Cityscape

A Journal of Policy

Development and Research

Double Issue

ReentRy HousIng AfteR JAIl oR PRIson

Recent RefoRms In ZonIng

Volume 25, numbeR 2 • 2023

U.S. Department of Housing and Urban Development Office of Policy Development and Research

Managing Editor: Mark D. Shroder

Associate Editor: Michelle P. Matuga

Advisory Board

Dolores Acevedo-Garcia

Brandeis University

Ira Goldstein

The Reinvestment Fund

Richard K. Green

University of Southern California

Mark Joseph

Case Western Reserve University

Matthew E. Kahn

University of California, Los Angeles

C. Theodore Koebel

Virginia Tech

Jens Ludwig

University of Chicago

Mary Pattillo

Northwestern University

Carolina Reid

University of California

Patrick Sharkey

New York University

Cityscape

A Journal of Policy

Development and Research

Double Issue

ReentRy HousIng AfteR JAIl oR PRIson

Recent RefoRms In ZonIng

Volume 25, numbeR 2 • 2023

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

The goal of Cityscape is to bring high-quality original research on housing and community

development issues to scholars, government officials, and practitioners. Cityscape is open to

all relevant disciplines, including architecture, consumer research, demography, economics,

engineering, ethnography, finance, geography, law, planning, political science, public policy,

regional science, sociology, statistics, and urban studies.

Cityscape is published three times a year by the Office of Policy Development and Research

(PD&R) of the U.S. Department of Housing and Urban Development (HUD). Subscriptions are

available at no charge and single copies at a nominal fee. The journal is also available on line at

huduser.gov/periodicals/cityscape.html.

PD&R welcomes submissions to the Refereed Papers section of the journal. Our referee process

is double blind and timely, and our referees are highly qualified. The managing editor will also

respond to authors who submit outlines of proposed papers regarding the suitability of those

proposals for inclusion in Cityscape. Send manuscripts or outlines to cityscape@hud.gov.

Opinions expressed in the articles are those of the authors and do not necessarily reflect the

views and policies of HUD or the U.S. Government.

Visit PD&R’s website, huduser.gov, to find this report and others sponsored by PD&R. Other services of HUD USER, PD&R’s Research and Information Service, include listservs, special interest

and bimonthly publications (best practices, significant studies from other sources), access to public

use databases, and a hotline (1–800–245–2691) for help with accessing the information you need.

Contents

Symposium

Reentry Housing After Jail or Prison ........................................................................................... 1

Guest Editor: Calvin C. Johnson

Guest Editor’s Introduction

Reentry and Housing ................................................................................................................... 3

Addressing Barriers to Housing in Reentry Programs Working to Address a Variety

of Needs: A Qualitative Study of Second Chance Act Grantees .............................................. 13

by Elizabeth L. Beck, Natasha N. Johnson, Sommer Delgado, Victoria Helmly, Susan A. McLaren,

Alice Prendergast, Leigh Alderman, Lorenzo Almada, Brian Bride, Eric Napierala, and William J. Sabol

Rental Assistance and a Fresh Start to Spur Criminal Desistance: Evidence From

a Pilot Housing Experiment ...................................................................................................... 35

by David S. Kirk

Addressing Homelessness Among People With Justice Involvement:

Los Angeles County’s Just in Reach Pay for Success Demonstration Project ......................... 57

by Sarah B. Hunter and Stephanie Mercier

Public Housing Eligibility for People with Conviction Histories ............................................ 73

by Niloufer Taber, Jacqueline Altamirano Marin, and John Bae

Symposium

Recent Reforms in Zoning ........................................................................................................... 91

Guest Editor: Noah M. Kazis

Guest Editor’s Introduction

Learning From Land Use Reforms: Housing Outcomes and Regulatory Change ................... 93

Where Will Accessory Dwelling Units Sprout Up When a State Lets Them Grow?

Evidence From California ....................................................................................................... 107

by Nicholas J. Marantz, Christopher S. Elmendorf, and Youjin B. Kim

California’s Strengthened Housing Element Law: Early Evidence on Higher

Housing Targets and Rezoning ............................................................................................... 119

by Paavo Monkkonen, Michael Manville, Michael Lens, Aaron Barrall, and Olivia Arena

How Can Procedural Reform Support Fair Share Housing Production? Assessing

the Effects of California’s Senate Bill 35 ................................................................................. 143

by Moira O’Neill and Ivy Wang

Here Come the Tall Skinny Houses: Assessing Single-Family to Townhouse

Redevelopment in Houston, 2007–2020 ................................................................................ 171

by Jake Wegmann, Aabiya Noman Baqai, Josh Conrad

Cityscape

iii

Contents

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes? ................. 203

by Leah Brooks and Jenny Schuetz

Learning from Land Use Reforms: The Case of Ramapo, New York ..................................... 225

by Joseph Weil Huennekens

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate ... 257

by Jacob Krimmel and Betty Wang

Refereed Papers ................................................................................................................... 279

Seeing the Big Picture with Multisector Data: Factors Associated with Exiting

from Federal Housing Assistance by Exit Type ..................................................................... 281

by Alastair I. Matheson, Danny V. Colombara, Annie Pennucci, Tyler Shannon, Andy Chan,

Megan Suter, and Amy A. Laurent

Factors Affecting Spillover Impacts of Low-Income Housing Tax Credit Developments:

An Analysis of Los Angeles ..................................................................................................... 309

by Brian Y. An, Andrew Jakabovics, Jing Liu, Anthony W. Orlando, Seva Rodnyansky,

Richard Voith, Sean Zielenbach, and Raphael W. Bostic

Departments ......................................................................................................................... 365

Data Shop

A Statistical Machine Learning Approach to Identify Rental Properties From

Public Data Sources ................................................................................................................. 367

by Daniel Kuhlmann, Jane Rongerude, Lily Wang, and GuanNan Wang

Veteran and Nonveteran Homelessness Rates: New Estimates ............................................. 379

by Brent D. Mast

Graphic Detail

Exploring the Relationship Between Child Opportunity and Violent Crime Rates

in West Virginia Counties ....................................................................................................... 387

by Brent D. Mast and Tricia Ruiz

Exits From HUD Assistance and Moves to Higher Poverty Neighborhoods Following

the Camp Fire .......................................................................................................................... 395

by Alexander Din

Industrial Revolution

Hiding in Plain Sight: How Reconsideration of Codes for Existing and Historic

Buildings Can Expand Affordable Housing ............................................................................ 403

by Marilyn E. Kaplan and Mike Jackson

Policy Briefs

Mortgage Risk and Disparate Impact Associated With Student Debt ................................... 413

by Kevin A. Park and Joshua J. Miller

Referees 2022–2023 ................................................................................................................ 429

iv Contents

Symposium

Reentry Housing After Jail or Prison

Guest Editor: Calvin C. Johnson

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape

1

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

2

Reentry Housing After Jail or Prison

Guest Editor’s Introduction

Reentry and Housing

Calvin C. Johnson

U.S. Department of Housing and Urban Development

The views expressed in this article are those of the author and do not represent the official positions or

policies of the Office of Policy Development and Research, the U.S. Department of Housing and Urban

Development, or the U.S. Government.

In 2021, HUD Secretary Marcia L. Fudge stated the President’s and her position on reentry and

housing: “The President and I believe that everyone deserves a second chance and a stable home

from which to rebuild their lives. No person should exit a prison or jail only to wind up on the

streets.” She went on to clarify to HUD-assisted housing providers and Continuums of Care that

returning citizens at risk of homelessness were among the eligible population for the recently

awarded 77,000 new emergency housing vouchers. She continued by stating—

HUD is committed to taking a comprehensive approach to addressing the

housing needs of returning citizens and people with criminal records, and

by doing so, increasing public safety within our communities. Addressing

reentry housing needs also furthers the Biden Administration’s commitment

to advancing equity and reversing systemic racism, given the racial disparities

evident in the criminal justice system.

The following year (April 2022), Secretary Fudge charged the Department’s leadership with

conducting a comprehensive review of regulations and guidance to identify ways to reduce barriers

to HUD programs for people with criminal records. She acknowledged that—

Individuals with criminal histories too often face daunting and unnecessary

barriers to obtaining and maintaining housing, including public housing,

HUD-assisted housing, and HUD-insured housing, which are often the only

types of housing they can afford … Too often, criminal histories are used to

screen out or evict individuals who pose no actual threat to the health and

safety of their neighbors. And this makes our communities less safe because

providing returning citizens with housing helps them reintegrate and makes

them less likely to reoffend.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape

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U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Johnson

Within that context, PD&R Edge (HUD-PD&R’s online magazine) published a series of messages

from PD&R’s leadership on these topics (in April 2022, May 2022, and August 2022), and the

planning for this Cityscape symposium on reentry and housing emerged.

The research articles in this symposium are intended to support evidence-building and policy-

development activities on both issues identified by HUD’s Secretary: (1) housing supports

for returning citizens and (2) the enduring barrier to housing presented by a criminal record.

Furthermore, these papers are written with a recognition that housing is an important tool

for increasing public safety and connecting individuals to services associated with successful

reentry (e.g., employment, health and wellness, and other social services), especially in the days

immediately following release from correctional institutions.

Introduction

Despite having the lowest incarceration rate on record since 1995, the United States continues

to incarcerate more people per capita than any other country for which data are available—810

inmates for every 100,000 adult residents (Gramlich, 2021). That proportion amounts to nearly

2.1 million incarcerated individuals, with the majority (69 percent) confined to state or federal

prisons, where they typically serve a period of incarceration of more than 1 year, and the remaining

31 percent confined to local jails, where they typically serve 1 year or less (Carson and Kluckow,

2023). An additional 4.4 million individuals were being supervised in the community on probation

(3.5 million) or on parole (900,000). The impact of those corrections practices disproportionately

affects low-income communities and communities of color. No matter the form of correctional

supervision, the facts remain the same: nearly all of them will go home. And when they go

home—that is, return to their communities—they will have been labeled for their criminal justice

involvement and, as a result, will face extremely challenging circumstances navigating life outside

jail and prison walls.

The literature can be no clearer. Individuals with a criminal record confront barriers to

accessing the most basic needs of food and shelter and an array of needed support services (e.g.,

employment, behavioral health, and physical and mental health care) to facilitate successful

transitions from jails and prisons and reduce the risk of rearrest, relapse, or any negative behavioral

outcomes that might land them back in a correctional institution (Reentry Coordination Council,

2022). With stable housing or a viable housing plan, these individuals establish meaningful

connections to family and community-based services that support their reentry journey and slow

the rate of future criminal offending (Fontaine, Gilchrist-Scott, Roman et al., 2012; La Vigne et al.,

2009; Visher et al., 2010). Without such stable housing or a viable housing plan, these individuals

are likely to lose their connections to meaningful social networks and support services that mitigate

the risk posed by social and economic deficits known to contribute directly and indirectly to

criminal behavior (Fontaine, Gilchrist-Scott, Denver et al., 2012).

In addition to being one of the most important basic needs, housing is a key predictor of successful

community reentry (Burrowes, 2019). Returning citizens for whom safe and affordable housing is

inaccessible often experience a downward spiral immediately following release from incarceration

that increases the risk of returning to jail or prison. Without housing, accessing much-needed

4

Reentry Housing After Jail or Prison

Reentry and Housing

social services and health services is extremely challenging. Safe and affordable housing serves as

a protective factor by removing the search for housing as a primary need and therefore facilitating

access to other needed services (Criminal Justice Policy Group, 2019; Johnson, 2022). Therefore,

housing should qualify as a prescription for public safety that supports access to other protective

factors known to reduce the rate of return to jail or prison.

Returning Citizens (formerly incarcerated individuals) and

Barriers to Reentry

Each year, between 600,000 and 650,000 individuals return to the community following a period of

confinement in state or federal prisons (Carson, 2015), and another 4.9 million individuals return

to the community following a period of confinement in local jails (Sawyer and Wagner, 2023). Upon

return to the community, they experience the same set of social, economic, and health conditions

that contributed to their incarceration. A significant share of these individuals return to a relatively

small number of communities across the country (La Vigne et al., 2003; Olson and Anderson,

2020; Travis, Solomon, and Waul, 2001; University of Wisconsin, Institute for Research on Poverty,

2020). Those communities have an ever-shrinking supply of quality affordable housing and limited

access to employment opportunities and healthcare services (including physical, mental, and

behavioral health). Based on surveys of returning citizens in The Returning Home Study, more than

one-half returned to neighborhoods that have major drug problems (Visher, La Vigne, and Travis,

2004; Visher, Yahner, and La Vigne, 2010). Despite every effort by families, community nonprofit

organizations, faith-based institutions, local government service providers, and community

corrections officers committed to supporting successful transitions from correctional institutions

to the community, the structural conditions within those communities (e.g., inadequate affordable

housing stock, high poverty, and high unemployment) are significant contributors to crime and its

correlates and, as such, present program and policy challenges for reentry.

Nearly all individuals confined to a period of incarceration in either jails or prisons will go home

and return to the community, so where do they live upon their return? Housing arrangements

for citizens returning from prison or jail are fragile; they were fragile before incarceration and

are even more fragile upon return to the community. Upon release from jail or prison, returning

citizens might stay with family, stay in emergency housing shelters, or find themselves experiencing

a variety of unsheltered sleeping arrangements. For those fortunate enough to have housing

arrangements with family, those arrangements often are temporary. Families who rent their homes

and invite a person returning from incarceration to stay with them often do so in violation of

their lease agreements. Moreover, families who receive federally assisted rental housing often are

restricted from adding individuals who have a criminal record to the lease, even if the individual

was listed as a tenant on the lease before incarceration. Those temporary and unstable housing

arrangements and lease restrictions further exacerbate the housing challenges faced by the

more than 600,000 individuals released from state and federal prisons and the nearly 5 million

individuals released from local jails each year.

The literature consistently indicates that returning citizens are more likely to transition successfully

to the community when housing and support services are in place, connections to healthcare

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Johnson

resources are established (including physical, behavioral, and mental health), and employment or

vocational skill development plans are in place (Travis et al., 2001; Zhang et al., 2019). Further,

the interconnectedness of health, employment, and housing as part of the reentry process (Link,

Ward, and Stansfield, 2019) requires intentionality with respect to prerelease planning (Nelson,

Deess, and Allen, 2011) and policy and program development. When people returning from

incarceration are housed and connected to services that address their healthcare needs and other

service delivery needs, such as employment or vocational programs, they are more likely to remain

in the community without being reincarcerated for a new offense (Bureau of Justice Assistance

(BJA), 2022; Fontaine, Gilchrist-Scott, Roman et al., 2012; La Vigne, Shollenberger, and Debus,

2009; Bae, diZerega, Kang-Brown et al., 2016).

When returning citizens have access to safe and affordable housing, they can reunify with family

members willing to support the implementation of a reentry plan, engage in employment services

or maintain their employment, participate in behavioral health services, and better manage their

physical and mental well-being (BJA, 2022). A growing body of evidence highlights the importance

of well-structured and coordinated housing interventions plus support services in increasing the

likelihood of successful reentry. A few such programs are listed below.

• Denver Social Impact Bond Initiative

{

Program Description: Offers permanent housing and supportive services to “front end” or

frequent users of criminal justice and emergency medical services.

{

Partners: Colorado Coalition for the Homeless, Colorado Division of Housing, Denver

Continuum of Care, Denver Housing Authority, and Mental Health Center of Denver.

{

Outcome (experimental design): Compared with a control group, the treatment group

experienced a 34-percent reduction in police contact and a 40-percent reduction in arrests.

• New York City Frequent Users Service Enhancement (FUSE)

{

Program Description: Individuals experiencing homelessness who had experienced four jail

stays and four shelter stays in the past 5 years were offered permanent supportive housing.

{

Partners: Corporation for Supportive Housing, Department of Corrections, Department

of Health and Mental Hygiene, Department of Homeless Services, Housing Preservation

and Development, New York City Housing Authority, and nonprofit housing and

service providers.

{

Outcome (quasi-experimental design): Compared with a comparison group, FUSE

participants experienced a larger reduction in jail days, lower rates of hard drug and

alcohol use, and higher levels of family and social supports.

• Returning Home—Ohio (RHO)

6

Reentry Housing After Jail or Prison

Reentry and Housing

{

Program Description: A component of the Corporation for Supportive Housing’s Returning

Home Initiative, in which citizens returning from incarceration with unmet medical needs

and at risk of being homeless are offered permanent housing and supportive services.

{

Partners: Corporation for Supportive Housing, Ohio Department of Alcohol and Drug

Addiction Services, Ohio Department of Mental Health, and Ohio Department of

Rehabilitation and Corrections.

{

Outcome (quasi-experimental design): Compared with a comparison group, RHO participants

were less likely to be rearrested and to return to prison within 1 year of release.

In the absence of stable housing or a viable housing plan upon release from jail or prison, returning

citizens are nearly 10 times more likely to be homeless than the general public (Couloute, 2018).

The rates are significantly higher among those released from incarceration within the past 2 years.

Using HUD Point-in-Time estimates and the National Former Prisoner Survey, Couloute estimates

that the sheltered homeless rate is 98 per 10,000 for formerly incarcerated individuals, compared

with 13 per 10,000 for the general public. The unsheltered homeless rate is 105 per 10,000 for

formerly incarcerated individuals compared with 8 per 10,000 for the general public. An additional

367 per 10,000 formerly incarcerated individuals have marginal housing insecurities, living in

rooming housings, hotels, or motels.

The Enduring Effect of a Criminal Record

Nearly 1 in 3 adults (or 85.1 million adults) in the United States are estimated to have a criminal

arrest record. No matter the length of time since their arrest, their criminal record has an enduring

effect, causing barriers to housing, employment, and a range of social services. Landlords conduct

criminal background checks and deny housing because of the presence of a criminal record—even

when the applicant is financially qualified.

Criminal history data (the compilation of crime records) typically are used in risk assessment and

screening tools across criminal and juvenile justice settings, but little empirical evidence supports

their use to predict successful tenancy (Malone, 2009). Instead, housing providers and their

management agents routinely conduct criminal background checks as part of their tenant screening

processes, believing that criminal history is an accurate predictor of future criminal behavior that

could pose harm to other tenants or disturb their peaceful enjoyment of the property. Housing

providers and their management agents rely on findings from recidivism studies that take an event-

based approach using an arrest in the past to predict future arrests or other behavioral outcomes.

In so doing, housing providers and their management agents fail to acknowledge that the greater

the distance in time since the criminal activity, the more indistinguishable the risk of arrest is for a

person with a criminal record compared with a person with no prior arrest (Bushway et al., 2022;

Kurlychek, Brame, and Bushway, 2006, 2007).

Given disproportionate patterns of arrest in low-income communities and communities of color,

the use of criminal records to determine eligibility for housing is a disadvantage for renters who

are people of color. Even when criminal records are assessed for all potential renters, this practice

Cityscape

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Johnson

has a disparate impact on low-income people and people of color who are adversely affected by

discriminatory policing and sentencing practices.

The articles in this symposium address the challenges of reentry and housing and provide more

details about the challenges faced by citizens returning from incarceration and individuals with a

criminal record as they attempt to obtain housing—one of the most important basic needs.

Order of Articles and Summaries

• In their article, Elizabeth Beck and her research partners from Georgia State University share

research from their evaluation of Second Chance Act (SCA) grantees in three communities.

The research team interviewed local SCA program providers in each site and 31 program

participants across the sites. Program participants’ list of housing challenges included

housing affordability, barriers in the private and public housing market caused by a criminal

record, and the importance of family in providing a housing safety net. Program providers

relied on a variety of efforts to support connection to housing for their program participants,

including referrals to housing partners and providers that also offer case management

support and collaboration with local housing authorities to promote the use of vouchers for

returning citizens.

• David Kirk’s article presents findings from pilot randomized evaluations of a “fresh start” with

free housing—the Maryland Opportunities through Vouchers Experiment (MOVE). Kirk notes

that a large share of citizens released from incarceration return to the community or areas

close to the community where they were arrested. Essentially, they return to the places familiar

to them—places where they have social networks that facilitate their criminal involvement—

and do so facing housing challenges. Although resources limited the design of the pilot

evaluation, Kirk designed two pilots testing (1) the effect of moving to a new jurisdiction with

free housing and (2) the effect of moving to a new jurisdiction with free housing compared

with the usual reentry housing search process. These pilots provide promising signals for

followup research offering returning citizens a “fresh start” in jurisdictions that are less familiar

and where they have no existing social networks that might facilitate criminal involvement.

• The article by Sarah Hunter and Stephanie Mercier presents findings from Los Angeles

County’s first Pay For Success (PFS) initiative—Just in Reach (JIR). In general, PFS allows the

private sector to invest in public initiatives and receive their initial investment plus interest

if outcomes are achieved. JIR PFS is modeled after Los Angeles County’s Housing for Health

program and is administered by the Office of Diversion and Reentry. The program model

includes prerelease screening for potential eligibility, assignment to intensive case management

services with mental health services as needed, and assignment to transitional housing (up to

9 months) followed by permanent supportive housing. Using a quasi-experimental design,

the authors report positive program effects for jail days, housing and homeless services, and

healthcare services and related costs.

• The article by Niloufer Taber and her research partners highlights the significant barrier to

obtaining housing presented by a conviction history. Further, the research team identifies

8

Reentry Housing After Jail or Prison

Reentry and Housing

“1,300 documented local and state barriers to housing for people with conviction histories

and 26 federal barriers.” Using policies across public housing authorities in Michigan and

Oklahoma regarding housing eligibility for persons with a criminal history and criminal

conviction data in both states, the researchers estimate the number of individuals potentially

excluded from housing in the 116 housing authorities in Michigan and the 101 housing

authorities in Oklahoma if the lookback period1 was changed. The numbers are large and

speak to the deleterious effect of the lookback period.

Acknowledgments

I am thankful to the authors for their submissions to this symposium. Their research contributes to

evidence-building and policy development at the intersection of reentry and housing.

Guest Editor

Calvin C. Johnson, PhD, is the Deputy Assistant Secretary for the Office of Research, Evaluation,

and Monitoring at the U.S. Department of Housing and Urban Development.

References

Bae, John, Margaret diZerega, Jacob Kang-Brown, Ryan Shanahan, and Ram Subramanian. 2016.

Coming Home: An Evaluation of the New York City Housing Authority’s Family Reentry Pilot Program.

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report-032917.pdf.

Bureau of Justice Assistance (BJA). 2022. Opening Doors, Returning Home: How Public Housing

Authorities Across the Country Are Expanding Access for People with Conviction Histories.

https://bja.ojp.gov/doc/opening-doors-returning-home.pdf.

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1 The lookback period is how far back a landlord “looks back” when conducting a criminal background check of a

housing applicant.

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10 Reentry Housing After Jail or Prison

Reentry and Housing

Link, Nathan W., Jeffrey T. Ward, and Richard Stansfield. 2019. “Consequences of Mental and

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Health and Well-Being: Proceedings of a Workshop. Washington, DC: National Academies Press.

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from COVID-19 and Beyond. Report to Congress from the Reentry Coordination Council.

https://www.justice.gov/opa/press-release/file/1497911/download.

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Travis, Jeremy, Amy L. Solomon, and Michelle Waul. 2001. From Prison to Home: The Dimensions

and Consequences of Prisoner Reentry. Washington, DC: Urban Institute.

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Incarceration, and Inequality. Fast Focus Research/Policy Brief No. 48-2020. https://www.irp.wisc.

edu/resource/connections-among-poverty-incarceration-and-inequality/.

Visher, Christy, Nancy La Vigne, and Jeremy Travis. 2004. Returning Home: Understanding the

Challenges of Prisoner Reentry. Maryland Pilot Study: Findings from Baltimore. Urban Institute Research

Report, January. https://www.urban.org/sites/default/files/publication/42841/410974-Returning-

Home-Understanding-the-Challenges-of-Prisoner-Reentry.PDF.

Visher, Christy, Jennifer Yahner, and Nancy La Vigne. 2010. Life After Prison: Tracking the

Experiences of Male Prisoners Returning to Chicago, Cleveland, and Houston. Urban Institute Research

Brief, May. https://www.urban.org/sites/default/files/publication/28671/412100-Life-after-Prison-

Tracking-the-Experiences-of-Male-Prisoners-Returning-to-Chicago-Cleveland-and-Houston.PDF.

Zhang, Ryan, Swathi Srinivasan, Amisha Kambath, Venus Nnadi, Adiah Price-Tucker, Amy

Zhou, Andrew Charroux, Choetsow Tenzin, Emma Robertson, Hoda Abdalla, Jeffrey Gu, Jordan

Barton, Maria Keselj, Owen Bernstein, Paul Alexis, Sethu Odayappan, and Tabitha Escalante.

2019. Successful Reentry: A Community-Level Analysis. Harvard Kennedy School Institute of Politics

Criminal Justice Policy Group. https://iop.harvard.edu/sites/default/files/sources/program/IOP_

Policy_Program_2019_Reentry_Policy.pdf.

Cityscape 11

12 Reentry Housing After Jail or Prison

by Elizabeth L. Beck, Natasha N. Johnson, Sommer Delgado, Victoria

Helmly, Susan A. McLaren, Alice Prendergast, Leigh Alderman,

Lorenzo Almada, Brian Bride, Eric Napierala, and William J. Sabol

Addressing Barriers to Housing

in Reentry Programs Working

to Address a Variety of Needs:

A Qualitative Study of Second

Chance Act Grantees

Elizabeth L. Beck

Natasha N. Johnson

Sommer Delgado

Victoria Helmly

Susan A. McLaren

Alice Prendergast

Leigh Alderman

Lorenzo Almada

Brian Bride

Eric Napierala

William J. Sabol

Georgia State University

Abstract

Using data from an evaluation of three Second Chance Act grantees, we explore formerly incarcerated

people’s (FIP) access to housing. This study is unique in that it includes the perspectives of individuals

with lived experiences and the insights of the reentry program providers working to meet their overall

needs, including in the area of housing. The data come from reentry programs in three regions of the

United States. Although the needs of the people with lived experiences have similarities, regional

differences exist, particularly related to housing costs and supply, including the availability of transitional housing. Also, variations exist between FIP who are able to live with family compared with those who

do not have this option. The three programs this study examined worked to address housing needs in

distinctive ways and explores the housing needs of FIP and the strategies the three programs use to

address these needs. Incorporating a two-pronged approach, this article includes analyses of (1) interview

data with 31 FIP from 3 months to 3 years post-incarceration and (2) interviews and program materials

to support formulative case analyses of the housing-related work that program enacted. Through this

work, highlighting program efforts to remove barriers to housing for this population, the study seeks to

promote the advancement of relevant policy, practice, and research in this arena.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 13

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Beck, Johnson, Delgado, Helmly, McLaren, Prendergast, Alderman, Almada, Bride,

Napierala, and Sabol

Key Words: Barriers to Housing, Second Chance Act, Post-Incarceration, Post-Release, Formerly

Incarcerated People, Recidivism, Reentry, Transitional Housing

Introduction

In 2020, one in five U.S. households experienced housing insecurity, and the nightly shelter

count of homeless people in 2021 was 326,000. For formerly incarcerated people (FIP), stable

housing represents the foundation for successful reentry from prisons and jails (HUD, 2022). It

is against this backdrop that the 600,000 people released annually from prisons and the 6 to 7

million people who are booked and released from jail in a year must find housing (Kovera, 2019;

Rodríguez-Pose and Storper, 2020; Spaulding et al., 2011, 2009; Yousefi-Rizi et al., 2021).

Housing, a primary determinant in one’s ability to successfully reintegrate, is a challenging barrier

that FIP face (Bowman and Ely, 2020; Geller and Curtis, 2011; Gunnison and Helfgott, 2011;

Remster, 2019). For a person reentering from incarceration, safe and affordable housing is more

than mere shelter. Stable housing is associated with positive psychosocial outcomes, including

employment, physical and behavioral health, and lower rates of recidivism (Bostic et al., 2012;

Semenza and Link, 2019). A criminal record exacerbates FIP’s challenges in finding housing, as

Marcia Fudge, Secretary of the U.S. Department of Housing and Urban Development (HUD),

succinctly stated—

Individuals with criminal histories too often face daunting and unnecessary barriers to

obtaining and maintaining housing, including public housing, HUD-assisted housing,

and HUD-insured housing, which are often the only types of housing they can afford

[…] Too often, criminal histories are used to screen out or evict individuals who pose

no actual threat to the health and safety of their neighbors. And this [policy] makes our

communities less safe, because providing returning citizens with housing helps them

reintegrate and makes them less likely to reoffend. (Fudge, 2022, p. 1)

The search for shelter among FIP is fraught with resource barriers and exclusionary policies

(Remster, 2021). This general lack of access to housing can be considered a public safety issue. As

such, after the initial Second Chance Act (SCA) of 2008 expired, Congress reauthorized it within

the First Step Act of 2018 under Title V—Second Chance Act of 2008 Reauthorization.1, 2 Both

bipartisan bills provided assistance for programs for FIP to successfully return and reintegrate into

their communities. Through the SCA, the Bureau of Justice Assistance and the Office of Juvenile

Justice and Delinquency Prevention have made more than 800 awards across 49 states since 2009

(Ritter, 2014). Even in areas where grant recipients are working to support FIP, securing housing

remains difficult (Bae et al., 2016; Simmons University School of Social Work, 2016).

Although people with lived experiences of reentry often have difficulties securing housing, regional

differences exist, especially related to housing costs and supply. This article examines data from

three geographically different SCA grantees, explores FIP’s access to housing, and includes both the

perspectives of individuals with lived experiences and the insights of the reentry program providers

1 Second Chance Act of 2008. Public Law 110-199.

2 First Step Act of 2018. Public Law 115-391.

14 Reentry Housing After Jail or Prison

Addressing Barriers to Housing in Reentry Programs Working to

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working to meet their needs. While none of the reentry sites had a specific focus on housing,

according to the SCA legislation, grantees are to provide comprehensive services that include

support with social services, jobs, and housing. This article begins by reviewing the relevant

literature on the reentry-housing-community development nexus.

Literature Review on the Reentry-Housing-Community Development Nexus

Access to safe, stable, and affordable housing remains a prevalent—and foundational—need

among FIP as they reenter society. Geller and Curtis (2011), Remster (2021, 2019), and Smoyer

et al. (2021) found that FIP frequently experience prolonged, unstable housing situations and

“ping-pong” between friends, family, romantic partners, shelters, and other temporary living

arrangements (Smoyer et al., 2021). Those who do not have familial access to housing are often

led to housing programs such as halfway houses or shelters, and others will face unsheltered

homelessness (Baxter et al., 2019). Remster (2021, 2019) evidenced the prolonged nature of ping-

pong housing, reporting that 49.6 percent of FIP who used shelters did so in the 2 years following

release. This finding points to the lack of stability often associated with living with friends or family

post-release, as overcrowding, family tensions, and other issues surface. These issues are, in many

instances, exacerbated by the income constraints of those providing housing to FIP and the income

constraints of FIP (Fontaine, 2013).

Extensive literature discusses the relationships between poverty and crime and poverty and

recidivism (Goodley, Pearson, and Morris, 2022; Jacobs et al., 2022; Nivette, 2011; Olver, Stockdale,

and Wormith, 2014; Patel et al., 2018; Pratt and Cullen, 2005). Hayat (2022: 657) investigated

these relationships and noted that “poverty may be a more significant predictor of recidivism than

dangerousness.” In their study involving 3,000 low-income men, Geller and Curtis (2011) found

that FIP were twice as likely to fall into one of the most extreme forms of poverty and homelessness

than others with similar risk factors. This finding is concerning, given the strong relationship

between homelessness and reincarceration (Brown, 2004; Moschion and Johnson, 2019; Travis,

Solomon, and Waul, 2001). In a study of roughly 50,000 men released from New York State

prisons, Metraux and Culhane (2004) found that 11.4 percent experienced homelessness; of that

population, nearly one-third were reincarcerated (Metraux and Culhane, 2004; Metraux, Hunt, and

Yetvin, 2020). In addition, Travis, Solomon, and Waul (2001) observed that FIP living in homeless

shelters were seven times more likely to not meet parole expectations due to the compounding

effects of homelessness that put them at greater risk for rearrest. Compounding factors include

rearrests for so-called ‘quality-of-life offenses,’ often incurred as people attempt to meet their daily

needs while living unsheltered (Travis, Solomon, and Waul, 2001).

Recognizing that housing provides a foundation from which one can seek employment, maintain

sobriety, address behavioral, mental, and physical health needs, and make positive contributions

to their community, the United States began implementing the Rapid Rehousing model for people

experiencing homelessness in 2014 (Beck and Twiss, 2018; Padgett, Henwood, and Tsemberis,

2015). Rapid Rehousing swiftly provides housing based on the assumption that once persons

experiencing homelessness obtain housing, they have the space and support to mitigate other

issues. The model has proven successful across various matrices, including cost-effectiveness, and

can be considered a response to previous models that did not prioritize housing (Lee, Shinn, and

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Napierala, and Sabol

Culhane, 2021; Padgett, Henwood, and Tsemberis, 2015). Rapid Rehousing holds essential lessons

and implications for the future of reentry work in terms of its philosophy and the strategies used to

overcome barriers to housing (Fontaine, 2013).

A criminal record is a significant barrier to securing housing. This record allows property owners

to deny FIP rental applications, disproportionately affecting minoritized citizens’ efforts to

obtain housing (HUD, 2022). This exclusion extends to public housing agencies (PHAs), which

administer federally subsidized housing at the local level. Historically, PHAs had prohibitions

on renting to people with felony records, and in some locations, this prohibition includes

misdemeanors. Other policies limit the ability of friends and family participating in PHA programs

to support FIP through housing. In response, President Barack Obama issued guidance to PHAs

in 2015 to remove these constraints. However, the decision to follow this guidance was left in the

hands of PHAs; ultimately, few changes were made (Hayat, 2022; Voborníková, 2016). On June 23,

2021, Secretary Marcia Fudge made a second appeal to remove barriers to public housing. As part

of the American Rescue Plan, 7,000 housing vouchers were awarded to 600 PHAs for use by FIP at

risk for homelessness (Fudge, 2021).

In a randomized controlled trial, the Maryland Opportunities Through Voucher Experiment, 0

percent of the formerly incarcerated participants who received housing vouchers in the first wave

were rearrested. In subsequent waves, a few members of the treatment group were rearrested. In

this same vein, “rearrest was lower among the treatment group of movers than the nonmovers and

was also lower for nonmovers who received free housing versus nonmovers who did not receive

housing” (Kirk et al., 2018: 213). In contrast, 22 percent of individuals in the control group were

rearrested (Kirk et al., 2018). The New York City Department of Homeless Services and the New

York City Housing Authority created the Family Reentry Pilot Program, which resulted in the

rearrest of only 1 of the 150 participants who received a public housing unit and participated in a

program to support family reunification (Bae et al., 2016). As impressive as these studies are, they

did little to address subpopulations with unique housing struggles. The Maryland program was not

open to women, and only 8.5 percent of the participants in the New York program were women,

who frequently face additional barriers like seeking housing suitable for their children. The

programs also did not explicitly address access to housing for people convicted of sexual crimes.

The barriers to housing for both populations are substantial.

When considering the reentry-housing-community development nexus, it is necessary to

acknowledge the combination of the following policies. First, the austerity measures used to

dismantle public housing; second, the rise of mass incarceration; and third, the policing and

criminal justice policies that disproportionality affect the incarceration rates of people of color

(Alexander, 2010; Beck and Twiss, 2018). Black and Latinx people are incarcerated at five times

and one and one-third times the rate of White people, respectively. During the past 50-plus years,

these policies collided to create a reentry-recidivism cycle that fuels the prison industrial complex

(Beck and Twiss, 2018; Kovera, 2019; Williams, Wilson, and Bergeson, 2019). Instead of making

communities safer, the inability to securely house FIP often leads to reincarceration and the

perpetuation of policies disproportionally targeting people of color (Western and Sirois, 2019).

16 Reentry Housing After Jail or Prison

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Data and Methods

Data were collected from each SCA-funded site between the summer of 2021 and early 2023.

The data collection process included one multiday site visit and regular interaction between the

authors and the SCA-funded sites. In addition, program providers from each location participated

in a 1-hour interview via video conferencing software (for example, Zoom) that was recorded and

transcribed. During these interviews, program providers were asked a series of questions designed

to better understand the role and goals of each site in the reentry-housing-community development

nexus. Information was also collected through documentation the site personnel provided, and the

authors obtained information via site visits and ongoing conversations. Open coding was used to

identify categories from these data.

Data Collection and Analysis

In the summer of 2022, the evaluation team interviewed 31 FIP who participated in SCA programs.

The SCA site assisted with recruiting subjects by using recruitment materials the evaluation team

provided and that the institutional review board approved. As a part of this recruitment, the

evaluation team requested that the sites try to refer individuals with diverse experiences regarding

the length of time since exiting prison; specifically, the team sought an array of individuals with

diverse backgrounds, program perceptions, and transitional experiences. Participants were asked

about ongoing interactions with the SCA program providers and their reentry experiences,

including barriers to reentry, but were not explicitly asked about their housing situations.

Interviews were between 30 and 180 minutes each and were primarily conducted within the SCA

site. However, several participants elected to participate through video conferencing software or

telephone. All participants were compensated $50.00 per hour for the interviews. The data from

these interviews were recorded, transcribed, and then coded using an open coding method to

identify categories. Some authors worked together to code the data, and interrater reliability was

achieved with several of the remaining authors.

Participant Overview

Demographic data on the participants were collected during the interviews. A diverse set of

participants were interviewed, categorized by their self-classified gender, race and ethnicity, and age

group. Interviewers asked open-ended questions regarding these demographics. Exhibit 1 outlines

participants’ self-identities.

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Beck, Johnson, Delgado, Helmly, McLaren, Prendergast, Alderman, Almada, Bride,

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Exhibit 1

Participant Demographic Data*

Participants

n = 31

Gender

Men

22

Women

9

Nonbinary and Gender Nonconforming

0

Race and Ethnicity

Black

12

White and Caucasian

7

Hispanic*

7

Other or Multiracial

5

Age

18–25

4

26–35

12

36–45

9

46 or Older

6

* Based on participants’ self-identities.

Source: authors’ data

Program Overview

The three sites in this study are each uniquely situated in their community and generally use

different strategies and resources to support program participants. Each site implemented

its respective program to reduce recidivism. The first site, implemented at a local probation

department, is designed to reduce recidivism among FIP returning to their communities from

prison by initiating case planning, service referrals, and reentry preparation prerelease. In regard to

housing and other social service needs, the program used referrals and pursued memorandum of

agreements with more than 50 organizations to provide services to FIP. The referral process begins

during incarceration when program providers make referrals based on participants’ reentry needs,

including housing, employment, and other services.

The second site is a community-based organization with a pre and post-release reentry program

designed to reduce recidivism by identifying specific needs that are not currently available through

community support. The program is a partnership between the community-based organization and

a local law enforcement office. The program acts as a liaison between the network of post-release

service providers and FIP, providing case management services, education and training, housing

coordination, and mentoring services.

The third site is a local governmental organization with a long history of collaborative efforts

to support successful reentry using a coalition of service providers. The program is designed to

reduce recidivism and address systemic gaps in reentry services, focusing on the specific needs

of incarcerated parents. This program includes individualized needs assessments, intensive

18 Reentry Housing After Jail or Prison

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programming during incarceration, and referrals to community agencies that provide reentry

services post-incarceration.

Findings

The following sections detail the findings—first, those related to FIP and housing and, second, the

barriers programs worked to address to meet the housing needs of participants.

Affordability—Living Wages and Cost of Living

Participants mentioned several challenges related to the financial barriers to finding stable, safe

housing. Although the housing markets in the three cities varied significantly, housing affordability

was noted in all three cities. Some participants spoke of a shortage of housing, specifically, the lack

of safe, affordable housing options that would meet their needs. Conversations with participants

about housing affordability and other financial barriers often also included information about

available employment opportunities. One participant stated—

I mean, I’ve had a couple interviews, but honestly, my background isn’t the greatest, so

that I’m not going to get a good job [...] It’s just going to be something that’s minimum

wage. And when you think about it, minimum wage is nothing compared to how much

the cost of living is now.

Another participant, who lives with his mom as his caregiver, mentioned that their combined

incomes are so low that they rent out one of the rooms in their house to afford housing costs. He

further explained that they would be “going through hard times” without this additional money.

Whether their criminal record affected their employment search or the jobs available did not pay

enough to afford safe and stable housing in their communities, the link between employment

and housing in their reentry experiences was evident. These experiences varied by site; however,

in all locations, participants discussed how the local job market or the wages at their current job

influenced their ability to secure housing. Participants who lived with family but wanted to find

their own housing also often described their employability or wages as a barrier to securing a

place to live. In addition, participants with children sought housing that was safe and suitable for

their children, including the need for additional room, which affected the affordability of their

housing search.

Several participants mentioned the cost of housing and other financial requirements to apply for

housing in the private market, like deposit amounts, poor or no credit, and income requirements.

One participant mentioned that an apartment complex required him to prove that his income was

three times the monthly rent amount, which was impossible for him to do. Another participant

talked about the challenges of income requirements as a single parent. She said, “So that’s just my

main thing is that background and just having to make triple by yourself. I could see if it was you

and somebody else, with your guys’ income combined, but by yourself, that’s a ladder to climb.”

Other participants mentioned that credit checks were required in some of the housing they sought,

which was a barrier for them. One person explained, “Right now, I’m trying to find a house. I can

make the money for it. I got the money. The money is not the issue. It’s the credit thing. I’ve never

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had a bill in my life. I’ve never paid; I probably paid a phone bill when I was like 18. That was it.”

For participants who did have credit scores, the minimum credit score requirement to apply for

housing was too high.

The effect of discrepancies between clients’ wages and local housing costs was evident at each site.

One of these sites is in an area known for its very high cost of living, and the other sites reported

the cost of rental properties and housing skyrocketing with the COVID-19 pandemic. Because

of the cost of housing, one of the sites stated they are working on securing opportunities for

higher-wage jobs so that participants can afford a place to live. Two of the sites also discussed the

importance of credit history, and both worked with clients to obtain documents and credit scores.

One site encouraged participants to find building management positions, as they often include

housing as compensation.

Arrest Records, Administrative Barriers, and Other Nonfinancial Barriers to

Stable Housing

Participants described several administrative or nonfinancial barriers to housing, including criminal

history and paperwork requirements, like birth certificates, social security, or other identification

cards. These barriers relate to accessing housing programs designed to assist them with housing

needs and to apply for housing in the private market. As with Section 8 in general, there is often

a dearth of available housing and resistance to participating in the Section 8 program. Limited

access to housing is an additional strain for FIP who have Section 8 vouchers and criminal records.

Also, nonfinancial barriers exist to using housing vouchers or staying with family or friends

who use housing vouchers. Programs described how the inspection requirements of vouchers

made landlords less likely to accept them. One program explained how they work to convince

landlords who hesitate to accept Section 8 vouchers because of past experiences with other Section

8 residents. They assure landlords that the participants in their program have a lot of support

and that the program staff are involved in their lives, including doing home visits. A participant

described how Section 8 rules prevented him from staying with friends or family that received

benefits, saying, “if somebody has Section 8 or something, and I need a place, I can’t stay with

them ... because I’m a felon.”

For some participants, their criminal histories or the conditions of their parole or probation

create limitations on where they can live and their ability to access housing. For one participant,

living with a family member proved to create challenges with the conditions of his release. The

family member worked in security and had a firearm in the home, which was a violation for the

participant. Another participant was participating in drug court, with a requirement being that

he stay in a halfway house. He felt the drug court would not allow him to get his own apartment,

saying, “Like getting my own house, my housing, they won’t let you until they think you’re ready.

They don’t appreciate the good that somebody’s doing like they should do.” Another participant

said that although criminal records used to be a significant barrier to housing, affordability was the

biggest obstacle, and it mattered more whether one could make three times the rent than if one

had a criminal history when applying for housing.

20 Reentry Housing After Jail or Prison

Addressing Barriers to Housing in Reentry Programs Working to

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Other participants talked about how criminal record background checks affected their ability to

secure employment that would allow them to live independently. One stated—

I was actually interested in getting some of that expunged, because when people look

up my background, it’s like they meet me, and then when they look up my background,

they’re like, ‘I can see that you’re a good person, but’ ... I’ve done got fired from jobs

because of my background.

Aside from the barriers due to a person’s criminal history, other administrative challenges inhibit

securing stable housing. In this study, the paperwork involved in applying for financial assistance

for housing was prohibitive. Participants described exiting incarceration without any identification,

and programs required birth certifications, social security cards, or driver’s licenses to apply

for housing assistance. Some programs offering housing assistance scheduled meetings to help

participants with paperwork. However, one participant missed these appointments on multiple

occasions due to rigid scheduling timeframes and her inflexible parole officer.

All programs assisted participants with documentation, such as identification needed to apply for

housing programs. One program worked specifically on the expungement of criminal records. This

program indicated that the time to secure the documentation and complete everything required

for HUD housing is too short. To address documentation issues, for 1 week during the month, the

program convenes a large event that brings FIP and programs together. As part of this monthly

event, participants are provided onsite assistance with issues like driver’s license reinstatement,

child support modifications, and other documents needed to secure jobs and housing.

Family and Social Support

Living with a family member or a partner was common among the participants in this research

across all three sites. This arrangement included living with parents, siblings, extended family,

friends, and current or ex-partners. In one program, the staff estimated that more than one-half of

participants live with family or partners. For some of the study participants, living with families

was their only option for housing. One participant stated, “I’m basically homeless, and if it wasn’t

for my mom, I’d be living in my car.” Some participants were living with family or a partner,

providing care for loved ones with serious illnesses, including one participant who is a full-time,

paid caregiver for a partner with a terminal condition. Other participants noted that they were able

to help their families with housing costs by staying with them.

Although this living situation was favorable for some, the team learned that many participants

viewed this arrangement as temporary and not ideal due to conflicts or personality differences. In

some cases, the housing was unsafe due to both neighborhood and family members’ engagement

in illegal activities. Several participants just wanted a place of their own. For participants with

children, some wanted to establish a home on their own with their children. It was common

among our participants to stay with family members and their children. For example, one

participant described living in a full house (with mother, siblings, and siblings’ children) and not

having a private room.

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Several participants inherited or were given property from family members that provided them

with a stable and safe place to live. However, two participants were unstably housed and unable to

live in the family property because of the trauma they associated with the house. One participant

discussed finding his brother, who had overdosed, on the property his parents gifted them.

Another participant inherited her parent’s house, the home she grew up in, but felt she could not

live with the memories.

Site personnel also revealed that living with family is a widely used strategy but noted that it is

often a “very temporary to temporary” solution. One of the sites indicated that they generally do

not view living with family members as a real solution because the stay is typically short-term.

Program providers at each site were asked about participant exploitation; only one indicated that

they knew of one such experience, with each site saying that it likely happens. When asked, sites

also recognized that due to location parameters, staying with friends and family is often prohibitive

for people convicted of sexual crimes.

Need for Independence and a Place of Their Own

For most participants in various living situations (living with their families, in shelters, in halfway

houses, or with roommates), finding a place to live independently was vital to them. Nearly every

participant stated that finding a place to live independently was one of their short- or long-term

goals. For participants in unstable housing situations, securing stable housing was a priority. For

those with more stable housing in the short term, purchasing a home was one of their long-term

goals. Participants looked forward to having their own space or, at minimum, a place to store their

belongings. One participant noted that their main complaint about their current living situation

with a parent and younger siblings is that they lack privacy. They mentioned that being in one’s

own space offers freedom and time to “...be in my feelings, be in my thoughts, pray, cry….”

Participants who wanted a place of their own spoke about how their current living situations

lacked privacy, freedom, and safety. Some participants believed having their own place to live

would give them a sense of accomplishment and stability. One participant reported drug use in the

house and that his housemates were not supportive enough of his sobriety. Another participant

was sexually assaulted in her home and relocated to a battered women’s shelter. This participant

described being robbed several times since her release. Moving several times since her release for

safety reasons also made it difficult for her to stay connected to program providers and to keep

track of and complete the paperwork she had been given. In addition to overcoming financial

and other barriers, providing a safe home for their children was of the utmost importance to

interviewees. One participant said, “I have found a landlord who has a nice house on the south end

by Medical College. And if, indeed, I can get in that house, it is a decent neighborhood where I

don’t got to worry about my daughter getting gunned down by a stray bullet.”

Each site’s response to participants’ needs for independence varied. Two facilities saw long-term

independent housing as a goal, and another was concerned more about ensuring that participants

had roofs over their heads. The sites that worked toward long-term housing also acknowledged

that this type of housing had limitations. For one, it is generally found in low-income areas that are

problematic due, in part, to violence and proximity to criminal behavior.

22 Reentry Housing After Jail or Prison

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Interactions With the SCA-Funded Programs

Participants described several ways the programs in the SCA evaluation offered support in securing

housing. In some instances, the case managers were hands-on in helping participants search

for accommodations, including calling landlords on their behalf and taking the participants to

see available housing. Other examples included helping participants create a personal budget to

determine the housing options they can afford. In addition, some participants described how the

program linking them with employment—and, in some cases, higher-wage jobs—could help them

overcome some of the barriers to housing.

Some participants felt the housing programs could be more hands-on or proactive with housing

assistance. Participants described being referred to programs or handed a list of shelters or

apartments but doing most of the leg work. Other participants described being unaware of the

program’s housing assistance. One participant, for example, explained that he would not have

known about the housing assistance available if he had not known to ask. He said—

I know [the program] got a lot of things for us, but they’re not going to sit there and tell

you. You got to ask questions; you got to speak up, say something. You will never know

until you say something [...] For instance, with the housing thing, I never knew about

that. I found that out from word of mouth, from somebody else.

Participants discussed how the program financially assists with housing, including paying for rent

and application fees. However, the requirements or criteria for this assistance can be restrictive.

For example, one program secured rental assistance for up to 6 months for clients if they were

within 120 days post-release from jail. This requirement meant one participant was ineligible,

because she stayed with family post-release. She wanted to use the voucher to secure a place to live

independently but was unaware of the assistance until after learning she was ineligible.

Another participant talked about time limits in the housing program in which he was participating.

He stated that the transitional housing he was in was only available for a certain period. If he met

the program’s requirements, that time could be extended; however, after the first extension, the

participant would have to find another option. When the sites are not administering the services,

they must rely on their referral partners. One participant was enrolled in a program to pay her

rent but stated that her landlord never received the rent. This program receives referrals directly

from the site, although she engaged with the program on her own. She described child protective

services taking away her child after being evicted from her housing.

Second Chance Act-Grantees’ Efforts in Securing Housing

The staff at each SCA grantee site recognized the importance of housing, and two programs cited

it as the most crucial resource for the population. In one conversation, a staff person stated,

“Workforce development, it’s great. Substance abuse, great. All that stuff is needed. But none of it

matters at all if you don’t have a place to lay your head that’s safe.”

Program staff recognize the systemic challenges that make overcoming housing barriers so difficult.

A staff person explained, “[it’s] such a larger part of a systemic problem, and it all goes back to

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Beck, Johnson, Delgado, Helmly, McLaren, Prendergast, Alderman, Almada, Bride,

Napierala, and Sabol

poverty.” She described their clients’ layered disadvantages, largely tied to a lack of resources and

the barriers of a criminal record. Despite these challenges, each site worked to secure housing using

the following strategies: Partnerships, referrals, and case management; securing additional housing

units; and using HUD resources.

Referral, Case Management, and Partnership

Every site engaged in partnerships, referrals, and case management and had either a housing

coordinator or a case manager whose work focused on housing. One of the programs relied on

partners to provide more intensive case management than the program structure supported, which

included obtaining housing. Two of the three programs contracted other providers to set aside shelter

and transitional “beds.” The third program is working to develop contracts with transitional housing

units that are in development. This program was also able to secure 22 Section 8 vouchers annually.

In one of the sites, a community-based partner renovated a motel that now provides the SCA

program with 265 units of transitional housing. Two of the programs partnered with community-

based organizations that assisted clients with security deposits. One program provided the first and

last month’s rent, and a second community-based organization provided up to 6 months of rent.

In the latter situation, the SCA program negotiated services for 15 participants; however, the rental

assistance was provided through The American Rescue Plan Act 2021 and will end within a year.

One of the SCA programs used an innovative approach that had a family reunification component,

which provided cash support to family members that were housing participants. The hope is that

the money used to offset household expenses might increase program participants’ stability.

To varying degrees, each program indicated that, at times, they needed to use shelter space. In

one of the sites, the SCA case manager continued to provide direct support in finding better

housing alternatives, and one of the sites provided no additional support. The third site was in

a community that used a tiered shelter system. The first tier offered little to no services, and the

second tier provided case management, especially related to housing. When participants were in a

tier-one shelter, the SCA program staff worked with them until they were moved to the next level.

Sites Working to Expand Housing

Efforts to expand housing opportunities included engaging landlords and brick-and-mortar

development. Two of the SCA sites worked hard to develop partnerships with landlords. For

example, one program jotted down every “for rent” sign and scoured social media to look for

available housing. Two programs discussed the effort they needed to advocate for their clients

with landlords who are hesitant to rent to FIP. One of the SCA programs informs landlords that

the program will vet potential participants, as the program views this process critical to the

sustainability of this approach. This program is also considering developing a certificate program

for renters that would focus on what it means to be a reliable and successful tenant.

Another program attempted to develop new housing. The site sought to create a 14-bed supportive

housing program through its SCA grant. The program then purchased an office building and

planned to convert it into a mixed-use space to include housing. Because the property was

commercially zoned, it needed to be rezoned, which required a public hearing. Despite gaining the

24 Reentry Housing After Jail or Prison

Addressing Barriers to Housing in Reentry Programs Working to

Address a Variety of Needs: A Qualitative Study of Second Chance Act Grantees

support of local commissioners, the site faced strong opposition from community members, and

the rezoning effort was denied. Now, the site is raising money to develop supportive housing for

FIP 55 and older, which it sees as a proactive way to address the compassionate relief their state is

offering to senior incarcerated people. The program providers feel confident about their ability to

secure zoning, as they are already operating housing on this specific lot and have built rapport with

the community.

Using HUD Resources

Each site discussed the importance of rapid rehousing, and each indicated that this option is a long

shot for their client population, given the scarcity of space in the program and the limited time

in which new applications are accepted. However, one program has worked hard to break these

barriers and set aside Section 8 vouchers from their local housing authority. Regardless, a shortage

of Section 8 units and paperwork are some of the additional obstacles to using the vouchers. This

program is working hard to develop relationships with landlords and ensure that participants have

the credentials they need to apply for Section 8 housing, including supporting documentation such

as birth certificates. Program staff noted that the short timeline to provide the documentation was

an additional barrier to securing units and wished they could work with participants’ prerelease

on this process. Program staff also discussed the difficulties associated with finding housing for

women with children and families.

This program is also supported by a long collaborative relationship with the localities’ HUD

program and a forward-thinking housing authority, supporting Section 8 use for FIP since the

progression of relevant PHA guidelines from 1975 to the present (National Housing Law Project,

2008). For example, this housing authority offers a $500.00 incentive to landlords willing to accept

HUD vouchers. The program sees the incentive as one potential tool to counter landlords’ hesitancy

to accept vouchers due to the stringent nature of the annual housing inspection HUD requires.

This highly collaborative SCA program also supports state policy under discussion to develop a

certificate of qualification that will help protect landlords if they experience damages and losses

resulting from their tenants.

In addition, all programs discussed the difficulties of finding housing for people convicted of sexual

crimes and FIP with children, the costs of housing due to location, or rent hikes that occurred with

the COVID-19 pandemic. Neighborhoods were also discussed as an issue, as accessible housing is

often in problematic regions in terms of safety and proximity to illegal activities.

Major Findings

Although the sites in this study operate in different contexts and have different strategies to address

the housing needs of participants, the themes uncovered were very strong within and across sites.

In many instances, the participants and program staff were aligned in their views of the prominent

challenges and barriers to housing. Ultimately, it is evident from these interviews that housing is a

primary concern and has a significant effect on the lives of FIP and their families. Participants lived

in a variety of housing situations, from shelters to transitional housing to family members’ homes.

The people interviewed also had various family ties and responsibilities that affected their housing

Cityscape 25

Beck, Johnson, Delgado, Helmly, McLaren, Prendergast, Alderman, Almada, Bride,

Napierala, and Sabol

situations and needs. Aside from the financial challenges of securing housing, compounding

administrative barriers were present, including credit score requirements. A significant thread that

linked many of the participants was their desire and need to have housing they could call their

own. They sought independence and privacy, regardless of their current housing situation. The

existing barriers to housing stand in the way of achieving this goal.

Consistent with Bowman and Ely’s (2020: 423) findings, the provision of stable housing is a

critical need for FIP reentering society, and its absence can have a deleterious effect on physical,

mental, and behavioral health and can hinder job searches. Stable housing directly affects both

the reentering individual and the community at large, as housing often provides a critical space

in which one can enact life changes needed to reduce recidivism and desistance. Even though

addressing housing was not the primary objective for any of the three sites, each of the reentry

sites recognized the importance and key role of FIPs finding stable housing as a predicate to

successful reentry and each worked to support FIP across a variety of spectra, with one participant

highlighting the importance of housing and income compared with other needs—

I’m an ex-addict. Well, actually, I’m a recovering addict, but [I use] every excuse possible to

use. You know what I mean? Like me not having my kids right now or me just in a situation

where I’m living or anything, or anything, not the house or no job or whatever. It’s just a

reason to continue to f*** up, to where like, in [...] But if we had a house, or let’s say if I had

a home or let’s say I have this really nice job and then a decent place, or whatever, it’s almost

like that would trump that because you don’t want to mess it up. You know what I mean?

But it’s so hard to pull, because of, I’m an ex-felon, to pull myself from the bottom.

This quote demonstrates the importance of housing in this FIP’s life and the struggle to achieve

specific post-incarceration goals. It also signifies that housing cannot be divorced from employment

and income. For many individuals, housing provides a foundation for seeking employment, and

as the data show, living with a family member or with others can also serve as a temporary bridge

from which FIP can seek employment. However, given the cost of housing on the open market

and the difficulties in earning a living wage, connecting FIP with employment opportunities that

will allow them to secure stable housing is important. Without programmatic efforts that address

housing and employment, the lasting effect of interventions is perhaps more questionable.

Presently, many people without criminal records are ill-housed and in need of services and

living wage jobs. McChesney (1990) likened the search for housing among homeless people, for

example, to a game of musical chairs in which those who were more able-bodied and carried fewer

burdens were more likely to secure chairs, and those who had more difficulties were left standing.

The presence of a criminal record can be a deciding factor in a person’s efforts to secure housing,

given the limited availability of extant resources. Therefore, it is important to recognize that FIP

are often competing with less burdened people for already very limited resources. Among FIP, a

hierarchy of burdens also exists, as women tend to have a more difficult time securing housing

than men, largely due to the need to find space for children, coupled with histories of interpersonal

violence and exploitation. Although none of the sites in our study provided explicit information

about their efforts to work with people sentenced for sexual crimes, each site did mention the

challenges involved in finding spaces for these individuals, including shelters.

26 Reentry Housing After Jail or Prison

Addressing Barriers to Housing in Reentry Programs Working to

Address a Variety of Needs: A Qualitative Study of Second Chance Act Grantees

The current housing problem echoes broader employment, income, poverty, and housing-access

issues. In this regard, FIP are entering and navigating markets with the stigma and, in some cases,

exclusionary policies associated with a criminal record. Therefore, it is incumbent on stakeholders

to develop proactive ways to support the housing needs of FIP, assisting them with the logistics

of reentry and also addressing issues of desistance and recidivism. The following section offers

recommendations for improving the reentry-housing-community development nexus.

Recommendations for Improving the Reentry-Housing-Community Development Nexus

Both SCA participants with lived experiences and service providers alike emphasized the

importance of housing, particularly considering housing as it is connected to broader employment,

income, poverty, and housing-access issues. As such, what follows are recommendations provided

by participants, as well as information synthesized by the authors that build on the literature,

interviews, and additional data collected as part of the evaluation grant.

Recommendations at the Federal Level

1. Given the housing challenges FIP face, opportunities exist for HUD and the SCA grantors (the

U.S. Department of Justice [DOJ] and Bureau of Justice Assistance) to enhance collaboration in

support of SCA-funded programs. Collaborative efforts might include providing support for

SCA programs to work with local public housing agencies to ensure that they follow Secretary

Fudge’s guidelines allowing FIP to participate in voucher programs. Additional efforts might

include highlighting the positive effects of collaboration between local reentry programs and

PHAs as one of the sites discussed. The collaboration model set by this reentry site shows the

mutually beneficial nature of the partnership, as the site works to ensure that all vouchers are

used, thereby allowing the PHA to maintain its allotment.

2. Opportunities exist for DOJ and HUD to explore testing innovative reentry grant efforts.

Given the positive results of rapid rehousing for the general population of homeless people,

a HUD-offered demonstration grant for people reentering can be an important contribution.

It would be helpful if such a demonstration included a data and cost-benefit component

to test if rapid rehousing provides the same benefits to FIP as it does to those who are

homeless. Due to the costs associated with prison and jail, a cost-saving effect is likely

with this approach. Other possibilities for innovation include incentivizing landlords to

accept vouchers for FIP. As with one of the study sites, this might include processes for SCA

initiatives to vet future Section 8 renters.

3. The connection between housing and income is two-fold and provides ample opportunity to

use reentry grants for income support for housing. The team encourages DOJ to examine the

imbued opportunities presented here and consider testing this model.

Recommendations That Can Be Implemented at All Levels

1. The supplemental funding through efforts such as the American Rescue Plan Act of 2021

provided invaluable support to some of the sites. The inclusion of rental assistance allowed

FIP to secure or maintain housing. The programs developed with this funding need to be

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Beck, Johnson, Delgado, Helmly, McLaren, Prendergast, Alderman, Almada, Bride,

Napierala, and Sabol

continued through public or private dollars.

2. Innovative reentry efforts, such as providing incentives to family members with whom FIP stay

as one of the sites discussed, should be shared, and efforts used to obtain public and private

resources should be supported.

Recommendations for Programs

1. Programs working with FIP should prioritize stable and safe housing as FIP reenter post-

incarceration. If possible, the programs should begin this work prior to the FIP’s release to

ensure their needs are assessed, they have access to emergency shelter post-release if necessary,

and they can begin to establish the paperwork necessary to apply for critical housing

assistance. In addition, programs should work to ensure that FIP are aware of the housing

assistance available to them through the program or the program’s referral agencies. Even if

FIP do not ask for housing assistance or talk about their housing struggles, they may require

housing assistance.

2. Given the strong connection between employment and housing, programs should work to

assist participants in overcoming financial barriers, including their search for higher-wage

employment and application requirements, such as deposit amounts. Program participants

who remain unbanked or underbanked may not have a credit score. As a result, programs

should look toward services that will assist participants in establishing and building credit,

which will assist in removing an additional important barrier to housing.

3. To remove barriers to employment and housing, programs should work to establish access to

expungement, where available.

Conclusion

The work to improve the lives and trajectories of FIP and the communities in which they reside

continues as the team continues to build more fruitful relationships with housing providers,

local PHAs, and other relevant stakeholders. Consistent with the prior literature on the reentry-

housing-community development nexus, this study’s findings reiterate the need to prioritize

housing in the efforts to support FIP and reduce recidivism. By highlighting program efforts to

forge these relationships and remove barriers to housing for this unique population, the team

seeks to move this needle forward by promoting the advancement of relevant policy, practice, and

research in this arena.

Acknowledgments

This project was supported by Award No. 2020-CZ-BX-0014, awarded by the National Institute of

Justice (NIJ), Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and

conclusions or recommendations expressed in this publication are those of the authors and do not

necessarily reflect those of the Department of Justice.

28 Reentry Housing After Jail or Prison

Addressing Barriers to Housing in Reentry Programs Working to

Address a Variety of Needs: A Qualitative Study of Second Chance Act Grantees

The authors would like to thank the Bureau of Justice Assistance, the NIJ, the three collaboration

sites, the study participants, and all stakeholders who worked to see this product to fruition.

Authors

Elizabeth L. Beck is a professor in the Department of Social Work at the Andrew Young School

of Policy Studies, Georgia State University. Natasha N. Johnson is a clinical instructor in the

Department of Criminal Justice and Criminology at the Andrew Young School of Policy Studies,

Georgia State University. Sommer Delgado is a doctoral candidate in the Department of Criminal

Justice and Criminology at the Andrew Young School of Policy Studies, Georgia State University.

Victoria Helmly is a doctoral candidate in the Department of Criminal Justice and Criminology

at the Andrew Young School of Policy Studies, Georgia State University. Susan A. McLaren is

an assistant project director in the Georgia Health Policy Center at the Andrew Young School

of Policy Studies, Georgia State University. Alice Prendergast is a senior research associate in

the Georgia Health Policy Center at the Andrew Young School of Policy Studies, Georgia State

University. Leigh Alderman is an assistant project director in the Georgia Health Policy Center at

the Andrew Young School of Policy Studies, Georgia State University. Lorenzo Almada is a clinical

assistant professor in the Department of Economics at the Andrew Young School of Policy Studies,

Georgia State University. Brian Bride is a distinguished university professor in the Department

of Social Work at the Andrew Young School of Policy Studies, Georgia State University. Eric

Napierala is a research associate II in the Georgia Health Policy Center at the Andrew Young

School of Policy Studies, Georgia State University. William J. Sabol is a distinguished university

professor in the Department of Criminal Justice and Criminology at the Andrew Young School of

Policy Studies, Georgia State University.

Direct all correspondence to Natasha N. Johnson, Department of Criminal Justice and Criminology,

Andrew Young School of Policy Studies, Georgia State University at njohnson93@gsu.edu.

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021-00999-w.

Cityscape 33

34 Reentry Housing After Jail or Prison

by David S. Kirk

Rental Assistance and a

Fresh Start to Spur Criminal

Desistance: Evidence From a

Pilot Housing Experiment

David S. Kirk

University of Oxford

The Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National

Institutes of Health supported the research reported in this article under award number R03HD081515, as

did the Leverhulme Trust through the Leverhulme Centre for Demographic Science. The content is solely the

responsibility of the author and does not necessarily represent the official views of the National Institutes

of Health or the Leverhulme Trust. Similarly, the findings in this article do not reflect the views of the

Maryland Department of Public Safety and Correctional Services or its constituent agencies.

Abstract

Much of the research literature on prisoner reentry focuses on the importance of individual determinants

of reintegration of the formerly incarcerated back into society, such as education, job training, and

addiction treatment. Less studied are the consequences of housing and neighborhood context. Still,

research shows that the formerly incarcerated tend to have unstable residential patterns, and their

places of residence are often in close proximity to the same locations where they got into trouble with

the law in the past. This article argues that stable housing, particularly in an environment that provides

an opportunity for a fresh start and a separation from past criminal associates, is a crucial foundation

for successful prisoner reentry. Evidence in support of this argument is garnered from a pilot housing

mobility program called the Maryland Opportunities through Vouchers Experiment, or MOVE, which

aimed at lessening the risk of recidivism by using housing subsidies to provide participants with housing

in geographic areas some distance from where they lived in the past. This article reports on the outcome

of the pilot, including an assessment of the likelihood of rearrest and a qualitative comparison of the post-

release experiences of treatment and control group participants.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 35

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Kirk

Introduction

I send this letter in the sincere hope that I can obtain your assistance. I am scheduled to

be released back into the band of society and I am without a place to stay. I do not desire

to return to the area that I lived in the city of Baltimore prior to my incarceration, as this

would cause me to jeopardize my probation. I would appreciate your assistance in obtaining

a housing voucher in the Prince George’s County area, as this would afford me a fresh start

toward becoming a good member of the community.

—Charles Dupree

Letter to the Maryland Opportunities through Vouchers

Experiment (MOVE), September 15, 2015

Mr. Dupree’s (a pseudonym) letter was one of more than 100 the author received from people

incarcerated in state prisons in Maryland requesting participation in MOVE, an experimental

housing mobility program for the formerly incarcerated that the author piloted in Maryland in

2015 and 2016. As a pilot program, the recruitment and outreach efforts were minimal, consisting

solely of contacting the four participating prisons in advance of periodic recruitment visits to

the institutions to inquire which individuals were to be released from prison during the next 90

to 180 days. The program was not advertised, yet word of mouth about the program quickly

spread. Although the program was implemented in four male institutions, the author received

correspondence from individuals housed in prisons throughout the state, including occasional

letters from incarcerated women. The steady stream of letters, received even after the pilot had

ended, speaks to the intense demand for housing among the incarcerated and formerly incarcerated.

Mr. Dupree’s letter touches on two challenges people exiting prison face. First, many individuals do

not have a stable place to live. For instance, Harding, Morenoff, and Herbert (2013) observed that

parolees in Michigan move an estimated 2.6 times per year for the median parolee. Second, most

people exiting prison end up living within a few miles of where they resided prior to incarceration,

effectively returning to the very environments where they got into trouble with the law in the past

(Glaser, 1969; Harding, Morenoff, and Herbert, 2013; Kirk, 2020). Yet many people exiting prison

recognize that returning to familiar settings can be detrimental to their rehabilitation, often leading

to a return to criminal behavior. Many express a desire to move to different residential locations

to avoid the temptations and troubles associated with familiar neighborhood settings (Visher and

Farrell, 2005).

The MOVE program was designed to address these twin challenges by offering subsidized housing

to individuals newly released from prison in locations distant from where they resided prior to

imprisonment. This article reports on a randomized pilot of MOVE, including a description of the

program design, an assessment of rearrest outcomes, and an analysis of participants’ experiences

post-incarceration via repeated qualitative interviews. Before proceeding with a discussion of the

program, the article first provides further background on the theoretical and empirical rationale for

the development of MOVE.

36 Reentry Housing After Jail or Prison

Rental Assistance and a Fresh Start to Spur Criminal

Desistance: Evidence From a Pilot Housing Experiment

Background

The era of mass incarceration since the 1970s in the United States has been characterized by

as astonishing likelihood that individuals will serve time in prison at some point in their lives,

particularly undereducated males. Roughly 1 in every 4 African-American men in the United States

born in the late 1970s served time in prison by age 34, as well as 1 in 8 Latino males and 1 in 20

White males (Western and Pettit, 2010). For African-American male high school dropouts, the

figure balloons to 68 percent. In terms of raw counts, approximately 1.2 million individuals were

serving time in state and federal prisons and another 636,000 in local jails in 2021, figures that

actually represent substantial declines relative to the onset of the COVID-19 pandemic (Carson,

2022; Zeng, 2022).

A rapid increase in the number of individuals exiting prison each year coincided with the rise

of hyperincarceration. In 1979, roughly 166,000 prisoners were released from state and federal

prisons back into the community. By 2009, that number had reached 740,000, before declining to

614,000 a decade later (BJS, 2023).

The sheer scale of individuals exiting prison each year in the United States strains a deeply

oversubscribed system of affordable housing, resulting in the twin challenges addressed at the

outset of the article: (1) High levels of housing instability and (2) a return to familiar environments

given limited access to housing elsewhere, even if doing so exposes individuals to the criminal

peers and criminal opportunities that could undermine one’s attempt at desistance. On the first

challenge, numerous studies have found that housing instability is common among formerly

incarcerated individuals, with evidence of variation by race. For instance, Makarios, Steiner, and

Travis (2010) found that Ohio parolees live, on average, in two different residences in the first year

after release from prison, and 30 percent lived in three or more locations. Drawing on data from

the Fragile Families data collection, Geller and Curtis (2011) found that 31 percent of sampled

urban fathers with a history of incarceration experienced housing instability, such as eviction or

time in a shelter, versus 14 percent of otherwise similar fathers without an incarceration history.

Herbert, Morenoff, and Harding (2015) tracked the residential situations of parolees in Michigan,

finding that most periods of residence for the parolees lasted only a few months, with one-half

lasting at most 8 weeks. Using nationally representative data from the 1997 National Longitudinal

Survey of Youth (NLSY), Warner (2015) found that only for formerly incarcerated African-

American individuals is residential instability significantly greater post-imprisonment relative to

their preprison experience. Also drawing on NLSY data, Bryan (2022) examined whether housing

instability among the population of criminally convicted individuals is attributable to their felon

status, even in the absence of incarceration. She concluded that a felony conviction, independent

of whether it led to a punishment of incarceration, induces housing instability, and that the

association between incarceration and housing stability observed in many studies is likely to be

driven by individuals’ felony conviction status rather than the actual experience of incarceration.

With respect to the second challenge related to residential location, research focusing on Louisiana

found that normally about 75 percent of people exiting from prison originally from the New

Orleans metropolitan area return to the same parish (that is, county) where they resided prior to

imprisonment (Kirk, 2020). In research in Michigan, Harding, Morenoff, and Herbert (2013) found

Cityscape 37

Kirk

that roughly one-third of newly released prisoners resided within 0.5 miles of their preprison place

of residence, and that 60 percent resided within 5 miles of their preprison residence. In a study

of individuals released from federal prison, Glaser (1969) found that 45 percent returned to the

same neighborhood where they resided prior to incarceration, and another 38 percent returned

to a different neighborhood in the same metropolitan area where they lived in the past. Only 17

percent of former federal prisoners in his sample moved to different metropolitan areas than where

they had resided immediately prior to incarceration, but most of these individuals had at some

point in their past resided in the metropolitan area where they returned, even if it was not the

location where they were residing immediately prior to incarceration. Hence, people exiting prison

do not necessarily return to the exact houses and neighborhoods where they resided prior to

incarceration, but it is very common for them to return to familiar environments in close proximity

to where they resided in the past.

Thus far, this article has described the twin challenges of housing instability and residence in the

familiar environments of one’s past. Of what consequence are these challenges for outcomes such

as recidivism? A growing body of research has addressed this very question, finding that residential

instability increases the likelihood of recidivism, as do periods of homelessness (Makarios, Steiner,

and Travis, 2010; Meredith, Speir, and Johnson, 2007; Metraux and Culhane, 2004; Steiner,

Makarios, and Travis, 2015). Hence, it reasons that financial housing assistance could lower the

likelihood of recidivism by facilitating residential stability, yet relatively little research has rigorously

tested this question (see Hamilton, Kigerl, and Hays, 2015; Lutze, Rosky, and Hamilton, 2013).

Similarly, recent research suggests that residential change, away from the geographic locations of

one’s past, can lead to a reduction in recidivism. For instance, in prior work, the author used the

neighborhood destruction in New Orleans following Hurricane Katrina as a natural experiment to

investigate the potential effect of residential change on reincarceration (Kirk, 2020, 2012, 2009).

He found that individuals who moved away from their former parishes were 13 percentage points

less likely to be reincarcerated through 8 years post-release.

Other studies similarly find some benefit to moving, at least when the distance is substantial

enough for a fresh start. For instance, in a study of Chicago-area youths, Sharkey and Sampson

(2010) found that among adolescents who moved to different neighborhoods within Chicago

(that is, a short distance) that their likelihood of violent offending increased. However, moving

outside of Chicago reduced violent behavior. Similarly, in a study of delinquency in London,

Osborn (1980) found that those who moved away from London were significantly less likely to be

reconvicted of a crime than individuals who stayed in London.

Why exactly might moving to a new city or county lead to a change in someone’s behavior? Laub

and Sampson’s (2003) life-course theory provides a theoretical rationale (see also Sampson and Laub,

1993). Among the notable findings from their multidecade project is that individuals desist from

criminal activity in response to structurally induced turning points, such as work and marriage.

Turning points provide opportunities for individuals to separate from the people and situations

that facilitated prior criminal behavior. Moving to a new location, if of sufficient distance away from

familiar locations of one’s past, may provide helpful separation from nefarious temptations and

criminal opportunities and an opportunity to change one’s routine activities (Kirk, 2018).

38 Reentry Housing After Jail or Prison

Rental Assistance and a Fresh Start to Spur Criminal

Desistance: Evidence From a Pilot Housing Experiment

This logic of turning points is intuitively appealing. Indeed, Mr. Dupree, in the opening quote of

the article, was implicitly using turning-point logic in describing his hopes for finding housing

away from Baltimore, the site of his legal troubles. However, what are the barriers to securing stable

housing in a new environment for people exiting prison?

Barriers to Housing

A fundamental challenge to finding secure and stable housing is a lack of income, which is a

function of the relatively poor employment prospects of formerly incarcerated individuals and

restrictions on welfare benefits to individuals with criminal convictions (Geller and Curtis, 2011;

Harding et al., 2014; Herbert, Morenoff, and Harding, 2015; Kirk, 2019). A related challenge

is the collateral consequences of punishment, which refer to the “legal and regulatory sanctions

and restrictions that limit or prohibit people with criminal records from accessing employment,

occupational licensing, housing, voting, education, and other opportunities” (ABA, 2013; see also

Kirk and Wakefield, 2018). For instance, individuals convicted of certain felonies may be banned,

some permanently, from receiving public housing benefits or vouchers.

Stigma and both legal and illegal discrimination are also barriers to housing. Easy access to

criminal background information makes it readily possible for landlords, property owners, and

lenders to use such information when evaluating prospective tenants and customers (Evans and

Porter, 2015; Lageson, 2020). Moreover, because of the racial and ethnic disproportionality in

the use of criminal justice punishments, the use of criminal background information in housing

decisions has civil rights implications in accordance with the Fair Housing Act (HUD, 2016, 2015).

Another barrier to housing for the formerly incarcerated is the lack of affordable housing. The

Joint Center for Housing Studies of Harvard University (2022) reported that 30 percent of all

U.S. households are cost burdened, meaning they pay more than 30 percent of their incomes on

housing. Fourteen percent of households spend more than 50 percent of their income on housing.

This substantial rate of cost-burdened households is a function of many factors, including soaring

costs of housing in the rental and owner-occupied markets and inflation in recent years that has far

exceeded average wage growth. Additionally, rental vacancy rates in the apartment stock reached

an all-time low of 4.8 percent in the third quarter of 2021, with rates just slightly higher in 2022

(JCHS, 2022). When vacancy rates are low, individuals with criminal records, many of whom also

have bad credit histories, are competing with others for relatively few affordable housing options.

Federal assistance to offset the cost burden of housing or the lack of supply of affordable units is

not nearly adequate to cover need. Indeed, Fischer and Sard (2017) found that only one-fourth of

families who are eligible for federal rental assistance actually receive it.

In sum, hyperincarceration and its collateral consequences, in combination with stagnant

investment by the federal government in assisted housing and the troubling state of affordable

housing development in the United States, have created and perpetuated a housing crisis for

individuals with criminal records, with consequences for public safety. To follow, this article presents

a model for how to provide a foundation for desistance from crime among formerly incarcerated

individuals through the provision of housing assistance. The Discussion section later addresses

possibilities for funding the subsidies without a net increase in overall government spending.

Cityscape 39

Kirk

MOVE Background and Design

MOVE is a voluntary program designed to incentivize and facilitate opportunities for housing and

residential moves among people exiting prison by providing participants a housing subsidy and

housing search assistance. The housing subsidies were geographically restricted, such that they could

only be used in geographic areas different from where the participant resided prior to incarceration.

Given the two forms of intervention core to the MOVE model—housing subsidies and residential

relocation—optimally, an evaluation of the program would include at least three experimental

groups (randomly assigned): (1) A group of movers with a housing subsidy, (2) a group of stayers

with a housing subsidy, and (3) a group of stayers without a housing subsidy. A comparison of the

first two groups would yield an estimate of the effect of moving to a new residential location. A

comparison of the latter two groups would yield an estimate of the effect of the offer of a housing

subsidy. Although such a design would be advantageous for a full-scale implementation and

evaluation, resource constraints prohibited the implementation of a three-group evaluation during

the pilot phase of the program.

Two different designs of the program were implemented during the pilot, as exhibit 1 shows. In

each design, the treatment group received a housing subsidy equivalent to the U.S. Department of

Housing and Urban Development’s (HUD) fair market rent, with the aforementioned geographic

restriction that it could only be used in a designated geographic area. Specifically, individuals

originally from the city of Baltimore were offered a subsidy for housing in Prince George’s (PG)

County, Maryland, which is adjacent to Washington, D.C., roughly 40 miles from Baltimore.

Individuals who resided in PG County prior to their time in prison were offered a housing subsidy

for use only in the city of Baltimore. The impetus behind the multilocation design was to guarantee

that each jurisdiction would send and receive the same number of individuals, with no net increase

in total parolees in any area (that is, the number of formerly incarcerated people moving to

Baltimore from PG County would be the same as the number leaving Baltimore).

Exhibit 1

Maryland Opportunities Through Vouchers Experiment Research Designs

Variable

Design A

Design B

Treatment group

6-month housing subsidy to

6-month housing subsidy to

move to a new jurisdiction

move to a new jurisdiction

Control group

6-month housing subsidy in

No housing subsidy

home jurisdiction

Nature of intervention (i.e., difference

Moving to a new city

Moving to a new city and

between treatment and control)

receiving free housing

Sample size

6 Treatment, 9 Control

8 Treatment, 7 Control

Recruitment period

2015

2016

The rationale for specifying a 40-mile distance between pre and post-prison locations for the

treatment group relates to the importance of distance from one’s past for facilitating a true

change in circumstances. If an individual moved only a short distance, then he or she could

easily maintain ties with criminal peers even after a move and still have ready access to criminal

40 Reentry Housing After Jail or Prison

Rental Assistance and a Fresh Start to Spur Criminal

Desistance: Evidence From a Pilot Housing Experiment

opportunities back in the old neighborhood (Kirk, 2018). Indeed, long-term findings from HUD’s

Moving to Opportunity (MTO) program, which found no statistically significant difference in the

likelihood of arrest or incarceration between experimental group movers and the control group,

may be explained by the fact that many, if not most, experimental families moved a relatively

short distance from their origin neighborhoods (Rosenbaum and Zuberi, 2010; Sampson, 2008;

Sanbonmatsu et al., 2011). Moving to an adjacent neighborhood is likely too short a distance to

expect a real change in circumstances or to sever ties with a criminogenic social network. In fact,

in their evaluation of MTO, Briggs, Popkin, and Goering (2010: 18) summarized the problem well:

“Changing the social relations of participants was not a primary aim of the MTO experiment [...]

But many of the hoped-for positive outcomes anticipated changes in exposure to particular kinds

of peers, adult role models, and more successful neighbors.”

The implication of the MTO studies and research on the Gautreaux housing mobility program and

more recent research on forced displacement from public housing demolitions is that residential

relocation may foster desistance from crime, but there must be enough distance between the

origin and destination locations to provide a true catalyst for change (Chyn, 2018; Keels, 2008;

Rosenbaum and Zuberi, 2010). Forty miles is arguably sufficient distance so that an individual

is not regularly immersed back in a preprison neighborhood environment, yet not so far that

prosocial ties could not be maintained.

Within Design A of the pilot, the control group received a housing subsidy of equivalent value to

the treatment group, and the subsidy could be used in the location where they resided immediately

prior to incarceration. For instance, individuals who resided in the city of Baltimore prior to

incarceration were offered a housing subsidy that could be used in Baltimore.

In Design B, however, the control group did not receive a housing subsidy. With this design, the

control group represented the status quo in terms of the typical circumstances that people exiting

prison confront. To deliver some benefit to all participants in Design B of the program, both treatment

and control group participants received a $100 gift card, which they could use for necessities.

Housing Subsidies

Whereas the housing subsidies in the MOVE program were privately funded by a foundation grant,

the program was developed to resemble aspects of HUD’s Housing Choice Voucher program (that

is, Section 8) so that the housing subsidies were to be used in the private rental market. Another

motivation was to design the program similarly enough to existing housing assistance programs so

that public housing authorities could replicate the model with relative ease if they wished to do so.

Given the importance of housing search assistance and landlord outreach for the placement of

lower-income groups into decent housing, MOVE program staff facilitated residence in private-

market dwellings by providing housing location assistance to participants (Bergman et al., 2020).

Whereas participating landlords knew that the program guaranteed payment of the rent for a

defined period, participants signed their own leases. A key difference with the Housing Choice

Voucher program is that MOVE vouchers were time limited because of finite resources for the pilot.

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Kirk

Determining the duration of subsidies for the pilot included balancing available funding resources

against research ethics and providing enough incentive for individuals exiting prison to participate

in the program and recruiting a sufficient number of prospective landlords. In terms of ethics, as the

preceding sections describe, research reveals a positive association between residential instability and

the likelihood of reoffending and recidivism, so MOVE sought to avoid contributing to residential

instability and, therefore recidivism, which might have happened if the program had provided only

very temporary housing (for example, 3 months or less; Makarios, Steiner, and Travis, 2010; Steiner,

Makarios, and Travis, 2015; Vogel, Porter, and McCuddy, 2017). Moreover, in outreach efforts to

prospective landlords and property owners in the design phase of the pilot, it became apparent that

less than 6 months of a housing subsidy was often going to be an insufficient incentive for landlords

to rent to participants, given their interest in signing at least a 6-month lease with participants (and

ideally a 1-year lease). Prospective landlords were concerned that after the rent subsidy from the

MOVE program ended, participants would be unable to pay rent. Hence, for the design MOVE

ultimately implemented, the program provided 6 months of free housing up to the HUD fair market

rent rate for a one-bedroom dwelling for a geographic area (in 2015, the rate for a one-bedroom

dwelling in Baltimore was $985 per month and $1,230 per month in PG County).1

Given that a key element of MOVE for the treatment group was residence in a different location

relative to where participants lived in the past, it is a legitimate question whether 6 months is

sufficient time away from an old environment to produce a turning point in the life-course of

crime. The program did not stipulate that participants needed to remain in their new locations after

the subsidy ended, although there may be benefits in doing so. Even if criminal opportunities and

access to drugs are nearly ubiquitous with little effort, residential change to a new environment

may provide a short-term disruption to an individual’s access to drugs and criminal opportunities,

thereby reducing the likelihood of recidivism. Indeed, Osgood et al. (1996) argued that motivation

for crime is situational. An individual’s persistence in criminal activity is a function of the

situational contingencies that provide opportunities for crime in the absence of social controls.

Living in a new environment, even for only 6 months, may promote desistance from crime by

disrupting access to illicit temptations and criminal opportunities in the crucial period right after

someone exits prison.

Whereas criminological research generally finds that families can be a positive influence and a source

of support to the formerly incarcerated, family reunification is not universally beneficial (Harding,

Morenoff, and Wyse, 2019; Hassan, Kirk, and Andersen, 2022; La Vigne, Visher, and Castro, 2004;

Leverentz, 2014; Visher, La Vigne, and Travis, 2004). Fontaine and Biess (2012: 4) explain—

Considering that some share of formerly incarcerated persons’ family members also

struggle with substance abuse issues, their own criminal histories, limited incomes, and

other issues, housing with family might be a less-than-ideal housing option for many

individuals recently released.

Nevertheless, recognizing the potential benefit of living with family members (and the potential

negative consequences of living in isolation), MOVE participants were not required to live alone.

1 The program also provided a 1-month security deposit if necessary. All subsidy payments were provided directly to landlords, as opposed to a cash transfer to participating individuals.

42 Reentry Housing After Jail or Prison

Rental Assistance and a Fresh Start to Spur Criminal

Desistance: Evidence From a Pilot Housing Experiment

They had the option and choice to reside with other individuals, including immediate family

members. The program also offered a supplemental $100 per month in housing subsidy for

individuals residing with dependent children to help offset the cost of renting a larger dwelling.

As noted, in addition to the housing subsidy, participants received housing location assistance from

a case manager employed by Quadel Consulting, a housing specialist firm with broad experience

working with public housing authorities to administer housing voucher programs. Quadel also

offered post-release case management services to participants to help with transition back into

the community. One reason to do so was to be aware of any effects of social isolation among the

individuals who moved to an unfamiliar area.

Participant Eligibility

For the pilot, MOVE specifically avoided “creaming” the sample to only those individuals most

motivated and ready to change their behavior following incarceration. Furthermore, eligibility was

not restricted to people with relatively short incarceration histories to the exclusion of individuals

with repeated stints of incarceration. Although undoubtedly some individuals might benefit more

from the housing and a new environment, the author’s prior work in Louisiana revealed that both

first-time releases and individuals with multiple incarcerations benefit from residential relocation,

as do individuals with a history of addiction and those without (Kirk, 2020). Therefore, MOVE

attempted to limit any restrictions on the pilot sample. That said, should a housing authority

or other housing provider seek to replicate MOVE, it may be advantageous to use modern risk

prediction tools available via data science to target finite resources at those individuals most highly

predicted to recidivate in the absence of housing or in the absence of a move to a new location.

In terms of the few eligibility exceptions, because of the two-site design and the goal of producing

no net change in the number of parolees in Baltimore or PG County, participants must have been

residents of one of those locations immediately prior to incarceration. MOVE excluded those

individuals from participation who had a detainer in another jurisdiction that would prevent their

actual release from incarceration (for example, if they were required to serve a sentence in another

state or the District of Columbia). Per an agreement with the Maryland Department of Public Safety

and Correctional Services (DPSCS), felony sex offenders and individuals screened as very high risk

of violent recidivism by Maryland’s Violence Prevention Initiative forecasting system were ineligible

for the program. Finally, the pilot only included men. MOVE excluded women from the pilot

phase of the project because of the combination of the small sample size and the relatively small

proportion of women among the prison population in Maryland.

Most prisoners in Maryland are released either through a decision by the parole board or

mandatory release following the diminution of their sentence through good time credit (The Pew

Charitable Trusts, 2015). Although the mandatory release mechanism is not subject to the approval

of a parole board, people exiting prison via mandatory release are still supervised by a parole officer

in the community until the expiration of their full criminal sentence. For logistical reasons, the

MOVE pilot was limited to mandatory releases. It is challenging to plan for the timing of the release

of an individual when a parole board makes the decision. It is useful to know when an individual

will be released from prison, so that information can be communicated to a potential landlord to

Cityscape 43

Kirk

initiate the apartment search process prior to the individual’s release. Therefore, the pilot focused

on individuals released via mandatory release.

As a final comment about eligibility, with Design A of the pilot, the age range of participants was

not restricted, and participants ranged in age from 22 to 53, including 6 (out of the 15 total)

participants over 45 and only 2 in their 20s. Many participants were likely well past their crime-

prone years. Although a housing subsidy could surely be beneficial, it might be unnecessary for

curtailing recidivism if most participants were already well past the ages of typical criminal activity.

Accordingly, Design B restricted the age range of participants from 18 to 45 to target participants

most at risk of recidivism.

Recruitment, Sample, and Take-Up Rate

For the pilot study, MOVE recruited 30 participants from four Maryland prisons, three in central

Maryland and one in southern Maryland. The four particular prisons were chosen because they

admit and release a relatively large number of individuals from the city of Baltimore and PG County.

In 2015 MOVE recruited 15 new participants into Design A of the program out of 17 invited

to participate (88 percent enrollment). Program staff randomly assigned six participants to the

treatment group and nine to the control group. As a reminder, with Design A, the treatment group

received a housing subsidy for use only in a jurisdiction that is more than 40 miles from their

place of residence prior to prison, and the control group received a housing subsidy back in the

same jurisdiction (the city of Baltimore or PG County) where they resided prior to incarceration.

In terms of take-up, three of the six treatment group participants (50 percent) and all nine of

the control group were placed in housing. The 50 percent take-up rate is lower compared with

the performance of the Housing Choice Voucher program, which has an estimated take-up rate

of nearly 70 percent (Finkel and Buron, 2001). However, it is comparable with or higher than

recent experimental and quasi-experimental programs designed to use housing vouchers to

foster residential mobility among lower-income households (Rubinowitz and Rosenbaum, 2000;

Sanbonmatsu et al., 2011).

As noted, Design B sought to compare the treatment condition (that is, housing subsidy plus

moving) with the status quo (that is, without housing subsidy). MOVE also hoped to increase the

take-up rate into housing relative to Design A. Whereas MOVE staff encouraged people to enroll

only if they were willing to reside in either the city of Baltimore or PG County, it was possible

for a prospective Design A participant to determine that he could sign up in the hopes of getting

6 months of free housing in his preferred location, then just drop out without penalty if he did

not get his preference. With Design B, however, which provided no possibility of obtaining free

housing back in a home jurisdiction, the program would presumably appeal only to individuals

who would at least consider moving to a new area.

For Design B, 15 pilot participants were recruited in 2016. Eight individuals were randomly

assigned to the treatment group and seven to the control group, with a 50 percent take-up rate

again in the treatment group. Accordingly, it might be concluded that only about one-half of

treatment group participants can typically be placed in housing. That said, the 50 percent take-up

44 Reentry Housing After Jail or Prison

Rental Assistance and a Fresh Start to Spur Criminal

Desistance: Evidence From a Pilot Housing Experiment

rate could be an anomaly of the small pilot sample, and it should, therefore, be investigated further

during a full implementation of the program.

Qualitative Interviews

MOVE staff completed up to four interviews with the final nine participants recruited into the

program, with one interview prior to release, one within 2 weeks after release from prison, and two

more at approximately 3 and 6 months post-release. Six participants completed all four interviews,

two participants completed three interviews, and one participant completed two interviews.

The interviews intended to understand the reentry experiences of participants in the program.

Specifically, through the interviews, MOVE staff sought to understand whether moving facilitated

any change in behavior and, if so, to identify mediating mechanisms that explain why residential

change may lead to a reduction in the probability of recidivism. These mechanisms may include a

change in routine activities, a change in peer associations, a reduction in criminal opportunities,

exposure to more opportunities for employment or social services, identity change, and a decrease

in the level of scrutiny by the police. Interviews also sought to examine the role of social supports

and the potential consequences of social isolation in a new environment.

Pilot Results

Whereas the main purpose of the Maryland Opportunities through Vouchers Experiment pilot

was to verify the procedures and design of the program so that adjustments could be made in

advance of a full-scale implementation, this section provides a descriptive summary of rearrest

rates among sample participants.2 Rearrest is defined as an arrest anywhere in the State of Maryland

in the 12 months immediately following an individual’s release from incarceration. MOVE staff

obtained rearrest data from the DPSCS and double-checked these data with online records from

the Maryland Judiciary Case Search portal.

For Design A, in which both the treatment and control groups received 6 months of housing

and the housing location distinguished the groups, none of the participants in either group were

rearrested within the 1-year followup period. The proportion rearrested in both groups (0 percent)

is the same whether comparing the full sample (that is, the so-called “intention-to-treat” sample)

or the subset with those participants who were able to use the MOVE housing subsidy to take up

housing (that is, the three out of six individuals in the treatment group and all nine individuals

in the control group). As a point of comparison, the Bureau of Justice Statistics reports that,

nationally, an average of 37 percent of people released from prison are arrested within 1 year of

their release, and in Maryland, 20 percent are reincarcerated within a year (DPSCS, 2022; Durose

and Antenangeli, 2021). Hence, the 0-percent rearrest rate is far from the norm.

Findings could indicate that free housing, offered to both groups in Design A, is a key contributor to

reducing the likelihood of recidivism, regardless of the location of that housing. An alternative (or

additional) explanation is that because the sample of participants included several individuals well

past the typical crime-committing age, the sample may have been filled with some individuals who

2 The author is presently exploring opportunities and funding for continued implementation of MOVE, including in

locations beyond Maryland.

Cityscape 45

Kirk

would not have been rearrested regardless of whether they had received a housing subsidy. In fact,

the median age in the treatment group in Design A was 35.5 and 46 in the control group. Hence,

particularly among control group participants, age may partially explain why no one was rearrested.

For Design B results, recall that eligibility is capped at age 45. Exhibit 2 reveals that 25 percent of

the treatment group were rearrested within 1 year of release from prison versus 57 percent in the

control group. As in the results for Design A, the proportion rearrested in both groups is the same

whether comparing the full sample (that is, the intention-to-treat sample) or subset with those

participants who were able to use the MOVE housing subsidy (that is, the four of eight individuals

in the treatment group). Whereas the small sample size means that hypothesis testing lacks

statistical power, a one-tailed hypothesis test of the difference in proportions across groups using

the intention-to-treat sample yields a z-statistic equal to -1.268, with a p-value equal to 0.102,

which is slightly outside the typical range used to denote marginal significance.

Exhibit 2

One-Year Rearrest Rate in Maryland Opportunities Through Vouchers Experiment Design B

60%

57%

50%

ear 40%

ested in 1 Y 30%

25%

cent Rearr 20%

Per

10%

0%

Treatment (Housing, Movers)

Control (No Housing, Stayers)

Qualitative Findings

Although findings presented thus far are suggestive of the value of housing assistance for reducing

the likelihood of recidivism, it is less clear whether residence in a new environment provided

sufficient separation from one’s past and a new set of routine activities to facilitate desistance from

crime (Laub and Sampson, 2003). Still, qualitative interview data can be used to unpack the

results. Evidence from participant interviews reveals several reasons underlying the differences in

rearrest between treatment and control group members, including housing instability, destructive

relationships, and idle time.

46 Reentry Housing After Jail or Prison

Rental Assistance and a Fresh Start to Spur Criminal

Desistance: Evidence From a Pilot Housing Experiment

Housing Instability

Pierre, a PG County resident prior to incarceration and in his mid-30s as of his release, was a

control group member under Design B and, therefore, did not receive a housing subsidy. He

was open to the idea of residing somewhere besides PG County, and he attempted to enroll in a

different government-sponsored transitional housing program in Baltimore on release. However,

he stated that he was denied entry into the housing program because he was not from Baltimore.

These types of restrictions on housing program eligibility, presumably stipulated because of finite

resources available to house the formerly incarcerated, have the consequence of encouraging

individuals to return to the communities from which they came, even if it is detrimental to do so.

That is what Pierre did.

Pierre has a sister in PG County where he could stay as a last resort, although she has four children

and little room for him. As an alternative, he occasionally scraped together enough money for a

night at a motel but tended to stay with friends and acquaintances from PG County a few days at

a time. A highly unstable housing situation, embedded in his old surroundings, characterized his

limited time out of prison.

Perhaps as a result of his housing instability, Pierre struggled to find work. Without work, he

did not have the income to look for stable housing. This situation is the sort of catch-22 that

characterizes so many individuals in the period soon after prison release. Because he did not have

a place of his own, he felt awkward just hanging around at whoever’s house where he was staying.

Accordingly, he would find something to do out of the house each day. Sometimes that meant

going to the library so that he could access the internet and look for work. Other times, it meant

hanging out with friends and acquaintances. None of these individuals were big-time criminal

offenders, but the regular drinking and minor drug use caught up to him. He was rearrested

approximately 6 weeks after his release from prison on three drug charges.

Destructive Relationships

Eddie’s experience, a control group participant in his early 30s at the time of his release, resembles

that of Pierre. Eddie resided the bulk of his life in a town just outside the Northeast section of the

District of Columbia. He exited prison after serving time on a robbery conviction. On the day of

his release, his actual exit was delayed by several hours, and by the time he got out, his mother

was busy at her church, so he went to his father’s house (his father and his mother had long been

divorced), where he resided prior to incarceration. His father lives in the area that had been a

frequent site of his criminal activity.

Eddie’s release from prison was an occasion for an impromptu party at his father’s house. Eddie

explained, “They [my father, stepmom, and their friends] was just partying. I drank a few, but I drink

one every hour or so, and then I drink water. I’m not trying to get drunk. They’re still doing that.”

Hence, within a couple of hours of his release, Eddie was back in his old neighborhood, reluctantly at

a party hosted by his alcoholic father with many of Eddie’s acquaintances from the neighborhood.

That party at his father’s was the first of several occasions that reconnected Eddie to his old

neighborhood and network. At his interview less than 2 weeks after release, he noted that his old

Cityscape 47

Kirk

neighborhood peers were already tempting him with drugs, both for use and to sell, which he

initially tried to resist. He explained to his friends that he was on parole and subject to urinalysis

twice a week, and thus needed to abstain from the drugs offered to him, such as cocaine. Perhaps

it is telling that rather than communicate to his peers that he was abstaining from drug use because

of a choice to leave that behavior in the past, he mentioned on more than one occasion in the

interview that his abstinence was out of necessity to avoid getting caught through the urinalysis.

At his interview roughly 2 weeks post-release, Eddie explained that he was trying to spend as

much time as he could with family, particularly his former girlfriend and their 5-year-old son. He

described how he did not want to let his son down and did not want him to follow the same path

to criminality that he did. Eddie also said that he and his son’s mother had begun to rekindle their

relationship. Still, during the interview, he also recounted how they had already had a couple of

arguments, because he was hanging out with other people rather than her and their son. Before

too long, repeatedly confronted with temptations and opportunities to return to his old life, Eddie

was rearrested. During the next couple of years, he continued to get in trouble with the law, with

several relatively low-level arrests and convictions. These arrests, combined with his criminal

history and parole revocations, meant that he continued to be in and out of prison.

Structured Versus Idle Time

Pierre’s experience after his release reveals the ways in which housing instability may contribute

to poor employment prospects and, therefore, a lot of idle time. And Eddie’s experience reveals

how easy it can be to return to old relationships and the routines, sometimes criminal, embedded

in those relationships. In contrast, Randy’s post-incarceration situation reveals that structured

time through employment, facilitated by having a stable place to reside, is important for avoiding

trouble with the law.

Randy, a 24-year-old treatment group member, lived in PG County prior to incarceration but

moved to Baltimore through the MOVE program, with the 6 months of free housing. In contrast to

Pierre, whose pre-incarceration friends supported him with places to stay after his release but also

served as companions as he returned to drinking and drug use, Randy avoided the social ties of his

past once he was released. He explained—

I cut off almost everybody other than family. The friends that I did have, we weren’t really

doing what we were supposed to be doing [in the past]. I don’t want to go back to old

relationships. I don’t want to go back to old friendships. I just think it was unhealthy

then. I need to focus more on other things now.

In contrast to Pierre, Randy’s ability to cut off his negative peers was facilitated by the fact that

he had a stable place to live post-release. On the very few occasions he reconnected with his old

friends, one of them offered to get him into drug distribution. Randy resisted, which is perhaps

easier to do, because he was not immersed in his old environment in PG County.

Randy’s post-release neighborhood in Baltimore was not crime free by any means, but he says he

was able to keep to himself rather than “hanging in the streets,” as he did in his old neighborhood

in PG County. By his 3-month interview, he was working at a fast-food restaurant, and by his

48 Reentry Housing After Jail or Prison

Rental Assistance and a Fresh Start to Spur Criminal

Desistance: Evidence From a Pilot Housing Experiment

6-month interview he had added another job, as a stocking clerk at a discount store. Neither were

well-paying jobs, but by working a lot of hours, Randy was trying to save money to continue

paying rent after his MOVE housing subsidy ended. Hence, it appears that a very structured life

with little idleness facilitated Randy’s transition post-incarceration.

When asked about the potential benefits of receiving housing through the MOVE program, Randy

replied simply, “it means that there is one less thing I need to worry about.” Indeed, that is an

intent of the housing first model of social service delivery. Without the worry of securing housing,

Randy could then focus on finding gainful employment.

Discussion

On exiting prison, formerly incarcerated individuals tend to lack access to safe and stable housing

and, partly as a result, return in proximity to the neighborhoods where they resided before

incarceration for opportunities to shelter with family, friends, or acquaintances. Prior research has

shown that housing instability and returning to familiar residential environments contribute to

the cycle of recidivism (for example, Kirk, 2009; Steiner, Makarios, and Travis, 2015). MOVE was

designed to address these twin challenges.

This article described the design of the MOVE program and its pilot evaluation and provided a

preliminary assessment of its feasibility and efficacy. With the caveat that findings are based on a

small sample and firm inferences are necessarily premature, the descriptive results in this study,

including those in exhibit 2, preliminarily suggest that provision of 6 months of housing may lower

the likelihood of recidivism, and that the residential moves to a new city or county facilitated by

that housing may also reduce the likelihood of recidivism. Qualitative evidence presented in this

article suggests that housing subsidies allowed individuals to focus attention on the many other

challenges they face when exiting prison, including a quicker transition to employment. Moreover,

when housing was away from previous residential locations, it helped MOVE participants avoid the

routines and temptations associated with their old environments and peer networks.

Ultimately, if the hypotheses underlying the MOVE program are subsequently supported through

full-scale implementations, then strategies to increase access to housing assistance for people

exiting prison may be worth pursuing. However, given that only about one-fourth of families who

are eligible for federal rental assistance presently receive it (Fischer and Sard, 2017), how would it

be possible to fund housing for people coming out of prison?

One idea for funding rental assistance for the formerly incarcerated could be a version of justice

reinvestment—for example, reinvest the criminal justice savings from reduced use of incarceration

into housing subsidies for formerly incarcerated individuals for some defined time period

depending on resource availability. To maximize the benefit of finite resources, one possibility

is to use modern risk prediction tools via machine learning to target subsidies at individuals

who would be most at risk of recidivism in the absence of stable housing. It is worth noting that

the average yearly cost to imprison someone in Maryland in 2015, when MOVE launched, was

$44,601 (equivalent to $122 per day per imprisoned individual; Mai and Subramanian, 2017).

By comparison, recall that study participants housed in Baltimore received a subsidy of $985

Cityscape 49

Kirk

per month, and participants in PG County received $1,230 per month, for an average of slightly

more than $1,100 per month in cost (or $13,200 per year). Although a stable place to reside will

not resolve everyone’s criminal behavior, it is nevertheless far cheaper to house someone in the

community than behind bars.

It may be a hard political sell to dramatically increase funding for housing for people coming out

of prison, even if it can be shown to enhance public safety. Hence, an alternative idea is to make

a concerted effort to expand affordable housing opportunities for low-income households in

general, with some formerly incarcerated individuals benefitting from the greater availability of

affordable housing. One strategy for doing so is to expand the Low-Income Housing Tax Credit

(LIHTC) program. The LIHTC program, which HUD (2018) described as “the most important

resource for creating affordable housing in the United States today,” provides tax incentives

to real estate developers to encourage affordable housing development. Since its inception in

1986, approximately 50,000 LIHTC developments totaling more than 3 million housing units

have been placed in service. Research shows that the tax incentives have indeed worked to spur

affordable housing development in low-income neighborhoods (Baum-Snow and Marion, 2009).

Research also reveals that low-income housing development in impoverished neighborhoods is

associated with significant declines in violent crime (Freedman and Owens, 2011). Whereas a

variety of routes exist for expanding housing opportunities for people exiting prison, evidence

from the MOVE pilot and its intellectual precursors suggests that stable housing for the formerly

incarcerated in a location that facilitates a fresh start in life will help break the cycle of recidivism

(Kirk, 2020).

Acknowledgments

The author relied heavily on the dedicated employees of the Maryland Department of Public Safety

and Correctional Services to implement the MOVE program and is grateful for their support. The

author would like to thank Geoff Barnes, Jordan Hyatt, and Quadel Consulting for their assistance

with the implementation of the MOVE program. The author would also like to thank Brook

Kearley and Dana Segev for their assistance with collecting and initial analysis, respectively, of the

qualitative interview data presented in this article.

Author

David S. Kirk is a professor in the Department of Sociology and Professorial Fellow of Nuffield

College, University of Oxford.

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Cityscape 55

56 Reentry Housing After Jail or Prison

by Sarah B. Hunter and Stephanie Mercier

Addressing Homelessness Among

People With Justice Involvement: Los

Angeles County’s Just in Reach Pay

for Success Demonstration Project

Sarah B. Hunter

RAND Corporation

Stephanie Mercier

Corporation for Supportive Housing

Abstract

This article provides an overview of the implementation and outcomes of Los Angeles County’s first Pay

for Success (PFS) initiative. In 2017, the county initiated a program to provide long-term housing and

supportive services as an alternative to jail for individuals with a history of homelessness and chronic

health conditions using a PFS model. The PFS model included two primary investors, two funding

partners, an intermediary team that monitored the implementation and outcome metrics, and an

independent evaluation team responsible for evaluating the program’s overall effect. The county operated

the program with support from a housing rental assistance and navigation service provider and a team

of intensive case management service providers. Participants were enrolled during a 2-year period,

and outcomes following post-supportive housing placement were observed along with trends among a

comparison group of similar individuals who were not enrolled in the program. In this article, the authors

bring together information about program operations, the funding model and outcomes from the metric

monitoring, and the broader impact evaluation to highlight Los Angeles County’s experience of applying

PFS to supportive housing provision for people involved with the justice system.

Introduction

According to the latest estimates, more than 69,000 individuals in Los Angeles (LA) County

experience homelessness—defined as a lack of a fixed, regular, or adequate nighttime residence

(HUD, 2019). Moreover, the number of individuals living unsheltered in LA County, approximately

70 percent of the people experiencing homelessness, is larger than any other region in the United

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

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U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Hunter and Mercier

States. Given the scope of the issue in LA County, new approaches are needed to better address the

current trends.

Homelessness is often interrelated with incarceration, in that incarceration may increase the

likelihood of experiencing homelessness, and homelessness may increase the likelihood of

incarceration (Cusack and Montgomery, 2017). Formerly incarcerated individuals are nearly 10

times more likely to experience homelessness than the general population (Couloute, 2018).

According to a recent survey of people experiencing homelessness in LA, 64 percent have been

involved with the justice system (HPRI, 2020). Moreover, enforcement of quality-of-life ordinances

often results in the “revolving door” between homelessness and jail that reduces an individual’s

opportunity to become stably housed.

One potential pathway to reducing homelessness in LA County is to provide permanent supportive

housing (PSH) for people with chronic health conditions who are currently incarcerated in

the LA County jail. PSH combines affordable housing with supportive services to assist people

experiencing homelessness who often have one or more disabling conditions to become stably

housed. Another potential benefit of a supportive housing provision beyond housing stability is to

avoid the unnecessary use of costly public services, including jail stays. Few studies have examined

the use of this model for populations that are also incarcerated. Initial work in this area suggests

that connecting people who have had frequent jail and shelter stays with PSH may result in a

reduction of rearrests and reincarcerations (Aidala et al., 2014; Listwan, Hartman, and LaCourse,

2018; Thomas et al., 2020).

Los Angeles County Just in Reach Pay for Success Initiative

To provide support to individuals in LA County’s jail system who were experiencing homelessness

and chronic health conditions, the Corporation for Supportive Housing (CSH) designed Just in

Reach (JIR), a program that provided holistic supportive services starting when an individual

is still incarcerated (that is, the “in reach” component) coupled with PSH on post-release. The

program was first piloted in 2008, and a 2.0 version that yielded more robust mental health service

connections and greater involvement from the Los Angeles County Sheriff’s Department (LASD)

and PSH providers began in 2014. Preliminary data from the pilot showed a 20-percent reduction

in rearrest rates among participants who received PSH compared with participants who were not

housed (Lawrence et al., 2016).

In 2015, the LA County Board of Supervisors approved JIR as the county’s first Pay for Success

(PFS) initiative. As exhibit 1 outlines, PFS is a funding structure in which one or more private

sector entities invest in a public initiative. The government potentially pays back these funds to

the initial investors (plus interest) if specific performance metrics are achieved, such as improved

participant outcomes. Proponents of the PFS model suggest that it has the potential to create

win-win scenarios by which the government needs only to support the costs of the initiative if it

is successful, while granting private investors the opportunity to create meaningful social impact

(Lantz et al., 2016).

58 Reentry Housing After Jail or Prison

Addressing Homelessness Among People With Justice Involvement:

Los Angeles County’s Just in Reach Pay for Success Demonstration Project

Exhibit 1

Just in Reach Pay for Success Project Structure

CSH = Corporation for Supportive Housing. JIR = Just in Reach. LA = Los Angeles. PFS = Pay for Success.

Source: Adapted from GAO-15-646

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Project Participants and Services Delivered

As noted, the JIR PFS housing program operates within the larger Office of Diversion and Reentry

(ODR) portfolio of services that provide housing and related services to people involved with the

justice system in LA County. The program was modeled on the LA County Department of Health’s

“Housing for Health” program, an existing county program for frequent users of the health system.

The ODR supportive housing program was further tailored to address the unique needs of the ODR

population, including more clinical support, a lower case management load to participant ratio,

and communication with legal stakeholders. ODR program participants could be “enrolled” in the

JIR PFS initiative at the point they enter PSH.

Individuals are first identified as potential candidates for the program while incarcerated in the

LA County jail (exhibit 2). Those eligible for enrollment included individuals with a history of

homelessness and one or more diagnosable chronic behavioral or physical health condition, including

serious mental health disorder, substance-use disorder, developmental disability, post-traumatic stress

disorder, cognitive impairment resulting from a brain injury, or a chronic physical illness or disability.

Using a clinical court diversion approach, program staff determine clinical eligibility, and the court

system assesses the potential program candidate for legal suitability.1 Individuals who are found to be

both clinically eligible and legally suitable for diversion and who agree to enroll may be conditionally

released to ODR and are placed on probation supervision. ODR then assigns the individual to

an intensive case management services (ICMS) provider and to an interim housing or residential

care facility based on availability, preference, and fit to ensure that participants have a place to go

on release. Interim housing is provided in congregate settings with onsite services, including case

management, medication monitoring, meals, and other services (for example, laundry). Individuals

with mental health disorders also receive mental health services, typically in partnership with the LA

County Department of Mental Health’s (DMH) Full Service Partnership program.

Exhibit 2

Office of Diversion and Reentry Housing Services

ODR = Office of Diversion and Reentry.

Source: Holliday et al., 2021

1 More information about program criteria is available in Holliday et al. (2020).

60 Reentry Housing After Jail or Prison

Addressing Homelessness Among People With Justice Involvement:

Los Angeles County’s Just in Reach Pay for Success Demonstration Project

Next, ODR makes a plan for longer-term housing placement in collaboration with ICMS staff and

other key stakeholders, including interim housing providers, Full Service Partnership providers,

and legal stakeholders. On average, individuals spend around 9 months in interim housing before

they are placed in longer-term housing settings, including PSH, which may be provided in project-

based or scattered-site settings. Some individuals are not placed in PSH but receive a higher level

of care called “enriched residential services” that provides more intensive clinical support than PSH

settings. A more detailed overview of the ODR supportive housing program operation, not specific

to the JIR PFS initiative, is available (Holliday et al., 2021).

Brilliant Corners is contracted through the county to provide housing navigation services and

administer rental assistance through the county’s Flexible Housing Subsidy Pool. This pool

streamlines the housing process by centralizing core functions, including the housing search and

application process, housing unit inspections, and security payments. Then, once an individual is

housed, it provides rentals payments, tenancy support services to assist in housing stability, and

tenant aftercare, including housing damage mitigation services.

Each participant in the broader ODR housing initiative, inclusive of JIR PFS enrollees, collaborates

with their ICMS service provider to develop an individualized service plan. This plan outlines the

range of services available to help participants meet needs and goals they have identified. These

ongoing supportive services include, but are not limited to, connection to mental health and

substance-use treatment services, housing stabilization support, enrollment in income support or

other mainstream services benefits, mentoring, and other community support.

Program Implementation

Individuals in ODR’s supportive housing program who entered PSH between October 2017 and

September 2019 were eligible to be enrolled in the JIR PFS initiative. PFS projects require the

establishment of performance metrics that are tied to payments to investors. The performance

metrics for this project assumed that at least 300 people would be enrolled in the program between

October 2017 and September 2019. The established performance metrics were—

1. Housing retention at 6 and 12 months, defined as participant retaining stable housing at the

designated time points as reported by ICMS and ODR.2

2. Jail avoidance at 2 years, defined as participant not returning to jail for a qualifying offense,

using LASD county data.3

As participants were enrolled in the program, they were organized into quarterly cohorts that

were tracked on the performance (also called “success”) metrics. The JIR PFS program had 300

designated PSH slots available during the 2-year recruitment period. If an individual exited a PSH

2 Stable housing was defined as continuing to retain a PSH lease or achieving a “good” exit from the program, defined as retaining housing or rental with or without an ongoing subsidy, residing in long-term care, nursing home or other long-term behavioral health facility, or staying with family or friends with permanent tenure.

3 Qualifying offenses included misdemeanor arrests for which a new criminal filing or violation is in lieu of a new criminal filing, felony arrests in which a finding of probable cause is found through a preliminary hearing or grand jury indictment, convictions of a misdemeanor or felony for an event that occurred after PSH placement, and revocation of community

supervision or flash incarceration due to violation of parole conditions.

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unit during the recruitment period (October 2017–September 2019), then that slot was opened for

another individual to be placed into PSH. It was also the case that if a participant died during the

enrollment period, then a new participant could be enrolled to fill that available housing slot.

The CSH and Evident Change calculated the success payments for each cohort based on data they

received from the ODR (that is, on housing retention) and from LASD (that is, on jail avoidance).

These data were also shared with RAND, the evaluation partner who reviewed the CSH and

Evident Change calculations and provided validation of the findings prior to the county making

investor payments. Specific payment amounts for achievement of the 6- and 12-month housing

retention metrics were outlined in the agreement, along with amounts tied to whether participants

achieved zero, one, or two qualifying returns during the 2-year post-enrollment period (no

payments were made if participants had three or more qualifying returns).

In concert with the monitoring of the eight quarter cohorts of participants across the project life

cycle, CSH and Evident Change also managed quarterly executive steering committee meetings

in which key stakeholders from the involved entities met to discuss program implementation,

including progress on the performance metrics and quality improvement initiatives that were

taking place. Participants in the steering committee included representatives from investors,

intermediaries, LA County Department of Health Services Office of Diversion and Reentry and

Housing for Health Division, LASD, and the evaluation team. In addition to the steering committee,

an operating committee—composed of county, Brilliant Corners, CSH, and Evident Change staff—

met regularly to discuss program performance. These meetings were a key part of the continuous

quality improvement approach of the project through which the performance data served as a

jumping off point for robust conversations about challenges and opportunities.

Program Implementation Results

During the project period, 349 individuals were enrolled in the JIR PFS initiative. CSH monitored

the demographic composition of participants (CSH, 2022; exhibit 3). Of the group, 67 percent

were male, 30 percent were female, and 3 percent were transgender. More than 45 percent of

participants were classified as Black or African-American, with nearly 28 percent Hispanic and

Latino, and nearly 17 percent White, Non-Hispanic or Latino. Of note, these data reflect an

overrepresentation of African-American people compared with the Los Angeles County population

(that is, approximately 8 percent) and are consistent with previous reporting of this racial and

ethnic disparity (Appel et al., 2020).

62 Reentry Housing After Jail or Prison

Addressing Homelessness Among People With Justice Involvement:

Los Angeles County’s Just in Reach Pay for Success Demonstration Project

Exhibit 3

Racial and Ethnic Background of Just in Reach Pay for Success Participants (n = 349)

Number of Percentage

Race/Ethnicity (Group)

Persons

of Persons

American Indian or Alaska Native

3

0.9%

Asian

8

2.3%

Black or African American

159

45.6%

Don’t Know/Refused

6

1.7%

Hispanic/Latino

95

27.2%

Multiracial

19

5.4%

Native Hawaiian or Other Pacific Islander

1

0.3%

White, Non-Hispanic/Latino

58

16.6%

349

100%

Source: Corporation for Supportive Housing, 2022

The findings from the program metric calculations showed that 92 percent of JIR PFS program

participants achieved the 6-month housing stability criteria. The overall 12-month housing

stability metric was 78 percent; however, it increased during the project lifecycle with the last

cohort achieving an 86-percent, 12-month housing stability rate. In terms of the qualifying returns

metric, 56 percent of participants achieved zero returns during the 2-year period, with 21 percent

having one return to jail, 8 percent having two returns to jail, and 15 percent with three or more

returns to jail. These findings were seen as particularly impressive given that some data suggest that

the recidivism rate among similar populations is likely to be more than 70 percent (CDCR, 2019,

2015). Based on this performance, investors were repaid their entire $10 million investment and

earned approximately 2.9 percent in blended interest.

Impact Evaluation

The project supported a broader impact evaluation that was not specifically tied to the success

metrics. The impact evaluation included an estimation of the PSH effect on usage of a spectrum

of publicly funded health, housing, and justice services and the costs of those services using a

quasi-experimental design. In addition, the impact evaluation considered the PSH costs to answer

whether the program resulted in overall costs or cost offsets to the county. This section provides an

overview of the methods used.

RAND obtained information about demographics, service use, and associated costs from

administrative data that participating agencies shared and from the Chief Information Office of

LA County, which manages an enterprise data warehouse across the different county departments,

including the data sources in this analysis (that is, LA County Departments of Health, Public

Health, Mental Health, and Los Angeles Homeless Services Authority and LASD).4 These data were

organized using unique individual identifier numbers. ODR provided a list of participants’ names

4 RAND received data from the Los Angeles County Department of Public Health on the use of treatment services for

substance use, but the data were not at the individual level, so they were not incorporated into the main outcome analyses.

Group-level analyses are in Hunter et al., 2022.

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and jail release dates to the Chief Information Office to assist with creating the treatment group

dataset (that is, JIR PFS participants). Exhibit 4 outlines the different services and cost estimates in

this analysis.

Exhibit 4

Service Cost Estimates (Fiscal Year 2019–20)

Department

Category

Response

Unit Cost Estimates

Source

Los Angeles County

Healthcare

Primarya

• Inpatient, medical: $5,588 per day

Department of

• Emergency room, medical: $1,784 per encounter

Health Services

• Outpatient, medical: $1,356 per encounter

• Inpatient, psychiatric: $2,804 per day

• Emergency room, psychiatric: $4,506 per encounter

• Outpatient, psychiatric: $1,202 per encounter

Los Angeles County

Healthcare

Primaryb

• Inpatient: $802 per day; residential $280 per day

Department of

• Outpatient: Variableb

Mental Health

• Crisis stabilization: Variableb

Los Angeles

Housing

Primaryc

• All costs per unit per night:

Homeless Services

| Crisis housing and emergency shelter: $65

Authority

| Interim housing, including hotel and motel

vouchers: $105

| Housing choice voucher, tenant-based supportive

housing, project-based voucher: $35

| HUD-VASH: $28

| (Non-ODR) permanent supportive housing: $43

| Rapid Re-Housing: $40

| Shelter Plus Care: $26

| Transitional housing: $101

ODR

Housing

Primaryd

• Housing subsidy: $1,842 per unit per month

(FY 2019–20)

• Intensive case management: $734 per client per

month (FY 2019–20)

LASD

Justice

Secondarye • Booking fee: $266 per booking (FY 2019–20)

• Jail maintenance fee: $389 per jailee per day

(FY 2019–20)

Los Angeles

Justice

Primaryf

• Probation supervision: $174 per month (FY 2019–20)

County Probation

Department

FY = fiscal year. HUD-VASH = U.S. Department of Housing and Urban Development-Veteran Affairs Supportive Housing. LASD = Los Angeles County Sheriff’s Department. ODR = Office of Diversion and Reentry.

a Cost estimates represent mean values across eight facilities within the network, primarily affiliated with academic medical centers. The Los Angeles County Department of Health Services provided these facility-level cost estimates.

b Inpatient costs represented as average fee for service for psychiatric inpatient hospital contract rate. Residential costs represented as average Institute for Mental Disease contract rate. Outpatient, emergency, and crisis stabilization costs were unique to individual encounters and reported directly by the Chief Information Office.

c Cost estimates provided by the Los Angeles Homeless Services Authority.

d Cost estimates provided by ODR based on service provision in 2021. Service components varied for the duration of the project (2015–2021) and were reflected in cost estimates during this period.

e Cost estimate for jail maintenance fee includes the composite average of high and moderate observation housing at Twin Towers and Century Regional Detention Facility as a proxy for maintenance fees at facilities throughout Los Angeles County that provide care for those with moderate and serious mental illness. This cost estimate is conservative, as it does not include the cost of correctional health services. Cost estimates provided by the Chief Information Office.

f Cost estimate provided by the Los Angeles County Probation Department for fiscal years 2015 through 2019.

Notes: All cost estimates adjusted for inflation to 2021 dollars. In cases with missing cost estimates for fiscal years 2014 through 2021, the team performed linear interpolation.

64 Reentry Housing After Jail or Prison

Addressing Homelessness Among People With Justice Involvement:

Los Angeles County’s Just in Reach Pay for Success Demonstration Project

Of the 349 JIR PFS participants enrolled, 13 were excluded from the evaluation for one of the

following reasons, leaving the total treatment group sample size at 336.

• Five participants had a release date in 2015; the outcomes data spanned from roughly 2015–

2020, meaning that a full year of prerelease information was not available for these individuals.

• Ten participants had an ODR release date that conflicted with the release date in the Chief

Information Office dataset.

• Two participants met both criteria.

RAND used propensity methods to create a matched comparison group to examine whether the

program had effects on service use and associated costs. A matched comparison group of 672 was

identified (using a two-to-one matching strategy) from a larger pool of roughly 5,400 individuals

who met the following criteria:

• Had received a service for individuals experiencing homelessness recorded in the Los Angeles

Homeless Services Authority (LAHSA) data between January 2016 and October 2019.

• Had at least one continuous jail stay of 30 days or longer recorded in the LASD data between

January 2016 and April 2019.

• Had a serious mental, behavioral, or chronic health condition identified by a service record in

the Los Angeles County Department of Health Services or the DMH.

• Did not receive a permanent form of housing through the JIR PFS program or other housing

program recorded in the LAHSA data—that is, Housing Choice Voucher, project-based

voucher, tenant-based supportive housing, Shelter Plus Care, permanent supportive housing,

or the U.S. Department of Housing and Urban Development-Veteran Affairs Supportive

Housing—in the post-release period.

RAND used regression analyses with propensity score matching—commonly referred to as

propensity score analyses—as the primary analytic technique for identifying the program effect

(Austin, 2011). Propensity score approaches allow for an observational, nonrandomized design to

mimic a randomized controlled trial by seeking a balance between individuals in the treatment and

comparison groups on a set of predefined characteristics that could plausibly be associated with

the outcomes. In this case, RAND sought balance on a number of demographic characteristics,

including age, gender, race, and ethnicity, as well as prior health, homeless, and justice service use

(that is, 12 months prior to jail release) and associated costs.

Prior to performing the outcome analyses, it was necessary to define a baseline period for

the comparison group members, corresponding to their ODR release data among the JIR PFS

participants. For each potential comparison group member, hypothetical “release to ODR

custody” data were selected at random from the set of all jail release dates observed in the LASD

data corresponding to a jail stay of 30 days or more to calculate the service use metrics for the

comparison group.

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RAND used the “twang” package in the statistical software program R to calculate the propensity

scores via generalized boosted regression (The Comprehensive R Archive Network, 2021; Ridgeway et

al., 2014). RAND selected a 2-to-1 nearest-neighbor matched comparison group based on the scores

in the MatchIt package in R (Ho et al., 2011). RAND assessed the quality of the comparison group by

examining the covariate values between the treatment group to the weighted comparison group.

Next, RAND assigned hypothetical PSH move-in dates for the comparison group to define the JIR

PFS analytic period. RAND did this by taking each comparison group member’s selected ODR

release date and the number of days from ODR release to PSH move-in corresponding to the

treatment group individual to which the comparison group member was matched.5

RAND examined the JIR PFS program effects on service use and costs using regression models on

the differences in 12-month pre-ODR release and 12-month post-PSH entry outcomes for each

treatment and comparison group member.6 RAND modeled average changes in use and costs as a

linear function of treatment assignment, demographics (age, gender, race, and ethnicity), and other

selected variables to determine whether program enrollment was associated with more (or less)

service use and costs relative to the matched comparison group.7

Evaluation Findings

Exhibit 5 provides the estimated average treatment effects on service use and associated service

costs for JIR PFS participants during the 12-month PSH period.

Exhibit 5

Estimated Average Treatment Effects During the 12-Month Post-Housing Period (1 of 2)

Service Use

Service Cost

Point Estimate

Significance

Point Estimate

Significance

Service Characteristic

(SE)

(95% CI)

(SE)

(95% CI)

Justice

Los Angeles County

– 23.84

0.002

– $16,891

< 0.001

Sheriff’s Department

(7.73)

(– 39.01, – 8.67)

($4,609)

(– $25,935, – $7,846)

(days)

Probation (days)

124.51

< 0.001

$672

< 0.001

(13.38)

(98.25, 150.77)

($72)

($529, $814)

Homeless services

Homeless services

– 0.32

< 0.001

NA

NA

(proportion receiving)

(0.04)

(– 0.41, 0.23)

Shelter and temporary

– 0.22

< 0.001

– $1,643

< 0.001

housing (proportion

(0.03)

(– 0.28, – 0.16)

($334)

(– $2,298, – $988)

receiving)

5 More details about this process and the comparison group members are in Hunter et al., 2022.

6 Outcomes from the interim housing periods and the 2-year post-PSH entry periods are reported in Hunter et al., 2022.

7 The team also modeled binary outcomes for the homeless service outcomes using logistic regression. The results were consistent with the linear model, and for simplicity and consistency, the team presents the results from the linear models.

The team also explored whether adjusting for the model’s p-values to account for multiple testing changed the statistical significance, finding no evidence to that effect. For simplicity, unadjusted values are reported.

66 Reentry Housing After Jail or Prison

Addressing Homelessness Among People With Justice Involvement:

Los Angeles County’s Just in Reach Pay for Success Demonstration Project

Exhibit 5

Estimated Average Treatment Effects During the 12-Month Post-Housing Period (2 of 2)

Service Use

Service Cost

Point Estimate

Significance

Point Estimate

Significance

Service Characteristic

(SE)

(95% CI)

(SE)

(95% CI)

ODR housing

Permanent supportive

336.42

< 0.001

$27,295

< 0.001

housing (days)

(2.82)

(330.88, 341.96)

($233)

($26,838, $27,753)

Los Angeles County Department of Health Services

Outpatient (visits)

0.09

0.47

$132

0.37

(0.13)

(– 0.16, 0.35)

($147)

(– $157, $420)

Inpatient (days)

– 0.78

0.03

– $3,308

0.04

(0.36)

(– 1.49, – 0.06)

($1,575)

(– $6,399, – $217)

Emergency (visits)

– 0.31

0.004

– $691

0.005

(0.11)

(– 0.53, – 0.10)

($246)

(– $1,174, – $208)

Los Angeles County Department of Mental Health

Outpatient (visits)

20.85

< 0.001

$2,512

< 0.001

(2.69)

(15.58, 26.12)

($381)

($1,764, $3,259)

Inpatient (days)

– 2.36

0.01

– $1,275

0.007

(0.97)

(– 4.27, – 0.47)

($470)

(– $2,197, – $352)

Crisis stabilization (visits)

– 0.17

0.11

– $470

0.06

(0.11)

(– 0.39, 0.04)

($253)

(– $966, $27)

$6,202

0.21

Total

($4,966)

(– $3,543, $15,947)

CI = confidence interval. NA = not available. ODR = Office of Diversion and Reentry. SE = standard error.

Source: Hunter et al., 2022

Justice System Services. RAND estimated that JIR PFS participation was associated with a

statistically significant decrease of 24 days in jail per participant relative to the comparison

group during the 12-month post-housing period. This estimate was associated with a statistically

significant decrease in jail service costs of $16,891 per JIR PFS participant. RAND also found

a significantly greater number of average probation days—125 more days—among JIR PFS

participants relative to the comparison group during the 12-month post-housing period and an

associated increased cost of $672 per participant.

Housing and Homeless Services. RAND found that JIR PFS participants had an average

22-percent reduction in the use of shelter and temporary housing services, with a corresponding

cost savings of $1,643 per participant. RAND also found a significant increase among JIR PFS

participants related to the PSH costs. Specifically, in the first year of the program, the treatment

group had an average increase of 336 days of PSH relative to those in the comparison group. This

increase in PSH days among the treatment group translated to an average increased cost of $27,295

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Hunter and Mercier

per participant, given that the comparison group was restricted to individuals who did not obtain

any form of permanent housing in the post-housing period.8

Healthcare Services. Regarding the Los Angeles County Department of Health service use, JIR PFS

program participants had an average of 0.8 fewer days of inpatient care and 0.3 fewer emergency

department visits relative to the comparison group, resulting in average cost savings of $3,308

and $691 per participant, respectively. Outpatient visits were not different among the two groups.

Regarding Department of Mental Health service use, RAND found that the JIR PFS group had on

average an additional 21 mental health outpatient visits per participant relative to the comparison

group during the 12-month post-housing period, translating to an average increase in service

use costs of $2,512 per participant. In addition, RAND observed an average decrease of 2.36

days in inpatient mental healthcare that the DMH provided to JIR PFS participants relative to the

comparison group, resulting in an average savings of $1,275 per participant.

Total Cost. RAND also quantified the total cost of service use during the 12-month post-housing

period among those in the JIR PFS group relative to those in the comparison group. Although

RAND estimated a positive net cost of $6,202 for the JIR PFS group, this estimate has a high degree

of uncertainty, and ultimately, RAND found no statistically significant difference between the two

groups. This lack of significant difference between the two groups on overall service costs implies

that, despite program costs associated with PSH and more frequent use of mental health outpatient

care and probation services among those in the JIR PFS group, cost savings in other areas—most

notably, less county jail time, fewer inpatient stays and emergency department visits, and less

shelter and temporary housing use relative to the comparison group—potentially offset these

higher costs. In other words, it remains plausible that the PSH program may entirely pay for itself

in terms of reductions in overall service costs.

Summary

The LA County Just in Reach (JIR) Pay for Success (PFS) initiative successfully enrolled more

than 300 participants in supportive housing during a 2-year period, achieving its enrollment

goal. Furthermore, 6- and 12-month housing stability rates were high, 92 and 78 percent,

respectively. At 2 years, more than one-half of participants had not experienced a qualifying return

to jail, and only 23 percent had experienced two or more qualifying returns. Given these results,

investors received a return on their initial investment plus interest, demonstrating the “win-win

scenario” portrayed in the literature surrounding PFS approaches. The results from the evaluation

demonstrated that the costs of providing permanent supportive housing (PSH) could plausibly

lead to a neutral effect on the overall county budget, given that the reductions in jail and inpatient

or emergency health services offset supportive housing costs. This demonstration project creates

a significant opportunity for the county to both sustain and scale this initiative, bolstering its

diversion efforts and increasing the availability of supportive housing for this population. It also

serves as a model for other regions to consider using to address chronic homelessness.

8 The team conducted sensitivity analyses in which comparison group members were allowed to have obtained subsidized housing in the post-period. Only 3 percent of the matched comparison group (23 individuals) received some form of

subsidized housing, and therefore, it did not result in any large differences in the outcomes or overall results. For further details, see appendix E in Hunter et al., 2022.

68 Reentry Housing After Jail or Prison

Addressing Homelessness Among People With Justice Involvement:

Los Angeles County’s Just in Reach Pay for Success Demonstration Project

Limitations

Several limitations to the project are worth noting. The JIR PFS participants were diverse in

background, and the team was not given the information to determine how well it generalized to

others in LA County jail with a background of homelessness and a chronic condition. However, the

Office of Diversion and Reentry has served and continues to serve thousands of individuals through

its supportive housing program. The impact analyses used a quasi-experimental design indicating

that it is always possible that unmeasured (that is, unobserved) variables may influence the study

results. Although the propensity weighted approach was successful in identifying comparison

group members that were similar to the treatment group members,9 the possibility remains that

the two groups may have important differences that are not accounted for in this analyses. RAND

excluded comparison group participants who obtained subsidized housing in the post-period from

the primary analyses, but when the team conducted sensitivity analysis with their inclusion, it did

not substantively change the overall findings. This lack of substantive change in results between

the two sets of analyses is consistent with what is known about subsidized housing options in Los

Angeles County in that the demand greatly outweighs the supply, and many people who may be

appropriate for subsidized housing do not receive it (Mazzella and Rosenfeld, 2021). Also, it was

recognized that program participants may access other services that are not accounted for in this

study, including, for example, court costs, law enforcement costs related to the arrest and booking

charges in the field, and homeless and health services received from noncounty-funded entities

that are not included in these analyses. For example, other studies have shown that supportive

housing may increase pharmacy costs, but the team did not have access to such data to examine

this association as part of the study (DeLia et al., 2021).

Conclusions

Los Angeles County’s PFS demonstration project successfully achieved its projected goals. More

than 300 people were successfully diverted from the criminal justice system into PSH during

a 2-year period, meeting the performance metrics regarding housing stability and recidivism.

A broader impact evaluation demonstrated that the program led to substantial reductions in

costly county service use that offset the PSH program costs. Ongoing collaborations among

key stakeholders throughout the program’s implementation led to improvements over time that

contributed to achieving the project’s goals. Other regions may want to consider adopting this

approach to address chronic homelessness in their communities.

Acknowledgments

The authors would like to thank the many organizations that were instrumental to the successful

implementation of this initiative. These include the Los Angeles County Department of Health

Services (DHS) and the Office of Diversion and Reentry (ODR), Los Angeles County Sheriff’s

Department (LASD), Los Angeles County Chief Executive Office, Evident Change, Conrad N.

Hilton Foundation, UnitedHealthcare, Amity Foundation, Special Services for Groups LLC (SSG),

Telecare, The People Concern, Volunteers of America, Brilliant Corners, and Department of

9 More details on the results from the propensity weighting are available in Hunter et al., 2022.

Cityscape 69

Hunter and Mercier

Housing and Urban Development (HUD)/ Department of Justice (DOJ) Pay for Success Permanent

Supportive Housing Demonstration Program; the California Board of State and Community

Corrections (BSCC) Pay for Success provided additional grant funding for this initiative. The work

that provided the basis for this publication was supported by funding under an award with HUD.

The substance and findings of the work are dedicated to the public.

Authors

Sarah B. Hunter is a senior behavioral scientist at the RAND Corporation. Stephanie Mercier is

director of impact investment at the Corporation for Supportive Housing.

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72 Reentry Housing After Jail or Prison

by Niloufer Taber, Jacqueline Altamirano Marin, and John Bae

Public Housing Eligibility for People

with Conviction Histories

Niloufer Taber

Jacqueline Altamirano Marin

John Bae

Vera Institute of Justice

The views expressed in this article are those of the authors and do not represent the official positions or

policies of the Office of Policy Development and Research, the U.S. Department of Housing and Urban

Development, or the U.S. Government.

Abstract

Housing is a basic need for all people, but one that is too often denied for those who have a conviction

history because of policies that exclude this population from renting or joining a lease. This study

estimates the number of people in Michigan and Oklahoma who are potentially excluded from public

housing and who may regain public housing eligibility if public housing authorities (PHAs) change the

time a conviction history can be considered for admissions decisions, commonly known as a lookback

period. Results show that more than 3.5 percent of all adult Michiganders and 7.6 percent of all adult

Oklahomans are potentially excluded from public housing. These percentages are low estimates for both

states due to the study’s simplified assumptions and the omission of the impact that such rules have on

family members of people with a conviction history.

Introduction

Housing is foundational for everyone’s health and well-being. Access to housing is a social

determinant of health and is used as an intervention to improve health outcomes (Feinberg et

al., 2014; Milaney et al., 2022; Wolitski et al., 2010). Safe and affordable housing also promotes

economic mobility and helps families exit poverty (Ramakrishnan et al., 2021). In essence, housing

is a basic need and a right for every person and family. For people who become involved in the

criminal legal system, housing is equally critical and contributes to the successful transition from

system involvement to the community.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

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U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Taber, Altamirano Marin, and Bae

However, barriers are put in place that bar people with conviction histories from obtaining

housing. These barriers include discriminatory policies that deny housing to people with

conviction histories, the use of criminal background checks, and community and societal stigma

(Crowell, 2017; Ehman and Reosti, 2015; Prisoner Reentry Institute at John Jay College of Criminal

Justice et al., 2017). Across the country, there are 1,300 documented local and state barriers to

housing for people with conviction histories and 26 federal barriers (Lake, 2021). These laws are

frequently implemented for fear that people with prior criminal histories will reengage in crime.

However, success is more likely than recidivism—most people with conviction histories never have

another conviction (Bushway et al., 2022).

Additionally, there is no evidence that housing policies that exclude people with conviction

histories make housing complexes safer or that people with conviction histories have different

tenancy outcomes than others without a history of arrests or convictions (Johnson, 2022). These

restrictions create barriers for the millions of people that transition out of jails and prisons every

year and struggle to find safe and affordable housing (U.S. Department of Health and Human

Services, n.d.).1 It is unclear, however, just how many people are excluded from housing because

of their conviction histories. Past studies have surveyed people with a conviction history on their

ability to secure housing (deVuono-powell et al., 2015). Others have surveyed people experiencing

homelessness to determine the number of people with a past conviction or arrest history (Dean,

2011). Researchers have cataloged housing providers’ exclusionary policies, but there have been no

studies that examine the impact of exclusionary public housing policies on people with conviction

histories (Tran-Leung, 2011).

This study by the Vera Institute of Justice (Vera) estimates the number of people who are excluded

from public housing in two states, Michigan and Oklahoma, based on conviction history. Vera

selected these two states for this analysis because both states have recently passed local and

state-level criminal justice reform laws (e.g., Michigan’s Clean Slate Act) that were signed into law

during the past 2 years. In both states, there is momentum to remove the barriers for people with

conviction histories to thrive after their involvement with the criminal legal system. This study

contributes to the momentum, identifying the challenges and opportunities for housing people

with conviction histories.

Specifically, access to public housing was evaluated because—unlike other housing providers in

the private market—admissions policies for PHAs are published, public, and detail exclusionary

criteria for people with conviction histories. The availability of these policies allows for the analysis

and determination of the number of people who may be excluded from public housing because of

their conviction histories.

Housing for People with Conviction Histories

It is estimated that more than 100 million people—nearly one out of every three American

adults—have some sort of criminal record, including a history of arrest, conviction, and

incarceration (Greenspan and DeBacco, 2014). People with a criminal history face challenges

1 More than 600,000 people are released from state and federal prisons each year. Another 9 million cycle through local jails.

74 Reentry Housing After Jail or Prison

Public Housing Eligibility for People with Conviction Histories

securing employment, education, government services, and housing because of their conviction—

yet it is exactly these supports that reduce recidivism and promote health and wellbeing for these

people, their families, and their communities (Rosenfeld and Grigg, 2022).

Safe, stable housing often serves as the gateway for accessing further services and support. Housing

makes it more likely that a person returning to their community finds and keeps employment;

gets connected to medical, mental, and behavioral healthcare; rebuilds supportive and prosocial

networks within their community; and is able to reestablish mutually supportive bonds with family

(Lebel, 2017; Metreaux and Culhane, 2004). However, people with conviction histories frequently

face challenges finding housing in the private market because landlords are often reluctant to rent

to people with any criminal background, and because people with conviction histories are more

likely to have lower incomes than the general public (Evans and Porter, 2015; Leasure and Martin,

2017; Looney and Turner, 2018; Thacher, 2008; Western and Pettit, 2010).

Finding housing has proven to be exceedingly difficult due to affordability, lack of housing supply,

the absence of transitional housing, a shortage of capacity, and hazardous conditions in shelters.

People with criminal records also struggle to obtain housing because of the stigma associated with a

criminal record and the aftereffects of a conviction (Bradley et al., 2001). These barriers contribute

to a high rate of housing insecurity and homelessness for this population; formerly incarcerated

people are 10 times more likely than the general public to be homeless (Couloute, 2018).

Public Housing and People with Conviction Histories

There are nearly 3,350 public housing authorities (PHAs) in the United States serving 1.3 million

families, and public housing is a resource available in virtually every community (U.S. Department

of Housing and Urban Development, 2017). Federally assisted housing is intended to provide access

to affordable housing as a public service. In determining who is allowed to live in public housing,

PHAs have broad discretion over their own admissions criteria. By law, PHAs are required to deny

people who have two types of convictions: convictions for manufacturing methamphetamines

in public housing and convictions that require lifetime sex offender registration. For all other

conviction types, PHAs have broad discretion in setting admissions criteria.

Criminal convictions did not always have an impact on a person’s ability to obtain public housing.

Policies that denied people access to public housing grew out of public safety and crime concerns

starting in the mid-1980s. In response to growing concerns about violent crime and drugs in

public housing neighborhoods, several federal regulations were passed. In 1988, the Anti-Drug

Abuse Act authorized PHA funding from the U.S. Department of Housing and Urban Development

(HUD) Secretary for initiatives to eliminate drug crimes. In 1990, the Cranston-Gonzalez National

Affordable Housing Act was passed, allowing PHAs to use criminal records for admissions

determinations, and the Housing Opportunity Program Extension Act (1996) required law

enforcement agencies to comply with criminal background requests from PHAs. Before the turn of

the century, the Quality Housing and Work Responsibility Act (1998) expanded PHA discretion to

establish admissions eligibility and permanently banned people with a lifetime sex offense registry

requirement, and the Independent Agencies Appropriations Act (1999) established the exclusion for

people convicted of producing methamphetamines in federally subsidized housing (Silva, 2015).

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Taber, Altamirano Marin, and Bae

Public Housing in Michigan and Oklahoma

There are 116 public housing authorities in Michigan administering 145,436 subsidized

housing units. Traditional public housing makes up 13.0 percent of the units; 83.0 percent are

either Project Based Section 8 or Housing Choice Vouchers, and 3.5 percent are financed under

other arrangements. These PHAs are currently estimated to serve nearly 240,000 people across

Michigan’s 83 counties, with about 51 percent of residents identifying themselves as Black, non-

Hispanic; about 42 percent White, non-Hispanic; 3 percent Hispanic; and the remaining 4 percent

Native American, Asian/Pacific Islander (API), multiracial, or another race. However, Black, non-

Hispanic residents are more heavily represented among those who use Housing Choice Vouchers

than those who live in traditional public housing or Project Based Section 8, compared with other

racial and ethnic groups.

The state of Oklahoma has 101 public housing authorities that administer 52,479 subsidized

housing units. Of these PHAs, 22 percent (11,377) are traditional public housing, 50 percent

(26,193) are Housing Choice Vouchers, 25 percent (13,024) are Project Based Section 8, and

under 4 percent (1,885) are financed under other arrangements. Nearly 95,000 people across

77 counties reside in federally subsidized affordable housing in Oklahoma, with 40 percent of

residents identifying as Black, non-Hispanic, 46 percent White, non-Hispanic, 1 percent API, 5

percent Hispanic, 7 percent Native American, and 1 percent multiracial or another category. As

in Michigan, Black, non-Hispanic residents form the majority of residents of the Housing Choice

Vouchers program.

Convictions in Michigan and Oklahoma

This study estimates that between 40,000 to 50,000 people are convicted of a felony in Michigan

each year, and between 10,000 to 18,000 people are convicted of a felony in Oklahoma annually

(exhibit 1).2 These statistics give a conviction− not imprisonment−rate of 392 people per 100,000

in Michigan and 259 people per 100,000 in Oklahoma, with the differences in raw numbers at

least partially explained by the size of Michigan’s population, which is nearly 2.5 times that of

Oklahoma (United States Census Bureau, 2021). In both states, nonviolent offenses make up the

vast majority of all dispositions (see exhibit 2).

2 The exception to this statistic was 2020, the first year of the COVID-19 pandemic and the most recent year for which data are available in Michigan, which saw a dramatic drop in the number of convictions. This drop may be due to multiple reasons, such as a delay in reporting data to a central location, or an overall slowdown in court processing, preventing arrests from becoming convictions.

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Public Housing Eligibility for People with Conviction Histories

Exhibit 1

Total Felony Dispositions, Individuals Convicted of a Felony, and Individuals Convicted of a First

Felony, by State and Year of Disposition

Sources: Felony dispositions, people with a felony disposition, and people with a first felony disposition in Oklahoma were calculated through an analysis of publicly available data from the Oklahoma Department of Corrections (Oklahoma Department of Corrections. 2022. Public Inmate Data ). Felony dispositions were available from the Michigan Department of Corrections. (Michigan Department of Corrections. 2022. Statistical Reports https://www.michigan.gov/corrections/

public-information/statistics-and-reports/statistical-reports ). From these data, using state published recidivism rates and research on recidivism from the Bureau of Justice Statistics, Vera estimated the numbers of people with felony conviction and people with a first felony conviction Exhibit 2

Type of Felony Disposition Over Years: Percent of Total Dispositions

Sources: Using publicly available data from the Oklahoma Department of Corrections, felony dispositions were coded for whether they were assaultive, drug-related, or neither, by year of disposition (Oklahoma Department of Corrections. 2022. Public Inmate Data ). Felony dispositions by type were available from the Michigan Department of Corrections. (Michigan Department of Corrections. 2022. Statistical Reports https://www.michigan.gov/corrections/public-information/

statistics-and-reports/statistical-reports ) Aims of the Present Study

This study estimates the number of people with a conviction history who are potentially excluded

from public housing in two states—Michigan and Oklahoma—and projects the number of people

who might regain eligibility for public housing if PHAs change their lookback periods, the span of

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Taber, Altamirano Marin, and Bae

time in which conviction history can be considered for admissions decisions. In both states, these

figures are likely to be minimum estimates due to simplifying assumptions and the exclusion of the

effect that such policies have on family members of people with a conviction history who wish to

remain united as households.

Methods

To estimate the number of people living in Michigan and Oklahoma with conviction histories

that could make them ineligible for public housing, Vera researchers compiled the following

pieces of information:

1. The lookback periods used by public housing authorities in each state by type of conviction.

2. The number of people in each state with a conviction by year, county, severity (misdemeanor

or felony), and type (violent, drug-related, or other).

3. The number of people reentering and living in the community with a conviction by county,

offense severity, type of conviction (violent, drug-related, or other), and year of conviction or

release for those with a custodial sentence.

4. The number of people excluded from public housing due to federal mandates.

5. The proportion of people with a conviction who met income limits for public housing

eligibility, disaggregated by year since conviction or release from incarceration.

Lookback Periods

In both states, Vera visited each PHA’s websites to obtain Admissions and Continued Occupancy

Plans and/or Administrative Plans. Some PHAs did not have their documents available online

or did not have a website. For these PHAs, Vera contacted the PHAs to obtain either a written

or verbal description of its policy regarding eligibility for housing based on criminal history or

conduct. Verbal confirmation of policies took place over the phone. Several PHAs either did not

have the resources to provide written policies or were unwilling to provide a verbal description

of their policy. To obtain verbal policies, researchers used a strategy common to Fair Housing

Centers: assuming the role of a friend or neighbor of a prospective tenant with a conviction history

and requesting information about eligibility policies and background check procedures on behalf

of this nonexistent person. Through these methods, researchers obtained 31 written policies in

Michigan, and 32 written policies and 24 verbal descriptions of policies in Oklahoma.

Several written or verbally described policies did not specify the types of convictions that were

potentially excludable or the timeframe the PHA would use to look back for a conviction. The

research team assumed that PHAs with ambiguous or missing policies are following guidance

from HUD on the use of criminal background checks, which has suggested a lookback period of

5 years from the date of application for serious crimes—including felony convictions for drug-

related crime and violent crime—and prohibiting the use of arrest data alone from being used

as a basis for a denial (Kanovsky, 2016). In Oklahoma, some counties had more than one PHA.

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Public Housing Eligibility for People with Conviction Histories

Because conviction and release information are available at the county level, the research team

applied a single policy to the entire county. In most cases, PHA policies within a county did not

conflict. In cases where there was a conflict or lack of clarity, the team applied a written policy in

preference over a verbal description for the county; if both policies were verbal or written, the team

used the least restrictive policy. This approach maintained a minimum or conservative estimate of

the number of people potentially excluded from public housing due to their conviction histories.

In both states, several PHAs described potential exclusions of individuals based not only on a

conviction, but an eviction for criminal activity, a pattern of arrests, or other evidence. This study

used only information about convictions in each state, rather than data on arrests or evictions

related to crime, due to the challenges of obtaining and interpreting such data and to ensure that

estimates of people excluded due to a conviction history would be conservative.

The Type, Timing, and Location of Convictions and Releases

Data sources and methods for estimating the number of people potentially excluded from public

housing due to a misdemeanor conviction or a federal felony were common in both states.

However, data sources and methods for estimating the number of people potentially excluded due

to a state felony differed between Michigan and Oklahoma.

Michigan: State Felony Convictions

Publicly available statistical reports were used to estimate the number of people in Michigan who

were potentially ineligible for public housing due to PHA policies and to make projections of how

many Michiganders could regain eligibility for public housing if lookback periods were changed.

These projections required several simplifying assumptions, which were made based on existing

research about housing and criminal legal systems and to ensure that estimates were conservative

wherever possible. The research team gathered aggregated data on state felony convictions from the

Michigan Department of Corrections (MDOC) Statistical Reports from 2011 to 2020. These reports

describe the number of convictions by county of disposition and type of conviction (violent, drug-

related, or other) within the year (Michigan Department of Corrections, 2022). These counts of

felony convictions, however, represent court proceedings rather than individuals, because a person

may have more than one court proceeding in a given year. To estimate the number of people with

a conviction, the study factored into the analysis the possibility of multiple felony convictions per

person within a single year ( intra-year recidivism) and multiple convictions per person in different

years ( inter-year recidivism).

Michigan: Accounting for Intra-Year Felony Recidivism

The number of convictions in a jurisdiction exceeds the number of people with a conviction within

a given year due to intra-year recidivism, and a person may face multiple proceedings within a

year. This study sought to estimate the ratio of convictions to people with a conviction. By doing

so, the ratio would be used to scale down the number of convictions to the number of people with

a conviction. To make this estimate, the team used another ratio of events to people in the court

system in Michigan, where not all convictions lead to a term of incarceration, making convictions

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Taber, Altamirano Marin, and Bae

with a custodial sentence a subset of all convictions. MDOC Statistical Reports, in addition to the

aggregate data described previously, also provide data points on the number of convictions with

a custodial sentence and the number of prison intakes. From these two numbers, the team was

able to calculate a ratio of convictions with a custodial sentence (events) to individuals admitted

to prison (people) within each year. The study assumed that this ratio of events to people among

those with a custodial sentence was the same as the ratio of events to people among those without

a custodial sentence. This simplifying assumption is grounded in research showing that custodial

sentences are not more effective at reducing recidivism than non-custodial sentences (Villettaz

et al., 2015). The team then divided the number of total convictions by this ratio (a ratio of

convictions that carried a custodial sentence to people with a custodial sentence) in order to

estimate the number of people with a conviction in a given year.

Michigan: Accounting for Inter-Year Felony Recidivism

Individuals may have convictions in several years that are within a PHA’s lookback period. To avoid

counting people more than once, we scaled down the number of unique people convicted each year

by an inter-year reconviction rate, using return to incarceration as a proxy for reconviction. Although

MDOC produces estimates of the cumulative 3-year return to incarceration rate, reconviction and

reincarceration rates are not constant over time, they are highest in the year immediately following

release from incarceration, with increasingly smaller proportions of people returning to incarceration

in each subsequent year (Durose and Antenangeli, 2021). Therefore, the research team distributed

the Michigan cumulative return to incarceration rate unevenly across years from prison release based

on research from the Bureau of Justice Statistics describing the shape of the recidivism curve over

time from the point of release from incarceration (Durose and Antenangeli, 2021).

Michigan: Number of People Returning from Prison and

Living in Communities

Two situations occur in which someone with a conviction would be living in their community

following their conviction: if their sentence did not include a term of incarceration or if they were

released from incarceration within that year. The team assumed that all people with a misdemeanor

conviction were living in the community for at least part of the year of their conviction, because

misdemeanor custodial sentences are typically less than a year. The team also assumed that

individuals convicted of a felony but not incarcerated were living in the county of their conviction.

Finally, the team assumed that people convicted of a felony and admitted to prison were ineligible for

public housing in the year of their conviction, because felony sentences are typically a year or longer.

MDOC Statistical Reports provided the number of people released from incarceration each

year. The research team assumed that the total number of releases each year followed the

same distribution of convictions by county and type for the same year. The team did not have

information on Michiganders with an out-of-state criminal conviction who may also become

eligible for public housing through changes in lookback periods, and so did not add these people

to the estimates.

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Public Housing Eligibility for People with Conviction Histories

Oklahoma: State Felony Convictions

In Oklahoma, data on each person’s complete criminal history are publicly available, including the

year of conviction, statute code and a short description of the offense, the sentence type (incarceration

or probation) and length, whether the person is currently incarcerated or living in the community,

and the county from which they were convicted. These data are updated regularly; the team

downloaded these data on November 3, 2022. The team classified each conviction in the publicly

available data as violent, drug-related, or neither by reviewing Oklahoma criminal statutes and coding

each offense for each person, and noting whether the offense was for a felony or a misdemeanor.

Several situations occurred in which assumptions were necessary. First, some statutes exist under

which a violation could result in a misdemeanor or a felony, depending on the severity of the

offense (such as theft of property being above or below a certain value threshold). In these cases,

the researchers assumed the offense was for a misdemeanor. This approach was taken to minimize

the number of people estimated to be potentially excluded from public housing, because fewer

PHAs have policies to exclude individuals with misdemeanors, thus minimizing the number of

people potentially restored through changes in PHA policies. Second, for people currently on

probation or parole, Vera researchers assumed they lived in the county where their probation office

was located, but for people who had completed all requirements related to their convictions, the

team assumed they lived in the county of their most recent conviction. Third, 20,324 people did

not have a year attached to their single offense and were not subject to a federally mandated ban

from public housing. To avoid overestimating the number of people currently excluded from public

housing, the team assumed that all these individuals would not be excluded from public housing

due to their conviction status and did not include any of them in the estimates.

County-Level Misdemeanor Convictions

Vera researchers used statistical reports that provided aggregate information in Michigan and

publicly available, individual-level data in Oklahoma to estimate the number of people with state-

level offenses of the type, timing, and location that could make them ineligible for public housing.

However, no similar data source for county-level misdemeanors exists. To estimate the number of

people with a misdemeanor conviction per year, the team counted jail admissions for a sentence as

a proxy for a misdemeanor conviction. Jail admissions for a sentence serve as a minimum estimate

for misdemeanor convictions because many misdemeanor sentences do not include incarceration

or set the sentence as time already served. The team used Vera’s Incarceration Trends estimates

of jail admissions for sentences by county and year. The Incarceration Trends uses data from the

Bureau of Justice Statistics Annual Survey of Jails, Census of Jails, and Mortality in Correctional

Institutions (Vera Institute of Justice, 2022). Vera researchers then accounted for intra- and inter-

year misdemeanor recidivism within each PHA’s lookback period using estimates for misdemeanor

recidivism from the Brennan Center for Justice (Craigie et al., 2020).

In Oklahoma, there were several public housing authorities that might deny housing to people

who were currently serving a term of probation. For state felony convictions, the available data

indicated whether a person was on probation. However, the same was not possible for aggregate

data available on county misdemeanors. Vera, therefore, assumed there were no people on

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probation for county misdemeanors to minimize the estimated number of people potentially

excluded from housing and the numbers potentially restored should there be changes in admission

policies. As mentioned, considering only misdemeanor convictions with a custodial sentence

promotes a conservative estimate.

Federal Felony Convictions

The research team retrieved information on federal convictions and releases from the Bureau of

Justice Statistics Federal Criminal Case Processing Statistics and the Federal Bureau of Prisons

(Federal Bureau of Prisons, 2022; U.S. Department of Justice, 2020). In both states, the team

assumed the county-level distribution of convictions and reentries for federal felonies followed

the distribution for state felonies, which the researchers obtained through statistical reports in

Michigan or through an analysis of publicly available data in Oklahoma.

People Excluded From Public Housing Due to Federal Mandates

People who have a mandated lifetime sexual offender registry requirement and those convicted of

manufacturing methamphetamine in public housing are ineligible for federally subsidized housing.

In Michigan, the team took data on the number of people meeting these criteria from MDOC’s

statistical reports and data published by the Michigan State Police (Michigan State Police, 2017).

Michigan has three “tiers” (levels of severity) of registrants that determine the length of time people

must remain on the registry, how often they must report in person to law enforcement, and the

restrictions with which they must comply. Only Tier 3 registrants must remain on the registry

for their lifetime and are therefore excluded from public housing under federal law. Because data

on the number of Tier 3 registrants were unavailable, all people on the sex offense registry were

excluded from eligibility for public housing.

Similarly, although federal law prohibits people convicted for having manufactured

methamphetamine in public housing from ever living in public housing again, data on the

location of manufacture for people convicted for this offense were not available. Vera researchers,

therefore, assumed that all convictions for methamphetamine manufacturing in Michigan arose

from incidents in public housing, and all such people are excluded from public housing eligibility.

In Michigan, because no available estimate on recidivism rates for people with convictions for

manufacturing methamphetamine exists, the team assumed that each relevant disposition within

and across years represents a new person permanently excluded from public housing.

In Oklahoma, the names and Oklahoma Department of Corrections (ODOC) identifying numbers

for people whose convictions were for offenses that would result in a lifetime sex offender registration

requirement were publicly available in separate databases. The team matched these ODOC numbers

against the November 3, 2022, conviction dataset to exclude these people from eligibility for public

housing. As in Michigan, the location of methamphetamine manufacturing convictions was not

available, so the team assumed all convictions under statutes that referred to people manufacturing

or possessing, selling, or distributing precursors for the manufacture of methamphetamine took place

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Public Housing Eligibility for People with Conviction Histories

on public housing property, and all people with such convictions were permanently excluded from

public housing and unable to be restored through changes in state legislation.

These assumptions maximize the number of people with a conviction who are permanently excluded

from public housing, minimizing estimates of those excluded due to PHA discretionary policies.

Income Limits

People and households are eligible for public housing if they do not exceed certain income

limitations. People with a conviction whose incomes exceed those limits would not gain eligibility

for public housing through changes in admissions policies based on conviction histories and

were excluded from Vera’s estimates. The team took data on Area Median Income and eligibility

thresholds under the Low Income (LI), Very Low Income (VLI), and Extremely Low Income (ELI)

thresholds from HUD’s Fair Market Rents and Income Limits data for 2021 (U.S. Department of

Housing and Urban Development, 2023). The team produced estimates of the incomes of people

leaving incarceration and those with a conviction history in the years following contact with the

criminal legal system. The estimates were based on research from the Brookings Institute and

the Brennan Center for Justice; these institutions based their research on data from the National

Longitudinal Survey of Youth, 1997 (NLSY97) (Bureau of Labor Statistics, 1997; Craigie et al.,

2020; Looney and Turner, 2018). Because the NLSY97 survey captures income distributions for

people with a conviction history as a categorical variable for each year following release from

incarceration, Vera assumed that income followed a stepwise function with a uniform distribution

within each income category.3

To estimate the number of people leaving incarceration who would be eligible for public

housing, the team assumed that people leaving incarceration will live alone in public housing.

This assumption reduces the amount of income the person may earn before they lose income

eligibility for public housing, because income thresholds for public housing increase with the size

of the household in a non-linear fashion. Although many people may live with family members

in the period following their release from incarceration, it was not possible to make reasonable

assumptions about household size or the income of other potential household members.

This assumption also meant that researchers did not consider the number of people who might be

affected by a change in policy due to their status as family and household members of people with

conviction histories. If one member of a family is ineligible for public housing, the whole family

will be denied unless the person with a conviction history leaves the household.4 Not attempting

to count family members likely minimizes Vera’s estimates of the number of people currently

excluded from public housing due to exclusionary rules.

3 For example, if the income threshold for public housing in a county was $37,500, Vera researchers assumed that one-half of the people in the $25,000 to $50,000 income category were at or below $37,500 and the other one-half were above it, as $37,500 is the midpoint between the minimum and maximum value of the category.

4 Many PHAs have rules requiring that all adults living in a home undergo background checks, with some PHAs screening tenants as young as 13. If it is determined that a person is ineligible because of a conviction or arrest record, they are not allowed to reside in that unit. A whole family may be denied if a parent or caregiver is proven to be ineligible.

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Results

Vera estimates that a minimum of 284,000 adults in Michigan and 233,000 adults in Oklahoma

have conviction histories that make them potentially ineligible for public housing or housing

choice vouchers due to the policies of public housing authorities in the state. To put these

numbers in context, more than 3.5 percent of all adult Michiganders and 7.6 percent of all

adult Oklahomans are potentially excluded from public housing due to the eligibility rules of

public housing authorities. In both states, these percentages are likely to be minimum estimates

due to both simplifying assumptions and excluding the effect that such policies have on family

members of people with a conviction history who wish to remain united as households. At

a minimum, more than one-half million (517,000) Americans in just these two states are

potentially excluded from federally assisted housing due to the application of discretionary

policies by public housing authorities.

In Michigan, if PHAs were to look only at the past 2 years of convictions and releases from

incarceration, at least 139,000 people would automatically regain their eligibility for public

housing; if PHAs were to look back only at the past 6 months, a minimum of 230,000

Michiganders would regain their eligibility. Policies to increase housing access in Michigan could

affect at least 1.7 to 2.9 percent of the adult population in the state. In Oklahoma, if PHAs were

to reduce lookbacks to 2 years of convictions and releases from incarceration, at least 125,000

people, or 3.1 percent of all Oklahomans, could have their eligibility for public housing restored;

if lookbacks were restricted to 6 months, more than 160,000 could have their eligibility restored,

representing 4.0 percent of all Oklahomans.

These numbers are a minimum of the number of people affected. Most importantly, the researchers

do not consider family and household members of people with conviction histories who may be

affected. If one member of a family is ineligible for public housing, the whole family may be denied

unless the person with a conviction history leaves the household. Some families may be unwilling

to live apart; for others, it may be financially impossible to maintain more than one household;

or the person with a conviction history may be a single parent whose ineligibility renders their

children ineligible as well, by default.

Second, Vera assumed PHAs that do not have or do not publish explicit policies are following

guidance from HUD and only considering serious convictions for the past 5 years; however, this

practice is unlikely to be the case, as many PHAs across the country have indicated that they

consider significantly longer timescales for a criminal background check, up to and including

entire lifetimes; that minor convictions are also considered; or a history of arrest alone is sufficient

to reject an application for tenancy (Tran-Leung, 2015).

Third, Vera researchers have included the year 2020 in estimates of the number of people

potentially excluded from federally assisted affordable housing due to a conviction in Michigan

and the years 2020–2022 in Oklahoma. Due to the COVID-19 pandemic, the years 2020–2022

added far fewer people to the list of those facing exclusions from public housing due to a criminal

conviction than in a typical year. Although some jurisdictions have made conscious policy efforts in

84 Reentry Housing After Jail or Prison

Public Housing Eligibility for People with Conviction Histories

the past few years to reduce the rate at which people are convicted and incarcerated, some rebound

in the reach of the criminal legal system following the pandemic will likely occur.

Discussion

There is an absence of evidence that policies that deny housing to people with conviction

histories contribute to safer housing complexes and communities. The information that is used

to make the determination about the danger and risk a person may pose has some limitations.

Criminal background checks provide information about a person at the time of their most recent

conviction, but nothing is revealed about what has happened with them in the time since, such

as success in employment and connections forged in the community (Bushway et al., 2022). If a

criminal background is used during the admissions process, an individualized assessment should

accompany the background investigation that considers mitigating factors such as “the facts or

circumstances surrounding the criminal conduct; the age of the individual at the time of the

conduct; evidence that the individual has maintained a good tenant history before and/or after

the conviction or conduct; and evidence of rehabilitation efforts” (Kanovsky, 2016). Using an

individualized assessment when making housing decisions may decrease the discriminatory impact

of exclusions absent the consideration of mitigating evidence (Kanovsky, 2016). Although people

with conviction histories are not a protected class under the Fair Housing Act, guidance released by

HUD suggests that the use of arrest and conviction histories for the basis of housing denials may

violate the Fair Housing Act under the following theories of liability: disparate treatment, disparate

impact, and refusals to make reasonable accommodations (McCain, 2022). Recently settled cases

in Hesperia, California, and New York City test the applicability of these theories. In both cities,

legal filings claimed that crime-free ordinances and blanket exclusions for people with arrest and

conviction histories had a disparate impact on people of color and violated the Fair Housing Act

(The United States Department of Justice, 2023; U.S. District for Eastern District of New York,

2019). Both cases were settled before trial.

In recent years, several jurisdictions have changed their policies to increase housing access for

people with conviction histories. In 2020, the Champaign Housing Authority in Illinois announced

that it would no longer use criminal background checks in admissions decisions outside of

the federal requirements. Several states have also started to limit how landlords may consider a

conviction history when determining an applicant’s eligibility. In Colorado, the Rental Application

Fairness Act (2019) prevents landlords from considering arrests that do not lead to a conviction

and convictions older than 5 years, with some exceptions.5 In Illinois, the Public Housing Access

Bill (2021) limits the lookback period of PHAs to 6 months.6 In New Jersey, the Fair Chance in

Housing Act (2021) limits the use of criminal background checks to after an offer of housing

has been made and limits the types of convictions that can be used for admission decisions

(New Jersey Office of Attorney General, 2021). Since passing these laws, more states and local

municipalities are considering similar changes.

5 Rental Application Fairness Act, H.B. 19-1106, 2019 Reg. Sess. (Co. 2019).

6 The Housing Authorities Act, 101 General Assembly, H.B. 5574, 2019 Reg. Sess. (Il. 2019).

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Conclusion

Housing is a foundational and basic building block of civic life. For people returning to

communities after involvement in the criminal legal system, housing is often the most important

tenet of successful reentry. Communities are beginning to explore different strategies to remove

housing barriers for people with conviction histories. PHAs, as leaders in providing safe, stable,

and affordable housing, have a role and an opportunity to strengthen communities and provide

an essential need for one of our most vulnerable populations. The effort starts with ensuring that

all doors to housing are open for everyone.

Acknowledgements

The authors thank Eurielle Kiki, Asha Muralidharan, and Celia Strumph for their support;

Cindy Reed for providing feedback; and our anonymous reviewers.

Authors

Niloufer Taber is the associate director for research for initiatives focused on postsecondary

education in prison and connecting people returning to their communities with housing at the

Vera Institute of Justice (Vera). Jacqueline Altamirano Marin is a program associate for Vera’s

Opening Doors to Housing project. John Bae is a program manager with Vera’s Opening

Doors to Housing project.

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Symposium

Recent Reforms in Zoning

Guest Editor: Noah M. Kazis

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

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U.S. Department of Housing and Urban Development • Office of Policy Development and Research

92 Recent Reforms in Zoning

Guest Editor’s Introduction

Learning From Land Use

Reforms: Housing Outcomes

and Regulatory Change

Noah M. Kazis

University of Michigan Law School

Introduction

Zoning is changing. Paradigms that have stood for a century—like the predominance of single-

family-only residential zoning—are being questioned and, in some places, abandoned. Political

sacred cows, like regulatory mandates for new construction to provide that most valued of

amenities, off-street parking, have been gored. Major reforms to loosen zoning and increase the

supply of housing have taken place in cities big and small, in state houses and city halls, from

coast to coast. Without overstating the case—in most places, the status quo remains unchanged,

and even in the few, change has been incremental—there has been a groundswell of support for

rethinking the restrictiveness of the American land use system.

Indeed, interest in zoning reform is a rare spot of bipartisan agreement. Legislation to promote

housing supply has been enacted by states as blue as California and Massachusetts and as red

as Montana and Utah. At the federal level, leading legislation comes from Indiana Republicans

and New Jersey Democrats. Concerns about regulatory barriers to housing production have been

trumpeted, at least for a time, by the Obama, Trump, and Biden White Houses (White House,

2022, 2016).1 This is a moment of ferment—and experimentation—in land use policy.

That experimentation demands careful policy evaluation: rigorous research exploring why land

use reforms have or have not worked; which policy levers matter most and how they interact; how

different tools function in different housing market contexts; and overall, how policymakers can

incrementally learn from the experiences of their neighbors. This article series helps build this

knowledge base. Gathering authors from multiple disciplines—economics, law, urban planning,

and public policy—and using both quantitative and qualitative empirical methods, this collection

1 Executive Office of the President. 2019. Executive Order 13878, Establishing a White House Council on Eliminating Regulatory Barriers to Affordable Housing. https://www.federalregister.gov/documents/2019/06/28/2019-14016/establishing-a-

white-house-council-on-eliminating-regulatory-barriers-to-affordable-housing.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

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U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Kazis

of seven articles offers new insights (and raises new questions) for policymakers exploring land use

reforms to increase housing supply.

This series comes at an auspicious moment. Housing policy experts have warned of the regulatory

barriers to housing production without much interruption for a half-century. But two factors have

pushed the issue into the spotlight—and even allowed reformers to notch some victories. First,

the politics have changed. The housing shortage has simply gotten much more acute, especially

in states like California and New York. Even if lower-income households ultimately suffer the

most from a housing shortage, today, middle-class Americans—and even some rather affluent

ones—are feeling the bite of restrictive zoning, particularly as rent burdens have increasingly hit

higher-income households over time (Dougherty, 2021; Ellen, Lubell, and Willis, 2021). This

has produced a far broader and more powerful political coalition for reform. The politics of

affordability has also been bolstered by renewed attention on racial equity—which has long been

the basis for concerns about exclusionary zoning—and new concerns about climate change. In

many places, after all, zoning limitations preclude the most environmentally friendly forms of

development—like dense, multifamily housing; housing near transit; and housing in climate-

friendly locations like coastal California—while pushing growth instead to the sprawling periphery

(Jones and Kammen, 2013).

At the same time, a steady stream of research has bolstered the case for zoning reform, suggesting

that restrictive land use policies lead not only to problems in the housing market but also

contribute to serious macroeconomic harms and racial injustice. Economists have shown how

overly-restrictive zoning has dramatically increased the cost of housing, especially in California

and metro areas along the Northeast Corridor (Gyourko and Molloy, 2015). They have shown

how zoning has limited household mobility to rich regions with better opportunities, costing the

economy as much as 9 percent of the gross domestic product—or over a trillion dollars (Ganong

and Shoag, 2017; Hsieh and Moretti, 2019). Scholars have connected restrictive zoning to

increased racial segregation (Resseger, 2013; Rothwell and Massey, 2009). They have traced how

frequently this was the intent of those enacting zoning regulations (Rothstein, 2017; Trounstine,

2018). They have connected limitations on access to high-opportunity neighborhoods, a

downstream effect of exclusionary land use rules, to a slew of important long-term social outcomes

for children (Chetty and Hendren, 2017). Moreover, a new strand of research has brought these

findings from the macro level down to the level of the neighborhood, tracing the effects of the

construction of individual buildings and providing an improved understanding of the mechanisms

at play (Li, 2022; Mast, 2021).

Even given the increasing magnitude of the housing supply shortage and the ever-increasing

body of research pointing to the need for regulatory reforms, it is somewhat remarkable how

much political action has been taken to reform land use in recent years. As Ellickson (2022)

demonstrated, American zoning is characterized by its basic stasis: once neighborhoods are built as

single-family residential neighborhoods, they overwhelmingly tend to stay that way. In some sense,

this long-term stability is to be expected. Leading theories of land use politics all agree: people hate

change. They bought their neighborhood in a bundle with their house, and whether for economic

or psychological reasons, they are distinctly, and perhaps unreasonably, averse to the risks brought

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by any change (Fennell, 2009; Fischel, 2005). Whether voters fear new development will increase

housing costs (pricing them out of their neighborhoods) or decrease them (reducing the value of

their homes) they are against it.

Indeed, to a surprising extent, not just land use policies, but land use politics, too, are little

changed over a period of decades. Many accounts of zoning law and politics written more than

a half-century ago could be republished today with only limited amendments. Richard Babcock’s

1966 The Zoning Game remains the witty and conversational tour guide for so many observers of

land use, while reexamining the still-vital warnings of 1968’s Kerner and Douglas Commissions

serves to remind any reader how little progress has been made toward racial and economic equality

in housing. It is difficult to think of many important regulatory systems that have changed so little

over so long: imagine environmental law without climate change or cap-and-trade, or telecom

under Ma Bell. Of course, land use law has evolved over this period, but too often by leaning

into its preexisting shortcomings: imposing tighter controls, more delay, and more discretion.

(Sometimes, as with new environmental laws, this brought important benefits—but the direction

remains consistent.)

This long-term stability has shaped the research base guiding land use reform. Quite simply, there

have been too few examples of land use reforms intended to promote housing supply, and as a

result, there is too little evidence on what reforms accomplish. Much of the leading empirical

research uses creative ways to estimate what the effects of land use reforms would be, based

on existing variations across places: how wages might rise if New York City and San Francisco

loosened their zoning; how housing affordability would improve if Connecticut allowed smaller

lot sizes; how Greater Boston might be less segregated if it had permitted more multifamily

housing (Glaeser, Gyourko, and Saks, 2005; Resseger, 2013; Song, 2021). These articles are

methodologically sophisticated and offer extremely important insights into the harms of overly-

restrictive land use regulations. But of course, those counterfactuals never happened.

Accordingly, there is a need for more high-quality research evaluating the reforms that did, in fact,

occur. Given the messiness of the real world, what research exists in this vein is often ambiguous

and contested (Freemark, 2020). Unsurprisingly, it is often the older interventions where

researchers have been most able to pin down what has worked and what has not. For example,

important and influential studies have evaluated California’s decades-long struggles to successfully

implement either the legalization of accessory dwelling units (ADUs) or its “fair share” system for

allocating regional housing-need obligations to localities (Brinig and Garnett, 2013; Lewis, 2005).

Similarly, researchers have relatively stronger understandings of New Jersey’s famous Mount Laurel

doctrine and Massachusetts’ analogous “40B” fair share process (Marantz and Zheng, 2020; Massey

et al., 2013).

This collection is meant to help fill that gap. The articles in this series look at places that have

made policy-relevant reforms and try to draw policy-relevant conclusions. These are lessons about

policy design in the real world—and often in real-time. As a result, they focus more on descriptive

analysis and less on the hard work of definitively disentangling all the causal mechanisms. This

approach certainly has its limits, but it has payoffs as well. In some sense, the articles in this series

are meant to be the second drafts of history, coming after initial journalistic coverage and adding

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scholarly rigor and empirical analysis, but before the authoritative accounts of a deep qualitative

history or a dot-every-i social scientific causal analysis are possible. More time—perhaps decades—

will be needed for all outcomes to unfold and all data to be available.

This focus shaped the scope of this article series. Some of the most splashy, well-covered reforms—

Minneapolis’ legalization of two- and three-unit homes citywide, or Oregon’s similar elimination of

single-family-only zoning across much of the state—were too nascent to be adequately evaluated

for the outcomes of greatest interest.2 The evaluations are worth waiting on. Likewise, the

constraints of timing shifted these articles toward studying outcomes like housing production and

away from outcomes like segregation, which might change more slowly (and where early effects

might not reflect a longer-term equilibrium). Issues like segregation—or climate emissions, rent

burdens, homelessness, or social mobility—of course remain the ultimate reasons one would care

about land use reform, the ends toward which housing production is a means, but this series was

not intended to measure them.

The scope of this collection is limited in at least two other important ways. First, it is focused on

questions of residential development and housing supply. Given the acuteness of the housing

crisis today, those issues seem especially timely, and many recent innovations in land use policy

have been addressed to residential supply. Other land use matters, whether attempts to revitalize

distressed neighborhoods or the planning of commercial and industrial areas and its effect on labor

markets, are well-worth further investigation elsewhere.

Second, this series sticks to an orthodox land use policy paradigm that sees adequate housing

supply as important and land use restrictions as costly barriers to that supply. This paradigm

is consistent with a wide range of ideological perspectives and policy approaches to land use

reform. Some articles in this series, for example, examine policies to facilitate market-rate housing

production, and others focus on subsidized housing development; some involve state-level

intervention into local control of land use, whereas others examine bottom-up policies crafted

by localities themselves. Recognizing the costs of land use regulation is also consistent with a

range of perspectives on the benefits of zoning to be weighed against those costs, and the policy

recommendation of removing costly barriers to production can be mixed-and-matched with

any number of non-land-use housing policies, from community ownership and social housing

models to rental assistance, homeownership subsidies, and mortgage market reforms. Land use

liberalization is not a panacea, even if it is the topic of this symposium.

But this collection does not include the voices of “supply skeptics” who hold that increased

supply will do little to improve, and may even hurt, overall housing affordability (Been, Ellen, and

O’Regan, 2019). Such perspectives remain fairly popular among the public at large and with a

small-and-declining number of scholars (Nall, Elmendorf, and Oklobdzija, 2022). However, this

“supply skepticism” is not backed by the weight of the evidence. Nor does this collection spend

undue time with more traditional arguments against development: that it leads to overcrowding

or a poor quality of life. Where substantiated, these are important concerns—and ones grappled

with in this collection—but the articles proceed with the recognition that the United States has

room to grow and a need to grow, and moreover, that such concerns have often undergirded

2 Important preliminary results in Minneapolis are provided by Kuhlmann (2021).

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or excused exclusionary land use policies. In other words, these articles all proceed on the

shared understanding that land use law ought, somehow, to facilitate adequate—and therefore,

additional—housing supply. The questions concern how to do so.

Within this defined scope, the collection covers a broad range of topics. The articles cover changes

to the substance of land use law and to its procedures. The authors study regulatory changes that

applied in select neighborhoods, citywide, and at the state level. Close attention is paid to the

ongoing reform efforts in California, the current epicenter of both the housing affordability crisis

and efforts to tackle it through land use changes. Three of the seven articles examine different

elements of that state’s recent reforms—and these only cover a fraction of the ongoing efforts.3

There is still much more to study. This collection fails to include coverage of the Mountain West—a

region facing unique challenges as remote work and other shifts helped suddenly drive up housing

costs during the COVID-19 pandemic—or of the Rust Belt. There has been renewed interest in

understanding how building codes can act as an important regulatory barrier to housing; this is

another important topic for future research.

Even so, these articles should prove useful in thinking through an array of policy options for

promoting housing production across a range of types of place. The results will not generalize

directly. Different cities will face different patterns housing demand, different legal backdrops, and

different political interests. Indeed, in at least one of the articles here—concerning neighborhoods

in Ramapo, New York, populated by ultra-Orthodox Jews—the uniqueness of the place is very

much the point. Rather, the hope is that these case studies point to the kinds of questions that

policymakers need to ask about the mechanisms before them: How does a particular policy play

out in neighborhoods of different incomes or with different preexisting lot sizes? What tradeoffs

apply when cities attempt to mandate affordable housing be included in new construction, and

how might they be evaluated?

Symposium Articles

The series includes seven articles. A first set of articles examines some of California’s recent

interventions into local land use. Nicholas Marantz, Christopher Elmendorf, and Youjin Kim

(2023) study one of California’s most-heralded reforms: the state’s efforts to legalize ADUs across

most single-family neighborhoods. Given ADUs’ relatively low costs and compatibility with the

existing built environment, they have been widely touted as a promising reform. California, after

decades of unsuccessful attempts to force local governments to permit ADU construction, enacted

a slew of statutes between 2016 and 2020, repeatedly limiting localities’ ability to block ADUs and

effectively permitting the construction of ADUs smaller than 800 square feet as-of-right. Marantz,

Elmendorf, and Kim offer two important sets of insights about these latest reforms. First, they

quantify ADU production, showing it to be a considerable share of California’s total housing growth

in the last few years: around 13 percent of permits in the Bay Area and 19 percent in Southern

California. Second, they show what kinds of parcels are mostly likely to have an ADU, such as

3 Future research will certainly be needed to explore two of the state’s more ambitious experiments, its provisions for as-of-right lot splits and duplexes and for higher density along commercial corridors, which were too recently enacted to be evaluated in this series.

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those that are larger, those that are in neighborhoods with moderate rents (i.e. relatively low, but

not the lowest, rents), and those closer to job centers. These findings help to identify where this

type of reform is likely to be most efficacious.

Paavo Monkkonen, Michael Manville, Michael Lens, Aaron Barrall, and Olivia Arena (2023)

examine another of California’s attempts to make a long-ineffective effort to produce housing

more functional, specifically, recent reforms to the state’s Regional Housing Needs Assessment

(RHNA) process. Under longstanding state law, California localities are required to develop

plans, called Housing Elements, for how they will meet the need for new housing at various

income levels, as projected by state and regional bodies. The RHNA process is meant to ensure

that all municipalities do their “fair share” in meeting that housing need. But housing need has

historically been miscalculated and misallocated, and the state has failed to scrutinize local plans

for bad-faith (or inadvertent) evasion of local responsibilities. Again, between 2017 and 2019,

the state enacted a suite of reforms to tighten policy and address each of those problems. The

authors find those reforms to be substantial, though incomplete, successes. Under the most recent

planning cycle, Southern California cities have engaged in dramatically more land use changes

to meet their RHNA obligations, including in the high-demand locations where such rezonings

are most needed. Indeed, just the 93 first Southern California cities to have compliant housing

elements—representing just one-fifth of the state’s population—have rezoned to add space for

over 250,000 units. In contrast, in the previous, prereform cycle, rezonings statewide only created

space for 35,430 units and, moreover, concentrated those rezonings where they were least needed.

Although the system is still slow and labor-intensive—and seems still to provide some mechanisms

for not-in-my-backyard politics to reduce the obligations of Whiter and wealthier localities—the

improvements appear to be marked.

The third investigation of state-level reform in California comes from Moira O’Neill and Ivy Wang

(2023). They examine Senate Bill 35 (SB 35), a 2017 law that targets not the substance of local

zoning but its procedures. In cities that have not met their housing production obligations under

RHNA, certain mixed-income or fully-affordable multifamily housing developments can avoid local

discretionary review. Instead, these SB 35 projects may use a state-provided ministerial process

to receive their permits. These projects must comply with the bulk and use requirements of local

zoning; only the process changes. Gathering project-level data on the approval process and rich

context on individual city’s implementation of SB 35, O’Neill and Wang find preliminary evidence

that SB 35 is making the development process faster and more predictable—and therefore, cheaper

and more attractive for affordable housing developers. Comparing the kinds of developments that

would have been eligible for SB 35 before its enactment to those that used it subsequently, O’Neill

and Wang find, for example, that approval timelines were cut by more than half in Los Angeles

and San Francisco. Although such reforms are inherently limited to places that, on paper, allow for

dense development, this research highlights the independent importance of procedure in any land

use reform agenda.

Although state-level reforms, in California and elsewhere, are especially high-profile (and potentially

high-impact) changes, land use remains primarily a local prerogative, and much innovation in this

space comes at the local level. Jake Wegmann, Aabiya Noman Baqai, and Josh Conrad (2023) study

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an important land use reform in Houston ( not, as Houstonians would remind you, a rezoning,

because technically the city lacks zoning). Changes to the city’s mandatory minimum lot size,

first enacted in 1998 and then extended in 2013, permitted the widespread development of what

Houstonians call “townhouses,” skinny, single-family homes (whether attached or detached) on

lots as small as 1,400 square feet. Past research has catalogued the scale of the townhouse boom,

which has produced tens of thousands of units, and the neighborhoods where growth has been

fastest (Gray and Millsap, 2020). Wegmann, Baqai, and Conrad add to this literature by focusing

on an especially important set of townhouse redevelopments: those which replace existing single-

family housing. Given the political inviolability of such lots in many places and the especially high

barriers to their redevelopment, it is especially valuable to understand under what conditions such

single-family lots might be densified. The authors find that single-family redevelopment accounts

for about one-fifth of total townhouse developments—whether this is a lot or a little is a matter of

perspective—and that it tends to occur when large lots near the urban core are occupied by small,

old homes. Notably, this redevelopment tends to occur in areas with higher-than-average housing

prices, yet it provides relatively affordable and spacious housing options.

Although Houston’s liberal land use rules and sustained growth have received much attention,

Joseph Huennekens (2023) points to a much more unusual case study: the Monsey section of

suburban Ramapo, New York. Monsey is home to a fast-growing ultra-Orthodox Jewish population,

a group which, unlike most suburbanites, is extremely supportive of housing development.

Whereas most land use reforms take place in a political context that is, at best, apprehensive about

growth, Monsey illustrates what an enthusiastic embrace of density might look like in a traditional

suburban setting. Mixing qualitative and quantitative techniques, Huennekens traces the area’s

sustained efforts to permit housing development, which have transformed it from predominantly

single-family to primarily multifamily housing, and identifies what worked in this unique setting.

He finds, for example, more sustained housing production in 6–12 unit buildings and less success

with ADUs, and he pinpoints the importance in this context of allowing condominiums in addition

to rentals. Huennekens also examines the impact of this growth on suburban service provision,

exploring the most common complaints around water, sewer, and fire provision.

Jacob Krimmel and Betty Wang (2023) study Seattle to shed light on a common proposal for

land use reforms: mandatory inclusionary zoning. In 2017 and 2019, Seattle rezoned 33 of its

neighborhoods for greater density, while also requiring that all new development in those areas

either set aside units as below-market-rate housing or pay into a citywide affordable housing fund.

By using a difference-in-differences approach to compare the pace of housing production just

inside the rezoned areas to those just outside them, Krimmel and Wang find that Seattle’s policy

reduced development along the upzoned side of these borders. Instead, development shifted to

parcels just outside the rezoned area. In Seattle, it seems, the cost of the affordability component

outweighed the benefit of the relatively modest upzonings (at least during this period), but

neighboring areas had zoned capacity sufficient to allow continued housing production.

Finally, Leah Brooks and Jenny Schuetz (2023) flip the script in their article. Rather than ask

whether a given zoning change generated additional housing production, they ask whether,

in Washington, D.C., housing production was preceded by zoning changes. Washington, D.C.,

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they show, is a city that has experienced meaningful amounts of infill development—but not

especially as a result of rezonings. The District’s rezonings over the past two decades largely

left bulk and density rules unchanged, especially in single-family residential neighborhoods,

and the neighborhoods that grew did not usually do so because they had been rezoned. Brooks

and Schuetz find no association between the change in a neighborhood’s housing units and

the percentage of land in that neighborhood that was rezoned. Instead, they identify as critical

the fact that high-growth neighborhoods began with relatively few single-family homes (many

were nonresidential to begin with). Their work points to the likeliest path forward for infill

housing production absent regulatory reform: finding underutilized commercial and industrial

neighborhoods (with limited political opposition to housing from residents) to convert to large-

scale multifamily housing.

Symposium Themes

Despite the disparate institutional, political, and economic contexts for the reforms studied, some

common themes and sharp contrasts emerge across the seven articles. At the most basic level—

but still worth saying—these articles refute the idea that zoning reform is futile, as some scholars

have suggested, or that it is unnecessary given popular demand for the existing, low-density built

environment (Schragger, 2021). Both city and state reforms can facilitate the production of new

housing supply. Both changes to the substance and the procedures of zoning can contribute.

Indeed, as Monkkonen, et al. (2023) show, even legal strategies which had seemed utterly

ineffective—like mandates for local governments to plan for housing growth—can be reworked

into powerful levers for change.

Perhaps more to the point, reforms have achieved two more difficult tasks. Reforms have

successfully facilitated subsidized housing development, as seen most clearly in O’Neill and Wang’s

(2023) research finding faster development times for the affordable projects aided by California’s

SB 35. And new housing production is possible even in established single-family residential

areas, which are widely understood to be especially resistant to redevelopment (indeed, this

understanding is confirmed by Brooks and Schuetz [2023] in their study of Washington, D.C.).

As illustrated by Houston townhouses, Ramapo’s growth, and California’s ADU development, even

single-family neighborhoods can change. Densification is not easy and not without policy tradeoffs.

Nor is it an inevitable result of regulatory liberalization. Factors ranging from demand and location

to the ease of site assembly are critical. But well-designed reforms can promote the development of

new housing: at many income levels and in many kinds of neighborhoods.

The collection also points to the kinds of neighborhoods most affected by some contemporary

zoning reforms. In Houston, townhouse redevelopment is taking off in higher-income, non-

gentrifying neighborhoods. ADU construction in California has been concentrated in census

tracts with slightly lower median rents compared to their larger regions and not very-low-

income areas. It appears that, in general, zoning reforms may have smaller effects in the lowest-

income neighborhoods, where future development would bear relatively low prices, and in

the very highest-income neighborhoods, where wealthy residents either retain tools to inhibit

redevelopment or place such a high value on the amenities of low density that they (for now)

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eschew the returns to redevelopment. Whether this pattern holds for all types of reform is an

important question, but as a rough rule of thumb, it provides useful guidance for those concerned

with gentrification and displacement in low-income neighborhoods. Those neighborhoods appear

not to be the primary places affected by these reforms.

This collection also highlights the importance of forms of tenure and ownership. In Ramapo, for

example, permitting multifamily units to be sold separately, as condominiums, was necessary for

significant levels of investment, echoing the Houston experience, where density has been built

through a townhouse form that allows not just condominium ownership but ownership in fee

simple. While rentals play a critical role in the housing system, in certain contexts—perhaps

especially in more suburban settings—the ability to own one’s unit remains something of

economic, practical, and cultural value. Relatedly, California’s experience with ADUs indicates the

lasting importance of covenants and homeowners associations in limiting housing production, even

when those covenants are no longer legally enforceable. This echoes past research on the longer-

term effects of racial covenants after their being ruled illegal in Shelley v. Kraemer, and it points to

the need for close consideration of the private law devices in play (Brooks and Rose, 2013).

A long literature has considered the merits of pairing upzoning with affordability requirements

(Hamilton, 2021; Schuetz, Meltzer, and Been, 2009). This series of articles adds to that discussion,

though it hardly resolves it. Krimmel and Wang’s (2023) study of Seattle adds a note of caution,

showing how miscalibrated deals can leave the cost of affordability requirements higher than the

benefits of the added density—potentially impeding rather than promoting development. Ramapo,

too, abandoned its affordability requirements for similar reasons. At the same time, though,

California’s RHNA law—and its strengthened enforcement, both through SB 35’s ministerial

process and through the reforms to the target-setting and local rezoning processes—points in

another direction. A path exists for zoning reforms that specifically target below-market-rate units.

This path may not be an inclusionary zoning requirement in all cases, but the alignment of land

use reforms with subsidized housing programs remains an important opportunity for continued

policy innovation.

There are no silver bullets here—as there so rarely are. Where California has attempted to

restructure the local zoning process, its successes have only been partial, as the articles studying

those state-level reforms show. Many cities have had no projects proceed under SB 35, and many

have found ways to keep their housing targets under RHNA lower than they ought to be. Ramapo

has struggled to upgrade its infrastructure as it grows. Progress and meaningful policy successes

have occurred, however, in an area where, historically, many interventions have fallen short.

Moreover, as both Krimmel and Wang’s (2023) account of Seattle and Huennekens’ (2023) story

of Ramapo make clear—not to mention the decades-long sagas of California’s various housing

production strategies—there is always a need for tinkering and iterative improvement. In Seattle,

for example, the same broad policy framework would have had quite different implications with

different levels of affordability required, different amounts of density granted, or a different drawing

of the geographic boundaries.

There is much more to learn about what interventions work—and especially about how to pair the

right reforms with particular places. Case studies cannot show every conceivable permutation of

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policy detail, political context, and market conditions. This series of articles helps to build out the

body of evidence for policymakers looking to understand the current wave of reforms. But there is

more to learn about what cities and states have done—and much more importantly, more for cities

and states still to try. At a high level, the need for zoning reform remains clear. The hard questions

remain: How to select from an ever-growing menu of reform options? How to tailor those strategies

to local conditions? How to mix-and-match, and how to innovate further?

Then, perhaps the hardest question of all: How to get those reforms passed?

Acknowledgments

My greatest thanks go to the authors in this series, who worked diligently, creatively, collaboratively,

and flexibly to put this series together. It has been a pleasure to see these articles grow from ideas

to drafts to completion—and, I hope, to have helped along the way. I am also indebted to my

colleagues at the NYU Furman Center who aided at every stage of the editorial process and, in

particular, to Vicki Been, Ingrid Gould Ellen, and Matthew Murphy. Finally, thanks are due to the

Pew Charitable Trusts for their funding of, and active engagement with, this project.

Guest Editor

Noah M. Kazis is an assistant professor of law at the University of Michigan Law School.

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Rothwell, Jonathan, and Douglas S. Massey. 2009. “The Effect of Density Zoning on Racial

Segregation in U.S. Urban Areas,” Urban Affairs Review 44 (6): 779–806.

Schragger, Richard C. 2021. “The Perils of Land Use Deregulation,” University of Pennsylvania Law

Review 170 (1): 125–205.

Schuetz, Jenny, Rachel Meltzer, and Vicki Been. 2009. “31 Flavors of Inclusionary Zoning:

Comparing Policies From San Francisco, Washington, DC, and Suburban Boston,” Journal of the

American Planning Association 75 (4): 441–456.

104 Recent Reforms in Zoning

Learning From Land Use Reforms: Housing Outcomes and Regulatory Change

Song, Jaehee. 2021. The Effects of Residential Zoning in U.S. Housing Markets. Job market paper.

https://static1.squarespace.com/static/6160e89a75cba2217fc14866/t/61a3f4ea1898562e22cf3

8d8/1638135020614/Jaehee_Song_JMP_share.pdf.

Trounstine, Jessica. 2018. Segregation by Design: Local Politics and Inequality in American Cities. New

York City, NY: Cambridge University Press.

Wegmann, Jake, Aabiya Noman Baqai, and Josh Conrad. 2023. “Here Come the Tall Skinny

Houses: Assessing Single-Family to Townhouse Redevelopment in Houston, 2007–2020,”

Cityscape 25 (2): 171–202.

White House. 2022. President Biden Announces New Actions to Ease the Burden of Housing Costs. Press

release. https://www.whitehouse.gov/briefing-room/statements-releases/2022/05/16/president-

biden-announces-new-actions-to-ease-the-burden-of-housing-costs/.

———. 2016. Housing Policy Toolkit. https://obamawhitehouse.archives.gov/sites/whitehouse.gov/

files/images/Housing_Development_Toolkit%20f.2.pdf.

Additional Reading

Babcock, Richard. 1966. The Zoning Game: Municipal Practices and Policies. Madison, WI: University

of Wisconsin Press.

Kerner, Otto Jr. 1968. Report of the National Advisory Commission on Civil Disorders. Washington,

DC: Government Printing Office. https://www.ojp.gov/ncjrs/virtual-library/abstracts/national-

advisory-commission-civil-disorders-report.

National Commission on Urban Problems. 1969. Building the American City: Report of the National

Commission on Urban Problems to the Congress and to the President of the United States. Washington,

DC: Government Printing Office.

Cityscape 105

106 Recent Reforms in Zoning

by Nicholas J. Marantz, Christopher S. Elmendorf, and Youjin B. Kim

Where Will Accessory Dwelling Units

Sprout Up When a State Lets Them

Grow? Evidence From California

Nicholas J. Marantz

University of California Irvine, School of Social Ecology

Christopher S. Elmendorf

University of California Davis, School of Law

Youjin B. Kim

University of California Irvine, School of Social Ecology

Abstract

Since 2016, California has adopted several laws to facilitate the development of accessory dwelling units

(ADUs), which are secondary units on residential parcels. This article analyzes ADU permitting in the

Bay Area and southern California under the newly liberalized legal regime using data collected by the

state. The analyses indicate that ADUs represent a substantial share of recent housing permits, that

ADUs are typically permitted on parcels with relatively good access to jobs, and that the relationship

between a neighborhood’s ethnoracial composition and the prevalence of ADU permitting varies by

county. These findings provide guidance for state and local governments seeking to understand where

ADUs might be permitted following the liberalization of ADU regulation.

Introduction

Housing advocates have long touted accessory dwelling units (ADUs)—secondary units on

residential parcels—as a potential tool to address soaring housing costs in coastal metropolitan

areas. As compared with denser forms of infill development, ADUs have several potentially

appealing characteristics. First, ADUs are frequently invisible from the street; they are in backyards,

existing secondary structures, or converted interior spaces, such as attached garages. Thus, as

compared with multifamily housing, ADUs may be a more politically palatable way to add much-

needed housing supply in single-family neighborhoods of high-cost metropolitan areas. Second, in

any given neighborhood, ADUs tend to be more affordable than single-family housing because the

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 107

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Marantz, Elmendorf, and Kim

units are relatively small and typically have fewer amenities (e.g., lower ceiling heights, less natural

light). Third, ADUs provide an opportunity for multigenerational households to enable family

members to age in place.

Although ADUs may be a relatively politically palatable form of new development, they have

engendered plenty of opposition too. In 1982, California passed its first state law limiting local

governments’ authority to restrict ADU development, and in 2002 the legislature made cities

permit ADUs “ministerially,” that is, without subjecting project applications to any discretionary

standards or conditions of approval. But, even after the 2002 reforms, many cities still found ways

to thwart the state’s pro-ADU policy (Brinig and Garnett, 2013). Between 2016 and 2020, the

legislature enacted multiple statutes, again strengthening state ADU law, and it appears that the

new reforms are finally unlocking ADU development opportunities.

This article analyzes ADU permitting under the newly liberalized California regime to shed light

on the prevalence and geography of ADU permitting in the nine-county San Francisco Bay Area

and in five southern California counties (Los Angeles, Orange, Riverside, San Bernardino, and

Ventura). Collectively, these counties represent 67 percent of the state’s population and 82 percent

of the parcels receiving ADU permits from 2018 through 2021. The analysis is restricted to parcels

zoned for single-family development because these were the parcels that the California Legislature

targeted for regulatory relief. As detailed below, we find that ADUs represent a substantial share

of recent housing permits, that ADUs are typically permitted on parcels with relatively good

access to jobs, and that there are heterogenous relationships between a neighborhood’s ethnoracial

composition and the prevalence of ADU permitting.

Background

California has a significant housing affordability problem, which ADU development could mitigate.

As of January 2023, the median rent in California was roughly 41 percent higher than the national

median, and rents in the state’s high-cost cities were far higher (Zillow, 2023a). For example, in the

Silicon Valley city of Palo Alto, the median rent was 89 percent higher than the national median

(Zillow, 2023b). The high cost of housing in California stems largely from supply constraints,

including barriers to greater density in existing residential neighborhoods.

Moreover, California faces serious pressures to reduce the need for development at the urban

fringe, necessitating policies that can help to produce housing by intensifying residential densities

in areas that are already urbanized. The state confronts an increased risk of wildfires at the

wildland-urban interface and has adopted ambitious goals to reduce greenhouse gas emissions,

in part by cutting per capita vehicle miles traveled (VMT). In addition, as noted above, ADUs

provide an opportunity for multigenerational households to enable family members to age in place.

ADUs could thus respond to several pressing needs by facilitating more intense development of

already-developed places and providing a relatively affordable type of housing. Nevertheless, the

same forms of neighborhood opposition that frequently thwart efforts to build townhomes and

apartments have also, in the past, limited options for ADUs.

108 Recent Reforms in Zoning

Where Will Accessory Dwelling Units Sprout Up

When a State Lets Them Grow? Evidence From California

California legislators have long recognized the potential benefits of ADUs and the need to address

localized opposition. In 1982, the state adopted a law explicitly authorizing municipalities

to allow ADUs and prohibiting municipalities from barring ADUs, with some exceptions.1

Municipalities, however, could still limit ADU development by imposing cumbersome and

unpredictable discretionary review requirements on applications for ADUs.2 As a result, the

legislature revised the relevant statute in 2002 to compel nondiscretionary review processes for

ADUs, among other provisions.3

Nevertheless, a survey of local regulatory responses found that “most California cities appeared to

comply with the state mandate by amending their zoning rules to permit ADUs, but they imbedded

many costly regulatory requirements within the ‘authorization’ that dramatically curtail[ed] the

likelihood that ADUs [would] actually be developed” (Brinig and Garnett, 2013: 547). Local

constraints included “costly off-street parking and minimum lot size requirements, . . . restrictions

on the maximum size of the ADU[,] . . . [and] limits on the ability of owners to lease ADUs” (Brinig

and Garnett, 2013: 547).

To address such restrictions, the California Legislature again revised the relevant statute in 2016

and 2017. As described in a companion article:

The revisions capped the fees local governments could impose, limited the

stringency of dimensional standards (such as setback requirements), and

established a strict timeline for reviews of applications (Senate Bill [SB] 1069,

2016 Cal. Stat. 4945; Assembly Bill [AB] 2299, 2016 Cal. Stat. 5044; AB 494,

2017 Cal. Stat. 4725; SB 229, 2017 Cal. Stat. 4688). Moreover, these laws limited

(and in many cases eliminated) the authority of local governments to impose

parking requirements on ADUs. In 2019, the legislature shortened the approval

timeline, prohibited municipalities from restricting the right to build ADUs to

owner-occupiers, tightened the dimensional standards (e.g., by establishing

minimum and maximum square footage requirements for ADUs), and prohibited

the imposition of fees on ADUs of less than 750 square feet (SB 13, 2019 Cal. Stat.

5559). [Another 2019 bill entitled homeowners to add both an 800 square foot

ADU and a smaller “junior ADU” (AB 68, 2019 Cal. Stat. 655).] The Legislature

also barred homeowners associations (HOAs) from imposing any covenant,

condition, or restriction (CCR) that either “effectively prohibits or unreasonably

restricts the construction or use of an accessory dwelling unit . . . on a lot zoned

for single-family residential use” (AB 670, 2019 Cal. Stat. 2515, 2515), and, in

2020, prevented HOAs from restricting the rental of ADUs (AB 3182, 2020 Cal.

Stat. 3068). In sum, as of 2020, ADUs should have been allowed as-of-right,

provided that they were under 800 square feet, no more than 16 feet tall, and had

4-foot setbacks (Marantz et al., under review).

1 1982 Cal. Stat. 5500.

2 For examples, see Desmond v. County of Contra Costa, 25 Cal. Rptr.2d 842 (Ct. App. 1993) (denying ADU permit based on perceived architectural incompatibility); Harris v. City of Costa Mesa, 31 Cal. Rptr.2d 1 (Ct. App. 1994) (denying ADU

permit based on concerns with height and neighborhood character).

3 2002 Cal. Stat. 6847.

Cityscape 109

Marantz, Elmendorf, and Kim

This article analyzes data collected by the state to understand the prevalence of ADUs in five

southern California counties and the nine-county Bay Area since ADU liberalization. It compares

the ADU permit rate across counties, and it analyzes parcel sizes to test whether ADUs tend to be

built on larger parcels (where there is more physical space for another structure). It then examines

the characteristics of areas where ADUs are being built, including ethnoracial composition, median

rent, and jobs accessibility.

Patterns of Accessory Dwelling Unit Permitting in the Bay Area

and Southern California

The data on ADU permitting comes from the annual progress reports compiled by the California

Department of Housing and Community Development (HCD). Although cities in California have

long been required to submit information about their housing plans to HCD, a state law adopted

in 2017 significantly enhanced the reporting requirements. Most relevant to this study, cities

must annually submit a spreadsheet including new housing units that received an entitlement, a

building permit, a certificate of occupancy, or any “other form of readiness that was issued during

the reporting year” (HCD, n.d.: 9). A process described in the appendix identified 43,160 parcels in

the Bay Area and southern California with at least one ADU permit.

The dataset used for analysis merges the ADU permit data with tract-level data on jobs accessibility,

median rent, and ethnoracial characteristics. The latter two measures come from the 2012–2016

American Community Survey (ACS). Both the measure of jobs accessibility, described in detail in

the appendix, and the ACS data predate the first year of ADU data (2018), mitigating concerns

about endogeneity. This article compares jobs accessibility, median rent, and the size of parcels

with and without ADUs. It then analyzes the relationship between ADU permitting and ethnoracial

composition by county with tract-level pairwise correlations.

Exhibit 1 reports counts of parcels with at least one ADU permitted from 2018 through 2021 (in the

column marked “ADUs”) and the results of the U.S. Census Bureau’s Building Permits Survey (BPS)

for incorporated municipalities in the study area during the same period. The BPS data, which come

from surveys of jurisdictions, include 275 of the 280 municipalities in the sample analyzed in this

article. The survey instructions direct respondents to report all detached ADUs and some attached

ADUs.4 ADUs are not separately reported in the BPS data, so a detached ADU, for example, would

be placed in the “1-unit” structure category along with detached single-family houses.

Given that the BPS count of permits should include a significant (albeit indeterminate) proportion

of permitted ADUs, exhibit 1 suggests that ADUs represent about 13 percent of permits in the

Bay Area and around 19 percent of permits in the southern California study area. Within both

regions, there is significant variation. In the Bay Area, Marin County, located on the other side of

the Golden Gate Bridge from San Francisco, has the highest proportion of ADUs. That proportion

4 Jurisdictions are instructed to report ADUs that are “detached and built on same lot as existing main structure[;] attached and built at the same time the main structure is being constructed[;] attached to main structure via a walkway[;] detached from existing structure but share utilities with main structure[; or] built over an existing detached garage - using the detached garage as the foundation for the ADU.” Jurisdictions should not report ADUs that are additions, that require alterations (e.g., a changed roof line in the main structure), or “conversions” (U.S. Census Bureau, 2021).

110 Recent Reforms in Zoning

Where Will Accessory Dwelling Units Sprout Up

When a State Lets Them Grow? Evidence From California

is driven by the low number of total units permitted from 2018 through 2021 per capita in Marin

County (3 units per 1,000 people) as compared with the Bay Area region as a whole (11 units per

1,000 people).5 In Alameda, Contra Costa, and Santa Clara Counties, all of which have relatively

large numbers of total permits, ADUs account for roughly 12 percent to 15 percent of newly

permitted units. San Francisco, which had relatively high per capita permitting (15 units per 1,000

people), had a relatively low proportion of ADUs, perhaps because its housing stock predominantly

consists of multiunit buildings.

Exhibit 1

New Units Permitted in Incorporated Areas, 2018–21, by County

Units by Structure Type (Census Bureau)

County

ADUs/

Pop.

3–4

Census

ADUs

1 unit

2 units

5+ units

Census

(2018)

units

Tot.

Tot. (%)

Bay Area

7,734,987

11,575

29,830

736

700

56,391

87,657

13

Alameda

1,651,760

2,927

6,444

200

363

16,287

23,294

13

Contra Costa

1,143,188

1,143

5,412

132

28

3,615

9,187

12

Marin

262,179

495

418

54

3

204

679

73

Napa

140,340

245

443

4

20

2,433

2,900

8

San Francisco

885,716

840

104

154

102

12,541

12,901

7

San Mateo

770,927

1,899

1,371

38

28

3,619

5,056

38

Santa Clara

1,943,579

3,269

6,757

84

93

14,786

21,720

15

Solano

436,813

257

3,968

4

0

960

4,932

5

Sonoma

500,485

500

4,913

66

63

1,946

6,988

7

Southern

18,774,638

31,585

77,550

6,346

2,674

81,725

168,295

19

California

Los Angeles

10,192,593

26,383

23,623

4,654

669

54,694

83,640

32

Orange

3,186,254

3,032

13,059

738

926

14,310

29,033

10

Riverside

2,397,662

668

23,764

128

491

5,473

29,856

2

San Bernardino

2,150,017

669

14,734

692

448

4,783

20,657

3

Ventura

848,112

833

2,370

134

140

2,465

5,109

16

ADU = accessory dwelling unit.

Notes: The Census Bureau aggregates building permit data for the Bay Area municipalities of Clayton, Hercules, Lafayette, Orinda, and Moraga with unincorporated Contra Costa County. This exhibit omits these jurisdictions and all unincorporated areas.

Sources: ADU data: California Department of Housing and Community Development (2022); Building Permit Survey data : U.S. Census Bureau (2022); Population data: State of California, Department of Finance (2021)

As is the case in the Bay Area, in southern California ADU permitting was higher in the coastal

counties (Los Angeles, Orange, and Ventura), where undeveloped, unprotected land is scarcer, and

rents are higher than in the inland counties of Riverside and San Bernardino. Within Los Angeles

County, the City of Los Angeles accounts for 70 percent of ADU permits (and thus 59 percent

of total ADU permits in the southern California study area), even though it accounts for only 40

percent of the population of Los Angeles County and only 21 percent of the southern California

study area population.

5 The denominator for the per capita statistics is measured as of 2018, based on estimates from State of California, Department of Finance (2021).

Cityscape 111

Marantz, Elmendorf, and Kim

The data indicate that ADUs in the study areas are typically sited on parcels with good access

to jobs and with acreage sizes comparable to other parcels. ADUs generally receive permits in

tracts with slightly lower median rents compared to the region as a whole. Exhibit 2 displays the

distribution of tract-level jobs accessibility for the 4,797,176 residential parcels in the Bay Area and

the southern California study area. The x-axes indicate a measure of jobs accessibility: the distance-

weighted sum of jobs within 50 miles of the centroid for the tract in which a parcel is located.

This distance-weighted measure, detailed in the appendix, means that closer jobs are more heavily

weighted than jobs that are farther away. The y-axis indicates the proportion of parcels at each level

of jobs accessibility. The distribution of parcels by jobs accessibility differs substantially between

the Bay Area and the more sprawling southern California region, but in both regions, ADUs are

more likely to be built on parcels with good jobs accessibility.

Exhibit 2

Distribution of Jobs Accessibility for Residential Parcels by Accessory Dwelling Unit Status

Bay Area

Southern California

No ADU on parcel

ADU on parcel

0.4

nel Density

Ker

0.2

0.0

0.0

2.5

5.0

7.5

10.0 0.0

2.5

5.0

7.5

10.0

Distance-Weighted Sum of Jobs (x100,000) Within 50 Miles of Tract

ADU = accessory dwelling unit.

Source: See appendix

Exhibit 3 shows that the size of parcels where ADUs have been permitted generally mirrors that

of all other residential parcels, although the smallest residential parcels are relatively unlikely to

include an ADU. Exhibit 4 shows that, in both the Bay Area and southern California, ADU permits

tend to be issued in census tracts that have relatively low rents, although not the lowest rents. At

first glance, this is surprising since the rental or for-sale value of an ADU is obviously higher in

places with higher rents. The disamenity value of an ADU to the occupant of a parcel’s primary

112 Recent Reforms in Zoning

Where Will Accessory Dwelling Units Sprout Up

When a State Lets Them Grow? Evidence From California

residence (loss of privacy or yard space) may be greater in markets with higher rents.6 A separate

companion article (Marantz et al., under review), uses a regression model to assess whether

different city-, tract-, and parcel-level attributes are related to ADU permitting, finding that the

relationship illustrated in exhibit 4 is robust with the inclusion of other variables. Notably, this

finding contrasts with earlier research by Chapple et al. (2020), who examine ADU permitting

through 2019 and find that most permits were issued in tracts with median household incomes

in the top two quartiles statewide. (The findings are inconsistent with those of Chapple et al. even

when the analysis is restricted to 2018 and 2019.)

Exhibit 3

Distribution of Residential Parcel Acreage by Accessory Dwelling Unit Status

Bay Area

Southern California

10.0

No ADU on parcel

7.5

ADU on parcel

5.0

nel Density

Ker

2.5

0.0

0.0

0.5

1.0

1.5

2.0 0.0

0.5

1.0

1.5

2.0

Acres

ADU = accessory dwelling unit.

Sources: ADU data: California Department of Housing and Community Development (2022); Parcel size data: Southern California Association of Governments (2021) and Boundary Solutions (2022)

6 It is also possible that some owners of single-family homes in high-rent locations are opting not to develop ADUs because they anticipate that their parcels will be rezoned for denser, more valuable forms of development in the future, such as fourplexes or small apartment buildings. In 2021, California passed a law authorizing lot splits and duplexes in lieu of ADUs on most single-family home parcels (Senate Bill 9), and a state policy to affirmatively further fair housing is also putting some pressure on local governments to allow multifamily housing in neighborhoods where it has been excluded in the past (HCD, 2021). Given the longstanding resistance to dense development in single-family home neighborhoods, it would be surprising if homeowner expectations about future multifamily development opportunities accounted for the lack of ADU development in high-rent areas. (The lot-split and duplex bill has generated very little development activity thus far

[Garcia and Alameldin, 2023].)

Cityscape 113

Marantz, Elmendorf, and Kim

Exhibit 4

Distribution of Tract-Level Median Gross Rent by Accessory Dwelling Unit Status for

Residential Parcels

Bay Area

Southern California

0.0012

0.0009

No ADU on parcel

ADU on parcel

0.0006

nel Density

Ker

0.0003

0.0000

1,000 2,000 3,000 4,000

1,000 2,000 3,000 4,000

Median Gross Rent (Tract-Level)

ADU = accessory dwelling unit.

Sources: ADU data: California Department of Housing and Community Development (2022); Median rent data: 2012–2016 American Community Survey Exhibit 5 presents statistical relationships between the ethnoracial characteristics of neighborhoods

(census tracts) and the prevalence of ADU permitting. In most of the counties in the sample,

there is a negative correlation between a census tract’s proportion of parcels with an ADU and the

percentage of the tract’s population identifying as Asian. Conversely, tracts that have relatively large

populations identifying as Hispanic or Latino tend to have more ADUs, although this relationship

is more prevalent in southern California than in the Bay Area. No consistent relationship exists

between ADU permitting and Black or White population shares.

114 Recent Reforms in Zoning

Where Will Accessory Dwelling Units Sprout Up

When a State Lets Them Grow? Evidence From California

Exhibit 5

Tract-Level Pairwise Correlations Between the Proportion of Residents in Ethnoracial Categories

and the Proportion of Single-Family Parcels With Accessory Dwelling Unit Permits

% Black or

% Hispanic

County

% Asian

% White

African American

or Latino

Bay Area Counties

Alameda

– 0.32

0.19

0.20

Contra Costa

Marin

– 0.46

Napa

– 0.37

San Francisco

San Mateo

– 0.19

Santa Clara

– 0.16

0.16

Solano

0.43

0.22

– 0.30

Sonoma

– 0.33

Southern California Counties

Los Angeles

– 0.08

0.08

Orange

0.35

0.22

– 0.42

Riverside

– 0.13

– 0.14

0.17

San Bernardino

– 0.12

0.26

– 0.18

Ventura

– 0.24

0.38

– 0.33

Note: This chart displays only coefficients that are statistically significant at p < 0.05.

Sources: ADU data: California Department of Housing and Community Development (2022); Ethnoracial characteristics: 2012–2016 American Community Survey As noted above, a separate companion article (Marantz et al., under review) uses a regression model

to assess whether different city-, tract-, and parcel-level attributes are related to ADU permitting. The

attributes are lot size, number of structures on a lot, tract-level median rent, the proportion of vacant

land in a tract, the log of city population, the proportion of occupied housing units in a city that are

owner-occupied, and HOA density (i.e., the proportion of mortgaged housing units in a city that

are covered by an HOA’s CCRs). The regression model indicates that larger lot sizes and additional

structures on a parcel are associated with increased odds of an ADU permit after controlling for

other variables. Those results are sensible. Homeowners may perceive converting an existing

structure to be the least expensive option for creating an ADU, or one which minimally impinges

on their yard space. (A larger yard naturally means that there is more room to accommodate a new

structure.) In addition, city-level HOA density is negatively associated with ADU permitting, even in

the years after state law prohibited HOAs from restricting the construction or rental of ADUs.

Conclusion

The above analyses indicate where ADUs are now in greater supply: in coastal counties, on

parcels of average size, and in jobs-accessible neighborhoods with relatively low median rents

(but not the lowest rents). The relationship between neighborhood ethnoracial composition and

ADU permitting varies by region and, within regions, by county. The only relatively consistent

relationship is that tracts with more residents identifying as Hispanic or Latino generally have

Cityscape 115

Marantz, Elmendorf, and Kim

more ADUs, although even this relationship varies by region and is more prevalent in southern

California than in the Bay Area. This analysis demonstrates that state laws liberalizing ADUs can

have differential effects across a state, suggesting that such a state intervention may be a more

powerful tool in some places than in others. Mandates for local governments to liberalize ADU

permitting should be accompanied by data collection requirements, as has been the case in

California, so that researchers and policymakers can assess those differential effects.

Appendix A7

Accessory Dwelling Unit Data

The data on accessory dwelling unit (ADU) permitting come from the annual progress reports

(APRs) compiled by the California Department of Housing and Community Development (HCD).

Each city’s APR must include the current assessor parcel number (APN) and street address for

every reported development project. A city’s APR must also report the type of project based on a

list that includes ADUs. A single project may appear multiple times in HCD’s compiled APR dataset

if, for example, the project receives a building permit in one year and a certificate of occupancy in

a subsequent year. In addition, HCD does not validate the APR data, and, as a result, the dataset

includes some erroneous APNs.

The process for generating an unduplicated count of parcels on which at least one ADU was

approved from 2018 through 2021 involves filtering the compiled APR data from HCD to include

only ADUs in the study counties; selecting rows that are uniquely identified by jurisdiction,

APN, and street address; and merging this dataset with parcel data from the Southern California

Association of Governments (SCAG) (2021) and Boundary Solutions (2022), which maintains

a proprietary database of digitized parcel boundaries. SCAG parcel data include consistent

information on zoning and land use as of 2016, but the Boundary Solutions data (which covers the

Bay Area) do not. Thus, the process for the Bay Area data involves the additional step of combining

geodata compiled by the Othering and Belonging Institute (Menendian et al., 2020), which

categorizes residential zoning as of 2020. The merge rate is 96 percent (i.e., 52,480 of the 54,584

ADU observations from HCD). The final step involves creating a unique ID for each parcel and

reducing the dataset to one observation per unique ID, yielding an unduplicated count of 43,160

parcels with at least one ADU permit.

Jobs Accessibility Measure

The measure of jobs accessibility is generated by calculating the distance-weighted sum of jobs

within 50 miles of census tract centroids. The distance-weighting is derived using a linear decay

function, following Salon (2014: 18), who notes that weighting by inverse distance squared

“quickly renders jobs beyond 10 miles to have little effect on the [jobs accessibility] variable,”

which is problematic in the California context. The census tract and block group distances come

from the National Bureau of Economic Research (2014), and the job counts come from the 2016

vintage of the Workplace Area Characteristics dataset from the Longitudinal Employer-Household

Dynamics database (U.S. Census Bureau, n.d.).

7 This appendix draws extensively on Marantz et al. (under review).

116 Recent Reforms in Zoning

Where Will Accessory Dwelling Units Sprout Up

When a State Lets Them Grow? Evidence From California

Acknowledgments

The authors thank Tom Brinkhuis of the California Department of Housing and Community

Development for answering questions about the agency’s data on accessory dwelling units; Noah

Kazis, Ingrid Ellen, and Betty Wang for helpful comments; and the Pew Charitable Trusts and the

Furman Center for Real Estate and Urban Policy for financial support for this project.

Authors

Nicholas J. Marantz is an associate professor of urban planning and public policy at the University

of California Irvine, School of Social Ecology. Christopher S. Elmendorf is a Martin Luther King,

Jr. Professor of Law at the University of California Davis, School of Law. Youjin B. Kim is a Ph.D.

candidate in Urban and Environmental Planning and Policy at the University of California Irvine,

School of Social Ecology.

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Brinig, Margaret F., and Nicole Stelle Garnett. 2013. “A Room of One’s Own? Accessory Dwelling

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California Department of Housing and Community Development (HCD). n.d. Housing Element

Annual Progress Report Instructions. Sacramento, CA. https://perma.cc/F8XV-A3FE.

———. 2022. “Housing Element Annual Progress Report (APR) Data by Jurisdiction and Year.”

https://data.ca.gov/dataset/housing-element-annual-progress-report-apr-data-by-jurisdiction-and-year.

———. 2021. Affirmatively Furthering Fair Housing: Guidance for All Public Entities and for Housing

Elements. Sacramento, CA: HCD. https://www.hcd.ca.gov/community-development/affh/docs/affh_

document_final_4-27-2021.pdf.

Chapple, Karen, David Garcia, Eric Valchuis, and Julian Tucker. 2020. Reaching California’s ADU

Potential: Progress to Date and the Need for ADU Finance. Berkeley, CA: Terner Center for Housing

Innovation & UC Berkeley Center for Community Innovation. https://ternercenter.berkeley.edu/

wp-content/uploads/pdfs/Reaching_Californias_ADU_Potential_2020.pdf.

Garcia, David, and Muhammad Alameldin. 2023. “California’s HOME Act Turns One: Data and

Insights from the First Year of Senate Bill 9.” Terner Center for Housing Innovation, UC Berkeley.

https://perma.cc/N9LT-GS25.

Marantz, Nicholas J., Christopher S. Elmendorf, and Youjin B. Kim. Under review. Overseeing Infill:

How State Agencies Can More Effectively Monitor Local Land-Use Administration.

Cityscape 117

Marantz, Elmendorf, and Kim

Menendian, Stephen, Samir Gambhir, Karina French, and Arthur Gailes. 2020. Single-Family Zoning

in the San Francisco Bay Area: Characteristics of Exclusionary Communities. Othering & Belonging

Institute. https://github.com/OtheringBelonging/BayAreaZoning.

National Bureau of Economic Research. 2014. “Block Group Distance Database. ” https://www.nber.

org/research/data/block-group-distance-database.

Salon, Deborah. 2014. Quantifying the Effect of Local Government Actions on VMT. Institute of

Transportation Studies, UC Davis. https://ww2.arb.ca.gov/sites/default/files/classic//research/apr/

past/09-343.pdf.

Southern California Association of Governments. 2021. “2016 Land Use Information for Los

Angeles, Orange, Riverside, San Bernardino, and Ventura Counties.” https://gisdata-scag.opendata.

arcgis.com/datasets/.

State of California, Department of Finance. 2021. E-4 Population Estimates for Cities, Counties, and

the State, 2011-2020 with 2010 Census Benchmark. Sacramento, CA. https://dof.ca.gov/forecasting/

demographics/estimates/e-4-population-estimates-for-cities-counties-and-the-state-2011-2020-

with-2010-census-benchmark-new/.

U.S. Census Bureau. n.d. “Longitudinal Employer-Household Dynamics. ” https://lehd.ces.census.gov/data/.

———. 2022. “Building Permit Survey. ” https://www2.census.gov/econ/bps/Place/West%20Region/.

———. 2021. “Form C-404, Report of Building or Zoning Permits Issued for New Privately-

Owned Housing Units.” OMB No. 0607-0094. Washington, DC: U.S. Census Bureau.

https://perma.cc/HP4B-4KVZ.

Zillow. 2023a. “Average Rental Price in California & Market Trends. ” https://perma.cc/9HMH-VWRV.

———. 2023b. “Average Rental Price in Palo Alto, CA & Market Trends.” https://perma.cc/L33W-FAPV.

118 Recent Reforms in Zoning

by Paavo Monkkonen, Michael Manville, Michael Lens, Aaron Barrall, and Olivia Arena

California’s Strengthened

Housing Element Law: Early

Evidence on Higher Housing

Targets and Rezoning

Paavo Monkkonen

Michael Manville

Michael Lens

Aaron Barrall

Olivia Arena

University of California, Los Angeles

Abstract

This article examines California’s strengthened housing planning system as an example of land use reform

impacts and intergovernmental conflict around housing policy. For the first time in its 50-year history,

the state’s plan mandate set local government housing targets for the 2021-through-2029 planning period

higher than many municipalities’ existing zoned capacity for new housing. Using administrative and

census data, we describe changes in housing targets and changes in the housing plans cities have made in

response. We analyze rezoning commitments in those plans, focusing on the 209 municipalities in southern

California, especially the 93 housing plans deemed compliant by the state as of February 10, 2023. These

municipalities, which represent less than one-third of the state’s population, have already committed to

over 10 times the amount of rezoning than in the previous planning period (in 2014). Using regressions

with different measures of targets and rezonings, we find that larger increases in a city’s housing target

are associated with more rezoning and that increases in targets that require land zoned for multifamily

housing have a stronger association. This assessment is important not only for the state’s 40 million

residents but also for national discussions about state-level intervention in local housing planning. Existing

evidence suggests that state affordable housing appeals systems have been more effective than plan

mandates, yet mandates have not yet been aggressively implemented until now. We also assess the actions

by presumably exclusionary cities: those with more expensive housing, non-Hispanic White residents,

homeowners, and elderly residents than the rest of the region. The results confirm that these cities had

received relatively low targets previously but do not differ in their rates of rezoning.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 119

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Monkkonen, Manville, Lens, Barrall, and Arena

Introduction

Federal and state governments in the United States have a long-standing interest in compelling

jurisdictions with expensive housing, which tend to be more affluent and more homogenous than

their regions, to allow more housing production. This interest arises from a desire to increase

affordability, advance racial integration, and foster equality of opportunity. Specific approaches to

this problem have varied across the country, but to date the results have been mixed at best.

On paper, local governments are creatures of the state and subject to the higher government’s

will. In practice, higher levels of government find it exceedingly difficult to change the behavior

of local ones, at least with respect to land use. State laws designed to change local behavior are

sometimes written poorly and other times written well but watered down—all state representatives

are also local residents—so states rarely bring the full force of their authority to bear on localities.

Even well-designed and strong laws, moreover, are often unevenly enforced. Local governments

that fail to comply with state housing laws may face few consequences, and often it is the same

affluent communities these laws target that are also best able to evade or circumvent them

(Zheng et al., 2021). The result is initiatives undertaken with great fanfare that deliver little of

substance. Massachusetts’ anti-snob zoning ordinance, for example, was passed in 1969 with the

intent of eventually having every city and town in the state offer 10 percent of its housing stock

as affordable. More than 40 years later, only 39 of the state’s 351 municipalities had reached that

benchmark (Fisher and Marantz, 2015).

This article examines California’s recent attempts to override local opposition. Specifically, we study

reforms passed in 2017 and 2018 that were designed to give more teeth to the state’s ineffective

and complicated “fair share” housing planning system. Under California’s long-standing existing

system (described in detail below), local governments were required to plan for projected housing

growth for households at a range of income levels. In theory, the system would ensure that every

jurisdiction contributed enough housing (including affordable housing) to meet regional needs. In

practice, the system was almost entirely ineffective.

The reforms we study identified and sought to correct three fundamental flaws in the existing

system: a low total target for statewide housing growth, an inequitable and often unrealistic

allocation of that total across local jurisdictions, and a plan update process that all but invited

strategic behavior by allowing local governments to submit highly unrealistic plans for growth. As

we will show, the first two flaws facilitated the third. The process that held down the total number

of housing for which jurisdictions needed to plan directly enabled the strategic behavior in that local

planning. Reforming the system to generate a higher initial number made evasion more difficult.

We wish to emphasize that point. Targets for total housing production are rarely part of state

housing policies (Elmendorf, 2019). The two most widely known fair share policies—in New

Jersey and Massachusetts—focus on subsidized units. California, by contrast, has long required

that its cities plan for a total amount of housing, which includes market-rate and subsidized units;

before reform, however, those targets were low. In the planning cycle that immediately preceded

the 2017 reforms, for example, the statewide target for housing growth—despite occurring during

a period of massive economic expansion—was 1 million units, many of which were allocated to

120 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

places with low demand for housing (Monkkonen and Friedman, 2019). Many affluent cities

near job centers received low housing targets, whereas low-demand cities in outlying parts of

metropolitan areas were expected to plan for thousands of units.

After the reform, the total target jumped to 2.5 million housing units, and compared to previous

planning cycles, regional governments allocated larger shares to affluent jurisdictions. Those

higher targets, combined with increased state scrutiny of how cities plan to meet them and new

consequences for failing to do so, have changed housing planning in the nation’s largest and least

affordable state.

This article has two empirical components, both of which examine cities in southern California.

First, we describe the recently adopted and certified housing plans under the reformed system.

Our focus here is on whether the new system prompted more cities to rezone. Some jurisdictions

could demonstrate to the state that their existing zoning provided sufficient capacity to meet their

new housing targets. Cities that could not demonstrate as much would need to rezone land to be

in compliance. If localities do not have to rezone, the planning mandate will continue to have no

impact on housing production. To assess the relationship between the reformed housing targets and

rezoning, we create an accurate measure of the change in housing targets standardized across cities.

Our second empirical exercise is to test the idea that more exclusionary cities were able, even in the

face of those reforms, to evade the state mandates and maintain their regulatory barriers to housing

(that is, avoid rezoning). We do so by measuring the association between rezoning and a series

of characteristics commonly associated with regulatory exclusion: high incomes, high housing

values, and larger shares of the population that are elderly and White. Doing so allows us to assess

whether those cities are systematically engaging in less rezoning.

Because only half of the cities in southern California have had their plans certified (in many cases

because the State found them inadequate), we also test whether these places differ on average from

cities without certified plans.

In the next sections of the article, we review literature on the effectiveness of plan mandates,

describe California’s planning system, and summarize recent changes to it. The article then

describes the challenges of assessing impact through a review of housing plans, how we dealt with

these challenges, and what we can say about the 2021-through-2029 housing plans. We then turn

to our empirical analysis and conclude.

Principal-Agent Problems and California’s Housing

Element System

This article contributes to the broad literature on housing affordability and exclusionary local

jurisdictions and also to the smaller literature on state housing laws and plan mandates—state-

level efforts to direct local planning actions. Plan mandates can address housing permitting, but

they may also be focused on issues such as wetlands protection, transportation behavior, or other

areas in which state officials believe that local incentives might be at odds with the state’s broader

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interests. In this case, it may be in an individual city’s interest to block housing production, but an

absence of housing undermines the state economy.

Some state housing laws take the form of ad hoc actions: California and Massachusetts, for

example, both require local governments to offer regulatory relief for developments that include

affordable housing. Other laws are more expansive and baked into the local planning process. At

least 15 U.S. states require local governments to adopt a comprehensive plan to govern land use

development (OECD, 2017). Some states—such as California, Florida, Oregon, and Washington—

require local municipalities to plan for the development of new market-rate and subsidized

housing. Expectations and requirements for plan content vary dramatically, as does how state

agencies supervise local planning.

The research on the efficacy and impact of state planning mandates is mixed. Decades into their

existence, the affordable housing laws in Massachusetts and California have produced thousands

of units.1 On the other hand, in most localities in both states, developers have never actually

used those laws; the affordable units created by those statutes are concentrated in a handful of

places. Arguments about comprehensive planning take a similar form: some scholars argue that

comprehensive planning mandates improve the quality of local planning and the management of

urban development generally (Burby et al., 1993; Jun, 2017), whereas others contend that such

mandates have little practical effect for most municipalities (Bunnell and Jepson, 2011; Deyle,

Chapin, and Baker, 2008; Yin and Sun, 2007).

More relevant for our purposes are studies that examine how well plan mandates are implemented.

In a review, Burby et al. (1993) point to the three elements that lead to effective plan mandate

implementation: a strong commitment by the state, clarity in monitoring and enforcement

bureaucracy, and incentives to participate and enforcement actions for failing to comply.

Those criteria hint at the situation’s broader contours. Any attempt by a state to compel local action

faces a principal-agent problem. A state wants a locality to do something, but in many cases—

especially in housing policy—both that outcome and the locality’s effort are hard to observe. The

outcome is hard to observe because the state cannot mandate housing production. Housing units

are easy to count, but the biggest determinant of housing production, all things being equal,

is demand, which is something localities have little control over. What the state (the principal)

wants, then, is for a locality (the agent) to make it easier for developers to build housing if demand

exists. A local government’s conduciveness to housing production, however, is much harder to

measure than housing production itself. Even if a state prohibits particular local restrictions (e.g.,

it says no localities can ban apartments), states have a hard time observing all the ways that local

governments regulate development. The diverging priorities of the state and its localities make it

difficult for states to measure progress toward housing production and fair housing goals and create

incentives for localities to obfuscate that progress.

In theory, a principal-agent problem can be mitigated if the principal clearly defines the desired

outcome and aligns accurate measures of effort with guaranteed consequences. Conversely,

1 Assessing direct connections to housing production is hard for plan mandate approaches. See Marantz and Zheng (2020) for an example of comparative research on affordable housing appeals systems.

122 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

principal-agent problems can be exacerbated if effort is not accurately measured or if low levels of

effort are not penalized, which, as we will show, California did for decades. Prior to reform, the

statewide housing mandate process conflated low effort with low demand and thereby rewarded

low levels of effort.

How Does California’s Housing Element Process Work?

The state-level housing planning framework in California, called the Regional Housing Needs

Assessment or the Regional Housing Needs Allocation (shortened either way to RHNA), consists

of three planning exercises. In the first exercise, the state, in cooperation with regional councils

of government,2 determines regional housing needs on the basis of projections of household

growth. Those needs are divided into housing that is affordable to households of different incomes,

including low, moderate, and above-moderate income.3

In the second exercise, each regional government then allocates the regional housing targets to its

constituent local governments (Cal. Gov. Code S 65584.05). The third exercise involves each local

government, in turn, incorporating those targets into the housing element of its general plan. All

California local governments must have a general plan, which can be thought of as a blueprint for

a community’s vision of its growth, divided into seven or more elements—including a housing

element (Cal. Gov. Code S 65580 et seq.). After the local government receives its housing target, it

must update the housing element of its general plan and demonstrate in that element that the city

can accommodate its targeted number of units.

The housing element must demonstrate to the state’s department of housing and community

development (HCD) that the jurisdiction has the capacity, within its existing zoning, to not just

meet but exceed its targets (Elemendorf et al., 2020).4 Jurisdictions must identify specific parcels

zoned as residential on which new housing could be built. To satisfy the requirements for low-

income housing, parcels must have certain size and permitted density characteristics, depending on

the type of city. For example, in metropolitan areas, parcels must be larger than one-half acre and

zoned for housing at 30 dwelling units per acre to satisfy the capacity for low-income housing.

A hypothetical example might make the RHNA process more concrete. The first exercise might

determine that the San Francisco Bay Area needs room for 300,000 housing units, the second

might involve the Bay Area’s regional government assigning 10,000 of those units to the City of

Oakland, and the third might involve Oakland planners demonstrating that their city’s existing

zoning can easily accommodate those 10,000 additional units. That demonstration would typically

involve identifying sites in the city that could hold more units (vacant lots, parcels ripe for

redevelopment, and so on).

2 California’s councils of government take various forms, as some are also Metropolitan Planning Organizations. Eight councils of government represent more than 10 jurisdictions, and the largest three councils of government represent 197, 101, and 29 jurisdictions, respectively.

3 In the recent RHNA process, roughly 40 percent of housing needs are for low-income households. Affluent cities get a larger share of low-income housing as a target.

4 In addition, the housing element must contain other required chapters in which local governments analyze housing needs, identify potential constraints to housing production, and develop programs to address those needs and constraints.

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If a city’s existing zoning code lacks sufficient capacity for the additional housing units the regional

plan has allocated to it, however, the government must commit to rezoning land to create space for

the new units. The government in this case must list sites that will be rezoned within 1 to 3 years,

although because the housing element is part of the general plan, the densities reported in sites’

inventories are effectively changes to municipal rules. For the majority of local governments, the

RHNA process repeats every 8 years, and the housing targets are for production during that period.

The Effectiveness of California’s State Housing Planning

Historically, the process described above has not worked. Although the state tried to strengthen

regulation over time, the RHNA process consistently failed to encourage housing production (Baer,

2008). A comprehensive study from 2005 presented strong evidence that the process did not

matter: municipalities in compliance with RHNA were no more likely to produce new housing

than noncompliant cities (Lewis, 2005).5 Importantly, the RHNA process has also not noticeably or

measurably reduced exclusionary land use regulation in California cities. Such a reduction is the

primary mechanism through which RHNA would help increase production of both market-rate

and subsidized housing. As we will describe, unless cities change their zoning or housing project

review processes as a result of the housing element law, its impact will be minimal.

Three deficiencies have dulled RHNA’s impact; each exacerbates the state-local principal-agent problem.

First, the planning exercises that determined housing needs were flawed at multiple stages. The

state’s process for producing top-line regional numbers systematically underestimated housing need

by relying primarily on projected population growth and ignoring existing conditions that might

indicate unmet housing demand, such as overcrowding, high rent burdens, and job growth. The

state also treated increasing household sizes as an indicator of decreasing housing need when, in

fact, the opposite was likely true: high housing prices, resulting from scarcity, were creating higher

occupancy in the state’s housing units.

Once regional housing needs were established, moreover, the process for allocating them

across jurisdictions rewarded rather than penalized efforts to block housing construction. The

regional governments (which are composed of, and heavily influenced by, local governments)

made allocations based on projections of future growth. But future growth was estimated only

by referring to past growth, not by any metric of demand, such as price levels or appreciation

(Monkkonen, Manville, and Friedman, 2019). That approach implicitly rewarded expensive cities

that had successfully used land use regulation to stop new construction. Those cities, with low past

growth but high present demand, should have been a prime target for RHNA allocations. Instead,

RHNA offered them an escape valve by virtue of the very behavior it ostensibly sought to curb.

Whiter and more expensive jurisdictions received lower housing allocations (Bromfield and Moore,

2017), and jurisdictions with more vacant land (a sign of lower demand) got higher allocations.

Low effort was rewarded with lower expectations (Monkkonen and Friedman, 2019; Ramsey-

Musolf, 2020; Zheng et al., 2021).

5 Other studies offer a more optimistic assessment but are based on small, non-random samples and find what are likely to be spurious correlations (Ramsey-Musolf, 2016, 2018).

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Again, an example might help illustrate the problem. In the fifth RHNA cycle, Beverly Hills, an

affluent municipality of approximately 30,000 people in close proximity to the region’s major

job centers, received a housing target of just three new units. By contrast, the city of Coachella, a

lower-income municipality of approximately 40,000 people located hours into the desert (about as

far from the region’s job centers as possible), received a housing target of 6,771 new units.

The RHNA process’s second flaw was that local jurisdictions, once they received their allocations,

used faulty and perhaps disingenuous site selection processes to demonstrate the capacity to

accommodate them. Recall that a city that wishes to avoid rezoning had to show that it had either

ample vacant land or many sites that were likely to be redeveloped. More expensive cities tend to

have little vacant land, so they often complied with RHNA by submitting inventories of sites they

said were likely to be redeveloped. Many of those inventories strained credulity. Cities offered

up sites that held recently built commercial uses, extremely steep slopes, and, on one occasion,

a city hall (Collins, 2022). Whether those submissions were errors or lies is debatable, but the

overall result was that sites listed as likely to hold new housing almost never, in practice, ended

up holding new housing. One study found that only 10 percent of the sites listed in the Bay Area’s

fifth-cycle housing elements as likely to be redeveloped were, in fact, developed between 2015

and 2022. Indeed, most housing constructed in California cities during the cycle was built on sites

not listed in the housing element—a testament to how inaccurate the planning process has been

(Kapur et al., 2021).

The third flaw in RHNA was that enforcement and consequences were weak. In principle (again),

state oversight should stop cities from submitting housing elements with sites that are unlikely

to be developed. Effective enforcement, however, requires considerable local knowledge, and the

state agency responsible for reviewing housing elements and site inventories had been inadequately

staffed, making state oversight weak.

The inefficacy of the RHNA process was not by design. The problem, rather, was that RHNA was

designed in a different era. When the RHNA system began, almost every city in California had

substantial amounts of vacant land, which made growth politics less explosive and also made site

selection more transparent and easier to verify.

Today, however, vacant land is less evenly distributed and is a sign of low demand. As such, a

city with a lot of vacant land can comply with its RHNA sites requirement, regardless of whether

it wants or is likely to get more housing. It can do so by pointing to its vacant land as capacity

for housing development. A higher-demand city with little vacant land, though, faces two paths

toward compliance. First, it can rezone its land for more intensive development. Rezoning ensures

compliance but also invites new housing development. If the city wishes to comply but avoid new

housing development, it must take a second option: argue (honestly or not) that it has enough

existing sites ripe for redevelopment to meet its allocation. Because many high-demand cities

would prefer to avoid development, they take the second option and behave strategically.

Two factors have enabled this strategic behavior: lax state oversight and a low total target. The latter

is easier to fix than the former. Oversight is labor intensive, and predictions of future development

are intrinsically debatable. A big target, in contrast, has self-enforcing properties. Each city has

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only so many available sites that are likely to redevelop and only so many more that can plausibly

be labeled as such (even in bad faith). As the target rises, cities that are built out according to their

own zoning will, as a matter of math, be forced to rezone.

Reforms Affecting the Current Planning Period

Laws passed in 2017 and 2018 changed California’s housing planning system in at least four

substantial ways: the size of regional housing targets, the allocation of those targets to local

jurisdictions, the requirements placed on local governments to demonstrate their ability to

accommodate those targets, and the scrutiny with which the state agency was to review local plans.

We briefly describe each law.

First, Senate Bill 828 (2018) led to higher regional targets for housing production. This bill

also moved the projection process away from its sole reliance on projected household growth

(Elmendorf et al., 2020) and required government demographers to also consider existing housing

needs.6 In high-priced areas, the impact of this change was significant: in the two southern

California regions, overall regional housing targets nearly tripled.

Second, Assembly Bill 1771 (2018) reformed the way regional governments allocate regional

housing targets to their constituent jurisdictions. Regional governments were previously allowed

to develop their own methodology for allocating housing targets to local governments, with little

oversight. AB 1771 requires regional governments to allocate the regional target to cities and

counties in a way that advances specified objectives, such as increasing housing supply; increasing

the mix of housing types, tenure, and affordability; and doing so in an equitable manner. Regional

councils of government interpreted those instructions differently, but most used a combination of

proximity to employment, transit access, and some consideration of equity (for example, average

income) in allocating targets to local governments. The result was that municipalities that had

previously received housing targets of a handful or a few hundred units had targets of several

thousand units.7 To return to previous examples, Beverly Hills’ target for the 2021-through-2029

planning period was 3,104 units, an increase of roughly 1000 percent. The city of Coachella’s

target was 7,886, still much larger than that of Beverly Hills but an increase of 115 percent.

Third, Assembly Bill 1397 (2017) increased the level of evidence, analysis, and infrastructure

necessary for a local government to justify designating a parcel as a site for future housing

production in its housing plan. As discussed earlier, jurisdictions had been allowed to include

housing sites on the basis of a “potential for redevelopment,” with no evidence. In the current

planning period, jurisdictions are required to show that their sites have “realistic” and near-term

feasibility for redevelopment on the basis of past experiences with conversion of similar uses, a

given use’s current market demand, and existing leases. Jurisdictions meeting more than one-

half of their lower-income housing targets on non-vacant sites were required to make evidence-

based findings that existing uses were “likely to be discontinued” during the planning period.

Jurisdictions had to assess infrastructure on a site-by-site basis, and all sites needed to be served by

6 The bill left some openness to specific measures of existing housing needs but listed overcrowding and cost burden as factors. For details, see https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201720180SB828.

7 Roughly 10 percent of the jurisdictions in southern California got lower targets in 2021 than in 2014.

126 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

suitable utilities or be included in a program dedicated to expanding infrastructure service. Lower-

income sites were generally limited to parcels with sizes between 0.5 and 10 acres to match the size

of actual projects.

In addition, SB 166 (2017) required that, as on-the-ground conditions change, jurisdictions

maintain adequate capacity to meet remaining housing targets for each income level throughout the

cycle. For example, if a site identified for lower-income housing production is developed for above-

moderate units, a jurisdiction must either already have additional lower-income capacity identified

or find a new site with proper zoning within 180 days. In effect, that necessitated that jurisdictions

provide more capacity than their baseline targets to avoid rezoning multiple times throughout the

planning period.

Finally, the reforms also gave HCD greater oversight authority. HCD gained the power to issue

standards, protocols, and binding requirements on local governments to collect, report, and

analyze data on how, for example, cities determine a site’s development potential (Elmendorf et

al., 2020). That reform is important because it reduces the information asymmetry between the

principal and the agent and makes the job of state reviewers achievable. For example, one practical

change is that cities must now submit an Excel form with detailed information on the sites they

identify as likely to hold new housing and sites designated for rezoning. Previously, cities submitted

that information in inconsistent and hard-to-use formats.

Did the combination of reforms work? If they did, one would expect to see that local government

housing plans proposed rezoning land to allow more housing and also a relationship between the

increase in municipalities’ housing targets and their rezoning activity.

Research Design and Data

Our analysis has two goals: accurately describe the plans adopted under the new rules and

determine if cities that received larger new housing targets proposed more rezoning. In the latter

goal, our empirical approach emphasizes municipalities in southern California because the state

staggers the timeline of the housing element process such that jurisdictions in northern California

start their update process a year after those in southern California. Southern California, as a result,

offers a large number of cities for whom the state has made determinations; northern California, at

the time of this writing, does not.

We start by defining some terms. The state gave each city a number of housing units to plan for

in 2014 and 2021. To meet that target, cities must show the state their “total capacity” for new

housing. This total consists of “existing capacity” (the number of units that could be built on vacant

and underdeveloped sites under current zoning) and, potentially, “rezoned capacity” (the amount

of housing allowed as a result of rezoning).

Our main outcome variable is rezoned capacity—essentially, the number of units the city commits

to through rezoning.8 Analyzing this metric is not the only way one could assess the impact of

8 Not only does listing sites for rezoning represent a commitment to changing the city’s zoning ordinance but the density listed in the housing element can be the basis for entitlement applications by developers from the moment the housing element is adopted. For more on this topic, see Elmendorf et al. (2021a).

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RHNA reforms. An alternative, for example, would be to calculate the capacity for growth relative

to housing targets. The problem with this approach is that it includes existing capacity in the form

of housing capacity from vacant or underdeveloped parcels. Such existing capacity, as we have

noted, has traditionally been an unreliable guide to development and, more importantly, does not

indicate a change in a jurisdiction’s total potential stock of housing. The total potential stock only

changes as a result of rezoning land to allow residential use or to allow more density.

For that reason, our approach is preferable; it examines how often cities were able to rely on

existing capacity (for example, to rezone as little as possible in spite of new laws) and how often

reforms forced them to change their land use plans. The absolute number of units rezoned in a

municipality depends, in part, on its size, so we create a standardized dependent variable. We

consider both the share of a city’s total capacity that will come from rezonings and the share of a

city’s total 2021 housing target.

The treatment variable of interest, therefore, is a measure of policy change: the ratio of a city’s

2021 target to its 2014 target. The policy mechanism we are hypothesizing, moreover, is rooted in

a change in the total target, not in the level of the target or its size relative to some static indicator,

such as the existing housing stock. A city assigned 1,000 more units in the reform cycle than it had

been assigned in the previous cycle probably faces pressure to rezone. A city assigned 1,000 units

may or may not face such pressure. For that reason, our analysis focuses on the increase in targets

rather than the targets themselves.

We run two sets of models to test the hypotheses about the impact of the planning reforms and

the role of city characteristics. The first set is composed of logistic regression models, which test

whether a city rezoned at all, and the second uses ordinary least squares models to assess how

much cities rezoned, both as a function of their relative increase in housing targets and as city

characteristics, such as population size, median home value, share White, share older than 65 years

old, and share homeowners. Our underlying theory is that local governments differ in both the

level of land use regulation they have and the level of political pressure they face to resist changing

their existing land use patterns. On the basis of the extant literature on opposition to new housing,

we hypothesize that jurisdictions where more residents are older, White, higher income, and

more likely to be homeowners will be less likely to rezone in response to higher housing targets

(Einstein, Glick, and Palmer, 2019).

Data on Housing Targets, Plans, and City Characteristics

This study relies on census and administrative data for cities in southern California.9 Housing

targets are available on regional government websites,10 and data on local governments’ plans are

9 County governments are responsible for developing a housing element for their unincorporated lands, but we exclude them from this study because of their distinct political organization and land pressures compared to city governments. Also excluded are small municipalities outside metropolitan statistical areas because the housing development pressures they face are substantially different from those in metropolitan areas.

10 The Southern California Association of Governments (SCAG) website is https://scag.ca.gov/rhna, and the San Diego Association of Governments (SANDAG) website is https://www.sandag.org/projects-and-programs/regional-initiatives/

housing-and-land-use/regional-housing-needs-assessment. The current planning period for the 197 jurisdictions in the six-county SCAG is 2021 through 2029. The 19 jurisdictions in SANDAG initiated their process 6 months before the rest of Southern California.

128 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

taken from the plans themselves. We triangulate three sources of data on housing element sites: the

plans on local government websites, the electronic sites’ inventory forms that cities are required to

submit to the California Department of Housing and Community Development (HCD), and a new

Department of General Services/HCD website that uses data from electronic sites’ forms to map

housing element sites.11

We obtained Excel forms directly from the California HCD. They consist of two tables: one that

lists parcels on which housing could be developed today, both because they are zoned residential

and are vacant or underused, and one that lists parcels that the city has committed to rezoning.

In both tables, the local government records information about the parcels, including their size,

density regulations, and estimates of the number of units of housing they would hold if developed.

We sum these unit estimates for both tables.

We note that our measure of rezoned capacity is imperfect, with the potential for both

simultaneously overestimating and underestimating the total amount of new capacity a city creates

in its housing element. The overestimates occur because HCD considers all sites with use or

density changes to be “rezoned,” regardless of their capacity before zoning changes. That means

HCD would consider a site zoned for 7 units rezoned to accommodate 15 units to be 15 rezoning

units. Jurisdictions are not required to report previous capacity of sites, so we assume that all units

on rezoned sites are rezoned capacity, just as HCD does. Fortunately for accuracy’s sake, a scan of

rezoned sites in cities that report on previous uses to the state reveals that most rezoned sites were

previously non-residential uses. Thus, most of the rezoned capacity is new.

On the other hand, HCD’s information underestimates the amount of capacity created through

rezoning because it only includes sites listed in the inventory, even if a city proposes zoning

changes beyond these properties. Sites’ inventories represent the properties most likely to develop,

but zoning changes often apply to many more properties than are listed. In addition, site capacities

are typically not estimated at their maximum allowed; they are adjusted downward to match

historical development trends. Finally, cities may implement other zoning reforms (e.g., removal of

parking standards) that functionally result in more residential potential but are not required to be

quantified on a site-by-site basis to HCD.

With these limitations in mind, we use the rezoned site capacity that cities reported to HCD as an

effective proxy for estimating the magnitude of zoning changes across the state. Exhibit 1 shows the

location of our sample of 93 municipalities with compliant housing elements as of February 10,

2023, on a map of southern California.

11 The website is here: https://experience.arcgis.com/experience/670e112e04ae415e9755f2d65fded76c/. Where possible, we ensure data quality by checking all three sources.

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Exhibit 1

Southern California Cities with Certified Housing Elements as of February 10, 2022

Source: Map created by Chanaporn Tohsuwanwanich with data from authors

Results

This section first describes the new housing targets faced by local governments statewide. The

discussion then shifts to southern California and examines the challenge local governments faced

in preparing compliant housing plans and the differences between local governments that had a

compliant housing element by February 10, 2023 (our sample of municipalities) and those that

did not. Finally, we present an overall description of the scale of local rezoning action, assess

differences in municipalities’ plans to meet housing targets, and test hypotheses predicting more or

less rezoning based on higher targets and characteristics of cities.

How Large Are the New Housing Targets?

The statewide housing target of roughly 2.5 million units for the sixth-cycle planning period12

is 2.5 times larger than that for the 2014-through-2021 fifth cycle (about 1 million units). This

comparison, moreover, arguably underestimates the difference between the two cycles because the

planning system allocated units in the sixth cycle to cities in a very different manner. In the fifth

12 The dates of the 8-year planning period differ by region to stagger the review work of the state agency. For most southern California jurisdictions, the sixth-cycle period is 2021 through 2029 and the fifth cycle was 2014 through 2021. San Diego County’s period is 2020 through 2028, and the Bay Area Association of Governments is 2023 through 2031.

130 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

cycle, most cities in southern California got targets based on their estimation of growth over the

next 8 years. Many of those million units required no action by local governments or reflected

vacant land in far-flung locations less likely to be redeveloped. In the sixth cycle, by contrast, more

of the 2.5 million units targeted may require meaningful action because the targeted cities often are

built out according to their zoning. Indeed, the state gave many local governments targets that far

exceeded cities’ stated capacity for new housing from 8 years earlier.

Exhibit 2 shows how big a challenge the new housing targets represent. This exhibit is not

restricted to southern California and uses data from the more than 400 municipalities that make up

the state’s large urban regions. Whereas the median city’s target (like the state’s) grew by 2.5 times;

for the city at the 75th percentile, the target grew by more than five times.

Exhibit 2

Sixth-Cycle Housing Targets in Context, Municipalities in Eight Major California Regions

Variables

25th Percentile

Median

75th Percentile

Ratio of sixth-cycle target to fifth-cycle target

1.42

2.58

5.09

Ratio of sixth-cycle target to fifth-cycle total capacity

0.57

1.22

2.48

Ratio of sixth-cycle target to 2021 housing units

0.09

0.15

0.22

Notes: N = 408. Planning periods differ across regions; they are staggered so the state agency does not have to review hundreds of plans at the same time.

Source: Nine regional government websites

On average, those targets were also larger than cities’ stated capacity for new housing in 2014,

indicating that the average city cannot rely on its preexisting capacity. That statement was not true

for all cities, however; the second row shows that well over 25 percent of municipalities received

lower targets than their stated capacity in 2014. Nonetheless, many cities received substantially

higher targets than their capacity, as reflected in the 75th-percentile city, which received a target

about 2.5 times its 2014 capacity.

Another way to illustrate the changes is to present them as a share of existing housing stock. The

median jurisdiction received a housing target equivalent to 15 percent of its existing housing units,

and one-fourth received an allocation that was equivalent to 22 percent. For context, California

consistently built more than 250,000 units per year in the 1960s and 1970s, equivalent to adding

as much as 30 percent or more of its stock over an 8-year period. In recent years, California has

been adding slightly more than 100,000 units per year; that rate of production in an 8-year period

represents only 6 percent of its stock.13

What Kinds of Cities Received Larger Targets?

We now turn to the 210 municipalities in Southern California. Exhibit 3 presents the correlations

between select city characteristics and measures of the 2021 housing targets and the increase in

targets compared with 2014. Housing targets in 2021, relative to existing stock, were higher for

larger municipalities and in places with lower home values and lower shares of White and older

residents. All of those findings are consistent with the idea that places presumably resistant to

growth continue to successfully game the system and receive relatively lower targets.

13 For data on housing production in California, see https://statewide-housing-plan-cahcd.hub.arcgis.com/.

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Exhibit 3

Size of Housing Targets According to Select Municipal Characteristics (205 municipalities)

2021 Low-Income

2021 Target/

2021 Target/

Variable

Target/2014 Low-

Housing Stock

2014 Target (log)

Income Target (log)

Population (log)

0.29

– 0.01

– 0.01

Housing Density (log)

– 0.02

0.34

0.35

Median Home Value (log)

– 0.30

0.38

0.47

Renters (%)

0.09

0.04

– 0.01

White population (%)

– 0.44

0.12

0.17

Black population (%)

0.13

– 0.03

– 0.05

Asian population (%)

0.16

0.25

0.28

Hispanic population (%)

0.31

– 0.25

– 0.32

Population older than 65 (%)

– 0.25

0.25

0.26

Source: U.S. Census Bureau, 2015

On the other hand, exhibit 3 also shows that the increase in housing targets was larger in places

with high home values and with more White residents and residents older than 65 years old. In

addition, higher-density municipalities and those with more Asian residents saw larger increases.

When calculating increases in targets for low-income housing, correlations are in the same

direction but slightly higher. Put differently, traditionally exclusionary municipalities continue to

have relatively lower targets, but the recent reforms have successfully shifted course, and targets for

those places have increased substantially.

Are Cities with Compliant Plans Different?

One indicator of the scale of changes to the planning system is the widespread difficulty

jurisdictions have had in getting their housing plans approved by the state government’s

implementing agency. The deadline for local governments in southern California to have a

compliant housing element was October 15, 2021. But a full year later, only one-third of the

region’s 197 cities and counties were in compliance. Five jurisdictions had not yet even submitted a

housing element to the state for review by October 2022.

Before focusing on the 93 compliant plans as of February 10, 2023, we examine how they differ

from those out of compliance. A substantial majority of jurisdictions in southern California were

more than 1 year late in getting their housing plans certified by HCD. Roughly one-half of the

jurisdictions in San Diego County, which began its update process 6 months before the rest of the

region, are still not compliant.

We examine whether the jurisdictions that have approved plans differ in some way from those that

do not. Using a logistic regression14 model and data on the 209 southern California municipalities,

we assess the probability of being compliant as a function of seven factors: cities’ targets as a share

of existing stock, the increase in targets, population, home values, share White, share homeowners,

and share elderly. We find that only two factors are statistically significant in predicting compliance:

14 Results of the logistic regression are available upon request.

132 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

the increase in housing targets and city size. Jurisdictions with larger increases are less likely to

be compliant, and larger cities are more likely to be compliant. Those results likely reflect large

jurisdictions’ greater administrative capacity and the more substantial political challenges of

compliance when faced with a larger growth target.

The Scale of Local Action

The most relevant outcome of the RHNA process is the volume of rezoning by local governments.

As described previously, local housing plans must show to the state that a jurisdiction has a

sufficient number of potential sites for housing development to accommodate its numerical

housing target. If they cannot find parcels zoned for housing with development potential, they

must commit to rezoning land to allow residential development or increase permitted densities

to create that space within 1 to 3 years. In the previous planning period (roughly 2014 through

2021), jurisdictions across the entire state committed to only 35,430 units of rezonings to comply

with RHNA (personal communication, HCD). Moreover, most of those rezonings were not in

high-demand communities and were rezonings of vacant land. Those fifth-cycle rezonings mostly

did not create redevelopment opportunities in cities that believed themselves to be built out. For

example, three jurisdictions—Riverside County, Kern County, and the City of Coachella—made up

nearly one-half of the state’s rezoning count. All are places far from job centers.

By contrast, our summary analysis of planned rezonings in the first 93 certified housing elements

for 2021 through 2029 finds more than 500,000 units on rezoned sites—roughly half of which

are in the City of Los Angeles. The City of Los Angeles’ housing element outlines a plan that will

rezone for a minimum of 250,000 units but considers a potential rezoning of up to 1.5 million

units (City of Los Angeles, 2021). These cities with compliant local housing elements represent less

than one-third of the state’s population. Considering that the majority of the state has yet to finalize

its plans, the housing planning process in California has much more potential than ever before.

Not only is the overall volume of rezoning important, it is also spread across many jurisdictions.

We find that roughly two-thirds of the jurisdictions with certified plans committed to rezoning

some land in response to RHNA requirements. And because the regional governments allocated

housing targets to higher-demand cities than in previous cycles, we see a positive correlation

between the extent of rezoning and housing costs—unlike during the previous cycle, in which

localities far from job centers did most of the rezoning.

Do Cities with Larger Increases in Targets Rezone More?

To more directly assess the connection between increases in housing targets and rezoning, we

will now focus on the 93 municipalities with compliant plans. Exhibit 4 presents descriptive

characteristics for this sample of municipalities.

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Monkkonen, Manville, Lens, Barrall, and Arena

Exhibit 4

Descriptive Statistics for Housing Plans and City Characteristics (93 cities)

Variable

Median

Mean

Std. Dev.

Housing Plan Characteristics

Any rezoning (1,0)

1

0.65

0.48

Rezoning units/Total capacity

0.34

0.41

0.40

Rezoning units/2021 target

0.40

0.54

0.76

2021 target/2014 target (total units in thousands)

2.80

39.98

218.99

2021 target/2014 target (low-income units in thousands)

2.95

27.67

145.59

City Characteristics

Population (thousands)

55

134

425

Median home value (thousands of $)

$573

$630

$314

Share of population White non-Hispanic

32%

35%

22%

Share of households that are homeowners

60%

58%

15%

Share of population older than 65

14%

15%

6%

Std. Dev. = standard deviation.

Sources: California HCD; U.S. Census Bureau, 2015

The outcome of interest is rezonings, and we use two different denominators to standardize the

number of units in proposed rezonings across cities: the city’s total capacity for new housing,

including rezoning, and the 2021 housing target. For the median city, rezoning represents 36

percent of the total capacity for new housing and 37 percent of its target.

Our treatment measures are the increase in a city’s housing target, measured by the ratio of its

total 2021 target to its total 2014 target, and the increase in its target for low-income housing

specifically. We use the increase in low-income targets separately because the rules cities face

for satisfying those targets are more restrictive. They must identify larger sites with zoning at a

prescribed minimum density threshold (for example, 30 dwelling units per acre in urban areas),

whereas sites to meet targets for moderate- and above-moderate-income housing can be any size

and density. Moreover, the aforementioned equity adjustments made in the regional allocation of

targets gave slightly higher targets for low-income housing to more affluent cities, so we anticipate

that the regional allocation will have a slightly larger impact than the change in the total target.

First, we assess correlations between these measures of plans, targets, and city characteristics

individually to identify relationships. Then, we run a regression to test the hypothesis that higher

housing targets are associated with more rezonings even when controlling for characteristics of the

municipalities that are correlated with rezonings and housing targets.

Exhibit 5 reports bivariate correlations between measures derived from housing plans and

municipal characteristics. The correlations between changes in targets and rezonings are positive

and strong. The correlations between demographic characteristics of cities and their rezoning plans

or change in targets are not statistically significant, meaning that even without controlling for other

variables, cities with more homeowners or White and older residents did not systematically differ

in their rezoning activity.

134 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

Exhibit 5

Correlations Between Housing Plan Measures and Municipality Characteristics (93 Certified

Housing Elements)

Total Target Low-Income

Any

Rezonings/ Rezonings/

Variables

2021/2014

Target

Rezoning?

Capacity

Target

(log)

2021/2014 (log)

Rezoning units/Capacity 2021

0.87**

1.00**

Rezoning units/Target 2021

0.87**

0.95**

1.00**

Total target 2021/2014 (log)

0.38**

0.43**

0.38**

1.00**

Low-income target 2021/2014 (log)

0.42**

0.49**

0.43**

0.97**

1.00**

Population (log)

0.21*

0.13

0.13

– 0.02

– 0.04

Median home value (log)

0.20*

0.28*

0.28*

0.19

0.30*

White non-Hispanic (%)

– 0.18

– 0.07

– 0.05

– 0.18

– 0.07

Homeowners (%)

– 0.05

0.04

– 0.01

– 0.12

– 0.03

Older than 65 (%)

– 0.08

0.00

0.00

0.06

0.12

Notes: N = 93. Spearman correlation coefficients. * and ** indicate significance at the 0.01 and 0.05 levels, respectively.

Sources: Authors, with U.S. Census Bureau, 2015; California HCD

Exhibit 5 shows that places with higher home values committed to more rezonings and had higher

targets, presumably because they are more likely to be built out according to their existing zoning.

Because higher-demand (more expensive) cities cannot find existing capacity in their zoning, they

need to rezone. This finding especially reinforces the need for a statewide planning process to

provoke zoning reform because it creates much more potential for new production in higher-demand

areas. Housing target increases are also significantly larger for cities with higher home values.

Now we turn to the results of our regressions in exhibit 6, which presents the results of six models in

which we vary three measures of rezonings and two measures of the change in targets (overall targets

and targets specific to low-income housing). Models 1 and 2 are logistic regression models that assess

the probability of a city doing any rezoning, so the dependent variable is a yes (1) or no (0). Models

3 and 4 are ordinary least squares (OLS) regressions using rezonings as a share of total capacity, and

Models 5 and 6 are also OLS using rezonings as a share of the target. All models include county fixed

effects and a dummy variable indicating whether the city’s housing element is compliant.

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Monkkonen, Manville, Lens, Barrall, and Arena

Exhibit 6

Regression Results: Rezoning and Housing Target Increase

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Logit: DV = Logit: DV = OLS: DV =

OLS: DV =

OLS: DV =

OLS: DV =

Variables

Rezoning? Rezoning? (Rezonings/ (Rezonings/ (Rezonings/ (Rezonings/

(Y/N)

(Y/N)

Capacity)

Capacity)

Target)

Target)

0.767**

0.097***

0.203***

Target 2021/2014 (log)

(0.374)

(0.029)

(0.055)

Low-income target

0.913**

0.113***

0.238***

2021/2014 (log)

(0.417)

(0.032)

(0.060)

0.756**

0.704*

0.059

0.050

0.113

0.096

Population (log)

(0.363)

(0.360)

(0.046)

(0.045)

(0.085)

(0.085)

Median home

0.620

0.298

– 0.002

– 0.051

0.094

– 0.012

value (log)

(1.075)

(1.107)

(0.135)

(0.137)

(0.253)

(0.256)

0.004

0.005

0.002

0.002

0.012**

0.012**

Non-Hispanic White (%)

(0.022)

(0.022)

(0.003)

(0.003)

(0.005)

(0.005)

0.035

0.031

0.007**

0.006*

0.009

0.007

Homeowners (%)

(0.026)

(0.026)

(0.003)

(0.003)

(0.006)

(0.006)

– 0.091

– 0.096

– 0.014*

– 0.015*

– 0.027*

– 0.029*

Older than 65 (%)

(0.063)

(0.063)

(0.008)

(0.008)

(0.015)

(0.015)

– 17.220

– 12.611

– 0.698

0.0197

– 2.537

– 1.005

Constant

(12.887)

(13.251)

(1.581)

(1.613)

(2.963)

(3.014)

Observations

92

92

92

92

92

92

(Pseudo) R-squared

0.30

0.31

0.31

0.31

0.34

0.36

DV = dependent variable. OLS = ordinary least squares.

Notes: Models include county fixed effects. Standard errors in parentheses. *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively.

Sources: Authors, with U.S. Census Bureau, 2015; California HCD

The results of the first two models show that only two variables—a larger increase in housing targets

and a larger population—predict a city engaging in rezoning. The coefficients suggest that doubling

the housing target makes a city 2.5 times more likely to rezone, and doubling the population makes

it twice as likely it will rezone. As an example, the median city had a 64-percent chance of engaging

in rezoning. A 1-standard-deviation increase in its target would lead to an 84-percent chance of

rezoning, and a 1-standard-deviation increase in its population size leads to a 76-percent chance.

The mechanism for larger targets to provoke rezoning is clear, and research shows that the politics of

larger cities are more amenable to new housing (Marantz and Lewis, 2022).

Higher housing targets are also statistically significantly associated with more rezoning, and the

coefficients are twice as large when measuring rezonings relative to housing targets compared

with using a city’s capacity. For example, doubling the overall housing target is associated with a

10-percentage-point increase in rezonings as a share of capacity and a 20-percentage-point increase

in rezonings as a share of a city’s target.15 That difference likely results from cities expressing more

15 To allay potential concerns about interpretation challenges when using the 2021 target to standardize rezonings, we note that the correlation between 2021 targets and the change in rezonings between 2014 and 2021 (the treatment variable) is almost zero (0.02).

136 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

capacity than their target if they do not have to rezone; being ambitious in identifying capacity is

easier if it does not involve changing zoning and simply requires cataloguing more available sites.

The associations between target increase and rezoning are slightly larger if we use the increase in

low-income targets rather than total targets as the treatment variable. Doubling the low-income

target is associated with 11 and 24 percentage points more rezoning as a share of capacity and

target. As mentioned, low-income sites must have certain characteristics—most importantly, a

minimum density threshold. Given the scarcity of parcels zoned for higher density, larger low-

income targets spur more change.

In a few of the models, the coefficients for share White and share homeowner attain slight

significance and are positive, whereas the share older than 65 is associated with less rezoning. The

coefficients represent the increase or decrease in share of capacity or target rezoned for a 1-percent

increase in the city’s population. For example, model 3 shows that a 1-percent increase in the

share of a city who are homeowners is associated with a 0.7-percent increase in the share of a city’s

capacity met with rezoning. It also shows that a 1-percent increase in the share of a city who are

older than 65 years old is associated with 1.4 percent less rezoning as a share of capacity.

Given the low significance levels, however, it is hard to make strong claims about the complicated

interaction between previous success in gaming the system to get low targets, buildout according to

local zoning, and NIMBY (“not in my backyard”) characteristics that correlate with one another. We

could interpret the negative coefficient on elderly as being a NIMBY characteristic that leads to less

rezoning in the current cycle, whereas cities with more White residents and homeowners are more

likely to be built out and thus have to engage in rezoning to become compliant. An encouraging

finding is that cities with NIMBY characteristics are not systematically rezoning less. With more

data as more cities complete their housing elements, those relationships will be easier to untangle.16

Conclusion

This article describes the outcomes of reforms to California’s housing planning process. Reform

legislation in 2017 and 2018 changed the nature and intensity of a planning process that occurs

every 8 years. Southern California municipalities received substantially higher housing growth

targets in 2021 than they had previously, and we evaluate the way they accommodate those targets.

We focus on how much potential for new housing those municipalities created through rezoning

land for residential use or for higher density.

We find that cities were more likely to change their local zoning in 2021 compared with 2014—

and to do so more dramatically. The 93 (of more than 209) southern California cities that have

compliant housing elements have committed to more than 500,000 units of rezonings, compared

with fewer than 50,000 in the whole state in 2014. Moreover, those commitments to rezoning are

happening in cities with relatively high housing values, which means housing is more likely to be

16 We also ran the same regression analysis with a larger sample of cities (an additional 32 cities) that have completed their housing elements and have sites’ inventory data available but are not certified by the state. The results of those models are reported in an appendix exhibit. The changes in targets have smaller coefficients but are still significant, and the other coefficients display some differences. We prefer the models using the smaller sample of certified housing elements because the uncertified elements may change.

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built. For those cities with certified housing elements, higher housing targets were significantly

associated with more rezoning, especially higher targets for low-income housing.

We also find that the characteristics associated with NIMBYism play a predictable role in the

determination of housing targets but not in models of rezoning. Cities with higher housing values

and more White homeowners get relatively lower targets than cities with fewer Whites and lower

housing costs but react no differently to them—or, in some cases, rezone more—than other cities.

In other words, although the planning system still treats those cities differently in setting targets, it

is forcing cities to meet targets in land use plans through rezoning. We take this finding as evidence

that the legislative reforms have spurred action and accountability among California cities in how

they plan for housing. In particular, higher total housing targets seem to have stifled some of the

previously existing strategic behavior.

Yet problems with the system remain. California’s housing element process is expensive (in terms of

money spent on consultants and plan preparation and of people’s time) and slow (the process has

been underway for several years, and no new housing has been built as a result), and rules seem to

continue to be administered unequally across places (Collins, 2022). Moreover, rezoning is not yet

permitting the needed housing, although it is a first and necessary step.

Finally, housing targets within regions have been increasingly assigned to cities with high prices

and near jobs—such as Beverly Hills—as a relative matter. Despite that improvement, many

cities that should probably not be pressured to grow from an environmental or affordability

perspective—such as Coachella—are still receiving larger targets than places where the impact

of rezoning would be greatest. One resolution to that problem would be to shift toward a system

that explicitly considers the probability of housing development in regional decisions about where

targets are assigned (Elmendorf et al., 2021b). For any parcel of land, analysts can use data to

estimate how likely it is to be redeveloped, as Los Angeles did in its housing element (City of Los

Angeles, 2021). Then regional targets could be assigned on the basis of where redevelopment is

most likely—which is much higher in Beverly Hills than in Coachella. That practice would both

reduce the local staffing costs of the process—because the estimation of development probability

could be carried out at the state or regional level—and relieve pressure on lower-income cities.

Finally, this study recalibrates the debate between the plan mandate approach to land use

interventions compared with state governments more directly preempting local control over

land use in strategic locations or creating affordable housing appeals systems. As already noted,

California’s plan mandate system had historically proven ineffective. But if recent reforms indeed

prove successful, as this research indicates, plan mandates may offer at least some advantages

over alternative state land use strategies and can be complementary to them. That is, plan

mandates set goals for local governments but include residents in a community-based process

of deciding where growth should occur. We see a tradeoff between the potential for increased

local participation in zoning decisions and concurrent political acceptance of the process and

the possibility of planning decisions that compromise environmental sustainability and social

integration. How that process will unfold a priori is not yet clear, and measuring political

acceptance is challenging; thus, a useful next step for future research will be to assess where

138 Recent Reforms in Zoning

California’s Strengthened Housing Element Law:

Early Evidence on Higher Housing Targets and Rezoning

rezonings are occurring in cities, spatially, compared with what a more top-down approach would

have yielded, according to some objective criteria.

Appendix

Exhibit A1

Replication of Models in Exhibit 6 Using a Larger Sample of Cities (125) that Includes Data from

Complete but Uncertified Housing Elements

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Logit: DV = Logit: DV = OLS: DV =

OLS: DV =

OLS: DV =

OLS: DV =

Variables

Rezoning? Rezoning? (Rezonings/ (Rezonings/ (Rezonings/ (Rezonings/

(Y/N)

(Y/N)

Capacity)

Capacity)

Target)

Target)

0.328*

0.066***

0.115***

Target 2021/2014 (log)

(0.187)

(0.023)

(0.040)

Low-income target

0.494**

0.086***

0.151***

2021/2014 (log)

(0.226)

(0.026)

(0.045)

0.190

0.188

0.008

0.005

0.058

0.052

Population (log)

(0.237)

(0.239)

(0.038)

(0.037)

(0.065)

(0.065)

Median home

1.666**

1.377*

0.146

0.096

0.151

0.064

value (log)

(0.729)

(0.756)

(0.104)

(0.106)

(0.179)

(0.183)

– 0.028*

– 0.026

– 0.002

– 0.002

0.005

0.006

Non-Hispanic White (%)

(0.016)

(0.016)

(0.002)

(0.002)

(0.004)

(0.004)

1.431

1.368

0.566**

0.549**

0.736

0.706

Homeowners (%)

(1.837)

(1.843)

(0.282)

(0.276)

(0.488)

(0.478)

0.006

0.001

– 0.003

– 0.004

– 0.010

– 0.01

Older than 65 (%)

(0.039)

(0.040)

(0.006)

(0.006)

(0.011)

(0.011)

0.429

0.523

0.0716

0.0817

0.261*

0.278**

Certified (Y/N)

(0.510)

(0.520)

(0.0797)

(0.0789)

(0.138)

(0.137)

– 24.63***

– 21.13**

– 2.139

– 1.502

– 2.990

– 1.881

Constant

(9.495)

(9.792)

(1.307)

(1.330)

(2.264)

(2.303)

Observations

125

125

125

125

125

125

(Pseudo) R-squared

0.22

0.23

0.23

0.24

0.26

0.28

DV = dependent variable. OLS = ordinary least squares.

Notes: Models include county fixed effects. Standard errors in parentheses. *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively.

Sources: Authors, with U.S. Census Bureau, 2015; California HCD

Acknowledgments

This project was supported by the Pew Charitable Trusts and New York University. The authors

would like to thank Noah Kazis, Ingrid Gould Ellen, Salim Damerdji, and participants at the 2022

conference of the Association for Public Policy Analysis and Management for valuable feedback on

the research.

Cityscape 139

Monkkonen, Manville, Lens, Barrall, and Arena

Authors

Paavo Monkkonen is a Professor of Urban Planning and Public Policy at the University of

California, Los Angeles. Michael Manville is an Associate Professor of Urban Planning at the

University of California, Los Angeles. Michael Lens is an Associate Professor of Urban Planning

and Public Policy at the University of California, Los Angeles. Aaron Barrall is a Masters student

in Public Health at the University of California, Los Angeles. Olivia Arena is a Masters student in

Urban and Regional Planning at the University of California, Los Angeles.

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Only I Could’: How Cities Can Use California’s Housing Element Law to Overcome Neighborhood

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Early Evidence on Higher Housing Targets and Rezoning

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to Meet Lofty Housing Goals. UCLA Lewis Center for Regional Policy Studies Policy Brief.

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142 Recent Reforms in Zoning

by Moira O’Neill and Ivy Wang

How Can Procedural Reform Support

Fair Share Housing Production?

Assessing the Effects of California’s

Senate Bill 35

Moira O’Neill

University of California, Berkeley

University of Virginia

Ivy Wang

University of California, Berkeley

Abstract

Land use regulation that constrains housing production risks social, economic, and environmental

harm. California attempts to avoid these harms by mandating localities plan to accommodate their

fair share of housing production to meet local and regional needs. Decades of inadequate affordable

housing production galvanized the legislature to pass multiple laws to address regulatory obstacles to

affordable housing production. One of those laws, Senate Bill 35, creates a ministerial approval pathway

for qualifying housing in communities that have failed to meet their housing production targets in prior

years. Lawmakers hoped that making affordable housing development faster and more predictable would

facilitate building more housing at a lower cost. This article provides preliminary empirical support for

that theory. In some cities, Senate Bill 35 is, in fact, speeding up housing approvals.

Introduction

Land use regulation that constrains housing production risks exacerbating and perpetuating

economic and racial segregation, inhibiting economic growth, increasing the cost of housing, and

worsening environmental harm (Glaeser, Gyourko, and Saks, 2005; Hsieh and Moretti, 2019;

Lens and Monkkonen, 2016; Rothwell and Massey, 2009; Sterk, 2021).1 California’s housing law

1 For example, municipal land use regulation tends to lower housing density and increase sprawl (Levine, 2005). Sprawl, in turn, leads to more resource-intensive households that contribute disproportionately to carbon emissions (Jones and Kammen, 2014).

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 143

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

O’Neill and Wang

attempts to avoid these outcomes by imposing “fair share housing production” requirements on

local zoning and planning (Elmendorf et al., 2021a; HCD, n.d.a). Despite this planning framework,

housing need has outpaced housing demand in many California communities for decades.

Inadequate production, particularly affordable housing production, has galvanized the California

legislature to reform state housing law across many dimensions.2 This article discusses just one

of these recent reforms, enacted in 2017—Senate Bill 35 (SB 35).3 SB 35 builds on the state’s

existing fair share production law by limiting procedural obstacles to some housing production.

SB 35 preempts local power to impose a discretionary approval process on qualifying affordable

or mixed-income housing in localities that have failed to approve adequate, affordable housing in

prior years (Wiener, 2017). Lawmakers hoped that making affordable housing development faster

and more predictable would allow for more housing to be built in more communities at a lower

cost (Wiener, 2017). This article provides preliminary empirical support for that theory. In some

cities, SB 35 is, in fact, speeding up housing approvals.

This article describes the relevant California housing and planning law, then explains SB 35’s

intervention within that framework. Next the authors explain how the Comprehensive Assessment

of Land Use Entitlements Study (CALES) data (O’Neill-Hutson et al., 2022) lends itself to

exploring the effect of SB 35 in selected cities. Then, the article offers findings on how SB 35 has

operated in five important local jurisdictions: Berkeley, Los Angeles, Los Angeles County, Oakland,

and San Francisco.

Background

California’s Housing Element Law provides the state’s legal and planning framework to meet

housing demand and address residential segregation (Ramsey-Musolf, 2016). Housing Element

Law in California “took shape in the 1970s in an era in which there was increasing concern with

civil rights and the ability of minorities and low-income families to have an opportunity to live in

suburbia, not just in inner-city or rural enclaves” (Lewis, 2003). Housing Element Law attempts

to remedy economic segregation through comprehensive long-term planning processes that

theoretically force localities to plan and zone for each jurisdiction’s “fair share” of housing for all

income levels.

Housing Element Law operates within California’s broader comprehensive planning law. Cities

and counties must update their Housing Elements every 5 or 8 years (HCD, n.d.b).4 The Housing

Element sets forth how the locality will support the production of sufficient housing units at

2 California State Senate, Senate Committee on Transportation and Housing Analysis, March 2, 2019, page 4. Senate Bill 35.

Planning and Zoning: Affordable Housing: Streamlined Approval Process. 2017–18 Reg. Sess., 4. https://leginfo.legislature.ca.gov/

faces/billTextClient.xhtml?bill_id=201720180SB35.

3 In addition to SB 35, the 2017–18 California legislature passed 15 pieces of legislation that aimed to, among other things, raise money to finance low-income developments, incentivize cities to plan neighborhoods for new development, allow cities to implement low-income requirements on developments, preserve existing low-income housing, require cities to plan for more housing, and enhance enforcement against cities that deny housing projects (Dillon, 2017). The legislature has continued to work actively in this area in the intervening years. Perhaps the most prominent changes have been California’s allowing duplexes on most single-family lots statewide.

4 Cal. Gov’t. Code Sec. 65585(e)(3).

144 Recent Reforms in Zoning

How Can Procedural Reform Support Fair Share Housing

Production? Assessing the Effects of California’s Senate Bill 35

each of four income levels—very low, low, moderate, and above-moderate (HCD, 2020: 9).5 The

local government does not determine what is sufficient—production targets come from the state’s

Regional Housing Needs Assessment (RHNA) (Elmendorf et al., 2021a: 978; Lindgren and Mattas,

2022: Sec. 2.13).

The RHNA process begins with the California Department of Housing and Community

Development (HCD) first determining the overall housing need for each region of the state based

on state demographic data, working with regional planning bodies.6 Each region then allocates its

housing need between all the cities and counties within its region,7 assigning a number of housing

units to each locality for each income level.8 Localities must then demonstrate that enough parcels

in their jurisdiction are zoned to accommodate those targets and identify and correct for regulatory

constraints on housing production (Elmendorf et al., 2021b: 611–12).9

Local governments submit their housing elements to HCD for approval. Following approval,

localities must also submit annual reports on their progress in implementing the Housing

Elements (HCD, n.d.a). HCD also has enforcement authority when communities fail to meet their

obligations under Housing Element law.10 If HCD decertifies a Housing Element, state law provides

for several potential consequences: A local government may be unable to access state funding for

community development, infrastructure, housing, and transportation and be exposed to lawsuits,

with plaintiffs eligible for attorneys’ fees (Elmendorf et al., 2021c; HCD, n.d.b). A court may also

mandate the approval of building permits for affordable housing developments or suspend the

local government’s permitting authority altogether (HCD, n.d.c).11

Despite the ostensible force of Housing Element Law, California communities have failed to

meet housing production needs. Indeed, research indicates that between 1994 and 2000, local

compliance with Housing Element Law did not result in any more local housing production

5 Cal. Gov’t. Code Sec. 65585(c); California Department of Housing and Community Development, “Housing Element

Sites Inventory Guidebook,” p. 9, https://www.hcd.ca.gov/community-development/housing-element/docs/sites_inventory_

memo_final06102020.pdf.

6 Cal. Gov’t. Code Sec. 65584.01(a).

7 Cal. Gov’t. Code Sec. 65584.03.

8 These income categories are prescribed in the California Health and Safety Code, Section 50093, et seq. See also Cal. Gov.

Code Section 65583. Notably, some argue that the allocations exacerbate existing income and racial segregation; one study found that the regional planning process led to assigning a disproportionate share of very low- and low-income units to jurisdictions with larger minority populations. See Bromfield and Moore (2017).

9 At page 612, Elmendorf discusses Cal. Gov’t. Code Sec. 65583(a), (c).

10 Cal. Gov’t. Code Sec. 65585(i)-(j); Assem. Bill 72, 2017-18 Reg. Sess., requiring HCD to review written findings about actions inconsistent with an adopted housing element and authorizing HCD to revoke a prior finding of compliance for a housing element, and AB 215 (2021), expanding HCD’s mandate to notify the Attorney General to bring action to enforce state law violations in housing element and authorizes HCD to appoint its own counsel if the Attorney General declines to represent the department in such an action, including https://www.hcd.ca.gov/planning-and-community-development/

accountability-and-enforcement. That enforcement authority has grown over time. Compare McDougall (1987)—following amendments to state law in 1984, the HCD must give prior approval to all local fair-share plans—with Elmendorf et al.

(2021a) “The legislature has also authorized HCD to decertify housing elements midcycle for failures of implementation and has backstopped decertification with fiscal penalties and more.”

11 Cal. Gov’t. Code Sec. 65755.

Cityscape 145

O’Neill and Wang

(Lewis, 2005). Scholars offer multiple explanations for local failure to meet production targets.12

The critique that Housing Element Law did not dismantle the procedural obstacles that can block

the construction of new housing is of particular interest to this research (Monkkonen, Manville,

and Friedman, 2019: 3). In theory, the law required localities to identify and mitigate regulatory

constraints to housing production, but in practice, local governments offered little to no analysis

of local constraints and doing so led to no consequences (Elmendorf et al., 2021b: 612). Prior

research suggests that the systematic failure to identify and correct for procedural obstacles to

production may significantly curtail housing production, even in cities that zone a lot of land for

dense housing (O’Neill-Hutson et al., 2022).

Procedural obstacles in California communities can manifest in different ways. California

law allows local governments latitude in how they approve residential development. Many

communities use discretionary review processes when approving housing developments, even

those that conform to base zoning and planning standards (that is, the density, use, setback, and

other requirements that dictate what type of development can go on a parcel). Discretionary

review refers to a local government’s ability to impose conditions of approval—or deny approval

altogether—when deciding whether to approve proposed development.13 The discretionary

approval process is best understood by contrasting it with a ministerial process in which a

decisionmaker applies law to fact without using subjective judgment.14 A discretionary process

allows for uncertainty and delay, which can increase costs; a ministerial process suggests approval

is certain if a project proponent meets specified requirements.15

Discretionary design, architecture review, site development review, and historical preservation

review are all examples of discretionary processes that localities apply to development that

conforms to all base zoning requirements (O’Neill-Hutson et al., 2022: 18). In past research,

O’Neill-Hutson et al. found that nearly all dense developments studied went through a

discretionary approval process—even in areas that cities have identified for dense development

12 These factors include that Housing Element Law does not require localities to actually produce additional housing (Lewis, 2003; Monkkonen, Manville, and Friedman, 2019). Moreover, material consequences are few for failing to deliver on

housing shares, and the planning process bases local housing need on past population growth, perpetuating exclusivity and unaffordability (Elmendorf, 2019; Kazis, 2020). Finally, the process frequently defers to affluent cities that lobby to keep their RHNA shares low (Monkkonen, Manville, and Friedman, 2019: 3).

13 California’s Housing Accountability Act (HAA) codified in Cal. Gov’t. Code Sec. 65589.5 et seq. requires that a local government issue written health and safety findings when imposing conditions of approval that reduce density or outright denying approval for certain housing developments. The written findings must justify the denial “based on a preponderance of the evidence in the record” (65589.5(d)). Amendments to the HAA in 1999 also provided that the HAA limits the scope of local discretion over developments that conform to “objective” general plan and zoning requirements in Cal. Gov’t. Code Sec. 65589.5(j). Also, penalties exist for bad-faith disapprovals in Cal. Gov’t. Code Sec. 655589.5(l).

14 For a definition of ministerial in California law, see Prentiss v. City of S. Pasadena, 15 Cal. App. 4th 85, 90, 18 Cal. Rptr. 2d 641 (1993), citing Cal. Code Regs., title 14 section 15268 (b)(1) “‘Ministerial’ describes a governmental decision involving little or no personal judgment by the public official as to the wisdom or manner of carrying out the project. The public official merely applies the law to the facts as presented but uses no special discretion or judgment in reaching a decision. A ministerial decision involves only the use of fixed standards or objective measurements, and the public official cannot use personal, subjective judgment in deciding whether or how the project should be carried out.”

15 For a discussion of what characterizes a discretionary action, see Friends of Westwood, Inc. v. City of Los Angeles 181 Cal.

App. 3d 259, 269-74 (1987). When city employees can set standards and conditions for many aspects of a proposed

building, the approval process is discretionary.

146 Recent Reforms in Zoning

How Can Procedural Reform Support Fair Share Housing

Production? Assessing the Effects of California’s Senate Bill 35

through zoning and planning designations (O’Neill-Hutson et al., 2022: 51–52).16 Discretionary

processes may enable local governments and homeowners to effectively block projects by creating

costly delay and uncertainty (Elmendorf, 2019: 88).17 In earlier work, O’Neill-Hutson et al. also

found that median entitlement timeframes for discretionary projects can span a few months to

many years, with timeframes varying widely across neighboring cities for similar developments

subject to similar processes (O’Neill-Hutson et al., 2022: Table 8).

In California, local discretionary review also triggers state-mandated environmental review under

the California Environmental Quality Act (CEQA). The California legislature enacted CEQA

in 1970 as a tool to review and mitigate potentially significant environmental effects of public

actions.18 As relevant here, CEQA requires local governments to study and disclose the effects of

their decisions, including discretionary housing approvals, on the environment (defined broadly)

and to engage in a public participation process to guide that analysis of impacts.19 Projects that

are more likely to have a significant effect on the environment require more extensive study and

disclosures. CEQA review also applies to local legislation. For example, if the local legislature

would like to create a new ministerial pathway for housing approvals previously subjected to

discretionary review, CEQA applies.20

Critics of CEQA argue that this environmental review process adds significant expense, time, and

uncertainty to project development, potentially leading to fewer residential projects being pursued

and built.21 CEQA lawsuits have challenged or stalled the development of affordable housing, as

well (Gray, 2021; Sedonaen, 2018). Indeed, some scholars argue that CEQA’s deference to local

agencies in the face of extensive delays and bad-faith tactics may allow development opponents

to “launder” project denials through CEQA when the actual grounds for their option may have

nothing to do with environmental concerns (Elmendorf and Duncheon, 2022: 17–23).

16 The CALES studied over 2,000 housing approvals for five or more units of housing. More than 80 percent required

discretionary approvals.

17 A recent appellate opinion demonstrates that project proponents and housing advocates may successfully seek judicial relief from local-level bad-faith denials under the HAA, partially ameliorating this problem. See California Renters Legal Advocacy & Education Fund v. City of San Mateo, 68 Cal. App.5th 820 (2021).

18 See Cal. Pub. Res. Code Sec. 21002.

19 Prior research found that most dense development of five units or more across 20 jurisdictions was subject to

discretionary review, even in areas designated for dense development. As a result, most multifamily developments fell within CEQA’s ambit (O’Neill-Hutson et al., 2022). Moreover, courts have interpreted CEQA broadly. Notoriously, a state court of appeal recently found that the University of California (U.C.) Berkeley’s decision to increase student enrollment was a

“project” subject to CEQA review and considered environmental effects, including “increased use of off-campus housing by U.C. Berkeley students (leading to increases in off-campus noise and trash), displacement of tenants and a consequent increase in homelessness, more traffic, and increased burdens on . . . public safety services.” Save Berkeley’s Neighborhoods v.

Regents of University of California, 51 Cal. App.5th 226 (1st Dist. 2020). In this instance, the state legislature stepped in to supersede the court’s decision by amending CEQA to state that enrollment increases on their own do not constitute a project under CEQA. Cal. Pub. Res. Code Sec. 21080.09.

20 See, for example, Union of Med. Marijuana Patients, Inc. v. City of Upland, 245 Cal. App. 4th 1265, 1272 (4th Dist. 2016)

“Ordinances passed by cities are clearly activities undertaken by a public agency and thus potential ‘projects’ under CEQA,”

quoting Santa Monica Chamber of Commerce v. City of Santa Monica, 101 Cal. App.4th 786, fn. 2 (2nd Dist. 2002). However,

“a municipal ordinance that merely restates or ratifies existing law does not constitute a project . . .” Union of Med. Marijuana Patients, Inc. v. City of Upland, 245 Cal. App. 4th 1265, 1273 (4th Dist. 2016).

21 See, for example, Hernandez (2018) and Gray (2021).

Cityscape 147

O’Neill and Wang

How Senate Bill 35 Operates

SB 35 reduces procedural hurdles to production. SB 35 does so by eliminating both local

discretionary review and state mandated environmental review for qualifying urban housing

developments in jurisdictions that have not met their state-set housing production targets or

process requirements under the Housing Element Law.

If cities or counties failed to approve enough housing units in their most recent reporting period

to meet their need allocation for certain income levels, they lose their discretionary authority over

specified projects.22 Instead, housing developers can apply to have the city use a state-required

ministerial approval process.23

Under the SB 35 process, local governments may still impose their objective local zoning

and design review standards.24 However, SB 35 significantly reduces or eliminates parking

requirements.25 Local governments may not impose additional discretionary review, however.

Importantly, SB 35 also provides for strict timelines for the approval process. The local government

must adhere to 90- or 180-day time limits (depending on the size of the development) for design

review and public oversight processes.26 Affordable housing developments also receive extended

expiration periods to complete construction.27 If the project is not consistent with objective

standards, the city must inform the developer in writing within 60 or 90 days.28 Local governments

may not impose additional fees or inclusionary housing requirements on these developments.29

Because SB 35 does not allow for a discretionary approval process for qualifying projects, CEQA

review is no longer required.

Which projects are eligible? SB 35 applies a ministerial approval process only to urban multifamily

housing developments that meet specific affordability thresholds.30 Additional criteria attempt to

preserve affordability and prevent displacement: Eligible projects cannot involve the demolition

22 Cal. Gov’t. Code Sec. 65913.4(a)(4)(A). SB 35 initially provided that a jurisdiction that either permitted too few developments by income level or had not submitted their annual report for 2 consecutive years was required to perform a streamlined review for qualified developments. The state legislature later removed 2 consecutive years of nonreporting as a way for jurisdictions to fall under SB 35. Compare Stats.2017, c. 366 (S.B.35), Sec. 3, eff. Jan. 1, 2018 to Stats.2018, c. 92

(S.B.765), Sec. 2, eff. Jan. 1, 2019.

23 See Prentiss v. City of S. Pasadena, 15 Cal. App. 4th 85, 90 (1993).

24 Cal. Gov’t Code Sec. 65913.4(a)(5).

25 Localities may not impose parking standards for streamlined developments within one-half of a mile of public transit, within architecturally and historically significant historic districts, requiring on-street parking permits but not offering them to occupants of the development or within one block of a car share vehicle. For all other developments, parking standards cannot exceed one parking space per unit. Cal. Gov’t Code Sec. 65913.4(d).

26 Cal. Gov’t Code Sec. 65913.4(c).

27 Cal. Gov’t Code Sec. 65913.4(e).

28 Cal. Gov’t Code Sec. 65913.4(b)(1).

29 Localities cannot impose any increased fees or inclusionary housing requirements based solely or partially on the fact that the project has received streamlined approval under SB 35 per Cal. Gov’t Code Sec. 65913.4(f).

30 Cal. Gov’t Code Sec. 65913.4(a).

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of affordable or tenant-occupied housing.31 The development must not require subdivision.32 The

development may not be sited in environmentally sensitive or significant areas.33 A significant

portion of the law also ensures developers using this streamlined process pay prevailing union

wages to both contractors and subcontractors.34

Research Questions and Hypothesis

This article explores how SB 35 operated within specific study cities for project approvals in 2018,

2019, and 2020. The authors examine—

1. What types of developments benefited from SB 35 in these years in the study cities?

2. For development that benefited from SB 35, how did the SB 35 approval process unfold?

3. What effect does SB 35 have on approval processes within these cities?

In earlier research, O’Neill-Hutson et al. (2022) examined the pathways to approvals for

developments of five or more units of housing issued in 2014, 2015, 2016, and 2017 in 20

jurisdictions. More than 80 percent of the more than 2,000 approved developments navigated a

discretionary review process on the way to approval; that is, they required “entitlement” before they

could proceed to the building department for permits to build.

For developments subject to a discretionary process, O’Neill-Hutson et al. found extreme

differences in the time between a project’s application and its entitlement, even between

neighboring urban cities for similar housing development. The median timeframe to entitlement

for multifamily development that conformed to all local planning and zoning requirements in San

Francisco exceeded 25 months. Next door, in Oakland, the median for the same was 6 months.

Both cities had similar regulations “on the books” and applied identical CEQA streamlining to

satisfy state required environmental review.

The authors hypothesized that (as the legislature intended it to) SB 35 should curtail process time

lags and risk of opposition for at least some mixed-income development. The authors hypothesized

that SB 35 would have the greatest effect in cities with more onerous procedural hurdles—like

Berkeley or San Francisco.

Methods

To understand SB 35’s effect on entitlement processes in the study jurisdictions, the authors built

on the research from O’Neill-Hutson et al. (2022) by adding analysis and data from the existing 20

case studies, an additional 8 case studies, and annual progress reports (APRs) for all 28 jurisdictions

31 Cal. Gov’t Code Sec. 65913.4(a)(7). Indeed, the project is ineligible if the proposed project is on a site that used to have tenant-occupied housing, but that tenant housing was demolished within the past 10 years. The code has additional anti-displacement provisions, as well.

32 Cal. Gov’t Code Sec. 65913.4(a)(9).

33 Cal. Gov’t Code Sec. 65913.4(a)(6).

34 Cal. Gov’t Code Sec. 65913.4(a)(8).

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produced under state Housing Element Law. The CALES case studies used mixed-method research

to understand planning, zoning, and approval pathways in 28 jurisdictions throughout the State of

California. To identify which of these cities would provide insight into the potential effect of SB 35,

the authors first used 2018 and 2019 annual progress reports to identify which of the 28 CALES

study jurisdictions reported approving developments that used SB 35. Only five of the study cities

reported approvals through SB 35 in these years, so the authors restricted this research to those five

jurisdictions: Berkeley, Los Angeles, Los Angeles County, Oakland, and San Francisco. That said,

that the other 23 jurisdictions initially reported no approvals under SB 35 is a notable fact about the

law’s early implementation, meriting further investigation.

Local governments implement SB 35, not the state. Whether developers use SB 35 depends partly

on how localities provide information about their SB 35 procedures and whether they make

prompt determinations about whether projects meet objective criteria. Thus, to better understand

some factors that might drive local outcomes despite the state’s attempt to standardize approval

processes, the authors reviewed how these five local governments explain their implementation of

SB 35 and how SB 35 fits into each jurisdiction’s existing legal regime.

Next, the authors examined how SB 35-eligible projects navigated the SB 35 approval pathway

in each city. The goal was to compare these processes with processes for similar developments

in earlier years, which were explored using a housing approval dataset developed for each of the

CALES case study cities.

Reviewing both APR data and local data portals, the authors found 49 potential observations of

proposed developments across five jurisdictions.35 The authors then confirmed whether these

49 observations did, in fact, benefit from SB 35 and expanded the data collection to determine

if additional developments benefited from SB 35 in 2020 using documents each jurisdiction

makes available through their local public portals. The authors then coded the data to allow for

comparative analysis with similar developments entitled in 2014–17 in the same five jurisdictions.

The observations from prior years do not have all the details needed to conclusively determine that

a project would have met SB 35 requirements. For example, the authors do not have information

on whether developments entitled in 2014–17 met the statute’s prevailing wage requirements.

Therefore, the authors selected projects entitled prior to SB 35 that met SB 35’s affordability, density,

and siting requirements and that were consistent with objective zoning and design review standards.

The authors then compared these pre-SB 35 entitlements with SB 35 approvals in later years. The

authors focused on required steps and entitlement timeframes when comparing developments. Each

jurisdiction requires its planning department to review whether the proposed development qualifies

for review under SB 35, and approval under SB 35, to proceed to the next step of applying for a

35 A quick review of the APR data across the entire state for these same years suggests that the total number of SB 35

approvals statewide may not be very high. Most reported SB 35 approvals in the 2018, 2019, and 2020 APR data (Table A) are incorrect. Jurisdictions appeared to report SB 35 approvals for single-family development (not allowed under the law).

The APRs from those years indicate only around 125 possible SB 35 approvals across the state. The authors are unable in this research to fully explain why more development did not benefit from SB 35. Base zoning (density and use controls) in some urban communities could be the problem. Prevailing wage requirements could be problematic in some regional

markets. Site characteristics might also present an obstacle.

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building permit. The eligibility review process allows the authors to uniquely create a comparison

between state ministerial approvals with local discretionary approvals in prior years.

An important limitation of this study, and data, is that findings from each study city are not

representative of how entitlement (or SB 35) operates across the state. The authors rely on case

studies to explain entitlement (and the application of SB 35) in specific cities—and do not draw

conclusions about how SB 35 operates in California, more generally, although the authors hope

these findings help build toward that larger understanding. Also, these study observations are

limited to proposed developments that were successfully entitled in 2014–17 or successfully

qualified for SB 35’s ministerial process in 2018, 2019, or 2020. In other words, these data

represent those developments that developers likely believed had a high enough probability of

success that they were willing to pursue entitlement in the first place. Thus, the authors cannot

rigorously evaluate whether SB 35 is changing the quantity or type of project proposed by

developers, who make those decisions in light of the applicable local legal regime. Even so, to the

extent that this research shows a faster and more predictable approval process under SB 35, the

authors would expect that developers adjust their behavior accordingly. Finally, the authors did not

find complete data in all five cities that would allow for comprehensive timeframe calculations for

all five cities.

Findings

Although SB 35 provides criteria about when and where it applies and imposes time constraints

on planning department eligibility review, it is not possible to extract a fully standard approach to

reviewing and processing applications from the language of the statute. Indeed, none of the five

jurisdictions studied modified their procedural rules in exactly the same way. Thus, the authors

begin each city-specific discussion with how the local government complied with the state law’s

eligibility review requirements. How cities respond to the time constraints on the eligibility review

process is significant in California, because past efforts to impose time constraints on procedure

have had mixed results, at best.36 Fully understanding the findings with respect to SB 35’s project-

level effects requires contextualizing those findings with cities’ still-disuniform procedures for

accessing the SB 35 process. Having provided that context, the authors then discuss the effect of

SB 35 on development approvals, offering comparisons with similar developments in prior years

where possible.

36 For example, the Permit Streamlining Act requires that local governments make completeness determinations within 30 days of a project application date, or the project application is “deemed complete” and ready for planning department review. The authors observed that many cities ignore this requirement entirely, whereas others do not make completeness determinations but do capture “deemed completed” dates within planning review tracking software.

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The City of Los Angeles

Los Angeles’ Senate Bill 35 Processes Add More Steps to Determining Eligibility, but

the City’s Application of Density Bonus Law Allows More Developments to Qualify

Separate from SB 35, the city of Los Angeles generally provides a ministerial process for code

compliant development, up to 49 units.37 California’s Density Bonus law further allows some

code compliant developments larger than 49 units to benefit from Los Angeles’ local ministerial

process.38 A property owner that qualifies for ministerial approval under local law applies for a

building permit directly with the Department of Building and Safety. If the development does not

qualify for ministerial review, the building department refers the project to the Department of City

Planning for planning review.

In contrast, to access SB 35 ministerial review, a project proponent begins with the Department

of City Planning and navigates multiple steps before proceeding to the Department of Building

and Safety. Los Angeles created an administrative procedure, the Streamlined Infill Project (SIP)

process, to review and track housing developments that qualify for SB 35.39 The goal of the SIP

process is to confirm that eligible projects meet the city’s objective zoning standards necessary

to access SB 35’s ministerial review. The SIP process requires multiple reviews, including from

the Department of City Planning and the Housing and Community Investment Department,

before proponents submit plans to the Los Angeles Department of Building and Safety (City of

Los Angeles Department of Regional Planning, 2023). Under the current procedures, it appears

that applicants for ministerial review under SB 35 are required to pay an “expedite fee” for this

service, in addition to the regular fee for a plan check (City of Los Angeles Department of Regional

Planning, 2023). Notably, SB 35 forbids the levying of “increased fees” based solely or partially

on the fact that the project has received this ministerial approval process, although it is unclear

whether the prohibition extends to processing fees.40

Los Angeles states that it will determine whether the application is consistent with objective

design standards within SB 35’s required timeframes. Any amendment to the original submission,

however, restarts that review clock (City of Los Angeles Department of Regional Planning, 2023).

Moreover, the city requires a project proponent to complete a “Pre-Application Review Process” to

determine eligibility. The city interprets SB 35’s time limitations to apply only after this preliminary

review (Glesne, n.d.). It is unclear whether these preapplication review process requirements are

consistent with SB 35.

37 Site Plan Review (the blanket discretionary provision with Los Angeles’ local code) generally applies at 50 units per Los Angeles Municipal Code §16.05 (C)(1). Additional criteria (related to specified planning areas) may also pull what would be ministerial into a discretionary approval pathway. Community Design Overlays can also render what would be ministerial discretionary (see § 13.08 (E)).

38 Los Angeles Municipal Code §12.22 A.25(g).

39 The same process is used to approve supportive housing projects that qualify for streamlined review under Assembly Bill 2162 (2018).

40 Cal. Government Code § 65913.4(h)(1) provides “A local government shall not adopt or impose any requirement,

including, but not limited to, increased fees or inclusionary housing requirements, that applies to a project solely or partially on the basis that the project is eligible to receive ministerial or streamlined approval pursuant to this section.”

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A final interpretive issue arising in Los Angeles’ implementation of SB 35 concerns the application

of density bonuses to SB 35 projects. SB 35 applies only to projects where one-half of units are

affordable but, until recently, did not provide clear language on whether the minimum affordability

threshold was to be calculated prior to or after a density increase.41 The city interpreted SB 35

to allow for “[a] minimum of 50 percent of the total units in the development, calculated prior to

any density increase, must be affordable” (City of Los Angeles Department of Regional Planning,

2023). This calculation parallels how the city applies the state Density Bonus Law to its own local

ministerial process. Notably, in September 2022, the state legislature amended the law to provide

that the minimum affordability threshold should be calculated prior to the application of a Density

Bonus, consistent with what Los Angeles was already doing.42

Senate Bill 35 Appears to Cut Approval Times in Los Angeles

So, how did the city’s SIP operate? Overall, the SIP appears to have reduced approval timeframes for

18 developments (exhibit 1 and exhibit 2). The median approval timeframe for the developments

that qualified for SB 35 was less than 3 months (or 2.7 months). CALES data yielded 11 similar

developments entitled in prior years that have project and site characteristics that would seem to

qualify for SB 35—had the state law applied in those prior years. The median time to entitlement

for the developments in earlier years was approximately 7 months, which indicates that SB 35

reduces approval timelines for some multifamily affordable housing developments in Los Angeles.

To illustrate the effect of SB 35 on individual developments, the authors examined two examples

from the same neighborhood. The authors compared 459 Hartford Avenue S and 1218 Ingraham

Street; both are 100-percent affordable developments in the Westlake neighborhood. The

development at 459 Hartford Avenue S, a 101-unit development sited on what was once surface

parking, took 132 days to reach its final entitlement in 2017. The development at 1218 Ingraham

Street, a 121-unit development also sited on a former surface parking lot, took 71 days to approve

under SB 35.

In terms of affordability mix, the 11 comparable pre-SB 35 developments were all 100 percent

affordable.43 After SB 35, the city approved 18 developments through SB 35. Of the 18

developments, 16 were 100 percent affordable. The city also approved one development that

was 37 percent affordable and another that was 34 percent affordable. In both instances, the

developments received density bonuses, and the city appears to have calculated the affordability

mix prior to applying the bonuses based on its interpretation of SB 35.

41 Cal. Gov’t Code § 65913.4(a)(4)(B)(ii) (2021).

42 Stats.2022, c. 658 (Assembly Bill 2668), Sec. 1, eff. Jan 1, 2023.

43 Developments with all but one or two units designated as affordable units are defined as 100 percent affordable, because those units are typically set aside as managers’ units.

Cityscape 153

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Exhibit 1

City of Los Angeles 2014–17 Observations With Senate Bill 35 Qualifying Criteria

Months to

Opposition

Total

CEQA

Approval

Total

Through Local

Project

Affordable

Compliance

to Proceed

Units

Administrative

Units

Pathway

to Building

Appeal

Department

Mitigated Negative

7843 N Lankershim Blvd.

50

50

4.3

Not appealed

Declaration

CEQA Exempt

3200 W Temple St.

59

59

3.7

Not appealed

(Class 32)

Mitigated Negative

Appealed

307 N Wilmington Blvd.

176

174

10.8

Declaration

(land use)

Mitigated Negative

Appealed

1307 W 7th St.

76

75

8

Declaration

(land use)

Mitigated Negative

649 S Wall St.

55

54

6.9

Not appealed

Declaration

Mitigated Negative

2631 S Crenshaw Blvd.

50

49

11.4

Not appealed

Declaration

13366–13380 W

CEQA Exempt

21

20

4.5

Not appealed

Beach Ave.

(Class 32)

CEQA Exempt

655 San Pedro St. S

81

80

6.5

Not appealed

(Class 32)

CEQA Exempt

4306 Adams Blvd. W

38

37

2.6

Not appealed

(Class 32)

Mitigated Negative

4339 Adams Blvd. W

48

47

7.8

Not appealed

Declaration

Addendum to Prior

459 Hartford Ave. S

101

100

Mitigated Negative

4.4

Not appealed

Declaration

CEQA = California Environmental Quality Act.

Source: Comprehensive Assessment of Land Use Entitlements Study data (O’Neill-Hutson et al., 2022)

Exhibit 2

City of Los Angeles 2018–20 Senate Bill 35 Approvals (1 of 2)

Months to Approval to

Total Affordable

Project

Total Units

Proceed to Building

Units

Department

3200 Temple St. W

64

63

2.1

1218 Ingraham St. W

121

120

2.4

14142 Vanowen St. W

64

63

2.9

4200 Pico Blvd. W

54

53

2.8

4719 Normandie Ave. S

43

42

4.0

5627 Fernwood Ave. W

60

59

6.9

456 9th St. W

91

90

Unknown

7022–7026 South Broadway

52

51

2.6

and 235 W 71st St.

2106, 2108, 2112 South Central Ave.

57

56

2.8

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Exhibit 2

City of Los Angeles 2018–20 Senate Bill 35 Approvals (2 of 2)

Months to Approval to

Total Affordable

Project

Total Units

Proceed to Building

Units

Department

1601–1647 North Las Palmas Ave.

202

69

8

1104–11014 Santa Monica Blvd.

51

50

4.1

5501, 5511 South Main St.

57

56

3.5

401–411 E 6th St. and 522 S San Julian St.

94

93

5.6

4219–4227 S Broadway

87

87

2.3

1040 N Kenmore Ave.,

62

61

1.5

4904–4920 W Santa Monica Blvd.

3300–3322 W Washington Blvd.

84

31

2.9

407–413 E 5th St.

150

150

6.3

6576–6604 S W Blvd.

64

63

2.1

Source: Authors’ original data

San Francisco

Senate Bill 35 Simplifies San Francisco’s Project Application Procedures for

Qualifying Projects

San Francisco’s local application process provides important context to understand the effect of

SB 35. San Francisco applies discretionary review to all development. San Francisco’s procedural

requirements provide multiple opportunities for discretionary review, public hearings, and

neighborhood opposition that often begin before a developer applies for entitlement.44 These

preapplication hearing requirements are supposed to air out potential opposition to the proposed

development and mediate disputes with neighbors. However, they can add years to the entitlement

process before the start of formal planning review (and environmental review). Moreover, San

Francisco historically has not applied time constraints once formal planning review begins.45

Another unique feature of San Francisco local law is that it provides a catch-all opportunity for

“interested parties” to request Discretionary Review of any permit, including code compliant

development.46 This process is separate from the processes for local administrative appeal of

housing approvals, which offer additional opportunities for neighborhood opposition post-

entitlement. In practice, the opportunity to request Discretionary Review allows project opponents

anywhere within the city to present previously undisclosed complaints about the proposed

development when the proposed development is on the eve of entitlement.47 In the CALES study

44 San Francisco requires all development proposals of more than 10 units of housing to complete a Preliminary Project Assessment (PPA) before they may file a Project Application. (City of San Francisco Planning Department, 2022). The PPA triggers a mandatory public notice and hearing that precedes the formal planning review process (and required hearings).

45 Specifically, San Francisco has openly acknowledged its failure to make completeness determinations (consistent with the Permit Streamlining Act) when reviewing Project Applications (City and County of San Francisco Board of Supervisors, n.d.). The authors found that to be true in the 2014–17 dataset.

46 Municipal Business and Tax Regulations Section 26(a).

47 Although the Planning Commission may only “take” Discretionary Review under “extraordinary circumstances,” a hearing will allow the interested party to request Discretionary Review.

Cityscape 155

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of San Francisco, O’Neill-Hutson et al. (2022) found that when the Planning Commission takes

Discretionary Review, it imposes a new set of conditions of approval. O’Neill-Hutson et al. also

heard from stakeholders that even when the Planning Commission does not take Discretionary

Review, the request alone may trigger last-minute negotiations that alter the proposed development.

San Francisco’s implementation of SB 35 creates a comparatively simplified and time-constrained

initial review process and eliminates the notice and hearing requirements typically required

of development proposals. Importantly, SB 35 eliminates the preapplication hearings and the

opportunity for project opponents to request Discretionary Review. Applicants seeking to use SB 35

must complete only the appropriate applications and submit architectural plans to initiate review

of whether SB 35 applies (City of San Francisco Planning Department, 2020: 2–3). SB 35, thus,

eliminates substantial sources of unpredictability. Developers can be certain that if the proposed

development conforms to planning and zoning law and meets all SB 35 criteria, San Francisco

will approve the plans for development as proposed and allow the developer to proceed to the

Department of Building Inspection.

The city also provides an informational packet that summarizes the major objective criteria a

project must meet under the statute and describes the application process (City of San Francisco

Planning Department, 2020).48 Like Los Angeles, San Francisco specifies that any changes to the

application will restart the statutorily required 90- and 180-day review timelines.

SB 35 Shortens Approval Timeframes for Qualifying Developments in San Francisco

In San Francisco, the CALES data yield only one multifamily affordable housing development out

of 140 entitlements issued in 2014–17 that offered an opportunity for meaningful comparison. In

fact, there are five 100-percent affordable developments in the San Francisco 2014–17 entitlement

dataset. Four of them are not suitable for comparison because of process or site characteristics.49

Exhibit 3 shows that the pre-SB 35 development is a 94-unit, 100-percent affordable development

in the Mission/Dolores neighborhood for formerly homeless seniors who satisfied the city’s

application of its inclusionary ordinance on another parcel slated for mixed-use development.

This project took just more than a year to entitlement (367 days).50 This affordable development

conformed to all planning and zoning and qualified for a streamlined environmental review

process.51 The planning department applied its priority processing, as well. The proposed

affordable housing did not require any approvals other than environmental review.

48 San Francisco has also dedicated a section of its city website to information on SB 35 in English, Mandarin, Spanish, and Tagalog (San Francisco Planning, n.d.).

49 These four were not suitable for different reasons: One required demolition of housing for sensitive populations, one required a conditional use permit, and two required general plan amendments. These characteristics would have

disqualified these projects from benefiting from SB 35.

50 This calculation is from the date of application. San Francisco also requires a mandatory preapplication review process, a PPA, for large projects (more than 10 units). Calculating the total timeline from the PPA application date would add another 59 days to the entitlement timeframe.

51 The development qualified for tiering under section 15183.3 of the California Environmental Quality Act guidelines and Public Resources Code Section 21094.5.

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Exhibit 3

City and County of San Francisco 2014–17 Observations With Senate Bill 35 Qualifying Criteria

Opposition

Total

CEQA

Months to

Through Local

Project

Total Units

Affordable

Compliance

Entitlement

Administrative

Units

Pathway

Appeal

Tiering (15183

1296 Shotwell

94

94

Community Plan

12.2

Appealed

Exemption)

CEQA = California Environmental Quality Act.

Source: Comprehensive Assessment of Land Use Entitlements Study data (O’Neill-Hutson et al., 2022)

After entitlement, someone appealed the Planning Commission’s decision to use streamlined

environmental review52 (consistent with the planning department’s recommendation). 53 The Board

of Supervisors upheld the Planning Commission approval, but the local administrative appeal

hearing and decision added another 76 days to the 367 days to entitlement.

Following the implementation of SB 35, San Francisco approved 12 100-percent affordable

developments and 1 group housing development (with 53 percent of the beds below market

rate) under SB 35 in 2018, 2019, and 2020 (exhibit 4). The median timeframe to approval for

the 13 developments was 141.5 days, or approximately 4.5 months. There is one outlier in terms

of approval timeframes. The entitlement process for 4840 Mission Street began in 2016 several

months before California State Senator Scott Weiner proposed SB 35 and years before SB 35’s

applicability. If we calculate the timeframe to approval from the date the developer initiated a new

application for SB 35 eligibility in March 2019, the approval period was 3.5 months.

Exhibit 4

City and County of San Francisco 2018–20 Senate Bill 35 Approvals (1 of 2)

Months to Approval to

Project

Total Units

Total Affordable Units

Proceed to Building

Department

2340 San Jose Ave.

130

130

6.2

266 4th St.

70

69

4.5

3001 24th St.

45

45

5.2

457 Minna St.

270

143

6.1

681 Florida St.

130

130

1.6

833 Bryant St.

146

145

5.3

1360 43rd Ave. (originally

135

135

3.5

proposed as 1351 42nd Ave.)

921 Howard St.

203

203

1.4

78 Haight St.

63

63

3.1

52 Sec. 15183.3 of CEQA Guidelines and Section 21094.5 of the CA Public Resources Code allow for Community Plan

Exemptions (or the ability to “tier” off of a plan Environmental Impact Report (EIR) in specified circumstances).

53 San Francisco’s online portal provides no additional detail about the administrative appeal or the party that appealed the approval. The record states “an appeal was filed” instead of naming the party that filed the appeal. The archived website no longer provides access to 2017 hearings in front of the Board of Supervisors.

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Exhibit 4

City and County of San Francisco 2018–20 Senate Bill 35 Approvals (2 of 2)

Months to Approval to

Project

Total Units

Total Affordable Units

Proceed to Building

Department

180 Jones St.

70

70

3.9

4840 Mission St.

137

137

33.4

436 Geary Blvd.

130

130

1.5

1064–1068 Mission St.

260

260

4.9

Source: Authors’ original data

Comparing specific developments with the pre-SB 35 development reveals more. For example,

one SB 35 development was five blocks from the pre-SB 35 site previously described, and its

entitlement under SB 35 took 157 days. This approval timeframe is less than one-half of the

timeframe for its pre-SB 35 neighbor. Although a local administrative appeal process further

delayed the pre-SB 35 project, SB 35 removed this opportunity for administrative appeal as an

obstacle altogether.

The City of Oakland

Senate Bill 35’s Effect on Oakland’s Project Application Procedures Are Unclear

As of summer 2022, Oakland offered scant resources explaining its SB 35 procedures. The extent

of its SB 35 public education resources appears to be a two-page checklist and brief description of

the SB 35 ministerial process (City of Oakland, n.d.). The checklist details the major requirements

for accessing ministerial review.54 The checklist states that approval decisions will be made within

SB 35’s required timelines of 90 days for developments of 150 or fewer units and 180 days

for developments of greater than 150 units but does not mention the separate 60- and 90-day

deadlines for determining consistency with objective design standards. The document does not

provide information on what forms developers must submit to qualify. Unlike the city of Los

Angeles and San Francisco, Oakland does not specify whether application revisions restart the

clock (City of Oakland, n.d.).

Oakland’s application procedures are more difficult to ascertain from public written documents

than San Francisco’s, but it does not necessarily mean that they are more burdensome. In prior

work, O’Neill-Hutson et al. (2022) found Oakland’s discretionary timeframes to entitlement for

development (including noncode compliant development) comparatively short.55

54 The checklists track the law’s requirements and include the level of affordability, percentage of affordable units, zoning, siting, construction worker compensation, conformity with objective standards of the planning code, and potential for tenant displacement.

55 Timeline data are much more difficult to pull out of Oakland compared with San Francisco. Almost one-half of all the authors’ observations are missing applications and, therefore, application dates, whereas nearly all San Francisco observations have application documents that the authors used to determine application dates. Fortunately, the observations within Oakland with timeline data, combined with interviews, allowed the authors to draw some conclusions about how Oakland compared with its neighbors.

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It Is Unclear Whether Senate Bill 35 Shortens Approval Timeframes in Oakland,

Given a Lack of Pure Senate Bill 35 Developments

The authors found seven entitlements in the 2014–17 dataset that offer valuable comparisons

with developments that benefited from SB 35. All were 100 percent affordable. The authors have

complete timeframe data for only five. The entitlement timeframes ranged from 4 to 41 months

(exhibit 5).

Exhibit 5

City of Oakland 2014–17 Entitlement Observations with Senate Bill 35 Qualifying Criteria

Opposition

Total

Total

CEQA Compliance

Months to

Through Local

Project

Affordable

Units

Pathway

Entitlement

Administrative

Units

Appeal

Tiering (15183 Community

0 7th St.

79

79

18.8

Not appealed

Plan Exemption)

2126 Martin Luther

62

62

CEQA Exempt (Class 32)

4.8

Not appealed

King Jr. Way

2201 Brush St.

59

59

CEQA Exempt (Class 32)

41.9

Not appealed

445 30th St.

58

57

CEQA Exempt (Class 3)

–

Not appealed

0 35th Ave.

181

179

Tiering

–

Not appealed

Appealed on land

1415 Harrison St.

81

81

CEQA Exempt (Class 3)

4

use grounds

344 13th St.

66

65

CEQA Exempt (Class 3)

4

Not appealed

CEQA = California Environmental Quality Act.

Note: – means a timeframe calculation is not possible.

Source: Comprehensive Assessment of Land Use Entitlements Study data (O’Neill-Hutson et al., 2022)

In 2018–20, Oakland approved two developments under SB 35, both 100 percent affordable

(exhibit 6). It took 10 months to approve a project of 97 units and around 14 months for a project

of 60 units. These eligibility review timeframes are longer than the median pre-SB 35 timeframes.

Exhibit 6

City of Oakland 2018–20 Senate Bill 35 Approvals

Total Affordable

Months to Approval to Proceed

Project

Total Units

Units

to Building Department

2125 Telegraph Ave.

97

97

10.3

2372 International Blvd.

60

59

13.9

Source: Authors’ original data

However, a closer exploration suggests these projects were not approved entirely within the SB

35 framework (and, indeed, exceeded the statutory SB 35 time limits).56 These two developments

both required approvals outside of the SB 35 process. The 97-unit development revised its plans

to allow for more units after obtaining a lot line adjustment, which may have delayed the final

56 SB 35 holds local government to strict time limits. For projects of 150 units or fewer, they must respond within 60 days if a project conflicts with any standards and 90 days to complete design review and public oversight. Cal Gov’t. Code §

65913.4(c)(1)-(d)(1).

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O’Neill and Wang

approval. The 60-unit project involved subdividing an existing parcel into two lots, which required

a separate application outside the SB 35 process.57 The approval documents suggest that the city of

Oakland allowed the project to qualify for ministerial review under SB 35 while also requiring the

developer to separately process the subdivision application, a process that perhaps did not strictly

adhere to SB 35 but did facilitate approval. Moreover, the 60-unit project appears not to have

requested review under SB 35 when it first sought approval in 2019, only doing so in 2020.

Oakland’s approval process for the 97-unit project also raises a fundamental question of what

qualifies as “objective” design criteria under SB 35. In this case, the developer and city disagreed

over whether historic district design criteria were objective. The criteria required, for example, that

new construction be “compatible [. . .] in terms of massing, siting, rhythm, composition, patterns

of openings, quality of material, and intensity of detailing” and provide “high visual interest.”58 The

developer argued that the criteria were subjective and, therefore, should not be applied during

design review. The city maintained that the criteria were objective but ultimately found that the

project satisfied them.

The City of Berkeley

Berkeley’s Senate Bill 35 Eligibility Determination Processes Are Complex But

Senate Bill 35 Importantly Modified Berkeley’s Use Permit Requirements

The city of Berkeley provides several SB 35 specific forms and resources on its website with

considerable detail about requirements for the use of the ministerial process and the necessary

documentation to satisfy these requirements (City of Berkeley, 2022a). Berkeley’s checklist adds

documentation requirements beyond those of SB 35, including affordable housing, landscaping,

and green building documentation depending on the project’s specifications (City of Berkeley,

2022b). Although SB 35 does not enumerate these requirements, they fall under the local objective

criteria provision of SB 35. Notably, the Berkeley checklist also “strongly encourage[s]” the project

proponent to convene a “pre-application neighborhood meeting,” even though SB 35 does not

allow the imposition of additional requirements on projects solely or partially, because they are

receiving ministerial review under SB 35 (City of Berkeley, 2022b).

Berkeley’s local regulations are comparatively difficult to understand and access, relative to

the other cities studied, which makes determining whether a project qualifies for SB 35 more

difficult. In other words, determining what is “code compliant” is difficult in Berkeley. SB 35 does

not resolve this issue, because it does not modify local density and use controls—it intervenes

in process. Unlike the other four jurisdictions discussed in this article, Berkeley does not

automatically disqualify proposed development that is inconsistent with current zoning provisions.

Instead, it allows applicants an opportunity to “reconcile[e] those discrepancies and demonstrate

how the development will be consistent” (City of Berkeley, 2022b). SB 35 had another major

effect in Berkeley: Berkeley’s local law requires all developments to obtain a use permit, even

57 Under SB 35, lot subdivisions disqualify a project from ministerial review unless the subdivision falls under one of two exceptions per Cal. Gov’t. Code § 65913.4(a)(9).

58 Oakland Planning Code Sec. 17.136.055(B)(2).

160 Recent Reforms in Zoning

How Can Procedural Reform Support Fair Share Housing

Production? Assessing the Effects of California’s Senate Bill 35

when the project fulfills all objective criteria for development. One of SB 35’s provisions voids this

requirement for qualifying developments.59

Notably, Berkeley considered SB 35 a major imposition on its control of land use, arguing in

litigation against the constitutionality of the statute under California home rule law. The narrow

dispute at issue concerned whether a project proposed to be built on an Ohlone shellmound burial

ground could receive a ministerial permit under SB 35 (Huang, 2018). However, when a developer

sued because Berkeley declined to apply SB 35, Berkeley’s arguments extended far beyond the

contested issues of the case, which concerned whether the burial site was a historic “structure” and

challenged the state’s authority to intervene in charter cities’ land use processes.60 These arguments,

made by a liberal city in the context of a site of cultural significance to Native American tribes,

echoed those made by the conservative city of Huntington Beach in related litigation. In both cases,

courts ultimately found in favor of the state and affirmed that SB 35 was reasonably related to the

statewide issue of insufficient low-cost housing and narrowly tailored to address the issue (Szabo,

2021). However, this litigation may indicate the practical importance of SB 35 in Berkeley; the city

considered the law sufficiently intrusive to challenge the statute as a whole during litigation.

Senate Bill 35 Approvals Moved Quickly in Berkeley

In Berkeley, two multifamily affordable developments were approved prior to the passage of SB

35 that can be compared with three SB 35 developments (exhibit 7). Of the two developments

from 2014–17, the authors were not able to determine the application date for one and could not

calculate an entitlement timeframe. The other took more than 34 months to entitlement.61 In 2014,

the developer sought approval for modifications to reduce parking and make minor adjustments to

the building, which it secured in 2017. One of the pre-SB 35 projects was 100 percent affordable;

the other was 57 percent affordable.

Exhibit 7

City of Berkeley 2014–17 Entitlement Observations With Senate Bill 35 Qualifying Criteria

Total

CEQA

Months to Approval to Opposition Through

Total

Project

Affordable

Compliance

Proceed to Building Local Administrative

Units

Units

Pathway

Department

Appeal

CEQA Exempt

2748 San Pablo Ave.

23

13

34.7

Not appealed

(Class 32)

3132 Martin Luther

42

42

Unknown

Unknown

Not appealed

King Jr. Way

CEQA = California Environmental Quality Act.

Source: Authors’ original data

Two of the three projects approved under SB 35 were significantly larger (142 and 87 units) than

the two projects from the 2014–17 dataset (23 and 42 units). Two of the SB 35 projects moved

quickly (exhibit 8). One took approximately 3.4 months and the other just under 2 months to

59 Cal. Gov’t Code § 65913.4(a).

60 The City of Berkeley also unsuccessfully argued for other narrowing constructions of the statute. Ruegg, 63 Cal. App. 5th at 318–319 (2021).

61 It also appears that the development was entitled in prior years, as well. The authors found a similar entitlement from 2007.

Cityscape 161

O’Neill and Wang

secure planning approval. The authors were unable to confirm the application date for the third

SB 35 project, but press coverage described the process as swift (Hicks, 2019).62 All three of these

projects were 100 percent affordable.

Exhibit 8

City of Berkeley 2018–20 Senate Bill 35 Approvals

Total

Months to Approval to Proceed

Project

Total Units

Affordable Units

to Building Department

2012 Berkeley Way

142

141

1.9

2001 Ashby

87

86

3.4

1601 Oxford

37

34

Unknown

Source: Authors’ original data

Los Angeles County

Los Angeles County Provides Substantial Guidance on How to Quality for Senate

Bill 35

Los Angeles County created several SB 35 documents to explain how to access this process. At the

time of writing, the County provided English- and Spanish-language factsheets. It now offers those

at request but still provides information about the basics of SB 35, including the major criteria

for projects, what ministerial review entails, and SB 35’s approval timelines (Los Angeles County

Department of Regional Planning, n.d.a.). A “Preexisting Site Condition Questionnaire” takes

developers through six questions about the site location that determine whether or not it is eligible

for SB 35 review (Los Angeles County Department of Regional Planning, n.d.b). The county also

provides a frequently asked questions list and memorandum that go into greater detail about the

SB 35 process (Los Angeles County Department of Regional Planning, n.d.a).

Los Angeles County also provides more information than the previously discussed five

jurisdictions regarding approval expiration. In addition to notifying developers that approvals

are valid for 3 years, the county provides that privately funded project proponents can extend

the approval for 1 year and certain publicly funded affordable housing projects have no approval

expiration date (Los Angeles County Department of Regional Planning, n.d.a). Finally, Los Angeles

County updated its Housing Element in November 2021. The Housing Element includes basic

provisions relating to SB 35.

The county’s support for SB 35 applicants is not limited to written materials. The county’s

Department of Regional Planning also established a team of Affordable Housing Case Planners that

serves as the point of contact for all SB 35 applicants (Los Angeles County Department of Regional

Planning, 2021: 16).

62 https://www.berkeleyside.org/2019/01/17/berkeley-approves-two-affordable-housing-projects-in-record-time-under-new-

state-law-sb-35. The authors were able to locate the date of the SB 35 Checklist, but not the date for the main application document, so the authors cannot confirm the application date. If the Checklist date is the same as the application date, then the approval timeframe is 31 days.

162 Recent Reforms in Zoning

How Can Procedural Reform Support Fair Share Housing

Production? Assessing the Effects of California’s Senate Bill 35

Los Angeles County Applies Senate Bill 35 Broadly—Even to Small

Market-Rate Developments

Before the passage of SB 35, Los Angeles County entitled two multifamily affordable developments

between 2014 and 2017 (exhibit 9).

Exhibit 9

County of Los Angeles 2014–17 Observations With Senate Bill 35 Qualifying Criteria

Opposition

Total

CEQA

Months to Approval

Through Local

Project

Total Units Affordable

Compliance

to Proceed to

Administrative

Units

Pathway

Building Department

Appeal

6218 Compton Ave.

30

29

Unknown

7.9

Not appealed

Hybrid exemption

1854 E. 118th St.

100

100

(Transit Priority

4

Not appealed

Project)

CEQA = California Environmental Quality Act.

Source: Comprehensive Assessment of Land Use Entitlements Study data (O’Neill-Hutson et al., 2022)

Following SB 35, the county entitled seven qualifying projects between 2018 and 2020 (exhibit

10). What is also notable is the time range until approval across SB 35 developments. Although

many took only a few months, one took more than 9 months, and another took almost 20 months

to approval. Thus, among the seven SB 35 approvals, the timeframes vary considerably. Indeed,

one 10-unit development accounted for the near 20-month timeframe, whereas another 10-unit

development required only 2 months. In this county, SB 35 did not lead to predictable entitlement

timeframes for qualifying projects. However, the authors lack the detail needed to understand why

certain projects in Los Angeles County exceeded the statutory timelines in SB 35. Hypothetically,

the story may involve projects with non-SB 35 components, or which changed their approval

process midway through the entitlement process, as in Oakland, but it is unknown.

Exhibit 10

2018–20 Observations Los Angeles County

Total Affordable Months to Approval to Proceed

Project

Total Units

Units

to Building Department

10928 S. Inglewood Ave.

10

0

19.9

7220 Maie Ave.

192

29

9.2

1351 W 95th St.

57

56

3.6

1619 Firestone Blvd.

12

2

3.5

Valley Blvd. and Workman Mill Rd.

81

80

.1

4101–4111 Whittier Blvd.

34

33

1

11503 S New Hampshire Ave.

10

2

2.2

Source: Authors’ original data

The longest approval timeline of the county’s SB 35 projects may be explained by its small size

and lack of affordable units. Overall, the county’s SB 35 projects had a range of affordability levels.

Of the seven multifamily affordable developments approved using SB 35, three were 100-percent

Cityscape 163

O’Neill and Wang

affordable developments, but the remaining developments were mixed income, with affordable

rates at 15, 17, and 20 percent of total units. The final development, of 10 units, had no affordable

units, however. The application of SB 35 to the market-rate 10-unit building may suggest that Los

Angeles County interprets SB 35 to also apply to market-rate developments of under 10 units,

meeting the other site and project criteria. SB 35 requires that to be eligible for ministerial review,

a development must be “subject to a requirement mandating a minimum percentage of below

market-rate housing” based on one of several criteria.63 In one such scenario, SB 35 provides that a

project of more than 10 units must make 10 percent of units affordable but does not specify that

projects of 10 units or fewer do not need to meet the requirement of being subject to a minimum

percentage of below market-rate housing.64 Los Angeles County advises in its materials that to be

eligible for SB 35 review, a “project with more than 10 dwelling units must include a 10-percent

affordable housing set-aside for lower or very low-income households” (Los Angeles County

Department of Regional Planning, n.d.a). The development without any below market-rate units

had an approval timeframe of 597 days. Although the authors are unsure exactly how or why Los

Angeles County applied SB 35 to this project, it appears to be an outlier in multiple respects.

Discussion

SB 35 is a state intervention in local discretionary review of a select group of developments that

meet predetermined affordability, site, and other project criteria. It also provides local planning

departments and developers relief from state mandated environmental review. SB 35 does not

disrupt local choices around density, use, or design. For researchers and policymakers, the form

of SB 35 poses important questions about whether a procedural intervention alone can catalyze

meaningful or marginal increases in housing production. Because SB 35 requires planning review

to determine whether a proposed development qualifies for the state-level ministerial process,

the statute also creates a unique opportunity to compare the effect of state law on preentitlement

processes within that state ministerial framework with preentitlement processes under local

discretionary review. Future research may want to compare ineligible developments approved

within the same period.65

This preliminary review indicates that in the first years following the statute’s effective date, few

developments statewide used SB 35. Many explanations are plausible. First, SB 35 modifies

process, not density and use controls. The parcels that meet the state law’s site criteria may not

have zoning in place to allow for multifamily developments, or developments big enough to be

financially feasible given the required affordability thresholds. Second, in some places, only certain

developers can benefit from SB 35. In four of the cities studied (Berkeley, Los Angeles, Oakland,

and San Francisco), the threshold to qualify for SB 35 was 50 percent of units being affordable, but

most SB 35-approved development was 100 percent affordable.66 Third, it could be that developers

63 Cal Gov’t Code Sec. 65913.4(a)(4)(B).

64 Cal Gov’t Code Sec. 65913.4(a)(4)(B)(i).

65 Future research may want to compare the effect of SB 35 on qualifying developments with proposed developments that do not meet SB 35’s site criteria, for example, or prevailing wage requirements.

66 Each city had a requirement of 50 percent affordable, except Los Angeles County, which had a 10-percent

affordable requirement.

164 Recent Reforms in Zoning

How Can Procedural Reform Support Fair Share Housing

Production? Assessing the Effects of California’s Senate Bill 35

and planners were uncertain about how to apply the new state law within the context of local legal

regimes. Local use of SB 35 may change over time as various actors learn how SB 35 applies and

interacts with other laws (like the state density bonus law in question in Los Angeles) and as courts

and the state legislature perhaps provide more clarity.

Nonetheless, these case studies provide preliminary evidence that SB 35 likely will quicken

approval timeframes for qualifying developments. Certainly, SB 35 approval processes are

worth tracking statewide. San Francisco and Los Angeles best illustrate how SB 35 can shorten

approval timeframes, with what appear to be meaningful reductions in timelines and increases in

predictability.

Also important, in some cities, SB 35 will offer a ministerial pathway to approval for mixed income

and 100-percent affordable development where none existed. This ministerial approval pathway

is especially important to affordable developers in cities with complex local rules that repeatedly

invite opportunity for neighborhood opposition—like Berkeley and San Francisco. Thus, SB 35’s

effect on planning review processes is significant, even if it has been limited to only a handful of

jurisdictions in the first years of the statute’s implementation.

These case studies also signal that SB 35 is unlikely to fully standardize how cities and counties

determine eligibility. These five jurisdictions adjusted their local planning review processes to

implement SB 35 differently. For example, San Francisco’s process changes infused simplicity into

a procedural maze, whereas the city of Los Angeles imbued more complexity into its Planning

Department review process. These distinctions reflect interactions between SB 35 and the local

rules previously in place: the SB 35 eligibility review in Los Angeles is more complicated than

Los Angeles’ local ministerial review, while SB 35 eligibility review in San Francisco eliminated

lengthy preapplication requirements and San Francisco’s use of a sometimes disruptive, blanket

Discretionary Review provision. The city of Los Angeles’ SB 35 process, thus, highlights the

importance of functional differences between what local ministerial processes and the state

ministerial process created by SB 35 can offer developers in terms of ease and efficiency. Other

differences in local application of SB 35 included the calculation of affordability when density

bonuses are applied—an issue subsequently clarified by state legislation—as well as how to apply

SB 35 when approvals ineligible for SB 35 (like subdivision) are required project components.

Still, outcomes from these jurisdictions also suggest that SB 35 is working—at least in some cities—

to accelerate approval of affordable housing development. Reforms that affect only procedure

are inherently insufficient to promote housing production in all places. In many jurisdictions,

the underlying substance of the zoning code provides the binding constraints on development.

However, where the base zoning purportedly permits development—whether by local initiative or

due to separate state-level interventions—procedural reforms can play a critical role.

Cityscape 165

O’Neill and Wang

Appendix: Additional Tables

Exhibit A1

Comparable Developments (Neighborhoods and Affordability)

2014–17 Entitlement Observation

SB 35 2018–20 Observations

Jurisdiction

San Francisco

San Francisco

Neighborhood

Mission

Mission

Number of units

94

63

Percent affordable

100%

100%

Entitlement timeframe

367 days

92 days

Jurisdiction

Oakland

Oakland

Neighborhood

Uptown

Uptown

Number of units

62

97

Percent affordable

100%

100%

Entitlement timeframe

143 days

308 days

Jurisdiction

City of Los Angeles

City of Los Angeles

Neighborhood

Westlake

Westlake

Number of units

101

121

Percent affordable

100%

100%

Entitlement timeframe

132 days

71 days

Jurisdiction

City of Los Angeles

City of Los Angeles

Neighborhood

Westlake

Westlake

Number of units

76

64

Percent affordable

100%

100%

Entitlement timeframe

240 days

64 days

Jurisdiction

City of Los Angeles

City of Los Angeles

Neighborhood

West Adams-Baldwin Hills-Leimert West Adams-Baldwin Hills-Leimert

Number of units

38

54

Percent affordable

100%

100%

Entitlement timeframe

78 days

84 days

Jurisdiction

City of Los Angeles

City of Los Angeles

Neighborhood

West Adams-Baldwin Hills-Leimert West Adams-Baldwin Hills-Leimert

Number of units

48

64

Percent affordable

100%

100%

Entitlement timeframe

235 days

63 days

Jurisdiction

City of Los Angeles

City of Los Angeles

Neighborhood

Central City

Central City

Number of units

81

94

Percent affordable

100%

100%

Entitlement timeframe

198 days

169 days

SB 35 = Senate Bill 35.

Source: Comprehensive Assessment of Land Use Entitlements Study data (O’Neill-Hutson et al., 2022)

166 Recent Reforms in Zoning

How Can Procedural Reform Support Fair Share Housing

Production? Assessing the Effects of California’s Senate Bill 35

Acknowledgments

The authors are grateful to the Furman Center at New York University for providing financial

support for this paper, and to Vicki Been and Noah Kazis for their thoughtful review and feedback

on drafts. The authors acknowledge and thank the Silicon Valley Community Foundation, the

Chan Zuckerberg Initiative, and the California Air Resources Board for their financial support of

the CALES.

This article relies on CALES data. The authors thank the CALES team, including the collaborators,

professors Eric Biber and Nicholas Marantz, student researchers, including Vivek Adury and

Deborah Le-An Tan, and former research team members, including Giulia Gualco-Nelson and

Raine Robichaud.

Authors

Moira O’Neill is an associate research scientist at the Institute for Urban and Regional Development

and a senior research fellow at the Center for Law, Energy, and the Environment at the University

of California, Berkeley. O’Neill is also an associate professor of law, general faculty, at the University

of Virginia School of Law and in the Department of Urban and Regional Planning in the School

of Architecture. Ivy Wang is a graduate student researcher at the Institute for Urban and Regional

Development at the University of California, Berkeley.

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170 Recent Reforms in Zoning

by Jake Wegmann, Aabiya Noman Baqai, Josh Conrad

Here Come the Tall Skinny

Houses: Assessing Single-Family

to Townhouse Redevelopment in

Houston, 2007–2020

Jake Wegmann

Aabiya Noman Baqai

Josh Conrad

University of Texas at Austin

Abstract

Contemporary research has documented a recent and widespread boom in the construction of

“townhouses,” or land-efficient single-family houses, both detached and attached, in Houston. Contrary

to popular portrayals of Houston as a Wild West of unrestricted land use, a deliberate reform in 1998

that was subsequently extended in 2013, in fact, made this townhouse boom possible. It drastically

reduced permissible minimum lot sizes citywide. This article builds on this emerging body of literature

to specifically focus on cases in which formerly single-family parcels were subdivided into small lots for

townhouse construction between 2007 and 2020. It argues that Houston’s phenomenon of single-family-

to-townhouse (SF2TH) redevelopment offers a glimpse of what other U.S. cities might expect to occur

were they to repeal large lot single-family zoning and other binding restrictions to allow for widespread

construction of widely desired small-lot single-family housing products in formerly low-density

neighborhoods. The findings suggest that SF2TH redevelopment accounts for less than one-fifth of overall

townhouse development, that it tends to take place on larger lots in the urban core occupied by small,

old houses, that it produces relatively reasonably priced houses, and that it predominantly takes place

in neighborhoods with higher-than-average house values prior to the period analyzed. The latter result

is inconsistent with a view of gentrification as a primary driver of SF2TH redevelopment. This article

also examines the pattern of “block votes,” or the pattern of usage of a petition mechanism that allows

homeowners to opt out of townhouse development on their own blocks, and finds that clusters of block

votes generally adjoin clusters of SF2TH redevelopment but with relatively little overlap.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 171

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Wegmann, Baqai, and Conrad

Introduction

What would happen if a large U.S. city decided to open up parcels on streets dominated by

existing large-lot, detached single-family houses to denser development? Furthermore, what if this

redevelopment took the form of a replacement of existing freestanding houses with multiple houses

on much smaller lots? Given the century-long dominance of single-family zoning in the United

States (Hirt, 2015), these questions might seem like an exercise in alternative history. However, this

article argues that, in fact, the recent experience of Houston provides instructive answers.

Despite its libertarian image as the only big American city without zoning, Houston is decidedly

in the business of regulating land use. Starting in the late 1990s, policymakers made a deliberate

decision to drastically reduce required minimum lot sizes for houses—at first only within the city’s

urban core (15 percent of the city’s land area) and later citywide. As a foundational article by Gray

and Millsap (2020) convincingly demonstrates, this sea change in land use regulation set off an

urban townhouse boom that led to the construction of tens of thousands of tall, skinny houses in

less than two decades, introducing a new housing product type and changing the urban landscape

of entire neighborhoods in the process. The resulting houses are locally known as townhouses

regardless of whether they are attached (that is, they touch on their side walls) or are technically,

if barely, freestanding. They sit on lots that are minuscule by typical U.S. standards—even by

big city U.S. standards. Notably, however, Houston-style townhouses are usually owned “fee

simple,” that is, homeowners own their house and the land underneath it. In this regard, Houston

townhouses represent a different outcome than what other U.S. cities have been recently pursuing

in relaxing land use regulations on residential blocks, through which planners tend to work toward

redevelopment into small-lot, medium-density multifamily, or “missing middle,” development.

Therefore, this article examines the results of Houston’s townhouse boom with a view toward

drawing useful lessons for other cities contemplating similar reforms. This article builds on Gray

and Millsap’s (2020) research and focuses on a subset of Houston’s townhouse phenomenon:

Townhouses developed via the acquisition and teardown of existing single-family parcels and

the subsequent resubdivision of parcels to accommodate multiple new townhouses. This process

represents the type of redevelopment—which is shorthanded herein as single-family-to-townhouse

(SF2TH) redevelopment—that some proponents of single-family zoning repeal hope and that

detractors fear will happen in zoned cities.

The rest of this article proceeds as follows. It begins by reviewing debates over single-family zoning

and notes recent developments that suggest its longstanding impregnable status—the “zoning

straitjacket” in the memorable formulation of legal scholar Ellickson (2021)—is weakening.

Next, it relies on Gray and Millsap (2020) and others to provide a brief overview of the regulatory

system, and reforms to it, that have allowed SF2TH units to take root in Houston. After introducing

the datasets and methods, the article presents various descriptive statistics and logistic model

results in three successive sections that allow for answering basic questions about the quantity,

timing, physical characteristics, and spatial patterns of SF2TH units, plus their association with

neighborhood change. It also presents results about the extent of “block votes,” an only-in-Houston

regulatory mechanism that allows homeowners to opt out of townhouse redevelopment on their

own block—but only on their own block—and that arguably paved the way for the reforms to

172 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

be broadly acceptable to the public in the first place (Gray and Millsap, 2020). It ends with a

summary of the topline findings and some reflections on what they might mean for other U.S.

cities contemplating the end of single-family zoning.

In brief, SF2TH redevelopment is comparatively rare on the citywide scale but concentrates

in particular locations, particularly within the urban core—much more so than townhouse

development in general. The most likely parcels for SF2TH redevelopment are large (when

controlling for other characteristics) with old, small, existing single-family houses. New SF2TH

units are, although not cheap, on the whole relatively modestly priced in the median case. Contrary

to many common assumptions, gentrification is not a particularly illuminating framework for

predicting where SF2TH redevelopment will concentrate. Also, contrary to common assumptions,

SF2TH redevelopment does not appear to be associated with a gain in children despite the

relative spaciousness of the new houses and their desirable locations—in fact, locations with

concentrations of SF2TH redevelopment lost children more rapidly than the city as a whole in

the past two decades. The unexpectedness of some of the findings underscores that research on

Houston’s unique land use trajectory is a rewarding enterprise with lessons for other cities. The

article accordingly closes with several suggestions for further research.

Background

Houston is often posited as an exceptional case, given its unique status as the only large U.S. city

that eschews zoning. This section begins with an overview of the current state of zoning reform

efforts in low-density neighborhoods in the United States, then reviews the case of Houston and

how it both aligns with and departs from these broader trends. It closes with a brief terminological

note on the use of “R1” as a shorthand for single-family zoning.

Single-Family Zoning: No Longer Untouchable?

If an outside observer were asked to identify the single characteristic that best distinguishes how

land use is regulated in the United States compared with its peer countries, chances are high

that single-family zoning, or R1,1 would win (Hirt, 2015). The concept certainly is not unique

to the United States, but it has arguably been taken further here than anywhere else, even when

compared with other high-income, sparsely populated countries such as Canada and Australia

(Hirt, 2015; Whittemore and Curran-Groome, 2022). Concerted action from the emerging

professionalized real estate industry and the federal government starting about a century ago led

to the rapid adoption of R1 in most localities, a process that was largely complete after just several

decades (Weiss, 1987; Whittemore, 2021).

In the 1960s, the concept of R1 and related regulatory tools, such as minimum lot sizes,

particularly in suburbs, experienced a serious challenge on the grounds of racial exclusion. The

efforts of the advocacy planner, Paul Davidoff, and others led to a series of “anti-snob zoning”

reforms in such locations as New Jersey, where it was imposed by the courts, and Massachusetts,

where it was legislated. Some local governments, beginning with Montgomery County in

1 This article follows Manville, Monkkonen, and Lens’ (2020) use of this terminology; it refers to a common (although not universal) shorthand for the most restrictive single-family zones in municipal zoning ordinances.

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Maryland, adopted inclusionary zoning ordinances as a means of counteracting the exclusionary

effects of single-family zoning and related land use regulations such as minimum lot sizes. Still, the

net effect of this wave of reforms was to leave R1 almost entirely unchallenged by the time it had

receded (Whittemore, 2021).

The status quo persisted for decades, right up until the present day. A recent New York Times

analysis of 11 mostly large cities shows that the percentage of residentially zoned land reserved for

single-family uses is startlingly high in most of them, such as 81 percent in Seattle and 79 percent

in Chicago (Badger and Bui, 2019). Of the 11, only New York City, the great sui generis exception

to the general U.S. patterns of low-density land uses and automobility reliance, and to a lesser

extent Washington, D.C., stand apart as cities with less than most of their residential land zoned

R1 (King, Smart, and Manville, 2022). The predominant status quo in most places, such as the

suburbs of Chicago, is one in which a demolished single-family house is replaced (if it is replaced

at all) with the only economically viable option under R1: A new single-family house much

larger and pricier than the one it supplants (Charles, 2013). Trends for decades toward public

participation mechanisms of greater frequency and scope have largely served to amplify the most

vocal nearby homeowners’ objections to any net gain whatsoever in units on an R1 parcel (Einstein,

Glick, and Palmer, 2019; Lemar, 2021).

However, something shifted within only the past half decade or so, and cracks in the firmament of

R1 have begun to grow and spread. Whittemore (2021) argues that this time, unlike in the 1960s,

the critique has originated from the urban economics literature and has also emphasized racial

exclusion and housing unaffordability within large cities, as contrasted with the suburban emphasis

in Davidoff’s heyday (also see Mangin, 2014). Academics have recently launched direct attacks on

R1, even as several zoned cities, most notably Minneapolis but also the likes of Olympia and Walla

Walla in Washington have repealed it within their boundaries (Manville, Monkkonen, and Lens,

2020; Wegmann, 2020a). Most dramatically of all, entire states, including California, Maine, and

Oregon, now require a large number of their jurisdictions to jettison R1.

It would be a mistake to conclude from these recent trends that the disappearance of R1

throughout the United States in the medium term is a foregone conclusion. Even many planning

academics still oppose its eradication or at least view it with trepidation on various grounds,

from infrastructural effects to inequitable outcomes, such as gentrification to unaffordable post-

redevelopment housing to the lack of an aspirational housing package with the same universal

appeal as large-lot single-family living (Chakraborty, 2020; Etienne, 2020; Kendig, 2020; Searle

and Phibbs, 2020). Among the voting public, R1 may be more popular still. The sheer weight of

the status quo means that even if a stronger consensus around the benefits of doing away with R1

were to emerge, it would be acted on unevenly across jurisdictions and slowly in the aggregate.

One helpful historical analogy is with minimum off-street parking requirements. Thanks to

foundational research by Shoup (2021) and others, the concept retains few principled defenders

today. However, under the sheer weight of status quo bias and the popularity of parking among

ordinary people, parking requirements continue to remain the law of the land in most cities,

suburbs, and towns in the United States.

174 Recent Reforms in Zoning

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to Townhouse Redevelopment in Houston, 2007–2020

One of the obstacles to the further spread of R1 repeal is a paucity of evidence about its actual

effects. One reason is a classic chicken-and-egg problem: It has happened in few places, and in

those very recently, with a global pandemic drastically upending housing development in the

United States since early 2020. What limited evidence exists does not show dramatic results.

Kuhlmann (2021) used a clever research design that exploited boundary effects between

Minneapolis and its neighboring suburbs to find that the much-ballyhooed recent citywide

replacement of R1, with zones permitting triplex construction, had modestly boosted land prices

but yielded few triplexes. It is possible that the zoning reform was a necessary but not sufficient

condition to induce the actual construction of a novel housing product type. After all, although the

city increased the number of units that can be accommodated on a formerly R1 parcel, it did not

appreciably increase the total square footage that can be built on one of those parcels. If one thinks

of the envelope of a new building on a previously R1 lot in Minneapolis as a box, the box can split

into three, but the box cannot be any bigger.

In the specific case of Minneapolis, it may be that the demand is not sufficient for three small

units rather than one large house on a parcel (Hamilton, 2020). It is also possible that triplexes—

an example of so-called “missing middle,” or lowrise, middle-density housing—suffer from the

perception among buyers that it is “stuck in the middle,” in that it offers neither the privacy nor

individualized ownership and control of a single-family house nor the amenities, security, and

views of an apartment or condominium in large midrise or highrise developments (Furth, 2020;

Parolek, 2020). Another possibility that must be considered is that the reform is in its early days,

and small builders will take a while to pivot to building a product type unfamiliar to both them

and their buyers (Kuhlmann, 2021). Time should soon tell.

At any rate, an informed observer is left with vanishingly few empirically documented cases

demonstrating how housing developers might respond to R1 repeal in cases for which it is a

binding constraint for builders’ decisions (Gabbe, 2018). The average American now lives in a

census tract with less than 15 percent of the tract-level population-weighted density that prevailed

in 1940 (King, Smart, and Manville, 2022). Not surprisingly, cases in which densities within

a neighborhood, let alone a whole city, have increased rather than decreased are rare and even

rarer in areas previously developed with housing opposed to former industrial or commercial

lands (Romem, 2016).2 One possibility is to turn to historical analogues, such as instances of

the replacement of single-family houses with what are sometimes called “dingbat” apartments in

Oakland during the 1960s (Banham, 1971; Smith, 1964).3 However, these comparisons suffer

from the obvious drawback of the vast changes to both the supply and demand sides of housing

development that have unfolded during the past half century or more.

However, Houston provides an instructive, albeit counterintuitive, case to learn from. Although it

is famously the only large, unzoned city in the United States, it engaged in a large-scale effort to

2 Some of the most striking cases—above all in California—of cities in which densities have increased rather than decreased since the 1960s can be attributed at least to a substantial degree to widespread densification via the addition of unpermitted housing, through either conversion or construction. See Wegmann (2015) and Wegmann and Mawhorter (2017).

3 The construction of dingbats in Oakland and other California cities largely took place where zoning already permitted higher density construction on what had been originally developed as single-family lots; thus, the historical analogue with R1 repeal today goes only so far.

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increase the permitted density of residential development, and it did so relatively recently but also

long enough ago that its on-the-ground effects are observable. Recent scholarship has done a great

deal to illuminate this heretofore underreported story in U.S. big city land use reform, to which the

next subsection turns.

The Unique Case of Houston

A tradition of scholarship examines Houston’s unique system of land use regulation, dating back

to at least Siegan’s seminal book Land Use Without Zoning, published in 1972.4 Among U.S. cities

alone, Houston put zoning adoption to a plebiscite on three occasions—in 1948, 1962, and

1993—and its voters found it wanting each time (Gray and Millsap, 2020). A powerful narrative

has developed in which Houston’s voters, confronted with a choice between an orderly but

restrictive regulatory regime versus a messier but more dynamic pro-free enterprise system, opted

for the latter, perhaps in keeping with the city’s general ethos and nationwide reputation (Gray and

Millsap, 2020).

However, it would be a mistake to assume that Houston lacks land use regulation. Instead, it

regulates land use not with zoning but with a series of citywide ordinances that restrict much

of what is regulated in any other U.S. city, such as minimum lot sizes and off-street parking

requirements, but without reference to zone districts. Furthermore, private deed restrictions are

viewed as a central mechanism deployed by the city to control land use. Texas state law includes

a special provision that grants special powers to Houston (and to no other Texas city) to use its

regulatory and enforcement machinery to encourage and enforce private deed restrictions. These

deed restrictions are not transparent in the same way as zoning, and so their scope is much more

difficult to quantify than in zoned cities, but one informed observer estimated that private deed

restrictions cover about one-fourth of private parcels in Houston (Kapur, 2004).

Legal scholars such as Lewyn (2004) and Kapur (2004) have argued that what might be termed

“Houston exceptionalism” is overblown and that, in fact, on-the-ground land use patterns seen

there differ little from other automobile-dominated Sunbelt cities that similarly grew explosively

in the postwar era. Pendall, Puentes, and Martin (2006), in presenting a taxonomy of land use

regulatory regimes across the United States, viewed the cities and suburbs of Texas as belonging

to a distinctive category of land use regime but did not see fit to exclude Houston from it. For the

purposes of this article, however, Houston really is a place apart in one critical way: It has seen a

boom in townhouses since the turn of the millennium, likely unique among big cities in the United

States in its scale and extent.

Gray and Millsap (2020) provide the definitive account of the 21st century Houston townhouse

boom, with Park and Guajardo (2021) and Hamilton (2023) subsequently adding important

empirical and historical perspectives. The brief summary that follows relies heavily on their accounts.

As Stephen Fox (2000: 23) notes, from a nadir in the local oil and gas sector, “the recovery of

Houston’s economy in the second half of the 1990s resulted in a boom in new, expensive, inner-

4 As an indication of the resurgent interest in Houston’s unique system of land use regulation, George Mason University recently reissued an updated edition of Siegan’s book.

176 Recent Reforms in Zoning

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to Townhouse Redevelopment in Houston, 2007–2020

city row house construction.” Gray and Millsap (2020) observe that the townhouse boom preceded

the city of Houston’s seminal townhouse-promoting reform in 1998. Those legal reforms then

augmented this initial shift in market conditions. After 1998, townhouse development could

proceed more smoothly in a “by right” fashion. Developers no longer had to undergo the more

costly and uncertain process of seeking variances to the subdivision ordinance.

The heart of the 1998 reform was a reduction in the required minimum lot size for detached

single-family houses from 5,000 to 3,500 square feet inside Interstate 610, an area generally

regarded as the city’s urban core or “Inner Loop.” Furthermore, for cases in which developers

provided compensating open space within a redevelopment or else met several performance

standards, the resubdivided house lots could get as small as 1,400 square feet (Hamilton, 2023).

To put these sizes in context, one recent analysis shows that the median area for new single-family

house lots in U.S. metropolitan areas ranges from 6,098 square feet in El Paso to 43,560 square feet

(or exactly one acre) in Bridgeport, Connecticut (Kolomatsky, 2022).

The reduced minimum lot sizes exist alongside other regulations that are highly permissive,

such as a citywide height limit of 75 feet for structures adjacent to single-family houses and a

requirement of a scant three feet of separation (even less under some conditions) for a detached

townhouse from its neighbor to the side (Hamilton, 2023). Required front setbacks range from

none at all to 25 feet, depending on the type of street (Hamilton, 2023). Although the required

off-street parking of two spaces per unit is not unusually permissive, it is easily accommodated

in a “tuck-under,” or first-story, garage in a typical townhouse and, in any case, aligns with many

homebuyers’ expectations.

The upshot of the lot size reforms coupled with other key regulations was that developers could

now build, by right, a housing product that they wanted to build and that their customers

wanted to buy. Townhouses could not be built quite everywhere—for instance, preexisting deed

restrictions and more recent “block votes” (detailed explanation to follow) preclude some single-

family parcels from being redeveloped into townhouses. Still, an enormous area was opened to

“by-right” townhouse development.

Despite the locally used moniker, many of these so-called “townhouses” are technically fully

detached houses; for this reason, Park and Guajardo (2021) dubbed them “detached townhouses.”

Nevertheless, even the detached variant of townhouses à la Houston are unlikely to be confused

with the popular image of a typical American single-family detached house. They are much taller—

commonly three stories tall and sometimes four or even five stories, as permitted under a generous

citywide 75-foot height limit—occupy comparatively tiny footprints, and sit on tiny lots, very often

with minimal or nonexistent yard space (exhibit 1).

Cityscape 177

Wegmann, Baqai, and Conrad

Exhibit 1

Various Examples of Townhouse Developments in the Greater Heights Area of the Inner Loop to

the Northwest of Downtown Houston

Source: Sandra Wegmann

Houston’s minimum lot size reforms were so well received in the housing market and in the

political arena that, in 2013, they were extended to all areas inside the city served by sewers

(Hamilton, 2023). It is an intriguing, and to date unanswered, question as to why such substantial

reforms passed to begin with and were subsequently expanded. Gray and Millsap (2020) make

a convincing argument that the key to success was providing homeowners with a mechanism to

opt out of townhouse development on their own blocks, thus defusing what would otherwise be a

potent source of opposition.

In Houston, homeowners can petition for their own residential block to be incorporated into

one (or both) of two types of special districts, Special Minimum Lot Size (SMLS) and Special

Minimum Building Line (SMBL) district, that consist of either one or two adjoining block faces.

These districts peg land use regulations to preexisting lot sizes or setbacks rather than the

citywide standards. Both SMLS and SMBL districts, thereby, severely constrain or altogether halt

economically viable subdivision of existing large parcels containing single-family houses into

smaller parcels appropriate for townhouses. These districts are formed by city ordinance rather

than private deed restrictions and thus can be thought of as a form of opt-in zoning. Formation of

178 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

one of these districts is a straightforward process requiring at least majority support of the affected

homeowners (the specifics vary), but at a minimum, the proposed regulation must match what

exists on at least 70 percent of the existing lots (or 60 percent in a historic district). Forming a

SMLS or SMBL district is loosely analogous to the homeowner-initiated process commonly used

to form historic districts in many cities, but the former is more from the bottom up. Whereas new

historic districts normally need the approval of an elected or appointed body, a proposal for a

new SMLS or SMBL district in Houston is highly likely to pass if enough property owners support

it. On formation, both types of districts—hereafter jointly referred to as “block vote” districts—

automatically sunset after 40 years, whereupon a new petition would be required to renew them.

Gray and Millsap (2020) argue that block votes force homeowners to weigh the tradeoffs between

maintaining the physical character of their own block and the possibility of increased property

values unlocked by future townhouse redevelopment. By contrast, in conventional zoning, a

citywide elected body votes on whether or not to change zoning on a given parcel or in a particular

district, and so a homeowner’s ability to act on their preferences for their own block is less direct.

In Houston, homeowners who most vehemently object to townhouse redevelopment near them

have a hyperlocal mechanism to which they can turn, which may reduce their motivation for

changing the rules that prevail beyond their own blocks.

Estimates of how many townhouses were built since the seminal reform in 1998 vary according

to the exact time periods and methodologies used, but it is clear that the number is large. Gray

and Millsap (2020) found more than 25,000 townhouses built between 1999 and 2016, Park and

Guajardo (2021) found nearly 39,000 from 2005 to 2018, and Wegmann (2020b) found more

than 34,000 from 1998 to 2020. Regardless of the details of the differing methodologies and

estimates, what is clear is that the scale is considerable; for instance, one of these analyses found

that post-1998 townhouses now account for 4 percent of all housing units citywide, 8 percent

inside the Inner Loop, and no less than 43 percent of net housing units added citywide since

1990 (Wegmann, 2020b). The transformation is not evenly distributed—some neighborhoods

remain untouched by townhouse redevelopment, while others, like Rice Military, 3 miles north by

northwest from Downtown Houston, have experienced a total transformation in their urban form

in only two decades (Gray and Millsap, 2020).

Although recent research has revealed a great deal about townhouse redevelopment in Houston,

more is to be learned. After all, the studies previously mentioned deal with townhouse

redevelopment as a whole, whether it took place on large former single-family parcels, formerly

commercial or industrial land, or other large nonresidential parcels. Arguably a study is needed

that specifically examines the subset of Houston’s post-1998 townhouse development activity, in

which formerly single-family parcels have been resubdivided into townhouse lots. These cases

could provide additional clues as to how redevelopment might proceed in existing residential

neighborhoods at buildout—neighborhoods that are the locus of both reformers’ ambitions

and much popular opposition—after reforms to land use regulations. This subcategory of

redevelopment is the object of the empirical investigation described in what follows.

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A Brief Note on Terminology

This article is premised on our claim that Houston’s minimum lot size reforms represent one path

other cities could take toward R1 repeal. In actuality, this premise is not quite precise. Lot size

requirements, on their own, are far from the only mechanisms enforcing the dominance of large-

lot single-family uses across the landscape, and removing them will not necessarily permit major

changes to the status quo. Typical American land use regulations also include use restrictions (that

is, allowing but a single unit per parcel); setback requirements and impervious cover and height

restrictions; elements of other, nonzoning ordinances that favor large-lot single-family housing over

more land-efficient housing types; and procedural laws that do the same. Bronin (2023) calls this

status quo “zoning by a thousand cuts;” in her analogy, any form of housing other than a single-

family house on a large lot is bled to death.

Even so, the Houston reforms are a useful stand-in for R1 repeals. Houston, unlike most zoned

cities, before 1998, lacked many of the mechanisms that in other cities limit the density of single-

family housing. That left minimum lot size requirements as the sole binding constraint (Gabbe,

2018). Once it was lifted, townhouse development ramped up dramatically. For that reason, the

example of Houston is instructive because it is analogous to what might happen if a typical zoned

city simultaneously lifted the various interlocking barriers that prevent redevelopment of R1 lots

into townhouses—which is what most proponents of R1 repeal have in mind.

Moreover, the Houston reforms only represent one path away from R1 zoning. They still

produce single-family housing (that is, townhouses), just on smaller lots than before. Still, this

path is meaningful, even without reforms to legalize more multifamily housing. Houston-style

townhouses (as this article quantifies in the following) are arguably such a dramatic departure

from the R1 status quo that they are different in kind and not just degree from large-lot single-

family houses. They are not “missing middle” housing in the purist sense but do meet Hamilton’s

(2020) definition of “stickplex” housing, or housing forms that combine efficient use of land with

inexpensive construction techniques (Parolek, 2020). In this regard, they represent a singular case

in the recent evolution of single-family dominated city neighborhoods in the United States, one

which this article proceeds to examine in detail.

Data and Methods

This article poses the research question: “What are the observable characteristics of SF2TH

redevelopments in Houston?” This article tackles this research question by dividing it into

several subquestions.

1. What is the overall quantity of SF2TH redevelopment and incorporation of single-family

parcels into block vote districts?

2. What is the temporal pattern of SF2TH redevelopment, that is, how has it fluctuated over time?

3. What are the characteristics of SF2TH redevelopments in comparison with what they replaced?

4. What are the geographic patterns of SF2TH redevelopment and block votes, that is, in what

types of locations have they occurred?

5. How have the neighborhoods around SF2TH redevelopments changed over time?

180 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

To answer these subquestions, this investigation relies on an analysis of property tax records from

the Harris Central Appraisal District (HCAD) for the years 2005 (the earliest available in electronic

form) through 2020, inclusive.5 To identify parcels on which SF2TH redevelopments have

occurred, the analysis takes advantage of a supplementary dataset that HCAD maintains known

as a “tieback table,” in which for every year, lots that have been newly created via lot subdivision

are noted and linked to the property identifications of (now defunct) parcels from previous years.

SF2TH redevelopments are defined as cases in which (1) a parcel HCAD initially classified as one

of the “residential” categories is (2) linked to later parcels occupying the same land area, and where

(3) a house on a parcel of less than 5,000 square feet occupies each of the subsequent parcels (that

is, below the pre-1998 minimum lot size and, therefore, conforming to the vernacular definition

of a “townhouse” in Houston). By collecting information on both the pre- and post-SF2TH parcels

as linked groups of one parcel (pre-redevelopment) to two or more (post-redevelopment), it is

possible to address research subquestion 3.

All cases of SF2TH redevelopment are linked to a geographic information system spatial

representation (that is, a shapefile) of all 2020 parcels, performed with Quantum Geographic

Information System, or QGIS, software. Shapefiles were obtained from the city of Houston’s

website, representing both forms of block votes (SMLS and SMBL).6 These block votes were

combined into a single layer, that is, with no distinction in the analysis between SMLS and SMBL.

The analysis of block votes considers only single-family parcels that, as of 2007 (the year of the

earliest identified SF2TH redevelopment in the dataset), were not then incorporated in a block vote

district, and it considers only block vote districts that were either formed in 2007 or later or else

were pending as of August 2022.

Some of the analyses rely on sociodemographic characteristics at the census tract level. These are

obtained from decennial census data from the year 2000 (that is, shortly after the enactment of

the townhouse reforms and the most recent data available prior to the beginning of this analysis

period in 2005) and 5-year American Community Survey (ACS) data collected in the years from

2015 through 2019. The latter are preferred over the more recent 2016–2020 ACS data due to

difficulties in data collection during the pandemic year of 2020 and considerable missingness in

reported 2016–2020 ACS data. This analysis uses all census tracts in Harris County that contain at

least one parcel in the city of Houston in use as single-family housing as of 2005.

Some of the previously summarized calculations provide two binary dependent variables:

Redevelopment of a given initially single-family parcel from 2007 to 2020 and its actual or pending

incorporation into a block vote from 2007 to August 2022 for the logistic regression models

described in the following section. These models are performed with the general linear model

(glm), command in the R programming language. Other calculations previously noted create the

5 Most of the city of Houston lies within Harris County, although small portions are in Fort Bend and Montgomery

Counties. This study ignores the latter two counties, as Houston’s territories within both are small and lie at the most distant fringes of the city, thus are unlikely to have had significant SF2TH redevelopment.

6 As of October 6, 2022, these shapefiles were available from https://www.houstontx.gov/planning/Min-Lot_Size-Min_Bldg_

Line.html.

Cityscape 181

Wegmann, Baqai, and Conrad

dependent variables for each of the two models: One predicting SF2TH redevelopment and the

other predicting block votes.7 Appendix A provides more detail on the two logit models.

Hypotheses for Models

Model 1 tests a hypothesis that large original lots, old original houses, and small original houses

will be associated with greater probability of redevelopment into townhouses, as all these

characteristics would be expected to increase the likelihood that homeowners sell to developers.

One would expect a negative relationship with central business district distance, that is, for

parcels closer to downtown Houston to be likelier to redevelop. A quadratic relationship with

tract house prices in the year 2000 is also hypothesized, based on Gray and Millsap’s (2020)

finding, that overall, Houston townhouse development was most concentrated in middle-income

neighborhoods. These areas have property values high enough to support redevelopment but low

enough that their residents are less likely to have initiated or maintained deed restrictions that

would restrict redevelopment.

The relationships with sociodemographic variables test the proposition that townhouse

redevelopment in Houston is a story of gentrification. If gentrification is a dominant mechanism,

then one would expect a greater likelihood of redevelopment in tracts that have higher Black and

Hispanic populations, more children, and a less college educated population in 2000, prior to

the period examined. This analysis, on the other hand, hypothesizes that Houston’s unusually

permissive land use regulations allow small-lot redevelopment to concentrate in more privileged

neighborhoods (as measured before the time period under analysis) than would be typically seen

in a large U.S. city.

Because block votes are expected to occur in reaction to SF2TH redevelopment, although the

models as structured can only detect associations rather than a causal relationship, coefficients for

Model 2 are expected to be broadly similar to those in Model 1. For the added variable of SF2TH

units within the same census tract, a quadratic relationship is modeled because of an anticipated

saturation effect. More SF2TH redevelopment nearby leads to a greater likelihood of a block vote

in response but only up to a point. Once the area nearby is sufficiently saturated with townhouse

redevelopment, according to this view, enacting a block vote is less worthwhile and thus becomes

less likely past the threshold.

Results

This section reports the results in three parts. The first reports descriptive statistics that shed light

on the first three subquestions listed in the prior section—that is, (1) on the quantity of SF2TH

redevelopment and block votes, (2) the timing of SF2TH redevelopment, and (3) a comparison of

pre- versus post-redevelopment characteristics of parcels. The next reports the results of the two

logit models to shed light on subquestion 4 on the geographic patterns of SF2TH redevelopment

and block votes. The last analyzes sociodemographic trends from 2000 to 2015–2019 to

answer subquestion 5—that is, to show how neighborhoods containing SF2TH redevelopment

concentrations have changed over time.

7 Exhibit 8 in the appendix summarizes all these variables’ values.

182 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

Basic Characteristics of Townhouse Subdivisions and Block Votes

Using the methods described in the previous section, 1,392 SF2TH redevelopments were identified

in the HCAD data spanning from 2005 to 2020, yielding 5,359 identified townhouse units, each

lying on its own new parcel, produced via the SF2TH process. The earliest of these townhouse

units were built in 2007 and the most recent in 2020. It must be acknowledged that this method

has not identified all SF2TH units in Houston; those whose lots were subdivided prior to 2005

would not be identified. It is also possible that this technique relying on tieback tables may have

missed some SF2TH subdivisions due to inaccurate or incomplete records.

The figure of 5,359 SF2TH units is not directly comparable with Gray and Millsap’s (2020)

finding that 25,269 townhouse-style parcels were created between 1998 and 2016, because

the time periods do not entirely overlap. Still, a very rough comparison suggests that SF2TH

redevelopments yielded less than one-fifth of the total post-1998 townhouses developed in

Houston. Presumably the remainder have been constructed on larger parcels, many of them

commercial or industrial rather than residential. The results further suggest that, notwithstanding

Houston’s 21st-century townhouse boom, SF2TH redevelopment is a relatively rare event. Out of

282,770 identified single-family parcels in the dataset not included within a block vote district as

of 2007, only 0.5 percent underwent SF2TH subdivision between 2007 and 2020. A considerably

higher, but still small, number of parcels, or 13,302 (4.7 percent of the total), were incorporated

into block vote districts during the same period.

SF2TH Subdivision Over Time

Exhibit 2 shows the number of SF2TH redevelopment events by year between 2007 and 2020.

Starting from a minuscule number (only four), the phenomenon rises in the latter part of the

decade of the 2000s, then declines (although by no means disappears) during the Great Recession

after 2008.8 However, it quickly resumes its upward growth after 2010 and proceeds to skyrocket

through 2015. After 2015, a large decline ensues—although not to anywhere close to zero—

perhaps as a delayed effect of a downturn in the locally important oil and gas economy (of which

Houston is regarded as the global capital) due to a collapse in global crude prices during 2014.

From 2016 to 2020, activity is relatively steady.

8 Note that the very low number for 2007 should be treated with caution, because the dataset does not include

redevelopments of parcels classified as “residential” prior to 2005. This analysis would have missed any 2007

redevelopments linked to parcels before 2005.

Cityscape 183

Wegmann, Baqai, and Conrad

Exhibit 2

Completed Single-Family-to-Townhouse Redevelopments by Year in Houston, 2007 to 2020

200

180

160

140

120

100

80

60

40

20

0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Note: The figure for 2007 may be low, because the dataset does not include subdivided parcels that link to unsubdivided parcels from before 2005.

Source: Harris County Appraisal District (HCAD) data, with calculations by authors

Geography of SF2TH Subdivision

Exhibit 3 shows the locations of SF2TH units completed from 2007 to 2020, along with the

block votes enacted during that period. It is immediately notable that SF2TH redevelopment

is overwhelmingly concentrated inside the Inner Loop (visible as the white roadway circling

around Downtown Houston). Only a minuscule number of SF2TH units (just 3.4 percent of

the total) lie beyond the Inner Loop. This very small share is in contrast to the prior results that

Wegmann (2020b) found, in which only 53 percent of townhouses overall built from 1999 to

2015 were within the Inner Loop. Thus, SF2TH development appears to be a more intensely urban

phenomenon than townhouse development in Houston in general. Reasons could include higher

land prices in the Inner Loop that make custom-built designs built in small increments more

feasible for builders, the higher sales prices needed to justify teardowns of existing houses, or a

lower prevalence of homeowners’ associations that might serve to thwart such development.

184 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

Exhibit 3

Single-Family-to-Townhouse Redevelopments in Houston

Notes: Single-family-to-townhouse redevelopments are black circles, block votes are in dark grey, freeways are white lines, and the Houston city limits are in light grey. Some fringe areas of the city that contain no redevelopments or block votes are omitted from view. For reference, Downtown Houston, containing no block votes or redevelopments, is located inside the smallest freeway loop visible. The Inner Loop, commonly regarded as Houston’s urban core and containing the bulk of redevelopments and block votes, is the area inside the larger of the two visible complete freeway loops.

Source: Image by authors, using data from Harris Central Appraisal District

Block vote districts are mostly, although not exclusively, close to large concentrations of single-

family-to-townhouses, although there is little overlap between the two. Indeed, the dissimilarity

index for block votes and SF2TH redevelopment at the tract level is just under 0.84.9 Instead,

clusters of SF2TH units and nearby large patches of block votes seem to exist within clumps

of roughly 1 to 4 miles in diameter primarily to the northwest, west, and south of Downtown

Houston. In the macroscale, these clusters are spatially concentrated, in the sense that only 121

of the 660 Harris County census tracts in Houston have either at least one SF2TH unit, at least

one block vote, or both. One could summarize the spatial pattern by remarking that block votes

and SF2TH units are clustered together when one is zoomed out but separated from each other in

mostly homogenous clusters when zoomed in. Exhibit 4 provides an illustrative example.

9 The dissimilarity index, commonly used as a measure for residential racial segregation, ranges from 0 (perfectly unsegregated) to 1 (perfectly segregated). Intuitively, the index of 0.84 means that 84 out of 100 block vote parcels would have to move to different tracts to achieve a perfectly proportionate balance of block votes and SF2TH townhouses by tract.

Cityscape 185

Wegmann, Baqai, and Conrad

Exhibit 4

Single-Family-to-Townhouse Redevelopments in the West of Downtown Houston

Notes: Single-family-to-townhouse redevelopments are shown as red dots, and block votes are shown in dark grey in a swath of the northwest quadrant of Houston’s Inner Loop. Both redevelopments and block votes tend to group together in clusters that are relatively near each other but mostly nonoverlapping.

Source: Image by authors, using data from Harris Central Appraisal District

A small although nontrivial number of block votes are beyond the Inner Loop and, in some cases,

a considerable distance from any identified SF2TH redevelopments. It is possible that nearby

(non-SF2TH) redevelopment motivated the formation of these districts as a preemptive measure to

prevent it in the future, or perhaps as a means of restricting or altering other forms of anticipated

redevelopment, such as single-family teardown and replacements.

Pre Versus Post-Single-Family-to-Townhouse Redevelopment

Having examined the macro characteristics of the SF2TH redevelopments—their number, temporal

pattern, and geographic distribution—now to turn to the characteristics of the redevelopments.

First, their scale: Redevelopments are mostly very small. If one thinks of a redevelopment as an

event that begins with the demolition of one or more existing structures on a single-family lot, then

results in a number of townhouses built on smaller lots subdivided from the original lot, then the

most common version of this event yields only two townhouses. The median redevelopment event

yields four townhouses. Notwithstanding the small net gain in units, Houston is a rare example—

beyond the still relatively small number of cities in which permitted accessory dwelling units have

been built in nontrivial numbers—in which small-lot redevelopment processes yield net gains in

housing units. The typical counterfactual in many other large cities is a one-for-one replacement of

a (small and deteriorated) single-family house with a (large and new) single-family house.

186 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

Exhibit 5 summarizes a number of metrics that in various ways compare conditions before and

after SF2TH redevelopment for all 1,392 parcels in the dataset on which it occurred. The unifying

theme is a drastic intensification in the use of these parcels, as one would expect. After all, for a

builder to expend the capital to acquire an existing property, demolish its existing use, and replace

it with new housing units requires a substantial increase in value. Exhibit 5 allows for this dynamic

to be quantified via various indicators.

Exhibit 5

Various Indicators Comparing Single-Family-to-Townhouse Redevelopment Ex Ante Versus Ex Post

Indicator

Pre-redevelopment

Post-redevelopment

Median house construction date

1936

2014

Median lot size

6,500 sf

2,105 sf

1,348 sf

2,483 sf (all newly-created

Median built floor area

(pre-redevelopment parcel)

townhouse units)

Ratio of new total built square

4.2 (median; 1st quartile =

footage to previous total built

1

2.2; 3rd quartile = 6.6)

square footage

Improvement-to-land (I/L)

0.14 (median)

2.14 (median)

assessed value ratio

Ratio of new assessed property

3.2 (median; 1st quartile =

1

value to previous property value

1.7; 3rd quartile = 5.1)

Total citywide taxable

$319 million

$1.914 billion

property value

Source: Harris County Appraisal District (HCAD) data, with calculations by authors

First, in the median case, an old house (built in 1936) is demolished and replaced with new

townhouses (built in 2014). The median lot of 6,500 square feet is split into new, smaller lots with

a median size of only 2,105 square feet. The original, relatively small house with a median of 1,348

square feet of floor area is replaced with new townhouses that collectively have 4.2 times the floor

area as the one single-family house demolished to make way for them. Furthermore, in the median

case, each new individual townhouse unit includes considerably more living space than the original

house that was torn down (2,483 versus 1,348 square feet).

According to one metric, the improvement-to-land (I:L) ratio, which measures the value of the

building as HCAD assessed compared with the assessed value of the land on which it sits—and

whose significance Landis et al. (2006) explained—post-redevelopment sees the parcel far much

more efficiently used. The I:L ratio leaps from 0.14 predevelopment to 2.14 for the median

townhouse created in the process—a 15-fold increase. Measured in a different way, the total

assessed value on the parcel jumps more than threefold from before versus after redevelopment.

Aggregated together, the SF2TH parcels represented $319 million of taxable value for the city,

county, school district, and other entities reliant on property tax revenues prior to redevelopment

compared with $1.914 billion in value afterward. Although this analysis does not attempt to

quantify increased needs for municipal services generated by lot subdivision, it seems highly likely

that SF2TH units represent a considerable fiscal net positive for taxing entities, including the city,

county, and kindergarten through 12th grade school districts.

Cityscape 187

Wegmann, Baqai, and Conrad

Affordability

The median SF2TH unit had an assessed value of $340,000 ($133 per square foot) as of 2020—

much less than the median citywide assessed value of single-family houses built 2007 or later on

unsubdivided parcels, which was $545,000 ($176 per square foot).10 Assessed values, opposed

to sales price data, should be treated with caution, but rough comparisons are still instructive.

Values of SF2TH units varied greatly, with an interquartile range of $213,000. This wide range

suggests that SF2TH units in Houston span the full gamut from bargain to high-end products and

everything in between.

When one considers that most SF2TH units are easily spacious enough for a four-person

household, their newness, and their predominant location inside the Inner Loop, $340,000

is a comparatively modest price. Under a reasonable set of assumptions, it was affordable to a

household earning 105 percent of the metropolitan median household income in October 2020,

although, of course, major changes have transpired in interest rates and other market conditions

since.11, 12 Compared with other big and growing U.S. cities, it is notable that a newly built,

family-sized housing product with a central location is within reach of middle-income Houston

households in the median case.

Summary

To summarize, SF2TH redevelopment on the citywide scale is a relatively rare event; the same is

true of block votes—presumably mostly conducted in response to nearby SF2TH redevelopment—

although less so. Although precise quantification is not possible, it is clear that SF2TH

redevelopment represents a small share of townhouse development in Houston overall. After a slow

start during and immediately following the Great Recession, SF2TH redevelopment has steadily

produced new housing, albeit not without fits and starts. SF2TH redevelopment is overwhelmingly

concentrated in the urban core, more so than townhouse development as a whole. It represents a

considerable intensification of urban land use, whether measured from a built form or property

tax generation standpoint. Even so, the typical end product, although by no means cheap, is

reasonably affordable to many middle-income homebuyers. Having reviewed some of the general

characteristics of SF2TH redevelopment, the article now turns to an analysis of the locational

factors that predict where it—along with its close companion, block vote district formation—is

most likely to take place.

Predicting Townhouse Redevelopment and Block Votes From 2007 to 2020

Exhibit 6 summarizes the two logit model runs, predicting SF2TH redevelopment (Model 1) and

block vote incorporation (Model 2). A discussion of the results from Model 1 follows.

10 Unless otherwise indicated, all dollar figures are rounded to the nearest $1,000.

11 This calculation uses Fannie Mae’s (n.d.) Homebuying Mortgage Calculator and assumes the following: 5-percent

downpayment, 2.8-percent interest rate for a 30-year mortgage, and no homeowner’s association fees (as is typical for Houston townhouses) but includes property taxes, homeowner’s insurance, and private mortgage insurance in addition to principal and interest. These costs are assumed to total no more than 30 percent of gross household income. In 2020, the median family income for Greater Houston was $78,800 (HUD, 2020).

12 This calculation also does not account for the likely understatement of HCAD’s assessed house values compared with real-world prices.

188 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

Exhibit 6

Logit Model Results for Single-Family-to-Townhouse Redevelopment (Model 1) and Block Vote

Incorporation (Model 2)

Model 1 (SF2TH logit):

Model 2 (Block Vote logit):

Single-family parcel

Single-family parcel not in a

Description

(as of 2007) undergoes

block vote district (as of 2007) is

subdivision into townhouse incorporated into a block vote,

lots, 2007–2020

2007–August 2022

Intercept

– 52.58

2.97***

11.70

(1.01)***

Original Lot and Structure Characteristics

Original lot size (acres)

0.51

(0.14)***

0.47

(0.032)***

Original structure year built

– 0.028

(0.0015)***

– 0.011

(0.00051)***

Original structure interior area (sf)

– 0.00026

(0.000041)***

0.00015

(0.000013)***

Distance from CBD (miles)

– 0.55

(0.022)***

– 0.059

(0.0048)***

Tract-level Characteristics, Year 2000

Median house value (1999 USD)

0.000019

(0.0000024)***

0.000026

(0.00000085)***

Median house value (1999 USD), squared – 3.50E-11

(4.18E-12)***

– 5.56E-11

– 1.73E-12***

Population share under age 18

– 1.71

(0.96)

– 4.41

(0.26)***

Population share Black non-Hispanic

– 1.37

(0.35)***

5.23

(0.10)***

Population share Hispanic

– 0.58

(0.44)

7.88

(0.13)***

Share of people ages 25+ with

– 1.89

(0.60)**

5.89

(0.17)***

bachelors or higher

SF2TH Townhouses in Tract, Built from 2007–2020

SF2TH townhouses in tract

0.012

(0.00066)***

SF2TH townhouses in tract, squared

– 0.000057

(0.0000028)***

n

282,742

282,742

Nagelke pseudo r^2

0.293

0.259

***p < 0.001; **p < 0.01; *p < 0.05; p < 0.1

SF2TH = single-family-to-townhouse.

Note: Standard errors are shown in parentheses.

Sources: Harris County Appraisal District (HCAD) and American Community Survey data, with calculations by authors The four original lot and structure characteristic coefficients are all highly significant and have the

expected signs. As hypothesized, parcels that are larger and that contain older and smaller existing

houses and that are closer to downtown are all likelier to redevelop when controlling for the other

variables in the model. The relationship of house value to redevelopment also behaves as expected.

Confirming Gray and Millsap’s (2020) finding that townhouse development in general is most

common in upper-middle-income locations, the year 2000 median tract house value coefficient

and its square in Model 1 point toward the likelihood of SF2TH redevelopment peaking, all else

equal, in tracts with a median house value of $271,000 (with a 95-percent confidence interval

spanning from $165,000 to $442,000) in 2000. This value compares with an overall year 2000

average tract-level median income house price of $91,000.13

13 See exhibit 8 in the appendix.

Cityscape 189

Wegmann, Baqai, and Conrad

The sociodemographic variable coefficients tell a somewhat mixed story. Tracts with fewer

Black residents in 2000 are more likely to redevelop, cutting against the notion that SF2TH

redevelopment is a phenomenon of gentrification. On the other hand, parcels in tracts with lower

college education levels are likelier to redevelop. A very weak negative relationship is between the

number of children in the tract and the likelihood of SF2TH redevelopment; Hispanic population

share has no relationship at all. Although these results are mixed, taken in tandem with the finding

previously reported about tract house prices, certainly no compelling reason exists to strongly

associate SF2TH redevelopment from 2007 to 2020 with gentrification. Instead, it appears to

concentrate in tracts that had fewer Black and college educated residents but higher-than-typical

house prices, as of 2000.

The Model 2 results bear many similarities to Model 1, suggesting that many of the basic factors

that drive SF2TH redevelopment are also associated with greater likelihood of a given parcel being

incorporated into a block vote district. For instance, as with SF2TH redevelopment, block votes are

likelier on larger parcels, with older houses, closer to downtown, and in neighborhoods with above

median house prices as of 2000 (peaking at $233,000 compared with $271,000 for Model 1).

The differences between Model 2 and Model 1 are instructive. Unlike SF2TH redevelopment,

block votes are more likely on parcels that contain larger houses; perhaps their owners (being less

likely to resubdivide their own lot) are more motivated to act to prevent what they regard as out-

of-scale townhouse redevelopment from taking place nearby. Block votes are more likely to take

place in tracts that have more children and college-educated adults; one could imagine neighbors

organizing to thwart SF2TH redevelopment in the name of maintaining a tranquil, child-friendly

atmosphere. One could also imagine more educated residents having more ability to navigate

the block vote district formation procedures. Intriguingly, parcels in tracts with more Black and

Hispanic residents are sharply more likely to join a block vote district.

Model 2 has a variable not present in the SF2TH model (Model 1), along with its squared term:

The number of SF2TH units built during the period from 2007 to 2020 within the same census

tract as the parcel in question. As previously discussed, the supposition here is that the presence

of SF2TH redevelopment nearby may spur homeowners to organize a block vote as a defensive

measure against townhouse redevelopment on their own street, although this analysis ignores the

relative timing of these events and thus can only yield a loose association. The sign and significance

of the squared term suggest, as with the house value variable, a quadratic relationship between

SF2TH units and block vote likelihood. The probability of a block vote covering a parcel between

2007 and 2022 peaks with 104 townhouse units created in the same census tract from 2007 to

2020 (with a 95-percent confidence interval from 84 to 115). Below that amount, block votes, all

else equal, are less likely, presumably because townhouse encroachment is a less alarming prospect.

If more than 106 townhouses per tract, block votes also become less likely, suggesting a critical

level of townhouse saturation, past which homeowners begin to calculate that it is no longer

worthwhile to bother with organizing a block vote.

Models 1 and 2 have Nagelke pseudo r2 values of nearly 0.29 and 0.26, respectively. Thus, they

are reasonably predictive, given that many factors governing lot-by-lot redevelopment and block

190 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

vote formation, such as a given homeowner household’s readiness to sell, the presence of nearby

blighted properties, or social relations on a given residential block are idiosyncratic.

Townhouse Redevelopment and Neighborhood Change

Although some of the model results previously reviewed relate, at least indirectly, SF2TH

redevelopment with gentrification, it is also useful to use simple descriptive statistics to build a

portrait of how the neighborhoods in which this type of townhouse development predominates

have changed during this century. Exhibit 7 captures these changes from 2000 in the decennial

census to 2015 through 2019 in the ACS. Sociodemographics in the mean census tract containing

SF2TH units (middle column) are compared with the same for the mean census tract containing

unsubdivided single-family parcels (right column). To provide a concrete example from the top

row of exhibit 7, the average parcel that underwent SF2TH redevelopment was in a census tract

in which the share of the population under age 18 dropped 7.4 percentage points between 2000

and 2015–2019 (center column). Meanwhile, the average single-family parcel that did not undergo

SF2TH redevelopment was in a tract whose under-18 share decreased only 2.3 percentage points

during that same period (right column). Therefore, SF2TH parcels tended to be in tracts that lost

children at a faster rate than single-family parcels that were not redeveloped into townhouses.

Exhibit 7

Average Tract-Level Change From 2000 to 2015–2019 for Subdivided (SF2TH) Versus

Unsubdivided Parcels

Description

Subdivided Parcels

Unsubdivided Parcels

Percent of population under age 18

– 7.4 pp

– 2.3 pp

Percent of population over age 65

– 0.2 pp

+1.8 pp

Percent of population non-Hispanic Black

– 3.8 pp

– 5.2 pp

Percent of population non-Hispanic White

+16.8 pp

– 6.4 pp

Percent of population Hispanic

– 21.0 pp

+10.7 pp

Median Family Income (nominal dollars)

+ $109,667

+ $29,520

Percent of adults 25+ with bachelors degree or higher

+38.3 pp

+6.2 pp

Percent of occupied housing units owner-occupied

+11.6 pp

– 3.1 pp

Median owner-occupied house price (nominal dollars)

+$315,401

+$134,195

Population density per square mile

+1,986

+270

Number of parcels

1,371

281,400

pp = percentage point change.

SF2TH = single-family-to-townhouse.

Sources: Harris County Appraisal District (HCAD), US Decennial Census, and American Community Survey data, with calculations by authors The mean SF2TH unit in the dataset is in a census tract that in the 21st century has, by all

indicators, disproportionately gained in Houston’s most historically advantaged populations.

White14 residents are up by 17 percentage points, although Black and Hispanic residents are down

4 and 21 percentage points, respectively. These demographic trends took place in the context of

substantial densification: The median density of the average SF2TH tract leapt by almost 2,000

14 “White” refers to “non-Hispanic White” throughout.

Cityscape 191

Wegmann, Baqai, and Conrad

extra people per square mile (an increase of nearly 39 percent) compared with less than 300, on

average, in the unsubdivided parcels’ tracts.

The average SF2TH tract gained in median family income by a whopping almost $110,000

compared with under $30,000 for the tract housing the average unsubdivided single-family

parcel. The median house price shot up by $315,000 in the former versus only $134,000 in the

latter. Homeownership rate trends diverged, increasing nearly 12 percentage points in the former

compared with a drop of 3 percentage points in the latter.15

The share of senior households in the mean SF2TH tract remained basically unchanged versus

a slight increase (1.8 percentage points) in the tract of the mean unsubdivided parcel. The

divergence was greater for children, as previously noted, that is, a 7.4-percentage-point drop for

subdivided versus drop of 2.3 percentage points for unsubdivided parcels.

Do these changes represent gentrification as it is commonly understood? In the average case,

no: The mean SF2TH unit is in a tract that had substantially more White residents in 2000, was

essentially identical in income, and had considerably higher median house values compared to

unsubdivided parcels (40 percent White for tracts containing SF2TH units versus 30 percent White

for tracts containing unsubdivided parcels, $48,000 versus $47,000 of median family income,

and $128,000 versus $90,000 of house value, respectively). SF2TH redevelopment, it appears,

disproportionately took place in somewhat advantaged tracts near the urban core and helped

those neighborhoods grow their advantaged populations. Consider the typical pattern in most

growing U.S. cities, such as nearby Austin, where land use regulations largely shield advantaged

neighborhoods from infill development and housing unit densification and, instead, shunt it

to historically marginalized (and less heavily regulated) areas in the urban core, thus fueling

gentrification. Houston represents an entirely different trajectory (Tretter, 2016).

Takeaways and Lessons

How should one summarize the Houston experience of SF2TH redevelopment—something

that could be thought of as Houston’s two-decade long experiment in repealing (erstwhile) large

lot single-family zoning via allowing redevelopment into townhouses? Although it is of course

important to be cautious about generalizing from one city to another, some of the topline takeaways

from the empirical findings previously presented may be instructive for other large, hot market,

U.S. cities, or at least those seeking to legalize townhouse-style redevelopment of large single-family

lots. The following paragraphs summarize these takeaways.

The removal of binding constraints can spur change. At the risk of stating the obvious, the

removal of a binding constraint on a form of development can greatly speed up the proliferation of

that form of development (Gabbe, 2018). Just as prior research has demonstrated that minimum lot

size requirements altered the quantity and character of greenfield suburban development throughout

Texas and townhouse redevelopment in Houston in general, the 1998 reforms appear to have

15 Here, it is worth recalling that exhibit 7 presents simple associations between parcel status (subdivided into townhouses or not), with the various sociodemographic and other indicators shown. Causation cannot be inferred directly. Still, the simplest explanation for the observed trends is that locations with rapidly increasing incomes and property values were, all else equal, likely more attractive for redevelopment, including SF2TH, where regulations and private deed restrictions allowed.

192 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

facilitated substantial SF2TH redevelopment in Houston (Gray and Furth, 2019; Gray and Millsap,

2020). The results in this article do not permit a comparison before and after the 1998 reforms

because the data span only the period from 2005 to 2020, but SF2TH redevelopment in Houston

accelerated post-Great Recession to a pace likely not seen in any other U.S. city in recent times.

Single-family-to-townhouse redevelopment is a comparatively rare event on the citywide

scale. Even though the 1998 lot size reforms and their later extension could be viewed as major

success stories in spurring the production of townhouses, townhouse development on formerly

single-family lots is relatively rare even under Houston’s near-ideal conditions. Although it must

be recognized that these estimates are likely not perfect, the analysis in this article found that only

0.5 percent of single-family lots underwent SF2TH redevelopment between 2007 and 2020. Even

ambitious and successful reforms to R1 such as Houston’s, in other words, are unlikely to spur

rapid transformation in the single-family stock at the scale of a whole U.S. city. Most single-family

housing is likely to stay single-family housing in the first decades after reform.

The same might not be true, however, at the neighborhood scale. Certain neighborhoods may

be ideally situated for redevelopment and face more rapid change. Indeed, certain Houston

neighborhoods, such as Rice Military and Montrose, were radically transformed by SF2TH

redevelopment during the period analyzed.

In Houston, townhouses on formerly single-family parcels are less numerous than those built

on formerly nonresidential parcels. The share of overall post-1998 townhouse development

in Houston that took the form of redevelopment of formerly single-family parcels was likely

less than 20 percent, although due to data limitations, the true percentage cannot be calculated

precisely from the data analyzed for this article. This low share likely reflects multiple factors. The

economies of scale achievable on larger (i.e., nonresidential) tracts may be attractive to developers.

Existing residential neighborhoods may be subject to restrictive covenants limiting redevelopment.

Even in a liberal regulatory regime, opposition from neighbors may still impede the redevelopment

of parcels in established single-family neighborhoods. Moreover, the specific design of Houston’s

lot size reduction ordinances likely incentivizes the acquisition of larger parcels—developers

can reach smaller townhouse lot sizes, for instance, if they provide common open space in their

developments. This incentive could spur them to prefer larger, commercial parcels over smaller,

residential ones in many cases. Whatever the exact reason, it is obvious that even where the

development of a given housing product is allowed on both commercial and residential land,

Houston’s experience shows that it is not an inevitability that the latter will predominate.

Single-family-to-townhouse redevelopment represents a significant intensification of land

use and fiscal yield where it occurs. Although SF2TH redevelopment represents a less dramatic

physical transformation than, say, the replacement of a single-story strip mall with a midrise

apartment building, it still results in a notable increase in how intensively the affected parcels are

used. The median SF2TH redevelopment produces 4.2 times more floor area on the parcel than

what existed before the prior single-family house on it was torn down. Notably, redevelopment

increased both the density and the unit sizes of housing compared with what existed on the same

parcels pre-redevelopment. These redevelopments provided more housing per household, not

only per acre.

Cityscape 193

Wegmann, Baqai, and Conrad

The increased valuation for property taxation purposes for all the properties that underwent

SF2TH redevelopment was fully sixfold higher post-redevelopment compared with pre-

redevelopment, suggesting that SF2TH redevelopment is almost certainly a fiscal winner for the

city and other taxing districts even when considering additional demand for municipal services.

Single-family-to-townhouse redevelopment is much likelier in the urban core and on certain

parcels (large lots with old, small existing houses). Notwithstanding the rarity of SF2TH

redevelopment on the citywide scale, in Houston, it has concentrated in particular locations. It is

intensely concentrated in the urban core—much more so than townhouse development in general,

which already has a notable urban tilt. Likely, the acquisition and teardown of an existing single-

family house requires considerable land values for it to be justified for SF2TH redevelopment.

It is most likely to take place on larger single-family parcels where the existing house is small

and old or low in value relative to the land on which it sits. This most recent finding, of course,

has implications for those concerned about historic preservation in Houston or in other cities

contemplating repealing R1.

Block votes are comparatively rare citywide but apparently effective in keeping SF2TH

redevelopment out of certain neighborhoods. Block votes are comparatively rare on the citywide

scale. Only 4.7 percent of single-family lots not in a block vote district as of 2007 came to be covered

by one, or had one pending, by 2022. This percentage is higher than the share of such parcels that

underwent SF2TH redevelopment (0.5 percent), but it is obvious that providing the block vote

mechanism did not come anywhere close to halting SF2TH redevelopment on the citywide scale. If

one takes a “zoomed-out” perspective, then block votes do not impede SF2TH redevelopment.

However, a “zoomed-in” perspective gives a more nuanced interpretation. Areas with heavy

concentrations of block votes tend to border on, but mostly not overlap with, areas with a lot of

SF2TH redevelopment. Thus, it can be inferred that certain sizable areas of the city that might

otherwise be expected to have market conditions favorable for SF2TH redevelopment seem to

have many blocks where this redevelopment has been thwarted.16 This dynamic can be seen in

the patchwork spatial pattern of large areas of concentrated townhouse redevelopment adjoining

other large areas that are thick with block votes. This finding buttresses Gray and Millsap’s (2020)

supposition that block votes allow homeowners to confront tradeoffs between property values

and their aversion to immediately proximate redevelopment, with varying results according to the

particular mix of life circumstances and preferences of homeowners on a given block.

The spatial patterns of SF2TH redevelopment are not consistent with gentrification as a

primary explanatory factor. The hypothesis that gentrification was a primary driver of SF2TH

redevelopment does not fit well with our empirical results. For a neighborhood to be commonly

understood to be gentrifying during a given period requires two ingredients: (1) A population that,

16 Some evidence in favor of this supposition: Generally speaking, parcels with lower I:L ratios are less intensively used in relation to their land value, thus can be expected to be more prone to redevelopment (Landis et al., 2006). Among the parcels in the dataset with valid property values, analysis finds that unsubdivided properties incorporated into block votes during the time period analyzed here had a median I:L ratio of 0.94, significantly less than for unsubdivided properties never incorporated into block votes, or 1.90. As a point of reference, parcels that underwent SF2TH redevelopment had a median I:L ratio of 0.14 versus 1.85 for those that did not (exhibit 5). These results are consistent with the idea that block votes are likely suppressing at least some SF2TH redevelopment. The authors thank Emily Hamilton for suggesting this comparison.

194 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

at the beginning of the time period, includes an overrepresentation of historically marginalized

groups and (2) a trajectory of change over time in which the share of advantaged groups increases.

To be sure, SF2TH redevelopment in Houston is consistent with the second requirement. It is

concentrated in tracts that from 2000 to 2015–2019 lost children, Black, and Hispanic residents

and gained White residents, college educated adults, owner-occupied housing share, and house

values. However, it does not meet the first requirement: When controlling for other relevant

factors, SF2TH redevelopment was more likely to occur in neighborhoods with above average

(although not the highest) property values and with fewer Black residents, as of 2000.

In other words, the pattern of SF2TH redevelopment is not consistent with a narrative that an

influx of townhouse development led to Houston’s historically disadvantaged groups having

to leave their neighborhoods. Instead, it seems to have been a case of neighborhoods that were

modestly wealthy to begin with getting wealthier. Neighborhoods with low house values may have

had insufficient land values to attract developers or homebuyers, and the highest cost blocks may

have been more likely to already have protective deed restrictions in place. Block votes may have

played some role in the latter, although they were similarly most likely to be used in neighborhoods

in the upper middle, rather than the top, of the citywide distribution of house values. It seems

plausible that high-income blocks are more likely to have already had deed restrictions in place,

and thus have less need for block votes, but this supposition must be thought of as conjecture for

the time being.

Although novel building forms, such as the tall and narrow dimensions of townhouses, often

serve for many as a visual totem of gentrification by contrasting with existing familiar building

types, the evidence does not strongly support the association in the case of Houston. Certainly,

one can find examples of gentrifying neighborhoods, such as Third Ward immediately south

of Downtown Houston, which experienced a spate of SF2TH redevelopment from 2000 to

2015–2019, but this pattern was not predominant. An implication for other U.S. cities is that a

broad-based, citywide repeal of R1 that effectively sparks redevelopment may result in somewhat

more advantaged neighborhoods soaking up some of the housing demand now channeled to

gentrifying neighborhoods.

Single-family-to-townhouse redevelopment does not appear to be associated with a gain in

children. Many observers have lamented the lack of new housing in urban cores that is suitable

for families with children. Houston has vigorously addressed this issue by pursuing regulatory

changes that have allowed for the construction of tens of thousands of relatively spacious single-

family townhouses that, in principle, could accommodate families with children. However, the

tracts that have seen SF2TH redevelopment at the highest rates have lost children faster since 2000

than the city as a whole. It is impossible to know from our results if this loss of children is due to

other factors, such as fear of crime or perceived low quality of public schools, or if townhouses are

still not viewed in Houston as family-friendly housing (perhaps because of a lack of yard space or

vertical layouts), despite their comparative spaciousness vis-à-vis apartments or other land-efficient

housing types. It is also possible that the availability of townhouses resulted in a slower decline in

the child population than would otherwise have been the case. However, one takeaway for cities

Cityscape 195

Wegmann, Baqai, and Conrad

contemplating R1 repeal is that townhouse redevelopment may not on its own be a panacea for

attracting children to the urban core.

What Remains Unknown

Although the research reviewed in this article sheds light on the 21st century phenomenon

of SF2TH redevelopment in Houston, much more is to be learned. Although it produced a

reasonable estimate and portrait of SF2TH transformations, it would be valuable to compare these

transformations more precisely with other forms of townhouse redevelopment in Houston, such

as those originating from commercial or industrial parcels. It would also be valuable to compare

the various forms of townhouse redevelopment with other forms of small-lot redevelopment,

above all the teardown and one-for-one replacement of single-family houses or the construction of

single-family houses on vacant lots. Property tax data may offer opportunities to make inferences

about the characteristics of the people who sold SF2TH parcels versus the incoming townhouse

buyers. Finally, the recent proliferation of impactful scholarship from political scientists delving

into land use regulation suggests the potential for new insights from analyzing block vote patterns

against traditional precinct-level election data (Einstein, Glick, and Palmer, 2019; Trounstine,

2018). Of course, more broadly, one cannot say how the lessons learned from this path away

from R1 zoning extend to other reform strategies focused on “missing middle” housing or higher-

density development.

Conclusion

What should other cities contemplating R1 repeal learn from Houston’s experience of having

allowed townhouses to appear on single-family-dominated blocks a quarter century ago? Houston

shows that a robust supply response exists provided that market conditions are ripe and the

new land use regulations allow for the construction of a product that builders want to build and

homebuyers want to buy (one or both of which seems to have been absent in Minneapolis’ recent

much-celebrated repeal of R1). At the same time, R1 repeal is unlikely to transform the face of an

entire city over a short period. Instead, its rough equivalent in Houston proceeded incrementally,

lot by lot, in certain areas much more than others, but at a pace and scale that was relatively

modest in the aggregate. Houston’s experience suggests that R1 repeal is unlikely, on its own, to

exacerbate gentrification. There is even reason to think that repeal might alleviate gentrification by

channeling a higher share of new development to middle-income neighborhoods.

Small-lot townhouse development may also open up new possibilities, such as allowing for below-

market, family-friendly homeownership opportunities by nonprofit builders. The relatively modest

prices of many new SF2TH units implies that the public subsidies needed to bring them within

reach of below median households would not be outlandish. Even in its purely market-driven

form, townhouse redevelopment on single-family parcels offers considerable benefits, such as

intensified usage of urban land, an increased tax base, and the production of newly built, well-

located, family-sized housing units that in the median case are much cheaper than large-lot single-

family equivalents.

196 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

Ultimately, one must weigh the drawbacks and benefits of SF2TH redevelopment in Houston

against the status quo that exists in other high-demand large cities. Precisely where single-family-

townhouse redevelopment in Houston has been likeliest to take place—on spacious, centrally

located parcels, occupied by small, old houses, in neighborhoods that lean affluent—is where

one might expect to see the demolition of single-family houses and their replacement with large,

new, and expensive single-family houses in many other U.S. cities. If the loss of older, deteriorated

single-family housing stock in such locations is difficult, impossible, or perhaps even undesirable

to halt, then it is worth asking what is a worthwhile replacement. The recent experience of

Houston with infill townhouses offers an intriguing and, within the context of the United States,

unusual answer.

Appendix A

Model Specifications

The models for redevelopment are as follows:

Model 1

logit(pSF2TH) = β0 + β1 XORIG + β2 xCBDdist + β3 xthv 00 + β4 x2thv 00 + β5 Xtsd 00 + ε

Model 1 predicts pSF2TH, or the probability that a given single-family parcel in Houston undergoes

redevelopment into townhouse lots at any point between 2007 and 2020. β0 is the y-intercept, and

XORIG is a vector of original lot and house characteristics (original lot size, year original structure

was built, and original structure floor area). To account for location, xCBDdist, the “as the crow flies”

distance in miles from the centroid of the parcel’s tract to Houston’s City Hall, is included. xthv 00 is

the median house value, as of 2000, in the parcel’s tract. The model also includes a squared term,

because of the hypothesized quadratic relationship between tract house value and probability of

redevelopment. Finally, the model includes Xtsd 00, a vector of tract sociodemographic characteristics

in the year 2000 (population share under age 18, percent Black non-Hispanic, percent Hispanic,

and share of adults ages 25 and older with a bachelor’s degree or higher), plus an error term.

The model for block votes is almost identical to Model 1 but with one difference, noted in the

following paragraph.

Model 2

logit(pBV) = β0 + β1 XORIG + β2 xCBDdist + β3 xthv 00 + β4 x2thv 00 + β5 Xtsd 00 + β6 xSF2TH + β7 x2SF2TH + ε

Instead of modeling the probability of a lot undergoing redevelopment into townhouses, as in

Model 1, Model 2 models pBV, the probability that a lot that was not in a block vote district as

of the beginning of 2007 is incorporated into a block vote district sometime between 2007 and

August 2022, inclusive. The independent variables are all the same as in equation 1, except they

also include xSF2TH, the number of SF2TH units built within the same census tract as the parcel at

any time between 2007 and 2020, inclusive. A squared term for xSF2TH is included.

Cityscape 197

Wegmann, Baqai, and Conrad

Appendix B

Exhibit 8

Descriptive Statistics for the Dependent and Independent Variables in the Logistic Models

Standard

Description

Mean

Median

Min

Max

Deviation

Dependent Variables

Initially single-family lot as of 2005

0.00484

0

0

1

0.0694

underwent subdivision from 2007 to

2020 (dummy)

Initially single-family lot as of 2005 was

0.0467

0

0

1

0.211

incorporated into a block vote between

2007 and August 2022 (dummy)

Original Lot and Structure Characteristics

Original lot size (acres)

0.212

0.174

0.0230

9.975

0.223

Original structure year built

1960

1959

1840

2016

19

Original structure interior area (sf)

1,731

1,521

300

9,992

887

Distance from CBD (miles)

8.25

8.00

0.29

26.64

3.98

Tract-level Characteristics from U.S. Census, Year 2000

Median house value (1999 USD)

$90,442

$64,295

$17,500 $1,000,001

$85,481

Population share under age 18

0.278

0.292

0.0296

0.471

0.0623

Population share Black non-Hispanic

0.281

0.101

0.000595

0.983

0.327

Population share Hispanic

0.366

0.301

0.00283

0.972

0.275

Share of people ages 25+ with

0.221

0.132

0.00445

0.817

0.214

bachelors or higher

SF2TH Townhouses in Tract, Built from 2007–2020

SF2TH townhouses in tract

11.88

0

0

719.00

56.64

CBD = central business district. SF2TH = single-family-to-townhouse.

Sources: Harris County Appraisal District (HCAD), US Decennial Census, and American Community Survey data, with calculations by authors Acknowledgments

The authors are grateful to New York University’s Furman Center and The Pew Charitable Trusts

for their sponsorship of the research featured in this article. The Hogg Endowment also provided

valuable support. The authors thank Noah Kazis for shepherding the article from inception; Ingrid

Gould Ellen, Vicki Been, and Matthew Murphy for their detailed and insightful comments; and the

staff at Cityscape for their careful work in editing the article (and patience with the first author).

Emily Hamilton also generously provided feedback.

198 Recent Reforms in Zoning

Here Come the Tall Skinny Houses: Assessing Single-Family

to Townhouse Redevelopment in Houston, 2007–2020

Authors

Jake Wegmann is an associate professor in community and regional planning at the University

of Texas at Austin. Aabiya Noman Baqai is a doctoral candidate, also in community and regional

planning, at the University of Texas at Austin. Josh Conrad is a doctoral candidate in historic

preservation at the University of Texas at Austin.

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Wegmann, Jake, and Sarah Mawhorter. 2017. “Measuring Informal Housing Production in

California Cities,” Journal of the American Planning Association 83 (2): 119–130.

Weiss, Marc A. 1987. The Rise of the Community Builders: The American Real Estate Industry and

Urban Land Planning. New York: Columbia University Press.

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Whittemore, Andrew H. 2021. “Exclusionary Zoning: Origins, Open Suburbs, and Contemporary

Debates,” Journal of the American Planning Association 87 (2): 167–180.

Whittemore, Andrew H., and William Curran-Groome. 2022. “A Case of (Decreasing) American

Exceptionalism: Single-Family Zoning in the United States, Australia, and Canada,” Journal of the

American Planning Association 88 (3): 335–351.

202 Recent Reforms in Zoning

by Leah Brooks and Jenny Schuetz

Does Housing Growth in

Washington, D.C., Reflect

Land Use Policy Changes?

Leah Brooks

George Washington University

Jenny Schuetz

Brookings Metro

Abstract

Across the United States, rising housing costs have increased the political pressure on local elected

officials to encourage more housing production. Local and state governments are experimenting with

changes to land use regulations that could allow more housing to be built through infill development.

Between 2000 and 2020, Washington, D.C., engaged in substantial infill development, increasing the

housing stock by about 15 percent. This article examines whether areas in the city with particularly

high growth saw large zoning changes. The authors find that most housing development occurred where

underused commercial or industrial land was repurposed into high-density residential uses. Some high-

growth neighborhoods experienced rezoning; others saw conversions of existing structures with little

zoning changes. Notably, high-growth areas initially had very little land zoned for single-family homes.

Introduction

Since the Great Recession, the United States has failed to build enough homes to keep up with the

growth in demand driven by population and job increases (Bernstein et al., 2021; Up for Growth,

2022). Housing costs have risen faster than household incomes, creating more financial pressure

on low-to-middle-income households, especially renter households. This issue, in turn, has raised

the political salience of housing affordability among elected officials seeking ways to encourage

more housing production (Schuetz, 2022).

As part of this response, local and state governments across the United States are beginning to

experiment with zoning reforms. These efforts include attempts to legalize “gentle density,” such

as accessory dwelling units and duplexes, to statewide requirements to allow more dense housing

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

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U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Brooks and Schuetz

(“upzone”) around transit stations (Chesto, 2021; Grabar, 2018; Tobias, 2022). To guide future

policy decisions, it is important to understand which reforms have the most potential to expand

housing production, where, and under what market conditions. However, most of these reforms

are too recent to have yielded measurable results in housing outcomes.

To provide insights into the relationship between housing production and land use policies, this

article applies a different lens: looking retrospectively at 2 decades of rapid housing growth in

Washington, D.C., what can be inferred from the neighborhoods that saw the greatest increases

in housing? Did high-growth neighborhoods enact notable changes in zoning or other land use

policies? What was their initial zoning status?

The authors care about the answers to these questions because, although regulatory constraints

are one—potentially very important—determinant of housing production, they are certainly not

the sole determinant. Housing development also depends heavily on market factors, such as land

values, the cost of construction labor and materials, and the expected prices or rents of newly built

homes. Overly restrictive zoning can limit housing growth, but permissive zoning will not produce

growth if market factors are not favorable. Additionally, zoning is not the only possible regulatory

constraint on housing growth; current residents and elected officials have a variety of political and

legal channels to deter unwanted development.

Washington, D.C., provides an intriguing empirical setting to examine the relationship between

redevelopment and zoning. It has benefited from a strong labor market and increased housing

demand during the past 2 decades. However, aside from parks, the city has almost no undeveloped

land. Virtually all land was developed by the 1950s or designated as protected open space.

Therefore, nearly all housing growth occurs through infill development, redeveloping parcels

currently occupied by other uses (Brooks, Deneoux, and Schuetz, 2020). The District’s elected

leaders have been actively discussing how changes to zoning and housing approvals could facilitate

more infill housing production (Baca, 2020; McCartney, 2019). (Like most older cities in the

Northeast and Midwest, the District’s boundaries are fixed. It is surrounded by independent

political jurisdictions and cannot grow in land area by annexing unincorporated areas.)

As exhibit 1 shows, Washington, D.C., grew from slightly more than 570,000 residents in 2000

to about 700,000 in 2020. This 21-percent increase reversed more than 2 decades of population

decline. Although the population can grow without new housing, Washington, D.C., had increases

in population and housing units during this period. This growth occurred in the absence of any

wholesale upzoning to allow multifamily zoning in single-family neighborhoods; such single-family

neighborhoods account for most of the city’s land. Thus, Washington, D.C., provides insights into

where housing production takes place in the absence of large-scale changes to land use regulation.

204 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

Exhibit 1

Washington, D.C., Population Declines Through 2000 and Then Grows

Washington, D.C., Population by Decade

Sources: Decennial Census 1970–2000; American Community Survey 2010 and 2020

In the remaining sections, the authors provide descriptive statistics on where housing growth

occurred—and did not occur—in Washington, D.C. This study examines how growth correlates

with initial zoning status and zoning changes at the neighborhood level.1 To better understand

the mechanisms at work, the article undertakes qualitative case studies of two neighborhoods that

added unusually large amounts of housing during this period. The analysis relies primarily on data

from the 2000 Census and the 2020 American Community Survey; details on data sources and

empirical methods are provided in the technical appendix.

During the 2000–20 period, the District revised its zoning laws, with new zoning districts and

rules taking effect in 2016. Although this revision was a major legislative effort, the fundamental

components of zoning—what size structures and what uses are allowed by right—changed on only

a modest share of land in most neighborhoods (Flanagan, 2016). Notably, virtually all the land

previously zoned for single-family homes remained zoned as single-family. Nearly 15 percent of

land previously zoned for multifamily housing was downzoned—rezoned to allow less intensive

use—to allow only single-family. The neighborhoods with the largest share of downzoned land

are historically Black Wards 7 and 8, east of the Anacostia River. Most of the zoning changes that

added capacity for new housing occurred by rezoning the land from non-residential districts

(specifically a catch-all “Other” category) into single-family, multifamily, and mixed-use districts.

1 Throughout, block groups are the neighborhood unit. A census block group is a small neighborhood, designated by the Census for statistical purposes, designed to contain between 600 and 3,000 residents (see Census documentation).

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Examining the patterns of housing growth, the study found that most growth is concentrated

where previously industrial or commercial land—often underused—was repurposed into housing.

Relatedly, growth is also concentrated in areas with the least land zoned for single-family housing.

Growth was not, however, tied to large-scale neighborhood rezonings.

The District’s experience suggests that, in many cities, targeted land use interventions can

accommodate substantial amounts of new housing. In the District’s case, the city reallocated very

small amounts of land from non-residential to high-density residential uses. This analysis does not

shed light on the potential effects of upzoning existing residential areas because the District did not

undertake this type of rezoning. Instead, it points to the conditions under which infill development

can occur without such upzoning.

Which Washington, D.C., Neighborhoods Saw the Most

Housing Growth?

As expected of a city whose population expanded by more than 20 percent, most District

neighborhoods—measured in this analysis as Census block groups—saw increases in population

density between 2000 and 2020.2 Exhibit 2 shows a scatterplot of housing unit density (thousands

of housing units per square mile) in these 2 years. Dots in the shaded triangle represent

neighborhoods that increased in housing unit density, and dots outside the shaded area represent

block groups that lost housing density. If a dot were to lie on the diagonal of the triangle, it would

have the same housing density in 2020 as in 2000. More than 60 percent of the District’s 433 block

groups gained population during this time, although most saw only modest increases. However,

a few neighborhoods account for a strikingly large share of the District’s housing growth: block

groups noted with larger dark circles are the five with the greatest change in the total number of

housing units; block groups noted with squares are the neighborhoods with the next five-greatest

increases in total housing units.3 The article analyzes these fast-growing neighborhoods in greater

detail in the following sections.

2 See footnote 1 for definition of neighborhoods.

3 An alternative classification is to examine the 10 neighborhoods with the greatest percentage increase in housing units. Of the 10 neighborhoods with the greatest increase in absolute number of units, 5 are in the top 10 when ranked by percentage change in units, and 7 are in the top 13 by percentage change in units.

206 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

Exhibit 2

Most District Neighborhoods Saw Modest Increases in Housing Unit Density From 2000 to 2020

Thousands of Housing Units per Square Mile by Census Block Group, 2000 and 2020

Notes: Housing density is calculated as housing units per square mile. Larger, darker circles represent the five block groups with the largest increases in the total number of housing units; squares represent the block groups with the next five largest increases in total housing units. All remaining block groups are the lighter, smaller circles. Some lighter, smaller dots lie above the darker circles and squares because the highest-growth neighborhoods are called out by growth in total units rather than by population density.

Sources: 2000 Decennial Census; 2020 American Community Survey

This housing growth was not spread evenly across the District’s neighborhoods: the five greatest

growth block groups account for nearly 30 percent of additional housing created from 2000

to 2020, and the next five block groups account for roughly 10 percent of additional housing

(exhibit 3). For context, these 10 block groups account for well under 5 percent of land in the

District. The remaining 432 block groups make up more than 95 percent of the land but only 60

percent of new housing.

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Brooks and Schuetz

Exhibit 3

Housing Growth Was Highly Concentrated in a Handful of Neighborhoods

Percent of New Housing Units, 2000 to 2020, and Percent of Total Land Area, 2020

(a) Percent of New Housing Units

(b) Percent of Land Area

Notes: Exhibit 3(a) shows the share of new housing units in the five block groups with the greatest increase in the number of housing units, the share of new housing units in the five block groups with the next greatest increase in housing units, and the share of new housing units in the remaining 423 block groups.

Exhibit 3(b) shows the share of land area in each of those three categories using the same scheme.

Sources: 2000 Decennial Census; 2020 American Community Survey

Which Neighborhoods Saw the Greatest Increase in Housing?

Not only was housing growth concentrated in a few block groups, these block groups are quite

spatially clustered (exhibit 4). The first part of exhibit 4 shows the 10 neighborhoods with the

greatest housing growth, as measured by the increase in the absolute number of units. The second

part of the exhibit locates those neighborhoods within the city at large. Although the District is

10 square miles, this city map shows that all the high-development neighborhoods are reasonably

close to the central business district, in an area roughly 2 by 4 miles.

One large cluster of high-growth block groups appears adjacent to downtown near the Archives-

Penn Quarter-Convention Center area in near Northwest. Another includes the near Northeast

area slightly north of Union Station, near the NoMa metro (which opened for service in 2004)

208 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

and Union Market. Other areas of high growth include the Navy Yards area in Southeast, near the

Nationals baseball park; the Shaw and U Street corridors in Northwest; and slightly north of the

Foggy Bottom area in Northwest.

Exhibit 4

Fastest-Growing Neighborhoods Were Adjacent to Downtown, Near Union Station and Navy Yards

Top 10 Block Groups by Housing Growth, 2000-2020

Notes: The map shows 10 block groups with the largest increase in total housing units. Dots are Metro stations. See the data appendix for details on other map sources.

Sources: 2000 Decennial Census; 2020 American Community Survey

How Did Zoning Change in Washington, D.C., Between 2000

and 2020?

Cities promulgate zoning regulations to define allowed land uses, building dimensions, and density.

Planners assign a zone to each piece of land in a city; in the District, planners currently have 164

zones from which to choose. For example, the R-1-A zone limits a piece of land to single-family

homes, limits the total square footage of the building on the lot, requires a set number of parking

spaces, and sets a minimum lot size. Originally, zoning was intended to separate commercial and

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industrial uses from residential areas, protect homes from noise and pollution, and preserve light

and air by limiting building height and bulk (Hirt 2014). Many critics of zoning argue that it goes

much farther, reinforcing economic and racial segregation and limiting access to high-opportunity

communities (Hsieh and Moretti, 2019; Rothstein, 2017; Schertzer, Twinam, and Walsh, 2021;

Trounstine, 2020).

The share of land allocated to each zone varies considerably across the District’s neighborhoods.

Downtown and commercial corridors are zoned to allow offices, retail spaces, and various other

types of commercial land uses, in most cases also allowing new residential development. Much

of the District’s land outside downtown is reserved for residential uses, with some small-scale

neighborhood-serving commercial activity permitted.

This analysis focuses on the revisions to Washington, D.C., zoning that took effect in 2016. In most

cases, these revisions were only technical and left in place the basics of zoning: what structures

and uses are allowed by right. For this analysis, the study aggregates the District’s 164 zones into

five broad categories: single-family, multifamily, mixed-use, commercial/industrial, and other. This

method necessarily limits the precision of the analysis but leaves the authors with a tractable and

comprehensible set of categories. Exhibit 5a shows the share of District land allocated to these five

categories under the prior zoning rules (in 2003), and exhibit 5b shows how much land originally

zoned for each category stayed the same or was reallocated to a different broad category.

In 2003, about 36 percent of the District’s land was zoned exclusively for single-family residential

uses, whereas about 16 percent was zoned as multifamily residential.4 The “other” category was

the largest non-residential category, accounting for roughly 35 percent of District land. The largest

component of this land is parks, including the very large Rock Creek Park and the National Mall;

this category also includes land used for federal and local government buildings and other uses.

About 12 percent of the land was zoned as commercial-industrial, with a very small amount

designated as non-residential mixed-use.5

Notably, virtually all the land initially zoned for single-family housing was still zoned exclusively

for single-family homes as of 2016. (Land zoned initially as mixed-use or commercial-industrial

was also left in the same broad categories). Single-family exclusive zoning, particularly when paired

with large minimum lot sizes, has been identified as one of the greatest barriers to increasing

housing in already developed neighborhoods, where adding housing would require replacing

existing single-family homes with multifamily, multi-story buildings (Ellickson, 2021; Furth and

Gray, 2019; Glaeser, Schuetz, and Ward, 2006).

One limitation of broad-brush zoning categories is not being able to observe more nuanced

changes to zoning rules, such as revisions to dimensional requirements or procedural rules,

that could alter the effective housing capacity within zoning categories. In theory, keeping land

zoned for single-family but cutting the minimum lot size in half might allow property owners to

4 For this analysis, mixed residential-commercial zones were assigned to residential categories, according to which type of housing was allowed by right. The core downtown areas permit multifamily housing by right, so they are counted as

“multifamily” throughout the analysis.

5 Commercial-industrial zones do not allow residential uses, with a few exceptions (onsite janitor, artist studios, and homes built before 1958).

210 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

subdivide parcels and build additional homes on the new lots. In practice, D.C.’s rezoning made at

most modest changes to dimensional requirements that did not substantially alter housing capacity

in residential areas (Flanagan, 2016).

Exhibit 5

Zoning Changes Added Housing Capacity by Reallocating Non-Residential Land to Residential Uses

Initial Land by Broad Zoning Category and Subsequent Allocation by Initial Zoning Category

(a) Percent of Land Area by Zone, 2003

(b) Percent of 2003 Land Area, by 2016 Zone

Sources: Washington, D.C., 2003 and 2016 zoning shapefiles (full details are in appendices A and B)

Nearly 15 percent of the land previously zoned for multifamily was downzoned to allow only

single-family homes. The neighborhoods that experienced the largest amount of downzoning are

east of the Anacostia River in Wards 7 and 8; these historically Black neighborhoods are some of

the least affluent parts of the District.

Most of the increased housing capacity that resulted from rezonings came from reallocating land

previously in the “other” category: 7 percent of this land was changed to single-family, 5 percent

to multifamily, and roughly 1.5 percent to commercial/industrial. The “other” category accounts

for quite a lot of land, so in theory, the District could keep adding housing capacity by rezoning

parcels from this category to allow multifamily and mixed-use development. In practice, however,

much of this land is occupied by local or federal government facilities, including parks owned by

the National Park Service, and is unlikely ever to be converted.

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Brooks and Schuetz

How Was Housing Growth Correlated With Initial Zoning Status

and Zoning Changes?

In exhibit 6, the authors evaluate whether any zoning change at all—to any category—is correlated

with new construction. Specifically, they plot each block group’s share of land with zoning change

versus each block group’s change in total housing units during this period. They use the scheme as in

exhibits 3 and 4 to denote neighborhoods with the largest housing growth. Between 2003 and 2016—

the years for which zoning designations were observed—the median block group had zoning changes

to about 1.5 percent of its land. Among the top 10 block groups with the greatest increase in housing

units, 9 of these block groups had zoning changes to less than 7 percent of land area. Only one of the

highest-growth block groups, located in the Navy Yard, had nearly 25 percent of its land rezoned.

Many neighborhoods with very large changes in zoning had very small changes in housing units.

However, this result reflects the nature of the zoning changes in these locations. Of the 10 block

groups with the greatest percentage changes in zoning, all are east of the Anacostia River. In all 10,

most of the change was to downzone multifamily land to single-family, effectively reducing capacity.

Exhibit 6

High-Growth Neighborhoods Did Not Undergo Large Zoning Changes

Percent of Block Group Land That Changed Zoning and Change in Housing Units, 2000–20

Notes: Zoning change is defined as movement between one of the five broad zone codes employed in this analysis. Larger and darker circles are the five block groups with the greatest absolute increase in housing units from 2000 to 2020; squares are the second five block groups with the next greatest housing increase. More details on the methodology are in appendix A.

Sources: 2000 Decennial Census; 2020 American Community Survey

212 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

Although high-housing growth neighborhoods did not experience large rezonings, they share

another zoning trait: relatively little land initially zoned exclusively for single-family homes (exhibit

7). Across all District block groups, the median block group has nearly 60 percent of land zoned

exclusively for single-family homes. Among the 10 top housing growth neighborhoods, eight block

groups had less than 10 percent of land zoned for single-family homes. The two outliers, with

more than 50 percent of land zoned for single-family in 2000, had substantial vacant or extremely

underused land. These neighborhoods are slightly west of Logan Circle and one, now known as

NoMa, is slightly east of Union Station.

Exhibit 7

High-Housing Growth Neighborhoods Had Relatively Little Single-Family Zoning in 2000

Change in Housing Units, 2000 to 2020 Versus Percent of Block Group Land Zoned for Single-Family

Housing in 2003

Notes: Single-family zoning is the share of land in a block group covered by a single-family zoning designation and comes from the 2003 zoning designations.

Zoning data were measured in 2003 and 2016. The larger, darker circles are the five block groups with the greatest absolute increase in housing units from 2000

to 2020; squares are the second five block groups with the next greatest housing increases. More details on the methodology and sources are in appendix A.

Sources: 2000 Decennial Census; 2020 American Community Survey

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Brooks and Schuetz

Deeper Look: What Happened in Unusually High-Growth

Neighborhoods?

To better understand what happened in high-growth neighborhoods, this study provides context

for these block groups’ demographic, economic, and physical characteristics at the beginning

and end of the period. The focus now narrows to two block groups. One is in the Navy Yard and

experienced the greatest absolute growth in housing units for all District neighborhoods. The

other is in the West End and ranks in the next five-highest growth areas. These two neighborhoods

illustrate somewhat different stories about where housing growth occurred (exhibits 8 and 9).

Exhibit 8

West End and Navy Yard Had More Housing and Population Growth Than the District Average

(1 of 2)

Change in Population, Housing Units, Median Rent, and Median Income for Block Groups in the West

End and the Navy Yard, an Average of All District Block Groups, and the Average of Above-Median

Income (2000) Block Groups.

214 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

Exhibit 8

West End and Navy Yard Had More Housing and Population Growth Than the District Average

(2 of 2)

Change in Population, Housing Units, Median Rent, and Median Income for Block Groups in the West

End and the Navy Yard, an Average of All District Block Groups, and the Average of Above-Median

Income (2000) Block Groups.

Notes: Income and rent are in 2021 dollars. “Average” reports the average value across all District neighborhoods. “Above Median Income” neighborhoods are those with median income above the citywide median (as calculated from block group data).

Sources: 2000 Decennial Census; 2020 American Community Survey

The West End block group illustrates how a substantial amount of housing can be added by

converting existing structures, even without major zoning changes. The West End block group—

bordered by Pennsylvania Avenue to the west/southwest, New Hampshire Avenue to the east/

northeast, and N Street to the north—had a small but fairly affluent residential population as of

2000 and grew larger and richer during the subsequent decades.

The block group has seen substantial development of apartment and condominium buildings and

several high-end hotels, restaurants, and retail stores. It also saw at least two very large conversions

of commercial property to residential uses: one large office building (located at 1255 25th Street)

became a residential rental and a hospital (the Columbia Hospital for Women) became condos

(Foggy Bottom Association, 2021; Pennsylvania State University, 2009). Although the mixture of

zoning as measured by land area has not changed much during this period—roughly two-thirds

mixed-use and one-third multifamily in 2003 and 2016—the total number of units is strikingly

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Brooks and Schuetz

higher and does not account for the large number of hotel rooms built in this area during the past

2 decades (exhibit 9).

Although there is little change in land zoned by major category, it is not uncommon for large

development (or conversion) projects to be facilitated by variances or other minor changes to

zoning rules. The District government does not systematically record these variances, making them

difficult to measure.

Exhibit 9

Change in Land Use Composition for West End, Navy Yards, and Average Block Group

Percentage of Land by Broad Zoning Designation in 2003 and 2016

Note: Shares of land area are reported by zoning category.

Sources: 2000 Census block groups; 2003 and 2016 zoning shapefiles

Navy Yard presents a more intentional story of housing growth as part of a broader redevelopment

plan. During the course of 2 decades, joint efforts of the District local government, the federal

government, private sector developers, and a newly formed Business Improvement District have

transformed a low-density, low-value commercial-industrial site into a high-density, high-value,

mixed residential-commercial area (Cassius, 2019; Stevens, 2012). In the early 2000s, then-Mayor

Anthony Williams led efforts to recruit a major-league baseball team—formerly the Montreal

Expos, now the Washington Nationals—to the region with the promise of a new ballpark. In

addition to the ballpark (which opened in 2008), the neighborhood now has nearly 15,000

residents, extensive retail and entertainment corridors, hotels, and parks and recreation facilities

along the Anacostia River. The newly built residences include quite a diversity of housing types,

216 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

including rental and owner-occupied homes, market-rate and subsidized properties, and at least

one mixed-income subsidized property that replaced public housing (Lerner, 2019). Projects such

as Navy Yard demonstrate that rezoning even small amounts of land for high-density multifamily

units can yield substantial amounts of new housing.

As of 2000, the West End (but not the Navy Yard) had income above the citywide average (exhibit

8). Most neighborhoods that had above citywide median income likely had high demand.

However, these neighborhoods had modest changes in population density and very small changes

in housing units, combined with appreciable increases in rent.

Conclusion and Policy Recommendations

As housing affordability becomes a politically contentious issue in more parts of the United States,

cities and states are experimenting with different policy levers to boost housing production.

Targeted research is needed to fill in knowledge gaps about what types of policies are most effective

in different housing market conditions. This analysis focuses on the experience of Washington,

D.C., a city that has seen strong population growth and housing demand during the past 20 years

yet had little undeveloped land.

The District has built substantial amounts of new housing in recent decades, almost entirely

through infill development converted from other uses. New housing is spatially concentrated in a

handful of neighborhoods. Although most high-growth neighborhoods did not see large zoning

changes, they entered the period with very small amounts of land zoned exclusively for single-

family homes. Case studies of two high-growth neighborhoods illustrate two different patterns.

The West End added housing by converting a few large commercial and institutional buildings

into housing without a substantial change in zoning. By contrast, Navy Yard is an example of

long-term planned redevelopment, with intentional partnerships between local government and

private sector developers to transform underused land into high-density mixed-use housing,

retail, and entertainment.

These results offer several insights into how policies may constrain or support greater housing

production. First, growth is more likely in predominantly non-residential or mixed residential-

commercial neighborhoods. This outcome likely reflects the limits imposed by low-density

residential zoning and the political process that guides development: having fewer neighbors to

complain makes it easier for developers to build. This pattern may also reflect economies of scale

in construction—especially with infill development in high-cost markets—that drive concentrated

growth of large buildings in a few areas. Both soft costs of development (financing, design and

engineering, and legal approvals) and materials and labor costs of vertical development contribute

to economies of scale. Second, rezoning even small amounts of land can yield large increases in

housing supply—if the rezonings allow sufficiently high density and the rents are high enough to

support construction.

The District has so far chosen not to upzone its single-family neighborhoods—and some residents

of single-family neighborhoods raised considerable opposition to any zoning changes during the

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Brooks and Schuetz

2022 Comprehensive Plan revision6—so the ability to draw inferences from this analysis about how

much additional housing could be produced by legalizing “gentle density” (or for that matter, larger

multifamily development) across large swathes of residential areas is limited. Some moderate-

density neighborhoods (such as Columbia Heights, Shaw, and Petworth) have seen substantial

numbers of conversions or replacement of rowhouses into small multifamily buildings, which is

allowed under existing zoning. The District’s more affluent residential neighborhoods prohibit this

type of infill development. Traditionally, rezoning non-residential areas for residential has been

an easier sell than convincing affluent residents in low-density neighborhoods to accept more

development—although the pro-housing movement is gaining political momentum in the D.C.

region and across the country (Pearson and Schuetz, 2022).

The District has relatively little industrial land that could be repurposed for residential or mixed-

use space, because the District never had a large manufacturing sector (unlike many Northeast

cities). The Ivy City and Union Market areas, which did contain warehouses for shipping and

logistics, are already undergoing rapid redevelopment. This pattern of substantial housing growth

in previously industrial space is not limited to the District. For instance, notable redevelopments

of Atlantic Yards and Long Island City in New York follow this model. To this end, New York

City intentionally rezoned industrial spaces to better reflect changes in labor and land markets

(New York City Office of Planning, 2022). The New York City example shows the importance and

limitations of this approach: formerly industrial sites have driven the city’s housing production, but

the number of such sites is finite and increasingly few.

Thus, this analysis ends on a note of caution. Although relaxing zoning can potentially yield greater

density, it need not always. Relaxing zoning allows developers to build at higher density, but it

does not and cannot mandate them to do so. Ultimately, developers will determine what types

of structures and densities to build based on the financial returns of each project. Furthermore,

the low-hanging fruit for the creation of urban housing units on a large scale is now underused

commercial and industrial land. In the wake of the pandemic, neighborhoods with high

concentrations of vacant retail and office space that have been hit hard by the shift to remote work

may also be promising targets for new housing.

Appendix A. Data and Methodology Notes

Maps

Digital maps are used for 2000 and 2020 block group definitions.

Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS

National Historical Geographic Information System: Version 17.0 [data set]. Minneapolis, MN:

IPUMS. 2022. http://doi.org/10.18128/D050.V17.0.

6 The Comprehensive Plan is the city’s outline for its overall land use goals. All zoning must be consistent with the goals laid out in the Comprehensive Plan.

218 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

For exhibit 4, the following were used:

• Map of water in the District, from D.C. Open Data.

{

https://opendata.dc.gov/datasets/waterbodies-2017

• Map of building footprints in the District, from D.C. Open Data.

{

http://opendata.dc.gov/datasets/building-footprints, downloaded May 2019

• Metro map from D.C. Open Data.

{

http://rtdc-mwcog.opendata.arcgis.com/datasets/metro-rail-stations, downloaded May 2019

Demographic Data

The source for demographic data (population, housing units, etc.) is the U.S. Census Bureau.

• 1970 to 2020 at the city level, for exhibit 1.

{

Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles.

IPUMS National Historical Geographic Information System: Version 16.0 [data set].

Minneapolis, MN: IPUMS. 2021. http://doi.org/10.18128/D050.V16.0.

• 2000 block group data.

{

Summary File 3 at the block group level.

{

Downloaded via the Census Bureau’s Application Programming Interface (API), July 7, 2022.

• 2020 block group data.

{

American Community Survey, 2016–20.

{

Block group level data.

{

Downloaded via the Census Bureau’s API on July 7, 2022.

Zoning Data

These data detail which land in the District is subject to which zone code in 2003 and 2016.

• 2003 Zoning Data.

{

From https://opendata.dc.gov/documents/dcgis-data-archive-july-2003/explore.

{

We use layer ZoningPly.

• 2016 Zoning Data.

{

Shapefile from https://opendata.dc.gov/datasets/DCGIS::zoning-regulations-of-2016/.

Consumer Price Index

The consumer price index was used to convert all relevant dollar values into 2021-year dollars.

Cityscape 219

Brooks and Schuetz

The All Urban Consumers series was used, downloaded from https://data.bls.gov/pdq/

SurveyOutputServlet on May 16, 2022.

Annual average values are from Column N.

Appendix B. Analytical Methods

Consistent 2000 Block Groups

The goal of this study is to make time-consistent block groups to analyze the same area over time.

To do so, the 2020 block group data were put into 2000 block group terms.

The original 2000 block group map has 433 block groups, and the original 2020 block group map

has 571 block groups. The final analysis sample uses 433 block groups.

To make this sample, the maps of the 2000 and 2020 block groups were overlaid. Many block

groups have the same borders or change little. Usually, block groups split over time to ensure that

their population does not grow too much.

When a 2020 block group does not have the same borders as the 2020 one, the demographics of

the 2020 block group were weighted to attribute it to a 2000 block group. For example, suppose

that 40 percent of the land area of 2020 block group A lies in 2000 block group X, and the

remaining 60 percent of 2020 block group A lies in 2000 block group Y. In this case, the authors

assigned 40 percent of block group A’s population to 2000 block group X and the remaining 60

percent to 2000 block group Y.

Zoning and Zoning Change

To study zoning change, the authors first re-categorize zoning codes into five broad categories—

single-family, multifamily, commercial/industrial, mixed-use, and other—to facilitate analysis.

By overlaying the 2003 and 2016 zoning maps, the team then evaluated which pieces of land

changed from one of these broad categories to another. Therefore, each piece of land was classified

by combining its broad 2003 zoning code and its broad 2016 code.

The 2003 and 2016 zoning status of each block group was found by intersecting the 2003/2016

zoning map with the 2000 block group map. The authors then calculated, for each block group,

the amount of land in each combination of 2003 and 2016 zoning codes.

Appendix C. Information on Neighborhoods With Large

Zoning Changes

The map in exhibit C-1 shows the 10 neighborhoods with the largest shares of land rezoned from

one of the five broad categories to another.

220 Recent Reforms in Zoning

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

Exhibit C-1

Neighborhoods With the Largest Change in the Percent of Total Land Area Rezoned, 2003 to 2016

Notes: This exhibit shades the 10 neighborhoods with the largest change in the percent of total land area rezoned, 2003 to 2016. See appendices A and B for complete details and citations.

Sources: Washington, D.C., 2003 and 2016 zoning shapefiles (full details are in appendices A and B)

Acknowledgments

Thank you to Mia Dana for careful and thoughtful research assistance. We thank Pam Brown for

the information on Foggy Bottom’s recent history. We are very appreciative of help on the zoning

designations from Michael Sakinejad, Zoning Data Coordinator for D.C.’s Office of Planning, and

Alex Santos for connecting us with him. Our paper is materially improved thanks to editorial

advice from Noah Kazis and Ingrid Ellen.

Cityscape 221

Brooks and Schuetz

Authors

Leah Brooks is an associate professor at the Trachtenberg School, George Washington University.

Jenny Schuetz is a senior fellow at Brookings Metro.

References

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What You Need to Know,” Greater Greater Washington, April 24. https://ggwash.org/view/77268/dc-

releases-its-amendments-on-the-comp-plan.

Bernstein, Jared, Jeffrey Zhang, Ryan Cummings, and Matthew Maury. 2021. Alleviating

Supply Constraints in the Housing Market. White House Council of Economic Advisors.

https://www.whitehouse.gov/cea/written-materials/2021/09/01/alleviating-supply-constraints-in-

the-housing-market/.

Brooks, Leah, Genevieve Deneoux, and Jenny Schuetz. 2020. The Washington D.C. Region Needs

More Housing, and Satellite Data Can Tell Us Where to Build. Brookings Institution. https://www.

brookings.edu/blog/the-avenue/2020/06/03/the-washington-dc-region-needs-more-housing-and-

satellite-data-can-tell-us-where-to-build/.

Cassius, Sarah. 2019. “Public Space for All? Examining the Role of Privatization, Visions of

Redevelopment, and BID Governance in the Capitol Riverfront.” Ph.D. diss., Columbian College of

Arts and Sciences, George Washington University.

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Decades,” Boston Globe, February 8.

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Single-Family Houses. Unpublished paper. Yale Law School.

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July 27. https://ggwash.org/view/42304/get-to-know-dcs-new-zoning-with-this-map.

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Foggy Bottom Got Its Trader Joe’s.” https://www.foggybottomassociation.org/post/funkstown-no-7-

the-columbia-hospital-for-women-and-how-foggy-bottom-got-its-trader-joe-s.

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Texas? Working paper. Arlington, VA: George Mason University Mercatus Center.

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Greater Boston. Cambridge, MA: Rappaport Institute for Greater Boston, Harvard University.

Grabar, Henry. 2018. “Minneapolis Confronts its History of Housing Segregation,” Slate Magazine,

December 7.

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Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

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NY: Cornell University Press.

Hsieh, Chang-Tai, and Enrico Moretti. 2019. “Housing Constraints and Spatial Misallocation,”

American Economic Journal: Macroeconomics 2: 1–39.

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Income, Mixed-Use Development,” Washington Post, December 5.

McCartney, Robert. 2019. “DC Mayor Muriel Bowser Wants More Low-Cost Housing in Affluent

City Neighborhoods,” Washington Post, October 15.

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planning/planning-level/housing-economy/housing-economy.page.

Pearson, Cassidy, and Jenny Schuetz. 2022. Where Pro-Housing Groups are Emerging. Brookings

Institution. https://www.brookings.edu/blog/the-avenue/2022/03/31/where-pro-housing-groups-

are-emerging/.

Pennsylvania State University. 2009. West End 25 Senior Thesis. Capstone Project Electronic

Portfolio. https://www.engr.psu.edu/ae/thesis/portfolios/2009/cmm5035/.

Rothstein, Richard. 2017. The Color of Law: The Forgotten History of How Our Government Segregated

America. New York: Liveright Publishing.

Schertzer, Allison, Tate Twinam, and Randall Walsh. 2021. “Zoning and Segregation in Urban

Economic History,” Regional Science and Urban Economics 94.

Schuetz, Jenny. 2022. Fixer Upper: How to Repair America’s Broken Housing Systems. Washington DC:

Brookings Institution Press.

Stevens, Michael. 2012. “Redeveloping a Vibrant Waterfront in Washington DC/The Capitol

Riverfront,” Journal of Urban Regeneration and Renewal 5 (2): 132–145.

Tobias, Manuela. 2022. “Duplex Housing Law Met With Fierce Resistance by California Cities,”

CalMatters, April 11. https://calmatters.org/housing/2022/04/duplex-housing-resistance/.

Trounstine, Jessica. 2020. “The Geography of Inequality: How Land Use Regulation Produces

Segregation,” American Political Science Review 114 (2): 443–455.

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vision/housing-underproduction/.

Cityscape 223

224 Recent Reforms in Zoning

by Joseph Weil Huennekens

Learning from Land Use Reforms:

The Case of Ramapo, New York

Joseph Weil Huennekens

Columbia GSAPP

Abstract

Many state and local governments are currently reforming zoning to increase housing production,

especially of dense, small multifamily options (often known as missing middle housing ). However, not

all efforts to reform single-family zoning are new. For instance, the suburban town of Ramapo, New York,

has continuously loosened development rules for nearly 4 decades, providing an unusually long timeline

for a case study of zoning reform. This article uses quantitative and qualitative data to assess the impact of

zoning reform in Ramapo. The case shows that the introduction of multifamily zoning—even in built-out

suburban neighborhoods—can spur the large-scale production of new housing units. By contrast, laws

that allow only for accessory units have more limited effects. The town’s experience also demonstrates the

importance of infrastructural investment to serve new housing supply, especially when added in suburban

areas. Finally, it shows that, at least in an unusually pro-growth political environment, discretionary

review and parking requirements do not automatically hinder housing production.

Introduction

By some appearances, Lenore Avenue is an unremarkable suburban street. Trees shade the road,

sidewalks are intermittent, and through traffic is blocked. The houses on Lenore Avenue appear

conventional as well. Styles typical of New York’s Rockland County—such as ranches and split

levels—predominate. However, some houses on Lenore Avenue are much bigger than others.

At first glance, these three-story homes might represent the type of new construction dwellings

derisively referred to as McMansions. Looking closer, however, reveals that not all these homes are

single-family. Some of the buildings have parking lots in the front and an unusually large number

of doors and mailboxes—clear indicators that they are multifamily buildings. Other properties

appear almost exactly as single-family dwellings. Yet, according to town records, these properties

are also classified as multifamily dwellings.

Lenore Avenue is a typical block in the Monsey section of the suburban town of Ramapo, New

York. It is within the R-15C district, a zoning designation created by the town board in response

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 225

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Huennekens

to a demand by the growing ultra-Orthodox Jewish population in the area allowing for flexibility

regarding denser housing configurations. Since its creation in 1986, the R-15C district has

expanded, and the rules governing properties in the zone have been progressively loosened. Today,

a maximum of six units are permitted on a 10,000-square-foot lot within the R-15C district—

twelve units on a double lot. Collectively, these decades of zoning reforms have had a profound

impact on the Monsey area, leading to the creation of a unique, quasi-urban neighborhood in

suburban New York.

The R-15C district is an unusually advanced example of zoning changes that planners and

policymakers argue should occur across the country. As the housing affordability crisis has spread,

a growing number of experts and politicians have called on governments to loosen restrictive

single-family zoning requirements to allow for a more diverse mix of housing options (Greene and

Gould Ellen, 2020; Kazis, 2020; Wegmann, 2020). State and municipal governments have passed

several zoning liberalization measures in response (Badger and Bui, 2019). These policy shifts are

driven by the hope that, by allowing construction of the type of small multifamily options (often

known as missing middle housing) currently precluded in most American communities, governments

might increase housing supply and drive down costs.

Contemporary zoning reform efforts have a clear parallel with the policy changes that Ramapo has

pioneered for nearly 4 decades. However, Ramapo’s zoning reforms are also distinct from current

efforts in two ways. First, most current zoning policy shifts are ongoing experiments grounded

in assumptions about how developers will respond to supply and demand. By contrast, Ramapo’s

R-15C district is an existing empirical case study, providing a window into how housing markets

and the real estate development industry respond to reform over a period of decades. Second,

most contemporary zoning reforms—such as the celebrated triplex law in Minneapolis—have

been pursued by urban municipalities. By contrast, Ramapo is a middle-ring suburb constructed

along the familiar midcentury pattern of single-family homes, separation of uses, and a disjointed

street grid. It thus provides a window into how the low-density suburban built environments that

dominate the American landscape might evolve when zoning rules are changed.

This article analyzes Ramapo’s zoning reforms to show how zoning policy interacts with

neighborhood change in a real-world case. The analysis uses a mixed methods approach,

triangulating quantitative data analysis with archival research and qualitative interviews, to show

how policy change in Ramapo has affected neighborhood character, land use composition, housing

markets, and infrastructure. The article comprises five major parts. The first section compiles a

detailed timeline of the town of Ramapo’s zoning policy changes in the Monsey area, showing the

branching permutations of zoning and tracing the housing configurations that each zoning policy

change spurred. The second section introduces the data and methods used to analyze the changes

wrought by zoning reform. The third section introduces the analysis itself, using parcel-level data

to look at how subdivisions, land use classifications, and units changed in the years following

the zoning change. The analysis aims to isolate the effect of zoning changes on land use and unit

production using difference-in-differences regression.

The fourth section provides an overview of the challenges and opportunities that accompanied

neighborhood densification in Ramapo and the expected drawbacks of added density to the town’s

226 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

infrastructure. It also briefly addresses the fraught local conversation (also a national one) about

the relationship between dense housing and affordability. Finally, the last section of this article

discusses what other communities can learn from Ramapo’s experience legalizing multifamily

dwellings—both in terms of what kind of production might be expected and the externalities that

may follow. Four key lessons emerged from the empirical record: (1) in some markets, multifamily

zoning will be significantly more conducive to housing production and land use change than

the gentlest forms of density, such as permitting accessory dwelling units and two- or three-

unit buildings; (2) zoning changes may be more attractive to builders if the laws allow the new

units to be sold as condominiums, at least in suburban settings; (3) densification requires extra

infrastructure investment if undertaken in a suburban environment; and (4) in an unusually pro-

growth suburban jurisdiction, discretionary review and parking requirements do not necessarily

hinder housing production.

Zoning History

Ramapo, New York, is one of five towns1 in suburban Rockland County. Approximately 35 miles

northwest of New York City, the town covers an expansive geographic area, a large portion of

which is protected land in Harriman State Park. The unprotected portion of the town is diverse in

character. It includes older villages developed during the Industrial Revolution, swaths of mid-

20th century subdivision-style development, and exurban areas subject to large lot zoning. In the

center of Ramapo, between the older villages of Spring Valley and Suffern, is the area known as

Monsey. During the past 40 years, a dramatic densification effort has transformed one portion of

Monsey into a quasi-urban space. The area is now a predominately multifamily neighborhood with

a population density closer to New York City than to the rest of Rockland County—an anomalous

built environment at the heart of an otherwise ordinary suburban area.

Early Years: Anti-Growth Pioneer

Although today Monsey is dense and quasi-urban, 100 years ago, the whole town of Ramapo was

mostly rural. The region changed in the 1950s and 1960s when the construction of the New

York State Thruway and the Tappan Zee Bridge linked the area with New York City. These federal

infrastructure investments, combined with White flight from the cities, spurred rapid growth in

Ramapo. The town grew more than 100 percent between 1960 and 1970 (U.S. Census Bureau,

2022), triggering a strong anti-growth backlash. First, in 1966, the town of Ramapo eliminated

as-of-right multifamily zoning within its borders (Meck and Retzlaff, 2008). Then, in 1969, town

authorities went further by implementing a points-based growth management plan tying building

permits to infrastructure investment. This innovative plan was upheld in the 1972 case of Golden v.

Ramapo—setting a national precedent for performance zoning schemes (Meck and Retzlaff, 2008).

Ramapo’s government pursued a largely anti-growth line throughout the 1970s, but at the same

time, small pockets of informal multifamily housing began to emerge. This informal density was

especially (although not exclusively) found in Monsey, parts of which were populated by a growing

number of ultra-Orthodox Jews (Saeed and Incalcaterra, 2001). In the mid-20th century, a small

1 A town is a meso-level jurisdictional category in New York State that carries land use decisionmaking power for all areas that are not further incorporated into villages.

Cityscape 227

Huennekens

number of Hasidic Jews relocated from Brooklyn to Ramapo (Mintz, 1992), settling in portions

of the town that had previously housed summer bungalow colonies for Jewish residents of New

York City (Hollander, 2017). One sect, the Skver, moved en masse, forming their own village, New

Square, on the edge of Ramapo in 1961. Another sect, the Vizhnitz, settled in central Monsey,

forming their own village, Kaser, in 1991.

Over time, a diverse mix of religious Jews followed these pioneers to the suburbs, creating a

burgeoning religious Jewish community composed of Hasidic and non-Hasidic sects (Berger,

2014). The community began to grow rapidly, driven by natural population growth and

continuous migration from Brooklyn. However, this exceptionally high demand for housing

was met with an artificially constrained supply, because Jewish law requires families to walk to

synagogue on the Sabbath (Mintz, 1992). Some families responded to the shortage by illegally

converting their single-family homes into multifamily ones, a practice that eventually expanded

to include newly built houses (Laudor, 1980).2 For at least a decade, the town government looked

the other way at informal conversions, with some officials alluding to an official amnesty policy

(Sanderson, 1978). However, as conversions spread to include new construction, a ferocious

backlash from secular town residents emerged, with hundreds of residents organizing civic

associations to oppose multifamily buildings (Rifkin, 1980).

The Creation of the R-15C District

As the illegal conversions controversy spread, Ramapo’s government struggled to develop an

effective solution. Some elements of the town bureaucracy counseled crackdowns, such as the

town building official who said at the time that “in certain areas of Monsey the legal house is

the exception ... it must be stopped or, we’ll have an absolute slum” (Allan, 1986a). Others,

especially elected officials, favored accommodation, arguing that the town should “recognize the

lifestyle already established there” (Colton, 1979). Eventually, the town board floated a proposal

to formalize some extralegal construction by creating a multifamily district (Colton, 1979). Anti-

growth residents were scandalized, because many had also moved from Brooklyn to, in the words

of one activist, “get away from what this downzoning3 [sic] is bringing back to us” (Laudor, 1981).

They mobilized to block zoning reforms, threatening to form villages to take land use regulatory

power back from the town government if they did not prevail. NIMBY (that is, Not in My

Backyard) civic activists also turned to the courts. Their legal efforts were successful in 1981 when

the State Supreme Court threw out the town’s first attempt at multifamily zoning (Colton, 1981).

In 1986, a new town supervisor, Herbert Reisman, was inaugurated, promising “a more

harmonious Ramapo” (Dow, 1986). Reisman again took up the multifamily issue, launching a

housing task force that ultimately called for the town to try again to implement multifamily zoning

(Dow, 1986). This recommendation was partially a response to the growing political clout of the

ultra-Orthodox community (Fararo, 1986). However, the town’s move was not purely a result of

2 The Adar Homes development is one example. This instance was a group of ostensibly single-family homes that anti-growth citizen activists organized to block on the grounds that the development was constructed purposely to allow for immediate conversion into multifamily dwellings.

3 Confusingly, at this time in Ramapo, “downzoning” was used to refer to zoning changes that would allow for more

intensive uses (what is normally called “upzoning” today).

228 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

ultra-Orthodox political pressure. As Reisman’s “harmonious” language implies, the town board

was also inspired by a philosophical belief in pluralism, expressed in the idea that government

should accommodate all lifestyles. This framing found its way into the legal text of the multifamily

zoning law itself,4 which addressed accommodating “specialized households” with distinct “social

and cultural needs.”

Specialized lifestyles aside, not all Monsey residents (and especially not all Ramapo residents)

welcomed multifamily dwellings. The new multifamily proposal poured fuel on village formation

efforts, ultimately spurring the creation of five new jurisdictions: Wesley Hills, New Hempstead,

Montebello, Airmont, and Chestnut Ridge (Allan, 1986b). This time, however, anti-growth

advocacy and lawsuits were not enough to block the multifamily law. The town board officially

altered the zoning code at the end of 1986, creating a new R-15C ( C for conversion) zoning district in a portion of Monsey previously zoned R-15. Under the old R-15 zoning, only single-family

homes were allowed on 10,000-square-foot lots (the vast majority), with two-family homes

requiring lots larger than 20,000 square feet. In the new R-15C zone, property owners were

granted the right to convert their single- or two-family homes into three-unit dwellings—the

beginning of a 40-year zoning liberalization process in central Monsey.

Additional Changes, Additional Controversies

The 1986 creation of the R-15C district was a milestone for multifamily zoning in Ramapo.

However, the specific rules that governed the zone’s development continued to loosen. First, in

1987, the maximum floor area ratio (FAR) permitted in the district was slightly higher.5 Next,

in 1991, fees were lowered, and conversions were made subject to administrative review rather

than planning board approval ( The Journal News, 1991). Then, in 1992, the zoning code was

amended to allow for the construction of new 3-family homes in the R-15C district rather than

just conversions ( The Journal News, 1992). This change paralleled the creation of a new Hasidic-

dominated village, Kaser, in one portion of Monsey, which altered its zoning to allow even denser

configurations (Henderson, 1991).

Despite the town’s zoning reforms, informal densification continued throughout the 1990s

(Lieberman, 1996). The controversy about multifamily housing also continued unabated.

Some residents accused the town of looking the other way regarding housing and quality-of-

life violations in the Monsey area, claiming that “garbage-strewn streets, torn-up lawns, parking

on both sides of the narrow streets, and increased bus traffic leave one with a negative image of

Ramapo” (Kramer, 1999). Others, including town officials, felt differently. Recognizing the intense

need for housing within the ultra-Orthodox enclave, officials counseled that the town needed to

continue to accommodate additional density to “provide housing that in effect would meet those

[religious Jewish] needs but still maintain some semblance of safety” (Boylan, 1997).

In 2000, the town launched a master plan update, hoping to strike a balance between the continual

need for affordable housing, especially among ultra-Orthodox Jews, and the fears of many non-

Orthodox residents that density threatened their “quiet way of life” (Craddock, 2001). The town

4 Local Law No 7–1986

5 Local Law Amending Local Law 5–1985

Cityscape 229

Huennekens

was spurred to act, in part, by the passage of the federal Religious Land Uses and Institutionalized

Persons Act (RLUIPA) that same year. That law destabilized the balance of power between

municipal officials and ultra-Orthodox developers by granting religious organizations powerful

new tools to challenge local zoning.6

The consultants hired by the town to create the master plan were initially critical of the ad hoc

growth permitted in the R-15C zone. The first draft of the town’s new comprehensive plan,

released in 2002 and updated in 2003, called for the conversion district to be replaced by official

multifamily zones, with additional density tempered by conservation and open space acquisition in

more rural parts of town (Brophy, 2002). However, the final version of the plan, adopted in 2004,

took a different tack. It retained the R-15C district and expanded its borders (Frederick P. Clark

Associates, 2004).

The plan also recommended introducing accessory units to parts of Ramapo. In the R-15C district,

one accessory unit was allowed per parcel in certain areas. Given the required setbacks, this unit

was usually attached to the primary dwelling. This revision meant that a three-unit home in

the R-15C district could now become a four-unit property.7 The town board also created a new

zoning district called R-15A. This new district retained the original R-15 zoning in terms of bulk

but allowed one accessory unit per parcel. Initially, all the R-15 zones in the town were meant to

turn into R-15A. However, following the controversy, R-15A was applied only to two sections of

Monsey. This compromise created the three types of R-15 zoning seen in central Monsey today.8

One portion retains the original R-15 zoning, which permits one-family homes on 10,000-square-

foot lots, semi-attached one-family homes on 15,000-square-foot lots, and two-family homes on

lots more than 20,000 square feet. Another portion is designated R-15A, which differs from the

R-15 rules by allowing one accessory unit per parcel. The final portion is designated R-15C, which

allows more intensive multiunit development (exhibit 1).

6 In direct response to RLUIPA, the town also created four separate “adult educational zones” to accommodate kollels, or Jewish higher learning institutions, with attached multifamily housing dorms (Local Law 9–2004).

7 Local Law 10–2004

8 The comprehensive update also introduced new Multiple Residential (MR) zoning districts that allowed for larger, more conventional multifamily dwellings. A few larger parcels in the Monsey area were eventually rezoned to this new MR zoning, but this rezoning was to facilitate more conventional new multifamily construction, not the conversion of existing, built-out neighborhoods. This article, therefore, does not discuss MR zoning.

230 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

Exhibit 1

Map of Central Monsey

Note: Map shows the different zoning districts in the central Monsey area.

Source: Joseph Weil Huennekens, “Town of Ramapo Zoning Map.” Rockland County GIS Portal.

Recent History: Continued Modifications to the R-15C

Although Ramapo’s passage of the accessory unit law allowed four-unit buildings in the R-15C

area, central Monsey’s development did not take off until other rules were changed. First, in 2007,

accessory unit regulations were tweaked to allow one accessory unit per primary unit (rather than

parcel) within townhouse-style buildings in the R-15C zone. A parcel developed in such a style

Cityscape 231

Huennekens

could now have six units: three primary units and three accessories.9 The maximum allowable

size of an accessory unit was also increased at this time. Then, in 2012, the town board voted to

authorize separate ownership of accessory units and uncap the number of bedrooms allowed in an

accessory unit.10 This modification meant accessory units could now be included as condominium

offers, with the units subject to resale restrictions meant to ensure affordability.

The 2007 and 2012 accessory unit modifications set off a boom in new construction in the R-15C

zone. Developers increasingly purchased existing properties for demolition, constructing larger

multifamily buildings in their stead. Parcels were also increasingly subdivided to allow for semi-

attached multifamily condominium buildings on double lots, each with four or six units (exhibit

2). Most of the new buildings constructed in the area were wood-frame or “stick-built.” Developers

nearly always sought variances for new construction rather than adhering to the zoning envelope.

This deviation was done to construct as close to the maximum envelope allowed by New York State

building and fire code as possible. For example, builders might seek to exceed the maximum 35-

foot height allowed (at that point) by the town zoning code to get closer to the 40-foot maximum

height allowed in the state building code for non-fireproof stick-built construction.

Exhibit 2

Photographs of R-15C Development (1 of 2)

9 Local Law 1–2007

10 Local Law 1–2012

232 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

Exhibit 2

Photographs of R-15C Development (2 of 2)

Note: Multifamily homes in the R-15C district.

Source: Joseph Weil Huennekens

The large number of new units constructed following the 2004, 2007, and 2012 zoning reforms

fostered new types of land use controversy, which were heightened by anxieties about the town’s

demographic transition. The 2010s was a time of significant political upheaval in Ramapo, with

controversy swirling around the East Ramapo School District in particular11 (Bandler, Lieberman,

and Liebson, 2017). The town government was also buffeted by a series of scandals during this

time. In 2016, a town official (and former Zoning Board of Appeals member) was arrested, and

the state temporarily took control of the town’s buildings department (Lieberman, 2016). The

dysfunction continued in April of that year, with the arrest of then town supervisor Christopher

St. Lawrence on suspicion of corruption (Weiser and Williams Walsh, 2016). This situation was

followed, in 2017, by the arrest of the town’s former building inspector on allegations of fraud

related to the processing of building permits (Lieberman, 2017a).

11 Ramapo is divided between two school districts, the Suffern Central School District and the East Ramapo Central School District. As the town grew increasingly ultra-Orthodox, public school funding in the East Ramapo district, which had grown to serve an almost exclusively Black and Brown public school population, was deprioritized in favor of services for yeshivas, leading to the imposition of a state fiscal monitor (Clark, 2014). More recent litigation alleged that the East Ramapo school board’s electoral system was discriminatory toward the public school population because at-large seats guaranteed the board was dominated by the private-school (yeshiva) parent population (Feldman, 2020).

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In recent years, under the leadership of a new town supervisor, Ramapo’s politics have stabilized.

New construction has continued apace, and additional tweaks have been made to the R-15C

zoning rules. In 2018, resale restrictions were removed from accessory units, allowing them to be

sold on the open market.12 This change came about because resale restrictions, which limited value

appreciation, were allegedly hurting the ability of property owners to secure mortgages. In 2019,

the maximum size of accessory units was increased again.13 Then, in 2020, the town altered the

R-15C zoning code to allow developers to merge individual parcels into larger lots and construct

more standard multifamily buildings.14 So far, only a few projects have used this new “large lot

overlay.” However, it represents a remarkable culmination of Ramapo’s zoning story—an example of

how far the town has traveled from its past as an anti-growth pioneer to its contemporary status as

one of the most permissive municipal land use systems in the New York City metropolitan area.

Data and Methods

Mixed Methods Approach

This article combines qualitative and quantitative data to take a comprehensive look at Ramapo’s

40-year history of zoning reform. Quantitative data are the core of the third section of the article,

which looks at the impact that zoning policy changes had on housing production and land use.

This quantitative dataset was pieced together through a variety of municipal and private sector

sources, as detailed in the paragraphs below. Qualitative data are the core of the fourth section

of the article, which looks at the implications of zoning change on the town’s built environment.

These qualitative data include archival material from local newspapers; archival information from

state, county, and municipal plans; information gleaned from semi-structured interviews conducted

with local stakeholders; and informal knowledge gleaned through participant observation and

casual conversation.

Parcel Dataset

This article analyzes a set of different local policies that were applied to a disjointed and

fragmented area at different points in time. Individual parcels are the only unit of analysis that

can be manipulated to accurately represent the shifting boundaries of the town’s zoning districts.

However, these types of parcel-level data were not publicly accessible in any comprehensive

form that spanned the nearly 40-year history of zoning reform. Instead, a parcel level dataset was

manually constructed from a variety of sources, organized around four cross-sectional periods:

1986, 2006, 2012, and 2021. Land use data from 2006 and 2012 came from land use shapefiles

available through Rockland County’s open data portal. These shapefiles classify each parcel by the

land use that existed in the year in question. Land use data from 2021 came from the private sector

data provider PropertyShark. Parcel-level data in PropertyShark were downloaded for each address

within the central Monsey area (which included land use classifications) and then matched with

the relevant parcels using the Town of Ramapo’s 2019 parcels shapefile.

12 Local Law 7–2018

13 Local Law 4–2019

14 Local Law 5–2020

234 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

The 2006, 2012, and 2021 datasets included full land use classifications. However, no such data

existed for 1986. Instead, the analysis imputed land use data for 1986 using a 1986 buildings

shapefile from Rockland County. These data were cross-checked with 1986 assessment rolls

available on microfilm at the Rockland County archive and assigned parcels that were listed in

1986 as non-homestead, or owned by a congregation or nonprofit, as having an institutional

classification. Parcels with no buildings on them were assigned as vacant. All other parcels were

assumed to be residential, given the zoning district. However, after this research, the question of

whether a building was one- or two-family remained (because the initial residential zoning for

the area allowed for two-family homes on oversized lots). The question was addressed by flagging

properties that had a building on them circa 1986 and were more than 20,000 square feet in size

(the minimum lot size for a two-family dwelling in the 1986 zoning). If these properties were

classified as one-family in the 2006 land use shapefile, the analysis assumed that they were also

one-family in 1986. However, if the oversized property was listed as two- or three-family in 2006,

they were cross-checked with property deeds (available for some parcels on PropertyShark). Unless

a deed was found that indicated single-family use after 1986, it was assumed these 34 properties

were two-family in 1986.

The unit measurements required more imputation and assumptions than the land use

classifications because the multifamily land use classification contains properties with different

numbers of units. The land use data for the four time periods were used to calculate units. This

method was relatively straightforward for most land use classes: a one-family parcel was assumed

to contain one unit, a two-family parcel was assumed to contain two units, and a three-family

property was assumed to contain three units. However, properties with four or more units are listed

only as belonging in a multifamily land use class. Therefore, a more complex set of assumptions

was implemented to determine approximate unit counts for multifamily properties. First, a manual

windshield survey (via Google Maps) of the 436 properties listed as multifamily in 2021 was

conducted. Many multifamily buildings in Ramapo include large address signs listing the number

of (legal) units, and others include formal multiunit mailboxes (exhibit 3). In such cases, a manual

calculation for the number of units was inputted.

However, many other properties did not show clear indications of unit counts and were

more ambiguous. PropertyShark data, where available, were used to input unit counts for

these properties. However, for a couple of hundred properties (listed as multifamily), manual

estimates and PropertyShark unit counts were unavailable (exhibit 4). Some of these properties

had a multifamily parcel count higher than three, because the property had been “condoed” or

subdivided into condo parcel fractions. For these properties, the number of units was inputted

as the number of these parcel fragments. However, this method still left close to 250 properties

that did not have a manual count, a Property Shark count, or a parcel number equivalent to a

multifamily condo (exhibit 4). These properties were assumed to have four units, a conservative

assumption of the lowest possible number of units that would qualify the property as multifamily.

Cityscape 235

Huennekens

Exhibit 3

Example of Property with Clear Unit Numbers

Note: Image shows how some properties contain clear address numbers, facilitating a manual count of units.

Source: Google Maps Streetview

Exhibit 4

Unit Count Imputations for Multifamily Properties

Description

2006

2012

2021

Parent parcels with manual or Property Shark count

0

0

125

Parent parcels with multifam parcels > 3

15

145

101

Parent parcels with multifam parcels < 3, imputed as 4

23

81

248

Total

38

226

474

Note: Unit counts are assigned to parcels and assumptions are built into the count.

Sources: Manual windshield survey via Google Streetview; PropertyShark

All land use and unit data were aggregated at the parent parcel level. Normalizing the four

datasets at the parent parcel level allowed a comparison of change over time at a standardized

unit. This process was important to capture parcel-level change, because subdivisions and split

lots are a common feature of development in the area. For example, a single one-family parcel in

1986 might, by 2012, include one two-family parcel and one three-family parcel, with five units

overall. Lastly, two outlier areas were removed before performing the final data analysis. The first

236 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

was an area near the village of Kaser that was rezoned to R-15C in 2001 and redeveloped as a

large multifamily complex. The second was a vacant parcel in the R-15C exclusion area (the area

rezoned to R-15C in 2004) that was redeveloped as a single 132-unit complex. These parcels were

removed to establish more accurate counts of the type of land use change that could be expected

on a more typical 10,000- to 20,000-square-foot suburban lot.

Difference-in-Differences Models

In the following section of the article, quantitative data are analyzed in both a descriptive and

analytic manner. Descriptive statistics are used to demonstrate the magnitude of change following

zoning reform. Difference-in-differences regression is used to tell a more causal story: that is, to

deduce what land use and unit change could be traced specifically to the impact of rezoning by

measuring change over and above what would be expected otherwise.

The limited amount of public data meant that only four cross-sectional measures were available

for use in the difference-in-differences models: 1986, 2006, 2012, and 2021. This data limitation

presented a problem because the most important zoning reforms proceeded during a period from

2004 to 2012. In 2004, the areas were rezoned for greater density; in 2007, an accessory law was

expanded to grant even more units in the R-15C district; and in 2012, accessories were allowed to

be sold on the open market. Due to this ambiguity and factoring in the lag time of construction,

for purposes of the difference-in-differences model, 1986 and 2006 were labeled as pre-treatment

years, and 2012 and 2021 were labeled as post-treatment years. Assigning 2012 as a “before year,”

although theoretically justified given that it was the culminating year of the period of zoning

reform, would have produced much higher estimates in terms of units. However, for this article,

2012 was assigned as a post-treatment year to create a more conservative estimate, with one within-

the-reform-period year (2006) assigned as a pre-treatment year and one within-the-reform-period

year (2012) assigned as a post-treatment year.

The difference-in-differences models contained estimates for the impact of two treatments first

administered in 2004. The first treatment was multifamily zoning (which was first applied to the

R-15C exclusion area in 2004, with the “dose” increased by the 2007 and 2012 accessory reforms).

The second treatment was accessory zoning (first given to the R-15A area in 2004, with the “dose”

increased by the 2012 reforms). Parcels in the core R-15C area (the initial zone, created in 1986)

were removed from the sample to measure only areas affected by the 2004 changes. This removal

left 4,343 observations, representing parent parcels in the three areas (R-15C exclusion, R-15A,

and R-15) during the four time periods. There were two models, the first with a dependent value

of one-family parcels and the second with a dependent variable of units. The models measured

both treatments (R-15C and R-15A) together according to the following specification:

DV = α + β Post + β R 15 c + β R 15 a + β treatment 1 (Post * R 15 c) + β treatment 2 (Post * R 15 a) + ε

Difference-in-differences models work on the assumption that the treatment and control groups

display parallel trends prior to treatment. The first indication of parallel trend comes from census

data, which show that the census tracts that were the closest proxy for the R-15C, R-15A, and

R-15 zones moved in close tandem prior to 1990 (the first census year after the creation of the core

Cityscape 237

Huennekens

R-15C district in 1986) (exhibit 5). This finding gave confidence that, at least theoretically, these

areas of Monsey were historically similar prior to the commencement of zoning reform.

Exhibit 5

Census Tract Parallel Trends

Notes: Census unit counts normalized to 2010 census tract boundaries. 2010 tracts 121.02 and 121.05 correspond roughly with R-15C zoning district. 2010

tracts 121.03 and 121.06 correspond roughly with R-15 and R-15A areas.

Sources: 1970, 1980, 1990, 2000, and 2010 Census; American Community Survey 5-year data, 2015–19

However, the difference-in-differences models do not measure the impact of the 1986 change.

Instead, they measure the impact of the 2004 changes that rezoned small portions of the R-15

zone to R-15C (the area termed the R-15C exclusion zone) and rezoned another portion of R-15

to the new R-15A designation. The R-15C exclusion area is a small and geographically fragmented

area that does not overlap clearly with any census geography. This renders an additional parallel

trend test necessary. Luckily, the R-15C exclusion zone—along with the R-15 zone and the

remaining portion of Monsey that stayed R-15—all display parallel trends prior to 2006 within

the parcel dataset as well (exhibits 6 and 7). As expected for the parallel trend assumption to

hold, the R-15C core area diverged from the other areas immediately after 1986. The R-15C

exclusion area stays broadly congruent with the R-15A and R-15 until the 2012 reading. Together,

this information gives meaningful assurance that the areas of central Monsey all behaved similarly

prior to zoning treatment, whether that treatment was administered in 1986 or in the period

between 2004 and 2012.

238 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

Exhibit 6

Parent Parcel Parallel Trends, By Number of Parcels

Note: Number of parcels per zoning district over time, showing the four cross-sectional years of the difference-in-differences models.

Sources: Rockland County GIS portal; PropertyShark

Exhibit 7

Parent Parcel Parallel Trends, By Number of Units

Note: Number of units per zoning district over time, showing the four cross-sectional years of the difference-in-differences models.

Sources: Rockland County GIS portal; PropertyShark; manual windshield survey via Google Streetview

Cityscape 239

Huennekens

Land Use Change and Housing Production

The R-15C zoning district unleashed a wave of development that transformed Ramapo. Between

1970 and 2019, the two census tracts15 that most closely overlap with the R-15C area went from

having a population of 2,900 to more than 16,000 people—a stunning 456-percent increase (U.S.

Census Bureau, 2022). Those two tracts had a population density of more than 22,000 per square

mile, according to the 2015–19 5-year American Community Survey estimate, far closer to New

York City (which, as of 2020, had a population density of about 29,000 per square mile) than to

the rest of suburban Rockland (which as of 2020 had a population density of 1,950 per square

mile). Of course, part of this high population density figure is related to the exceptionally large

size of most ultra-Orthodox families. However, even the number of households per square mile

resembles an American city more than similar middle-ring suburbs.16

Monsey’s tremendous growth is clear in census data. However, such data only describe the changes

that occurred at the community level; they do not reveal where precisely these changes occurred nor

what caused them. Accordingly, this section of the article augments census data with parcel-level

land use data from 1986, 2006, 2012, and 2021 to get a more precise sense of land use change.

Using these parcel-level data, this section addresses two issues regarding residential densification.

First, this section explores a set of descriptive questions about how land use change progressed,

including which parcels turned from single-family to multifamily housing and what number of

parcels turned. Second, the section explores the role of policy change in spurring densification.

Parcels and Subdivisions

Although Central Monsey was already largely built by the 1970s, the number of parcels in the R-15C

area increased dramatically following rezoning. In 1986, the initial rezoned area (hereafter, core

R-15C) contained approximately 620 parcels. By 2006, the number of parcels in the core R-15C

area had grown to approximately 760, and by 2021 there were approximately 1,090 (exhibit 8).

This increase resulted from the subdivision of existing lots, especially the division of the older parent

parcel (that is, the original platted parcel as it existed in 1986) into fractions as part of condominium

offerings. Between 1986 and 2021, the number of single-family parcels in the core R-15C zone

declined from 530 to only about 150, whereas the number of multifamily parcels increased from 0 to

more than 710 (exhibit 9). The portion of central Monsey that was excluded from the initial rezoning

in 1986—but added into R-15C after the 2004 master plan update (hereafter, R-15C exclusion

zone)—showed the same trend, just on a delay. The number of single-family parcels was relatively

stable until 2006, after which single-family parcels declined, but multifamily ones increased.

15 Data use the 2010 census tract boundaries. For the 2020 census, central Monsey’s tract boundaries were redrawn and about four tracts now correspond with the area.

16 These tracts have slightly more households per square mile than Baltimore, Maryland, and slightly fewer than

Pittsburgh, Pennsylvania.

240 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

Exhibit 8

Parcel Change Over Time

Parcels

1986

2006

2021

R-15C (core)

620

760

1,093

R-15C (exclusion)

391

408

732

R-15A

329

330

327

R-15

365

381

394

Note: Number of parcels per zoning district over time, showing increase in parcel subdivisions within rezoned areas.

Sources: Rockland County GIS portal; PropertyShark

Exhibit 9

Number of Parcels by Land Use Type

Description

Year

Vacant

One Family Two Family Three Family Multifamily Institutional

1986

68

530

16

0

0

7

R-15C 2006

32

346

147

91

98

23

(core)

2021

61

153

108

58

713

32

1986

34

354

3

0

0

2

R-15C 2006

17

316

46

5

4

4

(exclusion)

2021

37

133

36

24

502

10

1986

31

312

13

0

0

8

R-15A

2006

23

302

33

3

4

9

2021

31

243

55

9

57

16

1986

30

299

0

0

0

0

R-15

2006

3

319

2

0

0

4

2021

2

309

9

2

5

3

Note: Land use classifications per parcel per zoning district over time, showing decrease in single-family parcels over time.

Sources: Rockland County GIS portal; PropertyShark

Land Use Change

The proliferation of multifamily parcels in the R-15C zone indicates how development proceeded

over time. However, because of extensive subdivision (and condo parcelization), parcel quantities

might inflate the magnitude of change in an experiential sense. For example, if a block initially had

10 single-family parcels, 3 of which converted into six-unit condominiums, the overall change

in parcel composition would be dramatic: the block would go from having 10 parcels, all single-

family, to having 25 parcels, 18 of which were multifamily. However, at the scale of the street, that

change might not feel as dramatic. After all, only 3 of the 10 original lots would be multifamily, and

most of the street would look the same.

Therefore, to measure on-the-ground change, looking at land use change at the parent parcel level

is useful—that is, how the individual plots of land that existed in 1986 changed over time. By this

measure, the change from single-family to multifamily in the R-15C district is still quite dramatic.

Only 27 percent of parcels in the core R-15C zone that had been single-family in 1986 remained

wholly17 single-family by 2021, and about 38 percent of parcels that had been single-family in

17 “Wholly” includes instances in which a single-family parcel would be subdivided. One new lot would remain single-family, and a two-, three-, or multifamily dwelling would be constructed on the other new lot.

Cityscape 241

Huennekens

1986 had at least one multifamily dwelling by 2021 (exhibit 10). As might be expected, vacant

parcels densified at an even faster rate. An estimated 49 percent of parcels that were vacant in 1986

in the core R-15C zone had a multifamily dwelling on them by 2021. By contrast, only 6 percent

of vacant parcels had a one-family home on them, indicating that, after the zoning change, the

incentive to build new single-family homes practically disappeared.

Exhibit 10

Land Use Change for Parent Parcels, 1986–2021

% One

% Two

% Three

%

%

% Vacant

Description

Description

Family

Family

Family MultiFamily Institutional

in 2021

in 2021

in 2021

in 2021

in 2021

in 2021

R-15C (core)

0.08

0.27

0.15

0.08

0.38

0.05

R-15C (exclusion) One Family

0.07

0.33

0.09

0.04

0.44

0.02

R-15A

in 1986

0.06

0.70

0.12

0.02

0.06

0.03

R-15

0.01

0.94

0.03

0.00

0.01

0.01

R-15C (core)

0.06

0.06

0.24

0.11

0.49

0.04

R-15C (exclusion)

Vacant

0.21

0.26

0.06

0.06

0.38

0.03

R-15A

in 1986

0.21

0.48

0.09

0.03

0.09

0.09

R-15

0.03

0.90

0.00

0.00

0.03

0.03

Notes: Land use classifications per parent parcel between 1986 and 2021 by zoning districts. Parcels that were classified as either vacant or one-family in 1986

were classified in 2021. The shift in classifications for the two R-15C areas are particularly notable.

Sources: Rockland County GIS portal; PropertyShark

The dramatic conversion of one-family parcels into multiunit properties seen in the R-15C zone

was not replicated in the R-15A zone (the area of central Monsey rezoned to allow one accessory

unit per parcel). More than 70 percent of one-family properties in the R-15A area stayed that

way after the area was rezoned, and only 12 percent of homes that were one-family in 1986 were

two-family by 2021. Even vacant parcels in the R-15A district were more likely to become one-

family dwellings than any other use—showing that, within the accessory zoning area, one-family

construction continued even after zoning was altered.

Unit Change

From a land use perspective, the character of the R-15C district changed dramatically following

zoning reform, although the R-15A accessory zone changed much less. But what of units?

Measuring unit change is not as easy as measuring land use change because publicly accessible

land use data in Ramapo does not include unit counts for multifamily properties. This study’s

estimates were created using a manual windshield survey, private sector data counts, and a set of

conservative assumptions for properties where data were missing. These caveats aside, the picture

that emerges is one of robust unit production in the R-15C district—and much more sluggish

production in comparison areas.

In the core R-15C zone, housing units increased from an estimated 560 in 1986 (pre-rezoning) to

an estimated 2,250 in 2021—a growth rate of about 300 percent in 35 years (exhibit 11). Growth

was nearly as high in the R-15C exclusion area after parts of Monsey were rezoned to R-15C in

2004. Although unit counts were relatively stable between 1986 and 2006 in that area prior to

242 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

rezoning, housing production exploded once the area was rezoned to R-15C—going from an

estimated 440 units in 2006 to more than 1,350 by 2021. By contrast, fewer units were created

in the R-15A zone (rezoned to allow for one accessory unit per parcel) and the R-15 area (which

was never rezoned). Estimates show that about 300 new units were created in the R-15A area

since 1986, or a growth rate of 89 percent during those 35 years (exhibit 9). The R-15 zone grew

even less. Estimates show that fewer than 80 new units were created in the area between 1986 and

2021, reflecting a 26-percent growth rate during those same 35 years.

Exhibit 11

Change in Units Over Time

Change

% Change

Description

1986

2006

2021

1986 – 2021

1986 – 2021

R-15C (core)

562

1,373

2,248

1,686

3.00

R-15C (exclusion)

360

439

1,358

998

2.77

R-15A

337

391

636

299

0.89

R-15

299

323

377

78

26.09

Note: Change in units per zoning district over time.

Sources: Rockland County GIS portal; PropertyShark; manual windshield survey via Google Streetview

Difference-in-Differences

Descriptive statistics about parcel subdivisions, land use, and housing units give a sense of the

variable rates of neighborhood change between districts; however, they do not provide a causal

explanation for the change. Urban change is constant, and not all the densification in a certain

district stems from zoning reforms. Difference-in-differences regression provides one way to get

a more precise measure of change that isolates the impact of the policy. In the method, change is

measured over and above what might be expected to have occurred otherwise, given preexisting

trends. This assessment is done by comparing a treated area that received a change with an

untreated comparison area (akin to a control) that did not receive treatment. Both areas are

compared before and after the point of treatment, with the untreated area providing a proxy for

how growth would have proceeded in the treated area absent the treatment.

The data for the difference-in-differences models make up a cross-sectional dataset of parent

parcels with four measurement years: 1986, 2006, 2012, and 2021. The years 1986 and 2006 are

defined as pre-treatment years, and 2012 and 2021 are defined as post-treatment years. Using four

periods gives a more conservative estimation than simply comparing before and after (exhibit 12).

Two different regressions were run with two different dependent variables. Each model in turn

measures the impact of two treatments. The first treatment is the shift from existing R-15 zoning

(single-family zoning with two-family homes allowed on large lots) to R-15C zoning (multifamily

zoning with four to six units allowed on a single lot); the second treatment is the shift from R-15

zoning to R-15A zoning (the same as R-15, but with an additional accessory unit allowed per lot).

The R-15C exclusion area is selected because it is the portion of R-15C that jumped directly from

single-family zoning to multifamily zoning when the area was rezoned in 2004. Parcels in the core

R-15C zone are not included in this model, so the model only measures the impact of changes

that were undertaken at the same time. In sum, the two models measure the effects of (1) R-15C

Cityscape 243

Huennekens

multifamily zoning on parcel subdivisions, (2) R-15A accessory zoning on parcel subdivisions, (3)

R-15C multifamily zoning on units, and (4) R-15A accessory zoning on units.

In the first model, the dependent variable is the number of one-family parcels. This model thus

measures the impact of the two zoning changes on the number of one-family parcels, with parent

parcels as the unit of analysis. The coefficient for treatment 1 (multifamily R-15C zoning area

after rezoning) in the one-family parcels model is -0.371 with a 95 percent confidence interval

(CI) of [-0.431, -0.312] (exhibit 13). This result indicates that multifamily zoning change induced

about 0.35 fewer one-family parcels per parent parcel than expected, given preexisting trends.

The coefficient for treatment 2 (accessory R-15A zoning area after rezoning) is -0.112, with a 95

percent CI [-0.172, -0.051]. This result means accessory zoning led to about 0.12 fewer single-

family parcels per parent parcel than expected.

The second difference-in-differences model measures the impact of the same two zoning changes

on the number of units. Treatment 1 is again the effect of shifting from existing R-15 zoning to

R-15C zoning (multifamily zoning with at least four units allowed on a single lot), and treatment

2 is again the effect of shifting from R-15 zoning to R-15A zoning (one accessory unit allowed per

lot). The coefficient for treatment 1 (multifamily R-15C zoning area after rezoning) in this model

is 1.445, with a 95 percent CI [1.262, 1.629] (exhibit 13). This result indicates that multifamily

zoning change induced about 1.5 new units per parent parcel on top of the unit growth that would

otherwise have been expected given preexisting trends. The coefficient for treatment 2 (accessory

R-15A zoning area after rezoning) is 0.299, with a 95 percent CI [0.112, 0.485] (exhibit 11),

which means that accessory zoning induced 0.3 more units per parent parcel on top of the unit

growth that would otherwise have been expected if no zoning changes had been made. The far

more modest coefficients of treatment 2 compared with treatment 1 highlight the same finding as

the descriptive data: accessory laws alone induced more modest unit production, whereas Ramapo’s

multifamily zoning laws spurred more dramatic unit growth.

Exhibit 12

Estimates of Parcel and Unit Change

Difference-in-

Description

Naive Estimate

Before/After Estimate

Differences Estimate

Multifamily Zoning

One family parcels

– 0.57

– 0.53

– 0.37

Units

2.62

2.38

1.45

Accessory Zoning

One family parcels

– 0.19

– 0.22

– 0.11

Units

0.82

0.59

0.30

Notes: Naïve estimate represents the change in means between 1986 and 2021 for the treatment area. The before/after estimate is the difference between the change in means between 1986 and 2021 for the treatment area minus the change in means in the control area for the same period. The difference-in-differences estimate is the change in means in the treatment area over the change in means in the control area. Four cross-sectional measures (1986, 2006, 2012, and 2021) were used.

Sources: Rockland County GIS portal; PropertyShark; manual windshield survey via Google Streetview

244 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

Exhibit 13

Difference-in-Differences Estimates

One Family Parcels

Coefficient Std. Error

t

p

95% conf. interval

Post zoning reform

0.012

0.023

0.540

0.589

– 0.032

0.056

R-15C exclusion zone

0.082

0.022

– 3.82

0.000

– 0.125

– 0.040

(multifamily zoning)

R-15A zone (accessory zoning)

– 0.099

0.022

– 4.53

0.000

– 0.142

– 0.056

Treatment1 (R-15C*post)

– 0.371

0.031

– 12.17

0.000

– 0.431

– 0.312

Treatment2 (R-15A*post)

– 0.112

0.031

– 3.6

0.000

– 0.172

– 0.051

N

4343

r squared

0.128

adjusted r squared

0.127

Units

Coefficient Std. Error

t

p

95% conf. interval

Post zoning reform

0.128

0.069

1.850

0.064

– 0.007

0.263

R-15C exclusion zone

0.076

0.066

1.160

0.248

– 0.053

0.206

(multifamily zoning)

R-15A zone (accessory zoning)

0.055

0.067

0.810

0.415

– 0.077

0.186

Multifamily treated

1.445

0.094

15.450

0.000

1.262

1.629

(R-15C*post)

Accessory treated

0.299

0.095

3.140

0.002

0.112

0.485

(R-15A*post)

N

4343

r squared

0.186

adjusted r squared

0.185

std. error = standard error. conf. interval = confidence interval.

Sources: Rockland County GIS portal; PropertyShark; manual windshield survey via Google Streetview

Policy Challenges

The creation of the R-15C district spurred a major increase in housing production in the Monsey

area, but what were the implications of this rapid growth on the town’s social and physical

environment? The potential for negative consequences of development is known to anyone who

has attended a public meeting: some neighbors fear increased traffic, others complain about the

loss of environmental features, and others lament that new development is just plain ugly. Missing

from the standard public meeting is the fact that housing production also carries with it positive

externalities, the most basic of which is an (expected) mitigation of housing costs as new supply

comes online. This section of the article analyzes these externalities using qualitative evidence

gleaned from archival sources and semi-structured interviews. The analysis starts by investigating

some of the challenges that accompanied the development boom in central Monsey, especially

regarding public infrastructure and the aesthetics of the built environment, then discusses the

impact of the zoning change on affordable housing discourse in the area.

Cityscape 245

Huennekens

Infrastructure

As central Monsey densified, concern often centered on the capacity of public infrastructure to

accommodate growth. In interviews, critics of development questioned whether the town’s public

sewers, water supply, or street grid—all initially built on assumptions of more limited suburban-

style development—could handle increased usage. More sympathetic observers countered

that infrastructure in Ramapo has been strained for decades—yet the town had made enough

improvements to continue to function and attract new residents. Tracing the cause of infrastructure

strain is no easy task. Not all issues can be traced back to development, let alone the specific

development in the R-15C district. Nonetheless, the historical record does provide some indications

about the relationship between infrastructure strain and development. Reviewing that record reveals

a mixed story: investment has not kept up with population growth in certain realms, such as sewage

and water capacity, whereas other systems, such as the road network, appear more resilient.

At the time of the 2004 master plan update, the capacity of Ramapo’s sewage infrastructure to

accommodate new development was already in question (Frederick P. Clark and Associates,

2004). Unfortunately, some of these fears came to pass. New Jersey residents living downstream

of Ramapo filed a multimillion-dollar lawsuit against the sewer district that serves the town,

successfully proving in court that the local sewer plant had overflowed into the Upper Saddle

River multiple times between 2006 and 2010. These discharges were tied to capacity issues during

storm events and on regular days. Some observers (including the local newspaper) connected the

problem with overdevelopment in areas such as the R-15C district (Incalcaterra, 2014). Despite

subsequent investments in capacity, emergency sewage discharge occurred again in 2022, which

environmental activists also connected with “extensive development in the area” (Castelluccio,

2022). Whether the problems were explicitly tied to the specific development in the R-15C zone

is unclear. However, the sewage overflows highlight the need for additional investment to keep up

with usage.

The town’s water pressure is a related capacity issue. Concern about water pressure in the town

took on new urgency following a fatal fire in 2021 at a nursing home in the village of Spring Valley,

New York. One firefighter lost his life battling the blaze (firefighters are volunteers in Ramapo—

another suburban holdover), as did one nursing home resident (Lieberman, 2022). This fatal fire

raised concerns, first and foremost, about lax building inspections in Ramapo18 (Lieberman and

Kramer, 2021). However, it also highlighted the water system in the town. The hydrant nearest

the complex did not have adequate water pressure to fight the blaze, and firefighters were forced

to stretch hoses from nearly two-thirds of a mile away at the closest functioning pump (Kramer

and Lieberman, 2021). Following the tragedy, state officials launched an inquiry into Ramapo’s

water system. As with sewers, many observers drew a connection between the town’s multifamily

development boom and water issues. One state assembly member argued that the inquiry needed

to focus on “how our water infrastructure is being taxed by development decisions” (Kramer and

Lieberman, 2021).

18 The fire preceded another major fire at an illegally constructed grocery store in Monsey. Firefighting is also made difficult in Ramapo by a preponderance of informal units. Multiple interview participants accused the town of turning a blind eye to the potential for additional illegal units during the permitting process, such as by approving plans showing additional doors or basement kitchens that might indicate future subdivision into more units than permitted.

246 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

Ramapo’s street grid is a third piece of infrastructure described as being over capacity. In qualitative

interviews, even relatively pro-development participants complained about traffic. However,

unlike sewage and water capacity, there is little evidence tying increased density to traffic in central

Monsey. At the time of the 2004 master plan update, traffic on the two arterial roads that flank the

R-15C zone, Route 59 and Route 306, was already a major issue. New York State’s annual average

daily traffic (NYS AADT) counts from the NYS Department of Transportation averaged about

20,000 and 18,000 on the two stretches of Route 59 that were closest to the R-15C district and

about 11,000 and 14,000 on the stretches of Route 306 closest to Monsey. However, these counts

represented something of a peak. Since the mid-2000s, traffic on both roads has declined: traffic

counts on Route 59 in 2021 measured around 15,000. This decline is surprising considering the

thousands of new housing units constructed in the vicinity of the two roads. However, the decline

may have arisen because the new inhabitants of multifamily units are far less likely to own cars

than the suburban norm. In 2020, 25 percent of respondents in Monsey did not have access to

a vehicle, compared with only 6 percent in Rockland County as a whole (U.S. Census Bureau,

2022). Although Route 59 and Route 306 are still considered congested (Spack, 2017), dense

development has not exacerbated traffic in the way that might be expected, given the relative

dearth of public transit in the Monsey area.

Aesthetics

The physical appearance of the built environment is another area of concern in Ramapo—

mentioned frequently in qualitative interviews and in the archival record. Aesthetic concerns may

seem trivial compared with the pressing issue of water pressure, sewer capacity, or even traffic.

However, struggles about design are a key component of Ramapo’s land use history—appearing

almost immediately on the creation of the R-15C district and escalating as more and more of

central Monsey was rebuilt. (Snel, 1986). Ramapo’s zoning code includes requirements for

landscaping and screening in the R-15C district and grants the local government the right to make

aesthetic evaluations of new construction on the basis of compatibility during the discretionary

review (Town of Ramapo Zoning Code, 376-91). Despite these safeguards, aesthetic issues were

frequently cited in qualitative interviews as one of the downsides of densification.

Some of the aesthetic issues in central Monsey are built into the converted nature of the R-15C

district. Development within central Monsey has occurred on a lot-by-lot basis, so a single block

in the area may contain a mix of typologies. The cascading set of land use changes in the area has

also led to a few different rounds of building and rebuilding, with new-construction six-family

buildings sitting next to standard high-ranch single-family homes or converted (and expanded)

former single-family homes. Developers’ tendency to subdivide lots also means that homes are

typically graded at different levels, which can exacerbate the sense of height difference on a block

and, according to some observers, create runoff challenges.

Parking is another aesthetic issue in the district. Although the town of Ramapo progressively

loosened bulk and use restrictions in central Monsey, parking regulations have remained essentially

unchanged. One parking space is mandated per unit (both primary and accessory) within the

R-15C district, in addition to one space per “nontransient roomer or boarder” (Town of Ramapo

Zoning Code, Chapter 376). As a result, new multifamily construction requires a fairly large

Cityscape 247

Huennekens

number of spots. Because developers in Monsey tend to build right to the required side setback (or

seek a variance to build even closer to the property line), parking is generally provided in the front

of the building. This lack of space means that parking typically covers the entirety of the front yard,

with little space left for trees or landscaping.

Affordability and Fair Housing

The town of Ramapo, which crafted the R-15C district in part to create additional affordable

housing options for the rapidly growing population of central Monsey, assumed that new

multifamily development would create additional housing options and temper price escalation in a

high-demand environment—an assumption fully grounded in the policy literature and in the basic

economic model of supply and demand. However, the relative affordability of new construction

has subsequently been a major point of contention in Ramapo. In interviews, civic activists have

contended that the new housing supply in Monsey is not truly affordable—especially not the

condominiums constructed in the wake of the 2012 rule change allowing accessory parcels to be

sold as separate units. The complex demographic landscape of the town further complicates these

debates. Social justice-oriented civic groups, including the local chapter of the National Association

for the Advancement of Colored People (NAACP), argue that most new construction in the area is

built for, and exclusively marketed to, ultra-Orthodox buyers. They thus question whether housing

options (even if they are affordable) ultimately benefit the full spectrum of the town’s population.

Determining a causal relationship between housing supply and housing cost is outside the scope

of this article. Although affordability is one of the most hotly contested issues in Ramapo, even a

rudimentary assessment of housing affordability in Monsey is hampered by a dearth of reliable

pricing data. Census data on housing costs are self-reported, and private sector pricing data for

the area are partial, because many of the property sales reported in Monsey are conducted through

direct personal transactions.19 This brief discussion is not meant to resolve the question of how

best to perceive the affordability of these units, nor to disentangle the contributions of supply and

demand to their pricing. It is meant only to provide valuable context for understanding the type of

development underway and the unique context of housing supply in Ramapo.

The information that can be drawn from sales data and from anecdotal evidence from local real

estate advertisements supports both arguments made in Ramapo: (1) new units provide affordable

options that otherwise would not exist in such a high-demand environment, and (2) affordable

options are inaccessible to large portions of the greater Ramapo population. On the one hand,

the R-15C district successfully spurred the creation of hundreds of multifamily missing middle

options. Condos in multifamily buildings in the area generally sell for less than single-family

homes in the same zone, and the multifamily zoning changes also spurred the creation of many

more rental options than would otherwise exist. Moreover, in most years, sale prices (normalized

by the number of units in a sale) are lower in the R-15C district than in surrounding areas of

Monsey that do not allow for multifamily buildings, although the zones do appear to be growing

more similar over time (exhibits 14 and 15).

19 Rental prices are also opaque. Apartments marketed to ultra-Orthodox families are often advertised in different channels than for the standard market: by word of mouth, free circulars available at stores, or ultra-Orthodox online forums.

248 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

On the other hand, in recent years, housing costs in the Monsey area have far exceeded those

of Rockland County as a whole (exhibits 12 and 13). Large, brand-new condominiums with

five or six bedrooms in central Monsey can sell for close to $1 million—obviously far outside

even the most generous definition of affordability (Zillow, n.d.). Soaring demand from the ultra-

Orthodox community has also spilled into other areas, including the relatively low-income and

racially diverse village of Spring Valley east of Monsey. Developers have expanded condominium

construction there, leading to accusations of gentrification, racism, and fair housing violations (and

counteraccusations of antisemitism) (Bandler, Lieberman, and Liebson, 2017; Brum, 2017). For

example, in 2013, the local chapter of the NAACP filed a lawsuit against one new Spring Valley

development, alleging that units were exclusively marketed to ultra-Orthodox buyers (Lieberman,

2017b). This lawsuit was settled in the plaintiffs’ favor in 2017, highlighting the legitimate

frustration of some residents about just how affordable and accessible the new housing supply in

the area really is.

Exhibit 14

Average Sales Price, 3-Year Moving Average

$1,400,000

$1,200,000

$1,000,000

$800,000

$600,000

$400,000

$200,000

$-

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

R-15

R-15A

R-15C

Rockland

Notes: Average sale price per zoning district is average for sales over $20,000. Apartment buildings are excluded. Rockland County data are home values, not sale prices, and are included only for reference.

Sources: PropertyShark; Zillow Home Value Index

Cityscape 249

Huennekens

Exhibit 15

Average Sales Price, Per Unit Basis, 3-Year Moving Average

$900,000

$800,000

$700,000

$600,000

$500,000

$400,000

$300,000

$200,000

$100,000

$-

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

R-15

R-15A

R-15C

Notes: Average sale price per zoning district is average for sales over $20,000. Unit price is sale price divided by number of units in the property.

Source: PropertyShark

Learning from Ramapo

Ramapo provides a multidecade window into what can happen to the land use, housing market,

and neighborhood character of a suburban community that pursues zoning reform. Its extended

timeline holds a host of lessons for other communities, many of which are just starting to

contemplate the types of reform that Ramapo pioneered decades ago.

However, the ability to learn from Ramapo is hindered by some of its unique qualities. Ramapo

is an extreme case for at least two reasons (Flyvbjerg, 2006). The first is cultural. The built

transformation of central Monsey has been accompanied by the transformation of the wider town

of Ramapo into what might be best characterized as an ultra-Orthodox Jewish ethnoburb (Li, 2009).

This entanglement between demographic and physical change makes it difficult to parse which

outcomes in the case are specific to the unique social conditions of the community (that is, its

predominantly ultra-Orthodox character) and which outcomes stem from more universal forces

or conditions (such as a high-demand housing market). The second reason is political. The town

government of Ramapo is exceptionally pro-growth, and the electorate includes a formidable ultra-

Orthodox voting bloc laser-focused on increasing the housing supply for their community. This

250 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

detail impedes generalization between Ramapo and other places, because pro-growth advocacy

is still quite rare in other suburban locations, even with the emergence of the YIMBY (that is, Yes

in My Backyard) movement, and few local governments are subject to the type of pro-supply

pressures that Ramapo is under.

As a result, Ramapo is only directly analogous to a small set of communities: a handful of other

ultra-Orthodox suburbs and extreme cases of pro-growth politics and high population growth,

such as in oil boomtowns. However, more typical communities still have much to learn from

the Ramapo case. Ramapo can serve as a theoretical best case (or worst, depending on one’s

perspective) for how densification might proceed in a high-demand environment with a pro-

growth planning regime. Ramapo provides a rare window into the long-term effects of densification

policies under ideal pro-supply political conditions, akin to what is called a reasonable worst-case

development scenario in environmental planning. Ramapo can also serve as a falsification test for

certain theories about zoning reform—that is, as a test showing what is necessary for a suburban

place to transform into a denser environment. Even where suburbs reach urban levels of density,

they do so in a suburban way. Ramapo provides a window into what that might look like.

Proceeding in that light, what are the lessons of land use reform in Ramapo? First, Ramapo’s

accessory unit laws triggered only modest change, even in a high-demand and pro-growth

environment. Rather, the upzoning of existing neighborhoods to multifamily zones triggered

widespread change. Second, laws allowing for condominiums appeared important in providing an

attractive housing product to builders in Ramapo and may be important in other suburban settings

as well. Third, the Ramapo case shows that densification requires extra infrastructure investment

in a suburban environment. Fourth, in Ramapo’s exceptionally pro-growth (and suburban) context,

discretionary review and parking requirements did not necessarily hinder housing production.

To elaborate on the first point, the Ramapo case indicates that, even under an extremely pro-growth

planning system, ordinances that allow only small increases in the permitted number of units per

parcel provide only gradual unit growth over time. The creation of the R-15A district (the section

of Monsey that allows one accessory unit per parcel) and the first 20 years of the R-15C ordinance

(which allowed dwellings to be converted into up to three units) triggered only minor land use

changes. In contrast, reforms that eventually allowed four- and six-unit properties on a single lot

in the R-15C district (and 8 and 12 units on a double lot) triggered the production of thousands

of new units. This construction was especially true after multifamily properties were authorized to

be parceled out as condominiums. Whether because of constraints related to financing or because

of resident preferences to own their own homes, removing residency requirements and allowing

accessory units to be sold as condos proved significant. Together, the multifamily reforms and the

lifting of condominium restrictions resulted in widescale land use change: the majority of parcels

switched to multifamily use after those changes, and developers practically ceased to construct

single-family (or even two- and three-family) homes within the multifamily zone.

The research presented in this article does not allow for a definitive answer as to why larger

multifamily development proved more successful at increasing supply than accessory unit laws and

conversions. For example, did the building typology allow for unit types more attractive to buyers

or lower production costs for developers? Were the additional profits that could be generated by

Cityscape 251

Huennekens

one or two additional units insufficient to prompt existing owners to sell? The answers to these

questions would help other jurisdictions assess whether the more limited production of accessory

dwelling units and triplexes reflected local conditions or something more generalizable. Even so,

the research serves as a reminder that certain land use reforms, focused on the gentlest forms of

additional density, may fail to generate substantial production, even in extremely high-demand and

pro-growth environments.

The Ramapo case also demonstrates that decades of sustained development carry costs. The town’s

suburban infrastructure, especially the suburban sewer and water system, has not always been

able to handle urban densities, especially without substantial (and costly) upgrading. Further,

the Ramapo case shows that multifamily construction in a high-demand environment does

not guarantee affordability. Although multifamily units may be affordable compared with what

prices would have been otherwise, they are not necessarily accessible to the full spectrum of the

community. These points show the importance of complementary housing and planning policies.

Lastly, the R-15C district sheds some intriguing light on the processes necessary for a place

to transform. This information is where Ramapo’s use as a falsification test comes in: a place

that shows that certain widespread assumptions about zoning reform may not hold under all

conditions. For example, conventional wisdom holds that discretionary review processes hold up

development and constrain housing production (Metcalf, Garcia, and Karlinsky, 2020). However, in

Ramapo, developers actively seek variances, preferring the flexibility (and extra building capacity)

of a site-specific variance despite the costs in terms of time and fees. The aesthetic review process

in Ramapo, another type of discretionary review, also has not precluded rapid development.20

Parking requirements are thought to constrain production (Gould 2022); however, in Ramapo,

developers tend to meet or exceed parking requirements. It follows that, under certain political

conditions, eliminating discretionary review or parking requirements may not be as important for

housing production as institutional reforms that alter the speed and ease of discretionary review.

This conclusion is not to say that discretionary reviews do not tend to add cost and uncertainty

to the housing production process; rather, it is a reminder that the impact of discretion is also a

function of whose discretion is being exercised.

Other communities, especially those built on the same midcentury suburban model as Ramapo,

should heed the lessons of the Ramapo case. Ramapo’s zoning reforms show that, with the right

institutional framework and housing market, single-family tract housing developments can

be upgraded to become much denser neighborhoods. The R-15C district, in particular, shows

that single-family neighborhoods are not necessarily built out. Municipalities can induce the

production of large amounts of missing middle housing if they are bold enough to legalize true

multifamily buildings. However, municipalities should enter these efforts cognizant of the special

challenges that dense housing brings when constructed atop existing suburban infrastructure.

These downsides are far from insurmountable—and are no excuse for inaction. They are real,

however, and responsible policymakers should ensure that densification proceeds along with the

requisite infrastructure upgrades and housing affordability policies necessary to sustain safe and

just residential environments.

20 Although, as relayed in the policy challenges section, the aesthetic review board was seen as a rubber stamp by some in the community.

252 Recent Reforms in Zoning

Learning from Land Use Reforms: The Case of Ramapo, New York

Acknowledgments

The author thanks the NYU Furman Center and the Pew Charitable Trusts for helping to fund the

research, and Noah Kazis, Vicki Been, Ingrid Gould Ellen, and Matthew Murphy for constructive

feedback on earlier drafts of this article. The author also thanks the Ramapo stakeholders who

shared their knowledge of the R-15C district and local planning issues.

Author

Joseph Weil Huennekens (ORCID: 0000-0002-4549-9509) is a Ph.D. candidate in urban planning

at Columbia University. His research interests include changing suburbs, exclusionary zoning, and

planning practice.

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Rockland County GIS Portal. n.d. https://www.rocklandgis.com/portal/.

Saeed, Khurram and Laura Incalcaterra. 2001. “Illegal Homes House Many, Are Difficult to

Counter,” The Journal News, March 12.

Sanderson, Linda. 1978. “Drainage, Housing Issues Stir Anger,” The Journal News, January 26.

Snel, Alan. 1986. “Ramapo Approves Architecture Board,” The Journal News, May 29.

Spack, Mike. 2017. “Traffic Engineering Briefing: When Is a Road Congested?” Mike on Traffic,

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The Journal News. 1992. “Zone Change Would Allow 3-Family Homes in Ramapo,” March 26.

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Zillow. n.d. “Buy.” Zillow.com.

256 Recent Reforms in Zoning

by Jacob Krimmel and Betty Wang

Upzoning With Strings Attached:

Evidence From Seattle’s Affordable

Housing Mandate

Jacob Krimmel

Federal Reserve Board

Betty Wang

University of Hong Kong

The views and analysis expressed here do not represent those of the Board of Governors or the Federal

Reserve System.

Abstract

This article analyzes the effects of a major municipal residential land use reform on new home

construction and developer behavior. It examines Seattle’s Mandatory Housing Affordability (MHA)

program, which relaxed zoning regulations while encouraging affordable housing construction in 33

neighborhoods in 2017 and 2019. The reforms allowed for more dense new development, or “upzoning,”

but they also required developers to either reserve some units of each project at below market rates or pay

into a citywide affordable housing fund. Using a difference-in-differences estimation comparing areas the

reforms affected versus those not affected, the authors show that new construction fell in the upzoned,

affordability-mandated census blocks. The quasi-experimental border design finds strong evidence of

developers strategically siting projects away from MHA-zoned plots—despite their upzoning—and

instead to nearby blocks and parcels not subject to the program’s affordability requirements. Lowrise

multifamily and mixed-use development drive these effects. The findings speak to the mixed results of

allowing for more density while simultaneously mandating affordable housing for the same project.

Introduction

The broad academic consensus is that stringent land use regulations are behind many major

sociopolitical issues facing cities today, including housing unaffordability, residential segregation,

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 257

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Krimmel and Wang

and lagging economic growth.1 Among the most common forms of regulation are limits to urban

density that may restrict the size or height of multifamily residences or ban multiunit buildings

outright. Such low-density zoning is viewed as the root cause of affordability issues for many in-

demand cities (Glaeser and Gyourko, 2018). For instance, land zoned for single-family detached

homes accounts for 94 percent of all land zoned for residential use in San Jose, 75 percent in Los

Angeles, and 70 percent in Minneapolis.2

Although the ill effects of tightening land use controls are well established, far less practical

knowledge is available on how to ameliorate the situation. It is not clear to academics or

policymakers exactly how existing zoning codes and regulations should be changed to spur new

construction where housing shortages are most acute; nor is it straightforward to enact such

reforms, even if consensus existed. At present, state and local policymakers around the country are

implementing or discussing a wide range of housing affordability reforms, from changing zoning

codes to enacting rent controls.3

A key challenge facing policymakers seeking to boost supply and lower housing costs, in the long

run, is finding a suite of reforms that are politically feasible in the short run. For example, although

agreement is widespread among economists that allowing more dense construction will, in theory,

boost supply and bring down prices, voters and politicians remain wary. A prominent concern

is that upzoning leads only to constructing expensive units that would not directly alleviate

affordability issues among rent-burdened existing residents. However, empirical evidence on the

effects of upzoning is scarce, mainly because these policy changes are rare, especially at larger

geographic scales.

This article analyzes Seattle’s Mandatory Housing Affordability (MHA) reform, one of the largest

citywide density and affordable housing reforms in the United States. Seattle presents an ideal

setting to answer the question of how to tackle housing shortfalls and affordability issues. The city’s

population has boomed, and house prices have soared in recent years. Although the metropolitan

area population has grown 30 percent during the past 2 decades, Seattle is building fewer new

units per year than when it had 1 million fewer inhabitants. As a result, since 2000, median house

prices have nearly tripled; one in seven residents is severely rent burdened. Although a growing

political will is for large-scale housing reform, much of Seattle’s land remains zoned only for

detached single-family residences.4

1 Although all cities in the United States have some form of land use regulatory rules, restrictiveness, and corresponding housing shortages vary widely across metropolitan areas. See Gyourko, Hartley, and Krimmel (2021) for variation in land use restrictions across metropolitan areas.

2 A small number of cities also adopt urban growth boundary lines to limit the extensive margin along which cities could grow (most notably, Portland, Oregon).

3 These reforms include, for example, sweeping accessory dwelling unit reforms in California and Oregon (https://www.

hcd.ca.gov/policy-and-research/accessory-dwelling-units/), rent control proposals in Boston (https://www.boston.com/real-

estate/real-estate-news/2023/01/18/boston-rent-control-michelle-wu-plan/), a ban on single-family zoning in Minneapolis (https://www.planning.org/blog/9219556/measuring-the-early-impact-of-eliminating-single-family-zoning-on-minneapolis-

property-values/), and inclusionary zoning in Washington, D. C. (https://dhcd.dc.gov/service/inclusionary-zoning-iz-

affordable-housing-program/).

4 The authors’ conversations with officials from the city of Seattle, community groups, and developers indicate that pro-density and pro-affordability reforms have been discussed for at least the past 10 years. The authors also note that Seattle recently adopted reforms to boost accessory dwelling unit production in single-family neighborhoods.

258 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

The MHA reform presents a case study of how one high-cost city struck a balance between

its efforts to alleviate affordability issues and local political opposition from both single-family

homeowners resistant to change and rent-burdened households fearing displacement. The MHA

program upzoned 33 noncontiguous neighborhoods between 2017 and 2019. In these areas, MHA

allowed for greater density while mandating that all new commercial and multifamily residential

construction contributes to affordable housing. The reform combines two policy levers that some

economists consider to conflict with one another: Increasing development capacity through

upzoning and requiring private development to create income-restricted affordable housing.

Geographically, the MHA touches very little single-family zoned land, thus making minimal

alterations to the city’s landscape while attempting to increase overall housing production. MHA

is applied almost exclusively to areas already zoned for multifamily, commercial, or high-density

single-family homes (for example, townhomes and rowhouses)—an important factor for MHA’s

political feasibility.

Seattle is one of the first large cities to adopt this “upzoning with strings attached” model, a

prominent policy vehicle being discussed across the county. Thus, Seattle’s MHA represents

an interesting example for other cities considering density reforms to alleviate affordability

issues. Whether (and when) such a policy would spur or stifle housing development, especially

affordable housing development, remains an empirical question. What is the “right mix” of sticks

(requiring affordability contribution) and carrots (allowing more development capacity and

density) for the developers?

This article first presents empirical evidence on the quantity response to the regulatory changes:

Are more housing units permitted and built following the rezoning? The main empirical finding

suggests strategic developer behavior following the reform at selected zones, guided by a cost-

benefit tradeoff. Using a differences-in-differences analysis on a quasi-random sample of geographic

areas that straddle the boundary of MHA zones, the results show no overall supply decline but

strong strategic substitution of new construction away from blocks and parcels subject to the MHA.

It is worth noting that the substitution is more likely for lowrise residential zones than highrise

residential zones. This strategic substitution is a mixed result, because supply increases but not

in all MHA zones as intended. In particular, the article finds that conditional on a permit being

issued. It is more likely to be issued to a non-MHA block zone after the policy took effect in April

2019. Looking at the number of units, this study finds that differentially fewer units are added to

MHA lowrise zones after the reform. Worryingly, most of the drop in the number of units in MHA

zones is coming from the multifamily segment of the market, where most of the housing products

are three- and four-story townhomes and duplexes. The multifamily segment is of particular note

because lowrise and small multifamily homes are seen as more affordable alternatives to luxury

apartments for low- and moderate-income renters.

Overall, this study’s findings suggest preliminary mixed results for the MHA. Although upzoning

allows for the construction of larger, taller multifamily buildings, it appears that MHA’s affordability

requirements act as a tax on some additional development. Importantly, any project on an MHA-

zoned parcel was subject to the affordability requirement, regardless of whether the project was

Cityscape 259

Krimmel and Wang

“taking advantage” of the upzoning reform.5 On balance, it appears that the cost of the affordability

requirement to developers outweighed the benefit of additional units via upzoning, especially in

lowrise zones.

Institutional Background

Seattle has experienced an intensifying housing affordability crisis during the past 2 decades,

driven in part by the growth of big technology companies like Microsoft Corporation and Amazon.

com, Inc., which have boosted labor demand and, therefore, housing demand. The median home

price has tripled since 2000, and rental rates for a one-bedroom have increased 35 percent during

the past 5 years. Also, a large racial gap exists in rent burdens: 35 percent of the city’s African-

American renter households are severely rent burdened compared with 19 percent of White renter

households. Seattle’s population grew 15.7 percent between July 2010 and July 2016, faster than

almost any other large city in the country. In the city’s 2035 Comprehensive Plan, Seattle housing

officials identified that 20-year growth estimates would require the production of an additional

27,500 to 36,500 units, not including existing unmet needs. The shortage was anticipated to be

particularly severe for units restricted to 30 and 50 percent of area median income. At this time,

approximately 65 percent of the city’s land was zoned for exclusively single-family usage.

The MHA reform allows for greater building heights and higher floor area ratio (FAR) limits in

designated MHA zones. It also requires a developer contribution in exchange for the density

bonus. The contribution comes in two forms that the developer could choose: “Payment” or

“performance.” Payment, a one-time monetary payment based on a predetermined schedule, goes

directly into the city’s affordable housing fund; performance requires developers to build rent- and

income-restricted units on site. The contributions are designed to mitigate the perceived negative

effects of new development.

The MHA reform upzoned 33 neighborhoods, allowing taller buildings and denser construction in

three major types of buildings: Commercial, multifamily, and mixed-use. Any project in an MHA-

rezoned area may choose between payment or performance. This program exempts single-family

zones, but every project in an MHA rezone triggers the affordability requirement. This program

design would ideally provide a distribution of mixed-income housing and a source of public

revenue that the city housing agency could leverage toward more private funding for targeted

development. The level of developer contribution required (“M,” “M1,” or “M2”), measured either

through units built (performance option) or dollars contributed to the affordable housing fund

(payment option), is determined by the extent to which the zone has changed from its prior

classification. The program generated $68 million (and roughly 850 affordable units) in its first full

year in 2020, with most developers taking the payment option.

One key thing to note is that this MHA program is not an unexpected “shock.” Two years of

community engagement and policy analysis informed the program’s details. The guiding principles

generated from that process for the MHA program include creating more rent-restricted affordable

housing for low-income people; minimizing displacement and the effects on neighborhood

5 For instance, it does not matter if a developer built a duplex that would have been allowed under pre-MHA zoning rules or a triplex that would have been only allowed under post-MHA zoning.

260 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

character; increasing the variety of housing choices, including homeownership and family-

size housing; developing additional housing opportunities near schools, transit, and jobs; and

coordinating growth with infrastructure investments.

In fact, the current program is an expansion of the city’s preexisting voluntary Incentive Zoning

program. The biggest difference from Incentive Zoning to MHA was that it became mandatory in

designated geographies, in that it applies to all new permits issued within MHA zones after the

reform. MHA was initially rolled out in 6 “urban villages” between 2015 and 2017 before being

expanded on the same terms citywide in an additional 27 urban villages in April 2019.6 This

article uses April 2019 as the “post” period, because the overwhelming majority of neighborhoods

affected and permits issued occurred after this wave of the reform. Importantly, the designers of

MHA see the program as a necessary steppingstone for more ambitious future upzonings, both in

terms of increases in allowable density and geographic scope.

Neighborhood assessments, completed as the city’s comprehensive planning process, informed

the geographic design of the MHA rezonings. The demographic and neighborhood trends of

each urban village were examined to determine whether the area has a low or high “risk of

displacement” and “access to opportunity.”7, 8 The architects of MHA intended to spur the most

housing production in areas with a low risk of displacement and high access to opportunity.

Exhibit 1 shows that MHA rezonings affect quite a wide geography of neighborhoods. The left

panel of exhibit 1 shows all MHA rezonings. The right panel breaks the rezoning down into three

tiers based on the intensity of the zoning change. In the majority of cases, called the “M tier,” the

zoning code did not change, but taller buildings or higher FAR, or both, were allowed. A suffix was

added to the zoning code after MHA took effect for these cases. For example, a lowrise 3, or LR3,

becomes LR3(M). These rezones allow for roughly one story of additional development capacity.

As a percentage of developable land in MHA rezoned areas, 78 percent of land falls under this mild

change. The other 22 percent of land falls under M1 and M2 tiers, providing for more significant

changes than the M tier.9 The right panel shows the three color shadings corresponding to the three

tiers. The most moderate M tier is the lightest shade. Housing officials carefully choose to map M

tiers, opposed to M1 or M2 tiers, in high-risk or low-opportunity areas to minimize displacement

risk and avoid hurting access to opportunity. The aim was to ensure that affordable units were

added in lowrise multifamily zones rather than allowing for highrise luxury apartments. However,

this modest upzoning, paired with MHA’s affordability mandate, did leave M-tier areas at particular

risk of lower supply responses.

6 An urban village is the city’s inventive term for certain mixed-use neighborhoods designated to accommodate common housing and business growth.

7 Risk of displacement is determined by 14 indicators, including the racial composition of a neighborhood’s residents, their educational attainment, its proximity to high-income neighborhoods, and development capacity.

8 Access to opportunity includes factors such as local educational outcomes, property value appreciation, and proximity to resources such as parks, healthcare facilities, or public transit.

9 Seattle classified its zones into five broad categories based on the density of permitted development. The M1 tier, which accounts for 20 percent of MHA rezoned areas, includes rezonings that moved the land up an entire category. The M2 tier, which accounts for only 2 percent of rezoned areas, involves upzones that shifted the land by two or more categories.

Cityscape 261

Krimmel and Wang

Exhibit 1

Mandatory Housing Affordability Areas and the Intensity of Upzoning

M = tier indicating zoning code did not change. M1 = tier including rezonings that moved the land up an entire category. M2 = tier involving upzones that shifted the land by two or more categories.

Source: Seattle GeoData and Department of Construction & Inspections zoning map, City of Seattle, 2017

To understand the size of the MHA rezoning treatment effect, this article looks at the actual

permitting activities following MHA rezones. At the permit level, 75 percent of all permits issued in

MHA-rezoned areas between April 2019 and July 2022 occurred in areas subject to the four most

common zoning changes. Exhibit 2 summarizes these four changes. These four types of rezoning

were also the most common when ranked by the total square footage of the lots on which new

buildings were permitted, although their ranking differed across the two measures. For example,

although the fourth largest number of permits were issued in places that changed from the LR3

zone to LR3(M), those permits accounted for the largest area on which new development occurred.

Of all 34 million square feet of MHA-rezoned land on which new development occurred, 52.3

percent belongs to one rezone of LR3 to LR3(M).

262 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

Exhibit 2

Most Commonly Observed Four Rezones by Number of Building Permits and Lot Size

Rezone

Count of

Percent (%) of

Lot Size

Rezone

Percent (%)

Full Name

Permits

Total Permits

(sq. ft.)

RSL to RSL(M)

Residential Small Lot

418

18.4

3,169,433

9.1

LR1 to LR1(M)

Lowrise 1

386

17.5

2,302,597

6.6

LR2 to LR2(M)

Lowrise 2

329

14.9

2,373,404

6.8

LR3 to LR3(M)

Lowrise 3

271

12.3

18,243,500

52.3

All others

803

36.3

8,787,566

25.2

Total

2,207

100

34,876,500

100

LR = lowrise. M = tier indicating zoning code did not change. RSL = residential small lot.

Note: Percentages may not total 100 percent due to rounding.

Source: City of Seattle, Mandatory Housing Affordability Director’s Report 2018

Exhibit 3 shows the four types of rezoning in which the largest number of net units were created.

Here, a partial overlap exists with the earlier set, but certain commercial areas also appear due to

the permitting of especially large developments in these areas.

Exhibit 3

Most Commonly Observed Four Rezones by Number of Net Units Created

Rezone

Rezone Full Name

Count of Permits

Percent (%)

DMC 240/290-400 to DMC

Downtown Mixed Commercial

3,188

12.0

240/290-440

LR3 to LR3(M)

Lowrise 3

1,887

7.1

LR2 to LR2(M)

Lowrise 2

1,301

4.9

Commercial to Commercial/

C1-655 to NC3-75(M)

1,142

4.3

Mixed use

All others (all fewer than 1,000 permits for one type of rezone)

18,996

71.1

Total

26,514

100

C = commercial. DMC = downtown mixed commercial. LR = lowrise. M = tier indicating zoning code did not change. NC = commercial/mixed use.

Note: Percentages may not total 100 percent due to rounding.

Source: authors’ calculations.

The key takeaway is that the increased development capacity that most MHA rezonings created

was relatively limited. Hence the “carrots” for developers—the development capacity increase—

might not be big enough to outweigh their costs from the affordability payment or performance

requirement.10 The allowable FAR increases from 2.0 to 2.2 after MHA took effect, which means

the height limit increases from 40 to 50 feet, adding another floor.11 Other zoning code changes

generally enjoy similar magnitude as LR3 does.

With a relatively modest density bonus, the “affordability tax” on developers is comparatively

high. Housing officials estimate that the legislation will result in 17,000 more total housing units

for 20 years than would be generated by development in its absence; 5,600 of those would be

10 Appendix exhibit A2 shows an example of the specific building prototype for the most prevalent rezone change, LR3 to LR3(M) (Assefa et al., 2018).

11 For LR3 inside Urban Villages.

Cityscape 263

Krimmel and Wang

rent- and income-restricted units. Importantly, estimates operated on the assumption that one-

half of developers would choose to build affordable units on site, and one-half would choose to

contribute to the affordable housing fund. However, in the first year that MHA was in full swing,

an overwhelming majority of developers (98 percent) chose the payment option. This response

suggests either that the performance option constitutes a large “affordability tax” on the developers

or that the payment option levels were set too low.

Data and Analysis

To examine the effect of the MHA reforms on new home permitting and construction, two publicly

available maps from Seattle GeoData (part of Open Data Seattle) are merged: the map of Residential

Building Permits Issued and Final since 1990 and the city’s MHA Zones map. The permit data are

address-level geocoded and include information on the development site, permitting stage, plans

for units created and demolished (by unit type), and other geographic data.

Then a panel dataset is constructed at the census block level over time. MHA zones do not always

perfectly overlap with census block polygons, so a census block is categorized as an MHA block

if at least 50 percent of its area falls within an MHA zone. Under this definition, only 11 percent

(3,960 out of 35,279) of census blocks are within the MHA. MHA zones account for a very small

share of census blocks, but many more blocks are geographically proximate to an MHA zone.

Although slightly more than 1 in 10 census blocks has at least 50 percent of its area in an MHA

zone, more than 31 percent are within a census tract that overlaps with an MHA zone somewhere

within its boundaries. In this way, many census blocks are not upzoned, but they are in the

neighborhood (census tract) of somewhere that is upzoned.12

Exhibit 4 presents population and socioeconomic summary statistics. The first three columns are

block groups that were completely rezoned under MHA, those partially rezoned under MHA, and

those that are fully non-MHA.13 These block groups correspond roughly to the preexisting-built

environment of Seattle neighborhoods, with MHA zones being in the most densely developed

areas. The summary statistics change monotonically across all socioeconomic variables. Moving

from the fully MHA block groups to fully non-MHA ones, people are fewer, housing units are

fewer, incomes are higher, poverty is lower, and the percentage of owner-occupied housing is

higher. Put differently, the fully MHA block groups—with a poorer population of more renters

located around major commute lines—are precisely where housing affordability and displacement

concerns are the greatest.

12 The appendix has more details on the construction of the dataset.

13 Note that no summary statistics by census block exist because population, income, and demographic variables at the block level are not available in 5-year ACS samples.

264 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

Exhibit 4

Summary Statistics by Census Block Group Mandatory Housing Affordability Status

Border MHA/

Description

Fully MHA

Fully Non-MHA

All

Non-MHA

1,627.34

1,395.45

1,513.35

1,491.63

Population

(615.13)

(405.84)

(614.88)

(571.77)

867.31

601.72

568.19

599.35

Housing Units

(420.51)

(215.70)

(243.76)

(266.20)

Median Income

66,810.58

93,625.62

96,007.03

93,320.46

(2017 dollars)

(26,051.03)

(37,717.84)

(41,502.25)

(40,300.85)

Median House Value

464.16

567.94

450.99

482.63

(Thousands of 2017 dollars)

(157.12)

(232.88)

(245.52)

(242.58)

0.59

0.6

0.44

0.49

Share College+

(0.19)

(0.19)

(0.22)

(0.22)

0.17

0.1

0.08

0.09

Share in Poverty

(0.16)

(0.09)

(0.08)

(0.10)

0.23

0.59

0.68

0.63

Share Owner-Occupied

(0.15)

(0.21)

(0.26)

(0.27)

0.6

0.67

0.62

0.63

Share White

(0.21)

(0.23)

(0.20)

(0.21)

Observations

107

365

948

1,420

MHA = Mandatory Housing Affordability.

Notes: Summary statistics at census block group (CBG) level, according to 2013–17 American Community Survey 5-year estimates. Means of CBG listed for each column (for example, mean of CBG median income) and standard deviation in parentheses.

Source: Authors’ analysis via 2013–17 American Community Survey 5-year data

Fully non-MHA block groups are not a suitable control group for fully MHA block groups. As

such, analysis is limited to the partial MHA block groups, which are defined as “border” MHA

blocks for identification. One drawback of the border analysis is that although fully MHA block

groups (column 1) do not have an adequate non-MHA comparison, these areas saw the most

intense upzoning (the M1 and M2 zones). In effect, the ‘treatment’ is stronger in areas that lack a

good control group and weaker in areas where an adequate control exists.

Empirical Findings

Consider two hypothetical outcomes of the policy change. The “first best” outcome is an increase

in overall supply in the MHA zones. With the mandatory affordability requirements, this increase

in overall supply also means an increase in affordable units. At the other extreme, if the reform

is not designed correctly, the change in developer incentives could deter new construction both

inside and outside MHA zones. The success of MHA rests on developers’ response to a cost-benefit

tradeoff. On the one hand, MHA allows developers to build and sell more units; on the other hand,

each MHA-zoned project comes with a “developer tax” from the affordable housing requirement

(either payment or performance).

Cityscape 265

Krimmel and Wang

The main empirical finding suggests that developers behaved strategically after the reform took

effect, which this tradeoff guides.14 In particular, while housing production did not decline overall,

there was strong strategic substitution of new construction away from MHA zones. This policy

outcome can be considered a middle ground between the initial hypothetical outcomes: MHA

enactment did not halt all new development, but new units were not built where intended.

A difference-in-differences regression is estimated at the census block level to quantify the

substitution effect. Exhibits 5 through 7 show the generalized difference-in-differences result on

different dependent variables. Estimates for key coefficient β3 are on the first row.15

Exhibit 5

Extensive Margin: At Least One Permit Is Issued, Block by Month

Description

(1)

(2)

(3)

(4)

(5)

(6)

– 0.004***

– 0.004***

– 0.004***

– 0.005***

– 0.005***

– 0.004***

Post X MHA

(0.001)

(0.001)

(0.001)

(0.001)

(0.001)

(0.001)

– 0.000

– 0.000

Post

(0.000)

(0.000)

0.009***

0.005***

0.005***

MHA Block

(0.001)

(0.001)

(0.001)

R–squared

0.002

0.007

0.028

0.003

0.023

0.022

DV Mean

0.002

0.002

0.002

0.007

0.007

0.007

Geo. Sample

All Tracts

All Tracts

All Tracts

Border Tracts Border Tracts Border BlkGrp

YearMo FE

Yes

Yes

Yes

Yes

Geo. FE

Tract

Block

Tract

Block

Block

Observations

3527900

3527900

3527900

1049600

1049600

862700

***p < .01.

BlkGrp = block group. DV = dependent variable—in this table, whether at least one permit is issued at the block-month level. FE = fixed effects. Geo.

FE = geographic fixed effects. Geo. Sample = geographic sample. MHA = Mandatory Housing Affordability. Mo = month.

Notes: Standard errors are in parentheses. Standard errors clustered at census tract. Sample limited to at most 5 years before MHA.

Source: Authors’ analysis

Exhibit 6

Number of Permits, Block by Month (1 of 2)

Description

(1)

(2)

(3)

(4)

(5)

(6)

– 0.007***

– 0.007***

– 0.007***

– 0.009***

– 0.009***

– 0.008***

Post X MHA

(0.002)

(0.002)

(0.002)

(0.002)

(0.002)

(0.002)

0.000*

0.000*

Post

(0.000)

(0.000)

0.015***

0.011***

0.012***

MHA Block

(0.002)

(0.002)

(0.002)

R–squared

0.002

0.005

0.025

0.002

0.022

0.020

DV Mean

0.003

0.003

0.003

0.011

0.011

0.010

14 Appendix exhibits A4 and A5 present summary statistics on the average monthly number of net units and value in MHA versus non-MHA zones in aggregate, before and after the policy change.

15 See the appendix for the formal difference-in-differences equation 1.

266 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

Exhibit 6

Number of Permits, Block by Month (2 of 2)

Description

(1)

(2)

(3)

(4)

(5)

(6)

Geo. Sample

All Tracts

All Tracts

All Tracts

Border Tracts Border Tracts Border BlkGrp

YearMo FE

Yes

Yes

Yes

Yes

Geo. FE

Tract

Block

Tract

Block

Block

Observations

3527900

3527900

3527900

1049600

1049600

862700

*p < .10. ***p < .01.

BlkGrp = block group. DV = dependent variable—in this table, the number of permits at the block-month level. FE = fixed effects. Geo. Sample = geographic sample. Geo. FE = geographic fixed effects. MHA = Mandatory Housing Affordability. Mo = month.

Notes: Standard errors are in parentheses. Standard errors clustered at census tract. Sample limited to at most 5 years before MHA.

Source: Authors’ analysis

Exhibit 7

Number of New Units, Block by Month

Description

(1)

(2)

(3)

(4)

(5)

(6)

– 0.069**

– 0.069**

– 0.069**

– 0.057***

– 0.057***

– 0.065***

Post X MHA

(0.030)

(0.030)

(0.030)

(0.018)

(0.018)

(0.016)

– 0.000

– 0.000

Post

(0.001)

(0.001)

0.189***

0.120***

0.119***

MHA Block

(0.026)

(0.016)

(0.013)

R–squared

0.001

0.001

0.014

0.001

0.016

0.015

DV Mean

0.023

0.023

0.023

0.052

0.052

0.041

Geo. Sample

All Tracts

All Tracts

All Tracts

Border Tracts Border Tracts Border BlkGrp

YearMo FE

Yes

Yes

Yes

Yes

Geo. FE

Tract

Block

Tract

Block

Block

Observations

3527900

3527900

3527900

1049600

1049600

862700

** p < .05. ***p < .01.

BlkGrp = block group. DV = dependent variable, in this table it is the number of new units at the block-month level as indicated in the title of the table.

FE = fixed effects. Geo. FE = geographic fixed effects. Geo. Sample = geographic sample. MHA = Mandatory Housing Affordability. Mo = month.

Notes: Standard errors are in parenthesis. Standard errors clustered at census tract. Sample limited to at most 5 years before MHA.

Source: Authors’ analysis

Exhibits 5 through 7 tables are organized in six columns. The first three columns use the sample

of all census tracts, with no fixed effects (column 1), tract fixed effects (column 2), and year-month

and block fixed effects (column 3). To get to the causal effect of MHA, the analysis is limited to a

quasi-experimental sample in columns 4 through 6 for estimating equation 1.16 In particular, the

sample of “Border Tracts,” is used, which are tracts that straddle an MHA boundary. This sample

eliminates tracts that are entirely within MHA zones and entirely outside of MHA zones. A total

397 tracts exist, and 118 are border tracts by this study’s definition. The estimation is at the

census block level, which is a finer geography than census tracts. All the variation in the “Post X

MHA” coefficients comes from the comparison within neighborhoods, where some blocks in that

neighborhood are upzoned and others are not.

16 See the appendix for details regarding equation 1.

Cityscape 267

Krimmel and Wang

If the MHA upzoning program worked as hypothesized, much more permitting activity would

transpire, and more new supply would exist in the upzoned blocks within a border tract.

However, the opposite occurs—the development is happening more in blocks not upzoned

within the same neighborhood.

The identifying assumption is that, within a tract, the growth potential of housing demand is the

same whether the housing project falls onto either side of the MHA line. A tract is a fine geographic

level that makes this assumption reasonable. Columns 4 and 5 use the quasi-random sample of

border tracts, with tract and year-month fixed effects (column 4) and block and year-month fixed

effects (column 5). Column 6 zooms into the finer geographic level of block groups and uses the

finer block groups at the border (partially MHA), with year-month and block-fixed effects.

The three variables that measure quantity response are whether a permit was issued at all (exhibit

5), the number of permits issued (exhibit 6), and the net units permitted (exhibit 7). These three

exhibits examine housing supply activity in Seattle’s census blocks from 5 years prior to Mandatory

Housing Affordability through the most recent data (April 2022). All three exhibits are set up

the same way: Columns 1, 2, and 3 examine effects for all Seattle, adding fixed effects across the

columns, and columns 4 through 6 limit the sample to areas on the MHA borders.

The analysis finds quantitatively strong and consistent empirical evidence for substitution of

supply away from MHA zones. Across all specifications on all three quantity-dependent variables,

the number of permits and the number of units permitted per month decreases in MHA blocks

after the reform takes effect.

Exhibit 5 examines whether at least one permit was issued in a census block, finding that MHA

rezonings decreased the likelihood of a permit being issued, particularly at the border tracts.

The dependent variable is defined as an indicator variable that equals 1 if at least one permit is

issued in that block-month or 0 if none are issued. The estimate of -.004 on “Post X MHA” is very

consistent across all specifications and the two geographic samples. The magnitude is economically

meaningful. It is twice as large as the dependent variable mean in the full sample of all tracts (the

first three columns).17

Interestingly, as the analysis moves to the finer geography of border tracts, the (absolute)

magnitudes are bigger, which suggests that the substitution action is happening in the border

tracts. That is, permitting activity is switching within and not across neighborhoods. Notice also

that the dependent variable means an increase from 0.002 in columns 1 through 3 (the all tracts

sample) to 0.007 in columns 4 through 6 (the border tract sample). One could interpret this

increase as an annual likelihood of receiving a permit increasing from 2.4 percent overall to 8.4

percent for border-MHA neighborhoods, indicating that throughout this period (including 5 years

before MHA), much more housing is permitted in these MHA-border neighborhoods than in

either fully non-MHA or fully MHA areas. These results are consistent with developers deciding

first to build in a certain neighborhood, then strategically choosing to build on parcels in that

17 Put differently, this magnitude means that after MHA goes into effect, an MHA block is differentially 4.8 percent less likely to receive a permit in a given year (-0.004 x 12 months) than a non-MHA block in that same timeframe. On average, the likelihood a block receives a permit on an annual basis is about 2.4 percent (0.002 x 12 months), which is why this magnitude could be described as both large and economically meaningful.

268 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

neighborhood not subject to the MHA’s affordability requirements. This strategic behavior [suggests

that the potential benefits accruing to developers from the MHAs upzoning component failed to

outweigh the costs to developers of the affordability requirements.

Exhibit 6 looks at the number of permits issued, again finding a reduction driven by MHA

rezonings. The magnitudes are small (-0.007 per month), but the base is also very small (0.003),

so economically, the effect is quite large. Columns 1 and 2 show that MHA blocks generally see

more permitting activity throughout the sample period. Column 3 adds block and month-fixed

effects, and the point estimate is unchanged, meaning that permitting differentially decreases in

MHA zones. The final column limits the sample only to those census tracts that at least partially

intersect an MHA zone. Because tracts are much larger than blocks, this specification removes all

control census blocks that are far from an MHA zone, thus potentially differing unobservably from

treatment areas. The point estimate is unchanged and even a bit stronger, but it remains statistically

significant. The overall permitting activity tended to move across MHA boundaries after the policy

took effect.

The results for the number of new units created is very similar and robust. This evidence implies

that substituting behavior occurs along the border of MHA zones, which suggests strategic

developer behavior is at play. Developers want to profit from the growing demand in these MHA

zones, but the construction cost is too high after the reform. By moving to the bordering blocks,

they avoid those costs but still enjoy the spillover from in-demand neighborhoods.

Event Study Plots

The four event study plots use the three variables previously discussed and the net change in

multifamily units to illustrate these findings visually (exhibit 8). Column 5 from the three quantity

tables in exhibits 5 through 7 is regarded as the preferred estimates for the event study plots. Even

though column 6 arguably represents the finest geography, column 5 provides the finest data for

the socioeconomic variables. However, these plots should tell the same story using either of the

estimates. The coefficient estimates are similar across columns 5 and 6; if anything, column 5

underestimates the true effect. Exhibit 8 plots the relative treatment effect of MHA zones, because

the non-MHA areas are normalized to 0.18

18 The red bands are 95-percent confidence intervals around the point estimates. The x-axis is recentered around the “event date” of April 2019, with the green vertical line as the partition for pre and post-reform.

Cityscape 269

Krimmel and Wang

Exhibit 8

Event Study Results

Source: Authors’ analysis based on Seattle permits data

These plots show that MHA enactment in April 2019 was associated with a lower likelihood of

permit issuance in MHA zones (top left), fewer permits being issued per block (top right), slightly

fewer units permitted overall (bottom left), and significantly fewer multifamily units permitted

(bottom right). This final result of fewer multifamily units is particularly worrying, considering that

a major goal of MHA was to encourage dense multifamily development.

Common striking patterns emerge from comparing the four plots. For all four event studies,

housing production activity gradually declines in the treated MHA zones after the reform. The

substitution is expected to continue, as the authors obtain more data for newer months (currently,

the data are updated until April 2022).

Conclusion

This article provides empirical evidence on whether upzoning leads to new home construction if

strings are attached to affordable housing. A difference-in-differences estimation is performed using

a within-neighborhood variation, where some census blocks in that neighborhood are upzoned—

and subject to affordable housing requirements—whereas others are not. The hypothesis was

that much more permitting activity would occur in the upzoned blocks, because the benefits of

270 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

being able to build more densely was intended to outweigh the costs of any inclusionary zoning

requirements. However, the opposite is observed in the data. A differentially larger supply response

is found in blocks where the zoning has not actually changed and where affordable housing

has not been mandated. Unfortunately, this result runs contrary to the program’s dual goals of

increasing overall housing supply in general and affordable housing units in particular.

Examining the locations of permitting activity, this article finds new construction is sited just across

MHA lines at very fine geographies. Developers appear to be strategically substituting away from

plots and parcels subject to the MHA. This result is interpreted as evidence of the MHA’s cost to

developers outweighing its benefits, especially in MHA border neighborhoods. Specifically, the

MHA’s affordability requirements act as a “tax” on developers, which appears to dominate potential

gains from higher-density projects.

However, several caveats must be mentioned. First, the program is in its infancy. These data allow

for examining only the first 3 years of MHA, 2 years of which were affected by COVID-19. The

pandemic likely negatively affected both the demand for multifamily housing and the speed of the

city’s housing permitting process. It may also have shifted the demand for housing across Seattle’s

geography, given changing patterns of working from home—and here in Seattle, perhaps for the 5

to 10 years or even longer. Certainly, policymakers did not account for the pandemic in their initial

efforts to pair the amount of MHA’s upzoning with the number of its affordability requirements.

Second, the MHA border block design has drawbacks. By design, MHA blocks near the border

were zoned for smaller increases in density than areas farther away from an MHA boundary. This

design necessitates a tradeoff between examining where the reform’s treatment is more powerful

(the interior, fully MHA neighborhoods) versus where its effects can be more precisely estimated

(the border, partial MHA neighborhoods). In choosing the latter, the authors acknowledge that the

estimates are potentially downward biased; MHA’s effects may be more positive in the interior areas.

Regardless, although the results do not speak to how MHA affected the interior, fully MHA and

higher-density neighborhoods compared with a counterfactual, it is worth noting that permitting

did continue in these areas in an absolute sense. Third, MHA may have large noneconomic or

difficult-to-quantify benefits. For example, given the size of the reform and the consensus it took to

implement it, MHA has provided Seattle a potential springboard to expand upzoning in magnitude

and geography—one that policymakers hope to use. These benefits may be institutional or political

in nature. Although such institutional and political benefits are outside the scope of this economic

analysis, they may be quite significant.

How similar inclusionary housing programs will function outside Seattle remains ambiguous. The

findings are a function not only of the size of the density bonus provided and the affordability

mandate Seattle imposed but also the shape of housing demand in Seattle and the city’s room for

development outside the rezoned areas. Each of these factors will vary across different cities and

across time. Even so, the findings point to the potential for unintended consequences when density

bonuses are too small, or affordability mandates are too onerous. In the future, policymakers are

recommending that pursuing similar inclusionary housing strategies implement stronger upzonings

(larger density bonuses) and lighter, more flexible affordability mandates.

Cityscape 271

Krimmel and Wang

Appendix

Data and Econometrics

To examine the effect of the Mandatory Housing Affordability (MHA) reforms on new home

permitting and construction, this analysis merges two publicly available maps from Seattle GeoData

(part of Open Data Seattle): The map of Residential Building Permits Issued and Final since

1990 and the city’s MHA Zones map. The permit data are address-level geocoded and include

information on the development site, permitting stage, plans for units created and demolished (by

type of unit), and other geographic data. The permitting data also include information on the lot

size and the estimated value of the project, from which one can infer the density of the final project

(on a units per acre basis) and obtain a proxy of the overall market value. The MHA map contains

MHA zone polygons, with their pre and post zoning designation.

The authors then construct a panel dataset at the census block level over time. MHA zones do

not always perfectly overlap with census block polygons, so a census block is categorized as an

MHA block if at least 50 percent of its area falls within an MHA zone. Only 11 percent (3,960

out of 35,279 census blocks) are within the MHA.19 MHA zones account for a very small share

of census blocks, but many more blocks are geographically proximate to an MHA zone. Whereas

slightly more than 1 in 10 census blocks have at least 50 percent of its area in an MHA zone,

more than 31 percent of census blocks are within a census tract that overlaps with an MHA zone

somewhere within its boundaries. In this way, many blocks are not upzoned, but somewhere in

their neighborhood will be.

Regression Equation

Equation 1 in our econometric analysis is estimated, where t is month, i is MHA status of the

census block (or block group in column 6 specification across all results tables from exhibits 5

through 7), and γ and δ are, respectively, month and block (or tract) fixed effects.

Equation 1

Yi,t = α + β1 • postt × MHAi + β2 • postt + β3 • MHAi + γ t + ẟ i + ε i,t (1) The analysis is limited to 5 years of preperiod (January 2014 to March 2019) and 3.5 years of

post-period (April 2019 to April 2022). Using the full preperiod back to January 2010 gives similar

results, as appendix exhibit A3 shows. A block is “Treated” if it is in the designated MHA zone,

whether entirely or partially. “Post” is a dummy variable that equals to 1 if it is after April 2019.

The key coefficient of interest is β3.

Exhibit A1 shows the map of MHA zones in two waves. Exhibit A2 provides a visual illustration of

MHA change for one of the most commonly observed zone changes in a lowrise 3 zone.

19 For reference, 84 percent of blocks do not overlap with the MHA zones at all, whereas about 4 percent of blocks have between 0 and 50 percent MHA coverage (and are, thus, categorized as non-MHA under this definition). Of the blocks that are categorized as MHA, more than 80 percent are entirely contained within an MHA. This categorization of MHA blocks is conservative so as to limit false positives of permits popping up near, but not within, MHA designated areas.

272 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

Exhibit A1

Map of Mandatory Housing Affordability Zones

Source: City of Seattle

Cityscape 273

Krimmel and Wang

Exhibit A2

Illustration of Lowrise 3 Zoning in a Large Site Under Mandatory Housing Affordability

Source: City of Seattle, Office of Planning and Community Development

Exhibit A3 plots the share of net units per month by MHA status. It shows preliminary evidence

that construction of new units is slowing down in MHA areas after the mandate went into effect.

Exhibits A4 and A5 provide summary statistics on the number of net units and values of permits

pre- and post-MHA, in MHA versus non-MHA areas.

Exhibit A3

Share of Net Units per Month, by Block Mandatory Housing Affordability Status

MHA = Mandatory Housing Affordability.

Source: Authors’ analysis

274 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

Exhibit A4

Number of Net Units, Mean, and Standard Deviation

Description

Jan 2010–Apr 2019

After Apr 2019

Non-MHA

85.4

104.2

(82.8)

(98.8)

MHA

670.6

466.6

(508.0)

(495.9)

MHA = Mandatory Housing Affordability.

Source: Authors’ analysis

Exhibit A5

Value (in $1,000), Mean, and Standard Deviation

(In $1,000)

Jan 2010–Apr 2019

After Apr 2019

Non-MHA

22,433

28,895

(19,401)

(20,300)

MHA

121,538

89,217

(123,860)

(110,165)

MHA = Mandatory Housing Affordability.

Source: Authors’ analysis

Appendix exhibits A6 and A7 show the specific height, density, and floor area ratio limit changes

for commonly observed types of rezones.

Exhibit A6

Mandatory Housing Affordability Development Capacity Change in Lowrise Zones

Zone

FAR Limit

Height Limit

Density Limit

Pre MHA

Post MHA

Housing Type Pre MHA Post MHA Pre MHA Post MHA Pre MHA

Post MHA

Lowrise 1 Lowrise 1 (M) Cottage

1.1

1.1

18

22

1/1,600

No Limit

Townhouse

1.2

1.1

30

30

1/1,600

1/1,350

Rowhouse

1.1

1.1

30

30

1/1,600

1/1,350

Apartment

1.0

1.1

30

30

1/2,000

No Limit

Lowrise 2 Lowrise 2 (M) Cottage

1.1

1.3

18

22

1/1600

No Limit

Townhouse

1.3

1.3

30

40

No Limit

No Limit

Rowhouse

1.2

1.3

30

40

No Limit

No Limit

Apartment

1.3

1.3

30

40

No Limit

No Limit

Lowrise 3 Lowrise 3 (M) Cottage

1.1

1.8

18

22

1/1,600

No Limit

Townhouse

1.4

1.8

30

40

No Limit

No Limit

Rowhouse

1.3

1.8

30

40

No Limit

No Limit

Apartment

1.5

1.8

30

40

No Limit

No Limit

FAR = floor area ratio. M = tier indicating zoning code did not change. MHA = Mandatory Housing Affordability.

Source: Policy Proposal Director’s Report, City of Seattle

Cityscape 275

Krimmel and Wang

Exhibit A7

Mandatory Housing Affordability Development Capacity Change in Residential Small Lot Zones

Zone

FAR Limit

Height Limit

Density Limit

Pre MHA

Post MHA Housing Type Pre MHA

Post MHA Pre MHA

Post MHA Pre MHA

Post MHA

Residential

RSL

None

0.75

25

30

1/2,500

1/2,000

small lot

RSL (M)

Tandem RSL

None

0.75

18

30

1/2,500

1/2,000

(RSL)

Cottage RSL

None

0.75

18

30

1/1,600

1/2,000

FAR = floor area ratio. M = tier indicating zoning code did not change. MHA = Mandatory Housing Affordability.

Source: Policy Proposal Director’s Report, City of Seattle

Acknowledgments

The authors thank the New York University Furman Center for Real Estate Research and The Pew

Charitable Trusts for research funding. The authors also thank Todd Baker for providing excellent

research assistance.

Authors

Jacob Krimmel is an economist at the Federal Reserve Board. Betty Wang is an assistant professor

in economics in the Business School at the University of Hong Kong.

References

Assefa, Sam, Kathy Nyland, Steve Walker, and Nathan Torgelson. 2018. “Mandatory Housing

Affordability (MHA) Citywide Implementation Director’s Report and Recommendation.”

https://www.seattle.gov/Documents/Departments/HALA/Policy/Directors_Report_MHA_Citywide.pdf.

Glaeser, Edward, and Joseph Gyourko. 2018. “The Economic Implications of Housing Supply,”

Journal of Economic Perspectives 32 (1): 3–30.

Gyourko, Joseph, Jonathan S. Hartley, and Jacob Krimmel. 2021. “The Local Residential Land Use

Regulatory Environment Across U.S. Housing Markets: Evidence From a New Wharton Index,”

Journal of Urban Economics 124: 103337.

Additional Reading

Asquith, Brian J., Evan Mast, and Davin Reed. 2021. ““Local Effects of Large New Apartment

Buildings in Low-Income Areas,” The Review of Economics and Statistics 105 (2): 1–46.

Glaeser, Edward L., Joseph Gyourko, and Raven Saks. 2005. “Why is Manhattan So Expensive?

Regulation and the Rise in Housing Prices,” The Journal of Law and Economics 48 (2): 331–369.

276 Recent Reforms in Zoning

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

Hsieh, Chang-Tai, and Enrico Moretti. 2019. “Housing Constraints and Spatial Misallocation,”

American Economic Journal: Macroeconomics 11 (2): 1–39.

Schleicher, David. 2021. “Exclusionary Zoning’s Confused Defenders,” Wisconsin Law Review:

1315–1372.

Trounstine, Jessica. 2020. “The Geography of Inequality: How Land Use Regulation Produces

Segregation,” American Political Science Review 114 (2): 443–455.

Cityscape 277

278 Recent Reforms in Zoning

Refereed Papers

Refereed papers that appear in Cityscape have undergone a thorough and

timely double-blind review by highly qualified referees. The managing editor

reviews submitted manuscripts or outlines of proposed papers to determine

their suitability for inclusion in this section. To submit a manuscript or

outline, send an e-mail to cityscape@hud.gov .

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 279

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

280 Refereed Papers

by Alastair I. Matheson, Danny V. Colombara, Annie Pennucci,

Tyler Shannon, Andy Chan, Megan Suter, and Amy A. Laurent

Seeing the Big Picture with

Multisector Data: Factors Associated

with Exiting from Federal Housing

Assistance by Exit Type

Alastair I. Matheson

Danny V. Colombara

Public Health – Seattle & King County

University of Washington

Annie Pennucci

Tyler Shannon

King County Housing Authority

Andy Chan

Seattle Housing Authority

Megan Suter

Amy A. Laurent

Public Health – Seattle & King County

This work was funded by the U.S. Department of Housing and Urban Development under funding

opportunity FR-6400-N-58.

Abstract

The U.S. Department of Housing and Urban Development (HUD) has a goal of increasing the proportion

of households that exit HUD-supported housing for positive reasons, but little is known about factors

associated with different exit types. Learning which tenants are likely to leave for positive or negative

reasons can inform policies and programs that aim to encourage positive exits.

The authors linked data from two large public housing authorities (PHAs) to an existing multisector

data system that contains behavioral health, homeless services, and Medicaid claims data. The authors

used logistic regression to examine factors associated with exiting from housing assistance and used

multinomial regression to explore factors associated with exit type, both at the head-of-household level.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 281

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

Abstract (continued)

The analysis consisted of 8,266 exits: 2,610 negative, 4,538 neutral, and 1,118 positive. Male gender,

homelessness within the previous 3 years, and having a behavioral health crisis event were all associated

with increased odds of exit. Being older than age 25, increased time in housing (more than 6 years),

larger household size, having a single-caregiver household, and having a disability were all associated

with decreased odds of exit. Being of working age (25–61) was associated with positive exits but not

negative exits. Heads of household in single-caregiver households, who had disabilities, experienced

behavioral health crisis events, or had recent emergency department visits were all more likely to have

negative exits and less likely to have positive exits compared with neutral exits.

Linked administrative datasets offer PHAs a means of routinely obtaining information about physical

or mental health conditions, prior homelessness, or other factors that could influence outcomes for

their tenants. This approach can serve as a model to PHAs for cross-sector partnerships with health

departments and other groups interested in the health and well-being of housing assistance recipients.

Introduction

Housing affordability continues to be a significant challenge facing many American households.

Nearly half of all renters are housing-cost burdened, defined as spending 30 percent or more of

their income on housing costs (Martinez, 2022). Federal housing assistance, primarily in the form

of housing choice vouchers (HCVs) or public housing (PH), reaches only 20 to 25 percent of

eligible low-income households, leaving many people struggling to afford stable housing (Turner

and Kingsley, 2008). One possible approach to ensuring as many people as possible get assistance

is to create pathways for people in public housing so that they can become economically self-

sufficient and no longer require public housing support. To that end, in 2019, HUD set a goal of

increasing the proportion of households that exit HUD-supported housing for positive reasons

(HUD, 2019).

To support that goal, public housing authorities (PHAs) must be able to identify which households

are likely to experience positive or negative exits. However, grouping reasons for exiting housing

assistance into positive and negative categories is not without challenges. First, it assumes that

positive exits are more likely to yield better outcomes and are more conducive to economic self-

sufficiency. However, as noted below, the issue has not been thoroughly tested. Second, there is

no standardized process whereby PHAs collect reasons for exit, making comparisons across PHAs

difficult. Third, PHAs may lack sufficient information to confidently categorize an exit as positive or

negative, inflating the use of a neutral category.

If those difficulties can be overcome, then learning which tenants are likely to leave for positive or

negative reasons could inform policies and programs that aim to encourage positive exits. However,

it may not be feasible for PHAs to use predictors of exit type in a daily operational sense—for

example, identify and target specific individuals or households at risk of negative exits based on a

statistical model. Although PHAs routinely collect a relatively limited set of demographic, income,

282 Refereed Papers

Seeing the Big Picture with Multi-Sector Data: Factors

Associated with Exiting from Federal Housing Assistance by Exit Type

and address information, they typically have limited access to data on other topics or limited

capacity to analyze it in a way that would provide a comprehensive view of the people they serve.

When additional contextual information is collected, it often takes the form of one-off surveys,

research projects, or programmatic activities that do not cover all residents. In addition, it may be

unethical or illegal under the Fair Housing Act and state legislations to target services based on

such predictors as race and gender, even if they are found to be associated with exit types.

The challenges are surmountable. Outreach and services that aim to facilitate positive exits

and reduce negative exits are practicable on a programmatic level for certain predictors. For

example, households entering assistance from referral pathways whose eligibility criteria include

homelessness or having a disability could be offered housing navigation and stability support and

connected with other resources and care available in the community. If household composition

is related to exit type, then services and outreach could be tailored appropriately. Linking

administrative data from other sectors such as health care could lead to a better understanding of

complex individual needs, provide insight into circumstances prior to exit, offer an opportunity

to assess post-housing assistance outcomes, and have the potential to be more sustainable than

PHA project-driven data collection. Successful cross-sector data linkages related to housing have

previously examined physical health, behavioral health, criminal activity, and income (Actionable

Intelligence for Social Policy, 2015; Albertson et al., 2020; Chetty, Hendren, and Katz, 2016; Ellen,

Dragan, and Glied, 2020; Laurent et al., 2020). However, most examples are limited in that they

linked across only one nonhousing sector or were one-off linkages of administrative data.

This article describes the results of a project that linked PHA data from a large, urban setting to

an existing integrated data hub for the purposes of examining exits from HUD-housing assistance.

The overall project, called Housing and Urban Development Health, Economic and Residential

Stability (HUD HEARS), addressed questions related to both exit events and outcomes following

exit. This article focuses on the linkage process, factors associated with exit and exit type, potential

implications for policies and programs, and the sustainability of cross-sector data work.

Exits from Housing Assistance: What Is Already Known

Exits Overall

Of the limited literature on exits from housing assistance, researchers have focused primarily

on two outcomes: duration of housing assistance and factors associated with exit from housing

assistance. Several studies used panel data or HUD data systems such as the Multifamily Tenant

Characteristics System and the Tenant Rental Assistance Certification System’s databases to explore

the topic, typically using survival analysis methods (Ambrose, 2005; Cortes, Lam, and Fein, 2008;

Dantzler and Rivera, 2019; Freeman, 2005; Geyer, Dastrup, and Finkel, 2019; Hungerford, 1996;

Kang, 2020; Lubell, Shroder, and Steffen, 2003; McClure, 2018; Olsen et al., 2005). Across the

studies there was general agreement that increased age, female gender, non-White race, disability,

and tighter rental markets were all associated with lower likelihood of exiting from housing. In

their study of New York City residents, Bahchieva and Hosier (2001) found somewhat similar

findings: that younger and White residents had higher exit rates. And even though that housing

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Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

market may not be typical of others in the United States, it has some similarities to this study

setting as a high-cost, urban environment.

Larger households were generally found to be more likely to exit, but evidence was mixed on the

presence of children. Ambrose (2005) found increased likelihood of exit among larger households

but only for project-based vouchers, whereas Cortes, Lam, and Fein (2008) found decreased

exits—especially if younger children were present. Geyer, Dastrup, and Finkel (2019) found that

the introduction of small-area fair-market rents increased the probability of exit and shortened

the median time to exit. Among Veterans Affairs Supportive Housing (VASH) participants, women

were more likely to still be housed after 1 year than were men (Kasprow et al., 2000), but having a

service-connected disability was associated with exiting (Montgomery et al., 2017).

Dantzler (2021) also used the Panel Study of Income Dynamics to examine factors associated with

exit but made a case that the policy setting should not be ignored. Although some demographic

findings reinforced earlier studies—with older age and longer time in housing being associated

with remaining in housing assistance—the largest effect was exit after welfare reforms were

introduced in 1996.

Exit Types

Little in the published literature focuses on positive or negative exits. HUD’s and states’ Family Self-

Sufficiency (FSS) programs might be expected to lead to positive exits, and Anthony (2005) found

that younger adults, single participants, those without children, those with high school diplomas,

and those who acquired more skills during the training were all more likely to succeed in the FSS

program and exit housing assistance. Rohe, Webb, and Frescoln (2016) found a small effect of

completion of the program on positive exits. However, the sample sizes in both evaluations were

small and the specific nature of the FSS programs in question limits generalizability to the wider

population receiving housing assistance.

Studies that concentrated on negative exits examined who was at risk of eviction or lease violation.

Among residents of Mercy Housing—a large affordable-housing organization—increased age, Asian

race, and living in senior or supported housing versus family housing were all associated with

reduced risk of lease violations, whereas female gender, Black or other race versus White, being a

larger household, and increased income were all associated with increased risk of lease violations

(Brisson and Covert, 2015). Because of the counterintuitive nature of the finding regarding income,

Brisson and Covert (2015) conducted further analyses and found that an increase in stable benefits

was associated with decreased risk of a lease violation but that increases in work income, variable

benefits income, and other income were all related to slightly higher likelihood of experiencing

lease violations. Richter et al. (2021) explored who received eviction orders compared with only

eviction filings. Though most of their findings were for all landlords combined, public housing and

nonprofit organizations were the landlords in more than a quarter of all filings; and an unknown

number of residents with private landlords would have been receiving HCVs. The authors found

that male gender, White race versus Black, having more children, and receiving an eviction filing in

the past were all associated with eviction orders versus receiving only eviction filings. Receiving an eviction filing by a public housing entity or nonprofit organization carried a lower risk of getting an

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eviction order—relative to a filing by a private entity. Among VASH participants, male gender, older

age, having alcohol and/or drug disorders, and having used acute care were all associated with

increased levels of eviction (Montgomery and Cusack, 2017).

Just one study examined differences between those with positive exits and those with negative exits

(Smith et al., 2014). There was no difference between positive and negative exits in terms of age,

gender, or household size. At the end of the study, those with positive exits were more likely to be

married, to have ever been married, and to be Hispanic, and those with negative exits were more

likely to be non-Hispanic Black. Those with positive exits were less likely to have ever been homeless,

less likely to live in overcrowded housing, and less likely to have a high housing-cost burden.

Methods

Setting and Participants

The authors included households served by the King County Housing Authority (KCHA) and the

Seattle Housing Authority (SHA) from 2012 to 2019 in King County, Washington. Reasons for exit

were available for SHA from 2012 onward and for KCHA from 2016 onward. The research was

approved by the Washington State Institutional Review Board.

SHA and KCHA serve clients situated predominantly in an urban or suburban setting, though King

County also encompasses a large rural area. Seattle and the surrounding area experienced huge

increases in population, growing at two to three times the national average of 7.4 percent from

2010 to 2020 (Office of Planning and Community Development, 2021). That population boom

has been accompanied by a large increase in wealth, with median annual income increasing from

$60,000 in 2010 to $102,000 in 2019 in Seattle and from $66,000 to $102,000 in King County

as a whole (not adjusted for inflation) (Public Health – Seattle & King County, 2022). Both the

population and income changes have put pressure on the housing market, leading to an average

rent increase of 43 percent from 2012 to 2017 (Regional Affordable Housing Task Force, 2019).

The tight rental market has been accompanied by increases in homelessness—with the Seattle/King

County Point-in-Time count growing by 50 percent since 2007—and greater demand for housing

assistance, which is available to only one in four eligible households (HUD, 2022b).

Both KCHA and SHA are Moving to Work (MTW) PHAs. MTW is a program that gives PHAs

greater flexibility in the ways they use federal funding, with the idea that they generate innovative

ideas and programs that can be rolled out nationally (HUD, 2022a). MTW agencies may be better

placed to respond to findings generated from cross-sector data analyses.

Data Sources and Variables

PHA demographic data come primarily from HUD Form 50058 Moving to Work, which collects

data on households and individuals receiving federal housing assistance. The authors used the

following variables from the 50058 data in the exit analyses: (1) head-of-household demographics:

gender—male, female, or both male and female reported over time, which the authors termed

multiple; age—younger than 25, 25–44, 45–61, and 62 or older, with senior-housing eligibility

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Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

beginning at age 62; race/ethnicity— American Indian/Alaskan Native, Asian, Black, Latina/o/x,

multiple race, Native Hawaiian/Pacific Islander, and White; self-reported disability; and length of

time in housing; and (2) household characteristics: household size, single caregiver—one adult and

one or more children in the household—and assistance type—project-based vouchers (PBVs), PH,

or tenant-based vouchers (TBVs). The authors used three categories of assistance type because the

policy and program implications vary across each group; PHAs have a much greater ability to work

with tenants in their own properties than they do with those renting in the private market or with

partner nonprofits.

Exit reasons are collected on a separate form and stored in a different data system but were

linked using the methods described below. Both KCHA and SHA had previously categorized exit

reasons as positive, neutral, negative, or part of an additional aging or health category. Study team

members with experience in working with housing data harmonized the existing categorizations

across both PHAs and made minor adjustments. Positive exits consisted of reasons perceived to

be likely associated with self-sufficiency—for example, increased income, homeownership, and

moving to nonsubsidized rentals. Negative exits such as eviction, lease violations, criminal activity,

or abandonment of a property were those expected to be associated with adverse life events and

poorer outcomes. Several exit reasons could not be clearly identified as positive or negative and

were classified as neutral. For example, exit for health reasons or moving in with family or friends

could be associated with a positive or a negative trajectory depending on the circumstances. A full

list of exit reasons and categories can be found in appendix A.

The authors restricted exits (1) to those with an at least 12-month gap between exit date and any

subsequent housing (termed true exits) and (2) to nondeath exits. If a head of household had

multiple exits during the study period, they used the most recent exit. If multiple exit categories

were recorded for a single event, they prioritized the reason that belonged to the smallest group:

positive, then negative, then neutral.

The HUD HEARS study looking at exits from housing drew from several other administrative

datasets: (1) Behavioral Health and Recovery Division (BHRD) service data that includes mental

health and substance use claims, (2) Employment Security Department (ESD) wage data, (3)

Healthcare for the Homeless Network (HCHN) data, (4) Homeless Management Information

System (HMIS), and (5) Medicaid claims data. The authors anticipated incorporating income when

looking at factors associated with exit data, but ESD wage data were not available at the time of

analysis and instead were used to examine post-exit outcomes. In addition, income data from the

50058 forms were not complete for all years and therefore could not be used.

Based on existing literature and PHA expertise, the authors hypothesized that health status

and prior housing instability would influence exits from housing and exit type. In addition

to the demographic factors listed above, the authors used BHRD data to identify people who

had experienced acute behavioral health crisis events in the 12 months prior to housing exit.

Homelessness was defined as one or more of the following in the 3 years prior to exit: appearing

in HMIS or HCHN data, having a housing status in BHRD data that indicated housing instability,

or having an address listed as “Homeless” in the Medicaid data (Johnson, McHugh, and Reimal,

2021). The authors used Medicaid data to identify those who had experienced emergency

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department (ED) visits or hospitalizations for any reason in the 12 months prior to housing exit

and those with one or more chronic conditions as defined by the Chronic Condition Warehouse

(Centers for Medicare & Medicaid Services, 2022). The authors also created an enhanced definition

of behavioral health crisis event that added behavioral-health-related ED visits from Medicaid to

the BHRD data. Collectively, the Medicaid-derived all-cause ED-visit, hospitalization, and chronic-

condition measures are proxies for a person’s health status.

Data Linkage

To link the data sources, the authors used an existing multisector data system. The King County

Integrated Data Hub (IDH) combines identities across several datasets, including BHRD, HCHN,

HMIS, and Medicaid. The IDH uses a mix of probabilistic and deterministic methods to match

individuals across data systems via a proprietary tool (Informatica, Redwood City, CA). PHA data in

the form of 50058 and exit data from both KCHA and SHA were probabilistically linked by name,

Social Security number, date of birth, and gender using the RecordLinkage package in R v4.2.0 and

RStudio v2022.2.3.492 (R Core Team, 2022; RStudio Team, 2022; Sariyar and Borg, 2020). IDH,

ESD, and PHA data were then linked using the same RecordLinkage approach. PHA addresses were

geocoded using an in-house Esri ArcGIS locator (Redlands, CA).

Statistical Analysis

The primary analyses aimed to answer two questions: What factors are associated with exiting

from housing assistance? And what factors are associated with each exit type? For both analyses,

the unit of analysis was the head of household. Although some exit reasons may apply to an entire

household, other reasons focus on the individual, and other household members may continue to

receive housing assistance.

To answer the first question, the authors randomly matched four controls who were heads of

household who remained in housing for each exit without replacement and assigned the controls

a pseudo-exit date that matched the exit date for the purposes of assessing demographic and other

variables noted above. The authors used a 4:1 ratio because greater ratios yield minimal gain in

power to detect differences and only a limited number of controls were available for matching

(Breslow, 2005). Controls were eligible for matching if they remained in housing for at least 12

months after the case exit date. Because the authors wanted to examine how each variable was

associated with exits, they did not match controls on any other characteristics. If the authors had

matched on a factor such as age, they would have artificially balanced the distribution of that factor

between those who exited and the controls, meaning that no relationship between the factor and

exiting would be found.

The authors first examined descriptive statistics to find programmatically meaningful differences in

characteristics as defined by subject matter experts who work with the PHA population. Then the

authors used a binomial logistic regression to evaluate the relationship between each variable and

exiting from housing. To examine factors associated with exit type, the authors used a multinomial

logistic regression with neutral exits as the reference category. The authors used the DHARMa R

package to conduct model checking (Hartig, 2022).

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Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

Secondary Analysis

Healthcare utilization data from ED visits, hospitalizations, and diagnosed chronic conditions were

available only for those enrolled in Medicaid prior to exiting. The authors therefore conducted

a secondary analysis of the subset of exiting and controls participants who had full, nondual—

meaning that they were not also enrolled in Medicare—Medicaid coverage for at least 7 of the

12 months prior to the exit or pseudoexit date. That minimum-coverage requirement ensures

that if a person visited an ED, was hospitalized, or was diagnosed with a chronic condition, the

event would likely be detected in the claims data (Washington State Health Care Authority, 2022).

Because the authors excluded Medicaid members with dual Medicare coverage, they also restricted

secondary analyses to those younger than age 62, because most older Medicaid recipients also have

Medicare, and Medicaid claims may be incomplete.

Results

Sample Size

There were 19,411 exit records in the KCHA and SHA data, 19,013 (97.9 percent) of which

matched to records in the 50058 data; multiple exit categories were found in 50 (0.3 percent) of

the matched exits; and extra categories were removed by prioritizing the smallest group: positive,

then negative, then neutral. An additional 260 exit events matched to individuals in the 50058 data

who were not recorded as heads of households and so were excluded, for a total of 18,748 heads

of household. After restricting to the study period during which exit reasons were consistently

recorded, after removing false exits wherein the individual remained receiving housing assistance

after a recorded exit, and after removing exits due to deaths, missing exit reasons, or other missing

variables, the authors analyzed 8,266 exits in the regression analysis: 2,610 negative, 4,538

neutral, and 1,118 positive (exhibit 1). The secondary analysis of PHA recipients who also had full,

non-dual Medicaid coverage prior to exit was limited to 3,001 households.

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Exhibit 1

Sample Size for Primary and Secondary Analyses

Head-of-household exits total: 18,748

Exits outside study period:

KCHA (<2016, >2018: 2,466

SHA (<2012, >2018): 2,511)

Exits in study period: 13,771

False exits (n = 3,201)

True exits: 10,570

Multiple exits per person (n = 194)

One exit per person: 10,376

Exits due to death (n = 1,931)

or missing exit reasons (n = 5)

Nondeath exits: 8,440

Missing demographics (n = 174)

Household demographics: 60

Program type: 20

Age: 105

Complete demographics

(primary analysis): 8,266

<7 months full Medicaid coverage

prior to exit (n = 5,083)

or aged 62+ (n = 182)

Nondual, full Medicaid coverage

(secondary analysis): 3,001

KCHA = King County Housing Authority. SHA = Seattle Housing Authority.

Source: NO HARMS integrated study dataset

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Descriptive Statistics

Heads of households who exited were more likely to have the following attributes than were those

who remained in housing (exhibit 2).

• Male (39.9 percent versus 34.5 percent).

• Shorter average tenure receiving housing assistance (median of 3.7 years versus 5.5 years).

• Receiving PBV assistance (43.4 percent versus 18.6 percent).

• Experienced recent homelessness (39.4 percent versus 22.8 percent).

• Have had a recent behavioral health crisis (6.9 percent versus 1.6 percent).

Exhibit 2

Demographics of Heads of Households Who Exited Versus Controls Who Did Not, by Exit Type (1 of 2)

Remained

Exited

Neutral Exit

Positive Exit Negative Exit

Variable

(N=25,162)

(N=8,266)

(N=4,538)

(N=1,118)

(N=2,610)

Age

Mean (years)

52.4

50.7

53.2

48.9

47.2

Median (years)

52

49

52

47

45

Senior (aged 62+)

29.9%

26.9%

33.3%

21.0%

18.3%

Gender

Female

16,117 (64.1%) 4,869 (58.9%)

2,628 (57.9%)

678 (60.6%)

1,563 (59.9%)

Male

8,692 (34.5%)

3,300 (39.9%)

1,862 (41%)

423 (37.8%)

1,015 (38.9%)

Multiple

353 (1.4%)

97 (1.2%)

48 (1.1%)

17 (1.5%)

32 (1.2%)

Race/Ethnicity

AI/AN

329 (1.3%)

158 (1.9%)

81 (1.8%)

<20

65 (2.5%)

Asian

2,464 (9.8%)

689 (8.3%)

421 (9.3%)

118 (10.6%)

150 (5.7%)

Black

8,558 (34%)

2,866 (34.7%)

1,413 (31.1%)

437 (39.1%)

1,016 (38.9%)

Latina/o/x

1,684 (6.7%)

561 (6.8%)

299 (6.6%)

72 (6.4%)

190 (7.3%)

Multiple

2,530 (10.1%)

737 (8.9%)

367 (8.1%)

114 (10.2%)

256 (9.8%)

NH/PI

203 (0.8%)

67 (0.8%)

34 (0.7%)

<10

25 (1%)

White

9,394 (37.3%)

3,188 (38.6%)

1,923 (42.4%)

357 (31.9%)

908 (34.8%)

Time in Housing

Mean time (years)

5.9

5

4.5

6.2

5.6

Median time (years)

5.5

3.7

3

5.6

4.5

Household Characteristics

Head of

44.3%

42.0%

45.4%

25.2%

43.3%

household disability

Mean household size

2.2

2

1.7

2.6

2.1

Median household size

1

1

1

2

1

Single caregiver

19.0%

17.3%

15.0%

14.5%

22.6%

Program Type

PBV

4,672 (18.6%)

3,586 (43.4%)

2,761 (60.8%)

308 (27.5%)

517 (19.8%)

PH

7,118 (28.3%)

1,840 (22.3%)

912 (20.1%)

331 (29.6%)

597 (22.9%)

TBV

13,372 (53.1%) 2,840 (34.4%)

865 (19.1%)

479 (42.8%)

1,496 (57.3%)

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Exhibit 2

Demographics of Heads of Households Who Exited Versus Controls Who Did Not, by Exit Type (2 of 2)

Remained

Exited

Neutral Exit

Positive Exit Negative Exit

Variable

(N=25,162)

(N=8,266)

(N=4,538)

(N=1,118)

(N=2,610)

Health and Homelessness Events

Experienced recent

5,726 (22.8%)

3,256 (39.4%)

1,972 (43.5%)

226 (20.2%)

1,058 (40.5%)

homelessness

Experienced 1+

behavioral health crisis

408 (1.6%)

570 (6.9%)

339 (7.5%)

18 (1.6%)

213 (8.2%)

events in year prior to exit

(excl. Medicaid ED visits)

Experienced 1+

behavioral health crisis

313 (0.9%)

240 (2.8%)

122 (8.0%)

<10

82 (7.2%)

events in year prior to

exit (inc. ED visits)1

Average # ED visits in

0.8

1

2

0.8

2.1

year prior to exit1

Experienced 1+ ED visits 13,435 (36.6%) 3,381 (40.0%) 862 (56.6%)

118 (34.7%)

689 (60.5%)

in year prior to exit1

Average # hospitaliza-

tions in year prior to exit

6.1

7.8

17.5

6.8

15.4

(per 100 people)1

Experienced 1+

hospitalizations in year

1,657 (4.5%)

440 (5.2%)

175 (11.5%)

19 (5.6%)

107 (9.4%)

prior to exit1

Average # of

1

0.9

1.8

1.5

2

chronic conditions1

AI/AN = American Indian/Alaskan Native. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

1 Health event data available for those aged <62 enrolled in Medicaid. Remained N = 9,234. Exited N = 3,001. Negative N = 1,139. Neutral N = 1,522.

Positive N = 340.

Source: NO HARMS integrated study dataset

Race, household size, whether there was a single caregiver, or whether the head of household had

a disability did not substantially vary between those exiting and those remaining in housing. In

the secondary analysis of Medicaid recipients, people exiting had greater healthcare use in the year

prior to exit when it came to both ED visits—55.6 percent had more than one versus 46.9 percent

of people remaining—and hospitalizations: 10.0 percent versus 8.8 percent.

In a comparison of exits by type, those with neutral exits tended to be older than those with

positive or negative exits (median age 52 years compared with 47 and 45 years, respectively), were

slightly more likely to be male (41.0 percent versus 37.8 percent and 38.9 percent), were more

likely to be White (42.4 percent versus 31.9 percent and 34.8 percent), and had shorter average

tenures in housing assistance (median of 3 years versus 5.6 and 4.5 years) (exhibit 2). Those with

positive exits had larger average household sizes (mean of 2.6 versus 1.7 and 2.1 for neutral and

negative exits, respectively), were more likely to be living in public housing (29.6 percent versus

20.1 percent and 22.9 percent), and were less likely to have experienced recent homelessness (20.2

percent versus 43.5 percent and 40.5 percent) or behavioral crises (1.6 percent versus 7.5 percent

and 8.2 percent). Among Medicaid recipients, those with positive exits had lower levels of recent

Cityscape 291

Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

ED visits (34.7 percent versus 56.6 percent and 60.5 percent), hospitalizations (5.6 percent versus

11.5 percent and 9.4 percent), and chronic conditions (average of 1.5 versus 1.8 and 2.0).

Among both those who remained and those who exited, people with 7 or more months of full

Medicaid coverage in the year prior to exit were younger (44/41 years for remained/exited and

had Medicaid versus 59/56 years for those without Medicaid), were more likely to be female

(70.2 percent/64.6 percent versus 60.0 percent/55.3 percent), were more likely to be Black (43.8

percent/43.5 percent versus 27.6 percent/29.1 percent), were more likely to have larger households

(mean 2.8/2.4 versus 1.8/1.7), and were more likely to have a single caregiver (30.1 percent/28.4

percent versus 11.8 percent/10.4 percent) but less likely to have disabilities (35.4 percent/37.0

percent versus 50.1 percent/45.2 percent) (exhibit B-1). Among those with Medicaid coverage,

those exiting were more likely to be receiving PBVs than were those who remained (49.4 percent

versus 22.2 percent).

Regression Results

Results from the regression analysis show that after adjusting for other factors, male gender, receiving

a project-based voucher, homelessness within the previous 3 years, and having behavioral health crisis

events were all associated with increased odds of exits of any type (exhibit 3). Being older than age

25, increased time in housing (more than 6 years), larger household size, having a single-caregiver

household, and having a disability were all associated with decreased odds of exit. Race/ethnicity was

not associated with exiting. For the secondary analysis of housing recipients who also had more than

7 months of full Medicaid coverage in the year prior to exit, experiencing one or more ED visits in

the year prior to exit was positively associated with exit (adjusted odds ratio (aOR): 1.27, 95 percent

confidence interval (CI): 1.16–1.40, p<0.001); experiencing a hospitalization in the same time frame

was not associated with exit; and having two or more chronic conditions was negatively associated

with exits (0.75, 95 percent CI: 0.68–0.83, p<0.001) (exhibit 3 and exhibit B-2).

Exhibit 3

Regression Output for Heads of Households Who Exited Versus Controls Who Did Not (1 of 2)

Variable

Odds Ratio1

95% CI

Age

<25

ref

—

25-44

0.67***

0.58–0.78

45-61

0.48***

0.41–0.55

62+

0.50***

0.43–0.58

Gender

Female

ref

—

Male

1.08**

1.02–1.15

Multiple

0.96

0.76–1.21

Race/Ethnicity

White

ref

—

AI/AN

1.25*

1.01–1.53

Asian

0.92

0.83–1.01

Black

1.06

1.00–1.13

292 Refereed Papers

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Exhibit 3

Regression Output for Heads of Households Who Exited Versus Controls Who Did Not (2 of 2)

Variable

Odds Ratio1

95% CI

Race/Ethnicity

Latino

0.97

0.87–1.09

Multiple

1.00

0.90–1.10

NH/PI

1.10

0.81–1.47

Time in Housing

<3

ref

—

3-5.99

1.15***

1.07–1.23

6-9.99

0.95

0.89–1.03

10+

1.16***

1.07–1.26

Household Characteristics

Head of household disability

0.70***

0.66–0.75

Household size

0.90***

0.89–0.92

Single caregiver

0.76***

0.70–0.82

Program Type

TBV

ref

—

PBV

2.94***

2.75–3.14

PH

1.20***

1.12–1.29

Health and Homelessness Events

Experienced recent homelessness

1.41***

1.32–1.51

Experienced 1+ behavioral health crisis event in

2.91***

2.53–3.35

year prior to exit (excl. ED visits)

Experienced 1+ behavioral health crisis event in

2.12***

1.69–2.66

year prior to exit (incl. ED visits)2

Experienced 1+ ED visit in year prior to exit2

1.27***

1.16–1.40

Experienced 1+ hospitalization in year prior to exit2

0.96

0.82–1.12

2+ chronic conditions2

0.75***

0.68–0.83

AI/AN = American Indian/Alaskan Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

1 * = p<0.05. ** = p<0.01. *** = p<0.001.

2 Health event data available only for those aged <62 enrolled in Medicaid (N = 9,234 for controls, 3,001 for exits).

Source: NO HARMS integrated study dataset

Among those who exited, male gender, a longer time in housing, and living in public housing were

all associated with both negative exits and positive exits when compared with neutral exits (exhibit

4). Older age was negatively associated with both positive and negative exits, and, although not

statistically significant, working-age adults aged 25–61 were more likely to have positive exits.

Race was generally not associated with positive versus neutral exits, though people who identified

as Black were slightly more likely to have positive exits than were Whites. People in the American

Indian/Alaskan Native, Black, and Latina/o/x groups were all more likely to have negative exits

than were Whites. Those receiving PBVs were substantially less likely to have negative or positive

exits compared with those receiving TBVs, and those in PH were also less likely to have negative

exits but were not significantly different with regard to positive exits. Having a single-caregiver

household, experiencing recent homelessness, and experiencing more than one crisis event or ED

visit were positively associated with negative exits and negatively associated with positive exits.

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Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

Exhibit 4

Regression Output for Exit Type

Negative/Positive Exits Versus Neutral Exits

(Neutral N=4,538)

Variable

Negative Exits (N=2,610)

Positive Exits (N=1,118)

Odds Ratio1

95% CI

Odds Ratio1

95% CI

Age

<25

ref

—

ref

—

25-44

1.02

0.78–1.33

1.43

0.95–2.16

45-61

0.87

0.66–1.15

1.43

0.94–2.17

62+

0.43***

0.32–0.58

0.59*

0.38–0.91

Gender

Female

ref

—

ref

—

Male

1.33***

1.18–1.51

1.34***

1.14–1.56

Multiple

1.00

0.61–1.64

1.16

0.64–2.11

Race/Ethnicity

White

ref

—

ref

—

AI/AN

1.86**

1.26–2.74

0.92

0.49–1.76

Asian

0.80

0.64–1.01

0.99

0.77–1.27

Black

1.25***

1.10–1.43

1.20*

1.01–1.43

Latino

1.30*

1.03–1.63

1.13

0.84–1.52

Multiple

1.10

0.90–1.35

1.14

0.87–1.48

NH/PI

1.27

0.69–2.32

0.85

0.37–1.94

Time in Housing

<3

ref

—

ref

—

3-5.99

1.18*

1.01–1.37

1.28*

1.05–1.56

6-9.99

1.14

0.97–1.34

1.36**

1.11–1.68

10+

1.20*

1.00–1.43

1.54***

1.24–1.92

Household Characteristics

Head of household disability

1.03

0.90–1.17

0.53***

0.45–0.63

Household size

0.98

0.94–1.02

1.11***

1.06–1.16

Single caregiver

1.33***

1.12–1.57

0.62***

0.50–0.77

Program Type

TBV

ref

—

ref

—

PBV

0.07***

0.06–0.09

0.31***

0.26–0.38

PH

0.45***

0.39–0.52

0.86

0.71–1.03

Health and Homelessness Events

Experienced recent homelessness

1.76***

1.53–2.03

0.63***

0.52–0.76

Experienced 1+ behavioral health crisis

1.68***

1.36–2.08

0.43***

0.26–0.71

event in year prior to exit (excl. ED visits)

Experienced 1+ behavioral health crisis

1.50*

1.06–2.12

0.70

0.31–1.56

event in year prior to exit (incl. ED visits)2

Experienced 1+ ED visit in year prior to exit2

1.30**

1.08–1.58

0.62***

0.47–0.82

Experienced 1+ hospitalization in year

0.79

0.59–1.06

0.74

0.44–1.26

prior to exit2

2+ chronic conditions2

0.91

0.75–1.11

0.96

0.72–1.29

AI/AN = American Indian/Alaskan Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

1 * = p<0.05. ** = p<0.01. *** = p<0.001.

2 Health event data available only for those aged <62 enrolled in Medicaid (N = 1,522/1,139/340 for neutral/negative/positive exits).

Source: NO HARMS integrated study dataset

294 Refereed Papers

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Associated with Exiting from Federal Housing Assistance by Exit Type

Conclusion

After adjusting for other factors, the authors found that male gender, receiving PBV or PH

assistance versus TBV, homelessness within the previous 3 years, and having a behavioral health

crisis event were all associated with increased odds of exit. Conversely, age older than 25, increased

time in housing (more than 6 years), larger household size, having a single-caregiver household,

and having a disability were all associated with decreased odds of exit. Demographics associated

with exit were similar to previous studies with regard to age, gender, disability, and time in housing

(Ambrose, 2005; Cortes, Lam, and Fein, 2008; McClure, 2018). Although there was no association

for most race/ethnicity groups. The factors with the strongest association with exit were (1) recent

behavioral health crisis events (aORs of 2.91 for all heads of household and 2.12 when restricted

to Medicaid members but including behavioral health ED visits) and (2) receiving PBV assistance

(aOR of 2.94).

Among those who exited, there was some commonality between positive and negative exits,

compared with neutral exits. Both male gender and longer time in housing were positively

associated with both positive and negative exits, and age older than 62 and receiving PBV

assistance were negatively associated with both positive and negative exits. It is unclear why these

factors have similar associations for both positive and negative exits, and a deeper analysis of

specific exit reasons may yield a better understanding of the finding.

There were also substantial differences in factors associated with positive and negative exits. Those

who are American Indian/Alaskan Natives, Black, or Latina/o/x were more likely to have negative

exits compared with Whites; and Asians were less likely to have negative exits. The reasons for

differences by race/ethnicity are unclear; there may be systemic factors that affect certain race/

ethnicity groups differently, or race/ethnicity may be a proxy for additional factors not included

in the model. Heads of household in single-caregiver households, who had disabilities, who

experienced behavioral health crisis events, or who had recent ED visits were all more likely

to have negative exits and less likely to have positive exits compared with neutral exits. These

associations suggest that single caregivers or those with health problems face barriers to working

and may also experience other obstacles to stable housing. Those with recent homelessness were

less likely to have positive exits, but there was no difference between negative and neutral exits.

The findings are similar to those found in a previous examination of factors associated with exit

type (Smith et al., 2014), which found no difference between age and gender and a negative

association between prior homelessness and positive exits. However, this was the first study to

look at factors associated with exit types among all federally supported housing recipients rather

than a Moving to Opportunity subset, which included very low-income households with children.

The large sample size facilitates a robust analysis and examination of smaller demographic groups.

Another major strength was the addition of data from Medicaid, behavioral health, and homeless

management systems. Factors derived from those sources had some of the strongest associations

with both exits in general and exit type.

Cityscape 295

Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

Limitations

There were some limitations to the study, which are shared by most of the previous studies on

this topic. First, the authors looked only at heads of households, which presents two challenges:

(1) other household members’ experiences may affect the entire household, but that does not get

captured in the factors examined; for example, an incarceration or health event could destabilize

the household; and (2) other household members exit housing for varying reasons, none of which

are captured in the existing data and some of which may differ from the head of household’s reason

for exiting. PHAs could consider expanding their data collection processes to include reasons for

each individual person’s exit.

A second limitation is lack of contextual information behind the recorded exit reason. Previous

studies have taken a mixed-methods approach to explore exit experiences in more depth (Smith et

al., 2014), and although the authors did not have the resources to incorporate qualitative methods

in this study, they strongly encourage that approach in future work—or even as a routine part of

data collection at the time of exit. Guidance for PHAs on the collection of exit information could

also improve data quality and completeness. KCHA and SHA had different sets of exit reasons that

had to be harmonized, and more than 20 percent of exits had nonspecific reasons such as “Client

would not disclose” or “Moved out, location unknown.” The revised 50058 MTW form will in the

future collect information on reasons for end of participation, but limitations in household-level

information and lack of context will remain.

The categorization of exit reasons may also be problematic. For example, households that are over

income are considered positive exits. However, in the Seattle metropolitan area, an exit for that

reason does not guarantee that the household is no longer rent burdened; a household earning

just over 80 percent of the area median income would still be paying 38 percent of income for the

median rental price (Ellis, 2022; KCHA, 2022).

Finally, the secondary analysis of health-related factors was restricted to those with Medicaid coverage,

who were not demographically representative of the entire housing population (exhibit B-1).

Policy and Program Implications

The authors’ analysis identified several factors associated with exiting from housing assistance and

exit type. How PHAs use that information will depend on policy imperatives, resources, and what

is legally permissible. With regard to policy, the framing for the undertaking of the HUD HEARS

project was in the context of HUD’s desire to increase the number of positive exits from assisted

housing, with the expectation that that would increase the number of people able to receive

housing assistance. The results show that there is a ways to go to achieving that goal; positive exits

made up only 13.5 percent of all nondeath exits in the study.

For PHAs that are working to increase positive exits while minimizing negative exits, the findings

present some challenges. First, certain factors such as gender and type of housing assistance had

the same associations for both positive and negative exit types, compared with neutral. Second,

some factors are generally immutable such as age, gender, and race/ethnicity; and it may be illegal

or unethical to dedicate services based solely on those factors. To address such challenges, further

296 Refereed Papers

Seeing the Big Picture with Multi-Sector Data: Factors

Associated with Exiting from Federal Housing Assistance by Exit Type

investigation into why those characteristics are associated with exits and exit types could help

adapt services accordingly.

When there was a more specific association between factors and exit type, policy and program

implications became clearer. Although not statistically significant, the relationship between being

of working age and positive exits suggests that emphasis on workforce training and other self-

sufficiency programs may be warranted. Longer tenure in housing assistance was associated with

increased odds of a positive exit, which suggests that a stabilization period is required before a

household can get itself into a position in which positive exits are more possible. PHAs may wish

to investigate what it is about the early years of housing assistance that is not conducive to positive

exit and then determine what can be done to help households through the transition period.

Knowing that recent homelessness is a risk factor for negative exits suggests that efforts to support

people in their transitions from homelessness into housing are crucial. Indeed, the PHAs in

this study are already participants in federal initiatives involving specific voucher types such

as Emergency Housing Vouchers and Veterans Affairs Supportive Housing vouchers that pair

housing with supportive services. The PHAs also (1) fund supportive initiatives through their own

programming, (2) contract with community-based organizations and local governments, and (3)

maintain referral partnerships with local providers. However, not all PHAs are in a position to do

this; KCHA and SHA can undertake these programs through grant funding and because their MTW

authority offers flexibility in the ways funds can be used. PHAs without MTW authority are less

able to resource these kinds of support.

Associations between both ED visits and crisis events with negative exits emphasize that housing

is interconnected with other aspects of a person’s life. The policy and programmatic implications

for PHAs are complicated by laws such as the Health Insurance Portability and Accountability

Act, which places some limits on how health data can be shared. However, such restrictions can

be ethically addressed by obtaining appropriate and informed consent from housing assistance

recipients, meaning that holistic interventions that encompass health elements and housing

elements are possible. Such undertakings will require collaborations between PHAs and healthcare

organizations that have mutual interests in avoiding both ED visits and negative exits.

Summary

The data platform described here offers a wealth of opportunities beyond this study. PHAs can

obtain information about physical or mental health conditions, prior homelessness, or other

factors that could influence outcomes for their tenants. The data platform also provides a means

of examining events after exit from housing assistance, and planned future studies will examine

health, economic, and residential-stability outcomes. By the very nature of an exit, housing

recipients disappear from PHA data systems, and PHAs know little about what happens to

households beyond that point of exit. By building on an existing administrative data linkage,

this analysis and any other kind of examination of postexit outcomes can become routine data

processes that PHAs use for evaluating programs and monitoring progress toward goals rather than

being one-off research projects. The authors believe that such an approach can serve as a model

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Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

that PHAs can follow for establishing cross-sector partnerships with health departments and other

groups that have an interest in the health and well-being of housing assistance recipients.

This study adds important information to the discussion about exits from housing assistance. It

also demonstrates the value of using cross-sector data linkages to gain more holistic pictures of the

people being served. PHAs can use the knowledge from this study to more completely learn who is

most likely to experience a negative exit and then adapt programs and policies accordingly.

298 Refereed Papers

Seeing the Big Picture with Multi-Sector Data: Factors

Associated with Exiting from Federal Housing Assistance by Exit Type

Appendix A

Exhibit A-1

Exit Reasons and Categories (1 of 2)

Exit Category

Exit Reason

Negative

180 Days Away From Assisted Unit

Negative

Absence – Extended Leave

Negative

Absence – Incarceration

Negative

Absence – Treatment/Hospital

Negative

Absence – Treatment/Hospital

Negative

Client Location Unknown/Abandoned Unit

Negative

Criminal Activity

Negative

Domestic Violence

Negative

Eviction – Abandonment

Negative

Eviction – Judgment/Physical

Negative

Eviction – Judgment/Physical – Criminal

Negative

Eviction – Judgment/Physical – Other

Negative

Eviction – Nonpayment

Negative

Failure to Provide Information

Negative

Failure to Complete Housing Quality Standards Inspection

Negative

Failure to Complete Reexamination

Negative

Fraud – Household Composition

Negative

Fraud – Household Income

Negative

Fraud – Other

Negative

Housing Quality Standards Breach

Negative

Inspection/Damages

Negative

Landlord Eviction

Negative

Lease Enforcement

Negative

Lease Violation – Criminal

Negative

Lease Violation – Noncriminal

Negative

Location

Negative

More than 60 Days Absent From the Unit

Negative

Moved – Shelter

Negative

Moved – Transitional Housing Program

Negative

Neighborhood Quality

Negative

Nonpayment of Rent

Negative

Nonpayment of Retro Rent

Negative

Noncompliance – Citizenship/Immigration

Negative

Noncompliance – Criminal Activity

Negative

Noncompliance – Housing Quality Standards

Negative

Noncompliance – Paperwork Violation

Negative

Noncompliance – Payment Plan/Debt to SHA

Negative

Noncompliance – Program Partnership

Negative

Other Violation of Participant Obligations

Negative

PB/MR Moved due to Incarceration

Negative

PB/MR Moved to Shelter

Negative

Rent Too High

Negative

Section 8 PB Failed Social Services Program

Negative

Serious/Repeated Lease Violations (Criminal)

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Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

Exhibit A-1

Exit Reasons and Categories (2 of 2)

Exit Category

Exit Reason

Negative

Serious/Repeated Lease Violations (Noncriminal)

Negative

Subsidy in Jeopardy Client Choice

Negative

Unit/Property Quality

Negative

Voucher Expired

Neutral

Client Would Not Disclose Reason

Neutral

Deceased

Neutral

Expired – Ported Out

Neutral

Expired – Term-Limit Program

Neutral

Family Unification Program Youth 18-Month Expiration

Neutral

Health

Neutral

Ineligible – Citizenship/Immigration

Neutral

Moved – Changed Subsidy Program Type

Neutral

Moved – Needed Higher Level of Services

Neutral

Moved In With Family or Friends

Neutral

Moved to Non-KCHA-Subsidized Rental

Neutral

Mutual Termination

Neutral

No Longer Used as of 2016-09-14 (other)

Neutral

Other

Neutral

Other Subsidized Housing/Housing Choice Voucher

Neutral

PB/MR Moved Out Location Unknown

Neutral

PB/MR Moved to Hospital or Assisted Living

Neutral

PB/MR Moved to Non-Time-Limited Subsidized Housing

Neutral

PB/MR Moved to Temporary Housing With Family or Friends

Neutral

PB/MR Moved to Transitional Housing Program

Neutral

PM Move to KCHA Section 8 Voucher

Neutral

Port Out Termination

Neutral

Section 8 Absorption

Neutral

Section 8 Cross-Absorption

Neutral

Section 8 Incoming Portability Move Out

Neutral

Section 8 Sponsor-Based Provider-Based Move Out

Neutral

Transitional Housing Graduate to any Section 8 Voucher

Neutral

Transitional Housing Graduate to KCHA Managed Units

Neutral

Transitional Housing Graduate to KCHA PBA

Neutral

Transitional Housing Graduate to Other Subsidized Rental

Neutral

Transitional Housing Nongraduate Early Program Exit

Neutral

Vacated PB/MR Unit

Neutral

Voluntary Self-Termination

Positive

180 Days $50 or Less Housing Assistance Plan

Positive

180 Days Zero Housing Assistance Plan

Positive

Graduated – 180 Days $50 or Less Housing Assistance Plan

Positive

Homeownership

Positive

Moved to Nonsubsidized Rental

Positive

PB/MR moved to Non-Time Limited Market Rate

Positive

Section 8 Over Income

Positive

Transitional Housing Graduate to Nonsubsidized Rental

KCHA = King County Housing Authority. MR = mod rehab. PB = project-based. PBA = project-based assistance. SHA = Seattle Housing Authority.

Source: KCHA and SHA

300 Refereed Papers

Seeing the Big Picture with Multi-Sector Data: Factors

Associated with Exiting from Federal Housing Assistance by Exit Type

Appendix B

Exhibit B-1

Demographics of Heads of Households Who Exited Versus Those Who Did Not—by Medicaid

Enrollment Status

Remained,

Remained,

Exited, no

Exited,

Variable

no Medicaid

Medicaid

Medicaid

Medicaid

(N=15,214)

(N=9,948)

(N=5,083)

(N=3,183)

Age

Mean (years)

58

44

56.5

41.4

Median (years)

59

44

56

41

Senior (aged 62+)

44.8%

7.2%

40.1%

5.7%

Gender

Female

9,131 (60%)

6,986 (70.2%)

2,813 (55.3%)

2,056 (64.6%)

Male

5,875 (38.6%)

2,817 (28.3%)

2,209 (43.5%)

1,091 (34.3%)

Multiple

208 (1.4%)

145 (1.5%)

61 (1.2%)

36 (1.1%)

Race/Ethnicity

AI/AN

171 (1.1%)

158 (1.6%)

75 (1.5%)

83 (2.6%)

Asian

1,763 (11.6%)

701 (7%)

522 (10.3%)

167 (5.2%)

Black

4,202 (27.6%)

4,356 (43.8%)

1,481 (29.1%)

1,385 (43.5%)

Latina/o/x

1,011 (6.6%)

673 (6.8%)

339 (6.7%)

222 (7%)

Multiple

1,539 (10.1%)

991 (10%)

468 (9.2%)

269 (8.5%)

NH/PI

119 (0.8%)

84 (0.8%)

45 (0.9%)

22 (0.7%)

White

6,409 (42.1%)

2,985 (30%)

2,153 (42.4%)

1,035 (32.5%)

Time in Housing

Mean time (years)

6.2

5.5

5.4

4.4

Median time (years)

6.2

4.5

4.3

3

Household Characteristics

Head of household disability

50.1%

35.4%

45.2%

37.0%

Mean household size

1.8

2.8

1.7

2.4

Median household size

1

2

1

2

Single caregiver

11.8%

30.1%

10.4%

28.4%

Program Type

PBV

2,462 (16.2%)

2,210 (22.2%)

2,013 (39.6%)

1,573 (49.4%)

PH

4,985 (32.8%)

2,133 (21.4%)

1,330 (26.2%)

510 (16%)

TBV

7,767 (51.1%)

5,605 (56.3%)

1,740 (34.2%)

1,100 (34.6%)

Health and Homelessness Events

Experienced recent homelessness

2,373 (15.6%)

3,353 (33.7%)

1,448 (28.5%)

1,808 (56.8%)

Experienced 1+ behavioral health

220 (1.4%)

188 (1.9%)

343 (6.7%)

227 (7.1%)

crisis events in year prior to exit

(excl. Medicaid ED visits)

AI/AN = American Indian/Alaskan Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

Source: NO HARMS integrated study dataset

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Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

Exhibit B-2

Regression Output for Heads of Households Who Exited Versus Controls Who Did Not

(Medicaid Population)

Variable

Odds Ratio1

95% CI

Age

<25

ref

—

25-44

0.67***

0.56–0.81

45-61

0.50***

0.41–0.61

Gender

Female

ref

—

Male

1.05

0.94–1.17

Multiple

0.97

0.65–1.43

Race/Ethnicity

White

ref

—

AI/AN

1.23

0.90–1.67

Asian

0.94

0.77–1.15

Black

1.03

0.93–1.15

Latino

0.92

0.76–1.10

Multiple

0.90

0.76–1.07

NH/PI

0.89

0.52–1.45

Time in Housing

<3

ref

—

3-5.99

1.18**

1.05–1.32

6-9.99

1.16*

1.01–1.32

10+

1.22**

1.05–1.42

Household Characteristics

Head of household disability

0.81***

0.72–0.90

Household size

0.93***

0.90–0.96

Single caregiver

0.82***

0.73–0.92

Program Type

TBV

ref

—

PBV

2.80***

2.52–3.11

PH

1.26***

1.11–1.43

Health and Homelessness Events

Experienced recent homelessness

1.74***

1.57–1.94

Experienced 1+ behavioral health crisis event in

2.12***

1.69–2.66

year prior to exit (incl. ED visits)

Experienced 1+ ED visit in year prior to exit

1.27***

1.16–1.40

Experienced 1+ hospitalization in year prior to exit

0.96

0.82–1.12

2+ chronic conditions

0.75***

0.68–0.83

AI/AN = American Indian/Alaskan Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

1 * = p<0.05. ** = p<0.01. *** = p<0.001.

Note: Health event data available only for those aged <62 enrolled in Medicaid (N = 9,234 for controls, 3,001 for exits).

Source: NO HARMS integrated study dataset

302 Refereed Papers

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Associated with Exiting from Federal Housing Assistance by Exit Type

Exhibit B-3

Regression Output for Exit Type (Medicaid Population)

Negative/Positive Exits Versus Neutral Exits

(Neutral N=1,522)

Variable

Negative Exits (N=1,139)

Positive Exits (N=340)

Odds Ratio1

95% CI

Odds Ratio1

95% CI

Age

<25

ref

—

ref

—

25-44

0.97

0.70–1.35

0.99

0.58–1.67

45-61

0.86

0.60–1.22

0.92

0.53–1.61

Gender

Female

ref

—

ref

—

Male

1.08

0.88–1.34

1.21

0.90–1.63

Multiple

0.77

0.33–1.82

2.14

0.85–5.37

Race/Ethnicity

White

ref

—

ref

—

AI/AN

1.67

0.98–2.85

0.56

0.17–1.92

Asian

0.85

0.54–1.33

1.35

0.82–2.22

Black

1.14

0.93–1.40

1.00

0.74–1.36

Latino

1.24

0.87–1.79

1.26

0.74–2.14

Multiple

0.95

0.68–1.33

0.93

0.57–1.54

NH/PI

2.58

0.90–7.36

1.19

0.23–6.12

Time in Housing

<3

ref

—

ref

—

3-5.99

1.41**

1.12–1.78

1.36

0.96–1.93

6-9.99

1.55**

1.18–2.02

1.61*

1.10–2.36

10+

1.85***

1.35–2.53

2.49***

1.63–3.82

Household Characteristics

Head of household disability

0.90

0.72–1.13

0.48***

0.33–0.68

Household size

0.93*

0.88–0.99

1.10*

1.02–1.18

Single caregiver

1.12

0.89–1.40

0.56***

0.40–0.78

Program Type

TBV

ref

—

ref

—

PBV

0.11***

0.09–0.14

0.59**

0.42–0.82

PH

0.82

0.63–1.07

2.08***

1.45–2.98

Health and Homelessness Events

Experienced recent homelessness

2.12***

1.69–2.65

0.87

0.63–1.20

Experienced 1+ behavioral health

crisis event in year prior to exit

1.50*

1.06–2.12

0.70

0.31–1.56

(incl. ED visits)

Experienced 1+ ED visit in year

1.30**

1.08–1.58

0.62***

0.47–0.82

prior to exit

Experienced 1+ hospitalization in

0.79

0.59–1.06

0.74

0.44–1.26

year prior to exit

2+ chronic conditions

0.91

0.75–1.11

0.96

0.72–1.29

AI/AN = American Indian/Alaskan Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

1 * = p<0.05. ** = p<0.01. *** = p<0.001.

Source: NO HARMS integrated study dataset

Cityscape 303

Matheson, Colombara, Pennucci, Shannon, Chan, Suter, and Laurent

Acknowledgments

The authors would like to thank Titus Chembukha, Tyler Corwin, Carolina Johnson, and Karen

Spoelman, all at the King County Department of Community and Human Services, for their

assistance with the behavioral health definitions.

Authors

Alastair I. Matheson is assistant chief, data and informatics strategy at the Assessment, Policy

Development, and Evaluation Unit, Public Health – Seattle & King County and affiliate lecturer

at the Department of Epidemiology, University of Washington. Danny V. Colombara is an

epidemiologist at the Assessment, Policy Development, and Evaluation Unit, Public Health

– Seattle & King County and affiliate assistant professor at the Department of Epidemiology,

University of Washington. Annie Pennucci is director of impact and evaluation at the Social Impact

Department, King County Housing Authority. Tyler Shannon is a senior management analyst at

the Social Impact Office, King County Housing Authority. Andy Chan is a strategic advisor at

the Housing Choice Vouchers Department, Seattle Housing Authority. Megan Suter is an applied

epidemiology fellow at the Assessment, Policy Development, and Evaluation Unit, Public Health

– Seattle & King County. Amy A. Laurent is a senior epidemiologist at the Assessment, Policy

Development, and Evaluation Unit, Public Health – Seattle & King County.

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Cityscape 307

308 Refereed Papers

by Brian Y. An, Andrew Jakabovics, Jing Liu, Anthony W. Orlando, Seva Rodnyansky, Richard

Voith, Sean Zielenbach, and Raphael W. Bostic

Factors Affecting Spillover

Impacts of Low-Income Housing

Tax Credit Developments: An

Analysis of Los Angeles

Brian Y. An

Georgia Institute of Technology

Andrew Jakabovics

Enterprise Community Partners

Jing Liu

Econsult Solutions

Anthony W. Orlando

California State Polytechnic University, Pomona

Seva Rodnyansky

Occidental College

Richard Voith

Econsult Solutions

Sean Zielenbach

SZ Consulting

Raphael W. Bostic

Federal Reserve Bank of Atlanta

Andrew Jakabovics is employed by Enterprise Community Partners, whose subsidiary, Enterprise

Community Investments (ECI), syndicates low-income housing tax credits. Although ECI may have

syndicated tax credits attached to properties analyzed in this study, all the data came from public datasets

and independent proprietary sources. No ECI employees or resources participated in the research.

JPMorgan Chase & Co. supported this work. The funders were not involved in conducting this research or

in the preparation of this article. The views in this paper do not necessarily reflect the views of the Federal Reserve Bank of Atlanta or the Federal Reserve System.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 309

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Abstract

The Low-Income Housing Tax Credit (LIHTC) program is one of the largest sources of financing for

affordable housing in the United States. Contrary to many residents’ fears, research typically shows

that LIHTC-financed properties generate positive spillover impacts in their surrounding communities

in the form of increased housing prices. Some critics yet suspect that the overall positive effects obscure

the properties’ negative impacts for a significant subset of neighborhoods. This article examines these

concerns by assessing the housing price effects of LIHTC properties in Los Angeles. The authors explore

how the effects differ based on various characteristics of the LIHTC property and of the surrounding

neighborhood. The authors supplement these statistical analyses with interviews of key affordable

housing developers to understand their decisionmaking process regarding the siting and structuring of

LIHTC properties. Regardless of the property or neighborhood characteristics, LIHTC developments in

the region have positive spillover price effects. These findings can help inform policymakers who strive to

maximize the secondary benefits of affordable housing developments.

Introduction

The country continues to suffer from a significant shortage of affordable rental housing, a problem

that has worsened since the onset of the COVID-19 pandemic. Households that struggle to pay the

rent tend to be more likely to suffer from poor health and chronic illness. They are more likely to

experience food insecurity, and their children are more likely to struggle academically. Perhaps not

surprisingly, many developers view the creation and rehabilitation of affordable housing not only

as an end in and of itself, but also as a central component of a strategy to stabilize and revitalize

lower-income communities.

At the same time, many homeowners continue to have a negative perception of affordable rental

housing properties. Influenced in part by demagogic politicians and well-publicized concerns

about crime, these homeowners fear that the presence of a publicly subsidized rental housing

development in their community will have negative effects on local property values and public

safety. This “not in my backyard” sentiment has been most evident in middle- to upper-income

neighborhoods, and it frequently has racial or ethnic overtones. (Interestingly, the sentiment

typically represents itself simply as opposition to a proposed project, not necessarily as a preference

for another use of the property.)

A growing body of research offers evidence that rebuts the negative perception of affordable

housing properties. The largest public subsidy in the country, the federal Low-Income Housing

Tax Credit (LIHTC) program, has facilitated equity investments in properties that collectively

have created or rehabilitated, or both, more than 3 million affordable rental units since 1986.

Researchers have found that LIHTC-financed developments generally have neutral to positive

effects on surrounding property values. Several studies have documented the relatively long-lasting

nature of these spillover effects.

310 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

The research also has documented considerable variation in the extent and duration of the property

value effects—not only across metropolitan areas, but also within individual cities. Neighborhoods

are inherently dynamic environments, with multiple internal and external factors that affect local

real estate values and quality of life indicators. Some of those factors affect multiple communities;

some are idiosyncratic in nature. It is not surprising, therefore, that developments in different

neighborhoods could or would have different spillover effects. Unfortunately, not enough is known

about the causes of these variations.

A better understanding of the factors that most influence the spillover effects of affordable

housing developments is important for several reasons. It can help policymakers better allocate

and target comparatively scarce housing and neighborhood development resources. It can help

developers focus activity in areas where the local dynamics create a more favorable environment

for positive project spillover. A greater understanding of the interplay between affordable housing

developments and local dynamics also can help inform—and ideally alleviate—the persisting

opposition to subsidized rental housing.

All these outcomes are in addition to the primary focus and benefit of the LIHTC and other

affordable housing programs: The creation of quality homes that do not impose cost burdens on

their residents. The intent is not to change the LIHTC program into something for which it was

not intended. Rather, this research seeks to determine how various actors can create and preserve

affordable housing in a way that best contributes to the stabilization, enhancement, or revitalization

of the surrounding community.

This study begins to tease out the factors that influence the type and extent of a LIHTC property’s

effects on its neighborhood. It uses changes in residential home values as a proxy for improvements

in the area. If a community becomes more attractive, it should increase the willingness and

desire of people to live there. That increased interest should translate into increased demand

for local property, which will bid up local real estate values. In the model used here, therefore,

the “treatment effect” is the percent change in nearby prices that occurs after a new LIHTC

development is completed, relative to the change in comparable non-LIHTC neighborhoods.

This study examines LIHTC price effects while controlling for a range of project-specific

and neighborhood-level factors. On the project side, it considers the role that the size of the

development (that is, its number of units) plays in changing local values. It assesses whether

spillover effects are greater around entirely subsidized LIHTC properties or around those

developments with a mix of subsidized and market-rate units. It also assesses whether the

corporate structure of the developer has an effect: Do for-profit sponsored projects have a different

effect on the surrounding area’s property values than those sponsored by nonprofit organizations?

With respect to neighborhood dynamics, this study examines whether LIHTC developments have

greater spillover effects in low-income, high-income, or more moderate-income communities. It

examines property effects in communities with higher and lower proportions of people of color and

in predominantly Black, Latino, and Asian neighborhoods. It also analyzes whether the concentration

of LIHTC properties in a community enhances or limits local property value appreciation.

Cityscape 311

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

This study focus on Los Angeles, a city with an extreme shortage of affordable housing and very

strong competition for relatively scarce LIHTC allocations. It finds that, across each of the different

property- and neighborhood-level dimensions, the average LIHTC property contributes to a

meaningful increase in surrounding property values. Not surprisingly, some factors lead to more

significant property value increases than others. Although the market and political dynamics in

Los Angeles may limit the generalizability of some of these findings to other markets, several of the

findings are actionable for both developers and policymakers.

Contribution to the Literature

This analysis focuses on the spillover effects of LIHTC-financed properties that involve either

new construction or rehabilitation (and potential expansion) of existing properties. Substantial

evidence shows that both types of residential projects positively influence local property values.

In Cleveland, for instance, new construction in the 1980s and 1990s increased the sale price of

nearby homes by about $5,000, whereas significant rehabilitation had a positive $4,000 effect

(Ding and Knaap, 2002; Ding, Simons, and Baku, 2000; Simons, Quercia, and Maric, 1998). One,

therefore, would expect similar outcomes for LIHTC developments, as they typically involve either

new construction on a previously vacant lot or the often-significant enhancement of one or more

existing residential, commercial, or mixed-use properties. Because a typical LIHTC development

contains 60 to 80 units, it has a large enough physical footprint to have a noticeable impact on its

surrounding area.

Indeed, a growing body of research has documented that LIHTC properties have neutral to positive

effects on surrounding real estate prices. Exhibit 1 summarizes several of these analyses.

Exhibit 1

Selected Analyses of Low-Income Housing Tax Credit Properties’ Effects on Home Prices (1 of 2)

Study

Market

Basic Spillover Findings

Green, Malpezzie,

Madison and

No evidence that LIHTC properties depressed surrounding

and Seah (2002)

Milwaukee, WI

home sale prices; some evidence that properties near LIHTC

developments in Madison appreciated more rapidly than

those elsewhere in the city.

Johnson and

Cleveland, OH;

Property values increased within a few blocks of LIHTC

Bednarz (2002)

Portland, OR;

developments after the developments had been placed

Seattle, WA

in service.

Furman Center (2006)

New York City, NY

Property values surrounding LIHTC buildings increased by as

Ellen and Voicu (2007)

much as 9% in the 5 years after the LIHTC property’s opening.

Ellen et al. (2007)

Ezzet-Lofstrom and

Dallas, TX

LIHTC developments had a small, positive effect on

Murdoch (2006)

surrounding single-family house prices.

Baum-Snow and

National

Home prices increased by an average 14.9% in census block

Marion (2009)

groups within 1 kilometer of a LIHTC property.

Woo, Joh, and

Cleveland, OH

Home values near LIHTC developments increased 15.4%

Van Zandt (2016)

relative to price trends elsewhere in the city.

Young (2016)

National (20

Proximity to LIHTC property had no significant effect on

highest cost

home values.

markets)

312 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit 1

Selected Analyses of Low-Income Housing Tax Credit Properties’ Effects on Home Prices (2 of 2)

Study

Market

Basic Spillover Findings

Edmiston (2018)

Kansas City, MO

LIHTC properties had little positive or negative effect on

surrounding property conditions.

Bostic et al. (2020)

Cook County, IL

Home values within 1/8 of a mile of a LIHTC development

experienced a 10.8 percentage point increase relative to the

countywide average.

LIHTC = low-income housing tax credit.

Although the average spillover price effects of LIHTC developments are generally positive,

the averages mask considerable variation within and across regions. Such variations are not

surprising, given the often-substantial differences in amenities and real estate trends affecting

even adjacent neighborhoods within the same city. Several researchers have taken specific local

factors into account in their analyses of affordable housing developments’ spillover effects. Others

have examined some of the factors specific to the affordable housing properties. The following

subsections highlight some of their key findings.

Project Size

Intuitively, one would expect larger residential developments to have commensurately larger

effects—either positive or negative—on surrounding neighborhood conditions than smaller

developments. Larger developments occupy more physical space and are consequently more

visible within an area. They also house more people and, therefore, increase the community’s

population density.

Multiple studies of affordable housing developments found that larger projects tend to have greater

spillover effects, as exhibit 2 summarizes.

Exhibit 2

Selected Analyses of the Size Effects of Low-Income Housing Tax Credit Properties

Study

Market

Key Project Size Findings

Ellen (2007)

New York City, NY

Affordable housing properties with more units generally

generated more positive spillover price effects, although the

marginal benefit decreased with project size.

Deng (2011a)

Santa Clara County, CA

LIHTC properties with 50 or more units boosted surrounding

home values 5 to 6%; smaller properties had no significant

spillover effect.

Dillman, Horn,

Review of 24 separate

Larger, well-managed affordable housing properties tend to

and Verilli (2017)

studies across

generate more significant spillover price effects, and they also

multiple markets

contribute to reductions in local violent crimes.

LIHTC = low-income housing tax credit.

At the same time, larger projects can be problematic in certain markets. Several analyses

documented the potential for poorly managed developments of scale to exacerbate local crime

issues and contribute to neighborhood decline (Dillman, Horn, and Verilli, 2017). Mid- to

large-sized multifamily properties placed in service in low-density areas can negatively affect

Cityscape 313

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

surrounding home prices, especially in more affluent communities (Ericksen and Yang, 2022).

Even in areas with relatively high population densities such as New York City, spreading affordable

units across multiple properties instead of concentrating them in one or two developments can

result in greater and more positive overall property value effects (Ellen and Voicu, 2007). Density

concerns drive much of the “not in my backyard” opposition to affordable housing. Local residents

fear that the increased population density associated with a larger development will irrevocably

alter the existing community dynamics.

Extent of Project Subsidization

The LIHTC program does not require all units within a tax credit financed property to be

affordable to low-income households. In fact, the income mandates can appear comparatively

modest. Statutorily, developers must commit to creating properties that meet one of three income

thresholds: (1) Households earning 50 percent or less of the area median income (AMI) occupy at

least 20 percent of the units; (2) households earning 60 percent or less of AMI occupy at least 40

percent of the units; (3) households earning an average of 60 percent or less of AMI occupy at least

40 percent of the units, and no household in the property makes more than 80 percent of AMI.

The regulations, therefore, give developers flexibility. A developer can opt to create a facility in

which a portion of the units rent at market rates. Alternatively, the developer can elect to have

all the units be affordable to households making 60 percent or less of AMI—in which case the

property becomes eligible for the maximum LIHTC subsidy. LIHTC developers have tended

to focus primarily on creating income-restricted units, in part, as a way of increasing their

competitiveness in the tax credit allocation process. An analysis of 12,228 LIHTC properties

containing more than 760,000 units in 16 different states found that households earning 60

percent or less of the prevailing AMI occupied 93 percent of the units (Furman Center, 2012).

Similarly, an examination of LIHTC properties in 18 states found that 81 percent of the properties’

tenants made 50 percent or less of AMI (O’Regan and Horn, 2013).

Still, certain areas contain a fair number of LIHTC properties with market-rate units. In Chicago,

for instance, 19.3 percent of the non-senior LIHTC properties placed in service between 1987 and

2016 contained at least five market-rate units. In those properties, unsubsidized units accounted

for an average 27 percent of all units (Bostic et al., 2020).

Both fully subsidized LIHTC properties and those with a mix of subsidized and market-rate units

have generated positive spillover property value effects. Exhibit 3 summarizes the key findings of

several studies of “mixed-income” and fully subsidized properties financed in part with LIHTC or

federal HOPE VI monies, or both.

314 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit 3

Selected Analyses of Fully Subsidized Versus Partially Subsidized Affordable Housing

Developments’ Spillover Property Value Effects

Study

Market

Key Subsidy-Related Findings

Turbov and

Atlanta, GA;

Home values in the areas surrounding the mixed-income HOPE

Piper (2005)

Louisville, KY;

VI developments increased more quickly than elsewhere in the

Pittsburgh, PA;

respective cities.

St. Louis, MO

Castells (2010)

Baltimore, MD

Of three HOPE VI communities analyzed, only the more mixed-

income community demonstrated positive and significant spillover

property value increases; no spillover effects were observed

surrounding the fully subsidized HOPE VI developments.

Funderberg and

Polk County, IA

Property value appreciation near fully subsidized family LIHTC

MacDonald (2010)

developments was 2 to 4% less than elsewhere in the county;

partially subsidized LIHTC properties had no significant effects on

price trends.

Zielenbach, Voith, Boston, MA;

Both partially and fully subsidized HOPE VI developments had

and Mariano

Washington, DC

positive property value effects, with the greatest values in areas

(2010)

already experiencing development pressures.

Cloud and

Denver, CO

The ¼-mile area around the downtown mixed-income HOPE VI site

Roll (2011)

had a greater increase in property values and homebuying, a greater

reduction in blight, and a greater increase in other investments than

other similar areas in city.

Bostic et al.

Cook County, IL

Spillover price effects for LIHTC properties with at least five market-

(2020)

rate units were higher than the effects of properties consisting entirely

of subsidized units; the price effects were positive in both cases.

LIHTC = low-income housing tax credit.

The relatively limited literature exploring differences in property value spillover effects of partially

versus fully subsidized affordable housing developments suggests that complexes that include

market-rate units have more positive effects on local home prices. It is important not to draw hard

conclusions at this stage, however. The most in-depth examination of the issue, by Bostic et al.

(2020) in Chicago, found that LIHTC properties with market-rate units had a disproportionately

high effect on nearby home prices in higher-income areas. In lower-income communities, home

values appreciated more near fully subsidized LIHTC properties than near partially subsidized ones.

For-Profit Versus Nonprofit Developer

The LIHTC statute requires that at least 10 percent of all tax credit allocations go to projects

sponsored by nonprofit developers. Several states and localities have allocated higher proportions

of credits to these organizations. Overall, nonprofits were responsible for about 22 percent of the

LIHTC properties placed in service between 1987 and 2004, although that proportion may have

declined since (Bratt, 2007). A 2015 national survey of 100 affordable housing developers found

that, among the 52 most active entities, for-profits were responsible for starting 89 percent and

completing 86 percent of the affordable units produced during the year (Bratt and Lew, 2016).

Several analyses have documented the differences between nonprofit and for-profit developers.

Not surprisingly, the fundamentally disparate goals of the two types of entities help explain much

of the variation. The quest for financial returns drives most for-profit activity, as the developers

Cityscape 315

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

need to generate profits for their shareholders. In contrast, nonprofit developers tend to focus more

on neighborhood improvements and affordable housing provision. Nonprofits, consequently, are

more likely to develop properties in poorer areas. Their properties frequently target lower-income

households, and they also are more likely to target people with disabilities, the homeless, seniors,

and other “special” populations (Johnson, 2012; Silverman and Patterson, 2011). Because they

serve more disadvantaged populations, nonprofit properties often charge lower rents. Bratt and

Lew (2016), for instance, found that nonprofit-sponsored LIHTC developments had a higher

proportion of units with a low rent-fair market rent ratio than for-profit developments.

Overall, the cost of developing a LIHTC project tends to be higher for a nonprofit developer than

for its for-profit counterparts. Some of that difference can be attributed to nonprofits being more

likely to engage in the rehabilitation of existing properties and offering more services to tenants.

Whereas for-profit developers are more likely to engage in new construction, with models that

can be replicated across sites, nonprofits often need to develop project-specific designs for existing

properties (Silverman and Patterson, 2011). At least in metropolitan Richmond, Virginia, nonprofit

developers of LIHTC properties have been more likely to incorporate rehabilitation, certified

property management, and standard-use terms in their properties than their for-profit counterparts

(Johnson, 2012). These factors can contribute to greater operating costs. Many nonprofit

developers also struggle to obtain capital from conventional lenders and, therefore, are forced to

piece together different subsidies, particularly if they try to serve very low-income households. This

process can take time and ultimately drive up overall project costs.

Development costs notwithstanding, some evidence exists that nonprofit-developed affordable

housing complexes have at least similar, and potentially more positive, effects on surrounding

home values than properties developed by for-profit firms. Exhibit 4 summarizes the relatively

sparse literature on the issue.

Exhibit 4

Selected Analyses of Spillover Price Effects from Nonprofit and For-Profit Affordable

Housing Developments

Study

Market

Key Developer-Related Findings

Goetz, Lam,

Minneapolis, MN

Subsidized multifamily properties developed by nonprofit CDCs

and Heitlinger

enhanced the value of surrounding market-rate homes by 86 cents per

(1996)

square foot. Publicly subsidized housing owned by private for-profits had

a negative 82 cent per square-foot effect on surrounding home prices.

Smith (2003)

Indianapolis, IN

For 13 years, home prices in areas with significant CDC activity increased

7.14% relative to homes in non-CDC neighborhoods.

Ellen and

New York City, NY Nonprofit-developed, smaller affordable housing properties had larger

Voicu (2007)

home price spillover effects than similar for-profit developed properties.

The price-value effects associated with nonprofit-developed projects

were more stable over time.

Deng (2011a) Santa Clara

LIHTC projects developed by HPN-member* nonprofits had 4 to 6

County, CA

percentage points higher effects on surrounding values than properties

developed by for-profits or by non-HPN member nonprofits.

Edmiston

Kansas City, MO

CDC investments in owner-occupied, single-family homes contributed to an

(2018)

11.8% increase in home prices within 500 feet of the targeted properties.

CDC = Community Development Corporation. HPN = Housing Partnership Network. LIHTC = low-income housing tax credit.

* HPN is a national network of high-capacity nonprofit developers.

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Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Neighborhood Income Level

Residential developments do not take place in a vacuum. Their ability to attract and retain tenants,

generate revenue streams, and improve (or worsen) conditions in the surrounding area depends

not only on their size, management, structure, and other characteristics, but also on the dynamics

of the neighborhood. A development’s capacity to generate spillover effects can be augmented or

constrained by, among other factors, the physical geography of its surroundings, the strength or

weakness of the local economy, the extent of real estate development activity, local public safety

issues, and various local demographic and socioeconomic characteristics. Not surprisingly, studies

of LIHTC and other affordable housing developments’ spillover found significant differences in the

direction and extent of property value effects across communities.

In general, research has documented more positive spillover price effects from LIHTC

developments in lower-income areas than in middle- to upper-income communities. Exhibit 5

summarizes the findings of several studies that examined impacts across different communities.

Exhibit 5

Selected Analyses of Low-Income Housing Tax Credit Developments’ Spillover Price Effects by

Neighborhood Income Level

Study

Market

Key Findings by Income Level

Baum-Snow and National

Median home prices increased 14.9% within 1 kilometer of a LIHTC

Marion (2009)

property, but the price increases were noticeably lower in stable

(10.6%) and gentrifying (5.6%) communities.

Deng (2011a)

Santa Clara

LIHTC developments in low-income neighborhoods had positive

County, CA

price effects, but the effects for the county overall were statistically

insignificant.

Deng (2011b)

Miami-Dade

Price effects were most positive around LIHTC developments in high-

County, FL

poverty neighborhoods and most negative around developments in

middle-class communities.

Woo, Joh, and

Charlotte, NC

LIHTC properties had negative effects on surrounding values, but the

Van Zandt (2016)

effects were much more noticeable in moderate- and upper-income

areas than in lower-income ones.

Woo, Joh, and

Cleveland, OH

The home price effects of LIHTC properties were much lower in lower-

Van Zandt (2016)

income areas than in more moderate- and upper-income communities.

Dillman, Horn,

Summary of 24

LIHTC and other affordable housing properties generally boosted

and Verilli (2017)

studies spanning values in low-income areas but had more mixed effects in moderate-

the country

and high-opportunity areas.

Diamond and

Multistate

Home prices within 1/10 of a mile of a LIHTC property increased

McQuade (2019)

6.5% during 10 years in low-income neighborhoods but declined

nearly 2.5% in higher-income areas.

LIHTC = low-income housing tax credit.

LIHTC properties tend to be developed in relatively distressed areas. Nationally, 32 percent of

LIHTC units placed in service prior to 2011 were in census tracts with poverty rates of at least 30

percent in 2010, and another 23 percent of the units were in tracts with poverty rates between

20 and 30 percent. In 12 sampled states, the average LIHTC unit sat in a tract where the poverty

rate was 6 percentage points higher than that of a tract housing a typical unsubsidized rental unit

in the same metropolitan area (Ellen, Horn, and Kuai, 2018). The concentration of properties in

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weaker markets reflects both LIHTC allocation criteria—which frequently give projects greater

points for being in more distressed communities—and some developers’ desire to use the LIHTC

developments to help catalyze other investments in the area.

The relatively greater spillover benefits of LIHTC properties in lower-income communities should

not mask the positive effects that these developments often have in more affluent areas, however.

For instance, an analysis of Cleveland in the 1990s and early 2000s found positive price effects

of LIHTC developments in moderate- and upper-income communities. In fact, the effects were

greater in those areas than in the city’s more distressed markets (Woo, Joh, and Van Zandt, 2016).

An evaluation of Chicago trends encompassing the same period found strong and enduring LIHTC

price effects in both lower- and upper-income neighborhoods (Voith et al., 2022).

It is also possible that the observed negative effects of LIHTC properties in some higher-income

neighborhoods may result less from the introduction of affordable housing per se and more

from the introduction of comparatively dense multifamily properties in lower-density areas

with a preponderance of single-family homes. A recent study reran the nonparametric models

that Diamond and McQuade used in their 2019 analysis (see exhibit 5), adding unsubsidized

multifamily developments and LIHTC properties to the analysis (Eriksen and Yang, 2022). The

new study found that all types of multifamily developments depressed surrounding property values

in higher-income areas. Once they controlled for population density, the authors found that the

negative effects of LIHTC properties dissipated and even became moderately positive.

Neighborhood Racial and Ethnic Composition

Most research on LIHTC spillover price effects has taken the subject neighborhoods’ racial and

ethnic composition into account as part of the evaluation structure. Because of the strong inverse

correlation between neighborhood incomes and the communities’ proportions of individuals of color,

the observed effects of racial or ethnic composition largely have tracked the observed income-related

effects. Spillover price effects tend to be greater in predominantly Black and Latino communities.

Comparatively, little analysis has been done on different effects in higher- versus lower-income

communities of color—the Bostic et al. (2020) analysis of mixed-income properties in Chicago

being a notable exception. Similarly, little in-depth examination has been done on the spillover

effects in different types of majority-minority neighborhoods. It is not clear, for instance, whether

LIHTC properties have different price effects in predominantly Black, predominantly Latino, or

predominantly Asian-American neighborhoods.

LIHTC Project Concentration

One of the challenges in assessing the spillover impact of LIHTC developments is that the properties

tend to be geographically concentrated. Quite often, some overlap is among the distance bands

surrounding individual LIHTC properties. A home sale transaction, therefore, may be included

in multiple analyses, which can complicate the assessment of any single LIHTC development’s

true impact. Such an issue affects evaluations of many affordable housing properties, but LIHTC

developments especially, as they tend to be much more concentrated than other subsidized housing

properties (Oakley, 2008). In New York City, for example, 71 percent of LIHTC properties were

318 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

clustered; in Boston, the proportion was 50 percent (Dawkins, 2013). More than 90 percent of the

non-senior LIHTC properties placed in service in Cook County, Illinois, between 1987 and 2016

were within one-half a mile of at least one other LIHTC development (Voith et al., 2022).

To date, few analyses have addressed the effects of this concentration directly. Deng’s (2011b)

analysis of LIHTC-related effects in south Florida found mixed effects of concentrated development

in Miami-Dade County. Some areas with multiple LIHTC properties showed improvement, but

the presence of multiple LIHTC developments was potentially worsening conditions in certain

suburbs. Two reviews of the affordable housing assessment literature raised concerns about the

property value implications of geographically concentrated subsidized housing and the low-

income households such complexes support (Dillman, Horn, and Verilli, 2017; Nguyen, 2005).

Those concerns were not based on LIHTC-specific findings. Moreover, some evidence exists that

clustering affordable housing properties can have more beneficial effects on a community than

introducing a single property, based on an analysis of a scattered-site public housing program in

Denver (Santiago, Galster, and Tatian, 2001).

The one study to date that deliberately addressed the impacts of LIHTC project clustering focused

on Chicago. Voith and his colleagues (2022) found that the introduction of a single LIHTC

property to a community had positive and sustainable impacts on surrounding home prices. They

did not find any evidence that placing subsequent LIHTC developments in the neighborhood

detracted from the positive benefits associated with the initial property. In some cases, the

subsequent LIHTC properties had positive and additive effects on surrounding values.

Working Hypotheses

As described previously, a range of studies has documented the generally positive (or at least

neutral) overall effects of LIHTC developments on surrounding home prices—findings that rebut

the perception that such developments have inherently negative effects on communities. At the

same time, they have demonstrated the range of project-specific and neighborhood-level factors

that can influence such developments’ spillover impacts. Most analyses have incorporated only

a few of these independent variables, generally ignoring the complexities associated with the

tendency of LIHTC properties to be geographically concentrated. This study represents an initial

attempt to account for this wider range of factors in a single analysis.

Based on previous findings, it is expected that the introduction of a LIHTC property in a

community typically will have a positive and lasting effect on surrounding home prices. That

positive effect is likely to be more pronounced in low-income communities than in more affluent

areas, and the introduction of one or more subsequent nearby LIHTC properties is likely to

augment it.

This study posits that larger LIHTC properties and those nonprofit entities develop are likely to

have somewhat greater spillover price effects than smaller properties and those for-profit firms

develop or rehabilitate. (These relationships should hold even after controlling for the fact that

larger, nonprofit-sponsored properties are more prevalent in lower-income communities.) It is also

anticipated that partially subsidized LIHTC properties—those containing a mix of market-rate

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and income-restricted units—will have somewhat greater spillover effects on area prices than fully

subsidized properties, in part, because the higher-income residents’ additional purchasing power

will contribute to the attraction and retention of a wider range of local retail and other amenities.

It is not expected that this analysis will find any meaningful difference in price effects across

neighborhoods that are predominantly Black, Latino, or Asian-American (again controlling for

neighborhood income level).

LIHTC Developments in Los Angeles

To understand better the variations in the spillover effects of LIHTC properties, this study examines

both property and neighborhood characteristics in Los Angeles County, California. Los Angeles

is the country’s largest county and contains 833 LIHTC properties. It has a widespread and

widely acknowledged need for affordable housing, with several public, private, and philanthropic

initiatives working to alleviate the shortage. It has considerable demographic and socioeconomic

diversity, and it continues to be one of the country’s strongest real estate markets. It also has strong

political support for creating and preserving affordable housing. A Los Angeles-based analysis,

therefore, can be beneficial for developers and policymakers looking to address affordable housing

needs in other large cities with strong real estate markets, diversity of population and income, and

a political commitment to helping address residents’ housing cost burdens.

Data

This study analyzes the spillover effects of LIHTC developments placed in service in Los

Angeles County between 1987 and 2015. Pre-development and post-development prices in the

neighborhoods with one or more LIHTC developments are compared to price trends during the

period in neighborhoods with no LIHTC properties.

Data was obtained from HUD for each of the 833 LIHTC properties placed in service during that

period. The information includes the property’s street address, the year it was placed in service,

and its total number of units. Data on all Los Angeles residential property sales from 1987 to

2015 (more than 1.8 million arm’s length transactions) was obtained from DataQuick Information

Systems, Inc. and CoreLogic, Inc., the transactions were geocoded, then the distance between each

sold home and nearby LIHTC developments was calculated. During the 28-year period, 145,056

transactions were within one-fourth of a mile of a LIHTC property, and 362,811 transactions were

within one-fourth to one-half of a mile.

Exhibit 6 highlights the differences between Los Angeles County census tracts that contain at least

one LIHTC development and those without any such properties during the study period. As the

exhibit shows, Los Angeles’s LIHTC properties tend to be in disproportionately low-income areas.

320 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit 6

Characteristics of Los Angeles Median Low-Income Housing Tax Credit and Non-Low-Income

Housing Tax Credit Census Tracts

Variable

Median LIHTC Tract

Median Non-LIHTC Tracts

Household income

$46,883

$71,750

Population

4,362

4,182

White non-Hispanic residents

9.6%

19.5%

Black non-Hispanic residents

5.6%

3.1%

Asian non-Hispanic residents

7.9%

9.8%

Hispanic residents

58.9%

41.5%

Poverty rate

21.1%

11.0%

Unemployment rate

6.4%

5.5%

Residential vacancy rate

5.3%

4.9%

Median gross rent

$1,259

$1,544

Median home value

$473,250

$545,400

LIHTC = low-income housing tax credit.

Source: 2016 American Community Survey 5-year estimates

These characteristics are similar to those Basolo, Huarita, and Won (2022) identified in their

recent analysis of LIHTC developments in the county. They found that, relative to residential

properties generally, LIHTC properties tend to be in neighborhoods that have more economic

hardship, higher population density, a higher proportion of renter-occupied units, and more racial

and ethnic diversity.

LIHTC properties in Los Angeles also tend to be clustered geographically. Of the county’s 833

properties, 679 are within one-half a mile of at least 1 other LIHTC property. The greatest

concentration of these developments is in south-central Los Angeles, as exhibit 7 illustrates. The

different dots indicate both non-overlapping properties in the city and the overlapping ones.

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An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit 7

Map of Sampled Low-Income Housing Tax Credit Properties and Surrounding One-Half-Mile Radii

LA = Los Angeles. LIHTC = low-income housing tax credit.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

322 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Methodology

The aim here is to illuminate the roles that different project- and neighborhood-level characteristics

play in influencing the spillover property value effects of LIHTC development. This analysis

focuses on six different characteristics: (1) development size (small, medium, and large); (2)

proportion of subsidized units (all versus some); (3) developer type (for-profit versus nonprofit);

(4) neighborhood income level; (5) neighborhood racial and ethnic composition; and (6) the

number of existing LIHTC developments within the neighborhood. These differences are examined

both quantitatively and qualitatively to understand how the various factors influence both the

developers’ decisionmaking and the ultimate spillover impacts.

Quantitative Approach

For the quantitative methods, this study builds on the standard difference-in-differences regression

models typically used in program evaluation studies of this kind (for example, Butts, 2022;

Chen, Glaeser, and Wessel, 2022; Keeler and Stephens, 2022; Voith et al., 2022). Initially, the

typical model used in the literature is created, focusing on the difference in residential prices after

constructing a LIHTC project between houses near the completed project and houses farther away.

This model considers two distance bands: One within one-fourth a mile of the LIHTC project

and the second in the area between one-fourth and one-half a mile from the LIHTC project.1 This

model is illustrated in the following equation 1.

(1) ln( P

α

α

itk) = ∑ d∈ D 0 dPreidt + ∑ d∈ D 1 dPostidt + β Xit + ε k + τ t + μ itk, Where—

ln ( Pitk)

is the natural log of the price of house I at time t in census tract k;

D

is a set of distance bands d, where D = {0–¼ miles, ¼–½ miles}

Preidt

is a dummy variable equal to 1 if the transaction of house I in distance band d at

time t is prior to the construction of a LIHTC project;

Postidt

is a dummy variable equal to 1 if the transaction of house i in distance band d at

time t is after the construction of a LIHTC project;

Xit

is a vector of hedonic characteristics of house i at time t;2

ε k

is a vector of k tract-specific fixed effects;

τ t

is a vector of t year-specific fixed effects;3 and

μitk

is a random error variable.

1 These distance bands are common in the literature (for example, Diamond and McQuade, 2019; Orlando and Welke,

2022). Other distance bands were tested, yielding very similar results.

2 These hedonic characteristics include living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall).

3 Time-specific fixed effects control for marketwide inflation, allowing the model to use nominal house prices as the outcome variable.

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All transactions are included—those within and outside of the distance bands—to provide a better

estimate of the price trends within the overall market and to provide more data on the value of

individual housing and neighborhood traits. The “average treatment effect”4 is the difference between

the pre-treatment and post-treatment coefficients (α1 d — α0 d) for a given distance band d.5 The pre-treatment baseline is included explicitly to show how the LIHTC neighborhoods were different from

the control neighborhoods before the introduction of the affordable housing property.

The resulting treatment effect compares the average change in property values of properties within

the distance band with the average change in values of homes more than one-half a mile away,

controlling for overall marketwide changes in prices. This control group offers a counterfactual for

the average pre-post LIHTC change in property values of homes outside the distance bands. This

distance distinction is a typical counterfactual for spatial difference-in-differences studies, as these

neighborhoods are within the same metropolitan area and experience many of the same supply and

demand shocks, especially after controlling for hedonic characteristics, tract-specific fixed effects,

and year-specific fixed effects. This approach focuses on the difference in levels before and after

development rather than the difference in trends, because previous research (for example, Voith

et al., 2022) has demonstrated that level changes are the dominant impact. Assessing changes in

levels also allows for a less complicated exposition of each model described in the following.

Having mirrored the standard model, the analysis then expands on it by incorporating different

factors associated with the treatment effect. One by one, each of the six subcategorizations are

considered, across which one might expect to find different LIHTC effects.

When assessing the importance of neighborhood income and race and ethnicity composition, the

sample is subdivided, and separate regressions are run for the different groups of neighborhoods.

For income, the analysis looks at trends in low-income communities (those census tracts whose

median household incomes fell in the bottom one-third of all Los Angeles tracts per the 2016

American Community Survey’s 5-year estimates) and medium- to high-income communities (the

remaining tracts). For race and ethnicity, the analysis uses the same census tract tercile approach

to analyze communities with high proportions of Black, Latino, Asian, and non-White residents,

respectively. In each case, the remaining two-thirds of the tracts serve as our “control group” for the

analysis. Equation 1 is then estimated separately for each subsample.

Note that a single LIHTC development can have both high- and low-income neighborhoods within

one-half of a mile of the development. The split-sample subgroup approach is a clean way of

estimating different LIHTC impacts in communities with different incomes and demographics.

Exhibit 8 shows the differences in sample sizes for each category of analysis. Of the 1.8 million

transactions in the whole sample, approximately 26 percent are within one-half a mile of a

LIHTC development, as shown in column 2 (487,453). This ratio ranges from 10 percent for the

low-income subsample to 45 percent for the high-Hispanic subsample. Even where it is lowest,

4 In other words, the typical price effect associated with a LIHTC development being placed in service.

5 As with any difference-in-differences analysis with treatments in multiple periods, the amount of “pre” and “post” years available for each treatment depends on the timing of the treatment (that is, the introduction of the LIHTC property).

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Factors Affecting Spillover Impacts of Low-Income

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however, there are still more than 75,000 transactions, ensuring enough statistical power to detect

significant effects.

Exhibit 8

Number of Transactions Near Low-Income Housing Tax Credit by Racial and Ethnic Subsample

Transactions Within

Transactions More

Sample

Total Transactions

½ Mile of LIHTC

Than ½ Mile Away

Whole sample

487,453

1,355,272

1,842,725

Low % non-White

282,395

1,059,632

1,342,027

High % non-White

205,058

295,640

500,698

High % Black

252,599

342,078

594,677

Low % Black

234,854

1,013,194

1,248,048

High % Asian

111,384

473,298

584,682

Low % Asian

376,069

881,974

1,258,043

High % Hispanic

200,613

250,017

450,630

Low % Hispanic

286,840

1,105,255

1,392,095

Low income

75,459

663,251

738,710

High income

411,994

692,021

1,104,015

LIHTC = low-income housing tax credit.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database; U.S. Census Bureau

It is not easy to subdivide the sample to analyze the property value effects associated with different

property-specific characteristics or for neighborhoods with multiple LIHTC developments.

Too many transactions involve properties that fall within distance bands of different properties

developed at different times. For these factors, a regression is run with the entire sample, each

time focusing on the subcategory under consideration by adding a new, factor-specific variable and

two different dummy variables. Exhibit 9 shows that the sample size for each of these interaction

variables is large enough to estimate these coefficients. This approach allows for the examination of

effects associated with each characteristic while controlling for nearby properties. See the following

equation 2.

(2) ln P

α

itk = ∑d∈ D 0 d Pre idt + ∑s∈ S∑d∈ D α1 dsPostidst + β Xit + ε k + τ t + μ idstk, Where—

Preidt

is a dummy variable equal to 1 if the transaction of house i in distance band d at time

t is prior to the construction of a LIHTC project; and

Postidst

is a dummy variable equal to 1 if the transaction of house i in distance band d at time

t is after the construction of a LIHTC project with either neighborhood or property

characteristic s.

S

is defined as one of the following sets of neighborhood or property characteristics—

1. S = {1 LIHTC project nearby, 2 LIHTCs projects nearby, 3+ LIHTC projects

nearby};

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An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

2. S = {small LIHTC project, medium LIHTC project, large LIHTC project};

3. S = {mixed-income LIHTC project, fully subsidized LIHTC project}; or

4. S = {for-profit LIHTC developer; nonprofit LIHTC developer}.

Exhibit 9

Number of Transactions Near Low-Income Housing Tax Credit by Interaction Variable

Interaction

For-Profit Versus

Project

Subsidy

Project Size

Variable

Nonprofit

Concentration

½+ mile away*

1,628,449

1,628,449

1,628,449

1,628,449

For-profit

130,312

Nonprofit

83,964

Partially subsidized

56,845

Fully subsidized

196,431

Small property

93,601

Medium property

70,935

Large property

49,740

1 LIHTC project

214,276

2 LIHTC projects**

55,791

3+ LIHTC projects**

18,058

LIHTC = low-income housing tax credit.

* Includes transactions of homes more than one-half a mile from a LIHTC property at the time of sale.

** Figures represent subsets of transactions within one-half a mile of a single property.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

Potential Endogeneity Issues

These findings could reflect some implicit site selection bias if developers have chosen to locate the

LIHTC properties in neighborhoods where values are already trending upward. The model could

potentially be revealing existing appreciation trends, not changes associated with the introduction

of the LIHTC property.

Two factors lend credence to the selection bias concern. First, developers are inherently more likely

to locate properties in areas where they can obtain the greatest tax credit benefit. In their national

analysis, Baum-Snow and Marion (2009) found that LIHTC properties in program-designated

qualified census tracts (QCTs) had an average of six more units than properties in tracts that

fall just below the QCT eligibility threshold. Basolo, Huarita, and Won (2022) found a positive,

statistically significant association between LIHTC neighborhoods and QCTs in Los Angeles

County. Second, private developers are more likely to select properties in gentrifying—or at least

appreciating—neighborhoods than in stable or declining ones (Baum-Snow and Marion, 2009;

Ellen and Voicu, 2007).

To the extent that developers seek to maximize profits from rents or property appreciation, an

incentive is for them to build or rehabilitate properties in improving neighborhoods. Although they

326 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

may earn additional points in the LIHTC allocation process for targeting properties in QCTs or

difficult development areas, they could deliberately target properties in distressed areas displaying

clear signs of improvement. If property values are already trending upward in these areas, it

becomes harder to demonstrate convincingly that the LIHTC development is responsible for the

observed appreciation of the local market. Well-crafted statistical models may be able to document a

post-development trend in values that is steeper than the pre-development trend, but such findings

prove inherently less noteworthy than those that document a distinct change in trends. Without

knowledge of the developers’ particular location decisions, it is hard to determine the extent to

which observed neighborhood effects should be attributed to the initial selection of the site.

Following standard difference-in-differences methodology, a test is run for any observable evidence

of such behavior by including a linear “pre-trend” (that is, price trends prior to the LIHTC

development) in the model. Within both the ¼- and ½-mile distance bands, the coefficient on this

pre-trend is statistically insignificant—and within the ½-mile band, it is even negative. Thus, no

empirical evidence exists indicating that price trends in the areas around LIHTC developments were

any different from trends elsewhere in the market prior to the LIHTC development completion.

Qualitative Approach

The quantitative analysis described above is supplemented with interviews with LIHTC developers

active in the Los Angeles market. Using the authors’ collective network of developers, lenders,

public officials, and affordable housing advocates, a list of individuals with extensive experience

developing LIHTC properties in the region was identified. The authors specifically sought

individuals who had experience with both for-profit and nonprofit developers—either by virtue

of their work in both types of firms or through their interactions, or both, and joint ventures

on particular projects. The authors ultimately were able to schedule interviews with six separate

developers. Although it cannot be claimed that these individuals speak for all developers in the

market, the authors’ conversations with individuals throughout their various networks give them

confidence that the interviewees are generally representative of Los Angeles area LIHTC developers.

The quantitative analyses were conducted prior to interviewing the developers. This approach

gave the authors the opportunity to obtain context and some interpretation of the findings. Each

developer was asked standard questions about the six subcategorizations identified previously,

using the questionnaire included in appendix A. As a way of teasing out the extent to which

the quantitative findings merely captured preexisting price trends (and, thus, were skewed by

endogenous factors), each interviewee was specifically asked about the factors underlying different

developers’ site selection decisions. The results of those interviews were incorporated into the

discussion of the findings.

Findings

This section is divided into subparts, each of which contains our analysis of one of the specific

project- or neighborhood-level factors described previously. Within each subsection, the quantitative

findings are summarized first, then key insights from the developer interviews are incorporated.

Doing so provides a more nuanced understanding of the mechanisms underlying the observations.

Cityscape 327

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

LIHTC Spillover Effects in Los Angeles

To create a baseline set of housing price spillover effects, the simplest difference-in-differences

model is used as a baseline (that is, equation 1). This model identifies the average effect of

all LIHTC developments on surrounding home values. The key results are shown in the

“Neighborhoods with Any LIHTC Properties” columns of exhibit 10. (The full regression results—

with all hedonic coefficients—are available in appendix B.) Here, the model does not account for

the implications of having LIHTC projects geographically concentrated and individual home sales

falling within multiple distance bands. Instead, a home sale is designated as a “pre” transaction if

it occurs before the first LIHTC project is built in the area and a “post” transaction if it occurs after

that initial project is placed in service.

Exhibit 10

Baseline Model for Low-Income Housing Tax Credit Price Effects in Los Angeles County

Neighborhoods with Any LIHTC Properties

Distance from

Variable

LIHTC Property

T Stat (Coefficients)/

Coefficient

F Stat (Treatment Effects)

Pre

– 0.037***

– 5.37

Post

0–¼ mile

– 0.004

– 0.66

Effect

0.034***

15.96

Pre

– 0.033***

– 5.68

Post

¼–½ mile

– 0.003

– 0.51

Effect

0.030***

11.23

Observations

1,842,725

R 2

0.7242

LIHTC = low-income housing tax credit.

*** p < 0.001.

Notes: Regressions control for census tract fixed effects, year fixed effects, and the following property traits—living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall). Full results (with all hedonic coefficients) are available in appendix B. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

Reading the exhibit from top to bottom, the first set of estimates focuses on the transactions within

a ¼ mile of the LIHTC development. The “pre” coefficient, -0.037, indicates that average home

sale prices in the LIHTC neighborhoods were 3.7 percent less than comparable transactions in

non-LIHTC neighborhoods before the LIHTC development was completed. After a given LIHTC

project was built, the “post” coefficient, -0.004, indicates that average sale prices near the LIHTC

development were only 0.4 percent less than comparable sale prices in non-LIHTC neighborhoods.

Thus, the treatment “effect” is 0.034, the difference between pre and post coefficients, indicating

that prices rose 3.4 percent more in LIHTC neighborhoods.

This positive, statistically significant effect is consistent with many of the studies cited previously—

and it is similar in magnitude to the most recent estimates, such as Diamond and McQuade

(2019) and Voith et al. (2022). If a negative, supply-driven effect exists as Eriksen and Yang

(2022) suggest, it is significantly outweighed by the positive spillover effect of the high-quality

LIHTC investment. Although it is not possible to disentangle these two competing effects, it is

328 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

possible to conclude that these results represent a lower bound on the positive effects of the LIHTC

investment, as it may or may not be attenuated by unobservable supply effects.

Moving farther down the table, the second set of estimates focuses on transactions within the ¼–½-

mile band, where prices increased by 3.0 percent after the LIHTC development. Again, this result

reflects the difference between the post (-0.003) and pre (-0.033) estimates, indicating that LIHTC

neighborhoods had 3.3 percent lower prices before development and only 0.3 percent lower prices

after development. In other words, once the LIHTC development was in service, the relative price

differences nearly disappeared.

In the following discussion, the regression tables have a similar format. They incorporate more

post and effect categories to document the estimates of LIHTC effects associated with each

category of factors.

Project Size

Regardless of their size, LIHTC properties in Los Angeles County have generated positive effects

on surrounding home values. In fact, the effects progressively increased with the size of the LIHTC

property, at least within the smallest distance band. Exhibit 11 presents the findings. “Small”

developments are those with 50 or fewer units, “medium” developments have between 51 and 100

units, and “large” developments have 101 or more units. Among the 833 Los Angeles properties

in the sample, 381 qualify as small, 266 qualify as medium, and 186 qualify as large. Specific post

variables are indicated for “small” and “large,” because “medium” is the reference category. In other

words, the standard “post” coefficient captures the “medium” project size, and the “small post” or

“large post” coefficient must be added to “post” to calculate the effect of small or large properties.

Exhibit 11

Property Value Effects of Different Size Low-Income Housing Tax Credit Developments (1 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

Pre

– 0.037***

– 5.37

Post

0.002

0.19

Small post

– 0.015

– 1.38

Large post

0–¼ mile

0.005

0.42

Small Property Effect

0.024*

5.75

Medium Property Effect

0.039***

13.34

Large Property Effect

0.044***

13.32

Cityscape 329

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit 11

Property Value Effects of Different Size Low-Income Housing Tax Credit Developments (2 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

Pre

– 0.033***

– 5.67

Post

0.005

0.80

Small post

– 0.018

– 1.56

Large post

¼–½ mile

– 0.003

– 0.22

Small Property Effect

0.021+

3.23

Medium Property Effect

0.038***

28.56

Large Property Effect

0.036**

7.34

Observations

1,842,725

R 2

0.7242

LIHTC = low-income housing tax credit.

+ p < 0.1. * p < 0.05. ** p < 0.01. *** p < 0.001.

Notes: The regression controls for census tract fixed effects, year fixed effects, and the following property traits—living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall). Full results (with all hedonic coefficients) are available in appendix B. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

Regression results indicate a greater LIHTC spillover effect from medium and large properties

than from small ones. Within one-fourth of a mile of the LIHTC project, the largest properties

have the highest spillovers, but in the one-fourth to one-half of a mile distance band, the medium

properties’ effect surpasses that of large ones. Within both distance bands, the difference in

spillover effects between small- and medium-sized LIHTC properties is greater than the difference

between medium and large developments. That suggests that, although larger projects generally

have a larger effect, the marginal benefit decreases—and potentially even stops or reverses—once

the project reaches a certain size. Because the analysis did not include a continuous unit number

variable, it is not possible to comment on what that threshold might be.

Importantly, no negative price effects associated with introducing larger LIHTC properties into

a neighborhood were found. This finding refutes the perception—identified in a few previous

studies—that larger properties could have deleterious neighborhood effects. That said, the

“success” of a larger project cannot be taken for granted. Several developers with whom the

authors spoke emphasized the importance of addressing community concerns about larger LIHTC

developments early in the planning process. The developers frequently encountered resistance

to larger planned projects from area residents concerned about the additional traffic and parking

difficulties that increased population density could bring. Some residents also had concerns about

increased crime and other negative stereotypes associated with “those people,” the low-income

people of color that tend to occupy many of the region’s LIHTC properties.

Alleviating the concerns often required conscious and concerted efforts on the part of the

developers to address and ameliorate local residents’ reservations. Contending that a “thoughtful

LIHTC project only enhances a neighborhood,” several interviewees described their emphasis

on extensive community programming when designing and carrying out a development. They

330 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

engage regularly with local residents to help them understand and (ideally) benefit from the new

housing. The developers believe that a project’s success depends in large part on the quality of its

construction and management, its architectural design, and the supportive services provided to

its tenants. Another interviewee noted that “we’ve only ever had positive impacts” for his firm’s

projects, attributing the positivity to the considerable time spent during the development process

educating nearby residents about “them” (the likely tenants) and the steps the developer takes to

help the tenants and the development integrate seamlessly within the community.

Fully Versus Partially Subsidized Developments

Both fully subsidized and partially subsidized LIHTC developments have positive and often

significant effects on surrounding property values.6 Partially subsidized or “mixed” developments

are defined as those with six or more market-rate units. In Los Angeles, 6.72 percent of the LIHTC

developments placed in service between 1987 and 2016 meet this “mixed” criterion.7 These

mixed developments are treated as the default “post” variable in the analysis and add another

dichotomous variable for properties with fewer than six market-rate units (that is, fully subsidized

developments). Again, the effect of fully subsidized properties on surrounding property values can

be determined by adding the coefficient of this dichotomous variable to the “post” variable. For

example, reading the coefficients from top to bottom in exhibit 12, the pre variable indicates that

property prices were 3.7 percent lower in LIHTC neighborhoods than non-LIHTC neighborhoods

before the LIHTC development was completed, the post variable indicates that they were 1.6

percent higher after a partially subsidized development, and therefore, the partially subsidized

property effect was an increase of 5.4 percent. By comparison, property prices were 2.2 percent

lower after a fully subsidized development (“fully subsidized post” coefficient), and therefore, the

fully subsidized property effect was an increase of 3.2 percent.

Exhibit 12

Property Value Effects of Partially Versus Fully Subsidized Low-Income Housing Tax

Credit Developments (1 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

Pre

– 0.037***

– 5.37

Post

0.016

0.57

Fully subsidized post

0–¼ mile

– 0.022

– 0.74

Partially Subsidized Property Effect

0.054+

3.17

Fully Subsidized Property Effect

0.032***

15.36

6 One cannot rule out the possibility that the low significance for the partially subsidized properties is a result of the limited number of such properties.

7 As discussed in more detail to follow, the severe shortage of affordable housing in southern California contributes to the relatively low proportion of partially subsidized properties in the Los Angeles market. Recent changes to the LIHTC

allocation process in the state have created additional incentives for developers to maximize the number of affordable units in their properties.

Cityscape 331

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit 12

Property Value Effects of Partially Versus Fully Subsidized Low-Income Housing Tax

Credit Developments (2 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

Pre

– 0.033***

– 5.70

Post

0.005

0.22

Primarily subsidized post

¼–½ mile

– 0.008

– 0.40

Partially Subsidized Property Effect

0.038

2.24

Fully Subsidized Property Effect

0.029***

12.82

Observations

1,842,725

R 2

0.7242

LIHTC = low-income housing tax credit.

+ p < 0.1.*** p < 0.001.

Notes: Regression controls for census tract fixed effects, year fixed effects, and the following property traits—living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall). Full results (with all hedonic coefficients) are available in appendix B. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

Partially subsidized developments have a larger, but not necessarily statistically significant, effect in

both distance bands, which suggests that including some market-rate units within a development

is likely to generate greater spillover effects within the surrounding neighborhood. In Los Angeles,

the competition for tax credits has led developers to move away from such “mixed” properties.

Each developer with whom the authors spoke now focuses primarily on fully subsidized properties

serving very low-income households. Several nonprofit developers always have focused on

providing housing to tenants well down the income ladder. Two of the interviewees’ current firms

have in their portfolios substantial numbers of supportive housing units affordable to tenants

earning 40 percent or less of area median income. One of the region’s larger nonprofit developers

typically serves households earning less than 50 percent of AMI in developments it has financed

with 9-percent LIHTCs, whereas primarily housing tenants with incomes closer to 60 percent of

AMI in properties financed with the shallower 4-percent credits.

One of the for-profit interviewees emphasizes that the competitiveness of the LIHTC program

drives developers’ decisions around unit affordability. His firm prefers to develop properties that

primarily serve households earning between 50 and 60 percent of AMI, because the profit margins

are much tighter when units are set aside for tenants earning closer to 30 percent of AMI (especially

if those tenants do not have vouchers to help subsidize their rents). To receive maximum points

on a tax credit application, the firm needs to commit to serving households earning as little as 30

percent of AMI.

The high costs of land and construction in the Los Angeles area make it difficult to finance LIHTC

properties containing market-rate units. Non-LIHTC public funds generally cannot be used for

market-rate units, so developing mixed property inevitably involves separating the market-rate and

affordable units into distinct condominium-like entities. It becomes necessary to attract private,

non-LIHTC-related equity to finance the market-rate properties.

332 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

The California Housing Finance Authority operates a Mixed Income Program that helps support

properties serving renters earning between 30 and 120 percent of AMI. For all practical purposes,

the program is useful only for properties with a relatively small proportion of affordable units.

Many municipalities in the state now have inclusionary zoning ordinances that require market-

rate apartment properties to set aside at least 15 percent of their units for low-income households.

According to one interviewee, including much more than the minimum requirement subjects the

developer to financing constraints. “There’s a real sensitivity among [conventional] lenders and

investors once a project has more than 20 percent affordable units,” and that sensitivity leads to a

reluctance to commit capital.

Another interviewee explains that “the economics don’t really support a mixed-income approach.”

In Los Angeles, the costs of development exceed the rents that are affordable to low- and moderate-

income households—even for households making as much as 140 percent of AMI. As a result,

all units in a development effectively need to be subsidized to be affordable. Given the limited

amounts of public subsidy available, it makes more sense financially to maximize the number of

units that can receive LIHTC-related capital. Moreover, property owners generally are exempt from

property taxes on units designated as affordable to households making 80 percent or less of AMI;

that exemption disappears for units renting to households above the 80-percent threshold. Not

surprisingly, LIHTC developers in the region now tend to undertake partially subsidized projects

only if they are large, part of a broader development, and present an opportunity for a substantial

financial return.

For-Profit Versus Nonprofit Developer

Although both for-profit and nonprofit-sponsored LIHTC developments have positive effects on

surrounding home values, the effects of the for-profit projects appear to be greater. Within one-

fourth of a mile of a for-profit LIHTC property, the observed increase in home values is nearly twice

as large as the effect on homes near a nonprofit development (4.0 versus 2.1 percent).8 Similarly,

for-profit properties have a greater effect on properties between one-fourth and one-half of a

mile from the LIHTC site. Exhibit 13 presents the results, with the post coefficient representing

nonprofit sponsored developments.

8 Throughout this article, the coefficients are interpreted as percentages, which is the common protocol in the literature when the outcome variable is a natural logarithm. It is possible to be slightly more precise by converting all coefficients using exponential functions, but readers often find this approach more confusing when they try to compare the exhibit with the text.

Cityscape 333

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit 13

Property Value Effects of Nonprofit Versus For-Profit Sponsored Low-Income Housing Tax Credit

Developments

Distance from

T Stat (Coefficients)/F

Variable

Coefficient

LIHTC Property

Stat (Treatment Effects)

Pre

– 0.038***

– 5.43

Post

– 0.017

– 1.35

For-profit post

0–¼ mile

0.019

1.42

Nonprofit Treatment

0.021+

2.84

For-Profit Treatment

0.040***

15.51

Pre

– 0.033***

– 5.72

Post

– 0.014

– 1.31

For-profit post

¼–½ mile

0.013

1.36

Nonprofit Treatment

0.019

2.42

For-Profit Treatment

0.032***

11.84

Observations

1,842,725

R 2

0.7242

LIHTC = low-income housing tax credit.

+ p < 0.1. *** p < 0.001.

Notes: Regression controls for census tract fixed effects, year fixed effects, and the following property traits—living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall). Full results (with all hedonic coefficients) are available in appendix B. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

What accounts for the observed difference between for-profit and nonprofit-developed properties?

One of the interviewees, who has worked for both nonprofit and for-profit development firms,

contends that a “decent LIHTC deal is no different from any other multifamily property,” at least

not architecturally. He believes that a well-designed and well-managed LIHTC property should

have the same effect as any other residential development on the surrounding community.

Developers have yet different goals when building or rehabilitating a property, and those differing

motivations likely influence both the extent and type of their spillover potential.

According to the nonprofit developers with whom the authors spoke, stabilizing or revitalizing the

surrounding community often is, at best, a secondary goal for a project. Their overriding interest

lies in ensuring that cost-burdened households have an affordable and safe place to live. One

organization, for example, focuses primarily on alleviating and preventing homelessness. It looks

for sites that can support both affordable housing units and a range of ancillary human services

for its targeted very low-income population; its principal (or even sole) concern is its clientele,

not the broader neighborhood. Given space needs, its projects frequently are in less residential

neighborhoods, areas where less obvious opportunities exist for influencing single-family home

prices. Another nonprofit development organization focuses chiefly on properties that can help

alleviate the region’s affordable housing shortage. Although the organization aspires to help

facilitate community development, it realizes that many of its projects are unlikely to have much

catalytic spillover effect. “Some developments are just developments—most, in fact—while others

have more possibility for catalyzing neighborhood revitalization,” explains the firm’s president

and chief executive officer. Some properties are inherently more self-contained by virtue of their

334 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

location or population (those serving senior citizens or people with disabilities, for instance),

whereas others are more clearly part of a neighborhood.

In contrast, the economic considerations underlying for-profit developments appear to lead such

developers to focus more consistently on the ramifications of their properties on the local market.

A typical for-profit firm frequently looks to secure properties—especially vacant sites—whose

development can help catalyze investment in the surrounding area. Development team members

join local community crime watch groups, erect fencing around the site, hire security, and generally

work to ensure a safe environment. Post-construction, the firm imposes very strict rules on who

can live in or visit the property, employs national property management companies, and offers

extensive programming for tenants’ children. These steps help ensure that the development is well

received within the community and contributes to its overall improvement. Ideally, that positive

experience can help translate into political support for subsequent developments by the firm—

either in that community or in others nearby. For-profit LIHTC developers often are engaged in

non-LIHTC development, as well, and are routinely seeking sites for their next projects. In the

competitive real estate environment that is Los Angeles, strong community support can make the

difference in bids for desirable sites.

Neighborhood Income Level

LIHTC developments have had positive price effects across both lower- and higher-income

neighborhoods throughout Los Angeles. “Low-income” communities are defined as those census

tracts whose median household incomes were in the bottom one-third of all census tracts

throughout Los Angeles. “Medium- and high-income” tracts are those in the top two-thirds. The

incomes are based on the 2016 American Community Survey 5-year estimates. Exhibit 14 presents

the findings for both sets of neighborhoods.

Exhibit 14

Neighborhood Income Models

Distance from

All Neighborhoods

Low Income

Medium to High Income

Variable LIHTC Property Coefficient T/F Stat Coefficient T/F Stat Coefficient T/F Stat

Pre

– 0.037***

– 5.37

– 0.099***

– 6.20

– 0.032***

– 4.91

Post

0–¼ mile

– 0.004

– 0.66

– 0.021

– 1.35

0.012*

2.05

Effect

0.034***

15.96

0.078***

18.91

0.044***

29.08

Pre

– 0.033***

– 5.68

– 0.077***

– 5.42

– 0.030***

– 5.61

Post

¼–½ mile

– 0.003

– 0.51

0.004

0.22

0.009+

1.77

Effect

0.030***

11.23

0.080***

21.25

0.039***

23.47

Observations

1,842,725

738,710

1,104,015

R 2

0.7242

0.7033

0.6237

LIHTC = low-income housing tax credit.

+ p < 0.1. * p < 0.05. *** p < 0.001.

Notes: Regressions control for census tract fixed effects, year fixed effects, and the following property traits—living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall). Full results (with all hedonic coefficients) are available in appendix B. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database; U.S. Census Bureau

Cityscape 335

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

The effects are greater in low-income communities, where values have increased between 7.7 and

8.0 percent relative to similar neighborhoods with no LIHTC developments. These effects are

roughly twice the size of those in more affluent communities. Even in these medium- and high-

income neighborhoods, the presence of a LIHTC development increases surrounding home values

by about 4 percent. In Los Angeles, fears that LIHTC properties will depress local home values do

not conform to the data.

Neighborhood Race and Ethnicity

The regressions find little difference in the direction or size of LIHTC price effects in predominantly

White and predominantly non-White neighborhoods.9 Drawing on the 2016 American Community

Survey data, “high non-White” tracts are defined as those whose proportion of minorities is among

the top one-third of all Los Angeles census tracts. Conversely, “low and medium non-White” tracts

are those in the bottom two-thirds of the distribution. In both types of neighborhoods, LIHTC

properties have positive effects on surrounding house prices, with the effects dissipating slightly as

the distance from the LIHTC site increases. Although the model shows slightly higher price effects

in communities with higher proportions of White residents, those differences are not statistically

significant. Exhibit 15 presents the findings.

Exhibit 15

Neighborhood Race and Ethnicity Models (1)

Distance

Low to Medium

Variable

from LIHTC

All Neighborhoods

High Non-White

Non-White

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.037***

– 5.37

– 0.024**

– 3.21

– 0.048***

– 4.86

Post

0–¼ mile

– 0.004

– 0.66

0.008

1.12

– 0.010

– 1.15

Treatment

0.034***

15.96

0.032***

15.56

0.038**

7.33

Pre

– 0.033***

– 5.68

– 0.026***

– 4.96

– 0.038***

– 3.85

Post

¼–½ mile

– 0.003

– 0.51

0.004

0.76

– 0.005

– 0.56

Treatment

0.030***

11.23

0.030***

29.68

0.033*

3.91

Observations

1,842,725

500,698

1,342,027

R 2

0.7242

0.6872

0.7273

LIHTC = low-income housing tax credit.

* p < 0.05. ** p < 0.01. ** p < 0.001.

Notes: Regressions control for census tract fixed effects, year fixed effects, and the following property traits—living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall). Full results (with all hedonic coefficients) are available in appendix B. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database; U.S. Census Bureau

A similar methodological approach is taken to identify any differences in price effects across

communities with high proportions of Asian, Black, and Hispanic residents. Again, census data is

used to determine the proportion of each population group within a census tract, then the model

is run with the top one-third and bottom two-thirds (by proportion) of tracts within the county.

Exhibit 16 presents these findings.

9 White is defined as those who identify as White regardless of ethnicity; non-White is everyone else.

336 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit 16

Neighborhood Race and Ethnicity Models (2)

Distance from

High Asian

Low to Medium Asian

Variable

LIHTC Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.050***

– 4.07

– 0.035***

– 4.44

Post

0–¼ mile

0.011

1.16

– 0.006

– 0.88

Treatment

0.061***

20.36

0.028**

9.56

Pre

– 0.033***

– 4.31

– 0.034***

– 5.00

Post

¼–½ mile

0.006

0.81

– 0.005

– 0.69

Treatment

0.039***

11.63

0.029**

7.91

Observations

584,682

1,258,043

R 2

0.7249

0.7194

Distance from

High Black

Low to Medium Black

Variable

LIHTC Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.032**

– 3.16

– 0.049***

– 4.75

Post

0–¼ Mile

0.008

0.98

– 0.002

– 0.27

Treatment

0.039***

13.10

0.047**

10.43

Pre

– 0.042***

– 6.32

– 0.032***

– 4.03

Post

¼–½ mile

0.001

0.11

0.005

0.60

Treatment

0.043***

18.10

0.037**

10.22

Observations

594,677

1,248,048

R 2

0.6476

0.7356

Distance from

High Hispanic

Low to Medium Hispanic

Variable

LIHTC Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.022*

– 2.43

– 0.055***

– 5.91

Post

0–¼ mile

0.019**

2.94

– 0.007

– 0.80

Treatment

0.040***

11.74

0.047***

12.45

Pre

– 0.023***

– 4.55

– 0.043***

– 4.33

Post

¼–½ mile

0.017*

2.48

– 0.006

– 0.60

Treatment

0.040***

18.26

0.037*

5.13

Observations

450,630

1,392,095

R 2

0.5930

0.7221

LIHTC = low-income housing tax credit.

* p < 0.05. ** p < 0.01. *** p < 0.001.

Notes: Regressions control for census tract fixed effects, year fixed effects, and the following property traits—living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall). Full results (with all hedonic coefficients) are available in appendix B. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database; U.S. Census Bureau

The more race- and ethnicity-specific models follow the same pattern as the initial non-White

model. Regardless of race or ethnicity to classify census tracts, the model shows that LIHTC

projects have a significant positive effect on surrounding house prices within both the 0- to ¼-mile

band and the ¼- to ½-mile band. In the narrowest band where LIHTC investment is most likely

to affect houses, within one-fourth of a mile of a development, the LIHTC price effect is largest

Cityscape 337

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

for neighborhoods with a high proportion of Asian residents (6.1 percent) and second largest for

areas with a low to medium proportion of Black and Hispanic residents (4.7 percent in both cases).

The latter statistic is particularly important, as it contradicts the common concern that LIHTC

investment will be less advantageous to neighborhoods with predominantly White residents.

LIHTC Project Concentration

The introduction of subsequent LIHTC properties in a neighborhood tends to build on the

positive price effects associated with the initial LIHTC development. Multiple “post” variables are

incorporated for each distance band, with each such variable representing whether one, two, three,

or more LIHTC projects are present nearby when a given transaction occurs. The coefficients in

the “neighborhoods with one, two, or three LIHTC properties” columns in exhibit 17 reflect the

marginal effect of each successive LIHTC project on homes within the overlapping distance band

areas. (The “neighborhoods with any LIHTC properties” column presents the original price effect

model findings as points of reference.)

Exhibit 17

Baseline Model Versus Neighborhood Low-Income Housing Tax Credit Concentration

(Overlap) Model

Neighborhoods with

Neighborhoods with

Distance from

Variable

Any LIHTC Properties

1, 2, or 3 LIHTC Properties

LIHTC Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.037***

– 5.37

– 0.035***

– 5.16

Post1

– 0.004

– 0.66

– 0.006

– 0.95

Post2

– 0.002

– 0.24

Post3

0–¼ mile

0.074**

2.70

Effect1

0.034***

15.96

0.030***

13.31

Effect2

0.027*

5.30

Effect3

0.101***

12.07

Pre

– 0.033***

– 5.68

– 0.032***

– 5.64

Post1

– 0.003

– 0.51

– 0.006

– 1.12

Post2

0.001

0.07

Post3

¼–½ mile

0.049***

4.70

Effect1

0.030***

11.23

0.026***

11.11

Effect2

0.027+

3.69

Effect3

0.076***

20.86

Observations

1,842,725

1,842,725

R 2

0.7242

0.7242

LIHTC = low-income housing tax credit.

+ p < 0.1. * p < 0.05. ** p < 0.01. *** p < 0.001.

Notes: The regressions control for census tract fixed effects, year fixed effects, and the following property traits—living area square footage, lot size square footage, floor-area ratio, age at sale, number of stories, distance to central business district, seller type, and seasonal dummies (spring, summer, fall). Full results (with all hedonic coefficients) are available in appendix B. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

338 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

The coefficient for the pre variable in the ¼-mile band indicates that home prices are 3.5 percent

lower in the LIHTC neighborhoods relative to non-LIHTC neighborhoods, prior to completing any

LIHTC projects. After the first LIHTC property is placed in service (post1), average prices in the

LIHTC communities are only about 0.6 percent less than those in the non-LIHTC areas. Therefore,

the first LIHTC project leads to a 3.0-percent (with rounding) increase in home prices. Adding a

second project to the neighborhood does not significantly change the impact observed from the

first project. The addition of a third LIHTC development in the area yet significantly increases the

overall spillover price effect. It is unclear why the introduction of a third LIHTC property has a

greater (and more positive) effect than the introduction of a second such property, but these results

are consistent with the recent study of Chicago LIHTC price effects, which took a similar modeling

approach (Voith et al., 2022).

The positive price effects from adding subsequent LIHTC properties to a neighborhood apply up

to one-half of a mile from the LIHTC sites. Not surprisingly, the price effects within one-fourth of

a mile from the LIHTC sites are greater than those between one-fourth and one-half of a mile from

the properties.10

Project Siting

Positive price effects are consistently associated with LIHTC developments in Los Angeles—

regardless of the characteristics of the properties or the surrounding neighborhoods. What remains

somewhat unclear are the underlying factors that help to bring about these improvements.

As noted previously, one possible explanation for some of the observed effects is simple

endogeneity: Developers are choosing to build or rehabilitate properties in neighborhoods where

prices already are trending upward. The model might simply be measuring baked-in effects. Each

developer downplayed the role local real estate market factors play in the selection of LIHTC

project sites. One interviewee has spent multiple decades in the affordable housing industry as an

investor, developer, and advocate. To him, the local market is “irrelevant” when considering sites

for prospective LIHTC development, saying “it doesn’t help the project in any way.” Another agrees

that “local price trends don’t come into play, because [LIHTC unit] rents are too deeply subsidized.”

Because rents in LIHTC-subsidized units are tied to AMIs, the only way a developer can realize

additional revenues from those units is through an overall increase in AMI. An increase in local

market rents has no effect on the economic returns from subsidized units. Furthermore, the rent

restrictions last for at least 15 years, well beyond the point at which current trends can predict

future rents and land values. LIHTC developments receiving certain state subsidies are subject to

California’s 55-year affordability requirements.

In theory, developers could undertake LIHTC projects with the expectation of selling their

interest after 15 years and realizing a significant capital gain from the property’s appreciated value.

Nonprofit developers frequently have no intention of ever selling their LIHTC properties; some

10 The actual price effects could be greater than those reported. It is possible that prices may begin trending upward once plans for the development are announced, when the developer receives a formal allocation of tax credits, or when ground is broken on the project. Thus, the actual pre-development, pre-announcement home values in the LIHTC neighborhoods may be less than the reported average.

Cityscape 339

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

impose ground leases that ensure the properties’ affordability for up to 99 years. Many for-profits

developers take a similar “long-term hold” approach, meeting their economic return thresholds

from developer fees and ongoing rents.

Local market conditions factor into site selection decisions for projects with a mix of subsidized

and market-rate units. Such projects tend to involve for-profit developers (either alone or in a joint

venture with a nonprofit), and those firms certainly look for properties that can command higher

rents for their market-rate units. Because of the intensity of the competition for LIHTC allocations

and related public bond financing and the reality that applicants generally receive more points in

the allocation process for promising higher proportions of affordable units, new mixed-income

affordable housing developments are relatively rare now in Los Angeles. (They were more prevalent

8 to 10 years ago, when competition for allocations was less severe.) In the current environment,

such projects tend to occur only when necessary to satisfy local zoning regulations. In effect,

a mixed-income property is “really a market-rate deal with a small amount of affordability for

political or financing reasons,” according to one interviewee.

Nonprofit developers also may deliberately target properties in gentrifying areas, such as the Boyle

Heights neighborhood and low-income communities near the University of Southern California.

They do so not for the property’s appreciation potential, but rather as a way of preserving existing

affordable housing and preventing the displacement of lower-income residents. LIHTC financing

becomes a tool to help residents afford to continue living in their communities.

If developers are not basing site decisions on local market trends, what are their primary

considerations? Four key factors influence the location of LIHTC developments in greater Los Angeles.

1. Site Availability

Although Los Angeles does not have the development density of some other markets, it has

relatively few sites available for multifamily rental properties. Some developers undertake projects

primarily in response to specific requests for proposals issued by local housing agencies. The

agency typically has control of one or more specific parcels of land and searches for the best

strategy for developing it as affordable housing. Requests for proposals respondents, therefore, have

limited, if any, flexibility in the location of their proposed development.

Other developers proactively seek properties for construction or rehabilitation, often in partnership

with a local community organization. The challenge is that many desirable properties either are not

for sale or are too costly for an affordable housing development. Few developers have the financial

luxury of waiting indefinitely for a favored site to come on line. Some for-profit firms will pay a

premium for a desirable property, but the success of that approach still depends on the willingness

of the existing landowner to work with the developer. For every property a typical firm acquires, it

analyzes between 25 and 50 potential sites.

2. Project’s Economic Feasibility

A potential site and the desired development must be both physically and economically feasible.

Each of the developers emphasized that “the deal has to pencil out financially” to be considered.

340 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Among the factors developers must account for are the shape and contours of the site, which help

determine the potential size of the building or buildings and the difficulty of the construction or

rehabilitation process. They also need to weigh the costs of gaining control of the property, carrying

out any necessary environmental remediation and (potentially) relocating tenants during the

construction period.

Separate from the actual development costs are zoning considerations. Local land use plans must

already allow for multifamily development or be flexible enough to incorporate such activities.

Moreover, developers need to identify any existing entitlements or other constraints that could

affect the proposed project. Not surprisingly, many potential properties are not suitable or feasible

for affordable multifamily housing.

3. Extent of Local Political Support

Perhaps the most salient factor when considering sites for LIHTC projects is the extent of local

support for the proposed development. Developers noted that they “almost always” encounter

some community opposition to a proposed affordable housing project. Developments targeting

seniors and working adults generate less concern than those designed for the homeless and families

with children, but virtually every proposed project generates some local opposition. For nonprofit

organizations developing “special needs housing,” achieving 70 percent or greater local support

represents the targeted benchmark. They consequently devote considerable time and energy to

local outreach during a project’s pre-development stage to help assuage resident concerns and

alleviate local opposition.

The support—or at least neutrality—of local public officials is critical for a project to move forward

in Los Angeles, particularly if the developer is pursuing any public funding for the project. This

principle effectively applies to all projects, because a commitment of local funding enhances

a project’s competitiveness for tax credits. As one interviewee attested, “political support is

mandatory” for a LIHTC development to be successful. “You don’t want to drive a square peg into

a round hole . . . and you’re looking for the least amount of resistance to complete a project in a

reasonable timeframe.”

Local political support is not always forthcoming, however, even in communities with an objective

need for more affordable housing. One of the nonprofit developers interviewed contends that

Angelenos generally are “very aware of the lack of affordable housing” in the city but have limited

knowledge of the steps that need to be taken to address the problem. Educating them about the

importance of taking advantage of favorable properties can be “difficult.” For an organization that

focuses primarily on housing for the homeless, the fate of its developments depends almost entirely

on the support of a local city council member. These individuals are not always supportive, and

if they are not, their colleagues will not overrule them. Various local dynamics play into council

members’ decisions. One current council member, for instance, has been supportive of affordable

housing development but has imposed an unofficial moratorium on new such developments in his

neighborhood, because he feels that it has too much affordable housing right now.

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4. Competitiveness for LIHTCs and Other Public Resources

In California, competition for LIHTC allocations, state affordable housing bonds, and various other

public subsidies has become hypercompetitive due to the broad acknowledgment of the imperative

to address the state’s growing homeless population and its expanding deficit of affordable

housing. The increased competition has changed the dynamics surrounding project selection and

prioritization, with developers increasingly focused on structuring planned developments in ways

that can maximize their likelihood of scoring well in the application review. With finite resources

available and a limited number of application periods, developers must be ready to make their best

presentations during those application windows.

Applicants receive additional points for projects in designated areas such as qualified census

tracts and difficult-to-develop areas—communities with high land, construction, and utility costs

relative to the median income. Allocators also look more favorably on proposed developments near

amenities such as public transit and grocery stores. Developments whose financing limits the use

of state bond proceeds to the portion of the property serving the lowest income renters also tend to

score higher.

Implications for Policy and Future Research

This analysis demonstrates the widespread, positive spillover price effects associated with LIHTC

properties in Los Angeles. It explicitly refutes the pervasive perception among certain politicians

and policymakers that such developments somehow worsen neighborhood economic conditions.

Even in predominantly White, middle- to upper-income neighborhoods, LIHTC developments

have positive effects on local home values. Moreover, residents should not be concerned about

introducing a subsequent LIHTC property in the community; the concentration of such properties

typically has an additive effect on values. This study finds that larger scale LIHTC projects and

fully subsidized developments tend to bring about greater spillover benefits to the surrounding

neighborhoods, and such positive effects are not only found in projects sponsored by nonprofit

developers, but also by for-profit developers.

From a policy perspective, the key takeaway is that LIHTC developments, in addition to creating

and preserving badly needed housing that is affordable to low-income households, consistently

have positive effects on surrounding property values. A “bad” place for such properties to be

developed does not exist, nor does a “bad” type of LIHTC development exist. Regardless of the

development’s size or neighborhood in which it is placed into service, a LIHTC property is likely to

have a positive spillover effect on its neighborhood.

Are there types of properties or types of neighborhoods that are likely to produce more positive

spillover effects than others? Perhaps, and this study identified some differences in spillover

price effects associated with some project- and neighborhood-level factors. It is important to note

that these differences, although potentially significant statistically, are not meaningfully different

economically. At most, they may reflect a percentage point or two difference. Although not

insignificant, the variation is hardly enough to spend considerable time and energy searching for

the “best” fit of development and neighborhood. After all, the property value effects ultimately are a

342 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

secondary benefit of the LIHTC development; the primary benefit remains the affordable housing it

supplies for low-income people.

Moreover, trying to identify the ideal project and neighborhood rarely is realistic, given the

inherent political and economic constraints developers must negotiate. In a city such as Los

Angeles, with relatively little land available for development, finding a suitable property in an area

where residents are supportive of affordable housing is its own challenge. Developers often have

to take advantage of whatever opportunities are available; they do not have the luxury of waiting

for the highest impact scenario, particularly because no guarantee is given that a development will

obtain a LIHTC allocation and other subsidies.

Some of these findings and accompanying conclusions could be specific to Los Angeles. The city’s

well-publicized problem with homelessness and its severe—and widely acknowledged—shortage

of affordable housing resulted in the passage of several public ordinances to encourage more

LIHTC and other affordable housing development. Los Angeles continues to be one of the country’s

strongest real estate markets, with many of its neighborhoods experiencing substantial home price

appreciation in the past few years. These and other factors create an environment that is conducive

to positive LIHTC spillover price effects. It is important to see if these findings can be replicated in

weaker and smaller urban markets throughout the country.

This study presented rising property values as inherently beneficial for a community. They certainly

benefit local property owners, but they simultaneously can disadvantage local renters. Rising values

typically translate into higher rents; like many other cities throughout the country, Los Angeles has

experienced double-digit average annual rent increases in the past few years. Ironically, introducing

a LIHTC property in a community could conceivably reduce the housing affordability for other

renters in the area. To date, little research—in Los Angeles or elsewhere—has been on the spillover

rental ramifications of creating affordable housing in a neighborhood.

More generally, it is important to understand the precise mechanisms that contribute to the

observed price appreciation around LIHTC properties. If site selection is not contributing

significantly to the observed changes—as this quantitative and qualitative research suggests—

then researchers need to identify the factors that are driving the change. How much is a result of

additional population density in the community—density that can shape investors’ perception

of the community’s appeal? To what extent is the improvement driven by the replacement of a

vacant or underutilized, potentially deteriorating, property into a more positive community asset?

How much of the effect results from active and capable property management? Answering these

questions provides fruitful avenues for future research.

Appendix A. Los Angeles Low-Income Housing Tax Credit

Developer Questions

1. First, how did you choose the particular location for your LIHTC development?

a. How did the LIHTC allocation criteria affect your decision?

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An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

b. How difficult was it to find available land or properties?

c. What local market dynamics affected your decision? For instance, did you consider local

crime rates? Did you focus more on areas with appreciating property values? Did you

focus on areas where community organizations were actively encouraging affordable

housing development?

d. How supportive was the community of your planned development? Did their support

or opposition affect your decision to develop the property—or the characteristics of the

development?

2. What was your targeted mix of tenant incomes in the property?

a. What was the financial and mission rationale behind that goal?

b. Were you successful in achieving the desired mix? Why or why not?

c. Since the building has been operational, how has the tenant mix changed? What has been

the rate of tenant turnover?

d. Has the turnover rate been about what you expected? What factors have you found to be

most important in attracting and keeping tenants?

3. How (if at all) has the property affected the dynamics of the surrounding neighborhood?

a. How has the community’s opinion of the project evolved since the property was placed in

service?

b. How has the neighborhood changed since you broke ground? Has it become more or less

appealing for investment?

c. Do you believe that the LIHTC property has had a significant effect on the surrounding

community? If so, what kind of effect? Why?

d. Have you contemplated or undertaken subsequent LIHTC developments in this

neighborhood? If so, are you considering other or different factors now than you did prior

to the first LIHTC investment in the area?

344 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Appendix B. Full Regression Results

Exhibit B-1

Baseline Model for Low-Income Housing Tax Credit Price Effects in Los Angeles County (1 of 2)

Neighborhoods with Any LIHTC Properties

Distance from

Variable

LIHTC Property

T Stat (Coefficients)/

Coefficient

F Stat (Treatment Effects)

Pre

– 0.037***

– 5.37

Post

0–¼ mile

– 0.004

– 0.66

Effect

0.034***

15.96

Pre

– 0.033***

– 5.68

Post

¼–½ mile

– 0.003

– 0.51

Effect

0.030***

11.23

Lot size

0.000***

4.04

Lot size2

– 0.000***

– 4.40

Living area

0.000***

35.86

Living area2

– 0.000***

– 24.06

Floor-area ratio

– 0.004

– 1.35

Age

– 0.001

– 0.54

Age2

0.000

0.60

2 Stories

0.019**

2.84

3 Stories

0.067**

3.17

Spring

0.004

1.63

Summer

0.037***

14.88

Fall

0.045***

19.79

Distance to central business district

– 0.010

– 1.06

Government seller

– 0.176***

– 4.21

Bank seller

– 0.141***

– 9.95

1989

0.168***

21.45

1990

0.193***

28.68

1991

0.202***

17.39

1992

0.208***

7.66

1993

0.124***

8.42

1994

0.103***

5.49

1995

0.039***

4.05

1996

0.041***

4.47

1997

0.078***

7.45

1998

0.174***

19.86

1999

0.270***

28.36

2000

0.347***

37.47

2001

0.446***

46.47

2002

0.585***

59.70

2003

0.764***

72.21

2004

1.009***

88.21

2005

1.198***

100.99

2006

1.267***

96.35

2007

1.012***

84.16

2008

0.801***

38.30

2009

0.801***

19.29

Cityscape 345

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit B-1

Baseline Model for Low-Income Housing Tax Credit Price Effects in Los Angeles County (2 of 2)

Neighborhoods with Any LIHTC Properties

Distance from

Variable

LIHTC Property

T Stat (Coefficients)/

Coefficient

F Stat (Treatment Effects)

2010

0.846***

26.53

2011

0.811***

24.84

2012

0.850***

26.20

2013

1.017***

35.33

2014

1.135***

40.76

2015

1.217***

40.76

2016

1.291***

45.13

Constant

11.439***

71.77

Observations

1,842,725

R 2

0.7242

LIHTC = low-income housing tax credit.

** p < 0.01. *** p < 0.001.

Notes: Regressions also control for census tract fixed effects, which are not listed due to the large number of tracts. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

Exhibit B-2

Property Value Effects of Different Size Low-Income Housing Tax Credit Developments (1 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

Pre

– 0.037***

– 5.37

Post

0.002

0.19

Small post

– 0.015

– 1.38

Large post

0–¼ mile

0.005

0.42

Small Property Effect

0.024*

5.75

Medium Property Effect

0.039***

13.34

Large Property Effect

0.044***

13.32

Pre

– 0.033***

– 5.67

Post

0.005

0.80

Small post

– 0.018

– 1.56

Large post

¼–½ mile

– 0.003

– 0.22

Small Property Effect

0.021+

3.23

Medium Property Effect

0.038***

28.56

Large Property Effect

0.036**

7.34

Lot size

0.000***

4.04

Lot size2

– 0.000***

– 4.40

Living area

0.000***

35.86

Living area2

– 0.000***

– 24.06

Floor-area ratio

– 0.004

– 1.35

Age

– 0.001

– 0.54

Age2

0.000

0.60

2 Stories

0.019**

2.84

346 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit B-2

Property Value Effects of Different Size Low-Income Housing Tax Credit Developments (2 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

3 Stories

0.067**

3.17

Spring

0.004

1.63

Summer

0.037***

14.88

Fall

0.045***

19.79

Distance to central business district

– 0.010

– 1.06

Government seller

– 0.176***

– 4.21

Bank seller

– 0.141***

– 9.95

1989

0.168***

21.45

1990

0.193***

28.68

1991

0.202***

17.38

1992

0.208***

7.66

1993

0.124***

8.42

1994

0.103***

5.49

1995

0.039***

4.06

1996

0.041***

4.47

1997

0.078***

7.45

1998

0.174***

19.85

1999

0.270***

28.36

2000

0.347***

37.46

2001

0.446***

46.48

2002

0.585***

59.69

2003

0.764***

72.22

2004

1.009***

88.23

2005

1.198***

100.96

2006

1.267***

96.33

2007

1.012***

84.16

2008

0.801***

38.30

2009

0.801***

19.28

2010

0.846***

26.53

2011

0.811***

24.82

2012

0.850***

26.19

2013

1.017***

35.35

2014

1.135***

40.78

2015

1.217***

40.76

2016

1.291***

45.13

Constant

11.439***

71.86

Observations

1,842,725

R 2

0.7242

LIHTC = low-income housing tax credit.

+ p < 0.1. * p < 0.05. ** p < 0.01. *** p < 0.001.

Notes: Regressions also control for census tract fixed effects, which are not listed due to the large number of tracts. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

Cityscape 347

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit B-3

Property Value Effects of Partially Versus Fully Subsidized Low-Income Housing Tax

Credit Developments (1 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

Pre

– 0.037***

– 5.37

Post

0.016

0.57

Fully subsidized post

0–¼ mile

– 0.022

– 0.74

Partially Subsidized Property Effect

0.054+

3.17

Fully Subsidized Property Effect

0.032***

15.36

Pre

– 0.033***

– 5.70

Post

0.005

0.22

Primarily subsidized post

¼–½ mile

– 0.008

– 0.40

Partially Subsidized Property Effect

0.038

2.24

Fully Subsidized Property Effect

0.029***

12.82

Lot size

0.000***

4.04

Lot size2

– 0.000***

– 4.40

Living area

0.000***

35.86

Living area2

– 0.000***

– 24.06

Floor-area ratio

– 0.004

– 1.35

Age

– 0.001

– 0.54

Age2

0.000

0.60

2 Stories

0.019**

2.85

3 Stories

0.067**

3.17

Spring

0.004

1.63

Summer

0.037***

14.88

Fall

0.045***

19.79

Distance to central business district

– 0.010

– 1.05

Government seller

– 0.176***

– 4.21

Bank seller

– 0.141***

– 9.95

1989

0.168***

21.45

1990

0.193***

28.67

1991

0.202***

17.37

1992

0.208***

7.66

1993

0.124***

8.42

1994

0.103***

5.49

1995

0.039***

4.05

1996

0.041***

4.46

1997

0.078***

7.44

1998

0.174***

19.84

1999

0.270***

28.36

2000

0.347***

37.47

2001

0.446***

46.46

2002

0.585***

59.67

2003

0.764***

72.18

2004

1.009***

88.16

2005

1.198***

100.95

2006

1.267***

96.29

348 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit B-3

Property Value Effects of Partially Versus Fully Subsidized Low-Income Housing Tax

Credit Developments (2 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

2007

1.012***

84.13

2008

0.801***

38.30

2009

0.801***

19.29

2010

0.846***

26.53

2011

0.811***

24.83

2012

0.850***

26.20

2013

1.017***

35.33

2014

1.135***

40.77

2015

1.217***

40.77

2016

1.291***

45.15

Constant

11.439***

71.74

Observations

1,842,725

R 2

0.7242

LIHTC = low-income housing tax credit.

+ p < 0.1. ** p < 0.01. *** p < 0.001.

Notes: Regressions also control for census tract fixed effects, which are not listed due to the large number of tracts. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

Exhibit B-4

Property Value Effects of Nonprofit Versus For-Profit Sponsored Low-Income Housing Tax

Credit Developments (1 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

Pre

– 0.038***

– 5.43

Post

– 0.017

– 1.35

For-profit post

0–¼ mile

0.019

1.42

Nonprofit Treatment

0.021+

2.84

For-Profit Treatment

0.040***

15.51

Pre

– 0.033***

– 5.72

Post

– 0.014

– 1.31

For-profit post

¼–½ mile

0.013

1.36

Nonprofit Treatment

0.019

2.42

For-Profit Treatment

0.032***

11.84

Lot size

0.000***

4.04

Lot size2

– 0.000***

– 4.40

Living area

0.000***

35.85

Living area2

– 0.000***

– 24.06

Floor-area ratio

– 0.004

– 1.35

Age

– 0.001

– 0.55

Age2

0.000

0.61

2 Stories

0.019**

2.84

3 Stories

0.067**

3.17

Cityscape 349

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit B-4

Property Value Effects of Nonprofit Versus For-Profit Sponsored Low-Income Housing Tax

Credit Developments (2 of 2)

Distance from

T Stat (Coefficients)/

Variable

Coefficient

LIHTC Property

F Stat (Treatment Effects)

Spring

0.004

1.63

Summer

0.037***

14.91

Fall

0.045***

19.80

Distance to central business district

– 0.010

– 1.06

Government seller

– 0.176***

– 4.21

Bank seller

– 0.141***

– 9.95

1989

0.168***

21.46

1990

0.193***

28.71

1991

0.202***

17.40

1992

0.208***

7.67

1993

0.124***

8.42

1994

0.103***

5.49

1995

0.039***

4.06

1996

0.041***

4.48

1997

0.078***

7.45

1998

0.174***

19.82

1999

0.270***

28.31

2000

0.347***

37.33

2001

0.446***

46.36

2002

0.585***

59.54

2003

0.764***

72.12

2004

1.009***

88.16

2005

1.198***

100.93

2006

1.267***

96.25

2007

1.012***

84.11

2008

0.801***

38.31

2009

0.801***

19.29

2010

0.846***

26.54

2011

0.811***

24.83

2012

0.850***

26.20

2013

1.017***

35.33

2014

1.135***

40.79

2015

1.217***

40.81

2016

1.291***

45.19

Constant

11.439***

71.69

Observations

1,842,725

R 2

0.7242

LIHTC = low-income housing tax credit.

+ p < 0.1. ** p < 0.01. *** p < 0.001.

Notes: Regressions also control for census tract fixed effects, which are not listed due to the large number of tracts. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

350 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit B-5

Neighborhood Income Models (1 of 2)

Distance

All Neighborhoods

Low Income

Medium to High Income

Variable

from LIHTC

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.037***

– 5.37

– 0.099***

– 6.20

– 0.032***

– 4.91

Post

0–¼ mile

– 0.004

– 0.66

– 0.021

– 1.35

0.012*

2.05

Effect

0.034***

15.96

0.078***

18.91

0.044***

29.08

Pre

– 0.033***

– 5.68

– 0.077***

– 5.42

– 0.030***

– 5.61

Post

¼–½ mile

– 0.003

– 0.51

0.004

0.22

0.009+

1.77

Effect

0.030***

11.23

0.080***

21.25

0.039***

23.47

Lot size

0.000***

4.04

0.000***

4.82

– 0.000

– 0.36

Lot size2

– 0.000***

– 4.40

– 0.000***

– 4.63

0.000

0.09

Living area

0.000***

35.86

0.000***

26.27

0.000***

28.95

Living area2

– 0.000***

– 24.06

– 0.000***

– 19.10

– 0.000***

– 17.65

Floor-area ratio

– 0.004

– 1.35

– 0.002

– 1.27

– 0.356***

– 10.35

Age

– 0.001

– 0.54

0.002

1.51

– 0.004

– 1.40

Age2

0.000

0.60

– 0.000

– 0.66

0.000

1.16

2 Stories

0.019**

2.84

0.004

0.56

0.029***

3.56

3 Stories

0.067**

3.17

0.052*

2.30

0.102+

1.86

Spring

0.004

1.63

0.015***

4.35

– 0.002

– 0.62

Summer

0.037***

14.88

0.047***

13.01

0.029***

10.00

Fall

0.045***

19.79

0.051***

15.07

0.041***

10.80

Distance to central

– 0.010

– 1.06

– 0.006

– 0.51

– 0.013

– 1.04

business district

Government seller

– 0.176***

– 4.21

– 0.317**

– 2.93

– 0.128**

– 2.94

Bank seller

– 0.141***

– 9.95

– 0.123**

– 3.07

– 0.126***

– 18.49

1989

0.168***

21.45

0.179***

18.20

0.160***

22.52

1990

0.193***

28.68

0.176***

17.87

0.202***

29.48

1991

0.202***

17.39

0.178***

11.86

0.220***

15.49

1992

0.208***

7.66

0.146***

9.82

0.251***

7.22

1993

0.124***

8.42

0.072***

5.08

0.165***

8.50

1994

0.103***

5.49

0.054***

3.84

0.144***

5.15

1995

0.039***

4.05

0.008

0.79

0.064***

5.05

1996

0.041***

4.47

0.022*

1.99

0.059***

4.82

1997

0.078***

7.45

0.087***

6.81

0.073***

5.03

1998

0.174***

19.86

0.201***

16.68

0.151***

13.90

1999

0.270***

28.36

0.298***

21.01

0.249***

23.95

2000

0.347***

37.47

0.381***

25.65

0.324***

32.82

2001

0.446***

46.47

0.464***

31.37

0.435***

37.03

2002

0.585***

59.70

0.586***

40.22

0.585***

49.01

2003

0.764***

72.21

0.749***

51.41

0.774***

57.59

2004

1.009***

88.21

0.965***

62.05

1.036***

73.62

2005

1.198***

100.99

1.135***

69.04

1.242***

91.63

Cityscape 351

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit B-5

Neighborhood Income Models (2 of 2)

Distance

All Neighborhoods

Low Income

Medium to High Income

Variable

from LIHTC

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Coefficient

T/F Stat

2006

1.267***

96.35

1.205***

72.48

1.320***

79.23

2007

1.012***

84.16

1.204***

71.51

1.338***

60.39

2008

0.801***

38.30

1.056***

51.46

0.985***

27.57

2009

0.801***

19.29

0.942***

48.34

0.736***

14.08

2010

0.846***

26.53

0.944***

48.29

0.795***

19.25

2011

0.811***

24.84

0.902***

39.93

0.761***

18.01

2012

0.850***

26.20

0.936***

38.18

0.800***

18.68

2013

1.017***

35.33

1.078***

44.68

0.975***

25.51

2014

1.135***

40.76

1.186***

45.80

1.100***

30.16

2015

1.217***

40.76

1.258***

44.97

1.187***

30.19

2016

1.291***

45.13

1.316***

47.34

1.273***

33.79

Constant

11.439***

71.77

11.425***

62.24

11.649***

54.78

Observations

1,842,725

738,710

1,104,015

R 2

0.7242

0.7033

0.6237

LIHTC = low-income housing tax credit.

+ p < 0.1. * p < 0.05. ** p < 0.01. *** p < 0.001.

Notes: Regressions also control for census tract fixed effects, which are not listed due to the large number of tracts. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems and CoreLogic; LIHTC HUD User database; U.S. Census Bureau

Exhibit B-6

Neighborhood Race and Ethnicity Models (1) (1 of 2)

Distance

Low to Medium

Variable

from LIHTC

All Neighborhoods

High Non-White

Non-White

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.037***

– 5.37

– 0.024**

– 3.21

– 0.048***

– 4.86

Post

0–¼ mile

– 0.004

– 0.66

0.008

1.12

– 0.010

– 1.15

Treatment

0.034***

15.96

0.032***

15.56

0.038**

7.33

Pre

– 0.033***

– 5.68

– 0.026***

– 4.96

– 0.038***

– 3.85

Post

¼–½ mile

– 0.003

– 0.51

0.004

0.76

– 0.005

– 0.56

Treatment

0.030***

11.23

0.030***

29.68

0.033*

3.91

Lot size

0.000***

4.04

0.000***

3.04

0.000***

3.44

Lot size2

– 0.000***

– 4.40

– 0.000*

– 2.38

– 0.000***

– 3.80

Living area

0.000***

35.86

0.000***

31.61

0.000***

29.85

Living area2

– 0.000***

– 24.06

– 0.000***

– 18.00

– 0.000***

– 20.85

Floor-area ratio

– 0.004

– 1.35

– 0.206**

– 3.19

– 0.003

– 1.33

Age

– 0.001

– 0.54

– 0.001

– 0.48

– 0.001

– 0.53

Age2

0.000

0.60

0.000

0.36

0.000

0.65

2 Stories

0.019**

2.84

0.049***

6.43

0.010

1.16

3 Stories

0.067**

3.17

0.048

0.87

0.062**

2.78

Spring

0.004

1.63

– 0.009

– 1.31

0.010***

3.98

352 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit B-6

Neighborhood Race and Ethnicity Models (1) (2 of 2)

Distance

Low to Medium

Variable

from LIHTC

All Neighborhoods

High Non-White

Non-White

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Coefficient

T/F Stat

Summer

0.037***

14.88

0.025***

3.70

0.041***

16.37

Fall

0.045***

19.79

0.036***

4.20

0.049***

23.84

Distance to central

– 0.010

– 1.06

0.021

1.25

– 0.014

– 1.39

business district

Government seller

– 0.176***

– 4.21

– 0.054

– 0.58

– 0.234***

– 4.88

Bank seller

– 0.141***

– 9.95

– 0.141***

– 9.27

– 0.141***

– 7.88

1989

0.168***

21.45

0.173***

8.86

0.166***

24.13

1990

0.193***

28.68

0.188***

20.93

0.195***

23.56

1991

0.202***

17.39

0.206***

17.94

0.201***

12.88

1992

0.208***

7.66

0.198***

13.92

0.212***

5.95

1993

0.124***

8.42

0.149***

9.91

0.115***

6.16

1994

0.103***

5.49

0.138***

5.16

0.091***

5.10

1995

0.039***

4.05

0.067***

5.18

0.029**

2.63

1996

0.041***

4.47

0.063***

4.24

0.034***

3.44

1997

0.078***

7.45

0.087***

4.16

0.075***

7.36

1998

0.174***

19.86

0.164***

13.37

0.178***

17.76

1999

0.270***

28.36

0.240***

15.43

0.281***

25.76

2000

0.347***

37.47

0.326***

27.68

0.355***

32.96

2001

0.446***

46.47

0.430***

31.06

0.453***

43.15

2002

0.585***

59.70

0.569***

44.58

0.592***

53.60

2003

0.764***

72.21

0.751***

54.70

0.770***

65.36

2004

1.009***

88.21

1.016***

67.68

1.007***

76.51

2005

1.198***

100.99

1.226***

67.86

1.188***

86.60

2006

1.267***

96.35

1.299***

56.02

1.257***

90.50

2007

1.012***

84.16

1.324***

50.12

1.268***

80.84

2008

0.801***

38.30

1.013***

25.62

1.013***

39.27

2009

0.801***

19.29

0.778***

12.15

0.812***

19.36

2010

0.846***

26.53

0.831***

17.42

0.854***

26.62

2011

0.811***

24.84

0.810***

16.57

0.812***

24.66

2012

0.850***

26.20

0.853***

17.37

0.851***

26.21

2013

1.017***

35.33

1.021***

23.45

1.017***

35.59

2014

1.135***

40.76

1.145***

27.03

1.133***

40.29

2015

1.217***

40.76

1.237***

28.09

1.211***

40.29

2016

1.291***

45.13

1.317***

31.51

1.283***

44.69

Constant

11.439***

71.77

11.428***

25.64

11.496***

65.75

Observations

1,842,725

500,698

1,342,027

R 2

0.7242

0.6872

0.7273

LIHTC = low-income housing tax credit.

* p < 0.05. ** p < 0.01. *** p < 0.001.

Notes: Regressions also control for census tract fixed effects, which are not listed due to the large number of tracts. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database; U.S. Census Bureau

Cityscape 353

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit B-7

Neighborhood Race and Ethnicity Models (2) (1 of 4)

Distance

High Asian

Low to Medium Asian

Variable

from LIHTC

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.050***

– 4.07

– 0.035***

– 4.44

Post

0–¼ mile

0.011

1.16

– 0.006

– 0.88

Treatment

0.061***

20.36

0.028**

9.56

Pre

– 0.033***

– 4.31

– 0.034***

– 5.00

Post

¼–½ mile

0.006

0.81

– 0.005

– 0.69

Treatment

0.039***

11.63

0.029**

7.91

Lot size

0.000***

7.67

0.000*

2.49

Lot size2

– 0.000***

– 7.00

– 0.000**

– 2.86

Living area

0.000***

27.40

0.000***

30.76

Living area2

– 0.000***

– 17.92

– 0.000***

– 19.76

Floor-area ratio

– 0.001

– 0.81

– 0.006

– 0.97

Age

0.000

0.14

– 0.002

– 0.60

Age2

– 0.000

– 0.84

0.000

0.705

2 Stories

0.031***

4.53

0.014

1.59

3 Stories

0.048

1.44

0.071*

2.54

Spring

0.010***

3.84

0.002

0.65

Summer

0.042***

16.13

0.034***

10.49

Fall

0.050***

22.11

0.043***

13.72

Distance to CBD

0.022*

2.54

– 0.017

– 1.61

Government seller

– 0.147

– 1.32

– 0.175***

– 3.83

Bank seller

– 0.106***

– 15.11

– 0.144***

– 9.56

1989

0.178***

18.87

0.164***

18.17

1990

0.189***

18.70

0.197***

24.75

1991

0.170***

17.58

0.219***

14.17

1992

0.133***

10.45

0.245***

6.78

1993

0.068***

5.34

0.153***

7.98

1994

0.055***

3.54

0.127***

5.11

1995

0.001

0.07

0.057***

4.58

1996

0.012

0.94

0.057***

4.68

1997

0.051***

3.50

0.092***

6.67

1998

0.150***

14.44

0.186***

15.42

1999

0.231***

19.48

0.290***

22.98

2000

0.316***

29.34

0.363***

27.72

2001

0.418***

37.97

0.461***

33.39

2002

0.555***

49.28

0.600***

43.29

2003

0.735***

61.72

0.779***

52.84

2004

0.969***

70.78

1.027***

68.71

2005

1.145***

73.37

1.222***

81.35

2006

1.203***

77.87

1.298***

74.19

2007

1.214***

78.68

1.313***

58.69

2008

1.017***

79.89

1.011***

26.17

2009

0.903***

65.34

0.763***

14.10

2010

0.919***

68.98

0.819***

18.93

2011

0.871***

60.49

0.789***

17.55

354 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit B-7

Neighborhood Race and Ethnicity Models (2) (2 of 4)

Distance

High Asian

Low to Medium Asian

Variable

from LIHTC

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

2012

0.905***

57.87

0.829***

18.37

2013

1.055***

64.63

1.001***

24.76

2014

1.165***

64.53

1.123***

28.77

2015

1.234***

66.48

1.211***

28.85

2016

1.293***

70.44

1.292***

31.82

Constant

10.952***

82.04

11.548***

60.85

Observations

584,682

1,258,043

R 2

0.7249

0.7194

Distance

High Black

Low to Medium Black

Variable

from LIHTC

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.032**

– 3.16

– 0.049***

– 4.75

Post

0–¼ mile

0.008

0.98

– 0.002

– 0.27

Treatment

0.039***

13.10

0.047**

10.43

Pre

– 0.042***

– 6.32

– 0.032***

– 4.03

Post

¼–½ mile

0.001

0.11

0.005

0.60

Treatment

0.043***

18.10

0.037**

10.22

Lot size

– 0.000

– 1.19

0.000***

6.54

Lot size2

0.000

1.17

– 0.000***

6.64

Living area

0.000***

22.66

0.000***

36.78

Living area2

– 0.000***

– 12.30

– 0.000***

– 24.06

Floor-area ratio

– 0.314***

– 4.88

– 0.002

– 1.29

Age

– 0.007+

– 1.69

0.002*

2.21

Age2

0.000+

1.69

– 0.000*

– 2.31

2 Stories

0.018

1.36

0.018***

3.41

3 Stories

0.197**

2.67

0.057**

2.68

Spring

– 0.006

– 0.88

0.010***

4.62

Summer

0.026***

4.48

0.042***

18.25

Fall

0.039***

5.78

0.048***

22.77

Distance to CBD

– 0.023+

– 1.69

– 0.003

– 0.28

Government seller

– 0.104*

– 2.19

– 0.208**

– 2.90

Bank seller

– 0.143***

– 17.75

– 0.113***

– 5.84

1989

0.154***

13.88

0.172***

30.50

1990

0.184***

21.93

0.196***

26.67

1991

0.233***

9.46

0.188***

19.04

1992

0.300***

5.23

0.164***

16.07

1993

0.193***

5.83

0.093***

9.42

1994

0.162***

3.34

0.077***

6.96

1995

0.053**

2.75

0.031***

3.30

1996

0.047*

2.41

0.039***

4.26

1997

0.077**

3.24

0.078***

7.97

1998

0.166***

9.57

0.176***

19.55

1999

0.261***

17.48

0.273***

25.47

2000

0.338***

20.09

0.351***

34.61

Cityscape 355

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit B-7

Neighborhood Race and Ethnicity Models (2) (3 of 4)

Distance

High Black

Low to Medium Black

Variable

from LIHTC

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

2001

0.438***

20.37

0.448***

46.00

2002

0.585***

28.26

0.583***

58.18

2003

0.770***

33.15

0.759***

75.53

2004

1.025***

43.69

0.996***

89.97

2005

1.233***

50.46

1.175***

96.74

2006

1.308***

44.63

1.243***

95.68

2007

1.322***

33.05

1.256***

92.46

2008

0.949***

15.39

1.041***

74.19

2009

0.654***

8.62

0.890***

61.10

2010

0.731***

11.38

0.910***

63.92

2011

0.705***

9.98

0.866***

55.86

2012

0.741***

10.46

0.905***

53.48

2013

0.923***

15.02

1.061***

62.01

2014

1.048***

18.19

1.177***

64.78

2015

1.141***

18.30

1.253***

64.98

2016

1.234***

20.83

1.318***

67.33

Constant

11.812***

36.64

11.271***

72.44

Observations

594,677

1,248,048

R 2

0.6476

0.7356

Distance

High Hispanic

Low to Medium Hispanic

Variable

from LIHTC

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.022*

– 2.43

– 0.055***

– 5.91

Post

0–¼ mile

0.019**

2.94

– 0.007

– 0.80

Treatment

0.040***

11.74

0.047***

12.45

Pre

– 0.023***

– 4.55

– 0.043***

– 4.33

Post

¼–½ mile

0.017*

2.48

– 0.006

– 0.60

Treatment

0.040***

18.26

0.037*

5.13

Lot size

0.000*

2.19

0.000***

3.69

Lot size2

– 0.000*

– 2.00

– 0.000***

– 4.03

Living area

0.000***

31.05

0.000***

28.99

Living area2

– 0.000***

– 18.11

– 0.000

– 21.06

Floor-area ratio

– 0.282***

– 9.31

– 0.003

– 1.33

Age

– 0.001

– 0.25

– 0.001

– 0.78

Age2

0.000

0.15

0.000

0.94

2 Stories

0.043***

5.28

0.012

1.34

3 Stories

0.113

0.91

0.061**

2.80

Spring

– 0.015*

– 2.30

0.011***

4.67

Summer

0.018*

2.52

0.043***

17.99

Fall

0.032***

5.16

0.050***

27.21

Distance to CBD

– 0.010

– 0.50

– 0.009

– 0.94

Government seller

– 0.106

– 1.45

– 0.228***

– 4.90

Bank seller

– 0.119***

– 11.37

– 0.143***

– 7.39

356 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit B-7

Neighborhood Race and Ethnicity Models (2) (4 of 4)

Distance

High Hispanic

Low to Medium Hispanic

Variable

from LIHTC

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

1989

0.168***

9.88

0.168***

22.38

1990

0.219***

25.21

0.183***

23.43

1991

0.238***

23.32

0.190***

12.79

1992

0.269***

8.67

0.188***

6.27

1993

0.180***

15.95

0.107***

5.70

1994

0.182***

8.49

0.080***

4.33

1995

0.114***

6.07

0.016+

1.70

1996

0.098***

5.26

0.025**

2.72

1997

0.103***

4.87

0.071***

6.88

1998

0.170***

12.78

0.174***

18.46

1999

0.253***

13.61

0.274***

24.33

2000

0.327***

24.72

0.352***

34.52

2001

0.435***

25.76

0.449***

44.72

2002

0.581***

32.76

0.585***

56.80

2003

0.767***

39.74

0.763***

70.08

2004

1.044***

51.96

0.995***

79.87

2005

1.277***

69.63

1.172***

86.28

2006

1.376***

51.35

1.236***

91.58

2007

1.397***

37.55

1.246***

87.65

2008

0.991***

19.42

1.017***

43.60

2009

0.713***

10.54

0.838***

19.87

2010

0.780***

14.31

0.873***

28.11

2011

0.760***

13.42

0.830***

26.12

2012

0.800***

13.54

0.867***

28.39

2013

0.963***

17.78

1.031***

38.73

2014

1.101***

20.80

1.143***

43.51

2015

1.197***

21.11

1.219***

43.55

2016

1.287***

23.34

1.289***

48.65

Constant

11.474***

32.36

11.434***

65.86

Observations

450,630

1,392,095

R 2

0.5930

0.7221

CBD = central business district. LIHTC = low-income housing tax credit.

+ p < 0.1. * p < 0.05. ** p < 0.01. *** p < 0.001.

Notes: Regressions also control for census tract fixed effects, which are not listed due to the large number of tracts. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database; U.S. Census Bureau

Cityscape 357

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

Exhibit B-8

Baseline Model Versus Neighborhood Low-Income Housing Tax Credit Concentration

(Overlap) Model (1 of 2)

Distance

Neighborhoods with

Neighborhoods with

Variable

from LIHTC

Any LIHTC Properties

1, 2, or 3 LIHTC Properties

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

Pre

– 0.037***

– 5.37

– 0.035***

– 5.16

Post1

– 0.004

– 0.66

– 0.006

– 0.95

Post2

– 0.002

– 0.24

Post3

0–¼ mile

0.074**

2.70

Effect1

0.034***

15.96

0.030***

13.31

Effect2

0.027*

5.30

Effect3

0.101***

12.07

Pre

– 0.033***

– 5.68

– 0.032***

– 5.64

Post1

– 0.003

– 0.51

– 0.006

– 1.12

Post2

0.001

0.07

Post3

¼–½ mile

0.049***

4.70

Effect1

0.030***

11.23

0.026***

11.11

Effect2

0.027+

3.69

Effect3

0.076***

20.86

Lot size

0.000***

4.04

0.000***

4.04

Lot size2

– 0.000***

– 4.40

– 0.000***

– 4.40

Living area

0.000***

35.86

0.000***

35.87

Living area2

– 0.000***

– 24.06

– 0.000***

– 24.06

Floor-area ratio

– 0.004

– 1.35

– 0.004

– 1.35

Age

– 0.001

– 0.54

– 0.001

– 0.53

Age2

0.000

0.60

0.000

0.58

2 Stories

0.019**

2.84

0.019**

2.85

3 Stories

0.067**

3.17

0.067**

3.16

Spring

0.004

1.63

0.004

1.63

Summer

0.037***

14.88

0.037***

14.88

Fall

0.045***

19.79

0.045***

19.79

Distance to central

– 0.010

– 1.06

– 0.009

– 1.05

business district

Government seller

– 0.176***

– 4.21

– 0.177***

– 4.21

Bank seller

– 0.141***

– 9.95

– 0.141***

– 9.95

1989

0.168***

21.45

0.168***

21.45

1990

0.193***

28.68

0.193***

28.69

1991

0.202***

17.39

0.202***

17.39

1992

0.208***

7.66

0.208***

7.66

1993

0.124***

8.42

0.124***

8.42

1994

0.103***

5.49

0.103***

5.49

1995

0.039***

4.05

0.039***

4.06

1996

0.041***

4.47

0.041***

4.48

1997

0.078***

7.45

0.078***

7.48

1998

0.174***

19.86

0.174***

19.90

1999

0.270***

28.36

0.270***

28.39

2000

0.347***

37.47

0.347***

37.51

2001

0.446***

46.47

0.446***

46.51

358 Refereed Papers

Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

Exhibit B-8

Baseline Model Versus Neighborhood Low-Income Housing Tax Credit Concentration

(Overlap) Model (2 of 2)

Distance

Neighborhoods with

Neighborhoods with

Variable

from LIHTC

Any LIHTC Properties

1, 2, or 3 LIHTC Properties

Property

Coefficient

T/F Stat

Coefficient

T/F Stat

2002

0.585***

59.70

0.585***

59.71

2003

0.764***

72.21

0.764***

72.22

2004

1.009***

88.21

1.009***

88.19

2005

1.198***

100.99

1.198***

100.90

2006

1.267***

96.35

1.267***

96.24

2007

1.012***

84.16

1.282***

84.09

2008

0.801***

38.30

1.012***

38.30

2009

0.801***

19.29

0.801***

19.29

2010

0.846***

26.53

0.846***

26.52

2011

0.811***

24.84

0.810***

24.81

2012

0.850***

26.20

0.850***

26.17

2013

1.017***

35.33

1.017***

35.29

2014

1.135***

40.76

1.135***

40.74

2015

1.217***

40.76

1.216***

40.74

2016

1.291***

45.13

1.290***

45.10

Constant

11.439***

71.77

11.437***

71.70

Observations

1,842,725

1,842,725

R 2

0.7242

0.7242

LIHTC = low-income housing tax credit.

+ p < 0.1. * p < 0.05. ** p < 0.01. *** p < 0.001.

Notes: Regressions also control for census tract fixed effects, which are not listed due to the large number of tracts. Treatment effect is calculated manually from the differences in the regression coefficients, as the Methodology section describes. T-statistics are used for regression coefficients, and F-statistics are used for treatment effects.

Sources: DataQuick Information Systems, Inc. and CoreLogic, Inc.; LIHTC HUD User database

Authors

Brian Y. An is an assistant professor of public policy and finance at the Georgia Institute of

Technology. Andrew Jakabovics is vice president for policy development at Enterprise Community

Investments. Jing Liu is an associate director at Econsult Solutions. Anthony W. Orlando is an

associate professor of finance, real estate, and law at California State Polytechnic University,

Pomona. Seva Rodnyansky is an assistant professor of urban and environmental policy at

Occidental College. Richard Voith is president and founding principal at Econsult Solutions. Sean

Zielenbach is president at SZ Consulting. Raphael W. Bostic is president and CEO at the Federal

Reserve Bank of Atlanta.

Cityscape 359

An, Jakabovics, Liu, Orlando, Rodnyansky, Voith, Zielenbach, and Bostic

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Factors Affecting Spillover Impacts of Low-Income

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Factors Affecting Spillover Impacts of Low-Income

Housing Tax Credit Developments: An Analysis of Los Angeles

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Cityscape 363

364 Departments

Departments

In this issue—

• Data Shop

• Graphic Detail

• Industrial Revolution

• Policy Briefs

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 365

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

366 Departments

by Daniel Kuhlmann, Jane Rongerude, Lily Wang, and GuanNan Wang

Data Shop

Data Shop, a department of Cityscape , presents short articles or notes on the uses of data in

housing and urban research. Through this department, the Office of Policy Development and

Research introduces readers to new and overlooked data sources and to improved techniques

in using well-known data. The emphasis is on sources and methods that analysts can use in

their own work. Researchers often run into knotty data problems involving data interpretation

or manipulation that must be solved before a project can proceed, but they seldom get

to focus in detail on the solutions to such problems. If you have an idea for an applied,

data-centric note of no more than 3,000 words, please send a one-paragraph abstract to

chalita.d.brandly@hud.gov for consideration.

A Statistical Machine Learning

Approach to Identify Rental

Properties From Public Data Sources

Daniel Kuhlmann

University of Arizona

Jane Rongerude

Iowa State University

Lily Wang

George Mason University

GuanNan Wang

College of William and Mary

Introduction

For academic researchers and practitioners alike, identifying individual rental properties can be

incredibly useful but is often difficult due to insufficient and incomplete data. Although some cities

have ordinances that require residential rental property owners (RRPOs) to register their properties,

the availability and completeness of these registries vary dramatically from place to place. In places

without rental registries, tax assessor data can provide some information but often not enough to

clearly distinguish residential rentals from owner-occupied units and other commercial properties.

As part of a larger project surveying RRPOs, the project team developed and tested statistical

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 367

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Kuhlmann, Rongerude, Wang, and Wang

(machine) learning methods and predictive models to identify potential rental properties from

existing data sources. This article describes the process for creating the models, suggests potential

applications for the methods, and discusses how researchers and practitioners can use these

models and methods in their work.

Background

Many policy and research contexts exist in which the ability to identify individual rental properties

and the owners of those properties are useful. For example, for this project, the team needed to

generate a sample of rental property owners across multiple jurisdictions for a survey-based study

investigating RRPO characteristics and behaviors. Similarly, identifying who owns rental properties

can help researchers track investment behavior, understand market dynamics, and study other

owner-related housing questions. Identifying locations and addresses of rental properties is useful

for researchers when tracking displacement and tenant mobility or contacting tenants for surveys

and interviews. Likewise, differentiating rentals from owner-occupied properties can be useful for

those studying property and tenant outcomes, such as property neglect and evictions.

In addition, being able to identify rental properties may be useful for practitioners and local

government officials. For example, local housing officials might want to identify likely rental

properties in their jurisdictions to track whether RRPOs comply with rental licensing and

inspection laws. Similarly, in places without rental licensing requirements that need full disclosure

of owner data, practitioners might use these methods to distribute program information and

other resources to tenants or RRPOs. Local jurisdictions can benefit from knowledge about the

individuals or entities investing in their rental housing markets, the location of those entities,

and whether their holdings are expanding or consolidating in particular neighborhoods. The

COVID-19 pandemic and the subsequent housing policy responses, such as the eviction moratoria

and Emergency Rental Assistance program, demonstrated that having basic contact information

for RRPOs is necessary during a catastrophe or disaster. To distribute pandemic-related rental

assistance efficiently, local governments needed inventories of rental properties within their

jurisdictions and contact information for RRPOs. However, in most jurisdictions in the United

States, this information is not readily available.

This article describes the project team’s method for identifying potential rental properties from

existing data sources. Exhibit 1 summarizes different methods and data sources considered

for the study. It is important to note that each project described here has unique goals, which

understandably influence the methodological choice. This article does not aim to provide an

exhaustive accounting of the various methods researchers have used to identify individual rental

properties; instead, the project team presents a new methodology developed for its specific

research needs.

368 Data Shop

A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources Exhibit 1

Methods for Identifying Rental Property (1 of 2)

Primary

Method Description

Strengths

Weaknesses

Example

Data Source

Rental

Use rental licensing

In places with

Not all jurisdictions

Kuhlmann et.

license

data to identify rental well-enforced rental

have ordinances

al. (2022) used

records

properties.

licensing programs,

requiring rental

rental registry

rental registries

licenses. Further, the data to contact

are likely the best

types of properties

property owners in

and most complete

and property owners Minneapolis, MN, for

source of information who must obtain

an online survey.

on individual rental

licenses vary from

properties.

place to place. Even

in jurisdictions that

require regulations,

there is variation in

how well jurisdictions

enforce rental

licensing.

Property

Identify patterns in

Most uniform source In places without

Travis (2019)

assessor

property assessment of data available

rental licensing laws, compared identified

administrative records, such as

across the country

assessment records

rental properties

files

mismatched tax and

for information on

rarely identify rental

based on mis-

situs addresses,

property ownership.

properties explicitly.

matched tax and

use of homestead

Relatively easy to

Assessment data

situs addresses,

exemptions, and

identify certain types vary from county

non-individual owner

property use data

of rental properties,

to county, making

names (LLCs, etc.),

to flag likely rental

such as large

cross-jurisdiction

and properties with

properties.

multifamily rental

analyses difficult.

multiple units to

buildings.

Likely misses some

identify likely rental

rental properties and properties in his

mis-identifies certain project examine the

types of ownership

association between

forms as rentals.

LLC ownership and

property upkeep.

Public

Identify rental

Efficient method to

These data are not

De la Campa, Reina,

program

properties based on

identify a targeted

always publicly

and Herbert (2021)

participation whether either the

subset of the rental

available. Only

used applications for

landlord or tenant

housing stock. For

captures tenants

the Emergency Rental

participated in a

example, using

and owners

Assistance program

public program.

program participation who submitted

to identify landlords in

is a direct method

applications to public Los Angeles, CA for a

to identify low-cost

programs. As a result, survey.

rentals, properties

they are unlikely to be

with physical

representative of the

deficiencies, and

rental housing stock

cost-burdened

in a particular place.

tenants.

Cityscape 369

Kuhlmann, Rongerude, Wang, and Wang

Exhibit 1

Methods for Identifying Rental Property (2 of 2)

Primary

Method Description

Strengths

Weaknesses

Example

Data Source

Online

Use web-scrapping

One of the few

Requires technical

Boeing and Waddell

listings

techniques to pull

methods that

expertise to set

(2017) and Boeing

detailed rental listing provides real-time

up web scrapping

et al. (2021) created

data from websites

data on asking rents, programs and can

web-scrapping

such as Craigslist.

utilities, vacancy,

be computationally

programs to pull

and other property

taxing. Not all

rental listing data

information not

properties are listed

from Craigslist.

generally captured in on online platforms

administrative data.

and increasingly sites

block web scrapping

programs. Only useful

for generating point-

in-time data on rental

listings; less useful to

capture information on

the full housing stock.

Proprietary

Use data from

Efficient method to

Access to these

Decker (2021) used

date sources proprietary sources

identify generally

data is limited and

contact investor

to identify rental

representative

expensive when

contact information

properties and

samples of rental

available. These

from the residential

property owners.

properties and

data sources tend

investment property

Possible sources are owners.

to be focused on

listing platform

from third-party data

certain geographies

Roofstock to draw

companies, online

and segments of

a national sample of

listing websites, and

the rental housing

residential landlords.

private research firms.

market, and are

Raymond et. al.

thus less useful for

(2021) and others

studies interested in

have relied on the real

either all the rental

estate data company

properties or those

CoreLogic, which

that attempt to draw

provides standardized

a representative

assessment records

sample of rental units and has internal

or owners.

(although unvetted)

methods to identify

likely investor

properties.

SEC fillings

Use public filings

Possible to identify

Only applicable

Colburn, Walter,

with the SEC to

rental portfolios

for entities subject

and Pfeiffer (2021)

identify various

nationally.

to SEC reporting

examined SEC filings

corporate entities

requirements. Useful study ownership and

associated with a

for describing the

investment patterns

particular corporation.

portfolios of specific

of large, publicly

Then match these

corporate owners but traded investors in

corporate names with

misses the majority

single-family rental

assessment and other

of rental properties

properties.

public data to identify

owned by non-

rental holdings of

corporate entities.

large, corporate

owners and REITS.

LLC = limited liability company. REITS = real estate investment trusts. SEC = U.S. Securities and Exchange Commission.

370 Data Shop

A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources From a review of the previous research on rental property ownership, the team identified the

following questions as those important to consider when selecting a method for identifying rental

properties or rental property owners, or both.

• Is the project’s goal to identify all rental properties in each jurisdiction or a sample of rentals?

• How sensitive is the project to possible misidentification? Relatedly, is the project particularly

sensitive to either false positives (identifying nonrentals as rentals) or false negatives (failing to

identify rentals)?

• Does the project have funding to purchase third-party data or access to proprietary data sources?

• Is the project focused on a single jurisdiction, or should the method be applicable to multiple

jurisdictions and data sources?

Case Study

In the spring of 2020, the team launched a new project to examine the question, “What is

influencing the decisionmaking of RRPOs during the COVID-19 pandemic?” The decisions that

RRPOs make during a disaster affect not only their tenants’ short-term housing stability but also the

composition and stability of the rental housing stock. At the time, little information existed on how

the ongoing pandemic and subsequent policy responses affected the businesses of RRPOs. To study

this question, the team collected data about the characteristics and behaviors of RRPOs to better

understand who they were and how their decisions contributed to rental housing stability. Time,

cost, and safety considerations required gathering data through an e-mail survey; however, the

team quickly ran into difficulty trying to identify the RRPOs and obtain their contact information.

To address this obstacle, in the first year of the study, the team limited the scope to four cities:

Cleveland, Des Moines, Minneapolis, and Tampa. Each of these cities had a rental registry the team

could use to identify a sample of rental properties, even when a corporatized ownership structure,

such as a limited liability company (LLC), obscured the name of an individual owner.

Even after limiting the study scope to cities with available registries, the team found that the

completeness and accessibility of these registries varied from city to city. For example, the

Minneapolis rental registry is easily accessible online and includes inspection ratings and contact

information for property owners. Compared with census records, it includes around 95 percent of

the city’s rental units. In contrast, the Des Moines registry was not publicly available and required

a formal request to access from the city’s inspections department. Further, the team found that the

Des Moines registry had incomplete or missing contact information for many property owners

and included less than two-thirds of the city’s rental units. The team’s data dashboard has more information about each city’s rental registry (Rongerude et al., 2021).

In 2022, the project team expanded this study to examine how a broader range of natural disasters

affect the businesses of RRPOs, adding five disaster-prone cities: Austin, Dallas, Houston, Miami,

and New Orleans. In the expanded study, the team also shifted from an e-mail to a primarily

physical mail survey with an online option. Because only some, not all, of these new cities had

rental registries, the team was concerned about potential bias from surveying only owners who

Cityscape 371

Kuhlmann, Rongerude, Wang, and Wang

comply with registration requirements and, thus, developed a method to identify likely rental

properties across a diverse set of cities. After switching to a mail-based survey, the team was

particularly interested in developing a method that minimized misidentification, because unlike

the team’s previous study, now a marginal cost was associated with sending survey invitations.

To maximize the probability that the survey reached actual RRPOs, prior to distribution, the

team considered a methodological concern: how to identify rental properties from data sources

available across multiple jurisdictions while minimizing the number of owner-occupied properties

misidentified as rentals.

In response, the interdisciplinary team, including planners, statisticians, and data scientists,

undertook the modeling exercise this article describes. The team first reviewed methods that other

researchers have used to identify rental properties, then created a new modeling technique to

address its specific research needs. The rest of this article describes the data the team’s modeling

efforts used, details the modeling process and how the accuracy of each model was assessed, and

ends with a brief discussion of the limitations of the models and other potential applications.

Data

To build the predictive models, the project team first searched for variables that correlate with

housing tenure and were available and uniform across the nine jurisdictions in this study. For

example, some counties report whether properties in their assessment rolls claim a homestead

exemption. Although claiming an exemption is likely a good indication that the property is owner-

occupied, not all counties in the study report this field. Using this variable to build a model in

Minneapolis, for example, likely would improve its predictive power, but doing so would prevent

the model from applying to the cities lacking such indicators.

The data in this study come from two primary sources: Regrid.com, a data services company

that collects and standardizes parcel, transaction, and assessment administrative data, and 5-year

American Community Survey (ACS) estimates. The project team elected to purchase standardized

assessment data rather than collect the data for two reasons. The first is one of expediency.

Collecting and combining assessment records is a time-consuming task and one that is made

particularly difficult by the scope of this analysis. Because this study includes nine large U.S. cities,

several of which contain multiple counties (for example, five separate counties fall within the

municipal boundary of Dallas, Texas), building this dataset would require contacting 19 different

assessor offices, collecting their respective records, and formatting each county’s data to create

consistency across the sample. The second reason to purchase the standardized assessments is

that Regrid.com, in addition to standardizing the assessment records, also combines parcel-level

data with several other data sources that could be useful predictors of property tenure, such as

occupancy indicators from the U.S. Postal Service (USPS).

Exhibit 2 lists all the variables included in the modeling process. In addition to standard assessment

fields, the process used several variables from secondary data sources, such as a vacancy indicator

and an indicator from the USPS of whether mail is deposited at the street level or in a highrise

building. The project team also used several proprietary measures Regrid.com, including a count of

all primary and secondary addresses at the parcel (a more consistent measure of unit count across

372 Data Shop

A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources the cities in this study) and Regrid.com’s calculation of parcel sizes, building footprints, and a count

of structures on the parcel. In addition to these pre-formatted fields included in the Regrid.com

files, the project team also created several new variables. When reviewing the literature, the team

identified two measures that serve as strong indicators of property tenure. The first is a measure of

whether a corporate entity owns the property, such as a corporation, limited partnership, or LLC.1

To create this variable, the team wrote code that searches for regular expressions matching common

nonindividual ownership forms. This process was iterative, because none of the counties in the

study standardized their ownership fields, resulting in small variations in ownership names.2

Exhibit 2

Model Variables

Variable

Type

Level

Source

Total Parcel Value

Numerical integer;

County Assessor’s office;

Parcel

dollars

Regrid.com

Do the property situs and taxpayer

County Assessor’s office;

Binary; 1 = same

Parcel

Regrid.com; author’s

billing address match?

address

calculations

Does the owner name contain a

County Assessor’s office;

Binary; 1 = corporate Parcel

Regrid.com; author’s

corporate indicator? (e.g., LLC, LP, INC) indicator found

calculations

Postal delivery type (street

Binary; 1 = corporate Parcel

USPS; Regrid.com

versus highrise)

indicator found

Is the property a residential address?

Binary; 1 = residential Parcel

USPS; Regrid.com

Is the property vacant?

Binary; 1 = vacant

Parcel

USPS; Regrid.com

How many primary and secondary

Count

Parcel

USPS; Regrid.com

addresses are at the parcel?

Building footprint

Numerical integer;

Parcel

Regrid.com

square feet

Number of structures on parcel

Count

Parcel

Regrid.com

Share of housing units owner occupied

2015–2019 5-year ACS

Percent

Census Tract

estimates

Median household income

Numerical integer;

2015–2019 5-year ACS

Census Tract

dollars

estimates

ACS = American Community Survey. INC = incorporated. LLC = limited liability company. LP = limited partnership. USPS = U.S. Postal Service.

The second measure is a field indicating whether the taxpayer’s billing address differs from the

property situs address. To create this field, the project team first parsed the billing and situs

addresses, pulling each of the address components (house number, unit, street prefix direction,

and so on) into separate fields using the R package “PostmastR,” which parses address formats and

also standardizing common spelling variants in addresses (for example, “South,” “So,” and “S”). The

team then looked for matches in the house numbers and street names (excluding directionals and

suffixes) of billing and situs addresses. The team experimented with more strict match conditions

(for example, requiring that all address components match) but noticed enough instances of slight

variations in ultimately matching addresses to justify a more lenient matching criterion.

1 The project team considered including but ultimately excluded “trust” and related terms from the corporate entity search.

This term often picked up living trusts, which can be used for ownership of rental investments, but are also for owner-occupiers during the estate planning process.

2 For example, the code checks for multiple variants of “limited liability corporation,” including abbreviations with and without punctuation and variations in spelling.

Cityscape 373

Kuhlmann, Rongerude, Wang, and Wang

Finally, the team used several census tract-level variables from the 2018 ACS; using census tracts

(geographies containing between 1,200 and 8,000 people) rather than the more granular census

boundaries ensured consistency across the sample. The U.S. Census Bureau suppresses data for

smaller geographies, particularly when the reported tabulations could potentially be used to

identify individual households. The team included two estimates from the ACS in its models: the

share of renter-occupied housing units and the median household income.

Method

The goal is to create a predictive model to accurately identify potential RRPOs and use the model

to guide the selection of survey participants. Correctly identifying the potential rental properties

required an accurate binary classifier (that is, a model that estimates a yes or no outcome), and

the team considered five popular classification methods. Exhibit 3 briefly describes each method,

along with their relative advantages and disadvantages.

Exhibit 3

Modeling Techniques

(partially linear)

Description

Logistic

Decision Tree

Naive Bayes

kNN

GAM

Sensitivity

0.6701

0.7444

0.6378

0.7304

0.6983

Specificity

0.9487

0.942

0.9273

0.9481

0.9463

GAM = generalized additive model. kNN = k-nearest neighbors.

Source: Author’s Calculations.

To build these classification models, the project team used the variables in the combined dataset

to predict if the Minneapolis rental registry database lists a property. Essentially, the team built a

binary classifier using the nearly complete list of rental properties in Minneapolis, then used this

model to predict likely rental properties in the sample cities that lacked complete rental registries.

The outcome variable is a binary measure of whether the Minneapolis rental registry lists the

property as a licensed, long-term rental property. To predict this outcome, the team included

property-level variables measuring the parcel’s total assessed value, counts of the number of

structures and addresses on the parcel, and a measure of the total square footage of the structures

on the parcel. In addition, the team included binary measures of whether the parcel’s tax and situs

addresses match, whether a nonindividual owns the property, whether the property is residential

and vacant, and whether the postal delivery is at street level or in a highrise building. Finally, the

team included two census tract-level measures from the ACS: the share of the tract’s housing units

that are renter-occupied and the median household income.

Model Assessment

To contrast the classifier model’s prediction accuracy, the project team used the Minneapolis data to

compare how often the model correctly identified properties in the city’s rental registry. The team

followed a fivefold cross-validation process, randomly and evenly splitting the Minneapolis data

into five portions called folds. The team then chose four folds for training and one fold for testing,

repeated this procedure five times, then compared the predicted value (that is, whether the model

374 Data Shop

A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources predicted that the property was a rental) with the truth (whether the observation had an active

rental license). Because using the actual registry results is necessary as a basis for comparison, the

team could only perform this exercise in Minneapolis.

A confusion matrix captured comparisons that include the four mutually exclusive measurements:

(1) True positives—the property is a rental, and the model correctly identified it as a rental;

(2) true negatives—the property is not a rental, and the model correctly identified it; (3) false

positive—the property is not a rental, but the model predicted it as a rental; and (4) false

negative—the property is a rental, but the model failed to predict it. From this confusion matrix,

the team calculated two additional evaluation measures: specificity and sensitivity. Specificity is the

ratio of rental properties the model correctly predicted to the total number of rental properties

in the Minneapolis registry. Sensitivity is the ratio of properties the model correctly identified as

nonrentals to the actual number of nonrentals, plus the number of properties the model identified

as rentals when they were not in the rental registry. A specificity of 100 percent indicates that the

model perfectly predicted all the actual rental properties, and a sensitivity of 100 percent suggests

that the model did not misidentify any nonrentals as rentals. Exhibit 4 reports these metrics. In

general, the prediction accuracy is similar for all the methods, with specificities ranging from 92.7

to 94.8 percent and sensitivities ranging from 63.7 to 74.4 percent.

Exhibit 4

Confusion Matrix

Main

Method

Advantages

Disadvantages

Citation

Technique

Logistic

Maximum

Easy to implement

Assumption of

Hosmer,

Regression

likelihood

linearity between the Lemeshow, and

No assumptions on distributions

estimation

response and the

Sturdivant (2013)

of classes in feature space

explanatory variables

Easy to interpret

kNN

Euclidean

Easy to implement

Testing is slow

James et al. (2013)

distance

Training is fast

Sensitive to noise

Decision Tree Splitting,

No requirements of

Unstable

Breiman (2017)

stopping,

domain knowledge

and pruning

Easy to interpret

Naive Bayes

Bayes rule

Easy to implement

Strong assumption

James et al. (2013)

on the shape of data

Does not require many data

distribution

GAM

Smoothing

Ability to model highly complex High computational

Hastie et al. (2009)

nonlinear relationships

complexity

GAM = generalized additive model. kNN = k-nearest neighbors.

To predict potential rental properties in cities without rental registries, the project team focused on

the binary classifier based on the logistic regression model, which provides the highest specificity.

The team was particularly interested in finding models with high specificities due to the limited

survey sampling budget and the need to minimize the number of negative examples (non-RRPOs)

that are incorrectly classified. The team trained the classifier using the data from Minneapolis, then

applied this classifier to other cities in the study to generate a sample of likely RRPOs.

Cityscape 375

Kuhlmann, Rongerude, Wang, and Wang

As a result of this process, the model created a probability measure for each property in the

database corresponding to the predicted likelihood that it is a rental property. The team then

used these probabilities to create a sample in a way that minimized the risk of sending a survey

invitation to a nonrental property. To generate the survey sample, the team created a list with

unique owner names, keeping only one property per owner, then created the survey sample based

on two factors. First, the team oversampled owners who hold properties in corporatized entities

(such as LLCs), because these owners might be less likely to respond to survey requests. The

team split the sample, sending two-thirds of the 2,000 invitations to corporatized owners and

one-third to those who held properties as individuals. With this split in mind, the team ordered

the properties by their predicated rental likelihood for each city, then created a cutoff yielding the

minimum two- or one-third split between ownership types. Second, the team randomly selected

owners within this group, preserving the preferred split. This process created a sample for each city

that both oversampled nonindividual owners and minimized the likelihood that survey invitations

reached owners of properties that were not, in fact, residential rentals.

Conclusion

Rental housing occupies a significant portion of the housing stock in U.S. metropolitan areas, yet

researchers know very little about the specific characteristics of the institutional and noninstitutional

entities that hold titles to those properties and determine housing supply, rents, and the conditions of

both buildings and units. This gap in knowledge related to RRPOs persists partly due to a problem of

insufficient and incomplete data. No comprehensive national or statewide public database exists that

contains information about who owns rental properties, how to contact them, and what types of units

they own. Some cities have municipal rental registry databases connected to rental unit certification

and inspection programs; however, these programs are not universal, and because the databases are

resource intensive to create and maintain, they are often incomplete. As a result, RRPOs can be difficult

to identify and contact, a difficulty shared by researchers, housing advocates, and local governments.

In this article, the project team describes a novel method to identify potential rental properties

from existing data sources. The modeling procedure is flexible, and users can tune the parameters

within each model depending on the research objectives to achieve the desired specificity or

sensitivity. The case study describes how these models identify owners of likely rental properties

in eight cities, with either incomplete or nonexistent rental registries. Because the objective was

to create a representative sample of RRPOs, the team focused on the likelihood that any given

residential property was a rental unit, then created a model that allowed the team to identify a

sample of property owners based on the registered owners of those units.

Although the method described here allows researchers to identify likely rental properties in

cities without complete rental registries, its effectiveness ultimately depends on the accuracy

and completeness of the rental registry in Minneapolis. Although this registry is one of the most

complete in the country, how representative the registry is of all property types is unknown.

For example, RRPOs with lower-cost and -quality properties may be more likely to evade the

registration requirements and, thus, be underrepresented in the Minneapolis data. If this scenario

is the case, using the Minneapolis registry to train the predictive model may systematically

misidentify these properties when applied to the other sample cities.

376 Data Shop

A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources Relatedly, whether the associations in Minneapolis used to train the model are consistent across

the sample is unknown. For example, in Minneapolis, having a mismatched tax and situs address

may be a strong predictor of a rental property, but the association may be weaker and less accurate

in predicting rentals in cities with warmer climates and more second homes. In future iterations

of this project, the team plans to partner with other cities that have relatively complete registries

to expand the training dataset outside of Minneapolis. The team also plans to use the results of

initial survey responses—specifically whether respondents completed the survey or responded that

their property is not a rental—to build smaller training datasets in subject cities to improve the

prediction accuracy of the models.

These limitations notwithstanding, this article can be useful for researchers and practitioners

interested in identifying individual rental properties. Many questions regarding RRPOs and

changing trends in the nation’s rental markets still need to be answered. Furthermore, as the

COVID-19 pandemic put in stark relief, having the ability to identify and reach out to both RRPOs

and their tenants during a disaster can be hugely useful to housing officials in developing effective

policy responses and distributing aid.

Acknowledgments

The research in this article is part of a larger, longitudinal project investigating the effects of

residential rental property owner decisionmaking on post-disaster housing stability. This study

received funding from the Polk County Housing Trust Fund and the National Science Foundation

(NSF RAPID #2050264 and NSF #2139816). The authors acknowledge the following people who

have contributed to the ongoing work of the project, either as co-principal investigators, senior

personnel, or graduate student researchers: Elizabeth Mueller, Jake Wegmann, Taylor Cook, Biswa

Das, Wanying Zhu, and Lin Quan.

Authors

Daniel Kuhlmann, Ph.D. is an assistant professor at the University of Arizona (dok@arizona.edu).

Jane Rongerude, Ph.D. is an associate professor at Iowa State University (jrong@iastate.edu). Lily

Wang, Ph.D. is a professor at George Mason University (lwang41@gmu.edu). GuanNan Wang, Ph.D. is an associate professor at the College of William and Mary (gwang01@wm.edu).

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Joint Center for Housing Studies, Harvard University. Cambridge, MA: Harvard University.

Decker, Nathaniel. 2021. The Uneven Impact of the Pandemic on the Tenants and Owners of Small

Rental Properties. Report prepared for the Terner Center for Housing Innovation. Berkeley:

University of California.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning:

Data Mining, Inference, and Prediction. New York: Springer.

Hosmer, Jr., David W., Stanley Lemeshow, and Rodney X. Sturdivant. 2013. Applied Logistic

Regression. New York: Wiley.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to

Statistical Learning with Applications in R. New York: Springer.

Kuhlmann, Daniel, Jane Rongerude, Biswa Das, and Lily Wang. 2022. Rental Property Owner Stress

During the COVID-19 Pandemic: Results from a Minneapolis, MN Survey.

Raymond, E.L., B. Miller, M. McKinney, and J. Braun. 2021. “Gentrifying Atlanta: Investor

Purchases of Rental Housing, Evictions, and the Displacement of Black Residents,” Housing Policy

Debate 31 (3-5): 818–834.

Rongerude, Jane, Lily Wang, Biswa Das, and Daniel Kuhlmann. 2021. “RAPID: Keeping

Shelters in Place: Understanding Landlord Decision-Making During the COVID-19 Pandemic.”

https://covidrental.design.iastate.edu.

Travis, Adam. 2019. “The Organization of Neglect: Limited Liability Companies and Housing

Disinvestment,” American Sociological Review 84 (1): 142–170.

Additional Reading

Awad, Mariette, and Rahul Khana. 2015. Efficient Learning Machines: Theories, Concepts, and

Applications for Engineers and System Designers. New York: APress.

Furukawa, Norrie, and Motoharu Onuki. 2022. “The Design and Effects of Short-Term Rental

Regulation,” Current Issues in Tourism 25 (20): 3245–3260.

Hastie, Trevor, and Robert Tibshirani. 1990. Generalized Additive Models. New York: Chapman and Hall.

378 Data Shop

by Brent D. Mast

Veteran and Nonveteran

Homelessness Rates: New Estimates

Brent D. Mast

U.S. Department of Housing and Urban Development

Introduction

When analyzing indicators of social problems, researchers usually measure the problems in terms

of rates. Examples include poverty, unemployment, crime, and cancer rates. Researchers analyzing

homelessness have also measured homelessness in terms of homelessness rates. For example,

Mast (2020) analyzed homelessness rates for total homeless populations, and Nisar et al. (2020)

analyzed homelessness rates for the unsheltered homeless.

The U.S. Department of Housing and Urban Development (HUD) has long collected data on

counts of homeless veterans at the state and continuum of care levels; these data are published in

HUD’s Point-in-Time (PIT) datasets. PIT data are collected for sheltered and unsheltered homeless

populations during a single night in January.1

However, until recently, researchers analyzing veteran homelessness have been unable to calculate

veteran homelessness rates because of a lack of veteran population data. The lack of veteran

homelessness rates made it impossible to compare the relative severity of homelessness among

veteran and nonveteran populations.

In 2022, HUD acquired veteran population estimates from the U.S. Department of Veterans Affairs

(VA) for all 50 states and Washington, D.C., covering the years 2020, 2014, and 2008.2 These data,

when combined with HUD’s PIT homelessness estimates and state population estimates from the

U.S. Census Bureau, allow for the calculation of state-specific homelessness rates, defined as the

number of homeless persons per 10,000 residents, for veterans and nonveterans for the first time.

This study uses these newly acquired data to calculate state homelessness rates for the years 2020,

2014, and 2008. Findings indicate that, in each year, the mean homelessness rate for veterans

is higher than that of nonveterans. In addition, a linked micromap (exhibit 3) displays the state

homelessness rates for veterans and nonveterans in 2020, along with the percentage difference

between the two. The map illustrates that, in most states, veterans have a higher rate of homelessness.

1 For more information on the PIT data collection process, see HUD (2023) and Mast (2020).

2 The VA population estimates can be obtained from HUD by contacting Brent Mast at Brent.D.Mast@hud.gov.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

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Mast

Furthermore, the study examines the relationship between differences in state homelessness

rates in 2020 between veterans and nonveterans, with rental vacancy rates and median incomes.

When data are analyzed across states, results indicate that rental vacancy rates have a statistically

significant positive effect on the differences in homelessness rates, whereas median incomes do

not have a statistically significant relationship. When within-state differences are analyzed, neither

rental vacancy rates nor median incomes have statistically significant effects.

The availability of these new data from the VA allow for a deeper understanding of the

homelessness rates among veterans and nonveterans at the state level. The findings of this study

contribute to the ongoing efforts to address and reduce homelessness among veterans.

Data

Nonveteran homeless population estimates by state are calculated by subtracting the VA state

veteran homeless population estimates for Census Bureau state population estimates from their

Population Estimates Program. Homelessness rates per 10,000 population are calculated by

dividing the HUD PIT homeless count for each group by the population estimate, then multiplying

this ratio by 10,000.

For example, the 2020 Decennial Census population estimate for Alabama is 5,039,877. The VA

veteran population estimate for Alabama in 2020 is 363,000. Therefore, the nonveteran population

calculation is 5,039,877 - 363,000 = 4,676,877. The HUD PIT 2020 nonveteran homeless

population estimate for Alabama equals 3,022, and the veteran homeless population estimate

equals 329. As such, the veteran homelessness rate for Alabama in 2020 equals (329/363,000) x

10,000 = 9.063. The nonveteran homelessness rate for Alabama in 2020 equals (3,022/4,676,877)

x 10,000 = 6.462.

Exhibit 1 presents means, standard deviations, minimums, medians, and maximums for each year.

The mean veteran homelessness rate exceeds the mean nonveteran homelessness rate in each year;

the same is true for the median rates. The mean rates for both groups decline slightly each year.

The median rate for veterans increases between 2008 and 2014, then has a large decrease between

2014 and 2020. The median rate for nonveterans decreases each year.

Exhibit 1

Summary Statistics

Standard

Year

Variable

Mean

Minimum

Median

Maximum

Deviation

Veteran homelessness rate

22.262

27.649

3.725

13.821

185.278

2008

Nonveteran homelessness rate

19.157

15.317

5.536

14.546

98.799

Veteran homelessness rate

21.985

16.946

8.158

17.104

116.000

2014

Nonveteran homelessness rate

17.585

16.661

7.007

12.060

117.036

Veteran homelessness rate

17.417

16.895

3.676

12.052

104.138

2020

Nonveteran homelessness rate

15.495

14.936

3.758

10.200

94.813

N = 51 in each year.

Sources: U.S. Census Bureau Population Estimates Program; U.S. Department of Veterans Affairs; HUD Point-in-Time data 380 Data Shop

Veteran and Nonveteran Homelessness Rates: New Estimates

Exhibit 2 displays a bar chart with mean rates by group and year.

Exhibit 2

Mean Homelessness Rates by Group and Year

20

15

10

Rate (Mean)

5

0

Nonveterans

Veterans

Nonveterans

Veterans

Nonveterans

Veterans

Group

Year = 2008

Year = 2014

Year = 2020

Sources: U.S. Census Bureau Population Estimates Program; U.S. Department of Veterans Affairs; HUD Point-in-Time data Exhibit 3 displays a linked micromap that visualizes the 2020 state data and reports veteran

homelessness rates, nonveteran homelessness rates, and percentage differences. The data are sorted

in ascending order of the veteran homelessness rate.

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Mast

Exhibit 3

Linked Micromap With 2020 State Homelessness Rates and Percentage Differences

States

Veteran Homelessness Rate

Nonveteran Homelessness Rate

Percentage Difference

Mississippi

Virginia

South Dakota

West Virginia

Iowa

Utah

Alabama

North Dakota

Arkansas

Maine

Wisconsin

Ohio

Minnesota

Kansas

Wyoming

South Carolina

Missouri

Georgia

Oklahoma

Delaware

Michigan

North Carolina

New Hampshire

Connecticut

Nebraska

Maryland

Illinois

Pennsylvania

Texas

Tennessee

Indiana

Louisiana

Alaska

Kentucky

Idaho

Rhode Island

Florida

New Mexico

Vermont

New York

New Jersey

Arizona

Montana

Massachusetts

Colorado

Washington

Nevada

Hawaii

Oregon

California

District of Columbia

0

20

40

60

80

100

0

20

40

60

80

100

-60-40-20 0 20 40

Rate per 10,000

Rate per 10,000

Percent

Sources: U.S. Census Bureau Population Estimates Program; U.S. Department of Veterans Affairs; HUD Point-in-Time data The percentage difference in rates equals 100 multiplied by the difference between the veteran

homelessness rate and nonveteran homelessness rate divided by the nonveteran homelessness rate.

For example, in 2020 in Nevada, the veteran homeless rate equaled 41.622 and the nonveteran

homeless rate equaled 20.452. The percentage difference equaled 100 x (41.622 - 20.452)/41.622,

which equals 50.863 percent.

The veteran homelessness rates range from 3.676 in Mississippi to 104.138 in Washington, D.C.,

with a median of 12.052 in Maryland. The nonveteran homelessness rates vary from 3.758 in

Mississippi to 94.813 in Washington, D.C., with a median of 10.200 in Maryland. The percentage

differences range from -178.069 percent in New York to 50.888 percent in Louisiana, with a

median of 16.221 percent in Idaho. In 35 out of 51 observations, the veteran homelessness rate

exceeds the nonveteran homelessness rate.

Data Analysis

This section presents data analysis examples by exploring the relationship between the percentage

differences in veteran and nonveteran homelessness rates, with median household incomes and

rental vacancy rates taken from the American Community Survey (ACS). ACS data from 2005

through 2009 are used for year 2008; 2012–2016 ACS data are used for year 2014; and 2016–

2020 ACS data are used for year 2020. Least squares regressions explore the relationships.

382 Data Shop

Veteran and Nonveteran Homelessness Rates: New Estimates

The first regression estimates the effects across states using data for 2020. Exhibit 4 reports

descriptive statistics for the across-state regression variables, and exhibit 5 reports least squares

regression estimates for the first regression.

Exhibit 4

Regression 1 Variable Descriptive Statistics

Standard

Variable

Mean

Minimum

Median

Maximum

Deviation

Percentage difference

between veteran

5.565

40.349

– 178.069

16.221

50.888

and nonveteran

homelessness rates

Median household

65,045.176

11,051.568

46,511

63,015

90,842

income

Rental vacancy rate

16.526

5.782

4.235

16.636

39.022

N = 51.

Sources: American Community Survey 2020; U.S. Census Bureau Population Estimates Program; U.S. Department of Veterans Affairs; HUD Point-in-Time data Exhibit 5

Regression 1 Estimates

Degrees of

Coefficient

Standard

Variable

t-Value

p-Value

Freedom

Estimate

Error

Intercept

1

24.755

33.731

0.734

0.467

Median household income

1

– 0.001

0.001

– 1.610

0.114

Rental vacancy rate

1

2.102

0.985

2.135

0.038

N = 51. R squared = 0.107. Adjusted R squared = 0.070.

Note: Dependent variable equals percentage difference between veteran and nonveteran homelessness rates.

Sources: American Community Survey 2020; U.S. Census Bureau Population Estimates Program; U.S. Department of Veterans Affairs; HUD Point-in-Time data As exhibit 4 shows, percentage differences between veteran and nonveteran homelessness rates in

2020 ranged from -178.069 to 50.888 percent, with a median of 16.221 percent and a mean of

5.565 percent.3 Mean median household incomes in 2020 ranged from $46,511 to $90,842, with a

median of $63,015 and a mean of $65,045.176. Rental vacancy rates in 2020 varied from 4.235 to

39.022 percent, with a median of 16.636 percent and a mean of 16.526 percent.

The median income regression coefficient in exhibit 5 is negative but is not statistically significant

at the 0.10 level; the rental vacancy rate coefficient is positive and statistically significant at the

0.05 level.

The second regression uses data for all years to estimate the effects within states. To control for

differences across states, the model includes state fixed effects (that is, state dummy variables). To

control for national trends, the model also includes dummy variables for years 2014 and 2020. To

control for inflation, median household incomes are adjusted with consumer price index data for

2008, 2014, and 2020.

3 See the map in exhibit 3.

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Mast

Exhibit 6 reports descriptive statistics for the second regression variables (excluding the state

dummy variables), and exhibit 7 reports least squares regression estimates for the second

regression. Exhibit 7 does not report coefficient estimates for the state dummy variables.

Exhibit 6

Regression 2 Variable Descriptive Statistics

Standard

Variable

Mean

Minimum

Median

Maximum

Deviation

Percentage difference between

veteran and nonveteran

1.941

57.128

– 296.903

16.737

60.234

homelessness rates

Median household income

62,580.688

10,547.053

44,231.662

59,896.000

90,842.000

(inflation adjusted)

Rental vacancy rate

17.874

5.733

4.235

17.497

39.022

Year 2014 dummy variable

0.333

0.473

0

0

1

Year 2020 dummy variable

0.333

0.473

0

0

1

N = 153.

Sources: American Community Survey 2020, 2016, 2009; consumer price index 2020, 2014, 2008; U.S. Census Bureau Population Estimates Program; U.S.

Department of Veterans Affairs; HUD Point-in-Time data

Exhibit 7

Regression 2 Estimates

Coefficient

Variable

Standard Error

t-Value

p-Value

Estimate

Intercept

28.376

114.619

0.248

0.805

Median household income

– 0.001

0.002

– 0.260

0.796

(inflation adjusted)

Rental vacancy rate

0.872

2.503

0.349

0.728

Year 2014 dummy variable

40.685

12.267

3.317

0.001

Year 2020 dummy variable

29.919

17.851

1.676

0.097

N = 153. R squared = 0.491. adjusted R squared = 0.211.

Sources: American Community Survey 2020, 2016, 2009; consumer price index 2020, 2014, 2008; U.S. Census Bureau Population Estimates Program; U.S.

Department of Veterans Affairs; HUD Point-in-Time data

As exhibit 6 shows, during all 3 years, percentage differences between veteran and nonveteran

homelessness rates varied from -296.903 to 60.234 percent, with a median of 16.737 percent and a

mean of 1.941 percent. Inflation-adjusted median household incomes varied from $44,231.662 to

$90,842, with a median of $59,896 and a mean of $62,580.688. Rental vacancy rates ranged from

4.235 to 39.022 percent, with a median of 17.497 percent and a mean of 17.874 percent.

Regression coefficient estimates reported in exhibit 7 indicate that neither inflation-adjusted

median household incomes nor rental vacancy rates had statistically significant effects on

percentage differences between veteran and nonveteran homelessness rates. The dummy variables

for both years are positive and statistically significant at the 0.10 level, revealing an increase in

percentage differences between 2008 and 2014 and a decrease between 2014 and 2020.

384 Data Shop

Veteran and Nonveteran Homelessness Rates: New Estimates

Conclusion

In 2022, the VA provided HUD with state veteran population estimates for 2020, 2014, and 2008.

These estimates allowed HUD to calculate state homeless rates for veterans and nonveterans for the

first time when combined with HUD Point-in-Time homeless count data and Census Bureau state

population estimates.

This article demonstrates how to calculate state homelessness rates for veterans and nonveterans

using the new VA data. The veteran homeless rates are higher on average each year compared with

the rates for nonveterans, and the mean rates decrease each year for both groups.

For data analysis examples, this study explores the relationship between percentage differences

in veteran and nonveteran homelessness rates and median household incomes and rental vacancy

rates with least squares regressions. Across-state regression estimates using data for 2020 indicate

that rental vacancy rates are positively related to percentage differences between homelessness

rates for veterans and nonveterans; estimates indicate that median incomes have no statistically

significant relationship. Within-state regression estimates using data for all years find no statistically

significant effect of either median household incomes or rental vacancy rates on percentage

differences between veteran and nonveteran homelessness rates.

Acknowledgments

The author thanks Dylan Hayden for helpful comments.

Author

Brent D. Mast is a social science analyst at the U.S. Department of Housing and Urban

Development, Office of Policy Development and Research.

References

Mast, Brent D. 2020. “Measuring Homelessness and Resources to Combat Homelessness With PIT

and HIC Data,” Cityscape 22 (1): 215–225. https://www.huduser.gov/portal/periodicals/cityscpe/

vol22num1/article7.html.

Nisar, Hiren, Natalie Morrissey, Cory Carr, Charles Horseman, and Dennis Okyere. 2020.

Implementing Approaches to Address Unsheltered Homelessness. https://www.huduser.gov/portal/

publications/Implementing-Aprroaches-to-Address-Unsheltered-Homlessness-2020.html.

U.S. Department of Housing and Urban Development (HUD). 2023. “Point-in-Time Count and

Housing Inventory Count.” https://www.hudexchange.info/programs/hdx/pit-hic/.

Cityscape 385

386 Departments

by Brent D. Mast and Tricia Ruiz

Graphic Detail

Geographic Information Systems (GIS) organize and clarify the patterns of human activities

on the Earth’s surface and their interaction with each other. GIS data, in the form of maps, can

quickly and powerfully convey relationships to policymakers and the public. This department

of Cityscape includes maps that convey important housing or community development policy

issues or solutions. If you have made such a map and are willing to share it in a future issue of

Cityscape , please contact alexander.m.din@hud.gov.

Exploring the Relationship Between

Child Opportunity and Violent Crime

Rates in West Virginia Counties

Brent D. Mast

U.S. Department of Housing and Urban Development

Tricia Ruiz

U.S. Department of Housing and Urban Development

Introduction

The relationship between childhood development and neighborhood quality has important

implications in terms of physical and behavioral well-being and long-term socioeconomic

outcomes (Acevedo-Garcia et al., 2020; Chetty et al., 2018). One aspect of neighborhood quality

can be measured with crime rates. Exposure to violent crime has been associated with child health

across multiple dimensions, such as chronic physical conditions, developmental disorders, and

mental health (American Public Health Association, 2018; Jackson, Posick, and Vaughn, 2019).

Violent crime is known to have geographically concentrated patterns, and exposure to violent

crime affects the health of victims, their families, and their communities (Sackett, 2016).

This report analyzes the relationship between child opportunity and violent crime using a

geographic case study of counties in West Virginia. It is the authors’ understanding that this is the

first study to conduct an analysis using these two measures: correlation analysis and mapping.

This combination contributes to broader discussions about the effects of local crime on children

and also illustrates how the less-common analytical tool of bivariate mapping may be useful in

future research.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

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Mast and Ruiz

Data

For this study, child opportunity was measured with Child Opportunity Index (COI) 2.0 data.

Specifically, this study uses the COI 2.0 national Z-score for 2015. The COI 2.0 is a composite

measure that includes resources and conditions related to children’s health development and is

tabulated at the Census tract level.1

The COI 2.0 includes 29 indicators, including access and quality of early childhood education,

school poverty rates, health insurance coverage, access to green space and healthy food, exposure

to toxins, and socioeconomic measures including poverty rate, household income, homeownership

rates, and high-skill employment rates (Noelke et al., 2020).2

Because the COI does not include measures of crime, the study used violent crime rate (VCR) data

from the Federal Bureau of Investigation Uniform Crime Reports (UCR) for 2014, which were

taken from the National Neighborhood Data Archive (2020). The crime rates represent the number

of incidents per 10,000 population in 51 counties in West Virginia for which data were available.

In the next section, the authors describe the data and present summary statistics and Pearson

correlation coefficients. The data visualization, using bivariate choropleth maps, is presented in the

following section. Concluding remarks are presented in the final section.

Summary Statistics and Correlation Analysis

Summary statistics are reported in exhibit 1. The COI 2.0 national Z-scores range from -.057 to

.017, indicating a fairly wide range across West Virginia counties. Because the index is a Z-score,

the mean and median are both very close to zero. Violent crime rates also have a wide range,

varying from around 2 to 388, with a mean of about 40 and a median approximately equal to 23.

Exhibit 1

Descriptive Statistics

Variable

N

Mean

StdDev

Min

Median

Max

COI 2.0 national Z-score

55

– 0.015

0.015

-0.057

– 0.013

0.017

Violent crime rate

51

39.907

56.787

2.358

22.686

388.350

COI = Child Opportunity Index. Max= maximum. Min =minimum. StdDev = standard deviation.

Sources: COI 2.0, 2015; Federal Bureau of Investigation Uniform Crime Reports, 2014

Pearson correlation coefficients and p-values are reported in exhibit 2. The correlation coefficient

between the COI 2.0 national Z-score and the VCR is -.196, but it is not statistically significant at

the .10 level. This statistic indicates little relationship between the COI 2.0 index and the violent

crime rate.

1 The authors aggregated the COI 2.0 index to the county level by taking a weighted mean, where the weight was

child population.

2 The data for COI 2.0 were developed by researchers at Brandeis University, Waltham, Massachusetts, and are available at

data.diversitydatakids.org/.

388 Graphic Detail

Exploring the Relationship Between Child Opportunity

and Violent Crime Rates in West Virginia Counties

Exhibit 2

Pearson Correlation Coefficients

Pearson Correlation

P-value

Coefficient

Variable

N

COI 2.0 national Z-score

Violent crime rate

COI 2.0 national Z-score

55

1

Violent crime rate

51

– 0.196

0.168

COI = Child Opportunity Index.

Sources: COI 2.0, 2015; Federal Bureau of Investigation Uniform Crime Reports, 2014

Data Visualization

Choropleth maps typically display one variable using colors or shades to illustrate geographical

patterns and differences between spatial units, such as counties or neighborhoods. However,

comparing more than one variable at a time can be difficult. The authors overcame this limitation

by displaying the COI 2.0 index and the VCR as a bivariate choropleth map to allow the map

reader to analyze two variables in the same map. This method allows the map reader to visualize

the spatial patterns of one variable compared with the other.

The child opportunity index and violent crime rate were mapped using tertiles to categorize each

measure. Each category contained one-third of all counties, ranked as low, moderate, and high. These

findings are detailed in the tables for exhibits 3 and 4. These categories were used to create the maps

for exhibit 5 (in color) and exhibit 6 (in black and white). Exhibit 6 provides the same visualization

as exhibit 5 but is in black and white, with three shades of gray for the child opportunity index and

different patterns of dots, hatch-marks, and cross-hatch marks for levels of violent crime.

Exhibit 3

Categories for Child Opportunity Index in West Virginia (Total Number of Counties: 55)

Category

Number of Counties

Minimum

Maximum

Low

18

– 0.0573

– 0.0205

Moderate

18

– 0.0199

– 0.0074

High

19

– 0.0073

0.0172

No Data Available

0

NA

NA

Sources: Authors’ calculations of data from Child Opportunity Index 2.0, 2015; Federal Bureau of Investigation Uniform Crime Reports, 2014

Exhibit 4

Categories for Violent Crime Data in West Virginia (Total Number of Counties: 55)

Category

Number of Counties

Minimum

Maximum

Low

17

2.3584

18.5992

Moderate

17

18.9415

31.5259

High

17

38.9964

388.3495

No Data Available

4

NA

NA

Sources: Authors’ calculations of data from Child Opportunity Index 2.0, 2015; Federal Bureau of Investigation Uniform Crime Reports, 2014

Cityscape 389

Mast and Ruiz

In the map legend in exhibit 5, the rankings for COI are displayed vertically, with low at the

bottom and high at the top of the legend. For VCR, the rankings can be read from left to right,

with low levels on the left-hand side and highest on the right side. Both measures converge to

create nine different categories: low COI-low VCR, low COI-moderate VCR, low COI-high VCR,

moderate COI-low VCR, moderate COI-moderate VCR, moderate COI-high VCR, high COI-low

VCR, high COI-moderate VCR, and high COI-high VCR.

Exhibit 5

Bivariate Choropleth Map (in color) of Child Opportunity Index (COI) and Violent Crime Rate in

West Virginia

Sources: Authors’ calculations of data from COI 2.0, 2015; Federal Bureau of Investigation Uniform Crime Reports, 2014

390 Graphic Detail

Exploring the Relationship Between Child Opportunity

and Violent Crime Rates in West Virginia Counties

Exhibit 6

Bivariate Choropleth Map (in black and white) of Child Opportunity Index (COI) and Violent Crime

Rate in West Virginia

Sources: Authors’ calculations of data from COI 2.0, 2015; Federal Bureau of Investigation Uniform Crime Reports, 2014

Exhibits 5 and 6 illustrate that five counties had a combination of high COI and high VCR. These

counties include the three contiguous counties of Putnam, Lincoln, and Logan, in addition to Ohio

and Morgan counties. In contrast, Tyler, Preston, and Lewis counties had low COI and low VCR.

Because of the wide variation across these counties, these maps support what was learned from

the correlation analysis—there does not appear to be a distinct association between the child

opportunity index and violent crime rate in West Virginia at the county level.

Discussion

Violent crime is one component of neighborhood characteristics that affect childhood

development. Although the COI is a useful composite measure that includes a broad array of

resources and conditions related to child development, it does not include a measure for crime.

This analysis looked at the COI combined with county-level measures of violent crime; however, it

did not find a strong correlation between the two.

For example, based on the known harmful effects of violent crime on childhood development,

it could be expected that there would be an inverse relationship. Areas with high rates of

violent crime would also be areas with low childhood opportunity. However, there was not an

Cityscape 391

Mast and Ruiz

overwhelming number of these low COI-high VCR counties. In these maps, only McDowell,

Gilmer, Doddridge, and Tucker counties (4 out of the 55 counties) fall into this category.

This finding does not suggest that there is no connection between violent crime and childhood

opportunity, which is a complex, multilevel relationship that includes measures beyond the scope

of this analysis. However, the findings do indicate a relatively weak relationship between the COI

2.0 index and violent crime rates at the county level in West Virginia. Therefore, because the COI

2.0 index does not include a measure of exposure to violent crime,3 it would be worth continuing

to explore additional measures that include this important aspect of child opportunity, including

evaluating other geographies and/or spatial units.

Authors

Brent D. Mast is a social science analyst at HUD’s office of Policy Development and Research. Tricia

Ruiz is a geographer at HUD’s office of Fair Housing and Equal Opportunity.

References

Acevedo-Garcia, Dolores, Clemens Noelke, Nancy McArdle, Nomi Sofer, Nick Huntington,

Erin Hardy, Rebecca Huber, Mikyung Baek, and Jason Reece. 2020. “The Geography of Child

Opportunity: Why Neighborhoods Matter for Equity, First Findings from the Child Opportunity

Index 2.0. ” https://www.diversitydatakids.org/research-library/research-report/geography-child-

opportunity-why-neighborhoods-matter-equity.

American Public Health Association. 2018. “Violence is a Public Health Issue: Public Health is

Essential to Understanding and Treating Violence in the U.S. ” https://apha.org/policies-and-advocacy/

public-health-policy-statements/policy-database/2019/01/28/violence-is-a-public-health-issue.

Chetty, Raj, John N. Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya R. Porter. 2018.

The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility. Working paper 18–42.

Center for Economic Studies: U.S. Census Bureau. https://opportunityinsights.org/wp-content/

uploads/2018/10/atlas_paper.pdf.

Diversitydatakids.org. n.d.“COI 2.0 Index Data—Child Opportunity Levels, Scores and Averaged

z-Scores for the Overall Index and Three Domains” https://data.diversitydatakids.org/dataset/coi20-

child-opportunity-index-2-0-database/resource/080cfe52-90aa-4925-beaa-90efb04ab7fb.

Jackson, Dylan, Chad Posick, and Michael G. Vaughn. 2019. “New Evidence Of The Nexus Between

Neighborhood Violence, Perceptions Of Danger, And Child Health,” Health Affairs 38 (5): 746–754.

3 The researchers have noted that this issue is a limitation in their methodology and that it is important to include measures of violent crime, but they could not acquire neighborhood-level data at the time the data were compiled (Noelke et.al., 2020).

392 Graphic Detail

Exploring the Relationship Between Child Opportunity

and Violent Crime Rates in West Virginia Counties

National Neighborhood Data Archive: Crimes by County, 2002–2014. Last updated 1/21/2020.

Downloaded from the International Consortium for Political and Social Research (ICPSR) at the

University of Michigan. https://www.icpsr.umich.edu/web/ICPSR/studies/38649/versions/V1.

Noelke, C., N. McArdle, M. Baek, N. Huntington, R. Huber, E. Hardy, and D. Acevedo-Garcia.

2020. Child Opportunity Index 2.0 Technical Documentation. https://www.diversitydatakids.org/

sites/default/files/2020-02/ddk_coi2.0_technical_documentation_20200212.pdf.

Sackett, Chase. 2016. “Neighborhoods and Violent Crime,” Evidence Matters.

https://www.huduser.gov/portal/periodicals/em/summer16/highlight2.html.

Cityscape 393

394 Departments

by Alexander Din

Exits From HUD Assistance

and Moves to Higher Poverty

Neighborhoods Following the

Camp Fire

Alexander Din

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

The views expressed in this article are those of the author and do not represent the official positions or

policies of the Office of Policy Development and Research, the U.S. Department of Housing and Urban

Development, or the U.S. Government.

Abstract

Little is known about U.S. Department of Housing and Urban Development (HUD)-assisted households

following a natural disaster, including continued participation status in low-income rental assistance and

post-disaster location outcomes. This article compares changes in participation in HUD assistance and

neighborhood poverty status between HUD-assisted households in Paradise and Magalia, California, and

the rest of Butte County following the 2018 Camp Fire. The wildfire destroyed most of the community,

making it the deadliest and most destructive wildfire in California’s history. Approximately one-half

of HUD-assisted households were not participating in HUD assistance in 2019. Of households that

remained assisted, most had moved out of their neighborhood, often to higher poverty neighborhoods.

This research suggests that further research is necessary to measure changes in participation in HUD

assistance and locational trends for low-income subsidized households following a natural disaster.

What Was the Camp Fire?

The Camp Fire in and around Paradise, California, was the most destructive wildfire in California’s

history (CAL FIRE, 2022), killing 85 people, injuring others, and destroying nearly 19,000

structures (CAL FIRE, 2019). Nearly 90 percent of Paradise’s housing was destroyed, and 83

percent of its residents were displaced (Kuczynski and Sharygin, 2019). Only one-fourth of

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 395

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Din

residential addresses were occupied by the end of 2021 (Din, 2022). Although some research has

been conducted on households assisted by HUD after other natural disasters, little is known about

HUD-assisted households after the Camp Fire or other natural disasters. This article compares the

2017 and 2019 spatial locations of HUD-assisted households in Paradise and Magalia, California,

with HUD-assisted households in the rest of Butte County, California.

HUD-Assisted Households After a Natural Disaster

Approximately one-half of California’s housing units are in the wildland-urban interface (WUI)

(Hammer, Stewart, and Radeloff, 2007), and housing growth inside the WUI outpaces housing

growth outside the WUI (Radeloff et al., 2018). However, government-subsidized housing in

California is disproportionately outside of the WUI (Gabbe, Piece, and Oxlaj, 2020). Some studies

have researched HUD-assisted households in disaster-prone areas in general, but fewer studies

have analyzed the intersection of HUD-assisted households in wildfire-prone areas or the spatial

locations of HUD-assisted pre- and post-disaster, regardless of disaster type.

Despite limited information about HUD-assisted households in either wildfire areas or post-

disaster location patterns, some information is known about HUD-assisted households following

a natural disaster in general. HUD-assisted households were disproportionately in neighborhoods

with greater flood extents following Hurricane Harvey in Harris County, Texas (Chakraborty et al.,

2021). Public housing residents in Galveston, Texas, were displaced due to Hurricane Ike and post-

disaster redevelopment efforts (Rongerude and Hamideh, 2019), similar to the displacement of

public housing residents in Lumberton, North Carolina, after Hurricane Matthew (Khajehei, 2019).

Insufficient resources for displaced residents, particularly those who are low-income or homeless,

growth in post-disaster rental costs, and housing reconstruction costs are frequent barriers to

remaining or returning to a community after a disaster (Peloton Research and Economics, 2020;

Rouhanizadeh, Kermanshachi, and Nipa, 2020).

Where Were HUD-Assisted Households After the Camp Fire?

After the Camp Fire destroyed most of Paradise, one-half (50.0 percent) of HUD-assisted

households in Paradise and nearby Magalia that were present in the HUD administrative data in

2017 were not present in HUD’s administrative data for 2019,1 more than double the rate of HUD-

assisted households elsewhere in Butte County (22.1 percent).2 In all categories of move types, as

shown in exhibit 1, households from Paradise and Magalia greatly exceed moves from the rest of

Butte County.

1 An assisted household not being present in later HUD administrative data defines an exit in this article’s context.

2 Exits from HUD assistance between 2017 and 2019 in the rest of Butte County were similar to the full universe of HUD-assisted households during the same time period.

396 Graphic Detail

Exits From HUD Assistance and Moves to Higher Poverty Neighborhoods Following the Camp Fire

Exhibit 1

Presence and Locations in 2019 of HUD-Assisted Households in Butte County, California, in 2017

HUD-Assisted Households

Location Category

Paradise/Magalia

Rest of Butte County

Count

Share (%)

Count

Share (%)

HUD-Assisted in 2017

370

100.0

2,597

100.0

Not HUD-Assisted in 2019

185

50.0

575

22.1

HUD-Assisted in 2019 and in

17

4.6

24

0.9

Different State

HUD-Assisted in 2019 and Same

69

18.6

48

1.8

State but Different County

HUD-Assisted in 2019 and Same

79

21.4

103

4.0

County but Different Census Tract

HUD-Assisted in 2019 and Same

20

5.4

1,847

71.1

Census Tract

Note: Percentages may not total 100 percent due to rounding.

Sources: HUD Longitudinal Files; Calculations by author

HUD households from Paradise and Magalia primarily moved to Chico and Oroville, with fewer

numbers going to Gridley, Durham, and Magalia outside of the Camp Fire, as shown in exhibit 2.

Half of these households moved between 11.6 and 17.6 miles from their original locations.

Exhibit 2

Moves of HUD-Assisted Households From Paradise and Magalia to Elsewhere in Butte County,

California, Between 2017 and 2019

Sources: HUD Longitudinal Files; Calculations and visualization by author

Cityscape 397

Din

Slightly more than one-third (37.3 percent) of HUD-assisted households that remained in

assistance between 2017 and 2019 moved elsewhere in California outside of Butte County. This

group mostly remained in northern California; however, a few moved to the San Francisco Bay

Area. One-half of the households in this group of movers moved 72 or fewer miles. Popular

destinations for this cohort were Eureka, Redding, Red Bluff, the Sacramento area, and Yuba City.

Exhibit 2 visualizes the moves of this group.

Exhibit 3

Moves of HUD-Assisted Households From Paradise and Magalia to Elsewhere in California

Outside of Butte County Between 2017 and 2019

Sources: HUD Longitudinal Files; Calculations and visualization by author

398 Graphic Detail

Exits From HUD Assistance and Moves to Higher Poverty Neighborhoods Following the Camp Fire

A small number (17) of HUD-assisted households from Paradise and Magalia that remained in

assistance from 2017 to 2019 left California for another state by 2019. Many went north to Oregon

and Washington, a few to nearby Reno, Nevada, and others scattered around the country, as shown

in exhibit 4.

Exhibit 4

Moves of HUD-Assisted Households From Paradise and Magalia to Elsewhere Outside of

California Between 2017 and 2019

Sources: HUD Longitudinal Files; Calculations and visualization by author

Did the Neighborhood Poverty Rate Change for HUD-Assisted

Households That Moved After the Camp Fire?

Evidence supports that living in neighborhoods with higher opportunity is associated with

increased benefits to HUD-assisted households (Chetty, Hendren, and Katz, 2016). A common

measurement of neighborhood opportunity is a census tract’s poverty rate, where a lower poverty

rate is equivalent to a supposed greater level of opportunity (Brazil, Wagner, and Ramil, 2022).

Contrary to HUD programs nationwide (McClure, 2010), most HUD-assisted households in

Paradise and Magalia lived in low-poverty census tracts prior to the Camp Fire. By 2019, most

households that remained in HUD assistance had moved to higher poverty rate census tracts.

Exhibit 5 shows that of HUD-assisted households in Paradise and Magalia in 2017, those that

Cityscape 399

Din

moved shorter distances (e.g., to nearby census tracts within the same county) tended to move

to higher poverty neighborhoods, suggesting a decrease in potential neighborhood opportunity

benefits for these assisted households.

Exhibit 5

Change in Neighborhood Poverty for HUD-Assisted Households in Paradise and Magalia,

California, in 2017

Neighborhood

Poverty Rate

HUD-Assisted Households

Miles Moved

Poverty Category

Change

Move Type

Within

Overall

Count

Mean Median

2017

2019

Move Type Share

Mean Median

(N=165)

(%)

(%)

Share (%)

(%)

Low-

Low-

28

35.4

20.4

– 0.2

– 6.8

13

12

Poverty

Poverty

Low-

High-

Different

34

43.0

24.8

16.1

16.6

15

15

Poverty

Poverty

census tract

within Butte

Extremely

Low-

County

High-

15

19.0

10.9

30.3

27.4

13

13

Poverty

Poverty

High-

Low-

2

2.5

1.5

– 6.8

– 1.1

7

7

Poverty

Poverty

Different

Low-

Low-

38

55.1

27.7

– 0.2

– 1.3

113

76

census tract

Poverty

Poverty

outside of

Butte County

Low-

High-

but within

31

44.9

22.6

13.2

11.8

96

45

Poverty

Poverty

California

Different

Low-

Low-

13

76.5

9.5

– 4.1

– 3.9

740

409

census tract

Poverty

Poverty

outside of

Low-

High-

California

4

23.5

2.9

16.9

17.6

316

189

Poverty

Poverty

Notes: This exhibit does not include the 20 HUD-assisted households that remained in the same census tract in 2017 and 2019. Percentages may not total 100

percent due to rounding.

Sources: HUD Longitudinal Files; American Community Survey; Calculations by author

What Else is There to Learn About HUD-Assisted Households

After the Camp Fire or Other Natural Disasters?

Little has been researched about HUD-assisted households following a disaster, including those

that have exited assistance. Although public housing authorities can offer some help following a

disaster, such as portability or reissuing the voucher (PIH, 2022), the surge in demand for rental

housing following the Camp Fire in the tri-county region further constrained an already tight

rental market (Peloton Research and Economics, 2020). The Stafford Act mandates that the Federal

Emergency Management Agency (FEMA) take the lead on the post-disaster mission. In contrast,

HUD’s efforts are directed toward long-term recovery via Community Development Block Grant

Disaster Recovery (CDBG-DR) grants, which are long-term and take time to be appropriated by

Congress and then enacted by local grantee communities (Martín, Teles, and DuBois, 2021).

400 Graphic Detail

Exits From HUD Assistance and Moves to Higher Poverty Neighborhoods Following the Camp Fire

The large exit from HUD assistance and moves to higher poverty neighborhoods after the Camp

Fire suggests this trend may occur following other disaster types. Analyzing post-disaster spatial

trends of HUD-assisted households may help to understand the effects of a natural disaster on

long-term outcomes for assisted renters in general. For some of the United States’ most vulnerable

renters, it would be beneficial to further measure post-disaster mobility and exits from low-income

subsidized rental assistance. This assessment would identify potential policy improvements to

maintain housing and protect them from harm following a disaster.

Notes

Prior to the Camp Fire, there were 2,967 HUD-assisted households in Butte County, including 370

households in Paradise or Magalia. A two-thirds majority of assisted households in Butte County

participated in the Housing Choice Voucher (HCV) program, followed by Project-Based Section 8

(PBS8) (17.5 percent), Public Housing (11.5 percent), Section 202 (3.3 percent), and Section 811

(1.0 percent). HUD-assisted households in Paradise and Magalia participated only in the HCV and

PBS8 programs.

Acknowledgments

The author thanks Cody Bowles in Public and Indian Housing for his helpful comments.

Author

Alexander Din is a social science analyst in the Office of Policy Development and Research at the

U.S. Department of Housing and Urban Development.

References

Brazil, Noli, Jenny Wagner, and Raziel Ramil. 2022. “Measuring and Mapping Neighborhood

Opportunity: A Comparison of Indices in California,” Urban Analytics and City Science 50 (3): 1–19.

California Department of Forestry and Fire Protection (CAL FIRE). 2022. “Top 20 Most Destructive

California Wildfires.”

———. 2019. “Camp Fire. ” https://www.fire.ca.gov/incidents/2018/11/8/camp-fire/.

Chakraborty, Jayajit, Ashley A. McAfee, Timothy W. Collins, and Sara E. Grineski. 2021. “Exposure

to Hurricane Harvey Flooding for Subsidized Housing Residents of Harris County, Texas,” Natural

Hazards 106: 2815–2205.

Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better

Neighborhoods on Children; New Evidence from the Moving to Opportunity Experiment,”

American Economic Review 106 (4): 855–902.

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Din

Din, Alexander. 2022. “What Do Visualizations of Administrative Address Data Show About the

Camp Fire in Paradise, California?” Cityscape 24 (3): 261–271.

Gabbe, C.J., Gregory Piece, and Efren Oxlaj. 2020. “Subsidized Households and Wildfire Hazards

in California,” Environmental Management 66: 873–883.

Hammer, Roger B., Susan I. Stewart, and Volker C. Radeloff. 2007. “Demographic Trends, the

Wildland–Urban Interface, and Wildfire Management,” Society and Natural Resources 22: 777–782.

Khajehei, Sayma. 2019. “Recovery Challenges of Public Housing Residents After Disasters:

Lumberton, North Carolina After Hurricane Matthew.” Master of Community and Regional

Planning Thesis, Iowa State University, Ames, Iowa.

Kuczynski, Doug, and Ethan Sharygin. 2019. California Tops 39.9 Million Residents at New Year

per New State Demographic Report. Sacramento, CA: California Department of Finance.

https://htv-prod-media.s3.amazonaws.com/files/e-1-2019-press-release-1556737607.pdf.

Martín, Carlos, Daniel Teles, and Nicole DuBois. 2021. “Understanding the Pace of HUD’s Disaster

Housing Recovery Efforts,” Housing Policy Debate 32 (1): 102–127.

McClure, Kirk. 2010. “The Prospects for Guiding Housing Choice Voucher Households to High-

Opportunity Neighborhoods,” Cityscape: A Journal of Policy Development and Research 12 (3): 101–122.

Peloton Research and Economics. 2020. The Impacts of Camp Fire Disaster on Housing Market

Conditions and Housing Opportunities in the Tri-County Region: Study Prepared for North Valley

Community Foundation. Chico: Peloton Research and Economics.

Public and Indian Housing (PIH). 2022. “Rehousing Families. ” https://files.hudexchange.info/

resources/documents/PHA-Disaster-Readiness-Response-and-Recovery-Rehousing-Families-Fact-

Sheet.pdf.

Radeloff, Volker C., David P. Helmers, H. Anu Kramer, Miranda H. Mockrin, Patricia M. Alexandre,

Avi Bar-Massada, Van Butsic, Todd J. Hawkbaker, Sebastián Martinuzzi, Alexandra D. Syphard, and

Susan I. Stewart. 2018. “Rapid Growth of the U.S. Wildland-Urban Interface Raises Wildfire Risk,”

Proceedings of the National Academy of Sciences 115 (13): 3314–3319.

Rongerude, Jane, and Sara Hamideh. 2019. “Post-Disaster Recovery of Public Housing in

Galveston, Texas: An Opportunity for Whom?” Lincoln Land Institute. https://www.lincolninst.edu/

sites/default/files/pubfiles/2019_inquiry_post_disaster_recovery_texas_rongerude_hamideh.pdf.

Rouhanizadeh, Behzad, Sharareh Kermanshachi, and Thahomina Jahan Nipa. 2020. “Exploratory

Analysis of Barriers to Effective Post-Disaster Recovery,” International Journal of Disaster Risk

Reduction 50 101735.

402 Graphic Detail

by Marilyn E. Kaplan and Mike Jackson

Industrial Revolution

Every home that is built is a representation of compromises made between different and often competing

goals: comfort, convenience, durability, energy consumption, maintenance, construction costs,

appearance, strength, community acceptance, and resale value. Consumers and developers tend to make

tradeoffs among these goals with incomplete information which increases risks and slows the process of

innovation in the housing industry. The slowing of innovation, in turn, negatively affects productivity,

quality, performance, and value. This department piece features a few promising improvements to

the U.S. housing stock, illustrating how advancements in housing technologies can play a vital role in

transforming the industry in important ways.

Hiding in Plain Sight: How

Reconsideration of Codes for

Existing and Historic Buildings Can

Expand Affordable Housing

Marilyn E. Kaplan

Fellow, Association for Preservation Technology International

Preservation Architecture

Mike Jackson

Fellow, American Institute of Architects

Fellow, Association for Preservation Technology International

Abstract

Current policy conversations regarding the critical need to increase the availability of affordable housing

rarely include discussions regarding the reuse of existing and historic buildings, including the thousands

of vacant housing units on Main Streets across the United States. Recapturing these spaces has many

advantages, including carbon-reduction, expanded use of existing infrastructure, and building or

strengthening communities. Nearly 50 years ago, the U.S. Department of Housing and Urban Development

(HUD) led many of the efforts to identify barriers to building reuse that were embedded in the building codes,

resulting in the framework that is now the basis of the widely adopted model for existing building code(s). A

half-century after these initial efforts, many factors have contributed to the reemergence of codes as a barrier to greater reuse of existing buildings. An updated assessment and strategy to address current code barriers

can make an unparalleled contribution to meeting the nation’s current and projected housing needs.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 403

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Kaplan and Jackson

Introduction

The nation’s shortage of affordable housing—estimated at 3.8 million housing units according to

a 2021 Freddie Mac estimate—is widely recognized and critical to the health, safety, and welfare

of the public and the social and economic well-being of families and communities. Although no

precise estimate exists quantifying the number of vacant housing units and other underutilized

real estate, it is common sense to envision that vacant buildings have tremendous potential to

ameliorate the housing shortage. Many efforts to address the housing shortage focus on new

construction, often undertaken by larger developers.

In the late decades of the 20th century, many factors contributed to the public’s acceptance of

reusing existing buildings. Federal policy, state and federal tax credits for the rehabilitation of

qualified historic structures, and a rediscovered appreciation of walkable communities created

opportunities throughout the United States to reuse buildings. Although this back-to-the-city

trend has continued, it has been affected by code requirements for fire and life safety and other

societal goals such as accessibility and resiliency. In many respects, it appears that the advances

which brought forth the nation’s first existing building codes 25 years ago have been thwarted by

advancing codes for all new and existing buildings and state and federal housing policies favoring

new construction. Reliance on new buildings may have become widespread due to the ready

ability to replicate standard designs and eliminate the problems created by the nuances and unique

conditions presented by existing buildings, including environmental concerns and the difficulties

encountered when trying to meet construction standards written for new construction.

404 Industrial Revolution

Hiding in Plain Sight: How Reconsideration of Codes for

Existing and Historic Buildings Can Expand Affordable Housing

Communities throughout the United States share the dual challenges of the inability to meet their

housing needs and the underutilization of vacant buildings in their traditional community cores.

Some vacancies can be attributed to the long-term effects of urban renewal and the expansion of the

suburbs; others can be considered the result of building and fire codes that are unevenly punitive to

older structures. Although not all buildings are candidates for reuse, the codes’ devaluing of existing

buildings in favor of new construction that is easier to standardize and codify has been shortsighted,

often creating conditions where projects are unaffordable or technically infeasible.

The primary building types discussed in this article—small two-, three-, and four-story buildings

along Main Street, as illustrated in Exhibit 1—are among those that are underutilized but present

great opportunity for addressing housing needs. The recent recession, pandemic, and changing

patterns in retail and the workplace have created high vacancy and underutilization rates in large

retail malls and downtown offices, ranging from high-to low-rise construction and buildings

with large to small floor plates. For all these building types, retrofits and adaptive reuse for new

occupancies will require new and creative approaches to the construction codes that govern their

rehabilitation. For larger buildings, reuse for residential purposes will need to address requirements

such as those for natural light, ventilation, and exit travel. Some of these requirements create

barriers shared with the smaller Main Street buildings, whereas others are unique to the building

or occupancy types. To unlock the opportunities and benefits of the reuse of these structures, a

broad and fresh look, as supported by HUD nearly 50 years ago, is required.

On Main Street, the opportunity to create housing on vacant upper floors is obvious. In some

buildings, new units will be added or reinstated above existing businesses and offices; in other

buildings that have no first-floor use, additional opportunities exist to create nonresidential

occupancies that support or enhance the residential use. Unfortunately, code requirements seeking

to have existing buildings function like new construction can present insurmountable challenges,

with the greatest penalty paid when the code classifies the rehabilitation effort as a Change of

Occupancy. Based on the framework of earlier efforts to encourage building rehabilitation through

improved building codes, this article presents an updated approach to removing code barriers to

meet the current critical housing needs within existing buildings.

How Building Codes Work

Until the 1997 forming of the International Code Council (ICC), model building codes adopted

by jurisdictions were published by one of three model code organizations: the Building Officials

and Code Administrators International (BOCA), formed in 1915; the International Conference

of Building Officials (ICBO), founded in 1922; and the Southern Building Code Congress

International, formed in 1941. Created to standardize the construction industry via a national

mode, the ICC now publishes 15 model codes (the I-codes), updated at 3-year cycles. The next

family of codes to be published will be the 2024 edition.

A model code only becomes law once adopted by the jurisdiction. A jurisdiction can adopt one,

many, or all of the model codes and is free to update to current editions when desired. Adoption

most often includes modifications to the administrative procedures of Chapter 1 of each model

code to reflect local policy, but it can also include modifications to any other code sections. Great

Cityscape 405

Kaplan and Jackson

variety is possible in adoption across the nation. States may adopt recent editions of the I-codes for

all construction. Alternatively, states or smaller jurisdictions might adopt select codes applicable

only to particular building or occupancy types. In addition, codes and reference standards available

for adoption may be published by other organizations, such as the National Fire Protection

Association; focus on particular priorities of a jurisdiction, such as energy conservation or wildfire;

or be in place as a requirement of the funding source, as is typical for state or federally funded

housing or hospitals.

The first full model code intended for the rehabilitation of existing buildings, the International

Existing Building Code (IEBC), was published in 2003 by the ICC. The IEBC was created by

merging and enhancing the codes or provisions for existing and historic buildings previously

published by the regional model code organizations. The IEBC applies to existing and historic

buildings of all occupancy and construction types, whether otherwise governed by the

International Building Code (IBC) or the International Residential Code (IRC).

Application of the IEBC requires interaction with the IBC, International Energy Conservation

Code (IECC), and ICC 117.1 Accessible and Usable Buildings and Facilities. Chapter 11 of the

International Fire Code (IFC), “Construction Requirements for Existing Buildings,” provides limited

retroactive requirements for building rehabilitation. In contrast, the simpler format of the IRC is

applicable to new one-and two-family dwellings and townhouses up to three stories in height, with

separate entrances and accessory structures, and it is largely a stand-alone document. Although the

IRC is easier to use, the only provisions for rehabilitation of an existing building are included in an

appendix that must be independently adopted by the jurisdiction.

Compliance with building-related requirements established under other federal policies is required

by reference in the code and/or other program requirements. Achieving the safety objectives

of these policies is integral to the approval and construction processes. However, difficulties in

addressing issues such as asbestos and lead paint have resulted in policy shifts that, in contrast to

the earliest objectives, resulted in the unintended consequences of reduction or abandonment of

rehabilitation programs.

HUD’s Nationally Applicable Recommended Rehabilitation

Provisions (NARRP)

Prior to the IEBC, the near-insurmountable challenges of bringing existing buildings to comply

with new construction standards were widely recognized, particularly for historic buildings

undergoing restoration. The 1968 Kerner and Douglas Commissions addressed the deplorable

conditions of existing housing and urban neighborhoods abandoned by urban renewal and other

federal housing programs. HUD’s initiatives included the publication of a series of Rehabilitation

Guidelines, including those addressing code administration and enforcement of housing and

property maintenance codes. HUD also supported research and code development, culminating

in the 1997 publication of a model rehabilitation code, the Nationally Applicable Recommended

Rehabilitation Provisions (NARRP).1 The NARRP served as the basis of the IEBC, and with

1 https://www.huduser.gov/Publications/pdf/HUD-7842.pdf

406 Industrial Revolution

Hiding in Plain Sight: How Reconsideration of Codes for

Existing and Historic Buildings Can Expand Affordable Housing

modifications to format, New Jersey and Maryland were among the early adopters of rehabilitation

codes based on NARRP (exhibits 1 and 2).

Exhibit 1

Rehabilitation Building Code Requirements Prior to NARRP

Source: https://www.huduser.gov/Publications/pdf/HUD-7842.pdf

Exhibit 2

Rehabilitation Building Code Requirements After NARRP

Source: https://www.huduser.gov/Publications/pdf/HUD-7842.pdf

Cityscape 407

Kaplan and Jackson

The IEBC intended to provide predictability to the code and design process, consider the unique

conditions presented by existing buildings, and establish a graduated basis for the imposition of

required improvements to address safety and building performance. This graduated basis was

achieved by an approach generally based on the footprint, or “Work Area,” of the property owner’s

proposed work. This approach contrasted with the widely accepted pre-IEBC “50 percent rule,”

wherein the imposed requirements were a function of comparing the cost of rehabilitation against

the value of the building. Only projects where the rehabilitation expense exceeded 50 percent of a

building’s value were required to meet the current code (i.e., the code for new construction).

Conceptually, little has changed in the IEBC since its original publication. However, like all codes

and standards, the minimum thresholds for safety and building performance have increased,

diminishing the overall intent of the IEBC and its NARRP predecessor of encouraging building

rehabilitation via the removal of code barriers. Unfortunately, as HUD’s active role in the

maintenance of its model rehabilitation code has neither continued nor been assumed by others,

code changes during the past 20-plus years have created new barriers to building rehabilitation.

Barriers Reemerged

Although the IEBC achieved its goal of removing many of the barriers to rehabilitation, its utility as

a tool to encourage rehabilitation has been hampered by 2 decades of increasing code stringency,

paralleling the trajectory of the codes written for new construction. Since the publication of the

2003 IEBC, the scope of both new and rehabilitation codes has also expanded to regulate newly

prioritized or emerging topics such as resiliency, energy efficiency, and rural fire protection. During

this 20-year period, it is arguable that the acceptable disparity in performance between existing

buildings and new construction has shrunk.

These reemerged code barriers stifle the opportunity to use existing vacant or underutilized

buildings to meet current housing needs. Some barriers are rooted in the complexity of the codes

and their administration. Others are associated with particular building types, such as the two- and

three-story Main Street buildings, which, by nature of their smaller scale, should be the easiest to

address with solutions that are widely applicable.

The Main Street Building

Nearly every older community has a core of empty and underutilized buildings that originally

contained housing units above a nonresidential first floor. Common code barriers to the

rehabilitation of these buildings have already been identified:

• Inadequate egress.

• Lack of a fire separation between occupancies, sprinklers, or elevator access to upper floors.

• Potential building science issues affecting long-term building durability when required to meet

new construction energy standards.

408 Industrial Revolution

Hiding in Plain Sight: How Reconsideration of Codes for

Existing and Historic Buildings Can Expand Affordable Housing

• The code’s inflexibility to accept even those minimal dimensional variations which could have

an insignificant impact on safety and building performance.

Perhaps most problematic is the penalizing of projects classified as a “Change of Occupancy,”

whereby rehabilitation projects must essentially meet or exceed standards for new construction.

Knowledge and Technical Expertise

Other barriers include the lack of awareness by many code officials and design professionals

of the three optional code compliance methods included in the IEBC: Prescriptive, Work Area,

and Performance. Although the ability to choose the compliance path most advantageous to a

project is beneficial to the existing and historic building project, this added complication to an

already complex process is one reason the IEBC is not recognized or adopted. This situation

is most common in smaller and rural communities where there may be little support for code

official training, a hesitancy to adopt additional codes or the most current codes, and few design

professionals with knowledge of the IEBC.

Leadership

In contrast with the commercial marketplace’s current focus on larger office buildings and malls,

no broad unified effort to study barriers and opportunities has been undertaken for the smaller-

scale commercial buildings of Main Street. Since the founding of the ICC, ready opportunities to

influence code have shifted upstream from localities to a national forum, and jurisdictions are often

reluctant to introduce innovative provisions that vary from the national model. Thus, the codes’

lack of focus on expanding opportunities to create housing is best understood by noting the ICC’s

much broader mission of developing a single set of model codes that provide minimum safeguards

for people at home, the workplace, and other places of commerce and assembly. The ICC has not

led the charge to address the gap between the little-used International Property Maintenance Code

and the IEBC. Housing advocates and others outside the ICC community are often reluctant or

unsuccessful in engaging in the highly organized ICC code development process.

Moving Forward

Twenty years of experience with the IEBC has demonstrated its great value and the barriers still

faced by existing buildings. This experience is particularly important now because vacant buildings

and vacant upper floors of smaller-scale structures on Main Street present unequaled opportunities

for addressing climate change and the nation’s housing shortages. The infrastructure and building

shells exist, and the time required for planning approvals may be significantly shorter than for

new construction projects. Other benefits include the creation and strengthening of walkable

communities and the transformation of vacant, underutilized, and unsafe structures to viable

community resources.

Although the impetus of the earliest rehabilitation codes was to address urban blight and

substandard housing conditions, much of the current conversation focuses on the insufficient

Cityscape 409

Kaplan and Jackson

number of housing units available and their affordability. These current needs can be partially met

through many of the benefits associated with an expanded use of existing and historic buildings:

• Adjacency to existing urban infrastructure, including public transportation, community

amenities, etc.

• Increased neighborhood safety results from increased occupancy.

• Many have demonstrated their inherent resiliency and construction with durable materials.

• Shortened time periods for planning, review processes, and construction processes.

• Smaller-scale projects can create ownership and wealth for individuals.

• Ability to couple with financial incentives such as state and historic tax credits for the

rehabilitation of historic buildings. Between 1976 and 2021, this federal program,

administered by the National Park Service, rehabilitated 302,506 housing units, created

334,367 new housing units, and resulted in a total estimated expenditure of $116.34 billion.

• Reductions in demolition costs, landfill, and carbon production.

• Durability. Current construction practices accept the use of less durable materials than those

present in existing and historic buildings.

To support greater use of these buildings, a reevaluation is needed that mirrors the investigative

and creative process that began nearly 50 years ago and was responsible for the 1980s’

Rehabilitation Guidelines and the publication of the NARRP.

Research is needed to evaluate current code barriers and performance criteria for existing versus

new buildings. Researchers must identify unique solutions adopted by jurisdictions, with the long-

term goal of influencing the model code(s). A comparison of the relative level of safety and building

performance provided by each code compliance method of the IEBC should occur, and a similar

study evaluating the continuum bookended by housing and other minimum property maintenance

codes and the IEBC. The dissemination of research findings and incorporation into the codes may be

most valuable for smaller Main Street buildings least likely to benefit from the engagement of design

professionals and code officials familiar with code options for rehabilitation projects.

Examples of unique solutions adopted by jurisdictions are listed below. Although these concepts

are not universally appropriate or adoptable, many merit serious consideration.

• Provisions permitting any documented previous use to be considered when determining

whether a project will be classified as a Change of (California State Historical Building Code).

• Greater acceptance of smaller housing units, including accessory dwelling units.

• Sprinklers as compensation for other code deficiencies, or alternate fire protection systems as

compensation for lack of sprinklers.

410 Industrial Revolution

Hiding in Plain Sight: How Reconsideration of Codes for

Existing and Historic Buildings Can Expand Affordable Housing

• For mixed-use Main Street buildings, expanded use of sprinkler systems that have lower cost

alternate materials and coverage, including those installed with plastic piping and connected

to the domestic water supply system.

• Expanded acceptance of the single-stair building.

• Use of the Residential Code for up to six-unit buildings or, in smaller Main Street buildings,

for upper-floor units above certain nonresidential occupancies.

• A jurisdiction’s greater commitment to the training of code officials and design professionals,

with a more proactive role in determining the optimum code path and permitting efficiency.

Finally, research should examine and disseminate current approaches and new technologies and

programs available to protect existing vulnerable housing, including monitoring and maintaining

existing housing units. Equally critical is the spectrum and interaction of minimum standards from

housing and property maintenance codes through new construction standards. Minimum housing

or property maintenance codes, applicable when public funds are involved, set a particular mark in

the continuum. Clearer definitions and an understanding of the relative performance requirements

are warranted.

The Role of Collaboration

The goals of rehabilitation are broad and require numerous champions and resources. Among

these resources, the National Main Street Center, a subsidiary of the National Trust for Historic

Preservation, has worked with more than 2,000 communities, encouraging them to prioritize

housing in their downtowns, including the recreation of the traditional mix of housing above retail

or business occupancies that decreased during the second half of the 20th century. Greater use of

these buildings can replace or complement current models for mixed-use development, such as the

ubiquitous five-plus-one housing block built beyond a community’s downtown. These smaller-

scale projects present accessible opportunities for smaller projects that may be more easily achieved

and are more financially feasible than large-scale housing projects. These projects can still benefit

from state and federal historic credits designed to encourage building rehabilitation.

The Association for Preservation Technology International (APTI) Technical Committee on Codes

and Standards is seeking support for a second Rehabilitation Code Summit, which will follow

the summit that occurred in 2019 that set the agenda for APTI’s most recent code-related efforts.

These initiatives included a 3-day training session on applying codes on Main Street buildings

and proposing code changes to the IEBC. The aims are to coordinate better with state and national

federal preservation programs and to codify alternatives to sprinkler systems in smaller buildings.

Additionally, early discussions have begun on a followup to APTI’s 2022 virtual conference Building

Codes on Main Street, albeit with a particular focus on housing.

One approach is for HUD and other federal entities engaged in housing research and policy to join

forces with organizations such as the National Main Street Center and APTI to advocate for better

codes that encourage building reuse.

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Kaplan and Jackson

The yet unmet opportunity to recapture the vacant upper floors of buildings along Main Street and

other redundant structures requires another reexamination of the codes to reshape them to help,

rather than hinder, the achievement of housing and related urban policy goals.

Further Reading

National Trust for Historic Preservation and the National Preservation Partners Network. 2021.

Issue Brief: Preservation Priority: Affordable Housing and Density, Fall 2021.

National Trust for Historic Preservation. 2014. Older, Smaller, Better: Measuring How the

Character of Buildings and Blocks Influences Urban Vitality. Preservation Green Lab, May 2014.

Authors

Marilyn Kaplan is an Architect and Principal of Preservation/Architecture in Albany, New York. She

is the co-chair of the Association for Preservation Technology International Technical Committee on

Codes and Standards. Mike Jackson is a preservation architect in Springfield, Illinois. He is currently

the co-chair of the Codes and Standards Technical Committee of the Association for Preservation

Technology and coordinator of a committee task force on Building Codes on Main Street.

412 Industrial Revolution

by Kevin A. Park and Joshua J. Miller

Policy Briefs

The Policy Briefs department summarizes a change or trend in national policy that may have

escaped the attention of researchers. The purpose is to stimulate the analysis of policy in the

field while the policy is being implemented and thereafter. If you have an idea for future Policy

Briefs, please contact david.l.hardiman@hud.gov.

Mortgage Risk and Disparate Impact

Associated With Student Debt

Kevin A. Park

Joshua J. Miller

U.S. Department of Housing and Urban Development

The views and opinions expressed in this article are those of the authors and do not necessarily reflect

the official policy or position of any agency of the U.S. Government. This work was authored as part

of the authors’ official duties as employees of the U.S. Government and is therefore a work of the U.S.

Government. In accordance with 17 USC 105, no copyright protection is available for such works under

U.S. Law. The U.S. Government is granting a nonexclusive license to publish the article.

Abstract

Student debt payments represent a barrier to homeownership because student loan debt increases

the difficulty in qualifying for a mortgage and decreases the amount of income available to sustain

homeownership. Yet student loans are different from other types of debt, such as automobile loans and

credit card debt, because student loans represent a direct investment in human capital, and higher

educational attainment is associated with higher lifetime earnings. To explore the effect of student loan

debt in mortgage performance, the authors disaggregate the back-end debt-to-income ratio commonly

used in mortgage underwriting into payments on mortgage, student debt, and other debt. The authors

find that the presence of student debt is associated with a lower risk of mortgage default, all else equal.

However, while disaggregating debt ratios improves the ability to assess default risk and could expand

overall access to credit, it also increases the disparate impact on most non-White borrowers.

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 413

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Park and Miller

Introduction

Federal student loan debt is owed by over 43 million borrowers with an average outstanding

balance of over $37,000 (Hanson, 2022). Many borrowers view student loan debt as a significant

barrier to major lifetime milestones, including homeownership.

Several researchers have examined the relationship between student loan debt and

homeownership; however, no researchers to the authors’ knowledge have examined the effect of

student loan debt on mortgage performance. Understanding the relationship between student loan

debt and timely mortgage payments is important because student debt payments affect the “back-

end” debt-to-income (DTI) ratio, a common risk factor in mortgage underwriting. The DTI ratio is

the sum of all required monthly debt payments as a share of the borrower’s income but does not

distinguish between types of nonmortgage debt. Payments on student loans are traditionally treated

the same as consumer debt in mortgage underwriting.

However, student debt may instead be considered an investment in human capital. Graduates earn

significantly more than workers that did not attend college. On the other hand, the net wealth

premium associated with higher education has declined, possibly due to the increasing cost of

college being financed with rising student debt (Emmons, Kent, and Rickets, 2019). To the extent

that student debt also hinders qualifying for a mortgage, it also indirectly limits borrowers from the

wealth-building potential of homeownership (Stegman, Quercia, and Davis, 2007).

To explore the effect of student loan debt in mortgage performance, the authors disaggregate the

back-end DTI ratio commonly used in mortgage underwriting into payments on mortgage, student,

and other debt. Findings show that student debt is associated with a lower risk of default overall.

This finding is likely because student debt is correlated with higher educational attainment, which

is not observed or used in mortgage underwriting. Obtaining a college or graduate degree increases

the potential income of the borrower. Borrowers with student debt that do not graduate likely

experience the worst outcomes.

However, while disaggregating debt ratios improves the ability to assess default risk and could

expand overall access to credit, it also increases the disparate impact on most non-White

borrowers. As with the debate over the progressiveness of student debt forgiveness,1 the disparate

impact of student debt in mortgage underwriting is complicated. Black borrowers are more likely

to have student debt than White borrowers, but Hispanic and other minority borrowers are less

likely. Therefore, discounting student debt in underwriting increases the likelihood of approval for

Black and White borrowers but not Hispanic and others relative to the baseline of only using the

overall DTI ratio.

Literature Review

The literature on student loan debt and economic outcomes is broad. The presence and

accumulation of student loan debt is shown to affect numerous milestones and economic

1 For example, Looney (2022) argues student debt forgiveness is regressive, whereas Perry, Steinbaum, and Romer (2021) argue it is not. See also Leonhardt (2018).

414 Policy Briefs

Mortgage Risk and Disparate Impact Associated With Student Debt

outcomes. Studies have found, for example, that student loan debt is associated with delayed

marriage (Bozick and Estacion, 2014; Gicheva, 2011; Stone, Van Horn, and Zukin, 2012) and

childrearing (Nau, Dwyer, and Hodson, 2015; Sieg and Wang, 2017).

Another relevant strand of the literature looks at the relationship between student loan debt

and the financial health of borrowers post-schooling, such as repayment difficulties. Using the

2007–2009 Survey of Consumer Finances, Elliott and Nam (2013) find lower net worth for those

with high student loan debt, and Thompson and Bricker (2014) find families with student loans

more likely to be 60 or more days late paying bills. In addition, the research consistently finds high

student loan debt is not a strong predictor of repayment difficulties (Baum and Johnson, 2016;

Dynarski and Kreisman, 2013). Instead, high student loan debt is associated with higher levels of

degree attainment and completion. A recent study from Baum and Looney (2020) found that those

with professional and doctorate degrees, only 3 percent of the population sampled, held 20 percent

of the outstanding student loan debt.

In the context of homeownership, the relationship between student loan debt and homeownership

is also well examined. However, the findings are mixed between studies finding no relationship

between student loan debt and homeownership (Velez, Cominole, and Bentz, 2019; Zhang,

2013), and others finding a negative relationship between student loan debt and homeownership

(Bleemer et al., 2017; Mezza et al., 2016; Miller and Nikaj, 2018). The conflicting results are likely

explained by two factors. First, student loan debt is not randomly assigned, and selection into

student loan debt and homeownership are correlated. Studies have addressed this concern through

instrumental variables (Houle and Berger, 2015; Mezza et al., 2020; Velez, Cominole, and Bentz,

2019). The second concern is omitted variables. Dynarski (2016) and Miller and Nikaj (2018) find

degree completion to be an important consideration.

The literature on student loan debt is extensive. Prior studies find a direct relationship between

student loan debt and adult milestones such as marriage, childrearing, and homeownership.

Although the student loan literature is informative, this article may be the first to examine the

relationship between student loan debt and mortgage performance.

Data

To conduct the analysis, the authors obtained information on borrower characteristics and loan

performance from the National Mortgage Database (NMDB). The NMDB program is administered

by the Federal Housing Finance Agency (FHFA) and combines credit attributes and performance

data from a 1-in-20 sample of residential first lien mortgages from one of the three primary credit

bureaus with administrative records and information from the Home Mortgage Disclosure Act.

The authors examined owner-occupied home purchase mortgages originated between 2014

and 2018 and observed performance through 2019, ending before the COVID-19 pandemic.

Observations are restricted to loans with at least one borrower with a credit score and where

the reported back-end DTI ratio used in underwriting is greater than or equal to the sum of the

mortgage and any student debt payments reported to the credit bureau. Borrowers with student

debt are defined as any nonzero student debt balances when the mortgage was originated. The

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Park and Miller

student debt payment is defined as the median nonzero student debt payment between the two

quarters prior to and after origination for those with a nonzero student debt balance at origination.

These parameters result in a sample of roughly 800,000 loan borrowers, of whom 29 percent had

student debt at the time of origination. Exhibit 1 provides descriptive statistics on the loans and

borrowers. Non-Hispanic White borrowers account for nearly three-fourths of all borrowers. Exhibit

2 shows Black borrowers are more likely to have student debt than White borrowers, but other

minority groups are less likely. This pattern among mortgage borrowers by race and ethnicity reflects

a similar distribution of debt among recent graduates. For example, among 2015–16 bachelor’s

degree recipients, 86.3 percent of Black graduates still owed on federal student loan debt 12 months

after completion—compared to 70.1 percent of Hispanic graduates, 67.7 percent of White graduates,

and 43.9 percent of Asian graduates (National Center for Education Statistics, 2021).

Exhibit 1

Descriptive Statistics

No Student Debt

With Student Debt

Description

Non-Hispanic

Non-Hispanic

All

All Other

All Other

White

White

Share of Loans

52.4

18.3

22.2

7.1

100.0

35.1

38.4

37.5

40.8

36.7

DTI Ratio (%)

(10.1)

(9.8)

(9.1)

(9.0)

(9.9)

21.4

25.6

20.5

23.5

22.1

Front-End

(9.3)

(9.9)

(7.8)

(8.5)

(9.2)

3.7

3.3

1.0

Student

(3.4)

(3.3)

(2.4)

734

718

719

699

725

Credit Score

(66)

(66)

(59)

(60)

(65)

82.3

85.5

89.3

91.2

85.1

CLTV Ratio (%)

(19.5)

(17.0)

(14.2)

(12.8)

(17.9)

ARM (%)

19.7

20.4

16.3

14.1

18.8

Term≤15 (%)

31.7

26.8

22.0

17.7

28.2

Default Rate (%)

12.3

14.4

11.8

16.6

12.9

ARM = adjustable-rate mortgage. CLTV = combined loan-to-value. DTI = debt-to-income.

Note: Standard deviation is indicated using parentheses.

Source: National Mortgage Database

416 Policy Briefs

Mortgage Risk and Disparate Impact Associated With Student Debt

Exhibit 2

Share of Mortgage Borrowers With Student Debt by Race/Ethnicity

50

42.5

40

29.8

30

29.3

26.4

20.3

20

e With Student Debt (%) 10

Shar

0

White

Hispanic

Black

Other/Two+

Total

Race/Ethnicity

Source: National Mortgage Database

The average back-end DTI ratio is nearly 37 percent. Student debt payments account for roughly 3.6

percent of borrower income on average. Borrowers with student debt have higher overall DTI ratios

even though the share of income devoted to mortgage payments is lower, which suggests student

debt is constraining housing consumption. Borrowers with student debt also have lower credit

scores and higher combined loan-to-value (CLTV) ratios than borrowers without student debt.

Methodology

To explore the effect of student loan debt in mortgage performance, the authors define default as

the first instance of a 90-day delinquency in the mortgage tradeline and utilize a Cox proportional

hazard model. The Cox proportional hazard model is defined as—

λ (t) = λ0 (t)ef(x)

f(x) = βΩ + γ DTI + δ STD

Where λ0 is an unspecified baseline hazard, and Ω represents a vector of common underwriting

factors at loan origination, including credit score, CLTV ratio, and binary indicators of adjustable

interest rates and loan terms less than or equal to 15 years. STD represents a binary indicator of a

nonzero student debt balance in the quarter in which the mortgage was originated. DTI represents

various formulations of the debt-to-income ratio. In addition to the commonly used overall back-

end DTI ratio, the authors also include separate ratios for mortgage principal, interest, tax, and

insurance payments (payment-to-income [PTI] ratio) and student debt payments relative to income

(STDTI ratio). The remaining back-end DTI ratio excludes these subcomponents when they are

included directly as separate explanatory variables.

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Park and Miller

The linear combination of observation values and the estimated coefficients from these

specifications are then used to create risk scores that can be evaluated for predictiveness of default

and disparate impact with respect to race and ethnicity. The authors use Kolmogorov-Smirnov (KS)

statistics to summarize both impacts. The statistic is computed as the maximum difference in the

empirical distribution functions of two subpopulations, F 1 and F 2, based on the linear combination of borrower characteristics and estimated coefficients.

For evaluating the predictiveness of difference specifications, F 1 is the empirical distribution of

mortgages that did not default within 24 months of origination, and F 2 is the distribution of loans

that defaulted. The maximum difference is referred to as the Risk KS statistic. For evaluating disparate

impact, F 1 is the empirical distribution of non-Hispanic White borrowers, and F 2 is the distribution of other racial or ethnic groups. The maximum difference is referred to as the Race KS statistic.

Findings

Exhibit 3 shows the cumulative default hazard by whether the borrowers have any student debt.

Overall, student debt is associated with a slightly higher cumulative default hazard.

Exhibit 3

Cumulative Default Hazard

Source: National Mortgage Database

418 Policy Briefs

Mortgage Risk and Disparate Impact Associated With Student Debt

Hazard Model

Exhibit 4 presents select results of the Cox proportional hazard model of default related to student

debt and DTI ratio. As expected, higher credit scores and shorter loan terms are associated with

lower default risk, whereas higher CLTV ratios are associated with greater risk. Controlling for

these risks, a 1-percentage-point increase in the back-end DTI ratio is associated with roughly a

2-percent increase in the likelihood of default.

Exhibit 4

Cox Proportional Hazard Model

(1)

(2)

(3)

(4)

(5)

(6)

Any Student

0.8974***

0.9533**

1.1240***

Debt

(0.0134)

(0.0145)

(0.0225)

1.0211***

1.0216***

1.0124***

1.0129***

1.0143***

1.0139***

DTI Ratio†

(0.0008)

(0.0008)

(0.0009)

(0.0009)

(0.0009)

(0.0009)

1.0319***

1.0318***

1.0318***

1.0320***

Front-End

(0.0009)

(0.0009)

(0.0009)

(0.0009)

0.9769***

0.9618***

Student

(0.0032)

(0.0044)

0.9840***

0.9840***

0.9839***

0.9839***

0.9840***

0.9840***

Credit Score

(0.0001)

(0.0001)

(0.0001)

(0.0001)

(0.0001)

(0.0001)

1.0112***

1.0115***

1.0105***

1.0106***

1.0108***

1.0106***

CLTV Ratio

(0.0005)

(0.0005)

(0.0006)

(0.0006)

(0.0006)

(0.0006)

0.7864***

0.7817***

0.8530***

0.8489***

0.8419***

0.8474***

Term≤15

(0.0244)

(0.0242)

(0.0264)

(0.0263)

(0.0260)

(0.0262)

0.5486***

0.5452***

0.5630***

0.5612***

0.5567***

0.5585***

ARM

(0.0382)

(0.0380)

(0.0392)

(0.0391)

(0.0388)

(0.0389)

AIC

14127671

14126530

14117171

14116959

14113836

14113056

²

30492***

30639***

32274***

32289***

32630***

32759***

AIC = Akaike information criterion. ARM = adjustable-rate mortgage. CLTV = combined loan-to-value. DTI = debt-to-income.

* Statistically significant at the 0.050 level. ** Statistically significant at the 0.010 level. *** Statistically significant at the 0.001 level.

† = back-end DTI ratio excluding components directly included.

Note: Standard errors are shown in parentheses.

Source: National Mortgage Database

The second column includes a binary indicator of whether the borrower has any student debt

at the time of origination. The estimated hazard ratio indicates borrowers with student debt are

associated with a 10-percent reduction in the likelihood of default, all else equal.

The third and fourth columns disaggregate the back-end DTI ratio into the front-end DTI ratio (PTI

ratio) and the remainder; this reveals that a 1-percentage-point increase in the share of income

devoted to the mortgage payment increases the likelihood of default more than a 1-percentage-point

increase in share of income devoted to other forms of debt. Having student debt is still associated

with a small but statistically significant reduction in the likelihood of default (fourth column).

Cityscape 419

Park and Miller

The fifth and sixth columns further disaggregate the back-end DTI ratio into the front-end ratio,

the STDTI ratio, and the remainder. A higher share of income devoted to student debt payments is

associated with a decrease in the likelihood of default.

NMDB provides the overall DTI ratio as reported in the administrative data. The PTI ratio is the

escrow payment reported by the credit bureau with some imputation by FHFA. As a robustness

check, the authors replace these ratios with the median overall debt and escrow payments only as

reported by the credit bureau data, comparable to how student debt payments are computed. The

results shown in appendix A are substantively similar.

Kolmogorov-Smirnov Statistics

The linear combination of borrower characteristics and estimated coefficients presented in exhibit

4 can be converted into measures of predicted risk. Exhibit 5A plots the cumulative distribution of

loans that defaulted within 24 months and all other loans by the risk score derived from the first

specification. Borrowers that defaulted generally have higher risk scores than borrowers that did

not. The maximum difference between the two cumulative distributions (Risk KS statistic) is 55.1

percentage points.

Exhibit 5B is a similar chart showing the cumulative distributions for non-Hispanic White

borrowers and borrowers of all other races and ethnicities. Based on their risk factors, White

borrowers have lower average levels of predicted risk. The maximum difference between the two

cumulative distributions (Race KS statistic) is 13.0 percentage points.

Exhibit 5

Cumulative Distributions and Kolmogorov-Smirnov Statistics (1 of 2)

A. By 24-Month Default

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Mortgage Risk and Disparate Impact Associated With Student Debt

Exhibit 5

Cumulative Distributions and Kolmogorov-Smirnov Statistics (2 of 2)

B. By Race/Ethnicity

Source: National Mortgage Database

Risk and Race KS statistics are found for scores derived from each of the six specifications shown

in exhibit 4. In addition, seventh and eighth scores are computed using the fifth and sixth

specifications, respectively, except excluding components related to student debt (that is, any

student debt indicator and student debt payment to income ratio). These scores represent scenarios

in which student debt is not included in DTI ratio calculations at all. The overall results are

reported in the first two columns of exhibit 6 and displayed in exhibit 7A with the Risk KS on the

x-axis and the Race KS on the y-axis. Disaggregating back-end DTI ratio into mortgage payments,

student debt payments, and other debt payments improves the predictiveness of the derived risk

score, exhibited by higher Risk KS statistics. However, the improvement in predictiveness comes

with greater disparate impact, exhibited by higher Race KS statistics. Disaggregating DTI ratio but

excluding student debt from the risk score (that is, not rewarding borrowers spending a high share

of income on student debt) actually reduces its predictiveness.

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Park and Miller

Exhibit 6

Risk and Race Kolmogorov-Smirnov Statistics

Non-Hispanic

All Others

Hispanic

Black

White Versus

(1)

(2)

(3)

(4)

(5)

(6)

Score

Risk

Race

Risk

Race

Risk

Race

1

55.10

13.00

54.75

20.32

55.97

30.86

2

55.26

13.24

54.93

20.62

56.07

30.70

3

55.37

14.26

54.77

21.62

56.16

31.43

4

55.45

14.34

54.83

21.74

56.20

31.29

5

55.60

14.61

55.11

22.13

56.51

31.17

6

55.50

14.55

54.96

22.06

56.45

31.28

7

55.51

14.38

54.92

21.80

56.32

31.35

8

55.49

14.39

54.92

21.83

56.31

31.32

Notes: Scores 1–6 are based on the specifications shown in exhibit 4. Scores 7 and 8 are based on the fifth and sixth specifications, respectively, but do not include components related to student debt.

Source: National Mortgage Database

Exhibit 7

Risk and Race Kolmogorov-Smirnov (1 of 2)

A. Non-Hispanic White Versus All Others

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Mortgage Risk and Disparate Impact Associated With Student Debt

Exhibit 7

Risk and Race Kolmogorov-Smirnov (2 of 2)

B. Non-Hispanic White Versus Hispanic (Any Race)

C. Non-Hispanic White Versus Non-Hispanic Black

KS = Kolmogorov-Smirnov statistic.

Source: National Mortgage Database

Exhibit 7B and the third and fourth columns of exhibit 6 show the KS statistics when comparing

non-Hispanic White and Hispanic borrowers only. The results are similar (disaggregation improves

predictiveness but worsens disparate impact), and the Race KS statistics are all notably higher.

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Park and Miller

Exhibit 7C and the fifth and sixth columns of exhibit 6 show similar statistics comparing non-

Hispanic White and Black borrowers only. The Race KS statistics are even higher; however,

disaggregating student debt from the nonhousing DTI ratio reduces disparate impact on Black

borrowers relative to White borrowers.

Approval Rates

Measuring the differences in score distributions by race does not account for how a more predictive

underwriting model allows a lender to approve more borrowers. Because the marginal borrower is

more likely to be a minority borrower, this extensive margin may help offset any disparate impact.

Exhibit 8A shows the share of loans that could be approved while keeping the cumulative average

predicted 24-month default rate at 1 percent or less. Exhibit 8B shows the change in number of

approvals relative to the first specification. As expected, a more predictive model allows more

borrowers to be approved while maintaining the same overall level of risk. However, the effects are

heterogeneous: more White and Black borrowers are approved when the DTI ratio is disaggregated

but fewer Hispanic and Other borrowers are approved. This pattern mirrors the differences in the

share of borrowers with student debt.

Exhibit 8

Approval

Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

A. Approval Rate (%)

White

92.4

92.4

92.7

92.7

92.8

92.8

92.7

92.7

Hispanic

86.7

86.6

86.1

86.1

86.0

86.0

86.1

86.1

Black

78.5

78.9

78.4

78.6

79.0

78.8

78.6

78.6

Other/Two+

95.3

95.4

95.2

95.2

95.2

95.2

95.2

95.2

Total

91.1

91.2

91.3

91.3

91.4

91.4

91.3

91.3

B. Change in Approvals (%) Relative to (1)

White

0.06

0.35

0.36

0.43

0.43

0.39

0.39

Hispanic

– 0.11

– 0.65

– 0.68

– 0.76

– 0.71

– 0.68

– 0.68

Black

0.55

– 0.11

0.16

0.74

0.41

0.14

0.17

Other/Two+

0.04

– 0.16

– 0.13

– 0.14

– 0.14

– 0.14

– 0.13

Total

0.06

0.17

0.19

0.26

0.25

0.21

0.21

Note: Approval rates assuming overall cumulative average predicted 24-month default rate of 1 percent or less.

Source: National Mortgage Database

Conclusions

Student loan debt is held by a significant number of Americans. Further, researchers have shown

student loan debt to be associated with delays in marriage, childrearing, and homeownership.

Although student loan research is broad, this article may be the first to look at the relationship

between student loan debt and mortgage performance.

424 Policy Briefs

Mortgage Risk and Disparate Impact Associated With Student Debt

Student loan debt is included in most mortgage underwriting. Traditionally, student loan debt is

not differentiated from other types of debt, including auto and credit card, in the underwriting

process. One could reasonably argue that student loan debt is distinct from other types of debt

because it represents an identifiable investment in human capital that is associated positively with

future earnings.

This article analyzes the effect of student debt on mortgage performance using data from NMDB.

The authors find that student loan debt is associated with a lower risk of delinquency. The

results are robust to several specifications of student loans, including separate ratios for mortgage

principal, interest, tax and insurance payments, and student debt payments relative to income.

The finding is consistent with the hypothesis that student debt and obtaining a college or graduate

degree increases the potential income of the borrower. Borrowers with student debt that do not

graduate likely experience worse outcomes.

The authors also look at the disparate impact of student debt in mortgage underwriting, finding

that because of variations in the presence and burden of student loan debt by race and ethnicity,

discounting student loan debt in underwriting would increase the likelihood of approval for Black

and White borrowers but not Hispanic and others relative to the baseline of only using the overall

back-end DTI ratio.

The findings of this article are an important first step in understanding the relationship between

student loan debt and mortgage performance. The results suggest that student loan debt is distinct

from other forms of debt, and mortgage underwriting would benefit from separate treatment.

Policy changes, however, to the traditional treatment of student debt should carefully consider

disparate impact.

Cityscape 425

Park and Miller

Appendix A

Exhibit A-1

Cox Proportional Hazard Model, Credit Bureau Debt-to-Income

Variable

(1)

(2)

(3)

(4)

(5)

(6)

0.8932***

0.9535**

1.1318***

Any Student Debt

(0.0132)

(0.0147)

(0.0227)

1.0152***

1.0156***

1.0076***

1.0080***

1.0098***

1.0096***

DTI Ratio†

(0.0005)

(0.0005)

(0.0006)

(0.0006)

(0.0006)

(0.0006)

1.0266***

1.0263***

1.0257***

1.0261***

Front-End

(0.0008)

(0.0008)

(0.0008)

(0.0008)

0.9703***

0.9546***

Student

(0.0032)

(0.0043)

0.9836***

0.9837***

0.9837***

0.9837***

0.9838***

0.9838***

Credit Score

(0.0001)

(0.0001)

(0.0001)

(0.0001)

(0.0001)

(0.0001)

1.0112***

1.0114***

1.0108***

1.0109***

1.0110***

1.0108***

CLTV Ratio

(0.0006)

(0.0006)

(0.0006)

(0.0006)

(0.0006)

(0.0006)

0.7855***

0.7816***

0.8463***

0.8422***

0.8339***

0.8396***

Term≤15

(0.0243)

(0.0242)

(0.0262)

(0.0261)

(0.0258)

(0.0260)

0.5683***

0.5657***

0.5709***

0.5698***

0.5672***

0.5686***

ARM

(0.0396)

(0.0394)

(0.0398)

(0.0397)

(0.0395)

(0.0396)

AIC

14108182

14106939

14099969

14099764

14096339

14095468

χ²

33004***

33290***

33414***

33558***

34242***

34299***

AIC = Akaike information criterion. ARM = adjustable-rate mortgage. CLTV = combined loan-to-value. DTI = debt-to-income.

* Statistically significant at the 0.050 level. ** Statistically significant at the 0.010 level. *** Statistically significant at the 0.001 level.

† = back-end DTI ratio excluding components directly included.

Note: Standard errors shown in parentheses.

Source: National Mortgage Database

Acknowledgments

The authors would like to thank the National Mortgage Database staff at the Federal Housing Finance

Agency, including but not limited to Robert Avery, Ian Keith, Saty Patrabansh, and Jay Schultz.

Authors

Kevin A. Park was an economist in HUD’s Office of Policy Development and Research. Joshua J.

Miller is an economist in HUD’s Office of Fair Housing and Equal Opportunity. He can be reached

at Joshua.J.Miller@hud.gov.

426 Policy Briefs

Mortgage Risk and Disparate Impact Associated With Student Debt

References

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Loan Repayment System. Washington, DC: Urban Institute.

Baum, Sandy, and Adam Looney. 2020. “Who Owes the Most in Student Loans: New Data from the

Fed,” Brookings, October 9.

Bleemer, Zachary, Meta Brown, Donghoon Lee, and Wilbert Van der Klaauw. 2017. Debt, Jobs, or

Housing: What’s Keeping Millennials at Home? Federal Reserve Bank of New York Staff Report No. 700.

Bozick, R., and A. Estacion. 2014. “Do Student Loans Delay Marriage? Debt Repayment and

Family Formation in Young Adulthood,” Demographic Research 30 (69): 1865–1891. doi:10.4054/

DemRes.2014.30.69.

Dynarski, Susan M. 2016. “The Dividing Line Between Haves and Have-Nots in Home

Ownership: Education, Not Student Debt.” Economic Studies at Brookings Evidence Speaks Reports

1 (17). https://www.brookings.edu/research/the-dividing-line-between-haves-and-have-nots-in-

home-ownership-education-not-student-debt/.

Dynarski, Susan M., and Daniel Kreisman. 2013. Loans for Educational Opportunity: Making

Borrowing Work for Today’s Students. Washington, DC: The Brookings Institution.

Elliott, W., and I. Nam. 2013. “Is Student Debt Jeopardizing the Short-Term Financial Health of

U.S. Households?” Federal Reserve Bank of St. Louis Review 95 (5): 405–424.

Emmons, William R., Ana H. Kent, and Lowell R. Ricketts. 2019. “Is College Still Worth It? The

New Calculus of Falling Returns,” Federal Reserve Bank of St. Louis Review 101 (4): 297–329.

Gicheva, D. 2011. Does the Student-Loan Burden Weigh into the Decision to Start a Family?

Department of Economics Working Paper Series. Greensboro, NC: University of North Carolina

at Greensboro.

Hanson, Melanie. 2022. “Student Loan Debt Statistics,” Education Data Initiative, February 10.

https://educationdata.org/student-loan-debt-statistics.

Houle, Jason N., and Lawrence Berger. 2015. “Is Student Loan Debt Discouraging Homeownership

Among Young Adults?” Social Service Review 89 (4): 589–621.

Leonhardt, David. 2018. “Eliminating All Student Debt Isn’t Progressive,” New York Times,

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Looney, Adam. 2022. Student Loan Forgiveness is Regressive Whether Measured by Income, Education,

or Wealth. Washington, DC: Hutchins Center on Fiscal & Monetary Policy at Brookings.

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by-income-education-or-wealth/.

Cityscape 427

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Homeownership,” Journal of Labor Economics 38 (1): 215–260.

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of Research & Statistics and Monetary Affairs, Federal Reserve Board.

Miller, Joshua J., and Silda Nikaj. 2018. “Student Loan Debt, Educational Attainment, and Tenure

Choice,” Education Economics 26 (4): 393–410.

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Conditions of Education. U.S. Department of Education, Institute of Education Sciences.

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428 Policy Briefs

Referees 2022–2023

The Office of Policy Development and Research gratefully acknowledges the contributions of the

following referees and their assistance in making Cityscape worth reading.

Jill Breysse

Jason Hackworth

Sue Popkin

Jan Brueckner

Justin Hollander

Chris Prener

Ann Carpenter

David Huffer

Amanda Reddy

Nina Castells

Stephanie Ivey

Jason Rivera

Mark Coggeshall

Ryun Jung Lee

Kimberly Skobba

Alexandra Curley

Jenny Liu

Scott Susin

Prentiss Dantzler

Feng Liu

Sarah Szanton

Casey Dawkins

Kirk McClure

Donald Vandegrift

Lan Deng

Jennifer Molinsky

Jennifer Vey

Margaret Dewar

Victoria Morckel

Cheryl Young

Judy Geyer

Dan O’Flaherty

Colin Gordon

Rolf Pendall

Cityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023

Cityscape 429

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Symposium

Reentry Housing After Jail or Prison .................................................................................................................... 1

Guest Editor: Calvin C. Johnson

Guest Editor’s Introduction

Reentry and Housing .........................................................................................................................................3

Addressing Barriers to Housing in Reentry Programs Working to Address a Variety of Needs: A Qualitative

Study of Second Chance Act Grantees by Elizabeth L. Beck, Natasha N. Johnson, Sommer Delgado, Victoria

Helmly, Susan A. McLaren, Alice Prendergast, Leigh Alderman, Lorenzo Almada, Brian Bride, Eric Napierala,

and William J. Sabol ................................................................................................................................................... 13

Rental Assistance and a Fresh Start to Spur Criminal Desistance: Evidence From a Pilot Housing Experiment

by David S. Kirk ...............................................................................................................................................35

Addressing Homelessness Among People With Justice Involvement: Los Angeles County’s Just in Reach Pay

for Success Demonstration Project by Sarah B. Hunter and Stephanie Mercier ....................................................57

Public Housing Eligibility for People with Conviction Histories by Niloufer Taber, Jacqueline Altamirano Marin,

and John Bae ..................................................................................................................................................73

Symposium

Recent Reforms in Zoning .............................................................................................................................91

Guest Editor: Noah M. Kazis

Guest Editor’s Introduction

Learning From Land Use Reforms: Housing Outcomes and Regulatory Change .................................................93

Where Will Accessory Dwelling Units Sprout Up When a State Lets Them Grow? Evidence From California

by Nicholas J. Marantz, Christopher S. Elmendorf, and Youjin B. Kim ................................................................107

California’s Strengthened Housing Element Law: Early Evidence on Higher Housing Targets and Rezoning

by Paavo Monkkonen, Michael Manville, Michael Lens, Aaron Barrall, and Olivia Arena ...................................119

How Can Procedural Reform Support Fair Share Housing Production? Assessing the Effects of California’s

Senate Bill 35 by Moira O’Neill and Ivy Wang .................................................................................................143

Here Come the Tall Skinny Houses: Assessing Single-Family to Townhouse Redevelopment in Houston,

2007–2020 by Jake Wegmann, Aabiya Noman Baqai, Josh Conrad.................................................................171

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes? by Leah Brooks and

Jenny Schuetz ................................................................................................................................................203

Learning from Land Use Reforms: The Case of Ramapo, New York by Joseph Weil Huennekens .......................225

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate by Jacob Krimmel

and Betty Wang .............................................................................................................................................257

Refereed Papers ........................................................................................................................................ 279

Seeing the Big Picture with Multisector Data: Factors Associated with Exiting from Federal Housing Assistance

by Exit Type by Alastair I. Matheson, Danny V. Colombara, Annie Pennucci, Tyler Shannon, Andy Chan,

Megan Suter, and Amy A. Laurent ...................................................................................................................281

Factors Affecting Spillover Impacts of Low-Income Housing Tax Credit Developments: An Analysis of

Los Angeles by Brian Y. An, Andrew Jakabovics, Jing Liu, Anthony W. Orlando, Seva Rodnyansky,

Richard Voith, Sean Zielenbach, and Raphael W. Bostic ....................................................................................309

Departments................................................................................................................................................. 365

Data Shop

A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources

by Daniel Kuhlmann, Jane Rongerude, Lily Wang, and GuanNan Wang ...........................................................367

Veteran and Nonveteran Homelessness Rates: New Estimates by Brent D. Mast ..............................................379

Graphic Detail

Exploring the Relationship Between Child Opportunity and Violent Crime Rates in West Virginia Counties

by Brent D. Mast and Tricia Ruiz ......................................................................................................................387

Exits From HUD Assistance and Moves to Higher Poverty Neighborhoods Following the Camp Fire

by Alexander Din ...........................................................................................................................................395

Industrial Revolution

Hiding in Plain Sight: How Reconsideration of Codes for Existing and Historic Buildings Can Expand

Affordable Housing by Marilyn E. Kaplan and Mike Jackson ............................................................................403

Policy Briefs

Mortgage Risk and Disparate Impact Associated With Student Debt by Kevin A. Park and Joshua J. Miller ........413

Referees 2022–2023 .................................................................................................................................429

Cityscape

Office of Policy Development and Research

VOLUME 25 NUMBER 2 • 2023

Document Outline

Cityscape

Advisory Board

Contents

Symposium Reentry Housing After Jail or Prison

Guest Editor’s Introduction Reentry and Housing

Introduction

Returning Citizens (formerly incarcerated individuals) and Barriers to Reentry

The Enduring Effect of a Criminal Record

Order of Articles and Summaries

Acknowledgments

Guest Editor

References

Addressing Barriers to Housing in Reentry Programs Working to Address a Variety of Needs: A Qualitative Study of Second Chance Act Grantees

Abstract

Introduction

Conclusion

Acknowledgments

Authors

References

Rental Assistance and a Fresh Start to Spur Criminal Desistance: Evidence From a Pilot Housing Experiment

Abstract

Introduction

Acknowledgments

Author

References

Addressing Homelessness Among People With Justice Involvement: Los Angeles County’s Just in Reach Pay for Success Demonstration Project

Abstract

Introduction

Acknowledgments

Authors

References

Public Housing Eligibility for People with Conviction Histories

Abstract

Introduction

Housing for People with Conviction Histories

Public Housing and People with Conviction Histories

Public Housing in Michigan and Oklahoma

Convictions in Michigan and Oklahoma

Aims of the Present Study

Methods

Lookback Periods

The Type, Timing, and Location of Convictions and Releases

Michigan: State Felony Convictions

Michigan: Accounting for Intra-Year Felony Recidivism

Michigan: Accounting for Inter-Year Felony Recidivism

Michigan: Number of People Returning from Prison and Living in Communities

Oklahoma: State Felony Convictions

County-Level Misdemeanor Convictions

Federal Felony Convictions

People Excluded From Public Housing Due to Federal Mandates

Income Limits

Results

Discussion

Conclusion

Acknowledgements

Authors

References

Symposium Recent Reforms in Zoning

Guest Editor’s Introduction Learning From Land Use Reforms: Housing Outcomes and Regulatory Change

Introduction

Symposium Articles

Symposium Themes

Acknowledgments

Guest Editor

References

Additional Reading

Where Will Accessory Dwelling Units Sprout Up When a State Lets Them Grow? Evidence From California

Abstract

Introduction

Background

Patterns of Accessory Dwelling Unit Permitting in the Bay Area and Southern California

Conclusion

Appendix A

Acknowledgments

Authors

References

California’s Strengthened Housing Element Law: Early Evidence on Higher Housing Targets and Rezoning

Abstract

Introduction

Principal-Agent Problems and California’s Housing Element System

Research Design and Data

Results

Conclusion

Appendix

Acknowledgments

Authors

References

How Can Procedural Reform Support Fair Share Housing Production? Assessing the Effects of California’s Senate Bill 35

Abstract

Introduction

Background

Research Questions and Hypothesis

Methods

Findings

Discussion

Appendix: Additional Tables

Acknowledgments

Authors

References

Here Come the Tall Skinny Houses: Assessing Single-Family to Townhouse Redevelopment in Houston, 2007–2020

Abstract

Introduction

Appendix A

Appendix B

Acknowledgments

Authors

References

Does Housing Growth in Washington, D.C., Reflect Land Use Policy Changes?

Abstract

Introduction

Which Washington, D.C., Neighborhoods Saw the Most Housing Growth?

Which Neighborhoods Saw the Greatest Increase in Housing?

How Did Zoning Change in Washington, D.C., Between 2000 and 2020?

How Was Housing Growth Correlated With Initial Zoning Status and Zoning Changes?

Deeper Look: What Happened in Unusually High-Growth Neighborhoods?

Conclusion and Policy Recommendations

Appendix A. Data and Methodology Notes

Appendix B. Analytical Methods

Appendix C. Information on Neighborhoods With Large Zoning Changes

Acknowledgments

Authors

References

Learning from Land Use Reforms: The Case of Ramapo, New York

Abstract

Introduction

Zoning History

Data and Methods

Land Use Change and Housing Production

Policy Challenges

Learning from Ramapo

Acknowledgments

Author

References

Upzoning With Strings Attached: Evidence From Seattle’s Affordable Housing Mandate

Abstract

Introduction

Appendix

Acknowledgments

Authors

References

Additional Reading

Refereed Papers

Seeing the Big Picture with Multisector Data: Factors Associated with Exiting from Federal Housing Assistance by Exit Type

Abstract

Introduction

Exits from Housing Assistance: What Is Already Known

Methods

Results

Conclusion

Appendix A

Appendix B

Acknowledgments

Authors

References

Factors Affecting Spillover Impacts of Low-Income Housing Tax Credit Developments: An Analysis of Los Angeles

Abstract

Introduction

Appendix A. Los Angeles Low-Income Housing Tax Credit Developer Questions

Appendix B. Full Regression Results

Authors

References

Departments

Data Shop

A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources

Veteran and Nonveteran Homelessness Rates: New Estimates

Graphic Detail

Exploring the Relationship Between Child Opportunity and Violent Crime Rates in West Virginia Counties

Exits From HUD Assistance and Moves to Higher Poverty Neighborhoods Following the Camp Fire

Industrial Revolution

Hiding in Plain Sight: How Reconsideration of Codes for Existing and Historic Buildings Can Expand Affordable Housing

Policy Briefs

Mortgage Risk and Disparate Impact Associated With Student Debt

Referees 2022–2023