A Journal of Policy

Development and Research

Small area Fair market rentS

Volume 21, number 3 • 2019

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

Small Area Fair Market Rents

Volume 21, Number 3 • 2019

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

Small Area Fair Market Rents ...................................................................................................... 1

Guest Editors: Adam Bibler, Chalita Brandly, Peter Kahn, Marie Lihn, and Lydia Taghavi

Guest Editors’ Introduction

Small Area Fair Market Rents ..................................................................................................... 3

Small Area Fair Market Rent Demonstration ...................................................................... 17

The Effects of Small Area Fair Market Rents on the Neighborhood Choices of

Families with Children.............................................................................................................. 19

by Samuel Dastrup, Meryl Finkel, and Ingrid Gould Ellen

Small Area Fair Market Rents, Race, and Neighborhood Opportunity ................................... 49

by Kirk McClure and Alex Schwartz

Impact of Expanded Choice on Attrition in the Housing Voucher Program .......................... 71

by Judy Geyer, Samuel Dastrup, and Meryl Finkel

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment? .......... 89

by Edgar O. Olsen

Reflections on Demand Assistance in the Rental Sector: A European Perspective ............... 103

by Marietta E.A. Haffner

Aspects of the Housing Choice Voucher Program and the Impact of Small Area

Fair Market Rents Ceilings: a British Perspective .................................................................. 113

by Christine M.E. Whitehead

Alternative Measures of Market Rents .............................................................................. 123

The Best Laid Plans Often Go Awry: An Analysis of the Implementation of Small

Area Fair Market Rents ............................................................................................................ 125

by Kelly L. Patterson and Robert Mark Silverman

Comparing Small Area Fair Market Rents With Other Rental Measures Across

Diverse Housing Markets ........................................................................................................ 159

by Christian Hess, Rebecca J. Walter, Arthur Acolin, and Sarah Chasins

Contrasting Different Geographies in Fair Market Rents: Implications for the

Housing Choice Voucher Program in Pittsburgh, PA ............................................................. 187

by Mike Blackhurst, Chris Briem, and Sabina Deitrick

Cityscape

iii

Contents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data .... 207

by Aksel Olsen

A Tweak to Housing Assistance Allows Low-Income Renters Access to

High-Income Neighborhoods .................................................................................................. 245

by Alexander Casey

Refereed Papers ................................................................................................259

Sexual Harassment of Low-Income Women by Landlords .................................................... 261

by Rigel C. Oliveri

Departments .....................................................................................................285

Affordable Design

2019 American Institute of Architects (AIA)—HUD Secretary’s Housing &

Community Design Awards .................................................................................................... 287

by Regina C. Gray

Data Shop

A New Cost-Based Index of Housing Quality and Repair Needs .......................................... 299

by Eliza Wallace, Eileen Divringi, and Keith Wardrip

Foreign Exchange

Observing New Zealand’s Housing Crisis: Efforts to Produce and Finance Affordable

Housing and its Implications for U.S. Policies ....................................................................... 311

by Jeffrey Mosley

Graphic Detail

Misalignment Between ZIP Codes and Municipal Boundaries: A Problem for

Public Health ........................................................................................................................... 335

by Richard C. Sadler

Industrial Revolution

Trenchless Technology: A New Way to Install and Renew Utilities ..................................... 341

by Mike Blanford

Protecting Low-Income Housing from Climate Risks ............................................................ 347

by Michael E. Canes

Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative ..................... 355

by Alex Schwartz

Extremely Low-Income: Has the New Measure Made a Difference? ..................................... 389

by Chalita Brandly

iv Contents

Symposium

Small Area Fair Market Rents

Guest Editors: Adam Bibler, Chalita Brandly, Peter Kahn, Marie Lihn,

and Lydia Taghavi

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape

1

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

2

Small Area Fair Market Rents

Guest Editors’ Introduction

Small Area Fair Market Rents

Adam Bibler

Chalita Brandly

Peter Kahn

Marie Lihn

Lydia Taghavi

U.S. Department of Housing and Urban Development

This Cityscape symposium focuses on multiple aspects of Small Area Fair Market Rents (SAFMRs).

These elements include outcomes based on the experience of early forays into the use of SAFMRs

along with insights regarding the implementation of SAFMRs among the public housing agencies

(PHAs) required to do so. Before delving into the research, we begin with a brief overview of the

importance of Fair Market Rents (FMRs) on the Housing Choice Voucher (HCV) Program and

a discussion of the history of SAFMRs. We conclude with a summary of the research contained

within the Symposium.

Introduction

Fair Market Rents (FMRs) are primarily used to determine payment standard amounts for the

Housing Choice Voucher (HCV) Program. Local administrators of the HCV program set payment

standards that are used to calculate the value of the housing subsidy for each voucher family.

FMRs are gross rent estimates; they include the shelter rent plus the cost of all necessary utilities,

but do not include telephones, cable or satellite television service, and internet service. The U.S.

Department of Housing and Urban Development (HUD) sets FMRs to assure that a sufficient

supply of rental housing is available to program participants. To accomplish this objective, FMRs

must be both high enough to permit a selection of units and neighborhoods and low enough to

serve as many low-income families as possible.

Traditionally, HUD calculated a single FMR1 for each FMR area. HUD defines FMR areas as

metropolitan areas and non-metropolitan counties. HUD’s FMR areas are based on the most

1 HUD estimates FMRs for units of different sizes as measured by the number of bedrooms and publishes FMRs for zero-bedroom (efficiency) units to four-bedroom units. For purposes of this discussion, the set of FMRs HUD

estimates for an area is referred to as “the FMR.”

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape

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

Bibler, Brandly, Kahn, Lihn, and Taghavi

current Office of Management and Budget (OMB) definitions of metropolitan areas2 with

some exceptions.3

Beginning in 2009, the Office of Policy Development and Research (PD&R) undertook the

task of developing FMRs that vary within metropolitan areas. After examining a variety of

levels of geography, PD&R developed Small Area Fair Market Rents (SAFMRs) for ZIP Codes

within metropolitan areas. SAFMRs are designed to enable HCV tenants to access more units in

neighborhoods of opportunity because they more accurately reflect the cost of rental housing in

these areas. At the same time, and for the same reason, SAFMRs will discourage HCV tenants from

locating in neighborhoods of concentrated poverty.

Fair Market Rents and the Housing Choice Voucher Program4

The HCV program is the federal government’s major program for assisting very low-income

families, the elderly, and people with disabilities in affording decent, safe, and sanitary housing in

the private market. Housing choice vouchers are administered locally by public housing agencies

(PHAs). PHAs receive federal funds from HUD to administer the voucher program.

A family that is issued a housing voucher is responsible for finding a suitable housing unit

of their choice where the owner agrees to rent under the program. This unit may include the

family’s present residence. Rental units must meet minimum HUD standards of health and safety,

as determined by the local PHA. The participant is free to choose any housing that meets the

requirements of the program and is not limited to units located in subsidized housing projects.

A housing subsidy is paid to the landlord directly by the PHA on behalf of the participating family.

The family then pays the difference between the actual rent charged by the landlord and the

amount subsidized by the program. Because housing assistance is provided on behalf of the family

or individual, participants can find their own housing, including single-family homes, townhouses,

and apartments.

At the most basic level, the amount of housing assistance provided to each family is a function

of two components: (1) the family’s level of income, and (2) the PHA payment standard. In the

HCV program, families are required to pay 30 percent of their income toward rent. The PHA

administering the voucher program sets the payment standard. With some exceptions, payment

standards are calculated between 90 and 110 percent of the FMR for the area. Payment standards

are the amounts generally needed to rent a moderately-priced rental unit in the local market. PHAs

may set multiple payment standards for different parts of their operating area.

2 HUD updates the metropolitan area definitions once the metropolitan area definitions are incorporated into the American Community Survey (ACS) data used in the calculation of FMRs.

3 In general, HUD makes exceptions to the OMB metropolitan area definitions when the OMB definition is larger than HUD’s definition of housing market areas. The annual Federal Register notices announcing the FMRs for the upcoming fiscal year (FY), typically published around September 1, contain explanations of how HUD constructs the FMR geography.

4 The description of the Housing Choice Voucher Program is adapted from information obtained at https://www.hud.

gov/program_offices/public_indian_housing/programs/hcv/about/fact_sheet.

4

Small Area Fair Market Rents

Guest Editors’ Introduction: Small Area Fair Market Rents

FMRs are calculated based on gross rents paid for standard quality rental units occupied by

recent movers collected locally through the American Community Survey (ACS).5 The level at

which FMRs are set is expressed as a percentile point within the rent distribution of standard-

quality rental housing units.6 The current definition used is the 40th percentile rent,7 the dollar

amount below which 40 percent of the standard-quality rental housing units are rented. The

40th percentile rent is drawn from the distribution of rents of all units occupied by recent movers

(renter households who moved to their present residence within the current or previous year of

responding to the ACS). HUD is required to ensure that FMRs exclude non-market rental housing

in their computation. Therefore, HUD excludes all units falling below a specified rent level

determined from public housing rents in HUD’s program databases as likely to be either assisted

housing or otherwise at a below-market rent, and units less than 2 years old.8

The History of Small Area Fair Market Rents

HUD has been calculating FMRs since at least 1974 (39 FR 43943). Over the years, the data

sources, calculations methods, and geographic area definitions have changed. Notwithstanding

these changes, two constants have remained: FMRs have been calculated for “market areas”9 and

there was a single FMR for each area.

Original Calculation of Small Area Fair Market Rents

A Government Accountability Office (GAO) report from 1994 titled “Rental Housing: Use of Small

Market Areas to Set Rent Subsidy Levels Has Drawbacks” (GAO/RCED-94-112)10 states “fair market

rents based on smaller geographic areas would better reflect the rent levels typically prevailing

within those smaller markets” (page 5 of the report). The drawbacks, as summarized below, were

too great to allow the use of smaller rent geographies in determining FMRs at that time.

• The cost of collecting the additional data needed to accurately and reliably determine and update

FMRs could be substantial—ranging from $5 million to as much as $750 million annually.

5 The ACS is the primary socioeconomic and demographic survey of the U.S. Census Bureau. It has been conducted since 2005. See https://www.census.gov/programs-surveys/acs.

6 Standard-quality rental housing units have the following attributes: occupied rental units paying cash rent; specified renter on 10 acres or less; full plumbing; full kitchen; unit more than 2 years old, and meals not included in rent.

7 There is no statutory requirement for the FMR percentile. Effective 9/14/95, HUD promulgated a regulatory change (60 FR 42222) which set the FMR percentile at the 40th, down from their estimation at 45th percentile as a cost savings measure.

8 The specified rent level is known as the “Public Housing Cut Off” and is described more fully at: https://www.

huduser.gov/portal/datasets/fmr/fmrs/FY2014_code/Public_Housing_Adjustments_for_FMRs_Final.pdf.

9 Little is known about how the earliest FMR calculations were completed. A visual inspection of the referenced Federal Register notice lists the FMRs for “Market Areas” which appear to be areas larger than single counties. As examples, market areas are listed as “Atlanta” or “Chicago” or “San Francisco.”

10 The report is available at https://www.gao.gov/products/RCED-94-112.

Cityscape

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Bibler, Brandly, Kahn, Lihn, and Taghavi

• The costs per assisted household could increase and result in a smaller number of households

being served by the Section 8 program unless the program’s total funding was increased,

which is unlikely. Program costs could increase if the assisted households moved from market

areas where the FMR was reduced to market areas where it was increased. In addition, costs

could rise if FMRs could increase but not decrease from the current levels.

• FMR could decrease in some areas, thereby restricting housing choices for the assisted

households seeking units in those areas.

50th Percentile Rents as a De-Concentration Tool

In 2000, HUD identified a pattern of high voucher concentration in relatively low-cost areas.

To provide a broad range of housing opportunities throughout the metropolitan area, HUD

established FMRs at the 50th percentile by an interim rule published on October 2, 2000. Areas

had to meet the following eligibility criteria to use 50th percentile FMRs:

• contain at least 100 census tracts;

• at most, 70 percent of the census tracts with at least 10 two-bedroom units are in census tracts

where at least 30 percent of two-bedroom rental units have gross rents at or below the 40th

percentile FMR;

• at least 25 percent of the tenants in the FMR area reside in the 5 percent of the census tracts

within the FMR area that have the largest number of program participants.

After an area was selected to use 50th percentile FMRs, they would have 3 years to show

measurable de-concentration of program participants. If de-concentration was not shown or if the

FMR area deconcentrated but fell below 25 percent of the tenants’ rule, the FMR area would not be

allowed to continue to use 50th percentile FMRs. The objective of 50th percentile FMRs was to give

PHAs a tool to assist them in de-concentrating voucher program use patterns. The theory behind

50th percentile FMRs was that by providing certain areas with larger subsidy thresholds, voucher

holders would be able to use higher subsidy levels to move into higher opportunity neighborhoods.

Unfortunately, as HUD would later discover, raising the level of FMRs uniformly throughout an

FMR area did not provide a suitable incentive structure to move and the implementation of 50th

percentile FMRs led to administrative complexities for PHAs.11

Current Analysis of Small Area Fair Market Rents

At the beginning of the Obama administration, PD&R was challenged to explore the possibility

of calculating FMRs for geographies smaller than metropolitan areas. The first 5 years of ACS

data became available in 2010. This allowed access to smaller geographies that would be updated

at least every 5 years, which negated the first and most important cost concern expressed in the

GAO report.

11 Establishing a More Effective Fair Market Rent System; Using Small Area Fair Market Rents in the Housing Choice Voucher Program Instead of the Current 50th Percentile FMRs (Small Area FMR Final Rule, 81 FR 80567), Section II – Background, page 80570.

6

Small Area Fair Market Rents

Guest Editors’ Introduction: Small Area Fair Market Rents

In 2009, when HUD began to research calculating FMRs for areas smaller than metropolitan areas,

one of the first questions to be addressed was “what level of geography should HUD target?” Some

possible candidates were counties, census tracts, congressional districts, school zones, and ZIP

Codes. There are plusses and minuses for each of these geographic definitions. Counties may still

be too large. For example, the Flagstaff, Arizona metropolitan statistical area (MSA) is comprised

solely of Coconino County, Arizona. There would be no additional geographic delineations for

Flagstaff if counties were selected. An individual census tract is typically too small to be considered

a housing market, and easily determining what census tract a housing unit is in is not trivial while

in the midst of a housing search. Identification is a similar issue for congressional districts and

school zones. In the end, HUD decided to use ZIP Codes because:

• ZIP Codes are widely understood by HUD’s clients;

• ZIP Codes are small enough to localize rents;

• ZIP Codes are large enough in many cases to have statistically reliable, annually updated data

available from the U.S. Census Bureau.

Originally, SAFMRs were calculated by establishing the relationship between rents in the ZIP Code

to rents in the metropolitan area where the ZIP Code is found. HUD identified this relationship as

a “rent ratio.” The rent ratio was determined by dividing the median gross rent across all standard

quality units for the small area (a ZIP Code) by the similar median gross rent for the metropolitan

area (the Core Based Statistical Area or CBSA) of the ZIP Code.12 HUD adopted the rent ratio

method for calculating SAFMRs under the assumption that inter-area rent relationships are stable

over time and to ensure that an SAFMR value would be calculatable for each small area.

The rent ratio was calculated using median gross rents provided by the Census Bureau for both

the small area and its encompassing metropolitan area. HUD restricted the use of ZIP Code level

median gross rents to those areas for which the margin of error of the ACS estimate is smaller

than the estimate itself. The rent relationship was calculated in the following manner for those ZIP

Codes within the metropolitan area that have a sufficiently small margin of error:

Rent Ratio = Median Gross Rent for ZIP Code Area/Median Gross Rent for CBSA

The rent relationship was capped at 150 percent for areas that would otherwise be greater. This

cap was instituted as a mechanism for ensuring that HCV program funds are used as judiciously

as possible. At the time of the institution of the SAFMR demonstration program, 2000 census data

showed that only 1 percent of all metropolitan ZIP Codes had rents above this 150 percent.

If the gross rent estimate for a ZIP Code within the CBSA either did not exist or had a margin of

error that is greater than the estimate, then the median gross rent for the county within the state

containing the ZIP Code was divided by the similar median gross rent for the CBSA of the ZIP

Code; the gross rent relationship is calculated as:

Rent Ratio = Median Gross Rent of the County/Median Gross Rent of the CBSA

12 Median gross rents were used in this analysis because no special tabulations were needed to acquire the data.

Cityscape

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Bibler, Brandly, Kahn, Lihn, and Taghavi

To estimate the rent for a two-bedroom unit in a small area for the current year, HUD multiplied

the rent ratio by the current estimate of the 40th percentile two-bedroom rent for recent movers,

who had moved into standard quality units, for the entire metropolitan area containing the

small area. HUD used data from the ACS tabulations for ZIP Code Tabulation Areas (ZCTAs).

The Census Bureau requires the use of ZCTAs to report data for ZIP Codes, because ZCTAs are a

standard census geography. In addition to Census Bureau defined-ZCTAs, HUD produces SAFMR

estimates for ZIP Codes obtained from the U.S. Postal Service where the number of residential

addresses is greater than zero. The rent ratio set for these ZIP Codes is based on the county-to-

metropolitan relationship for the ZIP Code in question.

To set the floor for SAFMRs in a metropolitan area, HUD compared two-bedroom SAFMR

estimates with the state nonmetropolitan minimum two-bedroom rent for the state in which

the area is located that is established as a floor for all FMRs. If the ZIP Code-rent determined

using the rental rate ratio is less than the state minimum, the ZIP Code-rent is set at this state

nonmetropolitan minimum. SAFMR for bedroom counts other than two-bedroom units are based

on the bedroom-size relationships estimated for the metropolitan area. The final calculated rents

were then rounded to the nearest $10.

The Use of Small Area Fair Market Rents in the Dallas, Texas HUD Metro Fair

Market Rent Area

In 2007, The Inclusive Communities Project (ICP), a non-profit fair housing focused organization

working in the Dallas metropolitan area, sued HUD alleging that “HUD is violating its obligation

to set market area fair market rentals for the Dallas-area Section 8 programs.”13 ICP objected to

HUD’s use of a multicounty area definition for the Dallas metropolitan area. While not admitting

guilt, HUD agreed to settle with the plaintiff by naming the Dallas, Texas HUD Metro FMR area

(HMFA) as an SAFMR Demonstration Participant. The intent to run an SAFMR demonstration

was announced in May 2010 through a Federal Register notice (75 FR 27808). HUD announced

the Dallas, Texas HMFA as a demonstration participant via the Federal Register notice announcing

proposed fiscal year (FY) 2011 FMRs (75 FR 46958). This marked the first time that SAFMRs were

required to be used in the administration of the HCV program.

The Volunteer Demonstration

Within the same Federal Register notice announcing Dallas’ required use of SAFMRs, HUD

solicited volunteers to participate in the demonstration. To be eligible to apply, “the PHA or a group

of PHAs must represent at least 80 percent of the Section 8 voucher tenants in a metropolitan area.

Any PHA that is part of the Demonstration Project must use payment schedules based on these

SAFMRs, beginning October 1, 2010, or when they are designated as a Small Area Demonstration

Project in a subsequent Federal Register Notice.”

Response to HUD’s call for volunteers may best be described as non-existent. From personal

conversations with the managers of apparently suitable PHAs, HUD leadership determined that

13 Paragraph 9 of Plaintiff’s complaint, Case 3:07-cv-00945, filed in the United States District Court Northern District of Texas Dallas Division, May 29, 2007.

8

Small Area Fair Market Rents

Guest Editors’ Introduction: Small Area Fair Market Rents

PHAs were unwilling to volunteer due to the financial uncertainties of operating their programs

using SAFMRs.

After further considerations, using some Transformation Initiative14 funding, HUD devised

a strategy to offer SAFMR demonstration participants additional funding to be used to offset

the necessary administrative expenses incurred due to the switch to SAFMRs. HUD further

decided that rather than ask for volunteers, HUD would randomly select PHAs to participate in

the demonstration. PHAs who were selected to participate had the opportunity to decline the

invitation. HUD made three rounds of offers to PHAs to participate in the Demonstration between

July and September 2012. In all, five PHAs accepted HUD’s invitation to participate in the SAFMR

demonstration and they joined the PHAs in the Dallas, Texas HMFA15 in using SAFMRs in their

service area beginning in 2012.

The Small Area Fair Market Rent Demonstration Evaluation

In 2015, an evaluation was launched to evaluate the extent to which SAFMRs could provide an

effective means for HCV holders to move into higher-opportunity areas without significantly raising

overall subsidy costs. The five PHAs16 that agreed to participate in the SAFMR Demonstration,

along with two PHAs from the Dallas metropolitan area, were examined in this SAFMR

Demonstration Evaluation study. These seven SAFMR PHAs were compared with a group of 138

PHAs that had similar economic and demographic factors as those in the demonstration. The

evaluation revealed that voucher families in the PHAs using SAFMRs were more likely to move

to areas of high-opportunity than the “Comparison PHAs,” that were using area-wide FMRs. This

was especially the case for families with children in the SAFMR PHAs. There was a loss of about 3

percent of the units available for the SAFMR PHAs.

The Rulemaking

Although the demonstration was still ongoing, research concerning the benefits of SAFMRs began to

appear. One aspect of this research was the benefits of SAFMRs as a tool to assist PHAs and voucher

holders in tackling the problem of high voucher concentration in high poverty or low-income areas.17

HUD began the process of changing the voucher program rules pertaining to FMRs (along with

ancillary program rules related to FMRs) through the publication of an Advanced Notice of

Proposed Rulemaking on June 2, 2015 (80 FR 31332). This notice was followed by a Notice of

Proposed Rulemaking issued June 16, 2016 (81 FR 39218), and the Notice of Final Rulemaking on

November 16, 2016 (81 FR 80567). The final rule became effective on January 17, 2017.

14 More information about the Transformation Initiative is available at https://www.huduser.gov/portal/about/

trans_init.html.

15 Additional details regarding the process HUD used to randomly select PHAs for the demonstration may be found in HUD’s November 20, 2012 Federal Register notice (77 FR 69651).

16 The five PHAs that agreed to participate in the demonstration included the Housing Authority of the City of Laredo (Texas), the Town of Mamaroneck Housing Authority (New York), the Chattanooga Housing Authority (Tennessee), the Housing Authority of Cook County (Illinois), and the City of Long Beach Housing Authority (California).

17 Collinson, Robert A., Ganong, Peter. “The Incidence of Housing Voucher Generosity” Cambridge, MA: Harvard University, 2015.

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Bibler, Brandly, Kahn, Lihn, and Taghavi

Through the rule, HUD established an alternative to its 50th percentile FMR criteria to evaluate the

concentration of voucher holders in metropolitan areas and to determine if SAFMRs would be a

good candidate to help solve the high concentration issues in each area.

In a Federal Register notice accompanying the Final Rule (81 FR 80678), HUD identified 2418

metropolitan areas where vouchers are highly concentrated in areas of high poverty or low-income

and where SAFMRs would likely help. PHAs with jurisdiction in these areas were ultimately

mandated to use SAFMRs to determine payment standards rather than metropolitan FMRs. Other

PHAs were given the opportunity to opt-in with HUD approval.

The Implementation of the Rule

HUD designated 24 metropolitan areas where the use of SAFMRs is mandatory. PHAs that

directly administered HCV assistance for families within the 24 designated metropolitan areas, or

“Designated SAFMR PHAs” areas were required to implement SAFMRs by April 1, 2018. In contrast

to FMRs, SAFMRs do not apply to any programs other than the HCV program. Other programs that

use FMRs (for example, the HOME Investment Partnerships Program) continue to use metropolitan

area-wide FMRs regardless of whether SAFMRs have been designated for HCV tenant-based

assistance within the same metropolitan area. Designated SAFMR PHAs are not required to, but have

the option to, use them for their Project-Based Voucher (PBV) Program as well.

Following a new SAFMR becoming effective, designated PHAs and opt-in PHAs have 3 months to

implement new payment standards that fall within the 90 to 110 percent range of the SAFMR.19

The rule offers PHAs the flexibility to group multiple ZIP Codes into larger payment standard areas

or to adopt unique payment standards for each ZIP Code within its jurisdiction.

In the year that a metropolitan area first implements SAFMRs, the SAFMR for each ZIP Code

cannot be less than 90 percent of the Metro Area FMR of the previous FY. In subsequent years, the

SAFMR for an area is not allowed to be less than 90 percent of the SAFMR for the previous FY.

Once an area has been designated as a SAFMR area, it remains so permanently. HUD will review

and update the list of designated SAFMR areas every 5 years as new data becomes available.

Current Calculation Methods

Current calculation of SAFMRs begins by examining each ZIP Code’s 40th percentile gross rent

estimates. If a ZIP Code has a statistically reliable (based on the margin of error and sample size)

gross rent in at least two of the most recent three ACS releases, the average of those rents is used as

the current year’s “base rent”. HUD uses the average to account for inherent volatility of estimates at

low levels of geography in the ACS. Because the base rent represents a 5-year, all-mover estimate,

the base rent is updated using the recent mover factor, inflation update factor, and inflation trend

factor of the ZIP Code’s parent metropolitan area. For ZIP Codes without a useable base rent, a

18 One of the areas identified through the rulemaking process was the Dallas, Texas HMFA. Consequently, only 23

additional areas were required to start using SAFMRs as part of the rulemaking.

19 The rule implements a 3-month window for setting payment standards within the basic range for all newly published FMRs, not only SAFMRs.

10 Small Area Fair Market Rents

Guest Editors’ Introduction: Small Area Fair Market Rents

rent ratio is calculated as discussed previously, although the current practice is to consider the

parent HMFA rather than the entire CBSA to further localize SAFMRs. If a ZIP Code does not have

a useable base rent or ratio and is bordered by ZIP Codes which do, an average of its neighboring

SAFMRs is used. Otherwise, a county-based ratio is used. SAFMRs remain subject to the state

minimum, 150 percent cap, and a year-to-year maximum decrease of 10 percent.

The Symposium Papers

Research conducted and presented within this symposium falls into two general categories. First,

several contributions extend on the research and findings related to the SAFMR Demonstration.

Second, researchers examined the activities pertaining to the implementation of SAFMRs in the

areas required by the rule to do so and to look at alternative measures of rent to assess the quality

of SAFMRs in these areas. Submissions for this Symposium were advertised through a Call for

Papers published on HUDUSER.gov. The guest editors received 14 proposals for the edition. The

guest editors reviewed the proposals and considered how each proposal would contribute to the

body of knowledge concerning SAFMRs. The guest editors selected 10 of the proposals to move

forward. Each of the nine submissions summarized below was reviewed by each of the guest

editors who provided comments on the initial submissions. Each submission was assigned a

specific corresponding editor who worked with each corresponding author to complete a finished

product. Finally, the guest editors reached out to the two international commentators asking

for their review of several of the submissions vis-a-vis their experience with housing assistance

programs on an international stage.

Small Area Fair Market Rent Demonstration Articles

Work in the first category includes research by Dastrup, Ellen, and Finkel which extends the

work completed by the SAFMR Demonstration Evaluation with a specific focus on the impacts

of the implementation of SAFMRs on families with children. The authors test whether varying

housing assistance subsidy caps with ZIP Code rent levels (that is, introducing SAFMRs) increases

the likelihood that voucher-holder families with children relocated in higher-opportunity

neighborhoods, as proxied by poverty rates, the proficiency levels of local elementary schools,

jobs proximity, and environmental hazards. Because Dastrup et al. focus on families with children,

they focus on school proficiency levels and poverty rates. To estimate impacts, the authors use

a difference-in-differences specification on a repeated cross-section of administrative data to

estimate the effect of the introduction of SAFMRs in seven PHAs as compared with a large group

of agencies that continued to operate under metro area FMRs. Five years after implementation,

SAFMRs do not appear to affect overall move rates, but they meaningfully affect the locational

outcomes among families with children who move. The share of such families settling in

neighborhoods in the top quartile of our opportunity index measure increases by 11 percentage

points (a 120 percent increase).

McClure and Schwartz examine the interplay between SAFMRs and locational choice for voucher

families of different races. McClure and Schwartz explore the idea that the efficacy of the SAFMR

program may ultimately hinge on the race and ethnicity of the voucher holder. The authors

consider the role that race will play in voucher holders maximizing the benefits of SAFMRs by

Cityscape 11

Bibler, Brandly, Kahn, Lihn, and Taghavi

acknowledging the persistence of racial segregation in the United States and the potential

implications of this given that a majority of voucher holders are either Black or Hispanic. Their

analysis of all metropolitan areas exceeding a population of 1 million or more shows that nearly

all the growth in HCV-eligible units would occur in ZIP Code areas that are either predominantly

White or that are integrated. The consequence of this finding is that widespread implementation

of SAFMRs could make it more difficult for minority voucher holders to find eligible units

because the maximum qualifying rents would be reduced in many neighborhoods with large

concentrations of minority voucher holders. McClure and Schwartz conclude that more will

need to be done to help Black and Hispanic HCV recipients learn about predominantly White

and integrated neighborhoods and their housing opportunities, because most people live in

segregated areas dominated by people of their own race or ethnicity. Most likely, these needs will

have to be addressed by PHAs and their nonprofit partners providing transportation assistance

and other forms of support to help HCV recipients in segregated low-income communities find

housing in opportunity neighborhoods and to provide services to help remain in their new

neighborhoods. Housing counseling and case management will need to be enhanced.

Geyer, Dastrup, and Finkel examine whether the implementation of SAFMRs impacts a voucher

family’s length of stay in the HCV program. This research looks at the seven SAFMR PHAs

compared with 138 “Comparison PHAs” from the SAFMR Demonstration Evaluation. The

authors find that using SAFMRs reduces the length of stay for voucher tenants. The study found

a median length of participation in the HCV program of 11 years. For those seven PHAs using

SAFMRs, this exit rate declined by about 2 years to 9 years. Increased attrition effects are largest

among households living in lower- and moderate-rent areas at the time of SAFMR introduction.

While tenants in lower-rent areas may be forced out by landlords refusing to rent at lower

SAFMRs, the greater attrition in moderate rent households is not easily explained. Although

households with a working-age adult, as opposed to households with seniors, are more likely

to have increased attrition under SAFMRs in the HCV program, this does not translate into

improved financial resources. This merits further study to determine why working adults are

leaving the program when they still need housing resources.

Edgar Olsen examines the assertion that HUD overpays for housing units in the HCV

program and opines on SAFMRs impact on this issue. Olsen’s research provides a

comprehensive theoretical analysis that leads to the expectation that the worst voucher units

and those in the worst neighborhoods will usually rent for more than the mean market rent

of identical units and the best units in the best neighborhoods will rent for less than this

amount. This paper summarizes and assesses the data, methods, and results of the major

studies examining overpayment. The evidence is consistent with the general pattern predicted

by the comprehensive theoretical analysis. It is also consistent with an alternative explanation

that challenges its interpretation as overpayments and underpayments for voucher units.

The mix of units with estimated overpayments and underpayments varies across studies. The

weight of the evidence is that these aggregate differences are modest. Finally, the evidence

available indicates that SAFMRs will decrease the rents paid for voucher units with any

specified set of characteristics in the worst neighborhoods and increase the rents of such

units in the best neighborhoods.

12 Small Area Fair Market Rents

Guest Editors’ Introduction: Small Area Fair Market Rents

Marietta Haffner, a senior researcher at the Delft University of Technology, and Christine Whitehead,

Emeritus Professor of Housing Economics at the London School of Economics, reviewed research

from this first category and lend their commentary from an international perspective.

Alternative Measures of Market Rents

In the second category of research, Patterson and Silverman evaluate the early implementation

strategies of the 24 PHAs required to implement SAFMRs with an emphasis on their payment

standard setting behavior. Overall, the authors find that the payment setting practices of these

PHAs show low fidelity to the SAFMR Rule’s opportunity advancement goals. Although average

payment standards hovered around 100 percent of the published SAFMRs, those in low-

opportunity areas were generally above 100 percent and offset by payment standards below 100

percent in high opportunity ZIP Codes. In addition, the practice of tiering payment standards,

adopted by at least one PHA in 22 of the 24 mandated areas further exacerbates the low fidelity

to opportunity advancement goals. The authors recommend that HUD improve its monitoring

of the 24 mandated areas, which are currently not required to provide HUD with copies of

their payment standards and establish rules for tiering payment standards that conform to the

opportunity advancement goals of the SAFMR rule. The authors also indicate that HUD should

ensure Moving to Work (MTW) agencies in the mandatory areas remain faithful to the goals of the

SAFMR rule. The guest editors note that all PHAs face tensions between supplying deep subsidies

that provide access to high opportunity areas—at the cost of serving fewer families —and providing

shallower subsidies so more families may be assisted —at the cost of potentially limiting access to

highest opportunity areas. Further, the guest editors believe that MTW agencies must continue

to serve as laboratories that test the most effective use of limited housing subsidy dollars against

many competing program goals, including ending homelessness, promoting self-sufficiency, and

improving access to opportunity areas.

In “Comparing Small Area Fair Market Rents to Other Rental Measures Across Diverse Housing

Markets,” Hess, Walter, Acolin, and Chasins compare SAFMRs with rents measured using

webscraped rental listings and other private sources of data for three markets in which the PHAs

have shifted to localized payment standards: (1) Fort Lauderdale, Florida; (2) San Antonio, Texas;

and (3) Seattle, Washington. They explore correlations among the different sources and synthesize

the private data into a combined ZIP Code-level rent estimate. They show spatial correlations

among the ratios between SAFMRs and combined rent estimates and examine the difference in

neighborhood-level housing stock characteristics. Finally, they explore the PHAs’ decision making

process in choosing how to incorporate SAFMRs and identify challenges and risks going forward

based on the data analysis.

Blackhurst, Briem, and Deitrick show that, in the case of Pittsburgh, Pennsylvania, moving from

SAFMRs has helped to increase the number of eligible units across different rental markets,

however, increases in eligible units are less noteworthy in markets with higher rents. The authors

observe that the disparity between SAFMRs and estimated rents for Pittsburgh, Pennsylvania (from

listings available through Rent Jungle), increases substantially in areas with higher estimated rents.

This finding offers insight on a potential implication of the 150 percent cap of the FMR on SAFMR.

Cityscape 13

Bibler, Brandly, Kahn, Lihn, and Taghavi

Lastly, this article evaluates the effectiveness of using ZIP Codes as a suitable geographical unit for

delineating rental markets and presents an approach for constructing rental market clusters that

are representative of varying spatial amenities. The authors conclude that using a combination of

clustering and nearest neighbor algorithms are better predictors of market rents than ZIP Codes

and require fewer market delineations.

Aksel Olsen examines the impact of implementing SAFMRs through comparisons with rental data

acquired from Craigslist. Nationally, the study found that FMRs represented 35 percent of the

rental distribution, which is reasonably close to the target 40 percent, but for the 24 mandatory

SAFMR areas FMRs provide only 32 percent. With the implementation of SAFMRs, however, the

rental distribution for the 24 SAFMR areas increases to more than 40 percent. The change from

using FMRs to using SAFMRs increases the count of units available in higher cost and higher

opportunity areas with only a relatively minor loss of availability in low opportunity areas. The

authors found that the highest opportunity areas have more than 45 percent of listings using

SAFMRs. While this boost was largest for the 24 mandatory areas, it was nonetheless significant

for a group of 625 metropolitan areas assuming the use of SAFMRs. This result shows that a

broader application of SAFMRs will increase housing availability, especially in higher opportunity

areas. Challenges remain in dealing with existing tenants in areas where payment standards will

decline on implementation of SAFMRs, however, especially in very tight markets. While the most

constrained markets, where rents are very high and availability very low, have the greatest need to

use SAFMRs to allocate scarce resources to higher opportunity areas, the tight housing market in

these areas may be an impediment to a successful SAFMR implementation.

Finally, Casey examines similar comparisons using Zillow data. Monthly lease prices for 1- to

4-bedroom rental units (about 12 million) are collected from the Zillow platform for the 24 metro

areas mandated to use SAFMRs. The author finds that under the SAFMR rule, there is an increase

in the number of units that are affordable to voucher holders compared with the number of units

when using the areawide FMR in low poverty ZIP Codes. On the other hand, the number of units

affordable to voucher recipients using SAFMR is less than the number of units using the areawide

FMR for high-poverty ZIP Codes. Because this is a case study, it remains to be seen if the findings

will hold true for other segments of advertised rental listings.

Conclusion

The research conducted as part of the first group of papers is conducted on a limited set of PHAs

and metropolitan areas. Furthermore, PHAs required to use SAFMRs as part of the rulemaking

criteria have been doing so for less than 2 years. The articles in this symposium tremendously

expand the body of knowledge surrounding the use of SAFMRs; however, we postulate that this is

but a fraction of the work to come.

Acknowledgments

The guest editors thank the authors of the articles in this edition and the Cityscape editorial staff.

14 Small Area Fair Market Rents

Guest Editors’ Introduction: Small Area Fair Market Rents

Guest Editors

Adam Bibler is a senior economist in the U.S. Department of Housing and Urban Development,

Office of Policy Development and Research.

Chalita Brandly is an economist in the U.S. Department of Housing and Urban Development,

Office of Policy Development and Research.

Peter Kahn is a supervisory economist in the U.S. Department of Housing and Urban Development,

Office of Policy Development and Research.

Marie Lihn is a senior economist in the U.S. Department of Housing and Urban Development,

Office of Policy Development and Research.

Lydia Taghavi is a social science analyst in the U.S. Department of Housing and Urban

Development, Office of Policy Development and Research.

Each of the guest editors is a member of the Program Parameters Research Division. The division is

responsible for calculating a variety of HUD program parameters including Fair Market Rents and

Small Area Fair Market Rents.

Cityscape 15

16 Small Area Fair Market Rents

Small Area Fair Market Rent

Demonstration

• The Effects of Small Area Fair Market Rents on the Neighborhood

Choices of Families with Children

• Small Area Fair Market Rents, Race, and Neighborhood Opportunity

• Impact of Expanded Choice on Attrition in the Housing Voucher Program

• Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce

the Overpayment?

• Reflections on Demand Assistance in the Rental Sector: A European

Perspective

• Aspects of the Housing Choice Voucher Program and the Impact of Small

Area Fair Market Rents Ceilings: a British Perspective

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 17

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

18 Small Area Fair Market Rents

by Samuel Dastrup, Meryl Finkel, and Ingrid Gould Ellen

The Effects of Small Area Fair

Market Rents on the Neighborhood

Choices of Families with Children

Samuel Dastrup

Meryl Finkel

Abt Associates Inc.

Ingrid Gould Ellen

New York University

Disclaimer: The authors are solely responsible for the accuracy of the statements and interpretations contained in this publication. Such interpretations do not necessarily reflect the views of the U.S. Government.

Abstract

This paper reports and extends the quantitative findings of the Small Area Fair Market Rent

Demonstration Evaluation, focusing on the important subgroup of families with children. We test whether varying housing assistance subsidy caps with ZIP Code rent levels (that is, introducing Small Area Fair Market Rents or SAFMRs) increases the likelihood that voucher-holder families with children locate in higher opportunity neighborhoods, as proxied by poverty rates, the proficiency levels of local elementary schools, jobs proximity, and environmental hazards. Because of our focus on families with children,

we pay particular attention to school proficiency levels and poverty rates. We estimate a difference-in-differences specification on a repeated cross-section of administrative data to estimate the effect of the introduction of SAFMRs in seven public housing agencies as compared to a large group of agencies that continued to operate under metro area FMRs. Five years after implementation, Small Area FMRs do not

appear to affect overall move rates, but they meaningfully affect the locational outcomes among families with children who move. The share of such families settling in neighborhoods in the top quartile of our opportunity index measure increases by 11 percentage points (a 120-percent increase).

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 19

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

Dastrup, Finkel, and Ellen

Introduction

The Housing Choice Voucher (HCV) program, the largest housing assistance program administered

by the United States Department of Housing and Urban Development (HUD), provides rental

subsidies to over 2.5 million children under the age of 18. The median of these children’s families’

annual income is approximately $15,000 (in 2008), with 73 percent having family incomes below

the federal poverty level (Horn, Ellen, and Schwartz, 2014).1

HCV subsidies allow households to rent units on the private market. In theory, voucher holders

can locate in a wide variety of neighborhoods, including low-poverty neighborhoods that offer

a rich set of resources and opportunities. In practice, however, households with vouchers are

frequently concentrated in high-poverty neighborhoods with limited access to the amenities and

services associated with economic opportunity.

A growing body of evidence, summarized below, documents that low-income children benefit

from spending more of their childhood living in neighborhoods with lower poverty rates and with

schools that have higher proficiency rates. So, a key question for policy makers is how to enable

more voucher families with children to move to higher-opportunity areas. Because resources for

such policies are limited, strategies that can encourage such moves without significantly raising

overall subsidy costs are especially needed.

We explore the efficacy of one particular policy reform aimed at encouraging moves to higher-

opportunity neighborhoods: Small Area Fair Market Rents (SAFMRs), voucher ceiling rents that

vary with ZIP Code rent levels.2 Traditionally, ceiling rents in the HCV program have been based on

a Fair Market Rent (FMR) set for an entire metropolitan area (metro FMR). We evaluate the effect of

a demonstration that HUD launched in late 2012 to test Small Area FMRs in five randomly selected

public housing agencies (PHAs): Chattanooga Housing Authority (Tennessee), Housing Authority

of Cook County (Illinois), Housing Authority of the City of Laredo (Texas), Housing Authority of

the City of Long Beach (California), Town of Mamaroneck Housing Authority (New York).3 We also

include two housing agencies in the Dallas, Texas metro area, where SAFMRs were introduced in

2011 as part of a legal settlement (Housing Authority of the City of Dallas and Housing Authority

of Plano). We compare pre to post changes in opportunity measures for the ZIP Codes where

voucher families with children locate in these seven SAFMR PHAs to the same location outcomes

over the same time period for a large sample of voucher families with children assisted by PHAs

that were eligible for the demonstration but were not randomly selected to participate.

Our empirical analysis shows that, as intended, SAFMRs increase the pool of units affordable to

voucher holders in high-opportunity neighborhoods and decrease the number affordable in low-

opportunity neighborhoods. Further, we find evidence that the shift to SAFMRs affects voucher

holder families’ choice of locations. We find that HCV families with children are no more or less

1 With an estimated 6.1 million families with children with incomes below the poverty line in 2008, the HCV program provides rental subsidies to approximately 12 percent of families with children with below-poverty incomes.

2 While the FMR plays a key role in determining the subsidy amount, the actual voucher ceiling, or payment standard, is set by the PHAs. Payment standards generally fall between 90 and 110 percent of the FMR.

3 Many of the findings we report here are also reported by the authors in the Small Area Fair Market Rent Demonstration Evaluation: Final Report, Dastrup et al. (2018).

20 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

likely to move after the implementation of SAFMRs. We find strong effects, however, among

those that do move. Using a difference-in-differences regression framework, we find that these

mover households are 6.7 percentage points more likely to locate in a ZIP Code that ranks at

least one decile higher (within the metro area) in our ZIP Code opportunity index as a result of

the introduction of SAFMRs. Although effects are largest among movers, we also see shifts in the

locational choices of new voucher holder families with children. Nonetheless, after 5 years, one-

third of voucher holders with children in our study area still live in neighborhoods in the bottom

quartile of our opportunity measure.

We find variation across housing authorities depending on local housing market conditions.

Specifically, the extent to which the catchment area of the housing agency includes rental units

where voucher holders can enjoy higher subsidies after the introduction of SAFMRs plays an

important role in whether the policy can encourage moves to higher rent neighborhoods. In some

cases, most ZIP Codes in the catchment area, or service area of a housing agency, have average rents

that are lower than the 40th percentile rent in the larger metro area. In such a housing agency, the

SAFMRs will largely fall below original FMRs, meaning that the number of units that rent below

the voucher ceiling will fall. Switching to SAFMRs in such a housing agency may be problematic,

as the pool of available units may shrink. At the other extreme, if the ZIP Codes in the housing

agency’s service area have rents that are generally above the metro area rents, a switch to SAFMRs

will generally increase the pool of available units.

In late 2016, HUD made SAFMRs optional for all PHAs, and beginning in 2018, it required

the housing agencies in 24 metro areas to administer their tenant-based voucher program using

SAFMRs. By mandating SAFMRs in entire metropolitan areas rather than individual housing

agencies, HUD limited the change in the pool of available units within a metro area. Given this

mandate, together with the greater flexibility nationwide, we will see increased use of SAFMRs in

the coming years, and this article sheds important light on their potential effect on voucher holder

families with children.

Background and Literature

Housing Choice Voucher Program and Neighborhood Access

One of the long-standing arguments for tenant-based subsidies is that they give people far more

choice about where to live and allow them to reach a broader set of neighborhoods (Orlebeke,

2000; Schwartz, 2006). Research shows that neighborhoods make a difference in people’s lives.

The strongest such evidence comes from the Moving to Opportunity (MTO) for Fair Housing

demonstration program experiment. Analysis of the MTO experiment found that children who

moved to lower-poverty neighborhoods through the program had improved academic outcomes

(Turner, Nichols, and Comey, 2012) and enjoyed significantly improved college attendance rates

and earnings in young adulthood (Chetty, Hendren, and Katz, 2016). Adults also benefited from

increased time in lower-poverty neighborhoods, with significant improvements to their health and

well-being (Ludwig et al., 2011) and some evidence of employment and earnings gains (Turner,

Nichols, and Comey, 2012).

Cityscape 21

Dastrup, Finkel, and Ellen

Despite these potential benefits, however, housing choice voucher holders, including families with

children, rarely use their vouchers to live in low-poverty neighborhoods. A number of research

papers have documented that voucher holders, especially Black and Hispanic voucher holders,

tend to live in highly disadvantaged areas (Devine et al., 2003; Galvez, 2010; McClure, 2008,

2011; Owens, 2012; Pendall, 2000). On average, voucher holders live in less disadvantaged

neighborhoods than the residents of public or other HUD-assisted housing (Devine et al.,

2003; Hartung and Henig, 1997; Kingsley et al., 2003; Pendall, 2000), but only in slightly less

disadvantaged neighborhoods than the average low-income household (Galvez, 2010; Pendall,

2000; Wood, Turnham, and Mills, 2008). These same patterns generally hold for neighborhood

poverty, socioeconomic disadvantage, violence, and school performance (Horn, Ellen, and

Schwartz, 2014; Lens, Ellen, and O’Regan, 2011).

One factor that is likely contributing to this concentration is the spatial distribution of

homes that are affordable to voucher holders under metro area-wide ceiling rents. In a recent

contribution to this body of research, McClure, Schwartz, and Taghavi (2015) conclude that “if

the nation wants to pursue poverty deconcentration through the [voucher] program, we cannot

rely on the program, as it is now structured, to accomplish this goal. Additional incentives and

constraints will be needed….” We test the impact of the new set of incentives offered by shifting

to SAFMRs.

Small Area Fair Market Rents

Historically, rental subsidies provided by the voucher program have been subject to a single, metro

area-wide cap, the area’s metro FMR, generally set at the 40th percentile of rents paid by recent

movers in the area. With a single rent cap, voucher holders have access to relatively few units in

high-rent neighborhoods. In late 2012, HUD launched the SAFMR demonstration to test whether

replacing traditional metro FMRs with subsidy limits that vary with ZIP Code rent levels enable

more voucher holders to reach neighborhoods of opportunity.4 Specifically, to set SAFMRs in

the demonstration, HUD multiplied the metro FMR by the ratio of the ZIP Code median rent to

the metro area median rent, the “rent ratio” that HUD calculates for each ZIP Code from special

tabulations of U.S. Census Bureau (Census) data:

SAFMR = Rent Ratio * Metro FMR

As explained in the Dastrup et al. (2018) report, switching from metro FMRs to SAFMRs should

make a higher share of units available to voucher holders in higher rent neighborhoods, and a

lower share in lower rent neighborhoods. The share is the same where the rent ratio equals 1, and

the SAFMR equals the metro FMR.5

When SAFMRs are implemented at the level of the housing agency rather than the metro area, it

is also possible that a disproportionate share of ZIP Codes in PHA catchment areas have median

rents that fall below the metro median. This will mean that most ZIP Codes in that PHA will see a

4 SAFMR became effective in demonstration PHAs in late 2012.

5 See Kahn and Newton (2013) for a contemporary description of the demonstration. Full documentation of how metro FMRs and SAFMRs are currently calculated by HUD is available at huduser.gov/portal/datasets/fmr.html and

huduser.gov/portal/datasets/fmr/smallarea/index.html.

22 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

reduction in the share of units renting under the SAFMR. This is much more likely to happen in a

PHA whose jurisdiction is small relative to the full metro area.6

Two prior studies have modeled the likely effects of SAFMRs (or a similarly structured policy

reform) on voucher holder location outcomes. Geyer (2017) and Collinson and Ganong (2018)

introduce useful models that show that allowing subsidies to vary with small geographies within

a metro area may be effective in encouraging voucher holders to move to higher opportunity

neighborhoods. However, there are reasons to question the extent to which switching to SAFMRs

will help families with children reach higher opportunity areas. First, while SAFMRs may be

necessary to help families reach higher-rent neighborhoods, they may not be sufficient; it is unclear

in practice if landlords in higher-rent areas will be willing to participate in the voucher program.

Further, higher rent does not necessarily mean higher opportunity for families with children;

higher rents may capitalize amenities that have little relevance for children.

Two prior studies have also empirically examined the effects of SAFMRs. Reina, Acolin, and

Bostic (2018) study the short-term impact of the introduction of SAFMRs on locational outcomes

in Dallas and in HUD’s demonstration sites. They find that after the shift to SAFMRs, voucher

households live in higher opportunity neighborhoods in Dallas than voucher holders in

surrounding counties, but they find no positive association with access to opportunity in the other

demonstration sites. Significantly, however, they only examine outcomes in 2014, just two years

after sites adopted SAFMRs, and the voucher program (including the demonstration) exempts

sitting tenants who stay in place from subsidy cap reductions for 1 to 2 years. Collinson and

Ganong (2018) also include an empirical analysis of the switch to SAFMRs in the Dallas metro

area and find that, after the adoption of SAFMRs, more voucher holders lived in high opportunity

neighborhoods, at no additional subsidy cost.

We build on these earlier papers in several ways. First, while Collinson and Ganong’s analysis is

restricted to the Dallas metro area, we offer an empirical analysis of the impacts of SAFMRs in

five additional jurisdictions across the country, which allows us to examine the effect of SAFMRs

when implemented in a variety of housing market contexts. Second, we analyze longer-term results

than any of the prior studies, observing voucher holders 5 years after implementation. This is

6 The ability of PHAs to set payment standards between 90 to 110 percent of FMRs (and SAFMRs) may ameliorate the effects of this reduction. Voucher holders are allowed to use their vouchers in other jurisdictions, but such portability can be administratively difficult and is relatively rare. In our analysis sample here, only a small fraction of households used their voucher in another jurisdiction, and preliminary analysis indicates that this did not change after the adoption of SAFMRs. We omit these households from our sample. We also note that the within-ZIP Code distribution of rents for rental units in ZIP Codes with median rents below and above the metro area median rent may also matter.

Consider, for example, a metro area in which the variance of rents is greater in ZIP Codes with median rents above the metro area median than it is in ZIP Codes with median rents below the metro area median. In such a metro area, a SAFMR that is 10 percent higher than the FMR in higher-rent neighborhoods will add fewer units than will be lost in a ZIP Code where the SAFMR is 10 percent lower than the FMR. To illustrate this possibility, consider a hypothetical geography of three ZIP Codes, A, B, and C, each with three rental units. The units in A rent for $10, $11, and $12, the units in B rent for $12, $13, and $14, while units in C are more dispersed, renting for $13, $15, and $17. The 40th percentile of all the rents is 12. Four of the units (three in A and one in B) have rents at or below this 40th percentile.

The median of all the rents is 13. The median rent of 11 in ZIP Code A is 15 percent lower, so a “SAFMR” for ZIP Code A would be 0.85*12 = 10.2, with one unit renting below this amount. ZIP Code B shares its median with the overall median, and still has one unit at or below 12. The median rent of 15 in ZIP Code C is 15 percent higher than the median of all rents. At a resulting SAFMR of 1.15*12=13.8, one unit is now below the threshold. With only one unit in each neighborhood now below SAFMR, the total number of units affordable has fallen from four to three.

Cityscape 23

Dastrup, Finkel, and Ellen

especially critical given that the voucher program contains provisions that existing voucher holders

are held harmless from declines in the subsidy cap that applies to them for 1 to 2 years after the

implementation of SAFMRs. Third, we focus on the key subgroup of families with children. As

discussed above, the potential for neighborhood opportunity to alter long-term outcomes makes

housing policy particularly relevant for these voucher holders. Finally, we also analyze the initial

location decisions of families with children newly receiving a voucher and distinguish them from

those of existing voucher holders who move.

Data and Methods

Our core interest lies in examining whether the shift to ZIP-Code level SAFMRs has enabled

voucher families with children to reach neighborhoods that offer a richer set of amenities and

opportunities for advancement, and at what, if any, additional cost. In this section we define the

opportunity index we use, introduce our sample and data, and review our methods.

Data and Sample

Our analysis requires a variety of datasets, both publicly available measures of neighborhood

opportunity and administrative data to which we gained access specifically for this study. Our

core dataset consists of HUD administrative records with individual-level detail on household

characteristics, income, and rent information for housing choice voucher holder families for the

fourth quarter of 2008 (a baseline period for identifying moves in 2009) through the last quarter

of 2017. The data include the ZIP Code and census tract where a voucher is used, tenant income

and rent payments, the total monthly housing assistance payment subsidy amount provided, and

household composition and demographics.

We also rely on the SAFMRs and metro FMRs that HUD publishes each year. For the analysis of the

potential of SAFMRs to alter the location of homes affordable to voucher holders, we use fiscal year

(FY) 2015 data only, since we want to capture changes that result from rent formula changes rather

than any shifts in underlying rents.7 For the analysis of voucher holder location outcomes over

time, we use FY2009 (pre-SAFMR) through FY2017 data.

We use a special tabulation of American Community Survey (ACS) data that the Census produces

for HUD in order to determine rent ratios and SAFMRs. The dataset reports estimates, by bedroom

size, of the number of units with rents in reasonably narrow ranges for each ZIP Code. We merge

2008–2012 ACS 5-year estimates, which provide a more accurate count of the total number of

rental units in each census tract, with HUD SAFMRs to determine the number of units below the

respective FMR based on the proportions observed in the special tabulations.

Our sample includes voucher holder families in seven PHAs where SAFMRs have been

implemented (SAFMR PHAs), together with all voucher holder families in a large set of

7 Of course, it is possible that neighborhoods with relatively high concentrations of voucher holders may see changes in rents over time with changes in the maximum subsidy available through the program in that neighborhood. We do not examine these market-level effects. The rent distribution data we rely on for the affordability analysis is based on the 5-year ACS estimates, which do not allow the year-over-year comparisons that would be required to address this question within our analysis framework.

24 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

Comparison PHAs, located in the same HUD-defined clusters as the treatment PHAs.8 We define a

voucher holder family with children as any household with at least one child below 18 years old.

In total, the Comparison PHAs include 138 housing agencies serving slightly more than 550,000

voucher holder families with children (in 2015). Dallas and Plano were not part of any cluster as

their programs pre-dated the demonstration program, but the clusters that they would have been

assigned to are included in our comparison group.9

Defining Neighborhood Rent and Opportunity Levels

We assign ZIP Codes to three categories based on the ZIP Code’s rent ratio. High-rent ZIP Codes

are those in which the median rent is at least 10 percent higher than the metro area median (that

is, the ZIP Code rent ratio is greater than 110 percent). In medium-rent ZIP Codes, the rent ratio

is between 90 percent and 110 percent, while in low-rent ZIP Codes, the rent ratio is less than

90 percent.

We also rely on four measures of neighborhood opportunity: poverty, school proficiency,

employment access, and environmental quality. The measures are detailed in exhibit 1. These

are all measures that HUD has used to capture neighborhoods that offer better quality of life and

greater opportunities for economic mobility. In all cases, we construct indices, so a higher number

indicates higher opportunity.10

8 HUD invited the five SAFMR PHAs participating in the demonstration by randomly selecting them from clusters defined to ensure a variety of PHAs were included in the demonstration. The clusters were defined based on the number of vouchers administered, fair market rent levels, and the share of working-age heads-of-household among voucher recipients. We use all the remaining housing agencies that were in the same clusters as the SAFMR PHAs as a comparison group (Comparison PHAs).

9 SAFMRs have been implemented in all 12 PHAs that administer HCVs in the Dallas metro area. Our analysis includes only the Housing Authority of the City of Dallas, which administers most of the vouchers in the area, and the Housing Authority of Plano, a smaller PHA which administers vouchers in a higher-rent area with high measures of opportunity. Finkel et al. (2017) provides more detail on cluster definition and assignment.

10 The opportunity measures designed for this study are all initially derived from census tract-level measures. SAFMRs are defined for ZIP Codes, in part because ZIP Codes are easier for families seeking housing to use. To make our opportunity measures match the geography of SAFMRs, we convert the census tract measures to ZIP Code measures using population-weighted tract to ZIP Code crosswalks.

Cityscape 25

Dastrup, Finkel, and Ellen

Exhibit 1

Opportunity Indicators

Opportunity Indicator

Description

(Data Source/s)

Overall Opportunity Index

The Overall Opportunity Index was created specifically for this

(Composite of the Other

evaluation. It is the percentile rank by renters in the metropolitan area

Opportunity Indicators)

of the simple average of the percentile rank indexes for the share

of nonpoor, public school proficiency, employment access, and

environmental hazards.

Percent Nonpoor

The ACS five-year estimates provide the percent nonpoor for each

(ACS 5-Year Estimate, 2010–2014)

census tract. The percent nonpoor is the ratio of the population above

the poverty level to the total population for whom poverty status was

determined.

Public School Proficiency

School-level data on state exams for grade 4 students approximate

(2011–2012)

the quality of local public schools. The measure is based on the

public school(s) nearest to each block group and school zone from

the School Attendance Boundary Information System. Block group

data were weighted by numbers of households to create census

tract-level data. The higher the score, the higher the school system

quality is in a neighborhood.

Jobs Proximity Access

This index measures the access a neighborhood has to employment

(HUD, 2010)

opportunities as measured by the distance between block groups

and job locations weighted by employment size. Block group data

were weighted by numbers of households to create census tract-level

data. The higher the index value, the better the access to employment

opportunities for residents in a neighborhood.

Environmental Hazards

The Environmental Health Hazard Index is a tract-level index of

(Environmental Health Hazard

potential exposure to toxins based on National Air Toxic Assessment

Index, 2005)

data from the U.S. Environmental Protection Agency. The higher the

value, the better the environmental quality of a neighborhood.

To facilitate comparisons across measures and geographies, we normalize the Percent Nonpoor,

School Proficiency, Employment Proximity Index, and Environmental Hazard Index to be the

percentile of the raw index among rental units in the metro area.11 To create an aggregate index, we

average these component index percentile scores and calculate the percentile of the average score,

again among rental units, in the metro area.12 Because prior research has focused on neighborhood

poverty rates and school proficiency measures as important markers of opportunity for families

with children, we report findings separately for these measures in addition to our overall

opportunity index.

11 We explored the possibility of including access to transportation in our neighborhood opportunity index. Data on this indicator are not available across all the SAFMR PHAs, and where data are available, they are highly correlated with employment access. Similarly, we explored using neighborhood crime rate data in our measurement of neighborhood opportunity but did not have uniform and consistent coverage in the data for our study geographies.

12 The Overall Opportunity Index ranges from 0 to 100. A score of 50 for a ZIP Code means that the average of the four component indexes percentile ranks for that ZIP Code is greater than the average in ZIP Codes that contain half of the rental units in the metro area. Data on public school proficiency is missing in 10 percent of the ZIP Codes in our analysis and in under 6 percent of ZIP Codes in the SAFMR PHAs. In these ZIP Codes, we construct the Overall Opportunity Index as the average of the three other component indexes.

26 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

Methods

Our first aim is to produce a single point-in-time estimate of how the adoption of SAFMRs

changes the set of housing units affordable to voucher holder families. We contrast the estimated

count of units with rents below SAFMRs to the count of units with rent below the metro FMR in

high-, medium-, and low-rent ZIP Codes. We also compare counts in high-, medium-, and low-

opportunity ZIP Codes, as defined by bottom quartile, 25th–75th percentile, and top quartile on

the aggregate opportunity index.13

Our second aim is to analyze changes in the neighborhoods where voucher holder families

with children live. We estimate difference-in-differences regressions using a repeated cross

section that includes 2 to 3 years before the implementation of SAFMRs, and 5 to 6 years

after the implementation. This approach compares the change in location outcomes after the

implementation of SAFMRs in the SAFMR PHAs with the change in location outcomes in the same

time period for the Comparison PHAs where SAFMRs were not implemented. We look separately

at families with children who newly acquired a voucher and existing voucher holders who moved,

since second movers have more time to search for housing and thus may be better able to take

advantage of opportunities provided by SAFMRs.14

Our primary outcome is whether a family with children moves to a higher (at least 10 percentile

points) opportunity neighborhood in a given year. We also directly examine the change in the level

of the continuous opportunity scores that result from a move. To isolate the effect of SAFMRs on

families with children, we limit our sample to HCV holder households that include a child under

the age of 18.15 We examine the difference in the opportunity index of the initial and destination

ZIP Codes for all voucher holder families with children and those who move. We organize our data

into a household by year panel. That is, we have repeated observations for households receiving

vouchers between 2009 and 2017. The panel is unbalanced because households enter and leave

the sample as they newly receive a voucher or exit the voucher program. Our regression sample is

all households in SAFMR or Comparison PHAs for these years. We fit the following model:

13 This analysis includes a simplifying assumption that PHAs set the payment standard, which generally must fall between 90 and 110 percent of the FMR or SAFMR, equal to 100 percent of the FMR or SAFMR. This gives PHAs some latitude in setting the maximum rent that is subsidized with the voucher (with higher payment standards resulting in fewer low-income households provided with subsidies). The findings we present here could be either mitigated or amplified by PHA decisions in setting payment standards.

14 For data privacy reasons, we do not have access to exact voucher holder addresses or geocode coordinates. We determine that a family has moved if both the ZIP Code and census tract in which the family lives changes in the administrative record during a given year (or relative to the prior year). A household administrative record is updated at least annually (on a rolling basis) as a result of required annual income recertification. Additional administrative actions can generate an updated record, including a household move.

To streamline our unadjusted analyses of the proportion of families with a voucher living in each neighborhood type, we aggregate our move data to reflect four 2-year periods, one before the implementation of SAFMRs, and three after.

15 Sensitivity analyses (not included) do not show meaningful differences in findings when the sample is restricted to households with children under the age of 13 or under the age of 5. The coefficient of interest in the models we estimate on a restricted sample are nearly identical to the analogous coefficients from models estimated on the full sample of HCV holders with a “families with children” subgroup indicator interacted with the difference-in-difference terms. We present the restricted sample results here for a cleaner exposition. The interacted model for the overall opportunity index outcome is reported in Dastrup et al. (2018).

Cityscape 27

Dastrup, Finkel, and Ellen

MovedUpist is the binary outcome variable indicating if household i in PHA type s (SAFMR or Comparison) moved to a ZIP Code with at least a 10-point higher opportunity index score as

compared with their initial ZIP Code in time period t. The term Dt is an indicator of whether period t is after SAFMR implementation (2013–2017 for all SAFMR PHAs, with an additional

2011–2012 indicator term added for the Dallas PHAs that implemented SAFMRs 2 years earlier).

The term SAPHAs * Dt indicates that an observation is for a household in a SAFMR PHA in the

periods after SAFMRs are implemented. The estimate for the parameter δ is our difference-

in-differences effect of interest. It measures the additional percentage point likelihood that a

household in the SAFMR PHAs will move to a higher opportunity ZIP Code as a result of the

policy change.16 Our identifying assumption is that no other factors are changing differentially in

SAFMRs relative to Comparison PHAs that are correlated with the MovedUpist outcome. Our model

includes a set of potentially time varying household-level characteristics Xist observable in HUD’s tenant-level data. We include PHA-level fixed effects, γ PHAs. The model has an individual, time, PHA error term, ε ist.

We also estimate the same model with Δ Opportunityist as the outcome of interest. This outcome

measures the change in neighborhood opportunity score that results from the move. Therefore, a

value of 10 indicates that a family moved to a ZIP Code with a one decile higher score within the

metro area in the given opportunity measure.

To capture the possibility that rent rules affect households in lower-rent neighborhoods differently

than those in higher-rent neighborhoods, we interact the SAPHAs term with indicators for whether the household lives in a high-, medium-, or low-rent neighborhood at the start of the period, as

well as including the indicators separately in the model and interacted with Dt. This method allows us to estimate different δ coefficients for each neighborhood type.

To test whether the adoption of SAFMRs affect the unconditional probability of moving, we expand

our sample to the full set of existing voucher families with children and estimate regressions where

our outcome variable is an indicator of whether a family with children moved at all. Now the

parameter δ is interpreted as indicating whether families with children are more likely to relocate

at all (whether to a higher, similar, or lower opportunity neighborhood) as a result of SAFMRs.

We also estimate our model on the outcomes of whether a household moved up a decile in

opportunity using the full unconditional sample.

Results

This section begins with our analysis of how SAFMRs alter the share of units that are affordable in

neighborhoods with different levels of rent and opportunity. We then present findings on actual

location outcomes for voucher families with children.

16 The estimated coefficient on the indicator for Dallas metro area PHAs in 2011–2012 captures the initial effect of SAFMRs in these two PHAs.

28 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

Location of Units Affordable to Voucher Holder Families with Children

We start with a ZIP-Code level analysis for the seven PHAs where SAFMRs were implemented

during our analysis period that explores shifts in the neighborhood distribution of rental units that

are potentially affordable to voucher holder families in that they charge rents below the applicable FMR. Exhibit 2 shows the shift to SAFMRs clearly increased the number of rental units in high-rent

neighborhoods that were affordable to voucher holder families (or renting under the applicable

FMR cap) in the seven SAFMR PHAs. Under SAFMRs, 174,000 units in high-rent neighborhoods

were affordable to voucher holder families, more than double the number that would have been

affordable under metro FMRs (82,000). At the other end of the spectrum, the number of units with

rents below the applicable FMR in low-rent neighborhoods fell with the switch from conventional

to SAFMRs, from 278,000 to 174,000. The exhibit also shows that the reduction in units

available in low-rent neighborhoods was larger than the increase in units available in higher rent

neighborhoods, meaning that the total number of rental units affordable to voucher holder families

fell slightly after the adoption of SAFMRs.

Exhibit 2

Number of Units Renting Below Voucher Rent Cap Under FMR and SAFMR, by Rent Ratio

700

600

500

400

649

300

626

(Thousands)

200

278

289

278

100

174

174

82

Units With Rent Below SAFMR and FMR

0

Lower

Moderate

Higher

Total

<90

90-110

>110

Rent Ratio Category

FMRs

SAFMRs

FMR= Fair Market Rent; Small Area FMR = Small Area Fair Market Rent.

Notes: Analysis dataset includes all ZIP Codes in the service areas of the seven Small Area FMR PHA’s in our analysis.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Markets Rents; 2012 American Community Survey 5-Year Estimates (special tabulation for HUD of rent and rental units by ZCTA); 2012 American Community Survey 5-Year Estimates (total rental units), converted to ZIP Code estimates using HUD

ZIP Code to census tract crosswalks

Exhibit 3 shows that there is a lot of variation across the seven geographic areas in the change in

the number of affordable units across neighborhood rent types that results from the introduction

of SAFMRs. The exhibit shows the percentage change in units renting below the applicable FMR in

each of the PHAs where they were implemented.17

17 Specifically, we calculated the number of units renting below the SAFMR minus the number of units renting below the metro FMR, divided by the number of units renting below the metro FMR.

Cityscape 29

Dastrup, Finkel, and Ellen

In the Mamaroneck, Dallas, and Cook County housing authorities, we see 14- to 17-percent declines

in lower rent neighborhoods and similarly sized gains in units in higher rent neighborhoods. For

these PHAs, relative to metro FMRs, SAFMRs shifted affordable units from lower to higher rent

neighborhoods, with the overall number of units affordable staying about the same. In Chattanooga

and Laredo, the direction of the change is the same, but magnitudes are smaller.

In Long Beach, by contrast, most of the affordable rental housing stock is in lower rent neighborhoods.

Introducing SAFMRs in PHA results in large losses (23 percent) in the share of units affordable in low-

rent neighborhoods that are not offset by gains in high-rent and moderate-rent neighborhoods. Very

few rental units are available in the higher rent ZIP Codes within the Long Beach PHA’s jurisdiction.

Meanwhile, in Plano, where very few rental units are in low-rent areas, SAFMRs result in substantially

more (nearly 40 percent) units that are affordable in high-rent neighborhoods relative to metro FMRs,

with only a modest decrease in the low- and moderate-rent neighborhoods. Note that these differences

are more pronounced because in the demonstration, SAFMRs were implemented in individual housing

agencies rather than full metropolitan areas (NYU Furman Center, 2018).

Exhibit 3

Percent Change in Number of Units Renting Under FMR After Implementation of SAFMRs,

by ZIP Code Rent Ratio

50%

40%

38%

30%

26%

20%

19%

14%

10%

10%

9%

5%

6%

6%

4% 3%

4%

0

0%

-2%

-1%

-1%

-2%

ence in Units Renting below SAFMR and FMR

-10%

-4%

-4%

fer

-7%

-6%

-8%

-10%

cent Dif

-20%

-14%

-14%

-13%

Per

-17%

-30%

-23%

Laredo

aroneck

am

M

Chat anooga

Cook County

Long Beach

Dal as

Plano

Lower

Moderate

Higher

Total

<90

90-110

>110

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis dataset includes all ZIP Codes in the service areas of the seven Small Area Fair Market Rent Public Housing Authorities in our analysis.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Markets Rents; 2012 American Community Survey 5-Year Estimates (special tabulation for HUD of rent and rental units by ZCTA); 2012 American Community Survey 5-Year Estimates (total rental units) 30 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

Exhibit 4 confirms that SAFMRs flatten the relationship between the share of units affordable with

a housing choice voucher and ZIP Code rent levels. Fewer units rent below SAFMRs than FMRs in

ZIP Codes with lower rent ratios and more units rent below SAFMRs than FMRs in ZIP Codes with

higher rent ratios.

Exhibit 4

Fitted Relationship Between Share of Units Affordable Under SAFMRs and FMRs and

ZIP Code Rent Ratio

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis dataset includes all ZIP Codes in the service areas of the seven Small Area Fair Market Rent Public Housing Authorities in our analysis.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Markets Rents; 2012 American Community Survey 5-Year Estimates (special tabulation for HUD of rent and rental units by ZCTA); 2012 American Community Survey 5-Year Estimates (total rental units) The switch to SAFMRs not only provides more options in high-rent areas but also in high-opportunity areas, or at least areas with lower poverty rates, higher performing schools, and

cleaner air. Exhibit 5 shows that, for each index, more units are potentially available to voucher

holder families in high-opportunity areas under SAFMRs compared with metro area FMRs, and

fewer units are available in low-opportunity areas. For example, the share of units renting under

the applicable FMR in low-poverty ZIP Codes rose from a third to over a half, while the share

renting under the applicable FMR in high-poverty ZIP Codes fell from 70 percent to 48 percent

under SAFMRs.

Cityscape 31

Dastrup, Finkel, and Ellen

Exhibit 5

Changing Share of Units Affordable by Opportunity Measure Quartiles

e of Units in ZIP Code with

Shar

Rents Below SAFMR and FMR

e of Units in ZIP Code with

Shar

Rents Below SAFMR and FMR

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Notes: All ZIP Codes in Small Area Fair Market Rent (SAFMR) public housing authority (PHA) service areas (seven PHAs where SAFMRs have been implemented).

Overall index calculated as percentile rank within Metro Area of average of component indexes percentile ranks.

Sources: HUD FY2015 metro Fair Market Rents and SAFMR; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZCTA and public extract of total rental units; 2014 ACS 5-year estimates (poverty rate/percent nonpoor); School Proficiency Index, 2011–2012

(from HUD Open Data); Job Proximity Index, 2010 (from HUD Open Data); Environmental Health Hazard Index, 2005 (from HUD Open Data) 32 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

Actual Location of Voucher Holder Families with Children

The analyses above suggest that Small Area Fair Market Rents are doing what they were intended to

do: opening up more opportunities for voucher holder families with children to move to high-rent

areas that offer a richer set of resources and greater racial diversity. They do not, however, tell us

whether voucher holder families with children are able to take advantage of those opportunities.

In this section, we examine the actual location outcomes of voucher holder families with children

before and after the adoption of SAFMRs.

Exhibit 6 reports the results of difference-in-differences regressions for the sample of voucher

holders that moved in a given year. Here we see that SAFMRs increased the likelihood that

voucher holder families with children who move reach higher-opportunity neighborhoods. In

column 1, families with children who move are nearly 7 percentage points more likely to move to

a neighborhood with a significantly higher (at least 10 points) opportunity index score than are

their counterparts in Comparison PHAs as a result of the introduction of SAFMRs (the coefficient

0.067*** in column 1).18 Given that 30 percent of moves in SAFMR PHAs meet this criterion

before the introduction of SAFMRs, this statistic represents a 22-percent increase in the incidence

of such moves.

Column 2 demonstrates that the effect is strongest for households that start in low-rent ZIP Codes

(column 2), although the coefficient estimates for the different starting rent ratio categories are not

statistically significantly different from each other.

The regression coefficients reported in Column 3 indicate an average 4.4-percentile increase

in our opportunity index resulting from the introduction of SAFMRs. This is a 175-percent

increase relative to the mean of 2.5 for pre-SAFMR moves in SAFMR PHAs. While families with

children with a voucher who move under metro area FMRs also tend to locate to ZIP Codes with

higher opportunity measures, the resulting increase in average metro area percentile ranking on

opportunity more than doubles as a result of SAFMRs.

18 This is 2.4 percentage points higher and statistically significantly different than the effect for households without children.

Cityscape 33

Dastrup, Finkel, and Ellen

Exhibit 6

Regression Estimates of Effect of SAFMRs on Overall Opportunity Index,

Conditional on Moving for HCV Holder Families with Children

Model

(1)

(2)

(3)

(4)

Move Up

Move Up, by

Change in

Change in

Starting Rent

Overall Index

Overall Index

Ratio

by Starting

Rent Ratio

Outcome Variable

Mean (SAFMR Pre-SAFMR)

Moved Up

Moved Up

(10 Percentile) (10 Percentile)

ΔOpportunity

ΔOpportunity

0.304

0.304

2.51

2.51

Coefficient

Coefficient

Coefficient

Coefficient

(Std Err)

(Std Err)

(Std Err)

(Std Err)

SAFMR PHA

0.067***

4.38***

Post-SAFMR

(0.017)

(1.11)

Lower

0.095*

6.00

SAFMR PHA

(<90)

(0.043)

(3.66)

Post-SAFMR

Moderate

0.073***

4.66**

ZIP Rent Ratio

(90–110)

(0.015)

(1.28)

Interaction

Higher

0.050

5.41**

(>110)

(0.026)

(1.89)

Dallas PHAs

0.042**

2.66**

Interacted

Interacted

2011–2012

(0.013)

(0.82)

– 0.004

– 0.99***

Year = 2011–2012

Interacted

Interacted

(0.003)

(0.20)

– 0.012***

Post-SAFMR

Interacted

(0.004)

Household Characteristics

Yes

Yes

Yes

Yes

PHA Fixed Effects

Yes

Yes

Yes

Yes

0.324***

0.316***

1.92***

1.13

Intercept

(0.008)

(0.010)

(0.42)

(0.68)

Sample Size

376,180

376,180

376,180

376,180

(Household Years)

Number of PHAs

145

145

145

145

HCV = Housing Choice Voucher. PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

*** = p<0.001. ** = <0.01. * = <0.05.

Sources: HUD FY2015 Fair Market Rents and HUD FY2015 Small Area Fair Markets Rents (rent ratios); HUD PIC data extract; opportunity index (see exhibit 1) Results for school proficiency and neighborhood poverty measures are consistent with those for the

average combined measure. As shown in exhibit 7, families with children with a voucher are about

6 percentage points more likely to move to a ZIP Code with local schools that are a decile higher

within the metropolitan area in terms of the percent of students that are proficient (column 1).

34 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

The average increase in percentile gains in school proficiency for movers goes up by 3.9 percentile

points (column 3) as a result of the introduction of SAFMRs.

Exhibit 7

Regression Estimates of Effect of SAFMRs on Move Outcomes with Respect to School

Proficiency, Conditional on Moving for HCV Holder Families with Children

Model

(1)

(2)

(3)

(4)

Move Up

Move Up, by

Change in

Change in

Starting Rent

Overall Index

Overall Index

Ratio

by Starting

Rent Ratio

Outcome Variable

Mean (SAFMR Pre-SAFMR)

Moved Up

Moved Up

School

School

ΔPctl School

ΔPctl School

(10 Percentile) (10 Percentile)

Proficiency

Proficiency

0.307

0.307

1.56

1.56

Coefficient

Coefficient

Coefficient

Coefficient

(Std Err)

(Std Err)

(Std Err)

(Std Err)

SAFMR PHA

0.059***

3.87**

Post-SAFMR

(0.015)

(1.24)

Lower

0.074***

4.34***

SAFMR PHA

(<90)

(0.015)

(1.01)

Post-SAFMR

Moderate

0.081**

5.51*

ZIP Rent Ratio

(90–110)

(0.024)

(2.32)

Interaction

Higher

0.017

2.34

(>110)

(0.037)

(1.93)

Dallas PHAs

0.013

1.38

Interacted

Interacted

2011–2012

(0.009)

(0.79)

– 0.006*

– 0.93***

Year = 2011–2012

Interacted

Interacted

(0.003)

(0.18)

– 0.010*

– 1.48***

Post-SAFMR

Interacted

Interacted

(0.004)

(0.25)

Household Characteristics

Yes

Yes

Yes

Yes

PHA Fixed Effects

Yes

Yes

Yes

Yes

0.324***

0.365***

1.51***

5.09***

Intercept

(0.007)

(0.012)

(0.42)

(0.91)

Sample Size

365,156

365,156

365,156

365,156

(Household Years)

Number of PHAs

137

137

137

137

HCV = Housing Choice Voucher. PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

***=p<0.001. **=p<0.01. *=p<0.05.

Sources: HUD FY2015 Fair Market Rents and HUD FY2015 Small Area Fair Markets Rents (rent ratios); HUD PIC data extract; opportunity index (see exhibit 1) Cityscape 35

Dastrup, Finkel, and Ellen

As shown in exhibit 8, families with children with vouchers are also more likely to move to lower

poverty ZIP Codes after the introduction of SAFMRs.

Exhibit 8

Regression Estimates of Effect of SAFMRs on Move Outcomes with Respect to Percent Nonpoor,

Conditional on Moving for HCV Holder Families with Children

Model

(1)

(2)

(3)

(4)

Move Up

Move Up

Change

Change in

Percent

Percent

Percent

Percent Nonpoor

Nonpoor

Nonpoor, by

Nonpoor

Percentile by

Starting Rent

Percentile

Starting Rent

Ratio

Ratio

Outcome Variable

Mean (SAFMR Pre-SAFMR)

Moved Up

Moved Up

Percent

Percent

Nonpoor

Nonpoor

ΔPctl Percent ΔPctl Percent

(10 Percentile) (10 Percentile)

Nonpoor

Nonpoor

0.320

0.320

3.20

3.20

Coefficient

Coefficient

Coefficient

Coefficient

(Std Err)

(Std Err)

(Std Err)

(Std Err)

SAFMR PHA

0.060***

4.27***

Post-SAFMR

(0.016)

(0.93)

Lower

0.014

3.34

SAFMR PHA

(<90)

(0.047)

(3.99)

Post-SAFMR

Moderate

0.097***

5.25***

ZIP Rent Ratio

(90–110)

(0.016)

(1.16)

Interaction

Higher

0.102***

8.36**

(>110)

(0.018)

(2.18)

Dallas PHAs

0.036**

3.13***

Interacted

Interacted

2011–2012

(0.012)

(0.71)

– 0.004

– 1.01***

Year = 2011–2012

Interacted

Interacted

(0.003)

(0.22)

– 0.012**

– 1.74***

Post-SAFMR

Interacted

Interacted

(0.004)

(0.26)

Household Characteristics

Yes

Yes

Yes

Yes

PHA Fixed Effects

Yes

Yes

Yes

Yes

0.34***

0.45***

2.65***

10.90***

Intercept

(0.007)

(0.01)

(0.42)

(0.96)

Sample Size

376,180

376,180

376,180

376,180

(Household Years)

Number of PHAs

145

145

145

145

HCV = Housing Choice Voucher. PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

*** = p<0.001. ** = p< 0.01. * = p< 0.05.

Sources: HUD FY2015 Fair Market Rents and HUD FY2015 Small Area Fair Markets Rents (rent ratios); HUD PIC data extract. Opportunity index (see exhibit 1) 36 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

As for families with children newly receiving a voucher, exhibit 9 shows that, after the

implementation of SAFMRs, families with children newly receiving a voucher locate in ZIP Codes

that are at a 4.8 higher percentile on average in the metropolitan area distribution of overall

opportunity (column 1), a 3.7 higher percentile in school proficiency (column 2), and a 5.3 higher

percentile in the percent of households that are nonpoor (column 3).

Exhibit 9

Regression Estimates of Effect of SAFMRs on Location Outcomes for New HCV

Holder Families with Children

Model

(1)

(2)

(3)

Overall Index

School Proficiency

Percent Nonpoor

Percentile

Percentile

Percentile

Outcome Variable

Mean (SAFMR Pre-SAFMR)

Overall Index

School Proficiency

Percent Nonpoor

Percentile

Percentile

Percentile

33.1

35.4

36.5

Coefficient

Coefficient

Coefficient

(Std Err)

(Std Err)

(Std Err)

SAFMR PHA

4.80***

3.72***

5.33***

Post-SAFMR

(1.39)

(0.89)

(1.26)

Dallas PHAs

0.55

– 0.55

1.32

2011–2012

(0.87)

(0.75)

(0.79)

– 0.15

– 0.35

– 0.18

Year = 2011–2012

(0.26)

(0.27)

(0.28)

– 1.17***

– 1.48***

– 0.92**

Post-SAFMR

(0.33)

(0.30)

(0.30)

Household

Yes

Yes

Yes

Characteristics

PHA Fixed Effects

Yes

Yes

Yes

33.76***

36.03***

Intercept

(0.71)

(0.67)

Sample Size

161,712

154,963

161,712

(Household Years)

Number of PHAs

144

136

144

HCV = Housing Choice Voucher. PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

*** = p<0.001. ** = p< 0.01. * = p< 0.05.

Sources: HUD FY2015 Fair Market Rents and HUD FY2015 Small Area Fair Markets Rents (rent ratios); HUD PIC data extract; opportunity index (see exhibit 1) The regression estimates presented in exhibit 10 show that SAFMRs do not change the overall

incidence of moves among all families with children with vouchers (column 1) and result in only

a small statistically significant decrease in moves from lower-rent ZIP Codes (column 2). However,

column 3 shows that the unconditional likelihood of moving to a higher-opportunity neighborhood

increases by a half a percentage point (on a base of 4.2 percent) after SAFMR adoption.

Cityscape 37

Dastrup, Finkel, and Ellen

Exhibit 10

Regression Estimates of Effect of SAFMRs on Location Outcomes for All HCV Holder Families

with Children

Model

(1)

(2)

(3)

(4)

Moved

Moved

Moved Up

Moved Up

Neighborhood Neighborhood,

a Decile in

a Decile in

by Starting

Opportunity

Opportunity,

Rent Ratio

Index

by Starting

Rent Ratio

Outcome Variable

Mean (SAFMR Pre-SAFMR)

Moved

Moved

Moved Up

Moved Up

0.139

0.139

0.042

0.042

Coefficient

Coefficient

Coefficient

Coefficient

(Std Err)

(Std Err)

(Std Err)

(Std Err)

SAFMR PHA

-0.004

0.007***

Post-SAFMR

(0.008)

(0.002)

Lower

– 0.007

0.010

SAFMR PHA

(<90)

(0.005)

(0.005)

Post-SAFMR

Moderate

– 0.001

0.009**

ZIP Rent Ratio

(90–110)

(0.010)

(0.004)

Interaction

Higher

– 0.003

0.005

(>110)

(0.010)

(0.003)

Dallas PHAs

– 0.012

0.003

Interacted

Interacted

2011–2012

(0.007)

(0.002)

0.005

0.001

Year = 2011–2012

Interacted

Interacted

(0.004)

(0.001)

– 0.018***

– 0.007***

Post-SAFMR

Interacted

Interacted

(0.003)

(0.001)

Household Characteristics

Yes

Yes

Yes

Yes

PHA Fixed Effects

Yes

Yes

Yes

Yes

0.141***

0.147***

0.045***

0.055***

Intercept

(0.004)

(0.005)

(0.002)

(0.003)

Sample Size

2,707,779

2,707,779

2,707,779

2,707,779

(Household Years)

Number of PHAs

145

145

145

145

HCV = Housing Choice Voucher. PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

*** = p<0.001. ** = p< 0.01. * = p< 0.05.

Note: Sample includes all families with children with a voucher in each year except new voucher holders in the first year they receive a voucher.

Sources: HUD FY2015 Fair Market Rents and HUD FY2015 Small Area Fair Markets Rents (rent ratios); HUD PIC data extract; opportunity index (see exhibit 1) The combined result of these effects in terms of neighborhood opportunity outcomes for families

with children with a voucher is summarized in exhibit 11. By 2017, 15 percent of voucher holder

families with children in SAFMR PHAs lived in neighborhoods in the highest quartile of our

opportunity index as compared with 10 percent in 2010. Changes were even larger for families

38 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

with children that moved and those initially receiving a voucher. By 2017, 20 percent of families

with children with a voucher that moved were locating to neighborhoods in the top quartile of the

opportunity distribution, and 18 percent of new voucher holder families with children were using

their voucher in these types of ZIP Codes. Meanwhile, in Comparison PHAs we see essentially no

changes over the same period.

Exhibit 11

The Share of Voucher Holder Families with Children Who Live (After Any Moves) in ZIP Codes by

Opportunity Level Over Time, in SAFMR and Comparison PHAs

PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

Notes: All voucher holder families with children in ZIP Codes in Small Area FMR PHA service areas (seven PHAs where Small Area FMRs have been implemented) and in 144 Comparison PHAs. Statistically significantly different from the same proportion in 2010 at p-value < 0.001 ***, < 0.01 **, and < 0.05 *.

Sources: HUD FY2015 Fair Market Rents and HUD FY2015 Small Area Fair Markets Rents (rent ratios); HUD PIC data extract (counts); opportunity index (see exhibit 1) Conclusion and Policy Implications

Our analysis shows that SAFMRs show promise as a strategy to enable more voucher holder

families with children to reach high-opportunity neighborhoods. First, SAFMRs increase the

pool of units potentially available to housing choice voucher holder families in high-opportunity

neighborhoods and reduce the number in neighborhoods that offer more minimal opportunities.

Further, the shift to SAFMRs appears to affect voucher holder families’ actual choice of locations.

Among voucher holder families with children who move, the share locating in ZIP Codes in the top

Cityscape 39

Dastrup, Finkel, and Ellen

quartile of opportunity levels increases by 11 percentage points after the introduction of SAFMRs.

No changes are observed in a set of comparison jurisdictions. Combined with prior research

findings on long-term benefits from moves to higher opportunity neighborhoods to children in

households receiving housing assistance, our research suggests that the implementation of SAFMRs

may transform the HCV program toward achieving some gains in intergenerational economic well-

being of low-income households. Notably, as detailed in Dastrup et al. (2018), these effects appear

to be achieved at no additional cost to the government 5 years after implementation, reflecting

lower subsidies to voucher holder families in low-opportunity neighborhoods more than offset the

increased subsidies in higher opportunity areas.

While effects are largest among movers, we also see shifts in the locational choices of new voucher

holder families with children. Given the time pressure that new voucher holder families face to

find a home that meets voucher program quality standards and rents for less than the ceiling rent,

it is perhaps not surprising that we see a more modest change for new voucher holder families as

compared with continuing voucher holder families who have more time to search for new homes.

As more PHAs adopt SAFMRs, they might pay more attention to helping new voucher holder

families navigate the market and reach better neighborhoods. They may also need to do more work

to recruit more landlords in high-rent areas, and at least for those with catchment areas dominated

by low-rent ZIP Codes, adopt reforms to make it easier for voucher holder families to move to

other jurisdictions.

40 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

Appendix A: Additional Exhibits

Exhibit A.1

Rental Units and ZIP Codes by ZIP Code Rent Ratio for SAFMR PHAs

ZIP Code Rent Ratio

n

<0.9

0.9-1.1

>1.1

All SAFMR

Units (%)

1,290,864

380,598

(29)

588,330

(46) 321,936

(25)

PHAs

ZIP Codes (%)

411

87

(21)

186

(45)

138

(34)

Units (%)

25,544

6,582

(26)

15,228

(60)

3,734

(15)

Laredo

ZIP Codes (%)

5

1

(20)

3

(60)

1

(20)

Units (%)

143,226

51,090

(36)

64,066

(45)

28,069

(20)

Mamaroneck

ZIP Codes (%)

67

9

(13)

32

(48)

26

(39)

Units (%)

53,390

8,638

(16)

36,152

(68)

8,600

(16)

Chattanooga

ZIP Codes (%)

30

6

(20)

21

(70)

3

(10)

Units (%)

291,302

96,374

(33)

130,023

(45)

64,904

(22)

Cook County

ZIP Codes (%)

127

37

(29)

53

(42)

37

(29)

Units (%)

107,946

60,531

(56)

35,990

(33)

11,425

(11)

Long Beach

ZIP Codes (%)

13

5

(38)

5

(38)

3

(23)

Units (%)

668,981

157,382

(24)

306,396

(46)

20,523

(31)

Dallas

ZIP Codes (%)

168

29

(17)

71

(42)

68

(40)

Units (%)

236,040

21,549

(9)

111,166

(47) 103,324

(44)

Plano

ZIP Codes (%)

52

1

(2)

21

(40)

30

(58)

PHA=Public Housing Agency. SAFMR=Small Area Fair Market Rent.

Notes: Analysis dataset includes all ZIP Codes in PHA service areas where SAFMRs have been implemented. Services areas determined based on review of PHA administrative documents. Percentage of total for each row in parentheses.

Sources: Rent ratios calculated using HUD FY2015 Fair Market Rents and HUD FY2015 Small Area Fair Market Rents; total rental units recovered from 2012

American Community Survey 5-year estimates, converted to ZIP Code estimates using HUD ZIP Code to census tract crosswalks Cityscape 41

Dastrup, Finkel, and Ellen

Exhibit A.2

Substantial Variation Across PHAs in Number of Units Renting Below FMRs and SAFMRs

Total Units with Rents Below

FMR, All ZIP Code Rent Ratios

Percent Change

Difference

SAFMR vs. FMR

SAFMR

FMR

All SAFMR PHAs

626,483

648,607

– 22,125

– 3.4

Laredo

14,163

14,317

– 208

– 1.4

Mamaroneck

90,665

90,955

– 290

– 0.3

Chattanooga

23,395

22,673

721

3.2

Cook County

152,749

155,401

– 2,652

– 1.7

Long Beach

54,140

62,575

– 8,435

– 13.5

Dallas

291,066

302,246

– 11,180

– 3.7

Plano

101,009

80,163

20,846

26.0

FMR = Fair Market Rent. PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

Note: Analysis dataset includes all ZIP Codes in PHA service areas where SAFMRs have been implemented.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents; 2012 American Community Survey 5-year estimates (special tabulation for HUD of rent and rental units by ZCTA); 2012 American Community Survey 5-year estimates (total rental units) 42 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

Exhibit A.3

Summary Statistics for Regression Sample of Families with Children with a Voucher That Move

Sample

Pre or Post SAFMRs

SAFMR PHAs

Comparison PHAs

Pre

Post

Pre

Post

N (Household Years)

18,886

20,293

252,832

269,859

Outcome Variables

Move Up at Least 10 Points in

31.2%

35.2%**

30.5%

29.5%**

Opportunity Index

Change in Overall Opportunity Index

2.9

4.5*

1.5

0.4***

Analysis Subgroup

Household Includes Children

69.2%

63.5%***

71.0%

64.7%***

Other Household Characteristics Regression Covariates

Single Female Head of Household

92.3%

90.0%***

90.5%**

87.9%***

Adults in Household

Ages 18-24

1

20.0%

19.9%

19.3%

16.7%***

2 or More

5.6%

5.6%

3.6%***

3.4%***

Ages 25-61

1

83.0%

80.3%***

79.6%***

80.0%*

2 or More

8.3%

8.7%

8.6%

9.2%***

Ages 62 Plus

1

8.0%

9.9%***

7.3%

9.5%***

2 or More

0.5%

0.6%

0.5%***

0.6%***

Presence of Children

Under Age 5

24.2%

23.0%*

32.1%***

26.6%***

Ages 5-12

48.4%

44.4%***

51.5%**

47.4%***

Ages 13-17

37.7%

34.9%***

32.3%***

32.0%

Race/Ethnicity

White (Non-Hispanic)

11.8%

11.3%

27.5%***

25.6%***

Black

84.9%

86.1%

70.5%***

72.8%***

Hispanic

6.8%

6.6%

14.8%***

13.0%***

Other

3.3%

2.4%*

1.9%

1.7%***

Note: Statistical tests compare: SAFMR Pre, SAFMR Post; SAFMR Pre, Comp Pre; Comp Pre, Comp Post.

*** = p<0.001. ** = p< 0.01. * = p< 0.05.

Cityscape 43

Dastrup, Finkel, and Ellen

Exhibit A.4

Summary Statistics for Regression Sample of All Families with Children with a Voucher

Sample

Pre or Post SAFMRs

SAFMR PHAs

Comparison PHAs

Pre

Post

Pre

Post

N (Household Years)

180,584

236,669

2,300,476

2,820,158

Outcome Variables

Move to a New ZIP Code

10.5%

8.6%***

11.0%

9.6%***

Move Up at Least 10 Points

3.3%

3.1%

3.4%

2.9%***

in Opportunity Index

Analysis Subgroup

Household Includes Children

53.8%

48.5%***

55.4%

50.5%***

Includes Adult(s) 62 or Older

18.8%

21.0%***

17.2%

19.9%***

Includes Head or Co-Head with

38.0%

39.4%**

37.8%

40.2%***

a Disability

Other Household Characteristics Regression Covariates

Single Female Head of Household

86.2%

83.6%***

83.9%*

82.0%***

Adults in Household

Ages 18-24

1

18.8%

18.1%*

17.2%**

15.6%***

2 or More

5.2%

4.9%

3.4%***

3.4%**

Ages 25-61

1

73.6%

71.2%***

71.9%

70.8%***

2 or More

8.5%

8.7%

9.0%

9.7%***

Ages 62 Plus

1

16.9%

19.1%***

15.8%

18.5%***

2 or More

1.9%

1.8%

1.3%

1.5%***

Presence of Children

Under Age 5

16.6%

15.1%***

21.8%***

17.7%***

Ages 5-12

35.4%

31.8%***

38.1%*

35.0%***

Ages 13-17

30.3%

26.8%***

26.5%***

25.9%***

Race/Ethnicity

White (Non-Hispanic)

19.0%

19.0%

38.3%***

35.9%***

Black

75.4%

75.9%

58.7%***

61.3%***

Hispanic

8.4%

9.0%

16.1%***

14.8%***

Other

5.4%

4.8%**

3.0%

2.9%*

Note: Statistical tests compare: SAFMR Pre, SAFMR Post; SAFMR Pre, Comp Pre; Comp Pre, Comp Post

*** = p<0.001. ** = p< 0.01. * = p< 0.05.

44 Small Area Fair Market Rents

The Effects of Small Area Fair Market Rents

on the Neighborhood Choices of Families with Children

Acknowledgments

The work that provided the basis for this publication was supported by funding under a grant

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

Research. The substance and findings of the work are dedicated to the public.

Authors

Samuel Dastrup is an associate at Abt Associates and can be contacted at Samuel_Dastrup@

AbtAssoc.com.

Ingrid Gould Ellen is the Paulette Goddard Professor of Urban Policy and Planning at NYU Wagner

and a faculty director of the NYU Furman Center. She can be contacted at Ingrid.Ellen@nyu.edu.

Meryl Finkel is a principal associate at Abt Associates and can be reached at Meryl_Finkel@

AbtAssoc.com.

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Cityscape 47

48 Small Area Fair Market Rents

by Kirk McClure and Alex Schwartz

Small Area Fair Market Rents, Race,

and Neighborhood Opportunity

Kirk McClure

University of Kansas

Alex Schwartz

The New School

Abstract

This article assesses the potential of Small Area Fair Market Rents (SAFMRs) to help Housing Choice

Voucher (HCV) recipients, especially Black and Hispanic recipients, secure housing in high-opportunity neighborhoods. Examining large metropolitan areas, it is estimated that increasing the availability of rental housing in high-opportunity neighborhoods may not work well, especially when HCV recipients

are Black or Hispanic. Racial segregation and discrimination may still discourage Black and Hispanic voucher holders from moving into high-opportunity neighborhoods when these neighborhoods are

predominantly White. Moreover, widespread implementation of SAFMRs could make it more difficult for

minority voucher holders to find eligible units because the maximum qualifying rents would be reduced in many neighborhoods with large concentrations of minority voucher holders. For the SAFMR program

to succeed, supporting transportation and housing counseling services will be needed in addition to

extensive landlord outreach.

Introduction

Although Housing Choice Vouchers (HCV) are used in the vast majority of neighborhoods in the

United States—80 percent of all Census tracts with rental housing as of 2017—most voucher

holders tend to live in areas with relatively high levels of poverty, and many live in racially

segregated neighborhoods (Schwartz, McClure, and Taghavi, 2016). The federal government

established Small Area Fair Market Rents (SAFMRs) as a way to reduce the concentration of HCV

holders in poor neighborhoods and help them access higher-income neighborhoods with good

schools, employment opportunities, low crime, and recreational amenities. SAFMRs are based

on the premise that neighborhoods with higher rents offer more opportunity for low-income

households than those with lower rents.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 49

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

McClure and Schwartz

The maximum rental subsidy that HCV recipients receive is keyed to the Fair Market Rent (FMR)

of their metropolitan area or the county in a non-metropolitan area. The U.S. Department of

Housing and Urban Development (HUD) currently defines the FMR as the 40th percentile of gross

rents for typical, non-substandard rental units occupied by recent movers in the local housing

market.1 Public Housing Authorities establish a Payment Standard that can range from 90 to 110

percent of the FMR to set the maximum allowable rent that can be covered by the HCV program—

tenants may pay rents in excess of this standard provided that they spend no more than 40 percent

of their income on rent.

With a single FMR set for an entire metropolitan area, neighborhoods with lower rents are

more likely to have substantially more housing that qualifies for the HCV program than would

neighborhoods with higher rents. With SAFMRs, each ZIP Code area is assigned its own FMR

(HUD provides a table listing SAFMRs for each metropolitan ZIP Code area in the United States

and its territories). These SAFMRs are calculated in a manner similar to the calculation of FMRs,

except that the unit of analysis is a ZIP Code area rather than a metropolitan area or county.

SAFMRs in the more expensive ZIP Code areas of a metropolitan area may be set above the metro-

wide FMR, and SAFMRs in the least expensive ZIP Code areas may be set lower. When the SAFMR

exceeds the metro-wide FMR, the availability of rental housing eligible for the HCV program would

increase, as units with rents above the metro-wide FMR, but at or below the SAFMR would now

be accessible. On the other hand, if the SAFMR falls below the metro-wide FMR, units that cost

less than the metro-wide FMR but more than the SAFMR would no longer be eligible for the HCV

program—unless the owner of these units lowered their rents to the new SAFMR.

SAFMRs were first implemented in the Dallas, TX, metropolitan area in 2011 as part of the

settlement of a fair housing lawsuit initiated by the Inclusive Housing Project ( Inclusive Communities Project, Inc. v. HUD, 2009)2. The settlement required all PHAs in the Dallas metropolitan area

to institute SAFMRs. In 2012, HUD launched a demonstration program to test the effect of the

SAFMR on the HCV program, including its efficacy in helping voucher recipients access higher

opportunity neighborhoods. The demonstration involved two PHAs in the Dallas metropolitan

area that had already adopted SAFMRs and five additional PHAs (Dastrup et al, 2018; Reina,

Acolin, and Bostic, 2018). In 2016, at the end of the Obama Administration, HUD issued a final

rule mandating that PHAs in 24 metropolitan areas adopt SAFMRs (HUD, 2016). In 2017 under

the Trump Administration, however, HUD decided to delay the implementation of this rule until

at least 2020 (Matthew, 2017; NYU Furman Center, 2018). This decision was subsequently

suspended by a court order, effectively requiring the implementation of SAFMRs in the 24

metropolitan areas to begin in 2018.

The purpose of this article is to assess the potential of SAFMRs to help HCV recipients, especially

Black and Hispanic recipients, secure housing in “opportunity” neighborhoods, neighborhoods

with low levels of poverty, high-performing schools, and other desirable characteristics. Our

hypothesis is that increasing the availability of rental housing in high-opportunity neighborhoods

1 Until recently, HUD set the FMR at the 50th percentile in 17 high-cost metropolitan areas; those areas will now use SAFMRs.

2 Inclusive Communities Project, Inc. v. HUD, 12–11211, 13–10306, (U.S. District Court of Northern District Texas Dallas Division, 2009).

50 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

may not be sufficient by itself in increasing the utilization of HCVs in these neighborhoods,

especially when HCV recipients are Black or Hispanic. Racial segregation and discrimination

may still discourage African-American and Hispanic voucher holders from moving into high-

opportunity neighborhoods when these neighborhoods are predominantly White. Moreover,

widespread implementation of SAFMRs could make it more difficult for minority voucher

holders to find eligible units because the maximum qualifying rents would be reduced in many

neighborhoods with large concentrations of minority voucher holders. In this article we estimate

how SAFMR would affect the availability of HCV-eligible units in ZIP Codes with varying levels of

opportunity and with varying racial and ethnic characteristics.

The article is motivated in part by the outcomes of the SAFMR Demonstration program

(Demonstration). Both the final evaluation of the Demonstration (Dastrup et al., 2018)

conducted for HUD and an independent study (Reina, Acolin, and Bostic, 2018) found that

the implementation of SAFMRs yielded a small but significant effect on the likelihood that

HCV recipients would reside in higher-opportunity neighborhoods. The final evaluation of

the Demonstration found that 14 percent of HCV recipients in SAFMR PHAs resided in high-

opportunity neighborhoods after the introduction of SAFMRs, compared with 9 percent before the

SAFMRs; in a control group of PHAs similar to those with SAFMRs, 9 percent of HCV participants

resided in higher-opportunity neighborhoods throughout the study period (Dastrup et al.,

2018). Three of the seven SAFMR PHAs accounted for most of the increase in higher-opportunity

residency; the other four PHAs experienced little change (Dastrup et al., 2018). Reina, Acolin,

and Bostic (2018), using a different analytic approach including a somewhat different measure

of opportunity, also found that some sites (most notably Dallas) saw significant increases among

HCV recipients in high-opportunity ZIP Code areas, while others saw little if any increase. Neither

study compared the effect of SAFMR on HCV recipients of different races and ethnicities, or the

relationship between opportunity and the racial/ethnic composition of the ZIP Code areas.

This article builds on these studies of the Demonstration by estimating how the implementation

of SAFMRs in the nation’s largest metropolitan areas would affect the availability of rental housing

in ZIP Codes with varying levels of “opportunity” and with varying racial and ethnic profiles.

Whereas the Demonstration focused on PHAs in six metropolitan areas (including two in the

Dallas area), we cover all metropolitan areas with populations of at least 1 million as of 2017—53

in total. These large metropolitan areas held 57 percent of all vouchers in 2017. The article also

builds on NYU Furman Center’s estimation of the impact effect of SAFMRs on the number of rental

units affordable to voucher holders in the 24 metropolitan areas that the Obama Administration

designated for SAFMRs (NYU Furman Center, 2018).

Like the HUD evaluation, we examine the extent to which SAFMR would affect the number of

rental units that would be eligible (assuming payment standards are set at the SAFMR) for the

HCV program in ZIP Code areas with varying levels of “opportunity.” Opportunity, as with the

HUD study, is defined in terms of poverty exposure, school performance (test scores), labor force

involvement, and environmental health hazards.

Unlike the HUD evaluation, however, we also examine how the implementation of SAFMR

would affect the number of voucher-eligible rental units in ZIP Code areas that are dominated

Cityscape 51

McClure and Schwartz

by a particular racial or ethnic group and that are “integrated.” Given the persistence of racial

segregation in the United States, we argue that the efficacy of the SAFMR program may depend on

the race and ethnicity of the voucher holder.

Voucher holders, like most households tend to live either in neighborhoods that are populated

mostly by people of their own race or ethnicity, or in integrated neighborhoods (Schwartz, McClure,

and Taghavi, 2016). Given the high degree of racial segregation among voucher holders, it is

important to understand the relationship between the racial/ethnic composition of ZIP Code areas

and the distribution of “opportunity” across areas with different racial or ethnic characteristics.

Segregation would be less important if predominantly White, predominantly Black, predominantly

Hispanic, and integrated ZIP Code areas shared similar distributions of “opportunity”—that is, if

similar proportions of each group were classified as high opportunity or low opportunity. But if ZIP

Code areas dominated by certain racial groups are more likely than other ZIP Code areas to rank as

high opportunity, then those ZIP Code areas are most likely to benefit from SAFMRs.

The persistence of racial segregation is particularly relevant for the HCV program since 72 percent

of all voucher holders in the largest metropolitan areas are either Black (53 percent) or Hispanic

(19 percent). Non-Hispanic Whites account for 24 percent of all voucher holders (see exhibit

1). Black and Hispanic voucher holders reside mostly in ZIP Code areas dominated by their

own racial/ethnic group or in integrated areas, so it is particularly important to examine how

opportunity levels vary across ZIP Code areas with different racial and ethnic compositions.

Exhibit 1

Households with Housing Choice Vouchers 2017 by Race, Ethnicity, Disability, and Age Located in

Core-Based Statistical Areas with a Population Larger than 1 Million

Household Race,

Ethnicity, Age, and

Households

Percent

Disability Status

Non-Elderly and

White Non-Hispanic

73,710

7

Nondisabled

Black Non-Hispanic

335,541

31

Other Non-Hispanic

15,453

1

Hispanic

94,114

9

Total Non-Elderly

518,818

48

Nondisabled

Elderly or Disabled

White Non-Hispanic

184,231

17

Black Non-Hispanic

238,930

22

Other Non-Hispanic

30,209

3

Hispanic

107,629

10

Total Elderly or Disabled

560,999

52

All Households

White Non-Hispanic

257,941

24

Black Non-Hispanic

574,471

53

Other Non-Hispanic

45,662

4

Hispanic

201,743

19

Total All Households

1,079,817

100

52 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

This article is organized as follows: The Methodology section summarizes the data sources and

analytic approach. The Racial/Ethnic Composition section compares the ZIP Code areas in the

nation’s largest metro areas (with populations of 1 million or more) in terms of their racial/ethnic

composition and their level of opportunity as indicated from the index developed for this study.

The Change in HCV-Eligible Units by Category section presents estimates of the aggregate change

in HCV-eligible rental units that would occur in ZIP Code areas in each opportunity category. This

is followed by an examination of the Change in HCV-Eligible Units by Opportunity Level and

Racial/Ethnic Category. The Gain and Loss of HCV-Eligible Units in Integrated ZIP Codes section

examines the effect of SAFMRs in integrated ZIP Code areas. The article concludes with a summary

of findings and a discussion of policy implications.

Methodology

This study focuses on the 53 metropolitan areas with populations of more than 1 million in 2017.

This analysis of the impact of SAFMRs on the availability of HCV-eligible housing in these metro

areas is based on the following data sources:

HUD: Location and race and ethnicity of HCV recipients in 2017;

Metro-wide FMRs in 2017 by county;

SAFMRs in 2017;

Poverty exposure, public school performance, labor force engagement, and health hazards

in 2017 by census tract. These indicators of neighborhood opportunity are taken from

HUD’s Affirmatively Further Fair Housing data and mapping tool. The tool provides

publicly available data for fair housing analysis (HUD, 2017).

Census (American Community Survey):

Median rents by ZIP Code area in 2017;

Racial and ethnic composition of ZIP Code areas.

The analysis required all data to be tabulated to ZIP Code areas. While some data were available

for ZIP Code areas, other data needed to be converted from census tracts. To do so we applied

“cross-walks” provided by HUD. To estimate current FMRs at the ZIP Code levels, it was necessary

to apply county-level FMRs to census tracts, and then allocate rental units from the tract to the ZIP

Code level using another crosswalk provided by HUD.

Categorization of ZIP Code Areas by Race and Ethnicity

Each ZIP Code was classified into one of the following categories:

Non-Hispanic White (75 percent or more of total population)

Non-Hispanic Black (50 percent or more of total population)

Non-Hispanic Other (50 percent or more of total population)

Hispanic (50 percent or more of total population)

Integrated (all other ZIP Code areas).3

3 Because most ZIP Code areas in the nation have a majority White population, we set the threshold for defining predominantly White areas at 75 percent to identify those areas with very-low levels of racial or ethnic integration.

Cityscape 53

McClure and Schwartz

Categorization of ZIP Code Areas by the Level of Opportunity

Following the approach taken in HUD’s evaluation of the SAFMR Demonstration program, we

constructed a composite index of opportunity based on poverty exposure, school quality, labor

force involvement, and health hazards. Z-scores were generated for each ZIP Code area for each

variable. The Z-scores were then summed. The approximately 9,000 ZIP Code areas in the large

metropolitan areas were then divided into the following quintiles based on their summed Z scores:

Very high opportunity (top quintile)

High opportunity (2nd quintile)

Moderate opportunity (3rd quintile)

Low opportunity (4th quintile)

Very-low opportunity (bottom quintile)

To estimate the impact of SAFMRs in each ZIP Code area, we subtracted the number of HCV-eligible

rental units that would be present with the current metro-wide FMR from the number that would

exist if SAFMRs were in effect. The results of these calculations were then aggregated for each racial/

ethnic category and for each opportunity category. In carrying out these projections we assumed that

PHAs set their payment standard for the HCV program at 100 percent of the FMR/SAFMR.

Racial/Ethnic Composition and Opportunity Levels

Nearly one-half of the 8,763 ZIP Code areas in the largest metro areas are predominantly non-

Hispanic White. Exhibit 2 shows that these ZIP Code areas, defined as having 75 percent or

more of the population as non-Hispanic White, comprise 48 percent of all ZIP Code areas.

Predominantly Black ZIP Code areas (50 percent or more non-Hispanic Black) account for 6

percent of the total, and predominantly Hispanic areas (50 percent or more) for 7 percent. ZIP

Code areas in which Asian and other racial groups make up 50 percent or more of the population

make up 1 percent of the total. Integrated areas, in which Whites constitute less than 75 percent

of the population and all other racial or ethnic groups less than 50 percent, are the second most

common category, accounting for 38 percent of the total (exhibit 2).

Exhibit 2

ZIP Code Areas in Core-Based Statistical Areas with Population Larger than 1 Million by Dominant

Racial and Ethnic Population

ZIP Code Areas

Percent

Predominant Racial

White Non-Hispanic

or Ethnic Group in

4,181

48

(75 Percent or More)

ZIP Code Area

Black Non-Hispanic

537

6

(50 Percent or More)

Other Non-Hispanic

86

1

(50 Percent or More)

Hispanic of Any Race

598

7

(50 Percent or More)

Integrated

3,361

38

Total ZIP Code Areas

8,763

100

54 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

Exhibit 3 presents the distribution of HCV recipients of different races and ethnicities across ZIP

Code areas with different racial/ethnic compositions. It shows that 33 percent of all White voucher

holders live in predominantly White ZIP Code areas, and 55 percent live in integrated ones. More

than 80 percent of all Black voucher recipients reside either in predominantly Black ZIP Code areas

(34 percent) or in integrated areas (47 percent). The great majority of Hispanic voucher holders

live either in predominantly Hispanic (48 percent) or in integrated (40 percent) ZIP Code areas.

Very few Black or Hispanic voucher recipients reside in predominantly White ZIP Code areas (6

and 5 percent, respectively), and similarly few White voucher holders reside in predominantly

Black or Hispanic areas. These patterns are nearly identical for voucher holders who are elderly or

disabled and for voucher holders who are not.

Exhibit 3

Percent of Housing Choice Voucher Households by Race, Ethnicity, Disability, and Age Located

in Core-Based Statistical Areas with a Population Larger than 1 Million by Dominant Racial and

Ethnic Population in 2017

Dominant Racial or Ethnic Group in ZIP Code

Area (Percent)

White

Black

Other

Integrated

All Areas

Non-

Non-

Non-

Hispanic

Hispanic

Hispanic

Hispanic

Race and

White

33

3

1

8

55

100

Household

Non-Hispanic

Type

Black

6

34

0

13

47

100

Non-Hispanic

Other

6

6

14

19

55

100

Non-Hispanic

Hispanic of

5

6

1

48

40

100

Any Race

Total

12

20

1

19

48

100

Note: The significant numbers are bold.

Exhibit 4 cross-tabulates the ZIP Code areas by opportunity category and dominant racial/ethnic

group. It shows that while more than 60 percent of all White areas rank in the top two opportunity

categories, the same is true for only 3 percent of all Black and Hispanic ZIP Code areas. Conversely,

whereas only 5 percent of all White tracts are in the lowest opportunity category, they are joined

by 77 percent of all Black areas and 75 percent of all Hispanic areas. Integrated ZIP Code areas, on

the other hand, are more evenly distributed across the opportunity categories, with each category

claiming from 12 to 25 percent of all integrated ZIP Code areas.

The near absence of predominantly Black and Hispanic ZIP Code areas in the top two opportunity

categories means that if Black or Hispanic voucher recipients wish to live in an opportunity area,

they almost always choose between predominantly White or integrated areas. If they reside in a

predominantly Black or Hispanic ZIP Code area, these areas will almost always rank in the lowest

opportunity categories.

Cityscape 55

McClure and Schwartz

Exhibit 4

Opportunity Level for ZIP Code Areas by Dominant Racial and Ethnic Population in 2017 in

eas

cent

Net

-1%

-9%

-3%

Core-Based Statistical Areas with a Population Larger than 1 Million

19%

10%

Per

Change

Units

-11%

in Eligible

Dominant Racial or Ethnic Group in ZIP Code

Area (Percent)

White

Black

Other

Integrated All Areas

Non-

Non-

Non-

Hispanic

All ZIP Code Ar

Net

Units

251,611

217,485

-22,582

Hispanic

Hispanic

Hispanic

Change

in Eligible

-263,400

-554,694

-371,580

Combined

Very High

37

0

26

1

12

22

Opportunity

Opportunity

Category of

High

ZIP Code

25

3

25

2

19

20

cent

Opportunity

-16%

-20%

-23%

-22%

-23%

-22%

Areas

Loss in

Eligible

Units

Moderate

Per

20

4

14

5

22

19

Opportunity

Low

12

16

25

17

25

18

Opportunity

Very Low

Total

Units

Below

FMR

402,775

919,608

5

77

11

75

21

21

1,358,374

2,096,852

3,525,741

8,303,351

Opportunity

-Eligible Units

Total ZIP

100

100

100

100

100

100

Code Areas

Losing Units

Number of

3,604

509

73

553

3,029

7,768

Lost

-64,221

Areas

-180,073

-311,835

-467,730

-817,280

Total Units

-1,841,139

Change in HCV-Eligible Units by Opportunity Category

eas

487

621

721

818

True to the expectations of SAFMR’s architects, the implementation of SAFMRs across all large

1,006

3,653

Total Zip

Code

Ar

metropolitan areas would increase the number of HCV-eligible units in high-opportunity ZIP Code

areas. Exhibit 5 shows that more than 250,000 additional rental units would become available

in very-high opportunity ZIP Code areas and nearly 220,000 additional units would be gained

cent

34%

30%

25%

21%

18%

25%

in high-opportunity areas. On the other hand, SAFMRs would cause the number of HCV-eligible

Per

Gain in

Eligible

Units

units to decrease in all other ZIP Code areas, especially in very-low opportunity areas, which

would see a decrease of nearly 555,000 units. In most low-opportunity areas, the SAFMR would

be less than the metropolitan-wide FMR. As a result, units that rent for more than the SAFMR but

Total

Units

Below

FMR

935,981

956,235

1,317,638

1,165,498

1,491,319

5,866,670

less than the metro FMR would no longer qualify for the HCV program. On net, implementation of

SAFMR in large metropolitan areas would engender a decrease of more than 370,000 HCV-eligible

units, as the increase of 1,470,000 units in ZIP Code areas gaining units falls short of the decrease

Gaining Units

Total

Units

315,832

397,558

289,253

204,330

262,586

of 1,840,000 units in ZIP Codes losing units.

-Eligible Units by Opportunity Level and Net Change in HCV

Gained

1,469,559

The correlation between rent levels and opportunity is not perfect. Exhibit 5 shows that while most

eas

high opportunity ZIP Code areas would gain HCV-eligible units, some will lose them. Similarly,

907

741

566

626

1,248

4,088

most but not all low-opportunity areas would lose such units. For example, while 1,248 very-high

Total Zip

Code

Ar

opportunity ZIP Code areas would gain HCV-eligible units, 315,832 in total, 487 other very-high

opportunity areas would lose them (64,221). As a result of these divergent outcomes, very-high

opportunity ZIP Code areas would realize an estimated net gain of 251,611 additional HCV-eligible

units. At the other extreme, 1,066 very-low opportunity ZIP Code areas are projected to lose a total

eas Gaining or Losing HCV

of 817,280 HCV-eligible units, but this loss will be partially mitigated by a gain of 262,586 units

among 626 very-low opportunity ZIP Code areas.

eas

Exhibit 5

ZIP Code Ar

Combined Opportunity

Category of ZIP Code

Ar

Very High Opportunity

High Opportunity

Moderate Opportunity

Low Opportunity

Very Low Opportunity

Total

56 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

Exhibit 4

Opportunity Level for ZIP Code Areas by Dominant Racial and Ethnic Population in 2017 in

eas

cent

Net

-1%

-9%

-3%

Core-Based Statistical Areas with a Population Larger than 1 Million

19%

10%

Per

Change

Units

-11%

in Eligible

Dominant Racial or Ethnic Group in ZIP Code

Area (Percent)

White

Black

Other

Integrated All Areas

Non-

Non-

Non-

Hispanic

All ZIP Code Ar

Net

Units

251,611

217,485

-22,582

Hispanic

Hispanic

Hispanic

Change

in Eligible

-263,400

-554,694

-371,580

Combined

Very High

37

0

26

1

12

22

Opportunity

Opportunity

Category of

High

ZIP Code

25

3

25

2

19

20

cent

Opportunity

-16%

-20%

-23%

-22%

-23%

-22%

Areas

Loss in

Eligible

Units

Moderate

Per

20

4

14

5

22

19

Opportunity

Low

12

16

25

17

25

18

Opportunity

Very Low

Total

Units

Below

FMR

402,775

919,608

5

77

11

75

21

21

1,358,374

2,096,852

3,525,741

8,303,351

Opportunity

-Eligible Units

Total ZIP

100

100

100

100

100

100

Code Areas

Losing Units

Number of

3,604

509

73

553

3,029

7,768

Lost

-64,221

Areas

-180,073

-311,835

-467,730

-817,280

Total Units

-1,841,139

Change in HCV-Eligible Units by Opportunity Category

eas

487

621

721

818

True to the expectations of SAFMR’s architects, the implementation of SAFMRs across all large

1,006

3,653

Total Zip

Code

Ar

metropolitan areas would increase the number of HCV-eligible units in high-opportunity ZIP Code

areas. Exhibit 5 shows that more than 250,000 additional rental units would become available

in very-high opportunity ZIP Code areas and nearly 220,000 additional units would be gained

cent

34%

30%

25%

21%

18%

25%

in high-opportunity areas. On the other hand, SAFMRs would cause the number of HCV-eligible

Per

Gain in

Eligible

Units

units to decrease in all other ZIP Code areas, especially in very-low opportunity areas, which

would see a decrease of nearly 555,000 units. In most low-opportunity areas, the SAFMR would

be less than the metropolitan-wide FMR. As a result, units that rent for more than the SAFMR but

Total

Units

Below

FMR

935,981

956,235

1,317,638

1,165,498

1,491,319

5,866,670

less than the metro FMR would no longer qualify for the HCV program. On net, implementation of

SAFMR in large metropolitan areas would engender a decrease of more than 370,000 HCV-eligible

units, as the increase of 1,470,000 units in ZIP Code areas gaining units falls short of the decrease

Gaining Units

Total

Units

315,832

397,558

289,253

204,330

262,586

of 1,840,000 units in ZIP Codes losing units.

-Eligible Units by Opportunity Level and Net Change in HCV

Gained

1,469,559

The correlation between rent levels and opportunity is not perfect. Exhibit 5 shows that while most

eas

high opportunity ZIP Code areas would gain HCV-eligible units, some will lose them. Similarly,

907

741

566

626

1,248

4,088

most but not all low-opportunity areas would lose such units. For example, while 1,248 very-high

Total Zip

Code

Ar

opportunity ZIP Code areas would gain HCV-eligible units, 315,832 in total, 487 other very-high

opportunity areas would lose them (64,221). As a result of these divergent outcomes, very-high

opportunity ZIP Code areas would realize an estimated net gain of 251,611 additional HCV-eligible

units. At the other extreme, 1,066 very-low opportunity ZIP Code areas are projected to lose a total

eas Gaining or Losing HCV

of 817,280 HCV-eligible units, but this loss will be partially mitigated by a gain of 262,586 units

among 626 very-low opportunity ZIP Code areas.

eas

Exhibit 5

ZIP Code Ar

Combined Opportunity

Category of ZIP Code

Ar

Very High Opportunity

High Opportunity

Moderate Opportunity

Low Opportunity

Very Low Opportunity

Total

Cityscape 57

McClure and Schwartz

The gains and losses are not trivial in scale. The gains in program eligible units in the very high

and high opportunity areas are estimated to be 34 percent and 30 percent gains over the number

eas

cent

6%

of units eligible under the FMRs. The losses in program eligible units with the adoption of SAFMRs

Net

21%

-3%

-6%

Per

Change

Units

-14%

-13%

in Eligible

are estimated to range from 16 to 22 percent.

As noted earlier, we find that implementation of SAFMRs would yield a net decrease in the number

of HCV-eligible rental units. The loss of 371,580 program eligible units is about 1.5 percent of

All ZIP Code Ar

Net

65,492

33,908

Change

Units

-19,464

in Eligible

-126,984

-160,406

-207,455

the occupied rental stock in these large metropolitan areas and about 2.6 percent of the HCV

program eligible rental stock. This finding is consistent with the Final Evaluation of the SAFMR

Demonstration, which estimated that SAFMRs caused the number of HCV-eligible units in the seven

cent

participating PHAs to decrease by a total of 22,000 (3.4 percent). Most of this decrease occurred at

-17%

-24%

-26%

-27%

-24%

-25%

-Eligible Units by Opportunity

Per

Loss in

Eligible

Units

two sites (Dastrup et al., 2018). The NYU Furman Center, however, in its analysis of the potential

impact of SAFMRs in the 24 designated metropolitan areas, notes that the Final Rule authorizes

PHAs to adopt several strategies to diminish if not eliminate the loss of HCV-eligible rental units.

Total

70,602

These strategies include the ability to set payment standards at 110 percent of the SAFMR (thereby

Units

Below

FMR

278,439

386,502

680,088

892,555

2,308,185

increasing the number of eligible units), and if that is not sufficient, PHAs may obtain permission

from HUD to increase payment standards above 110 percent of SAFMR. The Final Rule also allows

Losing Units

PHAs to set payment standards for HCV recipients who remain in place at an amount up to the

Lost

-12,080

-67,026

-99,420

family’s current payment standard at the time SAFMRs were implemented (NYU Furman Center,

Total Units

-181,830

-217,643

-578,000

2018; Treat, 2018). Finally, it is also possible that some landlords would cut rents in response to

reduced payment standards, thereby mitigating the potential loss of HCV-eligible units.

eas

109

195

229

291

318

1,142

HUD assessed all metropolitan areas for inclusion within the SAFMR rulemaking. Only 24

Total Zip

Code

Ar

metropolitan areas met the specified criteria: (1) 2,500 or more vouchers under lease, (2) HCV

families are found to be 55 percent more likely to live in high poverty or low-income areas than

cent

renters in general, (3) 20 percent of the rental stock in ZIP Code areas had rents such that SAFMRs

33%

34%

26%

24%

16%

26%

Per

Gain in

Eligible

Units

are more than 110 percent of the metropolitan FMR, and (4) rental vacancy rate was above 4

s Selected SAFMR Markets Gaining or Losing HCV

percent. HUD believes these are areas where voucher holders are much worse off than renters

in general and are in markets where SAFMRs are likely to be useful. Fourteen of the 24 selected

Total

Units

Below

FMR

238,561

298,429

310,591

231,027

347,549

markets are among the 53 large metropolitan areas with populations of 1 million or more. Exhibit

1,426,157

6 repeats the estimation of rental units gained and lost for the 14 large markets selected by HUD

for implementation of the SAFMRs. The results for these 14 metropolitan areas are very similar to

eas Among HUD’

Gaining Units

the results, detailed in Exhibit 5, among all large markets. The ZIP Code areas that gained units

Total

Units

Gained

77,572

100,933

79,957

54,846

57,237

370,545

typically realized a 26-percent gain in units. The ZIP Code areas that lost units typically realized

a 25-percent loss of units. The result was a net loss in rental units eligible for participation in the

opolitan Ar

HCV program.

-Eligible Units

eas

278

251

225

171

162

1,087

ge Metr

Total Zip

Code

Ar

eas in the Lar

eas

Exhibit 6

ZIP Code Ar

Level and Net Change in HCV

Combined Opportunity

Category of ZIP Code

Ar

Very High Opportunity

High Opportunity

Moderate Opportunity

Low Opportunity

Very Low Opportunity

Total

58 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

The gains and losses are not trivial in scale. The gains in program eligible units in the very high

and high opportunity areas are estimated to be 34 percent and 30 percent gains over the number

eas

cent

6%

of units eligible under the FMRs. The losses in program eligible units with the adoption of SAFMRs

Net

21%

-3%

-6%

Per

Change

Units

-14%

-13%

in Eligible

are estimated to range from 16 to 22 percent.

As noted earlier, we find that implementation of SAFMRs would yield a net decrease in the number

of HCV-eligible rental units. The loss of 371,580 program eligible units is about 1.5 percent of

All ZIP Code Ar

Net

65,492

33,908

Change

Units

-19,464

in Eligible

-126,984

-160,406

-207,455

the occupied rental stock in these large metropolitan areas and about 2.6 percent of the HCV

program eligible rental stock. This finding is consistent with the Final Evaluation of the SAFMR

Demonstration, which estimated that SAFMRs caused the number of HCV-eligible units in the seven

cent

participating PHAs to decrease by a total of 22,000 (3.4 percent). Most of this decrease occurred at

-17%

-24%

-26%

-27%

-24%

-25%

-Eligible Units by Opportunity

Per

Loss in

Eligible

Units

two sites (Dastrup et al., 2018). The NYU Furman Center, however, in its analysis of the potential

impact of SAFMRs in the 24 designated metropolitan areas, notes that the Final Rule authorizes

PHAs to adopt several strategies to diminish if not eliminate the loss of HCV-eligible rental units.

Total

70,602

These strategies include the ability to set payment standards at 110 percent of the SAFMR (thereby

Units

Below

FMR

278,439

386,502

680,088

892,555

2,308,185

increasing the number of eligible units), and if that is not sufficient, PHAs may obtain permission

from HUD to increase payment standards above 110 percent of SAFMR. The Final Rule also allows

Losing Units

PHAs to set payment standards for HCV recipients who remain in place at an amount up to the

Lost

-12,080

-67,026

-99,420

family’s current payment standard at the time SAFMRs were implemented (NYU Furman Center,

Total Units

-181,830

-217,643

-578,000

2018; Treat, 2018). Finally, it is also possible that some landlords would cut rents in response to

reduced payment standards, thereby mitigating the potential loss of HCV-eligible units.

eas

109

195

229

291

318

1,142

HUD assessed all metropolitan areas for inclusion within the SAFMR rulemaking. Only 24

Total Zip

Code

Ar

metropolitan areas met the specified criteria: (1) 2,500 or more vouchers under lease, (2) HCV

families are found to be 55 percent more likely to live in high poverty or low-income areas than

cent

renters in general, (3) 20 percent of the rental stock in ZIP Code areas had rents such that SAFMRs

33%

34%

26%

24%

16%

26%

Per

Gain in

Eligible

Units

are more than 110 percent of the metropolitan FMR, and (4) rental vacancy rate was above 4

s Selected SAFMR Markets Gaining or Losing HCV

percent. HUD believes these are areas where voucher holders are much worse off than renters

in general and are in markets where SAFMRs are likely to be useful. Fourteen of the 24 selected

Total

Units

Below

FMR

238,561

298,429

310,591

231,027

347,549

markets are among the 53 large metropolitan areas with populations of 1 million or more. Exhibit

1,426,157

6 repeats the estimation of rental units gained and lost for the 14 large markets selected by HUD

for implementation of the SAFMRs. The results for these 14 metropolitan areas are very similar to

eas Among HUD’

Gaining Units

the results, detailed in Exhibit 5, among all large markets. The ZIP Code areas that gained units

Total

Units

Gained

77,572

100,933

79,957

54,846

57,237

370,545

typically realized a 26-percent gain in units. The ZIP Code areas that lost units typically realized

a 25-percent loss of units. The result was a net loss in rental units eligible for participation in the

opolitan Ar

HCV program.

-Eligible Units

eas

278

251

225

171

162

1,087

ge Metr

Total Zip

Code

Ar

eas in the Lar

eas

Exhibit 6

ZIP Code Ar

Level and Net Change in HCV

Combined Opportunity

Category of ZIP Code

Ar

Very High Opportunity

High Opportunity

Moderate Opportunity

Low Opportunity

Very Low Opportunity

Total

Cityscape 59

McClure and Schwartz

Change in HCV-Eligible Units by Opportunity and Racial/Ethnic Category

As would be expected given the paucity of predominantly Black and Hispanic ZIP Code areas that

are classified as high or very-high opportunity, nearly all of the growth in HCV program-eligible

units would occur in ZIP Code areas that are either predominantly White or that are integrated.

Exhibit 7 shows that of the 252,000 additional HCV-eligible units that would be gained in very-

high opportunity ZIP Code areas, 97 percent would be located in predominantly White (53

percent) or integrated (44 percent) areas. Similarly, 94 percent of the additional HCV program-

eligible units in high-opportunity ZIP Code areas would also be located in White and integrated

areas—although integrated ZIP Code areas would account for most of the increase (87 percent).

Exhibit 7

Aggregate Gain or Loss of Rental Units Eligible to Participate in the HCV Program in ZIP Code

Areas Within Core-Based Statistical Areas with a Population Larger than 1 Million by Dominant

Racial and Ethnic Population and Opportunity Level

Opportunity Level

Net Gain

Very High

High

Moderate

Low

Very Low

or Loss

Predominant White Non-Hispanic

133,011

16,279

-45,246

-30,310

-13,639

60,095

Racial or

(75 Percent or More)

Ethnic

Black Non-Hispanic

Group in ZIP

73

1,158

-2,206

-18,202

-60,173

-79,349

(50 Percent or More)

Code Area

Other Non-Hispanic

4,753

2,117

3,950

-1,150

-2,743

6,928

(50 Percent or More)

Hispanic of Any Race

1,840

7,870

2,273

-11,064

-228,566

-227,647

(50 Percent or More)

Integrated

111,933

190,061

18,647

-202,675

-249,573

-131,606

All ZIP Code Areas

251,611

217,485

-22,582

-263,400

-554,694

-371,580

Whereas exhibit 7 shows that nearly all of the increase in HCV-eligible units in high- and very-high

opportunity ZIP Code areas would occur either in White or integrated areas, exhibit 8 arrives at the

same finding from a different perspective. Here, high- and very-high-opportunity ZIP Code areas

that are projected to see an increase in HCV-eligible units are sorted by their dominant racial/ethnic

group, as are the low- and very-low-opportunity areas that are projected to lose eligible units. It

shows that 65 percent of the high-opportunity ZIP Code areas projected to gain HCV-eligible units

are predominantly White and 33 percent are integrated. Less than 1 percent are predominantly

Black, and only 1 percent are Hispanic. Conversely, the exhibit also shows that 31 percent of the

low- or very-low-opportunity ZIP Code areas that would lose HCV-eligible units are predominantly

Black or Hispanic; predominantly White ZIP Code areas constitute 20 percent of all areas projected

to lose units, integrated areas, 48 percent. In sum, while White and integrated ZIP Code areas

account for virtually all of the high-and very-high-opportunity ZIP Code areas that would gain

HCV-eligible units, low- and very-low-opportunity areas that would lose eligible units are more

evenly divided across the racial categories.

60 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

Exhibit 8

Racial and Ethnic Composition of ZIP Code Areas Gaining or Losing HCV Program Eligible Units

by Opportunity Level

Exhibits 6–8 examine the distribution of ZIP Code areas that would gain and lose HCV-eligible

units on an aggregate level for all 53 metropolitan areas with populations of 1 million or more.

Exhibit 9 focuses on the change in HCV-eligible units in high- and very-high-opportunity ZIP

Code areas within each metropolitan area. It shows that 45 metropolitan areas’ high- and very-

high-opportunity ZIP Codes would experience an increase in eligible units and 8 would lose units.

In all but 7 of the 45 metropolitan areas with high- or very-high-opportunity ZIP Codes that would gain

HCV-eligible units, 95 percent or more of these ZIP Codes are either predominantly White or integrated.

In four metropolitan areas (Los Angeles, San Francisco, San Jose, and San Diego), ZIP Code areas that

are predominantly populated by Asian or other racial groups account for 6 percent or more of the gain.

In two metropolitan areas, Miami and San Antonio, predominantly Hispanic high-opportunity areas

account for 28 and 63 percent, respectively, of the total increase in HCV-eligible units.

Cityscape 61

McClure and Schwartz

cent in

1%

0%

0%

0%

0%

0%

0%

28%

0%

0%

0%

0%

0%

0%

0%

63%

Per

Hispanic

cent in

0%

6%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Other

-9%

17%

Per

cent

1

0

1

1

1

1

1

0

1

1

1

1

1

1

0

0

Per

White or

> 95%

Integrated

eas by Racial/Ethnic Composition

cent in

easAr

99%

94%

109%

98%

100%

100%

100%

72%

100%

95%

100%

100%

100%

100%

83%

37%

Per

White or

Integrated

eas

9,481

8,385

7,729

7,443

6,476

6,279

5,968

All ZIP

Code

Ar

113,518

101,203

30,502

22,088

18,496

17,445

15,070

14,842

13,812

eas

719

-461

Ar

3,944

2,819

9,418

6,158

9,878

7,792

1,646

4,454

1,966

Integrated

ZIP Code

73,173

75,120

29,770

20,979

14,730

0

0

0

0

0

0

0

0

5

0

0

0

eas (1 of 3)

eas

219

ery High Opportunity ZIP Code Ar

Ar

Hispanic

1,525

4,174

3,785

ZIP Code

0

0

0

0

0

0

0

0

0

0

0

0

eas

121

Other

Non-

Ar

5,579

1,097

Hispanic

ZIP Code

-2,848

eas

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Black

Non-

Ar

405

429

Hispanic

ZIP Code

Change in Units in High and V

eas

704

339

-826

-67

728

217

ery High Opportunity ZIP Code Ar

White

Non-

Ar

3,580

1,250

7,654

7,667

5,797

6,937

Hispanic

ZIP Code

38,698

20,285

14,552

14,626

d,

,

d, FL MSA

est Palm Beach,

n, MI MSA

owson, MD MSA

dale-W

ea

oodlands-Sugar Land,

orth-Arlington, TX MSA

en-Dearbor

A MSA

arr

e-Columbia-T

opolitan Ar

ork-Newark-Jersey City

A-MD-WV MSA

-NJ-P

oit-W

ashington-Arlington-Alexandria,

Metr

New Y

NY

Los Angeles-Long Beach-Anaheim,

CA MSA

Dallas-Fort W

W

DC-V

Minneapolis-St. Paul-Bloomington,

MN-WI MSA

Philadelphia-Camden-Wilmington,

PA-NJ-DE-MD MSA

Houston-The W

TX MSA

Miami-Fort Lauder

FL MSA

Detr

Baltimor

St. Louis, MO-IL MSA

Orlando-Kissimmee-Sanfor

Cleveland-Elyria, OH MSA

Indianapolis-Carmel-Anderson, IN MSA

San Francisco-Oakland-Haywar

CA MSA

San Antonio-New Braunfels, TX MSA

ge CBSAs by Change in Units in High and V

Exhibit 9

Lar

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

62 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

cent in

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Per

Hispanic

cent in

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Other

47%

Per

cent

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

1

1

Per

White or

> 95%

Integrated

eas by Racial/Ethnic Composition

cent in

easAr

100%

100%

99%

100%

100%

100%

100%

100%

100%

100%

100%

100%

53%

100%

100%

100%

100%

100%

Per

White or

Integrated

eas

All ZIP

Code

Ar

5,903

5,215

5,084

4,986

4,972

4,910

4,901

4,300

4,026

3,908

3,879

3,586

3,438

3,000

2,818

2,613

2,570

2,400

0

0

0

eas

560

562

117

976

34

Ar

4,764

3,644

2,108

4,528

2,359

3,690

4,566

-892

4,460

2,437

Integrated

ZIP Code

eas (2 of 3)

eas

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

ery High Opportunity ZIP Code Ar

Ar

Hispanic

ZIP Code

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

eas

Other

Non-

Ar

Hispanic

1,621

ZIP Code

eas

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Black

Non-

Ar

47

Hispanic

ZIP Code

Change in Units in High and V

eas

382

218

-687

176

ery High Opportunity ZIP Code Ar

White

Non-

Ar

1,139

5,215

1,393

4,426

2,864

4,901

3,739

1,667

4,478

2,882

2,818

1,594

2,366

Hispanic

ZIP Code

-2,644

A MSA

cade,

o-

, FL MSA

-IN MSA

o, OR-W

, KY

den-Ar

eesbor

-Hillsbor

g-Clearwater

-IN MSA

ea

, OK MSA

A MSA

A MSA

arwick, RI-MA MSA

ferson County

, UT MSA

ancouver

, NY MSA

gh, P

opolitan Ar

ginia Beach-Norfolk-Newport News,

ovidence-W

falo-Cheektowaga-Niagara Falls, NY

Metr

Sacramento-Roseville-Ar

CA MSA

Pittsbur

Richmond, V

Austin-Round Rock, TX MSA

Portland-V

Memphis, TN-MS-AR MSA

Nashville-Davidson-Murfr

Franklin, TN MSA

Cincinnati, OH-KY

Oklahoma City

Atlanta-Sandy Springs-Roswell, GA MSA

Vir

VA-NC MSA

Pr

San Diego-Carlsbad, CA MSA

Buf

MSA

Louisville/Jef

Tampa-St. Petersbur

Rochester

Salt Lake City

ge CBSAs by Change in Units in High and V

Exhibit 9

Lar

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

Cityscape 63

McClure and Schwartz

cent in

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Per

Hispanic

cent in

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Other

269%

Per

cent

1

0

1

1

1

1

1

0

1

1

1

1

1

1

1

1

1

1

1

Per

White or

> 95%

Integrated

eas by Racial/Ethnic Composition

cent in

easAr

100%

69%

100%

100%

100%

100%

100%

100%

99%

100%

100%

100%

100%

100%

100%

100%

97%

100%

Per

White or

Integrated

-169%

eas

994

813

715

508

484

457

456

179

-722

-863

-952

All ZIP

Code

Ar

2,059

1,239

1,144

-1,104

-1,318

-1,363

-1,517

-2,099

71

90

0

eas

391

162

275

Ar

1,102

1,437

4,620

-837

-515

-104

-1,052

1,594

2,676

-2,300

-1,256

2,057

Integrated

ZIP Code

-2,198

eas (3 of 3)

eas

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

ery High Opportunity ZIP Code Ar

Ar

Hispanic

ZIP Code

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

eas

Other

Non-

Ar

Hispanic

ZIP Code

1,300

0

0

0

0

0

0

0

0

6

0

0

0

0

0

0

0

0

eas

-44

Black

Non-

Ar

389

Hispanic

ZIP Code

Change in Units in High and V

eas

-252

-293

421

553

233

236

361

115

-863

-62

-848

ery High Opportunity ZIP Code Ar

White

Non-

Ar

1,988

2,655

1,196

Hispanic

ZIP Code

-3,626

-1,415

-3,629

-3,530

-1,995

d, CT

A MSA

est Allis, WI MSA

d-East Hartfor

, AL MSA

dino-Ontario, CA MSA

ea

d-Gastonia, NC-SC MSA

nar

aukesha-W

est Hartfor

ora-Lakewood, CO MSA

, MO-KS MSA

acoma-Bellevue, W

d-W

-Aur

opolitan Ar

egas-Henderson-Paradise, NV MSA

Metr

Charlotte-Concor

Hartfor

MSA

Tucson, AZ MSA

Boston-Cambridge-Newton, MA-NH MSA

Birmingham-Hoover

Raleigh, NC MSA

Las V

San Jose-Sunnyvale-Santa Clara, CA MSA

Jacksonville, FL MSA

New Orleans-Metairie, LA MSA

Phoenix-Mesa-Scottsdale, AZ MSA

Milwaukee-W

Grand Rapids-Wyoming, MI MSA

Seattle-T

Denver

Riverside-San Ber

Columbus, OH MSA

Chicago-Naperville-Elgin, IL-IN-WI MSA

Kansas City

ge CBSAs by Change in Units in High and V

Exhibit 9

Lar

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

64 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

Gain and Loss of HCV-Eligible Units in Integrated

ZIP Code Areas

We have seen that, if SAFMRs were adopted, nearly all of the projected increase in HCV-eligible units

found in high- and very-high-opportunity ZIP Code areas would occur either in predominantly

White or integrated areas. Integrated ZIP Code areas, however, also account for nearly half of all low-

and very-low opportunity areas that would lose units. Indeed, integrated ZIP Code areas would see an

overall loss of HCV-eligible units while White ZIP Code areas would post a net gain.

A key reason for this difference is that a much higher proportion (46 percent) of integrated ZIP

Codes fall in the low- and very-low opportunity categories—which are most likely to lose HCV-

eligible units—compared with predominantly White areas (17 percent). Moreover, only 12 percent

of all integrated ZIP Code areas fall in the very-high opportunity category, which would gain the

most HCV-units. In contrast, more than 37 percent of all White ZIP Code areas are in the very-high

opportunity group (see exhibit 4).

The integrated category covers a large and varied assortment of ZIP Code areas. It accounts for 38

percent of the 8,763 ZIP Code areas in large metropolitan areas, second only to predominantly

White ZIP Code areas, which account for 48 percent of the total. As noted earlier, integrated ZIP

areas are defined as those in which non-Hispanic Whites make up less than 75 percent of the

population and all other racial and ethnic groups comprise less than 50 percent.

To shed more light on the impact of SAFMRs on integrated ZIP Code areas, Exhibit 9 partitions

them into two categories: Majority White areas where non-Hispanic Whites constitute 50 to 75

percent of the population, and minority White areas where they account for less than 50 percent.

About two-thirds (65 percent) of all integrated ZIP Code areas are majority White.

The two subgroups of integrated ZIP Code areas diverge sharply in their representation within

the high- and very-high opportunity categories. Whereas 41 percent of all majority White

integrated areas are classified as high- or very high-opportunity (compared with 62 percent of all

predominantly White areas), the same is true for just 16 percent of all minority White areas. While

17 percent of all majority White integrated areas rank in the very-high-opportunity category, only

4 percent of all minority White areas fall in this category. Conversely, 36 percent of all majority

White integrated ZIP Code areas are in the low- and very-low-opportunity categories, as against 64

percent of all minority White areas.

Cityscape 65

McClure and Schwartz

Exhibit 10

Integrated Zip Code Areas by Majority-White and Minority-White by Opportunity Level

Majority White

Minority White

All Integrated

(50-75%)

(<50%)

ZIP Code

ZIP Code

ZIP Code

Percent

Percent

Percent

Areas

Areas

Areas

Very High

340

17%

37

4%

377

12%

Opportunity

High

468

24%

122

12%

590

19%

Opportunity

Moderate

453

23%

224

21%

677

22%

Opportunity

Low

448

23%

313

30%

761

25%

Opportunity

Very Low

266

13%

358

34%

624

21%

Opportunity

Total

1,975

100%

1,054

100%

3,029

100%

Partly as a result of these differences, more than one-half (55 percent) of all majority White ZIP

Code areas that are projected to gain HCV-eligible units are classified as a high- or very-high-

opportunity, compared to 23 percent of all minority White areas (see exhibit 11). Conversely,

23 percent of all majority White ZIP Code areas projected to gain HCV-eligible units are low- or

very-low opportunity, compared with 50 percent of all minority White areas. While both majority-

White and minority-White ZIP Code areas are projected to see net losses in HCV-eligible units with

SAFMRs, the latter account for 96 percent of this loss.

Exhibit 11

Increases and Decreases in HCV Program-Eligible Units in Integrated Zip Code Areas by Majority

White and Minority White

Areas Gaining HCV-Eligible Units

Areas Losing HCV-Eligible Units

Majority Number Minority Number

Majority Number Minority Number

White

of Areas

White

of Areas

White

of Areas

White

of Areas

Very High

110,635

283

19,041

32

-16,270

55

-1,472

5

Opportunity

High

220,558

302

57,055

88

-75,688

163

-11,864

34

Opportunity

Moderate

136,724

232

90,713

141

-148,877

219

-59,912

82

Opportunity

Low

60,136

167

80,393

138

-205,957

281

-137,247

175

Opportunity

Very Low

22,807

81

60,866

124

-109,136

184

-224,111

234

Opportunity

66 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

Conclusions

SAFMRs have the potential to make housing located in high “opportunity” neighborhoods

substantially more available to HCV recipients. In metropolitan areas with populations of 1 million

or more, nearly one-half million additional units in very-high- and high-opportunity ZIP Code

areas would become eligible for the HCV program if SAFMRs were adopted. This potential is

unlikely to be realized, however, if governments and nonprofit organizations do not also address

the barriers of racial discrimination and segregation.

This article shows that the great majority of ZIP Code areas that fall in the top two “opportunity”

quintiles are predominantly White or integrated. Only 3 percent of all predominantly Black ZIP

Code areas rank as very high- or high-opportunity (and only one of 509 Black ZIP Codes is in the

top opportunity quintile), as do 3 percent of all predominantly Hispanic ZIP Codes. About three-

fourths of all predominantly Black and Hispanic ZIP Codes sit in the lowest opportunity quintile.

The concentration of “opportunity” within predominantly White and integrated ZIP Code areas

means that if an HCV recipient wishes to live in an opportunity neighborhood, he or she would

essentially need to choose between White and integrated areas. At present, about one-half of all

HCV recipients in large metropolitan areas reside in integrated ZIP Code areas. Most of the rest live

in segregated areas dominated by people of their own race or ethnicity and are highly unlikely to

benefit from SAFMRs unless they move out of a segregated neighborhood.

Predominantly White ZIP Code areas stand to benefit the most from SAFMRs. They, along with the

much smaller category of other non-Hispanic ZIP Code areas, are the only ones estimated to post

net gains in HCV-eligible units. Predominantly White ZIP Code areas are especially well positioned

to gain HCV-eligible units in very-high-opportunity areas. Integrated ZIP Code areas also stand

to gain many HCV program-eligible units in opportunity areas; indeed, they would gain more

units than would predominantly White areas in very-high- and high-opportunity ZIP Code areas

combined. Unlike predominantly White ZIP Code areas, however, integrated areas are also likely to

see large decreases in HCV-eligible units. Much of this decrease is due to the fact that integrated ZIP

Code areas encompass many more low- and very-low-opportunity areas than their predominantly

White counterparts. For example, while 21 percent of all integrated ZIP Code areas are in the

bottom opportunity quintile, the same is true of just 5 percent of all predominantly White ZIP

Code areas. Thus, of ZIP Code areas that are projected to gain HCV program-eligible units, 65

percent are predominantly White, the same is true for just 33 percent of integrated ZIP Code areas

projected to gain program-eligible units.

We estimate that SAFMRs would result in a net loss of HCV program-eligible units, with most of

this loss occurring in low-opportunity ZIP Code areas. It is important to emphasize, however, that

this potential loss could be reduced or prevented through the various strategies included in HUD’s

Final Rule of 2016 for instituting SAFMRs in 24 metropolitan areas (NYU Furman Center, 2018;

Treat, 2018). Even if SAFMRs were implemented so as to minimize the loss of HCV-eligible units

in low-opportunity and other areas, these measures would do little if anything to address the racial

barriers that make it very difficult for many Black and Hispanic HCV recipients to access high-

opportunity neighborhoods.

Cityscape 67

McClure and Schwartz

We conclude that while SAFMRs may be necessary to improve the ability of the HCV program to

reach high-opportunity neighborhoods, they are not sufficient. In order to realize its potential,

more will need to be done to help Black and Hispanic HCV recipients learn about predominantly

White and integrated neighborhoods and their housing opportunities. Most likely, PHAs and

their nonprofit partners would need to provide transportation assistance and other forms

of support to help HCV recipients in segregated low-income communities find housing in

opportunity neighborhoods and to provide services to help remain in their new neighborhoods.

Housing counseling and case management will need to be enhanced. The Baltimore Housing

Mobility Program, established in the late 1990s as part of a court-ordered consent decree from

a fair housing lawsuit ( Thompson v. HUD),4 demonstrates the value of “emotionally supportive

counseling, housing search assistance, and landlord recruitment” in successfully encouraging

HCV recipients to relocate to high-opportunity neighborhoods (DeLuca and Rosenblatt, 2017).

Greater outreach to landlords will be needed. Expanding the potential supply of HCV program

eligible units will mean little if landlords continue to resist participation in the program. This

resistance can be overcome through education and incentivizing participation. If the SAFMR

program is to realize its potential and if the HCV program is to affirmatively further fair housing,

the SAFMR program will require significant improvements.

Authors

Kirk McClure is a professor of urban planning at the University of Kansas’ School of Public Affairs

and Administration.

Alex Schwartz is professor of urban policy at the New School’s Milano School of Policy,

Management, and Environment.

References

Dastrup, Samuel, Meryl Finkel, Kimberly Burnett, and Tanya de Sousa. 2018. Small Area Fair

Market Rent Demonstration Evaluation: Final Report. Report prepared by Abt Associates for the U.S.

Department of Housing and Urban Development (HUD). huduser.gov/portal/publications/Small-

Area-FMR-Evaluation-Final-Report.html.

DeLuca, Stefanie, and Peter Rosenblatt. 2017. “Walking Away from the Wire: Housing Mobility and

Neighborhood Opportunity in Baltimore,” Housing Policy Debate 27 (4): 519–546.

Matthew, Teresa. 2017. “The Fight Over Fair Housing Goes to Court (Again),” City Lab, November 7.

https://www.citylab.com/equity/2017/11/the-fight-over-fair-housing-goes-to-court-again/544826/.

NYU Furman Center. 2018. How Do Small Area Fair Market Rents Affect the Location and Number of

Units Affordable to Voucher Holders? New York: New York University Furman Center.

https://furmancenter.org/files/NYUFurmanCenter_SAFMRbrief_5JAN2018_1.pdf.

4 Thompson v. HUD, 04-1535 (4th Cir. 2005).

68 Small Area Fair Market Rents

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

Reina, Vincent, Arthur Acolin, and Rafael Bostic. 2018. “Do Small Area Fair Market Rent Limits

Increase Access to Opportunity Neighborhoods by Housing Choice Voucher Recipients? An Early

Evaluation,” Housing Policy Debate 29 (1): 44–61

Schwartz, Alex, Kirk McClure, and Lydia B. Taghavi. 2016. “Vouchers and Neighborhood Distress:

The Unrealized Potential for Families With Housing Choice Vouchers To Reside in Neighborhoods

With Low Levels of Distress,” Cityscape 18 (3): 207.

Treat, John. 2018. “Establishing a More Effective SAFMR System: The Cost and Benefits of HUD’s

2016 Small Area Fair Market Rent Rule,” University of Michigan Journal of Law Reform 51 (3): 643–668.

U.S. Department of Housing and Urban Development (HUD). 2017. “AFFH Data and Mapping

Tool. ” hudexchange.info/resource/4867/affh-data-and-mapping-tool/.

———. 2016. “Establishing a More Effective Fair Market Rent System; Using Small Area Fair

Market Rents in the Housing Choice Voucher Program Instead of the Current 50th Percentile

FMRs,” Federal Register 81: 221: 80567- 80587. huduser.gov/portal/datasets/fmr/fmr2016f/

SAFMR-Final-Rule.pdf.

Cityscape 69

70 Small Area Fair Market Rents

by Judy Geyer, Samuel Dastrup, and Meryl Finkel

Impact of Expanded Choice

on Attrition in the Housing

Voucher Program

Judy Geyer

Samuel Dastrup

Meryl Finkel

Abt Associates Inc.

Abstract

This paper examines whether expanding neighborhood choice by adding a more localized approach to

setting the rental payment standard affects length-of-stay in the U.S. Housing Choice Voucher (HCV)

program. Payment standards are typically constant within a metropolitan area, rather than small

geographic areas such as ZIP Codes. Using data from the Small Area Fair Market Rent (SAFMR)

Demonstration by the U.S. Department of Housing and Urban Development (HUD), we observed

changes in program exit rates over time in housing agencies adopting ZIP-Code-based payment

standards and compare these with changes in exit rates in programs that continued under metropolitan-area-based standards. We expand the analysis by looking at subgroups, specifically households with

children, seniors, adults with disabilities, and residents in lower, average, or higher rent neighborhoods.

We find that the introduction of SAFMRs increased program attrition, with exit rates that imply about a 2-year reduction in the median length of program participation (from a base of about 11 years).

Effects are largest among working-age adults and in households living in lower- and moderate-rent areas at the time of program introduction. We conclude with a discussion of how our findings on program

attrition and housing independence inform recent proposals to adopt more flexible payment standards or increase public housing agencies (PHAs) authority to change payment standards under Moving to Work

(MTW) authority.

Introduction

The Small Area Fair Market Rent (SAFMR) Demonstration by the U.S. Department of Housing and

Urban Development (HUD) set out to determine whether replacing metropolitan-area Fair Market

Rents (FMRs) with ZIP-Code-based SAFMRs increased voucher holders’ access to and location in

higher-opportunity neighborhoods. Evidence to date finds that, on average, switching to SAFMRs

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 71

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

Geyer, Dastrup, and Finkel

makes voucher holders more likely to locate in higher-opportunity neighborhoods (Dastrup, et

al., 2018; Reina, Acolin, and Bostic, 2018; Collinson and Ganong, 2018), particularly among

households with children (Dastrup, Ellen, and Finkel, 2019). There is not yet evidence, however,

on whether introducing SAFMRs affects voucher holders’ length of participation in the voucher

program; that is, whether maintaining subsidized tenancy over time is affected by the introduction

of SAFMRs. This article studies how the replacement of metropolitan-area rent standards with

ZIP-Code-level rent standards affects the average length of stay in the Housing Choice Voucher

(HCV) program.

Leaving the voucher program can represent a positive development in a household’s self-sufficiency

and economic well-being if it is the result of increases in household income. Although there are

no comprehensive data sets reliably detailing reasons for exit from the HCV program, a common

perception is that most exits are not associated with incomes so high as to make the voucher

recipient income ineligible or even to make the net subsidy so small as to make it not worth the

administrative requirements of the program (Gubits, Khadduri, and Turnham, 2009; Lubell,

Shroder, and Steffen, 2003). Rather, program exit typically represents a substantial loss in resources

that the household continues to need.1 Many factors may contribute to program attrition other

than income: for example, tenant-landlord relationships, quality of the unit and neighborhood,

availability of local informal welfare networks (family, friends), moves to nursing homes, death, and

proximity to employment.

SAMFRs may interact with many of these factors that affect a household’s continued receipt of

assistance. Relative to the metropolitan-area-wide FMRs, SAFMRs result in lower maximum

subsidies in neighborhoods with prevailing rents lower than the metropolitan-area-wide FMR,

and SAFMRs result in higher maximum subsidies in neighborhoods with prevailing rents above

the metropolitan-area-wide FMR. This may make the program less attractive to landlords and

voucher recipients in lower rent neighborhoods, where a relatively large share of voucher holders

has historically been located. Conversely, this may make the program more attractive to landlords

and voucher recipients in higher rent neighborhoods. Different subsidy caps in different ZIP Codes

within a metropolitan area also represent added complexity for both voucher holders and landlords

participating in the program. This added complexity may result in shorter program participation,

for example by making it more difficult for tenants to understand the applicable payment standard

when searching apartment listings for a new apartment, or by increasing landlords’ costs of

navigating the program.

In this article, we estimate the effect of the introduction of SAFMRs on the length of participation

in the HCV program. We estimate Weibull survival models on a large longitudinal administrative

data set representing approximately one million program participants in 145 public housing

agencies (PHAs), from 1995 through 2017. We find that the introduction of SAFMR increased

program attrition, particularly among working-age adults and in households living in low- and

average-rent areas at the time of program introduction. Our estimated model implies exit rates

1 A family with one full-time worker earning minimum wage cannot afford the local FMR for a two-bedroom

apartment anywhere in the United States (HUD, 2018). A recent study finds that for every 100 families with incomes less than 30 percent of the area median income, there are only 22 affordable units that are not already occupied by a higher income household or a household with children (Airgood-Obrycki and Molinsky, 2019).

72 Small Area Fair Market Rents

Impact of Expanded Choice on Attrition in the Housing Voucher Program

over time that would cumulatively result in about a 2-year reduction in median length of program

participation (from a base of about 11 years). Effects are largest particularly among working-

age adults and in households living in lower and moderate rent areas at the time of program

introduction. These findings highlight the need for additional research to determine the factors

resulting in program attrition.

Literature Review and the SAFMR Study

Previous studies have shown that length of stay in the voucher program varies by several household

characteristics. Senior heads of household are less likely to leave the program than households

headed by a nonsenior person; households headed by people with disabilities are less likely to

leave than households headed by people without disabilities; African-Americans are less likely to

leave than Whites; and women are less likely to leave than men (McClure, 2017; Lubell, Shroder,

and Steffen, 2003). Households with infants or toddlers are less likely to leave the voucher

program, but households with older children are more likely to leave (Cortes, Lam, and Fein,

2008). Our analysis controls for all these household characteristics when testing whether a policy

change affects the probability of a household leaving the program and examines whether SAFMR

affects program attrition among certain demographic groups compared with others.

Household income also influences attrition in the housing voucher program but less than one

might expect. The most positive reason for leaving the voucher program is through increased

financial self-sufficiency in the form of improved earnings. Only one-third of all participants

leaving the housing voucher program have earnings, however, and income is not a good

predictor of exit (Olsen et al., 2005; McClure, 2017). HUD cannot track a household’s income

after they leave the program, so most previous studies (and this current one) have imperfect

income measurement. One way SAFMR policy may affect attrition is through access to better

neighborhoods. If SAFMR policy can improve access to higher opportunity neighborhoods, we

might expect participants to improve financial self-sufficiency and for this to be associated with a

greater likelihood of leaving the program. The Moving to Opportunity study found mixed evidence

on how moving to higher opportunity neighborhoods affected the employment and earnings of

adults (Sanbonmatsu et al., 2011; Turner, Nichols, and Comey, 2012).2

Patterns of exit in the SAFMR Demonstration households are similar to those found in other

studies. The analysis of the SAFMR Demonstration included five Demonstration PHAs and two

PHAs in the Dallas area where SAFMRs were imposed due to a legal settlement; we will refer to

these seven PHAs collectively as “the seven SAFMR PHAs”. Exhibit 1 compares the distribution

of household characteristics in the seven SAFMR PHAs prior to the switch from FMR to SAFMR,

with the distribution of household characteristics of households that exited the program at some

point between the introduction of SAFMR and December 31, 2017. Using simple t-tests (Chi-

square tests for categorical variables), households that eventually left the program have different

characteristics compared with the full sample of households served by the program (p < .001 in

all cases). Households that eventually exited the program are less likely to be headed by a woman,

2 Chetty, Hendren, and Katz (2016) found that the future earnings of children is positively impacted by moving to lower poverty neighborhoods, but this evidence is too indirect to suggest a strong association between SAFMR and an increased rate of exit due to improved tenant self-sufficiency.

Cityscape 73

Geyer, Dastrup, and Finkel

less likely to have children of any age, more likely to be White (than African-American), more

likely to be Hispanic, less likely to be disabled, and more likely to be living in a neighborhood with

average or higher than average rent. Households that left the program are more likely to be senior

(we do not have sufficient detail to know how many of these exits are due to death) and to have a

higher income.

Exhibit 1

Characteristics of SAFMR PHA Program Participants: At Time of Intervention Introduction and at

Time of Exit

Mean at Intervention

Mean at Exit, Among Leavers

Characteristic

Introduction (%)

(%)

Female

85.9

84.1

Senior

18.5

22.7

No Kids

40.7

53.9

Kids Under Age 5

23.2

11.1

Kids Age 5 to 12

42.4

28.6

Kids Age 13 to 17

37.0

27.8

White

19.4

22.4

African-American

75.3

73.3

Hispanic

8.9

10.5

People with Disabilities

36.7

37.0

Lives in Neighborhood With

39.2

38.2

Average Rent Ratio <90% FMRa

Lives in Neighborhood With

Average Rent Ratio Between 90%

44.1

44.2

and 110% of FMRa

Lives in Neighborhood With

16.6

17.5

Average Rent Ratio >1.1a

Any Income

99.0

98.4

Average Income

$17,734

$20,624

N

48,819

19,793

FMR = Fair Market Rent. PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

a Sample sizes are 48,134 and 19,479 due to missing data on rent ratios.

Notes: This analysis includes only households participating in the voucher program at the time of the introduction of SAFMR policy. The difference in the means of each row is statistically significant (p<.001, using Chi-squared tests for binary variables and t-tests for continuous variables). The categories “kids under age 5,” “kids age 5 to 12,” and “kids age 13 to 17” are not mutually exclusive, as households have can children across a wide age range.

Source: SAFMR Demonstration Data, SAFMR PHAs only

In addition to household characteristics, some studies have found that neighborhood features are

associated with attrition in the voucher program. Ambrose (2005) found that greater neighborhood

poverty is associated with a lower probability of leaving the voucher program, but there is no

information on why this association arises. Olsen, Davis, and Carillo (2005) find that greater

vacancy rates are associated with lower probability of leaving the program, but, again, the reason

for this association is not clear. Under SAFMR, higher poverty neighborhoods become less

attractive because of the PHA’s lower payment standards in those neighborhoods, forcing either

74 Small Area Fair Market Rents

Impact of Expanded Choice on Attrition in the Housing Voucher Program

landlords to accept lower rents or program participants to make larger contributions to the rental

payment. Olsen, Davis, and Carillo (2005) found that increasing the tenant rent contribution leads

to small positive increases in the probability of leaving the program.

The switch from FMR to SAFMR reduces the percent of units affordable to program participants in

lower rent neighborhoods and increases the percent of units affordable to program participants in

higher rent neighborhoods. For neighborhoods with median rents that were less than 90 percent

of the metropolitan-area median rent, the SAFMR demonstration found that 73 percent of all

units had rents below FMR, but only 46 percent had rents below the SAFMR. This reduction in

the proportion of units affordable to participants suggests that the introduction of the SAFMR

payment standard may make it more difficult for program participants residing in lower rent/high-

poverty neighborhoods to remain in their housing unit, because landlords may be less motivated

to participate in the program (Dastrup et al., 2018). At the same time, a higher share of units was

affordable in higher rent neighborhoods (26 percent affordable under FMR and 64 percent under

SAFMR), increasing the value proposition of the voucher to landlords and program participants

who would like to reside in higher rent neighborhoods.3

Effect of SAFMR on Attrition in the Housing Choice

Voucher Program

In this section, we describe our approach to estimating the causal effect of SAFMRs on program

attrition in the voucher program. We estimate the impact of the change from metropolitan-area

FMRs to SAFMRs using a difference-in-differences approach within a survival analysis model. To

examine the robustness of our main specification, we test alternatives specifications of the hazard

function and use Monte Carlo analysis. We omit a detailed description of the data and the HUD

demonstration from which they derived, as these are extensively described in Dastrup et al. (2018).

The difference-in-differences component compares the exit rates of voucher program tenants

before the introduction of SAFMRs with exit rates of tenants after the introduction, and then it

compares this difference across treated PHAs and untreated PHAs. The unit of observation is a

household-“stay.”4

This approach offers causal estimates under the assumption that SAFMR PHAs would have had

parallel trends in exit rates during our study period in the absence of SAFMRs. The difference-in-

difference approach removes potential bias from the impact estimate due to unobservable PHA

3 A recent HUD study found that refusal rates were much higher in low-poverty neighborhoods compared with the rates in higher-poverty neighborhoods (Cunningham et al., 2018).

4 As we cannot observe long stays post-intervention, we restrict the analysis sample to households that entered the voucher program after 1980. We also restrict the estimation to time periods beginning in 2009 which is when our historical HUD data set begins, so that households entering prior to 2009 do not bias the estimation of the survival function. For example, consider two households that enter the voucher program in 2000, where Household A leaves the voucher program in 2005 and Household B leaves the voucher program in 2015. Household A is not observed in our data, because household-stays that ended prior to 2009 are not observed. Household B is observed, but we only use information about that household’s “survival” at t ≥108 months (108=12*(2009 – 2000)), so that Household B

does not contribute an upwards bias of the survival probability at 0 < t < 108. A household that entered, exited, and re-entered the program will appear in our data twice, if the exit took place during or after January 1, 2009, and the second re-entry took place between January 1, 2009, and December 31, 2017.

Cityscape 75

Geyer, Dastrup, and Finkel

characteristics that are constant over the analysis time period (for example, local PHA practices

and preferences in tenant selection). While the impact estimates could still be biased due to time-

varying characteristics that coincided with the introduction of SAFMR and that affected the SAFMR

PHAs and PHAs metropolitan-area-wide FMRs differently, we find that possibility unlikely, as the

comparison sample is very large and the SAFMR demonstration sites were selected within clusters

of PHAs that were similarly sized, in markets with similar FMRs, and had similar percentages of

voucher holders that were working age (see Dastrup et al., 2018).

We estimate a Weibull survivor function using Stata’s survival analysis packages, which models the

probability that an existing tenant is still in the voucher program after t months. We considered other specifications (including the more typical proportional hazard model), but the data yield

linear, parallel trends (straight lines) in log cumulative hazard over log time, suggesting that

Weibull is the most appropriate specification. Equation 1 shows the Weibull cumulative hazard

function, including household characteristics X; PHA characteristics Z; an indicator of PHA ever randomly assigned to using SAFMRs, SA; an indicator for pre- and post-introduction of the SAFMR

policy, POST ; and a random error term ϵ. The coefficient β2 is the primary coefficient of interest, t

as it provides the estimate of whether the introduction of SAFMRs affected tenants’ probability of

leaving the program. Our findings are robust to the use of alternative hazard models (Gompertz)

and consideration of correlated random errors at the PHA-level using a shared frailty parameter.

(1) H(t) = exp(β + β SA

+ β SA + β POST + β X + β Z + ϵ) ta.

1

2

* POSTt

3

4

t

2

3

All households are censored at 15 years duration to avoid longer term households’ outsized influence

on the shape parameters of the Weibull model (our main findings are robust to their inclusion).

To visualize the analysis, exhibit 2 shows the probability (and confidence intervals around those

probabilities) that a household will remain in the voucher program after t months (a) prior to the

SAFMR demonstration and (b) after the SAFMR demonstration. These graphs begin at 1.0 (a 100

percent probability of remaining in the voucher program at the time of move-in) and decrease to

roughly 0.25 by the 175th month of participation (a 25 percent probability of still being in the

voucher program after roughly 14.5 years). The median length of stay is the number of months at

which 50 percent (0.5 on the y-axis) are still in the program.5 The solid line shows this probability

for households participating in PHAs assigned to use the SAFMRs, and the dashed line shows the

probability for households participating in PHAs using the typical (metropolitan-wide) payment

standards. The solid line is higher than the dashed line in exhibit 2(a), showing that households

in PHAs that will later participate in the SAFMR demonstration are more likely to “survive,”

such as, persist in the program, prior to the introduction of the SAFMR policy than households

in PHAs that will not participate in the SAFMR demonstration. In an experiment with a larger

sample, we would expect the two lines in exhibit 2(a) to overlap perfectly, indicating no differences

between the treatment and comparison group prior to the demonstration. Two of the SAFMR sites

(Cook County and Mamaroneck) were the biggest influence in generating the gap in the pre-

demonstration survival curves, and note that this gap is not surprising given that a sample size of

5 McClure estimated that the median length of stay in the voucher program is 4 years. Our data suggest that the median is much longer at 11 years (2017). One reason for this discrepancy may be that we do not include stays of less than 1 month.

76 Small Area Fair Market Rents

Impact of Expanded Choice on Attrition in the Housing Voucher Program

seven PHAs is small and cannot be expected to reflect the average of a group of 138 comparison

PHAs. This difference could be due to the combination of the housing market and participant types

served in these PHAs.

The survival function of the SAFMR PHAs looks markedly different after the introduction of

SAFMR policy. It nearly overlaps with the survival function of the non-SAFMR PHAs, as shown

in exhibit 2(b). These data strongly suggest that the introduction of SAFMR led to induced

program exit.

Exhibit 2(a)

Probability of Remaining in the Voucher Program, by

Months Since Entry: Prior to SAFMR Demonstration

Exhibit 2(b)

Probability of Remaining in the Voucher Program, by

Months Since Entry: After SAFMR Demonstration

Cityscape 77

Geyer, Dastrup, and Finkel

Consistent with exhibits 2(a) and 2(b), estimation of the survival regression model reveals that the

introduction of SAFMR increased program exit. For any given length of time in the program and

household characteristic, the introduction of SAFMR increased the probability of exit by 27 percent

(a hazard ratio of 1.269). Exhibit A.1 in the appendix shows the full set of hazard ratios for all

covariates in the regression model. This result is robust to alternative specifications, including the

exclusion of random effects at the PHA-level and use of the Gompertz model instead of the Weibull

model (see exhibit A.1). Of course, the increased probability of program attrition leads to shorter

lengths of stay in the voucher program. Using the main model estimates, exhibit 3 shows the

regression-adjusted median length of stay in months for households in treatment and comparison

PHAs, both before and after the introduction of SAFMR. These medians are consistent with the

survival rates in exhibit 2 and suggest that the introduction of SAFMR decreased the median length

of stay by 20 percent (-27 divided by 134).6

Exhibit 3

Regression-Adjusted Median Length of Stay in the Voucher Program (Months)

SAFMR PHAs

Comparison PHAs

Difference

After SAFMR

92

93

– 1

Before SAFMR

134

108

26

Difference

– 42***

– 15***

– 27***

*** p < 0.001

Notes: For SAFMR PHAs, the data include 54,860 households participating in the voucher program prior to the intervention and 56,061 households participating in the voucher program after the introduction of the intervention, of which 37,021 participated in the program in both periods. For comparison PHAs, the data include 738,807 households participating in the voucher program prior to the intervention and 668,602 households participating in the voucher program after the introduction of the intervention, of which 474,863 households participated in the program in both periods.

We were surprised at the large estimated impact of the change from metropolitan area FMR to

SAFMR on attrition. One hypothesis we had is that our findings could be due to chance, owing to

the small number of PHAs in the treatment group (7 out of 145 in the full sample). To explore this

hypothesis, we conducted a Monte Carlo analysis using the 138 PHAs in the comparison group.

Over 100 iterations, we randomly assigned 6 to 7 comparison PHAs to a “treatment” group and ran

the same difference-in-difference Weibull model. On average, those simulations found no impact (a

hazard ratio of 0.997, i.e. nearly 1). Despite the small number of PHAs in the simulated “treatment”

groups, only one out of the hundred simulations yielded an impact estimate as large as the impact

in our main analysis. In fact, our main finding is 2.31 standard deviations larger than the average

simulated impact estimate. From this exercise, we conclude that although our main finding is

puzzlingly large, there is roughly a one percent probability that it is due to random chance.

We tested to see if the impact was different across various tenant groups. We use the standard

triple-difference specification, shown Equation (2), with W being a dummy (binary indicator)

variable for subgroup inclusion. Table A.2 in the appendix shows the full set of estimated hazard

ratios for all coefficients in the subgroup analyses.

(2)

H(t) = exp (β + β SA

SA * W + β POST

W + β SA

+

1

2

* POSTt * W + β3

4

t * W + β5

2

* POSTt

β SA + β POST + β X + β Z + ϵ) ta

3

4

t

2

3

6 The SAFMR demonstration data suggest longer median lengths of stay than previously reported (McClure, 2017).

78 Small Area Fair Market Rents

Impact of Expanded Choice on Attrition in the Housing Voucher Program

Similar to previous studies, our main finding and subgroup analysis finds that heads of household

with disabilities are less likely to exit, in general, compared to nondisabled heads of household.

Interestingly, we find no evidence that heads of household with disabilities were differently affected

by the introduction of SAFMR compared to nondisabled heads of household. Stated differently, the

introduction of SAFMR increased the hazard of exit for heads of household with disabilities just as

much as it did for nondisabled heads of household.

We also compared households with children, to households without children. Similar to previous

studies, our main finding and subgroup analysis find that households with children are less

likely to exit, in general, compared with households without children. We find no evidence that

households with children were differently affected by the introduction of SAFMR compared with

households without. Stated differently, the introduction of SAFMR increased the likelihood of exit

for households with children just as much as it did for households without children.

We next looked at households living in high rent neighborhoods at the time the SAFMR

demonstration began. (We do not do a subgroup analysis based on neighborhood type at time of

exit because neighborhood type at time of exit is endogenous to the policy change, as shown by

Dastrup, Ellen, and Finkel, 2019.) Similar to Dastrup et al. (2018), high-rent neighborhoods are

defined as neighborhoods where the median rent is 110 percent higher than the metropolitan area

FMR. Unlike for households living in neighborhoods with rents lower than the 110 percent of the

metropolitan area FMR, there is no evidence that households living in high-rent neighborhoods

are more likely to exit the program after the introduction of SAFMR than before the introduction

of SAFMR. The difference in impact for household participants in higher rent neighborhoods

compared with all other households is statistically significant (p < .001). This finding suggests that the introduction of SAFMR increased attrition in lower and moderate rent neighborhoods but not

in higher rent ones.

To investigate further, we closely examined households that live in lower and moderate rent

neighborhoods at the beginning of the SAFMR Demonstration. As in Dastrup et al. (2018), lower

rent neighborhoods are defined as ZIP Codes where the median rent is at most 90 percent of the

metropolitan area median rent. We found that in general (prior to the SAFMR Demonstration and

in non-SAFMR PHAs) households living in lower rent neighborhoods are more likely to leave the

program than households in moderate rent neighborhoods (median rents between 90 and 110

percent the metropolitan median). For households that live in lower rent neighborhoods, the

introduction of the SAFMR increased the probability of exit by 26 percent. For households that live

in moderate rent neighborhoods, SAFMR increased the probability of exit by 41 percent, which

statistical tests show is significantly larger than the increase in probability of exit for lower rent

neighborhood participants. Therefore, we reject the hypothesis that the main result is driven solely

by voucher participants living in lower rent neighborhoods; rather, it is driven both by participants

in lower rent neighborhoods and participants in moderate rent neighborhoods. This finding

is surprising since the SAFMR formula results in SAFMRs that are similar or identical to FMRs

in these ZIP Codes. Further research is needed to determine the contribution to this finding of

payment standards (set by PHAs within the 90 to 110 percent of either SAFMR or FMR), program

complexity, and other factors.

Cityscape 79

Geyer, Dastrup, and Finkel

Last, we examined subgroups defined by age. The introduction of SAFMR had a different effect on

program attrition for households with seniors (age 62 and over) compared with households with

no seniors. Both groups were more likely to exit after the introduction of SAFMR, but the increased

hazard was smaller for households with seniors than households with no seniors (hazard ratio of

1.106 for seniors, 1.303 for nonseniors). The difference is statistically significant (p < .001). We

can speculate on possible explanations for this difference (for example, seniors are otherwise less

likely to move), but future research should clarify how the introduction of SAFMR had a different

effect on these two types of households. In the SAFMR PHAs, a slightly smaller percentage of

households with seniors live in high-rent neighborhoods than households with no seniors (16.8

percent compared with 15.9 percent). Therefore, the differential result for seniors is not tied to the

results on average neighborhood rent levels.

Discussion

The results show increased rates of program attrition after the introduction of SAFMRs.

Further research is needed to shed light on the reasons for the increased program attrition. It is

theoretically ambiguous how the change from metropolitan area FMRs to SAFMRs would affect the

length of stay in the program. The change from metropolitan area FMRs to SAFMRs may decrease

attrition in higher rent neighborhoods because the program’s generosity has increased. At the same

time, if higher rent neighborhoods coincide with better economic possibilities for financial self-

sufficiency, SAFMRs would ultimately increase attrition in higher rent neighborhoods. We found

that households residing in higher rent neighborhoods were less likely to leave the program after

the introduction of SAFMR than before, suggesting that, in the first 5 years after implementation,

the effects of increased generosity of the subsidy in those neighborhoods outweigh factors that

might decrease participation.

Another reason SAFMR might affect attrition is that it may increase attrition in lower rent

neighborhoods because landlords may be less willing to accept the lower payment standards or

interface with a more complicated program. Also, HCV participants with a lower valued subsidy

might decide that the program’s paperwork requirements are too burdensome. Indeed, we found

that households residing in lower rent neighborhoods were more likely to leave the program after

the introduction of SAFMR than before. Puzzlingly, households in moderate rent neighborhoods

were also more likely (in fact, even more likely than those in lower rent neighborhoods) to

leave the program after the introduction of SAFMR. Prior to this analysis, HUD recognized the

potential burden to existing program participants at PHAs that switch from FMR to SAFMR. In

part to address this possibility, Section 107 of the recently enacted Housing Opportunity Through

Modernization Act (HOTMA) provides PHAs with flexibility in how they set payment standards

in areas where the new payment standard falls below the metropolitan-area FMR. Under this new

rule, PHAs are explicitly given the option of allowing higher PHA rental contributions for units

whose new payment standard falls below the previous metropolitan-area FMR payment standard.

These protections and additional experience with SAFMRs may result in different outcomes than

we find for the SAFMR demonstration PHAs in our analysis.

80 Small Area Fair Market Rents

Impact of Expanded Choice on Attrition in the Housing Voucher Program

Attrition from the voucher program is puzzling in general because most households are not

financially better off as a result of leaving the program. Researchers would be able to document

tenant experience and explain demonstration impacts on attrition if the HUD Form 50058 that

describes each participating household recorded reasons for program exits. This article and the

previous literature suggest that the list of categorical responses recording the exit reason should

include: (1) death, (2) moving to co-habit with partner, (3) leaving geographic service area, (3)

eviction, (4) no longer in need of housing subsidy, (5) difficulty finding an apartment, and (6)

moving to a nursing or other more intensive care facility, as well as others. Increased resources

and improved processes for gathering this exit information would likely allow for more focused

research conclusions to inform policies that can affect program attrition.

Conclusion

During the SAFMR demonstration, the seven participating PHAs saw increased attrition rates from

the HCV program that imply a decrease in median length of program participation of about 2 years

(from a median base of about 11 years of participation). The increase in attrition is attributable

to the change from metropolitan area rents to SAFMRs. Households in lower and moderate rent

neighborhoods were affected but households in high-rent neighborhoods were not. Working-age

adults were more affected than households with adults over age 62. Characteristics of participants

exiting the program suggest that exits represent a decrease in household resources. Additional

research and data collection are needed to understand the reasons for program exits and how the

introduction of SAFMRs interacts with these reasons. Although the demonstration evaluation and

related research have found that SAFMRs led to an increase in households—especially those with

children—moving to high-rent neighborhoods, a more complete welfare analysis of SAFMRs is

needed to take into account its impact on attrition from the voucher program.

Acknowledgments

The authors thank Amanda Steigman for her help in preparing the dataset for analysis, and Jill

Khadduri, Peter Kahn, Jacob Klerman, Austin Nichols, and Marie Lihn for their feedback on early

versions of this manuscript.

Authors

Judy Geyer is an associate at Abt Associates and can be contacted at: Judy_Geyer@abtassoc.com.

Samuel Dastrup is an associate at Abt Associates and can be contacted at: Samuel_Dastrup@

abtassoc.com.

Meryl Finkel is a principal associate at Abt Associates and can be contacted at: Meryl_Finkel@

abtassoc.com.

Cityscape 81

Geyer, Dastrup, and Finkel

Appendix A: Additional Exhibits

Exhibit A.1

Main Results (1 of 2)

Weibull Model Without

Gompertz Model

Weibull Model With

PHA-Cluster Robust

Without PHA-Cluster

PHA-Cluster Robust

Standard Errors

Robust Standard Errors

Standard Errors

Interaction of Post Dummy and

1.399***

1.387***

1.270***

SAFMR-PHA Dummy

[1.357,1.443]

[1.344,1.430]

[1.230,1.311]

Indicator for Time ≥

1.089***

1.106***

1.171***

Post-Intervention

[1.081,1.097]

[1.099,1.114]

[1.163,1.179]

0.720***

0.730***

0.795

PHAs Using SAFMRs

[0.701,0.739]

[0.711,0.749]

[0.589,1.074]

1.040***

1.038***

1.053

Cluster = 2

[1.028,1.052]

[1.026,1.050]

[0.900,1.232]

0.552***

0.558***

0.520***

Cluster = 4

[0.542,0.562]

[0.548,0.568]

[0.424,0.640]

Cluster = 5

(omitted)

(omitted)

(omitted)

0.766***

0.772***

-0.195*

Cluster = 6

[0.759,0.773]

[0.764,0.779]

[0.678,0.998]

0.456***

0.464***

0.438***

Cluster = 7

[0.448,0.464]

[0.456,0.472]

[0.322,0.596]

0.729***

0.745***

0.740***

Household Head is a Woman

[0.723,0.736]

[0.739,0.752]

[0.733,0.747]

Head of Household with

0.822***

0.833***

0.793***

Disability

[0.815,0.829]

[0.826,0.840]

[0.786,0.800]

1.419***

1.419***

1.265***

Household Head is White

[1.406,1.430]

[1.406,1.430]

[1.252,1.276]

0.950***

0.951***

0.870***

Household Head is Hispanic

[0.939,0.960]

[0.941,0.961]

[0.860,0.882]

0.974***

0.960***

0.947***

Children Under Age 5 Present

[0.966,0.983]

[0.952,0.969]

[0.939,0.955]

0.846***

0.851***

0.842***

Children Age 5 to 12 Present

[0.839,0.853]

[0.845,0.858]

[0.835,0.849]

0.918***

0.934***

0.928***

Children Age 13 to 17 Present

[0.910,0.926]

[0.926,0.942]

[0.920,0.936]

More Than One Adult in

0.844***

0.854***

0.877***

Household

[0.836,0.850]

[0.846,0.861]

[0.869,0.884]

1.005

1.018***

1.025***

At Least One Adult Age 62+

[0.994,1.015]

[1.008,1.029]

[1.014,1.036]

0.941***

0.944***

0.904***

Income > $0 and < $5K

[0.922,0.960]

[0.925,0.963]

[0.886,0.923]

0.947***

0.952***

0.921***

Income ≥ $5K and < $10K

[0.929,0.966]

[0.934,0.971]

[0.903,0.940]

82 Small Area Fair Market Rents

Impact of Expanded Choice on Attrition in the Housing Voucher Program

Exhibit A.1

Main Results (2 of 2)

Weibull Model Without

Gompertz Model

Weibull Model With

PHA-Cluster Robust

Without PHA-Cluster

PHA-Cluster Robust

Standard Errors

Robust Standard Errors

Standard Errors

0.964***

0.970**

0.960***

Income ≥ $10K and < $15K

[0.945,0.983]

[0.951,0.990]

[0.941,0.979]

1.052***

1.054***

1.046***

Income ≥ $15K and < $20K

[1.031,1.073]

[1.033,1.075]

[1.025,1.068]

1.195***

1.198***

1.206***

Income ≥ $20K and < $25K

[1.170,1.220]

[1.174,1.224]

[1.181,1.231]

1.735***

1.747***

1.818***

Income ≥ $25K

[1.699,1.770]

[1.713,1.782]

[1.782,1.857]

0.008***

0.010***

0.007***

Constant

[0.008,0.008]

[0.009,0.010]

[0.007,0.008]

1.036***

1.066***

Weibull Shape Parameter

[1.033,1.039]

[1.062,1.069]

0.999***

Gompertz Shape Parameter

[0.999,0.999]

0.151***

Theta

[0.120,0.189]

PHA = Public Housing Authority. SAFMR = Small Area Fair Market Rent.

* p < 0.05, ** p < 0.01, *** p < 0.001

Notes: This table displays the model results as hazard ratios, which are exp( coeff ) where coeff are the estimated model coefficients. For the SAFMR PHAs, there are 54,860 observations pre-intervention and 56,061 observations post-intervention. For the metropolitan area FMR PHAs, there are 738,807 observations pre-intervention and 668,602 observations post-intervention. Income is inflation adjusted to 2018 dollars. Ninety-five percent confidence intervals in brackets.

Cityscape 83

Geyer, Dastrup, and Finkel

0.895**

0.844***

1.135***

1.413***

1.210***

0.726*

1.048

0.510***

0.819*

0.421***

(omitted)

Lower Rent

ea Median Rent)

verage Rent A

[0.834,0.960]

[0.829,0.859]

[1.070,1.203]

[1.353,1.478]

[1.200,1.220]

[0.536,0.984]

[0.895,1.228]

[0.414,0.627]

[0.674,0.993]

[0.309,0.573]

Neighborhoods

<0.9 Ar

Neighborhood (Rent Ratio

ea

0.784***

1.111***

0.117**

1.309***

1.158***

0.782

1.053

0.520***

0.823*

0.438***

(omitted)

Higher Rent

[0.717,0.858]

[1.086,1.134]

[1.045,1.209]

[1.265,1.354]

[1.149,1.166]

[0.579,1.057]

[0.900,1.232]

[0.423,0.639]

[0.678,0.998]

[0.322,0.596]

Ratio >1.1 Ar

Median Rent)

verage and Low A

Neighborhood (Rent

Rent Neighborhoods

esent

1.023

1.082***

0.877***

1.255***

1.121***

0.854

1.054

0.521***

0.823*

0.437***

esent

en Pr

Pr

(omitted)

[0.962,1.088]

[1.069,1.095]

[0.833,0.924]

[1.197,1.314]

[1.110,1.132]

[0.631,1.155]

[0.900,1.234]

[0.424,0.640]

[0.678,0.998]

[0.321,0.595]

Households with

at Least One Child

Childr

Households with No

esent

0.848***

0.935***

1.078*

1.305***

1.183***

0.785

1.053

0.520***

0.438***

esent

-0.196*

Pr

(omitted)

[0.780,0.921]

[0.916,0.953]

[1.008,1.153]

[1.260,1.351]

[1.174,1.192]

[0.581,1.061]

[0.899,1.232]

[0.424,0.639]

[0.678,0.997]

[0.322,0.596]

Seniors Pr

Households with at

Least One Senior

Households with No

esent

0.94

0.967***

1.147***

1.297***

1.184***

0.761

1.055

0.521***

0.437***

esent

-0.195*

Pr

(omitted)

[0.880,1.004]

[0.953,0.982]

[1.085,1.212]

[1.249,1.347]

[1.174,1.195]

[0.563,1.029]

[0.901,1.234]

[0.424,0.640]

[0.678,0.999]

[0.321,0.595]

Households with at

Least One Person

with a Disability

Households with

No Persons with

Disabilities Pr

,

oup Dummy

oup Dummy

oup Dummy

oup

Subgr

, and SAFMR

(by Comparison)

oup Analyses (1 of 3)

Exhibit A.2

Subgr

Interaction of Subgr

Post Dummy

Dummy

Interaction of Subgr

and Post Dummy

Interaction of Subgr

and SAFMR Dummy

Interaction of Post Dummy and

SAFMR Dummy

Post Dummy (Indicator for Time

≥>= Post-Intervention)

SAFMR Dummy (Indicator for

PHAs Using SAFMRs)

Cluster = 2

Cluster = 4

Cluster = 5

Cluster = 6

Cluster = 7

84 Small Area Fair Market Rents

Impact of Expanded Choice on Attrition in the Housing Voucher Program

0.731***

0.796***

1.271***

0.860***

0.954***

0.840***

0.923***

0.874***

1.022***

0.906***

0.926***

Lower Rent

ea Median Rent)

verage Rent A

[0.725,0.739]

[0.789,0.803]

[1.259,1.284]

[0.848,0.871]

[0.945,0.963]

[0.833,0.848]

[0.914,0.932]

[0.866,0.882]

[1.010,1.034]

[0.887,0.926]

[0.907,0.945]

Neighborhoods

<0.9 Ar

Neighborhood (Rent Ratio

ea

0.740***

0.793***

1.264***

0.870***

0.947***

0.841***

0.928***

0.877***

1.025***

0.904***

0.921***

Higher Rent

[0.733,0.747]

[0.786,0.800]

[1.252,1.276]

[0.860,0.882]

[0.939,0.955]

[0.834,0.848]

[0.920,0.936]

[0.869,0.884]

[1.014,1.036]

[0.886,0.923]

[0.903,0.940]

Ratio >1.1 Ar

Median Rent)

verage and Low A

Neighborhood (Rent

Rent Neighborhoods

esent

0.736***

0.795***

1.266***

0.869***

0.933***

0.829***

0.917***

0.875***

1.028***

0.902***

0.920***

esent

en Pr

Pr

[0.730,0.743]

[0.789,0.803]

[1.255,1.278]

[0.858,0.880]

[0.925,0.941]

[0.822,0.836]

[0.909,0.925]

[0.868,0.883]

[1.017,1.039]

[0.884,0.921]

[0.902,0.938]

Households with

at Least One Child

Childr

Households with No

esent

0.740***

0.793***

1.265***

0.871***

0.947***

0.842***

0.928***

0.877***

1.064***

0.905***

0.922***

esentPr

[0.733,0.747]

[0.786,0.799]

[1.252,1.276]

[0.860,0.882]

[0.939,0.955]

[0.835,0.849]

[0.920,0.936]

[0.869,0.885]

[1.048,1.079]

[0.886,0.923]

[0.904,0.940]

Seniors Pr

Households with at

Least One Senior

Households with No

esent

0.740***

0.803***

1.265***

0.870***

0.946***

0.842***

0.928***

0.877***

1.023***

0.905***

0.922***

esentPr

[0.733,0.747]

[0.794,0.812]

[1.252,1.276]

[0.860,0.882]

[0.938,0.955]

[0.835,0.849]

[0.920,0.936]

[0.869,0.884]

[1.012,1.034]

[0.887,0.924]

[0.904,0.940]

Households with at

Least One Person

with a Disability

Households with

No Persons with

Disabilities Pr

oman

esent

esent

esent

oup

Subgr

(by Comparison)

oup Analyses (2 of 3)

en Under Age 5 Pr

en Age 5 to 12 Pr

en Age 13 to 17 Pr

e Than One Adult in

Exhibit A.2

Subgr

Household Head is a W

Head of Household with

Disability

Household Head is White

Household Head is Hispanic

Childr

Childr

Childr

Mor

Household

At Least One Adult Age 62+

Income > $0 and < $5K

Income ≥ $5K and < $10K

Cityscape 85

Geyer, Dastrup, and Finkel

vention and

0.963***

1.052***

1.221***

1.861***

1.197***

0.007***

1.065***

0.152***

Lower Rent

ea Median Rent)

verage Rent A

[0.943,0.983]

[1.030,1.075]

[1.195,1.250]

[1.820,1.900]

[1.183,1.213]

[0.007,0.008]

[1.062,1.068]

[0.121,0.190]

vations pre-inter

Neighborhoods

Ninety-five percent confidence

<0.9 Ar

Neighborhood (Rent Ratio

ea

0.960***

1.046***

1.206***

1.818***

0.962***

0.008***

1.065***

0.151***

the SAFMR PHAs have 40,254 obser

Higher Rent

[0.941,0.979]

[1.025,1.068]

[1.179,1.231]

[1.781,1.857]

[0.947,0.976]

[0.007,0.008]

[1.062,1.068]

[0.120,0.189]

The regression in the last column has smaller sample sizes because

Ratio >1.1 Ar

Median Rent)

verage and Low A

Neighborhood (Rent

Rent Neighborhoods

Income is inflation adjusted to 2018 dollars.

vention.

For this subsample,

esent

0.958***

1.043***

1.202***

1.815***

esent

0.008***

1.065***

0.151***

en Pr

Pr

vations post-inter

[0.940,0.978]

[1.022,1.065]

[1.176,1.228]

[1.779,1.853]

[0.007,0.008]

[1.062,1.068]

[0.121,0.189]

are the estimated model coefficients.

Households with

at Least One Child

Childr

Households with No

coeff

) where

vention introduction are omitted.

coeff

esent

vention and 574,464 obser

0.961***

1.047***

1.206***

1.820***

esent

0.007***

1.065***

0.151***

Pr

[0.942,0.980]

[1.025,1.068]

[1.181,1.232]

[1.782,1.857]

[0.007,0.008]

[1.062,1.068]

[0.120,0.189]

which are exp(

Seniors Pr

Households with at

Least One Senior

vations pre-inter

Households with No

esent

Area Fair Market Rent.

0.961***

1.047***

1.207***

1.820***

esent

0.007***

1.066***

0.151***

Pr

[0.942,0.980]

[1.026,1.068]

[1.181,1.232]

[1.784,1.859]

[0.007,0.008]

[1.062,1.069]

[0.120,0.189]

Households with at

Least One Person

with a Disability

Households with

No Persons with

Disabilities Pr

. SAFMR = Small

Authority

This table displays the model results as hazard ratios,

vention; the metropolitan area FMR PHAs have 594,308 obser

oup

PHA = Public Housing

*** p < 0.001

Subgr

fect Parameter

(by Comparison)

vations post-inter

oup Analyses (3 of 3)

** p < 0.01,

oduction > 1.1

oduction < 0.9

eibull Shape Parameter

Each column is a separate regression.

vals in brackets.

Exhibit A.2

Subgr

Income ≥ $10K and < $15K

Income ≥ $15K and < $20K

Income ≥ $20K and < $25K

Income ≥ $25K

Ratio of Neighborhood Rent

to FMR at Time of Intervention

Intr

Ratio of Neighborhood Rent

to FMR at Time of Intervention

Intr

Constant

W

Random Ef

(Frailty)

FMR = Fair Market Rent.

* p < 0.05,

Notes:

households living in neighborhoods with average rents greater than 110 percent of the area FMR at the time of the inter 45,791 obser

inter

86 Small Area Fair Market Rents

Impact of Expanded Choice on Attrition in the Housing Voucher Program

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Cunningham, Mary, Martha Galvez, Claudia L. Aranda, Rob Santos, Doug 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.

Dastrup, Samuel, Ingrid Gould Ellen, and Meryl Finkel. 2019. “The Effects of Small Area Fair

Market Rents on the Neighborhood Choices of Families with Children.” Cityscape 21 (3): 19-47.

Dastrup, Samuel, Meryl Finkel, Kimberly Burnett, and Tanya de Sousa. 2018. Small Area Fair

Market Rent Demonstration Evaluation Final Report. Prepared for the U.S. Department of Housing

and Urban Development, Office of Policy Development and Research.

Gubits, D., Khadduri, J., & Turnham, J. (2009). Housing Patterns of Low Income Families with

Children: Further Analysis of Data From the Study of the Effects of Housing Vouchers on Welfare Families.

Joint Center for Housing Studies of Harvard University.

Lubell, Jeffrey M., Mark Shroder, and Barry Steffen. 2003. “Work Participation and Length of Stay

in HUD-Assisted Housing,” Cityscape 6 (2): 207–223

McClure, Kurk. (2017). Length of Stay in Assisted Housing. Prepared for U.S. Department of Housing and Urban Development, Office of Policy Development and Research. https://www.huduser.gov/

portal/sites/default/files/pdf/LengthofStay.pdf

Olsen, Edgar O., Scott E. Davis, and Paul E. Carrillo. 2005. “Explaining Attrition in the Housing

Voucher Program,” Cityscape 8 (2): 95–113.

Olsen, Edgar O., Catherine A. Tyler, Jonathan W. King, and Paul E. Carrillo. 2005. “The Effects of

Different Types of Housing Assistance on Earnings and Employment,” Cityscape 8 (2): 163–187.

Cityscape 87

Geyer, Dastrup, and Finkel

Reina, Vincent, Arthur Acolin, and Raphael W. Bostic. 2019. “Do Small Area Fair Market Rent

Limits Increase Access to Opportunity Neighborhoods by Housing Choice Voucher Recipients? An

Early Evaluation,” Housing Policy Debate 29 (1): 44-61.

Sanbonmatsu, Lisa, Jens Ludwig, Lawrence F. Katz, Lisa A. Gennetian, Greg J. Duncan, Ronald C.

Kessler, Emma Adam, Thomas W. McDade, and Stacy Tessler Lindau. 2011. Moving to Opportunity

for Fair Housing Demonstration Program: Final Impacts Evaluation. Washington, DC: U.S. Department of Housing and Urban Development.

Turner, Margery Austin, Austin Nichols, and Jennifer Comey. 2012. Benefits of Living in High-

Opportunity Neighborhoods. Washington, DC: Urban Institute.

U.S. Department of Housing and Urban Development. 2018. Affordable Housing. https://www.hud.

gov/program_offices/comm_planning/affordablehousing/.

88 Small Area Fair Market Rents

by Edgar O. Olsen

Does HUD Overpay for Voucher

Units, and Will SAFMRs Reduce the

Overpayment?

Edgar O. Olsen

University of Virginia

Abstract

One argument for Small Area Fair Market Rents (SAFMRs) is that they would reduce overpayment for

voucher units in low-rent neighborhoods. This leads to the belief that the benefits of SAFMRs can be funded largely by reductions in landlord profits rather than by losses to voucher recipients who remain in low-rent areas. The usual theoretical argument that has led many to believe that voucher units are overpriced focuses on one implication of one feature of the Housing Choice Voucher program. This

article provides a more comprehensive theoretical analysis that leads to the conclusion that the worst voucher units and those in the worst neighborhoods will usually rent for more than the mean market

rent of identical units, and the best units in the best neighborhoods will rent for less than this amount.

The debate over this matter has ignored the bulk of the available evidence. This article summarizes and assesses the data, methods, and results of the major studies. The evidence is consistent with the general pattern predicted by the comprehensive theoretical analysis but also with an alternative explanation that challenges its interpretation of overpayments and underpayments for voucher units. The mix of units

with estimated overpayments and underpayments varies across studies, but the weight of the evidence

is that the aggregate differences are modest. Finally, the evidence available indicates that SAFMRs

will decrease the rents paid for voucher units with any specified set of characteristics in the worst neighborhoods and will increase the rents of such units in the best neighborhoods.

Introduction

Convincing evidence indicates that children in low-income households will have better outcomes

as adults if they grow up in better neighborhoods (Chetty, Hendren, and Katz, 2016; Chyn, 2018).

Evidence also indicates that Small Area Fair Market Rents (SAFMRs) will induce more voucher

recipients with children to move to better neighborhoods without any significant increase in

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

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

E. Olsen

taxpayer cost (Collinson and Ganong, 2018; Dastrup et al., 2018). If the decision makers in these

families undervalue the benefits of a better neighborhood for themselves or their children, or if

they fail to devote a sufficient share of the family’s resources to their children in the eyes of others who care about them, this evidence provides a strong argument for SAFMRs.1

Another argument in the debate over the desirability of SAFMRs is on shakier ground. At several

points in the Federal Register entries for the proposed SAFMR rule (81 FR 39218) and final rule

(81 FR 80567), it is argued that another benefit of SAFMRs is that they would reduce overpayment

of rent in areas where median neighborhood rent is below the average for the metropolitan area.

The belief that the voucher program overpays for units in these areas is widespread among housing

policy analysts and others involved in housing policy debates. This leads to the belief that the

benefits of SAFMRs can be funded largely by reductions in landlord profits rather than losses to

voucher recipients who remain in low-rent areas. If market rents are paid for voucher units in these

areas, reduced payment standards would force these recipients to pay higher rents or move to less

desirable units.

The usual theoretical argument that has led many to believe that voucher units are overpriced

focuses on one feature of the Housing Choice Voucher (HCV) program and ignores other features

that affect this outcome. The Federal Register entries for the proposed and final SAFMR rule are

not specific about the nature of the evidence on this matter. They cite an early version of a recently

published paper (Collinson and Ganong, 2015), however, that contained some evidence about the

extent of overpayment for voucher units. Desmond and Perkins (2016) provide recent results for

one county, but neither Collinson and Ganong nor Desmond and Perkins provide a good account

of the major studies funded by the U.S. Department of Housing and Urban Development (HUD)

that shed considerable light on this matter.

The purposes of this article are to explain the theoretical reasons for rents of voucher units to be

greater or less than the mean rents of similar units occupied by unsubsidized households and to

summarize and assess the data, methods, and results of the major empirical studies of this matter.

Because the voucher program has changed over time and voucher programs with different features

should be expected to have different outcomes, this assessment will account for the nature of

the voucher program at the time of the data underlying the results of each study. This article will

provide a comprehensive account of the state of the evidence on this matter.

Theory

The HCV program has features that lead to the expectation that HUD will pay more than the mean

market rent of identical units for some units in some locations and less than the mean market rent

of identical units for other units in other locations. This section analyzes the expected effect of

these features.

The argument that has led many to expect that voucher units would be overpriced is based on a

simplified version of the program. It assumes that a voucher recipient is allowed to occupy any

1 Otherwise, the evidence provides no rationale for incentivizing families with children to live in a better neighborhood at the expense of worse housing.

90 Small Area Fair Market Rents

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment?

unit that meets the program’s minimum housing standards and rents for less than a program

parameter called the payment standard, the voucher recipient can agree to pay the landlord any

rent up to the payment standard, and the recipient will contribute a fixed amount toward its rent,

usually 30 percent of their countable income. Under these assumptions, if the market rent of a unit

occupied by a voucher recipient is less than the payment standard, its tenant would have no reason

to resist paying the landlord a rent equal to the payment standard. This leads to the conclusion

that all landlords who serve voucher recipients will charge rents equal to this amount. Therefore,

all voucher units with market rents less than the payment standard would be overpriced, and the

worst units in the worst neighborhoods would be overpriced to the greatest extent.

The conclusion that all landlords who serve voucher recipients will set their rents equal to the

payment standard is counterfactual. Collinson and Ganong (2018, Online Appendix, Figure B.5)

reveal that this is true for only 12 percent of voucher recipients.2 About 52 percent have rents less

than the payment standard and, in many cases, substantially less. Their results also indicate HUD

pays more for better units (Online Appendix, Figure B.1). The Freestanding Housing Voucher

Demonstration produced similar results for the predecessor to the current HCV program.3

The argument that leads to the expectation that voucher units will be overpriced ignores an

important aspect of reality and an important incentive in the simplified version of the program.

Exhibit 1 illustrates their combined effect. The exhibit describes the relationship between the

desirability and rents of the units in a given neighborhood. The units in the neighborhood differ in

their desirability Q . Due to search cost, identical units in the neighborhood do not have the same

H

rent. The points in the shaded area represent combinations of unit desirability and asking rents to

unsubsidized tenants for units that meet the voucher program’s minimum housing standards

.

PS is the program’s ceiling rent. The units available to voucher recipients are the units in the shaded

area to the right of

and below PS. Landlords with asking rents above PS will not agree to rent

to voucher recipients. Based on the assumptions of the usual argument, landlords with asking rents

less than or equal to PS will agree to rent to voucher recipients for a rent equal to PS because they

realize that voucher recipients have no reason to resist this rent for their unit.

2 https://assets.aeaweb.org/asset-server/files/6861.pdf

3 Leger and Kennedy (1990b, Table G.17) at https://www.huduser.gov/portal/Publications/pdf/HUD-005555.pdf and Leger and Kennedy (1990c, Table E.25C) at https://www.huduser.gov/portal/Publications/pdf/HUD-005597.pdf

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Exhibit 1

Units of Different Quality Potentially Available to Voucher Recipients in a Neighborhood

Like unsubsidized tenants, voucher recipients looking for a unit will contact a number of landlords

and visit a number of units. Asking rent and a few rudimentary characteristics are nearly always

stated in advertisements, but it is necessary to visit units to determine their desirability. Voucher

recipients will limit their search to units in the shaded area to the right of

and below PS, but

due to search costs, they will not contact all landlords with vacant units. Because the rent that

they pay does not depend on the desirability of the unit, they will occupy the best unit that they

find during their search. This is a largely unrecognized implication of the assumptions of the

usual argument. Recognizing that units with the higher asking rent usually have more desirable

characteristics would motivate voucher recipients to search among units with asking rents slightly

below PS.

On the usual argument, if the best unit found in a search has desirability between and , the

recipient’s combination of unit desirability and landlord rent would be on the line segment BC.

Except for units with an initial asking rent equal to PS, these landlords would receive rents greater

than their initial asking rents. In that sense, they would be overpriced. They would also, however,

have rents less than the mean rent of identical units. In this sense, they would be underpriced. All

empirical work on this topic is based on the second concept of underpayment and overpayment. It

compares the actual rent of voucher units with an estimate of the mean market rent of units with

the same characteristics.

Therefore, the fact that the tenant contribution to rent does not vary with the rent paid to the

landlord does not imply that HUD pays rents greater than the mean market rent of identical

units for units occupied by voucher recipients. Under the assumptions of the usual argument,

two offsetting forces are at work—no incentive to avoid overpayment for units and an incentive

to occupy the best possible unit. This leads to the expectation that the worst units occupied by

voucher recipients in a neighborhood would be overpriced and the best units underpriced.

92 Small Area Fair Market Rents

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment?

By reinterpreting Q , it is possible to get similar results for the pricing of identical units in

H

neighborhoods of different quality or the pricing of a composite of unit and neighborhood

quality across the entire housing market. Among units of identical quality, those in the

worst neighborhoods will be overpriced and those in the best neighborhoods underpriced.

Among all units, those with the lowest market rent will be overpriced and those with the

highest, underpriced.

In assessing the likelihood of underpricing and overpricing in exhibit 1, it is important to realize

that many landlords are unwilling to serve voucher recipients and the fraction willing to serve them

is lower for landlords with higher asking rents (Phillips, 2017).4 This will probably induce voucher

recipients to modify their search in terms of neighborhoods and asking rent. As a result, the best

option that results from their search are less likely to be between and in exhibit 1. This

would lead to fewer underpriced and more overpriced units.

The simplified description of the voucher program ignores two important features of the HCV

program. First, the housing agency rather than the tenant decides on the rent that will be paid

for a voucher unit. HUD regulations require public housing agencies (PHAs) to conduct a rent

reasonableness determination before units are leased and before rent increases are granted to

owners. They use a variety of methods to comply with this requirement. The most common is

based on the rents of unsubsidized units in the same building or nearby, adjusted for differences

in their characteristics (ICF International, 2014, Exhibit IV-25).5 Presumably, the PHA sets an

upper limit on the amount it is willing to pay for each unit. Landlords who understand the

program’s operation might reasonably propose rents equal to the payment standard even for units

with market rents well below these levels, but the housing agency will not agree to rent above

its upper limit. Therefore, for the lowest quality units and units in the worst neighborhoods, the

payment standard is not the effective upper limit on the rents paid to landlords. The effective upper

limit is what the housing agency is willing to pay. In terms of exhibit 1, it varies with Q and is H

probably somewhere between MAX (RENT| Q ) and MEAN (RENT| Q ). Enforcement of the rent

H

H

reasonableness test would reduce and possibly prevent overpayment for the worst units in the

worst neighborhoods. If housing agencies were able to determine completely accurately the mean

market rent of identical units and established it as an upper limit on a reasonable rent, no voucher

units would be overpriced.

The HCV program differs from the simplified version in another important respect. A voucher

recipient is allowed to occupy a unit renting for more than the payment standard but must pay

the entire additional cost. On the initial lease, the maximum rent is the payment standard plus

10 percent of countable income. This ensures that the household will not devote more than 40

percent of its countable income to rent. This restriction does not apply beyond the initial lease.

A household with a voucher might choose to occupy a unit renting for more than the payment

standard if it places a particularly high value on better housing or a more desirable neighborhood,

or if it is not worth the effort to find a unit renting for less. In 2018, the program’s mean payment

4 In an online appendix at https://assets.aeaweb.org/asset-server/files/6861.pdf, Collinson and Ganong (2018) develop a model that focuses on this aspect of reality.

5 https://www.huduser.gov/portal/publications/pubasst/QCinc_fy13.html

Cityscape 93

E. Olsen

standard and mean countable income were about $1,100 a month. Therefore, the rent ceiling

exceeded the payment standard by about 10 percent.

This feature affects many HCV recipients. In 2009, 36 percent of voucher units had rents greater

than the applicable payment standard.6 Because voucher recipients would bear the full cost of

better housing over this range of asking rents, they face the same incentives as unsubsidized

tenants. We would not expect the mean rent paid for voucher units in this range to differ from the

mean market rent for identical unsubsidized units. Therefore, some voucher recipients living in the

best units and neighborhoods are in this group. Others may live in units that are underpriced.

Taking account of incentives and features of the HCV program that are ignored in the usual

argument suggests that the worst voucher units, and those in the worst neighborhoods, will

usually rent for more than the mean market rent of identical units, and the best units in the

best neighborhoods will rent for less than this amount. The following evidence supports that

general pattern. It also sheds light on the mix of units in the two categories and the magnitudes

of the differences.

Evidence

The best evidence about voucher rents relative to market rents comes from three major HUD-

funded studies and one recent journal article. The HUD-funded studies use random sampling to

estimate patterns for the country as a whole. The journal article produces results for one county.

Most studies predict the market rent of voucher units based on estimated hedonic equations, but

one used real estate appraisers.

Because there is no reason to believe that the HCV program has about the same effect in all

localities, it is important to consider the evidence from the best older studies. However, since

these studies produced results for different times in the history of the Section 8 Existing Housing

Program (1979, 1987, 2000), it is also important to consider how the versions of the program that

existed at the earlier times differed from the current program and how these differences are likely

to affect the outcomes of interest. We are primarily interested in the performance of the current

program, and the Section 8 program has undergone significant changes on several occasions

(Olsen, 2003).

The earliest studies produced results for the rent certificate program. The only significant difference

between the rent certificate program and the HCV program is that the certificate program’s payment

standard was its ceiling rent.7 Unlike current recipients, certificate holders were not allowed to

occupy a more expensive unit by paying the incremental cost. It is reasonable to believe that the

recipients who would have chosen this option under the HCV program would have lived in the

best units and neighborhoods under the certificate program. The earlier analysis suggests that

these households would typically have paid below-market rents for their units under the certificate

6 Rob Collinson provided this number based on the data underlying Figure B.5 in the online appendix to Collinson and Ganong (2018).

7 For most certificate holders, the payment standard was the applicable Fair Market Rent (FMR) that applied everywhere in a metropolitan area. Housing agencies were allowed to have payment standards called exception FMRs up to 10 percent greater than the applicable FMR for up to 20 percent of recipients, however.

94 Small Area Fair Market Rents

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment?

program. Under the HCV program, they have the same incentive to avoid overpriced units as

unsubsidized households. Therefore, they would, on average, pay market rents for their units, and

we expect aggregate overpayment to be larger in the HCV program than in the certificate program.

This is consistent with evidence from the only study that compared the two programs (ORC/Macro,

2001, Exhibit V-10).8

Participation and Benefits in the Urban Section 8 Program

Abt Associates (1981) provides the first evidence on the rents paid for voucher units relative to

the mean market rent of similar units in similar neighborhoods.9 This study is based on 1979

data for 276 randomly selected participants in the original housing certificate program from 16

randomly selected metropolitan areas. The authors predict the market rents of voucher units using

hedonic equations estimated with detailed information on the characteristics of unsubsidized

dwelling units and their neighborhoods from the Annual Housing Survey (AHS) and even better

information collected by the contractor for the study. The AHS data contained information on

40,560 rental units; the latter information on 1,365 apartments in 13 metropolitan areas. With the

smaller sample, the authors estimated separate hedonics for four regions with dummy variables

for the standard metropolitan statistical areas (SMSAs) in those regions. For the larger sample,

they estimated separate hedonics for each SMSA. The results indicate that the program’s gross

rent exceeded predicted rent by 4 to 5 percent.10 Under standard assumptions, the premium was

estimated with considerable precision. The authors did not estimate how the premium varied with

the desirability of the unit or neighborhood.

Freestanding Housing Voucher Demonstration

The second major HUD-funded study that sheds light on the rents paid for units under voucher

programs is the Freestanding Housing Voucher Demonstration, a random assignment experiment

that compared selected outcomes of the old certificate and voucher programs.11 The 1998 Housing

Act created the HCV program as a hybrid of these two programs. With minor exceptions, the old

voucher program provided a fixed subsidy to each household and placed no upper limit on the

rent of the unit occupied. Every additional dollar paid in rent reduced the recipient’s spending on

other goods by that amount. Unlike the certificate program, the old housing voucher program did

not have a rent reasonableness test.

The results of the experiment are based on data collected between 1985 and 1987 on recipients

served by 17 randomly selected large urban PHAs and two statewide voucher agencies. The

experiment assigned new recipients randomly to receive one of the two types of housing assistance.

Many outcomes were studied, including the rents paid for certificate and voucher units with the

same characteristics. Recipient housing is the subject of a lengthy report (Leger and Kennedy,

8 https://www.huduser.gov/portal/Publications/pdf/qualitycontrol.pdf

9 This report is not available on HUD’s website. A scanned version can be found at http://eoolsen.weebly.com/housing-

policy-info.html.

10 See tables 6-25 and 6-26 at http://eoolsen.weebly.com/housing-policy-info.html.

11 The experiment generated reports totaling more than a thousand pages. The two final reports that contain the detailed results (Leger and Kennedy, 1990b; 1990c) are posted on HUD’s website. Oddly, the summary report (Leger and Kennedy, 1990a) is not posted.

Cityscape 95

E. Olsen

1990c). Its authors used data on households in the old housing voucher program to estimate

a hedonic equation that was used to predict the market rents of units occupied by households

with rent certificates. Because voucher recipients faced the full marginal cost of their housing, the

authors assumed that they paid market rents for their units. The analysis is based on data from

the 10 urban PHAs that had a specified minimum number of recipients in each program. The

contractor assembled detailed information about the housing and its neighborhood for about 100

certificate and 100 voucher recipients in each site.

The study found that the mean rent paid for units in the certificate program was about 4 percent

less than the mean of the market rents of its units.12 Detailed results indicated a pattern of

overpayment and underpayment consistent with the implications of the theoretical model in the

preceding section. Certificate recipients paid higher than market rents for low-quality housing

(broadly conceived) and the opposite for high-quality housing.13 Specifically, the authors provide

estimates of the mean actual and predicted rent for 15 ranges of predicted market rent relative to

FMR to account for different rent levels in different localities.14 For the 28 percent of certificate

units that had predicted market rents less than 75 percent of the applicable local FMR, the mean

actual rent exceeded the mean predicted market rent. With a trivial exception, the percentage

difference was greater for units with the lower predicted market rent. The largest percentage

difference (15 percent) was for units with predicted market rents less than 55 percent of the FMR.

For units with predicted market rents between 75 and 85 percent of the FMR, there was essentially

no difference between mean actual and market rents. For the 55 percent of certificate units with

predicted market rents in excess of 85 percent of the FMR, the mean predicted market rent

exceeded the mean rent paid for the units. The percentage difference tended to be greatest for units

with the highest predicted market rent; the largest percentage difference (17 percent) was for units

with predicted market rents greater than 140 percent of the FMR. Therefore, in most cases, the

incentive of certificate recipients to find the best units renting for less than the payment standard

outweighed the absence of an incentive to resist overpaying for the unit.

Quality Control for Rental Assistance Subsidies Determination

For many years, HUD has funded studies to assess the performance of PHAs in administering its

rental assistance programs. To determine their effectiveness in enforcing the rent reasonableness

test, one of these studies compared the rents paid for voucher units with their estimated market

rents (ORC/Macro, 2001). Unlike other studies discussed in this paper, the contractor hired

real estate appraisers to estimate market rents. Appraisers attempt to find comparable units that

are nearby and account for differences in the characteristics of the units that significantly affect

market rent. Whether appraisers or hedonics are more effective in predicting market rents is an

open question.

12 Leger and Kennedy (1990b, Table D.21A, D.21B) at https://www.huduser.gov/portal/Publications/pdf/HUD-005555.pdf.

13 The authors did not distinguish between unit and neighborhood quality. They used market rent as an index that captures both.

14 Leger and Kennedy (1990b, Table D.22C) at https://www.huduser.gov/portal/Publications/pdf/HUD-005555.pdf

and Leger and Kennedy (1990c, Table E.25C) at https://www.huduser.gov/portal/Publications/pdf/HUD-005597.pdf

provide similar results based on ranges of predicted rent alone. The qualitative conclusions are the same.

96 Small Area Fair Market Rents

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment?

This study is based on data for 752 recipients served by 107 randomly selected housing authorities

in 2000. Different recipients were served by different voucher programs. In October 1999,

HUD began the transition from the rent certificate and old voucher program to the current HCV

program. At the time of the study, some units were under each of the three programs. The report

does not contain information on the percentage served by each program. The results reported in

exhibit V-10, however, imply that the rent certificate program served 53 percent of the total.15 The

study does not report results separately for the three programs, and the one result reported for

different programs does not distinguish between the old and new voucher programs. Because the

old voucher program was quite small relative to the certificate program, it seems likely that the

current voucher program served many voucher recipients in the study.

ORC/Macro (2001) found that in aggregate the three programs paid 10 percent less than market

rents for their units. Exhibit V-10 indicates that, on average, program units rent for less than

predicted market rents for all types of households, dwelling units, and program parameters

studied. Consistent with the implications of the model in the preceding section, the discount is

greater in the certificate program than in the voucher programs ($120 versus $66 a month) and

greater in high- than in low-cost submarkets ($244 versus $14 a month). Because each unit is

classified as living in a high- or low-cost submarket based on whether its estimated market rent

is greater or less than the FMR in the locality, the latter result is more accurately described as

reflecting the overall desirability of the unit rather than only neighborhood desirability.

Milwaukee Study

In a recent study based on a random sample of rental units in Milwaukee County, WI, Desmond

and Perkins (2016) estimated the difference between the rents of unsubsidized and voucher units

based on excellent information about the housing, neighborhood, and tenant characteristics of

1,046 renters in 2010. They found that landlords received rents about $51 to $68 a month more

than market rents for their units. The mean rent paid for voucher units was about $765 a month,

so this amounted to 7 to 10 percent higher than market rents. The authors also present results for

different quartiles of the distribution of an index of neighborhood disadvantage (Desmond and

Perkins, 2016: Figure 3). They find that the excess cost in dollar terms is greatest and about equal

for neighborhoods in the two quartiles with the greatest disadvantage. In the most advantaged

neighborhoods, voucher recipients paid slightly less than market rents. If the results had been

expressed as percentages, they undoubtedly would have indicated a monotonic relationship.

This study has several important virtues. All recipients participated in the current HCV program

and hence the results apply directly to this program. Furthermore, its estimated hedonic equation

is the equal of the best previous hedonics used to estimate differences between the rents of

unsubsidized and voucher units.

Two disadvantages of this study compared with the other studies concern the generalizability of

the results and the small sample of voucher recipients. The magnitudes of interest surely vary

15 The exhibit at https://www.huduser.gov/portal/Publications/pdf/qualitycontrol.pdf reports the mean difference between actual and market rent for all units and separately for certificate and voucher units. The percentage served by the certificate program can be calculated from these numbers.

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E. Olsen

from time to time and place to place. For example, some PHAs surely do a better job than others

in enforcing the program’s rent reasonableness test. The variance in outcomes could be large, and

Milwaukee County could be far from average in its outcomes. The best previous studies produced

estimates for a random sample of locations in an attempt to produce national average results.

Furthermore, these studies base their estimates on much larger samples of voucher units ranging

from 276 to about 1,000, as opposed to 57 in the Milwaukee study.

Another problem with this study is that some households identified as unassisted probably live in

privately owned subsidized housing projects. Because they pay below-market rents, their inclusion

in the analysis as unassisted households would bias the results in the direction of overstating the

excess rent of voucher units. Despite the authors’ efforts to avoid it, there are good reasons to

believe that this inclusion is a problem. The sample is a stratified random sample of households

in the county. About 57 of the 1,045 households used in the analysis were identified as voucher

recipients. According to HUD’s Picture of Subsidized Households, twice as many households in

Milwaukee County live in privately owned subsidized housing projects as have housing vouchers.

Therefore, the sample should be expected to contain about 114 households living in subsidized

projects. Using address matching, Desmond and Perkins were able to identify only four households

in their sample who lived in such projects. It is very unlikely that such a large difference resulted

from random sampling. It is more likely to have resulted from the difficulties of address matching.

The list of addresses of privately owned subsidized housing projects may have been incomplete.

The address listed for a project may have been the address of a business office rather than the

residences at the project. Only one address is listed for each project, and projects often contain

multiple buildings with addresses that differ beyond differences in apartment numbers. Despite

substantial efforts, HUD has been unable to identify most of the tax credit units in the American

Housing Survey, and identifying units served by HUD’s programs was no mean feat. This testifies to

the challenges involved. It seems likely that about 10 percent of households identified as unassisted

paid below-market rents. For this reason, the Desmond and Perkins (2016) results should be

regarded as an upper bound on the overpayment for voucher units.

How Do Changes in Housing Voucher Design Affect Rent and

Neighborhood Quality?

As suggested by its title, the most important contribution of Collinson and Ganong’s published

paper is its estimates of the effects of replacing uniform metro-wide FMRs with SAFMRs on the

neighborhood choices of voucher recipients. Their results indicate that SAFMRs will induce

voucher recipients to live in significantly better neighborhoods with minimal effect on the taxpayer

cost per recipient and hence the number of recipients who can be served with a fixed budget. This

contrasts sharply in both respects with the effect of uniform increases in FMRs across the entire

metropolitan area. The paper also contains evidence, however, about whether HUD overpays for

voucher units in the worst neighborhoods and whether SAFMR will reduce these overpayments.

The paper’s evidence on the pattern of overpayment and underpayment for voucher units appears

in Figure B.1 in their online appendix.16 The figure displays the relationship between the actual

16 https://assets.aeaweb.org/asset-server/files/6861.pdf

98 Small Area Fair Market Rents

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment?

rents and predicted market rents of voucher units in one of their event studies. It indicates that

the mean of the actual rents exceeded the mean of the predicted market rents for nearly all levels

of predicted market rent, and the difference is largest for units with the lowest predicted market

rent. For units with a predicted market rent of about $450 a month in 2010, the estimated

overpayment was about 90 percent. These large differences are well outside the range of other

estimates. The most plausible explanation is that Collinson and Ganong’s predicted market

rents for this analysis are based on a hedonic equation with only a few unit and neighborhood

characteristics, namely, number of bedrooms, structure age, structure type, and the unit’s Public

Use Microdata Area (PUMA). The other studies had much better information on housing and

neighborhood characteristics.

The paper devotes considerable effort to estimating effects of changes in payment standards on

(1) the rents paid for housing units, (2) indicators of neighborhood desirability, and (3) indices

of unit desirability and combined unit and neighborhood desirability. These results are directly

relevant to the effect of SAFMRs on the degree of overpayment and underpayment. The authors

find that exogenous increases in payment standards will lead to much greater percentage increases

in the rents of voucher units than in indices of the overall desirability of these units and their

neighborhoods. Similarly, exogenous decreases in payment standards will lead to much greater

percentage decreases in the rents of voucher units than in indices of the overall desirability of

these units and their neighborhoods. If true, replacing current FMR with SAFMR would decrease

net overpayment for voucher units in areas with less than average median rents and increase it in

other areas.

Although the authors studied the effects of three events that created exogenous variation in

payment standards, assembled the best available data for studying these events, and used it well,

some uncertainty remains about this conclusion. Because the information on the desirability of the

dwelling unit and its neighborhood available for studying two of the three events was meager, it is

entirely possible that the increased payment standards led to a much larger percentage increase in

the actual as opposed to the measured desirability of the unit and its neighborhood. A sensitivity

analysis that attempted to hold constant unit characteristics by using unit fixed effects suggested

a much smaller increase in voucher rent relative to housing desirability (Collinson and Ganong,

2018: 74–75). In the one event where detailed information about unit desirability was available,

the circumstances arguably did not permit estimation of the long run effect of the increased

payment standards. Increases in the payment standard were modest (about 5 percent on average

in the first year), the time period studied was short (3 years), and most observations involved

sitting tenants. It would not be surprising if few sitting voucher recipients found it in their interest to search for the very slightly better unit made available by the slightly higher payment standard.

The effect of the increased payment standards on the desirability of the unit and neighborhood

occupied might have been much larger after most recipients had moved from their initial units.

Caveat

It is important to realize that there is an alternative explanation for the preceding empirical results

that challenges their interpretation as overpayments and underpayments for voucher units. All of

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E. Olsen

the empirical literature on this topic is based on the implicit assumption that, among all units that

are the same with respect to the observed housing and neighborhood characteristics used to predict

the market rents of voucher units, voucher units are neither better nor worse than unsubsidized

units with respect to unobserved characteristics. It is reasonable to expect that this assumption

is violated in a way that gives the appearance of overpayment in the worst, and underpayment in

the best, neighborhoods. The voucher subsidy gives its recipients the resources and incentive to

demand much better housing in their current low-rent neighborhoods. Some landlords in these

neighborhoods substantially renovate their units to attract and retain voucher recipients (Rosen,

2014). The observed housing characteristics used in empirical studies will capture some of these

improvements, but it is reasonable to believe that, among units that are equally good with respect

to observed characteristics in these neighborhoods, units occupied by voucher recipients are better

in unobserved respects. In the best neighborhoods, voucher recipients live in units that are the

worst with respect to observed and unobserved characteristics in order to occupy a unit renting for

less than the payment standard. To some extent, the apparent overpayments and underpayments

reflect differences in unit quality. Empirical studies with the best data on the characteristics of the

dwelling units and their neighborhoods should be expected to have the smallest bias in estimating

overpayment and underpayment for voucher units.

Conclusion

The evidence available suggests that HUD overpays for voucher units in the worst neighborhoods.

More generally, the current HCV program pays rents to landlords that are usually greater than

the mean rents of similar unsubsidized units for the least desirable units and units in the worst

neighborhoods. The opposite is true for the most desirable voucher units and units in the best

neighborhoods. The mix of units in these categories varies across studies. Some find an aggregate

overpayment and others an aggregate underpayment. The weight of the evidence is that these

aggregate differences are modest.

These outcomes are the net result of several program features. Ignoring the program’s rent

reasonableness test and the possibility that tenants can occupy a unit renting for more than the

payment standard by paying the entire extra cost, the tenant’s rent does not depend on the total

rent paid to the landlord for the unit. If the best unit resulting from the tenant’s search has an initial asking rent less than the payment standard, the voucher recipient has no incentive to resist a rent

increase up to the payment standard. The tenant, however, does have an incentive to find the best

possible unit renting for no more than the payment standard. If the tenant’s limited search leads

to one of these units, the landlord’s initial asking rent will be only slightly less than the payment

standard and below the mean rents of similar units occupied by unsubsidized households. If the

landlord is paid the payment standard for these units, these units would still be underpriced. The

rent reasonableness test limits the extent to which HUD overpays for voucher units, especially

for the worst units and units in the worst neighborhoods. The possibility that tenants can occupy

a unit renting for more than the payment standard by paying the entire extra cost provides a

significant fraction of voucher recipients with a strong incentive to avoid overpriced units.

100 Small Area Fair Market Rents

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment?

Finally, Collinson and Ganong’s results indicate that replacing current FMR with SAFMR would

decrease net overpayment for voucher units in areas with less than average median rents and

increase it in other areas. They also imply that lowering FMR across-the-board would have modest

effects on the desirability of the units and neighborhoods occupied by voucher recipients. If these

results are accurate, lowering the FMR would enable us to serve many more households with the

same budget without significant negative effects on existing recipients. Since only about one-third

of households with extremely low incomes receive any housing assistance, this reform would have

considerable merit. It could be achieved gradually by freezing Fair Market Rent at current levels.

Alternatively, current recipients could be grandfathered and the new schedule applied only to new

voucher recipients. This reduction in base FMR could be combined with a tilting of the schedule

using Small Area Fair Market Rent methodology to incentivize families, especially with children, to

move to better neighborhoods.

Acknowledgments

The author would like to thank Peter Kahn and Marie Lihn for helpful comments and Mark

Shroder for suggesting an alternative explanation for the empirical results that does not involve

overpayment or underpayment for voucher units.

Author

Edgar O. Olsen is a professor of economics and public policy at the University of Virginia and a

visiting scholar at the American Enterprise Institute.

References

Abt Associates, Inc. 1981. Participation and Benefits in the Urban Section 8 Program: New Construction and Existing Housing. Vols. 1 and 2. Washington, DC: U.S. Department of Housing and Urban

Development, Office of Policy Development and Research. http://eoolsen.weebly.com/housing-

policy-info.html.

Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better

Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment,”

American Economic Review 106 (4): 855–907.

Chyn, Eric. 2018. “Moved to Opportunity: The Long-Run Effect of Public Housing Demolition on

Children,” American Economic Review 108 (10): 3028–3056.

Collinson, Robert and Peter Ganong. 2018. “How Do Changes in Housing Voucher Design Affect

Rent and Neighborhood Quality?” American Economic Journal: Economic Policy 10 (2): 62–89.

————. 2015. The Incidence of Housing Voucher Generosity. Working paper.

Cityscape 101

E. Olsen

Dastrup, Samuel, Meryl Finkel, Kimberly Burnett, and Tanya de Sousa. 2018. Small Area Fair

Market Rent Demonstration Evaluation: Final Report. Washington, DC: U.S. Department of Housing

and Urban Development, Office of Policy Development and Research.

Desmond, Matthew, and Kristin L. Perkins. 2016. “Are Landlords Overcharging Housing Voucher

Holders?” City & Community 15 (2): 137–162.

ICF International, Inc. 2014. Quality Control for Rental Assistance Subsidy Determinations: Final

Report for FY 2013. Washington, DC: U.S. Department of Housing and Urban Development, Office

of Policy Development and Research. https://www.huduser.gov/portal/publications/pubasst/

QCinc_fy13.html.

Leger, Mireille L., and Stephen D. Kennedy. 1990a. Final Comprehensive Report of the Freestanding

Housing Voucher Demonstration. Vol. 1. Washington, DC: U.S. Department of Housing and Urban

Development, Office of Policy Development and Research.

————. 1990b. Final Comprehensive Report of the Freestanding Housing Voucher Demonstration.

Vol. 2. Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy

Development and Research. https://www.huduser.gov/portal/Publications/pdf/HUD-005555.pdf.

————. 1990c. Recipient Housing in the Housing Voucher and Certificate Programs. Washington,

DC: U.S. Department of Housing and Urban Development, Office of Policy Development and

Research. https://www.huduser.gov/portal/Publications/pdf/HUD-005597.pdf.

Olsen, Edgar O. 2003. “Housing Programs for Low-Income Households.” In Means-Tested Transfer

Programs in the United States, edited by Robert A. Moffitt. Chicago: University of Chicago Press.

ORC/Macro. 2001. Quality Control for Rental Assistance Subsidies Determination. Washington, DC:

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

https://www.huduser.gov/portal/Publications/pdf/qualitycontrol.pdf.

Phillips, David C. 2017. “Landlords Avoid Tenants Who Pay with Vouchers,” Economics Letters 151: 48–52.

Rosen, Eva. 2014. “Rigging the Rules of the Game: How Landlords Geographically Sort Low-

Income Renters,” City & Community 13 (4): 310–340.

102 Small Area Fair Market Rents

by Marietta E.A. Haffner

Reflections on Demand

Assistance in the Rental Sector:

A European Perspective

Marietta E.A. Haffner

Delft University of Technology, Department of Architecture and the Built Environment

Delft, Netherlands

Abstract

Demand-side or demand assistance with housing costs is known as housing allowance, housing benefit,

or rent rebate in advanced economies and as housing vouchers in the United States. This type of

assistance, which is also called a subject or person-based subsidy, aims to safeguard access to housing by making it affordable for consumers whose income is insufficient to pay for their housing costs.

This contribution aims to contextualize the newest development in the United States housing voucher

implementation: the use of Small Area Fair Market Rents (SAFMRs) rather than metropolitan Fair

Market Rents (FMRs) in the determination of the tenant subsidy amount. Some possible outcomes of this change in the design of the instrument are reported in three articles in this issue of Cityscape : (1) “The Effects of Small Area Fair Market Rents on the Neighborhood Choices of Families with

Children” by Samuel Dastrup, Ingrid Ellen, and Meryl Finkel

(2) “Impact of Expanded Choice on Tenure in the Housing Voucher Program” by Judy Geyer, Samuel

Dastrup, and Meryl Finkel

(3) “Small Area Fair Market Rents, Race, and Neighborhood Opportunity” by Kirk McClure and Alex

Schwartz

This contribution summarizes these outcomes, after presenting a brief history of housing demand-side assistance schemes and their design characteristics. The contribution concludes by comparing different systems and the role played by demand-side assistance.

History in a Nutshell

In the second half of the 20th century, demand-side assistance schemes were introduced as

complementary assistance systems to supply-side (object or brick-and-mortar) subsidy systems and

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

Haffner

became important housing instruments in the housing policy tool box in many advanced welfare

states (Kemp, 1997, 2007; Turner and Elsinga, 2005).1 A manifold of reasons was put forward for

the introduction, such as the desire to keep housing affordable in any situation of housing scarcity

(Sweden), to be able to remove or lighten rent controls (Germany, the Netherlands), and to move to

demand-side assistance (United Kingdom, United States).

Even though the latter reason might not have been the reason for its introduction, the shift away

from production-oriented subsidization with which governments realized their public, social, or

nonprofit housing (official name depends on a country’s tenure system) became popular because of

targeting. Demand-side subsidies operate as income- or means-tested instruments with a focus on

lower to middle-income households. The subsidy type allows for adaptation of the subsidy amount

when a household’s resources change, while, as a typical example, tenants in public rental housing

usually were not evicted once their income increased. Many expanding northwestern European

welfare states welcomed this flexibility and decrease in their government budgets of the 1980s and

1990s (as in Britain, Germany, Sweden, and the Netherlands). The shift to demand-side subsidies

frequently went together with a shifting discourse of governments from housing needs to housing

affordability, and from housing as a good based on merit to housing as a commodity (Freeman,

Kiddle, and Whitehead, 2000; Linneman and Megbolugbe, 1992; Whitehead, 1991).

The United States also shifted its policy from supply to demand subsidies based on the outcomes

of an Experimental Housing Allowances Program (EHAP) that took 11 years (Gibb, 1995). The

United States introduced the Section 8 Program, later renamed the Housing Choice Voucher (HCV)

program (Priemus, Kemp, and Varady, 2005). At the time of writing, the HCV program is the

largest housing assistance program that the U.S. Department of Housing and Urban Development

(HUD) administers (Dastrup, Ellen, and Finkel, 2019).

Some Design Characteristics of Housing Demand Assistance: Tenure and Rent

One of the major design criteria for a demand subsidy is the main role it will serve. While in other

countries demand assistance predominantly functioned as a safety net for tenants in the rental

sector (the Netherlands and United Kingdom), tenants in the social rental sector (Belgium) or in

the private2 rental sector, as well as in the owner-occupied sector (Germany), the United States

enforced a mobility objective3 with the permanent introduction of the HCV program in 1987

(Reina and Winter, 2019). The expression “vouchering out” indicates that the voucher recipient is

to move from a (possibly stigmatized) public rental dwelling to a suitable, decent-quality private

rental dwelling in order to improve quality of life. The government ensures the quality by checking

the dwelling against HUD’s standard of housing quality (Priemus, Kemp, and Varady, 2005).

1 The conceptual and geographic contextualisation in this text is largely based on insights developed in Boelhouwer and Haffner (2002); Haffner and Boelhouwer (2006); Haffner, Henger, and Voigtländer (2013); Haffner, Hoekstra, Oxley, and Van der Heijden (2009); Priemus and Haffner (2017); Van den Broeck, Haffner, Winters, and Heylen (2017).

2 Officially, the rental sector in Germany is a private rental sector. The private rental units might be temporarily subsidized with a supply subsidy to improve affordability in comparison to market rents (Haffner, Hoekstra, Oxley, and Van der Heijden, 2009; Kofner, 2014).

3 Only in the case of a terminated place-based voucher, the tenant in question will be able to receive a voucher to safeguard from displacement or from paying a higher rent (Reina and Winter, 2019).

104 Small Area Fair Market Rents

Reflections on Demand Assistance in the Rental Sector: A European Perspective

Another design characteristic involves the criteria that determine the level of subsidization. A

tenant’s income will play a role, next to household size and composition (for example, age in

Sweden and the Netherlands; Boelhouwer and Haffner, 2002; Haffner and Boelhouwer, 2006;

Haffner, Henger, and Voigtländer, 2013; Priemus, Kemp, and Varady, 2005).4

Furthermore, the type of rent that is being considered can influence the amount of demand

assistance in two ways. In the ex post assistance (Gibb, 1995), demand assistance is linked to actual rent paid (the Netherlands; Boelhouwer and Haffner, 2002; Haffner and Boelhouwer, 2006). Ex

ante assistance, like the HCV program in the United States, implies notional rent, a standard, or reference rent that the subsidy calculation is based on. From a welfare-theoretical perspective,

the idea would be that using a notional rent rather than actual rent allows an optimum in

consumer choice.

Germany and the United Kingdom were examples of countries which operated both types of

systems at the same point in time (Haffner, Henger, and Voigtländer, 2013; Walker and Niner,

2012; see below). While the United Kingdom worked5 with median area market rents for demand

assistance (called Local Housing Allowance or LHA) in the private rental sector, Germany applied

six region types6 (absolute rent levels) across the country for determining the standard for rent that

was to be taken into consideration for the calculation of the demand subsidy in the rental sector. In

both cases, the notional rents were adapted to property size.

The United States used to set metropolitan FMRs that HUD designated to metropolitan areas

(Dastrup, Ellen, and Finkel, 2019; Priemus, Kemp, and Varady, 2005). Payment standards are set

by public housing agencies (PHAs) generally between 90 and 110 percent of FMR (Geyer, Dastrup,

and Finkel, 2019). A voucher ceiling is or may also be applicable.

Problems with the HCV program implementation, including the concentration of voucher holders

in low-opportunity areas, led to an overhaul of the calculation of the FMRs and the change from

metropolitan-wide FMRs to SAFMRs. FMRs set at a lower geographic level would be more closely

related to actual rents in ZIP Code areas and would therefore increase supply of voucher-eligible

dwellings in more expensive ZIP Code neighborhoods and areas (Dastrup, Ellen, and Finkel,

2019; McClure and Schwartz, 2019). Such a change would allow voucher holders better access

to these high-opportunity areas. As these areas are associated with access to better amenities and

services, they allow better economic opportunities for the voucher holder. Furthermore, SAFMRs

would be expected to counter any rent overcharging of voucher holders. Desmond and Perkins

(2016) identified this “voucher premium” in Milwaukee, WI, areas where market rent was lower

than FMR.

In 2012, the change to SAFMRs from metro FMRs took place, when HUD launched a SAFMR

Demonstration Evaluation Project in five PHAs, which were randomly selected (Dastrup, Ellen,

4 As the years of the references show, the examples are not always state of the art, but may be, as is largely the case for the Netherlands and Germany. Since late 2013, the United Kingdom has been rolling out Universal Credit which combines several means-tested benefits (Goering and Whitehead, 2017).

5 For the United Kingdom, Walker and Niner (2012) indicated that the government proposed to replace the median rent with the 30th percentile rent in the private rental sector.

6 It is not clear how these regions are determined.

Cityscape 105

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and Finkel, 2019). In 2011, two PHAs had already started with SAFMRs bringing the total up to

seven PHAs.7 From late 2016 on, SAFMRs became optional for all metropolitan PHAs and were

aimed to be set as obligation for the PHAs in 24 metropolitan areas in October 2017. This was

delayed until April 2018, however, as the Trump Administration attempted to delay the mandatory

implementation for 2 years to give PHAs more time to prepare for the conversion (McClure and

Schwartz, 2019).

Evaluation of Small Area Fair Market Rents

The implementation of SAFMRs in a couple of PHAs and not in others functions as an opportunity

to treat the impact measurement of the policy change as a natural experiment. Dastrup, Ellen,

and Finkel (2019) and Geyer, Dastrup, and Finkel (2019) both took the opportunity to apply an

experimental methodology—the difference-in-differences (DiD) specification—in their evaluation

of the introduction of the SAFMRs demonstration program in five plus an additional two 2011

PHAs (see previous section). DiD compares the outcomes of PHAs that applied SAFMRs with

those that did not apply SAFMRs in order to identify “the winners and losers” of the policy change.

The three articles listed above all present a quantitative assessment of the impacts of the SAFMR

introduction, which are now briefly summarized.

Given the evidence that higher opportunity areas provide for long-term benefits for the

development of children in low-income households, Dastrup and colleagues (2019) aimed to

explore whether SAFMRs compared with FMRs allow voucher-recipient families with children to

move to higher opportunity areas in the first 5 years after the introduction of SAFMRs. Dastrup

and colleagues (2019) operationalized the benefits for the children in terms of poverty, school

proficiency, employment access, and environmental quality in a composite indicator. Using

repeated cross section regressions, the authors concluded that SAFMRs seem to deliver on their

promises. In high-opportunity areas more supply of suitable units is created, while the share of

families with children locating in better-opportunity areas also increased. These effects are larger

for movers than for new voucher recipient households with children. Therefore, some PHA

guidance would be welcomed, as well as landlord recruitment.

This all seems to be achieved without additional costs for the government, Dastrup, Ellen, and

Finkel (2019) reported, implying that the savings achieved in low-opportunity areas compensate

the increases in high-opportunity areas. On a metropolitan level, such an effect will depend on the

composition of the rental stock and its distribution in the metropolitan area.

Also using data from the SAFMR demonstration project, Geyer, Dastrup, and Finkel (2019)

examined the duration that an HCV recipient stays in the program. As in the previous paper, the

outcomes of PHAs which continued operating with metropolitan FMR and those of the PHAs

using SAFMR are compared, this time with a survival analysis. The authors found that the switch

to SAFMR increases the program exit rate with a median of about 2 years (from a base of a median

11 years).

7 PHAs have the possibility to protect voucher recipients whose SAFMR is lower than FMR (Geyer, Dastrup, and Finkel, 2019).

106 Small Area Fair Market Rents

Reflections on Demand Assistance in the Rental Sector: A European Perspective

For working-age adults and households not living in high-rent areas at the time of the switch,

the effects are found to be largest. For the latter outcome, authors suggested that households are

leaving the program because of “a decrease in household resources,” which would make it an

unexpected outcome, particularly for those in moderate-income area. For those in a lower income

area, voucher takeup might be considered less attractive than before, while the number of total

available dwellings has also decreased.

Authors indicated running into difficulty in explaining their outcomes and attribute this to

missing data and research. On exit reasons, they proposed to improve data collection, particularly,

registering a set of reasons when exiting the HCV program.

McClure and Schwartz (2019) investigated the potential success of Black and Hispanic voucher-

eligible recipients in accessing high-opportunity areas because these target groups often live in

disadvantaged neighborhoods. They reasoned that because the SAFMR demonstration project

outcomes are very much differentiated across PHAs, with only some showing change, they

“simulated” a switch to SAFMR of all metropolitan areas with more than a population of 1 million.

Given the switch, more dwellings in high-opportunity areas would increase supply there, while less

supply would be created in low-opportunity areas. The question is: what is the balance?

McClure and Schwartz found that the new HCV-eligible units in high-opportunity areas would be

mostly located in non-segregated areas, and the supply of these voucher-eligible dwellings would

have decreased. Given the barriers that arise in practice for minorities to move into non-segregated

areas, these outcomes lead the authors to conclude that introducing SAFMR in these larger

metropolitan areas will not be a sufficient condition to solve segregation.

They proposed a counseling program along the lines of the successful Baltimore Housing Mobility

Program (DeLuca, Garboden, and Rosenblatt, 2013).

From the three papers, it can be derived that the change is a re-allocation of the budget rather than

an increase of assistance. As with any budget-neutral policy change, the shift from FMR to SAFMRs

produces winners (families with children and others successfully moving to high-opportunity

areas) and losers (Black and Hispanic minorities; lower and middle-income households; middle-

aged adults).

The new way of calculating notional rents on a geographic lower level—ZIP Code level rather than

metro-level—seems to be suitable to the aim of the HCV instrument of stimulating mobility toward

higher opportunity areas, given the lower estimated supply of voucher-eligible rental dwellings

in lower opportunity areas. Does such a result imply that the introduction of the SAFMR lays the

groundwork for those who have better life chances already rather than helping those most in need

of affordable and decent housing?

Notional Rent vs. Actual Rent Assistance: Final Observations

Considering means-tested demand assistance as more effective and more cost-efficient than supply

subsidies was one of the reasons for many governments to make the shift from supply to demand

support. The subsequent design choices were complex and impacts often remain difficult to

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Haffner

measure (Shroder, 2002). Concerns of safety net, income support, price deduction, and mobility

all can play a role, as the above shows. Also, what is considered affordable for a household (Stone,

2006) goes beyond the discussion here8 about actual rent versus notional rent.

From a social protection point of view, considering access to decent-quality housing as a human

and social right, targeting would take the actual rent a tenant pays as starting point, and it

would set housing allowance as an entitlement. This would allow for tailor-made solutions.

Conditions will be attached to the receipt of such a subsidy in order to make it an effective and

efficient instrument.

An example here is the Dutch system (Priemus and Haffner, 2017; Rijksoverheid, 2016). The

level of assistance is dependent on household size, composition, age, income, and rent. Housing

allowance does not subsidize the first band of the rent (about 200 euros in months’ rent)9, as it is

assumed that this will be covered by other income; in the case there is no income from work or

pension, this will be (implicitly) covered by state welfare income support or state pension. The

next band of rent of almost another 200 euros is paid in full by the housing allowance. For the

next band of about 200 euros, 65 percent of the band is subsidized by the housing allowance in

order to prevent the overconsumption of housing services. Above that amount of rent only single

heads of household and senior-headed households will receive 40 percent of subsidy for the band

up to the maximum amount of rent that is eligible for housing allowance (about 700 euros was the

cap for rent eligible for housing allowance). As an entitlement, about 38 percent (16 percent) of

Dutch tenants (households) received housing allowance, and on average, housing allowance paid

about 40 percent of their rent in 2014 (Rijksoverheid, 2016).

For such an actual-rent-based system, society and politics make several “paternalistic” assumptions,

such as when the quality consumed must be considered “too much.” In that case, the quality

discount (subsidy only covering 65 and 40 percent, respectively, as explained in the previous

paragraph) comes into play to prevent the overconsumption of housing services (see also Gibb,

1995; Kemp, 2000; Priemus, Kemp, and Varady, 2005). Furthermore, this type of housing

allowance is associated with a potentially big poverty trap, as the implicit marginal tax rate amounts

to 27 to 43 percent (2015) and to more than 90 percent for some recipients, if other income-based

subsidies are considered in the Dutch case.

A voucher system on the other hand, allows the “self-sufficient” recipients to make their own

choices. From an economics perspective, a household will not choose a dwelling because of

subsidy maximization (moral hazard) but because of preferences for a certain dwelling. This allows

the choice of a more expensive dwelling or a less expensive one without losing the right to the

voucher. Such an ex ante subsidy goes together with subsidizing some form of FMR rather than

the actual rent that a tenant pays. In this line of thinking, can the shift in the United States from

FMR to SAFMR be considered a move from notional rent towards actual rent? If so, will it become

afflicted with housing allowance types of undesired effect, like a moral hazard?

8 This also applies to the topic of capitalization of demand assistance into rents as one of the possible undesired effects associated with demand assistance (see the example, Virén, 2012, for Finland).

9 As of July 2019, 200 euros is equal to about $224 USD.

108 Small Area Fair Market Rents

Reflections on Demand Assistance in the Rental Sector: A European Perspective

In the selection of countries at the beginning of this contribution, the choices and developments

have variegated. Germany has implemented notional rents since the introduction of the scheme

(Haffner, Henger, and Voigtländer, 2013; Kofner, 2007, 2014). For recipients of welfare income

support, their housing assistance has been included in income support since 2005 and is based on

actual rent, given suitable quality of the dwelling in relation to household composition (a move to

a more suitable dwelling may be required). The housing allowance scheme remains serving those

with an income from employment.

The United Kingdom made the opposite move from the one that Germany made, also

implementing two types of demand assistance schemes in the end (Haffner, Henger, and

Voigtländer, 2013; Stephens, 2005; Walker and Niner, 2012). As housing benefit was paid for out

of welfare income support (called social security), and income support did not take housing costs

into account, recipients of income support or households earning a comparable or lower income

received housing benefit for 100 percent of actual rent and rent increases. Because of budgetary

and overconsumption concerns, LHA was introduced in the private rental sector in 2008 after two

rounds of experimenting that took place at the beginning of the century. LHA applies a notional

rent to calculate the allowance, as explained above. Meanwhile for recipients of housing benefit, as

in Germany, the number of rooms in the dwelling in relation to household size became important.

As of April 2013, the number of rooms had to match household size, or the recipient was going

to pay an under-occupancy charge, also known as the bedroom tax (Goering and Whitehead,

2017). This change implied that only when the dwelling was deemed suitable, actual rent and rent

increases were paid in full by housing benefit.

In the Netherlands, academia has been promoting a shift from actual to notional rent practically

in vain since the 2000s (Priemus and Haffner, 2017; Priemus, Kemp, and Varady, 2005). Only

recently an intergovernmental advisory report included such a scenario for the first time. Such a

scenario expected that the tenants would take responsibility upon introduction and the measure

would help to reduce overconsumption and deliver government budget savings (Priemus and

Boelhouwer, 2018). One of the questions that would need to be solved before the switch to

notional rents from actual rents, is the matter of the first band non-subsidization. As explained

above, this will be paid out of basic income support for citizens without any other or without

“sufficient” other income paid for by the national government.

These examples show that demand-side housing assistance schemes are heavily interlinked with

the welfare system in place in a country. Developments therefore differ across countries and are

not necessarily converging as the examples show. Voucher-like systems with notional rents, like in

the United States, are implemented, as are tailor-made schemes that subsidize actual rents. Some

countries combine both types. An entitlement scheme, like in Germany, the Netherlands, and the

United Kingdom, will be strong on horizontal equity. Academia generally support the notional

rent scheme over and above an actual rent scheme. The housing market reality of the “new, global

urban housing affordability crisis” will put equity and efficiency issues, as well as supply assistance, back on the agenda not only for those with the lowest incomes (Wetzstein, 2017: 3,160).

Cityscape 109

Haffner

Acknowledgments

A big thank you goes to all the author’s co-authors of publications on demand-side subsidization.

Author

Marietta E.A. Haffner is an economist, assistant professor at Delft University of Technology in

the Netherlands, an editor-in-chief of Housing Studies, a coordination committee member of the

European Network for Housing Research, and honorary research fellow at RMIT in Australia.

References

Boelhouwer, Peter J., and Marietta E.A. Haffner. 2002. Subjectsubsidiëring in de huursector onder de loep, Utrecht, the Netherlands: NETHUR.

Dastrup, Samuel, Ingrid Ellen, and Meryl Finkel. 2019. “The Effects of Small Area Fair Market

Rents on the Neighborhood Choices of Families with Children,” Cityscape 21 (3): 19-47.

DeLuca, Stefanie, Philip M.E. Garboden, and Peter Rosenblatt. 2013. “Segregating Shelter: How

Housing Policies Shape the Residential Locations of Low-Income Minority Families,” The ANNALS

of the American Academy of Political and Social Science 647 (1): 268–299.

Desmond, Matthew, and Kristin L. Perkins. 2016. “Are Landlords Overcharging Housing Voucher

Holders?” City & Community 15 (2): 137–162.

Freeman, A., C. Kiddle, and C. Whitehead. 2000. “Defining Affordability.” In Restructuring Housing

Systems: From Social to Affordable Housing? , edited by Sarah Monk and Christine M.E. Whitehead.

York: Joseph Rowntree Foundation: 100–105.

Gibb, Kenneth. 1995. “A Housing Allowance for the UK? Preconditions for an Income-Related

Housing Subsidy,” Housing Studies 10 (4): 517–532.

Geyer, Judy, Samuel Dastrup, and Meryl Finkel. 2019. “Impact of Expanded Choice on Attrition in

the Housing Voucher Program,” Cityscape 21 (3): 71–88.

Goering, John, and Christine M.E. Whitehead. 2017. “Fiscal Austerity and Rental Housing Policy

in the United States and United Kingdom, 2010–2016,” Housing Policy Debate 27 (6): 875–896.

Haffner, Marietta E.A., and Peter J. Boelhouwer. 2006. “Housing Allowances and Economic

Efficiency,” International Journal of Urban and Regional Research 30 (4): 944–959.

Haffner, Marietta E.A., Ralf Henger, and Michael Voigtländer. 2013. “A Tale of Two Housing

Allowance Systems: Evidence on Over-Consumption from Germany.” paper presented at the

European Network for Housing Research (ENHR) Conference Overcoming the Crisis. Integrating

the urban environment, Tarragona, Spain, June 19–22.

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Reflections on Demand Assistance in the Rental Sector: A European Perspective

Haffner, Marietta E.A., Joris Hoekstra, Michael Oxley, and Harry van der Heijden. 2009. Bridging

the Gap Between Market and Social Rented Housing in Six European Countries. Amsterdam: IOS Press BV. http://repository.tudelft.nl/view/ir/uuid:d35c0ed4-7874-4413-8b90-25352ec8c980/.

Kemp, Peter A. (ed). 2007. Housing Allowances in Comparative Perspective. Bristol: The Policy Press.

———. 2000. “The Role and Design of Income-Related Housing Allowances,” International Social

Security Review 53 (3): 43–57.

———. 1997. A Comparative Study of Housing Allowances. London: H.M. Stationery Office.

Kofner, Stefan. 2014. The Private Rental Sector in Germany. N.l.: Stefan Kofner.

———. 2007. “Housing Allowances in Germany.” In Housing Allowances in Comparative Perspective,

edited by Peter A. Kemp. Bristol: The Policy Press: 159–192.

Linneman, Peter D., and Isaac F. Megbolugbe. 1992. “Housing Affordability: Myth or Reality?”

Urban Studies 29 (3/4): 369–392.

McClure, Kirk, and Alex Schwartz. 2019. “Small Area Fair Market Rents, Race, and Neighborhood

Opportunity,” Cityscape 21 (3): 49–69

Priemus, Hugo, and Peter Boelhouwer. 2018. “Toekomst van de huurtoeslag,” Economisch-

Statistische Berichten 103 (4757): 88–91.

Priemus, Hugo, and Marietta E.A. Haffner. 2017. “How to Redesign a Rent Rebate System?

Experience in the Netherlands,” Housing Studies 32 (2): 121–139.

Priemus, Hugo, Peter A. Kemp, and David P. Varady. 2005. “Housing Vouchers in the United States,

Great Britain, and the Netherlands: Current Issues and Future Perspectives,” Housing Policy Debate

16 (3–4): 575–609.

Reina, Vincent J., and Ben Winter. 2019. “Safety Net? The Use of Vouchers When a Place-Based

Rental Subsidy,” Urban Studies 56 (10): 2092–2111.

Rijksoverheid, Central Government of the Netherlands. 2016. De prijs voor betaalbaarheid.

Opties voor meer doelmatigheid en effectiviteit in de huurtoeslag en het beleid voor de sociale huur. n.l.: Ministerie van Financiën.

Shroder, Mark. 2002. “Does Housing Assistance Perversely Affect Self-Sufficiency? A Review Essay,”

Journal of Housing Economics 11 (4): 381–417.

Stephens, Mark. 2005. “An Assessment of the British Housing Benefit System,” International Journal

of Housing Policy 5 (2): 111–129.

Stone, Michael E. 2006. “What is Housing Affordability? The Case for a Residual Income

Approach,” Housing Policy Debate 17 (1): 151–184.

Cityscape 111

Haffner

Turner, Bengt, and Marja Elsinga. 2005. “Housing Allowances: Finding a Balance Between Social

Justice and Market Incentives,” European Journal of Housing Policy 5 (2): 103–109.

Van den Broeck, Katleen, Marietta Haffner, Sien Winters, and Kristof Heylen. 2017. Naar een nieuw

stelsel van huursubsidies. Leuven: Steunpunt Wonen.

Virén, Matti. 2012. “Is the Housing Allowance Shifted to Rental Prices?” Empirical Economics 44 (3): 1497–1518.

Walker, Bruce, and Pat Niner. 2012. “Welfare or Work? Low-Income Working Households’

Housing Consumption in the Private Rented Sector in England,” Housing Studies 27 (3): 381–397.

Wetzstein, Steffen. 2017. “The Global Urban Housing Affordability Crisis,” Urban Studies 54 (14): 3159–3177.

Whitehead, Christine M. E. 1991. “From Need to Affordability: An Analysis of UK Housing

Objectives,” Urban Studies 28 (6): 871–887.

112 Small Area Fair Market Rents

by Christine M.E. Whitehead

Aspects of the Housing Choice

Voucher Program and the Impact

of Small Area Fair Market Rents

Ceilings: a British Perspective

Christine M. E. Whitehead

London School of Economics

Abstract

This paper reviews three of the four symposium papers on the Small Area Fair Market Rents (SAFMRs)

Demonstration Evaluation—those by Dastrup, Ellen, and Finkel; Geyer, Dastrup, and Finkel; and

McClure and Schwartz. These are all based on the very detailed data made available by the U.S.

Department of Housing and Urban Development (HUD) to enable initial evaluation of this initiative.

Together, these articles provide impressive, detailed approaches to different aspects of the program: the experience of family households in the areas where SAFMRs have been introduced as compared with

metropolitan-wide fair market rents (FMRs); the importance of race in determining who may or may

not be expected to benefit from the initiative; and the evidence on whether the introduction of SAFMR

has affected how long people stay in the voucher programme. Taken together the findings reflect three main issues:

1

how even extensive datasets, while producing interesting results, can only cover some aspects of a

full evaluation;

2

all the initial findings are mainly about what would normally be called outputs—that is, what

has happened as a result of the initiative, rather than outcomes—which, to the extent that the

objectives of the policy are clear, must be about the impact on the welfare of those affected, both in terms of housing and opportunity; and

3

whether, especially given the extent of locational segregation (between income groups as well as

race and other household attributes), such an approach can be expected to generate significant

changes in household decisions and outcomes.

Importantly, there is also no discussion of value for money from the point of view of government, which is often (usually) a major objective of such evaluations. Rather, success appears to be based on ensuring the money made available is used and used for the intended purposes.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 113

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

Whitehead

Abstract (continued)

This review has three main sections: my understanding of the demonstration project and more generally of the Housing Choice Voucher (HCV) programme; a discussion of the UK housing allowance programme

for private tenants, which includes the use of rent caps determined for broad market areas with a note on other European examples; and an attempt, which may be overambitious given the enormous differences

in approach between countries, to learn lessons in both directions.

The SAFMR Initiative in the Context of the Housing Choice

Voucher Programme

The starting point for any such commentary must be that housing systems and institutional

frameworks differ so greatly between countries that understanding someone else’s system is a

major issue, and understanding it from, in this case, a U.S. perspective, almost an impossibility.

In this note, I have not tried to bring that level of understanding to my comments—but rather to

identify what strikes me as important from a European and, more specifically, a UK perspective. In

particular, this means remembering that a federal system will inherently operate in a very different

manner from our over-centralised form of government in the United Kingdom which starts from

a belief that policies should, in principle, be operated consistently across the whole of the country

(although, of course, the outcomes will differ greatly). In other words, policy in the UK is often

more about equity, while in the United States, it is more about choice.

The core idea of the SAFMR initiative is to increase the extent to which households with

vouchers are enabled to live in better-quality areas measured in terms of income, schools, access

to employment, and environmental quality. Under the traditional voucher scheme, FMRs are

identified for the whole metropolitan area and, given spatial inequalities, the eligible properties are

almost inevitably concentrated in poorer areas where rents fall within the bottom 40 percent for

the whole area. A relatively small number of properties will be eligible in better-off neighbourhoods

(even allowing for the fact that households may top up their payments, as long as their rents

remain below 40 percent of their income) because the vast majority will have rents in the top 60

percent of the overall distribution (top 50 percent in some contexts). By breaking up the large area

into several much smaller ZIP Code areas and applying the bottom 40 percent rule to each area, the

potential supply of eligible housing in the more expensive areas must increase significantly. Equally,

however, the numbers of potential units in poorer areas will decline—and the evidence presented

in these article shows, unsurprisingly, that as a result, the total supply of eligible housing in the

metropolitan area also will decline. Whether that matters significantly is not clear to the outsider,

as that depends not on the number of eligible units but on the numbers of eligible dwellings that

landlords are actually prepared to put into the scheme in relation to the number of vouchers (an

issue to which I return in the UK context). All that I can be sure of from the analyses is that the

ratio will differ between different metropolitan areas and between types of small areas—although

a priori one would expect landlords in higher valued areas to be less prepared to make their

properties available.

114 Small Area Fair Market Rents

Aspects of the Housing Choice Voucher Program and the

Impact of Small Area Fair Market Rents Ceilings: a British Perspective

Although these findings raise significant issues not just about numbers but also about the

objectives of the program and how the trade-off between access to affordable housing and access to

particular types of area might be assessed, this is not the main research interest of any of the three

papers. The Dastrup et al. paper concentrates on family household decisions and finds that there is

a significant shift towards these higher value neighbourhoods which might, in principle, help the

children gain access to better schools and the families to live in a better environment with greater

opportunities. The Dastrup et al. paper stresses that even though significant numbers of families

have moved to higher valued areas, there is little evidence that these potential opportunities have

been taken up, and no positive evidence of better outcomes. The same is true of the Geyer et al.

paper on length of stay in the programme—which shows large reductions in how long people

remain in the program. At first glance—at least for someone taking an international perspective,

where most housing allowances are available as of right to all eligible households—one would

expect this to mean that the households are doing better and are thus able to fund their own

housing. The authors made it clear, however, that in the U.S. context, most of those who leave the

program end up with fewer resources. Thus, both articles stress that there are clear and significant

changes in household behaviour directly related to the policy change—but that there is no

evidence of welfare improvement.

Finally, the McClure and Schwartz paper on the implications of the introduction of SAFMRs for

racial segregation uses the data in a rather different way to compare small areas within metropolitan

areas and show that race is an important part of understanding these small areas and how they

differ from one another. The findings suggest strongly that the small area approach will be

likely to harm racial minorities as supply in lower priced areas where they normally live will be

reduced, while, it is likely to be far more difficult for them to find accommodation in the higher

income areas because these areas are mainly White-dominated, even when they are, to some

degree, integrated.

Overall, the evidence presented suggests that the small-area approach will limit rather than

expand choice for many households. On the other hand, evidence cannot yet be provided to

show positive effects, such as more landlords coming forward or households achieving better

schooling, jobs, and incomes. Overall, from an outsider’s perspective, what looked like a sensible

approach to expanding opportunity and one which does result in changing behaviour—especially

among family households—probably ends up making the situation worse for larger numbers of

households. Moreover, those who suffer are almost certainly, on average, at the poorer end of the

scale. However, it is early to make an overall assessment and it may be that positive outcomes,

especially for children, could take more time to emerge. More fundamentally, however, the negative

results stem from the extent of segregation to be found in large metropolitan areas which cannot be

effectively addressed by relatively limited policy initiatives of this kind.

The UK Approach to Housing Allowances in the Private

Rented Sector

In the United Kingdom, the question of how to develop housing-specific income support was

part of the debate around the development of social security in general, initiated in the Beveridge

Cityscape 115

Whitehead

Report in 1942 (Beveridge, 1942). When Beveridge published his report on developing an effective

post-war social security system, he recognised that the objective of ensuring that everyone would

be able to achieve a reasonable standard of living through the national insurance system he

was proposing was put at risk by rents—because of the extent to which the costs of minimally

acceptable housing varied across the country. No other element of the necessities of life was seen as

having variations that were significant enough to undermine his proposed nationally based system.

Beveridge therefore argued that a flat-rate benefit with an average allowance for housing (in line

with his general principles) would leave people in more expensive homes below a subsistence

level, once they had paid their rent, and people in cheaper homes with a surplus. The alternative—

paying the actual rent as part of the insurance benefit—was seen as creating perverse incentives for

people to move into more expensive accommodation just before they retired.

Throughout the post-war period until the early 1970s, the problem was addressed by holding

rents down in both the public and private rented sectors. In the public sector rents simply had to

cover the historic costs of provision less subsidies provided by central government across each local

authority’s stock of rental units. This applied to up to 30 percent of all dwellings in Great Britain

and meant that the vast majority of tenants could pay their rent without significant difficulty. The

vast majority of tenancies in the private rented sector were subject either to traditional rent controls put in place in 1917 or, to a “fair rents” regime put in place for new tenancies in 1965, which again

made rents affordable for the majority of lower income tenants.

By the mid-1970s, however, the position was changing quite rapidly with increasing rents in both

the social sector and much of private renting. Legal decisions also meant that rents had to relate

to dwelling attributes not to the individual circumstances of the tenant, which rules out income

related as a way forward. It was in this environment that housing allowances were introduced.

The scheme, ultimately known as Housing Benefit, not only took account of individual household

circumstances but meant that housing support was seen as a “residual” benefit taking the strain

across the overall welfare system. The principles behind the as-of-right scheme were that (1)

social security rules would determine the income necessary to cover basic needs for each type

of household at the national level (that is, with no allowance for variations in costs across the

country); (2) housing costs would be treated separately, because rents varied so greatly both

spatially and between similar households; (3) all those who paid rent were eligible to apply for

benefits; and (4) the rebate or allowance would cover the whole rent (and allowable service

charges) where the claimant’s income was equal to the basic needs allowance. Above that level,

a proportion of every additional pound was withdrawn until income reached a point where the

system presumed that the rent could be fully paid by the tenant.

Thus, under the scheme, all tenants obtained the minimum income required to meet the full range

of essential needs; differences in housing costs were fully addressed so basic housing standards

could be achieved; and the subsidy withdrawal rate above that level was designed to ensure some,

limited, incentive to work or to work longer hours.

116 Small Area Fair Market Rents

Aspects of the Housing Choice Voucher Program and the

Impact of Small Area Fair Market Rents Ceilings: a British Perspective

This approach, however, meant that Beveridge’s concerns about the distortionary effects of

households’ private-rented-sector housing decisions were left unchecked, except to the extent that

some basic limits on the size of the dwelling in relation to household needs were introduced. The

assumption made was that people on low incomes had few housing choices, so the incentive to live

in a more expensive dwelling or area was rarely implementable.

Over time, the Housing Benefit system enabled massive changes in the mix of subsidy provided

by government shifting support away from supply subsidies in the social rented sector to income-

related benefits (Hills, 2001; Gibb and Whitehead, 2007). In the 1970s, 85 percent plus of all

financial support helped supply and the impact of income related benefits was therefore quite

small. Now, however, £22 billion1—over 95 percent of a larger housing subsidies bill in real terms

than in the 1970s - goes to Housing Benefit and its successor Universal Credit (Stephens et al.,

2018). Thus the UK spends $27 billion on income related housing assistance, while the United

States with a population of around five times that of the UK, spends only $35 billion2 on housing

assistance in total—including tenant-based rental assistance, public housing operating funds, and

project-based rental assistance.

Even so, in the UK, especially since austerity became the norm in 2010, several policy changes

have limited the availability of income-related housing support, changing the picture almost

out of recognition. In the private rented sector, the restructuring started earlier, in 2008, under

a Labour government with the introduction of a maximum Local Housing Allowance (LHA),3

instead of the introduction of simply accepting the individual tenant’s rent bill for appropriately

sized accommodation. This approach has something in common with the U.S. system, in that it

uses the distribution of rents for those not on benefits in “Broad Market Areas” (defined as where a

person could reasonably be expected to live taking into account of access to facilities and services

such as schools) to determine the maximum support for which tenants could be eligible given the

appropriate-sized dwelling. The original rule was set at the 50th percentile of rents in the area,

which in itself raised issues as to the balance between demand and supply; given that in some

particularly poor areas, up to 90 percent of privately renting households would be in receipt of

housing benefit. In these areas, large numbers of households are therefore given a choice between

making up the gap from their other income or moving to somewhere they might be able to find

accommodation within the cap. In 2011 when austerity was at its height, the Coalition government

reduced the cap from the 50th percentile to the 30th so that this problem has become much more

widespread (Wilson, 2013).

Further in 2015, the government introduced a 4-year freeze on LHA levels. This inherently means

that as actual rents have gone up, those paying around or above the third decile set at the beginning

of the freeze will not be compensated for any rent increases.

1 As of July 2019, £1 equals $1.22. £22 billion equals approximately $26.8 billion.

2 See https://www.hud.gov/sites/dfiles/CFO/documents/3%20-%20FY19CJ%20-%20Dept.%20Summary%20-%20

Budget%20Outlays%20-%20Updated.pdf

3 There had been demonstration projects from 2002 in what were called pathfinder areas before the national roll-out in 2008.

Cityscape 117

Whitehead

Austerity policies underlie many of these changes, but there is also an attempt to increase

incentives to move to more inexpensive accommodation or locations. Importantly, the principle

of ensuring that residual income is enough to pay for the other necessities of life has been

undermined very significantly. Rather, it is now assumed that people have choice and, therefore, it

is their own decision to use money for housing rather than other necessities.

A report by the Institute for Fiscal Studies (IFS) published in 2017 (Joyce et al, 2017), gives

the best estimates currently available of how much some of these changes have impacted lower

income tenants. They showed that in 2013-2015, among private renters in the bottom 40 percent

of incomes, the fraction whose housing benefit does not cover all their rent has increased quite

steadily, from 74 percent in the mid-1990s to 90 percent in the mid-2010s. The biggest change

occurred among low-income working-age households with children, where it rose from 63 percent

to 90 percent over the same period. IFS estimated that reforms since 2011 have cut the Housing

Benefit entitlements of 1.9 million privately renting households, some two-thirds of low-income

private renters.

Another issue is how these constraints impact landlord preparedness to let to those on housing

benefit or its replacement Universal Credit. Many landlords say they will not let to beneficiaries—

although government statistics continue to show that more and more landlords are actually letting

to such households.

More generally, private renting is not regarded as a suitable tenure for families and vulnerable

households because of the very limited security of tenure available. At a minimum, the lease can

be as little as 6 months and landlords have the right to evict without reason. Yet the sector has

doubled since the turn of the century so that it now houses around one in five households in Great

Britain and one in four in London (Rhodes and Rugg, 2018).

Comparing Housing Allowances in Europe

The Organisation for Economic Co-operation and Development (OECD) Affordable Housing

Database (2016) provides an overview of housing allowances across OECD countries. Almost all

such countries (except those in Eastern Europe) have at least one form of Housing Allowance,

although in many countries they tend to be quite restrictive. Private tenants are the most likely

group to be eligible and allowances are normally as of right for eligible households.

In most northwestern European countries, housing support has generally been developed that

makes an allowance for housing costs within mainstream social security benefits. Housing

allowance systems are based on the gap principle whereby, for a given income, the housing

allowance meets a certain proportion of rent above a minimum contribution up to a maximum

level. In circumstances where unmet housing costs take residual income below the social assistance

minimum, the social assistance system itself often steps in (Kemp, 2007).

The biggest difference with the United Kingdom, however, is that most other European systems are

more generous in that their more general welfare systems are based on a high proportion of earlier

earnings. These higher benefits in countries such as Germany, Sweden, and France allow most

118 Small Area Fair Market Rents

Aspects of the Housing Choice Voucher Program and the

Impact of Small Area Fair Market Rents Ceilings: a British Perspective

people to pay for their accommodation, with a top-up available if necessary. In part, this is possible

because rents are generally a lower proportion of income in most of Europe (particularly Germany)

except for areas of housing pressure. It is thus the case that a basic standard of residual income

can generally be maintained because of these higher social security benefits, while enabling only a

proportional approach to additional support.

In this context, it has suggested that the UK system, where social security payments are set at

very low levels, means that it is inevitable that high proportions of housing costs must be covered

(Griggs and Kemp, 2012). The system, while it looks generous taken on its own, is very much less

so if instead one looks at housing benefit plus social security as a percentage of average income.

Lessons Across the Divide?

The most obvious lesson from this short review is that the need for income-related housing-

specific allowances is prevalent across countries with very different institutional and ideological

frameworks. This is for the fundamental reason that housing costs are often a large proportion of

income for poorer households and that welfare systems find it difficult to address large scale spatial

variations in cost without an additional element of support. Yet in the main we do not address this

problem by expecting people to move long distances to find affordable accommodation. So, one

important question is how much choice the individual household is given to use the allowance in

the way that they might wish as compared with the government trying to influence moves either to

lower cost areas or to areas of opportunity.

One of the most important issues raised by SAFMR in the United States is around the importance

of opportunity for a relatively small proportion of households with vouchers against housing

availability for a much larger proportion. This suggests the need for more research into whether

location can have such a positive effect as to make that choice worthwhile. The answer is

clearly linked with the more general question of the scale of the benefits associated with mixed

communities, which have been an important aspect of UK housing policy.

Clearly, a core issue in the U.S. model is around the extent of demand as compared with the supply

meeting the cap in ZIP Code areas. The United Kingdom faces similar problems—that the cap

means there simply are not enough dwellings in the area to match potential demand, so tenants

face additional costs. This is also an area where more research on how households find suitable

accommodation is needed.

There are several behavioural issues associated with these programmes, notably with respect to

which households take up the opportunity—are they families at the upper margin of the eligible

incomes, for instance? Another issue is around landlord response—are landlords as prepared to let

to those on vouchers or housing benefit despite administrative costs as they are to poor households

without vouchers? In the United Kingdom, landlords often state that insurance companies and

mortgage providers require landlords not to let to those on benefits—although they may “protest

too much.” More generally there are issues around the capacity to enforce quality standards given

the incentives landlords face.

Cityscape 119

Whitehead

A final, very important, issue is why people leave the allowance/voucher programmes especially

given the understanding, reflected in the Geyer et al. paper, that many are worse off when they

leave. In the United Kingdom, the evidence is that the main reason for leaving is that family and

particularly employment circumstances change for the better—although, we are very well aware of

the instability of these circumstances, so many people return. Understanding exactly what happens

in the U.S. context should be a high priority.

Acknowledgements

The author thanks the Cityscape editors for the positive approach taken, especially to making the review more accessible to U.S. readers. The author also thanks this edition’s other symposium

authors for their valuable and interesting contributions to the debate on income related

housing support.

Author

Christine M. E. Whitehead is Emeritus Professor in Housing Economics at the London School of

Economics and can be emailed at c.m.e.whitehead@lse.ac.uk.

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122 Small Area Fair Market Rents

Alternative Measures of Market Rents

• The Best Laid Plans Often Go Awry: An Analysis of the Implementation

of Small Area Fair Market Rents

• Comparing Small Area Fair Market Rents With Other Rental Measures

Across Diverse Housing Markets

• Contrasting Different Geographies in Fair Market Rents: Implications for

the Housing Choice Voucher Program in Pittsburgh, PA

• Examining the Transition to HUD Small Area Fair Market Rents Using

Craigslist Data

• A Tweak to Housing Assistance Allows Low-Income Renters Access to

High-Income Neighborhoods

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

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

124 Small Area Fair Market Rents

by Kelly L. Patterson and Robert Mark Silverman

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation

of Small Area Fair Market Rents

Kelly L. Patterson

Robert Mark Silverman

University at Buffalo

Abstract

The U.S. Department of Housing and Urban Development’s (HUD) new Small Area Fair Market Rents

(SAFMRs) rule sets fair market rents at the ZIP Code level as opposed to an entire metropolitan region.

The rule became effective on January 1, 2018. It is mandatory in 24 metropolitan areas and voluntary in the other metropolitan areas across the United States. SAFMRs allow for housing choice voucher

(HCV) payment standards to vary across ZIP Codes within a region. This is a change from previous

policy that based Fair Market Rents (FMRs) on the 40th percentile of gross rents in a region. This change opens properties in higher income areas to HCV holders because rents at the ZIP Code level often exceed regional FMRs. The use of SAFMRs is predicted to help to deconcentrate poverty and allow HCV holders to access high opportunity neighborhoods in core cities and their suburbs. SAFMRs have the potential to curb some of the effects of increasing rents in places experiencing gentrification, as well as promote housing mobility and fair housing across regions. This article examines the early implementation

strategies for SAFMRs in the 24 metropolitan areas where they are currently mandated. Data were

collected from the 180 public housing authorities (PHAs) in those 24 metropolitan areas. The analysis is based on 2018 HCV payment standards and other program documents related to tenant and landlord

notification collected from PHAs, as well as content analysis of archival materials and public documents.

The analysis is used to measure PHA fidelity to the SAFMR rule’s opportunity advancement goals,

identify best practices, and make policy recommendations for the broader implementation of SAFMRs.

Introduction

In November 2016, HUD issued its final rule for the implementation of Small Area Fair Market

Rents (SAFMRs) (Federal Register, 2016). The purpose of the rule was to begin the process of

changing the basis for setting payment standards for the Housing Choice Voucher (HCV) program.

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

Patterson and Silverman

Historically, payments standards were based on Fair Market Rents (FMRs), which represent the

40th percentile of gross rents paid in metropolitan areas. Under SAFMRs, payment standards

are based on the 40th percentile of gross rents paid in ZIP Codes. The new rule was adopted to

achieve two primary benefits (PRRAC, 2018). First, SAFMRs were expected to provide voucher

holders with greater access to high-opportunity neighborhoods (that is, places that offered a

better mix of employment, educational, and transportation opportunities). This outcome was

anticipated because payment standards would increase in ZIP Codes where rents were higher than

metropolitan FMRs. Second, SAFMRs were expected to make the voucher program more cost-

effective. This outcome was anticipated for two main reasons. The adoption of SAFMRs would

lower payment standards in ZIP Codes where rents were lower than metropolitan FMRs. This

adjustment to payment standards on the lower spectrum of a region’s rental market was expected

to reduce the likelihood of landlords setting rents above the market rate for comparable units in

these ZIP Codes. In addition, the adoption of SAFMRs had the potential to be more cost-effective

in the long run because agencies administering the HCV program would no longer need to seek

HUD’s approval of exception payment standards.1 This would be the case whether such approval

was sought to increase payment standards in high-rent areas or lower them in low-rent areas. The

adoption of SAFMRs would make these adjustments automatic.

Initially, the new rule became effective on January 1, 2018, with implementation in the 24

mandatory metropolitan areas beginning on April 1, 2018. The Dallas metropolitan area was one

of these, but it was already required to use SAFMRs in the implementation of the HCV program

under a separate court settlement in 2011. Implementation of the rule was voluntary in other

metropolitan areas across the United States. In August of 2017, HUD attempted to suspend the

new rule, but in December of 2017 a U.S. District Court judge ordered the rule to be reinstated

(Thorpe, 2018).

This article examines the implementation strategies adopted by public housing authorities (PHAs)

in metropolitan areas mandated to use SAFMRs. This analysis has a focus on the degree to which

the setting of payment standards in 2018 supported the dual goals of expanding voucher holders’

access to high-opportunity areas and enhancing the cost-effectiveness of the HCV program. In

addition to examining how payment standards were set during the initial implementation of HUD’s

new SAFMR rule, the article is also informed by a content analysis of other archival materials,

public documents, and primary materials supplied by local PHAs. The analysis of these data

identifies best practices and policy frameworks for future HCV program implementation in areas

mandated to use SAFMRs.

After results are presented, the article concludes with a set of recommendations. They focus

on lessons learned from the analysis of metropolitan areas mandated to use SAFMRs. The

recommendations had three targets audiences: administrators within HUD, local PHAs that

implement the HCV program, and applied researchers and policy advocates.

1 Although administrative costs go up significantly during the initial implementation of SAFMRs, they are expected to decline once the new system is in place. Whether they return to the level they were when areawide FMRs were used, exceed them, or fall below depends on local conditions and administrative practices adopted by the public housing authorities (PHAs).

126 Small Area Fair Market Rents

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Small Area Fair Market Rents (SAFMRs)

This section provides background on programs and policies that led to the promulgation of HUD’s

SAFMR rule. The first subsection summarizes the historic backdrop of relevant housing mobility

decisions in the courts and HUD actions in response to them. The second subsection focuses on the

settlement to address voucher concentration in low-income areas of the Dallas metropolitan area and the

SAFMR demonstration project. The third subsection discusses the genesis of HUD’s new SAFMR rule.

The Lead-Up to SAFMRs

HUD’s new SAFMR rule was developed in response to several concerns about the effectiveness of

the HCV program in deconcentrating poverty and providing low-income households with access

to upward mobility. Historically, these concerns date back to issues raised in cases like Gautreaux

v. Chicago Housing Authority (1969) and Southern Burlington County NAACP v. Township of Mount Laurel (1975). In response to those decisions, programs and public policies were developed to

increase opportunities for recipients of housing vouchers to move out of segregated, impoverished

neighborhoods. Some of these programs included the Gautreaux program in Chicago, the Moving to

Opportunity (MTO) for Fair Housing demonstration program, various fair share housing initiatives

that grew out of the Mount Laurel doctrine, and other housing mobility programs that were adopted

across the country on a demonstration basis. The development of policies scaling up these programs

has become increasingly salient because HCVs are one of HUD’s primary tools to provide affordable

housing to low-income households. In 2017, HCVs were used for over 2.2 million rental units

(HUD, 2017). These 2.2 million units represented over 48 percent of all rental units subsidized

across the eight federal programs designed to subsidize rental housing. In 2017, almost 5.3 million

people were housed using HCVs, comprising almost 55 percent of all renters receiving housing

assistance across the eight federal rental subsidy programs.

In addition to programs and policies adopted in response to landmark decisions in the courts,

advocates have encouraged HUD to pursue administrative rule changes to address shortcomings

in the implementation of the HCV program. Advocates have been critical of how the use of

metropolitan-wide FMRs, and the calculation of payment standards based on them, impede

geographic mobility and housing searches in high-opportunity neighborhoods (Thorpe, 2018). In

response to some of these concerns, HUD launched the Moving to Work (MTW) demonstration

program in 1996. This program gave participating PHAs flexibility in the administration of HCVs

in order to promote economic self-sufficiency and mobility to high-opportunity neighborhoods.

A common tool used by PHAs in the MTW program has been the adoption of exception payment

standards in high-opportunity geographic areas. Exception payment standards allow PHAs to

set payment standards in excess of FMRs so that tenants can lease housing in high-opportunity

areas. In some cases, PHAs in the MTW program have adopted exception payment standards for

targeted geographic areas in excess of 150 percent of local FMRs.2 In 1996, HUD approved 30

2 Although all PHAs can adopt exception payment standards, it is often easier for PHAs in the MTW program to have them approved under the stipulations of their contracts with HUD. In addition, exception payment standards for both MTW agencies and other PHAs may be over 150 percent of local FMRs. This amount is noteworthy, because SAFMRs are capped at 150 percent of the areawide FMR. This rule means that exception payment standards represent an additional tool available to PHAs in areas where SAFMRs do not provide access to high-opportunity neighborhoods in areas experiencing rapidly increasing rents.

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PHAs across the country for the MTW demonstration program; that number grew to 39 PHAs

by 2012 (HUD, 2019). Although the PHAs selected to participate in the MTW demonstration

program represent a fraction (1.1 percent) of all PHAs in the United States, they tended to be

in core cities and represent the largest unit shares in the metropolitan areas where they were

located. Nevertheless, most metropolitan areas where a PHA was selected to participate in the

MTW program have several other PHAs that were not selected to participate. For instance, the

Chicago Housing Authority is a participant in the MTW program, but the other 12 PHAs in that

metropolitan area are not. Similarly, the Atlanta Housing Authority is a participant in the MTW

program, but the other 11 PHAs in that metropolitan area are not. Consequently, the scale and

scope of the MTW demonstration program potentially limits regional mobility and raises additional

concerns about the portability of vouchers within a region.

Although MTW is a demonstration program and not tested across a spectrum of local contexts,

it has provided policymakers and administrators of affordable housing programs with several

insights. One such insight is that metropolitan-wide FMRs fall short of providing tenants with

adequate subsidies to rent in high-opportunity neighborhoods. Researchers have identified this as

problematic because metropolitan-wide FMRs fall below rents in the most expensive submarkets

in metropolitan areas (Palm, 2018; Thorpe 2018; Treat, 2018). This limitation of FMRs is

compounded by data lag because FMRs are calculated using data from the American Community

Survey (ACS) which is released 2 years from its date of collection. One rationale for HUD allowing

PHAs to set payment standards within a 90–110 percent range of its published FMRs is to address

some of these limitations. In localized housing markets experiencing gentrification and other forms

of upward pressure on rents, however, the ability to set payment standards at 110 percent of FMRs

still does not close the affordability gap. To address this issue, SAFMRs have been advocated for as a

tool to promote housing opportunity on a metropolitan-wide scale.

The Dallas Settlement and the SAFMR Demonstration Project

The first opportunity to test this tool came in 2011 as a result of a court settlement that resolved

a complaint charging that payment standards based on FMRs in the Dallas metropolitan areas

resulted in the concentration of vouchers in low-income areas (Ellen, 2018; Treat, 2018). Under

the settlement, all PHAs in metropolitan Dallas were required to use SAFMRs when setting

payment standards. An early analysis of outcomes in Dallas suggested that the adoption of

SAFMRs resulted in improved neighborhood quality for HCV recipients and modest cost savings

for PHAs (Collinson and Ganong, 2014). It is noteworthy that Palm (2018) and Treat (2018)

caution against assuming that those outcomes are easily replicable in other metropolitan areas.

During the timeframe that data from Dallas were examined, vacancy rates exceeded 8 percent. In

tighter housing markets, the improvements due to the adoption of SAFMRs are expected to be

more modest.

Shortly after the Dallas settlement, HUD created its own SAFMR demonstration program. The

purpose of the demonstration program was twofold (Kahn and Newton, 2013). First, it was created

to test the effectiveness of SAFMRs in improving tenants’ access to high-opportunity areas as well

as the residual impacts on tenants’ living in areas with SAFMRs below metropolitan FMRs. Second,

the demonstration program was created to understand the administrative and budget effects on

128 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

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PHAs that adopt SAFMRs. The demonstration program was designed to run from 2012–2016.

Five PHAs were included in the demonstration program (see exhibit 1).3 They were selected in

order to include PHAs that administered different volumes of HCVs and that served clients from

different demographic segments of the population. The five PHAs selected to participate in the

demonstration program had the option not to participate. In addition to the five PHAs selected

to participate in the SAFMR demonstration program, HUD added two PHAs from the Dallas

metropolitan area that were already mandated to implement SAFMRs under the court settlement.4

These two PHAs are also listed in exhibit 1.

Exhibit 1

2011 Characteristics of PHAs in the SAFMR Demonstration Program

HCV Recipients (%)

Tracts

with HCV

Total

62

Residents

PHA Name (State)

HCV

Years

Total

Units

Minority Hispanic

and

Population

Over

Minority (%)

SAFMR Demonstration Participants

Chattanooga Housing Authority (TN)

3,183

82

2

15

54

Housing Authority of Cook County (IL)

12,622

83

3

58

52

Housing Authority of the City of Laredo (TX)

1,368

100

99

20

95

City of Long Beach Housing Authority (CA)

6,556

88

11

23

83

Town of Mamaroneck Housing Authority (NY)

647

54

22

39

32

PHAs Added from the Dallas Metropolitan Area

Housing Authority of the City of Dallas (TX)

18,525

94

5

17

67

Housing Authority of Plano (TX)

908

65

3

29

39

HCV = Housing Choice Voucher. PHA = Public Housing Authority.

Source: Finkel et al, 2017

At the end of the demonstration program, HUD released an evaluation report (Finkel et al., 2017).

The report focused on the two foci of the demonstration program: the effectiveness of SAFMRs in

improving tenants’ access to high opportunity areas and the effects of SAFMRs on HCV

implementation costs. In terms of tenants’ access to high-opportunity areas, the results of the

evaluation indicated that SAFMRs had a positive impact. The switch to SAFMRs made HCV holders

slightly more likely to live in high-rent ZIP Codes than they were before the demonstration

program. This was the case for new HCV holders and tenants who already held vouchers and

3 The SAFMR demonstration program ended in 2016. At that time, four of the five PHAs participating in it continued to use SAFMRs on a voluntary basis; they included the Chattanooga Housing Authority, the Housing Authority of Cook County, the City of Long Beach Housing Authority, and the Mamaroneck Housing Authority. One of these PHAs, the Housing Authority of Cook County, is in the Chicago-Joliet-Naperville metropolitan area that was subsequently mandated to adopt the new SAFMR rule. The Housing Authority of Laredo was the only PHA to

discontinue the use of SAFMRs after the demonstration program ended.

4 Because the two PHAs in Dallas adopted SAFMRs due to a court settlement, they could not opt out. HUD added these two PHAs to the evaluation in order to increase the sample size for the evaluation.

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moved to new ZIP Codes after the demonstration program began. Tenants who moved to these ZIP

Codes accrued benefits due to the areas’ lower poverty, higher school proficiency, proximity to

employment, environmental quality, and lower crime.

In terms of PHA implementation costs, the switch to SAFMRs resulted in modest reductions in

overall costs for PHAs. Although there was variation across the PHAs, on average, increases in

payment standards in high-rent ZIP Codes were offset by lower payment standards in low-rent

areas. Moreover, when the PHAs in the demonstration program were compared with a control

group, the cost savings were greater in terms of payment standards and Housing Assistance

Payment (HAP) contract costs for demonstration program participants. One caveat that explains

the modest reduction in overall costs was that PHAs were required under the rules of the voucher

program to hold existing tenants in low-rent ZIP Codes harmless from reduced payment standards

until their units went through their second annual recertification. This meant that there was a lag

in PHA savings from reduced payment standards in low-rent ZIP Codes, as well as a delay in any

additional costs to tenants who chose to remain in those ZIP Codes after SAFMRs were adopted.

Additional administrative costs to PHAs involved those associated with changing software, systems,

and staff training. These represented the largest up-front expenditures made by PHAs due to

SAFMR implementation. One-time costs were often associated with the revamping of internal

systems, however, and these costs were offset by long-term savings that PHAs would accrue.

For instance, SAFMRs reduced administrative costs associated with collecting comparative data

to determine rent reasonableness because the more discrete ZIP Code level geography makes it

less difficult to find comparable rents, and baseline rents are embedded in SAFMR calculations.

Moreover, areawide rent reasonableness studies are only if there is a 10-percent decrease in

SAFMRs consistent with the floor for all FMRs under the Housing Opportunities Through

Modernization Act (HOTMA). SAFMRs also eliminated the need to petition HUD for exception

payment standards in many areas where rents exceeded FMRs, which allowed PHAs to reallocate

staff resources.

The results from the evaluation of the SAFMR demonstration program provided HUD with

guidance for the scaling up of SAFMRs, but they should be interpreted with some important

qualifications. For example, except for the two PHAs added to the evaluation from metropolitan

Dallas, the other participants in the demonstration program administered SAFMRs in regions

where multiple PHAs continued to use metropolitan areawide FMRs in their HCV implementation.

Two of these PHAs were in the largest metropolitan areas in the country. Consequently, issues

related to the portability of vouchers where SAFMRs are used metropolitan-wide were not fully

integrated in the evaluation. In addition, scholars have raised concerns about the representativeness

of PHAs in the demonstration program and the generalizability of the findings from the evaluation

to other PHAs across the United States (NYU Furman Center, 2018; Palm, 2018). A degree of

caution was recommended, in particular, when applying the results of the evaluation of the

demonstration program to metropolitan areas selected for the first phase of the mandatory

implementation of HUD’s new SAFMR rule.

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The New SAFMR Rule

In November of 2016, HUD published the final version of the new SAFMR rule (Federal Register,

2016). This was followed by a series of technical documents including an implementation

guidebook for PHAs that had adopted SAFMRs (HUD, 2018). The final version of the SAFMR

rule included provisions for PHAs mandated to adopt SAFMRs as well as options for their

voluntary implementation by other PHAs. In the SAFMR rule’s initial implementation in 2018, 24

metropolitan areas were mandated to use SAFMRs when setting payment standards for HCVs. The

Dallas metropolitan area was one of these areas but had already implemented SAFMRs under the

terms of the 2011 court settlement under the provisions of the SAFMR rule. The next opportunity

to expand the number of metropolitan areas mandated to use SAFMRs would occur after 5 years had

passed. This meant that in 2018, there were a total of 180 PHAs across the 24 metropolitan areas

mandated to use SAFMRs.5 Those PHAs administered 413,591 vouchers, which accounted for about

19 percent of all HUD vouchers. Exhibit 2 lists all the metropolitan areas mandated to use SAFMRs.

Exhibit 2

Metropolitan Areas Mandated to Use SAFMRs (1 of 2)

PHAs

Average HCVs

Metropolitan Area

Total PHAs

in MTW

Leased Q1 2017

Program

Atlanta-Sandy Springs-Roswell, GA

12

41,011

1

Bergen-Passaic, NJ

10

10,881

Charlotte-Concord-Gastonia, NC-SC

6

8,151

1

Chicago-Joliet-Naperville, IL

13

71,275

1

Colorado Springs, CO

2

2,512

Dallas, TX

11

29,467

Fort Lauderdale, FL

6

11,529

Fort Worth-Arlington, TX

5

12,308

Gary, IN

3

3,097

Hartford-West Hartford-East Hartford, CT

21

19,183

Jackson, MS

2

5,641

Jacksonville, FL

2

7,250

Monmouth-Ocean, NJ

9

5,314

North Port-Sarasota-Bradenton, FL

3

2,773

Palm Bay-Melbourne-Titusville, FL

3

2,714

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

19

37,610

1

Pittsburgh, PA

8

17,087

1

Sacramento-Roseville-Arden-Arcade, CA

4

12,837

San Antonio-New Braunfels, TX

6

15,095

1

5 Nine of the PHAs in the mandated areas were participants in the MTW program and could request an exemption from the SAFMR rule. These PHAs accounted for 33 percent of the vouchers in the 24 mandatory SAFMR areas and had the option to voluntarily adopt SAFMRs.

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Exhibit 2

Metropolitan Areas Mandated to Use SAFMRs (2 of 2)

PHAs

Average HCVs

Metropolitan Area

Total PHAs

in MTW

Leased Q1 2017

Program

San Diego-Carlsbad, CA

6

28,458

1

Tampa-St. Petersburg-Clearwater, FL

8

19,290

Urban Honolulu, HI

2

6,040

Washington-Arlington-Alexandria, DC-VA-MD

14

37,379

2

West Palm Beach-Boca Raton, FL

5

6,689

HCV = Housing Choice Vouchers. MTW = Moving to Work. PHA = Public Housing Authority. Q1 = First Quarter.

Note: Total sample size is 24.

Source: HUD, Voucher Management System (VMS), retrieved by the Policy Race and Research Action Council The SAFMR rule was published in its final form after a period of notice and comment from

stakeholders. The changes made to the final rule had implications for the degree to which SAFMRs

expanded HCV recipients’ access to high-opportunity neighborhoods and the cost-effectiveness

of voucher implementation. The changes also had implication for which metropolitan areas were

selected for mandatory implementation of SAFMRs.

One of the main changes made to the SAFMR final rule was the addition of tenant protections.

In the final rule, provisions were included to hold existing HCV holders harmless if the payment

standards in the ZIP Code they lived in fell below metropolitan area FMRs. HUD provided PHAs

with three options to hold existing tenants harmless during the SAFMR implementation process:

(1) Under existing rules of the voucher program, PHAs could delay the reduction in payment

standards until the second annual recertification of their rental contract. This would give tenants

up to two years to weigh whether they would move to a different location or remain in their

current unit with a lower subsidy; (2) PHAs could develop a schedule to gradually reduce payment

standards over a period of time until they were at the new level set using SAFMRs; or (3) PHAs had

the option to hold tenants harmless indefinitely. HUD was able to provide these tenant protections

in the final rule, especially the last one, because of provisions within HOTMA.

Another noticeable change to the SAFMR rule involved how metropolitan areas were selected for

mandatory implementation. Concerns were expressed about the feasibility of SAFMRs in regions

with tight rental housing markets. Some of these concerns were supported by analysis in studies

like Palm’s (2018), which raised questions about replicating outcomes found in high vacancy

metropolitan areas like Dallas when using SAFMRs in tighter housing markets like San Francisco

and Oakland. In response to these concerns, HUD exempted areas with rental vacancy rates at 4

percent or less from mandatory adoption of the SAFMR rule.

In addition to these changes, HUD made others that essentially gave relief to PHAs. For instance,

HUD exempted all current and future project-based vouchers from mandatory implementation of

SAFMRs. This effectively made mandatory implementation of the SAFMR rule only applicable to

portable vouchers, giving PHAs more flexibility in the setting of payment standards for properties

they managed. Similarly, the final rule allowed PHAs participating in the MTW program to request

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an exemption from mandatory implementation of SAFMRs. HUD also simplified the exception

payment standard rules for PHAs that did not operate in mandatory SAFMR areas. Instead of going

through the more rigorous exception payment standard approval process, PHAs were permitted

to voluntarily adopt SAFMRs in individual ZIP Codes and notify HUD of this decision by email.

Moreover, HUD made efforts to address concerns raised about PHAs and about the cost burden

associated with the transition to SAFMRs. Although this was not addressed in the final rule per

se, HUD offered PHAs up to $25,000 in reimbursements for costs directly related to mandatory

SAFMR adoption.

Finally, HUD provides PHAs in non-mandatory SAFMR metropolitan areas with two options to

use SAFMRs voluntarily. As noted earlier, PHAs had the option to set payment standards up to 110

percent of SAFMRs in individual ZIP Codes without going through the more rigorous approval

process for exception payment standards. The second option for voluntary adoption of SAFMRs

was to request HUD’s approval to opt-in and apply SAFMRs metropolitan-wide. This allowed

PHAs to both increase payment standards in high-rent ZIP Codes and reduce payment standards

in low-rent ZIP Codes. PHAs that opted-in for full implementation of SAFMRs would have

greater flexibility in pursuing the goal of providing tenants with opportunities to move to high-

opportunity areas and garner broader cost savings in HCV administration.

Methodology

The results section of this article focuses on implementation strategies used by PHAs in the 24

metropolitan areas mandated to use SAFMRs when setting payment standards for HCVs. The

results presented in this section are based on data collected following the initial implementation

of SAFMRs in these jurisdictions during 2018. This is a critical timeframe to examine because

jurisdictions were mandated to have their SAFMR strategy in place by April 2018. The scope and

scale of the initial implementation provide one measure of the degree of PHAs’ fidelity to the goals

of the new SAFMR rule. Attempts were made to collect data from each of the 180 PHAs located

in the 24 metropolitan areas mandated to use SAFMRs. Between June and September of 2018,

members of the research team contacted individuals responsible for the administration of the HCV

program by telephone, email, and mail. Requests were made for each PHA’s: SAFMR payment

standards, documentation of updates to their administrative plans since SAFMRs were adopted,

materials distributed to voucher holders and landlords describing the PHA’s SAFMR policies, and

the approach adopted to render existing HCV holders harmless during the changeover to SAFMRs.

In cases where PHAs did not respond to data requests by telephone, email, and mail, the research

team searched for materials on PHAs’ websites. At the end of the data collection period, materials

were collected from 48 percent of the PHAs. These materials consisted largely of the SAFMR

payment standards that were adopted by the PHAs. To a lesser extent, PHAs provided copies of

other materials such as information provided to tenants and landlords about SAFMRs, policies to

hold existing HCV holders harmless, and updates to their administrative plans. Although data were

only available for 48 percent of the PHAs, all 24 metropolitan areas were represented in this subset

and the PHAs that did provide data represented those with larger service areas in the metropolitan

areas mandated to adopt SAFMRs. These PHAs were responsible for the administration of 79

percent of the 413,591 HCVs issued in the 24 metropolitan areas. PHAs for which data were

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collected administered an average of 3,801 HCVs, compared with an average of 922 HCVs for PHAs

where data were not collected. The data collected also included information for 55 percent of the

3,881 ZIP Codes contained in those metropolitan areas.

Payment standard data collected from PHAs were aggregated and presented in tabular form. These

tables are discussed in the results section and presented in the appendix to this article. In addition

to payment standard data, other data were collected from PHAs and public documents. Content

analysis was used to examine these data following methods described in Silverman and Patterson

(2015). The content analysis focused on information provided to tenants and landlords about

SAFMRs, the degree to which participants in MTW programs plan to integrate SAFMRs into their

HCV implementation strategies, rationales PHAs used in their requests for waivers, and the scope

of HUD’s monitoring of SAFMR implementation.

Results

Analysis of Year 1 Mandatory SAFMR Implementation

This section focuses on implementation strategies used by PHAs in the 24 metropolitan areas

mandated to use SAFMRs. The section begins with an overview of how PHAs set payment standards

for HCVs during the initial implementation of SAFMRs in 2018. It then examines information

provided to tenants and landlords about PHAs’ SAFMR policies and approaches adopted to render

existing HCV holders harmless. Following this discussion, the article summarizes other topics

pertinent to the implementation of the SAFMR rule including the degree to which participants in

MTW programs plan to integrate SAFMRs into HCV implementation, requests for waivers to the

SAFMR rule, and the scope of HUD’s monitoring of SAFMR implementation.

Payment Standards

Data for payment standards were collected from 86 PHAs in the metropolitan areas mandated to

use SAFMRs. These PHAs constituted 48 percent of all agencies. This group of PHAs administered

79 percent of the 413,591 HCVs issued in the 24 metropolitan areas. The HCV administered by

this group of PHAs covered 55 percent of the 3,881 ZIP Codes in those metropolitan areas.

Under the new rule’s guidelines, PHAs can set payment standards between 90 percent and 110

percent of SAFMRs. This range allows PHAs to account for local market conditions when adjusting

payment standards. For example, in areas where market rents are changing rapidly and published

SAFMRs are not in line with current trends, the 90 to 110 percent range gives PHAs flexibility to

address data lag issues. This flexibility may be beneficial in areas experiencing upward pressure

on rents due to gentrification, as well as in areas where rents are declining due to deteriorating

neighborhood conditions. While taking these issues into consideration, at minimum, one would

expect payment standards to cluster near 100 percent of published SAFMRs if a PHA had fidelity

to the opportunity advancement goals of the new rule. Under this scenario, a PHA would strike a

balance between ZIP Codes where SAFMRs were less than metropolitan-wide FMRs and ZIP Codes

where SAFMRs were greater than metropolitan-wide FMRs. This is an important balance to strike

because it generates the program cost savings necessary in low-rent ZIP Codes to free up resources

134 Small Area Fair Market Rents

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needed to enhance HCVs in high-rent ZIP Codes. Striking this balance is critical to maintaining a

PHA’s volume of HCVs while expanding housing options in opportunity areas.

If a PHA diverges from payment standards clustering near 100 percent of published SAFMRs, how

payment standards are set can be viewed as an indication of relatively high or low fidelity to the

new rule. In instances where there is high fidelity, a PHA would set payment standards in low-rent

ZIP Codes closer to 90 percent of SAFMRs while setting payment standards closer to 110 percent

of SAFMRs in high-rent ZIP Codes. Setting payment standards in this manner would maximize the

incentive for tenants to move to high-opportunity areas while reducing possible overpayments to

landlords in low-rent ZIP Codes. In contrast, low fidelity would be most pronounced in instances

where a PHA sets payment standards in low-rent ZIP Codes closer to 110 percent of SAFMRs while

setting payment standards closer to 90 percent of SAFMRs in high-rent ZIP Codes. Setting payment

standards in this manner would minimize the incentive for tenants to move to high-opportunity

areas while increasing overpayments to landlords in low-rent ZIP Codes. This scenario would

effectively undercut the opportunity advancement goal of the SAFMR rule by bringing payment

standards back in line with something approximating metropolitan-wide FMRs.

In order to measure this aspect of fidelity to the SAFMR rule, payments standards as a percent of

SAFMRs were calculated for each ZIP Code in the 24 metropolitan areas. A total of 5,501 payment

standards were reported for SAFMRs by all the PHAs from which data were collected. It should be

noted that in several instances, multiple payment standards were reported for the same ZIP Codes.

This variance was because different PHAs in the same metropolitan area often calculated their

own unique payment standards for the same ZIP Codes. At least one set of payment standards was

reported for 2,134 (55 percent) of the 3,881 ZIP Codes in the 24 metropolitan areas. More than

one ZIP Code was reported in 679 (17.5 percent) of the cases. The presence of multiple payment

standards in the same metropolitan areas can make the HCV process confusing to tenants and

landlords. Exhibit 3 summarizes the number of payment standards reported per ZIP Code in all the

24 metropolitan areas combined.

Exhibit 3

Number and Percent of ZIP Codes Where Public Housing Authorities Reported Payment Standards

Number of PHAs Reporting

Number of ZIP Codes

Percent of ZIP Codes

Payment Standards

0

1,725

45.0

1

1,477

38.1

2

218

5.6

3

213

5.5

4

117

3.0

5

55

1.4

6

55

1.4

7

20

0.5

8

1

0.0

PHA = Public Housing Authority.

Note: Total sample size is 3,881.

Cityscape 135

Patterson and Silverman

Exhibit 4 presents payment standards as a percent of SAFMRs in the aggregate for all 24

metropolitan areas. Exhibit 4 also reports payment standards as a percent of SAFMRs above

metropolitan-wide FMRs and for SAFMRs below metropolitan-wide FMRs. Low-opportunity

ZIP Codes were defined as ZIP Codes with SAFMRs less than 100 percent of area FMRs. High

opportunity ZIP Codes were defined as ZIP Codes with SAFMRs greater than or equal to 100

percent of area FMRs. These data suggest that, in the aggregate, PHAs in the 24 metropolitan

areas had low fidelity to the opportunity advancement goals of the SAFMR rule. Although average

payment standards hovered around 100 percent of published SAFMRs, there was a divergence

between the setting of payment standards in low-opportunity and high-opportunity ZIP Codes.

This difference reflected the opposite of the pattern of setting payment standards that would be

predicted where PHAs had high fidelity to the opportunity advancement goals of the SAFMR

rule. Setting payment standards in this manner potentially creates disincentives for moves to

high-opportunity neighborhoods and reinforces existing patterns of HCV concentration in low-

opportunity areas. Moreover, setting payment standards in this manner increases the likelihood

that landlords will be overpaid in low-rent areas and PHAs will forego cost-savings that can be used

to enhance payment standards in high-opportunity ZIP Codes. The result is fewer HCVs overall,

and fewer HCVs in high-rent ZIP Codes.

Exhibit 4

Payment Standards as a Percent of SAFMRs

0 Bedroom

1 Bedroom

2 Bedroom

3 Bedroom

4 Bedroom

All Payment Standards

Reported in the 24

100.57

100.85

100.58

100.31

100.04

Metropolitan Areas

Payment Standards in

102.81

103.02

102.77

102.51

101.94

Low-Opportunity ZIP Codes¹

Payment Standards in

98.71

98.09

98.82

98.29

98.13

High-Opportunity ZIP Codes²

SAFMR = Small Area Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note: Total sample size is 5,501.

The data presented in exhibit 4 is in the aggregate. It is important to note that there was

variation in the degree to which payment standards diverged between high-opportunity and

low-opportunity ZIP Codes in individual metropolitan areas. There were three main findings for

payment standard behavior relative to the classification of ZIP Codes by opportunity. The first was

in metropolitan areas where the payment standards followed a similar pattern to the aggregate data

reflected in exhibit 4. Eleven of the 24 metropolitan areas fell into this category. They included

the following metropolitan areas mandated to use SAFMRs: Atlanta-Sandy Springs-Roswell,

Colorado Springs, Fort Lauderdale, Fort Worth-Arlington, Hartford-West Hartford-East Hartford,

Jacksonville, Monmouth-Ocean, Pittsburgh, Sacramento-Roseville-Arden-Arcade, Tampa-St.

Petersburg-Clearwater, and Urban Honolulu.

The second finding in payment standards involved eight metropolitan areas where payment

standards were at or above 100 percent of SAFMRs in high-opportunity and low-opportunity ZIP

136 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

Codes. This outcome was found in the following metropolitan areas: Charlotte-Concord-Gastonia,

Chicago-Joliet-Naperville, Dallas, Gary, Jackson, North Port-Sarasota-Bradenton, Philadelphia-

Camden-Wilmington, and Washington-Arlington-Alexandria. These metropolitan areas exhibited

a relatively moderate degree of fidelity to the opportunity advancement goals of the SAFMR rule

in the sense that they erred on the side of adopting payment standards that were at or higher than

100 percent of SAFMRs across the board. In some cases, this error was justified due to a region’s

tight rental housing market, but in other metropolitan areas, setting payment standards above 100

percent of SAFMRs raises concerns about potentially overpaying landlords in low-rent areas. The

payment standards reported for the Dallas metropolitan area in exhibit 5 are exemplar of this trend.

Exhibit 5

Payment Standards as a Percent of SAFMRs for the Dallas, TX Metropolitan Area

0 Bedroom

1 Bedroom

2 Bedroom

3 Bedroom

4 Bedroom

All Payment Standards

Reported in the

103.78

103.13

103.28

103.37

103.25

Metropolitan Area

Payment Standards in

103.41

102.99

102.86

102.91

102.84

Low-Opportunity ZIP Codes¹

Payment Standards in

104.11

103.23

103.67

103.80

103.64

High-Opportunity ZIP Codes²

SAFMR = Small Area Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note: Total sample size for Dallas is 1,071.

The third finding in payment standards involved five metropolitan areas where payment standards

were below 100 percent of SAFMRs in high-opportunity and low-opportunity ZIP Codes. This

outcome was found in the following metropolitan areas: Bergen-Passaic, Palm Bay-Melbourne-

Titusville, San Antonio-New Braunfels, and San Diego-Carlsbad. These metropolitan areas

exhibited a relatively low degree of fidelity to the opportunity advancement goals of the SAFMR

rule in the sense that they adopted payment standards that were below 100 percent of SAFMRs

across the board. This had the effect of encouraging the concentration of HCVs in low-opportunity

areas, particularly in metropolitan areas with tightening rental markets. One justification for setting

HCVs in this manner might be to stretch a PHA’s resources and issue the maximum number of

vouchers possible, but this strategy results in placing the greatest constraints on HCV holders who

seek to relocate to high-opportunity areas. The payment standards reported for the San Diego-

Carlsbad metropolitan area in exhibit 6 show an extreme example of this trend.

Cityscape 137

Patterson and Silverman

Exhibit 6

Payment Standards as a Percent of SAFMRs for the San Diego-Carlsbad, CA

Metropolitan Area

0 Bedroom

1 Bedroom

2 Bedroom

3 Bedroom

4 Bedroom

All Payment Standards

Reported in the

85.82

86.96

85.77

84.22

80.37

Metropolitan Area

Payment Standards in

89.04

91.13

87.79

88.81

84.36

Low-Opportunity ZIP Codes¹

Payment Standards in

84.32

85.01

84.83

81.59

78.50

High-Opportunity ZIP Codes²

SAFMR = Small Area Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note: Total sample size for San Diego-Carlsbad is 169.

Within the SAFMR rule, HUD preserved the ability of PHAs to set payment standards within the

basic range by ZIP Code. This policy allowed PHAs to tier their payment standards. The use of

tiers involves setting a single payment standard for a group of ZIP Codes in order to simplify the

HCV implementation process. Tiering payment standards were common in the 24 metropolitan

areas mandated to use SAFMRs. Tiers were adopted by 53 percent of the PHAs where data were

collected and at least one PHA adopted them in 22 (92 percent) of the 24 metropolitan areas. PHAs

adopted a variety of approaches to tiering. For example, some created zones based on jurisdictional

boundaries of municipalities and counties, further grouping ZIP Codes within those boundaries.

Others created payment standard zones based on land use characteristics, such as rural, business

district, and standard zones. In other cases, it was clear that tiers reflected groupings of ZIP Codes

based on broader geographies identified high to low rent areas.

On the surface, applying tiers presents HCV holders with a more discrete list of payment

standards to reference when searching for housing. If applied with low fidelity to the opportunity

advancement goals of the SAFMR rule, however, this approach may result in more limited access

to high-opportunity areas and reproduce patterns of HCV concentration. Exhibit 7 provides an

example of this outcome. This exhibit summarizes average payment standards across the five

tiers set by the Hawaii Public Housing Authority for the 57 ZIP Codes in its service area, which

encompasses the entire Urban Honolulu metropolitan area. Exhibit 7 provides a summary of the

divergence between payment standards in low to high rent tiers. Tier 5 represented the lowest

rent ZIP Codes in the metropolitan area, rents incrementally increased until the highest rents were

found in tier 1. The data summarized in exhibit 7 shows that there was an inverse relationship

between rents and payment standards as a percent of SAFMRs.

138 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

Exhibit 7

Average Payment Standards as a Percent of SAFMRs for the Hawaii Public Housing Authority

0 Bedroom

1 Bedroom

2 Bedroom

3 Bedroom

4 Bedroom

Tier 5 (N=4)—

109.77

109.40

109.40

109.48

109.65

Lowest Rent ZIP Codes

Tier 4 (N=40)

101.74

101.43

102.05

101.76

101.99

Tier 3 (N=10)

98.55

98.65

98.65

98.80

98.73

Tier 2 (N=2)

90.00

90.00

90.00

90.00

90.00

Tier 1 (N=1)—

90.00

90.00

90.00

90.00

90.11

Highest Rent ZIP Code

Note: Total sample size for the Hawaii Public Housing Authority is 57.

The example of tiering in the Urban Honolulu metropolitan area illustrates how low fidelity can

undercut the opportunity advancement goals of the SAFMR rule when the use of SAFMRs is

mandated. In metropolitan areas where all PHAs are not required to use SAFMRs, tiering can have

more noticeable effects. Exhibit 8 summarizes average exception payment standards set across the

three tiers set by the San Diego Housing Commission for the 33 ZIP Codes in its service area.6

Although this PHA is located in a mandatory SAFMR area, it can request an exemption from the

SAFMR rule because it is a participant in the MTW program. This policy means that the San Diego

Housing Commission was able to set its payment standards without the constraints of the SAFMR

rule. The PHA’s tiering showed a similar inverse relationship between rents and payment standards.

Without the safeguards built into the SAFMR rule, however, average payment standards as a

percent of SAFMRs fell below the 90-percent threshold in high-opportunity areas.

Exhibit 8

Average Payment Standards as a Percent of SAFMRs for the San Diego, CA Housing Commission

0 Bedroom

1 Bedroom

2 Bedroom

3 Bedroom

4 Bedroom

Tier of Low Rent ZIP Codes

96.77

96.50

96.47

96.48

85.20

(N=10)

Tier of Moderate Rent ZIP Codes

82.97

82.73

82.54

82.65

72.86

(N=8)

Tier of High Rent ZIP Codes

80.85

80.32

80.42

76.75

72.70

(N=15)

SAFMR = Small Area Fair Market Rent.

Note: Total sample size for the San Diego Housing Commission is 33.

Holding Tenants Harmless

The final SAFMR rule required PHAs to hold existing HCV holders living in low-rent areas

harmless as FMRs were phased out during the SAFMR implementation process. PHAs had three

options to do this: (1) they could delay the reduction in payment standards until the second

6 The San Diego Housing Commission based its tiers on ZIP Code boundaries making it possible to directly compare payment standards set by this PHA with others in the metropolitan area using ZIP Code based SAFMRs. This type of comparison was not possible with MTW participants that used different geographies when setting their exception payment standards.

Cityscape 139

Patterson and Silverman

annual recertification of their rental contract, (2) they could develop a schedule to gradually reduce

payment standards over a period of time, or (3) they could hold tenants harmless indefinitely. It

is important to stress that the requirement to hold tenants harmless only applied to existing HCV

contract holders. Payment standards based on SAFMRs would be applied to new recipients of

HCVs at the time they originated.

Tenant and landlord notification materials were used in the analysis to identify what option for

holding tenants harmless was adopted by PHAs. Tenant notification materials were provided by 22

PHAs. Landlord notification materials were provided by 12 PHAs. These materials were analyzed

using content analysis. Findings from the analysis indicated that the thrust of tenant notifications

was to alert tenants that they would be held harmless if payment standards were reduced in their

area due to the adoption of SAFMRs. The discussion of SAFMR opportunity advancement goals

was secondary. In 11 of the letters analyzed, PHAs indicated that tenants would be held harmless

until their second annual recertification took place. Similar language was included in most of

the notifications that landlords received from these PHAs. The option to hold tenants harmless

until their second annual recertification occurred was the only option explicitly mentioned in the

materials analyzed.

In addition to notifying tenants that SAFMRs were being adopted and that this change may affect

their level of rental assistance in the future, nine PHAs also included language explaining the

opportunity advancement goals of the new policy. For example, the Sacramento Housing and

Redevelopment Agency’s notification explained that, “with the SAFMRs you will be able to use

your voucher in more places than would have been possible before—including neighborhoods that

have high-performing schools, reduced crime, access to grocery stores, parks, medical facilities,

childcare, transportation, and other amenities.” Similar language was found in six of the notices

that went to landlords.

One contrast stood out in the materials circulated to tenants and landlords. Landlords received

more detailed information about how payment standards were set by the PHAs. In four of the

landlord notifications, tables showing the payment standards were included. This level of detail

was absent from materials circulated to tenants. In addition to this contrast, the letters to landlords

provided insights about the degree to which PHAs had fidelity to the SAFMR rule. For instance,

the Cecil County Housing Authority informed landlords that most of its service area will have

payment standards reduced, making it “more difficult for some tenants to rent your higher cost

units, more difficult for you to get higher rents for some units, and more difficult for us to lease

voucher holders in some areas.” This PHA is in the Philadelphia-Camden-Wilmington metropolitan

area, and 88 percent of the 16 ZIP Codes in its service area had SAFMRs below FMRs. This

notification went on to describe how the PHA tiered its payment standards to buffer landlords from

potential reductions in payment standards due to the use of SAFMRs. The PHA’s letter to landlords

said “in an effort to minimize the disturbance to your operations, limit our administrative burden,

and maintain as much simplicity as possible for the tenants—while also attempting to adhere to

the spirit and intention of the SAFMR program—using local authority we have reduced the 16

standards to 3 different rate areas.” An examination of the FMRs, SAFMRs, and tiers adopted by

this PHA indicated the effect of the tiers adopted by this PHA was to adjust payment standards

upward in low-rent areas and downward in high-rent areas. Payment standard reductions using the

140 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

PHA’s tiers were about one-third to one-half of what they would have been if posted SAFMRs were

adopted without making these adjustments.

PHAs in the MTW Program and Others Requesting Waivers

Nine PHAs in the 24 metropolitan areas that were mandated to use SAFMRs were participants

in the MTW program. These PHAs included the Atlanta Housing Authority, Charlotte Housing

Authority, Chicago Housing Authority, District of Columbia Housing Authority, Fairfax County

Redevelopment and Housing Authority, Philadelphia Housing Authority, Housing Authority of

Pittsburgh, San Antonio Housing Authority, and San Diego Housing Commission. These PHAs

could propose alternative payment standard policies to HUD and request exemptions from the

mandate to use SAFMRs. Eight of the PHAs requested an exemption for at least 1 year when the

rule was promulgated due to the administrative burden of adopting SAFMRs on short notice and

the potential confusion it would cause with the alternative payment standard policies authorized

under their MTW agreements. The Philadelphia Housing Authority did not request an exemption.

PHAs requesting an exemption either implemented plans to phase in SAFMRs over a period of

years, applied SAFMRs to a subgroup of ZIP Codes, developed exception payment standards using

more flexible criteria based on metropolitan-wide FMRs, or used other metrics to set payment

standards in a manner that aligned with the opportunity advancement goals of the SAFMR rule.

Four MTW participants, including the Philadelphia Housing Authority, the Pittsburgh Housing

Authority, the San Antonio Housing Authority, and the San Diego Housing Commission, adopted

various plans to transition to SAFMRs. These PHAs set HCV subsidies based on payment standards

applied to ZIP Codes (as opposed to other geographies like census tracts or locally demarcated

neighborhood boundaries).

Another MTW participant, the Chicago Housing Authority, argued that their use of exception

payment standards based on FMRs was more effective than SAFMRs. According to this argument,

the use of SAFMRs would raise payment standards in all high-rent areas and increase the PHA’s

costs per voucher without additional funding from HUD. This variance would mean that the PHA

could issue fewer HCVs. It was also argued that SAFMRs would lower payment standards in low-

rent areas and increase housing costs for HCV holders who remained in them. Consequently, this

MTW participant requested an exemption from the SAFMR rule in favor of the use of exception

payment standards in a more discrete set of geographic areas.

Although MTW participants were able to request exemptions from the SAFMR rule, requests for

exemptions by other PHAs were not granted. For example, the two PHAs in the Urban Honolulu

metropolitan area jointly requested an exemption from the SAFMR rule based on unique

characteristics linked to the Honolulu metropolitan area being located on a densely populated

Pacific island (Department of Community Services, City and County of Honolulu, 2018). The

request was denied. Other PHAs had greater success in obtaining temporary extensions to the

deadline for implementation, citing issues related to administrative obstacles to implementing

SAFMRs on-time. PHAs that requested temporary waivers of 3 months to 1 year in order to

make the transition to SAFMRs more seamlessly were typically granted extensions (Federal

Register, 2018).

Cityscape 141

Patterson and Silverman

HUD’s Monitoring of SAFMR Implementation

In addition to issues related to the setting of payment standards, holding tenants harmless, and the

parallel administration of the MTW program in mandatory SAFMR areas, the analysis found that

the implementation of the SAFMR rule was hampered by a lack of proactive monitoring by HUD.

For example, PHAs are not required to submit their administrative plans, payment standards, or

materials used to notify tenants and landlords about their internal implementation policies related to

the SAFMR rule to HUD. Instead, they are expected to keep these records in-house and available if

HUD requests to inspect them. This procedure is problematic because there is no central repository

where these materials are stored and made publicly available for inspection. Instead, information

must be gathered from individual PHAs. This approach impedes public interest and advocacy

groups from accessing information about the implementation of the SAFMR rule and shifts the

burden of public disclosure from HUD to members of the general public. The lack of a public

repository for implementation materials also hinders the free flow of information between PHAs

interested in identifying best practices to adopt when planning their implementation strategies.

Conclusions and Recommendations

The results presented in this article highlight how the successful implementation of SAFMRs hinges

on the degree to which PHA administrators show fidelity to the opportunity advancement goals of

the SAFMR rule. These goals focus on setting payment standards that provide HCV holders with

greater chances to move to high-opportunity neighborhoods. To forward these goals, ZIP Code-

based payment standards should be elevated in high-rent areas and lowered in low-rent areas.

This behavior would create an incentive structure that encourages moves to high-opportunity

neighborhoods. Importantly, the reduction of payment standards in low-rent areas provides PHAs

with the cost savings needed to pay for higher payment standards in high-rent areas. The reduction

in payment standards in low-rent areas also corrects for the tendency to overpay landlords when

FMRs are used. Payment standards based on SAFMRs bring HCV subsidies in line with market-

based rents across a metropolitan area.

The results presented in this article indicate that PHA administrators’ behavior in setting payment

standards lack high fidelity to the opportunity advancement goals of the SAFMR rule. This lack

has led to less than optimal implementation in the 24 metropolitan areas required to use SAFMRs.

Despite these findings, there are signs that once PHAs gain experience in the use of SAFMRs they

will apply the policy with greater efficacy. For instance, all but one of the PHAs that participated

in the SAFMR demonstration program continue to use them well beyond the end of that program.

Likewise, some of the highest levels of fidelity to SAFMRs were found in the place with the most

experience using them to set payment standards, the Dallas metropolitan area. Still, there is a

need to fine-tune the SAFMR rule in anticipation of the scheduled addition of metropolitan areas

mandated to adopt it. The expansion of mandatory SAFMR metropolitan areas is scheduled to

occur in the fifth year of the new rule’s implementation. Fine tuning the SAFMR rule would

allow for a more rapid scaling up to occur. Ideally, this expansion would encompass the full

implementation of the SAFMR rule nationally. With that goal in mind, recommendations are made

to three target audiences: (1) administrators within HUD, (2) local PHAs that implement HCV

programs, and (3) applied researchers and policy advocates.

142 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

Recommendations from the Analysis of PHAs Mandated to Use SAFMRs

Increase HUD’s Emphasis on the Opportunity Advancement Goals of SAFMRs. Successful

implementation of SAFMRs hinges on PHA administrators’ fidelity to the opportunity advancement

goals of the SAFMR rule. To foster this commitment to promoting HCV holders’ mobility, HUD

must invest more resources in educating PHAs, tenants, and landlords about these goals and their

relationship to the setting of payment standards in high-rent and low-rent areas.

Increase Funding to Cover the Costs of Transitioning to SAFMRs. HUD should increase

funding to support the transition to SAFMRs in two key areas. First, funding should be enhanced

to cover the costs of training staff, developing educational materials for tenants and landlords, and

upgrading software and other administrative systems. Currently, PHAs can request reimbursements

up to $25,000 for costs directly related to SAFMR implementation. These funds are intended to

cover the costs of outreach and briefing materials, hiring and training of staff, the development of

new methodologies for reasonableness determinations, and software. These funds are available for

PHAs adopting SAMFRs, but the level of funding and its continuation beyond the initial adoption

phase of SAMFRs are not adequate to sustain the requisite capacity of a PHA. Second, HUD

should reimburse PHAs for the costs of holding tenants harmless during the transition to SAFMRs,

so payment standards can be raised in high-rent areas without reducing the overall number

of vouchers.

Require More Metropolitan-Wide Collaboration. To curb the practice of individual PHAs setting

multiple payment standards in the same ZIP Codes, HUD should encourage more metropolitan-

wide collaboration across PHAs. Setting uniform payment standards in a metropolitan area will

have the benefit of reducing confusion for renters and providing landlords with a more predictable

environment. Metropolitan-wide collaboration can be encouraged with incentives to PHAs, such as

awarding additional vouchers, funding for mobility counseling, and technical support to PHAs that

join consortia and set uniform payment standards.

Scrutinize PHAs That Tier Payment Standards. HUD should establish a set of criteria for tiering

payment standards that conforms to the opportunity advancement goals of the SAFMR rule. The

establishment of criteria for tiering would provide PHAs with guidance on how to group PHAs.

It would also require PHAs to document how tiering was implemented so its effectiveness in

promoting opportunity advancement can be measured and evaluated. Generally, tiering payment

standards should be discouraged unless PHAs can demonstrate that this approach is more effective

at promoting moves to high opportunity ZIP Codes across a metropolitan area than setting distinct

payment standards for every ZIP Code in a PHA’s service area.

Reinforce the Purpose of Holding Tenants Harmless in all Communications with PHAs.

HUD should continue to stress its policy on holding tenants harmless in all communications with

PHAs. Communications with PHAs should continue to highlight that the policy of holding tenants

harmless relates exclusively to current HCV holders during the phase-in period for SAFMRs.

Reinforcement is needed so PHAs do not misinterpret this policy in ways that lead to increasing

payment standards in low-rent areas across the board in order to protect new HCV holders and

landlords. HUD should continue to stress that this policy was not designed to hold new HCV

Cityscape 143

Patterson and Silverman

holders and landlords harmless. It only applies to current HCV holders during the SAFMR phase-

in period.

Transition MTW Participants to SAFMRs. HUD should increase dialogue with MTW

participants about strategies to transition to SAFMRs. This dialogue should focus on requiring

MTW participants to show they tested alternative payment setting strategies when requesting

exemptions from SAFMRs. HUD should review and evaluate these alternative strategies. Following

the evaluation, if HCV families are not advancing to high opportunity areas in accordance with

non-MTW families, HUD should consider removing the MTW agency exemption and require the

agency to adopt SAFMRs.

Increase HUD’s Monitoring and Reporting Requirements. HUD should increase its monitoring

and reporting requirements for the implementation of the SAFMR rule. Under its current

administrative practices, PHAs are not required to submit their administrative plans, payment

standards, or materials used to notify tenants and landlords about their internal implementation

policies related to the SAFMR rule to HUD. A central repository needs to be created where these

materials are stored and made publicly available for inspection. This repository can be used as a

resource: by HUD when monitoring the implementation of the SAFMR rule, by public interest and

advocacy groups, by PHAs interested in identifying best practices, and by the general public. This

repository can be modeled after the one maintained on HUD’s website for the MTW program.7

Acknowledgments

The author thanks the Poverty & Race Research Action Council (PRRAC) for providing a grant to

fund this research. An earlier version of this analysis appears in a report by PRRAC and the Buffalo

Center for Social Research at the University at Buffalo. The author also thanks Pascal Buggs, an

urban planning doctoral student at the University at Buffalo, who assisted with the collection of

data from PHAs in the 24 metropolitan areas mandated to use SAFMRs.

Authors

Kelly L. Patterson is an associate professor at the School of Social Work, University at Buffalo, and

can be reached by email at klp27@buffalo.edu.

Robert Mark Silverman, is a professor in the Department of Urban and Regional Planning,

University at Buffalo, and can be reached by email at rms35@buffalo.edu.

7 HUD maintains an electronic repository of annual plans and other documents for the MTW program at the

following website: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/mtw/mtwsites.

144 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

oom

oom

96.66

100.15

95.51

92.10

94.76

90.00

4 Bedr

4 Bedr

oom

oom

96.79

99.87

95.77

91.98

95.00

89.59

3 Bedr

3 Bedr

ea

oom

oom

97.15

100.94

95.90

92.09

94.73

90.00

2 Bedr

2 Bedr

opolitan Ar

ea

oom

oom

97.66

102.07

96.24

92.09

94.73

90.00

1 Bedr

opolitan Ar

1 Bedr

oom

oom

97.00

100.82

95.74

92.03

94.59

90.00

0 Bedr

0 Bedr

gen-Passaic, NJ Metr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the Atlanta-Sandy Springs-Roswell, GA Metr

cent of SAFMRs for the Ber

Area Fair Market Rents.

Area Fair Market Rents.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

Atlanta-Sandy Springs-Roswell metropolitan area is 351.

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Appendix A: Additional Exhibits

Total sample size for the

Total sample size for the Bergen-Passaic metropolitan area is 136.

Exhibit A.1

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.2

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Cityscape 145

Patterson and Silverman

oom

oom

NA

101.35

101.35

98.83

98.11

99.44

4 Bedr

4 Bedr

oom

oom

NA

100.78

100.78

101.08

101.30

100.89

3 Bedr

3 Bedr

ea

oom

oom

NA

100.65

100.65

103.95

104.04

103.86

2 Bedr

ea

2 Bedr

opolitan Ar

oom

opolitan Ar

oom

NA

101.10

101.10

106.04

105.97

106.10

1 Bedr

1 Bedr

d-Gastonia, NC-SC Metr

oom

oom

NA

101.37

101.37

104.25

104.01

104.46

0 Bedr

0 Bedr

ea

ea

Area Fair Market Rents.

opolitan Ar

opolitan Ar

SAFMRs = Small

ville metropolitan area is 345.

cent of SAFMRs for the Charlotte-Concor

cent of SAFMRs for the Chicago-Joliet-Naperville, IL Metr

Area Fair Market Rents.

Available.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

NA = Data Not

SAFMRs = Small

Total sample size for the Charlotte-Concord-Gastonia metropolitan area is 119.

Total sample size for the Chicago-Joliet-Naper

Exhibit A.3

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.4

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

146 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

oom

oom

94.28

104.45

92.20

103.25

102.84

103.64

4 Bedr

4 Bedr

oom

oom

99.10

106.92

92.03

103.37

102.91

103.80

3 Bedr

3 Bedr

oom

oom

102.92

110.00

101.47

103.28

102.86

103.67

2 Bedr

2 Bedr

ea

oom

oom

94.16

opolitan Ar

101.68

110.00

103.13

102.99

103.23

1 Bedr

ea

1 Bedr

oom

opolitan Ar

oom

104.20

109.50

99.07

103.78

103.41

104.11

0 Bedr

0 Bedr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the Colorado Springs, CO Metr

cent of SAFMRs for the Dallas, TX Metr

Area Fair Market Rents.

Area Fair Market Rents.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the Colorado Springs metropolitan area is 60.

Total sample size for the Dallas metropolitan area is 1,071.

Exhibit A.5

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.6

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Cityscape 147

Patterson and Silverman

oom

oom

95.84

96.55

95.08

99.86

105.07

92.62

4 Bedr

4 Bedr

oom

oom

95.59

99.25

94.70

99.78

105.03

93.49

3 Bedr

3 Bedr

oom

oom

95.93

99.85

94.97

100.17

105.25

92.91

2 Bedr

2 Bedr

ea

ea

oom

oom

97.79

opolitan Ar

101.92

96.78

100.50

107.08

95.76

1 Bedr

1 Bedr

opolitan Ar

oom

oom

dale, FL Metr

97.27

100.93

96.45

98.93

106.71

94.44

0 Bedr

0 Bedr

orth-Arlington, TX Metr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the Fort Lauder

cent of SAFMRs for the Fort W

Area Fair Market Rents.

Area Fair Market Rents.

orth-Arlington metropolitan area is 302.W

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the Fort Lauderdale metropolitan area is 321.

Total sample size for the Fort

Exhibit A.7

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.8

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

148 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

oom

oom

100.81

101.82

100.00

101.31

107.03

98.22

4 Bedr

4 Bedr

oom

oom

100.92

101.72

100.00

101.87

106.15

99.55

3 Bedr

3 Bedr

ea

oom

oom

101.03

102.02

100.00

opolitan Ar

101.99

106.12

99.75

2 Bedr

2 Bedr

d, CT Metr

oom

oom

100.96

101.61

100.00

102.42

105.60

100.90

1 Bedr

1 Bedr

ea

d-East Hartfor

opolitan Ar

oom

oom

101.21

102.37

100.00

est Hartfor

101.60

105.75

99.08

0 Bedr

d-W

0 Bedr

, IN Metr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the Gary

cent of SAFMRs for the Hartfor

Area Fair Market Rents.

Area Fair Market Rents.

est Hartford-East Hartford metropolitan area is 125.

y metropolitan area is 56.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the Gar

Total sample size for the Hartford-W

Exhibit A.9

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.10

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Cityscape 149

Patterson and Silverman

oom

oom

100.00

100.00

100.00

96.77

104.44

91.38

4 Bedr

4 Bedr

oom

oom

100.00

100.00

100.00

96.77

104.43

91.38

3 Bedr

3 Bedr

oom

oom

100.00

100.00

100.00

96.80

104.49

91.39

2 Bedr

2 Bedr

oom

ea

oom

ea

100.00

100.00

100.00

96.79

104.46

91.39

1 Bedr

1 Bedr

opolitan Ar

opolitan Ar

oom

oom

100.00

100.00

100.00

96.80

104.16

91.13

0 Bedr

0 Bedr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the Jackson, MS Metr

cent of SAFMRs for the Jacksonville, FL Metr

Area Fair Market Rents.

Area Fair Market Rents.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the Jackson metropolitan area is 64.

Total sample size for the Jacksonville metropolitan area is 80.

Exhibit A.11

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.12

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

150 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

oom

oom

93.70

98.41

91.55

98.78

99.76

97.78

4 Bedr

4 Bedr

oom

oom

93.66

98.25

91.56

99.55

100.00

99.09

3 Bedr

3 Bedr

oom

ea

oom

99.05

108.19

94.85

98.72

99.39

98.05

2 Bedr

2 Bedr

opolitan Ar

ea

oom

oom

99.69

95.53

99.19

opolitan Ar

108.77

101.15

103.15

1 Bedr

1 Bedr

oom

oom

99.37

108.42

95.22

100.10

100.38

99.81

0 Bedr

0 Bedr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the Monmouth-Ocean, NJ Metr

cent of SAFMRs for the North Port-Sarasota-Bradenton, FL Metr

Area Fair Market Rents.

Area Fair Market Rents.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the Monmouth-Ocean metropolitan area is 88.

Total sample size for the North Port-Sarasota-Bradenton metropolitan area is 91.

Exhibit A.13

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.14

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Cityscape 151

Patterson and Silverman

oom

oom

86.07

84.48

86.95

103.83

104.62

102.84

4 Bedr

4 Bedr

oom

oom

85.93

85.89

85.94

ea

103.77

104.48

102.86

3 Bedr

3 Bedr

opolitan Ar

ea

oom

oom

86.89

87.33

86.64

103.81

104.59

102.84

2 Bedr

2 Bedr

opolitan Ar

A-NJ-DE-MD Metr

oom

oom

88.06

88.60

87.66

103.76

104.55

102.75

1 Bedr

1 Bedr

ne-Titusville, FL Metr

oom

oom

89.31

89.89

88.99

103.13

103.33

102.89

0 Bedr

0 Bedr

ea

ea

opolitan Ar

opolitan Ar

itusville metropolitan area is 42.

cent of SAFMRs for the Palm Bay-Melbour

cent of SAFMRs for the Philadelphia-Camden-Wilmington, P

Area Fair Market Rents.

Area Fair Market Rents.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the Palm Bay-Melbourne-T

Total sample size for the Philadelphia-Camden-Wilmington metropolitan area is 451.

Exhibit A.15

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.16

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR>=100 percent area FMR.

Note:

152 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

oom

oom

99.84

105.38

95.45

102.60

110.00

97.89

4 Bedr

4 Bedr

oom

oom

99.93

106.04

95.32

102.61

110.00

97.90

3 Bedr

3 Bedr

ea

oom

oom

100.13

105.92

95.55

97.79

opolitan Ar

102.54

110.00

2 Bedr

2 Bedr

oom

cade, CA Metr

oom

ea

100.38

106.79

95.76

102.55

110.00

98.17

1 Bedr

1 Bedr

den-Ar

opolitan Ar

oom

oom

A Metr

100.04

105.69

95.12

102.72

110.00

98.09

gh, P

0 Bedr

0 Bedr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the Pittsbur

cent of SAFMRs for the Sacramento-Roseville-Ar

Area Fair Market Rents.

Area Fair Market Rents.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the Pittsburgh metropolitan area is 369.

Total sample size for the Sacramento-Roseville-Arden-Arcade metropolitan area is 128.

Exhibit A17

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR>=100 percent area FMR.

Note:

Exhibit A.18

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR>=100 percent area FMR.

Note:

Cityscape 153

Patterson and Silverman

oom

oom

91.60

91.60

91.60

80.37

84.36

78.50

4 Bedr

4 Bedr

oom

oom

91.66

91.19

92.09

84.22

88.81

81.59

3 Bedr

3 Bedr

oom

oom

ea

91.76

91.50

92.01

85.77

87.79

84.83

2 Bedr

2 Bedr

ea

opolitan Ar

oom

oom

91.68

91.21

92.11

opolitan Ar

86.96

91.13

85.01

1 Bedr

1 Bedr

oom

oom

91.76

91.73

91.79

85.82

89.04

84.32

0 Bedr

0 Bedr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the San Antonio-New Braunfels, TX Metr

cent of SAFMRs for the San Diego-Carlsbad, CA Metr

Area Fair Market Rents.

Area Fair Market Rents.

Antonio-New Braunfels metropolitan area is 135.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the San

Total sample size for the San Diego-Carlsbad metropolitan area is 169.

Exhibit A.19

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.20

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

154 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

oom

oom

99.09

104.79

95.15

101.60

104.01

97.10

4 Bedr

4 Bedr

oom

oom

100.41

105.23

96.96

101.48

103.82

97.12

3 Bedr

3 Bedr

ea

oom

oom

98.70

104.42

94.74

101.58

104.00

97.08

2 Bedr

2 Bedr

opolitan Ar

ea

, FL Metr

oom

oom

98.42

103.74

94.62

101.29

107.08

99.05

1 Bedr

1 Bedr

opolitan Ar

g-Clearwater

oom

oom

96.32

99.68

94.00

101.47

103.89

96.97

0 Bedr

0 Bedr

ampa-St. Petersbur

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the T

cent of SAFMRs for the Urban Honolulu, HI Metr

Area Fair Market Rents.

Area Fair Market Rents.

Petersburg-Clearwater metropolitan area is 200.

ds Reported in the Metr

ds Reported in the Metr

ds as a Per

Tampa-St.

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the

Total sample size for the Urban Honolulu metropolitan area is 86.

Exhibit A.21

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.22

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Cityscape 155

Patterson and Silverman

oom

oom

105.85

106.68

103.58

98.97

99.45

98.22

4 Bedr

4 Bedr

oom

oom

105.67

106.63

103.00

98.55

98.76

98.22

3 Bedr

3 Bedr

ea

opolitan Ar

oom

ea

oom

105.87

107.14

102.36

98.50

98.60

98.35

2 Bedr

2 Bedr

A-MD Metr

opolitan Ar

oom

oom

104.70

105.51

102.38

98.72

99.00

98.28

1 Bedr

1 Bedr

oom

oom

103.98

104.54

102.46

100.00

100.00

100.00

0 Bedr

0 Bedr

ashington-Arlington-Alexandria, DC-V

est Palm Beach-Boca Raton, FL Metr

ea

ea

opolitan Ar

opolitan Ar

cent of SAFMRs for the W

cent of SAFMRs for the W

Area Fair Market Rents.

Area Fair Market Rents.

ds Reported in the Metr

ashington-Arlington-Alexandria metropolitan area is 491.

ds Reported in the Metr

est Palm Beach-Boca Raton metropolitan area is 221.

ds as a Per

W

W

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

ds as a Per

ds in Low-Opportunity ZIP Codes¹

ds in High-Opportunity ZIP Codes²

SAFMRs = Small

SAFMRs = Small

Total sample size for the

Total sample size for the

Exhibit A.23

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

All Payment Standar

Payment Standar

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

Exhibit A.24

Payment Standar

FMR = Fair Market Rent.

¹Low-opportunity ZIP Codes = ZIP Codes with SAFMR<100 percent area FMR.

²High-opportunity ZIP Codes = ZIP Codes with SAFMR≥100 percent area FMR.

Note:

156 Small Area Fair Market Rents

The Best Laid Plans Often Go Awry:

An Analysis of the Implementation of Small Area Fair Market Rents

References

Collinson, Robert A., and Peter Ganong. 2014. The Incidence of Housing Voucher Generosity.

Working paper. http://scholar.harvard.edu/files/ganong/files/collinsonganong105.

pdf?m=1446135036.

Department of Community Services, City and County of Honolulu. 2018. (May 15). Letter to Ben S.

Carson, M.D., Secretary of Housing and Urban Development. City & County of Honolulu, Hawaii

Public Housing Authority, Request for exemption from SAFMR implementation for Honolulu

metropolitan statistical area. Washington, DC: U.S. Department of Housing and Urban Development.

Ellen, Ingrid Gould. 2018. “What Do We Know About Housing Choice Vouchers?” Regional Science

and Urban Economics, Online First, 1–5. Accessed at: https://www.sciencedirect.com/science/

article/pii/S0166046217302983?via%3Dihub.

Federal Register. 2018. Department of Housing and Urban Development, Notice of Regulatory Waiver

Requests Granted to the Second Quarter of the Calendar Year 2018. Fed. Reg. § 83, 190 (notices Oct. 1, 2018) (Docket No. FR-6101-N-02).

———. 2016. “Small Area Fair Market Rents in Housing Choice Voucher Program Values for

Selection Criteria and Metropolitan Areas Subject to Small Area Fair Market Rents,” Fed. Reg. § 81,

221 (final rule Nov. 16, 2016) (to be codified as 24 CFR 888.113).

Finkel, Meryl, Samuel Dastrup, Kimberly Burnett, Thyria Alvarez, Carissa Climaco, and Tanya de

Sousa. 2017. Small Area Fair Market Rent Demonstration Interim Report. Washington, DC: HUD,

Office of Policy Development and Research.

Gautreaux v. Chicago Housing Authority. 1969. U.S. District Court for the Northern District of

Illinois, 296 F. Supp. 907 (N.D. Ill.).

Kahn, Peter B., and Geoffrey B. Newton. 2013. “The Small Area FMR Demonstration,” Cityscape 15

(1): 325–328.

New York University (NYU) Furman Center. 2018. Data brief: How Do Small Area Fair Market Rents

Affect the Location and Number of Units Affordable to Voucher Holders? New York: NYU Furman Center.

Palm, Matthew. 2018. “Scale in Housing Policy: A Case Study of the Potential of Small Area Fair

Market Rents,” Cityscape 20 (1): 147–166.

Poverty & Race Research Action Council (PRRAC). 2018. A Guide to Small Area Fair Market Rents

(SAFMRs): How States and Local Housing Agencies Can Expand Opportunity for Families in All Metro

Areas. Washington, DC: Poverty & Race Research Action Council.

Silverman, Robert M., and Kelly L. Patterson. 2015. Qualitative Research Methods for Community

Development. New York: Routledge.

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Southern Burlington County NAACP v. Township of Mount Laurel. 1975. Supreme Court of New Jersey

(67 N.J. 151, 336 A.2d 713, N.J.).

Thorpe, Deborah. 2018. “Achieving Housing Choice and Mobility in the Voucher Program:

Recommendations for the Administration,” Journal of Affordable Housing 27 (1): 145–160.

Treat, John. 2018. “Establishing a More Effective SAFMR System: The Cost and Benefits of HUD’s

2016 Small Area Fair Markets Rent Rule,” University of Michigan Journal of Law Reform 51 (3):

643–668.

U.S. Department of Housing and Urban Development (HUD). 2019. Moving to Work Program.

Washington, DC: U.S. Department of Housing and Urban Development, Office of Public and

Indian Housing. https://www.hud.gov/mtw.

———. 2018. Implementing Small Area Fair Market Rents (SAFMRs): Implementation Guidebook.

Washington, DC: U.S. Department of Housing and Urban Development.

———. 2017. Picture of Subsidized Households. Washington, DC: HUD, Office of Policy Development

and Research. https://www.huduser.gov/portal/datasets/assthsg.html#2009-2017_data.

158 Small Area Fair Market Rents

by Christian Hess, Rebecca J. Walter, Arthur Acolin, and Sarah Chasins

Comparing Small Area Fair Market

Rents With Other Rental Measures

Across Diverse Housing Markets

Christian Hess

Rebecca J. Walter

Arthur Acolin

University of Washington

Sarah Chasins

University of California—Berkeley

Abstract

Small Area Fair Market Rents (SAFMRs) are calculated at the 40th percentile of the U.S. postal ZIP

Code instead of the metropolitan area in an effort to capture localized rents to expand choice for voucher holders to access housing in higher-opportunity neighborhoods. Existing studies on the potential and actual outcomes of SAFMRs demonstrate that findings vary for different types of housing markets.

Furthermore, the decisions public housing authorities (PHAs) make in the implementation process affect PHAs’ program budget and the rent burden and locational outcomes for voucher households. This study

aims to address how these implementation factors are affected by local rental market conditions for three PHAs—Housing Authority of the City of Fort Lauderdale, San Antonio Housing Authority, and Seattle

Housing Authority—in diverse housing markets. By comparing different sources of market rent estimates with SAFMRs in each location, we contribute new information about how this rule is likely to produce different residential outcomes in terms of increased access to low-poverty neighborhoods and adjustments to payment standards in low-rent neighborhoods. The findings reveal differences across rent measures in terms of estimated levels and relative differences across ZIP Codes. These findings suggest that housing authorities may face challenges in meeting the objectives of the SAFMR final rule without some form of local adjustments.

Introduction

Over the past several decades, social science research has emphasized how neighborhoods matter

through findings across disciplines that reveal associations between an individual’s neighborhood

conditions and their life outcomes. For example, distressed neighborhoods are often associated

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Hess, Walter, Acolin, and Chasins

with lower educational attainment and poorer health, whereas resource-rich neighborhoods

provide opportunities for economic mobility and improved life outcomes (Brooks-Gunn et al.,

1993; Chetty, Hendren, and Katz, 2016; Ellen and Turner, 1997; Gennetian et al., 2012; Ludwig

et al., 2012). Scholars, policymakers, and practitioners have therefore emphasized the importance

of providing low-income households with access to high-opportunity areas, that is, neighborhoods

with attributes that foster economic mobility such as quality schools, low crime rates, employment

opportunities, community resources, and healthy environments. This theoretical perspective has

prompted housing strategies and policies that focus on reducing both income and racial residential

segregation for low-income households.

The Housing Choice Voucher (HCV) program and its policies are seen as a vehicle capable of

deconcentrating poverty and expanding housing opportunities for low-income renters. Low-

income households that receive a voucher can use it for any unit in the private market that will

accept a voucher and will pass U.S. Department of Housing and Urban Development (HUD)

inspection standards. Voucher recipients pay approximately 30 percent of their income towards

rent, with the housing authority administering the voucher covering the difference. The purpose

of the flexibility provided to voucher households in choosing their unit relative to place-based

housing subsidies is to provide them with the choice to select the neighborhood and unit that

best meets their needs. Early evaluation of the HCV program demonstrates the failure of voucher

households to benefit from this choice and access high-opportunity neighborhoods, however

(Newman and Schnare, 1997). Subsequent studies continued to highlight this outcome, with

voucher holders concentrating in high-poverty, minority-concentrated neighborhoods across varied

research contexts (Devine et al., 2003; Feins and Patterson, 2005; Galvez, 2011; McClure, 2008;

McClure, Schwarz, and Taghavi, 2015; Metzger, 2014; Varady and Walker, 2000; Walter, Li, and

Atherwood, 2015; Wang and Varady, 2005).

Many explanations exist for why voucher holders are not accessing higher-opportunity

neighborhoods even though, in theory, the voucher should remove geographic restrictions. For

example, perceived and real discrimination by race, source of income, or family structure may limit

choice for low-income households (Charles, 2005; Feins and Patterson, 2005; Ondrich, Stricker,

and Yinger, 1999; Popkin et al., 2004; Popkin et al., 2003; Smith et al., 2002; Varady and Walker,

2003). Few cities in the United States have enacted “source of income discrimination” laws, which

prevent landlords from discriminating against voucher-holding applicants who are otherwise

qualified, but lack of enforcement in these areas stifles positive outcomes (Tighe, Hatch, and Mead,

2017). Some landlords will not participate in the HCV program because of their perception of

voucher holders or other bureaucratic factors (Garboden et al., 2018). Informal search processes

are another factor that may cause reconcentration and perpetuate segregation. Voucher holders

often depend on their social networks, only consider neighborhoods that are familiar to them, or

have incomplete information during the housing search process (Darrah and DeLuca, 2014; Krysan

and Crowder, 2017). The design of the voucher program itself may also contribute to undesirable

locational outcomes because the voucher holder is only given a limited timeframe (usually 60 days)

to find and lease a unit that is within the allowable rent and meets the inspection requirements of

the program (Basolo and Nguyen, 2005; DeLuca, Garboden, and Rosenblatt, 2013).

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With Other Rental Measures Across Diverse Housing Markets

Furthermore, voucher holders’ choice is restricted by the limited number of affordable rental

units available in high-opportunity neighborhoods that offer access to employment, transit, and

amenities (Schuetz, 2009; Tremoulet, Dann, and Adkins, 2016). Tight housing markets with low

vacancy rates exacerbate this challenge (Briggs, Popkin, and Goering, 2010; Feins and Patterson,

2005). Fair Market Rents (FMRs), calculated by HUD for the HCV program, are used to determine

payment standards for housing authorities and are established at the 40th percentile of all market

rents in a metropolitan area (Kahn and Newton, 2013). These payment standards determine the

maximum amount of rental subsidy that a housing authority may provide for each household.

FMRs have contributed to the concentration of voucher holders in distressed communities because

assisted households are unable to rent units in high-cost areas and therefore are often priced out of

higher-opportunity neighborhoods (Collinson and Ganong, 2018; Fischer, 2015).

In response to this programmatic barrier, HUD issued a new rule called Small Area Fair Market

Rents (SAFMRs) to expand housing choice in neighborhoods of opportunity for voucher

households. SAFMRs are calculated at the 40th percentile of U.S. postal ZIP Codes instead of the

overall metropolitan area. The purpose of this new rule is to provide housing authorities with

estimates that capture localized rents in their jurisdiction in order to expand choices for voucher

holders to access housing units in higher-opportunity neighborhoods.1 Ellen, Horn, and Schwartz

(2016) found that when provided the option, voucher holders will move to higher-opportunity

neighborhoods with better schools. Studies on the potential impact of SAFMRs reveal they increase

access to high-opportunity neighborhoods by expanding the number of units available to voucher

holders in higher-income neighborhoods in most, but not all, metropolitan areas (NYU Furman

Center, 2018; Palm, 2018).

Emerging evidence on the outcomes of SAFMRs is limited by its recent implementation in only

24 metropolitan areas. Most of these studies are based on a few years of data available from six

demonstration markets. In the earliest evaluation of SAFMRs, Collinson and Ganong (2018)

found that payment standards established at smaller geographies improve locational outcomes

for voucher holders. Data from Dallas, Texas, the first metropolitan area mandated to implement

SAFMRs as part of a court settlement in 2010, indicates voucher households have been able to

move out of high-poverty, unsafe neighborhoods with the implementation of SAFMRs (Collinson

and Ganong, 2018). The two studies that have been conducted on the other demonstration areas,

however, highlight how outcomes may vary depending on the housing market in which SAFMRs

are implemented (Finkel et al., 2017; Reina, Acolin, and Bostic, 2019).

What is apparent from this early research is where and how SAFMRs are implemented has

implications for program outcomes. This research includes but is not limited to: the ability for

households to secure a unit in high-opportunity neighborhoods; the impact on the reduction in rent

paid by the housing authority to the landlord in lower-income neighborhoods; and the potential

cost increase per voucher to housing authorities administering the program, which may result in

fewer households being served by the voucher program over time. This study aims to address how

these implementation factors are affected by local rental market conditions for three public housing

authorities (PHAs)—Housing Authority of the City of Fort Lauderdale, San Antonio Housing

1 Published in the Federal Register as a final rule on January 16, 2016. 81 Fed. Reg. 80567.

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Hess, Walter, Acolin, and Chasins

Authority, and Seattle Housing Authority—in diverse housing markets. By comparing different

sources of market rent estimates with SAFMRs in each location, we contribute new information

about how this rule is likely to produce different residential outcomes in terms of increased access to

low-poverty neighborhoods and adjustments to payment standards in low-rent neighborhoods.

Background

Designated PHAs in 24 metropolitan areas with substantial voucher concentration were required

to adopt the new SAFMR rule for all tenant-based vouchers starting at the beginning of 2018.

Project-based vouchers, which are housing subsidies attached to the housing unit instead of issued

to a voucher household, are exempt from mandatory adoption. SAFMRs are used to establish

payment standards for the voucher program. Payment standards determine the maximum amount

that a housing authority pays towards rent for a voucher household. PHAs have some flexibility

in establishing the payment standard amounts between 90 and 110 percent of the SAFMR, which

is also known as the basic range. PHAs may also establish a payment standard for each ZIP Code

in their service area, or group ZIP Codes that have SAFMRs within the basic range of all other

ZIP Codes within the group (HUD, 2018). For example, Walter (2018) explored grouping ZIP

Codes for several PHAs based on the objectives of the SAFMR rule instead of only using the

basic range. Since the goal of the rule is to deconcentrate voucher holders and provide access

to high-opportunity neighborhoods, ZIP Codes were first grouped by opportunity and voucher

concentration, before grouping ZIP Codes by SAFMRs within the basic range. This strategy intends

to make it easier for housing authorities to establish local policies and mobility strategies for each

grouping and to better track program outcomes (Walter, 2018).

The number of units accessible to voucher holders depends on how the payment standards are

established. In high-rent areas, the payment standard needs to be set at an amount that will

maximize the availability of units. In lower-rent neighborhoods, PHAs must decide if payment

standards should be held constant or reduced. Setting the initial payment standards is likely the

greatest challenge for SAFMR PHAs (HUD, 2018). These decisions affect the rent burden for voucher

households, the program budget for PHAs, and the locational outcomes for voucher holders.

Potential and Actual Outcomes of SAFMRs

Research on the potential and actual outcomes of SAFMRs demonstrates findings vary for different

types of housing markets. Palm (2018) examined the number of rental listings that will become

accessible to voucher holders in high-opportunity areas with the implementation of SAFMRs in

five metropolitan statistical areas in California. The analysis reveals a range of results from a decline in accessible rentals in San Francisco ZIP Codes with low poverty rates (less than 10 percent) to a

34-percentage point increase in Sacramento. The decrease in San Francisco is likely due to SAFMR

calculations not keeping pace in a market that is experiencing rapidly increasing rents (Palm, 2018).

In response to an interim report commissioned by HUD which found the total number of affordable

rental units accessible to voucher holders would decrease by 3.4 percent with the implementation of

SAFMRs (Finkel et al., 2017), the New York University (NYU) Furman Center specifically analyzed

the 24 metropolitan areas mandated to adopt the rule by 2018. The Furman Center suspected the

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With Other Rental Measures Across Diverse Housing Markets

findings could differ for the set of mandated areas, because the areas were selected based on specific

market characteristics (for example, number of voucher holders concentrated in low-income areas,

the percentage of renter units in ZIP Codes where the SAFMR is more than 110 percent of the

metropolitan FMR, and vacancy rates higher than 4 percent). The findings of this analysis reveal

that these markets would likely see an overall 9.1 percent increase in the affordable units available

to voucher holders in the mandated metropolitan areas. This availability would vary by housing

market, however, with four metropolitan areas—Gary, Indiana; Hartford, Connecticut; Monmouth-

Ocean, New Jersey; and Sarasota, Florida—experiencing a decline in the number of affordable rental

units. Of the remaining 20 housing markets that will experience an increase, the range is substantial,

from a 1.1 percent increase in Pittsburgh, Pennsylvania, to a 28.3 percent increase in San Antonio,

Texas. These findings reinforce the conclusion that the results of implementing SAFMRs are likely to

vary across different housing markets.

The HUD-commissioned interim report also examines the performance of SAFMRs in the

demonstration areas. This study highlights that SAFMRs provide access to more rental units in

higher- opportunity areas in the demonstration areas, but again, these gains vary by housing

market. For example, very few units are available in high-opportunity neighborhoods in Long

Beach, whereas Cook County experienced large gains. In terms of low-opportunity areas, Laredo

and Chattanooga lost some units, but Dallas and Long Beach lost substantially more. Overall, the

percentage of voucher holders living in high-opportunity neighborhoods among the demonstration

areas has risen slightly, from 11 percent to 13 percent. Still, outcomes for new voucher holders vary

depending on the market. Laredo experienced the greatest gain; the share of households moving to

high-rent ZIP Codes increased from 5 percent to 22 percent, whereas Mamaroneck experienced a

decrease from 83 percent to 59 percent (Finkel et al., 2017).

Another study examining the ability of SAFMRs to improve locational outcomes for voucher

households found similar regional variation. Reina, Acolin, and Bostic (2019) used tenant

data from the six SAFMR demonstration sites—Dallas, Texas; Cook County, Illinois; Laredo,

Texas; Long Beach, California; Mamaroneck, New York; and Chattanooga, Tennessee—to

determine if increased payment standards allowed voucher holders to access higher-opportunity

neighborhoods. Their findings indicate that voucher households in Dallas had the largest gains

in moving to neighborhoods where the SAFMR rent was higher than the FMR. Otherwise, results

were mixed; Laredo, Long Beach, and Mamaroneck experienced gains but Cook County and

Chattanooga underwent decreases. In terms of neighborhood quality,2 again voucher holders in

Dallas experienced the greatest gains, while Chattanooga voucher holders experienced a decline

in neighborhood quality (Reina, Acolin, and Bostic, 2019). Furthermore, Reina (2019), using the

same demonstration sites, examined if SAFMRs reduce the number of voucher households living in

minority concentrated neighborhoods. Findings indicate that SAFMRs only slightly improved access

to lower poverty neighborhoods for Black voucher households, particularly in Dallas compared with

the other locations. Overall, these studies highlight how differences in the housing markets where

PHAs are required to implement SAFMRs are important to understanding variation in outcomes.

2 Neighborhood quality in this study is measured by: the poverty rate, unemployment rate, percentage of children living in households headed by single mothers, housing vacancy rate, percentage of fourth graders who are not proficient in math or reading, and violent crime rate (Reina, Acolin, and Bostic, 2018).

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Hess, Walter, Acolin, and Chasins

Implementation Challenges Relevant to SAFMR Outcomes

Neighborhoods with rapidly increasing rents present a problem for implementing SAFMRs. As

Palm (2018) demonstrated in San Francisco, SAFMRs may not keep pace with the actual market;

these neighborhoods will not gain units affordable to voucher holders because the payment

standard will be set too low. This problem stems from two factors. First, in most markets, SAFMRs

use regional and national instead of local inflation trend factors, ignoring the considerable

heterogeneity in housing cost trends between metropolitan locations.3 Second, SAFMRs are

calculated from American Community Survey (ACS) estimates, and these data may already

include unreliable local estimates with geographical bias and high uncertainty (Bazuin and Fraser,

2013; Folch et al., 2016; Treat, 2017). This issue may be especially true in neighborhoods that

are experiencing considerable growth, where housing costs are rapidly increasing (Boeing and

Waddell, 2016; Palm, 2018; Treat, 2017). These limitations have prompted researchers to compare

SAFMRs with proprietary rental listing data sources to potentially improve housing cost estimates

within local rental housing markets.

As an example, Palm (2018) used Rent Jungle data and compared it against Boeing and Waddell’s

(2016) Craigslist data, finding that the two proprietary sources are more aligned with each other

than with the ACS data. Walter (2018) compared SAFMRs with CoStar and Apartments.com data

in three housing markets—Fort Lauderdale and Jacksonville, Florida, and San Antonio, Texas. The

results highlight considerable rent variations from the SAFMR in some neighborhoods that could

lead to fewer families being served by the program or may continue to prevent some households

from being able to access units in higher-cost neighborhoods (Walter, 2018). Proprietary sources

allow for real-time rental listing estimates at very precise geographic scales, and combined temporal

and spatial granularity may be able to provide insight for establishing payment standards in areas

where SAFMRs are not accurately reflecting the current rental market.

Another implementation challenge is the impact on households in lower-income neighborhoods

where SAFMRs reduce payment standards. Landlords may refuse to decrease rents for current

voucher tenants. If the household stays, it will be responsible for paying more towards rent,

causing a greater housing cost burden. Housing authorities may decide to implement a “hold

harmless” policy for existing tenants that prevents a reduction in their payment standard, but

new households admitted to the program may find it difficult to lease units in lower-income

neighborhoods because of the payment standard reduction. Further, the interim report on the

demonstration areas indicates that such rent adjustments particularly impact households with

disabled and elderly members that do not have the means to move. The amount paid by tenants

towards rent increased by 16 percent in a 5-year period, with the largest increases (22 percent)

observed among lower-rent ZIP Codes (Finkel et al., 2017). In response, PHAs are implementing

hold harmless policies, but this approach has financial implications on the number of voucher

holders they are able to serve.

As a Moving to Work (MTW) agency with authorization from HUD to design strategies to use

federal funding more effectively and request exemptions on voucher rules, King County Housing

3 In 23 markets, the local consumer price index produced by the Bureau of Labor Statistics is used, but even in these markets, the adjustments will not be able to account for neighborhood specific trends.

164 Small Area Fair Market Rents

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With Other Rental Measures Across Diverse Housing Markets

Authority in Washington has been implementing policies that use multiple payment standards to

better reflect the local rental market since 2001. Although not required to adopt SAFMRs, King

County is currently using a six-tier payment standard system. Given the concern over the impact

on households in lower-cost areas, King County did not decrease payment standards in these areas

at the time of implementation but instead chose to increase the payment standard in these areas to

a lesser extent than high-cost neighborhoods. This decision prevented housing costs from rising

for households in low-rent areas at the onset of implementation. The payment standard in low-

cost areas is now held below the FMR (King County Housing Authority, 2017). This alternative

may be one implementation option for housing authorities because they are not required to reduce

the payment standard in low-rent areas (HUD, 2018). Holding the payment standard constant,

however, increases program costs.

The extent to which new and existing households change their locational patterns in response

to the implementation of the SAFMRs will affect the cost of the program and thus influence the

number of households the housing authority is able to serve. The HUD interim analysis on the

demonstration sites found that the amount housing authorities paid for rent and utilities on behalf

of voucher holders decreased on average by 13 percent over a 5-year period. This decrease was

substantial in lower-rent areas (30 percent), whereas higher-rent areas experienced an average

increase of 3 percent (Finkel et al., 2017). If payment standards are reduced in low-rent areas

during implementation, it is reasonable to assume that SAFMRs will result in cost savings for

housing authorities. As more voucher holders move to higher-cost areas over time, however, total

costs to agencies will likely increase. King County Housing Authority explored long-term cost

implications and the hypothetical average payment they could expect, based on the number of

households that move to higher-cost neighborhoods. They found that by 2019 the costs savings

gained from using multiple payment standards rather than the FMR may be eliminated, and costs

may rise based on future mobility trends (King County Housing Authority, 2017).

The points previously raised highlight the need for a comprehensive understanding of the rental

markets in which SAFMRs are implemented, as there are significant implications for a household’s

ability to secure a unit in the neighborhood of their choice and for the cost to the housing

authorities administering the program. This research aims to compare the SAFMRs with other rent

measures and analyze how they compare in three very different markets. The findings will provide

further insight for PHAs currently implementing and considering implementing SAFMRs.

Study Areas

The study areas were selected based on diverse market characteristics and the willingness of the

local housing authorities to participate in the study. The three housing authorities that participated

in this study vary by location, size, and programs. The Housing Authority of the City of Fort

Lauderdale (HACFL) administers over 3,000 vouchers and manages 1,250 tax credit units, 170

affordable housing units, and 95 public housing units. HACFL’s service area focuses on Fort

Lauderdale but they manage housing units in other cities in Broward County with the exception of

the City of Hollywood, which is the only housing authority in the county with which HACFL does

not have an interjurisdictional agreement. In the final rule, HACFL was in an area that met the

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Hess, Walter, Acolin, and Chasins

SAFMR area criteria and was mandated to adopt SAFMRs. HACFL implemented SAFMRs in 2018

based on grouping all ZIP Codes that had SAFMRs within 10 percent of one another resulting in

six different payment standards.

The San Antonio Housing Authority (SAHA) is one of the 39 public housing authorities that has

MTW status, which gives housing authorities the flexibility to implement innovative activities and

programs to enhance housing and services for residents. SAHA serves over 65,000 residents in the

city with average incomes of less than $12,500 annually. SAHA administers over 14,000 vouchers

and manages more than 6,000 public housing units along with approximately 7,000 mixed-income

housing units. In the final rule, SAHA was also mandated to adopt SAFMRs. Based on extensive

research, they established two payment standards in 2018 and established a SAFMR Committee to

assess the potential impact of making additional changes to their two submarkets.

The Seattle Housing Authority (SHA) also has MTW designation. SHA serves 34,000 residents

in the city of Seattle and administers over 10,000 vouchers. They also own and operate more

than 8,000 housing units spanning 400 sites throughout Seattle. Although SHA is not one of

the areas mandated to adopt SAFMRs, they have been working towards strategies to provide

additional housing opportunities to voucher households. As a high-cost market that has seen

rapidly increasing rents over the past few years, it has become difficult for voucher holders to

locate housing within the city. SHA has implemented strategies like longer search times and is

participating in pilot projects such as the Creating Moves to Opportunity that includes a counseling

program and higher payment standards for families looking to move to high-opportunity areas with

the goal of finding new approaches that will best assist their households and improve long-term

outcomes. SHA also participated in a local survey led by King County Housing Authority to update

FMRs for the region. The last local survey was completed in 2017 and was made effective for 2018.

It resulted in an average FMR increase of 22 percent compared with the original FMR.

The three rental housing markets are diverse in terms of size, rent levels, and vacancy as shown in

exhibit 1. Seattle is a higher-cost, lower-vacancy market with high income that results in the lowest

levels of rent burden (45 percent). San Antonio is a low-cost, low-income market with relatively

high vacancy and a 52 percent share of rent burden households. Fort Lauderdale, while having

lower housing costs than Seattle and substantially higher vacancy, has lower household income

resulting in a higher share of rent burden households (63 percent).

166 Small Area Fair Market Rents

Comparing Small Area Fair Market Rents

With Other Rental Measures Across Diverse Housing Markets

Exhibit 1

Rental Housing Market Profiles

Fort Lauderdale

San Antonio

Seattle

Total Housing Units

93,917

552,520

355,293

Total Housing Units per Square Mile

2,587

1,187

2,501

Renter Occupied Units/Percent of Total

33,690

226,675

174,245

Housing Units

(35.87%)

(41.03%)

(49.04%)

Rental Vacancy Rate

9.40%

9.10%

3.90%

Average Household Size of Renter-

Occupied Units

2.40

2.70

1.90

Median Gross Rent

$1,217

$926

$1,555

Median Household Income

$56,309

$50,044

$86,822

Cost Burden Renter Households (Paying

More Than 30 Percent of Income on Rent)

63.30%

51.90%

44.60%

Source: 2017 American Community Survey 1-year estimates

Data and Methods

Information on real estate markets is imperfect. In particular, measures of rent levels and changes

are difficult to establish because, unlike sales, rental contracts are not recorded publicly. This

lack of data means it is only possible to collect information about contract rent via an ad-hoc and

decentralized process. Establishing constant quality in rental estimates is further complicated by

the heterogeneous nature of housing units and cyclical variations in the units available for rent.

Traditionally, the main national source of rent estimates at small levels of geography (ZIP Code or

below) was the Decennial U.S. Census, with measures for intercensal years extrapolated from the

last Census figures. In the last decade, however, several new public and private national rental

measures available at small levels of geography and updated at least annually have emerged. Since

2009, the 5-year estimates of the U.S. Census Bureau ACS have provided median contract and

gross rent information down to the census tract level. These estimates are updated annually but

only available with a lag—even at the time of release they still rely on information that is up to six

years old. For example, a fifth of the data used for the 2012–2016 ACS estimates was collected in

2012 and the estimates were released in December 2017. Another limit is that the ACS provides

a reliable estimate of rent paid by all renters but does not produce a precise estimate of current

market rent because some of these rents reflect contracts that were signed several years ago. To

account for these issues with the ACS, the FMRs and SAFMRs released annually by HUD rely on

custom estimates of the data produced by the Census Bureau on recent movers in two-bedroom

units adjusted for inflation as discussed above.

Several private sources for rental information have also emerged with estimates of local rental

costs. Zillow, a residential real estate services firm, offers rental estimates down to the ZIP Code or

neighborhood level on a monthly basis with a one-month lag. These estimates include the median

asking rent and are available by building type or bedroom size along with their own adjusted rental

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Hess, Walter, Acolin, and Chasins

measures controlling for the characteristics of the units being advertised and seasonal variations.

A limitation of these estimates is that the data used are dependent on unmeasured biases in the

listings that are on the platform. A number of other online rental platforms also provide estimates

based on their listings or those of their affiliates with similar sources of bias (for example, CoStar/

Apartments.com, StreetEasy, Zumper).

More recently, the potential of using the listings on online platforms to produce custom estimates

with high levels of geographic specificity, unit type, and timeliness has emerged (Boeing and

Waddell, 2016). Scraping these sources still has limits similar to the estimates produced by

Zillow and other online listing platforms because they do not capture rental listings that are

advertised through other channels such as signs in windows, newspaper ads, or posting boards

at shops. The direction and extent of the bias of relying on online listings are unclear and further

work is admittedly needed to examine how differences in coverage by online sources vary across

communities. Another limit is that these data capture asking rent and not contract rent. Given that

the rental market is generally considered to be a spot market with transactions taking place quickly

for the advertised rent, this distinction is less of a concern than for sale transactions except in a few markets with oversupply in which renters might have some bargaining power. For the purpose of

establishing rent estimates for voucher recipients, another shortfall is that only limited information

about the quality of the units and utility payments will generally be provided in the listing.

In this article, existing rental estimates are compiled for three markets: Broward County, Florida;

San Antonio, Texas; and Seattle, Washington. Existing rental estimates from HUD’s SAFMRs, ACS,

Zillow, and CoStar are compared with estimates produced from scraped Craigslist and Apartments.

com listings (the methodology is described in the following paragraph). Next, rental estimates

are compared in terms of overall descriptive statistics, coverage, and correlation to understand

differences in the level and distribution of rents between market data sources and public data

estimates like the ACS or HUD’s SAFMRs. The differences between HUD’s 2018 SAFMRs and the

produced 40th percentile asking rent estimates are the presented summary evidence of systematic

differences between study areas in terms of increased neighborhood access and potential for net

reduction in subsidy, variations which we argue are related to the diverse resident and housing

stock characteristics across these locations.

In order to collect rental data from Craigslist and Apartments.com for this project, we are using

Helena, a novel programming by demonstration (PBD) tool that one of the authors is developing

(Chasins and Bodik, 2017). Non-programmers can interact with Helena to specify what data they

want to collect from a given webpage. To scrape a dataset, the user records himself interacting with

a standard browser, demonstrating how to collect the first row of the data. To collect all the rental

listings from a platform, the user demonstrates how to click on the first listing, then how to collect

the price, number of bedrooms, address, and other target data from the listing’s webpage.

We used Helena for collecting rental listings from February to August 2018. Each of these

programs ran continuously, re-executing the program from the start as soon as it reached the

end of the list of posts. Relative to scraping only periodically (for example, weekly), this method

ensures that all listings posted on the platform were captured, even if they are only online for a few

days. The listings are then geocoded based on street addresses included within metadata for the

168 Small Area Fair Market Rents

Comparing Small Area Fair Market Rents

With Other Rental Measures Across Diverse Housing Markets

listing. We have a very high geocoding success rate when listings include address information in

their metadata or within their advertisement text, with approximately 98 percent or more of listings

geocoded to a rooftop or range interpolated position on a road (if an address was present), or a

buffer location (if cross-streets were provided).

Helena allows us to capture listings on multiple platforms and in multiple markets. The collected

data still requires substantial post-collection processing to identify duplicate postings of the

same units over time, however, both on the same platforms and across platforms. We first

deduplicate scraped listings within each source to create sets of listings with unique values based

on bedroom sizes, square footage, and spatial locations. To assess the overlap between Craigslist

and Apartments.com, we explored different ways to match the listings. Our final implementation

requires that location fields, bedroom size, and square footage must be the same between data

sources, with an absolute difference of $50 allowed between the two listings being compared.

This approach finds match rates of approximately 10 to 25 percent (relative to the number of

Apartments.com listings). This assessment is an essential process as we found duplicates are

particularly concentrated among higher-rent properties, biasing estimates upward if not removed.4

Our final dataset has more than 11,000 unique observations from which to derive rent estimates.

We then compute bedroom-specific 40th percentiles of rent for each ZIP Code where we have

more than five observations for a given unit size.5 This function also estimates standard errors for

quantiles of interest using a bootstrap approach allowing us to provide a measure of the margin of

error for the estimates.

In an effort to produce robust estimates that account for differences in density of posting by

bedroom size and spatial patterns in rental markets, we use Bayesian hierarchical smoothing

models estimated using Integrated Nested Laplace Approximations (INLA). This estimation

procedure is applicable to generalized linear regression models, which are themselves a case of a

broader set of models called Latent Gaussian Models (LGMs). These models are expressed in terms

of a conditionally independent likelihood function, a latent field which is Gaussian conditional

on the hyperparameters, and prior distributions of the hyperparameters. INLA approximates the

marginal posterior distributions of the parameters of interest, and in general, provides reliable

estimates. We follow the specification of Besag, York, and Mollié (1991) in using an Intrinsic

Conditional Autoregressive (ICAR) random effect and an exchangeable random effect for each

unit to model spatial structure. As spatial (or space-time) models, they partially pool variation

from contiguous spatial neighbors, other bedroom sizes or prior observations to smooth over

local deviations in the estimated rent surface. The benefit of these models is that partial pooling

induces shrinkage in the predicted estimates and also that they provide a model-based approach

for imputing values where direct estimates were missing. Shrinkage towards the conditional mean

reduces the bumpiness in the rent surface related to sampling variability (for example, low N). We

calculated the margin of errors around these estimates, generally around 10 percent of the estimate.

The estimates that are generated through this method using the Craigslist and Apartments.com data

are referred to in the remaining sections of the paper as the combined rent estimates.

4 On the other end, it is possible we miss some units if one posting is used to advertise several units.

5 This method is admittedly an arbitrary threshold based on the lowest number of observations for which the 40th percentile can be estimated.

Cityscape 169

Hess, Walter, Acolin, and Chasins

Results

Market Level Summary Measures

Exhibit 2 presents summary measures of the nine rent estimates we compared for the three

locations: HUD’s SAFMR, the 2012–2016 ACS at the ZIP Code and Public Use Microdata Area

(PUMA) level, Apartments.com, Craigslist, the combined rent estimates based on the Apartments.

com and Craigslist data (our preferred estimate and the one used for comparison in the remaining

section of the paper), CoStar, and Zillow.6 When possible, we present the 40th percentile estimates

for two-bedroom units.7 Exceptions include the ACS data which uses the median for all units at

the ZIP Code level, the 40th percentile for two-bedrooms at the PUMA level, and for Zillow where

the only available estimate is the median value. All measures are based on asking rent for current

listings with the exception of the ACS-derived measures that are based on the gross rent amount

(contract rent plus utility costs) and CoStar that captures rents for existing tenants.

The first observation made is in terms of coverage. Only the estimates based on the ACS and

the combined rent estimates have complete coverage in all three locations, providing a rent

estimate for all ZIP Codes in these jurisdictions. CoStar estimates are also extensive with Zillow

and Apartments.com having lower levels of coverage with variation across locations. Developing

measures that ensure extensive coverage is crucial to support the decision process of public

agencies that must cover their entire jurisdiction.

The second observation is that all sources are consistent on relative rent level of markets from

lowest to highest based on mean or median. Rent estimates vary, however, even when excluding the

ACS measures that are lagged. Average estimates range from $952 to $1,240 in San Antonio, from

$1,403 to $1,860 in Broward, and from $1,683 to $2,547 in Seattle. Variations at the market level

are also found through the variations in subareas’ minimum and maximum levels across sources.

6 All rent measures are 2018 estimates in 2018 dollars, except for the ACS measures that are based on the 2012–2016

5-year estimates in 2016 dollars (which is used to create the 2018 SAFMR estimate).

7 Throughout our analysis, we focus on rent estimates for two-bedroom units for two reasons: (1) it is the most common size for rental units; and (2) it is the size of units used for the custom ACS from which the SAFMR for all unit sizes are derived using a set ratio.

170 Small Area Fair Market Rents

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With Other Rental Measures Across Diverse Housing Markets

Exhibit 2

Summary Rent Estimates

Mean

SD

Median

Min.

Max.

N

Broward

SAFMR

1,531

252

1,460

1,180

2,080

47

ACS 2012–2016

1,191

298

1,073

854

2,130

47

[Median, All Unit Sizes]

Apartments.com

1,860

525

1,758

1,138

3,114

26

Craigslist

1,576

234

1,156

1,175

2,110

45

Combined Smoothed Estimate

1,626

210

1,597

1,271

2,261

47

CoStar

1,403

305

1,314

960

2,127

45

Zillow [Median]

1,598

304

1,500

1,250

2,500

42

ACS 2012–2016 Contract Rent

1,117

147

1,100

1,000

1,500

14

[2 Bed, PUMA]

San Antonio

SAFMR

1,041

175

995

870

1,500

60

ACS 2012–2016

752

212

711

454

1,354

60

[Median, All Unit Sizes]

Apartments.com

1,240

421

1,077

783

2,295

33

Craigslist

952

158

949

673

1,375

56

Combined Smoothed Estimate

1,015

226

947

780

1,718

60

CoStar

976

284

909

610

2,041

52

Zillow [Median]

1,109

322

1,005

880

2,208

20

ACS 2012–2016 Contract Rent

788

152

800

550

1,200

16

[2 Bed, PUMA]

Seattle

SAFMR

2,072

330

2,070

1,500

2,820

26

ACS 2012–2016

1,144

174

1,193

831

1,466

26

[Median, All Unit Sizes]

Apartments.com

2,547

522

2,557

1,604

3,697

18

Craigslist

1,786

257

1748

1,399

2,552

26

Combined Smoothed Estimate

2,163

355

2,099

1,532

3,102

26

CoStar

1,683

455

1,574

1,195

2,961

26

Zillow [Median]

2,239

394

2,223

1,673

2,891

11

ACS 2012–2016 Contract Rent

1,533

233

1,500

1,200

1,900

5

[2 Bed, PUMA]

ACS = American Community Survey. PUMA = Public Use Microdata Areas. SAFMR = Small Area Fair Market Rents. SD = Standard Deviation.

Note: The year of the data is 2018 except for the ACS data that is based on 2012–2016, 5-year estimates.

Cityscape 171

Hess, Walter, Acolin, and Chasins

In terms of variations across sources, the 2012–2016 ACS and CoStar measures tend to be the

lowest while the Apartments.com and Zillow measures are generally the highest. The SAFMR,

Craigslist, and the combined rent estimates are generally in between. This finding suggests that

the bias in the data sources has a systematic component, likely linked to the data generation

process. Potential sources of the differences include current asking rent versus contract rent for

existing tenants, selection of higher or lower end of the housing stock, and median versus 40th

percentile rents.8

Exhibit 3 reports the correlation between the ZIP Code-level rental estimates for the three locations

for two-bedroom rent measures (except for the ACS measure that is for all unit sizes). The SAFMR

and the ACS are highly correlated (.9 or above) as would be expected because the former is derived

from the latter with most of the adjustments being made at the national level and not changing

the relative estimates within a given market. The combined rent estimate is highly correlated with

Zillow’s median estimate with a correlation of .8 in Broward and close to 1.0 in San Antonio and

Seattle. The correlation of the combined rent estimate with the Apartments.com, Craigslist, and

CoStar measures are also generally high (somewhat less so in the case of CoStar). The high level of

correlation between the combined rent estimate and other market measures in all three locations

suggests that this similarity is a robust measure of local rent levels and variations despite the

potential for biases due to differences in coverage and type of units posted on different platforms.

The correlation between the SAFMR measure and the other rental market measures varies by

housing market. The correlation of the SAFMR with the combined rent estimate is positive and

significant in the three locations but varies from .8 in Seattle and .6 in Broward to less than .4 in

San Antonio. Similarly, the correlation of the SAFMR with CoStar and Zillow measures vary from

.3 to .8. This variation shows that in a market like San Antonio, the SAFMRs substantially differ

from other rental measures in ways that are not systematic while the other market measures (the

combined rent estimate and the measures from CoStar and Zillow) are more closely correlated.9

8 Zillow and the Census summary data report median rent. HUD estimates that the difference between the median and the 40th percentile is about 11 percent based on the national-level gross rents for two-bedroom units in special tabulations of the 2017 ACS.

9 If the difference between the SAFMR and other measures are systematic, for example if the difference is always 20

percent lower or higher, the correlation would be 1.0 even if the absolute difference was substantial.

172 Small Area Fair Market Rents

Comparing Small Area Fair Market Rents

With Other Rental Measures Across Diverse Housing Markets

Exhibit 3

Correlation Between ZIP Code Rent Estimates

Combined

ACS

Apartments.

SAFMR

Craigslist

Rent

CoStar

Zillow

2012-2016

com

Estimate

Broward

SAFMR

1.00

ACS 2012–2016 [Median,

0.94

1.00

All Unit Sizes]

Apartments.com

0.35

0.31

1.00

Craigslist

0.51

0.50

0.75

1.00

Combined Smoothed

0.61

0.62

0.81

0.87

1.00

Estimate

CoStar

0.78

0.83

0.31

0.52

0.59

1.00

Zillow [Median]

0.34

0.30

0.85

0.75

0.80

0.23

1.00

San Antonio

SAFMR

1.00

ACS 2012–2016 [Median,

0.95

1.00

All Unit Sizes]

Apartments.com

0.35

0.31

1.00

Craigslist

0.51

0.50

0.75

1.00

Combined Smoothed

0.36

0.48

0.81

0.87

1.00

Estimate

CoStar

0.47

0.58

0.81

0.72

0.90

1.00

Zillow [Median]

0.35

0.76

0.85

0.75

0.96

0.94

1.00

Seattle

SAFMR

1.00

ACS 2012–2016 [Median,

0.89

1.00

All Unit Sizes]

Apartments.com

0.75

0.70

1.00

Craigslist

0.64

0.49

0.86

1.00

Combined Smoothed

0.78

0.60

0.87

0.87

1.00

Estimate

CoStar

0.82

0.57

0.85

0.89

0.94

1.00

Zillow [Median]

0.85

0.88

0.86

0.97

0.98

0.89

1.00

ACS = American Community Survey. SAFMR = Small Area Fair Market Rent.

Cityscape 173

Hess, Walter, Acolin, and Chasins

Several ZIP Codes have estimated market rents that fall outside the basic range of 90 to 110

percent across the three study locations. Exhibit 4 summarizes the share of ZIP Codes in which the

combined estimate is 10 percent or more below the SAFMR, within the basic range of 10 percent

of the SAFMR, and more than 10 percent higher. In all three locations, the patterns are generally

similar across bedroom size but with some variation. In Broward and Seattle, most estimates are

within the basic range of the SAFMR. Among those that are not, there are generally more ZIP

Codes in which the combined rent estimates are above the basic range of the SAFMR (over one-

fourth of ZIP Codes for two-bedroom units in both locations) rather than below it. In San Antonio,

more than one-third of all ZIP Codes have combined rent estimates that are below the basic range

of the SAFMR for all unit sizes and less than 10 percent of the ZIP Codes are above the basic range.

These variations indicate that, depending on local market dynamics, the difference between the

SAFMR and other measures of market rents might not occur in the same direction.

Exhibit 4

Share of ZIP Codes Above, Within, and Below the SAFMR Basic Range, by Number of Bedrooms

Number of

Below

Within

Above

Bedrooms

0.9

0.9-1.1

1.1

1

3.8%

56.6%

28.3%

Broward

2

3.8%

56.6%

28.3%

Total ZIP Codes = 47

3

47.2%

34.0%

7.5%

4+

17.0%

58.5%

13.2%

1

38.4%

37.0%

6.8%

San Antonio

2

34.2%

38.4%

9.6%

Total ZIP Codes = 60

3

42.5%

32.9%

6.8%

4+

42.5%

32.9%

6.8%

1

19.4%

51.6%

12.9%

Seattle

2

6.5%

51.6%

25.8%

Total ZIP Codes = 26

3

29.0%

48.4%

6.5%

4+

12.9%

51.6%

19.4%

Exhibits 5, 6, and 7 show the ratio of the combined rent estimates relative to the SAFMR for two-

bedroom units in the ZIP Codes covering the study locations. Areas in which the combined rent

estimates are outside the basic range of the SAFMR are highlighted. Spatial patterns emerge in areas

in which the combined rent estimates differ substantially from the SAFMRs suggesting that some

of the differences may be systematically associated with housing stock and resident characteristics.

The next subsection reveals that areas with lower or higher estimates compared with SAFMRs share

common characteristics.

174 Small Area Fair Market Rents

Comparing Small Area Fair Market Rents

With Other Rental Measures Across Diverse Housing Markets

Exhibit 5

Difference Between Combined Rent Estimates and SAFMRs in Broward County,

Two-Bedroom 40th Percentile Rent

Broward County

ZIP Code, 40th Percentile Rent, 2 Bedroom,

Combined Rent Estimates as Percent SAFMR

Estimates/SAFMR

(2 Bedroom 40th Percentile Rent)

<= 90%

91 - 110%

> 110%

NA

SAFMR = Small Area Fair Market Rent.

Cityscape 175

Hess, Walter, Acolin, and Chasins

Exhibit 6

Difference Between Combined Rent Estimates and SAFMRs in San Antonio,

Two-Bedroom 40th Percentile Rent

San Antonio

ZIP Code, 40th

Percentile Rent,

2 Bedroom,

Combined Rent

Estimates as

Percent SAFMR

Estimates/SAFMR

(2 Bedroom 40th Percentile Rent)

<= 90%

91 - 110%

> 110%

NA

SAFMR = Small Area Fair Market Rent.

176 Small Area Fair Market Rents

Comparing Small Area Fair Market Rents

With Other Rental Measures Across Diverse Housing Markets

Exhibit 7

Difference Between Combined Rent Estimates and SAFMRs in Seattle,

Two-Bedroom 40th Percentile Rent

Seattle

ZIP Code, 40th

Percentile Rent,

2 Bedroom,

Combined Rent

Estimates as

Percent SAFMR

Estimates/SAFMR

(2 Bedroom 40th Percentile Rent)

<= 90%

91 - 110%

> 110%

NA

SAFMR = Small Area Fair Market Rent.

Cityscape 177

Hess, Walter, Acolin, and Chasins

Characteristics of ZIP Codes Where SAFMRs and Combined Rent Estimates Differ

Exhibits 8 and 9 report differences in building stock and neighborhood characteristics where

SAFMRs and the combined rent estimates differ. Exhibit 8 shows areas based on whether the

estimated 40th percentile for two-bedroom units is below (dots), within (solid), or above (stripes)

the basic range of the SAFMR. For ZIP Codes where the combined rent estimate is below the basic

range, on average the combined rent estimates relative to those within the basic range are—$287

higher in Broward, $10 lower in San Antonio, and $406 lower in Seattle. The ZIP Codes where the

combined rent estimate is above the basic range have higher rents on average and this observation

holds across the three study locations—$103 higher in Broward, $487 higher in San Antonio, and

$247 higher in Seattle.

Exhibit 8

Combined Rent Estimates for Two-Bedroom Units Below, Within, and Above the Basic Range

40th Percentile Two-Bedroom Rent

$3,000

$2,500

$2,000

$1,500

$1,000

$500

0

Broward

San Antonio

Seattle

.9 or less

.9-1.1

Above 1.1

Exhibit 9 summarizes the neighborhood characteristics of ZIP Codes with combined estimates

below, within, and above the basic range using ACS 5-year estimates for: poverty rate,

homeownership rate, vacancy rate, share of multifamily units, and share of rental units built

since 1990. The differences between the ZIP Codes that fall within each category are in the same

direction across the three locations. In ZIP Codes that the combined rent estimate is above the

basic range, there are higher poverty rates, vacancy rates, and share of multifamily buildings, with

lower homeownership rates. The reverse relationship is found in ZIP Codes in which the combined

rent estimate is below the basic range of the SAFMR. These findings suggest that the SAFMR is

more likely to be underestimated compared with asking rents in neighborhoods with higher-

vacancy rates and share of multifamily buildings. This outcome might be because multifamily

buildings have higher turnovers and are therefore overrepresented in listings relative to their share

of the housing stock. At the same time, the fact that the SAFMR is more likely to be above the

combined rent estimate in areas with lower poverty rates and fewer rental options might support

voucher recipients’ access to these neighborhoods. Further work is needed to examine what might

178 Small Area Fair Market Rents

Comparing Small Area Fair Market Rents

With Other Rental Measures Across Diverse Housing Markets

explain these systematic differences between the combined rent estimate and the SAFMR. Some

of the systematic differences could be driven by the adjustments made to produce the SAFMR

estimates that, for example, exclude recent units, units lacking full kitchen and bathrooms, or that

are below a certain rent level. The exclusion of these units is justified by regulations and policy

goals but might explain why the SAFMR departs from other rental market measures.

We also looked at the relationship between the share of rental properties that were recently built

(since 1990) and find that in the three locations the combined rent estimate is more likely to be

above the SAFMR basic range in areas that have a higher share of recently built renter-occupied

housing units. This outcome suggests that one of the contributors to the differences may be the

fact that the SAFMR removes properties built within the last 2 years of the ACS to avoid including

potential rent premiums associated with new construction. Further, Broward and San Antonio ZIP

Codes where the combined rent estimate is below the SAFMR basic range also have a higher share

of apartments built after 1990.

Discussion

The comparison of rental measures across three diverse housing markets—Broward, San Antonio,

and Seattle—indicates SAFMRs broadly reflect local market conditions and HUD adjustments

applied to the ACS data adjust point estimates closer towards asking rents. This study provides

evidence of substantial differences across rent measures in terms of estimated levels and relative

differences across ZIP Codes, however. Rent measures derived from asking rents on online

platforms—Apartments.com, Craigslist, and Zillow—are generally more correlated to each other

than compared with the SAFMRs or ACS data.

The combined rent estimates based on scraping and processing listings posted on Apartments.com

and Craigslist are closely aligned with Zillow’s rent estimates. The combined rent estimates have

the benefit of providing a measure for every ZIP Code and can be produced for diverse geographies

and time frames. When compared with the SAFMR, the combined rent estimates are below or

above the basic range of the SAFMR in a substantial share of ZIP Codes. This finding suggests that

housing authorities may face challenges in meeting the objectives of the SAFMR final rule without

some form of local adjustments.

As an example, Broward County has a large share of ZIP Codes where the combined rent estimates

are higher than the SAFMR. Many of these ZIP Codes are in areas that are opportunity rich in

terms of accessibility to jobs and transit and score high in neighborhood quality.10 If the Housing

Authority of the City of Fort Lauderdale continues to base their payment standards on the SAFMRs

in these particular ZIP Codes, the objectives of deconcentration and mobility to high-opportunity

areas may be stifled because the payment standard is still too low for voucher holders to access

these neighborhoods. The housing authority may need to consider an adjustment to the payment

standard by providing evidence that the SAFMR is not keeping pace with rapidly increasing rents

in these ZIP Codes. Seattle also has the same issue in a large number of ZIP Codes clustered in the

southern half of the city whereas this issue is only prevalent in two distinct areas in San Antonio.

10 See Walter, Evans, and Atherwood (2016) for the accessibility and neighborhood quality indices in Broward County.

Cityscape 179

Hess, Walter, Acolin, and Chasins

On the other hand, SAFMRs above the combined rent estimates present another potential problem

for housing authorities. For example, the San Antonio Housing Authority has many ZIP Codes

throughout the city where the combined rent estimates are substantially below the SAFMR. This

situation is problematic for the San Antonio Housing Authority as an MTW agency because they

must continue to serve the same number of households without additional funding for the voucher

program. If the payment standard is set too high and overcharging is systematically occurring,

the housing authority will not be able to continue to serve the same number of households while

increasing payment standards in higher-opportunity, higher-cost areas. To balance the various

goals of program implementation, which include maintaining the number of households served

while minimizing any negative impacts to exiting voucher households in low-cost neighborhoods,

the San Antonio Housing Authority has proposed the following in their 2020 Annual MTW Plan:

(1) setting the payment standard schedule between 81 to 90 percent of SAFMRs to make sure the

program is financially feasible; and (2) including a hold harmless and exception overlay policy.

This policy protects existing voucher recipients from a payment standard reduction so they can stay

in place if they so choose.

Further work is needed to understand why SAFMRs systematically differ from market conditions to

guide adjustments to better align SAFMR estimates with local housing market conditions. Absent

appropriate measures to make these adjustments, there is a risk that households will have difficulty

accessing neighborhoods in which the combined rent estimates are substantially higher than the

SAFMRs. Without adjustments, the stated goals of the SAFMR to deconcentrate voucher recipients and

increase choice through the creation of localized rental estimates may be compromised. Conversely,

as indicated by the San Antonio Housing Authority example, without careful consideration of local

rental market data and setting payment standards solely based on SAFMRs, the financial feasibility of

the program may be threatened. If not carefully monitored, payment standards based only on SAFMRs

may result in a lower number of households being served by the voucher program.

The ability to monitor rental trends and have reliable estimates is essential for housing authorities

using SAFMRs. This verification is particularly important in markets with high growth and in

neighborhoods with rapidly changing conditions. Take, for example, the characteristics of ZIP

Codes with combined rent estimates that are above the basic range of the SAFMR. These areas have

a higher share of rental units in multifamily buildings, higher vacancy rates, and a higher share of

apartments recently built. These characteristics are potentially contributing to mismeasurement

and voucher recipients may have difficulty finding eligible units and successfully leasing in these

neighborhoods if adjustments are not made. Therefore, it is vital for housing authorities to have

access to and track local rental listing data in neighborhoods that are experiencing rapid growth.

This monitoring will provide guidance for adjustments to payment standards that will better reflect

the local market. ZIP Codes with combined rent estimates that are below the basic range of the

SAFMR are just as important for housing authorities to monitor. Landlords may be able to extract

higher rent from housing authorities than what is justified based on local market conditions. Biases

in the underlying data sources that rental estimates are created from should be carefully considered

to maximize the potential of using small area rental estimates to expand choice for voucher holders

to access high-opportunity neighborhoods while maintaining the ability of housing authorities to

continue to serve as many voucher households as possible.

180 Small Area Fair Market Rents

Comparing Small Area Fair Market Rents

With Other Rental Measures Across Diverse Housing Markets

Exhibit 9

Differences in Neighborhood Characteristics for ZIP Codes Below, Within, and Above the Basic

Range

Broward County

100%

80%

60%

40%

20%

0%

Poverty Rate

Homeownership

Vacancy Rate

Share Multi-Family

Share Rental Built

Rate

Since 1990

.9 or less

.9-1.1

Above 1.1

San Anotnio

100%

80%

60%

40%

20%

0%

Poverty Rate

Homeownership

Vacancy Rate

Share Multi-Family

Share Rental Built

Rate

Since 1990

.9 or less

.9-1.1

Above 1.1

Seattle

100%

80%

60%

40%

20%

0%

Poverty Rate

Homeownership

Vacancy Rate

Share Multi-Family

Share Rental Built

Rate

Since 1990

.9 or less

.9-1.1

Above 1.1

Cityscape 181

Hess, Walter, Acolin, and Chasins

Acknowledgments

Partial support for this research came from a University of Washington Center for Studies in

Demography & Ecology Tier 2 seed grant and from a Eunice Kennedy Shriver National Institute

of Child Health and Human Development research infrastructure grant, P2C HD042828, to the

Center for Studies in Demography & Ecology at the University of Washington.

Authors

Christian Hess is a PhD candidate in the Department of Sociology at the University of Washington.

Rebecca J. Walter is an assistant professor in the Runstad Department of Real Estate at the

University of Washington.

Arthur Acolin is an assistant professor in the Runstad Department of Real Estate at the University of

Washington.

Sarah Chasins is a PhD candidate in the Department of Computer Science at the University of

California, Berkeley.

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186 Small Area Fair Market Rents

by Mike Blackhurst, Chris Briem, and Sabina Deitrick

Contrasting Different Geographies

in Fair Market Rents: Implications

for the Housing Choice Voucher

Program in Pittsburgh, PA

Mike Blackhurst

Chris Briem

Sabina Deitrick

Center for Social and Urban Research, University of Pittsburgh

Abstract

Local public housing authorities define the payment standards—the voucher amounts paid to

landlords—for renting their property under the Housing Choice Voucher (HCV) Program. Payment

standards have been historically based on 40th percentile Fair Market Rents (FMRs) calculated by the U.S. Department of Housing and Urban Development (HUD) for metropolitan areas and non-metro

counties. To better align payment standards with market rents, HUD has developed 40th percentile

Small Area Fair Market Rents (SAFMRs) at the ZIP-Code level and have mandated their use in 24

metropolitan areas. Public housing authorities using SAFMRs in lieu of FMRs must maintain payment

standards within 10 percent of the SAFMR. This study compares the efficacy of SAFMRs with rents

listed for Pittsburgh, PA, by Rent Jungle, a commercial aggregator of rental data. Correlations between SAFMRs and the sampled rents were relatively low at 37 percent. Results indicate that small area

markets defined using a combination of clustering and nearest neighbor algorithms are better predictors of market rents than ZIP Codes and require fewer market delineations, as shown by the adjusted

R-squared exceeding 60 percent with only three clusters (compared with the 26 ZIP Codes in Pittsburgh).

Results suggest that SAFMR achieves its goal of increasing the eligible units relative to FMR. Those increases were disproportionately in low-rent areas, however, where the proposed SAFMR is competitive with market rents. In contrast, in high-rent areas, the SAFMR is more than 50 percent lower than

market rents, on average, resulting in few eligible units. These observations suggest SAFMRs are likely to increase the number of landlords interested in the HCV program in low-rent areas, but not in high-rent areas. To increase the use of vouchers in high-rent areas, payments to landlords should adequately compete with market rents. Otherwise, only landlords in high-rent areas that have trouble renting in the private market, such as those that offer properties of marginal quality, are likely to participate in the HCV program.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 187

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

Blackhurst, Briem, and Deitrick

Introduction

The U.S. Department of Housing and Urban Development (HUD) funds the Section 8 Housing

Choice Voucher (HCV) program. Section 8 uses a public-private partnership model that allows

qualified tenants to rent on the open market from willing landlords of private properties that meet

federal housing quality standards. HUD disperses funds to public housing authorities (PHAs) who

are responsible for administering Section 8 at the local level.

Landlords are compensated through a combination of public assistance (vouchers issued by

housing authorities or the payment standard) and rent paid by tenants. HUD defines a market

rent basis that, in turn, limits the range of voucher amounts—or payment standards—allowable

by PHAs. Historically, HUD has defined the fair market rent (FMR) as the 40th percentile of gross

rents by FMR area. The FMR area consists of either the parent metropolitan statistical area (MSA)

surrounding a PHA or subset of geographies within the parent MSA customized by HUD for HCV

called the “HUD Metro Fair Market Area” or “HMFA.” Local PHAs are allowed to set payment

standards to vary from 90 percent to 110 percent of the FMR. Pinning payment standards to

90 percent to 110 percent of the FMR kept payment standards relatively affordable but has also

concentrated HCV tenants in lower market rent areas (per the 40th percentile FMR criteria), which

is often in areas associated with fewer economic opportunities.

Recognizing that rental market boundaries are difficult for housing authorities to define, HUD

proposed ZIP Codes serve as small area market boundaries and designed a method for estimating

market rents by ZIP Code (HUD, 2019a), or Small Area Fair Market Rents (SAFMRs). In November

2016, HUD mandated the use of SAFMRs as opposed to FMRs in deriving payment standards for

PHAs in 24 metropolitan areas. Notably, the minimum and maximum SAFMR is restricted to 50

percent and 150 percent of the FMR, respectively.

ZIP Codes were partly selected for convenience in that they are familiar to renters, landlords, and

housing authorities. ZIP Codes are also sufficiently large such that rents can be estimated using the

American Community Survey (ACS; Reina, Acolin, and Bostic, 2019). HUD also relied on early

results from demonstrations of SAFMRs in Cook County, IL; Long Beach, CA; Chattanooga, TN;

Mamaroneck, NY; Laredo, TX; and Dallas, TX, to suggest that ZIP Codes would increase access to

opportunity in markets nationwide (Finkel, 2017; Reina, Acolin, and Bostic, 2019).

HUD’s method for estimating 40th percentile SAFMRs by ZIP Code uses a series of reliability and

consistency standards applied to rents collected by the U.S. Census Bureau through the ACS (HUD,

2019a). By defining market rents at geographies smaller than MSAs and counties, HCV tenants are

more likely to find units in higher rental areas with more economic opportunities because PHAs

can set payment standards better aligned with local market rents. The smaller geography associated

with the SAFMR is anticipated to accommodate the twin goals of expanding access to higher

income markets for HCV tenants and thus reducing the concentration of HCV tenants in higher

poverty areas.

A primary concern regarding SAFMRs is determining how representative ZIP Codes are of true

rental market boundaries. Ideally, market areas would be intuitive to PHAs, voucher holders, and

landlords, and align ZIP Codes, which could create incentives for landlords to participate in the

188 Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents:

Implications for the Housing Choice Voucher Program in Pittsburgh, PA

HCV program and for households to move to areas of opportunity. Otherwise, payment standards

would be misaligned with market rents.

Researchers and affordable housing stakeholders have expressed concern about the appropriateness

of ZIP Codes for defining small area markets (CFR, 2016; Treat, 2018; Walter, 2018). ZIP Codes

are geographic boundaries established to administer postal delivery. Thus, prima facie concerns are

that ZIP Codes will not align with rental markets, where market variation within ZIP Codes could

be significant, and ZIP Code boundaries could cross municipal boundaries.

In theory, additional concerns associated with transitioning to SAFMRs are (1) landlords

participating in the HCV program under FMRs will exit the program because payment standards

in many of these areas should decrease by definition (HCV properties whose rents are below the

40th percentile of gross FMR are likely in ZIP Codes with even lower market rents); (2) it will

be harder to attract landlords in areas of opportunity given market competition and a history of

discrimination toward voucher holders; (3) the total voucher budget may not support increased

vouchers associated with significant moves to areas of opportunity; and (4) the transition may

facilitate a net decline in total HCV units.

The limited research exploring these issues is mixed. SAFMR demonstration projects in five PHAs

indicate households moving to higher rent areas increased from 18 percent in 2010 to 28 percent

in 2015, where most of the cost increase was borne by tenants. The net housing units eligible

for subsidies across the SAFMR demonstration regions declined by 3.4 percent, however, due

to reductions in the eligible units in lower rent areas (Finkel et. al., 2017). Significant variation

across regions was observed. These results suggest that moves to higher rent areas, with improved

neighborhood conditions, may not have been motivated by tenants’ interest in areas of opportunity

but more simply by geographic shifts in eligible units. In contrast to the decline in overall units

estimated by SAFMR demonstration efforts, the NYU Furman Center (NYU, 2018) estimated that

the number of HCV-eligible units in the 24 areas mandated to adopt the new SAFMRs is expected

to increase by 9 percent.

Reina, Acolin, and Bostic (2019) used a difference-in-differences method to examine the

geographic distribution of subsidized households before and after the SAFMR demonstration,

finding increased voucher holders in higher rent neighborhoods in Dallas, TX; Long Beach, CA;

and Mamaroneck, NY. Collinson and Ganong (2014) similarly reported that voucher recipients

in Dallas moved to neighborhoods improved with respect to crime, poverty, unemployment,

educational attainment, and fourth-grade test scores in public schools. Reina, Acolin, and

Bostic found marginal to negative impacts of SAFMR in Cook County, IL, and Chattanooga, TN,

however. The authors reported a potential decline in neighborhood quality for voucher holders

in Chattanooga following SAFMR changes, suggesting that there are too few neighborhoods

of opportunity in Chattanooga for payment standards to have a meaningful effect. In Cook

County, the authors indicated that an aggressive private rental market may have limited access to

neighborhoods of opportunity.

Inconsistencies in either the FMR or SAFMR and the private market rents can significantly affect

housing outcomes. First, in areas where enough HCV housing adopts payment standards that

Cityscape 189

Blackhurst, Briem, and Deitrick

exceed the market rate, rents may be artificially increased above market rates expected in the

absence of HCV housing (Collinson and Ganong, 2018; Susin, 2002). The mechanisms for this are

unclear but likely involve a combination of reducing the supply of market housing (housing not

enrolled in HCV) and reactions to price ceilings associated with payment standards. Price increases

to market housing would reduce affordability for renters that do not have vouchers. Previous

research in Allegheny County, PA, has found that subsidized housing (public housing and HCV

units) can constitute a significant portion of affordable housing supply for individuals below the 50

percent area median income (Deitrick et al., 2011).

Conversely, if payment standards are lower than the market, landlords are more likely to prefer

to rent on the private market and thus opt out of Section 8. Stakeholders have emphasized this

particular concern in the transition to SAFMR in that small area markets will depress voucher

amounts in low-rent areas, where most existing subsidized households are located, potentially

resulting in decreases in the number of total subsidized units (Walter, 2018; Treat, 2018).

Palm (2018) used commercially available rental data (sourced from the same supplier of data for

this study) to estimate the counts of eligible units before and after the administration of SAFMRs

in five metropolitan areas in California. Except for San Francisco, Palm found that using SAFMR’s

increased eligible units in low-poverty areas and decreased eligible units in high-poverty areas,

with net increases in total eligible units reported by metropolitan area. San Francisco reported a

loss of eligible units throughout the city, where the author suggested rents were rising faster than

could be recorded by the ACS.

If voucher amounts are higher than the market, then landlords will be more likely to participate

in Section 8 but may overcharge (Desmond and Perkins, 2016; Collinson and Ganong, 2014;

McClure, Schwartz, and Taghavi, 2015) or even manipulate the HCV process to effectively strand

tenants (Rosen, 2014), thus decreasing the cost-effectiveness of subsidized housing and the

number of total families who can be served.

It is not yet clear how well-subsidized housing can compete with the market in areas of

opportunity. Previous studies have shown, however, that landlords experience real and perceived

barriers to enrolling in the HCV program, including, but not limited to, financial risks, challenges

of meeting HCV inspection requirements, and an incomplete understanding of HCV (Greenlee,

2014; Pashup et al., 2005). Moreover, many studies have found discrimination against voucher

holders and vouchers as a source of income (Pendall, 2000; Turner and Ross, 2005; Yinger, 1995).

It is unclear how such discrimination will influence movement to opportunity. One early study in

Chicago found that voucher holders were 10 percent more likely to be denied housing in mixed-

income communities targeted for new subsidized housing than those seeking housing throughout

Chicago (Lawyers Committee for Better Housing, 2002).

It is also unclear to what extent existing HCV tenants are interested in moving to higher rent areas

and under what circumstances they would move. Schwartz, Mihaly, and Gala (2017) conducted an

experiment in which HCV voucher holders were offered a $500 grant and mobility counseling to

make opportunity moves in Chicago, IL, finding that these incentives did not increase the share of

opportunity moves beyond the control group, which was 12 percent. Schwartz, Mihaly, and Gala

190 Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents:

Implications for the Housing Choice Voucher Program in Pittsburgh, PA

suggested 12 potential barriers influencing opportunity moves, including increased transaction

times (including search costs) to find units in opportunity areas, insufficient resources to move

or rent units in opportunity areas, no landlord recruiting in opportunity areas, and too stringent

standards for areas of opportunity.

Housing authority staff have also expressed concern about administering a payment standard that

needs to conform to SAFMRs across many ZIP Codes. Walter (2018) examined the implications

of aggregating ZIP Codes in San Antonio, TX; Fort Lauderdale, FL; and Jacksonville, FL, finding

significant disparities between commercially available rents and allowable payment standards for

aggregated ZIP Codes. Walter ultimately concluded that ZIP Codes were simply too aggregate to

align with commercially available rent, even though this would make it more difficult for landlords

and housing authorities to administer HCV housing and for tenants to “shop” for leases.

In a review of HUD’s SAFMR rule changes, Treat (2018) recommends giving housing authorities

more flexibility to use “local knowledge” when geographically defining small area markets and

improving the accuracy of payment standards. This study follows these recommendations by

comparing observed and modeled prices of 11,214 market rental listings for the city of Pittsburgh,

PA, to SAFMR and FMRs by MSA in light of moving to opportunity policy objectives.

Materials and Methods

Data used for this analysis were purchased from Rent Jungle (Rainmaker Group, 2017), a

commercial aggregator of rental market data. The raw data include 1,006,857 listings aggregated

from different websites advertising online properties for rent in the city of Pittsburgh for October

2016 and October 2017. As a result of pooling listings from multiple sources and landlords that

repeatedly list properties, the raw data include duplicate listings. Rent Jungle applies a proprietary

algorithm intended to distinguish unique listings from duplicate listings. The expected number

of available unique listings in the city of Pittsburgh should roughly match those estimated using

tenure data for rental units (U.S. Census, 2019) or 16,500 listings. We found Rent Jungle’s

algorithm indicated approximately 30,000 unique listings, or twice those expected using Census

data. As a result, we allow only one advertised prototype per address to eliminate duplicates,

where a unit prototype is defined by unique counts of bedrooms and bathrooms, floor space

(where available), and price. While these criteria would exclude listings with the same floorplan

simultaneously listed at the same address, their application leads to approximately 15,000

unique listings available for rent, which better matches the 16,500 available units estimated using

Census data.

In addition to triaging duplicate listings, we also removed listings that did not include bedroom

and bathroom information and 53 outliers where the monthly asking rent was below $25 or

above $10,000. The final sample used for analysis consists of 11,214 listings, thus representing

approximately 68 percent of estimated available listings across 84 neighborhoods and 26 ZIP

Codes in the city of Pittsburgh. Exhibit 1 summarizes variation in price by counts of bedrooms

and bathrooms. Counts by bedroom size are 436 efficiencies; 2,158 one-bedroom units, 3,898

two-bedroom units; 2,831 three-bedroom units; 983 four-bedroom units; and 308 units with five

or more bedrooms. Nearly 19 percent of addresses in the sample reflect multiple units available;

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Blackhurst, Briem, and Deitrick

approximately 1 percent report more than 10 units available; and three addresses report more than

80 units available. These diagnostics suggest our criteria for identifying unique listings did not

eliminate addresses presenting multiple listings.

Exhibit 1

Variation in Advertised Rent by Bedrooms and Bathrooms for 11,214 Listings in the City of

Pittsburgh between October 2016 and October 2017

Gross rent—the rent value that includes both the lease amount and utilities—is the basis for HUD’s

fair market rent definitions (FMR and SAFMR). Combinations of the words “electric,” “gas,” “water,”

“utilities,” “heat,” “included,” and “not” were used to identify listings likely to include utilities.

Approximately 6 percent of listings in the Pittsburgh MSA were estimated as including utilities.

None of these listings were in the city of Pittsburgh, however. Thus, expenditures on natural

gas and electricity were estimated as the mean by bedroom and bathroom from pooled energy

consumption survey data at broader geographic scales (urban homes in Pennsylvania taken from

the U.S. Department of Energy [DOE], 2012; urban homes in the Middle Atlantic Census taken

from DOE, 2018). Expenditures on water were estimated using water rates for the local Pittsburgh

utility (PWSA, 2017) applied to a consumption of 62 gallons per capita per day (Dunham-

Whitehead and Moya, 2007) and an average of 0.9 people per bedroom (DOE, 2012; DOE,

2018). On average, estimated utility expenditures resulted in electricity, natural gas, and water

contributing to 5.4 percent, 2.5 percent, and 3.6 percent of gross rent, respectively, or a total share

of 11.5 percent of gross rent spent on utilities.

To approximate spatial boundaries in rental markets, we first regress market prices in the

Rent Jungle sample (n = 11,214) against bedrooms and counts of bathrooms (modeled as

nominal variables shown in exhibit 1 and as continuous variables using equation 1).

192 Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents:

Implications for the Housing Choice Voucher Program in Pittsburgh, PA

(1) log(price) = B0 + B1 * beds + B2 * baths + B3 * beds * baths +

In equation 1, the error (or residuals) term encompasses all contributions to market prices not

explained by counts of bedrooms and bathrooms. Much—but not all—of these contributions will

include renters’ valuations of spatial amenities, such as access to transit. In order to approximate

spatial boundaries in rental markets, we then cluster (k-means) the listings latitude, longitude, and

residuals ( ) from equation 1 to estimate small area markets. The k-means clustering algorithm

characterizing the “closeness” of listings with respect to space (latitude and longitude) and residual

( ) which largely represents spatial amenities.

We test between 2 and 26 clusters, as there are 26 unique ZIP Codes in the city of Pittsburgh. The

initial cluster assignments provide point estimates indicating the cluster with which the listing

is associated. Such point estimates derived from the Rent Jungle sample are helpful but do not

completely account for all units that could potentially participate in the HCV program. To derive

spatially continuous, complete coverage across the city, we assign to each parcel the most frequent

cluster associated with its 50 nearest rental listings in our sample, which, in turn, involved re-

assigning the cluster for some observations. We then regress listing prices according to equation 2.

(2) log(price) = B0 + B1 * beds + B2 * baths + B3 * beds * baths + B4 * geography +

In equation 2, the geography was modeled using either ZIP Codes, the initial cluster assignment,

or the clusters reassigned based on its nearest neighbors to evaluate the efficacy of different

geographic representations of small area markets. Both geographic definitions (ZIP Codes, initial

clusters, and reassigned clusters) and cluster counts were evaluated using the model adjusted

R-squared estimated from applying the sample to equation 2.

We compare observed and fitted rents to the SAFMRs and FMRs1 for the city of Pittsburgh (HUD,

2019b; HUD, 2019c) based on consistency, counts of households eligible for assistance, and

differences in the estimated price. For point comparisons, we assume all payment standards are

equal to 100 percent of SAFMRs or FMRs. Where ranges are compared, we assume payment

standards vary from 90 percent to 110 percent of SAFMRs. Where fitted results were used as

a comparison, the model fits reported herein were randomly drawn from the fitted mean and

standard deviation, assuming the fitted results are normally distributed.

To estimate the effect of the SAFMR transition on a broader group of properties that could

participate in HCV, we also applied our models to all 1–4 bedroom units not in large multi-

family complexes, where the stock and location of 1–4 bedroom units were taken from Allegheny

County (2018). This broader group of properties include 98,090 or approximately 73 percent of

households in Pittsburgh (U.S. Census, 2019). In broadening our model to other potential HCV

properties, we excluded multi-family complexes because the available data (Allegheny County,

2018) do not list bedroom and bathroom counts for each unit in multi-family complexes.

Analysis and results were prepared using the R programming language (R Core Team, 2018) in the

R Studio software (RStudio Team, 2018), including using R packages authored by Wickham et. al.

1 The 2017 FMRs used for this study are those for the Pittsburgh HMFA geography.

Cityscape 193

Blackhurst, Briem, and Deitrick

(2019), Wickham (2016), and Zhu (2018).

Exhibit 2

Data Sources and Their Respective Geographic and Property Scopes Relevant for this Study

Smallest

Scope of Properties Relevant for this Study

Data Set or Rent

Available

Benchmark

Geography

Geography

Properties Represented

Resolution

Market Rents

All Online Listings in Rainmaker

Individual Unit

City of Pittsburgh

(2017) that Meet the Criteria

Described Herein

Small Area Fair Market

All Rents Reported to the U.S.

Rent (SAFMR)

ZIP Code

City of Pittsburgh

Census (see HUD, 2019c for Details)

Fair Market Rent (FMR)

HUD Metro Fair

HUD Metro Fair

All Rents Reported to the U.S.

Market Area

Market Area

Census (see HUD, 2019b for Details)

(HMFA)

(HMFA)

Property Tax

All Property Types Equal to or Smaller

Assessments

Parcel

City of Pittsburgh

than Four-Family Units (Allegheny

County, 2018)

Results

Exhibit 3 compares distributions of two-bedroom market rents with SAFMRs for two-bedroom

units by ZIP Code. We limit the presentation of results to two-bedroom units for clarity. Two-

bedroom units were the most frequently occurring unit size and largely represent trends observed

for units of other sizes. Exhibit 3 indicates that SAFMRs are below the corresponding 40th

percentile market rents for all ZIP Codes except 15235 and 15220. The market estimate tends to

be increasingly higher than the SAFMR with increasing market price. Exhibit 3 indicates ZIP Codes

crossing the city boundary may explain some of the discrepancies between listed rents; however,

the most extreme differences occur where ZIP Codes do not cross city boundaries. Exhibit 3

also indicates that variation within ZIP Codes is much more significant in high-rent ZIP Codes

completely contained by the city (see legend titled “Share ZIP in the city” in exhibit 3).

HUD’s constraining SAFMR definitions to within 50 percent to 150 percent of the parent FMR (the

40th percentile FMR criteria) is likely influencing the degree to which market rents exceed the

SAFMR in very high-rent markets (for example, 15222).

Exhibit 4 shows the adjusted R-squared estimated when applying equations 1 and 2 assuming

different geographic variables during regression. As a point of reference, the correlation between

the market data and SAFMR is approximately 37 percent, which is marginally better than the

variation in SAFMR explained solely by bedrooms and bathrooms (the R-squared for equation 1 is

35 percent). Modeling spatial amenities using ZIP Codes as a proxy (applying equation 2 with ZIP

Codes equal to geography) increases the adjusted R-squared to 50 percent. The model improves

194 Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents:

Implications for the Housing Choice Voucher Program in Pittsburgh, PA

Exhibit 3

Variation in Market Rent for Two-Bedroom Units by ZIP Code in the City of Pittsburgh Compared

to Fair Market Rents Published by HUD

Note: Rents reflect 11,214 listings advertised online between October 2016 and October 2017 (Rainmaker, 2017). The fair market rents published by HUD

include the FY17 SAFMRs (HUD, 2019c) and 40th percentile for Pittsburgh FMR (HUD, 2019b). The variable “share ZIP in city” reflects those ZIP Codes completely contained by the city (a value of “all”), ZIPs whose area is 50 percent or more in the city (a value of “mostly in”), and ZIP Codes whose area is more than 50 percent outside of the City (“mostly out”).

Cityscape 195

Blackhurst, Briem, and Deitrick

considerably, however, when spatial amenities are modeled by cluster. Compared with modeling

all 26 ZIP Codes in Pittsburgh, only 2 clusters are needed to improve on the ZIP Code model.

Moreover, if 26 clusters are assumed, the adjusted R-squared values are 90 percent and 71 percent

assuming initial cluster assignments and clusters reassigned using nearest neighbors, respectively.

Exhibit 4

Adjusted R-Squared Applying Sample to Equation 1 and Equation 2 Assuming Different

Geographic Variables Represent Spatial Amenities

0.9

0.8

0.7

0.6

0.5

0.4

Adj. R2 Applying Data to Equations 1 and 2

Correlation Between Observations and SAFMRs = 0.37

0

10

20

Count of Geographies Modeled

None (Equation 1)

Raw Cluster (Equation 2)

Re-assigned Cluster (Equation 2)

ZIP Code (Equation 2)

Note: Equation 1 does not include spatial amenities. Equation 2 assuming spatial amenities are represented by either ZIP Codes or clusters estimated using k-means, where both original cluster assignments and clusters re-assigned by nearest neighbors where modeled.

For subsequent analysis, we select a model with eight clusters that have been reassigned using

nearest neighbor, which led to re-assigning the cluster for approximately 12 percent of the

observations. Exhibit 5 summarizes this model. The selected model is relatively simple and

administratively feasible given it covers the entire city and is reasonably predictive with an adjusted

R-squared of 65 percent, which is considerably higher than the correlation between the SAFMR

and market rents of 37 percent. Increases in R-squared beyond eight clusters are marginal (see

196 Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents:

Implications for the Housing Choice Voucher Program in Pittsburgh, PA

exhibit 4). The goal of this analysis is not to prescribe the number of clusters that Pittsburgh

should employ but demonstrate how these data can be useful in transitioning to SAFMR.

Exhibit 5

Regression Results from Fitting the Sample to Equation 2 Assuming Eight Clusters

Variable

Estimate

Std. Error

P-value

(Intercept)

6.27

0.021

<0.001

Bedroom—1

0.34

0.023

<0.001

Bedrooms—2

0.37

0.021

<0.001

Bedrooms—3

0.52

0.022

<0.001

Bedrooms—4

0.62

0.027

<0.001

Bedrooms—5 or more

0.71

0.026

<0.001

Bathrooms

0.18

0.015

<0.001

Cluster 2

0.10

0.011

<0.001

Cluster 3

0.24

0.010

<0.001

Cluster 4

0.25

0.010

<0.001

Cluster 5

0.36

0.010

<0.001

Cluster 6

0.59

0.010

<0.001

Cluster 7

0.62

0.010

<0.001

Cluster 8

0.70

0.012

<0.001

Bedroom—1:Bathroom

-0.16

0.019

<0.001

Bedrooms—2:Bathrooms

-0.02

0.017

0.262

Bedrooms—3:Bathrooms

-0.01

0.016

0.414

Bedrooms—4:Bathrooms

-0.02

0.018

0.339

Notes: The initial cluster assignments were reassigned using nearest neighbors to ensure continuous and complete geographic coverage. Residual standard error: 0.23 on 11196 degrees of freedom. Adjusted R-squared: 0.649. F-statistic: 1220.06 on 17 and 11196 DF with a p-value < 2.2e-16.

Exhibit 6 maps the clusters from the selected model against ZIP Codes. Exhibit 6 indicates that ZIP

Codes rarely align with the cluster boundaries and are more likely to contain a complete cluster at

the outskirts of the city where geographic variation in cluster assignment is relatively low. Exhibit

6 also indicates more variation in cluster assignments for those ZIP Codes completely contained by

the city, which include those areas in Pittsburgh that are more commercially active. For example,

Cityscape 197

Blackhurst, Briem, and Deitrick

ZIP Code 15219—which is between the most commercially active neighborhoods in the city—

encompasses six of eight clusters.

Exhibit 6

Map of Spatial Clusters of Market Rents Compared with ZIP Code Boundaries

Note: Example ZIP Code 15219 is relatively centrally located and includes six different clusters, an example demonstrating that market rent variation within ZIP

Codes is more pronounced near the city center.

Exhibit 7 also suggests that markets can be extremely small, extending only several blocks in some

cases. This occurs even after smoothing out discontinuities using nearest neighbors. While it is

beyond the scope of this study to identify the contributions of spatial amenities to rents, readers

familiar with Pittsburgh will note several potential causes for these micro markets, including access

to transportation, green space, rivers, or entertainment.

Exhibit 7 compares SAFMRs with prices fitted to all two-bedroom units (Allegheny County, 2018)

applying equation 2 to the selected model of eight clusters derived by re-assigning the initial

clusters using nearest neighbors. Exhibit 7 indicates that the fitted rents match relatively well the

SAFMR for clusters 1 and 2. The fitted rents match SAFMRs for only a few ZIP Codes that overlap

clusters 3 and 4. In clusters 5 through 8, which include the high-rent areas, SAFMRs are biased

198 Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents:

Implications for the Housing Choice Voucher Program in Pittsburgh, PA

15222

15220

15213

15211

15201

8

15233

15212

15214

15219

90% of 40th perc.

15217

15232

15213

15201

15224

7

15221

15206

40th perc.

15208

15219

15218

15203

15217

15213

15211

6

15210

15219

15207

110% of 40th perc.

15217

15232

15213

15221

15206

5

15210

15208

15218

15207

Fitted Estimate

15222

15217

15232

15213

15235

15201

ZIP Code

4

15224

15221

15212

15206

15208

oom SAFMR

15219

15222

ZIP Primarily out of City

15203

15220

15213

wo-Bedr

15211

15226

3

15205

15212

15216

ed with T

15210

15219

15222

ZIP Primarily in City

15220

15211

15205

15233

2

15204

ooms Compar

15212

15214

15219

15203

15217

wo-Bedr

Entire ZIP in City

15220

15211

15226

15205

1

15234

15216

15210

SAFMR

15227

15207

0

3000

2000

1000

Estimated Price for a Two-Bedroom Unit

Exhibit 7

Fitted Rents for Market T

Cityscape 199

Blackhurst, Briem, and Deitrick

low such that the allowable high payment standard (110 percent of the 40th percentile by ZIP

Code) are all below 90 percent of the 40th percentile of the fitted estimates.

Exhibit 8 compares the distribution of listings with rents below the FMR and SAFMRs for

Pittsburgh, PA, to the (a) all 1–4 bedrooms in the Rent Jungle sample (11,214 potentially eligible

units) and (b) the modeled estimates for all 1–4 bedrooms (98,090 potentially eligible units).

Exhibit 8 suggests that the SAFMR does achieve the goal of increasing eligible units in higher rent

areas. The increase in eligible units, however, declines considerably with increasing rent. Using

fitted values for all 1–4 bedroom units (exhibit 8b), the transition from FMR to SAFMR increases

by eligible units in the two highest rent clusters from 20 (7 + 13) to 66 (47 + 19), which accounts

for less than 1 percent of total units in these two clusters. In contrast to the highest rent clusters, an increase in 4,400 units (2,500 + 1,900) is estimated for the lowest two clusters, which accounts for

22 percent of the total units in these two clusters. More importantly, the price differences between

the SAFMR and the market rents vary considerably in magnitude between low- and high-rent

200 Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents:

Implications for the Housing Choice Voucher Program in Pittsburgh, PA

ence

ence

fer

Estimates

-11%

-17%

-22%

-28%

-27%

-42%

-49%

-50%

fer

-10%

-17%

-24%

-29%

-34%

-43%

-52%

-52%

Dif

Dif

Fitted Price

Relative to Mean

Observed Price

Relative to Mean

-110

-190

-290

-360

-400

-800

-940

-100

-200

-310

-400

-540

-770

-1,000

-1,100

-1,100

The sample of units included in exhibit 8b

Fitted Price)

Mean (SAFMR-

Observed Price)

Mean (SAFMR-

e SAFMR with Market Prices

e SAFMR with Market Prices

Compar

Compar

Mean

1,000

1,100

1,300

1,300

1,500

1,900

1,900

2,000

Price

1,000

1,200

1,300

1,400

1,600

1,800

2,100

2,100

Mean Fitted

ence in Market and SAFMR Price by Cluster for

Observed Price

fer

otal

otal

Units

10%

11%

Units

12%

10%

10%

Fitted values were randomly assigned estimates within two standard deviations of the mean fit.

Change in

9.10%

3.30%

6.80%

0.77%

0.89%

0.60%

Change in

4.60%

5.80%

1.40%

0.38%

0.41%

Eligible Units

Eligible Units

Relative to T

Relative to T

ogram, and the Dif

The sample of market rents in exhibit 8a are those summarized in exhibit 1.

Allegheny County (2018).

71 (180%)

62 (180%)

10 (600%)

17 (180%)

5 (270%)

34 (360%)

12 (270%)

Change in

110 (200%)

150 (810%)

89 (3100%)

Change in

820 (240%)

640 (670%)

63 (1200%)

oom Units

Eligible Units

ooms*

Eligible Units

2,500 (160%)

1,900 (200%)

1,300 (380%)

-Bedr

units as described by

8

69

47

19

170

220

170

130

92

12

39

740

6,900

3,900

1,800

1,400

Less than

SAFMR

than FMR

“four family”

Observed Price

Fitted Price Less

Counts of All 1–4 Bedr

6

13

7

97

3

2

3

470

590

110

110

21

71

22

FMR

4,400

2,000

Counts of Observations as Summarized in Exhibit 1

Observed

Price Less

than FMR

Fitted Price

Less than

.

710

840

Total

Units

1,000

1,600

1,900

1,300

1,300

1,900

Total

Units

21,000

19,000

13,000

18,000

11,000

4,500

9,000

2,900

-bedroom units in properties equal to or smaller than

1

2

3

4

5

6

7

8

1

2

3

4

5

6

7

8

Units estimated as eligible for the HCV program assuming payment standards of 100 percent of FMR and SAFMR.

Cluster

Cluster

Exhibit 8

Total Counts of Households, Counts of Households Eligible for the HCV Pr

(a) the Sample of Market Rents and (b) All One- to Four

a.

b.

Note:

includes one- to four

rounded to two significant figures for clarity

Cityscape 201

Blackhurst, Briem, and Deitrick

clusters. The SAFMR payment standard in low-rent clusters is approximately 10 percent below

market rent, but more than 50 percent below market rent in high-rent areas.

Conclusions and Implications

Since its conception, affordable housing stakeholders have expressed concern about using ZIP

Codes and latent American Community Survey (ACS) rent data as the basis for defining small

area markets to define payment standards under HUD’s Housing Choice Voucher (HCV) program.

Inherent in the use of ZIP Codes to represent small area markets are prima facie concerns about

misalignment with spatial amenities and the inclusion of rents from nearby but otherwise

unserved municipalities as the basis for payment standards. Concerns about ACS data include

representativeness and latency.

This study uses market rents in Pittsburgh, PA, to define small area markets and explore the

implications of inconsistencies between market rents and Small Area Fair Market Rents (SAFMRs)

and Fair Market Rents (FMRs). While correlations between market rents and SAFMRs are relatively

low at 37 percent, correlations using markets derived from a combination of clustering and nearest

neighbor algorithms can be as high as 71 percent. The model selected for this study includes only

eight clusters, which is fewer than the 26 ZIP Codes in Pittsburgh, demonstrated by an adjusted

R-squared of 65 percent.

Results suggest that the SAFMR achieves its goal of increasing the eligible units relative to HUD’s

previous standard using FMRs. Unlike previous studies using the same commercial data set,

however, increases in eligible units were observed in all areas but were disproportionately in

lower rent areas. The change in standard increased by only 46 eligible 1–4 bedroom units in the

two highest rent areas. While this is a 330-percent absolute increase (from 20 to 66) the increase

represents only 1 percent of total units.

The SAFMR is below the market rent in all ZIP Codes and generally increasingly so with the

increasing rental price. All else equal, differences between market rent and the voucher amounts

allowable under HUD’s requirements are likely to affect landlords’ willingness to participate in the

HCV program. A competitive payment standard combined with a high count of eligible units is

likely to increase HCV landlords in low-rent markets and, in some marginal units, may enhance

established landlord behaviors of overcharging or manipulating the HCV program to make it more

difficult for existing voucher holders to move. If landlords see an opportunity to overcharge in low

rent areas, this SAFMR payment standard may increase HCV housing and, therefore, expenditures

of public resources in low-rent areas.

High-rent markets demonstrate very few increases in eligible units combined with payment

standards well below market rents. Thus, only landlords that have trouble renting to the private

market, such as those that offer properties of marginal quality, are expected to participate in the

HCV program. It is not fully clear how landlords and tenants will interact in leasing marginal

properties in high-rent areas, but these findings suggest that search costs for tenants will be higher

in high-rent neighborhoods. Moreover, it is also unclear how tenants will trade better onsite

amenities in low-rent areas, such as a yard, with the neighborhood amenities typically associated

202 Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents:

Implications for the Housing Choice Voucher Program in Pittsburgh, PA

with the area of opportunity. These trade-offs are important given that the few properties in high-

rent areas expected to become eligible under the proposed SAFMR standards are expected to be

of marginal quality (assuming prices partly reflect quality). These results may explain previously

observed declines in high-rent areas following the introduction of SAFMR into some markets

(Reina, Acolin, and Bostic, 2019).

ACS ZIP Code rent data from nearby but otherwise unserved municipalities appears to not be

significantly influencing inconsistencies between market rents and SAFMRs unless rents from these

nearby municipalities are simply compensating for other inaccuracies in the payment standard.

Conversely, it is not clear why the payment standards are biased low in higher rent areas associated

with ZIP Codes completely within the city. Misalignment between the SAFMR and the market rents

may be due to HUD’s restricting SAFMRs to within 50 percent and 150 percent of the FMR. Future

work should explore the potential misrepresentations of market rent introduced by the 50-percent

to 150-percent FMR restriction. It could be that the ACS data are latent or simply inaccurate or that

the market data are biased upward. Results do suggest significantly more variation in rent in high

rent areas in ZIP Codes completely contained by the city, suggesting micro markets appear closer to

areas of high commercial activity.

This study indicates that rental markets in Pittsburgh can be extremely local, extending for only

several blocks in many locations. The underlying amenities (for example, access to education,

transit, entertainment) reflected in these observations are speculative, but distinguishing their

contribution to rental prices could better help interested families move to areas of opportunities.

By better understanding the contributions of individual amenities to market rents, housing

authorities could theoretically prioritize subsidies to towards amenities more aligned with

opportunity (for example, access to education or employment).

As previously reported by Palm (2018), the use of proprietary rental data, such as those employed

in this study, in administering an HCV may not be feasible in that these data do not conform

with the Government Accountable Office standards transparency, reproduction, and geographic

consistency (GAO, 2005). These data could, however, guide improved data collection by HUD

or the Census Bureau in support of moving to opportunity. There are also limitations to the raw

Rent Jungle data, including, but not limited to, demonstrating an uncertain degree of duplicate

listings, too limited coverage of onsite amenities outside of bedrooms and bathrooms (for example,

floor space), and no sampling procedure such that the data may be unrepresentative.

Similarly, it should be noted that future work should explore other spatial clustering methods

outside of k-means, such as including a distance function that attenuates the contribution of

neighboring rents with distance or methods that reflect stark geographical barriers (for example,

rivers and hillsides) characteristic of Pittsburgh.

Acknowledgments

The authors would like to thank the Housing Authority of the City of Pittsburgh for partly funding

this work. The authors would also like to thank Dr. Randy Walsh for his helpful guidance when

Cityscape 203

Blackhurst, Briem, and Deitrick

completing this work.

Author

Dr. Michael Blackhurst is the Co-Director of the Urban and Regional Analysis program at the

University Center for Social and Urban Research at the University of Pittsburgh. He can be reached

at mfb30@pitt.edu.

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206 Small Area Fair Market Rents

by Aksel Olsen

Examining the Transition to HUD

Small Area Fair Market Rents Using

Craigslist Data

Aksel Olsen

Association of Bay Area Governments and Metropolitan Transportation Commission

Abstract

The limitations of the U.S. Department of Housing and Urban Development’s (HUD) metropolitan-scale,

American Community Survey (ACS)-driven annual Fair Market Rent (FMR) estimates are familiar to

local housing officials. Each year, scores of comment letters are received by HUD as FMRs are updated and implications for local housing markets become known. The transition to Small Area Fair Market

Rent (SAFMRs) holds great promise in mitigating key shortcomings of using areawide geography, offering a much more submarket-specific variable payment standard for use by public housing authorities

(PHAs). This potentially opens up more high-opportunity areas to the program’s users. A more formal, large-scale assessment of this key rental housing policy, however, has been difficult due to paucity of current national yet sufficiently local, datasets describing rental housing markets. Using recent and spatially comprehensive rental data from Craigslist, a listing website that includes housing, we analyze HUD data for 2,600 FMR areas nationwide and show rental gaps between the actual cost of rentals and

what PHAs will pay per the FMR payment standard. We analyze how a shift to SAFMRs changes the

potential availability of units, focusing on both the 24 HUD rule areas and the nation at large. Based on our findings, we argue that more areas should be included in the program if appropriate safeguards can be instituted.

Introduction

In a report to Congress in 2017, the U.S. Department of Housing and Urban Development (HUD)

quantified the scarcity of affordable rental housing, noting that, “[n]ationwide, only 66 affordable

units exist for every 100 extremely low-income renters” (Watson et al., 2017). The situation is not

improving. Renters have faced a faster increase in rents relative to incomes, with 4 out of 10 renters

paying rents that used to be in the top quartile in 2000. In coastal markets, affordability challenges

are even more pronounced, with affordability having deteriorated considerably since 2000 (Myers

and Park, 2019). While it is true that many markets could be substantially helped by an increase

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 207

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

A. Olsen

in the supply of affordable housing, how the existing stock is managed and available to different

income groups, including through federal programs such as the HUD’s Housing Choice Voucher

(HCV) program, remains critical to low-income families throughout the country.

HUD has long provided a lifeline to millions of low-income renter households, subsidizing

their housing each year. While the department has a range of programs supporting the poorest

Americans, the largest by far in terms of outlays, is the HCV program (McClure, Schwartz, and

Taghavi, 2015). The HCV program, administered by HUD, supports over 2.2 million households,

or about 4.5 percent of all rental households, each receiving rental subsidies ensuring that their

rent does not exceed 30 percent of their income. Forty percent of HCV holders are female-headed

households with children (HUD, 2019). The program has undergone changes in funding and scope

since its inception in the Housing and Community Development Act of 1974, yet it remains a key

pillar of today’s tenant-focused (as opposed to project-focused) federal housing policy.

One of the determinants for access to users of the voucher program is a key regulatory feature of

the program’s implementation: the scales and geographies of Fair Market Rents (FMRs), which are

the HUD-provided metropolitan-scale maps denoting what a unit would rent for in a fair market

transaction in a given FMR area. By definition, FMRs are set at the 40th percentile of rents for

units of different bedroom counts, meaning about 4 out of 10 units should be nominally available

to voucher holders. Two challenges have long been associated with the program. First, there

is the issue of whether sufficient numbers of units are available to voucher holders in different

markets. If a much smaller sliver than the 40th percentile would be available, it would create more

competition and limit options for program participants (as well as to low-income households in

general). Second, and relatedly, a limited stock typically translates to a limited geography, with

households more prone to concentration in high-poverty, high-segregation neighborhoods.

Both factors—scarcity and concentration in high-poverty areas—have been demonstrated

empirically over the years by HUD and others. As a way to address both, HUD recently transitioned

a small number of areas to Small Area Fair Market Rents (SAFMRs), a much finer-grained estimate

of rents. SAFMRs allow subsidies to be higher in more expensive areas, while conversely reducing

them in more affordable ones. This could open access to new markets for many low-income

Americans. The Final Rule on establishing SAFMRs was issued in the second half of 2016, requiring

implementation for 24 HUD-defined metropolitan areas. HUD has issued interim and final reports

on six pilot study areas, with early implementation of the shift from FMRs to SAFMRs. While

interim and final evaluation reports suggest promising outcomes in terms of offering more units in

higher opportunity neighborhoods, assessments including larger geographies have yet to be done.

This study relies on a recent national sample of rental data scraped from Craigslist, a listing

website, to provide early insights into HUD’s transition to SAFMRs. The rental listings are geocoded

and can thus be classified by both the old “large area” FMRs as well as by the new SAFMRs,

allowing us to identify transitions—listings that were too expensive in the old classification but fall

below SAFMRs per the new schema. Although in many high-cost markets, the existing FMR system

means many neighborhoods are de facto rental deserts, with few rental listings available below

applicable FMRs, we find the situation to be much improved with SAFMRs. We discuss limitations

with the analysis as well as offer suggestions for the program.

208 Small Area Fair Market Rents

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We start the article with a review on the background of the FMR program, highlighting key

challenges, from concentration to measurement issues. We motivate the study in the context of

data limitations and the value in triangulating with an independent source. We then describe the

data and report on findings for the United States as a whole and for the 24 mandatory areas under

the SAFMR final rule (Rule Areas). We suggest other areas that could be added and then discuss

our findings.

Background: HUD’s Housing Choice Voucher Program in Brief

The HCV program, also known as the Section 8 program, helps households afford housing in the

private market by topping off the rent they are able to pay (set at 30 percent of their income), up to

the going market rate for a standard quality unit.

The program hinges on annually published FMRs for each of about 2,600 metropolitan and

non-metropolitan areas nationwide, determining the typical cost of a non-luxury unit. Local

public housing authorities (PHAs), in turn, use FMRs to set the payment standard for how much

a unit should rent for and, accordingly, what the size of the subsidy should be for individual

voucher holders at lease-up. The tenant pays 30 percent of their income, and the program pays

the difference up to the lesser of the gross rent for the unit or the payment standard amount set by

the PHA.

The voucher program is not an entitlement where every eligible family receives a voucher but a

benefit subject to waiting lists for vouchers to become available. Implementation details of the

program, such as how FMRs are determined, have a big bearing on how many households can be

supported and where those households will end up living within regions. As an example, potential voucher tenants accessing the Berkeley Housing Authority’s website during the spring of 2019

would find the waiting list closed; it was last open for 5 days during the summer of 2010 and some

37,000 people applied for a spot there (Berkeley Housing Authority, 2019). Nationally, an eligible

family that has secured a spot on the waiting list will wait an average of 2.5 years for a voucher

(Watson et al., 2017). Some markets see much longer waiting times; for example, in 2017, Santa

Cruz reported a waiting period of 8 years (Panetta, 2017). An unfortunate lack of centralized data

on waiting lists compiled from individual PHAs precludes systematic analysis of the predictors of

waiting list length. For many, the program is all but off-limits and not a predictably reliable plank

on which to build a family’s housing career.

In addition to being consequential for individuals, implementation details matter to the overall

fiscal health of the local PHAs managing the programs for HUD. If FMRs are set too low,

underestimating the “true” cost of rentals, many households won’t be able to secure a lease as they

cannot compete with non-subsidized renters; that would negatively affect the program’s “success

rate,” which hovers in the mid-30s in percentage terms in a large national assessment (Finkel

and Buron, 2001). To the extent that the payment standard is set too low in some FMR areas

with scarce affordable housing stock, those markets may, from the vantage point of low-income

renters and voucher holders, effectively function as rental deserts. In rental deserts, expansive

and expensive housing searches are conducted, with considerable difficulties securing leases,

particularly in neighborhoods offering opportunities. This scarcity is exacerbated by low turnover,

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loss of landlords to the HCV program, as well as gentrification of typically amenity-rich, centrally

located areas historically affordable to low-income individuals (Hwang and Lin, 2016; Somerville

and Holmes, 2001).

Conversely, if rents are set too high with higher FMR levels, landlords may absorb the higher

rents payable rather than provide more housing service, getting more money from the federal

government in the process (Collinson and Ganong, 2018). This would deplete funds and could

ultimately make fewer vouchers available for families in that area. Many local PHAs from high-cost

areas watch, not surprisingly, with great interest as rents are published ahead of each fiscal year

(FY), writing comment letters challenging local FMR determinations, using pointed language such

as “unfathomable” to describe the published rents (Fredericks and Havlicek, 2017).

Well-Known Voucher Program Challenges

Metro-Level Rents Ignore Submarkets

High-cost areas with rapid rent increases will, all other things equal, have a harder time getting

FMRs to catch up with local markets, but geography matters, too. The larger the region, the more

internal variation of rental rates from one neighborhood to the next. This variation is attributable

to a range of factors related to accessibility to jobs, open space, and other amenities, as has long

been well documented by the hedonic housing price literature (Bayer et al., 2016; Knaap, 1998;

Rosen, 1974). FMR areas as regions in their own right, typically have a number of relatively

distinct housing submarkets, each with their own characteristics and cost structures (Bourassa et

al., 1999). While PHAs can set payment standards from 90 to 110 percent of FMRs, many FMR

areas exhibit a much larger variation in rental costs between submarkets. Having uniform FMRs for

such areas means that the voucher subsidy will be the same in the most expensive areas as in the

most affordable parts of the FMR area. This effectively limits the geography of where the vouchers will likely be used, increasing the likelihood that lease-ups will be in the least desirable parts of

the region. This is in contrast with program objectives of poverty deconcentration, while certainly

flying in the face of the key premise of the program: that households be given a meaningful choice

of housing options.

Deconcentration

Deconcentration has long been recognized as an important objective of the affordable housing

programs,1 due to poor outcomes on a range of development indicators from growing up in

high-poverty, segregated neighborhoods. While there was little doubt that living in concentrated

poverty was not beneficial, the record on dispersal programs had long been less than convincing

(Goetz and Chapple, 2010). HUD’s own Moving to Opportunity (MTO) for Fair Housing

demonstration program has provided important experimental data underlying the policy

importance of neighborhood quality, even if the mechanisms may not be fully understood.

Leveraging these longitudinal data, Chetty and collaborators (2015), in a set of groundbreaking

studies, have provided new insights from the MTO program data linked to administrative records.

They convincingly demonstrated the long-term, positive effects of moving away from poverty-

1 “Fair Housing Act of 1968.” 1968. 42 U.S.C. 3601.

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Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

stricken neighborhoods before children reach adolescence, profoundly influencing individual life

trajectories (Chetty, Hendren, and Katz, 2015). Whether the key policy implication of MTO is to

address the root causes of poverty, fix the social fabric of existing neighborhoods, or encourage

moving residents, concentration in high-poverty neighborhoods remains a reality for many

voucher holders.

More than 20 years ago, Newman and Schnare (1997) found that, by giving tenants choices not

present with a policy based on place-bound, project-based assistance, the voucher programs

appeared to do little to help improve neighborhood quality of residents, although the voucher

program appeared to “reduce the probability that families will live in the most economically

and socially distressed areas.” Almost twenty years later, McClure and Johnson (2015) revisited

Newman and Schnare’s work, noting some success in terms of moving more households into

low-poverty, less distressed areas—including suburbs—though still finding much room for

improvement on racial integration and other factors.

A considerable amount of research, including research from HUD, has documented this very

challenge of deconcentration of voucher holders away from the most impoverished neighborhoods.

A number of studies have assessed particular markets with respect to the deconcentration goals

motivating the program (De Souza Briggs, Comey, and Weismann, 2010; Lens, Ellen, and O’Regan,

2011; McClure, 2008; McClure, Schwartz, and Taghavi, 2015; Varady et al., 2010). Section 8

households are concentrated in relatively high-poverty neighborhoods (Wang and Varady, 2005).

McClure and colleagues (2015) found that one in five voucher households situate in low-poverty

neighborhoods, although voucher holders were a small share of the housing stock and are not

particularly spatially concentrated. Pendall (2000) documented an association between high

rates of poverty and the concentration of voucher holders in neighborhoods of distress. Higher

vacancy rates, however, were found to increase the ability of households to move to areas of

higher opportunity (Galvez, 2010), a finding consistent with the concerns raised by commenters

on HUD’s Proposed Rule on SAFMRs: That the program would be less successful at providing

deconcentrating in markets with very low vacancy rates (HUD, 2016c).

Why are Voucher Holders Persistently Concentrated?

Lack of deconcentration is thought to be due in no small part to the payment standard being too

low for program design reasons. With the payment standard uniformly set for a metropolitan area,

higher cost areas will automatically be off-limits. Recent supporting evidence comes from Wang’s

analysis of survey data from Florida, which show a marked difference between voucher holder

stated preferences for safe and clean neighborhoods with good schools and the neighborhoods they

could actually afford (Wang, 2018).

The assumption is that concentration has persisted for financial reasons, with payment standards

set such that good neighborhoods were off-limits, although other plausible reasons have

been identified.

Landlords may not actually lease to voucher holders, as was recently reported by both the Los

Angeles Times and Pew Research (Khouri, 2019; Wiltz, 2018). In a landmark study of landlord

behavior, particularly whether would-be voucher holders would be treated differently than other

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prospective tenants, researchers found the housing search process fraught with denials of voucher

holders in more than 75 percent of cases in some markets (Cunningham et al., 2018). This is a

longer standing challenge, having accompanied the program perhaps since its inception (Tighe,

Hatch, and Mead, 2017). Building trust and long-term relationships with landlords is accordingly

critical to overall program success (Varady, Jaroscak, and Kleinhans, 2017). Many landlords are

anecdotally leaving the program, however, representing an erosion of long-term relationships

with PHAs.2

The program may fail to further deconcentration objectives for a number of reasons not necessarily

directly related to the program itself, such as lack of social networks in higher opportunity

neighborhoods or limited transportation options (McClure, 2008; Ruel et al., 2013).

Further, beyond payment standards, counseling appears to be an important factor for families

to successfully secure housing in low-poverty neighborhoods (Turner and De Souza Briggs,

2008). Voucher holders generally have 60 days to search, select, and secure a lease after voucher

assignment. Whether it is search difficulties, preferences, or discrimination, it follows that not all

searches will be successfully turned into a lease, even if the listing price is technically within reach, or leases may not lead to the most promising neighborhoods given the typically higher cost they

command (Shroder, 2002). In 2001, in a nationally comprehensive study, researchers found that

the “success rate,” or the rate of success of securing a lease for voucher families, was just 37 percent (Finkel and Buron, 2001).

A more structural reason for lack of deconcentration success is that demand-side programs such

as vouchers are unable to address a key underlying reason for high housing costs in many areas,

such as limited supply and low vacancies. In particular, as many high-cost markets are supply

constrained, vouchers are of less use in those areas.

HUD, however, has long focused on addressing this programmatic challenge. Already in 2000,

the agency issued an interim rule stipulating both that (1) some areas could base FMRs on 50th

percentile levels, departing from the typical 40th percentile standard, and (2) that PHAs would

have more flexibility in diverging from the areawide rent ceiling, allowing them to set the voucher

“payment standard” to between 90 and 110 percent of the published FMR for each unit size (HUD,

2000). This devolution of authority to the local level could mean PHAs could be much more

responsive to local conditions and knowledge. Ultimately though, HUD assessed that the 50th

percentile approach failed to sufficiently reduce the concentration of voucher holders in high-

poverty areas.

Toward Small Area Fair Market Rents

As the 50th percentile approach failed to sufficiently deconcentrate voucher holders, the most

recent evolution involves adjustments to the geography of the FMRs. In tandem with yearly small-

area data from the American Community Survey (ACS) that started to be available in 2011, HUD

issued a notice that it would begin a pilot demonstration project for a small number of PHAs to

2 Contra Costa County, for example, reported a drop of 631 landlords in recent years as they could lease to non-HCV

renters (Villareal, 2016).

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Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

use a new methodology of ZIP Code-based FMR areas, called SAFMRs (HUD, 2010).3 By shifting to

ZIP Codes instead of metropolitan statistical areas (MSAs), rents would be able to track submarkets

better, instead of treating housing markets as wholly uniform within an MSA-wide FMR area (HUD,

2016a). The premise of geographically rescaling FMRs to the much smaller ZIP Code tabulation

area geographies is to allow voucher payments to track actual rents much closer than the one-size-

fits-all per the FMRs, meaning a much more variable payment standard within each FMR area.

With the more nimble SAFMRs, households would therefore, in principle, be better able to locate

in relatively higher opportunity areas, which typically are more expensive, than what they would

have been able to with existing policy under prevailing FMRs. At the same time, the SAFMRs

would also “prevent undue subsidy in lower-rent areas (HUD, 2010). Where the 2000 Interim

Rule also increased the FMR ceiling for MSAs to the 50th percentile rent, the difference is the

finer geographic scale. An areawide increase in allowable rents did little to combat the locating of

voucher holders in areas of concentrated poverty and economic and racial segregation, while likely

subsidizing landlords offering substandard units. In practical terms, whereas there are around

625 unique metropolitan area-based FMRs, the number of SAFMRs is almost 25,000, a forty-fold

increase in geographic resolution, which is substantially better able to track real estate submarkets

than the metro-wide delineations they may replace.

To test the effect of migrating to smaller-scale FMR areas as a way to better enable voucher holders

to reside near jobs, transportation, and educational opportunities, five PHAs with different

demographic and economic characteristics agreed to participate in the SAFMR demonstration in

2012 (Finkel et al., 2017). The demonstration project would test key outcomes for a handful of

PHAs before rolling out the program as a replacement to the 50th percentile FMR areas.

Expectations and Early Assessments

By 2017, an Interim Report on the pilot areas was issued, demonstrating that this was indeed

the case; that by re-carving metropolitan area geographies into ZIP Code-level specificity, the

distribution of rental units tended to shift to relatively more expensive areas, often doubling

as areas of higher opportunity (Finkel et al., 2017). The study also noted, however, an overall

decline in units affordable to voucher holders in those areas.4 Still, based on those findings, HUD

issued a final rule that SAFMRs would become active in the 24 Rule Areas, generally higher cost

FMR areas, or areas with a high concentration of voucher holders in poverty (HUD, 2016c). After

pushback from stakeholders and some PHAs, HUD announced a delay in the implementation

of SAFMRs for the 24 Rule Areas to FY 2020, citing the desire to complete the full pilot study

and more fully analyze potential downsides of the transition (HUD, 2017b). A legal challenge

was filed by two voucher holders and a nonprofit organization devoted to providing housing

opportunities for low-income people in Connecticut, with the District of Columbia Circuit

3 The SAFMR demonstration consisted of five PHAs: The Housing Authority of the County of Cook (Illinois), the City of Long Beach (California) Housing Authority, the Chattanooga (Tennessee) Housing Authority, the Town of Mamaroneck (New York) Housing Authority, and the Housing Authority of Laredo (Texas). In addition, the evaluation of the demonstration study included two PHAs from the Dallas, Texas metropolitan area, the Housing Authority of the City of Dallas and the Housing Authority of Plano, which have both been using SAFMRs since 2011

as the result of a legal settlement.

4 The opportunity index constructed for this study includes percent nonpoor, school proficiency, job proximity, and environmental quality.

Cityscape 213

A. Olsen

Court enjoining HUD to continue with the SAFMR as HUD had not made the proper procedural

findings necessary for a delay.5

As SAFMRs were officially rolled out in 2018, an early assessment came from the New York

University (NYU) Furman Center. They expanded HUD’s Interim Report analysis of the pilot

SAFMRs demonstration to the 24 FMR areas mandated to use SAFMRs under the 2016 SAFMR

Final Rule (NYU Furman Center, 2018). They analyzed the introduction of SAFMRs in these 24

Rule Areas, using ACS data tabulated for HUD on rental units and their rent distribution at the Zip

Code Tabulation Area (ZCTA) level, and found that switching to SAFMRs furthered housing options

in higher-rent ZIP Codes while reducing them in lower-rent ZIP Codes, which was consistent with

program purposes. Somewhat in contrast with the pilot, their analysis also found that, in total,

the number of units affordable to voucher tenants would increase with the use of SAFMRs when

looking at the 24 Rule Areas as a whole (NYU Furman Center, 2018).

Palm (2018), in an assessment of the program using non-census rental data from Rent Jungle, a

web listing aggregator, analyzed rents from two time periods in five FMR areas and similarly found

that the switch to SAFMRs would positively influence availability of units in higher opportunity

neighborhoods; that finding stresses the importance of the geographic scale of the program. He

further found different trajectories in different areas. Sacramento, for example, would benefit

from inclusion in the SAFMR program as the switch would mean a substantial increase in listings

affordable in low-poverty neighborhoods, without an offsetting “cataclysmic loss” of listings in

higher poverty areas. Overall, the assessments of the switch so far are encouraging, though issues of

measurement will remain a challenge.

Key Measurement Challenges and Motivation

A difficulty in assessing FMRs comes from the data used to calculate FMRs in the first place; ACS

data. While nationally comprehensive, the data are collected by an ongoing survey throughout

the year with 1-year data released for larger geographies (areas representing more than 65,000

persons). For all its wonders as a timely, repeated, and comprehensive data resource for

researchers, a sample-based survey such as ACS presents unique analytical and programmatic

challenges when using it for program development and, in particular driving regulatory

geographies of FMRs. HUD, in 2018, indeed noted that “[i]n general, it is difficult to measure the

accuracy of FMRs for the simple reason that no single, widely accepted measure of gross rents

exists to use as a benchmark to compare with the FMRs” (HUD, 2018). While it is instructive to

assess unit availability below FMRs using the special tabulations of the inventory of rental units

by rent at the level of ZCTAs provided by HUD, by definition, availability largely follows FMRs

quite closely as program and evaluation is defined using the same source. Many PHAs, in their

comments on annual releases of FMRs, indeed note the challenges related to using ACS data, the

lags it necessarily entails, and the challenge of tracking fast-moving markets with higher than

average increases in prices not captured by the current usage of national trend factors.

“In 2013 and 2015 the eight PHAs in the [Oakland-Hayward-Berkeley, CA] Metro Division

paid for and conducted a statistically valid rental survey in order to refute proposed FMRs

5 Open Communities Alliance Et Al v. Carson Et Al, Civil Action No. 17-2192 (BAH). 2017. Washington, D.C.

214 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

that either were drastically low given our rental market or reduced from the previous year.

In both 2013 and 2015, the FMRs were significantly increased as a result of the study

data (approximately 16 percent in 2013 and approximately 34 percent in 2015), thus

demonstrating the inadequacy of HUD’s FMR methodology” (Villareal, 2016).

While SAFMRs represent a clear conceptual and policy-wise great leap forward recognizing the

significant intra-regional housing market heterogeneity, questions of measurement will nonetheless

continue to be an issue. The core challenge, only made more acute when going to the smaller

ZCTA level, is intrinsically related to capturing at relatively fine spatiotemporal detail the behavior

of the rental housing market with a survey with a locally modest sample size, coupled with the

requirement for both spatially and temporally detailed data on rents for different unit sizes. The

need for timely data requires further subsetting to recent movers to capture recent inflation,

further limiting the sample. HUD’s guidelines are such that estimates with a margin of error ratio

larger than 50 percent are not to be used for calculating base rents and recent move factors. This

means a wide band around a point estimate would be acceptable, and necessary (HUD, 2015).

HUD caps possible year-over-year decreases to 10 percent, effectively smoothing the effect of such

measurement volatility (HUD, 2017a).

Apart from sampling error, there is the challenge of using a survey that is not ideally suited as a

housing survey. Some commenters on annual FMR notices in the Federal Register have noted, in

connection with the requirement that HUD exclude subsidized households from the ACS rental

data, the difficulty of properly identifying and discarding them. HUD, however, handles that by

truncating the bottom of the rental distribution using administrative data on assisted housing rents

before calculating the 40th percentile. Similarly, some major cities, typically in expensive coastal

markets, have rent control, which could serve to downward bias FMRs in exactly the costliest

markets. In sum, inasmuch as these factors affect FMR levels, having external data to compare

against FMR levels is critical, highlighting the value of separating training and validation data

for FMRs.

Such assessments with ACS-independent data are rare, however, mainly because few nationally

comprehensive datasets exist on rental markets. While there is a strong data infrastructure

associated with home sales in the form of recorded transactions, rents leave far fewer traces to track

them effectively and across geographies. It is typical for vendors to exist in particular markets.

While the localized nature of rental information makes generalized, consistently measured

assessments difficult, it by the same token makes it hard to do larger scale accounting of the

housing program. Assessments have accordingly typically focused on individual areas due to data

limitations. Holding some promise, but nonetheless representing the problem of aggregation, as

well as of fair use, the increase in the number of web platforms has given rise to big data collection

efforts; that potentially offers insights into a range of domains, including rental markets, if the data are available to harvest and prove to be reliable.

A few researchers have relied on such big datasets to answer questions related to housing markets

and FMRs, offering a triangulation independent of ACS data. Boeing and Waddell (2017) used

Craigslist to assess FMRs nationally in a demonstration project of using big data to address

substantive social science research questions, while at the same time comparing the rental data to

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federal sources. While they found 37 percent of listings to be below FMR levels, close to the 40th

percentile defining the program, they found many variations between metropolitan areas, with

costlier FMR areas falling in the single digits. More recently, Palm (2018) provided preliminary

evidence that suggested the transition to SAFMRs would overall lead to more units in higher

opportunity areas. While Boeing and Waddell analyzed listings relative to FMRs, this study

extends the work by reporting on the transition from FMRs to SAFMRs. This article expands the

conversation and provides more detail on the potential for the program to move households into

higher opportunity neighborhoods with the transition to a more fine-grained regulatory geography.

The Current Study

What will Small Area Fair Market Rents mean for would-be voucher holders across the 24

Rule Areas? This study explores differences in rental listing availability using a national dataset

following the introduction of SAFMRs in the 24 Rule Areas. While there are only 24 areas that

were mandated to use SAFMRs under the final rule, we expand the comparative analysis to

include all areas for which SAFMRs are published, to cast a wider net on the effect of this type of

geographical-regulatory reclassification, including to assess which non-rule areas would be well

suited for using SAFMRs.

To gauge the availability of units at the relevant price point, this study relies on data scraped from

Craigslist, a rental listings site, to characterize the voucher program. Using alternate sources of

data to illuminate large scale urban phenomena is part of a wider emergence of “urban analytics”

(Goodspeed, 2017). These alternatives rely on an array—often implied to be “bigger” and more

“real time”—of sources of data and are often of a volunteered nature from “citizen sensors,”

with questions as to both motivation and accuracy (Goodchild, 2007). In the case of data from

Craigslist, listings are provided for business reasons by owners of units or companies managing

units on an owner’s behalf.

Description of Dataset

The national sample6 covers the first 6.5 months of the federal FY 2019, beginning October 1,

2018. We note that using data covering the first half of the FY, should all other things be equal,

leads to a better match with the FMRs, as it will cover the period least affected by inflation over the

course of the year.7

While there are issues of accuracy and duplication, perhaps the most salient issue is that of

coverage, since not all listings end up on Craigslist. As a non-research volunteered geographic

information dataset, there is no standard for inclusion, no published benchmarks of market

saturation and share, and coverage will vary over time and region, with usefulness needing to be

determined on a case-by-case basis. Using data from a non-scientific sample, or in our case a form

of “volunteered geographic information” (VGI) data, raises additional challenges, as the extensive

6 The listing data is scraped from Craigslist by UC Berkeley researchers using the Python-based Scrapy

library. See Boeing and Waddell (2017) for details.

7 The listing data have not been adjusted for inflation or seasonality, nor would it be appropriate to do so: the rents such as they are, over the course of a year, will be what is compared with the payment standard over the course of the year.

216 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

quality control measures associated with the federal statistical infrastructure are entirely absent; the data generating process is not a neatly curated, purpose-driven sample, but rather one from data

“in the wild.” The data are an artifact from the rental listing process; digital ephemera not meant to

leave a footprint; and are in many ways the equivalent of looking at historical yellow page listings.

To clean these, we went through a process similar to that described in Boeing and Waddell (2017),

dropping formal duplicates (landlords reposting the same listing days later to generate more views).

We similarly limited records to ones with valid geocoding. We filtered outliers using percentiles

as well, but instead of defining outliers relative to the national distribution, we calculated outliers

separately for each county to more closely reflect the norm for local markets.

To assess the dataset, we compared listings in our sample with the latest 1-year ACS release for

2017 at the core-based statistical area (CBSA) level for two pieces of information:

• Do the data roughly correspond to counts of recent mover households?

• Do the listing rents approximate those reported by ACS?

Listings and moves are distinctive conceptual worlds: Some people move more than once per year,

but this is not captured by the ACS survey, asking “Where did this person live 1 year ago?” By the

same token, the same unit may be listed more than once and appear more than once in the sample

legitimately without being a duplicate. We would accordingly expect a larger number of listings

than movers if the rental data represented the entire universe of listings, which of course they do not. Another difference is that listings may be akin to asking prices, and a lease may ultimately be

signed with a rent below the advertised price, depending on the conditions of local markets.

In the aggregate, we found sufficient alignment in the two datasets to suggest reasonable accuracy.

We found, first, a strong correlation (R2: 0.84) between Craigslist listings and recent movers per

ACS, and second, a strong correlation (R2: 0.86) between median rents, both measured at the CBSA

level. While these correlations were both strong, there were outliers particularly in the relationship

between listings and moves. We mark a number of those on exhibit 1 and note that New York falls

substantially below the regression line, having far fewer listings than the norm. New York’s rental

market is heavily dominated by brokers, with accordingly a lower utilization of services such as

Craigslist (Boeing and Waddell, 2017). On the other hand, the Los Angeles area has more listings

than expected, as do MSAs around Seattle; Washington, DC; Denver; San Francisco; and Portland,

to name the larger ones. Those housing markets could either see above average relocation activity,

or those areas could be more prone to duplicates not caught by the heuristic approach sketched in

exhibit 1.

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Exhibit 1

Comparing Craigslist, ACS on Listings and Median Rent, CBSA Level

ACS = American Community Survey. CBSA = Core-Based Statistical Area.

Notes: Recent mover households comes from ACS table ACS_17_5YR_B25026, with the assumption that the share of recent mover renter households is the same as the share of recent mover renter population. The median rent ACS estimate covers renters who moved since 2015, per table B25113_002E.

Geographical duplication was widespread in the dataset. The scraped data comes with the

listing ID assigned internally for tracking purposes by Craigslist. This ID will appear repeatedly

if users resubmit the same listing days or weeks later to appear as a more current listing. These

formal duplicates are removed. A more subtle class of duplicates involves new listings beyond

resubmitting an earlier listing. In this case, in substance, the same unit is offered through several

listings each with different IDs making them appear distinct. Depending on the market, this may

take place over the course of several months as landlords may wait for a tenant to take the offered

price, even on occasion lowering the rent to entice. We filter these by assuming that units on the

same location (latitude/longitude) that have the same size in square feet, the same bedroom count

and roughly the same price (within $100 intervals) within a quarter is a duplicate. There will be

boundary effects: Should the same listing be offered at the last day of the quarter and then a week

later, they will be treated as distinct and kept in the dataset, whereas if both listings had been in the same quarter, they would have been flagged as duplicates.

This may falsely identify some listings as duplicates when they are in fact distinct units in larger

multifamily buildings, though as the relisting practice appeared to be pervasive, this de-duplication

approach was preferable to leaving them in the dataset. Absent a way to uniquely identify units in

a building at the national scale, any practical use of such data will have to weigh the trade-offs of

discarding too many or too few listings for filtering of the data for the purpose at hand. All said,

as seen in exhibit 2, the sample went from 9.3 million to 2.8 million following de-duping and

filtering procedures. A number of descriptive statistics are provided in the data appendix.

218 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

Exhibit 2

Key Descriptives for Dataset Before and After Filtering

Square Feet

Rent

Listings

Subset

Area

(Count)

Standard

Standard

Median

Mean

Median

Mean

Deviation

Deviation

Unique

U.S.

9,392,930

900

1,084

27,169

1,324

10,040

4,545,939

Geo

Deduped

U.S.

2,845,445

967

1,331

41,665

1,400

25,276

7,843,509

Outlier

Filtered

U.S.

2,816,757

967

1,063

523

1,405

1,646

1,003

Notes: Unique data contains one listing per listing ID. Geo Deduped data removes likely geographical duplicates of the same listing. The Outlier Filtered data excludes outliers measured on a rent per square foot basis.

Data on Opportunity Areas

Researchers have long called for better accounting of what constitutes quality in a neighborhood.

As bigger datasets have become available, researchers are better able to come up with measures

of neighborhood quality that go beyond the most typical proxies for neighborhood quality such

as poverty (Pendall, 2000). To classify listings, we largely follow the approach set out in the HUD

interim report, creating a composite index based on census-tract level components obtained from

HUD’s open data site (Finkel et al., 2017). Opportunity is understood as a resource or amenity

available to residents in a given neighborhood.8 The components of the opportunity index include:

• A school proficiency index9 measuring neighborhood performance of fourth-grade students on

state exams,

• An environmental health hazard index,10 measuring potential exposure to harmful toxins at a

neighborhood level, including carcinogenic, respiratory, and neurological hazards,

• A jobs proximity index,11 a gravity-based measure of jobs access within a CBSA,

• A low poverty index,12 measuring share below the federal poverty limit.

Each is normalized on a scale from 0–100, with 100 considered higher opportunity. All indices are

defined at the tract level. We average the components at the tract level to produce the composite

opportunity index and then normalize it to a percentile ranking within each FMR area. A tract is

accordingly classified in relative terms within the opportunity distribution of each FMR region with

the implication that two tracts in different regions can have the same opportunity score though

different underlying components. As we are interested in the relative opportunity for voucher

8 These are not to be confused with “opportunity zones,” which denote areas offering tax preferential treatment to investors. See https://www.irs.gov/newsroom/opportunity-zones-frequently-asked-questions.

9 http://hudgis-hud.opendata.arcgis.com/datasets/school-proficiency-index.

10 http://hudgis-hud.opendata.arcgis.com/datasets/c7e2c62560bd4a999f0e0b2f4cee2494_0.

11 http://hudgis-hud.opendata.arcgis.com/datasets/4e2ef54b88084fb5a2554281b2d89a8b_0.

12 http://hudgis-hud.opendata.arcgis.com/datasets/3419cb4c7aa144b2bc54671f58b580f4_0.

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A. Olsen

holders searching for housing within a region, the normalization is appropriate, and we report on

listings availability by four opportunity categories.

Findings

In the following sections when comparing the two, we refer to area-wide Fair Market Rent areas as

MAFMRs, and the ZCTA-based FMRs as SAFMRs to avoid confusion. All other things equal, the

expected number of listings below the FMR should represent 40 percent of the rental distribution

as that threshold is used in their definition.

General Effect of Transition

Did the introduction to SAFMRs increase unit availability, overall, from MAFMR levels? Per exhibit

3, taken as a whole, nationally, we found that the share of listings was 5 percentage points below

40, at 35 percent, slightly smaller than the finding of 37 percent by Boeing and Waddell (2017).13

While the national count is reasonably close to the target 40 percent, many individual FMR areas

see well below 40 percent of units available below the FMR level. For the 24 Rule Areas, the share

is just 32 percent versus 35 percent for the United States as a whole. That availability is poorer in

the 24 Rule Areas is not surprising given the selection criteria’s focus on housing stock in relatively

high-cost areas (HUD, 2016b). Consistent with program expectations, per exhibit 3, we note that

the SAFMR transition, in the aggregate, moves availability up to the mid-40s, in percentage terms,

for both the 24 Rule Areas and the larger list of 625 metro-based SAFMR regions, with more of an

average increase for the 24 Rule Areas: Here, SAFMR would translate to an increase in available units

by 14 percentage points given their lower starting point, consistent with the Rule Areas’ selection

criteria based in part on the low availability of units. Exhibit 3 further suggests benefits for a wider universe of areas than those identified in the Final Rule if the disruptive effects could be mitigated.

13 For FY 2019, only three areas relied on the 50th percentile FMRs, with many of the others having transitioned to SAFMRs. In the assessment of rents relative to FMRs, we use the 40th percentile FMRs for the areas currently using SAFMRs, which will mean poorer performance in those areas.

220 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

Exhibit 3

Overall Change in Listings Below Fair Market Rent Levels, by Area Category

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Sources: Rental listings from Craigslist; FMR/SAFMR data from HUD

In some metropolitan areas, particularly in California, the share of units offered below FMR

levels was markedly lower, with Sacramento and Los Angeles having the lowest shares in the state

(exhibit 4). Of those two, Sacramento–Roseville–Arden-Arcade is one of the rule areas, whereas Los

Angeles-Long Beach is not. Both areas are in California where the state’s legislative analyst’s office

proclaimed a statewide under-production of housing by 100,000 units, severely impacting the

availability of units (Alamo and Uhler, 2015), underlining the importance of supply-side issues as

well to the success of the HUD program.

Notably, there is considerable variation with respect to availability even within high-cost areas.

For the San Francisco Bay Area, core FMR areas differ considerably in their placement, with San

Francisco having around 21 percent of units below FMR levels, while San Jose lands closer to the

national average, at 39 percent. Seattle has above-average unit availability, suggesting that it is not

simply a matter of the FMR methodology being unable to capture price increases in coastal tech-

based economies: here, San Francisco and San Jose differ in how they perform on the FMR score,

perhaps due to San Francisco’s long-standing rent control policy, which could downward bias the

payment standard.14

14 At the same time, it is conceivable rent control could upward bias FMR levels for a region insofar as the recent mover adjustment based on 1-year ACS data in the numerator is compared to the baseline rent data based on 5-year ACS data in the denominator. Rent control would likely impact the denominator more, leading to a larger upwards adjustment per the recent mover adjustment factor.

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A. Olsen

Exhibit 4

Share of Listings, Top 50 CBSAs Below FMR

CBSA = Core-Based Statistical Areas. FMR = Fair Market Rent. MSA = Metropolitan Statistical Area.

Notes: For the top 50 CBSAs by population, the share of listings falling below FMRs. CBSAs in the 24 Rule Areas marked in red.

Sources: Rental listings from Craigslist; FMR/SAFMR data from HUD

To better appreciate the nature of the transition to SAFMRs in a spatial sense, exhibit 5 shows, at

the ZCTA level of aggregation, the difference between FMR and SAFMRs for the 24 Rule Areas.

Negative values, from the left side of the key, show that SAFMRs are below the FMR for the subarea, so subsidy payments for units in these areas will go down. The right side of the key denotes an

increase in subsidy payments.

Overall, the map serves to illustrate the variety of submarkets in each region. In San Diego,

for example, coastal areas tend to be the most expensive and inland areas the least, reflecting

considerable geographic differences in rental costs; this is tracked more closely by a finer-grained

SAFMR standard. Importantly, the submarket-specific location of listings and associated rents on

those maps will determine the shift of the overall count of listings below FMR levels. If a plurality

of units happens to fall in low-cost areas with a lower payment standard, it would lower the count

222 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

available below FMR level. Conversely, more units in high-cost areas would mean an overall

increase in units available below FMR levels.

While the SAFMR data has been made available for a few years, making it possible to compare

the specific areas of change (exhibit 5), those maps only tell a partial story. Areas that may appear to see dramatic changes in FMR levels may turn out to lead to ultimately modest changes if few

rental units exist there, or if turnover is low. The address-level geographic specificity and ultimately microdata-nature of Craigslist data provides literal weights to those maps, telling us about where

listings are, how much they rent for, and where any one particular listing falls in the price brackets

defined by both the conventional FMR geography as well as by the potential SAFMR geographies.

For example, a listing in an above-average price neighborhood may have been above FMR levels in

the area-level schema and thus likely out of reach, but below SAFMR levels in the ZIP Code-based

schema. That same listing can be accounted for as having “transitioned” from out of reach to within

reach on monetary terms alone. We leverage the microdata nature of the data to analyze those

transitions by comparing geocoded listing rents with both the areawide FMRs as well as with the

SAFMRs. We subtract FMRs from the listing rent, where 0 means parity, positive means the listing

is above (out of reach) FMR levels, and negative means it falls below FMR levels.

Cityscape 223

A. Olsen

.

s methodology

per HUD’

A-level rent to the FMR-level rent,

eas

Area.

Tabulation

which in effect is the same as the ratio of the ZCT

A = Zip Code

ZCT

Census Bureau

Area Fair Market Rent.

A shapefile from U.S.

A Level, FMR to SAFMR, in Dollars, for 24 Rule Ar

SAFMR = Small

A-level differences between SAFMRs and FMRs,

ence at ZCT

FMR/SAFMR data from HUD; ZCT

fer

Maps shows ZCT

Exhibit 5

Dif

FMR = Fair Market Rent.

Note:

Sources:

224 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

As an example of how a particular area has seen a change in the distribution of listings as SAFMRs

were introduced, exhibit 6 shows the shift in units for San Diego, one of the 24 Rule Areas. We

see a shift of listings in the lower, pricier rows in the figure where rents are well above FMR

levels, to higher ones with SAFMR. Particularly for San Diego, we see a substantial upward shift

in the availability of one-bedroom units, owing to listings in areas that are now subject to the

higher SAFMRs.

Exhibit 6

Example Distributions, San Diego FMR Area

BR = Bedroom. FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Notes: Listings in thousands. The y-axis is listing rent minus FMR level (left panel) or SAFMR level (right panel). Positive categories (bottom three rows) means the listing rent for more than the prevailing FMR level; the top three rows, marked by a black rectangle, indicate listings costing less than the prevailing FMR

level. Labels show count, in thousands, of listings.

To see the general distribution for the 24 Rule Areas, exhibit 7 shows the areas sorted by share

below SAFMR in percentage points below the respective FMR level (FMR and SAFMR). The span

between the dots shows the movement for each area. The overall impression is that a SAFMR

transition for the 24 Rule Areas leads to a larger share of units falling below FMR levels and

thereby being, in principle, accessible to voucher holders, while there is considerable between-

area variation. Just 6 of the 24 Rule Areas have less than 40 percent of listings available below SAFMR, with Sacramento remaining in the bottom of the list. While SAFMRs shifted availability

upwards by nearly 20 percentage points, the levels are substantially lower than what was reported

using 2012–2013 data, whether due to inflation or data source coverage differences (Palm, 2018).

Overall, however, as far as the basis for the payment standard goes, the number of units and areas

available to voucher holders has increased.

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A. Olsen

Exhibit 7

HUD Final Rule Areas, Overall Shift in Share of Listings Below FMRs

FMR = Fair Market Rent. MSA = Metropolitan Statistical Area. SAFMR = Small Area Fair Market Rent.

Note: The length of the line denotes the movement in percentage points of listings falling below FMRs in each classification.

Sources: Rental listings from Craigslist; FMR/SAFMR data from HUD

Effect by Opportunity Areas

How are listing rents relative to FMRs and SAFMRs distributed, and what is the relation to

neighborhoods of opportunity? Exhibit 8 compares the distribution of listings in FMR areas but

also under SAFMRs.

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Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

Exhibit 8

Distribution of Listing-Level Difference, to FMR and SAFMR, by Opportunity Index Category

Opportunity Category

FMR

SAFMR

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Sources: Rental listings from Craigslist; FMR/SAFMR data from HUD

For each listing, the difference to the applicable FMR is calculated. Zero means parity; a positive

value means the Craigslist listing is more expensive than the FMR; a negative one means it is

priced below FMR. We further segment the data into four different opportunity categories. Each

listing inherits the score from the containing census tract. Scores are quartiles at the tract level,

but not necessarily at the rental listing level, and a panel is devoted to each segment. The left

panel of the top row shows that for MAFMR areas, much of the distribution is below the zero line,

meaning that listings are typically available at the fair market rent level on offer. The bottom row

shows the listings classified according to SAFMR geographies. As we head rightward in the figure,

toward higher opportunity areas, the share of units below parity generally drops: fewer units have

traditionally been affordable to voucher holders in higher opportunity neighborhoods. This is most

noticeable in the top row, with FMRs. The bottom row reveals that, with the SAFMR classification,

as we move to higher opportunity categories, the number of units falling below parity declines

much less than is the case in the top row: as the payment standard goes up in more costly, higher opportunity areas, listings are counted as reachable.

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Exhibit 9

Share of Listings Above FMR Limits, FMR and SAFMR Variants, by Opportunity Index Category

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Sources: Rental listings from Craigslist; FMR/SAFMR data from HUD

Whereas exhibit 8 showed distributions of the difference between FMR levels and rent levels

by opportunity category, exhibit 9 shows the number of listings by opportunity category as

percentages above or below FMR levels. The top panel accounts for just the 24 Rule Areas,

whereas the bottom panel shows the full national sample. As before, there is a clear progression

from low to high opportunity categories, with relatively fewer units available, and, within each

opportunity quartile, relatively more listings are available in the SAFMR classification. The 24 Rule

Areas differ mainly from the national sample in availability per FMR; overall, SAFMR availability

shows a remarkable constancy even as we move up opportunity categories. While this may seem

a remarkable shift, it just reflects that the payment standard goes up, following higher cost areas

more closely.

The last way we explore listings by opportunity areas allows us to track explicitly the number of

units changing “state,” from above FMR, or unattainable, to below SAFMR, by showing flows as

ribbons from one distribution to the next.

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Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

Exhibit 10

Listings, by Change of Status, to Above / Below FMR Level, by Opportunity Category

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Sources: Rental listings from Craigslist; FMR/SAFMR data from HUD

The ribbons show the scale of that transition, with the width of the ribbon proportional to the

number of units being reclassified from above FMR to below FMR levels (exhibit 10). Notably, the

middle categories covering the 25th to 75th percentile opportunity area bands show that a lot of

units are above the FMR, meaning many units are off-limits. At the same time, the ribbon shows

a considerable transition of listings into the below SAFMR bucket: Nationally, about 14 percent of listings switch from being unavailable to available in mid- and high-opportunity areas. The bands

of key interest are those that originate in “above” but transition to “below,” and the biggest switch

appears in the higher opportunity areas on the right, with a substantial number of units that

transition from above to below FMR levels with SAFMRs.

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Exhibit 11

Listings, by Change of Status, to Above / Below FMR Level, by Rent Quartile Category

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Sources: Rental listings from Craigslist; FMR/SAFMR data from HUD

Exhibit 11 is analogous to exhibit 10 but instead of segmenting by opportunity category, we show

the rental price quartile calculated within each metropolitan area. Overall, the progression from

quartile one through four is fairly marked: There are progressively fewer units below FMR levels

as we move up the rental cost distribution. Note the transition from above to below FMRs is about

equivalent to the transition in the reverse direction for the first quartile. In the second quartile, this is no longer true, and a substantial number of units becomes available below SAFMRs. Similar to

what we saw with higher opportunity areas, higher listing price areas, by definition, will have fewer

units below the FMR, although SAFMRs still offer more units than would be the case with the

areawide FMR system.

While SAFMRs have been applied to a limited number of areas, in part due to concerns related to

negative consequences in areas where the payment standard would be lowered, it is nonetheless

instructive to briefly explore non-rule areas where considerable counts of listings would switch

to being below FMR levels per the new SAFMR system. To do this, we examined non-rule areas

with respect to the transition. Of the top 50 FMR non-rule areas by population, we show the top

25 non-rule FMR areas sorted by the percentage point of listings moving to below FMR levels

subtracting any units that fell above the threshold.

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Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

Exhibit 12

Biggest Net Increase of Listings Below FMR, Non-Rule FMR Areas

FMR = Fair Market Rent. MSA = Metropolitan Statistical Area.

Notes: Net increase compares listings that move “ABOVE FMR” levels and listings that move “BELOW FMR” levels, assuming SAFMRs were applied. The areas with the largest net gains in listing counts are shown.

The Santa Ana-Anaheim-Irvine, California FMR Area tops the list, with more than one-fourth

of its listings crossing the threshold to be reachable below the FMR level. The Seattle-Bellevue,

Washington and Oakland-Fremont, California FMR areas are in the top five, as is Houston-

The Woodlands-Sugarland, Texas, and Birmingham-Hoover, Alabama. The lowest increase on

this top 25 list is Riverside-San Bernardino-Ontario, California, with about 12 percent of units

transitioning. Overall, the list contains a diverse array of areas and economies, spanning the

country, but with the strongest gains seen in some of the more dynamic regional economies. We

found a small but positive association between areas with higher personal incomes per capita

and the share of listings transitioning to falling below FMRs. Future work should explore which

particular characteristics account for this finding.

Discussion

This research uses listing data from Craigslist to offer insights into the transition to SAFMRs

for both the 24 Rule Areas and FMR areas more generally. While the data come with a range of

limitations due to their nature as a VGI dataset subject to a number of quality control issues, the

data have the advantage of currency and granularity and they also represent what a would-be

tenant could actually see when searching for an apartment.

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A. Olsen

We found that a switch to SAFMRs, consistent with earlier studies and objectives of the program,

increases the count of units available in higher cost and higher opportunity areas. While further

investigation is needed to better understand the downsides and risks, let alone the considerable

variation in benefits associated with transitioning to finer scaled geographies for different types of

areas, these findings suggest the switch to SAFMRs could generally prove beneficial not just for the

24 Rule Areas required to use SAFMR as the basis for setting payment standards, but indeed for a

larger swath of FMR areas where high costs have persistently been an issue.

The switch to SAFMRs led to a boost of listings available in generally higher opportunity areas with

only a relatively minor loss of availability in low opportunity areas. On its face, the upside was

considerable, with the highest opportunity area category seeing more than 45 percent of listings

falling below SAFMR levels. While this boost was largest for the 24 Rule Areas, it was nonetheless

significant for non-rule areas as well, with solid boosts in availability for a range of generally higher cost areas; those areas included the tech hotspots of Seattle, Denver, and the San Francisco Bay

Area. Before a wider rollout, it goes without saying that careful safeguards should be included to

avoid disruption of existing households in areas where payment standards would drop, causing risk to renewals. If the voucher opportunity map changes as implied, and lease-ups in these wider areas

prove successful in the coming years, local PHAs may find budgets even more strained, barring

more resources to the program overall.

Longer Term Challenges

In many ways, the HCV program shows the limits both of a housing policy heavily focused on

demand-side solutions and of how variable the outcomes of the program are. That is not because

the program treats FMR areas differently but because FMR areas have substantially different

housing markets. The hot coastal markets are much more difficult to fix with demand-side

measures when the challenge is a complex mix of low incomes, low supply, and spillover effects

from well-to-do tenants. The most critical need is in the areas with the most constrained rental

markets where rents are high and availability accordingly low. This is the typical situation in the

hot coastal markets, such as Los Angeles and the San Francisco Bay area, where vacancies are low

and talk of housing crises perennial. In those cases, the bottom of the housing market cannot

be easily remedied with an administrative fix and realignment like SAFMRs. As one PHA official

put it, landlords have a choice of tenants, and with rental vacancies hovering around perhaps 2

percent, landlords will have many options to rapidly fill their units without having to face the extra

risk, perceived or real, of subsidized tenants. In HUD’s phrasing,

[a] major question regarding the Small Area FMR approach is the willingness of owners

with rental units in the higher cost areas to participate in the program. If owners in higher-

cost areas have enough demand for their units from higher income unassisted families,

they may have little interest or incentive to participate in the HCV program (HUD, 2017b).

It was for this reason that some areas that would otherwise seem great candidates for inclusion

in the SAFMR version of the program balked—low vacancies would effectively preclude success

and could end up wasting money at the top of the rental distribution while causing disruption for

lower-income tenants. Ultimately, in those types of areas where the need may be the greatest, the

232 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

restrictive supply regime of the expensive coastal areas will remain an impediment to a successful

housing policy framework across levels of government—although SAFMRs appear to be a great

methodological realignment to allocate scarce resources to higher opportunity areas while limiting

landlord subsidies in lower cost submarkets. Whether the program adjustment will be successful

and actually translate the increased availability listings reachable by the program into higher lease-

up success rates in high-opportunity areas remains to be seen in the coming years. The 24 Rule

Areas may in effect help us understand more about the extent to which difficulties leasing up in

higher opportunity neighborhoods were of the financial sort, or instead related to a wider set of

issues, such as search costs, transportation challenges, or landlord behavior.

Acknowledgments

The author is indebted to Paul Waddell and Geoff Boeing for sharing data and insights on Craigslist

data, and to the Cityscape reviewers and editors for helpful comments that improved the paper.

This work was supported in part by a Doctoral Completion Fellowship at UC Berkeley. The views

expressed in this article are those of the author and are not necessarily those of the Association of

Bay Area Governments or the Metropolitan Transportation Commission.

Author

Aksel Olsen is currently a senior planner at the Association of Bay Area Governments and

Metropolitan Transportation Commission in San Francisco, California. He can be reached at

aolsen@bayareametro.gov.

Data Appendix

The data appendix provides key summaries by FMR area of the filtered listings data, including

the number of listings, mean or median bedroom counts, asking rent, and rent per square foot.

The tables also show the difference between listing rent and MAFMR and SAFMR, respectively. A

positive number means the listing rent is above the FMR; a negative means below. The median of

this difference is provided. The last four columns show the effect of the transition; the four columns

sum to 100 percent and show the four possible states: A listing could be, for MAFMRs and SAMFRs

in turn, available or not available at that price point. Some listings would be available or not under

both systems, while others would transition to becoming either available or not available.

The two tables differ only in terms of areas covered: exhibit A1 shows the 24 Rule Areas, whereas

exhibit A2 presents data for the top 50 (by population) non-rule FMR areas.

Cityscape 233

A. Olsen

Within

Reach

in Both

34%

24%

26%

15%

24%

31%

25%

39%

45%

40%

44%

35%

Systems

4%

3%

4%

2%

10%

3%

2%

7%

4%

3%

4%

5%

Became

Out of

Reach

ransitions

Listing T

15%

11%

16%

16%

14%

25%

14%

16%

11%

10%

21%

9%

Became

Within

Reach

Out of

Reach

in Both

48%

62%

54%

67%

51%

41%

59%

39%

40%

46%

32%

51%

Systems

$3

$267

$45

$225

$55

$30

– $52

$105

– $25

– $30

– $12

– $90

Rent Less

SAFMR

(Median)

$9

Rent

$128

$513

$148

$553

$136

$154

$265

$27

$40

$10

$96

Less FMR

(Median)

(Mean)

$1.1

$1.9

$1.1

$1.9

$1.2

$1.3

$1.5

$1.1

$0.9

$1.1

$0.8

$1.0

Rent/Sq Ft

Rent

$905

$900

(Median)

$1,239

$2,095

$1,195

$1,700

$1,192

$1,270

$1,800

$1,037

$1,200

$1,050

ooms

1.9

1.8

1.8

1.6

1.8

1.6

2.1

1.8

2.1

1.9

2.2

1.9

(Mean)

Bedr

900

936

925

920

996

Sq Ft

eas (1 of 2)

(Median)

1,100

1,003

1,034

1,151

1,000

1,130

1,081

3,096

2,243

5,486

2,260

Listings

(Count)

47,066

21,640

78,567

13,724

61,128

24,366

18,485

17,069

d,

ransitions for 24 Rule Ar

ea

ea

ea

ea

ea

ea

ea

ea

ea

ea

d-East Hartfor

ea

ea

d-Gastonia,

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

dale,

o FMR Ar

est Hartfor

ea

orth-Arlington,

d-W

,

Appendix A: Additional Exhibits

Exhibit A.1

Summary Statistics and T

FMR Ar

Atlanta-Sandy Springs-Roswell,

GA HUD Metr

Bergen-Passaic,

NJ HUD Metr

Charlotte-Concor

NC-SC HUD Metr

Chicago-Joliet-Naperville,

IL HUD Metr

Colorado Springs,

CO HUD Metr

Dallas,

TX HUD Metr

Fort Lauder

FL HUD Metr

Fort W

TX HUD Metr

Gary

IN HUD Metr

Hartfor

CT HUD Metr

Jackson,

MS HUD Metr

Jacksonville,

FL HUD Metr

234 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

Within

Reach

in Both

42%

28%

27%

21%

28%

15%

47%

31%

35%

25%

28%

Systems

2%

5%

4%

4%

3%

2%

2%

10%

4%

3%

3%

Became

Out of

Reach

ransitions

Listing T

7%

15%

6%

11%

12%

12%

21%

25%

11%

26%

19%

Became

Within

Reach

Out of

Reach

in Both

49%

52%

62%

63%

57%

71%

30%

34%

50%

46%

50%

Systems

$10

$40

$155

$160

$70

$185

$24

$20

– $70

– $13

Rent Less

SAFMR

(Median)

– $102

$2

Rent

$71

$115

$208

$325

$175

$327

$105

$84

$276

$200

Less FMR

(Median)

(Mean)

$1.3

$1.2

$1.1

$1.5

$1.1

$1.5

$1.1

$2.2

$1.2

$2.1

$1.4

Rent/Sq Ft

Rent

$989

(Median)

$1,500

$1,299

$1,275

$1,472

$1,015

$1,550

$2,050

$1,205

$1,853

$1,725

ooms

2.1

2.0

2.1

1.8

1.8

1.9

1.7

1.8

1.9

1.5

2.2

(Mean)

Bedr

975

950

975

908

910

882

Sq Ft

eas (2 of 2)

(Median)

1,055

1,103

1,104

1,028

1,224

Area Fair Market Rent.

2,062

6,822

3,251

Listings

(Count)

38,379

11,148

30,720

28,097

77,357

32,110

103,205

17,416

SAFMR = Small

Area.

cade,

,

ea

ransitions for 24 Rule Ar

den-Ar

ea

ea

ea

ea

o FMR Ar

ea

ne-Titusville,

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

MSA = Metropolitan Statistical

ea

A-MD HUD Metr

ashington-Arlington-Alexandria,

est Palm Beach-Boca Raton,

Exhibit A.1

Summary Statistics and T

FMR Ar

Monmouth-Ocean,

NJ HUD Metr

North Port-Sarasota-Bradenton,

FL MSA

Palm Bay-Melbour

FL MSA

Philadelphia-Camden-Wilmington,

PA-NJ-DE-MD MSA

Pittsburgh,

PA HUD Metr

Sacramento–Roseville–Ar

CA HUD Metr

San Antonio-New Braunfels,

TX HUD Metr

San Diego-Carlsbad,

CA MSA

Tampa-St. Petersburg-Clearwater

FL MSA

W

DC-V

W

FL HUD Metr

FMR = Fair Market rent.

Cityscape 235

A. Olsen

Within

Reach

in Both

50%

41%

49%

33%

38%

23%

30%

31%

39%

40%

31%

28%

28%

38%

29%

Systems

3%

5%

4%

2%

3%

5%

3%

4%

3%

5%

4%

1%

3%

4%

1%

Became

Out of

Reach

ransitions

Listing T

15%

13%

17%

18%

29%

17%

7%

8%

16%

9%

14%

5%

24%

9%

18%

Became

Within

Reach

Out of

Reach

in Both

32%

42%

30%

48%

31%

55%

60%

57%

43%

46%

51%

66%

45%

49%

52%

Systems

$60

$0

$30

$19

$15

– $64

– $30

– $11

$145

$129

– $30

$270

– $20

Rent Less

SAFMR

(Median)

– $120

– $128

Rent

$35

$176

$40

$403

$154

$87

$50

$31

$107

$317

$167

$73

$144

Less FMR

(Median)

– $13

– $31

(Mean)

$1.0

$1.4

$0.9

$1.4

$0.9

$2.7

$1.4

$0.9

$0.9

$0.9

$1.0

$1.5

$1.7

$1.0

$1.1

Rent/Sq Ft

eas (1 of 4)

Rent

$810

$950

$925

$950

$927

$900

(Median)

$1,291

$1,475

$2,540

$1,599

$1,000

$1,650

$1,562

$1,002

$1,225

ooms

1.7

1.7

2.1

1.8

2.0

1.9

2.1

2.1

2.0

2.1

1.9

1.7

1.6

2.1

2.1

(Mean)

Bedr

Sq Ft

855

900

950

900

(Median)

1,000

1,000

1,075

1,000

1,040

1,000

1,050

1,000

1,000

1,030

1,026

528

710

op 50 (by Population) Non-Rule FMR Ar

Listings

(Count)

14,288

62,621

3,178

25,421

4,005

63,525

7,250

12,048

10,115

16,254

97,588

19,891

8,864

o

ea

o FMR

ransitions for T

, MA-NH

o FMR Ar

owson, MD

ea

ea

ea

, AL HUD Metr

ea

-IN HUD Metr

o FMR Ar

o FMR Ar

o FMR Ar

ora-Lakewood,

en-Livonia,

que, NM MSA

o FMR Ar

e-Columbia-T

,

arr

ea

ea

-Aur

oit-W

ockton, MA HUD Metr

falo-Cheektowaga-Niagara Falls,

ea

esno,

Exhibit A.2

Summary Statistics and T

FMR Ar

Albuquer

Austin-Round Rock, TX MSA

Bakersfield, CA MSA

Baltimor

MSA

Birmingham-Hoover

FMR Ar

Boston-Cambridge-Quincy

HUD Metr

Br

Buf

NY MSA

Cincinnati, OH-KY

Ar

Cleveland-Elyria, OH MSA

Columbus,

OH HUD Metr

Danbury

CT HUD Metr

Denver

CO MSA

Detr

MI HUD Metr

Fr

CA MSA

236 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

Within

Reach

in Both

40%

53%

47%

31%

11%

40%

59%

13%

28%

28%

19%

31%

27%

36%

Systems

3%

3%

4%

4%

2%

2%

1%

2%

4%

2%

2%

2%

2%

2%

Became

Out of

Reach

ransitions

Listing T

24%

17%

10%

8%

12%

10%

15%

18%

20%

14%

9%

11%

6%

11%

Became

Within

Reach

Out of

Reach

in Both

32%

27%

39%

57%

76%

47%

25%

66%

49%

56%

69%

56%

64%

51%

Systems

$70

$25

$55

$58

$10

– $86

– $45

$390

– $5

$240

$195

$265

Rent Less

SAFMR

(Median)

– $110

– $195

Rent

$53

$134

$704

$31

$548

$149

$152

$314

$147

$410

$92

Less FMR

(Median)

– $28

– $13

– $143

(Mean)

$1.2

$0.9

$1.0

$1.1

$2.6

$0.9

$0.8

$2.0

$1.6

$1.1

$1.4

$1.2

$2.0

$1.2

Rent/Sq Ft

eas (2 of 4)

Rent

$860

$900

$865

$840

(Median)

$1,110

$1,129

$2,299

$1,950

$1,800

$1,095

$1,420

$1,273

$2,250

$1,100

ooms

1.7

1.8

1.8

1.8

1.6

1.9

2.1

1.8

1.8

1.9

1.8

1.9

1.8

1.9

(Mean)

Bedr

Sq Ft

933

997

954

910

950

970

(Median)

1,000

1,034

1,030

1,088

1,000

1,050

1,043

1,000

op 50 (by Population) Non-Rule FMR Ar

Listings

(Count)

49,073

12,853

15,546

46,922

7,502

4,370

3,846

129,255

10,163

47,317

12,691

37,323

21,831

10,239

o-

ea

ea

don,

ransitions for T

ea

ea

ea

est Allis, WI

eesbor

ea

ea

ea

ea

o FMR Ar

ea

ea

o FMR Ar

ea

o FMR Ar

o FMR Ar

oodlands-Sugar Land,

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

aukesha-W

o FMR Ar

o FMR Ar

,

o FMR Ar

folk,

ea

egas-Henderson-Paradise,

-IN HUD Metr

Exhibit A.2

Summary Statistics and T

FMR Ar

Houston-The W

TX HUD Metr

Indianapolis-Carmel-Anderson,

IN HUD Metr

Kansas City

MO-KS HUD Metr

Las V

NV MSA

Los Angeles-Long Beach-Glendale,

CA HUD Metr

Louisville,

KY

Memphis,

TN-MS-AR HUD Metr

Miami-Miami Beach-Kendall,

FL HUD Metr

Middlesex-Somerset-Hunter

NJ HUD Metr

Milwaukee-W

MSA

Minneapolis-St. Paul-Bloomington,

MN-WI HUD Metr

Nashville-Davidson-Murfr

Franklin, TN HUD Metr

Nassau-Suf

NY HUD Metr

New Orleans-Metairie,

LA HUD Metr

Cityscape 237

A. Olsen

Within

Reach

in Both

14%

26%

18%

66%

31%

32%

48%

20%

40%

38%

17%

31%

Systems

3%

3%

1%

2%

3%

6%

2%

3%

2%

3%

3%

3%

Became

Out of

Reach

ransitions

Listing T

9%

12%

24%

11%

12%

15%

18%

7%

14%

22%

15%

9%

Became

Within

Reach

Out of

Reach

in Both

74%

60%

58%

21%

54%

48%

33%

70%

45%

37%

66%

57%

Systems

$450

$130

$100

$47

$10

$235

$55

– $26

– $61

$150

Rent Less

SAFMR

(Median)

– $140

– $115

$0

Rent

$675

$387

$422

$125

$87

$340

$46

$63

$438

$145

Less FMR

(Median)

– $111

(Mean)

$3.1

$1.6

$2.6

$0.8

$1.2

$1.1

$1.6

$1.3

$1.1

$1.2

$1.5

$1.1

Rent/Sq Ft

eas (3 of 4)

Rent

$775

(Median)

$2,550

$1,775

$2,500

$1,319

$1,129

$1,445

$1,400

$1,129

$1,125

$1,670

$1,100

ooms

1.7

1.9

1.8

1.9

2.0

1.8

1.8

2.0

1.9

1.9

2.1

2.0

(Mean)

Bedr

Sq Ft

850

898

960

952

915

940

(Median)

1,000

1,079

1,000

1,070

1,000

1,090

op 50 (by Population) Non-Rule FMR Ar

Listings

(Count)

28,919

4,356

50,763

14,851

27,223

60,433

81,347

9,001

15,441

13,859

33,854

8,403

d,

o,

ransitions for T

ea

ea

ea

ea

ea

ea

,

dino-Ontario,

-Hillsbor

nar

o FMR Ar

o FMR Ar

,

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

emont,

ancouver

,

ea

ork,

A MSA

ovidence-Fall River

Exhibit A.2

Summary Statistics and T

FMR Ar

New Y

NY HUD Metr

Newark,

NJ HUD Metr

Oakland-Fr

CA HUD Metr

Oklahoma City

OK HUD Metr

Orlando-Kissimmee-Sanfor

FL MSA

Phoenix-Mesa-Scottsdale,

AZ MSA

Portland-V

OR-W

Pr

RI-MA HUD Metr

Raleigh,

NC MSA

Richmond,

VA MSA

Riverside-San Ber

CA MSA

Rochester

NY HUD Metr

238 Small Area Fair Market Rents

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

Within

Reach

in Both

23%

24%

35%

19%

39%

44%

52%

50%

14%

Systems

2%

2%

5%

6%

2%

1%

3%

2%

4%

Became

Out of

Reach

ransitions

Listing T

20%

19%

20%

34%

26%

15%

12%

11%

12%

Became

Within

Reach

Out of

Reach

in Both

56%

55%

41%

41%

32%

40%

33%

37%

70%

Systems

$45

$143

– $76

– $35

– $50

– $55

– $96

$265

Rent Less

SAFMR

(Median)

– $165

Rent

$132

$530

$157

$188

$112

$26

– $28

– $14

$422

Less FMR

(Median)

(Mean)

$1.3

$3.8

$3.0

$2.2

$2.2

$1.0

$1.0

$1.1

$2.2

Rent/Sq Ft

eas (4 of 4)

Rent

$900

$795

(Median)

$1,159

$3,450

$2,899

$2,087

$1,850

$1,123

$2,197

ooms

1.7

1.6

1.8

1.7

1.5

1.8

1.7

1.9

1.7

(Mean)

Bedr

Sq Ft

899

870

935

950

820

935

810

1,000

942

(Median)

Area Fair Market Rent.

op 50 (by Population) Non-Rule FMR Ar

Listings

(Count)

14,247

47,456

46,972

57,545

2,907

130,398

19,106

21,892

15,806

SAFMR = Small

Area.

ea

ransitions for T

ea

ea

ea

ea

ea

ea

ea

o FMR Ar

,

o FMR Ar

,

MSA = Metropolitan Statistical

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

o FMR Ar

A-NC HUD Metr

ea

A HUD Metr

estchester County

Exhibit A.2

Summary Statistics and T

FMR Ar

Salt Lake City

UT HUD Metr

San Francisco,

CA HUD Metr

San Jose-Sunnyvale-Santa Clara,

CA HUD Metr

Santa Ana-Anaheim-Irvine,

CA HUD Metr

Seattle-Bellevue,

W

St. Louis,

MO-IL HUD Metr

Tucson,

AZ MSA

Virginia Beach-Norfolk-Newport

News, V

W

NY Statutory Exception Ar

FMR = Fair Market Rent.

Cityscape 239

A. Olsen

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244 Small Area Fair Market Rents

by Alexander Casey

A Tweak to Housing Assistance

Allows Low-Income Renters Access

to High-Income Neighborhoods

Alexander Casey

Zillow Group Economic Research

Abstract

In this article, I use Zillow Group’s proprietary rental listing data to measure the proportion of rental units advertised at prices at or below both the units’ corresponding metro-wide Fair Market Rent (FMR) and Small Area Fair Market Rent (SAFMR). I find that, within the 24 metropolitan areas affected by

recent SAFMR litigation, calculating voucher payments based on SAFMRs will increase the proportion of units listed at prices affordable to voucher holders in low-poverty neighborhoods. In most areas, SAFMRs also decrease the proportion of affordable listings in high-poverty neighborhoods. Findings from pilot programs suggest that, although more options became available in high-rental cost ZIP Codes, decreased options in low-rent ZIP Codes could lead to an overall decline in the number of affordable rental units for voucher holders within a metropolitan area. This analysis shows that, according to Zillow’s online listings, that is not the case. In all 24 areas analyzed, the overall share of listings suitable for voucher recipients increased under the new SAFMR rule relative to the share suitable under metro-wide Fair

Market Rent calculations.

Introduction

Where you grow up can be a strong predictor of your future opportunities and outcomes (Chetty,

Hendren, and Katz, 2016).

For low-income households, expensive rents often prohibit access to the opportunities that are

afforded residents of high-cost neighborhoods, including better schools, less crime, better job

prospects, and lower poverty rates. Even the housing assistance offered to a lucky few (Casey,

2018a) is often only enough to afford rent in a few deeply segregated, high-poverty areas.

A new policy tweak might help fix that, however.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 245

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

Casey

Using Zillow Group data, I analyzed a recently adopted federal housing assistance rule crafted

by the Obama Administration to combat residential segregation. I found the new Small Area Fair

Market Rent (SAFMR; HUD, 2018) policy, which alters the maximum value of some Housing

Choice Vouchers (HCVs), could significantly increase the number of rental units listed at affordable

prices for voucher recipients in areas with relatively lower poverty. That increase would provide a

wider array of housing choices in places affected by the policy change—metropolitan areas with a

high concentration (Center on Budget and Policy Priorities, 2018) of voucher recipients who rent

in high-poverty areas and have limited options to move elsewhere with their housing voucher.

Switching from Metros to ZIPs

Recipients of HCVs, vouchers from the nation’s largest rental assistance program, typically pay 30

percent of their income for housing and the voucher pays the remainder up to a certain cap. That

cap is typically within 10 percent of what the U.S. Department of Housing and Urban Development

(HUD) determines is a Fair Market Rent (FMR) for each metropolitan area. Typically, the FMR

equals the 40th percentile of rents among recent movers. For the purpose of this analysis, a home

is considered affordable to a voucher holder if the landlord advertises rent prices that are below the

FMR or SAFMR. Cases in which the gross rent is above the payment standard and the tenant pays

beyond 30 percent of their income are excluded from this analysis.

The new SAFMR rule (HUD, 2016) recalculates that maximum cap, basing that maximum on the

rental prices in a ZIP Code rather than across the entire metropolitan area. The policy was formally

issued (HUD, 2017) near the end of the Obama Administration in 2016 and rolled out in 2019

after the Trump Administration delayed implementation; that spurred a lawsuit1 against HUD.

Ultimately, the Washington, DC Circuit Court ordered HUD to begin implementing (Jan, 2017)

SAFMR in two dozen metropolitan areas.2

In addition to enabling more rental options in less-impoverished ZIP Codes, the SAFMR

calculation also appears to increase the total share of rental options affordable to voucher holders in

all metropolitan areas except one, relative to the previous metro-wide FMR limits.

Results

Using metro-relative definitions for high- and low-poverty ZIP Codes, across the 24 metropolitan

areas analyzed and under the previous metro-wide FMRs, 7 percent of the rental listings affordable

to voucher recipients were in low-poverty areas. Under the SAFMR rule, that share rose to 19

percent of affordable listings. Under previous metro-wide FMRs, 33 percent of affordable listings

across the metropolitan areas analyzed were in high-poverty ZIP Codes, but with SAFMR limits,

that share drops to 20 percent.

1 Open Communities Alliance, Carter, & Moore v. Carson & U.S. Department of Housing and Urban Development. 2017.

United States District Court District of The District of Columbia, October 23.

2 U.S. Department of Housing and Urban Development, The Secretary. n.d. “Suspension of Small Area Fair Market Rent (FMR) Designations.” Small Area FMR Final Rule and 24 CFR 888.113(c)(4). https://www.huduser.gov/portal/

portal/sites/default/files/pdf/SecretarysDetermination.pdf.

246 Small Area Fair Market Rents

A Tweak to Housing Assistance Allows

Low-Income Renters Access to High-Income Neighborhoods

All metropolitan areas saw an increase in the share of eligible rental listings in low-poverty areas

under the SAFMR rule (exhibit 1). Jackson, Mississippi, and North Port-Sarasota-Bradenton,

Florida, demonstrate the largest percentage point increase: the share of each metropolitan area’s

affordable listings in low-poverty ZIP Codes rose by roughly 30 percentage points. In Jackson, the

previous voucher payment structure meant that only 18 percent of the area’s affordable rentals were

in low-poverty ZIP Codes. With SAFMR limits, that share rose to 52 percent.

Despite the overall trend, not all neighborhoods experienced significant changes. Even under

the SAFMR rule, in Hartford-West Hartford-East Hartford, Connecticut, and Bergen-Passaic,

New Jersey, less than 4 percent of the voucher-suitable units in the area are in low-poverty

neighborhoods, fairly similar to the share available under the metro-wide FMR calculation.

Exhibit 1

Percent of Each Metropolitan Area’s Voucher Suitable Listings in Low-Poverty ZIP Codes

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Source: Zillow Analysis of Zillow Rental Listings, HUD Fair Market Rents and Small Area Fair Market Rents and U.S. Census Bureau, American Community Survey, 2013-2017

Except for Pittsburgh, all metropolitan areas will have fewer voucher-suitable homes in high-

poverty areas (exhibit 2), and the Sacramento-Roseville-Arden-Arcade, Colorado Springs, and

Chicago-Joliet-Naperville metropolitan areas lead the pack. In Sacramento, the metropolitan area

with the largest percentage point shift, the proportion of voucher-suitable listings in high-poverty

areas dropped from 45 percent to 17 percent with SAFMR calculations.

Cityscape 247

Casey

Exhibit 2

Percent of Each Metropolitan Area’s Voucher Suitable Listings in High-Poverty ZIP Codes

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Source: Zillow Analysis of Zillow Rental Listings, HUD Fair Market Rents and Small Area Fair Market Rents and U.S. Census Bureau, American Community Survey, 2013-2017

This shift in the location of the greatest share of voucher-suitable units is most evident upon

examining ZIP Code maps of the affected metropolitan areas. For example, in the Chicago

metropolitan area (exhibit 3) under the SAFMR rule, the ZIP Codes north of downtown along Lake

Michigan see a significantly larger share of their rental listing suitable for voucher holders, and ZIP

Codes directly southwest of downtown see their share decrease.

248 Small Area Fair Market Rents

A Tweak to Housing Assistance Allows

Low-Income Renters Access to High-Income Neighborhoods

Exhibit 3

Share of Listings Below Fair Market Rent: HUD Region Chicago-Joliet-Naperville, IL HUD Metro

FMR Area

Small area fair market rents allow more rental options in low-poverty areas.

Source: Zillow Analysis of Zillow Rental Listings, HUD Fair Market Rents and Small Area Fair Market Rents The map of the Atlanta metropolitan area ZIP Codes (exhibit 4) also demonstrates a significant

geographic shift in the location of voucher-suitable listings. Most notably, ZIP Codes north and

northeast of downtown Atlanta significantly increase the share of voucher-suitable listings when

using the SAFMR rule.

Cityscape 249

Casey

Exhibit 4

Share of Listings Below Fair Market Rent: HUD Region Atlanta-Sandy Springs-Roswell, GA HUD

Metro FMR Area

Small area fair market rents allow more rental options in low-poverty areas.

Source: Zillow Analysis of Zillow Rental Listings, HUD Fair Market Rents and Small Area Fair Market Rents I analyzed all impacted ZIP Codes for the purpose of metro-wide calculations but did not map

individual ZIP Codes with fewer than 50 unique rental listings in 2018.

Although the new rule appears to offer more affordable options in high-opportunity areas,

policymakers grapple with potential drawbacks. Findings from pilot programs suggest (NLIHC,

2017) that although more options became available in high-rent ZIP Codes, even fewer rentals

(Finkel et al., 2017) in low-rent ZIP Codes were eligible for vouchers under the new rule, leading

to an overall decline in the number of options for voucher holders. Zillow’s analysis shows that is

not necessarily the case. In all 24 areas I analyzed, the overall share of listings suitable for voucher recipients increased under the new SAFMR rule (exhibit 5). Bergen-Passaic, New Jersey, had the

smallest increase and would see voucher-suitable listings remain roughly constant, rising only

slightly from 23.7 percent of rental listings to 24 percent. The share of listings under the FMR and

SAFMR also remained similar in the Philadelphia-Camden-Wilmington metropolitan area, where

proportions rose from 32.6 percent to 34.3 percent using SAFMRs limits.

250 Small Area Fair Market Rents

A Tweak to Housing Assistance Allows

Low-Income Renters Access to High-Income Neighborhoods

Exhibit 5

Share of Listings Below FMR, by HUD Metropolitan Area

Small Area Fair Market Rents Allow More Rental Options Within Metropolitan Area

Share of Listings Share of Listings

HUD Area Name

Below Metro-

Below SAFMR

Wide FMR (%)

(%)

Atlanta-Sandy Springs-Roswell, GA HUD Metro FMR Area

17.0

29.5

Bergen-Passaic, NJ HUD Metro FMR Area

23.7

24.0

Charlotte-Concord-Gastonia, NC-SC HUD Metro FMR Area

14.5

30.4

Chicago-Joliet-Naperville, IL HUD Metro FMR Area

13.4

27.5

Colorado Springs, CO HUD Metro FMR Area

27.3

43.3

Dallas, TX HUD Metro FMR Area

11.0

33.5

Fort Lauderdale, FL HUD Metro FMR Area

19.6

30.0

Fort Worth-Arlington, TX HUD Metro FMR Area

27.8

41.8

Gary, IN HUD Metro FMR Area

48.6

56.1

Hartford-West Hartford-East Hartford,

CT HUD Metro FMR Area

35.6

42.2

Jackson, MS HUD Metro FMR Area

28.0

50.7

Jacksonville, FL HUD Metro FMR Area

24.3

37.4

Monmouth-Ocean, NJ HUD Metro FMR Area

16.3

22.2

North Port-Sarasota-Bradenton, FL MSA

8.0

16.0

Palm Bay-Melbourne-Titusville, FL MSA

32.2

44.2

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA

32.6

34.3

Pittsburgh, PA HUD Metro FMR Area

31.2

41.6

Sacramento-Roseville-Arden-Arcade, CA HUD Metro FMR Area

9.5

20.8

San Antonio-New Braunfels, TX HUD Metro FMR Area

26.3

45.7

San Diego-Carlsbad, CA MSA

21.9

26.6

Tampa-St. Petersburg-Clearwater, FL MSA

15.1

27.9

Urban Honolulu, HI MSA

52.9

62.1

Washington-Arlington-Alexandria,

DC-VA-MD HUD Metro FMR Area

37.6

51.8

West Palm Beach-Boca Raton, FL HUD Metro FMR Area

22.6

27.0

FMR = Fair Market Rent. MSA = Metropolitan Statistical Area. SAFMR = Small Area Fair Market Rent.

The Jackson, Dallas, and San Antonio metropolitan areas saw the largest percentage point bump in

the share of units eligible for voucher use across the region. In Dallas, under previous metro-wide

FMRs, only 11 percent of rental listings could be afforded with a voucher, but under the SAFMR

rule, 23 percent of rental listings are affordable with a voucher. In Jackson, the share rose from 28

percent to 51 percent when comparing list prices to the SAFMR.

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Implications and Recommendations

Because of the limited rollout of the SAFMR, an obvious implication from findings that suggest

the policy is meeting its stated goals is the expansion of the policy to more metropolitan areas.

However, expansion of the SAFMR rule should also be coupled with additional reforms to federal

and local housing policy alike.

Potential for Expansion

In addition to the 24 metros included in the SAFMR litigation, other HUD metropolitan areas

could see substantial shifts in the share of voucher-suitable rental listings advertised in low-poverty

areas if voucher payment standards were calculated at the SAFMRs.

Across the entire country, under the metro-wide FMRs calculations, 9 percent of rental listings

affordable to voucher recipients are within low-poverty ZIP Codes. Under the SAFMR rule, that

share rises to 18 percent of affordable listings. Under previous voucher rules, 34 percent affordable

listings across the metros analyzed are in high-poverty ZIP Codes, but with SAFMR, that share

drops to 26 percent.

Policymakers likely look to other factors, including, but not limited to, the concentration of

voucher holders in certain neighborhoods, when determining suitable metropolitan areas for

further SAFMR implementation. Thus, these data do not necessarily pinpoint the best metropolitan

areas to expand the SAFMR policy. Information on the geography of advertised market-rate rental

properties, however, can provide a useful signal of the relative impact adjustments to FMR formulas

can have on the availability of advertised listings meeting the SAFMR rule’s criteria.

Among the larger metropolitan areas with at least 5,000 rental listings on Zillow during 2018,

some very large communities could see substantial increases in the share of affordable listings in

low-poverty ZIP Codes. To cherry-pick a few populous metros purely for illustrative purposes, this

analysis shows that the Nashville-Davidson-Murfreesboro-Franklin metropolitan area would see

an increase in the share of affordable listings in low-poverty ZIPs from 8 percent to 36 percent; Los

Angeles-Long Beach-Glendale would increase from 10 percent to 34 percent; and the Houston-The

Woodlands-Sugar Land metropolitan area would increase from 14 percent to 36 percent.

Conversely, unlike the 24 metropolitan areas included in the initial analysis, a few of these larger

metropolitan areas would be expected to experience a small decrease in the share of the region’s

affordable listings in the low-poverty ZIPs under the SAFMR. Examples include Boulder, Colorado;

Madison, Wisconsin; and Stockton-Lodi, California. Examples of this are relatively uncommon (32

of the 211 metropolitan areas analyzed here) and counterintuitive, so further investigation into the

characteristics of the low-poverty ZIPs in these areas is required before concluding any negative

impacts of SAFMR in these metropolitan areas. For example, many of the metropolitan areas, such

as Iowa City, Iowa; Champaign-Urbana, Illinois; Ann Arbor, Michigan; and College Station-Bryan,

Texas, are home to large universities where it is possible that ZIP Code poverty rates and rent prices

may not be as closely correlated.

252 Small Area Fair Market Rents

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Low-Income Renters Access to High-Income Neighborhoods

Further Policy Recommendations

Further data aggregation and research are needed to fully quantify the universe of rental listings

affordable at prices at or below the Fair Market Rent standards, beyond what appears on online real

estate platforms like Zillow. Data from platforms like Zillow will not capture the entire universe

of rental advertisements, as many landlords still advertise offline or via word-of-mouth. Even if

the entire universe of affordably priced rentals is known, however, there is a large operational gap

between a unit meeting the price limitations of a voucher holder and a landlord ultimately signing

a lease with a voucher holder. Increased access to rentals in higher-income areas will do little to

improve mobility if landlords do not accept vouchers (Cunningham et al., 2018), which means

there needs to be better laws or better enforcement of existing laws to protect voucher holders from

discrimination based on their use of housing assistance.

An analysis by the Center of Budget and Policy Priorities (CBPP, 2018) and the Poverty and Race

Research Action Council (PRRAC) explains how SAFMR also made voucher programs more cost-

effective for public housing agencies involved in a SAFMR pilot program. Cost-efficiency is vital,

but so is adequate investment in the program given the significant underfunding (Rice, 2018) of

HCVs relative to the need; that has prompted years-long waiting lists (Smith, 2017) or dead ends

for people seeking help.

Some local officials have expressed concern (Belanger and City Bureau, 2018) that households

that currently rent in low-cost areas could see their voucher ceiling fall below their current rents,

potentially prompting displacement. The Center on Budget and Policy Priorities (2018), Poverty &

Race Research Action Council guide outlines several policies that would protect families in low-rent

ZIP Codes, including methods for phasing in the new payment standards.

The switch to SAFMR could allow greater access to opportunities that have been found to

improve (Chetty, Hendren, and Katz, 2016) outcomes for children. By opening more units across

each metropolitan area to voucher eligibility, the new rule could ease the increasing strain and

competition voucher holders face that is caused by tight markets with limited rental supply. At some

point in certain metropolitan areas, however, new funding formulas and rent subsidies will go only

so far if prices continue to balloon and the dwindling supply of rental housing is not addressed.

To keep rents from skyrocketing in the long term, pro-growth land-use decisions at the

neighborhood and municipal levels—and challenges to exclusionary zoning policies—are

necessary to create rental housing that meets the robust demand. Without a sufficient supply of

rentals, the federal government will continually spend rapidly increasing amounts of money to

house the same number of low-income families with housing vouchers.

Finally, broad public policies must address persistent poverty to make a difference in affordable

housing. That means investments in policies that grow the economy and combat recessions and

unemployment, alongside a revamped commitment to safety-net social and economic policies that

supplement incomes.

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Methodology and Limitations

This analysis focuses on the advertised monthly price of rental listings advertised on Zillow Group

platforms from January 1, 2018, to December 31, 2018, in the 23 metros immediately affected by

the rule change, plus Dallas, where the SAFMR policy has been in place for years. I analyzed the

advertised lease price of nearly 12 million listings posted to Zillow Group’s platforms during that

time period, for one- to four-bedroom units and compared each list price with its corresponding

FMR and SAFMR published by HUD.

In general, local housing authorities can adjust the actual value of an HCV, called the payment

standard, within 10 percent of the FMR value. Public housing agencies can set a voucher payment

standard above 110 percent of the FMR with certain permission from HUD. For this analysis, I

assume the payment standard is set at 100 percent of the FMR and SAFMR for any given area. A

different payment standard would yield different results.

One method of measuring the share of affordable listings in high-poverty or low poverty areas is

to use a standard national measure that aligns with the HUD definition of high-poverty areas. In a

previous version of this analysis posted on Zillow’s Economic Research blog (Casey, 2018b), I used

that approach. HUD defined high-poverty ZIP Codes for SAFMR as ZIP Codes with a poverty rate

above 25 percent. For that previously published analysis, I chose to define low-poverty ZIPs as

those with a poverty rate below 5 percent. Those two poverty rates represent the 15th percentile

and the 85th percentile of the national ZIP Code poverty rate distribution. Poverty rates were

assessed using the U.S. Census Bureau 2013–2017 American Community Survey (ACS) 5-Year Estimates Poverty Status in the Past 12 Months, percent below poverty level.

Using those definitions across the 24 metros analyzed under the metro-wide FMRs calculation, 8

percent of the rental listings affordable to voucher recipients were in low-poverty ZIP Codes. Under

the SAFMR calculation, that share rose to 15 percent of affordable listings. Under previous voucher

rules, 18 percent of affordable listings across the metropolitan areas analyzed were in high-poverty

ZIP Codes, but with SAFMR limits, that share dropped to 10 percent. Another discrepancy from

the previous analysis posted is that this analysis uses all rental listings for 2018, the previous

analysis posted on the Zillow Research blog was posted in October 2018 and used rental listings

from between January 1, 2018, and August 13, 2018.

Because a goal of the SAFMR program may be to allow rental access in the lowest-poverty areas of a

community, rather than the lowest-poverty areas across the entire country, a more useful definition

involves relative measures of high- and low-poverty ZIP Codes unique to each metropolitan area.

Therefore, for this analysis, I characterize high-poverty areas as the top quintile of HUD’s FMR area

ZIP Codes when ranked by the share of the population below the poverty level—as designated

by 2013–2017 5-year ACS estimates. Low-poverty ZIPs are defined as an area’s ZIP Codes in

the bottom quintile when ranked by the share of the population in poverty. Thus, all HUD

metropolitan areas have an approximately equal number of ZIP Codes designated as high poverty

and low poverty.

254 Small Area Fair Market Rents

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Low-Income Renters Access to High-Income Neighborhoods

Poverty rate data from the 5-year ACS estimates at the ZIP Code level are somewhat crude

instruments for measuring the impact of a policy designed to increase rental access to high

opportunity areas. For one, the 2013–2017 5-year estimates can be significantly lagged and may

not reflect the economic characteristics of a region that has undergone rapid change. Second,

ZIP Codes can be rather large areas with relatively diverse economic and housing market

characteristics. HUD metropolitan areas also can be quite large, so it is possible that some of the

ZIP Codes comprising the bottom quintile of poverty rates in a metropolitan area represent some

far-flung regions away from job centers, transportation access, or other amenities—which may

be antithetical to some of the policy goals. Third, a ZIP Code’s poverty rate is a crude, incomplete

measure of an area’s relative access to institutions, forms of capital, or other mechanisms that can

provide for increased chances of economic mobility. In other words, attaining rental housing in the

ZIP Code with the lowest possible poverty rate should not be the only goal or benchmark for the

success of a program designed to increase access to opportunity.

While it is possible that online real estate listings may miss some less-expensive rental properties

that are instead advertised in online classifieds, offline, or by word-of-mouth, to my knowledge

there is no comprehensive national database of rental listings, online or otherwise, and Zillow

Group Rentals is the nation’s largest online rental network. Additionally, unlike some other online

propriety rental data sources, Zillow Group Rentals includes listings from both multifamily

buildings and single-family home rentals, whereas other data sources of rental list prices may only

capture listings from multifamily buildings.

It is possible that I may undercount some affordable and unaffordable listings available on Zillow.

While not typical, it is possible for landlords in multi-unit buildings to post only one listing for

multiple units available at the same time matching the characteristics and price. Finally, listing

prices do not always reflect the final contract rent, as landlords may ultimately make concessions

or otherwise change the final monthly price agreed upon before a lease is signed.

While the exact share of units available to voucher holders may be somewhat different when

considering listings not on Zillow, I find that voucher holders can expect more affordable rental

options under the SAFMR rule. These data cannot be used to evaluate the SAFMR program as

either an unalloyed success or failure, but they do provide a useful benchmark. The findings

demonstrate that the ideals devised in the policy laboratory for creating access to particular types of

neighborhoods are indeed reflected in the reality of the market-rate rental trends.

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Casey

Acknowledgments

The author would like to thank the members of the Zillow Group Economic Research team who

supported this work, especially Melissa Allison, Cory Hopkins, Elizabeth Kimn, and Joshua Clark.

Author

Alexander Casey is a senior policy advisor on the Zillow Group Economic Research Team.

References

Belanger, Christian, and City Bureau. 2018. “Why Is Chicago Sticking with Its Housing Voucher

Rules as the Nation Shifts?” Chicago Magazine, May 14. www.chicagomag.com/city-life/May-2018/

Why-Is-Chicago-Sticking-With-Its-Housing-Voucher-Rules-as-the-Nation-Shifts/.

Casey, Alexander. 2018a. How Improvements to Housing Vouchers Could Lift More Boats. Zillow

Research, January 18. www.zillow.com/research/improvements-vouchers-lift-boats-16155/.

———. 2018b. A Tweak to Vouchers Allows Low-Income Renters Access to High-Income Areas. Zillow

Research, October 17. www.zillow.com/research/vouchers-neighborhoods-21074/.

Center on Budget and Policy Priorities, Poverty & Race Research Action Council. 2018. A Guide to Small Area Fair Market Rents (SAFMRs). Washington, DC: Center on Budget and Policy Priorities.

www.cbpp.org/research/housing/a-guide-to-small-area-fair-market-rents-safmrs.

Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better

Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment,”

American Economic Review 106 (4): 855–902.

Cunningham, Mary K., Martha Galvez, Claudia L. Aranda, Rob Santos, Doug 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, Office of

Policy Development and Research.

Finkel, Meryl, Samuel Dastrup, Kimberly Burnett, Thyria Alvarez, Carissa Climaco, and Tanya de

Sousa. 2017. Small Area Fair Market Rent Demonstration Evaluation: Interim Report. Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy Development and Research.

Jan, Tracy. 2017. “Hundreds of Thousands of Poor Americans Will Soon Be Able to Move to

Better Areas, Thanks to This Judge,” The Washington Post, WP Company, December 28. www.

washingtonpost.com/news/wonk/wp/2017/12/28/hundreds-of-thousands-of-poor-americans-

will-soon-be-able-to-move-to-better-areas-thanks-to-this-judge/?utm_term=.3d8d4e5e904e.

256 Small Area Fair Market Rents

A Tweak to Housing Assistance Allows

Low-Income Renters Access to High-Income Neighborhoods

National Low Income Housing Coalition (NLIHC). 2017. “HUD Releases Interim Report on Small

Area FMR Demonstration.” Washington, DC: National Low Income Housing Coalition. nlihc.org/

resource/hud-releases-interim-report-small-area-fmr-demonstration?ct=t%28Memo_08_21_20

17%29&utm_campaign=ec574b69ae-Memo_08_21_17&utm_medium=email&utm_source=

NLIHC%2BAll%2BSubscribers&utm_term=0_e090383b5e-ec574b69ae-291622717.

Rice, Douglas. 2018. “Congress Should Increase HUD Funding in 2019 to Prevent Voucher Cuts,

Help Children Escape Poverty.” Washington, DC: Center on Budget and Policy Priorities. www.

cbpp.org/research/housing/congress-should-increase-hud-funding-in-2019-to-prevent-

voucher-cuts-help-children.

Smith, Doug. 2017. “Up to 600,000 Expected to Apply When L.A. Reopens Section 8 Housing List

This Month after 13 Years,” Los Angeles Times, October 1. www.latimes.com/local/lanow/la-me-ln-

section-8-waiting-list-20170922-htmlstory.html#nws=mcnewsletter.

U.S. Department of Housing and Urban Development (HUD). 2018. “FY2019 Small Area FMRs.”

www.huduser.gov/portal/datasets/fmr/smallarea/index.html.

———. 2017. “Establishing a More Effective Fair Market Rent System; Using Small Area Fair

Market Rents in the Housing Choice Voucher Program Instead of the Current 50th Percentile

FMRs.” 24 CFR Parts 888, 982, 983, and 985. https://www.huduser.gov/portal/datasets/fmr/

fmr2016f/SAFMR-Final-Rule.pdf.

———. 2016. “News Releases: HUD Announces New Approach to Expand Choice and

Opportunity for Section 8 Voucher Holders in Certain Housing Markets.” HUD Archives. archives.

hud.gov/news/2016/pr16-173.cfm.

Cityscape 257

258 Small Area Fair Market Rents

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 21, Number 3 • 2019

Cityscape 259

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

260 Refereed Papers

by Rigel C. Oliveri

Sexual Harassment of Low-Income

Women by Landlords

Rigel C. Oliveri

University of Missouri School of Law

Abstract

This article examines the results of a qualitative study of low-income women who have been sexually

harassed by their landlords. The study involved detailed interviews of 100 low-income women who

were clients of the Columbia (Missouri) Housing Authority. These interviews revealed a picture of the tenants who experienced sexual harassment in housing and how they responded; the characteristics of

landlords who engage in such harassment; the forms the harassment is likely to take; and the effects the harassment had on the tenants’ housing situation. The results suggest the need for more targeted outreach to low-income women who may be victimized and more oversight of landlords who may operate with

little accountability. More research is needed to determine how prevalent this problem is and what risk factors contribute to it.

Introduction

In recent years, high-profile and influential figures in media, government, and entertainment

have faced very public allegations of sexual misconduct, creating a watershed moment for public

awareness of sexual harassment. These revelations have sparked an important national discussion

about the prevalence of sexual harassment in American society and the systems that enable powerful

people both to exploit their vulnerable targets and to escape the consequences of their actions.

This article focuses on the sexual harassment and exploitation of low-income women by their

landlords—a problem of which both advocates and academics are aware. A number of published

legal cases have dealt with the issue, and the U.S. Department of Justice (DOJ) has filed numerous

complaints against alleged harassers. Multiple informative academic articles in legal and social

science literature have discussed the subject from a largely theoretical perspective. Unfortunately,

there have been no reliable empirical studies about the nature and prevalence of sexual harassment

in housing. As a result, policymakers and legislators have difficulty addressing sexual harassment

in housing because they do not know the basic facts, such as how common it is, who is likely to

experience or perpetrate it, and what forms it takes.

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

Oliveri

This article, and the underlying study (which was recently published in the Missouri Law Review

[Oliveri, 2018]), represent a first attempt at supplementing the available information by revealing

empirical data that challenge and improve on the assumptions in theoretical scholarship.

The Legal Background of Sexual Harassment in Housing section provides a brief introduction to

the law. The What We “Know” About Sexual Harassment in Housing section canvasses the state of

our knowledge of sexual harassment in housing, including the gaps in that knowledge that require

further research and the problems created by those gaps.

The Study section presents the methodology and results, which both add to and challenge some

of the prevailing assumptions about sexual harassment in housing. A significant number of study

participants—10 percent of the sample—had experienced actionable sexual harassment by their

landlords. All these women were living in private rental housing at the time they were harassed;

none lived in public housing, shelters, or other institutional facilities. Whether or not they were

receiving a housing subsidy did not appear to increase the likelihood of harassment, although

it did correlate to whether they remained in the housing after experiencing harassment. The

landlords who perpetrated the harassment were all owner-operators of their rental properties;

they did not work for or employ a rental management company. The harassment took two forms:

(1) nearly all the women described being explicitly asked to provide sex in lieu of rent, and (2)

one-half of the women also reported experiencing serious (likely criminal) conduct such as home

invasion, indecent exposure, and unwanted touching.

The Analysis and Implications section analyzes the results of the study and draws implications for

policy and further research. From a policy perspective, the study results reveal the consequences

of the lack of regulation of the landlord-tenant relationship, which has led to a regime allowing

private landlords to harass their tenants with virtual impunity. The study determined that greater

oversight of landlords and more targeted resources for the most vulnerable group of female

renters is necessary to address this problem. Ultimately, policymakers must address the root cause

of this problem, which is the serious lack of affordable housing and housing support programs in

this country.

Legal Background of Sexual Harassment in Housing

This section provides a brief introduction to the law. To begin, it is important to recognize that

sexual harassment law was first developed in the context of the workplace. Courts later applied

similar doctrines and definitions to sexual harassment in the housing context.

Employment Law Roots

The legal doctrine of sexual harassment originated in the employment context. Title VII of the Civil

Rights Act of 1964 (hereafter known as Title VII) prohibits employment discrimination based on

protected characteristics, including sex. Cases recognizing that racial and ethnic harassment in the

employment setting can violate Title VII date as far back as 1971. In 1982, the Eleventh Circuit

issued an influential sexual harassment opinion in favor of the plaintiff in Henson v. City of Dundee.1

1 Henson v. City of Dundee, 682 F.2d 897 (11th Cir. 1982).

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Sexual Harassment of Low-Income Women by Landlords

Henson set forth a binary classification of sexual harassment claims: (1) “quid pro quo” claims,

where a defendant has based the provision of job benefits on the plaintiff’s compliance with sexual

demands or causes the plaintiff tangible harm if he or she refuses to comply with such demands,

and (2) “hostile environment” claims, where unwelcome sexual advances occurred but did not lead

to loss of employment or other economic injuries.

In 1986, the United States Supreme Court adopted the Henson framework when it addressed

workplace harassment for the first time in Meritor Savings Bank v. Vinson. 2 A bank employee brought a Title VII claim against her employer, alleging that her branch manager made unwelcome sexual

advances toward her. The bank argued that sexual harassment was only actionable if it affected

tangible, economic aspects of the employment relationship and harassment that “only” affected the

work environment could not support a claim. The court disagreed, concluding that “a plaintiff may

establish a violation of Title VII by proving that discrimination based on sex has created a hostile or

abusive work environment.”

The hostile environment theory is rooted in the Title VII provision that bans discrimination in

the “terms, conditions, or privileges of employment.” The court held that harassment violates this

provision when it is shown to be “sufficiently severe or pervasive ‘to alter the conditions of [the

victim’s] employment and create an abusive working environment.”’ Subsequent U.S. Supreme

Court guidance instructed courts to determine whether an environment is sufficiently hostile or

abusive by “looking at all the circumstances ... includ[ing] the frequency of the discriminatory

conduct; its severity; whether it is physically threatening or humiliating, or a mere offensive

utterance; and whether it unreasonably interferes with an employee’s work performance.”3

Sexual Harassment and the Fair Housing Act

The law of sexual harassment in housing developed later and was predominantly in step with

Title VII. The first reported decision involving sexual harassment in housing was Shellhammer v.

Llewellyn in 1985.4 The plaintiffs in Shellhammer were a married couple who were evicted from

their apartment allegedly because Mrs. Shellhammer refused her landlord’s requests to pose for

nude photographs and to have sex with him. The magistrate judge who heard the case noted the

lack of any housing precedents for sexual harassment claims and the similarity between the Title

VII ban on discrimination in the “terms, conditions, or privileges of employment” and the Fair

Housing Act’s (FHA) prohibition on discrimination in the “terms, conditions, or privileges of sale

or rental of a dwelling.” Therefore, the judge turned to employment decisions under Title VII for

guidance and ruled that both quid pro quo and hostile environment claims were also actionable

under the FHA.

Subsequent courts followed Shellhammer’s approach. They found it appropriate to rely on Title VII

precedents to establish the contours of sexual harassment law under the FHA. Additionally, all the

presiding courts agreed that if the plaintiff’s complaint involved only a hostile environment claim (and 2 Meritor Savings Bank v. Vinson, 477 U.S. 57 (1986).

3 Harris v. Forklift Systems, 510 U.S. 17, 23 (1993).

4 Shellhammer v. Llewellyn, 770 F.2d 167 (6th Cir. 1985); Prentice Hall, Inc. et al., 1 Fair Housing—Fair Lending 15, 472 (1994).

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Oliveri

not the loss of a tangible housing benefit), then the defendant would only be liable if his behavior was

“severe or pervasive” enough to alter the terms and conditions of the plaintiff’s residency.

In 2016, the U.S. Department of Housing and Urban Development (HUD) issued a final rule

formalizing the definitions of and standards for quid pro quo and hostile environment sexual

harassment in housing.5 The definitions and standards largely conform to existing court precedent.

The purpose of the rule was to provide consistency and clarity to investigators, housing providers,

and victims.

What We ‘Know’ About Sexual Harassment in Housing

Little reliable data is available about the incidence of sexual harassment in housing, although there

is plenty of anecdotal evidence from cases, and scholars have written theoretical articles about the

problem based largely on assumptions from the cases. This section summarizes the small amount

of research that exists on the topic of sexual harassment in housing.

Official Statistics and Early Studies

The National Fair Housing Alliance (NFHA) provides the most comprehensive statistical

picture of fair housing complaints in the United States. Its annual report contains data on

housing discrimination complaints filed with government agencies such as HUD, Fair Housing

Assistance Program agencies, DOJ, as well as private fair housing organizations that process the

vast majority of housing discrimination complaints.6 Even so, NFHA’s report has limitations.

NFHA recognizes that due to the extremely high rate of underreporting, their figures represent

only a small fraction of the actual discrimination that occurs in the housing market. In 2017,

NFHA reports there were 1,017 complaints in which “sex” was listed as a possible basis for

discrimination (NFHA, 2018). These complaints, however, are not broken down by the type of

discrimination—for example, sex-based differential treatment (such as when a landlord refuses

to rent to someone because of sex) versus sexual harassment. The report separately identifies 747

harassment complaints, which can be based on any protected characteristic. Of these complaints,

only 200 are identified as being based on sex. It is unclear whether these allegations are in

addition to, or overlap with, the sex discrimination complaints. What is clear, given the small

numbers, is that this statistic is an undercount.

There is a similar shortage of academic studies on the topic, with only four scholarly articles that

analyze the problem of sexual harassment in housing in an empirical manner. Only two of the

articles attempt to discern prevalence data; both rely on returned surveys, and each is more than

20 years old.

The only known attempt to assess the frequency of sexual harassment in housing in the United

States was conducted nearly 30 years ago. In 1987, Regina Cahan surveyed 150 public and private

fair housing organizations across the country to see whether they had received complaints of sexual

5 24 Code of Federal Regulations (CFR) § 100.600 (2017).

6 NFHA does not track housing discrimination lawsuits filed by private lawyers who do not work for fair housing organizations.

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Sexual Harassment of Low-Income Women by Landlords

harassment (Cahan, 1987).7 Of the 87 centers that provided useable responses, 57 centers (65

percent) reported receiving a collective total of 288 complaints of sexual harassment, whereas 30

centers (35 percent) reported never having received such a complaint. Citing a recent survey which

found that fewer than 3 percent of victims of workplace sexual harassment sought help through

formal institutional processes, Cahan estimated that between 6,818 and 15,000 cases of sexual

harassment in housing may have occurred between 1981 and 1985 (the period of time that the

survey results covered).

A smaller number of centers provided Cahan with specific information about the income of the

victims and the nature of the harassing conduct. The victims were overwhelmingly poor, with 75

percent earning less than $10,000 per year and 23 percent earning between $10,000 and $20,000

per year. More than two-thirds (67.7 percent) of the complaints involved a landlord requesting

some form of sexual activity, nearly 39 percent involved abusive remarks, and 34 percent involved

unwanted touching. Cahan did not elicit additional information about the women, such as race or

age, nor did she elicit any information about the perpetrators. Cahan asked about the size (number

of units) and type of housing in which the women lived, but her questions were limited.

Although her article was groundbreaking, Cahan’s study is of limited use in determining true

prevalence due to her reliance on reported complaints to fair housing centers and, even then, only

the centers that responded to her survey rather than a population sample. Sexual harassment is

notoriously underreported in other settings such as the workplace (Johnson, 2016; Welsh and

Gruber, 1999), the military (Chema, 1993), and academia (National Academies, 2018).

The only other prevalence study of sexual harassment in housing was conducted more than 20

years ago in Canada. In 1991, a doctoral student in sociology, Sylvia I. Novac, mailed surveys

to 1000 rental households in Ontario (Novac, 1994). She received 352 useable surveys from

the responses, and of these surveys, 25 percent of the respondents reported experiencing sexual

harassment in housing.

Again, this methodology which relied on returned surveys, failed to measure true prevalence.

Moreover, the survey questions were based on workplace sexual harassment and may not have

adequately sampled the type of sexually harassing behaviors experienced by tenants. For example,

in an open-ended portion of the survey, 29 percent of respondents reported that their landlord had

entered their home without notice. Although unauthorized entry into the home is not necessarily

indicative of harassment, it may constitute part of a pattern of harassment and intimidation.

Similarly, behaviors such as refusing to allow women to have male visitors, looking through

windows, or being abusive toward household members are types of harassment unique to the

housing context that will not be captured in a typology based on employment harassment.

Recent Studies

Two more recent studies did not seek prevalence data but instead examined existing cases and

complaints to determine common characteristics of harassment, victims, and perpetrators. In 2005,

7 Thus, the survey answers were not provided by the victims but by the organization based on the material contained in their files.

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Oliveri

Drs. Maggie Reed, Louise Fitzgerald, and Linda Collinsworth reviewed deposition testimony given

by 39 victim-witnesses in three cases prosecuted by DOJ (Reed, Fitzgerald, and Collinsworth,

2005). The authors then analyzed all published federal sexual harassment in housing cases that

contained details about the sexually harassing conduct (a total of 18 cases). They compared the

conduct described in those cases with the conduct found in their deposition sample and noted a

significant overlap.

Between the reported cases and the depositions, the researchers identified 389 separate instances

of misconduct. These instances were then grouped generally into three categories: (1) gender

harassment (sexist hostility), (2) unwanted sexual attention (sexual behavior and imposition/

assault), and (3) sexual coercion (sexual threats and bribery). The authors found that the majority

of the instances were classified as unwanted sexual attention (60 percent), followed by sexual

coercion (18 percent), and gender hostility (13.9 percent). These findings were in dramatic

contrast with similar research done in the employment context where most harassing behavior

(59.5 percent) fell into the gender hostility category. Unwanted sexual attention (36.9 percent) was

the second most frequent type of conduct in the workplace sample, whereas sexual coercion (3.6

percent) barely registered. The researchers concluded that sexual harassment in housing was much

more likely to consist of unwanted sexual attention and sexual coercion when compared with

sexual harassment in the workplace, which was much more likely to consist of gender hostility

with very little sexual coercion. This study did not focus on victim or perpetrator characteristics

and did not analyze the type of housing the victims were living in at the time.

In 2008, Dr. Griff Tester analyzed 137 housing sexual harassment complaints made to the Ohio

Civil Rights Commission (OCRC) between 1990 and 2003 (Tester, 2008). He was the first to

obtain data on the race of the victims and perpetrators and found that 68 percent of the reported

victims were Black or “other women of color” whereas virtually all the perpetrators were White

men. The type of housing in which most of the harassment occurred were private rentals as

opposed to public housing, although OCRC files were not clear whether the victims were using

Housing Choice (Section 8) Vouchers at the time. The landlords tended to represent small,

privately owned housing as opposed to large rental companies with structured management and

procedures. OCRC did not collect specific data about the complainants’ socioeconomic status,

although information in the files indicated that many of the women were poor and in need of

housing assistance.

Both studies contributed valuable insights into the nature of sexual harassment in housing claims.8

The 2005 study was significant because it was the first to rigorously analyze the harassing behavior

and to compare sexual harassment in housing claims with sexual harassment in employment

claims. The 2008 study was valuable because it was the first to analyze the perpetrators, the type

8 A third, relatively recent, study also addressed a subset of housing harassment: sexual assault and rape. In 2006, Theresa Keeley, an attorney at the National Law Center on Homelessness and Poverty, sent a survey to rape crisis centers and sexual assault advocates asking if they had ever received complaints from tenants alleging rape or sexual assault by a landlord. Recipients of the survey were encouraged to share it with others and it was widely circulated. Of the 112

surveys that were returned, 58 percent described at least one tenant report of sexual assault or rape by a landlord in the previous year. The returned surveys contained 161 tenant complaints as some surveys described more than one report of harassment (Keeley, 2006). Because the survey was not distributed in a controlled way and participation was voluntary, assessing the statistical significance of these numbers is difficult. Nonetheless, they are disturbing.

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of housing, and the victim’s characteristics, such as intersectional factors like race. Both studies,

however, had methodological limitations because they relied on reported and/or litigated claims.

In sum, solid information about sexual harassment in housing, particularly prevalence data,

remains elusive. Given the methodological limitations of the early studies, which relied on reported

claims, filed cases, and survey returns, the basis for a reliable estimate of how often harassment in

housing occurs in the population of low-income women is still lacking. Although an analysis of a

small set of reported or prosecuted claims gives a sense of what sexual harassment in housing can

look like, it is not known how representative these claims are of the “typical” victim’s experiences.

Significantly, no information is available on the population of women who experience sexual

harassment by a housing provider but do not report it. What form does their harassment take?

What effect does it have on their lives? Why do they not report it? The answer to this latter

question, in particular, is crucial to developing reforms and interventions.

The Study

This study attempts to fill this research gap and its results are intended to support more wide-

ranging research on this topic in the future.

Purpose, Design, and Methodology

The purposes of the study were: (1) to gain an appreciation of the rough magnitude of sexual

harassment in housing that low-income women experience; (2) to observe the form(s) that the

harassment takes; (3) to get a sense of the characteristics of the women who experience the

harassment, the housing providers who perpetuate it, and the housing in which it occurs; and (4)

to understand women’s responses to the harassment, including why they may not report it and the

effect it has on their housing.

A survey instrument in the form of an interview script was devised with these objectives in mind.9

One hundred women were individually interviewed during a period of 3 months in Columbia,

Missouri. The interviews were conducted by a law professor (a former fair housing lawyer with

extensive experience interviewing victims of housing sexual harassment) and a law student with a

bachelor of science in social work.10

Interview subjects were solicited in the office of the Columbia Housing Authority (CHA); they were

all either clients of the CHA or applying to be a client. Thus, this study was a convenience sample

of women who were low-income and in need of housing assistance at the time of the interview.11

The subjects were selected as follows: every woman who came to the CHA reception desk, for any

9 The completed surveys are on file with the author.

10 Both interviewers were White women under the age of 40.

11 A decision was made to focus specifically on low-income women rather than the population of female tenants, and only women were interviewed. This decision was primarily because, as described in the text, both logic and the existing evidence indicate that sexual harassment in housing is primarily experienced by low-income women whose housing options are limited. Determining the prevalence of housing harassment among the population of all female renters, or all people, might be the subject of future research.

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reason, was asked if she wished to participate in a survey about her “experiences with housing”12

and would receive a small cash payment ($20) for participation. Interested subjects were then

referred to the interviewer.13

Of the 100 survey participants, 84 percent identified as Black or multiracial, 15 percent as non-

Hispanic White, and none as any other racial or ethnic group. This representation is not consistent

with the racial make-up of CHA clients and applicants, who are more evenly distributed between

Black and White. CHA records indicate that 56.8 percent of the residents are Black, whereas 41.8

percent are White.14 Similarly, CHA reports that Blacks constitute 70 percent of applicants for

vouchers and 51 percent for public housing, whereas Whites constitute 24 percent and 45 percent,

respectively. According to HUD data, in Missouri, Blacks constitute 41 percent of public housing

residents and 59 percent of voucher-holders; in the United States as a whole, Blacks constitute

41 percent of public housing residents and 47 percent of voucher-holders.15 Although nothing

was done to consciously bias the interview pool, this method was a sample of convenience and a

statistically representative sample was beyond the scope of the project.

The interviews were conducted in a private conference room in the CHA office. The only people

present were the interviewer and the subject. At the beginning of each interview, the subject was

informed that the interviewer worked at the University of Missouri and was not affiliated with the

CHA in any way. Subjects were further informed that their answers would be confidential and their

surveys kept anonymous.

After a number of background questions, the interview subjects were asked if they had ever16

experienced “sexually inappropriate” behavior from a landlord, including specific conduct that

would likely constitute sexual harassment such as inappropriate touching, sexual comments, and

requests for sexual activity. An additional category for “other inappropriate behavior” was included

that allowed the subjects to describe other behaviors that made them uncomfortable but were not

included in the list.17 The interview subjects were also asked if they had ever experienced “annoying

or disturbing” behavior from a landlord, including specific conduct that might be part of a pattern

of sexual harassment such as the landlord prohibiting male visitors, looking through the windows,

12 The reasons that the women were in the CHA office varied. Most were there to fill out paperwork or to meet with caseworkers.

13 It is not known what percentage of women who were given the opportunity to participate in the interview actually chose to do so. On most days, however, a long line of subjects were waiting to participate. Every interview subject completed the interview, which is to say that no one dropped out or reached a point where she refused to continue.

14 The data are provided as part of the City of Columbia’s Consolidated Plan provided to HUD as part of the Community Development Block Grants and HOME Investment Partnership Program. See http://www.columbiaha.

com/wp-content/uploads/2018/10/CHA-FYE2019-PHA-Plan-HP-Final-2018-10-16.pdf.

15 The data can be found in HUD’s Resident Characteristics Report at https://pic.hud.gov/pic/RCRPublic/rcrmain.asp.

16 It is important to underscore the fact that survey participants were asked about their lifetime experiences. This term is significant because, although virtually all the women interviewed were clients of the Columbia Housing Authority at the time of the interview, all those who reported harassing conduct experienced it prior to becoming clients of the Columbia Housing Authority.

17 The specific behaviors were identified as behaviors which were likely to constitute sexual harassment based on the research described in the previous section and a review of published cases. The survey question avoided using the legal term “sexual harassment” and instead used the term “sexually inappropriate behavior,” in order to allow the respondents to identify conduct that they personally found to be offensive or problematic, even if it might not reach the legal definition of sexual harassment.

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or entering the unit unannounced. Any woman who answered affirmatively was then asked whether

she believed these behaviors were “sexual in nature” and/or done “because [she is] a woman.”18

Women who responded affirmatively to any of the questions indicating sexual harassment were

then asked several followup questions in which they were prompted to describe:

• the conduct in detail, including frequency;

• their own characteristics, including how old they were, other occupants in the household, and

their source of income at the time the conduct occurred;

• the type of housing they were living in at the time the conduct occurred, whether it was

public housing, private rental housing, or some other type of housing (such as project-based

Section 8 housing, a homeless or domestic violence shelter, or another institutional setting); if

the woman was living in private rental housing, she was asked whether she received a Section

8 Voucher;

• the characteristics of the perpetrator, including estimated race, age, and role in the housing

(that is, whether he was the owner, a manager, or a maintenance worker) at the time the

conduct occurred;19

• their responses to the conduct, including whether and to whom they reported it, results or

outcomes of reporting, reasons for not reporting it, and any lasting emotional or psychological

effects the experience had on them.

Results

The study’s results were at times consistent with prevailing assumptions about sexual harassment in

housing. In other ways, the results challenged the accepted knowledge and provide insight into the

way harassment in housing “typically” manifests, who it involves, and what happens as a result.

Frequency, Severity, and Type of Conduct

Of the 100 women interviewed, 16 gave responses indicating that they had experienced some type

of sexually harassing or otherwise problematic conduct. These surveys were then sorted according

to whether the conduct described would likely constitute actionable sexual harassment under

current federal caselaw. Actionable claims were those likely to survive a motion to dismiss for

failure to state a claim.20

Ten women described conduct serious enough to meet the legal standard for sexual harassment

or at least support a legally actionable claim. The other six women described conduct that they

18 These questions were asked in order to distinguish ordinary disputes between landlords and tenants from situations that potentially involved sexual harassment.

19 Race and age had to be approximated by the respondents based on their observations of the perpetrator.

20 This determination was complicated by the fact that, for hostile environment sexual harassment, there is no bright-line rule but rather a standard—“severe or pervasive”—which may be applied differently by different courts. Although a few cases classified as “actionable” were borderline—that is, the women could have stated claims but might not have prevailed on the merits—most were quite clearly violations of the Fair Housing Act.

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believed was sex-based and that annoyed or upset them but would likely not meet the current legal

standard for sexual harassment.21

This article will focus on the 10 subjects with actionable claims.22 The following are brief

summaries of the conduct they described:

#20: The woman was 48 years old and caring for her granddaughter. Her sole source of

income was Social Security Disability Insurance (SSDI). Her landlord said that her rental

situation could be “cheaper and easier” if she would give him sexual favors. The landlord

watched her home and told her that she could not have male visitors. She did not comply

with his requests. She eventually “snuck out” of the house in the middle of the night. She

moved in with friends, although this meant she was unable to care for her granddaughter.

At the time of the interview, she was applying for public housing.

#21: The woman was 18 years old and in college. She did not have any children, and she

lived with a roommate. The landlord asked for sexual acts in lieu of rent and as a way

to expedite repairs. He made comments about the woman’s body and kept track of her

comings and goings. The woman eventually told him to stop, and nothing else happened.

#29: The woman was 21 years old and unemployed, although occasionally she worked as

an exotic dancer. She did not have any children and lived with a boyfriend. Her landlord

made multiple demands that she have “oral and regular sex” with him, because she was

behind on her rent and threatened her with eviction if she did not comply. He would

use his key to enter her apartment, without warning, while she was home, including

multiple times while she was in the shower. He touched her in ways she thought were

inappropriate. She never acquiesced to his demands and ultimately moved out before he

could evict her.

#37: The woman was 21 years old and a single mother of two. She was employed as an

aide in a facility for the disabled. She was attempting to rent an apartment. After showing

her the unit, the landlord locked the door and asked for oral sex, saying that she could

do that instead of paying the security deposit. The woman refused and chose not to rent

from the landlord.

#39: The woman was 27 years old and a divorced mother of six. She was paying for part

of her rent using a Section 8 Voucher. The landlord told the woman she could avoid

paying her portion of the rent if she had sex with him. She refused and continued to rent

the apartment.

#41: The woman was 27 years old and worked part-time as a housekeeper. She moved

into an apartment with her fiancé after spending 3 months in a domestic violence shelter.

The woman’s landlord requested that she have sex with him and watch him masturbate to

21 For example, one woman described being “creeped out” by her landlord, because the way he looked at her “was just plain wrong,” but did not provide any specific behaviors that would support a legal claim.

22 A chart with demographic characteristics, claims, and outcomes for each of these 10 women is attached at appendix A.

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help pay the rent. He threatened to evict her if she refused. She never complied with his

demands, however, on more than one occasion she woke up at night to find him in her

house (sometimes her bedroom) masturbating. The woman eventually moved out and

went to live with her sister.

#75: The woman was 23 years old and employed part-time as a hotel housekeeper. Her

landlord would ask her and her roommate for sex in lieu of rent. He would come into

their house uninvited, and he prohibited them from having male visitors. She called

the police to make a report about the landlord’s repeated unauthorized entry into her

apartment. An officer came by to take her statement but did nothing further. The woman

eventually moved out and went to live with her mother. Her roommate continued living

in the apartment and, according to the interviewee, her former roommate “did what he

wanted so she could afford the rent.”

#93: The woman was 24 years old, married with three children, and she worked as a hotel

housekeeper until she lost her job. Her husband also lost his job, and both were struggling

with drug addiction. The landlord said he would reduce the rent if the woman had sex

with him. She believed that he was having sex with other women in the complex. The

landlord watched her unit, made unannounced visits, and came into her apartment when

she was not home and removed items from her underwear drawer. The woman refused the

offers of sex for rent and, eventually, she and her family moved out and into a hotel.

#95: The woman was 35 years old, unemployed, and receiving SSDI. She also had a

Section 8 Voucher. She was looking at an apartment with her 10-year-old daughter when

the landlord made sexual comments and talked about how “sexy” he thought both of

them were. He tried to grope the daughter and make the woman sit on his lap, but the

woman pushed him away, and she and her daughter ran out of the apartment. She did

not rent the apartment.

#99: The woman was 30 years old and a single mother of four who worked as a school

bus driver. She had been living in a homeless shelter until she received a Section 8

Voucher. Her new landlord frequently directed sexual comments toward her and asked

to watch her engage in “girl on girl” sexual activity with another tenant. She said no, the

landlord eventually stopped making advances, and she continued living in the apartment.

Victim Characteristics

The women who reported experiencing harassment by their landlords were likely to be racial

minorities. Nine of the ten women who reported experiencing sexual harassment by their landlords

identified as Black or multiracial, and one identified as White; thus, 90 percent of the women with

positive responses were members of a minority group. This finding is consistent with the population

of the survey participants (85 percent Black, 15 percent White). It is important, however, to note

that the group of survey participants contained a disproportionate number of Black subjects when

compared with the total CHA population as previously described, for reasons that are not clear.

Although Blacks are disproportionately likely to be poor, Whites make up a majority of the low-

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income people in the United States in absolute numbers. (DeNavas-Walt and Proctor, 2015). Similar

trends are evident in rates of housing assistance. (Irving and Loveless, 2015).

The study identified another factor that was not addressed by any of the previous studies—the age

of the women at the time they experienced the harassment. Most of the women were young. The

median age at the time of the harassment was 25.5. Three of the women were 21 or younger when

they experienced the harassment.

Five of the women were caring for children and were the only adults in the household at the time

they were harassed. Four of the women did not have children and were living with roommates or

boyfriends. Only one household contained both children and another adult (#93 reported living

with her husband and three children, but she also reported that she and her husband were dealing

with drug addiction at the time).

All the women were low-income, or had no source of income at all, at the time they experienced

the harassment. One was unemployed, one was in college and living on student loans and help

from her family, two received SSDI payments, and the remaining six were employed in low-wage

jobs (three worked as hotel housekeepers, two worked as nurse’s aides, and one was a school bus

driver). Despite this level of income insecurity, only three of the ten were receiving rental assistance in the form of Section 8 Vouchers—an SSDI recipient, a nurse’s aide, and the bus driver. Of the

seven who did not receive Section 8 Vouchers, three relied on their wage earnings, one relied on

monthly SSDI payments, and three (who were unemployed) relied on assistance from family and

friends to pay rent.

The fact only three of the ten were receiving Section 8 Vouchers at the time of their harassment

might seem surprising considering that all ten were receiving housing assistance at the time

of the interview—either using Section 8 Vouchers or living in public housing. (Indeed, one of

the concerns about the project design was that it was likely to oversample women in public or

Section 8 housing because the interview subjects were recruited from the CHA office and the vast

majority were current CHA clients.) The fact that so few were receiving assistance at the time of

their harassment, however, is consistent with the rates at which low-income women, in general,

receive housing assistance. Due to resource limitations, only one in four low-income people who

qualify for rental assistance in the form of Section 8 Vouchers or public housing actually receives

it (Fischer and Sard, 2017). These study findings run contrary to assumptions made by other

commentators about the population of women at risk for sexual harassment in housing. Some

scholars assert—without evidence—that women are more likely to be harassed if they use vouchers

or live in public housing (George, 2016; Maxwell, 2006; Reed, Fitzgerald, and Collinsworth 2005).

Although the numbers involved in this study are too low to draw any assumptions, they suggest

that receiving housing subsidies does not make low-income women more likely to be harassed

than other women. On the contrary, it appeared that receiving a housing subsidy or other financial

support actually made it easier for some of the women in the study to reject their landlord’s

advances and remain in their housing.

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Perpetrator and Housing Characteristics

The perpetrators of the harassment were much different, demographically, than the women who

were targeted. They were more evenly distributed by race with five who appeared White and five

who appeared Black.23 Perhaps the most dramatic difference was age. Although their exact ages

were not known, the women estimated the ages of the perpetrators in each case, and all were

estimated to be between the ages of 40 and 70, with an average estimated age of 50. In all but one

case, there was at least a 10-year estimated age difference between the woman and perpetrator.

As noted previously, all the cases involved private rental housing; none of the women were living

in public housing, project-based Section 8 housing, or group setting housing, such as a shelter,

at the time they experienced the harassment. These findings are also consistent with the results

of Dr. Tester’s study; most of the reported complaints he found were in private rentals, although

he could not determine whether the rentals were participating in the Section 8 Voucher Program

(Tester, 2008).

All the women believed that the person who harassed them was the owner of the property and also

served as its manager. This status meant that the landlord did not employ a property manager or

management company and was the sole point of contact for the women with respect to their housing.

Responses and Consequences

Only one woman (#75) attempted to report her situation to someone in a position of authority.

After her landlord repeatedly asked her for sex in lieu of rent and came into her apartment

uninvited, she called the police. The police came to her apartment and interviewed her. She

was not aware of the police taking any further action. The remaining women did not report the

inappropriate behavior to anyone. This response is consistent with research findings of sexual

harassment in other contexts, such as the workplace and academia. In the study, the most common

reasons given for failure to report were that the woman did not know where, or to whom, to make

a report (five women); did not want to jeopardize her housing situation (four women); or did not

want to involve others in the situation (three women).

The emotional and, in some cases, physiological consequences for the women could be quite

serious. All reported feeling negative emotions at the time of the harassment, ranging from anger,

shock, depression, shame, and disgust. Five women also experienced physical symptoms such as

sleeplessness, stomach upset, headaches, and anxiety.

Four women reported experiencing serious and ongoing emotional problems. For example, #93

described how the harassment brought up issues related to molestation she had experienced as

a child. The harassment also exacerbated her drug use, about which she was deeply ashamed.

Moving her family into a hotel was extremely stressful, and her relationships suffered. She became

seriously depressed and attempted suicide. Ten years later she still struggles with depression and

trust issues. One participant, #41, reported that the harassment caused her relationship with

her fiancé to fall apart. Even 15 years later she still feels intense anger and disgust about what

23 These observations were based on the characterization of the landlord’s race by the women, who were asked to state what race the person appeared to be.

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happened. One woman, #75, experienced extreme anxiety, sleeplessness, and stress, particularly

due to the fact that her landlord repeatedly entered her apartment without authorization. She felt

helpless when the police apparently took little action after her report. Twenty years later she still

feels paranoid in her apartment and mistrustful of landlords. Another woman, #20, reported that 2

years later she still “prays every day, just trying to forget” her landlord.

Analysis and Implications

The findings, although based on a small number, reveal some important insights about sexual

harassment in housing. In particular, virtually all the women reported being asked by their

landlords to exchange rent for sex. This harassment took place against a backdrop in which several

of the women were having difficulty paying their rent; thus, an eviction for cause was a credible

threat. Whether or not a woman was able to rebuff the landlord’s advances and also remain in her

housing appeared to come down to whether she had assistance, usually through a HUD voucher, to

pay her rent.

In addition, all the landlord-perpetrators appeared to own and operate their properties themselves,

without the sort of oversight one might find in a large rental management company or more

institutionalized setting.

Analysis of the Findings

The Conduct

Eight of the ten cases involved explicit requests or demands by their landlords to trade sex for rent

(#20, #21, #29, #37, #39, #41, #75, and #93). A ninth woman (#99) described being subjected

to repeated sexual comments and requests by her landlord, although she was never specifically

propositioned to trade sex for rent. Five women described landlord behaviors that also fall into

the hostile environment category and are likely criminal in nature, including: home invasion (#29,

#41, #75, and #93), indecent exposure (#41), and sexual battery of a child (#95).

Sexual Requests: The Reality of Low-Income Housing

All 10 women identified in the study were subjected to sexual overtures by their landlords. Most

landlords were explicit about trading rent for sex, and some made aggressive or repeated advances.

All the women rejected these overtures. None of the women reported any direct, tangible, negative

actions taken by the landlords because of their refusals. In other words, none of the landlords

evicted or failed to rent to a woman because she refused his advances.

This finding is not to say that the sexual harassment had no effect on their housing situations. On

the contrary, two women (#39 and #95) refused to rent the apartments they had been considering

after their prospective landlords crudely propositioned or groped them, and five women (#20, #29,

#41, #75, and #93) ultimately moved out of their housing after their landlords propositioned them.

The fact that the landlords never took negative action is important for several reasons. Because

the landlords took no tangible, negative action against the women, under existing Supreme Court

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caselaw, their cases would be classified as alleging hostile environment harassment.24 In order for

conduct to constitute a hostile environment, it must be considered severe or pervasive. Thus, the

legal question for a court analyzing the issue would be whether the described behavior rises to the

level of “severe or pervasive” conduct. This showing should easily be met by the women who also

alleged serious and/or criminal behavior such as indecent exposure, home invasion, and sexual

battery. For one-half of the women, however, the sexual requests were the only form of harassment

they experienced.

Such requests are undoubtedly offensive, as any random request for sex from a relative stranger

would be. They take a much more sinister character, however, in light of the vulnerable position

the women were in at the time their landlords propositioned them.

Indeed, all the women in the study were in tenuous financial positions at the time they were

harassed. Although they were all low-income, only one of the ten was receiving Food Stamps or

Supplemental Nutrition Assistance Program (SNAP) benefits and none was receiving Temporary

Aid for Needy Families benefits. Four women had help paying their rent. Three had a portion of

their rent paid through the Section 8 Voucher Program. Two of those three were also working, and

the third received SSDI. The fourth was a college student who was receiving student loans and

help from family. The remaining six women had no rental assistance, from neither government nor

family, at the time they were harassed. Two were unemployed and had no source of income, one

received SSDI, and three were working.

All the women who worked had low-wage and/or part-time jobs that were almost certainly

insufficient to pay for market-rate housing on the private rental market. For example, in Columbia,

Missouri, a person earning minimum wage would have to work 76 hours per week, 52 weeks per

year, to afford the rent on a two-bedroom apartment. In fact, as the National Low-Income Housing

Coalition (NLIHC) has exhaustively documented, there is no state in the United States where a

low-wage employee, working full-time, can rent a two-bedroom apartment at the 40th percentile of

area rents without spending more than 30 percent of her income on rent (NLIHC, 2017).

Consequently, the six women in the study who were not receiving Section 8 Vouchers or other

rental assistance had difficulty consistently paying the rent for their apartments—a fact their

landlords should have known at the time of the lease. This information is significant because a

woman who received rental assistance appears to be more likely to remain in her housing after the

harassment occurred.

Five of the six women who had no assistance (#20, #29, #41, #75, and #93) moved out of

their apartments after the harassment. The sixth (#39) never rented the apartment because

the harassment occurred while she was viewing the unit. The circumstances described in the

interviews make clear that all were having difficulty making their rent payments. After declining

the “option” of sex in lieu of rent, all five moved out, most to a less desirable housing situation.

Although it is accurate to say that the landlords did not directly evict these women for their

refusals, it is also misleading to conclude that their refusals had no effect on their housing status.

Although the landlords would have had legitimate grounds for eviction due to failure to pay rent, if

24 Burlington Industries v. Ellerth. 524 U.S. 742, 753-54 (1998)

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the women had acceded to the landlord’s requests, then they presumably would have been able to

remain in their homes.

Three of the women were receiving vouchers or other assistance when they were propositioned.

The two women who were renting with Section 8 Vouchers25 (#39 and #99) and the woman who

was renting with student loans and family support (#21) also declined sexual requests from their

landlords but did not move out of their housing. None of them indicated having difficulty paying

the rent. If the women were not in arrears on their rent, then their landlords lacked legitimate

grounds to evict them.

Thus, it appears that the women who had resources to help them pay rent were able to turn down

their landlords’ requests without jeopardizing their housing situations. The women who did not

have such resources faced a much harder choice because, for them, saying “no” meant having to

move or be evicted.

Other Conduct

Five of the women described additional harassing conduct, including unauthorized entry and/

or home invasion (#29, #41, #75, and #93), indecent exposure (#41), and unwanted touching

(#29 and #95, the latter involving the woman’s 10-year-old daughter). Much of this behavior is

likely criminal in nature. The home invasions are particularly disturbing. One woman (#29) came

out of the shower to find the landlord inside her apartment multiple times. Another (#41) woke

up at night to find her landlord in her apartment masturbating. Even apart from these dramatic

episodes, simply having a landlord who would let himself into their apartments without warning

was extremely unsettling to these women. This behavior was particularly disturbing because

these women had been sexually propositioned by these same landlords. This combination—

unauthorized entry coupled with sexual propositions—was terrifying to all the women who

experienced it.

Lack of Oversight

All the harassment took place within private rentals, not in public housing, homeless shelters, or

other institutionalized settings. Three of the ten women were using Section 8 Vouchers to help

pay for rent at the time they were harassed, so there was at least a Housing Authority involved in

overseeing the rental. In the remaining seven cases there was no governmental, administrative, or

charitable entity involved in the rental relationship.

It appears that the perpetrators are likely to be independent owner-operators–that is, landlords who

both own and manage the properties without using a rental manager or management company.

All 10 of the women reported that this situation was the case. This finding is also consistent with

Dr. Tester’s study, which observed that most of the offenders were landlords who both owned and

managed their properties (Tester, 2008).

25 A third woman, #95, also had a voucher, but she and her young daughter were harassed while looking at the apartment and, as a result, she never actually rented from the landlord. She was also not explicitly propositioned by the landlord.

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Sexual Harassment of Low-Income Women by Landlords

This conclusion makes sense if it is assumed that a larger, more formal management apparatus—

of the sort that one would find either with public housing or with a private rental management

company—is more likely to contain some oversight and accountability mechanisms. The tenants

might have multiple points of contact with different employees, the employees would have

supervisors, and decisionmaking power about various aspects of the tenancy (rent payments, repairs,

lease status, and so on) would be less likely to rest with a single person. In the owner-operator

scenario, particularly where the tenant is not using a Section 8 Voucher, no mechanisms are in place.

This conclusion is not to suggest that harassment does not occur in public housing or other

institutional settings—anecdotal evidence and caselaw shows that it does.26 The same goes for

harassment committed by employees of rental management companies.27 On the whole, however,

it seems that sexual harassment in housing is most likely to occur in a specific setting—private

rentals—and that it is carried out by a specific type of perpetrator–a man who both owns and

manages his properties and who is operating without oversight or accountability.

Lack of Response

It is striking that essentially nothing happened to the landlords who committed the harassment.

The only woman to make any sort of complaint called the police who apparently took little

action. This result is not surprising. Police are trained to investigate criminal activity. They may

well view a complaint from a woman about her landlord as a landlord-tenant dispute and not a

law-enforcement matter. Police officers may view the property as belonging to the landlord and

therefore may be less willing to take complaints of home invasion by landlords seriously. Much of

the law review literature about police involvement with landlords and tenants involves the situation

in which police are summoned to help evict a tenant. This effect may be magnified by the fact

that the complainants are likely to be young, low-income women of color. Unless the police are

specifically trained on this issue, they may not be equipped to take appropriate action.

Fair housing organizations and lawyers who specialize in fair housing have the expertise to handle

complaints of this nature. HUD also processes sexual harassment in housing complaints, as do

state civil rights agencies.28 None of the women interviewed were aware of these resources.

Even if they had been aware of available complaint mechanisms, it is unlikely that the women in

the study would have used them. Of the nine women who made no formal complaint, all stated

that one reason was their reluctance to jeopardize their housing situation. This rationale was likely

a valid concern. As discussed previously, the women who were not receiving rental assistance were

having difficulty paying their rent. Their landlords may have had legitimate reasons to evict them

but may have held off to extort sex. A complaint from a fair housing center or a HUD investigation

could have potentially triggered an eviction. Women with Section 8 Vouchers might not have felt

the same danger of eviction but were still likely to be concerned about jeopardizing their vouchers.

26 See, for example, Banks v. Housing Authority of Bossier City, No. 11–0551, 2011 WL 4591899, at *1 (W.D. La. Sept.

30, 2011) (female public housing tenant alleged sexual harassment by maintenance technician); Woods v. Foster, 884 F.

Supp. 1169, 1171 (N.D. Ill. 1995) (female residents of homeless shelter sexually harassed by shelter directors).

27 See, for example, West v. DJ Mortgage, LLC, 164 F. Supp. 3d 1393, 1395–96 (N.D. Ga. 2016) (female tenant alleged harassment by landlord’s property manager).

28 42 United States Code (U.S.C.) § 3604 (2012); MO. REV. STAT. § 213.040.1(2) (2016).

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If a landlord evicts a woman because she filed a fair housing complaint against him, it can

constitute a separate violation of the Fair Housing Act’s (FHA) anti-retaliation provision.29 If the

record contains legitimate reasons for an eviction, it creates a question of causation for the fact-

finder, who will decide the true reason for the eviction. Unfortunately, this aid may come too late

for the woman if she has already been evicted.

A private lawyer might file for a temporary restraining order to prevent a complainant from being

evicted while her claim is pending. Similarly, it is possible for HUD to authorize the attorney

general to go to court to seek temporary or preliminary relief, which is referred to in FHA’s

regulations as “prompt judicial action.”30 If the complainant is in arrears, however, the court may be

unwilling to grant such a remedy. Moreover, a woman’s difficulty paying rent may provide fodder

for a landlord to argue that she is fabricating her complaints to avoid paying.

Ramifications for Policy

These findings suggest a number of policy improvements for targeted outreach and intervention.

Ultimately, increasing the amount of affordable housing and housing assistance for low-income

families would go a long way toward alleviating this problem.

The Need for More Targeted Outreach and Intervention

FHA and its state law equivalents prohibit discrimination in housing, including harassment

in housing,31 but HUD and the state civil rights agencies that enforce these laws operate on a

complaint-driven model and do not affirmatively regulate private rental housing (Johnson, 2011).

In many jurisdictions, there is little oversight of the landlord-tenant relationship. Regulation of

rental housing is conducted by local zoning authorities and typically focuses on the physical

condition of the properties. Landlord-tenant laws vary from state to state. They usually focus

heavily on the particulars of rent and security deposit collection, duties to repair, and eviction

procedures.32 They are almost always enforced through litigation (which is typically initiated by

landlords against tenants).

Individual owner-operators of private rental housing, who are the likely perpetrators of sexual

harassment in housing, therefore exist in a regulatory gray zone. Unfortunately, the women most

vulnerable to housing harassment—young, low-income, minority women who are not receiving

housing subsidies—are also among the hardest individuals to reach. These women are among the

most marginalized in society and have few social, economic, or institutional supports. Serving this

population remains one of the biggest challenges for social service providers.

For women who are receiving housing assistance, there are obvious agencies that could provide

oversight of landlords and offer resources to tenants who are harassed, specifically the housing

29 42 U.S.C. § 3617 (2012) (“It shall be unlawful to coerce, intimidate, threaten, or interfere with any person in the exercise or enjoyment of, or on account of his having exercised or enjoyed, or on account of his having aided or encouraged any other person in the exercise or enjoyment of, any right granted or protected.”)

30 24 C.F.R. § 103.500(a) (2016).

31 For example, 42 U.S.C. § 3604(b) (2012); MISSOURI REVISED STATUTE (MO. REV. STAT.), § 213.040.1(2) (2016).

32 For example, MO. REV. STAT. Chapter 535 (2016).

278 Refereed Papers

Sexual Harassment of Low-Income Women by Landlords

authorities that implement the Section 8 Voucher Program. The ability and willingness of housing

authorities to respond effectively to complaints may vary, however. Although some housing

authorities may have effective methods for receiving and acting on complaints, others may be

unresponsive to, or even perpetrators of, such harassment (Lussenhop, 2018). As one commentator

argues, housing authorities should develop standard procedures for training landlords about

their obligations under FHA; educating tenants about sexual harassment; and providing effective

methods to receive, investigate, and act on tenants’ complaints of harassment. HUD can and should

monitor how the housing authorities perform on this basis through its Section 8 Management

Assessment Program (SEMAP; Maxwell, 2006).

These sensible measures, however, will only reach the 25 percent of low-income women

who receive housing assistance. For the other 75 percent, avenues must be considered for

tenant education, regulation, and oversight of the landlord-tenant relationship, and complaint

mechanisms that can be made available at a variety of points—particularly at the local level. As a

starting point, states could require landlords to make mandatory disclosures to their tenants that

clearly spell out the right to be free from sexual overtures by landlords. Local governments could

initiate public awareness campaigns designed to reach the low-income population. In addition,

local code enforcement authorities could create and operate a hotline to receive complaints

from women who have experienced harassment. Hotline information should be available to the

women through mandatory disclosures and public education. The hotline would refer the women

to appropriate resources and lead to investigations of the landlords who are the subjects of the

complaints. Problem landlords could be identified and penalized just as they would for repeated

citations about maintenance or habitability.

Similarly, police departments should be trained on how to deal with tenants who allege criminal

harassment by their landlords. Specifically, they should be trained to not automatically view such

disputes as landlord-tenant problems and to take seriously allegations that the landlord is invading

the woman’s home.

In April 2018, the DOJ announced a nationwide initiative to combat sexual harassment in housing

(DOJ, 2018). The initiative contains three components: (1) a joint task force between DOJ and

HUD to coordinate and improve training, data-sharing, and outreach, (2) a toolkit for U.S.

Attorney’s Offices to use for enforcement, and (3) a public awareness campaign. This project is a

welcome move, but DOJ engages in only a fraction of fair housing enforcement. Most enforcement

comes from private non-profit housing agencies or state and local government agencies that rely

heavily on funding from HUD’s Fair Housing Initiatives Program (Schrupp and Olshan, 2005).

These agencies provide a crucial service, and their funding must be stronger and more consistent.

The Need for More Affordable Housing and Housing Assistance

Even the most robust interventions are unlikely to bring about significant change without

addressing its root cause: the fact that so many low-income women are left to their own devices to

find housing in a private rental market that is ill-suited for meeting the existing need.

Experts, advocates, and commentators have long agreed that the United States is in desperate need

of more affordable housing and housing assistance for low-income people (Cummins, 2001).

Cityscape 279

Oliveri

As discussed previously, 75 percent of people who qualify for housing assistance do not receive

it because of resource constraints. Waiting lists for public housing and Section 8 Vouchers are

frequently closed to new applicants, so new families may have to wait for years to sign up.

Although the numbers are small, the study results suggest that the women who were receiving

vouchers when they were harassed had better outcomes, in that they were able to remain in their

housing. Women with vouchers may have more resources which enables them to move away

from a harassing landlord, as well as clearer avenues for reporting the behavior. They may also

be less likely to fall into arrears on rent, making them less vulnerable to sexual coercion. Without

a subsidy, low-income women who have difficulty paying rent are easy prey for landlords who

recognize their vulnerability.

Study Limitations

This project was a small and localized study; its size and scope are not large enough to allow

for broad generalization, particularly across different populations and geographic areas. Indeed,

the study does not even give a snapshot of harassment in Columbia, Missouri. Although all the

interviews were conducted in Columbia, some of the interview subjects stated they had been living

elsewhere when they were harassed—usually Chicago or St. Louis—and that they had later moved

to Columbia. It seems likely that areas with a shortage of rental housing, particularly affordable

housing, would have higher rates of sexual harassment. In order for this theory to be tested, future

studies will need to isolate where the harassment occurred.

In addition, study participants were not reflective of low-income women as a whole because the

study contained a much higher percentage of Black women than the general population in Missouri

or in the United States, but no Hispanics or members of other racial minorities were interviewed.

Conclusion

This study allowed for an analysis of sexual harassment in housing through interviews with women

who have experienced it, but who never pursued legal action. The women were all low-income

at the time they were harassed, and most were quite young. Nearly all reported that they were

explicitly asked to trade sex for rent. Five of the women also experienced behavior that would

likely be criminal, including home invasion, indecent exposure, and groping. All the offending

landlords appeared to be sole owner-operators of their properties and most were significantly older

than their targets.

The only woman to report her experience to authorities did so to the police, who appeared not

to take any action. The remaining women did not report the harassment because they did not

know who to report it to, and/or because they did not want to jeopardize their housing situation.

Although none of the women were evicted for refusing to acquiesce to the landlord’s requests, five

of the women moved out, usually to less desirable living situations. It appears that all five of these

women were having difficulty paying their rent, and they moved out before they could be evicted.

In contrast, women who were receiving housing assistance did not move out of their housing. Two

280 Refereed Papers

Sexual Harassment of Low-Income Women by Landlords

women were propositioned when they were looking at apartments to rent, and the experience

caused them not to go forward with the lease.

All the women reported feeling negative emotions at the time of the harassment, ranging from

anger, shock, depression, shame, and disgust. Five women also experienced physical symptoms

such as sleeplessness, stomach upset, headaches, and anxiety, and four women reported

experiencing serious and ongoing emotional problems.

These results suggest that sexual harassment in housing exacts a toll on the women who experience

it, both in terms of their emotional well-being and in limiting their housing opportunities. They

suggest the need for more targeted outreach to low-income women who may be victimized and

more oversight of landlords who may operate with little accountability. Greater access to affordable

housing and housing assistance would almost certainly provide women like those in the study with

an improved ability to avoid and escape harassment without placing their housing situation at risk.

Many research questions on this subject still need to be answered. In particular, a large-scale study

is needed to determine how prevalent this problem is for low-income women as a whole and

within different subgroups. Future research should also focus on a variety of different geographic

locations with different vacancy rates, housing costs, and affordable housing options in order to

determine whether these factors play a role. A larger study could also seek to identify risk factors

among the women who experience sexual harassment in housing, for example, whether having a

background of evictions, domestic violence, substance abuse, disability, or criminal convictions is

associated with a greater likelihood of harassment.

Finally, future research might explore the prevalence of sexual harassment in housing for all female

renters, not only those who are low-income. Other groups may be vulnerable to sexual harassment

in housing for reasons other than income (for example, students, military spouses, or non-citizens).

Acknowledgments

The study on which this article is based was supported by the University of Missouri’s Research

Incentive Fund Program. The author would like to thank Melissa Hamilton for her assistance

in conducting the interviews, and the Columbia Housing Authority for allowing us to approach

subjects in the waiting room and to conduct the interviews onsite. The referees for this article

provided valuable feedback and suggestions.

Author

Rigel C. Oliveri is the Isabelle Wade and Paul C. Lyda Professor of Law at the University of Missouri

School of Law.

Cityscape 281

Oliveri

e

ent unit

ent unit

Outcome

Moved out (stayed with

friends, could not car

for granddaughter)

Remained in housing,

conduct stopped

Moved out (found

another apartment)

Did not r

Remained in housing

Moved out (stayed

with sister)

Contacted police.

Moved out (stayed with

mother)

Moved out (stayed

in a hotel)

Did not r

Remained in housing,

conduct stopped

ent

ent,

ent

ent,

ent,

, attempted

e (masturbation)

Claim

equests in lieu of r

equests in lieu of

equests in lieu of r

equests in lieu

equests in lieu of r

equests in lieu of r

equests in lieu of r

equests in lieu of

-old daughter

equests,

eatened eviction, home invasion,

eatened eviction, home invasion,

oping of both

Sexual r

Sexual r

rent, sexual comments

Sexual r

thr

unwanted touching

Sexual r

of security deposit

Sexual r

Sexual r

thr

indecent exposur

Sexual r

unauthorized entry

Sexual r

rent, unauthorized entry

(taking underwear)

Sexual comments to mother and

10-year

gr

Sexual r

sexual comments

ype/

oucher

oucher

oucher

oucher

oucher

oucher

oucher

Assistance

Housing T

oucher

oucher

oucher

Private/No V

Private/No V

Private/No V

Private/No V

Private/Section

8 V

Private/No V

Private/No V

Private/No V

Private/Section

8 V

Private/Section

8 V

e facility)

ce of Income

Sour

, educational stipends

e facility)

SSDI

Family

(college student)

Unemployed

Full-time employment (aide in

residential car

Full-time employment (aide in

residential

car

Full-time employment

(housekeeper)

Part-time employment (hotel

housekeeper)

Part-time employment (hotel

housekeeper), lost job due to

drug addiction

SSDI

Part-time employment (school

bus driver), SNAP benefits

en

en

en

en

en

Household

Composition

ee childr

Single, caring for

granddaughter

Lived with

roommate

Lived with

boyfriend

Single, two

childr

Single, six

childr

Lived with

boyfriend

Lived with

roommate

Lived with

husband and

thr

Single, two

childr

Single, four

childr

Age

48

18

21

21

27

27

23

24

35

30

Black

Black

White

Black

Black

Black

Black

Black

Black

Black

Race of

Subject

Appendix A

Exhibit A.1

Demographic Characteristics, Claims, and Outcomes of Subjects with Actionable Claims

#20

#21

#29

#37

#39

#41

#75

#93

#95

#99

282 Refereed Papers

Sexual Harassment of Low-Income Women by Landlords

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in the United States: 2014. Washington, DC: U.S. Government Printing Office. https://www.census.

gov/content/dam/Census/library/publications/2015/demo/p60-252.pdf.

Fischer, Will, and Barbara Sard. 2017. “Chart Book: Federal Housing Spending is Poorly Matched

to Need,” Center on Budget and Policy Priorities, March 8. https://www.cbpp.org/sites/default/files/

atoms/files/12-18-13hous.pdf.

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Johnson, Stefanie K., Jessica Kirk, and Ksenia Keplinger. 2016. “Why We Fail to Report Sexual

Harassment,” Harvard Business Review, October 4. https://hbr.org/2016/10/why-we-fail-to-report-

sexual-harassment

Keeley, Theresa. 2006. “Landlord Sexual Assault and Rape of Tenants: Survey Findings and

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Maxwell, Jill. 2006. “Sexual Harassment at Home: Altering the Terms, Condition, and Privileges of

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284 Refereed Papers

Departments

In this issue—

• Affordable Design

• Data Shop

• Foreign Exchange

• Graphic Detail

• Industrial Revolution

• Policy Briefs

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 285

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

286 Departments

by Regina C. Gray

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.

2019 American Institute of Architects

(AIA)—HUD Secretary’s Housing &

Community Design Awards

Regina C. Gray

U.S. Department of Housing and Urban Development

The Jury:

Simon Ha, AIA (Chair)

Principal, Steinberg Hart, Los Angeles, CA

Kai-Uwe Bergmann, FAIA

Partner, Bjarke Ingels Group (BIG)

Hans Butzer, AIA

Director, Butzer Architects and Urbanism

Mary Cerrone, AIA

Mary Cerrone Architects

Regina Gray, Ph.D.

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

Rachelle Levitt, Retired

Office of Policy Development & Research, 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.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 287

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

Gray

In this edition, we feature the winners of the 2019 American Institute of Architects—HUD

Secretary’s Housing & Community Design Awards. Recipients were recently honored at the annual

meeting of the American Institute of Architects in Las Vegas, Nevada.

Introduction

For nearly two decades, the Office of the Secretary of Housing and Urban Development has

collaborated with the American Institute of Architect’s (AIA) Housing Knowledge Community to

sponsor four prestigious housing design awards. During that time, the housing industry, in general,

and the architectural community, in particular, faced challenges during a period of economic decline

but has recently witnessed a resurgence of activity in response to the demand for affordable, quality

housing. The four awards represent HUD and AIA’s commitment to seeking feasible solutions to the

affordable housing challenges that many low-income communities confront.

This is not just an awards competition that rewards excellent design. For each of the four award

categories, applicants must also address how their submission responds effectively to the following

AIA-HUD established goals: (1) implementing sustainable development practices; (2) ensuring

that low- and moderate-income families have access to quality affordable housing; (3) promoting

innovative design; (4) integrating social and cultural norms into the built environment; and (5)

responding to client or resident needs. This Departmental piece will highlight the winners of this

competition. The following four recipients demonstrate how affordable housing does not have

to be limited to a certain building type or design format. Rather, innovation can occur through

thoughtful design that is also reflective of the goals of a community, while at the same time

providing a model for how other communities can expand affordable housing without sacrificing

aesthetics or ingenuity.

Excellence in Affordable Housing Design Award

Winner: Williams Terrace, Charleston, South Carolina (David Baker Architects)

The Excellence in Affordable Housing Design Award recognizes projects that demonstrate how

affordable housing design promotes equitable development and proves that good design is not

exclusive to higher-income community projects. Williams Terrace is an adaptive reuse project that

replaces an older development that sustained damage from Hurricane Hugo. This project was

specifically planned as housing for low-income seniors. Although located in a high-velocity flood

zone, the builders incorporated environmentally sustainable elements into the building itself and

the surrounding natural environment that protects the conditions of the property and residents in

the event of a disaster.

Sustainability. In light of the potential for the recurring natural disasters and extreme temperatures that often characterize Charleston, the judges thought that the effort to incorporate sustainability

elements throughout the development was particularly noteworthy. All the newly built homes are

oriented to the northwest to avoid heat loads from eastern and western exposure. The housing

units incorporate glazing (a wall or window made of glass) at north and south to optimize daylight

and enable solar control. At the east and west, glazing was minimized. The south-facing windows

have box frames with calibrated overhangs and sides to shield from high-angle sunlight and are

288 Affordable Design

2019 American Institute of Architects (AIA)—

HUD Secretary’s Housing & Community Design Awards

shaded 100 percent of cooling hours. West-facing windows are shaded approximately 65 percent of

cooling hours.

Both historic and contemporary building design common to Charleston’s cultural traditions were

interwoven into the development. For example, all the homes have high ceilings and are shaded

by the deep piazzas and double overhangs on south-facing window boxes and cooled with cross-

ventilation and ceiling fans. Outdoor circulation and inviting open-air stairs minimize elevator

use and reduce the amount of conditioned space. Local and durable materials were used, which

reduced shipping costs and minimized maintenance needs.

Recycled building materials are interwoven in the physical structure of the Williams Terrace homes.

Innovation. This is an interesting approach to addressing the needs of a senior community. Every amenity of the development incorporates some aspect of traditional design. One innovative

feature, however, is the sliding shutters that cover all the windows. The sliding feature allows the

resident some flexibility to either dim or open the shades to sunrays, but the feature itself also

provides for multi-level circulation on the porches and through every floor. The 41 one-bedroom

apartments are a version of the southern “shotgun” typology, with bedrooms toward the rear for

privacy and social living areas connected to the shared porches, extending the living space outward

and allowing for through-ventilation in every unit. The careful attention to the character of the

community and the history of Charleston is to be lauded.

Affordability and Social Impact: Williams Terrace Senior Housing is the first dedicated housing for low-income seniors in the city of Charleston. The demand for senior housing is particularly high in

the city, as Charleston is a popular retirement community. For even the residents who have lived in

the city all their lives and picked up the pieces after the Hurricane Hugo disaster, however, aging in

place is the ultimate goal. For these seniors, in particular, living in the city is their choice.

Cityscape 289

Gray

Creating Community Connection Award

Winner: Anchor Place, Long Beach, California (The Architects Collective)

The second award category, called the Creating Community Connection Award, acknowledges projects that use housing as a platform to transform a community, either through large-scale redevelopment

or by creating an entirely new community with new housing options that connect housing to the

larger community. In either case, winning submissions are recognized for demonstrating how

important place-based planning is for communities affected by major challenges. Anchor Place was

selected in this category for addressing the homelessness crisis in the Long Beach neighborhood.

Essential features of the planned community are a recently renovated naval housing development

used by homeless vets, a new community center, a much-needed upgrade to the existing

greenspace, and improvements made to the street landscape to enhance connectivity. This holistic

approach to community planning serves as a model for communities similarly situated.

Sustainability. Anchor Place design architects and partner owner, Century Villages at Cabrillo

(CVC), became one of the first projects in Southern California to receive a Platinum certification

from the USGBC LEED for Homes Multifamily Midrise rating system. The rating reflects the

partners’ commitment to creating communities that connect people to place, with supportive

services located conveniently close or within the development. Addressing sustainability, there

are four strategies that the team implemented to improve building performance throughout

the development: (1) integrating building systems, (2) increasing the efficiency of the building

envelope, (3) reducing energy loads through the use of on-site renewable energy systems, and (4)

incorporating carbon-neutral fuel sources.

Anchor Place is a fairly large multifamily housing complex that has been recognized for its high energy performance.

The pursuit of high-performance, integrated building systems approaches included strategic

solar orientation, window performance, window shading, natural lighting, ventilation, water

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heating, and mechanical system efficiency. As a result of these efforts, Anchor Place was identified

for having exceeded the 2008 California Energy Code (Title 24) by 15.8 percent. The building

insulation, glazing U-value, and solar heat gain coefficient exceeded the 2013 Title 24 minimum

standards. Energy loads were reduced for plugs, heating, cooling, lighting, and water heating. In

addition, plug load was reduced through a vacancy sensor. High-efficiency HVAC units (SEER 15.5

or better) and filters (MERV 13) were used to improve indoor air quality. All buildings incorporate

LED lighting, and the Energy Star lighting system includes a cut-off optic for lighting spillage

reduction. The result is that the lighting control system reduces energy usage to 50 percent through

the vacancy sensor. To reduce gas usage, in particular, a solar hot water pre-heating system was

installed with a 44 percent solar fraction, thereby reducing the amount of gas needed.

Anchor Place boasts high-resolution, high-performing windows and low volatile organic compounds, or VOC, flooring.

Innovation. It is not surprising that the most innovative aspects of Anchor Place involve the

additional sustainability features that not only aim to improve building performance but also to

enhance healthy living. For example, building materials, such as linoleum tile with low-to-no

VOCs, were installed throughout the building, while durable polished concrete flooring with no

VOCs was used in the common areas. The use of these healthier materials ensures better indoor

air quality. Nearly 90 percent of the construction waste was diverted from landfills, thus protecting

residents from potential health hazards. Low flow plumbing fixtures were used throughout the

building. The project provides a 64-percent reduction in estimated irrigation water demand.

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The Anchor Place project demonstrates how balancing goals of affordability and environmental sustainability can be achieved.

Affordability and Social Impact. The owner’s primary need was to deliver a multi-unit housing

project that helps break the cycle of homelessness, works synergistically within the existing campus

context and encourages residents to take part in the services and activities provided, thereby

strengthening individual life outcomes and community cohesion. Arbor Place boasts 120 units

of affordable housing, which includes 95 one-bedroom, 20 two-bedroom, and 5 three-bedroom

units. All units are restricted between 30 percent and 60 percent AMI; those include one-bedroom

units with rents ranging from $507 to $845; two-bedroom units ranging from $608 to $1,216;

and three-bedroom units ranging between $703 and $1,406. In response to the need for more

affordable housing units, about 45 percent of all the units are priced at 30 percent of the AMI.

In this Long Beach, California neighborhood, multimodal transportation options are a must for

most residents in the development. For this community, aside from health and housing challenges,

transportation has been a major barrier. To address this challenge, Anchor Place partners expanded

bus routes that connect the residents to the central business district. The residents also noted that

bus stops should be located within the development itself so that those with mobility concerns

can also use the service. Moving about the development is made easier through the use of an

open-air grand staircase that connects to a network of auxiliary staircases, enabling residents to

travel from the ground level to any floor in the building. Disabled residents can use any number of

connecting elevators, widened sidewalks, and ample open space for pedestrians to move about the

development. With four wings, the building design creates three courtyard gathering spaces that

connect to community-centered amenities located prominently on the ground floor. The landscape

design, including circulation and courtyards, reinforces the strategy of prominently locating

support services and amenities by leading residents directly to them.

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Community-Informed Design Award

Winner: 8869 Avis, Detroit, Michigan (Detroit Collaborative Design Center)

The Community-Informed Design Award recognizes design that supports physical communities as

they rebuild social structures and relationships that may have been weakened by outmigration,

disinvestment, and the isolation of inner-city areas. Applicants for the award are asked to address

how they address disinvestment in their communities by incorporating creative ideas from those

in the community who are most marginalized or disadvantaged. This project—8869 Avis—was

selected as one of the jury’s favorite entries.

Sustainability: A centerpiece of the 8869 Avis project is the use of existing building materials, uniquely incorporated through the use of graffiti art. For this model, the porch is enclosed by

geometric-patterned ornamental ironwork screens that reference the wide fencing structures

prevalent in the neighborhood; they enhance open community while also protecting the residents.

This feature was identified by the community of residents, some of whom contributed art to the

structure or building materials laying around from vacant properties. Local metalworkers fabricated

the screens. The mural that wraps throughout the building was designed by a late street artist and

completed in tribute by an Inside Southwest Detroit program alumnus.

The objective of the Avis center is to provide a place where youth may illustrate cultural heritage through artistic expression.

The renovated project is beautified by new, colorful but high-performing insulated glass windows

to maximize natural daylight and reduce the need to use artificial lights. LED lights were integrated

throughout the building to increase efficiency in the main interior spaces and on the exterior.

Bathroom and exterior lights were installed with sensors and timers to minimize overuse. Finally,

an energy-efficient water heater rooftop unit cut utility usage by half in 2018.

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Innovation: One of their flagship initiatives, “The Alley Project,” transformed a neighborhood

alley and surrounding vacant lots into an inspirational graffiti art gallery that connects neighbors

and youth to each other as well as to community assets. The 8869 Avis development provides an

anchor to The Alley Project through the renovation of an existing 2,400-square-foot building into

a community center, the Inside Southwest Detroit headquarters, and a leasable tenant area. Rather

than tear down the old building, the new 8869 Avis development not only rids the community of

a former eyesore, the center aids in offering at-risk youth a variety of social activities, job training and education-based services, and programs to prevent homelessness or displacement. The art is

not only provided by area artists—Avis extends opportunities to the area youth who contribute

their artistic talents to the project.

Affordability and Social Impact: The project began with a participatory process that engaged

representative stakeholders—graffiti artists, skateboarders, kids, grandparents, and other neighbors

of all ages—in project planning and design decisionmaking, resulting in a community space that

responds to local culture, needs, and opportunities. A series of community workshops, focus

groups, and neighborhood celebrations gave people the agency and opportunity to guide the

design of the building. The participatory process included activities that facilitated collaborative

visioning and programming, informed site design including key contextual relationships linking

to The Alley Project, and envisioned material and facade choices that support both security and

transparency. This collaborative design process resulted in spaces and design elements that reflect

the neighborhood’s identity and vibrancy, embodied in a well-used community hub for creative

programming and ongoing collaboration.

The Alley Project, ultimately, provides a safe haven for artists to express themselves and gives them

additional occasions to engage with the community and potentially expand their artistic endeavors to

other opportunities.

Housing Accessibility: The Alan J. Rothman Award

Winner: IFF Access Housing, Chicago, Illinois (Landon Bone Baker Architects)

Named in remembrance of a HUD engineer who championed affordable housing for the disabled,

the Alan J. Rothman Award is given to exemplary projects that demonstrate excellence in improving housing accessibility for people with disabilities. The IFF Access Housing project allows people

with disabilities to live in environments that resemble conventional or traditional single-family

neighborhoods. This is a different approach to the multifamily projects that we often see in the

accessible housing space. The jury focused on the universal design features of the project, as well

as visit able features that the disabled can use that are modern and contemporary.

Sustainability: The IFF Access Housing project was recognized, in part, for both rehab and new

construction that meet Energy Star testing requirements and surpass minimum requirements

for insulation and sealing for rehabs, resulting in low-cost energy bills for residents. Choosing

to rehab existing buildings saves countless pounds of material from the landfill and reduces the

amount of embodied energy required to manufacture and transport new materials to the site as a

replacement. Salvageable materials were reused and incorporated throughout the project where

possible during construction.

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IFF Access Housing emphasizes the importance of accessible design features that do not resemble traditional housing for persons with disabilities.

Innovation: Recognizing that disabilities take many forms, each apartment features both universal and accessible design features. Ground-floor units are all fully accessible with zero-step thresholds

and low peepholes; wheelchair-friendly kitchens with roll-under sinks and pull-downs in upper

cabinets; front-loading washers and dryers in the units; and thoughtfully designed bathrooms with

roll-in showers. Every unit in the project features design elements that serve all, including attractive contrast flooring borders for people with low vision; visual doorbells and strobe alarms for the

deaf, and soothing color palettes for those with sensory difficulties.

The physical design of IFF Access Housing fits with the character of other houses in the neighborhood.

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Affordability and Social Impact: The IFF Access Housing project’s goal is to preserve affordable housing while creating accessible options for people with disabilities. In Illinois, a disproportionate

number of low-income disabled persons live in institutions because they lack community-based

accessible housing alternatives.

At IFF, the focus is on creating a community where persons living with various disabilities are not isolated from other residents in the community; rather, they live, work and enjoy social spaces as a community.

The IFF Access Housing project provides housing alternatives for individuals with disabilities

to live independently. What sets this project apart is its human focus and scale. Rather than

concentrating people with disabilities in a single site, Access Housing gives people the opportunity

to thrive in charming and attractive homes that blend into communities of their choosing.

The AIA/HUD Secretary’s Housing Community Design Awards program is one of several

award programs that the Office of Policy Development and Research launched with national

organizations where the mission aligns with Departmental policy priorities. This year’s winning

projects were screened through the Department’s internal vetting process which consisted of

the Departments of Community Planning & Development in Charleston, South Carolina; Long

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Beach, California; Chicago, Illinois; and Detroit, Michigan; and the Office of Fair Housing and

Equal Opportunity in San Francisco, California; the Office of Assessment Management and

Portfolio Oversight in Washington, D.C.; and, the Inspector General’s Office, Washington, D.C. as

appropriate. No adverse findings were reported. The awards were announced on June 7, 2019, in

a special ceremony and reception during the A’19, or the 2019 American Institute of Architects

Conference in Las Vegas, Nevada.

Postscript

The competition is thoroughly documented online.

To learn more about the award:

https://www.huduser.gov/portal/about/housingCommDesign-2019.html

To read about the AIA/HUD award guidelines:

https://www.huduser.gov/portal/sites/default/files/pdf/AIASecretaryAwardsBrochure2020.pdf

To learn more detail about this year’s winning submissions, as well as prior years’ winners:

https://www.huduser.gov/portal/about/housingCommDesign-2019.html

Author

Regina C. Gray is a Social Science Analyst in the Affordable Housing Research and Technology

Division of the U.S. Department of Housing and Urban Development.

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298 Departments

by Eliza Wallace, Eileen Divringi, and Keith Wardrip

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.

A New Cost-Based Index of Housing

Quality and Repair Needs

Eliza Wallace

PolicyMap

Eileen Divringi

Keith Wardrip

Federal Reserve Bank of Philadelphia

Abstract

The American Housing Survey (AHS) has long been a powerful tool for understanding housing problems

in the United States. Traditionally, researchers have relied on subjective measures of housing adequacy or other abstract indices for quantitative analyses of housing quality. In addition to the characteristics of households or units with repair needs, when crafting policy solutions or guiding resources, decision makers need to know how much investment might be needed to bring the housing stock up to standard.

PolicyMap and the Federal Reserve Bank of Philadelphia have addressed this need by developing a

novel method to assign estimated repair costs to housing problems reported in the AHS. In this article, we describe how we merged a proprietary repair cost dataset with microdata from the AHS public use

file (PUF) to estimate the total cost of repairs that would be needed for each surveyed housing unit, using survey weights to aggregate estimates at various geographic levels. We briefly present key national findings on repair needs. Summary tables of these findings and more are available to the public for

download.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

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Wallace, Divringi, and Wardrip

Introduction

Building on recent efforts to quantify housing quality issues, researchers and data analysts at the

Federal Reserve Bank of Philadelphia and PolicyMap have developed a cost-based index of repair

needs that combines detailed data on housing conditions from the American Housing Survey

(AHS) with estimates of the costs of needed repairs. This article, adapted from a technical appendix

(Wallace et al., 2019) to a Federal Reserve Bank of Philadelphia Special Report (Divringi et al.,

2019), provides an in-depth overview of the development of this index, detailing the motivation

for the approach, the methodology for creating the index, and the basic summary statistics.

Background

Despite the widespread finding that severe housing problems have declined in the United States over

the past half-century, adequate quality housing is still inequitably distributed across socioeconomic

lines (Kutty, 1999; Holupka and Newman, 2011; Mundra and Sharma, 2015; Boehm and

Schlottmann, 2008; Jacobs et al., 2009). The primary dataset used to track both housing quality

trends and socioeconomic disparities in housing is the AHS, administered by the U.S. Department

of Housing and Urban Development (HUD) and the U.S. Census Bureau. Since 1984, the AHS has

published ZADEQ, a composite measure of housing quality—since renamed ADEQUACY.1 This

measure classifies housing units as adequate, inadequate, or severely inadequate. Housing units

flagged as inadequate have one or more severe problems in the unit’s plumbing, electrical, or heating

systems, or have a number of maintenance problems such as water leaks, holes in the floor, or rats.2

This measure is widely cited and is included in HUD’s biennial Worst Case Housing Needs report to Congress (Watson et al., 2017). Recent research suggests, however, that this measure understates

the extent of problems in the U.S. housing stock (Emrath and Taylor, 2012; Eggers and Moumen,

2013b), and some have questioned whether its criteria meaningfully reflect an underlying housing

quality construct (Newman and Garboden, 2013; Eggers and Moumen, 2013a).

Considering these limitations, there have been several recent attempts to create more nuanced

measures of housing quality. Three recent papers have informed our approach to the cost-based

index. Emrath and Taylor (2012) applied a hedonic model to AHS data to create a measure

based on the degree to which reported problems reduced expected home values and rents. Their

approach indicated that housing inadequacy was likely more common than the ADEQUACY

measure suggested, particularly among single-family homes and units where children are present.

Eggers and Moumen (2013b) proposed the Poor Quality Index (PQI), which assigned weights

based on subjective criteria to unit-level housing problems reported in AHS. Their results revealed

a heavily right-skewed distribution in which most units reported no housing deficiencies, a

significant portion were assigned low PQI values, and a small segment was found to have high PQI

values indicative of severe disrepair. Lastly, Newman and Holupka (2017) developed and tested

several potential quality indices, finding a strong correlation between Eggers and Moumen’s PQI

and more empirically-derived weighting schemes.

1 Email correspondence with HUD staff confirm that both variable names pertain to the same measure. For details, see the AHS online codebook, available at https://www.census.gov/data-tools/demo/codebook/ahs/ahsdict.html.

2 The specific criteria for determining whether a unit is inadequate or severely inadequate have changed slightly over time. See appendix E, page 73 of Watson et al. (2017) for the full, current definition.

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As one of few papers to integrate repair cost information with AHS data, Listokin and Listokin

(2001) similarly informed the development of our index. They categorized each housing unit

included in AHS into one of four repair intervention levels based on the intensity of its rehab

needs—none, minor, moderate, or substantial. They then applied flat repair cost estimates

provided by housing industry experts to units in each category, aggregating to estimate regional

and national summary figures. The cost-based index outlined in this article adapts Listokin and

Listokin’s practical emphasis on estimating repair costs with the recent efforts to develop more

meaningful summary measures of housing disrepair. The resulting index provides an intuitive and

policy-relevant indicator of repair needs.

Data Sources

To develop our cost-based index, we combined two data sources: (1) the AHS PUF, and (2) a

custom RSMeans dataset from Gordian that estimates the costs of repairs for each type of housing

problem reported in the AHS PUF.

American Housing Survey

The AHS, established in 1973, is conducted every other year through personal interviews either of

the householder or, in the case of vacant units, a landlord or other person with detailed knowledge

of the property. The AHS includes information on a range of housing-related topics, including

housing characteristics, housing costs, home improvements, demographics of heads of household

and tenants, reasons for recent moves, and housing problems. It is currently the only publicly

available, nationally representative source of data with highly detailed information on these

characteristics of housing units.

The cost-based index outlined in this article was developed based on the 2015 AHS PUF, which

includes anonymized survey responses, and applied to the subsequently released 2017 PUF, for

which the microdata codebook is identical for the variables used to calculate the index. For the

purposes of this analysis, we considered only housing units that were occupied as a primary

residence and for which an interview was conducted. Out of a total of 66,752 housing units

surveyed, 57,984 were occupied housing units where an interview was conducted (INTSTATUS =

1), 1,054 were occupied and interviewed but categorized as “usual residence elsewhere,” and 7,714

were vacant.

Custom RSMeans Database from Gordian

The research team worked with Gordian, a company that provides residential and facilities

maintenance, construction, and repair cost data for real estate professionals, to assign specific

repair interventions to each housing problem identified in AHS and to estimate the associated

costs. The majority of these estimates were based on the RSMeans 2018 Contractor’s Pricing

Guide database of Residential Repair & Remodeling Costs (Gordian, 2017). A subset of repair

interventions was not available in the Contractor’s Pricing Guide data. The missing interventions

largely pertained to major structural and plumbing repairs and were omitted either because the

scale or type of repair required the use of a contractor accustomed to working on larger commercial

projects or because RSMeans could not produce reliable estimates for smaller contractors. For

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these, RSMeans substituted estimates from the 2018 Facilities Maintenance & Repair Costs

database. Estimates based on the Facilities Maintenance & Repair Costs database assume the use

of open shop labor, which is associated with lower hourly wages than union labor but still higher

than the wage rates used in the Contractor’s Pricing Guide estimates. Both sources of estimates

reflect national average costs inclusive of materials, labor, contractor overhead, and contractor

profit. For estimates aggregated at the metropolitan statistical area (MSA)-level, dollar values are

adjusted using regional multipliers derived from Gordian (2017).

Methodology

The following sections describe the development and validation of the repair cost index. In most

cases, mapping repair cost estimates to reported housing quality issues was a simple one-to-one

merge with adjustments for unit or household size, depending on the intervention. In some

housing units, however, one repair might obviate the need for another repair. In these instances,

a hierarchy of repairs was established to avoid redundancies. Finally, using survey weights

provided in AHS, unit-level repair estimates were aggregated to produce national-, regional-, and

MSA-level estimates of repair costs according to various demographic, economic, housing unit,

and other characteristics.

Identifying Housing Deficiencies in AHS

The research team began by identifying the AHS variables associated with physical deficiencies

in the housing unit, primarily from the “Housing Problems” module.3 Several additional variables

provided context that further specified the types of repairs needed in each surveyed housing unit.

For example, repairs to a cracked or crumbling foundation would vary based on whether the unit

has a full basement, crawlspace, or some other foundation type. Similarly, the cost of repairs for

broken heating equipment would be determined by the type of heating system. For certain housing

problems, developing estimates required assumptions about the size and occupancy of the housing

unit. Accordingly, we used AHS variables describing the size of the unit, number of floors, and

number of residents to scale the repair estimates, as described in the table accompanying the full

technical appendix table.4

The initial combination of housing problems and context variables resulted in 99 repair scenarios.

The research team dropped those with frequencies below 0.05 percent in the unweighted data,

resulting in the 66 combinations of reported problems and repairs presented in the technical

appendix table.

3 There are 48 variables categorized under the “Housing Problems” topic in the AHS code book. Of these, several are excluded from this analysis because they represent a more generalized version of related variables (for example,

“Flag indicating unit was uncomfortably cold for 24 hours or more last winter”) or are not clearly related to structural deficiencies. Although not listed under the “Housing Problems” topic, the variables HEATTYPE and HOTWATER

were included, since they capture responses that have historically been considered housing quality issues (such as lack of heating equipment).

4 Appendix table available at https://philadelphiafed.org/-/media/community-development/publications/special-

reports/home-repair-costs-technical-appendix-table.xlsx?la=en (Note: this URL will initiate a download of an Excel workbook.)

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Repair Cost Assumptions

Once the repair scenarios were established, experts at Gordian outlined the details of the materials

and labor hours required for each repair. The AHS provides limited information about building

materials (for example, it does not specify the type of roofing materials). Accordingly, Gordian staff

assumed the use of common, cost-effective building materials and used their experience to supply

information on any secondary tasks such as demolitions or excavations associated with these repairs.

Certain repairs also required assumptions about the proportion of the area affected or the number

of items in need of repair. For certain roofing repairs, for instance, we assumed that 5 percent of

the roof would need repair. For units reporting broken or boarded windows, the AHS does not

specify how many are damaged, so we assumed an average of 1.5 would need repair. Similarly,

when a unit reports a lack of functioning electrical outlets in each room, we assumed an average

of 1.5 plugs would need to be installed. Such assumptions were arrived at in conversation with

industry experts at Gordian and are detailed for each applicable intervention in the technical

appendix table.

For the small number of repair scenarios that were not priced out by Gordian, we substituted

the costs of comparable repairs. For example, the AHS reports on mold problems in bathrooms,

kitchens, living rooms, basements, and bedrooms. For these repairs, we asked Gordian to estimate

the cost of remediating mold in a fixed square foot area of a bathroom, and we applied that cost to

any room type.

Adjustments for Unit and Household Size

In the AHS PUF, the variable for unit total square footage is provided as a binned categorical

variable. To scale repair costs that were provided on a per square foot basis, we assumed the

midpoint of each category. For units for which the size variable was missing or not reported,

we imputed 1,500 square feet based on the overall median unit size reported in the AHS Table

Creator.5 For repairs pertaining to the dimensions of the unit, we assumed that the footprint of the

housing unit would be equal to the total area of the unit divided by the number of reported floors

and conservatively estimated that the length of an outside wall of the housing unit would be equal

to the square root of that footprint size.

For replacements of heating systems or water heaters, the number of potential interventions was

limited by the range of equipment size options available in the RSMeans dataset. Accordingly,

for heating equipment replacements, unit size categories were collapsed into two-to-five groups

depending on the type of system, and those groups were assigned a corresponding equipment size.

For water heater replacement, we used the number of residents reported in the AHS to inform the

size of water heater required for each unit. Equipment specifications are detailed in the technical

appendix table.

5 See https://www.census.gov/programs-surveys/ahs/data/interactive/ahstablecreator.html?s_areas=00000&s_

year=2017&s_tablename=TABLE2&s_bygroup1=1&s_bygroup2=1&s_filtergroup1=1&s_filtergroup2=1.

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Assigning Repair Interventions

For several equipment-related repairs, variables recording the frequency or severity of the problem

indicated whether a repair or replacement would be most appropriate. For example, when a

house on well water has one or two interruptions in water service (NOWATFREQ), the well

piping may need to be replaced. Another house experiencing more breakdowns may need a much

more expensive well pump replacement. Similarly, for units reporting one or two heating system

breakdowns that resulted in uncomfortably cold temperatures, we applied a low-cost maintenance

intervention. For more frequent breakdowns, we assumed the equipment needed to be replaced.

Repair Hierarchy

Housing units may have more than one problem that could be solved by a single repair. To avoid

overestimating repair costs, we developed decision rules on which repairs supersede others.

For example, four different AHS variables denote roofing problems (ROOFSAG, ROOFSHIN,

ROOFHOLE, and LEAKOROOF) and needed repairs would overlap. Repairing a sagging roof

requires costly structural work that likely offsets other roof repair needs. For a detailed description

of which repairs superseded others, see the technical appendix table.

National, Regional, and Metropolitan Estimates

After summing the total repair costs for each surveyed unit, we used the full sample weighting

variable (WEIGHT) to calculate weighted summary statistics and aggregate repair costs tabulated

by a number of demographic, geographic, and housing type variables. The National PUF includes

variables that specify the Census Division6 and, for units in the 15 largest metropolitan areas,

MSAs, which can be used to tabulate estimates at these geographies.

All tabulations at the national and Census Region levels use the weighted national average costs

provided by Gordian. To account for regional variations in construction costs, dollar-value

estimates for the 15 largest MSAs are adjusted using location factors for the largest principal city.

These location factors were drawn from Gordian (2017), which provides cost multipliers at the

three-digit ZIP Code-level for most major cities.

Validation

To assess the internal validity of our repair cost index, we evaluated its relationship to other AHS

variables that can be reasonably expected to correlate with housing quality. First, as anticipated, we

found that each level of the categorical HUD housing quality indicator (ADEQUACY) is associated

with a progressively higher median and average repair cost estimate (exhibit 1).

6 Census Divisions are collections of states and the District of Columbia grouped by proximity. For more information, see https://www2.census.gov/geo/maps/general_ref/pgsz_ref/CensusRegDiv.pdf.

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A New Cost-Based Index of Housing Quality and Repair Needs

Exhibit 1

Comparison of Repair Cost Index to HUD ADEQUACY Composite Variable

ADEQUACY Value

Mean Repair Cost

Median Repair Cost

Adequate

$847

$0

Moderately Inadequate

$4,361

$2,440

Severely Inadequate

$6,487

$3,346

Notes: Means and medians include both units with and without repair needs.

Sources: Authors’ analysis of 2017 AHS PUF; 2018 RSMeans Data from Gordian

The research team also examined the respondent’s rating of the unit as a place to live (RATINGHS).

This is an ordinal variable ranging from one (worst) to ten (best). As expected, the repair cost index

has a highly statistically significant (p<0.0001) negative correlation with this measure (-0.21).7

While this association is moderate in strength, a large portion of units have an estimated repair cost

of $0. For these units and those with modest repair needs, resident ratings are likely to be heavily

influenced by other location and neighborhood factors.

Lastly, assuming that older units are more likely to experience housing quality issues, we

examined the association between the repair cost index and the binned ordinal variable that

denotes the decade the structure was built.8 Again, we found the expected, highly significant

(p<0.0001) negative association (-0.13),9 indicating that newer units are associated with less costly repair needs.

Limitations

In addition to the lack of contextual information on the building materials and magnitude of

certain reported issues as described earlier, there are housing deficiencies that may present threats

to the safety and well-being of residents that are not reported in the standard AHS modules. These

include missing or broken stairs and banisters, which present major injury risks.10 Furthermore,

PUF does not capture the need for adaptive modifications that may be critical to a resident’s ability

to safely navigate their unit and perform everyday tasks. Housing deficiencies that are unlikely to

be observed in residents’ everyday lives, such as lead exposure, water contaminants, and indoor

air quality issues, are similarly unavailable. Additionally, our cost estimates do not include local

and national regulatory factors that may significantly affect the cost of repairs, such as lead removal

requirements or environmental performance standards, though some of these variations may be

reflected in regional cost adjustments. Lastly, our inability to develop estimates for vacant units

likely understates the total magnitude of disrepair in the national housing stock (Emrath and

Taylor, 2012).

7 Pearson correlation coefficient based on weighted data.

8 The precise year the structure was built is not available in the PUF.

9 Pearson correlation coefficient based on weighted data.

10 Questions regarding these and other household health and safety issues have been included in supplementary modules in prior surveys, but are not part of the standard battery of survey questions.

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Due to data constraints, our repair cost index likely understates the magnitude of repair needs

for the multifamily housing stock. Many of the repair cost estimates supplied by Gordian assume

the intervention applies to a single-family home unless otherwise specified (for example, certain

manufactured housing-specific repairs). For many interventions, repair costs are likely to be

comparable in different unit contexts (such as repairing a crack in an interior wall), though for

others there may be substantial differences (such as repairing a 10th story window). Furthermore,

AHS respondents in multifamily housing are not asked most questions pertaining to structural

housing issues (for example, issues related to roofs, foundations, exterior walls, and building

systems). As a result, we are unable to capture the need for more extensive repairs to larger

residential buildings.

Given these limitations, our cost-based index should be understood as an approximate measure of

the costs to mitigate the substandard conditions reported in AHS.

Selected Findings

The following section provides tabulations of our repair cost index. More extensive results are also

available in Divringi et al. (2019) and Wallace et al. (2019). Many of our results align with those of

other studies on who is disproportionately living in lower quality housing (Holupka and Newman,

2011; Mundra and Sharma, 2015; Jacobs et al., 2009; Watson et al., 2017). Our results add nuance

by revealing which groups are more likely to need the most expensive repairs. The following results

are uncontrolled, weighted tabulations.

Despite the decline over the last several decades in inadequate housing, our analysis indicates that

approximately 35.8 percent of housing units needed repair in 2017, though many of these repair

needs were modest (exhibit 2). Our analysis reveals that the plurality of housing units that needed

repairs had repair needs between $1,000 and $5,000 (15.7 percent of housing units), and the

median cost of repairs was $1,449. The distribution of repair costs has a long tail to the right. Only

0.2 percent of housing units needed repairs totaling more than $20,000 per unit. Based on housing

quality problems reported in the 2017 AHS, we estimate that repair needs for the occupied housing

stock totaled $126.9 billion.11

11 Cost estimates are reported in 2018 dollars.

306 Data Shop

A New Cost-Based Index of Housing Quality and Repair Needs

Exhibit 2

Percent of Housing Units by Repair Cost Category

70

64.2

60

50

40

30

cent of Housing Units

20

Per

13.7

15.7

10

4.3

1.9

0.2

0

No Repair Cost

$1-$1,000

$1,001-$5,000

$5,001-$10,000

$10,001-$20,000

Mor

e than $20,000

Repair Costs

Sources: Authors’ analysis of 2017 AHS PUF; 2018 RSMeans Data from Gordian

Frequency and cost of repair needs vary greatly by type or by affected system. Structural repairs

were the second most common repair need but made up more than half of the national aggregate

repair costs. Leaks, in contrast, were the most commonly required repair but constituted only

one-fourth of aggregate costs. The least expensive repair type was pest remediation which was

reportedly needed by nearly 5 percent of housing units but only made up approximately 2.2

percent of repair costs (exhibit 3).

Exhibit 3

Repair Costs by Category

Percent of

Number of Units

Aggregate Repair

Repair Category

Percent of Units

Aggregate Repair

(Millions)

Cost (Billions)

Cost

Electrical

6.7

5.5

$8.9

7.0

Heating

6.1

5.0

$5.4

4.3

Leak

20.3

16.7

$31.7

25.0

Pests

6.0

4.9

$2.8

2.2

Plumbing

4.7

3.9

$5.9

4.7

Structural

19.1

15.7

$72.1

56.8

Sources: Authors’ analysis of 2017 AHS PUF; 2018 RSMeans Data from Gordian

Cityscape 307

Wallace, Divringi, and Wardrip

Next Steps

To help inform the development and targeting of strategies for improving the quality of the

U.S. housing stock, we have developed national typologies of housing units with repair needs

in a companion report (Divringi et al., 2019). Building on this work, members of the research

team from the Federal Reserve Bank of Philadelphia are working with the AHS Internal Use

File (IUF), which provides detailed unit-level geographic identifiers, to conduct a multivariate

analysis of housing quality using the new repair cost estimates in combination with neighborhood

characteristics. This analysis will enable us to understand and describe the relationship between

neighborhood characteristics and housing repair needs. Ultimately, the objective of this analysis

will be to statistically model small area estimates of housing repair costs. These geographically

granular estimates will enable community development-oriented investors, developers, and

government officials to target the neighborhoods with the greatest need for housing improvements.

Acknowledgments

The authors would like to thank project partner Elizabeth Nash for her contributions to this

research. We would also like to thank George Carter, Ira Goldstein, Lauren Lambie-Hanson, Joseph

Kelble, and Matthew Streeter for their thoughtful comments and feedback that informed our

methodology.

Authors

Eliza Wallace is the Data Development Lead at PolicyMap. Eileen Divringi is a Community

Development Research Associate at the Federal Reserve Bank of Philadelphia. Keith Wardrip is the

Community Development Research Manager at the Federal Reserve Bank of Philadelphia.

References

Boehm, Thomas P., and Alan Schlottmann. 2008. “Housing Tenure, Expenditure, and Satisfaction

Across Hispanic, African-American, and White Households: Evidence From the American Housing

Survey,” Cityscape 10 (2): 95–158.

Divringi, Eileen, Eliza Wallace, Keith Wardrip, and Elizabeth Nash. 2019. Measuring and

Understanding Home Repair Costs: A National Typology of Households. Philadelphia: Federal Reserve Bank of Philadelphia and PolicyMap. https://www.policymap.com/issues/housing-quality/.

Eggers, Frederick J., and Fouad Moumen. 2013a. Housing Adequacy and Quality as Measured by

the AHS. Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy

Development and Research.

308 Data Shop

A New Cost-Based Index of Housing Quality and Repair Needs

———. 2013b. American Housing Survey: A Measure of (Poor) Housing Quality. Washington, DC:

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

Emrath, Paul, and Heather Taylor. 2012. “Housing Values, Costs, and Measures of Physical

Adequacy,” Cityscape 14 (1): 99–125.

Gordian. 2017. 2018 Contractor’s Pricing Guide: Residential Repair & Remodeling Costs with RSMeans Data. Rockland, MA: The Gordian Group, Inc.

Holupka, C. Scott, and Sandra J. Newman. 2011. “The Housing and Neighborhood Conditions

of America’s Children: Patterns and Trends Over Four Decades,” Housing Policy Debate 21 (2):

215–245.

Jacobs, David E., Jonathan Wilson, Sherry L. Dixon, Janet Smith, and Anne Evens. 2009. “The

Relationship of Housing and Population Health: A 30-Year Retrospective Analysis,” Environmental

Health Perspectives 117 (4): 597–604.

Kutty, Nandinee K. 1999. “Determinants of Structural Adequacy of Dwellings,” Journal of Housing

Research 10 (1): 27–43.

Listokin, David, and Barbara Listokin. 2001. Barriers to the Rehabilitation of Affordable Housing:

Volume 1 Findings and Analysis. Washington, DC: U.S. Department of Housing and Urban

Development, Office of Policy Development and Research.

Mundra, Kusum, and Amarendra Sharma. 2015. “Housing Adequacy Gap for Minorities and

Immigrants in the U.S.: Evidence from the 2009 American Housing Survey,” Journal of Housing

Research 24 (1): 55–72.

Newman, Sandra J., and Philip M.E. Garboden. 2013. “Psychometrics of Housing Quality

Measurement in the American Housing Survey,” Cityscape 15 (1): 293–306.

Newman, Sandra, and C. Scott Holupka. 2017. The Quality of America’s Assisted Stock: Analysis of the 2011 and 2013 American Housing Surveys. Washington, DC: U.S. Department of Housing and Urban

Development, Office of Policy Development and Research.

Wallace, Eliza, Eileen Divringi, Keith Wardrip, and Elizabeth Nash. 2019. Technical Appendix: A

Repair Cost-Based Index of Housing Quality. Philadelphia: Federal Reserve Bank of Philadelphia and PolicyMap. https://www.policymap.com/issues/housing-quality/.

Watson, Nicole Elsasser, Barry L. Steffen, Marge Martin, and David A. Vandenbroucke. 2017.

Worst Case Housing Needs: 2017 Report to Congress. Washington, DC: U.S. Department of Housing

and Urban Development, Office of Policy Development and Research. https://www.huduser.gov/

portal/sites/default/files/pdf/Worst-Case-Housing-Needs.pdf.

Cityscape 309

310 Departments

by Jeffrey Mosley

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

Katherine.C.Marinari@hud.gov .

Observing New Zealand’s Housing

Crisis: Efforts to Produce and

Finance Affordable Housing and its

Implications for U.S. Policies

Jeffrey Mosley

Principal, Jeffrey Mosley Community Development Consulting, LLC and Fulbright Specialist

Introduction

Homelessness, overcrowding, and cost burden are common crises facing people with no or low

incomes. The number of families who are homeless or struggling to reliably afford a place to live

remain at crisis levels across the United States. Replace the reference to the housing crisis in the

United States with New Zealand, but picture it to a greater degree. For decades, the United States

has maintained public and, increasingly, private investment through a generally consistent housing

finance regulatory framework, particularly the Community Reinvestment Act and investment

tools like the Low-Income Housing Tax Credit Program. Not-for-profit housing developers have

been mainstays in the ownership, development, and sponsorship of affordable housing across the

United States and have benefited from public-private capacity-building investments. New Zealand

has no such housing finance regulatory framework and has an inconsistent history of supporting

not-for-profit housing providers’ capacity to develop real estate. The net result is a chronically

underfunded public-private investment across the affordable housing continuum. This article

provides a cautionary tale to U.S. housing policymakers and stakeholders from another western,

albeit smaller, nation facing its own housing crisis and the level to which it is willing to commit

development capital resources to meet the needs of those most vulnerable.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 311

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

Mosley

This article summarizes a larger report from 2018, “Priming the Pump: Access to Capital and

Capacity to House New Zealanders,” identifying factors necessary for New Zealand to enable

sustained private capital to support affordable housing development. The country’s government

alone does not invest enough capital to meet society’s growing public and affordable housing needs

and has not developed a regulatory framework to strategically secure private sector investments.

The article also explores not-for-profit housing developer performance and capacity development

needs. Readers will learn the implications of not having the necessary policy settings to secure

private sector investments and understand capacity requirements for mission-based developers

who endeavor to address the crisis.1 The particular role and capacity needs of community-based

organizations provide a clear picture of the community development ecosystem and its needs

(Department of Internal Affairs, 2019). This research sought to refine considerations for investing

in communities that have exhibited challenges accessing capital and building local capacity.

New Zealand has long shown great pride in its egalitarian spirit, Māori culture, and sporting-

outdoor heritage. As a society that promotes its outdoor wilderness and pioneering spirit, the

country, in fact, is a fast-growing, urbanized nation struggling to meet the needs of longtime

residents and new arrivals. Despite its egalitarian ethos—the idea of “Kiwis helping Kiwis,” a spirit

that to some extent remains—large shares of society, particularly Māori and Pacific peoples, live in

entrenched poverty and experience greater social deficits in housing, health care, education, and

other areas of life. New Zealand, in fact, has greater income inequality and a higher proportion of

residents—especially Māori and Pacific peoples, who are homeless or living in insecure housing

tenures (defined by cost burden) when compared with the United States. Families across the

country2 face housing challenges, including lack of affordable stock, poor quality and/or unsafe

conditions (referred to as old, cold, and with mold), excessive cost burdens, and the need for

financial literacy education and housing counseling.

Efforts to address the New Zealand housing crisis have stalled. Government-led efforts, like

KiwiBuild—a 10-year government-led plan to deliver 100,000 new homes across New Zealand

in 2017—have been slow to market, build, and sell. High land prices have made KiwiBuild

unaffordable to first-time homebuyers; a reset was announced for July 2019, less than 2 years

after it launched. The government also struggles to meet the growing demand for social “public”

housing for those families who need it. Housing officials and industry stakeholders cite a lack of

development capital as a key constraint to resolving the undersupply. Access to affordable, buildable

land and a skilled construction labor force are also key development challenges. Smart urban

planning and local zoning codes are also key policy matters that the government is reviewing.

Understanding the capacity of mission-based housing developers was a principal element of

the author’s research. The community housing provider (CHP) sector serves a variety of roles

in providing affordable secure tenure housing: developer, builder, sponsor, and varying forms

of tenancy, property, and asset manager. New Zealand has 46 registered CHPs, including Māori

housing providers, operating across the country, most of which are primarily supportive service

1 The author’s 2018 report also looks at government’s history of operating social/public housing and the impact of varying leadership on the current state of the portfolio (Mosley, 2018).

2 Aotearoa is the Māori name for New Zealand. The author uses New Zealand throughout this article but notes the cultural significance of the indigenous people and their language.

312 Foreign Exchange

Observing New Zealand’s Housing Crisis: Efforts to Produce

and Finance Affordable Housing and its Implications for U.S. Policies

providers (CHRA, n.d.). Approximately 10 CHPs have the capacity to develop at scale, from

5–10 units to up to about 200–300 units of mixed-income, mixed-tenure residential units every

1 to 2 years depending on their access to public and private financing and land. The capacity of

local organizations to secure land and capital is a key component to further local development

and community progress. Not-for-profit developer capacity, in particular, has been identified as

a crucial element to meeting local need in New Zealand (Housing Shareholders Advisory Group,

2010). Officials see CHPs as a small but influential part of the development sector; however, in

the overall demand for housing supply, CHPs struggle for recognition as a valued partner. In fact,

most of the recent housing policy innovations, such as inclusionary zoning, shared equity, lease-

purchase, and other initiatives, have come from forward-thinking CHPs that have learned lessons

from overseas.

Setting the Stage

Demographics and Housing in New Zealand and the United States

Key differences separate New Zealand and the United States. The United States has a landmass

almost 37 times larger and has 67 times more people (see exhibit 1); however, the countries have

a similar share of those living in cities and suburbs versus rural areas (86 percent urban in New

Zealand and 81 percent urban in the United States). The author chose to compare New Zealand

and its largest city, Auckland, with California’s Los Angeles and San Francisco because the city

represents New Zealand’s most populous jurisdiction and provides similar comparisons at the state

and local levels.

Exhibit 1

General Demographic Snapshot: New Zealand and the United States

New Zealand

Auckland United States

California

Los Angeles San Francisco

Landmass (km2)

268,021

1,102

9,857,348

423,972

1,213.86

121.4

Population

4,844,400

1,657,200

325,719,178

39,536,653

3,949,776

884,363

Urban (%)

86.3

92.6

80.7

87

100

100

Rural (%)

13.7

7.4

19.3

13

**

**

Māori (%)

15

10.7

**

**

**

**

Pacific (%)

7.4

14.6

7.4

0.4

0.2

0.4

Asian (%)

11.8

23.1

5.2

13.9

11.7

34.2

Native

American (%)

**

**

1.0

1.1

0.7

0.4

African-

American/

1.2

1.9

12.6

5.5

8.9

5.3

MELAA (%)

Hispanic/

Latino (%)

**

**

17.3

38.9

48.7

15.3

** Mainly Samoan, other Pacific Islanders—including Māori—and Native Hawaiian.

Notes: Landmass information sourced from Wikipedia. MELAA describes the population that is Middle Eastern, Latin American, and African. For New Zealand, the population share of Hispanic/Latino individuals is included within MELAA.

Sources: United Nations, 2015; Landmass information from Wikipedia and CityMayors.com; Urban population data from World Population Review, Auckland and U.S. Census Bureau; New Zealand population from Stats New Zealand of December 2017; L.A. County 2017 population—U.S. Census American Factfinder Cityscape 313

Mosley

The U.S. population grew 16,973,640 (5.5 percent) from 2013 through 2017; New Zealand’s

population grew by 351,600 (7.5 percent) during the same period, essentially adding the population

of a new Christchurch to the country in a 5-year span. Auckland absorbed 47 percent of this increase

(Johnson, 2018). The strong population growth comes mainly from in-migration, with almost

248,000 net new arrivals recorded during this 5-year period, or about a 2.5-percent increase annually

(Johnson, 2018).3 States in the United States that recorded the fastest growth in 2018 were Nevada

(2.1-percent annual increase) and Idaho and Utah (both at 1.9-percent increase); none of these states

is nearly as populous as California (U.S. News and World Report 2018 rankings).

Some of New Zealand’s housing characteristics and challenges are similar to those in the United

States (see exhibit 2). For example, homeownership, a long-sought goal of families in both

countries, has declined in both nations mainly because of substantially increased house values in

major urban markets and the inability of household income to keep pace (exhibit 2). In addition

to rising house values, population growth and the lack of housing construction have further

constrained access to affordable housing for those at the lower income rungs. The shortage of

quality affordable homes for those with lower incomes has resulted in overcrowding and higher

cost burden. The rate of those experiencing homelessness has been an increasingly visible sign

that access to affordable housing is failing. Los Angeles, San Francisco, and Auckland reported

percentages of homeless populations that exceed the national averages. The share of homeless

people in the United States was 0.2 percent, compared with almost 1.0 percent in New Zealand.

Of Auckland’s residents, 1.4 percent were visibly homeless compared with 0.9 percent each for

Exhibit 2

General Snapshot: 2017 Housing in New Zealand and the United States

New Zealand Auckland United States California Los Angeles San Francisco

Homeownership Rate

63.2

61.5

64.2

55.2

36.8

43.4

Median Household

Income (New

54,558

61,452

78,668

67,169

54,501

86,265

Zealand $)

Median House Value

(New Zealand $)

323,169

700,400

262,643

443,400

549,800

927,400

Median Monthly Rent

(New Zealand $)

1,130

1,421

1,350

1,358

1,302

1,709

Number of Homeless

41,207

23,409

553,742

134,278

34,189

7,499

(% of Population)

(0.9)

(1.4)

(0.2)

(0.3)

(0.9)

(0.85)

Notes: United States: 64.2 percent, Fourth Quarter 2017 (U.S. Census); highest was 69.2 percent, Second Quarter 2004; California: 55.2 percent, First Quarter 2018; highest was 60.2 percent in 2006.; there were 28,649 homeless in 2001 and 34,000 in 2006. The estimated ratio of homeless in the United States in 2017 was 17 out of every 10,000 (PIT Estimate of People Experiencing Homelessness by Sheltered Status).

Sources: LA (2018) —So. CA Assn. of Governments. S.F. rate from FRED Economic Data (Fed. Reserve Bank of St. Louis). Number of homeless for New Zealand comes from OECD, Homeless estimate 2015: Number of homeless for Auckland comes from Auckland Council (2017). HUD—The Annual Homeless Assessment Report to Congress: Part 1, p. 8, Exhibit 1.1. Estimate of Homeless Californians (PIT Estimate, p. 12).2017 Greater LA Homeless Count Results—LA County homeless estimate for L.A—LA Homeless Services Authority; 2017 San Francisco homeless count—S.F. 2017 Homeless Count and Survey Comp. Report—

Applied Survey Research

3 For 2017, the largest sources of in-migration for New Zealand were returning New Zealanders, China, India, the U.K., Australia, and various African countries.

314 Foreign Exchange

Observing New Zealand’s Housing Crisis: Efforts to Produce

and Finance Affordable Housing and its Implications for U.S. Policies

the cities of Los Angeles and San Francisco. The combined number of people who are homeless in

these U.S. cities—41,688—exceeds the 41,207 reported across New Zealand.

In an international comparison of housing affordability (the comparison looked at 91 cities with

populations of 1 million or more), New Zealand is rated the second least affordable country—

measured “severely unaffordable” at 9.0.4 Hong Kong is the least affordable jurisdiction at 20.9;

the United States overall is measured as moderately unaffordable at 3.9 (Demographia, 2019: 2).

Unaffordability, as measured by the ratio of house price to income, jumped in New Zealand from

the late 1980s or early 1990s to 2018—from about 2.8 to 6.5—because of rising migration, low

housing production, rising home prices, and New Zealand’s prior adoption of urban containment

policies. By comparison, unaffordability in the United States rose from 2.6 to 3.5 percent

(Demographia, 2019).

According to the Organization for Economic Co-operation and Development’s (OECD) Better Life

Index, the United States and New Zealand present similar general housing profiles and pressures.

For example,

• Rooms per person: 2.4 rooms: The United States ranked 2/38; New Zealand ranked 3/38;

• Dwellings with basic facilities: The United States at 99.9 percent (ranked 7/38); New Zealand

at 99.7 percent (ranked 12/38);

This is juxtaposed to the following example:

• Housing expenditure (ratio of housing cost to household’s gross adjusted disposable income,

latest available year): The United States at 18 percent (ranked 7/38); New Zealand at 26

percent (ranked 38/38).

Looking internationally at income inequality, measuring the ratio of income to housing costs, New

Zealand generally is among the most unequal of the developed countries. The country’s Ministry

of Social Development (MSD, 2016) found that, in 2012, New Zealand ranked in the top third

of the most unequal countries at 33—the same as Australia, less unequal than the United States

and United Kingdom (ranked 40 and 35, respectively), but more unequal than Canada (32).

The income inequality and associated housing affordability crisis are linked mainly to the lack of

supply for those at all income ranges, resulting in escalating values. Housing affordability across

New Zealand, according to public opinion, was the top public policy priority for New Zealanders.

(Dodd and Griffiths, 2018). Most Democratic candidates in the 2020 U.S. presidential race have

raised housing as a key issue (NLIHC, 2019; CityLab, 2019). President Trump, citing the systemic

lack of affordable housing across the United States signed an Executive Order on June 25, 2019, to

create a council to eliminate regulatory barriers to affordable housing.5 Political attention and efforts to address the dearth of affordable housing in both countries must support strategies that recognize

the role of effective policies and regulations that promote production and preservation and reduce

income inequality.

4 Demographia rating uses the “median multiple”—median house price divided by median household income.

5 https://www.whitehouse.gov/presidential-actions/executive-order-establishing-white-house-council-eliminating-

regulatory-barriers-affordable-housing/.

Cityscape 315

Mosley

The United Nations Sustainable Development Goals

The United Nations (UN) developed a set of Sustainable Development Goals (SDGs) to address

global challenges, particularly those related to poverty and inequality, adopted by world leaders in

2015. The UN developed the 17 goals to create a pathway enabling a better and more sustainable

future, with the goals to be met by 2030.6 Sustainable Development Goal 11 focuses on Sustainable

Cities and Communities; Target 11.1 of this Goal is to ensure access for all to adequate, safe,

and affordable housing and basic services, and to upgrade slums. In July 2019, New Zealand

committed to the following indicators to meet SDG 11 and ensure it is responding to its current

housing crisis:

• Homelessness—This indicator will show the stability and security of New Zealanders’

accommodation. It will measure the number of people without safe and secure

accommodation. The specific indicator is being formulated.

• Housing affordability—This indicator will measure the proportion of households spending

more of their gross income on housing costs; 22 percent of households in 2018 reported

spending over 30 percent gross income on housing costs.

• Housing quality—This indicator will measure the physical condition of accommodations. The

specific indicator is being formulated.

• Overcrowding—This indicator measures households living in overcrowded conditions using

the Canadian National Occupancy Standard (CNOS), where a household is considered

crowded if it needs one or more extra bedrooms to sufficiently accommodate all household

members. Data to be available in 2020 (StatsNZ, 2016; NZ-HUD, 2019).

The United States’ official SDG 11 goal is to reduce the percentage of occupied housing units in

urban areas that are inadequate, measured by a lack of access to improved sanitation facilities.

From 2001 through 2015, this percentage decreased from 6.05 to 5.48.7 This comparison presents

an opportunity for the United States to consider expanding its set of indicators to increase its

measure of accountability in addressing its housing crisis; many of these indicators are currently

measured. SDG USA and Sustainable Development Solutions Network (SDSN), a global initiative

for the United Nations, conduct their independent analysis and track additional housing measures,

providing state-level metrics:

• SDG1—End Poverty—Affordable Housing (per 100 extremely low-income renter households)

• SDG 11—Sustainable Cities and Communities—Overcrowded Housing (percent of occupied

housing) and Rent Burdened Population (percent).8

6 For more information on the SDGs, see https://sustainabledevelopment.un.org/?menu=1300.

7 https://sdg.data.gov/sustainable-cities-communities/; American Housing Survey.

8 Sustainable Development Report of the United States 2018; SDG USA and Sustainable Development Solutions Network; September 2018.

316 Foreign Exchange

Observing New Zealand’s Housing Crisis: Efforts to Produce

and Finance Affordable Housing and its Implications for U.S. Policies

Accessing Capital for Affordable Housing

Accessing Development Capital in New Zealand

Communities across the United States and New Zealand struggle to provide affordable housing that

is safe and secure in tenure. The lack of sufficient public housing and the strain on families and

other households living with limited resources to secure private market rentals and homeownership

calls for continued and increased investment for affordable housing options. In New Zealand,

government and councils have created joint venture development partnerships, such as Tamaki

Regeneration Corporation, through which they launched a community regeneration program in

South Auckland. HLC (Homes. Land. Community.), a wholly owned subsidiary of Housing New

Zealand Corporation (HNZC), continues to pursue large-scale community redevelopment projects

designed to include market rate and affordable rental and homeownership opportunities and to

redevelop existing public housing. These government, quasi-public, and private sector entities are

interested in developing more affordable and mixed-income housing.

The author’s previous report, “Priming the Pump” (Mosley, 2018), explored the settings to enable

systematic engagement from private capital sectors. Unlike the United States, New Zealand does

not have a regulatory framework encouraging strategic investment supporting affordable housing

development. In the New Zealand study, it was important to understand what factors are needed

to incentivize lenders and investors to participate at a greater level. The author interviewed

representatives from banks, private equity firms, foundations, and public pension annuity funds to

understand their interest in investing in projects that include public and affordable housing. Banks,

some foundations, and limited equity funds currently lend and, to a smaller extent, invest equity, in

projects; however, based on the need for added capital, substantially larger and strategic infusion is

necessary to address New Zealand’s housing pressures. Financiers interviewed (Mosley, 2018) cited

several factors that could encourage them to play a larger role, including the following:

• Rate of return—Traditionally all actors say they require a market rate of return on their

investments as their responsibility to shareholders or board members, but several suggested

they’d consider at least no economic loss level as long as there is a social return.

• Product design—Lenders stated that the design of a new financial loan or equity product

must not be complex or “boutique”; it must be easy for institutions to consider it alongside

existing products and tools without requiring substantive changes in business practices and

risk measures.

• Scale—Lenders, investors, and funders seek, or prefer, large projects (hundreds of units

compared with 10–50).

• Risk—Parties need a clear understanding and expectation of risk-sharing.

• Developer/Team—The developer must have a proven track record.

• Government—Each sector expected that government would participate in these projects as a

condition but questioned how reliable a partner government would be over the long term.

Cityscape 317

Mosley

New Zealand is home to private capital sources, in addition to public pension annuity funds,

but the country has seen limited appetite, enticements, or mandates for greater investment to

help meet its affordable housing need. Accessing conventional debt capital was not reported

to be an issue; however, the uncertainty of government shifts in ideology and priorities brings

development pipeline uncertainty for CHPs. Lenders have also expressed concern about serving

a greater role given the lack of certainty of long-term government engagement. Two key elements

cited as possible reasons for the limited appetite are, first, that most of the main banks are

Australian-owned and, although chartered with local governance, are still seen to have a business

focused more on Australian priorities. Second, investors and lenders see New Zealand’s relatively

small population (nearing 5 million) and economic scale as a factor motivating them to invest in

other ventures that would provide a reliable and greater rate of return than affordable housing

development. To help address the concerns raised by capital providers, the author identified

examples of successful financing models used in Australia, the United Kingdom, and the United

States, including:9

• Housing loan/bond aggregators,

• Infrastructure bonds,

• Social impact bonds,

• The Low-Income Housing Tax Credit program,

• Tax Increment Financing,

• Housing Trust Funds,

• Uplift America Fund,

• Rural Home Loan Partnership/Leveraged Loan, and

• California’s Cap and Trade Program.

Specific Capital Development Efforts in New Zealand

On October 1, 2018, the government formed NZ-Housing and Urban Development (NZ-HUD).

This new ministry, which consolidated several housing programs from other ministries, focuses

on three areas: ending homelessness, creating growth opportunities in urban areas, and creating

thriving communities (NZ-HUD, n.d.). One of these key efforts is NZ-HUD’s creation of a Housing

and Urban Development Authority (HUDA), or Kainga Ora. Through HUDA, the government

is creating the pathway to transform cities through streamlined consenting processes, funding

infrastructure and development and land assembly (NZ-HUD, n.d.). HUDA is expected to be

operational by late 2019 or early 2020.

9 The author’s full report, “Priming the Pump: Access to Capital and Capacity to House New Zealanders, ” describes each of these housing financing tools.

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New Zealand’s structural response to its housing supply and affordability response is generally

framed around quickly infusing new housing supply into cities and creating the mechanism to

more quickly redevelop land and communities. Soon after the current government assumed

leadership in October 2017, it took several steps to increase and incentivize housing production.

First, before it created NZ-HUD, and soon after it came into leadership, the current government

launched KiwiBuild in 2017 as an initiative to build 100,000 homes for first-time homebuyers

over 10 years. Through this initiative, the government is investing $2 billion to help catalyze the

development of these homes across the country. As of June 2019, Prime Minister Jacinda Ardern’s

Administration reported 10,497 (10.5 percent of the program goal) homes completed, sold, under

construction, or contracted/committed to build; 141 of these homes were actually built.10 The pace

of completion was deemed a failure and key Ministry leadership was changed in early July 2019

to jumpstart KiwiBuild and addresses the backlog of social/public housing construction. In fact,

Prime Minister Ardern expanded the number of Ministers with oversight of housing and urban

planning from two to five, acknowledging the failures and complexity of housing challenges. The

change in Ministerial leadership, however, has not yet created the settings to effectively increase

housing delivery, such as the use of public and privately owned land, increasing public financing

and incentivizing private sector debt and equity investment, and providing reliable development

pathways for developers/builders, including not-for-profit housing providers. Central government,

though, is working with local jurisdictions to enable more effective urban planning processes.

Community Housing Providers are advocating for new efforts to bring affordable ownership

opportunities to New Zealanders unable to afford a conventional mortgage. Four organizations

created KiwiBuy in 2018, a partnership initiative to help create pathways to affordable

homeownership and encourage government to do more to enable affordable ownership. KiwiBuy

promotes two forms of alternative ownership strategies, shared ownership and progressive

homeownership.11 These models, similar to U.S. shared equity and lease-purchase, are seen as a

way to leverage KiwiBuild, but also incentivize public and private sector participation; several

community housing providers currently offer these types of products. KiwiBuy is an example of

the energy and innovation from affordable housing stakeholders to encourage government to help

deliver affordable housing on a greater scale.

Separate from this housing reset, New Zealand government had already started a review of its tax

code in 2017, in particular exploring the implementation of a capital gains tax (CGT) as a means

to temper spiraling house prices. As part of the government’s tax working group review of its

overall tax policies and structure, the group analyzed and considered ways to address house prices.

Specifically, the tax working group recommended taxing vacant residential land and levying the

tax locally rather than on a national level (Tax Working Group, 2019). Early analysis suggests that,

while the tax may have minimal effect on overall housing affordability, house prices may soften

as investors sell properties to avoid paying the CGT, which may open opportunities in the short

term for first-time homebuyers. Reports also suggest, however, that rents may increase as investor

properties come off the market, further limiting supply (The Spinoff, 2019). The government

ultimately decided not to implement the CGT, citing no consensus for it.

10 For more information, see hud.govt.nz/news-and-resources/news/first-kiwibuild-homes-in-the-bay-of-plenty/.

11 For more information, see https://kiwibuy.kiwi/.

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New Zealand investors see residential real estate ownership as an attractive, stable investment

opportunity, producing a reliable stream of rental income. An increasing number of investors have

purchased residential properties as a long-term investment strategy and a “negative gearing” vehicle

to offset gains, prompting the government to enact new tax legislation to help balance housing

opportunities between these stakeholders and owner-occupiers, particularly those seeking to

become first-time homebuyers.12 Negative gearing in this context is a practice in which investors

purchase rental investment properties with borrowed funds even when the net rental income,

minus operating expenses, is less than the interest on the borrowed funds; the shortfall from the

rental income earned and interest due can be deducted from income taxes (Australian Taxation

Office, 2015). The expectation is that the expected reliable long-term capital gains compensates

for the short-term losses; Canada and Australia also allowed for this tax deduction (Kenton, 2018).

It is recognized as a factor in escalating house prices than would otherwise occur (Equality Rights

Alliance, 2011). The country passed loss ring-fencing legislation to mitigate the practice of negative

gearing to better control investor-owned property ownership and create conditions enabling first-

time homebuyers to purchase (Jolliff, 2018). Loss ring fencing restricts investors to offset rental

tax gains only from losses from another rental property rather than from other business activities;

residual losses can, however, be carried forward to offset future gains or applied to taxable gains on

future sales (Cantin, 2019).

Other legislative changes facing the housing market include a plan to limit or exclude foreign buyers

(who have been seen as being responsible for driving up house prices) and a ban on letting fees.

Following up on the recommendation to tax vacant, or otherwise unused, land, the Productivity

Commission has been tasked to explore options for this effort to encourage infrastructure projects

(Vaughan, 2019). The government has also implemented its Healthy Homes Standards requiring

landlords to improve the quality of rental housing across the country, focusing on heating,

insulation, ventilation, moisture, and drainage and eliminating drafts in their properties.

New Zealand’s willingness to explore, even if it ultimately rejects, changes to its tax code to address

its housing challenges is a testament to how far it is prepared to go to make significant steps to

face this crisis. While the government did not move forward on the CGT, it will still explore

other tax options, particularly those that could incentivize putting vacant land to productive use,

such as infrastructure and housing. Policymakers, however, see tax credits or other incentives

employed in the United States and other countries as overly complicating New Zealand’s tax

system. Several stakeholders interviewed said that tax credits are seen as burdensome, and with

the relatively small population, that such a financing tool is more appropriate in more populous

countries. The reluctance of successive governments to address increasing residential property

investment continues to affect housing supply and affordability. For example, politicians have

long debated implementing a CGT on rental investment properties but have repeatedly delayed

acting on this and other possible measures that may ultimately ease pressures on housing supply

and affordability. Its enactment of loss ring fencing is a step, in lieu of the CGT, that should create

balanced access to housing between investors and those seeking to buy their first homes.

12 New Zealanders generally view owning and managing rental housing as a secure investment strategy due to perceived volatility and risk in traditional stock and bond investments.

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Other key elements resulting in New Zealand’s housing market shortfalls include inconsistent urban

planning, land banking, infrastructure funding, and building sector reliability. Central government

and local councils look to updated urban planning to increase density to meet the housing demand,

but the lack of resources and pressures on the local consent process, along with land-banking

practices by both government and other sectors, have significantly constrained development activity.

The building industry is seen as very volatile, small-scale, and subject to upturns and downturns

in the economy, with inconsistencies in project funding and land-banking practices. In 2012, The

New Zealand Salvation Army’s Alan Johnson cited low productivity of the construction sector and

lack of competition as also contributing to fast-rising building costs and sales prices between 2002

and 2007, and to some extent in 2008 (Johnson, 2012). During boom times, there are not enough

workers, driving up prices to attract skilled labor, and during downtimes, workers are known to

leave for opportunities in Australia or leave the industry altogether (Johnson, Howden-Chapman,

and Eaqub, 2018). Those who own, or hold, land—including the government—report that it can

only be sold at market value based on New Zealand accounting standards and government rules,

which in light of other development costs, including infrastructure, makes building affordably

require greater amounts of subsidies, and at greater density.

Accessing Capital in the U.S. Affordable Housing Sector

In the United States, current federal housing policies and regulations continue to drive the major

response to address the needs of those who are housing insecure, but since the 1980s, public

funding levels have not kept pace with those in need (Kingsley, 2017). The Joint Center on

Housing Studies’ (JCHS) 2018 State of the Nation’s Housing Report cites land prices, increasing

construction costs, and regulatory barriers as challenges to developing new affordable housing.

For example, regarding affordability, the JCHS concluded in April 2019 that the supply of rental

dwellings affordable to low-income families remains insufficient across metropolitan areas and

that, without sufficient options, higher-income renters occupy units that could be within reach of

households with low incomes (Airgood-Obrycki and Molinsky, 2018).13

U.S. Department of Housing and Urban Development’s (HUD’s) priorities and resources, dating

back to the 1970s, have generally shifted from a focus on the production of new public and

affordable housing stock toward a portable voucher-oriented approach to housing households

earning below 30 percent of area median income (AMI). This shift has helped put emphasis on

private and not-for-profit sector development and the housing finance regulatory framework,

particularly on the Community Reinvestment Act (CRA) of 1977, government-sponsored

enterprises (GSEs), the HOME Investment Partnerships block grant, Housing Trust Fund, and tax-

credit incentives such as the Low-Income Housing Tax Credit (LIHTC). In addition to the federal

focus on updating and strengthening current regulations, states have created and bolstered their

housing financing programs and local jurisdictions are increasing adoption of inclusionary zoning

and providing density bonuses to developers building affordable housing.

The United States has benefitted since the 1970s from key legislation and tax policy to promote

affordable housing production. Without delving into the history of federal housing policy that

13 See also NHLIC’s 2018 Gap Report, which reported a national deficit of almost 8 million affordable rental homes.

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gave rise to federal interventions, it is important to focus on what these acts, institutions, and

tools mean to have an established pathway for public, private, and not-for-profit actors to produce

and finance housing and enterprises. The CRA requires federal regulators to ensure that the

financial institutions they oversee meet the credit needs of the communities they serve, including

low- and moderate-income neighborhoods.14 The Act’s regulations were substantially revised in

1995 to strengthen and expand its reach and effectiveness.15 The revised regulations provided

a greater statistically measured performance in banks’ lending; the changes required banks to

perform harder to get their grades (Erickson, 2009; Fishbein, 2003). The National Community

Reinvestment Corporation (NCRC, 2018) reports that banks have invested nearly $2 trillion in

small enterprise and community development loans over the past 20 years. “About 85 percent of

LIHTC investments come from banks, and CRA is a primary motivator,” says National Association

of Affordable Housing Lenders’ (NAAHL) President and CEO Benson Roberts, adding, “It’s unlikely

that LIHTC would have gained traction without CRA.”16

CRA, which is under review as of this writing, is cited as a key element encouraging private

investments into underserved areas. While many stakeholders agree that CRA needs to be

improved, it is also important to note the impact when there is no CRA. A recent paper points to

consistent research results that lenders are “responsive to the incentives that CRA provides, and

that CRA designations matter in a changing financial landscape” (Ding, Lee, and Bostic, 2018:

2). In looking at small business lending for example, the study concluded that banks—from a

CRA lens—are sensitive to communities deemed low- to moderate-income (LMI), and borrowers

operating there may be treated more favorably (Ding, Lee, and Bostic, 2018).

The LIHTC is widely regarded as the underpinning of financing U.S. affordable rental housing.

In the New Zealand report (Mosley, 2018), the author touted the LIHTC as a successful model of

using a tax mechanism, and thus incentivizing private capital, to develop and preserve affordable

housing. Its flexible structure ensures local priorities are addressed in a highly competitive

investor-pricing, cost-efficient process. The long-term certainty of the LIHTC, leveraging of federal

housing programs like the USHUD HOME Investment Partnership Program, and Housing Choice

Vouchers has provided a level of funding certainty for developers, lenders, and investors, helping to

ensure pipelines of affordable units in cities, rural areas, and Indian Country. Used throughout the

United States, between 1987 and 2015, the LIHTC helped finance the development of 2.3 million

homes in 38,000 projects (HUD, 2017). The Federal Housing Administration’s (FHA) mortgage

insurance program provides mortgage credit for large private multifamily rental properties,

including LIHTC projects; in FY15 alone, mortgage insurance for rental and cooperative housing

covered 192 projects that included 30,412 units for almost $3 billion. (HUD, n.d.) Collectively,

LIHTC, CRA, HUD’s HOME, FHA programs and voucher subsidies provide an affordable housing

ecosystem. Banks provide equity and mortgage financing via LIHTC and CRA. Financing gaps are

addressed by HOME, housing trust funds, and other sources. Vouchers help low-income families

reliably afford rent. The net result is a complex, layered, public-private funding and financing

14 For more information, see https://www.federalreserve.gov/consumerscommunities/cra_about.htm.

15 Darryl E. Getter, 2019, The Effectiveness of the Community Reinvestment Act: Congressional Research Service Report for Congress.

16 Comms with Benson Roberts, 2019.

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system highly reliant on private sector investment, but one that has offered a level of certainty

around which developers and investors can forecast a pipeline of projects.

Community Development Financial Institutions (CDFIs) are a growing and vital segment of the

capital market providing responsible, affordable lending capital to low-wealth communities and

their residents.17 Part of the U.S. community economic development industry since 1973, CDFIs

help finance residential and commercial real estate projects, small businesses, microenterprises,

and other not-for-profit firms. From 1985 through 2017, CDFIs have provided $5 billion in

lending to low-income communities and individuals and over $65 billion overall across the

United States, resulting in 2 million homes created, 1.3 million jobs created or retained, and

322,000 businesses and microenterprises developed or expanded. Targeted beneficiaries include

those who are low income, people of color, women, and those in rural communities (Opportunity

Finance Network, 2017).

Summary

The difference in how New Zealand and the United States approach financing affordable housing

is substantial. While both countries have their respective housing challenges, the settings around

accessing private sector capital serve an instructive role on the importance of maintaining a

regulatory framework to have the private sector participate fairly to help develop and preserve

critically needed affordable homes in both high- and low-wealth communities. Most notably,

however, in New Zealand, the attention to and investment in social/public and affordable housing

varies by governing party. As such, production ebbs and flows, ultimately leaving the government

and CHPs struggling to keep pace with the needs of vulnerable families. There is an array of

financing methods used internationally to secure private capital, but it is necessary to have “carrots

and sticks” encouraging these investments. The New Zealand research clarified the effect of not

having a systematic regulatory and financing approach on the country’s ability to deliver sufficient

housing. Cities and towns across the country are studying or working to expand inclusionary

zoning, ensuring that more production results in greater income-diverse communities. The growth

and success of the nascent KiwiBuy initiative will be telling to understand how the country is

willing and able to increase affordable housing options.

Building Capacity for Affordable Housing Development

CHP Production and Capacity Building in New Zealand

CHPs are small but recognized agents in New Zealand’s social/public affordable housing

framework. Along with HNZC, the government’s main public housing developer and landlord,

CHPs are the main entities receiving government funding to deliver public housing.18 The country’s

Ministry of Housing and Urban Development Community Housing Regulatory Authority (NZ-

HUD-CHRA) registers CHPs as Class 1 Social Landlords, certifying them to develop and/or

17 CDFI is a designation made by the U.S. Department of the Treasury CDFI Fund.

18 New Zealand officially referred to public housing as “social” housing until the current government came to power.

Referring to the stock as “public” housing is meant to signal the government’s ideology that it owns the dwellings and is responsible for meeting the needs of vulnerable New Zealanders.

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manage social/public housing units. These CHPs and additional housing and service providers,

including Māori Housing Trusts and supportive service providers, deliver assistance ranging from

emergency shelters to affordable housing rental and ownership opportunities. The Social Housing

Reform Program enacted in 2010 was designed to boost CHPs’ ability to deliver more of the social/

public housing alongside HNZC. This program included a plan to boost CHPs’ balance sheets

by transferring portions of the social/public housing stock to the not-for-profit organizations to

manage. Only one transfer, however, was completed, and the program ended in December 2017

as the current coalition government sought to keep social/public housing controlled, or owned,

by the government. Prior governments, mainly national, facilitated the sale of public housing

directly to families seeking to own their homes and transferred management to CHPs; this was

their method to increase ownership opportunities for families and CHPs’ capacity, while also

reducing government’s liabilities. The current government stopped this practice, solidifying their

“ownership” of social/public housing.

CHPs, as a sector, seek to increase their scale of development but have been hampered by their size

and inability to reliably access capital, land that is developable at a below-market rate, and reliable

trades. Community Housing Aotearoa (CHA), the national association for CHPs, has determined

that the nation’s developers must deliver 15,000 public and 85,000 affordable homes by 2030

in order to meet housing needs. Both lenders and government officials regarded most CHPs as

immature and risk-averse, preferring to minimize exposure of their balance sheets to greater debt.

Several CHPs are reluctant to jeopardize their core nonhousing operations. The risk tolerance of

CHP boards and the basis for their positions need to be better understood to identify under what

conditions these organizations may consider developing additional housing. To understand CHPs’

current and future development plans, as well as identify their organizational and project-related

capacity challenges to deliver this needed volume, the author surveyed CHRA-registered CHPs

(see exhibit 3). Responses show that 27 CHPs, including Māori housing providers, currently have

plans to deliver 5,640 public and affordable homes within 24 months and another 970 homes

thereafter. One explanation for the significantly low long-term production was uncertainty about

funding contracts with the government. The results suggested that those CHPs with portfolios in

the hundreds of units showed the most appetite for growth. Four CHPs have portfolios of between

2,000 and 3,000 units; 14 CHPs have hundreds of units; and 26 have unit counts below 100.19

CHPs with portfolios in the thousands planned to develop some units but indicated a future focus

on strengthening their assets and property management functions.

19 CHA database as of May 2018.

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Exhibit 3

CHP Reported Capacity-Related Challenges

Organizational Capacity

Project-Related Capacity

Lack of skilled staff and its effect on

Financing projects (7)

undertaking required operations, admin,

and pursuing opportunities (6)

Ability to address lack of government

Developing a project pipeline (6)

financial support for development projects—

need effective development and financing

pipeline and assurances (5)

Creating and managing effective

Government purchasing strategy—lack of

partnerships (4)

commitment, continuity, and changing funding/

procurement mechanisms (5)

Improve and balance tenancy management with

Developing and managing effective partnerships (4)

property and asset management functions (4)

Identifying and training board members (3)

Timing delays in funding decisions (4)

Financial management (2)

Consenting (4)

Addressing councils’ capacity to engage

Negotiating contracts (3)

and support CHPs in delivering affordable

and social housing (1)

Maximizing best practices to ensure efficient

Securing land, including at competitive pricing with

and effective outcomes (1)

HNZC and HLC (3)

Increasing waiting lists (1)

Review of legislative and regulatory framework (2)

Managing volunteers (1)

Local development contribution costs and facility

supply fees (2)

Succession planning (1)

Project management (2)

Not enough rental supply (1)

Other topics and issues raised by CHPs in the survey include lack of access to affordable land,

prohibitive costs and delays for projects in Auckland, delays caused by government turnover,

changing government requirements, lack of capital, and the need for CHPs to be seen as well-run

businesses. CHPs rely heavily on government funding for delivering public housing in a contractual

framework, but the government often sees itself as a partner, which makes the relationship difficult to

navigate. Government could promote CHP production by directing HNZC, and its subsidiary HLC, to

engage CHPs as co-developers, but without explicit directives from ministers, these entities saw CHPs

mainly as a purchaser of improved lots that they could develop themselves; this excluded CHPs from

actively participating in a pipeline of large-scale mixed-income residential developments. CHPs also

experienced a gap in access to capital funding when the Social Housing Fund ended in early 2014

and when the government began capital support (which existed only for Auckland developments

until early 2019) in December 2015; these gaps have left CHPs wary of government reliability to

fund projects (Barber, 2017). Dwell Housing Trust’s Alison Cadman and staff said that the settings

do not provide the ability to develop a pipeline, that the government moves from one policy change

to another, and that there is no consistency or traction on policy.20 CHPs see that the government

expects them to address capacity building through their contract fee income and other sources.

20 Comms. with Dwell Housing Trust staff.

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Other sources of funding for CHPs include government departments and not-for-profit

organizations. The Department of Internal Affairs (DIA) offers organizational capacity grants and

funding for secondments. Rata Foundation’s Karyn McLeod reported that the Foundation’s support

for and investment in capacity-building efforts have increased significantly since 2017.21 The

Foundation is looking at practices that build capacity, with not-for-profit leadership development

as a particular focus. Several other foundations have committed operating grants to local charitable

organizations but have not invested in larger sector-wide capacity-building efforts; several

foundations directly finance housing development projects.

The Institute of Directors (IOD) is a New Zealand not-for-profit that supports corporate governance

capacity development. IOD provides training and coaching to board or council members to ensure

they have the necessary skills to effectively guide their organizations, including understanding

their strategic responsibilities, financial management, and other functions. Te Puni Kōkiri (TPK)

contracts with IOD to provide board governance training to Māori housing organizations.

CHA’s technical support subsidiary, Community Housing Solutions (CHS), also provides board

development training as part of its array of services.

Funding Capacity Building in the United States

U.S. Government, lenders, and foundations support the capacity of local mission-based community

developers. HUD and the U.S. Department of Agriculture (USDA) fund capacity-building activities

of community-based not-for-profit organizations through loans, grants, and other project-related

financial assistance to further key development priorities. HUD works through three direct

grantees, known as intermediaries, that direct support to and invest in community development

corporations (CDCs) and other local organizations.22 Several major banks and insurance companies

have for decades invested in the capacity of community-based organizations to undertake

affordable housing and economic development projects. Finally, several United States-based

philanthropies—from those with global initiatives to community foundations—have adopted

principals such as double- or triple-bottom-line investing, seeking multiple types and levels of

returns and benefits, to meet their mission, diligence in investing oversight, and community

impact. For example, one intermediary, the Local Initiatives Support Corporation (LISC), has

alone worked with thousands of CDCs and communities since 1979, investing over $20 billion

and delivering almost 400,500 affordable homes and 66.8 million square feet of commercial and

community space (LISC, n.d.). CRA, LIHTC, and program-related investments were key factors in

these accomplishments.

Summary

New Zealand’s CHPs are flexing their muscle to develop, own, and manage housing that

is affordable to New Zealanders and to influence housing policy. As New Zealand’s central

government housing leadership underwent significant changes in the wake of KiwiBuild’s failure,

community—and Māori—housing providers sought to leverage this change in leadership to come

21 Comms. with Karyn McLeod, 2018.

22 For more information, see HOME Community Housing Development Organization (CHDO), HUD Exchange, and

USDA.gov.

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together in July 2019 to develop themselves into a sector. Through a housing conference, a CHP

promotional video, and a series of planned convenings with public and private stakeholders, these

providers are articulating their role, not as a vendor, but as a regulated partner with a distinct

track record of delivering community-oriented delivery and housing policy. These organizations

are developing a sector-wide approach to increase production, strengthen property and asset

management, and deepen their capabilities. The government’s new housing leadership has

signaled their support to the housing providers’ aspirations to be recognized as capable providers

and policy shapers.

Investing in CHPs’ capacity is essential if the sector is to continue to grow and reliably provide

dynamic, effective community-based housing. New Zealand’s central government currently

provides minimal capacity-building support.23 CHPs seeking to increase their scale of development

or further hone their tenancy and property/asset management functions need access to sources

of capital as well as the ability to charge all applicable development and management fees. The

evidence of the not-for-profit sector’s growth in the United States shows the promise and effect of

a focused and well-regulated investment and supportive ecosystem that also invests in capacity

building. Having consistent regulatory and funding expectations has enabled developers to

envision, plan, and execute and to meet the needs of those most vulnerable.

Recommendations and Observations

The following are recommendations and brief observations that the author shared with the

Minister overseeing housing policy and delivery, other key officials, and not-for-profit stakeholders

in New Zealand. Although these recommendations and observations respond directly to New

Zealand’s situation, they can easily be adapted for other countries.

Recommendation 1: Commit to long-term engagement and consistency in housing policy settings

and programming. The nature of planning and developing affordable housing requires committed

and consistent long-term policy support and investment.

Recommendation 2: Create a permanent public investment fund to help finance public and

affordable housing development. A fund that is “insulated” from political term lengths would help

create a level of funding certainty to developers to plan a pipeline of projects designed to meet current and forecasted demand. Private, philanthropic, and faith-based investors could invest in this pool to

help ensure the government’s commitment to a multisector funding partnership. The government

alone does not have the funding to deliver all the new supply for public and affordable housing. It

must actively participate in long-term funding collaboratives to ensure multisector investment and

realize the funded outcomes, meeting SDG and Treasury metrics, and create mutually agreed-upon

financial and social return benchmarks for developers under a multisector investment pool.

Recommendation 3: Invest in community housing development partner capacity to ensure

expected growth and housing production, outcomes, and quality. Develop and strengthen not-for-

profit capacity-building infrastructure. Create pathways to ensure the participation of nonpublic

23 The New Zealand government has committed capacity-building funding related to Māori housing production.

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partners in community revitalization projects (starting with initial planning), particularly when

government wholly owned subsidiaries and public joint ventures own or manage these projects.

Recommendation 4: Promote and incentivize innovation and creative capital by replicating

existing models.

Recommendation 5: Invest in housing education and transitioning households from emergency/

transitional housing and public housing toward market-rate homeownership. Success would be

more assured by mandating financial and housing education with benchmarks.

Conclusion

New Zealanders consider themselves to be 10 to 15 years behind the United States in addressing

housing challenges, but the two countries face very similar crises of housing insecurity, across

all incomes in most urban centers and particularly for those at the lowest income strata. Despite

significant differences in how affordable housing is financed and who develops it, the net effect is

that both societies have struggled to develop effective long-term consistent strategies to increase

and preserve supply while also ensuring that households with the least means can affordably rent

or own housing that meets their needs. New Zealand’s efforts have gone only so far in developing

housing at the needed scale. The country has researched housing programs and models, created a

new ministry and consolidated related functions, and launched efforts like KiwiBuild. Prior history

shows that, when the government changes party leadership and creates or recreates ministries and

shifts portfolios, it has the net effect of temporarily slowing or stopping development for months.

With national elections occurring every 3 years, the potential change in government results in a

periodic braking of social/public—and now affordable—housing production.

This research shows that efforts to maintain and increase housing production for those who are

vulnerable are highly dependent on government direction and investment and ultimately lack

consistency and reliability. Citing international examples to engage private sector investment

to leverage public funding and acknowledging that no regulatory framework exists to require

private sector investments in projects serving vulnerable families and underserved communities,

the author recommended that the government commit to long-term engagement and settings

to signal its certainty in this industry. Private capital representatives suggested that they would

consider investing at a higher scale, and with some added flexibility, in affordable housing, if the

government would commit to long-term engagement, provide transparent intentions, and agree

to risk sharing. Private sector capital providers report that, although they acknowledge the social

benefit of making capital available at a discounted rate, they must deliver a market rate of return

on their investments and that New Zealand has no requirements that they consider a below-

market return in lieu of social benefit. The United States, in contrast, created housing policies,

regulations, and financing mechanisms to produce and preserve affordable housing. While the

United States generally does not keep pace with the needs of those who are housing insecure,

the current housing system provides a financing framework, albeit complex, for developers to

plan and execute a pipeline of projects, consistently and reliably working with public and private

sector investors.

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This article provides the United States with a cautionary tale of the difficulty in securing private

sector investments from New Zealand, which lacks the consistent funding and regulatory

framework that—like CRA—has existed for decades. This tale means that the United States should

maintain (and strengthen) its existing regulatory framework after seeing what happens in a country

that doesn’t have one. U.S. not-for-profit housing developers, as a sector, have decades of evidence

showing the effect of delivering affordable housing relying on fairly consistent settings from public

sector agencies, the private sector, and foundations. Current U.S. capacity-building efforts should

be continued and increased to help ensure that this sector continues to evidence the effect of its

development work and effectively leverage public-private investments.

There are opportunities for future comparative research, with topics including the following—

Housing-Related:

• Economic analysis of future social benefit to family and society from households living with

secure tenure in affordable, safe, and warm homes.

• Best valuing land to purpose for affordable housing.

• Evidence of construction trade labor programs that effectively recruit and retain trainees.

• Strategies to enable greater development for indigenous peoples and other cultural and

racial minorities.

• Evidence of financial literacy and housing counseling programming.

• Comparative analysis of barriers to secure housing from societies with and without fair

housing laws.

Capital Investment-Related:

• Further analysis of the impact of housing regulation on catalyzing affordable housing development.

• Modeling housing financing tools in a no to low regulatory environment.

• Performance of the social impact of investing in producing affordable housing.

Acknowledgments

The author acknowledges the guidance and support from staff at New Zealand’s Community

Housing Aotearoa, NZ-HUD, and the Department of Treasury. Leaders from community housing

providers across New Zealand provided invaluable insight into their organizations and the sector

for this article. The author appreciates the feedback from reviewers on this article: National

Association of Affordable Housing Lenders’ President and CEO Benson Roberts, the Urban

Institute’s Senior Fellow and Research Director, Urban-Greater DC Peter Tatian, and writer, lecturer,

consultant and former Housing Assistance Council’s Deputy Director Joe Belden. Fulbright

New Zealand, and The Fulbright Specialist Program, a program of the U.S. Department of State,

Cityscape 329

Mosley

administered by World Learning, provided oversight during the author’s research. The author also

thanks Nora Mara for editing this article.

Author

Jeffrey Mosley is Principal with Jeffrey Mosley Community Development Consulting, LLC, and

Fulbright Specialist.

References

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and Available Rental Units for Families.” Cambridge, MA: Harvard University, Joint Center for

Housing Studies.

Auckland Council—The Community Empowerment Unit and Community and Social Policy

Teams. 2017. “Auckland Council’s Role in Addressing Homelessness”. Parity - Council to Homeless

Persons 30 (8): 32–33. Auckland, NZ: Auckland Council.

Australian Taxation Office (ATO). 2015. Rental properties 2015: 27. Sydney, Australia: ATO.

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Barber, Paul. 2017. ‘’Less Social Housing and Greater Need.” Kete Kupu News. Wellington, NZ: New Zealand Council of Christian Social Services.

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Cantin, John. 2019. Tax Mail – GST on imports and loss ring fencing Bill update. KPMG.

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update.html.

CityLab, and Kriston Capps. June 18, 2019. Will Housing Swing the 2020 Election? Citylab.com.

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trump-campaign/591604/. The Atlantic Monthly Group.

Clarke, Sara. 2018. U.S. News and World Report 2018 Rankings – “These Are the 10 Fastest-

Growing States in the U.S.” U.S. News and World Report. https://www.usnews.com/news/best-

states/slideshows/the-10-fastest-growing-states-in-the-us-in-2018.

Community Housing Aotearoa. 2018. Community Housing Provider Housing Supply Database.

Wellington, NZ.

The Community Housing Regulatory Authority (CHRA). n.d. “The CHRA Register.” https://chra.

hud.govt.nz/about-chra/register/about-the-chra-register/. Wellington, NZ: Ministry of Housing and Urban Development.

330 Foreign Exchange

Observing New Zealand’s Housing Crisis: Efforts to Produce

and Finance Affordable Housing and its Implications for U.S. Policies

Demographia. 2019. “15th Annual Demographia International Housing Affordability Survey.” Data

for 3rd Quarter 2018. Belleville, IL: Demographia.

Department of Internal Affairs, Community Matters. 2019. Wellington, NZ.

https://www.communitymatters.govt.nz/.

Ding, Lei, Hyojung Lee, and Raphael Bostic. 2018. Effects of the Community Reinvestment Act

(CRA) on Small Business Lending. Working paper 18-27. Philadelphia, PA: Federal Reserve Bank of

Philadelphia. https://doi.org/10.21799/frbp.wp.2018.27.

Dodd, Jonathan, and Griffiths, Richard. 2018. The IPSOS New Zealand Issues Monitor: An

IPSOS Survey - October 2018. IPSOS Public Affairs. https://www.ipsos.com/sites/default/files/

ct/news/documents/2018-11/3rd_ipsos_new_zealand_issues_monitor_released_22nd_

november_2018.pdf.

Erickson, David James. 2009. The Housing Policy Revolution: Networks and Neighborhoods.

Washington, DC: Urban Institute Press.

Equality Rights Alliance. 2011. Future Tax – Statements from Participants – Australia. October 4–6,

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Fishbein, Allen J. 2003. “What’s Next for CRA?” Journal of Housing and Community Development.

Washington DC. National Association of Housing and Redevelopment Officials.

Getter, Darryl E. 2019. The Effectiveness of the Community Reinvestment Act. R43661 – Version 10 –

Updated. Washington, DC: Congressional Research Service. https://fas.org/sgp/crs/misc/R43661.pdf.

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Department of Building and Housing.

Johnson, A. 2018. State of the Nation Report 2018. Wellington, NZ: The Salvation Army Social

Policy and Parliamentary Unit.

———. 2012. Adding it all up: The Political Economy of Auckland’s Housing. The Salvation Army

Social Policy and Parliamentary Unit.

Johnson, Alan, Philippa Howden-Chapman, and Shamubeel Eaqub. 2018. A Stocktake of New

Zealand’s Housing. Wellington, NZ: Prepared for the New Zealand Ministry of Business, Innovation and Employment.

Jolliff, Emma. 2018. “Property Investors Lose out as Government Ends Negative Gearing.” https://

www.newshub.co.nz/home/politics/2018/05/property-investors-lose-out-as-government-ends-

negative-gearing.html.

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Kaushal, Arjun, and David Mitchell. 2019. “The Community Reinvestment Act and the Future of

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Financial Inclusion.” Washington, DC: The Aspen Institute. https://www.aspeninstitute.org/blog-

posts/the-community-reinvestment-act-and-the-future-of-financial-inclusion/.

Kingsley, G. Thomas. 2017. “Trends in Housing Problems and Federal Housing Assistance.”

Washington, DC: Urban Institute. https://www.urban.org/sites/default/files/publication/94146/

trends-in-housing-problems-and-federal-housing-assistance.pdf.

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NZ: The Salvation Army New Zealand, Fiji, Tonga and Samoa Territory, Community Housing

Aotearoa, Habitat for Humanity New Zealand and New Zealand Housing Foundation New

Zealand. https://www.kiwibuy.kiwi/.

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http://www.lisc.org/impact/.

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Wellington, NZ: Ministry of Social Development. http://socialreport.msd.govt.nz/.

Mosley, Jeffrey. 2018. Priming the Pump: Access to Capital and Capacity to House New Zealanders.

Wellington, NZ: Fulbright New Zealand.

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Reinvestment Act; Changes Could Impact Billions in Loans and Investments Annually.”

Washington, DC. https://ncrc.org/occ-to-review-community-reinvestment-act-changes-could-

impact-billions-in-loans-and-investments-annually/.

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Diane Yentel, NLIHC President and CEO.” Washington, DC. https://nlihc.org/resource/point-

view-our-homes-our-votes-2020-diane-yentel-nlihc-president-and-ceo.

———. 2018. 2018 Gap Report – A Shortage of Affordable Homes. Washington, DC.

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Wellington, NZ. https://www.hud.govt.nz/about-hud/.

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and Government Policy. Wellington, NZ: Office for the Community and Voluntary Sector.

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Member Data Analysis; 20th Edition. Washington, DC.

Roberts, Benson. 2019. Communications with author. Washington, DC: National Association of

Affordable Housing Lenders.

332 Foreign Exchange

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and Finance Affordable Housing and its Implications for U.S. Policies

The Spinoff. 2019. “Five Commentators Weigh in on the Tax Working Group’s Recommendations.”

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going-to-please-everyone/.

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Wellington, NZ: Tax Working Group. https://taxworkinggroup.govt.nz/sites/default/

files/2019-03/twg-final-report-voli-feb19-v1.pdf.

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population.un.org/wup/Publications/Files/WUP2014-Report.pdf.

U.S. Department of Housing and Urban Development (HUD). 2017. HUD’s National Low-

Income Housing Tax Credit (LIHTC) database: Projects Placed in Service through 2015.

Washington, DC: HUD.

___. n.d. Mortgage Insurance for Rental and Cooperative Housing: Section 221(D)(4). https://

www.hud.gov/program_offices/housing/mfh/progdesc/rentcoophsg221d3n4.

Vaughan, Gareth. 2019. “Land Banking: Government tasks Productivity Commission with

exploring options for taxing vacant land, to prioritize work on ways to encourage investment in

significant infrastructure projects.” https://www.interest.co.nz/property/99202/government-

tasks-productivity-commission-exploring-options-taxing-vacant-land.

Cityscape 333

334 Departments

by Richard C. Sadler

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 john.c.huggins@hud.gov .

Misalignment Between ZIP Codes

and Municipal Boundaries: A

Problem for Public Health

Richard C. Sadler

Assistant Professor, Division of Public Health, Michigan State University

Abstract

While useful for mail delivery, ZIP Codes are flawed as a geographic metric for public health research.

This paper quantifies the magnitude of potential error inherent in using ZIP Codes as a unit of

analysis in the state of Michigan. ZIP Codes are intersected with municipality boundaries in ArcGIS

to determine the degree of misclassification. Results showed that 49 percent of the population had their municipality misclassified by their ZIP Code. This creates potentially huge errors when ZIP Code is the only geographic identifier, because actual exposure may vary from the exposure to which an individual is assigned based on ZIP Code. The Flint, Michigan, Water Crisis is a prime example of this error and the need to consider finer units of analysis whenever possible. Collaboration with experts in geographic information science is therefore essential for any public health research project where location is a factor.

Background

ZIP Codes are an arbitrary geographic designation assigned by the United States Postal Service

for the purpose of delivering mail (Grubesic, 2008). As such, they do not correspond to political,

social, or economic divisions, and have more heterogeneity than census units or neighborhood

boundaries (Grubesic and Matisziw, 2006; Krieger et al., 2002). Even so, they are frequently used

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

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

Sadler

uncritically as a unit of analysis in public health studies (Beyer, Schultz, and Rushton, 2008;

Drewnowski, Rehm, and Solet, 2007; Gordon-Larsen et al., 2006; Acevedo-Garcia, 2001), owing to

the ease with which this data can be gleaned from surveys or medical records.

Spatial error was front and center at the outset of the 2014 and ongoing Flint water crisis (Hanna-

Attisha et al., 2016), when state officials initially downplayed the significance of an uptick in

elevated blood lead levels among children because they were using ZIP Codes to define the

exposed area rather than a more precise geographic metric (Sadler, 2016). In that particular case,

using the city of Flint as the unit of analysis was much more accurate for defining the exposed

population because the water system aligned almost exactly with the municipal boundary. Because

of the possibility for error in other studies, the goal of this article is to quantify the magnitude of

potential error inherent in using ZIP Codes as a unit of analysis.

Methods

Shapefiles containing Michigan’s ZIP Codes (n = 987) and minor civil divisions, or municipalities

(n = 1,517), were intersected in ArcGIS, a geographic information system, to determine the degree

of overlap (or lack thereof) between the two. This operation led to the creation of 6,051 distinct

ZIP Code-municipality pairs. The area of each region was calculated, and populations were

assigned by joining each census block group centroid (n = 8,104) to the overlapping ZIP Code-

municipality pair. For reference, 2,045 (34 percent) ZIP Code-municipality pairs were smaller than

one-tenth of a square mile and 4,101 (68 percent) contained no census block group centroids, but

all were included in the analysis.

Exhibit 1 illustrates the land area and population of three different “zones” (according to the

type of overlap between ZIP Code and municipality). These zones include: (1) match: when the

ZIP Code and municipality share the same name (whether they correspond to a city or another

municipality); (2) false match: when the ZIP Code name is the same as that of two municipalities with similar names (for example, a ZIP Code named “Flint” covering “the City of Flint” and “Flint

Township”); and (3) non-match: when the ZIP Code and municipality do not share a name (that is,

the ZIP Code corresponds to another municipality).

Exhibit 1

Breakdown of Land Area and Population by ZIP Code-Municipality Match Type

Match Type

n

Area (Square Miles)

Population

Match

582

8,197

14%

5,071,822

51%

False Match

136

3,349

6%

679,177

7%

Non-Match

5333

45,958

80%

4,098,740

42%

Results

Exhibit 2 illustrates the match-non-match patterns spatially, with the “match” category shown in

dark gray, the “non-match” category shown in light gray, and the “false match” category shown in

medium gray. A quick examination reveals that most matching occurs in center cities throughout

the state and townships, especially near Detroit in the southeast.

336 Graphic Detail

Misalignment Between ZIP Codes and Municipal Boundaries: A Problem for Public Health

Exhibit 2

Concordance Between ZIP Code and Municipal Boundary Map Showing Michigan’s ZIP Codes,

Municipalities, and Overlap Type

The distribution of non- and false matches varies across the state. To give a better impression

of this variation, a closer look at Genesee County (where Flint is located) is shown in exhibit

3. Municipalities are labeled in regular text, and corresponding ZIP Code names are labeled in

italicized text. Note that matches are found in only a small amount of land area, and many false

match areas exist.

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Sadler

Exhibit 3

Concordance Between ZIP Code and Municipal Boundary Map Showing Higher Degree of Overlap

in Genesee County

338 Graphic Detail

Misalignment Between ZIP Codes and Municipal Boundaries: A Problem for Public Health Overall, only 14 percent of Michigan’s land area matches ZIP Code and municipality name. While

this small area includes 51 percent of the population, it means that 49 percent of the population is

misrepresented in some way by their ZIP Code.

From a public health reporting perspective, the “false” and “non-match” categories are troublesome

for different reasons. The “false match” category includes people living in townships with the same

name and ZIP Code as the nearby center city (for example, Flint-Flint Township), which would

lead many people to assume those residents live in the city if not specified. This category includes

6 percent of the state’s land area and 7 percent of its population. Referencing exhibit 3 again, these

false matches tend to occur where a township has the same name as the central city.

The non-match category poses similar problems—it contains 80 percent of the state’s land areas

and 42 percent of the population. When a non-match includes a reference to a center city, estimates

for public health problems, such as a “city’s” disease prevalence, crime incidence, or another

phenomenon, could be skewed by including outlying areas. Many of these non-matches include

rural areas where township names do not correspond to ZIP Code names. Although these rural non-

matches would pose slightly fewer problems for public health reporting, it is worth raising this issue

in general, because stakeholders and policymakers need to be aware of this source of error.

Conclusions

The importance of this misclassification to public health should not be forgotten: while ZIP Codes

can absolutely be useful tools for public health planning and policymaking, they can misrepresent

health statistics when looking at phenomena that may not coincide well with ZIP Code boundaries

(as described in Sadler and Lafreniere, 2017), including those related to municipal services.

The example of the Flint water crisis—and the results of this article showing how misaligned ZIP

Codes are from municipal boundaries—suggest that geographers, epidemiologists, and others

with expertise in geographic information science should be closely consulted on any public health

research where the location may play an important determining factor.

Acknowledgments

This publication was supported by a grant from the National Institute on Minority Health and

Health Disparities under award number U54MD011227.

Author

Richard C. Sadler is an Assistant Professor in the Division of Public Health at Michigan State

University. He can be contacted via phone at (810) 600-5674 or via e-mail at sadlerr@msu.edu.

Cityscape 339

Sadler

References

Acevedo-Garcia, Dolores. 2001. “ZIP Code-Level Risk Factors for Tuberculosis: Neighborhood

Environment and Residential Segregation in New Jersey, 1985-1992,” American Journal of Public

Health 91 (5): 734.

Beyer, Kirsten M., Alan. F. Schultz, and Gerald Rushton. 2008. 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.

Drewnowski, Adam, Colin D. Rehm, and David Solet. 2007. “Disparities in Obesity Rates: Analysis

by ZIP Code Area,” Social Science & Medicine 65 (12): 2458–2463.

Gordon-Larsen, Penny, Melissa C. Nelson, Phil Page, Barry M. Popkin. 2006. “Inequality in the

Built Environment Underlies Key Health Disparities in Physical Activity and Obesity,” Pediatrics 117

(2): 417–424.

Grubesic, Tony H. 2008. “ZIP Codes and Spatial Analysis: Problems and Prospects,” Socio-economic

Planning Sciences 42 (2): 129–149.

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.

Hanna-Attisha, Mona, Jenny LaChance, Richard Casey Sadler, and Allison Champney Schnepp.

2016. “Elevated Blood Lead Levels in Children Associated with the Flint Drinking Water Crisis:

A Spatial Analysis of Risk and Public Health Response,” American Journal of Public Health 106 (2): 283–290.

Krieger, Nancy, Pamela Waterman, Jarvis T. Chen, Mah-Jabeen Soobader, Subu V. Subramanian,

and Rosa Carson. 2002. “ZIP Code Caveat: Bias Due to Spatiotemporal Mismatches Between ZIP

Codes and U.S. Census–Defined Geographic Areas—The Public Health Disparities Geocoding

Project,” American Journal of Public Health 92 (7): 1100–1102.

Sadler, Richard Casey. 2016. “How ZIP Codes Nearly Masked the Lead Problem in Flint,” The

Conversation. http://theconversation.com/how-zip-codes-nearly-masked-the-lead-problem-in-

flint-65626.

Sadler, Richard Casey, and Don J. Lafreniere. 2017. “You Are Where You Live: Methodological

Challenges to Measuring Children’s Exposure to Hazards,” Journal of Children and Poverty 23 (2): 189–198.

340 Graphic Detail

by 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. If you have an idea for a future department feature, please send your diagram or photograph, along with a few well-chosen words, to michael.j.early@hud.gov.

Trenchless Technology: A New Way

to Install and Renew Utilities

Mike Blanford

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.

Abstract

Installing effective utilities infrastructure is an essential practice that enables, sustains, and enhances the living conditions in communities. Utilities that support housing include water supply, sewers/

stormwater management, electrical grids, fuel for heating and cooking, and telecommunications.

Housing would not be as useful without the necessary utilities to support it. The trenchless industry offers new technologies that help mitigate the cost, enhance safety, limit effects, and speed the

installation of utilities. In this article, we discuss how this invention supports the goal of promoting resiliency without sacrificing affordability.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

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

Blanford

Introduction

Innovative housing needs utilities. Identifying cost-effective strategies for installing those utilities increases affordability and value for homebuyers, as a greater portion of the construction cost can

be focused on the home. Rather than attempting to “go cheap” on infrastructure, an approach

that will likely prove to be a false economy, approaches that minimize the overall construction

and restoration costs merit consideration. The trenchless method offers an alternative, new way

to install utilities. As the name implies, a traditional trench may no longer be needed to install

underground utilities. The trenchless method offers a number of advantages, possibly including

reduced overall cost, improved safety, and increased speed of installation.

Trenchless methods can be applied to a wide range of utility types. Wet utilities, such as water,

sanitary sewer, and storm sewer are common, as well as other underground utilities, including

natural gas, electric, and telecommunications. Trenchless approaches also can be used to restore

aging infrastructure, thus enhancing the vitality of the surrounding community. Trenchless

methods may enable communities to reduce the effects of moving electrical utilities underground,

reducing or preventing power outages in areas with trees.

The North American Society for Trenchless Technology (NASTT) defines trenchless technology

as “a family of construction techniques for installing or rehabilitating underground infrastructure

with minimal disruption to surface traffic, businesses, and residents. Also includes technologies for

inspection, leak location, and leak detection with minimal disruption and minimal excavation from

the ground surface” (NASTT, n.d.).

The most popular trenchless technologies for housing infrastructure are Horizontal Directional

Drilling, Microtunneling, Horizontal Auger Boring, Pipe Ramming, and Lining. The following

exhibit provides a description of these technologies.

Exhibit 1

Popular Trenchless Technologies for Housing Infrastructure

Trenchless Method

Description

Horizontal Directional Drilling

A steerable system for the installation of pipes, conduits, and cables in

a shallow arc using drilling equipment from the surface. This method

is typically used for large scale crossings of features, such as rivers,

sensitive areas, roadways, and other critical infrastructure.

Microtunneling

A trenchless construction method for installing pipelines using a small

unmanned tunnel boring machine. The piping is used to push the

microtunneling boring machine to the end of the pipe run.

Horizontal Auger Boring

A method of forming a bore from a launch pit to a reception pit, by

means of a rotating cutting head. While some excavation is necessary, it

can be sited to avoid disruptions or excessive costs. This method may

have limited steering capability but might be more economical for short

and straight drives compared to other methods.

Pipe Ramming

A percussion hammer is used to force a strong (steel) pipe through the

ground, essentially using the force of the hammer to drive the pipe.

Lining

A liner is installed in an existing water or sewer pipe to reduce leakage

and improve flow.

342 Industrial Revolution

Trenchless Technology: A New Way to Install and Renew Utilities

Exhibit 2

Exhibit 3

Microtunneling Boring Machine

Horizontal Directional Drilling Machine

Photo courtesy the National Utility Contractors Association

Photo courtesy the National Utility Contractors Association

Exhibit 4

Liner in a Cast Iron Pipe

Photo courtesy the National Utility Contractors Association

Cost Mitigation

New infrastructure projects can often affect other community activities or installed infrastructure.

Significant costs may be incurred in an effort to mitigate those impacts. Consider the traffic

patterns in your community when a new building project near a major intersection needs to

install a trench for a new sewer line. The costs of working at night (a typical approach to reduce

traffic impacts), traffic control, worker protection (while trenching), and the necessary roadway

repairs must ultimately be borne by the owner or developer. Costs such as roadway repair do

not contribute directly to the actual construction project. For an owner or developer on a fixed

budget, those secondary costs may necessitate cost reductions on some more tangible portion of

the project.

Additionally, roadway repairs are rarely as durable as the original construction. Repairs can, hence,

lead to accelerated deterioration of the roadway, which increases costs for the community. While

the costs of using trenchless methods may be initially higher than other approaches, when the

full cost of the project is considered, the potential for avoiding many of the trenching and related

Cityscape 343

Blanford

secondary costs may tilt the decision to use trenchless technology.

Exhibit 5

The Two-stage Process in the Horizontal Directional Drilling Under a River

Image courtesy Brian Dorwart

Enhanced Safety

Trenching presents significant safety risks to the workers and the public. The Occupational Safety

and Health Administration (OSHA) notes that “cave-ins pose the greatest risk and are much more

likely than other excavation related accidents to result in worker fatalities. Other potential hazards

include falls, falling loads, hazardous atmospheres, and incidents involving mobile equipment.

Trench collapses cause dozens of fatalities and hundreds of injuries each year” (OSHA, n.d.).

While any construction project has risks, avoiding those that are known is always a good strategy.

Removing the need for a trench greatly reduces the safety hazards to the utility crew and the public.

344 Industrial Revolution

Trenchless Technology: A New Way to Install and Renew Utilities

Community Impacts

As we stated earlier, construction in a community is naturally disruptive. The disruption can

complicate the process of infill development despite the many benefits of building in existing

neighborhoods. The incorporation of trenchless technologies reduces the negative impacts to the

neighborhood, however, while providing modern infrastructure for the new home.

In areas that are environmentally, culturally, or otherwise significant, trenchless technologies

may permit construction with fewer effects to these important areas. Imagine a scenario where a

homeowner desires to renovate or replace a home on a lot with mature landscaping. Trenching for

the utilities might damage that landscaping, which could affect the entire neighborhood. Replacing

mature landscaping after that damage is typically quite expensive.

Installation Speed

There are a number of factors that contribute to the pace of utility installation when using

trenchless technologies. First, and foremost, is the type of trenchless technology being employed.

The utility application also merits consideration, such as the pipe size and bore length, the site

soil conditions, and the directional drilling tolerances. The speed of installation for trenchless

technologies versus trenched is most notable when the project requires minimal effects to existing

construction. In this case, trenchless technologies are the preferred method.

Conclusion

Trenchless technologies should be considered for utility installation because these technologies can

provide cost savings, enhanced safety, reduced community impacts, and speedy installation. This

is especially true for infill development, where upgrading utilities may cause significant damage

to the landscaping, sidewalks, or roadways. Trenchless technologies can help to avoid the damage,

minimizing the costs of installation as well as maintaining positive relationships between the

developer and the neighboring community.

Acknowledgments

The author would like to thank Dana Bres (HUD) and Anne Luzier (NUCA) for reviewing this

article and their thoughtful comments.

Author

Mike Blanford is a Research Engineer at the U.S. Department of Housing and Urban Development.

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Blanford

Additional Resources

North American Society for Trenchless Technology (NASTT)

NASTT is an engineering society. NASTT’s mission is to advance trenchless technology by

technical information dissemination, research and development, education, and training.

http://www.nastt.org/

Trenchless Technology Center (TTC)

The TTC is a Center of Excellence for trenchless technology at Louisiana Tech University. TTC

conducts research and development, education, and technology transfer.

https://www.latech.edu/research-enterprise/centers-of-excellence/trenchless-technology-center/

National Utility Contractors Association (NUCA)

NUCA is a trade association for utility construction and excavation industry in the United States.

NUCA’s membership uses both trenchless and trenched utility installation technologies.

https://www.nuca.com/

References

North American Society for Trenchless Technology (NASTT). n.d. “Glossary of Trenchless Terms.”

https://www.nastt.org/resources/glossary/.

Occupational Safety and Health Administration (OSHA). n.d. “OSHA FactSheet Trenching and

Excavation Safety.” https://www.osha.gov/Publications/trench_excavation_fs.html.

346 Industrial Revolution

by Michael E. Canes

Protecting Low-Income Housing

from Climate Risks

Michael E. Canes

Logistics Management Institute

Abstract

Buildings are vulnerable to short term extreme weather events such as flooding, high winds, wildfires and heavy precipitation, and to longer term forces such as soil erosion and extended heat waves. A

warming global climate increases the risks of these types of events, necessitating a strategy to manage them. Protocols have been developed to incorporate climate risks into risk management strategies in

response to rising interest from public authorities, bond rating agencies, insurance companies and

others. By incorporating climate risks into their risk management strategies and acting to mitigate

such risks, public housing authorities can (1) recognize the emergence of such risks, (2) quantify

them via localized climate impact projections, (3) develop plans to mitigate them, and (4) engage in strengthening or other strategies to protect buildings and their residents and to save money when severe weather events occur.

Introduction

Buildings can be severely damaged or even destroyed by severe weather events such as flooding,

high winds, wildfires, and heavy precipitation. Projections of a warming climate and rising ocean

levels indicate that the risks of these types of events are likely to increase over time. Those who

manage buildings such as public housing authorities can deal with these risks by incorporating

them into their overall risk management strategies and taking actions to address them. This article

provides background regarding what others are doing to deal with climate risks and then discusses

how managers of the nation’s low-income housing can deal with it in their overall risk management

plans and actions.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 347

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

Canes

Exhibit 1

Building Damage from a Hurricane

A warming climate will increase the frequency of severe high-wind events, with potentially devastating consequences for buildings.

Photo courtesy DailyCaller.com .

Background

Increasing risks related to climate change have become ever more apparent to governments

and to private sector entities as worldwide efforts to control carbon and other greenhouse gas

emissions have languished. Assets vulnerable to severe weather such as buildings, factories and

communication infrastructure therefore have become increasingly subject to scrutiny. Entities such

as the Climate Disclosure Project and the Task Force on Climate-related Financial Disclosures

(TCFD) are calling upon firms to disclose the potential impacts of climate risks on their assets.1

Meanwhile, insurers and investors have become aware that their liabilities or holdings may be

adversely affected by climate risks and are putting increased pressure on private firms to respond

accordingly. Many firms now report on climate risks affecting their assets although few have

publicly identified the potential impacts.

On the public-sector side, a system called the Climate Risk and Adaptation Framework and

Taxonomy (CRAFT) has been developed by two nonprofit entities, C40 and Arup, to offer cities a

way to report what they have done to anticipate and deal with climate risks. So far, more than 500

cities have utilized this system. They have been incentivized by Moody’s Investors Service, which

has warned municipalities that it will take account of their climate risk awareness and actions in

rating their bond offerings (Flavelle, 2017). Standard & Poor (S&P) likewise has indicated that it will take climate risks into account in rating private and public sector bond sellers (S&P, 2019).

1 The TCFD was created by the Financial Stability Board, an international organization that monitors and makes recommendations about the global financial system.

348 Industrial Revolution

Protecting Low Income Housing from Climate Risks

Policymakers also have taken an interest. Since 2013, the Government Accountability Office (GAO)

has recommended to Congress that the federal government identify significant climate risks and

create appropriate responses (GAO, 2019). Office of Management and Budget (OMB) Circular A-11,

a guidance document for federal agencies, at one point required climate adaptation planning and

resilience analysis for construction and renovation projects, although the language was removed in

a 2018 update. In addition, some in Congress have proposed a series of policies called the “Green

New Deal” to confront climate change, indicating legislative interest in climate-related actions.

Managing Climate Risk

Risk management has become a standard technique to deal with the various risks faced by asset

owners or those who manage the assets for them. The technique involves assessing the risks of

various events and the impacts of those risks if they materialize and developing plans to deal with

them. Financial accounts and planning documents are written to incorporate identified risks and

monies are set aside for purposes of taking actions to ameliorate the effects of the events, should

they transpire. If especially severe events occur, monies can be saved by having avoided sudden, very

large losses. In addition, disruptions to ongoing activities can be reduced if not altogether avoided.

Housing authority management of climate risks in particular involves incorporating such risks

into overall risk management planning by identifying the types of events that may occur because

of climate change, their impacts upon local publicly subsidized housing, and steps that are being

taken to reduce such potential damage. With federally subsidized housing, the welfare of residents

is of particular importance because their choices can become even more limited should drastic

weather events occur that suddenly damage or destroy the housing they occupy.

Exhibit 2

Public Concern over Low-Income Housing Resident Welfare

Severe weather events may impose difficult choices upon low-income residents and generate concern among their supporters.

Photo courtesy MarketMadHouse.com .

Cityscape 349

Canes

How can climate risks be dealt with? There are both general and specific steps to be taken. Among

the more general steps, first, treat the issue seriously. There is plenty of evidence that the climate

is changing and with it the probabilities of various weather events. Such probabilities may make

little difference in some geographic areas, but have clear implications for others, particularly coastal areas subject to flooding and frequent hurricanes.

Second, identify probable local impacts from a changing climate. A number of climate models have

been developed that project the local impacts of future climate change to at least the county level.

They are inexact, to be sure, but are still a useful tool that is accessible for those engaged in climate risk identification. Further, their accuracy has improved with time to the point where localized

quantitative risk assessment is feasible.2

Third, incorporate climate risks into reports and plans and set monies aside to execute such plans. This reporting, planning, and budgeting not only inform internal staff of the importance of recognizing

climate risks but also lets outside parties such as policy makers, investors, and bond rating agencies

know that actions to ameliorate such risks are being anticipated if not already implemented.

Fourth, communicate with local authorities to discover what plans are in place to deal with severe

weather events. Depending on what form these plans take, a housing authority may be able to

make use of local resources (for example, emergency housing) in its own response to such events.

A number of specific actions also can be taken to protect residents and structures from climate-

related events. Among these are to:

• Site and design buildings to minimize their exposure to severe weather events.

• Use fire-resistant building materials such as concrete made with tough, flame retarding aggregate.

• Protect building points of entry from floodwaters, for example, with portable flood barriers.

• Protect glass windows or doors in areas subject to high winds with shutters, for example,

accordion shutters.

• Perform electrical upgrades to ensure continued power in case of prolonged outages, for

example, installation of backup generators. Also, be sure switches, sockets, circuit breakers

and wiring are placed well above the expected flood levels in your area.

• Provide means for residents to communicate with the housing authority in case of severe

weather damage to facilities, for example, emergency telephone lines.

• Maintain secure means of access to and egress from damaged buildings to enable emergency

personnel and residents to get in or out in worst-case scenarios.

2 The Federal Emergency Management Agency (FEMA) offers a broad tool called RiskMAP (available at https://toolkit.

climate.gov/tool/risk-mapping-assessment-and-planning-risk-map-program) while a number of firms offer models with more specific localized information (for example, The Climate Service, LLC offers a tool called Climanomics, at

http://www.theclimateservice.com/).

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Protecting Low Income Housing from Climate Risks

• Conduct comprehensive maintenance on structures more frequently and completely

than otherwise.

• Prepare plans to temporarily house residents while rebuilding occurs.

• Maintain a list of trusted contractors the housing authority has worked with who can assist

with rebuilding damaged properties.

• Make sure key suppliers of goods or services to the housing authority’s buildings (for

example, trash collectors) are themselves taking measures to protect their assets against

severe weather events.

Exhibit 3

Building Construction Using Concrete

Concrete construction has low thermal conductivity and protects against the spread of fire.

Photo courtesy DesigningBuildings.co.uk.

Cityscape 351

Canes

Exhibit 4

Removable Doorway Flood Barrier

Removable flood barriers can retard or prevent water access into buildings.

Photo courtesy DesigningBuildings.co.uk.

Exhibit 5

Accordion Shutters

Accordion or other high-quality shutters protect glass from intense winds and flying objects.

Photo courtesy SecurityRollingShutters.com.

Conclusion

Climate risks are real and public sector entities who recognize them, understand their implications,

and meet them head-on will have a leg up with policy makers, investors, bond ratings agencies,

and others with direct concerns. Housing authorities who assess the local consequences of

such risks and take actions to manage these will better protect not only the buildings they are

responsible for but the low-income residents who inhabit those buildings as well. Housing

authorities also are likely to save money over the longer term. Tools exist to help manage climate

risks within broader risk management strategies and can be readily accessed by the public housing

authorities who wish to do so.

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Protecting Low Income Housing from Climate Risks

Acknowledgments

The author would like to thank Kelli Canada, Frank Reilly, and Erwin Villiger of LMI’s analytic staff

for their help in the preparation of this article.

Author

Michael E. Canes is a consultant at LMI, a not-for-profit government consulting firm located in

McLean, Virginia. He specializes in economic analyses of energy and environmental issues.

References

Flavelle, Christopher. 2017. “Moody’s Warns Cities to Address Climate Risks or Face Downgrades,”

Bloomberg News, November 29. https://www.bloomberg.com/news/articles/2017-11-29/moody-

s-warns-cities-to-address-climate-risks-or-face-downgrades.

Government Accountability Office (GAO). 2019. High-Risk Series: Substantial Efforts Needed

to Achieve Greater Progress on High-Risk Areas, GAO-19-157SP. Washington, DC: GAO.

https://www.gao.gov/assets/700/697245.pdf.

Standard & Poor (S&P). 2019. “Our Approach to Assessing ESG in Ratings.” New York: S&P

Global Ratings. https://www.spratings.com/en_US/products/-/product-detail/our-approach-to-

esg-in-ratings.

Cityscape 353

354 Small Area Fair Market Rents

by Alex Schwartz

Policy Briefs

The Policy Briefs department summarizes a change or trend in national policy that may have

escaped the attention of researchers. The purpose is to stimulate the analysis of policy in the

field while the policy is being implemented and thereafter. If you have an idea for future Policy

Briefs, please contact david.l.hardiman@hud.gov .

New York City’s Affordable

Housing Plans and the Limits

of Local Initiative

Alex Schwartz

The New School

Abstract

New York City invested more than $18.9 billion, after inflation, from its capital budget for the

development and preservation of more than 490,000 affordable housing units from 1987 to 2018. This

article assesses New York City’s various affordable housing programs during this period in light of the city’s longstanding affordability problems, focusing in particular on Mayor Bill de Blasio’s housing plan and the results of this plan from its inception in 2014 through the first quarter of 2019. This article discusses those results while considering criticisms that the plan does not provide sufficient housing for households with the lowest incomes and that its use of inclusionary zoning may exacerbate housing

affordability pressures. The article concludes that, while New York’s programs have produced affordable homes for thousands of residents, the amount of expenditure that would be required to resolve New York’s affordability crisis is far too large for New York to afford without federal support.

Introduction

New York City invested more than $18.9 billion, after inflation, from its capital budget for the

development and preservation of more than 490,000 affordable housing units from 1987 to 2018.

The city has spent billions more on housing through private activity bonds, block grants, and

other sources. Despite this investment, New York City has confronted a severe housing crisis for

many years. As of 2017, nearly one-third of all renters paid 50 percent of their income on rent, and

more than 60,000 people were homeless. The persistence of New York’s housing crisis in the face

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

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Schwartz

of its massive investments and other measures to provide affordable housing have prompted some

to suggest that these efforts are not only ineffective but may exacerbate the very problem they are

intended to solve.

My aim in this paper is to assess New York’s various affordable housing programs in light of the

city’s longstanding affordability problems. I argue that, while the city’s programs have provided

affordable homes for thousands of residents, and in some cases revitalized entire communities, the

amount of expenditure that would be required to resolve New York’s affordability crisis is far too

large for a city to afford without federal support. While a growing number of cities and states have

recently established affordable housing programs or increased the size of existing programs, the

New York City experience suggests that local initiatives are highly unlikely to be sufficient.

This article is organized as follows. First, I provide a brief overview of New York’s housing market,

focusing on the severity of the city’s affordability problem and some of its key causes. The second

section provides an overview of the city’s affordable housing initiatives, starting with Mayor Ed

Koch’s Capital Budget Plan of 1987 and continuing through Mayor de Blasio’s current Housing

New York Plan. The third and longest section examines the de Blasio plan in more detail. That

section describes the plan’s major elements, reviews its accomplishments over its first 4 years

of implementation, and discusses the primary criticisms that have been leveled against it. The

concluding section discusses how the New York City experience shows both the potential for local

governments to produce affordable housing and also their limited ability to fundamentally address

the housing affordability crisis.

Housing Market Overview

New York City has been in a housing crisis for nearly 100 years. As documented by Jacob Riis

(1997) and many others, the city has long struggled to provide decent and affordable housing

for its low-income residents. The city’s rent regulations, in place almost continuously since the

1940s, are premised on the existence of a housing emergency, currently defined as having a rental

vacancy rate of no more than 5 percent (Collins, 2018: 32). As of 2017, the average renter1 paid 32

percent of his or her income on rent and utilities, and more than 29 percent of all renters—totaling

511,736—confronted severe housing cost burdens, paying at least one-half of their income on

housing costs. Among extremely low-income renters,2 defined as earning no more than 30 percent

1 All data on housing affordability (income, rents) come from the Housing Vacancy Survey (HVS), a study conducted every 2 or 3 years by the U.S. Census Bureau for New York City. It is a representative random sample of about 19,000

households. The most recent survey was conducted in 2017. The figures reported in this paragraph do not include renters receiving federal Housing Choice Vouchers and other forms of tenant-based rental assistance. The HVS does not report the rents paid directly by these households; it indicates the total rents charged to the households, most of which is covered by the subsidy. As a result, the rent burdens for these households as reported by the HVS is inaccurately high (for example, 79 percent are indicated as spending 50 percent or more of their income on rent).

2 This definition of the Extremely Low-Income category, which New York City employs for its housing programs, differs from the official definition adopted by the federal government. The latter (amended by the 2014

appropriations act) defines an extremely low-Income family as a very low income family whose incomes do not exceed the higher of (i) the poverty guidelines or (ii) 30 percent of median family income for the area (adapted from 42 USC 1437a (b)(2)(C) - https://www.law.cornell.edu/uscode/text/42/1437a). For the New York, NY HUD Metro FMR Area, the poverty guidelines control the extremely low-income limits for larger family sizes. Thus, NYC income limits for larger households may be lower than the corresponding Federal limits.

356 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

of the area median family income (AMI), the incidence of severe housing cost burden exceeded

78 percent. As of October 2018, more than 63,000 people were part of the city’s homeless shelter

system, and that does not include several thousand more homeless people sleeping on the streets

and in other public spaces (Coalition for the Homeless, 2019).

There are numerous reasons for New York’s chronic housing affordability crisis. It reflects, among

other factors, the intensity of demand for housing among households of all incomes, the high cost

of land and construction, the weakening of rent regulation, the loss of lower-cost housing, the low

incomes of many renters, and the paucity of federal rent subsidies. It is not the goal of this article

to parse the influence of each of these elements, but it is important to frame the city’s efforts to

provide affordable housing within this broader context.

New York is one of the nation’s most expensive housing markets. At $1,789, the federally

designated fair market rent for New York City in 2018 was the 14th highest in the nation. Rents

would surely be higher were it not for the fact that about 1 million of the city’s 2.1 million rental

units are subject to rent regulation, and more than 250,000 are subsidized by federal, state, and

local programs, including 180,000 units of public housing. The average rent for a non-regulated

unit in 2017 was $1,700. Rents for vacant, non-regulated units are generally much higher, with the

average two-bedroom apartment costing $3,721 as of November 2018 (Rent Jungle, 2018).

Purchase prices of houses and apartments are also very high. According to the real estate service

Zillow Group, Inc., in June 2019, the median list price for a co-op or condominium apartment

was $850,000, and the median list price for a single-family house was $719,000 (Zillow, 2019).

Almost every week, The New York Times reports on apartments selling for tens of millions of dollars (Marino, 2019).

Fueling the demand for housing is the city’s population growth over the past several decades.

As of July 2017, New York’s estimated population stood at 8.6 million. This figure represented a

5.5-percent increase of more than 447,000 people since 2010 and a 22-percent increase of more

than 1.5 million since 1980. Much of this growth has been driven by foreign immigration, but

it also reflects the city’s enduring attraction as a mecca for the arts, finance, and other fields. In

addition, wealthy families frequently purchase apartments in New York for part-time residences.

Hundreds, if not thousands, of apartments have been acquired by foreign investors, often using

shell companies to cloak their identities and obscure the source of the funding (Story and Saul,

2015). The appetite for residential real estate investment is by no means limited to the high end

of the market. Investors have acquired hundreds of rental buildings in low- and moderate-income

neighborhoods, with the purchase prices frequently far higher than what the rent rolls would

justify. What motivates most of these investors is the prospect of removing the property from rental

regulation, enabling them to charge much higher rents (Association for Housing and Neighborhood

Development, 2011; Savitch-Lew and Spittal, 2017). The entry of Airbnb, Inc., an online, short-term

housing rental service, has further added to rent pressures by effectively converting thousands of

units from permanent housing to temporary accommodation (Ferré-Sadurní, 2019).

The cost of producing housing in New York City is extremely high. Construction costs are among

the highest in the nation, and land costs are very high as well. As a result, most new construction,

Cityscape 357

Schwartz

unless it is subsidized or subject to inclusionary zoning (discussed later), is only affordable to

affluent households. Without subsidy, newly built housing is unaffordable to most New York

City residents. Moreover, many households lack the income necessary to afford the basic costs of

operating a housing unit—that is, upkeep, insurance, utilities—much less the costs associated with

the acquisition or development of the housing.

For example, the New York University Furman Center estimates that the minimum monthly rent

necessary to support the construction and operation of a 400-square-foot studio apartment

amounts to $1,480, assuming the developer acquired the land at no cost. If the land costs $200 per

square foot, the minimum rent rises to $1,820. Under the free-land scenario, the rent is affordable

to a household earning at least $59,000, or 89 percent of New York’s Area Median Family Income

(AMI) for single-person households.3 With land costing $200 per square foot, the same apartment

becomes affordable only when incomes exceed $72,800 (110 percent of AMI). The same study

estimates the minimum rents for even smaller units, including ones with shared kitchens and

bathrooms: assuming no land costs, the minimum rent for a 160-square-foot “efficiency” apartment

with shared kitchen and bath amounts to $841 a month, which is affordable to households earning

at least $33,600 (51 percent of AMI). If the unit has its own kitchen and bath, the minimum rent

rises to $1,170, affordable to households with incomes of at least $46,800 (71 percent of AMI).

Rents for the same unit become more expensive when the underlying land must be purchased

(exhibit 2). In other words, even the smallest newly built apartments with shared facilities are out

of financial reach for hundreds of thousands of low-income New Yorkers.4 Larger units, of course,

are even more costly.

Exhibit 1

Maximum Incomes in 2016 for Each Income Category in the New York Metropolitan Area

Income Group

1-Person

2-Person

3-Person

4-Person

5-Person

Extremely Low Income

(<30% of AMI)

$19,050

$21,800

$24,500

$27,200

$29,400

Very Low Income

(30-50% of AMI)

$31,750

$36,250

$40,800

$45,300

$48,900

Low Income

(50-80% of AMI)

$50,750

$58,000

$65,250

$72,500

$78,300

Source: HVS, 2017

3 It is customary in the United States to classify household incomes in terms of the metropolitan area’s median family income (AMI) as adjusted by the U.S. Department of Housing and Urban Development. Eligibility for housing subsidy programs is almost always defined by a maximum percentage of AMI. In exhibit 1 are the maximum incomes in 2016

for each income category in the New York metropolitan area, which, besides the five boroughs of New York City, also includes Putnam, Rockland, and Westchester counties. Note that the federal department of Housing and Urban Development (HUD) uses slightly different terminology (and acronyms) to define income categories—Area Median Family Income (Area MFI).

4 According to the HVS, there were nearly 342,000 one-person households in 2017 with incomes below 50 percent of AMI, including more than 233,000 with incomes below 30 percent of AMI—far too low to afford even very small apartments with shared bathrooms and kitchens.

358 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

Exhibit 2

Minimum Rents, and Associated Income Requirements for Small Apartments, Alternative Scenarios

Efficiency

Efficiency

Shared Kitchen

Small Studio

Micro Unit

Shared Kitchen

& Bath

Square Feet

400

300

225

160

Land Costs = $0

Min. Rent

$1,480

$1,240

$1,170

$840

Min. Income

$59,200

$49,600

$46,800

$33,600

Corresponding AMI

89%

75%

71%

51%

Land Costs = $200/sq. ft.

Min. Rent

$1,820

$1,500

$1,410

$1,040

Min. Income

$72,800

$60,000

$56,400

$41,600

Corresponding AMI

110%

90%

85%

63%

Land Costs = $450/sq. ft.

Min. Rent

$2,360

$1,920

$1,780

$1,340

Min. Income

$94,400

$76,800

$71,200

$53,600

Corresponding AMI

142%

115%

107%

81%

Source: Stem and Yager 2018

The supply of housing affordable to lower income households has steadily dwindled over time. In

just 3 years, from 2014 to 2017, the number of units renting for less than $1,500 a month fell by

nearly 166,000 (12.3 percent). The stock of units renting for less than $1,000 a month dropped

by more than 87,000 (14.5 percent; exhibit 3). Not surprisingly, vacancy rates for these lower cost

units are very low. As of 2017, the vacancy rate for units renting below $800 a month stood at just

1.2 percent and, for units renting between $800 and $1,100 a month, it amounted to 2.1 percent.

The only rental housing with vacancy rates above 5 percent goes for $2,000 a month or more.

Exhibit 3

Change in Total Units from 2014 to 2017 by Monthly Contact Rent (Constant 2017 Dollars)

2017 Percent

Change 2014 - 2017

2014

2017

Share

Total

Percent

Less than $500

161,776

143,488

7%

-18,288

-11.3%

$500-799

188,006

160,238

8%

-27,768

-14.8%

$800-999

253,337

211,673

10%

-41,664

-16.4%

$1000-$1,499

740,732

662,466

31%

-78,266

-10.6%

$1,500-$1,999

351,847

401,444

19%

49,597

14.1%

$2000-$2,499

130,871

176,370

8%

45,499

34.8%

$2,500 +

228,877

282,972

13%

54,095

23.6%

No Rent

53,391

65,223

3%

11,832

22.2%

Total

2,108,837

2,103,874

100%

-4,963

-0.2%

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

Source: Housing Vacancy Survey

Cityscape 359

Schwartz

One reason for the loss of lower cost rental housing is the deregulation of rent-stabilized

housing. Over a quarter century, from 1994 to July 2019, New York State law allowed housing

to be removed from the rent regulation if the regulated rent exceeded a specific level ($2,700

per month in 2018) after a vacancy or if the tenant’s annual income exceeded $200,000 in each

of the previous 2 years and the rent exceeds a specified amount ($2,734 in 2018). Under rent

stabilization, the Rent Guidelines Board sets the maximum allowable rate of increase landlords

may charge for lease renewals. Landlords could increase rents by up to 20 percent, however,

during a turnover in occupancy, and they could increase rents to cover a portion of building-wide

capital improvements (for example, a new roof) and apartment-specific investments (for example,

new appliances or bathroom or kitchen renovations). As a result of these and other provisions,

nearly 291,000 units were deregulated from 1994 through 2017 (New York City Rent Guidelines

Board, 2018a). This loss has been counterbalanced by the addition of more than 143,000 units

into the rent-stabilization program. These latter units, however, usually part of luxury buildings

that received property tax abatements, charge much higher rents than that of housing lost to the

rent-stabilized housing stock (the luxury decontrol provisions do not apply to this housing). As

noted previously, the prospect of deregulating housing has tempted investors to acquire thousands

of rent-stabilized buildings, sometimes resulting in tenant harassment and other illegal measures to

increase rents and force tenants to move out of the building (Savitch-Lew and Spittal, 2017).

In June 2019, the state passed new legislation that, among other things, eliminated high-rent and

high-income deregulation, and additional rental increases allowed for new tenancies (vacancy

bonuses). It also reduced the rent increases allowed for major capital improvements and individual

apartment improvements (Murphy, 2019; Otterman and Haag, 2019).

Another fundamental aspect of New York’s housing affordability crisis is that hundreds of

thousands of households have very low incomes and far fewer apartments are available at rents

those households can afford. In 2017, 20 percent of all renters, 409,000 in total, earned less than

$18,000 annually, sufficient to afford no more than $450 in monthly rent. Ten percent of all renters

earned less than $11,000, which can support no more than $270 in rent. Another 10 percent

earned between $18,000 and $27,000, sufficient for rent of up to $675. Very few units are available

at these rents. Only 15 percent (300,000) of all rental units cost less than $800 a month in 2017,

and only 7 percent (143,500) cost less than $500 (exhibit 3).

Finally, the supply of federal rental subsidies has increased little over the past decade. From 2010

to 2017, the number of households in New York City with federal Housing Choice Vouchers

(HCV) went from about 130,000 to 136,000, an increase of less than 5 percent.

New York City’s Affordable Housing Initiatives from

1986 to 2019

New York City has a long history of promoting affordable housing, advocating for federal and

state support, and drawing from its own resources when necessary. The city accounts for a

disproportionately large share of the nation’s public housing. This is especially true today because

New York is one of the very few large cities that has not demolished its public housing, but it

360 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

was also true in the heyday of the public housing program. The city successfully advocated for

public housing, and it and the state financed the construction of additional public housing from

the 1950s to the 1970s (Bloom, 2008). New York also secured a large amount of other federally

subsidized housing under the federal Section 236 and Section 8 programs (Bach, 1999). The

Mitchell-Lama Housing Program, a city-state partnership created in 1955, provided low-cost

financing and tax abatements for moderate-income rental and cooperative housing. More than

105,000 units were built under the Mitchell-Lama program before the fiscal crisis of 1975 led to

the program’s demise (Bach, 1999).

In 1986, Mayor Ed Koch launched what was then called the Capital Budget Housing Plan or “The

Ten-Year Plan.” Faced with growing homelessness, widespread disinvestment and abandonment

of residential buildings, and declining federal housing subsidies, Mayor Koch decided to allocate

$4 billion ($8.9 billion in 2017 dollars) from the city’s capital budget for the construction and

preservation of housing targeted primarily to low- and moderate-income households (Schwartz,

1999). Every subsequent mayor, regardless of major party affiliation, also used the capital budget to

finance affordable housing. In total, the city invested more than $18.9 billion in inflation-adjusted

(2017) dollars on housing construction and preservation through 2018. These figures pertain

only to the city’s capital budget, expenditures funded largely through property tax, and other

general revenue. They do not include the billions of dollars more in tax-exempt and taxable private

activity bonds issued by New York’s Housing Development Corporation to underwrite permanent

mortgages and construction loans for hundreds of affordable housing developments, nor do they

include federal tax expenditures through the Low-Income Housing Tax Credit (LIHTC) program.

Exhibit 4 shows the annual amount of capital-budget expenditures on housing and the city-funded

housing starts from 1987 to 2018. Exhibit 5 shows average annual expenditures and housing starts

under each of the five mayors over this 31-year period. The data show that the city has consistently

spent hundreds of millions from its capital budget on housing. New York City spent less than

$400 million (in 2017 dollars) on housing in only 5 of the years in the time period. In 8 of the

years, expenditures exceeded $700 million. Exhibit 5 shows that average annual expenditures were

highest under Mayor Dinkins (1991–1994) at more than $932 million, followed by $914 million

under the current mayor, Bill de Blasio. Expenditures were lowest under Mayor Giuliani at $458

million, slightly less than the annual average of $459 million under Mayor Bloomberg.

Cityscape 361

Schwartz

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Source:

362 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

From the perspective of total city-funded housing starts, average annual production is highest

under Mayor de Blasio, at more than 25,000 units per year, followed by almost 21,000 units under

Mayor Koch. Mayor de Blasio’s administration has funded the most new construction, averaging

more than 7,900 units per year, compared with 4,600 units under Mayor Bloomberg and about

2,500 units or less under the preceding mayors. At more than 17,000 average annual starts, Mayor

de Blasio’s housing preservation activity is far higher than that of Mayors Bloomberg, Giuliani, and

Dinkins. It is eclipsed only by the 18,500 units preserved annually under Mayor Koch.

Exhibit 5

Average Annual Housing Starts and Capital Expenditures Under City Housing Plans

New Construction

Preservation

Total

Capital Expenditures

Mayor

Starts

Starts

Starts

(thousands of $2017)

Koch (FY 87-90)

2,337

18,531

20,869

$591,561

Dinkins (FY 91-94)

2,454

11,785

14,239

$932,059

Giuliani (FY 95-02)

2,554

6,909

9,463

$457,515

Bloomberg (FY 03-14)

4,619

9,937

14,556

$459,124

de Blasio (FY 15-18)

7,958

17,179

25,137

$914,020

Source: Mayor’s Management Report and Comptroller’s Budget Report

New York City classifies its affordable housing investments as either “New Construction” or

“Preservation.” These categories are not monolithic, however, and involve various forms of

investment. New construction can refer to housing that exclusively serves low- and/or moderate-

income households or to properties that combine market-rate and subsidized units. Some provide

housing for families; others serve populations with special needs. New construction projects vary

in terms of their size, the mix of income groups that are targeted, and the types of organizations

involved in their development and management.

Preservation refers to various levels and types of physical improvement to existing housing and also

to the extension or replacement of subsidies that would otherwise expire. Preservation programs

provide grants and low-cost loans to private landlords to replace roofs, windows, and upgrade

plumbing, electrical, and heating systems. They can involve “gut” rehabilitation of vacant, severely

dilapidated buildings or, more frequently, “moderate” rehabilitation of mostly occupied buildings,

in which the work is limited in scope. Preservation programs can involve for-profit landlords as

well as community development corporations and other nonprofit organizations.

Over the past decade or so, New York’s preservation efforts have also sought to prevent housing

from losing its existing subsidies. For example, the city has provided various financial and tax

incentives to encourage the owners of Mitchell-Lama developments to remain in the program and

not convert them to market-rate occupancy. The city also provides incentives for owners of housing

financed with federal LIHTC to keep the housing affordable to low-income households after the

30-year income-restriction period for the credits expires. In 2015, the city committed $220 million

to preserve the affordability of 5,000 units in Stuyvesant Town-Peter Cooper Village, a storied

development of more than 11,000 apartments that a real estate developer and a financial firm

purchased in 2006 from Metropolitan Life Insurance Company, its original owner. The new entity

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Schwartz

subsequently declared bankruptcy (the largest multifamily real estate bankruptcy in U.S. history),

reflecting the owner’s overly aggressive assumptions regarding its ability to take units out of rent

regulation (Stefanski, 2018; Bagli, 2013).

Originally, much of the city’s housing efforts centered on its extensive inventory of occupied and

vacant tax-foreclosed property (Braconi, 1999; Schwartz, 1999). Starting in 1977, in an effort to

discourage landlords from falling behind on their property taxes, the city decided to foreclose on

properties after 12 months of arrears instead of the 36 months that were previously allowed. The

policy did not succeed. Occurring at a time of population loss and disinvestment, landlords in

low-income, predominantly minority neighborhoods of the Bronx, Harlem, and Central Brooklyn

abandoned hundreds of apartment buildings, sometimes burning them down in the process. As a

result of the city’s tax-foreclosure policy change, it soon became the owner of thousands of vacant

and occupied units. These tax-foreclosed (“in rem”) properties were often in terrible physical

condition, with high levels of vacancy. Tenants had very low incomes, and rents, if collected, were

seldom sufficient to cover basic operating expenses.

By 1987, and the start of Mayor Koch’s Capital Budget Program, the city owned 53,000 units of

occupied in rem housing and 49,000 units in vacant buildings (Schwartz, 1999). Much of the city’s investments involved this housing. It paid for the physical upgrading of in rem occupied buildings, often transferring them to new ownership in the process—including community development

corporations, for-profit developers, and tenant cooperatives. The city funded the gut rehabilitation

of vacant buildings,5 creating new communities in the process (Lamberg, 2018). It also provided

financing for one-to-three family houses built on city-owned land—much of which was targeted to

moderate- and middle-income families. Not all of the city’s housing expenditures involved in rem

housing. It also spent millions of dollars each year to help private landlords improve and preserve

their buildings.

The amount of in rem property gradually diminished as the city rehabilitated it and transferred

ownership to private and nonprofit organizations. In the mid-1990s, the city changed its

foreclosure policy and stopped taking possession of most properties in arrears (Schwartz, 1999).

Instead, it auctioned tax liens of properties in arrears. The tax-lien buyers would then collect back

taxes from the owners and take possession of the property if they failed to pay. For property in

arrears that housed low-income and other “at-risk” tenants, the city created a new program, Third

Party Transfer, through which it conveyed control of the property to a qualified nonprofit or for-

profit entity that would rehabilitate the property if needed and provide improved management

(New York City Rent Guidelines Board, 2018b).

As a result of the city’s investments in the rehabilitation of in rem property, its cessation of most foreclosures, and perhaps most importantly, improvements in the city’s economy and real estate

market that greatly reduced incentives to lose property to foreclosure, the supply of in rem

housing declined from 102,000 units in 1987, 30,000 in 1997, less than 1,000 in 2007, and only

323 in 2018 (Schwartz and Vidal, 1999; City of New York, 2007; New York City Rent Guidelines

Board, 2018b).

5 Gut rehabilitation accounted for 29 percent of all housing units renovated or constructed with city assistance from FY1987 to FY1996 and for 61 percent of the city’s capital budget expenditures (Schwartz and Vidal, 1999: 239).

364 Policy Briefs

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Over time, reflecting the diminishment of in rem property, more of New York affordable housing

development has taken place on privately owned land. Some new construction has taken place

on land owned by various city agencies, but an increasing proportion is owned by nonprofit

organizations or for-profit entities. From the second half of fiscal year (FY) 2014 through 2018, less

than 10 percent of all city-funded housing starts took place on city-owned land (City of New York

2018b: 15). Developers have used city funds to build housing on sites owned by churches and

other nonprofit organizations. Some buildings have been constructed on open spaces (for example,

parking lots) on some of the city’s public housing campuses (not without controversy). Most

sites, however, have been obtained on the private market. Frequently, as will be discussed in the

following paragraphs, housing developments on privately owned land is mixed-income, combining

market-rate units with housing allocated for moderate- and low-income households.

Mayor Bill de Blasio’s Housing New York

In May 2014, 4 months after his inauguration, Mayor Bill de Blasio launched “Housing New

York,” his plan for developing and preserving affordable housing. The plan followed that of his

predecessor, Michael Bloomberg. Under Mayor Bloomberg’s “New Housing Marketplace” plan,

the city supported the development and preservation of 165,000 housing units (53,000 new

construction, 112,00 preservation) from FY2004 through FY2013 (NYC HPD, 2013). Expanding

on Mayor Bloomberg’s plan, Mayor de Blasio called for the development and preservation of

200,000 units (80,000 new construction, 120,000 preservation) over 10 years (City of New York,

2014). In 2018, just after his reelection to a second term, Mayor de Blasio announced that the

plan’s original goals would be achieved in 2022, 2 years ahead of schedule, and added 100,000

units to the plan. In total, the revised plan aimed to build or preserve 300,000 units by 2026

(120,000 new construction, 180,000 preservation; City of New York, 2018a).

In addition to its higher numerical goals, the de Blasio plan also differed from the Bloomberg plan

in giving greater priority to low-income households. Whereas 68 percent of the housing developed

or preserved during the Bloomberg Administration was designated for low-income households

earning up to 80 percent of AMI, the de Blasio plan designated 79.5 percent for this population.

Moreover, unlike the Bloomberg plan, the de Blasio plan specifies goals for different subsets of the

low-income category, distinguishing between households with “extremely low income” (0 to 30

percent of AMI), “very low income” (30 to 50 percent), and “low-income” (50 to 80 percent). The

Bloomberg plan, in contrast, set goals for the overall low-income category and did not indicate

the mix of incomes within this category that would be assisted. Nevertheless, as will be discussed

below, a major criticism of the de Blasio plan is that it does not provide sufficient housing for

households with the lowest incomes.

As with the housing plans of the previous mayors, de Blasio’s Housing New York is a compendium

of various programs and initiatives (Schwartz, 1999). As noted above, the de Blasio plan, like its

predecessors, calls for both the construction of new affordable housing and for the preservation

of existing affordable housing. To reach its goal of producing 120,000 new units by 2026, the

plan relies on a combination of inclusionary zoning, tax incentives, and direct subsidy. The

preservation goals are to be realized by replacing or extending subsidies that are currently in place

Cityscape 365

Schwartz

and by providing financial assistance to owners of affordable housing to help them pay for major

renovations and replace building systems (for example, boilers, plumbing, windows, roofs).

New Construction Programs

The aspect of Housing New York that has received the most attention (and criticism) is its embrace

of mandatory inclusionary zoning. Most of the new construction undertaken during the first 5

years of the plan, however, has involved direct subsidy and has not involved inclusionary zoning.

New York City has had a voluntary inclusionary zoning program since the 1980s, which had

yielded little new affordable housing. The program enabled developers to build at higher densities

than would be otherwise allowed, provided they reserve a portion of the additional space for low-

income households—and with “low income” defined at 80 percent of AMI. Few developers chose

to participate in this program, in part because there were other, less costly ways by which they

could build at densities greater than what would be normally allowed (Stein, 2018).

Mayor de Blasio’s Housing New York plan augmented this voluntary inclusionary zoning program

with a mandatory inclusionary housing (MIH) program that applied to neighborhoods that are

rezoned for higher densities and for individual sites for which developers obtain zoning variances

to build at higher densities. The City Planning Commission and the City Council approved

the MIH program in 2016 after months of negotiation. The program offers several options

for developers, all of which specify minimum percentages of units that must be provided to

households earning various percentages of AMI.

One option requires developers to allocate 25 percent of total floor area to households earning

an average of 60 percent of AMI, including 10 percent for households with incomes of no more

than 30 percent of AMI. A second option requires developers to allocate 40 percent of all units

for lower-income households, but the income limit is higher, at an average of 80 percent of AMI.6

The program allows developers to satisfy their affordable housing requirements by providing the

units off-site, but the amount of affordable housing that must be provided is increased under the

two options to 30 and 35 percent, respectively. Unlike most inclusionary zoning programs in

the United States (Thaden and Wang, 2017), the affordability period for New York’s program is

indefinite. All affordable units produced under the city’s Mandatory Inclusionary Housing program

must remain affordable in perpetuity. As of July 2019, six neighborhoods, starting with East New

York, have been rezoned at higher densities, thereby effectuating mandatory inclusionary housing.

Rezoning proposals were in process or anticipated for at least three additional neighborhoods (Ali

Kully, 2019a; Ali Kully, 2019b).

In addition to mandatory inclusionary housing, the de Blasio Administration plan also calls for the

city to invest directly in the development of new affordable housing. The administration launched

several programs that provide funds from the capital budget and other sources for new housing.

6 The City Council and City Planning Commission can also decide to apply two other options. The “Deep

Affordability” option requires developers to allocate 30 percent of total residential floor area for households earning an average of 40 percent of AMI. The “Workforce” option, which cannot be applied in most of Manhattan, requires 30

percent of all residential floor space to be reserved for households earning an average of 115 percent of AMI and no higher than 130 percent of AMI (New York City Department of City Planning, 2019).

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These programs include mixed-income developments that accommodate households from several

income groups and developments that focus on seniors and populations with special needs.

Exhibit 6 provides a summary of several of the mixed-income programs that include various kinds

of subsidies. For example, the Extremely Low- and Low-Income Affordability program (ELLA)

includes several income tiers. In one option, 10 percent of all units must go to formerly homeless

households, 10 percent to households with incomes up to 30 percent of AMI, 10 percent to

households with incomes up to 40 percent of AMI, 10 percent with incomes of up to 50 percent

of AMI, and 30 percent of the households must have incomes up to 60 percent of AMI. Developers

have the option of designating some or all the remaining 30 percent of the units to households

earning 70 to 100 percent of AMI; otherwise, they must be slated for households earning up to

60 percent of AMI. The city provides $130,000 to $150,000 in subsidy per unit, depending on

the overall income mix in the development. All units designated for households earning up to 60

percent of AMI are also eligible for federal LIHTC.

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Schwartz

Exhibit 6

Income Mixes and Subsidies for Selected Mixed-Income Programs in New York City (1 of 2)

1. Mandatory Inclusionary Zoning

Income Mix:

Option 1: 25 percent of total floor area to households earning an average of 60 percent of Area

Median Income (AMI), including 10 percent for households with incomes of no more than 30

percent of AMI.

Option 2: 40 percent of total floor area to households earning an average of 80 percent of AMI.

Deep Affordability Option: 20 percent of total floor area to households earning an average of 40

percent of AMI.

Workforce Option (inapplicable in Manhattan Community Districts 1 through 8): 30 percent

of total floor area to households earning an average of 115 percent of AMI, with a minimum of 5

percent at 70 AMI and 5 percent at 90 percent AMI.

Off-Site Option: additional 5 percent of the floor area must be designated for households earning up to 60 percent to 80 percent of AMI.

Subsidies:

None required

2. 50/30/20 Mixed-Income Program

Income Mix:

20 percent of units affordable to households earning up to 50 percent of AMI, including 15 percent

that are affordable to households earning no more than 40 percent of AMI;

Minimum of 30 percent of units affordable to households earning up to 130 percent of AMI;

Maximum of 50 percent of units can be set at market rents without regard to income.

Subsidies:

Tax-Exempt bond financing for first mortgage; 4 percent Low-Income Housing Tax Credits (LIHTC);

Subordinate loan of up to $65,000 to $85,000 per affordable unit, capped at $15 million per

project; property-tax exemption.

3. Mixed-Income Program: Mixed Middle (M2)

Income Mix:

20 percent of the units affordable to households earning up to 50 percent AMI, or 25 percent of the

units affordable to households earning up to 60 percent AMI.

Minimum of 30 percent of the units affordable to households earning between 80 percent and 100

percent of AMI.

Maximum of 50 percent of units affordable to households earning between 130 percent and 165

percent of AMI.

Subsidies:

Tax-Exempt bond financing for first mortgage; 4 percent LIHTC; Subordinate loan of up $85,000 to

$95,000 per affordable unit, capped at $15 million per project; property-tax exemption.

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Exhibit 6

Income Mixes and Subsidies for Selected Mixed-Income Programs in New York City (2 of 2)

4. Extremely Low & Low-Income Affordability (ELLA) Program

Income Mix:

Option 1:

10 percent of the units serving formerly homeless households,

10 percent of the units affordable to households up to 30 percent of AMI,

10 percent of the units affordable to households up to 40 percent of AMI,

10 percent of the units affordable to households up to 50 percent of AMI,

(Optional): up to 30 percent of the units with rents affordable to households earning

70–100 percent of AMI, and

Remaining units affordable to households up to 60 percent of AMI.

Option 2:

30 percent of the units serving formerly homeless households,

5 percent of the units affordable to households up to 40 percent of AMI,

5 percent of the units affordable to households up to 50 percent of AMI,

(Optional): up to 30 percent of the units affordable to households earning 70–100 percent

of AMI, and

Remaining units serving households up to 60 percent of AMI.

Subsidies:

$130,000 to $150,000 in capital funds per unit, depending on option and percent of units

affordable to households earning 70–100 percent of AMI; Federal LIHTC; tax-exempt financing;

property-tax exemption.

5. Mixed-Income Program: Mix & Match

Income Mix:

40–60 percent of the units affordable to households earning up to 60 percent of AMI, including at

least 10 percent of units serving formerly homeless households.

A minimum of 10 percent of units must be affordable to households earning 30–50 percent of AMI.

Remaining 40–60 percent of units affordable to households earning up to 130 percent of AMI.

Projects must have at least four affordability tiers.

Subsidies:

$10,000 to $225,000 in capital funds per unit depending on the income designation.

Federal LIHTC; tax-exempt financing; property-tax exemption.

In the Senior Affordable Rental Apartments (SARA) program and the Supportive Housing Loan

Program (SHLP), the city provides low-interest loans as gap financing to support the development

of special-needs housing. The maximum loan amounts range from $75,000 to $120,000 per unit,

depending on additional subsidies (tax-exempt financing with 4-percent LIHTC or 9-percent

LIHTC). Tenants in properties supported by these programs must spend no more than 30 percent

of their income on rent. In most cases, they receive project- or tenant-based rent subsidies.

A key element of Mayor de Blasio’s affordable housing programs, and of those created by his

predecessors, are property tax exemptions. Because property taxes for rental housing in New York

City are high, especially by comparison with most owner-occupied housing (New York University

Furman Center, 2016), it is very difficult for rents to be affordable to low-income households

if they must cover property taxes in full. In recognition of this problem, the city has frequently

provided long-term property tax exemptions for new and preserved housing for low-, moderate-,

and sometimes middle-income households. In addition, the city has also provided tax exemptions

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Schwartz

as an incentive to build market-rate housing. In the early 1970s, when the city was experiencing

population loss7 and disinvestment, the city, with state approval, created the “421-a tax exemption”

for property as an incentive to build rental and condominium buildings anywhere in New York

City. Over the years, the city revised the program so that properties built in the most affluent

sections of the city (for example, Manhattan south of 96th street) would only be eligible for

property tax exemptions if developers designated 20 percent of the units for low-, moderate-, and,

in some cases, middle-income households. The program expired in 2016 and was replaced in 2017

with a similar program, Affordable Housing New York, which further limited the availability of tax

exemptions for properties that do not offer affordable units (Nixon Peabody LLP, 2017).

More than 160,000 rental and condominium units received the 421-a property tax exemption in

2014, costing the city more than $1.07 billion in forgone property tax revenue (Bach and Waters,

2015). The program has been controversial, especially since many buildings that received the

subsidy included no affordable housing at all, and some of the buildings that received the tax

exemptions are composed of multi-million-dollar condominiums. The program took on more

notoriety in 2015 when it became known that developers had structured properties that received

the tax exemption so that the residents of the affordable units were required to enter through a

separate door—soon coined as a “poor door”—and were prohibited from using the gym and other

amenities available to the market-rate residents. The program has also been criticized because units

designated as “affordable” can be keyed to incomes as high as 130 percent of AMI, or $122,000 for

a family of three in 2018.

The city subsequently prohibited the use of “poor doors” in buildings that receive tax exemptions.

Affordable Housing New York, the tax exemption program that replaced 421-a focuses mostly on

rental buildings, nearly all of which must include some amount of affordable housing—although

the targeted incomes range from 40 to 130 percent of AMI. Luxury condominiums are no longer

eligible for tax exemptions. As with 421-a, the new program can be used alone or in combination

with other city subsidies.

Preservation Programs

As noted earlier, preservation refers both to the renewal or replacement of existing housing

subsidies and to loans, grants, and tax abatements to help owners of low-income housing

rehabilitate or otherwise upgrade their property. New York currently operates 30 preservation

programs (Murphy, 2017), some of which were established decades ago. Of the 300,000 units

in the expanded de Blasio plan, 60 percent involve preservation. Preservation also accounted for

60 percent of the housing funded under Mayor Bloomberg’s administration, as opposed to the 44

percent envisioned in the mayor’s original New Housing Marketplace Plan (NYC HPD, 2013). The

housing market crash and subsequent recession caused the city to pivot from an emphasis on new

construction to preservation.

In addition to the $220 million subsidy (mostly in the form of property tax exemptions) to

preserve the affordability of 5,000 units in Stuyvesant Town-Peter Cooper Village, the city has also

provided funds and technical assistance to help preserve housing financed with federal LIHTC so

7 From 1970 to 1980, New York’s population fell by more than 800,000, from 7.9 to 7.1 million (NYC OpenData, 2019).

370 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

that the housing can remain affordable and physically sound when it approaches the end of its

initial compliance period (NYC HPD, 2016). The city also offers low-interest loans and/or property

tax exemptions for private landlords to rehabilitate housing for low-income tenants. For example,

the Participation Loan Program began in 1977 and has supported moderate and substantial

rehabilitation of thousands of housing units. Another preservation program, Neighborhood Pillars,

was initiated in 2017 to provide funds and property-tax exemptions for nonprofit organizations

to acquire and rehabilitate privately owned residential buildings that house low- and moderate-

income renters.

Related Housing Initiatives under Mayor de Blasio

Closely aligned with Housing New York are several related initiatives that complement the plan’s

goals. Of particular note is the city’s decision to provide free legal assistance in housing court for

low-income renters, a city-state task force to combat tenant harassment, and the expansion of two

“rent-freeze” programs.

In August 2017, the city launched the Universal Access to Counsel (UAC) program to provide

free legal counsel for eligible renters. It is the first program of its kind in the United States and

has attracted the interest of many other cities (Been et al., 2018). As Desmond (2016) and

other researchers have documented, tenants seldom can afford to hire an attorney to represent

them when faced with eviction. Tenants are substantially more likely to avoid eviction when

legal counsel has been provided, and, if they are evicted, are given more time to move. Legal

representation is especially important when the grounds for eviction are spurious or if they

constitute outright harassment. UAC began operation in 15 ZIP Codes and is being phased in over

5 years (Been et al., 2018). When fully implemented, the program will serve 400,000 tenants at

an estimated annual cost of $155 million. In its first year of operation, 56 percent of tenants in

the designated ZIP Codes had legal counsel in housing court compared with 1 percent of tenants

citywide in 2013. During this period, evictions in New York declined by 27 percent (Fertig, 2018;

New York City Human Resource Administration, Office of Civil Justice, 2018).

In addition to providing free legal counsel in housing court, the city, in collaboration with New

York State, established a Tenant Harassment Prevention Task Force (THPT). Involving the New

York State Attorney General; the New York State Department of Homes and Community Renewal;

and the city’s departments of Housing Preservation and Development, Buildings, Health and

Mental Hygiene, and Law, THPT aims “to coordinate City and State agencies for joint inspections,

enforcement actions, and litigation strategies to intervene in buildings where harassment may be

occurring in order to prevent tenants from being forced out” (NYC HPD, 2019a).

In 2014, New York expanded two “rent freeze” programs for seniors and people with disabilities

by increasing the maximum eligible income level from $29,000 to $50,000. These programs

protect eligible low-income residents of rent-regulated housing who spend more than one-third

of their income on rent from additional rent increases. Instead, the city reduces the landlord’s

property taxes by the amount of rent increase that would otherwise be allowed under the city’s rent

stabilization and rent control programs (City of New York, 2019).

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Schwartz

Finally, it is important to note that New York spends billions of dollars annually on other housing-

related initiatives that are separate from Housing New York. These initiatives include about $200

million on rental assistance for homeless individuals and families and funding to help pay for

renovations and other capital improvements for New York’s public housing. In addition, the

city’s Housing Development Corporation (HDC) issued more than $8 billion in bonds from 2014

through 2018 for construction and permanent financing of affordable housing. HDC has also

invested millions of dollars from its corporate reserves to build and preserve affordable housing.

Housing New York’s Performance through the First Quarter of 2019

From the plan’s onset in 2014 through March 31, 2019, the city had started work on more than

123,000 units, surpassing the halfway point of the original plan (exhibit 7). Of the 123,456 starts,

38,793 (32 percent) involved new construction and 83,126 (68 percent) involved preservation.

The city posted 34,160 starts in 2018, an increase of nearly 10,000 from the previous year and the

most since 1989 (NYC HPD, 2019a).

372 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

cent of

5.9

9.7

0.5

Total

32.4

67.6

100.0

16.6

24.4

42.9

100.0

Per

597

Total

39,949

83,497

7,233

123,446

20,479

30,171

53,013

11,953

123,446

512

58

2019

(Q1)

5,077

8,212

13,289

2,713

4,442

4,099

1,465

13,289

691

154

2018

9,268

22,976

32,244

6,126

12,240

11,481

1,552

32,244

119

2017

7,765

16,588

24,353

4,014

6,453

9,964

1,533

2,270

24,353

87

2016

6,330

17,313

23,643

3,835

3,054

9,508

1,890

5,269

23,643

118

2015

8,732

11,839

20,571

2,869

2,588

12,234

1,394

1,368

20,571

oup, 2014-2019 (Q1)

922

352

890

61

2014

2,777

6,569

9,346

1,394

5,727

9,346

ype and Income Gr

vation and Development

ype

oup

of Housing Preser

emely Low ( <30% AMI)

eservation (P)

Area Median Family Income

NYC Dept.

Exhibit 7

Housing Starts by Construction T

Construction T

New Construction (NC)

Pr

Total

Income Gr

Extr

Very Low (31-50% AMI)

Low (51-80% AMI)

Moderate (81 - 120% AMI)

Middle (121-165% AMI)

Other

Total

AMI -

Source:

Cityscape 373

Schwartz

The de Blasio administration has invested $5.05 billion through 2018 on affordable housing

construction and preservation. This amount does not include $8.1 billion in tax-exempt and

taxable bonds issued by the New York City HDC, nor does it include HDC’s use of corporate

reserves to support the city’s housing programs. Total development costs exceed $25 billion. Much

of the funding not covered by the city’s capital budget and HDC derives from equity payments

from LIHTC investors.

Of the new construction and preservation units started through the end of March 2019, 41

percent were designated for households earning up to 50 percent of AMI, including 17 percent for

extremely low-income households with incomes up to 30 percent of AMI and 24 percent for very

low-income households earning 30 to 50 percent of AMI. Of the 32,214 starts in 2018, 57 percent

served these two income groups (NYC HPD, 2019a). The original plan, in contrast, designated 26

percent of all units for these two groups.

Of all the city-funded starts, 17,938 units (15 percent of the total) were constructed or preserved for

formerly homeless households (9 percent) or for low-income seniors (6 percent; NYC HPD, 2019c).

Exhibit 8 organizes the total housing starts (through March 31, 2019) by basic category. The largest

single category, accounting for 27 percent of total starts, involved the extension of affordability for

existing housing, including properties facing the expiration of LIHTCs, Mitchell-Lama properties

eligible for conversion to market-rate occupancy, as well as the previously mentioned deal to keep

5,000 units in Stuyvesant Town-Peter Cooper Village affordable to low-income households. The

second largest category, accounting for more than 21 percent of all starts, involves the moderate

rehabilitation of existing housing. The next three largest categories refer to new construction

and together account for 34 percent of total starts. Of these, the two largest elements involve

developments subsidized by the city, New Construction Finance Multifamily and Housing

Development Corporation Projects. They account for more than 32,000 units, 26 percent of

total starts.

374 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

Exhibit 8

Total Housing Starts by Category

Program Types

Units

Percent of Total

Extended Affordability Only (Includes HDC and State Extended

Affordability Deals)

33,409

27.1

Moderate Rehab

26,402

21.4

New Construction Finance Multifamily

20,903

16.9

Housing Development Corporation Projects

11,177

9.1

Housing Incentives Programs (Inclusionary Housing, 421-a

Standalones, etc.; Does Not Include IH or 421-a Units that are

9,714

7.9

Part of Projects with HPD Financing)

Special Needs Housing

7,691

6.2

State Projects

5,337

4.3

Major Rehab

7,419

6.0

Homeowner Assistance Programs

1,030

0.8

New Construction Finance Small Homes

364

0.3

Grand Total

123,446

100.0

IH = Inclusionary Housing

Source: New York City Department of Housing Preservation and Development

The third new-construction category encompasses all “housing incentives programs” and

constitutes less than 10,000 starts, 8 percent of the total. Housing incentives refer to affordable

housing constructed through inclusionary housing and tax-exemption programs that do not

receive other subsidies or financing through the city. Mandatory inclusionary zoning accounts for

1,431 of the starts in this group. Even though it comprises a relatively small share of total housing

starts, inclusionary zoning has received far more attention and criticism than the larger elements of

Mayor de Blasio’s housing plan.

Critiques and Challenges

Mayor de Blasio’s housing plan, its quantitative achievements notwithstanding, has been roundly

criticized. Its critics include the City Comptroller (New York City Comptroller, 2018) and other

government officials, academics (Angotti and Morse, 2017; Busa, 2017; Stein, 2018), journalists

(Feuer, 2017; Greenberg, 2017; Murphy, 2017), and community-based organizations and activists

(Dulchin, 2019; Real Affordability for All, 2017). They argue that while the plan claims to promote

affordable housing, most of the housing produced under the plan is not affordable enough. There

are two dimensions to this argument. First, many critics claim that most of the housing that is

preserved and constructed under the plan is not affordable to residents with the lowest incomes

who confront the most severe housing affordability problems. Secondly, critics argue that the

rezonings that are integral to the city’s mandatory inclusionary housing program have the perverse

effect of exacerbating housing affordability problems, even if the new housing built in rezoned

neighborhoods includes affordable units.

Cityscape 375

Schwartz

Although 17 percent of the 123,446 housing starts through the end of March 2019 (20,479 in

total) were slated for Extremely Low-Income (ELI) households earning up to 30 percent of AMI,

and an additional 24 percent went to Very Low-Income (VLI) households earning between 30

and 50 percent of AMI, critics argue that these income groups account for much larger shares of

households with housing cost burdens. As of 2017, these two income groups accounted for 65

percent of all households spending more than 30 percent of their income on rent. Focusing just

on renters with the most severe housing needs, spending 50 percent or more of their income

on housing, ELI households comprise 62 percent of the total and VLI households comprise 25

percent. In contrast, households earning more than 50 percent of AMI account for 35 percent of all

renters spending more than 30 percent of their income on housing, but they constitute 59 percent

of the plan’s beneficiaries to date.

Some critics state that the affordability gap between the rents of housing produced and preserved

through the city’s plan and the incomes of renters with the greatest needs is even larger than

indicated by citywide statistics. The demarcations for the various income categories are based

on the median income for the eight-county region of New York City and Putnam, Rockland,

and Westchester counties. The average incomes in many of the neighborhoods that are seeing

substantial amounts of construction under the plan, however, are much lower than the regional

median. For example, the median income in East New York in 2017 was $38,620 (NYU Furman

Center, 2018), compared with $66,200 for the eight-county region. What is classified as affordable

for an ELI household as defined by the regional median income, is out of reach for many residents

of low-income communities.

In light of these discrepancies, critics as diverse as the City Comptroller Scott Stringer and the

advocacy group Real Affordability for All have argued that the plan should allocate a higher

percentage of its housing for households with lower incomes. Comptroller Stringer, for example,

proposed that the housing construction yet to be started under the plan (85,706 units as of June

30, 2018) mostly benefit ELI households. Whereas the plan calls for 8 percent of the remaining

units to be built for ELI households, the Comptroller would allocate 77 percent to this group. The

Comptroller would also increase the share going to VLI households from 16 to 21 percent. To cover

the additional costs for providing more housing to lower income renters, the Comptroller would

increase the city’s annual capital commitments for new construction programs by about 60 percent,

an annual increase of $370 million in capital funding (New York City Comptroller, 2018).

The second criticism of the plan contends that it not only fails to provide sufficient amounts

of housing affordable to low-income residents, it also exacerbates the city’s affordability crisis.

More specifically, these critics argue that by rezoning low-income neighborhoods to promote

the development of large mixed-income buildings, the plan puts increased market pressure on

vulnerable communities. The influx of higher income households, combined with the tangible

changes in neighborhood fabric as low-rise and vacant properties—residential and otherwise—

are replaced by much larger, glitzier residential and mixed-use buildings, will tempt owners of

existing residential buildings to raise their rents and, if that is not possible under the city’s rent

regulations, attempt to push current tenants out of the unit. While the buildings put in place

under inclusionary zoning will include a number of units of affordable housing (notwithstanding

376 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

arguments about their actual affordability), the rezoning process may ultimately result in a net loss

of affordable housing. Here is how journalist Abigail Savitch-Lew summarizes the argument:

Say you live in an upzoned neighborhood . . . the 20 or 30 percent of new development

that’s rent-restricted might not actually be affordable to you. The other 70 or 80 percent is

so expensive that it’s unaffordable to everyone in the neighborhood. Wealthier newcomers

move in, with the disposable income to support new expensive amenities. As the

neighborhood becomes increasingly popular, landlords begin harassing rent-stabilized

tenants and real-estate brokers hound longtime homeowners with all-cash offers. Soon

your landlord is giving you the ultimatum: agree to a whopping rent increase, or move out

(Savitch-Lew, 2017).

Mayor de Blasio and other city officials have responded to this argument in two ways. First, they

point out that the city has strengthened its protections for tenants who might face illegal rent

increases, harassment, and eviction. As noted previously, the city is rolling out free legal counsel in

housing court for low-income renters. Secondly, the city and state created a task force to combat

landlord harassment. Through these and other means, the city believes that it can shield existing

residents in rezoned neighborhoods from unlawful rent hikes and various forms of harassment.

The city’s second response to the claim that rezoning and inclusionary zoning aggravates rather

than improves housing affordability is to point out that gentrification is occurring throughout the

city, whether or not the neighborhood has been rezoned. Although there are few if any studies

that show the effect of rezonings in New York City during the de Blasio era on rents and home

prices,8 it is true that rents have increased sharply in numerous neighborhoods throughout the

city. Adjusting for inflation, the median rent in the city as a whole increased by 20 percent from

2006 through 2016. In Manhattan, rents rose by 29 percent; Brooklyn, 25 percent. Of the city’s

55 sub-borough areas (roughly similar to its community districts), 26 saw rents rise by 20 percent

or more, including 13 where they increased by at least 30 percent (exhibit 8). There seems to be

ample justification for the claim that preserving the existing zoning of a neighborhood will not

keep gentrification at bay.

8 Yonah Freemark’s research (2019) on zoning changes in Chicago found that increases in density, controlling for other factors, was associated with increases in housing costs. These rezonings, however, unlike the case in New York, did not require developers to designate a portion of the new housing for lower income households. It is also uncertain if the experience in Chicago is directly applicable to New York.

Cityscape 377

Schwartz

Exhibit 9

Change in Median Rent 2006-2016 (2017$): Sub-Borough Areas

Sub-Borough Areas

90%

80%

Williamsburg/Greenpoint

70%

Brooklyn Heights/Fort Greene

60%

Staten

50%

Manhattan

Bronx

Brooklyn

Queens

Island

40%

30%

20%

NYC AVG

10%

0%

-10%

Source: NYU Furman Center

While critics of the de Blasio plan have focused mostly on neighborhood rezoning and inclusionary

housing, this component accounts for a relatively small share of the affordable housing produced

to date. Inclusionary housing and other incentive programs account for 8 percent of all new

construction and preservation housing starts through March 2019. Mandatory Inclusionary

Zoning, the subject of so much controversy and debate, accounts for only 1,431 starts as of March

31, 2019, less than 4 percent of all city-funded new construction starts. Moreover, most if not

all of the new housing subject to inclusionary zoning in East New York—the first neighborhood

to be rezoned—has been targeted to low- and moderate-income households. Most of the new

construction started under the plan has received direct subsidies from the city and other sources.

The fact that the residents of most of the neighborhoods slated for rezoning tend to have low

incomes and to be predominantly non-White has no doubt contributed to the plan’s hostile

reception. Some observers have suggested that the plan might have received more support if the

city had also included more affluent and more White neighborhoods among those to be rezoned

(Savitch-Lew, 2017).

There is no disputing that the de Blasio plan does not come close to meeting the need for

affordable housing in New York City. Compared with the previous mayoral housing plans, de

Blasio’s initiative targets a larger number, and larger share, of its housing for extremely low-income

households. As we have already discussed, however, the plan has been widely criticized for not

making more housing affordable for the lowest income residents, including the homeless. Critics

argue that the income eligibility standards for the affordable units are set too high, and that a larger share of the units being produced should go to households with very low incomes. In total, the

378 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

plan’s goal is to construct 12,600 units affordable to ELI households and 17,400 units affordable to

VLI households by 2028. This goal amounts to less than 7 percent of all ELI and VLI renters who

paid 50 percent or more of their income on housing in 2017, and that does not include the more

than 63,000 people who were in the city’s homeless shelters. Comptroller Stringer’s previously

mentioned recommendation to build 66,000 units for ELI renters and 18,000 for VLI renters still

pales by comparison with the 441,000 ELI and VLI renters with severe cost burdens.

The gap between the amount of housing targeted by the plan to the lowest income New Yorkers

and the number of such households in need of affordable housing underscores a fundamental

limitation in the ability of cities and states to adequately address their housing needs. As with

nearly all other state and local housing programs, the de Blasio plan aims to expand and preserve

the existing stock of affordable housing. They enable existing housing to remain affordable for years

into the future and create new units of affordable housing. In other words, each year, the city’s

expenditures increase either the number of affordable units that are preserved or that are created.

For the city to primarily assist households with the lowest incomes, it would need to provide rent

subsidies to cover the difference between what these households can afford to pay and the total

cost of operating the housing and covering the debt-service expenses connected to the housing’s

acquisition and/or development or rehabilitation. As rental subsidies are expensive, the city would

need to spend hundreds of millions or billions of dollars to support the same number of renters

every year. Rather than spend money to expand the stock of affordable housing, the city would

need to spend even more year after year to keep the same number of units affordable.

On average, depending on unit size and other factors, it costs between $500 and $700 every month

to cover the basic operations of a housing unit eligible for city subsidy—that is, upkeep (labor and

supplies), insurance, utilities, and taxes (if any). It can cost several hundred dollars more to cover

the debt-service expenses on the unit’s pro rata share of the building’s mortgage (that is, on the debt

associated with the cost of acquiring, renovating, or constructing the property). For example, as

shown in exhibit 2, the monthly costs of operating and paying the debt on a newly built efficiency

apartment with shared bathroom and shared kitchen amounts to $840 a month, and that assumes

that the development cost of the property did not include any expenses for land acquisition.

Even under this bare-bones scenario, the minimum monthly rent is only affordable to households

earning at least $2,800 a month ($33,600 annually), equivalent to 51 percent of New York’s AMI

for single-person households. All other households would require rent subsidies to afford this

apartment. Excluding the portion of the rent that would cover debt service, even the unit’s basic

operating costs of $332 per month would be unaffordable to households earning less than $13,267

a year, or 25 percent of all single-person households. The operating costs of a 400-square-foot

studio apartment with private kitchen and bath amount to $418 per month, affordable only to

people earning at least $16,700 (Stern and Yager, 2018).

Inclusionary housing and other forms of mixed-income housing can enable a small percentage of

ELI families and individuals to be included, with the rents paid by higher income residents “cross-

subsidizing” the low-income tenants. However, only a small number of ELI and VLI tenants can

be subsidized in this way; otherwise, the models are not financially viable. As discussed earlier,

the city’s mixed-income housing programs designate up to 20 percent of all units to households

Cityscape 379

Schwartz

earning up to 30 percent of AMI, and the bottom income group for some programs is set at 40

percent of AMI or higher (exhibit 6).

How much would it cost to provide rent subsidies so that all low-income New Yorkers do not

confront housing cost burdens? In 2017, there were 286,000 unsubsidized ELI renters and

250,000 VLI renters. Of these renters, 87 percent spent more than 30 percent of their income on

rent, and 62 percent spent more than 50 percent of their income on rent.

If New York were to subsidize all VLI households who are currently rent burdened so that they

paid 30 percent of their income on rent, it would cost $5.3 billion annually. The cost would rise

to $6.6 billion if the subsidy applied to all households earning up to 80 percent of AMI that spend

more than 30 percent of their income on rent. Conversely, if the subsidy were limited to ELI

households spending 50 percent or more of their income on housing, the annual cost would be

$3.2 billion (exhibit 10).9

Exhibit 10

Total Annual Subsidy Needed to Ensure Affordability for Rent-Burdened Households

Income Group and Current Rent Burden

Total Subsidy ($ Billions)

ELI, VLI, LI (<80% AMI)

Cost Burden >30%

6.6

Cost Burden >50%

5.3

ELI, VLI (<50% AMI)

Cost Burden >30%

5.3

Cost Burden >50%

4.5

ELI (<30% AMI)

Cost Burden >30%

3.6

Cost Burden >50%

3.2

ELI = Extremely Low Income. LI = Low Income. VLI = Very Low Income.

Source: Mayor’s Management Report and Comptroller’s Budget Report

To put these amounts in perspective, New York City spent $5.4 billion in FY2019 on police, $2.1

billion on fire protection, $1.7 billion on sanitation, and $461 million on parks (Citizens Budget

Commission, 2019). In other words, the costs of providing rental subsidies to eliminate housing

cost burdens for most low-income renters would equal or exceed the annual budget for numerous

city services. Moreover, the city’s public housing currently needs $31.8 billion in renovations and

other essential capital improvements (New York City Housing Authority, 2018) and the subway

system requires an estimated $8 billion (Metropolitan Transit Authority, 2018).

9 These estimates are based on the amount of money that would be required to augment the gross rent payments of rent-burdened households so that they pay 30 percent of their income on rent. The estimates do not consider any potential behavioral responses that such rent subsidies might elicit—such as renters reducing their incomes to qualify for rental assistance or moving into a more expensive home or landlords increasing their rents. Nevertheless, the estimates provide an order-of-magnitude sense of the overall cost of providing rental assistance to all eligible renters.

380 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

It is, of course, a political decision whether to use city funds to subsidize rents. Given New York

City’s operating budget of $90.2 billion, the $3–7 billion cost of providing rent subsidies for tens of

thousands of low-income New Yorkers would be substantial but not necessarily prohibitive. In fact,

the city already provides about $200 million annually in rent subsidies to help homeless families, and

families at risk of becoming homeless, secure permanent housing. Given the city’s many other critical

needs, however, it is unlikely that rent subsidies of this magnitude would be politically palatable.

Rental cost burdens are a national problem, afflicting low-income renters throughout the country,

no matter how expensive local housing markets may be (Joint Center for Housing Studies, 2018).

In numerous European countries, most low-income households are automatically entitled to rent

subsidies from the national government (Scanlon, Whitehead, and Arrigoitia, 2014). In the United

States, federal rent subsidies are not an entitlement, and only about one in four eligible low-income

households receive them, whether in the form of Housing Choice Vouchers, public housing, or

project-based Section 8. New York City’s housing affordability challenges, and those of all other

cities and states, would be far less severe if the federal government provided rental assistance to

all eligible low-income families (Desmond, 2016; Mallach, 2018). Alan Mallach (2018) estimated

that the annual cost of making rental assistance an entitlement would total about $90 to $100

billion, which is in the same ballpark as the estimated $85 billion cost in 2018 of tax expenditures

associated with homeownership—and this estimate is after the federal “Tax Cuts and Jobs Act” of

2017 decreased the value of these tax expenditures by 38 percent (Joint Committee on Taxation,

2018).10 If federal housing subsidies, whether in the form of vouchers, income-tax credits, or other

means, were more readily available, there would be less pressure to build housing affordable to ELI

households. Instead, New York City could invest its resources in building and preserving housing

that would be eligible for federal rent subsidies, as well as for households with somewhat higher

incomes but who still confront severe shortages of affordable housing.

Conclusions

More than three decades have passed since Mayor Koch launched New York City’s Capital Budget

program in 1987. Since then, the city has invested more than $17.5 billion (adjusted for inflation)

of its own capital resources on the construction and preservation of more than 490,000 housing

units. No other city has drawn on its own tax base to anywhere near this extent to help finance

affordable housing, and no other city has sustained an analogous level of commitment for so many

years under both Democrat and Republican mayoral administrations. New York City’s experience

in subsidizing affordable housing shows what is possible, but it also highlights fundamental

limitations in the ability of cities and other subnational jurisdictions to address their affordable

housing needs.

10 In 2018, estimated tax expenditures for the mortgage interest deduction, property taxes, and exclusion of capital gains on sale of primary residences totaled $85.8 billion, down from $130.1 billion in 2017. The estimation of property tax deductions in 2018 was based on the property tax share of total combined tax expenditures in 2017 for property taxes, state and local income taxes, and sales taxes. The Joint Committee on Taxation stopped publishing estimates of tax expenditures for property taxes after passage of the Tax Cuts and Jobs Act of 2017, which capped total deductions in these areas at $10,000. The Joint Committee does provide estimated tax expenditures in 2018 for these three deductions combined (Joint Committee on Taxation, 2018).

Cityscape 381

Schwartz

Many of the city’s housing investments from 1987 until the early 2000s programs involved in

rem property—occupied and vacant buildings as well as vacant land that the city had acquired

through tax foreclosure. Tapping the capital budget, the city has rehabilitated thousands of

in rem buildings and constructed thousands of new housing units on city-owned land. In so

doing, the city’s housing programs were essential in restoring the urban fabric of numerous

neighborhoods in the Bronx, upper Manhattan, Central Brooklyn, and elsewhere that had been

devastated by disinvestment, abandonment, and arson. Today, there is barely a trace of the physical

decay that was so prevalent in the 1970s, 80s, and 90s. Poverty remains high in many of these

neighborhoods, especially in the Bronx, but physical conditions are vastly improved (Schwartz,

1999; Lamberg, 2018).

Over the past 15 or so years, with in rem property largely depleted, the city’s housing investments have become less concentrated in particular neighborhoods. As before, the city continued to

provide low-cost loans and tax exemptions to help preserve privately owned housing. The city also

developed programs to renew or replace housing subsidies facing expiration. The city continued

to invest in the development of housing for very-low-income people with special needs—usually

providing HCVs and other rent subsidies to ensure the housing was deeply affordable. Most of

the new housing produced with city subsidies, however, targeted people with higher incomes.

These include buildings that receive federal Low-Income Housing Tax Credits, which generally

accommodate households earning between 40 and 60 percent of Area Median Income, as well

as various forms of mixed-income housing. While some new housing subsidized with city funds

is situated on city-owned sites, it increasingly involves the acquisition of expensive privately

owned land.

The administration of Mayor de Blasio has sharply increased the scale of New York’s investments

in affordable housing. During the Mayor’s first 4 years in office, annual city-supported housing

starts for new construction and preservation averaged 25,000 units—far more than what previous

mayors achieved. Compared with the administration of the previous mayor, Michael Bloomberg,

the de Blasio Administration has produced and preserved 72 percent more affordable housing

units per year and invested nearly twice as much from the capital budget for affordable housing.

In addition, the de Blasio Administration’s housing programs have generated and preserved far

more housing for the lowest income households. While the Bloomberg Administration grouped

all housing targeted for households earning less than 80 of AMI into a single category and did not

report on the housing produced or preserved for households earning less than 30 or 50 percent of

AMI, the de Blasio Administration designed several of its mixed-income programs so that at least

some of the units would be affordable to, and reserved for, households in these income groups. In

addition, the de Blasio Administration launched the nation’s first municipal initiative to provide

free legal counsel to low-income renters at risk of eviction.

Despite the larger scale and deeper subsidies of the de Blasio Administration’s housing programs,

New York’s affordable housing crisis remains as acute as ever. More than 29 percent of all renters

spend at least one-half of their income on housing, including 78 percent of all renters earning

less than 30 percent of AMI. More than 60,000 people sleep in the city’s homeless shelters. Some

critics argue that the persistence of the city’s affordability problems highlights both the city’s failure 382 Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative

to provide housing that is sufficiently affordable to low-income New Yorkers and the perverse

tendency of inclusionary zoning and other mixed-income programs to exacerbate market pressures

in low-income neighborhoods.

I argue that there is insufficient evidence to show that the inclusionary zoning program—which

accounts for less than 1,500 of the nearly 40,000 new housing units started through March

2019—has worsened the affordability situation. Rents are rising and displacement pressures

mounting throughout New York City, including in many neighborhoods that have not seen

changes in zoning. I also argue that although the de Blasio Administration has done more than its

predecessors and more than other U.S. cities to make units affordable to extremely low-income

renters, the tens of thousands of deeply affordable units produced so far pale by comparison with

the overall need. To fully address the city’s affordable housing needs would cost $3–7 billion

annually—a price that is politically if not fiscally prohibitive. While New York City demonstrates

that it is possible for cities and other subnational units of government to produce and preserve some

housing that is affordable to the lowest income residents, the New York City story also illuminates

the limits of local initiative. Only the federal government commands the financial resources

necessary to solve the affordable housing crisis—in New York City and in the rest of the nation.

Acknowledgments

The author thanks Marc Jahr and David Reiss for their insightful comments on previous drafts

of this article. The author is also grateful for the questions and suggestions he received at

presentations of this work at the University of Pittsburgh, Cambridge University, University College

London, the University of Calgary, the New School, and the U.S. Department of Housing and

Urban Development.

Author

Alex Schwartz is professor of urban policy at the New School’s Milano School of Policy,

Management, and Environment. In March 2019 he was appointed to the New York City Rent

Guidelines Board as a public member. The views expressed in this article are those of the

author only.

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388 Policy Briefs

by Chalita Brandly

Extremely Low-Income: Has the

New Measure Made a Difference?

Chalita Brandly

Department of Housing and Urban Development, Office of Economic Affairs

Abstract

Extremely Low-Income (ELI) Limits have been used in the Housing Choice Voucher (HCV), Public

Housing, and Multifamily programs to prioritize housing assistance for the poorest households since

1998. The original measure of ELI was calculated as 30 percent of area median income. In 2014, the

definition of ELI was modified to consider the official U.S. poverty measure. This policy brief discusses the goal of this definition change and how the new measure differs from the old measure. The extent to which households in these programs benefited as a result is evaluated using U.S. Department of Housing and Urban Development (HUD) administrative data. Findings shared in the brief reveal that 5 percent

of new households benefit from this definition change. These households are more likely to consist of more than one person and reside in non-metropolitan portions of the country. Findings also show that while ELI targets are being met across HUD’s three largest programs, many public housing agencies

(PHAs) in the HCV program are unable to meet their targets.

Introduction

The Quality Housing and Work Responsibility Act of 1998 (QHWRA) was a piece of major

housing reform legislation passed in 1998 that sought, among other things, to reduce the

concentration of poverty in public housing while protecting access to housing assistance among

the poorest households.1 One way which QHWRA addressed this was by modifying eligibility and

targeting requirements for housing programs. Prior to QHWRA, eligibility for the public housing

and Section 8 programs of HUD were subject to “federal preferences” that prioritized admission

to the program for certain categories of low-income persons and households. Federal preferences

were sometimes blamed for increased concentration of poverty, particularly in public housing. In

addition, the statutory system of federal preferences became increasingly complex over time, as

1 See H.R.4194 - Departments of Veterans Affairs and Housing and Urban Development, and Independent Agencies Appropriations Act, 1999. 105th Congress (1997–1998) https://www.congress.gov/105/plaws/publ276/PLAW-

105publ276.pdf.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 3 • 2019

Cityscape 389

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

Brandly

Congress added new categories of eligibility and added new rules that governed waiting lists used

by public housing agencies (PHAs).

QHWRA replaced the previous system of federal preferences with a new system under which PHAs

could set their own local admissions preferences, based on local needs and adopted though the

local PHA plan requirement (Hunt, 1998). At the same time, a portion of subsidized units that

became available through annual turnover would be reserved for the lowest income families—

defined as “extremely low-income” (ELI) or with incomes less than 30 percent of HUD’s local

area median income. For the public housing and project-based Section 8 programs, not less

than 40 percent of the units that became available per fiscal year (FY) should be made available

for occupancy by ELI households, under QHWRA. For Section 8 tenant-based vouchers—more

commonly referred to as Housing Choice Vouchers (HCV)—not less than 75 percent of new

admissions should be ELI households (Sard, 2000).

HUD’s area median income limits are generally recognized as having several positive advantages for

establishing basic eligibility for federal housing assistance. Chief among these advantages is that

the income limits are based on actual local economic conditions. Also, area median income limits

have been adopted for a wide variety of programs administered by HUD as well as for Low-Income

Housing Tax Credits. Policy advocates, however, did identify one way that HUD’s income limits

were under-inclusive of actual need for affordable housing (Olsen, 2014; Leopold, 2015).2 In some

very low-income regions of the country, the ELI threshold of 30 percent of local median income

could sometimes be lower than the federal poverty rate. These areas included the Mississippi Delta,

the Southwest border, and other rural areas. In the years following the implementation of QHWRA,

policy advocates urged an amendment to the income targeting requirements for housing assistance

to take this phenomenon into account (NLIHC, 2005).3

In 2014, Congress amended the definition of ELI households as households with incomes at or

below the greater of either: (1) 30 percent of HUD’s median family income (L30) for the local area;

or (2) the federal poverty threshold.4 The new definition was anticipated to provide households

living below the federal poverty line better access to federal rental programs in very low-income

regions, where HUD’s previous income limits might have unintentionally excluded households.

This article examines the effects of the 2014 statutory change to HUD’s income limits for ELI

households, and how those changes may have affected access to HUD rental programs.

Comparing the L30 Limit to the Poverty Guidelines

HUD sets ELI limits that establish household income targeting requirements for PHAs and landlords

using assisted housing programs including the Public Housing, Section 8 project-based, Section

8 HCV, Section 202 housing for the elderly, and Section 811 housing for persons with disabilities

2 See Poverty and the Safety Net: Hearing before the Committee on the Budget: House of Representatives, 113th Cong. (2014) (Testimony of Robert Greenstein, President of Center on Budget and Policy Priorities). pp. 22.

3 For a related discussion on the impact of QHWRA’s income targeting provisions, see Dawkins (2007).

4 See Consolidated Appropriations Act for FY 2014 (Public Law 113-176). Division L—Transportation, HUD and Related Agencies, Section 238, amending Section 3(b) of the United States Housing Act of 1937. https://www.

congress.gov/113/plaws/publ76/PLAW-113publ76.pdf.

390 Policy Briefs

Extremely Low-Income: Has the New Measure Made a Difference?

programs. An ELI limit is calculated for each Fair Market Rent (FMR) area and helps ensure that

assistance is set aside for households with the lowest incomes. Understanding the differences

between how the L30 limit and poverty guidelines are derived offers important context for analyzing

the extent to which the ELI limits have changed across FMR areas since 2014.

The base L30 limit for an FMR area is calculated as 30/50ths (60 percent) of its Section 8 Very

Low-Income Limit (VLIL) for four-person households; the VLIL is based on 50 percent of the

median household income estimated for an area (HUD, 2017). L30 limits for other household sizes

are subsequently calculated by applying a percentage adjustment to the preliminary four-person

ELI limit and then rounding up to the nearest $50.

In contrast, poverty guidelines are developed by the U.S. Department of Health and Human

Services (HHS) from the latest published final weighted average poverty thresholds from the U.S.

Census Bureau. Generally, this measure is calculated as three times the cost of a minimum food

diet in 1963 in today’s prices as inflated by the Consumer Price Index for All Urban Consumers

(CPI-U; National Academy of Sciences, n.d.). For two-person households, the threshold is based

on 3.7 times the cost of food. The poverty threshold for one-person households is set at 80 percent

of the corresponding thresholds for two-person households. One-person households are the only

households not directly calculated using a food cost multiplier (Fisher, 1997).

Thus, the principal difference between the two calculations is that the L30 limit is based on the

area median household income while the poverty guidelines are generally based on the cost of a

minimum food diet. Because of this, the L30 limit varies across approximately 2,600 FMR areas for

which HUD estimates area median household income, while the poverty guidelines are the same

for all FMRs areas within the contiguous United States.5

Exhibit 1 shows how the adjustments for various household sizes relate to a four-person base

for both measures. It is apparent that the adjustment increases at a higher rate for the poverty

guidelines as household size increases. It is important to note that income limits for additional

household sizes for the L30 are derived from the four-person L30 income limit. The household

size adjustments for the poverty guidelines shown below are simplified for easier comparison to

the L30.

Exhibit 1

Household Size Limits Compared to a Four-Person Base

One-

Two-

Three-

Four-

Five-

Six-

Seven-

Eight-

Measure

Person

Person

Person

Person

Person

Person

Person

Person

(%)

(%)

(%)

(%)

(%)

(%)

(%)

(%)

L30 (Preliminary ELI)

70

80

90

Base

108

116

124

132

Poverty Guidelines

50

62

78

100

118

134

152

170

ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure.

5 Alaska and Hawaii have their own state-wide poverty guidelines. Poverty guidelines are not defined for Puerto Rico, and other island territories and are set at the discretion of the federal office which administers the federal program in question for that jurisdiction.

Cityscape 391

Brandly

Determining the Basis of the ELI

Exhibit 2 illustrates how consideration of the poverty guidelines in the calculation of the ELI limit

affected various FMR areas in FY2017. Cells with no shading indicate where the L30 limits are the

basis for the ELI limit. This means that the poverty guidelines did not exceed the L30 limit for the

household size within its FMR area.

On the other hand, household sizes in light gray shading did have poverty guidelines that exceeded

the L30 limit for their respective FMR areas. In these cases, the poverty guidelines are used as the

basis of the ELI limit. Exhibit 2 illustrates how household sizes using the poverty guidelines are using the same national figure as other FMR areas using the poverty guidelines for the same household size.

Lastly, exhibit 2 illustrates how the use of the poverty guidelines may require a cap at the VLIL

level. Because the L30 is calculated as 30/50ths (60 percent) of its Section 8 VLIL, it does not run

the risk of exceeding its VLIL level. However, because of differences in how the L30 limits and

poverty guidelines are calculated, which were discussed in the prior section, the poverty guidelines

may exceed the VLIL in many cases, especially for larger household sizes.

Cells with dark gray shading indicate where the ELI equals the VLIL. The Abilene, TX metropolitan

statistical area (MSA) is an example of an FMR area where the poverty guidelines exceed the VLIL

caps for larger household sizes. In this example, the ELI is capped at the VLIL for household sizes

of six or more people. The poverty guideline for a six-person household was $34,960 dollars in

2017. However, the six-person VLIL for the Abilene, Texas MSA was $32,550.

If the poverty guidelines apply at a particular household size, poverty guidelines will be the ELI

limit for larger household sizes, unless the VLIL ceiling is hit.

Exhibit 2

Extremely Low-Income Basis, Fiscal Year 2017

One-

Two-

Three-

Four-

Five-

Six-

Seven-

Eight-

FMR Area

Person

Person

Person

Person

Person

Person

Person

Person

Abilene, TX MSA

12,060

16,240

20,420

24,600

28,780

32,550

34,800

37,050

Akron, OH MSA

13,800

16,240

20,420

24,600

28,780

32,960

37,140

41,320

Philadelphia-Camden-

Wilmington, PA-NJ-

17,500

20,000

22,500

24,950

28,780

32,960

37,140

41,320

DE-MD

Boulder, CO MSA

20,650

23,600

26,550

29,450

31,850

34,200

37,140

41,320

Washington-

Arlington-Alexandria,

23,200

26,500

29,800

33,100

35,750

38,400

41,050

43,700

DC-VA-MD

FMR = Fair Market Rent. MSA = Metropolitan Statistical Area.

392 Policy Briefs

Extremely Low-Income: Has the New Measure Made a Difference?

Insight 1: The New ELI Definition Impacts Most Income Limits

Exhibit 3 shows the distribution of the ELI limit basis across household sizes for all FMR areas in

2017. Because the 2014 Appropriations Act specified that the ELI limit could not exceed the VLIL,

in some cases the ELI limit is equal to the VLIL. In general, the poverty guidelines are more likely

than the L30 to be used as a basis for the ELI limit.

More frequent application of the poverty guidelines can be anticipated given that about 75 percent

of FMR areas are in non-metropolitan counties versus metropolitan areas. As mentioned earlier, the

poverty guidelines are based on a national measure whereas the L30 is locally based. On average,

metropolitan areas have higher incomes compared to their surrounding non-metropolitan or rural

areas (Weicher, 2012). As a result, one might expect that using a nationally based limit would

likely raise the limit for many non-metropolitan counties, which make up a majority of FMR areas.

Exhibit 3 also shows that as household size increases, so does the use of the poverty guidelines

in lieu of the L30 limit. Moreover, the use of the poverty guidelines for the ELI limit increasingly

requires the use of VLIL caps as household size increases. This is due to the difference in how the

L30 and the poverty guidelines are calculated for different household sizes as shown earlier.

Exhibit 3

Basis of Extremely Low-Income Limits, Fiscal Year 2017

L30 = HUD’s 30 Percent of Local Area Median Income Measure. VLIL =Very Low-Income Limit.

While most income limits are impacted by the use of the poverty guidelines, this does not

necessarily indicate that most households currently assisted by applicable HUD programs will be

impacted as well. Exhibit 4 illustrates the share of FMR areas where the ELI limit exceeds the L30

limit, weighted by their subsidized household population. The number of instances where the

poverty guidelines are used in the ELI limit are fewer when weighted by number of households,

especially for one- and two-person households.

Altogether, 15 percent of assisted households live in an FMR area using the poverty guidelines in

lieu of the L30 in the determination of the ELI limit for their respective household size. Exhibit 5

further illustrates that the ELI limit for non-metropolitan areas has been impacted more than metro

areas by incorporating the poverty guidelines into the definition of ELI.

Cityscape 393

Brandly

Exhibit 4

FMR Areas Where the ELI Limit Exceeds the L30 Limit, Fiscal Year 2017

One-

Two-

Three-

Four-

Five-

Six-

Seven-

Eight-

Person

Person

Person

Person

Person

Person

Person

Person

Count*

1,014

2,064

2,365

2,485

2,520

2,552

2,562

2,569

Share of Total

FMR Areas

39.2%

79.8%

91.5%

96.1%

97.5%

98.7%

99.1%

99.4%

Household-

Weighted Share

4.7%

27.9%

50.1%

59.6%

64.6%

73.3%

84.0%

94.8%

ELI = Extremely Low Income. FMR = Fair Market Rent.

*FMR areas where the ELI exceed the L30 by at least $200. 6

Exhibit 5

FMR Areas Where the ELI Limit Exceeds the L30 Limit by Metro Status, Fiscal Year 2017

One-

Two-

Three-

Four-

Five-

Six-

Seven-

Eight-

Person

Person

Person

Person

Person

Person

Person

Person

Metropolitan Areas

Count*

149

379

492

541

566

592

600

605

Share of All

Metropolitan

24.0%

61.1%

79.4%

87.3%

91.3%

95.5%

96.8%

97.6%

Areas

Household-

Weighted Share

1.5%

16.5%

38.6%

49.4%

56.0%

67.3%

79.9%

92.5%

Non-Metropolitan Counties

Count*

865

1,685

1,873

1,944

1,954

1,960

1,962

1,964

Share of All Non-

Metro Counties

44.0%

85.8%

95.3%

98.9%

99.4%

99.7%

99.8%

99.9%

Household-

Weighted Share

22.5%

77.3%

92.7%

97.8%

98.8%

99.7%

99.8%

99.9%

ELI = Extremely Low Income. FMR = Fair Market Rent.

*Areas where the ELI exceed the L30 by at least $200.

Insight 2: Five Percent of Newly Admitted Households Benefit

from Use of the Poverty Guidelines

For the purpose of this brief, households benefiting from the 2014 definition change will be

referred to as reclassified households. These are households that would not have been defined as

ELI under the old L30 definition, but following the 2014 definition change, were classified as ELI

as a result of the use of the poverty guidelines.

6 It is important to consider whether a change in the ELI limit is meaningful in the context of tenants’ income determinations. A study prepared for HUD (2014), identified rent errors by recalculating tenant rent based on verified quality-checked information and subtracting that amount from the tenant rent recorded on file. The actual monthly rent and quality-checked monthly rents were considered different if the difference between the two were greater than $5. This $5 rent error translates to $200 of annual income ($5*12 months/30 percent of income). Using this $200

criterion, the percentage of areas with an ELI limit exceeding the L30 limit declined only slightly.

394 Policy Briefs

Extremely Low-Income: Has the New Measure Made a Difference?

Between 2007 and 2017, ELI households made up approximately 78 percent of total new

admissions across programs (exhibit 6). The dotted line in the exhibit represents the share of new

households that had incomes below the L30 limit for their FMR area during this time period.

Because the L30 was the basis of the ELI prior to 2014, the share of new admissions that are ELI

and L30 are equal prior to 2014.

Exhibit 6

Households as a Share of New Admissions

90

76

77

78

77

78

78

76

78

80

79

79

80

70

75

75

74

74

60

ELI Households

50

(%)

L30 Households

40

Reclassified

30

Households

20

10

3

5

5

5

0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure.

In 2014, the share of new households that were extremely low income began to increase slightly from

its previous 7-year average of 77 percent. Since the definition change, the share of new admissions that are L30 has decreased by approximately 2 percentage points (except for in 2014, because the policy

was implemented mid-year), while overall ELI new households increased about 3 percentage points.

This 5-percentage point difference between the ELI share and the L30 share in exhibit 6 signifies

new households that were reclassified as ELI under the new definition. In 2017, the median

amount that reclassified households’ annual income exceeded the L30 by approximately $1,800.

The following section will compare characteristics of new reclassified households to new L30

households following 2014, to gain better insight about household qualifying for ELI targeting that

previously would have not qualified. Trends on ELI families (also L30 prior to 2014) prior to 2014

will be provided to ensure that there were no significant changes in the L30 population following

2014 that should be noted.

Insight 3: Location and Household Size are Distinguishing

Characteristics of Households Reclassified as a Result of use

of Poverty Guidelines

Examining tenant characteristics offer more detail about households who are currently designated as

ELI but would not have been under the L30 definition. The following highlights such characteristics.

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Metropolitan Designation Where Households Live

Seventy-six percent of FMR areas are in non-metropolitan counties with most of these counties

having an ELI limit that exceeds the L30 limit. Only 19 percent of ELI households live in non-

metropolitan counties, however. Exhibit 7 shows that this figure more than doubles for households

reclassified due to the definition change.

Exhibit 7

Share of Households in Non-Metropolitan Counties

45

42

40

35

30

25

(%)

19

20

20

15

10

5

0

2007-2013 (ELI Households)

2014-2017 (L30 Households)

2014-2017 (Reclassified

Households)

ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure.

Census Division Designation Where Households Live

In 2017, the Northeast, Midwest, South, and West made up 7 percent, 35 percent, 42 percent,

and 16 percent of FMR areas, respectively. Exhibit 8 shows that the highest proportion of new ELI

households (40 percent) live in the South. A greater proportion of reclassified households live in

the South as well (65 percent).

Exhibit 8

New Households by Census Division

25

23

22

20

20

1819

1616

15

14

1313

13

11

(%)

10

9

10

10

9

9 8

5

6

7

5

5

5 5 5

5

2 3

1

0

0

Mid

New

West

East

West

East

South

Pacific

Mountain

Island

Atlantic

England

North

North

South

South

Atlantic

Central

Central

Central

Central

2007-2013 (New ELI Households) 2014-2017 (L30 Households) 2014-2017 (Reclassified Households) ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure.

396 Policy Briefs

Extremely Low-Income: Has the New Measure Made a Difference?

Concentration of Households Reclassified Due to the Definition Change by Area

Exhibit 9 shows the share of newly assisted households that were reclassified in 2017 by FMR area

and quintile. Areas with the darkest shading have the highest share of new households that were

reclassified. Areas with no shading did not have households reclassified by the definition change.

The map below illustrates what the data in exhibit 8 showed; areas in the South have the highest

concentration of reclassified households.

Exhibit 9

Reclassified Households as a Share of New Households, 2014–2017

Q = Quintile.

Household Size

While 88 percent of ELI limits are set to the poverty guidelines across all FMR areas and household

sizes, only 42 percent of FMR areas had one-person limits affected by the definition change.

Exhibit 10 shows that 47 percent of new ELI households are in one-person households. In

contrast, reclassified households are more likely to be in households of two to four persons.

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Exhibit 10

Size Composition of New Households

60

51

50

48

40

(%) 30

22

20

27

21

21

20

15

13

14

11

10

9 8

4

4

5

1 1

2

1

0 0

0 0

0 One-Person Two-Person Three-PersonFour-Person Five-Person Six-Person Seven-Person Eight-Person

2007-2013 (New ELI Households) 2014-2017 (L30 Households) 2014-2017 (Reclassified Households ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure.

Income

Exhibit 11 shows that on average over 52 percent of new ELI households were in the first

income quintile of their FMR area for their household size. Another third was in the second

income quintile with less than 15 percent in the remaining quintiles. In contrast, only 7 percent

of reclassified households were in the first income quintile. Fifty-five percent of reclassified

households were in the second quintile, and 38 percent were in the remaining quintiles.

Because the poverty guidelines are generally higher than the L30 limit, it would be expected

that reclassified households would be in higher income quintiles compared to the rest of new

participating households.

Exhibit 11

Share of New Households Within Fair Market Rent Area Income Quintile

70

65 68

60

55

50

40

(%)

31

30

29

26

20

10

7

8

4

3

4

0.4 0.3

0.3 0.2

0

Q1

Q2

Q3

Q4

Q5

2007-2013 (ELI Households) 2014-2017 (L30 Households) 2014-2017 (Reclassified Households ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure. Q = Quintile.

398 Policy Briefs

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Race

Exhibit 12 shows that the racial composition of reclassified households varied little from that of

new ELI households prior to the definition change. Reclassified households were slightly more

likely to be Black/African-American.

Exhibit 12

Racial Composition of New Households

50

43

40

40

40

40

38

39

30

(%) 20

14

14

13

10

7

6

4

0

Black

Hispanic

Other

White

2007-2013 (ELI Households)* 2014-2017 (L30 Households) 2014-2017 (Reclassified Households ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure.

*Note: Years between 2007 and 2009 were not used in this instance because of discrepancies in data categories from subsequent years.

Household Type

Exhibit 13 shows that 75 percent of new ELI households are headed by females. A slightly higher share

of reclassified households (81 percent) was headed by females. While 31 percent of new ELI households

had children under the age of 18, far more reclassified households included children (69 percent).

This notable difference between the two populations is likely related to household size. Households

with children will inherently consist of more than one person, and exhibit 10 showed that reclassified

households were more concentrated in households with more than one person. Senior-headed and

households headed by people with disabilities only make up about one-fifth of new ELI households.

Reclassified households were less likely to be headed by a senior or to include a member with disabilities.

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Exhibit 13

Composition of New Families by Household Type

90

81

80

75

73

70

69

60

50

(%) 40

31

30

28

20

19

21

20

22

16

11

10

0

Female-Headed

w/Children

Senior-Headed

People with Disabilities

2007-2013 (ELI Households) 2014-2017 (L30 Households) 2014-2017 (Reclassified Households ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure.

Total Tenant Payments

Total tenant payment (TTP) is the amount that households pay their landlord towards rent.

Generally, TTP is the higher of 30 percent of the household’s monthly adjusted income; or 10

percent of the household’s monthly gross income; or payments specifically designated by the public

welfare agency to meet the household’s housing costs (also known as “welfare rent”) (HUD, n.d.).

Exhibit 14 shows that the median TTP for households has been relatively flat between 2007 and

2017. During this period, all new households and new ELI households on average paid close to

$220 in TTP. In contrast, tenant payments of reclassified households are twice as much, averaging

around $400. This is in line with exhibit 11, which illustrates reclassified households having

higher incomes than other ELI households. Because reclassified households have higher incomes,

their TTP will be higher accordingly.

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Exhibit 14

Median Total Tenant Payment of New Households (2017 Dollars)

ELI = Extremely Low Income.

Housing Assistance Payment

In the HCV and Section 8 Project-Based Rental Assistance (PBRA) programs, Housing Assistance

Payments (HAP) are subsidies that are paid to landlords in rent on behalf of program households.

HAP is not applicable in the public housing program. In the HCV program, HAP equals the lower

of (1) the payment standard7 for the household minus the total tenant payment; or (2) the gross

rent minus the total tenant payment. In PBRA programs, HAP is simply gross rent minus the TTP.

Gross rents and payment standards are driven by market conditions of the unit’s location (housing

value, utility costs, and so on) and the quality of the unit itself (number of bedrooms, unit size,

age of unit, and so on) (Albouy and Lue, 2015). Exhibit 15 shows an inverse pattern from exhibit

14 for HAP payments made to landlords. Landlords receive less HAP for reclassified households

because these households generally have higher incomes than most ELI households and therefore

pay more rent themselves (exhibit 11).

7 In the HCV program, a PHA establishes payment standards based on the HUD-established FMRs for the area. The payment standards must be within an established range (90–110 percent) of the FMR. Payment standards are not used in PBRA Section 8 properties.

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Exhibit 15

Median Total Tenant Payment of New Households (2017 Dollars)

ELI = Extremely Low Income.

Insight 4: Use of Poverty Guidelines May Aid Smaller Public

Housing Agencies in Meeting Extremely Low-Income Targets

Prior to the 2014 ELI limit definition change, the public housing project-based vouchers (PBV) and

PBRA Section 8 programs were meeting their ELI target of 40 percent and continued to do so as of

2017 at 79 percent. ELI targets of 75 percent were also being met for HCV households. Today, the

goals continue to be met. For the HCV and public housing programs, the share of new households

that are ELI has slightly increased since 2014. While data shows that these targets were being met

prior to the ELI definition change, since the change there has been a convergence in the share of

ELI households assisted via these programs (exhibit 16). This could be tied to internal enforcement

of the policy (Dawkins, 2007).

402 Policy Briefs

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Exhibit 16

Share of New Households That are Extremely Low-Income

Targeting Outcomes by Public Housing Agency Size

Evaluating targeting outcomes by PHA size sheds light on the extent to which targets are being met

in the HVC and public housing programs. Exhibit 17 shows that most new ELI households are

serviced by large PHAs as opposed to smaller ones. Size categories represent the number of units

operated under PHA and have been frequently used in prior published reports analyzing HUD

administrative data on the size of PHAs.

Exhibit 17

Distribution of New Extremely Low-Income Households by Public Housing Agency Size, 2007

50

46

45

40

35

30

25

26

(%) 25

20

20

19

18

17

15

10

10

9

7

5

3

1

0

Very Small

Small

Medium Low

Medium High

Large

Extra Large

(1-49)

(50-249)

(250-499)

(500-1,249)

(1,250-9,999)

(10,000+)

Housing Choice Voucher Public Housing

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Exhibit 18 shows that, in 2017, most PHAs operating the public housing program were meeting

their ELI targets. On average, more than 90 percent of PHAs operating 50 public housing units

or more were meeting their targets. This figure dropped to 79 percent for PHAs overseeing less

than 50 units in their public housing program. In the HCV program, individual PHAs were less

successful at meeting ELI targets than in the public housing program, which is not unexpected

given that the public housing program has a lower ELI target. Exhibit 18 also shows that as the

program size of PHAs increases, the expected rate of success generally increases as well.

Exhibit 18

Share of Public Housing Agencies Meeting Extremely Low-Income Targets, by Public Housing

Agency Size, 2017

Exhibits 19 and 20 show how successful PHAs have been in meeting ELI targets for the HCV

and public housing programs by PHA size. Each circle on the map represents a PHA having new

admissions in 2017, and the size of the circle corresponds to the size class of the PHA for the

program. PHA sizes are combined for easier interpretation of the map. The large circles represent

PHAs with 1,250 units or more, and the small circles represent PHAs with less than 250 units.

The color variation on the map represents the success of PHAs meeting ELI targets. Lighter

dots represent areas that met ELI target goals and darker dots represent PHAs that did not meet

ELI targets.

Exhibit 19 illustrates that successful targeting is not limited to a region of the country or PHAs of

certain sizes for the HCV program. While the HCV program is meeting ELI targets overall, there are

several PHAs needing improvement. Thirty-five percent of PHAs did not meet the ELI target during

2017, and 25 percent missed by more than 5-percentage points.

In contrast, income targets in the public housing program are largely being met across housing

authorities of various sizes (exhibit 20), which could be expected since public housing has a lower target than the HCV program. Ninety-six percent of PHAs not meeting the public housing ELI target are small.

404 Policy Briefs

Extremely Low-Income: Has the New Measure Made a Difference?

Exhibit 19

Extremely Low-Income Targeting Outcomes by Public Housing Agency Program Size (Housing

Choice Voucher, 75 Percent Target), 2017

PHA = Public Housing Agency.

Exhibit 20

Extremely Low-Income Targeting Outcomes by Public Housing Agency Program Size (Public

Housing, 40 Percent Target), 2017

PHA = Public Housing Agency.

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The magnitude of how the targets are being met differ by PHA size. Exhibit 21 shows that extra-

large PHAs have the highest share of new households that are ELI. The chart also shows that as

PHA size increases, so does the share of new households that are ELI. There has been a general

increase in the share of new households that are ELI for small- to medium-sized PHAs between

2013 and 2017.

Exhibit 21

Share of New Households That are Extremely Low-Income by Public Housing Agency Size

Note: Data for smaller PHAs are prone to exhibit data anomalies (GAO, 2017), represent a smaller number of observations (exhibit 17), and are sensitive to changes in data reporting. The spike for very small PHAs in 2011 may be related to administrative or reporting changes that were implemented during the year.

Exhibit 22 shows that, from 2014 to 2017, about 41 percent of reclassified households

were served by large PHAs, which is comparable to the share for ELI and L30 households.

Reclassified households were slightly more likely to be served by smaller PHAs in comparison

to L30 households.

406 Policy Briefs

Extremely Low-Income: Has the New Measure Made a Difference?

Exhibit 22

Size of Public Housing Agencies Serving New Households, 2017

50

45

42 44 41

40

35

30

23

(%) 25

20 21

20

18 18

15

13

13

10

10

8 8

9

8

5

1 1

2

0

Very Small

Small

Medium Low

Medium High

Large

Extra Large

(1-49)

(50-249)

(250-499)

(500-1,249)

(1,250-9,999)

(10,000+)

2007-2013 (ELI Households) 2014-2017 (L30 Households) 2014-2017 (Reclassified Households ELI = Extremely Low Income. L30 = HUD’s 30 Percent of Local Area Median Income Measure.

Final Thoughts

In many FMR areas, the use of the poverty guidelines in the determination of the ELI results in

an income-limit that is higher compared to the prior limit that was based on 30 percent of area

median income. This occurs in most cases for households of two or more people and areas in

non-metropolitan counties. Residing in an FMR area where the ELI is different due to the definition

change, however, does not guarantee that a household living within that FMR area will be

reclassified by the change.

Approximately 5 percent of new admissions are classified as ELI as a result of the use of the poverty

guidelines. Under the old L30 definition of ELI, these households would not have been defined as

ELI. These reclassified households are more likely to live in non-metropolitan areas and southern

portions of the country. In addition, they are more likely to consist of households greater than

one-person compared with the total new admission population that consists of mostly one-person

households. For this reason, households reclassified by the change are more likely to consist of

households with children. There is no substantial difference in the racial/ethnic background of

households reclassified by the change, and they are slightly less likely to be male-headed, senior-

headed, or include a member with disabilities.

The definition change also did not appear to have a significant impact on tenant payments made

by households entering HUD programs. Payments were relatively flat between 2007 and 2017.

Households reclassified by the definition change were more likely to have higher rent payments

because of their higher incomes compared to other newly admitted households. PHAs are more

likely to pay less HAP to landlords of reclassified households because these households typically

have higher incomes than average ELI households to cover their rent.

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Brandly

Prior to the 2014 ELI limit definition change, the public housing and PBRA Section 8 programs

were meeting their target of 40 percent and continued to do so as of 2017. ELI targets of 75 percent

are being met in the HCV program, however, individual PHAs are less successful at meeting ELI

targets than in the public housing program. This policy brief showed that ELI targeting improves

as the size of the PHA increases, and the share of new residents that are ELI has increased, most

notably for smaller PHAs, since the 2014 definition change.

Acknowledgments

The author thanks Kurt Usowski, Peter Kahn, Elizabeth Rudd, and other colleagues for their

constructive feedback on this article. The author also thanks Lydia Taghavi and Dan Hardiman for

their technical assistance and research support.

Author

Chalita Brandly is an economist in the U.S. Department of Housing and Urban Development,

Office of Economic Affairs.

References

Albouy, David, and Bert Lue. 2015. “Driving to Opportunity: Local Rents, Wages, Commuting, and

Sub-Metropolitan Quality of Life,” Journal of Urban Economics 89: 74–92.

Dawkins, Casey J. 2007. “Income Targeting of Housing Vouchers: What Happened After the

Quality Housing and Work Responsibility Act?” Cityscape 9 (3): 69–93. https://www.huduser.gov/

periodicals/cityscpe/vol9num3/ch4.pdf.

Fisher, Gordon M. 1997. The Development of the Orshansky Poverty Thresholds and Their

Subsequent History as the Official U.S. Poverty Measure. https://www.census.gov/content/dam/

Census/library/working-papers/1997/demo/orshansky.pdf.

Hunt, Louise, Mary Schulhof, and Stephen Holmquist. 1998. Summary of the Quality Housing

and Work Responsibility Act of 1998. Washington, DC: U.S. Department of Housing and Urban

Development, Office of Public and Indian Housing. Page 6. https://www.hud.gov/sites/

documents/DOC_8927.PDF.

Leopold, Josh, Liza Getsinger, Pamela Blumenthal, Katya Abazajian, and Reed Jordan. 2015. The

Housing Affordability Gap for Extremely Low-Income Renters in 2013. Washington, DC: The Urban

Institute, Urban Institute Policy Advisory Group. https://www.urban.org/sites/default/files/

publication/54106/2000260-The-Housing-Affordability-Gap-for-Extremely-Low-Income-

Renters-2013.pdf.

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Extremely Low-Income: Has the New Measure Made a Difference?

National Academy of Sciences. n.d. Measuring Poverty. https://www.census.gov/content/dam/

Census/topics/income/supplemental-poverty-measure/sum.pdf.

National Low Income Housing Coalition (NLIHC). 2005. National Housing Voucher Summit 2005

Final Statement, Recommendations, and Options. https://prrac.org/pdf/NLIHC_VoucherSummit.pdf.

Olsen, Edgar. 2014. Alleviating Poverty Through Housing Policy Reform, Innovating to End Urban

Poverty Conference, Los Angeles 2014. https://socialinnovation.usc.edu/files/2014/03/Olsen-

Alleviating-Poverty-through-Housing-Policy-Reform.pdf.

Sard, Barbara, and Jeff Lubell. 2000. How the Statutory Changes Made by the Quality Housing and

Work Responsibility Act of 1998 May Affect Welfare Reform Efforts. Center on Budget and Policy

Priorities (2000). https://www.cbpp.org/archives/12-17-98hous.htm#N_3_

U.S. Department of Housing and Urban Development (HUD). 2017. FY 2017 HUD

Income Limits Briefing Material. https://www.huduser.gov/portal/datasets/il//il17/

IncomeLimitsBriefingMaterial-FY17.pdf.

———. 2014. Quality Control for Rental Assistance Subsidy Determinations, FY 2013 Study. P.

IV-13 https://www.huduser.gov/portal/Publications/pdf/QC_2013_final.pdf.

———. n.d. Housing Choice Voucher Program Guidebook. “Chapter 6: Calculating Rent and

HAP Payment,” Washington, DC: U.S. Department of Housing and Urban Development, Office of

Housing Choice Vouchers. https://www.hud.gov/sites/documents/DOC_35616.PDF.

U.S. Government Accountability Office (GAO). 2017. Report to Congress. Housing Choice Voucher

Program: Limited Indications of Potential Fraud against Participants Identified. GAO-18-53.

December 2017: 25–26. https://www.gao.gov/assets/690/688718.pdf.

Weicher, John. 2012. Housing Policy at a Crossroads: The Why, Who, and How of Assistance

Programs. Washington, DC: American Enterprise Institute.

Cityscape 409

Symposium

Small Area Fair Market Rents ................................................................................................................................ 1

Guest Editors: Adam Bibler, Chalita Brandly, Peter Kahn, Marie Lihn, and Lydia Taghavi

Guest Editors’ Introduction

Small Area Fair Market Rents .............................................................................................................................3

Small Area Fair Market Rent Demonstration ...............................................................................................17

The Effects of Small Area Fair Market Rents on the Neighborhood Choices of Families with Children

by Samuel Dastrup, Meryl Finkel, and Ingrid Gould Ellen ...................................................................................19

Small Area Fair Market Rents, Race, and Neighborhood Opportunity by Kirk McClure and Alex Schwartz ...........49

Impact of Expanded Choice on Attrition in the Housing Voucher Program by Judy Geyer, Samuel Dastrup,

and Meryl Finkel ..............................................................................................................................................71

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment? by Edgar O. Olsen .............89

Reflections on Demand Assistance in the Rental Sector: A European Perspective by Marietta E.A. Haffner ........103

Aspects of the Housing Choice Voucher Program and the Impact of Small Area Fair Market Rents Ceilings:

a British Perspective by Christine M.E. Whitehead ............................................................................................113

Alternative Measures of Market Rents .......................................................................................................123

The Best Laid Plans Often Go Awry: An Analysis of the Implementation of Small Area Fair Market Rents

by Kelly L. Patterson and Robert Mark Silverman ..............................................................................................125

Comparing Small Area Fair Market Rents With Other Rental Measures Across Diverse Housing Markets

by Christian Hess, Rebecca J. Walter, Arthur Acolin, and Sarah Chasins .............................................................159

Contrasting Different Geographies in Fair Market Rents: Implications for the Housing Choice Voucher

Program in Pittsburgh, PA by Mike Blackhurst, Chris Briem, and Sabina Deitrick ...............................................187

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data by Aksel Olsen ...............207

A Tweak to Housing Assistance Allows Low-Income Renters Access to High-Income Neighborhoods

by Alexander Casey .......................................................................................................................................245

Refereed Papers ........................................................................................................................................ 259

Sexual Harassment of Low-Income Women by Landlords by Rigel C. Oliveri ....................................................261

Departments................................................................................................................................................. 285

Affordable Design

2019 American Institute of Architects (AIA)—HUD Secretary’s Housing & Community Design Awards

by Regina C. Gray..........................................................................................................................................287

Data Shop

A New Cost-Based Index of Housing Quality and Repair Needs by Eliza Wallace, Eileen Divring,

and Keith Wardrip ..........................................................................................................................................299

Foreign Exchange

Observing New Zealand’s Housing Crisis: Efforts to Produce and Finance Affordable Housing and its

Implications for U.S. Policies by Jeffrey Mosley ................................................................................................311

Graphic Detail

Misalignment Between ZIP Codes and Municipal Boundaries: A Problem for Public Health

by Richard C. Sadler ......................................................................................................................................335

Industrial Revolution

Trenchless Technology: A New Way to Install and Renew Utilities by Mike Blanford ..........................................341

Protecting Low-Income Housing from Climate Risks by Michael E. Canes ........................................................347

Policy Briefs

New York City’s Affordable Housing Plans and the Limits of Local Initiative by Alex Schwartz ...........................355

Extremely Low-Income: Has the New Measure Made a Difference? by Chalita Brandly ....................................389

Cityscape

Office of Policy Development and Research

VOLUME 21 NUMBER 3 • 2019

Document Outline

Cityscape

Contents

Symposium

Small Area Fair Market Rents

Small Area Fair Market Rent Demonstration

The Effects of Small Area Fair Market Rents on the Neighborhood Choices of Families with Children

Small Area Fair Market Rents, Race, and Neighborhood Opportunity

Impact of Expanded Choice on Attrition in the Housing Voucher Program

Does HUD Overpay for Voucher Units, and Will SAFMRs Reduce the Overpayment?

Reflections on Demand Assistance in the Rental Sector: A European Perspective

Aspects of the Housing Choice Voucher Program and the Impact of Small Area Fair Market Rents Ceilings: a British Perspective

Alternative Measures of Market Rents

The Best Laid Plans Often Go Awry: An Analysis of the Implementation of Small Area Fair Market Rents

Contrasting Different Geographies in Fair Market Rents: Implications for the Housing Choice Voucher Program in Pittsburgh, PA

Examining the Transition to HUD Small Area Fair Market Rents Using Craigslist Data

A Tweak to Housing Assistance Allows Low-Income Renters Access to High-Income Neighborhoods

Refereed Papers

Sexual Harassment of Low-Income Women by Landlords

Departments

2019 American Institute of Architects (AIA)—HUD Secretary’s Housing & Community Design Awards

A New Cost-Based Index of Housing Quality and Repair Needs

Observing New Zealand’s Housing Crisis: Efforts to Produce and Finance Affordable Housing and its Implications for U.S. Policies

Misalignment Between ZIP Codes and Municipal Boundaries: A Problem for Public Health

Trenchless Technology: A New Way to Install and Renew Utilities

Protecting Low-Income Housing from Climate Risks

New York City’s Affordable Housing Plans and the Limits of Local Initiative

Extremely Low-Income: Has the New Measure Made a Difference?