A Journal of Policy

Development and Research

The Moving To Work reTrospecTive evaluaTion

voluMe 22, nuMber 3 • 2020

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

The Moving to Work Retrospective Evaluation

Volume 22, Number 3 • 2020

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

The Moving to Work Retrospective Evaluation ............................................................................. 1

Guest Editor: Elizabeth Rudd

Guest Editor’s Introduction

The Moving to Work Retrospective Evaluation .......................................................................... 3

A Picture of Moving to Work Agencies’ Housing Assistance ..................................................... 9

by Martha M. Galvez, Ruth Gourevitch, and Benny Docter

Moving to Work Agencies’ Use of Funding Flexibility ............................................................ 29

by Diane K. Levy, Leiha Edmonds, and David Long

Moving to Work Agencies’ Use of Project-Based Voucher Assistance ..................................... 55

by Martha M. Galvez, Daniel Teles, Alyse D. Oneto, and Matthew Gerken

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice

Voucher Program ....................................................................................................................... 85

by Nina Castells

Designing an Alternative Rent Policy for the Housing Choice Voucher Program ................. 139

by James Riccio

Can Diverse Activities Have a Combined Impact? Examining the Effects of the Moving

to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes ....................... 163

by Mark Treskon, Matthew Gerken, and Martha M. Galvez

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness ..................... 193

by Christina Stacy, Josh Leopold, Daniel Teles, Alyse D. Oneto, Yipeng Su, Matthew Gerken,

and Ruth Gourevitch

A German Perspective on Objectives of the Moving to Work Demonstration, Housing

Provision, and Targeting ......................................................................................................... 219

by Max-Christopher Krapp

Improving Access to Housing Amid Exclusionary Housing Markets: A Latin American

Viewpoint of the Moving to Work Demonstration ................................................................. 229

by Adriana Hurtado-Tarazona

Disparities and Similarities in U.S. and U.K. Rent Subsidy Programs: A British

Perspective on the Moving to Work Demonstration .............................................................. 235

by Becky Tunstall

Cityscape

iii

Contents

Refereed Papers ................................................................................................................... 239

Documenting Racially Restrictive Covenants in 20th Century Philadelphia ....................... 241

by Larry Santucci

Applying Performance Management Tools to Understand and Improve Rapid

Re-Housing Program Outcomes .......................................................................................................269

by Christopher Weare

Departments ......................................................................................................................... 297

Affordable Design

2020 Innovation in Affordable Housing Student Design and Planning Competition:

Camino de Jacobo in Santa Fe, New Mexico .......................................................................... 299

by Jagruti D. Rekhi

Annual HUD Secretary’s Opportunity and Empowerment Award: Ebeid

Neighborhood Promise Initiative, Toledo, Ohio ..................................................................... 311

by Regina C. Gray

Data Shop

Learning More About HUD-Assisted Tenants Through Data Linkage .................................. 317

by Shawn Bucholtz, Emily Molfino, and Quentin Brummet

American Enterprise Institute Housing Market Indicators ................................................... 327

by Tobias Peter and Edward Pinto

Foreign Exchange

Policy Considerations on Housing, Wealth, and Inequality .................................................. 339

by Orsetta Causa and Nicolas Woloszko

Graphic Detail

Using HUD Crosswalk Files to Improve COVID-19 Analysis at the ZIP Code

and Local Level ........................................................................................................................ 365

by Alexander Din and Ron Wilson

The Changing Geography of Spatial Mismatch ...................................................................... 373

by Christina Stacy and Brady Meixell

Urban Greenery and Public Housing ...................................................................................... 379

by Brian Stromberg

Industrial Revolution

Reviving Rammed Earth as a Sustainable Construction Technique ...................................... 387

by Hota Gangarao, David Johnson, Ray Liang, and Mike Blanford

iv Contents

Symposium

The Moving to Work Retrospective Evaluation

Guest Editor: Elizabeth Rudd

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape

1

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

2

The Moving to Work Retrospective Evaluation

Guest Editor’s Introduction

The Moving to Work

Retrospective Evaluation

Elizabeth Rudd

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.

Introduction

This symposium includes seven articles about Moving to Work (MTW), a demonstration with 39

public housing agencies (PHAs) that gives participating agencies enormous flexibility to determine

how to provide low-income housing assistance in pursuit of the demonstration’s three statutory

objectives: (1) to improve cost effectiveness, (2) to promote self-sufficiency of assisted households, and (3) to increase housing choice for low-income families. Six articles report research conducted

by Urban Institute and MDRC for a HUD-sponsored retrospective evaluation of MTW.1 Congress

requested an assessment of the MTW demonstration as a whole, rather than studies of initiatives

at individual agencies. This was difficult to achieve because MTW encourages participating

agencies to exercise local decisionmaking—this means that averages across agencies of outcomes

embody agencies’ divergent goals and circumstances, and thus likely hide more information than

they reveal. Nevertheless, the six retrospective studies reported on in this symposium offer the

first longitudinal analysis and the most comprehensive view to date of MTW agencies, MTW

housing assistance, MTW activities, and the performance of MTW agencies in relation to statutory

objectives. The essay on HUD’s Rent Reform Demonstration also included here explains MTW’s

contribution to a major, groundbreaking study on rent setting in HUD programs.2

1 These reports will be available on the HUD User website, www.huduser.gov.

2 Information on HUD’s Rent Reform Demonstration is available on huduser. gov and at www.mdrc.org/project/rent-

reform-demonstration#overview.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

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

Rudd

The Moving to Work Demonstration 1996 – Present

MTW was authorized as a demonstration in 1996 amidst the Clinton-era initiatives that cut

welfare rolls and put time limits on assistance to needy families.3 Reform of housing assistance

for low-income families was also on the policy agenda of the time. Concerns included the “fast

approaching train wreck” of rising costs,4 the poor quality of public housing and its contribution

to concentrated poverty and racial segregation, the notion that housing assistance disincentivizes

work, and housing authorities’ claims that federal regulations were unnecessarily costly and stifled

innovation. Congress did not attempt wholesale reform of HUD’s programs in the 1990s but

enacted a few different types of reform efforts.

Hope VI (1993) gave housing agencies grants to facilitate the construction of mixed-income

communities and, later, allowed grantees to do mixed-finance projects. The Quality Housing and

Work Responsibility Act (QHWRA) (1998) reformed management of public housing and combined

existing tenant-based assistance programs to create the housing choice voucher program. MTW

(1996) was unique in that it proposed to tackle concerns about HUD’s programs by allowing PHAs,

as stated in the Omnibus Consolidated Rescissions and Appropriations Act of 1996, “the flexibility

to design and test various approaches for providing and administering housing assistance.” The idea

was to try deregulation on a small scale for a short time to see how it worked to

… reduce cost and achieve greater cost effectiveness in federal expenditures; give

incentives to families with children where the head of household is working, seeking

work, or is preparing for work by participating in job training, educational programs,

or programs that assist people to obtain employment and become economically self-

sufficient; and increase housing choices for low-income families.5

MTW has lasted for nearly 25 years, and today there are 39 PHAs with MTW Agreements that

go through 2028. These 39 MTW agencies have a wide scope in how they may provide housing

assistance and use federal funds to pursue MTW’s statutory goals, albeit within constraints that

HUD approves MTW activities, MTW activities must pursue statutory objectives, and MTW

agencies must continue to serve substantially the same number of families as they would absent

the demonstration. MTW agencies design their own rent policies, streamline administrative

requirements, provide short-term and time-limited assistance, build up reserves, and more. Specific

conditions of their MTW Agreements also give these 39 MTW agencies more generous funding

formulas than at traditional PHAs.

3 MTW was included in the Omnibus Consolidated Rescissions and Appropriations Act of 1996 (Public Law 104-134, 110 Stat 1321), dated April 26, 1996. The Personal Responsibility and Work Opportunity Reconciliation Act of 1996 replaced Aid to Families with Dependent Children (AFDC) with Temporary Assistance to Needy Families (TANF), leading to a decline in families receiving assistance from the historic peak of 5.1 million in 1994 to today’s approximately 1 million. For details, see the Congressional Research Service report “The Temporary Assistance for Needy Families (TANF) Block Grant: Responses to Frequently Asked Questions” available at https://fas.org/sgp/crs/

misc/RL32760.pdf.

4 From the Congressional Record—Senate, available at https://www.congress.gov/crec/1998/10/08/CREC-1998-10-08-

pt1-PgS11833-2.pdf, statement by Senator Bond.

5 Section 204(a) of the Omnibus Consolidated Rescissions and Appropriations Act of 1996 (Public Law 104-134, 110

Stat 1321), at 110 Stat. 1321-281, dated April 26, 1996.

4

The Moving to Work Retrospective Evaluation

The Moving to Work Retrospective Evaluation

The Moving to Work Expansion Authorized in 2016

In 2016, Congress told HUD to add 100 agencies to MTW and to require these agencies to

participate in studies of specific policy innovations. Consequently, there will be two groups of

MTW agencies: (1) The current MTW agencies (sometimes labeled “legacy agencies”), and (2) the

100 agencies that Congress mandated in the 2016 Consolidated Appropriations Act that have not

yet joined MTW, usually referred to as “expansion agencies.” The expansion agencies will include

many more small agencies than the legacy group, will have funding formulas similar to those

of traditional PHAs, and for the first few years in MTW, each expansion agency will be part of a

cohort devoted to studying one type of policy innovation.6

The studies included in this symposium are about the 39 current MTW agencies that joined the

demonstration before 2016 and have agreements that go through 2028.

Six Moving to Work Retrospective Studies and One Essay on

the Rent Reform Demonstration

A Picture of Moving to Work Agencies’ Housing Assistance, by Martha M. Galvez, Ruth Gourevitch, and Benny Docter, describes MTW agencies and the housing assistance they provided from 2008

– 2016 in comparison to traditional PHAs. This article’s exhibit 1 displays the names of the MTW

agencies and when they entered the demonstration. One topic covered is that MTW agencies,

over time, are taking up a greater proportion of total Housing Choice Voucher and Public Housing

program funding. This is likely due partly to differences between the legacy MTW agencies’ and

traditional PHAs’ funding formulas, a situation that will not be replicated for the MTW expansion

agencies, and the fact that legacy MTW agencies include some of the largest PHAs in the country.

How MTW agencies do things differently than traditional PHAs in the areas of fund flexibility,

project-based vouchers, and rent setting is covered in four separate articles. “Fund fungibility,” or fund flexibility, is a basic component of MTW that allows MTW agencies to use funds appropriated

for the Housing Choice Voucher and Public Housing programs for any allowable use under either

program, as well as for local, non-traditional activities.7 In Moving to Work Agencies’ Use of Funding Flexibility, Diane K. Levy, Leiha Edmonds, and David Long explain what fund fungibility is and how MTW agencies use it to pursue the MTW statutory objectives of cost effectiveness, self-sufficiency, and housing choice.

Attaching voucher funds to specific units through long-term contracts (also known as “project-

basing”) seems like an oxymoron—isn’t the whole point of the Housing Choice Voucher program

to allow assisted households to choose their own homes? Yet, as tenant-based housing assistance

increased as a share of HUD’s rental assistance, Congress enhanced opportunities for PHAs to use

6 Funding for expansion MTWs will generally be calculated as follows: Public Housing will be funded using the same formulas as traditional PHAs; HCV funding will be calculated based on expenditures, similar to traditional PHAs.

Please refer to the MTW Operations Notice for a complete description of the funding calculation for MTW expansion PHAs. See www.hud.gov/mtw for details on all aspects of MTW and the MTW expansion.

7 These activities are described in PIH Notice 2011-45, “Parameters for Local, Non-Traditional Activities Under the Moving to Work Demonstration Program.”

Cityscape

5

Rudd

project-based vouchers (PBVs). Traditional PHAs may only project base a small portion of their

voucher funding, but MTW agencies may, with HUD approval, attach up to 100 percent of their

voucher funding to specific, hard units.8 In Moving to Work Agencies’ Use of Project-Based Voucher Assistance, Martha M. Galvez, Daniel Teles, Alyse D. Oneto, and Matthew Gerken examine the

extent of, locations of, and factors associated with project-basing of voucher funding, including

the placement of PBV-subsidized units in Low-Income Housing Tax Credit and Rental Assistance

Demonstration projects.

How to set rents is at the heart of HUD housing assistance programs. In 1969, Senator Edward

Brooke got an amendment to the U.S. Housing Act passed that limited rent in federally assisted

housing to 25 percent of a family’s income; later, this increased to 30 percent. Before the Brooke

amendment, rents in public housing were set to cover the full operating costs of public housing

developments, and rents were often unaffordable for the lowest-income families. Pegging tenant

rent contributions to income helps keep rent affordable, but critics argue that it disincentivizes

work because every dollar earned by a tenant is really only 70 cents. The notion that income-based

rents discourage tenants from earning more money is investigated in Nina Castells’s study of Santa

Clara County Housing Authority’s (SCCHA) rent reform, which raised the tenant contribution from

30 to 32 percent of income. In The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program, Castells reports findings of her quasi-experimental analysis of the impact of SCCHA’s rent reform on employment, earnings, and housing subsidy.

HUD’s Rent Reform Demonstration tries the opposite of SCCHA’s approach. In Designing an

Alternative Rent Policy for the Housing Choice Voucher Program, James Riccio describes how HUD

and other stakeholders of HUD’s Rent Reform Demonstration decided to test a policy that could

incentivize earnings by keeping the tenant contribution the same for 3 years, irrespective of

fluctuations in income. Families that increase their income during the 3 years do not put 30

cents of every added dollar toward rent, and families that earn less do not get a break on the rent.

Tenants whose incomes drop too much, however, could suffer increased material hardship or

eviction. Riccio’s essay details the trade-offs weighed to produce a policy for HUD’s Rent Reform

Demonstration that could incentivize work yet maintain the social safety net function of HUD

housing assistance.

HUD’s Rent Reform Demonstration and Castells’s study of SCCHA’s rent policy contribute to a

growing body of research on rent setting in HUD programs. The MTW expansion will be used to

study other rent policy approaches such as tiered and stepped rents.9

Assessing the performance of the demonstration in relation to MTW’s statutory objectives of

cost effectiveness, self-sufficiency, and housing choice is the focus of two studies included in this symposium. Can Diverse Activities Have a Combined Impact? Examining the Effects of the Moving to

Work Demonstration on Housing Choice and Self-Sufficiency Outcomes by Mark Treskon, Matthew

Gerken, and Martha M. Galvez uses comparative interrupted time series models to see if groups

8 Congress requires PHAs to offer assisted households the opportunity to switch to a tenant-based voucher after a year of project-based assistance. MTW agencies may waive or modify that requirement.

9 See PIH Notice 2019-04 “Request for Letters of Interest under the Moving to Work Demonstration Program for Fiscal Year 2019: Cohort #2 – Rent Reform.”

6

The Moving to Work Retrospective Evaluation

The Moving to Work Retrospective Evaluation

of MTW agencies performed better than matched groups of traditional agencies in terms of

selected indicators. The authors measured average outcomes for selected groups of MTW and

traditional agencies, but conclude by emphasizing the mismatch between the goals of MTW—to

foster innovation responsive to local conditions—and looking at average outcomes for a group

of agencies. This study, and the studies reported in A Picture of Moving to Work Agencies’ Housing Assistance and Fund More, Serve More, Save More: MTW and Cost Effectiveness, finds that MTW

agencies added households more rapidly than matched traditional agencies. This fact must be

interpreted in light of the more generous funding received by the 39 legacy MTW agencies.

The cost-effectiveness of MTW agencies has been assessed before by comparing MTW to traditional

agencies at one point in time.10 The study reported in Fund More, Serve More, Save More: MTW

and Cost Effectiveness is different. It uses panel data spanning 2003 to 2017 and tests whether joining the MTW demonstration is associated with a change in the number of households served,

the amount of HUD funding, and the number of households served per HUD dollar received.

It concludes that MTW agencies are finding cost efficiencies, but the study cannot identify how.

As of this writing, this study is by far the most comprehensive and rigorous assessment of cost

effectiveness at MTW agencies.

The MTW retrospective evaluation studies form a foundation for future research designed to find

out how MTW agencies achieve cost efficiencies and use reserves and to learn from innovations

at individual MTW agencies. The retrospective studies also underscore, highlight, and starkly

emphasize the methodological challenges to the evaluation of MTW, which, in turn, should be a

warning to HUD: HUD needs data that support measures of key variables that are consistent over

time and across agencies. A consistent measure of rent burden, for instance, depends on measures

of income, but MTW agencies may change the definitions of adjusted income and total annual

income. Measures of turnover in the Housing Choice Voucher program depend on recertification

schedules, but MTW agencies may change the recertification schedule. As PHAs gain more freedom

from federal regulation, how will HUD keep track of policies that vary across hundreds of PHAs,

and how will those who monitor and evaluate HUD programs be able to determine which policies

apply to which households?

International Perspectives from Colombia, Germany,

and England

Three scholars of housing policy, including housing assistance for low-income households, offer

perspectives from very different contexts on the MTW retrospective studies.

10 Reports from the study by Abt Associates include Buron, Larry, Melissa Vandawalker, Tyler Morrill, Jill Khadduri, Jeffrey Lubell, and Azim Shivji. 2017. Testing Performance Measures for the MTW Program. Abt Associates; and Khadduri, Jill, Melissa Vandawalker, Rebecca Cohen, and Jeffrey Lubell. 2014. Innovations in the Moving to Work Demonstration. Abt Associates. The Government Accountability Office’s recent report on MTW is “Rental Housing: Improvements Needed to Better Monitor the Moving to Work Demonstration, Including Effects on Tenants.”

GAO-18-150. January 2018.

Cityscape

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Rudd

Adriana Hurtado-Tarazona, assistant professor at the University of the Andes, has studied urban

inequality, informality, and social housing in Colombia and Italy. Ciudad Verde, a “social housing

megaproject” in Soacha, a suburb of Bogota, Colombia, was the subject of her doctoral dissertation.

Max-Christopher Krapp works for the Institute for Housing and Environment in Darmstadt,

Germany, and focuses on European housing policies and housing security. He recently co-authored

a discussion of challenges for housing policy posed by SARS-CoV-2.

Becky Tunstall is Professor Emerita of Housing at the University of York. Her new book, The Fall

and Rise of Social Housing: 100 Years on 20 Estates, explains the transformation of crime-ridden and neglected social housing developments into successful mixed-income neighborhoods.

Guest Editor

Elizabeth Rudd, Ph.D., is a social science analyst in HUD’s Office of Policy Development and

Research. She served as the government technical representative for the HUD grant under which

Urban Institute and MDRC conducted the MTW retrospective evaluation.

Acknowledgments

So many colleagues provided indispensable assistance to me and to the research team for the

MTW evaluation, especially with understanding the MTW demonstration and HUD’s data. They

know so much, shared their expertise, and were always patient, helpful, and good-humored. I

cannot thank them enough. I also thank the several readers who helpfully criticized symposium

article drafts.

8

The Moving to Work Retrospective Evaluation

by Martha M. Galvez, Ruth Gourevitch, and Benny Docter

A Picture of Moving to Work

Agencies’ Housing Assistance

Martha M. Galvez

Ruth Gourevitch

Benny Docter

Urban Institute

Abstract

This article describes the 39 public housing authorities with Moving to Work (MTW) designation as of 2016

and the households they serve. Together, the MTW agencies served 12 percent of all households assisted by public housing agencies (PHAs) in that year. MTW agencies tend to be larger than traditional PHAs and in more densely populated urban housing markets. Compared with comparably sized traditional PHAs, MTW agencies provide a similar mix of housing assistance, serve similar populations, and assist households in neighborhoods with similar levels of poverty. MTW agencies provide more project-based housing choice voucher (HCV) assistance compared with traditional PHAs and added new households to their assistance portfolios between 2008 and 2016, whereas the traditional agencies did not. The MTW agencies also received increased funding over the 2008–2016 period, whereas traditional agencies did not.

The Moving to Work Demonstration and Moving to

Work Agencies

The Moving to Work (MTW) demonstration allows a small group of public housing agencies

(PHAs) to design and test innovative strategies for providing housing assistance when those

strategies are intended to achieve the demonstration’s statutory objectives of cost-effectiveness,

self-sufficiency, and housing choice.1 Congress enacted the demonstration in 1996. As of 2020, 39

PHAs participate in the MTW demonstration.

In this article, the authors describe MTW agencies, the types of housing assistance they provided,

and the households the agencies served as of 2016. This article contains the first detailed

analysis to be conducted of MTW agencies since the demonstration was launched. It uses

1 Omnibus Consolidated Rescissions and Appropriations Act of 1996. Pub. L. No. 104-134, tit. II, § 204, 110 Stat.

1321, 1321-281 (codified as amended at 42 U.S.C. § 1437f note).

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

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

Galvez, Gourevitch, and Docter

HUD administrative data spanning 2008 to 2016—including Public and Indian Housing (PIH)

Information Center (PIC) data, Voucher Management System (VMS) data, U.S. Census Bureau

data, and HUD-provided counts of housing assistance unique to MTW agencies—to contrast the

housing assistance provided by MTW agencies to that of comparably sized traditional PHAs.

The authors worked closely with HUD throughout the analysis process to understand data quality

and coverage and variations in reporting for MTW agencies compared with traditional PHAs. This

article is based on the full-length report A Picture of Moving to Work Agencies’ Housing Assistance (Galvez, Gourevitch, and Docter, forthcoming), which details the data sources used for this study. This article is complemented by an online feature that presents MTW agency-level information for selected measures

of housing assistance for each of the 39 agencies and provides access to the data used in this report.2

Moving to Work Agencies

The first three MTW agencies executed contracts with HUD in 1998, and the four newest MTW

agencies executed contracts in 2013 (see exhibit 1). One current MTW agency, the San Diego

Housing Authority, exited the demonstration in 2003 and reentered in 2008. Two PHAs, the

Greene Metropolitan Housing Authority and the High Point Housing Authority, entered and left

the demonstration before 2008 and are excluded from this study.

Exhibit 1

Timeline of Moving to Work Agreements

1999

2004

Cambridge

Oakland

Delaware

2008

Greene

Alaska

Greene and

High Point

Baltimore

High Point

Keene

San Bernadino

left

Lawrence-Douglas County

San Jose

demonstration

Lincoln

Santa Clara County

Louisville

San Diego (reentered)

Massachusetts

2001

Portage

New Haven

Philadelphia

2010

Portland

Champaign County

San Antonio

Tacoma

2013

Tulare County

2003

Columbus

Vancouver

Atlanta

Fairfax

Washington, DC

2011

Holyoke

1998

2000

King County

Boulder

Reno

Minneapolis

Chicago

Lexington-

San Diego

Pittsburgh

San Diego left

2007

Fayette

Seattle

San Mateo

demonstration

Charlotte

Orlando

Note: Year shown is the year Moving to Work (MTW) contracts were executed, which may differ from the year agencies were selected for MTW.

Sources: Documents retrieved from HUD’s MTW portal, hud.gov/mtw

As a group, the 39 MTW agencies differ from traditional PHAs in several ways. First, most

PHAs nationally are in metropolitan areas, but MTW agencies tend to be in densely populated

cities as opposed to suburban or exurban areas. Of the 39 MTW agencies, 24 are in the

2 The online feature and data are forthcoming at www.urban.org/policy-centers/metropolitan-housing-and-communities-policy-center.

10 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

principal city of a Metropolitan Statistical Area (MSA).3 MTW agencies also tend to be in

stronger housing markets compared with traditional PHAs, as measured by lower average

vacancy rates and higher housing costs (see exhibit 2).

In addition, MTW agencies tend to be larger than the typical PHA. None of the MTW agencies

served fewer than 750 households in 2016 (the smallest MTW agency is Keene Housing, in

Keene, New Hampshire, which served approximately 780 households that year). In contrast,

approximately 80 percent of all traditional PHAs serve fewer than 750 households. Smaller

traditional PHAs may face different constraints and costs than larger agencies. For example,

Turnham et al. (2015) found that smaller PHAs (with fewer than 500 vouchers) had substantially

higher administrative costs per household than did larger PHAs.

Finally, MTW agencies are more likely than traditional PHAs to operate both public housing and

HCV programs (see exhibit 3 for an overview of the housing assistance programs that MTW agencies

administer). As of 2016, only 7 percent of MTW agencies provided only one of the two main housing

assistance programs, compared with 80 percent of all traditional PHAs.

Exhibit 2

Characteristics of Jurisdictions and Portfolios for Moving to Work Agencies, All Traditional Public

Housing Agencies, and Comparison Traditional Public Housing Agencies, 2016

Comparison

MTW Agencies

Traditional

PHAs (All)

Traditional PHAs

(> 750 HHs)

N = 39

N = 3,837

N = 779

Total Assisted Households (2016)

435,205

3,626,708

2,451,254

Characteristics of PHA Jurisdictions (2011–2015, ACS 5-Year Estimates)

Average Population Density

4,914

1,852

3,548

Average Poverty Rate

16%

17%

17%

Median Rent

$961

$612

$768

Average Percent Rental Housing

39%

30%

35%

Average Vacancy Rate

10%

15%

12%

Average % White, Non-Hispanic/Latino

63%

74%

67%

Average % African-American, Non-Hispanic/Latino

14%

12%

14%

Average % Other Race, Non-Hispanic/Latino

8%

4%

5%

Average % Hispanic/Latino, Any Race

14%

10%

13%

PHA Portfolio Mix (2016)

Public Housing Only

0%

40%

1%

Housing Choice Vouchers (HCVs) Only

13%

40%

17%

Public Housing and HCVs

87%

20%

82%

ACS = American Community Survey. HH = households. MTW = Moving to Work. PHA = public housing agency.

Notes: Jurisdictions are approximated as the counties containing the largest share of each PHA’s assisted households. Population density is the number of people per square mile. “Other” race category includes Asian, Native Hawaiian, and other Pacific Islanders.

Sources: 2011–2015 ACS 5-year data; HUD Public and Indian Housing Information Center data

3 For more information about MSAs and how they are defined, see https://www.census.gov/programs-surveys/metro-

micro/about.html.

Cityscape 11

Galvez, Gourevitch, and Docter

Exhibit 3

Assisted Housing Programs Administered by Moving to Work Agencies

The public housing and the housing choice voucher (HCV) programs are the two largest low-income rental assistance programs administered by public housing agencies (PHAs). In addition, MTW agencies may

provide local, non-traditional assistance unique to MTW.

Public Housing. Originating in 1937, public housing is the nation’s oldest housing subsidy program.

Approximately 1.04 million public housing units are owned and managed by PHAs, and tenants pay rent

directly to a PHA each month. Households must have income below 80 percent of the area median income

(AMI) to qualify, but PHAs are required to target at least 40 percent of new admissions to households that meet either HUD’s definition of extremely low-income, defined as below 30 percent of AMI, or the poverty threshold established by the U.S. Department of Health and Human Services. In addition, housing agencies often give preference to households that are homeless, elderly and/or disabled, or working families. Most families pay 30 percent of their income in rent or a minimum rent of up to $50 per month.

Housing Choice Vouchers. The HCV program provides rental assistance to approximately 2.3 million low-income households annually. HUD requires that not less than 75 percent of families admitted to a PHA’s HCV

program in a year have incomes at or below the extremely low-income limit. The program includes tenant-and project-based voucher assistance. For both types of vouchers, households typically pay 30 percent of their income or a minimum rent of up to $50 per month.

• Tenant-Based Vouchers (TBVs): TBVs enable individuals or households to rent privately owned housing. Once a household receives a voucher from the local PHA, it has a minimum of 60 days to find

a unit that meets federal quality standards and whose landlord will accept the voucher. When an HCV

holder leases a unit, the HCV holder (that is, the tenant) pays a portion of the gross rent (rent plus any tenant-paid utilities), and the PHA pays the remainder. The program allows households to rent housing in any jurisdiction where a PHA administers an HCV program and a landlord will accept a voucher.

• Project-Based Vouchers (PBVs): PBVs are attached to specific units and properties through contracts with property managers or owners who rent units to eligible families. The rent is subsidized by the PHA through the PBV program. As with TBVs, the tenant pays a portion of the rent, and the PHA pays the

remainder. In some cases, PHAs own the PBV properties.

Local, Non-Traditional (LNT) Programs. LNT assistance is unique to MTW agencies and includes four forms of assistance: rental subsidy programs, including supportive housing; homeownership programs, such as ones in which the PHA acts as a mortgagee; housing development programs, including gap financing and tax credit partnerships; and service provision, wherein the PHA provides access to self-sufficiency or supportive services. All four types of assistance target families with incomes at or below 80 percent of the area median and must meet the MTW program’s statutory objectives. LNT assistance is not captured in administrative data systems. HUD’s MTW office tracks the total number of households served annually by each MTW agency. For those counts, HUD considers any assistance to be linked to a household regardless of whether the subsidy provided is shallow or deep, property-based or tenant-based, tied to a unit, or an investment in a property.4

With the differences between MTW agencies and traditional PHAs in mind, the authors selected a

subset of traditional PHAs as a point of comparison for the MTW agencies. This comparison group

includes only traditional PHAs that have more than 750 assisted households in any year between

2008 and 2016. These larger traditional PHAs are in slightly more expensive markets and more

densely populated counties and are more likely to provide both public housing and HCV assistance

than smaller PHAs. Combined, the MTW and comparison PHAs represent about 21 percent of all

PHAs nationally but account for three-fourths of all PHA-assisted households in 2016. Exhibit 2

presents selected housing market and housing assistance portfolio characteristics of MTW agencies,

all traditional PHAs, and the subset of traditional PHAs with at least 750 households served

4 U.S. Department of Housing and Urban Development, Office of Public and Indian Housing (HUD PIH). 2011. PIH

Notice 2011-45: Parameters for Local, Non-Traditional Activities under the Moving to Work Demonstration Program.

Washington, DC: HUD.

12 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

annually that are included in the comparison group. The households counted are housing choice

voucher (including special purpose voucher), public housing, and LNT families.5

Moving to Work Housing Assistance

In 2016, the 39 MTW agencies accounted for approximately 12 percent of all households assisted

by PHAs, reflecting an increase from 8 percent in 2008 (exhibit 4). The increase in the MTW share

of all PHA-assisted households reflects a combination of trends over time: increases in the number

of agencies with an MTW designation, a slight loss in the number of households served by the

traditional PHAs, and a moderate increase in the number of households served by MTW agencies

after they joined the demonstration.

Exhibit 4

Share of Assisted Households Served by Moving to Work and Traditional Public Housing

Agencies, 2008–2016

MTW = Moving to Work. PHA = public housing agency.

Notes: Includes all households assisted through public housing; housing choice vouchers (including special purpose vouchers); local, non-traditional assistance; and other unspecified program types. PHAs are included as MTW beginning in the first calendar year after contracts are executed.

Sources: Urban Institute analysis of HUD Public and Indian Housing Information Center data; local, non-traditional program data 5 See the appendix in Galvez, Gourevitch, and Docter (forthcoming) for details on data underlying this report. There is no unit- or household-level information available for LNT households, so they cannot be included in analyses requiring that information.

Cityscape 13

Galvez, Gourevitch, and Docter

Understanding the Increase in the Number of Households Assisted by Moving to

Work Agencies

In 2016, the 39 MTW agencies served 435,205 households—an increase of 60 percent, or 163,225

unique households, during the 8-year study period (exhibit 5). Fifteen PHAs joined the MTW

demonstration during the study period (six in 2008, two in 2010, three in 2011, and four in 2013;

see exhibit 1).

The addition of the 15 new MTW agencies—and the households they served at the point of MTW

designation—accounts for roughly three-fourths of the increase in households served during the

study period (111,263 households, or 74 percent of total growth). The remaining growth reflects

new households added to MTW agencies’ assisted housing portfolios between 2008 and 2016. For

example, the Atlanta Housing Authority reported an almost 5,000-household increase in assisted

households between 2008 and 2016. Other MTW agencies, such as the Housing Authority of

Champaign County (Illinois), had more modest increases. Several agencies, however, such as the

Housing Authority of Columbus (Georgia), recorded declines in households served after the point

of MTW designation. Exhibit 5 shows changes in households served between 2008 and 2016 by

MTW agencies, grouped on the basis of when they executed agreements with HUD. The online

feature that accompanies this report provides MTW agency-level numbers of households served

between 2008 and 2016.6

Exhibit 5

Change in Total Households Assisted by Moving to Work Agencies, 2008–2016

Year MTW

MTW Agency

Total Assisted

Total Assisted

Difference in

Percentage

Contract

(N)

Households

Households in

Households

Change

Executed

the Year MTW

2016

Assisted

Contracts were

Between Time

Executed

Periods

1998 to 2007

24

271,982*

323,941

51,959

19

2008

6

67,488

77,804

10,316

15

2010

2

6,615

7,302

687

10

2011

3

10,698

11,299

601

6

2013

4

15,404

14,858

-546

-4

Totals

39

MTW = Moving to Work. PHA = public housing agency.

* For agencies that executed MTW contracts between 1997 and 2007, the “total assisted households the year MTW contracts were executed” column is the number of households assisted in 2008.

Notes: Includes all households assisted through public housing and housing choice vouchers (including special purpose vouchers) and households missing program information. PHAs are included as MTW beginning in the first calendar year after contracts are executed.

Source: Urban Institute analysis of HUD Public and Indian Housing Information Center data

6 The online feature and data are forthcoming at www.urban.org/policy-centers/metropolitan-housing-and-

communities-policy-center.

14 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

Relationship Between Funding and Moving to Work Status

As a group, MTW agencies received nearly $2.3 billion in HUD funding in 2003 and $4.4 billion

in 2017—accounting for 9 and 17 percent, respectively, of all HUD funding to PHAs in those years

(exhibit 6).7 The total funding provided to MTW agencies increased slightly over time and as a

share of all PHA funding. In contrast, funding to traditional PHAs decreased from roughly $23.1

billion in 2003 to $21.5 billion in 2017.8

Exhibit 6

HUD Funding to Moving to Work and Traditional Public Housing Agencies, 2003–2017

HUD Funding to

HUD Funding to All

Share of HUD

Number of MTW

MTW Agencies

Traditional Agencies

Funding Allocated to

Agencies

MTW Agencies (%)

2003

$2,335,942,273

$23,125,233,221

9

22

2004

$2,880,298,542

$22,968,914,383

11

23

2005

$3,099,464,306

$22,282,477,664

12

24

2006

$3,038,859,385

$22,349,575,436

12

24

2007

$3,175,640,215

$22,149,305,232

13

24

2008

$3,268,879,633

$22,084,185,475

13

25

2009

$3,992,215,337

$21,925,248,088

15

29

2010

$4,297,130,473

$22,919,209,104

16

30

2011

$4,315,533,967

$22,164,180,149

16

32

2012

$4,208,962,565

$21,057,537,403

17

35

2013

$4,104,554,510

$20,892,163,136

16

35

2014

$4,242,998,614

$20,739,100,304

17

39

2015

$4,363,418,112

$21,115,350,833

17

39

2016

$4,415,498,374

$21,592,907,299

17

39

2017

$4,390,927,949

$21,473,228,619

17

39

MTW = Moving to Work.

Notes: Agency-level data are adjusted to account for missing data and inflation. HUD funding is defined as all HUD public housing agency (PHA) operating and capital grants for public housing operating fund, public housing capital fund, and housing choice vouchers. The HUD funding metric excludes funding through the special purpose voucher program. Traditional PHAs (N = 3,673) exclude PHAs that joined the MTW demonstration at any time. PHAs are included as MTW agencies in the year their contract is executed.

Source: Urban Institute analysis of HUD Financial Data System data

7 Stacy et al. (2020) directly examine the causal relationship between MTW status and the number of assisted households served. The authors of this report use the HUD Financial Data Schedule (FDS) dataset developed for Stacy et al. (2020) to estimate the funding received by all 39 MTW agencies between 2003 and 2017. See Stacy et al. (2020) for a description of how PHA funding levels were identified, but note that their method includes PHAs as MTW agencies in the same year they sign an MTW agreement, as opposed to the following calendar year (the method applied in this report).

8 Dollar amounts are stated in 2015 dollars.

Cityscape 15

Galvez, Gourevitch, and Docter

Assistance Provided by Moving to Work and Comparison

Traditional Public Housing Agencies

In 2016, about three-fourths of all housing assistance provided by the Moving to Work (MTW)

and comparison traditional public housing agencies (PHAs) was through housing choice vouchers

(including tenant- and project-based vouchers), and the approximately one-fifth remaining was

through public housing (exhibit 7).

MTW and comparison traditional PHAs provided similar shares of tenant-based vouchers (TBVs).

MTW agencies administered more project-based vouchers (PBVs) compared with traditional PHAs.

Exhibit 7

Share of Households Assisted by Moving to Work and Comparison Traditional Public Housing

Agencies by Program, 2016

Program

MTW Agencies

Comparison Traditional PHAs

N = 39

N = 779

Public Housing (%)

21

27

Tenant-Based Vouchers (%)

66

69

Project-Based Vouchers (%)

12

4

MTW = Moving to Work. PHA = public housing agency.

Notes: The comparison traditional PHA column includes traditional PHAs with >750 assisted households in the year reported. Shares for MTW agencies do not sum to 100 because local, non-traditional housing assistance is excluded.

Source: Urban Institute analysis of HUD Public and Indian Housing Information Center data

Between 2008 and 2016, the share of public housing assistance decreased among MTW agencies

(from 30 percent to 21 percent), and the share of PBV housing assistance increased (from 4 percent

to 12 percent). By comparison, the share of public housing (dropping from 29 to 27 percent) and

PBV assistance (rising from 2 to 4 percent) remained fairly steady for the comparison traditional

PHAs (exhibit 8).

A portion of the shift to voucher assistance for both MTW and traditional PHAs may be attributable

to participation in the Rental Assistance Demonstration (RAD; see exhibit 9). The RAD program,

authorized under the Consolidated and Further Continuing Appropriations Act of 2012, allows PHAs

to convert units to either PBVs or project-based rental assistance (PBRA).9 Between 2013 and 2016,

more than 90,000 public housing units converted to PBVs or PBRAs through RAD; MTW agencies

converted 11,327 units. Most of the MTW public housing units converted through RAD shifted to PBVs

(77 percent). Among comparison traditional PHAs, about one-half of RAD conversions were to PBRAs.10

9 PBVs are part of the HCV program, with voucher funding allocated to specific properties selected through a process managed by individual PHAs. PBRA is a HUD housing program in which HUD contracts with private landlords who provide affordable units to low-income tenants at specific properties. For more information on the PBV program, see

https://www.hud.gov/program_offices/public_indian_housing/programs/hcv/project. For more information on PBRA, see https://www.hud.gov/hudprograms/rs8pbra.

10 See Galvez et al. (forthcoming) for a detailed assessment of MTW agencies’ use of PBV assistance, including public housing conversions through RAD.

16 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

Exhibit 8

Share of Assistance by Program Type and Moving to Work Status, 2008–2016

2008

2009

2010

2011

2012

2013

2014

2015

2016

Project-based Vouchers

MTW Agencies (%)

4

4

5

8

8

9

8

11

12

Comparison PHAs (%)

2

2

2

2

3

3

3

4

4

Public Housing

MTW Agencies (%)

30

26

26

25

25

25

25

23

21

Comparison PHAs (%)

29

28

28

28

28

29

28

27

27

Tenant-based Vouchers

MTW Agencies (%)

66

70

69

67

66

66

66

66

66

Comparison PHAs (%)

69

70

70

69

69

68

68

68

69

PHA = public housing agency. MTW = Moving to Work.

Note: Numbers may not add to 100 due to rounding.

Source: Urban Institute analysis of HUD Public and Indian Housing Information Center data

Exhibit 9

The Rental Assistance Demonstration

Congress authorized the Rental Assistance Demonstration (RAD) in 2012 to stem the loss of public housing units due to lack of funding for repairs to deteriorated properties. RAD allows public housing agencies (PHAs) to convert public housing properties to project-based Section 8 contracts. That provides a more predictable, long-term funding stream and allows PHAs to use a wide range of financing from both public and private sources to pay for rehabilitation of the properties. PHAs using RAD choose Section 8 contracts that are project-based vouchers (PBV) or project-based rental assistance (PBRA). As of this writing, HUD

reports that 113,540 public housing units have been converted through RAD, and many more units are in the process of being approved for RAD.

Local, Non-Traditional Program Assistance

MTW agencies can design unique housing assistance models known as local, non-traditional (LNT)

programs (see exhibit 3). Traditional PHAs are not permitted to provide those types of assistance.

LNT programs may include both tenant- and property-based assistance. Examples include rental

subsidies administered through third-party providers, homeownership programs, and services to

people eligible for but not receiving public housing or HCV assistance (HUD PIH, 2011).

Both the number of PHAs administering LNT assistance and the number of households receiving

this form of assistance have increased over time. As of 2016, 23 MTW agencies served almost

10,000 households through LNT program assistance.

The extent to which MTW agencies use LNT program assistance varies. The Atlanta Housing

Authority accounts for between 30 and 40 percent of all LNT assistance. Alternatively, MTW

agencies in the District of Columbia and Lincoln, Nebraska, do not provide any LNT assistance.

Characteristics of Moving to Work Households

Moving to Work and the comparison group of traditional public housing agencies serve similar

populations, with minimal differences in household characteristics or trends in the types of

households served over time. Exhibit 10 presents measures of household composition, head of

Cityscape 17

Galvez, Gourevitch, and Docter

household characteristics, income, and rent burdens for households served by MTW agencies and

traditional PHAs. In both 2008 and 2016, the two sets of agencies served households of similar

size and composition, with similar incomes and similar rent burdens. MTW agencies served

larger shares of African-American households and fewer White and Hispanic/Latino households

compared with traditional PHAs. Measures are defined and discussed in detail in Galvez,

Gourevitch, and Docter (forthcoming).

Exhibit 10

Household Characteristics for Moving to Work and Comparison Traditional Public Housing

Agencies, 2008 and 2016

Traditional PHAs

Household Type

MTW Agencies

N = 39

(>750 Households)

N = 779

2008

2016

2008

2016

Households with more than one adult and no children

10%

12%

9%

10%

Households with one adult and no children

41%

46%

40%

45%

Households with children

49%

43%

51%

45%

Households with children ages birth–5 years

46%

39%

48%

41%

Households with children ages 6–17 years

44%

37%

43%

38%

Average number of children in households with children

1.3

1.1

1.3

1.1

Households with a disabled head of household

37%

43%

35%

40%

Households headed by an elderly individual

21%

27%

21%

25%

(62 or older)

Single-parent households

45%

36%

46%

40%

Households headed by a female

79%

75%

81%

78%

Households headed by a White, non-Hispanic/Latino

20%

19%

30%

28%

Households headed by an African-American,

62%

60%

46%

48%

non-Hispanic/Latino

Households headed by an Asian, non-Hispanic/Latino

3%

5%

3%

2%

Households headed by a non-Hispanic/Latino

1%

1%

1%

1%

of another race

Households headed by a Hispanic/Latino individual,

13%

15%

21%

21%

any race

Work-able head of household

54%

48%

55%

51%

Total average annual income

$13,093

$14,394

$12,935

$14,412

Households with extremely low incomes (L30)

−

78%

−

74%

Median rent burden

26%

29%

26%

29%

MTW = Moving to Work. PHA = public housing agency.

Notes: Data for households with extremely low incomes using HUD’s income standards are available only for 2016 and exclude households that exited housing assistance in 2016. The measure of extremely low income used here is 30 percent of area median income, labeled “L30”. See Brandly (2019) for information on definitions of extremely low income.

Source: Urban Institute analysis of HUD Public and Indian Housing Information Center data

18 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

Household Composition and Characteristics

As shown in exhibit 10, in both 2008 and 2016, the MTW agencies and comparison traditional

PHAs served households of similar size and served similar shares of households with children, with

a disabled head of household, with a head of household age 62 or older, and with a female head

of household. Any differences between MTW agencies and comparison traditional PHAs for those

measures in 2008 and 2016 tend to be within 1 to 3 percentage points.

Some changes occurred over time in household characteristics or composition for both sets of

PHAs, with similar trends. For example, the shares of households with children declined over

time for both MTW agencies and traditional PHAs, by roughly 6 percentage points for each set of

agencies. The shares of single-parent households and work-able households also declined over time

for both sets of PHAs, with a marginally larger decline in both measures for the MTW agencies.

Similarly, the share of households headed by an elderly person increased at a slightly faster rate

for MTW agencies between 2008 and 2016 compared with the traditional PHAs but was only 2

percentage points larger for MTW agencies in 2016.

Household Income and Rent Burdens

MTW and comparison traditional PHA households had similar average total (unadjusted) incomes

and median rent burdens in 2008 and 2016. The two sets of agencies also served similar shares of

extremely low-income households.

Average household income increased over time for each set of agencies. The MTW households’

incomes were slightly lower on average than those of the comparison traditional households in

nearly every year in the analysis period (see exhibit 11).

Exhibit 11

Average Annual Income of Assisted Households for Moving to Work and Comparison Traditional

Public Housing Agencies by Year, 2008–2016

$16,000

$14,000

$12,000

$10,000

$8,000

$6,000

$4,000

2008

2009

2010

2011

2012

2013

2014

2015

2016

MTW PHAs

Comparison Traditional PHAs

MTW = Moving to Work. PHA = public housing agency.

Notes: MTW agencies: N=39. Traditional PHAs: N=779. Income is total household income.

Source: Urban Institute analysis of HUD Public and Indian Housing Information Center data

Cityscape 19

Galvez, Gourevitch, and Docter

Median rent burden for households served by both the MTW agencies and comparison traditional

PHAs was about 29 percent between 2008 and 2016. A larger share of MTW-assisted households

had slightly lower rent burdens—between 27 and 29 percent—during that period, whereas a larger

share of households served by traditional PHAs had slightly higher rent burdens—between 30 and

32 percent.

HUD calculates income limits annually that determine eligibility for assisted housing programs

on the basis of median family income estimates and fair market rent definitions for metropolitan

areas and at the county level for nonmetropolitan areas. HUD’s PIH (Office of Public and Indian

Housing) Information Center (PIC) data indicate whether each assisted family’s income meets local

income limits of 30, 50, or 80 percent of area median income (AMI). In 2016, both MTW agencies

and comparison traditional PHAs served mainly extremely low-income (ELI) households—defined

as households with incomes at or below 30 percent of local AMI—with MTW agencies serving

a slightly larger share of ELI households compared with traditional PHAs (78 percent and 74

percent, respectively; see exhibit 12). The MTW agencies and traditional PHAs served similar

shares of very low-income households (30–50 percent of AMI), low-income households (up to 80

percent of AMI), and households with incomes above 80 percent of AMI.

Exhibit 12

Share of Assisted Households by Income Level for Moving to Work and Comparison Traditional

Public Housing Agencies, 2016

Extremely Low Income

Very Low Income

Low Income

Not Low Income

100%

4%

2%

3%

5%

90%

16%

18%

80%

70%

60%

50%

78%

74%

40%

30%

20%

10%

0%

MTW Agencies

Comparison Traditional PHAs

MTW = Moving to Work. PHA = public housing agency.

Notes: MTW agencies: N = 39. Traditional PHAs: N = 779. Data exclude households that exited assistance in 2016, as exiting households are missing income limits data. HUD’s income limit calculations are based on area median family income estimates (which are area median income estimates adjusted for family size) and Fair Market Rent area definitions at the metropolitan level and at the county level for nonmetropolitan areas. Extremely Low Income = at or below 30 percent of area median income (AMI) or the federal poverty threshold, whichever is greater; Very Low Income = 30 to 50

percent of AMI; Low income = 50 to 80 percent of AMI. Households with incomes above 80 percent of AMI are not considered to be low income. See

www.huduser.gov/portal/datasets/il.html and www.huduser.gov/portal/datasets/il/il18/FAQ-18r.pdf for more information on HUD income limits.

Source: Urban Institute analysis of HUD Public and Indian Housing Information Center data

20 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

Head of Household Race and Ethnicity

In 2016, more than 80 percent of MTW agencies’ heads of household were African-American,

Hispanic/Latino, or Asian, with White heads of household representing the remaining

approximately 19 percent. For the comparison traditional PHAs, approximately 72 percent of all

heads of household were people of color (exhibit 13).

Exhibit 13

Race and Ethnicity of Moving to Work Agency and Comparison Traditional PHA-Assisted

Households, 2016

MTW Agencies

Traditional PHAs

N=39

(>750 assisted households)

N=779 PHAs

White African-American Asian Hispanic Other

MTW= Moving to Work. PHA = public housing agency.

Notes: Race and ethnicity information corresponds to the head of household. Households are identified in HUD Public and Indian Housing Information Center data as belonging to a single race category. White, African-American, Asian, and other race categories are exclusive of Hispanic/Latino ethnicity.

Hispanic/Latino heads of household may be of any race.

Source: Urban Institute analysis of HUD Public and Indian Housing Information Center data.

For both sets of PHAs, African-American households represented the largest proportion of assisted

households overall and in each program, although the racial and ethnic composition varied by

assistance program (exhibit 14). Within MTW agencies, public housing residents are more likely

to be African-American and less likely to be White, compared with the populations of TBV or PBV

program participants. For the comparison traditional PHAs, TBV program participants are more

likely to be African-American compared with PBV and public housing residents. Public housing

residents in traditional PHAs are more likely to be Hispanic/Latino than households served through

other assistance programs or compared with MTW-assisted households.

Cityscape 21

Galvez, Gourevitch, and Docter

Exhibit 14

Race and Ethnicity by Program Type at Moving to Work and Comparison Traditional Public

Housing Agencies, 2016

Project-Based

Tenant-Based

Head of Household

Public Housing

Vouchers

Vouchers

MTW

Traditional

MTW

Traditional

MTW

Traditional

Agencies

PHAs

Agencies

PHAs

Agencies

PHAs

White, non-Hispanic/

13

21

25

32

20

29

Latino (%)

African-American,

68

46

55

43

57

50

non-Hispanic/Latino (%)

Asian, non-Hispanic/

4

3

6

3

5

2

Latino (%)

Hispanic/Latino individual,

13

30

12

20

16

18

any race (%)

Non-Hispanic/Latino

2

1

2

1

1

1

of another race (%)

MTW = Moving to Work. PHA = public housing agency.

Notes: MTW agencies: N = 39. Traditional PHAs: N = 779. Race and ethnicity information corresponds to the head of household. Households are identified in HUD Public and Indian Housing Information Center data as belonging to a single race category. White, African-American, Asian, and other race categories are exclusive of Hispanic/Latino ethnicity. Hispanic/Latino heads of household may be of any race.

Source: Urban Institute analysis of HUD Public and Indian Housing Information Center data

Access to Low-Poverty Neighborhoods

Previous research shows that neighborhoods influence well-being and long-term success (Chetty

and Hendren, 2015; Turner and Gourevitch, 2017). To document the extent to which Moving

to Work (MTW)-assisted households access low-poverty neighborhoods, the authors looked at

their census-tract locations in 2016 and calculated average tract-level poverty rates for all assisted households with 2011–2015 American Community Survey (ACS) data, as a proxy for overall

neighborhood quality.

In 2016, households assisted by MTW agencies lived in neighborhoods with an average poverty

rate of 28 percent, which was nearly identical to the average for comparison traditional household

neighborhoods (see exhibit 15, first column). That poverty rate is lower than the thresholds

of 30 or 40 percent for residents living in poverty that the literature typically associates with

neighborhood distress but substantially higher than the 10-percent threshold typically considered

to offer access to economic or educational opportunities (Galvez, 2010).

Average neighborhood poverty rates for MTW and traditional public housing agency households

also were nearly identical for each housing assistance program (exhibit 15). Consistent with the

literature on locations of PHA-assisted households (Devine et al., 2003; McClure, Schwartz, and

Taghavi, 2015; Pendall, 2000), public housing households lived in higher poverty neighborhoods

compared with housing choice voucher recipients (whether project-based vouchers or tenant-based

vouchers). That was the case for both MTW agencies and the comparison traditional PHAs.

22 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

Exhibit 15

Average Poverty Rate of Census Tracts Containing Assisted Households by Program Type for

Moving to Work and Comparison Traditional Public Housing Agencies, 2016

MTW Agencies

Comparison Traditional PHAs

35%

30%

25%

20%

34%

33%

15%

28%

28%

29%

27%

27%

25%

10%

5%

0%

Total

Public housing

Project-based vouchers Tenant-based vouchers

MTW = Moving to Work. PHA = public housing agency.

Notes: MTW Agencies: N = 39. Traditional PHAs: N = 779. Excludes households with missing geographic tract identifiers in HUD Public and Indian Housing Information Center data.

Sources: Urban Institute analysis of HUD Public and Indian Housing Information Center data; 2011–2015 American Community Survey 5-year data Nearly identical shares of households assisted by MTW and comparison traditional PHAs lived in

low-poverty census tracts in 2016, whereas a slightly higher share of MTW-assisted households

lived in high-poverty tracts. In 2016, only 15 percent of all MTW-assisted households and 14

percent of comparison traditional PHA-assisted households lived in areas with census-tract poverty

rates below 10 percent. Approximately 43 percent of all MTW households and 38 percent of

households served by comparison traditional PHAs lived in areas with poverty rates above 30

percent. See exhibit 16 for the shares of MTW and comparison agency households living in low-,

moderate-, and high-poverty census tracts.

Cityscape 23

Galvez, Gourevitch, and Docter

Exhibit 16

Share of Assisted Households by Census-Tract Poverty Level for Moving to Work and

Comparison Traditional Public Housing Agencies, 2016

Comparison

MTW Agencies

Traditional PHAs

Household Type

N = 39

(>750 Households)

N = 739

Percent of households in low-poverty census tracts

14%

15%

(<10% poverty rate)

Percent of households in low- to moderate-poverty census

22%

24%

tracts (10–20% poverty rate)

Percent of households in moderate- to high-poverty census

21%

23%

tracts (21–30% rate)

Percent of households in high-poverty census tracts

19%

17%

(31–40% poverty rate)

Percent of households in extremely high-poverty census tracts

24%

21%

(>40% poverty rate)

MTW = Moving to Work. PHA = public housing agency.

Note: Excludes households that do not have geographic tract identifiers in the HUD Public and Indian Housing Information Center dataset.

Sources: Urban Institute analysis of HUD Public and Indian Housing Information Center data; 2011–2015 American Community Survey 5-year data Household Locations by Moving to Work Agency Characteristics

Among MTW-assisted households, residential locations varied based on city type, PHA size,

and region of the country (exhibit 17). MTW agencies operating in central cities tended to

have households in census tracts with higher average poverty rates compared with other MTW

agencies (average poverty rate of 30 percent compared with 23 percent). Large MTW agencies

also tended to have households in census tracts with higher average poverty rates compared

with smaller MTW agencies (average poverty rate of 29 percent for PHAs with 10,000 or

more assisted households annually compared with 19 percent for PHAs with 1,250 or fewer

households). MTW-assisted households in the Midwest experienced the highest average

neighborhood poverty rates (33 percent on average), whereas MTW households in the West

experienced the lowest (23 percent on average).

24 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

Exhibit 17

Average Poverty Rate of Census Tracts Containing MTW-Assisted Households by Type of City,

Size of Agency, and Region of Country, 2016

30%

33%

29%

29%

30%

23%

25%

23%

19%

Principal

Other

500-1,250 1,251-10,000 10,001 or more Midwest

Northeast

South

West

City

Cities

households

households

households

MTW = Moving to Work

Notes: Principal city: N = 24; Other cities: N = 14. Size: 500–1,250 households: N = 5; 1,251–10,000 households: N = 19; 10,001 or more households: N = 14. Regional designations are created by the U.S. Census Bureau. Regions: Midwest N = 6; Northeast N = 7; South N = 11; West N =

14. “Principal city” public housing agencies appear as a central city named in the Metropolitan Statistical Area (MSA) name for 2015; “other” cities are smaller, often suburban jurisdictions included but not named in the MSA.

Sources: Urban Institute analysis of HUD Public and Indian Housing Information Center; 2011–2015 American Community Survey 5-year data Conclusion

This study provides a detailed, descriptive analysis of restricted-use HUD administrative data for

2008 through 2016 to describe the housing assistance provided by the 39 public housing agencies

(PHAs) with Moving to Work (MTW) designation as of 2016.

Considered together, the findings show that MTW agencies are larger and more likely to be in

dense urban areas than the typical traditional housing authority. When measures of MTW housing

assistance are compared with those of a subset of comparably sized traditional PHAs, the MTW

agencies seem to provide a similar mix of housing assistance, serve similar populations, and

have households in areas with similar levels of poverty. Some characteristics, such as the share of

single-adult households and the share of work-able households, have changed over time for both

MTW agencies and traditional PHAs. Some differences between MTW agencies and traditional

PHAs emerge in the data. MTW agencies provide more project-based HCV assistance compared

with traditional PHAs and seem to have added new households to their assistance portfolios

between 2008 and 2016, whereas traditional PHAs did not. In addition, the comparison traditional

PHAs served a larger proportion of White households compared with the MTW agencies. An

accompanying online data feature provides MTW agency-level data for selected measures described

in this report.

Cityscape 25

Galvez, Gourevitch, and Docter

Acknowledgments

The authors appreciate the extensive feedback and support that Elizabeth Rudd of the HUD

Office of Policy Development & Research (PD&R) provided. In addition, the authors thank Lynn

Rodgers and Lydia Taghavi of PD&R for their guidance and data expertise and Carol Star and David

Hardiman for feedback on early iterations of this study. The authors acknowledge the support of the

Moving to Work (MTW) Expert Panel and feedback from MTW agencies. Lastly, the authors thank

Diane Levy, Daniel Teles, Mark Treskon, and Matthew Gerken for their feedback and assistance.

Authors

Martha Galvez is a principal research associate in the Metropolitan Housing and Communities

Policy Center at Urban Institute. Her expertise is in housing and homelessness policy.

Ruth Gourevitch was formerly a research analyst in the Metropolitan Housing and Communities

Policy Center. Her research focuses on homelessness, place-based strategies for economic mobility,

neighborhood change dynamics, and the intersection of housing, health, and education.

Benny Docter is a research assistant in the Metropolitan Housing and Communities Policy Center.

His research focuses on access to banking and credit, public housing, community economic

development, neighborhood, and place-based education initiatives.

References

Brandly, Chalita. 2019. “Extremely Low-Income: Has the New Measure Made a Difference?”

Cityscape: A Journal of Policy Development and Research 21 (3): 389–409.

Chetty, Raj, and Nathaniel Hendren. 2015. The Impacts of Neighborhoods on Intergenerational

Mobility: Childhood Exposure Effects and County-Level Estimates. Cambridge, MA: Harvard University and the National Bureau of Economic Research.

Devine, Deborah J., Robert W. Gray, Lester Rubin, and Lydia B. Taghavi. 2003. Housing Choice

Voucher Location Patterns: Implications for Participants and Neighborhood Welfare. Washington, DC: HUD Office of Policy Development and Research.

Galvez, Martha M. 2010. What Do We Know About Housing Choice Voucher Program Location

Outcomes? Washington, DC: Urban Institute.

Galvez, Martha, Ruth Gourevitch, and Benny Docter. Forthcoming. A Picture of Moving to Work

Housing Assistance. Washington, DC: Urban Institute.

Galvez, Martha M., Daniel Teles, Alyse D. Oneto, and Matthew Gerken. Forthcoming. Moving to

Work Agencies’ Use of Project-Based Voucher Assistance. Washington, DC: U.S. Department of Housing and Urban Development.

26 The Moving to Work Retrospective Evaluation

A Picture of Moving to Work Agencies’ Housing Assistance

McClure, Kirk, Alex F. Schwartz, and Lydia B. Taghavi. 2015. “Housing Choice Voucher Location

Patterns a Decade Later,” Housing Policy Debate 25 (2): 215–33.

Pendall, Rolf. 2000. “Why Voucher and Certificate Users Live in Distressed Neighborhoods,”

Housing Policy Debate 11 (4): 881–910.

Stacy, Christina, Josh Leopold, Daniel Teles, Ruth Gourevitch, Alyse D. Oneto, Yipeng Su, and

Matthew Gerken. 2020. The Impact of the Moving to Work Demonstration on the per Household Costs of Federal Housing Assistance. Washington, DC: U.S. Department of Housing and Urban Development.

Turner, Margery Austin, and Ruth Gourevitch. 2017. How Neighborhoods Affect the Social and

Economic Mobility of Their Residents. Washington, DC: Urban Institute U.S. Partnership on Mobility from Poverty.

Turnham, Jennifer, Meryl Finkel, Larry Buron, Melissa Vandawalker, Bulbul Kaul, Kevin Hathaway,

and Christ Kubacki. 2015. Housing Choice Voucher Program Administrative Fee Study: Final Report.

Washington, DC: HUD.

U.S. Department of Housing and Urban Development, Office of Public and Indian Housing (HUD

PIH). 2011. “PIH Notice 2011-45: Parameters for Local, Non-Traditional Activities under the

Moving to Work Demonstration Program.” Washington, DC: HUD-PIH.

Cityscape 27

28 The Moving to Work Retrospective Evaluation

by Diane K. Levy, Leiha Edmonds, and David Long

Moving to Work Agencies’ Use of

Funding Flexibility

Diane K. Levy

Leiha Edmonds

Urban Institute

David Long

Princeton Policy Associates

Abstract

The Moving to Work (MTW) demonstration allows participating public housing agencies to implement

innovative approaches to achieving three statutory objectives: cost-effectiveness, household self-sufficiency, and housing choice. MTW funding flexibility is a cornerstone of agencies’ ability to work toward federally required and locally determined objectives. This flexibility allows the current 39 MTW agencies to treat the separate funding streams of public housing operations, capital improvements, and Housing Choice Voucher (HCV) programs as fungible, moving funds among the separate streams and from them into

local, non-traditional activities. This article examines how MTW agencies have used funding flexibility and includes a detailed examination of funding shifts from a sample of agencies.

Introduction

Our analysis indicates that all Moving to Work (MTW) demonstration agencies have used funding

flexibility and have undertaken more activities focused on increasing housing choice than on the

other statutory objectives. A plurality of agencies has used the flexibility to leverage additional

funding for priority activities. Among agencies in the sample, most of the funds shifted came from

the housing choice voucher (HCV) program stream, and the majority of shifted funds were used

for capital projects. Findings are drawn from a research report produced for the U.S. Department

of Housing and Urban Development (HUD)-funded retrospective evaluation of the MTW

demonstration (Levy, Long, and Edmonds, 2019). The report includes a comparative analysis of

MTW agencies’ use of funding flexibility and outcomes associated with the statutory objectives, as

well as detailed information on methods used and data produced for this study.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 29

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

Levy, Edmonds, and Long

What Is Moving to Work Funding Flexibility?

Public housing agencies receive their annual HUD funding through three distinct revenue streams:

operating funds used to operate and maintain public housing units; capital improvement funds for

public housing development, rehabilitation, and management improvements; and HCV funds used

to administer the voucher program.1 MTW agencies may apply flexibility to these three funding

streams upon receipt or maintain them separately and shift funds from one stream to another, as

depicted in the diagrams in exhibit 1.2 Whichever approach agencies take, they have greater leeway

in how they use federal resources compared with traditional agencies.

Exhibit 1

Traditional and Moving to Work Agencies’ Funding Streams and Uses

MTW = Moving to Work.

Source: Moving to Work Standard Agreement Attachment C, “Statement of Authorizations” (modified), retrieved from HUD’s MTW portal. https://www.hud.

gov/program_offices/public_indian_housing/programs/ph/mtw/mtwsa

Agencies that undertake activities with funding flexibility can do so with this flexibility alone or

combined with waivers of HUD regulations granted to MTW participants.3 Examples of activities that

require a waiver include changing the minimum rent and changing housing inspection procedures.

1 PHAs may also receive funds from other sources, such as HUD grants and voucher awards, and from states, localities, and foundations.

2 Traditional agencies may use up to 20 percent of their public housing operating funds annually for capital projects, as indicated by the dashed line in exhibit 1. This flexibility was extended to traditional agencies by the Housing Opportunity Through Modernization Act of 2016.

3 For details on waivers, see www.hud.gov/program_offices/public_indian_housing/programs/ph/mtw.

30 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

There are constraints on MTW agencies’ use of funding flexibility. The agencies must abide by the

statutes and regulations governing all public housing agencies except those for which an agency

has received waivers. Agencies must document the activities that use funding flexibility alone and

those that use the flexibility along with waivers. Also, agencies are required to serve substantially the same number and income mix of households as they would serve in the absence of funding

flexibility. In addition, since 2010, the use of funding flexibility to support local, non-traditional forms of assistance4 is allowed only if the activity serves eligible households and pursues one or

more of MTW’s three statutory objectives.

These features of funding flexibility lead to questions about MTW agencies’ actual use of it. What

type of and how many activities do agencies undertake with shifted funds? Do the activities address

some or all of the three statutory objectives? How and to what extent do agencies use funding

flexibility with waivers? What are the dollar amounts of shifted funds? Furthermore, are funds

shifted more or less from each traditional funding stream?

Research on Moving to Work Funding Flexibility

During the first few years of MTW, agencies used funding flexibility to address financial challenges, free resources for purposes already allowed, and increase the supply of affordable housing by

developing new public housing units or adding vouchers. MTW agencies often were cautious and

made modest changes to their funding use, such as using funding flexibility to address budget

shortfalls. Some agencies used funding flexibility more intensively to pursue agency priorities by,

for example, increasing resident services or providing additional affordable housing (Abravanel et

al., 2004). Agencies were found to value funding flexibility because it enabled them to use funds to

leverage financing for construction projects that otherwise would not have been possible (Brick and

McCarty, 2012). Subsequent research further documented the activities MTW agencies take and

considered the role funding flexibility plays in carrying them out, but research focused on funding

flexibility has been limited (Brick and McCarty, 2012; Khadduri et al., 2014; Webb, Frescoln, and

Rohe, 2015).

We know little about differences in agencies’ use of funding flexibility alone compared with

their use of the flexibility combined with waivers. The use of waivers has been discussed in

descriptions of agencies’ activities but not studied systematically (Khadduri et al., 2014). Similarly, we know little about funding shifts that involve funds freed up through cost savings. Research

has documented that agencies use funds accrued through administrative cost savings for various

purposes, but we do not know how frequently such actions are taken (Khadduri et al., 2014).

Neither do we know the dollar amounts associated with funding flexibility. There is no published

4 Local, non-traditional activities use MTW funds for activities outside of the HCV and public housing programs, as set out in sections 8 and 9 of the U.S. Housing Act of 1937. Non-traditional activities fall into one of four categories: (1) rental subsidy programs that provide a subsidy to a third-party for supportive housing, transitional housing, and other programs; (2) homeownership programs that allow an agency to act as a mortgager to provide homeownership assistance; (3) housing development programs that acquire, renovate, or build units that are not public housing or HCV units; and (4) service provision activities, such as self-sufficiency or supportive services, that are not permitted under the Housing Act and regulations or are provided to eligible individuals who do not receive public housing or HCV assistance (PIH Notice 2011-45).

Cityscape 31

Levy, Edmonds, and Long

evidence on the estimated dollar amounts of funds shifted across traditional funding streams or

funds accrued from cost savings or leveraging activities.

Research Questions and Challenges

We set out to systematically study agencies’ uses of funding flexibility to determine what they have

done with the flexibility and for what purposes, how they have used it alone or with waivers, and

the dollar amounts agencies shift among the traditional funding streams. MTW activities have been

documented, but we sought to go beyond existing documentation of MTW activities by identifying

those undertaken with funding flexibility alone and with a waiver, and associating each activity

with an MTW statutory objective. Our aim was to develop a more complete understanding of the

uses of funding flexibility and its importance related to meeting local and federal goals. We also

set out to detail how agencies use funding flexibility to shift funds, looking into data on funds

shifted from one traditional funding stream to another stream or to local, non-traditional efforts,

examining how agencies accrue savings and use those funds, and how the flexibility has been used

to leverage additional resources. Finally, we wanted to examine dollar amounts of shifted funds.

There has been no prior examination of the evidence on direct shifts of funds across traditional

funding streams or on the use of funding available through cost savings.

Challenges emerged early in designing the study. We first grappled with how to describe funding

flexibility. The conceptual diagram in exhibit 1 depicts straightforward shifts across the traditional funding streams. Our initial examination of agencies’ annual reports, along with reviews of past

research, made clear that funding flexibility also involves activities undertaken with waivers that

result in savings, which can be used later to fund activities in the same or different streams. It became clear that a discussion of funding flexibility must include what we came to call indirect shifts.

Identifying shifts in funds presented another challenge. MTW agencies report direct shifts of funds and how the funds were used in their annual reports, but funds freed for use because of savings are difficult to identify because they might not be used immediately. Instead, these freed funds are placed in

reserves for later use for an activity that may or may not be associated with the same funding stream in which the savings occurred. When there is evidence of using these freed funds, it may not be possible to identify the original funding stream from which they derived because, at the time of use, they are drawn from reserves. Once funds are pooled, the connection to the original stream can be broken.

Difficulties specifying funding shifts and uses of funds were highlighted in the U.S. Government

Accountability Office’s (GAO’s) MTW review that found HUD’s financial data system could not be used

to determine how agencies used funding they transferred from a traditional funding stream (GAO,

2018).5 Nevertheless, understanding funding flexibility requires more than tracking direct shifts. Fund accruals and uses of these funds are an important part of funding flexibility.

We also faced the challenge common to studies of the MTW demonstration: how to pursue

research across 39 agencies when the agencies act in accordance with local priorities. There

was variation as well in agencies’ reporting practices, with some providing more complete and

descriptive information in their annual reports than others.

5 According to HUD staff, HUD acknowledges this finding and has agreed to continuing the conversation with GAO

in an effort to reach a mutually agreeable resolution.

32 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

Methods

In this section, we describe our approach to studying funding flexibility, including how we tried to

mitigate the design and research challenges.

Data and Measures

We compiled data on funding flexibility from a review of all 39 MTW agencies’ 2015 annual

reports and 2016 annual plans submitted to HUD.6 The reports and plans include information on

activities being implemented and closed out since each agency joined the MTW demonstration,

which enabled us to identify a comprehensive list of funding flexibility-related activities through

2015. Data come from two sections of Form 50900 of the annual reports: section IV, approved

MTW activities, which includes activity descriptions, whether an activity includes the use of a

waiver, and whether it frees resources or uses resources; and section V, sources and uses of funds

which includes descriptions of activities undertaken with funding flexibility only. To gauge the

completeness and accuracy of data drawn from agencies’ annual reports, we compared these data

from a sample of agencies with data from HUD’s Financial Data Schedule (FDS). FDS contains the

annual financial statements submitted by public housing agencies. The FDS variables we looked at

differed by agency. For example, we looked at line 70710 (management fee) to help identify freed

resources for several agencies whose annual reports showed management fees that were higher

than related expenses. We looked at line 92100 (resident services) for several agencies to confirm

the uses of shifted funds for self-sufficiency and other programs.

We distinguished between direct and indirect shifts of funds to capture the range of agencies’ use

of funding flexibility. Direct shifts are shifts of funds from one traditional HUD funding stream

to another or to local, non-traditional activities to cover costs for a specific activity. These shifts reflect budgetary decisions. Agencies report direct shifts of funds in section V (Form 50900) of

their annual reports, though they do not consistently report all small direct shifts or provide full

information on dollar amounts shifted. Agencies report direct shifts of funds in relation to the

activities to be supported as “activities that used only MTW single fund flexibility.” We refer to

such initiatives as funding-flexibility-only activities. Indirect shifts result from policy decisions that free resources in one funding stream for spending in other streams or within the same stream

for different purposes. Funds are accumulated over time through MTW activities that result in

cost savings, new revenue, or unit cost reductions. These activities involve the use of regulatory

waivers to change policy or practice. The freed resources may or may not be shifted during the

same accounting period—they may be placed in operating reserves for future use, which may or

may not involve a shift across revenue streams when the funds in reserve are eventually spent. For

these reasons, it is difficult to track indirect shifts of funding. The potential for indirect shifts of funding to occur or to have occurred is inferred from information about whether an activity saved

costs, generated new revenue, reduced unit costs, or used resources. We identified potential shifts

from the information agencies reported under “implemented MTW activities” in section IV of Form

50900 in annual reports. We validated indirect shifts through interviews in a sample of agencies.

6 This method did not use data from HUD program and financial systems such as the Line of Credit Control System, Voucher Management System, HUD Central Accounting Program System, or the Public and Indian Housing Information Center.

Cityscape 33

Levy, Edmonds, and Long

We identified activities implemented with funding flexibility, with and without a waiver, based on

information drawn from Form 50900 in MTW agencies’ annual reports. Activities described in

section IV of Form 50900 in the annual reports usually are clear about the use of waivers and the

freeing or use of resources because HUD guidelines for completing this section are specific. The

activities described in section V of the form are often not as clear; guidelines only say to “describe activities that use only single fund flexibility,” without guidance on what descriptions should include.

We used these data to create accounts of activities that relied in whole or in part on funding

flexibility.7 For each activity identified in the reports and plans as using funding flexibility, the accounts include a brief description and information on activity dates and type, whether resources

were leveraged, whether the activity is intended to respond to local needs, involves a local,

non-traditional program, has outcomes related to the statutory objectives, and involves direct or

potential indirect shifts in resources.

We conducted in-depth interviews with leaders from eight MTW agencies, including executive

directors and the finance director or director of public housing or voucher programs. We used

these discussions to confirm the information we drew from the annual reports and plans and to

gather details on leveraged resources. Interviews also explored agencies’ purposes for using funding

flexibility and whether funding flexibility has influenced how the agencies respond to local needs.

Finally, the discussions included staff perspectives on the effect of flexibility on statutory objectives. A comparison of information from the interviews with annual reports for the eight agencies confirmed

that the activities reported as using large amounts of shifted funding did occur and that the funding amounts were reported accurately, though not always in enough detail to tie dollar amounts of shifted funds to specific activities.8 We also reviewed potential uses of indirectly shifted funds we identified in agency reports to determine whether the activities occurred. We also interviewed five HUD

MTW coordinators responsible for oversight of the agencies in the sample of eight. These interviews

explored coordinators’ perspectives on funding flexibility and agencies’ leveraging activities.

Sampling

Descriptive analyses of activities implemented with funding flexibility—with and without

waivers—and analyses on funding shifts use data from all 39 MTW agencies. To examine details

of agencies’ funding shifts, and to explore agencies’ perspectives on funding flexibility, we drew

a purposive sample of eight agencies based on information compiled in the accounts. Agencies

sampled included the Housing Authority of Baltimore City (HABC), Cambridge Housing Authority

(CHA), King County Housing Authority (KCHA), Lawrence-Douglas County Housing Authority

(LDCHA), Lexington-Fayette Urban County Housing Authority (LFUCHA), Minneapolis Public

Housing Authority (MPHA), Housing Authority of the City of Pittsburgh (HACP), and Home

Forward Portland. These agencies are diverse in size and geographic location, the number and type

of funding flexibility activities, and the purposes for which activities were pursued as identified by these agencies in their annual reports.

7 The 39 accounts will be accessible on HUDUSER.gov through the website for the MTW Retrospective Evaluation.

8 Documented direct funding shifts were reported accurately though some agencies did not include the dollar amount of the shifts. Some relatively smaller shifts discussed during interviews had not been included in annual reports.

34 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

Analysis

Once data and information on the 39 MTW agencies’ activities using funding flexibility were

tabulated in the accounts, we grouped activities associated with an objective into activity categories so that similar activities could be examined together. We used the account and FDS data to

produce descriptions, in numbers and percentages, of agencies’ uses of funding flexibility and

leveraging activities. We then compared direct and indirect shifts in funds with data from annual

reports and FDS to assess how agencies shifted funds across programs. Analysis of qualitative data

from interviews with HUD staff and staff from the sample of eight agencies for which we completed

validation of direct and indirect shifts provided additional insights into the use of funding

flexibility, including how agencies use leveraging.

Uses of Moving to Work Funding Flexibility

MTW agencies use funding flexibility alone and with waivers to pursue HUD’s statutory objectives

and local priorities. Analysis of the agencies’ 2015 annual reports finds that all 39 agencies used

funding flexibility to undertake a diversity of activities related to each of the three MTW statutory objectives. Most of the agencies reported using funding flexibility to access external resources. The flexibility helped agencies secure favorable financing terms and amounts for major projects, meet

a funder’s matching requirement, and partner with other organizations to provide resident services

more easily.

Use of Funding Flexibility by Statutory Objectives

All 39 MTW agencies have used funding flexibility, alone or with waivers, to pursue each of the

statutory objectives—to increase cost-effectiveness, self-sufficiency, and housing choice. (See

appendix A for the number of activities using funding flexibility by objective in fiscal year 2015.)

All but four of the agencies have engaged in at least 10 funding flexibility activities. Three of the four exceptions—Champaign County, Columbus, and Holyoke—had been in MTW for a relatively

short time. Overall, agencies pursued more activities associated with increasing housing choice

(41 percent of all activities using funding flexibility) than those associated with increasing cost-

effectiveness or self-sufficiency (30 and 29 percent, respectively). This focus on housing choice

aligns with what staff from the sample of agencies identified as priorities—increasing the number

of households served and increasing the number of affordable housing units.

Exhibit 2 lists the types of activities the 39 agencies have pursued with funding flexibility and

whether the activities freed funds or used resources. Nearly all activities pursuing cost-effectiveness involve potential cost savings, which frees resources for other uses. In some cases, these activities may use resources in the short term, for example, to fund additional staff training to achieve longer-term savings. Every MTW agency engaged in cost-effectiveness activities. Most of the agencies

changed their household certification and housing inspection processes, and more than one-half

modified other processes.

Cityscape 35

Levy, Edmonds, and Long

Exhibit 2

Features and Funding Flexibility Aspects of Moving to Work Activities (1 of 2)

% of

Activity

Activities Free

Agencies

Objective

Included Activities

Category

or Use Resources

with These

Activities

Cost-

Household

Certification Schedule

Free (most activities

95

Effectiveness Certification

Income Deductions, Exclusions,

produce cost savings)

Process

Verification

Asset Exclusions and Verification

Housing

Inspection Schedule

Free (most activities

90

Inspection

Self-Certification

produce cost savings)

Process

PHA Inspection

Inspection Rules

Housing Quality Standards (HQS)

Other

Rent Payment System

Free (most activities

Procedures

Utility Allowances

produce cost savings)

Rent Reasonableness

Use (some activities have

59

short-term expenses)

Waiting List Management

Referral System

Eligibility Rules

Administration/Training/Accounting

Customer Service

Self-

Rent

Minimum Rent

Free (most activities

95

Sufficiency

Calculation

Flat Subsidy

increase revenue or

Rent Reform

produce cost savings)

Other Calculation Changes

Use (some activities have

short-term expenses)

Additional

Self-Sufficiency Services

Free (some activities

93

Services

Resident Services

increase revenue or

produce cost savings)

Education

Use (most activities have

Case Management

short- and longer-term

Family Self-Sufficiency

expenses)

(FSS) Program

FSS Escrow Policies

Requirements Work Requirements

Free (most activities

54

& Limits

Service Use Mandates

increase revenue or

produce cost savings)

Exemptions/Work-Able Definitions

Use (some activities have

Housing Assistance Time Limits

short- and longer-term

and Restrictions

expenses)

36 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

Exhibit 2

Features and Funding Flexibility Aspects of Moving to Work Activities (2 of 2)

% of

Activity

Activities Free

Agencies

Objective

Included Activities

Category

or Use Resources

with These

Activities

Housing

Development Modernization/Revitalization

Free (some debt

54

Choice

& Renovation Shifts to Capital Fund/Capital

payment activities

Fund Expenditures

produce cost savings)

Debt Payment for Capital

Use (most activities

Fund Expenditures

have short- and longer-

term expenses)

Repairs/Improvements

Project

Cap on Voucher Funds that

Free (some activities

77

Basing

Can Be Project-Based

produce cost savings)

Vouchers

Cap on PBV Units in a

Use (some activities have

Development

short-term expenses)

PBV Assignment

PBV Rules, including PBV

in PHA-Owned Units

Affordable

Non-Traditional Housing

Use (most activities

77

Housing

(excluding PBVs)

have short- and

Housing with No Subsidies

longer-term expenses)

Non-PHA Housing Developments

Home Ownership

Pertinent Property Acquisition

Landlord

Landlord Recruitment

Use (most activities

23

Participation

and Retention

have short- and

Landlord Incentives and Payments longer-term expenses)

Landlord Outreach

Targeted

Housing for Targeted Households

Use (most activities

77

Populations

(for example, domestic

have short- and

abuse victims)

longer-term expenses)

Services for Targeted Households

Supportive Housing and

Sponsor-Based Vouchers

MTW = Moving to Work. PBV = Project-Based Voucher. PHA = public housing agency.

Source: MTW funding flexibility accounts prepared by Urban Institute based on data collected from review of MTW 2015 annual reports, sections IV and V.

Most funding flexibility activities that pursued cost-effectiveness involved streamlining procedures, and nearly all required waivers from federal regulations. There is a marked similarity in agencies’

approaches to streamlining, with many agencies making the same changes in the frequency

and provisions of certifications and inspections. Examples of commonly made changes include

revisions to the Housing Quality Standards (HQS) certification process, in cases of minor

deficiencies, to allow landlords to self-certify that they have made required corrections, thereby

reducing the number of housing inspections performed by agency staff and expanding the time

between household recertifications, especially for elderly and disabled households. Other process

changes agencies made include combining waitlists across public housing and HCV programs,

simplifying or eliminating utility allowance policies, merging public housing and HCV program

Cityscape 37

Levy, Edmonds, and Long

administration, and upgrading information technology systems. Streamlining activities reduced

the costs of the procedures, as documented by agencies in their annual reports, thereby freeing

resources for other uses. Exhibit 3 includes agency-specific examples of cost-effectiveness activities.

Exhibit 3

Examples of Cost-Effectiveness Activities

After joining Moving to Work (MTW) in 2013, the Fairfax County Redevelopment and Housing Authority

streamlined its household recertification process by reducing the frequency of reexaminations from once a year to every 2 years for work-able families and to every 3 years for non-working families. The agency also streamlined its processes for inspecting housing units rented by housing choice voucher (HCV) households.

Consistent with the experience of other agencies, both activities achieved cost savings.

Lawrence-Douglas County Housing Authority was one of the first MTW agencies to combine its public

housing and HCV programs into a single entity, called General Housing. This action created one waitlist for both types of housing assistance and a single organizational structure for program operations. The agency has reported cost savings from this change for nearly 20 years.

In contrast to the similarity of activities intended to improve cost-effectiveness, the activities

designed to improve self-sufficiency are diverse. Some agencies have sought to motivate self-

sufficiency through changing rent policies in ways that increase tenants’ payments, establishing

work requirements, setting time limits for assistance, and offering services to residents.9 Even

among agencies pursuing similar activities, such as changing rent calculations or establishing

work requirements, details of the approaches vary widely. Exhibit 4 includes examples of agencies’

activities to encourage increased work effort using rent policy and service-provision activities.

Exhibit 4

Examples of Self-Sufficiency Activities

Lincoln Housing Authority includes a minimum earned income amount for work-able adults when calculating income to determine a household’s rent contribution—whether or not a family member is working. Based on 25 hours of work per week at the minimum wage, the amount serves as a de facto work requirement. The

agency has reported substantial additional revenue from the policy, reducing the net subsidy cost of housing assistance and freeing resources for other uses.

Home Forward, the agency in Portland, Oregon, is part of the Action for Prosperity partnership, which includes Worksystems, Inc., the Multnomah County Anti-Poverty system, and the State Department of Human Services.

Each partner delivers the core services in which it specializes. Home Forward households receive employment and training assistance, childcare, and other services through this arrangement. The agency provides financial support for Action for Prosperity with funding shifted from the housing choice voucher funding stream.

Activities that agencies pursue to increase housing choice also vary. Some agencies, including

those in Pittsburgh, Baltimore, and Minneapolis, have prioritized improvements to existing public

housing properties using funds shifted from the HCV and public housing operating funding

streams for capital improvements. For example, Baltimore used funding flexibility to shift resources

from the HCV funding stream to help cover the costs of rehabilitating units. Other agencies used

funding flexibility to leverage financing for the development of new housing units. For example,

9 A requirement for agencies participating in MTW is developing a “reasonable rent policy” that “encourages employment and self-sufficiency by participating families” (Public Law 104-134, 110 Stat 1321). The requirement is noteworthy in part because agencies report in their 2015 annual report that these activities free money that can be used for other purposes.

38 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

Lawrence-Douglas matched MTW funds with the city’s affordable housing trust funds to build eight

units of American Disabilities Act (ADA)-compliant housing in 2007. King County also shifted

funds from its HCV funding stream to purchase older buildings for redevelopment in opportunity

areas. Based on interviews with the sample of agencies, those located in areas with strong housing

markets marked by rapidly rising housing costs, such as Cambridge, Portland, and King County,

have prioritized alternative housing arrangements. The alternatives vary across agencies, reflecting

local real estate opportunities (such as acquiring newly available properties) and partnering

opportunities (such as working with community-based organizations to provide housing and

intensive services for target populations). Agencies noted that partnerships allowed them to

increase the number of households served and increase households’ access to needed services.

While the approaches to increasing housing choice are diverse, staff from all eight agencies

emphasized the importance of funding flexibility to preserve and develop affordable housing. For

some agencies, this meant shifting funding to increase the capital available for rehabilitating aging housing stock, which they said they would not have been able to do without funding flexibility

unless other funding sources became available. Exhibit 5 includes examples of agencies’ efforts to

increase affordable housing options and to address homelessness.

Exhibit 5

Examples of Housing Choice Activities

Housing Authority of the City of Pittsburgh shifted millions of dollars from its Housing Choice Voucher and public housing programs in 2015 to support two scattered-site housing development deals—at Northview

Mid-Rise and Larimer—and to fund various public housing modernization projects.

King County Housing Authority partnered with the Highline school district to pilot a rapid re-housing approach for addressing the growing number of homeless students in the county’s public schools. Through 2015, the program provided short-term rental assistance to re-house 44 homeless families with 108 children and included wrap-around services.

The way activities engage funding flexibility differs across the three objectives in terms of whether they free or use funding, as shown in exhibit 2. All activities addressing cost-effectiveness, and

some activities pursuing self-sufficiency, make resources available, while many self-sufficiency and

housing choice activities use some of these resources. Exhibit 6 visually depicts the relationship

between funding flexibility activities and shifts in resources. Most direct shifts of funds involve

moving money from the HCV funding stream and using it for other purposes. This is indicated by

the dark shading of the cell in the second column (Direct Fund Shifts) and the row for Existing

Vouchers. Less often, agencies make direct shifts of funds from the public housing operations

funding stream, which is indicated by the light shading. Indirect shifts of funds made available by

changes in agency policies and practices that lead to cost savings or additional revenues, or that

come from leveraged resources, are shown in the next three columns. Most cost savings come

from cost-effectiveness activities that change certification and other processes and change rent

calculations. Additional revenues mostly come from rent calculations and occupancy terms and

requirements, with less coming from existing properties. Leveraging reflects the resources agencies

accessed from external sources for services and housing choice activities. The last column depicts

activities that use resources that were shifted, or potentially shifted, from one funding stream to

serve a purpose outside of that funding stream or within the same funding stream at a later time.

Cityscape 39

Levy, Edmonds, and Long

Exhibit 6

Sources and Uses of Funds Directly and Indirectly Shifted by Type of Activity, Fiscal Year 2015

Relative Use of Activities with these Features

Activities

Activities that Freed Resources

that Used

Objective/Activity Category

Resources

Direct Fund

Cost

Additional Leveraging

Shifts

Savings

Revenues

Cost-Effectiveness

Housing Certifications &

Housing Inspections Processes

Other Procedures

Self-Sufficiency

Rent Calculation

Additional Services

Requirements & Limits

Housing Choice

Existing Properties (Affordable

Housing, Development

& Renovation)

Existing Vouchers (Landlord

Participation, Affordable Housing)

Alternative Housing Options

(Project-Based Vouchers,

Targeted Populations)

Notes: Dark shaded cells indicate relatively more engagement of activities that directly or indirectly shift resources or that used shifted resources. Light shaded cells indicate relatively less engagement. Activity categories in parentheses under Housing Choice match the categories with those shown in exhibit 4.

Sources: Moving to Work funding flexibility accounts prepared by Urban Institute based on data collected from review of the 39 Moving to Work agencies’

2015 annual reports, sections IV and V

The activities intended to advance household self-sufficiency both freed and used resources.

Activities such as rent reform initiatives freed resources, most often by increasing tenants’ rent

contributions. Activities used resources most often for services to help tenants prepare for, obtain, and retain employment.

Most of the activities intended to increase housing choice used resources. Public housing

renovations and the acquisition and development of affordable housing used substantial amounts

of freed resources, sometimes over the course of several years. A few activities in this category also freed resources: Activities that used funding flexibility to pay down debt on housing investments or

to improve the terms of debt freed funding that otherwise would have gone toward debt service.

Leveraging Activities

For purposes of this study, “leveraging” refers to an agency’s use of funding flexibility to increase access to external funding and other resources for programs and activities. In the more traditional

use of the term, agencies have used funding flexibility to improve their position when seeking

40 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

capital to finance large projects. Agencies also have used funding flexibility to secure grants or enter cost-sharing arrangements with foundations, nonprofit organizations, and other public agencies.

According to MTW agency staff, funding flexibility increases their ability to access resources

because they can more easily bring money to the table compared with traditional housing agencies

that operate without funding flexibility. Staff from agencies using the flexibility to access funding said they have been able to negotiate better financing deals for capital projects that might not have been possible otherwise because they can provide their own gap financing for a construction or

renovation project, which makes them more attractive to equity financers. They also said they

can access funding more easily from foundations that require matched contributions toward the

funding of resident services because of their ability to shift funds.

Among the 39 MTW agencies, 26 reported that funding flexibility helped them access external

funds during 2015. We gathered detailed information on leveraging from the sample of agencies.

Resources came from several sources—financial institutions, private companies, other public

agencies, and nonprofit organizations—and the funds obtained were used for a variety of purposes.

Loans from private lenders tended to be used for renovating or building public housing units,

project-based voucher (PBV) conversions, and other affordable housing projects. For example,

Baltimore shifted $7.6 million into a capital project, which helped secure $15.6 million from other

sources, including a private loan, Low-Income Housing Tax Credit (LIHTC) program funding, and

deferred development fees, for a total of $23.2 million for the redevelopment of the O’Donnell

Heights public housing development. Minneapolis supported its rent-to-own initiative with funds

leveraged from nonprofit and public sources, including an American Recovery and Reinvestment

Act (ARRA) grant. The agency shifted funds as part of an initiative involving the purchase of a

20-unit development. Staff believed the ability to shift funds was one factor in the agency’s receipt of the ARRA grant, though the role played by the funding shifts is difficult to pin down. The

additional funding supported the redevelopment of housing units for families.

Agency staff also said they used funding flexibility to shift resources to meet foundation and public agency matching requirements, thereby increasing access to grants and services and to provide

base funding for initiatives on which other funding can be built. Lawrence-Douglas County, for

example, shifted $111,518 from HCV funds to increase funding for resident services. This shift to

services helped secure an additional $7,230 in grants for services from philanthropies, including

the Blue Cross Blue Shield Foundation.

Agencies documented in their annual reports and confirmed during interviews that they had

increased access to external funding, at least in part because of funding flexibility. We do not

know the exact contribution funding flexibility makes for leveraging efforts, however. Leveraging

is different from other agency efforts to use funding flexibility. When an MTW agency reports

achieving cost savings or additional revenue, its estimate of the dollar amount is a reasonable

approximation of the net value attributable to its MTW status. For example, a cost saving is

generally measured as the cost of a program or administrative function in the reporting year

compared with the cost in the baseline year. When an agency reports leveraging, it reports the

gross dollar amount of resources secured in the reporting year; no net value estimate can be made

because there is no baseline value to subtract.

Cityscape 41

Levy, Edmonds, and Long

Shift in Funds

Previous sections of this article have focused on shifts in funds documented in agencies’ 2015

annual reports. To gather detailed information to quantify direct and indirect shifts in funds across the statutory funding streams, we reviewed data from their annual reports and interviews with

staff from eight MTW agencies. Each agency provided additional detail on their use of funding

flexibility and updated data on the amounts shifted. Although the resulting estimates of shifts

in funds cannot be generalized to all 39 MTW agencies, these estimates offer insights regarding

funding shifts.

Most of the shifts among the eight agencies moved funds from the HCV revenue stream for

activities covered by the public housing operations and capital improvements funding streams for

which additional funding was needed. Three agencies also shifted funds from the public housing

operations stream to help fund capital projects.

Funding Shifts by Eight Agencies

The eight sampled agencies shifted roughly $81 million in 2015, as shown in exhibit 7. Baltimore,

the agency in our sample with the largest budget and most households served, shifted the largest

amount, $39.2 million, and Lawrence-Douglas County, with the smallest budget and fewest

households served, shifted the smallest amount, $714,000. Expressed as a percent of its overall

revenue, including HUD funding and income from rents, Pittsburgh shifted 17 percent, the highest

percentage shift among these agencies.

Agencies receive more funding for their HCV program than for the other two funding streams, and

the voucher program is the source for most of the funds shifted. Agencies shifted approximately

$62 million from this stream in 2015. Two agencies, Baltimore and Pittsburgh, account for most of

this shift, having moved approximately $22 million and $18 million, respectively. The amount of

funds shifted from the HCV funding stream makes up between 5 and 37 percent of the agencies’

HCV budgets for the year. Pittsburgh shifted the highest percentage of its HCV funding.

None of the agencies shifted funds from their public housing capital funding stream. Three

agencies shifted a total of about $19 million from the public housing operating stream. Baltimore

shifted nearly $18 million of this amount. Pittsburgh shifted $1.4 million, and Lawrence-Douglas

County shifted $19,000. The other five agencies all freed some public housing operating resources

through cost savings and new revenues, but they used those resources on public housing, so no

shifts resulted.

The largest share of shifted funds, roughly $58 million across seven of the eight agencies, was used

for capital projects. As presented in the detailed shifts by agencies displayed below, these funds

supported improvements made to public housing properties and modifications to newly acquired

properties to provide local, non-traditional affordable housing. Four agencies shifted close to $12

million into public housing operations to support the public housing budget generally (that is, to

fill gaps) or to pay for specific items, such as housing security systems. Six agencies shifted about $7 million for local initiatives, most of which involved resident services and local, non-traditional housing assistance, and two agencies shifted a total of less than $4 million for HCV administrative

42 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

purposes.10 Where agencies discussed the use of reserve funds, we include details in the description

of their funding flexibility activities.

Not all potential shifts identified in agencies’ information on activities that freed resources or used freed funds happened, even if the activity itself occurred. That is, an agency’s accounting indicated that an activity was funded with resources available within the pertinent budget category, rather

than with funds freed from another budget category using funding flexibility, or did not need the

amount of funding expected. For example, some changes in resident services or administrative

procedures identified as requiring additional resources were funded with resources available

within the respective funding streams. According to agency staff, some of the activities involving

project-based voucher conversions did not require the level of capital improvements agencies

anticipated based on past conversions, so the costs were lower. Similarly, some activities that

agencies undertook with partners appeared at first to need funds shifted directly or indirectly

from the public housing operations revenue stream, such as the provision of housing assistance

with services through project-based or sponsor-based developments. When the deal was finalized,

however, the services turned out to be covered entirely by partner organizations.

10 Additional spending on HCV administration technically is not a shift in funds—spending more money on administration and less on subsidy does not move money from the HCV funding stream. Staff from the two agencies, Baltimore and Minneapolis, however, identified their shifts in this way. In some cases, the additional administrative expense is associated with other shifts in which the agency engaged. For example, Baltimore incurred additional costs in connection with the Rental Assistance Demonstration, which involved significant capital improvements supported with funding shifts.

Cityscape 43

Levy, Edmonds, and Long

so

Total

$3,522,784

$6,771,054

$187,304,963

$62,556,939

$532,941,944

$782,803,846

$61,586,720

$19,041,641

$80,628,361

$58,380,889

$11,953,634

$80,628,361

d

3%

4%

Home Forwar (Portland)

$12,495,874

$4,434,494

$66,329,260

$83,259,628

$2,535,006

$2,535,006

$2,535,006

$2,535,006

17%

37%

Housing

$1,422,291

$6,214,730

$1,947,258

Authority of the City of Pittsburgh

$45,096,283

$17,804,045

$48,509,366

$111,409,694

$17,910,174

$19,332,465

$11,170,477

$19,332,465

8%

14%

$44,372

Public Housing

$925,853

Authority

$6,065,463

$6,065,463

$1,309,039

$3,786,199

$6,065,463

Minneapolis

$19,497,294

$10,916,192

$44,840,800

$75,254,286

4%

5%

e were unable to schedule a follow-up discussion of funding flexibility details with Lexington,

, and 14.HCV). . W

Fayette Urban County

$744,000

$744,000

$744,000

$744,000

Lexington-

Housing Authority

$3,866,086

$1,765,495

$14,213,504

$19,845,085

14.CFP

ence-

11%

13%

$736,594

$415,931

$694,645

$19,350

$713,995

$599,707

$114,288

$713,995

provided by each agency

Lawr Douglas County Housing Authority

$5,412,378

$6,564,903

item_70000 (14.OPS,

Sample of Eight MTW Agencies

7%

8%

ear 2015

Housing Authority

$7,141,415

$6,900,870

$8,743,403

$8,743,403

$4,893,495

$2,599,306

$1,250,602

$8,743,403

HUD Form 50900,

King County

$105,009,772

$119,052,057

5%

7%

Housing

$879,528

Cambridge

Authority

$13,314,663

$2,228,415

$3,297,758

$3,297,758

$2,418,230

$3,297,758

ork Agencies, Fiscal Y

$44,943,879

$60,486,957

PH = public housing.

ork.

e

W

13%

11%

City

Housing

$2,213,745

$2,213,745

.

Authority of Baltimor

$85,156,754

$18,091,497

$203,682,985

$306,931,236

$21,596,271

$17,600,000

$39,196,271

$34,768,781

$39,196,271

. MTW = Moving to

a

Voucher

b

eams

otal Funds

om:

b

om Statutory

cent of T

cent of HCV Funds

Financial data supplied by the sample of eight MTW housing agencies

Funding Categories

Exhibit 7

Funding Shifts at Eight Moving to W

2015 HUD Funding:

Public Housing

Operating Fund

Public Housing

Capital Fund

Housing Choice

Vouchers Funding

Total fr

Funding Str

Shifted fr HCV Subsidy (includes

administrative fee)

PH Capital

PH Operating

Total Funds Shifted

Total Funds Shifted as

Per

HCV Funds Shifted as

Per

Shifted to: HCV Administration

PH Capital

PH Operating

MTW Local Initiatives

Total Funds Received

HCV = Housing Choice Funding data comes from information agencies reported in the 2015 annual report, Data on the value and source of funds shifted comes from HUD Form 50900 and from detailed information a

b

we have less information for that agency Source:

44 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

Funding Shifts by Individual Agencies in 2015

A more detailed look at the experiences of the eight agencies shows how resources are shifted,

directly and indirectly, and used in local, high-priority initiatives. All shifts and uses of funds

presented here occurred in 2015. The funding that was shifted could have accumulated in reserve

over multiple years, though it was shifted in the agencies’ accounting in 2015. The activities could

extend over multiple years, though the expenditures included here were for 2015. The 39 accounts

with information on specific activities will be on HUDUser.gov, accessible through the website for

the MTW retrospective evaluation.

Housing Authority of Baltimore City. Baltimore shifted nearly $40 million from its HCV

and public housing operating streams—about 13 percent of its overall funding—into capital

expenditures and other initiatives. Nearly all of this total constitutes direct shifts (for additional details, see HABC Funding Flexibility Account Activities A, B, C, and D in appendix B of the

research report), and the majority of the shifted funds were devoted to capital improvements at

eight developments. The redevelopment of O’Donnell Heights, a public housing development built

in 1942, was the largest of these projects. The remainder of the shifted funds were spent on an

administrative change related to the agency’s participation in the Rental Assistance Demonstration.11

Cambridge Housing Authority (CHA). Cambridge spent $34 million of its $37.3 million HCV

funding in 2015 for HCV subsidies and administration, leaving $3.3 million for other uses. Despite

spending less than its full allotment, the agency spent more of the funding for HCV subsidies and

administration and provided voucher assistance to more households in 2015 than it did in the

previous year. The shifted funds were used for housing development and rehabilitation activities,

support of public housing operations, and local MTW initiatives. Most of the development and

rehabilitation in 2015 involved conversions to PBVs (see CHA Funding Flexibility Account,

activity A, in appendix B). About two-thirds of the local initiatives involved resident services (see CHA Funding Flexibility Account, activities 1 and 4, in appendix B), and the remainder paid for

Cambridge’s Policy + Technology Lab and other endeavors.

King County Housing Authority (KCHA). King County shifted $8.7 million from its HCV

budget and, like Cambridge, still provided more households with voucher assistance than it did

the previous year. Much of the subsidy fund movement in this and previous years was made

possible by the rent payment standards changes it made more than a decade ago (KCHA Funding

Flexibility Account, activity 15, appendix B). Also, the agency’s actual administrative costs were

lower than the embedded fees, in part due to several cost-saving measures (see, for example,

KCHA Funding Flexibility Account, activity 10, appendix B) making HCV administrative funds

available. King County also shifted money from its operating reserves, which accumulated based

on shifts from HCV in previous years. The public housing operating budget in 2015 faced a deficit,

so the agency moved funds previously reserved to fill the gap. The agency elected to use funding

previously shifted from the HCV program to reserves in lieu of capital funds for some of its public

housing capital improvement work. The capital fund has a longer spending horizon than MTW

11 This refers to an expenditure on the agency’s Voluntary Retirement Program (VRP), required for staff realignment due to the agency’s participation in the demonstration. In exhibit 13, the expenditure has been divided between HCV

administration and public housing (PH) operation.

Cityscape 45

Levy, Edmonds, and Long

funds and is not subject to possible recapture. This allowed KCHA to retain capital funds for

spending on projects extending beyond 2015. King County’s activities using funding flexibility

included local, non-traditional rental subsidy programs, support of public housing community

facilities, and facilitation of a loan for construction of a public housing senior building.

Lawrence-Douglas County Housing Authority. Lawrence shifted about $655,000 from its

operating reserves, which accumulated from direct shifts from the HCV revenue stream and

resources freed through administrative costs savings from the HCV and public housing programs

in 2015 and previous years (see Lawrence-Douglas County Funding Flexibility Account, activities

1, 2, 4, 6, 13, and 14, appendix B). Most of these funds were shifted into capital improvements

in Clinton Place (a development for the elderly) and the purchase of a property for affordable

housing. The funds shifted to local initiatives were spent on resident services (staff working on the Resident Opportunity and Self-Sufficiency [ROSS] and Family Self-Sufficiency [FSS] programs and

for the youth program operated jointly with Douglas County Housing Incorporated) and assistance

(for vehicle repairs and GED fees).

Lexington-Fayette Urban County Housing Authority. Lexington shifted funds from its HCV

allotment to partially repay a subordinated loan for development costs of Centre Meadows. This

development was rehabilitated as part of the Rental Assistance Demonstration. Centre Meadows

provides project-based vouchers to more than 200 households.

Minneapolis Public Housing Authority (MPHA). Minneapolis moved $6 million from its

HCV budget, with much of the shift enabled by other, previous activities, such as the agency’s

rent reform initiative, which produced cost savings (see MPHA Funding Flexibility Account,

activities 1 and 10, appendix B). The funds shifted into HCV administration supported the HCV

mobility program (Funding Flexibility Account activity 9, encouraging families to move to areas of

opportunity), creation of an interactive HQS enforcement reporting system, a supportive housing

initiative for youth, and a shelter-to-housing initiative for the homeless. The HCV funds moved to

support capital improvements were spent on the rehabilitation of public housing units. The funds

shifted into public housing operations were mostly spent on security systems (staff and equipment)

in public housing properties. The funds spent on resident services were used chiefly for counseling

and job training.

Housing Authority of the City of Pittsburgh (HACP). Pittsburgh shifted $17.9 million from

HCV and $1.4 million from public housing operating funds. A large share of these funds was

shifted to the capital fund to support development deals at Northview Mid-Rise and Larimer

developments ($8,080,784) and various modernization projects ($3,089,693). Funds were shifted

into public housing operating funds to support an energy performance contract, extraordinary

expenses, and administrative costs ($3,038,882), and to pay for protective services ($3,175,848).

Finally, $1,947,258 was shifted for local non-traditional activities to support resident services.

Home Forward (Portland). Home Forward shifted $2.5 million from HCV for several local

initiatives. Approximately one-half of the shifted funds were used for the agency’s local blended

subsidy initiative (see Portland Fund Flexibility Account, activities K and 3, appendix B). The

blended subsidy used HCV and public housing operating funds to subsidize rent for households at

46 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

or below 80 percent of area median income to increase the number of public housing units. It used

nearly $729,000 for three short-term rental assistance programs, including the “I Have a Dream”

program, which provides individualized social, emotional, and academic support to young people

from low-income communities.12 The agency also used about $59,000 of shifted funds for the

Veterans Affairs Supportive Housing deposit program (see Portland Funding Flexibility Account,

activity G, appendix B), $46,000 for the landlord retention program (see Portland Funding Flexibility Account, activity 7, appendix B), and $268,000 for the rent assistance component of the Action

for Prosperity program (see Portland Funding Flexibility Account, activity B, appendix B). It used

$32,000 for the Fast Track and Aging in Place programs (see Portland Funding Flexibility Account,

activity D, appendix B), more than $52,000 for resident services in the Aging in Place and Neighbor

to Neighbor programs (see Portland Funding Flexibility Account, activities D and F, appendix B),

$19,000 for the Families Forward program (see Portland Funding Flexibility Account Locally defined

goals, activity C, appendix B), and $39,000 on the Family Unification program.

Factors Motivating the Use of Funding Flexibility

We identified three key factors that affect agencies’ use of funding flexibility from the analysis of activities and interviews with staff from the sample of agencies: (1) reduced appropriation funding

by Congress for housing authorities; (2) local market and community conditions; and (3) HUD

statutory and other requirements. Staff identified funding reductions as a key driver of their

funding flexibility use. Specifically, staff talked about shifting funds from the HCV revenue stream

to cover costs associated with public housing capital improvements, maintenance, and security

measures that became increasingly challenging to cover. For example, the Minneapolis agency

faced a $2 million a year funding gap for security services in its public housing developments. The

agency had covered security costs with funding received through a City of Minneapolis property

tax levy, which was eliminated in 2010. It now uses funding initially received in the HCV revenue

stream to supplement funds from the public housing operations funding stream.

The strength of local housing markets also strongly influenced the activities agencies undertook

with funding flexibility. Staff from five agencies said strong housing markets, with increasing

housing costs and lack of a sufficient quantity of affordable housing units, motivated their use of

funding flexibility. Agencies made direct and indirect shifts of funds for efforts to preserve existing public housing units and develop new affordable housing. For example, the CHA moved funds to

support initial rents during the first phase of a conversion of public housing under HUD’s Rental

Assistance Demonstration, thereby satisfying financial requirements and allowing the agency to

secure larger loans. In its 2015 report, the agency reported using $2.4 million in funds for initial

rents, which supported over $45 million in additional debt. As of the spring of 2018, the agency

had leveraged $400 million to preserve 1,200 existing public housing units and develop 325 new

housing units. Agencies also have used funding flexibility to support RAD conversions of public

housing to PBV properties to ensure the ongoing existence of hard units in decent repair.

Local labor market conditions influenced the Lawrence-Douglas County agency’s decision to

implement a work requirement. Staff said that having a strong work requirement was possible

12 https://www.ihaveadreamfoundation.org/

Cityscape 47

Levy, Edmonds, and Long

because jobs were available for which residents could successfully apply. The agency uses

funding flexibility to provide enhanced self-sufficiency related services in support of residents’

employment efforts.

All eight agencies talked about the need to respond to housing insecurity among specific

populations as motivating their use of funding flexibility. Agencies worked with partners to offer

housing through project-based vouchers or non-traditional affordable housing strategies, such as

sponsor-based voucher housing, for people who are homeless, survivors of domestic violence, or

young adults exiting the foster care system. Funds were shifted, directly or indirectly, in pursuit of the strategies. For example, King County shifted money directly into local, non-traditional rental

subsidy programs; Portland shifted funds directly into its Action for Prosperity program (providing

rent assistance and project-based vouchers packaged with extensive services) and indirectly into

its local blended-subsidy programs. Organizations partnering with the agencies to offer targeted

housing also offered services to households to increase their housing stability and overall well-being.

For example, Portland partnered with Worksystems, Inc. and the Multnomah County Anti-Poverty

system in implementing Action for Prosperity, leveraging its investment in housing assistance (using

shifted funds) to bring intensive self-sufficiency services to those receiving assistance.

Statutes and regulations combined with budget cuts limit agencies’ use of funding flexibility. Agency staff especially noted constraints stemming from the statutory requirement to assist substantially

the same number of low-income households and maintain a similar household mix as they served

before entering their MTW agreement. Meeting this maintenance of effort requirement with reduced

revenues limited, according to staff, their ability to use funding flexibility for other purposes.

Discussion

This study examines how Moving to Work (MTW) agencies use the flexibility to shift funds across

the statutorily separate funding streams. Using information drawn from agencies’ 2015 annual

reports and 2016 annual plans and other sources, we identified all activities that involved a direct

shift in funds across funding streams, and those that involved actual or potential indirect shifts, by accruing funds from cost savings or resource leveraging. As we have defined the term, all indirect

shifts rely on the combination of funding flexibility and regulatory waivers.

All 39 MTW agencies have used funding flexibility to undertake a variety of

activities. More activities focused on increasing housing choice than on agencies’

cost-effectiveness or residents’ self-sufficiency.

The 39 agencies made use of funding flexibility with waivers, and the majority pursued activities

with funding flexibility alone. All 39 pursued activities related to cost-effectiveness, and about one-third of the documented activities focus on this objective. These activities typically involve funding flexibility with waivers. Examples include expanding the number of years between household

recertifications and reducing the frequency of housing inspections for landlords with a history of

strong inspections. Streamlining recertification and inspection processes reduced the associated

administrative costs, which freed resources for flexible use.

48 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

About one-third of the documented activities focused on increasing households’ self-sufficiency,

and all 39 agencies used funding flexibility to pursue this objective. Agencies used accrued cost

savings from changes made to rent calculations and other policy changes, and they leveraged

resources for resident services associated with improving self-sufficiency. About two-fifths of the

documented activities focused on increasing residents’ housing choice, and all 39 agencies used

funding flexibility to pursue this objective. Agencies shifted resources directly, leveraged new

resources, and potentially used funds accrued from cost savings to improve existing properties,

create new housing options, or increase housing choice in other ways.

Agencies have used funding flexibility to improve their access to financing with

favorable terms and to meet matching requirements of funders or partners.

Based on the information from agencies’ reports to HUD, 26 agencies leveraged funds in 2015. In

general, agencies shifted funds from the public housing operations or the HCV program streams to

close project financing deals with larger loans and better terms than they otherwise would receive.

They used funds leveraged from private lenders to renovate or build public housing units, convert

properties to project-based voucher units, or pursue other affordable housing activities. Agencies

shifted funds to meet a funder’s matching requirement or to provide base funding for initiatives to

attract other funders. They used funds leveraged from private companies and public and nonprofit

organizations to support tenant services.

Analysis of direct and indirect shifts of funds completed for eight agencies showed

that most funding was shifted out of the HCV program and shifted into capital

projects, with smaller amounts going to public housing operations, to support local

initiatives, and for HCV administrative purposes.

The analysis of verifiable shifts of funds in 2015, direct and indirect for the sample of eight

agencies, found that the agencies shifted about $81 million, most of which, $58 million, was used

for capital projects. Of the remaining amount, $12 million was used for public housing operations,

$7 million was used to support local initiatives such as resident services and non-traditional

housing assistance, and about $4 million was used for HCV administrative purposes.

Most of the shifted funds, $62 million, came from the HCV program funding stream; all eight

agencies tapped that stream. Three agencies shifted a total of approximately $19 million from

the public housing operations stream, while none of the agencies shifted funds from the capital

improvement stream.

Funding flexibility is credited with enabling agencies to act more quickly than

otherwise would be possible, to undertake a greater range of activities, and to

work toward longer-term outcomes.

Agency staff expressed the importance of funding flexibility for improving their ability to take

actions related to local goals and statutory objectives within a context of reduced funding. They

reported being better able to preserve and develop affordable housing and to develop partnerships

with nonprofit and for-profit entities around activities related to self-sufficiency and housing

Cityscape 49

Levy, Edmonds, and Long

choice. Agencies said that funding flexibility made some activities easier to accomplish or allowed

them to pursue activities more quickly than they could have done otherwise, though they could

have undertaken the activities without the flexibility. For other activities, especially efforts to

make housing improvements, agencies identified funding flexibility as critical. Staff from several

agencies summed up the overall effect of funding flexibility as enabling them to become more

entrepreneurial—to act more strategically and with longer-term outcomes in mind.

Limitations

This study’s primary limitation concerns the completeness and accuracy of data available from

MTW agencies’ 2015 annual reports. Two sections of these reports, sections IV and V, were the

primary data sources used to create the 39 activity accounts. Review of reports, especially section V

of Form 50900 that describes funding-flexibility-only activities, found variation across agencies in

the completeness and consistency of information. In some cases, the list of flexibility-only activities is incomplete, partly because the pertinent reporting requirements are not as demanding as those

for activities described in section IV of the reports13 and because of the difficulty some agencies

have in identifying such activities. Some agencies do not maintain an accounting of direct shifts.

Even for agencies that do track these shifts, tracking funding first shifted to operating reserves and used later to fund multiple activities is challenging.

To address this data limitation, we used other data sources to assess the quality of annual report

data and to complete data for analyses where possible. Sources include agencies’ 2016 annual

plans, FDS data, and discussions with staff from eight MTW agencies. A comparison of the

qualitative data collected from the sample of agencies with the data and information from the

same agencies’ annual reports confirmed that the report data were incomplete and, in some

instances, inaccurate. The data inconsistencies, though relatively small, raise questions about the

completeness and accuracy of information drawn from the reports of the other 31 agencies, but we

were not able to gather data from the other agencies in a similar way.

For the analysis of funding shifts, we conducted detailed analysis only for the eight agencies in

our sample because agency staff corrected information we drew from their respective annual

reports and filled in gaps. For the other 31 agencies, we looked at any funding they reported as

having been used for funding flexibility activities in their 2015 Annual MTW report, including

accumulated reserve funds, which represent potential funding shifts.

Conclusion

This study is the first to examine in detail MTW funding flexibility, the ability of MTW agencies

to shift funds across the traditional funding streams of public housing operations, capital

improvements, and HCV programs and from the streams to local, non-traditional activities. We

expand the definition of funding flexibility to include activities conducted with waivers that lead

13 Guidance provided to agencies for completing section V of Form 50900 is sparse, stating that agencies are to

“provide a thorough narrative of each activity that uses Single Fund Flexibility in the body of the Plan … [and they]

are encouraged to provide metrics to track the outcomes of these programs or activities.” Agencies have not completed this section of the form uniformly. Guidance is more detailed for completing section IV of the form, which includes benchmarks, outcomes to be tracked, and other details.

50 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

to cost savings and additional revenue, thereby increasing funds available for use toward other

activities. Including these indirect shifts as well as direct shifts from one traditional funding

stream to another allowed us to better capture MTW agencies’ use of funding flexibility to meet

statutory objectives.

Agencies have used the flexibility broadly, pursuing activities related to each of the three statutory objectives. They have paid particular attention to increasing housing choice, though they have also

sought to improve agencies’ cost-effectiveness and residents’ self-sufficiency. Though we cannot

generalize from the sample of eight agencies, those agencies shifted most funding from the HCV

program to support capital projects. A majority of the agencies have been able to improve their

access to financing and other funder support by using funds from the traditional streams to close

short-term project gaps, provide base funding for activities, or otherwise shore up their position,

making them an attractive investment.

The sample of agencies credit funding flexibility for improving their ability to take locally

appropriate actions to meet MTW objectives, especially within the context of reduced funding.

Staff say that with funding flexibility, they can work more quickly to make decisions on the use of

funding and are better able to plan and implement activities geared toward longer-term outcomes.

The number and range of activities the agencies pursue, and the benefits attributed to funding

flexibility, suggest it is an important tool for agencies’ pursuit of policy and programmatic goals.

Cityscape 51

Levy, Edmonds, and Long

Appendix A: Activities Using Funding Flexibility by Objective,

Fiscal Year 2015

Total Number

Number of Activities by Objective

Year Executed

of Funding

MTW Agency

MTW

Flexibility

Cost-

Self-

Housing

Agreement

Activitiesa

Effectiveness

Sufficiency

Choice

Cambridge

1999

13

1

4

8

Housing Authority

Delaware State

1999

10

2

7

1

Housing Authority

Keene Housing

1999

20

6

8

6

Lawrence-Douglas

County Housing

1999

25

6

13

6

Authority

Lincoln

1999

12

5

4

3

Housing Authority

Louisville Metropolitan

1999

18

3

8

7

Housing Authority

Massachusetts

Department of Housing

1999

15

8

3

4

and Community

Development

Minneapolis Public

1998

10

4

2

4

Housing Authority

Portage Metropolitan

1999

13

3

7

3

Housing Authority

Home Forward

1999

22

6

8

8

(Portland)

San Antonio

1999

18

5

7

6

Housing Authority

Housing Authority

of the County of

2000

31

11

3

17

San Mateo

Seattle

1998

13

4

1

8

Housing Authority

Tulare County

1999

5

2

2

1

Housing Authority

Vancouver

1999

23

7

7

9

Housing Authority

Atlanta Housing

2003

25

5

6

14

Authority

Chicago Housing

2000

36

2

21

13

Authority

District of Columbia

2003

21

8

4

9

Housing Authority

King County

2003

29

8

3

18

Housing Authority

Housing Authority of

2001

16

6

1

9

the City of New Haven

Oakland Housing

2004

19

6

2

11

Authority

52 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Funding Flexibility

Total Number

Number of Activities by Objective

Year Executed

of Funding

MTW Agency

MTW

Flexibility

Cost-

Self-

Housing

Agreement

Activitiesa

Effectiveness

Sufficiency

Choice

Philadelphia

2002

12

4

1

7

Housing Authority

Housing Authority of

2000

14

1

3

10

the City of Pittsburgh

Alaska Housing

2008

19

6

3

10

Finance Corporation

Housing Authority

2008

15

4

3

8

of Baltimore City

Boulder Housing

2011

15

5

6

4

Partners

Housing Authority of

2010

9

2

2

5

Champaign County

Charlotte Housing

2007

14

4

5

5

Authority

Housing Authority

2013

7

4

1

2

of Columbus, GA

Fairfax County

Redevelopment and

2013

10

4

2

4

Housing Authority

Holyoke Housing

2013

8

5

2

1

Authority

Lexington-Fayette

Urban County

2011

11

3

5

3

Housing Authority

Orlando Housing

2011

14

4

4

6

Authority

Housing Authority

2013

12

4

4

4

of the City of Reno

Housing Authority

of the County of

2008

23

4

11

8

San Bernardino

San Diego Housing

2009

25

11

6

8

Commission

Housing Authority

of the County of

2008

24

12

3

9

Santa Clara and the

City of San Jose

Tacoma Housing

2010

21

7

6

8

Authority

Total / Percent

647

192 (30%)

188 (29%)

267 (41%)

MTW = Moving to Work.

a The count of activities includes only those we identified as using funding flexibility, whether funded by direct or indirect shifts and undertaken with or without waivers.

Source: MTW funding flexibility accounts prepared by Urban Institute based on data collected from review of MTW 2015 annual reports, sections IV and V

Cityscape 53

Levy, Edmonds, and Long

Acknowledgments

This article is based on our research report funded by the U.S. Department of Housing and Urban

Development (HUD). We are grateful to them and to all our funders, who make it possible for

Urban Institute to advance its mission.

The authors appreciate the feedback and support that Elizabeth Rudd, the Government Technical

Representative (GTR) for the MTW Retrospective Evaluation, provided throughout the course

of the study. In addition, the authors thank the staff from the public housing authorities who

discussed the details of their use of funding flexibility and MTW program staff from HUD who

participated in interviews.

Authors

Diane K. Levy is a principal research associate with Urban Institute, Leiha Edmonds is a

research associate with Urban Institute, and David Long is the managing director at Princeton

Policy Associates.

References

Abravanel, Martin D., Robin E. Smith, Margery Austin Turner, Elizabeth Cove, Laura E. Harris,

Carlos A. Manjarrez. 2004. Testing Public Housing Deregulation: A Summary Assessment of HUD’s’

Moving to Work Demonstration. Washington, DC: Urban Institute. https://www.urban.org/research/

publication/testing-public-housing-deregulation.

Brick, Carmen, and Maggie McCarty. 2012. Moving to Work (MTW): Housing Assistance

Demonstration Program. Washington, DC: Congressional Research Service.

Khadduri, Jill, Melissa Vandawalker, Rebecca Cohen, and Jeffrey Lubell. 2014. Innovations in the

Moving to Work Demonstration. Cambridge, MA: Abt Associates.

Levy, Diane K., David Long, and Leiha Edmonds. 2019. Moving to Work Agencies’ Use of Funding

Flexibility. Unpublished manuscript.

U.S. Government Accountability Office (GAO). 2018. “Improvements Needed to Better Monitor

the Moving to Work Demonstration, Including Effects on Tenants.” GAO-18-150. Washington, DC:

U.S. Government Accountability Office. https://www.gao.gov/products/GAO-18-150.

Webb, Michael D., Kirstin Frescoln, and William M. Rohe. 2015. Innovation in Public Housing: The

Moving to Work Demonstration. Prepared for the Charlotte Housing Authority. Chapel Hill, NC:

University of North Carolina, Center for Urban and Regional Studies.

https://curs.unc.edu/files/2015/01/The-Moving-to-Work-Demonstration-Center-for-Urban-and-

Regional-Studies-Report.pdf.

54 The Moving to Work Retrospective Evaluation

by Martha M. Galvez, Daniel Teles, Alyse D. Oneto, and Matthew Gerken

Moving to Work Agencies’ Use of

Project-Based Voucher Assistance

Martha M. Galvez

Daniel Teles

Alyse D. Oneto

Matthew Gerken

Urban Institute

Abstract

The Moving to Work (MTW) demonstration gives selected public housing agencies greater flexibility with their spending and the ability to provide innovative housing assistance to low-income households. This paper explores multiple aspects of MTW agencies’ use of project-based voucher (PBV) assistance, including the share of assistance and Housing Choice Voucher budget authority devoted to PBVs, the relationships between PBVs and the Low-Income Housing Tax Credit (LIHTC) and Rental Assistance Demonstration

(RAD), the locations of PBV-assisted units, and motivations for using PBVs through case studies of three MTW agencies. We use a combination of HUD administrative data, publicly available neighborhood-level data, MTW plans and reports, and qualitative data. Our findings show that MTW agencies are more

likely to use PBVs and RAD than traditional agencies, and that PBVs are used more in tighter housing markets and have a significant overlap with LIHTC properties. The study also finds that there is little evidence that PBVs reach lower-poverty, opportunity-rich neighborhoods. Three case studies underscore the diverse ways that MTW agencies use PBVs to pursue the MTW program’s statutory objectives and

highlight the importance of local contexts and priorities in agency decisionmaking about PBV use.

Introduction

The Housing Choice Voucher (HCV) program is the largest federal housing assistance program,

serving more than 2.3 million households (HUD, 2019). Through the HCV program, the U.S.

Department of Housing and Urban Development (HUD) funds both tenant-based vouchers (TBVs)

and project-based vouchers (PBVs). With either voucher type, households pay up to 30 percent

of their income towards rent and utilities, and the voucher covers the difference. In either case,

at least 75 percent of participants must have extremely low incomes, using HUD’s income limits,

when they are admitted to the program. Unlike TBVs, however, PBVs are attached to specific

housing units or properties and are administered through contracts with property owners for

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 55

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

Galvez, Teles, Oneto, and Gerken

specified periods of time. When a household moves out of a PBV unit, the assistance remains with

the unit for the length of the PBV contract.

Systematic research of Moving to Work (MTW) agencies’ PBV-use—and PBV-use by traditional

public housing authorities (PHAs)—is relevant to HUD and policymakers for several reasons. First,

PBVs have become more available to traditional PHAs. The introduction of the Rental Assistance

Demonstration (RAD) and other pieces of legislation (discussed below) allow traditional PHAs to

designate more vouchers as project-based than was previously permitted and also ease some of the

challenges to using PBVs. Second, in some markets, PBVs may be appealing because portable TBVs

are difficult to use—whether due to tight rental markets or landlords refusing to accept them. It is

difficult to predict, however, whether PHAs will shift from TBVs to PBVs and what the implications

of that shift might be. Notably, PBVs limit neighborhood choice, raising concerns about PBV

households’ exposure to high-poverty neighborhoods. MTW agencies’ PBV activities and locations

can shed light on potential challenges, opportunities, and tradeoffs of expanded PBV-use. Lastly,

documenting the extent to which PBVs are tied to RAD or the Low-Income Housing Tax Credit

(LIHTC) program can help policymakers and practitioners understand potential constraints on

PBV locations.

This study explores PBV use at MTW agencies through a mixed-methods approach. We examine

several questions about PBV locations and the roles of RAD and LIHTC in the PBV program. We

use a combination of HUD administrative data, document review, and interviews with staff at three

MTW agencies. We focus on MTW PHAs but also examine PBV use among comparably sized

traditional PHAs. This article summarizes the research report “Moving to Work Agencies’ Use of

Project-Based Voucher Assistance” (Galvez et al., forthcoming), which includes additional detail on

the research methods and findings.

Background and Literature Review

Why and How Agencies use Project-Based Vouchers

There are several reasons that PHAs might choose to convert a portion of their TBV assistance

to PBVs (CBPP, 2017; Cunningham and Scott, 2010). PBVs might be attractive to PHAs in tight

or expensive markets and offer more predictable rent costs compared with TBVs. For example,

long-term PBV contracts set rent increases over time, even in places where private market rents are

rising rapidly.

PBVs may also be a promising option in places where voucher holders have difficulty finding

voucher-affordable units or landlords that will accept vouchers—recent research suggests that

landlords commonly refuse to rent to TBV holders (Cunningham et al., 2018). PBVs may also allow

agencies to serve higher-need households by co-locating supportive services.

They may provide a consistent revenue stream to help agencies finance new housing or rehabilitate

existing affordable housing—including through the RAD program, which allows PHAs to renovate

and preserve public housing units by converting them to PBVs or Project-Based Section 8 Rental

56 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

Assistance (PBRA).1 Finally, PHAs might view PBVs as an opportunity to create or preserve

affordable housing in high-opportunity neighborhoods. As discussed below, HUD provides

incentives to traditional PHAs for project-basing in lower-poverty areas.

There are several constraints on the use of PBVs by traditional PHAs.2 First, PHAs may not allocate

more than 20 percent of their total authorized number of HCVs to PBVs. Second, no more than 25

units or 25 percent of all units in a development (whichever is greater) may be assisted through

PBVs unless the property is in a census tract with a poverty rate below 20 percent (in which

case the cap is 40 percent of all units). Third, the maximum PBV contract term is capped at 20

years, with the option to renew for an additional 20 years. Finally, to retain neighborhood and

housing choice for families in the PBV program, HUD’s Family Right to Move requirement allows

households to request a TBV once they have lived in their PBV unit for 1 year.3 PHAs must provide

the family with the next available TBV.

Constraints differ somewhat for units converted from public housing through the RAD program.

RAD PBVs are not included in the 20-percent cap, and agencies can project-base an additional 10

percent of vouchers if they are connected to supportive services or serve vulnerable populations.

Additionally, RAD contracts are renewed indefinitely, and residents living in RAD-converted units

have a right to choice mobility, which is similar to HUD’s Family Right to Move requirement.4

MTW agencies have greater flexibility in their use of PBVs, conditional upon approval from HUD’s

MTW program office. With approval, MTW agencies may devote more than 20 percent of HCV

program funds or allocations to PBVs; devote more than 25 percent of the units in a single project

to PBVs; create initial PBV contract terms that extend beyond 20 years; establish a “local MTW PBV

program,”5 including project-basing units at properties owned by the agency (directly or indirectly)

and using simplified or existing local property selection processes for project-basing units; and

waive or revise the Family Right to Move requirement.

Additional HCV program flexibilities available to MTW agencies include the ability to waive or

revise operational policies and procedures, such as the terms of Housing Assistance Payment (HAP)

contracts and portability processes; rent policies and term limits; income verification procedures;

waitlist policies, such as procedures for maintaining waiting lists, and tenant selection procedures

1 The Office of Multi-Family Housing Program’s Section 8 Project-Based Rental Assistance (PBRA) provides long-term contracts to private for-profit or non-profit owners (including PHA owners) who rent some or all the units in the properties to low-income families. Costs of maintaining and operating the units with low-income tenants are covered by a monthly Section 8 PBRA payment to the private owner. This study does not include PBRA units in its analyses.

2 Some of the current restrictions were revised or relaxed from previous program regulations through the Housing Opportunity Through Modernization Act of 2016 (HOTMA). For more information, see https://nlihc.org/resource/hud-

provides-guidance-implementing-hotma-project-based-voucher-provisions. The data used in this study come from before HOTMA’s implementation in 2017.

3 See 24 CFR 983.261 for more information on the Family Right to Move provision: https://www.govinfo.gov/content/

pkg/CFR-2017-title24-vol4/pdf/CFR-2017-title24-vol4-part983.pdf.

4 All properties that convert assistance using RAD must provide residents the choice of moving with continuing tenant-based rental assistance using a Housing Choice Voucher (HCV) within an established time after conversion, which is 1 year for PBVs. Unlike the Family Right to Move Requirement, MTW PHAs are not able to waive or modify this provision.

5 For more information on the creation of an agency MTW Section 8 project-based program, see Section D.7, Attachment C, of the Standard MTW agreement (https://www.hud.gov/sites/documents/DOC_10242.PDF).

Cityscape 57

Galvez, Teles, Oneto, and Gerken

and criteria; Housing Quality Standards (HQS) certification and inspection procedures; and

processes to determine what types of funds may be used to rehabilitate or construct units, and

changes to procedures to determine a unit’s eligibility for PBVs.

MTW agencies document their activities and use of MTW flexibilities in annual plans and reports,

but reporting and the level of detail vary by agency, and agencies may bundle activities for the

purpose of reporting. For example, Boulder Housing Partners has implemented one activity that

covers eight elements of their PBV program and uses a combination of PBV-specific and broader

HCV authorizations. The flexibilities include waiving the 20-percent PBV cap on their HCV-

authorized units; using a local definition of exception units; waiving the competitive bidding process; establishing local rent limits and reasonableness; allowing owners or service providers to hold the

waitlist for their property; allowing Boulder Housing Partners staff to conduct their HQS inspections rather than a third party; and allowing tenants not receiving a subsidy to retain their voucher.

Existing Evidence on Project-Based Voucher Assistance

Prior research has established that MTW agencies use PBVs more than traditional agencies

(Galvez, Gourevitch, and Docter, forthcoming; Mast and Hardiman, 2017). Galvez, Simington,

and Treskon’s (2017) review of MTW agency plans and reports found that nearly all (36 of the 39)

MTW PHAs were engaged in at least one PBV activity as of 2015. In 2016, PBVs represented about

12 percent of all assisted units at MTW agencies compared with about 4 percent of all assisted

units at comparable traditional PHAs (those serving 750 or more households annually) (Galvez,

Gourevitch, and Docter, forthcoming). The share of MTW PBV assistance increased by roughly

8 percentage points from 2008 to 2016 (from about 4 to 12 percent), while the share of PBVs at

traditional PHAs increased by only 2 percentage points over the same period (from about 2 to 4

percent). Mast and Hardiman (2017) had similar findings and attributed MTW agencies’ more

frequent use of PBVs to their ability to use their MTW flexibilities.

Although the rate of PBV usage differs between MTW and traditional agencies, prior research

suggests that both types of agencies serve similar populations through their PBV programs, with

negligible differences in terms of the share of work-able household heads, head of household

average age, household composition and size, and the share of households headed by a person

with disabilities. PBVs at both MTW and traditional agencies tend to serve more elderly households

and fewer disabled households or households with children compared with TBVs or public

housing (Galvez, Gourevitch, and Docter, forthcoming; Mast and Hardiman, 2017). At both MTW

and traditional agencies, PBV-assisted household heads were slightly more likely to be White and

to be male, and slightly less likely to be work-able than public housing or TBV-assisted household

heads (Galvez, Gourevitch, and Docter, forthcoming).

There is no systematic evidence on how PHAs make decisions about PBV use. Prior literature

suggests PHAs may use PBVs to preserve or finance new affordable housing stock, to overcome

challenges finding landlords that will accept TBVs, or to pair housing with supportive services

(CBPP, 2017). No research has directly examined what generally motivates PHAs, or MTW agencies

specifically, to use PBVs.

58 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

There is a limited body of research on the geographic distribution of PBVs. One study finds that,

on average, PBVs tend to be in higher-poverty neighborhoods and are less dispersed than TBVs,

although they tend to be in lower-poverty neighborhoods and less concentrated than public

housing units (Devine et al., 2003; McClure, Schwartz, and Taghavi, 2015). Looking specifically

at households with children, Mast and Hardiman (2017) find the median poverty rate for tracts

with PBVs was marginally higher than the median for TBVs (28 percent for PBVs versus 24 percent

for TBVs). Similarly, Galvez, Gourevitch, and Docter (forthcoming) find that MTW PBVs and TBVs

were in neighborhoods with nearly identical poverty rates. There is limited evidence on MTW

agencies’ efforts to use PBVs in high-opportunity neighborhoods, but Galvez, Simington, and

Treskon (2017) did find that only four MTW agencies—Cambridge, Holyoke, Reno, and King

County—were intentionally using MTW flexibilities to reach low-poverty or high-opportunity

areas with PBVs.

None of these analyses examine PBV location patterns by race or ethnicity to determine if PBVs may

offer different neighborhood opportunities for Black or Latino households compared with TBVs or

public housing. Furthermore, no prior analyses examine the role of public housing conversions in

PBV locations (as discussed in the following discussion) or the types of neighborhoods in which

those conversions are occurring. In tighter or more racially segregated housing markets, it may be

challenging for low-income or non-White households to find housing that will accept vouchers

outside of high-poverty neighborhoods. PBVs could provide a mechanism for PHAs to identify

more promising location options than might be feasible with TBVs. PBVs that originate through

RAD public housing conversions, however, will likely resemble the higher-poverty locations of

public housing.

Interaction with the Rental Assistance Demonstration and the Low-Income Housing

Tax Credit

The potential interaction between RAD unit conversions and the use of LIHTC should be

considered when examining PBV use, particularly as they relate to PBV locations. Both RAD

conversion and LIHTC-supported rehabilitation and new construction projects typically involve

layering PBVs onto new or existing affordable housing properties. There is no research, however,

documenting the extent to which PBVs are connected to RAD conversions or are co-located in

LIHTC properties.6

RAD was authorized under the Consolidated and Further Continuing Appropriations Act of 2012

to help PHAs preserve and improve public housing in need of major rehabilitation. Through

RAD, PHAs can convert public housing to either PBVs or PBRA. Because RAD is a housing

preservation program, HUD waives the PBV program’s poverty deconcentration goal. RAD was

initially authorized up to 60,000 units for conversion, and Congress has gradually raised the cap to

455,000 units as of 2019. The uptake by PHAs has followed this expansion. An interim evaluation

published in 2016 identified 39,042 RAD conversions in 359 projects (Econometrica, Inc., 2016),

a number that has grown to approximately 135,210 as of July 2020. For both MTW and traditional

6 HUD’s RAD Resource Desk provides project-level information on RAD projects, including the number of units converted, whether the conversion used tax credits, and whether the conversion used PBVs or PBRA. For more information or to download the data, see: https://www.radresource.net/pha_data.cfm.

Cityscape 59

Galvez, Teles, Oneto, and Gerken

PHAs, the extent to which PBV-assisted units are in converted public housing properties may be

important in explaining PBV locations. If a large proportion of PBV units nationally are in former

public housing properties, PBV locations will very likely resemble public housing locations.

LIHTC gives private investors a federal income tax credit in return for making equity investments

in affordable rental housing.7 State policies for awarding tax credits vary widely and may include

additional tax credits awarded to projects in high opportunity neighborhoods as well as areas

with a higher poverty rate, a large number of low-income households, or with particularly high

development costs (Ellen et al., 2015; Scally, Gold, and DuBois, 2018). Research and anecdotal

evidence suggest there is considerable overlap between LIHTC and the HCV program (Climaco et

al., 2009; O’Regan and Horn, 2013), although no data source comprehensively overlays voucher

and LIHTC assistance or differentiates TBVs from PBVs.8 Prior research also shows that LIHTC

properties are more likely to be found in suburban areas compared with HCVs (Ellen, O’Regan,

and Voicu, 2009; Freeman, 2004; McClure, 2006). As with RAD, the degree of overlap between

the PBV and LIHTC programs could have implications for PBV neighborhood locations, but it is

difficult to estimate whether the co-location of PBV units in LIHTC properties might expand or

impede access to lower-poverty neighborhoods.

Research Approach

The remainder of this article explores five research questions, detailed below.

Research Question 1: How extensively do Moving to Work agencies use project-

based vouchers?

The first research question quantifies MTW agencies’ PBV activity using 2016 HUD administrative

data from the Public and Indian Housing Information Center (PIC), the Voucher Management

System (VMS), the RAD Resource Desk, and a database of MTW activities developed for the

evaluation. Specifically, we examine:

• How many MTW agencies report PBV-assisted households in HUD administrative data?

• Which MTW agencies have the most active PBV programs?

• How frequently do MTW agencies use their PBV flexibilities?

We identify four measures of PBV activity as of 2016. For the measures calculated using HUD

administrative data, we contrast PBV use by MTW agencies to that of the comparison group of

similarly large traditional PHAs. The four measures are:

1. The number and percent of MTW agencies with PBV-assisted households.

2. The number and percent of all MTW-assisted households served through PBVs.

7 See Scally, Gold, and DuBois (2018) for information on the LIHTC program.

8 For example, O’Regan and Horn (2013) had access to subsidy information for LIHTC properties in one state and estimated that roughly 23 to 26 percent of households in LIHTC properties had vouchers.

60 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

3. The percent of HCV budget authority that MTW agencies devoted to PBVs.

4. The number and percent of agencies that used their MTW flexibilities to exceed the cap of 20

percent of HCV budget authority allocated to PBVs.9

Research Question 2: What factors are associated with Moving to Work and

traditional agencies’ use of project-based voucher assistance?

We use linear regression to explore factors associated with PBV use. To increase our sample size

and statistical power, the regression model is estimated using a sample of MTW agencies only

(N=34) as well as a larger sample that includes large PHAs and MTW agencies (N=446).10 We

use data from PIC, the American Community Survey (ACS), HUD’s Affirmatively Furthering Fair

Housing (AFFH) database, Real Estate Assessment Center (REAC) Physical Assessment Subsystem

(PASS), and the Zillow Rent Index (ZRI).

The model uses the linear form:

Percent PBV

= α + β

+ β + ⋯ + β + γ

+ ϵ

(1)

2016

1 * x 1

2 * x 2

n * x n

* PercentPBV 2009

to estimate the relationship between the share of assisted households reported in PIC that were

assisted through PBVs in 2016 (Percent PBV ) and several factors ( x to x ) that might motivate 2016

1

n

PHAs to expand their use of PBVs, while accounting for a baseline level of PBV use ( PercentPBV ).

2009

Specifically, we examine the following motivating factors. First, we include logged average rental

prices measured with ZRI11 in 2016 and percent change in rents between 2011 and 2016 to

determine if agencies in tight, competitive housing markets, where it may be harder to use TBVs, have a greater incentive to use PBVs. In addition, we measure public housing distress prior to the RAD

launch in 2012, using REAC PASS scores from 2008, and include the share of assisted households

that lived in public housing in 2009, since we expect that PHAs with more distressed public

housing would be more motivated to take advantage of HUD programs such as RAD or Section 18

Demolition and Disposition that would allow them to improve and convert their public housing stock

and transition units to PBVs. Finally, the model includes indicator variables for U.S. Census Bureau

regions. The model also includes a regression constant, α, and heteroskedastic error term, ϵ.

Research Question 3: To what extent are Moving to Work agencies’ project-based

vouchers located in Rental Assistance Demonstration or Low-Income Housing

Tax Credit properties?

Three measures capture the extent to which MTW agencies’ PBV programs interact with RAD and

LIHTC as of 2016:

9 Prior to 2017, traditional PHAs were able to allocate 20 percent of their budget authority to PBVs. HOTMA shifted the formula and cap to 20 percent of agencies’ voucher allocations.

10 The full sample of 39 MTW agencies with MTW designation as of 2019 is reduced to 34 agencies because of a combination of data limitations. The housing authorities of the County of Santa Clara and the City of San Jose report data jointly into PIC and were entered into our analysis as a single PHA. Missing PASS and Zillow data required removing four PHAs. The analysis includes 412 comparison PHAs for whom PIC, PASS, and Zillow data were available.

11 ZRI and Zillow Home Value Index data were acquired from www.zillow.com/data on November 28, 2018.

Aggregated data on this page is made freely available by Zillow for non-commercial use.

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Galvez, Teles, Oneto, and Gerken

1. The number and share of each MTW and traditional agency’s PBV units that were former public

housing units converted to PBVs through RAD (regardless of occupancy status) as of 2016.

2. The number and share of each MTW agency’s PBV-assisted households living in former public

housing units converted to PBVs through RAD as of 2016.

3. The number and share of each MTW agency’s PBV-assisted households living in LIHTC

properties in 2016.

We use 2016 data from PIC, the RAD Resource Desk, and the National Housing Preservation Database.

HUD administrative data does not directly identify which PBV units were converted from public

housing, so it is necessary to use a combination of administrative data sets to differentiate the

RAD-converted PBVs from other vouchers and to identify households living in those units in 2016.

We first identified all MTW agency public housing addresses reported in PIC in 2012 through

2016 to create an inventory of properties in existence immediately prior to the availability of RAD

(which was enacted in 2012). We then matched the MTW public housing addresses to RAD “First

Component” address data12 for more than 44,000 units converted and “closed” through 2016 to

identify the properties converted during the first 4 years of the program. We then use 2016 PIC

data to identify all households reported as living in PBV-assisted units and to identify those in PBVs that were converted through RAD.

To identify the overlap between PBVs and LIHTC properties, we used ArcGIS to map the addresses

of all MTW PBV-assisted households in 2016 PIC data, and all LIHTC properties active as of 2015

in the National Housing Preservation Database.13, 14 We drew a radius of 200 feet around each

LIHTC property—the equivalent of about one city block—and defined all PBV addresses that fell

within that radius as located in the LIHTC property.15 We then determined the share of each MTW

agency’s PBV-assisted households located in LIHTC properties. We repeated this analysis for MTW

TBV-assisted households for comparison.

Research Question 4: Are Moving to Work agency project-based vouchers in

lower-poverty, higher-quality neighborhoods, and do project-based voucher

locations vary by household race or ethnicity?

To answer these questions, we first assess whether PBV-assisted households are in higher- or lower-

poverty neighborhoods (census tract) relative to three comparison points: (1) other neighborhoods

in their same housing markets; (2) households assisted by the same PHA but with TBVs or living

12 RAD’s First Component allows PHAs to convert public housing properties to either PBVs or PBRA (see PIH

Notice 2012-32).

13 We use all properties placed in service between 1987 and 2015 with active LIHTCs as of 2015.

14 This method was chosen after preliminary analysis showed that issues with data quality, changes in street addresses after redevelopment, and differences in coordinate precision and formats prevented exact location matching between PIC, the RAD Resource Desk, and the National Housing Preservation Database.

15 City block sizes vary widely across the country (Handy, Butler, and Paterson, 2003), and can be as small as 200

feet to 800 feet or more. We use a radius of 200 feet since existing studies treat 200 feet as a lower bound for the size of an average city block (Galvez et al., 2014). We conducted a sensitivity analysis using varying radii for the LIHTC

matching, which we include in appendix G of the full report (Galvez et al., forthcoming).

62 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

in public housing; and (3) the locations of PBV units at traditional agencies. We also compare

differences in neighborhood characteristics for PBV, TBV, and public housing locations by the race

and ethnicity of the assisted households.

To account for regional variation, we construct indicators of neighborhood quality that are

normalized by county, which allow us to compare location outcomes across MTW agencies while

accounting for the poverty levels or other characteristics of the housing markets that each agency

serves. The county-normalized neighborhood poverty rate of each household is calculated by

dividing the poverty rate of the household’s census tract by the county poverty rate, using estimates from the 2012–2016 ACS 5-year sample.

The average county-normalized neighborhood poverty rate for MTW PBV households is

then compared with that of households assisted through TBVs by the same MTW agency and

households in public housing assisted by the same MTW agency. We then compare the following:

the county-normalized neighborhood poverty levels of MTW PBV locations to that of comparison

traditional PHAs’ PBV locations; the difference between PBV and TBV county-normalized

neighborhood poverty levels for MTW agencies and that of the group of comparison traditional

PHAs; and the difference between PBV and public housing county-normalized neighborhood

poverty levels at MTW agencies with that of comparison traditional PHAs.

Each comparison is repeated using six additional county-normalized measures of neighborhood

(census tract) quality drawn from a combination of ACS and AFFH data. The measures are labor

force participation rate (2012–2016 ACS 5-year estimates); the percent of adults with a bachelor’s

degree (2012–2016 ACS 5-year estimates); Labor Market Engagement Index (HUD AFFH data);

Environmental Health Index (HUD AFFH data); School Proficiency Index (HUD AFFH data); and

Low Transportation Cost Index (HUD AFFH data).

The AFFH labor market engagement, environmental health, and low-cost transportation indices

are percentile ranks, nationally. The school proficiency index is a percentage rank by state. For

all four indices, a higher score represents a more desirable or higher-quality area. That is, higher

values mean more labor market engagement, fewer environmental hazards, better schools, or lower

transportation costs. A county-normalized value of 1 means that the assisted households are in

neighborhoods that are typical for the county.

We then examine locations for Black or African-American (non-Hispanic/Latino), Hispanic/Latino,

and White households. These analyses allow us to examine whether assisted households’ race

or ethnicity is associated with differential access to lower-poverty, higher-quality neighborhoods,

depending on the form of housing assistance.

Research Question 5: What are the agencies’ motivations for project-based

voucher use?

Based on our initial data collection and analysis for this study, we select three agencies with

extensive or innovative PBV programs: Boulder Housing Partners, the Cambridge Housing

Authority, and the Seattle Housing Authority. These agencies are among the highest users of PBVs

among all PHAs nationally; the average MTW agency with any PBV units devotes approximately 13

Cityscape 63

Galvez, Teles, Oneto, and Gerken

percent of their HCV budget authority to PBVs, while these three agencies, on average, devote 47

percent. These case studies are not exhaustive, but they identify common themes across a subset of

agencies with substantial PBV use.

To understand the agencies’ PBV efforts, we reviewed publicly available documents such as MTW

annual reports and plans16 and agency strategic or administrative plans. Each of the PHAs reviewed

and verified PIC data summarizing their PBV use. We also conducted group phone interviews

with three to four people in senior leadership roles at each agency who were knowledgeable about

the origin and priorities of the agencies’ PBV programs. These group phone interviews included

executive directors, HCV program directors, asset management directors, or policy staff.

Six interviews and follow-up calls were conducted with MTW agency staff in fall 2018. Interview

topics focused on an overview of the agencies’ PBV programs, agencies’ motivations and goals for

using PBVs, benefits and tradeoffs of PBV use, and how PBVs help meet MTW statutory objectives

or other goals. We also discussed the specific MTW PBV flexibilities that were the most useful and

local partnerships that involve PBVs.

Findings

Research Question 1: How extensively do Moving to Work agencies use project-

based vouchers?

MTW agencies are more likely to administer PBVs compared with traditional PHAs and, on

average, dedicate a larger share of their housing assistance to PBVs (exhibit 1). Nearly all MTW

PHAs reported at least one PBV-assisted household in PIC in 2016, versus 56 percent of the

comparison traditional PHAs. MTW agencies served more than 41,000 PBV households that

year, representing almost 1 in 10 MTW-assisted households. The group of comparison traditional

agencies served slightly more than 105,000 PBV households that year, accounting for about 4

percent of their overall housing assistance.

On average, the MTW agencies devoted 13 percent of their budget authority to PBVs, compared

with 5 percent at comparison traditional PHAs. Nevertheless, most MTW agencies’ PBV use falls

below the 20-percent budget authority cap applied to traditional PHAs, and extensive use of PBVs

among MTW agencies was rare. As of January 2017, only 9 of the 39 MTW agencies devoted more

than 20 percent of their budget authority to PBVs.

16 MTW housing authority plans and reports are available on HUD’s website. See “Moving to Work (MTW)–

Participating Sites,” HUD, http://portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_housing/programs/

ph/mtw/mtwsites.

64 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

Exhibit 1

Project-Based Voucher Use at Moving to Work Agencies and Comparison Traditional Public

Housing Authorities

Comparison

Measures

MTW Agencies

Traditional PHAs

Total PBV households (2016)

41,270

105,669

Percent PBV assisted households (2016)

9.7%

4.0%

Percent of PHAs with any PBVs (2016)

92.1%

56.1%

Average budget authority to PBVs (January 2017)

13.1%

5.4%

MTW = Moving to Work. PBV = project-based voucher. PHAs = public housing agencies.

Notes: We exclude Moderate Rehabilitation units. Comparison of traditional PHAs served more than 750 total households in 2016. The average percent of budget authority devoted to PBVs includes agencies with zero budget authority devoted to PBVs.

Sources: 2016 HUD Public and Indian Housing Information Center data; January 2017 Voucher Management System data; 2015 Moving to Work Annual Plans There is considerable variation in PBV use across MTW agencies. In 2016, 12 MTW agencies used

PBVs relatively sparingly (fewer than 5 percent of their assisted households), and three MTW agencies had no PBV-assisted units (exhibit 2). At the other extreme, four MTW PHAs— Cambridge Housing

Authority, Keene Housing, Boulder Housing Partners, and the Atlanta Housing Authority—served more

than one-fourth of their assisted households through PBVs. These four agencies combined represent

9,554 units, or 23 percent, of all MTW PBVs. Additionally, the MTW agencies with larger shares of

households assisted with PBVs also devoted greater shares of their HCV budget authority to PBVs.

Exhibit 2

Moving to Work Agencies’ Project-Based Vouchers as Percent of Assisted Households, 2016

Number of Moving to Work Agencies

14

12

10

8

6

4

2

0

No PBVs

Fewer than 5% 5 to 10%

10 to 15%

15 to 20%

20 to 25% More than 25%

Percent of Assisted Households in Project-Based Vouchers

PBV = project-based vouchers

Notes: Sample excludes Moderate Rehabilitation and includes only project-based vouchers. The Housing Authority of the County of Santa Clara and the Housing Authority of the City of San Jose jointly report data into HUD Public and Indian Housing Information Center (PIC) (for a total of 38 Moving to Work observations).

Source: Urban Institute analysis of 2016 HUD PIC data

Cityscape 65

Galvez, Teles, Oneto, and Gerken

Research Question 2: What factors are associated with Moving to Work and

traditional agencies’ use of project-based voucher assistance?

MTW agencies’ use of PBVs grew substantially between 2009 and 2016, from about 3 percent

of their total assisted households to 11 percent.17 During this time, 10 additional MTW agencies

began offering PBV units. Among the group of comparison traditional PHAs, the increase in PBV

use was more modest: from about 1 percent of all assisted households in 2009 to approximately 4

percent in 2016. Additional summary statistics are presented in Galvez et al. (forthcoming).

Linear regression clarifies which factors are associated with the growth in PBV use across agencies.

The model predicts PBV use in 2016 using the agency’s region, the quality and size of the agency’s

public housing stock in 2008 and 2009, the extent of PBV use in 2009, the level of rents in the

agency’s service area in 2016, and the growth of rents between 2011 and 2016.

Using a sample of both MTW and comparison traditional PHAs (exhibit 3, column 1), we find that

PHAs with more distressed public housing in 2008 used more PBVs in 2016. The model shows

that receiving five fewer points on a PASS score in 2008 is associated with having an additional

0.7 percent of households in PBV units 8 years later. Although that is a small percentage of total

assisted households, considering that the average large PHA (MTW or traditional) relied on PBVs to

support only 4 percent of households, it represents a nearly 18 percent increase in PBV units.

Exhibit 3

Model Results: Factors Related to the Percent of Assisted Households Assisted by Project-

Based Vouchers in 2016 (1 of 2)

MTW and Comparison

MTW PHAs

Traditional PHAs

-0.011

-0.019

Percent Public Housing (2009)

(0.018)

(0.232)

-0.002*

0.000

REAC PASS Score (2008)

(0.001)

(0.006)

0.040***

0.093

Rent Index (2016)

(0.013)

(0.091)

-0.036

-0.264

Change in Rents (2011 to 2016)

(0.028)

(0.206)

-0.009

-0.169*

South

(0.010)

(0.093)

-0.008

-0.173*

Midwest

(0.010)

(0.100)

17 We use a different calculation of total PBV use for the regression analysis compared with the assessment of MTW

agencies’ PBV use in research question 1, resulting in a slightly different share of PBV use. For the regression analyses, we calculate an average of the percent PBV use at each MTW PHA.

66 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

Exhibit 3

Model Results: Factors Related to the Percent of Assisted Households Assisted by Project-

Based Vouchers in 2016 (2 of 2)

MTW and Comparison

MTW PHAs

Traditional PHAs

-0.000

-0.131

West

(0.011)

(0.113)

1.181***

1.019***

Percent of HHs in PBVs in 2009

(0.143)

(0.365)

-0.027

0.001

Constant

(0.033)

(0.300)

Observations

446

34

HHs = households. MTW = Moving to Work. PBV = project-based voucher. PHAs = public housing agencies. REAC = real estate assessment.

PASS = physical assessment.

* p<0.10. ** p<0.05. *** p<0.01.

Notes: Samples include MTW PHAs and traditional PHAs with at least 750 households in 2016 and for which both REAC and Zillow data were available.

For MTW PHAs, this excludes four PHAs. Housing Authority of the City of San Jose and Housing Authority of the County of Santa Clara household counts in HUD Public and Indian Housing Information Center (PIC) are reported jointly and listed here as a single PHA. Standard errors are heteroskedastic robust and displayed in parentheses.

Sources: Urban Institute analysis of 2016 PIC data, 2011 and 2016 Zillow Rent Index data, and 2008 Real Estate Assessment Center Physical Assessment Subsystem data

We also find that agencies in cities with higher rents use PBVs more extensively. Even after

controlling for geographic region and the rate at which rents are rising, we find that a 10-percent

difference in the price of rent is associated with a 0.4-percentage-point difference in the share of

households served with PBVs (exhibit 3). Our analysis does not identify a relationship between the

percent of assisted households in public housing in 2009 and the percent in PBVs in 2016 or any

regional differences in the expansion of PBV use between 2009 and 2016.

The regression model lacks precision when it uses the smaller sample of only MTW agencies

(exhibit 3, column 2). It is unable to determine whether the estimated relationships between PBV

use and public housing quality and the cost of rent are applicable to MTW agencies specifically.

Yet, the model shows that, among MTW agencies, PBVs are used more extensively in the Northeast

than in the South or the Midwest, with smaller differences between the Northeast and the West.

Research Question 3: To what extent are Moving to Work agencies’ project-based

vouchers located in Rental Assistance Demonstration or Low-Income Housing

Tax Credit properties?

MTW agencies frequently use RAD, and there is considerable overlap between MTW agencies’ PBVs

and LIHTC properties. In 2016, almost 40 percent of all occupied PBV units at MTW agencies,

representing 16,331 households, were former public housing units converted through RAD or

located at LIHTC properties. The remaining 60 percent of PBVs (24,939 households) were not

associated with RAD or LIHTC properties.

Cityscape 67

Galvez, Teles, Oneto, and Gerken

By the end of 2016, 15 MTW agencies (39 percent) converted 11,272 public housing units

through RAD, compared with 32,996 units converted by 97 of the traditional PHAs in our

comparison group (12 percent of 788 PHAs). MTW RAD conversions represented over one-fourth

of all RAD conversions among our sample of PHAs. In total, in 2016, about 14 percent of all MTW

PBV-assisted households reported in PIC were living in former public housing developments

converted through RAD (about 5,700 MTW PBV-assisted households).18

The MTW agencies were more likely than the traditional PHAs to convert units to PBVs than to

PBRA. MTW agencies converted about 77 percent of their RAD units to PBVs, whereas traditional

agencies converted 55 percent of their RAD units to PBVs. The average number of units per RAD

property converted to PBVs was similar for MTW agencies and the group of comparison traditional

PHAs (105 units on average versus 107).

Differences in RAD use remain if conversions that are still in progress as of March 2018 are included in the analysis: 22 of the 39 MTW agencies (about 56 percent) had RAD projects in progress or

completed as of March 2018, compared with 183 of the 788 traditional PHAs (23 percent).

LIHTC properties accounted for more than one-fourth of all MTW agencies’ PBVs (about 27

percent, or 10,984 households) in 2016, using addresses within 200 feet of a LIHTC property

address as a proxy for co-location. The share was substantially smaller for TBVs: about 8 percent of

MTW TBVs, or 21,334 households, were in LIHTC properties.

A small number of the MTW agency PBVs (387 of all PBVs, or about 1 percent) were RAD-

converted units that include LIHTC. Some anecdotal evidence suggests the RAD process can be

difficult to coordinate with LIHTC, which may explain the low overlap of RAD and LIHTC in the

relatively early years of the RAD program.19

RAD use and the extent of overlap between the PBV and RAD or LIHTC programs varied

considerably across MTW agencies. Among the MTW agencies with closed RAD PBV units by

the end of 2016, the total number of units ranged from 88 to 2,083. Twenty-four of the 35 MTW

agencies with any PBV-assisted households reported in 2016 had PBVs located in properties with

LIHTC. The overlap ranged from 3 percent of their PBV-assisted households to 65 percent. All

MTW agencies had at least some TBVs located in properties with LIHTC, ranging from about 0.3

percent of all TBV units to slightly more than one-fourth.

Research Question 4: Are Moving to Work agency project-based vouchers in

lower-poverty, higher-quality neighborhoods, and do project-based voucher

locations vary by household race or ethnicity?

Our analysis finds that MTW agency PBV-assisted households lived in neighborhoods with higher

poverty rates, lower levels of educational attainment, lower labor market engagement, lower

environmental quality (more potential exposure to environmental toxins), and lower performing

18 A small number of MTW RAD units were identified in PIC data as TBVs, likely due to PIC data entry or reporting errors. We omit these units from our analyses.

19 See, for example, Lessons from RAD https://www.huduser.gov/portal/pdredge/pdr-edge-featd-article-010818.html.

68 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

schools compared with the county average (exhibit 4). The comparative analysis with TBV and

public housing shows mixed results, however.

Unlike the national measures, county-normalized measures account for differences in cost of living,

access to education, and labor markets across regions. For context, the average county poverty rate

across the sample is about 15 percent, and 90 percent of PHAs are in counties with poverty rates

between 10 and 23 percent (using data from 2016). The average poverty rate for neighborhoods

with PBV households at MTW agencies is 28 percent—about 85 percent higher than if PBV units

were distributed evenly across richer and poorer neighborhoods.

Exhibit 4 shows a comparison between PBV locations and the locations of TBVs or public housing

at the same MTW PHA. This analysis shows that PBVs are in tracts with higher poverty rates than

TBVs. The average difference in county-normalized poverty rates between PBV and TBV tracts

is 27 percent of the county poverty rate (exhibit 4). This difference is statistically significant

(p=0.001). MTW agencies’ PBVs are in neighborhoods with similar poverty rates as public housing

neighborhoods. Although public housing neighborhoods have higher poverty rates than PBV

neighborhoods (an average of 2.0 times the county average compared with 1.85 for PBVs), the

difference is not statistically significant (exhibit 4).

We found that PBV households at MTW agencies live in neighborhoods with higher educational

attainment and lower transportation costs in comparison to both TBV and public housing

households. The average MTW PBV household lives in a neighborhood in which the share of

adults with a bachelor’s degree is 17 percent below the county average. Yet, the typical MTW

household assisted with TBVs is in a neighborhood in which the share of adults with a bachelor’s

degree is 26 percent below the county average, and the typical MTW household in public housing

lives in a neighborhood in which the share of adults with a bachelor’s degree is 29 percent below

the county average. Lower transportation costs were expected because census-tract poverty rates

and the transportation cost index are inversely correlated.

Cityscape 69

Galvez, Teles, Oneto, and Gerken

Exhibit 4

County-Normalized Measures of Neighborhood Quality for Assisted Households at Moving to

Work Agencies by Program, Averages and Differences, 2016

Differences

Differences

Neighborhood

Means

Means

(p. value)

(p. value)

Quality Measures

PBV

TBV

PBV – TBV

PBV

PH

PBV – PH

1.85

1.58

0.27***

1.87

2.03

-0.16

Poverty Rate

(0.001)

(0.124)

0.83

0.74

0.10**

0.82

0.71

0.11

Percent with Bachelor’s Degree

(0.029)

(0.064)

0.96

0.97

-0.02

0.96

0.94

0.01

Labor Force Participation

(0.123)

(0.334)

0.67

0.69

-0.01

0.66

0.60

0.05

Labor Market Engagement Index

(0.729)

(0.202)

0.61

0.77

-0.15***

0.60

0.64

-0.05

Environmental Health Index

(0)

(0.383)

0.71

0.67

0.03

0.69

0.66

0.03

School Proficiency Index

(0.232)

(0.416)

1.24

1.14

0.10***

1.23

1.22

0.02

Transportation Cost Index

(0.001)

(0.58)

Observations

35

32

* p<0.10. ** p<0.05. *** p<0.01.

MTW = Moving to Work. PBV = project-based voucher. TBV = tenant-based voucher.

Notes: All statistics are normalized to the county mean. Raw values for the labor market engagement index, environmental health index, and transportation cost index are national percentile ranks with higher values signifying better outcomes. School proficiency index is percentile-ranked at the state level. This exhibit excludes Delaware State Housing Authority, Lawrence-Douglas County Housing Authority, and Home Forward (Portland, Oregon), which do not have any PBV units. The Housing Authority of the City of San Jose and Housing Authority of the County of Santa Clara household counts in the HUD Office of Public and Indian Housing Information Center data are reported jointly; they are listed here as a single public housing agency.

Sources: Urban Institute analysis of 2016 PIC data, 2012–2016 American Communities Survey 5-year estimates, and HUD AFFH data Comparisons between PBV households at MTW agencies and at traditional agencies are an

important component of this analysis. Exhibit 5 compares the neighborhoods of MTW PBV

households to the neighborhoods of PBV households at traditional PHAs. Both sets of PBVs are

in neighborhoods with higher poverty relative to the average for their counties. Moreover, at

both MTW and comparison PHAs, the average PBV-assisted household lives in a neighborhood

with a higher poverty rate than TBV-assisted households and a lower poverty rate than public

housing residents.

Both MTW and traditional agencies’ PBVs are in neighborhoods that score lower on the AFFH

Environmental Health Index than the county average, but after accounting for regional differences,

MTW PBVs are in neighborhoods with poorer air quality than PBVs at traditional PHAs. The

index ranks census tracts based on potential exposure to harmful toxins as measured in the 2005

National Air Toxins Assessment. At comparison agencies, the typical PBV household lives in a

70 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

neighborhood that ranks 23 percent lower than the county as a whole. At MTW agencies, the

typical PBV household lives in a neighborhood that ranks 39 percent lower than the national

average. The gap in normalized measures of environmental health between MTW and comparison

agencies is statistically significant.

Exhibit 5

County-Normalized Measures of Neighborhood Quality for Project-Based Vouchers at Moving to

Work and Traditional Public Housing Agencies, 2016

Means

Difference (p-value)

Neighborhood Quality Measures

MTW PBV –

MTW PBV

Traditional PBV

Traditional PBV

1.85

1.67

0.17

Poverty Rate

(0.175)

0.83

0.74

0.05

Percent with Bachelor’s Degree

(0.424)

0.96

0.97

0.00

Labor Force Participation

(0.855)

0.67

0.69

-0.02

Labor Market Engagement Index

(0.768)

0.61

0.77

-0.16***

Environmental Health Index

(0.003)

0.71

0.67

-0.01

School Proficiency Index

(0.856)

1.24

1.14

-0.03

Transportation Cost Index

(0.604)

Observations

35

Varies with Measure

* p<0.10. ** p<0.05. *** p<0.01.

MTW = Moving to Work. PBV = project-based voucher.

Notes: All statistics are normalized to the county mean. Raw values for the labor market engagement index, environmental health index, and transportation cost index are national percentile ranks with higher values signifying better outcomes. School proficiency index is percentile ranked at the state level. Among MTW agencies, this exhibit excludes Delaware State Housing Authority, Lawrence-Douglas County Housing Authority, and Home Forward (Portland, Oregon), who do not have any PBV units. Housing Authority of the City of San Jose and Housing Authority of the County of Santa Clara household counts in Public and Indian Housing Information Center are reported jointly; they are listed here as a single PHA. Poverty Rate, Percent with a Bachelor’s Degree, and Labor Force Participation were available for 417 traditional PHAs. Labor Market Engagement was available for only 413

traditional PHAs; Environmental Health Index was available for only 396 traditional PHAs, School Proficiency index was available for 409 traditional PHAs; and Transportation Cost Index was available for 413 traditional PHAs.

Sources: Urban Institute analysis of 2016 PIC data, 2012–2016 American Communities Survey 5-year estimates, and HUD AFFH data The second part of research question 4 asks about the relationship between PBV usage and race

or ethnicity. Exhibit 6 shows differences in the county normalized poverty rate for neighborhoods

accessed by Black (non-Hispanic/Latino), Hispanic/Latino, and White (non-Hispanic/Latino)

households. With each type of assistance, households headed by a non-Hispanic, White person

reach lower-poverty neighborhoods than those with a Black or Hispanic/Latino household head,

and differences between housing types are relatively consistent for Black, White, and Hispanic/

Cityscape 71

Galvez, Teles, Oneto, and Gerken

Latino-headed households. Results using the other five measures of neighborhood quality can

be found in Galvez et al. (forthcoming). In sum, we find that historical patterns of segregation

and concentration of minorities in neighborhoods with poor environmental conditions, fewer

amenities, and higher poverty rates are unchallenged by the use of PBVs.

Exhibit 6

County-Normalized Neighborhood Poverty Rate for Assisted Households at Moving to Work

Agencies by Program and Race/Ethnicity, 2016

Means

Differences Observations

Means

Differences Observations

PBV

TBV

PBV-TBV

PBV

PH

PBV-PH

Black (non-Hispanic)

1.88

1.61

0.27***

35

1.92

2.10

-0.18

31

Hispanic

1.81

1.53

0.29***

34

1.81

1.97

-0.16

31

White (non-Hispanic)

1.70

1.43

0.27***

33

1.72

1.80

-0.08

30

* p<0.10. ** p<0.05. *** p<0.01.

MTW = Moving to Work. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

Notes: All statistics are normalized to the county mean. Additional measures appear in appendix B (Galvez at al., forthcoming). This exhibit excludes Delaware State Housing Authority, Lawrence-Douglas County Housing Authority, and Home Forward (Portland, Oregon), who do not have any PBV units.

Housing Authority of the City of San Jose and Housing Authority of the County of Santa Clara household counts in Public and Indian Housing Information Center are reported jointly; they are listed here as a single public housing agency.

Sources: Urban Institute analysis of 2016 PIC data and 2012–2016 American Communities Survey 5-year estimates RAD and LIHTC are also major considerations for us in looking at differences between PBV

and TBV. Additional analysis in Galvez et al. (forthcoming) found no statistically significant

differences in neighborhood quality measures between RAD and non-RAD PBV units. MTW

households in RAD and LIHTC-financed PBV properties live in similar neighborhoods to

households in other PBV properties. Among the MTW agencies with LIHTC-financed PBV

properties, only two measures showed statistically significant differences: households in LIHTC-

financed PBV properties live in neighborhoods with somewhat better access to transportation

and somewhat worse air quality. Otherwise, PBVs in LIHTC-financed properties were in similar

neighborhoods as other PBV households with regards to poverty, educational attainment, labor

force participation, and school proficiency.

Case Studies of Three Moving to Work Agencies’ Project-Based Voucher Use

In this section, we summarize findings from our review of three PHAs’ PBV programs. The three

agencies included are Boulder Housing Partners, Cambridge Housing Authority, and the Seattle

Housing Authority. We first discuss common themes that emerged from the interviews with the

three agencies about how and why they use PBV assistance. We then present summaries of each

agency’s PBV activities along with additional detail on each agency.

Common Perspectives on Project-Based Voucher Use

Several common themes emerged about how and why the three agencies use PBV assistance.

The three PHAs maximize their MTW flexibilities to pursue MTW housing choice and cost-

effectiveness objectives. All three agencies included in our case studies use at least two of the five 72 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

main PBV flexibilities available to MTW agencies (summarized in the background section above),

combined with flexibilities available for HCV program administration generally. In MTW plans

and reports, all three agencies tie their PBV activities and waivers to the housing choice and cost-

effectiveness objectives—although each agency noted in interviews that PBVs can also indirectly

impact the MTW self-sufficiency objective.

Specifically, all three agencies waive the cap on the share of HCV budget authority that may be

applied to PBVs and the cap on the number of PBV units that may be in a single property. Among

the three agencies, the share of budget authority applied to PBVs in 2016 was the lowest for Seattle

(31 percent) and highest for Cambridge (67 percent). Each of the agencies subsidizes properties

that are 100 percent PBVs. Each of the agencies also applies additional HCV program flexibilities to

their PBVs, and each retains partial or full ownership in at least one property with PBVs.

Agencies use PBVs to facilitate partnerships. Each of the three PHAs described PBVs as

facilitating a variety of partnerships with community stakeholders to further local affordable

housing priorities or initiatives. For example, this included a longstanding partnership between the

Seattle Housing Authority and the city of Seattle to use PBVs to augment local Housing Tax Levy

funds to address homelessness. The ability to pursue common goals with local partners and be

responsive to local housing needs was noted as a key motivation for all three agencies’ PBV use.

PBVs allow the PHAs to be more effective in tight housing markets. Each of the three agencies is in

expensive housing markets with low vacancy rates. Agency staff noted advantages to PBVs compared

with TBVs in such market contexts. For example, all three noted that PBVs provide opportunities to

preserve or secure affordable units in areas with high or rapidly rising rents, whereas TBV families

have difficulty finding voucher-affordable housing or landlords that accept vouchers.

Staff from each of the agencies also said that year-to-year increases in housing assistance payment

costs could be more predictable for PBVs compared with TBVs in areas where rents are rising

quickly. Whereas individual landlords may raise rents substantially at the end of a lease period,

PBV HAPs and annual increases are established in PBV contracts. This allows agencies to build

increases in their longer-term financial planning.

PBVs offer opportunities for administrative efficiency. Agency staff said the MTW PBV

flexibilities offered opportunities for administrative efficiencies—and subsequent cost offsets for

the agencies—that TBVs do not. For example, Boulder Housing Partners’ staff discussed site-based

waiting lists administered by individual property owners and managers as allowing the PHA to

free up staff time for other tasks, in addition to helping the agency efficiently connect households

to suitable units and properties. The Seattle Housing Authority staff also noted that conducting

inspections at properties with multiple PBV-assisted units is more efficient than inspecting

geographically dispersed units or units that require interacting and coordinating with multiple

owners or managers. In addition, by allowing owners to conduct their own turnover inspections

for mid-year vacancies, the Seattle Housing Authority reduced the number of annual staff hours

spent conducting inspections.

Cityscape 73

Galvez, Teles, Oneto, and Gerken

Maintaining a balance of TBVs and PBVs. Each of the PHAs discussed the need to maintain a

portfolio of TBVs, as well as PBVs, and the limitations on PBV use. None expected to transition to

100 percent PBVs, and all acknowledged the importance of maintaining the residential mobility

opportunities that TBVs offer. Agency leadership said they periodically discuss the appropriate

balance of HCV use and whether to expand PBVs.

Project-Based Vouchers and Neighborhood Location

None of the three agencies explicitly use PBVs to target low-poverty or high-opportunity

neighborhoods. Each of the agencies, however, tied their PBV activities to a broad definition of

housing choice—emphasizing that PBVs increase local affordable housing options for low-income

people citywide and the benefits associated with their current PBV locations.

For example, the Cambridge Housing Authority and Boulder Housing Partners described their

jurisdictions as generally wealthy and opportunity-rich. The Cambridge Housing Authority staff

viewed the city as a whole as a resource-rich environment that is difficult for TBV holders to

access. This characterization of their PBV efforts as generally offering access to opportunity areas is reflected in the MTW activities reported to HUD. Boulder Housing Partners similarly highlighted

the city’s relatively low-poverty rates as a reason for not explicitly prioritizing low-poverty

neighborhood locations.

The Seattle Housing Authority staff noted the city’s downtown area, where many of their PBV units

are located, was identified as an opportunity area by a Kirwan Institute and Puget Sound Regional

Council analysis—in part because of the proximity to public transportation and social services.20, 21

For formerly homeless PBV residents, who account for most of their PBV occupants, access to these

resources can be essential to helping them be successful.

Boulder Housing Partners: Public Housing Conversion and Local Partnerships

“They’re bringing their services and we’re bringing the housing, so it’s a match made

in heaven.”

—Boulder Housing Partners

Since receiving MTW designation in 2011, the Boulder Housing Partners has focused their PBV

efforts on converting their public housing stock to PBVs and transitioning fully to HCV assistance.

They also have partnered with local housing and service providers to pursue a comprehensive

place-based education initiative, and in their 2015 MTW plan, they committed to contributing

2,000 new affordable units to Boulder’s permanently affordable inventory by 2025.

Staff said in interviews that MTW PBV flexibilities were a motivation for pursuing MTW status.

MTW status generally, and MTW PBV flexibilities specifically, are central to pursuing the agency’s

20 For more information, see the Puget Sound Regional Council’s “Opportunity Mapping”: https://www.psrc.org/

opportunity-mapping.

21 Seattle Housing Authority is also piloting a neighborhood mobility program using TBVs to support moves to opportunity-rich neighborhoods. For more information, see: https://www.seattlehousing.org/creating-moves-to-

opportunity-seattle-king-county-pilot-project-fact-sheet.

74 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

goals. Waivers to the PBV budget authority and units per property caps were included in the

agency’s first MTW Annual Plan after receiving MTW designation (BHP Annual Plan, 2012). The

Boulder Housing Partners formally ties its PBV use to the housing choice and cost-effectiveness

MTW statutory objectives, although staff said in interviews that their PBV program also addresses

family self-sufficiency. In 2018, the agency consolidated MTW activities enacted in previous

years into a single PBV program activity. In addition to waiving the PBV caps, the activity allows

Boulder Housing Partners to project-base 100 percent of units in a single project and to use site-

based waitlists. The activity also modifies several administrative policies for their HCV program,

including local rent reasonableness tests, rent limits, Housing Quality Standards inspections, and

income requirements.

Staff described how PBVs and the combination of PBV flexibilities help the agency impact

the MTW statutory objectives. For example, site-based waiting lists can help the agency more

efficiently place households into properties and units that meet their needs and offer more housing

choices. Staff said that converting units to PBVs as opposed to TBVs also allowed Boulder Housing

Partners to maintain the same demographic mix in their properties as in their traditional public

housing. The Bringing School Home initiative, located at five former public housing communities

that were converted to 100 percent PBVs, is intended to help close gaps in educational achievement

for low-income children and support long-term economic self-sufficiency.

Agency staff identified three specific programmatic efforts, which they believe have been supported

by MTW PBV flexibilities.

Partnerships. Strong partnerships with local service providers are central to Boulder Housing Partner’s organizational goals. Staff noted that the re-naming of the organization in 2001 from

the Housing Authority of the City of Boulder to Boulder Housing Partners reflects a longstanding

emphasis on service partnerships that predates their MTW status. These partnerships do not

usually come with additional service dollars attached; rather, staff emphasized that both the

Boulder Housing Partners and their affiliates see the value in connecting families already receiving

services to Boulder Housing Partners-assisted units. For example, Boulder Housing Partners

joins with the Boulder Shelter for the Homeless, which provides case management for homeless

individuals and families and helps them transition into PBV-assisted housing.

Project Renovate. Completed in 2017, Project Renovate converted 279 public housing units

in six properties to PBV units using RAD and Section 18 Demolition and Disposition. Boulder

Housing Partners converted units to PBVs instead of PBRA through RAD to retain the use of

MTW PBV flexibility for these units. Staff said that a goal for the conversions was to retain the

same households, demographic mix, and level of affordability for the converted properties as in

their original public housing portfolio. The housing authority has converted 135 units to PBVs

through RAD, amounting to about 33 percent of their PBV-assisted units. Two additional PBV

public housing property conversions have been completed through the Section 18 Demolition and

Disposition program (2018 annual report).

Bringing School Home. Bringing School Home is a place-based initiative currently operating in five former public housing properties that were converted to 100 percent PBVs. Local partners

Cityscape 75

Galvez, Teles, Oneto, and Gerken

manage the PBV communities and provide a variety of on-site services for children up to 6

years old through a variety of educational and enrichment supports for them and their families.

The Emergency Family Assistance Association manages the waitlist for these properties and is

responsible for the screening and admission. An example of services offered to residents includes

the “I Have a Dream” Foundation’s programming. The program seeks to reduce the gap in

educational outcomes between low-income students and their peers by maximizing the amount of

time children spend in educationally enriching activities.22

Cambridge Housing Authority: Preserving Affordable Housing with Project-

Based Vouchers

“The Project-Based Voucher (PBV) Program is considered a community resource, both to

support and preserve existing housing, and to expand affordable housing development

in Cambridge.”

—Administrative Plan for the Federal HCV Program

Cambridge Housing Authority (2013)

Rapidly rising rents, and extreme pressure on the affordable housing stock in and around the city

of Cambridge, provides the motivating context for the PHA’s MTW and PBV program priorities. In

2014, only 54 units of housing were available to every 100 extremely low-income households in

Middlesex County; 35 of these units were HUD-subsidized (Poethig et al., 2017). Approximately

30 percent of Cambridge’s population is students, which places additional demands on the lower

end of the rental housing market.23 Rents have been rising rapidly in the Cambridge area since

1994 when rent control ended in the state of Massachusetts.

The housing authority’s MTW and PBV programs center on creating and preserving affordable units

in Cambridge. In interviews, staff emphasized that TBVs are difficult to use locally, with 47 percent of TBVs porting out of the jurisdiction. TBV holders who remain in Cambridge may face annual

rent increases beyond the TBV voucher payment standard—set at 126 percent of fair market rent—

potentially triggering a move, higher HAPs, or additional rental costs and higher rent burdens for

assisted households.

Agency staff identified three specific programmatic efforts that they believe are facilitated by MTW

PBV flexibilities.

The Expiring-Use Preservation Program. About one-half of the Cambridge Housing Authority

PBVs—about 1,800 vouchers in 18 properties—were issued through the Expiring-Use Preservation

Program, which focuses on preserving units in and around Cambridge. Through this program, the

Cambridge Housing Authority identifies units in the private rental market with an existing subsidy

that are nearing their expiration date (for example, HUD legacy programs like the Rent Supplement

program and Rental Assistance Payment). Upon expiration of these subsidies, eligible residents may

22 For more information about the Bringing School Home program, see: https://boulderhousing.org/bringing-school-home.

23 “Demographics and Statistics FAQ.” Cambridge Development Department, accessed February 2019,

https://www.cambridgema.gov/CDD/factsandmaps/demographicfaq.

76 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

receive an Enhanced Voucher,24 which allows them to remain in their unit; however, if the resident

leaves their original unit, the Enhanced Voucher converts to a mobile voucher, and the original

unit becomes unsubsidized and likely converted to a market-rate unit. Through the Expiring-Use

Preservation Program and their MTW authority, the Cambridge Housing Authority is able to work

with the owner to preserve the tenancies of the existing residents in addition to preserving the

long-term affordability of these units.

Public housing conversion through RAD. Just under 30 percent of the agency’s PBV-assisted

units are in former public housing properties converted through RAD. The Cambridge Housing

Authority converted units to PBVs rather than PBRA to retain MTW flexibilities for rent

simplification and to retain voucher administrative fees, providing additional cash flow to leverage

debt for capital improvements. Staff asserted that it was a priority to retain the same assisted

households through the conversion and avoid disrupting their experience with the housing

authority. According to agency staff, few residents, if any, have taken advantage of Choice Mobility25

TBVs because of the challenges of finding housing with a mobile voucher in Cambridge.

Partnerships. The Cambridge Housing Authority has several partnerships with service providers and housing developers that incorporate PBV assistance to develop or preserve affordable units.

Many of these partners provide services on-site in PBV-assisted properties. For example, the

Cambridge Housing Authority partners with Just-A-Start to place their PBV-assisted units. As a

community development corporation, Just-A-Start provides resident services in all their affordable

rental developments, including supportive services and education programs.26 The Cambridge

Housing Authority noted that their development partners can access properties or neighborhoods

that the housing authority may not be able to access alone. They also stated that most of their

partnerships are long-standing, formalized through Memoranda of Understanding, and were

formed when organizations approached the PHA with collaboration ideas.

Seattle Housing Authority: Using Project-Based Vouchers to Serve People

Exiting Homelessness

“Our primary interest in the project-based voucher has been in the population that it

serves and the services that come with it.”

—Seattle Housing Authority

Since the early 2000s, the Seattle Housing Authority has partnered closely with the city, county,

and local service providers to address homelessness and support service-enriched housing for

high-need populations. Most of the Seattle Housing Authority’s 3,600 PBVs are connected to these

efforts, with a small share going to replacement vouchers in their HOPE VI communities. The

24 For more information about HUD’s Enhanced Vouchers, see: https://www.hud.gov/sites/documents/ENHANCED_

VOUCHERS_ENG.PDF.

25 Residents living in RAD-converted units have a right to choice mobility, which is similar to HUD’s Family Right to Move requirement. All properties that convert assistance must provide residents the choice of moving with continuing tenant-based rental assistance using an HCV within an established time after conversion, which is 1 year for PBVs.

Unlike the Family Right to Move Requirement, MTW PHAs are not able to waive or modify this provision.

26 For more information on Just-A-Start, see: https://www.justastart.org/.

Cityscape 77

Galvez, Teles, Oneto, and Gerken

Seattle Housing Authority staff stated that to date, their use of PBVs has, in large part, been guided by community priorities identified by local partners.

A tradeoff discussed by the Seattle Housing Authority staff of the focus on homeless and high-need

households is that the Seattle Housing Authority’s PBVs disproportionately house White, single

adult men compared with their TBV program. The PBV population mix is driven by priorities set

through the city’s Housing Tax Levy efforts and the county’s 10-Year Plan to End Homelessness,

and not by explicit Seattle Housing Authority targets. Although staff said that single adults can

inherently carry some cost efficiencies because they tend to live in studios that have lower HAP

costs than larger units, it has also meant serving fewer families than might be expected through the

TBV program. Unlike the PBV population, staff said, roughly half of the TBV waiting list tends to

be families with children.

Agency staff identified two main programmatic efforts as facilitated by MTW PBV flexibilities.

Local partnerships to end homelessness. Two main partnerships were discussed as driving the

Seattle Housing Authority PBV use: the Seattle Housing Tax Levy and the King County 10-Year

Plan to End Homelessness.27 The Housing Tax Levy raises funds to support affordable housing

creation and preservation. The first levy was passed in 1986, and Seattle residents vote every 7

years to provide funding to create and preserve affordable housing. The Seattle Housing Authority

leadership said that for each levy process, they determine whether to participate and at what scale.

The Seattle Housing Authority has contributed PBVs to each of the levies passed since they received

MTW status—in 2002, 2009, and 2016—committing 500, 500, and 300 new PBVs, respectively.

In total, roughly one-half of the Seattle Housing Authority PBVs are units connected to the levy.

Staff said that prior to the Tax Levy collaboration and through approximately 2009, additional

ad hoc partnerships were formed that account for the balance of the Seattle Housing Authority

PBV units. Many centered on the county’s Plan to End Homelessness and efforts to braid housing

assistance with service dollars from the county or other sources to serve high-need populations.

Cost-effectiveness through PBVs. The Seattle Housing Authority has made several efforts to pursue

efficiencies through its PBV program specifically and HCV program generally. Staff highlighted two

PBV flexibilities as particularly useful to sustaining service-enriched housing: waiving the PBV exit voucher requirements (or the “Family Right to Move”) and site-based waiting lists. Waiving exit

vouchers was described as allowing continuity and predictability for service partners and removing

pressure from the TBV waitlist to absorb households exiting PBV units. Staff said that site-based

waiting lists allowed high-need populations to be connected to properties that offered appropriate

services. Additionally, the Seattle Housing Authority has implemented combined program

management, which enables the Seattle Housing Authority to streamline management and policies

for PBV and public housing units that are co-located at the same property and to ensure that their

residents do not see a difference in their services or program management, no matter what kind of

assistance they receive.

27 For more information on the Seattle Housing Levy, see: https://www.seattle.gov/housing/levy. For more information on the King County 10-year Plan to End Homelessness, see: http://www.cehkc.org/plan.html.

78 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

Discussion

This study documents various aspects of PBV use for MTW agencies and a group of comparably

sized traditional agencies using administrative data and case studies of three MTW agencies with

large PBV portfolios.

PBV use is more common among MTW agencies than among traditional PHAs, but extensive

PBV use is not the norm. Most MTW agencies used PBVs to some extent as of 2016 and reported

activities requiring MTW PBV flexibilities. Yet, only nine agencies exceeded the 20-percent budget

authority cap on PBV use in 2016, and only four of those agencies used PBVs for more than 25

percent of the assisted housing they provided. More MTW agencies used PBVs and their flexibilities

sparingly. Even among the three case study agencies with extensive PBV use, operating in markets

where TBVs are challenging to use, staff discussed the importance of maintaining a portfolio of

TBVs and the residential mobility opportunities they offer.

Local housing markets play an important role in PBV use and agency decisions, which was

evident in both the quantitative and qualitative findings. Across MTW agencies, PBV use increased

more in the Northeast than in the South and Midwest. Furthermore, our analysis of MTW and

traditional PHAs shows that agencies in areas with higher rents increased their PBV use more than

agencies in more affordable markets. Staff at the three case study agencies stated that PBV costs are more predictable than TBV costs when rents are rising quickly. All three agencies discussed the

challenges TBV holders face finding private market housing citywide or in specific submarkets as

motivating their PBV use.

Our results also show a relationship between distressed public housing and PBV use. MTW and

traditional agencies with lower-quality public housing in 2008 (measured as PASS scores) were

more likely to increase their PBV use by the end of our study period (2016). The MTW agencies

were more likely than the traditional agencies to use RAD to convert public housing and convert

to PBVs. This may reflect MTW agencies’ ability to use funding or other flexibilities to navigate the RAD conversion process, and that MTW agencies can retain their funding and other flexibilities for

converted PBV units (but not for PBRA).

We find no evidence that PBVs are used as a tool to improve access to low-poverty or opportunity-

rich neighborhoods by MTW agencies or our sample of traditional PHAs. For both sets of agencies,

PBVs are in more distressed neighborhoods compared with TBVs and tend to be in areas that more

closely resemble public housing neighborhoods. Also, on average, both MTW and traditional PHAs’

PBV-assisted households live in more distressed neighborhoods than the typical neighborhood in

their counties.

Results were similar across neighborhood quality measures, with two exceptions. MTW PBVs were

in neighborhoods with better access to affordable transportation compared with the average for

their counties, most likely because MTW agencies tend to serve dense central cities with better

public transportation compared with other parts of their counties. Moreover, MTW agencies’ PBVs

are in neighborhoods with higher educational attainment than TBVs or public housing.

Cityscape 79

Galvez, Teles, Oneto, and Gerken

We did not find any differences in location patterns by race or ethnicity for PBVs compared

with other assistance programs. As is the case for the TBV program, non-White PBV-assisted

households tend to live in higher-poverty, more distressed neighborhoods than White PBV-assisted

households. We found that MTW agencies in more racially segregated areas are more likely to have

PBV units in higher-poverty neighborhoods. Notably, PBV locations appeared more sensitive to

racial segregation than TBVs in the same jurisdictions. It may be that, in highly segregated cities,

developing PBV properties outside of high-poverty neighborhoods is more difficult than renting in

those same neighborhoods with a TBV.

The case study agencies, although not representative of all MTW agencies, did not approach PBVs

as a tool to create housing in opportunity-rich neighborhoods. The agencies’ primary PBV goals

were to preserve and expand housing opportunities more broadly and to improve cost efficiency—

and PBV use tended to be tied to specific local priorities, partnerships, target populations, and

market considerations. The agencies discussed their PBV use as consistent with neighborhood

choice goals in that PBVs were in areas that likely offered tangible benefits to assisted households.

The three case studies underscore the diverse and creative ways that MTW agencies may use

PBVs to pursue the MTW program’s statutory objectives and the importance of local contexts and

priorities in agency decisionmaking.

Limitations

A significant limitation of this study is that it does not examine the extent to which PBVs are

combined with supportive services or used to house high-need households. MTW agencies have

unique opportunities to provide supportive services by making creative use of funding or policy

flexibilities—and staff from the case study agencies discussed tying supportive services to PBV

properties for veterans, people experiencing homelessness, and others. HUD also encourages

traditional PHAs to use PBVs to provide supportive services and serve “hard to house” families. No

data source documents whether PBVs house high-need families or are linked to services. MTW

annual plans and reports provide some relevant information, but the scale or nature of services

cannot be determined consistently.

Our analysis of the relationships between the PBV and LIHTC programs was also limited by

available data. Administrative data do not identify vouchers used in LIHTC properties, and issues

with data quality, changes in street addresses after redevelopment, and differences in coordinate

precision and formats prevented exact location matching between PIC and the National Housing

Preservation Database. We estimate the intersection of these programs for MTW agencies based

on addresses, but these estimates are sensitive to assumptions about property and block sizes and

could be improved by adjusting for local contexts.

The small number of MTW agencies itself was a limiting factor for cross-agency analysis of PBV use

or locations. We include comparison traditional PHAs in regression analyses to increase our sample

sizes and our ability to detect statistical relationships. As a result, the extent to which the estimated relationships between motivating factors and PBV use are representative of MTW agencies,

specifically, could not be determined. Similarly, we include case studies of just three MTW agencies

that are not typical of the average PHA because they are the highest PBV users among all PHAs

80 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

nationally and in some of the nation’s most competitive rental markets. Their insights are useful

to understand the benefits of PBVs in tight markets and ways that PBVs can be used to facilitate

partnerships or increase administrative efficiencies—but they tell us little about PBV use in weaker

markets or decisionmaking among agencies that do not use PBVs.

Policy Implications and Future Research

Our findings suggest some cause for concern about PBV locations and PBV-assisted households’

access to low-poverty, opportunity-rich neighborhoods. More research is needed to understand

ways to improve PBV neighborhood locations, including the mechanisms driving PBV location

options and MTW agency decisionmaking. For example, more qualitative research with MTW

and traditional agencies could shed light on how agencies select PBV properties and identify

opportunities for HUD to encourage improved locations. More work is also needed to examine the

relationship between PBV locations and racial segregation—for example, to understand whether

local opposition to affordable housing development contributes to PBVs’ concentration in high-

poverty areas. Finally, research is needed to fully document how often MTW agencies waive or

revise the PBV Family Right to Move requirement, the agencies’ reasons for doing so, and ways to

make it more feasible for agencies to implement it in the way HUD intends.

Second, the case studies identified examples of unique approaches to PBV use and productive local

partnerships from three high-capacity agencies, but a rigorous investigation of promising MTW

agency activities was not possible. More information about innovative practices and partnerships

from a diversity of agencies could help identify replicable models. Future case studies or qualitative work should include a range of PHA sizes, local market characteristics, and agencies with different

levels of PBV use to understand the challenges and opportunities that PBVs present to pursue

MTW’s objectives.

Third, our analyses begin to document the relationships between the HCV program and LIHTC

properties, but more work is needed to fully understand the extent to which the HCV and LIHTC

programs are mutually dependent. A more precise estimate of PBV and TBV co-location in LIHTC

properties at both MTW and traditional PHAs would shed light on the role that LIHTC properties

play in the HCV program.

Finally, future work should examine the extent to which MTW agencies and traditional PHAs

combine PBVs with supportive services or use PBVs to support high-need households. HUD

should consider ways to strengthen data and reporting from both MTW and traditional agencies,

to support research on the availability of supportive services and examine outcomes for households

with access to services.

Acknowledgments

This report was funded by the U.S. Department of Housing and Urban Development (HUD). We

are grateful to them and to all our funders, who make it possible for Urban Institute to advance

its mission.

Cityscape 81

Galvez, Teles, Oneto, and Gerken

Many people reviewed and contributed to this report. The authors appreciate the feedback and

support that Elizabeth Rudd, the government technical representative for the Moving to Work

(MTW) Retrospective Evaluation, provided throughout the course of the study. Wilton Oliver

provided valuable research assistance. In addition, the authors thank leadership from the Seattle

Housing Authority, Boulder Housing Partners, and Cambridge Housing Authority for contributing

their time and insights to this report. We also thank the MTW Program Office and other HUD staff,

including Mike LaRiccia, Patrick Hatch, William Lavy, Michael Hollar, and Jean Reed, for their

assistance in understanding administrative data sources. We thank our reviewers, Dr. Ingrid Gould

Ellen of the Furman Center for Real Estate and Urban Policy, Dr. Christina Stacy of Urban Institute,

and Dr. Nandita Verma of MDRC. We acknowledge the support of the MTW Expert Panel in the

design of this work. Other members of Urban’s MTW Evaluation team provided insights, data, and

comments on this study along the way. Finally, we thank the 39 MTW agencies for their review of

an early version of this report.

Authors

Martha M. Galvez is a principal research associate in the Metropolitan Housing and Communities

Policy Center at Urban Institute, where she specializes in housing and homelessness policy.

Daniel Teles is a research associate in the Metropolitan Housing and Communities Policy Center at

Urban Institute, where he specializes in applied microeconomic policy analysis.

Alyse D. Oneto is a research analyst in the Metropolitan Housing and Communities Policy Center at

Urban Institute, where her work focuses on housing and homelessness.

Matthew Gerken is a research analyst in the Metropolitan Housing and Communities Policy

Center at Urban Institute, where he focuses on affordable housing, homelessness, and

community development.

References

Boulder Housing Partners (BHP). 2012. “Boulder Housing Partners 2012 MTW Annual Report.”

Boulder, CO: Boulder Housing Partners. https://boulderhousing.org/sites/default/files/2012%20

MTW%20Annual%20Report.pdf.

Center on Budget and Policy Priorities (CBPP). 2017. Policy Basics: Project-Based Vouchers.

Washington, DC: Center of Budget and Policy Priorities.

Climaco, Carissa, Meryl Finkel, Bulbul, Kaul, Ken Lam, Chris Roger. 2009. Updating the Low-Income Housing Tax Credit (LIHTC) Database: Projects Placed in Service Through 2006. Washington, DC: U.S.

Department of Housing and Urban Development, Office of Policy Development and Research.

82 The Moving to Work Retrospective Evaluation

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

Cunningham, Mary K., Martha M. Galvez, Claudia. L. Aranda, Robert Santos, Douglas Wissoker,

Alyse Oneto, Rob Pitingolo, and James Crawford. 2018. A Pilot Study of Landlord Acceptance of

Housing Choice Vouchers. Washington, DC: U.S. Department of Housing and Urban Development.

https://www.huduser.gov/portal/pilot-study-landlord-acceptance-hcv.html.

Cunningham, Mary, and Molly M. Scott. 2010. The Resident Choice Option: Reasons Why Residents

Change from Project-Based Vouchers to Portable Housing Vouchers. Washington, DC: Urban Institute.

Devine, Deborah J., Robert W. Gray, Lester Rubin, and Lydia B. Taghavi. 2003. Housing choice

voucher location patterns: Implications for participants and neighborhood welfare. Washington, DC: U.S.

Department of Housing and Urban Development.

Econometrica, Inc. 2016. Evaluation of HUD’s Rental Assistance Demonstration (RAD): Interim Report.

Washington, DC: U.S. Department of Housing and Urban Development. https://www.huduser.gov/

portal/sites/default/files/pdf/RAD-InterimeRpt.pdf.

Ellen, Ingrid Gould, Keren Horn, Yiwen Kuai, Roman Pazuniak, and Michael David Williams.

2015. “Effect of QAP Incentives on the Location of LIHTC Properties.” Washington, DC: U.S.

Department of Housing and Urban Development.

Ellen, Ingrid Gould, Katherine O’Regan, and Ioan Voicu. 2009. “Siting, Segregation, and Spillovers:

A Re-examination of the Low Income Housing Tax Credit Program.” In Housing Markets and the

Economy: Risk, Regulation, Policy; Essays in Honor of Karl Case, edited by Edward Glaeser and John Quigley. Cambridge, MA: Lincoln Institute for Land Policy.

Freeman, Lance. 2004. Siting Affordable Housing: Location and Neighborhood Trends of Low-Income

Housing Tax Credit Developments in the 1900s. Washington, DC: The Brookings Institution.

Galvez, Martha, Ruth Gourevitch and Benny Docter. Forthcoming. A Picture of Moving to Work

Agencies’ Housing Assistance. Washington, DC: U.S. Department of Housing and Urban Development.

Galvez, Martha, Sharon Estee, Irina Sharkova, Qinghua Liu, Barbara Felver, and Jim Mayfield. 2014.

Characteristics and Experiences of TANF Clients by Housing Status. Olympia, Washington: Research and Data Analysis Division, Department of Social and Health Services. Urban Institute. Retrieved from:

https://www.dshs.wa.gov/sites/default/files/SESA/rda/documents/research-11-211.pdf.

Galvez, Martha, Daniel Teles, Alyse D. Oneto, and Matthew Gerken. Forthcoming. Moving to Work

Agencies’ Use of Project-Based Voucher Assistance. Washington, DC: U.S. Department of Housing and Urban Development.

Galvez, Martha, Jasmine Simington, and Mark Treskon. 2017. Moving to Work and Neighborhood

Opportunity: A scan of mobility initiatives by Moving to Work public housing authorities. Metropolitan Housing and Communities Brief. Washington, DC: Urban Institute. https://www.urban.org/

sites/default/files/publication/88171/galvez_2017.01.04_moving_to_work_and_neighborhood_

opportunity_formattedv2_final_2.pdf.

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Handy, Susan, K.S. Butler, and Robert Paterson. 2003. Planning for Street Connectivity - Getting from Here to There. Chicago, IL: American Planning Association.

Mast, Brent D., and David Hardiman. 2017. “Project-Based Vouchers,” Cityscape: 19 (2): 301–322.

McClure, Kirk. 2006. “The low-income housing tax credit program goes mainstream and moves to

the suburbs,” Housing Policy Debate 17 (3): 419–446.

McClure, Kirk, Alex F. Schwartz, and Lydia B. Taghavi. 2015. “Housing choice voucher location

patterns a decade later,” Housing Policy Debate 25 (2): 215–233.

O’Regan, Katherine M., and Keren M. Horn. 2013. “What Can We Learn About the Low-Income

Housing Tax Credit Program by Looking at the Tenants?” Housing Policy Debate 23 (3): 597–613.

Poethig, Erika, Liza Getsinger, Josh Leopold, Graham MacDonald, Lily Posey, Pamela Blumenthal,

Reed Jordan, and Katya Abazajian. 2017. Mapping America’s Rental Housing. Urban Institute,

updated 2017, https://apps.urban.org/features/rental-housing-crisis-map/.

Scally, Corianne Payton, Amanda Gold, and Nicole DuBois. 2018. The Low-Income Housing Tax

Credit: How it Works and Who it Serves. Research Report. Washington, DC: Urban Institute.

U.S. Department of Housing and Urban Development (HUD). 2019. “Picture of Subsidized

Households.” Database. Washington, DC: U.S. Department of Housing and Urban Development.

http://www.huduser.org/portal/datasets/picture/yearlydata.html.

84 The Moving to Work Retrospective Evaluation

by Nina Castells

The Effects of Increasing the Tenant

Rent Contribution in the Housing

Choice Voucher Program

Nina Castells

MDRC

Abstract

This study examines the effects of a rent reform in the Santa Clara County Housing Authority (SCCHA) on Housing Choice Voucher (HCV) program residents’ employment rates, average earnings, and housing subsidies using a quasi-experimental design. In the face of federal budget cuts to the HCV program in 2013, SCCHA reduced subsidies for all households rather than cutting some households from the program.

The primary component of its rent reform was to increase the tenant rent contribution rate from 30

percent of adjusted income (equivalent to about 27 percent of gross income) to 35 percent of gross income (eliminating all deductions and allowances) for all subsidy households. A risk was that if tenants reduced their earnings in response to the higher “tax” rate (since they keep a smaller portion of their earnings under the new policy), their subsidies would increase, counteracting the housing agency’s expected savings from increasing tenant rent contributions. A second rent reform component changed the voucher size policy, which resulted in a smaller voucher size (fewer bedrooms) for some households. The findings indicate that, on average, the SCCHA rent reform did not affect residents’ employment rates and average earnings throughout the 4 years following the implementation of the rent reform. Thus, the rent reform reduced households’ average housing subsidies as intended, and SCCHA was able to meet its projected savings. Since households did not increase their earnings to compensate for the reduction in their subsidies, these findings suggest that households absorbed their increased housing costs; however, whether they did so by reducing spending on necessary goods or by increasing debt and whether they experienced increased material hardship is unknown.

Introduction

This study contributes to an emerging body of evidence on the effects of alternative rent policies in subsidized housing by evaluating the impact of a rent reform at the Santa Clara County Housing

Authority (SCCHA). In 2013, federal budget cuts significantly reduced the SCCHA budget for

housing choice vouchers (HCVs), and SCCHA no longer had sufficient funds to continue subsidies

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 85

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

Castells

to all households at the same level as before. To avoid ending subsidies for some households,

SCCHA chose to increase the tenant rent contribution rate from 30 percent of household adjusted

income to 35 percent of gross income.1 It also changed the voucher size policy—the rules used to

determine the number of bedrooms on which a household’s subsidy is based—which resulted in

a smaller voucher size for 17 percent of all SCCHA’s HCV households and 23 percent of SCCHA’s

nonelderly, nondisabled households, the SCCHA households included in the study cohort.2

A central question of this study is how the rent reform implemented by SCCHA affected

households’ employment and earnings. There were three possible effects of the rent reform: (1)

the increased tenant contribution rate would act as a disincentive that would cause households to

decrease their earnings; (2) households would increase earnings to cover their increased housing

costs; or (3) there would be no effect on earnings or employment because households would adjust

spending in other areas to cover their higher housing costs, increase their debt, or experience

increased material hardship. If there had been a reduction in household earnings, it would have led

to an increase in housing subsidy amounts, counteracting the intended savings from the increase

in the tenant contribution rate. These policy changes affected all HCV households, regardless of

elderly or disability status, but the study focuses on nonelderly, nondisabled households because

elderly and disabled households would not have had the same flexibility to change their work

behavior in response to the policy changes.

The present study found no evidence that these policy changes had any effects, on average, with

respect to the employment and earnings of nonelderly, nondisabled SCCHA residents. Because

households’ earnings did not decrease in response to the policy change, SCCHA realized its projected

savings. Nonelderly, nondisabled households received approximately $1,600 less in housing

subsidies, on average, during the first year, $1,550 less in the second, and $1,330 less in the third year after the rent reform than they would have received without the rent reform. There was also

no evidence that the rent reform caused households to lose their housing subsidies, on average.

This article summarizes the study’s rationale, research questions, analytic approach, and findings.

A longer report published earlier this year, “Evaluating the Effects of Santa Clara County Housing

Authority’s Rent Reform,” includes a more comprehensive discussion of the methodology,

comparison group selection, and additional sensitivity analyses (Castells, 2020).

Background

The HUD HCV program is the nation’s primary rental subsidy program, assisting approximately

2.2 million low-income households in paying for housing in the private rental market. HCV

households generally pay 30 percent of their income toward rent, and HUD subsidizes the

remaining amount of the households’ rent up to the payment standard, a threshold based on area

housing costs. This rent policy aims to protect assisted households from excessive rent burden, but

critics of the policy argue that pegging tenant contributions to income can disincentivize work.

1 This change was tempered in 2014 when the rate was reduced to 32 percent of gross income, although this is still substantially higher than the previous rate.

2 HUD defines an elderly household as one where the head of household, spouse, or co-head is at least 62 years old, and a disabled household as a household where the head of household, spouse, or co-head is a person with a disability (Code of Federal Regulations, 24 CFR 5.403).

86 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Launched in 1996, HUD’s Moving to Work (MTW) demonstration grants selected public housing

agencies (PHAs) special statutory authority to change many HCV program rules, including rent

rules.3 As an MTW agency, SCCHA could respond to budget cuts by increasing the proportion of

income that HCV households paid toward their rent.

HCV is a housing subsidy paid directly to the landlord by the agency on behalf of the participating

household. The subsidy amount is called the Housing Assistance Payment (HAP) and is equal to

the difference between the Total Tenant Payment (TTP) and the payment standard. TTP is typically

30 percent of the household’s adjusted income, after accounting for various allowable deductions.

The household is responsible for their TTP, plus any amount by which the gross rent exceeds the

local payment standard.4, 5 As a result, when tenants’ earnings increase, their share of the rent is

increased by 30 percent of the additional amount they earn.

Exhibit 1

Definitions of Housing Subsidy Terms

Adjusted Income: A household’s gross income minus deductions for the following: allowances for dependents, status as an elderly family or family with members with disabilities; unreimbursed childcare expenses; unreimbursed medical expenses (for elderly families and families with members with disabilities only); and unreimbursed disability assistance expenses.

Fair Market Rent (FMR): HUD publishes, annually, an FMR schedule for the FMR area in which the PHA has jurisdiction. FMRs are based on the 40th percentile of rents charged for standard rental housing in the FMR area.

Gross Income: A household’s total income before taxes and other deductions, received by all members of the household. It includes total income from wages, social security payments, retirement benefits, military and veteran’s disability payments, unemployment benefits, welfare benefits, and asset income. It excludes certain forms of income, such as earnings from minors and income from live-in aides.

Gross Rent: The total contract rent paid to the landlord plus any utility allowances.

Payment Standard: Payment standards are used to calculate the housing assistance payment (HAP) that the PHA pays to the owner on behalf of the family leasing the unit. Each PHA has latitude in establishing its schedule of payment standard amounts by bedroom size. A PHA may set its payment standard amounts

from 90 percent to 110 percent of the published FMRs for the area and may set them higher or lower with HUD approval.

Port-Out: A household relocating to a unit within another PHA jurisdiction.

Utility Allowance: The utility allowance is an estimate of the amount needed for a household to cover its reasonable utility costs, based on the unit’s number of bedrooms, which utilities the tenant is responsible for outside the contract rent, the type of utilities (for example, gas versus oil heat), and other unit characteristics such as structure type.

Voucher Size: The number of bedrooms on a household’s voucher, calculated based on the household composition. For determining the payment standard applicable to the household, the HCV program uses the lower of (1) the number of bedrooms on a household’s voucher, or (2) the number of bedrooms of the actual unit rented.

Minimum Rent: The minimum amount set by the PHA that households must contribute towards rent and utilities.

3 Subject to public notification, approval of each PHA’s Board of Directors, and HUD approval.

4 The payment standard is the maximum subsidy a PHA can pay on behalf of a household, and payment standards are set by the PHA between 90 and 110 percent of the area’s fair market rents (FMRs). HUD sets FMRs annually at the 40th percentile of gross rents in the area.

5 See exhibit 1 for definitions of relevant housing subsidy terms.

Cityscape 87

Castells

The rule that households pay 30 percent of adjusted income pre-dates the Section 8 program’s

enactment in 1974 and was based on a judgment about reasonable housing cost burden at the time

(Schwartz and Wilson, 2008). It was initially set at 25 percent by the 1969 Brooke Amendment

to the 1968 Housing and Urban Development Act and was raised to 30 percent in 1981. This

30-percent-of-income rule is also now known as the Brooke Rent.6 Variations of this tenant

contribution rate—such as the one implemented by SCCHA—have so far not been rigorously

tested. If increasing the tenant portion of rent does not affect households’ employment decisions

and does not significantly increase tenants’ material hardship, but does succeed in reducing HAP

expenditures, this could be one way of providing housing assistance to more families within a fixed

budget amount.

Exhibit 2

General Housing Policy Terms

Brooke Amendment: The Brooke Amendment to the 1968 Housing and Urban Development Act was

enacted in 1969. It amends the United States Housing Act of 1937 to cap subsidy households’ tenant rent share at 25 percent of their adjusted income. This percentage was raised to 30 percent in 1981.

Brooke Rent: Thirty percent of adjusted income that households pay toward their gross rent.

Section 8 Housing Choice Voucher Program (HCV): The HCV program is the federal government’s primary rental subsidy program, assisting approximately 2.2 million low-income households in paying for housing in the private rental market. HCV households generally pay 30 percent of their income toward rent, and HUD

subsidizes the remaining amount of the households’ rent up to a certain threshold based on area housing costs. The HCV program was enacted as Section 8 of the United States Housing Act of 1974.

The Santa Clara County Housing Authority Rent Reform

Until September 2013, SCCHA required its tenants to pay 30 percent of adjusted income for rent

(plus any amount over the payment standard). Then, in response to federal budget cuts resulting

from the sequestration in March of that year, SCCHA increased tenants’ share to 35 percent of gross

income. Under traditional rent rules, income is first adjusted by subtracting various allowances

and deductions—including dependent and childcare allowances and deductions for medical

expenses—and then multiplying the adjusted income by 30 percent to arrive at the TTP. Under

the new rules, TTP was a flat 35 percent of the household’s gross income with no allowances or

deductions.7 SCCHA estimated that this changed households’ rent contributions from 27 to 35

percent of gross income (HACSC, 2013). In addition to eliminating deductions and allowances,

SCCHA also eliminated utility allowances, so that households do not receive an extra subsidy for

utilities that are not included in their contract rent.8 In effect, those households were paying more than 35 percent of their gross income. In September 2014, when the budget situation improved,

6 See exhibit 2 for definitions of general housing policy terms used in this report.

7 SCCHA has a minimum tenant rent contribution of $50 that was in place before the rent reform (and before the beginning of the study period). Under both traditional and new rent rules, the tenant rent share is the greater of the calculated percentage of income or the $50 minimum rent.

8 Under the traditional rent rules, for units where utilities are not included as part of the contract rent, a utility allowance is added to the contract rent amount to calculate the gross rent. The HAP calculation is then based on this gross rent amount rather than the contract rent amount and equals the gross rent minus the household’s TTP

(assuming the unit does not exceed the payment standard). Using the new rent calculation rules, HAP is calculated as the contract rent minus the household’s TTP (again, assuming the unit does not exceed the payment standard).

88 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program the percentage of gross income to be contributed was reduced from 35 to 32 percent. As a result of

the policy change, therefore, the overall tenant contribution was drastically increased in 2013, then slightly decreased a year later.

Exhibit 3 shows TTP as a percentage of gross income in the last month before the policy change

and over the followup period for the study cohort (all nonelderly, nondisabled households), which

is the measure that most directly reflects the rent policy changes over time.9 This measure reflects

the treatment whose effects are evaluated in this study. In the month before the rent reform was implemented, the 30 percent of adjusted income that households were paying toward rent (up to

the unit’s payment standard) under the traditional rent rules translated, on average, to 27 percent

of gross income. The actual percentage during this month was 30.6 percent because this average

includes households for whom 30 percent of adjusted income (or 10 percent of gross income) is

less than the PHA’s minimum rent. At the end of the first year after rent reform, tenants’ TTP was

on average equal to 37.8 percent of their gross income.10 It decreased slightly to 35.1 percent in

Year 2 because of the reduction in TTP from 35 to 32 percent of gross income 1 year after the rent

reform was first implemented.

Exhibit 3

Total Tenant Payment as a Percentage of Monthly Gross Income Among Nonelderly, Nondisabled

Households Receiving Subsidies 2013 to 2016

TTP Among Households Receiving Subsidies

SCCHA

(% of Monthly Gross Income)

Last Month of Baseline

30.6

Last Month of Year 1

37.8

Last Month of Year 2

35.1

Last Month of Year 3

34.3

Sample Size

7,109

SCCHA = Santa Clara County Housing Authority. TTP = total tenant payment.

Notes: Samples consist of households headed by adults who were not elderly or adults with disabilities. Sample sizes may vary by year because of missing values. TTP is the minimum amount a family must contribute toward rent. Outcomes shown describe only those households receiving any housing subsidies in the specified month.

Source: MDRC calculations using HUD Public and Indian Housing Information Center data

While SCCHA increased the tenant contribution rate, it also changed its policy for determining

a household’s voucher size. The new policy allocated one room for the head of household

(with spouse or partner) plus one additional bedroom for every two persons regardless of age,

generation, relationship, or gender. Before this policy change, the household members of different

generations (such as grandparents and their grandchildren), of the opposite sex over the age of

5, and unrelated adults (other than significant others) were allocated separate bedrooms. For

example, a household that included a household head, her 7-year-old daughter, and her 9-year-

old son would have had a voucher size of three bedrooms using the old rule and a voucher size of

9 This measure is only available for households in the study cohort who are receiving subsidies at the time.

10 Again, this percentage is higher than 35 percent because for some households, 35 percent of gross income was below the minimum rent.

Cityscape 89

Castells

two bedrooms under the new policy. This policy change decreased the voucher size for 23 percent

of nonelderly, nondisabled households served by SCCHA. In other words, under the new policy,

23 percent of the study cohort’s voucher households were housed in a unit where the number of

bedrooms exceeded the number of bedrooms on their vouchers. For these households, if they did

not move before the effective date of the new policy, their expected tenant contribution would

sharply increase because their subsidy was now based on the fair market rent (FMR) for a smaller

unit than the one they occupied.

In anticipation of the potential hardship that HCV households might face because of the rent

reform, SCCHA offered two means of assistance: hardship exemptions for households that

experienced a sharp increase in rent share because of the new rent calculation, and financial and

legal assistance for households at risk of eviction due to the rent reform.

The hardship exemption policy allowed households to have childcare or medical deductions

temporarily included in the calculations for their TTP. These expenses were deducted from the

household’s gross income (the exemption did not include other deductions, such as the dependent

deduction). A household was eligible to apply for a hardship exemption if its monthly rent portion

increased by at least $50 because of the new rent calculation method. Since the households with

larger deduction amounts were most affected by the rent reform, eligible households consisted

mostly of households with children under the age of 13 that were paying for childcare, or elderly and disabled households. Hardship exemptions were granted to 414 households (out of 754 requested),

most of them immediately after the rent reform went into effect.11 Households that were granted the

exemption paid the lower TTP for 90 days, after which it was reset to the regular amount under the

new rent rules, based on 35 percent of the household’s gross income with no deductions.

SCCHA also collaborated with the Law Foundation of Silicon Valley and other local organizations to

create the Sequester Eviction Prevention Program (SEPP) to assist HCV households that were facing

eviction because of the SCCHA rent reform. The program provided financial assistance to cover

unpaid rent or to cover a security deposit if a household had to move because of the rent reform.

The program also included free legal services to prevent eviction. While the program was primarily

designed to assist households affected by the voucher size rule change, the program was open to any

household facing eviction because of an inability to pay the higher rent under the new rent policy.

Households that experienced a substantial increase in their rent share (at least $300 monthly), or

that were otherwise at risk of eviction because of the rent reform, were eligible to receive assistance from the program. SCCHA committed $500,000 of MTW funds for SEPP, which was supplemented

with additional funds from the county of Santa Clara, the city of San Jose, and the Housing Trust

Silicon Valley, for a total funding commitment of $1,820,000. Of this committed funding, $808,078

was spent on the program, and all payments were made by August 2014. The program assisted 293

households comprising 805 people, including 260 households in the study cohort.

11 The total of 414 hardship exemptions includes exemptions for all households regardless of elderly or disabled status. SCCHA did not track the elderly and disabled status of households granted hardship exemptions, so it is unknown how many of these households are in the study sample, which only includes nonelderly, nondisabled households and individuals.

90 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program To avoid terminating the participation of some households in the HCV program due to budget

cuts, SCCHA chose to increase the tenant contribution rate.12 SCCHA assumed that this would

not reduce tenants’ employment and earnings, which would have made the policy change

counterproductive.13 In fact, SCCHA did not know how its policy would affect HAP expenditures,

households’ employment decisions, and hardships, and SCCHA could not rely on previous

experience. The present study is the first study of the effects of increasing the tenant contribution rate in a housing subsidy program.

Existing Evidence About the Effects of Housing Subsidies on

Employment and Earnings

Given the increase in the percentage of income that tenants paid toward rent under the new

SCCHA rent policy, there was a risk that tenants might reduce their earnings, which would

have resulted in SCCHA having to increase the subsidy levels, effectively counteracting the HAP

savings the housing agency hoped to achieve. The economic theory behind these expectations

is not straightforward, however. Economic theory suggests that, on the one hand, increasing the

percentage of income that tenants pay toward rent can reduce employment in response to the

increased “tax” on their earnings (the “substitution effect” in labor economics). The tenants would

gain less disposable income with each extra hour of work than they would have in the absence of

that tax. On the other hand, households might increase their employment to compensate for the

reduction in their subsidy and maintain the level of disposable income they had before the policy

change (the “income effect” in labor economics).

Before this study, existing evidence was limited to studies about providing housing vouchers to

households that had not been receiving housing subsidies. For such households, both the income

and substitution effects point to housing subsidy receipt as a work disincentive.14 The income effect suggests that providing housing subsidies can reduce employment among recipients by increasing

households’ disposable income because the subsidy covers a large part of the housing costs for

which households were previously paying. The substitution effect suggests a further disincentive

to work by imposing an implicit “tax” on their earnings since the 30-percent-of-income rule for

determining tenant rent share means that households would only “take home” 70 cents for every

dollar they earn. (This tax is in addition to the explicit taxes that earnings bring.) This “tax” is

effectively an increase from 0 to about 30 percent of the households’ income when they begin

receiving subsidies.

Three large-scale random assignment studies conducted in the past decade provide reliable

evidence on new housing subsidies’ effects on households’ employment behavior and suggest some

disincentive effect on employment and earnings consistent with the economic theory described

12 SCCHA also put a freeze on issuing new vouchers at the time of the 2013 rent reform, so households that left the program after the rent reform was first implemented were not replaced in the subsidy program for a few years. There were two exceptions: an allotment of 500 vouchers issued to waiting list applicants in 2015 and an allotment of 500

vouchers issued to the chronically homeless in 2016. The voucher freeze was lifted at the end of February 2017.

13 It was confirmed in phone calls with the executive staff at SCCHA that the estimates of HAP savings that they calculated before implementing the rent reform did not account for any potential reductions in earnings from the reform.

14 See Shroder (2012) for a discussion of these and other factors and a literature review up to 2012.

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above. The first of these recent studies is the random assignment evaluation of the Welfare to

Work Voucher (WtWV) program, which studied the effects of receiving housing vouchers for

households selected from the HCV waiting list that were receiving or had received Temporary

Assistance to Needy Families (TANF). The study found that households randomly assigned to

receive a housing voucher worked less than households that were randomly assigned to the control

group, but only during the first year following random assignment (Mills et al., 2006). A later

study leveraged a lottery used by the Chicago Housing Authority Corporation to allocate newly

available housing vouchers in 1997 (Jacob and Ludwig, 2012). This natural experiment found

more persistent negative impacts on labor supply. The study found that receiving a voucher caused

subsidy recipients to reduce their employment by 6 percent (4 percentage points) and reduce

their quarterly earnings by 10 percent ($329 in 2007 dollars). The Chicago study found that the

impacts were significantly larger for households that were not receiving TANF than for those that

were, which may be an important reason their results differed from those of WtWV (whose sample

consisted of all TANF recipients and found less negative impact on employment).15

The most recent of the three studies, the Family Options Study, offered vouchers to homeless

families as one arm of the intervention. This study also found that housing vouchers reduced

employment for voucher recipients. In the short-term, 11 percentage points fewer households

who were randomly assigned to receive housing vouchers were working than was the case

for households that were randomly assigned to the study’s control group. (Fifty percent of the

intervention group had any employment during the first year after random assignment compared

with 61 percent of the control group.) After 3 years, this difference fell to 6 percentage points (64

percent of treatment group members had any employment in the prior year and a half compared

with 58 percent in the control group) (Gubits et al., 2015, 2016).

In all three of these studies, randomly selected households were given access to housing subsidy

programs that used the traditional 30-percent-of-income rent rule. Therefore, as described earlier,

these studies estimated the effects of providing housing assistance to households that were not yet

receiving any assistance. That research question is somewhat distinct from the question addressed

by this SCCHA study, which examines the effects of changing rent policies for households that are

already receiving housing assistance under the traditional rent rules. Also, while the earlier studies provide evidence for a potential work disincentive from obtaining a housing voucher at least in the

short term, this evidence does not necessarily imply that reducing the housing subsidy (as in the

SCCHA rent reform) would provide an incentive to work.

The SCCHA rent reform provided an opportunity to bring empirical evidence to bear on these

economic theories. Would nonelderly, nondisabled adults respond to the increased tenant

contribution rate by working less, because they could keep less of their earnings? Or would they

increase their earnings to compensate for lost income? The net outcome would depend on which of

the two effects is dominant. Households may also respond by maintaining their earnings levels and

15 Both the WtWV and Chicago studies found that estimated effects varied according to several baseline characteristics, including household size and age of the household head. In Chicago, Jacob and Ludwig (2012) used their employment and income findings to estimate an income elasticity of -0.09 and a compensated wage elasticity of 0.15. In this case, the response of individuals determined by the elasticity of substitution was more important than the one caused by the elasticity of income.

92 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program managing higher housing costs by reducing other spending or incurring debt. The present study

is the only study to date to assess the effects of increasing the percentage of income that tenants

pay toward rent in a housing subsidy program. It estimates the effects on tenants’ employment

behavior and housing subsidies and examines tenants’ rent burden levels after the policy change

was implemented.

Research Design

This study aimed to answer the question: How does an increase in tenant rent share affect HUD-

assisted tenants’ work and earnings, and how does it affect housing? This overarching question can be answered through careful consideration of the following specific research questions:

1. What was the effect of the SCCHA rent reform on household members’ employment and earnings?

2. What was the effect of the SCCHA rent reform on housing assistance subsidy amounts and

continued housing subsidy receipt?

3. Was there any suggestion of potential effects of the SCCHA rent reform on households’

housing decisions?

4. To what extent did SCCHA households experience selected housing-related hardships after the

rent reform?

These research questions are first addressed for the full study sample of nonelderly, nondisabled

adults living in HCV-subsidized households. The study then explores whether these effects differ

for households only affected by the increase in the tenant contribution rate (77 percent of the study sample) and for the households affected by the change in the voucher size policy in addition to the

increase in the tenant contribution rate (23 percent of the study sample).

Effects on Employment and Earnings

The primary research question for this study is: “How did the SCCHA policy changes affect HCV

subsidy recipients’ employment and earnings?” Adults in subsidy households who were able to

work could have responded to the rent reform by changing their work behavior to adjust their

earnings (and therefore adjust their tenant contribution amount and net income) in three ways:

1. Households may have increased their employment and hours worked in response to the policy change. Doing so could increase their earnings, compensating for all or part of the reduction

in disposable income so they would experience less change in their standards of living.

2. Households may have maintained their current levels of employment and hours worked

(by choice or not), either finding a way to manage with less disposable income or perhaps

experiencing increased material hardship.

3. Households may have reduced their employment and earnings in response to the increased

disincentive to work, namely the increased “tax” on earnings, reflecting the fact that they got

to keep less of each dollar they earned than they did under the former policy.

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In the first two scenarios, HAP expenditures would have decreased in the amount estimated by

SCCHA, and their expected savings would have been realized.16 If instead, households reduced

their employment in response to an increase in the percentage dedicated to rent (the third

scenario), HAP expenditures would not have decreased as much as expected, and SCCHA would

not have met its budgetary targets. Depending on the extent of the reduction in employment, HAP

expenditures might even be greater than previous levels, and the policy change would have been

counterproductive.

These different forces could have immediate effects (someone could reduce the number of hours

worked because of the increased implicit tax on earnings), or they could affect employment

decisions later in the followup period (someone could choose not to accept an opportunity to

increase working hours because the increase in net income would not be as large as it would

have been under the previous rent policy). It is also possible that households responded to the

increased tenant contribution rate differently over time. For example, households may have first

responded to the unanticipated income shock by working more to compensate for the lower-

income and to maintain longer-term financial commitments, but over time they may have reduced

their employment as they adjusted to the change, possibly down to their pre-2013 employment

level, or even lower, in response to the increased “tax” on earnings. This study estimates the effects of the SCCHA rent reform on employment and earnings for the 4 years after the rent reform was

implemented and therefore can capture both immediate and longer-term effects.

Effects on Average Housing Subsidy Amounts and Continued Housing Subsidy Receipt

As described in the previous section, the SCCHA rent reform could have led to various changes

in households’ employment behavior. If many households had reduced their earnings in response

to the rent reform, then average household HAP amounts may not have decreased substantially.

In other words, the rent reform could have directly affected households’ subsidy levels through its

change in the tenant contribution rate and indirectly affected households’ subsidy levels through

households’ employment responses to the changes in their housing costs.17

16 The elimination of utility allowances may contribute to the income effect (as households may have to increase income to cover increased utility costs) but would not contribute to the substitution effect (reducing earnings would not increase HUD coverage of utility costs; it would only increase HAP to cover rent).

17 See exhibit 4 for definitions of subsidy outcomes included in the present study.

94 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Exhibit 4

Definitions of Housing Subsidy Measures Used in This Study

Housing Assistance Payment (HAP): The amount that the public housing agency (PHA) provides in subsidy to pay for rent and utilities. It is calculated as the lower of (1) the payment standard for the family’s unit minus TTP, or (2) the gross rent minus TTP. Under the Santa Clara County Housing Authority (SCCHA) new rent calculation rules, HAP is calculated as the lower of (1) the payment standard for the family’s unit minus TTP, or (2) the contract rent minus TTP.

Total Tenant Payment (TTP): The minimum amount that the household must contribute towards rent and utilities. Under traditional rent rules, it is calculated as the greatest of (1) 30 percent of monthly adjusted income, (2) 10 percent of monthly gross income, and (3) the PHA minimum rent. Under the SCCHA new rent rules, it is calculated as the greater of 35 percent (or 32 percent, after July 2014) of monthly gross income and (2) the PHA minimum rent.

Tenant Rent Share: The household’s total contribution toward the contract rent. For households living in units with contract rents at or below the payment standard, the tenant rent share is equal to the household’s TTP. For households with contract rents that exceed the payment standard, the tenant rent share is calculated as TTP plus the amount by which the contract rent exceeds the payment standard.

Continued Subsidy Receipt: A household is classified as receiving housing subsidies if their HAP amount is greater than zero (in any PHA). If a household’s income increases to the threshold where their HAP amount is zero because their calculated TTP is greater than the gross rent, they have a grace period of 180 days during which they are not receiving any subsidies but are still considered active in the program. If their income drops before their grace period ends, they can have their TTP recalculated and will begin receiving subsidies again. A household whose HAP is $0 and is in their grace period is not classified as receiving any housing subsidies at that time.

Rent Burden: Rent burden is typically defined as the portion of a household’s income used toward total housing costs, including rent and utilities. Because data on utility costs were not available for two PHAs in the sample, the present study uses an alternative measure of rent burden, which is calculated as the monthly tenant rent share (rent only, not including utilities) divided by the household’s monthly gross income.

The SCCHA rent reform might also have caused households to leave the HCV program. The

increase in the tenant contribution rate might have driven some households’ HAP down to

zero, leading them to exit from the HCV program. If the rent reform increased employment

and earnings, some households might have “earned their way off of” housing subsidies (if their

increased income effectively reduced their subsidy payments to $0). Households whose HAP was

significantly reduced, but not reduced fully to $0, might have chosen to leave the HCV program

because they found the smaller subsidy no longer justified the burden of staying in the program

and complying with program rules. While there were strong reasons to expect direct effects on

average household housing subsidy amounts (as discussed before), effects on receiving any housing subsidy (“attrition” from the program) would have been less direct.

Households’ Housing Decisions While in the Voucher Program

In contrast with the housing subsidy amounts and receipt, which are near-term outcomes of the

SCCHA rent reform, households’ housing decisions are more removed, and the effects on these

outcomes are less direct. The tenant contribution rate change could have encouraged households to

seek other housing, if their gross rent was above the payment standard, in order to offset the increase in housing costs. The study explored whether households moved to smaller units, moved to different

types of neighborhoods, or relocated to units outside the jurisdiction of SCCHA (defined as “porting

out”) and whether they changed their housing composition by adding or removing family members.

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Household Rent Burden While in the Voucher Program

Because the policy change effectively reduced the subsidy amounts provided to households for

rent, an important research question is whether the rent reform led to housing-related hardship.

Due to data limitations, the study was able to examine rent burden over time on a descriptive level,

but it could not measure other aspects of material hardship, such as rent arrears, evictions, food

insecurity, inability to pay utility bills or medical expenses, and other important expenses. The

study relied on conversations with SCCHA leadership and internal agency documents to explore

the types of assistance SCCHA provided to alleviate housing-related hardship and prevent eviction

due to the rent reform.

Effects for Households Affected by the Tenant Contribution Rate Change Only

Versus Households Also Affected by the Voucher Size Rule Change

The hypotheses described earlier relate to the potential effects of the tenant contribution rate

change. As noted earlier, 23 percent of the study sample of nonelderly, nondisabled HCV

households in SCCHA were immediately affected, not only by the change in subsidy levels but also

by the new voucher size policy. The new voucher size policy enforced a minimum of two family

members per bedroom, excluding the head of household’s bedroom, regardless of age, generation,

gender, or relationship of the household members.

All households in both subgroups were affected by the tenant contribution rate increase; therefore,

all households faced the potential earnings and employment incentives and disincentives, and

all households whose rent exceeded the payment standard faced some incentive to move to a

less expensive unit to offset higher housing costs. It is possible, however, that the rent reform’s

effects on employment and earnings for the subgroup of households affected only by the tenant

contribution rate increase may have differed from the effects for the subgroup of households

affected by both policy changes. Households also affected by the voucher size rule change might

have stayed in their current units, and faced much steeper out-of-pocket housing costs, because

they had to fully cover the difference between the new lower payment standard and the previous

one, or they might have moved to a smaller unit whose gross rent exceeded the area payment

standard by a larger amount than their previous larger unit. For these reasons, households in the

double policy change subgroup could have had stronger incentives to change their employment

behavior in addition to stronger incentives to move to a new unit. Furthermore, if double policy

change households who did not move to smaller units were unable to afford their much higher

rent shares, and this hardship led them to leave the subsidy program (either by voluntarily moving

to a more affordable housing situation, like moving in with other family members or through

eviction), the turmoil of their housing situation may also have affected households’ employment

and earnings.

Study Sample

The analysis sample includes all nonelderly, nondisabled households and individuals who were

receiving HCV subsidies at the time of the SCCHA policy change in July 2013 from SCCHA or

from one of the three selected comparison PHAs: the Housing Authority of the County of Alameda,

96 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program the Housing Authority of the County of San Mateo, and San Francisco Housing Authority.18, 19 The

main criteria for the Comparative Interrupted Time Series (CITS) method used in this analysis is

that the housing authorities’ voucher holders must be subject to similar labor and housing market

forces as those in SCCHA. The study considered local labor and housing market information,

baseline earnings and employment levels and trends, and the characteristics of households in

candidate housing agencies to select the sample. The process used for selecting these PHAs for the

comparison group is described later in this article.

Exhibit 5

HUD Definitions of Sample Terms

Adult: An individual who is 18 years of age or older or a minor under the age of 18 who has been emancipated to act on his/her own behalf, including the ability to execute a contract or lease.

Household with Disabilities: A household where the head of household, spouse, or co-head is a person with a disability.

Elderly Individual: A person at least 62 years of age.

Elderly Household: A household whose head, co-head, spouse, or sole member is at least 62 years of age.

Quarterly and annual effects on employment and earnings were estimated for the cohort of all

nonelderly, nondisabled individuals who were living in households receiving HCV subsidies at the time of the rent reform (July 2013). Annual effects on household subsidy amounts and continued

housing subsidy receipt were estimated for the cohort of all nonelderly, nondisabled households

receiving HCV subsidies from study PHAs at the time of the rent reform.

A different sample was required for the subgroup analysis that compares intervention effects

on households that experienced only a change in the tenant contribution rate with households

that were also affected by the voucher size rule change. The subgroup of SCCHA households

affected by the voucher size rule change was identified by comparing households’ voucher sizes

at baseline (in early July 2013, before the rent reform was implemented) with their voucher sizes

under the new policy. Using actual subsidy data, a simulation applied the new voucher size rules

to each household according to its baseline unit size and household composition. A household

was included in the subgroup of households affected by the voucher size rule change if its new

rent calculation would use the payment standard of a smaller voucher size under the new policy.

All other households were included in the subgroup of households that were only affected by the

tenant contribution rate increase.

At the time of the SCCHA rent reform, only one PHA in the study sample other than SCCHA—the San

Francisco Housing Authority (SFHA)—had not yet moved to a more conservative voucher size policy.

Therefore, only SFHA could be included in the comparison group for this subgroup analysis. The

method described earlier was used to identify the subgroup of households (based on baseline voucher

size, unit size, and household composition) in SFHA that would have been affected by the new voucher 18 The study uses HUD’s definitions of elderly and disabled households and adults. See exhibit 5 for definitions of these terms.

19 As described earlier, the SCCHA policy changes affected all HCV households, regardless of elderly or disability status. The study focuses on nonelderly, nondisabled households because elderly and disabled households would not have had the same flexibility to change their work behavior in response to the policy changes.

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size rules if those rules had been applied to them. Twenty-three percent of nonelderly, nondisabled

households in SCCHA, and 21 percent in SFHA, were identified for this subgroup analysis. The

subgroup analysis also differs from the main analysis in that, due to data limitations, the sample was defined at the household (not individual) level. The subgroup analysis estimates effects for adults

living in nonelderly, nondisabled households—this group includes a small number of disabled and

elderly individuals (approximately 5 percent of the sample) and excludes some nonelderly, nondisabled individuals who were living in households headed by an elderly or disabled person.

Study Period

The CITS design used in this study (described in detail later) requires a sufficient number of time

points (ideally, at least 4 years) before the policy change in order to reliably estimate baseline

trends. As exhibit 6 shows, the SCCHA rent reform was implemented in early July 2013 (when

SCCHA sent letters to subsidy households notifying them of the rent reform and their new tenant

rent share under the new rent policy), and the baseline period is defined as the 4 years before the

start of the SCCHA rent reform implementation: July 1, 2009, to June 30, 2013. The followup period

includes the 4 years after the start of the SCCHA rent reform and is defined as July 1, 2013, to June 30, 2017. In other words, the analysis used 4 years of historical (pre-rent reform) and 4 years of

followup (post-rent reform) data on the study cohort to estimate the effects of the rent reform.

Exhibit 6

Timeline of Policy Changes

Sept 2013:

35% rent

July 2013: effective Sept 2014:

Household

32% rent

Notification

effective

July 2009–June 2013

July 2013–June 2017

Baseline period

Follow-up period

Source: Housing Authority of the County of Santa Clara, 2013

Data Sources

The study primarily relies on two data sources for understanding the rent reform’s effects on

employment, earnings, housing subsidy receipt and amounts, plus housing characteristics: state

unemployment insurance (UI) wage data and the HUD Inventory Management System/Public

Indian Housing Information Center (IMS/PIC) data. It also uses other data sources—including

U.S. Census Bureau data and internal SCCHA documents and discussions with SCCHA staff

members—to provide context for these findings.

98 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Employment and Earnings

Employment and earnings data obtained from the California Employment Development

Department (EDD) consist of employer-reported UI wage data for all employment covered by UI in

the state of California. These data do not include wage data for jobs outside of California, federal

employment, or informal or self-employment not covered by UI.

The data are quarterly and were received in de-identified, aggregated form grouped to serve

analysis purposes. The employment and earnings data for the main analysis (which estimates

effects for all nonelderly, nondisabled adults receiving HCV subsidies) were grouped by PHA

and whether individuals were elderly or disabled. These data were used to construct the average

quarterly employment rate and average quarterly earnings measures. Since CITS analysis requires

the calculation of a trend in the outcome over time, average quarterly earnings were adjusted for

inflation to 2017 dollars using the U.S. Bureau of Labor Statistics Consumer Price Index (CPI).20

For the subgroup analysis, which looks at effects on employment and earnings for adults living

in households that were only affected by the increased tenant contribution rate separately from

households also affected by the new voucher size policy, the sample was defined at the household

level. The data were grouped by PHA, household elderly or disabled status, and whether the

payment standard on which the household’s rent calculation was based was reduced because of

the new voucher size determination rules. Therefore, this analysis estimated effects on earnings

and employment for all adults (regardless of elderly or disabled status) living in nonelderly,

nondisabled households that were subject to the voucher size policy change. Employment and

earnings measures were constructed in the same way as for the main analysis.

Housing Subsidies and Housing Characteristics

Quarterly IMS/PIC snapshot data were used to identify the study sample, to describe household

and individual characteristics at the time of the policy change, and to create covariates for the

impact models. Snapshot files from the second and third quarters of 2013 were used to identify

which households in the selected PHAs were receiving subsidies on July 1, 2013, just before the

SCCHA rent reform was implemented.

IMS/PIC transactional data were used to investigate effects on housing subsidy amounts and

housing subsidy receipt and to provide descriptive information on other housing variables,

including rent burden and unit characteristics. The transactional files include a record for each

certification that occurred during the study period. Monthly measures from July 2009 through June

2017 were created using these data. Data were acquired for all transactions recorded from January 1,

2009 (6 months before the study period) to establish baseline levels of all the housing-related measures.

The IMS/PIC data for the last year of the followup period (July 1, 2016, to June 30, 2017) did not

benefit from updates recorded in the following year, nor from certifications that were recorded in the following year but effective during Year 4. Therefore, results using the fourth year of IMS/PIC data are not presented in this report, but the overall results using Year 4 data are reported in footnotes.

20 All Urban Consumers (Current Series) database (https://www.bls.gov/cpi/data.htm).

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MTW agencies have fewer 50058 reporting requirements and use a shortened 50058 form to

collect data at each certification. It was necessary, therefore, to construct some measures that are

available for traditional PHAs but are not included in HUD Form 50058 MTW: HAP, TTP, and

family share. These measures rely on the payment standard at the time of the rent calculation,

which is not included in the IMS/PIC data.21 MDRC collected payment standard amounts for the full

study period from SCCHA and from the Housing Authority of the County of San Mateo (HACSM), one

of the MTW agencies selected for the comparison group.22 Exhibit 7 describes how MDRC calculated

the subsidy measures used in the analysis that were not already included in the IMS/PIC data.

Exhibit 7

Calculated Housing Subsidy Measures Used in this Study

Calculation Method Used to Construct Measures for Analysis

Measure

Definition

Traditional PHAs

MTW PHAs

The minimum amount that the No calculation

Under traditional rent rules, it is calculated

household must contribute

needed / Included

as the greatest of (1) 30 percent of monthly

toward rent and utilities

in the IMS/PIC data. adjusted income, (2) 10 percent of monthly

gross income, or (3) the PHA minimum rent.

TTP

Under the SCCHA new rent rules, it is

calculated as the greater of 35 percent (or

32 percent, after July 2014) of monthly gross

income or (2) the PHA minimum rent.

The amount that the PHA

No calculation

Under the traditional rent policy, it is calculated

provides in subsidy for rent

needed / Included

as the lower of (1) the payment standard for

and utilities

in the IMS/PIC data. the family’s unit minus the TTP or (2) the gross

rent minus the TTP.

HAP

For SCCHA households after the new rent

policy went into effect, it is calculated as the

lower of (1) the payment standard for the

family’s unit minus the TTP or (2) the contract

rent minus the TTP.

The household’s total con-

No calculation

Family

For households with gross rent at or below

tribution (rent plus utilities)

needed / Included

the payment standard, the family share is

Share

toward the gross rent

in the IMS/PIC data. equal to the household’s TTP.

The household’s total

No calculation

Tenant

TTP plus the amount over the payment

contribution toward

needed / Included

Rent

standard that the household pays toward

contract rent

in the IMS/PIC data. contract rent. (Contract rent is gross rent

Share

minus utilities.)

Traditional definition: The total The total amount household pays in rent (tenant share) divided by

amount a household pays in

household gross income.

Rent

rent and utilities (family share)

Burden

as a proportion of household

adjusted income

HAP = Housing Assistance Payment. IMS/PIC = HUD Inventory Management System/Public Indian Housing Information Center.

MTW = Moving to Work. N/A = data not available. PHA = public housing agency. SCCHA = Santa Clara County Housing Authority.

TTP = total tenant payment.

21 The payment standard amounts and the household HAP, TTP, family share, and tenant share of contract rent amounts are included in the IMS/PIC data for traditional PHAs.

22 The selection process for the comparison group is described in detail later in this chapter.

100 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Accounting for utility allowances in this study posed measurement challenges. At traditional

PHAs, if a tenant’s contract rent does not cover utilities, the tenant receives a utility allowance from the PHA. The contract rent plus the utility allowance equals the gross rent for a unit, and HAP is

calculated based on gross rent. MTW agencies have the option to eliminate or restructure their

utility allowances, and one of the comparison agencies did so early in the study period.23 Therefore, the calculation of HAP for that housing agency does not include utility allowances.24, 25

To ensure that the measure of rent burden is consistent over time—both before and after the

SCCHA rent reform—and that this measure is comparable between SCCHA and the comparison

group PHAs—an alternative measure of rent burden that does not rely on the measurement of

utility allowances is used. This measure is the tenant rent share (equal to the TTP plus any amount

by which the contract rent, not including tenant-paid utilities, exceeds the payment standard) divided by the household’s gross income.26

A 40-percent rent burden is commonly used as the threshold for a high burden in the literature

discussing HCV households (Dawkins and Jeon, 2017; Ellen and Torrats-Espinosa, 2017). The measure

used in this study—the tenant rent share as a proportion of household gross income—departs from this

standard definition in two ways. First, the threshold used is typically 40 percent of adjusted income—

which would be a lower threshold than 40 percent of gross income. Second, the measure typically

uses tenant rent share plus tenant-paid utilities as a proportion of household income. The measure in the present study uses tenant rent share and does not include tenant-paid utilities.

Neighborhood Poverty, Local Context, and Hardship Policies

To measure neighborhood quality, the study uses the census tract poverty rate. The poverty rate is

the most widely used measure of neighborhood quality and distress (Galvez, 2010). The geocoded

HUD IMS/PIC data were merged with U.S. Census Bureau data. The census-tract poverty rates (for

individuals age 18 to 64 years) from either the 2007–2011 American Community Survey (ACS)

5-year estimates or the 2012–2016 ACS 5-year estimates, depending on the date that the record

was entered, were merged with the census tracts in the IMS/PIC data. ACS data on county-level

population and housing market data were used for comparison group selection. These measures

included the median household income, median household rents, rental vacancy rates, housing

unit density, population density, education levels, and racial and ethnic composition.

The study used two additional public data sources during the process of selecting the comparison

group PHAs (described later). Public data from the California EDD website was one source

for measures of labor information for 2012–2016 for the Bay Area counties where the PHAs

23 HACSM restructured its utility allowances as part of a more comprehensive rent reform that introduced a tiered rent structure.

24 SCCHA’s elimination of utility allowances when it implemented its rent reform in 2013 does not pose problems for estimating impacts of the rent reform on housing subsidies because the calculations of HAP post-rent reform accurately reflect the exclusion of utility allowances.

25 Several sensitivity analyses described in Castells (2020) confirm that results are not affected by the exclusion of utility allowances in the calculation of HAP at HACSM.

26 For households whose tenant rent share (TTP plus the amount that the contract rent exceeds the unit’s payment standard) exceeds their gross income, this measure is set to 100 percent.

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in the present analysis were located.27 The other source was 2013 data from the HUD Picture

of Subsidized Households dataset, available publicly on the HUD website, to describe the

characteristics of households living in candidate PHAs.

Lastly, the study relied on discussions with SCCHA leaders and internal SCCHA documentation

to better understand PHA hardship policies and eviction assistance program and the overall policy

and economic context at the time the rent reform was implemented.

Analytic Approach

A combination of analytic methods was used to assess the effects of the SCCHA 2013 rent

reform, using the most rigorous approach feasible for each set of outcomes. Each method is

explained narratively in what follows, and appendix B contains model specifications.28 Exhibit

8 shows which outcome measures were used in each type of analysis, and which research

questions they aimed to address.

Exhibit 8

Outcome Measures by Research Questions and Analytic Approach

Outcomes by Analytic Approach

Research Question

Comparative

Autoregressive

Interrupted Time

Difference-in-

Descriptive (Exploratory)

Series

Differences

What was the effect of

Total annual

the SCCHA rent reform

earnings

on household members’

Average quarterly

employment and earnings? employment rate

What was the effect of the

Total Annual

Received Any Housing Subsidies

SCCHA rent reform on

Housing Subsidy

Among Households Receiving Subsidies:

housing assistance subsidy

Received

• Total Tenant Payment (TTP)

amounts and continued

Any Housing

• Housing Assistance Payment (HAP)

housing subsidy receipt?

Subsidies

• Total Tenant Rent Share

Was there any suggestion

Among Households Receiving Subsidies:

of potential effects

• Neighborhood Poverty Rate

of the SCCHA rent

• Total Number of Bedrooms

reform on households’

• Ported Out to a Different PHA

housing decisions?

• Household Size

To what extent did

Rent Burden (Tenant Rent Share

SCCHA households

Contribution as Percent of Monthly

experience selected

Gross Income)

housing-related hardships

Total Hardship Exemptions Granted

after the rent reform?

(SCCHA only)

Total Households Assisted with Eviction

Prevention Assistance (SCCHA only)

PHA = public housing agency. SCCHA = Santa Clara County Housing Authority. Employment and Earnings—Comparative Interrupted Time Series 27 Data were compiled from the “REPORT 400 C, Monthly Labor Force Data for Counties, Annual Average (Data Not Seasonally Adjusted)” reports, produced by the Labor Market Information Division of California EDD, available on the California EDD website: https://www.labormarketinfo.edd.ca.gov/data/unemployment-and-labor-force.html.

28 See Castells (2020) for more details on each of the analyses used in the present study.

102 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Employment and Earnings—Comparative Interrupted Time Series

The most rigorous method used is a CITS analysis, which is a quasi-experimental design, used in

this study to estimate effects on the study’s key outcomes: employment and earnings.

The CITS analysis measures the impact of the SCCHA rent reform on earnings or on employment

as the difference between the post rent reform SCCHA deviation from its pre-rent reform trend and the corresponding comparison group deviation from its pre-rent reform trend.29 The focus of the

present analysis is whether the SCCHA rent reform caused a deviation in predicted employment

rates or average quarterly earnings for SCCHA’s study cohort that differed from any deviation from

predicted employment rates and average annual earnings for the study cohort at comparison PHAs.

The SCCHA rent reform meets important criteria for a successful CITS analysis. First, the policy

change or changes must be consequential, capable of producing a substantial impact in a relatively

short time. The reform was consequential in that it was expected to increase the average tenant rent

share from 27 to 35 percent of gross income and decrease HAP payments by 12 percent. Second,

the policy change must be implemented all at once for the entire study cohort. The rent reform

affected all HCV households, and, according to the SCCHA, all households were notified and

therefore potentially affected at the same time. Letters were sent out to households in the first week of July, and the changes went into effect on September 1, 2013. Third, for the comparison with

other housing authorities to be valid, those housing authorities’ voucher holders must be subject to

similar labor and housing market forces as SCCHA (the baseline levels of employment and earnings

do not have to be the same). The study used a systematic comparison group selection process that

identified comparison PHAs that faced similar local economic forces during the study period.

The first step in the CITS analysis was to measure the average earnings or employment trajectory

of all the nonelderly, nondisabled adults in households receiving subsidies in the SCCHA HCV

program over the 4 years before its policy changes in 2013. This baseline trend was then used to

extrapolate what earnings levels or employment rates would have looked like in the absence of the

policy changes in 2013. The second step was to measure the earnings levels or employment rates

after these changes occurred to estimate the magnitude and direction of any subsequent deviations

from this baseline trend. A parallel analysis was conducted for comparison PHAs. The final step

of the design was to measure the magnitude and direction of the treatment and control group

difference in these estimated deviations from the trend. These observed differences represent the estimates of the impact of the SCCHA rent reform on individual earnings and rates of employment.

Subsidy Amounts and Receipt of Subsidy—Autoregressive Difference-in-Difference

An autoregressive difference-in-difference model was used to estimate effects on the average

amount of subsidy that households received, and the percentage of households in the sample still

receiving subsidies each year after the SCCHA rent reform was first implemented. Difference-in-

difference estimation is also a rigorous, quasi-experimental design, but unlike a CITS analysis, it

does not account for differences in baseline trends.

29 See Somers et al. (2013) for a review of this method.

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While the lack of 4 full years of historical data for the full study cohort ruled out using a CITS

analysis for these outcomes, the comparison group selection process provides a reasonable level

of confidence that the comparison PHAs had similar baseline trends in housing subsidy receipt

and amounts and were subject to the same labor and housing market forces. It is important to

remember the caveat that if there were any differences in the trends of these housing outcomes

before the rent reform, those differences, rather than the policy changes, could be the cause of

estimated impacts, or they could mask a true effect when none is detected.

Conceptually, autoregressive difference-in-difference measures the difference between the outcome

of interest at the time of the policy change and the outcome at specific points during the followup

period (for example, 1 year after the policy change) for both the treatment and comparison groups,

and then compares those differences. Unlike a simple difference-in-difference model, however, the

models used for this analysis are autoregressive in that they also control for preintervention values of the outcome measure. The model estimating effects on average HAP includes four covariates

representing HAP amounts in the 4 years before the rent reform (for those households not yet

receiving HCV subsidies in each of the 4 years, these values of HAP are $0), along with four binary

covariates indicating whether the household was receiving any HCV subsidy in each year before

the rent reform. The model specifications for these autoregressive difference-in-difference models

are provided in appendix B.

Descriptive Analysis of Rent Burden, Number of Bedrooms, Neighborhood Quality,

and Household Size

The study describes outcomes that may reflect housing decisions that SCCHA households made

in response to the rent reform and that reflect any subsequent housing-related hardship they may

have faced, including rent burden, number of bedrooms, neighborhood quality, and household

size. Average levels of these outcomes for the sample households in Santa Clara are compared

with averages in the comparison group PHAs in the last month of baseline and annually thereafter.

Discussions with SCCHA leadership provided information on safeguards that SCCHA offered to

households severely affected by the rent reform.

The trends in the descriptive exhibits should be interpreted with caution because data are only

available for households still receiving subsidies and therefore represent only a partial picture.

Also, households that leave the subsidy program each year are likely to differ systematically

from households that remain, meaning this picture gets less representative later in the study

period as the percentage of households still receiving subsidies gets smaller.30 The comparison

group’s outcomes are presented for context, but they should not be interpreted as counterfactual

representations of the expected outcomes for SCCHA households in the absence of the rent reform.

The descriptive analysis serves two main purposes. First, it examines the averages in the housing

subsidy outcomes as an additional check on the validity of the study. The SCCHA rent reform is

30 Drawing conclusions from the patterns of these outcomes can be especially problematic if the rent reform had an effect on the proportion of households that lost their subsidies. While the analysis did not suggest an overall effect on households leaving housing assistance, the SCCHA rent reform may have influenced which types of households left the subsidy program during the followup, even if this change is not captured by the data on changes in the percentage of households receiving subsidies over time.

104 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program expected to have direct effects on households’ housing subsidies. If there is no clear pattern of an

increase in TTP and reduction in HAP (especially 1 year after the rent reform was implemented),

the findings might call into question the face validity of the study—whether the SCCHA rent reform

was implemented as expected and whether key outcomes are being measured correctly. Second, the

analysis examines patterns in averages of housing outcomes during the followup period that may

reflect households’ responses to the SCCHA rent reform to see whether any stark deviations from

pre-rent reform levels are evident, especially if this deviation does not exist in the comparison group.

Even if such a stark difference is evident, however, it alone would not provide compelling evidence

that the change was caused by the SCCHA rent reform. Instead, it would suggest that the rent

reform may have led to changes in SCCHA households’ housing decisions and may warrant further

exploration in future studies with more rigorous methods directed to this question.

Single and Double Policy Change Subgroup Analysis

The subgroup analysis examines whether the effects of the SCCHA rent reform for the 77 percent

of nonelderly, nondisabled households who were only affected by the tenant contribution rate

increase (hereafter referred to as the “single policy change” subgroup) differed from that for the

23 percent of nonelderly, nondisabled households who were affected by both the increased tenant

contribution rate and the reduction in voucher size (hereafter referred as the “double policy

change” subgroup).

Only one of the comparison PHAs—SFHA—could be used for the subgroup analysis comparison

group. Analyses reported in Castells (2020) confirm that SFHA by itself would provide a valid

comparison group for the subgroup analysis of impacts on earnings and employment. The CITS

method used to estimate impacts on employment and earnings in the full study sample was used to

estimate impacts for each subgroup in the smaller sample of households drawn from only SCCHA

and SFHA.

The analyses showed, however, that the autoregressive difference-in-difference model used to

estimate impacts on average household HAP and whether households were still receiving subsidies

in the full sample would not be valid for the subgroup analysis using only SCCHA and SFHA.

As explained in Castells (2020), it is possible to estimate how the effects of SCCHA’s rent reform

differ (in direction and magnitude) between the single and double policy change groups. These

differential effects can be estimated by examining the difference in the separate estimated effects for the two subgroups. This method is known as a difference-in-difference-in-difference design and is

presented in this article as an exploratory analysis for the present study.31

Comparison Group Selection

One of the main conditions for a CITS design to be an effective method for measuring the impact

of the SCCHA rent reform is that the CITS comparison group be subject to labor and housing

market forces that are similar to those for SCCHA. Because the present study identified comparison

PHAs in the Bay Area in counties with similar labor and housing markets, one can be reasonably

31 See Wing, Simon, and Bello-Gomez (2018) for a description of this method.

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confident that this condition is met.32 This condition is important for a CITS analysis because

the mean outcome of the comparison group does not serve as the counterfactual outcome for the

treatment group, as it does in most quasi-experimental methods. Instead, the deviation in the mean outcome of the comparison group from its estimated baseline trend serves as the counterfactual

for the deviation of the mean treatment group outcome from its estimated baseline trend.

Consequently, the difference between these two deviations from the trend identifies the impact of

the SCCHA rent reform on the employment and earnings of SCCHA subsidy recipients.

In addition, it is desirable for the CITS comparison group to have baseline trends for the primary

outcome measures (employment and earnings) that are similar to those for SCCHA and for

the characteristics of their households to be similar to those of SCCHA residents. While these

similarities are not necessary for a CITS analysis to produce valid impact estimates (because the

CITS design implicitly controls for these baseline differences), the closer the alignment between the treatment and comparison groups on baseline levels and trends of earnings and employment, and

the more similar the households in the two groups, the more one can be confident that the groups

would respond similarly to changing local economic conditions.

In contrast, the similarity of baseline trends is quite important for the difference-in-difference

method used to estimate effects on housing subsidy levels and whether households continued to

receive subsidies since this method does not account for any treatment and comparison group

differences in baseline trends.

With the preceding considerations in mind, a three-stage process was used to identify PHAs

to serve as the comparison group. As exhibit 9 illustrates, the first stage narrowed down the

counties considered by examining the local labor market and housing market conditions. The

map in appendix A shows the counties’ locations in the Bay Area. The second stage examined

characteristics of households in the remaining candidate PHAs to determine which differed

appreciably from those in SCCHA. Last, baseline earnings and employment trends for the

remaining pool of PHAs were examined to select those whose baseline trends were most closely

aligned with the SCCHA baseline trends. Castells (2020) describes the details of each stage of the

process, including the rationale for narrowing down the specific counties or PHAs at each stage.

32 While it would have been possible to identify PHAs in areas with similarly tight housing markets and strong labor markets, like New York City or Los Angeles, that closely match SCCHA in baseline characteristics and employment and earnings trends, selecting the comparison group PHAs from locations outside the Bay Area increases the likelihood that the comparison group PHAs experienced economic forces or policy changes that were different from those that SCCHA experienced during the study’s followup period.

106 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Exhibit 9

Comparison Group Selection

Stage 1

Compare Local Labor and

Housing Market Conditions

Candidate Counties: 11

Selected Counties: 5

Stage 2

Compare PHAs’ Aggregate

Household Characteristics

Candidate PHAs: 12

Selected PHAs: 11

Stage 3

Compare PHAs’ Average Baseline

Earnings and Employment Trends

Candidate PHAs: 11

Selected PHAs: 3

Housing Authority of the County of Alameda,

Housing Authority of the County of San Mateo,

and San Francisco Housing Authority

PHA = public housing agency.

Notes: Stage 1 conditions were calculated using county-level U.S. Census Bureau (2010), 2009–2013 American Community Survey 5-Year Estimates, and California Employment Development Department Monthly Labor Force Data for Counties (2012–2016) data. Stage 2 characteristics were calculated using HUD housing agency-level Picture of Subsidized Households (2013) data and HUD household-level Public and Indian Housing Information Center data. Stage 3 trends were calculated using California Employment Development Department’s individual-level aggregate unemployment insurance data.

Trends were calculated across the 4-year period from July 1, 2009, through June 30, 2013.

Based on local labor and housing market conditions in candidate counties, and aggregate

household characteristics in candidate PHAs, and most importantly, patterns of baseline

employment and earnings among residents in candidate PHAs before the SCCHA rent reform,

the sample was narrowed down to the three PHAs with trends most similar to Santa Clara’s: The

Housing Authority of the County of Alameda, HACSM, and SFHA. Exhibits 10 and 11 present

these trends for Santa Clara and the selected counties.

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Exhibit 10

Baseline Trends in Quarterly Employment Rates of Nonelderly, Nondisabled Adults in the Santa

Clara County Housing Authority and Selected Comparison Housing Agencies

HA = housing authority.

Note: Sample consists of adults in the Housing Choice Voucher program who were not elderly or adults with disabilities.

Source: California Employment Development Department individual-level aggregate unemployment insurance data Exhibit 11

Baseline Trends in Average Quarterly Earnings of Nonelderly, Nondisabled Adults in the Santa

Clara County Housing Authority and Selected Comparison Housing Agencies

HA = housing authority.

Notes: Sample consists of adults in the Housing Choice Voucher program who were not elderly and did not have disabilities. Earnings not adjusted for inflation.

Source: California Employment Development Department aggregate unemployment insurance data

108 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Exhibits 12 and 13 presents baseline characteristics for households and individuals in SCCHA

and the three selected comparison PHAs in early July 2013, just before the rent reform was

implemented.33 It shows that households in both groups have, on average, two adults, with more

than one-half having more than one adult in the household, and 65 percent in both groups have

children in the household. About 62 percent of study households in SCCHA and 60 percent

in comparison PHAs have at least one household member who is working. In both groups, 23

percent of households are receiving TANF.

Exhibit 12

Characteristics of Study Nonelderly, Nondisabled Households at Baseline

Characteristic

SCCHA

Comparison PHAs

Average Number of Family Members

Adultsa

1.9

1.8

Children

1.4

1.3

Families with More than One Adult (%)

58.3

50.7

Families with Any Children (%)

64.7

64.6

Current/Anticipated Annual Family Income ($)

17,368

18,525

Income Sourcesb (%)

Wages

61.7

60.4

TANF

23.2

22.9

Social Security/SSI/Pension

12.8

16.5

Other Income Sources

29.1

35.5

Average Monthly Family Sharec ($)

523

561

Average Monthly Housing Subsidyd ($)

1,397

1,244

Monthly Gross Rent Exceeds Payment Standard (%)

53.2

56.7

Neighborhood Poverty Ratee (%)

0%–10%

41.5

41.8

11%–20%

40.1

47.2

21%–30%

14.3

8.1

More than 30%

4.1

3.0

Household Sample Size (Total = 15,499)

7,111

8,388

PHA = public housing agencies. SCCHA = Santa Clara County Housing Authority. TANF = Temporary Assistance for Needy Families.

SSI = Supplementary Security Income.

aAdults are defined as individuals age 18 and older who were not classified on the HUD-50058 form as a live-in aide.

bIncome source categories are as defined on the HUD-50058 form. Wages include one’s own business, federal wages, PHA wages, military pay, and other wages. Other income sources include child support, medical reimbursement, Indian trust/per capita, unemployment benefits, and other nonwage sources.

cFamily share is the family’s contribution toward the gross rent.

dHousing subsidy is the full subsidy amount paid by the housing agency on the household’s behalf. It includes any utility allowance payments made to the tenant in addition to rent paid to the owner by the housing agency.

ePoverty rate is defined as the percentage of individuals ages 18 to 64 years whose income in the previous 12 months was below the poverty threshold.

Notes: Samples consist of households that were not headed by elderly adults or adults with disabilities. Sample sizes may vary because of missing values. Rounding may cause slight discrepancies in sums and differences. The set of comparison group public housing agencies (PHAs) includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Housing subsidy characteristics represent monthly averages. Utility allowance data were not available for San Mateo and are therefore not included in San Mateo’s housing subsidy measures. Averages for the comparison group are weighted based on each PHA’s sample size.

Sources: MDRC calculations using HUD Public and Indian Housing Information Center and 2009–2013 American Community Survey 5-year estimates data 33 A more detailed set of baseline characteristics are presented in Castells (2020).

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Exhibit 13

Characteristics of Study Nonelderly, Nondisabled Adults at Baseline

Characteristic

SCCHA

Comparison PHAs

Female (%)

63.9

66.3

Average Age (Years)

35.6

35.4

Race (%)

White, non-Hispanic

9.4

16.7

Black, non-Hispanic

14.7

40.6

Asian or Hawaiian or Pacific Islander, non-Hispanic

34.7

21.2

Hispanic

40.4

20.1

Other Race or More than One Race, non-Hispanic

0.8

1.4

Income Sourcesa (%)

Wages

41.1

41.4

TANF

11.5

11.9

Social Security/SSI/Pension

1.8

4.2

Other Income Sources

14.3

21.0

Average Annual Income from Wages for Individuals

16,840

19,247

with Any Wage Income ($)

Individual Sample Size (Total = 34,075)

16,133

17,942

PHA = public housing agency. SCCHA = Santa Clara County Housing Authority. SSI = Supplementary Security Income. TANF = Temporary Assistance for Needy Families.

aIncome source categories are as defined on the HUD-50058 form. Wages include one’s own business, federal wages, PHA wages, military pay, and other wages. Other income sources include child support, medical reimbursement, Indian trust/per capita, unemployment benefits, and other nonwage sources.

Notes: Samples consist of adults who were nonelderly and did not have disabilities. Sample sizes may vary because of missing values. Rounding may cause slight discrepancies in sums and differences. The set of comparison group PHAs includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Averages for the comparison group are weighted based on each PHA’s sample size.

Source: MDRC calculations using HUD Public and Indian Housing Information Center data

The two groups are also quite similar at the individual level. Exhibit 13 presents the baseline

characteristics of nonelderly, nondisabled adults in the HCV program in SCCHA and the

comparison PHAs. The sample is majority female (64 percent in SCCHA and 66 percent in

comparison PHAs), and the average age is about 36 years old in SCCHA and 35 in comparison

PHAs. Forty-one percent of adults in both groups were working during the last month of the

baseline period, and 12 percent in both groups were receiving TANF. Average annual earnings were

higher in the comparison PHAs: $19,247 compared with $16,840 in SCCHA.

Although both groups are racially and ethnically diverse, there are differences in their racial

and ethnic compositions. SCCHA has a larger Asian population (35 percent compared with 21

percent in the comparison group) and a larger Hispanic population (40 percent compared with 20

percent in the comparison group). The comparison group PHAs have a higher White (17 percent

compared with 9 percent in SCCHA) and Black (41 percent compared with 15 percent in SCCHA)

population. As mentioned, while there are some differences in racial and ethnic composition

between SCCHA and the comparison group, the fact that the trends in employment and earnings

throughout the 4-year baseline period are similar provides assurance that households in both

groups respond similarly to local economic forces despite these differences.

110 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program The fact that households in both groups live in tight housing markets is evident by their high

level of average household subsidies: $1,397 monthly for SCCHA households and $1,244 for

the comparison PHAs. Also, 53 percent of households in SCCHA and 57 percent in comparison

PHAs have a gross rent that is greater than the payment standard. On average, households pay a

total of $523 monthly in SCCHA and $561 in comparison PHAs toward their housing costs (rent

plus utilities). Only a very small proportion of households (4 percent in SCCHA and 3 percent

in comparison PHAs) live in neighborhoods with poverty rates greater than 30 percent. Most

households (82 percent in SCCHA and 89 percent in comparison PHAs) live in neighborhoods

with relatively low poverty rates of no more than 20 percent.

The validity of the CITS analysis that investigates the impact of the rent reform on employment

and earnings is not based on how similar the comparison group is to the intervention group but on

the level of confidence that the comparison group PHAs are subject to the same labor and housing

market forces, and that they generally respond similarly to such changes. The baseline trends in

employment and earnings presented in exhibits 10 and 11 provide a reasonable level of confidence

that subsidy recipients in each of the two groups respond similarly to changing economic

conditions. Even so, it is useful to note that the study households in the comparison PHAs are

quite similar to the study households in SCCHA.

Findings

Impacts on Employment and Earnings

The CITS design tests whether the SCCHA rent reform caused a deviation from the predicted

baseline trend in employment rates and average earnings for the nonelderly, nondisabled adults in

the SCCHA HCV program. Exhibits 14 to 15 graphically illustrate this analysis. The first step in

the CITS analysis was to use the observed quarterly employment rate of the adults in the SCCHA

sample (the solid line in exhibit 14) over the 4-year baseline period before the SCCHA rent reform

to measure the employment rate trend over those 4 years, which is shown by the dotted line over

the same baseline period. This baseline trend was used to project what employment rates would

have looked like for this SCCHA sample in the absence of the 2013 rent reform, as shown with the

dotted line continuing throughout the 4-year followup period. The second step of the analysis was

to measure the quarterly employment rates after the SCCHA rent reform (the solid line) to estimate

whether there was a deviation from this baseline trend, indicated by the distance between the solid

line during the followup period and the dotted line during the followup period. Visually, this gap

indicates that employment rates were slightly higher than the trend would predict in the first 2

years of implementing the new SCCHA rent policies, and then somewhat lower.

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Exhibit 14

Quarterly Employment Rates for Nonelderly, Nondisabled Adults in the Santa Clara County

Housing Authority

SCCHA = Santa Clara County Housing Authority.

Notes: Samples consist of adults who were not elderly and did not have disabilities. Impacts were estimated using a comparative interrupted time series model. Average quarterly earnings were adjusted for inflation to 2017 dollars using the Bureau of Labor Statistics Consumer Price Index.

Source: California Employment Development Department individual-level aggregate unemployment insurance data 112 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program

Exhibit 15

Quarterly Employment Rates for Nonelderly, Nondisabled Adults in the Comparison Group

Notes: Samples consist of adults who were not elderly and did not have disabilities. The set of comparison group public housing agencies includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Impacts were estimated using a comparative interrupted time series model. Average quarterly earnings were adjusted for inflation to 2017 dollars using the Bureau of Labor Statistics Consumer Price Index.

Source: California Employment Development Department individual-level aggregate unemployment insurance data Cityscape 113

Castells

Exhibit 16

Quarterly Employment Rates for Nonelderly, Nondisabled Adults in the Santa Clara County

Housing Authority and Comparison Group

SCCHA = Santa Clara County Housing Authority.

Notes: Samples consist of adults who were not elderly and did not have disabilities. The set of comparison group public housing agencies includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Impacts were estimated using a comparative interrupted time series model. Average quarterly earnings were adjusted for inflation to 2017 dollars using the Bureau of Labor Statistics Consumer Price Index.

Source: California Employment Development Department individual-level aggregate unemployment insurance data A parallel analysis was then conducted for the comparison group sample, illustrated in exhibit

15. The deviation of the actual employment rates from the predicted employment rates over the

followup period looks similar to the deviation for SCCHA in exhibit 14. The final step of the

analysis was to test whether the deviation in the employment rates for SCCHA was statistically

discernible from the deviation for the comparison group PHAs. In other words, the differences in the deviations were examined. Exhibit 16 overlays the graphs for the SCCHA and comparison

groups. Again, the relevant comparison is not between the two [solid lines], but between the two

sets of deviations from each [solid line].

Exhibit 17 to 19 illustrate the same CITS analysis for average earnings. The difference between the

gaps for the two groups was not statistically significant for either the employment rate or average

earnings outcome throughout the followup period. The sharp upward trend in average earnings

for both SCCHA and the comparison group reflect the rapidly growing local economy during the

study, which covers a time period when the economy was rebounding from the 2008 recession, the

overall median household income was accelerating more rapidly in the area compared with the rest

of the state and the country, and where the latter half of the study period saw a series of increases in local (and state) minimum wages.

114 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program

Exhibit 17

Average Quarterly Earnings of Nonelderly, Nondisabled Adults in the Santa Clara County

Housing Authority

SCCHA = Santa Clara County Housing Authority.

Notes: Samples consist of adults who were not elderly and did not have disabilities. Impacts were estimated using a comparative interrupted time series model. Average quarterly earnings were adjusted for inflation to 2017 dollars using the Bureau of Labor Statistics Consumer Price Index.

Source: California Employment Development Department individual-level aggregate unemployment insurance data Cityscape 115

Castells

Exhibit 18

Average Quarterly Earnings of Nonelderly, Nondisabled Adults in the Comparison Group

Notes: Samples consist of adults who were not elderly and did not have disabilities. The set of comparison group public housing agencies includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Impacts were estimated using a comparative interrupted time series model.

Source: California Employment Development Department individual-level aggregate unemployment insurance data 116 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program

Exhibit 19

Average Quarterly Earnings of Nonelderly, Nondisabled Adults in both the Santa Clara County

Housing Authority and the Comparison Group

SCCHA = Santa Clara County Housing Authority.

Notes: Samples consist of adults who were not elderly and did not have disabilities. The set of comparison group public housing agencies includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Impacts were estimated using a comparative interrupted time series model. Average quarterly earnings were adjusted for inflation to 2017 dollars using the Bureau of Labor Statistics Consumer Price Index.

Source: California Employment Development Department individual-level aggregate unemployment insurance data Exhibit 20 presents the estimated effects of the SCCHA rent reform on average employment rates

and average annual earnings for adults for each year of the followup period.34 Yearly effects on

employment rates for each year in the followup period were calculated by averaging the quarterly

employment rate impact estimates across the four quarters in each followup year. Yearly effects on

average earnings were calculated by summing quarterly earnings impact estimates across the four

quarters in each followup year. Standard errors were computed accordingly.

The first column of results in exhibit 20, labeled “SCCHA Mean,” represents the average outcome

for the study cohort in SCCHA. For example, the average quarterly employment rate in Year 1

(which is an average of the quarterly employment rate for each of the four quarters in Year 1) is

50.8 percent. In other words, 50.8 percent of the SCCHA households had some earnings in a

given quarter, on average, in Year 1. The second column, labeled “Impact,” represents the estimated

impact of the SCCHA rent reform. For example, the 50.8-percent employment rate observed

34 See Castells (2020) for estimated quarterly impacts on employment rates and average earnings.

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Castells

for SCCHA is estimated to be a 0.5 percentage point lower than what it would have been in the

absence of the SCCHA rent reform. This impact estimate is an estimate of the true effect, which

is unknown. The third column labeled “Std. Error” represents the standard error of the impact

estimate, which measures the uncertainty which exists about its corresponding impact estimate.

The final column reports the p-value of the impact estimate, which represents the likelihood that

an estimated effect at least as large as the one observed would have occurred by chance if there was

no true effect.

Exhibit 20

Impacts on Average Quarterly Employment Rate and Annual Earnings of Nonelderly,

Nondisabled Adults

SCCHA

Estimated

Std.

Outcome

P-Value

Mean

Effect

Error

Employment Rate

Year 1

50.8

-0.5

0.9

0.604

Year 2

54.5

0.0

1.2

0.972

Year 3

56.6

0.1

1.5

0.929

Year 4

57.5

0.1

1.8

0.941

Earnings

Year 1

11,187

46

349

0.897

Year 2

13,549

143

474

0.763

Year 3

16,198

200

597

0.738

Year 4

18,538

509

725

0.484

Sample Size

34,075

SCCHA = Santa Clara County Housing Authority.

Notes: Samples consist of adults who were not elderly and did not have disabilities. The set of comparison group public housing agencies includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Effects were estimated using a comparative interrupted time series model. All estimated earnings effects are reported in 2017 dollars. The p-value indicates the likelihood that the estimated impact (or larger) would have been generated by an intervention with zero true effect. Statistical significance levels are indicated as: *** = 1

percent; ** = 5 percent; * = 10 percent.

Source: California Employment Development Department individual-level aggregate unemployment insurance data The results show no evidence of effects on the percentage of nonelderly, nondisabled adults who

are employed or on their average earnings across the 4 years of followup. The estimated effects on

both outcomes are small and not statistically significant. For example, in the first year after the rent reform, the estimated impact on average earnings was $46, but this difference was not statistically

significant. This estimate could be interpreted to mean that the SCCHA average quarterly earnings

of $11,187 for this first followup year was $46 higher than it would have been in the absence

of the rent reform. The magnitude of the effect is very small, however, and the standard error

and p-values are very large, suggesting that this small difference is very likely due to chance.

In other words, there is no evidence that the SCCHA rent reform caused a deviation from its

predicted average earnings trend in the first year after the rent reform was implemented that was

118 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program appreciably different from deviation from the predicted average earnings trend for the comparison

group during the same time period, where no rent reform was implemented. As exhibits 14 to

19 illustrate, employment rates and average earnings increased steadily throughout the 4 years

following the SCCHA rent reform for both SCCHA and comparison group subsidy recipients.

Impacts on Housing Subsidies

The SCCHA rent reform was intended to directly decrease the average household HAP by

increasing the tenant contribution rate from 30 percent of adjusted income to 35 percent of gross

income, eliminating utility allowances for all households, and by reducing the voucher sizes for

a smaller percentage of households (23 percent) by applying a new voucher size policy. This part

of the analysis examines whether the rent reform did in fact decrease average household HAP and

whether HAP remained lower over time. If households had reduced their earnings in response

to the rent reform (there was no evidence they did so, as discussed in the previous section), the

SCCHA HAP savings would have been smaller than what it had projected. The analysis also tested

whether the SCCHA rent reform increased the rate of households leaving the HCV program.

The rent reform could have caused attrition from the HCV program if it drove some households’

HAPs down to zero, reduced subsidies to the extent that some households decided the subsidy no

longer justified the hassles of complying with program rules or increased household rent burden

to a level that was not sustainable for households.

Exhibit 21 presents the estimated effects of the SCCHA rent reform on the average amount of

housing subsidy and the percentage of households receiving any housing subsidy at followup,

using autoregressive difference-in-difference estimation. The findings in this exhibit can be

interpreted in the same way as described in the previous section for exhibit 20, with the caveat

that the research design used to estimate impacts for exhibit 21 is weaker and that less confidence

should be placed in the estimates.

The average household housing subsidy in the month before the implementation of the rent

reform was $16,764 per year, or $1,397 per month (as shown in exhibit 12). As expected,

the SCCHA rent reform reduced the average amount of housing subsidy that households

received. This effect is probably a direct result of the increase in the tenant contribution rate

from 30 percent of adjusted income to 35 percent of gross income and the other policy changes

implemented as part of the rent reform, which resulted in the PHA paying less of a subsidy on

behalf of each household. In the first year, the average annual household subsidy was estimated

to be $1,593 less than it would have been in the absence of the rent reform. This annual effect is

equivalent to $133 monthly.35 In the second year after the rent reform was implemented, when

the tenant contribution rate was reduced to 32 percent of gross income, the effect was slightly

smaller: the average monthly household subsidy was $1,548 less annually, or $129 less monthly,

35 This first-year impact estimate does not account for a 90-day hardship exemption that allowed some households to deduct medical and childcare expenses from their gross income for their temporary HAP calculation. A total of 414

households were granted a hardship exemption, but this total includes elderly and disabled households. SCCHA did not retain data on the elderly and disabled status of these households, so the precise number of households in the study’s nonelderly, nondisabled sample is unknown.

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Castells

than it would have been in the absence of the rent reform. In the third year, the rent reform

reduced annual HAP by $1,329 and monthly HAP by $111.36,37

Exhibit 21

Impacts on Housing Subsidies of Nonelderly, Nondisabled Households

SCCHA

Estimated

Std.

Outcome

P-Value

Mean

Effect

Error

Total Annual Housing Subsidy ($)

Year 1

14,335

-1,593

65

0.000

***

Year 2

13,414

-1,548

99

0.000

***

Year 3

13,481

-1,329

123

0.000

***

Receipt of Housing Subsidy (%)

Year 1

99.2

0.5

0.2

0.013

**

Year 2

92.9

-0.4

0.5

0.368

Year 3

87.5

-0.9

0.6

0.137

Sample (Total = 15,490)

7,109

SCCHA = Santa Clara County Housing Authority.

Notes: Samples consist of households that were not headed by elderly adults or adults with disabilities. The set of comparison group public housing agencies (PHAs) includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority.

Sample sizes may vary because of missing values. Effects were estimated using an autoregressive difference-in-differences model, controlling for the past receipt of housing subsidies and other baseline characteristics of sample households. To assess differences between the research groups, chi-square tests were used for categorical variables and two-tailed t-tests were used for continuous variables. The p-value indicates the likelihood that the estimated impact (or larger) would have been generated by an intervention with zero true effect. Statistical significance levels are indicated as: *** = 1

percent; ** = 5 percent; * = 10 percent. Utility allowance data were not available for San Mateo and are therefore not included in San Mateo’s housing subsidy measures.

Source: MDRC calculations using HUD Public and Indian Housing Information Center data

The bottom panel of exhibit 21 presents the estimated effects of the rent reform on whether

households are still receiving subsidies at followup.38 Overall, there is no clear evidence that the

SCCHA rent reform had an effect on the percentage of nonelderly, nondisabled households that

lost their subsidies. There was a small (0.5 percentage point) but statistically significant increase in the percentage of households continuing to receive subsidies in the first year of followup, but this

effect is probably inconsequential because of its small magnitude and the fact that it disappears

after the first year.39 (This 0.5 percentage point impact estimate can be interpreted to mean that

36 The effect continues to decrease in the fourth year, though this finding is likely due to the data limitation for Year 4

housing data described in the “Data Sources” section of chapter 2.

37 Results from a sensitivity test that estimated effects on HAP using utility allowance imputations for HACSM in the HAP calculations had very similar results (Castells, 2020).

38 Any household that is receiving a HAP amount greater than zero (in any PHA) is counted as still receiving a subsidy at that time. If a household’s income increases to the threshold where its HAP amount is zero because its calculated TTP is greater than the gross rent, the household has a grace period of 180 days during which it is still considered active in the program. If the household income drops during this period, TTP can be recalculated, and the subsidy can resume; otherwise, the household’s participation is terminated from the HCV program and would have to go back on the waiting list to rejoin if its situation changes. Households that leave the HCV program are included in the average with a subsidy amount of $0. If 10 percent of the sample left the HCV program and the PHA replaced them all with new households, the subsidy receipt rate for the group would be 90 percent (not 100 percent).

39 The statistical significance for an impact estimate of such a small magnitude reflects the high precision that exists when the mean of a binary outcome variable is near zero or one.

120 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program 99.2 percent of the cohort of households who were receiving HCV subsidies from SCCHA when

the rent reform was implemented in July 2013 still received subsidies during the first year after the rent reform was implemented, which is 0.5 of a percentage point more than what the percentage

would have been in the absence of the rent reform.) Two years after the implementation of the

rent reform, there was no difference between the percentage of households in the sample receiving

subsidies in SCCHA and the percentage that would have been receiving subsidies in the absence of

the rent reform. This remained true 3 years after rent reform was implemented.40, 41, 42

The patterns in household housing subsidy outcomes over time align with expectations based

on the nature and timing of the rent reform, indicating that the rent reform was implemented

as intended.43 The average household TTP and family share (TTP plus the amount by which

a household’s gross rent exceeds the payment standard) increased more steeply than they did

for comparison group PHAs when the tenant contribution rate changed from 30 percent of

adjusted income to 35 percent of gross income in the first year of the rent reform. The increase

was tempered in the second year when the tenant contribution rate was reduced to 32 percent

of gross income. As expected, the household HAP—which is directly related to the TTP in that a

household’s HAP decreases by the same amount that the TTP increases—mirrors these patterns

in TTP and family share. Housing market trends are also reflected in these patterns of housing

subsidy outcomes: housing prices were steeply increasing during the followup period, so there is

a general upward trend in subsidies independent of any policy change (across both SCCHA and

comparison PHAs) as payment standards increased in response.

Households’ Housing Decisions and Rent Burden

The findings discussed so far show that SCCHA rent reform did not have an overall effect on tenants’

employment and earnings and that it did lead to deep and lasting cuts in housing subsidies as

intended. This section explores measures of households’ housing characteristics over time to gain

further insight into how households may have responded to the SCCHA rent reform and to describe

households’ levels of rent burden in the years following the rent reform. The rent reform could have

encouraged households to seek other, less expensive housing, possibly with fewer bedrooms or in

higher poverty neighborhoods in some cases, or to transfer to an area in the jurisdiction of a different PHA that still used traditional rent rules. These types of housing decisions would have tempered the

increase in households’ rent burden resulting from the reduction in housing subsidies.

The findings in this section should be interpreted with a great deal of caution. The exhibits in this section present average housing outcomes over time and causal inferences should not be drawn

40 It also remained true 4 years after the rent reform was implemented. While the data limitations with the Year 4

IMS/PIC data would have affected measures of average subsidy amounts more than the measures of any subsidy receipt, the overall data limitations also reduce the reliability of this Year 4 impact estimate.

41 As a sensitivity test, logistic regressions were run to estimate effects on this dichotomous outcome. The results were very similar.

42 Results from a sensitivity test that included utility allowance imputations for HACSM in the HAP calculations were very similar in magnitude to these main findings except for the Year 1 impact estimate, which was smaller (but still positive) and was not statistically significant.

43 Patterns in household housing subsidy outcomes over the study period are presented in Castells (2020).

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from this descriptive analysis. Comparison group levels are presented to provide context for the

SCCHA findings.

Households’ Housing Decisions While in the Voucher Program

The patterns in measures related to households’ housing decisions throughout the followup

period give no obvious indication that households are, on average, relocating to poorer quality

neighborhoods, moving to smaller units, moving to units outside the jurisdiction of SCCHA,

or changing their household composition.44 There was a general upward trend in households’

neighborhood poverty rate over the followup period, suggesting that households that moved

tended to move to poorer neighborhoods, but this trend also existed for households in the

comparison group PHAs. The SCCHA households’ average number of bedrooms dropped only

slightly, from 2.7 to 2.6 bedrooms. (The comparison group’s average number of bedrooms

remained constant throughout the followup period.) There is also no strong indication that a

significant portion of households changed their household composition by adding or removing

household members in response to the rent reform. Household size decreased slightly over the

followup period, but a similar trend is evident for the comparison group households. Last, there is

no indication that a large portion of households chose to relocate to a unit outside the jurisdiction of SCCHA to avoid the higher out-of-pocket housing costs (defined as “porting out”); SCCHA had

a lower rate of port-outs than the comparison group PHAs throughout the study period.

Household Rent Burden While in the Voucher Program

The lack of effects on employment and earnings discussed earlier in this chapter means that

households were not increasing their incomes to cover their greater tenant rent share due to the

rent reform. These findings suggest that households were absorbing their increased housing costs

into their current budgets, either by reducing spending or incurring debt. Considering material

hardship is therefore an important part of understanding the effects of a rent reform that increases

the tenant contribution rate toward rent, but material hardship is only partially measured in this

study. This section looks at patterns of rent burden over the study period to better understand

the extent to which households’ rent burdens increased after the rent reform was implemented.45

As described earlier, the present study uses a measure of rent burden that is the tenant rent share

(equal to the TTP plus any amount over the payment standard that the household pays toward the

contract rent, not including tenant-paid utilities) as a proportion of the household’s gross income.

Exhibit 22 shows patterns in households’ rent burden from the last month of the baseline period

(before the rent reform was implemented) through the 4 years of followup.46 In the month before

SCCHA’s rent reform was implemented, the percentage of nonelderly, nondisabled households

44 See Castells (2020) for details.

45 As described earlier, this study focused on nonelderly, nondisabled households and individuals in the HCV

program. Although it was not feasible to conduct an impact analysis for the cohort of elderly and disabled households and individuals in the HCV program due to the data limitations described earlier, the rent burden measure was available for this cohort. Castells (2020) presents the rent burden measures for the cohort of elderly and disabled households and shows similar patterns as the nonelderly, nondisabled sample, though the increases in the percent of gross income these households pay toward rent are not as steep.

46 Castells (2020) presents alternative measures of rent burden, including average, median, and distributions of rent burden in the last month of baseline and of each followup year.

122 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program whose tenant rent share exceeded 40 percent of their gross income looked fairly similar to the

percentage for the comparison group households; 12 percent in SCCHA and 14 percent in

comparison PHAs were paying more than 40 percent of their gross income toward rent. In the

first year following the implementation of the SCCHA rent reform, the percentage of households

in SCCHA paying more than 40 percent of their income toward rent increased steeply from 12

to 30 percent, compared with a three percentage points increase for households in comparison

group PHAs. These patterns largely held up during the second year; the differences declined in

the third. That decline may not reflect a true decline in rent burden, if it was at least in part due to households with an extreme rent burden leaving the subsidy program (voluntarily or through

eviction) and therefore not being counted in the averages. It is notable that a downward trend in

the percentage of households exceeding this 40-percent threshold also existed for the comparison

group in this subsample, possibly also because of the changing composition of households

continuing to receive subsidies over time.

Exhibit 22

Rent Burden Among Nonelderly, Nondisabled Households Receiving Subsidies

Outcome

SCCHA

Comparison PHAs

Tenant Rent Share Exceeds 40% of Monthly Gross Income (%)

Last Month of Baseline

12.3

14.0

Last Month of Year 1

30.1

17.4

Last Month of Year 2

30.5

14.2

Last Month of Year 3

19.4

11.2

Tenant Rent Share Exceeds 50% of Monthly Gross Income (%)

Last Month of Baseline

8.9

9.6

Last Month of Year 1

21.4

11.9

Last Month of Year 2

21.9

9.3

Last Month of Year 3

13.9

7.7

Sample Size (Total = 15,490)

7,109

8,381

PHA = public housing agency. SCCHA = Santa Clara County Housing Authority.

Notes: Samples consist of households headed by adults who were not elderly and did not have disabilities. The set of comparison group PHAs includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Sample sizes may vary because of missing values. Outcomes shown describe only those households receiving any housing subsidies in the specified month. Utility allowance data were not available for San Mateo and are therefore not included in San Mateo’s housing subsidy measures.

Source: MDRC calculations using HUD Public and Indian Housing Information Center data

Effects for the Single and Double Policy Change Subgroups

As described earlier, SCCHA implemented a new voucher size policy that enforced a minimum of

two family members per bedroom, excluding the head of household’s bedroom. Before this policy

change, the age, generation, gender, and relationship of other household members were considered

when determining voucher size. The group of households in SCCHA that were immediately

affected by this change constituted approximately 23 percent of the nonelderly, nondisabled

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Castells

voucher population. This section presents the findings of a subgroup analysis of the effects on

earnings and employment and describes housing characteristics and rent burden separately for the

households only affected by the tenant rent contribution increase (referred to as the “single policy

change” subgroup) and households affected by the voucher policy change in addition to the tenant

contribution rate increase (referred to as the “double policy change” subgroup).

The single policy change subgroup differed from the double policy change subgroup in some

important ways. It is important to keep this in mind when interpreting the subgroup findings, as

differences in effects between the two subgroups may not be entirely due to being differentially

affected by just the tenant contribution rate versus both policy changes, but they may also

reflect how different types of households respond to the policy changes. Exhibit 23 presents

characteristics of households just before SCCHA implemented its rent reform for the single policy

change and the double policy change subgroups separately. Households that were directly affected

by the voucher size rule change tended to be larger and have more children than households

that were only affected by the tenant contribution rate change. In SCCHA, the average number

of household members is 3.7 for this subgroup (compared with 3.2 for the single policy change

subgroup), and 77 percent of households in the double policy change group in SCCHA included

children, compared with 61 percent in the single policy change subgroup. A slightly larger

proportion of households in the double policy change subgroup in SCCHA had at least one adult

working at baseline than those among the single policy change subgroup: 65 percent compared

with 61 percent. The double policy change subgroup in SCCHA also had a higher level of out-of-

pocket housing costs ($574 monthly) than the single policy change subgroup ($508 monthly) and

a larger average subsidy ($1,711 per month compared with $1,304).47

47 See Castells (2020) for more detailed exhibits of baseline characteristics of households and adults in each of these subgroups.

124 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Exhibit 23

Characteristics of the Sample at Baseline by Policy Group Nonelderly, Nondisabled Santa Clara

County Housing Authority Households

Characteristic

Single Policy Change

Double Policy Change

Average Number of Family Members

Adultsa

1.8

2.2

Children

1.4

1.5

Families with More than One Adult (%)

53.6

74.1

Families with Any Children (%)

61.2

76.7

Current/Anticipated Annual Family Income ($)

16,511

20,247

Income Sourcesb (%)

Wages

60.7

65.2

Temporary Assistance for Needy Families (TANF)

22.6

25.4

Social Security/SSI/Pension

11.2

18.0

Other Income Sources

28.1

32.4

Average Monthly Family Sharec ($)

508

574

Average Monthly Housing Subsidyd ($)

1,304

1,711

Monthly Gross Rent Exceeds Payment Standard (%)

53.5

52.0

Neighborhood Poverty Ratee (%)

0%–10%

39.0

50.1

11%–20%

41.1

36.8

21%–30%

15.5

10.4

More than 30%

4.4

2.7

Household Sample Size (Total = 6,725)

5,183

1,542

SCCHA = Santa Clara County Housing Authority. SSI = Supplementary Security Income.

aAdults are defined as individuals age 18 and older who were not classified on the HUD-50058 form as a live-in aide.

bIncome source categories are as defined on the HUD-50058 form. Wages include one’s own business, federal wages, public housing agency (PHA) wages, military pay, and other wages. Other income sources include child support, medical reimbursement, Indian trust/per capita, unemployment benefits, and other nonwage sources.

cFamily share is the family’s contribution toward the gross rent.

dHousing subsidy is the full subsidy amount paid by the housing agency on the household’s behalf. It includes any utility allowance payments made to the tenant in addition to rent paid to the owner by the housing agency.

ePoverty rate is defined as the percentage of individuals ages 18 to 64 years whose income in the previous 12 months was below the poverty threshold.

Notes: Samples consist of households headed by adults who were not elderly and did not have disabilities. Sample sizes may vary because of missing values. Rounding may cause slight discrepancies in sums and differences. The set of comparison group PHAs includes the San Mateo County Housing Authority, the San Francisco Housing Authority, and the Alameda County Housing Authority. Housing subsidy characteristics represent monthly averages.

Utility allowance data were not available for San Mateo and are therefore not included in San Mateo’s housing subsidy measures.

Sources: MDRC calculations using HUD Public and Indian Housing Information Center and 2009–2013 American Community Survey 5-year estimates data Subgroup Impacts on Employment and Earnings

The estimated effects of the SCCHA rent reform on employment rates and average earnings for the

subgroup of individuals living in households that were affected only by the tenant contribution

rate increase were very similar to the impact estimates for the full sample. There is no suggestion

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Castells

of positive or negative effects on employment rates or average earnings for this subgroup. This is

unsurprising, given that these households constitute 77 percent of the full study sample.

As exhibit 24 shows, households affected by the change in the voucher size policy in addition to the tenant contribution rate increase—the remaining 23 percent of the full study sample—showed a

pattern of negative estimated effects on employment rates, but these estimates were not statistically significant. They were also not statistically significantly different from those for the single policy change subgroup. The impact estimates in the “single policy change” panel and the “double policy

change” panel of exhibit 24 represent impact estimates using a CITS analysis for each subgroup

separately and can be interpreted in the same way as the impact estimates in exhibit 20. Exhibit

24 contains an additional column labeled “Difference Between Groups.” This column presents the

results of the statistical comparison between the difference in the impact estimates between the two subgroups. The numbers in the column represent the p-values, and the daggers represent the levels

of statistical significance of the estimate of the difference in impacts.

There is some evidence, however, that the household members also affected by the voucher size

rule change may have reduced their earnings in response to the rent reform. For this double policy

change subgroup, average annual earnings were estimated to be lower in the first 2 years after

the rent reform was implemented than they would have been in the absence of the rent reform,

though these estimated effects were not statistically significant from zero. By the third year, SCCHA residents in this subgroup were earning $2,220 less annually on average than they would have

been in the absence of the rent reform, and this effect was sustained in the fourth year (when the

estimated decrease was $2,341). These third- and fourth-year effects are statistically significant at the 0.05 level. They are also statistically significantly different from the impact estimates for the single policy change subgroup at the 0.05 level.

As exhibits 14 to 19 from the full sample analysis illustrated, employment rates and average

earnings increased over the followup period for both SCCHA and the comparison group. In the

context of these earnings trends, an estimated negative effect on average earnings means that,

although average earnings did increase over time for SCCHA subsidy residents, they did not

increase as quickly or as much as they would have in the absence of the rent reform. An estimated

negative effect on average earnings could be caused either by some SCCHA residents reducing

their employment in response to the rent reform or by some SCCHA residents not obtaining

employment or not increasing their hours or wages because of the rent reform.

126 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Exhibit 24

Impacts on Average Quarterly Employment Rate and Annual Earnings of Adults in Nonelderly,

Nondisabled Households by Policy Change

One Policy Change Group

Two Policy Change Group

Difference

Outcome SCCHA Estimated

SCCHA Estimated Std.

Between

Std. Error P-Value

P-Value

Mean

Effect

Mean

Effect

Error

Groups

Employment Rate

Year 1

49.9

-0.2

1.4

0.881

51.0

-1.3

1.3

0.323

0.573

Year 2

53.1

-0.2

1.9

0.907

54.6

-1.7

1.7

0.322

0.560

Year 3

54.9

-0.6

2.4

0.801

56.4

-3.5

2.2

0.112

0.374

Year 4

55.6

-0.4

2.9

0.886

57.7

-4.2

2.6

0.112

0.334

Earnings

Year 1

11,223

111

496

0.824

11,572

-663

521

0.208

0.284

Year 2

13,206

212

674

0.755

13,761

-960

695

0.172

0.228

Year 3

15,618

223

859

0.796

16,206

-2,220

875

0.014 ** 0.048

††

Year 4

17,606

736

1,046

0.484

18,487

-2,341

1,058

0.031 ** 0.041

††

Sample Size

12,439

4,438

SCCHA = Santa Clara County Housing Authority.

Notes: The comparison group public housing agency in this exhibit is the San Francisco Housing Authority. Effects were estimated using a comparative interrupted time series model. All estimated earnings effects are reported in 2017 dollars. The p-value indicates the likelihood that the estimated impact (or larger) would have been generated by an intervention with zero true effect. Statistical significance levels are indicated as: *** = 1 percent; ** = 5

percent; * = 10 percent. A two-tailed t-test was applied to differences between the outcomes of the policy subgroups. Statistical significance levels for differences across subgroup impacts are indicated as: ††† = 1 percent; †† = 5 percent; † = 10 percent.

Source: California Employment Development Department household-level aggregate unemployment insurance data This negative effect of the rent reform on average earnings for the households affected by both the

tenant contribution rate change and the voucher size rule change seems at least partly due to the

decrease in employment rates that the impact estimates (though not statistically significant) on

employment rates suggest. The negative effect on employment and earnings for this subset of the

sample did not occur immediately after the rent reform was implemented, suggesting that residents

did not reduce their employment right away.

An exploratory analysis examined whether these employment and earnings effects may also be

related to the double policy change households’ housing experiences following the rent reform.

The column labeled “Difference Between Groups” can be interpreted as the difference in impact estimates (the double policy change subgroup impact estimate minus the single policy change

subgroup impact estimate). The column to the right of that column contains their p-values, which

are marked with daggers to indicate the level of statistical significance.

The findings of this exploratory analysis suggest that households in the double policy change

subgroup experienced much larger reductions in HAP than the single policy change group.

Households in the double policy change subgroup experienced reductions in subsidies that were

more than $4,000 larger than the single policy change group in the first 2 years following the rent

reform, and this difference declined somewhat in the third year. These differences in effects on

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Castells

average HAP are statistically significant at the 0.01 significance level. The differential effects on continued subsidy receipt are not statistically significant between the two groups (although they

approach statistical significance by the third year), but the pattern of these differential effects may suggest that the SCCHA rent reform led a small percentage of households in the double policy

change group to leave the subsidy program or lose their housing subsidies. The timing of these

estimated impacts on continued subsidy receipt aligns with the timing of the estimated negative

impacts on employment and earnings for this double policy change subgroup, suggesting that

they may be related. The patterns in housing subsidies for each of the subgroups (in relation to

their corresponding comparison groups) aligns with these suggestive findings. HAP decreased

significantly more, and tenant rent share increased significantly more, for the double policy change

subgroup than for the single policy change subgroup. By the third year after the SCCHA rent

reform was implemented, 86 percent of SCCHA households in the double policy change subgroup

were still receiving subsidies compared with 91 percent in the comparison group.48

Households’ Housing Decisions and Rent Burden While in the Voucher Program

by Subgroup

Households affected by only the tenant contribution rate increase may have had an incentive to

move to a more affordable unit (whose contract rent exceeds the payment standard by a lesser

amount than their current unit) to offset increases in housing costs. Households affected by the

voucher size rule change, in addition to the tenant contribution rate increase, had an additional

very strong incentive to relocate to smaller units to avoid the steeper out-of-pocket housing cost

they would have faced if they remained in their units.

Especially in a tight housing market, finding a unit that is affordable may require moving to a more

affordable neighborhood. The trends among both single policy change households and double

policy change households look similar to each other: a slight increase in neighborhood poverty

over the 3 years following the SCCHA rent reform that mirrors the full sample results. Similar to

the full sample findings, the differences between each subgroup of SCCHA households and their

corresponding comparison group households (whose trends fluctuate only slightly) are not stark,

and it is impossible to draw any conclusion about effects from these purely descriptive data. The

trends in the number of bedrooms over time suggest more strongly that households in the double

policy change group moved to smaller units, with households’ average unit size dropping from

3.4 to 3.2 bedrooms in the first year after the SCCHA rent reform was implemented, and then

continuing to drop to 3.1 bedrooms by the end of the third year, compared with the single policy

change comparison group, whose average remains steady at 3.1 starting before the SCCHA rent

reform was implemented and remaining at 3.1 throughout the 3 years following the rent reform.

For the single policy change group, the number of bedrooms remains steady both for SCCHA

households and comparison group households. There is no apparent pattern of households

changing their household composition following the policy changes for either subgroup.49

One particularly striking pattern is that the double policy change households’ tenant rent share (as

measured by TTP plus the amount that the contract rent exceeds the payment standard) increased

48 See Castells (2020) for more details.

49 See Castells (2020) for details.

128 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program very steeply in the year following implementation of the rent reform for households continuing to

receive subsidies compared with the single policy change subgroup. Tenants’ rent shares increased

slightly in the comparison groups for both the single and double policy change households. The

SCCHA households’ tenant rent share in the single policy change subgroup increased from $447

to $529 between the month before the SCCHA rent reform and 1 year later (which was a larger

increase than the single policy change comparison group, as expected). In contrast, the SCCHA

households’ tenant rent share in the double policy change subgroup rose from $511 per month just

before the implementation of the rent reform to $810 per month 1 year later. (Only a tiny fraction

of households in the double policy change subgroup—0.3 percent—stopped receiving housing

subsidies between these two time points, so these averages are a relatively reliable measure of the

sample’s housing costs.) Tenant rent shares did decrease in subsequent years (in part because of the

reduction in tenant contribution from 35 to 32 percent of gross income), but they remained high.

This pattern supports SCCHA’s observation that while many double policy subgroup households

moved to smaller units, many other households stayed in place and paid the much higher housing

costs as a result.

The increase in tenant rent share for double policy change households from $511 to $810 per

month is quite large for this subset of SCCHA households, which have an average household

income of $1,687 per month. This apparent hardship is explored more formally in exhibit 25,

which presents households’ rent burden just before the SCCHA implementation of the rent reform

and throughout the followup period.50 The levels of average rent burden demonstrate that this

subset of households experienced very high levels of housing-related hardship after the rent reform

was implemented. Just before the rent reform was implemented, just 6 percent of these households

had tenant rent shares that exceeded 40 percent of their gross income. The percentage jumped

to 61 percent 1 year after the rent reform was first implemented. (In contrast, this percentage

only increased from 15 to 21 percent for single policy change households.) This percentage then

decreased over the rest of the followup period, to 50 percent by the end of the second year and 33

percent by the end of the third. This decrease reflects the tenant contribution rate being reduced

from 35 to 32 percent of gross income in the second year of its implementation, but may also

reflect the fact that some households may have moved to smaller or more affordable units after the

end of the first year, or that households with the highest levels of rent burden may have left the

subsidy program following the rent reform. The same pattern is evident using a higher threshold.

The percentage of SCCHA households in the single policy change subgroup paying over one-half

of their gross incomes toward rent rose only slightly from 11 to 14 percent, but for households

in the double policy subgroup, this percentage rose steeply from 4 percent just before the start of

implementation to 47 percent 1 year later.

50 Additional measures of rent burden are presented in Castells (2020).

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Castells

Exhibit 25

Tenant Rent Burden Among Nonelderly, Nondisabled Households Receiving Subsidies, by Policy

Subgroup in Santa Clara County Housing Authority

Single Policy

Double Policy

Outcome

Change

Change

Tenant Rent Share Exceeds 40% of Monthly Gross Income (%)

Last Month of Baseline

14.9

5.8

Last Month of Year 1

21.4

60.5

Last Month of Year 2

24.5

50.2

Last Month of Year 3

15.6

32.9

Tenant Rent Share Exceeds 50% of Monthly Gross Income (%)

Last Month of Baseline

10.9

4.1

Last Month of Year 1

14.0

47.3

Last Month of Year 2

16.8

38.7

Last Month of Year 3

10.9

25.0

Sample Size (Total = 6,723)

5,181

1,542

Notes: Sample sizes may vary because of missing values. Outcomes shown describe only those households receiving any housing subsidies in the specified month.

Source: MDRC calculations using HUD Public and Indian Housing Information Center data

Discussion

This study analyzed the impact of SCCHA’s rent reform on employment, earnings, housing subsidy,

and housing characteristics of the group of nonelderly, nondisabled SCCHA tenants who were

receiving HCV subsidies in July 2013, when SCCHA’s rent reform was implemented. There were

three possible effects of the rent reform on tenant earnings: (1) households would reduce their

earnings in response to the disincentive of higher tenant contribution rate; (2) households would

increase earnings to cover their increased housing costs; or (3) households would not change their

employment behavior and instead would reduce spending in other areas to cover their higher

housing costs, or they would increase their debt, or do neither and experience increased material

hardship. The analysis found no evidence that SCCHA’s rent reform had any effects, on average, on

the employment and earnings of nonelderly, nondisabled SCCHA residents. Since the rent reform

did not lead to a decrease in earnings, SCCHA realized its projected HAP savings. Nonelderly,

nondisabled households received approximately $1,600 less in housing subsidies, on average,

during the first year, $1,550 less in the second, and $1,330 less in the third year after the rent

reform than they would have received without the rent reform. There was also no evidence that the

rent reform caused households to lose their housing subsidies, on average.

There are two factors specific to the SCCHA context that might have strengthened any incentive

inherent in the rent reform’s policy change for households to increase their employment and

earnings. First, Santa Clara County and its surrounding counties had a robust job market during

the study period. While this limits the ability to generalize the findings from this study to areas

with weaker job markets, it is worth noting that even in this robust job market, households did

130 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program not increase their employment or earnings in response to an increased tenant contribution rate.

Second, SCCHA has a 24-month recertification period, which is longer than the typical 12-month

recertification period used in traditional PHAs. SCCHA households that were considering

increasing their income in response to the rent reform (to compensate for lost net income)

may have had a stronger incentive to do so than they would have in a PHA with a 12-month

recertification period since they have a longer period of time for which their increased income

would not have increased their rent share. If positive effects had been estimated on employment

and earnings, these two factors would have needed to be taken into consideration when

interpreting results. There was no evidence, however, of effects on employment and earnings,

despite these two conditions that could have made it more possible or more likely for households

to respond to the “income effect,” that is, to increase their income to compensate for their higher

housing costs. This absence of earnings effects could mean that they were not important factors

in households’ employment decisions. For example, the overall robust job market may not have

translated to many employment opportunities for SCCHA tenants, who generally have lower

education levels and less work history than non-subsidized residents in the county. And, the

24-month recertification period, which had already been in place for about 4 years before the

rent reform was implemented, may not have provided a significant incentive to increase earnings

beyond that which households may have already responded to before the rent reform. If they were

meaningful factors in households’ employment decisions, it is unknown the extent to which they

balanced out a disincentive to earn more.

The combination of findings that overall the SCCHA rent reform did not increase residents’ average

earnings levels and that it did, as intended, reduce households’ housing subsidies, means that

households experienced reductions in their net income. For households that did not increase

their income over time, these reductions in net income could have been persistent. Households

who did increase their income over time would have experienced smaller increases in net income

compared with the increases they would have experienced in the absence of the rent reform. This

interpretation is consistent with SCCHA leadership’s impression that voucher households generally

did not change their employment behavior but instead absorbed their increased housing costs.

This study did not include measures of households’ material hardship beyond rent burden,

and therefore cannot assess how this increase in rent burden translated into experiences with

material hardship. It is an important consideration, however, because increased housing costs

for households without increased income to cover those costs could mean that households are

reducing spending on other necessary goods and services, which may lead to increased material

hardship or increased debt.

One study—the Rent Reform demonstration—that is currently underway in four PHAs can

provide some context for potential effects on households’ material hardship. A baseline survey

of the households participating in the demonstration—a population comparable to the sample

for the present study, consisting of nonelderly, nondisabled households in the Housing Choice

Voucher (HCV) program—revealed that households receiving housing subsidies under traditional

rent rules commonly experience material hardship: almost 70 percent of survey respondents said

they had experienced at least one form of material hardship during the last year. Forty-six percent

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said they were not able to pay a utility bill, 34 percent said they were not able to pay a telephone

bill, and 20 percent said they were not able to pay the (subsidized) rent in at least 1 month of

the past year. Furthermore, 28 percent said they were not able to buy food, and 23 percent said

they were not able to see a doctor or buy prescription drugs they needed during at least 1 month

in the past year (Riccio, Deitch, and Verma, 2017). Given that HCV households in SCCHA were

likely to be experiencing similar material hardship before the rent reform, there is a risk that the

increase in housing costs and consequent decrease in net income caused by the rent reform may

have worsened such hardship. A subgroup analysis explored how the SCCHA rent reform may

have affected households subject to only the change in the tenant contribution rate differently from

households who were also affected by the voucher size rule change. Like the findings for the full sample, there was no evidence that the rent reform had any effects on employment or earnings

for the subgroup only affected by the tenant contribution rate change. There was, however, some

evidence that the SCCHA rent reform reduced earnings for the subgroup of households that was

affected by both policy changes. These households experienced particularly high levels of rent

burden following the rent reform (measured as the tenant rent share as a proportion of household

gross income). The negative effect on earnings for this subgroup did not occur immediately after

the rent reform was implemented, suggesting that residents did not reduce their employment right

away, but perhaps struggled with their increased housing costs before reducing their earnings. An

exploratory analysis shows that, as expected, the SCCHA rent reform led to especially deep cuts

in housing subsidies for this double policy change subgroup. To the extent that the steep increases

in households’ housing costs led some households to leave the subsidy program (of which there

is only suggestive evidence), this housing instability might partially explain the employment and

earnings effects for this subgroup of households, if having to relocate to a new unit or leave the

subsidy program destabilized a person’s employment situation. The true drivers of these delayed

negative effects on household earnings for this subgroup cannot be identified within the scope of

the present study.

These findings hint at some potential consequences of implementing a voucher size policy that

causes steep increases in households’ housing costs if they do not move to a smaller unit. The

scarcity of affordable housing in Santa Clara meant that households had few options for less

expensive units. Although the trends in the average number of bedrooms over time suggest that

some households in this group did move to smaller units, it is likely that many were unable to, or

that if they did, those units might not have been as affordable as their previous units.51 In a location with more affordable housing options, households that have to move to a smaller unit may have an

easier time doing so.

SCCHA provided crucial safeguards for these households by partnering with local organizations

to provide legal and financial assistance to prevent eviction. It is important for any PHA that

implements a rent reform to identify the households that might be most adversely affected by

the policy changes and provide them with adequate protection from severe hardship. This study

provides evidence that can help inform that assessment for other PHAs considering these changes

to the rent policy. For example, the SCCHA eviction prevention assistance program (SEPP)

51 A new unit with fewer bedrooms under the new policy could be less affordable than the former unit with more bedrooms if the new unit’s gross rent exceeded the payment standard for the smaller voucher size more than the former unit’s gross rent exceeded the payment standard for the larger voucher size.

132 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program provided important support that likely helped many households avoid eviction during the time

that it was being operated. An attorney working in the program (and now a staff member of

SCCHA) recalled that of the 293 households that SEPP assisted, only two cases at most resulted

in an actual eviction. Other PHAs considering a similar policy change in a similar context (where

finding an affordable smaller unit may be difficult) might consider that households who are

vulnerable to adverse effects because of the policy change may greatly benefit from an effective

safeguard such as the SEPP program that is offered over an extended period of time.

Study Limitations

Findings from this study contribute to the emerging body of evidence on how changing the rent

policy for households receiving housing assistance can affect employment and earnings. Still,

the study has some limitations that should be kept in mind when interpreting the results. An

important limitation, as discussed earlier, is that aspects of material hardship beyond rent burdens, such as food insecurity and difficulty covering medical expenses or rent and utilities, are not

measured in this study. A full assessment of the implications of increasing the tenant contribution

rate—or any other rent reform that reduces housing subsidy levels—should consider the effects on

the level of material hardship that households experience in response to the policy change.

Another limitation is that the study does not examine how the effects of the rent reform may vary

for different types of individuals and households. It includes a subgroup analysis that compares

effects for households only affected by the tenant contribution rate and households affected by

both policies, but there may be other sources of variation that the study was not able to explore. It is possible, for example, that nonelderly, nondisabled adults who were already working at the time

the SCCHA rent reform was implemented would have more flexibility than those who were not

employed to increase or reduce their employment and earnings in response to the increased tenant

contribution rate. Nonelderly, nondisabled individuals who are not yet working may have less of

an incentive to begin a new job with a higher tenant contribution rate. The study was not able to

estimate effects separately for these subgroups due to data limitations.

The primary analytic method in this study is a rigorous one: a CITS to examine the effects of the

SCCHA rent reform on employment and earnings. The rent reform meets important conditions

for CITS in that it was a consequential change that occurred for the full study cohort all at once,

and there were data available for a comparison group that was subject to the same economic forces

as SCCHA. As noted in the Analytic Approach section, it was not feasible to use CITS to examine

effects on households’ average housing subsidies or continued subsidy receipt because 4 years of

historical housing subsidy data were not available for the full study sample, and therefore baseline

trends could not be estimated for these measures. The autoregressive difference-in-difference

design used to study the effects of the rent reform on these outcomes is not as rigorous as CITS

because it does not account for potentially differing baseline trends in housing subsidy measures.

Furthermore, it was not feasible to use either CITS or autoregressive difference-in-difference

to examine effects on housing characteristics (including the number of bedrooms in the unit

or neighborhood poverty) or household composition because these data were not available for

households that were no longer receiving subsidies. The descriptive analysis used to explore these

outcomes can only provide suggestive evidence of potential effects.

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This analysis relies on UI wage data to assess effects on employment and earnings, and these data

do not capture earnings from employment that is not covered by UI. While there is no strong

reason to believe that the SCCHA rent reform would have affected informal employment differently

from formal employment, this study cannot formally test that assumption. It is possible, however,

that the increased rent contribution rate could strengthen the incentive to underreport earnings to

the PHA, and underreporting would be easier with informal employment, especially if the worker

is not paying taxes on the earnings.

Finally, the present study focused on only one PHA in one location, Santa Clara County, that has

a tighter housing market and a more robust job market than most of the country, though there

are many PHAs in comparable situations—such as PHAs in New York City, Boston, Los Angeles,

Seattle, Denver, Washington, and San Diego—serving large numbers of households. These findings

provide an important contribution to the literature on the effects of rent reform on labor market

outcomes; still, additional studies in multiple locations with diverse contexts are needed to draw

more broadly applicable conclusions.

Conclusion

The present study of the effects of a rent reform implemented by SCCHA provides evidence, from

one location, that a public housing agency can increase the tenant contribution rate by a moderate

amount (30 percent of adjusted income to 35 percent of gross income, later to 32 percent of gross

income) without creating a substantial disincentive to work and without significantly increasing

the likelihood that households may lose their subsidies. It also provides suggestive evidence that

implementing a voucher size policy that requires some households to relocate to a different unit

(with fewer bedrooms) to avoid even deeper cuts in housing subsidies may have adverse effects in

the context of a housing market where affordable housing options are very limited, and that short-

term safeguards that PHAs provide to households may not fully prevent longer-term hardship.

Further research is needed in multiple locations that represent diverse housing markets and labor

markets to examine how the effects of this type of rent reform may be expected to vary based

on the context. Studies with a random assignment design would help overcome many of the

methodological limitations of this study described above, for example, by providing a reliable

counterfactual for outcomes where multiple years of pre-intervention data are not available for the

full sample. Finally, future studies of rent reforms that might result in reduced housing subsidies

for households should carefully measure households’ experiences with material hardship, including

food insecurity and their ability to cover medical expenses, in response to the rent reform.

The present study is the first to investigate how increasing the tenant contribution rate affects

households’ employment, earnings, and housing subsidies.

134 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program

Appendix A. Map of Santa Clara and Comparison Counties

Source: California State Association of Counties

Appendix B. Model Specifications

Comparative Interrupted Time Series Model

The following comparative interrupted time series (CITS) model was used to estimate effects on

quarterly employment rates and average quarterly earnings:

Where:

Y i = the value of the outcome variable for observation i, where observation i is defined as the value of the outcome variable for the public housing agency (PHA) p in quarter q,

PHA = a series of four indicator variables, one for each PHA p,

α = a series of four intercepts for baseline trends, one for each PHA p,

Q = the quarter (a continuous variable),

β0= a series of coefficients representing the slope of the baseline trend for each of the four PHAs,

F = an indicator value for followup quarter f,

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Castells

β1= a series of coefficients representing the comparison group deviation from its baseline trend for

each followup quarter f,

S = 1 if the PHA is the Santa Clara County Housing Authority (SCCHA) (the treatment group) and

0 otherwise,

β2 = a series of coefficients representing the treatment-comparison group difference between their

deviations from their baseline trends for each followup quarter f, and

e = a random error term.

Autoregressive Difference-in-Difference Model

The following autoregressive difference-in-difference (DinD) model was used to estimate effects on

housing subsidy outcomes:

Where:

Y h = the value of the outcome variable for sample household h,

α = the intercept, representing the conditional mean outcome for the three comparison PHAs

combined,

S = 1 if household h was in SCCHA (the treatment group) at the time of the rent reform and

0 otherwise,

β1 = estimate of the effect of the SCCHA rent reform on outcome Y,

Y bh = a set of four variables representing the lagged value of the outcome variable Y for the 4

baseline years prior to the rent reform for household h

β2 = a series of four regression coefficients, one for each of the four variables representing the

lagged values of the outcome for the 4 baseline years prior to the rent reform,

R = a set of four variables representing whether the household received any subsidies in each of the

4 baseline years prior to the rent reform,

β3 = a series of four regression coefficients for the 4 baseline years’ indicators of any housing

subsidy receipt,

X = a set of background characteristics for household h,52

β4 = the set of regression coefficients for the background characteristics,

e = a random error term.

52 The background household characteristics included in the model as covariates include: the head of household’s gender, age, race and ethnicity, and receipt of wage, Temporary Assistance for Needy Families (TANF), or Social Security/Supplementary Security Income/Pension (SSIP) income; number of adults in a household; age of the youngest child; annual household wage earnings; monthly family share; and whether the household’s monthly gross rent exceeds the payment standard.

136 The Moving to Work Retrospective Evaluation

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program Acknowledgments

The evaluation was sponsored by the Office of Policy Development and Research in the U.S.

Department of Housing and Urban Development (HUD) and was part of a broader Moving to

Work (MTW) evaluation led by Diane Levy at Urban Institute. At HUD, I am especially grateful to

Elizabeth Rudd, the government technical representative for the MTW retrospective evaluation,

who provided guidance and very thoughtful feedback throughout the study. Elaine Ng, regional

economist at the HUD San Francisco office, shared helpful insights on the labor and housing

markets of counties in the Bay Area, which provided important context for selecting appropriate

comparison sites and interpreting study findings.

This report reflects the efforts and important contributions of the study team at MDRC. James

Riccio and Nandita Verma—the project director and project manager—provided valuable guidance

and support throughout, from the research design to their careful reviews of each draft of this

report. Howard Bloom, the senior technical reviewer, worked closely with the study team on

developing the methodology and providing guidance and advice throughout the analysis. I am

very grateful for the critical insights and expertise that he contributed. Gordon Berlin also provided critical advice on the report and its findings. Audrey Yu and Kali Aloisi carefully processed and

analyzed all the earnings data and housing subsidy data for the study and contributed many

important insights on the data to the interpretation of the findings. Jennie Kaufman skillfully edited the report. David Long from Princeton Policy Associates also contributed important insights during

the research design, analysis, and report writing phases of the study.

This report has benefited greatly from the insights of the staff at the MTW housing agencies

included in this study, particularly the Santa Clara County Housing Authority (SCCHA). Katherine

Harasz, the executive director of SCCHA, and Janine Burrier, the assistant director of housing,

generously shared extensive details about the housing agency’s policies and safeguards and

the policy and economic context in which the SCCHA rent reform was implemented. Jennifer

Rainwater at the Housing Authority of the County of San Mateo and Dominica Henderson at the

Oakland Housing Authority also shared important information on their agencies’ policies and the

housing and employment contexts where their housing agencies were located.

Author

Nina Castells is a Research Associate at MDRC.

References

Castells, Nina. 2020. Evaluating the Effects of Santa Clara County Housing Authority’s Rent Reform Final Report. Prepared for U.S. Department of Housing and Urban Development Washington, DC.

Dawkins, Casey J., and Jae Sik Jeon. 2017. Rent Burden in the Housing Choice Voucher Program.

Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy

Development and Research.

Ellen, Ingrid Gould, and Gerard Torrats-Espinosa. 2017. Do Vouchers Protect Low-Income

Households from Rising Rents? Unpublished paper. New York, NY: New York University.

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Galvez, Martha M. 2010. What Do We Know About Housing Choice Voucher Program Location

Outcomes? Washington, DC: Urban Institute.

Gubits, Daniel, Marybeth Shinn, Michelle Wood, Stephen Bell, Samuel Dastrup, Claudia D. Solari,

Scott R. Brown, Debi McInnis, Tom McCall, and Utsav Kattel. 2016. Family Options Study: 3-Year

Impacts of Housing and Services Interventions for Homeless Families. Washington, DC: U.S. Department of Housing and Urban Development.

Gubits, Daniel, Marybeth Shinn, Stephen Bell, Michelle Wood, Samuel Dastrup, Claudia D. Solari,

Scott R. Brown, Steven Brown, Lauren Dunton, Winston Lin, Debi McInnis, Jason Rodriguez,

Galen Savidge, and Brook E. Spellman. 2015. Family Options Study: Short-Term Impacts of Housing

and Services Interventions for Homeless Families. Washington, DC: U.S. Department of Housing and Urban Development.

Housing Authority of the County of Santa Clara (HACSC). 2013. Moving to Work (MTW) 2014

Annual Report for the Housing Authorities of the County of Santa Clara and the City of San José. Santa Clara, CA: Housing Authority of the County of Santa Clara.

Jacob, Brian A., and Jens Ludwig. 2012. “The Effects of Housing Assistance on Labor Supply:

Evidence from a Voucher Lottery,” American Economic Review 102 (1): 272–304.

Mills, Gregory, Daniel Gubits, Larry Orr, David Long, Judie Feins, Bulbul Kaul, Michelle Wood, Amy

Jones, Cloudburst Consulting Associates, and the QED Group. 2006. Effects of Housing Vouchers on

Welfare Families: Final report. Prepared for the U.S. Department of Housing and Urban Development, Office of Policy Development and Research. Cambridge, MA: Abt Associates Inc.

Riccio, James A., Victoria Deitch, and Nandita Verma. 2017. Reducing Work Disincentives in the

Housing Choice Voucher Program: Rent Reform Demonstration Baseline Report. New York, NY: MDRC.

Schwartz, Mary, and Ellen Wilson. 2008. “Who Can Afford to Live in a Home? A Look at Data from

the 2006 American Community Survey.” Paper presented at the Population Association of America

2008 Annual Meeting, New Orleans, LA, April 18. Washington, DC: U.S. Census Bureau. https://

www.census.gov/housing/census/publications/who-can-afford.pdf.

Shroder, Mark D. 2012. “Housing Subsidies and Work Incentives.” In International Encyclopedia of

Housing and Home, edited by Susan J. Smith, Marja Elsinga, Ong Seow Eng, Lorna Fox O’Mahony,

and Susan Wachter. Oxford, United Kingdom: Elsevier Science: 632–637.

Somers, Marie-Andrée, Pei Zhu, Robin Tepper Jacob, and Howard Bloom. 2013. The Validity and

Precision of the Comparative Interrupted Time Series Design and the Difference-in-Difference Design in Educational Evaluation. New York, NY: MDRC.

U.S. Census Bureau. 2013. American Community Survey. https://www.census.gov/programs-surveys/

acs/data.html.

Wing, Coady, Kosali Simon, and Ricardo A. Bello-Gomez. 2018.“Designing Difference in Difference

Studies: Best Practices for Public Health Policy Research,” Annual Review of Public Health 39: 453–469.

138 The Moving to Work Retrospective Evaluation

by James Riccio

Designing an Alternative Rent

Policy for the Housing Choice

Voucher Program

James Riccio

MDRC

Abstract

In 2013, four Moving to Work (MTW) agencies joined the Rent Reform Demonstration to design and test an alternative approach for subsidizing private-market rents paid by families participating in the Housing Choice Voucher program. The new policy’s goals are to support tenants’ efforts to increase their earnings, reduce the administrative burden on public housing agencies, and protect families from increased hardship, all while remaining cost-neutral relative to the existing rent policy. This article describes the trade-offs associated with different reform options and the process through which the four agencies considered those trade-offs and came to a consensus on an alternative policy to test.

Introduction

The Housing Choice Voucher (HCV) program, funded by the U.S. Department of Housing and

Urban Development (HUD) and administered by local public housing agencies (PHAs), offers

tenant-based rental subsidies to approximately 2.2 million low-income households, enabling them

to live in privately-owned housing units. Authorized by an amendment to Section 8 of the U.S.

Housing Act of 1937 (and commonly referred to as “Section 8”), this roughly $24 billion program

is a vital component of the nation’s safety net.1

For decades, however, calculation and administration of rental subsidies for the voucher program

have been controversial, especially as applied to families headed by working-age, nondisabled

1 Funding level is for fiscal year 2020 (National Low-Income Housing Coalition, 2020). For a general description of the voucher program, see Center for Budget and Policy Priorities (2017a). Tenant-based vouchers are “portable”

vouchers, meaning that if a household moves, the family may take the subsidy with it and use the voucher with a new landlord of its own choosing, as long as the housing unit meets the PHA’s quality standards. The HCV program also includes “project-based vouchers,” which are subsidies tied to specific housing units through contracts between the PHA and managers or owners.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 139

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

Riccio

adults. Critics assail the policy as complex and expensive for PHAs to administer and difficult

for families to comprehend. Many believe it discourages, rather than supports, tenants’ efforts to

increase their employment and earnings.2 Numerous stakeholders have advocated reform of the

traditional rent subsidy system to simplify the administration of vouchers, support work, and

contain the system’s average per-family costs to save taxpayers’ dollars or serve more income-

eligible families.3 Policy reform has been elusive, however, because balancing competing objectives

related to the administrative burden on PHAs, system costs, tenant work incentives, and housing

affordability has made it hard to achieve agreement and because there is no strong evidence of the

effects of alternative approaches.

To produce evidence, HUD launched the Rent Reform Demonstration, an initiative to design and

carefully evaluate an alternative rent system for tenant-based vouchers. HUD selected MDRC to

coordinate the design process and evaluate the new policy through a randomized controlled trial,

working closely with HUD and a small number of PHAs that have Moving to Work (MTW) status.

Only MTW agencies were considered for the Rent Reform experiment because they are the only

PHAs authorized by Congress to make changes in rent rules. Although some MTW agencies (not

in the demonstration) were already experimenting with alternative policies, none of those reforms

had been subject to rigorous evaluations with random assignment research designs or other strong

research methods using comparison groups.

This article discusses how the demonstration design team, composed of staff from HUD, several

PHAs, and MDRC (including this author, who led the MDRC team) and its partners,4 formulated

the alternative rent policy. Drawing on the study’s baseline report that I co-authored (Riccio, Deitch, and Verma, 2017), I highlight the reforms the team considered, trade-offs associated with different

reform options, and the process for coming to consensus on a consistent alternative rent policy that

several PHAs agreed to test.

We began design of the demonstration in 2013, selected PHAs to participate in 2014, and

identified eligible families for the study sample in 2015. The evaluation is currently underway, and

early and interim results are available in published reports (Riccio and Deitch, 2019; Riccio, Verma, and Deitch, 2019). Four PHAs joined the demonstration:

• Lexington-Fayette Urban County Housing Authority in Lexington, Kentucky (generally

referred to as the Lexington Housing Authority)

• Louisville Metropolitan Housing Authority in Louisville, Kentucky

2 Abt Associates Inc., Urban Institute, and Applied Real Estate Analysis, Inc. (2010). See also Government Accountability Office (2012) and Public Housing Authorities Directors Association (2005).

3 Only about one-fourth of income-eligible families receive Housing Choice Vouchers due to funding limitations (Center for Budget and Policy Priorities, 2017b).

4 The MDRC design team included technical-assistance housing experts from the Bronner Group and Quadel Consulting, research experts from Urban Institute and Branch Associates, and professors John Goering (City University of New York) and Ingrid Gould-Ellen (New York University).

140 The Moving to Work Retrospective Evaluation

Designing an Alternative Rent Policy for the Housing Choice Voucher Program

• San Antonio Housing Authority in San Antonio, Texas

• District of Columbia Housing Authority in Washington, D.C.5

These four MTW agencies agreed to help finalize the new policy to be tested, to operate that policy

alongside HUD’s traditional rent policy, and to comply with the needs of the evaluation, which

included randomly assigning eligible households to a group that would be subject to the new

policy or to a control group that would remain subject to the existing rent rules. A total of 6,665

households are included in the study. Households that were headed by elderly or disabled adults

(as defined by HUD) were not eligible for the study. The nonelderly/nondisabled households that

were eligible for the study could include other members who were elderly or disabled, however.

The Rent Reform Debate

HUD’s traditional rent rules for HCV families establish how much of its income a family must

contribute toward its rent and utilities and how expensive a housing unit a family is permitted to

rent with a government subsidy.6 A typical voucher family is expected to contribute 30 percent of

its adjusted monthly income—that is, its net income after certain deductions are made from its pretax income—or 10 percent of gross income (whichever is greater).7 Moreover, monthly income

is derived from the family’s annual anticipated adjusted income, which itself is an annualized estimate of current income. The family’s expected contribution based on its current/anticipated

adjusted income is referred to as the family’s “total tenant payment” (TTP).

The concept of tying families’ rent contributions to their incomes was incorporated into law

through a provision of the Housing and Urban Development Act of 1969, which amended the

Housing Act of 1937. Sponsored by Massachusetts Senator Edward Brooke III, and commonly

known as the “Brooke Amendment,” it pegged tenant contributions in public housing at 25 percent

of adjusted household income. Brooke rents, as they were called, were applied to the Section 8

program when it was established in 1974 and were incorporated into the HCV program when it

succeeded Section 8. The tenant contribution was raised in 1981 to 30 percent of a household’s

adjusted income (or 10 percent of gross income, if greater).

Once a family’s TTP is established, the PHA calculates the amount of subsidy it will provide. The

PHA pays the difference between the family’s TTP and the housing unit’s “gross rent.” The gross

rent is the amount of rent charged by the landlord for the unit (referred to as the “contract rent”)

plus an allowance for basic utilities that are not included in the contract rent. The subsidy amount

cannot exceed the PHA’s payment standard (or maximum subsidy) for the local area, which is based

5 The Washington, D.C. PHA ended its participation in the demonstration in September 2019.

6 Throughout this paper, mentions of HUD’s “current” or “traditional” rent policy for voucher holders refer to the national rent policy in effect for traditional PHAs before the passage and implementation of the Housing Opportunity Through Modernization Act of 2016 (HOTMA).

7 HUD rules specify what resources count as income. For example, earnings and cash payments from welfare and other government benefit programs count, while food stamps and Earned Income Tax Credit payments do not. For a full explanation of HUD’s existing rent rules, see HUD’s Housing Choice Voucher Program Guidebook (U.S. Department of Housing and Urban Development, 2001).

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on Fair Market Rents in the area.8 Payment standards, which vary by the number of bedrooms in

a rental unit and by local housing markets, are intended to ensure that families have access to safe

and decent housing, while also limiting the amount of the subsidy provided to any given family (to

contain government costs). This subsidy is referred to as the housing assistance payment (HAP).

If the gross rent exceeds the payment standard, the family is responsible for that extra amount

in addition to its TTP. The TTP plus that extra amount make up the family’s total housing cost,

which HUD calls the “family share” of rent and utilities. At the beginning of a new lease, however,

a family’s total expenditures for the unit must not constitute more than 40 percent of its adjusted

income to ensure that families do not enter leases they cannot afford. A family is only permitted to

pay additional rent above 40 percent of adjusted income if the extra amount is necessary for the

family to remain in a current housing unit (for example, if the landlord raises the rent).

Families are required to undergo annual recertifications through which the PHA determines

whether they remain eligible for the voucher program and, if so, the PHA adjusts their TTPs and

subsidies to reflect their anticipated incomes for the coming year. The PHAs also conduct interim

recertifications to adjust families’ TTPs and subsidies when their incomes change before their next

annual review.

Since the enactment of the Quality Housing and Work Responsibility Act in 1998, PHAs have also

been permitted to establish minimum TTPs, typically referred to as “minimum rents,” of up to $50

per month. A family subject to a minimum TTP would pay at least that amount, regardless of its

income, unless it received a hardship exemption from the PHA.

Making housing affordable for low-income families has been the primary rationale embraced by

Congress for the percentage-of-income rent policy. While this policy means that a family will pay

more if its income grows, it will also pay less if its income falls—an important safety net feature.

Low-income-housing advocates have staunchly defended this policy as essential to protecting

vulnerable families and children.

At the same time, the percentage-of-income system has been criticized by public housing industry

groups and others as allegedly having unintended negative consequences. One major concern is

the belief that it depresses tenants’ work effort. This belief is because 30 percent of every extra

dollar they earn is added to their TTPs and reduces their subsidies. This implicit “tax” on their

earnings may lead some tenants to conclude that work “doesn’t pay,” especially because increasing

income may cause loss of other income-related government benefits, such as Supplemental

Nutrition Assistance Program (SNAP) benefits (food stamps). Moreover, housing subsidies phase

out entirely when income rises beyond a certain point. If, on average, tenants work less than they

otherwise would because of this policy, it might also tie up subsidy dollars that could otherwise be

used to provide housing assistance to other needy families. Income-based rents are also thought

by some critics to discourage family formation and to keep some adult household members off the

8 An area’s Fair Market Rent represents a point on the distribution of all rents charged by private landlords for standard housing units. It is typically set at the 40th percentile, meaning that 40 percent of all housing units in the area would rent for no more than that amount. A PHA may set its payment standards, for units of varying sizes, from 90 percent to 110 percent of the published Fair Market Rents for its area and may adopt higher or lower levels with HUD approval.

142 The Moving to Work Retrospective Evaluation

Designing an Alternative Rent Policy for the Housing Choice Voucher Program

lease because the incomes of those adults, if counted toward household income, would decrease a

family’s HAP and increase the family’s contribution to rent and utilities.

Other criticisms focus on the complexity of the rules that must be followed to determine and verify

families’ incomes and calculate their TTPs and subsidy amounts and the staff time and effort required to revise these calculations when families’ incomes rise or fall, generating a substantial administrative cost for PHAs. Many of these rules are also thought to be difficult for tenants to understand.

These criticisms, described in the HUD-funded Study of Rents and Rent Flexibility (Abt Associates

Inc., Urban Institute, and Applied Real Estate Analysis, Inc., 2010). and other papers (Castells, 2020; Government Accountability Office, 2012; Public Housing Authorities Directors Association, 2005;

Riccio, Deitch, and Verma, 2017), were a crucial touchpoint for HUD, the PHAs, and MDRC and its

partners in designing the alternative rent policy for the Rent Reform Demonstration.

Goals Established for an Alternative Rent Policy

HUD set four main goals for the new rent policy that would be tested with the Rent Reform

Demonstration. HUD sought a policy that would:

1. Increase the financial incentive for tenants to work, increase their earnings, and advance

toward self-sufficiency.

2. Simplify the administration of the voucher rent system to improve transparency, reduce the

burden on PHAs and households, and reduce administrative costs.

3. Minimize any increases in PHAs’ average housing-subsidy expenditures and, ideally,

reduce those costs, with the aim of making the alternative policy cost-neutral relative to the

traditional policy.

4. Continue to provide a safety net for tenants who cannot readily work, who lose jobs, or who

could not increase their incomes.

The Design Process

To develop a new rent model, the MDRC team worked closely with HUD and, initially, with nine

PHAs that had expressed interest in joining the demonstration; the final group was made up of

four agencies that actually joined.9 It was vital to design a policy in close partnership with PHAs

because they brought real-world expertise to the process, and also because it was unlikely any PHA

would implement a new rent policy and join an evaluation if it had little or no say in the policy

design and no sense of ownership over the policy. The consultation process sought to identify a

common set of approaches all candidate PHAs would be willing to adopt.

9 The housing agencies that did not join the demonstration are the Housing Authority of Baltimore City, the Cambridge Housing Authority, the Chicago Housing Authority, the Massachusetts Department of Housing and Community Development, and the Santa Clara County Housing Authority. Although these PHAs were helpful during the design process, they were contending with a variety of other policy reforms or new initiatives that made it difficult for them to participate simultaneously in the Rent Reform Demonstration.

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The MDRC team reviewed with HUD and the PHAs a range of possible rent reform ideas,

including those discussed in the Study of Rents and Rent Flexibility (Abt Associates Inc., Urban Institute, and Applied Real Estate Analysis, Inc., 2010). That study collected perspectives on rent

reform options from voucher recipients, residents of public housing, waiting-list applicants, and

PHA staff members.

When the four PHAs agreed to join the demonstration, HUD officials, the PHAs, and the MDRC

team had reached a preliminary agreement on a general approach to a new rent policy. This

approach included changes in how TTPs and subsidies would be calculated (such as eliminating

deductions and setting a lower percentage-of-income rate), introducing or increasing minimum

rents, extending the recertification schedule, limiting interim recertifications, establishing hardship policies and other safeguards to protect affordability for families, and simplifying the calculation

of utilities costs. MDRC then used statistics to assess how the new approach might affect two

types of outcomes: (1) families’ net income, taking into account earnings, work-related expenses,

housing subsidies, and other government income transfer benefits; and (2) PHAs’ housing-subsidy

expenditures. Each round of results was discussed with HUD and the PHAs as they considered

adjustments to the preliminary alternative rent model.10

Throughout the design process, the HUD-PHA-MDRC team conferred with national and local

representatives of the low-income-housing advocacy community and housing agency interest

groups about the design options under consideration. Some of these experts, along with several

academics and executive directors of other PHAs, were eventually included on an expert panel that

reviewed the near-final alternative rent model and the evaluation research design.11

The result of those consultations, analyses, and reviews is an alternative rent model that we arrived at through debate about the options and trade-offs described in detail in the next section. Exhibit 1

summarizes the end product of our design process, comparing HUD’s traditional rent policy to the

alternative tested in the Rent Reform Demonstration.

10 For details on these analyses, see Riccio, Deitch, and Verma (2017) and MDRC’s demonstration design paper (MDRC, 2016), available upon request from MDRC. The analyses concerning family net income used Urban Institute’s Net Income Change Calculator for a set of hypothetical families (for example, families where the number and ages of children varied). The PHA analysis used several years of national housing-subsidy expenditure data obtained from HUD covering all non-Moving to Work PHAs in the country, and similar housing-subsidy expenditure data covering several years from several Moving to Work agencies that were being considered for the Rent Reform Demonstration, including the four agencies that finally joined the study.

11 These representatives included the Center on Budget and Policy Priorities and the National Low Income Housing Coalition. Other expert panel members included representatives from the Public Housing Authorities Directors Association, the National Association of Housing and Redevelopment Officials, and the Council of Large Public Housing Authorities; the executive directors of the Cambridge and Seattle housing authorities; and several academic experts.

144 The Moving to Work Retrospective Evaluation

Designing an Alternative Rent Policy for the Housing Choice Voucher Program

Exhibit 1

Comparison of the Rent Reform Demonstration’s Alternative Rent Policy to HUD’s

Traditional Policy (1 of 2)

Component

Traditional HUD Policy

Alternative Rent Policy

Total tenant

30 percent of adjusted

28 percent of gross monthly retrospective income (that is,

payment (TTP) monthly income (that is,

gross monthly income over the previous 12 months), with no

total countable anticipated

deductions or allowances.

income, minus deductions) or Countable income estimate for setting a family’s

10 percent of gross income,

TTP and housing subsidy is based on 12-month

whichever is higher.

retrospective income.

Minimum TTP

Up to $50 per month, at

$50 to $150 per month, depending on the PHA.

public housing agency (PHA) All families pay a minimum amount of rent directly to their

discretion

landlords, to mirror the landlord-tenant relationship in the

unsubsidized rental market.

Assets

Family income from assets

Family income from assets is ignored when total asset value

is counted in determining a

is less than $25,000, and families do not need to document

family’s TTP.

those assets.

Recertification Annual recertifications.

Triennial recertifications.

period

Interim

At an agency’s discretion,

Earnings gains do not increase TTP for 3 years (that is, until

recertifications families report any income

the next triennial recertification).

when income

increases when they occur,

Interim recertifications to account for income reductions

changes

before the next scheduled

are limited to a maximum of one per year (referred to

recertification. Families may

as “restricted interim recertification”), and only when

request interim recertifications a family’s average gross income over the most recent

whenever their incomes fall

12 months drops by more than 10 percent from the

by any amount.

retrospective estimate that was used to establish the TTP

currently in effect.

Utilities

Where the contract rent does A simplified utilities policy that is tailored to a standard base

not include utilities, a utility

rate for utility costs that varies according to the voucher

allowance is provided based amount, with additional payments available to families paying

on a detailed schedule that

higher costs related to the type of heating (for example,

takes into consideration

electric or oil heat) and water and sewer charges.

voucher size (the number

of bedrooms covered by a

family’s voucher) and various

other aspects of the type of

housing unit.

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Exhibit 1

Comparison of the Rent Reform Demonstration’s Alternative Rent Policy to HUD’s

Traditional Policy (2 of 2)

Component

Traditional HUD Policy

Alternative Rent Policy

Hardship policy If the PHA has a minimum

Families qualify for consideration of a hardship-based

TTP, it must suspend that

remedy if:

minimum TTP for families

• The family’s monthly TTP exceeds 40 percent of its

who are unable to pay

current or anticipated monthly gross income.

it because of specified

• The hardship cannot be remedied by the one interim

financial hardships. Short-

recertification permitted each year.

term hardships (lasting 90

• The family faces eviction for not paying rent or utilities.

days or less) require the

• The family meets other criteria determined by the PHA.

suspended minimum to be

Hardship remedy options include the following

reinstated after the hardship

standardized list:

period ends and to be repaid • Allowing an additional restricted interim recertification

according to a reasonable

beyond the normal one per year.

payment plan.

• Setting the family’s TTP at the minimum level for up to

180 days. (This remedy can be renewed at the end of that

period if the hardship persists.)

• Setting the family’s TTP at 28 percent of its current gross

income (which may be less than the minimum TTP), for

up to 180 days (except in Lexington). (This remedy can be

renewed at the end of that period if the hardship persists.)

• Offering a “transfer voucher” to support a move to a more

affordable unit.

Grace period

Not applicable. TTP is always At the triennial recertification, if a family’s current gross

based on current income.

income is more than 10 percent lower than its average gross

retrospective income over the last 12 months, the family will

have its TTP calculated at that time based on current income

rather than retrospective income, and this TTP will remain in

effect for 6 months. During this grace period, families can still

qualify for a hardship-based remedy.

Note: The “Traditional HUD Policy” column shows the national policy in existence for the non-Moving to Work tenant-based Housing Choice Voucher program population before the enactment of the Housing Opportunity Through Modernization Act of 2016. With a few exceptions, the PHAs participating in the Rent Reform Demonstration have continued to implement that policy.

Rent Reform Options and Trade-offs

As it began its work, the design team recognized that any reforms it considered would come with

trade-offs related to the larger goals of the Rent Reform Demonstration. For example, some reforms,

taken by themselves, might increase the financial incentive for tenants to work, but also increase

the cost of the voucher program. They might even put some tenants at risk of greater hardship if

they cannot work. Some reforms might substantially reduce the PHAs’ administrative burden and

costs but come at the expense of work incentives, cost-containment, or tenant protection. The

design team weighed these trade-offs for each element of reform under consideration.

(1) Flat rents or income-based rents?

One fundamental question was whether to stick with income-based rents or switch to flat rents.

Flat rents base the subsidy a family receives on the size of the housing unit (for example, the

146 The Moving to Work Retrospective Evaluation

Designing an Alternative Rent Policy for the Housing Choice Voucher Program

number of bedrooms) rather than on a family’s income. This approach is simpler than the Brooke

rent, and it prioritizes work incentives but weakens a rent policy’s safety net functions. Because

a family’s rent contribution and subsidy amount would not be tied to its income, a family could

increase its income without penalty, creating a strong work incentive. At the same time, families

with the lowest incomes would experience a higher rent burden (that is, they would contribute a

larger proportion of their incomes toward rent and utilities). In addition, a drop in income would

cause families’ rent burden to increase, and possibly increase their risks of material hardship and

even eviction, unless mitigated by a hardship policy.

A stepped rent policy is a variant of this approach, whereby the flat rent based on the size of the

unit increases gradually over a period of years, regardless of changes in a family’s income. By

raising the rent gradually, it would give families time to increase their incomes.12 Still, fixed or

stepped rents might increase financial hardships and risk of eviction, especially for the lowest-

income families, even if the policy led some families to increase their earnings. Worried about this

possibility and the challenge of winning the support of housing advocates for such a policy, the

design team chose to stick with an income-based approach for the Rent Reform Demonstration.

(2) Tiered rents?

One popular reform idea that continues to link families’ rent contributions to their incomes is a tiered rent policy. Under this approach, income bands are created, and a fixed rent is established for each

band or tier. All families with incomes falling within the same tier pay the same TTP. The primary

purpose of a tiered rent is simplification. It is presumed to be a policy that is easier for PHA staff to explain to tenants and for tenants to understand. In addition, if income bands are wide enough,

families with incomes at the low end of the band would have some incentive to increase their earnings, because their rent contributions would not go up as long as their incomes stayed within the band.

One potential drawback to a tiered rent policy with wide bands is that, because all families within

a given band would pay the same TTP, those families at the low end of the band would have a

relatively higher rent burden than families at the high end. In addition, wide income bands with

large differences in TTPs from one band to the next could create sizable work disincentives for

tenants whose incomes approached the top of a band (so that a small jump in income resulting in a

shift up to the next band could result in a big increase in TTP). These problems could be mitigated

by creating narrow income bands. Narrow bands, however, would reduce the potential work

incentive derived from keeping TTPs constant within an income band.

The design team ultimately did not choose this approach primarily out of a concern that the

income tier strategy might not offer a powerful enough work incentive. Furthermore, the team was

also concerned that such a policy would not be much simpler to administer than HUD’s traditional

rent rules if other reforms in the rent determination process were not also addressed.13

12 A separate HUD demonstration with newly designated MTW agencies is intended to test a version of a stepped rent policy. For more information, see “Moving to Work (MTW) Expansion—Cohort #2” at https://www.hud.gov/program_

offices/public_indian_housing/programs/ph/mtw/expansion/cohort2.

13 HUD’s separate rent reform demonstration focused on new MTW agencies is expected to test a tiered rent strategy combined with a number of other reforms, alongside a stepped rent policy. For more information, see “Moving to Work (MTW) Expansion—Cohort #2” at https://www.hud.gov/program_offices/public_indian_housing/programs/ph/

mtw/expansion/cohort2.

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(3) A longer recertification period?

A potentially strong incentive to encourage increased work and earnings would be to allow more

years to pass before families are required to have their eligibility for the voucher program and

their TTPs redetermined—a process commonly referred to as “recertification.” A change in the

recertification timeframe from an annual recertification schedule to a longer recertification period

means that no matter how much families earned during that period, none of the increased earnings

would go toward higher contributions for rent and utilities (as would be true under traditional

rules). An extended recertification period could also reduce the administrative burden: PHA staff

would spend less time conducting one-on-one sessions with families, and families would spend

less time having to document and report their incomes to the PHA.

A 3-year recertification approach also risks increasing PHAs’ HAP expenditures. Some tenants will

increase their earnings from one year to the next, despite having to pay higher TTPs. A cap on

their TTPs, which prevents their subsidies being reduced, means that PHAs would forego naturally

occurring subsidy reductions for as long as the cap remains in place. These higher-than-normal

HAP expenditures could more than outweigh the savings that might be achieved in administrative

expenses. Thus, when lengthening the recertification time period, potential benefits of increasing

work incentives and reducing administrative burden must be balanced against the likelihood of

increased HAP expenditures. With these concerns in mind, the design team initially considered

limiting the extension of the recertification period to 2 years. It ultimately decided, however, to

adopt a 3-year recertification schedule, with the view that the demonstration should test a bolder

strategy for increasing work incentives.

The triennial recertification schedule adopted for the Rent Reform Demonstration means that families

assigned to the alternative rent are not required to report any increases in income to the PHAs during that 3-year period. In addition, any increases in income to the household that come from adding new

spouses, domestic partners, or other adults to the lease during that period will not affect their TTPs or housing subsidies (as long as those additions do not require a larger unit size).

Of course, some employed tenants may lose their jobs during the 3-year period, which would

make it difficult for them to pay their expected TTPs if those TTPs were to remain fixed. To protect

such families against financial hardship, it was important for the new policy to include a number of

safeguards, which are described in a later section.

According to the alternative policy, families whose earnings increased during the 3-year period

would begin paying a higher TTP in the fourth year, after completing their triennial recertifications.

This policy would allow the PHAs to begin recouping some of the subsidy reductions they had to

forego for families whose incomes grew while their TTPs were capped. Although these families

would begin paying higher TTPs in the fourth year, their new TTPs would be capped for another

3 years, allowing them to avoid until the next triennial recertification the implicit tax on their

subsequent earnings gains that might discourage them from working more. This cycle would

continue for as long as the families received Housing Choice Vouchers (assuming the alternative

rent rules were an ongoing policy).

148 The Moving to Work Retrospective Evaluation

Designing an Alternative Rent Policy for the Housing Choice Voucher Program

(4) Gross or adjusted income?

Under traditional rent rules in the HCV program, a family generally pays 30 percent of its adjusted

income (after certain deductions from its pretax income) for rent and utilities, or 10 percent of its gross income, whichever is higher. Under those traditional rules, the amount of annual income

counted toward the TTP is reduced using the following deductions: $480 for each dependent;

$400 (total) for having one or more elderly family members or family members with disabilities;

reasonable child care expenses that enable a family member to be employed, actively seek

employment, or further his or her education; certain medical, attendant, and auxiliary apparatus

expenses for elderly family members and those with disabilities; and expenses for the care of

household members with disabilities. (As previously mentioned, households in which the head

of household was elderly or disabled according to HUD’s definitions were not eligible for the Rent

Reform Demonstration, but otherwise eligible households could include other members who were

elderly or disabled.)

The process for computing a household’s TTP under these rules, particularly when it involves

estimating childcare and medical-related and disability-related expenses, is widely considered to be

complex and error-prone. For the Rent Reform Demonstration, the design team generally favored

eliminating all deductions and basing the calculation on gross income to simplify the calculation

process and reduce errors. The idea of eliminating childcare deductions, however, which could

represent a substantial loss to families with high childcare costs and possibly discourage parents

from working, raised special concerns.

Under the traditional rent rules, childcare deductions are based on anticipated unreimbursed

childcare expenses for the coming year (or until the next scheduled review of income). In practice,

actual costs can be difficult to anticipate, particularly for parents who move in and out of jobs,

whose childcare providers change, whose childcare needs change (for example, if their work shifts

change), whose children make a transition to a free preschool program, or who become eligible

for an external childcare subsidy during the course of the year, and when more than one adult

in the household is working.14 It is not clear how reliably these types of changes—some of which

might result in TTP increases or decreases—are reported to PHAs between annual income reviews

under the traditional rent policy. The design team recognized, however, that it would be vastly

more difficult to estimate anticipated childcare expenditures accurately 3 years into the future, as

14 The potential complexity is readily apparent from the following HUD guidance for PHAs on how to estimate the child care deduction when calculating a family’s TTP: “Reasonable child care expenses for the care of children including foster children, age 12 and younger, may be deducted from annual income if all of the following are true: the care is necessary to enable a family member to work, look for work, or further his/her education (academic or vocational); the expense is not reimbursed by an agency or individual outside the household; and the expenses incurred to enable a family member to work do not exceed the amount earned. When more than one family member works, the PHA must determine which family member is being enabled to work because child care is provided. This is necessary because the child care allowance cannot exceed the income that family member earns. A good general rule is to assume that the child care expenses enable the lowest paid individual to work, unless this is obviously not the case. When a family member works and goes to school, the PHA must prorate the child care expense so that the portion of the total child care expense that is specifically related to the hours the family member works can be compared with the amount earned. PHAs must determine whether child care costs are ‘reasonable.’ Reasonable means reasonable for the care being provided. Reasonable costs for in-home care may be very different from reasonable daycare center costs. Families may choose the type of care to be provided.” See Chapter 5 of HUD’s Housing Choice Voucher Program Guidebook (U.S. Department of Housing and Urban Development, 2001).

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would be required for a rent policy using triennial recertifications. Retaining such deductions in

the context of triennial recertifications, it was feared, might have imposed even more administrative burden on staff and more unfairness in the determination of families’ rent contributions.

The design team also considered that only a small percentage of households make use of the

existing childcare allowance under traditional rent rules. MDRC analyses showed that fewer than

9 percent of working-age, nondisabled voucher holders assisted by non-Moving to Work agencies

nationally, and fewer than 11 percent in the PHAs participating in the Rent Reform demonstration,

used these allowances.15 In part, these low rates reflected the fact that many families who might

have benefited from the deductions were not employed. It is also possible that some employed

parents relied on family members or friends to care for their children while they worked.

Although it eliminated the childcare deduction, the design team decided that, for the purposes of

the Rent Reform Demonstration, all families already receiving that deduction at the time of random

assignment would be excluded from the study so that they would not have to forfeit an existing

benefit. Families who were enrolled in the study and assigned to the new rent rules group, however,

would not have access to the childcare deduction as long as the study continued. Although this policy would constitute a loss to the minority of families who would otherwise have used the deduction,

those families, like others, stood to benefit from the 3-year cap on their TTPs, which could leave them with more resources to help cover at least some of their future childcare expenses.

(5) Exclude income from assets in calculating gross income?

Under the traditional rent policy, if a family has assets (such as bank accounts, stocks, and bonds), the income from those assets (such as interest or dividends) must be reported, verified, and

included in the income base used to calculate the family’s TTP. HUD guidelines state that when

assets are $5,000 or less, the actual income from assets is to be counted. When assets exceed

$5,000, the PHA must determine the actual income from those assets and an imputed income

based on a passport savings rate established by HUD. It then applies the greater of these two

estimates to the income base.16 Typically, however, few voucher holders have assets that produce

enough income to have a meaningful effect on their TTPs. Thus, in another step toward the

demonstration’s simplification goal, the design team decided that, under the alternative policy, if a family had assets worth less than $25,000 in total, any income generated by those assets would be

ignored for the purposes of computing the family’s TTP. In the rare instances that assets exceed that threshold, the income from those assets above the threshold would apply. Moreover, the families

would not be required to document assets that they attested were worth less than that amount.

In addition to reducing the administrative burden on staff (and families), ignoring income from

assets worth less than $25,000 might also encourage families to try to increase their assets through

increased earnings and savings.

15 This estimate is based on MDRC calculations using 2011 HUD national data from PHAs included in the modeling exercise.

16 See U.S. Department of Housing and Urban Development (2001). HUD streamlining provisions issued in 2016, however, allowed PHAs to accept families’ certification without third-party verification that they did not own assets valued at $5,000 or more.

150 The Moving to Work Retrospective Evaluation

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(6) What percent of gross income?

The decision to rely on gross income rather than adjusted income in calculating a family’s TTP under

the new rent policy raised some concerns that initial TTPs would be higher for families than they

would be under traditional rules. Although their TTPs would be capped for the subsequent 3 years,

families might experience a higher rent burden at the start of that period. To address this concern,

the design team considered applying to the income base a rate lower than the traditional 30 percent

to determine a family’s TTP. It first had to consider what the implications might be for PHAs’ subsidy payments because lower TTPs would mean higher subsidies, and the higher subsidy expenditures

would continue through the 3-year recertification period when TTPs were capped.

Before the design team agreed to adopt a lower rate, MDRC’s statistical modeling analysis compared

how the TTPs and net incomes of certain types of families might change if TTPs were calculated

using different percentages of family income (20 percent, 27 percent, and 28 percent), after taking

into account families’ housing costs, earnings, work-related expenses, taxes, and government

benefits. The analysis also showed how those different rates might affect each of the four PHAs’

total housing assistance payments on behalf of families during a 4-year period and the possible

effect of such a policy on national housing assistance expenditures. The 28-percent rate was

selected because a lower rate would have put the PHAs (and HUD’s national budget) at risk of

incurring much higher HAP costs, reducing the likelihood the alternative rent policy would achieve

its cost-neutrality goal over 4 years.17

(7) Current/anticipated or retrospective gross income?

The traditional rent policy’s method of calculating a family’s TTP applies the 30-percent-of-income

rate to the family’s annual current/anticipated adjusted income. Of course, families’ incomes can rise and fall unexpectedly. The traditional rent policy addresses income volatility by scheduling

income reviews annually and allowing unlimited interim recertifications to adjust TTPs between

annual reviews. The demonstration design team recognized, however, that offering families

the opportunity to lock in a TTP based on current/anticipated income for 3 years may create

an incentive for some families to lower incomes just before their scheduled recertifications. In

theory, some family members might be tempted to quit their jobs, reduce their hours of work, or

avoid looking for new jobs, so that the family’s base income used in calculating its TTP for the

next 3 years would be as low as possible (thus maximizing the family’s subsidy for that period).

This practice could result in unnecessarily low TTPs and unnecessarily high public subsidies.

The extent to which voucher holders would actually resort to such practices was unknown. The

design team had to weigh the relative merits of relying on an arguably simpler strategy (current/

anticipated income) that risked generating higher HAP expenditures versus a more administratively

burdensome strategy (retrospective income) that might help limit extra HAP expenditures and also

protect the new policy from claims that it encouraged families to “game the system,” potentially

harming its public reputation and support.

17 Further details on the modeling work are available in MDRC’s demonstration design paper (MDRC, 2016) available upon request from MDRC.

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With these trade-offs in mind, the design team decided to base TTP calculations on retrospective

income. In practice, this policy means that under the alternative rent policy, a family’s TTP is

calculated using their reported and verified income during the prior 12 months—the “look-back”

period—unless the family qualifies for a safeguard option.18 The average monthly gross income

during the prior 12 months is multiplied by 28 percent to determine the TTP.

Simply relying on retrospective income, however, could put some families at risk of excessive rent

burdens. For example, if a family member had been working steadily but was laid off just before

the family’s recertification that will set its TTP for the next 3 years, the family may not be capable of paying a TTP based on its retrospective income. Furthermore, a tenant may have difficulty finding

a new job quickly, or finding a new job that pays as much as the old one, no matter how hard the

person tries, especially during a weak economy. Alternatively, a family member may have recently

suffered a disability or may have retired from work and moved to a lower, fixed income. Thus,

simply setting a family’s TTP on the basis of its prior income—income that may be impossible to

restore in future years—could leave some families with too high a rent burden, creating financial

hardship for them and even putting them at risk of eviction. The alternative rent policy’s safeguards (discussed later in this paper) were intended to accommodate the fact that some losses in family

income will be permanent or long-lasting, whereas others will be transitory, and that it is not

always possible to tell in advance which will be which.

(8) A limited number of interim recertifications?

The adoption of a 3-year recertification schedule meant that interim reviews and adjustments

would no longer be made when families’ incomes grew during that period. What should be

done, however, when their incomes dropped? Under HUD’s existing rent policy, families who lose

income can get their TTPs reduced and subsidies increased at any time. Some PHA representatives

on the demonstration design team advocated changing this feature by placing restrictions on

interim recertifications when families’ incomes fall. In their experience, many tenants requested

TTP reductions repeatedly during the year, and often for small reductions in income, which took

staff time to complete. They and others on the design team recognized, however, that some access

to interim adjustments was necessary to help families avoid falling into hardship in the face of

substantial income losses, which they may have incurred through no fault of their own. Therefore,

a balance had to be found between minimizing frequent, and small, interim TTP adjustments and

protecting families from increased financial hardship.

It was decided that families would be allowed one interim recertification each per year, at which

time its TTP could be lowered. In addition, to keep the PHAs from having to make frequent

adjustments for relatively small changes in income, an interim reduction would only be permitted

when a family’s average income from the prior 12 months fell by more than 10 percent below the

18 This calculation excludes any nonwage sources that stopped providing income by the end of that period because the family can no longer count on them. For example, if a family had been receiving Temporary Assistance for Needy Families (TANF) or unemployment insurance benefits but is no longer receiving them, the income from those benefits would be excluded. Income from family members who were removed from the voucher program is also excluded (for example, income from a spouse or other adult who died, who was incarcerated, or who was removed for other reasons during the previous 12 months). Imputed welfare income—that is, TANF income forfeited when a parent does not meet her or his TANF work requirement—is still counted if the family is still enrolled in TANF.

152 The Moving to Work Retrospective Evaluation

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retrospective income previously used to compute its TTP. As discussed in a later section, however,

the new policy’s hardship provisions allowed families to apply for a TTP reduction anytime its TTP exceeded 40 percent of its current/anticipated gross income. In other words, the restriction on interim recertifications would limit families’ options for counteracting small-to-moderate income

losses, but another strategy would address severe income losses.

(9) A minimum TTP?

A potentially controversial issue was whether to include a minimum TTP (also commonly referred

to as a “minimum rent”) in the alternative rent policy, and if so, at what amount. All PHAs across

the country were already authorized to set minimum TTPs of $50 per month under the Quality

Housing and Work Responsibility Act of 1998, although not all did so. Given enduring policy

questions about minimum TTPs as part of a rent subsidy policy, HUD urged that this provision be

tested as part of the Rent Reform Demonstration. Among the four participating PHAs, however,

some were concerned that introducing this feature would provoke strong local opposition from

tenant advocacy groups. Two decisions helped to secure agreement on this issue: (1) each PHA

would be able to set the amount of its minimum TTP, and (2) the new rent policy’s hardship

provisions would protect the lowest income tenants from severe hardship.

The minimums the PHAs set varied widely. At the low end, the Louisville Metro Housing Authority

selected a $50 minimum, which is the minimum rent permitted under the existing federal

legislation. The District of Columbia Housing Authority implemented a $75 minimum, which was

roughly equivalent to the inflation-adjusted value of the $50 minimum permitted when that law

was enacted. The San Antonio Housing Authority introduced a $100 minimum, which was double

the $50 minimum that the PHA had already implemented for its general voucher population before

the Rent Reform Demonstration began. The Lexington Housing Authority implemented a $150

minimum TTP, which it had adopted before joining the demonstration.

If families paying the minimum TTP early in the 3-year period steadily increased their incomes,

they would continue to pay only the minimum TTP for the remainder of that period. This offered

a substantial financial benefit for such families, but it was also a reason not to set the minimum

too low because it could remain in place for several years, even as families’ earnings rose, a very

low minimum could substantially increase the PHAs’ housing subsidy expenditures (relative to

traditional rent rules). It was expected, however, that only a minority of families would be affected by the minimum TTP, because most were already paying above the specified levels before being

enrolled in the Rent Reform Demonstration.19

(10) Who should pay the landlord the minimum rent?

Under the traditional rent policy, families are responsible for paying the rent portion of their TTPs directly to their landlords, and for paying the utility companies for their utility costs (if utilities are not included in the lease). The PHAs pay directly to the landlords whatever rent subsidy amount is

19 Using HUD data from December 2012, MDRC estimated that about 69 percent of households in non-Moving to Work agencies paid $100 or more in rent, and 85 percent paid at least some amount to owners.

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owed to them. In some cases, however, families’ TTPs are so low that their utilities payments consume most or all of their TTPs, so the PHA directly pays most or all of the rent owed to the landlords.

During the demonstration design phase, HUD officials expressed concern that families in this

situation would have no direct financial relationship with their landlords. This aspect was

considered undesirable for at least two reasons. First, HUD officials worried that it would not help

families get in the habit of making an on-time rental payment each month, as they would need to

do if they rented in the private housing market after exiting the voucher program. Second, in the

absence of a regular, rent-paying relationship, they may be less inclined to demand from landlords

the level of maintenance and other services they are entitled to receive.

The design team therefore decided that under the new rent policy, all families would be required

to pay at least the minimum TTP amount to their landlords. This practice would not put families

at any additional risk of not paying or underpaying their utility bills because it does not change

the family’s total subsidy amount. Rather, part of the subsidy that the PHA would normally pay a

landlord in these circumstances would be redirected to the family in the form of a higher utility

allowance reimbursement payment (or UAP), allowing the family to meet its utility costs and pay the landlord the minimum rent.

(11) Protections from excessive rent burden?

The adoption of minimum TTPs, the elimination of deductions, and the reliance on retrospective

income posed a critical challenge for the demonstration design team: How would the new policy

ensure that families would not be placed at a greater risk of incurring an excessive rent burden

that would cause serious financial hardship, and possibly eviction? Here the goals of increasing

the financial incentive to work, simplifying the administration of the rent subsidy system, and

containing HAP expenditures came into some tension with the goal of protecting families. The

design team thus had to identify a set of safeguards to protect families from being required to

pay TTPs they simply could not afford, without disregarding the other goals of the Rent Reform

Demonstration. The approach they finally adopted included several basic safeguards that would be

routinely applied and a hardship policy that included standardized elements but also allowed some

PHA discretion.

One safeguard concerned disabilities. If at any time a family became designated as a disabled

household (according to HUD’s definition), the PHA would immediately recalculate the family’s

TTP based on its current/anticipated gross income (rather than retrospective income), without

waiting for its next triennial recertification.

For other families, interim recertifications were an important safeguard when reductions in income

occurred. As previously discussed, however, interim recertifications were restricted to one per

year and only when a family’s more recent retrospective income fell by more than 10 percent of its

previously calculated retrospective income.

Another provision applied at the beginning of each 3-year period between triennial recertifications. If at that time a family’s current/anticipated gross monthly income for the coming year was substantially lower than its average gross monthly income for the past 12 months (that is, more than 10 percent

154 The Moving to Work Retrospective Evaluation

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lower), the PHA would automatically set a temporary TTP based on the family’s current/anticipated

income (or the minimum TTP, whichever is higher) for a full 6-month “grace period.”

This grace period was intended to protect the family temporarily from a high rent burden while it

tried to restore its income to its prior level (for example, through a new job search for a voucher

holder who recently lost a job). At the end of the 6-month grace period, the temporary TTP expires

and the family is switched automatically to the “regular” TTP amount based on its retrospective income. Six months was chosen as a grace period to align with the normal period allowed for

recipients of federal unemployment insurance benefits to find new work. Of course, some tenants

will have difficulty replacing their lost earnings within 6 months—or, perhaps, ever—making other

protections necessary, which included an interim recertification or hardship remedy that would

commence at the end of the grace period.

The design team recognized that a family could suffer a severe drop in income at any time during

the 3 years before the triennial recertification, and that the more routine grace period and interim

recertification features would not be enough to protect them from an excessive rent burden. A more

robust hardship policy would be needed. To address this challenge, the design team had to make

decisions pertaining to three critical issues: (1) what circumstances should qualify families for a

hardship remedy, (2) what should those remedies be, and (3) what process should be followed in

administering the remedies and responding to complaints by tenants who contend that they were

unjustly denied a hardship remedy? The design team sought to standardize the hardship policy as

much as possible across the four PHAs, so that the protections offered to tenants by the rent policy

would not vary with contrasting ideologies or tendencies toward greater or lesser leniency, or with

idiosyncratic administrative practices.

Conditions that qualify a family for a hardship remedy

All four PHAs and HUD agreed that a family would be considered eligible for a hardship remedy if

at least one of the following criteria was met:

• The family’s total monthly rent exceeds 40 percent of its monthly current/anticipated gross

income (including imputed welfare income).

• The family faces a risk of eviction for nonpayment of rent—including utility shutoffs for

nonpayment of utility bills that could lead to eviction.

• Other exceptional circumstances, as determined by the PHA (expected to be rare).

In arriving at the 40-percent threshold, the design team recognized that how much of rent burden

was “excessive” was a subjective decision, and it looked to other benchmarks to help it decide.

For example, it took into account the fact that HUD regulations set an affordability standard that

allowed voucher holders to enter into new leases that would require them to pay up to 40 percent

of their adjusted income in rent and utilities. (In these cases, the household’s family share would

exceed its TTP.) The design team also looked at other subsidy programs and noted that SNAP rules

include excess shelter costs in calculating SNAP benefits when an applicant’s shelter costs exceed

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50 percent of net income. With these benchmarks in mind, 40 percent of gross income seemed to

be a reasonable threshold for defining excessive rent burden for the alternative rent policy.

This hardship criterion was intended to allow the PHAs to waive the minimum TTP when the

minimum TTP accounted for more than 40 percent of a family’s current/anticipated gross income,

and to reduce TTPs for families who were paying above the minimum but nevertheless met the

40-percent threshold. Three of the four PHAs agreed fully with these provisions; Lexington was

the exception. It objected to reducing families’ TTP below the agency’s previously established $150

per month minimum TTP under any circumstances short of a household becoming defined as

disabled. Thus, the hardship criterion that applies in Lexington specifies that a family’s TTP must

exceed the 40 percent threshold and be greater than the $150 minimum rent.

Two other options were considered but rejected in defining qualifying conditions. One was to

exclude from eligibility any families whose incomes fell because tenants quit their jobs. Concern

was expressed about using public subsidies to compensate for voluntary unemployment. It was

agreed, however, that the circumstances surrounding a tenant’s exit from a job were often murky,

and investigating those circumstances could take considerable staff time. Also, in some cases,

these investigations may never clearly conclude whether the tenant quit for what were deemed

“justifiable” reasons. This qualifying condition was thus considered impractical for the alternative

rent policy.

A second concern related to families hit with a household member’s unexpected medical bills

and drug costs not covered by insurance, making it difficult for the families to pay their rent and

utilities; this circumstance could possibly expose them to eviction, even if their incomes did not

fall. It was decided that the circumstances of such families could vary widely, and other families

may face serious financial emergencies unrelated to medical costs, making it difficult to establish a blanket policy that would be appropriate in such cases. Instead, the design team opted to include

two other qualifying conditions for a hardship remedy: a high risk of eviction and other special

(and unusual) circumstances. PHAs would have the flexibility to deem certain families facing those

circumstances as worthy of a hardship remedy on a case-by-case basis, with the expectation that

these criteria would be used sparingly.

Hardship remedies

As part of its effort to standardize the hardship provisions, the design team specified a menu of four remedies from which all PHAs would choose while allowing each agency the discretion to decide

which to apply in any given situation:

1. Allowing an additional interim recertification beyond the normal one-per-year option. This

additional recertification could lower a household’s TTP (but only as low as the minimum

TTP) until the next triennial recertification.

2. Setting the household’s TTP at the minimum TTP level for up to 180 days.

3. Setting the household’s TTP at 28 percent of current income (which may be less than the minimum

TTP, except in Lexington) for up to 180 days.

156 The Moving to Work Retrospective Evaluation

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4. Offering a “transfer voucher” to support a move to a more affordable unit (including a unit

with lower utility expenses).

At the end of the hardship period, the family’s regular TTP would be reinstated, and the family

would not be required to repay the amount it would have paid otherwise. If the hardship

continues, the family may request an extension of the hardship remedy. The hardship remedy

period cannot be scheduled to end after the family’s next scheduled triennial recertification.

The hardship process

Despite having standardized criteria for qualifying for a hardship remedy and a standardized menu

of remedies, PHAs retained considerable discretion in operationalizing aspects of this policy. In

most cases, families had to request a hardship remedy in writing by completing the hardship

request form and supply information and documentation that supported their hardship claims.20 It

was important that the PHAs establish processes for reviewing and acting on these requests in a fair

and impartial manner, and that families had a reasonable opportunity to appeal if their requests

were denied. To that end, the demonstration design team considered a multi-step process in which

each PHA would set up a similar hardship committee to review hardship requests and address

tenants’ grievances. The PHAs argued, however, that this process would be too cumbersome and

burdensome to administer, and that their pre-existing procedures for handling tenant disputes

of any kind would suffice. Ultimately, with the understanding that all PHAs would adopt the

same hardship criteria and set of remedies, and that families whose requests were denied would

have adequate opportunities to appeal, it was decided that each PHA would determine its own

procedural steps for granting hardships and processing appeals.

(12) What about utilities?

Utility expenses are a crucial component of shelter costs, and calculating them is complex for

PHAs. For many voucher holders, some or all utilities expenses are included in the contract rent

paid to the landlord, but for others, utilities are a separate cost. Under traditional rent rules, PHAs help to cover these expenses through a “utility allowance.”

PHAs review and, if necessary, update their utility allowance tables annually (through market

surveys and analyses that take into consideration the type of dwelling), and apply them in

determining each family’s gross rent, taking into account particular characteristics of the family’s

dwelling unit and type of heating system. The process is widely viewed as complicated and error

prone. The PHAs in the Rent Reform Demonstration therefore agreed that the new rent policy

should include a more streamlined (and less error-prone) approach to calculating the utilities

component of a family’s TTP.

20 For example, a family must provide proof of the following: loss of eligibility for a federal, state, or local assistance program; loss of employment or reduction in work hours; an eviction letter; a document indicating utilities may be shut off; or a document indicating the family is at risk of eviction. To request a hardship remedy based on the risk of eviction for nonpayment of rent or utilities, a family must provide to the PHA a notice from the landlord of nonpayment of rent and the landlord’s intent to terminate the family’s tenancy or a notice from a utilities company warning of a utilities shutoff. PHAs may set a time limit within which they must receive a copy of this notice from the tenant (for example, no more than 10 business days from the date that the tenant received the notice from the landlord or utility company).

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The design team explored a variety of options, looking most closely at the following four strategies: 1. Eliminating utility allowances altogether and basing a family’s subsidy on the payment

standard, even if its gross rent (that is, the contract rent owed to the landlord plus expected

utilities costs, if utilities are not included in the lease) is below the payment standard.

2. Eliminating utility allowances and basing a family’s subsidy on a payment standard set equal to

100 percent of the small area Fair Market Rent for rental costs in the area in which its unit is

located, and paying up to that payment standard.

3. Applying the SNAP (food stamp) standard utility allowance set by state or local SNAP agencies and used in calculating SNAP benefits.21

4. Paying a flat rate utility allowance based on the number of bedrooms required for a given

household size (voucher size), with some provision for extra costs.

MDRC’s statistical modeling exercise examined how these options were likely to affect families’ net

incomes and TTPs, as well as PHAs’ HAP expenditures (relative to the traditional rent and utilities

rules). The analyses revealed that the likely effects of the first three alternatives varied across housing agencies to a degree that was considered unacceptable for a potential national rental policy. In

addition, they were likely to increase HAP expenditures (relative to estimated expenditures under

traditional rules) by amounts deemed to be too high, putting the cost-neutrality goal beyond reach.

The design team settled on the fourth option, which was based on an approach previously

developed by the District of Columbia Housing Authority. According to this approach, using local

area utility rates (which were to be updated periodically), each PHA in the demonstration specifies

a standardized utilities base rate that varied only according to the size of the voucher (that is, the number of bedrooms covered by a family’s voucher). It then specified a few “add-on” amounts for

units that were dependent on more expensive utilities. The particular add-ons varied from agency

to agency depending on the types of utilities more common in the area. For example, the PHA in

Washington, D.C., includes an add-on payment for units relying on electric heating, which is more

expensive than gas heating. It includes another add-on for water and sewer costs when the tenant

is responsible for these expenses.22

It was hoped that the new utility schedule would result in fewer errors in calculating utility

allowances, primarily because it requires housing specialists to gather and take into account much

less information about the characteristics of a rental unit.

Predicting the overall effects of the alternative rent policy

Exhibit 1 summarizes the key features of the final policy that emerged from the design process

and how these features compare with HUD’s traditional rent policy. Taking all of these features

21 This number is a fixed dollar amount that states update annually. It applies statewide and is not based on an individual household’s actual costs. Some states set separate amounts for heating and cooling, non-heating/cooling (for example, other electricity, water, trash collection), and telephone only.

22 The PHA estimated that its new approach cost the agency about the same as the existing utility allowances.

158 The Moving to Work Retrospective Evaluation

Designing an Alternative Rent Policy for the Housing Choice Voucher Program

into account, MDRC’s statistical modeling showed that under the new rent rules, when families

increased their earnings (especially through full-time employment), their net family incomes were

likely to increase more than they would under the traditional rent rules. These increases would be achieved primarily by holding their TTPs and housing subsidies constant in the face of earnings

gains during the 3 years leading up to their next triennial recertifications, thus allowing them to

keep more of their increased earnings.

For PHAs, the statistical modeling predicted that the new policy would cause the agencies’ total

expenditures on housing subsidies for the families receiving the new rent rules to be higher during

the first 3 years than they would be under traditional rent rules. This increased expenditure was

expected largely because families who increased their earnings, and who would therefore have had

their subsidies reduced under traditional rules, would instead receive the same level of subsidy

until their triennial recertifications took place. However, the analysis also predicted that in Year 4, housing-subsidy expenditures under the alternative rent policy would be somewhat lower than

under the traditional policy, even assuming that the new policy did not have a positive effect on families’ earnings. In part, this prediction reflects the assumption (supported by the modeling

exercise) that, on average, TTPs recalculated in Year 4 would be based on average earnings that

would be higher than the average earnings of the same families’ recertifications 3 years earlier

because of normal increases in work and earnings over that period of time (that is, increases that

would have occurred even in the absence of the new policy). In addition, the policy’s minimum

rent, the absence of deductions, and limits on interim recertifications in the face of income declines would begin to generate higher TTPs for families still on the voucher program in Year 4. It is at

the point of that triennial recertification that PHAs would begin to recoup the housing-subsidy

reductions they had foregone during the previous 3 years when TTPs were capped.

The modeling exercise also showed that in the absence of an employment impact, the cumulative

housing-subsidy expenditures through the first 4 years of the policy may be somewhat higher for

families receiving the new rent rules compared with families subject to the traditional policy. If the new policy has a modest employment impact, however, those subsidy expenditures may reach (or

come very close to) a “break-even” level, achieving the cost-neutrality goal of the new policy.

Of course, it was impossible to predict with certainty what would really happen under the new

rent policy. The exercise was helpful to the policy designers, however, because it illustrated possible trade-offs among different options considered for the demonstration. The randomized trial now

underway is providing more definitive evidence on how the new policy affects families and PHAs.

Conclusion

Designing the new rent policy for the Rent Reform Demonstration was a challenging process

because it sought to balance multiple, sometimes competing, objectives related to tenants’ work

incentives, administrative simplification, cost-neutrality, and tenant protection. The demonstration

design team weighed the pros and cons of a variety of reform options, from entirely jettisoning

the Brooke Amendment linking rent to family income, to myriad ways of altering exactly how

rents could be tied to income. The process had to contend with initially different views on features

like minimum rents, adjusted vs. gross incomes, retrospective income, recertification schedules,

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hardship policies, and utilities policies. It is thus noteworthy that, despite some differences in

perspectives, all four very different PHAs and HUD reached a consensus on the core elements of

the new policy.

On many issues, these different views were not cleanly split along the lines of institutional

affiliation. Rather, they simply reflected different insights, ideas, and experiences that individual members of the design team brought to the discussions. Perhaps most critical to reaching a

consensus, however, was finding a way to accommodate some local adaptations important to the

PHAs. These adaptations especially involved the issue of minimum rents, which they knew would

be of great concern to the local advocacy groups in their communities, and on the procedures

for administering the agreed-upon hardship remedies. Also important to getting PHA buy-in on

a common policy was the care taken by HUD to avoid creating the impression that it was trying

to pressure the PHAs to adopt any particular approach, rather than allowing the collaborative

process to play out. The fact that HUD chose an intermediary (in this case, MDRC) to organize and

manage the design process may also have helped. The evaluation of the final model is slated to be

completed by 2024.23

Acknowledgments

The author would like to thank Elizabeth Rudd for her excellent guidance and feedback on earlier drafts of this paper. Thanks also go to other HUD staff, PHA staff, external advisors, and the MDRC team who were involved in the design of the Rent Reform Demonstration, and on whose work this paper draws.

Author

James Riccio is the Director of MDRC’s Low-Wage Workers and Communities Policy Area.

References

Abt Associates Inc., Urban Institute, and Applied Real Estate Analysis, Inc. 2010. Study of Rents and Rent Flexibility. Report prepared for the U.S. Department of Housing and Urban Development, Office of Public and Indian Housing. Washington, DC: U.S. Government Printing Office.

Castells, Nina. 2020. Evaluating the Effects of Santa Clara County Housing Authority’s Rent Reform.

Washington, DC: U.S. Department of Housing and Urban Development.

Center for Budget and Policy Priorities. 2017a. Policy Basics: The Housing Choice Voucher Program.

https://www.cbpp.org/research/housing/policy-basics-the-housing-choice-voucher-program.

23 An overview of the demonstration and links to published reports can be found on MDRC’s website at https://www.

mdrc.org/project/rent-reform-demonstration#overview. Links to individual reports can also be found on the HUD User website at https://www.huduser.gov/portal/home.html.

160 The Moving to Work Retrospective Evaluation

Designing an Alternative Rent Policy for the Housing Choice Voucher Program

Center for Budget and Policy Priorities. 2017b. Three Out of Four Low-Income At-Risk Renters Do Not Receive Federal Rental Assistance. https://www.cbpp.org/three-out-of-four-low-income-at-risk-renters-

do-not-receive-federal-rental-assistance.

Government Accountability Office (GAO). 2012. Housing Choice Vouchers: Options Exist to Increase

Program Efficiencies. Washington, DC: U.S. Government Printing Office.

MDRC. 2016. Rent Reform Data Collection and Analysis Plan. Unpublished design paper

submitted to the U.S. Department of Housing and Urban Development. New York: MDRC.

National Low Income Housing Coalition. 2020. FY21 Budget Chart for Selected HUD and USDA

Programs, July 7, 2020. https://nlihc.org/sites/default/files/NLIHC_HUD-USDA_Budget-Chart.pdf.

Public Housing Authorities Directors Association. 2005. Rent Reform: Fair and Simple Solutions.

Washington, DC: Public Housing Authorities Directors Association.

Riccio, James, and Victoria Deitch. 2019. The Rent Reform Demonstration: Early Effects on Employment and Housing Subsidies. Washington, DC. U.S. Department of Housing and Urban Development.

Riccio, James, Nandita Verma, and Victoria Deitch. 2019. The Rent Reform Demonstration: Interim

Findings on Implementation, Work, and Other Outcomes. Washington, DC. U.S. Department of

Housing and Urban Development.

Riccio, James, Victoria Deitch, and Nandita Verma. 2017. Reducing Work Disincentives in the

Housing Choice Voucher Program: Rent Reform Demonstration Baseline Report. Washington, DC: U.S.

Department of Housing and Urban Development.

U.S. Department of Housing and Urban Development (HUD). 2001. Housing Choice Voucher

Program Guidebook. Washington, DC: U.S. Department of Housing and Urban Development.

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162 The Moving to Work Retrospective Evaluation

by Mark Treskon, Matthew Gerken, and Martha M. Galvez

Can Diverse Activities Have a

Combined Impact? Examining

the Effects of the Moving to Work

Demonstration on Housing Choice

and Self-Sufficiency Outcomes

Mark Treskon

Matthew Gerken

Martha M. Galvez

Urban Institute

Abstract

Public housing agencies (PHAs) participating in the Moving to Work (MTW) demonstration can use

funding and policy flexibilities to further the three statutory objectives of the demonstration—to (1) reduce cost and achieve greater cost-effectiveness, (2) promote family self-sufficiency, and (3) increase housing choice. This report examines the success that MTW agencies had in meeting the housing choice and self-sufficiency objectives. Using longitudinal HUD administrative data and a database of publicly available MTW Annual Plans, we conduct comparative interrupted time series (CITS) analysis to

compare MTW groups with traditional PHAs to identify whether MTW status or policies are associated with progress toward meeting the second and third statutory objectives. Results indicate some signs of a positive relationship between MTW and outcomes of interest. Of the three indicators we detail in this paper, we find that MTW status and activity are associated with an increased share of new households, not associated with the share of tenant-based vouchers in low-poverty tracts, and may be associated with an increase in the share of households with incomes higher than when they entered housing.

Introduction

The Moving to Work (MTW) demonstration gives participating public housing agencies (PHAs)

funding and policy flexibility not available to traditional PHAs. MTW agencies can use flexibilities

to implement activities that further the three statutory objectives of the demonstration, which are

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 163

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

Treskon, Gerken, and Galvez

to (1) reduce cost and achieve greater cost-effectiveness in federal expenditures; (2) give incentives to families with children when the head of household is working, seeking work, or preparing for

work by participating in job training; and (3) increase housing choices for low-income families.1

This study examines the success that MTW agencies had in meeting the statutory objectives to

increase housing choice and encourage self-sufficiency (see Stacy et al., 2020, for an analysis of the cost-effectiveness objective).

HUD does not explicitly define those statutory objectives, and agencies may define them in their

own ways (Government Accountability Office, 2018; GAO, 2012). Agencies are expected to

experiment with policy reforms or housing assistance models that respond to local contexts and

needs. Further, MTW agencies vary widely in terms of the year they received MTW designation,

total households served, local housing market characteristics, the mix of housing assistance

provided, the characteristics of assisted households, the context in which they received the MTW

designation, and their goals.

Our review of agency MTW plans showed that as of 2015, three or more MTW PHAs included

20 distinct housing choice initiative types that were currently active and 14 distinct and active

self-sufficiency initiative types (Treskon, Gerken, and Galvez, forthcoming). For housing choice,

common initiatives were non-traditional programs (involving rental subsidy, housing development,

or service provision); comprehensive, project-based voucher programs; expedited acquisition of

public housing; housing mobility programs; local payment standards; and revised waitlist policies.

For self-sufficiency, local non-traditional service provisions were, again, prevalent; so were policies changing how income was used to calculate rent, alternate Family Self-Sufficiency (FSS) programs,

minimum rent, and work requirements.

Given the wide variety of goals and initiatives across the MTW agencies, this study analyzes the

effects of MTW on outcomes for two differently defined treatment groups: an “MTW status”

group to measure the effect of MTW designation itself, and an “MTW activity-specific group” to

measure the effect of activities directly related to self-sufficiency and choice. Our data come from a combination of longitudinal HUD administrative data and a unique database of publicly available

MTW annual plans.

To reflect the diversity of potential effects from MTW status or initiatives, we posed seven research questions—three about housing choice and four about self-sufficiency:

• Do MTW agencies promote housing choice?

1. Do MTW agencies create more housing opportunities relative to traditional agencies?

2. To what extent are households served by MTW agencies reaching lower poverty, higher

opportunity neighborhoods than households served by traditional agencies?

1 See Public Law Section 204 C(3) (A-E): http://www.gpo.gov/fdsys/pkg/PLAW-104publ134/pdf/PLAW-104publ134.pdf

(p. 283). Agencies participating in MTW are also required to have at least 75 percent of admitted families be very low income, create a rent policy encouraging self-sufficiency and employment, assist “substantially” the same number of low-income families and maintain a similar family mix as they would have otherwise, and ensure that housing meets quality standards determined by HUD.

164 The Moving to Work Retrospective Evaluation

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Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes

3. To what extent are households served by MTW housing agencies living in higher quality

public housing2 dwellings relative to households in traditional agencies?

• Do MTW agencies promote self-sufficiency?

4. How do incomes of existing work-able households served by MTW agencies compare

with those served by traditional agencies?

5. How does the use of escrow accounts as a tool for promoting self-sufficiency differ

between MTW and traditional agencies?

6. Are existing work-able households in MTW agencies moving to minimal housing subsidy

at greater rates than households at traditional agencies?

7. Are existing work-able households in MTW agencies making positive exits from housing

assistance at greater rates than households at traditional agencies?

After providing an overview of the literature on housing choice and self-sufficiency, this report

presents the study goals, methods, and findings related to MTW activities and outcomes of interest,

followed by a discussion and conclusions. This article provides an overview of a longer report,

“Housing Choice and Self-Sufficiency Outcomes at Moving to Work Agencies” (Treskon, Gerken,

and Galvez, forthcoming), which discusses the methodology and findings in more detail and

includes results from an analysis of the outcomes of individual MTW agencies.

Literature Review

This section situates the present study in the context of the existing work on how MTW agencies

approach the housing choice and self-sufficiency statutory requirements.

Housing Choice

With one exception, studies of MTW and housing choice are primarily descriptive. Those studies

show that most MTW agencies have pursued the housing choice objective but with wide variation

in how housing choice is defined and what the efforts entail (Buron et al., 2017; Galvez et al.,

forthcoming; Galvez, Gourevitch, and Docter, forthcoming; Galvez, Simington, and Treskon, 2017;

Khadduri et al., 2014; Oppenheimer, Haberle, and Tegeler, 2013; and Webb, Frescoln, and Rohe,

2015). One study that went beyond a descriptive approach assessed the effect of MTW on housing

choice and found that MTW agencies generally performed better than comparable PHAs in their

public housing and affordable housing programs—as measured by Real Estate Assessment Center

(REAC) physical inspection scores and number of rental units preserved—but had lower voucher

utilization rates (Buron et al., 2017).

2 Using a measure of housing quality for the housing choice voucher (HCV) program was not feasible because of data limitations.

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Treskon, Gerken, and Galvez

What is “Housing Choice”?

HUD does not define what housing choice is, so MTW agencies and researchers have defined it in a wide variety of ways. In a study of MTW innovations, Khadduri et al. (2014) identified three types

of initiatives relevant to housing choice: increasing the quantity and quality of affordable housing, promoting residential stability, and improving geographical choice. They described several categories of activities within each initiative, which informed the performance measure outcomes examined

separately by Buron et al. (2017). Buron et al. (2017), in turn, found that MTW agencies have lower

voucher utilization rates, higher public housing physical inspection scores, a smaller share of public housing units with unmet capital needs, and a higher share of project-based units. MTW agencies

have found success in areas that cannot be compared well to traditional agencies, such as the use by

MTW agencies of local, non-traditional assistance to stabilize hard-to-serve populations.

In one of the first comprehensive efforts to describe MTW activities related to statutory objectives, Webb, Frescoln, and Rohe (2015) found diverse categories of the efforts that MTW agencies

made to increase housing choice: broadening supportive housing options, improving access to

high-opportunity neighborhoods, administering assistance to households at risk of foreclosure,

project-basing units, improving access to housing, using landlord outreach, and promoting

homeownership programs.

Galvez, Simington, and Treskon (2017) examined the 2015 annual plans for all 39 MTW

agencies. The researchers identified 187 ongoing activities from 37 MTW agencies that indicated

increasing housing choice as an objective—with 45 of those activities (from 24 agencies) related to

neighborhood mobility. Those activities included some that restricted moves (by limiting the ability

of households to move to a different PHA jurisdiction), and some activities intended to encourage

moves to low-poverty areas. In their investigation of the extent to which MTW agencies promoted

neighborhood mobility, Oppenheimer, Haberle, and Tegeler (2013) found that some MTW

agencies defined neighborhood mobility to include self-sufficiency efforts that could indirectly

affect movement to new neighborhoods through economic mobility and redevelopment of assisted

housing in high-poverty areas that might improve housing quality but could also reinforce existing

residential segregation.

Measuring MTW Housing Choice Outcomes

Buron et al. (2017) developed performance measures to assess agency-level outcomes for MTW

agencies in 2014 compared with those of a subset of traditional PHAs. They created five measures

of housing choice, defined as increasing the quantity and quality of affordable housing: voucher

utilization and public housing occupancy; public housing physical inspection scores; unmet public

housing capital needs; the amount of affordable housing preserved; and the amount of local,

non-traditional assistance that MTW agencies provide.3 Those researchers also looked at measures

of neighborhood mobility, including portability, project-basing of voucher assistance, and census

tract poverty rates. Looking at data for one point in time, using fiscal years 2013 and 2014 Voucher

3 As described in PIH Notice 2011-45, MTW agencies can implement local, non-traditional activities that fall outside the HCV and public housing programs, provided those activities target low-income households and meet one of the three MTW statutory objectives.

166 The Moving to Work Retrospective Evaluation

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Management System reports and 2014 American Community Survey 5-year estimates, they found

mixed results.

Specifically, in relation to comparison PHAs, Buron et al. (2017) found that MTW agencies had

lower average voucher utilization rates but comparable outcomes for some measures (public

housing occupancy rates) and better outcomes on others (physical inspection scores, public

housing units with unmet capital needs, and the number of units preserved). They also assessed

how well MTW agencies expanded the geographic scope of assisted housing, considering

portability, project-basing of units, and neighborhood poverty rates. They found that MTW

agencies, with respect to comparison PHAs, ported out a smaller share of vouchers, had a higher

share of project-based units, and had comparable neighborhood poverty rates for voucher holders.

Two studies that compared MTW agencies to comparably sized traditional PHAs found that

location patterns for MTW-assisted households resembled those of households assisted by

traditional PHAs. MTW-assisted households lived in neighborhoods with an average poverty

rate that was almost identical to that of households served through comparably sized traditional

agencies. Results were consistent across the tenant-based voucher (TBV), project-based voucher

(PBV), and public housing programs (Galvez, Gourevitch, and Docter, forthcoming; Galvez et

al., forthcoming).4 Galvez et al. (forthcoming) compared PBV location outcomes using measures

adjusting for regional differences and found that, relative to the average neighborhood in their

jurisdictions (approximated as primary counties), MTW-assisted PBV units were in neighborhoods

with a greater concentration of poverty than were PBV units at traditional PHAs, although the

difference was not statistically significant.

Finally, Galvez, Gourevitch, and Docter (forthcoming) found that MTW agencies added relatively

more households between 2008 and 2016 compared with traditional agencies, whose assistance

remained fairly flat over the same time period. The rigorous study of cost effectiveness in the MTW

retrospective evaluation also found that MTW agencies added relatively more households than did

comparable traditional PHAs (Stacy et al., 2020). Both studies also documented increased funding

for MTW agencies relative to traditional agencies.

Self-Sufficiency

Six published studies explored MTW efforts to encourage self-sufficiency (Buron et al., 2017;

Castells, 2020; Khadduri et al., 2014; McClure, 2017; Rohe, Webb, and Frescoln, 2015; and Webb,

Frescoln, and Rohe, 2015). Four of those studies assessed MTW agencies as a group, whereas

Rohe, Webb, and Frescoln (2015) and Castells (2020) evaluated the effects of self-sufficiency-

related efforts at individual MTW agencies. The studies measured self-sufficiency primarily in terms

of MTW agency efforts to increase employment and income over time and to transition households

off housing assistance, such as through case management and self-sufficiency programming,

escrow accounts, and time limits. The studies considering MTW agencies as a group found that the

4 An abundance of literature documents the locations of PBV-assisted units—particularly in relation to the Housing Choice Voucher program—but those studies do not break out MTW agencies specifically to determine if MTW

agencies have improved location outcomes. See, for example, national and state housing data fact sheets and similar ongoing analysis by the Center on Budget and Policy Priorities (http://www.cbpp.org/research/national-and-state-

housing-data-fact-sheets), Devine et al. (2003), and McClure, Schwartz, and Taghavi (2015).

Cityscape 167

Treskon, Gerken, and Galvez

earnings of households were more likely to increase at MTW agencies than at comparable agencies

and found conflicting evidence on length of stay. A study of the Charlotte Housing Authority found

a higher likelihood of employment for public housing residents with a work requirement and case

management and self-sufficiency programming, compared with residents at the PHA not subject

to the work requirement (Rohe, Webb, and Frescoln, 2015). A study of the Santa Clara County

Housing Authority found its rent reform program had no significant effects on the relationship

between increased rent and employment and earnings (Castells, 2020).

Defining Self-Sufficiency

As with housing choice, Webb, Frescoln, and Rohe (2015) categorized activities found in MTW

annual reports that sought to promote self-sufficiency. They found that self-sufficiency efforts at

MTW agencies included case management and self-sufficiency programming (such as through

Family Self-Sufficiency programs and similar models), escrow accounts and other incentives

to promote work through work requirements, time limits on housing assistance, training and

vocational programming, and initiatives to improve educational and health outcomes.

Khadduri et al. (2014) defined self-sufficiency as increased earnings of work-able assisted

households and focused on initiatives supporting, incentivizing, or requiring work. As with the

housing choice performance measures, analyses by Buron et al. (2017) built off the Khadduri et

al. (2014) assessment to develop performance measures for agencywide outcomes related to self-

sufficiency for MTW agencies in 2014. Buron et al. (2017) identified three measures of increasing

self-sufficiency: earnings growth among nonelderly and nondisabled households,5 the share of

households without reported earnings, and the length of stay in assisted housing.

Measuring MTW Self-Sufficiency Outcomes

An evaluation of the effort by the Charlotte Housing Authority to promote self-sufficiency by

requiring public housing residents to work and offering them case management and supportive

services found that those subject to the work requirement were more likely to be employed. Case

management alone, when not coupled with work requirements, did not have any statistically

significant effect on employment. The authors did not find evidence that work requirements led to

more evictions or other types of negative move-outs (Rohe, Webb, and Frescoln, 2015).

Buron et al. (2017) found that household earnings at MTW agencies were more likely to increase

than were household earnings at comparable agencies and had a smaller share of households with

no earnings than did comparable agencies. They also found that HCV households, on average, had

a shorter length of stay at MTW agencies than at comparison agencies. MTW agencies, however,

also had a higher share of households with decreasing earnings than at comparison agencies.

Length of stay is one measure of self-sufficiency that considers how long households use housing

assistance. McClure (2017) examined the length of time that cohorts of assisted households stay in

assisted housing across several categories of housing assistance using household dates of admission

and exit. He found that the average length of stay had increased over time for all programs,

including HCV-assisted households at MTW agencies. When calculating the average and median

5 All residents of working age not identified as elderly or disabled were defined as work-able.

168 The Moving to Work Retrospective Evaluation

Can Diverse Activities Have a Combined Impact? Examining the Effects of the

Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes

lengths of stay by admission year, he found, in contrast to Buron et al. (2017), that HCV-assisted

households at MTW agencies had a higher average and median length of stay than did both HCV-

assisted and public housing households at traditional agencies.

Castells (2020) assessed the effects of the Santa Clara County Housing Authority rent reform

initiative that increased the percentage of income that tenants paid toward rent. Specifically, Santa Clara increased the rent contribution required of tenants from 30 percent of adjusted income

to 35 percent of gross income in September 2013 and then decreased that percentage of gross

income from 35 percent to 32 percent in September 2014. The rent increase did not affect average

employment and earnings of work-able HCV households in the 4 years following the rent reform.

HCV households increased their employment and earnings on average during that time, but that

increase was similar to the increase that households from surrounding comparison communities saw

who were not subject to rent reform. The study found that a subset of families who were affected by

a changed bedroom standard in addition to rent reform may have reduced their earnings, suggesting

families affected by both policy changes may have decreased their level of employment.

Goals of the Present Study

Although existing studies of the activities intended to promote housing choice and self-sufficiency

among MTW agencies have documented a broad range of definitions for those objectives and of

activities intended to achieve them, only one study to date, Buron et al. (2017), has attempted

to assess the effect of the MTW demonstration on housing choice and self-sufficiency outcomes

among MTW agencies as a group. Although two impact studies have examined specific MTW

initiatives (Castells, 2020; Rohe, Webb, and Frescoln, 2015), much is still unknown about the

overall effects that MTW has had in helping PHAs meet their statutory objectives.

This study builds on previous work by systematically examining both the diversity of ways in

which housing choice and self-sufficiency could be understood and measured and by analyzing the

effects of MTW in two ways: through MTW status and through MTW-specific activities. The root

question of this study is, are MTW agencies more effective at increasing housing choice and self-

sufficiency than comparable traditional PHAs?

Methods

Our analysis examines the effect of MTW by considering whether posttreatment outcomes for

groups of MTW agencies diverge in a statistically significant manner from those of matched

comparison groups of traditional agencies. We use comparative interrupted time series (CITS)

analysis, which is a type of multivariate regression that uses longitudinal data to compare

changes over time in an outcome measure for a group that experienced a treatment to changes

for a matched comparison group that did not receive the treatment.6 CITS tests for a change in

differences in an outcome between two groups at two points in time (level differences), and it

tests for differences in trends during two time periods (slope or trend differences). CITS relies on

multiple years of pre- and posttreatment data. Pretreatment data provide a baseline for analysis

6 See Bloom (2001), Bloom et al. (2005), Linden (2015), Somers et al. (2013), and St. Clair, Cook, and Hallberg (2014) for examples of CITS analysis and methodological discussion.

Cityscape 169

Treskon, Gerken, and Galvez

and for identifying a comparison group. Posttreatment data identify any significant divergence in

outcomes between the treatment and comparison groups.

Treatment

Given the substantial diversity in how MTW agencies interpret and approach the statutory

objectives, we selected the MTW groups for analysis in two different ways. First, we selected one

group of MTW agencies on the basis of when they received the MTW designation. Second, we

identified groups of MTW agencies on the basis of their engagement in broadly defined efforts

to increase housing choice or to encourage self-sufficiency. In separate analysis steps, each type

of MTW agency group is compared with a matched group of traditional PHAs for a set of seven

research questions.

Timeframe

Our analysis timeframe is broken into three periods: one pretreatment period and two

posttreatment periods. The time up to 2009 is the pretreatment period, 2010–2012 is the initial

posttreatment period, and 2013–2016 is the second posttreatment period. Those periods were

chosen in part due to data and analysis issues. We had access to data through 2016, so working

backward to obtain at least 3 years of data for each posttreatment analysis period resulted in the

2013–2016 and 2010–2012 period definitions. The pretreatment periods also vary depending

on data quality and availability. For agencies that joined MTW in 2008 or later, Public and Indian

Housing (PIH) Information Center (PIC) data are reliable back to at least 2001; however, agencies

that joined MTW before 2008 had limited reporting requirements before that year (Galvez,

Gourevitch, and Docter, forthcoming),7 so the exact pretreatment period analyzed depends on the

MTW agencies we included in a given analysis group.

Posttreatment Period 1 (2010–2012) reflects an initial posttreatment period when effects may

begin to emerge following the date that MTW-status group agencies first joined the program (from

2008 to 2011) or the MTW activity-specific groups first enacted relevant activities (from 2009 to

2012). In the CITS analysis, we measured changes and differences in outcome levels and trends.

The level difference compares the change from 2009 (the last pretreatment year) to 2010 for the

MTW group to that of the comparison group. The trend difference compares the 2010–2012 trend

for the MTW group with that of the comparison group.

Posttreatment Period 2 (2013–2016) reflects the period when MTW-status group agencies had

MTW status for at least 1 year, and activities started by agencies in the MTW activity-specific

groups had all been implemented for at least 1 year. In the CITS analysis, the level difference

compares the change from 2012 (the last year of the initial implementation treatment period)

to 2013 for the MTW group with the change for the comparison group. The trend difference

compares the 2013–2016 trend for the MTW group versus the comparison group.

7 Although MTW agency data are generally poor before 2008 (when reporting requirements changed for MTW

agencies), we assessed data coverage and quality for 2007 for the agencies in our MTW activity-specific group and found them to be reliable for that year.

170 The Moving to Work Retrospective Evaluation

Can Diverse Activities Have a Combined Impact? Examining the Effects of the

Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes

Sampling

The goal of sampling was to create a group of MTW agencies chosen solely for having received

MTW designation and groups composed of MTW agencies engaged in activities expected to affect

selected indicators of housing choice and self-sufficiency. The MTW-status group includes the nine

MTW agencies that signed an MTW agreement between 2008 and 2011.8 MTW agencies in that

study may belong to one or more groups (see exhibit 1).

MTW Activity-Specific Groups

To identify MTW agency activities expected to affect our selected indicators of housing choice

and self-sufficiency, we reviewed the MTW evaluation database and MTW plans and reports. The

review identified 143 activities implemented between 2009 and 2012 related to either housing

choice or self-sufficiency and active as of 2015, and we determined that 42 activities were likely

to affect assisted households agencywide and across housing programs. Through that review, we

identified for analysis 15 MTW agencies that undertook significant housing choice activities and

18 MTW agencies that undertook significant self-sufficiency activities. Agencies may be in multiple

groups (exhibit 1).

Exhibit 1

Moving to Work Agencies by Analysis Group (1 of 2)

MTW Activity-specific: New

MTW Activity-specific: New

Housing Choice, 2009-2012

Self-Sufficiency, 2009-2012

New

MTW

Income

PHA

State

Household Poverty

Quality

Escrow

HAP

Status

Increasing

Share

Alaska

AK





Oakland

CA





San Mateo

CA







San Bernardino

CA











Tulare

CA







Santa Clara/

CA









San Jose

San Diego

CA











Boulder

CO



Orlando

FL







Chicago

IL





Champaign

IL







Louisville

KY





8 We exclude one agency (Baltimore) from the MTW-status group due to data quality issues and its early participation in MTW as part of the Jobs Plus program. Restricting our MTW-status sample to those agencies also coincides with the implementation of MTW standard agreements in 2008, which standardized reporting requirements; before that implementation, reporting consistency and completeness varied for agencies already participating in MTW.

Cityscape 171

Treskon, Gerken, and Galvez

Exhibit 1

Moving to Work Agencies by Analysis Group (2 of 2)

MTW Activity-specific: New

MTW Activity-specific: New

Housing Choice, 2009-2012

Self-Sufficiency, 2009-2012

New

MTW

Income

PHA

State

Household Poverty

Quality

Escrow

HAP

Status

Increasing

Share

Lexington

KY



Massachusetts

MA





Minneapolis

MN





Charlotte

NC















Lincoln

NE



Portage

OH







Portland

OR





Pittsburgh

PA







San Antonio

TX



King County

WA







Tacoma

WA









Vancouver

WA







MTW = Moving to Work; HAP = housing assistance payments; PHA = public housing agencies

Notes: Although initially selected for MTW in 1999, we include Charlotte in the MTW Status Group because it did not sign an MTW agreement until December 2007 and has been actively in MTW for a similar length of time as others in this group. Jurisdiction names are used for MTW agencies; official agency names may be different. The housing authority of Portland and Multnomah Count, OR, for example, is named Home Forward.

Source: Analysis by the authors of activities related to statutory objectives identified through MTW agency annual plans Comparison Groups

For each MTW agency group, we identified a separate comparison group. We began by limiting

our comparison group selection to traditional PHAs with more than 500 households to exclude

small PHAs (which differ from the MTW agencies).9 To ensure that comparison groups are similar

in ways appropriate for CITS analysis, we selected agencies whose pretreatment levels and trends

for each outcome measure of interest closely resembled those of the average MTW group. We used

a Stata protocol that selects a matched comparison group on the basis of pretreatment levels and

trends, discussed in more detail in Treskon, Gerken, and Galvez (forthcoming).10

9 Galvez, Gourevitch, and Docter (forthcoming) document that MTW agencies tend to be larger than traditional comparison PHAs and that larger PHAs tend to more closely resemble MTW agencies than smaller traditional agencies in terms of program mix and local housing market characteristics.

10 We use the Stata protocol “itsamatch.”

172 The Moving to Work Retrospective Evaluation

Can Diverse Activities Have a Combined Impact? Examining the Effects of the

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Data

This study uses five data sources:

HUD Public and Indian Housing (PIH) Information Center (PIC) data

PHA staff regularly report detailed information on every assisted household to HUD through the

PIC data system using forms HUD-50058 and HUD-50058 MTW.11 We used household-level PIC

data for MTW agencies and traditional PHAs for 2001 through 2016 to identify total household

counts and shares of households in each assistance program (HCV and public housing) and to

identify household characteristics and locations. Unique household identifiers allowed us to track

movement across assistance programs, exits from assistance over time, and movement across

census tracts over time. Data for MTW agencies are not available before 2007 (see Treskon, Gerken,

and Galvez, forthcoming, for further discussion).12

PHA Performance Measure Indicators

We use Physical Assessment Subsystem (PASS) scores as indicators of public housing agency

performance for analysis of housing quality and standards. PASS scores only apply to the public

housing stock in a PHA and are determined by an inspection satisfying the HUD Uniform Physical

Condition Standards. A PHA can receive a maximum PASS score of 40 points. The PASS score is

one component of a larger Public Housing Assessment System, or PHAS score, which HUD uses to

assess how well PHAs manage their public housing programs.

Supplemental HUD-Assisted Unit Counts

The HUD Moving to Work office provided data on the number of households assisted through

MTW local, non-traditional housing assistance programs. Those units are not included in PIC data

and are added to the total household counts for each MTW agency.

Decennial Census and American Community Survey (ACS)

We used publicly available tract-level census data to assign poverty rates to the census tract location for each household to identify the number and percentage of assisted households living in lower-poverty neighborhoods. We used 2011–2015 ACS 5-year estimates to identify tract poverty rates.

Database of MTW Activities

For the MTW retrospective evaluation, we created an agency-level database of MTW activities

and flexibilities based on information reported in the 2015 and previous MTW annual plans and

reports. The MTW plans and reports include information on all MTW activities implemented,

such as activity name, activity status, year proposed, implementation year, the authorization(s)

involved, activity description, and the statutory objectives that the activity addresses. We use

11 Form HUD-50058 for traditional agencies can be found here: https://www.hud.gov/sites/documents/50058.PDF.

Form HUD-50058 MTW can be found here: https://www.hud.gov/sites/documents/DOC_10236.PDF.

12 MTW agencies did not consistently report household information into HUD’s PIC system before 2007 (for some, 2008), resulting in significant gaps in the administrative data available for agencies that received MTW designation in the first 10 years of the demonstration.

Cityscape 173

Treskon, Gerken, and Galvez

this information to identify significant housing choice and self-sufficiency activities implemented

between 2009 and 2012.

Variables

We examine seven main outcome variables of interest—three related to housing choice and four

related to self-sufficiency (exhibit 2).

Exhibit 2

Outcome Variables

Outcome Variable

Data Source

Outcome Definition

Newly admitted households PIC: all

Share of households with 50058 Field 2a action codes 1,

as a share of all households program types 4, or 1413

Share of tenant-based

PIC: tenant-

Percentage of tenant-based voucher households in

voucher households in low-

based

census tracts with poverty rates no higher than 10

poverty census tracts

vouchers

percent

Physical Assessment

PIC/ REAC:

Average PASS score per agency per year

Subsystem (PASS) scores

public housing

Housing Choice

and multifamily

assisted

Share of existing work-

PIC

Percentage of existing work-able households in a given

able households with

year with total annual incomes higher than at their year

rising income over time

of entry into housing assistance (in 2016 dollars—

(annualized rate of change)

adjusted for inflation). Includes only households assisted

at the point of designation of the PHA as MTW.

Percentage of work-able

PIC

Percentage of existing work-able households with

households reported to have

a non-zero FSS program escrow account balance.

an escrow account

Includes only households assisted at the point of

ficiency

designation of the PHA as MTW.

Share of existing work-able PIC

Percentage of existing work-able households with a

households with housing

HAP less than $50. Includes only households assisted at

Self-Suf assistance payment (HAP)

the point of designation of the PHA as MTW.

less than $50

Share of existing work-

PIC

Percentage of work-able households who reach a HAP

able households leaving

less than $50 and who exit in the following year (exit

PHA in year after

defined as having exit code or household missing in

attaining minimal HAP

subsequent year). Includes only households assisted at

the point of designation of the PHA as MTW.

FSS = Family Self-Sufficiency. HAP = Housing Assistance Payment. MTW = Moving to Work. PASS = Physical Assessment Subsystem. PHA = public housing agency. PIC = PIH Information Center. PIH= Public and Indian Housing. REAC = Real Estate Assessment Center.

Note: “Existing” households are households that are not new entrants to housing assistance in a given year.

Housing Choice Variables

The housing choice measures used reflect three approaches to interpret the objective, including

increasing the number of households served, expanding access to low-poverty neighborhoods, and

improving the quality of public housing. PHAs may view expanding housing availability and the

number of low-income households served as expanding housing choice. Housing choice also may

13 We used annual extracts of PIC data. Households completing an interim recertification in their first year of housing assistance may not be counted as new because the record in our extracts would have an action code that would identify them as an existing household rather than a new household.

174 The Moving to Work Retrospective Evaluation

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be interpreted as expanding the range of neighborhood locations that are accessible to low-income

households—particularly low-poverty neighborhoods. Finally, agencies may define expanding

choice as improving the quality of public housing units.

We identify newly admitted households as the share of all households served by an MTW

agency that had an action code in PIC data (Form HUD-50058, Field 2a) associated with a new

admission.14 We focus on new admissions as an indicator of the ability of agencies to expand the

pool of households they serve over time. Agencies with a higher share of their total assistance going to newly admitted households are arguably expanding opportunities for low-income households to

benefit from housing assistance.

To examine the share of tenant-based voucher households in low-poverty census tracts, we define

low-poverty neighborhoods as census tracts with poverty rates below 10 percent. That threshold

is commonly used to approximate neighborhood quality in the neighborhood effects and assisted

housing location literature (Galvez, 2010). The relationships between poverty rates and health

and economic outcomes are well documented, and census tract poverty rates are commonly relied

on as a proxy for overall neighborhood quality—particularly at very low and very high levels

(Galster, 2012).

We measure public housing quality using PHA average PASS scores, which are based on physical

inspections to determine if public housing units are decent, safe, sanitary, and in good repair.

PASS scores use a 40-point scale. Inspections are conducted in accordance with the HUD Uniform

Physical Condition Standards, or UPCS, on a sample of units within a given development; scores

are rolled into a composite PHA-level score.

Self-Sufficiency Variables

The self-sufficiency measures in this study are limited to work-able households and include the

share with increasing incomes, the share with a Family Self-Sufficiency (FSS) program escrow

account,15 the share with housing assistance payment less than $50, and the share of those

households who leave PHA assistance. Those measures reflect common goals for the self-

sufficiency of assisted households. PHAs may encourage self-sufficiency by promoting work

and through increases in wage income. They may also promote household savings, incentivize

employment, and prepare households for independence through FSS programs.

14 We consider a household as having entered PHA assistance in a year if they have an action code that denotes a new admission (action code 1), a portability move-in (action code 4), or (in cases where no entry code exists for a household) a historical readjustment (action code 14). We consider cases in which the household’s first appearance in the dataset does not have an action code associated with an entry to be newly assisted in that year.

15 Family Self-Sufficiency (FSS) programs are designed to enable assisted families to increase earned income and reduce dependency on welfare and housing assistance. These programs include an interest-bearing escrow account established by the PHA for each participating family. If participating households increase their income through wages, the resulting additional rent payments due to the PHA are instead credited to the family’s escrow account, which is available to the family upon graduation from the FSS program. HUD staff noted that households in agencies participating in Jobs Plus may opt out of FSS escrow accounts to make use of the Jobs Plus Earned Income Disregard.

Although we did not formally verify that assertion, Charlotte, an MTW agency that participates in Jobs Plus, had increases in escrow utilization during the analysis period.

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Treskon, Gerken, and Galvez

To measure the potential effects of MTW status or activities on resident incomes, we measured the

percentage of existing work-able households in a given year with total annual incomes higher than

at their year of entry into housing assistance (in 2016 dollars—adjusted for inflation).

To measure the extent to which households are participating in Family Self-Sufficiency (FSS)

programming, we measured the percentage of existing work-able households with a non-zero FSS

program escrow account balance. That indicator measures the effect of MTW status or activities on

FSS participation rather than longer-term effects of FSS participation.

We inferred positive exits from housing assistance from two indicators. First, we considered the

share of existing work-able households who approach minimal HAP to determine whether existing

work-able households at MTW agencies are moving to minimal housing subsidy at greater rates

than households at traditional agencies. We define minimal as a HAP of less than $50, based on our analysis of HAP amounts for assisted households as reported in PIC. We identify exits as cases

where there is no recertification record for at least 1 year after attaining minimal HAP; that is, for our purposes, if a household reaches minimal HAP and does not have another record in PIC for at

least 1 year, we count that household as having exited.16

Analysis

CITS analysis is designed to measure divergence between a treatment group and comparison group

after the introduction of an intervention. To be valid, CITS requires both groups to have similar

pretreatment levels and trends. Because we have two posttreatment periods of analysis, group

averages could diverge in four ways:

• The first posttreatment period (2010–2012):

{

The 2009–2010 1-year level change.

{

The 2010–2012 trend.

• The second posttreatment period (2013–2016):

{

The 2012–2013 1-year level change.

{

The 2013–2016 trend.

Below, we present topline findings for the seven indicators of interest and include comparative

graphs for three: the share of households that are new, the share of households in low-poverty

tracts, and the share of existing work-able households with total annual incomes higher than they

were at entry. For more detailed findings on all indicators (including regression tables) and details on the analysis approach used for this research, see Treskon, Gerken, and Galvez, forthcoming.

16 We defined exits in that way to account for how MTW agencies can use their MTW flexibilities to use biennial or triennial recertifications instead of annual recertifications. Based on internal analysis, MTW agencies that have revised their recertification schedules for work-able households have moved to a biennial calendar. Our approach may overestimate positive exits for households at MTW agencies who were not recertified within a 2-year window.

176 The Moving to Work Retrospective Evaluation

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Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes

Results

Identification of Activities

Our review of MTW plans identified a range of activities that had the potential to lead to significant effects on housing choice or self-sufficiency indicators. Exhibits 3 and 4 indicate the specific

activities used to identify the housing choice and self-sufficiency groups, respectively.

We identified 17 MTW agencies implementing one or more activities related to housing choice

and potentially producing measurable effects (we excluded two of these agencies, Lexington and

Minneapolis, from our final analysis due to data limitations).

Exhibit 3

Housing Choice Initiatives Used To Select Activity-Specific Groups

PHA

Activity Name

Alaska

Simplification of Utility Allowance Schedules (2011)

HCV Maximum Family Contribution at Lease Up Raised to 50 Percent (2012)

Charlotte

Increase of Acquisition and Rehabilitation of Existing Multifamily Properties (2009-7)

Land Acquisition for Future Use (2009-8)

Community-Based Rental Assistance (2009-4)

King County

Community Choice Program (2012)*

Lexington**

HCV Tenant-Based Special Partners Programs (2012)

Lincoln

RentWise Tenant Education (2012)

Minneapolis**

Section 8 HCV Mobility Voucher Program (2010/2009-6)

Oakland

Elimination of Caps on PBV Allocations (2012)

Portage

Maximum Rent (2009)*

Portland

Measures to Improve the Rate of Voucher Holder Lease Up (2010)

San Antonio

Preservation and Expansion of Affordable Housing (2011)

San Bernardino

Local PBV Program (2010)

Local Payment Standards (2012)

San Diego

Acquisition of affordable units (2010)

Development of public housing units using combination of funds (2010)

Choice Communities Component (2010)

San Mateo

MTW Funds for Leveraging Additional Affordable Housing (2012)

Santa Clara/

Creation of Affordable Housing Acquisition and Development Fund (2012-3);

San Jose

Creation of Affordable Housing Preservation Fund for HACSC and Affiliate-Owned

Properties (2012-4)

Tacoma

Creation and Preservation of Affordable Housing (2012)

Tulare

Development of Additional Affordable Housing (2009)

Vancouver

Renter Education Required for Applicants (2009)*

Simplified Utility Allowance Schedule in HCV (2009)*

* Indicates initiative potentially affecting both housing choice and self-sufficiency.

** Not included in our final housing choice activity-specific analysis group due to data limitations.

HACSC =Housing Authority of the County of Santa Clara; HCV = housing choice voucher; MTW = Moving to Work; PBV = project-based voucher; PHA = public housing agency

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Treskon, Gerken, and Galvez

In our review of 2015 (and previous) annual plans, we identified 18 MTW agencies implementing

one or more activities related to self-sufficiency and potentially producing measurable effects.

Exhibit 4

Self-Sufficiency Initiatives Used To Select Activity-Specific Groups

PHA

Initiative Name

Champaign County

Mandatory Local Family Self-Sufficiency Program (2011)

Tiered Flat Rents and Minimum Rents by Bedroom Size (2011)

Charlotte

Rent Reform and Work Requirement (2010)

Chicago

Public Housing Work Requirement (2009-2)

King County

Community Choice Program (2012)*

Louisville

Mandatory Case Management (2010)

Massachusetts

Rent Simplification (2012)

Minneapolis

Public Housing Works Family Incentive (2011/2010-1)

Oakland

Program Extension for Households Receiving $0 HAP (2010)

$225 Rent Floor for Nonelderly and Nondisabled Households (2012)

Orlando

Increase of Minimum Rent for Work-Able Households (2012)

Pittsburgh

Modified Rent Policy for Section 8 Housing Choice Voucher Program (2011)

Portage

Maximum Rent (2009)*

San Bernardino

Minimum Rent (2010); 5-Year Lease Assistance Program (2012)

San Diego

Adopt a Local Interim Certification Policy (2011)

San Mateo

Change Automatic Termination of HAP Contact from 180 to 90 Days (2012)

Santa Clara/San Jose

Expand Tenant Services at HACSC/Affiliate Properties (2012-5)

Tacoma

Local Policies for Work-Able Households (2012)

Tulare County

Encourage Self-Sufficiency/Transition Pre-1999 Families to MTW (2009)

Vancouver

Renter Education Required for Applicants (2009)*

Simplified Utility Allowance Schedule in HCV (2009)*

* Indicates initiative potentially affecting both housing choice and self-sufficiency.

HAP = housing assistance payment; HCV = housing choice voucher; HACSC = Housing Authority of the County of Santa Clara; MTW = Moving to Work Comparative Interrupted Time Series Analysis

Detailed Findings

Housing Choice

Do MTW Agencies Create More Housing Opportunities Relative to Traditional Agencies?

• Outcome measure: the share of MTW agency-assisted households that are new admissions

into an assisted housing program

This measure is an indicator of the capacity of an agency to serve more households over time

through either growth or increased turnover. Rather than count the new households served, we

178 The Moving to Work Retrospective Evaluation

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Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes

examined the share of households that were new admissions each year—to control for the size of

a PHA. MTW agencies have received additional funding relative to traditional agencies, so that can

also be seen as an examination of the extent to which MTW agencies are using additional funding to

assist additional households (see Galvez, Gourevitch, and Docter, forthcoming; Stacy et al., 2020).

During the pretreatment period and the initial posttreatment period (2010–2012), differences

between the MTW-status group (N = 9) and the comparison group (N = 18) were small (see exhibit

5) and not statistically significant. During the 2013–2016 posttreatment period, however, the

share of new households in MTW agencies increased from 10 percent to more than 15 percent

(with most of the increase between 2014 and 2015), whereas the comparison group share did not

increase. That divergence was statistically significant.

Exhibit 5

Share of Households That Are New: MTW-Status Group and Comparison Group

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

MTW-status Group: Predicted

Traditional Agencies: Predicted

MTW-status Group: Actual

Traditional Agencies: Actual

MTW = Moving to Work.

Source: Analysis of HUD Public and Indian Housing Information Center data

Results for the group of MTW agencies selected, based on their implementation of activities

expected to increase the share of new households, are similar to findings for the MTW-status group

(see exhibit 6). For that group, the share of new households grew from 8 percent to 12 percent

between 2013 and 2016, whereas during that period, the average share of new households at the

comparison group remained roughly stable at 10 percent.

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Treskon, Gerken, and Galvez

Exhibit 6

Share of Households That Are New: MTW-Activity Group and Comparison Group

16%

14%

12%

10%

8%

6%

4%

2%

0%

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

MTW Activity-specific Group: Predicted

Traditional Agencies: Predicted

MTW Activity-specific Group: Actual

Traditional Agencies: Actual

MTW = Moving to Work.

Source: Analysis of HUD Public and Indian Housing Information Center data

To What Extent are Households Served by MTW Agencies Reaching Lower Poverty, Higher

Opportunity Neighborhoods Than Households Served by Traditional Agencies?

• Outcome measure: the share of tenant-based voucher households in low-poverty census tracts

For the MTW-status group (N=8; comparison group of 6 traditional agencies), the share of

households in low-poverty census tracts dipped somewhat between 2005 and 2011 before

increasing again through 2016 (see exhibit 7). The comparison group followed a similar pathway,

dropping during the 2010–2013 period but increasing more quickly relative to the MTW group

between 2013 and 2016. Those differences were not statistically significant.17

17 Results of an outlier sensitivity analysis were consistent. Removing Boulder, Colorado, produced some significant results, but results from a secondary outlier sensitivity analysis (excluding Boulder and then removing remaining PHAs one at a time) resulted in no significant findings.

180 The Moving to Work Retrospective Evaluation

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Exhibit 7

Share of Tenant-Based Voucher Households in Low-Poverty Tracts: MTW-Status Group and

Comparison Group

25%

20%

15%

10%

5%

0%

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

MTW-status Group: Predicted

Traditional Agencies: Predicted

MTW-status Group: Actual

Traditional Agencies: Actual

MTW = Moving to Work.

Source: Analysis of HUD Public and Indian Housing Information Center data

The MTW activity-specific group analysis (N=6; comparison group of 16 traditional agencies)

showed no significant divergence in levels or trends from the comparison group during the study

period (see exhibit 8).

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Treskon, Gerken, and Galvez

Exhibit 8

Share of Tenant-Based Voucher Households in Low-Poverty Tracts: MTW Activity-Specific Group

and Comparison Group

16%

14%

12%

10%

8%

6%

4%

2%

0%

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

MTW Activity-specific Group: Predicted

Traditional Agencies: Predicted

MTW Activity-specific Group: Actual

Traditional Agencies: Actual

MTW = Moving to Work.

Source: Analysis of HUD Public and Indian Housing Information Center Data

To What Extent are Households Served by MTW Housing Agencies Living in Higher Quality

Public Housing Dwellings Relative to Households in Traditional Agencies?

• Outcome measure: public housing physical assessment subsystem scores

We explored public housing PASS scores to understand the extent to which households served

by MTW agencies are living in higher quality public housing dwellings relative to households

at traditional agencies. Only three MTW agencies had implemented initiatives related to public

housing quality, so we did the CITS analysis for the MTW-status group only. PASS scores for the

MTW-status group and the comparison group generally remained aligned throughout the analysis

period: steady at about 25 (out of a maximum of 40 points) before 2010, rising to about 34

from 2010 to 2013, and remaining between 32 and 34 from 2013 to 2016. The only statistically

significant result was when the average PASS score for the MTW group increased from about 30 to

34 between 2012 and 2013, whereas the average for the comparison group remained unchanged.

The substantive meaning of this result is minimal, however, because the average comparison group

score had simply risen from 30 to 34 sooner (with most of the increase between 2010 and 2011).

182 The Moving to Work Retrospective Evaluation

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Self-Sufficiency

How Do Incomes of Work-Able Households Served by MTW Agencies Compare With Those

Served by Traditional Agencies?

• Outcome measure: the share of assisted existing work-able households that have total annual incomes higher than they were at housing assistance entry18

We measured whether households worked more after agencies joined MTW by identifying the

share of work-able (nonelderly and nondisabled) households that have total annual incomes higher

than they did in their first year of housing assistance (their year of admission recorded in PIC).

Because MTW status could possibly be associated with changes in the overall composition of newly

admitted households, we included only existing households in this analysis.

We dropped two agencies from our MTW-status group due to missing or anomalous income

data.19 Of the seven MTW agencies analyzed here (comparison group of 21 traditional agencies),

the share of households with income greater than at housing assistance entry remained between

50 and 60 percent during the 2001–2009 pretreatment period for both the MTW-status group

and the comparison group. Between 2010 and 2012, the two groups diverged somewhat: both

saw a decrease in that measure between 2010 and 2011, but the drop was more pronounced and

statistically significant for the MTW group (see exhibit 9).

After 2013, the share of households with incomes higher than their baseline year increased for

both groups. That increase for the MTW group was larger than it was for the comparison group,

and that difference was statistically significant. The increase allowed MTW agencies to regain the

losses of the previous period and catch up to and converge with the level of the traditional public

housing agencies (PHAs) by 2016.

18 Households that exited in a given year are classified as an existing household for that year.

19 We removed Champaign because of missing income data for some of the analysis period years, and we removed Charlotte because our outlier sensitivity analysis determined that an anomalous 1-year drop in 2011 (from 51 percent of households with incomes higher than at entry in 2010, to 35 percent in 2010, and back to 50 percent in 2012) skewed the group results.

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Treskon, Gerken, and Galvez

Exhibit 9

Share of Existing Work-Able Households With Total Annual Incomes Higher Than at Housing

Assistance Entry: MTW-Status Group and Comparison Group

70%

60%

50%

40%

30%

20%

10%

0%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

MTW-status Group: Predicted

Traditional Agencies: Predicted

MTW-status Group: Actual

Traditional Agencies: Actual

MTW = Moving to Work.

Source: Analysis of HUD Public and Indian Housing Information Center data

Our activity-specific group compared 14 MTW agencies to a group of 77 traditional agencies.20 The

only significant divergence was in the 2013–2016 trend, when the MTW group share increased

relative to that of the comparison group; however, as with the MTW-status group, the result was that

the MTW group “caught up” to the comparison group rather than overtaking it (see exhibit 10).

20 We excluded Chicago from the analysis. An outlier sensitivity analysis found that the agency had a significant effect on the grouped results, leading the 2013–2016 trend findings to no longer be significant. A secondary sensitivity analysis found that after excluding Chicago, rerunning the grouped analyses by sequentially removing each of the remaining PHAs one at a time produced consistently significant and positive results.

184 The Moving to Work Retrospective Evaluation

Can Diverse Activities Have a Combined Impact? Examining the Effects of the

Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes

Exhibit 10

Share of Existing Work-Able Households With Total Annual Incomes Higher Than They Were at

Housing Assistance Entry: MTW-Activity Group and Comparison Group

70%

60%

50%

40%

30%

20%

10%

0%

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

MTW Activity-specific Group: Predicted

Traditional Agencies: Predicted

MTW Activity-specific Group: Actual

Traditional Agencies: Actual

MTW = Moving to Work.

Source: Analysis of HUD Public and Indian Housing Information Center data

How Does the Use of Escrow Accounts as a Tool for Promoting Self-Sufficiency Differ

Between MTW and Traditional Agencies?

• Outcome measure: the share of existing work-able households with Family Self-Sufficiency

program escrow accounts (a proxy for FSS program participation)

The share of existing work-able households with a positive FSS program escrow account

balance—a proxy for FSS program participation—indicates whether the use of escrow accounts

as a tool for promoting self-sufficiency differs between MTW and traditional agencies. Only three

MTW agencies reported activities with the potential to influence escrow use during our study

period, so we did CITS analysis only for the MTW-status group. We found no evidence that MTW

agencies are more likely to use escrow accounts than traditional agencies. The one statistically

significant divergence between the MTW group and the comparison group was that, between 2009

and 2010, the share of work-able households with escrow accounts remained steady for the MTW-

status group but increased for the comparison group, which resulted in a statistically significant

decrease for the MTW group relative to the comparison group. Subsequently, the share of work-

able households with escrow accounts increased for both groups in the same way. Findings were

robust to sensitivity analyses. The share of households with escrow accounts was small: for the

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Treskon, Gerken, and Galvez

MTW-status group, it remained between roughly 2 and 5 percent, and for the comparison group, it

remained between 2 and 7 percent. The share has varied over time, with peaks for both the MTW-

status group and the comparison group in 2005 and again in 2016.

Are Existing Work-Able Households in MTW Agencies Moving to Minimal Housing Subsidy

and Making Positive Exits From Housing Assistance at Greater Rates Than Households at

Traditional Agencies?

• Outcome measures: the share of existing work-able households who approached minimal

HAP (less than $50); the share of households who left assisted housing in the year after

reaching minimal HAP

Starting with the MTW-status group and its comparison group, we found that the share of existing

households at minimal HAP was small, generally remaining below 2 percent before 2010 for both

groups. Between 2010 and 2016, the share increased to nearly 7 percent for the MTW-status group

and nearly 3 percent for the comparison group, a difference that was not statistically significant.

For the MTW activity-specific group, the share of existing households with minimal HAP also

remained relatively small during the analysis period, exceeding 6 percent in 2008 and again in

2016. Many MTW agencies and traditional PHAs reported a 1-year spike in 2008, which may reflect

a data reporting or quality issue for that year (see Galvez, Gourevitch, and Docter, forthcoming,

for a discussion of data challenges). The share of existing households with minimal HAP increased

between 2012 and 2013 for the MTW activity-specific group while staying stable for the comparison

group; that difference was statistically significant. That result should be treated with caution for

several reasons, however, because the difference between groups was quite small, and, for both

groups, the total number of households included in this measure tended to be very small.

Our second measure for positive exits was the share of existing work-able households who left

assisted housing in the year after reaching minimal HAP. Households in the MTW-status group

were more likely to subsequently exit assistance than were similar households in the comparison

group—but there was no significant divergence between the MTW group and its comparison

group for the share of existing work-able households achieving minimal HAP in the first place.

Households in the MTW activity-specific group saw statistically significant increases relative to

households in comparison groups for both the share of households reaching minimal HAP and

those subsequently exiting housing assistance. For both traditional and MTW agencies, however,

households achieving minimal HAP represented a very small portion of their assisted populations

(7 percent or less). That means that the measure of those with minimal HAP who left assistance

must be interpreted cautiously, as it is based on few households and therefore is sensitive to year-

over-year random variation.

Discussion and Conclusions

The root question of this study is, are MTW agencies more effective at increasing housing

choice and self-sufficiency than comparable traditional PHAs? Our findings indicate that for

some measures (adding new households, increasing earnings, and positive exits from housing

assistance), MTW agencies are undertaking activities that are helping them meet those goals.

186 The Moving to Work Retrospective Evaluation

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Interestingly, the results tend to show up for both the MTW status group and the MTW activity-

specific group, which indicates that MTW status itself may be a pathway to making progress on

statutory objectives. Because this particular study focuses on MTW agencies as a group, it cannot

tease out the exact pathways that individual agencies are taking, but it does indicate that future

analyses should examine the suite of policies and programs that MTW agencies undertake and how

the sum total of MTW activities may be driving outcomes.

Housing Choice

Of the three main housing choice themes we studied—amount of housing, location of housing,

and quality of housing—we found the greatest indication of an effect from MTW in the first.

Between 2013 and 2016, newly assisted households represented a larger share of assisted

households in MTW agencies compared with traditional PHAs. That outcome had the most

consistent and substantive effects in our analyses and held for both the MTW-status group and

the activity-specific group. Results are also consistent with the descriptive findings by Galvez,

Gourevitch, and Docter (forthcoming) that the 39 MTW agencies added more new households to

their assisted housing portfolios between 2008 and 2016, whereas the number added by traditional

PHAs remained flat (Stacy et al., 2020, have similar findings). Given the increased funding that

MTW agencies have received relative to traditional agencies, this is not entirely unexpected;

however, it does indicate that at least some additional funding has been used to expand access to

new households rather than being fully allocated to programming or other purposes.

We found no evidence that MTW agencies differed from traditional comparison agencies in

the share of tenant-based voucher households in low-poverty census tracts. For both the MTW

groups and the traditional agency groups, the share of households in low-poverty neighborhoods

dropped between 2010 and 2012 before increasing between 2013 and 2016. Given the housing

market recovery and economic expansion across the 2010–2016 period, that inflection merits

closer attention.

We also found no substantially significant finding in terms of public housing quality as measured by

PASS scores: the one statistically significant divergence seems to be more of a lagging trend between the trajectory of the MTW-status group and the comparison group rather than an indicator of an

MTW-specific effect. Also, participation in the Rental Assistance Demonstration program during our

study time period—which allows housing authorities to convert public housing to project-based

vouchers or project-based rental assistance—may have removed the most distressed public housing

units from the portfolios of some of the MTW agencies. If so, that could have resulted in an apparent improvement in our PASS score measure for remaining units. Galvez et al. (forthcoming) find that

MTW agencies are more likely than comparison PHAs to convert public housing units through RAD.

The relationship between RAD conversions and public housing quality merits future research.

Self-Sufficiency

Results for outcome measures related to improving self-sufficiency (income gains over time for

assisted households and two measures related to positive exits from assistance) are positive but

preliminary, particularly regarding income. Specifically, for the share of households with income

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Treskon, Gerken, and Galvez

gains over time, the MTW agencies initially lagged behind the traditional PHAs before catching up

to the comparison PHAs by the end of 2016. Achieving parity with traditional PHAs may itself be a

positive outcome. Updating the current analyses with additional years of HUD administrative data

will help determine whether the promising trend has continued, and MTW agencies have begun to

outpace the comparison group, whether they have remained in parity or whether they have again

fallen behind.

The share of work-able households with escrow accounts increased after 2009 for the comparison

group but only after 2012 for the MTW group. That circumstance resulted in a statistically

significant divergence during the initial posttreatment period, but since 2012, the trend for both

groups has increased at a similar rate. Although that means that the MTW group has not “caught

up” to comparison groups, and given the relatively low share of households with escrows (in 2016,

the average was 4.8 percent for the MTW group and 6.8 percent for the comparison group), the

substantive implication of that finding is limited.

Finally, MTW-assisted households were more likely to exit assistance after obtaining a low level

of subsidy—but that finding is weak, and there is no difference between MTW agencies and

comparison agencies in the share of households that reach minimal housing subsidy. Given the

small numbers of households reaching minimal HAP and subsequently exiting housing assistance,

those outcome measures must be treated cautiously, as they can be sensitive to small changes.

Additional tracking over a longer time period can help clarify whether positive trends have

continued. Indepth analyses of the individual agencies included in our MTW activity-specific

sample would also be useful to assess what might be happening on the ground.

Implications for Research

This study is the most exhaustive effort to date to examine the effects of MTW across agencies

on housing choice and self-sufficiency outcomes. A challenge of this work was that assessing

aggregate effects can be at odds with the inherent diversity of MTW agencies, activities, and local

contexts. We adjusted for the complexity of the MTW program by selecting outcome measures

that seem relevant to a wide range of MTW agencies and selecting two types of treatment groups

of agencies—one group of agencies that received MTW designation at roughly the same time and

another group of agencies engaged in specific activities relevant to our outcomes of interest. That

approach provides several implications for future research examining the effects of MTW.

First, we found both commonalities and differences between the two MTW agency grouping

approaches. Both approaches may be useful in different contexts, and some measures are more

appropriate for status versus activity-specific treatment group approaches. For example, measures

of Family Self-Sufficiency program participation or public housing quality improvements may

benefit from activity-specific analyses because they require MTW agencies to be engaged in

narrowly focused efforts or programs. Other outcome measures, such as positive exits from assisted

housing or income gains over time, may be more appropriate for a status group approach because

they speak to common MTW agency objectives and changes that may be triggered through a

variety of MTW agency efforts. Future research using the status group approach could incorporate

the 100 agencies that are expected to be designated through the MTW expansion (although the

188 The Moving to Work Retrospective Evaluation

Can Diverse Activities Have a Combined Impact? Examining the Effects of the

Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes

funding formula for agencies in that expansion group is not yet known and must be incorporated

into any review and analysis of their activities).

Second, this work provides a useful starting point for additional research examining the findings

documented in this report. That research could include qualitative work examining what MTW

agencies consider to be the most relevant flexibilities, policy reforms, or initiatives for meeting their statutory objectives. This descriptive work can help identify practices that may be useful to test

rigorously at other PHAs and lay the foundation for more rigorous, targeted MTW agency-level or

grouped impact analyses. Likewise, additional quantitative analysis of the measures found to have

promising results can help shed light on whether the positive trends have continued since 2016

(the last year of data to which we had access for this study). Similarly, our assessment of MTW

agency activities provides a starting point for more comprehensive agency-level analyses. Our

grouped analysis approach could not be as closely tailored to the diversity of MTW agency contexts

as an agency-by-agency analysis. A rigorous assessment of outcomes for the individual agencies

in our activity-specific samples would require an indepth accounting of local agency contexts,

goals, and programs—both to measure outcomes precisely and to select appropriate traditional

comparison PHAs.

Third, this study and others clearly show that MTW agencies are undertaking many varied and

experimental activities, using a range of approaches and in diverse local contexts. Considerable

work has been done through the MTW retrospective evaluation and other research to emphasize

the breadth of activities, partnerships, and goals in which the MTW agencies are engaged. Even

when care is taken to adjust for the diversity of agencies and approaches, however, an aggregate

approach sheds little light on which innovative practices might be taken to scale. Additional

rigorous research is needed on individual MTW agency initiatives to tease out promising activities

or uses of MTW flexibilities that hold the most promise to help achieve the housing choice or self-

sufficiency goals of the MTW program. Examples of such research include the study of the Santa

Clara Housing Authority conducted through the retrospective MTW evaluation (Castells, 2020);

the study of work requirements at the Charlotte Housing Authority by Webb, Frescoln, and Rohe

(2015); and the randomized control trial study currently in progress at the neighborhood mobility

program in Seattle and King County, Washington.21 Other future work should highlight promising

Moving to Work agency practices through mixed-methods research, case studies of individual

agency efforts, or analyses of common approaches.

Finally, improved data and reporting requirements and the use of randomized control trials will

greatly improve knowledge about new agencies included in the MTW expansion. Similar rigor

should be applied to current MTW agencies.

Conclusion

This study found some signs that MTW positively affects some housing choice and self-sufficiency

related outcomes and discovered no evidence of negative effects on any of our outcomes of interest.

Specifically, both the MTW-status group and the MTW activity-specific group increased the share

21 For information on the Creating Moves to Opportunity program and research, see: http://creatingmoves.org/research/.

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Treskon, Gerken, and Galvez

of new households served relative to the traditional PHA comparison groups. That finding may

be driven at least in part by the additional funding MTW agencies have received compared with

traditional agencies, but it may also relate to the use of MTW flexibilities for cost-savings efforts.

We also found some evidence indicating modest positive effects from MTW on the share of

MTW-assisted households with income gains after entry into assisted housing, the share of work-

able households reaching minimal housing assistance payments (HAP), and the share of those

households who subsequently leave assistance. We found no evidence, however, of differences

between MTW and traditional PHAs for tenant-based voucher (TBV) neighborhood locations,

public housing quality, or use of Family Self-Sufficiency program escrow accounts.

This study did not examine the specific MTW activities that may be driving those outcomes or the

specific MTW flexibilities that may allow agencies to pursue successful initiatives. Future research

should focus on identifying the MTW flexibilities that are the most useful for pursuing initiatives

that positively affect choice and self-sufficiency outcomes, including the ways in which agencies

use any additional federal funding they receive from HUD. A more robust understanding of the

MTW flexibilities with the most effect would shed light on how best to apply MTW practices to

traditional PHAs.

Acknowledgments

The authors appreciate the extensive feedback and support that Elizabeth Rudd, the government

technical representative for the Moving to Work (MTW) retrospective evaluation, provided

throughout the course of this project. In addition, the authors thank Lynn Rodgers and Lydia

Taghavi for their assistance in the data collection and cleaning process. The authors acknowledge

the support of the MTW Expert Panel in the design and analysis of this work and technical readers,

including Breno Braga, Ingrid Gould Ellen, and Roberta Graham. Finally, the authors thank readers

from MTW agencies who reviewed a draft version of this report.

Authors

Mark Treskon is a senior research associate in the Metropolitan Housing and Communities Policy

Center at Urban Institute. Matt Gerken is a research analyst in the Metropolitan Housing and

Communities Policy Center at Urban Institute. Martha Galvez is a principal research associate in

the Metropolitan Housing and Communities Policy Center at Urban Institute.

References

Bloom, Howard. 2001. Measuring the Impacts of Whole-School Reforms: Methodological Lessons from

an Evaluation of Accelerated Schools. New York: MDRC. http://www.mdrc.org/publication/measuring-

impacts-whole-school-reforms.

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Bloom, Howard, James A. Riccio, Nandita Verma, and Johanna Walter. 2005. Promoting Work in

Public Housing: The Effectiveness of Jobs-Plus. New York: MDRC. https://files.eric.ed.gov/fulltext/

ED484619.pdf.

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2017. Testing Performance Measures for the MTW Program. Cambridge, MA: Abt Associates.

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the_mtw_program.pdf.

Castells, Nina. 2020. Evaluating the Effects of Santa Clara County Housing Authority’s Rent Reform.

Washington, DC: U.S. Department of Housing and Urban Development (HUD), Office of Policy

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Devine, Deborah. J., Robert. W. Gray, Lester Rubin, and Lydia B. Taghavi. 2003. Housing Choice

Voucher Location Patterns: Implications for Participant and Neighborhood Welfare. Washington, DC: HUD PD&R.

Galster, George C. 2012. The Mechanism(s) of Neighbourhood Effects: Theory, Evidence, and

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Springer Netherlands. https://doi.org/10.1007/978-94-007-2309-2.

Galvez, Martha M. 2010. What Do We Know about Housing Choice Voucher Program Location

Outcomes? Washington, DC: Urban Institute.

Galvez, Martha M., Jasmine Simington, and Mark Treskon. 2017. Moving to Work and Neighborhood

Opportunity. Washington, DC: Urban Institute.

Galvez, Martha M., Ruth Gourevitch, and Benny Docter. Forthcoming. A Picture of Moving to Work

Agencies’ Housing Assistance. Washington, DC: U.S. Department of Housing and Urban Development.

Galvez, Martha M., Daniel Teles, Alyse D. Aneto, and Matthew Gerken. Forthcoming. Moving to

Work Agencies’ Use of Project-Based Voucher Assistance. Washington, DC: U.S. Department of Housing and Urban Development.

Government Accountability Office (GAO). 2018. Rental Housing: Improvements Needed to Better

Monitor the Moving to Work Demonstration, Including Effects on Tenants. GAO-18-150. https://www.

gao.gov/assets/690/689583.pdf.

———. 2012. Moving to Work Demonstration: Opportunities Exist to Improve Information and

Monitoring. GAO-12-490. https://www.gao.gov/assets/600/590226.pdf.

Khadduri, Jill, Melissa Vandawalker, Rebecca Cohen, and Jeffrey Lubell. 2014. Innovations in the

Moving to Work Demonstration. Cambridge, MA: Abt Associates. https://www.abtassociates.com/sites/

default/files/migrated_files/b8bd4434-4303-408e-b696-874f821e66ea.pdf.

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Linden, Ariel. 2015. “Conducting Interrupted Time-Series Analysis for Single- and Multiple-

Group Comparisons,” The Stata Journal 15 (2): 480–500. http://lindenconsulting.org/documents/

ITSA_Article.pdf.

McClure, Kirk. 2017. Length of Stay in Assisted Housing. Washington, DC: HUD PD&R.

https://www.huduser.gov/portal/sites/default/files/pdf/LengthofStay.pdf.

McClure, Kirk, Alex F. Schwartz, and Lydia B. Taghavi. 2015. “Housing Choice Voucher Location

Patterns a Decade Later,” Housing Policy Debate 25 (2): 215–233. https://www.tandfonline.com/doi/

abs/10.1080/10511482.2014.921223.

Oppenheimer, Sarah, Megan Haberle, and Philip Tegeler. 2013. Increasing Housing Choices: How Can

the MTW Program Evolve to Achieve its Statutory Mandate? Washington, DC: Poverty & Race Research Action Council. http://www.prrac.org/pdf/MTW-HousingOpportunity.pdf.

Rohe, William M., Michael D. Webb, and Kirstin P. Frescoln. 2015. “Work Requirements in Public

Housing: Impacts on Tenant Employment and Evictions,” Housing Policy Debate 26 (6): 909–927.

Somers, Marie-Andrée, Pei Zhu, Robin Jacob, and Howard Bloom. 2013. The Validity and Precision

of the Comparative Interrupted Time Series Design and the Difference-in-Difference Design in

Educational Evaluation. Working paper. New York: MDRC. http://www.mdrc.org/sites/default/files/

validity_precision_comparative_interrupted_time_series_design.pdf.

Stacy, Christina, Josh Leopold, Daniel Teles, Ruth Gourevitch, Alyse D. Oneto, Yipeng Su, and

Matthew Gerken. 2020. The Impact of the Moving to Work Demonstration on the Per Household Costs of Federal Housing Assistance. Washington, DC: U.S. Department of Housing and Urban Development.

St. Clair, Travis, Thomas Cook, and Kelly Hallberg. 2014. “Examining the Internal Validity and

Statistical Precision of the Comparative Interrupted Time Series Design by Comparison with a

Randomized Experiment,” American Journal of Evaluation 35 (3): 311–327.

Treskon, Mark, Matthew Gerken, and Martha M. Galvez. Forthcoming. Housing Choice and Self-

Sufficiency Outcomes at Moving to Work Agencies. Washington, DC: U.S. Department of Housing and Urban Development.

Webb, Michael D., Kirstin Frescoln, and William M. Rohe. 2015. Innovation in Public Housing: The

Moving to Work Demonstration. Chapel Hill: Center for Urban and Regional Studies, University

of North Carolina at Chapel Hill. https://curs.unc.edu/files/2015/01/The-Moving-to-Work-

Demonstration-Center-for-Urban-and-Regional-Studies-Report.pdf.

192 The Moving to Work Retrospective Evaluation

by Christina Stacy, Josh Leopold, Daniel Teles, Alyse D. Oneto, Yipeng Su,

Matthew Gerken, and Ruth Gourevitch

Fund More, Serve More,

Save More: Moving to Work

and Cost-Effectiveness

Christina Stacy

Josh Leopold

Daniel Teles

Alyse D. Oneto

Yipeng Su

Matthew Gerken

Urban Institute

Ruth Gourevitch

Massachusetts Institute of Technology

Abstract

Moving to Work (MTW) is a U.S. Department of Housing and Urban Development (HUD)

demonstration that gives selected public housing agencies (PHAs) greater flexibility with their spending and the ability to provide innovative housing assistance to low-income households. This article examines the impact of MTW on cost-effectiveness, measured as the total funding PHAs receive from HUD for

public housing and housing choice vouchers divided by the number of households assisted by these

programs. We use 15 years of historical data, from 2003 through 2017, to measure pre- and post-MTW

trends for PHAs that joined or left MTW during the period. We also compare trends for MTW PHAs to traditional PHAs of comparable size during the same period. We find that MTW status has no significant impact on cost per assisted household. Although MTW status is associated with an increase in HUD

funding, the agencies use this funding to assist more households, resulting in no significant change in cost-effectiveness. We find no evidence that MTW agencies maintain their cost-effectiveness by shifting their program mix, reducing housing quality or affordability, or serving different households. We also find that MTW agencies experienced a large increase in dollars per household held in reserves while serving roughly the same number of assisted households per dollar of HUD funding as before joining the demonstration. Our analysis does not explore the mechanisms by which this increase occurred, although it can be inferred that MTW agencies were able to realize some cost efficiencies in areas other than household assistance.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 193

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

Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

Introduction

More than 3 million households receive housing assistance through HUD’s public housing and

Housing Choice Voucher (HCV) programs.1 Current funding, however, is sufficient to serve only

one in five eligible households (Scally et al., 2018). The Moving to Work (MTW) demonstration

aims to make these programs more cost-effective by easing regulations, encouraging innovation,

and providing greater flexibility to the local agencies that administer them.

Cost-effectiveness is not the only goal of MTW, however, and there is a risk that the greater

flexibility MTW provides reduces efficiency by allowing public housing agencies (PHAs) to

use HUD funds for purposes other than rental assistance and capital improvements. In fact,

most empirical studies of the demonstration show that MTW agencies spend more per assisted

household than traditional PHAs and use a lower proportion of their annual budgets on direct

housing assistance (Buron et al., 2017; Fischer, 2011; GAO, 2018). These studies, however, do

not account for spending levels before agencies joined the MTW demonstration, and thus do not

establish whether this difference is caused by the MTW demonstration.

This article provides an analysis of the impact of MTW on cost-effectiveness. We measure cost-

effectiveness within a PHA as the cost, in dollars received from HUD, per assisted household. Our

analysis overcomes the challenges of earlier studies by examining changes in cost-effectiveness

within PHAs that joined or left the MTW demonstration. To this end, we use a fixed-effects

model that estimates the effect of MTW on cost per assisted household relative to their expected

trajectory if they had not joined the demonstration. PHA fixed effects account for differences

between MTW and traditional PHAs in size, location, and other factors not associated with

the MTW demonstration that predate our analysis. We also control for changes in local rental

costs and public-sector wages in the PHAs’ service area that are known to affect the costs of

housing assistance. The analysis relies on HUD administrative data from 2003 to 2017. To better

understand how, if at all, MTW status affects cost-effectiveness, we create separate estimates of the effect of MTW status on PHAs’ annual funding from HUD and on the number of households that

PHAs assist with that funding. These estimates seek to isolate the impact of MTW status on funding

and households served from other factors that may influence these outcomes.

We find that MTW status has no significant impact on cost per assisted household when compared

with traditional PHAs of similar size.2 PHAs do receive significantly more funding after joining the

demonstration (an estimated 11-percent increase in annual HUD funding), but they use this money

to serve significantly more households (an estimated 10-percent increase in assisted households).

We also find that MTW status is associated with statistically significant increases in the amount

of funding PHAs hold in operating reserves of approximately $840 per assisted household. This

finding suggests that MTW agencies are able to serve the same number of households per dollar

1 Data accessed through HUD’s Picture of Subsidized Households, https://www.huduser.gov/portal/datasets/assthsg.html

2 Excluding small PHAs provides a more accurate comparison because there are many traditional PHAs that serve significantly fewer households than the smallest MTW PHA, and these small PHAs have different trends for HUD

funding and assisted households during the analysis period compared with MTW PHAs and larger traditional PHAs.

194 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

of HUD funding while also saving money in reserves for future developments and other uses.3 We

find no evidence that MTW agencies maintain their cost-effectiveness by shifting their program

mix, reducing housing quality or affordability, or serving different households.

This article summarizes a longer report, The Impact of the Moving to Work Demonstration on the Per Household Costs of Federal Housing Assistance (Stacy et al., 2020), published earlier this year, that includes a more comprehensive discussion of the methodology, comparison group selection, and

additional sensitivity analyses.

Background

Enacted by Congress in 1996, the Moving to Work (MTW) demonstration allowed designated

public housing agencies (PHAs) greater regulatory and funding flexibility to test innovations

in housing assistance. MTW agencies can apply for waivers from program regulations to test

innovations that meet one or more of the demonstration’s three statutory objectives: reducing

costs and increasing cost-effectiveness, promoting employment and economic self-sufficiency, and

increasing housing choices for low-income families. MTW agencies are also given greater flexibility

in their use of funds from the two largest housing assistance programs: public housing and housing

choice vouchers (HCVs).

Traditional PHAs are funded for discrete activities through a set of clearly defined formulas,

with strict specifications about how they can use their funding. Each year, they receive separate

funding allocations from HUD for HCV Housing Assistance Payments (HAP) and administrative

fees, public housing operations, and public housing capital improvements. With some exceptions,

funds cannot be used for anything other than the designated purposes, and programs must be

administered according to federal guidelines.

In contrast, each MTW agency is provided with fund flexibility and has its own funding agreement

with HUD that provides a base funding for public housing and HCVs each year, with an inflation

adjustment. As long as agencies remain compliant with the requirement to serve “substantially

the same” number of households as they assisted before joining MTW, their funding does not go

up if they serve more households or go down if they serve fewer households. HUD funding for

programs other than public housing and HCVs are not funded in this way and are not eligible for

fund flexibility.

Fund flexibility allows MTW agencies to apply fungibility to the HCV program, public housing

operations, and public housing capital funding streams. This feature allows MTW agencies to, for

example, use HCV program funds to build or preserve affordable housing or use their public housing

and voucher funds to leverage other funding sources for housing development or preservation (Levy,

Edmonds, and Long, this volume). MTW agencies report their spending on public housing, HCVs,

and local, non-traditional (LNT–described later) assistance collectively as MTW program spending,

and the fungible funding used for these activities is often referred to as the MTW fund. It is important to note, however, that not every MTW agency administers public housing.

3 There appears to be no impact of MTW on spending on administrative costs per household and tenant services per household, but there is too much variation within and between PHAs to measure these impacts with precision.

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The other mechanism not available to traditional PHAs is waiver authority. Waiver authority means

that MTW agencies may be allowed by HUD to waive parts of the U.S. Housing Act of 1937 (as

amended) to implement innovations intended to achieve one of the three statutory objectives.

Through waiver authority, MTW agencies can also offer LNT assistance. LNT programs may

provide a rental subsidy through a third-party, homeownership subsidies, affordable housing

development, or service provision. MTW agencies still must abide by statutory requirements and

regulatory requirements for which they do not have a waiver.

Findings from Prior Research

Previous studies have found that MTW agencies spend more per assisted household than

traditional PHAs, but these studies do not account for other factors that may drive spending

differences. The most rigorous prior studies have compared MTW agencies’ and traditional PHAs’

per household spending on specific aspects of providing housing assistance. These comparisons

included program administration, housing assistance payments, public housing operations, and

operating reserves, rather than looking at either total PHA expenditures or total HUD spending

per assisted household (Buron et al., 2017; GAO, 2018). These studies show that MTW agencies

spend more per assisted household on specific activities than traditional PHAs, but that these

differences diminish after accounting for differing housing and labor costs in the markets where

MTW agencies and traditional PHAs operate. This finding suggests that observable differences in

spending may be caused by differences between MTW and traditional PHAs that are unrelated

to the MTW demonstration itself. For example, MTW agencies tend to be larger than traditional

PHAs and are more likely to be in areas with high housing and labor costs (Galvez, Gourevitch,

and Docter, forthcoming). Tighter housing markets and higher local wages have been shown to

increase the costs per household in the HCV program (Finkel and Buron, 2001; Turnham et al.,

2015). Additionally, many MTW agencies, such as the Housing Authority of the City of Pittsburgh

and the Chicago Housing Authority, entered the demonstration with a large stock of aging public

housing developments, which studies have shown are more expensive to maintain or repair

(Stockard et al., 2003).

Administrative Costs and Reserves

The MTW demonstration was expected to create efficiencies in program administration costs

because MTW agencies have fewer administrative requirements. Recent studies by Abt Associates

and the U.S. Government Accountability Office (GAO), however, have shown that MTW agencies

spend more per household on administrative costs than similar traditional PHAs. Abt Associates

compared per-household costs between MTW and traditional PHAs by matching each MTW PHA

to three to five traditional PHAs that were most similar based on the number of HCV and public

housing units, fair market rents, poverty rates, area income, and unemployment. The results of

the comparison showed that, in 2014, the average MTW agency spent $163 more per assisted

household on administrative costs in the HCV program than the average comparable traditional

PHA (Buron et al., 2017). The report notes, however, that the difference in costs was driven

primarily by a few MTW agencies that used their funding flexibility to spend administrative funds

on resident services. Nearly one-half (15 of 35) of MTW agencies had lower administrative costs

per assisted household than their comparison traditional PHAs (Buron et al., 2017).

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The GAO (2018) constructed a comparison group of traditional PHAs that were as similar as

possible to MTW agencies based on several household, financial, and geographic characteristics.4

Rather than looking at a single year, the GAO report compared median costs per household

between MTW agencies and the matched comparison group between 2009 and 2015.5 GAO

estimated that the median per household administrative expenditure was $922 for MTW agencies

and $642 for traditional PHAs. GAO’s estimate of administrative costs includes median spending of

$37 per HCV household on resident services for MTW agencies, compared with a median of $0 for

traditional PHAs (GAO, 2018).

There is evidence, however, of MTW agencies using their flexibility to increase cost-effectiveness

in some areas and improve processes or services in others. An analysis of MTW agencies’ annual

administrative plans found that most agencies use their flexibility to scale back the frequency of

annual housing quality inspections or income recertifications (Galvez, Simington, and Treskon,

2017).6 Likewise, although implementing policy changes does incur short-term costs such as those

associated with updating forms and software, training staff, and educating residents, it may result

in long-term efficiencies (Khadduri et al., 2014). Moreover, MTW PHAs may use savings generated

from reduced regulations to shift how administrative staff spend their time rather than reduce

overall staffing. Officials from several MTW agencies reported that staff were still spending as much time with residents as before their agency joined the demonstration, but the relationship had

shifted from one of “auditor or investigator to one of mentor or advocate” (Abravanel et al., 2004).

MTW agencies can also use their flexibility to place more money into reserves, which they can

use to invest in affordable housing development or preservation, or as a “rainy-day fund” in case

of future funding shortfalls. The GAO’s 2018 report estimated that, as of June 2017, the 39 MTW

agencies had a total of $808 million in HCV reserves—more than all the 2,116 traditional PHAs

that administer the HCV program combined ($737 million) (GAO, 2018). This finding may

indicate MTW agencies are not using as much of their annual funding for direct housing assistance,

but it can give them advantages over traditional PHAs in completing deals to build or preserve

affordable housing (Abravanel et al., 2004; Levy, Edmonds, and Long, this volume). Abt Associates

conducted a survey of PHAs as part of its MTW evaluation and found that MTW agencies

preserved significantly more affordable housing units than traditional PHAs (Buron et al., 2017).

HAP and Public Housing Operations

The largest budget item for most agencies is HAP—the money paid from the PHA to the landlords.

For traditional PHAs, this funding must be used to provide rental subsidies to landlords in the

HCV program. MTW agencies have the flexibility to shift HAP funds into the public housing

operating or capital funds programs, build up reserves, or develop new types of assistance. They

4 GAO used a sophisticated statistical method called genetic matching to identify one traditional PHA most similar to each MTW agency on 12 variables, including 4 household characteristics, 4 financial characteristics, and 4 location measures (GAO, 2018).

5 Although GAO used multiple years of data, they pooled the years together and did not undertake an analysis of changes pre- and post-entry into the MTW demonstration.

6 Income recertification is the process through which a PHA determines a family’s income for purposes of setting the total tenant payment toward rent. Housing quality inspections are required to ensure that tenant- and project-based housing vouchers are used to house families in units that meet HUD housing quality standards.

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can also adjust the amount of subsidy that households in the HCV program receive or provide

financial incentives to landlords to participate in the program. The Center on Budget Policies

and Priorities (CBPP) estimated that, in 2010, MTW agencies left 16 percent of their HAP funds

unused compared with 4 percent for traditional PHAs (Fischer, 2015). This analysis was unable to

account for HCV funds used to assist households in local, non-traditional (LNT) or public housing

programs, however, nor did it control for other differences that could have contributed to lower

usage rates at MTW agencies. The GAO report found that the median rental payment subsidy was

about 25 percent higher at MTW agencies than for comparable agencies—$8,295 per household

for MTW agencies and $6,629 per household for the comparison group (GAO, 2018). The 2017

Abt Associates report, however, found no statistically significant differences in HAP costs between

MTW and traditional PHAs after adjusting for the fair market rent in the service area of each PHA

(Buron et al., 2017).

Program administration and the provision of public housing are funded and frequently examined

collectively as public housing operations. Capital improvements are funded separately, however,

and have received less research attention. In part because of differences in funding formulas,

CBPP estimated that, in 2010, MTW agencies received almost $3,000 more per unit for public

housing operations than traditional agencies (Fischer, 2015). This estimate, however, did not

account for variations between PHAs in local housing or labor markets. The GAO report, which

did attempt to control for some of these differences, found that MTW agencies spent about

$1,600 more per unit per year on public housing operations than traditional agencies (GAO,

2018). The Abt Associates report found no statistically significant differences in per unit public

housing operations costs between MTW and traditional PHAs after accounting for differences in

local wages (Buron et al., 2017).

Some research suggests that MTW agencies’ funding for public housing tends to be more

predictable, and potentially more generous, than it is for traditional PHAs. For example, CBPP

found that the funding formula for public housing operating costs used for 11 MTW agencies

accounts for $260 million in additional funding compared with the formula used to fund

traditional PHAs (Fischer, 2015). In years of reduced appropriations, funding for traditional

PHAs were reduced, but MTW agencies were still funded based on their MTW funding agreement

for both their HCV and public housing programs.7 By contrast, traditional PHAs are funded

according to a formula that accounts for local housing costs, past usage of HUD funds, and current

obligations. HUD then prorates each traditional PHA’s funding levels to adjust for changes in total

funding levels from Congress.

Limitations of Prior Research

Prior studies that examined the relationship between MTW status and cost-effectiveness show that

MTW agencies spend more per household than traditional PHAs. This study accepts these findings

and poses the question: Is participation in the MTW demonstration the cause of these higher costs?

7 For more information, see Title 24—Housing and Urban Development (2017) §982.503, Payment Standard Amount and Schedule, PIH Notice 2008-15(HA) “Implementation of the Federal Fiscal Year 2008 Funding Provisions for the Housing Choice HCV Program,” Notice PIH 2009-13(HA) “Implementation of the Federal Fiscal Year 2009

Funding Provisions for the Housing Choice HCV Program,” and PIH Notice 2012-9 “Implementation of the Federal Fiscal Year 2012 Funding Provisions for the Housing Choice HCV Program.”

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Earlier studies could not answer this question because they could not effectively identify the effect of MTW on agencies. The studies conducted by Abt Associates (Buron et al., 2017) and the GAO

(2018) did not control for unobserved differences that existed before MTW agencies joined the

demonstration. They also did not examine how cost-effectiveness or spending at agencies changed

after they joined the demonstration. Additionally, these studies were unable to account for shifts in spending between public housing and HCVs or to LNT housing programs. Our research overcomes

these limitations.

Research Approach

This analysis uses 15 years of administrative data to estimate the impact of the MTW demonstration

on cost-effectiveness at PHAs. We measure cost-effectiveness within a PHA as the cost, in dollars

received from HUD, per assisted household. This measure is defined to cover all households assisted

with MTW funds, including through local, non-traditional (LNT) programs. It is therefore not

affected if an MTW agency shifts funds from one program to another. The analysis uses many more

years of data than previous studies, tracking changes in MTW agencies and traditional PHAs from

2003 to 2017. With this additional data, the analysis is also able to examine costs per household

at MTW agencies both before and after joining MTW. The analysis compares changes in costs that

occur when agencies join MTW with changes at traditional PHAs over the same years. It separately

examines trends in funding and in the number of assisted households at PHAs before and after

they join MTW. The study also investigates whether changes in the mix of program types, housing

quality, or affordability explain changes in the average cost per assisted household.

Research Questions

What is the effect of MTW status on HUD cost per assisted household?

The first research question asks what effect participating in the MTW demonstration has on the

per household cost of housing assistance. The answer quantifies the impact of MTW status on the

number of dollars spent by HUD per household assisted by a PHA. We then separately determine the

impact of MTW status on HUD funding levels, and the number of assisted households PHAs serve, to

understand why MTW status is having its observed effect on the cost per assisted household.

Do changes in program mix, housing quality and affordability, or the

characteristics of assisted households explain the effect of MTW status on HUD

costs per assisted household?

Our second research question tests whether the estimated impact of MTW status on HUD funding,

households served, and cost per household changes after controlling for differences between

MTW and traditional PHAs in three areas: the mix of public housing, tenant-based, and project-

based vouchers in their portfolio; housing affordability and quality; and targeting of assistance to

households that may be costlier to serve. Each of these factors is accounted for in a separate model.

Each model tests a different mechanism through which cost per household could change.

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Does MTW status affect agencies’ total per-household operating and housing

assistance spending or per-household spending on program administration, tenant

services, or operating reserves?

Our third research question examines how MTW status affects spending by PHAs. We examine

both total per-household spending and spending on specific components of housing assistance.

This contrasts with the first two research questions, which focus on HUD funding per assisted

household. Differences in expenditure levels between MTW and traditional PHAs have been well

documented. Because this is the first study to examine how cost-effectiveness changes when an

agency joins the MTW demonstration, this research question offers the opportunity to provide

context to the spending differences described in prior research.

Sample

Ideally, we would examine the full history of the MTW demonstration, but data limitations

precluded this possibility. After extensive research and collaboration with HUD, we set 2003 as the

initial year for analyzing administrative data. This was the first full calendar year for which HUD

could provide voucher management data. This voucher data was needed to differentiate between

HCV households and households assisted by special purpose vouchers.

Our statistical models, therefore, include the 15-year period from 2003 through 2017, during

which 17 new PHAs entered the demonstration, two PHAs left, and one agency (San Diego

Housing Commission) exited and re-entered (exhibit 1).8 Each agency’s date of entry into MTW

is defined as the date that their first MTW agreement was executed.9 Exits are defined based on

the date on which the MTW agreement was terminated. The analysis period includes both the

increased investments from the 2009 stimulus package and the deep cuts created by sequestration.

Because our analysis begins with 2003, we do not estimate how MTW status affects the cost per

household for PHAs that joined prior to 2003. This analysis excludes some of the largest MTW

agencies such as the Chicago Housing Authority, the Cambridge Housing Authority, Home

Forward (Portland, OR), the Housing Authority of the City of Pittsburgh, the Minneapolis Public

Housing Authority, and the Seattle Housing Authority.

8 Descriptive statistics for these PHAs appear in appendix exhibit C of Stacy et al. (2020). The Housing Authority of the County of Santa Clara manages all housing programs for the Housing Authority of the City of San Jose, therefore, we treat them as a single agency in our analysis.

9 In the case of the San Diego Housing Commission’s reentry into MTW, we use the date that their 2008 MTW

agreement was executed for reentry.

200 The Moving to Work Retrospective Evaluation

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Exhibit 1

Timeline of Moving to Work Agreements And Evaluation Period (Evaluation Sample In Bold)

1999

Cambridge

2004

Delaware

2008

Oakland‡

Greene

Alaska*

Greene and

High Point

Baltimore

High Point

Keene

San Bernadino*

left

Lawrence-Douglas County

San Jose*‡

demonstration

Lincoln

Santa Clara County*‡

Louisville

San Diego (reentered)

Massachusetts

2001

Portage

New Haven

Philadelphia

2010

Portland

Champaign County

San Antonio

Tacoma‡

2013

Tulare County

2003

Columbus

Vancouver

Atlanta

Fairfax

Washington, DC

2011

Holyoke

1998

2000

King County

Boulder

Reno

Minneapolis

Chicago*

Lexington-

San Diego Pittsburgh*

San Diego left

2007

Fayette

Seattle

San Mateo

demonstration

Charlotte*

Orlando

Evaluation Period

* PHAs selected by Congress, not through a competitive process

‡Impact analyses do not include Oakland Housing Authority, Tacoma Housing Authority, Housing Authority of the County of Santa Clara, or Housing Authority of the City of San Jose because of incomplete data on households in public housing. The 18 public housing agencies included in the impact analyses are Alaska, Atlanta, Baltimore City, Boulder, Champaign, Charlotte, District of Columbia, Columbus (GA), Fairfax County, Greene, High Point (NC), Holyoke, King County, Lexington-Fayette Urban County, Orlando, Reno, San Bernardino, and San Diego. The event study regression includes only 12

public housing agencies due to data limitations (see Exhibit B5 in Stacy et al., 2020).

Notes: The graphic shows the evaluation period and the year that agencies signed their MTW agreement. Agencies included in our analysis are in bold.

Source: Documents retrieved from HUD’s Moving to Work (MTW) portal (https://www.hud.gov/mtw ) The comparison group is comprised of large traditional PHAs—those with more than 750

assisted households. Our analysis assumes that MTW agencies were similar to the comparison

PHAs, or at least followed similar trends, prior to joining the demonstration. Even before joining,

however, future MTW agencies tended to be larger and have higher costs per household than other

traditional PHAs. The smallest agency that joined MTW during the study period (after 2003)

assisted an average of 938 households in 2003. The average cost per household among these pre-

MTW agencies was $8,500 in 2003. Although the average cost per household among agencies

that did not join MTW was $5,925, the average cost per household within the comparison group

of large agencies was $7,148. Additional analysis in Stacy et al. (2020) also shows that cost per

assisted household fell among traditional PHAs between 2003 and 2017, with larger declines

among PHAs with 750 or fewer assisted households.

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Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

Data Collection and Assembly

This study relies on three HUD administrative datasets: (1) the Financial Data Schedule (FDS) to

track HUD funding and public housing agency (PHA) costs, (2) the Office of Public and Indian

Housing (PIH) Information Center (PIC) to track the number of households in public housing and

the characteristics of households in public housing and the voucher program, and (3) the Voucher

Management System (VMS) data to track the number of households with vouchers. Data from

the Decennial Census (Census), American Community Surveys (ACS), and the Bureau of Labor

Statistics (BLS) provide information such as local wages and housing and utility costs. The Public

Housing Assessment System (PHAS) provides data on public housing quality. We also include HUD

data on the number of households assisted by MTW agencies through LNT programs that are not

captured in PIC data. Additional information on the data used in this study can be found in Stacy

et al. (2020).

We merged data from PIC, VMS, LNT, and FDS with regional wage data from BLS and regional

demographic data from the census and ACS to construct a balanced panel dataset—a dataset with

every PHA in every year—of 3,726 PHAs and 55,890 observations. PHAs that do not appear at

least once in PIC and at least once in FDS are excluded, as are PHAs for which either county wage

or local rent data were unavailable.10

After constructing this initial dataset, we made several adjustments to account for missing or

incomplete data. These processes are described in detail in the “sample construction” section of

Stacy et al. (2020). Data issues were present in both MTW and traditional PHAs, particularly in the

early years of our analysis, and may represent early challenges PHAs faced in reporting to HUD. We

filled in for missing, zero, and “bad” data points from PIC, VMS, and FDS using nearest-neighbor

interpolation and extrapolation.11

After adjusting for missing and incomplete data, we constructed measures of the number of

assisted households, total HUD funding, and cost per assisted household. For consistency, we

converted the FDS data from fiscal to calendar year by using a weighted average of the two fiscal

years that overlap each calendar year. Because data from the 2018 fiscal year were not available for

all agencies, we used 2017 fiscal year data for calendar year 2017.

Finally, we excluded the 19 PHAs that entered the MTW demonstration before 2003 and remained

in the program through the observation period. For all our analyses, we compare MTW agencies

only with traditional PHAs that had at least 750 assisted households in 2003. Excluding smaller

agencies, and those with only 1 year of reliable public housing, voucher, or financial data, reduces

the sample to 727 PHAs—18 MTW agencies and 709 traditional PHAs—and 10,905 observations.

10 PHAs only appear in PIC or VMS when public housing or HCV households are reported. PHAs may appear in FDS to report funding streams other than those for public housing operations, public housing capital improvements, and HCVs.

11 Linear interpolation produced less plausible values, including some negative values in the earlier part of the sample period.

202 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

Defining Outcome Measures and Controls

This study defines cost-effectiveness as “cost per household,” or the total funding PHAs receive

from HUD each year for public housing and HCV programs divided by the number of households

assisted by these programs. This measure was selected as the most comprehensive measure of cost

per household because it includes all funding sources that are eligible for MTW fund flexibility,

and all households assisted through this funding, including households assisted through local,

non-traditional MTW programs. It also allows for a more direct comparison of cost-effectiveness

between MTW and traditional agencies than looking at expenditures because MTW agencies report

expenditures differently than traditional PHAs.

Our measure of HUD funding includes HUD PHA operating grants and capital grants for public

capital funds, HUD PHA operating grants for public housing operating funds, and HUD PHA

operating grants for the Housing Choice Voucher (HCV) program (exhibit 2). All funding data is

collected from the FDS, adjusted from fiscal year to calendar year, and converted from nominal

dollars to 2015 dollars using the Consumer Price Index for all Urban Consumers (CPI-U). (See

exhibit C2 in Stacy et al., 2020, for details on calculations using FDS data.)

Exhibit 2

Variables For Analysis And Data Sources (1 of 2)

Variable Description

Data Source

Definition / Notes

Outcome Measures

HUD Funding

FDS

HUD PHA operating grants and capital grants for public capital

funds, plus HUD PHA operating grants for public housing

operating funds, plus HUD PHA operating grants for the HCV fund

Assisted Households

PIC, VMS,

Total households assisted through public housing, the HCV program

LNT data

(excluding special purpose vouchers), and LNT programs

Cost per Assisted

FDS, PIC,

HUD funding/Assisted households

Household

VMS, LNT

data

Treatment Variable

MTW Status

Annual

The treatment variable is equal to one for agencies for years in which

Reports,

they are a part of the MTW demonstration and zero for years in

MTW

which they are not. If an agency has MTW status for part of a year,

agreements

the value is a fraction based on the number of months remaining in

the calendar year when the agency’s MTW agreement is executed.

For example, if an agency signs the MTW agreement in September,

then MTW status = .25 in the year the agreement was signed

because three months, or .25 of a year, remain in the calendar year.

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Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

Exhibit 2

Variables For Analysis And Data Sources (2 of 2)

Variable Description

Data Source

Definition / Notes

Internal (Endogenous) Cost Drivers

Percent of Households

VMS

Number of assisted households with TBVs/total assisted households

Using Tenant-Based

Vouchers (TBVs)

Percent of Households

VMS, PIC

Number of assisted households with PBVs/total assisted households

Using Project-Based

Vouchers (PBV)

Percent of Households

VMS, PIC

Number of assisted households in PH/total assisted households

in Public Housing (PH)

Quality of

PHAS

Physical Assessment Subsystem (PASS) score

Public Housing

Affordability (Median

PIC

Median of (total family contributionb x 12 / total annual income) for

Rent Burden)

new households

Household Size

PIC

Average number of individuals in new households

Median Income

PIC

Median annual total income of new households

High Need Households

PIC

Percent of new, assisted households in which the household head is

62 or older, the household head is disabled, or any other member of

the household is disabled.

Cost Components

Administrative Costs

FDS

Total operating administrative expenses

Tenant Services

FDS

Total tenant services expenses

Spending

Operating Reserves

FDS

Following the formulaa outlined in PIH notice 2011-055

Total Operating and

FDS

Total operating expenditures from the public housing, HCV, or the

Housing Assistance

MTW funds plus total housing assistance payments from the HCV

Spending

and MTW funds.

External Cost Drivers

Average Wage of Local

BLS

Average wage of local government employees in the county with

Government Employees

the most households assisted by a given MTW agency or traditional

PHA reported in PIC in 2003.

Median Rent in

ACS

Population weighted median rent in each year based on the census

Service Area

tracts of residents reported in PIC

a The sum of FDS line items 111 Cash Unrestricted, 114 Cash Tenant Security Deposits, 120 Total Receivables, 131 Investments Unrestricted, 142

Prepaid Expenses and Other Assets, 144 Inter-program – due from, and 145 Assets Held for Sale, minus the difference between line 310 Total Current Liabilities and line 343 Current Portion of Long-term Debt-capital Projects.

b For MTW agencies, the family contribution toward rent variable is constructed by HUD and includes the family’s contribution toward utilities when applicable. This variable is not included in the standard PIC data and was provided by HUD for the purposes of this study. To calculate the annual family contribution toward rent for non-MTW agencies, we multiply the monthly contribution toward rent as reported in PIC by 12 and divide this by the total household adjusted income in PIC.

Sources: Administrative data from the U.S. Department of Housing and Urban Development (HUD) include the Financial Data Schedule (FDS), the Office of Public and Indian Housing Information Center (PIC), the Voucher Management System (VMS), and the Public Housing Assessment System (PHAS); Public use data include the Decennial Census (Census), American Community Surveys (ACS), and the Bureau of Labor Statistics (BLS) Quarterly Census of Employment and Wages; Local, non-traditional (LNT) data was provided by the HUD Moving to Work (MTW) office and calculated based on data reported by agencies on form 50900; New households are identified using action code flags in PIC.

204 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

We count the number of assisted households as the sum of those served through public housing,

HCVs, and, for MTW agencies, through LNT programs. To do so, we combine public housing

data from PIC, voucher data from VMS, and LNT data calculated by HUD from agency reporting.

This calculation includes households assisted through LNT programs that are funded using MTW

fund flexibility; it excludes households assisted through HUD special purpose voucher programs

(such as the Family Unification Program or HUD-VASH) that are not covered by MTW agreements

and not funded through public housing operating, public housing capital, or the traditional HCV

funding streams.

To answer the second research question, we use data from PIC, VMS, and PHAS to examine

internal cost drivers (exhibit 2). Type of housing assistance is measured by calculating the percent

of assisted households in (1) public housing, (2) tenant-based vouchers, and (3) project-based

vouchers. Housing quality is measured by the most recent physical assessment subsystem (PASS)

score from PHAS.12 Housing affordability is measured as the percent of household income that

the median assisted household spends on housing. To assess whether MTW agencies are assisting

households that are costlier to serve, we use three metrics: (1) the median income, as a percent of

the area median income (AMI), of newly admitted households, (2) the percent of newly admitted

households with an elderly head of household, disabled head of household, or disabled family

member, and (3) average household size. We calculate these variables directly from PIC data.

To estimate the effect of MTW on spending, we examine total expenditure per household and three

additional cost components (exhibit 2). Total per household operating and housing assistance

spending is calculated by adding total operating expenditures associated with public housing, the

HCV program, or the MTW fund to total housing assistance payments associated with the HCV

program or the MTW fund, then dividing by the number of assisted households. It includes all

reported public housing operations, maintenance and administration spending, and all reported

spending on the administration of HCVs and LNT vouchers and units. It does not include

capital expenditures, transfers, depreciation, or accounting costs such as bad debts. We calculate

administrative costs and tenant services by totaling these spending categories from the funds

associated with HCV and public housing operations for traditional agencies, and for MTW agencies

with the MTW funds. Additionally, we calculate operating reserves based upon the guidance

provided in PIH notice 2011-055. The specific FDS line items we use appear in Stacy et al. (2020;

appendix C, exhibit C2).

Our estimation methods account for pre-existing differences between agencies, but we include

two cost drivers that change over time: median rent and local public sector wages. Prior research

has shown that local housing costs and wages impact per household costs of providing housing

assistance (Finkel and Buron, 2001; Turnham et al., 2015). We measure housing costs with median

rent, as reported in the Census/ACS. We measure wages using the Bureau of Labor Statistics

(BLS)-reported, county-level annual average pay in local government (all industries). Counties are

mapped to PHAs based on household-level county variables in the PIC. Each county in the United

States is assigned to the PHA that serves the most households in the area. State-level data are used

where county-level data are incomplete or missing. The service area of each PHA is defined here

12 This score measures the housing quality of all the public housing units that the PHA manages. We do not have a comparable measure of housing quality in the HCV program.

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Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

based on where residents live, as reported in PIC. We retrieved local rental costs at the tract level from the National Historical Geographic Information System (NHGIS) (Manson et al., 2018); for

each PHA, we take a weighted average based on the number of assisted households in each tract in

2003. We retrieved local wages by county; we assign each PHA the wage rate corresponding to the

county in which it assisted the most households in 2003.

Estimation Methods

We use fixed-effects models to estimate the impact of participating in the MTW demonstration on

cost per household and other outcomes. Fixed effects for each PHA control for urbanity, rurality,

and other time-invariant characteristics, including factors that may make a PHA more or less likely

to join the MTW demonstration. These fixed effects also control for the average mix of tenant-based

vouchers, project-based vouchers, and public housing administered by the PHA. The models also

include two control variables to capture factors that change over time that may affect the costs of

providing housing assistance: (1) median rent, and (2) the local public sector wages in the service

area of each PHA.

Estimation Method for Research Question 1: What is the effect of MTW status on

cost per assisted household?

To determine the impact of the MTW demonstration on cost per household, we estimate the

following fixed effects panel regression model:

Outcome = β

+ γ

+ λ + α + ϵ (1)

it

* MTWit

* ExternalCostDriversit

t

i

it

That is, each of our outcome variables for PHA i in year t (the natural log of HUD funding, the natural log of assisted households, the natural log of cost per assisted household) is a

function of MTW status, external drivers of cost—median rent and local public sector wages

( ExternalCostDrivers ), year fixed effects (λ ), PHA fixed effects (α ), and an idiosyncratic residual it

t

i

(ϵ ), clustered at the PHA level and robust to arbitrary forms of misspecification. MTW equals it

it

1 for PHA i in year t if the agency is an MTW agency in that year as defined by having a signed agreement; in the year that the agreement is signed, we set MTW equal to the fraction of the year it

remaining at the date of the second (HUD or PHA) signature. Variables measured in dollars and

households are log-transformed before they enter the equation; this step accounts for the skewness

of their distributions and produces estimates of the MTW effect in percentage terms. The measure

of cost per household is also log-transformed before entering the equation.

The coefficient β approximates the percentage change in average cost per household associated

with entering the MTW demonstration (a coefficient of 0.1 indicates a 10-percent change). A

positive and significant estimated value for β implies a higher cost per household. We also use

equation 1 to estimate the impact of MTW status on the amount of funding a PHA gets from HUD

and on the number of assisted households it serves.

206 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

Understanding the Timing of Changes in Cost per Household

To better understand the timing of MTW’s impact on cost per assisted household, we use an event-

study regression to isolate the impact of the MTW demonstration in the year in which the MTW

agreement was executed, 1 year after joining the demonstration, 2 years after, and then all other

years after joining the demonstration. In this model, we also estimate whether the trends at MTW

agencies were diverging from trends in traditional PHAs in the years before they signed the MTW

agreement. The event study model takes the following form:

Outcome = δ D

+ δ D

+ δ D + δ D

+ δ D

+ δ MTW

+ γ

+ λ + α

it

1

i( t+2)

2

i( t+1)

3

it

4

i( t–1)

5

i( t–2)

6

i( t+3)

* Controlsit

t

i

+ ϵ (2)

it

Here, we replace the indicator for MTW status with a series of dummy variables D

to D

i( t+2)

i( t-2)

indicating 2 years before, 1 year before, the year of, the year after, and 2 years or more after a

PHA’s first MTW agreement is executed. The variable MTW

is equal to 1 for MTW agencies

i( t+3)

beginning in the third year after entry into the demonstration. That is, it estimates long term

effects. Again, the model includes external drivers of cost ( Controls ), year fixed effects (λ ), it

t

PHA fixed effects (α ), and an idiosyncratic residual (ϵ ) clustered at the PHA level and robust to

i

it

arbitrary forms of misspecification.

Estimation Method for Research Question 2: Do changes in program mix, housing

quality and affordability, or the characteristics of assisted households explain the

effect of MTW status on HUD costs per assisted household?

To explore whether internal cost drivers, including program mix, housing quality and

affordability, and the characteristics of assisted households, explain the relationship between

MTW status and per household cost, we separately add each set of internal cost drivers to the

main model shown previously:

Outcome = β ’

+ δ

+ γ

+ λ + α + ϵ (3)

it

* MTWit

1 * InternalCostDriversit

* ExternalCostDriversit

t

i

it

where the outcome measure is the natural log of cost per assisted household. Here, the primary

outcome measure is a function of a set of PHA factors, external cost drivers, year fixed effects (λ ), t

PHA fixed effects (α ), and an idiosyncratic residual (ϵ ) clustered at the PHA level and robust to

i

it

arbitrary forms of misspecification. Of interest here is whether and how the coefficient on MTW

status changes once these endogenous characteristics are included. If the effect disappears, this

finding suggests that changes in cost per household related to MTW status may be due to changes

in program mix, housing quality and affordability, and household characteristics, rather than

simply due to serving fewer or more of the same households at the same quality level.

To determine how changes within the PHA affect costs, we include three sets of internal cost

drivers. The first set of cost drivers—the percent of total households funded with tenant-based

vouchers and the percent of total households funded with project-based vouchers (omitting

the percent of households in public housing as the reference group)—explores program mix.

The second set—median rent burden and public housing physical inspection scores—explore

affordability and quality. The third set of cost drivers—household income, household size, and

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Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

the share of households with an elderly household head or disabled household head or family

member—relate to the ability of PHAs to reach households that may require more resources to

serve. To explore each mechanism separately, we isolate each set of cost drivers in a distinct model.

The MTW effect (β’) estimated in this way can be interpreted as the change in cost per household

beyond, or that is not driven by, changes in the internal cost drivers included in the regression. We then test whether the estimated impact of MTW has changed when these factors are added to the model

(whether β’ = β). If the value of coefficient β is not the same with the internal cost drivers added to the model, we can conclude that changes in program mix explain at least some of the differences,

or the lack of a difference, between MTW agencies and traditional PHAs, in cost per household.

Estimation Method for Research Question 3: Does MTW status affect agencies’

total per household operating and housing assistance spending, or per household

spending on program administration, tenant services, or operating reserves?

To investigate how MTW status affects per household spending on specific spending categories,

we estimate equation 1 with four left-hand-side measures: (1) total expenditures per assisted

household, (2) administrative costs per assisted household, (3) spending on tenant services per

assisted household, and (4) changes in operating reserves per assisted household. We take the

natural log of total per household operating and housing assistance and per household spending

on program administration before they enter the model. Because tenant services are frequently zero,

and reserve balances can be negative, we do not take the natural log of these measures; instead, we

estimate a linear relationship on dollars per household.

Here, the estimated coefficients represent (1) the approximate percentage change in total per

household spending, (2) the approximate percentage change in per household administrative costs,

(3) the dollar value of increased (or decreased) per household spending on tenant services, and (4)

the dollar value of increased (or decreased) reserve balances per assisted household.

Findings

Estimates of the effect of MTW on cost per household, HUD funding, and the number of assisted

households appear in exhibit 3. These estimates approximate the percentage change attributable to

MTW.13 We find no statistically significant relationship between MTW status and cost per assisted

household. More specifically, controlling for baseline characteristics, national trends, and exogenous cost drivers, our statistical analysis shows that MTW status is associated only with a small and

statistically insignificant increase in cost per assisted household of 1.3 percent (exhibit 3).

This lack of impact on cost per household results from increases in both the level of HUD funding

and the number of assisted households for agencies after they join MTW. PHAs receive, on average,

11 percent more funding from HUD after joining the MTW demonstration and assist 10 percent

more households (exhibit 3). Because these effects are of a similar size, they have offsetting impacts on our primary outcome variable of cost per assisted household.

13 Because outcome measures entered the regression in log form, percentage change is calculated by exponentiating the coefficient and subtracting 1. For example, the coefficient for HUD funding is 0.106, and the estimated percentage change is (e^0.106)-1=0.112 or 11 percent.

208 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

Exhibit 3

The Effect of Moving to Work on HUD Cost per Assisted Household, HUD Funding, and Number

of Assisted Households

HUD Cost per

Assisted

HUD Funding

Assisted Household

Households

Impact of MTW

0.013

0.106***

0.092***

(0.030)

(0.024)

(0.028)

Control Variables

Area Median Rent

0.298***

0.144**

-0.154***

(0.071)

(0.060)

(0.047)

Government Wage

0.048*

0.023

-0.025

(0.026)

(0.026)

(0.020)

Number of PHAs

727

727

727

Adjusted Within R-Squared

0.096

0.083

0.039

Observations

10,905

10,905

10,905

* p<0.10, ** p<0.05, *** p<0.01.

MTW = Moving to Work. PHA = public housing agencies.

Notes: Standard errors (listed in parentheses) are heteroskedastic robust and clustered at the PHA level. Data cover 2003-2017. Regressions include only agencies with at least 750 households and exclude agencies that joined MTW before 2003. Impact analyses do not include Oakland Housing Authority, Tacoma Housing Authority, Housing Authority of the County of Santa Clara, and Housing Authority of the City of San Jose because of incomplete data on households in public housing. Regression includes year and PHA fixed effects. Cost per assisted household, HUD funding, assisted households, area median rent, and government wages enter the regression in logged form.

Sources: Urban Institute Analysis of HUD Office of Public and Indian Housing Information Center (PIC), Financial Data Schedule (FDS), and Voucher Management System (VMS) data

To better understand the timing of MTW’s impact on cost per assisted household, we used an

event-study regression to estimate the impact of the MTW demonstration by year. Using this

framework, we find no significant differences in cost-effectiveness in any year before or after

joining the MTW demonstration. We also estimated the effect of MTW by year for HUD funding

and households assisted (exhibit 4). HUD funding begins to rise the year before PHAs officially

enter the MTW demonstration (although this effect is not statistically significant at α=0.1), and

it continues to rise after the agency joins MTW, with the largest increase 1 year after joining.

The number of assisted households served by the PHA also increases 1 year before joining the

demonstration (statistically significant at α=0.1) and continues to rise after joining but more

smoothly than funding, which peaks the year after joining. Full results from the event-study

models appear in Stacy et al. (2020).

Cityscape 209

Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

Exhibit 4

The Effect of the Moving to Work Demonstration on HUD Funding and Number of Assisted

Households (Percent Change)

MTW = Moving to Work.

Notes: Vertical dotted line and “Year joined MTW” represent the year in which PHAs execute their first MTW contract. The solid line represents the point estimate, and the dashed lines on either side of this estimate represent the 90-percent confidence interval. Estimates are converted from log form to percent change. Regressions include only agencies with at least 750 households and exclude agencies that joined MTW before 2005 or after 2015, because they need to have 2 years of data prior to joining MTW and 3 years of data after joining enter this equation. Impact analyses do not include Oakland Housing Authority, Tacoma Housing Authority, Housing Authority of the County of Santa Clara, and Housing Authority of the City of San Jose because of incomplete data on households in public housing. All regressions include year and PHA fixed effects. Median rent and government wages are included as control variables. HUD cost per household, area median rent, and government wages enter the regression in logged form.

Sources: Urban Institute Analysis of HUD Office of Public and Indian Housing Information Center (PIC), Financial Data Schedule (FDS), and Voucher Management System (VMS) data

The increase in the number of assisted households the year before PHAs sign their MTW agreement

is both a surprising empirical fact and a potential cause of bias to our main regression model.

One way to address this bias is to estimate agency-specific long-term trends within the regression

model. As shown in Stacy et al. (2020), estimates using this type of model find no relationship

between MTW status and cost per assisted household. These models also confirm the findings from

the event-study regression and imply that increases in HUD funding and the number of assisted

households are part of longer-term trends that predate entry into the MTW demonstration.

Effect of the MTW Demonstration Controlling for Program Mix, Housing Quality and

Affordability, and Household Characteristics

Accounting for changes in (1) program mix, (2) housing quality and affordability, and (3)

household characteristics does not alter our primary finding of no significant relationship between

MTW status and cost per assisted household (exhibit 5). We reach this conclusion by comparing

the numbers in the top row (Impact of MTW) in columns 2, 3, and 4 of exhibit 5 with the number

in column 1.

210 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

Estimates in columns 2, 3, and 4 show the change in cost per household that is not driven by

changes in the internal cost drivers included in the regression. Looking at the result in column 2

(program mix), we see that after controlling for changes in program mix, joining the MTW

demonstration is associated with a statistically insignificant 0.8-percent increase in costs per

assisted household. The difference between this estimate and the estimated effect in the original

model (column 1) can be interpreted as the impact of MTW on cost-effectiveness through

the mechanism of changes in program mix. The difference here, 0.5 percent, is small and not

statistically significant. The estimated impact of MTW on cost-effectiveness changes only 0.2

percent after accounting for housing affordability and quality (column 3) and only 0.1 percent after

accounting for the characteristics of assisted households (column 4). Again, these differences are

small and not statistically significant. In other words, MTW agencies are not maintaining their cost-

effectiveness by shifting their portfolio to lower-cost assistance programs, offering lower quality or less affordable housing assistance, or providing assistance to households with fewer needs.

Exhibit 5

The Effect of Moving to Work on HUD Cost per Assisted Household Controlling for Program Mix,

Quality and Affordability, and Household Demographics

Controlling for:

Main

Housing

Regression

Household

Program Mix

Quality and

Characteristics

Affordability

Impact of MTW

0.013

0.008

0.015

0.014

(0.030)

(0.025)

(0.031)

(0.029)

Percent tenant-based HCV holder

-

0.613***

-

-

(0.075)

Percent project-based HCV holder

-

0.643***

-

-

(0.107)

Quality of public housing

-

-

-0.001

-

(0.001)

Affordability (median rent burden)

-

-

-0.007**

-

(0.003)

Median income (new residents)

-

-

-

-0.040***

(0.011)

High need households (new residents)

-

-

-

0.000

(0.000)

Household size (new residents)

-

-

-

0.018**

(0.008)

Control Variables

YES

YES

YES

YES

Observations

10,905

10,905

8,775

10,905

* p<0.10, ** p<0.05, *** p<0.01.

HCV = Housing Choice Voucher. MTV = Moving to Work.

Notes: There is no statistically significant difference between the four coefficients for the impact of MTW status on cost per household. Standard errors (listed in parentheses) are heteroskedastic robust and clustered at the PHA level. Regressions include only agencies with at least 750 households and exclude agencies that joined MTW before 2003. Impact analyses do not include Oakland Housing Authority, Tacoma Housing Authority, Housing Authority of the County of Santa Clara, and Housing Authority of the City of San Jose because of incomplete data on households in public housing. Regression includes year and PHA fixed effects. Median rent and government wages are included as control variables. Cost per assisted household, HUD funding, assisted households, area median rent, and government wages enter the regression in logged form. Regression (3) only includes PHAs with public housing units.

Sources: Urban Institute Analysis of HUD Office of Public and Indian Housing Information Center (PIC), Financial Data Schedule (FDS), and Voucher Management System (VMS) data

Cityscape 211

Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

Effect of the MTW Demonstration on per Household Spending by PHAs

Finding no impact of MTW status on our preferred measure of cost-effectiveness, we turn to

focus on spending categories highlighted in prior research. We find that MTW status is not

associated with an increase in PHAs’ total operating and housing assistance spending per assisted

household. Our model estimates a change in total spending of only 0.3 percent (exhibit 6, column

1). Variation in spending on administrative costs and tenant services, both between agencies and

within agencies over time, limit the precision of the model and prevent us from drawing strong

conclusions. The standard error for the model of administrative costs (exhibit 6, column 2) implies

a 90-percent confidence margin of error of roughly 17 percent. With an estimated effect of 0.137,

or about 15 percent, we cannot rule out the possibility that MTW has no effect on administrative

costs. Similarly, a 90-percent confidence margin of error for the tenant services model (exhibit

6, column 3) prevents us from drawing conclusions. It implies that MTW may lead spending on

tenant services to fall by 20 percent or increase by more than two-thirds.

In contrast, we find a statistically significant impact of MTW status on operating reserves. We

estimate that MTW increases the funds that agencies hold in operating reserves by about $840 per

assisted household (exhibit 6, column 4). In the first year of our study period, 2003, the average

future-MTW agency held $473 in operating reserves per household. Taken together, the estimated

increase in reserves combined with no estimated impact on cost per household (exhibit 3) implies

that MTW agencies are able to find some efficiencies that allow them to build up their operating

reserves while serving roughly the same number of assisted households per dollar of HUD funding

as they did before joining MTW.

Exhibit 6

The Effect of Moving to Work on per Household Total Spending, Administrative Costs, Tenant

Services Spending, and Operating Reserves

Total Spending (Operating

Administrative

Tenant Services Operating Reserves

and Housing Assistance) Costs per Assisted

Spending per

per Assisted

per Assisted Household

Household

Assisted Household

Household

Impact

0.003

0.137

22.4

839***

of MTW

(0.048)

(0.098)

(25.0

(197)

0.061

0.073

17.3

96.9

Median Rent

(0.146)

(0.091)

(24.7)

(170)

Government

0.111

0.060

-21.5

114

Wage

(0.080)

(0.048)

(18.8)

(119)

Observations

10,905

10,905

10,905

10,905

* p<0.10, ** p<0.05, *** p<0.01.

MTW = Moving to Work.

Notes: Standard errors are heteroskedastic robust and clustered at the public housing agency (PHA) level and listed in parentheses. Regressions exclude agencies that joined MTW before 2003 and agencies that had fewer than 750 assisted households in 2003. Impact analyses do not include Oakland Housing Authority, Tacoma Housing Authority, Housing Authority of the County of Santa Clara, or Housing Authority of the City of San Jose because of incomplete data on households in public housing. All regressions include year and PHA fixed effects. Total expenditures per assisted household, administrative costs per assisted household, median rent, and government wage enter our regression equation in natural log form, tenant services spending per assisted households and operating reserves enter our regression equation non-transformed. We turn all zero values to 0.0001 before taking the natural log. The natural logs of median rent and government wages are included as control variables.

Sources: Urban Institute Analysis of HUD Office of Public and Indian Housing Information Center (PIC), Financial Data Schedule (FDS), and Voucher Management System (VMS) data

212 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

Robustness Checks

We use a series of alternative models and samples to examine the strength of our results. To better

understand how the population of traditional PHAs in our sample impacts our estimates, we

performed a sensitivity analysis using alternative thresholds for the minimum number of assisted

households for PHAs in our comparison group. As soon as the smallest agencies—those with 150

or fewer assisted households—were removed from the sample, the relationship between MTW

status and cost per household became statistically insignificant at the α=0.1 level (Stacy et al.,

2020, Exhibits B1 and B2). We also constructed two comparison groups using propensity scores.

Estimating equation 1 using these two alternative comparison groups reaffirms the main results

(Stacy et al., 2020, Exhibit B4). Next, to confirm that our treatment of the underlying data did

not impact our results, we examined an alternative method for addressing missing data (multiple

imputations). Because LNT assistance may be less expensive, we explored weighting schemes for

families served through LNT assistance. These analyses also show that MTW has no impact on

cost per household but is associated with a greater number of households served and greater levels

of funding received (Stacy et al., 2020, Exhibit B4). Finally, we examined an alternative model in

which we relaxed the assumption of parallel trends for MTW and traditional PHAs and allowed

each PHA its own specific, long-term, linear time trend (Stacy et al., 2020, Appendix B). Estimates

from the model with PHA-specific time trends reaffirm that MTW-status is not associated with

change in cost per assisted household. The inclusion of PHA-specific time trends, however, reduces

the estimated relationships between MTW status and both funding and the number of assisted

households to near zero (Stacy et al., 2020, Exhibit B4).

Discussion

This study provides new insights into the relationship between MTW status and cost-effectiveness

and opens doors for other avenues of research. First, it shows that MTW agencies had higher costs,

as measured by HUD funding per assisted household, than traditional PHAs before they joined

the demonstration. Thus, the higher costs observed at MTW agencies in prior studies are probably

driven by differences in the costs unaffected by MTW status, such as labor and housing costs, and

not because of the regulatory or financial flexibility offered by the demonstration.

Second, although MTW status is not associated with a change in the per household costs of rental

assistance, it is associated with both an increase in HUD funding and an increase in assisted

households. Surprisingly, agencies began assisting more households the year before they joined the

demonstration. This finding may be part of a longer-term trend at agencies that would go on to join

the MTW demonstration. Alternatively, agencies may have changed their behavior in anticipation

of MTW status. MTW agencies are funded based on the number of households they were assisting

when they joined the demonstration. Thus, agencies could increase the base funding in their MTW

contracts by increasing the number of assisted households they served in the year before they joined

MTW. Additional qualitative data collection with MTW agency staff would be useful to determine

if they were intentionally serving more households in anticipation of their MTW contracts. In

addition, we find evidence of an adjustment period where cost per assisted household increases

slightly 1 year after PHAs join the demonstration, although the trend is not statistically significant.

Cityscape 213

Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

Third, although MTW status did not affect the overall costs to HUD of providing rental assistance,

it did allow agencies to significantly increase the amount of money held in reserves. Interviews

with a sample of MTW agencies suggest that reserves can be useful in financing the construction

or preservation of affordable housing, re-affirming prior research (Levy, Edmonds, and Long, this

volume). Furthermore, holding additional reserves may allow MTW agencies greater access to

financing or lower interest rates when they seek to acquire or develop additional housing units.

The increase in reserves could be a sign that MTW agencies are reducing their capital outlays in the

short term and building reserves to fund larger capital projects in later years. Supporters of MTW

may cite this as evidence that MTW improves the financial position of agencies, thereby helping

them to preserve or increase the stock of affordable housing in the community while maintaining

the same level of cost per household. Detractors may argue that the money MTW agencies hold in

reserves would be better spent assisting more households through rental subsidies.

Given that agencies increased their reserves while serving roughly the same number of households

per dollar, we should infer that they found some cost efficiencies. MTW agencies, however, did

not shift their portfolio to lower-cost assistance programs, offer lower quality or less affordable

housing assistance, or provide assistance to households with fewer needs as a result of joining the

demonstration. Further exploration is warranted to determine where these efficiencies arise.

It is important to note that we only examine MTW-eligible funding and therefore do not assess

whether MTW status affects how much funding PHAs receive from other programs. For example,

MTW status might help PHAs receive more funding because they can use their flexibility to hire

grant writers or because they can leverage funding to receive additional loans or grants from public

or private funders. This factor may be what enables them to build their operating reserves without

reducing their cost-effectiveness. Conversely, traditional PHAs may be able to use other funding

opportunities to pay for the enhanced services or development activities that MTW agencies pay

for with their funding flexibility.

Finally, our findings may inform thinking about how to strike the right balance between federal

regulation and local control. The finding that MTW status does not significantly impact cost-

effectiveness could be taken as evidence that strict regulation of PHAs is not necessary to manage

costs, at least among high-performing agencies. Conversely, our finding of no impact of MTW may

reflect a lack of contrast in the regulatory environment between MTW and traditional PHAs. During

our analysis period, 2003 to 2017, HUD was establishing more uniform standards and monitoring

for MTW agencies while also relaxing certain regulatory requirements for traditional PHAs.

Limitations

Lack of random assignment to MTW status limits our ability to estimate the causal impact of the

demonstration. PHAs were chosen for MTW through selection processes that shifted from year-

to-year before becoming more standardized in the later years of the demonstration. In some years,

PHAs self-selected into the demonstration by applying to join. PHAs that applied for the MTW

demonstration may be systematically different in unobservable and unaccounted-for ways from

agencies that did not try to join the demonstration. For instance, they may have leadership with

high levels of motivation to improve their agencies.

214 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

Data availability constrains the analysis in three important ways. First, the analysis only includes

agencies that joined or exited the MTW demonstration since 2003, and for whom there are enough

years of accurate data. It therefore excludes the first agencies to join MTW, some of which have

been singled out by critics of the demonstration for not using enough of their budget on housing

assistance (Fischer, 2015). It also excludes some of the largest MTW agencies and agencies with the

most ambitious MTW activities, such as the Chicago Housing Authority, Home Forward (Portland,

OR), and the Cambridge Housing Authority. We do not know how including these agencies would

affect our results. Second, inconsistent data reduces the accuracy and precision of our estimates.

Reliance on imputing missing and incomplete data adds uncertainty to our estimates. Third, FDS

data do not fully differentiate between spending on public housing, vouchers, or LNT assistance at

MTW agencies and do not allow us to track the flow of funds across accounts.

Conclusion

The MTW agencies we studied received higher levels of HUD funding after joining the

demonstration and increased the total number of assisted households served, resulting in no

significant change in overall cost per household. These agencies also experienced a large increase

in dollars per household held in reserves, suggesting that they were able to increase their savings

while still serving roughly the same number of assisted households per dollar of HUD funding as

before joining the demonstration. We did not find any evidence that this increase in efficiency was

caused by agencies shifting their portfolios to lower-cost assistance programs, offering lower quality or less affordable housing assistance, or providing assistance to households with fewer needs.

Future studies should examine cost-effectiveness in tandem with self-sufficiency or housing choice

to determine the overall effect of the MTW demonstration on its three statutory objectives. Future

studies should also estimate the relationship between MTW status and the number of affordable

units within the service area of each PHA to determine whether MTW agencies use their reserves to

build more affordable housing than other similar agencies.

Acknowledgments

We are grateful to HUD and to all our funders, who make it possible for Urban to advance its mission.

Many people reviewed and contributed to this report. The authors appreciate the feedback and

support that Elizabeth Rudd, the government technical representative for the Moving to Work

(MTW) Retrospective Evaluation, provided throughout the course of the study. Noah Strayer

provided valuable research assistance, and Emily Peiffer provided editorial support. In addition,

the authors thank leadership from Boulder Housing Partners, the Columbia (Missouri) Housing

Authority, the Charlotte Housing Authority, the Housing Authority of the City of Pittsburgh,

HomeForward (Portland, Oregon), the San Antonio Housing Authority, the San Diego Housing

Commission, the San Mateo County Housing Department, the Seattle Housing Authority, and the

Housing Authority of Tulare County for taking the time to speak with us. We also thank MTW

Program Office and other HUD staff, including Lynn Rodgers, Lydia Taghavi, Alison Christensen,

John Concannon, Debra Hamblin, Nick Miele, Marianne Nazarro, and many others for their

Cityscape 215

Stacy, Leopold, Teles, Oneto, Su, Gerken, and Gourevitch

assistance in understanding administrative data sources. HUD staff, including Mark Shroder and

Kurt Usowksi, also provided helpful feedback on our research design. Jason Casterline helped us

to understand administrative financial data. We thank our reviewers, Doug Wissoker and Martin

Abravanel, along with Diane Levy and Martha Galvez, for providing input throughout the research

process. Other members of Urban’s MTW Evaluation team provided insights, data, and comments

on this study along the way. Finally, we thank the 39 MTW agencies for their review of an early

version of this report.

Authors

Christina Plerhoples Stacy is a senior research associate in the Metropolitan Housing and

Communities Policy Center at Urban Institute, where she specializes in urban economics

and inclusion.

Josh Leopold is a senior research associate in the Metropolitan Housing and Communities Policy

Center at Urban Institute, where his work focuses on homelessness and affordable housing policy.

Daniel Teles is a research associate in the Metropolitan Housing and Communities Policy Center at

Urban Institute, where he specializes in applied microeconomic policy analysis.

Ruth Gourevitch is a graduate student in city planning at Massachusetts Institute of Technology.

Alyse D. Oneto, Yipeng Su, and Matthew Gerken are research analysts in the Metropolitan Housing

and Communities Policy Center at Urban Institute.

References

Abravanel, Martin, Robin Smith, Margery Turner, Elizabeth Cove, Laura Harris, and Carlos

Manjarrez. 2004. Testing Public Housing Deregulation: A Summary Assessment of HUD’s ‘Moving to

Work’ Demonstration. Washington, DC: Urban Institute.

Buron, Larry, Melissa Vandawalker, Tyler Morrill, Jill Khadduri, and Jeffrey Lubel. 2017. Testing Performance Measures for the MTW Program. Cambridge, MA and Bethesda, MD: Abt Associates.

Finkel, Meryl, and Larry Buron. 2001. Study on Section 8 HCV Success Rates: Volume I: Quantitative Study of Success Rates in Metropolitan Areas. Cambridge, MA: Abt Associates.

Fischer, Will. 2015. HUD Seeks Significant Improvements to “Moving to Work” Demonstration, But

Additional Changes Needed. Washington, DC: Center on Budget and Policy Priorities.

———. 2011. Expansion of HUD’s ‘Moving-to-Work’ Demonstration is Not Justified: Other Approaches

Would Promote Demonstration’s Goals More Effectively. Washington, DC: Center on Budget and

Policy Priorities.

216 The Moving to Work Retrospective Evaluation

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

Galvez, Martha, Ruth Gourevitch, and Benny Docter. Forthcoming. A Picture of Moving to Work

Agencies’ Housing Assistance. Washington, DC: U.S. Department of Housing and Urban Development.

Galvez, Martha, Jasmine Simington, and Mark Treskon. 2017. Moving to Work and Neighborhood

Opportunity: A Scan of Mobility Initiatives by Moving to Work Public Housing Authorities. Metropolitan Housing and Communities Brief. Washington, DC: Urban Institute.

Khadduri, Jill, Melissa Vandawalker, Rebecca Cohen, Jeffrey Lubell, Larry Buron, Lesley Freiman,

and Eliza Kean. 2014. Innovations in the Moving to Work Program. Cambridge, MA: Abt Associates.

December. https://www.abtassociates.com/sites/default/files/migrated_files/b8bd4434-4303-408e-

b696-874f821e66ea.pdf.

Levy, Diane, Leiha Edmonds, and David Long. 2020. MTW Agencies’ Use of Funding Flexibility.

Cityscape 22 (3): 29.

Manson, Steven, Jonathan Schroeder, David Van Riper, and Steven Ruggles. 2018. “IPUMS National

Historical Geographic Information System: Version 13.0 Database.” Minneapolis: University of

Minnesota. http://doi.org/10.18128/D050.V13.0.

Scally, Corianne, Samantha Batko, Susan J. Popkin, and Nicole DuBois. 2018. The Case for More,

Not Less: Shortfalls in Federal Housing Assistance and Gaps in Evidence for Proposed Policy Changes.

Washington, DC: Urban Institute. https://www.urban.org/research/publication/case-more-not-less-

shortfalls-federal-housing-assistance-and-gaps-evidence-proposed-policy-changes.

Stacy, Christina P., Josh Leopold, Daniel Teles, Ruth Gourevitch, Alyse D. Oneto, Yipeng Su, and

Matthew Gerken. 2020. The Impact of the Moving to Work Demonstration on the Per Household Costs of Federal Housing Assistance. Washington, DC: Urban Institute.

Stockard, J. G., G. A. Byrne, K. Day, G. A. Maneval, L. A. Nielsen, and K. James. 2003. Public

Housing Operating Cost Study. Cambridge, MA: Harvard University Graduate School of Design.

Turnham, Jennifer, Meryl Finkel, Larry Buron, Melissa Vandawalker, Bulbul Kaul, Kevin Hathaway,

and Chris Kubacki. 2015. Housing Choice HCV Program: Administrative Fee Study, Final Report.

https://www.huduser.gov/portal/publications/pdf/AdminFeeStudy_2015.pdf.

U.S. Government Accountability Office (GAO). 2018. Improvements Needed to Better Monitor the

Moving to Work Demonstration, Including Effects on Tenants: GAO-18-150. https://www.gao.gov/

assets/690/689583.pdf.

Cityscape 217

218 The Moving to Work Retrospective Evaluation

by Max-Christopher Krapp

A German Perspective on

Objectives of the Moving to

Work Demonstration, Housing

Provision, and Targeting

Max-Christopher Krapp

Institute for Housing and Environment, Darmstadt, Germany

Objectives of the Moving to Work Demonstration

and its Assessment

The Moving to Work (MTW) program aims to create greater flexibility for participating public

housing agencies (PHAs) in the management of their tasks, in particular, by relaxing budgetary and

instrumental requirements for the use of housing choice vouchers. The following objectives are set

by law for the agencies participating in MTW:

1. Reduce cost and achieve greater cost effectiveness in Federal expenditures;

2. Give incentives to families with children where the head of household is working, seeking

work, or is preparing for work by participating in job training, educational programs, or

programs that assist people to obtain employment and become economically self-sufficient; and

3. Increase housing choices for low-income families.1

These three objectives are not defined by the financing agency, the U.S. Department of Housing

and Urban Development (HUD), so it seems rewarding to examine the objectives and their

implementation and discuss them from a German perspective. The cost dimension of MTW

activities is examined in the contribution by Stacy et al. (this volume); it is primarily measured by the average amount of subsidy funds per household. The authors conclude that MTW agencies

can provide the same level of services to the households supported and, at the same time, increase

their reserves for housing development and maintenance. According to the authors, no evidence

of negative effects on housing quality exists. This finding may indicate an increase in the cost-

effectiveness of the MTW agencies. As Stacy et al. (this volume) point out, however, further

research is needed to better understand the causalities of the underlying efficiency gains. The

1 Omnibus Consolidated Rescissions and Appropriations Act of 1996 (note: 42 USC 1437f).

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 219

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

Krapp

question arises as to whether the average amount of subsidy funds per household is a suitable

indicator of cost-effectiveness, as this indicator tends to measure the intensity of subsidies. To take sufficient account of the effectiveness of MTW activities, it would be helpful to assess whether the

MTW objectives are achieved to the same extent with less funding: Is the same level of housing

cost relief achieved? Do MTW agencies serve the same types of households with similar supply

problems? Is funding provided in comparable housing market segments or locations? It makes

a difference whether the average subsidy or the achieved reduction in housing cost is measured.

Further, the average subsidy per household served does not reflect the extent of variation between

households in the level of benefits—do the households differ greatly in benefits received, or are the benefits similar for all recipient households?

One reason for improved cost-effectiveness could be the increased use of project-based vouchers

(PBVs) as opposed to tenant-based vouchers (TBVs), as noted in the contributions of Galvez et

al. (this volume) and Galvez, Gourevitch, and Docter (this volume). They conclude that MTW

agencies use their resources on PBVs to a greater extent, although typically not even close to the

extent allowed. As Galvez et al. (this volume) show, this shift in favor of PBVs also varies within the group of MTW agencies, with a larger share of PBVs being found in particular in contexts of higher

rent levels; the importance of local contexts of action and housing market conditions becomes

obvious here. It is possible that PBVs linked to specific projects or specific buildings not only imply a more predictable cost development but also enable more efficient administration (compare to the

qualitative surveys of Galvez et al. [this volume]). Similarly, a less favorable sociospatial location of housing provision by PBVs could also lead to supposedly better cost-effectiveness. Galvez et al. (this volume) conclude that the housing conditions of PBV households, in terms of renting dwellings in

lower-poverty, higher-quality neighborhoods, are worse than those of TBV households, both for the

MTW agencies and for the comparison agencies. According to this, PBVs tend to be located in less

favorable areas, which may also affect the individual labor market and educational opportunities of

the households concerned. This finding would therefore be critical, not only for housing choices

but also in terms of self-sufficiency.

In contrast to this, the analysis by Treskon, Gerken, and Galvez (this volume) concludes that the

MTW program has a positive impact on housing choices. They examine three dimensions: the

amount of housing, the location of housing, and the quality of housing, and they observe an effect

with regard to the first dimension. The fundamental question, however, is whether the expansion

of subsidized housing—or more precisely of supported households—through more vouchers

assigned by the agency, can actually be seen as an increase in housing choice.

The stronger establishment of PBVs could be linked to the perspective that project-based

management could be used to achieve a more favorable sociospatial distribution of subsidized

housing and thereby increase housing choice. Ultimately, however, it should be noted that PBVs

seem to contribute more to the stabilization of sociospatial segregation patterns. In contrast,

TBVs seem to enable a higher degree of housing choice by enabling renting housing in a greater

number of affordable areas. Nevertheless, the particular financial difficulties in developing or

securing affordable housing, especially in tight markets, must be acknowledged; this factor reveals

the conflict between cost-effectiveness—in a narrowly defined understanding—and housing

220 The Moving to Work Retrospective Evaluation

A German Perspective on Objectives of the Moving to

Work Demonstration, Housing Provision, and Targeting

choice. An expansion of housing choices, which opens up housing contexts with more favorable

conditions (and thus generally higher price levels), inevitably leads to an increase in expenditure if the level of benefits continues to be linked to a housing cost burden ratio.

On the one hand, the link to the housing cost burden is appropriate to the problem of

affordability. In contrast to flat-rate housing allowances, benefits are granted according to

individual conditions so that the actual need for support can be met. On the other hand, it can

amplify existing inequality. For example, the types of households more likely to be able to lease

up in a low-poverty neighborhood with good schools end up getting larger subsidies (assuming

that rents are higher in lower-poverty neighborhoods). The trade-off between cost-efficiency and

housing choice becomes even starker in the case of MTW agencies because, according to the

analyses by Galvez, Gourevitch, and Docter and Stacy et al. in this symposium, these agencies

are more often characterized by higher rent levels. The expansion of supported households in the

MTW agencies, as noted by Treskon, Gerken, and Galvez (this volume), also points to the greater

need for support in these agencies’ jurisdictions.

The target dimensions of cost-efficiency, self-sufficiency, and housing choice are in part explicitly and implicitly present in the German housing policy debate, but they are less associated with local

administrative flexibility and more strongly linked to the use of basic types of instruments. Thus,

different strengths or supply potentials are generally assigned to the so-called subject-oriented and object-oriented (project-based) funding approaches, also described as demand-side and supply-side instruments.2 Subject-oriented services have been of greater importance in Germany, especially

since the early 2000s. In Europe, too, they are used as housing allowances, housing benefits, and

other similar designations, with very different instrumental arrangements (OECD, 2019).3

Subject-oriented instruments are generally considered to be highly cost-efficient because support

services are allocated to the relevant households in a more targeted and needs-based manner (see

Gibb and Whitehead, 2007; Griggs and Kemp, 2012; Kemp, 2007; Lux, Sunega, and Boelhower,

2009; Turner and Elsinga, 2005). Despite the fact that this instrument is well-established in policy

and well-funded, the need for a more comprehensive social housing sector is acknowledged

in Germany and many European countries. This demand is attributed to a lack of affordable

housing in central areas, which, with rising rent levels, would mean a financial overload of

subject-oriented funding approaches. Moreover, despite the use of subject-oriented instruments,

a displacement of low-income households is assumed, which would have to be countered by

the expansion of subsidized housing with fixed rents. It should be noted that the term “social

housing” is used inconsistently and subsumes a wide variety of phenomena (Hansson and

Lundgren, 2018; Whitehead and Scanlon, 2007).

2 “Subject-oriented” housing policies for low-income households include tenant-based rental assistance, but also housing assistance for homeowners. Besides such subsidies to steadily reduce housing costs of households (housing allowances), further instruments for homeowners and homebuyers can be relevant; these are subject-oriented subsidies such as credits, grants, guarantees, or taxation to make property possible or financially secure private homes.

3 A European Union (EU)-wide survey of housing policy instruments is currently being conducted within the framework of the project “Housing Policies in the EU,” carried out by the Institute for Housing and Environment and the Technical University of Darmstadt, financed by the German Federal Ministry of the Interior, Building, and Community. The results are expected to be published at the end of 2020.

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The PBVs, which are more important for MTW agencies than for comparable agencies, are hybrid

in nature. These vouchers reduce the housing costs of households, according to the circumstances

of the individual situation, and thus have a clear subject-oriented character. At the same time, this instrument is indirectly used to finance specific properties that are the subject of a contractual

agreement between agency and owner. Sometimes the agency is the owner, which could be

linked to other funding elements relevant to the estimation of cost-effectiveness. Based on case

studies, Galvez et al. (this volume) identify two motives of MTW agencies for using PBVs that

are particularly relevant from a supply theory perspective. First, the interviewed MTW agency

staff believes that PBVs make it possible to provide housing in market situations where TBVs are

less effective due to lower acceptance by landlords. Accordingly, the basic supply for households

in need is, from that point of view, only ensured on a project basis. Second, the projects are not

limited to the formal-legal provision of housing for a regulated period of time, but contribute to

the establishment and strengthening of partnerships. These partnerships, for example, contribute

to the provision of particularly urgent care (especially for housing homeless people) and can thus

provide an essential added value for the housing supply. In this respect, the benefits of PBVs may

turn out to be greater, from a qualitatively detailed perspective, than the defined target triad of cost-efficiency, self-sufficiency, and housing choice suggests. Against this background, the instrumental

orientation of housing policy in Germany will be examined later in this report, taking into account

the triad of objectives.

Instruments of German Housing Policy—Cost-Effectiveness,

Self-Sufficiency, and Housing Choices?

In German housing policy, a distinction is made between tenancy law and the economic forms of

object promotion (project-based or supply-side) and subject promotion (demand-side). The social

object promotion ( soziale Wohnraumförderung) refers to the financial promotion of the construction or conversion of housing (“brick-and-mortar-subsidies”). In practice, housing promotion, in

contrast to subject promotion, focuses less on broader target groups with financial difficulties

and more on persons or households with access difficulties to the housing market (for example,

households with a migrant background or many children; ex-prisoners; persons with mental health

problems) or housing needs that are not adequately provided by the housing market (for example,

age-appropriate and handicapped-accessible housing). Nonetheless, political discussions at the

regional level—which is responsible for social housing promotion—often reveal efforts to expand

the target group by extending income limits, so that ultimately more and more eligible persons

tend to encounter a tendentially decreasing number of social housing units. The instrumental

logic of social housing promotion is based on the agreement that, in return for financial loans and

grants, the benefiting investor enters into occupancy and rent price commitments. In addition

to restricting the rent level of the subsidized housing, it thereby also serves to determine the

prioritization of supported households according to urgency by the municipalities (occupancy

rights). From the tenant’s point of view, only one limited choice remains. Households that are

registered as housing seekers at local authorities can only indicate preferences for the district in

which they would like to live. Due to the long waiting list, a housing offer can only be accepted or

rejected (which may mean that no social housing dwelling can be rented at all). Furthermore, when

222 The Moving to Work Retrospective Evaluation

A German Perspective on Objectives of the Moving to

Work Demonstration, Housing Provision, and Targeting

households are allocated social housing units, they are each allocated a standard amount of floor

space. Social housing units are all very similar to each other. A central problem of social housing

promotion in Germany is the small number of dwellings to be allocated. This circumstance is due

in particular to the current large number of expiring agreements. In addition, turnover in the social housing stock is extremely low because the relatively few (compared with the number of eligible)

tenants rarely move out. People are not required to move out if their incomes rise to exceed the

income limits for the subsidy. To reclaim the subsidy that such households receive, a special levy

on over-income households is imposed in some of the Länder.4 This levy does not immediately

cancel out the subsidy advantage when the income limits are exceeded, however, and it affects

only a part of the subsidy advantage. It can therefore be assumed that this special levy will only

marginally compensate for the subsidy expenditure. Against this background, the cost-effectiveness

of social housing is often criticized. In this context, the dimension of self-sufficiency in the sense of independence from transfer payments indirectly becomes the subject of the housing policy

discussion in Germany. For example, a lack of affordable housing is problematized in economically

prosperous regions where the labor market is generally more favorable. In this respect, the

expansion of a subsidized stock is increasingly understood as a measure to enable affordable

housing for low- and middle-income groups in these market contexts. At the same time, however,

it has to be stated that the completion of (subsidized) buildings is too low to make this goal of

providing housing for broader target groups realistic. With regard to housing choices, the decisive

factor in Germany is that new subsidy contracts are concluded mainly for new buildings in newly

developed districts of a city. Expiring subsidy agreements can sometimes be extended, but there is

no extensive purchase of occupancy rights in the existing stock. The anchoring of obligatory social

housing quotas in planning law for larger new housing developments has led to a spatial expansion

of the subsidized stock, although this is still at a relatively low level due to the small number of

new buildings.

Subject-based instruments are understood as financial support for households that are unable

to obtain sufficient supply on the housing market. Since the early 2000s, Germans have shifted

toward this bundle of instruments. The housing allowance ( Wohngeld), which subsidizes part of the housing costs, is intended to reduce the housing cost burden on low-income households. The

housing allowance is a priority social benefit that precedes the basic security scheme.5 Eligibility

and the amount of the benefit depend on the size of the household, the household income, and the

eligible rent or housing cost burden in the case of owner-occupied housing. The increase in housing

benefit payments in line with the level of rent is intended to induce housing consumption based

on demand and to prevent it from being minimized in favor of other goods. The funds are thus

indirectly linked to their purpose of improving housing provision. At the same time, the eligible

rent is capped by a maximum amount table, so that inappropriate housing consumption is equally

4 Länder are the regional units in Germany responsible for social housing promotion. They correspond to the states in the United States or to the provinces in Canada.

5 The social protection system in Germany consists of the basic security scheme and priority benefit systems (in particular, housing allowance). The two systems differ in terms of the scope of the benefit objectives, benefit administration, benefit assessment, and rights and duties of individual households. Households are obliged to prevent their need for basic security benefits by claiming priority benefits such as the housing allowance (compare to Cischinsky and Krapp, 2020).

Cityscape 223

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ineligible.6 Consequently, the design of this financial incentive system—the decisive factor is the

dependence on the level of rent—has a paternalistic tendency to prescribe appropriate housing

consumption. Nevertheless, it is a very market-oriented instrument which, by increasing the housing

budget, strengthens the demand possibilities and thus theoretically increases housing choice. To take into account the regional differences in rent levels, the housing allowance ceilings are differentiated according to seven defined rent levels, into which individual municipalities are grouped.

The second form of subject-oriented funding is the assumption of costs of accommodation ( Kosten

der Unterkunft, KdU) within the scope of the basic security scheme according to the Social Security Code. With the reorganization of the social benefit systems in 2005, these costs were transferred

to the municipal level, although the federal government participates in the financing. Although

the housing allowance is designed merely as a subsidy to housing costs and is a social benefit that

precedes the basic security scheme, the KdU is intended to ensure that all needs are met to secure

the minimum subsistence level. Due to this demanding supply target, which is constitutionally

guaranteed as an individual right of entitlement, and the simultaneous heterogeneity of the local

housing markets (with different supply and demand structures and different price levels and

dynamics), municipalities are in charge of setting the limits of accommodation adequacy. These

limits in the form of local cost restrictions are calculated differently in terms of their amount,

depending on the size of the household and the different costs of locations (infrastructural-

connected locations within individual administrative districts) (compare to Malottki et al., 2017).

Both subject-oriented instruments are attributed a higher cost-efficiency compared with object-

oriented funding because a more targeted and needs-based allocation of funding is assumed.

At the same time, it must be noted that the levels of support must be constantly adjusted to

the development of rents, and that expenditure on subject-oriented services has risen sharply

in recent years.7 Because the subject-oriented instruments increase the ability of households to

pay for housing, it is sometimes argued that they contribute to rent increases, but no reliable

studies on this exist. The strengthening of self-sufficiency, in the sense of work incentives to avoid benefit payments, is seen as a positive characteristic of housing benefit in particular. In principle, the economic and political discussion assumes that negative work incentives exist when higher

incomes lead to lower benefits or a loss of benefits (but compare to Castells [this volume] for the

absent impact of increasing the tenant contribution rate). To mitigate this negative incentive, the

housing allowance entails low transfer withdrawal rates. If households are able to improve their

income situation, the housing benefit is only reduced partially. In the context of KdU, which is

more important given the number of benefit recipients, such an incentive to work does not exist.

Only a small amount of additional income will not be considered in determining the benefit

amount. This circumstance is due to the existence-securing character of this service, which aims to

6 The federal government defines the level of services nationwide, whereas the benefits for individual households are administered at the local level. In the past, benefits were only adjusted at irregular intervals (2009, 2016, and 2020) to take account of price increases. This circumstance led to sharp fluctuations in the number of recipients and to the undesirable switching of households between the housing allowance system and the basic security scheme (compare to Cischinsky and Krapp [2020]). From 2022, the housing benefit will be updated automatically (so-called dynamization), on the basis of official price indices of housing costs and general consumer prices.

7 Expenditure on KdU under the Social Security Code and on housing benefit amounted to a total of approximately

€15 billion in 2008. In 2017, despite a tendency toward a decreasing number of supported households,

accommodation costs of approximately €18 billion were recognized (compare to German Federal Government, 2019).

224 The Moving to Work Retrospective Evaluation

A German Perspective on Objectives of the Moving to

Work Demonstration, Housing Provision, and Targeting

meet demand as precisely as possible. This limitation of the benefit is often also understood as an

argument for cost-effectiveness.

With regard to housing choice, the subject-oriented benefits are, similar to the TBVs, characterized

in principle by openness, which, in the sense of housing choice, allows the rental of different

types and different places of accommodation. In view of the capped benefit level, however, renting

housing in better locations is difficult even with these instruments.

Conclusions

In summary, it can be said that the MTW agencies may well provide poorer sociospatial services to

households supported by the project by using more PBVs than similar agencies, which are typically

located in less favorable districts. The MTW agencies, however, tend to be situated in tighter

housing market contexts, so that the challenges of housing provision are greater. Securing housing

via PBVs—or via social housing, as the German housing promotion scheme aims to do—is essential

here to open up real integration opportunities, especially for cases with access difficulties to the

housing market. If the use of PBVs leads to the provision of housing for such serious emergencies,

and even combines this with further supporting measures, the less favorable locations would

certainly be tolerable. The importance of residential locations should, of course, not be disputed.

Ultimately, it will depend on local housing allocation practice, and the quality of locally developed partnerships, where and which target group-specific support services are developed. The MTW

approach aims to give local agencies greater flexibility in this respect. In the future, however, the question of the sociospatial location of housing provision should be given more attention, and

the framework conditions and barriers for the location of PBVs should be investigated. It may

be possible that U.S. planning law enables a stronger interlocking of land use zoning on the one

hand and social housing provision on the other, as is becoming increasingly important in German

municipalities. In Germany, planning law is used in the form of urban development contracts to

require a considerable proportion of the planned housing for social housing provision. In some

municipalities, this requirement even results in a quota of up to 40 percent of the developed

dwellings in larger construction areas, which need a subsidy agreement with corresponding rent

and occupancy commitments.

Furthermore, against the background of the special project character of the PBVs, further research

is needed on the supported households and their specific supply problems in given market

situations. Galvez, Gourevitch, and Docter (this volume) already analyze an extensive catalog of

household structure characteristics, including household composition, income, and rent burden.

Further information, for example, on the initial housing conditions of the supported households

would be useful to better reflect the structure of local supply problems and the quality of support

provided by local agencies. Ultimately, this could also enable a more indepth assessment of the

work of MTW agencies.

Cityscape 225

Krapp

Author

Max-Christopher Krapp is a political scientist and senior research associate at the Institute for

Housing and Environment in Darmstadt, Germany, where his work focuses on instrumental design

of housing policy.

References

Castells, Nina. 2020. “The Effects of Increasing the Tenant Rent Contribution in the Housing

Choice Voucher Program,” Cityscape 22 (3): 85.

Cischinsky, Holger, and Max-Christopher Krapp. 2020. “Widersprüchlichkeiten bei der sozialen

Absicherung des Wohnens. Policy-Inkohärenzen zwischen Grundsicherung und Wohngeld –

Befunde und Reformvorschläge,” WSI-Mitteilungen 73 (2): 100–109.

Galvez, Martha M., Ruth Gourevitch, and Benny Docter. 2020. “A Picture of Moving to Work

Agencies` Housing Assistance,” Cityscape 22 (3): 9.

Galvez, Martha M., Daniel Teles, Alyse D. Oneto, and Matthew Gerken. 2020. “Moving to Work

Agencies’ Use of Project-Based Voucher Assistance,” Cityscape 22 (3): 55.

German Federal Government 2019: Wohngeld- und Mietenberich t 2018, Berlin, Germany.

https://www.bmi.bund.de/SharedDocs/downloads/DE/publikationen/themen/bauen/wohnen/

Wohngeld-und-Mietenbericht-2018.pdf?__blob=publicationFile&v=1.

Gibb, Kenneth, and Christine Whitehead. 2007. “Towards the More Effective Use of Housing

Finance and Subsidy,” Housing Studies 22 (2): 183–200.

Griggs, Julia, and Peter A. Kemp. 2012. “Housing Allowances as Income Support: Comparing

European Welfare Regimes,” International Journal of Housing Policy 12 (4): 391–412.

Hansson, Anna Granath, and Björn Lundgren. 2018. “Defining Social Housing: A Discussion on

the Suitable Criteria,” Housing, Theory and Society 36 (2): 149–166.

Kemp, Peter A. 2007. “Housing Allowances in Context.” In Housing Allowances in Comparative

Perspective, edited by Peter A. Kemp. Bristol: The Policy Press.

Lux, Martin, Petr Sunega, and Peter Boelhouwer. 2009. “The Effectiveness of Selected Housing

Subsidies in the Czech Republic,” Journal of Housing and the Built Environment 24: 249–269.

Malottki, Christian von, Max-Christopher Krapp, Joachim Kirchner, Günter Lohman, Galina

Nuss, and Markus Rodenfels. 2017. Ermittlung der existenzsichernden Bedarfe für die Kosten

der Unterkunft und Heizung in der Grundsicherung für Arbeitsuchende nach dem Zweiten

Buch Sozialgesetzbuch (SGB II) und in der Sozialhilfe nach dem Zwölften Buch Sozialgesetzbuch

(SGB XII). BMAS-Forschungsbericht 478 (Report for the Federal Ministry of Labour and Social

Affairs), Berlin.

226 The Moving to Work Retrospective Evaluation

A German Perspective on Objectives of the Moving to

Work Demonstration, Housing Provision, and Targeting

The Organisation for Economic Co-operation and Development (OECD). 2019. Affordable

Housing Database. Paris, France: OECD.

Stacy, Christina, Josh Leopold, Daniel Teles, Ruth Gourevitch, Alyse D. Oneto, Yipeng Su, and

Matthew Gerken. 2020. “Fund More, Serve More, Save More: The Moving to Work Demonstration

and the Cost-Effectiveness of Federal Rental Assistance,” Cityscape 22 (3): 193.

Treskon, Mark, Matthew Gerken, and Martha M. Galvez. 2020. “Can Diverse Activities Have a

Combined Impact? Examining the Effects of the Moving to Work Demonstration on Housing

Choice and Self-Sufficiency Outcomes,” Cityscape 22 (3): 163.

Turner, Bengt, and Marja Elsinga. 2005. “Housing Allowances: Finding a Balance Between Social

Justice and Market Incentives,” International Journal of Housing Policy 5 (2): 103–109.

Whitehead, Christine, and Kathleen Scanlon, eds. 2007. Social housing in Europe. London, UK:

London School of Economics and Political Science.

Cityscape 227

228 The Moving to Work Retrospective Evaluation

by Adriana Hurtado-Tarazona

Improving Access to Housing Amid

Exclusionary Housing Markets: A

Latin American Viewpoint of the

Moving to Work Demonstration

Adriana Hurtado-Tarazona

Cider, Universidad de los Andes

Undertaking a retrospective look at the Moving to Work (MTW) demonstration—a diverse and

ambitious policy demonstration, applied by different agencies and in diverse sociospatial and

socioeconomic contexts—is a valuable effort for the analysis and improvement of public policies.

In this sense, the articles in this symposium are immensely informative. Although the initiatives

included in MTW are complex, and sometimes difficult to understand from an international

perspective, the articles of this collection help not only to understand the U.S. initiative, but also to open an opportunity for knowledge transfer. Within the symposium, the essay on the Rent Reform

Demonstration (Riccio, this volume) is especially enlightening because decisionmaking processes

in public policy are crucial for understanding and replicating interventions, and this information is seldom published. Getting to know the discarded alternatives, and the reasons for doing so, opens

an opportunity to analyze policy decisions based on the particular contexts and conditions for

implementations, instead of what is more common: to state “best practices” or recipes for success

that end up being replicated in disparate contexts (Angotti and Irazábal, 2017).

How can the United States’ experience work with housing policy in other regions such as Latin

America? At first sight, the realities are so different that trying to make connections would be a

fruitless venture. Taking into account the broader context of global transformations of housing

markets and policies, however, one could identify common challenges and analyze how different

policy approaches have met, or not met, the ultimate goal: to guarantee the right to housing for all.

This commentary is an attempt to assess the MTW demonstration from a Latin American perspective.

First, I will mention some aspects of the broad context of housing policy in Latin America, and then

I will comment on what the articles of the symposium state about two of the three main objectives of

MTW, contrasting them with some experiences from Latin America and Colombia.

Housing policy in the United States mirrors global transformations that started during the last

decades of the 20th century, which allows us to identify common elements with other regions

of the world. The reforms of the Clinton Administration that led to MTW followed the logic of

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 229

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

Hurtado-Tarazona

transitioning toward deregulation and contraction of the welfare states, which generated housing

policies geared toward the private sector. In Latin America, this result was achieved— multilateral

banks played a prominent role in policy dissemination— through housing policies that replaced

public housing construction with partial subsidies for households to buy in the private market, in

combination with mortgage credits. This model dramatically reduced the supply of public housing

and left social rental housing programs to a marginal role. It also excluded the lowest income

households and those who could not demonstrate stable income due to being in precarious jobs,

and who, despite qualifying for a subsidy, did not qualify for mortgage credit. Additionally, this

market-oriented housing policy resulted in the construction of massive social housing projects in

peripheral locations. Building in these peripheral locations created serious problems of accessibility to urban goods and services because they lacked infrastructure. This situation deepened the

processes of sociospatial segregation and exclusion (Hurtado-Tarazona, Álvarez-Rivadulla, and

Fleischer, 2020).

The social rental programs that currently exist in the region are mainly rental subsidies for young

households (Chile and Argentina)—conceived as a first step to buying a home—and forms of

leasing or leases with an option to purchase (Brazil and Colombia) (Blanco, Fretes Cibilis, and

Muñoz, 2014). In most countries in the region, these are incipient efforts or programs that receive

very few resources compared with programs that promote the construction of new housing. Also,

with exceptions such as a social rental program in São Paulo, Brazil, in which the units are publicly owned and rent is set at between 10 and 15 percent of household income, the majority operate in

the private housing market and are subject to rental price increases in central areas.

Unlike the Latin American case, however, this symposium demonstrates the continuity of a rental

policy amid the neoliberalization and financialization of housing. These conditions are especially

relevant today; predatory rental markets are being documented throughout the world and show

that, increasingly, more households are being excluded from the possibility of accessing quality and

well-located housing. In the context of the COVID-19 pandemic, it has become clear that access

to adequate housing is crucial for sustaining social life and that continuing to develop fair rental

housing policies should be a priority for governments and housing authorities.

In the next paragraphs, I would like to comment on two of the MTW objectives that resonate

with some initiatives in Latin America and Colombia: to promote the self-sufficiency of assisted

households and to increase housing choice for low-income families.

Assisting Households while Promoting Self-Sufficiency

One of the main concerns of several of the articles in this symposium is to promote self-sufficient

households: to avoid the circumstance that obtaining a subsidy may discourage a household from

searching for a job to maintain or increase household income for fear of losing benefits. The article by Nina Castells (this volume) tests this assumption, reviewing a case in which the rent burden was

increased from 30 to 35 percent of household income. Castells evaluated employment and income

conditions of these households after 4 years. The study found that households absorbed this

rent increase without increasing their income or improving their employment conditions, giving

priority to rent payment over other expenses. This situation has some similarities with that of

230 The Moving to Work Retrospective Evaluation

Improving Access to Housing Amid Exclusionary Housing Markets:

A Latin American Viewpoint of the Moving to Work Demonstration

low- and middle-income households in Colombia. The households in Colombia received a subsidy

to become homeowners and ended up absorbing the additional costs of moving to peripheral,

inaccessible housing, without experiencing an increase of their incomes (Hurtado-Tarazona, 2019).

To promote self-sufficiency without putting a strain on households’ economic situations, and to

make it possible for them to truly capitalize on any improvement in their income and working

conditions, the Rent Reform Demonstration (Riccio, this volume) tested a policy that incentivizes

income growth by maintaining rent rates for 3 years without adjustment. This policy gives

households a margin for increasing their income without paying more rent. The policy challenge

here is to incentivize households to depend less on subsidies while protecting those households

that may need more help at any moment for a loss of income.

To promote household self-sufficiency, other alternatives exist beyond calculating rent rates

according to income. Some Latin American countries have implemented time limits for subsidies.

In Chile, the rental subsidy is given to households for 8 years, during which time they must save a

minimum monthly amount to acquire their own home. In Colombia, a new rental program grants

a monthly rent subsidy of around $150 (USD) for 24 months, during which time households

must save for the downpayment of a Vivienda de Interés Social (privately developed social housing, which has a price cap to maintain affordability). Although this type of program may promote

self-sufficiency due to the obligation to demonstrate monthly savings, not enough evidence

demonstrates the efficiency of these programs. Additionally, they disregard rental housing as a

long-term housing option because they are conceived as a step to homeownership, and this may

not meet the needs or aspirations of some households.

An additional aspect to take into account regarding household self-sufficiency is the ability of

these programs to react to crises or situations, such as the current health and economic emergency

related to the COVID-19 pandemic. Households benefiting from this type of program, many of

which are subject to precariousness and job instability, are especially vulnerable to economic crises.

Thus, the need for policy interventions to adapt for households with diverse needs and situations

leads us to the next MTW objective regarding housing choice.

Increasing Housing Choice and Reducing

Sociospatial Segregation

Housing location is an attribute of adequate housing that is related both to accessibility criteria

(transport and proximity to urban facilities, goods, and services) and to issues of social integration (to avoid exclusion and segregation). The question of whether to tie rental vouchers to specific

projects and locations is a relevant question, not only for the implementation of specific

housing programs but also for broader discussions on the right to the city (Lefebvre, 1996), or

granting equitable access to urban infrastructure, services, and amenities. The implementation

of Project-Based Vouchers (PBVs)—as documented by Galvez et al. (this volume), specifically in

their research question 4—shows that tying rent vouchers to specific projects (although it may

seem contradictory to the objective of increasing housing choice) could be a way to overcome

the barriers of housing markets that are socially and spatially exclusive. To do so, however, the

Cityscape 231

Hurtado-Tarazona

housing policy would have to meet additional requirements, not all of which lead to cost-effective

interventions, as I will show.

In several Latin American countries, including Colombia, the rate of the rental subsidy is

calculated based on the housing unit price—not on household income. This calculation means

that subsidized households can only access homes in a certain price range, which generally

turns out to be peripheral homes in large-scale housing projects. For both purchase and rental

subsidies, the label “social housing,” which in principle means only a price cap, as I mentioned

previously, translates into spatial limits in cities, especially in those with higher land prices. This situation seems to be similar to that of some applications of the PBV, according to the studies

included in the article; those studies indicate that some PBVs tend to be concentrated in higher

poverty neighborhoods compared with the average incomes of their counties and even in some

comparisons with the tenant-based voucher (TBV) program. The evidence collected shows that this

intervention has failed to reverse processes of territorial stigmatization and sociospatial segregation.

They also found, however, that “PBV households at MTW agencies live in neighborhoods

with higher educational attainment and lower transportation costs in comparison to both TBV

and public housing households” (Oneto et al., 2020). This points to some opportunities for

improvement. If the selection of projects to implement PBV is defined with additional criteria in

terms of location, accessibility, and quality of the environment (for example, access to schools,

social infrastructure, public spaces, and parks), such an approach could mitigate segregation,

stigmatization, and housing discrimination. Of course, this would be a more costly alternative,

especially in areas with expensive housing markets. This leads me to a final remark regarding the

cost-effectiveness of policy interventions.

Conclusion

Policy interventions need to be assessed not only internally, regarding their objectives,

interventions, and resources, but also regarding the general context of the housing market in which

they operate. In the context of housing financialization (Rolnik, 2013), part of the cost-effectiveness goal of interventions would be to avoid an excessive increase in rent. In various regions of the

world, these excessive increases are produced by “predatory” agents like corporate landlords,

private equity funds, and real estate investment funds (Aalbers, Rolnik, and Krijnen, 2020), which,

in some areas, capture much of the rental housing stock and make them unaffordable. In countries

like Colombia, the role of the state in housing financialization makes any subsidy-based policy

intervention more expensive, which ends up transferring public funds to private agents. A rental

housing program for low-income households could encourage self-sufficiency, increase housing

choice, and even be more cost-effective if it is implemented alongside broader measures (like rent

regulations) to overcome some of the barriers of the housing market in general.

Acknowledgments

The author would like to thank Malena Rinaudo for her help in reviewing Latin American

experiences on rental housing programs.

232 The Moving to Work Retrospective Evaluation

Improving Access to Housing Amid Exclusionary Housing Markets:

A Latin American Viewpoint of the Moving to Work Demonstration

Author

Adriana Hurtado-Tarazona is an assistant professor at Cider, Universidad de los Andes in Bogotá,

Colombia, and can be reached by email at a.hurtado10@uniandes.edu.co.

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Castells, Nina. 2020. “The Effects of Increasing the Tenant Rent Contribution in the Housing

Choice Voucher Program,” Cityscape 22 (3): 85.

Galvez, Martha M., Daniel Teles, Alyse D. Oneto, and Matthew Gerken. 2020. “Moving to Work

Agencies’ Use of Project-Based Voucher Assistance,” Cityscape 22 (3): 55.

Hurtado-Tarazona, Adriana. 2019. “Privatization of Urban Governance and Legitimacy Disputes in

a Social Housing Megaproject (Soacha, Colombia).” In Legitimacy: Ethnographic and Theoretical

Insights, edited by I. Pardo and G. B. Prato. New York: Palgrave Macmillan.

Hurtado-Tarazona, Adriana, María José Álvarez-Rivadulla, and Friederike Fleischer. 2020.

“The Normalization of Bogota Social Housing Residents. Class Tensions in Third World Urban

Peripheries,” City & Society. https://doi.org/10.1111/ciso.12338.

Lefebvre, H. (1996) Right to the City. In E. Kofman and E. Lebas (Eds), Writings on Cities:

Henri Lefebvre. Translated by Elenore Kofman and Elizabeth Lebas. (pp.61-181). Oxford:

Blackwell Publishing.

Oneto, Alyse D., Martha M. Galvez, Daniel Teles, and Matthew Gerken. 2020. “Moving to Work

Agencies’ Use of Project-Based Voucher Assistance,” Cityscape 22 (3): 55.

Riccio, James. 2020. “Designing an Alternative Rent Policy for the Housing Choice Voucher

Program,” Cityscape 22 (3): 139.

Rolnik, Raquel. 2013. “Late Neoliberalism: The Financialization of Homeownership and

Housing Rights,” International Journal of Urban and Regional Research 37 (3): 1058–1066.

https://doi.org/10.1111/1468-2427.12062.

Cityscape 233

234 The Moving to Work Retrospective Evaluation

by Becky Tunstall

Disparities and Similarities in U.S.

and U.K. Rent Subsidy Programs:

A British Perspective on the Moving

to Work Demonstration

Becky Tunstall

University of York

This collection is a valuable contribution to the evidence base on Moving to Work (MTW),

demonstrating high methodological standards, including randomization, quasi-experimental

methods, use of controls, and addressing several key policy issues raised by the program. The

details on MTW activities will be valuable to public housing agencies (PHAs), whatever their

current status, as well as to policymakers, residents’ groups, and advocates.

The collection builds on earlier research on MTW, going back to the mid-2000s. One of the papers

refers to at least seven studies of MTW and housing choice and five studies on MTW and self-

sufficiency in the past 7 years.1 Accessible evidence of this standard creates housing policy soft

power. Research articles on the HOPE VI and Moving to Opportunity (MTO) programs of the

1990s were so extensively published in international journals and searchable open-access pieces

that they also became well known to (if not always fully understood by) European academics and

policymakers. This evidence inspired 12 “mixed communities initiative demonstration projects” in

the United Kingdom (U.K.), which started in 2005 (and which I was involved in evaluating), and

more comprehensive programs in the Netherlands and France.

The robust quantitative evidence puts this group of reports into the international evidence gold

standard. The handful of interviews on PHA strategy in the use of project-based vouchers (PBVs)

are illuminating, however, and more qualitative elements could have added to understanding.2

Was the flexibility of MTW agencies used as envisaged by policymakers? Are there downsides to

flexibility? Do non-MTW PHAs find the lack of flexibility a problem, or, as the report on the topic

suggests, was flexibility mainly a tactical approach to funding cuts? Resident voices and, indeed,

1 Treskon, Mark, Matthew Gerken, and Martha M. Galvez. 2020. “Can Diverse Activities Have a Combined Impact?

Examining the Effects of the Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes,”

Cityscape 22 (3): 163

2 Galvez, Martha M., Ruth Gourevitch, and Benny Docter. 2020. “A Picture of Moving to Work Agencies’ Housing Assistance,” Cityscape 22 (3): 9

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

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Tunstall

resident interests are missing from these papers. Do residents prefer public housing, PBVs, or

housing choice vouchers (HCVs)—if they have a choice? How did residents react to the prospect—

or actuality—of losing $1,350 or more a year through increased contributions as required in Santa

Clara, California?3

The U.S. and U.K. public housing and rent subsidy programs are very different from one another.

From a U.K. point of view, the lack of flexibility for PHAs without MTW is notable. Despite

40 years of the Right to Buy policy—a U.K. policy that gives secure tenants of councils and

some housing associations the legal right to buy the council house in which they live at a large

discount, and has cut the housing stock in half—the United Kingdom still has considerably

more public housing than the United States. About 5 million “social rent” (council and housing

association) homes are available in the United Kingdom, at about 50 percent of the market-rent

level, providing for 17 percent of all U.K. households. Two-thirds of U.K. social renters receive

“housing benefit” to help to pay their already-low rent, as do a minority of private tenants, totaling about one-tenth of all U.K. households. Nonetheless, MTW’s statutory aims and many of the

initiatives that agencies have taken since the program started in 1998 are mirrored by similar

reforms (or attempts at reform) in the United Kingdom over the same period. U.K. governments

of various colors have tired of the cost of housing subsidy or its failure to tackle all social ills. A long-term shift has been occurring in the United Kingdom from subsidizing homes to subsidizing

people to pay their rent via housing benefit, which mirrors the growth of HCVs. Private rental

housing has been used increasingly to provide temporary accommodation for people eligible

for but waiting for social housing, and 6-month-long private rented tenancies are now a legal

alternative to more secure social housing. Ironically, that has resulted in increasing total housing

benefit costs, prompting further efforts at reform and savings elsewhere, and today one-third of

people in poverty (with incomes below 60 percent of the national median) are in private renting,

the same proportion as in social renting. MTW seems to have avoided those problems by making

efficiencies and by requiring tenants to do so.4 Also mirroring MTW, the United Kingdom has

made attempts to remove what are believed to be perverse incentives to tenants and to link

housing support to employment and training.

The different sizes and mix of the population mix in U.K. social housing residents have created

somewhat different politics. For example, just as some MTW agencies increase households’ rent

contributions if they are deemed to have too large a home, the U.K.’s 2010–2015 coalition of

Conservatives and Liberal Democrats decided to pay housing benefit only for the “right” number of

rooms for each household, which affected 10 percent of all social renters. This policy was quickly

nicknamed the “bedroom tax” by campaigners and mainstream media and became the best-known

and most-campaigned-against welfare reform of the 2010s. Castells says only that households

“absorbed their increasing housing costs.” In England, there is evidence of considerable distress

from this and other housing benefit reforms. Some people could move home, a smaller number

could increase their earned income, but the largest group cut back on essentials, borrowed from

3 Castells, Nina, 2020. “The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program,” Cityscape 22 (3): 85

4 Stacy, Christina, Josh Leopold, Daniel Teles, Alyse D. Oneto, Yipeng Su, Matthew Gerken, and Ruth Gourevitch.

2020. Fund More, Serve More, Save More: Moving to Work and Cost Effectiveness. Cityscape 22 (3): 193.

236 The Moving to Work Retrospective Evaluation

Disparities and Similarities in U.S. and U.K. Rent Subsidy Programs:

A British Perspective on the Moving to Work Demonstration

family or friends, or got into other debt—some developed anxiety and depression. Several cases

of suicide are under investigation. Although benefit policy is determined at the U.K. level, the

governments of Scotland and Northern Ireland chose to use their own funding to mitigate the

impact of the “tax” for their residents. As another example, whereas some MTWs increase tenant

rent contributions in response to budget cuts, the U.K.’s coalition government gave social landlords

the power to charge market rents to tenants with incomes over $80,000, a policy nicknamed “pay

to stay.” When those landlords showed little interest in that practice, the government tried to make

the policy compulsory, also lowering the threshold to $41,000 per year, which would have affected

about one-tenth of all tenants. The policy was dropped after widespread protest and evidence that

implementation would be difficult (because agencies do not know tenant incomes) and might add

to work disincentives and poverty.

I know all too well that evaluators have to stick to the brief, and that doing neutral research in an active policy area can be tricky. In the United Kingdom in the 2000s, my colleagues and I struggled

to convey to policymakers our unexciting, evidence-based assessment that the U.K. social housing

population was already fairly “mixed” and that the benefits of an additional mix (of housing tenure

and income) could not justify the financial and nonfinancial costs of creating this mix through

demolition, new build, and displacement (as inspired by HOPE VI). Nonetheless, these MTW

papers probably could have done more to point out the local, national, and temporal contexts of

their data gathering.

Inevitably, these papers cannot describe the wider policy context into which their carefully

assembled evidence will be thrust. The collection only touches on current steps to increase the

number of MTW agencies and does not say where the evidence supports widely held beliefs

about MTW—or does not support them. For example, it is worth saying bluntly that these papers

suggest that “Moving to Work” is a misnomer, as its homes and households are no more likely to

be in lower poverty (and presumably higher work opportunity) areas than those in comparable

traditional PHAs. Housing policy—public housing policy, in particular—can be eager to please

and to appear significant, but it should not try to do everything. How much time and money

should housing providers spend trying to get people childcare, education, or jobs? The economy,

economic policy, and housing markets must take some responsibility too. For example, no

voucher regime can provide choice if landlords commonly refuse to rent to tenant-based voucher

holders, as Galvez, Gourevitch, and Docter state.5 Discrimination has to be challenged directly. The

paper by Castells on the effects of increasing the tenant rent contribution in the HCV program is

fascinating and expert; however, the difference-in-difference method irons out variations in labor

markets.6 For me, the most interesting result was the dramatic increase in housing choice voucher

resident incomes in MTWs and nearby control PHAs alike, which far outweighed the differences

between them. It also limits the potential to generalize results in times and places with worse job

opportunities—like most places now.

5 Galvez, Martha M., Ruth Gourevitch, and Benny Docter. 2020. “A Picture of Moving to Work Agencies’ Housing Assistance,” Cityscape 22 (3): 9

6 Castells, Nina, 2020. “The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program,” Cityscape 22 (3): 85

Cityscape 237

Tunstall

Based on this excellent research, another urgent task awaits: to translate the results into a wise

policy for an expanded MTW scheme.

Author

Becky Tunstall is a professor emerita with the University of York.

238 The Moving to Work Retrospective Evaluation

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 22, Number 3 • 2020

Cityscape 239

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

240 Refereed Papers

by Larry Santucci

Documenting Racially

Restrictive Covenants in

20th Century Philadelphia

Larry Santucci

Federal Reserve Bank of Philadelphia

The views expressed here are solely those of the author and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. Nothing in the text should be construed as an endorsement of any organization or its products or services. No statements here should be treated as legal advice.

Abstract

One of the tools used by early 20th century developers, builders, and White homeowners to prevent African-Americans from accessing parts of the residential real estate market was the racially restrictive covenant. In this article, I present a newly constructed spatial dataset of properties in the city of Philadelphia with deeds that contained a racially restrictive covenant at any time from 1920 to 1932.

To date, I have reviewed hundreds of thousands of property deeds and identified nearly 4,000 instances in which a racial covenant had been included in the deed. The covenanted properties formed an invisible barrier to less densely populated areas sought after by White residents and around predominantly White neighborhoods throughout the city. I present the data in a series of geospatial maps and discuss plans for future enhancements to the dataset.

Introduction

One of the tools used by early 20th century developers, builders, and White homeowners to

prevent African-Americans from accessing parts of the residential real estate market was the racially restrictive covenant (hereafter, racial covenant). Racial covenants were obligations inserted into

property deeds that typically forbade the premises from being occupied or owned by persons not

of Caucasian descent.1 Covenants were most often written into a deed by a private developer but

1 Although the terms have been used interchangeably for many years, White and Caucasian share no etymological resemblance; White refers to skin tone, while Caucasian describes a taxon of people indigenous to Europe, Asia, and certain parts of Africa.

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Santucci

enforced through the state courts. Evidence of racial covenants in property deeds is present in

cities throughout the country, including Baltimore, MD; Boston, MA; Charlotte, NC; Hartford, CT;

Kansas City, MO; Los Angeles, CA; St. Louis, MO; and Sacramento, CA.2 In cities such as Chicago,

IL; Minneapolis, MN; Portland, OR; Richmond, VA; Seattle, WA; and Washington, DC, researchers

have compiled databases and built interactive maps of racially covenanted properties.3 There is

some evidence that property developers in Philadelphia, PA, also used racial covenants to prevent

African-Americans and other minority ethnic groups from owning, renting, or residing in homes

constructed in certain parts of the city (Gottlieb, 2015). References to “restricted sections” of the city in real estate advertisements from as early as 1911 demonstrate that builders were not only imposing

restrictions on new homes, but the restrictions were a primary selling point (exhibit 1). Also, careful searches through handwritten deeds of the time reveal the presence of covenants, stating that land

shall not be occupied by persons “other than those of the Caucasian Race” (exhibit 2).

Exhibit 1

Advertisement for New Homes (1911)

Source: The Philadelphia Inquirer , Sunday, March 5, 1911; courtesy of J.M. Duffin, University of Pennsylvania 2 See https://dcicblog.umd.edu/redliningbaltimore/, http://www.bostonfairhousing.org/, http://northcarolinahistory.org/

encyclopedia/restrictive-covenants/, https://ontheline.trincoll.edu/book/chapter/restricting-with-property-covenants/,

http://www.kansascity.com/news/local/article92156112.html, https://www.kcet.org/shows/city-rising/how-prop-14-

shaped-californias-racial-covenants, http://mappingdecline.lib.uiowa.edu/documents/, and https://www.researchgate.net/

publication/46541492_Redlining_Revisited_Mortgage_Lending_Patterns_in_Sacramento_1930-2004.

3 See http://digitalchicagohistory.org/exhibits/show/restricted-chicago/restrictive_covenants, https://www.mappingprejudice.

org/, https://www.google.com/maps/d/viewer?mid=1Tz4qsE1Pm-usvnLNQNnWirj41vt3lcwf&ll=45.44267388516265%2C-122.62427014343308&z=14, http://www.virginiamemory.com/online-exhibitions/exhibits/show/mapping-inequality, https://

depts.washington.edu/civilr/covenants.htm, and http://www.mappingsegregationdc.org/.

242 Refereed Papers

Documenting Racially Restrictive Covenants in 20th Century Philadelphia

Exhibit 2

Excerpt from Deed of Sale from James H. Watson to George W. Twaddell (April 10, 1912)

Source: City of Philadelphia Department of Records; courtesy of J.M. Duffin, University of Pennsylvania.

While much work has been done to document the existence of racial covenants, less is known

about their effects. This is beginning to change. Recent work by Sood, Speagle, and Ehrman-

Solberg (2019) presents evidence that racial covenants placed on properties during the 1940s had

significant and persistent effects on home prices (measured as of 2018) and African-American

spatial concentrations and homeownership rates in the Minneapolis area (measured as of 2010).

Their results add to a growing body of economic research that includes Aaronson, Hartley, and

Mazumder (2017), who find long-lasting effects of redlining during the 1930s, and Aliprantis,

Carroll, and Young (2018), who find that, conditional on income and wealth, household racial

composition predicts large differences in neighborhood racial composition. Their result is

consistent with the hypothesis that a legacy of racial segregation created persistent differences in

neighborhood outcomes.

As the research demonstrates, racial covenants and other tools of residential segregation are not

just part of a long-forgotten history; their effects are observed in today’s metropolitan residential patterns and in the vast and persistent wealth gap between African-Americans and Whites (Cho,

2018). Thus, the study of racial covenants and the generations of residents affected by them has

Cityscape 243

Santucci

important implications for current housing policy and policies seeking to address wealth inequality,

if only as a means of attaching names and neighborhoods to an otherwise intangible injustice of the

past. As noted by Sood, Speagle, and Ehrman-Solberg (2019), however, further research is required

to provide policy proposals to mitigate any persistent effects of racial covenants.

Against this backdrop, I began to examine the extent to which racial covenants had been used

to restrict homeownership in Philadelphia, a city that continues to be one of the nation’s most

residentially segregated cities.4 With the help of the City of Philadelphia Department of Records

(DOR), I began creating a spatial database of racial covenants written into Philadelphia property

deeds, starting in 1920. To date, I have reviewed deeds written from 1920 to 1932 and identified

nearly 4,000 instances in which a deed contained a racial covenant.

The spatial database allows me to make a number of interesting observations. First, visual pattern

analysis suggests that properties with racial covenants were not randomly distributed throughout the

city. Instead, the dispersion pattern suggests the covenants were put in place to restrict the movement of African-Americans into new developments and predominantly White neighborhoods.5

Second, analyzing the location of covenanted properties in relation to smaller geographical units

sheds more light on the patterns of neighborhood segregation and racial covenants than does

analysis at higher levels of aggregation. Although this may seem intuitive, researchers have only

recently been able to examine residential segregation prior to 1950 at civil divisions smaller than

the city ward. Studies conducted at the enumeration district (ED) level, as noted in Logan et al.

(2015), have generated results that cast doubt on longstanding beliefs about the patterns and

timing of residential segregation.6 For reasons I discuss in section 3, I initially report the locations of covenanted properties at the ward level and conduct a ward-level analysis with census data

from 1920. With some additional work, however, I was able to identify approximate latitude and

longitude coordinates for many of the covenanted properties and map them against demographic

data at the ED level.7 Analysis at the ED level paints a much richer picture of the dynamics at play

between property developers and owners, as well as the perceived threat posed by neighboring

African-American communities.

Third, there is evidence of a relationship between the location of covenanted properties and the

neighborhood grades subsequently assessed by the federal government’s Home Owners’ Loan

Corporation (HOLC) in 1937. Racial covenants can be found in areas of every grade, from A to D,

with more than 80 percent located in midgrade areas (see exhibit 4). This finding is consistent with

the work of Hillier (2005), Crossney and Bartelt (2005), and Greer (2013), who find that the racial

4 Massey and Denton (1988) define residential segregation as “the degree to which two or more groups live separately from one another, in different parts of the urban environment.” In 2000, the Philadelphia metropolitan area ranked 18th out of 318 in terms of African-American/White segregation, ahead of cities such as Kansas City (37), Pittsburgh (38), Baltimore (44), and Atlanta (67). Source: http://www.censusscope.org/us/rank_dissimilarity_white_black.html.

5 The spatial pattern of racial covenants may also reflect the preferences of particular developers and the properties they chose to purchase and develop during that period.

6 The 1950 Decennial Census was the first to capture information at the tract level. Logan et al. (2015), revisiting work by Cutler, Glaeser, and Vigdor (1999), show that segregation was much higher in the early 20th century than previously reported, when measured at the enumeration district level. Massey and Denton (1993) also underestimate the extent of residential segregation by race.

7 Enumeration districts are smaller than census tracts and typically contain fewer than 2,000 residents.

244 Refereed Papers

Documenting Racially Restrictive Covenants in 20th Century Philadelphia

composition of an area was an important factor in determining its grade and suggests that race may

have played an outsized role in the decision to assign a grade of B or C to an area. Racial covenants were probably not needed to discourage most African-Americans from relocating into grade A areas;

the barrier of home prices was sufficient (Kaul, 2019). Thus, only 4 percent of covenants were found

in grade A areas. In addition, the presence of covenants in the lowest graded areas could have resulted from developers including boilerplate racial covenants in all of their property deeds.

Exhibit 4

Location of Racial Covenants on HOLC Residential Security Map (1937)

HOLC = Home Owners’ Loan Corporation.

Note: Geographic boundaries are enumeration districts.

Sources: Author’s calculations and Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed.

Cityscape 245

Santucci

Racial Zoning and Racial Covenants

In the early 20th century, a confluence of factors instigated a large and extended migration of

African-Americans from southern states to northern cities, a movement commonly referred to as

“the first Great Migration.” From 1915 until 1940, hundreds of thousands of poor, rural African-

Americans left the southeastern United States for the northern cities of Baltimore, Chicago, Detroit, New York City, and others. Where the 1910 Census indicated that 90 percent of African-Americans

resided in the South, the rate had fallen by 13 percentage points by 1940 (Gibson and Jung, 2002).

The influx of African-Americans into northern cities had the effect of magnifying existing racial

disparities and residential segregation patterns. Logan et al. (2015) find evidence of residential

segregation in northern cities as early as 1880, a time when few African-Americans lived in the

northern states.8 Those who resided in the north tended to work as servants and housekeepers for

wealthy White families and often resided near their place of employment. Although White families

lived along main streets, African American residences were often clustered along side streets and

back alleys, however.

During the early 20th century, African-American neighborhoods grew larger and more

homogeneous. The question of how these early segregation patterns evolved is a topic of debate

among researchers. It was certainly the result of both sorting (residents choosing to collocate

in neighborhoods predominantly populated by people of similar race or ethnicity) and steering

(individuals of a certain race or ethnicity made to reside in less-desirable areas than they would

have otherwise chosen). African-American migrants, having ventured northward at the urging

of northern friends and family members, were likely to live in or near existing African-American

communities.9 Sorting and steering, however, were not the only means by which Whites sought

to influence African-Americans’ choice of residence. Often, the housing stock available to African-

Americans was in parts of the city that were no longer desirable to Whites due to proximity to

industry or physically deteriorating housing stock.

Urban Whites used a variety of tactics to prevent African-American migrants from settling in

predominantly White neighborhoods. Early methods of deterrence were both physical and

economic. Violence against African-Americans was common in low-income neighborhoods,

whereas home prices and the imposition of various fees and dues created an economic barrier

in upper-income neighborhoods. In many ways, steering practices became institutionalized. For

example, for more than 30 years, real estate brokers followed a code that urged them to maintain

neighborhood racial composition. Until 1956, the National Association of Real Estate Boards’ code

of ethics instructed member agents to never be instrumental in introducing into a neighborhood

“members of any race or nationality, or any individuals whose presence will clearly be detrimental

to property values in that neighborhood” (National Association of Real Estate Boards, 1928).

Over time, the tactics of residential discrimination also relied more heavily on local government

and law enforcement (Rose, 2013). In an early use of the legal system to exclude African-Americans

8 Their study includes data for 10 cities: Boston, Brooklyn (a separate city in 1880), Cincinnati, Chicago, Cleveland, Detroit, New York, Philadelphia, Pittsburgh, and St. Louis.

9 This process is often referred to as chain migration; see Boustan (2017).

246 Refereed Papers

Documenting Racially Restrictive Covenants in 20th Century Philadelphia

from White neighborhoods, White plaintiffs argued that the mere presence of African-Americans in a

neighborhood constituted a nuisance because it could reduce the value of their homes (Rose, 2013).

More often, plaintiffs bundled a racially motivated case with complaints of noise or congestion. In the end, the courts tended to side with the opinion that a person’s race—“a natural condition not in any

way traceable to human activity”—could not constitute a nuisance (Freund, 1904).

Although the tactic of using a nuisance law to execute residential segregation was mostly

unsuccessful, the concept of using the legal system as a means of restricting the free movement of

African-Americans took hold. The next facet of governance to be appropriated in the pushback

against integration was the zoning ordinance.

Zoning Ordinances

In the first half of the 20th century, zoning became an increasingly common means of regulating

land use. Brought to the United States from Germany in the 1890s, zoning ordinances dictated

what activities property owners could engage in, what structures they could erect, and where on

the property the structures could be erected (Hirt, 2014). Ironically, the use of zoning became

more pervasive after a realty company that owned land in the village of Euclid, OH, sued the

village to obtain relief from its zoning ordinance. The company’s 68 acres of land stretched across

multiple areas, each with its own use and building restrictions, creating significant impediments

to development and thereby, the company alleged, damaging its value. The case, Euclid v. Ambler, went before the Supreme Court in 1926. The Court upheld the constitutionality of the village’s

zoning ordinances, paving the way for the growth of zoning as a means of regulating land use

throughout the country.10

Zoning ordinances were also used to exclude African-Americans from purchasing homes in

majority White neighborhoods. In response to middle-class African-American families moving

out of crowded, predominantly same-race neighborhoods into more affluent, less-crowded White

neighborhoods, the city of Baltimore passed the nation’s first racial zoning ordinance in 1910

(Brooks and Rose, 2013). The ordinance prohibited African-Americans from buying homes on

majority White blocks and vice versa ( New York Times, 1910). The use of zoning ordinance to

segregate neighborhoods garnered widespread use in the early 20th century, with southern cities

including Atlanta, GA; Birmingham, AL; Miami, FL; Charleston, SC; Dallas, TX; Louisville, KY;

New Orleans, LA; Richmond, VA; and St. Louis, MO, following Baltimore’s lead.

In the 1917 case of Buchanan v. Warley, the Supreme Court ruled that municipally mandated racial zoning was unconstitutional. The court opinion notes that the 14th Amendment prevents state

interference with property rights, except by due process of law, and that the amendment protects

all U.S. citizens, regardless of color or race.11 Despite the ruling, cities including Atlanta, GA;

Austin, TX; Birmingham, Richmond, and West Palm Beach, FL continued to adopt and enforce

racial zoning ordinances. Such ordinances were again held unconstitutional in 1927;12 yet, the

ordinances were enforced in some cities well into the 1980s.

10 See Euclid v. Ambler, 272 U.S. 365 (1926).

11 See Buchanan v. Warley, 245 U.S. 60 (1917).

12 See Harmon v. Tyler, 273 U.S. 668 (1927).

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Santucci

With racial zoning prohibited, property developers turned to restrictive covenants in property

deeds (see exhibit 5) as the primary means of establishing residential segregation.

Exhibit 5

Definition of Property Deed

A property deed is a legal document whose purpose is to record a transfer of ownership from a grantor, or owner, to a grantee, or purchaser. Deeds typically include a description of the property and its boundaries, an indication of the type of conveyance embodied, and the names and signatures of the grantor and

grantee. They may also include clauses or covenants restricting the use and ownership of the property.

To see how restrictive covenants might be used, consider a real estate developer who is planning a new residential subdivision. The developer might prohibit nonresidential uses such as business, manufacturing, hospitals, or prisons as well as so-called nuisance activities such as keeping chickens or locating fuel tanks above ground (Monchow, 1928). In this way, the appeal and value of residential property could be preserved from the potential harm of industries known for emitting noxious fumes and waste (McGruder, 2015).

Racially Restrictive Covenants

Deed restrictions were originally used as a means of regulating land use. Restrictions covered such

things as building materials, setbacks, easements, and the minimum cost of any home built on the

property. In the same way, deed restrictions were used to insulate new residential subdivisions from

the potential harm of industrial use. By the mid-1920s, it became popular to use restrictions to insulate predominantly White subdivisions from African-American buyers. Clauses typically put into deeds by

land developers, racial covenants restricted the sale of new properties to Whites only and prevented

future generations of homeowners from selling or renting the property to African-Americans.

The first restrictive covenant to restrict the sale of a property on the basis of race or ethnicity

appeared in Brookline, MA, in 1843, where deeds from the Linden Place subdivision included a

clause stating that the residences could not be sold to “any Negro or native of Ireland” (McGruder,

2015). The pace of deed restrictions accelerated rapidly through the 1910s and 1920s. A 1926

Supreme Court decision may have accelerated the spread of racial covenants throughout the

country. In the matter of Corrigan v. Buckley, the Supreme Court declined to hear the case, allowing an earlier ruling against the defendant, Irene Corrigan, to stand. Corrigan had sought to sell her

covenanted property to an African-American woman. The Court argued that, while states could

not engage in racial zoning, nothing prohibited private individuals from agreeing not to sell their

homes to people of designated races, ethnicities, or nationalities (Brooks and Rose, 2013).

One study found that, from 1910 to 1920, the number of properties with racial covenants in

Minneapolis, MN, increased by almost 500 percent, from 1,436 in 1910 to 8,534 in 1920.13 By

1950, there were approximately 17,500 racial covenants on Minneapolis properties. Racially

restrictive covenants remained enforceable in state courts until 1948, when the U.S. Supreme Court

ruled that, while not illegal in and of themselves, racial covenants could not be enforced through

the state court system.14

13 See https://www.mappingprejudice.org/index.html.

14 See Shelley v. Kraemer, 334 U.S. 1 (1948).

248 Refereed Papers

Documenting Racially Restrictive Covenants in 20th Century Philadelphia

Racial covenants (and the less familiar but closely related racially restrictive condition) tended to fall into one of three categories (Monchow, 1928; and Scanlan, 1949).15 The first category includes

covenants that restricted ownership, more specifically the sale, lease, conveyance to, or ownership

by any member of a certain racial or ethnic group. The second category includes covenants that

prohibited use or occupancy by any member of such a group, and the third prohibited both

ownership and occupancy. Once a deed restriction was in place, it might remain with the property

for decades. This feat was often achieved by way of one or more workarounds to the rule against

perpetuities (RAP), a common-law doctrine that was intended to prohibit property restrictions

from remaining in place for more than one generation (about 21 years; Brooks and Rose, 2013).

Thus, many deed restrictions were written with an explicit duration of 20 or 21 years, particularly

around the turn of the 20th century.16 Real estate developers could also circumvent the RAP

doctrine by structuring their purchase agreements to retain a reversionary interest in the property.

A reversionary interest could be established by stating that, if one of the covenants is violated, the property would revert back to the developer. Thus, maintaining a residual ownership interest in the

property was another way to circumvent the RAP doctrine and extend deed restrictions for many

decades (Brooks and Rose, 2013).

A 1928 survey of 84 deeds found that deed restrictions typically contained a stated duration

and a set of conditions under which the restrictions could be extended (Monchow, 1928).

Some restrictions were designated to renew automatically at the frequency of the original term

unless certain conditions were met. For example, some deeds specified that either a majority or

supermajority (for example, two-thirds) of owners in the development had to agree in writing to

terminate the automatic extension. Automatic renewals and high thresholds for termination may

help to explain why racial covenants remain on deeds throughout the country, having followed

properties through many generations of ownership (WOSU Public Media, 2017).

The use of racial covenants was promoted by the National Association of Real Estate Boards

(NAREB), then a nascent trade group established for White brokers.17 In 1924, NAREB adopted

an amendment to its member code of ethics that required brokers to practice racial steering or

risk expulsion. A model real estate licensing act, adopted by 32 states, authorized state real estate commissions to revoke licenses of agents who violated the NAREB code of ethics (Plotkin, 2001).

Then, in 1927, in conjunction with the U.S. Department of Commerce, NAREB drafted a model

racial covenant (see exhibit 6).18 Restrictions based on the NAREB model were inserted into deeds

across the country. NAREB also encouraged local real estate boards to partner with homeowner

associations to spread the model covenant (Roithmayr, 2014).

15 The term condition may have been used more generally. In real estate, a covenant is a type of condition that is tied to the ownership or use of a piece of land.

16 In our data, covenants with an explicit (and finite) duration tended to be for 10, 25, and 30 years. More often, the covenant was said to extend “hereafter forever.”

17 The influence of NAREB and its affiliates on residential segregation patterns is, in part, a result of their quest for recognition as a profession, which became necessary as the housing market expanded, bringing with it unscrupulous brokers and agents (Fox Gotham, 2014). In the 1930s, a NAREB affiliate called the American Institute of Real Estate Appraisers began to formalize appraisal techniques that, in part, rested upon the belief that property value was linked to neighborhood racial composition.

18 See http://wbhsi.net/~wendyplotkin/DeedsWeb/philpott.html for additional information.

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Exhibit 6

Model Racial Covenant Text

Model Racial Covenant

1. No part of said premises shall in any manner be used or occupied directly or indirectly by any negro or negroes, provided that this restriction shall not prevent the occupation, during the period of their employment, of janitors’ or chauffeurs’ quarters in the basement or in a barn or garage in the rear, or of servants’ quarters by negro janitors, chauffeurs or house servants, respectively, actually employed as such for service in and about the premises by the rightful owner or occupant of said premises.

2. No part of said premises shall be sold, given, conveyed or leased to any negro or negroes, and no permission or license to use or occupy any part thereof shall be given to any negro except house

servants or janitors or chauffeurs employed thereon as aforesaid.

Source: National Association of Real Estate Boards, 1928

At their core, racial covenants served the purpose of maintaining separate residential areas for persons of different racial and ethnic groups.19 In suburban areas where residential real estate development

was just beginning, and in urban areas where aging housing stocks were being replaced with new

homes and subdivisions, racial covenants were a means of introducing social norms into places where

none existed previously, or of codifying social norms already in place. Because they were embedded

in deeds, racial covenants were contractual, and because they were entered into by private

businesses and citizens, they were not explicitly actions of the state and thus not public policy. The responsibility for enforcing racial covenants ultimately fell, however, on the judicial system, a public institution. It was on this basis that, in 1948, racial covenants were challenged in court and deemed unenforceable. In Shelley v. Kraemer, the U.S. Supreme Court ruled that any court that enforced a racial covenant violated the equal protection clause of the 14th Amendment, which prohibits states

from denying a person within its jurisdiction the equal protection of the law.20 The court’s decision meant that enforcing a racial covenant through the court system was akin to the state taking action

against citizens, violating their 14th Amendment rights (Brooks and Rose, 2013). Still, the court

ruling did not prevent racial covenants from being written into deeds. It was not until 1968, when

Title VIII of the Civil Rights Act made racial covenants illegal.21

Despite the demise of the racial covenant more than 50 years ago, research suggests that African-

Americans continue to face challenges in securing favorable residential opportunities. A feature of

modern residential discrimination is that it tends to occur via the home-buying process. A recent

paper by Christensen and Timmons (2018) found strong evidence that real estate agents continue

to steer similarly situated minority households toward worse neighborhoods than their White

counterparts. Controlling for income and locational preferences, discriminatory steering explains

a significant fraction of the disproportionate share of minority households in impoverished

19 Throughout this paper, I maintain the existing convention of describing covenants against certain racial and ethnic groups as racially restrictive covenants or racial covenants, although race and ethnicity are two distinct concepts, and race is commonly understood to be a social category, not a biological or ethnic classification.

20 See https://constitutioncenter.org/interactive-constitution/amendments/amendment-xiv/the-equal-protection-clause/

clause/20 for more information.

21 See Pub L. No. 90–284, 82 Stat. 73 (1968). Title VIII of the Civil Rights Act is known as the Fair Housing Act.

250 Refereed Papers

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neighborhoods and neighborhoods near contaminated land designated as Superfund sites by the

U.S. Environmental Protection Agency.22

Creating the Spatial Database

Beginning in 2018, I launched an initiative to discover the extent to which racial covenants had

been used to restrict homeownership in Philadelphia. My goal was to create a spatial database of

racial covenants in Philadelphia property deeds. The database would enable researchers to map the

location of properties that were once restricted by race and to analyze the effect of the covenants

on residential patterns and the disparities in economic outcomes between White and non-White

Philadelphians. The historical deed books are a matter of public record and are accessible through

the DOR’s historical land and vital records website.23 Although pages of the deed books have been

copied to microfilm and converted to electronic image files, the site was designed to facilitate the

retrieval of individual deed records. To create a spatial database, I would need to search millions of deeds spanning a period of 20 or more years. The DOR provided electronic records for the period

1920–1938. This window was selected because it coincided with the period during which racial

covenants were being written into deeds and because most Philadelphia deed records prior to

1917 were handwritten, making the task of optical character recognition (OCR) significantly more

challenging. I received more than 1.2 million files (146GB), each file containing a scanned copy of

two pages from the deed book.

To convert the images to searchable PDF files, I read small batches of image files (300–400) into

OCR software. The software was configured to automatically correct various aspects of the image

prior to executing the character recognition process. Because the image files varied widely in

fidelity, some text was well reproduced from the images, while images that were blurred, too dark,

or too light tended to generate long strings of nonsense characters.

Once a batch of deeds had been converted from images to searchable text, I began the process of

identifying deeds containing racial covenants. To do so, I searched for keywords such as: Caucasian, Negro, white, black, descent, and occupied. One way I attempted to overcome the image fidelity and clarity challenges was to search for keyword fragments. For example, I searched for the word

Caucasian as well as cau, cauc, asian, and ian. This approach yielded many more hits than searching by keyword alone.

As of the writing of this article, my search has yielded more than 3,800 distinct properties with

racial covenants, spanning the period 1920–1932.24 Properties that appeared more than one time in

the deeds data set were entered into the spatial database as different records, but only entered into the final tally once. In most instances, this double-counting occurred when a property changed

hands. For example, the property at 1031 Flanders Road in the 34th Ward was sold by Edward

Hoopes in April 1928, and by Albert Simon in February 1930. In some instances, I observed a

property transferring hands twice in one day, as on April 15, 1920, when the property at 3833

Walnut Street was bought and sold both by George E. Outhette and William Alexander Brown.

22 See https://www.epa.gov/superfund/what-superfund for more information.

23 See http://phila-records.com/historic-records/web/.

24 I found 15 racial covenants in deeds that were dated 1919.

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Because the deed records are not marked with a chronological identification number, I was unable

to compute the exact number of deed records searched. I estimate that 600,000 to 1.2 million deed

records were searched since most of the 1.2 million image files contained a left- and right-hand

page, and a single deed record tended to be between two and four pages in length.

Upon locating a keyword or keyword fragment, I noted the location of the deed in the electronic

records and the following information:

• Date of deed

• Race-related search term identified in the text

• First seller listed

• Location of plot(s)

• Ward number

With additional resources, I would have recorded several additional pieces of information for each

deed containing a racial covenant, including the exact text of the covenant, the stated covenant

duration, and the first listed buyer. This—and other areas for future work—will be discussed in the

final section.

Two Caveats

Two caveats bear mention. First, my findings should be interpreted as a lower bound on the true

number of racial covenants added to properties from 1920 until 1932. I can say with certainty that

there are zero instances in which my algorithm identified a covenant where one did not exist (that

is, Type I error). I am less certain, however, that my algorithm found all covenanted properties (that is, Type II error). The second caveat directs attention to the additional work needed to enhance the

precision of my coordinate estimates.

A Lower Bound

First, my tally likely underestimates the number of racial covenants present in the data sample.

As noted previously, varying image quality affected the OCR software’s ability to translate images

into words. The typewritten deed records also varied in type spacing and horizontal alignment.

Lines of text sometimes drift down the page, and letters of the same word are often not on the

same horizontal plane. All these factors created challenges in converting the images to searchable

documents. I attempted to mitigate the effects of these and other challenges by searching for word

fragments, but it is unlikely my process identified all racial covenants. In addition, deeds are not

required to be immediately recorded in the county deed books. Thus, although I reviewed files

from the deed books corresponding to the period 1920–1932, my sample contained some deeds

written before 1920 and probably excluded some that were not recorded until after 1932. Last,

some deed writers might have used uncommon or obscure words to refer to African-Americans or

persons of another religion or ethnicity. For example, other analyses of racial covenants have found

African-Americans broadly referred to as Ethiopians, a word that was initially on my search list but

was dropped for the sake of efficiency when it did not appear to be yielding hits.

252 Refereed Papers

Documenting Racially Restrictive Covenants in 20th Century Philadelphia

Imprecise Coordinate Estimates

Second, I was unable to ascertain the exact location of most properties on the basis of information

provided in the deed itself. Plots of land in the city of Philadelphia have historically been identified by a metes-and-bounds description—a surveying methodology that identifies a piece of land

by placing it in relation to nearby landmarks—rather than by parcel number or other unique

identifier. Philadelphia County was very much under development during the first half of the 20th

century; thus, many of the deeds in our sample convey plots of land to developers who would then

go on to subdivide the property and build streets and homes. After 1927, I observed more deeds

being associated with a particular address (or addresses). Thus, the need to match metes-and-

bounds descriptions to addresses may diminish as I progress further into the deeds data.25

When a street address was available, I used a geocoding application programming interface (API)

to generate latitude and longitude coordinates. Likewise, for a small number of deeds, I was able

to easily determine the street address based on the metes-and-bounds description and could then

retrieve its coordinates from the API.

When a street address was unavailable, I approximated the property location to either the nearest

street intersection or the intersection that marked the beginning of the metes-and-bounds

description. In the first case, I leveraged the order imposed by central Philadelphia’s gridiron-style layout; in instances where a north-to-south running numbered street intersected an east-to-west

running named street (for example, 18th Street and Walnut Street), the property was assigned the

coordinates of the first even-numbered property on the block (for example, 1800 Walnut Street).

In the second case, if a metes-and-bounds description began at the corner or intersection of two

streets, I approximated the property location with the set of coordinates corresponding to the

center of the intersection.

Comparison to Other Data Collection Projects

For many years, little was known about the prevalence of racial covenants and their long-term effects (Fox Gotham, 2014). Fortunately, this has changed in recent years, as teams of researchers have

undertaken efforts to identify, document, and map racial covenants in cities throughout the country.

Since 2005, Segregated Seattle, a project run by the Seattle Civil Rights and Labor History

Project at the University of Washington (UW), has been working to identify and document racial

covenants. Their database is the work of several teams of UW students and appears to be the result

of a very labor-intensive process. According to their website, the students have examined about

40 percent of deeds filed between 1923 and 1950, finding more than 500 covenants containing

racial restrictions that apply to at least 20,000 properties in King County, WA.26 An online database 25 Today, each property in Philadelphia has a unique, nine-digit parcel number, initially created by the Board of Revision of Taxes. Since 2010, the process has been administered by the Office of Property Assessment; however, current Philadelphia County document recording requirements state that properties must only be identified by the metes-and-bounds description and street address (https://www.phila.gov/records/DocumentRecording/

DocumentRecordingReq.html).

26 See http://depts.washington.edu/civilr/covenants.htm.

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Santucci

contains a list of 416 covenants from deeds filed between 1927 and 1948.27 The database includes

fields for neighborhood, plat, and seller names, deed date, the text of the covenant, and a link to a pdf copy of the original deed.

One of the more sophisticated efforts to identify and record racial covenants is Mapping Prejudice,

a group that began its work in Hennepin County, MN. The team first used OCR to identity over

30,000 property deeds containing racial language. Once the deeds were compiled, they used a

crowdsourcing platform to make the deeds available to volunteers who then examined the text

to gather several pieces of information. The information was then transferred to a database to

populate an interactive map that also includes a time-lapse heat map.28

In early 2019, Jordy Yager began a project to identify every covenanted property in Charlottesville,

VA. Unlike in Philadelphia, the city of Charlottesville had not scanned their historic deed records,

so Yager and a group of students at the University of Virginia scanned 33 deed books containing

records from 1909 until 1936.29 The scans were converted into PDF format and run through OCR

software. As with the Mapping Prejudice project, Yager used a crowdsourcing platform to make

the covenanted deeds available to volunteer researchers. He also received a batch of TIF image files

with more than 150,000 pages of property deeds from 1888 until 1964, finding racial covenants

from as far back as 1897.30

Preliminary Observations

Ward-Level Analysis

As noted in section 3, most deeds from the 1920s referenced properties that did not yet have

street addresses. In other cases, an address might not have been provided. Fortunately, each

deed included a reference to the ward in which the property was situated.31 Thus, my most

geographically accurate representation of the new data is at the ward level. Column (b) of exhibit

7 lists the number of covenanted properties found in each of Philadelphia’s 48 wards.32 I use data

from the 1920 census to calculate the number of covenants per thousand dwellings in column (c).

27 See http://depts.washington.edu/civilr/covenants_database.htm.

28 See https://www.mappingprejudice.org/.

29 See https://mappingcville.com/2019/02/07/scanning-scanning-scanning/.

30 See https://mappingcville.com/2019/01/28/1903-1948-charlottesvilles-first-racially-restrictive-covenants/.

31 The Census Bureau did not adopt the census tract as an official geographic entity until the 1940 census. See https://

www.census.gov/history/www/programs/geography/tracts_and_block_numbering_areas.html.

32 The city had 48 wards from 1914 until 1932. Today it is divided into 66 wards. See https://www.philadelphiavotes.

com/en/resources-a-data/political-maps.

254 Refereed Papers

Documenting Racially Restrictive Covenants in 20th Century Philadelphia

Exhibit 7

Racial Covenants Identified in Philadelphia Deed Books, 1920–1932 (1 of 2)

(a)

(b)

(c)

(d)

(e)

Ward

Number of

Racial Covenants

Percent African-

Percent African-

Covenanted

per Thousand

American, 1920

American, 1930

Properties

Dwellings

1

0

0

0.6%

2.4%

2

0

0

5.6%

17.9%

3

0

0

7.9%

21.1%

4

0

0

15.6%

33.0%

5

0

0

6.7%

11.9%

6

0

0

5.1%

19.8%

7

0

0

46.5%

41.5%

8

16

8.6

13.1%

8.5%

9

0

0

12.5%

6.0%

10

0

0

5.5%

8.7%

11

0

0

4.2%

21.3%

12

0

0

4.1%

20.3%

13

0

0

11.1%

26.4%

14

0

0

27.0%

46.0%

15

3

0.4

8.4%

10.7%

16

0

0

1.0%

6.8%

17

0

0

2.0%

3.6%

18

0

0

0.3%

0.1%

19

1

0.1

1.2%

3.0%

20

2

0.2

17.6%

35.9%

21

111

14.8

1.8%

2.6%

22

877

50.5

7.8%

9.1%

23

192

23.7

2.2%

2.9%

24

7

0.6

13.5%

23.7%

25

0

0

0.6%

1.3%

26

0

0

9.1%

8.9%

27

39

11.8

12.1%

13.0%

28

84

7.0

3.4%

12.7%

29

2

0.3

5.3%

21.9%

30

0

0

52.5%

70.3%

31

0

0

0.1%

0.1%

32

3

0.3

8.3%

31.7%

33

61

4.1

0.4%

0.2%

34

593

38.3

4.9%

16.0%

35

228

84.9

1.8%

1.5%

36

74

6.7

24.1%

29.8%

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Santucci

Exhibit 7

Racial Covenants Identified in Philadelphia Deed Books, 1920–1932 (2 of 2)

(a)

(b)

(c)

(d)

(e)

Ward

Number of

Racial Covenants

Percent African-

Percent African-

Covenanted

per Thousand

American, 1920

American, 1930

Properties

Dwellings

37

1

0.2

4.8%

8.0%

38

336

22.0

2.8%

3.8%

39

25

1.7

1.0%

2.9%

40

283

17.1

5.0%

6.8%

41

318

82.5

5.0%

5.4%

42

434

30.6

1.1%

0.7%

43

36

2.8

1.8%

1.7%

44

14

1.6

7.9%

26.2%

45

11

1.4

0.9%

0.8%

46

62

3.6

1.3%

1.7%

47

2

0.4

27.9%

40.9%

48

11

2.0

0.1%

0.6%

Totals

3,826

10.8

7.4%

11.3%

Notes: Number of racial covenants identified in property deeds found in City of Philadelphia Deed Books from 1920 to 1932. Multiple covenants on a single property are counted once. Totals include the population and dwellings of all 48 wards, including those in which no racial covenants were found.

Sources: Author’s calculations and U.S. Census Bureau

For the period 1920–1932, the largest number of covenants were found in Ward 22, an area of

northwest Philadelphia with a population that was 7.8 percent African-American in 1920, just

above the overall rate of 7.4 percent. The 877 covenanted properties in Ward 22 account for

about 23 percent of the total covenants in the 1920–1932 subsample. The next largest shares of

covenanted properties were found in Wards 34 and 42, with 593 and 434 covenants, respectively.

Taken together, Wards 22, 34, and 42 contained nearly 50 percent of all covenants in the sample.

The remainder of covenanted properties were found in another 20 wards, while no properties with

covenants were found in 20 of the 48 wards. The 20 wards in which no covenants were found

mostly fall along the Delaware River on the city’s eastern shoreline.

At the ward level, there is evidence that racial covenants could have reduced the inflow of African-

Americans into particular wards. The percentages of ward residents identified as African-American

in 1920 and 1930 are found in columns (d) and (e) of exhibit 7. The correlation between racial

covenants per thousand dwellings and the change in the percentage of African-American residents

from 1920 to 1930 is -0.258, suggesting that the relationship is directionally within expectations.

In addition, the percentage of African-Americans in wards in which 0 to 10 covenants were located

grew by 8.1 percent, compared with 2.3 percent in wards with 10 or more covenants.33

The 22nd Ward, with 877 covenanted properties, lies in the northwestern part of the city and

includes the neighborhoods of Germantown, Mount Airy, and Chestnut Hill. These neighborhoods

33 Calculations weighted by population totals from the 1920 census.

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were initially home to middle class and wealthy Philadelphians seeking to avoid the congestion of

Center City. As development expanded outward and more areas became accessible by streetcar, the

area became increasingly accessible to working-class citizens traveling along the old 23 streetcar

line that ran from Chestnut Hill to South Philadelphia.34 Situated between Chestnut Hill and

Germantown, Mount Airy is better known for its intentional efforts to be an integrated community

throughout the 1950s and 1960s (Ferman, Singleton, and DeMarco, 1998; and Rolland and

DeMaria, 2016).

Similarly, Ward 34 in West Philadelphia, containing 593 covenanted properties, began as a getaway

for upper-class Philadelphians. In the second half of the 19th century, its location on the western

banks of the Schuylkill River made it attractive to developers who built country homes for middle-

class buyers. Shortly after its incorporation in 1902, Philadelphia Rapid Transit began construction

of electric streetcar lines to West Philadelphia, earning the area the moniker of streetcar suburb.35

By the 1920s, many of the 19th-century homes were demolished to make way for the construction

of large, four- or five-story apartment houses.36

The number of covenanted properties in a ward is, in part, a function of the residential construction occurring there. Thus, I would expect to see fewer covenants in wards that experienced very little

construction. As late as 1937, much of the far northeast, from Pennypack Park to Bucks County, still

contained vast swaths of farmland.37 Exhibit 8 shows that, on a per-dwelling basis, the areas of the

city with the most covenanted properties were in Wards 35 and 41 in the northeast and far northeast

(dark red), respectively.38 While sparsely populated in 1920, these two wards had a combined density

of 866 persons per square mile, much lower than the overall county density of 14,000. By 1930, the

populations of the two wards had grown by 314 percent and 95 percent, respectively. Thus, exhibit 8

suggests that much of the new residential construction that occurred in the greater northeastern part of the city during the 1920s was covered by racial covenants.

34 See http://www.phillytrolley.org/1923map/1923_prt_map_z1.html.

35 In 1998, the West Philadelphia Streetcar Suburb Historic District was placed on the National Register of Historic Places.

36 See http://www.uchs.net/HistoricDistricts/wpsshd.html.

37 Source: HOLC Residential Security Map for Philadelphia, PA, available at https://dsl.richmond.edu/panorama/

redlining/#loc=12/40.038/-75.12&city=philadelphia-pa.

38 In 1920, the Bureau of Labor Statistics began collecting data on new building permits issued in the country’s largest cities. While Philadelphia is included in the dataset, the data are not available at the ward level. In lieu of data on building permits, I computed the number of covenanted properties as a percentage of the number of dwellings listed in the 1920 Census.

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Exhibit 8

Number of Covenanted Properties per Thousand Dwellings, by Ward, 1920–1932

Notes: Number of racial covenants identified in deeds books from 1920 until 1932. Dwellings are from 1920 Decennial Census.

Source: Author’s calculations

HOLC Map Analysis

Next, I replaced the ward-level map with Home Owners’ Loan Corporation’s 1937 residential

security map of Philadelphia. HOLC was established by Congress in 1933, with a mandate to

refinance residential mortgages in default.39 Beginning in 1935, HOLC staff created maps of cities

throughout the country as a reference to assist in the collection and disposition of the mortgage

debt it had purchased from lenders. Areas marked as grade A were expected to be racially

39 It did so by exchanging government bonds for defaulted mortgages. See Hillier (2002) for additional information.

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Documenting Racially Restrictive Covenants in 20th Century Philadelphia

homogeneous and have space for new construction (Aaronson, Hartley, and Mazumder, 2017).

Grade B areas were similar to green but no longer had space for new residential construction, while

grade C areas were declining and might be racially or ethnically mixed or have the potential to

become mixed. Last, grade D areas were undesirable and often had high concentrations of Jewish

or African-American residents.

Exhibit 4 shows where covenanted properties fell on the HOLC map, bearing in mind that the

covenants were written several years prior to the HOLC map and were certainly considered by

HOLC staff when grading an area. The latitude and longitude coordinates associated with each

of the covenanted properties are superimposed. As noted previously, I do not currently have

exact coordinates for many of the covenanted properties. For those properties, I chose to append

the coordinates of the nearest intersection. Thus, the size of each red dot signifies the number

of covenanted properties that have been mapped to that particular location, with larger red dots

signifying more properties.

Only about 4 percent of covenants were in green areas, while over 60 percent of the covenants

were in blue areas and 23 percent in yellow areas. This suggests the hypothesis that HOLC staff

may have viewed the presence of covenants as a second-order factor in highly rated portions of the

city but weighed them more heavily when determining whether an area should receive a blue grade

or a yellow grade.

A second observation in exhibit 4 that warrants further examination is the location of covenanted

properties near the borders of differently colored areas. This phenomenon is particularly visible in

northeast Philadelphia, where covenants in the blue area dot the perimeter between it and several

yellow areas, and in southwest Philadelphia, where yellow areas border red areas. Covenanted

properties along area borders are further evidence that developers and builders used covenants to

create buffers between White neighborhoods and nearby African-American neighborhoods. Similar

patterns have been found in other cities, including Washington DC, St. Louis, and Chicago.40 The case was made explicit in Grady v. Garland, in which the DC Appeals Court upheld racial covenants as an effective “barrier against the…movement of colored population into the restricted area.”41

Enumeration District-Level Analysis

In their work on residential segregation, Allison Shertzer, John Logan, and their colleagues have

found that the geographic scale in which one examines residential segregation can have significant

effects on results. Shertzer, Walsh, and Logan (2016) argue that this issue—known as the Modifiable

Areal Unit Problem—has affected the work of previous researchers whose analyses relied on ward-

level data. For this reason, they recommend using EDs to examine racial composition prior to the

40 See the “Racial Covenants as a Barrier” section of https://www.arcgis.com/apps/MapJournal/index.html?appid=58c3e008

81374a7b8acddade025ade64, http://www.encyclopedia.chicagohistory.org/pages/1761.html, and Long and Johnson (1947).

41 89 F.2d 817 (D.C. Cir. 1937)

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introduction of census tracts in the 1940s.42 Moreover, they have created and made publicly available ED shapefiles for the period 1900–1930 for 10 cities, including Philadelphia.43

In exhibit 9, the city is divided into hundreds of EDs, many of which are no larger than a few city

blocks. Allison Shertzer also provided us with demographic data at the ED level, which I use to

map the racial composition of each ED. Exhibit 9 places the covenanted properties in relation to

the many small African-American neighborhoods that dotted the city in 1920, and, in doing so,

significantly refines the narrative. The covenanted properties tend to fall along a large horizontal

swath of land just south of the greater Northeast, with concentrations in neighborhoods such

as Dearnley Park (Shawmont), East Falls, East Mount Airy, East Germantown, West Oak Lane,

Lawndale, Juniata Park, Frankford, Mayfair, and Tacony.44 West of the Schuylkill River, the

covenants are clustered in the neighborhoods of Overbrook, Carrol Park, Wynnefield, Kingsessing,

Southwest Schuylkill, and Clearview. Just east of the river, across from Southwest Schuylkill,

covenanted properties were found in Grays Ferry. In the greater Northeast, covenanted properties

were found in Fox Chase and in the Somerton Gardens section of Somerton, a small development

along the northern edge of the city.

42 In addition, Smith (2016) recommends using intensive data representations—those that are comparable across areal units of different sizes and characteristics—to conduct areal data analysis. An example would be to use population per square mile in place of population count.

43 The shapefiles are available to download at https://s4.ad.brown.edu/Projects/UTP2/HGISDoc/Final_Version_All_ED_Maps.zip.

44 For readers unfamiliar with the neighborhoods of Philadelphia, please refer to Spector (2008).

260 Refereed Papers

Documenting Racially Restrictive Covenants in 20th Century Philadelphia

Exhibit 9

Location of Covenanted Properties Relative to African-American Population

Note: Geographic boundaries are enumeration districts.

Sources: Author’s calculations and Allison Shertzer

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Many of the covenanted properties are in neighborhoods adjacent to those with high

concentrations of African-Americans. For example, in Tacony—a neighborhood on the

northeastern waterfront of the city—the ED formed by Princeton Avenue, Gillespie Street, Route

73, and a railway line, was 35 percent African-American (exhibit 10). There, racially covenanted

properties dot the bordering streets, presumably to keep African-American residents from

advancing into adjacent, mostly White neighborhoods.45 Likewise, in exhibit 11, covenanted

properties located in the center of East Falls lie just north of two EDs with higher proportions of

African-American residents.

Exhibit 10

Tacony Neighborhood, Philadelphia

Notes: Scale 1:10,000. Coordinate markings not adjusted for concentration.

Sources: Author’s calculations and Allison Shertzer

45 Recall from section 3, A.2 that, when a street address was unavailable, I approximated the property location to either the nearest street intersection or the intersection that marked the beginning of the metes-and-bounds description. Thus, it is likely that a racial covenant located at an intersection is linked to an interior lot. I do not believe this affects the results presented here.

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Documenting Racially Restrictive Covenants in 20th Century Philadelphia

Exhibit 11

East Falls Neighborhood, Philadelphia

Note: Scale 1:10,000. Coordinate markings not adjusted for concentration.

Sources: Author’s calculations and Allison Shertzer

In some instances, the imposition of racial covenants appears to have been driven by an

existential—rather than imminent—threat. In the Germantown Westside neighborhood, a group of

44 homeowners living along either side of West Penn Street banded together in 1927 to voluntarily

impose a rather strict covenant, whereby violators could be evicted “by force of arms” (exhibit 12).

The full text of the covenant is reproduced below:

That at no time hereafter forever shall any of the said properties be owned, occupied, or leased

by any persons other than those of the Caucasian race. That at any time hereafter forever if any

person or persons of any other race occupy any of the said properties or any portion thereof, it

shall be permissible for any owner or occupier of any property hereinbefore mentioned to evict

the same by force of arms or by action at law, and any title given by any deed to any person other than that of the Caucasian race shall be null and void.

Such agreements—called petition covenants—were written into county deed books by groups of

White neighbors. Compared to deed restrictions, which were typically written at the time a plot

of land was transferred to developers, neighbors seeking to enforce a petition covenant were more

likely to have success in court. Our search yielded only a handful of petition covenants.

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Exhibit 12

Voluntary Neighbor Agreement, West Penn Street in Germantown

Note: Scale 1:15,000. Coordinate markings not adjusted for concentration.

Sources: Author’s calculations and Allison Shertzer

Concluding Remarks

The research presented here represents a critical first step in a broader initiative to ascertain the causal effects of racial covenants on a variety of economic and social outcomes, including the wide

and persistent racial wealth gap. I have begun the process of cataloging Philadelphia’s covenanted

properties, finding 3,826 distinct covenants placed on city properties from 1920 to 1932.

Going forward, I hope to better leverage our technological resources by automating key parts of the

discovery process, testing more powerful OCR software, and investigating how the process might

benefit from the tools of machine learning. With a refined process in hand, I hope to continue

to work with the Philadelphia Department of Records to expand my window back to 1910 and

forward to the Shelley decision of 1948 and beyond. My preliminary findings suggest there is a rich vein to be mined, and the information will only become more valuable as more information is

gathered and the geolocation process is refined.

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Acknowledgments

The author thanks Erik Ebinger and Caleb Hoover for their research assistance, and Alaina Barca,

Julia Cheney, Kyle DeMaria, J.M. Duffin, Amy Hillier, Bob Hunt, Jeffrey Lin, and James Wolfinger

for their feedback and suggestions. Special thanks to James Leonard and Irene Macchia at the City

of Philadelphia Department of Records for facilitating this research.

Author

Larry Santucci is an advisor and research fellow at the Federal Reserve Bank of Philadelphia,

Ten Independence Mall, Philadelphia, PA 19106-1574. He made be contacted by email at

larry.santucci@phil.frb.org.

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268 Refereed Papers

by Christopher Weare

Applying Performance Management

Tools to Understand and Improve

Rapid Re-Housing Program Outcomes

Christopher Weare

University of California, Berkeley

Abstract

This paper examines the potential for improvement in the performance of Rapid Re-Housing programs in terms of moving people experiencing homelessness quickly and effectively into stable housing. These programs have grown rapidly since their introduction in 2009 and have been evaluated extensively. These evaluations have had mixed results but have generally supported the conclusion that this intervention is equally effective and less expensive than transitional housing programs. Although this literature has clarified the overall effectiveness of this intervention, it provides less insight to local policymakers and program managers on whether their programs are performing as well as possible and what could be

done to improve housing outcomes. This analysis employs the tools of performance management—

including benchmarking, control charts, process mapping, and performance comparisons across time and providers—to analyze data from the Continuum of Care in Sacramento, California. These tools search for performance outliers that cannot be explained by the underlying variation in the data and then seek to identify the root causes of these deviations. The analysis does find that significant performance deficits have arisen over time and between program providers. If managers could reduce just one-half of the identified performance deficits, the system-level rate of moving clients to stable housing would increase by one-third, a much larger improvement than could be achieved by reasonable budgetary increases.

What are the prospects for improving the performance of Rapid Re-Housing (RRH) programs

through the application of performance management analytics? To date, researchers have

conducted multiple evaluations of the program (Brown et al., 2017; Byrne et al., 2016; Finkel

et al., 2016; Gubits et al., 2018; Rodriguez and Eidelman, 2017). These summative evaluations

seek to identify the impact of the intervention as it is operating during the assessment stage of the policy cycle. The results of this work have been mixed. The most rigorous analysis did not find

statistically significant program effects, although it found that the costs of RRH were less than

usual care (Gubits et al., 2018). Several observational studies have found more positive results, but

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

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

Weare

none have a randomized control group to assess program impacts (Byrne et al., 2016; Finkel et al.,

2016; Rodriguez and Eidelman, 2017; Spellman et al., 2014). In sum, the rapid growth of RRH

programs has been primarily propelled by the evidence that it is less expensive than shelter care

or transitional housing, although the existing evidence has not established it as more effective than other programs.

This article focuses on a related though distinct question: is there room to improve current outcomes?

These questions are the focus of formative evaluations or performance management processes

during the implementation stages of the policy cycle (McDavid, Huse, and Hawthorn, 2018;

Scriven, 1991). Specifically, it examines whether there are variations in the outcomes between RRH

program providers or over time that indicate the possible presence of performance deficits that, if

addressed, could improve system outcomes. This question is important and timely given that the

program remains relatively new and has grown dramatically. At the national level, the program

model has been in operation only since 2010. Since then, the total number of beds has almost

quintupled from 19,842 to 112,961 in 2019, and RRH now constitutes more than 12 percent of

the stock of beds devoted to addressing homelessness. The program is delivered in a highly flexible

manner wherein most programs apply a progressive engagement model that caters types and

amounts of services provided to each client according to their perceived needs (Dunton and Brown,

2019; Shinn and Khadduri, 2020). This level of administrative discretion can promote program

efficiency by only providing a minimal amount of support to resolve a client’s homelessness, but

it also risks producing unmanaged variation that can undermine program effectiveness. Finally,

the recent housing market boom has raised questions on whether and how this model can operate

effectively in tight housing markets (Batko, Gillespie, and Gold, 2019).

This article examines the prospects for improvement by applying the tools of performance

management to RRH data from the Continuum of Care (CoC) in Sacramento, California (Behn,

2014; Cole, 2011; Hatry, 2006). These tools include benchmarking against performance goals,

control charts, comparisons across subunits and over time, and process mapping. The goal

throughout is to identify anomalies in output and outcome measures that cannot be explained by

random variation in the underlying data. When anomalies are identified then the analysis seeks

to identify their root causes, thereby highlighting performance issues that require management

attention. The analysis identifies significant performance deficits and calculates that addressing the identified problems could increase the number of clients successfully rehoused by RRH programs

by 30 to 40 percent.

The article proceeds as follows. The next section describes RRH programs and reviews the existing

evaluations of the program. It continues to describe performance management and its application

to RRH. The next section describes the data that comes from the Homeless Management

Information System (HMIS) in Sacramento, California. The main analytic section begins with a

comparison of Sacramento to national performance benchmarks and finds that Sacramento does

not meet these benchmarks. The section then continues to diagnose the root causes of the failure

to meet these benchmarks. Throughout, the analysis emphasizes the graphical display of data

that facilitates communicating results to a wider audience. The conclusion contrasts the results to

previous work and provides policy recommendations.

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and Improve Rapid Re-Housing Program Outcomes

Rapid Re-Housing and Performance Management

RRH is an outgrowth of the housing first movement (Mackie, Johnsen, and Wood, 2017).

Traditionally, homeless individuals were moved through a progression of programs, including

emergency shelters and transitional housing, in the belief that these programs were necessary

before clients could be capable of living independently. Housing First, in contrast, seeks to provide permanent housing as quickly as possible while providing supportive services for substance abuse,

employment, and other issues before, during, and after the client is housed.

The RRH approach has been adopted by a range of funders. It was introduced at the national level

in 2009 when the American Recovery and Reinvestment Act created the Homelessness Prevention

and Re-Housing Program (HPRP). Since then, it has been incorporated into funding by the U.S.

Department Veterans Affairs’ (VA’s) Supportive Services for Veteran Families program and in the

U.S. Department of Housing and Urban Development’s (HUD’s) CoC and Emergency Solutions

grant programs. Further funding has been made available from state programs such as the 2018

Homeless Emergency Aid Program in California.

The specific program requirements vary between programs, but they share a common

approach—to place clients into housing as quickly as possible. The program provides short-term

rental subsidies for periods ranging between a few months up to 2 years. During enrollment,

programs provide a range of social services that helps clients find housing and prepares them to

independently maintain housing after program completion. Because RRH seeks to help clients who

are capable of living independently, but who do need short-term assistance, it has been targeted to

those with a middle range of needs.

The programs are designed to be flexible, incorporating a progressive engagement approach that seeks

to provide just enough supports to enable the client to succeed. Consequently, caseworkers maintain

discretion concerning the types of social services provided (such as substance abuse treatment,

employment services, housing search services) and the amounts of financial support provided (such as

relief from debts, security deposits, and the amount and length of housing subsidies).

Evaluations of RRH Programs. A sizable literature has evaluated RRH programs. This work has sought to isolate the effects of RRH programs in comparison to alternative treatments that include

usual treatment entailing stays in emergency shelters in combination with other available benefits

or enrollment in transitional housing programs or receiving long-term housing subsidies (Brown et

al., 2017; Burt et al., 2016; Byrne et al., 2014; Cunningham and Batko, 2018; Finkel et al., 2016;

Gubits et al., 2018; Mackie, Johnsen, and Wood, 2017; Rodriguez and Eidelman, 2017; Spellman

et al., 2014). The results of this work, however, remain inconclusive. The most rigorous random

control trial of the intervention did not find statistically significant program effects compared to

usual care in terms of housing status and family welfare (Gubits et al., 2018). The point estimates

of the study found that RRH achieved better housing outcomes compared to usual care, but the

standard errors were too large to draw strong conclusions. This result is due in part to the fact that the study employed an intent-to-treat design and was affected by a significant crossover between

treatment groups. For example, 22 percent of the families assigned to the usual care group accessed

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RRH services, and only 58 percent of the RRH group enrolled in the program. These factors

combined to decrease the statistical power of the trial (Evans, Philips, and Ruffini, 2019).

In an observational study, Rodriguez and Eidelman (2017) employed propensity score matching to

compare RRH clients to those served by shelter or transitional housing programs. They found that

RRH was more effective than shelters at preventing returns to homelessness, but that RRH was no

more effective than transitional housing. Other descriptive studies have found that the program

operated as intended. Enrollees who made use of RRH were housed more rapidly than those who

do not, and a high proportion of clients achieved housing independence and remained stably

housed after program completion (Cunningham and Batko, 2018). More importantly, this work has

found that RRH produces similar outcomes compared to traditional transitional housing programs

but at a lower cost (Gubits et al., 2018; Rodriguez and Eidelman, 2017).

Two main caveats attach to this work. First, it examines the early stages of program performance,

using data primarily prior to 2014 when the national housing market was still recovering from

the Great Recession. Early evidence did find that the program achieved higher levels of success

in communities with higher apartment vacancy rates (Spellman et al., 2014). Second, these early

programs often screened their clients, focusing on families with children who could meet certain

income minimums. Thus, it is less certain whether these results may be attained for broader

subpopulations and in high-cost housing markets.

Although these studies were not focused on improving program performance, they did highlight

several potential avenues for doing so. Rodriguez and Eidelman (2017) found that there is

significant variation between programs in terms of returns to homelessness, suggesting that some

programs could be improved. The findings concerning the effects of program characteristics are

mixed. Some found that program features did not correlate with outcomes (Finkel et al., 2016),

though others found weak evidence that increasing household income did improve success rates

(Brown et al., 2017). Overall, these authors conclude that there is a need for additional study that

examines the impacts of specific program features.

Performance Management and RRH. In addition to efforts to determine the effectiveness of RRH programs, there have been calls to apply performance management to maintain and

improve program performance (Cepiku, 2017; NAEH, 2016; Turner, 2015). Performance

management practices come in a variety of modalities, each with its own nomenclature, including

PerformanceStat, Compstat, managing for results, lean/six sigma, to name just a few. This

management strategy incorporates several key elements (Behn, 2014; Cole, 2011). It identifies a

series of performance metrics of an organization’s processes, outputs, and outcomes that are tied to

the organization’s strategic objectives. It analyzes these metrics to highlight anomalies that may be associated with performance deficits or performance exemplars. Then, managers conduct regular

meetings to review analyses of the metrics and to develop and followup on strategies to improve

the program’s effectiveness based on these data.

Performance management complements evaluation studies. Evaluations tend to be conducted at

the assessment stage of the program cycle after a program has been in operation sufficiently long

to generate its intended effects. Evaluators come from outside of management structures, either in

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Applying Performance Management Tools to Understand

and Improve Rapid Re-Housing Program Outcomes

a specialized research office or outside consultants. These studies emphasize research designs that

control for confounding factors that may influence observed program outcomes and, thus, require

significant resources and time to complete.

Performance management relies on the evaluation framework to identify metrics that are valid

performance indicators, but its objectives and the manner in which it is conducted differ.

Performance management is conducted close to program managers and provides ongoing feedback

rather than one-time assessments. It focuses less attention on controlling confounding factors and

devotes more attention to finding actionable information. Experimentation with management

strategies and rapid feedback on results substitute for more rigorous research designs. This learning-by-doing strategy is not immune to inferential errors that more rigorous evaluations seek to filter

out, but it does provide opportunities to correct issues with program operations in real-time.

The application of performance management in the public and nonprofit sectors has been widely

touted (Behn, 2014; Forsythe, 2001; Osborne and Gaebler, 1992). These advocates argue that

greater attention to output and outcome metrics in public sector management keeps public

agencies focused on key goals. Furthermore, performance management permits greater managerial

discretion to find methods for achieving those goals while maintaining accountability through

transparent metrics. There is little doubt that data-driven management has had a profound impact

on private sector firms (Womack, Jones, and Roos, 2007).

There have been notable successes with performance management in the public and nonprofit

sectors where agencies have improved the quality and availability of services and strengthened

management practices (Lee, McGuire, and Kim, 2018; Poister, Pasha, and Edwards, 2013; Walker,

Damanpour, and Devece, 2011). CompStat, a performance management system developed for

policing in New York City, is credited with helping reduce crime in that city and has been widely

replicated (Behn 2014; Bratton and Malinowski, 2008; Smith and Bratton, 2001; Willis, Weisburd,

and Mastrofski, 2003). HUD developed and operated its own performance management system

called HUDStat beginning in 2011, and it employed its continuous review of data to tackle veteran

homelessness (HUD PD&R, 2012). Also, Culhane et al. (2008) reported on two local initiatives in

Arizona and Columbus, Ohio, that developed metrics and employed them to strive toward system-

level performance goals.

Nevertheless, the record of successful application of performance management in the public

and nonprofit sectors has been spotty at best (Moynihan, 2008; Radin, 2006). For example,

Sanger (2013) examined 190 cities that published performance metrics online and found that

only 27 cities (14 percent) applied best practices to their use of metrics. Also, after a change in

administrations in Washington, D.C., HUDStat was discontinued in 2016.

A number of factors make it difficult to maintain performance management regimes. Public

agencies and nonprofits are typically staffed by line workers who are experts in program

administration but lack analytic training to manage and analyze data. They also often lack modern

data processing capabilities that restrict the range and quality of metrics that may be brought to

bear in managerial decisionmaking. The rise of HMIS systems has improved this situation greatly,

though many communities continue to be challenged by low data quality and participation levels

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Weare

in their HMIS. Finally, budgetary and time constraints often conspire to force managers to focus on

short-run fixes rather than long-run program improvements (Behn, 2013).

Despite these constraints, homelessness policy and RRH programs, in particular, constitute a policy

area that demonstrates some promise for escaping this web of impediments. The main goals of the

program—helping people back into stable housing—receives wide support, allowing for greater

emphasis on outcome measures. High-quality data is becoming increasingly available due to the rise

of homeless management information systems promoted by HUD. The structure of programs with

services provided by multiple agencies provides an excellent basis for performance comparisons.

In addition, the flexibility, which is a hallmark of the program, offers extensive potential for

experimentation with different packages of services directed at specific subpopulations.

Based on these prospects, the practitioner community has published guides on how to use available

data to monitor and approve programs. Canada has published a performance management guide

that provides a high-level overview of the analytic approach (Turner, 2015). The National Alliance

to End Homeless (NAEH), in coordination with the U.S. Interagency Council on Homelessness

(USICH), HUD, and VA, has promulgated benchmarks for three key performance metrics: (1)

days homeless prior to attaining housing (average days homeless), (2) percent of clients that

exit to permanent housing situations (successful exits), and (3) percent of clients that return to

homelessness within a year after a successful exit (returns) (NAEH, 2016). Based on stakeholder

consultations, NAEH put forth the following system goals:

1. On average, clients should take 30 days or less to move into permanent placements after

program enrollment.

2. Eighty percent of clients should exit to permanent placements.

3. No more than 15 percent of clients who exit to permanent placements should become

homeless again within a year.

The report then details program core competencies in the areas of housing identification, rental

assistance, and case management that would enable CoCs to achieve these performance benchmarks.

Building on these benchmarks, HUD has recently introduced a strategy and analysis toolkit called

Stella that presents CoC data in a manner that permits analysis of these metrics. The dashboards

are based on longitudinal system analysis data that each CoC will upload each year. It provides

overviews on the three NAEH performance metrics and allows users to analyze performance by

client subgroups and by pathways, combinations of programs employed by clients. In particular,

the system points out specific pathways and user groups that are impacting performance to point

managers to important problems. A second analytic toolkit that is intended to facilitate resource

planning is in development.

The analysis in this paper seeks to facilitate the diffusion of performance management by homeless

policymakers and administrators by demonstrating the analytic strategies that can be applied to

highlight performance issues and illustrating the scope of the performance improvement that can

be achieved by acting on these insights.

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and Improve Rapid Re-Housing Program Outcomes

Data

The data for this study comprises a comprehensive, de-identified dataset from the Sacramento CoC

HMIS provided to the researcher by Sacramento County. All the metrics employed in the analysis

are taken directly from HMIS or are calculations using HMIS data fields.1 The analysis focuses on

4,839 program enrollments between 2015 and 2018. Pre-2015 RRH enrollments are excluded

due to concerns with data quality, although these earlier data are used to record clients’ previous

experience in emergency shelter and street outreach programs. The data runs through May 2019

for the purposes of tracking returns to homelessness. Thus, only clients who exit an RRH program

into stable housing prior to May 2018 are included in the calculations for returns to homelessness

within 1 year. Within these enrollments, 516 households enrolled in the RRH program more than

once during these 4 years, leaving the number of distinct households at 4,268.

The demographic characteristics of the heads of households served by Sacramento RRH programs

are shown in exhibit 1. This group is similar to the populations analyzed by previous work on

RRH based on HMIS data from Indianapolis, Indiana, and the state of Georgia (Brown et al., 2017;

Rodriguez and Eidelman, 2017). The average age and the percent of male clients fall between the

percentage from the other two studies. In terms of race and ethnicity, the proportion of White

clients is similar, although the proportion of African-Americans is lower while the proportion

of Latinos is higher in Sacramento, reflecting the different ethnic composition of California.

Compared with the Georgia sample, the proportion of households who enroll in Sacramento RRH

with no income (9.8 percent versus 21.7 percent) and who have children (57.5 percent versus 65.4

percent) are comparable.

There are relevant differences in the Sacramento population. The percent of enrollees with

disabilities is much higher in Sacramento, 47.0 percent compared to 15.1 percent in Georgia and

24.2 percent in the Indianapolis study, although it is uncertain whether these differences are due

to data entry practices or real differences in population characteristics. Sacramento clients are

less likely to have had previous emergency shelter stay, 14.6 percent compared to 23.5 percent in

Georgia. One striking difference between these Sacramento data and the data from Indianapolis

and Georgia is that Sacramento’s rental market is significantly tighter, during the study period.

From 2017 to the present, Sacramento rents based on the Zillow Rent Index have increased over 40

percent, the third-highest rate of increase among the 50 largest metropolitan areas.

1 There are instances in which calculations yielded anomalous results, most likely due to data entry errors. For example, the days spent homeless prior to housing sometimes yielded negative results, probably due to a coding error for the date client moved into housing. Similarly, observations for some program enrollments lacked a housing move-in date, but the client then exited to permanent housing after a year or more in the program. These records are probably due to missing move-in dates. In both of these cases, the variables were coded as missing.

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Exhibit 1

Demographic Characteristics of Head of Household

Sacramento

Georgia State

Indianapolis

Demographic Variables

(n=4268)

(n=379)

(n=203)

Age

40.3

37.1

45.1

Male (%)

41.0

25.9

62.6

White (%)

29.8

23.5

28.0

African-American (%)

48.6

73.1

67.9

Latino (%)

13.8

3.4

n/a

No Cash Income Source at Enrollment (%)

9.8

21.7

n/a

Total Household Income

1,067.9

n/a

578.1

Households with Children (%)

57.5

65.4

n/a

Veteran (%)

29.9

4.8

15.8

Disabled (%)

47.0

15.1

24.2

Previous Shelter Stay (%)

14.6

23.5

n/a

N/A = data not available.

Analysis

The performance management paradigm broadly seeks to identify anomalies in program

performance that cannot be explained by the natural variation in metrics. It then focuses on the

negative anomalies to find problems that may need to be addressed and on the positive anomalies

that may provide clues on how to improve performance. This search for anomalies can take on

many forms and search down many paths, but the performance management paradigm offers

a rich tool kit of methods, borrowing extensively from descriptive, exploratory, and inferential

statistics, for presenting and analyzing data. Once anomalies are identified, managers are then

guided to investigate likely reasons for the anomalies through root cause analysis and other analytic heuristics and to develop and test improvement strategies based on the root causes of identified

issues (Cole, 2011).

This article focuses on a subset of performance management’s analytic tool kit that is most

commonly employed. It begins with benchmarking, the comparison of performance metrics with

standards that have either been established externally or internally through strategic planning. It

then turns to the use of control charts, a common technique that examines performance variations

over time to assess whether a process is controlled, in a statistical sense, or not. It then continues the search for anomalies by expanding these comparisons across time and between subunits (for

example, program providers), two of the most common comparisons employed in performance

management (Behn, 2014). Then the analysis introduces process mapping, which tracks the steps

through which clients proceed when enrolled in an RRH program. Once these steps are identified,

metrics to evaluate the performance of each step are identified to analyze the entire program

process. Based on these performance analyses, estimates of potential performance improvement are

developed based on the reasonable corrections that can be made to program performance.

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The analysis begins with benchmarking Sacramento’s performance with the standards set forth by

NAEH. These comparisons are based on all program enrollments that began on or after January 1,

2015, and ended before May 30, 2018. Later exits are excluded to ensure that all records have a full

year’s worth of data to track returns to homelessness. The results are presented in exhibit 2.

The dashed lines in each graph represent the NAEH benchmark targets, and the whiskers on the

top of each bar represent the 95 percent confidence interval for the sample mean or proportion.

Sacramento only meets one of these three benchmarks, “returns to homelessness.” The average

time to housing of almost 50 days is nearly 66 percent longer than the 30-day benchmark, and the

percent of clients that exit to permanent housing placements is less than 55 percent, far below the

80 percent benchmark. The percentage of returns to homelessness, on the other hand, is below the

benchmark of 15 percent.

Exhibits 2a–c

Comparisons to National Alliance to End Homelessness Benchmarks

Exhibit 2a

Exhibit 2b

Exhibit 2c

System Average Days Homeless

Percent Successful Exits

Percent Returns

2015-2018

2015-2018

2015-2018

60

9

.2

NAEH Benchmark

8

50

7

NAEH Benchmark

.15

40

6

5

NAEH Benchmark

30

cent

cent .1

4

Per

Per

verage DaysA 20

3

.05

2

10

1

0

0

0

NAEH = National Alliance to End Homelessness.

These results are cause for concern. Although most clients who exit into permanent placements

appear to remain stably housed, Sacramento’s system takes a longer than expected time to find

permanent housing for RRH clients, and fewer than expected clients are stably housed when they

exit the program.

The insights that are drawn from these benchmark comparisons, nevertheless, are limited. As

NAEH recognizes, their benchmarks may not be appropriate for all communities. In their review

of early results from RRH programs, Shinn and Khadduri (2020) found that many programs did

meet these benchmarks or came close. Nevertheless, Sacramento’s performance may be the result

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of particular regional or historical characteristics that hamper the effectiveness of RRH, rather than issues with the system itself. Thus, further investigation of the root causes of these performance

deficits is warranted to test the degree to which these deficits can be connected to the operations

of Sacramento RRH programs and, if that is the case, to identify areas in which improvement

strategies should be targeted.

Control Charts. The next step is to examine the control charts for these three key performance metrics. These charts graph metrics over time and compare them to upper and lower control

bounds that are based on the natural variation of outcomes. There are many versions of control

charts, but here, we employ basic ones that set the upper and lower control bounds at three

standard deviations above and below the mean. The goal of these charts is to provide a clear, visual

evaluation of whether a process is under control or whether there are outcomes that are so far

outside of the norm (for example, more than three standard deviations from the mean) that they

require immediate attention.

The control charts from the three NAEH metrics are presented in exhibits 3a to 3c. The short-

dashed line is the outcome for the cohort of clients entering RRH programs each month. The

long-dashed lines with dots are the average outcome, and the thick black lines present the upper

and lower control limits.2 The NAEH benchmark for each metric is shown as a short-dashed line.

In all three cases, outcomes are considered stable from a statistical perspective because in no month does the measure cross the upper or lower control limits. Nevertheless, the control charts do reveal

problems. For the days in the program prior to being housed, the average number of days is far

above the 30-day benchmark, and in only 2 months does the system meet the benchmark. For the

percent of clients exiting to stable housing, the system did briefly meet the benchmarks in early

2015, but since then, performance has steadily declined.

The analysis of returns to homelessness is more nuanced. Exhibit 3c indicates that the rate of

returns is typically below the NAEH benchmark except for a few months in late 2016 and early

2017. It appears, nevertheless, that the percentage of returns was trending upward until the

beginning of 2017 and has since trended down strongly. There are, however, two major caveats

that need to be considered. First, the results for April and May of 2018 are based on clients that

enrolled RRH in those months and exited by May. These fast exits are biased toward clients that

have greater resources that enable them to find stable housing on their own, which makes the lack

of any returns less noteworthy. Second, unlike the other two control charts, this one ends in May

of 2018 instead of extending throughout the year because of the need for a full year of data to

determine whether a return has occurred.

2 When the lower control limit is negative, it is set just below zero.

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Exhibits 3a–c

Control Charts for National Alliance to End Homelessness Benchmarks

Exhibit 3a

Exhibit 3b

Mean Days Homeless by Month, 2015-2018

Percent Successful Exits by Month, 2015-2018

100

1.2

UCL

90

UCL

80

1

70

.8

60

NAEH Benchmark

50 Average

cent

.6 Average

40

Per

30

.4

NAEH Benchmark

20

Mean Days Homeless

LCL

.2

10

LCL

0

0

2015m1

2016m1

2017m1

2018m1

2019m1

2015m1

2016m1

2017m1

2018m1

2019m1

Month

Month

Exhibit 3c

Percent Returns by Month, 2015-2018

.3

.25

UCL

.2

cent .15

NAEH Benchmark

Per .1 Average

.05

0

LCL

2015m1

2016m1

2017m1

2018m1

Month

m1 = January. NAEH = National Alliance to End Homelessness. UCL = upper control limit. LCL = lower control limit.

Although returns to homelessness remains an important metric and it has been the primary metric

employed in evaluations of RRH (Brown et al., 2017; Byrne et al., 2016; Rodriguez and Eidelman,

2017), this feature of the metric limits its usefulness for ongoing program management because it

reflects relatively dated program performance. The average length of stay in RRH is about 155 days.

Adding that program stay to the 365-day projection required to determine whether a client has

returned to homelessness means that this metric is primarily reporting on program performance

for clients that enrolled almost a year and one-half prior to the analysis. Any performance issues

that the metric may signal may no longer be relevant to current management decisions.

On the other hand, examining the returns metric in conjunction with the successful exit metric

demonstrates the value of taking multiple perspectives on program performance. As the successful

exit rate declined through 2017, the returns to homelessness also decreased, suggesting that the

clients that did find stable housing had a greater capacity to remain housed. Conversely, as a

community works to improve their rate of successful permanent housing placements out of RRH,

managers would need to check the returns metric to ensure that their efforts to increase successful

exits did not lead programs to the exit of clients who were not prepared to maintain housing on

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their own and therefore were more likely to return to homelessness. In this way, the two metrics

work in conjunction to provide checks on such unintended consequences.

Comparisons Over Time. To further investigate the significance of the trends observed in the control charts for successful exits and returns, exhibits 4a and 4b show the average rates for

the four cohorts enrolling in RRH in each of the years 2015 to 2018. In these bar charts, the

confidence intervals are adjusted such that visual inspection of the overlap between confidence

intervals is a valid statistical test (Goldstein and Healy, 1995). When the confidence intervals

overlap between 2 years, one cannot reject the null hypothesis at the 5-percent level of significance that there is no difference between the years.3 A key goal of performance management is to only

focus on performance anomalies that cannot be explained by the normal variation in performance,

making normal hypothesis testing of group differences essential. Presenting these differences in bar

charts with confidence intervals facilitates the communication of these tests to a broader audience.

Exhibit 4a shows clearly that there has been a deterioration in the percentage of clients with successful exits from RRH programs. In 2015, Sacramento met the NAEH benchmark, but the success rate

tumbled to 57 percent in 2016 and experienced a further, statistically significant decrease in 2017 and 2018. Returns to homelessness in exhibit 4b provides some additional nuance beyond the control chart.

Sacramento achieved the NAEH benchmark in all years, although the rate did increase by a statistically significant amount between 2015 and 2017. The year 2018 saw a large decline but also has a large

confidence interval due to the smaller number of exits in 2018. Thus, although the rate in 2018 is

statistically significantly lower than 2017, it does not differ from the other years once the margin of error is taken into account. In sum, positive changes may have occurred in 2018 that enabled RRH clients to retain housing, and although the evidence remains weak, further investigation may be warranted.

Exhibits 4a and 4b

National Alliance to End Homelessness Benchmarks by Year

Exhibit 4a

Exhibit 4b

NAEH = National Alliance to End Homelessness.

3 When comparing just two groups, the confidence intervals can be adjusted exactly to yield accurate hypothesis tests.

When a data visualization includes multiple groups, the adjustment is averaged over every pair-wise comparison.

Consequently, there are comparisons for which the significance level is slightly higher or lower than 5 percent.

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In sum, the performance of RRH is stable in a statistical sense. Nonetheless, in terms of successful

exits, the performance has deteriorated in recent years. The next step is to investigate the possible causes of this deterioration.

Program Comparisons. One tactic for investigating the root causes of observed performance deficits is to compare performance across subgroups within a system. RRH programs in Sacramento have

been provided by 16 different providers, although not all have been active all years. This analysis

focuses on 2017 data from nine programs that have been active in 2015 through 2018 and have

served on average at least five clients per year. The comparisons are presented in exhibits 5a–c.

These charts begin to provide more useful clues on where performance improvement efforts should

be focused. First, though, it is important to note two caveats that arise as the analysis examines

ever more narrowly defined subgroups. First, as seen in the charts in exhibit 5, the estimates of

the group means become significantly less precise and have much wider confidence intervals

compared to previous graphs. This issue arises because point estimates from smaller samples have

larger standard errors. These errors limit the distinctions that can be drawn between subgroups.

For example, in exhibit 5a, the average days of homelessness for programs B to I vary widely from

63 to 39 days. Considering the overlaps in confidence intervals though, the average performance

of programs B through G cannot be distinguished because of the large standard errors in the

estimates. Similarly, programs E through I cannot be distinguished. Second, as the number of sub-

group comparisons in the analysis increases, the probability of incorrectly identifying performance

differences where none exists (for example, Type I errors) increases dramatically (Ioannidis, 2005).

This multiple testing problem is well understood in evaluation research (Miller, 2012), although it

does not feature prominently in the performance management literature.

Acknowledging these limitations, exhibit 5a provides two potentially important insights about days

to housing. First, no program meets the NAEH standard of 30 days of homelessness prior to moving

into housing, with most hovering between 45 and 55 days. This consistent level of performance

indicates that managers may face challenges in efforts to achieve NAEH benchmarks. There are

no existing examples of strong performance over time or across programs that could provide a

model method by which the system could identify housing and move clients more quickly. Rather,

managers will have to develop and implement wholly new strategies to address this problem.

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Exhibits 5a–c

Comparisons of Program Performance, 2017

Exhibit 5a

Exhibit 5b

Exhibit 5c

NAEH = National Alliance to End Homelessness.

Second, the data do point out a possible problem in program A. Clients in that program take a

significantly longer amount of time to find housing, over 130 days on average, and the fact that the

confidence interval does not overlap with any other confidence intervals indicates that this level of poor performance cannot be attributed to random variation.

Exhibit 5b demonstrates that there is much great inter-program variation in terms of successful

exits. Program performance ranges between 87.5 percent of clients being placed in stable housing

to less than 35 percent. Although the width of the confidence intervals indicates that there is much

uncertainty with these data, there are stronger and poor performers. Programs E, B, A, and C

constitute a group of higher performers, and programs D, G, H, and I are performing less well. It

is less certain how well program F is faring in comparison because its confidence interval overlaps

both high and low performing programs.

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Exhibit 5c shows a similar division of programs between high and low performers in the term

of returns to homelessness. Four programs (C, I, D, and B) meet the NAEH benchmark of 15

percent or fewer clients returning to homelessness. Two programs (G and E) have significantly

more of their clients who fall back into homelessness, whereas the results for three programs (A,

F, H) are too uncertain to determine whether they should be classed with the higher or lower

performing programs.

These comparisons begin to reveal potential strategies for performance improvement by

distinguishing areas of strong and poor performance. The poor performers, such as program A in

terms of length of time spent homeless or program G that combine low rates of successful exits and

a high rate of returns, need attention to see if their poor performance can be improved. Program

E reports very high rates of successful exits, but also has the second highest rate of returns, which indicates a possible issue that this provider exits its clients too quickly or is overly optimistic

concerning the permanence of their placements at the time of exit. Also, programs that are faring

better, such as programs B and C that combine relatively high rates of successful exits and low rates of return, can be further examined to determine whether they employ specific strategies that can

and should be replicated by lower-performing programs.

Process Mapping. Process mapping is a technique commonly employed in performance

management analyses to identify the root causes of high or low performance, and HUD has

recommended its application for managing RRH programs (HUD, 2014). It involves mapping out

the steps of helping clients within RRH programs and identifying metrics that can be used to assess

the performance of each step of the process. The goal is to identify whether any specific steps are

leading to overall problems and to determine whether the process is working well as a whole.

Specifically, given the analysis that shows that performance has deteriorated, these metrics can

assess which if any steps in this process have functioned more poorly over time.

Exhibit 6 presents a basic description of the RRH process.4 At each step, metrics that can be

calculated based on HMIS data are proposed. Other metrics based on HMIS data are possible

and, with additional data gathering, a CoC could implement still others.5 Nevertheless, this list

provides a useful first-cut analysis of the process. Clients begin the RRH program by first engaging

in the homelessness crisis system. Then there is a triage process by which clients that are well

suited to benefit from RRH are referred to an RRH provider. Once clients are enrolled in RRH,

they are provided with a range of services. Caseworkers may engage in diversion practices that

seek to identify solutions to a client’s homelessness that does not require a stay in subsidized

rental housing. They are also offered help with housing identification and leasing and other social

services. Once they identify and occupy a subsidized housing unit, social services continue to

strengthen the client’s ability to live independently after the program is completed. Finally, clients are exited from the program.

4 Sacramento has not fully implemented a centralized coordinated entry system. Thus, not all RRH clients enter the program in the same manner. Some are referred directly into RRH, whereas others may be referred to RRH through emergency shelters or street outreach caseworkers. These steps still provide a rough guide to the process.

5 Often, process mapping analyzes the time required for each step in the process to identify bottlenecks. In a system with a fully developed coordinated entry system, such time markers can be collected with the dates of evaluation and of referral actions. Unfortunately, these data do not have these timestamps.

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Exhibit 6

Process Map and Relevant Metrics

Engagement

• VI SPDAT scores

Triage

• Income

• Previous homelessness services

• Days spent homeless

Housing

• % exiting to stable housing prior to

Search &

moving in to subsidized housing

Early Support

• % of clients who find subsidized housing

Housing &

• Increase in income

Support

• Days in subsidized housing

Services

• Successful

exit after stay

Program Exit

in subsidized

housing

VI-SPDAT = vulnerability index–service prioritization decision tool.

For the triage stage, the metrics examine whether appropriate clients are being sent to the RRH

program. RRH is a short-term program that assumes that clients will be able to remain housed

independently at the end of the program. Thus, it targets clients that have no more than a medium

level of vulnerabilities and demonstrate the capacity to earn enough to afford housing in the long

run. Three metrics are available. The first is the client’s VI-SPDAT score (Vulnerability Index–

Service Prioritization Decision Tool), which is a commonly used instrument to rate the health,

medical, and social vulnerabilities of clients. The scores range between 0 and 20, with higher

scores indicating a higher level of need. There are slightly different instruments for transition-aged youth and families. Thus, all scores are standardized to make them comparable. The second is an

indicator of whether the household had any income at the time of enrollment. The third is whether

the client had previously been enrolled in an emergency shelter or street outreach. Previous bouts

of homelessness may indicate that the client is more vulnerable, and previous research has shown

that previous episodes increase the risk of a return to homelessness (Byrne et al., 2016; Rodriguez

and Eidelman, 2017). The metric employed here counts any previous enrollment in an emergency

shelter or street outreach as experience with homelessness. This specific metric differs from

Rodriguez and Eidelman (2017) in that it includes street outreach and differs from Byrne et al.

(2016) in that it does not include other contacts with the Veterans Administration. Including street

outreach enrollments in this metric has a major impact. Exhibit 1 shows that 14.6 percent of RRH

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clients had a previous shelter stay. In contrast, when one examines both shelter and street outreach

enrollments in 2018, almost 80 percent of RRH clients had been previously homeless.

To assess the quality and effectiveness of housing search and early support services, there are

three metrics. The first is the number of days until housing is secured. This metric is similar to the NAEH benchmark, although it is restricted to clients who do find subsidized housing. The second

is the percentage of clients that exit the program into stable housing before receiving subsidized

housing. This metric captures the effectiveness of diversion services that may be provided. The

third metric is the percentage of households that find and move into a rental property through

their RRH program. Ideally, information on the range of services and the amounts of cash supports

provided to each client would be telling. Although these data are recorded, the quality of these data was not sufficient to include them in the analysis.

Once housed, RRH seeks to help the client prepare for life on their own. One goal is to increase

their income, and another is to move them to self-sufficiency as rapidly as possible, which is

tracked by the length of time spent in subsidized housing. Finally, at the time of exiting the

program after receiving the full suite of services, a metric is the percent of clients who exit into

stable housing (this percentage only includes clients that have moved into a rental through their

RRH program).

To assess whether the deterioration in performance in exits to stable housing may be attributable

to specific steps in the program process, we examine the trends of these process metrics over time,

examining whether issues have arisen that correlated with the decrease in successful exits. Exhibit

7 presents the changes of metrics in the triage step to check whether the triage process has been

referring more difficult to serve clients to RRH. The evidence suggests that that has not changed.

Exhibit 7a shows that the average VI-SPDAT scored has changed little over the 4 years, ranging

between 7.8 and 8.6 on the 20-point scale. Similarly, exhibit 7b shows that the proportion of

clients with no income at the start of the program remained steady at about 10 percent. There was

a statistically significant, although small, increase in the proportion of households with no income

between 2017 and 2018, but the proportion in 2018 does not differ from earlier years. In contrast,

there is an indication in exhibit 7c that the proportion of clients that have previously experienced

bouts of homelessness had increased from 2016 to 2018, although the 2018 percent was the same as

in 2015 when program performance was stronger. The consistently high percentage of households

that have previously experienced homelessness, nevertheless, is possibly a sign of system-level

difficulties. Under housing first principles, RRH programs are intended to engage clients early in

their experience with homelessness and to provide them housing quickly to avoid the accumulation

of problems that arise from homelessness. These data, however, indicate that Sacramento is only

engaging clients in RRH after they have struggled with housing instability for a longer period.

Exhibit 8 shows the metrics for pre-move-in services to assess whether there are any problems

with the services provided to households early after program enrollment. Two metrics (exhibits

8a and 8b), the number of days until move in and percent housed prior to moving into a rental

subsidized by the RRH program, show little change over time and are unlikely to be the root causes

of decreased performance. The decrease in the percent housed without RRH subsidies in 2018

is likely due to data truncation caused by the shorter time some of these households have been

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enrolled. In contrast, the percentage of clients that do find housing after enrolling in RRH has

declined sharply over the years, from 78 percent in 2015 to 38 percent in 2017 and 2018. This is

an anomaly that requires further investigation.

Exhibits 7a–c

Triage Process Metrics

Exhibit 7a

Exhibit 7b

Exhibit 7c

First, though, we examine the metrics for services after the client is placed in housing and upon

exit. Exhibit 9a shows that for the subset of clients that do move into subsidized housing, success

rates have been steadily at or above 90 percent over time. As seen in exhibit 9b, they have averaged

about 180 days in subsidized housing from 2015 through 2017. The much lower number for 2018

is due to data truncation, in which clients that enter later in the year did not have time to complete the program and have their stay recorded in these data. The percentage of clients that increase their income shown in exhibit 9c, in contrast, is another source of concern. The percentage of clients that increased their income during the program dropped from 20 percent in 2015 to less than 8 percent.

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Exhibits 8a–c

Early Program Services Metrics

Exhibit 8a

Exhibit 8b

Exhibit 8c

RRH = rapid re-housing.

In sum, the review of the program process pointed out two issues that require attention. The first

is that an increasing proportion of clients fail to find a housing option even after they are enrolled in the program, and that the program is not providing sufficient help to clients to enable them to

increase their earning potential and afford housing on their own once the program is completed.

To delve deeper into the root causes of these process issues, comparisons between providers

over time can be made. Unfortunately, not all providers operated in all years, and some had

less than five clients in a year. Thus, exhibit 10 focuses on the five providers with sufficient

data (Providers B, C, D, H, and I in previous graphs). Looking at successful move-ins, a clear

distinction is seen between three providers (D, H, and I) that have had increasing problems

finding housing for their RRH clients compared to two others (B and C) that have consistently

performed better than D, H, and I since 2016. In terms of the percent of clients who increase

their incomes, a similar pattern emerges. Although all programs are only able to increase

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the incomes of a minority of their clients, two programs, B and C, sustained higher levels of

performance. Program C consistently achieved desired outcomes for between 25 and 30 percent

of its clients, and program B steadily improved its performance. In 2018, they both approached

or exceeded 15 percent, a benchmark employed by some continuums (Batko, Gillespie, and

Gold, 2019). In contrast, programs D, H, and I, as with their performance in finding housing,

experienced dramatic and consistent deterioration in the proportion of clients for whom they

were able to increase their incomes.

Exhibits 9a–c

Post-Housing Program Services Metrics

Exhibit 9a

Exhibit 9b

Exhibit 9c

To summarize, the analysis followed a path delineated by the questions raised at each level

of inquiry. First, significant performance deficits were identified through comparisons with

NAEH benchmarks. The root causes of these deficits were further examined with control charts

that indicated that, although the processes were in statistical control, the results for successful

exits showed worrisome deterioration in performance over time. To examine the source of this

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deterioration, comparisons between program providers were made that revealed significant variance

in performance. Delving into the causes through process analysis placed further focus on issues

related to successfully completing the housing search process and helping clients increase income.

Exhibits 10a and 10b

Outcomes by Program and Year

Exhibit 10a

Exhibit 10b

These insights provide useful and actionable insights for program-level and system-level managers.

There is room to address issues with finding housing for RRH clients and for program supports

that seek to increase clients’ capacity to afford housing independently. Moreover, the analysis

identifies three programs, D, H, and I, that deserve particular attention. NAEH and HUD have

collated best practices for housing identification, including staffing, policies, and activities and case management (HUD, 2015; NAEH, 2016). NAEH suggests that programs should ensure that staff

members are properly trained in housing identification services. Such training should ensure that

staff progressively increase the supports provided in response to client needs and that staff set the subsidy level for rent at levels that enable clients to acquire a lease. It suggests that programs need to actively recruit and manage relationships with landlords. Strategies include signing master leases, guaranteeing short vacancy periods between tenants, and increasing the amount of security deposit

provided. NAEH also recommends maintaining clearly defined relationships with employment and

income programs to promote the capacity of clients to afford rent after program completion.

In a fully developed performance management system, managers would employ regular review

meetings to focus on the poorly performing programs identified in this analysis. They could

consult with program-level managers to compare their current practices to these best practices and

develop appropriate strategies based on these comparisons. Once promising avenues are identified,

system managers could work with program managers to implement these strategies and track their

effectiveness through continued surveillance of the trends in performance metrics.

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Prospects for Improvement

What is the potential magnitude for improved system-level performance if managers were to act on

this analysis? To develop estimates, one needs to differentiate between two types of improvements:

systemic improvements and error corrections. Systemic improvement moves the performance of

an entire system to a higher level. Take, for example, a CoC with an average successful exit rate of

65 percent and in which no program in any year achieved higher than a 70 percent successful exit

rate. For that CoC to raise its average rate to the NAEH benchmark of 80 percent would require

systemic changes that increased the performance of providers beyond historical levels. Although

this level of improvement is possible, it is challenging given that managers have no readily available models on which to build improvement strategies. Error corrections, in contrast, compare current

performance to performance levels that have been achieved and seek to close the gap between

current performance and past peak performance. The feasibility of these types of improvements is

greater because the goals have been achieved previously in certain years and by certain programs,

providing models to replicate.

Even with a more conservative focus on error correction improvements, the potential for

improvements is significant. In this CoC, there have been programs that have met the 80-percent

NAEH benchmark for successful exits. Thus, this goal is reasonable for the system as a whole.

If programs were able to close one-half of the gap between their 2017 success rates and this

benchmark, the number of clients stably housed would increase by 33 percent, from 747 to 995.

Considering that HUD CoC grants have only increased at a 2.5-percent annual rate between 2010

and 2019, such performance improvements offer continuums a significantly greater avenue for

increasing the program impacts compared to increasing budgets.

Discussion and Conclusions

The analysis presented here supports the contention that performance management can have

significant positive effects on efforts to address homelessness in the United States. A guided

search through performance metrics making comparisons over time and across subunits revealed

weaknesses in program performance. Further process mapping then pinpointed the likely root

cause of performance deficits as problems with identifying housing for program participants. Just

partially closing the gap between low- and high-performing programs has the potential to improve

overall program outcomes by between 30 and 40 percent.

The results also reinforce the findings of existing evaluation studies. As Rodriguez and Eidelman

(2017) found that there is substantial variation in the performance of individual providers of

RRH services, and the current analysis demonstrates how reducing the gaps between low and

high performers can improve system-level outcomes. Also, the data presented here show that

RRH can be an effective program. Certainly, in 2015, the year closest to the cohorts analyzed in

the evaluation studies, the Sacramento program exceeded or came close to meeting the NAEH

standards for housing placements and returns to homelessness. On the other hand, these data

reveal a distinct decline in program performance from 2016 onward. These trends may be unique

to Sacramento, given that its housing market has been impacted with particularly rapid increases

in rents. Nevertheless, further study is warranted to see how pervasive these trends are especially

290 Refereed Papers

Applying Performance Management Tools to Understand

and Improve Rapid Re-Housing Program Outcomes

given the strong evidence that homelessness is strongly impacted by tight housing markets (Glynn

and Fox, 2019; Quigley and Raphael, 2001).

The adoption of robust performance management regimes does involve risks. First, the insights

derived from these analytic tools can suffer from issues of internal validity. Managers may

misattribute performance differences to program features that are in fact due to extraneous

factors. The use of confidence intervals limits the dangers of misidentification of problems. Also,

ongoing assessment of strategic initiatives can root out strategies based on faulty assessments of

the underlying problems. Performance comparisons, however, do not fully control for alternative

explanations for observed performance deficits such as differences in client characteristics and

environmental factors. Future work could focus more on controlling for confounding factors

through propensity score matching or other statistical controls. Nevertheless, the use of real-time

programmatic data in performance management limits the use of random treatment assignment

to control for differences in clients and environmental conditions. Consequently, performance

management systems cannot replace rigorous program evaluations that take care to isolate

program effects.

Second, as performance management systems strive to increase accountability and improve

resource allocations, they create incentives to game the system either by cream-skimming,

where programs boost outcomes metrics by enrolling easier to serve clients while turning away

individuals who are less likely to succeed, or by outright distortion of reported data (Hood, 2006;

Musso and Weare, 2020). Avoiding such perverse consequences requires deft management that

emphasizes the degree to which performance management can lift the entire system to better

achieve common goals over negative attention from rigid accountability. It is also important to

maintain oversight over data quality to detect any issues in reporting.

These results from Sacramento are also not generalizable. The particular performance issues found

in these data would not necessarily be found in other communities, and each community is likely

to have its own set of idiosyncratic issues with program operations. In contrast, the methods

pursued in this analysis, the process of broadly searching for performance outliers and then

seeking to understand their root causes, can be replicated. The search for performance deficits will

likely lead down different paths for other CoCs, and there may well be CoCs that meet or exceed

NAEH benchmarks, leaving little room for improvement. Nevertheless, the magnitude of potential

performance improvements, in this case, suggests that the opportunities for improvement in other

CoCs are significant.

The main policy recommendation arising from this analysis is simply that more systems need to

apply these analytic tools to the management of homelessness programs. HUD’s development of

Stella models is a major step forward in this process. Given that CoCs frequently lack expertise

in data extraction and analysis, the Stella models provide decisionmakers powerful tools while

avoiding imposing heavy data analytic costs on CoCs. On the other hand, there are limitations to

the Stella model. It provides a specific set of analyses focused on subpopulations and pathways

through the homelessness crisis system. The analysis presented here, in contrast, highlights

the importance of analytic flexibility. Problems may pop up in multiple components of specific

programs or a system as a whole. Thus, a broad approach that examines multiple key performance

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indicators is more likely to identify areas of concern. Then, a search for the root causes of

performance deficits requires a combination of views into the data, including control charts,

comparisons across time and programs, and process analysis. The relevant analyses are not evident

until data anomalies are identified, making it difficult to define analytic packages ex-ante.

A second recommendation is the need to develop administrative controls of homelessness

programs that are capable of effectively responding to data-analytic insights. Performance

management is as much an exercise in leadership as in analytics (Behn, 2014). Major examples of

successful performance management reforms involved skilled and committed leaders that operated

in environments with centralized executive control. These examples include police departments

with their military-like chain of command and parliamentary governments in other countries,

such as New Zealand and Great Britain, where the ruling party has greater control to implement

management changes (Moynihan, 2008). CoCs, in contrast, are loosely connected coalitions of

providers and funders that lack a unitary executive function. Managing such governance networks

with a focus on key performance metrics is a challenge, but one that must be confronted if

communities are to realize the potential performance improvements illustrated by this analysis.

Acknowledgments

The author would like to thank Sacramento County for sharing their HMIS Data, and the

anonymous reviewers for their insightful remarks.

Authors

Christopher Weare is a lecturer at the University of California Berkeley’s Goldman School of

Public Policy and the President of the Center for Homeless Inquiries. His research focuses on the

use of performance management in the public and nonprofit sectors with particular attention to

issues of homelessness.

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296 Refereed Papers

Departments

In this issue—

• Affordable Design

• Data Shop

• Foreign Exchange

• Graphic Detail

• Industrial Revolution

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

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

298 Departments

by Jagruti D. Rekhi

Affordable Design

The U.S. Department of Housing and Urban Development sponsors or cosponsors three annual

competitions for innovation in affordable design: The Innovation in Affordable Housing Student

Design and Planning Competition; the American Institute of Architects – HUD Secretary’s

Housing Community Design Awards; and the HUD Secretary’s Opportunity & Empowerment

Award, co-sponsored with the American Planning Association. This Cityscape department

reports on the competitions and their winners. Each competition seeks to identify and develop

new, forward-looking planning and design solutions for expanding or preserving affordable

housing. Professional jurors determine the outcome of these competitions.

2020 Innovation in Affordable

Housing Student Design and

Planning Competition: Camino de

Jacobo in Santa Fe, New Mexico

Jagruti D. Rekhi

U.S. Department of Housing and Urban Development

The Jury:

Danielle Arigoni, Director of Livable Communities, AARP

Christie DeSanctis, Director, Business and Conventional Finance Policy

Kathleen Dorgan, FAIA, LEED-AP, Principal, Dorgan Architecture and Planning

Rob Hazelton, CEO, Dominion Due Diligence Group

Elizabeth Plater-Zyberk, FAIA, LEED AP, Professor of Architecture and Director of the Master of

Urban Design Program, University of Miami

Joe Ventrone, Vice President, Federal Policy and Industry Relations

Winning Team: Yale University, New Haven

Helen Farley

Kelley Johnson

Eva Leung

Jackson Lindsay

Runner-Up Team: University of Maryland, College Park

Sam Bohmfalk

Margaret Curran

Tochi Ohakawa

Shayne Piltz

Andrew Walker

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 299

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

Rekhi

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.

Introduction

The seventh annual HUD Innovation in Affordable Housing (IAH) Student Design and Planning

Competition challenged multi-disciplinary graduate student teams to respond to an existing

affordable housing design and planning issue. The IAH Student Design and Planning Competition

is open to graduate students in architecture, planning and policy, finance, and other disciplines.

The competition challenges the students to address social, economic, and environmental issues in

responding to a specific housing development problem identified by a partnering public housing

agency (PHA).

The overarching goal is to advance innovation in the design of affordable housing. The competitors’

plans and designs must address the issues outlined by the PHA. The designs should identify

improvements that could be implemented at the site, and the plans must promote durability,

reduce energy consumption, increase the quality of housing, and enhance the social and economic

vitality of the surrounding community.

For the 2020 challenge, HUD partnered with the Santa Fe County (New Mexico) Housing Authority.

Teams were challenged not only to innovate but also to preserve and celebrate the unique culture of

Santa Fe. They were asked to design a new mixed-use development for low- and moderate-income

residents, with a particular focus on expanding housing for women with children, with the usual

planning constraints: zoning requirements, local economic conditions, financial feasibility, the built environment, and the larger social needs of the community. Executive Director, Joseph Montoya,

envisioned a development similar to the Santa Fe city center, with a mixture of businesses, housing,

and centers of faith all within the immediate area and consistent with an architectural heritage going back to the 1573 Law of the Indies promulgated by the King of Spain.1

The County of Santa Fe purchased a vacant lot of 6.6 acres of land in the fastest-growing part

of the city of Santa Fe, which has a very uncoordinated pattern of development in that area (see

exhibit 1). The parcel also adjoins some infill sites and a commercial power center. The developer

of the power center is working on zoning that will allow for more pedestrian-oriented, high-density

mixed-use, and mixed-income residents. The land is zoned to allow for up to 29 units per acre, the

highest allowable density in the city of Santa Fe. The Housing Authority would like to use low-

income housing tax credits and other sources of capital to finance the development.

1 https://www.huduser.gov/portal/sites/default/files/pdf/The-Laws-of-the-Indies.pdf

300 Affordable Design

2020 Innovation in Affordable Housing Student Design and

Planning Competition: Camino de Jacobo in Santa Fe, New Mexico

Exhibit 1

Site Location and Surrounding Area

Source: www.CPI.NM.com

The competition is designed in two phases. During Phase I, a jury of six practitioners (a planner,

builder, realtors, finance specialist, and architects) evaluated the first-round proposals submitted by teams from 32 universities electronically. The jury selected four finalist teams from the 32 proposals to move on to Phase II of the competition. In Phase II, the finalist teams further refined their

proposals—addressing complex issues, incorporating more detail, improving their design plans,

and conducting additional analyses on the financing needed to create viable housing, following

the site visit to Santa Fe. The site visit enabled the finalists to expand on their original proposal and submit a revised final project. Several weeks after the site visit, the original plan would have

required the jurors and the four final teams to travel to HUD headquarters in Washington, DC,

to present their plans and the awards ceremony. Due to the COVID-19 pandemic, the event was

held virtually on April 16, 2020. At this event, finalist teams presented their revised project plans.

Following the presentations, the jury selected the team from Yale University as the winner and the

team from the University of Maryland, College Park, as the runner-up.

For this article, the winning student teams and members of the jury shared their thoughts about

the competition. The students reflected on the biggest challenges the team faced and how they

attempted to address them, opportunities to learn from mistakes, their concept of innovation,

elements observed that provided value to the design of the project, and any tradeoffs that had

to be made to get a feasible site plan. The Yale teams commented that their “team really tried to

work together to create a complete design where the social, financial, and sustainable models all

worked together to create a strong community fabric. It was through our many interdisciplinary

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conversations that some of our best ideas came through.” Jurors commented that Yale’s project was

“rooted in a communal lifestyle that can enrich the quality of tenants lives, and pays homage to

New Mexico with its thoughtful integration of community at many scales,” and that the University

of Maryland’s project design “provides social connection, stability, and support for some of Santa

Fe’s most vulnerable community members.”

The Winning Team: Yale University

Helen Farley, Kelley Johnson, Eva Leung, Jackson Lindsay, and Miguel Mauricio

The award-winning site plan from Yale University, called Jacobo Commons—Community at Many

Scales, would create 158 units, 62 percent of them affordable, with 10 percent of the affordable units reserved for households earning less than 30 percent of the area median income (AMI). The

remaining 38 percent would be market rate. The total development cost of the project is $44.4

million, $245 per square foot, or approximately $281,000 per unit.

The team’s design promotes communal living to enrich the tenants’ lives. They wanted their design

to reflect the rich history of the indigenous people of New Mexico. Inspired by the pueblos,

the project endeavors to develop a strong community among the residents and the surrounding

neighbors by creating community paths providing access to the local shopping centers and

neighbors (see exhibit 2). Team member Jackson Lindsay commented, “Our team really tried to

work together to create a complete design where the social, financial, and sustainable models all

worked together to create a strong community fabric.”

Exhibit 2

Overview of Jacobo Commons

Note: Some walkthroughs are highlighted on the right.

302 Affordable Design

2020 Innovation in Affordable Housing Student Design and

Planning Competition: Camino de Jacobo in Santa Fe, New Mexico

The design incorporates features to promote better health, economic, and environmental

outcomes, such as including flexible spaces for work, community courtyards, community gardens,

playgrounds, a gym, and daycare facilities. As a juror commented, “The courtyard concept provides great integration of space, and community support spaces are distributed throughout the buildings along an access that makes the community more walkable and inclusive.”

Outdoor Living: The design provides visual variety and open courtyards as inviting spaces while

providing views out to the local mountains, especially the Sangre de Cristo range. The buildings

themselves step down into the courtyards, replicating Pueblo architecture. The design would allow

adults to supervise children at play in the courtyard from their windows (see exhibit 3).

Exhibit 3

Placement of the Courtyard at Jacobo Commons

Healthy Living and Connectivity: The public outdoor area consists of a series of active spaces,

including a basketball court and playground. The paths (see exhibit 2) create connections to

local walking and bike trails to the east and south of the site. The project features a community

garden and dedicated space to host local farmers’ markets. The paths would create access not only

to the trail network in the adjoining lot but also to the cluster of retail stores across the lot. Two courtyards open up to the neighborhood off of the central axis, providing public recreation spaces

for the surrounding families, and encouraging walking and biking to and from the site.

On the Environment: The team designed the hardscaped surfaces to be as porous as possible, with

permeable pavers on the streets and a bioswale in the southern courtyards to help with stormwater

retention and on-site filtration (see exhibit 4). The building uses solar energy for heat through the colder months, and in the hot summer months, window frames block solar rays. The Yale team

kept Santa Fe’s climate in mind in striving to achieve energy and water requirements on-site. The

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jury was impressed with the green, energy-efficient construction with passive cooling, solar energy,

and geothermal heating (see exhibit 5).

Exhibit 4

Jacobo Commons Water Drainage and Retention

Exhibit 5

Jacobo Commons Building Solar Panels

304 Affordable Design

2020 Innovation in Affordable Housing Student Design and

Planning Competition: Camino de Jacobo in Santa Fe, New Mexico

On Innovation: The team opted for durable, sustainable, resilient adobe block and plaster

construction materials, which resulted in a highly energy-efficient building with higher

construction costs. They proposed master metering of the buildings to allow the housing authority

to capitalize on the saving achieved. Head Juror Rob Hazelton was particularly inspired by the

team’s forethought, noting that the team “included a Year 15 exit strategy of converting portions of

the property to a Limited Equity Co-op, providing a method for tenants to build sweat equity or

pay-in over their resident tenure to achieve future homeownership.” The team designed housing

to allow for intermingling between different age groups, households, and incomes. In their final

presentation, they noted that care was given to creating mixed housing so no one group— such as

singles, households with children, elderly units, or market rent units—was isolated.

Jurors observed that Jacobo Commons would offer a blueprint for communities that place a

strong emphasis on the preservation of local architectural traditions in places where people would

want to live and retire, while adhering to the goal of extending housing options for residents

regardless of income.

The Runner- Up Team: University of Maryland, College Park

Sam Bohmfalk, Margaret Curran, Tochi Ohakawa, Shayne Piltz, and Andrew Walker

The University of Maryland, College Park’s Nueva Acequia was selected as the runner-up this year.

Their development plan proposed a mixture of multifamily residences, townhomes, garden-style

apartments, and permanent supportive housing for 210 units (see exhibit 6). The total cost of

construction is $47,338,693, or $257 per square foot. The team proposal addressed three goals: (1)

increasing the availability and affordability of housing, (2) extending a pathway to homeownership,

and (3) reducing homelessness. Drawing from the Taos Pueblos tradition of shared irrigation

systems, Nueva Acequia is designed with shared community resources to provide residents with

economic opportunity, diversity, health and wellness, and sustainability.

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Rekhi

Exhibit 6

Overview of Nueva Acequia Site with Building Typology

Source: University of Maryland presentation, April 16, 2020

The plan would establish an on-site wrap-around services center where residents could go to get

assistance with job searches, health care, and homelessness prevention. Nueva Acequia would

also provide flexible live-work units that can be used for work-live, live-live, or work-work,

where families might also operate small businesses. The plan also includes space for both a youth

education center and a daycare center with an enclosed outdoor play area.

On Building: Nueva Acequia’s architecture merges contemporary design with the Pueblo tradition.

The structures would be wood-frame with layered wood-and-stucco veneers. Production

techniques would allow all buildings to achieve standards beyond the established International

Energy Conservation Code. The project will use low-e windows2 positioned to provide maximum

sunlight, decreasing the use of lighting, and reducing the need for air conditioning.

On Health and Wellness: The community would feature both a fitness facility and a Green Living

Center utilizing innovative indoor vertical farming techniques that save water and space (see

exhibit 7). The center leads to a terrace with community garden plots available to the residents.

The community is buffered on three sides by complete streets, prioritizing pedestrians over cars.

The streets bridge the currently disconnected neighborhoods to the east and west. Head Juror Rob

Hazelton congratulated the team on providing the community with a resident-only fitness center

for the community, giving residents a way to maintain a healthy lifestyle.

2 A low-e glass (or low emissivity) window is a windowpane coated in microscopic layers of metallic oxides, invisible to the naked eye. It allows natural light to come through while minimizing the infrared and ultraviolet (UV) rays from coming through. This controls radiant heat (infrared light) as it enters and leaves a room, keeping your house warmer in the winter by reflecting certain segments of the sun’s light spectrum back into the home, and cooler in the summer by reflecting particular sections outside.

306 Affordable Design

2020 Innovation in Affordable Housing Student Design and

Planning Competition: Camino de Jacobo in Santa Fe, New Mexico

Exhibit 7

Services Available at Nueva Acequia

Source: University of Maryland presentation, April 16, 2020

On Environment: The facility would have industrial composting to reduce waste. Solar panels and

optimized building performance would lower the community’s electricity costs by 55 percent. The

landscaping further minimizes the impact on the environment by creating a stormwater-retention

dry creek, fully permeable surface treatments, low water xeriscaping,3 and native vegetation.

On Financing: The community is financed with low-income housing tax credits (LIHTC) and

mixed gap funding, including Rental Assistance Demonstration (RAD) conversion vouchers. The

addition of RAD vouchers, in particular, is expected to increase the supply of affordable housing

units and also include a diversity of housing types that will be made available to residents at

various income levels. At 16 years, a portion of the units can be purchased by the tenants who

have lease to purchase agreements. The homes will be available for purchase at an affordable price.

Joe Ventrone, a juror representing the National Association of Realtors, remarked that he “loved

the project’s unit mix and financing package.” Collectively, the jurors felt that Nueva Acequia

featured a sustainable design that included housing, studios, live-work space, fitness spaces, and

community areas supporting seniors and young families alike. The team featured 14 townhouse

condominiums, which in year 15 present potential homeownership opportunities for residents.

3 Landscaping that uses native, drought-resistant plants arranged in efficient, water-saving ways.

Cityscape 307

Rekhi

Thoughts from the Jury

Rob Hazelton, Danielle Arigoni, Christie DeSanctis, Kathleen Dorgan, Elizabeth Plater-Zyberk,

and Joe Ventrone

The jury for the 2020 IAH Student Design and Planning Competition faced the difficult task of

deciding which of the four outstanding student site plans best exemplified an innovative design.

The members were asked specifically to consider how well the student teams successfully and

convincingly addressed the following critical elements—

• If the proposed design is reasonable and feasible in its design and planning, demonstrating

knowledge and understanding of codes and zoning.

• Whether the proposed design is resilient and environmentally responsive to local climate and

site conditions (for example, healthy, energy efficient, water efficient, resource efficient, and

low impact). Also, whether the proposal contains an economic life cycle analysis.

• If the proposed solution is affordable (cost effective to construct and operate).

• If the design innovates in a way that integrates the design into the neighborhood

and community.

• Does the design promote social responsiveness, such as creating a sense of neighborhood or

cohesive community, facilitating access to employment and services, addressing accessibility,

demonstrating the opportunity for social networking, control, and comfort?

• Is the approach innovative in all aspects of the solution (for example, planning, design,

construction, environmental concerns, and durability)?

• Is the design innovatively addressing the needs of singles and women with children?

• Were innovative approaches employed to integrate the design into the neighborhood

and community?

The jurors found two of the four teams’ proposals addressed nearly all of the issues discussed above

clearly and with forethought. After eliminating two of the four presentations, the jurors emphasized

that the deciding factor was how well the students identified and discussed innovation in their site

plans. Narrowing the competition down to the University of Maryland and Yale University teams,

the jury set about identifying elements of the site plans they thought were particularly innovative

while keeping an eye on the critical elements listed previously. They quickly decided that the Yale

University site plan was both inviting for the residents and provided innovative financing.

The Executive Director of the Housing Authority of the County of Santa Fe commended the

students’ hard work and their contribution toward finding innovative solutions to the affordable

housing challenge that Santa Fe confronts: “Beyond the exceptional work and expertise that

HUD staff, consultants, and jurors brought to the table, it was a joy to work with young, positive

professionals who passionately pushed some exciting ideas. We will definitely be questioning some

of our original assumptions and viewpoints with a fresh outlook as we move forward.”

308 Affordable Design

2020 Innovation in Affordable Housing Student Design and

Planning Competition: Camino de Jacobo in Santa Fe, New Mexico

Acknowledgments

The U.S. Department of Housing and Urban Development (HUD) thanks the award-winning

student teams from Yale University and the University of Maryland, College Park, for sharing their

thoughts and for all the hard work they put into their submissions for this year’s competition. We

also thank the remaining two teams that were selected to participate this year: The University of

Michigan, Ann Arbor, and the University of California-Berkeley. HUD greatly appreciates the 2020

Innovative Affordable Housing jury members’ dedication and hours devoted to the awards selection

process. Finally, HUD thanks Schatz Publishing Group LLC for planning and logistics efforts

under the constraints of the COVID-19 pandemic. Their hard work and flexibility made this year’s

competition a success.

Author

Jagruti D. Rekhi is a social science analyst in the Affordable Housing Research and Technology

Division of the U.S. Department of Housing and Urban Development.

Postscript

The competition is thoroughly documented on the web.

To learn more about the award: https://www.huduser.gov/portal/challenge/home.html.

To read about the 2020 design guidelines: https://www.huduser.gov/portal/challenge/

competition_2020.html.

Cityscape 309

310 Departments

by Regina C. Gray

Annual HUD Secretary’s Opportunity

and Empowerment Award: Ebeid

Neighborhood Promise Initiative,

Toledo, Ohio

Regina C. Gray

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.

The American Planning Association (APA) was established in the late 1970s with the central

mission to create great communities for all through effective planning, education, and advocacy.

Since its inception, the organization has fostered partnerships with national, state, and local entities committed to a similar vision. For more than two decades, the U.S. Department of Housing and

Urban Development (HUD) and the APA have co-sponsored the HUD Secretary’s Opportunity and

Empowerment Award, which recognizes communities that adopt creative and effective strategies

for improving the quality of life for area residents. In their submissions, nominees discuss the role of planners throughout the process and how the submission actively engaged with residents, the

public, and partnering organizations. Nominees are required to address how their project, plan,

or proposal incorporates innovative approaches to problemsolving; how it achieves measurable

results; and how those results might be replicated in communities similarly situated. This year, a

jury of industry professionals selected the Ebeid Neighborhood Promise Initiative and its partner

organization, ProMedica, as the award recipient.

The UpTown neighborhood is an old exurb nestled between historic Downtown Toledo and

the Old West End community. UpTown residents face a number of challenges: disinvestment

in commercial corridors; lack of infrastructure to support a grocery store; clusters of subsidized

housing with few supportive services; high rates of unemployment, poverty, and homelessness;

and poor health conditions. Although UpTown is a diverse community, the vast majority of the

residents are renters, many are low-skilled workers, and the prospects for educational attainment

are limited. Those challenges were documented in the UpTown neighborhood master plan. Local

planners called for a holistic, “all-in” strategy focused entirely on improving the community’s

socioeconomic determinants of health.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 311

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

Gray

ProMedica, a mission-based organization in upper northwest Ohio, teamed with philanthropist

Russell Ebeid to establish the Ebeid Neighborhood Promise Initiative (ENP). The initiative reflects

a 10-year, $50 million commitment to improve health outcomes, provide stable housing, and

expand access to educational and employment opportunities. Although the program is relatively

young, ENP has demonstrated remarkable progress toward building a socially and economically

sustainable community. Using existing community plans, a robust and creative placemaking

strategy, unique partnerships, and innovative funding models, ENP demonstrates that it is possible

to build on the existing community landscape and create a more welcoming environment for all

who live there.

Planning

The planning award category requires that nominees explain how their submission addresses

a real-world challenge in the community, as identified by an existing comprehensive,

regional, or neighborhood plan. Applicants must describe the role of planners in working

with local decisionmakers, partnering organizations, and the public in achieving success

from implementation and beyond. In the spirit that this award represents, submissions must

demonstrate how the community engaged and empowered residents throughout the process.

The Ebeid Neighborhood Program is a place-based initiative that reflects two decades of

planning activities that have resulted in a shift toward improving the conditions of the UpTown

neighborhood. ProMedica’s model envisions a comprehensive approach to planning that centers

on the social determinants of health. In the earliest phase, ProMedica and its partners instituted a

series of signature programs meant to empower residents by improving access to quality housing,

health care, job training, employment opportunities, education, and small business enterprise. The

strategy entailed creating a one-stop-shop at a facility that offers a range of supportive services for residents seeking opportunities for a better life. The ProMedica Ebeid Institute, pictured below,

serves residents by increasing

access to healthy food, delivering

nutritional education, and

offering job training, among other

programs and cultural events.

Partner organizations, including

the Local Initiatives Support

Corporation (LISC) and

AmeriCorps, worked with

residents to identify community

needs and priorities. For instance,

ProMedica health providers

surveyed residents and found

that among their biggest concerns

was food insecurity. In 2015, the

Source: ProMedica

The ProMedica Ebeid Institute is an all-in-one community hub that houses the market and provides

institute opened Market on the

job training, education, health screenings, family planning, and financial and housing counseling.

312 Affordable Design

Annual HUD Secretary’s Opportunity and Empowerment Award:

Ebeid Neighborhood Promise Initiative, Toledo, Ohio

Green, a nonprofit co-op that offers a variety of healthy, affordable food options. The market targets area residents for onsite job training. ProMedica and partners expanded access through a mobile

market that delivers food to seniors and people with disabilities and offers online ordering, delivery, and pickup. A community garden adjacent to the facility provides fresh produce for the store.

Investment capital of $86.7 million was deployed for housing at all income levels. To expand and

preserve affordable housing, ENP offers loans of up to $7,500 to residents looking to purchase or

renovate units in designated Low-Income Housing Tax Credit (LIHTC) program properties that

have reached the end of their 15-year compliance period. To combat blight, the ENP community

revitalization program incentivizes builders by offering up to $40,000 per unit1 to renovate

vacant properties. A new mixed-income housing development called Village on the Green offers

a variety of housing types, such as garden-style or high-rise. Education and training programs are

also available onsite, targeted to residents who either work at the grocery co-op or are searching

for jobs in the medical field. Those programs include housing counseling, financial literacy,

mentorship, and coaching.

Results

The second award category requires that applicants clearly describe how the project, plan, or

initiative achieves goals through measurable outcomes. Examples include the number of jobs

created and retained, improvement in education outcomes (for example, graduation rates), or

data showing a reduction in crime or poverty. The following examples of outcomes are reported

from ProMedica’s recent report, Embracing an Anchor Mission: ProMedica’s All-In Strategy (Oostra, Zuckerman, and Parker, 2018).

Health

• ENP partners invested $600,000 annually to hire at least one school nurse for every public

elementary school in Toledo.

• To address food insecurity, about 2,600 meals were provided to low-income persons and

families who were screened for acute or severe hunger, and 600 pre-packaged meals were

delivered to food clinics (Oostra, Zuckerman, and Parker, 2018: 24–5).

• Together with Mercy Health, the University of Toledo Medical Center, and the Hospital

Council of Northwest Ohio, ENP created Northwest Ohio Pathways HUB. This program used

medical home teams to screen 20,000 pregnant mothers. Of those screened—

{

Sixty-nine percent were referred to additional services, including the Pathway HUB and a

home visiting program.

{

Women enrolled for more than 90 days in the program had a 90-percent rate of healthy

birth outcomes.

1 The program is part of a ProMedica-LISC and Key Bank initiative, which allows former renters to borrow between $30,000–40,000 per unit to make necessary renovations. The program infuses LIHTC program tax credit funds, local bonds, and other sources to encourage a path to homeownership (Oostra, Zuckerman, and Parker, 2018: 42–43).

Cityscape 313

Gray

{

Those who enrolled in the program for more than 90 days had lower rates of low-birth-

weight deliveries than the state or county average (Oostra, Zuckerman, and Parker, 2018:

36). African-American mothers tend to have higher rates of low birth weight and infant

mortality than do White mothers.

Housing2

• ENP created 178 units of new affordable housing in the UpTown neighborhood.

• Individuals who receive housing and financial counseling have witnessed an 89-percent

increase in net income.

• A 300-unit housing development was created to house seniors and persons with disabilities.

Education

• Fifty-two percent of enrollees in the ProMedica Ebeid Institute financial counseling program

have engaged with a financial coach at least once a month.

• Participants in the program achieved a 25-percent increase in net income and a 17-percent

increase in credit score.

• Three hundred individuals received free tax preparation, receiving $510,000 in federal tax

savings and $200,000 in Earned Income Tax Credit (Oostra, Zuckerman, and Parker, 2018: 30).

Employment3

• Between 2017 and 2019, 55 percent of Ebeid job trainee participants obtained employment

after 12 months, with 39 percent employed by ProMedica.

• Forty-two former job trainees achieved full-time employment.

Innovation

The third category challenges nominees to convince jurors that their project is innovative or

advances a new, effective way of solving a community’s problems. APA and HUD jurors were most

impressed with how the ENP and ProMedica collaboration worked to implement a long-term,

sustainable plan of investment for the UpTown community. Partnering with LISC, the collaborative

established an investment portfolio that includes a business incubator program for small, minority-

and women-owned businesses and more than $45 million devoted to capital improvement projects

along the downtown corridor (ProMedica, 2018). According to the most recent annual report,

the initiative has invested nearly $90 million for housing, commercial, and public infrastructure.

To further diversify its investment portfolio, the partnership offers microloans of up to $3 million

to small banking institutions to support job creation, new or existing businesses, and other

2 See Oostra, Zuckerman, and Parker (2018), page 42.

3 See Oostra, Zuckerman, and Parker (2018), page 34.

314 Affordable Design

Annual HUD Secretary’s Opportunity and Empowerment Award:

Ebeid Neighborhood Promise Initiative, Toledo, Ohio

community programs. The funds are replenished as long as the bank demonstrates a continued

commitment to expanding services and opportunities for the town residents.4

Equity

Nominees must explain how their submission advances social equity throughout the planning

process. They must clearly describe how historically underrepresented groups were empowered

by these efforts, leading to improved life outcomes. The ProMedica Ebeid Institute facility

provides free financial coaching and has served more than 1,000 individuals by offering financial

counseling and job training to neighborhood residents. The job training program provides 12

months of training on technical and soft skills, plus an additional 4 hours per week of GED

(general educational development) classes, vocational training, digital literacy training, or other

development opportunities. Once a participant graduates from the program, he or she is paired

with a “job navigator” to assist in the search. The program has met with some success, connecting

more than one-half of program graduates with viable employment options. Residents are connected

with a social worker, or “neighborhood navigator,” to address health and wellness, homelessness

prevention, motherhood preparation, and childhood development. A new walking trail designated

for seniors promotes physical activity and beautification projects.

Source: ProMedica

Arts and afterschool programs are offered to area residents at the ProMedica Ebeid Institute.

4 See Oostra, Zuckerman, and Parker (2018), page 38. The partners use certificates of deposit through the Certificate of Deposit Account Registry Service to invest in local banks while maintaining protection of the original deposits through Federal Deposit Insurance Corporation insurance. The banks are then required to use funds to invest in the community by offering valuable job training skills, expanding after school programs, and encouraging homeownership, among other activities. To ensure long-term commitment, lending institutions are also required to report on measurable outcomes, such as the number of residents served by their programs.

Cityscape 315

Gray

Transferability

The fifth and final award category asks award nominees to explain how, and to what extent, their

submission serves as a model for other localities working to address similar challenges. To satisfy

this requirement, the response should describe prescriptive measures that communities should

employ to achieve success over time. The ProMedica model has been replicated in other local

jurisdictions adjacent to Toledo and in the neighboring state of Michigan. In 2017, with a new

neighborhood plan underway and various planning efforts in adjacent neighborhoods taking

place, ProMedica, LISC, and other stakeholders brought all neighborhood activity under the ENP

umbrella for continued—but greater—implementation efforts to take place, with an additional

focus on improving health outcomes in the neighborhood. Beyond that, ProMedica and its partners

continue to expand the program to other jurisdictions that seek to improve community outcomes

through a health and wellness lens.

The HUD Secretary’s award recipients were recognized in a virtual celebration on the APA Facebook

page earlier this year: https://www.facebook.com/login.php?next=https%3A%2F%2Fwww.facebook.

com%2Fwatchparty%2F2453728664939060%2F. To learn more about the HUD Secretary’s Opportunity and Empowerment Award, visit HUD User: https://www.huduser.gov/portal/

publications/pdf/hud_583_2015.pdf.

Author

Regina C. Gray is the Director of the Affordable Housing Research and Technology Division in the Office of Policy Development and Research of the U.S. Department of Housing and Urban Development.

References

Oostra, Randy, David Zuckerman, and Katie Parker. 2018. Embracing an Anchor Mission:

ProMedica’s All-In Strategy. Toledo, OH, and Washington, DC: ProMedica and The Democracy

Collaborative. www.democracycollaborative.org/learn/publication/embracing-anchor-mission-

promedicas-all-strategy.

ProMedica. 2018. “ProMedica, LISC Launch $45M Partnership.” March 13. Toledo, OH:

ProMedica. www.promedica.org/news/promedica-lisc-launch-45m-partnership.

316 Affordable Design

by Shawn Bucholtz, Emily Molfino, and Quentin Brummet

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 david.a.vandenbroucke@

hud.gov for consideration.

Learning More About HUD-Assisted

Tenants Through Data Linkage

Shawn Bucholtz

U.S. Department of Housing and Urban Development

Emily Molfino

U.S. Census Bureau

Quentin Brummet

NORC at the University of Chicago

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.

Introduction

The U.S. Department of Housing and Urban Development (HUD) administers several rental

assistance programs that help low-income households afford their rental units, aiding

demographics such as seniors, people with disabilities, and veterans. These programs include the

Public Housing (PH), Housing Choice Voucher (HCV), and project-based rental assistance (PBRA)

programs.1 Altogether, HUD’s rental assistance programs provide housing for over 4.6 million

households or about 10.5 percent of U.S. renter households.

To administer rental assistance programs in a manner consistent with statutory, regulatory, and

program-specific requirements, HUD must collect information from its beneficiaries. Like many

federal programs, however, HUD’s information collection is generally limited to the information

1 For the purposes of this article, other small project-based programs are included in the PBRA total.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 317

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

Bucholtz, Molfino, and Brummet

necessary to implement the program—a legal requirement stemming from the Paperwork

Reduction Act of 1995.2 The limited information collected by an agency is often not sufficient to

fully monitor ongoing program performance or evaluate longer-term program impact, including

both impacts on the beneficiaries themselves and the public.

Linking administrative records to existing surveys provides a promising method for low-cost

evaluation of program performance and impact, and HUD has been a leader in this area. As one

example, HUD rental assistance administrative records were linked to the Center for Disease

Control’s National Health and Nutrition Examination Survey and National Health Interview Survey.

Researchers using this linked data have produced several important findings about HUD-assisted

households, including findings about blood lead levels (Ahrens et al., 2016); cigarette smoking

(Wang et al., 2015); levels of physical activity (Wong et al., 2018); health insurance uptake (Simon

et al., 2017); health services use (Brucker, Helms, and Souza, 2018); and overall adult health

(Fenelon et al., 2017).

Numerous other researchers have used the U.S. Census Bureau’s linking infrastructure to link

administrative records to existing surveys to study a range of topics, including the impact of

Medicaid expansion on mortality (Miller et al., 2019); family relationships (O’Hara, Shattuck, and

Goerge, 2017); economic inequality and mobility (Medalia et al., 2019); minority-owned and

women-owned businesses (Jarmin, Krizan, and Luque, 2016); the effect of pollution exposure

on adult wages, education attainment, and incarceration (Voorheis, 2017); and the impact of

transportation on physical and mental health and the environment (Cavoli et al., 2015).

Following these numerous examples, social and data scientists at HUD and the Census Bureau

developed a procedure to link HUD rental assistance administrative records to the American

Community Survey (ACS), thereby identifying ACS households as receiving HUD rental assistance.

ACS contains a wealth of household and demographic information that is not currently collected

by HUD. Some examples of information in ACS include—

• Type of occupation and commuting mode.

• Veteran status.

• Health insurance status.

• Expanded racial categories and household relationship types.

• Internet access.

This new linked dataset allows HUD to gain insights into HUD-assisted housing units and

households that would otherwise not be possible with current rental assistance administrative

records, potentially leading to more robust program evaluation. Our record linkage process takes

full advantage of both address- and person-level matching to overcome HUD address quality issues.

2 U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Assistance, Paperwork Reduction Act (PRA), 44 U.S.C. § 3501 et seq.

318 Data Shop

Learning More About HUD-Assisted Tenants Through Data Linkage

A full technical description of the data sources, linking process, and linking performance metrics is available in Bucholtz, Molfino, and Brummet (2020).

The remainder of this article describes the data and record linkage process, presents an evaluation

of the linkage quality, and discusses early insights gleaned from the linked data.3 We conclude with

a discussion of how to access the linked data for further research.

Data

We linked the HUD rental housing assistance administrative records to ACS. To accomplish this,

we relied on two other Census Bureau data sources integral to the linking process: the Master

Address File (MAF) and the Social Security Administration (SSA) Numerical Identification

(Numident) File. A fuller description of the data sources is available in Bucholtz, Molfino, and

Brummet (2020).

ACS internal use file (IUF) microdata differ from the public versions of the ACS microdata in three

important ways. First, the ACS IUFs contain precise location information (that is, housing unit

address), which can be used to link to other data sources through address matching or other spatial

matching techniques. Second, the ACS IUFs contain respondent names, which can be used to link

to other data sources through person-matching techniques. Finally, the ACS IUFs include all the

survey responses, whereas the ACS public use microdata typically includes about two-thirds of the

actual number of respondents (U.S. Census Bureau, 2009).

The HUD rental assistance administrative records are derived from HUD forms 50058 (for PH

and HCV) and 50059 (for PBRA). These forms are used to collect information from tenants

who are participating or seeking to participate in rental assistance programs. Although the data

collection forms are paper, virtually all PHAs implement an electronic version of the forms.

The electronic data are transferred to HUD daily, and HUD uses them to monitor program

compliance and performance.

The Census Bureau’s MAF is a collection of all addresses in the United States. MAF was originally

built for the 2000 Decennial Census, using addresses from the 1990 Decennial Census as well as

the U.S. Postal Service (USPS) Delivery Sequence File (DSF) (National Research Council, 2004).

Currently, the MAF is updated twice a year using the USPS DSF and other USPS information, as

well as information gathered during other census surveys and decennial preparation operations

(U.S. Census Bureau, 2014a). A key feature of MAF is that every address it contains is assigned a

unique key called a MAF identifier, or MAFID.

The SSA Numident file contains the name, date, place of birth, and parent’s names for each social

security number (SSN). It also contains all transactions for an SSN, such as name changes and

deaths. Using the SSA’s Numident file as a base, the Census Bureau builds their own version of a

Numident file regularly. This process augments the SSA’s Numident with information from other

3 The U.S. Census Bureau reviewed all tables in this article for unauthorized disclosure of confidential information and approved the disclosure avoidance practices applied to this release.

Cityscape 319

Bucholtz, Molfino, and Brummet

federal and state administrative records to include current address and household composition

(Wagner and Layne, 2014).

Record Linkage Process

At a high level, the record linkage process included four steps, which are outlined below. Some

steps of the linkage process are considered probabilistic, meaning a linkage between two records

is considered “acceptable” if a “probability of linkage” threshold is achieved. Other steps are

considered deterministic, meaning the linkage is either a “yes” or “no.” A complete description of

the record linkage process, as well as potential problems with the linkage process, is available in

Bucholtz, Molfino, and Brummet (2020).

The first step of the linking process is to extract HUD rental assistance administrative records

from HUD’s systems. One extract is made per year and represents all households receiving HUD

assistance at any time during the calendar year.

The second step in the linking process is address matching. This step begins by “cleaning” the

addresses in HUD rental assistance administrative records. The Census Bureau uses standardization

algorithms that edit and standardize the addresses. The standardization algorithms are themselves

probabilistic.4 Then, the HUD rental assistance administrative record addresses are probabilistically matched to the Census Bureau’s MAF addresses using a Census Bureau address matching

algorithm. The result is that each HUD rental assistance administrative record receives a unique

MAFID based on its address. Finally, we deterministically match the HUD rental assistance

administrative records to ACS using MAFID. It is important to note that each ACS record already

had a MAFID because the ACS housing unit sample is derived from housing unit addresses in MAF.

The third step in the linking process is person-level matching. This step starts with a process to

deterministically match HUD rental assistance administrative records (which include SSNs) to the

Numident file using SSNs. This results in each HUD rental assistance administrative record receiving

a “PIK or Protected Identification Keys,” which is functionally a pseudo-SSN used to safeguard real

SSNs throughout the matching process. Then, we probabilistically match the ACS households to the

Numident file. The Census Bureau developed an algorithm to match ACS respondents to the Census

Bureau’s Numident file using the information provided by the ACS respondent. This algorithm is

embedded in a system called the Person Identification Validation System, or PVS. As discussed in

Wagner and Layne (2014), PVS uses the respondent’s name, gender, address, date of birth, and

household relationship to match a respondent to the Numident file probabilistically. This step results in each ACS person record receiving a PIK value. Finally, we deterministically matched HUD rental

assistance administrative records to ACS using the PIK values.

The fourth and final step in the linkage process is to make a final linkage determination. In our

linking process, any ACS housing unit that matches to a HUD rental assistance administrative

record by a MAF address match is considered a valid link. Any additional ACS household that

matches to a HUD rental assistance administrative record by SSN match is also considered a valid

link, so long as the HUD administrative record and the ACS record are in the same county.

4 Refer to Brummet (2015) for further descriptions of this process.

320 Data Shop

Learning More About HUD-Assisted Tenants Through Data Linkage

Exhibit 1 shows the number of ACS records linked to a HUD administrative record by the type

of link. Although not the subject of this article, we speculate that the downward trend in the total

number of ACS records linked to a HUD administrative record reflects a general downward trend

in response rates for HUD-assisted households. The authors have observed a similar trend in

another household survey, the American Housing Survey.5

Exhibit 1

Breakdown of American Community Survey/HUD links made by Address (MAF) and Person (PIK)

Year

2011

2012

2013

2014

2015

2016

2017

MAF-Matched

60,000

63,500

55,500

58,000

57,000

54,500

51,500

81.6%

81.9%

82.2%

82.9%

82.6%

83.8%

84.4%

PIK-Matched

13,500

14,000

12,000

12,000

12,000

10,500

9,500

18.4%

18.1%

17.8%

17.1%

17.4%

16.2%

15.6%

Total

73,500

77,500

67,500

70,000

69,000

65,000

61,000

MAF = Master Address File. PIK = Protected Identification Key.

Note: Numbers are rounded to comply with Census Bureau disclosure guidelines.

Source: 2011-2017 Linked HUD/American Community Survey Internal Use Files

Finally, it is important to note that the record linkage process is not perfect. There are numerous

ways in which the process can fail to link an ACS and HUD record (false negative) or incorrectly

link an ACS and HUD record when they do not represent the same housing unit (false positive).

For instance, the addresses in HUD’s rental assistance administrative records may not be of

sufficient quality to be cleaned (step 2) or uniquely matched to a MAF address (step 2). Likewise,

the address matching algorithm (step 2) may fail. The person-matching algorithm (step 3) used to

match ACS household members to the Numident file can produce both false positives and false

negatives, as persons could move across units, and there is a small amount of error inherent in the

PVS process (Layne, Wagner, and Rothhaas, 2014).

Record Linkage Quality Assessment

To determine whether the multifaceted linking process performed well, we compared the “pre-

linking” count of HUD rental assistance administrative records with the “post-linking” ACS

weighted estimate of ACS housing units identified as HUD-assisted. All else being equal, if the

linking process performs well, the post-link ACS weighted estimate of HUD-assisted units should

be very similar to the pre-link known record count.

Exhibit 2 presents linking quality metrics for 2015 through 2017. The table shows that HUD

provided the Census Bureau with 4.74 million rental assistance administrative housing unit

records in 2017. When these records were linked to ACS housing units, the weighted estimate of

HUD-assisted housing units was 4.62 million, or 97.3 percent of the real total. Across all years of

5 Based on unpublished analysis of 2015, 2017, and 2019 American Housing Survey internal use files.

Cityscape 321

Bucholtz, Molfino, and Brummet

the data linkage (2011–2017), the ACS-weighted estimate of HUD-assisted housing units ranges

from 97.0 to 99.4 percent. There is some variation in linkage rate across the three categories of

HUD programs, however, with the PBRA program consistently performing worse than PH or

HCV. Reasons for this difference are explored in Bucholtz, Molfino, and Brummet (2020), but are

generally due to variations in the quality of HUD addresses.

Given the results in exhibit 2, a reasonable conclusion is that the linking process performed well

enough to ensure that the ACS housing units flagged as HUD-assisted units are a representative

cross-section of all possible ACS housing units that are truly HUD-assisted units. In statistical

terms, although there are false negatives (positives), they appear to be limited in quantity, and

we feel their omission (inclusion) should not result in biased estimates of housing or household

characteristics of HUD-assisted households. Regardless of the extent to which our linking process

introduced any bias, a method for overcoming this bias is described in Bucholtz, Molfino, and

Brummet (2020).

Exhibit 2

Results of American Community Survey/HUD Administrative Linking

All

PH

HCV

PBRA

HUD records provided to Census

4,757,000

998,200

2,265,000

1,494,000

ACS estimate of HUD-assisted households

4,678,000

1,021,000

2,256,000

1,400,000

2015 ACS estimate as share of HUD records

98.3%

102.3%

99.6%

93.7%

ACS 90% Margin of Error

0.7%

1.4%

1.2%

1.3%

HUD records provided to Census

4,760,000

1,014,000

2,300,000

1,446,000

ACS estimate of HUD-assisted households

4,623,000

1,001,000

2,248,000

1,374,000

2016 ACS estimate as share of HUD records

97.1%

98.7%

97.7%

95.0%

ACS 90% Margin of Error

0.7%

1.5%

1.1%

1.1%

HUD records provided to Census

4,744,000

977,100

2,313,000

1,453,000

ACS estimate of HUD-assisted households

4,615,000

979,700

2,268,000

1,367,000

2017 ACS estimate as share of HUD records

97.3%

100.3%

98.1%

94.1%

ACS 90% Margin of Error

0.8%

1.5%

1.1%

1.3%

ACS = American Community Survey. HCV = Housing Choice Voucher program. PH = public housing.

PBRA = project-based rental assistance.

Source: 2011-2017 Linked HUD/American Community Survey Internal Use Files

Uses of the Linked Data

In this section, we illustrate two uses of the linked data to produce estimates that are otherwise not feasible to derive using HUD rental assistance administrative records alone.

On HUD forms 50058 and 50059, current and prospective HUD-assisted renters supply a host

of demographic information, including age, sex, race, and ethnicity. Consistent with federal

guidelines governing the collection of race and ethnicity data, HUD collects race information using

five standard categories: White, Black or African-American, Asian-American, American Indian or

322 Data Shop

Learning More About HUD-Assisted Tenants Through Data Linkage

Alaska Native, and Native Hawaiian or Pacific Islander. ACS follows the same federal guidelines but

expands the number of categories for Asian-American from one (Asian American) to six detailed

Asian race categories and offers respondents a write-in option.

The linked ACS/HUD data can be used to estimate the number of HUD-assisted householders

reporting their race as Asian, by detailed Asian race category. Exhibit 3 below presents these results from the 2013–2017 ACS 5-year data. The results reveal significant variation within the Asian

race groups in the share of households receiving HUD assistance relative to those eligible for HUD

assistance. While it is beyond the scope of the article to explain these differences further, this

analysis illustrates the potential value of the data linkage for better understanding who is served by HUD rental assistance programs.

Exhibit 3

Detailed Asian Race for HUD-Assisted Households, 2013–2017

Share of Eligible

HUD-Assisted

Households Eligible for

Householder Race

Households Receiving

Households

HUD Assistance

HUD Assistance (%)

Asian Indian

5,973

105,533

6

Cambodian

5,480

16,640

33

Chinese

53,810

259,710

21

Filipino

12,340

82,330

15

Hmong

4,515

14,389

31

Japanese

3,117

34,187

9

Korean

24,000

116,480

21

Laotian

2,289

9,847

23

Other Asian or Two Groups

9,879

116,379

8

Vietnamese

32,370

89,910

36

Source: 2011-2017 Linked HUD/American Community Survey Internal Use Files

As another example, HUD and the U.S. Department of Veterans Affairs (VA) partner to implement

the VA Supportive Housing (VASH) program, which provides housing vouchers to homeless

veterans. As of 2017, the HUD-VASH program provided housing to nearly 88,000 households with

a veteran (Montgomery and Cusack, 2017). HUD leaders long suspected that other HUD rental

assistance programs provided housing to many additional veterans that were not part of the VASH

program. As is the case with detailed race and ethnicity data, however, HUD forms 50058 and

50059 do not collect information on veteran’s status.

The linked ACS/HUD data can be used to estimate the number of HUD-assisted households with

a veteran to inform this program. Exhibit 4 presents these results by year from the 1-year ACS

for 2011 through 2017. The results reveal that HUD is serving between 285,000 and 324,000

households with a veteran.

Cityscape 323

Bucholtz, Molfino, and Brummet

Exhibit 4

Number of HUD-Assisted Households with Veterans by Year, 2011–2017

Year

HUD-Assisted Households with a Veteran

2011

302,000

2012

314,200

2013

289,900

2014

288,200

2015

286,000

2016

285,600

2017

291,900

Source: 2011-2017 Linked HUD/American Community Survey Internal Use Files

Conclusion

Using a multifaceted approach, we have linked administrative data from HUD’s rental housing

assistance programs to ACS housing units using address and SSN information for years 2011 through

2017. In the future, this work will continue, and we plan to link HUD administrative records to

future years of the ACS as they become available. In each year of the ACS, we identify 61,000 to

78,000 ACS households as being HUD-assisted. Our analysis of the data linkage quality suggests that

false-positive links and false-negative links are minimal, enabling high-quality analysis of the linked data. A full technical report on the process is found in Bucholtz, Molfino, and Brummet (2020).

Our goal with this project was to develop the linkage process and build the linked data sets so

researchers can further explore the data. Access to the ACS/HUD IUFs is available to researchers

through a Federal Statistical Research Data Center (FSRDC), after obtaining Special Sworn Status

and approval for their project. The process is as follows:

1. Identify the FSRDC located nearest to you (https://www.census.gov/fsrdc).

2. Contact the FSRDC administrator to explain your interest in using the linked ACS/HUD IUF.

3. Complete a proposal following the proposal guidelines (https://www.census.gov/ces/pdf/

Research_Proposal_Guidelines.pdf).

4. If your proposal is approved, complete additional application materials and submit to a

background check.

5. Conduct your research at the FSRDC.

6. Submit your results to the FSRDC for clearance.

Acknowledgments

The authors would like to thank Paul Joice for helpful comments to an earlier draft of this article.

324 Data Shop

Learning More About HUD-Assisted Tenants Through Data Linkage

Authors

Shawn Bucholtz is the Head Statistical Official and Director of Housing and Demographic

Analysis in the Office of Policy Development and Research, U.S. Department of Housing and

Urban Development.

Emily Molfino is a Survey Statistician in the Associate Directorate for Demographic Programs at the

U.S. Census Bureau.

Quentin Brummet is a Senior Research Methodologist in the Statistics and Methodology

Department at NORC at the University of Chicago.

References

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Administrative Data to Improve Research on Families,” The ANNALS of the American Academy of

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“HUD Housing Assistance Associated With Lower Uninsurance Rates And Unmet Medical Need,”

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———. 2014b. American Community Survey Design and Methodology: Chapter 3 Frame Development.

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of Data for American Indians and Alaska Natives Need to Know. Washington D.C.: U.S. Census Bureau, U.S. Government Printing Office.

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Environmental Inequality at Birth. Working paper 2017-05. U.S. Census Bureau, Center for

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Applying the Center for Administrative Records Research and Applications’ (CARRA) Record

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326 Data Shop

by Tobias Peter and Edward Pinto

American Enterprise Institute

Housing Market Indicators

Tobias Peter

Edward Pinto

American Enterprise Institute

Abstract

The American Enterprise Institute Housing Market Indicators (HMIs) provide analysts a holistic view of the single-family housing market. The indicators cover home prices and supply, mortgage risk, measures of affordability, land prices, and new construction sales. The HMIs deliver reliable data at fine geographic levels to address the local nature of housing markets. Due to innovative methodologies, the data have minimal latency. The HMIs come with interactive visualization tools, and most data, aggregated to fine levels of geography, are available to download, allowing for data-driven decisionmaking and analysis by governments, the private sector, and consumers.

The American Enterprise Institute (AEI) Housing Center has amassed one of the deepest and

most robust sets of housing market indicators (HMIs) available. Through utilizing numerous data

sets, the HMIs provide analysts a holistic view of the single-family housing market. The data are

unprecedented in their availability at fine levels of geography and their minimal latency. All data

can be accessed at no cost and can be downloaded from the AEI Housing Center’s website.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 327

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

Peter and Pinto

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328 Data Shop

American Enterprise Institute Housing Market Indicators

Several features distinguish HMIs from other data sources. First, as mentioned, HMIs provide

a holistic picture of the housing market; they measure demand (the number of home sales and

the level of mortgage risk of these sales), supply (new construction sales and remaining months

of existing inventory for sale), and home price trends. Second, HMIs deliver reliable data at fine

geographic levels to address the local nature of housing markets. For instance, the number of new

construction sales is available at the ZIP Code and census-tract level. Third, due to innovative

methodologies, the data have minimal latency. For example, home price appreciation (HPA) data

are available for the previous month, while trends in underwriting are available in near real-time.

Fourth, since borrower access to credit (leverage) has a profound impact on market trends, many

indicators are divided into four leverage-based price tiers, rather than into tertiles, quartiles, or quintiles. Fifth, HMIs come with interactive visualization tools, and most data, aggregated to fine

levels of geography, are available free-of-charge to download.

Underlying Data

The primary data for HMIs are national public records data from First American via DataTree.com.

The authors utilize the deed file, which provides information about the sale and mortgage, as well as the tax assessor file, which provides information on the characteristics and location of the structure.

We limit the data to single-family home sales from 2012 forward, but in the coming months, previous

years will be added. Other datasets used, such as Optimal Blue rate lock data, are described below.

Price Tiers

A defining feature of HMIs is that the data are segmented into dynamic, leverage-based price tiers.

Housing trends are driven by lending standards and the availability of supply, which may not map

neatly onto evenly sized price tiers.1

HMIs divide homes into four flexible price tiers that are set quarterly at the metro level. The low

and low-medium tiers consist of sales at or below the 40th- and 80th-percentile of Federal Housing

Administration (FHA) sales prices, respectively. The medium-high tier consists of sales at or below

125 percent (to account for an 80 percent loan-to-value [LTV]) of the government-sponsored

enterprise (GSE) loan limit, and the high tier consists of all other sales.

Within the lower two price tiers—often referred to as entry-level—around 30 percent are financed

with FHA loans. As seen in exhibit 2, these tiers have by far the highest mortgage risk with a

mortgage risk index (MRI) of 14–15 percent. (The MRI measures how the loans originated in a

given month would perform if subjected to severe stress—more on MRI later). The medium-high

tier has only a 10 percent FHA share and an MRI of about 9 percent as it consists of higher-priced

homes and borrowers with generally lower loan-to-value ratios or debt-to-income ratios or higher

credit scores. The high price tier is largely outside the reach of government lending due to loan

limits. This tier only represents about 6–8 percent of the share of all sales, and it is very low risk.

1 Lending standards vary depending on borrowers’ credit profiles, house prices, and competition among government housing finance agencies. Prices are set by the marginal buyer: borrowers who receive additional leverage drive up prices for all borrowers. In this way, extending extra leverage to some borrowers has spillover effects for entire neighborhoods (Davis et al., 2020).

Cityscape 329

Peter and Pinto

Exhibit 2

National Price Tiers by Guarantor Type, Market Share, and Mortgage Risk Index: 2019

Percent of loans

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Low (27%)*

Low-Med (29%)*

Med-High (38%)*

High (6%)*

VA Portfolio GSE FHA

Price Tier

2019

Entry Level

Move-up

Low

Low-Med

Med-High

High

Mortgage Risk Index

15.5%

14.3%

8.9%

3.1%

Market Share*

27%

29%

38%

6%

FHA = Federal Housing Administration. GSE = government sponsored enterprise. VA = U.S. Department of Veterans Affairs

*Market share of all institutionally financed home sales in 2019 by tier.

Note: Data excludes Rural Housing Service. In 2017, Rural Housing Service loans made up 3 percent of the low tier, 2 percent of the low-medium tier, and a negligible amount of the two upper tiers.

Source: American Enterprise Institute Housing Center, www.aei.org/housing

House Price Appreciation Housing Market Indicators

The AEI HPA Index differs from the most widely known home price indexes (HPI), which are

either repeat sales pair (for example, S&P CoreLogic Case-Shiller HPI or Federal Housing Finance

Agency [FHFA] HPI [Calhoun, 1996]) or hedonic (for example, Zillow Home Value Index [Hryniw,

2019]) indexes. AEI creates a “quasi” sales pair consisting of one actual sale and a second reference

“sale” as measured by the home’s estimated sale price using an automated valuation model (AVM).

AVM approximates a property’s sale price at a given point in time. The current AVM used is from

December 2018. AVMs come from First American DataTree LLC (DataTree.com) and, on average, provide accurate and unbiased estimates (Davis et al., 2020). The HPA Index’s results are similar to

price indexes from the FHFA HPI and Case-Shiller HPI, which confirms this approach’s validity.

330 Data Shop

American Enterprise Institute Housing Market Indicators

AEI’s quasi-repeat sales index approach comes with several benefits. Unlike a true repeat sales

model, it includes virtually all existing homes sold for which an AVM is available.2 This process

results in a large sample size, which can be used to construct separate indexes by price tier and at

fine geographic levels and with much less latency. For example, the AEI HPAs released in May are

for the prior month (April). The Case-Shiller HPI released in May is for March; however, the data

are a 3-month rolling average of January, February, and March. Also, by requiring only one AVM at

a given point in time, this methodology is a cost-effective way to produce an HPA index. Notably,

the HPA Index is incorporated into our other HMIs.

The HPA Index data are found at the Housing Center’s HPA Index and Months’ Remaining

Inventory interactive data explorer. The data are available monthly for the largest 40 metro areas in the United States. The listings and sales data are provided by Zillow Group Inc. (Zillow).

Land Price and Land Share Housing Market Indicators

The price of land represents a net market value for a variety of factors such as size and shape of

the lot, access to jobs, type of street, commute, schools, crime, weather, neighborhood, amenities,

and more. Land share, or land price expressed as a percentage of a residential property’s value, is

a particularly useful indicator for public policy research. Previous work (Davis et al., 2017) found

that the increase in the land share of house value prior to the Great Recession was a significant

predictor of the decline in house prices during the bust (the “canary in the coal mine”).

While land prices and shares are a crucial component in assessing house price risk, reliable and

accurate data are hard to come by and generally have only been available for relatively small subsets of the country (Davis et al., 2017; Nichols, Oliner, and Mulhall, 2013). Due to a new working

paper, this is no longer the case (Davis et al., 2019a). Davis et al. use millions of appraisal records that separate total property value into the value of land and the depreciated value of the structure, making land data available for nearly the entire country for the first time.

As Davis et al. acknowledge, appraisals anchored to tax assessments and limited to GSE financed

sales can potentially bias the results. Therefore, the data likely understate the amount of home

price appreciation3 and, by extension, the amount of increase in land prices.

To overcome these potential biases from anchoring, the AEI Housing Center only relies on land

prices and shares for 2012 as a stake in the ground. We then enhance these data using AEI’s

constant-quality HPA index. 4 The 2012 land values are then rolled forward in time using various AEI metrics and assumptions.5

2 New home sales are excluded for two practical reasons: due to a lagging assessment, the AVMs for new homes are often missing. We also exclude interfamily sales and sales that occur within 6 months of each other.

3 When appraisers anchor their estimates to tax assessments, this may understate house price appreciation because (1) the rate of tax assessments may be capped to prevent rapid tax increases in high home-price growth environments, and (2) low tax assessments are likely to go unchallenged, while high ones are challenged (Lutz, Molloy, and Shan, 2011).

4 Other benefits are that since the AEI constant-quality HPA Index is based on all financed sales, we include a larger part of the market than the GSE financing in the Davis et al. (2019a) dataset. Finally, since the AEI constant-quality HPA Index is updated monthly, AEI-adjusted land prices and land shares may also be updated monthly.

5 To read the detailed methodology, see https://www.aei.org/wp-content/uploads/2020/03/Land-Price-Interactive-QA-FINAL.pdf.

Cityscape 331

Peter and Pinto

The AEI-adjusted data are available on the Land Price and Land Share Indicators interactive data explorer on the AEI Housing Center’s website. The interactive data explorer features land prices,

land shares, land share change, and land panel data. The data are available at census-tract, county,

metro, state, and national levels and are downloadable.

For instance, the Housing Center’s map of the Washington, D.C. metro highlights how land share

varies across the region. Land makes up a larger share of the total value in the District of Colombia (D.C.) and wealthier suburbs in Maryland and northern Virginia than in more distant suburbs and

exurbs. Since 2012, however, land shares have risen more in the less affluent parts of the metro,

such as Prince George’s County and southeast D.C. (not shown).

Exhibit 3

Land Share by ZIP Code in the Washington, D.C. Core-Based Statistical Area: 2019

Source: American Enterprise Institute Housing Center, www.aei.org/housing

New Construction Housing Market Indicators

Another innovative aspect of the AEI HMIs is its methodology for identifying new construction

home sales in near real-time at the property level. Granular and timely data on new construction

trends are particularly relevant for policy analysts given dramatic home price increases and the lack of new homebuilding in recent years.

The AEI data are available monthly. They have minimal latency because the underlying data

come from the county public records data (deed and assessor files) and listings data from Zillow,

which are frequently updated. The key field to identify a newly constructed home is the home’s

“year built” variable in the county assessor file. If the “year built” is missing, the authors check

the home’s seller name from the county deed file. If the seller matches a name in a list of over 400

332 Data Shop

American Enterprise Institute Housing Market Indicators

builders or it includes a generic keyword that helps identify smaller builders (such as “Builder” or

“Construction”), then the sale is most likely a new construction that has not yet been assessed.6

We have verified the accuracy of our methodology through extensive random sampling and

checking newly constructed and existing homes using Zillow data, Google Street View, and satellite

images. We find around 2 percent false positives and around 1 percent false negatives. Moreover,

the process yields similar results to the totals published in the U.S. Census Bureau’s new home sales data, providing further confirmation that the AEI methodology is sound.

The nature and methodology of the new construction data offer benefits compared to the Census

Bureau’s data that may be of interest to researchers. Aggregated AEI data are available at the

census-tract/ZIP Code level. Pivotally, the AEI data represent a comprehensive count of properties,

rather than relying on estimation from survey data. Moreover, since homes are segmented by new

construction status, analysts can identify how new homes differ from the existing housing stock

by location, price, square footage, and more. One can also plot the data to observe trends in new

construction sales over time.

Exhibits 4 and 5 illustrate changes in new construction activity in the Seattle metro. Between 2012

and 2019, entry-level new construction sales in King County have fallen from over 1,100 sales

to just under 200. In Snohomish County, they have fallen from 1,200 in 2012 to 600 in 2019. In

Pierce County, by contrast, they have only fallen from 1,100 to 800.

Exhibit 4

Entry-Level New Construction Sales Heat Map in the Seattle, WA Core Based Statistical Area

2012

2019

Source: American Enterprise Institute (AEI) Housing Center, www.aei.org/housing

6 In the case that “year built” or seller name are missing, listings data are checked for a “year built” or “land use code,”

which helps determine the new construction status of the home. Only the first sale of a home is counted as a new construction.

Cityscape 333

Peter and Pinto

Exhibit 5

New Construction Sales in the Seattle, WA Core Based Statistical Area

County

Segment

2012

2013

2014

2015

2016

2017

2018

2019

Overall

6,303

7,076

6,736

7,116

7,824

7,922

7,717

6,843

Entire Metro

Entry-level

3,363

3,415

2,584

2,622

2,438

1,955

1,910

1,635

Move-up

2,940

3,661

4,152

4,494

5,387

5,968

5,807

5,207

Overall

3,107

3,630

3,494

3,592

3,877

3674

3,313

3,015

King, WA

Entry-level

1,126

1,201

795

721

508

229

148

184

Move-up

1,981

2,430

2,699

2,871

3,369

3,446

3,165

2,831

Overall

1,306

1,796

1,744

1,727

1915

2,038

2,242

1,675

Pierce, WA

Entry-level

1,084

1,436

1,286

1,309

1,305

1,163

1,242

856

Move-up

222

360

458

418

610

875

1,000

819

Overall

1,890

1,650

1,498

1,796

2,033

2211

2,162

2,153

Snohomish, WA Entry-level

1,154

779

503

592

625

563

520

596

Move-up

737

871

995

1,204

1,407

1,647

1,641

1,558

Source: American Enterprise Institute Housing Center, www.aei.org/housing

All summary data are available upon request, and heat maps are available in the State of the

Housing Market interactive data explorer.

National Mortgage Risk Index and Housing Market Nowcast

The National Mortgage Risk Index (NMRI) tracks demand and underwriting standards. It

illuminates the buildup of risk in the mortgage market to prevent a repeat of the housing crash by

fostering transparency. The risk-rated NMRI dataset covers 99 percent of the agency market and

consists of over 43 million loans that are released monthly as mortgage-backed security data by

GSEs and Ginnie Mae.

NMRI measures how loans originated in a given month would perform if subjected to the same

stress as in the financial crisis that began in 2007. This is similar to stress tests routinely performed to ascertain an automobile’s crashworthiness or a building’s ability to withstand severe hurricane-force winds.

The expected stressed default rates come from Davis et al. (2019b) and measure stress by dividing

loans into 320 risk buckets defined by credit score, combined LTV ratio (CLTV), total debt-to-

income ratio (DTI), and more for four loan types. Then, for each risk bucket, the authors calculate

the share of loans originated in 2007 that defaulted by December 31, 2018.7

Risk rating loans in NMRI and Nowcast is objective and transparent. Every month, loans

are risk rated against a known stress event (the 2007–08 financial crisis). By tracking actual

underwriting, analysts can monitor changes in mortgage risk. Many loan characteristics such as

credit score, DTI, and CLTV are available for download online at the AEI Housing Center’s MRI

interactive data explorer.

7 The original methodology was first developed by AEI with expected stressed default rates that were based on Freddie Mac primary owner-occupied, 30 year fixed rate, fully amortizing, fully documented loans originated in 2007

Loan Performance data through 2012. This methodology was later adopted and refined in Davis et al. (2019b).

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American Enterprise Institute Housing Market Indicators

Since NMRI data are released with a lag, the AEI Housing Center has also developed a new dataset

to measure risk in real-time using mortgage rate lock data from Optimal Blue. After extensive

historical analysis of Optimal Blue data going back 7 years, the AEI Housing Center concludes that

while the Optimal Blue data only cover roughly one in three loans in the U.S., they follow similar

trends to NMRI.8 As a result, the rate lock data are used to construct the Housing Market Nowcast,

which provides an advanced look at borrowing trends that will not be available in the NMRI data

until 3 months later. These data are particularly valuable during a fast-changing environment, such

as the COVID-19 pandemic. Weekly reports are available on the Housing Center’s Nowcast page.

Tracking Affordability with the Housing Market Indicators

In an era of generally declining housing affordability, the AEI Housing Center has developed

two interactive data explorers to track housing affordability at the metro level. The first is The

Carpenter Index, which measures if the workers who build homes can afford to buy a home of their own. The Index compares housing affordability in the largest 100 metros across the nation.

The report concludes that in two-thirds of the 100 largest metros, entry-level or starter homes are

still affordable for the average carpenter household—a proxy for a blue-collar worker—in 2018.

The Best and Worst Metro Areas to Be a First-Time Homebuyer (FTB) is the Housing Center’s second housing affordability interactive data explorer. The report ranks the largest 50 metro areas

for FTB affordability. Cities are evaluated by observing the ratio of home prices to buyer income for over 2.5 million FTB sales from 2013 to 2018. In 2018, Pittsburgh was the most affordable metro,

while San Jose was the least affordable one.

These affordability interactive data explorers are useful because they take local conditions into

consideration. The key to understanding affordability is observing its two key components: house

prices and income or wages. Metros with higher house prices need higher wages to be affordable.

Moreover, the indexes utilize data specific to blue-collar workers and data from individual FTB

purchase transactions, providing a far more accurate picture than taking simple averages for wages

and purchase prices in an entire metro area.

Other Housing Market Indicators

The State of the Housing Market

For those interested in state and county data, the State of the Housing Market is a comprehensive interactive report that features HMIs for the 50 states and nearly every county. The annual data

includes housing affordability, months’ supply of available homes for sale, new construction,

mortgage risk, and outcomes for low-income and minority buyers.

The report has a special focus on housing affordability and the drivers of growing house price

appreciation. For instance, there are data examining how house prices have risen faster than

8 We checked credit score, DTI, LTV, sale price, and note rate by loan type and observed that the data follow very similar trends to the ones observed in the NMRI. There are slight level differences, however, due to the lower coverage of the Optimal Blue data.

Cityscape 335

Peter and Pinto

incomes in the majority of counties from 2012 to 2019. Additionally, there are data examining the

positive correlation between mortgage risk and a census tract’s minority share of the population.

National and Metro Housing Market Indicators

The National and Metro HMIs is another far-reaching interactive data explorer that provides HMIs for the nation’s 60 largest metro areas. The quarterly data include house price appreciation,

months’ supply, mortgage risk, new construction, and average sale prices, among others. There is

an emphasis on differences between entry-level and move-up market segments. A key finding is

that entry-level markets tend to have fewer months’ supply remaining and higher HPA compared to

the move-up segment in the same metro.

Conclusion

Housing markets are inherently local, making them notoriously difficult to analyze due to the lack

of accurate and reliable data at fine geographic levels. The AEI Housing Center aims to fill this

void by compiling a variety of HMIs. All historical data are available to housing researchers and the public for download or upon request.

While each indicator is useful in its own regard, collectively, HMIs present a holistic picture of the housing market, allowing for data-driven decisionmaking and analysis by governments, the private

sector, and consumers.

Acknowledgments

The authors would like to thank Steve Oliner for his thoughtful comments and feedback that

informed our methodology, as well as Neil Filosa for his excellent research assistance on this

article. In addition, we would like to thank our data providers. We are solely responsible for the

datasets, HMIs, analysis, and any errors therein. Lastly, the Housing Center is part of the American

Enterprise Institute, and we are most grateful for its support.

Authors

Edward J. Pinto is a resident fellow and the director of the AEI Housing Center at the American

Enterprise Institute.

Tobias J. Peter is a research fellow at AEI and the director of research of the AEI Housing Center.

References

Calhoun, Charles A. 1996. “OFHEO House Price Indexes: HPI Technical Description.” Office of

Federal Housing Enterprise Oversight: 1–15.

336 Data Shop

American Enterprise Institute Housing Market Indicators

Davis, Morris A., Stephen D. Oliner, Tobias J. Peter, and Edward J. Pinto. 2020. “The Impact

of Federal Housing Policy on Housing Demand and Homeownership: Evidence from a Quasi-

Experiment,” Journal of Housing Economics 48: 101670. doi: 10.1016/j.jhe.2020.101670.

Davis, Morris A., William D. Larson, Stephen D. Oliner, and Jessica Shui. 2019a. “The Price of

Residential Land for Counties, ZIP Codes, and Census Tracts in the United States.” No. 19-01.

Federal Housing Finance Agency Staff Working Paper Series. Washington D.C.: Federal Housing

Finance Agency.

Davis, Morris A, William D. Larson, Stephen D. Oliner, and Benjamin R. Smith. 2019b. “A Quarter

Century of Mortgage Risk.” No. 19-02. Federal Housing Finance Agency Staff Working Paper Series.

Washington D.C.: Federal Housing Finance Agency.

Davis, Morris A., Stephen D. Oliner, Edward J. Pinto, and Sankar Bokka. 2017. “Residential Land

Values in the Washington, DC Metro Area: New Insights from Big Data,” Regional Science and Urban

Economics 66: 224–246. doi: 10.1016/j.regsciurbeco.2017.06.006.

Hryniw, Natalia. 2019. “Zillow Home Value Index Methodology, 2019 Revision: Getting Under the

Hood.” Zillow, 18 December. https://www.zillow.com/research/zhvi-methodology-2019-deep-26226/.

Lutz, Byron, Raven Molloy, and Hui Shan. 2011. “The Housing Crisis and State and Local

Government Tax Revenue: Five Channels,” Regional Science and Urban Economics 41(4): 306–319.

doi: 10.1016/j.regsciurbeco.2011.03.009.

Nichols, Joseph B., Stephen D. Oliner, and Michael R. Mulhall. 2013. “Swings in Commercial

and Residential Land Prices in the United States,” Journal of Urban Economics 73 (1): 57–76. doi:

10.1016/j.jue.2012.06.004.

S&P Global, CoreLogic, Inc., and Case-Shiller. 2020. “S&P CoreLogic Case-Shiller Home Price

Indices Methodology.” S&P Dow Jones Indices. https://us.spindices.com/documents/methodologies/

methodology-sp-corelogic-cs-home-price-indices.pdf.

Cityscape 337

338 Departments

by Orsetta Causa and Nicolas Woloszko

Foreign Exchange

Foreign Exchange, a department of Cityscape, reports on what the U.S. Department of Housing and

Urban Development’s Office for International and Philanthropic Innovation has learned about new

departures in housing and development policy in cities and suburbs throughout the world that might have value if applied in U.S. communities. If you have a recent research report or article of fewer than 2,000 words to share in a forthcoming issue of Cityscape, please send a one-paragraph abstract to

Bradley.A.Weaver@hud.gov .

Policy Considerations on Housing,

Wealth, and Inequality

Orsetta Causa

Nicolas Woloszko

Organisation for Economic Co-operation and Development

Disclaimer: The views expressed and arguments employed herein are solely those of the authors and do not necessarily reflect the views of the OECD or its member countries.

Abstract

This paper discusses policies on housing from wealth accumulation and wealth distribution perspectives, relying on new evidence and stylized facts from recent Organisation for Economic Co-operation and Development (OECD) research (Causa, Woloszko, and Leite, 2019). A key policy issue is whether and how housing-related policies affect wealth distribution. Another related issue is whether housing-related policies raise potential trade-offs between equity, inequality reduction, and other policy objectives; these other objectives include employment growth, productivity growth, and macroeconomic resilience.

Informed by the stylized facts and existing evidence, this paper discusses housing-related policy reforms to promote inclusiveness and social mobility, to enhance efficiency in the allocation of labor and capital, and to strengthen macroeconomic resilience.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 339

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

Causa and Woloszko

Introduction and Motivation

Housing is an important social indicator of development along several dimensions: access to affordable housing for different socioeconomic groups, poverty and deprivation risks, and spatial inequalities

such as housing segregation in metropolitan areas. One important dimension less explored in the

literature, especially on a cross-country basis, is the distributional implications of housing from

a wealth perspective. Recent OECD work (Causa, Woloszko, and Leite, 2019) fills in this gap by

delivering new insights on housing and wealth across OECD countries, allowing readers to draw policy

implications across a range of policy objectives such as inequality, efficiency, and resilience.

Housing and wealth distribution warrants attention for several reasons. Housing is the largest

asset in household portfolios. It is therefore a fundamental driver of the accumulation and the

distribution of assets and wealth across the lifecycle and generations, hence contributing to wealth

inequality. Assessing housing from a wealth distribution perspective is all the more important in a

context where inequality has been rising, the capital share of income has increased relative to labor, and wealth inequality is much higher than income inequality, potentially undermining equality of

opportunity and social mobility (OECD, 2018c).

Housing debt is also the largest liability in household portfolios. One of the reasons why housing

is a major vehicle of wealth accumulation is because it can be acquired with leverage. Housing-

related debt enables households with low income and few assets—such as young households—to

accumulate wealth. The benefits of leverage need to be balanced against its risks—one major lesson

from the 2008 financial crisis. Assessing housing from a wealth distribution perspective requires

looking at housing assets and liabilities, with particular attention focused on the bottom of the

income and wealth distributions.

A number of public policies affect the housing market and therefore wealth and its distribution.

Such policies intend to repair market failures, pursue broader economic efficiency goals, and

promote affordable quality housing for citizens. They include fiscal measures, macroprudential

regulations on mortgage markets, the provision of social housing, regulations aimed at influencing

rental markets and the quantity and quality of dwellings through land-use policies, urban planning,

and the enforcement of competition in related activities (such as construction or real estate).

Promoting homeownership is a policy objective for many governments, whether stated explicitly

or not. Public policy tends to favor ownership relative to renting and other investments, typically

via the preferential tax treatment of owner-occupied housing (Andrews and Caldera Sánchez,

2011; Salvi del Pero et al., 2016).1 The main economic argument for favoring homeownership over

renting is that it may give rise to positive spillovers for society. For instance, homeownership is a vehicle for wealth accumulation, leads to better outcomes for children, and is associated with more

community engagement and voting behavior. Empirical evidence does not consensually support

the existence of these channels; a common problem is establishing causality, because correlation

between homeownership and a variable of interest may reflect the influence of a third omitted

factor and self-selection bias.

1 Also, the vast majority of OECD countries offer financial assistance to households to support the purchase of a home (Salvi del Pero et al., 2016).

340 Foreign Exchange

Policy Considerations on Housing, Wealth, and Inequality

Still, the argument that homeownership provides the most stable tenure arrangement to satisfy

basic household needs could justify pursuing higher homeownership as a public policy goal.

Yet this policy goal can conflict with other policy goals, such as efficiency, by distorting labor

and capital from their most productive use; unemployment reduction by slowing down labor

adjustment in a downturn; and social mobility throughout the lifecycle and across generations by

discouraging people from relocating and benefiting from new opportunities.

This paper takes stock of empirical evidence in Causa, Woloszko, and Leite (2019) to frame a

policy discussion on housing and wealth distribution. Reforms affecting housing wealth and its

distribution tend to be unpopular. In this context, this paper attempts to analyze housing from a

wealth distribution perspective by taking into account the political economy angle. The discussion

focuses on a wide range of policy areas that affect housing and its distribution, such as taxation,

housing market regulations, and borrower-based macroprudential policies. These focuses enable

us to draw some policy implications of housing-related reforms to promote inclusiveness and

social mobility, to enhance efficiency in the allocation of labor and capital, and to strengthen

macroeconomic resilience.

Stylized Facts in a Nutshell

The contribution of housing to wealth inequality varies significantly across countries, but the

following facts stand out from the data:

• Wealth inequality is much higher and much more dispersed across countries than

income inequality. On average across OECD countries, the bottom 40 percent of households

receive around 20 percent of disposable income but only 3 percent of net wealth. The higher

level of wealth compared to income inequality partly reflects lifecycle effects.

• There is a strong negative cross-country association between homeownership and wealth

inequality. Low homeownership countries exhibit high wealth inequality, even when income

inequality is low.

• Housing tends to equalize the distribution of wealth from a static cross-country

perspective. This finding is because housing is the most important and most widely-owned

asset in household balance sheets, representing a much higher source of wealth among

middle-class households than at the top.

• The data do not lend strong support to the argument that housing acts as a vehicle to

encourage higher long-term savings.

Access to mortgage markets enables credit-constrained households to have a better chance of

owning their own home, but it entails risks:

• Housing-related debt is the most important liability in households’ portfolios,

particularly for young homeowners and homeowners at the bottom of the distribution.

OECD countries exhibit stark variation in the extent to which households hold mortgage debt,

ranging from almost 50 percent in the Netherlands to less than 10 percent in Slovenia.

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Causa and Woloszko

• Mortgage debt is both an opportunity and a risk. Although it allows households, especially those with little initial assets, to accumulate wealth, it could expose households, especially

those at the bottom of the distribution, to economic and social vulnerabilities.

Informed by the stylized facts delivered in Causa, Woloszko, and Leite (2019) and summarised

here, the remainder of this paper discusses policy implications of housing reform to promote

inclusiveness and social mobility, to enhance efficiency in the allocation of labor and capital, and to strengthen macroeconomic resilience.

Reforming Property Taxes to Make the Overall Tax System

More Progressive and Efficient

Shifting the Tax Mix Towards Property Taxes

Policy analysts and international organisations have increasingly advocated reforms to shift the

tax burden toward property taxes to switch to a more growth- and equity-friendly tax system (for

example, OECD, 2019a). The case for shifting towards property taxes is based on vast empirical

evidence showing that greater reliance on property taxes boosts growth and tends to reduce or have

neutral effects on income inequality. From an efficiency perspective, recurrent taxes on immovable

property (such as taxes levied regularly on the ownership of immovable property) have been found

to be the least damaging to economic growth, followed by consumption taxes, other property taxes,

personal income taxes, and corporate income taxes (Brys et al., 2016, OECD, 2010). Compared

with recurrent taxes on immovable property, non-recurrent taxes on immovable property, such

as property transaction taxes, can have distortionary effects. For instance, they can discourage

the owner of a house from moving to an area with better labor market opportunities. Transaction

taxes, however, can have the advantage of discouraging speculative behavior and thereby cooling

down house prices. From a distributional perspective, Akgun, Cournéde, and Fournier (2017)

have recently found that greater reliance on recurrent taxes on immovable property has no effect

on disposable income inequality and that greater reliance on inheritance taxes tends to reduce

disposable income inequality.

Despite their growth and equity benefits, OECD countries make little use of property taxes (exhibit

1). Overall, property taxes make up slightly more than 5 percent of tax revenues on average,

ranging from less than 2 percent in Estonia, Austria, and the Slovak Republic and Slovenia, to

around 10 percent in Korea, Canada, and the United Kingdom. The share of property tax revenues

in the OECD average tax mix has declined over time, reflecting the widespread repeal of net wealth

taxes, inheritance taxes, and gift taxes and the failure to update property values (OECD, 2018b).

All in all, there is scope for shifting the tax burden toward property taxes across the OECD. Such

reforms would be particularly relevant in countries where the tax mix is particularly skewed toward

income relative to property, as can be seen by comparing the share of tax revenue raised from labor,

capital income, social security contributions and payroll to that raised from recurrent taxes on

immovable property and on estate, inheritance and gift (exhibit 2).

342 Foreign Exchange

Policy Considerations on Housing, Wealth, and Inequality

Exhibit 1

OECD Countries Have Ample Room to Shift the Tax Burden Towards Property Taxes

Tax Revenue From Property Taxes in % of Total Tax Revenue

OECD = Organisation for Economic Co-operation and Development.

Source: OECD Tax Revenue Statistics

Exhibit 2

Some Countries Could Move Away from Taxing Income to Taxing Immovable Property and Inheritance

om

operty and Inheritance

ax Revenue Raised Fr

e of T

Shar

Immovable Pr

Share of Tax Revenue Raised From Income and Payroll

Note: Share of tax revenue raised from labor, capital income, social security contributions, and payroll (categories 1000, 2000, 3000 of OECD Tax revenue statistics); share of tax revenue raised from recurrent taxes on immovable property and on estate, inheritance and gift (categories 4100, 4300

of Organisation for Economic Co-operation and Development [OECD] Tax revenue statistics).

Source: OECD Tax Revenue Statistics

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Causa and Woloszko

Enhancing the Efficiency and Progressivity of Immovable Property Taxation

Housing taxation can be made more efficient and progressive. Owner-occupied residential property

is highly tax-favored in most countries compared to other forms of household savings, with the

exception of retirement plans (OECD, 2018a). This preference is due to the exemption of imputed

rent and of capital gains from taxation, whereas mortgage interest is often deductible. This favorable tax treatment of owner-occupier property is economically inefficient by creating several distortions in investment decisions, capital and labor allocation, and excessive leverage (Fatica and Prammer, 2017).

Equity considerations would not justify the favorable tax treatment of owner-occupied property,

because it is unlikely to benefit low-income people most. In particular, the literature has shown

that mortgage interest rate deductibility has a regressive impact in most cases (Fatica and Prammer,

2017). This finding reflects the fact that high-income households are much more likely to finance

their houses with mortgage debt, as documented in this paper (exhibit 3). Another argument

against mortgage interest rate deductibility is that generous tax relief can be capitalized in house

prices, thereby redistributing income from new entrants in the housing market to insiders

(Andrews, Caldera Sánchez, and Johansson, 2011).

The presumption that the favorable tax treatment of owner-occupied housing is regressive or at least

flat is confirmed by comprehensive modeling of property taxation. New estimates of marginal effective tax rates on various components of household savings and wealth show that in most countries owner-occupied property taxes are not progressive (OECD, 2018a). This finding is illustrated in exhibit 3,

which provides estimated average effective tax rates on owner-occupied housing for three income

levels: 67, 100, and 500 percent of the average wage. In most OECD countries, the tax rates are flat

across the distribution, and in the United States, they are even higher for low-income households.

Exhibit 3

Owner-Occupied Property Taxes Could be Made More Progressive

Marginal Effective Tax Rates on Owner-Occupied Residential Property (%)

Notes: Estimates from Organisation for Economic Co-operation and Development (OECD), 2018a. Marginal effective tax rates on owner-occupied residential property, equity-financed. Personal tax rate: 67, 100, and 500 percent of the average wage (AW). These taxes include recurrent taxes on immovable property, transaction taxes, possible taxes on income, and capital gains taxes, when applicable.

Source: OECD, 2018a

344 Foreign Exchange

Policy Considerations on Housing, Wealth, and Inequality

Housing should ideally be taxed in the same way as other assets by taxing imputed rental income

while allowing for mortgage interest deductibility. In practice, few countries tax imputed rental

income, and using recurrent property taxes as a substitute is most often not sufficient as these taxes are not large enough to offset the mortgage subsidy. In these cases, a “second best” approach is to

remove the mortgage subsidy or to scale up recurrent property taxes (OECD, 2010).

Removing or reducing mortgage interest rate deductibility would increase the progressivity of

the tax burden on owner-occupied property.2 This removal should be done gradually to prevent

a crash in house prices insofar as mortgage deductions tend to be capitalized in house prices

(Andrews, Caldera Sánchez, and Johansson, 2011). If removing mortgage interest rate deductibility

is not an option, granting the rebate as a capped tax credit (for example, a capped reduction of

the tax liability), rather than a tax allowance (such as, a reduction of the taxable income) is one

way to make the tax relief less regressive. A more direct way to achieve progressivity in owner-

occupied property taxation is to apply a progressive recurrent tax rate schedule and introduce a

tax allowance or income-tested property tax credit. Another approach is to allow deferral of the

tax payment until the death of the taxpayer or sale of the property for older taxpayers, but one

major drawback in this case is the risk of lock-in effects. OECD countries have used these types of

measures to increase the progressivity of their property taxes (Brys et al., 2016)

Increases in recurrent taxes on immovable property must be accompanied by regular updating

of property values to market values. Denmark recently introduced a property tax reform that

includes a new system for housing valuation and replaces a nominal freeze of property taxes with

proportional taxation, maintaining a progressive element for the most valuable homes (OECD,

2019c). Reforms in this area can be designed to address liquidity constraints for people with

low incomes and non-liquid assets, for instance, by making it possible to spread tax payments

throughout the year or by introducing escrow accounts.

Going further, tax reforms to shift from labor to immovable property taxation are likely to enhance

tax efficiency, progressivity, and labor market inclusiveness in countries where the taxation of low

wages is relatively high and the taxation of owner-occupied property for high-income households is

relatively low (exhibit 4). This likelihood implies recurrent taxes on immovable property featuring

generous allowances and a progressive tax schedule, especially when homeownership is widespread.

2 The current low interest environment may strengthen the case for removing mortgage deductibility.

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Causa and Woloszko

Exhibit 4

Higher Progressivity in the Tax System Could be Achieved by Raising Owner-Occupied Property

Taxes at High-Income Levels while Reducing Labor Taxation at Low-Income Levels

W

edge at 67% A

ax W

ginal Labour T

Mar

Marginal Effective Tax Rates on Owner-Occupied

Residential Property at 500% AW

AW = average wage

Note: Marginal labor tax wedge is defined as marginal personal income tax and social security contribution rate on gross labor income. Marginal effective tax rates on owner-occupied residential property, equity-financed.

Source: Organisation for Economic Co-operation and Development (OECD) Tax Database and OECD, 2018a There is also scope to review taxation of secondary and rented residences. Indeed, the distribution

of other real estate is extremely unequal (exhibit 5), with households in the top 10 percent of the

net wealth distribution owning 34 percent of net housing wealth and 69 percent of net other real

estate wealth. Available tax indicators suggest that the taxation of other real estate is higher and

more progressive than that of an owner-occupied residential property (OECD, 2018a). Comparing

the marginal effective tax rates on an owner-occupied residential property relative to those on

rented property suggest that: (1) marginal effective tax rates on rented property are significantly

higher than those for primary residences because of the non-taxation of imputed rental income,

as opposed to actual rental income, and because most countries apply capital gains tax to rented

residential property; and (2) marginal effective tax rates on rented property tend to be progressive

346 Foreign Exchange

Policy Considerations on Housing, Wealth, and Inequality

across the income distribution because rental income is most often taxed at progressive marginal

personal income tax rates.

The fact that rented property exhibits higher and more progressive taxation compared with

owner-occupied property does not necessarily imply that reforms in this area are not needed. As

discussed in Causa, Woloszko, and Leite (2019), the ability to debt-finance a property may open

up tax-planning opportunities that benefit wealthier households the most. Real estate is also a

potential asset class that can be attractive for hidden wealth. More broadly, how to tax the buy-

to-let property market at the individual and corporate level is becoming a topical question, for

instance, given the increasing presence of institutional investors and buyers in globalized cities

that have experienced rising house prices [see chapter 3 in (IMF, 2018)]. More work needs to be

done to properly document the policy features at stake, but reviewing the taxation of real estate

investments—in the broader context of alternative investment vehicles—is warranted on efficiency,

equity, and resilience grounds.

Exhibit 5

Household Main Residence and Other Real Estate: A Tale of Two Inequalities

Share of Net Housing Wealth and of Other Real Estate Wealth

Held by the Top 10 Percent of the Net Wealth Distribution (%)

Notes: Households are ranked by net wealth. Therefore, this exhibit shows the share of net housing /net other real estate wealth held by households at the top of the net wealth distribution.

Source: Organisation for Economic Co-operation and Development Wealth Distribution Database (oe.cd/wealth) Taxing Inherited Wealth: The Role of Housing

Taxing inherited wealth is justified on equity and efficiency grounds. From an equity perspective,

well-designed inheritance taxes may increase intergenerational mobility and equality of opportunity

by reducing and dispersing wealth holdings at death. Indeed, wealth transfers can be viewed as

a source of opportunity that is not linked to the recipient’s effort and should therefore be taxed,

regardless of whether the donor has already paid income tax or capital gains tax on the assets. In

cases where the main residence is a significant portion of the estate’s wealth, it may even not have

faced income or capital gains taxes prior to the donor’s death.

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From an efficiency perspective, inheritance taxes tend to be less distortive than other forms of

wealth taxation because, for example, their effects on savings are smaller than in the case of

recurrent taxes on personal net wealth.3 Another argument in favor of inheritance taxes is that the

double taxation argument is weaker than for recurrent taxes on net wealth; there is no double

taxation of the donor, and the inherited wealth is also only taxed once in the hands of the recipient.

Finally, inheritance taxes are also easy to administer and comply with as they are only levied once.

A recent report on net wealth taxes argues that capital income taxes alone will most likely not be

enough to address wealth inequality, suggesting the need to complement capital income taxes with

inheritance taxes (OECD, 2018b).

Despite the strong case for wealth transfer taxes, revenues from inheritance or estate and gift

taxes are very low and have been declining over time on average in OECD from 1.1 percent

of total taxation in 1965 to 0.4 percent today (OECD, 2018b). Low revenues reflect the fact

that inheritance/estate and gift tax bases are often narrowed by numerous exemptions and

deductions, and that avoidance opportunities are widely available. The decline in tax revenues

also reflects the fact that a number of countries have either abandoned or scaled back their

wealth transfer taxes. Differences, however, across countries—for instance higher revenues

collected in Belgium and France—suggest that the revenue potential of these taxes could be

further exploited in many countries.

Designing efficient and fair wealth transfer taxes calls for progressive inheritance taxes. This

taxation involves taxing large inheritances, but not taxing (or taxing at low rates) small

inheritances received by poor taxpayers and allowing for deferred payments and installments

to address liquidity constraints. One question is whether inheritance taxes should involve a

favorable tax treatment when the transmitted asset is the home in which the recipient lives.4 Such

treatment could take the form of a higher exemption threshold for the home than for other assets

transmitted. This treatment may be justified on distributional grounds, because low-income

households tend to inherit their houses, whereas high-income households tend to inherit other

assets (exhibit 6).

3 Akgun, Cournéde, and Fournier (2017) find net wealth taxes have a negative effect, whereas inheritance taxes have no effect on long-term output.

4 Other important questions arise in the design of inheritance taxes, such as the treatment of family-owned business.

These questions are beyond the scope of this paper.

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Policy Considerations on Housing, Wealth, and Inequality

Exhibit 6

High-Income Households Have Much Higher Chances of Inheriting Other Assets Than the Main

Residence

Odds Ratio of Inheriting: High-Income Relative to Low-Income Households

Notes: High and low incomes refer to the top and bottom income quintiles. How to read this figure: in the Netherlands, households in the top income quintile are 2.6 times more likely to receive any inheritance or gift than households in the bottom income quintile; households in the top income quintile are 1.3 times more likely to receive the main residence as inheritance or gift than households in the bottom income quintile; and households in the top income quintile are 10.5 times more likely to receive assets other than the main residence as inheritance or gifts than households in the bottom income quintile.

Source: Household Finance and Consumption Survey (HFCS)

Among countries that have inheritance taxes, the main residence generally receives special

treatment in the form of higher tax-exemption thresholds (in the United Kingdom, for example),

preferential valuation rules (such as in France), or even full exemptions under strict rules on usage of the home (as in Ireland). The level of the general inheritance tax exemption threshold is often

used to ensure that small inheritances can be passed on tax-free. In addition, there can be measures

to address liquidity constraints when it comes to the payment of inheritance tax on the main

residence, such as allowing tax payment deferral until the property is sold for individuals who still occupy the home or allowing tax payments in installments.

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Exhibit 7

Across European Countries, Housing Inheritance is Negatively Correlated with Inheritance

Tax Revenues (1 of 2)

Inheritance/Gifts of Housing and Non-Housing Assets

and Tax Revenue Raised From Inheritance and Gift Taxes

Panel A. Proportion of Households Having Received Their House as Inheritance or Gift in

Age Group <35 and Tax Revenue Raised From Inheritance and Gift Taxes

Tax Revenue Raised From Inheritance and Gift Taxes (%) of Total Tax Revenue)

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Policy Considerations on Housing, Wealth, and Inequality

Exhibit 7

Across European Countries, Housing Inheritance is Negatively Correlated with Inheritance

Tax Revenues (2 of 2)

Panel B. Proportion of Households Having Received Assets Other Than Their House as

Inheritance or Gift in Age Group <35 and Tax Revenue Raised From Inheritance and Gift Taxes

Tax Revenue Raised From Inheritance and Gift Taxes (%) of Total Tax Revenue)

Note: Tax revenue from estate, inheritance, and gift taxes, average over the period 2009-2014.

Source: Household Finance and Consumption Survey and Organisation for Economic Co-operation and Development Tax Revenue database Full exemption of the main residence from inheritance taxes is likely to have regressive effects by

allowing rich households to transmit expensive houses for free. It may also open up tax planning

opportunities (such as providing incentives to hold more wealth in housing in anticipation of

favorable inheritance tax treatment). Moreover, it risks locking-in recipients in their house,

thereby reducing residential mobility. Indeed, the data indicate that households that have received

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Causa and Woloszko

their houses as inheritances or gifts tend to be less mobile than those that have acquired them.5

Finally, this exemption will narrow the tax base and reduce revenues from inheritance taxes on

houses (OECD, 2018b). In fact, across European countries, inheritance tax revenue is negatively

correlated with inheritance of the main residence and positively correlated with inheritance of

other assets (exhibit 7).

Political Economy Considerations in Housing Taxation

Political economy considerations affect the design and implementation of housing taxation.

One reason why OECD countries make little use of immovable property taxes, and even less of

inheritance taxes, is because those taxes are highly unpopular, and distributional concerns are

major reform obstacles. To start with, this paper has shown that housing is the chief asset of the

middle class.6 In virtually all OECD countries, the median voter is a homeowner. Concern is often

raised that property taxes impose an unfair burden on middle-income families because middle-

income families tend to hold a high proportion of their wealth in the family home, whereas top

earners may hold a significant proportion of their wealth in more liquid assets that are not subject

to property taxes. Concern is also raised about the impact of inheritance taxes on asset-rich but

cash-poor households, especially in the case where the house is being inherited; a substantial tax

bill combined with a low income may result in a property needing to be sold to pay the tax. These

concerns are not unjustified:

• Although being a homeowner drastically reduces the risk of being asset poor, it does not

affect the risk of being income poor (exhibit 8).7 Going further, in a number of OECD

countries, especially high-ownership ones such as Eastern European countries, Spain, and

Japan, homeowners are over-represented among the income-poor (exhibit 9).

• Housing is transmitted from one generation to the other, and in most European countries,

more than one in five low-income homeowners has inherited their houses (exhibit 10).

5 Not shown to save space. However, causality is hard to infer, as lack of mobility could reflect other confounding factors such as low education.

6 For a discussion on housing and the middle class see for example (Wolff, 2017).

7 One potential limitation and explanation of this finding is that the income poverty measure used here does not include imputed rents. This definition of income poverty is in line with standard practice due to the difficulty of properly estimating imputed rents in a comparable way across countries.

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Policy Considerations on Housing, Wealth, and Inequality

Exhibit 8

Being a Homeowner Reduces the Risk of Being Asset-Poor but Not the Risk of Being Income Poor

Share of Individuals That Are Income Poor, Asset Poor

and Income and Asset Poor, OECD Average

Notes: For the purpose of poverty measurement, both income and wealth are equalized so that the unit of analysis is the individual. (1) income-poor individuals are defined as those with equivalized annual income below the income poverty line (50 percent of median); (2) asset-poor individuals are defined as those with equivalized net worth insufficient to cover 3 months of the income poverty line; and (3) income and asset poor individuals as those with equivalized net worth insufficient to cover 3 months of the income poverty line and with equivalized annual disposable income below the income poverty line. Different wealth concepts and reference periods can be used to derive asset poverty measures, which has an impact on the estimated poverty levels. For instance, when net wealth is used, measures of asset-based poverty are around two-thirds lower than those based on the liquid financial wealth concept. As expected, the share of the population identified as asset poor increases with longer reference periods, although the relative ranking of countries is insensitive to the reference period used. See Balestra and Tonkin (2018) for details.

Source: Organisation for Economic Co-operation and Development Wealth Distribution Database (oe.cd/wealth) Cityscape 353

Causa and Woloszko

Exhibit 9

Homeowners are Over-Represented Among the Income Poor in Some OECD Countries

Housing Tenure Mix of Income-Poor Households (in %)

Note: How to read this figure: in Chile, 25 percent of individuals are income-poor, out of which 15 percent are homeowners and 10 percent are renters.

Source: Organisation for Economic Co-operation and Development (OECD) Wealth Distribution Database (oe.cd/wealth) Exhibit 10

In Most European Countries, More than One in Five Low-Income Homeowners Have Inherited

Their Houses

Proportion of Homeowners Having Inherited or

Received as Gift Their House Across the Income Distribution

Note: How to read this figure: in Germany, 23 percent of homeowners have inherited their house, 36 percent of homeowners in the bottom income quintile and 19 percent of homeowners in the top income quintile have inherited their house.

Source: Household Finance and Consumption Survey

Still, as discussed, housing taxation reforms can be designed in a way that addresses these obstacles, ultimately producing a more efficient and more progressive tax system. No approach is one-size-fits-all, and tax reform will depend on country-specific context, challenges, and social preferences.

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Housing Policy Reforms to Promote Resilience and

Labor Mobility

Reducing Household-Level Vulnerabilities Through Prudential Regulation

This paper has shown that access to mortgage debt allows households with little assets a chance

to own their own home and to accumulate wealth, but it can expose households at the bottom of

the distribution to financial vulnerabilities. This section discusses preliminary policy implications focusing on borrower-based prudential policies alongside their potential differential effects

across the distribution (see Alam et al. [2019] for recent evidence on the effects of loan-targeted

instruments on aggregate household credit and consumption).

The implementation of borrower-based prudential regulation may raise distributional concerns. As

shown in this paper, borrowers with high loan-to-value ratios are concentrated at the bottom of the

wealth distribution, and borrowers with high loan-to-income ratios at the bottom of the income

distribution. Subsequently, caps on loan-to-value and debt-to-income may exclude low-income and

low-wealth households from the mortgage market. The downpayment constraint resulting from

more restrictive caps will be particularly binding for first-time buyers and liquidity-constrained

households, such as younger and low-income households (see, for example Ortalo-Magne and

Rady, 2006). Recent analysis by Kelly, Le Blanc, and Lydon (2019) on the effect of tightening credit

standards on the distribution of borrowers shows that European countries that experienced a

boom-and-bust in the housing market saw the composition of buyers shifting from young and low-

income to old and high-income households after 2010.

However, distributional concerns associated with the implementation of borrower-based

macroprudential policies are likely to disappear over a longer term horizon. Excessive expansions

of mortgage credit can trigger higher house price increases, which reduce housing affordability

and thus price out low-income households from the market. By curbing the joint increase of credit

volume and house prices during leverage cycle booms, macroprudential caps may enhance housing

affordability (Glick and Lansing, 2010; Kohl, 2018; Mian and Sufi, 2009).

The policy implication is that macroprudential policies can enhance micro-resilience, especially

for those households most vulnerable to price and income shocks. Although associated credit

constraints may prevent young households from accumulating wealth through homeownership,

long-term positive gains are likely to outweigh short-term costs, and therefore such instruments

can improve welfare by (1) preventing young households from prematurely investing in housing,

hence reducing vulnerability to price and income shocks, ultimately allowing better consumption

smoothing (Xiong and Mavropoulos, 2018); and (2) more generally, contributing to housing

affordability by curbing leverage-induced increases in house prices. The effectiveness of such

instruments will ultimately depend on specific policy design: more data and work are needed to

properly evaluate the micro distributional effects of macroprudential instruments.

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Promoting Residential Mobility by Reducing Relocation Costs

The ease of moving residence geographically has positive efficiency implications by enhancing

the functioning of the labor market through the job-matching process and therefore the efficient

allocation of human resources. It can also have positive inclusiveness implications, especially from

a dynamic perspective. Moving can be an opportunity for people from disadvantaged areas and

backgrounds to find better jobs and achieve a better quality of life, and available evidence tends to support this argument (Chetty, Hendren, and Katz, 2016).

Ideally, housing markets and policies affecting them should not hinder residential mobility. The

data used in this paper allow for shedding some light on this topical issue. Keeping in mind

that the data do not distinguish residential turnover within the same geographical area from

geographical mobility, the evidence is that of a strong negative cross-country association between

homeownership and households’ mobility (exhibit 11).8 In the average European country, 6

percent of households change their residence over a 1-year period. Such mobility is low in high-

ownership countries in the East and South of Europe, compared with low-ownership countries in

the middle and North of Europe, where households move twice as often.

Exhibit 11

Across European Countries, High Homeownership is Associated with Low Residential Mobility

Homeownership and Residential Mobility

ear Period

centage of Households That Change

Residence Over a One-Y

Per

Homeownership

Notes: Residential mobility is defined as the proportion of households that change their main residence over a 1-year period. Restricted to age group 35–64.

Source: Household Finance and Consumption Survey

8 The sample is restricted to the age group 35–64 to reduce the impact of differences in demography, notably in the share of older households. That said, country rankings are unaffected by using the whole sample.

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Policy Considerations on Housing, Wealth, and Inequality

The negative association between homeownership and residential mobility directly reflects cross-

country differences in the housing tenure mix to the extent that homeowners tend to be less mobile

than private renters (exhibit 12).9 A common conjecture is that mobility is lower among owner-

occupiers than renters; owners face higher transaction costs of moving homes and therefore spend

a longer time in their residence to spread the costs over a longer time period. Causation cannot be

easily established, and differences in mobility across tenure types could also reflect self-selection into various tenures. For example, some households may have a preference for stability and be

more likely to choose owner occupancy. The negative association between homeownership and

residential mobility can also reflect that when the tenure mix is skewed toward owner-occupancy,

the size of the rental market, and therefore turnover in the rental market, is limited, which reduces mobility among renters. Indeed, the lowest level of mobility among renters is observed in high

homeownership countries such as Eastern European countries, Portugal, and Spain. One crude

implication from the negative association between homeownership and residential mobility would

be that there is a trade-off between promoting homeownership and encouraging residential mobility.

Exhibit 12

Owner-Occupied Households Tend to be Less Mobile Than Renters

Residential Mobility by Housing Tenure (%)

Notes: Residential mobility is defined as the proportion of households that change their main residence over a 1-year period. Restricted to age group 35-64.

Source: Household Finance and Consumption Survey

Reducing policy-driven residential mobility costs can help mitigate the trade-off between

promoting homeownership and encouraging labor mobility. One relevant area is property

transaction costs. For instance, stamp duties and registration taxes are typical transaction costs

in buying and selling a property—together with real estate agent fees and legal fees, which are

also influenced by government regulations. Data from Global Property Guide’s in-house research

published online10 and used in World Bank (2018) suggest that such transaction costs differ

9 This finding is in line with a number of papers such as Causa and Pichelmann (2020) and Caldera Sánchez and Andrews, (2011).

10 https://www.globalpropertyguide.com/home

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Causa and Woloszko

considerably across OECD countries: they are comparatively high in Belgium, France, Italy, and

Greece and comparatively low in the Nordic and Anglo-Saxon countries—in line with replies

to the 2009 OECD questionnaire on housing. High transaction costs may discourage property

transactions and thus curb the liquidity of housing markets, with potentially negative repercussions

for residential mobility. Empirical evidence has indeed shown that high transaction costs tend to

reduce residential mobility (Caldera Sánchez and Andrews, 2011; Causa and Pichelmann, 2020;

World Bank, 2018).

The existing literature has suggested reforms to reduce transaction costs in two areas:

• Shifting from transfer taxes and stamp duties to (progressive) recurrent taxes on residential

property. Reforms in this area are likely to make the housing market more liquid and efficient,

but they could also make it more volatile and therefore less resilient. Governments need

to seek an appropriate balance, taking into account country-specific conditions because

transaction taxes can be useful at curbing over-heated housing markets.

• Liberalising professional services to reduce notarial, legal, and real estate agency fees linked

to housing transactions. This result can be achieved by reforming conveying procedures to

allow for more competition among the providers of housing transaction services. For example,

in some countries, the use of notaries is mandatory in real estate transactions. The case for

reducing the role and cost of professional services in this area is all the more justified in the

context of digitalization that allows using new technologies (such as blockchain) to secure

property transactions.

Curbing excessively strict rental regulations can also increase residential, and therefore labor,

mobility. Empirical evidence has shown that stricter rent controls and tenant-landlord regulations

significantly reduce residential mobility by discouraging the supply of rental housing and by

locking-in tenants (Caldera Sánchez and Andrews, 2011; World Bank, 2018). Recently developed

indexes of rental regulations suggest that rent control is comparatively strict in countries with a

relatively large rental sector such as Denmark and Germany, possibly reflecting that, in countries

with large rental sectors, the demand for regulation is greater. Tenant-landlord regulation, however, is measured as comparatively strict both in countries with large (Austria and France) and small

(Italy and Spain) rental sectors (exhibit 13). Reforms in the area of rental regulations need to

strike a balance between landlords’ and tenants’ interests to create a security of tenure and avoid

market segmentation between sitting and new tenants (Andrews, Caldera Sánchez, and Johansson,

2011). On the one hand, the absence of rent regulations can lead landlords to hold up tenants by

unexpectedly raising rents. On the other hand, very strict rental regulations can hold up landlords’

property and reduce incentives for investing in rental housing, maintenance, and upkeep.

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Policy Considerations on Housing, Wealth, and Inequality

Exhibit 13

Excessive Rental Market Regulation May Hamper Residential Mobility

Rental Market Regulation Indexes

Note: The index varies between 0 and 1 and increases in the level of regulation.

Sources: Kholodilin (2018), Deutsches Institut für Wirtschaftsforschung (DIW)

Reforms to land-use regulations can influence housing supply and, in turn, residential mobility.

In particular, where housing supply is more responsive to demand, residential mobility is higher

(Caldera Sánchez and Andrews, 2011). This finding may reflect that higher supply responsiveness

reduces housing affordability differentials and price gaps across regions, potentially easing

relocation. In this context, policies to increase the responsiveness of housing supply are likely to

deliver more efficient and inclusive housing markets by curbing excessive house prices and making

housing more affordable, reducing geographical disparities and urban sprawl, and encouraging

residential mobility. Reforming land-use regulations and building restrictions is key in this respect, while balancing economic, social, and environmental aspects. Security of property rights and better

quality of land administration (such as coverage of registration system, reliability of administrative infrastructure, and accessibility of information) have also been found to lead to higher residential

mobility across European countries (World Bank, 2018).

Housing-related social transfers and subsidies aimed at addressing inclusiveness and redistributive

concerns also influence residential mobility and require careful design to reconcile efficiency and

equity objectives. Several studies have found that tenants in social housing are less mobile than

private tenants, possibly reflecting the reluctance to give up their below-market rents and their

generally more secure tenancies (Caldera Sánchez and Andrews, 2011; World Bank, 2018). This

circumstance has been found to be particularly the case in countries where social housing is highly

targeted. The causality is unclear, however, since households that are inherently less mobile to

begin with—possibly due to unobserved characteristics such as cultural and or social attachment to

their local area—may self-select into social housing.

Well-designed income-based portable housing allowances may be preferable to the direct provision

of social housing as they do not seem to directly hinder residential mobility. Governments could

also consider providing housing or rent subsidies for targeted groups, such as young people who

are more likely to move, potentially making benefits conditional on job search responsibilities.

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Causa and Woloszko

Finally, experimenting with housing vouchers to encourage low-income households to move

to higher income neighborhoods is another policy option to encourage residential and social

mobility (see Chetty, Hendren, and Katz [2016] for empirical evidence based on the United States).

That said, housing allowances have limitations; they cannot guarantee good housing and may

adversely affect rent prices. They require careful design in terms of efficiency and targeting to avoid discouraging labor market participation and ensure take-up by households in greatest need for

housing (World Bank, 2018).

In this context, social housing is needed, but it should prevent residential segregation by ensuring

that it is well integrated in the urban structure with appropriate access to transport sectors and

public services. Urban transport planning policies are key complementary instruments, and they

should aim at desegregating and connecting people in disadvantaged communities. In addition,

frequent reassessment of eligibility of social housing incumbent tenants with appropriate action if

eligibility has changed is important, as it frees up accommodation for needier households. Such

reassessments may also help encourage residential mobility, but they should be designed to avoid

possible disincentives to labor market participation among incumbent tenants.

Conclusion

Political economy considerations affect the design and implementation of housing-related reforms

and often make them unpopular:

• The median voter is a homeowner in many countries. Besides providing shelter,

homeownership is the most important source of wealth accumulation for middle-class

households. For low-income households, it is often the only source of wealth transmission

across generations through inheritance.

• One often stated challenge to housing reform is the fact that homeowners can be asset-

rich and income-poor. Indeed, being a homeowner significantly reduces the risk of being

asset poor, but it does not affect the risk of being income poor.

• Public policy tends to favor homeownership relative to renting, typically via the

preferential tax treatment of owner-occupied housing relative to rented housing. Yet the

case for departing from housing tenure neutrality in policy design is not clear, neither on

efficiency nor on equity grounds.

Informed by the stylized facts in Causa, Woloszko and Leite (2019) this paper discusses policy

implications of housing reform to promote inclusiveness and social mobility, to enhance

efficiency in the allocation of labor and capital, and strengthen macroeconomic resilience:

• Making the overall tax system more progressive and efficient, for instance, by (1) shifting from income to progressive recurrent taxes on immovable property and on inheritance and

gifts; and (2) phasing out the regressive features associated with the preferential tax treatment

of owner-occupied housing such as mortgage interest deductibility.

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Policy Considerations on Housing, Wealth, and Inequality

• Reducing household-level financial risks associated with mortgage debt through

borrower-based prudential regulation such as loan-to-value or debt-to-income caps.

• Promoting residential mobility by (1) reducing housing transaction costs associated

with taxation and the regulation of professional services; (2) curbing excessively strict

rental regulations; and (3) reforming social housing programs with a view to avoiding

lock-in effects and residential segregation and expanding well-designed portable housing

allowances. These actions require complementary investments in public transportation and

effective urban planning.

Authors

Orsetta Causa is the deputy head of the Structural Surveillance Division and the head of the

Inclusive Growth Unit Economics Department at the Organisation for Economic Co-operation

and Development.

Nicolas Woloszko is an economist in the Macroeconomic Analysis Division and an associate

in the Innovation Lab of the Economics Department at the Organisation for Economic Co-

operation and Development.

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———. 2013. Pensions at a Glance 2013: OECD and G20 Indicators. Paris, France: OECD

Publishing. http://dx.doi.org/10.1787/pension_glance-2013-en.

———. 2010. Tax Policy Reform and Economic Growth. Paris, France: OECD Publishing.

https://doi.org/10.1787/19900538.

Ortalo-Magne, François, and Sven Rady. 2006. “Housing Market Dynamics: On the Contribution

of Income Shocks and Credit Constraints,” Review of Economic Studies 73 (2): 459–485,

http://dx.doi.org/10.1111/j.1467-937X.2006.383_1.x.

Salvi del Pero, Angelica, Willem Adema, Valeria Ferraro, and Valérie Frey. 2016. Policies to Promote

Access to Good-Quality Affordable Housing in OECD Countries. Working paper No. 176. OECD

Social, Employment and Migration, Paris, France: OECD Publishing.

https://dx.doi.org/10.1787/5jm3p5gl4djd-en.

Wolff, Edward. 2017. Household Wealth Trends in the United States, 1962 to 2016: Has Middle

Class Wealth Recovered? Working paper No. 24085. National Bureau of Economic Research,

Cambridge, MA.

World Bank. 2018. Living and Leaving: Housing, Mobility and Welfare in the European Union. World Bank, Washington DC.

Xiong, Qizhou, and Antonios Mavropoulos. 2018. Housing Consumption and Macroprudential

Policies in Europe: An Ex Ante Evaluation. Discussion paper 17/2018. Halle Institute for Economic

Research (IWH), Halle. http://www.iwh-halle.de.

Cityscape 363

364 Departments

by Alexander Din and Ron

Wilson

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.

Using HUD Crosswalk Files to

Improve COVID-19 Analysis at

the ZIP Code and Local Level

Alexander Din

U.S. Department of Housing and Urban Development

Ron Wilson

University of Maryland, Baltimore County

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

As the novel coronavirus disease (COVID-19) continues to infect, harm, and kill thousands of

Americans, many jurisdictions and institutions are publishing data at the ZIP Code-level, including counts of tests performed, people infected, hospitalizations, and deaths. These data are leading to quickly produced publications with strong conjectures about the forming of geographic patterns. We present an alternative to ZIP Codes when working with local COVID-19 data.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

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Din and Wilson

The large, ambiguous shapes and skewed underlying data of ZIP Codes adversely affect statistical

analyses, which can lead to incorrect conclusions, particularly in the health sciences (Beyer,

Schultz, and Rushton, 2007; Cudnick et al., 2012; Grubesic and Matisziw 2006; Krieger et. al.,

2002; Oregon Health Authority, 2020; Sadler, 2019; Wilson, 2015). In particular, a recent study by

Harris (2020) from the National Bureau of Economic Research (NBER) simply overlaid New York

City (NYC) subway ridership patterns with ZIP Code data to suggest the subway is responsible for

COVID-19 concentration patterns across the city.

Using COVID-19 data from the NYC Department of Public Health1 and subway exit turnstile data

from the NYC Metropolitan Transit Authority (MTA),2 we examine if any spatial relationships exists

between the two, with a more statistically robust analysis than other authors. The COVID-19 data

are cumulative to April 30, 2020. The turnstile data are between November 1, 2019, and March

15, 2020; Staten Island data were unavailable for this analysis.3

Using the HUD 2020 quarter 1 ZIP-to-census-tract crosswalk file,4 we created positive COVID-19

case density estimates for a more local-level analysis at the census tract-level, thus escaping the

adverse effects of ZIP Codes. Our disaggregated estimates from these larger geographic units are

robust because of numerous empirical results that exemplify Gibrat’s law, which states that a growth

rate is proportional to the size of the distribution with which it is in contact (Santarelli, Klomp, and Thurik, 2006; Yigit, 2020). With respect to the growth of COVID-19 in specific neighborhoods, it is

expected that the virus growth is clustered in census tracts with a higher population.

The bivariate cluster map in exhibit 1 shows the statistical relationship between census tract

distance to the subway station and positive COVID-19 estimate densities (per square kilometer).

The Short Distance-High Density (dark orange) are the clusters of interest, which represent census

tracts near subway stops that are surrounded by census tracts with high positive COVID-19

estimate densities.

The Bronx and upper Manhattan are the only two boroughs that show a systemic relationship

between proximity to the subway stations and high COVID-19 density estimates. However, the

subway exit averages, shown as red circles, in these positive density hot spots vary from low to high, with many non-hot-spot subway stops having consistently high exit averages. This is inconsistent

with the idea that COVID-19 hot spots would be near high-use subway stops and lines.

1 https://github.com/nychealth/coronavirus-data

2 http://web.mta.info/developers/developer-data-terms.html

3 The New York City Subway does not connect to Staten Island. While Staten Island does have passenger rail service via the Staten Island Railway, it does not connect to any of the other four boroughs.

4 https://www.huduser.gov/portal/datasets/usps_crosswalk.html

366 Graphic Detail

Using HUD Crosswalk Files to Improve COVID-19 Analysis at the ZIP Code and Local Level

Exhibit 1

COVID-19 Cluster Relationships between Subway Exits Levels and Positive Density Estimates

Source: Metropolitan Transit Authority Turnstile Data November 1, 2019 – March 15, 2020 http://web.mta.info/developers/turnstile.html

Cityscape 367

Din and Wilson

Lower Manhattan does not show hot spots near subways, while Brooklyn and Queens only show

three hot spots. Further, Brooklyn and Queens either show no relationship between distance to

the subway and positive densities (light grey) or have Long-Distance Low-Density (dark blue) cold

spots that are census tracts far from the subway, surrounded by tracts with low positive densities.

Queens has two large cold spots, one in-between a series of subway stops, indicating no in-fill of

positive density clusters emanating from being surrounded by the subway.

With some of the above studies suggesting a relationship between high positive densities and

population density (population per square kilometer), we examine this relationship in conjunction

with distance to the subway (exhibit 2). The first two correlations are the distance to the subway

with positive densities and population densities, which primarily indicate that positive and

population densities are moderately associated with closer proximity to the subway at about the

same levels. However, the third correlation between positive and population densities shows a

strong relationship, suggesting that COVID-19 densities are more associated with population

density than proximity to the subway.

Exhibit 2

Correlation of Densities of COVID-19 Cases with Distance to the Subway and

Population Densities

Distance & Positive

Distance & Population

Positive & Population

Geography

Densities

Densities

Densities

r

t

p

r

t

p

r

t

p

The Bronx

-0.41

-8.13

< 0.001

-0.42

-8.33

< 0.001

0.88

33.52

< 0.001

Brooklyn

-0.32

-6.47

< 0.001

-0.37

-10.92

< 0.001

0.72

27.99

< 0.001

Manhattan

-0.15

-2.63

0.009

-0.12

-1.93

0.055

0.77

19.97

< 0.001

Queens

-0.37

-10.31

< 0.001

-0.49

-14.47

< 0.001

0.82

37.85

< 0.001

Staten Island

n/a

n/a

n/a

n/a

n/a

n/a

n/a

n/a

n/a

New York City

-0.32

-15.64

< 0.001

-0.43

-21.35

< 0.001

0.75

51.48

< 0.001

A simple regression of positive COVID-19 density estimates (y) in relation to the distance to the

subway (x ) and the population density (x ) shows no statistical relationship between the positive

1

2

estimates when controlling for the population density (exhibit 3). This baseline model indicates

that increased COVID-19 estimates are related more to increased population density than to

proximity to subway stations. The adjusted R-squared of 0.57 shows that this initial baseline model

indicates that it has a strong explanatory power of the positive estimates being unrelated to tracts

near subway stations.

368 Graphic Detail

Using HUD Crosswalk Files to Improve COVID-19 Analysis at the ZIP Code and Local Level

Exhibit 3

Regression Coefficients Between Distance to Subway, Positive Case Density, and Population Density

Variable

Estimate

Std. Error

t-Value

Pr(>|t\)

intercept

0.0524

0.0081

6.429

0.0000 ***

Distance to Subway

-1.2E-06

3.2E-06

-0.371

0.7100

Population Density

0.0124

0.0003

46.295

0.0000 ***

Exhibit 4, a 3D scatter plot, exemplifies the regression relationship between near distance to

subway (x), population density (y), and positive estimates (z). The pattern in the data cloud reveals a higher correlation between the positive COVID-19 estimates and population density than with

distance to the subway. The relationship trend in exhibit 4 shows that the positive estimate density

rises steeply at very close distances to subway stations but is pulled away and spread widely on the

population density axis.

Exhibit 4

Correlations between Distance to Subway, Positive Case Density, and Population Density

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Din and Wilson

This pattern indicates that positive COVID-19 densities increase with population density, and

population density increases as distance to the subway stations decreases. With the color gradient

showing changes in positive estimate density, the pattern shows that the highest positive density

estimates correspond with the highest population densities near the subway stops.

Correspondingly, the pattern revealed in the data cloud in exhibit 4 corresponds with the

regression results in exhibit 3. That is, the positive COVID-19 estimates are more associated with

higher population density than being close to subway stations.

Our analysis aligns with other research that suggests COVID-19 clusters may be related to

something other than public transportation such as places where people spend a more significant

amount of time (Bromage, 2020; Kay, 2020). With COVID-19 primarily requiring longer periods

of exposure than typical subway rides, it is not yet proven that public transportation is the culprit for spreading the virus. More so, the virus seems to be associated with higher population densities,

which is in line with the nature of a communicable outbreak (Yigit, 2020).

Acknowledgements

The authors thank Mark Shroder for his helpful comments.

Authors

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.

Ron Wilson is an adjunct faculty member of the Geographic Information Systems Program at the

University of Maryland, Baltimore County.

References

Beyer, Kirsten, Alan Schultz, and Gerard Rushton. 2007. “Using ZIP Codes as Geocodes in Cancer

Research.” In Geocoding Health Data: The Use of Geographic Codes in Cancer Prevention and Control, Research and Practice, edited by G. Rushton, M. P. Armstrong, J. Gittler, B. R. Greene, C. E. Pavlik, M. M. West, and D. L. Zimmerman. Boca Raton, FL: CRC PRESS: 37–64.

Bromage, Erin S. 2020. The Risks - Know Them - Avoid Them. Working paper.

https://www.erinbromage.com/post/the-risks-know-them-avoid-them?fbclid=IwAR31ts4XTZnIJh4

mJlZXX8_rZLAYgMDhCw6chpUT6F12avhv4n17LeBAXkQ.

Cudnik, Michael T., Jing Yao. Dana Zive, Craig Newgard, and Alan T. Murray. 2012. “Surrogate

Markers of Transport Distance for Out-of-Hospital Cardiac Arrest Patients,” Prehospital Emergency Care 16 (2): 266–272.

370 Graphic Detail

Using HUD Crosswalk Files to Improve COVID-19 Analysis at the ZIP Code and Local Level Grubesic, Tony H. and Timothy C. Matisziw. 2006. “On the Use of ZIP Codes and ZIP Code

Tabulation Areas (ZCTAs) for the Spatial Analysis of Epidemiological Data,” International Journal of Health Geographics 5 (1): 58.

Harris, Jeffrey E. “The Subways Seeded the Massive Coronavirus Epidemic in New York City.” April

2020. working paper. National Bureau of Economic Research. https://www.nber.org/papers/w27021.pdf.

Kay, Jonathan. 2020. COVID-19 Superspreader Events in 28 Countries: Critical Patterns and

Lessons. Working paper. https://quillette.com/2020/04/23/covid-19-superspreader-events-in-28-

countries-critical-patterns-and-lessons/.

Krieger, Nancy., Pamela Waterman, Jarvis T. Chen, Mah-Jabeen Soobader, S. V. Subramanian, and

Rosa Carson. 2002. “ZIP Code Caveat: Bias Due to Spatiotemporal Mismatches Between ZIP Codes

and US Census–Defined Geographic Areas—The Public Health Disparities Geocoding Project,”

American Journal of Public Health 92 (7): 1100–1102.

Oregon Health Authority. (2020). COVID-19 Weekly Report, May 5, 2020.

https://www.oregon.gov/oha/PH/DISEASESCONDITIONS/DISEASESAZ/Emerging%20

Respitory%20Infections/COVID-19-Weekly-Report-2020-05-05-FINAL.pdf.

Sadler, Richard. 2019. “Misalignment Between ZIP Codes and Municipal Boundaries: A Problem

for Public Health.” Cityscape, A Journal of Policy Development and Research 21 (3): 335–340.

Santarelli, Enrico, Luuk Klomp, and Roy Thurik. 2006. “Gibrat’s Law: An Overview of the

Empirical Literature.” In Entrepreneurship, Growth and Innovation, The Dynamics of Firms and

Industries, edited by Enrico Santarelli: 41–73.

Wilson, Ronald E. 2015. “The Neighborhood Context of Foreclosures and Crime,” Cartography and

Geographic Information Science 4 (2): 162–177.

Yigit, Aydogan. 2020. “Are Covid-19 Cases Independent of the City Sizes?” MPRA Paper No. 99697:

1–9. https://mpra.ub.uni-muenchen.de/99697/.

Cityscape 371

372 Departments

by Christina Stacy and Brady Meixell

The Changing Geography of

Spatial Mismatch

Christina Stacy

Brady Meixell

Urban Institute

Abstract

Using data from Snagajob, we analyze spatial mismatch for low-wage workers in Detroit, Michigan, and Seattle, Washington. In Detroit, more low-wage job seekers than jobs exist in the central city, while the suburbs have a larger number of low-wage jobs than workers seeking those jobs. In Seattle, there is an overabundance of low-wage job seekers in the suburbs and an overconcentration of low-wage jobs in the central city.

In many cities in the United States, low-wage workers live far from available jobs (Stacy et al.,

2019). This phenomenon, called spatial mismatch, causes high unemployment rates and longer

spells of joblessness among lower-paid workers (Andersson, Klaesson, and Larsson, 2014;

Bruekner and Zenou, 2003), particularly Black residents, women, and older workers (Andersson

et al., 2018). Although spatial mismatch theory was initially developed through the lens of racial

discrimination, the mechanisms are also relevant to households with low incomes.

To measure spatial mismatch, we use data from Snagajob, the largest online marketplace for hourly

jobs. We use Snagajob applicant and posting data from 2015 to calculate a measure of spatial

mismatch—specifically, the number of job seekers minus the number of job postings within a

reasonable commuting distance of each ZIP Code.1 We define the reasonable commuting distance

to be a 6.3-mile radius around the population-weighted center of each ZIP Code because that is

the average distance (after removing outliers) in the Snagajob data between job seekers’ home ZIP

Codes and the ZIP Codes in which they apply to jobs. We calculate spatial mismatch for the 16

metropolitan statistical areas (MSAs), which were selected to create a diverse group of places based

on geography, population growth, and labor market conditions.

1 In accordance with the data-sharing agreement with Snagajob, we were provided data aggregated to the ZIP Code, as the smallest level of geography they were comfortable releasing.

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Stacy and Meixell

When this phenomenon was first studied in the 1960s, 1970s, and 1980s, the type of mismatch

that was most prevalent followed a pattern in which low-income workers lived in central cities

and jobs predominantly existed in the suburbs (Ellwood, 1986; Holzer, 1991; Kain, 1968; Wilson,

1987). This kind of spatial mismatch pattern is still visible today in some cities, such as Detroit

(exhibit 1). In Detroit, more low-wage job seekers than jobs exist in the central city, while the

suburbs have a larger number of low-wage jobs than workers seeking those jobs.

Exhibit 1

Spatial Mismatch in the Detroit, Michigan Area, 2015

Note: “Reasonable distance” is 6.3 miles from the population-weighted centroid of each ZIP Code, the average distance between job seekers and jobs for each application in our dataset.

Sources: Urban Institute analysis of 2015 Snagajob data; map layers from Esri, HERE, Garmin, OpenStreetMap contributors, and the GIS user community Since the early 2000s, however, many cities have begun to face a new type of spatial mismatch,

with lower-wage workers overconcentrated in the suburbs and job opportunities located in the

urban core. This pattern is at least partially due to a residential and employment resurgence,

where younger and higher-skilled individuals have increasingly chosen to live closer to downtown

areas (Baum-Snow and Hartley, 2017; Couture and Handbury, 2017; Edlund, Machado, and

Sviatschi, 2015). This influx of relatively high-income earners has led to the gentrification of many historically low-income neighborhoods, putting pressure on incumbent residents, especially low-income renters, to move elsewhere in search of affordable housing (Brummet and Reed, 2018).

374 Graphic Detail

The Changing Geography of Spatial Mismatch

In some cities, this increased density of higher-income residents in the urban core has led to the

displacement of lower-income residents into the suburbs.

This form of spatial mismatch is visible in Seattle (exhibit 2), where there is an overabundance of

low-wage job seekers in the suburbs, and an overconcentration of low-wage jobs in the central

city. This mismatch could be due to the high cost of rental housing in Seattle, forcing lower-wage

workers to seek housing farther outside of the city. In 2017, Seattle had the 4th highest gross rent

of any city in the country at $1,555.2

Exhibit 2

Spatial Mismatch in the Seattle, Washington Area, 2015

Note: “Reasonable distance” is 6.3 miles from the population-weighted centroid of each ZIP Code, the average distance between job seekers and jobs for each application in our dataset.

Sources: Urban Institute analysis of 2015 Snagajob data; map layers from Esri, HERE, Garmin, OpenStreetMap contributors, and the GIS user community To address spatial mismatch, cities should use data to better understand their own mismatch

patterns and design policy and practice solutions to link people to jobs. Investments in affordable

housing and increasing urban density (Durst, 2020), transit connections between areas of

opportunity and pockets of poverty (Ong and Miller, 2005), and advancements in career pathways

2 Balk, Gene. 2019. “Seattle now most expensive city for renters outside California, census data shows.” The Seattle Times. July 30. https://www.seattletimes.com/seattle-news/data/seattle-now-most-expensive-city-for-renters-outside-

california-census-data-shows.

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Stacy and Meixell

and wages for low-wage workers may be ways to reduce the negative impacts of spatial mismatch.

Special consideration should also be given to those living in public housing and federally

subsidized housing who have experienced increased levels of spatial mismatch compared with

similar populations of unassisted households (Stacy et al., 2020). More research is needed to

understand the specific patterns of mismatch in different cities and to identify solutions that work

to reduce the negative impacts of mismatch.

Authors

Christina Plerhoples Stacy is a senior research associate in the Metropolitan Housing and Communities Policy Center at Urban Institute, where she specializes in urban economics and inclusion.

Brady Meixell is a research analyst in the Metropolitan Housing and Communities Policy Center at

Urban Institute.

References

Andersson, Fredrik, John C. Haltiwanger, Mark J. Kutzbach, Henry O. Pollakowski, and Daniel

H. Weinberg. 2018. “Job Displacement and the Duration of Joblessness: The Role of Spatial

Mismatch,” Review of Economics and Statistics 100 (2): 203–18.

Andersson, Martin, Johan Klaesson, and Johan P. Larsson. 2014. “The Sources of the Urban Wage

Premium by Worker Skills: Spatial Sorting or Agglomeration Economies?” Papers in Regional Science 93 (4): 727–47.

Baum-Snow, Nathaniel, and Daniel Hartley. 2017. Accounting for Central Neighborhood Change,

1980-2010. Chicago: Federal Reserve Bank of Chicago.

Bruekner, Jan K., and Yves Zenou. 2003. “Space and Unemployment: The Labor-Market Effects of

Spatial Mismatch,” Journal of Labor Economics 21 (1): 242–262.

Brummet, Quentin, and Davin Reed. 2018. The Effects of Gentrification on Original Neighborhood

Residents: Evidence from Longitudinal Census Microdata. Technical report. Chicago: NORC at the University of Chicago.

Couture, Victor, and Jessie Handbury. 2017. Urban Revival in America, 2000 to 2010. Working

paper 24084. Cambridge, MA: National Bureau of Economic Research.

Durst, Noah. 2020. Residential Land Use Regulation and the Spatial Mismatch between Housing and

Employment Opportunities in California Cities. Berkeley, CA: Center for California Real Estate and the Terner Center for Housing Innovation.

Edlund, Lena, Cecilia Machado, and Maria Micaela Sviatschi. 2015. Gentrification and the Rising

Returns to Skill. Working paper 21729. Cambridge, MA: National Bureau of Economic Research.

376 Graphic Detail

The Changing Geography of Spatial Mismatch

Ellwood, David T. 1986. The Spatial Mismatch Hypothesis: Are There Teenage Jobs Missing in the

Ghetto? In The Black Youth Employment Crisis, edited by Richard B. Freeman and Harry J. Holzer.

Chicago, Illinois: University of Chicago Press.

Holzer, Harry. 1991. “The Spatial Mismatch Hypothesis: What Has the Evidence Shown?” Urban

Studies 28 (1): 105–122. https://doi.org/10.1080/00420989120080071.

Kain, John F. 1968. “Housing Segregation, Negro Employment, and Metropolitan Decentralization,”

Quarterly Journal of Economics 82 (2): 175–97.

Ong, Paul M., and Douglas Miller. 2005. “Spatial and Transportation Mismatch in

Los Angeles,” Journal of Planning Education and Research 25 (1): 43–56. https://doi.

org/10.1177/0739456X04270244.

Stacy, Christina, Christopher Davis, Benny Docter, Leiha Edmonds, Jorge Gonzalez, Ananya

Hariharan, Brady Meixell, Alexander Peffley, Nancy Pindus, Prasanna Rajasekaran, Daniel Teles,

Brett Theodos, and Mark Treskon. 2020. Spatial Mismatch and Federally Supported Rental Housing: Do Public Housing and Vouchers Help People Live Closer to Available Jobs? Washington, DC: Urban Institute.

Stacy, Christina, Terry-Ann Craigie, Brady Meixell, Graham MacDonald, Sihan Vivian Zheng, and

Christopher Davis. 2019. Too Far from Jobs: Spatial Mismatch and Hourly Workers. Urban Institute.

https://www.urban.org/features/too-far-jobs-spatial-mismatch-and-hourly-workers.

Wilson, William Julius. 1987. The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy. Chicago: University of Chicago Press.

Cityscape 377

378 Departments

by Brian Stromberg

Urban Greenery and Public Housing

Brian Stromberg

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.

Abstract

Urban greenspace has demonstrably beneficial impacts on mental and physical health. Greenspace

and tree canopy also play a role in reducing local temperatures, which reduces heat-related mortalities.

This is particularly important in cities that act as “urban heat islands.” This short article reviews the research on these two topics and provides a simple analysis of the location of public housing buildings in Washington, D.C. in relation to tree canopy and greenspace.

Urban greenery in its various forms (urban forestry, city parks, urban agriculture, greenspace, and

so on) has been shown to provide a wide range of physical and mental health benefits to urban

populations (Beyer et al., 2014; Thompson et al., 2012). This evidence ranges from improved

recovery of hospital residents with view of trees outside their windows (Ulrich, 1984) to stress

reduction prompted by the Japanese practice of Shinrin-yoku (Park et al., 2007).

Research on the mental and physical well-being of HUD-assisted residents suggests they would

benefit from increased access to greenspace. Helms, Sperling, and Steffen (2017) compared the

physical and mental health of HUD-assisted adults to unassisted low-income adults and the general

adult population. The results of this research indicate that HUD-assisted households tend to have

poorer health than other households in the country. Almost 36 percent of adults in HUD-assisted

housing reported their health as fair or poor, compared with 24 percent for unassisted low-income

renters and 14 percent for the general adult population (Helms, Sperling, and Steffen, 2017). This

self-reporting was supported by the fact that HUD-assisted adults suffered from higher rates of

various physical health conditions such as diabetes, asthma, heart disease, and obesity. The mental

health of HUD-assisted adults was also markedly worse than the two comparison groups; 12

percent of HUD-assisted adults experienced serious psychological distress, compared with 9 and 4

percent for unassisted low-income renters and the general population.

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Stromberg

These increased rates of physical and mental stress in HUD-assisted adults could be reduced by

expanded access to greenspace, according to the research that has been done. One of the earliest

contemporary experimental analyses of the health impact of green space was Maas et al. (2006).

The authors looked at the connection between health outcomes and the percentage of green space

in individuals’ immediate environment. More recently, Beyer et al. (2014) looked at the population

of Wisconsin and found, quite simply, that “higher levels of green space correspond to better mental

health outcomes” (p. 3466). This positive impact is comparable to the difference between individuals

with and without private health insurance. The authors also suggested that the greening of

neighborhoods could help offset the stress of high unemployment rates and residential segregation.

Simple access to green space was shown in Miami to reduce the incidence of chronic medical

conditions in Medicare recipients (Brown et al., 2016; 2018). Those medical conditions included

both physical and mental conditions such as diabetes, hypertension, obesity, Alzheimer’s disease,

and depression. A study in California found that the presence of tree canopy resulted in better self-

reported health; a lower prevalence of obesity; stronger neighborhood cohesion; and lower rates of

diabetes, high blood pressure, and asthma (Ulmer et al., 2016). Additional studies abound in the

literature that connect greenspace to positive mental and physical health outcomes.

The presence of tree canopy can also help mitigate the health impacts of extreme heat events,

which are in turn amplified by the urban heat island (UHI) effect that causes significantly higher

temperatures in developed areas that lack vegetation (Mallen et al., 2020). For example, a 2017

analysis of temperature and vegetative land cover in Washington, DC, connects neighborhood-

level variations in temperature with the presence of green land cover (McCo, 2018; see also

Smith, 2017).

Extreme heat exacerbates existing health issues and can increase mortality (Bowler et al., 2010;

Buchin et al., 2016). These temperature variations can have particularly significant consequences

for low-income and minority communities, who tend to have higher mortality rates and higher

healthcare costs during extreme heat events (Schmeltz, Petkova, and Gamble, 2016; Schwartz,

2005; Smith, 2017; Wondmagegn et al., 2019). HUD-assisted households can generally be

described as part of these more vulnerable populations.

The maps included in this article show the relationship between urban greenspace and tree canopy

and public housing buildings in Washington, DC. The land cover data comes from the Chesapeake

Conservancy, and provides 1-meter resolution coverage for the entirety of the Chesapeake Bay’s

watershed (Chesapeake Conservancy, 2020). This raster data was used to produce a series of maps

that compare the distribution of public housing to tree canopy coverage.

Exhibit 1 shows public housing building locations and the land cover raster data for DC classified

according to specific types.1 Exhibit 2 generalizes the raster according to whether the cells are

classified as any type of tree canopy or some other type. Exhibit 3 further generalizes the raster data into hexagonal cells, shaded according to the total amount of tree canopy cells in each hexagon.

1 A live, colorized version of the same data can be found on the Chesapeake Conservancy’s website here:

https://chescon.maps.arcgis.com/apps/webappviewer/index.html?id=9453e9af0c774a02909cb2d3dda83431

380 Graphic Detail

Urban Greenery and Public Housing

Exhibit 1

Urban Greenery and Public Housing, Washington, DC

Sources: HUD, Chesapeake Conservancy, National Park Service

Cityscape 381

Stromberg

Exhibit 2

Urban Greenery and Public Housing, Washington, DC

Sources: HUD, Chesapeake Conservancy, National Park Service

382 Graphic Detail

Urban Greenery and Public Housing

Exhibit 3

Urban Greenery and Public Housing, Washington, DC

Sources: HUD, Chesapeake Conservancy, National Park Service

Cityscape 383

Stromberg

A simple summary of land cover types suggests that public housing buildings have less tree canopy

and more impervious surfaces in their immediate area than the District has as a whole. Exhibit 4

compares the percentage of land cover types across DC to the percentage of land cover in a quarter-

mile radius around public housing buildings. The area around public housing also has a much

higher percentage of low vegetation and barren land cover.

Exhibit 4

Percentage of Land Cover Types Across DC and Within a Quarter-Mile of Public Housing

Land Cover Type

Districtwide Percentage (%)

Public Housing Percentage (%)

Tree canopy

30.36

23.49

Impervious surfaces

16.71

21.60

Low vegetation

16.43

17.87

Structures

15.42

21.27

Impervious roads

10.68

14.72

Water

10.00

0.51

Barren

0.24

0.39

Shrubland

0.14

0.13

Wetlands

0.03

0.00

Source: Author’s calculations using Chesapeake Conservancy data

This analysis is basic and could be significantly improved in several ways. Potential improvements

include dimensions of population density and other socioeconomic characteristics and the

distribution of impervious surfaces throughout the neighborhoods would give a better sense of

where tree canopy is found across the city. Spatial regression analysis would also provide a more

detailed sense of how tree canopy is distributed. These more advanced methods may be pursued in

future research.

This article focuses on tree canopy, but greenspace in general is the main focus of a large part of

the literature on the benefits of urban greenery. A broader analysis of greenspace is complicated

by questions of access. A project that includes remote sensing imagery with on-the-ground data

collection would provide a better sense of where greenspace is located and how accessible it may be.

Acknowledgments

The author would like to thank John Huggins, Dylan Sweeney, Michael Early, Veronica Helms, and

Alexander Din for their participation in the project that produced this article.

Author

Brian Stromberg is a Social Science Analyst in the Office of Policy Development and Research at HUD.

384 Graphic Detail

Urban Greenery and Public Housing

References

Beyer, Kirsten M. M., Andrea Kaltenbach, Aniko Szabo, Sandra Bogar, F. Javier Nieto, and Kristen

M. Malecki. 2014. “Exposure to Neighborhood Green Space and Mental Health: Evidence from the

Survey of the Health of Wisconsin,” International Journal of Environmental Research and Public Health 11 (3): 3453–3472.

Bowler, Diana, Lisette Buyung-Ali, Teri M. Knight, and Andrew S. Pullin. 2010. “Urban Greening

to Cool Towns and Cities: A Systematic Review of the Empirical Evidence,” Landscape and Urban

Planning 97 (3): 147–155.

Brown, Scott, Tatiana Perrino, Joanna Lombard, Kefeng Wang, Matthew Toro, Tatjana Rundek,

Carolina Marinovic Gutierrez, Chuanhui Dong, Elizabeth Plater-Zyberk, Maria I. Nardi, Jack

Kardys, and José Szapocznik. 2016. “Health Disparities in the Relationship of Neighborhood

Greenness to Mental Health Outcomes in 249,405 U.S. Medicare Beneficiaries,” International Journal of Environmental Research and Public Health 15 (3): 430.

Brown, Scott C. Tatiana Perrino, Joanna Lombard, Kefeng Wang, Matthew Toro, Tatjana Rundek,

Carolina Marinovic Gutierrez, Chuanhui Dong, Elizabeth Plater-Zyberk, Maria I. Nardi, Jack

Kardys, and José Szapocznik. 2018. “Health Disparities in the Relationship of Neighborhood

Greenness to Mental Health Outcomes in 249,405 U.S. Medicare Beneficiaries,” International Journal of Environmental Research and Public Health 15 (3): 430–441.

Buchin, Oliver, Marie-Therese Hoelscher, Fred Meier, Thomas Nehls, and Felix Ziegler. 2016.

“Evaluation of the Health-Risk Reduction Potential of Countermeasures to Urban Heat Islands,”

Energy and Buildings 114: 27–37.

Chesapeake Conservancy. 2020. Land Cover Data Project. Annapolis, MD: Chesapeake

Conservancy. https://chesapeakeconservancy.org/conservation-innovation-center/high-resolution-

data/land-cover-data-project/. Accessed on 7/25/2020.

Helms, Veronica, Jon Sperling, and Barry Steffen. 2017. A Health Picture of HUD-Assisted Adults,

2006-2012. Washington, DC: U.S. Department of Housing and Urban Development.

Maas, Jolanda, Robert A Verheij, Peter P Groenewegen, Sjerp de Vries, and Peter Spreeuwenberg.

2006. “Green Space, Urbanity, and Health: How Strong is the Relation?” Journal of Epidemiology and Community Health 60 (7): 587–592.

Mallen, Evan, Joshua Bakin, Brian Stone, Ramachandra Sikavumar, and Kevin Lanza. 2020.

“Thermal Impacts of Built and Vegetated Environments on Local Microclimates in an Urban

University Campus,” Urban Climate 32.

McCoy, Terrence. 2018. “‘I Don’t Want to Die’: As the Country Bakes, Studies Show Poor City

Neighborhoods Are Often Much Hotter Than Wealthy Ones.” Washington Post. September 2.

https://www.washingtonpost.com/local/social-issues/i-dont-want-to-die-as-the-country-bakes-

studies-show-poor-city-neighborhoods-are-often-much-hotter-than-wealthy-ones/2018/09/02/21d-

cf796-abd8-11e8-b1da-ff7faa680710_story.html?utm_term=.53ec2e09361b.

Cityscape 385

Stromberg

Park, Bum-Jin, Yuko Tsunetsugu, Tamami Kasetani, Hideki Hirano, Takahide Kagawa, Masahiko

Sato4) and Yoshifumi Miyazaki. 2007. “Physiological Effects of Shinrin-yoku (Taking in the

Atmosphere of the Forest)—Using Salivary Cortisol and Cerebral Activity as Indicators,” Journal of Physiological Anthropology 26 (2): 123–128.

Schmeltz, Michael T., Elisaveta P. Petkova, and Janet L. Gamble. 2016. “Economic Burden of

Hospitalizations for Heat-Related Illnesses in the United States, 2001-2010,” International Journal of Environmental Research and Public Health 13 (9): 894–905.

Schwartz, Joel. 2005. “Who Is Sensitive to Extremes of Temperature?: A Case-Only Analysis,”

Epidemiology 16 (1): 67–72.

Smith, Randy. 2017. “DC’s Heat Islands.” Washington, DC: D.C. Policy Center.

https://www.dcpolicycenter.org/publications/urban-heat-islands/.

Thompson, Catharine Ward, Jenny Roeb, Peter Aspinall, Richard Mitchell, Angela Clowd, and

David Miller. 2012. “More Green Space Is Linked to Less Stress in Deprived Communities:

Evidence From Salivary Cortisol Patterns,” Landscape and Urban Planning 105 (2012): 221–229.

Ulmer, Jared M., Kathleen L. Wolf, Desiree R. Backman, Raymond L. Tretheway, Cynthia J.A.

Blaine, Jarlath PM O’Neil-Dunne, and Lawrence D. Frank. 2016. “Multiple Health Benefits of Urban

Tree Canopy: The Mounting Evidence For a Green Prescription,” Health & Place 42 (2016): 54–62.

Ulrich, R.S. 1984. “View through a window may influence recovery from surgery,” Science 224

(4647): 420–421.

Wondmagegn, Berhanu Y., Jianjun Xianga, Susan Williams, Dino Pisaniello, and Peng Bi. 2019.

“What Do We Know About the Healthcare Costs of Extreme Heat Exposure? A Comprehensive

Literature Review,” Science of the Total Environment 657: 608–618.

386 Graphic Detail

by Hota Gangarao, David Johnson, Ray Liang, and Mike Blanford

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.

Reviving Rammed Earth as a

Sustainable Construction Technique

Hota Gangarao

David Johnson

Ray Liang

West Virginia University

Mike Blanford

U.S. Department of Housing and Urban Development

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

The status quo for single-family home construction has been wood frame construction, commonly called

“stick framing” because of the dominant use of 2” x 4” dimensional lumber. Wood frame construction has served the homebuilding community well; however, alternative building approaches are beginning to catch on. The alternative discussed in this article—rammed earth—is actually a historical construction technique that practitioners are reviving.

Cityscape: A Journal of Policy Development and Research • Volume 22, Number 3 • 2020

Cityscape 387

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

Gangarao, Johnson, Liang, and Blanford

Introduction

Rammed earth is a construction technique in which subsoil1 is compressed within a vertical

formwork to form the walls (generally, exterior) of a building. The subsoil is added layer by

layer, rather than all at once. These layers are called “lifts” (see exhibit 1). Compacting each lift ensures even compression across the entire wall section. In modern use, rammed earth is typically

stabilized with a stabilizing agent—Portland cement being the most common. This technique is

called stabilized rammed earth.

Exhibit 1

Corner of wall with lifts clearly shown.

Source: West Virginia University

1 Subsoil is the layer of soil under the topsoil. Subsoil is typically composed of a mixture of sand, silt, and clay.

388 Industrial Revolution

Reviving Rammed Earth as a Sustainable Construction Technique

Over the past quarter century, rammed earth construction has received renewed attention due

to its desirable, green characteristics. The North American Rammed Earth Building Association

(NAREBA) and other green advocates have led the movement for increased attention. This interest

led to a standardization effort for the use of rammed earth and other earthen wall construction

techniques the civil engineering community uses. In 2010, the American Society for Testing

and Materials International (ASTM) created the Standard Guide for Design of Earthen Wall Building Systems, which “provides guidance for earthen building systems, also called earthen construction, and addresses both technical requirements and considerations for sustainable development.”

History of Rammed Earth

The basic construction techniques involved in rammed earth construction have been used for

centuries. In China, rammed earth structures built 500–1,000 years ago remain standing and

in use today (Liang et al., 2013). Several hundred years ago, the indigenous inhabitants of the

southwest region of the United States were adept at using local materials (primarily earth/clay

mixtures) to build durable structures that provided relative comfort against both the heat of

summer and bitter cold of winter (Hardin, Merry, and Fritz, 2003).

The U.S. Department of Agriculture published one of the earliest modern technical documents for

rammed earth construction in May 1937. The report noted that “no permanent building material

is cheaper, and when spare-time farm labor is employed, very little cash outlay is required to erect

durable structures” (Betts and Miller, 1937).

No single event started the modern era of rammed earth construction. Some practitioners never

stopped employing it because of the low cost and low technology of the technique. The 2003

report by Maniatidis and Walker gives an excellent overview of the literature on rammed earth

(Maniatidis and Walker, 2003).

Benefits

Rammed earth is a sustainable and natural construction material providing many environmental

benefits. Possible benefits of rammed earth over wood framing include:

• Durability of the technique, which is demonstrated by historical buildings that were

constructed using the same technique and are still standing to this day.

• The construction skills required to build a rammed earth wall can be easily acquired without

formal training, which is especially beneficial in rural areas with unskilled labor availability.

• High thermal mass coupled with modern insulation materials of the rammed earth walls

allows for the modulation of indoor temperatures. This feature makes the home more

comfortable during the spring and fall.

• Low material and transportation costs due to the nearly universal availability of suitable subsoil.

Cityscape 389

Gangarao, Johnson, Liang, and Blanford

Drawbacks

• One major limitation of rammed earth construction is its labor intensiveness.

• Another limitation, related to the high thermal mass of the rammed earth walls, is the slow

temperature adjustment of the living space. This characteristic may happen in the winter

or summer if the home’s heating or cooling equipment is turned off or reduced and then

turned back on. Indoor comfort can also be impacted if the heating or cooling capacity of the

equipment is not able to quickly overcome the thermal mass of the wall system.

A Rammed Earth Home in Alaska

In 2016, HUD supported the construction of a stabilized rammed earth home by the Aleutian

Housing Authority (AHA) in Butte, Alaska (see exhibit 2). AHA was interested in demonstrating a

stabilized rammed earth (SRE) affordable home because of their desire to produce the most energy-

efficient and healthiest homes possible at the lowest possible cost.

Exhibit 2

Roof assembly being constructed on the Aleutian Housing Authority stabilized rammed earth home.

Source: West Virginia University

Because the AHA home was built in a cold climate region, builders sandwiched a layer of insulation

between two stabilized rammed earth layers to provide a highly insulative wall assembly; this

method is suitable for residential or commercial building types (Windstorm and Schmidt, 2013).

One major benefit of insulated stabilized rammed earth construction (sometimes called SIRE, for

Stabilized Insulated Rammed Earth) is high R-values2 (Windstorm and Schmidt, 2013).

2 R-value is the measure of resistance to thermal conduction from one side of the material to the other; in this case, it is the temperature difference between the inside and outside walls.

390 Industrial Revolution

Reviving Rammed Earth as a Sustainable Construction Technique

Furthermore, the home was designed to have maximum passive solar heating. Energy modeling

suggested that passive solar heating will satisfy 20 percent of the annual space heating

requirements in the home. An innovative natural gas-fired heating system will provide for the

portion of the space heating demand not met by passive solar heating. The heart of the heating

system is a condensing storage tank-type water heater that provides both domestic hot water for

bathing and washing and heat for in-floor radiant heating. This heating system was selected for

a variety of reasons. First, because annual space heating loads and heating demand for the home

are roughly 75 percent lower than average homes in the surrounding area (a function of the high

levels of insulation and air-tightness provided by the building envelope), the heating system needs

to provide only minimal levels of heat output to maintain comfortable conditions inside the home.

Hot water heating demand, by contrast, is more a function of occupant behavior (shower length,

laundering practices, and so on) and thus does not vary significantly between more and less

energy-efficient homes.

Performance Validation of Aleutian Housing Authority Home

West Virginia University (WVU) has been evaluating the performance of the AHA SIRE home since

its construction. The evaluation includes testing of the wall system for its structural properties in a mechanical properties laboratory at WVU and monitoring its energy use, thermal performance,

living comfort, and durability at the Butte, Alaska site.

Researchers evaluated SRE from 75 specimens consisting of cylinders, beams (reinforced and

unreinforced), and blocks for its compressive and flexural strengths, fatigue, freeze-thaw durability, and thermal performance. For example, researchers evaluated three 8” x 14” x 123” reinforced SRE

beams designed to simulate the load carrying capacity of window lintels in Alaska in a three-point

bending-shear test after two of the beams were exposed to fatigue and creep conditions that would

be present in the field (see exhibit 3). Researchers also tested the SRE wall sections with several

simulated earthquake loads. The result shows that walls displayed minimal deformation under

earthquake load types and were deemed adequate for the specific service loads evaluated.

Researchers cycled samples for a number of freeze and thaw cycles and then evaluated them under

compression to determine the loss in strength when compared with uncycled control specimens.

The addition of fibers in the mix design was found to enhance the flexural capacity of the beam

and its freeze-thaw durability, whereas the freeze-thaw evaluations demonstrated very promising

long-term freeze-thaw durability for the building in Alaska. Thermal performance studies of

the wall assembly concluded that an R-value of 40 could be achieved with two sections of SRE

measuring 8 inches each with, 8-inch interstitial foam as insulation.

Cityscape 391

Gangarao, Johnson, Liang, and Blanford

Exhibit 3

A stabilized rammed earth (SRE) beam being tested at WVU.

Source: West Virginia University

The mechanical property testing concluded that SRE is nearly as strong as low-strength (~2500 psi)

concrete but has a near-zero impact on our environment. Highly energy-efficient rammed earth

wall construction can be durable and earthquake-resistant.

Ongoing performance monitoring of the AHA SIRE has demonstrated the comfortable living

environment of the home, including stable temperature and humidity and good indoor air quality.

As noted earlier in the drawbacks, however, the large thermal mass of the rammed earth walls can

tax the heating system of the home under certain conditions.

The AHA SIRE home has demonstrated that rammed earth construction can be adapted for Alaska.

A safe and energy-efficient housing unit can be built using local materials and local unskilled labor.

Further Reading

Ciancio, Daniela, and Christopher Beckett. 2015. Rammed Earth Construction: Cutting-Edge Research on Traditional and Modern Rammed Earth. London, UK: Taylor & Francis Group.

Easton, David, and Cynthia Wright. 1996. The Rammed Earth House. Chelsea Green Publishing.

392 Industrial Revolution

Reviving Rammed Earth as a Sustainable Construction Technique

Krahn, Tim J. 2019. Essential Rammed Earth Construction: The Complete Step-by-Step Guide. British Columbia, Canada: New Society Publishers.

North American Rammed Earth Building Association (NAREBA). http://nareba.org.

Acknowledgments

The authors would like to thank the leadership team Dan Duame, Steven Mandt, and Kennedy Serr

at the Aleutian Housing Authority and Bly Windstorm of NAREBA for their contributions leading

to the construction of the SRE house and their support to the ongoing monitoring activities. The

authors appreciate the cooperation from the tenants of the SRE house for allowing us to test and

evaluate their home.

Authors

Hota Gangarao is the Maurice A. and Joann Wadsworth Distinguished Professor of civil and

environmental engineering at West Virginia University.

David Johnson is a graduate research assistant at the Constructed Facilities Center at West

Virginia University.

Ray Liang is a research professor at West Virginia University.

Mike Blanford is a research engineer at the U.S. Department of Housing and Urban Development,

Office of Policy Development and Research.

References

ASTM Standard E2392/E2392M. 2016. Standard Guide for Design of Earthen Wall Building Systems.

West Conshohocken, PA: ASTM International.

Betts, Morris C., and Thomas A. H. Miller. 1937. Farmers’ Bulletin No. 1500, Rammed Earth Walls for Buildings. Washington, DC: U.S. Department of Agriculture.

Hardin, Mary C., Dr. Scott Merry, and Wolfgang Fritz. 2003. Towards and Affordable Rammed-Earth

Dwelling. PLEA 2003 – The 20th Conference on Passive and Low Energy Architecture. Santiago, Chile.

Liang, Ruifeng. 2013. “Historic Hakka Architecture Could be Key for Future Sustainable Buildings,”

SangSaeng 36 (1): 10–13.

Maniatidis, Vasilios, and Peter Walker. 2003. A Review of Rammed Earth Construction. Bath, UK: Natural Building Technology Group.

Windstorm, Bly, and Arno Schmidt. 2013. “A Report of Contemporary Rammed Earth Construction

and Research in North America” Sustainability 5: 400–416.

Cityscape 393

Symposium

The Moving to Work Retrospective Evaluation .................................................................................................... 1

Guest Editors: Elizabeth Rudd

Guest Editor’s Introduction

The Moving to Work Retrospective Evaluation .....................................................................................................3

A Picture of Moving to Work Agencies’ Housing Assistance by Martha M. Galvez, Ruth Gourevitch, and

Benny Docter ........................................................................................................................................................ 9

Moving to Work Agencies’ Use of Funding Flexibility by Diane K. Levy, Leiha Edmonds, and David Long .............29

Moving to Work Agencies’ Use of Project-Based Voucher Assistance by Martha M. Galvez,

Daniel Teles, Alyse D. Oneto, and Matthew Gerken ...........................................................................................55

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program

by Nina Castells ...............................................................................................................................................85

Designing an Alternative Rent Policy for the Housing Choice Voucher Program by James Riccio ........................ 139

Can Diverse Activities Have a Combined Impact? Examining the Effects of the Moving to Work

Demonstration on Housing Choice and Self-Sufficiency Outcomes by Mark Treskon, Matthew Gerken,

and Martha M. Galvez ...................................................................................................................................... 163

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness by Christina Stacy,

Josh Leopold, Daniel Teles, Alyse D. Oneto, Yipeng Su, Matthew Gerken, and Ruth Gourevitch .......................... 193

A German Perspective on Objectives of the Moving to Work Demonstration, Housing Provision, and

Targeting by Max-Christopher Krapp ................................................................................................................. 219

Improving Access to Housing Amid Exclusionary Housing Markets: A Latin American Viewpoint of the

Moving to Work Demonstration by Adriana Hurtado-Tarazona .......................................................................... 229

Disparities and Similarities in U.S. and U.K. Rent Subsidy Programs: A British Perspective on the Moving

to Work Demonstration by Becky Tunstall .......................................................................................................... 235

Refereed Papers ........................................................................................................................................ 239

Documenting Racially Restrictive Covenants in 20th Century Philadelphia by Larry Santucci .............................241

Applying Performance Management Tools to Understand and Improve Rapid Re-Housing Program

Outcomes by Christopher Weare ....................................................................................................................269

Departments................................................................................................................................................. 297

Affordable Design

2020 Innovation in Affordable Housing Student Design and Planning Competition: Camino de Jacobo

in Santa Fe, New Mexico by Jagruti D. Rekhi ..................................................................................................299

Annual HUD Secretary’s Opportunity and Empowerment Award: Ebeid Neighborhood Promise Initiative,

Toledo, Ohio by Regina C. Gray .....................................................................................................................311

Data Shop

Learning More About HUD-Assisted Tenants Through Data Linkage by Shawn Bucholtz, Emily Molfino,

and Quentin Brummet ...................................................................................................................................317

American Enterprise Institute Housing Market Indicators by Tobias Peter and Edward Pinto ..............................327

Foreign Exchange

Policy Considerations on Housing, Wealth, and Inequality by Orsetta Causa and Nicolas Woloszko ..................339

Graphic Detail

Using HUD Crosswalk Files to Improve COVID-19 Analysis at the ZIP Code and Local Level by

Alexander Din and Ron Wilson .......................................................................................................................365

The Changing Geography of Spatial Mismatch by Christina Stacy and Brady Meixell ........................................373

Urban Greenery and Public Housing by Brian Stromberg ..................................................................................379

Industrial Revolution

Reviving Rammed Earth as a Sustainable Construction Technique by Hota Gangarao, David Johnson,

Ray Liang, and Mike Blanford .........................................................................................................................387

Cityscape

Office of Policy Development and Research

VOLUME 22 NUMBER 3 • 2020

Document Outline

Cityscape

Contents

Symposium

The Moving to Work Retrospective Evaluation

Introduction

The Moving to Work Demonstration 1996 – Present

The Moving to Work Expansion Authorized in 2016

Six Moving to Work Retrospective Studies and One Essay on the Rent Reform Demonstration

International Perspectives from Colombia, Germany, and England

Guest Editor

Acknowledgments

A Picture of Moving to Work Agencies’ Housing Assistance

The Moving to Work Demonstration and Moving to Work Agencies

Moving to Work Agencies

Moving to Work Housing Assistance

Understanding the Increase in the Number of Households Assisted by Moving to Work Agencies

Relationship Between Funding and Moving to Work Status

Assistance Provided by Moving to Work and Comparison Traditional Public Housing Agencies

Local, Non-Traditional Program Assistance

Characteristics of Moving to Work Households

Household Composition and Characteristics

Household Income and Rent Burdens

Head of Household Race and Ethnicity

Access to Low-Poverty Neighborhoods

Household Locations by Moving to Work Agency Characteristics

Conclusion

Acknowledgments

Authors

References

Moving to Work Agencies’ Use of Funding Flexibility

Introduction

What Is Moving to Work Funding Flexibility?

Research on Moving to Work Funding Flexibility

Research Questions and Challenges

Methods

Data and Measures

Sampling

Analysis

Uses of Moving to Work Funding Flexibility

Use of Funding Flexibility by Statutory Objectives

Leveraging Activities

Shift in Funds

Funding Shifts by Eight Agencies

Funding Shifts by Individual Agencies in 2015

Factors Motivating the Use of Funding Flexibility

Discussion

All 39 MTW agencies have used funding flexibility to undertake a variety of activities. More activities focused on increasing housing choice than on agencies’ cost-effectiveness or residents’ self-sufficiency.

Agencies have used funding flexibility to improve their access to financing with favorable terms and to meet matching requirements of funders or partners.

Analysis of direct and indirect shifts of funds completed for eight agencies showed that most funding was shifted out of the HCV program and shifted into capital projects, with smaller amounts going to public housing operations, to support local initiatives, and for HCV administrative purposes.

Funding flexibility is credited with enabling agencies to act more quickly than otherwise would be possible, to undertake a greater range of activities, and to work toward longer-term outcomes.

Limitations

Conclusion

Appendix A: Activities Using Funding Flexibility by Objective, Fiscal Year 2015

Acknowledgments

Authors

References

Moving to Work Agencies’ Use of Project-Based Voucher Assistance

Introduction

Background and Literature Review

Why and How Agencies use Project-Based Vouchers

Existing Evidence on Project-Based Voucher Assistance

Research Approach

Findings

Case Studies of Three Moving to Work Agencies’ Project-Based Voucher Use

Discussion

Limitations

Policy Implications and Future Research

Acknowledgments

Authors

References

The Effects of Increasing the Tenant Rent Contribution in the Housing Choice Voucher Program

Introduction

Background

The Santa Clara County Housing Authority Rent Reform

Existing Evidence About the Effects of Housing Subsidies on Employment and Earnings

Research Design

Effects on Employment and Earnings

Effects on Average Housing Subsidy Amounts and Continued Housing Subsidy Receipt

Households’ Housing Decisions While in the Voucher Program

Household Rent Burden While in the Voucher Program

Effects for Households Affected by the Tenant Contribution Rate Change Only Versus Households Also Affected by the Voucher Size Rule Change

Study Sample

Study Period

Data Sources

Employment and Earnings

Housing Subsidies and Housing Characteristics

Neighborhood Poverty, Local Context, and Hardship Policies

Analytic Approach

Employment and Earnings—Comparative Interrupted Time Series

Subsidy Amounts and Receipt of Subsidy—Autoregressive Difference-in-Difference

Descriptive Analysis of Rent Burden, Number of Bedrooms, Neighborhood Quality, and Household Size

Single and Double Policy Change Subgroup Analysis

Comparison Group Selection

Findings

Impacts on Employment and Earnings

Impacts on Housing Subsidies

Households’ Housing Decisions and Rent Burden

Effects for the Single and Double Policy Change Subgroups

Discussion

Study Limitations

Conclusion

Appendix A. Map of Santa Clara and Comparison Counties

Appendix B. Model Specifications

Comparative Interrupted Time Series Model

Autoregressive Difference-in-Difference Model

Acknowledgments

Author

References

Designing an Alternative Rent Policy for the Housing Choice Voucher Program

Introduction

The Rent Reform Debate

Goals Established for an Alternative Rent Policy

The Design Process

Rent Reform Options and Trade-offs

Conditions that qualify a family for a hardship remedy

Hardship remedies

The hardship process

Predicting the overall effects of the alternative rent policy

Conclusion

Acknowledgments

Author

References

Can Diverse Activities Have a Combined Impact? Examining the Effects of the Moving to Work Demonstration on Housing Choice and Self-Sufficiency Outcomes

Introduction

Literature Review

Housing Choice

Self-Sufficiency

Goals of the Present Study

Methods

Treatment

Timeframe

Sampling

Data

Variables

Analysis

Results

Identification of Activities

Comparative Interrupted Time Series Analysis

Detailed Findings

Discussion and Conclusions

Housing Choice

Self-Sufficiency

Implications for Research

Conclusion

Acknowledgments

Authors

References

Fund More, Serve More, Save More: Moving to Work and Cost-Effectiveness

Introduction

Background

Findings from Prior Research

Limitations of Prior Research

Research Approach

Research Questions

Sample

Data Collection and Assembly

Defining Outcome Measures and Controls

Estimation Methods

Findings

Effect of the MTW Demonstration Controlling for Program Mix, Housing Quality and Affordability, and Household Characteristics

Effect of the MTW Demonstration on per Household Spending by PHAs

Robustness Checks

Discussion

Limitations

Conclusion

Acknowledgments

Authors

References

A German Perspective on Objectives of the Moving to Work Demonstration, Housing Provision, and Targeting

Objectives of the Moving to Work Demonstration and its Assessment

Instruments of German Housing Policy—Cost-Effectiveness, Self-Sufficiency, and Housing Choices?

Conclusions

Author

References

Improving Access to Housing Amid Exclusionary Housing Markets: A Latin American Viewpoint of the Moving to Work Demonstration

Assisting Households while Promoting Self-Sufficiency

Increasing Housing Choice and Reducing Sociospatial Segregation

Conclusion

Acknowledgments

Author

References

Disparities and Similarities in U.S. and U.K. Rent Subsidy Programs: A British Perspective on the Moving to Work Demonstration

Author

Refereed Papers

Documenting Racially Restrictive Covenants in 20th Century Philadelphia

Introduction

Racial Zoning and Racial Covenants

Zoning Ordinances

Racially Restrictive Covenants

Creating the Spatial Database

Two Caveats

A Lower Bound

Imprecise Coordinate Estimates

Comparison to Other Data Collection Projects

Preliminary Observations

Ward-Level Analysis

HOLC Map Analysis

Enumeration District-Level Analysis

Concluding Remarks

Acknowledgments

Author

References

Applying Performance Management Tools to Understand and Improve Rapid Re-Housing Program Outcomes

Rapid Re-Housing and Performance Management

Data

Analysis

Prospects for Improvement

Discussion and Conclusions

Acknowledgments

Authors

References

Departments

2020 Innovation in Affordable Housing Student Design and Planning Competition: Camino de Jacobo in Santa Fe, New Mexico

Introduction

The Winning Team: Yale University

The Runner- Up Team: University of Maryland, College Park

Thoughts from the Jury

Acknowledgments

Author

Postscript

Annual HUD Secretary’s Opportunity and Empowerment Award: Ebeid Neighborhood Promise Initiative, Toledo, Ohio

Planning

Results

Health

Housing

Education

Employment

Innovation

Equity

Transferability

Author

References

Learning More About HUD-Assisted Tenants Through Data Linkage

Introduction

Data

Record Linkage Process

Record Linkage Quality Assessment

Uses of the Linked Data

Conclusion

Acknowledgments

Authors

References

American Enterprise Institute Housing Market Indicators

Underlying Data

Price Tiers

House Price Appreciation Housing Market Indicators

Land Price and Land Share Housing Market Indicators

New Construction Housing Market Indicators

National Mortgage Risk Index and Housing Market Nowcast

Tracking Affordability with the Housing Market Indicators

Other Housing Market Indicators

The State of the Housing Market

National and Metro Housing Market Indicators

Conclusion

Acknowledgments

Authors

References

Policy Considerations on Housing, Wealth, and Inequality

Introduction and Motivation

Stylized Facts in a Nutshell

Reforming Property Taxes to Make the Overall Tax System More Progressive and Efficient

Shifting the Tax Mix Towards Property Taxes

Enhancing the Efficiency and Progressivity of Immovable Property Taxation

Taxing Inherited Wealth: The Role of Housing

Political Economy Considerations in Housing Taxation

Housing Policy Reforms to Promote Resilience and Labor Mobility

Reducing Household-Level Vulnerabilities Through Prudential Regulation

Promoting Residential Mobility by Reducing Relocation Costs

Conclusion

Authors

References

Using HUD Crosswalk Files to Improve COVID-19 Analysis at the ZIP Code and Local Level

Acknowledgements

Authors

References

The Changing Geography of Spatial Mismatch

Authors

References

Urban Greenery and Public Housing

Acknowledgments

Author

References

Reviving Rammed Earth as a Sustainable Construction Technique

Introduction

History of Rammed Earth

Benefits

Drawbacks

A Rammed Earth Home in Alaska

Performance Validation of Aleutian Housing Authority Home

Further Reading

Acknowledgments

Authors

References