A Journal of Policy

Development and Research

Double Issue

HabItat III

tHe CRa tuRns 40

Volume 19 numbeR 2 • 2017

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

Managing Editor: Mark D. Shroder

Associate Editor: Michelle P. Matuga

Advisory Board

Dolores Acevedo-Garcia

Brandeis University 

Ira Goldstein 

The Reinvestment Fund 

Richard K. Green 

University of Southern California 

Mark Joseph 

Case Western Reserve University 

Matthew E. Kahn 

University of California, Los Angeles 

C. Theodore Koebel 

Virginia Tech 

Jens Ludwig 

University of Chicago 

Mary Pattillo 

Northwestern University 

Carolina Reid 

University of California 

Patrick Sharkey 

New York University

Cityscape

A Journal of Policy 

Development and Research

Double Issue

HabItat III

tHe CRa tuRns 40

Volume 19 numbeR 2 • 2017

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: Habitat III ......................................................................................................... 1 

Guest Editor: Katrina Durbak

Guest Editor’s Introduction 

Looking Beyond Habitat III ........................................................................................................ 3

Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool........................... 9 

by Rachel Fyall and Alexander Casey

Boundary Issues: The 2016  Atlas of Urban Expansion Indicates Global Dedensification ...... 21 

by John Wihbey

Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s  

Housing Crisis ........................................................................................................................... 31 

by Kira Intrator and Kaustubh Shivdikar

Inclusion and Innovation: The Many Forms of Stakeholder Engagement in Habitat III ....... 45 

by Eugenie L. Birch

Housing Perspectives on Habitat III: Canada .......................................................................... 53 

by Janet Kreda

Symposium: The CRA Turns 40 ........................................................................................ 57 

Guest Editor: Carolina Reid

Guest Editor’s Introduction 

The Community Reinvestment Act at 40 ................................................................................. 59

Small Business Lending Under the Community Reinvestment Act ........................................ 63 

by Raphael W. Bostic and Hyojung Lee

Using Credit Reporting Agency Data To Assess the Link Between the Community 

Reinvestment Act and Consumer Credit Outcomes ................................................................ 85 

by Kristin F. Butcher and Ana Patricia Muñoz

Bridging the Gap to Scalable Community Reinvestment Lending Programs ....................... 109 

by Roberto G. Quercia and Sarah Riley

Commentary: Encouraging Housing Equity ........................................................................... 129 

by Dan Immergluck

Community Reinvestment Act and Local Governance Contexts: Advancing the Future  

of Community Reinvestment?  ................................................................................................. 137 

by Colleen Casey, Joseph Farhat, and Gregory Cartwright

Data, Accountability, and the Public: Using Community Reinvestment Act Data for  

Local Community Development ............................................................................................. 161 

by Marijoan Bull

Cityscape

iii

Contents

Commentary: The Community Reinvestment Act Must Be All About Public Participation,  

but It Still Doesn’t Feel That Way ............................................................................................ 177 

by Josh Silver

Commentary: FinTech and the Liberation of the Community Reinvestment Act Marketplace .. 187 

by Michael Gaughan

Commentary: What Can We Learn From Government Attempts To Modify the  

Allocation of Mortgage and Consumer Credit in the United States?  .................................... 199 

by Anthony Yezer

Commentary: Filling a Gap in the Community Reinvestment Act Examiner Toolkit .......... 207 

by Mark A. Willis

Refereed Papers ................................................................................................................... 213

Are Homeowners Better Neighbors During Housing Booms? Understanding Civic and  

Social Engagement by Tenure During the Housing Market Cycle ........................................ 215 

by Deirdre Pfeiffer and Eric A. Morris

A Research Note: The Housing Choice Voucher Program and Access to Opportunity in 

Florida’s Nonmetropolitan Areas ............................................................................................ 239 

by Rebecca J. Walter and Ruoniu Wang

Departments ......................................................................................................................... 255

Data Shop 

Measuring Disability ............................................................................................................... 257 

by Debra L. Brucker and Veronica E. Helms

Graphic Detail 

A Cartographic Perspective on the Correlation Between Redlining and Public Health in 

Austin, Texas—1951 ............................................................................................................... 267 

by John C. Huggins

Impact 

Reducing the Flood Hazard Exposure of HUD-Assisted Properties...................................... 281 

by Michael K. Hollar

Policy Briefs 

Project-Based Vouchers ........................................................................................................... 301 

by Brent D. Mast and David Hardiman

SpAM 

Measuring Distance to Resources ........................................................................................... 323 

by Ron Wilson and Alexander Din

Evaluation Tradecraft 

Use of Genetic Matching in Program Evaluation: The Case of RAD ..................................... 337 

by David Ruiz, Dennis Stout, and Christine Herlihy

Call for Papers 

Symposium on Housing and Health ....................................................................................... 351 

Referees 2016–17 .................................................................................................................... 352 

iv Volume 19 Number 1

Symposium

Habitat III 

Guest Editor: Katrina Durbak

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape

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

2

Habitat III

Guest Editor’s Introduction

Looking Beyond Habitat III

Katrina Durbak  

U.S. Department of Housing and Urban Development

The views expressed in this article are those of the guest editor 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.

What follows a major conference convened by the United Nations? How will the call to action 

put forward at Habitat III, the third United Nations (UN) Conference on Housing and Sustainable 

Urban Development, throughout its robust preparatory processes and in its formal outcome docu-

ment be realized? Although it is too early to speak about the long-term legacy of this conference, 

the broad efforts leading up to it and recent innovative approaches as presented in this Symposium 

offer some insights into what lies beyond Habitat III. 

The first United Nations Programme for Human Settlements (UN-Habitat) conference met in 1976 

in Vancouver, Canada, sparking an international dialogue on urban issues. Twenty years later at 

Habitat II in Istanbul, Turkey, world leaders adopted the Habitat Agenda, a global action plan 

aimed at providing adequate shelter for all. In October 2016, more than 30,000 representatives 

of the nations of the world and nongovernmental organizations gathered for Habitat III in Quito, 

Ecuador. 

The Habitat III conference was intended to reinvigorate the global commitment to sustainable 

urbanization, at a moment in history of rapid global urbanization. The global process leading up 

to Habitat III centered on engaged participation, organized both regionally and topically, from 

stakeholders, representatives, and technical experts to draft a  New Urban Agenda, the outcome 

document of Habitat III.

The UN opened its process to input from a broad base of countries, interest groups, technical 

experts, and the general public. In all, 11 regional and thematic preparatory conferences were held 

around the world. A series of issue papers were created for public feedback. Two hundred technical 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

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

Durbak

experts worked to develop foundational documents to feed into the  New Urban Agenda, which was 

negotiated and ultimately agreed on as a nonbinding, consensus document of the member states of 

the UN.1 The  New Urban Agenda is organized around the following—

•  Three transformative commitments.

 � Sustainable urban development for social inclusion and ending poverty.

 � Sustainable and inclusive urban prosperity and opportunities for all.

 � Environmentally sustainable and resilient urban development.

•  Effective implementation.

 � Building the urban governance structure: establishing a supportive framework.

 � Planning and managing urban spatial development.

 � Means of implementation.

•  Followup and review—organized by UN-Habitat, with national and local governments and 

other partner organizations.

U.S. Preparations for Habitat III

The United States participated in the Habitat III conference and preparatory processes for two 

primary reasons: (1) to further domestic efforts at providing affordable and accessible housing 

and fostering sustainable, inclusive communities, and (2) to contribute to the global sharing of 

knowledge and practices and diplomacy around these issues. The U.S. Department of Housing 

and Urban Development (HUD), at the request of and in partnership with the U.S. Department of 

State, led the official U.S. Habitat III preparatory process from September 2014 through October 

2016 and the U.S. delegation to Habitat III in October 2016. 

Cities and housing are of course domestic policy concerns. Thus, the Habitat III conference 

touched on several critical domestic issues. Although the  New Urban Agenda contains some discus-

sion of national urban policies in the document, which is not applicable to governments with a 

federal structure, including Australia, Canada, the United States, and others, the principles put 

forward in the  New Urban Agenda are nonetheless generally aligned with interests that the U.S. 

government wished to promote, even if their legal implementation may require participation across 

federal, state, and local governments. With input from the U.S. Habitat III National Committee, 

HUD identified three main themes to guide the U.S. domestic preparations for Habitat III— 

1.  Investing in people and communities for upward mobility.

2.  Securing housing options for all.

3.  Building resilience in an era of change.

1 See https://www2.habitat3.org/bitcache/97ced11dcecef85d41f74043195e5472836f6291?vid=588897&disposition=

inline&op=view.

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Habitat III

 Looking Beyond Habitat III

HUD assembled a Habitat III National Committee, comprising 44 federal government agencies, 

civil society, research organizations, affinity groups, and philanthropic organizations. Members 

of the U.S. Habitat III National Committee provided inputs to the U.S. negotiating position on 

the  New Urban Agenda at numerous stages in its development. They contributed to HUD’s two 

reports—a U.S. National Report for Habitat III, in line with UN-Habitat guidelines, for purposes 

of comparable data across countries and regions, and a U.S. 20/20 Habitat III Report. The 20/20 

Report presented the U.S. story around housing, community development, and the fostering 

of inclusive, sustainable, and resilient communities across the United States, spanning the 20 

years following Habitat II. HUD cohosted five domestic convenings to engage local and regional 

practitioners in the preparatory process. A subcommittee organized five simultaneous convenings 

informing the international affairs community about the intersection of their goals with those of 

urban development and housing. These sessions covered topics related to sustainable and safe 

urban development and aimed to advance U.S. policy for an increasingly urbanized world.

The United States actively participated in the Habitat III conference, with HUD Secretary Julián 

Castro leading the delegation to the conference and addressing the main plenary. Secretary Castro 

chaired an event, Equity and Inclusion: A Mayor and Minister Discussion of National-Local Policies 

To Combat Inequality and Achieve Opportunity for All. At this event, Secretary Castro and his Ca-

nadian counterpart, Minister of Families, Children and Social Development Jean-Yves Duclos, led a 

discussion among U.S. and Canadian mayors around opportunities to reduce inequality and to find 

collective, innovative solutions to challenges. Other events were organized by other U.S. federal 

agencies, and members of the U.S. delegation served on panels and presentations with government 

and nongovernment partners. 

Multiple nongovernmental organizations from the United States also independently participated in 

the international processes run by UN-Habitat leading up to Habitat III and attended the Habitat 

III conference independently of the official U.S. delegation. 

The U.S. Habitat III Documents and This Symposium

The U.S. National Report for Habitat III provided brief descriptions of efforts across the United 

States and numeric data for a series of indicators, per the UN-Habitat guidelines (HUD, 2015). The 

U.S. 20/20 Habitat III Report (HUD, 2016) described policies, programs, and initiatives across the 

United States organized by the three themes of the U.S. domestic preparations for Habitat III. It 

contains a variety of contributed essays by outside partners. 

My goal in developing this symposium was to share some of the creative and scientific work that 

Habitat III either stimulated or brought to HUD’s attention. The authors have all taken up the call 

to action of Habitat III to find innovative, yet feasible, approaches to the complex challenges of 

equitable, sustainable, and inclusive housing and urban development. 

The first article, “Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool,” by 

Rachel Fyall and Alexander Casey, presents a very local, innovative approach for addressing chal-

lenges of housing affordability—through local zoning tools. This peer-reviewed article considers 

Cityscape

5

Durbak

the hurdles around providing affordable housing associated with land value. Fyall and Casey 

(2017) propose an alternative policy that enables cities to capture a portion of this land value 

increase in order to fund housing equity priorities. 

The second article, “Boundary Issues: The 2016  Atlas of Urban Expansion Indicates Global Deden-

sification,” by John Wihbey, presents a technical approach for documenting and analyzing spatial 

urban growth. The importance of data, its reliability and availability, and its role in helping develop 

and refine policies and programs around land use, urban planning, and housing were recurring 

themes in the Habitat III effort (Wihbey, 2017).

The third article, “Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s 

Housing Crisis,” by Kira Intrator and Kaustubh Shivdikar, explores the challenge of data from a dif-

ferent perspective—namely the complexity of gathering adequate, accurate, and appropriate data. 

This peer-reviewed article proposes an innovative approach of utilizing unmanned aerial vehicles 

and artificial intelligence to gather more detailed information about housing conditions, so that 

local and national governments and international organizations can better target interventions and 

specific needs (Intrator and Shivdikar, 2017).

The fourth article, “Inclusion and Innovation: The Many Forms of Stakeholder Engagement in 

Habitat III,” by Eugenie L. Birch, discusses the stakeholder engagement within the Habitat III effort 

and provides a commentary on how to further build on this approach. This article positions the 

importance of the role of stakeholder participation in tackling these issues from the UN to the local 

government level (Birch, 2017). 

The final article is a perspective by Janet Kreda from the Canada Mortgage and Housing Corpora-

tion on the recent and forthcoming changes in Canada’s policies and approaches toward housing. 

The Trudeau administration’s development of a New Housing Strategy for Canada occurred in 

parallel with the development of the  New Urban Agenda. Given the structural similarities between 

the governments of Canada and the United States, specifically the power of the provinces within 

Canada’s federal system, the Canadian perspective can be seen as particularly relevant for U.S. 

readers (Kreda, 2017).

Acknowledgments

The guest editor acknowledges the input of the authors who contributed to this symposium and 

the anonymous referees who provided timely and thoughtful reviews.

Guest Editor

Katrina Durbak is a program analyst in the U.S. Department of Housing and Urban Development, 

Office of Policy Development and Research.

6

Habitat III

 Looking Beyond Habitat III

References

Birch, Eugenie L. 2017. “Inclusion and Innovation: The Many Forms of Stakeholder Engagement 

in Habitat III,”  Cityscape 19 (2): 45–51.

Fyall, Rachel, and Alexander Casey. 2017. “Urbanizing for Equity: Harnessing Upzones as a Redis-

tributive Policy Tool,”  Cityscape 19 (2): 9–19. 

Intrator, Kira, and Kaustubh Shivdikar. 2017. “Missing ‘Middle Scenarios’: Uncovering Nuanced 

Conditions in Latin America’s Housing Crisis,”  Cityscape 19 (2): 31–43.

Kreda, Janet. 2017. “Housing Perspectives on Habitat III: Canada,”  Cityscape 19 (2): 53–56.

U.S. Department of Housing and Urban Development (HUD). 2016.  The U.S. 20/20 Habitat III 

Report. Washington, DC: U.S. Department of Housing and Urban Development. https://www.

huduser.gov/portal/sites/default/files/pdf/Habitat-III-National-Report.pdf.

———. 2015. “National Report for the Third United Nations Conference on Housing and Sustain-

able Urban Development (Habitat III).” https://unhabitat.org/wp-content/uploads/2014/07/United-

States-of-America-National-Report.pdf.

Wihbey, John. 2017. “Boundary Issues: The 2016  Atlas of Urban Expansion Indicates Global Deden-

sification,”  Cityscape 19 (2): 21–30.

Cityscape

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8

Habitat III

Urbanizing for Equity: Harnessing 

Upzones as a Redistributive Policy Tool 

Rachel Fyall  

University of Washington

Alexander Casey  

Zillow

Abstract

Alternative land use policies are needed to ensure equitable housing. This article explores 

the often lottery-like characteristics of significant land value increases that certain 

property owners realize following local zoning changes. Traditionally, property owners 

enjoy a financial windfall from land use modifications when provided the opportunity to 

redevelop to a higher density or sell to a developer. We propose an alternative policy that 

enables cities to capture a portion of this land value increase in order to fund housing 

equity priorities.

Introduction

The  New Urban Agenda urges the reconsideration of equity as a fundamental component of urban-

ism. Although cities have long been places of opportunity, urbanization often prompts an increased 

cost of living, housing shortages, and other externalities that disproportionately burden low-

income and poverty-stricken individuals and families. To respond to this paradox, the  New Urban 

Agenda declares, “We commit ourselves to promoting equitable and affordable access to sustainable 

basic physical and social infrastructure for all” (UN, 2016: 7). If urban developments are to be 

sustainable, they must “leave no one behind” (UN, 2016: 5). 

Beyond envisioning equitable cities as a desired outcome, the process of urbanization also offers 

equity-related challenges and opportunities. At a micro level, cities grow by the parcel or lot. A 

family farm begets a group of single-family homes, which begets an urban community of mixed 

use, multistory buildings. Even historical city centers grow taller and denser. The new develop-

ment that enables urbanization creates an inequitable economic bounty; few mechanisms exist to 

redistribute the profits associated with new construction and rising land values. 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

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

Fyall and Casey

In growing economies, changes in allowable land use often facilitate increased land values. This 

phenomenon is simplest as a function of density—as the allowable density of a land parcel ex-

pands, the value of the land usually increases. In the United States, local zoning processes typically 

determine land use and associated changes. Traditional zoning policies enable landowners to enjoy 

the financial benefits associated with zoning changes by selling or redeveloping properties to take 

advantage of newly allowed uses. In either scenario, the public action of zoning creates private 

wealth through increased land values. 

Zoning changes can be a boon to private landowners, but land value increases create additional 

costs for renters and first-time homebuyers. Redevelopment can catalyze gentrification and the 

displacement of low-income renters. Although affordable residential density fosters access to the 

benefits of cities, the urbanizing process tends to exacerbate economic inequalities. 

In response to the inequality inherent to urban growth, this article proposes a policy that enables 

cities to incorporate equity priorities into zoning changes. Rather than allowing for zoning 

changes to enrich private landowners, the proposed policy captures this wealth for redistributive 

purposes. The creation of Upzone Development Rights (UDR) separates the ownership of newly 

created allowable uses from the ownership of the land and its existing use. This article explores the 

potential for how such a policy might work in practice, acknowledging the likely political and legal 

challenges to such a radical innovation. This article contributes to the implementation of the  New 

Urban Agenda by proposing an innovative policy tool that integrates equity into the development 

agenda. 

Upzone Development Rights Policy Overview 

In tight property markets with relatively few housing vacancies, existing zoning policies enable 

development demand and potential to build up in desirable locations. Unrealized demand per-

colates until cities grant permission to build new densities or uses. When governments allow for 

denser development in certain areas, they create bottlenecks of development demand. This excess 

demand drives up the market value for the properties within newly zoned areas. 

Zoning decisions are (usually) strategic and tend to reflect existing or future infrastructure or 

growth trends. However, we use the term  lottery-like to describe the somewhat arbitrary nature of 

exactly where zoning boundaries exist. An  upzone refers to a defined geographic area within an 

urban setting rezoned for a higher, denser, or more profitable use. 

Zoning boundaries can distinguish two parcels that may experience the characteristics of their 

neighborhood in very similar ways. For instance, two neighboring plots may benefit from a neighbor-

hood’s transportation access, safety, schools, and commercial establishments, but one lot may be zoned 

for midrise development and the other may permit only a single-family home. On either side of the 

zoning boundary, stark differences are likely for properties and property owners. Whereas a newly up-

zoned home may become a target for prospective developers previously unable to develop multifamily 

or commercial development in the area, the value of a similar home outside the zoning boundary 

remains relatively unchanged. This phenomenon creates winners who, much like those in a lottery, 

receive significant wealth despite taking the same action (or inaction) as their unlucky neighbor. 

10 Habitat III

Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool

Rezoning can transfer significant value to property owners who win this lottery. We draw on the 

conceptual framework of Hagman and Misczynski (1978) and refer to property value gains created 

by public action as  windfalls. Property owners enjoy some legal protections against zoning changes 

that decrease their property values, but they typically enjoy the entire increase in value that zoning 

and other publicly funded improvements create. Governments do expect indirect financial benefits 

due to increased property tax assessments, but greater tax revenues are comparatively small and 

often delayed by several years. 

This article proposes that the public should benefit from the spikes in property values created by 

zoning policies, and once captured this wealth should help increase equity. A dedicated revenue 

source for redistributional purposes can serve as a meaningful antidote to the negative impacts of 

the urbanizing process on low-income populations. This practice embeds an equity component 

into urbanization, extracting value for redistribution only when market forces recognize land value 

increases. 

The idea of windfall recapture is not novel in theory, and cities already possess some tools for 

capturing value from property owners. Several municipal finance tools harness the newly gained 

property values from public-sector investments to help finance those investments. Cities across the 

United States use instruments like tax increment financing, local improvement districts, impact 

fees, and the sale of development permissions to pay for investments within particular communi-

ties. Our proposed policy, UDR, is a variation on the sale of development permissions. 

Our proposal differs from existing policies on a few key points. First and most important, we 

specifically address increased value for landowners resulting from zoning changes rather than 

capital projects. Unlike tools used to finance infrastructure projects, zoning increases do not 

require financing. Through a UDR system, cities obtain revenues not directly offset by correspond-

ing costs. If windfalls fund equity investments, property owners within an upzone may not receive 

private financial gains from city zoning policy. Instead, we argue that the benefits of the wealth 

(property value increases) created through public action (upzones) need not accrue exclusively for 

the neighborhood where the wealth was created. Capturing windfalls for public purposes can fund 

broader issues of urban equity such as housing subsidies or targeted investments in less prosperous 

neighborhoods. This represents a significant departure from other common tools for capturing 

land value.

Second, we advocate that cities allow for market-based pricing systems when granting develop-

ment permissions. Through the similar value-capture tools mentioned previously, government 

agencies determine the price or rate that community members pay for the public investments 

from which they benefit. The market-based approach, through a bidding or negotiation system, 

could maximize the value capture without imposing costs that disincentivize development or 

skew the market. This component is essential for ensuring the intended outcome of urbanization 

(that is, fostering the growth of cities so that more people may access benefits and lower prices 

from increased supply). To harness urbanization without impeding it, a UDR policy must not 

substantially delay or reduce multifamily development, because exacerbating current multifamily 

supply-demand imbalances could unintentionally offset equity benefits. However, if administered 

effectively, UDR could equitably redistribute gains without affecting the incentive structure to build. 

Cityscape 11

Fyall and Casey

Third, we argue for collecting an unmaterialized value by imposing a new cost to the buyer, rather 

than selling a development standard of value—like density bonuses—or taxing a realized property 

value increase. We expect that developers have a finite willingness to pay for parcels to develop 

newly allowable higher-density projects, so they will cut acquisition costs elsewhere in order to 

secure UDR. Imposing a new expense to bid for and secure the development permissions for the 

newly acquired parcel should lead to a corresponding decrease in the price the developer offers 

to the seller. Simply, we expect the developer to offer less to the property owner, thus transferring 

most of the windfall to the government for redistributive purposes. 

Although a UDR policy reduces the incentive for homeowners to sell, the number of upzoned 

parcels likely remains a finite resource. Therefore, a homeowner may still extract a modest windfall 

compared with similar parcels outside the upzone. Rather than eliminating windfalls, this proposal 

merely enables a city to collect revenue to reinvest toward equity goals. Many cities already experi-

ence neighborhood opposition to upzones, and a UDR system may even foster broader support for 

upzones as the resulting wealth creates citywide benefits. Regardless, it is essential that cities main-

tain their commitment to upzones in order to achieve the positive outcomes density can provide. 

Finally, we firmly assert that the recaptured windfall must advance “equitable and affordable ac-

cess” to a city. Although we stated previously that a UDR system enables cities to obtain revenues 

not directly offset by corresponding costs, we view certain urbanizing trends such as rent increases, 

gentrification, and the displacement of low-income households as the costs of upzones. The 

upzoned parcels may not exemplify these trends, but a more holistic perspective reveals these 

aggregate effects of urbanization. The specific needs of a city should determine how exactly to 

use these funds, but programs such as rent subsidies, downpayment assistance, and community 

revitalization projects may appropriately promote equity goals while supporting urbanization and 

reaping the benefits that upzones and density provide.

To present a compelling argument for such a policy innovation, we first establish the connection 

between zoning, windfalls, and equity. We then describe a brief case of windfalls in practice. We 

then present a review of existing mechanisms for capturing windfalls. We conclude with our policy 

proposal for a UDR system based on market forces.

Zoning, Windfalls, and Equity 

Our policy proposal echoes long-ignored calls to better moderate the effect of public policy on 

land values. Hagman and Misczynski (1978) described the lottery-like nature of the status quo: 

planning, even when conducted in the public interest, can be perceived as an “arbitrary and 

capricious […] non-system of planning” through which a landowner could win or lose millions by 

“the decision of a government body or by the stroke of a planner’s pen” (Hagman and Misczynski, 

1978: 21). Although city- or neighborhood-level scrutiny may convey logical land use policies, 

parcel-level decisions about zoning boundaries rarely result from such careful analysis. 

Land use regulation is an effective tool for balancing the density needs of a growing city with 

other urban priorities, such as open spaces, the preservation of historic buildings, and affordable 

housing. Changes in land use policy, however, often create new inequities. When residential 

12 Habitat III

Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool

neighborhoods are upzoned, seemingly random homeowners may sell their properties at inflated 

prices, reflecting new development potential. Under this system, homeowners capture the entire 

windfall attributed to zoning actions. Neighbors outside of a rezoned area lack this opportunity. 

Mitigating the ability of existing property owners to capitalize on zoning changes would lessen the 

inequities currently inherent to zoning changes. 

By offsetting the lottery-like effects of upzones, a windfall capture scheme also dampens the poten-

tial for politically motivated zoning boundaries. Developers and influential property owners have 

less incentive to disrupt planning if the parcel-level decisions have lower financial stakes. Planning 

can focus on maximizing public benefit rather than engaging in disputes centered on private 

benefit concerns. Thus, recapturing windfalls would both diminish the inequitable fortunes caused 

by zoning and reduce the likelihood that private interests drive parcel-level planning decisions. 

The link between zoning and geospatial inequity related to housing affordability also justifies har-

nessing zoning as tool for the redistribution of land values. In many parts of the country, the price 

of housing generally reflects the price of construction; however, in particular high-cost cities, the 

traditional land-value and construction-cost model poorly predicts the price of housing (Glaeser 

and Gyourko, 2002). Instead, evidence suggests that zoning and land use restrictions prompt 

higher housing prices (Glaeser and Gyourko, 2002). Although zoning provides a variety of public 

benefits related to the strategic growth of a city, its link to increased housing costs and geospatial 

inequity validate distributing the financial gains from zoning to those adversely affected by zoning 

and land use restrictions. Other city planning strategies can mitigate these negative externalities, 

but we advocate harnessing the windfalls through a value capture mechanism and investing them 

in equity-focused strategies. 

A Brief Case of Windfalls in Practice 

To illustrate the phenomenon of windfalls, we examine the market value changes of four single-

family homes within or adjacent to the Roosevelt Upzone in Seattle, Washington. In 2012, the city 

of Seattle increased density and height allotments in several zones surrounding a future light rail 

station in the Roosevelt neighborhood. Large public investments like light rail generally increase 

property values, but examining the vastly different resale values of a few similar properties illumi-

nates how zoning affects values independently from public investments. 

This case presents four properties that, due to their proximity, should benefit similarly from the 

public and private investments in the area, including the forthcoming major infrastructure im-

provement to the neighborhood. All four homes are in the same neighborhood; however, homes A 

and B were not rezoned for higher allowable building uses, whereas homes C and D were. Exhibits 1 

and 2 illustrate the increase in market value and percentage increase in market value, respectively, 

before and after the Roosevelt Upzone. The median sale prices of homes in the Roosevelt area 

increased at an average rate of 6.84 percent per year during the past 5 years.1 

1 http://www.trulia.com/real_estate/Roosevelt-Seattle/6869/market-trends/.

Cityscape 13

Fyall and Casey

Exhibit 1

Pre- and Post-2012 Roosevelt Upzone Market Value 

$5,000,000  

$4,000,000  

$3,000,000  

$2,000,000  

$1,000,000  

$0  

Home A 

Home B 

Home C 

Home D 

Pre 

Post 

Source: King County Assessor’s Office, parcels 922140-0695 (A), 922140-0555 (B), 952810-2875 (C), and 952810-2640 (D) Exhibit 2

Market Value as a Percentage of Pre-Upzone Sale Price (2012 Roosevelt Upzone)

1,200% 

1,000% 

800% 

600% 

400% 

200% 

0% 

2004 

2006 

2008 

2010 

2012 

2014 

2016 

2018 

Home A 

Home B 

Home C 

Home D 

Source: King County Assessor’s Office, parcels 922140-0695 (A), 922140-0555 (B), 952810-2875 (C), and 952810-2640 (D) Home A has two bedrooms, one bathroom, and 1,020 square feet. It was built in 1919 and is in 

fair condition.2 Home A sits on a 2,400-square-foot lot that remains zoned for single-family homes 

and is three blocks from the heart of the Roosevelt Upzone. Home A sold for $332,000 in 2006 

and resold for $525,000 in 2015. This represents a 58-percent market value increase over 9 years. 

This increase breaks down to $21,444 per year, or 6.5 percent per year of the 2006 value equally 

distributed over 9 years, similar to the overall neighborhood average. 

2 Parcel records and condition quality designations are according to the King County Assessor’s Office.  

14 Habitat III

Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool

Home B sits one block east of home A in the same residential neighborhood surrounding the 

Roosevelt neighborhood commercial development. Home B is a three-bedroom, two-bathroom, 

1,750-square-foot single-family home built in 1920. Home B is also in fair condition and, 

like home A, its zoning did not change during the 2012 rezone. Unlike home A, home B’s 

3,807-square-foot lot is currently zoned for lowrise development, although the only existing struc-

ture is a single-family home. Home B sold for $550,000 in 2007 and resold for $673,000 in 2015. 

This represents a market value increase of 22 percent 8 eight years. This increase breaks down to 

$15,375, or 2.8 percent, per year of the 2007 value equally distributed over 8 years. 

Because no single-family zones were upzoned during the 2012 Roosevelt Upzone, the closest 

comparisons are single-family homes built in lowrise zones, like home B, that sold after their lots 

transitioned from lowrise to midrise zoning. Home C spent the better part of the past two decades 

as a single-family home on a 4,120-square-foot lot zoned for lowrise development. During the 

2012 rezone, home C’s zoning designation changed to midrise. Home C is a 960-square-foot, two-

bedroom, one-bathroom single-family home built in 1919. In 2010, home C sold for $388,500. 

In 2015, after the rezone, home C sold for $815,900, which represents an increase of 110 percent 

over 5 years. The increase breaks down to $85,480, or 22 percent, per year of the 2010 value 

equally distributed over 5 years. Home C sold again in August 2016 for $4.133 million, with no 

property improvements since the previous sale. Comparing this second sale to the 2010 sale price 

represents an increase of 964 percent over 6 years. This increase breaks down to $624,083, or 161 

percent, per year of the 2010 value equally distributed over 6 years.

Home D experienced a similar transition as home C and previously existed as a single-family home 

on a 4,634-square-foot lot zoned for lowrise. The structure, built in 1904, featured two bedrooms, 

two bathrooms, and 1,410 square feet of finished space. During the 2012 Roosevelt Upzone,  

home D’s zoning changed to midrise. Home D was demolished 2 months after its most recent sale. 

The demolition illustrates the divergence of land values from the value of property improvements 

after land suddenly inherits additional development potential. Home D best depicts this land value 

increase after an upzone, because it sold for a highly inflated price almost immediately before 

being torn down. Home D sold for $377,500 in 2006 and, following the rezone, sold for $1.946 

million in 2014. This price represents an increase of 415 percent over 8 years, which breaks down 

to $196,000, or 52 percent, per year of the 2006 value equally distributed over 8 years. On the 

former site of home D, construction began on a midrise apartment building 2 weeks after demoli-

tion of the single-family home. 

A comparison of market values for the properties outside of the rezone (homes A and B) with the 

values of those within the rezone (homes C and D) illustrates divergent value trends. Whereas this 

brief case cannot account for an isolated monetization of zoning changes in this neighborhood, the 

examples of a few single-family homes that were upzoned to midrise and subsequently sold dem-

onstrate the significant discrepancies in land value increases compared with neighboring homes 

outside of the upzone. Rather than allow for zoning to drive such disparate trends, recapturing and 

redistributing the zoning-induced windfall would harness that value for the public good. 

Cityscape 15

Fyall and Casey

Existing Mechanisms for Land Value Capture 

Land value recapture is already a well-established policy tool. Municipal financing tools such as tax 

increment financing, local improvement districts, and impact fees exploit the presumed value en-

gendered by public investment in order to help pay for the improvement. Existing policy tools take 

advantage of the positive financial forecast attributed to the public benefit infrastructure project. 

Through tax increment financing, governments finance infrastructure improvements by earmarking 

property tax revenues from the area that the improvement is likely to benefit (Dye and Merriman, 

2006). Local improvement districts enable benefiting properties to finance capital improvements 

through bond issues by forming special assessment districts and paying the debt obligations for the 

projects that benefit them over time through assessments on their property (MRSC, 2009). Some 

local governments require impact fees from new developments to provide public capital facilities 

for that development.

Local governments throughout the country are increasingly using impact fees to shift 

more of the costs of financing public facilities from the general taxpayer to the benefi-

ciaries of those new facilities. As a general matter, impact fees are capitalized into land 

values, and thus represent an exaction on the incremental value of the land attributable to 

the higher and better use made possible by the new public facilities. (APA, 1997)

Our policy proposal relies on a sale of development permission system as an administratively 

feasible and effective structure to frame UDR for equity reinvestment. A sale of development 

permission system is predicated on government controlling the right to develop and granting 

such permission in exchange for contributions toward public goods—in this case, purchase of the 

right to build on newly awarded allotments within a particular zone. Under a sale of development 

permission system, a government can recapture unearned windfalls as a return to the community 

that created the value (Hagman and Misczynski, 1978). 

The experience of São Paulo, Brazil, provides a clear example. During a budget crisis in the 1990s, 

the São Paulo municipality began granting the right to exceed building height restrictions within 

certain zones in exchange for “equivalent value in monetary resources, land, or public works” 

(Froes and Rebelo, 2006: 1). The funds generated through the sale of additional occupancy allot-

ments by the São Paulo municipality helped finance part of the São Paulo Metro Line 4 project 

(Froes and Rebelo, 2006). São Paulo’s regulations do not require density fee payments from 

developers “for buildings that fall within the normal limitations on floor space.” However, in speci-

fied high-development zones, developers must pay for additional floor space above normal density 

(Peterson, 2009: 76). 

Under São Paulo’s system, once the total stock of new development potential was tallied, the 

system allows for the “transfer of individual lots … through property certificates … which are not 

linked to any specific lot” (Froes and Rebelo, 2006: 5). The prices of these permissions are set on 

an area-by-area basis by the municipality (Froes and Rebelo, 2006). The funds generated from this 

system are dedicated to financing the urban investments outlined in the law that created the sale 

of development permission system (Peterson, 2009). An additional benefit to the development 

permission systems is the speed at which government funds can be materialized. 

16 Habitat III

Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool

This form of syndication of additional construction area allows the anticipation of 

resources for the implementation of public works, which are going to enhance the ap-

preciation of the value of the areas which they serve. The sale of potential construction 

through auction by the Stock exchange also allows for a faster process and gets significant 

resources in volume in a shorter period of time. (Froes and Rebelo, 2006: 5) 

A similar policy in the state of Maharashtra, India, allows developers to buy additional floor space 

beyond the maximum allowable amount under current policy within two specific districts. Ac-

cording to Peterson (2009: 77), for “upper-income housing, the cost per square foot of additional 

building area will be set at 80 percent of the price per square foot of land in the assessment zone.” 

Systems based on transfer of development rights (TDR) operate similarly, although they are 

founded on different principles. TDR systems allow low-density property owners to monetize their 

forgone development potential by selling the potential to enable extra density in a high-density 

area. TDR systems can incentivize the preservation of open space, historic buildings, and rural ar-

eas, but they do not account for the negative externalities of high-density spaces on disadvantaged 

populations nor the inequitable process that designates some property owners as TDR-receiving 

sites whereas others lack that opportunity. In short, TDR systems help preserve some public 

benefits, but the wealth created through density increases stays in private hands. 

Upzone Development Rights: A Market-Based System 

Although windfall capture mechanisms exist, they often finance infrastructure investments more 

directly beneficial to those paying for development permissions, thus resembling the theoretical 

framework of a special purpose assessment. Using these revenues to redistribute zoning-induced 

wealth to achieve intracity equity creates an additional degree of separation between payer and 

beneficiary. 

The aforementioned examples of sale of development permission systems in Brazil and India rely 

on unilateral pricing, whereby local governments set the prices for development permissions. 

However, unilateral price controls risk stifling or significantly altering the development landscape 

if pricing schemes do not reinforce market preferences. Alternatively, a market-oriented pricing 

system for development rights would maximize the efficiency of the value capture and minimize 

the disincentives for development (Hagman and Misczynski, 1978). 

A market-based approach could involve blind bidding, public auction, or a negotiation system for 

development permissions, thus enabling a city to recapture the maximum windfall while protecting 

incentives to develop. 

Under this market-based proposal, ownership of development rights for achieving new zoning 

maximums could be purchased divorced from ownership of the land, which could trigger a 

secondary negotiation between the landowner and the holder of newly acquired development 

permission (Hagman and Misczynski, 1978). Market-based TDR systems and private purchases of 

air rights provide precedents for enabling market mechanisms to determine a monetary value for 

development rights unique to each transaction. 

Cityscape 17

Fyall and Casey

Compared with a government pricing system, a market-based system minimizes the risk that a 

development permission price is set too low and substantial windfalls are not captured, or that 

the price is set too high and development is stifled in areas prioritized for denser development. 

However, a few pitfalls to this system persist. A bidding or auction system may lead to a slowdown 

of development if owners of development rights do not utilize them or are unable to negotiate suc-

cessfully with landowners, although this risk mirrors dynamics in the existing real estate market. 

Existing city permitting structures could incorporate a market-based UDR system. Although politi-

cal hurdles are inevitable with any large-scale policy change, especially one with redistributive in-

tentions, the untethering of the value of increased development potential from property acquisition 

costs can be streamlined. Once zoning authorities adopt upzones, construction-permitting agencies 

could create permits specifically for achieving new zoning potential, which the market could 

then monetize for density-increasing redevelopment. With marketable development permissions 

a new requirement, the value of the permissions, which the developers pay the city to acquire, 

should lead to a corresponding decrease in the price the developer offers to the seller if we assume 

developers have a maximum of land acquisition costs based on a project’s earning potential. At this 

point, the buyer can make an offer to the seller and negotiate a price for the home. 

Zoning changes that allow for additional density, height, or uses of new developments in an area 

will trigger the creation of UDR. Each parcel within the upzone area is bestowed affiliated UDR; 

however, consistent with the aforementioned proposals for market-based pricing systems, the 

UDR for each parcel is marketed as a separate, severable entity. A negotiation system between the 

government and one or multiple bidders will determine the UDR value from that upzone, thus 

reflecting the market value for increasing density on a particular lot. Through processes outlined 

within adopted city policy, developers must purchase from the city the UDR of each parcel on 

which it wishes to build to the newest zoning potential. The city will transfer all revenue from 

the sale of UDR, net administrative costs, to the designated agencies, programs, or redistributive 

mechanisms charged with improving urban equity. UDRs would capture value increases before 

transactions with, or redevelopment by, property owners; therefore, this system would enable most 

of the capital previously granted to homeowners to provide funding for redistributive purposes 

administered by the local government. 

Conclusion

The Habitat III United Nations conference sought innovative approaches to urban sustainability, 

prioritizing the role of equity in development. A UDR system responds to this challenge by radi-

cally altering land transactions in urbanizing areas. By harnessing the financial windfalls created 

through public land use decisions, a UDR system can simultaneously tame the lottery-like reper-

cussions of current zoning practices and fund investments in more equitable cities. Although the 

political barriers for transferring private gain into redistributive policies are likely to be high, this 

policy innovation that embeds equity into the urbanizing process deserves consideration.

18 Habitat III

Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool

Acknowledgments

The authors acknowledge the efforts of the students enrolled in the Evans School of Public Policy 

& Governance’s “Housing and Social Policy” course at the University of Washington in the winter 

quarter of 2016.

Authors

Rachel Fyall is an assistant professor at the University of Washington, Evans School of Public Policy 

& Governance. 

Alexander Casey is a policy analyst with Zillow.

References 

American Planning Association (APA). 1997. “APA Policy Guide on Impact Fees.” https://www.

planning.org/policy/guides/adopted/impactfees.htm.

Dye, Richard F., and David F. Merriman. 2006. “Tax Increment Financing: A Tool for Local Eco-

nomic Development,”  Journal of Housing and Community Development 63 (3): 22–24, 26–29.

Froes, Marilda, and Jorge M. Rebelo. 2006.  Urban Operations and the São Paulo Metro Line 4. Wash-

ington, DC: World Bank Group.

Glaeser, Edward L., and Joseph Gyourko. 2002. The Impact of Zoning on Housing Affordability. 

NBER Working Paper No. 8835. Cambridge, MA: National Bureau of Economic Research.

Hagman, Donald G., and Dean J. Misczynski, eds. 1978.  Windfalls for Wipeouts: Land Value Capture 

and Compensation. Chicago: American Society of Planning Officials.

Municipal Research and Services Center (MRSC). 2009.  Local and Road Improvement Districts 

Manual for Washington State, 6th ed. Report Number 59, Revised. Seattle: Municipal Research and 

Services Center. 

Peterson, George E. 2009.  Unlocking Land Values To Finance Urban Infrastructure. Vol. 7. Washing-

ton, DC: The International Bank for Reconstruction and Development; The World Bank.

United Nations (UN). 2016.  New Urban Agenda. Quito, Ecuador: United Nations Conference on 

Housing and Sustainable Urban Development (Habitat III).

Cityscape 19

20 Habitat III

Boundary Issues: The 2016 Atlas 

of Urban Expansion Indicates 

Global Dedensification

John Wihbey   

Northeastern University

This article was originally published in the October 2016 issue of  Land Lines , the quarterly magazine of the Lincoln Institute of Land Policy.

Abstract

Cities around the world are consuming land at a rate that exceeds population growth, 

according to the 2016  Atlas of Urban Expansion —a precise analysis of 200 global 

urban growth boundaries drawn from satellite images, population figures, and other 

data. Produced through a partnership among the United Nations Programme for Human 

Settlements, the New York University Urban Expansion Program, and the Lincoln Insti-

tute of Land Policy, the study parses the drivers and effects of sprawl and creates the basis 

for a science of cities. This article examines some of the critical findings of the  Atlas of 

Urban Expansion , such as a general trend of falling density across the world’s cities, as 

well as the identification of a variety of aerial visual signatures of unplanned settlements. 

Future data collection challenges and implications for land use policy are discussed. 

Introduction

When city growth comes up in public discourse, the conversation nearly invariably focuses on 

population. We speak of booming cities that have grown from, for example, 2 to 5 million in 

population in only a few decades or declining cities that are hollowing out and losing residents at a 

rapid rate.

The common unit of understanding and measurement, in other words, is nearly always the 

number of people. Measures of land use are often missing from the picture, despite the fact that 

cities grew much more in land use than in population between 1990 and 2015, according to data 

(UN-Habitat, 2016a) from the United Nations Programme for Human Settlements (UN-Habitat). 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 21

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

Wihbey

In developed countries, urban population grew 12 percent while urban land use increased 80 

percent. In developing countries, population expanded 100 percent and urban land use rose 350 

percent.

Land use issues will become more critical as the world population exceeds 9 billion and 2.5 billion 

persons migrate to cities by 2050, according to United Nations (UN) projections. Configuring 

urban areas and their available resources to support this massive inflow will be crucial to sustaining 

human life on the planet (Buhaug and Urdal, 2013; Angel, 2012). 

How exactly are these rising urban populations changing global maps? Further, can we observe 

regular, even predictable, patterns? Are these trend lines, such as they are, sustainable over time?

To date, little scientific understanding exists of broad global patterns related to how city borders, 

systems, and land use patterns are changing. The online  Atlas of Urban Expansion (hereafter, the  Atlas) aims to fill this crucial gap in knowledge (Angel et al., 2016). Produced through a partnership 

among UN-Habitat, the New York University Urban Expansion Program, and the Lincoln Institute 

of Land Policy, the new  Atlas performs precise analysis of satellite imagery, population figures, and other data to study the changing nature of cities from 1990 to the present. The full report and data 

were unveiled in October 2016 at the Habitat III global cities summit in Quito, Ecuador, as part of 

the implementation of the UN’s  New Urban Agenda.

The new  Atlas analyzes 200 cities (up from 120 in the 2012 sample), selected from among the 

4,231 cities in the world with populations greater than 100,000 (as of 2010), which constitute a 

stratified sample of large urban areas. The 200 cities in question contain about 70 percent of the 

world’s urban population (Angel et al., 2016). 

The UN statistics division has now accepted and adopted this UN Sample of Cities as a way to 

conduct ongoing analysis of urbanization trends (UN-Habitat, 2016b). “Cities, how they form, and 

the effects of urbanization on the quality of human life must now be treated as a science. The un-

precedented confluence of climate change, population boom, and the rush to live in cities means 

that our critical human development will take place in cities” (UN News Centre, 2016). 

With unplanned settlement fluidly redefining many urban boundaries, experts and planners say 

that producing a consistent method for studying cities as contiguous spatial units, not only ad-

ministrative jurisdictions, is crucial (Angel, 2016). The UN Sample of Cities also enables transition 

from an urban agenda based on country-level data to one predicated on city-based data collection 

and analysis. 

Studying such a sample enables us to infer some generalizable rules about large urban areas. “The 

sample accurately represents that universe, so you can actually make statements about that universe 

given information about the sample. That’s the more scientific contribution of this  Atlas” (Angel, 2016). 

Land Consumption and Dedensification

One reliably observed pattern is that cities around the world are stretching out physically and con-

suming land at a rate that exceeds population growth. This tendency corroborates the findings of the 

first-edition  Atlas, which indicates falling density (Angel et al., 2016). In the past, falling density was 22 Habitat III

Boundary Issues: The 2016  Atlas of Urban Expansion  Indicates Global Dedensification

termed “sprawl,” and some refer to it now as “dedensification.” In any case, for a planet increasingly 

concerned with sustainability, energy efficiency, climate change, and resource scarcity, falling density 

is a troubling trend. Density generally allows for greener and more sustainable living patterns.

Qingdao, China, had among the fastest rates of population growth and outward urban expansion 

in the UN Sample of Cities. Population grew at an average annual rate of 7.2 percent per year 

between 1990 and 2013, rising from 853,000 to 4.5 million, and the built-up area associated Qin-

gdao’s urban extents grew even faster, at 11.6 percent per year. Qingdao’s expansion areas revealed 

mostly planned, orderly extensions. Exhibit 1 shows the city in 1990, 2000, and 2013. 

Exhibit 1

Urbanization in Qingdao, China, in 1990 (Top), 2000 (Middle), and 2013 (Bottom)

Note: The brown sections are built-up urban areas, red sections are built-up suburban areas, black sections are built-up rural areas, light green sections are urbanized open space, and green sections are rural open space.

Source: New York University, Urban Expansion Program

Cityscape 23

Wihbey

Angel (2016) noted a kind of rough statistical rule that emerges from the new  Atlas work—as 

populations double, land use triples. Many policymakers have been unable, or unwilling, to see 

this reality unfolding in recent decades. 

Chen (2016) observed that the issue of sustainable growth is “very uneven in terms of planning 

officials’ awareness.” In many countries, “various orthodoxies are battling it out,” and frequently 

the “cards are stacked against us” in terms of changing norms and official attitudes. “For many, 

many decades, and in some countries for centuries, there have been incentives [for] building on 

virgin land.” Even where political will for change exists, there are “multiple dimensions of capabil-

ity to build upward, such as in-ground infrastructure” (Chen, 2016). Wider complex systems must 

be coordinated from a policy perspective in order to achieve greater density and land conservation.

In any case, the data analysis effort undertaken in the  Atlas—which at root is intended to help 

define a new “science of cities”—may serve as a wakeup call. Angel said the  Atlas can be a “tool for convincing policy makers that the expansion they must prepare for is considerably larger than their 

own little back-of-the-envelope calculations, or what their planners have in their master plans” 

(Angel, 2016).

Increasing density again will necessitate sacrifice and modification of existing norms for living standards 

in many places. It will require people to live in smaller apartments and homes, in multifamily housing, 

and in higher buildings. It also will frequently require redevelopment of low-density areas in cities.

McCarthy commented that the data are “a little bit chilling,” as they reveal a pervasive pattern that 

signals substantial trouble ahead. “It’s something that we have to stop—whether we call it ‘sprawl’ 

or ‘de-densification’ or something else. We can’t continue to consume all of our best land with ur-

ban development. We still have to feed ourselves. We still need to collect water” (McCarthy, 2016).

McCarthy also noted many ill-fated attempts to build large housing units far outside denser urban 

areas, leaving millions of units across the world largely empty. These failed attempts have happened 

in many countries, from Mexico and Brazil to South Africa and China. “Why is it that we continue 

to build these developments in the middle of nowhere and expect people to live there?” McCarthy 

(2016) noted. The lesson is that it is vital to link jobs and industrial activity with housing.

More proactive planning clearly is required for growth across the world, according to  Atlas 

researchers. That means finding the right ways to channel city growth spatially and to create the 

infrastructure—transportation, water, sewer, and other necessities—so the new settlements and 

housing units are serviced appropriately. 

Moreover, it is also necessary,  Atlas researchers say, for many of the big cities around the world—

from Lagos, Nigeria, to Mexico City, to Zhengzhou, China—to adopt more next-generation think-

ing about so-called “polycentric” cities. That will require moving beyond the traditional paradigm 

of hulking, monocentric cities with a huge urban core and instead creating polycentric networked 

hubs, whereby a metropolitan area will have many interlinked urban centers.

24 Habitat III

Boundary Issues: The 2016  Atlas of Urban Expansion  Indicates Global Dedensification

Signatures of Unplanned Settlement

The satellite imagery analyzed in the  Atlas also highlights other key patterns that are both drivers and symbols of the overall dedensification trend worldwide.

One granular mark is a lack of four-way intersections, a clear sign that roads are being laid out 

haphazardly. Such informality and unplanned development have been increasing over time across 

the world. The pattern, however, is strongly correlated with lower gross domestic product, or GDP, 

per capita, and therefore is more pronounced in the developing world and global south. Linked to 

this observed pattern is an increase in urban block size, as shantytowns and unplanned settlements 

of many kinds grow without regard to transportation needs.

Exhibit 2 shows clear qualitative differences in the layouts of streets and blocks in the expansion 

areas of Kozhikode, India (top), which were mostly unplanned, and Qingdao (bottom), which 

were mostly planned. From 1990 to 2014, 28 percent of Qingdao’s built-up area was in roads 

and streets compared with 8 percent in Kozhikode; four-way intersection and average block sizes 

were 51 square kilometers and 4.7 hectares, respectively, in Qingdao compared with 10 square 

kilometers and 7.5 hectares, respectively, in Kozhikode. The amount of residential development in 

formal land subdivisions or housing projects accounted for 76 percent of Qingdao’s expansion area 

compared with 1 percent of Kozhikode’s.

Exhibit 2

Layout of Expansion Areas in Kozhikode, India (Top), and Qingdao, China (Bottom)

Source: Bing

Cityscape 25

Wihbey

The  Atlas also suggests a pervasive lack of orderly connections to arterial roads, which are key to 

facilitating transportation to employment and economic networks. Built-up areas within walking 

distance of wide arterial roads are less frequent than they were in the 1990s, according to data from 

that decade (Angel, 2012). More generally, there is simply not enough land being allocated for 

roads.

In many unplanned, rapidly growing cities such as Kozhikode, poor road network connectivity 

and the low share of land in streets compromises mobility and poses serious economic challenges 

for residents (exhibit 3). Obtaining city-level estimates for urban layout metrics required analyzing 

dozens of quasi-randomly located 10-hectare study areas, or locales, distributed throughout a city’s 

entire expansion area. 

In addition, low-density tracts and small dwellings are unnecessarily consuming precious urban 

open space—parks and green spaces that can make dense urban areas more livable. 

Angel (2016) noted that planners need to get ahead of the coming wave of urban migration and 

secure land for transportation, affordable housing, arterial roads, and open space. That kind of 

planning needs to happen before settlement, after which land prices soar and the logistics of mov-

ing populations become trickier. “This can be done at a relatively small cost,” said Angel (2016), 

who suggested that planners begin to “make some minimal preparations for it.” 

Even in countries where a high degree of central planning occurs, the data contained in the  Atlas 

may prove helpful for diverse land management challenges. According to Liu (2016), “Compared 

to most cities in the developing world, Chinese cities are better managed. The  Atlas is still useful for China, as it provides accurate, visual urban expansion data and analytics to planners that could 

strengthen their understanding of the scale and patterns of urban expansion in their cities.” 

Exhibit 3

Digitization of Road Features in Kozhikode, India (Left), and Qingdao, China (Right)

Source: Google

26 Habitat III

Boundary Issues: The 2016  Atlas of Urban Expansion  Indicates Global Dedensification The Atlas Data Challenge

The data collection and analysis that went into producing the  Atlas highlight future challenges 

for urban theory and monitoring of global cities, especially in developing nations. Blei (2016) ac-

knowledged that assembling the 200 cities for the stratified sample was difficult, as no universally 

accepted definition exists for a metropolitan area. Researchers had to account for variables such as 

regional location, growth rate, and population size in order to ensure the sample was valid, and 

they had to create a careful and defensible methodology.

The National Aeronautics and Space Administration (NASA) Landsat database, a satellite imagery 

program running since the 1970s, was the basis for the spatial analysis (NASA, 2017) Although 

that methodical, scientific data set is of exceedingly high quality, the underlying population data, 

which were key for establishing migration- and settlement-related patterns, were frequently less 

than perfect. 

“Some countries have very well-established data programs,” Blei (2016) noted. In other cases, 

however, the data are very coarse, and large cities, particularly in the developing world, have only 

broad census zones. It is therefore difficult, at times, to make fine-grained insights about popula-

tion changes in connection with land use shifts, because the researchers had to assume equal 

density over large tracts of the metropolitan area in question.

Scanning the NASA pictures, the researchers analyzed pixels to identify impervious coverage 

surface or soils. They performed this task with powerful software according to well-established 

methods, but correlating it with population data was not always smooth. “Unfortunately, there’s 

not very much we can do if the data are not very good, but we did the best we could under the 

circumstances,” Blei (2016) noted.

Evidence suggests the need for less variation in population data collection and synthesis across 

countries to derive more actionable insights for policymakers in every country. More global 

consensus is needed around the definition of cities. The U.S. Census Bureau (Wilson et al., 2012) 

defines them very precisely as “urbanized areas,” or “metropolitan statistical areas,” but they are 

frequently defined in more scattered ways by other countries’ data collection agencies. Asia and 

Africa—home of many of the fastest-growing cities, both in terms of population and geographic  

extent—suffer from a lack of granular city population data that speak to neighborhood-level change.

Global Nuances and Uncertain Futures

The  Atlas contributes to a long debate in policy and academic circles about how to measure sprawl, 

both high and low density, and the best models for addressing related issues. The  Atlas also speaks 

to a long research literature on the consumption of resources and quality of life in urban contexts. 

Silva (2016) said that the  Atlas research will continue to help advance understanding of govern-

ment planning and rulemaking and of residential pricing. The 2016  Atlas project includes surveys 

conducted with various stakeholders in cities that might yield insights on planning policies and 

markets, among other issues.

Cityscape 27

Wihbey

Silva (2016) noted, “it’s definitely an effort that is needed. It’s a first-mover type of project. The 

measure of success will be the extent to which other researchers, whether through critique or sup-

port of the initial idea, can improve upon it and contribute to our understanding of how cities are 

growing, or even contracting.”

It will also help ground-level understanding for those studying or making policy in particular 

cities. Silva (2016) pointed to Buenos Aires, Argentina, which he called a “classic case” where the 

expansion of territory is occurring faster than the population growth—and where many people are 

being displaced outward from the denser city core. Silva said that research by Goytia et al. (2015) 

has shown how lax land use regulation affects settlement patterns. Land markets and their regula-

tions affect affordability, and these dynamics can result in unplanned settlements.

Kudva (2016) praised the “very careful work” performed in the  Atlas effort. She worries, however, 

that smaller cities—those with populations of less than 100,000 and therefore excluded from the 

analysis—may see different dynamics that are subject to more variable patterns and experiences. 

In trying to create “one science of cities,” she noted, planners and policymakers may miss 

significant differences between small and big metropolitan areas, limiting the capacity to imagine 

creative interventions. “The difference between small and big can be the ability to influence politi-

cal processes, the ability to garner funds, to organize, to intervene. For a person like me who is 

interested in smaller places, things like the  Atlas provide important suggestions, important points 

of reference, important counterpoints, but they are not always useful” (Kudva, 2016).

Kudva also noted that it is possible large-scale, emerging changes related to energy systems, global 

warming, sea-level rise, and political upheaval may alter worldwide land use patterns compared 

with those observed in the past. The issue of falling density is potentially reversible, she said. “That 

trend could change. We need to play a more interventionist role” (Kudva, 2016).

Nonetheless, better data and a more detailed picture of settlement patterns could help substantially 

address challenges common to cities of many different sizes. Chen (2016) noted that research like 

the  Atlas is necessary to combat issues such as unequal access to opportunity. “We need baseline 

data, and we need to understand the relationship between how we use land and other things.” 

The issue of global inequality, which McCarthy (2016) called the biggest “unassailable challenge” 

of cities, looms in all the data. Beyond the layers of the global maps in the  Atlas are facts and 

dilemmas that researchers and policymakers are only beginning to understand and address. “The 

biggest one is the absolute concentration of poverty and geographic isolation of large segments of 

the population,” McCarthy (2016) observed, noting that sometimes 30 to 50 percent of residents 

in many large cities live in “deplorable conditions.” 

Decent affordable housing that is meaningfully integrated into the economic network and flow of 

cities has to be a priority. Many national efforts to date have failed to achieve that goal, however. 

“That’s the thing that I find most vexing,” McCarthy (2016) said. 

That issue—and many others affecting the world’s fast-growing cities—is framed more precisely 

and powerfully by the new, comprehensive data.

28 Habitat III

Boundary Issues: The 2016  Atlas of Urban Expansion  Indicates Global Dedensification Acknowledgments

The author thanks Maureen Clarke of the Lincoln Institute of Land Policy.

Author

John Wihbey is an assistant professor of journalism and new media at Northeastern University.

References

Angel, Shlomo. 2016. Personal communication (interview). Professor and senior research scholar, 

New York University.

———. 2012.  Planet of Cities. Lincoln Institute for Land Policy. http://www.lincolninst.edu/ 

publications/books/planet-cities.

Angel, Shlomo, Alejandro M. Blei, Jason Parent, Patrick Lamson-Hall, and Nicolás Galarza Sán-

chez. 2016.  Atlas of Urban Expansion, Vol. 1,  Areas and Densities. New York: New York University Urban Expansion Program; United Nations Programme for Human Settlements; Lincoln Institute 

of Land Policy.

Blei, Alejandro. 2016. Personal communication (interview). Research scholar, New York University, 

Marron Institute.

Buhauga, Halvard, and Henrik Urdal. 2013. “An Urbanization Bomb? Population Growth and 

Social Disorder in Cities,”  Global Environmental Change 23 (1): 1–10.

Chen, Don. 2016. Personal communication (interview). Director of Equitable Development, Ford 

Foundation.

Goytia, Cynthia, Guadalupe Dorna, Jonathan Cohen, and Ricardo Pasquini. 2015. An Empirical 

Analysis of Land Use Regulation Determinants. Working paper. Cambridge, MA: Lincoln Institute 

of Land Policy.

Kudva, Neema. 2016. Personal communication (interview). Associate professor, Cornell University. 

Liu, Zhi. 2016. Personal communication (interview). Director of Program on People’s Republic of 

China, Lincoln Institute of Land Policy. 

McCarthy, George W. 2016. Personal communication (interview). President and CEO, Lincoln 

Institute of Land Policy. 

National Aeronautics and Space Administration (NASA). 2017. “Landsat Science.” https://landsat.

gsfc.nasa.gov/data/.

Silva, Enrique R. 2016. Personal communication (interview). Senior research associate, Lincoln 

Institute of Land Policy. 

Cityscape 29

Wihbey

UN-Habitat. 2016a. “Global Urban Observatory (GUO).” https://unhabitat.org/urban-knowledge/

guo/. 

———. 2016b. “Global Urban Indicators Database.” https://unhabitat.org/publications-listing/

global-urban-indicators-database/.

UN News Centre. 2016. “UN Tool Will Map 'Science of Cities' as Rapid Urbanization Emerges 

as Force in Sustainable Development.” http://www.un.org/apps/news/story.asp?NewsID=54188#.

WSSNihNVhBc. 

Wilson, Steven G., David Plane, Paul J. Mackun, Thomas R. Fischetti, and Justyna Goworowska. 

2012. “Patterns of Metropolitan and Micropolitan Population Change: 2000 to 2010.” https://www.

census.gov/library/publications/2012/dec/c2010sr-01.html.

30 Habitat III

Missing ‘Middle Scenarios’:  

Uncovering Nuanced Conditions  

in Latin America’s Housing Crisis

Kira Intrator   

Massachusetts Institute of Technology

Kaustubh Shivdikar  

Veermata Jijabai Technological Institute

Abstract

This article proposes a novel approach to capturing housing deficiencies in rapidly 

urbanizing regions that is more suitable for data capture, policymaking, and redevelop-

ment. Housing deficit data need to be accurately captured and categorized to adequately 

act on them. As underscored in the  New Urban Agenda , urban policymakers need to 

be able to set accurate and realizable targets to address the housing crisis. Local govern-

ments require precise knowledge of ground realities to strategically allocate money and 

resources, target solutions, and avoid needless waste. Planners and architects require 

detailed data and plans to respond to complex conditions. Citizens and nongovernmen-

tal organizations should be informed of their communities’ needs to engage and col-

laborate in enhancing local urban service delivery. The goal of this article is threefold: 

(1) to explore a new method to rapidly capture high-quality housing data in the region; 

(2) to discuss how these deficits and spatial patterns could be clustered into a new form 

of place-based deficiency typologies; and (3) to contribute to a more precise analysis of 

housing shortfalls for planners, policymakers, and local governments using the Latin 

American region as a case-study scenario. 

The proposed solution has the potential to be lower cost, more accurate, rapid, and scal-

able compared with currently applied techniques and technologies such as census data 

and satellite imagery. This research serves as a call to action and an exploration in the 

potential to leverage unmanned aircraft systems and a leading artificial intelligence ma-

chine learning algorithm applied to its output data to test, program, and study geospatial 

data for the purpose of capturing and categorizing rapidly changing qualitative housing 

typologies in Latin America.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 31

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

Intrator and Shivdikar

Introduction 

Latin America and the Caribbean are plagued by massive housing deficits. Currently, more than 59 

million individuals—one in three families—live in deficient housing that lacks basic infrastructure, 

is unsuitable to inhabit, or built with low-quality materials (Bouillon, 2012). This dilemma is 

compounded by the fact that Latin America is one of the fastest-growing and most-urbanized de-

veloping regions in the world (Bredenoord, van Lindert, and Smets, 2014). Formal housing has not 

been able to keep pace; illegal or informal settlements have remained one of the more common, 

and in some regions the primary, means of providing housing for low-income households. In addi-

tion, housing occupies the most sizeable surface area in cities. Although figures vary considerably, 

the principal land use in all cities is residential, occupying from 65 to 75 percent of the surface 

(Rodrigue, 2013). At the urban scale, tackling the housing challenge directly addresses the most 

predominant land use in cities. However, deficits in Latin America and the Caribbean are calcu-

lated and collapsed into a simplistic binary of qualitative and quantitative shortfalls. An absence 

of shared definitions, inconsistent methodologies, and outdated information compound this issue. 

Although directionally helpful, the binary definition of qualitative and quantitative shortfalls is 

unable to accurately capture complex urban realities. Binary definitions are static and do not help 

pinpoint acute needs. Moreover, binary definitions do not measure the magnitude of specific char-

acteristics of the housing crisis at a neighborhood, urban, or regional scale. Categorizing housing 

deficiencies through a binary lens helps produce binary responses. Nuanced and targeted solutions 

that address ground realities are likely neglected. 

Among the main pillars examined and problematized at the 2016 United Nations Conference on 

Housing and Sustainable Urban Development (Habitat III) were guidelines and improvements to 

national urban policy and housing policies (clustered under the key areas of urban frameworks, 

and urban housing and basic services respectfully). Habitat III also addressed the need for ac-

curate and timely data capture to ensure closer alignment between local needs and national urban 

planning frameworks. As stated in Habitat III’s  New Urban Agenda, articles 111 and 156 affirm the 

importance of capturing “differentiated analysis of housing supply … based on high-quality, timely, 

and reliable disaggregated data at the national, sub-national, and local levels” and encourage the 

use of digital tools such as “geospatial information systems … to improve long-term integrated 

urban and territorial planning and design” (United Nations, 2017).

Drivers for Change: Why Is a New Approach Needed? 

The current method used to define and analyze the housing deficit in Latin America is binary 

and, therefore, limited. The housing shortage in Latin America is neither a monolithic statistic nor 

binary in nature. Regional housing deficits are calculated and collapsed into a simplistic binary of 

qualitative and quantitative shortfalls. Qualitative shortfalls represent the inadequacy of housing 

units with respect to structural shortfalls, such as poor housing materials and quality; insufficient 

space; privation of basic infrastructure (for example, electricity, sanitary disposal of waste, and po-

table water); poor location; and insecure tenure. Quantitative deficiencies denote a lack of shelter, 

severe overcrowding, or a need for a completely new dwelling. Although directionally helpful, the 

binary definition is unable to accurately capture complex urban realities. The definitions are static 

32 Habitat III

Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s Housing Crisis and do not pinpoint acute needs, nor do they measure the magnitude of specific characteristics 

of the housing crisis at the neighborhood, urban, or regional scale. Examining and categorizing 

housing deficiency through a binary lens helps produce binary responses. Nuanced, targeted, and 

effective solutions that address the ground reality likely are overlooked or neglected. As under-

scored throughout the  New Urban Agenda, the development of robust and accurate urban policies 

is vital to providing the necessary framework and support to urban development, acting as a key 

instrument to capture and target current and future urban priorities and to coordinate and guide 

the actions of national and local actors, as well as private and public investments. 

Housing deficiencies do not exist in a binary bubble. The urban reality is much more complex, 

with a wide variety of conditions that constitute “middle scenarios.” This article argues that these 

missing middle scenarios—which cut across regional, urban, and neighborhood scales—need 

to be explored and exposed. Imagine an agglomeration of poorly ventilated slum housing in an 

urban center with weak roofs and collapsing walls surrounded by highrise buildings. Compare this 

scenario with decrepit lowrise social housing in a periurban region rebuilt with deteriorating walls 

and roofs and with little to no access to public transportation or schools. Although both display 

similar measures of qualitative shortfalls (for example, weak walls and roofs), these two scenarios 

have very different sets of conditions and solutions. An analysis of housing shortfalls more suitable 

for redevelopment is desperately needed. 

To generate accurate policies, urban plans, and architectural or landscape designs, middle scenario 

typologies are needed to reflect the complexity of the housing challenge and condition. Although 

a significant range of complex urban conditions exists, patterns of typologies likely will emerge. 

These patterns need to be captured, learned, and analyzed. This article explores a solution to this 

challenge; it proposes that a catalog of local, regional, and national housing deficiency typologies 

be developed through pattern-recognition and machine learning on the back end of an unmanned 

aerial vehicle (UAV). The goal of cataloging these deficits is to generate a new method of clustering 

typologies and a more nuanced analysis of the housing deficit. In sum, the proposed solution 

would help address the shortcoming of current measurements and provide a novel approach to 

collect and parse housing deficiency data.

Institutional Challenges

In Latin America, an absence of shared definitions and a prevalence of inconsistent methodologies 

and outdated information characterize the quantitative and qualitative shortfalls regarding housing 

data. Little agreement exists in the region as to what defines qualitative deficiencies and adequate 

shelter or as to the methodology of how to capture these shortfalls. Definitions vary by country and 

by researcher in the field of housing, from setting a minimum predefined livable space—wherein 

households or individuals living beneath this minimum requirement are classified as inadequate—

to assessing housing conditions by measuring levels of deprivation (Rojas, 2015).

There is a paucity of data. The Economic Commission for Latin America and the Caribbean 

completed the most recent Latin American regional assessment in 2000, primarily by drawing on 

census data from the 1990s (UN-Habitat, 2011). Census data largely exclude informal areas and 

settlements. Ad hoc or independent efforts have been launched to address this data gap by the 

Cityscape 33

Intrator and Shivdikar

Center for Social Research at the nonprofit TECHO or through an interim housing census launched 

in 2015 in Brazil and Mexico (to be carried out every decade).1 Nevertheless, there remains a lack 

of timely, high-quality data capture and analysis. Specifically, local and national data on informal 

settlements tend to be imprecise and fragmented (Fernandes, 2011). Moreover, despite the fact that 

numerous regional and international agencies attempt to provide reliable statistics and encourage 

standardization and updated methodologies to measure deficiencies, a desperate need remains for 

the production of standardized and periodic data.2 Census data are captured every decade. Region-

ally, the average amount of time dating to the most recent census is more than 6 1/2 years (see 

exhibit 1). Outdated, inaccurate, and binary information needs to be replaced, and the absence of 

shared definitions and inconsistent methodologies needs to be countered. 

Exhibit 1

Census Data Captured by Year, Latin America 

Notes: At the time of this research, Guatemala’s Instituto Nacional de Estadistca (INE) website was disabled and not open to the public. All countries that conduct the housing census collect qualitative and quantitative housing data. In 2015, Mexico and Brazil introduced an interim housing census that will be carried out every decade. Chile will create a new one-off census in 2017 because the data captured in 2012 did not adhere to the required data standards. 

Sources: Map by Kira Intrator and Ricardo Martínez Campos using data from Instituto Nacional de Estadística y Censos (INEC; Argentina); Instituto Nacional de Estadística de Bolivia (Bolivia); Instituto Brasileiro de Geografia e Estadística (Brazil); Instituto Nacional de Estadística (INE; Chile); Departamento Administrativo Nacional de Estadística (Colombia); INEC (Costa Rica); Oficina Nacional de Estadística (Dominican Republic); INEC (Ecuador); Dirección General de Estadística y Censos (El Salvador); INE (Guatemala); INE (Honduras); Instituto Nacional de Geografía, Estadística e Informática (Mexico); INEC (Nicaragua); INEC (Panama); Dirección General de Estadísticas, Encuestas y Censos (Paraguay); Instituto Nacional de Estadística e Informática (Peru); INE (Uruguay); INE (Venezuela) 

1 To counter the lack of data on informal settlements, the Center for Social Research conducted alternative census studies in Argentina, Chile, Colombia, and Nicaragua in 2015.

2 Examples of regional and international agencies seeking standardized definitions, updated housing indicators, methodologies, and regional coordination around indicators include the Regional Office on Latin America and the Caribbean, the Latin American and Caribbean Demographic Centre, and the Global Housing Policy Indicators. 

34 Habitat III

Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s Housing Crisis

General Scope and Scale of Challenge

The regional, urban, and neighborhood-scale impact of Latin America’s housing crisis is massive. 

Latin America’s housing deficit and the concomitant rise of central and peripheral informal housing 

settlements has heightened large-scale urbanism challenges. The dynamics that determine polycen-

tric or periurban expansion are complex—ranging from housing policies, sociospatial issues, a lack 

of serviceable land, and weak urban planning and infrastructure—a chief result of which is the 

unavailability of suitable, affordable housing.

Urban areas in Latin America suffer primarily from qualitative housing deficiencies. Of these quali-

tative deficiencies, the most pervasive is inadequate infrastructure, followed by a shortfall in the 

quality of the materials used for shelter. Basic infrastructure provision includes sanitary disposal 

of waste and access to electricity and potable water. Additional qualitative shortfalls include over-

crowding; poor-quality or impermanent materials; inadequate roof, wall, or floor conditions; and 

tenure. Rojas and Medellin (2011) corroborated that the region’s greatest unmet need is qualitative 

(exhibit 2). In addition, statistics published at a major continental ministerial meeting indicate 

that 60 percent of households have access to suitable housing in the region. Of the remaining 40 

percent, 22 percent live in dwellings that are structurally deficient and the final 18 percent need 

entirely new houses (MINURVI, 2007). According to more recent studies in the category of infra-

structure, the leading challenge is inadequate sanitation, affecting 13 percent of urban households 

(about 17 million households), and 6 percent (or 8 million households) lack piped water. Electric-

ity access appears to be inconsequential, with nearly universal coverage (Bouillon, 2012).

Exhibit 2

Urban Housing Deficiencies by Type, Latin America

35

30

25

20

15

cent of total households

Per

10

5

0

Quantitative

Infrastructure

Materials / Overcrowding

Tenure

Quality

1995

2009

Source: Adapted from Rojas and Medellin (2011)

Cityscape 35

Intrator and Shivdikar

Uncovering ‘Missing Middle’ Scenarios in Latin America’s 

Housing Crisis

A UAV, commonly known as a drone, is a pilotless aircraft. UAVs are a component of unmanned 

aircraft systems (UAS), which include a UAV, a ground-based controller, and a system of communi-

cations between the two. The flight of UAVs may operate with various degrees of autonomy, either 

remotely by a human operator or fully or intermittently autonomously by onboard computers 

(ICAO, 2011). UAS provide a cheaper and faster alternative to conventional mapping techniques. 

The advantage of using these systems is demonstrated in the speed and accuracy with which the 

provide geospatial data and their capacity to generate accurate data of existing formal or informal 

spatial developments. UAS are a logical means of collecting and parsing spatial patterns and data 

due to the fast pace of Latin America’s urban growth, the presence of undocumented informal 

housing settlements, and the fact that housing census data are collected once every decade. The 

proposed solution is for the UAS data to feed into a new form of image-based machine learning, 

developed from current urban conditions. Machine learning, using the long short-term memory 

(LSTM) algorithm, would help surface patterns and provide the primary input to the formulation 

of these missing middle scenarios and urban spatial typologies.

UAS counter the cost, complexity, and time of conventional mapping. Multilateral agencies, 

researchers, the private sector, and governments are testing the nascent field of UAS mapping. Dur-

ing the past year, for example, The World Bank used UAS to generate geospatial data for surveying 

land in countries with weak or outdated geospatial data infrastructure (World Bank, 2016). At 

a smaller scale, independent research initiatives experiment with UAS as a primary input for 

participatory mapping and to collect geospatial data for informal housing settlements in unmapped 

regions (Barnes, Volkmann, and Muller, 2015). Through seed funding from initiatives such as 

Drones for Good from the United Arab Emirates, private-sector startups use UAS to undertake 

a range of activities such as three-dimensional mapping, ocean navigation and surveillance, and 

emergency surveillance in remote geographies (UAE Drones for Good, 2017). 

In Latin America, governments primarily use UAS for activities related to intelligence, surveillance, 

and reconnaissance operations, with a few governments beginning to apply alternative purposes 

such as monitoring environmental crimes in the Brazilian Amazon rainforest and architectural 

digs in Peru (Cawley, 2014, 2013; Emol, 2013). However, these data collection approaches remain 

binary in nature and do not employ or combine artificial intelligence with UAS. The proposed 

approach and solution has yet to be to be applied to defining or addressing the housing crisis in 

developing regions.

The UAS could be used in a new way—to test, program, and study geospatial data for the purpose 

of capturing and categorizing qualitative housing typologies in Latin America. UAS have yet to be 

deployed in Latin America for the specific use of cataloging housing and their surrounding urban 

spatial characteristics. This idea responds to a critical need for urban and housing deficit data that 

are accurate and rapidly generated. The proposed solution could use and expand on current data 

libraries as a baseline input (for example, census data, local survey data, infrastructure provision, 

36 Habitat III

Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s Housing Crisis and so on) related to qualitative urban housing conditions. Moreover, the solution aims to capture 

the types of shortages compromising an individual home, as well as examine the larger urban 

fabric in which it resides.

The proposed concept explores a novel approach to the data mining process by using the latest 

LSTM model. Predictive models, such as deep neural networks, have the potential to generate 

a real-time heat map of city characteristics and housing deficiency needs. Many of the recurrent 

neural network algorithms, such as the LSTM model that contains prolific nodes, have made an 

impact in real-life applications. LSTM is extensively used and adapted through algorithms involv-

ing speech recognition and are applied in the field of robotics for vision analysis, scene labeling, 

and face recognition. For example, in their work of speech recognition, Sak, Senior, and Beaufays 

(2014) dissected acoustic frames that were analyzed using a phoneme database with the LSTM 

model. Unlike the simple feed-forward neural networks, LSTM models are tantamount to cyclic 

connection-based modeling sequences; through the use of LSTM, these sequences are converged 

quickly, and the data are processed in real time. The proposed model is unique because of the 

parameters that are input into the algorithm. Although a simple LSTM network, this model will 

be peculiar in that the data provided by the drone will be processed in real time using its array of 

various sensors (see exhibits 3 and 4).

Exhibit 3

Input and Output Samples for the Long Short-Term Memory Algorithm

Input 

   Process

Output

Camera feed

Infrastructure

water electricity

Thermal imaging

School

Cor

rela

Temperature data

tion

Transportation

LiDAR

Roof and walls

Census

Ventilation

LiDAR = Light Detection and Ranging.

Cityscape 37

Intrator and Shivdikar

Exhibit 4

Snapshot of Long Short-Term Memory Algorithm (or Reoccurring Neural Network) 

Functions

Input

Process

Output

Node

Camera feed

Infrastructure

Node

Node

water electricity

Node

Node

School

Node

Thermal 

Node

imaging

Transportation

Node

Node

Roof and walls

Node

Node

Node

Ventilation

Node

Node

Temperature 

data

Node

Node

LiDAR

Node

Node

Latin American

Node

Census

Census

LiDAR = Light Detection and Ranging.

Implementing the Model 

LSTM models are well known for finding both closely related and sparsely related dependencies. 

This important characteristic of LSTM models can be directly correlated to our problem statement. 

The short-term dependencies will develop logic nodes for medium-sized databases with immediate 

connection to the output—for example, connecting light detection and ranging (LIDAR) data 

to calculate the distance between objects and gather information on the number of floors of a 

building. In contrast, the long-term dependencies of the model will be capable of relating abstract 

concepts to one another.3 

The input to the proposed solution includes UAS equipped with camera feed, thermal imaging, 

and LIDAR sensors (exhibit 3). Existing census and temperature data would further be fed into the 

LSTM system as external data sources. The camera feed from the UAS is of higher quality, more 

detailed, less expensive, and more timely than census data collection and has the potential to be 

much more scalable than existing satellite or Landsat imagery. Thermal imaging enables estimates 

of how many people live in a given house or area, and LIDAR would provide basic input such 

as the topography of the region and the height of buildings (for example, LIDAR combined with 

thermal imaging data could be cross-referenced to calculate how many individuals live in a given 

dwelling and space).

3 For example, a long-term dependency could include connecting temperature data from a region with the availability of public transportation. Even though a heat map of a particular area may seem superfluous in identifying the availability of public transport, such data help determine the relationship by linking the heat map data to previous data samples. If a similar pattern of heat map variations with respect to time were found after the introduction of public transport in another region, the algorithm would conclude the presence of public transport in an area with similar heat-map-to-time data patterns.

38 Habitat III

Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s Housing Crisis A prototype of the LSTM model could be conducted in a small preselected urban agglomeration 

in which input parameters are collected and the LSTM model trained through data tagging of 

the region. In its maiden flight, the drone gathers data and streams it to a local database. The 

LSTM model would use this data to correlate and drive conclusions about the spatial patterns 

and clusters of housing deficiencies of that area. Previous census data would then be compared 

with these findings and the aberrations used to fine-tune the LSTM model. Once such a network 

is established, it would use the correlation (also known as learned neural network nodes) on the 

surrounding houses in the area (see exhibit 4). The UAS, when deployed, will transmit massive 

amounts of data to online servers for large-scale computations. An important feature of the LSTM 

model is the ability to process data in real time. The model is capable of processing gathered intel-

ligence and correlating it with previous data, thus providing output in real time. Real-time output 

makes the data processing faster. The algorithm would be deployed on rented servers, as the server 

environment would enable easy and, in most cases, automatic expansion of the algorithm through 

the self-replication of the code. This ability to extrapolate its computational capability would not 

only enable multiple drone deployments but would keep the downtime of the model to a mini-

mum, due to the fact that servers have multiple redundancies. Such a model of running algorithms 

on low-cost, dedicated rental servers is advantageous, as it would allow for building on the readily 

available high-tech infrastructure of premium server services such as Amazon Web Services and 

Google Cloud Platform. With respect to the financials of deploying the UAS, the initial hardware 

costs incurred for the drone, LIDAR sensors, and thermal imagers would be substantial. After this 

initial investment, longer-term recurring charges would be minimal and include only hardware 

maintenance and server infrastructure charges. Moreover, the pool of big data that this type of 

initiative would generate would be massively helpful to multiple sectors across industries. For 

example, the real estate industry would be able to locate regions requiring redevelopment, the food 

and supply industries would be able to better allocate resources, and governments would be able 

to map and create requisite policies to abate high levels of pollution.

To generate correlations and categorize spatial housing deficiency patterns, the algorithm receives 

data from the UAS. The algorithm will initially generate nodes, or so-called virtual decisionmakers, 

and start interconnecting the nodes based on the type of data collected in real time from the drone. 

This interconnection of nodes will enable users to conclude connections and map out high-quality 

housing deficiency patterns through the use of remotely related parameters. For example, UAS 

send data of a select region to the servers. The algorithm uses the primary data from the camera 

feed to process telltale signs that certain objects are present on roofs by comparing them with its 

database of objects. For example, the UAS discovers the existence of large ventilation shafts and 

air-conditioning units on a roof, and the algorithm concludes that the building is well-ventilated. 

Object recognition and image annotation will be carried out by matching object features, including 

speeded-up robust features (SURF) and Fast Retina Keypoint (FREAK) features. The SURF and 

FREAK features enable the LSTM algorithms to match corner points and edges of objects in the 

camera feed. This feature comparison makes it possible for the model to detect ventilation, the 

availability of water supply by detecting water pipes, and structural integrity of the buildings 

through the detection of crevices in buildings and discoloration of walls. Other data, such as the 

LIDAR sensor feeds, enable a topographical analysis of the region and a calculation of the height 

of given infrastructure and buildings. Another critical feature of the neural network is its ability 

Cityscape 39

Intrator and Shivdikar

to predict certain outputs in the absence of direct inputs; it extrapolates the data it has retrieved 

to predict parameters it has never been fed. It does so by extrapolating from indirect inputs. For 

example, if no image or sensor data of the interiors of houses exist to provide direct information 

regarding levels of overcrowding or the quality of floors or ceilings, it uses thermal images and 

LIDAR data to relate them to floor and ceiling conditions. Although the accuracy of the model 

reduces when relying on indirect parameters, it is an added advantage nevertheless.

A limitation of the proposed model is that the sensors used by the UAS are susceptible to weather 

changes. One way to tackle this issue is by adding or subtracting Delta errors to the sensor read-

ings. However, this adjustment would eliminate the error only if these weather-induced errors 

remained constant when data are fed to the network. In contrast, weather induced errors fluctuate 

considerably when covering a large area. This limitation would be addressed as the network grows; 

with a substantial amount of data, the error is reduced by taking the simple average of repetitive 

data. An additional implementation challenge to be addressed would be military areas and no-fly 

zones. To address this issue, the data channeled through the network could be preprocessed to 

avoid legal and privacy issues. Such an attempt can be made by selectively muting onboard sensors 

when a drone flies in those areas. Many Latin American countries have their own drone regula-

tions. Panama, for example, divides drones into four categories based on weight (AAC, 2016). 

Argentina requires drones to stay out of a 5-kilometer radius around airports (ANAC, 2015).

Conclusion

Housing deficit data need to be captured and categorized accurately for policymakers, urban plan-

ners, architects, citizens, and nongovernmental organizations to adequately act on it. This article 

outlined a novel approach to capturing and recategorizing missing middle scenarios of rapidly 

changing qualitative housing typologies in Latin America by leveraging UAVs and neural network 

machine learning. The proposed solution is more suitable for data capture, policymaking, and 

redevelopment in rapidly urbanizing regions and has the potential to be lower cost, more accurate, 

rapid, and scalable compared with currently applied techniques and technologies such as census 

data and satellite imagery.

Authors

Kira Intrator is the principal of the Civic Strategy Group and is a global partnership manager and 

urban planning researcher at the Massachusetts Institute of Technology, School of Architecture and 

Planning.

Kaustubh Shivdikar is an electrical engineer in the Department of Electrical Engineering at Veer-

mata Jijabai Technological Institute.

40 Habitat III

Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s Housing Crisis References

Administración Nacional de Aviación Civil (ANAC). 2015. “Proyecto Normativa Para Vehículos 

Aéreos No Tripulados (UAV).” http://www.anac.gov.ar/anac/web/index.php/1/1088/raacdnar-bajo-

proceso-de-elaboracion-participativa-de-normas-decreto-117203/-ltpre-gtproyecto-normativa-

para-vehiculos-ltbr-gtaereos-no-tripulados-uav-ltpre-gt.

Autoridad Aeronautica Civil (AAC). 2016. “Resolucion No. 120/DSA/DJ/DG/AAC.” http://www.

aeronautica.gob.pa/seguridad_aerea_nacional/index.php?c=seguridad_aerea_drone.

Barnes, Grenville, Walter Volkmann, and Anna Muller. 2015. “Drones in Support of Upgrading 

Informal Settlements: Potential Application in Namibia.” Paper presented at 2015 World Bank 

Conference on Land and Poverty, Washington, D.C., March 23–27. 

Bouillon, César, ed. 2012.  Room for Development: Housing Markets in Latin America and the Carib-

bean. New York: Palgrave Macmillan.

Bredenoord, Jan, Paul van Lindert, and Peer Smets. 2014. “Introduction: Governance, Sustain-

ability and Affordability of Low-Income Housing.” In  Affordable Housing in the Urban Global South: 

Seeking Sustainable Solutions, edited by Jan Bredenoord, Paul van Lindert, and Peer Smets. New 

York: Routledge: 1–14. 

Cawley, Marguerite. 2014. “Drone Use in Latin America: Dangers and Opportunities,”  InSight 

Crime: Investigation and Analysis of Organized Crime, April 18. http://www.insightcrime.org/news-

analysis/drone-use-in-latin-america-dangers-and-opportunities. 

———. 2013. “Rising Eco-Trafficking Threatens Brazil Amazon,”  InSight Crime: Investigation 

and Analysis of Organized Crime, November 15. http://www.insightcrime.org/news-briefs/eco-

trafficking-threatens-brazil-amazon.

Emol. 2013. “Peruanos Amplian Uso de ‘Drones’ en Arqueologia y Agricultura,”  Emol.com, August 

10. http://www.emol.com/noticias/internacional/2013/08/10/613929/drones-surcan-cielos-de-

peru-para-usos-en-arqueologia-y-agricultura.html?utm_source=twitterfeed&utm_medium=twitter.

Fernandes, Edesio. 2011.  Regularization of Informal Settlements in Latin America. Cambridge, MA: 

Lincoln Institute of Land Policy.

General Assembly of Ministries and High Level Authorities on Housing and Urbanization in Latin 

America and the Caribbean (MINURVI). 2007. “Declaration of Santiago de Chile.” Paper presented 

at MINURVI XVI, Santiago, Chile, October 8–9.

International Civil Aviation Organization (ICAO). 2011.  Unmanned Aircraft Systems (UAS), CIR328 

AN/190. Quebec, Canada: International Civil Aviation Organization. http://www.icao.int/Meetings/

UAS/Documents/Circular 328_en.pdf.

Rodrigue, Jean-Paul. 2013.  The Geography of Transport Systems, 3rd ed. New York: Routledge. 

Cityscape 41

Intrator and Shivdikar

Rojas, Eduardo. 2015. “Housing Policies, Quality of Housing and Urban Development: Lessons 

From the Latin American Experience 1960–2010.” Paper presented at 2015 World Bank Confer-

ence on Land and Poverty, Washington, D.C., March 23–27.

Rojas, Eduardo, and Nadine Medellín. 2011. Housing Policy Matters for the Poor: Housing 

Conditions in Latin America and the Caribbean, 1995–2006. Working paper. Washington, DC: 

Inter-American Development Bank.

Sak, Hasim, Andrew Senior, and Françoise Beaufays. 2014.  Long Short-Term Memory Based Recur-

rent Neural Network Architectures for Large Vocabulary Speech Recognition. Mountain View, CA: 

Google.

United Arab Emirates (UAE) Drones for Good. 2017. “The UAE Drones for Good Award.” https://

www.dronesforgood.ae/.

United Nations. 2017. “Resolution Adopted by the General Assembly on 23 December 2016,” 

71/25. New Urban Agenda. http://habitat3.org/wp-content/uploads/New-Urban-Agenda-GA-

Adopted-68th-Plenary-N1646655-E.pdf. 

United Nations Programme for Human Settlements (UN-Habitat). 2011. A ffordable Land and 

Housing in Latin America and the Caribbean. Nairobi, Kenya: United Nations Programme for Human 

Settlements.

World Bank. 2016. “Drones Offer Innovative Solution for Local Mapping.” http://www.worldbank.

org/en/news/feature/2016/01/07/drones-offer-innovative-solution-for-local-mapping. 

Additional Reading

Aguilar, Adrián, Peter Ward, and C.B. Smith. 2003. “Globalization, Regional Development, and 

Mega-City Expansion in Latin America: Analyzing Mexico City’s Peri-Urban Hinterland,”  Cities 20 

(1): 3–21. 

Angel, Shlomo. 2000.  Housing Policy Matters: A Global Analysis. New York: Oxford University Press.

Benítez, Griselda, Arturo Pérez-Vázquez, Martha Nava-Tablada, Miguel Equihua, and José Álvarez-

Palacios. 2012. “Urban Expansion and the Environmental Effects of Informal Settlements on the 

Outskirts of Xalapa City, Veracruz, Mexico,”  Environment and Urbanization 24 (1): 149–166. 

Da Gama Torres, Haroldo. 2008.  Social and Environmental Aspects of Peri-Urban Growth in Latin 

American Megacities. New York: United Nations Expert Group Meeting on Population Distribution, 

Urbanization, Internal Migration and Development.

Fay, Marianne, ed. 2005.  The Urban Poor in Latin America. Washington, DC: The International Bank 

for Reconstruction and Development; The World Bank. 

Girshick, Ross, Jeff Donaue, Trevor Darrell, and Jitendra Malik. 2013.  Rich Feature Hierarchies for 

Accurate Object Detection and Semantic Segmentation. Berkeley, CA: University of California. 

42 Habitat III

Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s Housing Crisis Harvey, David. 1973.  Social Justice and the City. Athens, GA: University of Georgia Press.

Karpathy, Andrej. 2015. “The Unreasonable Effectiveness of Recurrent Neural Networks,”  Andrej 

Karpathy blog, May 21. http://karpathy.github.io/2015/05/21/rnn-effectiveness/.

Mnih, Volodymyr, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu. 2014.  Recurrent Models of 

Visual Attention. London: DeepMind Technologies Limited. 

Olah, Christopher. 2015. “Understanding LSTM Networks,”  Colah’s blog, August 27. http://colah.

github.io/posts/2015-08-Understanding-LSTMs/.

Ruprah, I.J. 2009.  The Housing Gap in Latin America: 1995–2015. Washington, DC: Inter-American 

Development Bank. 

Secretaría de Desarrollo Social (SEDESOL). 2011. “Estado Actual de la Vivienda en México” Secretaría 

de Desarrollo Social, Gobierno de los Estados Unidos de México. México City, México: Secretaría de 

Desarrollo Social.

Shivdikar, Kaustubh. 2015. “Automatic Image Annotation Using a Hybrid Engine.” Paper pre-

sented at 2015 Annual IEEE India Conference (INDICON), New Delhi, India: December 17–20.

United Nations. 2010a.  World Economic and Social Survey 2010: Retooling Global Development. New 

York: United Nations.

———. 2010b.  Home at Last? The State of the Homeless in Today’s Cities. New York: United Nations, 

Department of Public Information and Non-Governmental Organizations.

Winchester, Lucy, and Raquel Szalachman. 2009.  The Urban Poor’s Vulnerability to the Impacts of 

Climate Change in Latin America and the Caribbean: A Policy Agenda. New York: United Nations, 

Economic Commission for Latin American and the Caribbean.

Cityscape 43

44 Habitat III

Inclusion and Innovation:  

The Many Forms of 

Stakeholder Engagement  

in Habitat III

Eugenie L. Birch 

University of Pennsylvania

Abstract

With an unprecedentedly high level of stakeholder participation, Habitat III offered 

many opportunities for contributions to the  New Urban Agenda . This innovative and 

inclusive process created a strong framework for continued involvement in the subse-

quent implementation of the agenda. These efforts are an important and lasting part of 

the Habitat III legacy. 

Introduction

In 2013, as the United Nations (UN) General Assembly approved the details for the Conference on 

Housing and Sustainable Urban Development (Habitat III) preparations, the member states made 

an explicit request to Joan Clos, the Habitat III Secretary General charged with overseeing the ar-

rangements along with a bureau composed of representatives of 10 member states. They called for 

“improved participation of local authorities and other stakeholders in the preparatory process and 

the conference itself” (UN General Assembly, 2013: 4). Clos quickly transformed this request into 

an innovative, 18-month, multistep, multiparty process that involved thousands of participants 

in specific activities aimed at delivering a strong conference outcome. Clos also knew, as a former 

mayor, that if the conference was to be effective, he needed to develop a broadly accepted outcome 

document (named the  New Urban Agenda) through intense and widespread involvement of many 

stakeholders (Scruggs, 2016a). Finally, he knew that beyond the formal commitments of the 

member states to the  New Urban Agenda at Habitat III, the implementation to take place during the 

following two decades would require strong multiparty stakeholder participation, the foundations 

for which would be laid during the preparatory process. 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 45

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

Birch

Thus, the preparatory process developed under his direction and embedded in ongoing courses of 

action had three types of components: (1) knowledge sharing and reflection, (2) policy recommen-

dations, and (3) engagement activities.1 Each component had stakeholder involvement depending 

on the nature of the desired contribution to the preparations, the conference, and later implemen-

tation. The result was the involvement of thousands of participants, even before the conference 

that had some 30,000 attendees, whose interests were ignited for ongoing work (Scruggs, 2016b). 

This article provides an overview of these efforts and discusses how they constitute an important 

legacy on which to build strong implementation programs. It will have a special focus on the 

development of the General Assembly of Partners (GAP), a new engagement platform created for 

Habitat III. Exhibit 1 illustrates the elements of the preparatory process. 

Exhibit 1

The Components of Stakeholder Engagement

Knowledge Sharing and Reflection: Stakeholders Contribute

The knowledge sharing and reflection component involved national governments and regional 

commissions. It asked them to evaluate their work on housing and sustainable urban development 

since Habitat II, sharing this evaluation with stakeholders. The United States, for example, as-

sembled a national committee of governmental and nongovernmental stakeholders who reviewed 

1 The other ongoing processes were the Habitat III preparatory conferences held in October 2015 (New York, United States), April 2016 (Nairobi, Kenya), and July 2016 (Surabaya, Indonesia), and the UN deliberations around disaster risk reduction, financing development, sustainable development goals, and climate change.

46 Habitat III

Inclusion and Innovation: The Many Forms of Stakeholder Engagement in Habitat III

a seven-chapter national report, largely written by the U.S. Department of Housing and Urban 

Development (HUD), and opted to develop an annex to the U.S. report in time for the conference. 

In addition, the U.S. National Committee solicited U.S. aspirations to be reflected at Habitat III by 

convening five regional conferences and sponsoring a discussion series on global agreements in 

Washington, D.C., for national leaders. The National Committee met three times during the prepa-

ratory period, was invited to become part of the U.S. delegation to Habitat III, and now constitutes 

an organized body (open to other members) to contribute to implementation as individual organi-

zations or collectively. Their next target will be the United Nations Programme for Human Settle-

ments (UN-Habitat) biannual World Urban Forum (WUF), one of the monitoring mechanisms for 

the  New Urban Agenda. In the past, HUD’s Office of International and Philanthropic Innovation 

has led in organizing stakeholder contributions to WUF, a role it is likely to continue for the next 

convening in Kuala Lumpur, Malaysia, in 2018.

Policy Recommendations: Stakeholders Involved

To collect policy recommendations for the  New Urban Agenda, the UN General Assembly accepted 

the Secretary General’s plan of soliciting two official input streams derived from the Habitat III 

Policy Units and the Habitat III Regional and Thematic conferences. The policy unit concept was 

new to the UN. It brought in 200 experts nominated by the member states and civil society orga-

nizations to synthesize material from 22 issue papers written by UN staff from agencies throughout 

the organization. The topics for both the papers and the policy units were organized under six 

large headings: (1) social cohesion and equity, (2) urban frameworks, (3) spatial development, 

(4) urban economy, (5) urban ecology and environment, and (6) housing and basic services. 

Exhibit 2 illustrates this format. The policy units had a presence in official hearings held at the UN 

prior to the issuance of the zero draft of the  New Urban Agenda and had special sessions at Habitat 

III. UN-Habitat, the coordinator of the  New Urban Agenda implementation, now is calling on them 

to contribute to an emerging strategy document, tentatively entitled  Action Framework for the New 

Urban Agenda, to serve as guidance for member states. 

The 11 Habitat III Regional and Thematic conferences, which convened between the fall of 2015 

and the spring of 2016, developed declarations that served as the second official input for the 

New Urban Agenda. Four regional conferences, sponsored by member states, took place in the 

Czech Republic, Indonesia, Mexico, and Nigeria. Seven thematic conferences, sponsored by local 

governments, took place in Abu Dhabi, United Arab Emirates (renewable energy); Barcelona, Spain 

(public space); Cuenca, Ecuador (intermediate cities); Mexico City, Mexico (financing develop-

ment); Montreal, Canada (metropolitan governance); Pretoria, South Africa (informal settlements); 

and Tel Aviv, Israel (civic engagement). These meetings involved local stakeholders, thus magnify-

ing stakeholders’ ability to participate in crafting the outcome document. The momentum gained 

in these conferences will be integrated into the programming of the WUF through its plenary and 

other events.

Cityscape 47

Birch

Exhibit 2

Habitat III Issue Papers and Policy Units

Engagement Activities: Stakeholders Active in Formal 

Channels

To ensure representation of key stakeholder groups, the Habitat III secretariat encouraged the cre-

ation of an innovative engagement platform, the General Assembly of Partners. The idea for GAP 

originated in the workings of UN-Habitat’s World Urban Campaign (WUC), the members of which 

quickly realized that WUC, a UN-Habitat project formed to advocate that agency’s work, was not 

be able to act as an independent entity for a UN-wide conference. To remedy this problem, they 

conceived of GAP at the first Urban Thinkers Campus held in Caserta, Italy, in October 2015. They 

devised a constitution, consulted among the likely stakeholders, and proposed the organization at 

the second Habitat III preparatory conference in April 2015. As its designers intended, GAP was 

to emulate the UN General Assembly in structure and purpose. It was a representative body, led 

by a president and vice president, composed of 14 GAP Partner Constituent Groups (PCGs)—the 

UN’s 9 major groups recognized in 1992 at the UN Conference on Environment and Development 

(Earth Summit), the Habitat Agenda Partners (formed after Habitat II), and other interested groups. 

It aimed to contribute to Habitat III and the  New Urban Agenda by providing an official channel for 

stakeholder engagement and collaboration, developing and advocating for common positions, and 

elaborating and adopting position statements while respecting the individual differences among the 

PCGs. The GAP constitution called for elected co-chairs to head each PCG and an executive com-

mittee composed of the PCG co-chairs and the officers. The constitution called for GAP members 

to meet in plenary as a decision-making body. The UN General Assembly acknowledged GAP in its 

December 2015 resolution recognizing the modalities for Habitat III (UN General Assembly, 2015).

48 Habitat III

Inclusion and Innovation: The Many Forms of Stakeholder Engagement in Habitat III

During the months prior to Habitat III, the executive committee met six times, and the GAP full 

membership met four times to develop its position. GAP issued two declarations—the Nairobi 

Declaration, presented to the Bureau of the Preparatory Committee (hereafter, the Bureau), 

articulating its presence and commitment to developing collective contributions to the  New Urban 

Agenda and the New York Declaration, detailing GAP’s intended legacy document. In May 2016, 

GAP issued its legacy document,  Partnerships for the New Urban Agenda, which identified areas 

of contributions from the partners in the implementation of the  New Urban Agenda (knowledge, 

advocacy, piloting, and innovation, and monitoring). This document became the basis for GAP 

interventions with member states as they developed the  New Urban Agenda.

GAP was extremely active in the deliberations for the  New Urban Agenda in several ways beyond 

promoting its partnership agreement. Its involvement increased with growing recognition of GAP 

by the member states. GAP representatives were official members of the drafting committees for 

the Habitat III Regional and Thematic conferences. GAP mounted side events at the conferences. 

The regional conference in the Czech Republic invited the GAP president to address the opening 

and closing ceremonies. Further, GAP members were well represented as individual experts in the 

Habitat III Policy Units. In June 2016, GAP organized the stakeholder hearings mandated by the 

UN General Assembly modalities in which each PCG and more than 40 stakeholders commented 

on the zero draft of the  New Urban Agenda. At this time and forward, GAP had multiple, lengthy 

meetings with the Bureau and co-facilitators (UN ambassadors charged with assembling the final 

drafts of the  New Urban Agenda). GAP PCG chairs were recognized to make statements at the 

informal meetings held in New York City in May and June and at the Third Preparatory Committee 

of the Habitat III Conference. Throughout this time, GAP increased the number of PCGs, adding 

one recognizing older persons and another recognizing persons with disabilities; its membership 

exploded, rising from about 100 at its formation in April 2015 to more than 1,200 unique organi-

zations and individual members in September 2016. Overall, its membership encompassed 58,000 

networks and had an outreach in the millions. 

For Habitat III, GAP was quite visible. It organized 16 roundtables and 4 assemblies, with the 

president’s and vice president’s speeches included in the opening and closing ceremonies. UN 

Secretary General Ban Ki-moon met privately with the GAP Executive Committee. In an analysis 

of the  New Urban Agenda, GAP identified 20 paragraphs in which its interventions on behalf of 

knowledge, advocacy, innovation, and monitoring were present. Each PCG, in similar analyses, 

found many areas of support for their respective interests. Finally, the  New Urban Agenda recog-

nized GAP by name. 

GAP was originally intended to be a short-term project, to self-extinguish with the end of the Habi-

tat III Conference, as reflected in its constitution. However, at its Fifth Plenary meeting, held at the 

Habitat III Conference, its members decided otherwise. Encouraged by the Habitat III Secretary 

General to capitalize on the positive momentum generated in the preparatory process and at the 

conference, and by a sense of accomplishment as enumerated previously, they voted to extend the 

GAP’s life into the  New Urban Agenda implementation stage. 

Currently, GAP leadership is engaged in the adaptation process. Its executive committee met in 

January 2017 in New York City, where they hammered out the revised constitution and pledged to 

Cityscape 49

Birch

develop focused strategic implementation frameworks built on intra- and inter-PCG relationships 

and commitments. A follow-up meeting held in New York City in April yielded the final program 

that was approved at GAP’s Sixth Plenary prior to UN-Habitat’s Governing Council meeting in 

Nairobi in May 2017. Forster (2017) underlined the importance of the ongoing GAP work.

Although GAP organized the 16 PCGs, it was not the only stakeholder activity recognized in 

the Habitat III preparations and conference. Notably, the Global Taskforce of Local and Regional 

Governments—which formed in 2013 to offer a platform for subnational governments at UN 

conferences, represents thousands of localities throughout the world, and drives its messages 

straight to the community level—was extremely active. It participated in the Habitat III Regional 

and Thematic conferences; co-chaired one of the Habitat III Policy Units; organized the mandated 

hearing for mayors, county, and provincial leaders to comment on the zero draft in May; and put 

together a roundtable and the multiday Second Global Assembly of Local and Regional Authori-

ties toward Habitat III. It is now engaged in multiple follow-up activities around the  New Urban 

Agenda. For example, it brought mayors to the 61st Session of the Commission on the Status of 

Women in March to discuss the  New Urban Agenda and will continue to do so in UN meetings 

related to urbanization and sustainable development throughout this year. 

Conclusion: The Many Forms of Stakeholder Involvement

The many entry points for stakeholder involvement is a hallmark of Habitat III, born out of the 

desire to mobilize strength for the implementation of the  New Urban Agenda. Its predecessors, 

Habitat I and II, did not support the extensive preparatory experiences demonstrated by Habitat 

III. The creation of the Habitat III Policy Units and their work in developing one of two official 

input streams to the  New Urban Agenda, the organization of Habitat III Regional and Thematic con-

ferences with drafting committees empowered by recognition as the other official input stream to 

the  New Urban Agenda, were innovative. As were the preparatory conference modalities that called 

for expert and stakeholder hearings and permitted stakeholder visibility via making statements at 

member state meetings. The recognition by the Bureau and the co-facilitators of stakeholders in 

their dialogues with stakeholders were exceedingly important in opening up Habitat III and the 

New Urban Agenda to voices beyond the member states. 

Although the  New Urban Agenda reflects these inroads, which represent greater recognition of the 

importance of stakeholder engagement in crafting global agreements—especially those related to 

cities and human settlements—the real proof of this progress is yet to come. Responsibilities will 

lie on both sides: the stakeholders will need to continue to engage seriously in the implementation 

activities, and member states will need to retain openness to these activities, distribute powers to 

the appropriate level of government, and engage in dialogues to make it possible. GAP represents 

more than 58,000 networks and the Global Taskforce of Local and Regional Governments— 

representing thousands of subnational governments worldwide—which lay the foundation for 

bringing the aspirations and commitments of the  New Urban Agenda to the local level. The follow-up 

work, engaging multiparty stakeholders with all spheres of government, will need to be contextual 

and tailored to the localities in which it will take place. This effort will involve not only growing 

the number of stakeholders energized through the Habitat III, a process requiring substantial 

50 Habitat III

Inclusion and Innovation: The Many Forms of Stakeholder Engagement in Habitat III

organizational and communication skills, but also working out the allocation of tasks to the 

appropriate groups, governmental entities, and partnerships that will make achieving sustainable 

urbanization a reality. The framework exists, and energetic leadership on all sides will be necessary.

Acknowledgments

The author thanks her GAP colleagues, the Secretary General and Coordinator of the Habitat III  

Secretariat, members of the U.S. National Committee for Habitat III, partners in the U.S. Depart-

ments of State and Housing and Urban Development, and the Government of Germany for 

engaging in and supporting the civic engagement activities that not only made this article possible 

but also (and most importantly) realized GAP’s recognition in the  New Urban Agenda. For more 

information about GAP, see http://generalassemblyofpartners.org.

Author

Eugenie L. Birch is the Lawrence C. Nussdorf Professor of Urban Research at the School of Design 

and Co-Director of the Penn Institute for Urban Research at the University of Pennsylvania.

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———. 2016b. “50,000 Gather in Quito for Habitat III, A Once-in-a-Generation Summit on the 

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Cityscape 51

52 Habitat III

Housing Perspectives  

on Habitat III: Canada

Janet Kreda

Canada Mortgage and Housing Corporation

Abstract

Canada was one of more than 170 countries to participate in Habitat III and embrace 

the principles of the  New Urban Agenda . Implementing the  New Urban Agenda  will 

mean addressing the social, economic, and environmental sustainability of our cities, 

with housing a key priority. This article describes Canada’s approach to developing its 

National Housing Strategy, sets out the key priorities and themes, and describes the 

synergy between the Strategy and the principles of the  New Urban Agenda .

Intensity and excitement marked the buildup to and participation in the United Nations (UN) 

Conference on Housing and Sustainable Urban Development (Habitat III) in Quito, Ecuador. Now 

governments, nongovernmental organizations, communities, public servants, academics, and citi-

zens must get down to the hard work of translating the good intentions of the  New Urban Agenda 

into concrete actions and meaningful change for people and the planet. 

The government of Canada embraced the guiding principles set for the Habitat III summit, 

which align well with Canada’s interest in improving access to housing and building healthier 

communities across the country. Those principles will guide work as our government embarks on 

the development of important national strategies and solutions related to housing, poverty reduc-

tion, childcare, infrastructure, climate change, and other challenges in both the urban and rural 

contexts. 

Prime Minister Justin Trudeau said recently at the UN General Assembly, “Diversity is a source of 

strength, not weakness. Our country is strong not in spite of our differences, but because of them” 

(Trudeau, 2016). Working together is something that Canada does well and will be vital as we un-

dertake the important work of implementing the  New Urban Agenda. Safe, adequate, and affordable 

housing is an essential component of inclusive, sustainable urban centers and requires continued 

partnerships with provinces, municipalities, academia, civil society, youth, indigenous peoples, and 

others. Going forward, we will engage with our partners to share what we learned from Habitat III 

and discuss how we can implement the  New Urban Agenda. 

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Kreda

Throughout the negotiation process on the  New Urban Agenda, Canada fought hard to ensure an 

inclusive approach—to have groups like youth, children, immigrants, people with disabilities, 

indigenous peoples, women, and people who are lesbian, gay, bisexual, transgender, and intersex 

officially recognized in the  New Urban Agenda. Canada strives to be an inclusive society, but it faces challenges. Although its housing system is fundamentally sound, Canada can and must do better. 

High levels of homelessness continue to plague many Canadian communities, and new approaches 

to community building are needed to ensure all people have equal opportunities to thrive in 

society and contribute to the economy. A lack of affordable housing is a significant contributing 

factor to social challenges such as poverty and homelessness, unemployment and poor health, and 

poor educational outcomes. 

At the same time Canada was preparing for Habitat III in Quito, the government was also in the 

midst of consulting widely with Canadians on the vision, priorities, and outcomes of a National 

Housing Strategy (NHS). Thousands of Canadians took part by filling in an online survey, submit-

ting ideas in writing, and joining the conversation on social media. Many also participated in 

formal consultations, such as a series of expert and stakeholder roundtables and focus groups to 

get the views and input of Canadians facing some of the most difficult housing challenges, includ-

ing people who have been homeless, indigenous peoples, people living in the north, newcomers to 

Canada, and persons with disabilities. 

In November 2016, Canada released  What We Heard, which summarizes the themes and priorities 

identified by Canadians throughout the consultation process (Conference Board of Canada, 2016). 

For example, the government heard consistently that the NHS should consider the entire housing 

system and spectrum of housing needs while prioritizing the most vulnerable. The government 

heard of a need for better integration of housing and support services and access to capital to 

facilitate more affordable housing options. It heard a strong partiality for policies that enable local 

communities to drive local solutions to housing problems.

A number of themes also emerged from the consultations covering topics Canadians felt needed to 

be addressed in the NHS.

•  Every effort should be made to reduce or eliminate homelessness in Canada.

•  The strategy needs to prioritize the housing needs of low-income and vulnerable Canadians.

•  Special attention needs to be given to addressing the unique challenges facing indigenous 

peoples, wherever they may live.

•  The strategy should respond to the growing housing affordability challenges faced by low- and 

middle-income Canadians, especially in large cities.

•  Housing should shape communities that are both inclusive and sustainable. 

•  A strong social housing sector needs to be a pillar of the NHS.

For the most part, Canadians across the country ranked outcomes related to affordability, sustain-

ability, and inclusivity as the top three priority outcomes of the NHS, which are very much in line 

with those outlined in the  New Urban Agenda. In particular, achieving greater social inclusion of 

54 Habitat III

Housing Perspectives on Habitat III: Canada

those who are often not able to fully participate in the prosperity of the country is a key outcome 

for which to strive. Canada looks forward to creating meaningful change in the social and eco-

nomic outcomes for its most vulnerable citizens (notably for seniors, indigenous peoples, veterans, 

victims of domestic violence, and those with mental health issues).

The government is committed to release a comprehensive NHS in 2017 that aims to—

•  Mobilize partnerships across the housing system. 

•  Achieve better housing outcomes for Canadians with indicators and targets to track progress 

and a strong performance measurement framework.

•  Optimize government investments and assets (for example, lands and buildings) in order to 

ensure a fiscally sustainable approach to achieving outcomes.

•  Improve housing affordability for those who need support and provide pathways toward greater 

self-reliance and prosperity.

•  Identify gaps and provide a continuum of flexible policy and program responses built on best 

evidence of what will produce the desired outcomes.

•  Promote innovation and new approaches, including financing approaches and private sector 

partnerships.

•  Situate housing as part of a holistic approach to achieve broader socioeconomic and 

environmental outcomes.

The 2017 federal budget, released on March 22, proposed $11.2 billion over 11 years for new 

federal programs and initiatives that will address important housing needs and shape Canada’s 

long-term vision and priorities for a NHS. A proposed National Housing Fund (NHF) includes 

allocations for the renewal of the social housing sector, innovative approaches to the development 

of affordable housing, a coinvestment fund to promote partnerships where housing can support 

complementary social objectives, and direct lending, which will increase the impact of these invest-

ment dollars. Funding will also go toward renewing the federal-provincial-territorial partnership in 

housing and supporting shared affordable housing priorities.

The proposed NHF also includes targeted funding to— 

•  Reduce chronic and episodic homelessness.

•  Support efforts to improve housing conditions in the north.

•  Make surplus federal lands and buildings available to housing providers at low or no cost for the 

development of affordable housing. 

•  Support expanded housing research and improve data collection and analytics, including 

developing and implementing a new Housing Statistics Framework.

•  Address critical repair and affordability needs, and encourage development of new housing, for 

indigenous people living off reserve. 

Cityscape 55

Kreda

In addition to substantial new investments in housing, approximately $1.7 billion in annual housing 

funding, which was scheduled to decline as long-term operating agreements end, has been preserved. 

Options for reinvesting these funds in support of sustainable social housing are being considered. 

The 2017 budget also proposed new investments for indigenous peoples, including $4 billion 

for infrastructure in indigenous communities, a significant portion of which will go to housing, 

where it is most needed. In 2017, Canada Mortgage and Housing Corporation and Indigenous 

and Northern Affairs Canada will work with First Nations, Inuit, and Métis groups to codevelop 

distinct initiatives to improve outcomes and move toward greater autonomy and responsibility for 

their housing on the part of indigenous peoples.

The  New Urban Agenda challenges governments to make bold changes to the way they plan and 

invest in infrastructure, social services, and economic development in urban centers. Canada rec-

ognizes the importance of housing as the foundation for social and economic opportunities, which 

lead to better lives for Canadians. The 2017 budget demonstrates this commitment to housing 

through significant proposed investments, which would build on existing programs and the $4.3 

billion for new and existing housing initiatives included in the 2016 budget. Details of Canada’s 

NHS will be released later this year. 

Habitat III brought government leaders and civil society from around the world together to agree on a 

vision for socially, economically, and environmentally sustainable cities that put people and adequate 

housing at the center. The NHS will be one part of Canada’s integrated approach to creating healthier 

and more sustainable communities and a significant step toward realizing the vision and goals of the 

New Urban Agenda. Achieving the desired outcomes of the NHS and of the  New Urban Agenda will depend on us—individuals at all levels of government and civil society—to take the vision to heart, 

to promote it on a daily basis in our work, and to take actions that make a difference. 

Acknowledgments

The opinions expressed in this article reflect public statements of the Canada Mortgage and Hous-

ing Corporation’s official positions. The author thanks Katrina Durbak and HUD for inviting the 

Canada Mortgage and Housing Corporation to contribute this article. 

Author

Janet Kreda is a senior researcher in the Housing Needs division of the Canada Mortgage and 

Housing Corporation.

References

Conference Board of Canada. 2016.  What We Heard: Shaping Canada’s National Housing Strategy. 

Ottawa, Canada: Government of Canada.

Trudeau, Justin. 2016. “Prime Minister Justin Trudeau’s Address to the 71st Session of the United 

Nations General Assembly.” http://pm.gc.ca/eng/news/2016/09/20/prime-minister-justin-trudeaus-

address-71st-session-united-nations-general-assembly.

56 Habitat III

Symposium

The CRA Turns 40 

Guest Editor: Carolina Reid

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 57

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58 The CRA Turns 40

Guest Editor’s Introduction

The Community 

Reinvestment Act at 40

Carolina Reid

University of California, Berkeley

In 1977, Congress enacted the Community Reinvestment Act1 (CRA) in an effort to expand access 

to credit and to encourage private capital to return to formerly redlined neighborhoods. CRA 

established that federally insured banks and thrifts have a “continuing and affirmative obligation” 

to meet the credit needs of the communities that they serve, including low- and moderate-income 

neighborhoods, consistent with safe and sound banking practices. Since its enactment, CRA has 

been subject to significant debate, reflecting differing ideological views of the role of government 

in overcoming market failures (Barr, 2005; Chakrabarti et al., 2009). Forty years after the law’s 

passage, the question of whether banks have an obligation to serve low-income communities—and 

what form that obligation should take—is no less salient. This symposium seeks to inform that 

debate by bringing together new research and commentaries on CRA in an effort to better under-

stand the impact of the law and to provide insights into how it could be improved to better serve 

the credit needs of low- and moderate-income communities.

Three themes emerge from the contributions in this symposium. The first is that CRA has a measur-

able impact on access to credit but that larger market forces often mute its effect. Bostic and Lee 

(2017) examine an important, underappreciated aspect of CRA—its role in expanding access to credit 

for small businesses. Analyzing data from nearly two decades of small business lending, they find that 

CRA increases access to small business loans but that the impact is influenced by macroeconomic 

market conditions and varies over time. Butcher and Munoz (2017) use newly accessible credit bu-

reau data to examine the impact of CRA on a broad set of consumer credit outcomes. They find that 

CRA leads to increased credit market activity, including increases in the total number of loans, the 

number of people covered by the credit bureau data, and the fraction of individuals with a valid risk 

score. Importantly, they find no effect of CRA on increased delinquencies or foreclosures, adding to 

a growing body of evidence that CRA did not play a role in the subprime crisis (Avery and Brevoort, 

2011; Canner and Bhutta, 2008; FCIC, 2011; Reid and Laderman, 2011; Reid et al., 2013).

Two other contributions to this symposium look at CRA’s impact from a different angle, asking, 

“what can we learn from CRA to inform future housing policy”? In their article, Quercia and Riley 

(2017) reflect on the lessons learned from an innovative effort to scale CRA mortgage lending using 

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII.

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Reid

the secondary market: the Community Advantage Program (CAP) run by Self-Help. Building on 

their extensive research on the impacts of CAP, Quercia and Riley explore how policymakers can 

balance risk management with efforts to expand the scale of CRA lending to low-income families. 

In his commentary, Immergluck (2017) examines the strengths and weaknesses of CRA against 

those of another key piece of legislation designed to increase housing equity: the Affirmatively 

Furthering Fair Housing (AFFH) rules. His analysis reveals limitations in the AFFH regulatory 

structure, including the lack of consistency and specificity of the quantitative measures. It also 

highlights that, despite uneven implementation over the years, CRA’s approach to regulatory 

compliance has worked relatively well.

A second key theme in this symposium is the importance of public participation and advocacy in 

the implementation of CRA. The “stick” of CRA is relatively weak; ratings largely come into play 

only during mergers and acquisitions or branching activities, and most banks receive “satisfactory” 

or “outstanding” ratings (Avery, Courchane, and Zorn, 2009). As a result, the role of community-

based coalitions has been critical to holding banks accountable for their CRA activities. Casey, 

Farhat, and Cartwright (2017) provide empirical evidence for this important aspect of CRA, 

illustrating how community mobilization in St. Louis, Missouri, was associated with positive lend-

ing outcomes in CRA-eligible tracts. However, the pieces by Bull (2017) and Silver (2017) point 

out critical challenges to the public accountability aspects of CRA. Bull highlights how the data 

collected as part of a CRA exam are often hard to access, and the reporting of CRA activities is at a 

geographic scale that does not allow for community development corporations to engage effectively 

with local banks about the credit needs in their communities. In his commentary, Silver shines 

light on the regulators and the processes by which public comments are considered as part of CRA 

exams and bank merger applications, and he offers tangible suggestions for how these processes 

could be strengthened.

The third theme relates to the question of how CRA could be improved, particularly given the 

changes in the banking sector that make parts of CRA seem anachronistic. Gaughan (2017) 

looks specifically at the rise of financial technology (FinTech) companies. He argues that FinTech 

provides an opportunity to broaden CRA coverage and leverage technological innovation to more 

equitably distribute CRA investments to the communities most in need. Yezer (2017) takes a differ-

ent view, suggesting that the changes in banking have made CRA obsolete. He argues that, over the 

years, political interests have held sway over economic analysis in the development of housing and 

mortgage market policy, to the detriment of households and neighborhoods. Willis (2017) asks one 

of the most enduring questions confronting CRA: how do we actually know if a bank has done an 

adequate job of meeting the credit needs of the community it serves? He proposes that examiners 

develop new metrics that account for local housing market conditions and provides one example 

of how such a metric could work in New York City.

Given the continuing disparities in access to credit and the importance of credit for economic 

mobility, the goal of CRA is as critical and relevant today as it was 40 years ago. The contributions 

in this symposium provide welcome additions to the interdisciplinary literature on CRA and will 

hopefully help to inform ongoing policy efforts to expand access to opportunity for low- and 

moderate-income households.

60 The CRA Turns 40

The Community Reinvestment Act at 40

Acknowledgments

The guest editor thanks all the authors for their dedication and contributions to this symposium 

and thanks the reviewers who provided critical feedback on draft articles and commentaries.

Author

Carolina Reid is an assistant professor in the Department of City and Regional Planning at the 

University of California, Berkeley.

References

Avery, Robert B., and Kenneth P. Brevoort. 2011.  The Subprime Crisis: Is Government Housing Policy 

to Blame?  Finance and economics discussion paper. Washington, DC: Board of Governors of the 

Federal Reserve System. https://www.federalreserve.gov/pubs/feds/2011/201136/201136pap.pdf.

Avery, Robert B., Marsha J. Courchane, and Peter M. Zorn. 2009. “The CRA Within a Changing 

Financial Landscape.” In  Revisiting the CRA: Perspectives on the Future of the Community Reinvestment 

Act, edited by Prabal Chakrabarti, David Erickson, Ren S. Essene, Ian Galloway, and John Olson. 

Boston; San Francisco: Federal Reserve Banks of Boston and San Francisco: 30–46.

Barr, Michael. 2005. “Credit Where It Counts: The Community Reinvestment Act and Its Critics,” 

New York University Law Review 80 (2): 513–652.

Bostic, Raphael W., and Hyojung Lee. 2017. “Small Business Lending Under the Community 

Reinvestment Act,”  Cityscape 19 (2): 63–84.

Bull, Marijoan. 2017. “Data, Accountability, and the Public: Using Community Reinvestment Act 

Data for Local Community Development,”  Cityscape 19 (2): 161–175.

Butcher, Kristin F., and Ana Patricia Muñoz. 2017. “Using Credit Reporting Agency Data To Assess 

the Link Between the Community Reinvestment Act and Consumer Credit Outcomes,”  Cityscape 

19 (2): 85–108.

Canner, Glenn, and Neil Bhutta. 2008.  Staff Analysis of the Relationship Between the CRA and the 

Subprime Crisis. Washington, DC: Board of Governors of the Federal Reserve System.

Casey, Colleen, Joseph Farhat, and Gregory Cartwright. 2017. “Community Reinvestment Act and 

Local Governance Contexts: Advancing the Future of Community Reinvestment?”  Cityscape 19 (2): 

137–160.

Chakrabarti, Prabal, David Erickson, Ren S. Essene, Ian Galloway, and John Olson, eds. 2009.  Re-

visiting the CRA: Perspectives on the Future of the Community Reinvestment Act. Boston; San Francisco: Federal Reserve Banks of Boston and San Francisco.

Cityscape 61

Reid

Financial Crisis Inquiry Commission (FCIC). 2011.  The Financial Crisis Inquiry Report. Washington, 

DC: Government Printing Office. http://fcic-static.law.stanford.edu/cdn_media/fcic-reports/fcic_ 

final_report_full.pdf.

Gaughan, Michael. 2017. “Commentary: FinTech and the Liberation of the Community Reinvest-

ment Act Marketplace,”  Cityscape 19 (2): 187–198.

Immergluck, Dan. 2017. “Commentary: Encouraging Housing Equity,”  Cityscape 19 (2): 129–136.

Quercia, Roberto G., and Sarah Riley. 2017. “Bridging the Gap to Scalable Community Reinvest-

ment Lending Programs,”  Cityscape 19 (2): 109–128.

Reid, Carolina K., and Elizabeth Laderman. 2011. “Constructive Credit: Revisiting the Performance 

of Community Reinvestment Act Lending During the Subprime Crisis.” In  The American Mortgage 

System: Rethink, Recover, Rebuild, edited by Marvin M. Smith and Susan M. Wachter. Philadelphia: 

University of Pennsylvania Press: 159–186.

Reid, Carolina K., Ellen Seidman, Mark Willis, Lei Ding, Josh Silver, and Janneke Ratcliffe. 2013. 

Debunking the CRA Myth—Again. Raleigh, NC: UNC Center for Community Capital. http://ccc.unc.

edu/contentitems/debunking-the-cra-myth-again/.

Silver, Josh. 2017. “Commentary: The Community Reinvestment Act Must Be All About Public 

Participation, but It Still Doesn’t Feel That Way,”  Cityscape 19 (2): 187–198.

Willis, Mark. 2017. “Commentary: Filling a Gap in the Community Reinvestment Act Examiner 

Toolkit,”  Cityscape 19 (2): 207–212.

Yezer, Anthony. 2017. “Commentary: What Can We Learn From Government Attempts To Modify 

the Allocation of Mortgage and Consumer Credit in the United States?”  Cityscape 19 (2): 199–205.

62 The CRA Turns 40

Small Business Lending Under 

the Community Reinvestment Act

Raphael W. Bostic   

University of Southern California

Hyojung Lee  

Joint Center for Housing Studies of Harvard University

Abstract

This article reviews small business lending patterns from 1996 to 2015 and examines 

the role of the Community Reinvestment Act (CRA) in shaping small business lending 

patterns. Using the data that have been reported pursuant to the CRA, we observe that 

the number of small business loans dramatically increased from 1996 to 2015, while the 

loan volumes remained essentially unchanged, which translates into a significant decline 

in the size of the average small business loan. Next, using a regression discontinuity design, 

we seek linkages between changes in small business lending in a census tract during three 

distinct periods—1996 to 2002, 2003 to 2011, and 2012 to 2015—and that tract’s status 

as being covered by the CRA. We observe a positive association between small business 

lending and the CRA from 1996 to 2002 and from 2012 to 2015 and observe a negative 

association from 2003 to 2011. The findings are consistent with a view that banking 

institutions strategically respond to the CRA, but that the incentives presented by 

macroeconomic market conditions can overwhelm any incentives the CRA provides.

Introduction

Capital is the lifeblood of every community. For a community’s residents, capital is critical for pur-

chasing durable goods, such as appliances and a car, that are essential for a family’s day-to-day life. 

In addition, families generally need capital in order to achieve homeownership, which is a primary 

vehicle for building wealth and avoiding the costs associated with housing instability (Green and 

White, 1997; Gyourko, Linneman, and Wachter, 1999; Linneman and Wachter, 1989; Sinai and 

Souleles, 2005; Turner and Luea, 2009). Capital is also essential for a community’s businesses, 

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which use capital to acquire land and equipment that are needed to stand up and operate their 

enterprises. Business success is essential for communities, as businesses are an important source of 

jobs and provide the basic services that enable communities to function and thrive.

Given this essential role of capital for community health, access to capital and credit has been a 

concern of policymakers for decades. A large volume of evidence has made clear that capital mar-

kets have fallen short where equal access is concerned, which has triggered legislative action to try 

to improve the state of capital access (Blanchflower, Levine, and Zimmerman, 2003; Munnell et 

al., 1996). The Community Reinvestment Act1 of 1977 (CRA) is one such piece of legislation. The 

CRA seeks to ensure that banking institutions reinvest a significant portion of the deposits they 

take from local communities back into those communities (Garwood and Smith, 1993). The CRA 

also establishes that a banking institution’s record of reinvestment will be considered as a factor 

when that institution seeks to acquire other institutions or complete another activity that requires 

regulatory approval.

A revision to the CRA in 1995 gave the act an additional role—to monitor the performance of 

banking institutions in providing small business credit. The CRA revision established that banking 

institutions must report on their lending to small businesses and their provision of small loans to 

businesses (Canner, 1999). The article uses the data that have been compiled under the CRA to 

describe changes in small business lending from 1996 to 2015 and examine the role of the CRA in 

shaping small business lending patterns.

We begin by reviewing the literature examining how the CRA has influenced banking activity, par-

ticularly mortgage and small business lending. We then introduce our methodology and data set 

that are used in this article. The subsequent section describes the patterns in small business lend-

ing from 1996 to 2015 at a national level. The next-to-last examines the effectiveness of the CRA 

on small business lending, using a regression discontinuity design (RDD). The final section con-

cludes by summarizing the findings and considering some potential implications of the findings.

Literature Review—The Effect of the CRA

The literature on the effects of the CRA has focused on how banks engage with local community 

organizations, the volume and distribution of mortgage loans, and the volume and distribution of 

small business loans. One response of banking institutions to the introduction of CRA has been 

to enter into agreements with local organizations, whereby banking institutions pledge to devote 

significant resources to support CRA objectives. Several studies have sought evidence of whether 

these agreements are associated with increases in lending activities. Bostic and Robinson (2003) 

found a positive association between the number of new CRA agreements in a county and lending 

in that county during a 3-year period. Bostic and Robinson (2005) similarly found that banking 

institutions that enter into CRA agreements increase their lending activity when the agreement is in 

force and maintain higher levels of lending even after the agreement has expired.

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII.

64 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

Regarding mortgage lending, a number of studies have sought to quantify the impact of the CRA 

on the volume and distribution of loans. We highlight only a few here. Based on a survey of bank-

ing institutions, Avery, Bostic, and Canner (2005) found that the CRA induced institutions to 

engage in lending that they would not have and that most of these new activities were profitable. 

Bhutta (2011) found that the CRA was associated with increases in mortgage lending in large met-

ropolitan areas in the late 1990s and early 2000s, although effects were not significant on average 

for the entire nation. Bhutta concluded that government interventions to offset information exter-

nalities that suppress credit supply may be justified. Gabriel and Rosenthal (2008) compared the 

effects of the CRA, which targets banking institutions, with similar regulations that target activities 

of Fannie Mae and Freddie Mac. Their results are mixed, although they found weak evidence that 

the CRA increases homeownership in CRA-eligible tracts. More recently, some have argued that the 

CRA was the main catalyst of the financial crisis of the mid-2000s (Agarwal et al., 2012), although 

the evidence strongly suggests otherwise (Avery and Brevoort, 2015; Reid et al., 2013).

The literature on the CRA’s impact on small business lending is much smaller than the literature 

on the law’s impacts on mortgage lending. Zinman (2002) was the first paper, to our knowledge, 

that examined the (causal) effects of the CRA on small business lending. Using a framework that 

is analogous to a triple difference approach, the author compared the dollar amount of small busi-

ness loans outstanding of banks and commercial borrowers by their bank asset size and regulator 

toughness in the wake of the 1995 CRA reforms. Also, Zinman restricted the bank sample to those 

around the CRA asset size cutoff (between $150 million and $350 million) to control for any un-

observed bank characteristics (regression discontinuity sample). The estimates found in the paper 

suggest that the CRA (specifically, the 1995 CRA reforms) increased small business lending by 15 

percent of base period lending, which ultimately led the CRA-affected areas to increase payroll and 

reduce bankruptcies.

A more recent attempt to assess the effectiveness of the CRA on small business lending is found 

in Bates and Robb (2015). From an analysis based on the Kauffman Firm Survey, the paper found 

positive associations between minority residential areas and loan availability between 2004 and 

2011. Although the authors interpreted the regression results as impacts of CRA, the methodology 

does not allow for a causal interpretation of the findings because the paper (1) equates minority 

communities with the CRA eligible low- and moderate-income (LMI) neighborhoods and (2) uses 

ZIP Codes rather census tracts as the geographic unit of analysis.

In sum, relatively little documentation exists of how small business lending has evolved since the 

introduction of the small business lending data reporting mandate, as part of the major reforms 

to the CRA in 1995. Relatively little work comprehensively describes patterns in small business 

lending and empirically examines the effects of the CRA on small business lending. Using the most 

comprehensive and current data available, we will attempt to fill the gap in the existing literature.

Method and Data

To examine the role of the CRA on small business lending, we use RDD. One of the key character-

istics of the law is that the threshold for eligibility is clear: to be a “CRA-eligible” neighborhood, the 

Median Family Income (hereafter, median income) in a tract should be less than 80 percent of the 

Cityscape 65

Bostic and Lee

median income for the surrounding area. Given this regulatory framework, neighborhoods slightly 

below and above the CRA threshold are theoretically quite similar, save for their CRA designation. 

This close similarity enables us to view any discontinuities in outcomes at the threshold as effects 

of the CRA. The RDD approach has been often used to evaluate the effectiveness of the CRA on 

home mortgage lending (Avery and Brevoort, 2015; Berry and Lee, 2007; Bhutta, 2011; Gabriel 

and Rosenthal, 2008), but this article is the first to apply this approach to examine the CRA effects 

on small business lending.

In a standard RDD, we estimate the impact of the CRA on small business lending as—

,                                                 (1)

where         is small business loan originations in census tract  i in county  j in year  k.            is an indicator that is equal to 1 if a census tract is a CRA-eligible LMI neighborhood, and the indicator 

is equal to 0 otherwise.           is a vector of neighborhood characteristics that are associated with 

small business lending, including the median income ratio between tract and corresponding met-

ropolitan area. The model also includes county and year-fixed effects to control for any year- and 

location-specific heterogeneities.

For the model, we use four samples: (1) all census tracts, (2) census tracts with a median income 

ratio within 10 percentage points of the CRA cutoff, (3) tracts within 5 percentage points of the 

cutoff, and (4) tracts within 3 percentage points of the cutoff. The results based on all census tracts 

provide a reference, but we focus on the narrower samples to estimate the effects of the CRA on 

small business lending. The narrower sample will enable a more accurate comparison, but at the 

cost of statistical power due to smaller sample size.

The primary data used for the analysis are the 1996–2014 CRA aggregate flat files provided by the 

Federal Financial Institutions Examination Council (FFIEC).2 The CRA aggregate flat files include 

information on the number and dollar amount of small business loans originated, aggregated at 

census tract level, by banks and thrifts. In the CRA report, small business loans are defined as 

business loans of $1 million or less. The data also provide the number and dollar amount of those 

loans to businesses with gross annual revenues of $1 million or less. Although they provide rela-

tively limited information as compared with the Home Mortgage Disclosure Act3 data, the CRA 

data are the most comprehensive publicly available data on small business lending. Greenstone, 

Mas, and Nguyen (2015) reported that the CRA data cover approximately 86 percent of all loans 

amounting $1 million or less.

Using the data set, we test the role of the CRA on four outcome variables: (1) number of small 

business loans ($1 million or less), (2) dollar amount of small business loans, (3) number of small 

business loans to small firms (with gross annual revenues of $1 million or less), and (4) dollar 

2 The 1995 CRA reform made depository institutions with assets above a certain asset-size threshold report small business, small farm, and community development lending activity that they originate. Since 2005, the CRA asset-size threshold is adjusted on an annual basis. It was $1 billion in 2005 and is $1.226 billion in 2017. 

3 Pub. L. 94–200, 89 Stat. 1124.

66 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

amount of small business loans to small firms. In this article, we focus on small business loan origi-

nations, rather than loan purchases, and restrict our sample to census tracts within metropolitan 

statistical areas or metropolitan divisions.

We supplement the CRA data with data from various other sources. First, we use data on every 

tract’s median income as a percentage of the median income for its surrounding metropolitan 

area, included in the 1996-to-2014 census and demographics files provided by FFIEC. These data 

enable us to identify those tracts that receive attention under the CRA based on the 80-percent 

threshold. In addition, we use decennial censuses and the American Community Survey (ACS) to 

link various neighborhood characteristics of the census tracts in which the small business loans 

were originated to the CRA data. These characteristics include population, number of housing 

units, vacancy rate, homeownership rate, minority population share, share of the population age 

25 and over, share of the population that has a bachelor’s degree, share of the population with 

income less than the poverty rate, the ratio of the tract median income to the metropolitan area 

median income, median house value, and median gross rent. We link data from (1) the 1990 cen-

sus to the CRA data from 1996 to 2002, (2) the 2000 census to the CRA data from 2003 to 2011, 

and (3) the 2010 census and the 2006–2010 ACS to the CRA data from 2012 to 2014. Lastly, we 

obtain the number of establishments from the ZIP Code Business Patterns database from 1996 to 

2014. As the data set is reported at ZIP Code level, we converted the data into census tracts using 

population as weights.

Trends in Small Business Lending

We begin our analysis by presenting trends in the small business lending from 1996 to 2015, fo-

cusing on the small business loans originated in LMI neighborhoods. Panel A of Exhibit 1 shows 

the number and dollar volume of small business loans by income category of census tracts. In 

2015, the depository institutions subject to the CRA originated 5.7 million small business loans 

totaling $219.2 billion.4 The number and dollar amounts have changed dramatically during the 

period from 1996 to 2015, corresponding to some extent to the business cycle fluctuations during 

the period. The number of small business loans exploded from 2.3 million in 1996 to 13.1 million 

in 2007 before plummeting to 4.0 million in 2010. The average loan size shrank dramatically be-

tween 1996 and 2015; the total dollar volume of lending is effectively the same in 2015 as it was in 

1996, despite the fact that the number of loans in 2015 is more than double the number in 1996. 

As a consequence, the average loan size in 2015 ($38,200) is only about 40 percent of the average 

loan size in 1996 ($93,650).

The data for small business loans to small businesses show the same pattern (Panel B). Current 

loan volumes are much higher in 2015 than in 1996, whereas real loan dollars are smaller in 2015 

than in 1996. Thus, the average small loan to businesses in 2015 ($26,600) was only 37 percent of 

the average small loan in 1996 ($70,900).

Exhibit 2 shows how small business loans and loans to small businesses are distributed across 

census tracts grouped by relative median income. Although the number of loan originations has 

4 All monetary amounts throughout this article are in 2016 dollars.

Cityscape 67

Bostic and Lee

Exhibit 1

Number and Dollar Amount of Small Business Loans by Income, 1996 to 2015

Panel A. All Small Business Loans

Number of Small Business Loans 

Dollar Amount of Loans

(in thousands)

(in $ billions)

All 

Below 

50 to 

80 to 

All 

Below 

50 to 

80 to 

120+%

120+%

Tracts

50%

79%

119%

Tracts

50%

79%

119%

1996

2,332

121

372

1,157

683

218.4

13.8

34.9

102.5

67.2

1997

2,494

128

399

1,228

739

232.3

14.2

37.2

108.3

72.6

1998

2,488

121

388

1,234

745

227.3

13.4

35.6

106.7

71.6

1999

3,091

123

451

1,554

962

242.0

13.6

37.6

114.2

76.5

2000

4,867

191

711

2,446

1,520

237.1

13.0

36.4

113.1

74.7

2001

5,727

250

872

2,871

1,735

292.6

15.9

44.5

139.7

92.6

2002

7,000

287

1,062

3,534

2,118

324.9

17.0

49.4

155.8

102.7

2003

7,429

282

1,260

3,540

2,347

347.6

16.8

61.9

158.2

110.7

2004

7,708

285

1,283

3,677

2,463

358.0

17.0

63.8

161.7

115.4

2005

7,601

257

1,245

3,594

2,505

322.3

15.3

56.6

142.8

107.7

2006

12,238

402

1,926

5,504

4,406

350.9

15.9

60.3

153.5

121.2

2007

13,079

414

2,013

5,931

4,721

369.7

15.9

62.3

161.9

129.6

2008

10,021

318

1,504

4,505

3,695

311.5

13.5

51.8

137.1

109.1

2009

4,455

150

665

1,975

1,665

208.8

9.7

35.7

92.1

71.3

2010

4,048

137

598

1,780

1,533

187.6

8.9

32.2

82.9

63.6

2011

4,852

167

725

2,130

1,830

202.7

9.4

34.8

89.0

69.5

2012

4,924

245

801

1,972

1,906

203.7

13.8

37.8

80.3

71.8

2013

4,743

247

789

1,889

1,818

206.7

14.0

38.7

81.4

72.7

2014

5,302

272

895

2,101

2,034

207.4

13.8

38.7

81.3

73.6

2015

5,741

299

983

2,265

2,194

219.2

14.2

40.6

85.4

78.9

Panel B. Small Business Loans to Businesses With  

Gross Annual Revenues of $1 Million or Less

Number of Small Business Loans 

Dollar Amount of Loans

(in thousands)

(in $ billions)

All 

Below 

50 to 

80 to 

All 

Below 

50 to 

80 to 

120+%

120+%

Tracts

50%

79%

119%

Tracts

50%

79%

119%

1996

1,334

57

201

688

388

94.6

4.8

14.1

46.1

29.5

1997

1,260

52

186

647

375

98.1

4.8

14.1

47.1

32.2

1998

1,451

57

211

745

438

107.7

5.0

15.2

52.2

35.4

1999

1,858

68

262

950

579

117.7

5.3

16.6

57.5

38.3

2000

2,022

72

286

1,042

621

110.0

4.9

15.5

54.2

35.5

2001

2,481

104

373

1,259

744

134.9

5.8

18.6

66.5

44.0

2002

2,215

82

310

1,137

686

145.2

6.1

19.8

71.5

47.9

2003

2,851

95

465

1,386

905

161.4

6.3

26.3

76.1

52.8

2004

2,919

92

464

1,419

944

160.0

6.1

26.0

74.7

53.2

2005

3,646

110

565

1,749

1,221

146.7

5.7

23.4

66.8

50.7

2006

4,483

134

686

2,102

1,561

154.7

5.8

24.4

69.6

54.8

2007

4,988

146

763

2,345

1,735

154.2

5.5

24.1

69.8

54.8

2008

3,135

91

470

1,486

1,088

115.4

4.1

17.6

53.1

40.6

2009

1,527

44

223

721

538

77.6

2.7

11.8

35.6

27.4

2010

1,422

41

206

667

509

68.9

2.5

10.3

31.6

24.5

2011

2,192

67

312

983

831

76.1

2.7

11.5

34.5

27.4

2012

2,183

96

341

877

869

75.2

3.9

12.7

30.4

28.1

2013

2,315

104

364

921

926

75.2

3.9

12.9

30.3

28.1

2014

2,494

116

406

990

982

72.7

3.9

12.4

29.1

27.2

2015

3,015

143

500

1,194

1,179

80.1

4.2

13.5

31.9

30.5

Notes: Based on the Federal Financial Institutions Examination Council’s tract classification rules, neighborhoods are categorized by Median Family Income (MFI) into low- (less than 50% of MFI), moderate- (50 to 79% of MFI), middle- (80 to 119% of MFI), and upper- (equal to or more than 120% of MFI) income. The sample is restricted to small business loans in metropolitan statistical areas or metropolitan divisions, reported by the lending institutions that are subject to the Community Reinvestment Act (CRA) of 1977. Dollar figures are adjusted to 2016 dollars.

Source: 1996–2015 CRA aggregate flat files

68 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

Exhibit 2

Distribution of Small Business Loan Dollars Across Tracts Grouped by Relative 

Median Income, 1996 to 2015

Panel A. All Small Loans

60.0 

50.0 

Upper-income tracts  

40.0 

(120+ %) 

Middle-income tracts  

30.0 

(80 to 119%) 

20.0 

Moderate-income tracts  

(50 to 79%) 

Share of loan dollars (%)  10.0 

Low-income tracts  

(below 50%) 

0.0 

1 

1996 

1997 

1998 

1999 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

201

2012 

2013 

2014 

2015 

Panel B. Small Loans to Small Businesses

60.0 

50.0 

Upper-income tracts  

40.0 

(120+ %) 

Middle-income tracts  

30.0 

(80 to 119%) 

20.0 

Moderate-income tracts  

(50 to 79%) 

Share of loan dollars (%)  10.0 

Low-income tracts  

(below 50%) 

0.0 

1 

1996 

1997 

1998 

1999 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

201

2012 

2013 

2014 

2015 

Notes: Based on the Federal Financial Institutions Examination Council’s tract classification rules, neighborhoods are categorized by Median Family Income (MFI) into low- (less than 50% of MFI), moderate- (50 to 79% of MFI), middle- (80 to 119% of MFI), and upper- (equal to or more than 120% of MFI) income. The sample is restricted to small business loans in metropolitan statistical areas or metropolitan divisions, reported by the lending institutions that are subject to the Community Reinvestment Act (CRA) of 1977.

Source: 1996–2015 CRA aggregate flat files

Cityscape 69

Bostic and Lee

fluctuated during the period, the dollar-calculated share of loans has been relatively stable, even 

during the financial crisis of 2007 through 2008. Upper- and middle-income tracts have received 

between 75 and 80 percent of small business loan dollars, while LMI tracts have received from 20 

to 25 percent of small business loan dollars. The skew of loan dollars to more affluent neighbor-

hoods may not come as a surprise, but the lack of variation through the difference stages of the 

business cycle does.5 One might have expected increasing concentrations in higher-income tracts 

as the economy weakened. One sees similar trends when shares are calculated based on the num-

ber of loans.6 

Regression Discontinuity Analysis

To examine whether the evidence is consistent with the view that the CRA influences small busi-

ness lending, we pooled the CRA aggregate flat files from 1996 to 2014 and conducted regressions 

using samples that vary in terms of the bandwidths around the 80-percent CRA eligibility thresh-

old (regression discontinuity samples). Exhibit 3 presents the results for regressions including four 

distinct dependent variables: (1) number of small business loans (panel A), (2) dollar amount of 

small business loans (panel B), (3) number of small business loans to firms with gross annual rev-

enues of $1 million or less (panel C), and (4) dollar amount of small business loans to firms with 

gross annual revenues of $1 million or less (panel D).

Before turning to the main relationships of interest, we discuss the relationships between the de-

pendent variables and the covariates. The relationships are largely consistent across the four regres-

sions and conform to expectations. Small business lending activity, whether measured in terms of 

the number of loans or the loan dollar volume, is greater in census tracts with more business estab-

lishments and more residents with college degrees and less in tracts with higher homeownership 

rates, minority population shares, unemployment rates, median rents, and median house value. 

We also generally observe less small business lending activity in tracts with larger populations and 

in tracts with higher vacancy rates. The one exception is that the results show that small business 

lending activity is higher in tracts that have higher poverty rates. This finding is somewhat surpris-

ing but could be explained by the fact that small businesses often lack the capital and cash flows 

required to locate in more affluent communities. It could also be the result of general land use pat-

terns, as higher-income neighborhoods tend to be more residential and not have large commercial 

corridors. These relationships, which almost uniformly conform to what is seen in other research 

on small business lending, suggest that the regression results represent legitimate relationships 

(Bates, 1997, 1991; Bates and Robb, 2015; Bostic and Lampani, 1999; Cavalluzzo and Cavalluzzo, 

1998; Immergluck, 1999, 2004).

Turning to the variable of interest—being in a CRA-covered LMI tract—we see that the full sample 

shows small significant positive relationships, which would generally be consistent with the idea 

that the CRA had significant positive effects on small business lending activity. However, further 

examination calls this interpretation into question. Coefficient estimates on LMI status become 

5 We observe two discontinuities in the data, in 2002–2003 and in 2011–2012. We believe these discontinuities are a result of updates to the census tract boundaries that occur with each decennial census. 

6 The calculation, not shown in this article, is available on request from the authors.

70 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

Exhibit 3

Results of Regression Discontinuity Analysis, 1996–2014 (1 of 4)

Panel A. Number of Small Business Loans

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

1.368 

***

0.559 

– 0.076 

0.142 

Neighborhood characteristics

Population

0.001 

***

– 0.003 

***

– 0.001 

***

– 0.001 

Housing units

0.007 

***

0.010 

***

0.002 

– 0.000 

Establishments

0.512 

***

0.504 

***

0.577 

***

0.593 

***

% vacancy

0.224 

***

– 0.257 

***

– 0.076 

0.021 

% homeownership

– 0.181 

***

– 0.285 

***

– 0.285 

***

– 0.264 

***

% minority

– 0.347 

***

– 0.291 

***

– 0.320 

***

– 0.321 

***

% bachelor’s or higher

0.399 

***

0.297 

***

0.153 

**

0.155 

*

% unemployed

– 0.136 

***

0.015 

0.062 

– 0.385 

**

% poverty

0.354 

***

0.296 

***

0.364 

***

0.116 

Median income ratio

0.372 

***

0.395 

***

0.217 

0.468 

Median gross rent

– 0.008 

***

– 0.022 

***

– 0.021 

***

– 0.029 

***

Median value

– 0.000 

***

– 0.000 

***

– 0.000 

**

– 0.000 

***

Year FEs (Ref. 1996)

1997

0.237 

– 0.198 

– 0.538 

– 1.219 

1998

– 1.387 

***

– 1.544 

**

– 1.624 

– 2.180 

1999

7.999 

***

5.404 

***

5.420 

***

4.522 

***

2000

36.977 

***

28.536 

***

28.488 

***

26.724 

***

2001

50.671 

***

41.134 

***

41.220 

***

38.900 

***

2002

70.340 

***

57.738 

***

57.699 

***

53.809 

***

2003

66.064 

***

53.628 

***

55.936 

***

50.026 

***

2004

69.124 

***

55.496 

***

58.269 

***

52.310 

***

2005

66.596 

***

51.717 

***

54.216 

***

48.514 

***

2006

137.182 

***

102.317 

***

104.463 

***

98.313 

***

2007

149.185 

***

110.945 

***

112.906 

***

106.781 

***

2008

102.890 

***

72.249 

***

74.669 

***

68.497 

***

2009

18.422 

***

6.805 

***

9.554 

***

3.437 

*

2010

12.367 

***

1.820 

**

4.649 

***

– 1.394 

2011

25.126 

***

11.909 

***

14.880 

***

8.744 

***

2012

24.957 

***

15.872 

***

17.913 

***

15.516 

***

2013

21.770 

***

13.758 

***

15.653 

***

13.198 

***

2014

28.631 

***

19.806 

***

22.069 

***

19.144 

***

County FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.410

0.357

0.350

0.359

Number of observations

1,213,980

264,734

133,133

79,091

Cityscape 71

Bostic and Lee

Exhibit 3

Results of Regression Discontinuity Analysis, 1996–2014 (2 of 4)

Panel B. Dollar Amount (in Thousands) of Small Business Loans

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

157.284 

***

– 13.621 

– 43.193 

10.004 

Neighborhood characteristic

Population

– 0.179 

***

– 0.369 

***

– 0.244  ***

– 0.179  ***

Housing units

– 0.094 

***

0.202 

**

– 0.381  ***

– 0.507  ***

Establishments

32.654 

***

31.599 

***

35.071  ***

34.648  ***

% vacancy

18.537 

***

– 4.303 

9.988 

**

13.483  **

% homeownership

– 36.723 

***

– 34.483 

***

– 35.554  ***

– 34.592  ***

% minority

– 8.516 

***

– 7.396 

***

– 11.817  ***

– 14.364  ***

% bachelor’s or 

– 11.465 

***

– 20.361 

***

– 24.934  ***

– 26.071  ***

higher

% unemployed

– 13.881 

***

– 4.937 

– 13.469 

– 38.580 

*

% poverty

3.193 

***

17.702 

***

22.183  ***

9.124 

Median income ratio

24.015 

***

21.545 

***

14.200 

52.839  **

Median gross rent

– 0.119 

***

– 0.001 

0.159 

– 0.528  ***

Median value

– 0.001 

***

– 0.001 

***

– 0.001 

*

– 0.003  ***

Year FEs (Ref. 1996)

1997

95.139 

***

62.077 

67.704 

23.825 

1998

– 34.070 

– 40.764 

– 48.726 

– 79.750 

1999

169.949 

***

147.719 

**

153.953 

120.315 

2000

44.323 

76.579 

58.426 

– 72.395 

2001

952.542 

***

747.526 

***

704.688  ***

562.681  ***

2002

1,392.043 

***

1,120.210 

***

1,092.542  ***

897.420  ***

2003

1,631.522 

***

1,519.169 

***

1,600.939  ***

1,130.636  ***

2004

1,711.392 

***

1,569.795 

***

1,691.739  ***

1,205.396  ***

2005

1,105.599 

***

996.182 

***

1,094.211  ***

607.780  ***

2006

1,492.491 

***

1,231.292 

***

1,354.065  ***

903.729  ***

2007

1,722.410 

***

1,382.915 

***

1,480.346  ***

1,021.885  ***

2008

876.474 

***

728.665 

***

832.337  ***

353.352 

2009

– 619.574 

***

– 416.929 

***

– 308.445 

**

– 787.210  ***

2010

– 929.205 

***

– 664.522 

***

– 563.048  ***

– 1,040.692  ***

2011

– 675.790 

***

– 464.771 

***

– 345.630 

**

– 825.632  ***

2012

– 634.690 

***

– 375.300 

***

– 364.694  ***

– 533.726  ***

2013

– 623.969 

***

– 339.416 

***

– 327.930  ***

– 506.278  ***

2014

– 658.897 

***

– 368.798 

***

– 342.551  ***

– 561.497  ***

County FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.270

0.197

0.186

0.153

Number of 

1,213,980

264,734

133,133

79,091

observations

72 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

Exhibit 3

Results of Regression Discontinuity Analysis, 1996–2014 (3 of 4)

Panel C. Number of Small Business Loans to Small Firms

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

– 0.163 

0.213 

0.402 

0.358 

Neighborhood characteristics

Population

0.001 

***

– 0.001 

***

– 0.000 

– 0.000 

Housing units

0.004 

***

0.005 

***

0.002 

***

0.002 

**

Establishments

0.179 

***

0.171 

***

0.198 

***

0.201 

***

% vacancy

0.004 

– 0.190 

***

– 0.115 

***

– 0.088 

***

% homeownership

– 0.062 

***

– 0.095 

***

– 0.089 

***

– 0.086 

***

% minority

– 0.132 

***

– 0.118 

***

– 0.128 

***

– 0.134 

***

% bachelor’s or 

0.216 

***

0.207 

***

0.163 

***

0.162 

***

higher

% unemployed

– 0.013 

– 0.023 

– 0.024 

– 0.115 

**

% poverty

0.113 

***

0.132 

***

0.154 

***

0.099 

***

Median income ratio

0.121 

***

0.161 

***

0.186 

***

0.185 

Median gross rent

– 0.004 

***

– 0.010 

***

– 0.009 

***

– 0.012 

***

Median value

– 0.000 

***

– 0.000 

0.000 

– 0.000 

**

Year FEs (Ref. 1996)

1997

– 2.100 

***

– 2.276 

***

– 2.340 

***

– 2.542 

***

1998

0.645 

***

– 0.107 

– 0.197 

– 0.334 

1999

7.222 

***

5.005 

***

5.057 

***

4.882 

***

2000

9.621 

***

7.218 

***

7.216 

***

6.843 

***

2001

17.072 

***

13.730 

***

13.627 

***

12.984 

***

2002

12.000 

***

8.556 

***

8.406 

***

7.574 

***

2003

17.912 

***

14.033 

***

15.099 

***

13.246 

***

2004

18.528 

***

14.175 

***

15.327 

***

13.562 

***

2005

29.422 

***

22.286 

***

23.273 

***

21.402 

***

2006

42.023 

***

31.260 

***

32.130 

***

30.183 

***

2007

49.472 

***

37.472 

***

38.301 

***

36.245 

***

2008

21.202 

***

14.502 

***

15.531 

***

13.625 

***

2009

– 3.132 

***

– 5.197 

***

– 4.132 

***

– 6.097 

***

2010

– 4.685 

***

– 6.583 

***

– 5.446 

***

– 7.344 

***

2011

7.331 

***

1.797 

***

2.925 

***

0.925 

2012

5.508 

***

1.775 

***

2.921 

***

2.487 

***

2013

7.106 

***

2.787 

***

3.815 

***

3.438 

***

2014

9.295 

***

5.077 

***

6.282 

***

5.815 

***

County FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.401

0.335

0.348

0.341

Number of 

1,213,980

264,734

133,133

79,091

observations

Cityscape 73

Bostic and Lee

Exhibit 3

Results of Regression Discontinuity Analysis, 1996–2014 (4 of 4)

Panel D. Dollar Amount (in Thousands) of Business Loans to Small Firms

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

85.835 

***

– 9.557 

9.043 

10.991 

Neighborhood characteristics

Population

– 0.038 

***

– 0.130 

***

– 0.078 

***

– 0.069 

***

Housing units

0.195 

***

0.291 

***

0.090 

***

0.103 

***

Establishments

10.252 

***

9.407 

***

10.337 

***

9.830 

***

% vacancy

2.184 

***

– 8.414 

***

– 3.483 

***

– 3.971 

***

% homeownership

– 13.259 

***

– 13.062 

***

– 13.052 

***

– 12.448 

***

% minority

– 3.264 

***

– 2.809 

***

– 3.978 

***

– 4.860 

***

% bachelor’s or 

3.387 

***

0.199 

– 0.754 

– 1.919 

higher

% unemployed

– 5.173 

***

– 4.473 

***

– 6.287 

***

– 12.043 

***

% poverty

3.021 

***

10.520 

***

11.568 

***

9.336 

***

Median income ratio

10.950 

***

10.432 

***

13.837 

***

18.362 

**

Median gross rent

– 0.180 

***

– 0.192 

***

– 0.119 

***

– 0.277 

***

Median value

– 0.000 

***

0.000 

0.000 

– 0.000 

*

Year FEs (Ref. 1996)

1997

13.359 

– 36.431 

*

– 37.483 

– 62.449 

1998

161.172 

***

86.519 

***

93.639 

***

81.821 

*

1999

311.794 

***

222.048 

***

235.004 

***

222.586 

***

2000

163.312 

***

119.800 

***

119.730 

***

78.528 

*

2001

572.105 

***

415.413 

***

405.562 

***

365.309 

***

2002

711.259 

***

517.698 

***

505.751 

***

455.856 

***

2003

798.475 

***

703.885 

***

748.147 

***

623.367 

***

2004

752.324 

***

661.010 

***

726.854 

***

607.474 

***

2005

528.510 

***

449.287 

***

507.231 

***

384.615 

***

2006

635.176 

***

504.336 

***

574.988 

***

459.529 

***

2007

609.207 

***

484.533 

***

544.791 

***

413.074 

***

2008

27.721 

**

36.656 

101.241 

***

– 27.467 

2009

– 529.544 

***

– 404.294 

***

– 355.247 

***

– 476.979 

***

2010

– 659.078 

***

– 516.316 

***

– 461.213 

***

– 583.210 

***

2011

– 540.328 

***

– 429.045 

***

– 368.175 

***

– 493.373 

***

2012

– 599.313 

***

– 439.903 

***

– 406.926 

***

– 443.850 

***

2013

– 610.139 

***

– 441.388 

***

– 407.550 

***

– 444.015 

***

2014

– 661.552 

***

– 490.633 

***

– 443.572 

***

– 497.518 

***

County FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.285

0.229

0.228

0.213

Number of 

1,213,980

264,734

133,133

79,091

observations

FE = fixed effects. LMI = low- and moderate-income. 

*  p  < 0.1. **  p  < 0.05. ***  p  < 0.01.

Notes: Robust standard errors are in parentheses. The fixed effects regression models were conducted for county fixed effects. The sample is restricted to small business loans, in metropolitan statistical areas or metropolitan divisions, reported by the lending institutions that are subject to the Community Reinvestment Act of 1977. Headers of columns 3–5 indicate interval rotations for the samples used.

74 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

statistically insignificant as we narrow the bandwidth around the 80-percent CRA threshold. It 

would seem that the link between the CRA and small business lending activity from 1996 to 2014 

is, at best, weak.

However, the pooled cross-sectional regressions, which present average effects of the CRA on small 

business lending from 1996 to 2014, might conceal changes in the CRA impacts during a period 

of nearly two decades that included two recessions and two long periods of extended growth. To 

find out, we repeated this regression discontinuity analysis on an annual basis, and the results 

based on the sample 77-to-83 bands are shown in exhibit 4.7 The error bars, in light gray, show the 

95-percent confidence intervals and indicate that the estimated coefficients on the LMI dummy are 

not statistically different from 0 in general. However, we can observe certain cycles in the effects of 

the CRA over time. Although statistically not significant, positive estimated coefficients are mainly 

found in the 1996-to-2003 and 2012-to-2014 regressions, and negative coefficient estimates are 

found in the 2003-to-2011 regressions.

The three distinctive periods coincide with the years when the LMI status was determined based 

on the 1990 census, 2000 census, and 2006–2010 ACS 5-year estimates, respectively. Thus, we 

pooled the CRA data into those three time periods and repeated the regression analysis. The results 

are shown in exhibits 5 through 7. As one might expect, the CRA impact generally is found to be 

positive for both the number and amount of small business loans in the 1996-to-2002 sample, 

even within a narrow range around the income threshold (exhibit 5). During this period, the effects 

are strongest in the regressions using the narrowest band, which is the cleanest test of the role of 

the CRA in influencing small business lending activity. Although no clear relationship between the 

CRA and the number of small business loans from 2003 to 2011 was seen, we do observe signifi-

cantly lower small business loan dollar volumes in the LMI neighborhoods as compared with non-

CRA eligible tracts (exhibit 6). Finally, during the most recent 3 years of 2012 to 2014, we observe 

an opposite pattern from that of the 2003-to-2011 period (exhibit 7). Here again, the number of 

loans is not statistically different in LMI tracts than in other tracts, but the size of the loan dollars is significantly larger.

7 The relationships for the covariates (available on request) are qualitatively unchanged with varying bandwidths.

Cityscape 75

Bostic and Lee

Exhibit 4

Estimated Coefficients on LMI Dummy From Stratified Regressions by Year, 77 to 83 

Percent (1 of 2)

Panel A. Number of Small Business Loans

25.000  

20.000  

15.000  

10.000  

5.000  

0.000  

–5.000  

ficient estimates  –10.000  

Coef –15.000  

–20.000  

–25.000  

–30.000  

1 

1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  201 2012  2013  2014 

Panel B. Dollar Amount (in Thousands) of Small Business Loans

2000.000  

1500.000  

1000.000  

500.000  

0.000  

ficient estimates  –500.000  

Coef –1000.000  

–1500.000  

–2000.000  

1 

1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  201 2012  2013  2014 

76 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

Exhibit 4

Estimated Coefficients on LMI Dummy From Stratified Regressions by Year, 77 to 83 

Percent (2 of 2)

Panel C. Number of Small Business Loans to Small Firms

10.000  

8.000  

6.000  

4.000  

2.000  

0.000  

ficient estimates  –2.000  

–4.000  

Coef –6.000  

–8.000  

–10.000  

1 

1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  201 2012  2013  2014 

Panel D. Dollar Amount (in Thousands) of Business Loans to Small Firms

800.000  

600.000  

400.000  

200.000  

0.000  

ficient estimates 

Coef –200.000  

–400.000  

–600.000  

1 

1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  201 2012  2013  2014 

LMI = low- and moderate-income.

Notes: Estimated coefficients are based on the regression discontinuity analysis, with the sample 77–83% bands, repeated on an annual basis. The error bars, in light gray, show the 95% confidence intervals of the estimates. 

Cityscape 77

Bostic and Lee

Exhibit 5

Results of Regression Discontinuity Analysis, 1996–2002

Panel A. Number of Small Business Loans

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

– 0.533 

0.618 

0.031 

3.685 

**

 (0.435)

 (1.032)

 (1.283)

 (1.689)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.364

0.245

0.221

0.240

Number of observations

414,714

92,906

47,371

27,934

Panel B. Dollar Amount (in Thousands) of Small Business Loans

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

177.884 

***

– 89.343 

8.937 

  410.028 

**

 (41.508)

 (95.883)

 (129.149)

 (161.287)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.247

0.128

0.118

0.089

Number of observations

414,714

92,906

47,371

27,934

Panel C. Number of Small Business Loans to Small Firms

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

– 0.273 

0.036 

0.457 

2.263 

***

 (0.175)

 (0.355)

 (0.472)

 (0.633)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.332

0.213

0.250

0.233

Number of observations

414,714

92,906

47,371

27,934

Panel D. Dollar Amount (in Thousands) of Business Loans to Small Firms

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

99.436 

***

– 57.241 

**

24.809 

  167.638 

***

 (13.544)

 (27.246)

 (38.573)

 (51.035)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.253

0.173

0.172

0.148

Number of observations

414,714

92,906

47,371

27,934

FE = fixed effects. LMI = low- and moderate-income. 

**  p  < 0.05. ***  p  < 0.01.

Notes: Robust standard errors are in parentheses. The fixed effects regression models were conducted for county fixed effects. The sample is restricted to small business loans, in metropolitan statistical areas or metropolitan divisions, reported by the lending institutions that are subject to the Community Reinvestment Act of 1977. Headers of columns 3–5 indicate interval rotations for the samples used.

78 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

Exhibit 6

Results of Regression Discontinuity Analysis, 2003–2011

Panel A. Number of Small Business Loans

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

– 1.213 

**

– 1.016 

– 2.303 

– 3.297 

*

 (0.532)

 (1.031)

 (1.462)

 (1.916)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.421

0.397

0.410

0.413

Number of observations

583,102

127,974

63,474

37,604

Panel B. Dollar Amount (in Thousands) of Small Business Loans

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

80.884 

***

– 19.232 

  – 193.724 

**

– 287.597 

**

 (30.023)

 (61.382)

 (89.751)

 (120.825)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.282

0.250

0.258

0.256

Number of observations

583,102

127,974

63,474

37,604

Panel C. Number of Small Business Loans to Small Firms

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

– 1.381 

***

– 0.801 

**

– 0.656 

– 1.192 

*

 (0.180)

 (0.370)

 (0.524)

 (0.686)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.430

0.396

0.406

0.408

Number of observations

583,102

127,974

63,474

37,604

Panel D. Dollar Amount (in Thousands) of Business Loans to Small Firms

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

56.669 

***

– 28.554 

– 44.186 

  – 95.308 

**

 (10.438)

 (20.984)

 (30.615)

 (39.887)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.301

0.254

0.257

0.258

Number of observations

583,102

127,974

63,474

37,604

FE = fixed effects. LMI = low- and moderate-income. 

*  p  < 0.1. **  p  < 0.05. ***  p  < 0.01.

Notes: Robust standard errors are in parentheses. The fixed effects regression models were conducted for county fixed effects. The sample is restricted to small business loans, in metropolitan statistical areas or metropolitan divisions, reported by the lending institutions that are subject to the Community Reinvestment Act of 1977. Headers of columns 3–5 indicate interval rotations for the samples used.

Cityscape 79

Bostic and Lee

Exhibit 7

Results of Regression Discontinuity Analysis, 2012–2014

Panel A. Number of Small Business Loans

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

1.284 

***

2.437 

**

4.723 

***

3.604 

*

 (0.483)

 (1.141)

 (1.599)

 (2.080)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.364

0.273

0.276

0.285

Number of observations

216,162

43,773

22,149

13,403

Panel B. Dollar Amount (in Thousands) of Small Business Loans

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

80.604 

**

88.211 

  384.676 

***

317.680 

**

 (32.802)

 (76.705)

 (104.817)

 (129.645)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.232

0.192

0.193

0.210

Number of observations

216,162

43,773

22,149

13,403

Panel C. Number of Small Business Loans to Small Firms

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

0.614 

***

1.346 

***

2.065 

***

1.349   

 (0.198)

 (0.492)

 (0.698)

 (0.935)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.408

0.280

0.263

0.271

Number of observations

216,162

43,773

22,149

13,403

Panel D. Dollar Amount (in Thousands) of Business Loans to Small Firms

All Tracts

[70%, 90%)

[75%, 85%)

[77%, 83%)

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

Coef.

Sig.

LMI tract

50.702 

***

45.896 

**

111.915 

***

75.031 

**

 (9.022)

 (21.874)

 (29.691)

 (36.889)

Neighborhood characteristics

Yes

Yes

Yes

Yes

County FEs

Yes

Yes

Yes

Yes

Year FEs

Yes

Yes

Yes

Yes

Within  R-squared

0.253

0.185

0.161

0.178

Number of observations

216,162

43,773

22,149

13,403

FE = fixed effects. LMI = low- and moderate-income. 

*  p  < 0.1. **  p  < 0.05. ***  p  < 0.01.

Notes: Robust standard errors are in parentheses. The fixed effects regression models were conducted for county fixed effects. The sample is restricted to small business loans, in metropolitan statistical areas or metropolitan divisions, reported by the lending institutions that are subject to the Community Reinvestment Act of 1977. Headers of columns 3–5 indicate interval rotations for the samples used.

80 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

Conclusion

The CRA was enacted in response to concerns that banking institutions were not allocating suffi-

cient capital to neighborhoods in their service areas that had been historically underserved. As part 

of the evolution of the CRA, starting in 1995, banking institutions were required to report on the 

geographic distribution of their small business lending activity, both in terms of numbers of loans 

and the total volume of lending in dollars. The hope was that greater transparency in actual bank 

activity would trigger scrutiny of the lending sector and trigger changes in practices that would 

lead to increased small business lending in targeted geographies.

This article reviews how small business lending activity has evolved since the small business data 

reporting requirement was introduced. Between 1996 and 2015, the number of small business 

loans increased dramatically, but the total dollar volume of small business lending in 2015 was 

largely unchanged from 1996. Thus, the size of the average small business loan was smaller in 

2015 than in 1996. The data also show that 75 to 80 percent of all small business lending occurred 

in upper- and middle-income census tracts, and this share was consistent throughout the entire 

period.

Regarding the effect of the CRA, analyses using the pooled sample suggest that the CRA has had 

minimal effect on the geographic distribution of small business lending activity since the introduc-

tion of the data-reporting requirement. However, subsequent analysis shows that this initial con-

clusion is a function of three different trends. We observe significant positive associations between 

small business lending activity and being a CRA-covered census tract from 1996 to 2002 and from 

2012 to 2014. These periods are marked by steady economic growth. By contrast, we observe a 

negative relationship between small business loan dollar volume and being a CRA-covered census 

tract during the 2003-to-2011 period. During the same period, however, we find no significant 

relationship between the number of small business loans and CRA designation.

The positive relationships observed in the 1996-to-2002 period support the view that the CRA has 

influenced the distribution of small business credit, as does the relationship in the 2012-to-2014 

period. In the latter case, the relationship is significant for loan dollars but not for the number of 

loans, which could reflect a desire for reinvestment by businesses that had survived the Great Re-

cession and were positioned to become larger and more mature, coupled with weakened aggregate 

demand by small businesses generally in the wake of the Great Recession. The pattern of the rela-

tionships in the 2003-to-2011 period is interesting and suggests that banks may have responded to 

the more turbulent economic period by sustaining loan numbers so as not to trigger CRA concerns 

but reducing loan volumes to limit their exposure to perceived higher risks posed by businesses in 

CRA-designated neighborhoods. The insignificant result for number of loans could also be a func-

tion of fewer applications for loans, as businesses might have been less likely to seek financing in 

the midst of a deep recession.

These results have some interesting implications. First, they suggest that banking institutions 

respond affirmatively to CRA incentives during times of economic growth. At least two possible 

dynamics might be at work. First, periods of economic growth will be times when banking institu-

tions might be considering making strategic moves, such as acquisitions and expansions, that will 

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Bostic and Lee

be subject to CRA scrutiny, so banking institutions will have an incentive to score well along CRA 

dimensions. Second, it is during periods of economic growth that conventional notions of marginal 

borrowers might no longer hold, such that borrowers who might not have been deemed good 

prospects might be viewed in a different light. In such an instance, we might observe experimenta-

tion and a willingness to expand the margin such that loans in geographies that were previously 

“off limits” become acceptable. Our results suggest that the CRA establishes markers about where 

the thresholds for acceptability might lie. The significant break in small business lending activity 

that occurs at the 80-percent threshold established by the CRA strongly suggests that lenders view 

the CRA cutoff as a clear delineation of where such experimentation should occur.

Another important implication of our results is that broader macroeconomic forces swamp the 

specific social incentives introduced by the CRA. During times when economic performance is 

particularly uncertain, we observe that banking institutions “flee to safety,” and retrench their 

lending activity such that places that are widely viewed to be marginal, whether that perception is 

legitimate or not, receive significantly less lending. Thus, market and risk perceptions trump social 

purpose when economic uncertainty is broader.

It is important to emphasize that this study tests only indirectly whether the CRA influenced de-

cisionmaking at an institutional level. We examine whether small business lending activity was 

elevated in CRA-covered census tracts, which is different from examining whether institutions 

covered by the CRA increased their lending activity in CRA-covered census tracts. Future research 

should conduct this more direct test of the CRA’s effect on small business lending activity, as well 

as the effects of this investment on the revitalization of LMI neighborhoods. 

Authors

Raphael W. Bostic is the Judith and John Bedrosian Chair in Governance and the Public Enterprise 

and Chair of the Department of Governance, Management and the Policy Process at the University 

of Southern California, Sol Price School of Public Policy. 

Hyojung Lee is a postdoctoral fellow at the Joint Center for Housing Studies of Harvard University. 

The research for this article was conducted when Lee was a doctoral student at the Sol Price School 

of Public Policy at the University of Southern California.

References

Agarwal, Sumit, Efraim Benmelech, Nittai Bergman, and Amit Seru. 2012. Did the Community 

Reinvestment Act (CRA) Lead to Risky Lending? NBER Working Paper No. w18609. Cambridge, 

MA: National Bureau of Economic Research.

Avery, Robert, Raphael W. Bostic, and Glenn B. Canner. 2005. “Assessing the Necessity and Effi-

ciency of the Community Reinvestment Act,”  Housing Policy Debate 16 (1): 143–172.

Avery, Robert, and Kenneth Brevoort. 2015. “The Subprime Crisis: Is Government Housing Policy 

to Blame?”  Review of Economics and Statistics 97 (2): 352–363.

82 The CRA Turns 40

Small Business Lending Under the Community Reinvestment Act

Bates, Timothy. 1997. “Unequal Access: Financial Institution Lending to Black- and White-Owned 

Small Business Startups,”  Journal of Urban Affairs 19 (4): 487–495.

———. 1991. “Commercial Bank Financing of White and Black-Owned Small Business Start-

Ups,”  Quarterly Review of Economics and Business 31 (1): 64–80.

Bates, Timothy, and Alicia Robb. 2015. “Has the Community Reinvestment Act Increased Loan 

Availability Among Small Businesses Operating in Minority Neighbourhoods?”  Urban Studies 52 

(9): 1702–1721.

Berry, Christopher, and Sarah Lee. 2007. The Community Reinvestment Act: A Regression Discon-

tinuity Analysis. Harris School Working Paper Series No. 704. Chicago, IL: University of Chicago.

Bhutta, Neil. 2011. “The Community Reinvestment Act and Mortgage Lending to Lower Income 

Borrowers and Neighborhoods,”  The Journal of Law and Economics 54 (4): 953–983.

Blanchflower, David G., Phillip B. Levine, and David J. Zimmerman. 2003. “Discrimination in the 

Small-Business Credit Market,”  Review of Economics and Statistics 85 (4): 930–943.

Bostic, Raphael W., and K. Patrick Lampani. 1999. “Racial Differences in Patterns of Small Business 

Finance: The Importance of Local Geography.” In  Business Access to Capital and Credit: A Federal 

Reserve System Research Conference, edited by Jackson L. Blanton, Alicia Williams, and Sherrie L.W. 

Rhine. Washington, DC: Federal Reserve System: 149–179.

Bostic, Raphael W., and Breck L. Robinson. 2005. “What Makes Community Reinvestment Act 

Agreements Work? A Study of Lender Responses,”  Housing Policy Debate 16 (3–4): 513–545.

———. 2003. “Do CRA Agreements Influence Lending Patterns?”  Real Estate Economics 31 (1): 23–51.

Canner, Glenn B. 1999. “Evaluation of CRA Data on Small Business Lending.” In  Business Access to 

Capital and Credit: A Federal Reserve System Research Conference, edited by Jackson L. Blanton, Alicia Williams, and Sherrie L.W. Rhine. Washington, DC: Federal Reserve System: 53–84.

Cavalluzzo, Ken, and Linda Cavalluzzo. 1998. “Market Structure and Discrimination: The Case of 

Small Businesses,”  Journal of Money, Credit, and Banking 30 (4): 771–792.

Gabriel, Stuart A., and Stuart Rosenthal. 2008. “The GSEs, CRA, and Homeownership in Targeted 

Underserved Neighborhoods.” In  Conference on Built Environment: Access, Finance, and Policy. Cam-

bridge, MA: Lincoln Institute of Land Policy: 202–229.

Garwood, Griffith L., and Dolores S. Smith. 1993. “Community Reinvestment Act: Evolution and 

Current Issues,”  Federal Reserve Bulletin 79: 251–267.

Green, Richard K., and Michelle J. White. 1997. “Measuring the Benefits of Homeowning: Effects 

on Children,”  Journal of Urban Economics 41 (3): 441–461.

Greenstone, Michael, Alexandre Mas, and Hoai-Luu Nguyen. 2015. Do Credit Market Shocks Af-

fect the Real Economy? Quasi-Experimental Evidence From the Great Recession and Normal Eco-

nomic Times. NBER Working Paper No. w20704. Cambridge, MA: National Bureau of Economic 

Research.

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Gyourko, Joseph, Peter Linneman, and Susan Wachter. 1999. “Analyzing the Relationships Among 

Race, Wealth, and Home Ownership in America,”  Journal of Housing Economics 8 (2): 63–89.

Immergluck, Dan. 2004.  Credit to the Community: Community Reinvestment and Fair Lending Policy in 

the United States. Armonk, NY: M.E. Sharpe.

———. 1999. “Intrametropolitan Patterns of Small-Business Lending: What Do the New Commu-

nity Reinvestment Act Data Reveal?”  Urban Affairs Review 34 (6): 787–804.

Linneman, Peter, and Susan Wachter. 1989. “The Impacts of Borrowing Constraints on Homeown-

ership,”  AREUEA Journal 17 (4): 389–402.

Munnell, Alicia H., Geoffrey M.B. Tootell, Lynn Browne, and James McEneaney. 1996. “Mortgage 

Lending in Boston: Interpreting HMDA Data,”  The American Economic Review 86 (1): 25–53.

Reid, Carolina, Ellen Seidman, Mark Willis, Lei Ding, Joshua Silver, and Janneke Ratcliffe. 2013. 

Debunking the CRA Myth—Again. UNC Center for Community Capital Working Paper. www.ccc.

unc.edu/abstracts/debunkingCRAMyth.php.

Sinai, Todd, and Nicholas S. Souleles. 2005. “Owner-Occupied Housing as a Hedge Against Rent 

Risk,”  Quarterly Journal of Economics 120 (2): 763–789.

Turner, Tracy M., and Heather Luea. 2009. “Homeownership, Wealth Accumulation and Income 

Status,”  Journal of Housing Economics 18 (2): 104–114.

Zinman, Jonathan. 2002.  The Efficacy and Efficiency of Credit Market Interventions: Evidence From the 

Community Reinvestment Act. Cambridge, MA: Joint Center for Housing Studies of Harvard Univer-

sity.

84 The CRA Turns 40

Using Credit Reporting Agency 

Data To Assess the Link Between 

the Community Reinvestment Act 

and Consumer Credit Outcomes

Kristin F. Butcher   

Wellesley College and National Bureau of Economic Research

Ana Patricia Muñoz  

Grupo FARO

Abstract

We use a regression discontinuity design to investigate the effect of the Community 

Reinvestment Act (CRA) on consumer credit outcomes, using data from the Federal 

Reserve Bank of New York’s Consumer Credit Panel database (hereafter, CCP/Equifax 

data) for the years 2004 to 2012. A bank’s activities in census tracts with Median 

Family Incomes (MFIs) that are less than 80 percent of the metropolitan statistical 

area (MSA) MFI count toward a lending institution’s compliance with CRA rules. 

Discontinuous changes in consumer credit outcomes at this threshold—assuming census 

tracts with MFIs at 79.9 percent of the MSA median are the same as census tracts at 

80.0 percent, except for CRA eligibility—are evidence of the CRA’s impact. We find no 

statistically significant effects of the CRA on mortgages or foreclosures, either before or 

after the financial crisis. We do, however, find evidence that the CRA expanded broad 

measures of credit market activity; at the CRA threshold, the total number of loans 

increased 9 percent, the number of people covered by the CCP/Equifax data increased, 

and the fraction of individuals with a valid risk score increased. Despite expanded credit 

activity, which may increase consumers’ risk for adverse outcomes, delinquencies did not 

significantly increase, nor did credit risk score worsen at the CRA threshold.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 85

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

Butcher and Muñoz

Introduction

Access to credit is critical to a well-functioning economy. It enables consumers to smooth their 

consumption over good and bad times, businesses to invest and expand, and individuals to 

become homeowners. Consumers and businesses in lower-income areas tend to have less access to 

credit than their higher-income counterparts. Although it may make sense that lenders are more 

willing to lend to those whom they have high confidence will be able to repay the loan, it is pos-

sible that, from a societal perspective, access to credit is less in low-income areas than is optimal 

for economic growth. If market failures occur in low-income areas such that lenders do not serve 

individuals who are nonetheless “credit worthy,” then policy intervention has a role in increasing 

lenders’ incentives to lend in those areas. The Community Reinvestment Act1 (CRA), enacted in 

1977, is an example of such a policy. The goal of the CRA was to encourage depository institutions 

to help meet the credit needs of their local communities, including low- and moderate-income 

(LMI) neighborhoods. 

Over the years, the CRA has attracted broad interest from researchers and policymakers. We seek 

to determine if it has been effective in expanding access to credit in lower-income areas.

Much of the research on the CRA has focused on mortgage lending. This article adds to the 

literature by examining broad measures of consumer credit market activity available in the Federal 

Reserve Bank of New York’s Consumer Credit Panel (CCP). The longitudinal database, comprising 

individual credit records maintained by Equifax, is a nationally representative 5-percent sample of 

individuals with consumer credit records (hereafter, CCP/Equifax data). The database allows for 

us to examine a rich set of consumer credit outcomes, including mortgages and foreclosures, total 

number of trades (accounts), delinquencies, and credit risk scores.

The main challenge in determining whether a policy like the CRA has had a salutary or adverse 

effect is that people living in LMI areas who are the targets of the policy are likely to have different 

outcomes from people living in higher-income areas, for reasons that have nothing to do with 

the policy. We need a way to compare outcomes in areas that are likely to be the same, with the 

sole exception being that one group is affected by the CRA and one is not. In this study, we use a 

feature of the CRA eligibility rules to create only such comparisons. A census tract is considered 

LMI, and activity in that census tract will count toward a depository institution’s CRA activities, 

if the MFI in that census tract is less than 80 percent of the Area Median Family Income (AMFI).2 

This creates the potential for using a regression discontinuity design; we can examine whether a 

discontinuous change in consumers’ outcomes exists for those in neighborhoods that are slightly 

below that 80-percent cutoff—which are thus in CRA-eligible areas—compared with those in 

neighborhoods at or above 80 percent , which are not in CRA-eligible areas. If we assume that 

people in neighborhoods where MFI is at 79 percent of the AMFI are unlikely to be very different 

from people in neighborhoods where MFI is at 80 percent of AMFI, this comparison gives insight 

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII. 

2 For census tracts in metropolitan areas, the AMFI is the median for the metropolitan area. For census tracts outside of metropolitan areas, the AMFI is the median for all tracts outside of metropolitan areas. 

86 The CRA Turns 40

Using Credit Reporting Agency Data To Assess the Link Between  

the Community Reinvestment Act and Consumer Credit Outcomes

into the causal effect of the CRA on credit outcomes. Further, we can use this methodology to 

examine whether the effect of CRA eligibility is different before and after the financial crisis.3 

We find evidence that the CRA expanded access to credit in LMI neighborhoods. Neighborhoods 

(census tracts) that barely meet the CRA-eligibility criteria have 9 percent more accounts overall 

than do neighborhoods that are immediately outside the eligibility threshold. Further, more people 

in the CCP/Equifax data are in neighborhoods that barely meet the CRA eligibility threshold, and 

the data are of higher quality with fewer missing values. We see no statistically meaningful increase 

in delinquencies, mortgages or risk of foreclosures, or changes in credit risk scores in census tracts 

at the CRA eligibility threshold. Additionally, no evidence emerges of an adverse  change in foreclo-

sures at the CRA eligibility threshold with the financial crisis in 2007.

The next section presents background on the CRA. The section that follows describes the CCP/

Equifax data and presents summary statistics. We then discuss the regression discontinuity methodol-

ogy. The subsequent section presents the results, followed by a discussion and conclusion section.

Background

The CRA was enacted in 1977 to encourage depository institutions to help meet the credit needs of 

their local communities, including LMI neighborhoods.4 The CRA created an affirmative obligation 

for banks to provide credit in LMI communities without establishing minimum targets of lending or 

investment.5 Institutions that are regulated by the federal government are affected by CRA rules.6 The 

CRA is enforced through regulators’ periodic examination of banks’ records, and an institution’s CRA 

rating is taken into account when it applies for deposit facilities, including for mergers and acquisi-

tions. Thus, an institution’s CRA compliance has an effect on its future business options.7

The CRA has gone through three major changes over the past three decades. In 1989, Congress 

required regulators to prepare a detailed written evaluation of lenders’ CRA performance and man-

dated public disclosure8 of CRA ratings and evaluation, making it easier for the public to observe 

whether banks were compliant. Further, regulatory changes in 1995 (effective in 1997) revised 

CRA examination of banks, establishing a three-pronged test for large institutions based on perfor-

mance in the areas of lending, investments, and services, a change intended to make the examina-

tions more objective. As part of the CRA reform, the spatial emphasis was modified to include in 

3 Note that CRA eligibility applies to both LMI individuals and individuals who live in LMI census tracts. In this study, we focus only on the latter because the CCP/Equifax data do not include information on family income, only on the current census tract. 

4 The CRA was enacted in response to claims of  redlining practices: that is, banks’ refusal to lend to potential borrowers living in low-income, minority communities (Barr, 2005).  

5 Congress argued that the CRA would be a reasonable quid pro quo for the federal benefits that banks receive, such as federal deposit insurance and the Federal Reserve’s discount window (Bernanke, 2007). 

6 In addition, six states have enacted CRAs at the state level: Connecticut, Massachusetts, New York, Rhode Island, Washington, and West Virginia. Connecticut and Massachusetts have also enacted similar laws that apply to credit unions. 

Massachusetts is the only state that, since 2007, has also had CRA-like exams for residential mortgage lenders. 

7 In the 1980s, 8 of 40,000 applications were denied due to CRA concerns (Essene and Apgar, 2009). 

8 Public disclosure of CRA ratings and evaluations began in 1990.

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Butcher and Muñoz

the evaluation loans to LMI  borrowers regardless of the economic status of their neighborhoods 

(Friedman and Squires, 2005). In 2005, a new category of small banks,  intermediate small institu-

tions, was created; they are subject to a lending test and a new community development test.

Enforcement: CRA Examinations

Three federal agencies are responsible for enforcing the CRA. The Board of Governors of the 

Federal Reserve System (FRS) supervises banks with state charters that are members of the FRS; 

the Federal Deposit Insurance Corporation, or FDIC, supervises banks with state charters that are 

not members of the FRS; and the Office of the Comptroller of the Currency, or OCC, supervises 

banks with charters from the federal government. All three regulators follow nearly identical rules 

to implement the CRA.

Examiners review lenders’ activities in lenders’ assessment areas. Assessment areas consist of met-

ropolitan statistical areas (MSAs), metropolitan divisions, or contiguous political subdivisions in 

which the institution has its main office, branches, or deposit-taking ATMs, as well as surrounding 

geographies in which the institution originated or purchased a substantial portion of its loans.9

Regulators evaluate the compliance of large institutions through tests of their lending, investments, 

and banking services.10 Under the lending test, regulators look at the volume of each type of loan 

made or purchased by the institution within its assessment area. Examiners analyze the loans’ 

geographic and income distribution, looking at the share of consumer and mortgage loans made in 

LMI geographies and the share of loans made to LMI borrowers whose median income is less than 

80 percent of the median for their MSA, regardless of their neighborhoods’ LMI status (Friedman 

and Squires, 2005). Examiners take into account mortgages, small business loans, and community 

development lending.

Under the investment test, examiners review the degree to which investments serve LMI areas or 

individuals. The service test determines whether the institution provides adequate services to LMI bor-

rowers and in LMI areas by looking at the distribution of the institution’s branches, its record of open-

ing and closing branch offices, and the accessibility and use of alternative systems for delivering retail 

banking services.11 Although much of the research on the CRA focuses on mortgage lending, keep in 

mind that large institutions get CRA credit for services provided to LMI borrowers and borrowers 

in LMI areas. There are reasons to think that the CRA may have effects beyond mortgage lending.

Based on the outcome of the review process, regulators give lending institutions a rating of “Out-

standing,” “High Satisfactory,” “Low Satisfactory,” “Needs to Improve,” or “Substantial Noncompli-

ance.” According to the Federal Financial Institutions Examination Council (FFIEC) database, of 

the 69,792 banks examined between 1990 and 2012, 15.3 percent were rated Outstanding and 

80.7 percent were rated either High Satisfactory or Low Satisfactory. The vast majority of banks 

were in compliance with CRA rules.

9 Pub. L. 95–128, 91 Stat. 1147, Title VIII, § 228.41, assessment area delineation, as amended effective January 1, 2010. 

10 Large institutions are defined as those with $250 million or more in assets. Small banks are assessed on lending activities. 

More information on examination procedures is available at the CRA examination overview page on the Federal Financial 

Institutions Examination Council (FFIEC) website, http://www.ffiec.gov/cra/exam_overview.htm.  

11 Examples of alternative delivery systems for services include proprietary and nonproprietary ATMs, loan production offices, banking by telephone or computer, and bank-at-work programs.

88 The CRA Turns 40

Using Credit Reporting Agency Data To Assess the Link Between  

the Community Reinvestment Act and Consumer Credit Outcomes

Effects of the CRA

The effect of the CRA on neighborhoods, consumers, and banks has been a topic of controversy. If 

the credit market is fraught with market failures, then private banks that seek to maximize profits 

will not supply the optimal amount of credit; thus, government intervention has an important 

role to play.12 Theoretically, it is reasonable to believe that credit markets bear many of the traits of 

markets that would be plagued by market failures; information is incomplete and asymmetric, for 

example, about consumers’ ability to pay back a loan. If information is difficult to gather and ana-

lyze, then banks may end up using rules of thumb about the likelihood of a consumer being able 

to pay back a loan, and those practices may result in “statistical discrimination,” whereby, because 

individuals belong to a race or ethnic group, for example, that is statistically more likely to default 

and less likely to be able to pay back a loan, they are less likely to get a loan, despite the fact that if 

their own circumstances were fully understood, they would be deemed creditworthy. Proponents 

of the CRA and other interventions argue that these policies help to address these market failures 

and lead to better economic outcomes. Critics say government intervention in the credit markets, 

including (but not limited to) the CRA, played a major role in the financial crisis (see, for example, 

Leibowitz, 2008). These critics suggest that government policies, like the CRA, gave banks an 

incentive to adopt unsafe lending practices in the name of extending credit to underserved 

communities. Criticism from another perspective suggests that the CRA has had little impact on 

access to credit because bank lending may simply crowd out lending that would have taken place 

through other institutions, resulting in little overall increase in lending to LMI neighborhoods and 

consumers. These two streams of criticism suggest two important and basic questions for research 

to answer; first, does the CRA actually expand access to credit, and second, if so, does it do so in a 

way that contributed to the financial crisis that preceded the Great Recession?

Empirical research on the second question suggests that the CRA had little to do with the subprime 

crisis. Kroszner (2009: 11), for example, writes—

Two key points emerge from all of our analysis of the available data. First, only a small 

portion of subprime mortgage originations are related to the CRA. Second, CRA-related 

loans appear to perform comparably to other types of subprime loans. Taken together 

… we believe that the available evidence runs counter to the contention that the CRA 

contributed in any substantive way to the current mortgage crisis.

Of course, if the CRA had no real effect on the supply of credit, then it would be unlikely to have 

been part of the reason for the mortgage crisis and ensuing financial crisis.

The few papers that attempt to empirically examine the effect of the CRA on housing outcomes 

show mixed results. In particular, three papers use similar methodology to this article: Berry and 

Lee (2007), Gabriel and Rosenthal (2009), and Bhutta (2011). All three papers used a regression 

discontinuity design to examine the effect of the CRA on homeownership, mortgage applications, 

and mortgage originations, respectively, for example.

Berry and Lee (2007) found that the CRA had very little effect on outcomes in Home Mortgage 

Disclosure Act (HMDA) data on loan applications for the 1993-to-2003 period. However, they 

12 See Barr (2005) for a discussion of the credit market failures the CRA was designed to address.

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Butcher and Muñoz

focused on pairs of census tracts that are geographically adjacent, as well as having MFIs slightly 

less or more than 80 percent of AMFI. Their sample was is relatively small, and the estimated 

impacts have large standard errors and are statistically insignificant.

Gabriel and Rosenthal (2009) found some evidence that the CRA expanded home lending in the 

nonconforming13 loan sector, but not in the conforming sector, and that the CRA had a small 

positive impact on homeownership. Finally, Bhutta (2011) used HMDA data to examine mortgage 

originations in CRA-eligible neighborhoods and to CRA-eligible consumers. He focuses on two 

different periods: 1994 to 2002 and 2004 to 2006. The CRA may have had a different effect in 

these different periods because of both regulatory changes and changes in the economy. Bhutta 

also performed the analysis separately by metropolitan area size, because regulatory enforcement 

(or market forces that make it more or less likely that regulations have an impact) may differ 

depending on area population. Bhutta found that mortgage originations and applications were both 

higher in CRA-eligible neighborhoods in large metropolitan areas during the 1994-to-2002 period. 

Further, he found that this increase was not simply among regulated institutions, suggesting that 

the effect might be one of “crowding in” rather than “crowding out.”14 He found little evidence that 

the CRA affected housing-related credit outcomes in other areas or time periods.

In this article, we build on this prior research and similarly use a regression discontinuity design to 

measure the CRA’s effects. Following Bhutta’s findings, we focus on the effects in large metropolitan 

areas (that is, metropolitan areas with a population of at least 2 million) and allow the results to 

differ by time period.15 This article adds to the existing research by shifting the focus from out-

comes solely in the home lending market to the broader set of consumer credit outcomes available 

in the CCP/Equifax data.

Data and Descriptive Statistics

The CCP/Equifax data contains data beginning in 1999; the database reports information on loan 

performance and consumer debt on a quarterly basis. Although the data are anonymous, individuals 

can be tracked over time using a unique individual identification code. CCP/Equifax is updated every 

quarter adding individuals who have entered the credit report database using the same randomized 

sampling methods to maintain the representativeness of the sample as new people enter the Equifax 

database.16 Keep in mind that this data set is not representative of the entire U.S. population; it is rep-

resentative of individuals with a credit report. Thus, simply being in the data set may be an outcome 

of interest.

13  Nonconforming loans are those that either do not conform to the Fannie Mae or Freddie Mac guidelines, typically because the dollar amount is more than the purchasing limit—so-called  jumbo loans—or because they do not conform to loan-to-value guidelines. 

14 A  crowd-in effect can happen in the following manner: if one lender is induced by regulation to make loans to individuals or areas that nonregulated lenders have been reluctant to serve, due to lack of sufficient information for wise lending decisions, then one lender’s actions may provide the information necessary to make it worthwhile for the nonregulated institutions to extend credit in those areas or to those individuals as well. 

15 Appendix exhibit A-2 provides results for all metropolitan areas regardless of population for completeness.  

16 One can get information not only on those in the 5-percent sample (primary individuals), but also on individuals with credit reports who share a household with a primary individual. In this article, we focus only on the primary individuals.

90 The CRA Turns 40

Using Credit Reporting Agency Data To Assess the Link Between  

the Community Reinvestment Act and Consumer Credit Outcomes

Limitations of the CCP/Equifax Data Set

The data set contains richly detailed information on individuals’ credit records and the status of 

their loans, which can be tracked quarterly. However, the data contain very little information about 

the actual individuals; we know the years of their birth and their geographic locations each quarter, 

including the metropolitan area and census tract, but no other personal characteristics.17 Knowing 

the geographic information, however, makes it possible to merge the CCP/Equifax data with infor-

mation on each census tract’s characteristics, including the ratio of tract MFI to AMFI.

For this analysis, we use information from the first quarter of 2004 through the second quarter 

of 2012 (the latest information available when the project began). We merge the CCP/Equifax 

data with information on census tract characteristics that the FFIEC puts together from census or 

American Community Survey data. The FFIEC creates LMI census tract designations using census 

information on the relative Median Family Income (RMFI) in the census tracts, updated after each 

census.18 The LMI designations were updated in 2004 to reflect the 2000 census. Note that in our 

final data set, being an LMI census tract is a fixed characteristic of each tract in the time period we 

analyze. The LMI designation does not change between 2004 and 2012.

The CCP/Equifax data contain about 409 million quarterly observations from the first quarter of 

2004 through the second quarter of 2012. In the regression discontinuity analysis, we keep all in-

dividuals who are in census tracts that have MFIs between 75 and 85 percent of the AMFI. Recall that 

the CRA cutoff is at 80 percent of the AMFI, so this focuses our analysis on neighborhoods that have 

MFIs close to the cutoff. Within that narrow band, nonlinearities are less likely in the relationship 

between RMFI and the consumer credit outcomes of interest that might adversely affect the regression 

discontinuity estimates.19 We aggregate the individual-level data to census tract-level for each quarter.

Summary Statistics on Consumer Credit Outcomes

In this section, we present summary statistics describing how consumer credit outcomes changed 

over time by MFI, relative to the AMFI, which is helpful to know as groundwork for considering 

the impact of the CRA on consumer credit outcomes. We focus on a 10-percent random subset of 

the individuals who are 18 to 85 years old.20 We present outcomes by year and by six categories of 

RMFI: less than 50 percent, 50 to 80 percent, 80 to 110 percent, 110 to 140 percent, 140 to 170 

percent, and more than 170 percent of AMFI.

Exhibit 1 shows how the average number of primary individuals—that is, individuals represented 

in the CCP/Equifax data—in a census tract varies by RMFI. The average number of primary 

17 If individuals move, their credit outcomes move with them. An individual’s location is mapped to the address associated with current loan activity, which could cause problems for our estimation strategy if credit activity induces geographic mobility. Two points mitigate our concerns about this strategy. First, our estimation strategy is valid if mobility across census tracts generally differ with different income levels; it is only if differential mobility occurs right at the CRA threshold that our estimates could be affected. Second, research using census data examining whether mobility at the CRA threshold differs finds no evidence of this (Johnson, 2012).  

18 Until 2012, the FFIEC classifications were updated every 10 years using the decennial census data. Starting in 2012, classifications have been updated every 5 years using the American Community Survey. 

19 When we examine whether the estimates are robust to nonlinear specifications, we find they are when we limit the analysis to this narrow band of RMFI.  

20 Because the data set contains repeated observations on individuals, we take a 10-percent random subset of the individuals, not the observations.

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Exhibit 1

Average Number of Primary Individuals in Tract

Average Number of Primary Individuals in Tract

for Census Tract Median Family Income Groups

35

30

25

Number of  CIDs" in Tract" 20

15

2004

2005

2006

2007

2008

2009

2010

2011

2012

Tract Median Fam Inc as % of MSA Median Fam Inc

<50%

50% to 80%

80% to 110%

110% to 140%

140% to 170%

over 170%

CID = primary individuals in the data set. MSA = metropolitan statistical area.

Source: Federal Reserve Bank of New York Consumer Credit Panel/Equifax data

individuals in the data set for the top three RMFI categories—those above 110 percent of AMFI—

ranges between 25 and 35. On average, fewer than 15 primary individuals are in in the lowest-

income census tracts (those with MFIs less than 50 percent of AMFI). Because census tracts were 

originally constructed to have similar-sized total populations, and lower-income neighborhoods 

tend to have a lower adult-to-child ratios, this result is possibly driven by demographic differences 

across these neighborhoods.21 On the other hand, an individual must have a certain amount and 

type of economic activity connected to him or her in order to be included in the CCP/Equifax data. 

It makes sense that census tracts with higher income levels would be more likely to have more 

people with the type of economic activity that Equifax monitors and thus be more likely to be in 

the CCP/Equifax data.22 When we turn to the regression discontinuity analysis, the methodology 

will implicitly control for differences in the adult-to-child ratio across CRA-eligible and CRA- 

ineligible tracts, enabling us to see whether the CRA has an effect on individuals’ presence in the 

credit data. As credit report data are used for purposes other than to determine one’s risk of defaulting 

on a loan—for example, many employers request credit reports as part of the employment screening 

process—simply being in the data may be an important outcome. 

The top-line information on credit activity in the CCP/Equifax data is the total number of trades. A 

trade is defined as activity associated with an account (for example, an automobile loan, a mortgage, or a student loan), and  all trades is an aggregate measure of all the different types of activity captured in the CCP/Equifax data. Exhibit 2 shows how the all trades measure changes over time; the decline 

in all trades is stark with the beginning of the Great Recession, consistent with the decline in overall 

economic activity during a recession. Exhibit 2 also shows the average number of trades or accounts 

(from the all trades information) by RMFI by year. In all years, trades, on average, are more numerous 

21 In exhibit 4, we examine whether the fraction of adults in a census tract changes abruptly at the CRA-eligibility threshold and find that it does not. 

22 We also find that the fraction missing the top line information on “all trades” is lower in the higher-income census tracts (Muñoz and Butcher, 2013). 

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in census tracts with higher RMFIs. Further, in results not shown, we find the fraction of individuals 

missing information on all trades is higher in lower-income census tracts as well. In census tracts with 

MFIs above 110 percent of AMFI, missing values for all trades are relatively rare, generally less than 

5 percent. However, as MFI falls, the incidence of missing data on all trades increases. In neighbor-

hoods below 50 percent of AMFI, the incidence is about 15 percent (Muñoz and Butcher, 2013). 

Exhibit 3 provides further evidence on how the data vary by MFI and over time. The CCP/Equifax 

data are turned into a risk score by FICO and other credit score organizations.23 Although the precise 

Exhibit 2

Average Number of Trades

Average Number of Trades

for Census Tract Median Family Income Groups

7

6

5

4

Number of Trades

3

faminc_missrisk_new_aug15

6/28/17, 4:54 PM

2004

2005

2006

2007

2008

2009

2010

2011

2012

Tract Median Fam Inc as % of MSA Median Fam Inc

<50%

50% to 80%

80% to 110%

110% to 140%

140% to 170%

over 170%

MSA = metropolitan statistical area.  

Source: Federal Reserve Bank of New York Consumer Credit Panel/Equifax data

Exhibit 3

Fraction Missing Risk Score by Year

Fraction Missing Risk Score by Year

for Census Tract Median Family Income Groups

.2

.15

.1

Mean Risk Score .05

0

2004

2005

2006

2007

2008

2009

2010

2011

2012

Tract Median Fam Inc as % of MSA Median Fam Inc

<50%

50% to 80%

80% to 110%

110% to 140%

140% to 170%

over 170%

MSA = metropolitan statistical area.  

Source: Federal Reserve Bank of New York Consumer Credit Panel/Equifax data

23 The risk score in the CCP/Equifax data is not the official FICO score, but it has the same range and behaves in a similar way. 

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Butcher and Muñoz

methodology used to arrive at credit scores is proprietary, it is known to use information collected 

on outstanding credit activity to predict the probability that an individual would be more than 60 days 

late on a loan. Because not all individuals’ information is equally easy to find, some individuals 

have patchier records in the CCP/Equifax data than others. For example, some individuals have missing 

risk scores, presumably indicating that not enough information was available for them for the methodol-

ogy to produce a reliable risk score.24 Exhibit 3 shows the fraction of individuals in a census tract who 

are missing their risk scores.25 For census tracts in the highest three categories of RMFI (at or above 110 

percent of AMFI), about 5 percent or fewer are missing risk scores. For those in census tracts with MFIs 

less than 50 percent of AMFI, between 15 and 20 percent of the individuals are missing risk scores.

Keep in mind then, that MFI is correlated with being in the CCP/Equifax data at all and with the 

amount of actual information in the data set about one’s economic activities. In the sections that 

follow, we will look at these as outcomes and examine whether a neighborhood being eligible for 

the CRA has an effect on these margins, as well as on other consumer credit outcomes.

Methodology

We would like to estimate the causal effect of the CRA on consumer credit outcomes. By  causal 

effect, we mean those changes in consumer credit outcomes that are due to CRA regulations. A 

starting point is the following linear regression.

                                              , 

(1)

where        is a given credit market outcome for individual  i in neighborhood  n at time  t.          is a variable indicating whether the individual lives in an LMI neighborhood or not,       is a vector 

of individual and neighborhood characteristics that may vary over time, and       is the error term. 

The error term contains all the factors not explicitly included in the regression that affect    . If these factors are correlated with whether one lives in an LMI neighborhood, then the estimated impact of 

        on consumer credit outcomes from equation (1) will be biased and will pick up many differ-

ences between consumers in LMI and non-LMI neighborhoods that have nothing to do with CRA.

Because individuals in LMI and non-LMI neighborhoods are likely to differ on many dimensions 

that will affect their credit outcomes, to estimate the causal effect of CRA on consumer credit 

outcomes, we exploit the discontinuity in the way banks get CRA credit, based on the legislated 

definition of an LMI neighborhood. Neighborhoods will be deemed LMI, and banks will receive 

CRA credit for activities in that neighborhood, if they are below the 80-percent threshold on the 

ratio of MFI in that census tract to AMFI in the MSA. Thus, we define LMI as in equation 2.

24 This finding is consistent with the results on missing information on all trades discussed previously (Muñoz and Butcher, 2013).  

25 The denominator here, and in all the other fraction outcomes, is the number of individuals in the CCP/Equifax data in the census tract, not a measure of the tract population from the census.

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                if                                                                                            , (2)

                if                                                                                            , where  n indexes the census tract (or neighborhood) and  c the metropolitan area. This equation is the source of the discontinuity; as RMFI in the census tract passes from slightly less than 80 percent to 80 

percent or more, its designation flips from LMI to not LMI (or from 1 to 0 in our data categories).

We also define a scaled version of the census tract’s RMFI.

                                                                                                    .   

(3)

We scale the variable by the 80-percent threshold for ease of interpretation. When RMFI is 0, LMI 

is 0, so in the following analyses, we can center the data on 0.

Using the variables LMI and RMFI, we have the following regression equation.

                                                      .   

(4)

One would expect consumer credit outcomes to increase with RMFI—so      is expected to be positive 

(for good outcomes). The question posed in this study is whether the response of credit outcomes to 

income right at the LMI boundary is discontinuous, which would suggest something special about 

the CRA designation, as no other reason exists to expect a discontinuous change in the relationship 

between credit outcomes and RMFI at that threshold.26 If no discontinuity exists, then      will be 

estimated to be 0, implying no estimated effect of the CRA on individuals’ credit outcomes. If      is 

estimated to be a positive (negative) number, then this indicates that outcomes in LMI neighborhoods 

are better (worse) than in neighborhoods that are incrementally better off in terms of RMFI.

The assumption that must hold for a regression discontinuity design to give insight into the impact of 

the CRA on consumers’ credit outcomes is that nothing else that might affect credit outcomes changes 

discontinuously at the 80-percent RMFI threshold. Exhibit 4 examines whether observable charac-

teristics change at the LMI threshold, holding constant RMFI. We use the same sample restrictions as 

in the main analysis using the consumer credit data.27 Columns 1 through 5 use tract-level data from 

the 2000 census to examine whether a discontinuous change occurs in tract characteristics at the LMI 

threshold for the following:28 population, fraction of the population that is more than 25 years old, 

fraction of the population with a college degree, fraction of the population that reports being His-

panic, and fraction of the population that reports being African-American (non-Hispanic). No change 

in these tract-level characteristics is statistically significant at the LMI threshold. Previous research 

using regression discontinuity to study effects of the CRA has also presented evidence that the regres-

sion discontinuity design is valid by examining characteristics at the CRA eligibility threshold (Berry 

26 In the regression discontinuity framework, the continuous variable—in our case, RMFI—is referred to as the “running variable.” 

27 We focus on census tracts with MFIs between 75 and 85 percent of AMFI; further, we focus on large MSAs with populations greater than 2 million. 

28 The data are from the 2000 census, with one observation per census tract.

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Butcher and Muñoz

Exhibit 4

Estimated Regression Coefficients for Census Tract Characteristics

(1)

(2)

(3)

(4)

(5)

(6)

Fraction of 

Fraction of 

Fraction of 

Fraction of 

Population 

Population  Median Age 

Population

Population 

Population 

College 

African-

in Tract

Older Than 25

Hispanic

Degree

American

LMI tract

145.7

0.00387

0.000638

0.00150

– 0.0110

0.058

(Standard error)

(171.8)

(0.00615)

(0.00365)

(0.0123)

(0.0171)

(0.3934)

p-value

0.397

0.530

0.861

0.903

0.521

0.884

RMFI

60.43

0.00291

0.00204

– 0.00647

– 0.00615

0.111

(Standard error)

(29.51)

(0.00106)

(0.000627)

(0.00212)

(0.00295)

(0.0681)

p-value

0.0407

0.00598

0.00116

0.00226

0.0368

0.102

Observations

2,518

2,518

2,518

2,518

2,518

85,221

R-squared

0.137

0.193

0.236

0.555

0.203

0.1981

LMI = low- and moderate-income. RMFI = relative Median Family Income.

Notes: The regressions in columns 1 through 5 use census tract-level data, available from the Federal Financial Institutions Examination Council from 2009 (based on the 2000 census and revisions made to LMI tract definitions in 2004). Columns 1 

through 5 are estimated including metropolitan statistical area (MSA) fixed effects. Observations are at the tract level, with one observation per census tract. Columns 1 through 5 include census tracts where the Median Family Income (MFI) is between 75 and 85 percent of the MSA median; only large MSAs are included. Column 6 reports results of a regression using Consumer Credit Panel/Equifax data, and the same specification used for the main results in exhibit 5. The outcome is median age in the census tract. Column 6 includes census tracts where MFI is between 75 and 85 percent of the MSA median; only large MSA are included. Column 6 includes MSA fixed effects and quarter fixed effects. In Column 6, standard errors clustered at the census tract level. 

and Lee, 2007;29 Bhutta, 2011; Gabriel and Rosenthal, 2009; Johnson, 201230).31 The last column 

of exhibit 4 uses median age in the census tract in the CCP/Equifax data, which similarly shows no 

discontinuous change in median age at the CRA threshold.32

Although the regression discontinuity design is rigorous and has the potential to establish causal 

relationships, it is not without drawbacks. It is a “data hungry” methodology; it needs a large 

number of observations in order to get an estimate that is precise enough to be able to determine 

an effect with some certainty.33

29 Berry and Lee (2007) show no discontinuity at the CRA threshold in percent female, percent Black, percent Hispanic, percent Asian, or income. 

30 Johnson’s (2012) undergraduate honors thesis at Wellesley College examined evidence of an impact of the CRA on demographic characteristics of a neighborhood. As Johnson points out, changes in credit outcomes in a neighborhood could generate changes in neighborhood demographic and real estate characteristics. However, Johnson’s findings indicate no statistically significant difference at the threshold in mobility rates or home values; she found some evidence for a change in college education and commute times. 

31 Note that, under these conditions, a regression discontinuity design is similar to a randomized controlled trial and can give sound insight into the causal effects of the CRA on credit outcomes (Lee, 2008).  

32 Appendix exhibit A-1 investigates the robustness of results to alternative specifications. Columns 3 and 4 show the results with and without controlling for median age in the census tract. The results are robust; in the main results, we include median age as a control.  

33 We need enough data around the threshold to allow for this estimation strategy. In results not shown, we estimated a kernel density function and showed good support in the data in the 75- to 85-percent range of AMI (Muñoz and Butcher, 2013: 24).  

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Finally, this technique focuses on changes at the LMI threshold—and the neighborhoods slightly 

below the 80-percent RMFI cutoff are the relatively better-off LMI neighborhoods. It is possible that 

the CRA has a different effect farther away from this threshold; this technique cannot tell us that.

Results

Our main results focus on data that are aggregated from the individual level to the census tract 

level for the years 2004 to 2012. We restrict the sample to census tracts between 75 and 85 percent 

of AMFI and model the relationship between credit outcomes and RMFI as linear.34,35 We further 

restrict our analysis to large metropolitan areas, those with at least 2 million people (Bhutta, 

2011).36,37 Our main analysis data set consists of the 85,221 quarterly census-tract observations 

that fall within this range.

Equation 4 enables us to hold RMFI constant and ask whether a discontinuous jump occurs in the 

relationship between each consumer credit outcome in question,    , and being in a neighborhood 

that barely passes into CRA eligibility. Our regression discontinuity design will also enable us to 

test whether the relationship between the consumer credit outcomes and LMI is different before 

and after the financial crisis, as in equation 5. Estimates of      in equation 5 will show whether the 

discontinuous jump at the LMI threshold is significantly different after the financial crisis.

(5)

The Regression Estimates of the Discontinuity at the LMI Threshold 

Exhibit 5 presents the results of estimating the regression discontinuity for different consumer 

credit outcomes38 and enables us to see whether the size of the jump at the CRA threshold is 

statistically different from 0. We control for MSA fixed effects, quarter effects, and median age in 

the census tract, and we cluster the standard errors at the census tract.39 The results in column 1 of 

exhibit 5 hold constant MSA fixed effect, quarter effects, and median age in the census tract. The 

estimated jump in all trades at the CRA eligibility threshold is about 9 percent, which is statistically 

significantly different from 0 at the 10-percent level, suggesting that the CRA has expanded access 

to credit in LMI neighborhoods.

34 Regression discontinuity estimates can be sensitive to functional form. We tested different functional forms, including allowing the slope to differ on either side of the discontinuity, and a cubic in RMFI. When the data are limited to RMFIs within 5 points of the cutoff, a linear functional form fits the data, and the results are robust to other choices of functional form. 

35 Recall that RMFI is based on information from the 2000 census and does not vary within a census tract from 2004 to 2012.  

36 Results that include other areas are in appendix exhibit A-2. We find no statistically significant effects at the CRA threshold in this sample. 

37 The analysis includes 42 metropolitan areas. 

38 Appendix exhibit A-1 shows that the results for the sum of all trades are robust to various functional form assumptions about the relationship between the sum of all trades and RMFI and to the inclusion of different controls. 

39 We cluster the standard errors at the census tract-level, as tracts may have systematically correlated shocks in their consumer credit outcomes. This sample includes 2,509 different census tracts. Results are robust to clustering standard errors at the MSA level.

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Exhibit 5

Estimated Regression Coefficients for Selected Consumer Credit Outcomes:  

Large Metropolitan Areas

(1)

(2)

(3)

(4)

(5)

(6)

(7)

log(Sum log(Number  Fraction 

log(Sum 

Average 

log(Sum of 

log(Sum of 

of All 

of People  

Missing  

of Auto 

Risk 

Mortgages)

Delinquencies)

Trades)

in Tract)

Risk Score

Loans)

Score

LMI tract

0.0899

0.0714

– 0.00526

0.0275

0.0604

0.0566

1.742

(Standard error)

(0.0466)

(0.0426)

(0.00261)

(0.0478)

(0.0456)

(0.0478)

(1.775)

p-value

0.0539

0.0938

0.0443

0.565

0.185

0.236

0.327

RMFI

0.0376

0.0262

– 0.00230

0.0356

0.0338

0.0158

1.429

(Standard error) (0.00814) (0.00751)

(0.000447)

(0.00855)

(0.00805)

(0.00832)

(0.306)

p-value

0.0000 0.000487

0.0000

0.0000

0.0000

0.0570

0.0000

Standard errors 

Yes

Yes

Yes

Yes

Yes

Yes

Yes

clustered

Quarter 

Yes

Yes

Yes

Yes

Yes

Yes

Yes

indicators

MSA fixed 

Yes

Yes

Yes

Yes

Yes

Yes

Yes

effects 

Median age in 

Yes

Yes

Yes

Yes

Yes

Yes

Yes

census tract

Observations

85,203

85,221

85,221

84,924

85,021

84,940

85,207

R-squared

0.207

0.204

0.223

0.354

0.396

0.219

0.447

LMI = low- and moderate-income. MSA = metropolitan statistical area. RMFI = relative Median Family Income.

Notes: The dependent variable is the indicated consumer credit outcome in a census tract in a given quarter. LMI tract is a dummy variable equal to 1 if the Median Family Income (MFI) in the census tract is less than 80 percent of the Area Median Family Income (AMFI), indicating the census tract is eligible for the Community Reinvestment Act, and 0 otherwise. RMFI is MFI in the census tract divided by AMFI, multiplied by 100 to obtain the percentage, then scaled by subtracting 80, so that at RMFI = 0, LMI turns from 1 to 0. All regressions include a constant. Standard errors are in parentheses and are clustered at the census tract level; the clusters numbered 2,509.

In columns 2 through 7 of exhibit 5, we examine additional consumer credit outcomes: the log of 

the number of individuals in the CCP/Equifax data in each census tract in each quarter, the fraction 

of individuals in a census tract in a quarter who are missing their risk scores, the log of the sum 

of mortgages in a census tract in a quarter, the log of the sum of auto loans in a census tract in a 

quarter, the log of the sum of delinquent loans in a census tract in a quarter, and finally the average 

credit risk score in a census tract in a quarter. Although other credit outcomes are available in the 

CCP/Equifax data, we believe these measures capture a broad set of interesting outcomes.

The estimates in column 2 of exhibit 5 indicate that 7 percent more individuals are represented in 

the CCP/Equifax data set in census tracts that are only barely eligible for the CRA than are in cen-

sus tracts that are only barely ineligible. Recall from the discussion of exhibit 1 that more individu-

als in the CCP/Equifax data are in higher-income census tracts, possibly because those tracts have 

a higher adult-to-child ratio, leading to more people with credit records, or possibly because more 

of the types of economic activities that generate a credit market footprint are in higher-income 

neighborhoods. The regression discontinuity methodology controls for potential differences in 

demographic makeup of the tracts, adult-to-child ratios should not differ significantly between 

tracts that are barely below and above the CRA threshold (and, as presented in exhibit 4, no direct 

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evidence was found of a change in the fraction of the population that is more than 25 years old). A 

marginally statistically significant 7-percent discontinuous jump emerged in the data at the eligibil-

ity threshold, suggesting that CRA eligibility has expanded credit market activity in these areas.

Column 3 of exhibit 5 presents coefficient estimates for the fraction of individuals in a census 

tract or quarter who are missing a risk score. As discussed previously, in order to generate a risk 

score for an individual, the scoring companies need to have enough information about a person’s 

interaction with the credit markets to predict his or her risk of default. Thus, a missing risk score is 

an indication that an individual has a relatively thin record (or that the person’s activities were dif-

ficult to track for some reason). We see a statistically significant discontinuous -0.0053 jump in the 

fraction of individuals who are missing their risk scores at the CRA eligibility threshold. The mean 

of the fraction that represents missing risk scores is about 9.5 percent. The coefficient indicates 

that going from non-LMI to LMI status (controlling for RMFI) reduces this by about 0.5 percentage 

point, or by about 5 percent of the mean. We take the results for these three outcomes—total num-

ber of trades, the number of individuals in the CCP/Equifax data, and the fraction of individuals 

with a missing risk score—to show that, in general, more credit market activity is present among 

individuals who live in areas that barely meet the criteria for CRA eligibility.

In columns 4 through 7 of exhibit 5, we examine some specific credit outcomes: the sum of all 

mortgages, sum of automobile loans, sum of delinquencies, and the average credit risk score. 

The results40 in column 4 indicate that the estimated size of the jump in mortgages at the CRA 

eligibility threshold is about 2.75 percent, but although this estimate is positive it is not statistically 

different from 0. Recall from the background section that Bhutta (2011) found a statistically signifi-

cant 4-percent increase in mortgage originations using a regression discontinuity methodology and 

focusing on a similar sample from large metropolitan areas.

Why the difference in results? First, note that our 95-percent confidence interval ranges from -6.6 

percent to 12.1 percent, which includes Bhutta’s estimate. In addition, the measures and the time 

periods of the two studies differ substantially. Our measure should be thought of as a measure 

of the stock of mortgages—the number of existing mortgages—in a given census tract in a given 

quarter, whereas Bhutta’s measure is a measure of mortgage originations or flows—new mortgages 

in census tract in a given year.41 In any given time period, no reason exists to think that the effect 

on the stock of mortgages and the flow of mortgages will be the same. In addition, our study 

begins in 2004, during the housing boom, when many mortgages were being extended and thus 

being on the correct side of the CRA threshold may not have been a determining factor in lending 

institutions’ decisions to extend credit for housing. It may not be surprising that we do not find a 

statistically significant effect at the CRA threshold for mortgages during this period.

Column 5 of exhibit 5 presents evidence for the log of the sum of auto loans in a census tract in a 

quarter. The coefficient on LMI indicates that about 6 percent more automobile loans are made in 

areas that cross the threshold of CRA eligibility, but—as with the estimate for mortgages—although 

it is positive, this coefficient is not statistically different from 0.

40 The sample size varies across the columns because not all census tracts have all outcomes. For example, not all census tracts have mortgages in the CCP/Equifax data in all quarters and, because we are taking the log of the sum of mortgages and the log of 0 is undefined, the sample size is different for mortgage outcomes and for the number of people in a census tract. 

41 HMDA data used by Bhutta reports the number of new mortgages issued in a given year. 

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The results so far suggest that the CRA expands some types of credit market activities in LMI 

neighborhoods. If credit activity increases, does the CRA put more consumers at risk of undesir-

able outcomes? Column 6 presents evidence for one adverse outcome: delinquencies. The depen-

dent variable is calculated as the log of the sum of all delinquencies in a census tract in a quarter. 

Delinquencies are defined as the total number of trades on record minus the total number of trades 

that are defined by Equifax as being current (that is, not more than 30 days past due). Thus, the 

sum of delinquencies in a neighborhood can be higher if more people have a single delinquency, a 

single person has many delinquencies, or both. As the number of people and the number of trades 

in a census tract increase, the risk of a delinquency will increase, and we know from the results 

in columns 1 and 2 that both of these variables are higher at the CRA threshold. However, the 

evidence in column 6 shows that the total number of delinquencies in a census tract at the CRA 

threshold did not have a statistically significant change.

Finally, column 7 of exhibit 5 presents estimates for the impact of CRA eligibility on the average 

risk score in a census tract in a quarter. A higher credit risk score indicates that the individual is 

considered more likely to pay back a loan. It is unclear how the CRA, if it were to increase credit 

supply in a neighborhood, might affect risk scores. If the CRA encourages lenders to make loans to 

individuals with poor credit histories, drawing more marginal borrowers into to the credit market, 

then one might expect the coefficient on LMI to be negative. On the other hand, if CRA encour-

ages lenders to  find individuals who are no riskier than other customers but who happen to live 

in a CRA-eligible neighborhood, then individuals’ credit scores may not be affected. Finally, it is 

possible that the CRA could improve individual credit scores if it made appropriate loans available 

to relatively low-income individuals such that they were able to keep up with the payments and 

establish healthy credit records.

The estimate in column 7 of exhibit 5 is inconclusive; the point estimate is a positive 1.74, but it is not 

statistically different from 0. The 95-percent confidence around the point estimate ranges from –1.74 

to 5.22. Because the mean of the average risk score in a census tract and year is 671.5, the range of 

estimates implies that we can rule out large changes in average credit risk score at the CRA threshold.

In sum, the results from exhibit 5 suggest that CRA eligibility expanded LMI neighborhoods’ access 

to credit markets in the 2004-to-2012 period. Census tracts that barely qualify for CRA eligibility 

by virtue of having RMFI slightly below 80 percent of the AMFI have 9 percent more total trades, 

7 percent more people in the CCP/Equifax data, and are 0.5 percentage points less likely to have 

missing risk score information (about a 5-percent reduction) compared with census tracts that 

have slightly  higher RMFI.

In the next section, we look more closely at housing-related credit outcomes and examine whether 

the effect of the CRA was different before and after the beginning of the financial crisis and Great 

Recession.

Housing Outcomes Before and After the Financial Crisis

Exhibit 6 uses the same basic regression discontinuity framework to investigate the impact of 

the CRA on housing-related consumer credit outcomes: mortgages and foreclosures. Because the 

financial crisis arguably began with the bursting of the housing bubble, and because government 

100 The CRA Turns 40

Using Credit Reporting Agency Data To Assess the Link Between  

the Community Reinvestment Act and Consumer Credit Outcomes

-

es

s s s s

Ye Ye Ye Ye

(8)

0.161 0.0102

0.0296

0.0352

0.342

84,924 0.226

Fraction 

eclosur

– 0.00551 (0.00393)

(0.00470)

– 0.00129 (0.000612)

0.000697 (0.000733)

For

r indicators. All 

es

s s s s

Ye Ye Ye Ye

(7)

0.710

0.225

0.00110

0.0780

84,924

Fraction 

eclosur

(0.00297)

– 0.000840 (0.000476)

For

es in 

s s s s

ears)

Ye Ye Ye Ye

(6)

0.0336

0.393

0.488

0.169

0.684

45,180 0.225

e and After the Financial Crisis: 

(0.0393)

– 0.0336 (0.0485)

0.00954 (0.00694)

(0.00866)

log(Sum of 

eclosur

– 0.00352

For

Past 2 Y

es in 

s s s s

ears)

Ye Ye Ye Ye

(5)

0.783

0.187

0.225

0.00798 (0.0290)

0.00685

45,180

(0.00519)

log(Sum of 

eclosur

For

Past 2 Y

. In each of the paired columns (1 and 2, 3 and 4, and so on), the first set of re

edit Outcomes, Befor

es)

s s s s

Ye Ye Ye Ye

(4)

0.510

0.387

0.167

0.487

0.292

eclosur

0.0335 (0.0508)

0.0122

66,576

– 0.0450 (0.0519)

log(Sum of 

(0.00878)

– 0.00636 (0.00914)

For

, indicating the census tract is eligible for the Community Reinvestment Act, and 0 otherwise. RMFI 

Median Family Income.

t comes from estimating equation 5. LMI is a dummy variable equal to 1 if the Median Family Income 

tract in a given quarter

en scaled by subtracting 80, so that at RMFI = 0, LMI turns from 1 to 0. Post2007 is an indicator that 

t the census tract level.

es)

s s s s

Ye Ye Ye Ye

(3)

0.954

0.269

0.292

eclosur

0.00231 (0.0402)

0.00774

66,576

log(Sum of 

(0.00700)

For

s s s s

Ye Ye Ye Ye

(2)

0.0399

0.408

0.275

0.745

0.354

(0.0482)

0.0362

0.0000

84,924

– 0.0191 (0.0175)

(0.00850)

(0.00307)

log(Sum of 

Mortgages)

– 0.000998

, 0 otherwise. It interacts with both LMI and RMFI. The postcrisis main effect on the outcome is controlled for with the quarte s s s s

Ye Ye Ye Ye

(1)

0.0275

0.565

0.354

(0.0478)

0.0356

0.0000

84,924

log(Sum of 

Mortgages)

(0.00855)

eas

ed

ession Coefficients for Housing-Related Consumer Cr

vation is from 2007 or later

opolitan Ar

or)

or)

or)

or)

ors cluster

fects 

d err

d err

d err

d err

d err

ed

ge Metr

Exhibit 6

Estimated Regr

Lar

-value

-value

-value

-value

-squar

LMI tract (Standar  p  LMI * Post 2007 (Standar  p  RMFI (Standar  p  RMFI * Post 2007 (Standar  p  Standar Quarter indicators MSA fixed ef Median age in census tract Observations  R

LMI = low- and moderate-income. MSA = metropolitan statistical area. RMFI = relative 

Notes: The dependent variable is the indicated consumer credit outcome in a census 

sults is from estimating equations as in column 4 of exhibits 4 and 5, and the second se

(MFI) in the census tract is less than 80 percent of the Area Median Family Income (AMFI)

is MFI in the census tract divided by AMFI, multiplied by 100 obtain the percentage, th

equals 1 if the obser

regressions include a constant. Standard errors are in parentheses and are clustered a

Cityscape 101

Butcher and Muñoz

interventions in the credit markets related to housing have come in for particular criticism as 

exacerbating (or creating) the financial crisis, it is worthwhile to examine whether the estimated 

impact that living in a CRA-eligible neighborhood has on housing outcomes differs before and after 

the financial crisis. Exhibit 6 presents two sets of results for each housing outcome. The first set of 

results is from estimating equations, as in exhibit 5. The second set comes from estimating equa-

tion 5, enabling interactions between the LMI indicator and an indicator variable for the postcrisis 

period (as well as an interaction between RMFI and the postcrisis indicator). 

We will focus on the estimates of     and     :    tells us whether a discontinuous jump occurred 

at the CRA threshold prior to the crisis, and     tells us whether the jump at the LMI threshold is 

different after the crisis begins. If the estimate of     is not statistically different from 0, then the data indicate that the jump is not statistically different in the two time periods. The estimated size of the 

jump at the CRA threshold in the postcrisis period is given by adding the estimates of     and     .42 

As in the previously stated results, we control for quarter, for metropolitan area, and for median 

age in the census tract.

The housing outcomes we examine are the log of the sum of mortgages in the census tract in a 

quarter, the log of the sum of foreclosures in a quarter, the log of the sum of foreclosures within the 

past 2 years, and the foreclosure rate. Recall that the sum of mortgages in a census tract in a year 

is a measure of the stock of mortgages among individuals in that tract. Foreclosures are similarly a 

stock, but an individual would be coded as having a foreclosure on his or her record if a foreclo-

sure had occurred in the past 7 years. Also, the CCP/Equifax data have a measure of foreclosures 

initiated in the past 2 years, so these can be thought of as a stock of recently initiated foreclosures. 

Finally, the foreclosure rate measure is the stock of foreclosures initiated in the past 7 years divided 

by the stock of mortgages on record. Given that the CRA may increase mortgages, and one needs 

to have a mortgage in order to be at risk of foreclosure, we are interested in whether the fraction 

of foreclosures is affected by CRA eligibility. However, getting the timing right for these measures, 

especially in terms of what we would have expected to change (and when) after the financial 

crisis, is difficult. Perhaps we should investigate only newly initiated foreclosures and whether 

that changed in 2007. However, if outcomes were worse earlier in LMI neighborhoods, limiting to 

newly initiated foreclosures might miss that. We think the measures we have chosen are a sensible 

starting point. We consider all the years from 2007 to 2012 to be postcrisis years, so if housing 

outcomes got relatively worse on average during that period in neighborhoods that are at the CRA 

eligibility threshold, our measures should pick that up.

Columns 1 and 2 of exhibit 6 show the results for the log of the sum of mortgages. No change 

was statistically significant in mortgages at the CRA threshold (results in column 1 are a repetition 

of exhibit 5, column 4). In column 2, we see that the interaction between LMI and the postcrisis 

period is negative but not statistically different from 0. In the discontinuity at the CRA eligibility 

threshold after the financial crisis, the change is not statistically significant. Overall, no evidence of 

a change in mortgage stock is present in the 2004-to-2012 period due to CRA eligibility.

Columns 3 and 4 present results for the log of the sum of foreclosures in a census tract in a 

quarter. Column 3 indicates CRA eligibility has no statistically meaningful relationship with the 

42 Any year after 2006 (2007 and later) is a postcrisis year. 

102 The CRA Turns 40

Using Credit Reporting Agency Data To Assess the Link Between  

the Community Reinvestment Act and Consumer Credit Outcomes

stock of foreclosures. Column 4 also indicates no statistically meaningful change in the relationship 

between the precrisis and postcrisis years. Results using the log of the sum of foreclosures in the 

past 2 years follow a similar pattern. Finally, the estimated impact of CRA eligibility on foreclosure 

rate (column 7) is a small, imprecisely estimated, positive number (a coefficient of 0.0011, and 

the mean of the dependent variable is 0.051) for all time periods combined. When we allow the 

effect of CRA eligibility to differ by precrisis and postcrisis period (column 8), we see an estimated 

change in the size of the jump at the CRA threshold. The coefficient on LMI is negative but not 

statistically different from 0 for the precrisis period. The interaction term between LMI and the 

postcrisis period is positive and statistically different from 0, indicating a statistically meaningful 

change in the size of the jump, and that change was toward a higher fraction of foreclosures in 

CRA-eligible neighborhoods in the postcrisis period. However, the estimated size of the jump in 

the postcrisis period is small, 0.005,43 and is not statistically different from 0.44 Thus, no evidence 

exists that being in a CRA-eligible neighborhood affected the housing market either beneficially or 

adversely in the 2004-to-2012 period.45

Discussion and Conclusions

The research presented here uses CCP/Equifax credit data to examine the effect of the CRA on 

consumer credit outcomes. These data enable us to expand on previous research by focusing on a 

broad set of consumer credit outcomes for which little prior evidence of the CRA’s impacts exists. 

Using a regression discontinuity design, we find evidence that the CRA expanded consumers’ 

credit market footprint: a 9-percent increase in all trades, or accounts, at the CRA threshold. Also, 

the number of people who have a record in the CCP/Equifax data at the CRA threshold increased 

7 percent, and the incidence of individuals missing a risk score—a likely indication of a thin 

record of activity in the CCP/Equifax data—at the CRA threshold decreased 0.5 percentage point 

(around 5 percent of the mean). These results suggest that the CRA is having an effect on consumer 

credit market activity. As credit reports are becoming broadly used—for example, by employers 

as a screening method during the hiring process—it is possible that even having a presence in the 

43 To see the size of the jump in the postcrisis period, one must add the coefficient on LMI (-0.0055) and the coefficient on the LMI*postcrisis interaction term (0.0102), to get 0.0047. Although this number is positive, it is not statistically significantly different from 0.  

44 The regression in column 8, and in the rest of the table, restricts the effects of the other variables in the regression—the MSA codes and the median age in the census tract—to be the same in the precrisis and postcrisis era. To ensure that this is not somehow affecting the coefficient on LMI, we also ran the regressions separately for the precrisis and postcrisis periods. When we do this, the coefficients ( p-values) for LMI pre- and post-2007 are -0.0055 (0.141) and 0.0047 (0.177), respectively, which are very similar to the estimates in column 8. 

45 If CRA generated mortgages that were more likely to be foreclosed on, which then generated additional mobility to neighborhoods with RMFIs farther away from the CRA eligibility thresholds, our methodology would not pick up the increased foreclosure activity because we are measuring whether, in a tract in a given quarter, more people in the CCP/

Equifax data have a foreclosure on their records. Evidence against this potential problem is that direct measures of mobility at the CRA threshold are no different (Johnson, 2012) and that we find more people in the CCP/Equifax data at the CRA threshold. Nonetheless, future research might fruitfully use the CCP/Equifax data to track individuals over time and examine whether those who are initially observed in neighborhoods at the CRA threshold have different long-term outcomes. Similarly, using the panel data during long periods to examine whether changes in the LMI designation that occur with releases of new census or American Community Survey data generate changes in outcomes for individuals may shed new light on these issues. 

Cityscape 103

Butcher and Muñoz

credit report data is an outcome with potential implications for individuals’ well-being. We find 

no evidence that adverse outcomes, such as loan delinquencies, poor credit risk scores, or foreclo-

sures, increase at the CRA threshold.

Although we find broad evidence of the CRA’s impact on consumers’ interactions with credit mar-

kets, we do not necessarily see these effects where one might have expected them. For example, 

because mortgages are an important piece of what regulators consider in assessing compliance with 

the CRA, one might expect to see an impact on housing-related outcomes. Indeed, much of the 

research on the CRA has focused on its impact on housing market outcomes. That research finds 

that the estimated impacts are sensitive to the sample’s geographic area and time period, and the 

time frame we examine is one in which many mortgages were being extended by many types of 

lending institutions not subject to the CRA, so perhaps it is unsurprising that we find no effect on 

the stock of mortgages at the CRA eligibility threshold. However, we also find no adverse effects; 

we find no evidence of a significant jump in foreclosures at the CRA threshold, whether we focus 

on the time period before or the period during and after the financial crisis.

Our finding of a change in broad measures of consumer credit market activity at the CRA threshold 

suggests a role for CRA beyond the housing market. This finding does, of course, beg the question 

of what the mechanism might be. Large institutions, at least, are examined for CRA compliance 

in terms of services extended in LMI neighborhoods. Our results are consistent with institutions 

proffering expanded services in areas at the CRA eligibility threshold, leading to more overall credit 

market activity.

Finally, keep in mind that the regression discontinuity methodology does have drawbacks. In par-

ticular, it only tells us what happens right at the threshold where we cross over from CRA eligibility 

to CRA ineligibility. If the CRA gives regulated lending institutions an incentive to extend credit to 

individuals in the highest-income neighborhoods among the CRA-eligible neighborhoods, then the 

effects could be large at the threshold but zero elsewhere. On the other hand, the CRA could drive 

institutions to create products that are appropriate for LMI consumers (loans that receive CRA 

credit), many of whom can be found in neighborhoods with MFIs at 80 percent, 81 percent, and 

so on of the AMFI. This would tend to erode any differences between neighborhoods right below 

and above the thresholds. In short, the fact that the CRA eligibility threshold in our broad mea-

sures of consumer credit market activity jumps suggests an effect of the CRA, but the magnitudes 

should be interpreted with caution. 

Appendix

Exhibit A-1 shows that results for the log(sum of all trades) are robust to the inclusion of controls 

for metropolitan statistical area fixed effects, quarter fixed effects, and median age in the census 

tract. Further, the point estimates are similar when we allow for a cubic in relative Median Family 

Income (RMFI). Column 5 shows results when we use a cubic model. The coefficient on low- and 

moderate-income (LMI)—the estimate of the break at the Community Reinvestment Act (CRA) 

eligibility threshold—is still very similar to the coefficient in column 4 (9-percent increase in trades 

versus an 8-percent increase for columns 4 and 5, respectively). The coefficients in columns 4 and 5  

are not statistically different from one another; however, the estimated break at the LMI threshold 

104 The CRA Turns 40

Using Credit Reporting Agency Data To Assess the Link Between  

the Community Reinvestment Act and Consumer Credit Outcomes

Exhibit A-1

Estimated Regression Coefficients for log(Sum of All Trades in Census Tract): 

Large Metropolitan Areas

(1)

(2)

(3)

(4)

(5)

(6)

LMI tract

0.0803

0.0803

0.0900

0.0899

0.0799

0.0910

(Standard error)

(0.00931)

(0.0515)

(0.0466)

(0.0466)

(0.0622)

(0.0467)

p-value

0.000

0.119

0.0535

0.0539

0.200

0.0516

RMFI

0.0380

0.0380

0.0378

0.0376

Cubic in 

0.0469

RMFI

(Standard error)

(0.00160)

(0.00896)

(0.00811)

(0.00814)

(0.0123)

p-value

0.000

0.000

0.000

0.000

0.000139

LMI * RMFI

– 0.0181

(Standard error)

(0.0163)

p-value

0.267

Standard errors 

No

Yes

Yes

Yes

Yes

Yes

clustered

Quarter 

No

No

Yes

Yes

Yes

Yes

indicators

MSA fixed 

No

No

Yes

Yes

Yes

Yes

effects 

Median age in 

No

No

No

Yes

Yes

Yes

census tract

Observations

85,203

85,203

85,203

85,203

85,203

85,203

R-squared

0.013

0.013

0.207

0.207

0.207

0.207

LMI = low- and moderate-income. MSA = metropolitan statistical area. RMFI = relative Median Family Income.

Notes: The dependent variable is the natural log of the sum of all consumer credit trades in a census tract in a given quarter. 

The sample includes metropolitan areas with populations of 2 million or more. LMI is a dummy variable equal to 1 if the Median Family Income (MFI) in the census tract is less than 80 percent of the Area Median Family Income (AMFI), indicating the census tract is eligible for the Community Reinvestment Act, and 0 otherwise. RMFI is MFI in the census tract divided by AMFI, multiplied by 100 to obtain the percentage, then scaled by subtracting 80, so that at RMFI = 0, LMI turns from 1 to 0. All regressions include a constant. Standard errors are in parentheses and are clustered at the census tract level where indicated; the clusters numbered 2,509.  

in column 5 is not statistically different from 0. Although the coefficients for the cubic in RMFI are 

not shown in the table, the estimates suggest that a linear relationship fits the data as well as the 

cubic. 

Another possible way in which the relationship between RMFI and the  Y variable may differ from 

linear is that the relationship may be different on either side of the LMI threshold. For example, it 

is possible that the relationship between the Median Family Income and the total number of trades 

is steeper in lower-income census tracts than in higher-income tracts. If that is the case, then the 

data may indicate a jump at the threshold, not because of the effect of the CRA but because of a 

naturally occurring difference in the linear relationship between RMFI and  Y on either side of the 

threshold.

Column 6 allows for this possibility. We have interacted the continuous variable RMFI with the 

dummy variable LMI, which allows for the coefficient on RMFI to take on a different value when 

LMI = 1 and when LMI = 0. If the coefficient on this interaction term is statistically different from 

0, it suggests a different slope to the relationship between RMFI and Y on either side of the thresh-

old. However, this coefficient is not statistically different from 0 ( p-value = 0.267). Further, the coefficient on LMI still indicates about 9 percent more trades or accounts in census tracts that are 

Cityscape 105

Butcher and Muñoz

CRA eligible. Columns 5 and 6 suggest that the simple linear relationship between log(sum of all 

trades) and RMFI does not violate the assumptions under which a regression discontinuity design 

provides an unbiased estimate of the effect of the CRA on consumer credit outcomes. We use the 

specification in column 4 of exhibit A-1 to investigate the relationship between CRA eligibility and 

consumer credit outcomes going forward.46 

Other Geographic Areas

Like Bhutta (2011), we focused on large metropolitan areas (those with populations of 2 million 

or more) for our research. Bhutta’s research found statistically significant effects of CRA eligibility 

on mortgage originations in these larger areas but not in others. Bhutta argued that CRA enforce-

ment activities may be greater in larger cities and thus one may see a greater effect there. It is also 

plausible that the differences in lenders’ responses to the CRA are driven by something other than 

enforcement. For example, if lenders have to do more outreach to potential borrowers in order 

to fulfill their responsibilities under the CRA, it is likely less costly to do that in more densely 

populated areas.

For completeness, we repeat the preceding analysis for all areas, not only those in large metropoli-

tan areas. The results, which are presented in exhibit A-2, follow closely the outcomes presented 

in exhibit 5. Our results are similar to those of Bhutta (2011); we find no statistically significant 

jumps in credit outcomes at the CRA threshold when we consider all areas combined, despite the 

fact that the sample size is considerably larger.

Two studies, to our knowledge, find an impact at the CRA threshold when focusing on large met-

ropolitan areas. Although investigating why the CRA has a different effect depending on the size of 

the metropolitan area is beyond the scope of this particular project, the question deserves further 

attention.  

46 For the other outcomes in exhibit 5, we have performed the same robustness checks and linearity, combined with the narrow 75- to 85-percent band, seems to fit the data and return results that are not sensitive to functional form. 

106 The CRA Turns 40

Using Credit Reporting Agency Data To Assess the Link Between  

the Community Reinvestment Act and Consumer Credit Outcomes

Exhibit A-2

Estimated Regression Coefficients for Selected Consumer Credit Outcomes:  

All Areas

(1)

(2)

(3)

(4)

(5)

(6)

(7)

log(Sum  log(Number  Fraction 

Average 

log(Sum of  log(Sum of 

Fraction 

of All 

of People   Missing  

Risk 

Mortgages) Foreclosures) Foreclosures

Trades)

in Tract)

Risk Score

Score

LMI tract

0.0349

0.0252

– 0.0018

0.0197

0.0312

0.0029

– 0.2268

(Standard error) (0.0293)

(0.0275)

(0.0018)

(0.0319)

(0.0245)

(0.0019)

(1.0876)

p-value

0.233

0.358

0.311

0.537

0.203

0.121

0.835

RMFI

0.0254

0.0166

– 0.0020

0.0306

0.0093

– 0.0004

1.2028

(Standard error) (0.0050)

(0.0047)

(0.0003)

(0.0055)

(0.0042)

(0.0003)

(0.1884)

p-value

0.0000

0.0000

0.0000

0.0000

0.0280

0.142

0.0000

Standard errors 

Yes

Yes

Yes

Yes

Yes

Yes

Yes

clustered

Quarter 

Yes

Yes

Yes

Yes

Yes

Yes

Yes

indicators

MSA fixed 

Yes

Yes

Yes

Yes

Yes

Yes

Yes

effects

Median age in 

Yes

Yes

Yes

Yes

Yes

Yes

Yes

census tract

Observations

240,554

240,679

240,679

239,697

179,529

239,697

0.4568

R-squared

0.2399

0.2074

0.2139

0.3104

0.2709

0.1694

0.447

LMI = low- and moderate-income. MSA = metropolitan statistical area. RMFI = relative Median Family Income.

Notes: The dependent variable is the indicated consumer credit outcome in a census tract in a given quarter. The sample includes all areas. LMI is a dummy variable equal to 1 if the Median Family Income (MFI) in the census tract is less than 80 

percent of the Area Median Family Income (AMFI), indicating the census tract is eligible for the Community Reinvestment Act, and 0 otherwise. RMFI is MFI in the census tract divided by AMFI, multiplied by 100 to obtain the percentage, then scaled by subtracting 80, so that at RMFI = 0, LMI turns from 1 to 0. All regressions include a constant. Standard errors are in parentheses and are clustered at the census tract level; the clusters numbered 7,088.

Acknowledgments

The authors thank Prabal Chakrabarti, Chris Foote, Jeff Fuhrer, Erin Graves, Anna Steiger, and 

seminar participants at the Federal Reserve Bank of Boston, the Federal Reserve Bank of Chicago, 

and Wellesley College for helpful comments. All errors are our own. The views expressed herein 

are those of the authors and do not represent those of the Federal Reserve Bank of Boston or the 

Federal Reserve System.

Authors

Kristin F. Butcher is the Marshall I. Goldman Professor of Economics at Wellesley College and a 

research associate at the National Bureau of Economic Research.

Ana Patricia Muñoz is a consultant at Grupo FARO in Quito, Equador. She conducted this research 

when she was the Assistant Vice President of the Federal Reserve Bank of Boston.

Cityscape 107

Butcher and Muñoz

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108 The CRA Turns 40

Bridging the Gap to Scalable 

Community Reinvestment 

Lending Programs 

Roberto G. Quercia

Sarah Riley

University of North Carolina at Chapel Hill

Abstract

The Community Reinvestment Act requires commercial banks and savings institutions 

to help meet the credit needs of borrowers in their communities, including low- and 

moderate-income neighborhoods. The CRA has been controversial since its enactment, 

with calls for both repealing and expanding it. One rationale for expanding CRA 

lending activities is the distinction between conforming and nonconforming CRA 

lending, and the fact that a large secondary market exists for the former but not the 

latter, essentially creating a dual market. In this market, marginal borrowers potentially 

face higher borrowing costs that may be disproportionate to their actual credit riskiness, 

can only obtain loans with alternative features that may increase default risk, and may 

suffer from predatory lending practices. However, not much is known about the lending 

risks associated with the nonconforming CRA segment of the market. In this article, we 

summarize the lessons learned from one large nonconforming community reinvestment 

mortgage program and provide insight into how the challenges associated with special 

community reinvestment lending might potentially be managed on a larger scale, via 

increased capital reserves, a mortgage insurance mechanism, and enhanced market 

liquidity in the special community reinvestment product space.

Introduction

Community Reinvestment Lending in Context 

A substantial mortgage finance infrastructure is available to low- and moderate-income households 

in the United States (Nothaft and Surette, 2001). This infrastructure includes initiatives supported 

by federal, state, and local governments, as well as initiatives in the nonprofit and for-profit sectors. 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

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Quercia and Riley

One such initiative is the Community Reinvestment Act1 (CRA),which was enacted in 1977 in 

response to concerns about  redlining, a form of geographically based credit rationing. The CRA 

requires depository institutions to provide credit in a “safe and sound manner” to the communities 

in which they collect deposits, including low- and moderate-income households and neighbor-

hoods. Institutions subject to the CRA are periodically evaluated on the extent to which they meet 

this goal, and the results of the evaluation are considered by regulators when reviewing bank 

applications for mergers or changes in the location of branches (Ardalan, 2006; Ardalan and Davis, 

2006; Hossain, 2004). Since 1996, CRA-regulated banks are estimated to have lent more than 

$973 billion in small business loans and $883 billion in community development loans to support 

affordable housing and economic development projects.2

Two other federal government initiatives amplify the potential impacts of the CRA: the housing goals 

of the government-sponsored enterprises (GSEs), Fannie Mae and Freddie Mac, and the work of the 

Federal Housing Administration (FHA). Under the Federal Housing Enterprises Financial Safety and 

Soundness Act3 of 1992, as amended by the Housing and Economic Recovery Act4 of 2008, Fannie 

Mae and Freddie Mac are subject to affordable housing goals covering their purchases of single-family 

and multifamily mortgages, including CRA-eligible loans. These mandates are intended to provide 

liquidity and a secondary mortgage market for prime, conforming mortgages made to low- and 

moderate-income households, as well as those in underserved geographic areas and markets (DiVenti, 

2009).5 Therefore, greater involvement by the GSEs may lead to the origination of more CRA-eligible 

prime, conforming loans. The GSEs are currently under the conservatorship of the Federal Housing 

Finance Agency (FHFA). In addition, the FHA insures nonconforming mortgages and thereby 

provides credit enhancements for borrowers who may have impaired credit history or liquidity con-

straints (Nothaft and Surette, 2001); these same borrowers are often served by CRA lending efforts. 

A secondary market for FHA loans exists through Ginnie Mae, which is a government corporation. 

Thus, the FHA may amplify the impacts of the CRA through its insurance programs.

The definition of a conforming mortgage has varied over time but generally reflects GSE guidelines 

stipulating loan and borrower characteristics, such as loan amount, downpayment as a fraction of 

the property value, credit score, debt-to-income ratio, loan-to-value ratio, and the extent of sup-

porting documentation required.6 These guidelines are intended to standardize both loan and bor-

rower characteristics, thereby reducing or eliminating informational asymmetries for investors and 

permitting very accurate prediction of default and prepayment risks (Green, 2008). CRA-eligible 

loans, namely those for which institutions that are subject to the CRA regulations can receive low- 

and moderate-income lending credit during their CRA assessments, can be either conforming or 

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII.  

2 See http://www.ffiec.gov/craadweb/aggregate.aspx for data on community development lending and small business lending 

by year. 

3 Pub. L. 102–550, 106 Stat. 3941, Title XIII.  

4 Pub. L. 110–289, 122 Stat. 2654. 

5 The 2015–2017 housing goals are described at https://www.fhfa.gov/Media/PublicAffairs/Pages/FHFA-Adopts-Final-Rule-

on-2015-to-2017-Housing-Goals-for-Fannie-Mae-and-Freddie-Mac.aspx. 

6 For example, see Fannie Mae’s 2016 eligibility matrix at https://www.fanniemae.com/content/eligibility_information/

eligibility-matrix.pdf.

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Bridging the Gap to Scalable Community Reinvestment Lending Programs

nonconforming (Avery, Bostic, and Canner, 2000). During the 1990s, approximately 40 percent of 

prime, conforming loans purchased by the GSEs were made to low- or moderate-income borrow-

ers, and about two-thirds of those loans were also CRA eligible (Nothaft and Surette, 2001).7

What little available evidence exists regarding nonconforming CRA-eligible mortgage lending sug-

gests that, historically, this segment of the market has been relatively small and is served through 

special CRA lending programs (Quercia and Ratcliffe, 2009). Such programs have been adopted by 

CRA-regulated institutions for the purpose of enhancing CRA assessment ratings and primarily tar-

get low-income borrowers living in low-income neighborhoods. Special CRA loans typically carry 

nonconforming features that are designed to relax credit constraints or underwriting standards 

and thereby make credit available to marginal borrowers who would not qualify for a conforming 

mortgage. Such loans may permit higher loan-to-value ratios, higher debt-to-income ratios, lower 

borrower credit scores, or a combination of the three, and they may carry lower interest rates. A 

survey of large retail banking institutions conducted by the Federal Reserve Board of Governors 

suggests that special CRA mortgage lending represented about 5.6 percent of total CRA mortgage 

lending in 1999 (Avery, Bostic, and Canner, 2005, 2000). A more recent estimate suggests that 

special CRA programs originate about 20,000 to 30,000 loans per year (Herbert, 2009).

Loans made via such special programs are often referred to as “prime” mortgages, despite the facts 

that many of the borrowers served have subprime credit ratings and the flexible underwriting 

features of such mortgages cause them to carry somewhat higher default risk than prime, conform-

ing mortgages (LaCour-Little, 2007). The borrowers who receive nonconforming loans also tend to 

be more heterogeneous than borrowers who receive prime, conforming loans, thus complicating 

the management of associated mortgage termination risks (Green, 2008). Special CRA loans can, 

however, be distinguished from subprime loans, due to the fact that they do not carry alternative 

loan features, such as prepayment penalties, balloon payments, reverse amortization, teaser rates, 

or high-cost interest rates, which tend to increase default risk irrespective of borrower credit 

quality (Aalbers, 2009; Ding et al., 2011). Subprime loans have tended to be favored by investors, 

higher-income households, and borrowers who are able to provide substantial downpayments but 

are unable or unwilling to provide complete documentation due to self-employment or temporary 

adverse financial shocks (Aalbers, 2009; LaCour-Little, 2007).

Innovation in Community Reinvestment Lending

Numerous proposals have been put forward over the years concerning how CRA lending in 

general, and special CRA lending in particular, could potentially be expanded. One such proposal 

that surfaced in the late 1990s involved the creation of a secondary market for special CRA-eligible 

mortgages, which did not exist at that time (Quercia, Freeman, and Ratcliffe, 2011). In the absence 

of such an outlet, primary lenders must keep marginal CRA loans in portfolio (Bhutta, 2011), 

where they can cause an increase in required capital reserves, because higher default rates and 

higher economic capital requirements are associated with loans that have high loan-to-value ratios, 

are made to borrowers with lower credit scores, are geographically concentrated, or a combination 

7 Note that the GSE housing goals have historically used higher income thresholds for targeted low- and moderate-income borrowers and communities than those specified by the CRA, so some loans purchased by the GSEs in satisfaction of their housing goals were not CRA eligible. See An et al. (2007) for a discussion.

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(Calem and LaCour-Little, 2004). Some evidence suggests that the existence of a secondary mort-

gage market can increase loan originations and that this effect may be particularly pronounced for 

loans made to low-income borrowers (Gabriel and Rosenthal, 2007).

Thus, the proposal was to involve the GSEs in providing liquidity for special CRA lending activities 

and to reduce the need for lending institutions to hold special CRA loans on their books. Such 

a secondary market would essentially expand conforming loan criteria to include the type of 

nonconforming loan features that are typical of special CRA-eligible lending programs and thus 

potentially permit many more such loans to be made. It could also reduce the risk of lending to the 

special CRA borrower population by standardizing underwriting practices and providing geograph-

ic diversification. As a test of this idea, a targeted lending program, the Community Advantage Pro-

gram (CAP), was implemented in 1998 as a demonstration of how a secondary mortgage market 

for special CRA-eligible mortgages might function in practice. The demonstration was intended to 

address concerns about whether such loans were too risky for investors and whether the borrowers 

receiving such loans would be financially better off as renters, given the relatively higher costs and 

personal financial risks associated with homeownership.8 

To date, the performance of CAP mortgages and the housing experiences of CAP borrowers have 

been documented at length. CAP loans tend to have somewhat higher default risk than prime, 

conforming mortgages but substantially lower default risk than subprime loans made to similar 

borrowers (Ding et al., 2011). Most CAP borrowers have sustained homeownership, accumulated 

home equity, and characterized their decisions to become homeowners as positive (Quercia, 

Freeman, and Ratcliffe, 2011). In the years since CAP started, the GSEs have also implemented 

other special secondary market programs that provide an outlet for special CRA-eligible loan 

products. Examples include Fannie Mae’s Community Home Buyer’s Program and Freddie Mac’s 

Affordable Gold Alt 97 program (LaCour-Little, 2007), which were in place in 2002; Fannie Mae’s 

HomeReady mortgage program, which was implemented in 2015;9 and Freddie Mac’s Home 

Possible mortgage program, which was implemented in 2016.10 Research from CAP was taken into 

consideration during the design of the more recent programs.

Current Policy Challenges

Despite these changes in the secondary mortgage market, some researchers continue to believe that 

special CRA lending activities need to be greatly expanded. In particular, one rationale for expand-

ing special CRA lending activities that has been proposed in the aftermath of the financial crisis is 

that the distinction between conforming and nonconforming CRA lending, and the fact that a large 

secondary market exists for the former but not the latter, essentially creates a dual market. In this 

market, marginal borrowers potentially face higher borrowing costs that may be disproportionate 

to their actual credit riskiness, can only obtain loans with alternative features that may increase 

8 Although some lending programs targeted to low- and moderate-income households have historically been associated with objective increases in average wealth and higher standards of living among the program participants, considerable outcome variability exists, which has raised questions about the suitability of such lending (Galster and Santiago, 2008).  

9 See the HomeReady product matrix at https://www.fanniemae.com/content/fact_sheet/homeready-product-matrix.pdf. 

See also general information about Fannie Mae’s flexible lending programs at https://www.fanniemae.com/content/tool/

flexibilities-for-creditworthy-borrowers.pdf. 

10 See an overview of Home Possible at http://www.freddiemac.com/homepossible/. 

112 The CRA Turns 40

Bridging the Gap to Scalable Community Reinvestment Lending Programs

default risk, and may suffer from predatory lending practices (Levitin and Ratcliffe, 2014; Quercia 

and Ratcliffe, 2009). Concerns about this dual market structure reflect the observation that a 

number of low-income and minority homeowners took out subprime loans during the housing 

market expansion of the early 2000s and might have faced lower default risk from more traditional 

mortgage products (Bostic and Lee, 2008; Ding et al., 2011). An expansion of special CRA lending 

through GSE secondary market programs would potentially serve this segment of the market.

In addition, the public mortgage finance infrastructure that supports lending to low- and 

moderate-income households has come under increasing scrutiny since the housing market slump 

that began in 2006 and the associated recession from 2007 to 2009. The CRA has always been 

controversial due its ambiguous wording, subjective implementation, limited mortgage market 

coverage, and tendency to prevent banks from achieving economies of scale through specialization 

(Hossain, 2004; Hylton and Rougeau, 1999; Marsico, 2005; Richardson, 2001). Assessing the 

effectiveness and adequacy of the CRA has proved difficult due to its lack of quantitative compli-

ance criteria and its interaction with other financial regulations in determining lender incentives 

(Getter, 2015). Another longstanding controversy concerns the impact of the GSE housing goals 

with respect to housing market liquidity (Ambrose and Thibodeau, 2004; Bostic and Gabriel, 

2006; Gabriel and Rosenthal, 2010). However, additional questions have recently been raised as to 

the extent to which the CRA, the GSEs, and the FHA may have contributed to economic volatility 

through the relaxation of underwriting standards (Nichols, Hendrickson, and Griffith, 2011). In 

particular, the contribution of the CRA (or lack thereof) to the financial crisis has been debated at 

length (Aalbers, 2009; Reid, 2013). Moreover, given that the GSEs currently remain in conservator-

ship, various proposals have been made for how to reorganize the U.S. housing finance system 

(Duca, Muellbauer, and Murphy, 2016; Seidman et al., 2013).11 Thus, the future of the secondary 

market for special CRA-eligible lending activities remains unclear.

Roadmap

We contribute to the ongoing policy discussion by reviewing some of the information that has 

been gathered from CAP and providing insights as to how the challenges associated with special 

CRA-eligible lending may be managed. We believe that any expansion or reorganization of the 

housing finance system that seeks to incorporate special CRA-eligible lending on a significant scale 

should be carefully designed with risk management in mind. In brief, the design and outcomes 

of CAP, combined with our reading of the relevant literature, suggest that increasing the scale of 

special CRA lending would require increased capital reserves, a mortgage insurance mechanism, 

and enhanced market liquidity in the special CRA product space.

The remainder of the paper is divided into four sections. First, we briefly discuss the historical 

background and implementation of the CRA. Second, we describe CAP and the data collection 

associated with the evaluation of this program. Third, we describe the demographic profile of CAP 

borrowers, the program’s underwriting considerations, and the mortgage performance and wealth-

building experiences of participating borrowers. Finally, we discuss implications for the scalability 

of special CRA lending.

11 See also a synthesis of proposed reforms at http://www.urban.org/policy-centers/housing-finance-policy-center/projects/

housing-finance-reform-incubator/jim-parrott-clarifying-choices-housing-finance-reform. 

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Community Reinvestment Act Background

The CRA was passed in 1977 in response to beliefs that lenders were rationing mortgage credit in 

low- and moderate-income and minority neighborhoods. Such credit rationing was believed to 

result from racial discrimination, statistical discrimination, informational asymmetries, the inability 

of lenders to achieve economies of scale, or some combination thereof (Ardalan, 2006; Ardalan and 

Davis, 2006; Avery, Bostic, and Canner, 2005; Hossain, 2004; Hylton and Rougeau, 1999; Klaus-

ner, 1995). In particular, economic theory suggests that an equilibrium in which profit-maximizing 

lenders rationally engage in credit rationing can exist as a result of either thin markets or imperfect 

information about the credit risks of heterogeneous borrowers (Lang and Nakamura, 1993; Stiglitz 

and Weiss, 1981). Further motivation for the CRA was provided by the belief that access to credit 

tended to carry positive community-level externalities that were not internalized by lenders and 

that the level of mortgage lending in the relevant neighborhoods might thus be socially suboptimal 

(Lang and Nakamura, 1993; Richardson, 2001). Some proponents of the CRA have also argued 

that banks benefit from the public sector with respect to deposit insurance and should therefore be 

required to provide credit broadly as a public service (Levitin and Ratcliffe, 2014).

In light of these beliefs, the CRA was intended to induce depository institutions to provide more 

credit in the locations in which they received deposits and particularly to induce such institutions 

to expand lending activity in low- and moderate-income and minority neighborhoods. The CRA 

reaches beyond existing fair lending laws by not only prohibiting discrimination in lending but also 

creating an obligation to proactively make credit broadly available to the target population groups 

(Ardalan and Davis, 2006; Levitin and Ratcliffe, 2014). The intention was that CRA loans might be 

less profitable than mainstream lending activities, but they should not undermine lender solvency 

(Avery, Bostic, and Canner, 2005).

The CRA was revised in 1995 in an effort to address concerns of both lenders and community groups 

with respect to the challenges of CRA assessment and enforcement (Hossain, 2004). In its current 

form, the CRA provides for a government assessment of lenders that varies based on institutional 

size and involves up to three tests—namely a lending test, an investment test, and a service test. The 

lending test consists of an evaluation of how loans are distributed across neighborhoods and income 

groups within the assessment area; loan frequencies, amounts, and product mixes are considered. The 

investment test evaluates lender investments in local economic development activities. The service 

test considers lender systems and processes for providing credit to various constituencies within the 

assessment area (Ardalan and Davis, 2006). The lending test is more heavily weighted than the other 

tests (Quercia and Ratcliffe, 2009). Lenders can meet their CRA obligations in a variety of ways,12 

including by providing funding for low-income housing initiatives, small businesses or farms, disaster 

recovery and neighborhood revitalization projects, and community services that are specifically 

targeted to lower income households or residents of distressed areas. Lenders can receive credit by 

lending directly or by purchasing qualifying loans originated by other lenders.

The CRA assessment assigns each bank a rating on a four-point scale, and this rating is taken into 

consideration during the approval process when bank applications are submitted for “new charters; 

12 See a fact sheet describes the many activities for which banks can receive CRA credit at https://www.occ.treas.gov/topics/

community-affairs/publications/fact-sheets/fact-sheet-cra-loans.pdf.

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Bridging the Gap to Scalable Community Reinvestment Lending Programs

new branches or relocation of an existing branch; bank mergers and consolidation; and other 

similar corporate activities” (Office of the Comptroller of the Currency, 2014: 2). Small banks are 

evaluated primarily based on their lending activities. Larger banks are assessed based on lending, 

investment, and service activities. In each case, a given lender can opt to submit a strategic plan for 

meeting CRA objectives; if approved by the assessor, this strategic plan can provide context for the 

assessment so that it takes into consideration the unique characteristics of the lending institution in 

relation to its service area. The results of each assessment are made available to the public, which 

allows for secondary enforcement of CRA compliance and encourages collaboration with com-

munity groups via the pressure to maintain a favorable public image (Marsico, 2005).13

Community Advantage Secondary Market Demonstration 

Program

The CAP is a secondary mortgage market demonstration program initiated in 1998 via a partnership 

among the Ford Foundation, Fannie Mae, and Self-Help, a nonprofit lender with headquarters in 

Durham, North Carolina. Under CAP, Self-Help purchased qualifying loans from the originating lend-

ers and resold them to Fannie Mae while retaining recourse for an agreed period of time. Qualifying 

loans were those made to households with annual incomes of no greater than 80 percent of the Area 

Median Income (AMI) at the metropolitan statistical area level, or to minority households with annual 

incomes of no greater than 115 percent of AMI. CAP was designed to provide policy-relevant insights 

with respect to community reinvestment lending, as defined by the CRA, and to inform future hous-

ing policy.

The risk associated with CAP lending was managed in several ways. First, because it was acknowl-

edged that special CRA mortgage lending posed a higher risk of default than prime, conforming 

mortgage lending, the Ford Foundation provided a $50 million grant for the purposes of under-

writing CAP loans and providing capital reserves that could compensate Fannie Mae in the event of 

unexpected losses. Second, Self-Help added a small risk fee to the mortgage interest rate; this risk 

fee ranged between 0 and 150 basis points and took the place of mortgage insurance. Third, the 

recourse arrangement of Self-Help permitted the return of loans with low credit quality; if a loan 

became delinquent very shortly after purchase, Self-Help returned it to the originator. Self-Help 

also retained recourse on each loan for a given period of time after reselling it to Fannie Mae, an 

arrangement that provided additional default risk protection for the GSE.

More than 46,000 loans totaling more than $4 billion were purchased through CAP. Purchased loans 

were primarily 30-year, fixed-rate, low-downpayment mortgages. Origination dates range from 1983 

to 2010, with 95 percent originated after 1995. CAP loans had a median original loan-to-value ratio 

of 97 percent and were originated at a median interest rate of 7 percent. The median original loan 

balance was $79,000. In some cases, Self-Help purchased qualifying loans that had already been 

originated, and in other cases Self-Help collaborated with originating lenders to create a program-

specific lending channel that would permit the future origination and resale of qualifying loans.

13 See also a presentation from the Federal Deposit Insurance Corporation at https://www.fdic.gov/regulations/resources/

director/presentations/cra.pdf.

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With further funding from the Ford Foundation, a subset of approximately 3,700 CAP borrowers 

who received loans between 1999 and 2003 were surveyed annually beginning in 2003. The sur-

vey collected information about homeownership experiences for the purposes of academic research 

and program evaluation. In 2004, a comparison group of about 1,500 renter households was also 

recruited and interviewed. These renter households were selected to be in the same metropolitan 

areas as the CAP homeowners and to be similar with respect to an income ceiling. The comparison 

panel was primarily intended to provide a point of reference from which to evaluate the wealth 

accumulation of the CAP owners. Details about the wealth accumulation and asset holdings of 

survey participants were collected by surveys administered in 2005, 2009, and 2012. The last year 

of survey data collection was 2014. Altogether, the survey data collected between 2003 and 2014 

constitute the Community Advantage Panel Survey (CAPS).

Like other panel surveys, CAPS has experienced some attrition of participants over time. As of 

2014, the final sample sizes were approximately 1,800 owner sample members and 800 renter 

sample members. Male and Hispanic respondents were the most likely to drop out during the 

survey period (Riley et al., 2015).

Sample Characteristics

Exhibit 1 presents demographic and underwriting summary statistics for CAP participants and 

both CAPS samples. At the time of loan origination, the median CAP borrower was 32 years 

old and had an annual income of $31,000 (or 60 percent of AMI), a credit score of 681, and a 

debt-to-income ratio of 37 percent. The median CAP property was valued at $84,000 at the time 

of purchase, and the median CAP borrower’s original equity in the property was approximately 

$2,500. About 40 percent of borrowers were members of a racial or ethnic minority group, with 

about 19 percent Black and about 14 percent Hispanic representation. Thus, much of the sample 

comprises households from groups that have traditionally been underserved by financial markets. 

At the time of loan origination, about 14 percent of CAP properties were in rural areas, about 32 

percent were in low-income census tracts, and about 30 percent were in minority census tracts. 

Nearly 70 percent of CAP properties were in the South, with approximately 30 percent in North 

Carolina. The subset of CAP borrowers who participated in CAPS presents a similar profile overall, 

except that survey participants were more likely to be in the Midwest and less likely to purchase a 

property in a minority tract.

The CAPS renters sample differs from the CAPS owners sample with respect to both demographic 

and geographic characteristics at survey baseline. Compared with the CAPS owners sample, survey 

participants in the CAPS renters sample had a higher median age at the survey baseline (39 versus 

32) and had a lower median household income ($19,000, or 30 percent of AMI, versus $31,000, 

or 54 percent of AMI). In addition, renters were more likely to be Black (33 versus 20 percent) or 

Hispanic (19 versus 16 percent). Renters were also less likely to be married (28 versus 46 percent), 

less likely to have completed education beyond high school (25 versus 39 percent), and less likely 

to be employed (63 versus 92 percent). Finally, renters were more likely to be in the South (74 ver-

sus 62 percent). Because of these differences, existing analyses comparing the financial outcomes 

of these two samples have tended to employ selection models or matching methods as a means of 

correcting for sample imbalance on key respondent attributes.

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Bridging the Gap to Scalable Community Reinvestment Lending Programs

Exhibit 1

Owner and Renter Characteristics by Sample

CAP  

CAPS Owners 

CAPS Renters 

Portfolio

Sample

Sample

N = 46,547

N = 3,743

N = 1,529

Age (median, years)

32

32

39

Income (median, $)

30,792

30,672

19,000

Income (median, % of AMI)

60

58

33

Male (%)

57

54

30

Race (%)

White

61

61

44

Black

19

20

33

Hispanic 

14

16

19

Other

6

3

4

Marital status (%)

Married

—

46

28

Widowed/divorced/separated

—

20

32

Never married

—

35

40

Educational attainment (%)

Less than high school

—

9

20

High school diploma or GED

—

51

55

Associate’s or trade school degree

—

14

8

Bachelor’s degree

—

19

13

Graduate degree

—

6

4

Employment status (%)

Working

—

92

63

Unemployed (looking for work)

—

3

12

Out of labor force

—

3

20

Retired

—

2

5

Geographic region (%)

Midwest

16

26

14

Northeast

4

3

0

South

69

62

74

West

12

10

12

Rural (%)

14

18

—

Low-income tract (%)

32

30

—

Minority tract (%)

30

25

—

Purchase price (median, $)

84,000

77,500

—

Loan amount (median, $)

79,000

74,775

—

Loan-to-value ratio (median, %)

97

97

—

Debt-to-income ratio (median, %)

37

37

—

Credit score (median)

681

673

—

AMI = Area Median Income. CAP = Community Advantage Program. CAPS = Community Advantage Panel Survey. GED = 

general equivalency diploma.

Differences also exist between CAPS participants and the larger population of low-income 

households. Compared with similar low-income participants in the 2003 Current Population 

Survey, CAPS respondents tend to be much more likely to live in the South; they also appear to 

be somewhat more educated and more attached to the labor force. Compared with low-income 

homeowners in the Current Population Survey, the CAPS owners sample also tends to be younger 

and exhibit greater representation of males. However, these two groups appear to be similar with 

respect to distributions of race, income, and household size. Compared with low-income renters 

in the Current Population Survey, the CAPS renters sample demonstrates higher representation of 

Black and female participants but similar distributions of income and household size (Riley, Ru, 

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Quercia and Riley

and Quercia, 2009). These differences between CAPS participants and the general low-income 

population should be kept in mind when considering the generalizability of analyses based on 

CAPS data.

Mortgage Performance

As of the end of 2016, approximately 17 percent of the CAP loan portfolio remained active. An 

additional 71 percent had prepaid, 5 percent had been returned to the originator, and 7 percent 

had been terminated via foreclosure sale. With respect to worst-ever payment delinquency status, 

more than 80 percent of CAP loans had never been 90 or more days delinquent. Approximately 

62 percent had never been delinquent at all, an additional 15 percent had been at most 30 days 

delinquent, and about 4 percent had been more than 30 but at most 60 days delinquent.

When compared with the broader mortgage market, CAP rates of serious delinquency (that is, at 

least 90 days late on payments or in foreclosure) during the financial crisis generally fell between 

those for fixed-rate prime loans and those for other mortgage products. This relationship is il-

lustrated in exhibit 2; CAP serious delinquency rates over time are compared with those for prime 

fixed-rate loans, prime adjustable-rate loans, subprime fixed-rate loans, subprime adjustable-rate 

loans, and FHA loans. Exhibit 2 covers the period from the second quarter of 2006, when the 

housing market peaked, to the end of 2009, when subprime serious delinquency rates reached 

their maximum. In the fourth quarter of 2009, active CAP loans exhibited a serious delinquency 

rate of about 10 percent. By contrast, prime fixed-rate loans exhibited a serious delinquency rate 

of about 5 percent, whereas prime adjustable-rate loans, subprime fixed-rate loans, and subprime 

adjustable-rate loans exhibited serious delinquency rates of 18, 22, and 43 percent, respectively.14

CAP loans thus exhibit default risk that is higher than that of fixed-rate prime loans but lower 

than that of adjustable-rate prime loans or subprime loans. The intermediate risk profile of CAP 

mortgages reflects the similarities between CAP borrowers and subprime borrowers with respect to 

credit characteristics but also the differences in loan products received. The former received loans 

at fixed rates, high loan-to-value ratios, and near-prime interest rates, whereas the latter received 

subprime or adjustable-rate loans that could generate mortgage payment shocks. In consequence, 

CAP mortgage performance reflects less risk layering than that of subprime loans. The mortgage 

product can mediate the relationship between borrower origination credit profiles and observed 

mortgage performance (Ding et al., 2011; Pennington-Cross and Ho, 2010). This pattern is 

consistent with the idea that special CRA lending may pose somewhat higher costs to lenders than 

conforming prime lending and may thus require higher loss reserves or subsidization, either from 

other business lines or from external organizations, in order to be viable.

However, CAP mortgage performance does generally reflect the effect of standard underwriting 

factors, including household income, credit score, loan-to-value ratio, and debt-to-income ratio 

(Quercia, Pennington-Cross, and Tian, 2012). Thus, lower incomes, lower credit scores, higher 

loan-to-value ratios, and higher debt-to-income ratios are all associated with increases in default 

risk for CAP loans, as they are for other loan products.

14 See the Mortgage Bankers Association’s National Delinquency Survey at https://www.mba.org/news-research-and-

resources/research-and-economics/single-family-research/national-delinquency-survey. 

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Exhibit 2

Serious Delinquency (90+ or Foreclosure) Rates by Mortgage Product, 2006–2009

ARM = adjustable-rate mortgage. CAP = Community Advantage Program. FHA = Federal Housing Administration. FRM = 

fixed-rate mortgage.

Sources: CAP database; Mortgage Bankers Association

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Appreciation and Return on Equity

Exhibit 3 shows the real FHFA purchase-only house price index for the period of 1991 to 2017. 

All nominal index values are adjusted to 1991 values using the Consumer Price Index for all urban 

consumers less shelter. As illustrated, national house prices exhibited considerable volatility during 

the period of CAP program evaluation. Thus, many of the CAP borrowers who sold their homes 

during the study period experienced historically high rates of house price appreciation and return 

on equity. Among those CAPS owners who sold their CAP properties during the survey period, 

89 percent sold their houses for at least what they initially paid, and the remaining 11 percent 

incurred a loss from sale. Those who experienced a gain from the sale reported a median nominal 

gain of nearly $26,000, whereas those who experienced a loss from sale reported a median nominal 

loss of about $10,000.

The CAP borrowers who retained their CAP properties experienced modest gains more consistent 

with long-term rates of market appreciation. As of the fourth quarter of 2016, CAP properties had 

appreciated at a nominal annualized rate of about 2 percent since loan origination, which cor-

responds to a potential annualized return on equity of approximately 19 percent. This latter lever-

aged rate of return on equity compares favorably with returns on alterative unleveraged investment 

vehicles, as the Dow Jones Industrial Average returned a nominal annual return of about 4 percent 

during the study period, and the 10-year Treasury bill rate was about 5 percent at the time of CAP 

loan origination.

More specifically, exhibit 4 illustrates the potential gains in home equity that CAP borrowers 

remaining in their CAP properties could have achieved as a result of the evolution of house prices 

between CAP property purchase and the end of 2016. We calculate potential home equity as 

the difference between the market value of the property15 and the most recent observed unpaid 

principal balance on the CAP mortgage. At the median, CAP borrowers potentially experienced 

nominal equity growth of about $44,000 after loan origination; including the original equity held 

in the property at the time of purchase, the median CAP borrower would have accumulated about 

$46,000 in home equity. The upper 75th percentile represents accumulated total equity of about 

$76,000, whereas the lower 25th percentile represents accumulated total equity of about $25,000. 

This variation across CAP properties primarily reflects geographic differences in local and regional 

housing market conditions.

15 We use the FHFA metropolitan-level housing price indices to calculate market value for the CAP properties based on the original purchase price.

120 The CRA Turns 40

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Exhibit 3

FHFA Purchase-Only House Price Index, 1991–2017

FHFA = Federal Housing Finance Agency. 

Notes: Adjusted for inflation to 1991 values. Data as of the first quarter of 2017.

Sources: Bureau of Labor Statistics; FHFA

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Exhibit 4

Home Equity Potentially Accumulated by CAP Participants, 2003–2016 

CAP = Community Advantage Program. 

Sources: CAP database; Federal Housing Finance Agency

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Wealth Accumulation

As of the end of 2012, which was the most recent year in which comprehensive wealth accumula-

tion and asset holdings information was collected from CAPS participants, the owners sample 

reported a median net worth of approximately $70,000. This wealth represents primarily the 

value of home equity and retirement accounts, with liquid assets and the value of vehicles making 

secondary contributions. As of the end of the survey period, home equity represented about 23 

percent of total net worth for the CAPS owners sample as a whole, and retirement account balances 

represented about 40 percent; net liquid assets (that is, liquid assets less unsecured debt) contrib-

uted an additional 20 percent, followed by the net value of vehicles, at 7 percent. More than 70 

percent of CAPS owners reported having retirement accounts during the study period. In contrast, 

the renters sample reported median net worth of approximately $11,000 in 2012, and about 

40 percent reported having retirement accounts. In addition, when differences in 2005 wealth 

are considered in conjunction with tenure status during the period of the survey, the wealth gap 

between the two samples is particularly pronounced. For example, when those CAPS participants 

with less than $10,000 in net worth in 2005 are considered, CAPS owners who remained owners 

reached a median net worth of about $41,000 in 2012, whereas CAPS renters who remained 

renters accumulated less than $1,500 at the median (Freeman and Quercia, 2014). Thus, CAP 

homeowners accumulated considerably more wealth than similar renters during the study period 

and were more likely to hold investment assets other than home equity.

These differences in wealth across the owners and renters samples in CAPS reflect the fact that the 

user cost of owning compared with renting a comparable property was lower for most of the study 

period. The user cost for owners reflects not only mortgage principal payments, but also mortgage 

interest, taxes, insurance, maintenance expenses incurred, opportunity costs, and house price 

appreciation. The user cost for renters is the monthly rent. The primary driver of the user cost for 

owners is house price appreciation, and therefore market house price trends determine whether 

owning is preferred to renting during any given period of time. Thus, the observed pattern in 

the relative user costs of CAPS owners and renters samples reflects the high levels of house price 

appreciation toward the beginning of the study and the fact that the CAP mortgages carried very 

low downpayments; the latter feature of these mortgages tended to reduce the opportunity cost of 

owning and to increase the benefit from even small amounts of house price appreciation (Riley, Ru, 

and Feng, 2013). A second factor contributing to difference in wealth gains between CAPS owners 

and renters involves gains in equity markets, as those CAPS participants with retirement savings 

have experienced increases in net worth as a result of appreciation in nonhousing investments.

Implications for the Scalability of Special CRA Lending

Overall, the accumulated evidence from CAP suggests that many borrowers who take out special CRA 

loans can sustain mortgage payments and build home equity and that they will tend to accumulate 

more wealth than similar renters. In addition, it suggests that the default risks associated with special 

CRA mortgage lending can be managed. The key features of CAP that contributed to program viabil-

ity were mechanisms for credit risk management and borrower credit enhancement, combined with a 

functional secondary market (Ding et al., 2011; Quercia, Freeman, and Ratcliffe, 2011).

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First, CAP was made possible largely due to an underwriting grant from the Ford Foundation that 

was designed to bridge the gap between the projected default risks of CAP borrowers and those 

of more conventional prime borrowers. Self-Help also managed the elevated risks associated with 

CAP lending by adding a risk fee to the interest rate in lieu of mortgage insurance. Although CAP 

borrowers have demonstrated that they are of lower risk than similar borrowers who received sub-

prime loans, they remain somewhat higher risk than prime borrowers who receive prime, conform-

ing loans. Therefore, in order to serve the special CRA population, lenders must generally either 

cross-subsidize special CRA lending activities using profits from other business lines, increase their 

internal capital reserves, or receive external capital as a means of underwriting the increased risk 

associated with making special CRA loans.

Second, CAP was made possible through the collaboration of Fannie Mae, which agreed to pur-

chase CAP loans from Self-Help and thereby create a secondary market for special CRA mortgages. 

This arrangement created greater liquidity in the special CRA lending space and permitted originat-

ing lenders to make many more CRA loans than would have been possible if they had kept these 

loans on their books. The ability of lenders to make special CRA loans on a rolling basis requires 

that they be able to sell the loans to external investors, such as the GSEs or buyers of mortgage-

backed securities, from whom they can recover their initial capital. In addition, a centralized 

secondary market facilitates geographic risk pooling and the standardization of mortgage product 

offerings, which can reduce informational asymmetries for investors and the costs of lending to the 

target population.

These two pillars of program success suggest that any significant expansion of special CRA lending 

activities among depository institutions would likely require increased capital reserves, a mortgage 

insurance mechanism, and enhanced market liquidity in the special CRA product space. These 

implications are consistent with prior evidence from the broader literature suggesting that capital 

requirements for nonconforming loans made to lower-income borrowers may be substantial 

(Calem and LaCour-Little, 2004; Deng, Quigley, and Van Order, 1996), that lenders may be more 

willing to lend in lower-income neighborhoods when loans carry mortgage insurance (Ross and 

Tootell, 2004), and that lenders may increase loan supply in the presence of a secondary market 

(Ambrose and Thibodeau, 2004; Gabriel and Rosenthal, 2007).

Within the context of the current policy discussion concerning the uncertain future of the GSEs, 

one possible means of achieving these goals may be to leverage the existing integrated mortgage 

insurance and secondary market infrastructure provided by the FHA and Ginnie Mae. In practice, 

special CRA loan products and FHA-insured mortgages tend to serve a similar clientele and share 

many of the same loan features (LaCour-Little, 2007; Nothaft and Surette, 2001; Spader and Quer-

cia, 2012). Thus, consolidating these lending activities has the potential to reduce administrative 

overhead by eliminating redundancy in program administration. It could also permit economies of 

scale, product standardization, and more accurate prediction of mortgage termination risks. This 

consolidation could be achieved by broadening the mortgage insurance function of the FHA to 

cover the special loan products that GSEs currently purchase to provide liquidity for special CRA 

loans and by transferring the secondary market function for these mortgages from the GSEs to 

Ginnie Mae.

124 The CRA Turns 40

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Along these lines, note that FHA insurance activities expanded significantly in response to the 

subprime crisis (Immergluck, 2011); this expansion suggests that the integration of special CRA 

lending activities into the FHA insurance framework may be a natural extension of its historical 

function. Although FHA loans tend to be somewhat more expensive for borrowers than some 

special CRA loan products, they are arguably less expensive than many of the subprime loans that 

were issued during the early 2000s. Thus, if one is concerned about nonprice credit rationing or 

the existence of a dual mortgage market, in which the special CRA target population has access 

only to alternative and significantly more risky mortgage products, then the former type of loan 

product would seem to be preferable to the latter (Karikari, Voicu, and Fang, 2011).

Nevertheless, a key question for future research concerns the optimal scale of special CRA lending. 

With the exception of the Federal Reserve Board of Governors’ 2000 survey of special CRA lending 

programs and the insights obtained from CAP, relatively little is known about the actual scale of 

special CRA lending at the national level, the features of such programs, how loan volume evolves 

in response to changes in economic conditions, or how much such activities can or should be 

expanded in practice. Moreover, the language of the CRA provides little quantitative or systematic 

guidance as to what level of lending in this space should ideally be achieved. A socially optimal 

level of special CRA lending would reflect and balance the tradeoffs inherent in lending to marginal 

borrowers, and identifying this social optimum should be a key priority for regulators before any 

large-scale expansion of special CRA lending activities. Further data collection concerning the 

demand for credit that exists among low- and moderate-income borrowers who would not qualify 

for conforming mortgages, the factors that influence their ability to manage credit, and the broader 

economic costs and benefits associated with lending to this population would facilitate this process.

Acknowledgments

The authors wish to thank the Ford Foundation for funding for this research. 

Authors

Roberto G. Quercia is a Distinguished Professor of City & Regional Planning and Director of the 

UNC Center for Community Capital at the University of North Carolina at Chapel Hill.

Sarah Riley is a senior research economist at the University of North Carolina at Chapel Hill.

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128 The CRA Turns 40

Commentary:  

Encouraging Housing Equity 

Dan Immergluck

Georgia State University

On the 40th anniversary of the Community Reinvestment Act (CRA)1, it is worth thinking about 

how federal regulations have worked to address inequities in housing markets. The civil rights 

movement launched a period of activism and optimism for government intervention into housing 

discrimination, leading to the passage of The Fair Housing Act (FaHA)2 of 1968 and the 1977 

CRA. FaHA and CRA share some common traits; they are both federal regulatory tools that rely on 

a notion of minimum obligation and encourage innovations that promote greater access by lower-

income and protected-class households to housing and economic opportunities. In addition, both 

statutes employ sanctions to motivate regulated entities toward nondiscriminatory behavior and 

help establish standards for what is considered responsible behavior in housing markets.

This article reviews the similarities and differences between one significant component of FaHA, 

the 2015 Affirmatively Furthering Fair Housing (AFFH) rules, and CRA. Substantial evidence 

suggests that CRA has had a positive impact on home lending practices (Apgar and Duda, 2003; 

Bhutta, 2008), despite the fact that it has often been laxly implemented over the years (Immer-

gluck, 2004; Silver, 2016). It is too early to say definitively whether or not the 2015 AFFH regula-

tions will generate such an impact. By examining AFFH alongside CRA, however, it is possible to 

identify a number of structural and design characteristics of the rules that may limit their effects.

Most critically, AFFH is limited by the fact that its primary potential sanction—the withholding of 

funding from the U.S. Department of Housing and Urban Development (HUD)—may not prove 

much of a consequence to the local governments against which it is most likely to be applied, a 

problem exacerbated by continual declines in HUD funding. It is unclear whether such a sanction 

would ever be exercised, and it is unclear whether the offending localities would be likely to care 

much if it was. Conversely, CRA’s main sanctions—the possible delay or denial of a bank merger or 

acquisition and also the risk to a bank’s reputation for being socially responsible—are significant, 

if underutilized, consequences that can be used to encourage compliance from most banks and 

savings institutions.

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII. 

2 42 U.S.C. § 3601–3619.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 129

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

Immergluck

Background

The AFFH provision, originally part of FaHA, has often been viewed as a dead letter. Until the 

2015 AFFH rule was produced, the provision was criticized for weak implementation and enforce-

ment. In particular, the Analysis of Impediments (AI) process, which was supposed to identify 

barriers to fair and affordable housing in a jurisdiction, has been characterized as ineffective (GAO, 

2010; National Commission on Fair Housing and Equal Opportunity, 2008). AIs frequently were 

issued with minimal critical oversight and effectively no community engagement. AIs were not 

required to be submitted to or reviewed by HUD, and compliance with the AI process was weak. 

GAO (2010) examined more than 400 AIs and found that 29 percent of entitlement communities’ 

AIs were more than 5 years old. Many AIs did not include timeframes for implementation, and top 

officials did not sign off on others. More fundamentally, the sense that the AI process generated 

improvements in fair housing across the U.S. is scarce.

With the advent of the new AFFH rules in July 2015, the idea that local jurisdictions might take 

the AFFH provision more seriously holds some promise. However, the AFFH rules suffer from a 

variety of limitations, some of which are the creatures of the FaHA statute, the result of the struc-

ture and design of the regulations, or derived from fundamental issues of power relations between 

the regulator and the regulated party. Comparing the AFFH process with that of CRA serves to 

illustrate some of these limitations.

In a general sense, FaHA and CRA have some similarities. Arguably, both statutes have helped 

promote a more socially responsible business culture that has influenced housing markets in 

important ways. In the case of FaHA, most real estate agents understand what is considered accept-

able or not acceptable under the law. The National Association of Realtors®—despite its historic 

opposition to FaHA and the 1988 improvements—regularly offers a wide variety of training and 

programs related to the law (Burke, 2016; NAR, 2016). Although some real estate agents may pre-

fer that the law not exist, and some may overtly flout it, most have generally accepted the norms of 

fair housing that FaHA introduced and helped standardize. Fair housing is an area in which large 

segments of the private sector generally accept the law as good business practice.

CRA raised the consciousness of financial institutions, in terms of their responsibilities to serve the 

banking and credit needs of lower-income communities and households. Arguably, the change to 

CRA under the Financial Institutions Reform, Recovery, and Enforcement Act3 of 1989—in which 

CRA exams and ratings were made public—was the most important policy driver of that change. 

Large banks, especially, are not only at risk of having a merger delayed or having their reputations 

damaged if they do not do well on their CRA exams, but they often strive for “outstanding” ratings 

to maintain their profile as socially responsible corporate citizens.

In addition, both types of law can be seen as  social contract regulation, in which parties benefiting from federal government support (not necessarily direct financial aid) are held to a set of standards 

and are subject to evaluation under these standards. The AFFH rules specify the process by which 

recipients of federal housing and community development funding assess their own efforts at 

3 Pub. L. 101–73, 103 Stat. 183.

130 The CRA Turns 40

Commentary: Encouraging Housing Equity

affirmatively furthering fair housing by completing an Assessment of Fair Housing (AFH; HUD, 

2015). HUD is then required to review the AFH for completeness and for whether the recipient is 

making adequate steps to further fair housing. The AFH can be seen as very roughly equivalent to 

the CRA performance evaluation.

In the case of AFFH, the receipt of federal housing and community development monies is the 

benefit accruing to state and local governments that are subject to AFFH rules. In the case of 

CRA, banks and savings institutions benefit from deposit insurance and a host of other federal 

benefits—access to the Federal Reserve System, Fannie Mae, Freddie Mac, the Federal Housing 

Administration (FHA), and the Home Loan Banks; the support and credibility of the federal bank 

supervisory system; and, as evidenced since 2007, access to many potential emergency assistance 

programs. In fact, CRA historically was viewed as a specification of banks’ responsibilities to serve 

the credit needs of their communities, with a particular focus on underserved neighborhoods and 

communities (Immergluck, 2004).4

Despite the similar goals of the AFFH and CRA regulations, they have fundamental differences, 

including whom is being regulated, the power of the sanctions to change behavior, and the data 

and metrics that are used to measure progress.

Differences in the Regulated Parties and Their Roles in the 

Assessment-and-Evaluation Process

The first key difference between the AFFH and CRA regulations relates to the regulated parties. 

CRA covers federally insured depository institutions, whereas the AFFH rules apply to state and 

local governments who are “entitlement communities” (that is, receive HUD funding) and public 

housing authorities (PHAs). The motivations, political power, and context of banks and savings in-

stitutions (most of which are small institutions) are quite different from those of local governments 

and PHAs, which influences the degree to which these regulations may induce positive actions. 

Although the financial industry holds a great deal of political power, the typical bank, especially if 

it is not large, tends to have limited ability to call on its congressional representatives to defend it 

if it receives a poor CRA rating. Federal banking regulators are also arguably more insulated from 

local political pressure than is a line agency like HUD. Moreover, a poor CRA rating may put the 

bank at odds with voters in the bank’s service area, especially when local advocacy organizations 

are involved in critiquing the bank’s practices, and further constrain its access to political power. 

Conversely, many local governments found to perform poorly under the AFFH rules might find 

their congressional representatives or political constituencies quite willing to take their side against 

HUD.

4 In fact, since at least the early 2000s, community reinvestment advocates have called on federal policymakers to expand CRA to a much broader array of financial institutions, because most receive some form of significant federal support (Immergluck, 2004). Most nonbank mortgage companies, for example, utilize Fannie Mae, Freddie Mac, 

FHA, or a combination of the three for a significant share of their home lending. Many finance companies rely on large federally insured banks for their warehouse lines of credit, without which they could not make business or other types of loans. Investment banks, even if they are not affiliated with insured depositories, rely heavily on a federally regulatory infrastructure, without which they could not attract many investors.

Cityscape 131

Immergluck

The Value of the Sanctions to the Regulated Parties

A second, and closely related, difference between AFFH and CRA is the value of the potential sanc-

tions. A weak CRA rating—a rating of “substantial noncompliance” (SNC) or “needs to improve” 

(NTI)—may imperil or delay current or future plans to merge with or acquire another bank, which 

could be costly. In contrast, an “outstanding” CRA rating can work as a sort of immunization from 

the risk of having a merger or acquisition delayed on CRA grounds. Even during times when 

banks are less likely to engage in mergers or acquisitions, a strong CRA rating serves as a signal to 

the bank’s customers and investors that it is a socially responsible corporate citizen. Institutional 

investors, including pension funds, religious institutions, state and local governments, and large 

nonprofit organizations, may be less likely to do business with banks that do not maintain strong 

CRA ratings.5 A bank receiving a rating of SNC or NTI is especially likely to suffer from significant 

reputational risk. The evidence suggests that, when faced with a poor CRA rating, most banks are 

motivated to improve that rating. Exhibit 1 shows that of the 357 banks receiving a SNC or NTI 

CRA rating since 2000, only 85 (less than 25 percent) received two or more such ratings in a row. 

This finding suggests that most banks are motivated to shed poor CRA grades.

In contrast, in the case of AFFH, the value of the potential sanction is likely to vary widely, based 

on the socioeconomic and political context of the entitlement community or PHA. In large central 

cities or suburbs that greatly value HUD funding (for example, the Community Development 

Block Grant and HOME programs), the risk of losing such funding will comprise a substantial 

sanction. In some other communities, however, especially ones that are not very reliant on HUD 

funding and where political leadership may not be greatly concerned with issues of affordable 

housing or community development, the value of the sanction is much less clear.6 Some communi-

ties may not want to be sanctioned by HUD for fear of being labeled exclusionary, but in other 

contexts it is conceivable that some local political leaders may be rewarded politically by residents 

who are unwelcoming of housing or community development subsidies.

More fundamentally, the question remains of whether HUD will have the political will or ability to 

withdraw funds from localities, even on an infrequent basis. Two systematic attempts have been 

Exhibit 1

NTI and SNC Ratings From CRA Performance Evaluations Published  

January 1, 2000—July 1, 2016

Number of Banks

Number of Exams

CRA rating of NTI or SNC

357

539

Repeated CRA rating of NTI or SNC

85

161

Percent of CRA NTI or SNC ratings or banks that increase 

76.2

70.1

to satisfactory or outstanding rating by next exam

CRA = Community Reinvestment Act. NTI = needs to improve. SNC = substantial noncompliance.

Note: Author’s calculations for all bank CRA ratings from January 1, 2000, through July 1, 2016.

Source: Federal Financial Institutions Examination Council Interagency CRA Ratings Database (http:// www.ffiec.gov/%5C/

craratings/Rtg_spec.html)

5 For example, several large cities have passed “responsible banking ordinances” in recent years, and many of these ordinances look to CRA ratings as a part of their evaluation of socially responsible banking (Holeywell, 2012). 

6 If the loss of a broader array of federal funding were brought to the table as a sanction—as in George Romney’s Open Communities proposal—then AFFH would contain a much more powerful sanction, but that has not been proposed.

132 The CRA Turns 40

Commentary: Encouraging Housing Equity

made to deny funding to localities on fair housing grounds, but both were met with substantial 

resistance. George Romney’s Open Communities plan—in which federal funding would be with-

held from exclusionary localities—was key to his eventual exit from the Nixon Administration 

(Hannah-Jones, 2015). In 1998, HUD Secretary Andrew Cuomo briefly suggested denying HUD 

funding to communities that did not comply with AFFH, but he met swift resistance from the local 

government lobby (Hannah-Jones, 2015). In general, HUD has never consistently held back fund-

ing based on fair housing grounds in any systematic way.

Specificity of Quantitative Indicators and Comparability Across Assessments

The third difference between CRA and AFFH is in their approaches to data collection and perfor-

mance metrics. Federal regulators evaluate banks every 2 to 5 years on their CRA performance. 

Regulations and procedures governing CRA performance evaluations (“exams”) specify the sorts 

of measures that bank examiners should use in conducting an exam. An examiner develops an 

analysis of the “performance context” of the bank being evaluated, which situates the bank’s CRA 

activities within the local area’s economic conditions and needs. Using the performance context, 

examiners have significant discretion in what a particular exam might emphasize or precisely what 

indicators or ratios it might display.

Despite the flexibility to take into account local conditions, the regulations and associated guidelines 

specify a good deal of common approaches to evaluating key data on lending patterns. For example, 

examiners are generally instructed to calculate an “institution's share of reported loans made in low- 

and moderate-income geographies versus its share of reported loans made in middle- and upper-

income geographies within the assessment area(s)” (OCC, FRB, and FDIC, 2014: p. 7). Exhibit 2 

is an excerpt of a table from a “large bank” CRA exam conducted by the Office of the Comptroller 

of the Currency (OCC) in 2014. It examines the geographic distribution of home purchase loans 

across census tracts of different income levels. The table examines the bank’s home lending in the 

Greenville, South Carolina metropolitan area, where the bank is based.

Charter Number: 24975 

Exhibit 2 shows that the bank made 0.81 per

cent of all home purchase loans in upper-income 

census tracts but only 0.48 per

Table 1. Lending Volume 

cent of all loans in moderate-income census tracts and 0 percent 

Lending Volume     

Geography: South Carolina     

Evaluation Period: January 1, 2012 To December 31, 2013 

in low-income census tracts. Thus, the bank’

% of   

Rated   

s market shar

e in higher-income neighbor

Community 

hoods was % of Rated 

Area Loans 

Home Mortgage 

Small Loans to 

Small Loans to Farms 

Development Loans** 

Total Reported Loans 

Area 

much lar

(#) in 

Businesses 

Deposits in 

Assessment ger than its market shar

 Area (2012): 

MA/AA* 

e in lower

-income neighbor

hoods. The ratio of the market shar

e in 

MA/AA*** 

# 

$ (000’s) 

# 

$ (000’s) 

# 

$ (000’s) 

# 

$ (000’s) 

# 

$(000’s) 

Full Review: 

Exhibit 2

Greenville 

76.44 

 493 

95,444 

22 

4,975 

 0 

 0 

1 

1,460 

516 

101,879 

81.50 

Limited Review: 

Anderson 

7.86 

 48 

8,192 

4 

480 

 0 

 0 

1 

300 

53 

8,972 

5.85 

Excerpt Fr

Spartanburg 

om a Table in a Lar

15.70 

 95 

ge Bank CRA Performance Evaluation, 2014

15,559 

11 

3,222 

 0 

 0 

 0 

 0 

106 

18,871 

12.65 

Table 2. Geographic Distribution of Home Purchase Loans 

 Geographic Distribution: Home Purchase      

 Geography: South Carolina   

Evaluation Period: January 1, 2012 To December 31, 2013 

Total Home 

Low-Income Geographies 

Moderate-Income 

Middle-Income 

Upper-Income 

Market Share (%) by Geography* 

Purchase Loans 

Geographies 

Geographies 

Geographies 

Assessment 

# 

% of 

% Owner 

% BANK 

% Owner 

% 

% Owner 

% 

% Owner 

% 

Area: 

Total** 

Occ 

Loans**** 

Occ 

BANK 

Occ 

BANK 

Occ 

BANK 

Overall  Low  Mod  Mid  Upp 

Units*** 

Units*** 

Loans 

Units*** 

Loans 

Units*** 

Loans 

Full Review: 

Greenville 

 324 

79.22 

3.37 

0.62 

14.95 

7.41 

47.78 

36.11 

33.91 

55.86 

0.72  0.00  0.48  0.69  0.81 

Limited Review: 

Anderson 

 27 

6.60 

2.08 

0.00 

19.37 

0.00 

47.91 

66.67 

30.64 

33.33 

0.19  0.00  0.00  0.34  0.10 

Spartanburg 

 58 

14.18 

1.73 

0.00 

17.16 

12.07 

53.25 

55.17 

27.86 

32.76 

0.18  0.00  0.00  0.27  0.08 

CRA = Community Reinvestment Act.

* Loan Data as of December 31, 2013. Rated area refers to either state or multi-state MA rating area. 

** The evaluation period for Community Developm

Source: Reproduced from OCC (2014)  ent Loans is from January 01, 2012 to December 31, 2013. 

*** Deposit Data as of June 30 2013. Rated Area refers to either the state, multi-state MA, or institution, as appropriate. 

* Based on 2012 Peer Mortgage Data (USPR) 

** Home purchase loans originated and purchased in the MA/AA as a percentage of all home purchase loans originated and purchased in the rated area. 

*** Percentage of Owner Occupied Units is the number of owner occupied units in a particular geography divided by the number of owner occupied housing units in the area based on 2010 Census information. 

**** Data shown includes only One to Four-family and manufactured housing. (Property type of 1 or 2)  

Cityscape 133

App

endix D-30 

Immergluck

higher-income neighborhoods to the market share in lower-income neighborhoods is not necessar-

ily the dominant criterion on which a bank’s CRA lending is judged, but it is one commonly used 

indicator. More importantly, these sorts of measures are specified in the regulations and operating 

procedures of the regulatory agencies.

In the case of the AFFH process, the local governments complete the AFH document. The 

guidebook for preparing an AFH is on the order of 200 pages long (HUD, 2015). The guidebook 

does instruct localities to conduct certain common measurements, such as the level of segregation 

within the metropolitan area. In terms of measuring local government activities and determining 

how well they further fair housing, however, the guidebook is quite vague; it provides a variety 

of data and a mapping tool but very little guidance on what measures should be constructed with 

such data. In this aspect, the AFH involves more discretion on the part of the authors than does a 

CRA exam. Moreover, because AFHs are performed by the localities and not by trained examiners, 

the variability in indicators and methods across AFHs is likely to be much greater than the vari-

ability across CRA exams. The ability to generate AFHs that are consistent across 4,600 entitlement 

communities and PHAs could be a major challenge, one that might severely tax HUD staff.

CRA exams also focus on outcomes that are largely (although not completely) under a bank’s con-

trol. That is, whereas lending to lower-income neighborhoods may be limited by the demand for 

homeownership in such neighborhoods (although demand is partly a function of mortgage avail-

ability), the CRA examiner compares the bank with its peers (or the “aggregate” market), through 

the market share comparison described previously and by comparing a bank’s lending with various 

proxies for potential demand (for example, the number of owner-occupied homes in lower-income 

and higher-income census tracts).

In the case of AFFH, localities are being assessed for their actions across a much wider range of 

governmental activities—including funding decisions and local regulations—that may affect fair 

housing outcomes. The rules also call for them to be assessed for whether levels of segregation 

within their communities have improved or worsened. Arguably, some localities—especially ones 

with predominantly minority or lower-income populations—have limited influence over these 

broader governmental activities or the ultimate outcomes. Perhaps understandably, the rules are 

vague on the extent to which localities should focus more on end outcomes or on intermediate 

objectives such as distributing housing subsidies in a desegregated fashion.

The Staffing and Resources of HUD Compared With Bank Regulators

Finally, CRA and AFFH differ in terms of evaluative capacity and the scale of review that is 

required. Approximately 6,000 commercial banks and savings institutions are regulated for CRA. 

Most banks fall under the “small bank” CRA evaluation process, which is much less detailed and 

extensive than the “large bank” CRA exam. Some CRA exams—especially of banks that serve 

multiple states—can run into the hundreds, in terms of page count. Moreover, a bank is evaluated 

for CRA by one of three federal bank regulators—the OCC, the Federal Reserve, or the Federal 

Deposit Insurance Corporation—none of which has a budget subject to annual congressional ap-

propriations. These regulatory agencies devote hundreds of high-level staff to the CRA evaluation 

process.

134 The CRA Turns 40

Commentary: Encouraging Housing Equity

Although HUD does not need to conduct AFHs in the same way that regulators evaluate banks 

under CRA, with more than 4,600 entitlement communities and PHAs, HUD may need to review 

900 or more AFHs per year.7 HUD has 60 days to notify a locality whether it is or is not accepting 

the AFH. The two classes of reasons for not accepting the AFH are (1) that it is inconsistent with 

fair housing or civil rights requirements, or (2) that it is substantially incomplete. Obviously, these 

two grounds for rejection of an AFH leave a great deal of uncertainty. One might expect that the 

second class of reasons for not accepting an AFH would be the most common, at least in the first 

5 years of the new rule, so the interpretation of “substantially incomplete” is likely to be a critical 

one. It may also be possible that some AFHs will go through more than two iterations before being 

deemed acceptable. These issues suggest that the administrative and staffing challenges for HUD 

will be substantial, especially because HUD is subject to congressional appropriations and its avail-

able resources to review and direct thousands of AFHs will likely pale in comparison to resources 

commanded by the bank regulators.

Conclusion

The differences between CRA and AFFH regulations reveal some key limitations in the AFFH 

regulatory structure. Although the new AFFH rules and data tool may prove to be an improvement 

over the previous AI process, they suffer from a number of key challenges, some of which are 

evidenced in comparing the AFFH rules with the CRA regulatory process. These include issues 

involving intergovernmental regulation and related power structures, the lack of consistency and 

specificity of the quantitative measures in the AFH, the unclear focus of the assessment measures, 

and the staffing and resource challenges that lie ahead for HUD.

The most important flaw in the AFFH structure, however, is the likelihood that some localities— 

especially those most likely to merit critical action under the AFFH process—may also be those 

least worried about any loss of HUD funding. Therefore, the AFFH regulations may have limited 

power to break down barriers to fair housing in the very places where those barriers are the highest. 

Acknowledgments

Thanks to Carolina Reid, Alex Schwartz, and one anonymous reviewer for comments on an earlier 

version of this article.

Author

Dan Immergluck is a professor in the Urban Studies Institute at Georgia State University.

7 Joint or regional AFHs are possible, which may reduce the number of AFHs that HUD needs to review, but such AFHs may also be more complex.

Cityscape 135

Immergluck

References

Apgar, William, and Mark Duda. 2003. “The Twenty-Fifth Anniversary of the Community Rein-

vestment Act: Past Accomplishments and Future Regulatory Challenges,”  Economic Policy Review 9 

(2). http://www.ssrn.com/abstract=793486.

Bhutta, Neil. 2008.  Giving Credit Where Credit Is Due? The Community Reinvestment Act and 

Mortgage Lending in Lower Income Neighborhoods. Finance and Economics Discussion Series 

2008-61. Washington, DC: Federal Reserve Board. http://www.federalreserve.gov/pubs/

feds/2008/200861/200861pap.pdf.

Burke, Jennifer. 2016. “The National Association of REALTORS and the Fair Housing Mandate, 

1961–2001.” Ph.D. Diss., University of California Santa Cruz.

Government Accountability Office (GAO). 2010.  HUD Needs To Enhance Its Requirements and Over-

sight of Jurisdictions’ Fair Housing Plans. GAO-10-905. Washington, DC: Government Accountability 

Office. http://www.gao.gov/new.items/d10905.pdf.

Holeywell, Ryan. 2012. “Cities Using Deposits To Gain Leverage Over Banks,”  American Banker, 

May 2. http://www.governing.com/blogs/view/responsible-banking-ordinances-2012.html.

Hannah-Jones, Nicole. 2015. “Living Apart: How the Government Betrayed a Landmark Civil 

Rights Law,”  Propublica, July 8. http://www.propublica.org/article/living-apart-how-the-government- 

betrayed-a-landmark-civil-rights-law.

Immergluck, Daniel. 2004.  Credit to the Community: Community Reinvestment and Fair Lending Policy 

in the U.S.  Armonk, NY: M.E. Sharpe. 

National Association of Realtors® (NAR). 2016. “Fair Housing Resources for Associations.” http://

www.realtoractioncenter.com/for-associations/fair-housing/.

National Commission on Fair Housing and Equal Opportunity. 2008.  The Future of Fair Housing. 

Washington, DC: The Leadership Conference on Civil and Human Rights. www.civilrights.org/

publications/reports/fairhousing/.

Office of the Comptroller of the Currency (OCC). 2014. “Community Reinvestment Act Perfor-

mance Evaluation: Certusbank, National Association.” May 12.

OCC, Federal Reserve Board (FRB), and Federal Deposit Insurance Corporation (FDIC). 2014. 

“Large Institution CRA Examination Procedures.” April. http://www.occ.treas.gov/news-issuances/

bulletins/2014/bulletin-2014-16a.pdf.

Silver, Josh. 2016. “CRA Exams Aren’t Cutting It for Communities in Need,”  Rooflines, the Shelter-

force Blog, January 21. http://www.rooflines.org/4364/cra_exams_arent_cutting_it_for_communities_ 

in_need/.

U.S. Department of Housing and Urban Development (HUD). 2015  AFFH Rule Guidebook. 

Washington, DC: U.S. Department of Housing and Urban Development. https://hudexchange.info/

resources/documents/AFFH-Rule-Guidebook.pdf.

136 The CRA Turns 40

Community Reinvestment Act 

and Local Governance Contexts: 

Advancing the Future of  

Community Reinvestment?

Colleen Casey   

University of Texas at Arlington

Joseph Farhat  

Central Connecticut State University 

Gregory Cartwright  

University of Texas at Arlington

Abstract

One main goal of the Community Reinvestment Act (CRA) of 1977 is to stimulate 

reinvestment by increasing CRA-regulated lending activity in low-income and minority 

communities. CRA has been applauded for democratizing credit (Barr, 2005) however, 

its regulatory shortcomings, namely the need to strengthen and expand federal enforce-

ment, have also been acknowledged (Fishbein, 1992; Immergluck, 2004). The design of 

CRA actively encourages community-based groups and coalitions to participate in regu-

lation and oversight. In many cities across the country, organized nonprofit networks 

and community-based groups have played pivotal roles in making the goals of commu-

nity reinvestment a reality. Community mobilization can be a positive externality, as it 

allows for the public and the local community to provide input and participate in CRA 

governance. However, reliance on mobilization at the local level can also produce nega-

tive externalities, particularly in places that lack the capacity to mobilize around CRA 

or as this capacity changes over time.

This article suggests that a changed governance context at the local level may be associat-

ed with the achievement of community reinvestment goals. We conducted a longitudinal 

analysis (from 2007 to 2014) of the case of St. Louis, Missouri, a city that experienced 

a change in community governance around CRA in 2009. Historically, St. Louis could 

be characterized as a city in which CRA mobilization was lacking; however, in 2009, 

a reinvestment coalition formed and took actions such as negotiating agreements with 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 137

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

Casey, Farhat, and Cartwright

Abstract (continued)

lenders, providing public comments to regulators, and building partnerships and relation-

ships with lenders. The results of the analysis suggest that community mobilization around 

CRA is associated with some positive outcomes—such as increases in the likelihood of 

loan approvals in the post-CRA governance context and by lenders with CRA agreements. 

However, we also find that the post-CRA governance context is associated with differential 

effects in terms of which racial and ethnic groups benefit from the CRA agreements. Com-

munity mobilization around CRA appears to be associated with a stimulation of community 

reinvestment activity and may be a positive part of CRA design; however, alone, it may not 

be enough to achieve the broader ideals of community reinvestment. We conclude with a 

discussion of future policy implications.

Introduction

The Community Reinvestment Act1 (CRA) of 1977 is designed to stimulate reinvestment by 

increasing lending and investment activity in low-income and minority communities. Previous 

studies have recognized CRA as democratizing credit (Barr, 2005), but others have acknowledged 

its regulatory shortcomings (Fishbein, 1992; Immergluck, 2004). Organized community-based 

groups have been identified as a driving force behind CRA regulation, and in this article we 

provide empirical evidence that mobilization around CRA continues to be a critical factor in 

community reinvestment. We utilize a quantitative case study research design to analyze how 

lender and applicant behavior changes when community mobilization around CRA is present. We 

find that community mobilization around CRA changes the behavior of lenders and applicants; 

however, these changes are not confined to areas or lenders covered by a CRA agreement. Rather, 

these changes expand to minority applicants throughout the city. We also find differential effects 

in terms of which racial and ethnic groups benefit from CRA mobilization. Mobilization around 

CRA appears to stimulate community reinvestment activity; however, not all applicants benefit 

equally. We conclude that mobilization around CRA can stimulate reinvestment activity and can be 

a positive part of CRA design; however, alone it may not be enough to achieve the broader ideals of 

community reinvestment.

Our analysis is framed from a governance perspective, which assumes that formal governmental 

agencies rarely administer policies in a neutral state (Salamon and Elliott, 2002). Rather, policies 

and resulting outcomes are embedded in dynamic social and political contexts at the local level. As 

such, we argue that recommendations on regulation or the design of public policies must take into 

consideration the broader, institutional context in which policies and regulations are enacted, the 

different institutional arrangements through which policy objectives are pursued, and the influence 

of the actors included in governance.

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII.

138 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

It can be argued that the response at the local level is the result of a lack of regulatory enforcement 

or attributable to the vague nature of the CRA, which provides lenders with many ways to meet 

regulatory requirements. The purpose of this article is not to debate what has prompted organizing 

around CRA, but rather to understand the effects of regulation from outside, or from below, influ-

ence community reinvestment outcomes. We analyze a case in which CRA governance has recently 

changed—a city that was once nearly devoid of mobilization around CRA but has recently formed 

a coalition to address reinvestment needs throughout the city. This change allows us to better 

understand how governance structures, particularly those that include local community-based 

organizations, around CRA can influence reinvestment.

We consider two community reinvestment outcomes in our analysis: loan approvals and the type 

of lender to whom applicants apply. The first outcome represents a direct source of reinvestment, 

the provision of access to credit to borrowers or areas that have historically been the targets of 

redlining. The second outcome is critical to understanding the role of CRA mobilization in the new 

financial institution landscape. Federal bank regulation policy has created a financial marketplace 

no longer dominated by conventional or traditional savings institutions, but that now includes 

mortgage brokers and financial service providers that are not subject to CRA regulation (Temkin, 

Johnson, and Levy, 2002), many of which specialize in offering subprime credit. Research suggests 

that lenders not regulated by CRA originate a higher percentage of loans and higher percentage 

of subprime loans in minority communities (Courchane, Surette, and Zorn, 2004; HUD, 2000). 

Thus, as the financial institution landscape has changed, it has produced new ways in which 

communities are redlined. Areas once starved for access to credit are now flush with a supply of 

higher-priced, riskier loan opportunities. However, mobilization around CRA may, in effect, dis-

courage applicants from pursuing credit from alternative or non-CRA-regulated lenders. Increased 

loan approvals and increases in applicants applying for loans from CRA-regulated lenders are 

considered indicators of increased community reinvestment activity.

The primary research question we ask is, does a change in the governance around CRA—namely, 

increased community-based mobilization—improve community reinvestment outcomes along 

these two dimensions? We find that the presence of agreements is associated with an increase in 

loan approvals. However, we also find that the benefits are not necessarily limited to a spatial, 

geographic level but may occur at a broader level. Furthermore, not all races and ethnicities may 

benefit equally. Although regulation from below provides a number of positive externalities, CRA’s 

current design also leads to a number of negative externalities that must be addressed to enhance 

the functioning of the law across all assessment areas to ensure equal opportunity to access to 

banks for all. 

Formal and Informal Regulation Under the Community  

Reinvestment Act

CRA is intended to encourage depository institutions to help meet the credit needs of the commu-

nities in which they operate. It was enacted in response to concerns over  redlining practices, which can be defined as banks penalizing or refusing to lend to borrowers based on the characteristics of 

the community to which they belong. Redlining not only has consequences for individuals, but it 

Cityscape 139

Casey, Farhat, and Cartwright

also has place-based, spatial consequences. It can trigger a cycle of spatial disinvestment as access 

to credit within a geographic boundary becomes difficult or impossible for individual homeowners 

and businesses to obtain (Immergluck, 2004; Squires, 2003, 1992). The current design of CRA 

addresses this spatial component by requiring lenders to define a geographical assessment area, 

commonly referred to as a  CRA assessment area. CRA assessment areas generally refer to the geog-

raphies in which a bank has its main office, its branches, and its deposit-taking automated teller 

machines, or ATMs.2 

Formal regulation under CRA is divided among three federal agencies responsible for regulatory 

oversight in the financial industry—the Federal Reserve Board (FRB), the Federal Deposit Insur-

ance Corporation (FDIC), and the Officer of the Comptroller of Currency (OCC). Originally, four 

agencies handled regulatory oversight; however, effective July 21, 2011, the Office of Thrift Super-

vision merged with OCC. In addition, in 2008, the Consumer Financial Protection Bureau (CFPB) 

was formed (CFPB, 2017). The CFPB has power to enforce existing laws against discrimination in 

consumer finance and was formed with the intent to provide a single point of accountability for 

enforcing federal consumer financial laws. The CFPB has taken action around CRA. For example, 

in 2015, the CFPB joined with the Department of Justice (DOJ) to take action against a bank head-

quartered in New Jersey alleged to have engaged in illegal redlining based on the manner by which 

the bank delineated its CRA assessment area (Davenport and Epstein, 2015).

Formal agencies responsible for CRA oversight have interpreted CRA as holding lenders account-

able in four areas: lending, investment, community development, and service. Regulators review 

the performance of the lender based on an established exam schedule and can impose costs on 

lenders when their performance is deemed to be suboptimal. The regulatory agency considers a 

bank’s CRA rating when the bank applies to open or close a domestic branch, seeks to merge with 

another lending institution, consolidates, acquires assets, or assumes liabilities. If a bank has a less-

than-satisfactory CRA rating, a regulator could deny or delay a merger application, an acquisition 

request, or the opening of a new branch. In general, satisfactory ratings for CRA are high.

However, the other side to CRA regulation, which can be argued to be the more powerful source 

of CRA enforcement, has expanded the nature of governance around community reinvestment. 

Regulation—and, arguably, progress—under CRA cannot solely be attributed to formal regulatory 

channels, but must recognize the important role that local community-based groups and nonprofit 

organizations have played in its enforcement. In many cities across the country, strong nonprofit 

networks and community-based groups have played a pivotal role in achieving the goals of com-

munity reinvestment. This involvement can be a positive externality, as it allows for local knowl-

edge and the local community to inform lenders’ behavior. In some cases, such as in Cleveland, 

Ohio, local governments have also stepped in to enhance CRA regulation. Under CRA regulation, 

community-based groups and other public entities are encouraged to challenge or inform the 

behavior of lenders under CRA—efforts that may lead to a negotiated agreement.

In a number of cities across the country, community-based and local groups have used CRA as a 

lever to negotiate agreements with lenders for programs or policies intended to increase access to 

minority borrowers and to pressure regulatory agencies to review the lenders’ activity in certain 

2 12 C.F.R. §228.41(a).

140 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

communities (Immergluck, 2004; JCHS, 2002; Squires, 2003, 1992; Williams and Nesiba, 1997). 

Over time, the scale of CRA negotiations has changed, moving away from smaller neighborhood 

efforts and protests to larger collaborative efforts among multiple groups (JCHS, 2002). In a 

number of cases, actors include active organizing and advocacy groups, development corporations, 

and support groups to coordinate community-based efforts and often forge a larger coalition 

(JCHS, 2002; Squires, 2003, 1992; Taylor and Silver, 2003). Agreements have also expanded in 

some cases to cover a larger geographic area, beyond a lender’s CRA assessment area, or to provide 

provisions for targeted groups and populations of borrowers.

The Community Reinvestment Act Governance Context and 

Community Reinvestment

Understanding the relationship between community investment outcomes and the local CRA gov-

ernance context is important for several reasons. Applying a governance perspective to understand 

reinvestment outcomes raises questions about the relationship between the local capacity to mo-

bilize and its influence on those regulated. Pluralistic theories suggest that public policy outcomes 

are realized largely through the political process, whereby interests inform policy preferences, these 

interests are representative of the racial and ethnic makeup of the residents in a community, public 

officials enact policy in response to these interests, and responsible agencies neutrally administer 

policies through the use of rules and standard operating procedures that create the desired condi-

tions (Dahl, 1961). Conversely, a governance perspective recognizes the potential for policies at 

the federal level to be shaped or characterized by local interests and the values espoused by these 

interests, and, in turn, the involvement of these interests may shape not only policy preferences 

and implementation, but also outcomes and behaviors of targeted groups (Salamon and Elliott, 

2002; Schneider and Ingram, 1997). As such, governance perspectives suggest that different rein-

vestment outcomes and levels of community reinvestment activity might emerge in an environment 

governed by different interests and perspectives. Furthermore, as the governance context changes, 

so too might reinvestment outcomes and community reinvestment activity. As it applies to CRA, in 

cities where mobilization around CRA is greater, the governance context might result in increased 

community reinvestment activity.

Previous support exists for this argument; however, it does not allow for a before-and-after assess-

ment. Casey, Glasberg, and Beeman (2011) found that minority applicants living in an area with 

a history of organizing around CRA were more likely to seek mortgage credit from CRA-regulated 

lenders than non-CRA-regulated lenders. The analysis focused on two cities, Cleveland and St. 

Louis, Missouri, which were identified as similar along a number of economic, demographic, and 

socioeconomic dimensions. The authors tested whether disadvantaged applicants have a greater 

likelihood of pursuing access to credit from traditional lending institutions in a context where 

local capacity around CRA exists. The empirical results suggested that access to credit from CRA-

regulated lenders was more likely in a place with local capacity around CRA.

The aforementioned study is limited in that it relies on a cross-sectional analysis of governance 

contexts and is unable to capture the before and after effects of mobilization around CRA. In 

2009, the CRA governance context in St. Louis changed, creating the opportunity to conduct a 

Cityscape 141

Casey, Farhat, and Cartwright

longitudinal analysis of the effects of a changed governance context on community reinvestment. 

The St. Louis Equal Housing and Community Reinvestment Alliance (SLEHCRA) formed in 2009 

with a focus on building local capacity around CRA. The coalition effort was led by the Metropoli-

tan St. Louis Equal Housing Opportunity Council (EHOC), with a primary objective to hold lend-

ers more accountable to CRA. Since SLEHCRA’s launch in 2009, the coalition has negotiated 46 

contracts with lenders, the first of which went into effect by 2010. This change in the governance 

context around CRA provides the opportunity to analyze the effect of CRA on community reinvest-

ment in St. Louis.

The Community Reinvestment Act Governance Context in 

St. Louis

Mobilization around CRA in St. Louis was virtually nonexistent between the 1990s and SLEHCRA’s 

formation in 2009. Justine Petersen, who began advocating for banks to increase lending to low- 

and moderate-income communities, largely spearheaded some CRA mobilization in the 1980s and 

1990s. She successfully negotiated the first CRA agreements in St. Louis, and as Sherraden (1996: 

7B) said —

… was one of the nation’s path breaking organizers in community reinvestment. Working 

for ACORN [the Association of Community Organizations for Reform Now] in the 1980s 

and early 1990s, she negotiated hundreds of millions of dollars in lending packages for 

low-income and minority home ownership in St. Louis. She believed that the large banks, 

which had abandoned the poorest neighborhoods, could learn to serve these areas and 

also make a profit in doing so. 

One of the major community development corporations (CDCs) in St. Louis bears Petersen’s name 

today.

However, between the late 1990s and 2009, no other CRA organizing efforts were in place (Casey, 

2009). SLEHCRA was formed in response to the same concerns expressed by Peterson in the 

1980s, that many low-income and minority communities in St. Louis did not have equal access 

to mainstream financial services and that redlining was still a major issue in the area. Community 

disinvestment remains a problem in St. Louis 40 years after the enactment of CRA. EHOC took 

the lead on building and developing the coalition. EHOC, a nonprofit fair housing organization, 

focuses on enforcing fair housing laws and the obligation to affirmatively further fair housing 

among financial institutions, as required by the federal Fair Housing Act.3 EHOC spearheaded the 

formation of SLEHCRA and identified its mission as a new coalition to hold banks accountable for 

CRA and fair lending performance. The mission of EHOC integrates the principles of CRA and fair 

lending laws—to promote investment in low- and moderate-income communities, regardless of 

race, and in minority communities, regardless of income (SLEHCRA, 2012).

SLEHCRA has grown since its inception and includes over 25 organizations as of 2017  

(SLEHCRA, 2017). SLEHCRA is a broad-based coalition, with members representing diverse 

3 7 C.F.R. §1901.203.

142 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

interests, missions, and purposes, who work on community development issues at various scales; 

that is, from within the city of St. Louis only across county boundaries and throughout the state 

of Missouri. Members include organizations that are focused on policy advocacy and civil rights, 

such as the Coalition of Concerned Citizens and Ready, Aim, Advocate! Committee. Members also 

include CDCs and housing service providers. The coalition also includes members representing the 

interests of racial and ethnic groups, such as the NAACP St. Louis City and the Latinos Ex Axion. 

Coalition membership also includes interests representing business and the workforce.

SLEHCRA has focused on engaging all banks active in the St. Louis area on matters relating to CRA 

and the Fair Housing Act. SLEHCRA adds to formal regulation and oversight of CRA in a number 

of ways. SLEHCRA conducts analysis of banks’ CRA activities, writes public comment letters to be 

considered by federal regulators in CRA evaluations and applications, and engages in dialogues and 

partnerships with banks about their performance in providing services to all communities. Since 

2009, SLEHCRA has conducted more than 80 reviews of bank performance and has written more 

than 60 public comment letters, including the first public comment letter ever to be received by 

the St. Louis field office of OCC (SLEHCRA, 2015, 2012).

SLEHCRA (2015) identified a correlation between its work and enhanced regulation of CRA. For 

example, two banks received “Needs to Improve” CRA ratings, the first below-satisfactory CRA 

rating for an institution based in St. Louis since 1994. SLEHCRA’s work has also led to increased 

scrutiny of lending institutions by the DOJ and the U.S. Department of Housing and Urban Devel-

opment (HUD). SLEHCRA attributes these investigations and fair lending cases as originating from 

the efforts of their work.

SLEHCRA’s work also expands beyond mere regulation and enforcement from below; it also 

aims to educate and inform borrowers and lenders about CRA lending opportunities. SLEHCRA 

also builds partnerships with other organizations to address issues borrowers in the community 

face, such as lack of financial education or awareness about banking products. Examples of these 

organizations include the Greater St. Louis Financial Education Collaborative and the St. Louis 

Regional Unbanked Taskforce. Likewise, SLEHCRA also works to educate lenders on the needs of 

the communities and valuable opportunities for CRA investments. An example of this outreach is 

the formation of the St. Louis Metropolitan CRA Association in 2012, which provides professional 

development and collaborative activities for CRA officers at banks or those engaged in relevant 

work (SLEHCRA, 2015).

As mentioned previously, SLEHCRA has negotiated 46 CRA agreements with banks serving 

potential borrowers in communities throughout the city of St. Louis. The CRA agreements often 

target the activities to low-income or minority census tracts throughout the city. The agreements 

are relatively standard and include provisions aimed at the following.

•  Increasing lenders’ commitment to providing support for community development activities. 

•  New bank branch openings.

•  The development of new banking products, including new deposit products like second-chance 

checking accounts, new affordable mortgage products, and products  such as small-dollar loans 

and credit-building products.

Cityscape 143

Casey, Farhat, and Cartwright

•  Financial education resources and partnerships.

•  Increased outreach to low-income communities and predominately minority communities, in-

cluding increased marketing and advertising to African-American and Hispanic communities.

•  Increased diversity and community development within bank staff and directors, including new 

positions created for CRA and community development, increased diversity among bank em-

ployees, and banks adding members to their board of directors. 

•  Increased partnerships with nonprofit and community organizations to provide financial sup-

port and investments, financial education resources, and partnership programs.

Anticipated Outcomes of a Changed Community  

Reinvestment Act Governance Context

The emergence of SLEHCRA provides the opportunity to evaluate the impact of this governance 

change on community reinvestment activity in St. Louis. How did this change in governance 

around CRA influence community reinvestment in the city of St. Louis? Has community reinvest-

ment activity increased? If so, at what geographic level does this change occur and who benefits?

We anticipate the change in CRA governance to have a positive relationship with increased com-

munity reinvestment activity in St. Louis. As mentioned previously, we define increased communi-

ty reinvestment activity in two ways: an increase in the likelihood of loan approvals and a decrease 

in the likelihood that applicants pursue credit from alternative or non-CRA-regulated lenders. Key 

subquestions include the following: do only lenders in negotiated agreements drive community 

reinvestment activity in the post-CRA governance context? Does community reinvestment activity 

occur only in the tracts covered by the agreements, or does a post-CRA governance effect stimulate 

community reinvestment activity that cuts across tracts and lenders? We utilize interaction terms 

to determine if these effects are equal across all races and ethnicities. The logic that informs the 

different expectations is discussed in the following sections.

The Changed CRA Context: Lenders With Negotiated Lending Agreements

The changed CRA governance context now includes lenders that have agreed to a negotiated CRA 

agreement with SLEHCRA. The most direct outcome from the post-CRA governance context is the 

negotiation of a lending agreement between the coalition members and targeted lenders in the city 

of St. Louis. We test the relationship between lenders with a CRA agreement and loan approvals. 

We test this relationship because a loan approval is one way a lender can fulfill its CRA obligation, 

increasing community reinvestment activity by increasing lenders’ investments to fulfill the terms 

of the agreement. We anticipate that lenders with a CRA agreement will be more likely to approve 

loan applications in order to satisfy their CRA agreement requirements.

The Changed CRA Context: Tracts Covered by Negotiated Lending Agreements

The changed CRA governance context in the case of St. Louis also results in an increase in the 

number of census tracts covered by negotiated lending agreements. Our expectations related to the 

144 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

relationship between the coverage of the CRA agreements and community reinvestment activity is 

informed by signaling theory and information externalities, which suggest the presence of effects 

of CRA agreements that extend beyond the lender involved in the agreement. Signaling theory 

addresses information asymmetries between two parties, whereby the sources of asymmetric infor-

mation are mainly concerned with information about quality or intent (Stiglitz, 2000). The changed 

governance context may exert a signaling effect to other CRA-covered institutions and to applicants.

Two primary signaling effects are of interest in this analysis—those that influence the behavior of 

lenders and those that influence the behavior of applicants. First, the post-CRA governance context 

can improve the information lenders have about applicant characteristics and how regulators 

are going to evaluate their CRA performance. The mechanisms behind the signaling and lenders’ 

responses to it are not of primary concern in this analysis, but rather the intent is to understand 

if any CRA governance effects extend beyond the parties and geographic areas subject to the 

agreement. We anticipate that some will, and that this signaling effect will exert an influence on the 

lender’s behavior, resulting in increased loan approvals in tracts covered by the agreement.

Secondly, the post-CRA governance context can have a signaling effect on the behavior of ap-

plicants. It can also result in improved information for applicants as lenders engage in outreach or 

other activities to market to applicants or as the coalition and its partners improve the information 

flow to applicants about CRA and CRA-regulated institutions. We anticipate that this signaling 

effect can reduce the likelihood that applicants pursue credit from non-CRA-regulated lenders.

The Post-CRA Governance Context

Finally, we also aim to understand the geographic reach of a post-CRA governance context. Su et 

al. (2014) found that the strength of a signal, for example, the presence or absence of a coalition, 

may change for different institutional environments. In a context with an active coalition, we 

anticipate that the effects of the signal may not be limited only to the lenders or the geographic 

areas covered by the agreement but that it may be strong enough to change the overall lending 

environment. We anticipate that the post-CRA governance context may represent a changed insti-

tutional context that exerts a signaling effect that extends beyond the individual lenders involved in 

agreements or the geographic areas covered by CRA agreements. Rather, community reinvestment 

activity may reach across the city. The local coalition is involved in efforts beyond the negotiation 

of CRA agreements, such as providing public input on lenders’ performance and informing regula-

tors about the behavior of lenders in the area. Thus, it is plausible that this increased scrutiny has 

changed the response and behavior of lenders and that they have enhanced their efforts to meet 

the objectives of CRA. Likewise, CRA agreements can enhance information in a given context that 

improves the loan outcomes for applicants throughout the city.

SLEHCRA has also been engaged in educating potential borrowers about CRA, CRA lending prod-

ucts, and financial education. This community engagement can improve the information potential 

borrowers have about the opportunities available through CRA-regulated lenders, leading to a 

reduced likelihood of applying to non-CRA-regulated lenders.

Finally, although CRA does not have a specific race or ethnicity requirement, we anticipate that the 

composition of this particular coalition—and the socioeconomic and demographic characteristics 

Cityscape 145

Casey, Farhat, and Cartwright

of St. Louis—will exert an effect, and hence agreements and outcomes will also be targeted toward 

minority borrowers. The historical correlation among race, ethnicity, income, and redlining in 

the city of St. Louis is strong (Gordon, 2008), so we anticipate that lending goals are also targeted 

toward minorities. Furthermore, as the  New York Times reported in 2016, citing a National Com-

munity Reinvestment Coalition study, “only 3 percent of loans to low- and moderate-income 

borrowers were made in St. Louis neighborhoods where minorities made up 80 to 100 percent of 

the population” (Eavis, 2016: B3). Finally, the coalition, as mentioned previously, is led by EHOC, 

which has as its mission equal and fair housing and reducing discriminatory barriers.

Methods

We use Home Mortgage Disclosure Act (HMDA)4 data, census data, and qualitatively coded data 

from CRA agreements for the analysis. We use the HMDA database to obtain data on loan applica-

tions in the city of St. Louis from 2007 to 2014, as it is the most comprehensive public database 

of mortgage loans and lenders. Loans are restricted to conventional loans, one- to four-family 

property types, home purchase loan types, and owner-occupied as a principal dwelling. We collect 

supplementary borrower data from the census 2000 Summary File 3 and the census 2010 Sum-

mary File 1, available via the U.S. Census Bureau. We use the census data from 2000 and 2010, 

as opposed to other data sources such as the American Community Survey, to align with the data 

used in the HMDA database. HMDA uses 2000 Census definitions through 2011 and does not 

apply the updated definitions (2010) until 2012. Census data are used to control for characteristics 

of the census tracts that could impact the analysis. The controls include the percentage of college 

education in a tract, African-American percentage in the tract, and number of housing units.

SLEHCRA provided copies of all CRA agreements negotiated since 2010. All the contracts provide 

the terms of the agreement, the targeted geographic location, the target demographic hoped to be 

reached, and the years of coverage. Although most contracts targeted lower-income borrowers, 

some specified all minorities and others predominantly targeted African-American borrowers, 

as we anticipated, given the coalition’s focus on fair housing and lending and given the historic 

correlation in the city of St. Louis among race, ethnicity, and income (Gordon, 2008). Other provi-

sions in the contracts include increasing promotion and lending education in minority areas and 

increasing the level of minority employee representation.

Dependent Variables

Two dependent variables are considered in the analysis: loan approval and type of lender. Loan 

approval is a binary variable that equals 1 if a loan is originated or is approved by the lender but 

not accepted. Loan denials are those applications that are denied by the financial institution and 

are coded with a 0.

Type of lender is a categorical variable that indicates if the loan application was made to a CRA-

regulated lender, a credit union, or a non-CRA-regulated lender. A 1 indicates a loan application 

made to a CRA-regulated lender, defined as a lender subject to regulatory oversight by FRB, FDIC, 

4 Pub. L. 94–200, 89 Stat. 1124.

146 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

or OCC. A 2 indicates a credit union lender that is governed by the National Credit Union.  

A 3 indicates a lender that is not regulated as a depository institution, is not subject to CRA,  

and is governed by HUD.

Independent Variables

Three independent variables of interest are in the analysis, which are hereafter referred to as a group 

as “CRA variables.” The first is the binary variable “lender with a CRA agreement,” which represents 

a loan application made to a lender with a negotiated CRA agreement. A loan application to a lender 

with a CRA agreement is coded with a 1. All other loan applications are coded with a 0.

The second independent variable of interest is “CRA Agreement,” which indicates if the loan ap-

plication is for a home in a tract covered by a CRA agreement. A value of 1 is assigned to census 

tracts covered by a CRA agreement during a given year. Census tracts covered by CRA agreements 

in this study are those that were  majority-minority or  low-income, as specified in the original CRA agreement.  Majority-minority is determined by the presence of 50 percent minority population 

(FFIEC, 2015).  Low-income is defined as less than 50 percent of the Area Median Income (Brison, 

2016). The definitions of low-income and minority are specified in many of the agreements, and 

for those not specified we applied the same definitions.

The third independent variable of interest is “post-CRA,” which indicates a loan application made 

after the change in CRA governance occurred. The pre-CRA governance period included the 2007-

to-2010 period. The post-CRA period was from 2011 to 2014. All loan applications made from 

2011 to 2014 are coded with a “1” to represent those loans made after the first CRA agreement was 

enacted.

Data Analysis

We used logistic and multinomial regression the analysis; we estimated several specifications of 

each model for robustness purposes. We used interaction terms to understand how the relation-

ship between minority borrower status and loan approvals and type of lender changes (1) when 

CRA agreements cover the tract in which the loan is made, (2) when applicants apply to a lender 

with a CRA agreement, and (3) in the post-CRA governance context. The presence of a significant 

interaction indicates that the effect of one predictor variable on the response variable is different for 

different values of the CRA variables. When adding the interaction term, the effect of a borrower’s 

minority status will depend upon the value of the interaction term, and as such the interpretation 

of the coefficients is different. Model summary statistics are presented as applicable. A  p-value of 

.05 is used to determine significance.

Results

Exhibit 1 presents the descriptive statistics for the sample overall (n = 14,483) and indicates the 

significant differences in means for the pre- and post-CRA governance context. A significant, 

positive increase in the percentage of applications approved in the post-CRA governance context 

(mean [M] = 89.91 percent, standard deviation [SD] = 0.30) emerges as compared with the 

Cityscape 147

Casey, Farhat, and Cartwright

e 

***

*** ***

*** *** ***

*** *** *** **

***

*** *** ***

***

*** *** *** *** ***

***

ence

9.89

3.21

4.3

0.91 0.00

3.64

3.73

6.2

fer

20.18 15.27

– 5.38 – 9.89

17.01

17.12

12.28

25.95

52.50

16.12

– 17.94 – 0.09 – 15.92

– 11.21

Post vs. Pr

Dif

– 183.59

1.00

1.00

1.00 1.00 1.00

1.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00

1.00

71.10

99.69

1.00

Max

153.71

1,460.00

2,566.00

9,154.00 4,925.00

0.00

1.00 0.00

0.00 0.00 0.00

6.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.00

4.33

0.00

Min

358.00

14.64

1,006.00

0.30

0.34

0.18 0.30 0.23

0.48 0.33 0.20 0.00 0.25 0.14 0.49 0.25 0.46

0.44

0.16

SD

112.96

101.27

713.84 16.42 30.04 23.17

Post (2011–2014)

1,350.09

89.91

86.71

3.21 10.09 5.40

92.45 64.8 87.14 4.07 0.00 6.86 1.93 61.89 6.64 31.48

25.95

27.46 90.21 34.24

97.4 4,867

Mean

160.99

3,815.80 2,156.32

1.00

1.00

1.00 1.00 1.00

1.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00

0.00

65.50

99.69

1.00

Max

143.65

1,360.00

3,675.00

9,154.00 4,925.00

0.00

1.00 0.00

0.00 0.00 0.00

5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.50

4.33

0.00

Min

865.00 358.00

17.10

0.40

0.45

0.28 0.40 0.15

0.49 0.46 0.17 0.00 0.43 0.14 0.42 0.17 0.39

0.00

0.28

e (2007–2010)

SD

100.28

96.36

957.75 15.26 25.96 31.19

Pr

1,815.43

80.02

71.43

8.59 19.98 2.19

75.44 60.5 70.02 3.16 0.00 24.80 2.02 77.81 3.00 19.20

0.00

23.72 74.09 45.45

91.2 9,616

Mean

140.81

3,999.39 2,103.83

1.00

1.00

1.00 1.00 1.00

1.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00

1.00

71.10

99.69

1.00

Max

153.71

1,460.00

3,675.00

9,154.00 4,925.00

0.00

1.00 0.00

0.00 0.00 0.00

5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.00

4.33

0.00

Min

865.00 358.00

14.64

2007–2014

0.37

0.42

0.25 0.37 0.18

0.49 0.43 0.18 0.00 0.39 0.14 0.45 0.20 0.42

0.28

0.25

SD

105.14

98.36

883.66 15.75 28.44 29.23

1,675.75

83.35

76.57

6.78 16.65 3.27

81.16 62.0 75.77 3.47 0.00 18.77 1.99 72.46 4.22 23.32

8.72

24.98 79.51 41.68

93.2

Mean

147.59

14,483

3,937.69 2,121.47

eement 

p  ≤ .001.

oved not 

egulated 

Variables

p  ≤ .01. *** 

egulated lenders (%)

oved or appr

oved (%)

oved not accepted (%)

eements (%)

accepted (%)

(%)

edit union (%)

lenders (%)

agr

tract 

Exhibit 1

Characteristics of Loan Applicants in St. Louis, 2007–2014

Appr

Loan amount ($ in 000) Appr

Appr Denied (%) Lender with CRA agr

Applicant income ($ in 000) Applicant sex (1=Male) (%) White, non-Hispanic (%) Asian (%) Other race (%) African-American (%) Hispanic (%) CRA-r Cr Non-CRA-r Loans in tracts with CRA 

Tract population Housing units in the tract  College in the tract (%) Tract-to-income ratio  Minority population in the Lien status (0 = no lien) (%) Total loans (n)

p  ≤ .05. ** 

CRA = Community Reinvestment Act. SD = standard deviation.

* 

148 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

pre-CRA governance context (M = 80.02 percent, SD = 0.40);  p < .001. Likewise, applications to 

lenders with CRA agreements are significantly higher in the post-CRA governance context (M = 5.4  

percent, SD = 0.23) as compared with the pre-CRA governance context (M = 3.27 percent, SD = .15), 

p < .001. However, applications to CRA-regulated lenders significantly decline in the post-CRA 

governance context (M = 61.89 percent, SD = 0.49) from the pre-CRA context (M = 77.81 percent, 

SD = 0.42),  p < .001, whereas loans to non-CRA-regulated lenders significantly increase (M = 31.48 

percent, SD = 0.46) in the post-CRA governance context,  p < .001.

White applicants account for the majority of the loan applications in the overall sample, 75.77 per-

cent. African-American applicants account for the second largest group of applicants in the sample 

overall, 18.77 percent, followed by Asian applicants, at 3.47 percent. Hispanic applicants represent 

a very small percentage of the overall sample, about 2 percent. The percentage of applications from 

White borrowers significantly increases in the post-CRA context to 86.71 percent (SD = 0.34) from 

71.43 percent (SD = 0.45),  p < .001. However, African-American applications significantly decline in the post-CRA governance context (M = 6.86 percent, SD = 0.25) as compared with the pre-CRA 

governance context (M = 24.8 percent, SD = 0.43),  p < .001. Conversely, Asian applicants have 

significantly higher loan activity in the post-CRA context by about 1 percent,  p < .001. Hispanic applications also decline in the post-governance context; however, the decline is not significant.

Several significant changes occur in the characteristics of census tracts and applicant characteristics 

in the pre- and post-CRA context. The percentage of college-educated residents in census tracts 

increases significantly by 3.73 percent in the post-CRA context,  p < .001. The mean tract popula-

tion decreases significantly,  p < .001. The mean percentage of minority population in census tracts significantly decreases in the post-CRA context by 11.2 percent,  p < .001. To summarize, in the 

post-CRA context, the minority population in census tracts slightly decreases, and the percentage 

of college-educated residents increases.

Exhibit 2 summarizes the descriptive statistics of minority applicants in the sample (n = 3,509). 

Minority applicants experience a significant, positive increase in the percentage of applications ap-

proved in the post-CRA governance context (M = 76.2 percent, SD = 0.43),  p < .001 as compared 

with the pre-CRA governance context (M = 61.2 percent, SD = 0.49). Likewise, the percentage of 

minority applicants applying to a lender with a CRA agreement is significantly higher in the post-

CRA governance context (M = 7.5 percent, SD = 0.26),  p < .001 as compared with the pre-CRA 

governance context (M ≤ 1 percent, SD = 0.08). However, the percentage of minority borrowers 

applying to CRA-regulated lenders significantly declines in the post-CRA governance context (M = 

70.5 percent, SD = 0.46),  p < .001 when compared with the pre-CRA context (M = 76.34 percent; 

SD = 0.43).

Several differences are evident when comparing the tract and applicant characteristics of minority 

borrowers (exhibit 2) with characteristics of the sample overall (exhibit 1). The mean percentage of 

college-educated residents in the tracts where minority applicants apply (M = 16.5 percent, SD = 14.88) 

is lower than in the sample overall (M = 25 percent, SD = 15.75). Minority applicants apply for loans 

in tracts with a higher percentage of minority population (M = 67.35 percent, SD = 29.82) when 

compared with the sample overall (M = 42 percent, SD = 29.23). Loan amounts and incomes of 

minority applicants also tend to be lower than the sample overall.

Cityscape 149

Casey, Farhat, and Cartwright

e 

***

** ***

*** *** ***

*** **

***

*** *** *** ***

***

***

*** *** ***

ence

6.92

5.3 0.00

0.00

8.29

7.42

7.42

2.1

fer

14.98

 9.79  22.04 – 7.06

19.92

21.08

49.04

58.59

17.53

– 14.98

– 29.37

– 5.90

– 1.52

– 20.64

Post vs. Pr

Dif

– 318.89

1.00

1.00

1.00 1.00 1.00

1.00 0.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00

1.00

71.10

99.69

1.00

Max

153.71

1,292.00

1,003.00

9,154.00 4,925.00

0.00

1.00 0.00

0.00 0.00 0.00

9.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.00

4.33

0.00

Min

358.00

14.64

1,006.00

0.43

0.45

0.20 0.43 0.26

0.50 0.00 0.47 0.00 0.50 0.36 0.46 0.30 0.40

0.50

0.21

SD

113.93

98.06

710.01 18.42 32.00 29.17

Post (2011–2014)

1,232.04

626

76.20

71.88

4.31 23.80 7.51

78.14 55.1 0.00 31.63 0.00 53.35 15.02 70.45 9.74 19.81

49.04

22.64 77.85 50.39

95.4

Mean

 127.00 

3,450.38 1,927.34

1.00

1.00

1.00 1.00 1.00

1.00 0.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00

0.00

65.50

99.69

1.00

Max

143.65

1,000.00

1,460.00

9,154.00 4,925.00

0.00

1.00 0.00

0.00 0.00 0.00

5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.50

4.33

0.00

Min

865.00 358.00

17.10

0.49

0.50

0.32 0.49 0.08

0.50 0.00 0.31 0.00 0.38 0.25 0.43 0.15 0.41

0.00

0.25

e (2007–2010)

SD

80.56

62.18

752.10 13.64 21.54 28.67

Pr

1,457.49

61.22

49.84

11.38 38.78 0.59

58.22 49.8 0.00 10.54 0.00 82.73 6.73 76.34 2.32 21.33

0.00

15.22 60.32 71.03

93.3 2,883

Mean

 117.21 

3,769.28 1,868.75

1.00

1.00

1.00 1.00 1.00

1.00 0.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00

1.00

71.10

99.69

1.00

Max

153.71

1,292.00

1,460.00

9,154.00 4,925.00

0.00

1.00 0.00

0.00 0.00 0.00

5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.00

4.33

0.00

Min

865.00 358.00

14.64

2007–2014

0.48

0.50

0.30 0.48 0.13

0.50 0.00 0.35 0.00 0.42 0.27 0.43 0.19 0.41

0.28

0.24

SD

87.51

70.34

745.01 14.88 24.67 29.82

1,424.97

63.89

53.78

10.12 36.11 1.82

61.77 50.8 0.00 14.31 0.00 77.49 8.21 75.29 3.65 21.06

8.75

16.54 63.45 67.35

93.7 3,509

Mean

118.96 

3,712.39 1,879.20

eement 

p  ≤ .001.

oved not 

Variables

p  ≤ .01. *** 

egulated lenders (%)

oved or appr

oved (%)

oved not accepted (%)

eement (%)

accepted (%)

(%)

edit union (%)

lenders (%)

agr

tract 

Exhibit 2

Characteristics of Minority Loan Applicants in St. Louis, 2007–2014

Appr

Loan amount ($ in 000) Appr

Appr Denied (%) Lender with CRA agr

Applicant income ($ in 000) Applicant sex (1=Male) (%) White, non-Hispanic (%) Asian (%) Other race (%) African-American (%) Hispanic (%) CRA-r Cr Non-CRA-Regulated Loans in tract with CRA 

Tract population Housing units in the tract  College in the tract (%) Tract-to-income ratio Minority population in the Lien status (0 = no lien) (%) Total loans (n)

p  ≤ .05. ** 

CRA = Community Reinvestment Act. SD = standard deviation.

* 

150 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

However, despite these differences, minority borrowers experience the same directional changes 

in tract, applicant, and loan characteristics in the post-CRA context as the sample overall. In the 

post-CRA context, the percentage of college-educated residents in census tracts where minority 

applicants apply increases significantly by 7.42 percent,  p < .001. The mean percentage of minority population in census tracts decreases in the post-CRA context 20 percent,  p < .001. In summary, 

in the post-CRA governance context, applicants tend to apply for loans in tracts with a higher per-

centage of college-educated residents and a lower percentage of minority residents. This suggests 

some demographic and socioeconomic shifts are possibly occurring throughout the census tracts 

in the city over the years, which are controlled for in the analysis by adding the relevant control 

variables and year controls.

Exhibits 3 and 4 present the results of the regression analyses, reporting the untransformed coef-

ficients. Exhibit 3 presents the results of the analysis of the relationship between the CRA variables 

and loan approvals. Exhibit 4 presents the results of the analysis of the relationship between the CRA 

variables and type of lender to which applicants apply. As presented earlier, three main CRA variables 

of interest are in the analysis—loan applications to lenders with CRA agreements, loan applications 

in tracts covered by a CRA agreement, and the post-CRA governance context. For each dependent 

variable, five models are presented, which consider the different CRA variables and interaction terms.

Exhibit 3 illustrates the relationship between the three CRA variables and the likelihood of loan 

approvals. Overall, the results suggest that Asian, African-American, and Hispanic applicants are 

significantly less likely to be approved for a loan when compared with the base group of White 

borrowers. This fact is evidenced by the significant, negative coefficients across models 1a through 

1e on the race variables, suggesting this relationship holds when adding all the variables of interest 

and when including the control variables. Model 1b includes the CRA variables of interest along 

with the race and control variables. Loan applications made in the post-CRA context ( ß = 0.583, 

p = .001) and applications to lenders with a CRA agreement ( ß = 1.408,  p < .001) have a positive, significant relationship with loan approvals. This finding suggests that loan applications are more 

likely to be approved in the post-CRA context. However, loan applications for homes in a tract 

covered by a CRA agreement does not have a significant effect ( ß = 0.011). This suggests that the 

odds of a loan approval, although greater in the post-CRA context, do not differ if the loan is made 

for a home in a tract covered by a CRA agreement. The direction and significance of the relation-

ships stay consistent as additional variables and interaction terms are added, as evidenced in 

models 1c through 1e. Models 1c through 1e indicate the results when the CRA variables interact 

with race and ethnicity. None of the interaction terms are significant, which indicates that the CRA 

variables do not exert a mediating effect on minority applicant status and loan approvals.

Exhibit 4 illustrates the relationship between these variables and likelihood of applying to a CRA-

regulated lender compared with a credit union or a non-CRA-regulated lender. The likelihood of 

applying to a certain type of lender differs by applicant race. As illustrated in model 2b, with the 

CRA variables and controls included, Asian applicants are less likely to apply to a credit union than 

White applicants ( ß = -0.992,  p < .01) and are less likely to apply to a non-CRA-regulated lender than White applicants ( ß = -0.845,  p < .01). Hispanic applicants do not significantly differ from White applicants in the likelihood of applying to a credit union versus a CRA-regulated lender 

or to a non-CRA-regulated lender versus a CRA-regulated lender. Also as evidenced in model 2c, 

Cityscape 151

Casey, Farhat, and Cartwright

Exhibit 3

Loan Approvals in St. Louis by Governance Context, 2007–2014

Coefficient

1a.

1b.

1c.

1d.

1e.

White, non-

Base

Hispanic

Asian

– 0.672 (0.120) *** – 0.654(0.120) *** – 0.698(0.127) *** – 0.537(0.148) *** – 0.653(0.120) ***

African-American – 1.257 (0.072) *** – 1.254(0.072) *** – 1.257(0.076) *** – 1.233(0.079) *** – 1.254(0.072) ***

Hispanic

– 0.342 (0.166) *

– 0.368(0.166) *

– 0.448(0.169) ** – 0.305(0.190)  – 0.368(0.166) *

Pre-/Post-CRA 

0.614 (0.105) ***

0.583(0.115) ***

0.584(0.115) ***

0.617(0.118) *** 0.584(0.115) ***

governance 

Tract covered by 

0.011(0.111)

– 0.037(0.130)

0.031(0.119)

0.011(0.111)

agreement

Lender with CRA 

1.408(0.255) ***

1.410(0.256) ***

1.415(0.256) *** 1.473(0.382) ***

agreement

Interactions: Tract covered by agreement and race/ethnicity

Asian

0.378(0.385) 

African-

0.025(0.188)

American

Hispanic

1.551(1.065) 

Interactions: Pre-/post-CRA governance and race/ethnicity

Asian

– 0.337(0.249) 

African-

– 0.086(0.159)

American

Hispanic

– 0.229(0.381)

Interactions: Pre-/post-CRA governance and lender with CRA agreement

Lender with 

– 0.115(0.514)

CRA 

agreement

Pseudo  R 2

0.122

0.125

0.126

0.125

0.125

N

14,483

14,483

14,483

14,483

14,483

Log pseudo 

– 5,729

– 5,705

– 5,703

– 5,704

– 5,705

likelihood

Control 

Yes

Yes

Yes

Yes

Yes

variables

Year control

Yes

Yes

Yes

Yes

Yes

 Applicant sex  – 0.121(0.051) * – 0.119(0.051) * – 0.120(0.051) * – 0.119(0.051) * – 0.119(0.051) *

(1=Male)

Tract population

0.000(0.000) 

0.000(0.000) *

0.000(0.00) 

0.000(0.000) 

0.000(0.000) 

Housing units in 

0.000(0.000) 

0.000(0.000) *

0.000(0.000) 

0.000(0.000) 

0.000(0.000) 

the tract 

Percentage 

0.010(0.003) ***

0.011(0.003) ***

0.011(0.003) ***

0.011(0.003) *** 0.011(0.003) ***

college in the 

tract

Tract-to-income 

0.005(0.002) ***

0.006(0.002) ***

0.006(0.002) ***

0.006(0.002) *** 0.006(0.002) ***

ratio 

Minority 

– 0.006(0.001) *** – 0.005(0.001) *** – 0.005(0.001) *** – 0.006(0.001) *** – 0.005(0.001) ***

population in 

the tract 

Applicant income  0.000(0.000)

0.000(0.000)

0.000(0.000)

0.000(0.000)

0.000(0.000)

Lien status (0 = 

– 0.368(0.105) *** – 0.375(0.105) *** – 0.375(0.105) *** – 0.379(0.105) *** – 0.375(0.105) ***

no lien)

CRA = Community Reinvestment Act.

Note: Logistic regression coefficients with standard deviations in parentheses (*  p  ≤ .05; **  p  ≤ .01; ***  p  ≤ .001).

152 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

Exhibit 4

Applications to Type of Lender by Governance Context, 2007–2014 

Coefficient

2a.

2b.

2c.

2d.

2e.

Credit Union 

White, non-

Base

Hispanic

Asian

– 0.944(0.311) ** – 0.947(0.310) ** – 0.992(0.311) *** – 1.425(0.418) *** – 1.862(0.721) **

African-American

0.345(0.155) *

0.357(0.156) *

0.366(0.156) **

0.184(0.185) 

0.010(0.207)

Hispanic

0.263(0.276) 

0.265(0.276) 

0.324(0.277) 

0.302(0.296) 

0.292(0.376) 

Pre-/Post-CRA 

1.885(0.178) ***

1.880(0.187) ***

1.846(0.187) *** 1.764(0.189) ***

governance

Tract covered by 

0.107(0.159) 

0.124(0.159) 

– 0.121(0.191)

– 0.038(0.173)

agreement

Interactions: Tract covered by agreement and race/ethnicity 

Asian

1.575(0.647) *

African-

0.579(0.298)t

American

Hispanic

0.056(0.816)

Interactions: Pre-/post-CRA governance and race/ethnicity

Asian

1.278

African-

0.623*

American

Hispanic

0.018

Non-CRA-Regulated 

White, non-

Base

Hispanic

Asian

– 0.813(0.137) *** – 0.808(0.137) *** – 0.845(0.138) *** – 0.864(0.152) *** – 1.049(0.216) ***

African-American

0.084(0.068) 

0.063(0.069) 

0.065(0.069) 

0.186(0.072) **

0.255(0.076) ***

Hispanic

– 0.114(0.150) 

– 0.117(0.150) 

– 0.053(0.150)

0.050(0.154)  – 0.016(0.192)

Pre-post 

1.033(0.068) ***

1.125(0.074) ***

1.147(0.075) *** 1.172(0.076) ***

agreement 

Tract covered 

– 0.177(0.084) ** – 0.158(0.085)t

0.025(0.092)

– 0.018(0.089)

by CRA 

agreement

Interactions: Tract covered by CRA agreement and race/ethnicity 

Asian

0.066(0.372)

African-

– 1.112(0.217) ***

American

Hispanic

– 1.240(0.650)t

Interactions: Pre-/post-CRA governance and race/ethnicity

Asian

0.366(0.286) 

African-

– 0.976(0.174) ***

American

Hispanic

– 0.066(0.310)

Pseudo  R 2

0.038

0.039

0.057

0.059

0.059

N

14,483

14,483

14,483

14,483

14,483

Log pseudo 

– 9,840

– 9,838

– 9,653

– 9,629

– 9,627

likelihood

Control variables

Yes

Yes

Yes

Yes

Year control

Yes

Yes

Yes

Yes

CRA = Community Reinvestment Act.

Note: Logistic regression coefficients with standard deviations in parentheses (*  p  ≤ .05; **  p  ≤ .01; ***  p  ≤ .001;  p  ≤ .10).

Cityscape 153

Casey, Farhat, and Cartwright

African-American applicants are significantly more likely than White applicants to apply to a credit 

union rather than to a CRA-regulated lender ( ß = 0.366,  p < .05), however, they are not more likely than White applicants to apply to a non-CRA-regulated lender ( ß = 0.065,  p = not significant). 

Overall, applying for a loan in a tract covered by a CRA agreement does not significantly influence 

the likelihood an applicant applies to a credit union as opposed to a CRA-regulated lender. How-

ever, applicants applying for a loan in a tract covered by a CRA agreement are less likely to apply 

to a non-CRA-regulated lender, a marginally significant difference ( ß = -0.158,  p < .10). In the post-CRA governance context, applicants are significantly more likely to apply to a credit union  

( ß = 1.880,  p < .001) and more likely to apply to non-CRA-regulated lenders ( ß = 1.125,  p < .001).

However, as evidenced in models 2d and 2e, race and ethnicity, interacted with the CRA variables, 

make a significant difference. CRA agreements and the post-CRA context have differential effects 

based on applicant race and ethnicity. Overall, the post-CRA governance context appears to 

increase the likelihood that applicants apply to a non-CRA-regulated lender, however, this increase 

appears to be mitigated by the race and ethnicity of the borrower. Model 2d illustrates the effects 

of applicant race and applying for a loan in a tract covered by a CRA agreement on the type of 

lender. African-American loan applicants in tracts covered by CRA agreements are significantly less 

likely to apply to non-CRA-regulated lenders when compared with White borrowers ( ß = -0.926, 

p < .01). Model 2e illustrates the effects of applicant race and the post-CRA governance context on the type of lender. Model 2e illustrates that in the post-CRA governance context, African-American 

applicants are significantly less likely to apply to non-CRA-regulated lenders than White borrow-

ers ( ß = -0.721,  p < . 01). Hispanic applicants are marginally significantly less likely to apply to non-CRA-regulated lenders in tracts with CRA agreements ( ß = -1.19,  p < .01), but the post-CRA governance context does not seem to exert a significant effect ( ß = -0.082). The CRA variables exert less influence on the behavior of Asian applicants, with the exception that Asian applicants who 

apply for a loan in a tract covered by a CRA agreement are significantly more likely to apply to a 

credit union than to a CRA-regulated lender ( ß = 0.05,  p < .05).

In summary, the analysis suggests the post-CRA governance context is associated with a change in 

the lending environment—the likelihood of loan approvals in a post-CRA governance context in-

creases. The overall post-CRA governance context and applying to a lender with an agreement are 

associated with an increased probability of loan approval. Conversely, applying for a loan in a tract 

covered by a CRA agreement does not seem to be associated with loan approval. The CRA variables 

do not appear to exert a mediating effect on race and ethnicity and loan approval.

The results also suggest that the post-CRA governance context is associated with the types of 

lenders to which applicants apply. Overall, in the post-CRA governance context, applicants are 

more likely to apply to a non-CRA-regulated lender. However, there is marginal support that 

the presence of a CRA agreement in the tract reduces the likelihood of a borrower applying to a 

non-CRA-regulated lender. Perhaps more importantly, the post-CRA governance context appears 

to have differential effects based on borrower race and ethnicity, particularly on the behavior of 

African-American applicants. African-American borrowers experience different outcomes in the 

post-CRA governance context, both when applying for loans in a tract covered by an agreement 

and during a period when agreements are in effect. Under these two conditions, African-American 

applicants are less likely to apply for loans from a non-CRA-regulated lender.

154 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

Taken together, the results suggest that the post-CRA governance context may be associated with 

community reinvestment activity, particularly by increasing access to CRA-regulated lenders for 

African-American borrowers. This finding suggests that the signaling effect may change applicant 

behaviors, and efforts of the coalition may be improving information flows between CRA-regulated 

lenders and African-American borrowers. Likewise, lenders with CRA agreements may be associ-

ated with stimulating community reinvestment activity as they increase the likelihood of loan ap-

provals. However, these activities do not necessarily translate to benefits for all minority applicants. 

Although the post-CRA governance context is associated with a higher level of loan approvals, it 

is also associated with more loans being made by non-CRA-regulated lenders. The longitudinal 

analysis also suggests that the post-CRA governance context may improve community reinvestment 

outcomes for African-American borrowers but not necessarily for Hispanic or Asian borrowers.

It is important to note the limitations of the study. First, other factors that cannot be ruled out, 

such as personal credit history or other unique features of the property for which data are not 

available, may influence a loan approval or denial decision. However, these data are not available 

from HMDA or a publicly available data source. Even so, it is plausible that barriers, such personal 

credit history, or other individual factors, such as length of employment, that typically lead to the 

exclusion of credit may be reduced in a post-CRA governance context due to the partnerships and 

programs enacted that provide a bridge to lenders. Secondly, this analysis considers only one CRA 

governance context, the city of St. Louis. The history of racial segregation that characterizes the city 

of St. Louis may explain why some particular races or ethnicities benefit more than others in the 

post-CRA governance context.

Finally, considering only one city does not allow for us to account for macro-level forces that 

may explain these results. To address this limitation, we conduct a descriptive analysis of lending 

patterns using data from Kansas City, Missouri, during the same time periods. The purpose of 

the descriptive analysis is to understand if the pattern of loan approvals and types of lender looks 

similar or different from the pattern in the city of St. Louis. Kansas City is different than St. Louis 

in that it cuts across several county boundaries, whereas St. Louis city is its own county. The 

descriptive analysis of loan applications and types of lenders to which borrowers applied indicates 

a minimal change in loan approvals from the time period of 2007 to 2010 (M = 86.00 percent, 

SD = 0.35) to the period of 2011 to 2014 (M = 87.00 percent, SD = 0.34). The number of loans 

to CRA-regulated lenders also declined significantly during the 2011-to-2014 time period (M = 

36.00 percent, SD = 0.49) as compared with the 2007-to-2010 period (M = 66.00 percent, SD = 

0.49). Loan applications to non-CRA-regulated lenders increased from 2007 to 2010 (M = 15.00 

percent, SD = 0.36) to 2011 to 2014 (M = 59.00 percent, SD = 0.49). The percentage of minority 

loan applications (n = 13,898) approved during the 2011-to-2014 period (n = 4,283, M = 76.00 

percent, SD = 0.43) to that of the 2007-to-2010 period (n = 9,615, M = 75.00 percent, SD = 

0.43) remained almost constant. As in St. Louis, the percentage of minority borrowers applying to 

CRA-regulated lenders declined in the years of 2011 to 2014 (M = 33.00 percent, SD = 0.47) as 

compared with 2007 to 2010 (M = 60.00 percent, SD = 0.49). Although the analysis of Kansas City 

is descriptive in nature, it does suggest that macro-level forces alone may not be the only factor 

explaining the results. However, it is still limited in proving causality, in that we do not control for 

nuances about variations in local lending markets in the descriptive analysis.

Cityscape 155

Casey, Farhat, and Cartwright

Discussion and Conclusion

The results suggest that applicant outcomes differ in the post-CRA governance context and that 

community-based mobilization around CRA can stimulate community reinvestment activity. CRA 

has fueled the creation of nonprofit partnerships and networks that serve as valuable links between 

lenders and borrowers and, in many cases, can make the regulation to work better. Community 

involvement in its implementation gives voice to a community, and allows for local-level influ-

ence in decisionmaking. For example, Casey (2009) found that challenges and negotiations in 

Chicago, Illinois, and in Cleveland led to agreements that contained a number of provisions that 

moved beyond increasing the number of loans. These agreements included community-level input 

from groups representing multiple segments of society and included the provision of education, 

resources, and outreach to improve borrower awareness of opportunities and options.

However, organizing around CRA has been highly variable across time and place, which may 

lead to further inequities when community-based coalitions or organizations are absent. Casey 

(2009) argued that the indirect element of CRA regulation is positive in that it can foster broader 

connections between lenders and community-based groups to develop community reinvestment 

responses. However, the previously referenced study that analyzed four different cities—Cleveland; 

Chicago; Indianapolis, Indiana, and St. Louis—revealed inequities across cities in mobilization 

around CRA. Indianapolis and St. Louis did not organize around CRA; hence, the benefits afforded 

to communities in Chicago and Cleveland were not realized (Casey, 2009).

A major shortcoming in CRA regulation is its variation across places, and this variation is further 

complicated by a changed regulatory landscape. The regulatory landscape has changed and has 

led to the emergence of a host of non-CRA-regulated lending entities and banks that do business 

across state lines and outside the communities in which they are chartered. Arguably, these changes 

produce positive externalities; however, it is also critical to reflect on the implications of these 

changes on reinvestment in urban areas, potential negative externalities, and the potential of CRA 

in today’s environment. The current design of CRA regulation has both positive and negative 

externalities. A positive is that CRA’s current design provides local groups and organizations with 

the opportunity to better inform lenders of the banking needs of low- to moderate-income and 

minority communities. Community development advocates in the 1960s and 1970s argued for 

more community-based participation in programs and policies in response to top-down initiatives 

(Phillips and Pittman, 2009); however, it is necessary to consider how to strike a more appropriate 

and just balance between formal and informal regulation. The balance is necessary to address the 

variation across metropolitan areas in the capacity for community-based groups and nonprofit 

organizations to take regulation into their own hands.

What, then, do the findings from this analysis suggest for the future of community reinvestment and, 

more specifically, the design of CRA for the future? The findings suggest that the appropriate policy 

design lies somewhere between top-down and bottom-up regulation. Too much reliance on bottom-

up regulation may reproduce inequities or shift the burden to nonprofit and community-based 

organizations that may lack the capacity or leadership to undertake mobilization efforts. Conversely, 

too much reliance on top-down regulation has the potential to diminish community participation in 

reinvestment activity, proscribing one-size-fits-all approaches for meeting CRA objectives.

156 The CRA Turns 40

Community Reinvestment Act and Local Governance Contexts:  

Advancing the Future of Community Reinvestment?

Thus, the design of the policy (and formal regulatory enforcement of it) needs to address the orga-

nizational capacity equities and inequities that are built into the context in which community rein-

vestment is vital. A value of CRA is that it allows for flexibility to best address reinvestment market 

conditions and community needs; however, CRA in its current form seems to be more effective in 

areas with a strong community-based capacity or network. This aspect of CRA is important when 

considering the democratic value of CRA, but it neglects to consider the inequities in capacities 

and reinvestment needs that exist across places. In effect, the unevenness of community-based 

capacity and networks around CRA can lead to CRA assessment areas that are more desirable for 

lenders to target, which may deplete lender activity in areas that lack community capacity. 

The resulting recommendation is the need to balance bottom-up and top-down regulation and 

address organizational capacity at the local level of the community. The three recommendations 

that follow suggest several roles for formal regulatory agencies, including the newly formed CFPB 

and HUD. The first recommendation is for formal regulators to more stringently define and restrict 

what qualifies as a CRA assessment area. Some precedent for this exists, as the CFPB and DOJ 

have recently taken action against a bank in New Jersey for selectively picking its CRA assessment 

areas (Davenport and Epstein, 2015). Formal regulatory agencies can strengthen their enforcement 

by requiring lenders to define CRA assessment areas as those that not only possess the historical 

characteristics related to redlining but also are areas that are overly serviced by non-CRA-regulated 

lenders. Fulfilling this recommendation requires an expansion of the definition of CRA assessment 

areas to include criteria related to the current level of investment in the area and the types of lend-

ers in operation.

Second, formal regulatory agencies can strengthen enforcement by analyzing CRA mobilization 

capacity in specific areas, and other agencies such as HUD can develop grant programs geared to-

ward building capacity around CRA. Areas that are weak in CRA capacity should be prioritized for 

capacity building. The goal of these efforts should be twofold: to develop internal, local networks 

aimed at developing local knowledge and capacity in these CRA assessment areas, and to leverage 

the knowledge and experience of successful CRA networks from outside the CRA assessment area.

Finally, the design of CRA policy needs to recognize the diverse composition and lending experi-

ences of minority borrowers, and also the variation that exists across different metropolitan 

contexts. The results from this study suggest that organization around CRA can lead to differential 

effects for borrowers, which could be due to the types of groups mobilized around CRA or the 

historical disparities that have persisted. However, formal regulation needs to ensure that when 

CRA activity occurs, it creates reinvestment opportunities equally for all.

Acknowledgments

The authors thank and acknowledge Elisabeth Risch, Director of Research and Evaluation, Metro-

politan St. Louis Equal Housing and Opportunity Council, for providing data on the Community 

Reinvestment Act agreements negotiated by the St. Louis Equal Housing and Community Reinvest-

ment Alliance. 

Cityscape 157

Casey, Farhat, and Cartwright

Authors

Colleen Casey is a former associate professor, program director, and interim department chair in 

the College of Architecture, Planning and Public Affairs at the University of Texas at Arlington.

Joseph Farhat is a professor of finance and department chair at Connecticut State University.

Gregory Cartwright is an adjunct professor in the College of Architecture, Planning and Public 

Affairs at the University of Texas at Arlington.

References

Barr, Michael S. 2005. “Credit Where It Counts: The Community Reinvestment Act and Its Crit-

ics,”  N.Y.U. Law Review 80 (2): 513–652.

Brison, Tina M. 2016. “Your Assessment Area and CRA Performance Context.” Federal Deposit 

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Call.pdf.

Casey, Colleen. 2009. “Linking Social Capital and Indirect Policy Tools: Fostering Equitable Com-

munity Reinvestment Responses?”  Journal of Planning Education and Research 28 (4): 413–425.

Casey, Colleen, Davita Glasberg, and Angie Beeman. 2011. “Racial Disparities in Access to Credit: 

Does Governance Matter?”  Social Science Quarterly 92 (3): 782–806.

Consumer Financial Protection Bureau (CFPB). 2017. “Consumer Financial Protection Bureau: 

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Courchane, Marsha, Brian Surette, and Peter M. Zorn. 2004. “Subprime Borrowers: Transition and 

Outcomes,”  Journal of Real Estate Finance and Economics 29 (4): 365–392.

Dahl, Robert. 1961.  Who Governs? Democracy and Power in the American City. New Haven, CT: Yale 

University Press.

Davenport, Erin, and Eric Epstein. 2015. “CFPB Deems Federally-Approved CRA Assessment 

Area To Be Discriminatory.” JD Supra. https://www.jdsupra.com/legalnews/cfpb-deems-federally-

approved-cra-96888/.

Eavis, Peter. 2016. “Race Strongly Influences Mortgage Lending in St. Louis, Study Finds,”  The New 

York Times, July 19, B3.

Federal Financial Institutions Examination Council (FFIEC). 2015. “Home Mortgage Disclosure 

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HMDA2012.htm.

Fishbein, Allen J. 1992. “The Ongoing Experiment With Regulation From Below: Expanded 

Reporting Requirements for HMDA and CRA,”  Housing Policy Debate 3 (2): 601–636.

Gordon, Colin. 2008.  Mapping Decline: St. Louis and the Fate of the American City. Philadelphia: 

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Advancing the Future of Community Reinvestment?

Immergluck, Daniel. 2004.  Credit to the Community: Community Reinvestment and Fair Lending Policy 

in the United States. Armonk, NY: M.E. Sharpe.

Joint Center for Housing Studies (JCHS). 2002.  The 25th Anniversary of the Community Reinvestment 

Act: Access to Capital in an Evolving Financial Services System. Cambridge, MA: Joint Center for 

Housing Studies of Harvard University. http://www.jchs.harvard.edu/research/publications/25th-

anniversary-community-reinvestment-act-access-capital-evolving-financial.

Phillips, Robert H., and Rhonda Pittman. 2009.  An Introduction to Community Development. New 

York: Routledge.

Salamon, Lester M., and Odus V. Elliott. 2002.  The Tools of Government: A Guide to the New Gover-

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Schneider, Anne L., and Helen M. Ingram. 1997.  Policy Design for Democracy. Lawrence, KS: 

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Sherraden, Michael. 1996. “Two Missourians Who Made a Difference,”  St. Louis Post Dispatch, 

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Squires, Gregory D. 2003.  Organizing Access to Capital: Advocacy and the Democratization of Financial 

Institutions. Philadelphia: Temple University Press.

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St. Louis Equal Housing and Community Reinvestment Alliance (SLEHCRA). 2017. “Public Com-

ments.” http://www.slehcra.org/banks-lending/bank-reports-public-comments/.

———. 2015. “SLEHCRA 2015 Report to the Public.” http://www.slehcra.org/wp-content/

uploads/2015/11/Report-to-the-Community-FINAL-rvs.2-11-11-15.pdf.

———. 2012. “Report to the Community: An Examination of SLEHCRA’s Impact on Community 

Investment in the St. Louis Metro Area.” http://www.slehcra.org/wp-content/uploads/2012/09/

SLEHCRA-Reports-to-the-Community-FINAL-091312.pdf.

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Economics,”  The Quarterly Journal of Economics 115 (4): 1441–1478. DOI: 10.1162/003355300555015.

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160 The CRA Turns 40

Data, Accountability, and the Public: 

Using Community Reinvestment  

Act Data for Local  

Community Development

Marijoan Bull

Westfield State University

Abstract

Community development corporations (CDCs) are place-based, resident-led organiza-

tions committed to revitalizing neighborhoods and expanding opportunities for low- and 

moderate-income residents. The Community Reinvestment Act (CRA) shares a place-

based focus, as it was intended to hold banks accountable for their activities in all parts 

of their service areas—including the low- and moderate-income neighborhoods where 

CDCs operate. This article discusses the disconnect between the mandated CRA process 

and the type of information on banking activity that would be useful for local orga-

nizations working on community revitalization. It presents a case study from the city 

of Holyoke, Massachusetts, detailing a partnership between a university and a CDC, 

focused on federal CRA performance in the low- and moderate-income area the CDC 

serves. Students reviewed performance evaluations of seven banks and, working with 

the CDC, prepared an examination of the findings. Conclusions of the research include 

the inadequacy of CRA performance evaluations and data for local CDCs and the lack 

of a documented deep analysis of community needs. Actions that regulators, banks, and 

CDCs can take to improve the CRA process are discussed.

Introduction

The federal Community Reinvestment Act1 (CRA) of 1977 was part of a larger multipronged feder-

al response to discriminatory practices in real estate and lending, urban disinvestment, and a con-

sumer protection movement (Squires, 1992). Adopted shortly after the Home Mortgage Disclosure 

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 161

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

Bull

Act2 (HMDA) of 1974,CRA was based on the principle that “regulated financial institutions have 

a continuing and affirmative obligation to help meet the credit needs of the local communities in 

which they are chartered.”3 CRA had multiple purposes, but one received a lot of traction with 

community groups: to assess and correct for  deposit redeployment—banks failing to invest in areas 

where they collected deposits (Bradford and Cincotta, 1992; Marsico, 2005). Beyond this purpose, 

CRA was seen as a market-correction action (Avery, Bostic, and Canner, 2005; Olson, Chakrabarti, 

and Essene, 2009), surfacing information on local needs and conditions to get beyond the banks’ 

ignorance of certain geographies and perceptions that there was no demand nor sound basis for 

loans in low- and moderate-income (LMI) neighborhoods (Ludwig, Kamihachi, and Toh, 2009; 

Marsico, 2005). CRA was meant to make banks more knowledgeable about and responsive to the 

financial needs of local communities. In addition, CRA was seen as a logical quid pro quo—public 

services in exchange for the “federal safety net” provided to banks and thrifts (Immergluck, 2004; 

Ludwig, Kamihachi, and Toh, 2009; Marsico, 2005; Olson, Chakrabarti, and Essene, 2009).

CRA’s intent to ensure banks attend to the needs of all parts of their service areas—including those 

places that are home to LMI households—dovetails with the work of community development 

corporations (CDCs). As place-based, resident-led organizations, CDCs have worked tirelessly to 

address community needs in distressed and declining areas (Mallach, 2009). CDC-led programs 

are very diverse and may include affordable housing development, small business entrepreneur-

ship, vocational training, youth programming, community greening and local food system 

improvements, environmental cleanups, and other quality of life concerns. The work of these 

programs requires funding and often relies on leveraging an assortment of sources. Banks can assist 

CDCs by providing some of the capital, loans, and technical assistance needed for community 

improvement efforts to succeed. In addition, banks traditionally have played a role in the vitality 

of neighborhoods by meeting the ongoing day-to-day financial needs of local businesses and resi-

dents. CDCs often advocate, on behalf of the community members, for access to services such as 

business loans, payroll processing, lines of credit, home mortgages, car loans, and check cashing, 

at affordable rates.

Discussions on the effectiveness of CRA have consistently noted the critical role that organized 

groups, such as CDCs, and the wider public play in the CRA process. CRA was written with 

very weak enforcement authority (Immergluck, 2004). The bank regulators were charged with 

“encouraging” banks to fulfill the objectives of CRA. The law requires banks to periodically submit 

data for a regulatory review, and this review leads to a rating of the bank’s performance. CRA only 

has true teeth when a regulator is considering a banking institution’s application to open or close a 

branch or to acquire or merge with another institution (Marcy and Miller, 1993; U.S. Department 

of the Treasury, 2003). It is then that the regulating authority may consider the bank’s CRA rating, 

and the public and community organizations have a formal opportunity to comment on the bank’s 

CRA performance.

As expressed by Gale Cincotta, one of the main citizen leaders behind the adoption of CRA, “full 

responsibility for the enforcement of CRA has always been the job of people in the neighborhoods” 

2 Pub. L. 94–200, 89 Stat. 1124.

3 12 U.S.C. § 30.

162 The CRA Turns 40

Data, Accountability, and the Public: Using Community  

Reinvestment Act Data for Local Community Development

(quoted in Immergluck, 2004: 3). Similarly, former Ohio Congressman Denis Kucinich observed 

CRA “was borne out of public protest and sustained by public participation” (House Subcommittee 

on Domestic Policy, 2009: 4). A large part of the power of CRA, then, has come from an account-

ability to the public facilitated by the disclosure of data (Littrell and Brooks, 2010; Squires, 1992). 

The requirement to publish figures on the geography and borrower profiles of mortgage lending, 

small business loans, and community development activity has forced banks to consider and docu-

ment their activity in LMI areas—the very places where CDCs work. 

Today’s less-local banking environment, combined with CRA’s generalized reporting requirements, 

often fails to provide the accountability CDCs seek. NACCED (2005) estimated that more than 

4,000 CDCs are in the United States, and two-thirds of them work within one neighborhood, 

several neighborhoods, or one city. For these small-scale organizations, the nature of the aggregated 

data reported under CRA is not useful for assessing the activity of banks in their neighborhood 

target areas. Although the transparency of data is important, the form and content of information 

also determines its suitability for assessing impacts. A lack of precision can weaken accountability.

Background on Community Reinvestment Act Data and 

Ratings 

The CRA implementing regulations specify the types of data banks must report. These require-

ments have evolved over time, both to be more specific and to streamline the process for smaller 

institutions (Alexander, Dahl, and Spivey, 2009; Getter, 2015). Although the CRA domain is 

complex (it involves three regulating bodies, four different bank categories, varying examination 

schedules, and pages of definitions and examples), regulators have developed a common explana-

tory narrative—the “Interagency Questions and Answers” and other manuals—to assist banks in 

meeting their reporting obligations.4 A performance evaluation (PE) is generated by the appropriate 

regulator, is based on a review of a bank’s submittal and, due to a 1989 amendment to the original 

CRA legislation, must be available for public review.5

Exhibit 1 summarizes the data banks report annually through CRA, based on the size of assets. In 

general, larger banks must report more information with greater specificity. All banks are required 

to report on their lending activity in terms of home mortgages, small business loans, and consumer 

loans. Banks report figures annually, and, depending on their size, the data indicate the total dol-

lars, number of loans, percentage of loans in LMI census tracts, and loans by income of borrowers. 

Small business loans are defined as nonfarm loans with original amounts of $1 million or less.

Bank reviewers consider the reported overall loan-to-deposit ratio as part of their assessment of 

how well the banks are meeting the credit needs of their service areas. Regulators are looking for 

a “reasonable” loan-to-deposit ratio (FFIEC, 2015). A low loan-to-deposit ratio (for example, less 

than 80 percent) would be reason to look more closely to see if a bank is not making enough loans 

4 See http://www.ffiec.gov/cra/ and http://www.ffiec.gov/cra/pdf/2010-4903.pdf.

5 With the 1989 amendment, CRA data mirrored the expansion of HMDA filings. When first adopted, HMDA reported only mortgage originations by census tract; in 1989, reporting was expanded to require applications also and to break down the data by race, gender, and household income.

Cityscape 163

Bull

Exhibit 1

Generalized CRA Data Reporting by Bank Type

Bank Size

Lending

CD Investments

CD Services

Small (less than  

Loan: Deposit ratios and the number, 

—

—

$305 million  

amount, and geographic (LMI CT 

in assets)

level) and income distribution of 

mortgage, small business, and 

consumer loans (as applicable). 

Intermediate small 

Loan: Deposit ratios and the 

—

Branch locations within 

($305 million– 

geographic and income distribution 

AA and other types 

$1.221 billion 

of mortgage, small business, and 

of services provided, 

in assets)

consumer loans (as applicable). 

including technical 

Small business loans include the 

assistance to community 

number, total amount, and percent 

organizations.

in LMI CT.  

Mortgage loans: HMDA 

requirements.

Large (more than 

Loan: Deposit ratios and the 

Listing of number  Branch locations within 

$1.221 billion in 

geographic and income distribution  and dollar amount. AA and other types 

assets)

of mortgage, small business, and 

of services provided, 

consumer loans (as applicable). 

including technical 

Small business loans include the 

assistance to community 

number, total amount, and percent 

organizations.

in LMI CT.  

Mortgage loans: HMDA 

requirements.

Wholesale/limited 

Can choose one: CD investment,  

Can choose one:  Can choose one: CD 

purpose 

CD loans, or CD services.

CD investment, 

investment, CD loans, or 

CD loans, or CD 

CD services.

services.

AA = assessment area. CD = community development. CRA = Community Reinvestment Act. CT = census tract. HMDA = 

Home Mortgage Disclosure Act. LMI = low- and moderate-income.

Note: The asset limits that define categories fluctuate with the Consumer Price Index.

Sources: Avery, Bostic, and Canner (2005); FFIEC (2015)

in its service area; a high ratio (for example, greater than 90 percent) could be reason for concern 

about whether the bank has adequate funds on hand to cover unexpected demands (Trefis Team, 

2015). Banks that must comply with HMDA complete multiple tables, breaking down home 

mortgage lending activity by borrower race, borrower income classification, and home location. 

Larger banks may also list community development loans, including loans to support the develop-

ment of affordable housing or the establishment of a community facility to serve LMI residents. 

Large banking institutions list any “community development investments” they have made. This 

category includes investments or grants for a wide range of activities, including affordable housing 

development, homelessness services, daycare and job training, youth programs, and investing in a 

local Community Development Finance Institution (CDFI).

Banks that are classified as “intermediate small” and “large” must also include information in the 

category of “community development services.” Services are assessed by considering the location 

of bank branches and whether the bank has worked with local community groups to support 

community development activities. According to official guidance, these services could include 

164 The CRA Turns 40

Data, Accountability, and the Public: Using Community  

Reinvestment Act Data for Local Community Development

providing technical assistance on financial matters by having bank personnel serve on the board of 

a nonprofit organization, offering financial literacy classes, providing credit and foreclosure preven-

tion counseling, reviewing CDC rehabilitation loans, and more (OCC, 2014).

The legislation deliberately avoided establishing set quotas for community development lending, 

investments, and services, and thus regulators and community groups have no benchmarks to use 

in assessing activity in these categories (Avery, Bostic, and Canner, 2005; Bradford and Cincotta, 

1992). Nevertheless, community groups successfully used CRA (and its sister, HMDA) to improve 

the performance of banks in LMI areas during the late 1980s and 1990s, when banks were growing 

rapidly and mergers were frequent (Holyoke, 2004; Squires, 1992). Agreements reached between 

CDCs and banks established special pools of funding and new loan initiatives for community and 

economic development activity.

In PEs, regulators review all the data (as outlined in exhibit 1) and assign one of four overall CRA 

ratings; they consider the performance context in which the bank operates, for example, overall mar-

ket conditions, the bank’s structure, the amount and nature of bank competition, and the economics 

and demographics of the service area. From highest to lowest, the possible ratings are outstanding, 

satisfactory, needs improvement, and substantial noncompliance (FFIEC, 2015). These ratings are 

widely seen as subjective and have been documented as inconsistently interpreting the same figures 

across regulatory agencies and reviewed institutions (Alexander, Dahl, and Spivey, 2009; Immergluck, 

2004; Marcy and Miller, 1993; Marsico, 2005). Today, the landscape after the banking crisis consists 

of five main banks with 44 percent of the assets and nonbanking financing institutions (which are 

not subject to CRA) on the rise; the community reinvestment climate is subdued as banks find their 

footing, and the role of CRA appears to have declined (Rosengren and Yellen, 2009; Schaefer, 2014).

Research Methodology

Working in the spring of 2013 with the CDC Nueva Esperanza of south Holyoke, Massachusetts, 

a team of three undergraduates and one faculty member researched the CRA performance of 

banks operating within the city. Nueva Esperanza sought a general profile of banking services for 

residents of the neighborhood and a review of the performance of banks in Nueva Esperanza’s 

service area along two criteria: the nature and level of community development loans, investments, 

and services; and loan activity levels to small businesses. Given that the housing stock of the 

neighborhood presented extremely few opportunities for homeownership, the CDC was not inter-

ested in reviewing the HMDA data.6 Students completed the data gathering and analysis, meeting 

regularly with the staff and board members of Nueva Esperanza for feedback and direction. The 

objective of the CDC was to understand the banking activity and services in the area before directly 

approaching banking institutions. Nueva Esperanza was thinking strategically about which banks 

to approach for support with a Latino small business entrepreneurship initiative and with the re-

vitalization of mixed-use properties in the heart of the neighborhood. The strategy was to focus on 

presenting an ask to the banks with a less-stellar performance in the neighborhood, which would 

be informed by a knowledge of the activity of all the banks in the city.

6 Massachusetts has a state Community Reinvestment Act, but these reviews are coordinated with the federal CRA so, for purposes of this research and given time constraints, only the federal CRA documents and processes were examined.

Cityscape 165

Bull

The first step was to determine which banks should be reviewed. An assessment of bank locations 

(using bank websites, Google map searches, and field observations) found no bank branches in 

Nueva Esperanza’s immediate focus area, census tracts 8114 and 8115, but seven banks within the 

city as a whole (see exhibit 2). A part of the assessment included locating and mapping all alternative 

financial service providers serving the area, including automated teller machines (ATMs) and payday 

loan operations nearby. Following the identification of the banks operating in Holyoke,7 the online 

CRA Performance Evaluations data bank was used to locate the most current PE for each institution,8 

and the online PE documents were reviewed prior to the arranged site visits. After the review, phone 

calls were made to the financial institutions to request an opportunity to review the public CRA files. 

The public CRA files contain the same online PE, and any correspondence received by the bank con-

cerning its performance—including any complaints or commendations. As none of the files reviewed 

contained any correspondence, the following findings were based on the PEs alone. 

Exhibit 2

Summary of Federal CRA Performance Evaluations for Banks in Holyoke, 

Massachusetts

Overall CRA Rating/

Bank Name

Exam Date

City, State HQ

Regulator

Bank Size

Bank of America

3/31/2009

Charlotte, NC

Outstanding/

OCC

large bank

RBS Citizens, NA 

4/03/2010

Providence, RI

Satisfactory/

OCC

large bank

Peoples Bank

11/07/2011

Holyoke, MA

Satisfactory/

FDIC

large bank

People’s United Bank

10/05/2009

Bridgeport, CT

Outstanding/

OCC

large bank

TD Bank, USA

12/31/2011

Portland, ME

Outstanding/

OCC

large bank

United Bank

10/29/2012

West Springfield, MA Satisfactory/

OCC

large bank

Westfield Bank

4/05/2010

Westfield, MA

Satisfactory/

OTS

intermediate small bank

CRA = Community Reinvestment Act. FDIC = Federal Deposit Insurance Corporation. HQ = headquarters. OCC = Office of the Comptroller of Currency. OTS = Office of Thrift Supervision.

Note: Two other banking institutions, First Niagara Bank and Holyoke Credit Union, are not included, because First Niagara Bank was new to Massachusetts, and the Holyoke Credit Union is not subject to federal review.

7 Credit Unions are exempt; one bank was new to the area and thus did not have a PE. 

8 See https://www.occ.gov/topics/compliance-bsa/cra/perfomance-evaluations-by-month/index-perfomance-evaluations-by-

month.html.

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Findings

From this research of online sources, face-to-face meetings, and document review, themes emerge on 

the shortcoming of CRA PEs. The shortcomings, described here, have to do with the nature of data 

collected, the ease of access to the data, and the mismatch between the data and the work of CDCs.

Mismatched Scale of Data Reporting

Under CRA regulations, banks report information for a self-defined  assessment area (AA), which is 

generally quite large. Guidance on defining the AA stipulates—

… assessment areas must consist generally of one or more metropolitan statistical divi-

sion (MSA/MD) or one or more contiguous political subdivisions such as counties, cities, 

or towns. An institution must include the geographies in which its main office, branches, 

and deposit-taking ATMs are located as well as the surrounding geographies in which it 

has originated or purchased a substantial portion of its loans. (FFIEC, 2015: 7) 

The AA scale of data makes it difficult to determine community development activity within the 

much smaller target area of the CDC. Of the seven institutions reviewed, five were large banking 

institutions with multiple AAs and multistate AAs, one 1 was a large institution using the local 

metropolitan statistical area (MSA; 2 counties for a total of 124 census tracts) for an AA, and one 

was an intermediate small bank, with an AA of 1 county plus 6 political subdivisions (a total of 94 

census tracts)., The regulators reviewed only a sampling of the AAs of the most geographically ex-

tensive of the large institutions. Even when the AA chosen included the 2 census tracts of interest 

to Nueva Esperanza, the size of the AA was at least that of the entire county. The PEs included the 

total number and amount of small business loans, along with an overall percentage of the banks’ 

small business loans in LMI census tracts within the defined AA. It was not possible to determine 

if a bank’s small business loans went to census tracts 8114 and 8115.9 Community development 

loans, investments, and services were reported in various forms. For four banks, the community 

development information consisted of the minimal requirements—the total number, amount, 

and generic type of project (for example, homeless shelter)—and only three banks included 

references to the specific community location, organization involved, or project name. Thus, in 

general, it is not possible to determine if the community development activity happened within 

census tracts 8114 or 8115, or even more generally within the city of Holyoke (which has 11 

total census tracts). Of the seven banks reviewed, only one bank specifically reported community 

development loans or investments for projects in the city of Holyoke. It is important to realize that 

the PEs that were reviewed covered such large areas that a CDC in a separate city within the same 

MSA (Springfield, MA) would be looking at exactly the same information. Given that the financial 

institutions serving the census tracts in question, and the city in general, are large, the defined AAs 

are geographically extensive.

9 The CRA Aggregate Reports, however, provide the ability to see the overall (from all banks) small business activity by 

census tract (see http://www.ffiec.gov/cra/online_rpts.htm). Small business loans may legitimately require a level of privacy that would preclude identifying information. Privacy would seem less applicable to community development activities, however, as community development loans and grants are most often part of a larger public funding or regulatory process that includes public disclosure of information.

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Generalized Level of Detail

As stated, the information in the PEs on community development loans, investments, and services 

was extremely generalized and included no specific details on the structure of the loan, investment, 

or services. It was not always possible to determine in which census tract the community develop-

ment activity was located. For all but three of the listings, the specifics of the organization receiving 

the loan, the project location, or both were not disclosed. Typical listings included language such as—

An officer serves on the Board of a local housing organization that provides programs 

targeting LMI individuals and families. (OTS, 2010)

Twenty-four community development grants made by the foundation were reviewed to 

verify eligibility. These grants totaled $379 thousand, and each provided significant as-

sistance to LMI persons, affordable housing assistance, and/or neighborhood stabilization. 

(OTS, 2009)

The largest investments in the Springfield MSA totaled $3.9 million and consisted of 

LIHTC [low-income housing tax credit] projects which provided more than 400 housing 

units affordable to LMI families. The bank also invested $290 thousand in one CDFI. 

Other investments consisted primarily of contributions to local or regional organizations 

providing economic development, affordable housing, community services or activities 

that revitalize or stabilize LMI geographies. (OCC, 2009)

These overly generalized descriptions make it impossible to determine if any of the aforementioned 

efforts occurred in the focus area of the CDC.

Lack of Community Development Benchmarks

PEs combine quantitative and qualitative information and do not establish minimum benchmarks 

for community development activity. In this study, it was not possible to assess if PE ratings 

reflected differing standards based on bank size. For instance, Peoples Bank had local deposits 

far exceeding many of the other banks (anywhere from 4 to 25 times greater than the deposits of 

other banks in the city), yet its community development portfolio did not seem to reflect such a 

comparable level of activity (see exhibit 3). Loan and investment activity were not substantially 

greater, but the PE does note Peoples Bank had a higher level of community development 

grantmaking than other institutions.10 Given the subjective evaluation of loans, investments, and 

services and the regulators’ directive to consider the “performance context,” it is difficult to draw 

a direct conclusion about the level of community development activity reported or about how the 

bank evaluators used performance context in the assessment, which garnered the bank an overall 

“satisfactory” rating. The comparison, in terms of number of loans and total amount, would not 

appear to be proportionate to the level of deposits the institution holds. The findings in the study 

team’s final report to Nueva Esperanza highlighted the importance of reaching out to Peoples Bank.

10 Note that the community development activities are for the entire AA, whereas the deposits are for the city of Holyoke alone.

168 The CRA Turns 40

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Exhibit 3

Total Deposits in Bank Branches as of June 30, 2012, and CD Activity From PEs

Percent of 

Percent of 

Deposits—

Bank’s Total  Bank’s Total 

Qualified CD Activity  

Bank

FDIC # All Holyoke 

Hampden  Massachusetts 

in AA From PE

Locations ($)

County 

Deposits

Deposits

Bank of America

3510

75,461,000

7.1

0.1

Not Specified for AA 

Peoples Bank

90213 344,801,000

32.7

25.5

8 loans $1.2 million

Investments $1.6 million 

419 grants $1.2 million

People’s United Bank 27334

35,435,000

16.0

1.1

21 loans $12.7 million

Grants $264 thousand

RBS Citizens, NA

57957

14,499,000

4.4

0.05

5 loans $5 million

2 investments $434 thousand

27 grants $358 thousand

TD Bank, NA

18409

24,836,000

1.8

0.2

17 loans $16.9 million

94 Investments $13 million

United Bank

26486

90,438,000

8.8

7.1

9 loans $1.1 million

Westfield Bank

90300

23,033,000

3.1

3.1

7 loans $2.2 million

Investments $2.6 million

28 grants $283 thousand

AA = assessment area. CD = community development. FDIC = Federal Deposit Insurance Corporation. PE = performance evaluation.

Note: To get these data, visit http://www2.fdic.gov , choose Industry Analysis, Banks and Statistics, then enter bank of interest, and once selected, choose Summary of Deposits, CD Activity from PE reports.

Source: http://www2.fdic.gov

Unfriendly Data Portal Design

Although the regulating bodies have made efforts to make the PEs available and have websites 

with useful guides and information, the very nature of banking structures meant the PE database 

was not easy to use. Challenges included determining the precise name of the banking institution 

as listed in the database and matching it to the local bank under review. This issue is exacerbated 

by the multistate institutions, which may be headquartered in some distant city and do business 

under a variety of names. In one instance, the bank’s most recent PE (2 years old) was not available 

through the database and had to be requested through the bank. Although the database enables 

searching in a variety of ways, it does not provide contact information for the CRA-regulating agen-

cies. It is a multistep process to first determine the bank’s regulating agency and then search for 

its PE. An additional finding about online access had to do with the banks’ own websites. None of 

the websites had any information on CRA officers or PE access. Most bank websites are structured 

as retail outlets and department and personnel listings are nonexistent. In fact, in some cases, the 

websites did not even list branch addresses.

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Defensive Gatekeeper Attitudes

Although we reviewed online PEs before going to the banks, when making the site visit, we re-

quested the public CRA file and the most recent PE. Callers identified themselves as students, and 

if asked also identified with which CDC we were working. The reaction and treatment we received 

varied greatly. At most institutions, our initial phone inquiry caused confusion, as few of those 

answering the phone knew what CRA was or to whom to direct us. When we arrived for our file 

reviews, two banks provided a private space for our use, and one gave us the PE to review on our 

laps in a crowded office while the manager sat at the desk and conducted business. One, appearing 

disquieted, asked for details on our research and followed up by searching Nueva Esperanza on-

line. Further, all the bank employees with whom we interacted (at least one of whom had worked 

for the institution for 20 years) noted that they had never had a request, prior to ours, to review the 

public CRA file. All the bank employees seemed to be unaware that the PEs are available online, 

and some provided inaccurate information regarding the policy for providing us with copies. None 

of the CRA files had any public comments or letters from organizations; in each case the entire file 

consisted only of the PE.

Narrow Breadth of Information

Some information relevant to the study was not available (and is not required) in the PE. One no-

table exclusion is information about whether the bank had declined to participate in any community 

development activities. In addition, PEs provide little explicit information on the bank’s evaluation 

of community needs, despite the CRA legislation stipulating that banks must consider and respond 

to such needs. One connection that was lacking was any reference to government plans or analyses 

that could be relevant, including housing or economic development plans, urban renewal strategies, 

affordable unit preservation efforts, or Community Development Block Grant planning documents. 

These documents are good sources of housing need evaluations, often drawing from census data and 

mapped U.S. Department of Housing and Urban Development (HUD) analyses.

One PE contained a statement attributed to an unnamed local community development person that 

explained, for one community within the AA, “further development opportunities were limited due 

to the town having already developed most available land” and thus that community did not pro-

vide any opportunities for affordable housing (OTS, 2010: 6). This statement distorts the potential, 

as affordable housing opportunities can be created through redevelopment and adaptive reuse, and 

the community noted was only one of many in the AA. Further, Massachusetts is a housing “fair 

share” state, and the community in question fails to meet the Massachusetts housing goal of 10 

percent of the housing stock being affordable. In fact, for the municipality in question, the figure 

was only 3.5 percent (publicly available information). To clarify, this statement was reported by a 

bank evaluator—not the bank.

In addition, the Massachusetts Community and Banking Council has developed a voluntary 

program titled the Basic Banking Checking/Savings Account. Banks are encouraged to offer very 

low-fee, low-minimum-deposit accounts to meet the needs of low- and working-class households 

for affordable banking (MCBC, 2016). None of the banking institutions mentioned this program, 

although two of them participate by offering Basic Banking checking accounts. Nueva Esperanza 

was interested in bank participation in this program, given the needs of unbanked households in 

170 The CRA Turns 40

Data, Accountability, and the Public: Using Community  

Reinvestment Act Data for Local Community Development

their target area. The Corporation for Enterprise Development reports that 17.8 percent of Holyoke 

households are unbanked and another 18.9 percent are underbanked (CFED, 2016), and Nueva 

Esperanza wanted to see how Basic Banking was addressed in CRA PEs.

Conclusions

Research on CRA has highlighted the critical watchdog role community groups can play in keeping 

banks accountable to the communities they serve. If CDCs are to play this role, however, the CRA 

PE data must be accessible, comprehensible, complete, and relevant. Our research made it clear 

that the PE data fall short of those goals in some communities, and that some basic ways of access-

ing the data could be improved to simplify and improve community-level monitoring of banks. 

Although website availability is an improvement over having to travel and collect or view CRA 

documents at bank locations, the current websites are not particularly user friendly. Determining 

the proper name to use for the banks in the search functions is not obvious, nor is knowing how 

to follow the multistep process of identifying the regulatory agency to locate the most recent PE. 

Although the websites likely work well for the well-informed and repeat users, for CDC staff 

attempting to add CRA monitoring to their workload, the websites are difficult to use. If banks 

simply included CRA contacts on their websites, made regulatory contacts available at the CRA 

search websites, or both, access to the data would be easier. A list that connects the local bank 

name with the formal listing name would be helpful.

One of the well-known strengths of CDCs is their place-based nature (von Hoffman, 2012). A 

trend toward citywide or regionwide CDCs has occurred, but many CDCs still strongly identify 

with (or are eponymously named for) a portion of a city. Given the size of today’s banks (and 

the streamlining of CRA reporting for smaller banks) few data are reported at a scale that can be 

aligned with the scale at which CDCs often operate. Although some of the banks we reviewed pro-

vided identifying information about community development activities (for example, organization 

involved and location of project), this information is not required, and many left the information in 

the public document at a very generalized level. Scale is also a concern when it comes to determin-

ing a rating for banks. In a testy 2009 exchange between then-Congressman Denis Kucinich and 

Sandra Braunstein, then Director of the Division of Consumer and Community Affairs for the 

Board of Governors of the Federal Reserve System, Braunstein explained how large AAs can mask 

problems in portions of the areas that large banks serve.

This is a rating that is done by looking at the totality of banks serving their community 

needs and depending on the size of the institution that would make a big difference. … 

If you have one of these huge national institutions and they have a problem in one little 

market, and then in the other 150 markets they are serving they are doing just fine, how 

much do you weigh that—I mean these are subjective judgments. (House Subcommittee 

on Domestic Policy, 2009: 97)

Although the data in the PEs are presented in clear, standardized summary tables, the data are 

often at an aggregated level that does not align with the target areas of CDCs. For the CDC we were 

working with, it was not possible to determine if the community development activities reported 

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were geographically dispersed across a large area or concentrated in a portion of the bank-defined 

AA. The low level of information PEs provide on community development activities, and the lack 

of benchmarks, leaves the impression that CRA undervalues community development investments 

and services. Mapping community development activities, or at least identifying the census tract 

(as is required for home mortgage and small business loans), would provide a more useful picture. 

Another approach would be for CDCs to consider the promotion of a local responsible banking 

ordinance that could seek more specific data. Such regulations reward banks receiving high PE 

ratings with municipal banking business.11 

Despite the pubic availability of PE reports, some of the banks with which we interacted during 

our research were defensive about sharing the reports. CRA is used poorly—if at all—by commu-

nity groups in western Massachusetts, and some banks are uncomfortable with the public review 

of the documents. Once we connected with the right people at the banks, the information was 

forthcoming, but most preliminary-level bank personnel were unaware of CRA. Banks could pro-

mote more positive and effective interactions by posting the CRA officer online and in the premises 

so the interested public can begin with the person who knows the most about the PE reports. Local 

community organizations should take up the role of regularly reviewing and commenting on PEs 

for banks operating within their focus areas. Despite the mismatch between data reporting scale 

and CDC actions, CDCs should reach out to banks to let them know of local- or neighborhood-

level needs and to comment on the types of community development work reported in the PE 

reports. Not communicating is a missed opportunity to increase the banks’ awareness.

Finally, our review of seven bank CRA PEs found that many sources of information are not 

referenced in the PE reports, including sources of data on community needs and the ways banks 

can provide services for LMI households. In an age of big data, it seems inefficient for the large 

investments in data preparation completed by a federal agency (for example, HUD data on 

housing needs or Federal Deposit Insurance Corporation survey results on the unbanked) to go 

unutilized in federally mandated reviews. The PEs do not present a detailed analysis of needs, yet 

many relevant sources of readily prepared data on housing needs could be referenced and used as 

comparisons to the bank-reported data. Having banks more explicitly engage with these analyses 

(if available), or having regulators use them to inform the performance context portion of reviews, 

seems like an appropriate way to make sure banks and regulators are aware of unmet needs and 

gaps in community development finance. Several recent publications have stressed the value of 

complete and insightful performance context narratives. A publication by the National Com-

munity Reinvestment Coalition stated, “the foundation of a rigorous CRA exam, particularly for 

the analyses of community development lending and investment, is rigorous performance context 

analysis” (Silver, 2016: 26). This report notes the typical PE lacks the indepth and unique analysis 

needed for a meaningful comparison between needs and services. In addition, a recent report by 

the Federal Reserve Bank of San Francisco suggests that banks, although not required to, could 

benefit from completing and sharing their own performance context analysis with regulators (Choi 

and Dowling, 2014).

11 The ordinances are aimed at empowering municipalities with better data, but a recent New York State case has put their 

fate in limbo. See http://www.ncrc.org/media-center/press-releases/item/1047-ncrc-statement-on-court-ruling-to-overturn-

new-york-city’s-responsible-banking-act.

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In many ways, CRA is an awkward and cumbersome structure for banks to report on how they are 

meeting the needs of the communities in which they operate. The fact, however, that regulators 

conduct performance reviews, and that these reviews are public, is a good thing. Certainly, the 

banking landscape has changed dramatically since the conception and passage of CRA, and thus 

room for improvement exists. Considering the changes in the economy and banking, revisiting the 

usefulness of the present PEs is appropriate. Concerns about banking access continue, and options 

for affordable and community responsive banking continue to be explored (see, for example, 

Servon, 2017). As CDCs remain strong local agents of change, making sure data on bank activity 

are more easily accessible and meaningful can translate into informed advocacy for equal access 

and creative cooperation in the design of needed community development initiatives. 

Acknowledgments 

The author acknowledges the contributions to the original research for this work completed by 

students Ashley Eaton, Ivette Morillo, and Jimmy Pereira, and the support of Nueva Esperanza 

associates Joshua Garcia and Carlos Gonzalez.

Author

Marijoan Bull is an associate professor of geography and regional planning at Westfield State University.

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176 The CRA Turns 40

Commentary: The Community 

Reinvestment Act Must Be All  

About Public Participation, but  

It Still Doesn’t Feel That Way

Josh Silver

National Community Reinvestment Coalition

Under the Community Reinvestment Act (CRA)1 of 1977, banks have an affirmative and continu-

ing obligation to serve the convenience and needs of the communities in which they are chartered 

to do business.2 The best way to determine if banks are serving the credit and deposit needs of the 

communities is to listen to the residents assess the banks’ records of doing so. As Marijoan Bull’s 

article in this symposium shows, however, the public input mechanisms established by the federal 

bank agencies remain incomplete and, in many instances, are difficult to use (Bull, 2017). Thus, 

the full potential of CRA is frustrated in terms of increasing responsible lending, investing, and 

bank services in low- and moderate-income communities.

Two aspects of public participation are related. First, are the public input mechanisms easy to use 

so that members of the public know how and when to offer input on banks’ reinvestment records? 

Second, what is the impact of public comments on bank performance, and do the agencies take the 

comments seriously?

In considering CRA and the role of public input on the 40th anniversary of CRA, it is necessary to 

consider if legislative and regulatory reluctance to update CRA frustrates public input and ultimately 

impedes CRA’s effectiveness. A lack of updating law impedes progress. As Thomas Jefferson said—

I am not an advocate for frequent changes in laws … but laws … must go hand in hand 

with the progress of the human mind. As that becomes more developed, more enlight-

ened … new truths discovered … institutions must advance also to keep pace with the 

times. We might as well require a man to wear still the coat which fitted him when a boy 

as a civilized society to remain ever under the regimen of their barbarous ancestors.3  

The current status of CRA resembles more a straightjacket that suffocates public input than a suit 

that empowers community residents and enables them to make comments that count and influ-

ence bank behavior in a positive manner under CRA.

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII. 

2 12 U.S.C. 30, available via https://www.fdic.gov/regulations/laws/rules/6500-2515.html#6500hac801.  

3 https://www.nps.gov/thje/learn/photosmultimedia/quotations.htm.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

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

Silver

This commentary describes and assesses the formal mechanisms of public input on CRA exams 

and merger applications, and it explores whether legislative and regulatory constructs impede 

public input or create possibilities of public participation having significant impacts.

Formal Mechanisms for Public Input

Federal banking regulatory agencies have established formal mechanisms for public input during 

CRA exams and merger applications. Agencies solicit and accept comments while examiners con-

duct CRA exams.4 Likewise, bank regulatory agencies have established procedures during merger 

application proceedings for accepting comments on the past and likely future CRA and fair-lending 

performance of a bank (OCC, 2017).5 Public input is a primary mechanism of CRA enforcement; 

the CRA statute requires federal banking agencies to consider a bank’s CRA performance in decid-

ing whether to approve or deny a bank’s application to merge.6 

However, the ease of commenting on CRA exams and merger applications is uneven across and 

within agencies. Although agencies have web page features that facilitate commenting, other as-

pects of the web pages often frustrate commenting, like a lack of contact information for regulatory 

staff. For example, the Federal Reserve provides a list of quarterly exams on its web page, where 

a member of the public is directed to another section of its website to submit a comment with a 

1,500-character maximum (which is insufficient for comments on complex topics or large banks 

spanning several states). The site gives no clear indication about whom to contact if a member of 

the public has basic or technical questions about the upcoming exam. Other regulators are not 

much better. The National Community Reinvestment Coalition (NCRC) recently produced a white 

paper that evaluated and rated the public comment process of the agencies. Overall, NCRC gave 

the agencies low and failing ratings for their public comment process (NCRC, n.d.).

In addition to the process for commenting, the second critical aspect of the public participation 

process is the extent to which the agencies take the comments seriously. The agencies tend to elevate 

the visibility of comments to a greater extent on merger applications than on CRA exams. When 

agencies issue decisions on merger applications, they discuss past CRA performance and the merg-

ers’ impact on future reinvestment and fair-lending performance in a section called “Convenience 

and Needs.” In this section of the merger decision, it is common to see numerous footnotes sum-

marizing community group comments on specific aspects of CRA and fair-lending performance. 

The footnotes and the body of the narrative include the agencies’ responses to the community group 

comments. Through this discussion and the final decision, it is possible to discern whether com-

munity group comments affected the agencies’ analyses, conclusions, and final orders.

In contrast, community group influence on CRA exams is more difficult to discern. CRA exams 

have a section called “Community Contacts.” This section often offers generic and cursory 

4 See the CRA regulations 12 CFR Part 25 §25.26. The most explicit statement of this procedure is in the regulations for small-bank CRA exams, in which public comment on a bank’s record is one criterion for determining the rating of a small bank. 

5 Federal Reserve rules for public comment and hearings on mergers are in 12 CFR Part 225, specifically see §225.16, Public notice, comments, hearings, and other provisions governing applications and notices. See http://www.ecfr.gov/cgi-

bin/text-idx?c=ecfr&sid=635f26c4af3e2fe4327fd25ef4cb5638&tpl=/ecfrbrowse/Title12/12cfr225_main_02.tpl. 

6 See Section 804 of the CRA statute (12 U.S.C. 30 §804).

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About Public Participation, but It Still Doesn’t Feel That Way

observations of community groups regarding the credit needs and economic conditions of a 

locality. From these general observations, it is usually not possible to determine if the public input 

influenced the CRA examiner’s analyses, conclusions, or ratings. NCRC commented about a year 

ago on the CRA exam of Texas Capital Bank. The comments concerned the low levels of lending 

to businesses with revenues of less than $1 million. The comments caught the attention of the 

bank’s CRA staff, who had some conversations with NCRC. The Office of the Comptroller of the 

Currency, however, did not contact NCRC for any followup regarding its concerns, leading NCRC 

to wonder whether its comments had any effect.

The seriousness with which community input is solicited and reflected on CRA exams and merger 

applications motivates or discourages public involvement. If members of the public regard either 

CRA exams or merger applications as rubber stamp processes—in which public input is formally 

solicited in a minimalistic manner, acknowledged in a brief manner, but then largely discarded—

then public participation will be infrequent. On the other hand, if substantive comments are 

elevated in importance, are treated courteously, and influence analyses or conclusions, then public 

input will be more frequent.

Impact of Public Comment on Community Reinvestment 

Act Exams—Constraints and Opportunities

In addition to the difficulty of figuring out how to comment on CRA exams, limits on CRA exams’ 

coverage and flexibility, due to legislative and regulatory impediments, curtail the effectiveness of 

public comments. In particular, three features of CRA exams currently constrain the influence of 

public comments: (1) assessment areas or geographical areas on CRA exams, (2) the treatment of 

bank affiliates on exams, and (3) fair lending reviews accompanying exams.

Assessment Areas

Assessment areas are geographical areas on CRA exams that are usually metropolitan areas or coun-

ties that contain bank branches. In the case of traditional banks that conduct most of their lending 

through branches, assessment areas will cover the great majority of a bank’s retail lending activity. 

In contrast, assessment areas will be less effective and cover only a minority of a bank’s lending 

activity when a bank is a nontraditional lender using the internet, brokers, or loan officers to 

make large numbers of loans. Retail lending outside of assessment areas does not factor in a bank’s 

CRA rating. If a community group therefore raises a legitimate issue concerning retail lending to 

low- and moderate-income borrowers outside an assessment area, the comment will not factor on 

the CRA exam.

It is possible to fix the assessment area constraint in a number of ways. The more difficult one in 

the current political environment is to amend the CRA statute to stipulate that CRA exams consider 

most bank retail lending, regardless of whether it is conducted through branches. Another fix that 

could be readily implemented is to follow the example of a few outlier CRA exams that considered 

lending outside of assessment areas. These exams rely on the current regulation and interagency 

guidance indicating that a small percentage of lending in the assessment areas will reflect poorly on 

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a bank (Silver, 2015). The exams then check to see if lending outside the assessment areas is serv-

ing low- and moderate-income borrowers. NCRC has yet to see an exam conducted in this manner 

lower a rating for poor retail lending performance outside of assessment areas. However, it would 

seem that if an examiner observes poor performance, the exam narrative could point it out and 

encourage the bank to improve performance. Although a more effective and sweeping procedure 

is needed, this approach would at least be able to incorporate and respond to community group 

concerns about substantial volumes of bank lending wherever it is occurring.

A second problem with assessment areas is the distinction between full-scope and limited-scope as-

sessment areas. Full-scope areas have a considerable influence on the final rating, whereas limited-

scope areas have little influence on the final rating. In the case of several large-bank CRA exams, 

full-scope assessment areas are the minority of assessment areas. Moreover, full-scope areas tend 

to be large metropolitan areas, whereas medium-sized or small cities and rural counties are usually 

limited-scope areas. NCRC members from areas like Dayton, Ohio; Gary, Indiana; or Upstate New 

York often have less influence than other members in larger cities on a CRA exam rating, only 

because of where they live and work.

CRA examination procedures could be changed to facilitate influence from community organiza-

tions in smaller metropolitan areas or rural counties by increasing the number of full-scope areas.7 

This change would not significantly increase examiners’ workloads or exhaust resources because 

examiners conduct data analysis for all areas already. Alternatively, agencies could use a random 

process for designating some full-scope assessment areas so that a bank would be compelled to 

perform reasonably well in all areas.8 Still another approach would be to group all limited-scope 

areas in a state together and count them as one full-scope area, which would give these areas a 

greater chance to influence a rating.

The CRA examiner could also expand the exam narrative for limited-scope areas, indicating where 

performance could be improved. Overall, NCRC believes that expanding CRA exam narrative to 

include discussions of expected improvements in CRA performance—for example, in a new sec-

tion on CRA exams called “Expectations for Improvement”—would actually be beneficial for both 

limited- and full-scope exams.

The relatively few times that NCRC and its members have affected a CRA rating involved a 

systemic issue, meaning that it was significant enough to capture regulator attention. In the case of 

Woodforest National Bank, for example, NCRC commented on a low loan-to-deposit ratio; that is, 

the bank was taking deposits but not making loans. The bank failed its exam (OCC, 2012a). Like-

wise, several NCRC members commented on a restrictive policy involving credit scores and FHA 

lending in the case of BBVA Compass Bank, which contributed to low levels of lending to low- and 

moderate-income and minority borrowers (Federal Reserve Bank of Atlanta, 2013).9

7 Presently, full-scope areas tend to be where the most loans are made, deposits are received, or both. However, although a large bank may have a small minority of its loans and deposits in a limited-scope area, the bank, by virtue of its size and market presence, might be a major lender in the limited-scope area. Hence, the case can be made that a number of limited-scope areas on large-bank CRA exams should receive more consideration. 

8 The bank’s largest markets, where it has its highest numbers of loans or deposits, will likely remain full scope. However, an examiner could create a random process for smaller markets (whether they be smaller metropolitan areas or rural areas) in which some of these areas are assured full-scope status. 

9 Compass received a needs-to-improve and passed its subsequent exam.

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However, identifying systemic issues is a high bar that will not be met in all cases, particularly in 

the cases of uneven lending across assessment areas. Having an “Expectations for Improvement” 

section may point to an unmet need that the examiner could agree that the bank has the capacity to 

address, even when community groups’ concerns do not lead to a lower rating. Examiner narrative 

indicating expectations for improvement would increase the chances that nonsystemic issues, which 

will probably not impact a rating, will nevertheless be addressed. Also, if a need remains unaddressed 

and community organizations issue comments in the bank’s CRA public file during subsequent years, 

these comments should influence a bank’s rating in one or more states or overall on the next exam.

Affiliates

The current examination procedure of allowing banks to decide whether their nondepository af-

filiates are included on CRA exams also limits community-group input on CRA exams. Banks tend 

to include their affiliates if they are making responsible loans and are effective in serving low- and 

moderate-income borrowers. However, banks will tend to exclude affiliates from their CRA exams 

if they have compliance concerns or if the affiliates are not particularly effective in reaching low- 

and moderate-income borrowers. In the early 2000s, leading up to the financial crisis, a number of 

nondepository affiliates made high numbers of questionable subprime loans that were not included 

on CRA exams. Clearly, this limited the CRA exams’ effect on the behavior of these affiliates and 

also prevented community organizations from commenting during CRA exams about the affiliates. 

Community groups would have to wait for merger applications to make their views known about 

the affiliates. Thus, timely community input on these important matters was limited.

The agencies have not mandated inclusion of affiliates on CRA exams. If they believe action is 

needed by Congress to mandate nondepository affiliates be included on CRA exams, the agencies 

could nevertheless amend CRA examination procedure. At the very least, if community groups 

believe that affiliates are violating antidiscrimination and consumer protection law, they should be 

able to present their concerns on CRA exams. If the agencies identify violations or inadequate com-

pliance controls, they should downgrade the bank’s rating. The inadequate legal compliance would 

ultimately affect the bank because management does not have strong enough controls throughout 

the entire organization to ensure legal and responsible operations.

Currently, affiliate violations can lead to downgrades in ratings only if the violations occur in 

the assessment areas and the bank has included the affiliates in the exam. A recent example of 

this involves SunTrust Bank, whose affiliate, SunTrust Mortgage Company, was involved in two 

settlements involving price discrimination10 and violations of the Home Affordable Modification 

Program (HAMP)11 program that occurred during a time period covered by two consecutive CRA 

exams. The settlements did not affect the bank’s CRA exam because the affiliate was not included 

on the CRA exam. The mortgage company, however, was making high volumes of home loans that 

the bank was purchasing (Federal Reserve Bank of Atlanta, 2010).

10 https://www.justice.gov/opa/pr/justice-department-reaches-21-million-settlement-resolve-allegations-lending-discrimination.  

11 HAMP was a federal program that provided subsidies to banks to restructure mortgages of distressed borrowers in order to prevent foreclosure. See https://www.justice.gov/opa/pr/suntrust-mortgage-agrees-320-million-settlement. 

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Fair Lending Reviews

CRA does not consider lending to people of color or communities of color. Ironically, before the 

CRA regulatory reform of the mid-1990s, analysis of lending to people of color would occasionally 

be included on CRA exams (Silver, 2015). Now, a fair-lending review section affirms that either the 

bank regulatory agency or the Consumer Financial Protection Bureau has conducted a fair-lending 

review. In a few brief sentences, the section will state that neither review detected discrimination 

or other illegal activity. Sometimes, the section will state whether occurrences of discrimination or 

other violations were widespread, thus resulting in a rating downgrade. Occasionally, the exam will 

describe the nature and type of the violation and which protected classes were victimized. Recently, 

some very large banks, including Wells Fargo (OCC, 2012b), failed their CRA exams due to serious 

violations of fair lending and consumer compliance, which were described in the fair-lending re-

view section. However, in many—if not most—instances, the exam will not describe the violation.

When violations are not described, community group input is stunted. Fair-housing and fair-

lending groups are unable to engage in testing or mystery shopping to determine if the violations 

have indeed ceased. They are thus unable to report findings from their investigations on future 

CRA exams or merger applications. Also, when CRA examiners do not describe community group 

comments on bank performance concerning communities of color, the community group does not 

know if the agencies took these comments into account when conducting the fair-lending review. 

Even if the agencies find that no discrimination occurred, the exam narrative should describe that 

community organizations raised concerns but that the agencies did not find violations. This would 

provide reassurance that serious concerns of the public were at least addressed. Overall, the CRA 

examiner narrative on the fair-lending section needs improvement to be more transparent.

Impact of Public Comments on Mergers and Applications—

More Effective Than Community Reinvestment Act Exams

In contrast to CRA exams, the regulators’ responses to public comments on merger applications 

have resulted in public input achieving significant impacts. The Bank Merger Act12 requires the 

federal agencies to consider the “probable effect of the merger transaction in meeting the conve-

nience and needs of the community to be served.” The “probable effect” is also referred to as the 

“public benefit” standard. Although not in most cases, the agencies have acted on public comments 

regarding public benefit.

Past performance will provide a guide to likely future performance. If a bank performed poorly in 

the past and received a low CRA rating, the future performance is likely to be poor or mediocre at 

best. Thus, commenting on CRA exams is also important for merger applications. If community 

groups influence the CRA rating, they can also influence the outcome of a merger proceeding by 

pointing out weaknesses or strengths in bank CRA performance and exams.

12 12 U.S.C 1828 (b). https://www.law.cornell.edu/uscode/text/12/1828.

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About Public Participation, but It Still Doesn’t Feel That Way

It is also the case that future performance could be different from past performance. For example, 

suppose a bank did well on its CRA exam and received a good rating. The future performance 

could decline if the bank closed several branches after its merger in a cost-cutting move. Hence, 

careful review of the merger application by federal agencies and the public is important to deter-

mine the likely impacts on future performance.

A number of banks try to satisfy the forward-looking requirement with vague promises that they 

will continue to comply with their CRA and fair-lending requirements. However, NCRC and its 

member organizations maintain that assessing the probable effect of a merger entails using objec-

tive measures of performance in determining whether a proposed merger will have public benefits 

in the future. For example, will the number and percent of branches in low- and moderate-income 

communities increase or decrease after the merger is consummated?

Four years ago, NCRC and its member organizations opposed a proposal by Renasant Bank in Mis-

sissippi to acquire another midsize bank. Renasant’s percentage of loans to minorities and low- and 

moderate-income borrowers was much lower than that of its peers in a number of metropolitan 

areas. NCRC asked for a specific plan and performance measures indicating how the bank would 

improve. The Federal Deposit Insurance Corporation (FDIC) responded in a conditional merger 

approval requiring the bank to develop an action plan that would indicate how it would improve 

lending performance to be at or above the level of its peers within 3 years.

NCRC applauded the FDIC’s move but noted that the action plan was to be submitted to the agency 

directly, without the public being able to comment on its adequacy before submission. Ultimately, 

NCRC and its members want banks to develop such plans regularly and submit them as part of their 

application. This would help significantly in the implementation of the public benefit standard.

In subsequent mergers, NCRC and its members were not content to wait for the agencies to 

require action plans submitted by banks. In recent cases involving large banks, NCRC saw an 

opportunity to work with forward-thinking banks in jointly developing public benefit plans. In 

early 2016, Keybank proposed to acquire First Niagara, which was a major lender in New England. 

Communities throughout Keybank’s footprint, but particularly in New England, were concerned 

about branch closures and reductions in lending. Taking advantage of a positive relationship with 

Keybank, NCRC approached the bank regarding a public benefits agreement.

During the merger application process over a period of several months, NCRC, 100 NCRC 

member organizations, and Keybank negotiated a public benefits agreement that lasts 5 years 

that commits Keybank to significant increases over the premerger lending of it and First Niagara. 

Committed totals include $5 billion in mortgage lending, $2.5 billion in small business lending, 

and $8.8 billion in community development lending in low- and moderate-income communities. 

Also, Keybank agreed to halt the closure of four branches in low- and moderate-income tracts and 

to open a branch in a low- and moderate-income tract in Buffalo (NCRC, 2016).

To increase the chances that the benefits plan can be achieved, Keybank and NCRC established a 

national advisory committee and several local advisory committees consisting of bank officials and 

community group representatives. These committees will review the bank’s progress in meeting 

plan goals and work together to magnify successes and also address any obstacles or challenges 

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impeding goal attainment. The Federal Reserve Board issued a conditional merger approval of Key-

bank’s merger, acknowledging the execution of the benefits agreement reached with community 

organizations.

Shortly after the Keybank merger, NCRC and Huntington worked together to strike a similar 

public benefit commitment to be implemented after Huntington’s acquisition of First Merit. The 

total commitment of $16 billion was quite similar to Keybank’s. Other important provisions 

included the addition of dedicated community mortgage loan officers and the formation of a dedi-

cated mortgage processing team to handle unique underwriting opportunities. Reforms in bank 

processes and additions of specialized loan officers will help banks reach traditionally underserved 

communities.

The merger application process is far from perfect. Public benefit plans and commitments do not 

happen regularly. Community groups need to advocate vigorously for their adoption. The agencies, 

however, recognize the importance of considering convenience and needs in the merger process 

to make sure that mergers do not benefit banks at the expense of communities. During NCRC’s 

annual conference in 2016, Comptroller of the Currency Thomas Curry called conditional merger 

approvals a “unique tool” and said that community groups like NCRC have a “critical role” in ad-

dressing public benefit concerns (NCRC, 2016).

Conclusion

A law, no matter how well constructed, is only as good as its implementation. CRA and the merger 

application process have conferred significant benefits on traditionally underserved communities, 

but the constrained and tentative regulatory implementation of these laws has frustrated their full 

potential. Integral for the full attainment of these laws is the active participation of the communi-

ties these laws are intended to empower. The participation of the communities, however, will be 

stunted if CRA exam procedures continue to limit the scope and application of community group 

comments due to blinkered procedures regarding assessment areas, affiliates, and fair-lending 

reviews. Although the procedures regarding public input and the consideration of public input are 

more developed and effective on merger applications, inconsistencies remain. CRA and the merger 

application process should ultimately be regarded as means for creating dialogue and conversation 

among stakeholders (community groups, banks, and regulators) about the extent to which banks 

are meeting community credit needs. If the conversation is stilted and options are limited for re-

sponding to the conversation, then CRA and the merger application process will fall short of their 

potential to increase access to credit, investment, and bank services for traditionally underserved 

communities. The recommendations in this commentary are geared toward promoting a rich and 

powerful dialogue that leads to mutually beneficial results for communities and banks.

Acknowledgments

The author thanks the hard-working staff at National Community Reinvestment Coalition who 

work to make CRA more effective on a daily basis. 

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Commentary: The Community Reinvestment Act Must Be All  

About Public Participation, but It Still Doesn’t Feel That Way

Author

Josh Silver is a senior advisor at the National Community Reinvestment Coalition.

References

Bull, Marijoan. 2017. “Data, Accountability, and the Public: Using Community Reinvestment Act 

Data for Local Community Development,”  Cityscape 19 (2): 161–175.

Federal Reserve Bank of Atlanta. 2013.  Community Reinvestment Act Performance Evaluation: BBVA 

Compass Bank. Washington, DC: Office of the Comptroller of the Currency. https://www.frbatlanta.

org/-/media/documents/banking/cra_pes/2014/697633.pdf.

———. 2010.  Community Reinvestment Bank Performance Evaluation: SunTrust Bank. Atlanta: 

Federal Reserve Bank of Atlanta. https://www.frbatlanta.org/-/media/Documents/banking/

cra_pes/2010/675332.pdf.

National Community Reinvestment Coalition (NCRC). n.d.  Performance Evaluation: Facilitating 

Public Input on CRA via Federal Agency Websites. White paper. Washington, DC: National Com-

munity Reinvestment Coalition. http://www.ncrc.org/resources/reports-and-research/item/1116-

performance-evaluation-facilitating-public-input-on-cra-via-federal-agency-websites.

———. 2016. “NCRC and Partners Secure Over $32B in Community Development Commitments 

as Part of Two Merger Agreements,”  Reinvestment Works Summer 2016: 1–4.

Office of the Comptroller of the Currency (OCC). 2017.  Comptroller’s Licensing Manual: Public 

Notice and Comments. Washington, DC: U.S. Department of the Treasury.

———. 2012a.  Community Reinvestment Act Performance Evaluation: Woodforest National Bank. 

Houston: Office of the Comptroller of the Currency, Houston Field Office. https://occ.gov/static/

cra/craeval/dec13/16892.pdf.

———. 2012b.  Community Reinvestment Act Performance Evaluation: Wells Fargo Bank, National 

Association. Washington DC: Office of the Comptroller of the Currency, Large Bank Supervision, 

Washington DC. https://www.occ.gov/static/cra/craeval/apr17/1.pdf.

Silver, Josh. 2015. Testimony Regarding the Regulatory Review Mandated by the Economic Growth 

and Regulatory Paperwork Reduction Act (EGRPRA). Washington, DC: EGRPRA Outreach Meet-

ing, December 2. http://www.ncrc.org/images/ncrc_egrpra_testimony_dec_2015_web.pdf.

Cityscape 185

186 The CRA Turns 40

Commentary: FinTech and the 

Liberation of the Community 

Reinvestment Act Marketplace

Michael Gaughan

National Development Council

Abstract

Observers of the Community Reinvestment Act (CRA) have for many years noted the 

pressing need for reform of the law’s principal unit of analysis—the assessment area. 

As noted on prior anniversaries of CRA, the geographic basis for assessment areas has 

become increasingly irrelevant, given the disparity between where deposits are held and 

where the business of banks actually takes place. What was once an observation on a 

growing trend is quickly becoming a historical footnote, thanks to the way financial tech-

nology, or  FinTech , is rewriting how and where financial services are provided. To date, 

FinTech organizations are without community reinvestment obligations. The impending 

oversight of the FinTech market presents an historic opportunity to rethink assessment 

areas, while using technological innovation to more equitably distribute CRA invest-

ments to the people and communities most in need.

Introduction

The ongoing disruption of the banking industry has raised a lot of questions, including how the 

banking industry will continue to fulfill its community reinvestment obligations in the face of 

relentless change. As described throughout this issue of  Cityscape, the Community Reinvestment 

Act (CRA)1 of 1977 places one or more requirements related to lending, investment, and service 

requirements on banks. This legislation was passed to combat practices of denying credit to 

business and residents (often minority and/or low income) within defined geographies by literally 

mapping a red line of exclusion (that is,  redlining) around these areas (Federal Reserve Bank of 

Minneapolis, 2017). 

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII.

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

Gaughan

For 40 years, CRA has provided a critical lifeline for community development efforts in under-

served communities, while ensuring stable and accessible funding for small businesses, housing, 

and community facilities. An effective solution at the time, this framework for the equitable dis-

tribution of financial services has become complicated by two related trends in the ensuing years: 

changes in who provides financial services and where those services are provided.

In particular, changes in technology have led to anywhere-and-anytime financial services enabled 

by web or mobile apps, created by banks and nonbanks alike, that make branch networks less 

relevant than in 1977—particularly for granting credit. This trend predates the 2007-to-2009 

financial crisis (hereafter, the recession); data compiled by authors at the Federal Reserve Board 

found an increase in the median distance for a consumer loan between the location of the borrower 

and the location of the financial institution from 7 to 22 miles, or 214 percent, from 1992 and 

2004 (Amel, Kennickell, and Moore, 2008).

The recession contributed to further reducing the reliance on bank branches, as the subsequent 

low-interest-rate environment pressured margins, and alternative means of providing financial 

services became more widely adopted. 

Between the historical peak in 2009 and 2016, the number of bank branches nationally declined 

nearly 8 percent, an average of more than 1,000 branch closings per year (FDIC, 2017). The 

impact of technology on this consolidation is hard to ignore; mobile banking participation rates 

increased from 22 to 43 percent in the overlapping period between 2011 and 2015. Meanwhile, 

a rough parity developed during the same period in how customers interact with a bank. A 2016 

Federal Reserve Board survey of banking customers polled respondents on whether they had 

used a branch, automated teller machine (ATM), and online banking during the past 12 months. 

The results indicated uses of 84, 75, and 71 percent, respectively, demonstrating relatively equal 

willingness to use alternative access to financial services (Federal Reserve, 2016).

Reflecting on the pressure to traditional banking caused by new technology, the former Chief 

Executive Officer (CEO) of Barclays, Antony Jenkins, noted that “the number of branches and 

people employed in the financial services sector may decline by as much as 50 percent over the 

next 10 years” (Williams-Grut, 2015), which is corroborated by World Bank estimates that the 

total number of U.S. branch locations will contract an additional 33 percent by 2025, when taking 

into account population changes (Citi, 2016).

Despite these trends, CRA’s primary unit of analysis is unchanged since 1977 and continues to 

focus on a bank’s geographically defined  assessment area, where branches, ATMs, and offices are 

located. Banks self-define the assessment area, which represents the geographic area in which 

regulators measure, both quantitatively and qualitatively, a bank’s CRA activities. The problematic 

nature of the assessment area as the unit of analysis for CRA has been well understood by the 

community development field for many years—including previous reviews of CRA (Chakrabarti et 

al., 2009). 

More recently, the emergence of the financial technology, or  FinTech, sector raises a fresh set of 

questions as to whether CRA’s regulatory framework is outdated. The FinTech sector includes 

diverse applications, including crowdfunding platforms such as Kickstarter, education lending 

188 The CRA Turns 40

Commentary: FinTech and the Liberation of the Community Reinvestment Act Marketplace

platforms such as SoFi, and payment tools such as Stripe. The landscape of FinTech expands with 

each passing day. In broad strokes, FinTech includes everything in financial services to which 

technology can be applied and is most commonly focused on enhanced speed and better user 

experiences related to loan origination, credit evaluation, and payment services.

In this article, I highlight the challenges and opportunities of FinTech, given its influence in rewrit-

ing the rules of lending and eviscerating the geographic limitations of banking. I first discuss why 

a CRA framework is critical for FinTech and then why assessment areas are already falling short. I 

conclude by reflecting on opportunities for FinTech to help alleviate the geographic mismatch of 

CRA activities. Taken together, it is my argument that CRA continues to be a critical law but that 

it should be modernized through a broad and a fair framework that acknowledges how financial 

services are provided in 2017 and how they will be provided in the decades to come.

Why FinTech Is Key to the Future of Banking

The growth of FinTech is best illustrated by Lending Club, which was founded in 2006 and is 

arguably the most well-known FinTech participant. Lending Club falls in a subcategory of FinTech 

called  marketplace lending, in which the company originates the loan and unrelated individuals or 

firms then fund the loan by purchasing it in real time through the online platform. This model is 

in contrast to a  traditional lending model, in which a bank accepts deposits at a brick-and-mortar 

location, originates a loan, and then funds the loan with those deposits.

Lending Club expanded rapidly following the recession (Crunchbase, 2016). The company’s total 

originations grew from approximately $77 million in 2009 to nearly $19.5 billion by the fourth 

quarter of 2016 (Lending Club, 2016). In the process, Lending Club helped pave the way for other 

FinTech startups by showing venture investors the value of the sector. Its initial public offering was 

the one of the largest for 2014 and netted the company $900 million at an $8.5 billion valuation 

(Whitehouse, 2014).

Lending Club is only one example in a diverse and rapidly growing field. McKinsey estimates that 

between April 2015 and February 2016, the number of FinTech startups grew from 800 to more 

than 2,000—with upper-tier estimates putting the number of related startups as high as 12,000 

(Dietz et al., 2016). Industrywide loan origination activity provides another measure of growth. 

FinTech data aggregator Orchard estimates that unsecured consumer origination in the sector 

totaled approximately $9.8 billion in 2016, which represents a 312-percent increase over total 

origination in 2013 (Orchard, 2016). Despite this rapid growth, FinTech is still small compared 

with conventional bank lending, accounting for only 1 percent of all consumer lending activity, but 

it is expected to increase to 17 percent during the next 6 years (Citi, 2016).

Traditional lenders have not been passive in the growth of FinTech, however, and are both collabo-

rating with and adopting innovations from the sector (Citi, 2016). Representative examples of this 

adoption include Citibank’s internal Citi FinTech division and JPMorgan Chase’s agreement with 

OnDeck for small business loan origination (Gandel, 2016; Rudegeair, Glazer, and Simon, 2015). 

More generally, banks have focused on adopting the best practices of FinTech in technology related 

to payments, credit evaluation, and mobile services.

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Unlike traditional lenders, however, CRA compliance is not required of financial service providers 

that are not Federal Deposit Insurance Corporation-insured depositories, which includes most 

FinTech participants. As noted previously in the collaboration examples, FinTech often plays 

a complementary—and, in some cases, substitute—role for traditional banking services. This 

dynamic calls into question the ability of CRA to ensure equal access to financial services while 

relying on an outdated framework of banks servicing defined geographic areas.

New Frontiers in Redlining: The Case for Subjecting FinTech 

to the Community Reinvestment Act

Until recently, the most widely applicable regulations for FinTech are laws related to consumer 

protection. Following the 2010 Dodd-Frank Wall Street Reform and Consumer Protection Act,2 the 

Consumer Financial Protection Bureau (CFPB) is the primary oversight body for consumer protec-

tion. Its mission is to enforce and to make rules related to consumer financial protection acts, 

which include the Truth in Lending Act3 of 1968 and Truth in Savings Act4 of 1991, which require 

terms and conditions disclosure; the Equal Credit Opportunity Act5 of 1974, a precursor to CRA 

that prevents discrimination on the basis of race, color, religion, national origin, sex, marital status, 

age, and receipt of public assistance; the Real Estate Settlement Procedures Act6 of 1974, which 

requires disclosure and fee restrictions with regard to home loans; and the Fair Debt Collection 

Practices Act7 of 1977, which restricts debt collection activities (Carpenter, 2014).

In addition to the laws mentioned previously, many states have usury laws that limit the maximum 

interest rate charged by institutions. State-specific laws, however, can be avoided when the lender 

is a bank based in another state with more flexible interest rate regulations (Rudegeair, 2015; 

Beam, Kaplan, and Weissgold, 2011). Many FinTech lenders use the latter exception to avoid 

usury laws through partnerships with nationally chartered banks. In these instances, the bank 

serves as the legal entity that originates the loans, which are then sold to the FinTech company 

with payment provided by either their own funds or funds from an outside investor (in the case 

of marketplace lenders). To date, most efforts to regulate FinTech lenders have focused on these 

basics, such as a 2015 New York State court case that challenged avoidance of state usury laws for 

nonbank entities (Wack, 2016).

The dynamic mentioned previously is changing rapidly since the December 2016 announcement 

by the Office of the Comptroller of the Currency (OCC) that it would grant special-purpose banking 

charters for FinTech companies. Insight into the OCC’s intent with regard to CRA-like obligations are 

outlined in a white paper that accompanied the announcement and a draft (at the time of writing) 

licensing manual released in March 2017 (OCC, 2017, 2016). Both documents acknowledge the 

necessity of accommodating responsible and inclusive applications of new technology.

2 Pub. L. 111–203, 124 Stat. 1376. 

3 Pub. L. 90–321, 82 Stat. 146, Title I. 

4 Pub. L. 102–242, 105 Stat. 2236. 

5 15 U.S.C. § 1691 et seq. 

6 Pub. L. 93–533, 88 Stat. 1724. 

7 Pub. L. 95–109, 91 Stat. 874.

190 The CRA Turns 40

Commentary: FinTech and the Liberation of the Community Reinvestment Act Marketplace

Notably, the draft OCC FinTech manual includes a requirement to specify, as part of a special pur-

pose charter application, a Financial Inclusion Plan that would identify markets and communities 

touched by the organization’s products and services. Appendix B of the document outlines regula-

tory expectations, including identification of geographies and customers served and the need for 

measurable goals for meeting the financial services needs of these populations. In draft form only 

and without applicants to review at this time, it is unclear how this Financial Inclusion Plan will be 

used in practice and whether it can provide the same protections afforded by CRA.

In particular, it is unclear as to how new regulation will address the most concerning aspects 

of FinTech, which are buried in the algorithms and data aggregation that are the foundation of 

new approaches to credit evaluation. The CFPB noticed this concern and has focused efforts on 

ensuring that third-party data used by financial institutions is available to consumers and that 

consumers grant permission prior to data use (Cordary, 2016). This line of inquiry is particularly 

important, given the many alternative metrics FinTech uses to evaluate creditworthiness. 

Discoverable examples (not buried in proprietary models) include indicators such as majors for 

international students, post-college moves, and trust scores based on social-network data (Dietz et 

al., 2016; ZestFinance, 2014).

On one hand, new approaches to granting credit have the potential to help previously underserved 

and unbanked communities, members of which may not currently qualify based on traditional 

credit metrics that conventional lenders use. On the other hand, it may lead to scenarios in which 

lenders deny credit because of a lack of access to broadband or a cultural avoidance of social 

media. 

Moreover, rather than outright discrimination based on religion or race, one common link among 

these alternative metrics is that they bear closer resemblance to CRA’s emphasis on spatial service 

areas. Data on individuals with both many post-college moves and seemingly untrustworthy social 

networks have a spatial aspect that could be pinpointed on a map and evoke the redlining of years 

past. Unlike years past, however, the identification of these groups is buried in big data algorithms 

that, without transparency and a regulatory mandate for inclusion, are impossible to characterize 

as either fair or exclusionary.

Beyond the invisible boundaries discussed previously that potentially limit (or expand) credit 

access is the unique threat posed by marketplace lenders. In this subsector of the FinTech sector, 

entities such as LendingClub, Prosper, and FundingCircle credit score and originate the loan. Criti-

cally, the loan is not funded until outside investors, either institutions or accredited investors, make 

a decision to purchase the loan. This method essentially puts the credit decision in the hands of 

thousands of individuals who bring their own sources of potential bias in deciding to fund a loan. 

Marketplace lenders derive income primarily from origination and service fees and are ultimately 

dependent on these investors for funding the loans.

Dugan and Demos (2016), writing in the  Wall Street Journal, noted this potential pitfall of the 

platform while describing a widespread practice on the part of marketplace-lending investors of 

avoiding certain states like Nevada and Florida. The article focuses on LendingClub, which does 

not provide neighborhood-specific locational data for loans, limiting the extent to which individu-

als can engage in de facto redlining. Nevertheless, redlining is clearly the intent of the investors 

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identified in the article, as evidenced through investment advice articles specific to marketplace 

lending with titles such as, “The Joy of Redlining: Why I Never Lend Money to Florida” (Dugan 

and Demos, 2016). Notably, these investors have made it impossible for a marketplace lender to 

penetrate all areas of its service area and highlight the potential role CRA could serve in ensuring 

that borrowers in Florida or Nevada receive fair technology-enabled access to credit.

The incorporation of FinTech into CRA cannot work under the current paradigm of geographi-

cally defined assessment areas, as these nonterritorial entities seek the largest coverage possible. 

However, FinTech has the potential to liberate CRA-related spending by reaching borrowers that 

traditional banking structures cannot. Expanding the assessment area from its narrow historical 

boundaries to conform to the changing landscape of how financial services are offered is key to the 

liberation of CRA-related spending.

How Assessment Areas Hurt Community Development

Alongside pressures on the assessment area stemming from FinTech is another issue related to the 

equity of spending spurred by CRA. One of the more problematic outcomes of CRA is that related 

activities fall in geographies where bank deposits are concentrated. Because the banking and 

financial services industry is concentrated in large urban areas, the major banks have assessment 

areas that overlap among and within major population centers, such as the Northeast corridor and 

California. The consequence is outsized demand for investments that may qualify for regulatory 

credit in those markets, whereas states in the Midwest and the Southeast rely on a smaller deposit 

base for equally meaningful community development needs.

This trend is particularly true of opportunities for investment credit, which is largely facilitated 

through purchase of tax credits, such as the Low Income Housing Tax Credit (LIHTC) and New 

Markets Tax Credit, and of mortgage-backed securities (MBS) that include home loans for low- and 

moderate-income borrowers. The LIHTC program in particular is one of the most liquid of all 

CRA-related investments, thanks to a process for generating equity for affordable housing develop-

ment that has been refined in the 30 years since it was created in the Tax Reform Act8 of 1986. As 

a result, the market and related pricing for LIHTCs provide the best measure of the geographic 

mismatch in CRA investments.

Cohn Reznick (2013) explored the geographic mismatch in LIHTC tax credit pricing due to CRA 

in detail, finding that 76 percent of tax credit properties are in areas where the top 20 U.S. com-

mercial banks have CRA responsibility. Translated to tax credit pricing, this concentrated demand 

resulted in a 35- to 60-cent premium in the purchase price of each tax credit dollar if the project’s 

location was in a favorable area for CRA. In the fall of 2016, the CRA yield premium was estimated 

to fall in the range of 1.25 to 1.50 percent (resulting in a higher tax credit price), according to 

industry observers (Garcia, 2016).

The consequence of this pricing discrepancy is that deserving projects outside of population 

centers where deposits are concentrated get less money. Cohn Reznick (2013) also noted this 

8 Pub. L. 99–514, 100 Stat. 2085.

192 The CRA Turns 40

Commentary: FinTech and the Liberation of the Community Reinvestment Act Marketplace

conclusion, arguing that geographic tax credit pricing gaps could be closed, and projects funded, 

by allowing for banks to invest in states and broader regions surrounding assessment areas.

For the purpose of this article, the question is not only how geography can be expanded, but also 

how other investments outside of the commonly used tax credit investments and MBS can become 

a more common part of the CRA toolbox, thanks to the specialization and the efficiencies gained 

through FinTech. One way to accelerate the integration of old and new financial services is to bring 

them into the same regulatory framework while reviewing each based on their furtherance of CRA’s 

mandate to provide equitable access to financial services.

Comparative Advantage: A New Community Reinvestment 

Act Framework

Fortunately, a framework for collaboration and alignment of incentives already exists, as two 

concurrent events—FinTech regulation and discussion of CRA reform—emerge. In early 2015, 

Lending Club and Citibank teamed up to arrange for a $150 million credit facility that facilitated a 

third party’s purchase of loans to low- and moderate-income neighborhoods and borrowers within 

Citi’s assessment area (Lending Club, 2015). Critically, the arrangement has Citi providing credit 

to the third-party firm, rather than borrowers, and begs the question of why Citi did not directly 

engage with the population it had hoped to serve.

Nevertheless, the transaction provides an example of how the new and the old providers of finan-

cial services can build on their own comparative advantages to both meet regulatory requirements 

and produce better community-development outcomes. The former Lending Club CEO noted how 

this interaction is common when interfacing with traditional lenders.

We work with a lot of banks now and CRA often comes in conversation about something 

banks would like to more of. They are having trouble reaching these populations because 

they don’t necessarily have branches in a particular area, so having an online lending 

platform is a way to reach them. (Banjo, 2015)

In this case, FinTech provided its ability to originate loans in difficult-to-reach populations, and a 

bank provided its access to capital. Unfortunately, this partnership did not expand CRA markets, as 

Citi’s interest was in meeting the requirement of the existing regulatory framework.

One way to move beyond geographic mandates is to conceptualize CRA in an outcome-based 

framework that enables regulatory dollars to have the largest impact—wherever those dollars are 

needed most. In this framework, the power of big data harnessed for alternative credit metrics 

could also be applied to creating meaningful impact measures. 

Pay for Success financing models, for example, provide a promising analogue for the outcome-

based approach. In these models, loan payments are tied to outcomes, like reducing stormwater 

runoff volume or increasing the number of homeless individuals in stable housing. A typical CRA 

evaluation period spans 3 to 5 years and provides an appropriate timespan to measure the impact 

of community interventions tied to an institution’s business model.

Cityscape 193

Gaughan

Aiding this potential framework is the large amount of data ready to be analyzed for CRA purposes. 

The U.S. Census Bureau, among other agencies, has application programming interfaces available 

for use in dynamic models, and the Federal Financial Institutions Examination Council publishes 

a number of CRA-related data sets that could be combined to understand where need exists but is 

not being met under current activities. Most promising, technology could enable community de-

velopment technology to go where individual practitioners cannot by searching through regulator-

authored CRA performance evaluation reports to identify local patterns and gaps—a desperately 

needed outcome as aggregated reporting on CRA-related community development lending and 

investments is scarce at a usable scale. 

For on-the-ground practitioners of community development, this change would be welcome in 

all but a few markets. High-quality housing for low- and moderate-income populations may be as 

important to rural Mississippi as it is to cities on the Acela corridor, but it is often extremely dif-

ficult to attract any part of the $87 billion in annual bank-related community-development lending 

to out-of-the-way places (FFIEC, 2016). Local banks may provide appropriate dispersion of loans 

and investment through their service areas, but they lack the scale of the 20 largest banks noted in 

the Cohn Reznick study.

In contrast, a small portion of one of the largest banks’ CRA investment goals could go a long way 

toward increasing demand, and the amount of funding raised, for any number of important com-

munity objectives in markets that do not have oversubscription for qualifying projects.

Behind the compliance officer and regulator curtain, rumors abound that regulators expect to see 

CRA loan and investment targets in proportion to the amount of high-quality capital, such as U.S. 

Treasury bonds and deposits, known as Tier 1 capital, required by Dodd-Frank and Basel III. In 

reality, no formal targets exist within CRA, and the issue highlights the wide amount of discretion 

on the part of CRA examiners. An obvious alternative to the assessment area is to move from the 

map to the balance sheet and formalize the Tier 1 target (or others developed with stakeholder par-

ticipation). Banks would then have the burden of proof to make the most meaningful investments 

within these capital targets.

FinTech can then help locate the most promising investments and loans to low- and moderate-

income individuals. The potential for FinTech to aid CRA loans is already established in the 

Citibank-Lending Club partnership. Investments are an untested, but potentially critical, applica-

tion of a new approach. A significant amount of activity related to meeting the investment criteria 

is concentrated in the purchase of qualifying secondary-market securities, like federally guaranteed 

MBS—hardly a good use of the CRA price premium.

It is not difficult to imagine an alternative wherein banks seek securities with better impact through 

FinTech start-ups like Neighborly or Point. The first of these is a municipal bond issuance platform 

that uses technology to gain efficiency in the issuance of municipal securities while opening the 

door to investments in small-scale infrastructure, like streetscapes and parks. The second is a 

platform to help owners monetize home equity by selling a fraction of their equity to Point, in 

turn, the owner avoids using a home equity loan and the start-up does not receive a return until 

the home sells or the owner buys back the equity. 

194 The CRA Turns 40

Commentary: FinTech and the Liberation of the Community Reinvestment Act Marketplace

As discussed previously, a price premium (note that a higher price means a lower yield and po-

tentially a better rate for borrowers) for CRA spending clearly exists, which can provide additional 

financial incentive for FinTech to become involved in originating investment and loan opportuni-

ties. Meanwhile, FinTech will similarly be evaluated for its ability to reach populations in need, 

based on the appropriate macro outcome—origination, credit, and so on.

A symbiotic relationship between banks and FinTech around CRA could be one way to further the 

appropriately broad guidance given by the OCC in its framework for innovation in financial products.

The OCC may also issue guidance on its expectations related to products and services 

designed to address the needs of low-to moderate-income individuals and communities 

and may encourage innovative approaches to financial inclusion by promoting awareness 

of other activates that could quality for Community Reinvestment Act consideration. 

(OCC, 2016)

Emerging practices in a data analysis can fuel this new framework by shedding light on the actual 

impact of dollars generated by regulatory requirements. FinTech participation is not the cure-all for 

better community investments, but it does represent a source of leverage to bring relative impact to 

the forefront of CRA spending.

Data-enabled guidance and regulatory approval on where banks and FinTech are going before 

they begin could build on early efforts at providing regulator-defined performance contexts and 

preapproval of CRA investments. Both of these approaches have anecdotally proven to be a success 

in facilitating higher quality and new types of activity (Choi and Dowling, 2014; Cisneros, 2015). 

Data, the fuel of FinTech, can help expedite these early and time-intensive efforts by reducing the 

risk of banks in considering new methods of CRA compliance.

A data-driven process would ease the time burden of regulators who would be required to evaluate 

a dispersed book of lending and investment that extends beyond their current geographic spe-

cializations. As always, enthusiasm for the elegance of data is tempered by the risk of spending in 

only those categories that are measurable, but it is one starting point for regulators to complement 

time-tested methods that have made CRA such an effective law.

Undoubtedly, we will see unimaginable changes in how financial services are provided in the next 

40 years, but it is clear that the geographic basis of CRA is increasingly problematic. An outcome-

based approach tied to the regulated entities’ primary business plans, such as capital, loans 

originated, or whatever subspecialization emerges, is one method to ensure flexibility. This new 

framework would be only a start, but it would be critical in helping reallocate current spending 

related to CRA in a more equitable distribution for the benefit of all involved.

Acknowledgments

The author gratefully acknowledges the support of the guest editor, Carolina Reid, for her com-

ments and suggestions without which this commentary would not be possible. The author also 

thanks Will Lambe at the Federal Reserve Board of Atlanta for encouraging the development of the 

commentary. 

Cityscape 195

Gaughan

Author

Michael Gaughan is a director at the National Development Council.

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198 The CRA Turns 40

Commentary: What Can We Learn 

From Government Attempts To 

Modify the Allocation of Mortgage 

and Consumer Credit in the 

United States?

Anthony Yezer

George Washington University

Introduction

This commentary considers the Community Reinvestment Act (CRA) in historical context. CRA 

reflects one of many government attempts to influence the allocation of mortgage and consumer 

credit, but many of these interventions have had adverse outcomes. This commentary is written 

in the hope that those who are aware of history will stop repeating it. Specifically, I argue that 

lawmakers have been too quick to succumb to political pressures and have failed to follow basic 

economic principles when creating mortgage market policies. As a result, expanding access to 

credit has been prioritized over the safety and soundness of the housing and mortgage markets.

The Legacy of Past Housing Policies

Until the 1990s, restrictions on banks limited their geographical expansion. The policy of not al-

lowing interstate branching was formalized in the McFadden Act1 of 1927 and strengthened by the 

Bank Holding Company Act2 of 1956. Although these restrictions seem absurd today, they had im-

portant implications for mortgage finance. Because they could not branch across state lines, banks 

held local mortgages in their portfolios and were forced to take substantial geographic risk that 

could not easily be diversified away. The lack of portfolio diversification was magnified because 

deposits were also local. As a result, a downturn in the local economy could result in bank failure, 

because customers would withdraw deposits and loan performance would deteriorate. Banks could 

not market the poorly performing local loans, and liquidity problems would turn into insolvency.

1 Pub. L. 69–639. 

2 Pub. L. 84–511, 70 Stat. 133.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 199

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

Yezer

Given the political unpopularity of branching, the answer to geographic risk diversification was 

to get mortgages out of the portfolios of the depository institutions that underwrote and endorsed 

them. The National Housing Act3 of 1934, which established the Federal Housing Administration 

(FHA) and introduced mortgage insurance to make mortgages more marketable, accomplished this 

goal. In the beginning, insurable mortgages had a maximum term to maturity of 20 years and a 

maximum loan-to-value (LTV) ratio of 80 percent, based on strict appraisals and required property 

inspections. The founding of the Federal National Mortgage Association (Fannie Mae) in 1938 to 

purchase both FHA-guaranteed and conventional mortgages was the second answer to the problem 

of diversifying geographic risk. Fannie Mae enabled housing to be financed by ultimate lenders who 

held a well-diversified portfolio. In many cases, banks, which could not diversify geographically due 

to statutory limits, purchased the mortgage-backed securities back from Fannie Mae.

The prohibition against branching provided a justification for federal involvement to diversify 

geographic risk, but it introduced other problems. Initially, FHA Section 203(b) mutual mortgage 

insurance was seen as a success. FHA was designed to protect homebuyers and taxpayers, but 

the limits on both maturity and LTV ratio crept upward as house prices rose and memories of the 

Great Depression faded.  Redlining—which was designed to manage FHA’s risk by avoiding neigh-

borhoods where house prices were likely to decline—came under attack for discriminating against 

minority neighborhoods.

Yet another policy was added in response: Section 235 of the Fair Housing Act4 of 1968. It relaxed 

lending criteria, reduced property inspection requirements (increasing the risk that mortgages 

were made on flawed units), and provided interest rate subsidies. The next 5 years were marked 

by scandal; more than 240,000 units went into default, resulting in a foreclosure rate five times 

that of FHA insurance. The effects of dilapidated and abandoned structures on neighborhoods 

turned residents against FHA and raised demands that the private sector become more involved in 

financing higher-risk loans. In my opinion, the primary impetus for passage of the Home Mortgage 

Disclosure Act5 (HMDA) of 1975 and the Community Reinvestment Act6 (CRA) of 1977 was the 

complete failure of Section 235, which was in turn a reaction to deficiencies in FHA Section 203(b) 

mutual mortgage insurance.

Given the failures of these FHA programs, public policy turned to the thrift industry to provide 

mortgage credit to lower-income borrowers and, again, set up policy conditions that worked 

against sound economic principles. Thrifts were given valuable competitive advantages. First, 

Regulation Q interest-rate ceilings were set to keep the cost of capital for thrifts artificially low but 

high enough to give them an advantage over commercial banks in attracting small savers. Second, 

restrictions on branching, particularly convenience and advantage regulations, gave thrifts some 

degree of local market power. However, a combination of rising interest rates and financial innova-

tion that provided small savers access to market returns through Money Market Mutual Funds 

prompted disintermediation and destroyed the thrift business model. Economists had forecast 

these effects, but regulators ignored them.

3 Pub. L. 73–479. 

4 Pub. L. 90–284, 82 Stat. 73. 

5 Pub. L. 94–200, 89 Stat. 1124. 

6 Pub. L. 95–128, 91 Stat. 1147, Title VIII.

200 The CRA Turns 40

Commentary: What Can We Learn From Government Attempts To Modify  

the Allocation of Mortgage and Consumer Credit in the United States?

The thrift crisis of the 1980s gave the banking system a reprieve from the regulatory effects of 

HMDA and CRA, as the government’s problem was not how to finance more housing, but how to 

dispose of all the mortgages and properties acquired in the financial crisis. Eventually, about 750 

insolvent institutions with assets of $800 billion (in 2016 dollars) closed. The Resolution Trust 

Corporation, established under the Financial Institutions Reform, Recovery, and Enforcement Act7 

of 1989, was involved in disposing of defaulted housing assets not unlike that which followed the 

demise of the Section 235 program. Once again, history repeated itself.

The Post-1990 Public Policy Record

Since 1990, a steady technological transformation of mortgage and consumer credit markets has 

taken place. Brick-and-mortar branches are closing. Lending is accomplished on the internet. 

Property appraisals and tax assessments are automated. When HMDA passed in 1975, property 

records were recorded on paper and filed in local courthouses. Now, property-transfer records 

are available on the web, easily scraped, and matched with HMDA records, so that today there is 

virtually no privacy in HMDA data. Indeed, the publication of HMDA data is inconsistent with U.S. 

Census Bureau standards for preserving privacy.8 CRA has also failed to respond to technological 

change. CRA is based on the presumption that deposit insurance is so valuable to banks with 

brick-and-mortar branches that they will assume substantial examination and compliance costs 

and will adjust lending, investment, service practices, or a combination of the three to achieve an 

“outstanding” or “satisfactory” CRA rating. That presumption, however, is technologically obsolete 

and financially unsound for both borrower and lender. Equally troubling is that economists have 

been unsuccessful in determining that having institutions with high CRA ratings makes a signifi-

cant difference in overall community economic performance. Given that 97 percent of institutions 

examined achieve high ratings, the opportunity to study the effects of unsatisfactory performance 

on local economies is scarce.9 Paradoxically, it may be that CRA has actually discouraged branching 

that could expand the definition of market area. Given that branches have been closing rapidly, 

perhaps having CRA impose extra burdens on banks that branch is not a good idea.10 

Despite the fact that the financial landscape had changed in a way that made CRA’s original 

premises invalid, the 1990s instead saw a move to combine CRA with the now revitalized and 

recapitalized government-sponsored enterprises (GSEs; that is, Fannie Mae and the Federal Home 

Loan Mortgage Corporation, or Freddie Mac) to provide high-risk mortgage credit. The Federal 

Housing Enterprise Safety and Soundness Act11 of 1992 enabled HUD to set mortgage purchase 

7 Pub. L. 101–73, 103 Stat. 183. 

8 For example, Gerardi and Willen (2008) were able to identify more than 70 percent of HMDA respondents by matching census tract, lender, and loan amount with readily available property records purchased from a commercial firm. This finding contrasts sharply with CFPB’s (2016) assurance to the public regarding privacy of their HMDA data, which stated, 

“This provides enough information about the location to be useful, but still provides protections for individual privacy.” 

9 For an excellent discussion of the nature of CRA examinations and attempts to find economic effects, see Getter (2015). 

10 For example, Bank of America’s annual reports indicate that it has 4,600 branches today compared with 6,100 in 2009. 

Of course, institutions can earn CRA points by selective branching, so the net effect of CRA on branching is difficult to determine. The intent of CRA is a complete reversal of previous public policy that discouraged branching. Indeed, under convenience and advantage regulation, banks were allowed to branch only into fast-growing, higher-income areas, because the concern was to preserve the safety and soundness of the banking system. 

11 Pub. L. 102–550, 106 Stat. 3672, Title XIII.

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Yezer

goals for the GSEs and established the Office of Federal Housing Enterprise Oversight (OFHEO) 

to monitor their safety and soundness. Again, sound economic principles were ignored. Initially, 

OFHEO calibrated a stress test using GSE mortgages acquired between 1979 and 1997 and was 

required to publish the results.12 However, OFHEO never updated it. Frame, Gerardi, and Willen 

(2015) found that, if the stress test had been updated, by 2004 it would have been apparent that 

the GSEs had a capital adequacy problem. Yet, in 2004, HUD raised the GSE affordable housing 

goals at precisely the time when the GSEs should have been contracting. In mid-2007, the GSEs 

reported $65.5 billion in book value of equity against $1.7 trillion in assets (3.9-percent ratio). In 

June 2008, they reported $54 billion in equity supporting $1.8 trillion in assets. On September 7, 

2008, they were put into receivership. OFHEO and the Federal Reserve had allowed them to 

expand, rather than contract, based on faulty modeling and political pressure. Impartial economic 

analysis would have curtailed their operations years earlier, but political forces always triumph over 

economic analysis in mortgage market policy.

During the 1990s, another episode occurred in which political pressure caused large FHA losses 

in a policy initiative at least as flawed as the Section 235 program: the seller-funded downpayment 

program. Originally designed to expand access to homeownership, the seller-funded downpayment 

program enabled sellers to “voluntarily” contribute the downpayment for FHA-insured mortgages 

to an approved nonprofit organization, which used part of the contribution to help finance a 

downpayment. The Housing and Economic Recovery Act13 of 2008 finally terminated the program 

due to high default rates. Hard experience demonstrated the economic unsoundness of assuming 

that sellers would voluntarily contribute funds to a third party to pay the downpayment without 

raising the asking price by the amount of the contribution. Simple economic analysis would have 

demonstrated the fallacy of the program’s expectations and prevented the high rates of default and 

foreclosure.

Since the housing crisis of the mid-2000s, the gap between economic analysis and public policy 

toward credit markets has only grown wider. For example, the Dodd-Frank Wall Street Reform and 

Consumer Protection Act14 of 2010 (Dodd-Frank Act) limits the fees that mortgage brokers can 

charge to take, underwrite, and endorse mortgages. Fees are now expected to be uniform; no yield 

spread premium can be paid, regardless of whether the applicant applies online and has perfect 

financial records, a large downpayment, high credit score, and ample income or whether the 

applicant is unable to use a computer, keeps financial records in a shoe box, and has a low credit 

score.15 Discovering the lack of creditworthiness of individuals whose financial records are in a 

shoe box can be very expensive. The response to regulation of fees for brokerage services has been 

a predictable decline in mortgage brokers serving low-income, less-educated borrowers. 

12 The requirement to publish a stress test is problematic and shows a profound misunderstanding of the problem of bank regulation because the test, once published, invites institutions to engage in regulatory capital arbitrage. The process is like giving students the questions on the final examination at the start of a course. They are likely to learn the answers and nothing else. 

13 Pub. L. 110–289, 122 Stat. 2654. 

14 Pub. L. 111–203, 124 Stat. 1376. 

15 The Federal Reserve proposed the original broker compensation rule in July 2009. It is now part of the Dodd-Frank Act. Section 129 B of the Truth in Lending Act was modified, adding section (c), which prohibits a mortgage originator from receiving and no person from paying compensation based on the terms of the loan except for the amount of a residential mortgage loan.

202 The CRA Turns 40

Commentary: What Can We Learn From Government Attempts To Modify  

the Allocation of Mortgage and Consumer Credit in the United States?

GSE regulation also continues to be problematic, for example, when political pressure does not 

allow for GSEs to price mortgages based on geographic risk. In 2008, the GSEs asked Congress 

to be allowed to require more equity in declining housing markets, a logical step to protect them 

against default risk. As Hurst et al. (forthcoming) explained—

The declining market policy was announced in December of 2007 and was implemented 

in mid-January of 2008. After receiving large amounts of backlash from a varied set of 

constituents, the policy was abruptly abandoned in May of 2008. Consumer advocacy 

groups rallied against the policy, arguing that it was a form of space-based discrimination. 

Real estate trade organizations used their political clout to protest the policy because it 

was hurting business. For example, the Wall Street Journal summarized the GSEs’ aban-

doning the declining market policy by saying, “The change comes in response to protests 

from vital political allies of the government sponsored provider of funding for mortgages, 

including the National Association of Realtors, the National Association of Home 

Builders, and organizations that promote affordable housing for low-income people.” 

The Washington Post reported, “Critics, including the National Association of Realtors 

and consumer advocacy groups, had charged that Fannie Mae’s policy further served to 

depress sales and real estate values in the areas tainted as declining.

A further attempt in 2014 by the GSEs to add a 25-basis-point origination fee that would help 

cover additional losses in five states where foreclosure delays were long was also defeated by the 

same political forces.

Recently, Luan (2017) demonstrated that the spread between interest rates on jumbo mortgages 

(loans above the GSE and FHA ceiling) and conventional (GSE and FHA) rates can be used to pre-

dict future changes in house prices. This important finding shows that the jumbo market responds 

effectively to differences in local risk. In contrast, these risks are ignored in pricing conforming 

mortgages. The result is that the normal role of interest rates in attenuating housing price bubbles 

is short-circuited by the GSE and FHA pricing policy. How important was this in the housing 

bubble that precipitated the mortgage crisis? That is difficult to determine, but Tian reported that 

a 1-standard-deviation change in the jumbo- and conforming-mortgage spread is associated with 

a 2.5-percent lower rate of subsequent house price appreciation. Clearly, current federal housing 

policy accommodates local housing bubbles, with CRA contributing to the dynamic by encourag-

ing banks to make loans even in areas where rising house prices threaten housing affordability.

Contrasting an Economic Perspective and Current 

Regulatory Policy

The fundamental basis for disagreement between economic analysis and current regulatory policy 

is that economists believe in using prices—that is, interest rates—to ration credit among alternative 

uses and users. Economists believe that raising interest rates is the proper response to a local hous-

ing bubble, but public policy views higher interest rates as a bad thing because they make housing 

less affordable just as prices of housing units are rising.

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Yezer

CRA is based on the notion that the banking system is responsible for making housing affordable, 

even when market prices are rising. Economists believe that, if housing is less affordable, that is 

a housing market issue. If housing costs $300 or more per square foot, the banking system is not 

responsible for making it affordable for low- or moderate-income households. Economists say that 

lawmakers should examine policies influencing the supply (zoning, building codes, transportation 

access, and so on) and demand (tax treatment of mortgage interest and property taxes and the 

exclusion from capital gains taxation) for housing rather than the local bank branch.

In addition to insisting on geographic uniformity of mortgage interest rates, public policy tends to 

oppose variation in rates across individuals. Borrowers are urged to shop for credit, presumably 

in the hope of lowering cost. However, current examination practices penalize lenders if interest 

rates are determined to be correlated with borrower demographic characteristics. Given that demo-

graphic characteristics are correlated with financial and numerical literacy, the correlation of rates 

with demographic characteristics is inevitable if borrowers earn positive returns from shopping in 

credit markets, something that an economist would see as a positive.

Also, attitudes toward applicant competence differ between the regulatory environment and 

economics. Applicants face two challenges: (1) finding investment opportunities that are attractive, 

and (2) determining the best debt instrument to use in financing the opportunity. Most economists 

would say that the first challenge is greater and that many borrowers make bad investment choices. 

Indeed, one function of responsible lending is to stop borrowers from making bad decisions 

by denying them credit or making the credit so expensive that the problematic nature of the 

investment decision is clear. CRA does not credit lenders who prevent financial failure by denying 

applications or offering credit only at high rates. Loan denials are not seen as a valuable deterrent 

to overly optimistic loan applicants.

Another problem concerns the economic view of appropriate mortgage instruments. Advocates 

who focus on expanding access to credit often discount the risks and costs of making mortgages; 

some expect the financial system to provide homebuyers with 30-year, self-amortizing mortgages 

with no prepayment penalties, no deficiency judgments, debt-to-income ratios of 0.43, and LTV 

ratios of 0.95 or more. Credit scores of 620 (even 600) should be adequate, and an interest rate 

within 200 basis points of the 10-year treasury rate is expected. These mortgages may be economi-

cally sound as long as nominal housing prices are rising, but if house prices fall, as happens 

periodically, this mortgage product could result in substantial losses. Accordingly, the financial 

system is generally not willing to hold such mortgages as an asset in significant quantity unless the 

government offers some form of guarantee that causes losses to fall on taxpayers or to be passed on 

to future homebuyers in the form of higher guarantee fees.

Finally, the premise of CRA is inconsistent with modern economic thought. The idea that local 

depository institutions should reinvest local deposits by holding local liabilities is completely 

inconsistent with modern portfolio theory and sound banking practice. Modern technology has 

broken the link between local deposit taking and lending. The great mystery about CRA is how 

such a flawed view of banking has survived so long in a country that leads the world in both 

internet technology and financial economics.

204 The CRA Turns 40

Commentary: What Can We Learn From Government Attempts To Modify  

the Allocation of Mortgage and Consumer Credit in the United States?

Acknowledgments

The author acknowledges suggestions and encouragement provided by Carolina Reid, Mark Shro-

der, Edgar Olsen, and Steve Malpezzi.

Author

Anthony Yezer is a professor of economics in at George Washington University.

References

Consumer Financial Protection Bureau (CFPB). 2016. “About HMDA.” Transcript of a video on the 

Home Mortgage Disclosure Act. http://www.consumerfinance.gov/data-research/hmda/learn-more.

Frame, W. Scott, Kristopher Gerardi, and Paul S. Willen. 2015. The Failure of Supervisory Stress 

Testing: Fannie Mae, Freddie Mac, and OFHEO. Working Paper 15-4. Boston: Federal Reserve 

Bank of Boston.

Gerardi, Kristopher S., and Paul S. Willen. 2008. Subprime Mortgages, Foreclosures, and Urban 

Neighborhoods. Working Paper 08-6. Boston: Federal Reserve Bank of Boston.

Getter, Darryl E. 2015.  The Effectiveness of the Community Reinvestment Act. Congressional Research 

Report 7-5700. Washington, DC: Congressional Research Service.

Hurst, Erik, Benjamin J. Keys, Amit Seru, and Joseph S. Vara. Forthcoming. Regional Redistribu-

tion Through the U.S. Mortgage Market,  American Economic Review. 

Luan, Tian. 2017. Did Investors Price the Housing Bubble? Evidence From the Jumbo/Conforming 

Rate Spread. Working paper. Washington, DC: George Washington University, Institute for Interna-

tional Economic Policy.

Cityscape 205

206 The CRA Turns 40

Commentary: Filling a Gap in the 

Community Reinvestment Act 

Examiner Toolkit

Mark A. Willis 

NYU Furman Center

Critical to the success of the Community Reinvestment Act1 (CRA) of 1977 is how bank regulators 

evaluate a bank’s performance in meeting the credit needs of low- and moderate-income (LMI) bor-

rowers. Failure to measure the right outcomes results in a less-effective, if not ineffective, CRA, as 

banks focus on simply checking off boxes that may make little difference in the communities they 

serve. A particular problem is if the examiners cannot fully take into account variations in local 

market conditions and credit opportunities. As noted in Willis (2009), the examination process is 

critical if CRA is to continue to play a meaningful role in strengthening LMI households and com-

munities. This commentary considers one aspect of the challenge of keeping CRA effective: the cri-

teria for determining how well large banks (banks with assets of $1 billion or more) are serving the 

home purchase mortgage needs of LMI households. More specifically, I examine how well a bank is 

doing in helping to meet the credit needs of the LMI community compared with its performance in 

serving higher income groups.

The burden of determining a bank’s CRA performance falls on bank examiners, who currently need 

better guidance for making evaluations across the vast array of housing market conditions in the 

United States, ranging from cities plagued with disinvestment and abandonment to those with high 

housing costs.2 The lack of adequate guidance results in a lack of clarity that does not serve any of 

the stakeholders well. Advocates want banks to be held to a consistent and high standard. Bank 

examiners value standardized tests to increase consistency and to help arm them against second-

guessing by advocates. Bankers are looking for standardization and predictability to enable them 

to develop a forward-looking business plan for their CRA activities.3 Therefore, the language and 

results of the evaluation process (which appear in the performance evaluation published with a 

bank’s ratings) show a heavy reliance on a narrow set of standardized statistics. Information on lo-

cal market and business conditions supplied by the bank as part of its performance context seems 

incapable of swaying the examiner to reward the bank with a rating any higher than “adequate.”4 

1 Pub. L. 95–128, 91 Stat. 1147, Title VIII. 

2 The standard criterion for  affordable is housing costs of no more than 30 percent of income. 

3 A lag in completion of prior exams exacerbates the challenge for banks in planning their CRA activities, by delaying any feedback from the results of those exams. 

4 The terms “excellent,” “good,” “adequate,” “poor,” and “very poor” correspond to CRA ratings of “outstanding,” “high satisfactory,” “satisfactory,” “needs to improve,” and “substantial noncompliance.”

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 207

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

Willis

Reliance on only a few standard statistical tests can be a particular problem in high-housing-cost 

areas; what is a bank to do if market conditions do not make buying a home feasible for an LMI 

household?

To judge a bank’s CRA performance with regard to home mortgage lending to LMI households, 

examiners look at three benchmarks: (1) the LMI share of the population, (2) the LMI share of 

homeowners, and (3) the performance of the bank, as measured by its distribution of home mort-

gages across households by income compared with that for all the lenders in that locality. None of 

these three benchmarks, however, provides a reliable basis for assessing the extent to which a bank 

helps to meet the home purchase mortgage credit needs of LMI households in high-housing-cost 

markets.5 

The shortcomings of these three benchmarks are made clear by the example of New York City 

(NYC) where, since 2000, median home prices have more than doubled while incomes have con-

tinued to stagnate (NYU Furman Center, 2016). Exhibit 1 presents the benchmarks traditionally 

used as part of a CRA exam. Benchmark 1, the LMI share of households in NYC (51.6 percent), 

is meaningless as an indicator for credit demand unless the distribution of sales prices of housing 

allows for equal proportions of households at all income levels to sustain homeownership—clearly 

not the case for NYC. Benchmark 2, the percentage of LMI households that are homeowners (30.9 

percent), is equally problematic as a target because existing homeowners reflect an amalgam of 

sales made over many years, including sales made well before house prices boomed. Benchmark 3, 

the percentage of home purchase mortgages made to LMI households by all lenders in NYC, re-

mains. Is 8.7 percent good or bad? Are banks in NYC meeting local credit needs? There is no way 

to know if an 8.7-percent share of home purchase mortgages meets the credit needs of LMI house-

holds, and the first two benchmarks do little to help.

I argue that a better, localized benchmark is needed for judging whether a bank carries out the 

intent of CRA and helps to meet the credit needs of its community, and I propose one approach 

to doing so.6 To be useful, a benchmark needs to account for differences across cities in home 

prices and in the factors that go into a lender’s ascertainment of how much it can lend. Based on 

the assumption that most LMI homebuyers take out a mortgage, this commentary demonstrates 

how such a benchmark can be developed for NYC using 2014 home sales and Home Mortgage 

Exhibit 1

Comparison of Benchmarks for New York City, 2014

Benchmark

Percent

LMI share of households

51.6

LMI share of homeowners

30.9

LMI share of home purchase mortgages

8.7

LMI = low- and moderate-income.

5 Although refinancing mortgages is also part of the home mortgage market, the income distribution of customers for home purchase mortgages can differ greatly from that for refinancing mortgages. The home purchase market is sensitive to the price distribution of the homes for sale, whereas the refinancing market depends on the distribution of existing homeowners and, in particular, those who already have a mortgage. 

6 Although this commentary is able to provide only an overview of the methodology, the specific details are available on request from the author. 

208 The CRA Turns 40

Commentary: Filling a Gap in the Community Reinvestment Act Examiner Toolkit

Disclosure Act7 (HMDA) data.8 The home sales data provide us with the price distribution of 

homes sold—in other words, the potential “stock” of homes LMI families could buy.9 HMDA provides 

data on income levels and mortgage amounts by borrower and so enables us to determine the distri-

bution of mortgage-to-income ratios (MIRs) for borrowers within different income ranges. By making 

assumptions regarding LMI borrower incomes and downpayments, we can use an MIR distribution 

to estimate a distribution of purchasing power for LMI homebuyers and then match it to the distribu-

tion of sales prices to determine a share of sales that LMI homebuyers are likely able to purchase. The 

distribution can then be used as a benchmark against which to judge lender performance.

The first step is to calculate how much LMI families can realistically borrow. To guard against using 

an MIR distribution that embodies a failure by banks and other lenders to meet the home purchase 

mortgage credit needs for the LMI community, we compare the MIR distribution for LMI borrow-

ers with incomes from 70 to 80 percent of the Area Median Family Income (AMFI) with the MIR 

distributions for borrowers with higher incomes: 80 to 90, 90 to 100, and 100 to 120 percent of 

AMFI (exhibit 2).10 Although the shapes of the MIR distributions vary across these income groups, 

Exhibit 2

Distribution of Mortgage-to-Income Ratios by Income Range of the Borrowers, New 

York City, 2014

Sources: Home Mortgage Disclosure Act (2014 data); NYU Furman Center 

7 Pub. L. 94–200, 89 Stat. 1124. 

8 LMI households that purchase two- to four-family homes can often use a portion of the rental income in their mortgage application, thus potentially raising the income that is reported in HMDA above the maximum income for an LMI household, in which case the borrower would be placed in a higher income category.  

9 For more information on the sales data, see NYU Furman Center (2016). 

10 The 2014 AMFI for NYC is compiled using American Community Survey 2006–2010 estimates for the New York, NY 

Metropolitan Statistical Area. 

Cityscape 209

Willis

the bulk of the MIRs lies between 2 and slightly more than 5 in all cases.11,12  It is clear, however, 

that households earning between 70 and 80 percent of AMFI have, on average, lower MIRs than 

those who earn more. This difference may reflect tighter lending standards for LMI borrowers than 

for higher-income borrowers but can also be explained by numerous other factors. Such factors 

include the potentially higher cost of credit (with LMI borrowers possibly having to pay higher 

interest rates, given that their credit histories tend to be weaker than those of higher-income 

households), borrower concerns about having sufficient residual income after making mortgage 

payments (for example, needing to make sure they have enough to cover other debts and living 

expenses, including house repairs and rehab), and the fact that LMI homebuyers may be limited in 

the amount of house they can purchase because they likely have less savings to devote to a down-

payment, closing costs, and lender-required reserves (often two to three times projected monthly 

housing costs).

The power of this analysis is that it is now possible to map each of the four MIR distributions 

against the distribution of home sales prices to derive estimates of the share of homes for sale 

that LMI households could be expected to buy. To simplify the calculation for translating the MIR 

distributions into LMI borrowing power, I assume that LMI borrowers are evenly split between 

those with incomes at 80 percent of AMFI and those at 70 percent. That assumption somewhat 

inflates the borrowing power of LMI borrowers who, based on the HMDA data, were more heavily 

weighted toward those with incomes less than 70 percent of AMFI than toward those with incomes 

between 70 and 80 percent of AMFI.

Exhibit 3 shows the results when applying the four different MIR distributions based on that split 

of LMI borrower incomes, assuming downpayments of either 3 or 20 percent of the purchase price. 

Although I do not have direct information on the size of downpayments, the low end of this range 

Exhibit 3

Estimated Share of 2014 Home Sales That Could Have Been Bought by LMI 

Households Based on HMDA-Derived MIR Distributions 

AMFI Range for the MIR 

Estimated LMI Market Shares (%)

Distribution Used (%)

Assuming Borrowers Able To Put 

Assuming Borrowers Able To Put 

Down 3 Percent of the Sales Price Down 20 Percent of the Sales Price

70–80

6.7

10.2

80–90

7.6

11.6

90–100

7.5

11.5

100–120

7.3

11.2

AMFI = Area Median Family Income. HMDA = Home Mortgage Disclosure Act. LMI = low- and moderate-income. MIR = 

mortgage-to-income ratio.

11 Note that the graphs reflect the “cleaning” of the data of ratios that seemed unreasonably high by truncating the HMDA-generated MIR distribution at the 90th percentile and truncating the bottom 10 percent to guard against biasing the results downward.  

12 Interestingly, using the maximum “ability-to-repay,” or QM, ratio of 0.43 and the 2014 average mortgage interest rate of 4.5 percent (and no mortgage insurance), Realtor.com yields what might be considered a maximum possible MIR of 5.3, based on a 20-percent downpayment and machine-generated projections for NYC property taxes and insurance costs for a mortgage of $288,000. 

210 The CRA Turns 40

Commentary: Filling a Gap in the Community Reinvestment Act Examiner Toolkit

is likely to predominate, especially for the first-time homebuyers, given the generally low level of 

savings of LMI households (Federal Reserve, 2015; see page 23).13 Assuming a 3-percent downpay-

ment, the MIR distribution for LMI borrowers yields an estimated share of sales of 6.7 percent. Us-

ing the MIR distributions of higher-income borrowers yields estimates as high as 7.6 percent, with 

the highest produced by the MIR distribution for borrowers with incomes between 80 and 90 per-

cent of AMFI. Interestingly, the MIR distributions for the next two higher-income groups actually 

yield smaller LMI market shares, suggesting that 7.6 percent may reasonably be considered an up-

per estimate for a benchmark of the potential LMI share of home sales in NYC in 2014, if borrow-

ers put down only 3 percent of the sales price. If LMI borrowers are able to put down 20 percent, 

the estimated shares of loans are higher, ranging from 10.2 to 11.6 percent.

The numbers in exhibit 3 provide an estimated market share of LMI households and a more real-

istic benchmark to serve as a target for CRA evaluation. If a bank is working to effectively expand 

access to credit, and a 3-percent downpayment seems standard, then 6.7 percent of home purchase 

loans to LMI households would be a good benchmark for judging performance. This calculation 

also suggests that the industrywide LMI share of 8.7 percent in NYC (exhibit 1) might reflect a 

strong performance. Such a conclusion would be valid even if it turns out that as many as 25 per-

cent of LMI homebuyers have sufficient savings to put down 20 percent. Even using the highest 

estimates in exhibit 3—7.6 and 11.6 percent—and weighing them accordingly, the benchmark 

estimate would be 8.6 percent. In other words, based on this metric, examiners could more ef-

fectively conclude that, overall, lenders in NYC are abiding by the spirit of CRA, so any bank that 

meets or exceeds that standard should also get credit for stepping up to help meet LMI credit needs 

for home purchase mortgages.

This proposed metric is far from perfect and is based on a lot of assumptions that can surely be 

further documented and refined. For example, more information is needed on the size of down-

payments made by LMI borrowers, but examiners could probably ascertain that information by 

working with the banks in a local market. In addition, relying on HMDA data of the actual experi-

ence of mortgage borrowers where housing costs are high may bear further scrutiny to make sure 

that this analysis does not simply replicate whatever shortcomings may exist in the way lenders 

make credit available. Clearly, if lenders are not helping to meet the credit needs of LMI borrowers, 

then the HMDA data cannot shed light on the true borrowing potential for LMI households. Nev-

ertheless, the existing benchmarks do not provide much guidance in high-housing-cost locations for 

examiners who are tasked with determining whether a bank meets its CRA obligations. For CRA 

to work as effectively as it should, regulators need to establish metrics that can effectively capture 

whether banks meet market demand across the diversity of housing market contexts in the United 

States.

Acknowledgments

The author thanks Maxwell Austensen for his incredible data work.

13 As a result of low saving rates, it is not surprising that surveys of prime, working-age household heads show only minimal net worth among those with incomes in the bottom four deciles (Thompson and Suarez, 2015; see page 39).

Cityscape 211

Willis

Author

Mark A. Willis is a senior policy fellow at the NYU Furman Center.

References

Board of Governors of the Federal Reserve System (Federal Reserve). 2015.  Report on the Economic 

Well-Being of U.S. Households in 2014. Washington, DC: Board of Governors of the Federal Reserve 

System. https://www.federalreserve.gov/econresdata/2014-report-economic-well-being-us-households- 

201505.pdf.

NYU Furman Center. 2016.  Report on Homeownership & Opportunity in New York City. New York: 

NYU Furman Center; Citi. http://furmancenter.org/files/NYUFurmanCenterCiti_ 

HomeownershipOpportunityNYC_AUG2016.pdf.

Thompson, Jeffrey P., and Gustavo A. Suarez. 2015.  Exploring the Racial Wealth Gap Using the Survey 

of Consumer Finances. Finance and Economics Discussion Series 2015-076. Washington, DC: Board 

of Governors of the Federal Reserve System. http://www.federalreserve.gov/econresdata/feds/2015/

files/2015076pap.pdf.

Willis, Mark. 2009. “It’s the Rating Stupid, A Banker’s Perspective on the CRA.” In  Revisiting the 

CRA: Perspective on the Future of the Community Reinvestment Act, edited by Prabal Chakrabarti, Da-

vid Erickson, Ren S. Essene, Ian Galloway, and John Olson. Boston; San Francisco: Federal Reserve 

Bank of Boston and Federal Reserve Bank of San Francisco: 59–70. http://www.frbsf.org/community-

development/files/its_rating_stupid1.pdf.

212 The CRA Turns 40

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 19 Number 2 • 2017

Cityscape 213

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

214 Refereed Papers

Are Homeowners Better Neighbors 

During Housing Booms? 

Understanding Civic and Social 

Engagement by Tenure During the 

Housing Market Cycle

Deirdre Pfeiffer   

Arizona State University

Eric A. Morris  

Clemson University

Abstract

Research has shown that homeowners’ concern with their property values may lead them 

to have greater civic and neighborhood social engagement than renters. However, it has 

not been well examined whether changing property values during the housing market 

cycle affect this purported social benefit of homeownership. Using 2003 to 2013 Ameri-

can Time Use Survey data and Cragg two-part hurdle regression analysis, we assess 

differences in homeowners’ and renters’ civic and neighborhood social engagement during 

the stages of the recent housing market cycle. We find that, holding other characteristics 

constant, homeowners were more likely than renters to volunteer but spent less time in 

social activities with neighbors than renters, regardless of the housing market stage. Dif-

ferences in civic and social engagement by tenure did not vary in consistent or expected 

ways across the stages of the cycle. These findings reinforce claims that homeownership 

has civic benefits, but evidence is lacking that homeowners make better neighbors than 

renters. Our findings also suggest that policies to intervene in the housing market to pro-

mote neighborhood cohesion need not vary across the housing market cycle.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 215

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

Pfeiffer and Morris

Introduction

The United States is emerging from a decade of housing market flux in which most major regions 

underwent three distinct periods—expansion, recession, and recovery—in residential investment 

from the mid-2000s through the early 2010s. Increasing housing production and demand defined 

the expansion period. Falling housing production and demand, and increasing foreclosures and 

vacancies, characterized the recession period. Declining foreclosures and vacancies and changing 

property ownership and tenure composition, largely through the disposition of foreclosures, 

punctuated the recovery period. 

These stages offer an opportunity to deepen our understanding of how housing market cycles 

affect the social benefits of homeownership (Lindblad, Manturuk, and Quercia, 2013; Rohe and 

Lindblad, 2013). A common rationale for subsidizing homeownership is that it builds strong 

communities due to homeowners’ greater civic and social engagement. Because homeowners are 

eager to increase their property values and are less transient, they may be more likely than renters 

to become involved in local affairs and to get to know their neighbors. Existing research largely 

confirms these suppositions (Blum and Kingston, 1984; Cox, 1982; DiPasquale and Glaeser, 1999; 

Lindblad, Manturuk, and Quercia, 2013; Lyons and Lowery, 1989; Manturuk, Lindblad, and 

Quercia, 2010, 2012; McCabe, 2013; Rohe and Basolo, 1997; Rohe and Lindblad, 2013; Rohe 

and Stegman, 1994; Rohe, Van Zandt, and McCarthy, 2001; Rossi and Weber, 1996; Shlay, 2006). 

However, these studies overlook the role that changing economic conditions during the housing 

market cycle may play in these outcomes. It is unknown whether differences between homeown-

ers’ and renters’ civic and social engagement narrow or widen during the housing market cycle, 

particularly during recessions when homeowners may experience declines in their property values 

(Rohe and Lindblad, 2013).

This research helps to fill this gap by using pooled data on civic and social engagement in U.S. 

metropolitan areas from the 2003 to 2013 American Time Use Surveys (ATUS). We use a two-stage 

Cragg hurdle analysis to test whether homeowners have greater civic and social engagement than 

renters across the housing market cycle and then specifically during expansion periods, when 

property values are rising or stable, during recessions, when property values are falling, and during 

recovery periods, when the market returns to equilibrium. Examining this question sheds light on 

whether a social justification exists for countercyclical or even procyclical policies to intervene in 

the housing market to promote homeownership. Might policies to shore up housing markets in 

times of distress help to maintain and enhance community social fabric?

In what follows, we first discuss the reasons why homeowners may have greater civic and social 

engagement than renters and synthesize the findings of existing research testing these claims. Then 

we discuss theory on how changing economic conditions during the housing market cycle may 

alter homeowners’ engagement relative to renters. This discussion is followed by a description of 

the data and methodology and then a summary of the results. We conclude by highlighting key 

takeaways from the study and implications for further research and policymaking.

216 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

The Link Between Tenure and Civic and Social Engagement

The social benefits of homeownership have long been the focus of debate (Dietz and Haurin, 2003; 

Rohe and Lindblad, 2013; Rohe, Van Zandt, and McCarthy, 2001; Rossi and Weber, 1996; Shlay, 

2006). One key purported social benefit of homeownership is heightened civic and social engage-

ment, which together may lead to stronger communities that are better able to make improvements 

and respond to threats. 

Civic Engagement

We define  civic engagement as formal, goal-oriented participation in neighborhood, charitable, 

church, or government organizations or processes, such as helping out at a church soup kitchen 

or attending a public meeting on a development proposal. Research, most notably from Putnam 

(2001), has documented a dramatic drop in American civic engagement during the past decades. 

Putnam attributed some of the cause for this decline to suburbanization and sprawl, claiming there 

is a “‘sprawl civic penalty’ of roughly 20 percent on most measures of community involvement” 

and that “the direct civic penalty associated with sprawl probably accounts for something less 

than one-tenth of the total disengagement” (Putnam, 2001: 215). Putnam reasons that sprawl and 

suburbanization have eroded community life for several reasons, such as greater dependence on 

socially isolating automobile travel, the social homogeneity of suburban enclaves, and a lack of 

spatial bounding as suburbanites live, work, and shop in far-flung locations. 

American suburbanization, particularly in the post-World War II era, has gone hand in hand with 

rising homeownership. Glaeser (2011) argued that policies that favor homeownership contribute 

to sprawl, because the owner-occupied housing market consists overwhelmingly of detached 

single-family units, whereas rentals strongly tend to be in multifamily, denser structures. Thus, 

assuming Putnam is correct (see Morris and Pfeiffer [2017] for a critique), suburbanization and 

poorer community life may be linked indirectly. 

However, the literature strongly suggests that the more direct effects of homeownership on 

individuals’ social and community lives dwarf any such sprawl civic penalty. A strong consensus 

exists that homeownership is positively linked to diverse measures of civic engagement (Cox, 

1982; DiPasquale and Glaeser, 1999; Lyons and Lowery, 1989; Manturuk, Lindblad, and Quercia, 

2012; McCabe, 2013; Rohe and Basolo, 1997; Rohe and Stegman, 1994; Rossi and Weber, 

1996). The civic behavior that is most positively linked to homeownership is belonging to local 

or neighborhood organizations (Cox, 1982; DiPasquale and Glaeser, 1999; Lyons and Lowery, 

1989; Manturuk, Lindblad, and Quercia, 2012; McCabe, 2013; Rohe and Basolo, 1997; Rohe 

and Stegman, 1994; Rossi and Weber, 1996). Other civic behaviors that are positively associated 

with homeownership include attending meetings (Cox, 1982; Lyons and Lowery, 1989; Rohe 

and Basolo, 1997); organizing or signing petitions and communicating with public officials (Cox, 

1982; Lyons and Lowery, 1989); voting (DiPasquale and Glaeser, 1999; McCabe, 2013); working 

to solve community problems; and attending church (DiPasquale and Glaeser, 1999). However, 

some counter evidence suggests that homeownership is negatively linked to working to solve 

community problems and voting (Kingston and Fries, 1994), and some research has found that 

Cityscape 217

Pfeiffer and Morris

homeownership is not linked to being involved in religious organizations (Kingston and Fries, 

1994; Rohe and Basolo, 1997; Rossi and Weber, 1996) or other local or neighborhood organiza-

tions (Blum and Kingston, 1984; Rohe and Stegman, 1994).

Social Engagement

We define  social engagement as informal social interactions that homeowners have with neighbors, 

such as talking across the fence, going for a walk together, and taking care of one another. These 

interactions may lead neighbors eventually to develop trust, shared norms, and social capital—the 

ability to rely on their relationships to access information or resources (Briggs, 1997; Coleman, 

1988). A neighborhood’s collective efficacy, or its ability to respond to threats or improve condi-

tions such as rescuing neighbors from flooded homes after a hurricane or lobbying against threats 

to neighborhood character, derives in large part from residents’ social capital (DeFilippis, 2001; 

Sampson, Raudenbush, and Earls, 1997). It is important to examine social engagement in tandem 

with civic engagement to fully assess whether homeownership leads to stronger communities. 

Evidence exists for a positive association between homeownership and social engagement 

(Blum and Kingston, 1984; Lindblad, Manturuk, and Quercia, 2013; Lyons and Lowery, 1989; 

Manturuk, Lindblad, and Quercia, 2010; Rohe and Basolo, 1997), but some studies question 

this link (Manturuk, Lindblad, and Quercia, 2012; Rohe and Basolo, 1997; Rohe and Stegman, 

1994; Rossi and Weber, 1996). Measures of collective efficacy, such as talking to neighbors about 

problems or people’s perceptions of neighbors’ ability to solve problems, are most positively linked 

to homeownership (Lyons and Lowery, 1989; Lindblad, Manturuk, and Quercia, 2013; Manturuk, 

Lindblad, and Quercia, 2010). Homeownership also is associated with having a higher proportion 

of one’s social network comprised of neighbors (Blum and Kingston, 1984) and growth in social 

ties with neighbors over time (Rohe and Basolo, 1997). Conversely, homeowners tend to have less 

intense interactions with their neighbors. For example, they may know fewer people on their block 

by name or may consider fewer neighbors close friends (Rohe and Basolo, 1997; Rohe and Steg-

man, 1994). They may also have less frequent conversations with neighbors (Manturuk, Lindblad, 

and Quercia, 2012) and spend fewer evenings with neighbors (Rossi and Weber, 1996). Thus, 

although existing research generally has reached a consensus that an association exists between 

ownership and formal civic engagement, evidence on social engagement and ownership is decid-

edly mixed.

How Concern for Property Values and Residential Stability May Promote Civic 

and Social Engagement

Homeowners’ (1) concern for their property values and (2) greater residential stability are thought 

to drive their purported greater civic and social engagement. Homeowners differ from renters in 

that their homes have exchange (monetary) value in addition to use value (Logan and Molotch, 

1987). Home equity is the largest repository of wealth for American households, accounting for 

about two-thirds of middle-income households’ wealth in 2010 (Mischel et al., 2012). Property val-

ues affect home equity and are influenced by local conditions, such as the strength of the economy, 

the quality of the schools, the presence of crime, and nearby amenities. Thus, homeowners have an 

218 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

incentive to get involved in civic activities that help protect and bolster their property values, such 

as volunteering for a neighborhood beautification committee, attending a community long-range 

planning meeting, or forming a committee to protest the construction of a nearby homeless shelter 

(Blum and Kingston, 1984; Fischel, 2001). They also have a financial incentive to get to know their 

neighbors to forge shared social norms as a way of enhancing their collective efficacy (Dietz and 

Haurin, 2003; Lindblad, Manturuk, and Quercia, 2013; Sampson, Raudenbush, and Earls, 1997). 

Another way that homeowners differ from renters is that their moving costs are much higher. 

Although renters have to cover the costs of searching for an alternative place to live, moving pos-

sessions, and potentially paying a security deposit and other fees, homeowners incur these costs 

plus the additional cost in time, effort, and money of selling their homes. As a result, homeowners 

stay in place longer than renters. For instance, from 2005 to 2010, about two-thirds of renters 

moved compared with less than one-fourth of homeowners (Ihrke and Faber, 2012). According 

to the National Association of Home Builders, typical home purchasers will remain in their home 

for 13 years (Emrath, 2013). Staying in place for longer has three implications for civic and social 

engagement. First, because homeowners cannot easily change their neighborhood, they have the 

incentive to become involved in community activities to maintain and improve their neighbor-

hoods as a means of preserving and enhancing their quality of life (Cox, 1982). Second, staying in 

place for longer may lead homeowners to become more attached to the use values of their com-

munities (Logan and Molotch, 1987), which include amenities and services, such as stores, parks, 

and transit, and informal networks for support and social control. As both a cause and an effect 

of these amenities, homeowners may have greater self-identification with their community. They 

may be more likely to form relationships with neighbors and get involved in community affairs to 

maintain residential stability, community conditions, and sense of self. Finally, staying in a place 

longer allows more time for social ties to flourish, because one has a better chance of getting to 

know one’s neighborhood and neighbors.

The Role of the Housing Market Cycle

The recent U.S. housing market cycle, spanning from the mid-2000s through the early 2010s, 

provides a rare opportunity to explore the role that changing economic conditions and residential 

investment may play in shaping the social benefits of homeownership, particularly because the 

fluctuations in home prices and foreclosure rates across this recent cycle were so dramatic. Real 

estate economists typically define the housing market cycle as taking place in four stages, although 

their drivers and components are still debated (Mueller, 1995). The first stage is a recovery stage 

characterized by declining housing vacancies and stagnant housing construction. The second 

stage is an expansion stage characterized by continued declines in housing vacancies and increases 

in housing construction. The third and fourth stages are defined by hypersupply and eventual 

recession, with increasing housing vacancies and continued new housing construction in the short 

term—the third stage—but declines in new housing construction and increases in vacancies in the 

long term—the fourth stage (Mueller, 1995).

Cityscape 219

Pfeiffer and Morris

Stages of the Recent Housing Market Cycle

During the recent housing market cycle, the expansion and hypersupply stages—which we amal-

gamate to call the expansion stage—spanned from 2004 to 2006 in most U.S. regional housing 

markets, although in some places the stage began as early as 2003 and extended to as late as 2007. 

Falling interest rates and new mortgage products sparked rapid investment in housing. The median 

sales price of existing single-family homes increased from about $190,000 to $220,000 from 2004 

to 2006 (JCHS, 2007). The percentage of homes that were owner-occupied reached an all-time 

high of 69 percent in 2004 (JCHS, 2007). 

The economic recession officially spanned from late 2007 to mid-2009. However, in some regional 

markets, the housing downturn lasted through 2010 and 2011. This period was initially defined 

by rising home prices, then by growing unemployment, declining income and housing demand, 

and resetting mortgage loan interest rates. These conditions culminated in a recession and caused 

widespread disinvestment in housing, with high rates of foreclosures. About $2.5 trillion in home 

equity was lost in 2007 and 2008 (JCHS, 2009), and nearly 7 million households were displaced 

by 2014 (CoreLogic, 2014). By 2009, about 11.3 million homeowners owed more on their 

mortgages than what their homes were worth ( under water); about 5 million homeowners had 

loan-to-value ratios that exceeded 125 percent (JCHS, 2011). 

During the recovery period from 2010 to 2013, a mix of lower home prices and stringent mortgage 

lending increased the role of investors in neighborhood housing markets. In 2004, companies 

bought less than 1 percent of single-family homes for sale. By 2012, this share had increased to 

more than 6 percent, with rates reaching more than 10 percent in Sun Belt metropolitan areas such 

as Phoenix and Las Vegas (Molloy and Zarutskie, 2013). These actors ranged from local mom-and-

pop operations, such as individuals or family trusts, to out-of-state Wall Street corporations, such 

as private equity firms, hedge funds, and real estate investment trusts. Investor activity helped 

to bolster property values. The number of the 100 largest metropolitan areas nationwide posting 

annual increases in home prices grew from 73 in 2012 to 97 in 2013 (JCHS, 2013). The number 

of underwater homeowners declined from 11.3 million in 2009 to 6.5 million by the end of 2013 

(JCHS, 2011, 2013). Home prices in many metropolitan areas, however, remained below their 

mid-2000 peak (JCHS, 2013). 

Expectations on Civic and Social Engagement by Tenure During the Housing 

Market Cycle

Four potential hypotheses exist about how civic and social engagement may vary by tenure during 

the housing market cycle. 

1. Differences in engagement may fluctuate, assuming that concern for property values is driving 

any difference between homeowners’ and renters’ civic and social engagement. This fluctuation 

is because the value of the assets homeowners seek to protect vary during the cycle, so concern 

for protecting their value also varies. 

2. Differences in engagement may remain constant if the driver behind differences between 

homeowners’ and renters’ civic and social engagement is homeowners’ greater residential 

stability, as stability does not vary as much as property values during the housing market cycle.

220 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

3. Differences in engagement may not be present if, contrary to prior research, no actual 

differences exist in homeowners’ and renters’ commitment to community despite homeowners’ 

concern with property values or differences in residential stability.

4. Differences in engagement may manifest and fluctuate in unpredicted ways due to an unknown 

factor affecting civic and social engagement by tenure.

We explore these hypotheses in further detail in the following sections and summarize their 

expected patterns in exhibit 1.

Our preferred hypothesis is hypothesis 1. Hypothesis 1 assumes that homeowners may be more 

civically and socially engaged than renters because they seek to protect their property values by 

improving neighborhood quality through civic and social engagement. Property values fluctuate 

during the housing market cycle, as previously discussed. If homeowners’ concern with their 

property values contributes to their civic and social engagement, then the intensity of homeowners’ 

civic and social engagement also should vary during the housing market cycle as prices vary. Rent-

ers are not directly invested in their properties, so their civic and social engagement should occur 

more independently of property value dynamics. Thus, if homeowners’ civic and social engage-

ment fluctuates, but renters’ engagement remains relatively constant, differences in civic and social 

engagement by tenure should vary during the housing market cycle. 

We have two subhypotheses on how these dynamics might manifest. Our first, and preferred, 

prediction (hypothesis 1a) is derived from rational utility maximization behavior.1 This theory 

suggests first that homeowners should be more engaged in protecting the value of their properties, 

Exhibit 1

Hypotheses on Civic and Social Engagement by Tenure During the Housing Market 

Cycle

Predicted Effect of Homeownership  

on Civic and Social Engagement 

Constant Effect 

Additional Effects Within the Stages of the 

Influencing  

Hypothesis

Across the  

Housing Market Cycle

Factor

Housing Market 

Cycle

Expansion

Recession

Recovery

1a

Positive

Positive 

Negative  

Less negative  

Change in  

or zero

or zero

property values

1b

Positive

Negative  

Positive

Less positive  

Change in  

or zero

or zero

property values

2

Positive

Zero

Zero

Zero

Lower residential 

mobility

3

Zero

Zero

Zero

Zero

None

4

Negative

Positive, 

Positive, 

Positive, 

Unknown

negative,  

negative,  

negative, 

or zero

or zero

 or zero

1 For an intellectual history and critique of this fundamental tenet of neoclassical economics, see McCormick (1997).

Cityscape 221

Pfeiffer and Morris

because they have a greater financial stake in them than renters. Prior research generally finds that 

homeowners maintain their dwellings better than renters (Dietz and Haurin, 2003; Galster, 1983; 

Gatzlaff, Green, and Ling, 1998); the most obvious reason is that homeowners reap the financial 

benefits of home maintenance and improvements when their properties sell, whereas renters do 

not. If the protection of asset values does indeed drive homeowner concern for the dwelling and its 

environs, the intensity of these concerns should vary as the value of the asset fluctuates. 

High and rising, or at least stable, property values during the expansion stage of the recent housing 

market cycle should have led homeowners to feel confident in their investment and may have 

given them more incentive to preserve and further grow that investment by participating in local 

affairs and getting to know their neighbors. In short, one should work harder to protect something 

that is more valuable. Conversely, falling or depressed property values during the recession and 

recovery periods should have led homeowners to feel less incentive to protect their investment as 

it lost, or failed to fully regain, value. Thus, according to our preferred hypothesis, homeowners 

should have exhibited greater levels of civic and social engagement than renters during the expan-

sion stage of the cycle, when property values were high and rising or stable, and similar or lower 

levels of engagement during the recession and recovery stages of the cycle, when property values 

were falling or depressed (see exhibit 1). 

A second possible prediction (hypothesis 1b) is derived from the theory of satisficing behavior, 

which suggests that individuals may have a minimum outcome they seek to achieve but will cease 

efforts to maximize their returns when that minimum acceptable outcome is attained (Simon, 

1955, 1956). In our case, homeowners may seek to improve their property values if they fall below 

a certain minimum threshold but lose interest in continuing to improve their property values once 

they rise above this threshold. Falling or depressed property values during the recession and recov-

ery stages of the recent housing market cycle may have led homeowners to become more civically 

and socially engaged in an effort to curtail further declines and restore prior levels. However, high 

and rising or stable property values during the expansion period may have restored homeowners’ 

confidence about their property values and led them to lose interest in civic and social engagement. 

If this hypothesis has merit, homeowners should have exhibited greater levels of civic and social 

engagement than renters during the recession and recovery stages of the cycle, when property 

values were falling or depressed, and similar or lower levels of engagement during the expansion 

stage of the cycle, when property values were high and rising or stable (see exhibit 1).

The second reason (hypothesis 2) why homeowners may be more civically and socially engaged 

than renters is that they tend to stay in place longer, which may lead them to become more 

attached to their communities and want to improve them through civic and social engagement. 

Homeowners’ residential mobility is arguably less strongly linked to the housing market cycle than 

their concern for their property values. Homeowners historically have had lower mobility than 

renters, even during the recent recession (Bucks and Bricker, 2015). Thus, if homeowners’ greater 

residential stability contributes to their civic and social engagement, some differences in civic and 

social engagement by tenure should remain constant during the housing market cycle. Thus, we 

would expect that homeowners on balance would always exhibit greater civic and social engage-

ment than renters, regardless of the stage of the housing market cycle, and that this difference 

should not change much across the cycle (see hypothesis 2 in exhibit 1).

222 Refereed Papers

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Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

Another possibility (hypothesis 3) is that neither property values nor residential mobility affect home-

owners’ civic and social engagement. There may be no difference in civic and social engagement by 

tenure; strong evidence of the link between civic engagement and tenure in the existing literature makes 

this scenario unlikely, although it is possible that homeowners are self-selected from people who have 

a preexisting tendency to be civically involved, and this trait may not vary across the housing market 

cycle. Moreover, the ambiguous evidence for links between ownership and neighborhood social engage-

ment suggest that no difference by tenure in terms of social engagement may be more plausible. In any 

event, in these cases, we would expect no difference in civic and social engagement by tenure during 

the housing market cycle (see hypothesis 3 in exhibit 1). 

A final possibility (hypothesis 4) is that an unknown factor may lead homeowners to have less civic 

and social engagement than renters across the stages of the housing market cycle. Homeownership may 

have a positive, negative, or statistically insignificant varying effect on engagement during the housing 

market stages depending on what this unknown factor is. Hypothesis 4 is an exploratory hypothesis, 

as no strong theoretical basis currently exists for another contributing factor to differences in civic and 

social engagement by tenure.

Thus, the relationship between differences in civic and social engagement by tenure and the housing 

market cycle is potentially complex, with countervailing tendencies depending on which causal factors 

(financial incentives versus residential stability) are paramount in causing homeowners’ observed high 

levels of civic and social engagement and which principle of behavioral economics (rational utility 

maximization versus satisficing) guides homeowners’ decisionmaking. Nevertheless, we expected to 

find that homeowners had greater civic and social engagement than renters during the expansion stage 

of the cycle and lower engagement during the recession and recovery stages of the cycle due to owners’ 

incentive to protect their monetary investment through rational utility maximization behavior, which 

would lead to more efforts to protect that investment when it is more valuable.

Data and Methods

We tested our preferred hypothesis against the four competing hypotheses using pooled data from the 

2003 to 2013 ATUS (Bureau of Labor Statistics, 2014; Hofferth, Flood, and Sobek, 2013). The ATUS 

provides detailed, micro-level information on how a representative sample of noninstitutionalized 

people ages 15 and older living in the United States spend their time. The ATUS selects its respondents 

from the pool of people who have completed the Census Bureau’s and Bureau of Labor Statistics’ Cur-

rent Population Survey (U.S. Census Bureau, n.d.). The ATUS selects a stratified sample from this group 

(stratifying by state of residence, race and ethnicity, and household structure). Of those contacted by the 

CPS, 90 percent participate in that survey, an extremely high response rate. In turn, about 50 percent of 

those CPS respondents agree to participate in the ATUS, yielding an aggregate response rate of roughly 

45 percent; this response rate is quite high by the standards of survey research, suggesting limited prob-

lems due to response bias. The valid sample size per year is approximately 13,500 individuals; thus, in 

our models, we observe a very large sample of more than 133,633 individuals.2

2 Due to missing data, 10 percent of the observations were excluded.

Cityscape 223

Pfeiffer and Morris

The ATUS is primarily conducted by telephone, but special efforts are made to reach those who 

are impossible to contact by phone (about 5 percent of the sample). Surveys are conducted in 

English and Spanish. The ATUS generates sampling weights to reflect the fact that some groups (for 

example, those reporting weekend days) are oversampled. 

Professional interviewers, who guide respondents in reconstructing the day prior to the interview, 

gather the ATUS data. The use of trained interviewers to code the activities greatly reduces pos-

sible error. Interviewers code where each activity took place (with 26 separate codes for places 

and travel modes), the times the activity took place, and with whom the activity was conducted 

(broken down into 24 types of companion). Our research particularly makes use of the “neighbors, 

acquaintances” category, which best reveals respondents’ level of community connectedness. The 

interviewers also assign each activity to one of more than 460 activity types, providing a very fine-

grained look at time-use patterns. These categories cover a range of civic and social activities that 

are underrepresented in existing research on the social benefits of homeownership. Civic activities 

include attending government hearings and sessions and meeting with political representatives, 

whereas volunteering activities include undertaking unpaid work for a formal institution, such as 

church or charity. The rest of the activity categories are self-explanatory. The ATUS collects reason-

ably rich demographic data, including variables commonly used in social science model specifica-

tions (age, race and ethnicity, sex, income, employment status, health, and so on). Also collected is 

housing tenure, one of our variables of interest.

Examining civic and social engagement with the ATUS data has several limitations. The first relates 

to variable construction. Our measures of social activities performed with neighbors include activi-

ties undertaken with acquaintances, because the ATUS combines neighbors and acquaintances 

together in one category. Moreover, the ATUS includes a separate category, “friends,” which we 

exclude from our measure. The respondent determines whether a companion is categorized as a 

“neighbor, acquaintance” or a “friend.” Thus, activities undertaken with nonneighbor acquain-

tances, or especially friendly neighbors, may not be included in the analysis. These factors should 

not introduce systemic bias into our results unless patterns of socialization with neighbors and 

acquaintances or the predisposition to label people as friends or neighbors and acquaintances vary 

based on tenure or the stage of the housing market cycle. 

A second limitation relates to the fact that we are observing only a single day in the lives of 

participants. Obviously, many respondents may have had atypical days on the study day. However, 

in a sample as large as ours, it can be expected that this noise will cancel out. The size of the 

sample also means that we are able to capture a reasonable number of instances of participation in 

rare activities. However, some of the activities that we planned to observe are excluded from the 

following analysis, simply because they are so rare. These rare activities were instances of socially 

engaging with neighbors while watching sports, providing care, attending arts or entertainment 

events, participating in school extracurricular activities, and attending volunteering meetings.

We classified respondents as either homeowners, meaning they lived in an owner-occupied house-

hold, or renters, meaning they did not live in an owner-occupied household. Homeowners were 73 

percent of our sample, which is slightly higher than the national homeownership rate during the 

224 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

study period (about 65 to 69 percent; Schwartz, 2015). We accounted for the stage of the hous-

ing market cycle during which the respondent was interviewed by defining 2003 to 2006 as the 

expansion stage, 2007 to 2009 as the recession stage,3 and 2010 to 2013 as the recovery stage. 

We used a logit model to determine the demographic, temporal, and geographic characteristics 

associated with being a homeowner. Homeownership was more common among respondents who 

were interviewed prior to the recession; older; married and living with their spouse; a parent of 

children under 18; non-Hispanic White; born in the United States; higher income; employed; and 

living in the Midwest or South. These associations were all as expected. These estimates are not 

reported in the exhibits in order to conserve space but are available on request. 

We assessed civic and social engagement by tenure during the housing market cycle using descrip-

tive statistics and a two-stage Cragg hurdle model (Cragg, 1971). The Cragg model is useful in 

time-use research, primarily because it copes with the censoring of activity times at zero (most 

respondents do not participate in most activities on any given day) and because the model assumes 

that separate psychological processes determine, first, whether an individual decides to participate 

in an activity, and then second, how long he or she participates in the activity conditional on 

having decided to undertake it. For example, it is reasonable to assume that a person who attends 

community meetings frequently does not necessarily spend a longer period of time at those meet-

ings than a person who attends them infrequently. This feature makes the Cragg technique superior 

to other models used for dealing with censored data, such as forms of tobit regression. 

The Cragg method involves the calculation of two separate models. First, a probit model is used 

to identify the factors that help determine whether respondents participate in a particular activity 

on any given day; these factors include the demographic control variables and the independent 

variables of interest in this study (homeownership and stage of the housing market cycle). Second, 

a truncated linear or exponential model is used to determine the factors that contribute to activity 

duration for those who engaged in the activity. We use exponential models in this research, because 

nearly all time uses are positively skewed for those who undertook them. Most people participate 

in the activities for relatively short periods of time, whereas a small number of people participate 

for long amounts of time, yielding a long tail to the right for the distribution. 

Separate coefficients and  t-statistics are generated for each part of the model. Because the coef-

ficients are difficult to interpret, we generate predictions for the probability of engaging in the 

activity and the conditional duration of activity participation (activity time, provided the activity 

is engaged in) for homeowners and renters in all three time periods, holding the demographic 

control variables at their means. Further, we amalgamate these outcomes to produce predicted 

unconditional activity times, or the average amount of time per day homeowners and renters in all 

three periods are predicted to spend on our civic and social activities. 

As has been noted, the main variables of interest in the models are whether or not a respondent 

was a homeowner and whether or not a respondent was interviewed in the expansion, recession, 

or recovery period. We interacted tenure with the stage of the housing market cycle to assess how 

3 This period, which coincides with the official duration of the recession (December 2007 through June 2009) as defined by the National Bureau of Economic Research, is commonly used to study the effects of the recession on neighborhoods, housing, and social life, because it allows for geographic variability in timing of the recession. 

Cityscape 225

Pfeiffer and Morris

the differential effect of homeownership varied during the housing market cycle. We compared 

these effects with our preferred hypothesis and four competing hypotheses to determine which 

hypothesis best fits the pattern from our results. 

Results

In this section, we evaluate the results to determine whether they favor our preferred hypothesis—

that homeowners have lower levels of engagement than renters during the recession stage of the 

cycle, when property values are falling. First, we use  t-tests of differences in proportions and means to explore changes in respondents’ civic and social engagement during the housing market cycle, 

regardless of tenure. Then, we assess results from the Cragg models to determine whether being 

a homeowner has a differential effect on the likelihood and extent of civic and social engagement 

during the stages of the housing market cycle.

Civic and Neighborhood Social Engagement During the Housing Market Cycle

Civic and neighborhood social engagement was not common among the respondents during the 

stages of the housing market cycle (exhibit 2). Volunteering was the most commonly reported 

activity of the civic engagement variables. About 6 to 7 percent of the respondents volunteered 

on the day prior to their interview across the stages, typically for about 2 hours. Respondents’ 

unconditional average time spent volunteering on a given day, including participants and nonpar-

ticipants in the activity, was between 8 and 9 minutes. Less than 0.5 percent of the respondents 

engaged in civic activities, typically for about an hour, or an unconditional average of about 0.2 

minutes per day. About 4 to 5 percent of the respondents participated in social activities with their 

neighbors (110 conditional minutes and 4 to 7 unconditional minutes spent). The most common 

type of social activity was informal socializing with neighbors (2 to 3 percent of the respondents 

participating, an average of 2 to 3 unconditional minutes spent).

There were modest differences in respondents’ civic and social engagement during the stages of 

the housing market cycle. Asterisks in exhibit 2 denote when the likelihood or average minutes of 

engagement in one stage was statistically different enough from the other two stages at the 5-percent 

level or higher. Respondents were more likely to volunteer during the recession stage. About 7 percent 

of respondents interviewed during the recession stage volunteered on the previous day compared 

with about 6 percent of respondents interviewed during the expansion and recovery stages. 

Respondents spent an average of 9 unconditional minutes volunteering during the recession stage 

compared with 8 unconditional minutes during the expansion and recovery stages. Respondents 

also were more likely to participate in social activities with neighbors and spent more time on these 

activities during the recovery stage. About 5 percent of respondents interviewed during the recovery 

stage participated in social activities with neighbors on the previous day compared with about 

4 percent of respondents interviewed during the expansion and recession stages. Furthermore, 

participants spent more time on social activities with neighbors during the recovery stage than the 

expansion or recession stages (126 conditional and 7 unconditional minutes spent compared with 

105 conditional and 4 unconditional minutes spent respectively). Most of the disaggregated social 

activities with neighbors were more intensively engaged in during the recovery stage (eating and 

drinking, playing sports, attending religious activities, volunteering, socializing, and traveling). 

226 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

 **

**

 **

**

**

**

**

0.1         

8.4          

6.6        

0.7           

0.7         

0.1         

0.7          

0.9          

2.9           

0.4           

verage 

Total 

(0.0)

(0.3)

(0.2)

(0.1)

(0.1)

(0.0)

(0.1)

(0.1)

(0.1)

(0.0)

Minutes

ing the 

A

 *

**

**

verage 

70.1      

(20.2)

(3.2)

(3.1)

(3.7)

(6.6)

(8.4)

(7.4)

(8.2)

(3.5)

(5.3)

137.4      

125.9         

62.9       

110.3       

43.8         

125.4            

136.2      

103.1         

42.8       

Recovery

A

Minutes for 

Participants

 **

 **

**

 **

 *

 **

 **

 **

 **

cent 

0.2        

(0.0) 6.1         

(0.1) 5.2        

1.1           

(0.1) 0.6          

(0.1) 0.2           

(0.0) 0.6          

(0.0) 0.7        

(0.0) 2.8           

(0.1) 0.9       

(0.1)

Per

 (0.1)

Engaging 

 *

**

**

 *

 **

 **

0.1          

9.3         

4.1            

0.4           

0.4          

0.1         

0.3       

0.8        

1.8         

0.3        

verage 

Total 

(0.0)

(0.3)

(0.2)

(0.0)

(0.1)

(0.0)

(0.0)

(0.1)

(0.1)

(0.0)

A

Minutes

**

 **

verage 

57.1       

(17.8)

(3.6)

(3.9)

(4.0)

(7.4)

(4.4)

(5.6)

131.6         

105.1       

54.4         

105.7       

(11.6) 54.9         

(18.1) 108.8       

141.8        

(13.1) 80.9         

38.3        

Recession

A

Minutes for 

Participants

 **

**

 **

 *

 **

**

cent 

0.2        

(0.0) 7.1        

(0.2) 3.9          

(0.1)

0.6         

(0.1) 0.4           

(0.0) 0.2        

(0.0) 0.2       

(0.0) 0.5        

(0.1) 2.2          

(0.1) 0.7        

(0.1)

Per

. 

Engaging 

r higher

 **

 *

 *

 **

**

 **

0.2          

8.4         

4.1        

0.5        

0.4        

0.1         

0.3         

0.5           

2.0         

0.3          

TUS-X Extract Builder

verage 

Total 

(0.0)

(0.2)

(0.2)

(0.0)

(0.1)

(0.0)

(0.0)

(0.1)

(0.1)

(0.0)

A

Minutes

. Standard errors in parentheses. Asterisks denote when the likelihood or average minutes of engagement in one 

*

**

**

verage 

71.5            

(22.1)

(2.7)

(3.1)

(3.7)

(6.9)

(9.4)

(9.4)

(4.4)

126.1         

105.5         

61.4      

119.3        

(11.3) 53.2       

114.4       

125.7        

82.5       

(3.5) 43.4       

Expansion

A

Minutes for 

Participants

**

 **

 **

 **

 **

 **

cent 

0.2         

(0.0) 6.6         

(0.1) 3.9           

(0.1)

0.7        

(0.0) 0.4       

(0.0) 0.2          

(0.0) 0.3        

(0.0) 0.4      

(0.0) 2.4         

(0.1) 0.6         

(0.0)

verage Daily Likelihood and Minutes of Civic and Neighborhood Social Engagement Dur

Per

Engaging 

vey sampling design and weights accounted for

eligious events

ences in Participants’ A

Types of Engagement

fer

(sum of time spent on activities 

below) Eating and drinking

Playing sports

Shopping

Attending r

Volunteering

Socializing

Travel

Exhibit 2

Dif

Housing Market Cycle

Civic activities with anyone

Volunteering activities with anyone

Social activities with neighbors 

* p < 0.05. ** p < 0.01. 

Notes: n = 133,633. Sur

stage is statistically different enough from the other two stages at the 5-percent level o

Sources: Bureau of Labor Statistics (2014); Hofferth, Flood, and Sobek (2013); A

Cityscape 227

Pfeiffer and Morris

Effects by Tenure During the Housing Market Cycle

We next conducted the two-stage Cragg hurdle regression analysis to examine whether being a 

homeowner has an effect on the likelihood and duration of civic and social engagement during the 

housing market cycle, controlling for other factors. Statistically significant results are shaded in 

light gray (see exhibits 3 and 4). Estimates for controls included in the models are suppressed but 

listed on bottoms of exhibits 3 through 7. These estimates are available on request.

Exhibit 3

Effect of Being a Homeowner on Participants’ Average Daily Likelihood of Civic and 

Neighborhood Social Engagement During the Housing Market Cycle

Interaction Effect Model                         

(Homeownership Separately Interacted  

With Recession and Recovery)

Effect of Being 

Additional 

Additional 

Best Fit 

a Homeowner 

Effect of Being  Effect of Being 

Types of  

Hypothesis 

on Likelihood  Effect of Being  a Homeowner  a Homeowner 

Engagement

(Refer to 

of Engaging in  a Homeowner 

During the 

During the 

Exhibit 1)

Activity

on Likelihood 

Recession 

Recovery 

of Engaging in 

Period 

Period 

Activity

Relative to 

Relative to 

the Expansion  the Expansion 

Period

Period

Civic activities with 

0.044              

0.118                   

–  0.123                 

–  0.096                  

3

anyone

(0.073)

(0.113)

(0.157)

(0.150)

Volunteering activities 

0.104           **

0.124               **

–  0.035                 

–  0.028                   

2

with anyone

(0.022)

(0.031)

(0.049)

(0.042)

Social activities with 

0.013         

0.006                   

0.061                   

–  0.014                

3

neighbors (sum 

(0.021)

(0.032)

(0.047)

(0.041)

of time spent on 

activities below)

Eating and drinking

–  0.035             

0.015                   

0.070                  

–  0.120                 

3

(0.036)

(0.058)

(0.084)

(0.076)

Playing sports

–  0.004             

–  0.074                 

0.185                 

0.063                 

3

(0.051)

(0.072)

(0.120)

(0.096)

Shopping

–  0.060             

–  0.075                      

0.129                  

–  0.020                     

3

(0.055)

(0.099)

(0.140)

(0.138)

Attending religious 

0.090           *

0.219                 **

0.008                     

–  0.205                      *

2

events

(0.041)

(0.071)

(0.103)

(0.098)

Volunteering

0.032             

0.039                    

0.008                        

–  0.017                               

3

(0.053)

(0.091)

(0.132)

(0.115)

Socializing

–  0.002              

0.017                    

0.020                     

–  0.049                         

3

(0.025)

(0.036)

(0.053)

(0.048)

Travel

–  0.072              *

–  0.071                    

0.064                         

–  0.032                     

4

(0.035)

(0.057)

(0.089)

(0.074)

* p < 0.05. ** p < 0.01.  

Notes: n = 133,633. Survey sampling design and weights accounted for. Standard errors in parentheses. Demographic, socioeconomic, temporal, and geographic characteristics associated with homeownership controlled but estimates suppressed. 

Controls included stage of housing market cycle (expansion stage omitted), age, sex, household income (adjusted by household size), education, race and ethnicity, nativity status, marital status, number of children, employment status, geographic type (urban/rural), and region. 

Sources: Bureau of Labor Statistics (2014); Hofferth, Flood, and Sobek (2013); ATUS-X Extract Builder

228 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

Exhibit 4

Effect of Being a Homeowner on Participants’ Conditional Average Daily Minutes of Civic 

and Neighborhood Social Engagement During the Housing Market Cycle

Interaction Effect Model                         

(Homeownership Separately Interacted  

With Recession and Recovery)

Effect of Being 

Additional 

Additional 

Best Fit 

a Homeowner 

Effect of Being  Effect of Being 

Types of  

Hypothesis 

on Minutes 

Effect of Being  a Homeowner  a Homeowner 

Engagement

(Refer to 

Spent on  

a Homeowner 

During the 

During the 

Exhibit 1)

Activity

on Minutes 

Recession 

Recovery 

Spent on  

Period 

Period 

Activity

Relative to 

Relative to 

the Expansion  the Expansion 

Period

Period

Civic activities with 

–  0.600                 *

–  1.318              **

1.143                 *

0.935                

4

anyone

(0.245)

(0.402)

(0.567)

(0.495)

Volunteering activities 

–  0.058        

–  0.052                 

0.009                   

–  0.019                   

3

with anyone

(0.048)

(0.065)

(0.112)

(0.089)

Social activities with 

–  0.173             **

–  0.189               **

0.009                     

0.028                 

4

neighbors (sum 

(0.048)

(0.072)

(0.112)

(0.093)

of time spent on 

activities below)

Eating and drinking

–  0.191         **

–  0.173                 

–  0.054                 

–  0.014                    

4

(0.063)

(0.131)

(0.168)

(0.153)

Playing sports

–  0.031               

0.148                    

–  0.088                        

–  0.284                            

3

(0.088)

(0.145)

(0.217)

(0.171)

Shopping

0.014           

0.347                   

–  1.093                *

–  0.237                  

3

(0.204)

(0.323)

(0.435)

(0.387)

Attending religious 

0.020             

–  0.177                 

0.240                   

0.241                    

3

events

(0.098)

(0.170)

(0.250)

(0.183)

Volunteering

–  0.313            *

0.012                    

–  0.509                  

–  0.373                        

4

(0.127)

(0.185) 

(0.308)

(0.209)

Socializing

–  0.176         **

–  0.291                **

0.090                       

0.199                   

4

(0.057)

(0.089)

(0.133)

(0.116)

Travel

–  0.002           

0.090                       

–  0.110                               

–  0.129                   

3

(0.123)

(0.170)

(0.222)

(0.207)

* p < 0.05. ** p < 0.01.  

Notes: n = 133,633. Survey sampling design and weights accounted for. Standard errors in parentheses. Demographic, socioeconomic, temporal, and geographic characteristics associated with homeownership controlled but estimates suppressed. 

Controls included stage of housing market cycle (expansion stage omitted), age, sex, household income (adjusted by household size), education, race and ethnicity, nativity status, marital status, number of children, employment status, geographic type (urban/rural), and region. 

Sources: Bureau of Labor Statistics (2014); Hofferth, Flood, and Sobek (2013); ATUS-X Extract Builder

Two observations are in order. First, homeownership was in most cases not significantly associated 

with propensities to engage in civic and social activities after controlling for factors associated with 

being a homeowner. Homeownership was, however, significantly associated with a higher propen-

sity to volunteer (see exhibit 3). The typical homeowner had a 5- to 6-percent chance of reporting 

volunteering on the previous day during the stages of the housing market cycle compared with a 

4- to 5-percent chance for the typical renter (predictions generated using marginal effects at the 

means of the control variables; see exhibit 5). Although homeowners spent less time volunteering, 

Cityscape 229

Pfeiffer and Morris

Exhibit 5

Predicted Daily Likelihood of Civic and Neighborhood Social Engagement for 

Homeowners and Renters During the Housing Market Cycle

Predicted Probability of Engaging in Activity (%)

Types of 

Expansion

Recession

Recovery

Engagement

Typical 

Typical 

Typical 

Typical 

Typical 

Typical 

Homeowner 

Renter 

Homeowner 

Renter 

Homeowner 

Renter 

Civic activities 

0.2

0.1

0.2

0.2

0.2

0.2

with anyone

Volunteering 

5.8

4.5

6.1

5.1

5.1

4.2

activities with 

anyone

Social activities 

3.8

3.8

3.9

3.4

4.9

5.0

with neighbors 

(sum of time 

spent on 

activities below)

Eating and 

0.6

0.6

0.6

0.5

0.9

1.1

drinking

Playing sports

0.3

0.4

0.3

0.2

0.5

0.5

Shopping

0.2

0.2

0.2

0.1

0.2

0.3

Attending 

0.3

0.1

0.2

0.1

0.5

0.5

religious 

events

Volunteering

0.3

0.3

0.5

0.4

0.6

0.5

Socializing

2.4

2.4

2.3

2.1

2.6

2.8

Travel

0.5

0.6

0.5

0.5

0.7

0.9

Notes: n = 133,633. Survey sampling design and weights accounted for. Standard errors in parentheses. "Typical" denotes a person at the mean for all control variables except tenure. Demographic, socioeconomic, temporal, and geographic characteristics associated with homeownership controlled but estimates suppressed. Controls included stage of housing market cycle (expansion stage omitted), age, sex, household income (adjusted by household size), education, race and ethnicity, nativity status, marital status, number of children, employment status, geographic type (urban/rural), and region. Predictions are based on mariginal effects at the means. All estimates are statistically significant at the 1-percent level and higher. 

Sources: Bureau of Labor Statistics (2014); Hofferth, Flood, and Sobek (2013); ATUS-X Extract Builder

when they did engage in it (see exhibit 4), in total these two effects translate into a meaningful 

additional amount of time spent volunteering by homeowners across the cycle (on average, about 

1.25 minutes a day, or 38 minutes a month, a nontrivial amount; see exhibit 7). Homeowners 

also were more likely to attend religious events with neighbors (see exhibit 3) and spent much 

more time in total doing so, particularly in the expansion and recession phases of the cycle. These 

findings are consistent with evidence of the civic benefits of homeownership reported by existing 

research, considering that some scholars group religious activities with civic activities (DiPasquale 

and Glaeser, 1999). 

Homeownership versus renting was not associated with the propensity to participate in social 

activities with neighbors, but homeownership was associated with less time spent on social activi-

ties for participants (see exhibits 3 and 4). Renters on average were predicted to spend about 45 

more seconds a day (24 minutes a month) on social activities with their neighbors (see exhibit 7). 

230 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

Exhibit 6

Predicted Daily Conditional Minutes of Civic and Neighborhood Social Engagement 

for Homeowners and Renters During the Housing Market Cycle

Predicted Minutes Spent on Activity

Types of 

Expansion

Recession

Recovery

Engagement

Typical 

Typical 

Typical 

Typical 

Typical 

Typical 

Homeowner 

Renter 

Homeowner 

Renter 

Homeowner 

Renter 

Civic activities 

40

152

40

47

48

70

with anyone

Volunteering 

153

161

157

164

164

177

activities with 

anyone

Social activities 

108

130

110

132

137

161

with neighbors 

(sum of time 

spent on 

activities below)

Eating and 

56

67

52

65

62

75

drinking

Playing sports

107

92

104

98

106

121

Shopping

67

47

42

88

43

38

Attending 

111

133

108

101

124

116

religious 

events

Volunteering

143

141

134

220

152

218

Socializing

78

105

81

99

108

118

Travel

41

37

38

39

37

39

Notes: n = 133,633. Survey sampling design and weights accounted for. Standard errors in parentheses. "Typical" denotes a person at the mean for all control variables except tenure. Demographic, socioeconomic, temporal, and geographic characteristics associated with homeownership controlled but estimates suppressed. Controls included stage of housing market cycle (expansion stage omitted), age, sex, household income (adjusted by household size), education, race and ethnicity, nativity status, marital status, number of children, employment status, geographic type (urban/rural), and region. Predictions are based on mariginal effects at the means. All estimates are statistically significant at the 1-percent level and higher. 

Sources: Bureau of Labor Statistics (2014); Hofferth, Flood, and Sobek (2013); ATUS-X Extract Builder

Homeownership was associated with a lower likelihood of engaging in travel with neighbors and 

less time spent socializing, volunteering, and eating and drinking with neighbors, conditional on 

performing these activities.4 No other meaningful differences emerged between homeowners’ and 

renters’ civic and social engagement across the housing cycle (see exhibits 3, 4, and 7). Taken as 

whole, our findings on neighborhood civic engagement confirm prior research, but our findings on 

social time run counter to the narrative in some of the literature that homeowners are more socially 

engaged in their communities than renters.

4 It is possible that the effect of homeownership on traveling is a false negative or artifact rather than a true effect. 

Researchers risk obtaining false positive or negative results when they model many effects. We have no expectations on what the effect of homeownership should be on traveling with neighbors, as the activity is not usually studied in existing research on the social benefits of homeownership.

Cityscape 231

Pfeiffer and Morris

Exhibit 7

Predicted Daily Unconditional Minutes of Civic and Neighborhood Social Engagement 

for Homeowners and Renters During the Housing Market Cycle

Predicted Minutes Spent on Activity

Types of 

Expansion

Recession

Recovery

Engagement

Typical 

Typical 

Typical 

Typical 

Typical 

Typical 

Homeowner 

Renter 

Homeowner 

Renter 

Homeowner 

Renter 

Civic activities 

0.1

0.2

0.1

0.1

0.1

0.1

with anyone

Volunteering 

8.9

7.2

9.6

8.4

8.4

7.3

activities with 

anyone

Social activities 

4.1

4.9

4.3

4.5

6.8

8.1

with neighbors 

(sum of time 

spent on 

activities below)

Eating and 

0.4

0.4

0.3

0.3

0.5

0.8

drinking

Playing sports

0.3

0.3

0.4

0.2

0.5

0.6

Shopping

0.1

0.1

0.1

0.1

0.1

0.1

Attending 

0.3

0.2

0.3

0.1

0.6

0.6

religious 

events

Volunteering

0.5

0.4

0.6

0.9

0.9

1.2

Socializing

1.9

2.5

1.9

2.1

2.8

3.4

Travel

0.2

0.2

0.2

0.2

0.3

0.3

Notes: n = 133,633. Survey sampling design and weights accounted for. Standard errors in parentheses. "Typical" denotes a person at the mean for all control variables except tenure. Demographic, socioeconomic, temporal, and geographic characteristics associated with homeownership controlled but estimates suppressed. Controls included stage of housing market cycle (expansion stage omitted), age, sex, household income (adjusted by household size), education, race and ethnicity, nativity status, marital status, number of children, employment status, geographic type (urban/rural), and region. Predictions are based on mariginal effects at the means. All estimates are statistically significant at the 1-percent level and higher. 

Sources: Bureau of Labor Statistics (2014); Hofferth, Flood, and Sobek (2013); ATUS-X Extract Builder

Second, differences in the likelihood of civic and social engagement by tenure usually did not vary 

during the stages of the housing market cycle. Thus, hypothesis 3, that no difference exists in civic 

and social engagement by tenure during the housing market cycle, is most supported by our analysis 

(see exhibit 1 and the rightmost column of exhibit 3). Of the 20 interaction terms between period 

and tenure, 19 are insignificant in our models predicting likelihood of activity participation, and 18 

of these terms were insignificant in the conditional time models. These outcomes strongly suggest 

that the effect of homeownership on community engagement changed little during the housing cycle. 

Two exceptions were that, relative to renting, homeownership was associated with (1) a modestly 

lower likelihood of attending religious events with neighbors in the recovery period and (2) less 

conditional time spent shopping during the recession period. However, these effects do not fit our 

expectations on how homeowners’ civic and social engagement should vary during the housing 

market cycle if changing property values influences their engagement. Specifically, hypotheses 1a  

232 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

and 1b do not predict that (1) homeowners have a lower propensity to engage in religious activities 

during recovery periods but not recession periods relative to expansion periods, or that (2) home-

ownership has no constant positive effect on conditional minutes spent shopping across the stages 

of the housing market cycle (see exhibit 1). It also is noteworthy that homeownership was associ-

ated with more conditional time participating in civic activities during the recession. However, 

being a homeowner had a statistically significant negative effect on civic time during the entirety 

of the cycle (hypothesis 4). This finding is contrary to the main thrust of existing research. 

Hypothesis 2, that homeownership has a constant, positive effect on civic and social engagement 

during the housing market cycle due to homeowners’ greater residential stability, is fully supported 

by our findings on volunteering and largely supported by our findings on attending religious 

events, as engagement in both activities is positively associated with homeownership across the 

housing cycle. However, our findings show that homeownership is associated with less time eating 

and drinking, volunteering, and socializing with neighbors, and less time in overall social engage-

ment with neighbors. These results best fit hypothesis 4. The reason why homeowners’ intensity of 

participation in neighborhood social life should be less than that of renters is unclear, but in any 

event this finding supports some prior research that calls into question the common assertion that 

homeownership builds stronger communities, at least to the extent that informal socialization is an 

important contributor to neighborhood cohesion. 

These nuances illustrate the complexity of dynamics between civic and social engagement and 

tenure. The fact that many of the effects for variables measuring the extent of civic and social engage-

ment by tenure do not match to our existing theories (hypotheses 1a, 1b, 2, and 3) reveals the need 

for additional theory building on this topic. However, these nuances should not overshadow two 

notable trends: (1) homeowners were more likely than renters to volunteer but spent less time on 

social activities with neighbors than renters, regardless of the housing market stage; and (2) differ-

ences in civic and social engagement by tenure did not vary in consistent or expected ways across 

the stages of the cycle.

Discussion

This research helps to fill a gap in the literature on the social benefits of homeownership by test-

ing whether (1) homeowners were more civically and socially engaged than renters with similar 

characteristics during a 10-year period, and (2) whether any differences between homeowners’ 

and renters’ civic and social engagement fluctuated during the recent housing market cycle. In this 

section, we discuss the key findings and suggest directions for future research and policymaking.

Like previous studies, we find that homeowners were more likely to volunteer than renters when 

accounting for demographic differences between homeowners and renters. Thus, it is possible that 

homeownership may have some sort of morally uplifting effect, influenced potentially by home-

owners’ greater residential stability, or that persons more likely to be engaged in volunteering are 

more likely to become homeowners. Even if an uplifting effect exists, it is important to note that 

we cannot disaggregate volunteering to see which volunteering activities specifically relate to the 

local community, and thus we cannot confirm that homeownership contributes to neighborhood 

public spiritedness. 

Cityscape 233

Pfeiffer and Morris

We find little reason to believe that homeowners make better neighbors, a finding that calls into 

question whether homeownership really does contribute to community life and positively affect 

the private provision of public goods. Contrary to some existing studies, we did not find that 

homeownership was associated with more social engagement with neighbors. Being a homeowner 

actually had a negative association with the overall time spent on neighborhood social engagement 

across the cycle. One explanation is that social engagement is less visible than civic engagement, 

so homeowners may feel fewer rewards from time invested in these activities. Another explanation 

is that homeowners simply have less time to get to know their neighbors than renters or find less 

value in doing so. Overall, our findings suggest that although there may be good reasons for the 

large subsidies for homeownership in the United States, promoting neighborhood cohesion may 

not be one of them.

Finally, we found that fluctuation in property values during the housing cycle may have little effect 

on the social benefits of homeownership, contrary to our expectations. Our preferred hypothesis 

(hypothesis 1a), which predicts a greater positive effect of homeownership on engagement during 

expansion periods, was wholly unsupported in the analysis. Hypothesis 1b, which predicts a 

greater positive effect of homeownership on engagement during recession periods, also lacked 

support. Our results on the likelihood of civic and social engagement by tenure most closely 

matched hypotheses 2 and 3, which predict a constant difference, or, more usually, a constant lack 

of difference in engagement by tenure during the housing market cycle. Our results on the extent 

of civic and social engagement by tenure also support hypothesis 4, which predicts a constant 

negative effect of homeownership on engagement during the housing market cycle unexplained by 

existing theory.

Further analyzing and explaining these different patterns, particularly for activities that vary in un-

expected ways during the housing market cycle, is a worthy direction for further research. Another 

important direction is to examine potential subgroup variation in civic and social engagement (Ho-

lupka and Newman, 2012; Newman and Holupka, 2013). For instance, broad evidence exists that 

the stages of the recent housing market cycle transformed low-income and minority homeowners 

and neighborhoods the most, which may lead these groups to exhibit different community engage-

ment patterns (Bocian et al., 2011; Rugh and Massey, 2010; Immergluck and Law, 2014). 

Exploring variation in the potential social benefits of homeowners’ greater civic engagement, for 

which we do find some evidence, is also worthwhile. To the extent that homeowners are volunteer-

ing for organizations that benefit their neighborhoods, homeowners’ civic engagement may make 

places more inclusive or exclusive and regions more equitable or inequitable. For example, civic 

engagement may contribute to exclusionary zoning or protests against mixed-income development 

coming into a neighborhood (Fischel, 2001). There are reasons to believe that homeowners’ civic 

engagement may be more exclusionary during recession periods, as homeowners may try to pro-

tect their property values and quality of life from people or land uses seen to diminish these public 

goods. Testing the link between more exclusionary civic engagement and recessions is a pressing 

pursuit. Because we cannot observe the character of neighbors or neighborhoods in our data set, 

we cannot comment on the effects of neighborhood homogeneity on civic and social engagement 

or on how the housing cycle may change that engagement over time. Future research should 

investigate these questions. 

234 Refereed Papers

Are Homeowners Better Neighbors During Housing Booms?  

Understanding Civic and Social Engagement by Tenure During the Housing Market Cycle

A final direction is to explore the drivers of changes in civic and social engagement during the 

housing market cycle, regardless of tenure. Homeowners and renters in our study were more likely 

to socialize with their neighbors, and spent more time socializing with them, during the recovery 

stage. One possible explanation is that, in the aftermath of the economic flux and residential transi-

tion of the recession, people tried more than in the other stages to get to know newcomers on their 

block, such as renters moving into former foreclosures.

Overall, it is important for housing scholars, planners, and policymakers to differentiate between 

civic and social engagement in conveying the social benefits of homeownership. Our finding that 

homeownership is associated with a higher likelihood of working to improve society through 

volunteering does suggest that homeownership may have benefits for the society as a whole, which 

in turn implies that public policy to promote homeownership has merits in this regard. In contrast, 

policies to promote homeownership may do little to maintain neighborhoods through the exercise 

of informal social control or improve sense of community. We do not find compelling evidence 

that the degree of intervention to promote homeownership should vary during the housing market 

cycle. 

Acknowledgments 

The authors thank the three anonymous reviewers and managing editor for their insightful com-

ments, which strengthened the contributions of this article.

Authors

Deirdre Pfeiffer is an assistant professor in the School of Geographical Sciences and Urban Planning 

at Arizona State University.

Eric A. Morris is an assistant professor in the Department of City and Regional Planning at Clem-

son University.

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238 Refereed Papers

A Research Note: The Housing 

Choice Voucher Program and 

Access to Opportunity in Florida’s 

Nonmetropolitan Areas 

Rebecca J. Walter

Ruoniu Wang 

The University of Texas at San Antonio

Abstract

Previous research on the Housing Choice Voucher program has found that recipients 

tend to be clustered in high-poverty, minority concentrated areas. Although these find-

ings are consistent across study areas, much of the research has been conducted in large 

metropolitan areas. This study accounts for all locations across the state of Florida, 

including small metropolitan, micropolitan, and rural areas, to examine if place mat-

ters for voucher holders’ ability to access high-opportunity neighborhoods. The findings 

reveal that voucher households in nonmetropolitan areas have lower incomes and tend 

to be younger with higher percentages of single mothers and families with children; 

however, nonmetropolitan voucher households are also less concentrated and access 

higher-opportunity neighborhoods. These differences in nonmetropolitan areas warrant 

the consideration of place for both future research and policy. 

Introduction

The Housing Choice Voucher (HCV) program provides housing assistance to more than 2 million 

low-income households and is the largest tenant-based rental housing subsidy in the nation. A 

national objective of the HCV program is to provide voucher holders with the ability to move to 

the location of their choice. A body of research has emerged that focuses on the importance of 

providing low-income households access to opportunity-rich neighborhoods that include, for ex-

ample, quality schools, access to employment, and healthy environmental conditions that facilitate 

self-sufficiency. Recent research documents that younger children who moved to higher-opportunity 

neighborhoods were more likely to attend college and experience greater earnings as adults 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 239

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

Walter and Wang

(Chetty, Hendren, and Katz, 2015). Nevertheless, previous nationwide studies have questioned 

the HCV program’s ability to deconcentrate poverty (Devine et al., 2003; McClure, Schwartz, and 

Taghavi, 2015) and provide low-income households the ability to move to neighborhoods with 

higher opportunity after receiving a voucher (Walter, Li, and Atherwood, 2015). 

A large body of research has found that voucher holders tend to be clustered in high-poverty, 

minority concentrated neighborhoods (Newman and Schnare, 1997; Pendall, 2000) and have 

limited access to high-opportunity neighborhoods. However, much of this research has examined 

large metropolitan statistical areas (MSAs). Little is known about locational patterns of voucher 

recipients who reside in small metropolitan, micropolitan, and rural areas.1 This gap in the research 

raises an interesting question about the performance of the HCV program outside of large MSAs 

and if voucher recipients in nonmetropolitan areas access neighborhoods with higher opportunity 

than urban households. Furthermore, very little research focuses on small cities and regions that 

are not connected to the global network, and generalizing theories for large MSAs can lead to 

biased approaches for understanding localized areas (Bell and Jayne, 2009). 

This study extends the query by examining voucher locational patterns in the state of Florida at 

a more granular scale, offering the opportunity to capture location heterogeneity across varying 

housing markets. This study utilizes administrative data from the U.S. Department of Housing 

and Urban Development (HUD) documenting location and sociodemographic information of 

individual voucher households residing in Florida in 2013. The data set is coupled with the op-

portunity indices provided in HUD’s Affirmatively Furthering Fair Housing (AFFH) tool to address 

the following research questions: (1) What share of HCV households reside in smaller MSAs, 

micropolitan areas, and rural areas, and do household characteristics vary by location? (2) Does 

voucher dispersion or clustering in affordable rental neighborhoods vary by place? (3) Are voucher 

holders in MSAs accessing higher-opportunity neighborhoods than nonmetropolitan households?

Four large MSAs (population of more than 1 million) are in Florida: Jacksonville, FL; Orlando-

Kissimmee-Sanford, FL (Orlando); Miami-Fort Lauderdale-West Palm Beach, FL (Miami); and 

Tampa-St. Petersburg-Clearwater, FL (Tampa) (based on the MSA definitions in OMB Bulletin 

15-01, dated July 15, 2015). This study uses these MSAs for comparison purposes, but they are 

not the focus of the study. The remaining areas in the state of Florida are assigned to one of three 

categories: (1) small MSAs, (2) micropolitan areas, and (3) rural areas (counties that are not in a 

metropolitan or micropolitan area). Micropolitan and rural areas are also referred to as nonmetro-

politan areas in this study. Taking Thrift’s (2000) approach to the city that one size does not fit all, 

this study intends to advance rental housing policy for very low-income households by accounting 

for place. Understanding the dynamics of voucher household characteristics and locational 

outcomes in nonmetropolitan areas can help direct policy interventions to particular voucher 

subgroups in regions that have been given less attention. 

1 The Office of Management and Budget defines metropolitan and micropolitan statistical areas by grouping counties, including relatively high-density counties in urban areas, plus adjacent counties that have strong social and economic ties with the urban area. MSAs contain an urban area with a population of 50,000 or more, and the population within the urban area of a micropolitan statistical area is between 10,000 and 50,000 on average.

240 Refereed Papers

A Research Note: The Housing Choice Voucher Program and  

Access to Opportunity in Florida’s Nonmetropolitan Areas 

Literature Review

The body of literature on the HCV program is heavily focused on urban areas, leading to calls for 

more research on voucher holders who reside in different types of urban or nonurban settings 

(Carlson et al., 2012b). Only one study has focused on rural areas exclusively (Pistilli, 2001). The 

few studies that have accounted for voucher holders in nonmetropolitan areas found differences in 

household composition and neighborhood quality (Carlson et al., 2012b; Ross, Shlay, and Picon, 

2012), suggesting the importance of location in analyzing the HCV program. No study, however, 

has examined the ability for voucher holders to access opportunity by location, comparing large 

MSAs, small MSAs, micropolitan areas, and rural areas. 

The following information describes the differences in location that have been gathered from 

national studies. First, the share of voucher holders in nonmetropolitan census tracts in 2010 made 

up only 13.3 percent of all vouchers, with the majority residing in central city tracts (52.2 percent) 

and the remaining residing in suburban tracts (34.4 percent). Voucher holder presence in non-

metropolitan areas declined from 21.0 percent in the mid-1990s to 19.2 percent in 1997 and 13.3 

percent by 2010 (McClure and Johnson, 2015; Newman and Schnare, 1997). Second, substantially 

more rent-burdened HCV households (families who spend more than 31 percent of income on 

rent) reside in nonmetropolitan areas (47.4 percent) than in metropolitan areas (36.0 percent; Mc-

Clure, 2005). Third, although HCV households move from a nonmetropolitan area to a metropoli-

tan area more often than in the other direction, from 1998 to 2005, portability from metropolitan 

areas to nonmetropolitan areas accounted for 5.5 percent of all portability moves (Climaco et al., 

2008). Fourth, nonmetropolitan areas have the fewest high-opportunity neighborhoods by both 

census tract and block group as compared with central cities and suburban cities. Nonmetropolitan 

areas also have the fewest neighborhoods with low poverty and minimum assisted housing as 

compared with central cities and suburban cities (McClure, 2011). HCV households residing in 

rural and suburban areas were less satisfied with their housing unit than those residing in central 

cities but were more satisfied with their neighborhoods (Ross, Shlay, and Picon, 2012).

State HCV studies are less common, and only two state studies distinguish between rural and 

urban regions (Carlson et al., 2012a, 2012b). Carlson and colleagues found the number of house-

holds that applied and did not receive a rent subsidy in Wisconsin was lower in rural areas. Also, 

nearly one-third of urban-residing applicants did not receive assistance (excluding Milwaukee) 

compared with only slightly more than one-fifth of applicants in rural areas (Carlson et al., 2012a). 

Household composition and neighborhood quality for voucher holders were different in rural areas 

than in large urban areas in Wisconsin. One year after voucher receipt, households in rural areas 

moved to neighborhoods of lower quality, whereas households in urban areas moved to neighbor-

hoods with higher quality (Carlson et al., 2012b).

The only study the authors found that focused on rural areas was conducted nearly 15 years ago and 

commissioned by HUD. The five areas include: Troy, Alabama; Creston, Iowa; Great Falls, Montana; 

Blossburg, Pennsylvania; and Del Rio, Texas. That study largely focused on voucher success rates 

and found that HCV waiting lists tended to be shorter than the average wait time for the voucher 

program and had fewer applicants (Pistilli, 2001). Turnover was high in the program, creating issues 

of program utilization for housing authorities. Success rates varied from 96 percent in Del Rio to  

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Walter and Wang

35 percent in Creston. Most rental units passed the initial inspection or required minor repairs, 

deflating the perception that rural areas contain a large stock of substandard units. Housing avail-

ability was a concern with fewer rental units than the national average. The degree of rurality did not 

appear to affect lease-up rates for voucher holders, but proximity to colleges and universities created 

competition for voucher holders to find affordable rental units. Manufactured housing in rural areas 

increased the number of affordable housing options for voucher holders. Discrimination based on 

family size and race may have lowered lease-up rates in rural areas (Pistilli, 2001).

Past research indicates that the HCV program shares similarities in nonmetropolitan areas and 

urban areas, albeit with stark differences. The limited information comparing neighborhood 

conditions between urban and rural regions reveals some indication that the socioeconomics of 

neighborhoods in which voucher holders reside are similar (high poverty, cheaper housing, and 

low levels of educational attainment). Factors such as discrimination based on family size and race 

appear to reduce lease-up rates for both urban and rural households. Significant differences are 

seen in neighborhood and housing unit satisfaction, the extent of the rent burden, the demand for 

vouchers, and mobility. These differences warrant research that accounts for place to help guide 

policymaking. This study expands on the previous literature on the HCV program by examining 

voucher household characteristics, degrees of clustering, and the ability for voucher holders to 

access opportunity by location, comparing large MSAs, small MSAs, micropolitan, and rural areas.

Data and Methodology

The authors obtained calendar year 2013 HCV administrative data for the entire state of Florida 

from HUD. The variables include individual tenant characteristics (age, gender, race or ethnicity, 

income sources and amounts, disability status, and relation of each household member to the head 

of household); household characteristics (whether any member was formerly homeless, household 

income, and total tenant payment); and unit and location information (number of bedrooms, home 

address, geocoded latitude and longitude, gross rent, and utility allowance). Gross rent includes 

contract rent and a utility allowance estimate. Total tenant payment equals gross rent minus the 

voucher amount. Only tenant-based vouchers were used. The moderate rehabilitation category was 

excluded, because it provides project-based rental assistance. This procedure resulted in a study 

population of 105,466 voucher households that were geocoded in ArcGIS based on the geocoordi-

nates provided by HUD. 

To determine the share of Florida HCV households that reside in places outside large MSAs, the 

state’s counties were classified into the following categories: large MSA, small MSA, micropolitan 

area, and rural area. A county population of at least 1 million distinguished a large MSA from a 

small MSA. The definitions used by the Office of Management and Budget were applied to classify 

micropolitan counties. Out of 67 counties in Florida, 44 fall within a MSA. Four MSAs, includ-

ing Jacksonville, Miami, Orlando, and Tampa, meet the definition for a large MSA and cover 16 

counties. The remaining 18 small MSAs cover 28 counties and have populations of approximately 

between 100,000 and 1 million. The 7 micropolitan areas in Florida have populations of approxi-

mately 10,000 to 100,000 and encompass 7 counties. The remaining 16 counties that did not fall 

within a metropolitan or micropolitan area were categorized as rural. All but 1 of the rural counties 

are in northern Florida (exhibit 1).

242 Refereed Papers

A Research Note: The Housing Choice Voucher Program and  

Access to Opportunity in Florida’s Nonmetropolitan Areas 

Exhibit 1

Study Area by Location

MSA = metropolitan statistical area.

To understand the heterogeneity of voucher households by location, descriptive statistics from the 

2013 HUD data on household characteristics were examined. Specifically, voucher distribution 

was analyzed by household racial or ethnic composition; household type (including age, disability 

status, whether children are present, and household size); economic status including income level 

and income source; and unit size (number of bedrooms). Although the focus of this article is on 

voucher holders outside of large MSAs, data on voucher holders in the four large MSAs, as well as 

statewide information, are provided for comparison.

To determine if HCV recipients are more dispersed in affordable rental neighborhoods by location, 

the proportion of HCV households to total fair market rental units was calculated at the census 

block group level. The total fair market rental units were estimated using the total rental units de-

rived from 2009–2013 American Community Survey 5-year estimates and 2013 Fair Market Rents 

(FMRs) published by HUD. Specifically, the total rental units were generated by adding occupied 

units with cash rent, occupied units without cash rent, vacant for-rent units, and rented-not-

occupied housing units. The calculation was based on two-bedroom FMRs, because HUD typically 

uses the two-bedroom FMRs to derive the other bedroom sizes (HUD, 2007). 

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Walter and Wang

To calculate the number of fair market rental units, the first step was to calculate the number of 

occupied units with cash rent of less than the two-bedroom FMR. For example, if the FMR was 

$780, all units in the rent categories from $1 to $749 were included, plus 60 percent of the units 

falling under the $750–$799 category. Next, a multiplier was generated by dividing the number 

of occupied units with cash rent of less than the FMR (results from step one) by the total number 

of occupied units with cash rent. The last step was to apply this multiplier to the number of total 

rental units. Census block groups with no vouchers and no fair market rental units were removed. 

Because the count of fair market rental units was an estimate, some census block groups had zero 

fair market rental units and at least one HCV household. In this case, the ratio of the number of 

HCV households to the number of fair market rental units was assigned the value 1.

Taking a similar approach as Wang and Varady (2005), hot spot analysis was conducted at the state 

level using the HCV household and fair market rental unit ratio to determine statistically significant 

hot spots (areas that have high concentrations of HCV households) and cold spots (areas that have 

a low number of HCV households relative to under-FMR units). The parameters for the hot spot 

analysis included a fixed distance band with a band size of 15,000 meters to conceptualize the 

spatial relationship.2 The results of the hot spot analysis were overlaid on a map of Florida’s 67 

counties to determine if HCV households are concentrated in areas that have affordable rental units 

or if location reveals differences.

Neighborhoods of opportunity are defined and measured by using the opportunity indices provided 

in HUD’s AFFH tool and include the School Proficiency Index, Jobs Proximity Index (provided at the 

census block group level), Low Poverty Index, Labor Market Engagement Index, Low Transportation 

Cost Index, Transit Trips Index, and Environmental Health Index (census tract level). The values in 

the indices range from 0 to 100, and higher scores indicate more opportunity.3 For each index, the 

median and 25th and 75th percentile scores were calculated by location (large MSA, small MSA, 

micropolitan, and rural) based on where voucher holders reside using the 2013 geocoded data set. 

This process allows for each index to be assessed by location to determine if voucher holders in MSAs 

access higher-opportunity neighborhoods than their counterparts in nonmetropolitan areas. 

The median and 25th and 75th percentile scores were also calculated for each county, and the 

opportunity indices were converted to  z-scores. This approach was used to standardize the data 

to produce an overall average opportunity score for each county in which the seven opportunity 

indices were weighted equally. The same process was repeated for each county but only accounting 

for the census tracts and block groups where voucher holders reside using the 2013 geocoded 

data set as done previously. The index scores for each county were then compared with the index 

scores for where voucher holders reside in that county. The purpose of this analysis was to further 

understand how voucher holders preform relative to the overall opportunity index score for 

each county to determine where voucher holders access the highest or lowest opportunity and to 

provide additional insight on variation across location. 

2 The fixed distance method was selected because it is a robust method for the large variation in size of census block groups, which is important for this study as it covers all census block groups in the state. The band size was selected with multiple trials to ensure that it reflects maximum spatial autocorrelation.

3 Variables, data sources, formulas, and a complete description of the opportunity indices are in HUD’s AFFH Data Documentation (HUD, 2016).

244 Refereed Papers

A Research Note: The Housing Choice Voucher Program and  

Access to Opportunity in Florida’s Nonmetropolitan Areas 

Results

This section first presents results on HCV household characteristics by location, then demonstrates 

how voucher clustering in affordable rental neighborhoods varies by place, and lastly discusses the 

findings comparing voucher holder access to opportunity neighborhoods in MSAs and nonmetro-

politan areas.

Housing Choice Voucher Household Characteristics by Location 

Approximately 98 percent of Florida HCV households live in large and small MSAs, and nearly 

three-fourths of those live in large MSAs specifically. Only 0.9 percent of HCV households live in 

micropolitan counties and 1.1 percent reside in rural counties, accounting for slightly more than 

2,000 HCV households in 2013. The proportion of voucher households to households that earn 

50 percent of the Area Median Income (AMI) is higher in large MSAs (7.0 percent) and small MSAs 

(5.0 percent) compared with 2.9 percent in micropolitan areas and 3.9 percent in rural areas. 

This finding is consistent with Arnold’s (1990) argument that fewer households receive subsidy in 

nonmetropolitan areas.

Statewide, more than one-half of HCV households are Black, approximately 25 percent are 

Hispanic, and about 15 percent are White. Heterogeneity is prominent by location in household 

racial or ethnic composition, even though Black households are the primary racial group that use 

vouchers in Florida across the four different locations. Large MSAs have only about 10 percent of 

White HCV households, and this proportion substantially grows as the location becomes more 

rural. Nearly one-half, 43.5 percent, of all HCV households in rural areas are White. The difference 

between the proportions of Hispanic households in large MSAs compared with rural areas is also 

substantial (30.8 versus 2.0 percent, respectively). Black households make up more than one-half 

of the HCV population in all areas except micropolitan areas, where the proportion drops to 42.4 

percent. The large proportion of White residents and few Hispanic residents in rural areas reflect 

the demographics in northern Florida.

Household type by age is fairly consistent across all locations. Large MSAs and micropolitan areas 

are similar in terms of age distribution. Rural areas, closely followed by small MSAs, have the most 

household heads or spouses who are young adults (34 years of age or younger). Large MSAs and 

micropolitan areas have the largest share of household heads, spouses, or both who are 62 years 

of age or older. Approximately one-half of all heads of household or spouses (whomever is older), 

regardless of location, are between the ages of 25 and 61. Nearly one-half of voucher households 

in MSAs have at least one disabled member, most with disabled members as the primary member 

in the household. This number drops slightly to approximately 39 percent in micropolitan and 

rural areas. The variation by location is very small when looking at only disability of the primary 

member in the household.

Nearly one-half of voucher households have children, and a large portion of these households consist 

of single mothers with children. Rural areas and small MSAs have relatively high proportions of 

families with children (58.0 and 53.5 percent, respectively). Both areas also have many single mothers 

with children, with one-half of all households in rural areas representing this dynamic. Statewide, the 

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Walter and Wang

primary household size for voucher holders is one member, which represents more than one-third 

of the population, followed by four or more members at 26 percent. Micropolitan areas have 

higher proportions of one- and two-member households compared with all other areas. The largest 

household sizes (four or more members) are most often in MSAs. The primary bedroom sizes for 

HCV households are two and three bedrooms (34 and 32 percent, respectively). In rural areas, a 

significantly lower proportion of HCV households live in one-bedroom units (7 versus 23 percent 

statewide), but a much higher percentage live in three-bedroom units (47 versus 32 percent statewide).

Statewide, the median income is about $10,300 for voucher households. The highest median 

income is in micropolitan areas, at $12,300, and the lowest median income is in rural areas, at 

$9,400. In terms of the primary source of income, about one-third of households have no income, 

which is consistent across all locations. Another one-third of voucher households’ primary income 

is from wages, and for another one-third, it is from social security or supplemental security income. 

Very little variation exists by location in all income sources. On average, across the state, the HCV 

program serves households with income levels at 25 percent of the AMI; this percentage ranges 

from 24 percent in large MSAs to 27 percent in micropolitan and rural areas. More than 70 percent 

of HCV households in Florida are extremely low-income and earn 30 percent of the AMI or less, 

and about 23 percent of HCV households are very low-income, which ranges from 31 percent to 

50 percent of the AMI (exhibit 2).

In summary, a larger share of households that earn less than 50 percent of AMI are served in MSAs 

compared with nonmetropolitan areas. A greater share of minorities comprises HCV households 

in urban areas, and, as the location changes and becomes more rural, White households make up 

a larger share in the program. Rural areas contain more HCV households with single mothers and 

children. The largest share of extremely low-income HCV households is in large MSAs. Many of 

these findings are consistent with and reflect state demographics. 

Exhibit 2

Florida Housing Choice Voucher Profiles by Location (1 of 2)

Large  

Small  

Micropolitan 

Rural  

State

MSAs

MSAs

Areas

Areas

Overall HCV count

105,466

77,578

25,857

906

1,125

Percentage of HCV households

100.0

73.6

24.5

0.9

1.1

Land area (square miles)

56,778

15,462

24,331

6,052

10,933

Percentage of land area

100.0

27.2

42.9

10.7

19.3

HCV households as percent of 

6.3

7.0

5.0

2.9

3.9

households with 50% AMI 

or less

Race/ethnicity (%)

Blacka

55.6

54.1

60.7

42.4

51.0

Whitea

14.3

10.2

24.5

31.0

43.5

Hispanica

25.3

30.8

10.1

22.1

2.0

Otherb

4.9

5.0

4.7

4.5

3.6

Household type (%)

Age categoryc

34 or younger

24.0

21.9

30.0

22.2

33.8

35–61

51.5

51.3

52.0

53.4

49.1

62 or above

24.5

26.8

17.9

24.4

17.2

246 Refereed Papers

A Research Note: The Housing Choice Voucher Program and  

Access to Opportunity in Florida’s Nonmetropolitan Areas 

Exhibit 2

Florida Housing Choice Voucher Profiles by Location (2 of 2)

Large  

Small  

Micropolitan 

Rural  

State

MSAs

MSAs

Areas

Areas

Disabled households (any 

46.5

48.5

41.1

39.4

39.1

member)

Disabled households (any 

38.5

39.4

35.8

36.1

37.7

primary member)d

Households with child(ren)

49.5

48.0

53.5

45.6

58.0

Single mother with child(ren)

45.2

44.0

48.6

40.4

51.1

Household size

1 member

36.5

36.9

35.1

40.7

32.6

2 members

20.8

21.1

19.9

23.0

20.9

3 members

17.1

16.7

18.2

14.6

21.4

4 or more members

25.6

25.3

26.8

12.0

15.1

Number of bedrooms (%)

0 bedrooms

1.8

2.2

0.7

0.7

0.9

1 bedroom

23.4

25.1

19.1

25.1

7.4

2 bedrooms

34.3

33.4

36.8

38.0

39.1

3 bedrooms

31.7

30.0

36.2

31.0

47.0

4 or more bedrooms

8.7

9.3

7.2

5.3

5.6

Median income ($)

10,284

10,008

10,872

12,278

9,360

Income source (%)

No income

32.1

31.3

34.6

32.8

30.0

Primary income—wage

31.3

30.6

33.3

34.5

31.4

Primary income—welfare

2.2

2.0

2.7

1.4

4.3

Primary income—social 

34.4

36.1

29.4

31.2

34.4

security/SSI

Income as percent of AMI

Average percent of AMI

24.8

24.3

26.3

27.4

27.2

30% AMI or less

70.4

72.1

65.8

63.4

68.1

31–50% AMI

22.7

21.2

26.9

29.8

26.0

51–80% AMI

6.3

6.1

6.8

6.7

5.6

81% AMI or more

0.6

0.6

0.5

0.1

0.4

AMI = Area Median Income. HCV = Housing Choice Voucher. MSA = metropolitan statistical area. SSI = supplemental security income.

a All household members have the same race or ethnicity.

b Other includes all other racial groups, interracial households, and mixed-race households.

c The age of household head or spouse, whoever is older.

d Primary member is defined as household head, spouse, or co-head.

Housing Choice Voucher Distribution by Location

Exhibit 3 reveals statistically significant hot spots and cold spots of clusters of voucher holder 

concentration relative to under-FMR rental housing units. Hot spots have a high concentration of 

voucher holders relative to the number of fair market rental units. Cold spots have a low number 

of voucher holders relative to the number of fair market rental units, which indicates areas that are 

underrepresented by voucher holders. The confidence intervals indicate that a less-than 1-percent 

chance (99 percent confidence) or less-than 5-percent chance (95 percent confidence) that the 

clustering occurs by random chance.

Cityscape 247

Walter and Wang

Exhibit 3

Hot Spot Analysis of Voucher Holder Concentration Relative to Under-FMR Rental 

Housing

FMR = Fair Market Rent. MSA = metropolitan statistical area.

Most hot spots and cold spots are in large MSAs, and the distribution pattern is quite different 

across each large MSA. The Jacksonville and Tampa MSAs have mainly hot spots, indicating the 

proportion of HCV households compared with under-FMR rental units is relatively high, which 

represents a clustered pattern of a high concentration of voucher holders. The Orlando MSA has 

a very large cold spot, meaning the share of voucher holders is low where under-FMR rental units 

are. The Miami MSA includes a combination of hot spots and cold spots, with concentrations of 

voucher holders in the middle portion of South Florida counties in underserved neighborhoods. 

This pattern raises the question as to why voucher holders are not accessing neighborhoods where 

affordable units are. Although beyond the scope of this study, reasons that may be further assessed 

to explain this finding are that the lack of voucher acceptance in these areas, discrimination in the 

market, or the local housing authority’s service area may be constricting voucher holders to certain 

areas during the first year of voucher receipt.

248 Refereed Papers

A Research Note: The Housing Choice Voucher Program and  

Access to Opportunity in Florida’s Nonmetropolitan Areas 

Besides focusing on where hot spots and cold spots exist, we also examine where no hot or cold 

spots exist. A lack of hot spots and cold spots indicates areas where voucher holders are randomly 

distributed relative to under-FMR units. Both micropolitan and rural areas have no hot spots or 

cold spots. This absence means voucher holders are randomly disbursed throughout areas that 

provide rental units for less than the FMR. The absence of visibly large clusters of poverty in 

nonmetropolitan areas helps explain why rural poverty is often considered forgotten in mainstream 

America. For more than five decades, the percentage of low-income people in nonmetropolitan 

areas has consistently been higher than in MSAs (USDA, 2017).

Housing Choice Voucher Access to High-Opportunity Neighborhoods

Exhibit 4 reveals that micropolitan areas provide the most opportunity to voucher holders in terms 

of access to employment, school quality, and labor force participation and human capital. On 

the other hand, large MSAs provide the least opportunity to employment and school quality for 

voucher holders, whereas rural voucher holders are where labor market engagement is the lowest. 

The Low Poverty Index, which assesses exposure to poverty in a neighborhood, performs the best 

in small MSAs and worst in large MSAs, although it is important to note that this index varies very 

little by location; the scores range from approximately 20 to 28. These scores are relatively low, 

which indicates voucher holders across the state of Florida are in neighborhoods with high poverty. 

It is not surprising that the Low Transportation Cost Index and Transit Trips Index scores for 

voucher holders vary by location, with the highest scores in large MSAs and lowest scores in rural 

areas. This disparity is a result of the decline of public transit and higher transportation costs from 

inner cities to rural areas. The Environmental Health Index, which assesses environmental quality 

in neighborhoods using the National Air Toxics Assessment data, indicates that voucher holders 

in rural areas live in neighborhoods with the highest scores. The score drops rapidly for all other 

locations and is fairly consistent for large MSAs, small MSAs, and micropolitan areas, although 

voucher holders in small MSAs access neighborhoods with slightly better environmental quality. 

Again, this result is likely due to the fact that environmental quality is generally better farther away 

from urban areas where there are fewer pollutants.

In summary, the finding that voucher holders live in neighborhoods that have high exposure to 

poverty is consistent with other studies (Devine et al., 2003; McClure, Schwartz, and Taghavi, 

2015) and does not vary much by location. The transit and environmental results are also as 

expected and reflect the general locational differences of these factors. However, the ability for 

voucher holders to access the best employment opportunities and schools in micropolitan areas 

is a new finding. Is this finding also a reflection of overall locational differences? Do micropolitan 

areas generally provide more opportunity than large MSAs, where voucher holders have the 

hardest time accessing neighborhoods of opportunity? Or do voucher holders successfully access 

neighborhoods with higher opportunity in micropolitan areas? To better understand this finding, 

we consider the opportunity scores of voucher holders relative to the overall score for the location 

in which they live. 

When accounting for the overall county index scores for each location, voucher holders in 

micropolitan and rural counties access higher-opportunity neighborhoods than their counterparts 

Cityscape 249

Walter and Wang

Exhibit 4

AFFH Opportunity Indices by Location

Jobs  Proximity  Index  

School  Proficiency  Index  

 90    

 90    

 80    

 80    

 70    

 70    

 60    

 60    

 50    

 50    

 40    

 40    

 30    

 30    

 20    

 20    

 10    

 10    

 0        

 0

Large  MSA  

Small  MSA  

Micro  (906)  

Rural  (1,119)  

Large  MSA  

Small  MSA  

Micro  (906)  

Rural  (1,119)  

(77,578)  

(25,857)  

(77,578)  

(25,857)  

Median  

25th  percenKle  

75th  percenKle  

Median  

25th  percenIle  

75th  percenIle  

Low  Poverty  Index  

Labor  Market  Engagement  Index  

90  

90  

80  

80  

70  

70  

60  

60  

50  

50  

40  

40  

30  

30  

20  

20  

10  

10  

0  

0  

Large  MSA  

Small  MSA  

Micro  (906)  

Rural  (1,119)  

Large  MSA  

Small  MSA  

Micro  (906)  

Rural  (1,119)  

(77,578)  

(25,857)  

(77,578)  

(25,857)  

Median  

25th  percenIle  

75th  percenIle  

Median  

25th  percenHle  

75th  percenHle  

Environmental  Health  Index  

Low  TransportaDon  Cost  Index  

90  

90  

80  

80  

70  

70  

60  

60  

50  

50  

40  

40  

30  

30  

20  

20  

10  

10  

0  

0  

Large  MSA  

Small  MSA  

Micro  (906)  

Rural  (1,119)  

(77,578)  

(25,857)  

Large  MSA  

Small  MSA  

Micro  (906)  

Rural  (1,119)  

(77,578)  

(25,857)  

Median  

25th  percenHle  

75th  percenHle  

Median  

25th  percenDle  

75th  percenDle  

Transit  Trips  Index  

90  

80  

70  

60  

50  

40  

30  

20  

10  

0  

Large  MSA  

Small  MSA  

Micro  (906)  

Rural  (1,119)  

(77,578)  

(25,857)  

Median  

25th  percenGle  

75th  percenGle  

AFFH = Affirmatively Furthering Fair Housing. MSA = metropolitan statistical area.

250 Refereed Papers

A Research Note: The Housing Choice Voucher Program and  

Access to Opportunity in Florida’s Nonmetropolitan Areas 

in metropolitan counties (large and small MSAs). Voucher holders in 13 of the 16 rural counties 

and 6 of the 7 micropolitan counties reside in neighborhoods that have higher overall opportunity 

scores than the counties containing them. In large MSAs, voucher holders are in neighborhoods 

that have higher overall opportunity in only 4 of 16 counties, although the ratio is 9 to 28 counties 

in small MSAs. The top 10 counties where the overall index score for voucher holders is greatest 

compared with the average county index are all rural or micropolitan (exhibit 5).

Exhibit 5

Ten Highest Voucher Index Scores Relative to Overall County Opportunity Index

County Index  

Voucher Index 

County

Location

Difference

Score

Score

Holmes

Rural area

– 0.9749

0.6450

1.6198

Jackson

Rural area

– 0.1378

1.2307

1.3685

Columbia

Micropolitan area

– 0.4906

0.6748

1.1654

Bradford

Rural area

– 1.0747

– 0.0444

1.0302

Hamilton

Rural area

– 0.5772

0.4490

1.0262

Glades

Rural area

– 1.1198

– 0.1299

0.9899

Hendry

Micropolitan area

– 0.1430

0.7768

0.9197

DeSoto

Micropolitan area

– 0.5151

0.3882

0.9033

Washington

Rural area

– 0.4046

0.4554

0.8600

Taylor

Rural area

– 0.1887

0.6057

0.7944

Note: Scores in other Florida counties are available from the authors.

Source: Authors’ computations

Discussion

The goal of this study is to better understand voucher household characteristics, concentration, 

and access to neighborhoods of opportunity by accounting for location, operationalized at the 

county level by degree of rurality. Many studies on the HCV program have evaluated large MSAs, 

implying that policy is guided by metropolitan findings. However, this study finds that the HCV 

program performs differently—at least in the state of Florida—across the rural-metropolitan gradi-

ent in terms of household composition, concentration, and ability for HCV households to move to 

neighborhoods of opportunity. Location may be an important consideration in the HCV program, 

and caution may be justified before assuming national HCV policies are equally effective for public 

housing authorities that service nonmetropolitan areas.

A primary finding of this study is that HCV households are not concentrated relative to units 

that rent for less than the FMR in micropolitan and rural areas, and the HCV program performs 

better in terms of allowing HCV households to access higher-opportunity neighborhoods in non-

metropolitan areas. This finding is particularly interesting given that McClure (2011) found that 

nonmetropolitan areas have the fewest high-opportunity neighborhoods as compared with central 

cities and suburban areas. This finding implies nonmetropolitan HCV households can access 

higher-opportunity neighborhoods, even though fewer are available. 

Importantly, the federal deconcentration goal may not be as relevant to nonmetropolitan areas. 

For example, HCV households in rural areas have lower incomes, tend to be younger, and 

comprise higher percentages of single mothers and families with children than the statewide HCV 

average. A wider range of supportive services, rather than mobility options to move to a different 

Cityscape 251

Walter and Wang

neighborhood, are likely needed for these households to reach self-sufficiency. Ross, Shlay, and 

Picon’s (2012) finding that HCV households residing in rural areas are more satisfied with their 

neighborhoods than their urban counterparts supports this conclusion. 

Equally important is the issue of housing availability in nonmetropolitan areas. Pistilli (2001) 

researched the HCV program’s performance in rural areas and found that rural housing availability 

was a concern for renters, with fewer rental options compared with national averages. Also, recall 

that Ross, Shlay, and Picon (2012) found that HCV households residing in rural areas were less 

satisfied with their housing units. Because Pistilli (2001) used data that are now more than 15 

years old and examined only six rural areas, a national paper that examines the HCV program and 

housing availability in nonmetropolitan areas is warranted.

Furthermore, the finding that voucher households access higher-opportunity neighborhoods 

in certain Florida counties is worth further exploration. For instance, what residential mobility 

push-and-pull factors enable voucher holders to access more high-opportunity neighborhoods in 

micropolitan and rural areas than in MSAs? A more indepth qualitative analysis is necessary to gain 

insight to the factors and barriers that enable or prevent access to high-opportunity neighborhoods 

for future HCV mobility policy.

Acknowledgments

The authors thank the editor and anonymous reviewers for their valuable remarks that improved 

the manuscript.

Authors 

Rebecca J. Walter is an assistant professor of urban and regional planning at the University of Texas 

at San Antonio in the College of Architecture, Construction and Planning.

Ruoniu (Vince) Wang is a postdoctoral fellow in the College of Architecture, Construction and 

Planning at the University of Texas at San Antonio.

References

Arnold, Craig A. 1990. “Ignoring the Rural Underclass: The Biases of Federal Housing Policy,” 

Stanford Law and Policy Review 2: 191–206.

Bell, David, and Mark Jayne. 2009. “Small Cities? Towards a Research Agenda,”  International 

Journal of Urban and Regional Research 33 (3): 683–699.

Carlson, Deven, Robert Haveman, Tom Kaplan, and Barbara Wolfe. 2012a. “Long-Term Earnings 

and Employment Effects of Housing Voucher Receipt,”  Journal of Urban Economics 71 (1): 128–150.

———. 2012b. “Long-Term Effects of Public Low-Income Housing Vouchers on Neighborhood 

Quality and Household Composition,”  Journal of Housing Economics 21 (2): 101–120.

252 Refereed Papers

A Research Note: The Housing Choice Voucher Program and  

Access to Opportunity in Florida’s Nonmetropolitan Areas 

Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2015.  The Effects of Exposure to Better Neigh-

borhoods on Children: New Evidence From the Moving to Opportunity Experiment. Washington, DC: 

Internal Revenue Service; U.S. Department of the Treasury.

Climaco, Carissa, Christopher N. Rodger, Judith Feins, and Ken Lam. 2008. “Portability Moves in 

the Housing Choice Voucher Program, 1998–2005,”  Cityscape 10 (1): 5–40.

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

Voucher Location Patterns: Implications for Participant and Neighborhood Welfare. Washington, DC: 

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

McClure, Kirk. 2011.  Housing Choice Voucher Marketing Opportunity Index: Analysis of Data at the 

Tract and Block Group Level. Washington, DC: U.S. Department of Housing and Urban Develop-

ment, Office of Policy Development and Research.

———. 2005. “Rent Burden in the Housing Choice Voucher Program,”  Cityscape 8 (2): 5–20.

McClure, Kirk, and Bonnie Johnson. 2015. “Housing Programs Fail To Deliver on Neighborhood 

Quality, Reexamined,”  Housing Policy Debate 25 (3): 463–496.

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

Patterns a Decade Later,”  Housing Policy Debate 25 (2): 215–233.

Newman, Sandra J., and Ann B. Schnare. 1997. “‘... And a Suitable Living Environment’: The 

Failure of Housing Programs To Deliver on Neighborhood Quality,”  Housing Policy Debate 8 (4): 

703–741. 

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

ing Policy Debate 11 (4): 881–910.

Pistilli, Linda. 2001.  Study on Section 8 Voucher Success Rates. Vol. 2,  Qualitative Study of Five Rural Areas. Washington, DC: U.S. Department of Housing and Urban Development. 

Ross, Lauren M., Anne B. Shlay, and Mario G. Picon. 2012. “You Can’t Always Get What You Want: 

The Role of Public Housing and Vouchers in Achieving Residential Satisfaction,”  Cityscape 14 (1): 

35–53.

Thrift, Nigel. 2000. “Not a Straight Line but a Curve: Or Cities Are Not Mirrors of Modernity.” In 

City Visions, edited by David Bell and Azzedine Haddour. Harlow, United Kingdom: Prentice Hall: 

233–263. 

U.S. Department of Agriculture (USDA). 2017.  Poverty Overview. A Note About the Data Sources. 

Washington, DC: U.S. Department of Agriculture, Economic Research Service.

U.S. Department of Housing and Urban Development (HUD). 2016.  Affirmatively Furthering Fair 

Housing (AFFH) Data Documentation. Version 3.1. Washington, DC: U.S. Department of Housing 

and Urban Development.

Cityscape 253

Walter and Wang

———. 2007.  Fair Market Rents for the Section 8 Housing Assistance Payments Program. Washington, 

DC: U.S. Department of Housing and Urban Development, Office of Policy Development and 

Research.

Walter, Rebecca, Yanmei Li, and Serge Atherwood. 2015. “Moving to Opportunity? An Examina-

tion of Housing Choice Vouchers on Urban Poverty Deconcentration in South Florida,”  Housing 

Studies 30 (7): 1064–1091. 

Wang, Xinhao, and David Varady. 2005. “Using Hot-Spot Analysis To Study the Clustering of Sec-

tion 8 Housing Voucher Families,”  Housing Studies 20 (1): 29–48.

254 Refereed Papers

Departments

In this issue—

• Data Shop

• Graphic Detail

• Impact

• Policy Briefs

• SpAM

• Evaluation Tradecraft

• Call for Papers

• Referees 2016–17

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 255

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

256 Departments

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 ana-

lysts 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. 

Measuring Disability

Debra L. Brucker   

University of New Hampshire 

Veronica E. Helms 

U.S. Department of Housing and Urban Development

The views expressed in this article are those of the authors and do not represent the official positions or policies of the U.S. Department of Housing and Urban Development or the U.S. government.

Abstract

Housing policy researchers studying the intersection of housing and disability must 

understand the relative strengths and limitations of the various types of administra-

tive and survey data that can be used to identify persons with disabilities. This article 

describes traditional ways that disability has been measured in U.S. Department of 

Housing and Urban Development (HUD) administrative data and in relevant federally 

funded household surveys in the United States, while also highlighting newly available 

linked administrative survey data that can better identify persons with disabilities who 

participate in HUD-assisted housing programs. The article addresses various methods of 

measuring disability, including measures that are common across data sources (such as 

the sequence of six disability questions now included in the American Community Sur-

vey, American Housing Survey, and other federally funded surveys) and measures that 

are unique to specific sources of data (including HUD administrative data linked with 

population health surveys that include more detail on activity, functional, and social 

limitations). The article also discusses the strengths and limitations of various measures.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 257

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

Brucker and Helms

Introduction

Persons with disabilities represent a sizable and diverse population in the United States. Recent esti-

mates suggest that 40 to 53 million Americans living in the community have some form of ambula-

tory, cognitive, developmental, intellectual, mental health, or sensory disability (Courtney-Long et al., 

2015; Lauer and Houtenville, 2017a). In many cases, disability is associated with poverty (Brucker, 

Mitra et al., 2015). Persons with disabilities face a number of housing-related challenges, including 

disproportionately high levels of participation in federal rental housing assistance programs, reduced 

access to accessible and safe housing units, and lower levels of homeownership (Brucker and Houten-

ville, 2014); Brucker, Helms, and Souza, 2016; Hoffman and Livermore, 2012; Martin et al., 2011). 

Housing policy researchers must therefore consider disability status as a key demographic variable of 

interest.

Conceptual Models of Disability

Historically, disability has been defined using different conceptual models, which, in turn, led to 

the development of distinct measures to identify persons with disabilities. The medical model of 

disability posits that disability is caused by disease, injury, or other health conditions. Persons with 

any impairment are therefore considered to have a disability, regardless of whether their impair-

ment is associated with limitations in their daily lives (WHO, 1980). Disability can alternatively 

be considered as a function of the social environment. The social model of disability suggests that 

an environment that is not inclusive of all persons and that limits participation for persons with 

impairments can result in disability (Shakespeare and Watson, 2001). A final conceptual model 

of interest is the International Classification of Functioning, Disability and Health (ICF) model 

developed by the World Health Organization. The ICF model integrates the models mentioned 

previously by considering impairments, functional limitations (such as difficulty walking), and 

participation restrictions (such as restrictions in employment) as equally important in defining 

disability. The ICF model has been adopted as an international standard for measuring disability 

(Altman, 2001; WHO, 2001).

In addition to the conceptual models described previously, specific definitions of disability exist 

that government programs have operationalized to meet legislative directives, eligibility criteria, 

and administrative necessity. Some examples of these definitions, included within HUD-assisted 

housing administrative data, are described in the following section.

Data Sources

Housing policy researchers seeking to explore empirical data that include measures of disability 

have the option of using administrative data, survey data, or some combination of the two.

Administrative Data

HUD administrative data capture detailed information about all household members participating 

in HUD-assisted programs, including HUD’s three main program categories—public housing (PH), 

258 Data Shop

Measuring Disability

Housing Choice Voucher (HCV), and multifamily (MF) programs. In all cases, the amount of infor-

mation collected about disability is negligible, as data collection processes generally only include a 

yes-no question about disability.1 Program staff are directed to gather information about disability 

status for every member of a HUD-assisted household as residents enter housing assistance and 

with every annual recertification.2

For households that participate in the PH or HCV program category, a person is considered to have 

a disability if they have—

•  A disability as defined in section 223 of the Social Security Act.3

•  A physical, mental, or emotional impairment, which is expected to be of long-continued and 

indefinite duration, substantially impedes his or her ability to live independently, and is of such 

a nature that such ability could be improved by more suitable housing conditions.

•  A developmental disability as defined in section 102 of the Developmental Disabilities 

Assistance and Bill of Rights Act.4

•  Acquired immune deficiency syndrome (AIDS) or any condition that arises from human 

immunodeficiency virus, the etiologic agent for AIDS.

The definition of disability used in MF programs varies according to specific programs, but gener-

ally overlaps with the definition used by PH and HCV programs.5

Household-level information on disability is aggregated and reported within the annually released 

“A Picture of Subsidized Households”: (1) the percentage of households younger than age 62 in 

which the head, spouse, or co-head has a disability; (2) the percentage of households age 62 or 

older in which the head, spouse, or co-head has a disability; and (3) the percentage of all persons 

in assisted households who have a disability (HUD, 2016). Access to HUD restricted-use data, in-

cluding the binary disability indicator, is accessible for researchers using HUD’s data license process.6

Survey Data

Recognizing the need to standardize methods for measuring disability across household and 

population-based surveys, federal statistical agencies began proceedings in 2008 to develop and 

adopt a standardized series of questions to measure disability. The finalized metric that transpired 

from these discussions, alongside more comprehensive measures of disability, is discussed in the 

1 The disability data field is included in HUD FORM 50058 ( Family Report), HUD MTW FORM 50058 ( MTW Family Report), and HUD Form 50059 ( Owner’s Certification of Compliance with HUD’s Tenant Eligibility and Rent Procedures). 

2 Program directives are outlined in the Housing Choice Voucher Program Guidebook (https://portal.hud.gov/hudportal/

HUD?src=/program_offices/public_indian_housing/programs/hcv/forms/guidebook), Public Housing Occupancy Guidebook (https://portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_housing/programs/ph/rhiip/

phguidebook), and the Occupancy Requirements of Subsidized Multifamily Housing Programs (https://portal.hud.gov/

hudportal/HUD?src=/program_offices/administration/hudclips/handbooks/hsgh/4350.3). 

3 Pub. L. 74-271, 49 Stat. 620, § 223. 42 U.S.C. 423. August 14, 1935.

4 Pub. L. 106-457, 114 Stat. 1957, § 102. November 7, 2000.

5 For more detail, consult appendix F of Lloyd and Helms (2016).

6 For more information, see huduser.gov/portal/research/pdr_data-license.html.

Cityscape 259

Brucker and Helms

following sections. Additionally, given the recent availability and promotion of the use of linked 

survey and administrative data for research purposes (for example, the Evidence-Based Policymak-

ing Commission Act of 2016),7 the following discussion highlights available data sources of interest 

to housing policy researchers.

Survey Data: Six-Question Screener

The adoption of the ICF model of disability is tied closely to the development of a series of survey 

questions currently used by many federally funded household surveys. A sequence of six ques-

tions, initially developed by the U.S. Census Bureau and the National Center for Health Statistics 

(NCHS) for inclusion in the American Community Survey, was designed to identify the population 

with disabilities (Sirken, 2002). The questions, shown in exhibit 1, include a mix of activity, func-

tional, and sensory limitation questions. Persons identifying as having any one of these limitations 

are considered to have a disability. In 2010, Section 4302 of the Patient Protection and Affordable 

Care Act8 mandated that all federally funded population-based health surveys adopt this standard-

ized set of questions to identify people with disabilities.

As of 2017, these six questions are now available in many cross-sectional population-based surveys 

including the American Community Survey, American Housing Survey, Annual Social and Economic 

Supplement of the Current Population Survey, and the National Health Interview Survey (NHIS). 

Panel surveys such as the Survey on Income and Program Participation also include the six questions.

The benefit of the six questions is that they facilitate the standardized incorporation of disability 

measurement into federally funded population surveys. As an improvement over prior questions that 

narrowly focused solely on economic, medical, or social factors, the six questions reflect a broader 

understanding of disability as a condition which reflects the interaction of an individual person’s 

health condition with his or her environment, a concept widely accepted by disability researchers.

The six questions have some limitations. First, when applied to the working-age population, the 

six questions fail to identify up to one-third of persons who receive public disability benefits, such 

as Social Security Disability Insurance or Supplemental Security Income (Burkhauser, Houtenville 

Exhibit 1

Standardized Set of Disability Questions Used in Federally Funded Surveys

Limitation Type

Questions

Activity limitations

•  Do you have difficulty dressing or bathing?

•  Because of a physical, mental, or emotional condition, do you have difficulty 

doing errands alone such as visiting a doctor’s office or shopping?

Functional limitations •  Do you have serious difficulty walking or climbing stairs?

•  Because of a physical, mental, or emotional condition, do you have serious 

difficulty concentrating, remembering, or making decisions?

Sensory limitations

•  Do you have serious difficulty hearing?

•  Are you blind or do you have serious difficulty seeing even when wearing glasses?

7 Pub. L. 114-140, 130 Stat. 317. March 30, 2016.

8 Pub. L. 111-148, 124 Stat. 119, § 4302. March 23, 2010.

260 Data Shop

Measuring Disability

and Tennant, 2014). Second, the six questions do not provide sufficient detail about either specific 

types of disabilities or the severity of disabilities. Researchers seeking information about persons 

with specific types of conditions, such as intellectual or developmental disabilities or psychiatric 

conditions, must use a broader array of questions. In addition, although some researchers have 

used the two activity limitation questions as proxies for severity (Brucker, Houtenville, and Lauer, 

2015), additional detail, which is available from using a longer set of questions, can improve the 

measurement of severity. The next section describes examples of surveys that include some of these 

more detailed measures. Lastly, prior research suggests that population prevalence estimates vary 

slightly across surveys due to sampling strategies and instrument design features. When comparing 

disability prevalence for adults using these six questions across the American Community Survey, 

Annual Social and Economic Supplement of the Current Population Survey, NHIS, and Survey on 

Income and Program Participation, Lauer and Houtenville (2017b) found the highest estimates 

(17 percent) of disability in NHIS and the lowest (12 percent) in the Annual Social and Economic 

Supplement of the Current Population Survey.

Of importance for housing policy researchers, however, is how the measures of disability men-

tioned previously are integrated with available housing data. The inclusion of the six questions 

in the American Community Survey can help researchers interested in examining the association 

of disability with housing information, including financial and occupancy characteristics. The 

six-question screener is also incorporated into the American Housing Survey, a biannual, nation-

ally representative survey that collects information about housing conditions, costs, supply, and 

demand in the United States (Eggers and Moumen, 2011). The inclusion of the six questions on 

other surveys that attempt to measure participation in public programs, such as federal rental 

assistance (for example, the Annual Social and Economic Supplement of the Current Population 

Survey and the Survey on Income and Program Participation), may also be of interest to housing 

policy researchers, although some underreporting of program participation is known to limit 

results (Gordon et al., 2005).

Survey Data: Expanded Measures of Disability

Researchers seeking more detailed information about disability can access a number of surveys, 

including the National Survey on Drug Use and Health (NSDUH) and the Medical Expenditures 

Panel Survey (MEPS). Each of these surveys contains more detailed questions about disability, 

including those that can be used to identify persons with specific conditions or persons with dis-

abilities that vary in levels of severity. The NSDUH includes information not only about substance 

use disorders, but also about mental health conditions. The MEPS includes detailed information 

about health-related expenditures, including special healthcare needs. Both surveys, however, 

include only a limited amount of information about housing characteristics. This article highlights 

one population health survey that is particularly relevant for housing policy researchers, given its 

recent linkage with HUD administrative data: NHIS.

Prior to the adoption of the six-question disability screener, the nation’s largest population health 

survey, the NHIS, utilized dozens of questions to assess disability. Although the NHIS now 

also includes the six-question screener, researchers interested in examining disability in more 

depth or from a historical perspective can utilize two detailed measures of disability that were 

Cityscape 261

Brucker and Helms

operationalized by NCHS: Basic Actions Difficulty (BAD) and Complex Activity Limitation (CAL) 

(Altman and Bernstein, 2008). These measures are based on the concept of disability as a multidi-

mensional health problem associated with physical, mental, or social limitations.

The BAD disability metric measures disability by assessing the primary functioning that is neces-

sary for a person to accomplish daily tasks, maintain independence, and successfully participate 

in social activities. NHIS data do not cover the full range of functional levels for all classes of basic 

actions, but the available questions can identify a range of difficulty levels in the following core 

areas of functioning, including movement and sensory, emotional, and cognitive functioning.

The CAL metric measures disability based on an individual’s physical, mental, and emotional func-

tioning as it integrates and coexists with participation in the environment and social world. More 

than other traditional disability measures, this method captures social participation. The elements 

identified in the CAL metric that are available in the NHIS include questions based on self-care, 

social limitation, and work limitation.

Linked Administrative and Survey Data

The NHIS is traditionally used by public health researchers; however, a recent interagency col-

laborative between HUD and NCHS resulted in a newly available linked data source for researchers 

interested in housing policy and disability. By linking HUD administrative data from the agency’s 

largest housing assistance programs with NHIS, researchers can now examine disability among 

HUD-assisted residents for the first time. Preliminary research using these linked data suggests 

that adults with disabilities are dispersed throughout HUD assistance programs, not only within 

programs targeting persons with disabilities (Brucker, Helms, and Souza, 2016), suggesting that 

housing policy researchers who study housing assistance programs must consider disability as a 

key demographic variable regardless of program focus.9

Conclusion

Given the strong association among poverty, housing, and disability, housing policy researchers 

must consider persons with disabilities as a key population with unique housing needs. Exhibit 2 

provides examples of disability prevalence from the key sources mentioned previously. 

Due to varying theoretical models and the lack of consistent disability measurement adoption 

until recently, many measures exist to assess the relationship between disability status and housing 

across U.S. population-based, household surveys and HUD administrative data. Researchers should 

cautiously assess the strengths and limitations of disability metrics.

9 To access this linked data source, researchers can visit https://www.cdc.gov/nchs/data-linkage/hud.htm.

262 Data Shop

Measuring Disability

Exhibit 2

Relevant Administrative and Survey Estimates of Disability 

Percent With a 

Source

Unit of Measure

Disability (Year)

American Community Survey

Civilian noninstitutionalized population

13 (2015)a

American Housing Survey

Occupied housing units

22 (2015)b

Annual Social and Economic 

Civilian noninstitutionalized population

12 (2015)c

Supplement of the Current 

Population Survey

Picture of Subsidized Housing

HUD-assisted households of age 61 and 

34 (2015)d

younger with head or spouse with a disability

HUD-assisted households of age 62 and older 

42 (2015)d

with head or spouse with a disability

HUD administrative data linked with  Civilian adults ages 18 and older

44 (2010–2012)e

National Health Interview Survey

National Health Interview Survey

Civilian noninstitutionalized adults

15 (2014)f

a Using the six disability questions screener and data from https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml. 

b Using the six disability questions screener and data from https://www.census.gov/programs-surveys/ahs/data/interactive/

ahstablecreator.html#?s_areas=a00000&s_year=n2015&s_tableName=Table1&s_byGroup1=a1&s_byGroup2=a1&s_

filterGroup1=t1&s_filterGroup2=g1.

c Using the six disability questions screener and data from King et al. (2010).

d Using HUD programmatic definitions of disability and data from https://www.huduser.gov/portal/publications/mdrt/disability-

designatedHousing.html. 

e Brucker, Helms, and Souza (2016).

f Lauer and Houtenville (2017b) using the six disability questions screener. 

Appendix A. Data Sources

Administrative Data

A Picture of Subsidized Households: huduser.gov/portal/datasets/assthsg.html.

U.S. Department of Housing and Urban Development restricted-use administrative data: huduser.

gov/portal/research/pdr_data-license.html.

Survey Data

American Community Survey: http://www.census.gov/programs-surveys/acs/.

American Housing Survey: https://www.census.gov/programs-surveys/ahs/.

Annual Social and Economic Supplement of the Current Population Survey: http://www.census.

gov/programs-surveys/cps/.

Medical Expenditures Panel Survey: https://meps.ahrq.gov/mepsweb/.

National Health Interview Survey: https://www.cdc.gov/nchs/nhis/.

National Survey on Drug Use and Health: https://www.samhsa.gov/data/population-data-nsduh.

Survey on Income and Program Participation: http://www.census.gov/sipp/.

Cityscape 263

Brucker and Helms

Linked Data

U.S. Department of Housing and Urban Development administrative data linked with National 

Health Interview Survey: huduser.gov/portal/datasets/nchs-hud-data-linkage.html.

Acknowledgments

A portion of the work on this project was funded by the U.S. Department of Health and Human 

Services’ National Institute on Disability, Independent Living and Rehabilitation Research, coopera-

tive agreement number 90RT5022-01-00.

Authors

Debra L. Brucker is a research assistant professor at the University of New Hampshire, Institute on 

Disability.

Veronica E. Helms is a social science analyst at the U.S. Department of Housing and Urban Devel-

opment.

References

Altman, Barbara. 2001.  The Handbook of Disability. Thousand Oaks, CA: Sage.

Altman, Barbara, and Amy Bernstein. 2008.  Disability and Health in the United States, 2001–2005. 

Hyattsville, MD: National Center for Health Statistics.

Brucker, Debra, Sophie Mitra, Navena Chaitoo, and Joseph Mauro. 2015. “More Likely To Be Poor 

Whatever the Measure: Working-Age Persons With Disabilities in the United States,” S ocial Science 

Quarterly 96 (1): 273–296. DOI: 10.1111/ssqu.12098.

Brucker, Debra L., Veronica Helms, and Teresa Souza. 2016. Health and Health Care Access 

Among Adults With Disabilities Who Receive Federal Housing Assistance. Under review.  Housing 

Policy Debate. 

Brucker, Debra L., and Andrew J. Houtenville. 2014. “Living on the Edge: Assessing the Economic 

Impacts of Potential Disability Benefit Reductions for Social Security Disability Beneficiaries,” 

Journal of Vocational Rehabilitation 41(3): 209–223. DOI: 10.3233/JVR-140714. November 2014.

Brucker, Debra L., Andrew J. Houtenville, and Eric Lauer. 2015. “Using Sensory, Functional and Ac-

tivity Limitation Data To Estimate Employment Outcomes for Working-Age Persons With Disabilities 

in the U.S.,”  Journal of Disability Policy Studies 27(3): 131–137. DOI: 10.1177/1044207315578949.

Burkhauser, Richard, Andrew Houtenville, and Jennifer Tennant. 2014. “Capturing the Elusive 

Working-Age Population With Disabilities: Reconciling Conflicting Social Success Estimates From 

the Current Population Survey and the American Community Survey,”  Journal of Disability Policy 

Studies 24 (4): 195–205.

264 Data Shop

Measuring Disability

Courtney-Long, Elizabeth, Dianna Carroll, Qing Zhang, Alissa Stevens, Shannon Griffin-Blake, 

Brian Armour, and Vincent Campbell. 2015. “Prevalence of Disability and Disability Type Among 

Adults: United States, 2013,”  Morbidity and Mortality Weekly Report 64 (29): 777–783.

Eggers, Frederick, and Fouad Moumen. 2011.  Disability Variables in the American Housing Survey. 

Report prepared by Econometrica, Inc. Washington, DC: U.S. Department of Housing and Urban 

Development. huduser.gov/publications/pdf/AHSTask8.pdf.

Gordon, Erika, Sandra Chipungu, Lisa Marie Bagley, and Sophia Zanakos. 2005.  Improving Housing 

Subsidy Surveys: Data Collection Techniques for Identifying the Housing Subsidy Status of Survey Respon-

dents. Washington, DC: U.S. Department of Housing and Urban Development.

Hoffman, Denise W., and Gina A. Livermore. 2012. “The House Next Door: A Comparison of 

Residences by Disability Status Using New Measures in the American Housing Survey,”  Cityscape 

14 (1): 5–33.

King, Miriam, Steven Ruggles, J. Trent Alexander, Sarah Flood, Katie Genadek, Matthew B. 

Schroeder, Brandon Trampe, and Rebecca Vick. 2010. “Integrated Public Use Microdata Series, 

Current Population Survey.” Version 3.0. Machine-readable database. Minneapolis, MN: Minnesota 

Population Center.

Lauer, Eric, and Andrew Houtenville. 2017a.  Annual Compendium of Disability Statistics. Durham, 

NH: University of New Hampshire.

———. 2017b.  Estimates of Prevalence, Demographic Characteristics, and Social Factors Among People 

Identified With Disabilities in United States Surveys. Durham, NH: University of New Hampshire. 

Lloyd, Patricia, and Veronica E. Helms. 2016.  NCHS-HUD Linked Data: Analytic Considerations and 

Guidelines. Hyattsville, MD: National Center for Health Statistics.

Martin, Marge, Barry L. Steffen, David A. Vandenbroucke, Yung-Gann D. Yao, Maria T. Souza, and 

Robert A. Collinson. 2011.  Worst Case Housing Needs of People With Disabilities: Supplemental Find-

ings of the Worst Case Housing Needs 2009 Report to Congress. Washington, DC: U.S. Department of 

Housing and Urban Development. huduser.gov/portal/publications/affhsg/wcn_disability.html.

Shakespeare, Tom, and Nicholas Watson. 2001. “The Social Model of Disability: An Outdated 

Ideology?”  Research in Social Science and Disability 2: 9–28. 

Sirken, Monroe. 2002. “Integrating Measurements of Disability in Federal Surveys: Seminar 

Proceedings,”  Vital Health Stat 4 (32): 1–44.

U.S. Department of Housing and Urban Development (HUD). 2016.  Data Dictionary for Picture of 

Subsidized Households: 2015. Washington, DC: U.S. Department of Housing and Urban Development. 

World Health Organization (WHO). 2001.  International Classification of Functioning, Disability and 

Health. Geneva, Switzerland: World Health Organization. 

———. 1980.  WHO Classification of Impairments, Disabilities and Handicaps. Geneva, Switzerland: 

World Health Organization.

Cityscape 265

266 Departments

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.

A Cartographic Perspective on the 

Correlation Between Redlining and 

Public Health in Austin, Texas–1951

John C. Huggins 

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.

Consequences of historic redlining—the once federally sanctioned denial of services to residents 

of predominantly non-White neighborhoods—are often measured in terms of structural decay and 

economic stagnation. However, the effects of redlining are also evident in the health disparities 

observed between maligned neighborhoods and surrounding communities (Gee, 2008). With this 

fact in mind, in this article I juxtapose selected historic maps and data in an effort to examine the 

correlation between redlining and incidence of tuberculosis in 1950s Austin, Texas. 

The impetus for this article was a 1952 fundraising flyer for a campaign sponsored by the Travis 

County Tuberculosis Association. On the back of the flyer was a supplemental map of reported 

cases and deaths resulting from the disease in the Austin area for the previous year (exhibit 1). 

On examining the map, I was struck by a clear concentration of reported cases and deaths from 

the disease in the southeast area of the city, north of the Colorado River between East 1st and East 

11th Streets. The high density of cases reported for that area suggested higher rates of the disease 

in those communities when compared with the rest of the map.

Certainly, maps drawn on the back of antique fundraising flyers found in used bookstores 

hardly constitute vetted data. However, the high density of cases shown for that part of the city 

was consistent with both the historic characteristics of the area and the behavior of the disease. 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 267

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Huggins

Exhibit 1

1952 Travis County Tuberculosis Association Fundraising Flyer (a) and 

Supplemental Map of Reported Cases and Deaths for the Previous Year (b)

(a)

(b)

Southeast Austin, which contains the city’s traditional enclaves of African-American and Hispanic 

communities, has typically exhibited higher rates of socioeconomic distress than have surrounding 

neighborhoods. For example, data gathered from the 1950 U.S. census (exhibit 2) describe high 

rates of dilapidated housing with no private baths and no running water for tracts in southeast 

Austin (Minnesota Population Center, 2016). 

Moreover, tuberculosis—an airborne pathogen commonly thought of as a disease of low-income 

people—is more readily transmitted between people living in poorly ventilated, close quarters 

(Schmidt, 2008). Although the data illustrated in exhibit 2 provide no indication of indoor air quality, 

we can assume that, given the lack of basic services in the area like running water, housing in those 

particular neighborhoods was older, dilapidated, and likely occupied by low-income residents.

While looking over the fundraising flyer, I was reminded of another historic map well known to 

those concerned with housing policy—The Home Owners’ Loan Corporation (HOLC) map for 

the city of Austin. Published in 1934, the map (and several like it) classified sections of the city 

according to an appraised risk for home financing. The HOLC map of Austin categorized and 

268 Graphic Detail

A Cartographic Perspective on the Correlation Between  

Redlining and Public Health in Austin, Texas–1951

Exhibit 2a

Map of 1950 U.S. Census Tracts in Austin, Texas, Depicting Dilapidated Homes 

With No Running Water

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Huggins

Exhibit 2b

Map of 1950 U.S. Census Tracts in Austin, Texas, Depicting Dilapidated Homes 

With No Private Bath

270 Graphic Detail

A Cartographic Perspective on the Correlation Between  

Redlining and Public Health in Austin, Texas–1951

mapped neighborhoods as either best (green), still desirable (blue), definitely declining (yellow), 

and hazardous (red).1 Communities were often deemed hazardous by the HOLC solely on the basis 

of the race and ethnicity of its residents. Hence the term “redlining.” 

Studying both the tuberculosis and HOLC maps makes it clear that the high rates of tuberculosis 

reported on the flyer coincided with Austin’s historic redlined communities. To examine the relation-

ship further, I georeferenced digital images of both maps relative to spatially referenced data of 

present-day Austin. Fortunately, many of Central Austin’s present-day rights of way have changed 

little in the past 75 years, and the digital images of the tuberculosis and HOLC maps overlaid present-

day geographies with little error. Once the maps were aligned, I digitized reported cases of the disease 

(exhibits 3 and 4) and the boundaries of HOLC-redlined neighborhoods (exhibits 5 and 6).

After digitizing point data from the tuberculosis flyer, area data from the HOLC map, and gathering 

socioeconomic information from the 1950 U.S. Census, I added the data sets to a single model to 

view the information in tandem (exhibits 7 and 8). Although the final maps do not demonstrate 

causation, they do present what appears to be a clear alignment of socioeconomic distress, 

structural dilapidation, and poor physical health with the institutional maligning of minority com-

munities that officially began nearly 20 years earlier.

To more effectively demonstrate contrasts in the distribution of cases throughout the city relative 

to redlined neighborhoods, I aggregated cases according to an overlaid mesh of 1/4-mile hexagons, 

a process known as hexbinning (exhibit 9).2 I then used graduated symbols classified at equal 

intervals of five to visualize the resulting count per hexbin (exhibit 10).

In examining the occurrences of cases from this perspective, it becomes clear that (in general) the 

prevalence of the disease appears to have peaked at between one and five cases within a given 

1/4-mile radius. As expected, the exception to this occurred in the redlined neighborhoods in the 

southeast area of the city, where the number of cases per 1/4-mile was as many as 16 to 20.

However, rates were not consistently higher across all of Austin’s redlined communities, which 

raises additional questions about the differences between redlined communities that perpetuated or 

prevented transmission of the disease.

1 A color version of the map can be found at the University of Texas’ Perry-Castañeda Library Map Collection. http://www.

lib.utexas.edu/maps/texas/austin-redlining-small-1935.jpg.  

2 Hexagons measured from the center of the shape.

Cityscape 271

Huggins

Exhibit 3

Digital Copy of the 1951 Tuberculosis Map Georeferenced and Overlaid on a Base 

Map of Austin, Texas 

272 Graphic Detail

A Cartographic Perspective on the Correlation Between  

Redlining and Public Health in Austin, Texas–1951

Exhibit 4

Reported Cases and Deaths From Tuberculosis in 1951 Digitized From the 

Georeferenced 1951 Map of Austin, Texas

Cityscape 273

Huggins

Exhibit 5

Digital Copy of the 1934 Home Owners’ Loan Corporation Map Georeferenced 

and Overlaid on a Base Map of Austin, Texas

274 Graphic Detail

A Cartographic Perspective on the Correlation Between  

Redlining and Public Health in Austin, Texas–1951

Exhibit 6

Digitized Areas of “Hazardous” Redlined Communities From the Georeferenced 

1934 Map of Austin, Texas

Cityscape 275

Huggins

Exhibit 7

Reported Cases of Tuberculosis in 1951 Relative to Dilapidated Homes With No 

Running Water and HOLC-Designated Redlined Communities in Austin, Texas

HOLC = Home Owners’ Loan Corporation.

276 Graphic Detail

A Cartographic Perspective on the Correlation Between  

Redlining and Public Health in Austin, Texas–1951

Exhibit 8

Reported Cases of Tuberculosis in 1951 Relative to Dilapidated Homes With No 

Private Bath and HOLC-Designated Redlined Communities in Austin, Texas

HOLC = Home Owners’ Loan Corporation.

Cityscape 277

Huggins

Exhibit 9

One-Fourth Mile Hexagonal Partition of City and Reported Cases of Tuberculosis 

in 1951 in Austin, Texas

278 Graphic Detail

A Cartographic Perspective on the Correlation Between  

Redlining and Public Health in Austin, Texas–1951

Exhibit 10

Graduated Symbols Indicating the Number of Reported Cases of Tuberculosis in 

1951 per One-Fourth Mile in Austin, Texas

Cityscape 279

Huggins

Author

John C. Huggins is a social science analyst and research geographer with the U.S. Department of 

Housing and Urban Development, Office of Policy Development and Research, Program Monitor-

ing and Research Division.

References 

Gee, Gilbert C. 2008. “A Multilevel Analysis of the Relationship Between Institutional and Indi-

vidual Racial Discrimination and Health Status,”  American Journal of Public Health 98 (1): S48–S56. 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2518571/.

Minnesota Population Center. 2016. “National Historical Geographic Information System: Version 

11.0 [Database].” http://doi.org/10.18128/D050.V11.0.

Schmidt, Charles. 2008. “Linking TB and the Environment: An Overlooked Mitigation Strategy,” 

Environmental Health Perspectives 116 (11): A478–A485. https://www.ncbi.nlm.nih.gov/pmc/

articles/PMC2592293/.

280 Graphic Detail

Impact

A regulatory impact analysis must accompany every economically significant federal rule 

or regulation. The Office of Policy Development and Research performs this analysis for 

all U.S. Department of Housing and Urban Development rules. An impact analysis is a 

forecast of the annual benefits and costs accruing to all parties, including the taxpayers, 

from a given regulation. Modeling these benefits and costs involves use of past research find-

ings, application of economic principles, empirical investigation, and professional judgment.

Reducing the Flood Hazard Exposure 

of HUD-Assisted Properties

Michael K. Hollar 

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.

Background

Following the extensive flood damage caused by Hurricane Sandy, Executive Order 136901 

directed federal agencies to reduce risk associated with floodplain development. In response, the 

Mitigation Framework Leadership Group (MitFLG), which coordinates flood mitigation efforts 

across Federal agencies, studied evidence on past and predicted sea level changes and riverine 

flooding and established the Federal Flood Risk Management Standard (FFRMS).2 The new 

standard requires that buildings be constructed with at least 2 feet of freeboard3 above base flood 

elevation (BFE), or BFE+2. Critical actions, such as construction or substantial rehabilitation of 

hospitals and assisted living facilities, should be constructed with at least 3 feet of freeboard above 

1 “Establishing a Federal Flood Risk Management Standard and a Process for Further Soliciting and Considering Stakeholder Input.”

2 “Revised Guidelines for Implementing Executive Order 11988, Floodplain Management, and Executive Order 13690, Establishing a Federal Flood Risk Management Standard and a Process for Further Soliciting and Considering Stakeholder Input.”

3 Freeboard is defined by FEMA in 44 CFR 59.1 as “a factor of safety usually expressed in feet above a flood level for purposes of floodplain management. ‘Freeboard’ tends to compensate for the many unknown factors that could contribute to flood heights greater than the height calculated for a selected size flood and floodway conditions, such as wave action, bridge openings, and the hydrological effect of urbanization of the watershed.” 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

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BFE (BFE+3) or to the 0.2 percent annual chance (500-year) floodplain,4 whichever is higher. Finally, 

nonresidential structures that are not critical actions, including multifamily structures without resi-

dential units below BFE+2, may be floodproofed instead of elevating to the new standard. The U.S. 

Department of Housing and Urban Development (HUD) proposed the adoption of this standard.5

Impetus for Policy Change 

HUD’s previous standard, which did not require elevation beyond BFE, was insufficient for two 

reasons. First, the rise in sea level and riverine flooding is slowly increasing the BFE. According to 

U.S. Global Change Research Program (2014), the global sea level has risen about 8 inches since 

reliable recordkeeping began in 1880 and is expected to rise another 1 to 4 feet by 2100. The 

frequency of inland and riverine floods is also increasing. IPCC (2013) also confirmed that the sea 

level is expected to continue rising throughout the 21st century. 

Second, flood maps drawn by the U.S. Federal Emergency Management Agency (FEMA) are up-

dated infrequently and therefore do not indicate the true BFE at the time of construction for much 

of the country. Thus, many buildings are inadvertently constructed with the lowest floor below the 

1-percent annual chance (100-year) flood plain. Requiring an additional 2 feet of elevation would 

offset these deficiencies.

The proposed floodplain standard not only addresses the rising sea level and flood map deficien-

cies; it also addresses inefficiencies created by federal disaster assistance and the provision of 

flood insurance. Federal flood insurance suffers from a market failure of information in which 

homeowners do not understand the true flood risk and therefore do not take actions, particularly 

maintaining active flood insurance policies, to reduce this risk. Flood insurance, similar to other 

types of insurance, also suffers from problems arising from asymmetric information and moral 

hazard. Many sellers and developers know the risk of flood damage, but buyers or—in the case of 

multifamily buildings—renters do not always know the risk. Moral hazard occurs as policyholders 

decrease flood mitigation efforts, knowing that flood insurance will cover the costs of flood dam-

age. This section explains the inefficiencies inherent in the provision of federal disaster assistance 

and the market for flood insurance.

Market Failure

Many homeowners lack information and understanding regarding the risks of locating in the flood-

plain, resulting in inefficient pricing whereby house and land prices do not reflect the true flood 

risk, leading to overdevelopment in the floodplain and underinsured homes. Numerous studies 

indicate that many homeowners are either not fully aware of the risk of a flood occurring or that 

they discount the cost of a flood if it occurs. In some cases, owners simply underestimate the risk 

of flooding. Rosenbaum (2005) noted that many studies find homeowners underestimate the risk 

of floods, and even when informed few owners react to offset the risk. For example, Chivers and 

4 As defined in 24 CFR§55.2(b)(3)(i), “critical action” means any activity for which even a slight chance of flooding would be too great, because such flooding might result in loss of life, injury to persons, or damage to property. Applicable critical actions include but are not limited to HUD-insured hospitals and assisted care facilities. 

5 81 FR 74967. October 28, 2016.

282 Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties

Flores (2002) surveyed homebuyers in Boulder, Colorado, and found a market failure of informa-

tion in which homeowners did not fully understand the flood risks or cost of insuring against the 

risk when purchasing their homes. Michel-Kerjan (2010: p.179) also noted that, despite the occur-

rence of floods or natural disasters, homeowners take no action to fortify their homes, likely due 

to “a lack of accurate knowledge about risk; budget constraints; and myopia.” This effect is evident 

through the actions of homeowners. Michel-Kerjan, Lemoyne de Forges, and Kunreuther (2012) 

found that homeowners allowed their flood policies to lapse typically after 2 to 4 years, even for 

federally backed mortgages, which require flood insurance.6 This lapse occurs despite the “one bite 

rule,” which prohibits parties from applying for flood disaster assistance a second time unless flood 

insurance has been maintained.7

This market failure is also found empirically in the price of homes in floodplains. In an efficient 

market, in which the flood risk is fully known, the sales prices of residential properties in the 

floodplain should reflect this risk, selling for a lower price than identical homes located outside of 

the floodplain. Further, the occurrence of a 1-percent annual chance (100-year) flood should not 

affect house prices. However, several studies, including Muckleston (1983), Holway and Burby 

(1990), and Tobin and Montz (1997), found that residential property values in the floodplain do 

not incorporate this risk. Chivers and Flores (2002) concluded that, despite the notification of 

flood risk per National Flood Insurance Program (NFIP) regulations, this risk is only incorporated 

after a flood has occurred. If house prices do not reflect full flood risks, then more settlement 

would occur in floodplains than if the market were efficient.

Evidence suggests, however, that home prices do include the flood risk discount in some markets. 

Kousky (2010) examined the response of home prices in St. Louis County, Missouri, following 

the Great Flood of 1993. Prior to the flood, she found a flood risk discount between 3.2 and 4.5 

percent for homes in the 1-percent annual chance (100-year) floodplain, but no such discount 

for homes in the 0.2-percent annual chance (500-year) floodplain. Home prices did not change 

in response to the flood in the 1-percent annual chance (100-year) floodplain. Prices did decline, 

however, by 2.6 percent in the 0.2 percent annual chance (500-year) floodplain. Since NFIP 

requires homebuyer notification of flood risk in the 1-percent annual chance floodplain but not the 

0.2-percent annual chance floodplain, this finding indicates that home prices reflected the risk in 

the former, but the flood event provided new information to owners in the latter. This implies that 

not all markets are priced inefficiently, and owners in these markets more fully internalize the costs 

related to flooding.

6 For loans made by federally regulated lenders, lenders are responsible for enforcing the flood insurance requirement. 

However, as explained by Michel-Kerjan, Lemoyne de Forges, and Kunreuther (2012), some banks do not ensure the policies remain in force.

7 Under section 582 of the National Flood Insurance Reform Act of 1994 (42 U.S.C. 5154a), HUD disaster assistance that is made available in a special flood hazard area may not be used to make a payment (including any loan assistance payment) to a person for repair, replacement, or restoration for flood damage to any personal, residential, or commercial property if (1) the person had previously received federal flood disaster assistance conditioned on obtaining and maintaining flood insurance, and (2) the person failed to obtain and maintain flood insurance.

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Asymmetric Information

Asymmetric information about the precautionary behavior of an insured household allows 

homeowners and property managers to reduce flood mitigation efforts. Flood insurance policies 

are priced at an actuarially fair rate to reflect the risk of damage based on location, elevation, and 

other structural characteristics. However, insurers do not observe the behavior of individuals, 

who may act riskier or safer than the insurer expects. For riskier individuals, additional flood risk 

is transferred to the insurer. As explained by Stiglitz (1983: p.6), “the more and better insurance 

that is provided against some contingency, the less incentive individuals have to avoid the insured 

event, because the less they bear the full consequences of their actions.” The inability to adjust 

rates for individual risk allows some owners and developers to act riskier without bearing the full 

cost of their actions.

Moral Hazard

In the absence of disaster assistance and federal flood insurance, owners would bear the full cost of 

locating in a floodplain. The federal government, however, has a long history of providing disaster 

assistance and relief to individuals affected by flooding, which has allowed individuals to avoid the 

full cost of flooding. In order to reduce the cost of this assistance and ensure landowners assume 

more of the inherent flood risk, Congress created NFIP in 1968. A government-run insurance 

program was necessitated to resolve market failures caused by information asymmetries. Although 

Congress initially limited FEMA’s ability to charge actuarial rates, allowing owners to bear only part 

of the cost, recent statutory changes require FEMA to phase out these subsidies. 

The financial damage imposed upon a household by a severe flood can be extensive, and the 

potential severity underlying the loss should lead households to guard against this risk through 

insurance, preventative measures, or both. Households can reduce their vulnerability by living in 

less risky areas and by investing in floodproofing. However, many private citizens do not appear 

to take precautions that would be in their own best interest. As an example, Moore (2016) found 

that approximately 2,100 properties across the United States that are enrolled in NFIP have been 

flooded and subsequently rebuilt more than 10 times since 1978. An outlier in Louisiana flooded 

40 times. Some of the residents of these homes may have been able to reduce their losses through 

flood mitigation.

In addition, many homeowners implicitly expect disaster assistance in the event of severe flooding, 

even if they are perfectly informed of flood risks (Burby, 2006; Kunreuther and Michel-Kerjan, 

2010). This expectation leads to the underprovision of flood mitigation, encourages underinsur-

ance, and increases the liability to the federal government. 

Two studies (Browne et al., 2015; Cordes and Yezer, 1998) studied flood insurance specifically. 

Cordes and Yezer (1998) estimated a model of development in 42 beach communities from Florida 

to New York across three decades and found strong evidence of induced development during the 

emergency phase of a community’s participation in NFIP. After controlling for various demand 

factors, storm damage and shore protection projects, the authors found that building permits were 

50 percent higher in the emergency phase of participating NFIP communities. Development during 

284 Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties

the regular phase of the program, however, was unchanged. The large effect found by Cordes and 

Yezer contrasts with the marginal impact concluded by the U.S. Government Accountability Office 

but is not inconsistent with the positive influence. Cordes and Yezer found that initial approval of a 

community for NFIP appears to be the driving factor of development, underscoring the importance 

of having national standards in place before any further development surges. The findings of these 

studies illustrate the excessive risk sharing of the government and the corresponding need for 

development standards.

Browne et al. (2015) studied the effect of NFIP in coastal and noncoastal Florida communities 

from 1975 to 1998 during the regular phase of these communities’ NFIP participation. Estimating 

a model of development similar to the one employed by Cordes and Yezer, except at the county 

level, the authors found that NFIP participation increased development in noncoastal counties by 

55 to 60 percent but decreased development in coastal counties by the same amount, despite the 

higher risk associated with coastal areas. The authors believe this may result from the higher costs 

of regulations in coastal communities. However, if the coastal development occurred earlier than 

otherwise planned in communities that participated in an emergency phase of the program, as 

indicated by Cordes and Yezer, it is not surprising to find that the development during the regular 

phase was lower. In addition, estimating the model at the county level, rather than the community 

level, may bias the results toward areas out of the floodplain or near the edge, where the flood risk 

is lower but more stringent building codes still apply.

Analysis

HUD’s assistance in floodplains generally consists of only noncritical actions, assisting or insuring 

the new construction or substantial improvement of single-family and multifamily properties. 

Because HUD does not routinely insure or fund critical actions, such as mortgage insurance of hos-

pitals and assisted-living facilities, in flood zones, this analysis focuses on the impact to noncritical 

actions in a typical production year. Developers receiving HUD assistance who are not currently 

building to the proposed standard of BFE+2 can meet the proposed standards either by elevating 

the lowest floor of the structure or by floodproofing to the new standard and limiting the first floor 

to nonresidential uses. Alternatively, developers could choose to locate outside of the floodplain 

and the affected horizontal expansion or reduce substantial improvement projects to less than 50 

percent of the market or predisaster value of the structure, which would no longer classify the 

project as “substantial.”

The proposed standards will increase the construction cost of HUD’s assisted and insured new 

construction and substantially improved properties located in the 1-percent annual chance flood-

plain. This amends HUD’s current standard, which requires elevation to at least the BFE. Thus, the 

elevation standards are not new, but rather revised to an increased height.8 Therefore, the costs of 

the new standard increase the marginal cost of construction for the affected properties.

8 The determination of whether a property is located in the floodplain is made using FEMA’s flood maps, as HUD already requires. The determination of whether a property is located in the horizontal expansion can be determined by comparing the property’s elevation, using Google Earth or topographic maps, for example, and the height of BFE identified in FEMA’s flood maps. 

Cityscape 285

Hollar

Developers of new single-family and multifamily structures are expected to meet the new standard 

by elevating the lowest floor.9 Owners of existing single-family homes undergoing substantial 

improvement would also likely meet the standard through elevation due to the relatively low 

cost. Although the cost of elevating multifamily apartment buildings could be prohibitive, most of 

HUD’s assisted and insured multifamily properties located in the floodplain are lowrise, garden-

style buildings. Thus, HUD also expects these properties to elevate. However, for larger buildings, 

developers of substantially improved multifamily structures would likely choose to meet the new 

standard by floodproofing and excluding residential units below the new standard from the build-

ing’s first occupied floor. 

Twenty states, plus the District of Columbia and Puerto Rico, already require elevation exceeding 

HUD’s current standard of BFE+0.10 A further four states—Indiana, Montana, New York, and 

Wisconsin—already require residential structures elevated with a minimum of BFE+2. Thus, the 

cost of compliance in these states would be less than in those that have no minimum elevation 

requirements in the floodplain. In addition, its likely that some of the HUD-assisted properties in 

the remaining states, which do not impose elevation above BFE, do in fact build to a higher eleva-

tion than HUD’s minimum standards. To the extent that such building occurs, the effect of this rule 

would be less than the following estimate.

The costs of floodproofing for a substantial improvement project are greater than for new construc-

tion because an existing structure has less flexibility for modification. The economic benefit of 

elevation—that is, the avoided damage—will be the same whether newly built or rehabilitated (see 

exhibit 3 in the Costs of Rule section). Because a rehabilitation project brings the level of housing 

capital to the same level of newly built housing, the cost of restoring a building is the same for 

newly built or redeveloped housing. Thus, we would expect the net benefit of floodproofing to be 

greater for new construction because the cost is lower.

Exhibit 1 shows the estimated number of single-family and multifamily properties that would be 

affected by the standard proposed at their current state elevation standards. During Federal Hous-

ing Administration (FHA) insurance year 2012, 116 newly constructed and 821 substantially reha-

bilitated single-family properties were located in flood zones of states that require less than 2 feet 

of freeboard. Geocodable information, which is required to determine location in floodplains, is 

available for HUD’s largest production programs, including public housing and properties financed 

with FHA multifamily insurance. Geocoded information for multifamily properties receiving funds 

9 Alternatively, a developer could choose to locate outside of the floodplain. Doing so would likely not change revenue since many of HUD’s multifamily programs set income and rent limits by metropolitan area. In addition, Holway and Burby (1990) found that land values decrease $74 per 1,000 square feet in response to elevation requirements of 1 foot. For a typical FHA-insured home of 1,800 square feet on a lot between three and four times the house size, the decrease in land value, before the developer purchases the land, would total approximately $400 to $500. This amount is within in the range of increased costs of elevation. Thus, the change in location is unlikely to result in different costs to the developer and therefore the developer is unlikely to choose an alternate location.

10 Arizona, Colorado, Georgia, Illinois, Iowa, Kansas, Maryland, Maine, Michigan, Minnesota, North Dakota, Nebraska, New Jersey, Oregon, Puerto Rico, and Rhode Island require BFE+1. The District of Columbia and Pennsylvania require BFE+1.5. Indiana, Montana, New York, and Wisconsin require BFE+2. See www.floods.org/ace-files/documentlibrary/

FloodRiskMngmtStandard/States_with_freeboard_and_CRS_Communities_with_Freeboard_in_Other_states_2-27-15.pdf.

286 Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties

Exhibit 1

Annual Average Number of FHA-Insured Single-Family and HUD-Assisted 

Multifamily Properties Located in 1-Percent Annual Chance Floodplain

HUD-Assisted  

FHA-Insured  

Multifamily Properties

State  

Single-Family Properties

Construction  

New  

Substantial  

Standard

Construction

Rehabilitation

New  

Substantial   Properties Units Properties Units

Construction

Rehabilitation

No standard specified

101

581

117      

1,725      

3      

117      

Coastal states

95

552

117      

1,725      

3      

117      

Inland states

6

29

0      

0      

0      

0      

BFE +1 foot

15

220

46      

366      

7      

128      

Coastal states

11

147

46      

366      

7      

128      

Inland states

4

73

0      

0      

0      

0      

BFE +1.5 feet

0

20

0      

0      

0      

0      

Coastal states

0

20

0      

0      

0      

0      

Inland states

0

0

0      

0      

0      

0      

All properties

232

1,622

163      

2,091      

10      

245      

BFE = base flood elevation. FHA = Federal Housing Administration. HUD = U.S. Department of Housing and Urban Development. 

from one of HUD’s grant programs, such as the Community Development Block Grant (CDBG)11 

and HOME programs, is available only for properties that also received low-income housing tax 

credits. Annual production averages for these programs rely on production years 2011 through 

2013, which are the most recent available. Overall, approximately 163 newly constructed and 10 

substantially rehabilitated multifamily properties each year were located in the 1-percent annual 

chance (100-year) floodplain of states that require less than 2 feet of freeboard. 

Costs of Rule 

The estimated costs of elevating newly constructed single-family and multifamily structures are 

shown in exhibit 2. HUD estimates the construction cost of elevating for new construction projects 

based on findings from two engineering studies conducted for FEMA.12 These studies examine the 

construction cost of elevation for new residential structures in Coastal A Zones, V Zones, and A Zones 

for various foundation types, building sizes, elevation levels of the lowest floor, and flood conditions. 

The term  Coastal A Zone is used to differentiate areas that are subject to breaking waves up to 3 feet and conditions similar to, but less severe than, V Zones. Base flood conditions in A Zones resemble 

those in riverine areas. Thus, although these studies focus on coastal areas, the results are equally 

applicable to riverine areas.

11 Nonresidential actions funded by CDBG that are located in the floodplain are subject to an eight-step environmental review process but not elevation requirements. Thus, expanding the affected area beyond the 1-percent annual change floodplain to include horizontal expansion would subject more CDBG projects to the review process. The cost of compliance for these projects, however, would be negligible. 

12 See Jones et al. (2006) and updated in FEMA (2013). 

Cityscape 287

Hollar

Exhibit 2

Cost of Elevation for Newly Constructed HUD-Assisted and HUD-Insured Properties 

in 1-Percent Annual Chance Flood Zone

Increase in 

New  

Aggregate Increase in 

State 

Cost of Freeboard Construction Costs 

Construction 

Construction Costs

Construction 

Per Property 

Standard

Properties  Units  Minimum  Maximum  Minimum  Maximum  Minimum  Maximum 

(#)

(#)

(%)

(%)

($)

($)

($)

($)

Single-family

No standard 

101

NA

specified

Coastal states

95

NA

0.30

4.80

317    

5,074    

30,125    

481,992    

Inland states

6

NA

0.30

4.50

317    

4,757    

1,903    

28,539    

BFE +1 foot

15

NA

Coastal states

11

NA

0.20

3.90

211    

4,122    

2,325    

45,345    

Inland states

4

NA

0.20

2.30

211    

2,431    

846    

9,724    

BFE +1.5 feet

0

NA

Coastal states

0

NA

0.10

1.95

106    

2,061    

0    

0    

Inland states

0

NA

0.10

1.15

106    

1,216    

0    

0    

All properties

116

NA

35,198    

565,601    

Multifamily

No standard 

117

1,725

0.30

4.80

4,423    

70,769    

517,500    

8,280,000    

specified

BFE +1 foot

46

366

0.20

3.90

1,591    

31,030    

73,200    

1,427,400    

BFE +1.5 feet

0

0

0.10

1.95

0    

0    

0    

0    

All properties

163

2,091

590,700    

9,707,400    

Increase due 

99,238    

1,630,843    

to horizontal 

expansion

Overall total

689,938     11,338,243    

BFE = base flood elevation. HUD = U.S. Department of Housing and Urban Development. NA = not applicable.

Based on these findings, the construction cost of increasing the base of a new residential structure 

2 additional feet of vertical elevation varies from 0.3 to 4.8 percent of the base building cost. The 

increased cost as a percentage of construction cost is highest in Coastal A Zones, although the 

cost range is generally similar across the three flood zones. Although HUD’s geocoding identifies 

whether buildings are located in the 1-percent annual chance floodplain, details are not available 

on which flood zone the building is located. Thus, for coastal states, HUD applies the full cost 

range across the three flood zones. For inland states, HUD applies only the A Zone cost.

Based on the median mortgage amount for FHA-insured properties in the 1-percent annual chance 

(100-year) flood zone, the estimated average cost of construction for FHA-insured single-family 

properties totals approximately $105,700 for newly constructed homes. Construction costs of 

new multifamily properties average approximately $100,000 per unit. Applying the previously 

discussed construction cost range, the cost of elevating a single-family home an additional 2 feet 

would add as much as $5,074 to the total cost of construction. The additional cost per multifamily 

property, based on a weighted average of HUD-assisted and HUD-insured properties located in 

the floodplain, for an additional 2 feet of elevation would cost up to $70,769 per property. These 

costs should generally be considered a maximum because most HUD-assisted or HUD-insured 

substantial improvement projects already involve elevation to the current standard, BFE+0. Thus, 

288 Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties

the cost of elevating a structure an additional 2 feet would be marginal compared with the initial 

cost of elevation to the floodplain level. The aggregate increased costs of construction for newly 

constructed FHA-insured properties would range from $0.035 million to $0.566 million for single-

family properties and from $0.591 million to $9.707 million for multifamily properties.

Adding 2 feet of freeboard would extend the floodplain and the area impacted by this rule 

horizontally, except in the case of the FHA single-family mortgage insurance program for one- to 

four-unit properties. The distance of this horizontal expansion depends on the topography of local 

jurisdictions. Based on available data from 20 coastal counties in six states,13 FEMA estimates that the 

horizontal expansion from the additional 2 feet of freeboard would increase the area of the floodplain, 

and thus the affected area, by 16.8 percent on average across the country. For purposes of this analy-

sis, HUD assumes that the number of affected properties would also increase by this percentage. This 

estimate, however, likely overestimates the impact because HUD’s assisted and insured properties are 

not evenly distributed across space, and the counties in the sample are more concentrated in higher-

flood-risk areas than HUD’s stock. Furthermore, none of these counties includes riverine floodplains, 

which would likely have a smaller extension. Also, less than 1 percent of HUD-assisted multifamily 

properties and less than 2 percent of single-family properties that are in the floodplain are in these 

counties. Including the 16.8-percent increase from horizontal expansion, the aggregate increased 

construction cost for multifamily properties ranges from $0.690 million to $11.338 million.

Substantial improvement projects require a more involved process of lifting the structure and 

extending the foundation. The cost of elevating single-family homes depends on factors such as the 

foundation type, but costs of elevation generally range from $7,682 to $22,022 per foot that the 

house is elevated.14 Owners of multifamily properties could choose to either elevate or floodproof 

and use any floor below the standard for nonresidential purposes. Because virtually all HUD-assisted 

and HUD-insured multifamily properties are two-story, garden-style apartments, HUD expects 

these properties to opt for elevating. Based on the elevation of larger structures noted in ASFPM 

(2002), HUD estimates that the cost of elevating small multifamily buildings ranges from $4,097 

to $11,745 per unit per foot of elevation. These costs should be considered a maximum because 

most HUD-assisted or HUD-insured substantial improvement projects already involve elevation to 

the current standard, BFE+0. Thus, the cost of elevating a structure as much as an additional 2 feet 

would be marginal compared with the initial cost of elevation to the floodplain level. 

As stated previously, HUD expects multifamily properties to meet the standard by raising the 

building base elevation rather than by eliminating residential units on the first floor. Choosing the 

latter, however, could lead to lost revenue either because fewer units are produced or because of 

the higher construction costs of adding a floor to maintain the same number of units. Alternatively, 

some developers may choose to locate outside of the floodplain and construct the same number 

of units. Revenue would likely remain the same in this case, because many of HUD’s multifamily 

programs set rent limits by metropolitan area. Any change in rent, however, would be offset by 

13 The counties included in the horizontal expansion calculation are San Francisco County, California; Flagler and Nassau Counties, Florida; Charlton, Chatham, Douglas, Effingham, and Forsyth Counties, Georgia; Beaufort, Brunswick, Carteret, Craven, Hyde, New Hanover, Onslow, Pamlico, and Pender Counties, North Carolina; Colleton and Horry Counties, South Carolina; and Grays Harbor County, Washington. 

14 See ASFPM (2002). Estimates are adjusted for inflation.

Cityscape 289

Hollar

further decreased flood insurance premiums and avoided damage. Furthermore, this change would 

affect only properties at the margin with viable alternatives to locating inside and outside the flood 

zone. HUD does not have an estimate for the number of properties that may choose this option.

Exhibit 3 shows the cost of elevating substantially rehabilitated, HUD-insured single-family homes 

and HUD-assisted multifamily properties. In FHA insurance year 2012, HUD insured 581 single-

family properties in the 1-percent annual chance floodplain in states that do not require elevation 

above the BFE. Another 220 homes were insured in states that require construction to at least 

BFE+1 and 20 homes in states that require at least BFE+1.5. The aggregate increased construction 

cost for these single-family homes to meet the proposed standard ranges from $10.693 million to 

$30.655 million. 

HUD assists far fewer substantial improvement projects of multifamily properties in the floodplain. 

Annually, only 10 properties containing 245 units are typically assisted in the floodplain. Most 

of these properties, comprising slightly more than one-half of the units, are in states that require 

construction to at least BFE+1. Overall, the aggregate costs of the proposed standard ranges from 

$1.483 million to $4.252 million, and from $1.732 million to $4.966 million if including horizon-

tal expansion.

Exhibit 3

Cost of Elevation for HUD-Assisted and HUD-Insured Substantial Improvement 

Properties in the 1-Percent Annual Chance Flood Zone

New  

Cost of Freeboard  

Aggregate Increase in 

State 

Construction 

per Unit

Construction Costs

Construction  

Standard

Properties 

Units 

Minimum  Maximum  Minimum  Maximum 

(#)

(#)

($)

($)

($)

($)

Single-family

No standard specified

581

NA

Coastal states

552

NA

15,365    

44,045    

8,481,396   24,312,824  

Inland states

29

NA

15,365    

44,045    

445,581  

1,277,304  

BFE +1 foot

220

NA

Coastal states

147

NA

7,682    

22,022    

1,129,316  

3,237,305  

Inland states

73

NA

7,682    

22,022    

560,817  

1,607,641  

BFE +1.5 feet

20

NA

Coastal states

20

NA

3,841    

11,011    

76,824  

220,225  

Inland states

0

NA

3,841    

11,011    

0  

0  

All properties

1,622

NA

10,693,934   30,655,300  

Multifamily

No standard specified

3

117

8,195    

23,491    

958,766    

2,748,406    

BFE +1 foot

7

128

4,097    

11,745    

524,453    

1,503,402    

BFE +1.5 feet

0

0

2,049    

5,873    

0    

0    

All properties

10

245

1,483,220    

4,251,808    

Increase due to horizontal 

249,181    

714,304    

expansion

Overall total

1,732,401    

4,966,112    

BFE = base flood elevation. HUD = U.S. Department of Housing and Urban Development. NA = not applicable.

290 Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties

Benefits of Rule

The standard proposed in this rule that requires that buildings be constructed with at least 2 feet 

of freeboard above BFE is intended to protect HUD-assisted and HUD-insured structures and the 

owners and tenants in these units. Thus, the benefits of the rule include reduced building damage 

and decreased costs to tenants temporarily displaced due to flooding, the latter of which includes 

avoiding search costs for temporary replacement housing and lost wages. The annual reduction in 

insurance premiums provides an adequate measure of the reduction in expected damages, assum-

ing that NFIP rates are calculated to maintain a nonnegative balance. In this case, the premiums 

for catastrophic insurance would be slightly higher than, but similar to, the expected value of the 

claim to pay for administrative costs. It is not clear whether NFIP will operate without deficits or 

need for appropriations after the most recent reforms15 (GAO, 2014). If the reduction in the insur-

ance premium is not equivalent to the change in the risk-adjusted expected cost of claim (higher or 

lower), then a transfer to or from the insured will come from or go to the insurer. 

Flood insurance premiums vary based on a variety of factors, such as the structure’s height above 

BFE. Estimation of the expected reduction in damage for both new construction and substantial 

improvement projects, measured by the reduction in premiums, rely on the engineering studies 

discussed previously, which examine the effect of freeboard on new residential buildings in 

floodplains. As previously mentioned, although the studies focus on coastal areas, they separately 

model the effects in the Coastal A Zone and the A Zone. Flood conditions in the A Zone are subject 

to stillwater flooding and have a low threshold for wave damage. Therefore, the results of these 

studies are equally applicable to riverine areas. Similarly, HUD also applies findings to both new 

construction and substantial improvement projects because substantial improvement increases the 

value of the structure, with all else equal, to be near the new construction value. 

FEMA (2013) estimated that the average annual premium savings as a percentage of construction 

cost ranges from 0.24 to 0.87 percent. The savings are greatest in the V Zone (0.87 percent) com-

pared with the Coastal A (0.24 percent) and A (0.26 percent) Zones. Exhibit 4 shows the aggregate 

savings for a range of BFE levels, based on a typical production year for FHA-insured single-family 

properties, and exhibit 5 shows the same for HUD-assisted multifamily properties. These estimates 

reflect compliance with the minimum standard of 2 feet of freeboard. On learning of the benefits 

related to elevation, owners may choose to elevate to a level higher than the minimum required or 

take other precautions to minimize the flood risk.

For single-family properties, moving to the BFE+2 standard will decrease NFIP premiums in ag-

gregate by a minimum of $142,987 ($28,687 + $114,300) and a maximum of $453,231 ($95,088 

+ $3,587,144) annually. Assuming a 30-year useful life, the aggregate discounted savings range 

from $1.717 million to $9.150 million, assuming a 3-percent discount rate, and $1.208 million to 

$6.018 million, assuming a 7-percent discount rate. 

15 The Biggert Waters Flood Insurance Reform Act of 2012 (Pub. L. 112–141, 126 Stat. 916. July 6, 2012) included provisions requiring FEMA to charge actuarial rates in order to allow the agency to repay the $24 billion loaned from the U.S. Treasury. The Homeowner Flood Insurance Affordability Act of 2014 (Pub. L. 113–89, 128 Stat. 1020. March 21, 2014), however, restricted the amount premiums could increase, slowing FEMA’s ability to charge full risk rates and repay the loan.  

Cityscape 291

Hollar

 b

0

0

9,507

7%

903,408 53,760

157,445 84,261

84,465

618,420 132,386

37,343

1,208,382

5,145,245

6,017,860

($)

0

0

3%

76,391

223,723 119,732

13,510

128,427

940,295 201,291

56,780

Discounted Savings

1,283,707

1,717,064

7,823,249

9,150,043

0      

0      

82,800       4,713      

16,538       9,125      

1,125      

4,713      

9,125      

2,813      

114,300      

300,150      

41,344      

358,144      

Substantial 

Rehabilitation

($)

0       0      

0       0      

egate Annual Savings

846      

846      

New 

24,100       1,649      

2,093      

28,687      

87,361       1,649      

5,232      

95,088      

Aggr

Construction

150         163        

113         125        

56         63        

544         163        

281         125        

141         63        

Substantial 

Rehabilitation

cent Annual Chance Floodplain

($)

operty Annual Savings 

95        

New 

254         275        

190         211        

106        

920         275        

476         211        

238         106        

gs.

Per Pr

Construction

a 

operties in 1-Per

verage 

emium 

(%)

0.24 0.26

0.18 0.20

0.09 0.10

0.87 0.26

0.45 0.20

0.23 0.10

A

Pr

Savings

0

0

552 29

147 73

20

801

552 29

147 73

20

801

operties

Substantial 

Rehabilitation

ed Single-Family Pr

95 6

11 4

0 0

95 6

11 4

0 0

Number of Pr

New 

116

116

Construction

d

d specified

d specified

State

Standar

Construction 

operties

operties

voided Damage to FHA-Insur

savings

Coastal states Inland states

Coastal states Inland states

Coastal states Inland states

savings

Coastal states Inland states

Coastal states Inland states

Coastal states Inland states

Exhibit 4

A

Miniumum estimated 

No standar

BFE +1 foot

BFE +1.5 feet

All pr

Maxiumum estimated 

No standar

BFE +1 foot

BFE +1.5 feet

All pr

BFE = base flood elevation. HUD = U.S. Department of Housing and Urban Development. 

 Decrease in annual flood insurance premiums as percentage of construction costs.

 Discounted annual insurance premium savings based on 30-year useful life of buildin

a

b

292 Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties

0

0

b

7%

5,664,734

1,012,396

6,677,130 7,798,888

20,534,660

2,530,990

23,065,650 26,940,679

($)

0

0

3%

Discounted Savings

8,613,122

1,539,329

10,152,451 11,858,063

31,222,565

3,848,323

35,070,889 40,962,798

0

0

12,636

10,368

23,004

45,806

25,920

71,726

Substantial 

Rehabilitation

($)

0

0

egate Annual Savings

New 

414,000

65,880

479,880

164,700

1,500,750

1,665,450

Aggr

Construction

0

0

cent Annual Chance Floodplain

4,212

1,481

15,269

3,703

Substantial 

Rehabilitation

($)

0

0

operty Annual Savings 

New 

3,538

1,432

12,827

3,580

gs.

Per Pr

Construction

a 

(%)

0.24

0.18 0.09

0.87

0.45 0.23

operties Located in 1-Per

verage 

emium 

A

Pr

Savings

0

0

117

128

245

117

128

Units

5,499,000

Substantial 

3

7 0

3

7 0

Rehabilitation

10

operties

117

Pr

0

0

366

366

Units

1,725

2,091

1,725

1,000

operties

46 0

46 0 47

Number of 

Pr

117

163

117

opertiesPr

d

d 

d 

State

Standar

operties

operties

voided Damage to HUD-Assisted Multifamily Pr

Construction 

estimated 

savings

specified

horizontal 

expansion

estimated 

savings

specified

horizontal 

expansion

Exhibit 5

A

Miniumum 

No standar

BFE +1 foot BFE +1.5 feet All pr Including 

Maxiumum 

No standar

BFE +1 foot BFE +1.5 feet All pr Including 

BFE = base flood elevation. HUD = U.S. Department of Housing and Urban Development. 

 Decrease in annual flood insurance premiums as percentage of construction costs.

 Discounted annual insurance premium savings based on 30-year useful life of buildin

a

b

Cityscape 293

Hollar

NFIP premiums for multifamily properties will decrease in aggregate from $0.503 million ($0.480 

million + $0.023 million) to $1.737 million ($1.665 million + $0.071 million) annually. Over a 

30-year structure life, the aggregate discounted savings ranges from $10.152 million to $35.071 

million, assuming a 3-percent discount rate, and $6.677 million to $23.066 million, assuming a 

7-percent discount rate. 

In addition to savings on insurance premiums, homeowners and tenants would also accrue signifi-

cant benefits by avoiding the costs of moving from a flooded property. The family cost of moving 

a two-bedroom apartment costs approximately $800 plus lost wages. According to the American 

Moving and Storage Association (Williams 2014), an average in-state move costs $1,170, based on 

an average move weight of 7,100 pounds, or approximately $0.16 per pound. Based on a review of 

typical apartment weights, the median estimate for a two-bedroom apartment is 5,000 pounds.16 

Thus, the cost of moving a two-bedroom apartment is approximately $800 ($0.16 x 5,000). Using 

the national median hourly wage reported by the U.S. Bureau of Labor Statistics of $16.71, if 

affected households’ wage earners are unable to work for a cumulative 40 hours each, due to time 

spent doing flood-related apartment searching and moving, a family would lose $668. Moving 

costs and lost wages combined would cost each tenant an estimated $1,468. 

Homeowners and tenants living in the 100-year floodplain face a 1-percent chance each year that 

a flood will occur and that they will need to temporarily relocate. Increasing the base elevation by 

2 feet would place the building, on average, at the 250-year floodplain, which has a 0.4-percent 

probability of experiencing a flood each year. Based on the weighted average of FHA-insured and 

HUD-assisted multifamily properties located in the floodplain each year—based on current state 

and local standards—this rule decreases the annual risk by 0.67 percent. The expected value of 

decreased owner and tenant costs is $9.88 per household ($1,468 x 0.67 percent). The discounted 

30-year value of these avoided costs is $199 per household, assuming a 3-percent discount rate, 

and $131 per household, assuming a 7-percent discount rate. Aggregating over the combined 

3,273 units (both single-family and multifamily), the total benefit to owners and tenants is $0.643 

million, assuming a 3-percent discount rate, and $0.423 million, assuming a 7-percent discount 

rate. Including horizontal expansion, the benefits increase to $0.751 million using a 3-percent 

discount rate and $0.494 using a 7-percent discount rate.

Exhibit 6 summarizes the expected benefits generated from the proposed standard of 2 feet of free-

board. Overall, the maximum valued benefits of this rule, including horizontal expansion (where 

applicable) and higher estimates of damage avoided, total $50.657 million, assuming a 3-percent 

discount rate, and $33.317 million, assuming a 7-percent discount rate.

In addition to the benefits discussed previously, tenants experience unvalued benefits of avoiding 

temporary relocation. Being forced to relocate on short notice creates considerable stress and 

uncertainty for families. Furthermore, some families may not be able to find affordable housing 

in their immediate area and could be forced to move farther away, sometimes out of state. Long-

distance moves remove a family from their local social network and add additional stress not only 

on adults, but also on children who may be required to enroll in different schools. 

16 Average apartment weights are calculated from a range of industry estimates, including www.citytocitymoving.us, www.

movers.com,  and www.movingcompanies.us.  

294 Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties

Exhibit 6

Valued Benefits of BFE+2 Standard

Excluding Horizontal Expansion 

Including Horizontal Expansion 

Benefits

Discount Rate ($)

Discount Rate ($)

3%

7%

3%

7%

Avoided damage

Minimum

11,869,515

7,885,512

13,575,126

9,007,270

Maximum

44,220,932

29,083,510

50,112,841

32,958,539

Avoided tenant costs

642,599

422,629

750,555

493,630

Total

Minimum

12,335,648

8,192,082

14,119,571

9,365,343

Maximum

44,687,065

29,390,080

50,657,285

33,316,613

BFE = base flood elevation. 

Coastal Versus Inland States

As explained previously, one concern about the applicability of the study (Jones et al., 2006) on 

which HUD’s analysis relies is that the data were collected from coastal states, where costs and 

damage due to flooding may differ from inland states. Thus, it is important to consider how using 

data from coastal states may affect our estimates. Coastal states will possess A Zones, Coastal A 

Zones, and V Zones; however, inland states will possess only A Zones.17 

The Coastal A Zone distinction was developed to encourage more effective flood mitigation. A 

FEMA (2005: p.1) study of damage during Hurricane Katrina found that “typical A Zone construc-

tion techniques … are subject to damage when exposed to less than 3-foot breaking waves, which 

is the current threshold for V Zone conditions.” Higher standards, roughly equivalent to V Zone 

standards, are recommended but not required for A Zones in coastal areas. The Coastal A Zone is 

not shown on Flood Insurance Rate Maps because the federally mandated design standards are the 

same for all A Zones.

A more recent report (FEMA, 2009: p.1) after Hurricane Ike defined the Coastal A Zone as one that 

is “landward of a V Zone, or landward of an open coast without mapped V Zones.” The Coastal A 

Zone’s source of flooding ranges from “astronomical tides, storm surges, seiches or tsunamis, not 

riverine flooding.” According to the report, “communities, designers, and owners, will have to 

determine whether a site lies within a Coastal A Zone” (FEMA, 2009: p.2). Without any geographic 

data, it is difficult to analyze the frequency of development in Coastal A Zones.

A Zones in coastal areas (as opposed to Coastal A Zones) are characterized as “areas with shallow 

flooding only, where potential for damaging waves and erosion is low” (FEMA, 2005: p.1). This 

definition is identical whether the zone is inland or near a coast. Fortunately, the FEMA report 

differentiates between A Zones and Coastal A Zones so that the increase in compliance costs can be 

calculated for properties in A Zones of inland states as well as those in coastal states.

In a typical production year, all HUD-assisted new construction in flood zones occurs in coastal 

states, and nearly 60 percent of HUD-assisted single-family development in flood zones occurs in 

17 HUD recognizes that flooding in inland states with major rivers may be more severe than in some coastal states.  

Cityscape 295

Hollar

coastal states. According to FEMA (2013), the cost of adding 2 feet of freeboard (BFE+2) is lower 

in A Zones than in Coastal A Zones (0.3 to 4.5 percent of construction cost for A Zones compared 

with 0.7 to 4.8 percent for Coastal A Zones). The maximum cost in V Zones of BFE+2 is 3.6 

percent of total construction cost. The benefits may be different as well. Although A Zones and 

Coastal A Zones are expected to experience similar insurance savings as a percentage of the cost of 

construction, development in V Zones may experience significantly greater benefits. Thus, both the 

costs and benefits of the rule could be lower in inland areas, which have only A Zones.

Deaths Prevented and Health-Related Benefits

Although the purpose and expected impact of this rule is the protection of HUD investments and 

the uninterrupted provision of affordable housing, it is reasonable to expect that building to higher 

construction standards would prevent injuries or deaths related to tenant evacuation during or 

soon after a dangerous flood. According to National Weather Service data (NWS n.d.a, n.d.b), 

from 1995 to 2015, 82 deaths occurred per year due to flash and river floods. A large portion of 

these deaths occurred in vehicles as drivers tried to cross flooded roads or hydroplaned. In 2015, 

176 deaths occurred due to floods, and 64 percent of these involved a vehicle. In contrast, 11 

deaths, or 6.3 percent, occurred within an individual’s permanent home. Since 1995, flooding was 

involved in 76 deaths in permanent homes and 35 deaths in mobile homes, making up 4.4 and 2.0 

percent of total flood-related deaths in that time period, respectively. Of these flood-related deaths, 

373, or 21.7 percent, were of elderly people (those over the age of 60).

Ashley and Ashley (2008) found that between 1959 and 2005, 4,586 flood-related fatalities oc-

curred in the contiguous United States. On average, approximately 97.6 flood-related fatalities oc-

curred per year, with a median value for reported deaths of 81. Most of these flood-related fatalities 

occurred due to flash floods, followed by traditional floods and tropical systems. Fewer than 200 

of the 4,586 flood-related deaths occurred in a permanent structure, although this report did not 

distinguish between residential and nonresidential structures. This report also noted that people 

between the ages of 10 and 29 and people older than age 60 were more vulnerable to floods. 

However, the data used for this study, from the National CDs of Environmental Information Storm 

Events database, contained unknown ages for 63 percent of the fatalities.

Rappaport (2014) examined the fatalities that occurred in the United States from Atlantic tropical 

cyclones and found that, between 1963 and 2012, approximately 2,544 people died in the United 

States due to tropical cyclones, equating to roughly 50 deaths per year on average.18 Roughly 90 

percent of these deaths occurred in water-related incidents like drowning, 49 percent were caused 

by storm surge, and 27 percent were caused by freshwater floods and mudslides. In an overview of 

deaths caused by Hurricane Katrina, the report noted than many died due to drowning and entrap-

ment in attics in the home.

Aside from decreasing deaths, the higher standards proposed in this rule will also decrease the 

incidence of mold. The additional 2 feet of freeboard will prevent water damage from a 1-percent 

annual chance flood. As FEMA, the Centers for Disease Control and Prevention (CDC), and other 

18 Atlantic tropical cyclones affect only coastal areas in the Eastern United States.  

296 Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties

federal agencies have noted (CDC, n.d.; Hurricane Sandy Rebuilding Task Force, 2015), mold 

after flooding can cause a number of health issues to residents. Children, elderly people, pregnant 

women, and people with respiratory conditions such as asthma are at high risk for adverse health 

effects from mold.

The CDC also notes that floodwater can bring harmful contaminants into a home. For example, 

contaminated water can contain bacteria that cause infectious diseases and diarrhea. Also, floods 

can wash chemicals; displaced insects, reptiles, and other animals; and sharp objects into a home. 

Residents also face electrical hazards from downed power lines and flooded electrical equipment; 

if not property ventilated, diesel- and gasoline-powered generators can cause carbon monoxide 

poisoning. Floods can also cause indirect impacts, such as destruction of infrastructure that could 

help provide medicine, food, public services, or other needed supplies in an emergency.

Summary of Benefits, Transfers, and Costs

HUD proposed new elevation standards for its funded and assisted residential properties, due to 

the increased sea level and frequency of riverine flooding. The new standard also addresses issues 

that affect the insurance industry, including a market failure of information, asymmetric informa-

tion on occupant flood mitigation efforts, and the moral hazard associated with insured properties 

and the assumption of governmental disaster relief. Increasing the required minimum elevation 

of HUD-assisted structures located in and around the floodplain will prevent or reduce damage 

caused by flooding and avoid relocation costs to tenants associated with temporary moves when 

HUD-assisted structures sustain flood damage and are temporarily uninhabitable. These benefits, 

which are realized throughout the life of HUD-assisted structures, are offset only by the one-time 

increase in construction costs.

As explained previously, HUD estimates that requiring developers to construct or floodproof HUD-

funded and HUD-insured properties to BFE+2 will increase construction costs by $12.803 million 

to $47.525 million total. Benefits of the increased standard include avoided damage to buildings, 

as measured by decreased insurance premiums, and avoided costs associated with tenants being 

displaced. These benefits occur annually over the life of the structures. Over a 30-year period, the 

present value of aggregate benefits total between $12.336 million to $50.657 million, assuming 

a 3-percent discount rate, and between $8.192 million to $33.317 million, assuming a 7-percent 

discount rate.

These estimates are based on the annual production of HUD-assisted and HUD-insured structures 

in the floodplain and accounts for the 20 states, plus the District of Columbia and Puerto Rico, 

with existing freeboard requirements. The cost of compliance and expected benefits are lower in 

these states than in states that have no minimum elevation requirements above the BFE. HUD’s 

analysis does not consider benefits due to further coastal sea level or riverine rise. Further increase 

in the sea level rise or inland and riverine flooding would increase the benefits of this rule.

Cityscape 297

Hollar

Acknowledgments

The author thanks Michael Flannelly for his capable research assistance and Jeremiah Sanders for 

sharing his insights and expertise on floodplain management. 

Author

Michael K. Hollar is a senior economist in the U.S. Department of Housing and Urban Develop-

ment, Office of Policy Development and Research.

References 

Ashley, Sharon T., and Walker S. Ashley. 2008. “Flood Fatalities in the United States,”  Journal of 

Applied Meteorology and Climatology 47(3): 805–818.

Association of State Floodplain Managers (ASFPM). 2002.  Mitigation Success Stories in the United 

States. Edition 4 (January). Madison, WI: Association of State Floodplain Managers.

Browne, Mark, Carolyn Dehring, Donald Eckles, and William Lastrapes. 2015. Does National 

Flood Insurance Program Participation Induce Housing Development? Submitted for publication at 

Journal of Risk and Insurance. Accessed version dated August 28.

Burby, Raymond J. 2016. “Hurricane Katrina and the Paradoxes of Government Disaster Policy: 

Bringing About Wise Governmental Decisions for Hazardous Areas,”  Annals of the American Acad-

emy of Political and Social Science 604 (1): 171–191.

Centers for Disease Control and Prevention (CDC). n.d. “Flood Waters or Standing Waters.” 

https://www.cdc.gov/healthywater/emergency/extreme-weather/floods-standingwater.html.

Chivers, James, and Nicholas E. Flores. 2002. “Market Failure in Information: The National Flood 

Insurance Program,”  Land Economics 78 (4): 515–521.

Cordes, Joseph, and Anthony Yezer. 1998. “In Harm’s Way: Does Federal Spending on Beach 

Enhancement and Protection Induce Excessive Development in Coastal Areas?”  Land Economics 74 

(1): 128–145.

Federal Emergency Management Agency (FEMA). 2013.  2008 Supplement to the 2006 Evaluation 

of the National Flood Insurance Program’s Building Standards. Washington, DC: Federal Emergency 

Management Agency.

———. 2009.  Design and Construction in Coastal A Zones. Washington, DC: Federal Emergency 

Management Agency.

———. 2005.  Design and Construction in Coastal A Zones. Washington, DC: Federal Emergency 

Management Agency.

Government Accountability Office (GAO). 2014. “Overview of GAO’s Past Work on the National 

Flood Insurance Program” GAO-14-297R: April 9. 

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Holway, James, and Raymond Burby. 1990. “The Effects of Floodplain Development Controls on 

Residential Land Values,”  Land Economics 66 (3): 259–271.

Hurricane Sandy Rebuilding Task Force, Indoor Environmental Pollutants Work Group. 2015. 

“Homeowner’s and Renter’s Guide to Mold Cleanup After Disasters.”

Intergovernmental Panel on Climate Change (IPCC). 2013. “Summary for Policymakers.” In 

Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth As-

sessment Report of the Intergovernmental Panel on Climate Change, edited by T.F. Stocker, D. Qin, 

G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley. 

Cambridge, United Kingdom; New York: Cambridge University Press: 3–29.

Jones, Christopher, William Coulbourne, Jamie Marshall, and Spencer Rogers. 2006.  The Evalu-

ation of the National Flood Insurance Program: Evaluation of the National Flood Insurance Program’s 

Building Standards. Report prepared by Christopher Jones and Associates. Washington, DC: Ameri-

can Institutes for Research.

Kousky, Carolyn. 2010. “Learning From Extreme Events: Risk Perceptions After the Flood,”  Land 

Economics 86 (3): 395–422.

Kunreuther, Howard C., and Erwann Michel-Kerjan. 2010. “Market and Government Failure in 

Insuring and Mitigating Natural Catastrophes: How Long-Term Contracts Can Help.” In  Public In-

surance and Private Markets, edited by Jeffrey R. Brown. Washington, DC: The AEI Press: 115–142.

Michel-Kerjan, Erwann O. 2010. “Catastrophe Economics: The National Flood Insurance Pro-

gram,”  Journal of Economic Perspectives 24 (4): 165–186.

Michel-Kerjan, Erwann, Sabine Lemoyne de Forges, and Howard Kunreuther. 2012. “Policy Tenure 

Under the U.S. National Flood Insurance Program (NFIP),”  USC CREATE Published Articles & 

Papers. Paper 169.

Mitigation Framework Leadership Group (MitFLG). 2014. “Costs and Benefits of the Federal Flood 

Risk Management Standard.” July 9. 

Moore, Robert. 2016. “Rebuild, Repeat: The Need for Flood Insurance Reforms,” NRDC.com, 

August 11.

Muckleston, Keith W. 1983. “The Impact of Floodplain Regulations on Residential Land Values in 

Oregon,”  Water Resources Bulletin 19: 1–7. 

National Weather Service. n.d.a. “Hydrologic Information Center—Flood Loss Data.” http://www.

nws.noaa.gov/om/hazstats.shtml. 

———. n.d.b. “Weather Fatalities.” http://www.nws.noaa.gov/hic/.

Rappaport, Edward N. 2014. “Fatalities in the United States From Atlantic Tropical Cyclones,” 

Bulletin of the American Meteorological Society March 2014. 

Rosenbaum, Walter. 2005.  The Developmental and Environmental Impact of the National Flood Insur-

ance Program: A Review of the Literature. Washington, DC: American Institutes for Research.

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Stiglitz, Joseph. 1983. “Risk, Incentives and Insurance: The Pure Theory of Moral Hazard,”  The 

Geneva Papers on Risk and Insurance 26 (8): 4–33.

Tobin, Graham A., and Burrell E. Montz. 1997.  Natural Hazards: Explanation and Integration. New 

York: The Guilford Press.

U.S. Global Change Research Program. 2014. “National Climate Assessment of 2014.” http://

nca2014.globalchange.gov/report.

Williams, Geoff. 2014. “The Hidden Costs of Moving,”  U.S. News & World Report, April 30.

300 Impact

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.

Project-Based Vouchers

Brent D. Mast

David Hardiman

U.S. Department of Housing and Urban Development

The views expressed in this article are those of the authors and do not represent the official positions or policies of the Office of Policy Development and Research, the U.S. Department of Housing and Urban 

Development, or the U.S. government.

Note From the Editor

David Hardiman

This article by Brent D. Mast presents a highly useful snapshot of project-based vouchers (PBVs), 

which includes information on both assisted household characteristics and the locations of PBVs. 

These snapshot data are also presented alongside a comparison with the larger Housing Choice 

Voucher (HCV) tenant-based program.

Both aspects of the article—the snapshot of PBVs and the comparison—present useful information 

to consider; however, in this introduction I present some caveats with the comparison aspect of the 

information provided.

Background and Legislative History

The Quality Housing and Work Responsibility Act of 19981 (QHWRA) enacted a major PBV pro-

gram authority that allowed public housing agencies (PHAs) to attach a portion of their allocated 

vouchers to specific buildings. This authorization was part of a larger set of streamlining improve-

ments in QHWRA that included consolidating two previously separate Section 8 programs (vouch-

ers and certificates) and delegating additional decisionmaking authority to the local level (PHAs), 

1 Pub. L. 105–276, 112 Stat. 2519, 42 U.S.C. 1437. October 21, 1998.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

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

Mast and Hardiman

while reducing U.S. Department of Housing and Urban Development (HUD) micromanagement.2 

This PBV authority was further amended, clarified and improved in the VA-HUD-Independent 

Agencies Appropriations Act for FY 2001.3

The number of PBVs has increased dramatically in only a few years, as PHAs have made increasing 

use of this flexible authority and as units have been converted from other rental programs. As shown 

in exhibit 1, PBVs have increased from only about 43,000 in 2010 to more than 140,000 in 2016.

This rapid increase is important to keep in mind while considering the information presented in 

this article, particularly those portions comparing PBVs to the overall HCV tenant-based program. 

For instance, in terms of the data on assisted households themselves, the comparisons will not 

always be parallel in nature. PBV units that are newly issued by a PHA often show a shorter “length 

of stay” for assisted tenants. Differences in length of stay, therefore, will likely reflect differences in 

when the units were issued rather than any difference in tenant outcomes or behavior. Alternative-

ly, PBVs used for a special purpose, such as supportive housing for the homeless, or PBVs issued 

in conjunction with Low-Income Housing Tax Credits as a set-aside for persons with disabilities or 

the elderly, will reflect the tenant characteristics of those specific populations.

In addition, for PBV units that converted from another HUD rental housing program, the compari-

sons do not include data tracking existing tenants’ experiences (for example, length of stay) while in 

those previous programs. I will discuss this concept more in the next part of this introductory note.

Exhibit 1

Project-Based Vouchers, MTW and non-MTW, 2010 to 2016

Year

2010

2011

2012

2013

2014

2015

2016

All

43,393

55,759

66,383

77,504

91,478

119,605

140,043

MTW PBV

11,383

16,163

20,917

21,987

25,557

32,033

38,210

Non-MTW PBV

32,010

39,596

45,466

55,517

65,921

87,572

101,833

MTW = Moving to Work. PBV = project-based voucher.

Note: Data are as of December of each year.

Sources: Public and Indian Housing Information Center; Voucher Management System

2 A previous version of the PBV program authority—project-based certificates (PBCs)—was seldom used, largely due to complex and time-consuming HUD requirements. PBCs were first enacted in the Housing and Urban-Rural Recovery Act of 1983 (Pub. L. 98–181) and later amended in the National Affordable Housing Act of 1990 (Pub. L. 101–625; better known as Cranston-Gonzalez). HUD itself stated in a 1995 attempt at regulatory improvement, “HUD agrees that the HUD 

oversight is excessive for a five-year subsidy commitment, especially considering the limited HUD field office staff capacity to perform PBC reviews. The final rule significantly decreases HUD review responsibilities for the PBC program, and simplifies program administration” ( Federal Register 60 (127): 34692).

3 Pub. L. 106–377, 114 Stat. 1441, 1 U.S.C. 12 note. October 27, 2000.

302 Policy Briefs

Project-Based Vouchers

Additional Considerations for Specific Types of PBVs: Moving to Work and 

Rental Assistance Demonstration Conversions

Moving to Work Demonstration

A large portion of PBVs are administered by PHAs that are participants in the Moving to Work 

(MTW) demonstration. MTW provides a range of programmatic flexibilities, including greater op-

portunities for PHAs to waive regulatory requirements and undertake innovative practices, many of 

which involve the use of PBVs.

As shown in exhibit 1, of the approximately 119,000 PBVs as of December 2015, approximately 

32,000 were in MTW PHAs and 87,600 were in non-MTW PHAs.

The high number of PBVs in MTW PHAs likely reflects the additional programmatic authority and 

flexibility available to these agencies. For instance, several MTW PHAs have undertaken innovative 

practices such as using sponsor-based vouchers for providing assistance to homeless persons in 

permanent supportive housing. Other agencies have converted their public housing units to PBVs 

while also pursuing partnerships for private management arrangements.

Rental Assistance Demonstration Conversions

Of the approximately 87,600 non-MTW PBVs, about 10,200 are units that were converted from 

other project-based rental programs. Thus, these units were previously either public housing or 

Rent Supplement/Rental Assistance Payments (Rent Supp/RAP) units. This has important implica-

tions for the findings in the article that relate to locational information (that is, poverty rate and 

proximity to opportunity assets).

In addition, PBV units that were converted from other programs do not include data for their 

assisted residents as related to their length of stay in a rental assistance program. The data used in 

this article consider only assisted households’ length of stay in the PBV program itself and do not 

count any previous time they may have been assisted by the housing program (public housing or 

Rent Supp/RAP) from which the unit was converted. Nevertheless, the information is included, as 

it still offers some utility, despite this noted limitation.4

Note also that other PBVs may have been converted from other housing programs that did not 

include a Rental Assistance Demonstration (RAD) transaction. Several MTW PHAs, for example, 

converted their public housing units to PBVs. Without an additional indepth inventory analysis, it 

is not known how many additional converted units fall into this category. The caveat that applies 

to the length of stay data therefore applies to an unknown additional number of PBVs that were 

converted without a RAD transaction.

4 A final consideration on the length of stay comparison is that both sides of the program data are based on programwide snapshots (that is, all PBVs compared to all tenant-based HCVs) and not individual cohorts of assisted residents tracked over time. As such, impact on tenant outcomes or behavior is hard to estimate from these data alone. At the time of writing, HUD is undertaking a separate study on this topic.

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Mast and Hardiman

Note Conclusion

The significant amount of information on PBVs in this article sheds additional light on the growing 

use of this important program flexibility. As more PHAs take advantage of the authority to use 

PBVs, including in conjunction with other programs and for specific public policy purposes, this 

article may help guide informed decisionmaking at the federal, state, and local levels.

The Utilization of Project-Based Vouchers 

Brent D. Mast

Introduction

This article explores the use of project-based vouchers (PBVs), primarily using HUD Public and 

Indian Housing Information Center (PIC) data for December 2015. The analysis is motivated by 

the conflict between public housing agencies (PHAs)—which would like more discretion to use 

PBV funding—and advocates that have pressed for restrictions on project-basing, because of con-

cerns about the consequences for fair housing and economic opportunity if project-basing absorbs 

more of the Housing Choice Voucher appropriations. I address the following questions—

•  What are the differences between the types of households that are served by PBVs and those 

served by tenant-based vouchers (TBVs)? 

•  Are there differences in the types of PHAs that use PBVs and those that use TBVs? I pay 

particular attention to the distinction between Moving to Work (MTW) demonstration PHAs 

and non-MTW PHAs, as the MTW PHAs are less constrained in the use of PBVs. 

•  Are there differences in household outcomes, such as subsidies, rent burdens, and length of stay, 

between PBV and TBV households? 

•  Are there differences in neighborhood indicators, such as poverty rates, environmental quality, 

and school proficiency, between PBV households and TBV households?

Background

PBVs are a type of voucher for which the subsidy is assigned to a unit, in contrast to TBVs, for 

which households can use the voucher to rent an affordable unit of their choice.5 Under the PBV 

program, a PHA enters into an assistance contract with the owner for specified units for a specified 

term. Any PHA operating a voucher program can have up to 20 percent of vouchers be PBVs. 

According to PIC data for December 2015, 119,613 vouchers were PBVs, representing 5.4 percent 

of total vouchers. Among the 604 PHAs using PBVs, 7.6 percent of vouchers were PBVs. Of the 40 

MTW PHAs, 35 operated PBV programs. Among the 35 MTW agencies with PBV programs, 11.8 

percent of vouchers were PBVs. Among the 569 non-MTW agencies with PBVs, 6.7 percent of 

vouchers were PBVs.

5 For more information on the PBV program, see portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_

housing/programs/hcv/project.

304 Policy Briefs

Project-Based Vouchers

Key Findings

In the next section, I explore differences in tenant and PHA characteristics in the PBV program 

with those in the TBV program. Some notable differences are— 

•  Elderly households are much more likely to use PBVs.

•  Nonelderly households with disabilities are not more common in the PBV program than the 

TBV program.

•  Households with children and large households are much more prevalent in the TBV program 

than the PBV program.

•  PBV use is much more common among MTW PHAs.

•  PBVs are most common in the Northeast and West census regions.

•  PBV use is much more prevalent in larger PHAs.

In the third section, I compare household and neighborhood outcomes for PBV households with 

children to those for a weighted national sample of TBV households with children. Results indicate 

that—

•  Rent burdens are considerably lower for PBV households. 

•  Length of stay is much shorter, on average, for PBV households, and length of stay is longer for 

elderly households in both programs.

•  Total income and wage income distributions are very similar between PBV and TBV households.

•  PBV households tend to live in higher-poverty neighborhoods than TBV households.

•  Environmental risks are greater for PBV households.

•  PBV households tend to have better job proximity.

•  PBV households tend to have lower transportation costs and better transit access.

In the fourth section, I compare length of stay, rent burden, and poverty rates for TBV families with 

children to those for PBV families with children within the 604 PHAs with PBV programs. I make 

comparisons with multivariate regression models with PHA controls, so that the comparisons of 

PBV and TBV households are within-PHA comparisons. 

I present my conclusions in the final section.

Comparison of PBV- and TBV-Assisted Tenants

In this section, I explore differences in the types of households and PHAs that the PBV and TBV 

programs serve. I present summary statistics on PBV and TBV households for numerous tenant 

and PHA characteristics, including disability status, elderly status, census region, and PHA size. All 

statistics except those on homelessness are reported for MTW and non-MTW agencies.

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Mast and Hardiman

Tenant Characteristics

PBVs comprise 5.4 percent of total vouchers; the share is much larger in MTW agencies. Exhibit 2 

shows percentage frequencies for program type (PBV or TBV) by MTW status. PBVs are 11.2 per-

cent of vouchers in MTW agencies compared with 4.5 percent in non-MTW agencies. One possible 

explanation for this difference, which I explore in a following section, is that MTW agencies may 

have clients that are harder to house with TBVs. Another possible explanation is that some ceilings 

on PBVs do not apply to MTW agencies, and those agencies prefer PBVs for one or more reasons; 

for instance, PBVs may be easier to administer than TBVs.

Two groups that may be harder to house, as compared to other voucher households, are elderly house-

holds and nonelderly households in which the household head or spouse has a significant disability. 

Elderly households comprise 39.3 percent of PBV households compared with 22.0 percent of TBV 

households. Elderly households’ share of total vouchers is slightly higher in MTW agencies (23.8 

percent) than in non-MTW agencies (22.8 percent). Exhibit 3 shows percentage frequencies for 

MTW status by program type and elderly status. The percentage of elderly households that are 

PBV households is very similar for both MTW and non-MTW PHAs. In non-MTW agencies, 39.3 

percent of PBV households are elderly households. In MTW agencies, elderly households are 39.0 

percent of PBV households. The shares of TBV households that are elderly are very similar between 

MTW and non-MTW agencies. 

Nonelderly households with disabilities constitute a slightly lower share of vouchers in the PBV 

program than in the TBV program. Nonelderly households with disabilities comprise 24.9 percent 

of PBVs compared with 27.7 percent of TBVs. Nonelderly households with disabilities, as a share 

of total vouchers, are 26.5 percent in MTW agencies compared with 27.7 percent in non-MTW 

Exhibit 2

Program Type by MTW Status Frequencies

Program

MTW Status

PBV (%)

TBV (%)

MTW

11.2

88.8

Non-MTW

4.5

95.5

Total

5.4

94.6

MTW = Moving to Work. PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

Exhibit 3

Elderly Household Frequencies

MTW Status

Program

Elderly (%)

Nonelderly (%)

Non-MTW

PBV

39.3

60.7

Non-MTW

TBV

22.0

78.0

MTW

PBV

39.0

61.0

MTW

TBV

21.9

78.1

Total

PBV

39.3

60.7

Total

TBV

22.0

78.0

MTW = Moving to Work. PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

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agencies. Exhibit 4 shows percentage frequencies for MTW status by program type and disability 

status. In non-MTW agencies, nonelderly households with disabilities comprise 25.9 percent of 

PBV households. In MTW agencies, nonelderly households with disabilities comprise 22.1 percent 

of PBV households.

Another group that may be relatively harder to house is families with children, yet families with 

children are more likely to use a TBV. Families with children are 30.4 percent of PBV households 

and 46.7 percent of TBV households. The share of total vouchers held by families with children 

is 2 percentage points lower in MTW agencies (44.1 percent) than in non-MTW agencies (46.1 

percent). Exhibit 5 shows percentage frequencies for MTW status by program type and presence 

of children. Households with children are more likely to use a PBV in MTW agencies than in non-

MTW agencies. In MTW agencies, households with children hold 32.6 percent of PBVs and 45.5 

percent of TBVs. In non-MTW agencies, households with children hold 29.7 percent of PBVs and 

46.9 percent of TBVs.

Large households may also be relatively hard to house. Large households, defined as those with 

at least five household members, comprise 10.6 percent of total vouchers—10.5 percent in 

non-MTW agencies and 10.9 percent in MTW agencies. Large households are more prevalent 

in the TBV program; they comprise 6.0 percent of PBVs and 10.8 percent of TBVs. Exhibit 6 

shows percentage frequencies for MTW status by program type and large household status. Large 

households are more likely to use a TBV in both MTW and non-MTW agencies. In MTW agencies, 

large households hold 6.3 percent of PBVs and 11.5 percent of TBVs. In non-MTW agencies, large 

households hold 5.9 percent of PBVs and 10.7 percent of TBVs. 

Exhibit 4

Nonelderly Households With Disabilities Frequencies 

Nonelderly Households With 

MTW Status

Program

Other Households (%)

Disabilities (%)

Non-MTW

PBV

25.9

74.1

Non-MTW

TBV

27.7

72.3

MTW

PBV

22.1

77.9

MTW

TBV

27.1

72.9

Total

PBV

24.9

75.1

Total

TBV

27.7

72.4

MTW = Moving to Work. PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

Exhibit 5

Households With Children Frequencies

MTW Status

Program

Households With Children (%)

Other Households (%)

Non-MTW

PBV

29.7

70.3

Non-MTW

TBV

46.9

53.1

MTW

PBV

32.6

67.4

MTW

TBV

45.5

54.5

Total

PBV

30.4

69.6

Total

TBV

46.7

53.3

MTW = Moving to Work. PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

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Exhibit 6

Large Household Frequencies

MTW Status

Program

Large Households (%)

Other Households (%)

Non-MTW

PBV

5.9

94.1

Non-MTW

TBV

10.7

89.3

MTW

PBV

6.3

93.7

MTW

TBV

11.5

88.5

Total

PBV

6.0

94.0

Total

TBV

10.8

89.2

MTW = Moving to Work. PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

Families that were homeless at admission may also have special circumstances that make them 

more likely to use a PBV. Unfortunately, homelessness data are not available in PIC for MTW 

agencies. Recent research has concluded that homeless-at-admission data for non-MTW agencies 

are not reliable for the period analyzed in this article, due to inconsistent collection and recording 

of household homeless status during participants’ program enrollment and recertification. The 

information cited in the following section should be considered with the understanding that HUD 

Office of Policy Development and Research staff members who are most familiar with PIC data are 

not comfortable with the accuracy of the data in the homelessness element for that period. 

In non-MTW agencies, 4.2 percent of all voucher households are coded as homeless at admission 

in the PIC system for December 2015. The share of PBV households coded as homeless at admis-

sion is much higher (10.6 percent) than the share of TBV households (3.9 percent).

Geography

The use of PBVs varies considerably in terms of geography. PBV use is highest in the Northeast 

and West census regions. In the Northeast region, 6.9 percent of vouchers are PBVs, and 30.1 

percent of PBVs are in the Northeast. In the West region, 5.9 percent of vouchers are PBVs, and 

25.6 percent of PBVs are used in the West. The geographic distribution of PBVs is driven in part 

by the location of MTW vouchers, which are concentrated in the west. Exhibit 7 shows percentage 

frequencies for MTW status by program type and census region (data for Puerto Rico are given 

separately in the table). 

Exhibit 7

Census Region Frequencies

MTW Status Program Northeast (%) Midwest (%)

South (%)

West (%)

Puerto Rico (%)

Non-MTW

PBV

31.4

18.8

25.2

22.4

2.3

Non-MTW

TBV

23.8

19.2

33.9

21.5

1.5

MTW

PBV

26.6

10.6

28.5

34.4

NA

MTW

TBV

17.5

20.4

26.7

35.4

NA

Total

PBV

30.1

16.6

26.1

25.6

1.7

Total

TBV

23.1

19.4

33.1

23.2

1.3

MTW = Moving to Work. NA = not applicable. PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

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•  Non-MTW agencies: 31.4 percent of PBVs are in the Northeast, 18.8 percent are in the Midwest, 

25.2 percent are in South, and 22.4 percent are located in the West.

•  MTW agencies: 26.6 percent of PBVs are in the Northeast, 10.6 percent are in the Midwest, 28.5 

percent are in South, and 34.4 percent are located in the West.

PHA Size

Large PHAs dominate the utilization of PBVs. Almost one-half (46.2 percent) of PBVs are admin-

istered by PHAs with more than 10,000 total units (vouchers and public housing) compared with 

32.3 percent of TBVs administered by those PHAs. MTW agencies greatly influence this discrep-

ancy; 73.4 percent of MTW vouchers are in agencies with more than 10,000 total units compared 

with 27.1 percent of vouchers in non-MTW agencies. 

Exhibit 8 shows percentage frequencies for MTW status by program type and by PHA size in five 

categories: 0 to 250, 251 to 550, 551 to 1,250, 1,250 to 10,000, and 10,001 or more units. “Quali-

fied” (that is, small) PHAs are defined as those with no more than 550 units.

•  Non-MTW agencies: Only 3.0 percent of PBVs are in qualified PHAs, although 11.2 percent of 

TBVs are in qualified PHAs. The largest concentration of PBVs and TBVs is in PHAs with 1,251 

to 10,000 total units. Slightly more than one-third (35.7 percent) of PBVs are in the largest 

PHAs compared with 26.7 percent of TBVs.

•  MTW agencies: No MTW agencies are qualified PHAs. About one-fourth of PBVs and TBVs are 

in medium-to-large MTW PHAs. The share of PBVs in the largest PHAs is 74.8 percent, which is 

similar to the share of TBVs (73.2 percent).

Exhibit 8

PHA Size Frequencies

PHA Size (%)

MTW Status

Program

0–250

251–550

551–1,250

1,251–10,000

> 10,000

Non-MTW

PBV

0.6

2.4

8.8

52.5

35.7

Non-MTW

TBV

4.0

7.2

14.5

47.6

26.7

MTW

PBV

NA

NA

2.1

23.1

74.8

MTW

TBV

NA

NA

1.1

25.7

73.2

Total

PBV

0.5

1.8

7.0

44.6

46.2

Total

TBV

3.5

6.4

12.9

44.9

32.3

MTW = Moving to Work. NA = not applicable. PBV = project-based voucher. PHA = public housing agency. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

Summary

To summarize, little evidence suggests PBVs are used for populations that are harder to house. PBV 

use is much more common among elderly households than nonelderly households. Nonelderly 

households with disabilities may also be harder to house, yet they comprise a greater share of TBVs. 

Families with children and large households may also be harder to house, yet they are much more 

prevalent in the TBV program than the PBV program. 

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Households that were homeless at admission may be the population that is hardest to house. Data 

availability and quality, however, make it difficult to draw conclusions regarding these households. 

No homelessness data are available for MTW agencies. In non-MTW agencies, households coded 

as homeless at admission are much more likely than other households to use a PBV.

PBV use is much more common in MTW agencies than in other PHAs. One possible explanation 

for the wider use of PBVs in MTW agencies is that their tenants are harder to house, yet little 

evidence supports this hypothesis. Elderly households are more likely to use PBVs, but elderly 

households’ share of total vouchers is very similar for both MTW and non-MTW agencies. In a 

logistic regression predicting PBVs, the coefficient for MTW agencies was still highly significant 

when controlling for elderly households, nonelderly households with disabilities, households with 

children, large households, census region, and PHA size. MTW agencies might have more tenants 

that were homeless at admission, but no data are available on this topic.

Geographically, PBV use is most common in the Northeast and West census regions, in part due to 

the large concentration of MTW agencies in the West. PBV use is also much more common among 

the largest PHAs. Possible explanations are that negotiating PBV contracts and/or administering 

two voucher programs are too burdensome for smaller PHAs.

National Comparison of Selected Outcomes for Households With Children 

Propensity Score Matching

In this section, I compare TBV families with children to PBV families with children on a national 

scale. To obtain a TBV comparison group more similar to PBV households, I weighted the TBV 

households by propensity score weights. The propensity scores were estimated by logistic regres-

sion. The model included two continuous predictors (household head age and number of chil-

dren); binary indicators for married household heads, female household heads, elderly households, 

nonelderly households with a disabled spouse or co-head, and households in MTW PHAs; and 

categorical variables for census region and PHA size. I would have liked also to include an indica-

tor for households homeless at admission, but this information is missing for MTW households.

Regression estimates are shown in exhibit 9. All predictors—except the indicator for nonelderly 

households with disabilities—had statistically significant predictive power. As household head age 

and number of children increased, the estimated probability of having a PBV decreased. Married 

household heads, elderly households, and MTW households were estimated to be more likely to 

have a PBV; female household heads were estimated to be less likely to have a PBV.

Households in the Northeast and West were most likely to use a PBV, and PBV use increased with 

PHA size.

The propensity score weight was computed as  N *  p / ∑  p, where  N is the number of TBV households with children and  p is the estimated probability of being a PBV household. The formula was 

chosen so that the sum of weights equals  N. 

Exhibit 10 shows summary statistics for the continuous and binary matching variables by program; 

exhibits 11 and 12 show frequencies for census region and PHA size, respectively. TBV data are 

310 Policy Briefs

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Exhibit 9

Logistic Regression Estimates

Effect

Odds Ratio Estimate

Lower 95% CI

Upper 95% CI

Household head age

0.950

0.949

0.952

Number of children

0.903

0.895

0.911

Married household head

1.217

1.171

1.265

Female household head

0.576

0.555

0.598

Elderly household

2.751

2.533

2.987

Nonelderly household with disabilities

0.971

0.941

1.002

Moving to Work PHA

2.513

2.448

2.580

Census region: Northeast vs. PR

3.938

3.365

4.607

Census region: Midwest vs. PR

1.803

1.539

2.112

Census region: South vs. PR

1.958

1.674

2.291

Census region: West vs. PR

3.612

3.087

4.226

PHA size: 0–250 vs. > 10,000

0.176

0.152

0.202

PHA size: 251–550 vs. > 10,000

0.326

0.302

0.352

PHA size: 551–1,250 vs. > 10,000

0.618

0.593

0.644

PHA size: 1,251–10,000 vs. > 10,000

0.854

0.833

0.875

CI = confidence interval. PHA = public housing agency. PR = Puerto Rico.

Note: – 2 log likelihood = 312,428.47. 

Source: Public and Indian Housing Information Center, December 2015

Exhibit 10

Matching Variable Summary Statistics

25th  

75th  

Variable

Program

N

Median Mean

Std Dev

Percentile

Percentile

Number of children

PBV

36,252

1

2

2.170

3

1.295

TBV

973,917

1

2

2.236

3

1.322

TBV (w)

973,913

1

2

2.179

3

1.267

Household head age

PBV

36,251

28

34

35.649

42

10.289

TBV

973,913

31

37

38.182

44

9.825

TBV (w)

973,913

29

34

35.818

41

9.177

Married household head

PBV

36,252

0

0

0.114

0

0.318

TBV

973,917

0

0

0.083

0

0.276

TBV (w)

973,913

0

0

0.111

0

0.314

Female household head

PBV

36,252

1

1

0.878

1

0.327

TBV

973,917

1

1

0.922

1

0.268

TBV (w)

973,913

1

1

0.881

1

0.324

Nonelderly household 

PBV

36,252

0

0

0.142

0

0.349

with disabilities

TBV

973,917

0

0

0.164

0

0.370

TBV (w)

973,913

0

0

0.144

0

0.351

Elderly household

PBV

36,252

0

0

0.022

0

0.148

TBV

973,917

0

0

0.026

0

0.159

TBV (w)

973,913

0

0

0.023

0

0.149

Moving to Work PHA

PBV

36,252

0

0

0.288

1

0.453

TBV

973,917

0

0

0.118

0

0.323

TBV (w)

973,913

0

0

0.272

1

0.445

PBV = project-based voucher. PHA = public housing agency. Std Dev = standard deviation. TBV = tenant-based voucher.  

Note: (w) indicates weighted TBV.

Source: Public and Indian Housing Information Center, December 2015

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Exhibit 11

Census Region Matching Statistics

Census Region (%)

Program

Northeast

Midwest

South

West

Puerto Rico

PBV

29.9

14.1

26.9

28.6

0.4

TBV

21.0

20.4

37.7

19.3

1.7

TBV: weighted

29.3

14.5

27.6

28.1

0.5

PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

Exhibit 12

PHA Size Matching Statistics

PHA Size (%)

Program

0–250

251–550

551–1,250

1,251–10,000

> 10,000

PBV

0.5

2.0

8.5

44.6

44.4

TBV

3.3

6.0

12.8

47.3

30.6

TBV: weighted

0.6

2.1

8.7

45.2

43.4

PBV = project-based voucher. PHA = public housing agency. TBV = tenant-based voucher. 

Source: Public and Indian Housing Information Center, December 2015

reported unweighted and weighted by propensity score weights. The gauge of the success of the 

propensity score weighting is how closely the weighted TBV matching variables mirror their PBV 

counterparts. In general, the distributions of the matching variables for TBV households are much 

closer to the PBV distributions after propensity score weighting. The mean number of children 

for PBV households with children is 2.171; the mean number for TBV households is 2.236 un-

weighted and 2.179 weighted. The mean household head age for PBV households with children is 

35.649; the mean number for TBV households is 38.182 unweighted and 35.818 weighted.  

The propensity score weighting was also quite successful for the indicator variables.

1.  Marital status: The share of PBV married household heads with children is 11.4 percent; the 

share for TBV households is 8.3 percent unweighted and 11.1 percent weighted.

2.  Gender: The share of PBV female household heads with children is 87.8 percent; the share for 

TBV households is 92.2 percent unweighted and 88.1 percent weighted.

3.  Disability: The share of PBV households with children that are nonelderly households with 

disabilities is 14.2 percent; the share for TBV households is 16.4 percent unweighted and 14.4 

percent weighted.

4.  Elderly households: The share of PBV households with children that are elderly households 

is 2.2 percent; the share for TBV households is 2.6 percent unweighted and 2.3 percent 

weighted.

5.  MTW: The share of PBV households with children in MTW PHAs is 28.8 percent; the share 

for TBV households is 11.8 percent unweighted and 27.2 percent weighted.

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Propensity score weighting was also successful for the census region and PHA size variables. For 

example, 29.9 percent of PBV households with children are located in the Northeast compared 

with 21.0 percent of TBV households. The weighted share of TBV households in the Northeast is 

29.3 percent. 

Comparisons for Matched Samples

Exhibit 13 shows summary statistic comparisons for four household-level variables: subsidy, total 

household income, rent burden, and length of stay. All data are for households with children, 

with TBV statistics weighted by propensity score weights. Rent burden is defined as the tenants’ 

contribution to gross rent (including utilities) divided by monthly adjusted income. Rent burden is 

undefined for households with 0 adjusted income.

Subsidies are slightly greater for PBV households with children (median of $787 and mean of 

$839) as compared with the matched sample of TBV households with children (median of $778 

and mean of $830). Note that subsidies are calculated differently for PBV and TBV tenants. In the 

PBV program, the PHA pays the owner the difference between 30 percent of family income and the 

gross rent for the unit.6

Incomes tend to be slightly higher for the weighted sample of TBV households. Median income is 

$13,650 for TBV households compared with $12,918 for PBV households, and mean income is 

15,718 for TBV households compared with $15,433 for PBV households.

Length of stay is noticeably longer for TBV households (median of 5.9 years and mean of 8.1 years) 

than for PBV households (median of 1.8 years and mean of 3.8 years). As discussed in the intro-

ductory note, this discrepancy may be due to considerations with the data and not due to actual 

differences in tenant experience or outcomes.

Exhibit 13

Families With Children Household Variable Comparisons

25th  

75th  

Variable

Program

N

Median

Mean

Std Dev

Percentile

Percentile

Subsidy

PBV

36,239

556

787

839.320

1,085

427.287

TBV (w)

973,888

527

778

830.637

1,089

431.179

Total household 

PBV

36,252

6,492

12,918

15,433.295

21,480

12,312.044

income

TBV (w)

973,913

7,113

13,650

15,718.443

21,938

11,879.412

Length of stay

PBV

36,252

0.822

1.755

3.825

3.775

11.631

TBV (w)

973,913

2.208

5.929

8.094

10.238

12.866

Rent burden

PBV

30,081

29.981

30.003

31.210

30.028

10.136

TBV (w)

906,656

30.000

30.081

36.874

37.760

16.233

PBV = project-based voucher. Std Dev = standard deviation. TBV = tenant-based voucher 

Note: (w) indicates weighted TBV.

Source: Public and Indian Housing Information Center, December 2015

6 http://portal.hud.gov/hudportal/documents/huddoc?id=DOC_9157.pdf. 

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Mast and Hardiman

Length of stay is longer for elderly households in both programs. The mean length of stay for 

elderly households is 5.0 years in the PBV program and 14.1 years in the TBV program compared 

with 3.8 and 8.0 years, respectively, for nonelderly households. 

TBV households are much more likely to be rent burdened than PBV households. Mean rent 

burden for TBV households is 36.9 percent compared with 31.2 percent for PBV households.

Exhibit 14 shows rent burden in five categories: 0 to 30 percent, 31 to 39 percent, 40 to 49 

percent, 50 percent and above, and missing.

1.  Rent burden of 0 to 30 percent: The share of PBV households with burdens in this category is 

76.1 percent compared with 49.5 percent of TBV households.

2.  Rent burdens between 31 and 39 percent: The share of PBV households with burdens in this 

category is 2.7 percent compared with 23.5 percent of TBV households.

3.  Rent burdens between 40 and 49 percent: The share of PBV households with burdens in this 

category 1.3 percent compared with 9.4 percent of TBV households.

4.  Rent burdens of 50 percent or more: The share of PBV households with burdens in this 

category is 2.9 percent compared with 10.4 percent of TBV households.

5.  Missing rent burden: Rent burden could not be computed for 17.0 percent of PBV households 

and 7.2 percent of TBV households.

Although total household incomes are similar between the two groups, differences in wage income 

are also policy relevant. Higher wage income might indicate better economic progress for assisted 

tenants. For these comparisons, I exclude elderly households and include only households with 

children. The 25th percentile of wage income is $0 for both TBV and PBV households. Median 

wage income is $4,864 for PBV households and $5,200 for TBV households. Mean wage income 

is $10,904 for PBV households and $10,308 for TBV households. The 75th percentile of wage 

income is $18,590 for PBV households and $17,999 for TBV households.

Comparing wage income for nonelderly households with children in PHAs with PBVs, median 

wage income is $4,864 (mean is $10,904) for PBV households compared with a median of $5,130 

(mean is $10,521) for the matched sample of TBV households.

Exhibit 14

Rent Burden Frequencies

Poverty Rate 

PBV (%)

TBV: Weighted (%)

Category

0–30%

76.1

49.5

31–39%

2.7

23.5

40–49%

1.3

9.4

50% and above

2.9

10.4

Missing

17.0

7.2

PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

314 Policy Briefs

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To summarize, the most noticeable differences in household variables between the groups are 

much shorter lengths of stay and much lower rent burdens for PBV households with children than 

for the matched sample of TBV households. Subsidies are similar for both groups, and differences 

in both total income and wage income are modest.

Neighborhood Comparisons

To compare neighborhood conditions for TBV and PBV households with children, I analyze 10 

variables: (1) Small Area Fair Market Rent (SAFMR), (2) the census tract poverty rate, (3) the per-

centage of the population that is non-White, (4) an indicator for  racially or ethnically concentrated 

areas of poverty (R/ECAPs), (5) a school proficiency index, (6) a jobs proximity index, (7) a labor 

market engagement index, (8) an environmental health index, (9) a low transportation cost index, 

and (10) a transit trips index.

Data on R/ECAPs and the six opportunity indices are taken from HUD’s Affirmatively Furthering 

Fair Housing (AFFH) database.7 In metropolitan areas, R/ECAPs are defined as having at least 50 

percent non-White residents and a poverty rate that is at least 40 percent or three times the average 

tract poverty rate for the metropolitan area. In nonmetropolitan areas, the non-White threshold is 

20 percent and the poverty threshold is 40 percent.

The six opportunity indices are measured on a 0-to-100 scale, with greater values indicating higher 

opportunity. The block-group school proficiency index is based on the percentage of fourth-grade 

students that is proficient on state math and reading exams.

The jobs proximity index for a given residential block group is based on a ratio of jobs to workers 

measured across the metropolitan area. Both numerator and denominator are distance weighted, 

with jobs and workers in block groups farther away receiving less weight.

Computed at the tract level, the labor market engagement index is based on the unemployment 

rate, the labor force participation rate, and the percent of the population ages 25 and older with at 

least a bachelor’s degree.

The tract-level environmental health index is a linear combination of standardized estimates of 

air-quality carcinogenic, respiratory, and neurological hazards.

Two indices measure transportation opportunity for a household profile consisting of a single-

parent family of three, renting, with an income equal to 50 percent of Area Median Income. The 

transportation cost index is based on modeled transportation costs as a share of household income. 

The transit trips index is based on modeled annual household transit trips. 

Exhibit 15 shows summary statistics for the neighborhood indicators for PBV and TBV households 

with children. TBV statistics are weighted by propensity score weights. 

SAFMRs are very similar for PBV households with children and TBV households with children. 

Median SAFMR is $1,090 (mean is $1,182) for PBV households compared with $1,100 median 

($1,183 mean) for TBV households.

7 Sources and other information for the R/ECAP variable and six opportunity indices are available in the AFFH data 

documentation, www.hudexchange.info/resource/4848/affh-data-documentation/.

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Mast and Hardiman

Exhibit 15

Neighborhood Variable Summary Statistics 

25th  

75th  

Variable

Program

N

Median

Mean

Std Dev

Percentile

Percentile

SAFMR

PBV

32,636

880

1,090 1,182.252

1,390

409.371

TBV (w)

842,085

890

1,100 1,183.297

1,370

423.901

Poverty rate

PBV

36,250

16.841

27.829

28.860

39.354

15.324

TBV (w)

973,112

14.574

23.588

25.201

34.058

13.583

Non-White %

PBV

36,250

33.333

65.156

61.191

90.313

30.561

TBV (w)

973,112

35.439

67.764

61.789

90.925

30.837

R/ECAP

PBV

36,250

0

0

0.233

0

0.423

TBV (w)

973,112

0

0

0.140

0

0.347

School proficiency 

PBV

33,920

12

28

33.496

53

25.598

index

TBV (w)

930,578

12

27

32.100

48

24.052

Jobs proximity index

PBV

33,615

26

53

51.618

77

29.316

TBV (w)

925,615

22

48

47.656

72

29.033

Labor market  

PBV

36,250

9

26

34.834

58

29.482

engagement index

TBV (w)

973,112

12

27

33.224

50

25.148

Environmental health 

PBV

36,181

12

25

32.978

53

26.043

index

TBV (w)

973,072

13

27

34.066

53

25.568

Low transportation 

PBV

36,030

55

79

70.350

91

24.726

cost index

TBV (w)

955,856

47

69

65.158

88

25.364

Transit trips index

PBV

36,030

52

75

68.365

90

25.318

TBV (w)

955,856

49

71

66.008

89

26.015

PBV = project-based voucher. PHA = public housing agency. R/ECAP = racially or ethnically concentrated area of poverty. 

SAFMR = Small Area Fair Market Rent. Std Dev = standard deviation. TBV = tenant-based voucher. 

Note: (w) indicates weighted TBV.

Sources: Public and Indian Housing Information Center, December 2015; U.S. Department of Housing and Urban Development (HUD), Small Area FMR database 2016; 2009–2013 American Community Survey 5-year estimates; Great Schools 2012; Longitudinal Employer-Household Dynamics 2012; U.S. Environmental Protection Agency, National Air Toxics Assessment 2005; HUD Local Affordability Index database, 2008–2012

Compared to TBV households with children, PBV households with children tend to live in higher-

poverty census tracts. The median tract poverty rate for PBV households is 27.8 percent (mean is 28.9 

percent) compared with 23.6 percent (25.2-percent mean) for TBV households. Exhibit 16 shows 

census tract frequencies in five categories of poverty rate: 0 to 9 percent, 10 to 19 percent, 20 to 29 

percent, 30 to 39 percent, and 40 percent and above. Of PBV households with children, 10.6 percent 

reside in tracts with poverty rates under 10 percent compared with 13.3 percent of TBV households 

with children. The share of PBV households with children living in tracts with poverty rates of at least 

40 percent is 23.8 percent compared with 15.1 percent of TBV households with children.

The minority share of tract population is slightly lower for PBV households with children than for 

TBV households with children. The median percentage of non-White residents in a tract is 65.2 

percent (mean is 61.2 percent) for PBV households with children compared with 67.8 percent 

(61.8-percent mean) for TBV households with children.

As measured by R/ECAPs, PBV households with children tend to live in more distressed neigh-

borhoods than the matched sample of TBV households. The share of PBV households residing in 

R/ECAPs is 23.3 percent compared with 14.0 percent of TBV households. 

The school proficiency index is slightly higher for PBV households with children than for TBV 

households with children. The median school proficiency index is 28 (mean is 33.5) for PBV 

households and 27 (32.1 mean) for the matched sample of TBV households.

316 Policy Briefs

Project-Based Vouchers

Exhibit 16

Census Tract Poverty Rate Categories for Families With Children

Poverty Rate 

PBV (%)

TBV: Weighted (%)

Category

0–9%

10.6

13.3

10–19%

22.0

27.0

20–29%

21.7

25.8

30–39%

21.9

18.9

40% and above

23.8

15.1

PBV = project-based voucher. TBV = tenant-based voucher.

Sources: Public and Indian Housing Information Center, December 2015; 2009–2013 American Community Survey 5-year estimates

The jobs proximity index is notably higher for PBV households with children than for the matched 

sample of TBV households. The median jobs proximity index is 53 (mean is 51.6) for TBV house-

holds compared with 48 (47.7 mean) for TBV households.

Labor market engagement is similar for both groups. The median labor market engagement index 

is 26 (mean is 34.8) for PBV households with children and 27 (33.2 mean) for TBV households 

with children.

PBV households with children score lower on the environmental health index as compared with 

TBV households with children. The median environmental health index is 25 (mean is 33.0) for 

PBV households with children compared with 27 (34.1 mean) for the matched sample of TBV 

households with children.

Transportation opportunity is much higher for PBV households with children than for the matched 

sample of TBV households with children. The median low transportation cost index is 79 (mean is 

70.4) for PBV households compared with 69 (65.2 mean) for TBV households. The median transit 

trips index is 75 (mean is 68.4) for PBV households and 71 (66.0 mean) for TBV households.

Overall, the results of the neighborhood comparisons are quite mixed. Although PBV households 

with children tend to live in higher-poverty neighborhoods, are more likely to reside in R/ECAPs, 

and have lower environmental quality, they also tend to have better proximity to jobs and better 

transportation opportunities. The categories of SAFMR, minority share of tract population, labor 

market engagement, and school proficiency are similar for both groups.

Within-PHA Comparison of Selected Outcomes for Households With Children

In this section, I compare length of stay, rent burden, and poverty rates for TBV families with 

children with those for PBV families with children, within the 604 PHAs with PBV programs. The 

comparisons are made with multivariate regression models with PHA controls, so that the compari-

sons of PBV and TBV households are within-PHA comparisons. To control for non-PHA-specific 

factors affecting outcomes, the regression models include two continuous predictors (household 

head age and number of children) and binary indicators for program type, married household 

heads, female household heads, elderly households, and nonelderly households with a disabled 

spouse or co-head.

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Mast and Hardiman

Length of Stay

I modeled length of stay with a linear regression with PHA fixed effects; estimates are shown in 

exhibit 17 (PHA fixed effects are not shown). Model estimates indicate that the average length 

of stay for PBV households with children is 4.6 years shorter than that of TBV households with 

children, within PHAs with PBV programs, with other things equal.

To compare length-of-stay predictions between PBV and TBV households, I compared model 

predictions evaluated at the means of all variables except program type. Predicted length of stay is 

8.7 years for TBV households and 4.2 years for PBV households.

Predicted length of stay is much longer for elderly households than for other households. For 

these predictions, I evaluated model predictions at the overall means for the PHA fixed effects and 

evaluated means according to elderly status for the remaining variables except for program type. 

In the TBV program, predicted length of stay is 8.6 years for nonelderly households and 12.9 

years for elderly households. The corresponding predictions for the PBV program are 4.0 years for 

nonelderly households and 8.3 years for elderly households.

Exhibit 17

Length of Stay Regression Estimates

Variable

Coefficient

Standard Error

t-value

p-value

Intercept

– 5.847

0.265

– 22.105

< 0.0001

Project-based voucher

– 4.574

0.059

– 77.536

< 0.0001

Household head age

0.241

0.002

155.048

< 0.0001

Number of children

0.024

0.010

2.520

0.012

Married household head

0.032

0.050

0.649

0.516

Female household head

1.743

0.050

34.636

< 0.0001

Elderly household

– 2.565

0.087

– 29.320

< 0.0001

Nonelderly household with disabilities

– 0.180

0.035

– 5.188

< 0.0001

Notes: The model includes 603 public housing agency fixed effects, which are not reported. N = 714,395,  R -Squared = .261.

Source: Public and Indian Housing Information Center, December 2015

Rent Burden

I analyzed rent burden with two regression models. First, I modeled continuous rent burden with 

a linear regression with PHA fixed effects; estimates are shown in exhibit 18 (PHA fixed effects are 

not shown). Estimates indicate rent burden is 5.1 percent lower for PBV households than for TBV 

households, within PHAs that have PBV programs, with other things equal.

To estimate rent predictions for PBV and TBV households, I compared model predictions evaluated 

at the means of all variables except program type. Predicted rent burden is 37.0 percent for TBV 

households and 32.0 percent for PBV households.

I also modeled the rent burden categories shown in exhibit 14 with a multinomial logistic 

regression with PHA random intercepts. For brevity, regression estimates are not reported but are 

available on request. Model predictions evaluated at the means of all variables except program type 

are shown in exhibit 19.

318 Policy Briefs

Project-Based Vouchers

Exhibit 18

Rent Burden Regression Estimates

Variable

Coefficient

Standard Error

t-value

p-value

Intercept

36.258

0.398

90.998

< 0.0001

Project-based voucher

– 5.081

0.096

– 52.839

< 0.0001

Household head age

0.015

0.002

6.251

< 0.0001

Number of children

– 0.469

0.015

– 31.233

< 0.0001

Married household head

– 3.033

0.077

– 39.523

< 0.0001

Female household head

0.468

0.078

6.022

< 0.0001

Elderly household

– 3.081

0.133

– 23.161

< 0.0001

Nonelderly household with disabilities

– 2.751

0.053

– 52.210

< 0.0001

Notes: The model includes 603 public housing agency fixed effects, which are not reported. N = 657,755,  R -Squared = .076.

Source: Public and Indian Housing Information Center, December 2015

Exhibit 19

Rent Burden Category Predictions

Rent Burden 

PBV (%)

TBV (%)

Category

0–30%

81.1

52.1

31–39%

2.8

24.1

40–49%

1.3

9.2

50% and above

2.5

9.5

Missing

12.3

5.1

PBV = project-based voucher. TBV = tenant-based voucher.

Source: Public and Indian Housing Information Center, December 2015

1.  Rent burdens of 0 to 30 percent: The predicted share of PBV households with burdens in this 

category is 81.1 percent compared with 52.1 percent of TBV households.

2.  Rent burdens between 31 and 39 percent: The predicted share of PBV households with 

burdens in this category is 2.8 percent compared with 24.1 percent of TBV households.

3.  Rent burdens between 40 and 49 percent: The predicted share of PBV households with 

burdens in this category 1.3 percent compared with 9.2 percent of TBV households.

4.  Rent burdens of 50 percent or more: The predicted share of PBV households with burdens in 

this category is 2.5 percent compared with 9.5 percent of TBV households.

5.  Missing rent burden: Rent burden could not be computed for 12.3 percent of PBV households 

and 5.1 percent of TBV households.

Poverty Rates

I modeled continuous census tract poverty rates with a linear regression with PHA fixed effects; 

estimates are shown in exhibit 20 (PHA fixed effects are not shown). Estimates indicate poverty rates 

are 4.4 percent greater for PBV households than for similar TBV households within the same PHAs.

To estimate tract poverty rates for PBV and TBV households within the same PHAs, I compared 

model predictions evaluated at the means of all variables except program type. Mean predicted 

poverty rates are 25.4 percent for TBV households and 29.8 percent for PBV households.

Cityscape 319

Mast and Hardiman

Exhibit 20

Poverty Rate Regression Estimates

Variable

Coefficient

Standard Error

t-value

p-value

Intercept

14.382

0.310

46.452

< 0.0001

Project-based voucher

4.400

0.069

63.603

< 0.0001

Household head age

– 0.027

0.002

– 14.544

< 0.0001

Number of children

0.243

0.011

21.540

< 0.0001

Married household head

– 0.203

0.059

– 3.458

0.0005

Female household head

– 1.413

0.059

– 23.905

< 0.0001

Elderly household

1.436

0.103

13.990

< 0.0001

Nonelderly household with disabilities

0.219

0.041

5.388

< 0.0001

Notes: The model includes 603 public housing agency fixed effects, which are not reported. N = 708,297,  R -Squared = .254.

Sources: Public and Indian Housing Information Center, December 2015; 2009–2013 American Community Survey 5-year estimates

I modeled the poverty categories shown in exhibit 14 with a multinomial logistic regression with 

PHA-random intercepts. For brevity, regression estimates are not reported but are available upon 

request. Model predictions evaluated at the means of all variables except program type are shown 

in exhibit 21.

1.  Poverty rates of 0 to 9 percent: The predicted share of PBV households with poverty rates in 

this category is 7.7 percent compared with 11.6 percent of TBV households.

2.  Poverty rates of 10 to 19 percent: The predicted share of PBV households with poverty rates in 

this category is 24.1 percent compared with 32.2 percent of TBV households.

3.  Poverty rates of 20 to 29 percent: The predicted share of PBV households with poverty rates in 

this category is 27.6 percent compared with 30.9 percent of TBV households.

4.  Poverty rates of 30 to 39 percent: The predicted share of PBV households with poverty rates in 

this category is 24.3 percent compared with 17.6 percent of TBV households.

5.  Poverty rates of 40 percent and above: The predicted share of PBV households with poverty 

rates in this category is 16.3 percent compared with 7.8 percent of TBV households.

Exhibit 21

Poverty Category Predictions

Rent Burden 

PBV (%)

TBV (%)

Category

0–9%

7.7

11.6

10–19%

24.1

32.2

20–29%

27.6

30.9

30–39%

24.3

17.6

40% and above

16.3

7.8

PBV = project-based voucher. TBV = tenant-based voucher.

Sources: Public and Indian Housing Information Center, December 2015; 2009–2013 American Community Survey 5-year estimates

320 Policy Briefs

Project-Based Vouchers

Summary

Within-PHA comparisons are consistent with the national comparisons from the previous sec-

tion. PBV households have much shorter lengths of stay than TBV households within the same 

PHAs, and elderly households have considerably longer stays in both programs as compared with 

nonelderly households within the same PHAs. PBV households are much less likely to be rent 

burdened than similar TBV households within the same PHAs. PBV households are more likely to 

live in high-poverty neighborhoods than are similar TBV households within the same PHAs.

Conclusions

To summarize, key findings include—

1.  PBV use is much more common among MTW PHAs. One possible explanation for greater use 

of PBVs in MTW agencies is that their populations are harder to house. Analyzing data for a 

variety of household types that may be harder to house, however, I find little support for this 

hypothesis. 

2.  PBV use is much more common in large PHAs. Possible explanations are that negotiating PBV 

contracts and/or administering two voucher programs are too burdensome for smaller PHAs.

3.  Elderly households are much more likely to use a PBV relative to other households. Elderly 

households, as a share of total vouchers, are very similar in MTW and non-MTW agencies, so 

this is not an explanation of greater PBV use by MTW agencies.

4.  Geographically, PBVs are most prevalent in the Northeast and West census regions. This 

finding could be in part due to the concentration of MTW agencies in the West and to local 

housing market factors (for example, tighter rental supply) in the Northeast.

5.  Rent burdens are lower for PBV tenants than for TBV tenants. Subsidies are similar for tenants 

in both programs.

6.  Length of stay tends to be much shorter for PBV households with children than for similar TBV 

households with children. As discussed in the introductory note, this finding may be due to 

considerations with the data and not due to actual differences in tenant experience or outcomes.

7.  Comparing neighborhood outcomes for PBV and TBV households with children, results 

are quite mixed. PBV tenants tend to live in higher-poverty neighborhoods, are much more 

likely to reside in R/ECAPs, and have greater environmental risks. PBV households, however, 

also tend to live closer to employment opportunities, have lower transportation costs, and 

have better transit access. As discussed in the introductory note, a large number of PBVs are 

units that were converted from older project-based programs (that is, public housing and 

Rent Supplement/Rental Assistance Payments) and thus will reflect the earlier locational 

development decisions of those programs.

8.  Results of within-PHA comparisons are very similar to national comparisons. Within the same 

PHAs, PBV households have shorter lengths of stay, lower rent burdens, and higher census 

tract poverty rates than TBV households. 

Cityscape 321

Mast and Hardiman

Acknowledgments

The authors thank Kathy O’Regan and Mark Shroder for helpful comments.

Authors

Brent D. Mast is a social science analyst in the U.S. Department of Housing and Urban Develop-

ment, Office of Policy Development and Research.

David Hardiman is a program analyst at the U.S. Department of Housing and Urban Development, 

Office of Policy Development and Research.

322 Policy Briefs

SpAM

SpAM (Spatial Analysis and Methods) presents short articles on the use of spatial 

statistical techniques for housing or urban development research. Through this depart-

ment of  Cityscape , the Office of Policy Development and Research introduces readers to 

the use of emerging spatial data analysis methods or techniques for measuring geographic 

relationships in research data. Researchers increasingly use these new techniques to 

enhance their understanding of urban patterns but often do not have access to short 

demonstration articles for applied guidance. If you have an idea for an article of no more 

than 3,000 words presenting an applied spatial data analysis method or technique, please 

send a one-paragraph abstract to rwilson@umbc.edu  for review.

Measuring Distance 

to Resources

Ron Wilson   

University of Maryland, Baltimore County

Alexander Din   

Dewberry

Abstract

Mapping counts or rates of residents by areal geographies is useful for visualizing 

distributions across regions. However, this approach limits the understanding of resource 

proximity to visual approximations. Taking advantage of exact location information in 

a geographic information system (GIS), direct proximity statistics can be created by geo-

processing residence locations to population centers. In this article, we demonstrate how 

to geoprocess location information to create a table of the distances between resident 

locations and the nearest population centers to gain a more precise understanding of 

how far people live, as groups, from their closest resource centers. 

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 323

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

Wilson and Din

Relationship Between Voucher Residences and Population 

Centers

Proximity analysis is an analytical approach used to capture the distance between neighboring 

locations. Measuring the proximity of a residential location to the closest population center (PC) is 

a common approach toward understanding general resource accessibility. Similarly, the proximity 

of PCs to residence locations is useful for understanding distance costs from a central location to 

nearby residences. Because residence locations and the center of a PC are represented as discrete 

spatial locations in a GIS, distance measurement techniques can succinctly capture proximity 

and present the results as a set of statistics. A proximity analysis also allows for the visualization 

of the geographic distribution of locations and their distances around a PC that can be used in 

conjunction with distance analysis tables that offer more comprehensive pictures of accessibility or 

outreach challenges. 

We demonstrate the proximity analysis approach by geoprocessing two sets of locations to create a 

distance statistics table to gauge how proximate Housing Choice Voucher (HCV) program partici-

pant locations are to their nearest PC. Assigning residential locations to the closest, or proximate, 

PC allows for deriving a set of summary statistics on the distance between individuals and the 

likely places they frequent for resources and services. The assumption rests on a complex interac-

tion between self-selection and economic realities that occurs in residential selection, wherein 

people often cluster together residentially due on similarities in social, demographic, and economic 

characteristics (Tittle and Rotolo, 2010). HCV recipients, as an example, typically cluster where 

housing is affordable. 

Data

The data we used in this example are the counts of 2016 HCV program participants by census 

tract in the Baltimore-Columbia-Towson, MD Core Based Statistical Area (CBSA)—Baltimore CBSA 

hereafter. We downloaded the data from the U.S. Department Housing and Urban Development 

(HUD) enterprise GIS storefront. Exhibit 1 shows the geographic distribution of program partici-

pants across the CBSA.

Because HUD does not provide program participant locations, we simulated the locations in a 

two-stage process to create residential locations based on where people actually live. First, we 

proportionally divided the counts of program participants according to the proportion of residents 

within each block group contained in each census tract. Second, we created a set of randomly 

distributed locations within each block group to simulate the locations based on known residential 

patterns. This two-stage process allows for a reasonable approximation of where HCV program 

participants live and reduces the risk of placing them in areas where populations do not reside (for 

example, forested portions, lakes, parks areas, and industrial sites). 

324 SpAM

Measuring Distance to Resources

Exhibit 1

Thematic Map of Housing Choice Voucher Program Participant Counts in the 

Census Tracts

HCVP = Housing Choice Voucher participant. 

Proximity Analysis

A proximity analysis of this type rests on the geographic theory of spatial interaction, in which 

individuals interact with places by traveling to nearby locations to obtain amenities, services, or 

resources. With HCV program participants, the interaction results from traveling to the closest PC 

where daily needs resources (for example, jobs, groceries, goods, and doctors) are concentrated. 

Interaction for outreach involves centralizing resources to deliver in a PC and venturing into sur-

rounding neighborhoods to reach the intended constituents. In each of these cases, the “least cost 

principle” is applied to the geographic theory of spatial interaction to assume that people— 

particularly those with limited abilities to travel—will travel to the closest place for any needs.

Method

We use ArcGIS software to develop this method, but any GIS software program can be used to 

replicate our method. The GIS software programs available contain the tools required to prepare 

the data, calculate proximity distances, and visualize the results. The method demonstrated in this 

article follows a series of six stages to create a table of summary distance statistics between HCV 

program participant residences and their closest PCs, and also output for visualizing the distances. 

We provide a script in the appendix to automate the following method.

Cityscape 325

Wilson and Din

Stage 1: Identifying Initial Population Centers

In the first stage, we selected a set of locations (point geometries) to represent the PCs that the 

HCV participant residence locations will be assigned. Our method gives analysts the quantitative 

and qualitative flexibility to identify PCs that serve as resource centers. The initial PCs can be 

selected objectively based on a population threshold, for example, that will ensure that PCs likely 

have a concentration of amenities, services, or resources. Alternatively, the initial selection of 

PCs can be determined subjectively by choosing sites that have importance or fill coverage gaps 

between places.

Any given coverage area, such as the Baltimore CBSA, likely has too many PCs for residential as-

signment. The initial centers should be places with a certain number of residents but recognizable 

by name to residents. Selecting too many PCs leads to an oversaturation with too many centers too 

close to each other, creating a large summary table with too few distances to each PC. Too large a 

table makes the statistics unreliable and unreasonable to communicate the likely centers to which 

residences travel for resources. Selecting too few PCs would aggregate residential locations to PCs 

that are unlikely to be places to which residences travel for resources. Both situations provide 

unrealistic pictures of people and the PCs with which they interact. The selection of PCs, therefore, 

must be well thought out.

Our first selection reduced the 72 PCs in the Baltimore CBSA to 30 by removing nearly all centers 

with fewer than 10,000 residents, making our initial analysis primarily quantitatively selective. We 

subjectively included additional PCs to cover large gaps between centers (see exhibit 2).

Exhibit 2

Initial Population Centers With Randomized Housing Choice Voucher Program 

Participant Locations

326 SpAM

Measuring Distance to Resources

For example, we kept Cockeysville (northern Baltimore County) and Centreville (Eastern Shore 

in Queen Anne’s County) to reduce gaps in coverage, even though their populations are less than 

10,000. Additionally, because the city of Baltimore is a single PC in the geometric center of the 

city jurisdiction boundary, we added three PCs (North, East, and West Baltimore) within the ad-

ministrative sections of the city. The initial selection of 30 PCs provided a comprehensive coverage 

across the Baltimore CBSA in relation to residential distributions of HCV program participants. 

Stage 2: Assigning HCV Recipient Locations to the Nearest Population Centers 

Before conducting a proximity analysis, we updated the HCV data to contain X and Y coordinates, 

to create and draw proximity links between HCV recipient locations and the nearest PCs. These 

proximity links are known as  spider diagrams, which aid in the visualization of the geographic 

distribution of locations around a central location. 

We identified the nearest PC to each participant location based on the direct (Euclidean) distance 

using the Near technique in the ArcToolbox. This technique identifies the closest PC to each 

participant location and records the following three geographic characteristics of the nearest PC to 

each participant location in the resident location attribute table.

•  Unique identifier of the nearest PC location. 

•  Distance from the HCV program participant location to the nearest PC.

•  X and Y coordinates of the nearest PC location. 

These three characteristics allow for a summarization of the linkage distances into a set of sum-

mary statistics of residences and the closest PCs. They also provide the data that creates the spider 

diagrams for visualization.

Stage 3: Calculating the Proximity Link Between Locations

In this stage, we calculated the distances between HCV recipient locations and PCs and created 

proximity links for the spider diagram. Using the X and Y coordinates of the closest PC recorded 

for each program participant location, we produced a proximity layer of lines between the resi-

dences and PCs showing those linkages.

We first had to generate an additional set of X and Y coordinates for the participant locations to 

create the proximity links. Creating a layer of proximity links requires start and end coordinates 

that allow for the lines to be drawn between locations. With the Add Geometry Attributes tool in 

the ArcToolbox, we added the centroid coordinates for the HCV resident locations. Doing so gave 

the program participant layer the coordinate pairs needed to create proximity link lines between 

the residences and the population centers.

Using the XY to Line technique in the ArcToolbox, we created a new geographic layer of lines be-

tween participant locations and PCs and loaded the links into ArcGIS to visualize (see exhibit 3). The 

links allow for a visual depiction of the proximity assignment to ensure that coverage is adequate.

The PCs can now be visually inspected in conjunction with the distance summary statistics table to 

begin evaluating whether the results are reasonable to meet the objectives of the analysis.

Cityscape 327

Wilson and Din

Exhibit 3

Initial Population Center Selection Links Between Housing Choice Voucher Program 

Participants and Nearest Population Centers

Stage 4: Summarizing the HCV Counts by Population Centers

In this stage, we summarized the distances between the HCV recipient locations and PCs into a 

table of proximity distance statistics. We used these statistics to evaluate the proximity assignment 

results and determine if PCs should be removed from, or added to the analysis.

We summarized the distances associated by the unique identifier for each PC, recorded from 

the near analysis of each resident location in stage 2. We then used the Summary Statistics tool 

in the ArcToolbox to create several statistical point estimates for each PC and specified Count, 

Sum, Mean, Standard Deviation, Minimum, and Maximum to be calculated using the NEAR_FID 

unique identifier as the Case field. The Case field enables ArcGIS to summarize distances by each 

individual PC and create a table of summary statistics. 

Stage 5: Assessing Proximity Results by Population Center

At the final stage, we imported the summary distances into an Excel spreadsheet to create a 

distance statistics table (exhibit 4) corresponding with the thematic map in exhibit 3 to visualize 

the frequencies of each PC. This visualization allows for an examination of the distance summary 

statistics. In this stage, we generated a set of links that show which HCV recipient locations are 

proximately associated with the PCs and inspected the attribute table of the PCs layer to ensure 

that we achieved a reasonable set of statistics. 

328 SpAM

Measuring Distance to Resources

Exhibit 4

Distance Statistics (in Kilometers) of Initial Housing Choice Voucher Program 

Participation Assignment to Nearest Population Centers

Pop Center

Count

Sum

Mean

Std. Dev.

Minium

Maximum Coeff. Var.

East Baltimore

4,002

8,452

2.1

0.9

0.1

4.7

0.43

North Baltimore

1,437

4,099

2.9

0.7

0.7

4.5

0.24

West Baltimore

3,514

7,590

2.2

0.9

0.0

5.0

0.44

Aberdeen

322

1,376

4.3

3.5

0.1

16.6

0.81

Annapolis

407

2,024

5.0

5.0

0.1

28.2

1.01

Bel Air

366

1,378

3.8

3.8

0.1

25.8

1.02

Brooklyn Park

845

2,177

2.6

1.1

0.2

5.6

0.44

Catonsville

458

1,432

3.1

1.4

0.2

8.2

0.44

Centreville

92

1,275

13.9

5.9

0.8

28.3

0.43

Cockeysville

96

232

2.4

2.0

0.0

14.6

0.81

Columbia

1,329

6,654

5.0

2.2

0.0

14.6

0.43

Dundalk

780

1,618

2.1

1.2

0.1

8.7

0.58

Edgewood

438

1,457

3.3

2.0

0.0

8.0

0.60

Essex

430

1,008

2.3

0.8

0.3

6.7

0.33

Ferndale

154

352

2.3

1.0

0.2

8.0

0.44

Glen Burnie

755

2,707

3.6

2.0

0.2

14.0

0.55

Middle River

624

1,409

2.3

1.4

0.2

7.3

0.61

Odenton

205

839

4.1

3.3

0.2

12.5

0.80

Overlea

412

981

2.4

1.0

0.2

4.6

0.41

Parkville

773

2,019

2.6

0.7

0.1

4.8

0.25

Perry Hall

168

409

2.4

1.6

0.2

9.8

0.64

Pikesville

1,090

2,804

2.6

0.9

0.4

6.5

0.36

Randallstown

746

1,732

2.3

1.6

0.2

7.5

0.68

Reisterstown

360

1,306

3.6

2.1

0.2

9.4

0.58

Rosedale

753

2,051

2.7

0.8

0.1

4.5

0.31

Severn

227

915

4.0

3.4

0.2

12.9

0.85

Sykesville

85

460

5.4

4.7

0.0

15.5

0.87

Towson

227

388

1.7

1.3

0.1

5.8

0.77

Westminster

708

5,194

7.3

6.3

0.1

25.2

0.86

Woodlawn

1,278

3,324

2.6

1.0

0.1

6.4

0.38

We added the coefficient of variation (CV) to the table to compare the spread of HCV program 

participant locations around the average from the PCs. The CV is particularly important in spatial 

analysis when comparing groups of locations or distances around a central point, because less space 

exists in which the locations closer to the center can be distributed. That is, locations toward the 

center are likely to be closer together and have naturally smaller distances, because less space exists 

for the locations to be distributed–, making their variation from the mean appear less varied. The 

converse is true for locations farthest aware from the center; that is, they appear more dispersed 

and have larger distances. Thus, using the standard deviation distances alone to compare the sum-

marized distances around each PC location is untenable, because the differences in space restrictions 

will make PCs with HCV participants closer to the PC to appear less dispersed than those farther 

away. Therefore, to determine if HCV residences are more dispersed around the different PCs, the 

distances around the average distance for each group are contextualized by dividing the standard 

deviation of the distances, the mean distance to create a CV of distances to make comparisons.

The results of our first analysis show that using 30 PCs created a large distance analysis table, 

including a number of PCs with few HCV program participants. Centreville, Cockeysville, Fern-

dale, Odenton, Perry Hall, Severn, Sykesville, and Towson are centers with a relatively few HCV 

Cityscape 329

Wilson and Din

program participants in proximity. Reducing the size of the table is not a simple matter of removing 

these small number of residences. First, removing each location will impact the counts of nearby 

PCs, because the program participants associated with one of the removed centers will get assigned 

to the next nearest PC. Second, removing a center because it does not meet a certain threshold 

may lead an unreasonable distance to be considered proximate. For example, removing Centreville 

would show many more HCV program participants on the Eastern Shore traveling to Annapolis for 

services, which is an unreasonable and unlikely assumption. As such, we subjectively determined 

whether to keep or discard a PC for the next analysis.

Stage 6: Adjustment Recalculations To Refine the Results

To refine the analysis, we removed five of the eight PCs with fewer than 300 associated HCV resi-

dent locations—Cockeysville, Ferndale, Odenton, Perry Hall, and Sykesville. We eliminated two 

additional PCs, Essex and Rosedale, because they are in close proximity to each other and other 

centers. We removed Sykesville in the west and Cockeysville in the north; although doing so left 

gaps in the coverage area, the HCV program participant locations were close enough to be assigned 

to a nearby PC on a regional scale. 

Most participants near Cockeysville were reassigned to Towson, thus increasing Towson’s numbers. 

Towson is reasonable reassignment choice because amenities, services, and other resources are 

more clustered in that center than in Cockeysville. We also removed Parkville and Overlea because 

of their proximity to Towson, which has greater name recognition and is more meaningful to 

residents. We replaced North Baltimore with Northeast Baltimore to provide coverage in the city’s 

northeast. Few to no HCV program participant locations actually lived close to the North Baltimore 

location, even though it was the most proximate PC for about 1,400 participants. Further, given 

the economic status of North Baltimore, resources for HCV participants are not in that section of 

the city. These two reasons made North Baltimore less than an ideal choice to represent a central 

location for participants to travel to for resources.

We removed Ferndale and Odenton, because they are in close proximity to Glen Burnie and 

Odenton to Annapolis, respectively. HCV participant locations around Parkville and Perry Hall, 

slightly northeast of the city of Baltimore, were reassigned to several PCs in the vicinity, which also 

increased the count of HCV program participants around Towson (see exhibit 5). 

The removal of the aforementioned five centers left Centreville, Severn, and Towson remaining in 

the second analysis.

In the second analysis, all PCs had resident counts at least above a reasonable threshold to create 

estimates that represent large enough groups, with no two PCs too close to each other. We repeated 

stages 2 to 4 and reexamined the results, as in stage 5.

330 SpAM

Measuring Distance to Resources

Exhibit 5

Final Population Center Selection Links Between Housing Choice Voucher Program 

Participants and Nearest Population Centers

Graphing the Final Proximity Analysis Results

Once we summarized the voucher holder distances, we assessed the resulting table to determine 

if we needed to adjust the number and inclusion of PCs. We imported the summary table into a 

Microsoft Excel spreadsheet with the distance statistics and calculated the corresponding CVs to 

evaluate the results (exhibit 6).

Exhibit 6 offers several statistics for reporting on approximately how far HCV program participants 

travel for services or other resources. These statistics provide the number of participants in proxim-

ity to each PC, how far on average they would have to travel to a particular PC, and how dispersed 

they are within the area. The minimum and maximum figures give the distance ranges of the 

closest and farthest HCV program participant residences from a PC. Finally, we calculated the CV 

so that the dispersion of the distances for each PC can be compared. 

Exhibits 7 and 8 show graphs of the average distances and CVs, respectively, to visually gauge the 

similarities and differences for each PC.

The summary table displayed in exhibit 6 can also be joined to the PC layer to thematically 

map HCV program participant volumes of those proximate to that PC or any of the summarized 

distance statistics. The final map to accompany exhibit 6 contains only the PCs with corresponding 

HCV program participant counts (exhibit 9).

Cityscape 331

Wilson and Din

Exhibit 6

Distance Statistics (in Kilometers) of Final Housing Choice Voucher Program 

Participation Assignment to Nearest Population Centers

Pop Center

Count

Sum

Mean

Std. Dev.

Minium

Maximum Coeff. Var.

East Baltimore

4,005

6,587

1.9

0.9

0.1

4.7

0.44

Northeast Baltimore

1,611

6,612

2.8

1.1

0.1

8.3

0.39

West Baltimore

3,514

9,861

2.5

1.3

0.0

6.5

0.51

Aberdeen

322

1,376

4.3

3.5

0.1

16.6

0.81

Annapolis

439

2,539

5.8

5.6

0.1

28.2

0.98

Bel Air

367

1,394

3.8

3.9

0.1

25.8

1.02

Brooklyn Park

926

2,464

2.7

1.1

0.2

5.7

0.42

Catonsville

461

1,456

3.2

1.4

0.2

8.3

0.45

Centreville

92

1,275

13.9

5.9

0.8

28.3

0.43

Columbia

1,400

7,527

5.4

2.7

0.0

21.8

0.51

Dundalk

805

1,732

2.2

1.3

0.1

8.7

0.59

Edgewood

439

1,466

3.3

2.0

0.0

8.9

0.60

Glen Burnie

1,157

6,149

5.3

3.6

0.2

19.4

0.67

Middle River

1,142

3,988

3.5

2.1

0.2

9.7

0.60

Randallstown

961

3,174

3.3

3.0

0.2

20.9

0.91

Reistertown

376

1,540

4.1

3.2

0.2

23.7

0.77

Rosedale

1,263

1,712

2.8

1.1

0.1

6.0

0.38

Towson

986

2,869

4.0

2.4

0.1

14.7

0.61

Westminster

722

5,516

7.6

6.6

0.1

27.3

0.87

Woodlawn

2,093

8,284

3.5

1.3

0.1

7.0

0.37

Exhibit 7

Average Distances of Housing Choice Voucher Program Participant Residences to 

Nearest Population Centers

Centreville 

13.9  

Westminster 

7.6  

Annapolis 

5.8  

Columbia 

5.4  

Glen Burnie 

5.3  

Aberdeen 

4.3  

Reistertown 

4.1  

Towson 

4.0  

Bel Air 

3.8  

Woodlawn 

3.5  

Middle River 

3.5  

Edgewood 

3.3  

Randallstown 

3.3  

Catonsville 

3.2  

Rosedale 

2.8  

Northeast Baltimore 

2.8  

Brooklyn Park 

2.7  

West Baltimore 

2.5  

Dundalk 

2.2  

East Baltimore 

1.9  

332 SpAM

Measuring Distance to Resources

Exhibit 8

Distance Coefficients of Variation of Housing Choice Voucher Program Participant 

Residences to Nearest Population Centers 

Bel Air 

1.02  

Annapolis 

0.98  

Randallstown 

0.91  

Westminster 

0.87  

Aberdeen 

0.81  

Reistertown 

0.77  

Glen Burnie 

0.67  

Towson 

0.61  

Middle River 

0.60  

Edgewood 

0.60  

Dundalk 

0.59  

West Baltimore 

0.51  

Columbia 

0.51  

Catonsville 

0.45  

East Baltimore 

0.44  

Centreville 

0.43  

Brooklyn Park 

0.42  

Northeast Baltimore 

0.39  

Rosedale 

0.38  

Woodlawn 

0.37  

Exhibit 9

Final Population Center Selection With Counts of Housing Choice Voucher Program 

Participants

HCVP = Housing Choice Voucher participant. PC = population center.

Cityscape 333

Wilson and Din

Summary

The proximity method is an approach that enables analysts, researchers, and others to transform 

location data into a set of a descriptive statistics for stakeholders, planners, and other officials to 

make informed decisions based on how proximate residents are to resources or how far outreach 

must be to reach specific populations. Information compiled as a set of statistics allows for that 

information to be better understood than by the visual display of numerous data points or color-

shaded areas that show concentrations of locations. The use of GIS enables analysts to implement 

this technique quickly, which gets information to users faster than before. If too many locations 

are assigned to a PC, many of those locations are likely not really proximate, which undermines 

the objective of providing local-level counts so that outreach or services can be centered in places 

that serve the maximum population and minimize travel distance. The advantage of the method 

presented in this article is that the analyst has control over data selection to make the results as 

generalized as needed but more accurate that other methods.

Appendix: Python Script of Proximity 

import arcpy

## set the workspace environment

arcpy.env.workspace       = "C:\my\workspace\and\geodatabase.gdb"

arcpy.env.overwriteOutput = True

## set variables:

## these variables you may need to change as the analysis changes

points     = “randomized_voucher_locations” ## HCV program locations

popCenters = “population_centers”           ## population centers

nearTable  = “Near_Table”                   ## the near distances table

## these variables always stay the same, no change required

## the below is the summarized table of the near table and the accompanying statistics

myFields = [

   [ 

“NEAR_DIST”, 

”COUNT”],

[ “NEAR_DIST”, ”MIN”  ],

[ “NEAR_DIST”, ”MAX”  ],

[ “NEAR_DIST”, ”MEAN” ],

[ “NEAR_DIST”, ”STD”  ]

 ]

## sumTable is the summary statistics of the near distances table

sumTable = "%s_SUMMARY" %(nearTable)

## generate near table

arcpy.GenerateNearTable_analysis(points,popCenters,nearTable,””,”LOCATION”,””,”CLOSEST”)

## summarize the near table

arcpy.Statistics_analysis(nearTable,sumTable,myFields,”NEAR_FID”)

## add coefficient of variation and name fields

arcpy.AddField_management(sumTable,”NEAR_DIST_CV”,”DOUBLE”)

arcpy.AddField_management(sumTable,”NAME”,”TEXT”)

## join in the population centers to get their names, it makes reading the table easier

arcpy.AddJoin_management(sumTable,”NEAR_FID”,popCenters,”OBJECTID”)

## calculate coefficient of variation and bring in the name of the population center

arcpy.CalculateField_management(sumTable,”NEAR_DIST_CV”, “!STD_NEAR_DIST! / !MEAN_NEAR_DIST!”, 

”PYTHON_9.3”)

expression   = “!%s.NAME!” %(popCenters)

sumTableName = “%s.NAME”   %(sumTable)

arcpy.CalculateField_management(sumTable,sumTableName,expression,”PYTHON”)

## remove the join

arcpy.RemoveJoin_management(sumTable,popCenters)

334 SpAM

Measuring Distance to Resources

Acknowledgments

The authors thank Katherine Condon of the U.S. Citizenship and Immigration Services for 

motivating us to develop and publish this method and for providing valuable comments toward 

improving the manuscript. The authors also thank several others who motivated us to publish this 

work.

Authors

Ron Wilson is an adjunct faculty member of the Geographic Information Systems Program at the 

University of Maryland, Baltimore County.

Alexander Din is a geographic information system analyst at Dewberry.

References

Tittle, Charles R., and Thomas Rotolo. 2010. “Socio-Demographic Homogenizing Trends Within 

Fixed-Boundary Spatial Areas in the United States,”  Social Science Research 39 (2): 324–340. 

Cityscape 335

336 Departments

Evaluation Tradecraft

Evaluation Tradecraft presents short articles about the art of evaluation in housing and 

urban research. Through this department of  Cityscape , the Office of Policy Develop-

ment and Research presents developments in the art of evaluation that might not be 

described in detail in published evaluations. Researchers often describe what they did 

and what their results were, but they might not give readers a step-by-step guide for 

implementing their methods. This department pulls back the curtain and shows readers 

exactly how program evaluation is done. If you have an idea for an article of about 

3,000 words on a particular evaluation method or an interesting development in the art 

of evaluation, please send a one-paragraph abstract to marina.l.myhre@hud.gov .

Use of Genetic Matching  

in Program Evaluation:  

The Case of RAD

David Ruiz

Dennis Stout

Christine Herlihy

Econometrica

In this article, we describe the use of genetic matching in program evaluation, define cases in 

which this approach would be appropriate, and detail the value that this approach can provide. 

In particular, we focus on how the researchers used genetic matching in the ongoing evaluation 

of the Rental Assistance Demonstration (RAD) program, the results they obtained, and how they 

assessed its success. Clinical researchers and social scientists have developed genetic matching as a 

sampling technique for conducting nonrandomized observational studies in a quasi-experimental 

fashion. The method matches each member of the treatment group with one or more members 

of the control group. The match uses a set of key covariates, which the analyst selects based on 

prior expectations about possible treatment group participation factors. In the RAD evaluation, the 

research staff used stratified random sampling to select the RAD project sample (treatment group) 

from the participating RAD population. For the non-RAD sample (control group), researchers 

used a genetic matching algorithm to select a matched group of non-RAD public housing projects 

from the nonparticipating public housing population. Postsampling analysis confirmed that, on 

covariates likely to impact participation in RAD, the control group and the treatment group were 

similarly distributed. This matching technique can be a useful tool in program evaluation when 

membership in the treatment or control group is not random; for instance, if participation is 

voluntary, as is the case in the RAD program.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 337

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

Ruiz, Stout, and Herlihy

Overview of RAD

RAD was authorized in 20121 as a pilot program for converting public housing projects that are 

subsidized through public housing programs to assisted housing projects that are subsidized through 

project-based Section 8 Housing Assistance Payment (HAP) contracts. Participation in the program 

is voluntary for public housing authorities (PHAs). For a PHA to participate, RAD requires that it 

submit a project application with supporting documentation and analysis. Over a period of several 

months, the U.S. Department of Housing and Urban Development (HUD) reviews and approves 

the RAD application, grants a Comprehensive Housing Assistance Plan (CHAP), and issues a RAD 

Conditional Commitment (RCC). During this process, a project can be withdrawn by the PHA or 

have its CHAP revoked by HUD. At the end of this approval process, the PHA and HUD agree to 

convert the project to a project-based Section 8 HAP contract. After conversion, the former public 

housing project will receive its program funding from the project-based Section 8 program instead of 

from public housing programs. The primary intent of the RAD program is to preserve and improve 

the quality of subsidized housing by enabling PHAs to use their long-term Section 8 HAP contracts to 

leverage external capital for rehabilitation or new construction and financial stabilization.

Congress requires HUD to assess how the RAD program has been implemented and its impact on 

the physical and financial condition of converted housing and tenants. The core research questions 

revolve around whether RAD has produced better-quality housing and put that housing on a firmer 

financial foundation while continuing to serve low-income tenants. The evaluation began in 2014 and 

will continue through 2018. An interim report on the evaluation was released in September 2016. 

Genetic Matching in Observational Studies 

Few program evaluations can replicate the research design used in typical clinical experiments to 

test the efficacy of drugs and other medical treatments. In such experiments, the treatment group is 

administered the test drug, while the control group is given a placebo. Such studies are  double blind 

in the sense that the assignment of each participant to the treatment or control group is random, 

and neither the research scientists nor the subjects of the experiment know to which group each 

subject has been assigned.

Random selection is the preferred approach for clinical research because any variation between 

the two groups after such assignment is random, rather than systematic. This allows researchers 

to more accurately attribute any difference in impact to the treatment alone (that is, receiving the 

drug as opposed to a placebo), rather than to potentially confounding variables.

Studies that use observational data rather than experimental data—as is the case with most program 

evaluations, including the evaluation of the RAD program—are more likely to produce biased results 

because assignment into the treatment and control groups has not been randomized. However, obser-

vational studies can be conducted in a quasi-experimental fashion by matching each member of the 

treatment group with one or more controls based on a set of key covariates that are postulated to have 

some effect on the propensity of a given individual to participate in the treatment. Using this method, 

1 Consolidated and Further Continuing Appropriations Act of 2012, Pub. L. 112–55. 

338 Evaluation Tradecraft

Use of Genetic Matching in Program Evaluation: The Case of RAD

the researcher selects a matched group of controls with a similar distribution of covariates to that of 

the treatment group. A high-quality match will minimize all observed sources of bias.2 The quality of 

the match is measured by calculating the bias for each variable, as follows.

                        ,    

(1)

where       and       are the means of a covariate     for the treatment and control groups, respectively, 

and       and       are their variances.3 This bias should not be statistically different from 0.

There are many ways to match treatment and control samples. No consensus has emerged in the 

research literature on which matching method is best, and empirical matching is as much art as 

science (Stuart, 2010). For the evaluation of the RAD program, research staff opted for a flexible 

matching method—the genetic matching algorithm GenMatch (GM) written in R (Sekhon and Me-

bane, 1998).4 GM is “a multivariate matching method that uses an evolving search algorithm de-

veloped to maximize the balance of covariates across matched treated and control units” (Diamond 

and Sekhon, 2013: 2).5 “Balance” means that the treatment and control groups have the same 

joint distribution of the covariates. GM minimizes a loss function6 that combines two statistical 

tests: (1) a parametric  t-test for the difference in means of each covariate and (2) a nonparametric Kolmogorov–Smirnov test (KS test) that minimizes the difference between the empirical cumulative distribution functions of each covariate (Diamond and Sekhon, 2013). It is helpful to think of 

genetic matching as a generalized matching algorithm that can incorporate both nonparametric (for 

example, Mahalanobis distance) and parametric (for example, propensity score matching [PSM]) 

matching algorithms, if appropriate.7 GM, through its iterations, allows us to identify the most 

relevant characteristics to match treatment and comparison entities. GM outperforms PSM and 

other, more simplistic nonparametric methods because it returns an optimal set of matches even if 

such optimal balance is best achieved by a differential combination of weights across characteristics 

on which the treatment and comparison groups are matched.8

2 It does not, of course, control for unobserved sources of bias. 

3 The bias is also known as the “standardized difference,” because the difference in means is “standardized” by dividing it by the pooled standard deviation.

4 R is a programming language and development environment for statistical computing and data visualization; see https://

www.r-project.org/about.html for more information.

5 Genetic algorithms (including but not limited to genetic matching) are tools that can be used in machine learning. These algorithms have their roots in and borrow concepts from evolutionary biology (for example, mutation, crossover, and selection).

6 The loss function that is minimized is the maximum  p-value from either the Kolmogorov–Smirnov (KS) test or the paired t-test over all variables that are matched over. One can write the loss function as— 

. 

The best match is the one that minimizes this loss function. Rather than relying on the  t-test alone, the GM algorithm combines the  t-test with the KS test to get results that are as well-balanced as possible with respect to both tests. Using p-values enables one to compare results from both tests on the same (probability) scale.

7 Mahalanobis matching, for example, minimizes a distance measure that does not rely on an econometric model or distributional assumptions—and the lack of distributional assumptions enables us to find matches among smaller sample sizes.

8 As a practical example, if propensity score methods were indeed the best method for identifying comparison entities that were statistically similar, the genetic matching algorithm would place a weight of 1 on the propensity score “characteristic” 

and a weight of 0 on all other characteristics. 

Cityscape 339

Ruiz, Stout, and Herlihy

The  t-test for the difference in means is familiar but subject to two crucial limitations. First, the t-test is based on a single parameter, the mean, even though distributions with identical means 

might have widely different underlying distributions. Second, the  t-test depends on the assumption 

that both underlying distributions are normal, which may be untrue. The KS test, on the other 

hand, is nonparametric in that it does not depend on any assumptions about the underlying dis-

tributions. It compares the empirical distribution of each variable for a given RAD project with its 

potential matches in the non-RAD population and calculates the maximum difference between the 

two cumulative distribution functions. The GM algorithm minimizes the largest discrepancy based 

on  p-values from KS tests and  t-tests for all covariates.9 For example, if one is trying to achieve balance on  A,  B, and  C characteristics, the algorithm begins with the assumption that  A,  B, and  C all matter equally in achieving balance. It assesses this by minimizing the  p-values of the  t-test and the KS test between the treatment and control groups. GM checks multiple weighting schemes—across 

A,  B, and  C—to identify which weighting scheme minimizes  p-values, thereby identifying which variables are most important to minimize statistical bias.

Research Design for Evaluation of the RAD Program

The research design for the evaluation of the RAD program called for analyzing data for a small 

sample of RAD projects (the treatment group) and a small sample of non-RAD projects (the control 

group) to identify how RAD might impact the physical and financial condition of converted prop-

erties. The analysis focused on changes in the RAD cohort, before and after conversion, compared 

with changes in the non-RAD cohort over a comparable period of time for a range of variables, 

such as short- and long-term capital needs, reserves, and cashflow. Small sample sizes were a 

necessity, given the need to manage the high cost and burden on PHAs of collecting primary data. 

Data included a project’s physical and financial condition, collected by professional engineers on 

site through a Physical Condition Assessment or similar format, and the PHA’s views and experi-

ences of the program, collected by online surveys and telephone interviews. The data collection 

burden on PHAs was managed by limiting the number of PHAs participating in the study to small 

sample groups. The research team decided on a minimum of 24 RAD projects in the treatment 

group and 48 non-RAD projects in the control group. The ratio of 2 non-RAD projects for every 

RAD project in the sample reflected the expectation that the control group would exhibit greater 

data variability and that PHAs with non-RAD projects would be less inclined to participate in the 

study, requiring substitutes.

The sample frame for the treatment group consisted of the universe of 132 RAD properties that had 

an approved CHAP as of December 31, 2013, and had either closed or reached the RCC stage by 

December 31, 2014. This sample frame meant that the resulting sample would be representative of 

9 The  p-value represents the probability of getting a value of the test statistic greater than the one obtained, given that the null hypothesis is actually true. As applied in the RAD study, the null hypothesis would be that no difference exists between RAD and non-RAD developments. A low  p-value (less than 5 percent) would result in rejection of the null hypothesis (with 95 percent confidence). A high  p-value, however, would mean that the null hypothesis could not be rejected. Using  p-values enables results for different test statistics to be compared on the same scale. That is, the GM algorithm can directly compare the  p-value from a  t-test with the  p-value from a KS test because both are measured in terms of probabilities. 

340 Evaluation Tradecraft

Use of Genetic Matching in Program Evaluation: The Case of RAD

RAD projects that had applied earlier and had moved from CHAP to RCC or had closed in a timely 

manner.10 To select the RAD sample, researchers used a stratified random selection methodology 

along two dimensions: (1) PHA size and (2) project performance rating. Each dimension was split 

into three subcategories, yielding nine (3 × 3 = 9) potential buckets. However, only eight of these 

buckets contained actual housing projects. Exhibit 1 shows the number and percentage of projects 

in each bucket for the RAD treatment group sample. The bucket of substandard-performing proj-

ects managed by small PHAs is empty, because no projects in the population of 132 RAD projects 

from which the RAD project sample was drawn were in that bucket.

The sample frame for the control group consisted of 5,993 public housing projects that had not 

applied to the RAD program based on the HUD’s inventory of all public housing projects and RAD 

program data on applications. To select non-RAD projects to serve as the control group from the 

non-RAD population, the genetic matching algorithm was used to select without replacement the 

two non-RAD properties that were the best matches for each participating RAD property. Because 

the sample included 24 RAD properties, the result was 48 matching, non-RAD comparison projects 

from the HUD inventory of non-RAD projects.

Exhibit 2 shows the number and percentage of projects in each bucket for the non-RAD control 

group. The bucket of substandard-performing projects managed by small PHAs is empty again 

because it is empty for the RAD sample, and the non-RAD control group is intended to match 

the RAD sample. The distribution of projects across the other buckets for both samples is broadly 

similar. Although not identical in all buckets, the distribution is the same for the PHA size 

subcategories. The slight differences reflect the use of other variables during the iterative genetic 

matching process.

Exhibit 1

Distribution of Projects in RAD Treatment Group Sample by PHA Size and Project 

Performance Rating 

Total RAD Projects 

Substandard

Standard

High-Standard

in Sample by  

PHA Size

PHA Size

No.

Pct.

No.

Pct.

No.

Pct.

No.

Pct.

Small

0

0

2

8

3

13

5

21

Medium

1

4

8

33

6

25

15

63

Large

1

4

2

8

1

4

4

17

Total RAD projects 

2

8

12

50

10

42

24

100

in sample by per-

formance rating

PHA = public housing authority. RAD = Rental Assistance Demonstration.

Source: Public and Indian Housing Information Center and RAD program data as summarized by Econometrica, Inc.

10 The RAD program was expanded at about the time of our sampling. Due to the time limitations of our study, we did not sample from the entire universe of 1,074 public housing projects that eventually applied to RAD. Projects that applied later, were progressing slowly, or dropped out were not in our sample frame because they would have offered little information on the impact of RAD. Researchers selected a supplemental sample of projects that withdrew from RAD or had their CHAPs revoked by HUD to analyze the factors contributing to that outcome.

Cityscape 341

Ruiz, Stout, and Herlihy

Exhibit 2

Distribution of Projects in Non-RAD Control Group by PHA Size and Project 

Performance Rating

Total Non-RAD 

Substandard

Standard

High-Standard

Projects in Control 

PHA Size

Group by PHA Size

No.

Pct.

No.

Pct.

No.

Pct.

No.

Pct.

Small

0

0

2

4

8

17

10

21

Medium

2

4

17

35

11

23

30

63

Large

3

6

3

6

2

4

8

17

Total non-RAD 

5

10

22

46

21

44

48

100

projects in control 

group by perfor-

mance rating

PHA = public housing authority. RAD = Rental Assistance Demonstration.

Source: Public and Indian Housing Information Center and RAD program data as summarized by Econometrica, Inc.

Accounting for Bias 

Drawing a simple random sample of 48 projects from the population of 6,664 non-RAD public 

housing projects would introduce  self-selection bias, because PHAs  choose to participate (or not participate) in RAD; they are not randomly assigned to RAD. Failing to account for this choice 

could result in biased estimates that would reduce the accuracy and reliability of the findings. 

However, at the start of the study, the research team expected that RAD projects could differ 

systematically from non-RAD projects due to self-selection bias. For instance, PHAs might prefer 

to submit a well-managed project to the RAD program because such a project would be less risky.11 

In addition, the goal of RAD is to generate capital for rehabilitation, and PHAs might therefore 

select projects that need more capital improvements than other public housing developments to 

take advantage of that feature of the program. The size of a PHA could also affect its participation 

in RAD; if smaller PHAs have less mixed-finance experience and therefore less familiarity with the 

financing tools that RAD makes available, they may not understand the advantages of RAD or they 

may feel they cannot make RAD work for them.

To eliminate potential self-selection biases, such as those described above, and to give more 

confidence to the findings, statisticians matched the 24 projects in our RAD sample with non-RAD 

public housing projects based on observable characteristics that could account for differences 

in the likelihood that a given project would participate in RAD. Using RAD program data, HUD 

administrative data from the Public and Indian Housing Information Center, and data from the 

2008–2012 American Community Survey 5-year estimates, the research team created a data set 

of non-RAD properties with usable information for 13 matching variables, or covariates. These 

variables were selected to capture key characteristics of PHAs, public housing projects, and the 

neighborhoods in which the projects are located. The covariates used for this matching are listed 

in exhibit 3, along with the rationale for each covariate and the source of the data. The only 

PHA-level variable was the size of the PHA based on the number of public housing units under 

11 Because RAD projects can assume project debt, which is repaid out of project cashflows, PHAs may consider better-managed projects to be less likely to default under RAD.

342 Evaluation Tradecraft

Use of Genetic Matching in Program Evaluation: The Case of RAD

management. Property-related variables included information on the property’s size (number of 

Annual Contributions Contract units), age (Date of Full Availability, construction date, or date of 

last modernization), structural type (building and development type), bedroom mix (percentage 

of zero-, one-, or two-bedroom units), physical condition (Real Estate Assessment Center inspec-

tion score), and vacancy rate. Neighborhood-level variables capture information on the strength 

or weakness of local affordable housing market conditions, such as rents that are high relative 

to average household income (cost-burden rate), overcrowded living conditions (overcrowding 

rate), degree of poverty in the community (poverty rate), extent to which households in the area 

rent rather than own their homes (percentage of renters), and the prevalence of vacant housing 

(vacancy rate) in the area. 

Exhibit 3

Covariates Used To Match RAD Properties With Non-RAD Properties (1 of 3)

Data 

Variable

Description

Rationale

Source

ACC_Unit_Cnt

Number of Annual 

Indicator of the size of the devel-

PIC  

Contributions Contract units 

opment. Property maintenance and  database

in a property

replacement costs are expected to 

be commensurate with the number 

of units in a property.

Bldg_Type_Code

Building type of project =  

Property maintenance and 

PIC  

1, 2, 3, 4, 5, where:

replacement costs are driven in 

database

1 = ES, elevator structure

part by building type, in that the 

2 = RW, rowhouse or  

cost of maintaining or replacing a 

townhouse style

physical asset such as an elevator 

3 = SD, semidetached

will impact the level of capital 

4 = SF, single-family detached

needs.

5 = WU, walkup/multifamily  

apartment

Dev_Type_Code

Development type of the 

According to  Capital Needs in 

PIC  

project = 1, 2, 3, where:

the Public Housing Program (Abt 

database

1 = elderly

Associates, 2010), average capital 

2 = mixed

needs vary by type of housing. For 

3 = family

example, the average amount of 

capital needs for an elderly unit is 

lower than that of a family unit.

DOFA

Date of Full Availability for  

Indicates the age of the building, 

PIC  

the project

which is important for determin-

database

ing replacement needs. DOFA 

establishes when a development 

can access the operating subsidy 

from a PHA’s Operating Fund. In 

most cases, this date is the same 

as the construction date. We also 

considered the last modernization 

date, if available.

Cityscape 343

Ruiz, Stout, and Herlihy

Exhibit 3

Covariates Used To Match RAD Properties With Non-RAD Properties (2 of 3)

Data 

Variable

Description

Rationale

Source

Percent_1_2_Bed

Percentage of units in the 

Indicator of the size of the unit. 

PIC  

project that have either zero, 

Costs associated with the unit size  database

one, or two bedrooms

of individual units are not equally 

distributed.

PHA_Size_Code

PHA size = 1, 2, 3, where:

Large PHAs differ from small PHAs.  PIC  

1 = small, ≤ 250 units

A PHA’s planning process is unique  database

2 = medium, 251–1,250 units

to the PHA but related to the size 

3 = large, > 1,250 units

of the PHA. The PHA plan includes 

policies, programs, operations, and 

strategies for meeting local housing 

needs and goals. Factors must be 

consistent with the housing and 

community development plans of 

the jurisdiction (as described in the 

Consolidated Plan); thus, PHA size 

matters.

Rounded_ 

Physical inspection score 

REAC conducts approximately 

REAC file

Inspection_

(rounded) for the project

20,000 physical inspections on 

Score

housing properties annually to 

ensure that families living in public 

housing have decent, safe, and 

sanitary housing that is in good 

repair. Scores range from 0 to 100. 

Properties that receive a Public 

Housing Assessment System score 

greater than 90 are considered 

high performers; properties that 

score between 70 and 89 are 

standard; properties that score 

lower than 70 are substandard or 

troubled. High-scoring properties are 

inspected every 3 years, standard 

performers are inspected every  

2 years, and troubled properties are 

inspected every year. The inspec-

tion score served as a proxy for 

estimating capital needs; properties 

with high scores are likely to have 

fewer capital needs than those with 

lower scores.

Vacancy_Rate

Vacancy rate in the project 

Calculated as the percentage of 

PIC  

units occupied. Indicator of both 

database

the condition of the development 

and the quality of PHA manage-

ment. One would expect that a 

well-managed development in 

good physical condition would be 

100% occupied.

344 Evaluation Tradecraft

Use of Genetic Matching in Program Evaluation: The Case of RAD

Exhibit 3

Covariates Used To Match RAD Properties With Non-RAD Properties (3 of 3)

Data 

Variable

Description

Rationale

Source

Cost_Burden_Rate Cost-burden rate in the 

Measures the percentage of rent-

ACS data— 

census tract

ers with gross rent greater than 

U.S. Census 

or equal to 35 percent of their 

Bureau; by 

income. Indicator of both the cost 

census tract

of housing in the local market and 

of poverty in the neighborhood in 

which the development is located.

Overcrowd_Rate

Overcrowding rate in the 

Calculated as number of persons/

ACS data— 

census tract

number of rooms. A ratio greater 

U.S. Census 

than 1 is defined as overcrowded. 

Bureau; by 

Indicator of local housing market 

census tract

conditions and poverty in the 

neighborhood in which the devel-

opment is located.

Poverty_Rate

Poverty rate in the census 

Percentage of neighborhood resi-

ACS data— 

tract

dents below the poverty level.

U.S. Census 

Bureau; by 

census tract

Renter_Rate

Renter rate in the census tract Percentage of neighborhood 

ACS data— 

housing stock occupied by renters.  U.S. Census 

Indicator of the type of housing 

Bureau; by 

available in the neighborhood in 

census tract

which the development is located.

Vacant_Rate

Vacancy rate in the census 

Percentage of vacant homes in the 

ACS data— 

tract

neighborhood in which the develop-

U.S. Census 

ment is located. Indicator of de-

Bureau; by 

mand and supply conditions in the 

census tract

local housing market. The vacancy 

rate determines the choices open to 

consumers in a market. As housing 

supply expands, housing vacan-

cies rise, and demand will either 

remain the same or decrease as 

more residents find available units; 

as vacancies decrease, the housing 

supply either remains the same or 

contracts while demand grows. 

ACS = American Community Survey. PHA = public housing authority. PIC = Public and Indian Housing Information Center. 

RAD = Rental Assistance Demonstration. REAC = Real Estate Assessment Center.

Cityscape 345

Ruiz, Stout, and Herlihy

One can see the amount of bias in the RAD sample directly by comparing the group of 24 RAD 

projects with the entire set of 6,644 non-RAD (NR) public housing projects. Exhibit 4 compares 

the number of projects in each group, the mean value for each variable for the RAD (Mean ) and 

RAD

non-RAD (Mean ) groups, the standard deviation (StdDev) and standard error (StdErr) for difference 

NR

in means, the  t-value, and the bias. The bias is similar to a  t-test for the difference in two means.12 

A high bias will often result in a  t-test that rejects the null hypothesis that the two means are equal. 

For example, Vacant_Rate has a bias of -41.9 percent and a  t-value of -2.05 (which would lead to a 

rejection of the null hypothesis that the two means are the same at the 95-percent confidence level). 

Exhibit 4 shows that the RAD sample and the population of non-RAD projects are fairly dissimilar. 

Even though the average bias of 4.2 percent is fairly low, the disaggregated results for each of the 

covariates show much higher levels of bias: Vacancy_Rate (within the project) has a 39.5-percent 

bias, and Poverty_Rate and Renter_Rate both have biases greater than 20 percent.

Due to the dissimilarity between RAD projects and the population of non-RAD projects, the 

research team gathered a matched sample of non-RAD projects for our control group. The goal 

was to establish a control group that has a lower overall bias and lower bias for individual covari-

ates. The means to achieve this goal was the GM algorithm. The target was a control group of 48 

non-RAD projects matched to the randomly selected sample of 24 RAD projects. However, to reach 

this target, statisticians implemented the GM algorithm using a 4-to-1 match (that is, 4 matches 

were selected for each of the 24 projects in the sample of RAD projects). This approach resulted 

in the selection of 96 unique non-RAD projects, which was twice the size of the desired sample of 

non-RAD projects in case some matches had to be rejected. Matches were selected without replace-

ment, so each non-RAD project could be selected as a control only once. When possible, the two 

best matches were selected for the study. However, in several cases, matched non-RAD projects 

Exhibit 4

Comparison of Means by Covariates for RAD Sample and Non-RAD Population

Variable

Mean

Mean

StdDev

StdErr

t-Value

Bias (%)

RAD

NR

ACC_Unit_Cnt

159.6

154.8

208.3

42.6037

0.11

2.3

Bldg_Type_Code

2.5833

2.4104

1.1134

0.2277

0.76

15.5

Dev_Type_Code

2.9167

2.8639

0.4234

0.0866

0.61

12.5

DOFA

1974.5

1976.6

16.9795

3.4722

– 0.60

– 12.4

Percent_1_2_Bed

0.6204

0.6610

0.3103

0.0635

– 0.64

– 13.1

PHA_Size_Code

1.9583

1.9738

0.8351

0.1708

– 0.09

– 1.9

Rounded_Inspection_Score

84.5417

84.8222

12.9316

2.6444

– 0.11

– 2.2

Vacancy_Rate

0.1658

0.0919

0.1868

0.0382

1.93

39.5

Cost_Burden_Rate

44.5833

42.1859

13.1244

2.6838

0.89

18.3

Overcrowd_Rate

0.0344

0.0375

0.0472

0.0097

– 0.32

– 6.4

Poverty_Rate

31.4750

27.8604

15.1196

3.0918

1.17

23.9

Renter_Rate

55.6292

51.0282

22.1308

4.5256

1.02

20.8

Vacant_Rate

10.9125

14.5284

8.6328

1.7653

– 2.05

– 41.9

Average bias

4.2

RAD = Rental Assistance Demonstration.

Source: Public and Indian Housing Information Center and American Community Survey data as analyzed by Econometrica, Inc.

12 The  t-value is calculated by dividing the difference in means by the standard error instead of the standard deviation. 

Loosely speaking, standard error = (standard deviation) × sqrt(1/n). The square root term causes the standard error to approach 0 as the sample size increases. The precise mathematical relationship between the bias and the  t-value is Bias = 

tValue * sqrt(1/n  +1/n ).

1

2

346 Evaluation Tradecraft

Use of Genetic Matching in Program Evaluation: The Case of RAD

had to be rejected for various reasons. For instance, one project was no longer a public housing 

project and was eliminated. Several projects turned out to be RAD projects (they had applied to 

the program after the cap was lifted) and thus were not appropriate as controls. One project was 

rejected because it was a single highrise building that had been matched against a project of scat-

tered townhouse units. In other cases, the PHAs declined to participate and were dropped from the 

study. When a potential control was rejected, staff selected the next best match in the list. In a few 

cases, the controls ran out. Consequently, for some of the projects in the RAD sample, staff reran 

the genetic matching program to select 4 more controls to complete the final sample of 48 projects.

Assessing Genetic Matching Results

Using genetic matching reduced bias in the samples. The results of the GM algorithm are given 

in exhibit 5. On one variable, PHA_Size_Code, the samples were a perfect match under both 

the  t-test for difference in means and the KS test. This means that the RAD sample and the 

non-RAD sample could be sorted into equal numbers of projects of the exact same size class. On 

Poverty_Rate, the samples were a near-perfect match—perfect in terms of the difference in means 

but not with respect to the KS test. The worst match in terms of  p-values was on Bldg_Type_Code, 

at 29.6 percent under the KS test and 48.8 percent under the difference in means test. Another 

way of describing this result is to say that one can reject the null hypothesis that the distribution 

of building types in the two samples is the same with 70-percent confidence. Typically, analysts 

demand a higher degree of confidence—in the 90- or 95-percent range. Bias might still be present 

with respect to the Bldg_Type_Code and Vacant_Rate variables. For the latter, one can accept the 

null hypothesis that the means are the same with only 45-percent confidence. 

Exhibit 6 compares the bias of the unmatched set of all non-RAD public housing projects against 

the final set of 48 matched non-RAD projects in the sample of comparison projects. For all 

but three covariates, bias decreased after the match. For two covariates—ACC_Unit_Cnt and 

Exhibit 5

Results of the Genetic Matching Algorithm

p-Values (%)

Variable

t-Test for Difference in Means

KS Test

ACC_Unit_Cnt

72.8

55.2

Bldg_Type_Code

48.8

29.6

Dev_Type_Code

81.2

92.2

DOFA

67.0

76.3

Percent_1_2_Bed

77.3

98.4

PHA_Size_Code

100.0

100.0

Rounded_Inspection_Score

76.6

79.0

Vacancy_Rate

84.3

98.8

Cost_Burden_Rate

87.4

81.6

Overcrowd_Rate

88.9

72.6

Poverty_Rate

100.0

99.8

Renter_Rate

77.3

82.4

Vacant_Rate

53.3

56.3

KS Test = Kolmogorov–Smirnov test.

Source: Based on analysis by Econometrica, Inc.

Cityscape 347

Ruiz, Stout, and Herlihy

Exhibit 6

Comparison of the Degree of Bias Before and After Match

Unmatched

Matched

Variable

Mean

Mean

RAD

NR

N

Mean

Bias (%)

N

Mean

Bias (%)

NR

NR

NR

NR

ACC_Unit_Cnt

24

159.6

6,644

154.8

2.3

48

149.4

8.8

Bldg_Type_Code

24

2.5833

6,644

2.4104

15.5

48

2.3958

17.4

Dev_Type_Code

24

2.9167

6,644

2.8639

12.5

48

2.9375

– 5.9

DOFA

24

1974.5

6,644

1976.6

– 12.4

48

1976.1

– 10.8

Percent_1_2_Bed

24

0.6204

6,644

0.6610

– 13.1

48

0.6378

– 7.3

PHA_Size_Code

24

1.9583

6,644

1.9738

– 1.9

48

1.9583

0.0

Rounded_ 

24

84.5417

6,644

84.8222

– 2.2

48

85.5208

– 7.4

Inspection_Score

Vacancy_Rate

24

0.1658

6,644

0.0919

39.5

48

0.1518

5.0

Cost_Burden_Rate

24

44.5833

6,644

42.1859

18.3

48

44.1458

3.9

Overcrowd_Rate

24

0.0344

6,644

0.0375

– 6.4

48

0.0332

3.6

Poverty_Rate

24

31.4750

6,644

27.8604

23.9

48

31.4583

0.1

Renter_Rate

24

55.6292

6,644

51.0282

20.8

48

56.8750

– 7.2

Vacant_Rate

24

10.9125

6,644

14.5284

– 41.9

48

11.7083

– 15.7

Averages

4.2

– 1.2

Source: Based on analysis by Econometrica, Inc.

Rounded_Inspection_Score—the bias was higher after the match (in absolute value) but still less 

than 10 percent. Given the results in exhibits 5 and 6, some further caution might be due with 

respect to three covariates: Bldg_Type_Code, DOFA, and Vacant_Rate. Our overall conclusion is 

that the genetic matching reduced bias. After-match average bias is less than one-third its original 

size, falling from 4.2 to -1.2 percent.

Concluding Remarks

As evidenced by the results outlined in this article, genetic matching is a powerful tool. It can 

help researchers mitigate systemic bias in quasi-experimental situations in which assignment to 

the treatment group is nonrandom and a specific set of covariates is believed to influence the pro-

pensity to participate. Within the public policy space, genetic matching is particularly well-suited 

for program evaluation in which participation in the treatment or exposure to the policy reform 

has not been randomized (that is, it may be voluntary or the result of participation in previous 

reforms) and the researchers seek to draw conclusions about the impacts of the policy in question 

(Sekhon and Grieve, 2008).

In such instances, genetic matching is used to control for imbalances that are expected to impact 

the propensity of selection to, or participation in, the treatment group and  not imbalances related 

to each group’s values for the dependent variables of interest. Of course, in some situations, genetic 

matching would not be an appropriate methodological choice. These situations include research 

questions that do not involve the identification or selection of a comparison group (for example, 

longitudinal analysis using data for a single population) and research designs that employ random 

selection to choose the units that will receive treatment.

348 Evaluation Tradecraft

Use of Genetic Matching in Program Evaluation: The Case of RAD

Acknowledgments

The authors thank Nathan Bossie of the U.S. Department of Housing and Urban Development (HUD) 

for his guidance in submitting this article and acknowledge the assistance of Bethany Hase of Econo-

metrica, Inc., and anonymous HUD reviewers in the preparation of this article for publication.

Authors

David Ruiz is the Lead of the Data and Analytics Practice at Econometrica, Inc.

Dennis Stout is Director of Housing and Community Development at Econometrica, Inc.

Christine Herlihy is a data scientist at Econometrica, Inc.

References

Abt Associates. 2010.  Capital Needs in the Public Housing Program. Washington, DC: U.S. Depart-

ment of Housing and Urban Development.

Diamond, Alexis, and Jasjeet S. Sekhon. 2013. “Genetic Matching for Estimating Causal Effects: A 

General Multivariate Matching Method for Achieving Balance in Observational Studies,”  Review of 

Economics and Statistics 95 (3): 932–945.

Sekhon, Jasjeet S., and Richard Grieve. 2008. “A New Non-Parametric Matching Method for Bias 

Adjustment With Applications to Economic Evaluations,”  SSRN Electronic Journal. DOI: 10.2139/

ssrn.1138926.

Sekhon, Jasjeet S., and Walter R. Mebane, Jr. 1998. “Genetic Optimization Using Derivatives,” 

Political Analysis 7: 189–203. 

Stuart, Elizabeth A. 2010. “Matching Methods for Causal Inference: A Review and a Look For-

ward,”  Statistical Sciences 25 (1): 1–21.

Cityscape 349

350 Departments

Call for Papers

Symposium on Housing and Health

The U.S. Department of Housing and Urban Development (HUD), Office of Policy Development 

& Research invites paper submissions for a symposium in  Cityscape: A Journal of Policy Development 

and Research on topics related to the link between housing and health. 

Housing is an important social determinant of individual and population health due to a range of 

factors including, but not limited to, housing affordability, neighborhood location, and physical 

conditions. Housing policies, including housing vouchers, project-based housing, inclusion-

ary zoning, place-based initiatives, and the like may have intended and unintended health 

consequences. The relationships between housing and health, and ways to leverage housing as a 

platform for health, remain incompletely understood. 

Papers accepted for the  Cityscape Housing and Health Symposium must be well-organized, original 

contributions. A range of topics at varying units of analysis will be considered including, but not 

limited to— 

•  National epidemiological studies (for example, using national health survey data to examine the 

link between housing assistance and healthcare utilization).

•  Local intervention studies (for example, the impact of smokefree policies on a multifamily unit 

or the evaluation of a health promotion program using public housing as the intervention site).

•  Environmental studies (for example, the impact of lead remediation programs or the overlap of 

large multiunit structures with Superfund sites).

•  The impact and implications of policy (for example, how federally qualified health centers can 

collaborate with housing agencies or how public health entities are strategically addressing 

health and housing). 

Submit a brief (100- to 300-word) abstract describing the proposed article by September 30, 2017. 

Each abstract will be peer reviewed and, if the abstract is approved, authors will be invited to 

submit a full manuscript for consideration under a rigorous review process. Full manuscripts will 

be due December 15, 2017. Direct abstracts and questions to guest editors Veronica Helms (HUD 

social science analyst; veronica.e.helms@hud.gov) and Craig Pollack (Johns Hopkins School of Medicine, associate professor of medicine; cpollac2@jhmi.edu). 

The HUD Office of Policy Development and Research publishes  Cityscape, a scholarly journal, 

three times a year. See the HUD User website for more information and past issues: huduser.gov/

portal/periodicals/cityscape.html.

Cityscape: A Journal of Policy Development and Research • Volume 19 Number 2 • 2017

Cityscape 351

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

Referees 2016–17

The Office of Policy Development and Research gratefully acknowledges the contributions 

of the following referees and their assistance in making  Cityscape  worth reading.

Dionissi Aliprantis

Daniel Hartley

Katherine O’Regan

Vicki Been

Luke Herrine

Theresa Osypuk

Cara Berton

Justin Hollander

Ann Owens

Evelyn Blumenberg

Derek Hyra

Danilo Pelletiere

Jessica Burley

Paul Jargowsky

Kathryn Pettit

Margaret Dewar

Gita Kuhn Jush

Jenny Schuetz

Keri-Nicole Dillman

Peter Kahn 

Katherine Shelton

Jamison Fargo

Kate Ko

Leslie Strauss

Will Fischer

Laura Leete

Michael Suher

Lance Freeman

Irene Lew

Ann Tickamyer

Peter Ganong

George McCarthy

Dan Treglia

Ira Goldstein

Bill McCarty

Ruoniu Wang

Daniel Gubits

Dan O’Flaherty

352 Departments

Contents

Symposium: Habitat III ...........................................................................................................1

Guest Editor: Katrina Durbak

Guest Editor’s Introduction: Looking Beyond Habitat III ...................................................................3

Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool  by Rachel Fyall and 

Alexander Casey ...........................................................................................................................9

Boundary Issues: The 2016  Atlas of Urban Expansion Indicates Global Dedensification  

by John Wihbey...........................................................................................................................21

Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s Housing Crisis   

by Kira Intrator and Kaustubh Shivdikar ..........................................................................................31

Inclusion and Innovation: The Many Forms of Stakeholder Engagement in Habitat III 

by Eugenie L. Birch .......................................................................................................................45

Housing Perspectives on Habitat III: Canada  by Janet Kreda ............................................................53

Symposium: The CRA Turns 40 ...........................................................................................57

Guest Editor: Carolina Reid

Guest Editor’s Introduction: The Community Reinvestment Act at 40 ................................................59

Small Business Lending Under the Community Reinvestment Act  by Raphael W. Bostic and  

Hyojung Lee ................................................................................................................................63

Using Credit Reporting Agency Data To Assess the Link Between the Community Reinvestment Act 

and Consumer Credit Outcomes  by Kristin F. Butcher and Ana Patricia Muñoz ....................................85

Bridging the Gap to Scalable Community Reinvestment Lending Programs  by Roberto G. Quercia 

and Sarah Riley .........................................................................................................................109

Commentary: Encouraging Housing Equity  by Dan Immergluck ......................................................129

Community Reinvestment Act and Local Governance Contexts: Advancing the Future of  

Community Reinvestment?  by Colleen Casey, Joseph Farhat, and Gregory Cartwright........................137

Data, Accountability, and the Public: Using Community Reinvestment Act Data for Local 

Community Development  by Marijoan Bull ................................................................................... 161

Commentary: The Community Reinvestment Act Must Be All About Public Participation,  

but It Still Doesn’t Feel That Way  by Josh Silver .............................................................................177

Commentary: FinTech and the Liberation of the Community Reinvestment Act Marketplace   

by Michael Gaughan .................................................................................................................187

Commentary: What Can We Learn From Government Attempts To Modify the Allocation  

of Mortgage and Consumer Credit in the United States?  by Anthony Yezer .....................................199

Commentary: Filling a Gap in the Community Reinvestment Act Examiner Toolkit  by Mark A. Willis ...207

Refereed Papers .......................................................................................................... 213

Are Homeowners Better Neighbors During Housing Booms? Understanding Civic and Social 

Engagement by Tenure During the Housing Market Cycle  by Deirdre Pfeiffer and Eric A. Morris .......... 215

A Research Note: The Housing Choice Voucher Program and Access to Opportunity in Florida’s 

Nonmetropolitan Areas  by Rebecca J. Walter and Ruoniu Wang .....................................................239

Departments ................................................................................................................255

Data Shop

Measuring Disability  by Debra L. Brucker and Veronica E. Helms ....................................................257

Graphic Detail

A Cartographic Perspective on the Correlation Between Redlining and Public Health in Austin, 

Texas—1951  by John C. Huggins .................................................................................................267

Impact

Reducing the Flood Hazard Exposure of HUD-Assisted Properties  by Michael K. Hollar .....................281

Policy Briefs

Project-Based Vouchers  by Brent D. Mast and David Hardiman .........................................................301

SpAM

Measuring Distance to Resources  by Ron Wilson and Alexander Din ............................................... 323

Evaluation Tradecraft

Use of Genetic Matching in Program Evaluation: The Case of RAD  by David Ruiz, Dennis Stout, 

and Christine Herlihy ......................................................................................................................337

Call for Papers

Symposium on Housing and Health ................................................................................................351

Referees 2016–17 ................................................................................................................352

Cityscape

U.S. Department of Housing

and Urban Development

Volume 19 numbeR 2 

Office of Policy Development and Research

• 2017

Document Outline

Cover

Title Page

Contents

Symposium: Habitat III

Guest Editor’s Introduction: Looking Beyond Habitat III 

Urbanizing for Equity: Harnessing Upzones as a Redistributive Policy Tool 

Boundary Issues: The 2016 Atlas of Urban Expansion Indicates Global Dedensification

Missing ‘Middle Scenarios’:  Uncovering Nuanced Conditions  in Latin America’s Housing Crisis

Inclusion and Innovation:  The Many Forms of Stakeholder Engagement  in Habitat III

Housing Perspectives  on Habitat III: Canada

Symposium: The CRA Turns 40

Guest Editor’s Introduction: The Community Reinvestment Act at 40 

Small Business Lending Under the Community Reinvestment Act

Using Credit Reporting Agency Data To Assess the Link Between the Community Reinvestment Act and Con

Bridging the Gap to Scalable Community Reinvestment Lending Programs 

Commentary:  Encouraging Housing Equity 

Community Reinvestment Act and Local Governance Contexts: Advancing the Future of  Community Reinves

Data, Accountability, and the Public: Using Community Reinvestment  Act Data for Local  Community De

Commentary: The Community Reinvestment Act Must Be All  About Public Participation, but  It Still Do

Commentary: FinTech and the Liberation of the Community Reinvestment Act Marketplace

Commentary: What Can We Learn From Government Attempts To Modify the Allocation of Mortgage and Cons

Commentary: Filling a Gap in the Community Reinvestment Act Examiner Toolkit

Refereed Papers 

Are Homeowners Better Neighbors During Housing Booms? Understanding Civic and Social Engagement by T

A Research Note: The Housing Choice Voucher Program and Access to Opportunity in Florida’s Nonmetrop

Departments 

Data Shop: Measuring Disability 

Graphic Detail: A Cartographic Perspective on the Correlation Between Redlining and Public Health in

Impact: Reducing the Flood Hazard Exposure of HUD-Assisted Properties

Policy Briefs: Project-Based Vouchers 

SpAM: Measuring Distance to Resources

Evaluation Tradecraft: Use of Genetic Matching  in Program Evaluation:  The Case of RAD

Call for Papers: Symposium on Housing and Health

Referees 2016-17 