A Journal of Policy

Development and Research

NatioNal Survey of Mortgage origiNatioNS

voluMe 21, NuMber 2 • 2019

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

Managing Editor: Mark D. Shroder

Associate Editor: Michelle P. Matuga

Advisory Board

Dolores Acevedo-Garcia

Brandeis University

Ira Goldstein

The Reinvestment Fund

Richard K. Green

University of Southern California

Mark Joseph

Case Western Reserve University

Matthew E. Kahn

University of California, Los Angeles

C. Theodore Koebel

Virginia Tech

Jens Ludwig

University of Chicago

Mary Pattillo

Northwestern University

Carolina Reid

University of California

Patrick Sharkey

New York University

Cityscape

A Journal of Policy

Development and Research

National Survey of Mortgage Originations

Volume 21, Number 2 • 2019

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

The goal of Cityscape is to bring high-quality original research on housing and community development issues to scholars, government officials, and practitioners. Cityscape is open to all relevant disciplines, including architecture, consumer research, demography, economics,

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

regional science, sociology, statistics, and urban studies.

Cityscape is published three times a year by the Office of Policy Development and Research (PD&R) of the U.S. Department of Housing and Urban Development (HUD). Subscriptions are

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

huduser.gov/periodicals/cityscape.html.

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

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

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

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

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

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

Visit PD&R’s website, huduser.gov, to find this report and others sponsored by PD&R. Other services of HUD User, PD&R’s Research and Information Service, include listservs, special interest and bimonthly publications (best practices, significant studies from other sources), access to public use databases, and a hotline (1–800–245–2691) for help with accessing the information you need.

Contents

Symposium

National Survey of Mortgage Originations ................................................................................... 1

Guest Editors: Robert B. Avery and Ron Borzekowski

Guest Editors’ Introduction

National Survey of Mortgage Originations ................................................................................. 3

by Robert B. Avery and Ron Borzekowski

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations ................................................... 9

by Tim Critchfield, Jaya Dey, Nuno Mota, and Saty Patrabansh

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations ....................................... 33

by Chad Redmer

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the

United States: Evidence from the National Survey of Mortgage Originations ........................ 51

by Robert B. Argento, Lariece M. Brown, Sergei Koulayev, Grace Li, Marina Myhre,

Forrest Pafenberg, and Saty Patrabansh

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit ........... 75

by Brian Bucks, Tim Critchfield, and Susan Singer

Refereed Papers ..................................................................................................................... 91

Road Map to a Unified Measure of Housing Insecurity ........................................................... 93

by Robynn Cox, Benjamin Henwood, Seva Rodnyansky, Eric Rice, and Suzanne Wenzel

The Effects of Flood Insurance

on Housing Markets ................................................................................................................ 129

by Agustín Indaco, Francesc Ortega, and Süleyman Tas¸pınar

Departments ......................................................................................................................... 157

Affordable Design

Annual HUD Secretary’s Opportunity and Empowerment Award:

Family Scholar House, Louisville, Kentucky .......................................................................... 159

by Regina Gray

Data Shop

Tracking Individuals Pre- and Post-Foreclosure..................................................................... 163

by Christos Makridis and Michael Ohlrogge

Cityscape

iii

Contents

Graphic Detail

Visualizing and Comparing Residential Permit Data Using Lollipop Plots .......................... 175

by Alexander Din

Impact

Acceptable Separation Distance Standards for Residential Propane Tanks .......................... 179

by Maria Chelo Manlagnit De Venecia

Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI

Mixed-Income Redevelopment ................................................................................................ 185

by Taryn H. Gress, Mark L. Joseph, and Seungjong Cho

SpAM

Where is the City’s Center? Five Measures of Central Location ........................................... 213

by Matthew J. Holian

Correction

Racially Concentrated Areas of Affluence: A Preliminary Investigation ............................... 227

Referees 2018–19 ..................................................................................................................... 229

iv Volume 21 Number 2

Symposium

National Survey of Mortgage Originations

Guest Editors: Robert B. Avery and Ron Borzekowski

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape

1

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

2 National Survey of Mortgage Originations

Guest Editors’ Introduction

National Survey

of Mortgage Originations

by Robert B. Avery and Ron Borzekowski

Robert B. Avery

Federal Housing Finance Agency

Ron Borzekowski

Consumer Financial Protection Bureau

Disclaimer

The views expressed in this article are those of the authors and are not necessarily those of the Consumer Financial Protection Bureau, the Federal Housing Finance Agency or any other agency

of the U.S. Government.

In 2012, the Federal Housing Finance Agency (FHFA) and the Consumer Financial Protection

Bureau (CFPB) began work on the National Mortgage Database (NMDB®) and a new quarterly

mail survey called the National Survey of Mortgage Originations (NSMO) associated with it.

In November 2018, the two agencies released a public use file covering the first 4 years

(2013–2016) of mortgage originations in NSMO.1 The public use file contains survey responses

and key administrative data obtained from other sources. The availability of matched administrative and survey data offers researchers and policymakers a unique view of the U.S. mortgage market,

arguably one of the most critical markets in the U.S. economy.

NSMO is unique for two reasons. First, it fills an information void. There is no other recurring

nationally representative survey of borrowers who have just obtained a mortgage. The U.S.

Department of Housing and Urban Development’s (HUD) American Housing Survey and the

Federal Reserve’s Survey of Consumer Finances and Survey of Household Economics contain

some information on mortgages, but the number of recent mortgage borrowers they capture

is small because of the different focus of those surveys. The National Association of Realtors®

annual survey of homebuyers and sellers covers home purchasers but not refinancers and is

not generally available to the public.

1 The public use NSMO data and documentation are available at https://www.fhfa.gov/nsmodata.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape

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

Avery and Borzekowski

Second, NSMO provides access to high-quality administrative data, as NSMO’s sampling frame is

derived from the NMDB. The NMDB contains data for a 5-percent random sample of closed-end,

first-lien residential mortgages active at any time in the credit files of one of the three national credit bureaus from January 1998 to the present. The database is updated each quarter with a

5-percent sample of newly reported mortgages—typically, those just opened. The quarterly

NSMO sample is a randomly drawn subset of 6,000 borrowers associated with those newly

reported mortgages.2 Based on the quarterly cycle, the typical respondent fills out the

questionnaire between 6 and 9 months after origination.

The NMDB data include origination and performance information for each NMDB sample

mortgage—and credit information about the borrowers associated with those mortgages—from

origination to closure. Although extensive, the credit bureaus’ files do not contain information

on a number of key mortgage features, such as the loan’s purpose (home purchase or refinance),

whether it had an adjustable or fixed rate, its securitization status, or whether it was backed by an owner-occupied property, vacation home, or investor property. That information is available from

a variety of sources, including high-quality matches to administrative file records maintained by

Fannie Mae, Freddie Mac, the Federal Housing Administration, the U.S. Department of Veterans

Affairs, the Rural Housing Service, and the Federal Home Loan Banks, which collectively account

for more than 70 percent of mortgages in the United States. Further information was obtained

from other public and proprietary data sources, including deed record filings, Home Mortgage

Disclosure Act filings, and commercially available servicing databases.

The NMDB database combines information from each of those sources into a single record for

each mortgage. Data merging was conducted behind a protected firewall at the credit bureau,

using information about the borrowers, such as their names, Social Security numbers, addresses,

and dates of birth, to facilitate high-quality matches. Variable values for the small percentage of loans that could not be matched to any of those sources were imputed using a variety of methods.

The resulting matched database, as delivered to FHFA and CFPB, is de-identified—it does not

identify specific borrowers.

The availability of high-quality administrative data for each sample loan means that NSMO

does not have to ask the respondents to provide factual information about the mortgage.

Thus, the survey instrument focuses on obtaining information about the borrowers’ experience,

perceptions, and expectations—which are not readily available anywhere else. The survey asks

sampled borrowers 94 questions (including more than 300 subquestions) about their experience

shopping for and closing on a mortgage; their knowledge and perceptions of the mortgage market

and the mortgage process; their use of housing counselors; and their future expectations about

house price appreciation and critical household and financial events. The survey also asks about

demographic details and household composition information that is not available in the NMDB.

2 At the time of this writing, that figure represents a sampling rate of about 1 in 300 new mortgages in the nation.

About one-half of the survey responses have been completed online and the other half by mail. The overall response rate of NSMO is about 33 percent, and the usable response rate is about 28 percent. The NSMO survey instrument was improved substantially in 2015, was refined moderately in 2016, and continues to receive minimal adjustments moving forward. In 2015, the NSMO was sent to additional borrowers associated with a special sample of 1,000 mortgages originated in 2014 in completely rural counties. That sample was used for one of the articles in this volume.

4 National Survey of Mortgage Originations

National Survey of Mortgage Originations

The public use NMSO data contain variables drawn from both the survey responses and the

NMDB administrative data file3 and consist of 24,847 loans originated from 2013 through 2016,

with additional years to be added in the future. When appropriately weighted, the data should be

representative of new mortgages originated over the 4-year period.4

The four articles in this volume use the public use NSMO data to address a set of related and

important questions about the knowledge and behavior of borrowers in the U.S. housing market

over the past few years.5 To make informed choices about buying a home or choosing and

closing on a mortgage, consumers need information on a multitude of factors, including their

own financial situation, the state of the housing market in their area, the details of available loan products, an understanding of the various service providers, and the process involved in the

mortgage transaction. Some of that information is provided by private sources, although search

costs or agency costs could be a factor in borrowers’ accessing that information; however, in some cases, policymakers have mandated that consumers, or at least certain consumers, be provided

particular information.

The first two articles focus on questions related to information provided primarily by private

sources. In the first article, “Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations,” Tim Critchfield, Jaya Dey, Nuno

Mota, and Saty Patrabansh use a special supplement of NSMO that oversampled borrowers in

completely rural areas—those without a significant population center—to compare the experience

of borrowers in those areas with borrowers in urban and larger rural markets. Rural mortgage

markets have been a particular concern for policymakers and researchers.6 One conjecture is that

because rural markets are relatively thin, they suffer from a lack of scale in information, and thus, mortgages are more expensive to originate. For example, lack of scale may make it more difficult

for lenders and the mortgage secondary market to properly assess home values and thus make

markets less competitive. In this article, the authors indirectly test those conjectures.

Collectively, the 644 counties covered by the special sample account for only 1 percent of

U.S. residential mortgage originations, but those are arguably the areas most likely to reveal

informational problems. Indeed, Critchfield and his colleagues show that borrowers in the special

sample were less likely than other borrowers to be satisfied with the mortgage closing process and the required disclosures. They also tend to be less confident or knowledgeable about some details

of mortgages. Together, those findings suggest that perhaps supply conditions lead to a poorer

3 Because mortgage filings are typically a matter of public record, releasing the detailed administrative data contained in the NMDB as part of NSMO with any degree of geographic detail would not have been possible without creating significant privacy concerns. Consequently, almost all geographic information is suppressed in the NSMO public use file.

Unfortunately, those concerns did not allow for the data of the special supplemental sample of completely rural borrowers used for one of the articles in this volume to be included in the public release. County-level house price indices and economic factors used for another article also could not be included in the public use file for the same reason.

4 The administrative data in the NMDB are used to calculate nonresponse adjustment weights for NSMO. Missing values for almost all variables in the public use file have been imputed using an iterative regression—or logistic model-based approach—that also relies heavily on administrative data. The incidence of missing values is less than 10 percent for almost all variables.

5 Use of information not in the public use NSMO is explicitly noted in two of the articles.

6 The congressional bill that established the FHFA mandated that it pay particular attention to rural markets in its regulation of Fannie Mae and Freddie Mac.

Cityscape

5

Avery and Borzekowski

information environment in which these rural consumers are making their decisions. The authors

also show that, collectively, the borrowers in the special sample have higher contract interest rates than otherwise comparable borrowers in other areas—another potential sign of higher costs or

poorer decisionmaking.

In the second article, “Perceptions and Expectations of Mortgage Borrowers: New Evidence from

the National Survey of Mortgage Originations,” Chad Redmer examines how cognizant different

types of borrowers are of changes in recent house prices in their communities and the extent to

which borrowers’ expectations of the future may help forecasters predict future price changes.

He finds, first, that borrowers overall have a good understanding of recent house price changes,

and second, that their future expectations seem to incorporate some knowledge beyond what is

available in public macro indicators. The first finding is important because borrowers who have

just obtained a mortgage (the sample frame for the NSMO) are the group for whom house price

knowledge may be most important. Poor decisions in either house purchases or refinances can have

long-term implications for consumers’ well-being. Redmer further finds that first-time homebuyers, the group with the most potential vulnerability, actually are among the more informed borrowers.

The second finding—that borrower expectations have additional predictive power over macro

indicators—is more difficult to interpret. It could mean that consumers have additional

information about their local markets that is not incorporated into traditional economic measures.

Alternatively, it could stem from a self-fulfilling process whereby borrowers with bullish views tend to drive up prices in their markets. More research must be done to understand this finding better.

The last two articles focus more specifically on market interventions mandated by statute and

regulations aimed at improving the information available to borrowers. The interventions

considered include (1) homebuying handbooks that government agencies mandate originators

give to borrowers and (2) homeownership education and counseling which is required for

certain homebuyers.7

More specifically, federal regulations require that purchase-mortgage borrowers get a “special

information booklet” to assist in their decisions. CFPB, which is now in charge of implementing the regulations, changed the booklet in October 2015. In “National Survey of Mortgage Originations

Survey Data on Your Home Loan Toolkit,” Brian Bucks, Tim Critchfield, and Susan Singer treat this change as a “natural experiment” and use the time series component of NSMO to examine the

effect of the booklet change. They find that the percentage of homebuyers who remember receiving

information from their lender increased with the introduction of the Toolkit. That increase was

larger for borrowers with lower credit scores, household income, education, or experiences with

prior mortgages. The percentage of respondents asking followup questions also increased after the

new Toolkit introduction so that the total number of borrowers “digging deeper” also seems to have increased with this change.

7 One can also see mandated standardized disclosures, such as the Loan Estimate and the Closing Disclosure, as similarly aimed at improving the information available to borrowers.

6 National Survey of Mortgage Originations

National Survey of Mortgage Originations

The fourth and final article presents a comparison of the outcomes, satisfaction, and knowledge

metrics of borrowers who underwent housing counseling and otherwise similar borrowers who did

not. Using propensity score methods, Robert B. Argento, Lariece M. Brown, Sergei Koulayev, Grace

Li, Marina Myhre, Forrest Pafenberg, and Saty Patrabansh, in “First-Time Homebuyer Counseling

and the Mortgage Selection Experience in the United States: Evidence from the National Survey

of Mortgage Originations,” compare borrowers who did and did not undergo counseling. They

find that first-time homebuyers who reported receiving homeownership education and counseling

also reported better mortgage knowledge, more confidence in their ability to explain the mortgage

process, and a higher level of satisfaction with the mortgage they received.

The four articles in this symposium each use the survey data from NSMO to begin to address

important questions about consumer knowledge and understanding in the U.S. housing and

mortgage markets. Although each of these articles is a first step in its respective area, they all find important variations by geography or borrower characteristics, and all offer new areas for

additional research. The articles chosen for this volume were authored by members of the team

that helped develop the NMDB and NSMO, but it is our hope that these articles will stimulate

researchers in the greater academic and the policy analysis community to use the NSMO data

for their own investigations.

Acknowledgments

We thank the authors of the articles in this volume and numerous reviewers for comments and suggestions.

Cityscape

7

8 National Survey of Mortgage Originations

by Tim Critchfield, Jaya Dey, Nuno Mota, and Saty Patrabansh

Mortgage Experiences of Rural

Borrowers in the United States:

Insights from the National Survey

of Mortgage Originations

Tim Critchfield

Consumer Financial Protection Bureau

Jaya Dey

Freddie Mac

Nuno Mota

Fannie Mae

Saty Patrabansh

Federal Housing Finance Agency

Disclaimer

The views expressed in this article are those of the authors and are not necessarily those of

the Consumer Financial Protection Bureau, Freddie Mac, Fannie Mae, or the Federal Housing

Finance Agency.

Abstract

To date, research on rural mortgage markets in the United States has been limited by a lack of data on the specific mortgage experiences of borrowers living in rural areas. To fill this data gap, the National Survey of Mortgage Originations (NSMO) conducted a survey that oversampled people who took out mortgages in completely rural counties in 2014. This article shows results from this survey, contrasting the characteristics, experiences, and loan terms of mortgage borrowers in completely rural counties to those of borrowers in metropolitan and other non-metropolitan areas. Completely rural counties are those with no urban cluster or an urban population less than 2,500. We find that borrowers in completely rural counties paid slightly higher interest rates on average and were less satisfied that their mortgage was the one with the best terms to fit their needs than borrowers in other areas. These results persist even after controlling for income, credit quality, and other borrower characteristics. Completely rural borrowers were less likely than other borrowers to be satisfied with the mortgage closing process, the timeliness of disclosures, and the disclosure documents themselves. Finally, compared with borrowers in more urban areas, borrowers in completely rural areas tend to be less confident or less knowledgeable about some details of mortgages, and they are more likely to initiate contact with their lender.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape

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

Critchfield, Dey, Mota, and Patrabansh

Introduction

There is a widespread belief that lenders and credit markets in rural areas of the United States differ from those in urban areas. The literature on “relationship lending” argues that lending in rural

areas differs fundamentally from lending in other areas because rural lenders have greater personal knowledge about their borrowers and local economic conditions.1 The literature on community

banking shows that lenders in rural areas tend to have fewer assets and generally have smaller

geographic markets than larger financial institutions (Critchfield et al., 2004). The smaller scale of lending in rural areas could potentially constrain the supply of mortgages, make mortgages more

costly to originate, and could adversely affect mortgages taken out by borrowers.2

To some degree, federal housing and mortgage policies reflect the distinctive features and challenges of mortgage lending in rural areas. For example, the Housing and Economic Recovery Act of 2008

(HERA) assigned Fannie Mae and Freddie Mac a Duty to Serve (DTS) three underserved markets by increasing the liquidity of mortgage investments and improving the distribution of mortgage

investment capital to those markets.3 One of the underserved markets specifically mentioned is the rural market, and another is manufactured housing—which is much more prevalent in rural areas

(CFPB, 2014). Similarly, the Consumer Financial Protection Bureau (CFPB) provided exceptions for

loans made by small creditors that operate predominantly in rural or underserved areas in several of its mortgage rules.

This article explores the underlying premise of these government policies: that mortgage borrowers in rural areas are potentially “less well-served” than those in other areas. Such an article has been difficult to write in the past because of a lack of data.4 There are few available data sets containing detailed information on the characteristics of borrowers and their mortgages that are both

representative and contain enough borrowers in rural areas to make meaningful comparisons. For

example, data reported under the Home Mortgage Disclosure Act (HMDA) provide data on millions

of mortgages and thus include many rural loans, but HMDA exempts small lenders and lenders with

branches exclusively outside metropolitan areas, making the coverage of rural loans incomplete

and potentially unrepresentative.

To fill the need for data on mortgages and mortgage borrowers in rural areas, the Federal Housing

Finance Agency (FHFA) and CFPB conducted a special supplemental survey of 1,000 mortgages

as part of the National Survey of Mortgage Originations (NSMO) which is based on the National

Mortgage Database (NMDB®).5 The supplemental sample that is the cornerstone of this article covers mortgages originated at any time in 2014 in counties defined as “completely rural.” This article uses data for 267 borrowers from the special sample of completely rural mortgages and 6,273 respondents to the regular NSMO survey who took out a mortgage in 2014.6

1 See, for example, https://www.stlouisfed.org/bank-supervision/2013-community-banking-conference/videos/small-

business-lending-and-social-capital-are-rural-relationships-different or https://www.stlouisfed.org/Publications/

Regional-Economist/April-2002/Community-Ties-Does-Relationship-Lending-Protect-Small-Banks-When-the-Local-

Economy-Stumbles.

2 See HAC (2012) for a description how mortgage finance in rural communities has evolved over the last decade.

3 See 12 U.S.C. 4565(a)(1).

10 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

NSMO data allow us to compare the expectations, perceptions, knowledge, experience, and

satisfaction of respondents in completely rural areas with those respondents in other rural areas, as well as urban areas. The remaining sections of the article summarize the relevant literature; define the three geographical groups used in the analysis; and describe the geographic differences in property, loan, and borrower characteristics, borrowers’ experience and knowledge, and borrower’s lender and mortgage choice.

Literature

This article bridges two largely distinct literatures, namely, (1) studies of rural housing and mortgage markets, and (2) the literature on borrower perceptions and experiences within the mortgage market.

The first set of studies have described the trends in employment, incomes, and housing market

characteristics across the urban-rural divide (HAC, 2012; Mota, 2016; USDA, 2016) along with the

effect and prevalence of government-sponsored enterprises in rural markets (Ambrose and Buttimer,

2005; MacDonald, 2001; Vandell, 1997). These studies compared rural and urban housing and

mortgage markets at an aggregate level but did not consider differences in the characteristics of

borrowers across rural and urban areas.

Because these studies did not consider the differences in characteristics between rural and urban

borrowers, they generally find differences in borrower perceptions and experience by income,

race and ethnicity, and prior experience with mortgages, rather than across area distinctions.

Bucks and Pence (2008) showed that consumers were aware of the general terms of their mortgage

(for example, adjustable-rate versus fixed-rate mortgages, number of months of required payment,

and required monthly payment amount); they were less aware of details about potential interest rate changes in their adjustable-rate mortgages. Huang et al. (2017) reported that there were common

misperceptions among borrowers about mortgage qualification criteria; specifically, borrowers

tended to overestimate minimum credit score and down payment requirements. Cai and Shahdad

(2015) found that one-third of homebuyers in their sample did not obtain quotes from multiple

lenders when shopping for a mortgage. In addition, Cai and Shahdad concluded that borrowers with

higher incomes, younger borrowers, and minority borrowers were more likely to obtain multiple

lender quotes compared with other borrowers; while lower-income and first-time homebuyers were

particularly likely to use family, friends, and co-workers when selecting a lender.

4 This lack of data has limited the academic and policy research on rural mortgage markets, and both Fannie Mae and Freddie Mac cite lack of data and research on rural mortgage lending as one of the critical issues in rural mortgage markets. See Freddie Mac’s “Freddie Mac Duty to Serve Underserved Markets Plan” from December 2017 and Fannie Mae’s “Introduction of the Duty to Serve Underserved Markets Plan for the Manufactured Housing, Affordable Housing Preservation, and Rural Housing Markets” from December 2017.

5 NSMO and NMDB are described in the guest editor’s introduction.

6 While the NSMO data for the regular sample is publicly available, the data for the supplemental NSMO sample is not publicly available for privacy considerations because membership in the supplemental sample conveys information about the location of the borrowers, which is suppressed in the public use NSMO. Out of the 1,000 surveys mailed in the supplemental survey, only 267 were useable records.

Cityscape 11

Critchfield, Dey, Mota, and Patrabansh

Definition of Urban and Rural Areas

We classify U.S. counties into three groups based on the U.S. Department of Agriculture’s (USDA)

county-level Rural Urban Continuum Code (RUCC) classification from 2013.7 The first group,

which we refer to as “metro” counties, consists of 1,167 counties in Metropolitan Statistical Areas (MSA) (RUCC codes 1, 2, or 3).8 The second group includes 1,332 counties that are not in

metropolitan areas, but have at least one urban cluster of 2,500 or more people (RUCC codes

4, 5, 6, or 7)—many of these counties, which we refer to as “non-metro” counties, are in

Micropolitan Statistical Areas.9 The last group, which we refer to as “completely rural” counties, comprises 644 counties that are designated as “completely rural or less than 2,500 urban

population” (RUCC codes 8 or 9). The supplemental NSMO sample used in this article was

drawn from mortgages in completely rural counties.

Using this classification, 37 percent are metro counties, 42 percent are non-metro counties, and

21 percent are completely rural counties. Exhibit 1 shows that most counties along the coasts are

metro counties.10 Non-metro counties are spread throughout the country, but the coasts have the

lowest share ranging from 33 percent of counties in the Middle Atlantic Census division to

35 percent in the Pacific division. Completely rural counties are primarily located in parts of

the Midwestern, Mountain, and Southern states.

Exhibit 2 shows the share of the 2014 population and housing units in each of the three county types according to the Census Bureau’s American Community Survey (ACS) 2010–2014 5-Year Estimates.

7 While there are many definitions of “rural” areas in the literature and government programs, including DTS, this article arises out of the supplemental NSMO sample and is restricted to how the sample was drawn. See USDA documentation

at https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/.

8 MSAs have at least one urbanized area of 50,000 or more population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting patterns.

9 Micropolitan Statistical Areas have at least one urban cluster of at least 10,000 but less than 50,000 population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting patterns. For the USDA, “non-metro” would include RUCC codes 8 and 9, but we have broken them out for the purpose of this study.

10 The Census Bureau divides the country into four regions and nine divisions.

12 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

Exhibit 1

Geographic Classification of Counties and County Equivalents

Source: USDA RUCC data, 2013

Most people and housing units in the United States—86 percent of people and 85 percent of housing

units—were in metro counties in 2014. The remaining population and housing units were primarily

located in non-metro counties. Completely rural counties accounted for only 2 percent (4.6 million people) of the U.S. population and 2 percent (2.6 million housing units) of housing units in 2014.

The final bars in exhibit 2 show the share of mortgage originations by geographic location. The

share of mortgage originations in metro counties is 88.4 percent, a few percentage points higher

than the share of people and housing units in these areas. Furthermore, just 1 percent of originations, or 55,000 loans, were in completely rural counties.11 This highlights the importance of drawing a

supplemental sample of mortgages in completely rural counties in order to obtain more accurate

estimates. In particular, about 5 percent of our sample (345 out of 6,540) is for mortgages in

completely rural areas, a sample size that is five times larger than a simple random sample of the country would be expected to yield for these areas.12

11 County-type shares of NMDB 2014 originations in the NMDB data are similar to those in HMDA 2014 data, where metro accounts for 89.4 percent of originations, non-metro 9.7 percent, and completely rural 1.0 percent of originations.

12 Analysis in this article, including the regressions, is based on analytic weights that account for both the sampling weight and the non-response adjustment. For each survey response, the sample weight adjusted for non-response was computed by multiplying the sampling weight and the non-response adjustment. Then, the analytic weight was computed separately for each of the following three groups: (1) mortgages in completely rural counties included in the special supplementary sample (267 mortgages), (2) mortgages in completely rural counties included in the regular sample (78 mortgages), and (3) mortgages in non-metro and metro counties included in the regular sample (6,273

mortgages). The analytic weight for a survey response was computed by multiplying the non-response-adjusted sample weight of that survey response by the total sample size of the group and dividing it by the sum of the non-response-adjusted sample weight of that group. The total number of completely rural mortgages in the analysis sample is 345 (267+78).

Cityscape 13

Critchfield, Dey, Mota, and Patrabansh

Exhibit 2

Population, Housing Unit, Mortgage Originations, and Sample Size by County Type, 2014

Metro Counties

Non-Remote Non-Metro Counties

Remote Non-Metro Counties

1.5

2.0

1.0

5.3

12.9

10.6

14.7

10.0

PERCENT

85.6

83.4

88.4

84.7

POPULATION

HOUSING

MORTGAGE

STUDY

UNITS

ORIGINATIONS

SAMPLE

Note: Study sample includes a special oversample of mortgages originations in completely rural counties.

Sources: Census Bureau ACS 2010–2014 5-year estimates data for population and housing units by county; NMDB® data for 2014 first-lien mortgage originations; NSMO 2014 data for sample sizes by county

Geographic Differences in Property, Loan, and Borrower

Characteristics

The primary data we use are survey responses from the regular quarterly NSMO samples and

a special supplemental sample of borrowers in completely rural areas.13 NSMO data cover a

representative sample of first-lien residential mortgages taken out since 2013.

Exhibit 3 shows property and mortgage characteristics for our sample. Because purchasers and

refinancers may have different expectations, knowledge, or experience, we present results for all

originations, purchase mortgages, and refinance mortgages, separately.

Compared with those in metro areas, properties associated with mortgage originations in

completely rural and non-metro areas were less likely to be single-family attached dwellings,

and were more likely to be manufactured homes.14 Manufactured housing is often titled as chattel (personal property) even though about three-fifths of manufactured-housing residents own the

13 The supplemental sample uses the same questionnaire as the regular sample for 2014 mortgages with a few exceptions noted later.

14 Single-family attached dwellings include townhouses, row houses, villas, apartments, and multi-unit dwellings. In the NSMO survey, mobile and manufactured homes are identified as manufactured housing because all such homes built after 1976 are defined by the HUD as manufactured housing.

14 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

83

2

13

2

17

50

25

8

38

15

73

13

21

25

17

20

3

75

26

74

26

91

9

Rural

(%)

NA

(n=184)

Completely

o

85

5

10

0

12

59

24

5

47

20

74

11

13

24

28

18

5

79

21

81

19

91

9

Non-

NA

Metr

(%)

(n=306)

Refinance Mortgages

o

1

0

4

7

9

NA

84

15

38

37

22

60

30

73

16

19

31

17

84

16

75

25

77

23

Metr

(%)

(n=2,497)

89

2

5

4

10

60

29

1

78

30

95

8

20

30

14

21

7

86

14

76

24

94

7

Rural

(%)

NA

(n=161)

Completely

o

88

8

2

2

8

60

27

5

82

30

90

12

16

21

21

21

8

86

14

75

25

89

11

Non-

NA

Metr

(%)

(n=348)

chase Mortgages

Pur

o

1

0

2

6

NA

82

17

34

41

23

89

30

88

11

20

18

29

17

90

10

76

24

77

23

Metr

(%)

ype (1 of 2)

(n=3,044)

2

9

3

4

5

8

Rural

(%)

53

86

14

55

27

59

30

80

10

21

28

16

20

81

19

75

25

92

(n=345)

Completely

o

52

86

7

6

1

10

60

25

5

65

30

80

12

15

23

25

20

7

83

17

78

22

90

10

Non-

Metr

(%)

(n=654)

All Mortgages

o

55

83

16

1

0

3

35

39

22

76

30

80

6

10

18

18

30

17

87

13

76

24

77

23

Metr

(%)

(n=5,541)

ower Characteristics by Mortgage and Market T

e

ed

ears)

e

e

e

TV) Ratio (Median)

e Full-Time

ype

ype

erm to Maturity

erms (in Y

, Mortgage, and Borr

ears or Mor

chase Mortgage Shar

operty T

Single-Family Detached

Attached

Mobile or Manufactur

Land

Less than $50,000

$50,000 to $149,999

$150,000 to $299,999

$300,000 or Mor

30 Y

Median T

Less than $35,000

$35,000 to $49,999

$50,000 to $74,999

$75,000 to $99,999

$100,000 to $174,999

$175,000 or Mor

One or Mor

None Full-Time

Couple

Single

Non-Hispanic White

Hispanic and Non-White

operty

Characteristics

Pur

Pr

Loan Amount

Mortgage T

Loan-to-Value (L

Household Income

Household Employment

Household T

Respondent Race/Ethnicity

Exhibit 3

Pr

Cityscape 15

Critchfield, Dey, Mota, and Patrabansh

2

8

8

8

5

9

7

Rural

(%)

13

20

24

25

17

52

26

22

30

13

10

14

39

(n=184)

714

Completely

o

17

24

23

21

15

49

1

29

9

22

25

13

14

3

4

9

10

9

11

40

Non-

Metr

(%)

720

(n=306)

Refinance Mortgages

o

13

23

29

22

13

51

2

11

5

22

34

26

6

4

7

7

8

7

10

50

Metr

(%)

739

(n=2,497)

45

25

10

12

8

37

3

14

8

21

40

14

9

4

12

7

9

11

17

31

Rural

(%)

(n=161)

717

Completely

o

39

24

16

14

8

39

2

13

8

24

33

20

7

6

6

7

10

10

9

46

Non-

Metr

(%)

729

(n=348)

chase Mortgages

Pur

o

40

23

17

12

7

39

1

8

4

19

38

29

4

4

5

6

8

8

11

54

Metr

(%)

747

ype (2 of 2)

(n=3,044)

3.0 credit score ranges from 300 to 850.®

3

8

9

6

9

8

9

Rural

(%)

30

23

17

19

12

43

19

21

35

14

12

12

35

(n=345)

716

VantageScore

Completely

o

29

24

19

17

11

45

2

21

9

23

29

17

10

5

5

8

10

9

10

43

Non-

Metr

(%)

725

(n=654)

All Mortgages

and multi-unit dwellings.

o

28

23

23

16

10

45

1

10

5

20

36

28

5

4

6

6

8

8

10

52

Metr

(%)

744

apartments,

(n=5,541)

villas,

ower Characteristics by Mortgage and Market T

row houses,

e

ee

edit Scor

ounger

, Mortgage, and Borr

2014

35 or Y

36 to 45

46 to 55

56 to 65

66 or Older

Median

Some School

High School

Technical School

Partial College

College Degr

Postgraduate

Lower Than 620

620 to 639

640 to 659

660 to 679

680 to 699

700 to 719

720 to 739

740 or Higher

Median

NSMO,

operty

Characteristics

Respondent Age

Respondent Education

Respondent Cr

Attached properties include townhouses,

Exhibit 3

Pr

NA = data not available.

Notes:

Source:

16 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

land it is sited on (CFPB, 2014). Because chattel loans generally are not identifiable as mortgages in the NMDB credit files, the NSMO sample misses most chattel loans and thus substantially

undercounts loans for manufactured housing. We found the percentage of initial purchases for

manufactured housing much lower than for resale or any refinancing transactions for the same.

Underrepresentation of manufactured housing may not be as severe for refinance mortgages or

purchase mortgages by repeat buyers. Homeowners may be able to obtain mortgage refinancing on

manufactured housing originally titled as chattel after a title change if the house is on a permanent foundation and especially if they also own the underlying land.15

Generally, mortgage loan amount was lowest in completely rural counties and highest in metro

counties. In addition, loans with terms of less than 30 years were more common in completely

rural areas. This partially reflects the greater share in completely rural areas of manufactured

housing loans, which tend to have shorter terms. Nonetheless, these differences in loan terms

persist even when manufactured housing are excluded from the samples. Purchase loans in

completely rural counties had a higher median loan-to-value (LTV) ratio.16

This is partly explained by the slightly higher share of purchasers in completely rural counties

that were first-time homebuyers, who tend to have loans with higher LTVs.17

NSMO is a representative sample of mortgages, but the survey is answered by a single respondent

who may be one of multiple borrowers. Seventy-five percent of mortgage loans involve multiple

borrowers. For simplicity, we refer to the respondents as “borrowers.” In characterizing borrowers, we focus on age, household type, number of co-borrowers, race and ethnicity, education,

employment, income, and credit scores.

Overall, borrowers in completely rural areas had lower incomes, were less likely to be employed

full-time, and were more likely to identify as non-Hispanic White. Age differences across areas

were small, though purchasers were slightly younger and refinancers were slightly older in

completely rural counties than in metro and non-metro areas. Educational levels differed more

noticeably with metro borrowers more likely than others to have a graduate degree. Although

median credit scores were only slightly lower in completely rural counties than in more populous

areas, the share of borrowers with a credit score of at least 740 was much lower in completely

rural counties (35 percent) than in non-metro (43 percent) and metro (52 percent) counties.18

15 According to the Manufactured Housing Resource Guide by the National Consumer Law Center, approximately three-quarters of the states have statutes that set forth a procedure to convert a manufactured home from personal to real property and document that conversion. See https://www.nclc.org/images/pdf/manufactured_housing/cfed-titling-homes.pdf.

16 The LTV ratio is obtained by dividing the mortgage loan amount by the property value, and the difference between 100 percent and the LTV indicates the borrower’s share of equity on the property. A typical mortgage will have an LTV

of 80 percent, which indicates that the borrower has 20 percent equity in the property.

17 First-time purchases are purchase mortgages taken out by borrowers who were younger than 55 years of age, who did not have any record of having a prior mortgage in the NMDB data, and who were buying a house they will primarily live in.

18 VantageScore® 3.0 is the credit scoring model developed jointly by Equifax, Experian, and TransUnion. The NMDB

contains VantageScore® 3.0 credit scores that range from 300 to 850. Generally, the higher a borrower’s credit score is, the less risky the borrower is assumed to be. See https://your.vantagescore.com/resource/52/understanding-credit-scores.

Cityscape 17

Critchfield, Dey, Mota, and Patrabansh

The smaller share of completely rural borrowers with scores of 740 or greater suggests that loans

in completely rural areas were likely to have higher interest rates because the best rates are typically offered to borrowers with scores above 740. To test this conjecture, we examine whether there

were statistically significant differences in Freddie Mac’s Primary Mortgage Market Survey (PMMS®) spread across the analysis groups (metro, non-metro, and completely rural).19 We consider both the observed difference in PMMS spreads as well as an adjusted difference based on a regression model

that accounts for borrowers, property, and loan attributes:

PMMS _ Spread = α + ρ

i,c

CR CRc + ρ NM NMc + β Xi + ε i,c

The equation models the PMMS spread of the loan originated to borrower i in analysis group c

( PMMS _ Spread ) on indicator variables of whether analysis group c is completely rural

i,c

( CRc) or non-metro ( NMc), as well as a vector of borrower, property, and loan attributes ( Xi).

The coefficient estimates for ρ CR and ρ NM indicate the average difference in the PMMS

spread for loans originated in completely rural and non-metro counties relative to metro counties.

Exhibit 4 displays the coefficient estimates and indicators of statistical significance for the sample of all mortgages, and then separately for purchase and refinance mortgages. This exhibit also provides the borrower, property, and loan attributes that are used as control variables for estimating the

differences between counties.20 Borrowers in completely rural counties indeed paid a slightly higher interest rate than borrowers in metro areas,21 and the spread over the PMMS rate for borrowers in

completely rural counties was 14 basis points higher than metro areas with a statistically significant difference. The results imply that completely rural borrowers paid 16 basis points more than

non-metro borrowers. While 14 basis points appears small for the average of completely rural

counties, a mortgage of $100,000 with a 4.00 percent rate that moved to 4.14 percent would cause

the monthly payment to rise $8.10, or $97.20 per year, and cost the borrower $2,916.00 over the

life of the loan. For purchase mortgages, the differences between completely rural and metro areas was 24 basis points and statistically significant, but the refinance mortgages difference was not

significant. Notably, in this regression framework, the PMMS spread does not vary significantly

with most borrower characteristics, whereas the purchase flag, loan amount, property type, and

credit score are statistically significant predictors of the interest rate spread.

Geographic Differences in Borrowers’ Experiences

and Knowledge

In examining differences in mortgage borrowers’ experience and knowledge by geography, we

use the framework of the PMMS equation and exhibit 4 to examine borrowers’ self-reported

satisfaction, knowledge, and lender selection. More specifically, exhibit 5 shows the results of the NSMO asking borrowers how satisfied they were with several aspects of their mortgage and the

19 Freddie Mac publishes the average PMMS rate by mortgage term on a weekly basis. The PMMS spread is calculated as the difference between the actual note rate of a mortgage and Freddie Mac’s PMMS average prime offer note rate for that term at that time. This spread indicates how expensive a mortgage is compared to the average mortgage of similar term taken out in that week. The spread used in this article is unbounded while the spread in the NSMO public use file is bounded for privacy considerations.

20 Some of the controls, such as race, ethnicity, age and gender, are characteristics that lenders do not or cannot use in loan pricing models. We include them to account indirectly for unmeasured characteristics that may be correlated with these controls.

21 The R-squared for the models ranged from 0.06070 to 0.08412.

18 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

Exhibit 4

Spread Regression

Purchase

Refinance

All Mortgages

Mortgages

Mortgages

Average for Metro (M) (Percentage Points)

0.22

0.19

0.27

Controlled Model

Parameter

Categories

Estimate

Estimate

Estimate

Intercept

0.22***(0.03)

0.15***(0.03)

0.23***(0.03)

Non-Metro (NM)

– 0.03 (0.03)

– 0.04 (0.04)

– 0.02 (0.04)

Completely Rural (CR)

0.14***(0.04)

0.23***(0.05)

0.05 (0.05)

Purchase Mortgage

– 0.06***(0.02)

NA

NA

Property Type:

Mobile or Manufactured

0.10*(0.06)

0.16*(0.10)

0.06 (0.07)

Attached

0.06**(0.02)

0.02 (0.03)

0.11***(0.03)

Land

0.06 (0.12)

0.03 (0.03)

NA

Loan Amount:

Less Than $50,000

0.31***(0.04)

0.37***(0.06)

0.27***(0.06)

$50,000 to $149,999

0.12***(0.02)

0.13***(0.03)

0.11***(0.03)

$300,000 or More

– 0.15***(0.02)

– 0.11***(0.03)

– 0.19***(0.03)

Reported Household

Income:

Less Than $35,000

0.01 (0.04)

0.008 (0.05)

0.02 (0.05)

$35,000 to $49,999

– 0.03 (0.03)

– 0.05 (0.04)

0.001 (0.05)

$50,000 to $74,999

– 0.01 (0.03)

– 0.04 (0.03)

0.02 (0.04)

$100,000 to $174,999

– 0.01 (0.02)

– 0.004 (0.03)

– 0.03 (0.03)

$175,000 or More

– 0.07**(0.03)

– 0.11***(0.04)

– 0.04 (0.04)

Household Type:

Single

– 0.001 (0.02)

– 0.007 (0.03)

0.006 (0.03)

Respondent

Hispanic and Non-White

– 0.03 (0.02)

– 0.03 (0.03)

– 0.02 (0.03)

Race/Ethnicity:

Respondent Age:

35 or Younger

– 0.01 (0.02)

– 0.002 (0.03)

0.008 (0.04)

46 to 55

– 0.01 (0.02)

0.05 (0.03)

– 0.08**(0.03)

56 to 65

– 0.05*(0.03)

– 0.02 (0.03)

– 0.08**(0.03)

66 or Older

– 0.04 (0.03)

0.03 (0.05)

– 0.10**(0.04)

Respondent Education:

Some School

0.03 (0.07)

0.07 (0.10)

0.008 (0.10)

High School

0.06**(0.03)

0.06 (0.04)

0.06 (0.04)

Technical School

0.05 (0.04)

0.05 (0.05)

0.06 (0.05)

Partial College

0.05**(0.02)

0.07**(0.03)

0.04 (0.03)

Postgraduate

– 0.02 (0.02)

– 0.01 (0.03)

– 0.02 (0.03)

Respondent Credit Score: Lower Than 620

0.16***(0.03)

0.05 (0.05)

0.24***(0.05)

620 to 639

0.06 (0.04)

– 0.05 (0.06)

0.19***(0.06)

640 to 659

0.12***(0.03)

0.04 (0.05)

0.20***(0.05)

660 to 679

0.08**(0.03)

0.08*(0.05)

0.08*(0.04)

680 to 699

0.11***(0.03)

0.06 (0.04)

0.18***(0.04)

700 to 719

0.01 (0.03)

– 0.02 (0.04)

0.03 (0.04)

720 to 739

0.04 (0.03)

0.04 (0.04)

0.04 (0.04)

Number of Observations

6,540

3,553

2,987

R-Squared

0.07

0.06

0.08

NA = data not available.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level. *** denotes significance at 1-percent level.

Notes: The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36

to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey question: “What is the interest rate on this mortgage?”

Source: NSMO, 2014

Cityscape 19

Critchfield, Dey, Mota, and Patrabansh

mortgage process.22 Most borrowers were “very satisfied” with their mortgage and the mortgage

process, but borrowers in completely rural counties were less likely be “very satisfied,” as most

of the completely rural–metro differences in exhibit 5 are negative and statistically significant.

Exhibit 5

Satisfaction of Borrowers with Mortgage and Mortgage Process

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

Share “Very Satisfied”

M

NM – M

CR – M

CR – NM

All Mortgages

Best Terms to Fit Needs

78

2

– 8***

– 10***

Lowest Interest Rates Qualified

70

3

– 3

– 6*

Lowest Closing Costs

58

1

– 6**

– 6*

Lender

76

1

0

– 1

Settlement Agent

70

1

– 4

– 5

Application Process

62

0

– 3

– 3

Loan Closing Process

66

0

– 8***

– 9***

Disclosure Documents

65

1

– 8***

– 8***

Timeliness of Documents

64

1

– 9***

– 11***

Purchase Mortgages

Best Terms to Fit Needs

78

2

– 7**

– 9**

Lowest Interest Rates Qualified

70

– 1

– 8**

– 7*

Lowest Closing Costs

55

– 2

– 5

– 3

Lender

76

– 3

2

5

Settlement Agent

69

2

– 4

– 6

Application Process

61

– 3

– 5

– 2

Loan Closing Process

64

– 1

– 9**

– 8*

Disclosure Documents

63

– 1

– 8**

– 7

Timeliness of Documents

63

1

– 10***

– 12***

Refinance Mortgages

Best Terms to Fit Needs

78

3

– 10***

– 13***

Lowest Interest Rates Qualified

70

7**

2

– 5

Lowest Closing Costs

61

3

– 7*

– 10**

Lender

76

5**

– 2

– 8*

Settlement Agent

71

0

– 3

– 4

Application Process

63

3

1

– 4

Loan Closing Process

69

1

– 7*

– 9*

Disclosure Documents

66

3

– 7*

– 10**

Timeliness of Documents

66

1

– 8**

– 10**

CR = completely rural counties. M = metro counties. NM = non-metro counties.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level. *** denotes significance at 1-percent level.

Notes: The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36 to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey questions: “Overall, how satisfied are you that the mortgage you got was one with the following? Overall, how satisfied are you with the following?”

Source: NSMO, 2014

22 The survey asked borrowers if satisfaction with the mortgage they got was: (1) the best terms to fit their needs, (2) the lowest interest rate for which they qualified, and (3) the lowest closing costs. The survey asked the borrowers about satisfaction with: (1) their lender or broker, (2) their settlement agent, (3) the application process, (4) the loan closing process, (5) the information in the disclosure documents, and (6) timeliness of mortgage disclosure documents.

20 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

The share of borrowers in completely rural counties who were “very satisfied” that they received

a mortgage with the best terms to fit their needs was 8 and 10 percentage points lower than the

shares in metro counties and non-metro counties, respectively. Completely rural borrowers were

also 6 percentage points less likely than those in metro areas to report that they were very satisfied they received the lowest closing costs. Completely rural borrowers were 8 to 9 percentage points

less likely to be very satisfied with the closing process, disclosure documents, and timeliness of mortgage documents. This pattern largely holds for refinance mortgages, and for both completely

rural/metro and completely rural/non–metro comparisons. For purchase mortgages, these

differences are less likely to be significant.

The NSMO data offer several measures of borrowers’ expectations at the start of the mortgage

process and familiarity with aspects of mortgage lending. These include borrowers’ indications

of how concerned they were about qualifying for a mortgage and how firm an idea they had

about the type of mortgage they wanted. The difference in concern about qualifying for a mortgage

between metro, non-metro, and completely rural borrowers was virtually nil. Exhibit 6, however,

shows that completely rural borrowers were less likely to have a firm idea about the type of

mortgage they wanted than borrowers in the other two areas.

The survey measured borrowers’ understanding of the mortgage process by asking about their

familiarity with their own credit history; available interest rates and mortgage products; and

requirements—such as income and down-payment requirements—to obtain a mortgage.

Exhibit 6

Borrower Concern about Qualifying and Idea about Mortgage Wanted

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

M

NM – M

CR – M

CR – NM

All Mortgages

Not at All Concerned

52

– 1

– 1

– 1

Have Firm Idea

59

0

– 6**

– 6*

Purchase Mortgages

Not at All Concerned

47

3

1

– 3

Have Firm Idea

54

– 1

– 4

– 3

Refinance Mortgages

Not at All Concerned

57

– 5*

– 3

2

Have Firm Idea

65

1

– 8**

– 9*

CR = completely rural counties. M = metro counties. NM = non-metro counties.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level.

Notes: The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36 to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey questions: “When you began the process of getting this mortgage, how concerned were you about qualifying for a mortgage? How firm an idea did you have about the mortgage you wanted?”

Source: NSMO, 2014

Cityscape 21

Critchfield, Dey, Mota, and Patrabansh

Generally, borrowers in completely rural counties were less familiar with aspects of mortgage

lending than borrowers in non-metro or metro counties, as shown in exhibit 7. Borrowers were

typically very familiar with their credit history with little differences among the areas. Completely rural counties had a smaller fraction of borrowers who were very familiar with interest rates

available, the types of mortgages available, the mortgage process, down-payment requirements,

and income requirements. Completely rural borrowers and refinancers among them were also

less likely to be very familiar with the money required for closing. These results were consistent with the notion that borrowers in completely rural counties have less information or fewer

lenders to choose from than borrowers in metro areas.

Exhibit 7

Borrower Familiarity with Aspects of Mortgage Lending

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

Share “Very Familiar”

M

NM – M

CR – M

CR – NM

All Mortgages

Credit History or Score

76

2

– 1

– 3

Interest Rates Available

58

1

– 8***

– 9***

Mortgage Types Available

48

– 2

– 9***

– 7**

Mortgage Process

55

0

– 10***

– 11***

Down Payment to Qualify

59

1

– 9***

– 9***

Income Needed to Qualify

57

0

– 8***

– 8**

Money Needed for Closing

51

– 1

– 9***

– 8**

Purchase Mortgages

Credit History or Score

75

– 1

0

1

Interest Rates Available

54

0

– 8**

– 9*

Mortgage Types Available

45

– 4

– 10***

– 6

Mortgage Process

49

1

– 9**

– 10**

Down Payment to Qualify

60

– 1

– 8**

– 7*

Income Needed to Qualify

54

2

– 6

– 8*

Money Needed for Closing

48

– 4

– 5

– 2

Refinance Mortgages

Credit History or Score

77

4

– 3

– 7*

Interest Rates Available

64

1

– 8**

– 10**

Mortgage Types Available

51

0

– 8**

– 8*

Mortgage Process

64

– 3

– 13***

– 10**

Down Payment to Qualify

59

0

– 10**

– 10**

Income Needed to Qualify

60

– 3

– 10**

– 7

Money Needed for Closing

55

0

– 13**

– 13**

CR = completely rural counties. M = metro counties. NM = non-metro counties.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level. *** denotes significance at 1-percent level.

Notes: The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36 to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey question: “When you began the process of getting this mortgage, how familiar were you with each of the following?”

Source: NSMO, 2014

22 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

The NSMO also probed borrowers on their knowledge about mortgage concepts by asking

them how well they could explain the concepts to someone else. Based on borrowers’ responses,

the mortgage concepts from the least to the most challenging are:

(1)

difference between fixed and adjustable rates,

(2)

consequences of not making required payments,

(3)

difference between interest rate and annual percentage rate (APR),

(4)

amortization of a loan, and

(5)

difference between prime and subprime loans.

We classify the first two as simple concepts and the last three as complex concepts.

Overall, borrowers were more knowledgeable about simple concepts than they were about

complex concepts as shown in exhibit 8. For the simple concept of adjustable versus fixed-rate

mortgages, borrowers in completely rural counties were less likely to say they could explain the

Exhibit 8

Borrowers’ Abilities to Explain Aspects of Mortgages

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

Share Able to Explain

“Very Well”

M

NM – M

CR – M

CR – NM

All Mortgages

Fixed Versus Adjustable Rate

70

– 1

– 8***

– 7**

Consequence of Not Paying

67

1

– 3

– 3

Amortization of Loan

39

0

– 6**

– 5*

Interest Rate Versus APR

28

0

– 4

– 4

Prime Versus Subprime

22

– 1

– 2

– 1

Purchase Mortgages

Fixed Versus Adjustable Rate

67

1

– 1

– 2

Consequence of Not Paying

66

2

1

0

Amortization of Loan

36

– 3

– 5

– 3

Interest Rate Versus APR

26

– 3

– 4

– 1

Prime Versus Subprime

20

– 2

0

2

Refinance Mortgages

Fixed Versus Adjustable Rate

72

– 4

– 17***

– 13***

Consequence of Not Paying

68

– 1

– 9**

– 7

Amortization of Loan

41

2

– 5

– 7

Interest Rate Versus APR

31

4

– 3

– 7*

Prime Versus Subprime

24

2

– 4

– 5

APR = annual percentage rate. CR = completely rural counties. M = metro counties. NM = non-metro counties.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level. *** denotes significance at 1-percent level.

Notes: The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36 to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey question: “How well could you explain to someone the following?”

Source: NSMO, 2014

Cityscape 23

Critchfield, Dey, Mota, and Patrabansh

differences very well. After adjusting for differences in characteristics of borrowers and loans, the share of borrowers in completely rural counties who could explain the differences very well was

8 percentage points lower than those in metro areas, and 7 percentage points lower than in non-

metro counties. Refinancing transactions drove the overall differences, as the share of refinancers in completely rural counties able to explain the difference between adjustable and fixed rates very well was 17 percentage points lower than those in metro areas and 13 percentage points lower

than those in non-metro counties. Fewer borrowers reported familiarity with complex mortgage

concepts and there were no significant differences among the geographic areas.

The NSMO asked several questions about the shopping and the mortgage application process,

namely, whether the borrower:

(1)

picked the lender or broker before the loan;

(2)

applied directly to a lender (as opposed to through a broker or a builder);

(3)

initiated contact with the lender or broker (as opposed to those who were contacted by

the lender or broker first, or those were who were put in touch with the lender or broker

by a third party);

(4)

seriously considered multiple lenders and brokers; and

(5)

applied to multiple lenders or brokers.

We interpret the first three items as reflecting how proactive borrowers were with lender selection.

The last two items reflect how much borrowers shopped across multiple lenders.

Exhibit 9 shows that most borrowers were proactive with lender selection—they picked the lender

before the loan, initiated contact, and applied directly with a lender. With respect to shopping

behavior, more than half of borrowers seriously considered applying to multiple lenders, but only

one-fourth or fewer applied to more than one lender.

Outside of metro areas, borrowers were more proactive in initiating contact and applying directly to a lender. Completely rural borrowers seeking a loan for home purchase were more proactive than

their home-purchase counterparts in metro areas, as they were 13 and 16 percentage points more

likely to have applied directly or to have initiated contact, respectively. These borrowers may have fewer options for lenders in their area as solicitation by lenders may be less common in completely rural areas or lenders in those areas may have less competition.

These differences, however, do not translate into differences in the likelihood that a completely

rural borrower considered or applied to multiple lenders. Borrowers who refinanced in non-metro

counties were more proactive than those in metro areas, but they did not show a different level of engagement with multiple lenders than other areas.

The NSMO asked borrowers how likely they were to: sell their property, move but keep the

property, refinance, or pay off the mortgage to have a mortgage-free property in the next couple of years. Borrowers’ responses demonstrated their expectations about their property and mortgage.

In our analysis, we combined borrowers who were very likely to take an action together with those

who were somewhat likely to take that action and referred to these borrowers as generally likely to take that action.

Borrowers were generally very unlikely to take any of these actions, as seen in exhibit 10.

Borrowers who refinanced were the most likely to report any of these anticipated actions, with 40

24 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

Exhibit 9

Borrowers’ Mortgage Shopping and Application Steps

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

M

NM – M

CR – M

CR – NM

All Mortgages

Picked Lender Before Loan

71

0

4

4

Applied Directly to a Lender

63

9***

10***

1

Borrower Initiated Contact

66

8***

12***

4

Considered Multiple Lenders

52

– 1

0

1

Applied to Multiple Lenders

23

– 2

0

3

Purchase Mortgages

Picked Lender Before Loan

73

0

3

3

Applied Directly to a Lender

58

9***

13***

4

Borrower Initiated Contact

64

10***

16***

6

Considered Multiple Lenders

55

– 1

3

4

Applied to Multiple Lenders

26

– 5*

1

6

Refinance Mortgages

Picked Lender Before Loan

68

1

5

4

Applied Directly to a Lender

69

10***

9**

– 1

Borrower Initiated Contact

69

5*

7**

3

Considered Multiple Lenders

49

– 2

– 4

– 2

Applied to Multiple Lenders

19

0

– 1

– 1

CR = completely rural counties. M = metro counties. NM = non-metro counties.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level.

Notes: The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36 to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey questions: “Which of the following best describes your shopping process? How did you apply for the mortgage? Who initiated first contact between you and the lender/mortgage broker you used for the mortgage you took out? How many different lenders/mortgage brokers did you seriously consider before choosing where to apply for this mortgage? How many different lenders/mortgage brokers did you end up applying to?”

Source: NSMO, 2014

Exhibit 10

Borrowers’ Mortgage and Property Expectations (1 of 2)

Controlled Difference Model

Percent

County Type Difference

Share Stating

(Percentage Point)

“Likely” to

M

NM – M

CR – M

CR – NM

All Mortgages

Sell Property

34

– 3*

– 2

2

Move but Keep Property

23

– 2

– 1

1

Refinance Mortgage

31

– 3*

– 2

1

Have Mortgage-Free Property

22

2

2

0

Purchase Mortgages

Sell Property

30

0

5

5

Move but Keep Property

23

– 2

4

6

Refinance Mortgage

34

– 1

– 5

– 4

Have Mortgage-Free Property

21

0

2

2

Cityscape 25

Critchfield, Dey, Mota, and Patrabansh

Exhibit 10

Borrowers’ Mortgage and Property Expectations (2 of 2)

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

Share Stating

“Likely” to

M

NM – M

CR – M

CR – NM

Refinance Mortgages

Sell Property

40

– 7**

– 9**

– 2

Move but Keep Property

23

– 3

– 6*

– 3

Refinance Mortgage

28

– 6**

0

6

Have Mortgage-Free Property

22

4

2

– 1

CR = completely rural counties. M = metro counties. NM = non-metro counties.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level. *** denotes significance at 1-percent level.

Notes: The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36 to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey question: “How likely is it that in the next couple of years you will do the following?”

Source: NSMO, 2014

percent reporting they might sell the property in the next few years. Refinancers in completely rural counties were 9 percentage points less likely to sell their property in the next few years. Refinance borrowers in non-metro counties were 7 percentage points less likely than borrowers in metro areas to anticipate selling their homes, and were also less likely to report that they expect to refinance again in the next few years compared to both metro and completely rural refinancers.

Geographic Differences in Borrowers’ Lender and

Mortgage Choice

The detail of the question on how borrowers chose their lenders is an advantage of the NSMO

survey. NSMO asked specifically about the importance of seven factors:

(1)

reputation of the lender or broker,

(2)

having an established banking relationship with the lender or broker,

(3)

having a local office or branch of the lender or broker nearby,

(4)

recommendation from a friend, relative or co-worker,

(5)

recommendation from a real estate agent or builder, and

(6)

whether the lender or broker was a friend.

Unfortunately, the survey questions changed from a three-point scale of “very,” “somewhat,”

and “not at all” to a two-point scale of “important” and “not important” in the seventh wave of

the survey when the supplemental sample was administered along with mostly 2015 mortgages

from the regular sample. Consequently, we examined lender and mortgage choice with a different

comparison group than that used in the earlier part of the article. Supplemental sample borrowers

(267) were combined with regular sample borrowers who took out their mortgage in 2015 (6,199)

to create an analysis file of borrowers who answered the question in the two-point scale. This led to a sample size of 6,466 mortgages with 5,508 (85 percent) in metro, 618 (10 percent) in non-metro,

and 340 in completely rural counties shown in exhibits 11 and 12.23

23 These county shares are comparable to the ones for the 2014 sample, reported in exhibit 2.

26 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

Borrowers most often identified lender reputation as an important factor for lender selection,

followed by having an established banking relationship and a local office or branch. Having an

established banking relationship was more important to borrowers in non-metro and completely

rural counties than in metro areas, shown in exhibit 11. In contrast, metro borrowers felt the

agent or builder recommendation was more important. Differences were even more stark in the

importance of an established banking relationship and agent or builder recommendation for

purchase mortgages. Furthermore, non-metro borrowers also stated that having a local office

or branch was important more frequently than metro borrowers, particularly for refinancers.

Exhibit 11

Importance of Factors in Choosing a Lender

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

Share Stating Factor

“Important”

M

NM – M

CR – M

CR – NM

All Mortgages

Reputation

71

– 4*

0

3

Established Bank Relationship

55

9***

9***

0

Local Office or Branch

49

8***

5

– 3

Friend/Relative Recommended

40

– 2

1

3

Agent/Builder Recommended

36

– 6***

– 12***

– 6**

Was a Friend or Relative

14

0

– 3

– 3

Purchase Mortgages

Reputation

72

– 4

– 4

0

Established Bank Relationship

50

12***

19***

7

Local Office or Branch

55

5*

0

– 5

Friend/Relative Recommended

48

– 1

– 5

– 3

Agent/Builder Recommended

55

– 11***

– 21***

– 10**

Was a Friend or Relative

15

– 1

– 4

– 3

Refinance Mortgages

Reputation

71

– 3

2

5

Established Bank Relationship

60

7**

0

– 7

Local Office or Branch

44

10***

7*

– 4

Friend/Relative Recommended

32

– 3

6*

9**

Agent/Builder Recommended

17

– 2

– 5*

– 3

Was a Friend or Relative

14

1

– 1

– 3

CR = completely rural counties. M = metro counties. NM = non-metro counties.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level. *** denotes significance at 1-percent level.

Notes: Given inclusion of 2015 NSMO observations, sample sizes for exhibits 8 and 9 differ from those in remaining tables. Sample sizes for this exhibit are: 5,508 for M, 618 for NM, and 340 for CR. The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36 to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey question: “How important were each of the following in choosing the lender/mortgage broker you used for the mortgage you took out?”

Source: NSMO, 2014-2015

Cityscape 27

Critchfield, Dey, Mota, and Patrabansh

NSMO also asked about the importance of seven factors in deciding on a mortgage:

(1)

lower interest rate,

(2)

fixed-interest rate for the life of the loan,

(3)

lower annual percentage rate (APR),

(4)

lower closing fees,

(5)

lower monthly payment,

(6)

a term of 30 years, and

(7)

no mortgage insurance.

Exhibit 12 shows the factors for selecting a mortgage are listed in the order of importance with

near universal agreement that getting a lower interest rate was very important. For all mortgages, one difference between borrowers in non-metro areas relative to metro borrowers was their ranking

of importance of having a lower monthly payment and not having mortgage insurance. The shares

reporting these features as being important for mortgage selection in non-metro counties was

between 4 and 6 percentage points lower than in metro counties. Completely rural purchasers

were 7 percentage points less likely to indicate that having lower monthly payment was important

than metro purchasers. Completely rural purchasers were also 8 to 10 percentage points less

likely to indicate that a mortgage term of 30 years was important than non-metro and metro

purchasers, respectively.

Exhibit 12

Importance of Factors in Choosing a Mortgage (1 of 2)

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

Share “Very Familiar”

M

NM – M

CR – M

CR – NM

All Mortgages

Lower Interest Rate

98

0

0

0

Fixed-Interest Rate

89

1

– 1

– 3

Lower APR

88

– 1

2

3

Lower Closing Fees

84

0

0

1

Lower Monthly Payment

80

– 4**

– 3

1

30-Year Term

61

– 2

– 3

0

No Mortgage Insurance

58

– 6***

– 2

4

Purchase Mortgages

Lower Interest Rate

97

– 1

0

1

Fixed-Interest Rate

89

0

– 1

– 1

Lower APR

87

0

1

1

Lower Closing Fees

82

1

– 2

– 3

Lower Monthly Payment

81

– 3

– 7**

– 4

30-Year Term

69

– 2

– 10**

– 8*

No Mortgage Insurance

54

– 6*

– 2

5

28 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

Exhibit 12

Importance of Factors in Choosing a Mortgage (2 of 2)

Controlled Difference Model

Percent

County Type Difference

(Percentage Point)

Share “Very Familiar”

M

NM – M

CR – M

CR – NM

Refinance Mortgages

Lower Interest Rate

98

1

0

– 1

Fixed-Interest Rate

90

2

– 1

– 3

Lower APR

88

– 2

3

6*

Lower Closing Fees

85

– 1

3

4

Lower Monthly Payment

80

– 4

1

5

30-Year Term

52

– 3

3

6

No Mortgage Insurance

62

– 6*

– 1

5

APR = annual percentage rate. CR = completely rural counties. M = metro counties. NM = non-metro counties.

* denotes significance at 10-percent level. ** denotes significance at 5-percent level. *** denotes significance at 1-percent level.

Notes: Given inclusion of 2015 NSMO observations, sample sizes for exhibits 8 and 9 differ from those in remaining tables. Sample sizes for this exhibit are: 5,508

for M, 618 for NM, and 340 for CR. The controlled difference model has the following covariates for the respondent: age, race/ethnicity, credit score, and education; and household: household type, annual income, mortgage loan amount, and property type. The controlled difference model intercept reflects the percentages for respondents who were 36 to 45 years old, non-Hispanic White, and in a coupled household; who had a college degree or higher, credit score of 740 or higher, annual household income from $75,000 to $99,999, and mortgage loan amount from $150,000 to $299,999; and who lived in a single-family detached house.

Survey question: “How important were each of the following in determining the mortgage you took out?”

Sources: NSMO, 2014-2015

Conclusions

Rural credit markets are commonly viewed as differing from those in more populous areas for

several reasons, including a greater share of smaller and locally focused lenders. This article

offers, to our knowledge, the first comprehensive analysis that contrasts mortgage borrowers’

expectations, knowledge, and outcomes in completely rural areas to those of borrowers in metro

and non-metro areas. To do so, we take advantage of survey data from the NSMO, including a

special sample of mortgage borrowers in completely rural counties.

The comparisons we provide do not paint a simple picture of how borrowers and mortgage

markets differ by geographic location, as completely rural borrowers differ from other borrowers

on some dimensions but not others. Nonetheless, the results provide some suggestive evidence

that completely rural borrowers may have been initially less familiar with the mortgage market

conditions, products, and requirements. Completely rural borrowers were significantly less

likely than borrowers in metro areas to say they were very familiar with elements of the mortgage

process—such as the down-payment requirements—when they began it. There were few

significant differences by geographic location, however, in borrowers’ self-reported level of concern in qualifying for a mortgage, or in their ability to explain specific mortgage features to someone else at the time of the survey. It is not clear how to reconcile these findings, but one possibility is that completely rural borrowers were less familiar at the outset because they expected to or

relied more heavily on the lender to qualify for a loan and to become familiar with the details

of getting a mortgage.

Cityscape 29

Critchfield, Dey, Mota, and Patrabansh

Our results may also point to a relatively greater importance of borrower-lender relationships

outside of metro areas. For example, borrowers in completely rural and non-metro areas were

more likely than those in metro areas to rate having an established relationship as important in

choosing their lender. Additionally, completely rural borrowers were similarly satisfied with the

lender, settlement agent, and the application process as were borrowers in more populous areas,

even though they paid somewhat higher interest rates and were less likely to be very satisfied

with the mortgage they obtained, or with the closing process.

We conclude this article with a note of caution. Results presented in this article are for three

groups of counties as a whole. Because counties are very heterogeneous in each of the three

groups, the results do not necessarily apply to any individual county.

Acknowledgements

The authors thank Robert B. Avery, Ron Borzekowski, Kenneth P. Brevoort, Lariece Brown,

Brian Bucks, Daniel E. Coates, Thomas Daula, Michael Eriksen, Samuel Frumkin, Ian H. Keith,

Michael LaCour-Little, Forrest W. Pafenberg, Shiv Rawal, David Sanchez, Susan Singer,

and Rebecca Sullivan for providing comments and Julia Nguyen for assisting with data.

Authors

Tim Critchfield is an interdisciplinary statistician in the Office of Research at the Consumer

Financial Protection Bureau.

Jaya Dey is a senior economist with the Affordable Lending Analytics and Research group at

Freddie Mac.

Nuno Mota is an economist with the Economic and Strategic Research group at Fannie Mae.

Saty Patrabansh is the manager of the National Mortgage Database Program at the Federal

Housing Finance Agency.

References

Ambrose, Brent, and Richard Buttimer. 2005. “GSE Impact on Rural Mortgage Markets,” Regional

Science and Urban Economics 35 (4): 417–443.

Avery, Robert B., Kenneth P. Brevoort, Tim Critchfield, Ian H. Keith, Ismail E. Mohamed, Forrest W.

Pafenberg, Saty Patrabansh, Jay D. Schultz, and Claudia E. Wood. 2017. National Mortgage Database Technical Report 1.2. Federal Housing Finance Agency and the Consumer Financial Protection Bureau. https://www.fhfa.gov/PolicyProgramsResearch/Programs/Documents/NMDB-Technical-

Report_1.2_10302017.pdf.

30 National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

Avery, Robert B., Mary F. Bilinski, Audrey Clement, Tim Critchfield, Ian H. Keith, Ismail E.

Mohamed, Saty Patrabansh, Chad Redmer, Jay D. Schultz, and Rebecca Sullivan. 2018. National

Survey of Mortgage Originations Technical Documentation: Technical Report 2. Federal Housing Finance Agency and the Consumer Financial Protection Bureau, National Mortgage Database Program.

https://www.fhfa.gov/DataTools/Downloads/Documents/NSMO-Public-Use-Files/NSMO-

Technical-Documentation-20181108.pdf.

Bucks, Brian, and Karen Pence. 2008. “Do Borrowers Know Their Mortgage Terms?” Journal of

Urban Economics 64 (2): 218–233.

Cai, Qiang, and Sarah Shahdad. 2015. What is the Mortgage Shopping Experience of Today’s

Homebuyer? Lessons from Recent Fannie Mae Acquisitions, Fannie Mae: Perspectives. http://

fanniemae.com/portal/research-insights/perspectives/041315-cai-shahdad.html.

Consumer Financial Protection Bureau (CFPB). 2014. Manufactured-Housing Consumer Finance

in the United States. Washington, DC: Consumer Financial Protection Bureau. http://files.

consumerfinance.gov/f/201409_cfpb_report_manufactured-housing.pdf.

Critchfield, Tim, Tyler Davis, Lee Davison, Heather Gratton, George Hanc, and Katherine Samolyk.

2004. “The Future of Banking in America – Community Banks: Their Recent Past, Current

Performance, and Future Prospects” [Vol. 16, No. 3], FDIC Banking Review Series. https://www.

fdic.gov/bank/analytical/banking/2005jan/article1.pdf.

Huang, Li-Ning, Michael LaCour-Little, Mark Palim, and Ying Pan. 2017. “How Much do U.S.

Households Know About Qualifying for a Mortgage?” Working Paper, May 31 2017. Washington,

DC: Fannie Mae. http://fanniemae.com/resources/file/research/datanotes/pdf/consumer-

knowledge-working-paper.pdf.

Housing Assistance Council (HAC). 2012. Taking Stock: Rural People, Poverty, and Housing in the 21st Century. Washington, DC: Housing Assistance Council. http://www.ruralhome.org/storage/

documents/ts2010/ts_full_report.pdf.

MacDonald, Heather I. 2001. “Fannie Mae and Freddie Mac in Nonmetropolitan Housing Markets:

Does Space Matter?” Cityscape 5 (3): 219–264.

Mota, Nuno. 2016. Rural Mortgage Lending Over the Last Decade. Working paper 10.25.2016.

Washington, DC: Fannie Mae. http://fanniemae.com/resources/file/research/datanotes/pdf/

working-paper-102716.pdf.

U.S. Department of Agriculture (USDA). 2016. “Rural America at a Glance, 2016 Edition.”

Economic Information Bulletin 162. Washington, DC: U.S Department of Agriculture, Economic Research Division. https://www.ers.usda.gov/webdocs/publications/80894/eib-162.pdf.

Vandell, Kerry D. 1997. “Improving Secondary Markets in Rural America.” In Proceedings –

Rural and Agricultural Conferences: Financing Rural America, April: 85–120. Kansas City, MO: Federal Reserve Bank of Kansas City.

Cityscape 31

32 National Survey of Mortgage Originations

by Chad Redmer

Perceptions and Expectations of

Mortgage Borrowers: New Evidence

from the National Survey of

Mortgage Originations

Chad Redmer

United States Naval Academy

The views expressed in this paper are those of the author and should not be attributed to the United States Naval Academy or Department of Defense. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Abstract

Research on house price expectations has been limited by a lack of data which have meaningful

cross-sectional variation in home purchase and refinance experiences across respondents. The

National Survey of Mortgage Originations (NSMO) fills this gap and allows for the exploration of whether members of different demographic groups exhibit different levels of knowledge of house price changes in their local areas and whether they have different levels of capacity to anticipate future price changes as a result. This study finds that first-time homebuyers, a demographic group considered potentially vulnerable during the origination process, are consistently more aware of recent and impending house price trends than repeat purchasers. This study also shows that borrowers with lower incomes have less awareness about price trends than borrowers with higher incomes, and less-educated borrowers have less awareness about price trends than borrowers with higher levels of education.

Additionally, this article provides evidence that expectations of future house price changes in consumers’

local markets have informational value for estimating future house prices beyond the value provided by local economic data. This study provides a basis for understanding whether consumers who are less knowledgeable about house prices in their area may make poorer decisions regarding their mortgage or home purchase and may require more support or protection as a result.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 33

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

Redmer

Introduction

Economists understand that consumer expectations of house prices can play a role in the

real economy. Several studies have shown that expectations have predictive information in

the aggregate. What is less understood is whether differences in cross-sectional house price

expectations can help predict cross-sectional variations in outcomes and whether there is

variation in the relationship across different demographic groups. That is, do members of

different demographic groups exhibit more or less knowledge of house price changes in their

local areas, and do they have more or less capacity to forecast future price changes as a result?

This article addresses these two questions.

These questions are important not just for macroeconomic purposes, but for consumer well-being

and policy formulation as well. Consumers who are less knowledgeable about house prices in their

area may make poorer decisions regarding their mortgage or house purchase. Because housing

is a significant portion of overall wealth for most households, the accuracy of perceptions and

expectations regarding house price trends could have lasting effects on the lifecycle consumption

decisions of households.

This article addresses the questions posed earlier using previously unavailable survey data from the newly released NSMO, which is based on the National Mortgage Database (NMDB®).1 The survey

database is large enough to provide meaningful cross-sectional variation in home purchase and

refinance experiences across respondents. The survey offers a rich set of questions about house

prices, including what respondents think has happened to prices in their local area (backcasting)

and what respondents expect to happen (forecasting). Because time has passed since the responses

reflected in the publicly released NSMO data were gathered, it is possible to compare both

respondents’ backcasts and forecasts with actual house price changes in their areas.

Another advantage of NSMO is that it surveys only borrowers who have just obtained a mortgage.

These borrowers are likely the most motivated and informed consumers among the overall

population of homeowners and their answers should be the most accurate since they experienced

the mortgage process recently. This makes NSMO better for comparisons of the accuracy of

backcasts and forecasts across groups than surveys of the whole population. House price

expectation data from comprehensive national surveys suffer from the fact that many respondents

may have little need to be informed since they are not active participants in the housing market.

The emphasis on borrowers active in the mortgage market is also important because these are

the consumers for whom consumer protection would have been most relevant. The identification

of subgroups who turn out to be systematically less informed may be particularly important in

designing consumer-focused policies.

The remainder of this article is organized as follows. The next section provides background

information on the literature and data used for the analysis. The two sections after that provide

results for backcasting and forecasting, respectively. The section following those examines

differences across groups in how they form expectations. The final section concludes.

1 NSMO and NMDB are described in the guest editors’ introduction.

34 National Survey of Mortgage Originations

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

Background

Despite the importance of understanding borrowers’ perceptions and expectations concerning

local house prices, little economic literature compares them with the price changes that actually

occurred in the relevant local areas. Case et al. (2015) evaluated survey responses regarding

house price trend perceptions and expectations collected from recent homebuyers in four specific

housing markets in the same season annually over the 2003-through-2014 period. The responses

are compared to changes in the Case-Shiller Home Price Index over the same period for those four

markets. Niu and van Soest (2014) used national survey responses from the RAND American Life

Panel, conducted quarterly from 2009 through 2013, to compare state level responses concerning

future price expectations with the Federal Housing Finance Agency (FHFA) House Price Index

(HPI) state-level indices. The latter study does not focus on respondents most likely to be

interested in house prices, and neither study uses a sample that is representative.

This article extends the existing literature using responses to NSMO, which was specifically

designed to provide detailed information on borrower perceptions and expectations. In addition

to questions concerning mortgage shopping behavior and mortgage closing experience, NSMO

includes questions concerning borrowers’ perceptions of recent house price trends in their

neighborhood and their expectations of future house price trends. The survey also contains

detailed information on borrower demographics and several questions which solicit the borrower’s

appetite for financial risk, concerns about loan qualification, and knowledge about mortgages.

Using NSMO responses from borrowers who originated mortgages from 2013 through 2016,

this article compares respondents’ perceptions of what happened to house prices in their local

area with what actually occurred prior to the survey, and compares what they forecasted would

happen with what actually did occur after the survey. The challenge in this exercise is to use a

definition of area (neighborhood) comparable to that used by respondents and which also has

available accurate summary information on house price changes. For this article, the county

is used as the representation of “neighborhood,” and actual house price changes are derived

from newly available FHFA county-level house price indices. This is less than ideal, as it defines an area which is surely larger than that generally meant by respondents when they refer to a

neighborhood. Indices, however, computed at more granular geographic levels (often based on

very few observations) would include too much unexplained variation for a meaningful analysis

of differences. In addition, the FHFA county indices are available at the quarterly level, which

is generally not true for indices with more granular geography. This is important because of the

relatively short window (“couple of years”) used in the NSMO questionnaire for items regarding

past and future house price change perceptions and expectations.

Another advantage in using a county-level definition of local area is that it allows for use of

other aggregate measures of economic activity including population change, unemployment rates,

per capita income changes, mortgage delinquency, and mortgage volumes. The metrics measuring

mortgage delinquency and volume are new and are constructed from the NMDB. These aggregates

serve as estimates of the “local economic fundamentals” driving county-level house price changes.

This allows for comparisons of respondent perceptions and forecasts with actual changes at both

the gross level and when controlling for these fundamentals.

Cityscape 35

Redmer

The statistics for the specific county-level variables used in this analysis are presented in exhibit 1.

NSMO data used in this analysis were solicited in 15 different waves of a quarterly survey.

Thus, there is considerable variation in the timing of when respondents provided their answers

(generally 6 to 9 months after origination). The county-level HPI estimates were available quarterly and thus could be aligned to when the survey was answered. Data regarding the county-level

seriously delinquent rate and average mortgage loan amount were also available quarterly, both

in terms of levels and changes.2 Percent change in population, percent change in per capita

income, and the level and change in the unemployment rate, however, were only available

annually. Perforce, this implies alignment issues in their use, which is partially accounted for by using quarterly dummy variables for time of response in the analysis. Another consequence of

the availability of data is a slight reduction in the number of observations that could be used for analysis. Population and unemployment data were not available for 72 observations in the full

NSMO sample and thus those respondents were not used. An additional 2,928 observations were

dropped for the forecasting section of the analysis since they were interviewed in the most recent waves of the survey considered for this study, and therefore, the 2 years of future price changes

data were not available at the time of this analysis.

Exhibit 1

Summary Statistics for County HPI and Economic Data

Local Economic Fundamental Variable

N

25th

50th

75th

Previous 2-Year County HPI Growth

24,775

4.46

9.34

17.29

Future 2-Year County HPI Growth*

21,847

6.73

10.97

16.10

Previous 2-Year County Population Growth

24,775

0.46

1.66

3.08

Previous 2-Year County Income Growth

24,775

4.55

6.38

8.61

Previous 2-Year County Mortgage Balance Growth

24,775

0.48

2.39

4.87

2-Year Lagged County Unemployment Rate

24,775

5.17

6.43

7.87

Change in 2-Year Lagged County Unemployment Rate

24,775

– 1.17

– 0.76

– 0.41

2-Year Lagged County Serious Delinquency Rate

24,775

2.11

3.18

4.66

Change in 2-Year Lagged County Serious Delinquency Rate

24,775

– 1.32

– 0.70

– 0.36

HPI = House Price Index.

Note: *2-year forward HPI comparison data is not yet available for surveys completed in 2017Q1 or 2017Q2.

2 A mortgage is considered to be seriously delinquent if it has been delinquent for 90 days or more or has entered the bankruptcy or foreclosure process.

36 National Survey of Mortgage Originations

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

Borrower Backcasts of House Price Changes

Borrowers were asked, “In the last couple of years, how have house prices changed in the

neighborhood where this property is located?”—a question referred to as a “backcast” in this

study. The distribution of their responses is presented in exhibit 2. The distribution of actual

percent change in county HPI over the 2 years prior to the quarter in which the borrower

completed the survey is presented in exhibit 3. The exhibits demonstrate that both borrower

backcasts and county-level HPIs skewed heavily toward price increases, though in both

instances there is some evidence of price decreases.

The alignment between borrower backcasting of local house price trends and actual house

price trends is examined in two ways: first, with a simple correlation of the percent change

in respondents’ county HPI with their backcast of local house price trends for corresponding

prior 2-year periods, and second, with a multivariate analysis examining the conditional

correlation of actual changes with respondents’ backcasts, controlling for local economic

fundamentals measured over the same 2-year period. In both the simple and multivariate

backcast tests, backcast information is represented by two dummy variables for the responses

“Significant Increase” and “Significant Decrease,” with “Little/No Change” treated as the

base response.

The two analyses test different hypotheses. The simple analysis tests how aware borrowers are

of general changes in house prices. The multivariate analysis tests a more subtle question:

do the borrowers possess information on actual local house price changes over and above that

which could be explained by the other economic variables? Additionally, both analyses test

how this information varies across groups.

Exhibit 4 presents estimates from the simple and multivariate backcast tests. The highly

significant coefficients on the two dummy variables in the simple test have appropriate signs

but are unbalanced in their effects. The coefficient on “Significant Increase” is nearly seven

times larger than the coefficient on “Significant Decrease” in absolute value, illustrating that

borrowers classify relatively smaller declines in prices as significant.

In the multivariate regression, all control variables are significant, providing evidence that

the selected fundamentals are good indicators of actual house price growth. Importantly,

the coefficients on the response dummy variables are still significant, albeit with somewhat

smaller magnitude for “Significant Increase.” This indicates that borrowers have additional

knowledge, beyond that gained from local economic conditions, that shapes their backcast

of recent local house price trends. One possible explanation is that a borrower may be aware

of a more recent appraisal or sale price of one of their neighbor’s homes that provides them

with an information advantage over just the macroeconomic variables.

Cityscape 37

Redmer

Exhibit 2

Perception of Recent Trends in Neighborhood House Prices

50

40

30

cent

Per

20

10

0

1 Significant Decrease

2 Little/No Change

3 Significant Increase

Note: Distribution of responses to the question, “In the last couple of years, how have house prices changed in the neighborhood where this property is located?”.

Source: NSMO

Exhibit 3

Distribution of Actual Recent Trends in County House Prices

8

6

cent

4

Per

2

0

-40

-20

0

20

40

Percent Change in County HPI

HPI = House Price Index.

Note: Distribution of actual change in county house prices in the 2 years prior to the quarter of survey completion.

Source: FHFA HPI

38 National Survey of Mortgage Originations

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

Exhibit 4

Parameter Estimates for Simple and Multivariate Backcast Regressions

Backcast Alignment with Actual House Price Trends

Parameter

Simple

Multivariate

N

24,775

24,775

R2

0.0908

0.4838

9.34

– 1.27*

Intercept

(0.08)

(0.53)

5.86***

3.16 ***

“Significant Increase”

(0.12)

(0.10)

– 0.84***

– 0.74 ***

“Significant Decrease”

(0.21)

(0.16)

-

1.82 ***

Population Growth

(0.02)

-

0.33***

Income Growth

(0.01)

-

0.77***

Unemployment Level

(0.03)

-

– 3.56***

Change in Unemployment

(0.11)

Average Mortgage Balance

-

0.10***

(0.01)

Delinquency Rate

-

– 0.74***

(0.03)

Change in Delinquency Rate

-

– 3.38***

(0.06)

Survey Quarter

Fixed Effects

No

Yes

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

Note: Standard errors are given in parentheses.

As indicated earlier, the simple and multivariate analyses are repeated for different subsets of

respondents. An evaluation of the relative “accuracy” of the collective backcasts of each group

is based on the relative R2 statistics of the estimated equations, with a higher R2 indicating more accuracy. The following demographic and behavioral groups are examined:

Cityscape 39

Redmer

Age3:

Less than 30, 30 to 54, 55 or more

Purchaser status4:

First-time homebuyer, repeat purchaser

Refinance status:

Purchaser, refinancer

Education5:

Four-year college degree or higher, less than four-year college degree

Household income:

Less than $50,000, $50,000 to $149,999, $150,000 or more

Number of borrowers:

Single borrower, borrower with spouse/partner

Property use:

Primary residence, second or investment home

Credit score6:

Below 720, 720 to 799, 800 or more

Financial riskiness:

Above average, average, no risk

Qualification concern:

Very, somewhat, not at all

Results for both the simple and multivariate analyses for the subgroups are presented

in exhibit 5. Like the overall analysis, both the simple and multivariate subset analyses

produce similar results. With a few exceptions, those subgroups which were more accurate

with the simple analysis were also more accurate when control factors were added.

Refinancers were collectively more accurate than purchasers, perhaps because they lived in

the area during the measurement period. Within the purchaser group, first-time homebuyers

appeared to be more accurate, perhaps because homeownership is a bigger decision for

them and they put more energy into the shopping process as a result.

Other groups that were collectively more accurate than comparison groups are borrowers

aged 30 to 54 (relative to borrowers younger than 30 or aged 55 or older), college graduates

(relative to borrowers with less formal education), higher-income households (relative to

lower-income households), owner-occupants (relative to investors/second-home borrowers),

borrowers who identified that they were willing to take above average financial risk (relative

to those with less appetite for risk), and borrowers who indicated that they were either

somewhat or not concerned about mortgage qualification (relative to those who were

very concerned). This analysis also showed that neither credit score nor the number of

borrowers on a mortgage was consistent in ranking accuracy when comparing the simple

and multivariate analyses.

3 Age is defined as the older of the respondent and spouse age if the spouse is a borrower on the loan.

4 The purchaser demographic excludes refinancers, while all other demographics capture all respondents.

5 Household education level is defined as the highest level indicated between the respondent and spouse.

The four-year college degree group includes respondents who indicated they were a “college graduate” or had completed “postgraduate studies.”

6 Credit score uses VantageScore® 3.0, which ranges from 300 to 850, with higher scores implying less risk.

The score used is the higher of the respondent and spouse at origination if the spouse is a borrower on the loan.

40 National Survey of Mortgage Originations

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

ease

5.6

7.9

9.7

8.4

8.6

8.6

8.5

8.3

8.9

8.7

8.6

8.2

8.4

8.9

8.6

7.9

9.1

8.6

8.1

7.6

8.7

8.9

8.5

8.5

8.5

8.5

Significant Decr

8.1

9.5

9.7

8.9

9.8

9.5

9.2

9.4

9.2

10.7

9.4

8.4

9.3

9.5

9.2

10.5

9.6

9.4

9.1

9.6

9.3

9.1

9.7

9.3

9.3

9.3

Mean HPI Change (%)

Little/No Change

Within Response Category

ease

13.8

15.4

15.3

14.5

14.9

14.8

15.5

15.4

14.5

16.0

15.3

13.8

15.2

15.1

15.1

16.0

15.2

15.6

14.7

15.3

15.4

14.7

15.0

15.5

15.1

15.2

Significant Incr

.4659

.4945

.4801

.4978

.4569

.4689

.5031

.4900

.4765

.5438

.4859

.4667

.4835

.4877

.4860

.4719

.5016

.4982

.4706

.5112

.4815

.4721

.4749

.4888

.4883

.4838

Multivariate2R

Simple

.0981

.1027

.0696

.0830

.0711

.0756

.1029

.1028

.0616

.0866

.0924

.0664

.0938

.0816

.0925

.0825

.0806

.1006

.0833

.1029

.0939

.0710

.0722

.0961

.0938

.0908

2 R

N

1,870

13,499

9,406

3,290

8,231

11,521

13,254

17,562

7,213

3,941

15,428

5,406

18,910

5,865

22,751

2,024

5,923

11,057

7,795

5,752

12,580

6,443

3,485

7,101

14,189

24,775

rend Backcast Within Demographic Subsets

ee

ee

verage

<30

55+

chase

2

1

Very

All

Subset

30–54

Repeat

ear Degr

ear Degr

<$50K

800+

<720

verage

First–Time

Pur

Refinance

$150K+

720–799

A

No Risk

Somewhat

Not at All

4–Y

<4–Y

$50K–$149,999

Above A

Owner/Occupants

Investment/Rental

n

e

Age

owers

Category

chase Status

Education

edit Scor

Number of Borr

operty Use

Demographic

Pr

Cr

Risk Appetite

Benchmark

Pur

Refinance Status

Household Income

Qualifying Concer

Exhibit 5

Relative Accuracy of House Price T

HPI = House Price Index.

Cityscape 41

Redmer

Borrower Forecasts of House Prices

Borrowers were asked, “What do you think will happen to the prices of homes in this

neighborhood over the next couple of years?”—a question referred to as a “forecast” in

this study. The distribution of their responses is presented in exhibit 6. The distribution of

actual percent change in county HPI over the 2 years after the quarter in which the borrower

completed the survey is presented in exhibit 7. Again, the figures demonstrate that both

borrower forecasts and county-level HPIs skewed heavily toward price increases, though in

both instances, there is some evidence of price decreases.

The alignment between borrower forecasting of local house price trends and actual house

price trends is examined in the same two ways as studied for backcasting (simple and

multivariate). A five-point scale was used, however, in soliciting respondents’ forecasts

versus a three-point scale for backcasts. Thus, forecast information is represented by

four dummy variables for “Increase a lot,” “Remain about the same,” “Decrease a little,

and “Decrease a lot,” with “Increase a little” treated as the base group since it was the

modal response.

Exhibit 8 presents estimates from the simple and multivariate forecast tests. The highly

significant coefficients on the four dummy variables in the simple test have appropriate signs

but are unbalanced in their effects. The effects of coefficients on “Increase a lot” and

“Remain about the same” have similar magnitude in absolute value. The coefficients on

“Decrease a little” and “Decrease a lot,” while greater in magnitude than on “Remain about

the same,” do not follow a linear pattern. This may just be a residual of the fact that house

prices generally rose over the sample period. It may also mean that borrowers are more

sensitive to the amount of a decline in prices than they are to a comparable increase.

In the multivariate regression, all local economic fundamental control variables except

for the index of average loan balance and lagged 2-year delinquency rate are significant,

providing evidence that the other selected fundamentals are good indicators of actual

house price growth. The failure of the mortgage market variables to show predictive power

suggests that these variables lag house price changes rather than lead them. The coefficients

on the response dummy variables “Increase a lot” and “Remain about the same” are still

significant, however the response dummy variables “Decrease a little” and “Decrease a lot”

are no longer significant. This could be an indication that borrowers possess information

on future house prices above what could be estimated when prices are trending upward.

42 National Survey of Mortgage Originations

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

Exhibit 6

Expectation of Future Trends in Neighborhood House Prices

60

40

cent

Per

20

0

1 Decrease

2 Decrease

3 Remain

4 Increase

5 Increase

a lot

a little

about the same

a little

a lot

Note: Distribution of responses to the question, “What do you think will happen to the prices of homes in this neighborhood over the next couple of years?”.

Source: NSMO

Exhibit 7

Distribution of Actual Future Trends in County House Prices

12

10

8

cent

6

Per

4

2

0

-40

-20

0

20

40

Percent Change in County HPI

HPI = House Price Index.

Note: Distribution of actual change in county house prices in the 2 years after the quarter of survey completion.

Source: FHFA HPI

Cityscape 43

Redmer

Exhibit 8

Parameter Estimates for Simple and Multivariate Forecast Regressions

Forecast Alignment with Actual House Price Trends

Parameter

Simple

Multivariate

N

21,847

21,847

R2

0.0407

0.5141

11.87***

6.57***

Intercept

(0.06)

(0.17)

1.96***

0.29***

“Increase a lot”

(0.12)

(0.09)

– 2.27***

– 0.25**

“Remain about the same”

(0.12)

(0.09)

– 2.49***

– 0.29

“Decrease a little”

(0.29)

(0.21)

– 3.10***

– 0.12

“Decrease a lot”

(0.55)

(0.39)

-

0.21***

Past HPI Growth

(0.00)

-

1.16***

Population Growth

(0.02)

Income Growth

-

0.36***

(0.01)

-

– 0.07**

Unemployment Level

(0.02)

-

– 0.81***

Change in Unemployment

(0.08)

Average Mortgage Balance

-

– 0.01

(0.01)

Delinquency Rate

-

0.14***

(0.02)

Change in Delinquency Rate

-

– 0.02

(0.05)

Survey Quarter

Fixed Effects

No

Yes

HPI = House Price Index.

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

Note: Standard errors are given in parentheses.

44 National Survey of Mortgage Originations

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

Conversely, this could also be an indication that borrowers may not possess information of future

prices over what could be estimated when there is a downturn in house price trends.

The simple and multivariate analyses of forecasting is repeated for the same subsets of respondents examined in the backcasting section. Results for both the simple and multivariate analyses for the subgroups are presented in exhibit 9. There is less consistency between the simple and multivariate analyses than in the backcasting section. In multiple cases, there were reversals among pairs or

a complete reverse ordering among triples.

Groups that were collectively more accurate than comparison groups at forecasting are young

borrowers (relative to borrowers 30 and older), first-time homebuyers (relative to repeat

purchasers), single-borrower households (relative to two-borrower households), owner-

occupants (relative to investors/second-home borrowers), and borrowers who indicated that

they were very concerned about mortgage qualification (relative to those that were somewhat

or not at all concerned).

Several groups reversed ordering between the simple and multivariate analyses. Based on the

simple analyses, the forecasts of the following groups were better aligned with actual price trends: refinancers (relative to purchasers), less formally educated (relative to college graduates), lower-income households (relative to higher-income households), and borrowers who take no financial

risks (relative to borrowers who accept financial risk). In the multivariate analyses, these categories had a complete reversal in ordering. In this case, purchasers, higher-educated borrowers, higher-income borrowers, and borrowers willing to take above average financial risks may provide some

information on future house prices above what could be estimated from fundamentals. Again,

the credit score comparison groups showed no consistency in patterns between the simple and

multivariate analyses.

Finally, there was very little consistency in the demographics that were relatively better at both backcasting and forecasting. The only group that was consistent in simple and multivariate test

results and relatively better at both backcasting and forecasting was the first-time homebuyer

group (relative to repeat purchasers).

How Expectations of Future House Prices are Formed

In the previous section, borrower responses regarding their expectations for future prices changes in their neighborhood were compared with actual future changes to determine if the respondent

data have forecasting value. This section examines how these expectations are formed. This

question is important in improving the understanding of how consumers process house price

information, particularly when the answers are found separately for different subsets.

Here, the responses to the question “What do you think will happen to the prices of homes in this

neighborhood over the next couple of years?” are treated as the dependent variable. There is no

reason to believe that the responses follow a cardinal ordering so they are treated as ordinal, and the process is modeled as an ordered logistic regression.7 Three sets of explanatory variables are 7 See Cameron and Trivedi (2005) for a detailed review of ordered logistic regression methodology.

Cityscape 45

Redmer

ease

5.2

7.0

6.0

8.1

8.2

8.2

6.0

6.6

6.5

8.0

4.4

7.8

5.1

6.5

7.2

9.6

5.7

6.7

7.1

6.5

6.5

5.9

7.1

6.8

6.7

“a lot”

10.1

Decr

ease

6.1

7.6

8.4

6.2

8.3

7.3

7.7

7.2

8.2

7.5

7.9

7.1

7.8

7.1

7.5

9.0

6.3

7.7

7.9

7.3

7.4

7.9

8.4

7.7

7.1

7.9

Decr “a little”

Mean HPI Change (%) Within Response Category

6.9

8.0

7.5

7.7

8.0

7.9

7.6

8.3

6.7

7.9

6.5

7.8

7.5

7.6

9.3

8.0

8.3

7.0

8.3

7.8

7.4

7.7

7.9

7.6

7.9

Remain Same

10.3

ease

9.9

Incr “a little”

11.9

11.9

10.8

11.6

11.4

11.9

11.8

11.3

13.0

11.7

10.5

11.6

11.9

11.6

12.3

11.7

11.9

11.3

11.8

11.7

11.4

11.8

11.8

11.6

11.6

ease

13.0

14.9

15.1

13.5

14.3

14.0

15.4

14.7

14.8

16.2

14.8

13.1

14.7

14.6

14.7

15.0

15.1

15.1

14.2

14.9

14.7

14.4

15.0

14.6

14.7

14.6

Incr “a lot”

.5369

.5280

.4860

.5532

.5036

.5192

.5116

.5220

.4999

.5252

.5183

.4995

.5084

.5363

.5215

.4729

.5197

.5170

.5133

.5290

.5217

.4887

.5389

.5061

.5151

.5141

Multivariate2R

Simple2

.0444

.0405

.0407

.0481

.0296

.0366

.0443

.0357

.0511

.0295

.0398

.0417

.0395

.0447

.0444

.0150

.0395

.0354

.0484

.0302

.0410

.0464

.0588

.0348

.0396

.0407

R

N

1,685

11,898

8,264

2,943

7,321

10,264

11,583

15,457

6,390

3,419

13,586

4,842

16,649

5,198

20,013

1,834

5,210

9,737

6,900

5,062

11,104

5,681

3,116

6,363

12,368

21,847

rend Backcast Within Demographic Subsets

ee

ee

verage

<30

55+

chase

2

1

Very

All

Subset

30–54

Repeat

ear Degr

ear Degr

<$50K

800+

<720

verage

First–Time

Pur

Refinance

$150K+

720–799

A

No Risk

Somewhat

Not at All

4–Y

<4-Y

$50K–$149,999

Above A

Owner/Occupants

Investment/Rental

n

e

Age

owers

Category

chase Status

Education

edit Scor

Number of Borr

operty Use

Demographic

Pr

Cr

Risk Appetite

Benchmark

Pur

Refinance Status

Household Income

Qualifying Concer

Exhibit 9

Relative Accuracy of House Price T

HPI = House Price Index.

46 National Survey of Mortgage Originations

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

included in the analysis: (1) dummy variables representing the respondent’s backcasts; (2) the set of county-level economic fundamentals variables; and (3) variables representing the demographic

and other subgroups while controlling for backcasting and local economic fundamentals. Exhibit

10 presents results for the three different models. Column 1 uses only those variables in set (1)

above; column 2 uses only the variables in (2); column 3 uses all three sets of variables. In each case, the columns present the log odds coefficients of the ordered logistic. Here, positive values should be interpreted as increasing the odds of a more optimistic response category and negative

values as decreasing the same.

Exhibit 10

Three Models of House Price Expectation Formation (1 of 2)

Models of House Price Expectation Formation

Model 1

Model 2

Model 3

Parameter

Past Perceptions

Local Fundamentals Demographic Effects

(N=24,775)

(N=24,775)

(N=24,775)

All ordered logistic regression coefficients are reported in log odds.

1.35***

-

1.14***

“Significant Increase”

(0.03)

(0.04)

– 0.69***

-

– 0.62***

“Significant Decrease”

(0.05)

(0.05)

-

0.03***

0.02***

Past HPI Growth

(0.00)

(0.00)

-

0.11***

0.09***

Population Growth

(0.01)

(0.01)

-

0.03***

0.02***

Income Growth

(0.00)

(0.00)

-

– 0.02

0.00

Unemployment Level

(0.01)

(0.01)

-

– 0.01

0.01

Change in Unemployment

(0.04)

(0.04)

-

0.02***

0.00

Average Mortgage Balance

(0.00)

(0.00)

-

0.01

0.02*

Delinquency Rate

(0.01)

(0.01)

-

– 0.01

– 0.02

Change in Delinquency Rate

(0.02)

(0.02)

Cityscape 47

Redmer

Exhibit 10

Three Models of House Price Expectation Formation (2 of 2)

Models of House Price Expectation Formation

Model 1

Model 2

Model 3

Parameter

Past Perceptions

Local Fundamentals Demographic Effects

(N=24,775)

(N=24,775)

(N=24,775)

-

-

0.04

Age <30

(0.06)

-

-

0.19***

Age ≥55

(0.03)

-

-

0.01

Spouse/Partner

(0.04)

-

-

– 0.26***

Income ≤$50K

(0.04)

-

-

0.01

Income ≥$150K

(0.04)

-

-

0.06

College Graduate

(0.04)

-

-

0.12***

Credit Score <720

(0.04)

-

-

– 0.05

Credit Score ≥800

(0.04)

First-Time

-

-

0.01

Homebuyer

(0.05)

-

-

– 0.25***

Refinance

(0.03)

-

-

0.03

Owner–Occupants

(0.05)

-

-

– 0.02

Very Concerned

(0.05)

-

-

– 0.07*

No Concerns

(0.03)

Above Average

-

-

0.23***

Financial Risk

(0.04)

-

-

– 0.18***

No Financial Risk

(0.04)

Survey Quarter

No

Yes

Yes

Fixed Effects

HPI = House Price Index.

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

Notes: Model 1 captures recent perceptions of borrowers through responses to the question: “In the last couple of years, how have house prices changed in the neighborhood where this property is located?” Model 2 includes local economic fundamentals as independent variables. Model 3 includes the perceptions from Model 1 and the fundamentals from Model 2 and adds demographic group dummy variables to capture differences relative to the to the base group which has the following characteristics: age 30 to 54, one-borrower household, income from $50,000 to $149,999, less than 4 years of college education, credit score from 720 to 799, repeat homebuyer, investor/second home owner, somewhat concerned about mortgage qualification, and takes average financial risks.

All ordered logistic regression coefficients are reported in log odds. Exponentiating the log odds coefficients returns the proportional odds ratio. Standard errors are given in parentheses.

48 National Survey of Mortgage Originations

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

It is not surprising that when regressing backcasts of recent house price changes on forecasts of

future changes (column 1), the coefficients on the dummy variables are highly significant and

properly signed. Holding all else constant, if the respondent selected “Significant Increase” as

a backcast on recent prices, the odds of that respondent selecting a more optimistic categorical

response as their forecast are nearly four times higher than selecting a less optimistic response.

Conversely, if the respondent selected “Significant Decrease” as a backcast, the odds of selecting a more optimistic categorical response as their forecast are less than half as high as the odds of selecting a lower categorical response.

The results presented in column 2 indicate that more positive economic fundamentals including

actual recent house price changes, population growth, income growth, and average mortgage

balances slightly increase the odds of a respondent selecting a more optimistic categorical response as a forecast. Somewhat unexpectedly, unemployment indicators did not seem to be related to

consumer forecasts.

The results presented in column 3 put these pieces together and measure the marginal, rather

than gross, impact of both backcasts and economic fundamentals variables as well as the impact

of variables representing demographic and other group factors. Somewhat surprisingly, the

coefficients on the backcasting and economic fundamental variables are relatively similar to

their gross effects shown in columns 1 and 2. This suggests that they have separate impacts on

respondent forecasts. Coefficients on the group variables show that older borrowers, borrowers

with low credit scores, and borrowers that take above average financial risk are significantly more likely to select a more optimistic response (conditioned on their backcast and fundamentals).

Other groups were less optimistic. Borrowers with low income, refinancers, and borrowers that

take no financial risk are more likely to select a more pessimistic response.

Conclusion

Assessing awareness of house price trends is an important part of consumer protection in the

mortgage market. This article extends the literature by identifying variation across recent mortgage borrowers—those with the greatest incentive to be informed—in their awareness of changes in the

house prices in their local markets. On a positive note, this study finds that first-time homebuyers, a demographic considered especially vulnerable during the origination process, are consistently

more aware of recent and impending house price trends than repeat borrowers. On the other hand,

this study finds that lower-income and less-educated borrowers have relatively less awareness about price trends.

This article also provides evidence that expectations of future house price changes in consumers’

local markets have informational value for forecasting beyond the value provided by local

economic data. Why this is true, however, is not clear and needs to be further researched. This

article finds that different demographic groups display varying degrees of optimism in forming

their forecasts even when controlling for their backcasts and economic fundamentals. Borrowers

willing to take above average risk stand out as the most optimistic forecasters, while lower income borrowers tend to be the least optimistic.

Cityscape 49

Redmer

It is important to note that this study defined a relatively aggregated area—the county—as the

“neighborhood” of the borrower. It is likely that there is neighborhood variation within counties that is not reflected in this definition, however, data at more local levels were unavailable for detailed exploration. Further, the results presented are in aggregate and may not reflect a particular locale.

Finally, this article should be treated as a “first stab” at the problem. NSMO contains a wealth of information on borrower behavior, expectations, and attitudes. While the article identifies the fact that consumers vary in the accuracy of their backcasts and forecasts, it does not address how this variation affects their behavior and, importantly, their outcomes in the mortgage market. These

issues remain topics for future research.

Author

Chad Redmer is a Ph.D. student in the Economics Department at George Washington University

and a Commander in the U.S. Navy assigned to teach in the Economics Department at the U.S. Naval

Academy. Please contact the author at caredmer@gwmail.gwu.edu with any questions or comments.

Acknowledgments

The author thanks Robert B. Avery, Timothy Critchfield, Ian Keith, Will Larson, Saty Patrabansh, Jay Schultz, and Rebecca Sullivan for providing comments, Anthony Yezer for providing continuous guidance and encouragement throughout my GWU experience, and Darryl Getter for a lifetime of advising.

References

Cameron, A. Colin, and Pravin K. Trivedi. 2005. Microeconometrics: Methods and Applications.

New York: Cambridge University Press.

Case, Karl E., Robert J. Shiller, and Anne Thompson. 2015. What Have They Been Thinking?

Home Buyer Behavior in Hot and Cold Markets. NBER Working Paper No. 18400. Cambridge, MA:

National Bureau of Economic Research.

Niu, Geng, and Arthur van Soest. 2014. House Price Expectations. IZA Discussion Paper No. 8536.

Bonn, NRW, Germany: Institute for the Study of Labor (IZA).

50 National Survey of Mortgage Originations

by Robert B. Argento, Lariece M. Brown, Sergei Koulayev,

Grace Li, Marina Myhre, Forrest Pafenberg, and Saty Patrabansh

First-Time Homebuyer Counseling

and the Mortgage Selection

Experience in the United States:

Evidence from the National Survey

of Mortgage Originations

Robert B. Argento

Lariece M. Brown

Freddie Mac

Sergei Koulayev

Consumer Financial Protection Bureau

Grace Li

Fannie Mae

Marina Myhre

U.S. Department of Housing and Urban Development

Forrest Pafenberg

Saty Patrabansh

Federal Housing Finance Agency

Disclaimer

The views expressed in this article are those of the authors and are not necessarily those of Freddie Mac, the Consumer Financial Protection Bureau, Fannie Mae, the U.S. Department of Housing and Urban Development, or the Federal Housing Finance Agency.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 51

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

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Abstract

The existing literature on homebuyer education and counseling (HEC) often focuses on the evaluation of specific programs, generally using mortgage loan performance as the metric of success. This article contributes to the literature in two ways. First, it provides evidence on the benefits of HEC to mortgage borrowers in aspects other than mortgage performance. Second, the article evaluates HEC in general, not just one specific program. It does so by drawing from a nationally representative sample of all first-time homebuyers in the United States who took out a mortgage between 2013 and 2016.

The study data comes from the National Survey of Mortgage Originations (NSMO), a new survey

co-sponsored by the Federal Housing Finance Agency (FHFA) and the Consumer Financial Protection Bureau (CFPB). We find that 17 percent of a nationally representative sample of first-time homebuyers reported receiving some form of HEC. In an analysis of early loan performance, we find that while borrowers reporting HEC had higher delinquency rates, these differences decrease and are not

statistically significant when controlling for observable differences between those reporting HEC

and the group without HEC. Using propensity score matching, we find that first-time homebuyers who reported receiving HEC also reported better mortgage knowledge, more confidence in their ability to explain the mortgage process, and higher level of satisfaction with the mortgage they received.

Introduction

Homebuyer education and counseling (HEC) is often viewed as a strategy for achieving

sustainable homeownership, particularly among low- to moderate-income households, and,

as such, an important aspect of providing access to sustainable mortgage credit. Advocates

of HEC promote these programs to better prepare first-time homebuyers for successful

homeownership by helping them make good home purchase and mortgage decisions and by

improving their financial management skills (DeMarco et al., 2016). Lenders may require HEC

for potential homebuyers with more marginal credit characteristics, such as higher loan-to-value

(LTV) ratios, to mitigate risk. Therefore, HEC has become a standard part of many government,

nonprofit, and industry programs geared toward low- to moderate-income first-time homebuyers

on the premise that both homebuyers and society will benefit.

However, there has not been definitive evidence or a consensus on the benefits and cost-

effectiveness of HEC. The existing literature on housing education and counseling often focuses

on the evaluation of specific programs, generally using mortgage loan performance as the metric

of success. The literature shows mixed results on HEC’s effectiveness. In addition, the question

of whether borrowers benefit in other ways from HEC, even when mortgage performance in the

short term may not be improved, is largely unaddressed. This article contributes to the literature in two ways. First, it provides evidence about potential benefits of HEC to nonperformance

aspects, such as mortgage knowledge and satisfaction with the mortgage terms and the mortgage

process. Second, it uses a new nationally representative sample of first-time homebuyers rather

than focusing on a specific program.

In this article, we examine the incidence and effectiveness of HEC among the general population

of first-time homebuyers who took out a mortgage for home purchases between 2013 and

2016. We use responses from the NSMO, a new survey of a nationally representative sample

of new mortgages in the United States. NSMO is a component of the National Mortgage

Database® (NMDB).1

1 NSMO and NMDB are described in the guest editors’ introduction.

52 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

We find that 17 percent of first-time homebuyers reported receiving some form of HEC. In an

analysis of early loan performance, we find that while borrowers reporting HEC had higher

delinquency rates, these differences decrease and are not statistically significant when controlling for observable differences between the groups. Using propensity score matching to account for

selection on observables, we find that first-time homebuyers who reported receiving HEC also

reported better mortgage knowledge, more confidence in their ability to explain the mortgage

process, and higher level of satisfaction with their mortgage.

Literature

The literature studying the effectiveness of prepurchase HEC is large and diverse; see Myhre and

Watson (2017), Mayer and Temkin (2016), and Collins and O’Rourke (2011, 2010) for reviews.

Nevertheless, the existing research is limited in several respects. First, nearly every study that we are aware of has focused on measuring counseling effectiveness among participants of a

particular lending program.2 An important exception is “The First-Time Homebuyer Education

and Counseling Demonstration” study currently being conducted by the U.S. Department of

Housing and Urban Development (HUD). This is a large-scale randomized experiment in which

participants are recruited from a pool of mortgage applicants from three large national lenders

(DeMarco et al., 2016).

Different lending programs have different counseling programs. There is a large variety of

prepurchase counseling programs, which differ by (a) mode of delivery (individual counseling,

classroom instruction, telephone instruction, home study); (b) program duration and content;

(c) who delivers the instruction (nonprofit, government, lender); and (d) whether it is voluntary

or mandatory. Typically, a study focuses on a specific counseling program (Agarwal et al., 2014a,

2014b, 2010; Brown, 2016) or by study design (Smith, Hochberg, and Greene, 2014; Carswell,

2009). Few studies span across counseling programs; see the article by Quercia and Spader

(2008) for an analysis of the relative effectiveness of various types of counseling; also, see Avila, Nguyen, and Zorn (2013), whose study utilized a large sample of loans not restricted to

a specific counseling program.

The selection specifics in the lending program and the specifics of the counseling program

can make it difficult to generalize results of a given study. Therefore, we believe there is a need for evidence about the effectiveness of HEC that spans across lending programs and modes

of instruction.

The existing studies of HEC have exclusively focused on the goal of home purchase or on the debt

repayment behavior (mortgage delinquency and default, credit score, repayment of nonmortgage

debt). Little evidence exists on the impact of HEC on borrower’s mortgage knowledge and

behaviors related to mortgage choice, such as mortgage shopping. The HUD experiment is the

first attempt in this direction, and early results point to improvements in financial literacy and a “greater appreciation for communication with lenders” (DeMarco et al., 2016).

However, beyond the HEC context, there is a large body of literature studying the effectiveness

of various financial education programs in improving financial literacy and related behaviors.

For instance, Collins (2013) examined the impact of a mandatory financial education program

2 See Avila, Nguyen, and Zorn (2013) for a study of Freddie Mac’s Affordable Gold and Home Possible programs and Mayer and Temkin (2016) for NeighborWorks America’s programs.

Cityscape 53

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

on very low-income households, finding improvements in self-reported behaviors. Broadly, low

levels of financial literacy have been linked to suboptimal financial behaviors (see review by

Lusardi and Mitchell, 2014). In the mortgage context, Alexandrov and Koulayev (2017) suggested

that a lack of mortgage shopping may have prevented borrowers from realizing significant price

savings. Moulton, Collins, Loibl, and Samak (2013) showed that many borrowers underestimate

their total or monthly nonmortgage debt and are overconfident in their ability to pay down their

debt, relative to the actual repayment behavior; and Gerardi, Goette, and Meier (2010) linked low

financial literacy to delinquencies on subprime mortgage loans. Thus, there is a need to study the effectiveness of HEC as a specific type of financial education in addition to its role in default

risk management.

This article addresses both aspects of HEC’s effectiveness. Our results apply to the general

population of first-time homebuyers who took out a mortgage between 2013 and 2016 and are

not specific to a particular lending or counseling program. We explore a variety of outcomes

beyond debt repayment, such as self-reported mortgage knowledge, shopping, mortgage selection,

satisfaction with the mortgage process, and the use of government-mandated mortgage disclosures.

Finally, we point out that results measuring the effect of HEC on loan performance vary greatly

across studies. Part of the difference in results across studies may be related to the time period when the loans were originated. In a natural field experiment of a Tennessee prepurchase

homebuyer education program in 2002 funded by a HUD housing counseling grant, Brown (2016)

found that borrowers who received HEC had a 42-percent reduced chance of foreclosure compared

with the control group but no statistically significant difference in default (defined as first incidence of becoming 90 days delinquent). On the other hand, in a randomized field experiment sponsored

by the Federal Reserve Bank of Philadelphia, Smith and colleagues (2014) found that one-on-one

prepurchase counseling conducted by a HUD-approved housing counseling agency in 2007

had positive long-term effects on credit score and debt levels of participants compared with a

control group who only received a 2-hour prepurchase workshop—but no impact on timeliness

of mortgage payments, as most borrowers stayed current on their mortgage. Avila, Nguyen, and

Zorn (2013) found that the effect of HEC on loan performance was largest among loans originated

between 2006 and 2008. Similarly, Mayer and Temkin (2016) found a 30-percent reduction in

90-plus days’ delinquency for 2007-vintage loans originated by NeighborWorks. Li and colleagues

(2016) analyzed the same program but for post-crisis originations in the period from 2010

to 2012 and found a 14-percent reduction.

Analysis Sample and Outcomes

For this article, we use data from the NSMO survey conducted for a sample of borrowers with

mortgages originated in 2013 through 2016. There were 24,847 respondents to this survey for

these origination years. We focus specifically on first-time homebuyers to identify those with less experience in the mortgage process relative to repeat borrowers. Given the detailed administrative and credit data available in NMDB, we can identify homebuyers as those borrowers who have not

had an active mortgage. We also restrict our analysis to homebuyers younger than 55 years

to increase the likelihood that the analysis is focused on first-time buyers instead of those who paid off an earlier mortgage.4

4 For NSMO, we match the survey respondent and the spouse (when one exists) to the borrowers in the credit file.

At least one of them must be the borrower on the mortgage. If both are borrowers, we require that both have no prior mortgage in the credit file for the loan type to be designated as a first-time homebuyer loan. If only one is the borrower, we require that that person has no prior mortgage in the credit file for the loan type to be designated as first-time homebuyer loan.

54 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

NSMO identifies the recipients of HEC by asking, “Did you take a course about homebuying

or talk to a housing counselor?” Followup questions ask what delivery mode was used for the

homebuying course or housing counseling (in-person, one-on-one; in-person, group; over the

phone; or online), how long the course or counseling lasted, and whether it was helpful. Exhibit

1 summarizes responses to these questions. Among the 3,305 first-time homebuyers that we have

identified, 562 reported receiving some form of HEC, which amounts to a rate of 17 percent.5

This is itself a novel finding; there is currently no reliable estimate of the percentage of recent mortgage borrowers who receive some form of HEC. Among those first-time homebuyers who

report receiving any kind of HEC, 43 percent report receiving it in a group setting, while

18 percent report a one-on-one counseling interaction, as shown in exhibit 1.6

Exhibit 1

Types of Counseling

Weighted Frequency Survey Counts

(%)

Did you take a course about homebuying or talk to a housing counselor?

No

83.18

2,743

Yes

16.82

562

How was the homebuying course or counseling provided?

In Person, One-on-One

18.49

102

In Person, in a Group

43.02

244

Over the Phone

12.39

68

Online

49.89

281

How many hours was the homebuying course or counseling?

Less Than 3 Hours

43.07

247

3–6 Hours

28.91

166

7–12 Hours

22.75

123

More Than 12 Hours

5.26

26

Overall, how helpful was the homebuying course or counseling?

Very

55.20

302

Somewhat

37.66

218

Not at All

7.15

42

Notes: Responses do not sum up to 100 percent because categories are not mutually exclusive. Frequencies are adjusted for population weights.

Survey counts are unweighted actual responses.

Source: NSMO, 2013–2016

The scope of this analysis is a comparison of the 562 first-time homebuyers reporting HEC with

the remaining 2,743 first-time homebuyers who did not report receiving HEC. Substantively,

the NSMO does not distinguish between homebuyer education (group classroom instruction

about the homebuying process) and homebuyer counseling (individual, one-on-one sessions

with a housing counselor that are tailored to a client’s financial situation and stage in the

homebuying process). For instance, 18 percent of respondents indicated a one-on-one session,

which probably corresponds to counseling rather than homebuyer education. It is unclear whether

the remaining 82 percent received education, counseling, or both. For instance, those who report

group instruction may have also received individual consultation. NSMO does not identify whether

5 Our definition of a first-time mortgage borrower is the following: (a) no mortgage in the previous credit history; (b) both the borrower and the coborrower, if present, are 55 years of age or younger. This approach results in approximately 36 percent of first-time mortgage borrowers among all borrowers for home purchase in 2013–2016.

6 Sample weights are used for frequencies, summary means, and the linear probability models. For each survey response, the sample weight is adjusted for non-response by multiplying the sampling weight and the non-response adjustment.

Cityscape 55

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

the provider of homebuyer education or counseling meets HUD or National Industry Standards

(NIS). In fact, the reported counseling could involve a for-profit organization or an online tool that does not meet HUD or NIS standards. For example, the provider could be the lender or mortgage

insurance company. Therefore, we chose not to compare groups who self-reported different types

of HEC in NSMO to one another.

The NSMO survey focuses on borrower experiences when obtaining a mortgage. We identify

a broad set of questions related to mortgage knowledge and mortgage-related behaviors. For

each question, we compare the responses of first-time homebuyers who reported receiving HEC

with those who did not report receiving HEC while controlling for the set of relevant borrower

covariates. We believe the observed differences in responses are informative of how HEC may

have affected the underlying mortgage knowledge and mortgage-related behaviors. For this

reason, we label these responses as “outcomes.”

Each outcome is an indicator variable equal to one if the respondent provides a certain answer

to the relevant question, and zero otherwise. The question, “How well could you explain to

someone the process of taking out a mortgage?” generates an indicator variable equal to one if the respondent replied “very well” and zero otherwise. The mean of the outcome variable represents

the share of respondents who provided a particular answer; for the question above, the weighted

sample mean is 41.32 percent, indicating that 41.32 percent of respondents said “very well,”

as shown in exhibit 2. Among HEC recipients, the share of respondents who said “very well,”

48.42 percent is much higher than the 39.89 percent of non-HEC respondents who said they

could explain the process of taking out a mortgage “very well”. For the complete list of questions and corresponding outcomes, see exhibit 2.7 Next, we provide a broad overview of the

categories of survey questions we analyze and how first-time homebuyers responded to them.

Exhibit 2

Summary Statistics of Responses for Select Questions (1 of 3)

Weighted Means (%)

Sample

No HEC

HEC

When you began the process of getting your mortgage, how familiar were you (and any cosigners) with each of the following? (=1 if very familiar)

The mortgage interest rates available at that time

33.58

32.81

37.39

The different types of mortgages available

23.66

22.84

27.75

The process of taking out a mortgage

15.59

14.68

20.10

The downpayment needed to qualify for a mortgage

35.99

36.26

34.65

The income needed to qualify for a mortgage

32.20

31.45

35.91

Your credit history or credit score

64.43

63.60

68.57

The money needed at closing

25.20

25.03

26.06

How well could you explain to someone the… (=1 if very well)

Process of taking out a mortgage

41.32

39.89

48.42

Difference between a fixed- and an adjustable-rate mortgage

55.59

55.01

58.49

7 The current survey instrument is available at https://www.fhfa.gov/nsmo.

56 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

Exhibit 2

Summary Statistics of Responses for Select Questions (2 of 3)

Weighted Means (%)

Sample

No HEC

HEC

How well could you explain to someone the… (=1 if very well)

Difference between a prime and a subprime loan

12.44

12.50

12.13

Difference between a mortgage’s interest rate and its APR

19.17

19.05

19.76

Amortization of a loan

23.98

24.41

21.86

Consequences of not making required mortgage payments

55.73

54.30

62.81

How many different lenders/brokers did you seriously consider before choosing where to apply

for this mortgage? (=1 if more than one lender/broker)

Seriously considered more than one lender/broker

54.64

54.26

56.52

How many different lenders/brokers did you end up applying to? (=1 if more than one

lender/broker)

Applied to more than one lender/broker

31.71

30.97

35.37

Did you seek input about your closing documents from any of the following people?

(=1 if yes, =0 otherwise)

Lender/mortgage broker

72.92

72.91

73.00

Settlement/closing agent

22.70

22.51

23.65

Real estate agent

60.78

61.13

59.04

Personal attorney

15.83

15.83

15.87

Title insurance agent

17.41

16.69

21.00

Trusted friend or relative who is not a cosigner on the mortgage

44.44

45.01

41.60

Housing counselor

2.45

0.77

10.73

Any of the sources

88.17

87.97

89.17

How important were each of the following in choosing the lender/broker you used for the mortgage you took out? (=1 if important or very important)

Having an established banking relationship

43.98

44.64

40.71

Having a local office or branch nearby

52.04

51.68

53.82

Used previously to get a mortgage

7.69

7.19

10.15

Lender/broker is a personal friend or relative

12.64

12.27

14.42

Recommendation from a friend/relative/co-worker

50.07

50.51

47.90

Recommendation from a real estate agent/home builder

51.44

51.07

53.28

Reputation of the lender/broker

59.12

58.73

61.04

Spoke my primary language, which is not English

9.64

8.89

13.31

Any of non-price factors are important

91.79

91.79

91.84

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Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Exhibit 2

Summary Statistics of Responses for Select Questions (3 of 3)

Weighted Means (%)

Sample

No HEC

HEC

Overall, how satisfied are you that the mortgage you got was the one with the…

(=1 if very satisfied)

Best terms to fit your needs

73.47

73.36

73.59

Lowest interest rate for which you could qualify

68.47

68.61

57.28

Lowest closing costs

56.53

62.05

62.40

Any option = very

84.26

87.47

63.62

Overall, how satisfied are you with the… (=1 if very satisfied)

Lender or mortgage broker you used

72.94

72.80

73.59

Application process

59.57

60.03

57.28

Loan closing process

61.71

61.58

62.40

Information in the mortgage disclosure documents

61.24

60.75

63.62

Timeliness of mortgage disclosure documents

60.35

60.91

57.61

Settlement agent

65.90

66.07

65.06

Overall satisfied with mortgage process

87.19

86.90

88.64

Did you face any unpleasant surprises at your loan closing? (=1 for “yes”)

Did you face any unpleasant surprises at your loan closing?

16.63

15.50

22.25

Administrative data

Mortgage interest rate spread at origination greater than 100 BPS

12.93

12.22

16.43

LTV ratio at origination greater than 95 percent

44.24

42.24

54.11

Mortgage debt to income ratio greater than 45 percent

12.57

12.29

13.94

Second lien

2.14

1.43

5.62

Ever 60 days delinquent

3.65

3.12

6.31

Ever 90 days delinquent

2.51

2.19

4.11

BPS = basis points. HEC = homebuyer education and counseling. LTV = loan-to-value.

Note: Sample size is 3,305.

Source: NSMO, 2013–2016.

Knowledge of Mortgage Process

Respondents were asked how familiar they were with their credit score, the current level of interest rates, available mortgage types, the mortgage process in general, and the downpayment needed

when applying for mortgages when they began the process of getting this mortgage. For each item,

we compare the response of “very familiar” to the responses “somewhat” or “not at all.” While 64

percent of first-time homebuyers responded they were very familiar with their credit history and

credit score, only 16 percent reported high familiarity with the process of taking out a mortgage.

58 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

Another survey question asked how well the respondent could explain to others the process of

taking out a mortgage, the difference between a fixed- and adjustable-rate mortgage, the difference between the mortgage interest rate and the annual percentage rate (APR), and the consequences of

not making required mortgage payments. We compare the self-reported “very well” to “somewhat”

or “not at all” responses. Most first-time homebuyers responded that they understood the

difference between a fixed- and an adjustable-rate mortgage (56 percent) and the consequences of

not making required payments (56 percent). Respondents were least comfortable explaining the

differences between prime and subprime loans (12 percent) and the difference between the interest

rate and the APR (19 percent).

Assigned Importance to Non-Price Factors

Respondents were asked if lender attributes—such as lender reputation, the lender’s online

presence, whether the lender has a branch nearby, and whether the lender can speak the borrower’s

language—were important or very important when choosing the lender. These attributes are

normative, non-price criteria. We create a combined indicator equal to one if at least one attribute was deemed very important. We created variables with a value of one if the respondent answered

“very important” for any of the non-price factors. A very high percentage, 92 percent, of first-time homebuyers indicated that at least one non-price attribute was “very important.” This suggests that their mortgage choice might not have been guided by mortgage cost alone.

Satisfaction with Mortgage Process and Mortgage Terms

The survey contains a variety of questions that focus on a borrower’s satisfaction with the mortgage experience, beginning with the application process and ending with mortgage terms. Generally,

borrowers are satisfied with both the process and mortgage terms. For example, close to 68

percent of respondents report being satisfied with the interest rate on the mortgage.

Mortgage Shopping

Shopping behavior is measured two ways. First, the respondent is asked, “How many lenders/

brokers did you seriously consider before taking out this mortgage?” and by this measure,

55 percent of first-time homebuyers reported that they seriously considered more than one

lender or broker. Borrowers were also asked, “How many different lenders/brokers did you

end up applying to?”. By this second measure, 32 percent indicated that they applied to more

than one lender or broker.

Seeking Input About Closing Documents

Eighty-eight percent of respondents reported they sought input about closing documents from

at least one source, with the lender and real estate agent being the most popular sources (73

percent and 61 percent, respectively). Forty-four percent asked for input from a friend or relative.

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Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Mortgage Terms

Using survey responses and the mortgage administrative data linked to the survey, we can observe

the characteristics associated with the mortgages selected by the borrower. Available information

includes underwriting information such as LTV at origination and debt-to-income ratio (DTI),

as well as product features such as interest rate, term, and fixed-rate versus adjustable-rate.

Between 2013 and 2016, the mortgage market was relatively homogeneous, with only a small

fraction of loans having “special” mortgage features, such as a balloon payment, prepayment

penalty, or interest-only payments.8 For this reason, we do not include these mortgage features

as part of our analysis.

For our analysis, we created indicator variables to represent a mortgage with an LTV at origination greater than 95 percent, a mortgage interest rate spread at origination greater than 100 basis

points,9 a DTI ratio greater than 45 percent, and a second lien present at origination. For the

first-time homebuyers in this study, 44 percent have an origination LTV greater than 95 percent,

2 percent are securing second liens with the first mortgage, and 13 percent have the higher DTI

and interest-rate spread at origination.10

Some mortgage terms, including LTV and DTI, may reflect decisions or circumstances of a

household that may predate HEC, such that any association between these terms and reported

HEC should be taken with caution. In the more detailed empirical analysis, discussed below,

we consider the possibility that these mortgage terms affect the selection into reported HEC as

an additional specification. This is particularly true if borrowers participated in HEC to qualify for a specific mortgage program. Again, we are not able to confirm the exact nature, timing,

or entity associated with the counseling.

Empirical Strategy

For each outcome discussed above, we examine the empirical relationship between the incidence

of that outcome among survey respondents and the self-reported HEC, controlling for relevant

borrower characteristics. Because the borrowers in our analysis were not randomly assigned to

receive HEC, we employ propensity score matching (PSM) to control for the observable borrower

characteristics related to which borrowers reported receiving homebuyer education or counseling.

PSM controls for observable borrower and loan characteristics.11 This is an improvement relative to other papers that were limited to detailed information for only borrowers who received counseling.

Such papers relied on loan characteristics (Agarwal et al., 2014a, 2014b, 2010) or credit attributes (Mayer and Temkin, 2016; Roll and Moulton, 2016) to address selection.12 Regardless, PSM

is not able to control for unobserved attributes that might affect both counseling choices and

mortgage selection, such as job security, mobility, available assets, or funds for a downpayment.

Further, borrowers may select into HEC based on pre-HEC levels of mortgage knowledge in a way

that is not fully captured by the included covariates. Overall, the results of our analysis are best 8 Note that only first-lien residential mortgages are in the NSMO sample, and second liens such as Home Equity Lines of Credit (HELOC) are not captured.

9 A basis point is one-hundredth of a percent.

10 We have 45 fewer observations associated with DTI because we omit DTI observations that exceed 60 percent.

The relevant counts are reported in exhibit 2.

11 For an explanation of the method, see, for example, Dehejia and Wahba (2002).

12 See Mayer and Temkin (2016) for a review.

60 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

interpreted as correlations or suggestive evidence of the relationship between HEC and aspects

of the mortgage selection process, not proof of causal relationships.

We begin by summarizing how borrowers who reported HEC differ from those who reported

that they did not participate in HEC. We have identified several borrower characteristics that

are predetermined and potentially related to self-reported education or counseling participation.

Exhibit 3 presents the breakdown of first-time homebuyers by borrower characteristics. The

first column presents the frequency of a borrower type, adjusted for population weight to be as

representative as possible to the general population of first-time homebuyers. The second column

presents the counseling rate among borrowers of a given type which can be compared with the

overall counseling rate of 17 percent. From this exhibit, we make the following observations.

• Credit Score: Borrowers with a lower credit score are generally more likely to report HEC

participation, with the highest participation rates reported for borrowers with a credit score

of 620–639.13 Among these borrowers, the reported HEC rate is 27 percent.

• Age: Younger borrowers (35 years or younger) are less likely to report HEC participation than

older borrowers. The younger borrowers’ reported HEC participation rate is 15 percent

compared with 21 percent for those 36 to 45 years old.

• Race and Ethnicity: The HEC rate among Blacks is 41 percent, more than twice the average

in the sample.

• Education: Borrowers with a college degree have a lower reported HEC rate, at 16 percent,

compared with a 24-percent rate among high school graduates.

• Household Income: Household income shows a strong relationship with HEC rate.

Households with less than $50,000 in combined yearly income exhibit HEC rates of

approximately 26 percent, well above that of higher income groups.

• Marital Status: At 15 percent, married couples have the lowest reported HEC rate, compared

with 19 percent for both not-married couples and singles.

Exhibit 3

Types of Borrowers and the Counseling Rate for Each Type (1 of 3)

Weighted Percent

Covariate

Frequency

Counseling

Rate

Loan Amount

Less Than $50,000

1.88

21.91

$50,000 to $99,999

16.51

20.38

$100,000 to $149,999

25.65

19.25

$150,000 to $199,999

19.91

15.93

$200,000 to $249,999

12.95

15.72

13 The credit score in the data is VantageScore® 3.0.

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Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Exhibit 3

Types of Borrowers and the Counseling Rate for Each Type (2 of 3)

Weighted Percent

Covariate

Frequency

Counseling

Rate

$250,000 to $299,999

8.18

16.91

$300,000 to $349,999

4.45

14.05

$350,000 to $399,999

3.08

10.21

$400,000 or More

7.41

7.84

Credit Score

Lower Than 620

6.51

19.61

620 to 639

5.36

26.83

640 to 659

8.71

20.98

660 to 679

8.03

21.28

680 to 699

9.18

19.94

700 to 719

10.62

16.17

720 to 739

12.55

14.29

740 or Higher

39.04

13.40

Age at Last Birthday

35 or Younger

73.81

15.47

36 to 45

18.60

21.32

Older Than 45

7.59

18.93

Race

White/Caucasian

81.68

15.10

Black/African-American

6.31

40.65

Asian

8.22

13.19

Other

3.79

22.20

Hispanic or Latino

No

13.08

21.60

Yes

86.92

16.10

Highest Level of Education Achieved

Some Schooling

1.50

33.27

High School Graduate

8.95

23.99

Technical School

5.30

19.76

Some College

17.06

19.96

College Graduate

41.05

15.75

Postgraduate Studies

26.14

12.46

Income Relied Upon in Underwriting

Less Than $35,000

8.39

25.72

$35,000 to $49,999

17.61

26.28

$50,000 to $74,999

26.62

19.02

$75,000 to $99,999

20.26

13.01

$100,000 to $174,999

19.74

8.35

$175,000 or More

7.37

9.32

62 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

Exhibit 3

Types of Borrowers and the Counseling Rate for Each Type (3 of 3)

Weighted Percent

Covariate

Frequency

Counseling

Rate

Household Type

Married

52.64

15.10

Not Married but With Partner

16.95

18.77

Single

30.41

18.73

Gender

Female

44.47

18.63

Male

55.53

15.37

Employment Type

Employed Full Time

87.78

16.22

Self-Employed

4.90

16.67

Other

7.33

24.20

LTV Ratio at Origination

95% or Below

55.76

13.84

Greater Than 95%

44.24

20.58

Mortgage DTI Ratio

45% or Below

87.43

16.51

Greater Than 45%

12.57

18.61

Has Second Lien

No

97.86

16.22

Yes

2.14

44.22

DTI = debt-to-income. LTV = loan-to-value.

Source: NSMO, 2013–2014

To implement PSM we first estimate a logistic regression model of the likelihood that a borrower

reports participating in HEC. The specification of this regression mirrors the specification used

in the linear probability model. Based on the estimates of the logistic regression, the propensity score is computed for each observation in the sample. Borrowers reporting HEC are then matched

to non-HEC borrowers with similar values for the probability of reporting HEC.14 Within each

matched group, the survey responses of those reporting HEC are compared with the responses

of borrowers who did not report HEC using a linear regression model.

14 We used the k-nearest neighbors method to identify matched observations. The results reported in the table include the 20 nearest neighbors. Additional sensitivity testing was conducted for k=10 and k=15.

Cityscape 63

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

In exhibit 4, we present results of a linear probability model of HEC with the variables used in

the logistic regression of PSM. Credit score, race, age, education, and income are statistically

significant predictors of reported HEC. When other factors, such as income, are controlled for,

loan amount, household type, and employment type are not statistically significant in the

counseling regression model.

As mentioned earlier, we consider an analysis in which higher LTV, higher DTI, and the presence

of a second lien are considered outcome variables and an alternate analysis in which these

variables are selection variables used in the logistic model of reporting HEC. The linear probability model of the alternate model is included on the right side of exhibit 4. Having a second lien

is a statistically significant predictor of counseling.

Exhibit 4

Weighted Linear Probability Model of HEC Choice (1 of 2)

Without Administrative Data

With Administrative Data

Control

Estimate Standard

Standard

(%)

Error

t-Statistic

Estimate

Error

t-Statistic

(%)

(%)

(%)

Intercept

26.90

7.69

3.50

24.62

7.69

3.20

Loan Amount

Less Than $50,000 (Omitted)

–

–

–

–

–

–

$50,000 to $99,999

0.62

4.89

0.13

0.60

4.91

0.12

$100,000 to $149,999

3.17

4.86

0.65

3.35

4.90

0.68

$150,000 to $199,999

2.84

4.97

0.57

2.86

5.02

0.57

$200,000 to $249,999

4.56

5.12

0.89

5.13

5.17

0.99

$250,000 to $299,999

8.50

5.35

1.59

9.48

5.40

1.75

$300,000 to $349,999

7.05

5.79

1.22

8.14

5.82

1.40

$350,000 to $399,999

5.17

6.18

0.84

5.70

6.20

0.92

$400,000 or More

3.04

5.75

0.53

3.39

5.80

0.58

Credit Score

Lower Than 620 (Omitted)

–

–

–

–

–

–

620 to 639

9.12**

3.71

2.46

9.36**

3.68

2.54

640 to 659

4.01

3.30

1.21

4.03

3.29

1.22

660 to 679

5.22

3.37

1.55

4.67

3.36

1.39

680 to 699

4.55

3.27

1.39

4.37

3.27

1.34

700 to 719

1.02

3.20

0.32

1.20

3.18

0.38

720 to 739

1.55

3.14

0.49

1.49

3.14

0.47

740 or Higher

1.97

2.81

0.70

2.39

2.85

0.84

Age at Last Birthday

35 or Younger (Omitted)

–

–

–

–

–

–

36 to 45

4.16**

1.71

2.43

4.31**

1.71

2.53

Over 45

1.05

2.47

0.43

1.26

2.45

0.51

64 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

Exhibit 4

Weighted Linear Probability Model of HEC Choice (2 of 2)

Without Administrative Data

With Administrative Data

Control

Estimate Standard

Standard

(%)

Error

t-Statistic Estimate

Error

t-Statistic

(%)

(%)

(%)

Race

White/Caucasian (Omitted)

–

–

–

–

–

–

Black or African-American

23.64***

2.69

8.79

23.40***

2.69

8.71

Asian

1.85

2.47

0.75

2.41

2.47

0.97

Other

6.79**

3.34

2.03

6.73**

3.33

2.02

Hispanic or Latino

Yes

–

–

–

–

–

–

No

2.68

1.95

1.37

2.71

1.96

1.38

Highest Level of Education Achieved

Some Schooling

–

–

–

–

–

–

High School Graduate

– 6.70

5.66

– 1.18

– 7.78

5.62

– 1.38

Technical School

– 8.60

5.95

– 1.44

– 8.42

5.91

– 1.42

Some College

– 10.19*

5.50

– 1.85

– 10.24*

5.47

– 1.87

College Graduate

– 10.54*

5.45

– 1.93

– 10.35*

5.41

– 1.91

Postgraduate Studies

– 11.60**

5.56

– 2.09

– 11.65**

5.52

– 2.11

Income Relied Upon in Underwriting

Under $35,000 (Omitted)

–

–

–

–

–

–

$35,000 to $49,999

0.80

2.71

0.30

1.83

2.74

0.67

$50,000 to $74,999

– 6.27**

2.70

– 2.32

– 5.34*

2.73

– 1.95

$75,000 to $99,999

– 12.20***

2.97

– 4.11

– 11.07***

3.00

– 3.69

$100,000 to $174,999

– 17.25***

3.19

– 5.41

– 16.63***

3.23

– 5.14

$175,000 or More

– 14.71***

4.17

– 3.53

– 15.65***

4.23

– 3.70

Household Type

Married (Omitted)

–

–

–

–

–

–

Not Married but With Partner

2.96*

1.80

1.65

2.93*

1.79

1.64

Not Married, No Partner

– 1.26

1.57

– 0.80

– 1.06

1.57

– 0.68

Gender

Female (Omitted)

–

–

–

–

–

–

Male

– 2.12

1.32

– 1.60

– 2.13

1.32

– 1.61

Employment Type

Employed Full Time (Omitted)

–

–

–

–

–

–

Self-Employed

0.95

2.98

0.32

2.04

2.97

0.69

Other

3.54

2.52

1.40

3.64

2.54

1.43

LTV Ratio at Origination

95% or Below (Omitted)

–

–

–

–

–

–

Greater than 95%

–

–

–

1.55

1.50

1.04

Mortgage DTI Ratio

45% or Below (Omitted)

–

–

–

–

–

–

Greater Than 45%

–

–

–

– 2.67

2.00

– 1.34

Has Second Lien

No (Omitted)

–

–

–

–

–

–

Yes

–

–

–

29.04***

4.38

6.63

– = not applicable. DTI = debt-to-income. LTV = loan-to-value.

* p<0.05, ** p<0.01, *** p<0.001

Source: NSMO, 2013–2016.

Cityscape 65

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Empirical Results

The NSMO survey responses are used to explore the relationship between reported participation

in HEC and aspects of the mortgage shopping and selection process. The categories we investigate

include knowledge of the mortgage process, importance associated with non-price lender

attributes, satisfaction with the mortgage process, number of lenders considered, seeking input

on closing documents, and the post-origination loan performance.

For each response or mortgage attribute, we estimate OLS and PSM models.15 Exhibits 5 and

6 present the results from all regressions. Exhibit 5 includes the results when higher LTV,

higher DTI, and the presence of a second lien are considered outcome variables, and exhibit

6 includes an alternate analysis where these variables are considered as potential selection

variables for reported HEC. We report the estimate of the HEC coefficient, multiplied by 100,

with standard errors. Because all outcomes are indicator variables, the interpretation of the

coefficient is the percentage point change in the response of interest. To help assess the economic magnitude of the change, the first column of the exhibit presents the sample average of the

response or attribute variable.

In every case in which a significant result is found, all models report similar magnitude. Using the numbers reported for the PSM model in exhibit 6, highlights of the results include the following:

1. Borrowers who reported receiving HEC also reported large positive and statistically significant differences in financial knowledge regarding available mortgage interest rates, the different

types of mortgages available, and their credit history or credit score. Holding other

characteristics constant, borrowers reporting HEC were 9-percentage points more likely to

report that they could explain the process of taking out a mortgage (with the population

average of 41.32 percent) and 7-percentage points more likely to report they could explain

the consequences of not making required mortgage payments (with the population average

of 55.73 percent).

2. Borrowers who reported receiving HEC were 9-percentage points more likely to consult a

housing counselor about their closing documents than were average first-time homebuyers

(with a population average of 2.45 percent). This is a non-trivial result because a counseling

course does not necessarily include an individual consultation, as it may have been in a group

setting or online.

3. Borrowers were generally satisfied with at least one aspect of the mortgage they received,

at 84.26 percent. Yet, borrowers who reported receiving HEC were 4-percentage points

more likely to report being satisfied with their mortgage.

4. A somewhat surprising result is that borrowers who reported HEC were 5-percentage points

more likely to report facing “unpleasant surprises” at the loan closing. It is not clear if the

first-time homebuyers who reported HEC had more challenging closing processes or were

more inclined to carefully review their loan estimates and closing documents.

14 The PSM regression includes weights based on the number of observations in each group of matched respondents.

66 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

Exhibit 5

Estimates of the Relationship Between Reported HEC and the Outcome of Interest (1 of 2)

OLS

PSM

Mean

Standard

Standard

(%)

Coefficient

Coefficient

(%)

Error

Error

(%)

(%)

(%)

When you began the process of getting your mortgage, how familiar were you (and any cosigners) with each of the following? (=1 if very familiar)

The mortgage interest rates available

at that time

33.58

7.46

***

2.48

4.68

**

2.37

The different types of mortgages

available

23.66

5.36

**

2.31

4.77

**

2.18

The process of taking out a mortgage

15.59

3.76

*

2.02

3.19

1.98

The downpayment needed to qualify

for a mortgage

35.99

– 0.21

2.46

– 0.35

2.40

The income needed to qualify for a

mortgage

32.20

4.56

*

2.48

3.55

2.40

Your credit history or credit score

64.43

6.95

***

2.41

5.77

**

2.36

The money needed at closing

25.20

– 0.83

2.27

– 1.87

2.23

How well could you explain to someone the… (=1 if very well)

Process of taking out a mortgage

41.32

9.93

***

2.58

9.03

*** 2.49

Difference between a fixed and an

adjustable-rate mortgage

55.59

7.71

***

2.48

5.79

**

2.49

Difference between a prime and

subprime loan

12.44

1.94

1.63

2.06

1.57

Difference between a mortgage’s

interest rate and its APR

19.17

2.84

2.01

2.84

2.02

Amortization of a loan

23.98

2.65

2.06

3.60

*

2.05

Consequences of not making required

mortgage payments

55.73

9.37

***

2.49

6.20

**

2.45

How many different lenders/brokers did you seriously consider before choosing where to apply

for this mortgage? (=1 if more than one lender/broker)

Seriously considered more than one

lender/broker

54.64

4.94

*

2.58

3.00

2.49

How many different lenders/brokers did you end up applying to?

(=1 if more than one lender/broker)

Applied to more than one lender/broker

31.71

3.77

2.47

3.99

*

2.41

Did you seek input about your closing documents from any of the following people?

(=1 if yes, =0 otherwise)

Lender/mortgage broker

72.92

1.72

2.27

1.39

2.28

Settlement/closing agent

22.70

1.01

2.17

3.12

2.15

Real estate agent

60.78

– 1.83

2.56

– 0.11

2.44

Personal attorney

15.83

1.04

1.93

-0.68

1.89

Title insurance agent

17.41

3.88

*

2.11

2.83

2.03

Trusted friend or relative who is not a

cosigner on the mortgage

44.44

– 1.13

2.56

0.25

2.45

Housing counselor

2.45

9.05 ***

1.46

8.91

*** 1.34

Any of the sources

88.17

2.52

1.65

1.90

1.61

Cityscape 67

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Exhibit 5

Estimates of the Relationship Between Reported HEC and the Outcome of Interest (2 of 2)

OLS

PSM

Mean

Standard

Standard

(%)

Coefficient

Coefficient

(%)

Error

Error

(%)

(%)

(%)

How important were each of the following in choosing the lender/broker you used for the mortgage you took out? (=1 if important or very important)

Having an established banking relationship 43.98

– 5.70

2.55

– 5.10

2.48

Having a local office or branch nearby

52.04

2.02

2.62

2.13

2.51

Used previously to get a mortgage

7.69

1.71

1.52

1.15

1.52

Lender/broker is a personal friend or relative 12.64

1.92

1.76

3.84

**

1.69

Recommendation from a friend/relative/

coworker

50.07

-0.94

2.61

– 1.08

2.49

Recommendation from a real estate agent/

home builder

51.44

2.05

2.57

3.38

2.50

Reputation of the lender/broker

59.12

3.24

2.55

4.32

*

2.46

Spoke my primary language,

which is not English

9.64

1.77

1.66

1.61

1.67

Any of non-price factors are important

91.79

0.31

1.37

– 1.55

1.43

Overall, how satisfied are you that the mortgage you got was the one with the…

(=1 if very satisfied)

Best terms to fit your needs

73.47

0.82

2.33

1.40

2.23

Lowest interest rate for which you

could qualify

68.47

0.04

2.41

0.10

2.37

Lowest closing costs

56.53

3.32

2.51

4.43

*

2.44

Any option = very

84.26

4.27

**

1.74

3.52

**

1.77

Overall, how satisfied are you with the… (=1 if very satisfied)

Lender or mortgage broker you used

72.94

0.96

2.29

0.69

2.22

Application process

59.57

– 3.16

2.60

– 1.39

2.47

Loan closing process

61.71

0.50

2.56

0.66

2.42

Information in the mortgage disclosure

documents

61.24

2.22

2.55

2.19

2.41

Timeliness of mortgage disclosure

documents

60.35

– 3.12

2.61

– 1.62

2.47

Settlement agent

65.90

– 2.53

2.48

– 1.17

2.39

Overall satisfied with mortgage process

87.19

2.16

1.61

1.83

1.67

Did you face any unpleasant surprises at your loan closing? (=1 for “yes”)

Did you face any unpleasant surprises at

your loan closing

16.63

5.83 ***

2.11

5.50

***

2.05

Administrative data

Mortgage interest rate spread at origination

greater than 100 BPS

12.93

2.38

1.73

2.77

2.05

LTV at origination greater than 95%

44.24

1.44

2.34

0.51

2.50

Mortgage DTI ratio greater than 45%

12.57

– 2.09

1.67

– 1.30

1.86

Second lien

2.14

4.56 ***

1.18

4.36

*** 0.95

Ever 60 days delinquent

3.65

1.41

1.23

0.59

1.20

Ever 90 days delinquent

2.51

0.91

1.05

0.53

1.00

BPS = basis points. DTI = debt-to-income. LTV = loan-to-value. OLS = ordinary least squares. PSM = propensity score matching.

* p<0.05, ** p<0.01, *** p<0.001

Note: Sample size is 3,305 for OLS and 3,134 for PSM.

Source: NSMO, 2013–2016.

68 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

Exhibit 6

Estimates of the Relationship Between Reported HEC and the Outcome of Interest with Selection

on LTV, DTI, and Second Lien (1 of 2)

OLS

PSM

Mean

Standard

Standard

(%)

Coefficient

Coefficient

(%)

Error

Error

(%)

(%)

(%)

When you began the process of getting your mortgage, how familiar were you (and any cosigners) with each of the following? (=1 if very familiar)

The mortgage interest rates available

at that time

33.58

7.74

***

2.53

5.20

**

2.41

The different types of mortgages

available

23.66

6.25

***

2.35

4.78

**

2.21

The process of taking out a mortgage 15.59

4.26

**

2.06

3.89

*

1.99

The downpayment needed to qualify

for a mortgage

35.99

0.38

2.48

– 0.44

2.44

The income needed to qualify for a

mortgage

32.20

5.12

**

2.52

3.36

2.45

Your credit history or credit score

64.43

7.14

***

2.43

5.68

**

2.41

The money needed at closing

25.20

– 0.25

2.31

– 0.83

2.26

How well could you explain to someone the… (=1 if very well)

Process of taking out a mortgage

41.32

10.17

***

2.61

8.77

***

2.53

Difference between a fixed and an

adjustable-rate mortgage

55.59

7.82

***

2.51

4.80

*

2.54

Difference between a prime and

subprime loan

12.44

2.03

1.67

2.27

1.63

Difference between a mortgage’s

interest rate and its APR

19.17

3.26

2.06

3.05

2.05

Amortization of a loan

23.98

2.48

2.10

3.40

2.10

Consequences of not making

required mortgage payments

55.73

9.26

***

2.52

6.88

***

2.51

How many different lenders/brokers did you seriously consider before choosing where to apply

for this mortgage? (=1 if more than one lender/broker)

Seriously considered more than one

lender/broker

54.64

5.17

**

2.61

2.08

2.54

How many different lenders/brokers did you end up applying to?

(=1 if more than one lender/broker)

Applied to more than one lender/broker 31.71

4.62

*

2.49

3.47

2.47

Did you seek input about your closing documents from any of the following people?

(=1 if yes, =0 otherwise)

Lender/mortgage broker

72.92

2.47

2.26

1.72

2.32

Settlement/closing agent

22.70

0.50

2.19

3.31

2.17

Real estate agent

60.78

– 2.03

2.59

0.25

2.49

Personal attorney

15.83

1.46

1.94

– 0.55

1.97

Title insurance agent

17.41

3.60

*

2.13

2.80

2.08

Trusted friend or relative who is not a

cosigner on the mortgage

44.44

– 1.50

2.58

0.51

2.50

Housing counselor

2.45

9.33

***

1.50

9.29

***

1.35

Any of the sources

88.17

2.83

*

1.60

2.25

1.64

Cityscape 69

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Exhibit 6

Estimates of the Relationship Between Reported HEC and the Outcome of Interest with Selection

on LTV, DTI, and Second Lien (2 of 2)

OLS

PSM

Mean

Standard

Standard

(%)

Coefficient

Coefficient

(%)

Error

Error

(%)

(%)

(%)

How important were each of the following in choosing the lender/broker you used for the mortgage you took out? (=1 if important or very important)

Having an established banking relationship

43.98

– 4.64

2.57

– 1.99

2.53

Having a local office or branch nearby

52.04

2.18

2.65

2.14

2.56

Used previously to get a mortgage

7.69

1.71

1.55

1.00

1.57

Lender/broker is a personal friend or relative

12.64

2.09

1.78

3.25

*

1.77

Recommendation from a friend/relative/

coworker

50.07

– 1.24

2.64

– 2.59

2.55

Recommendation from a real estate agent/

home builder

51.44

1.12

2.60

2.11

2.56

Reputation of the lender/broker

59.12

3.22

2.58

4.39

*

2.52

Spoke my primary language,

which is not English

9.64

1.76

1.67

1.97

1.67

Any of non-price factors are important

91.79

0.47

1.37

– 0.86

1.49

Overall, how satisfied are you that the mortgage you got was the one with the…

(=1 if very satisfied)

Best terms to fit your needs

73.47

0.81

2.36

1.91

2.28

Lowest interest rate for which you

could qualify

68.47

0.51

2.44

0.26

2.41

Lowest closing costs

56.53

2.89

2.54

5.08

**

2.50

Any option = very

84.26

4.39 **

1.77

3.49

*

1.82

Overall, how satisfied are you with the… (=1 if very satisfied)

Lender or mortgage broker you used

72.94

1.72

2.29

0.78

2.28

Application process

59.57

– 2.65

2.61

– 1.30

2.52

Loan closing process

61.71

0.60

2.59

1.05

2.47

Information in the mortgage disclosure

documents

61.24

2.35

2.57

1.97

2.46

Timeliness of mortgage disclosure

documents

60.35

– 2.92

2.63

– 2.02

2.52

Settlement agent

65.90

– 2.71

2.51

– 0.03

2.46

Overall satisfied with mortgage process

87.19

2.33

1.61

1.73

1.70

Did you face any unpleasant surprises at your loan closing? (=1 for “yes”)

Did you face any unpleasant surprises at

your loan closing

16.63

5.24 **

2.15

4.90

**

2.09

Administrative data

Mortgage interest rate spread at origination

greater than 100 BPS

12.93

2.81

1.76

2.28

2.10

Ever 60 days delinquent

3.65

1.20

1.21

0.82

1.20

Ever 90 days delinquent

2.51

0.73

1.01

0.66

1.04

BPS = basis points. DTI = debt-to-income. HEC = homebuyer education and counseling. LTV = loan-to-value. OLS = ordinary least squares. PSM = propensity score matching.

* p<0.05, ** p<0.01, *** p<0.001

Note: Sample size is 3,260 for OLS and 3,012 for PSM.

Source: NSMO, 2013–2016.

70 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

We also analyze whether HEC had any effect on early loan performance. We measure loan

performance as ever being 60 or more days delinquent since origination. For loan originations

to first-time homebuyers, this delinquency rate is 3.65 percent. We consider the full payment

history available for any given origination, resulting in comparatively longer performance

timelines for earlier originations. Without controls, the difference in the delinquency rate

between those reporting HEC and those not reporting HEC is 3.19 percentage points,

6.31 percent compared with 3.12 percent, as shown in exhibit 2. In both the OLS and PSM

modeling frameworks, the performance gap between the reported HEC and non-HEC group

decreases when controlling for observable differences between the groups. The difference

between HEC recipients and other first-time homebuyers is not statistically significant.

Our finding of no effect is in line with Smith, Hochberg, and Greene (2014).

It may be tempting to conclude that counseling does not matter for first-time homebuyers.

However, we should recognize that selection into counseling or reported HEC is not by

random assignment. In fact, the reported HEC group may not have been eligible for a mortgage

without completing some form of HEC. The reported HEC group was selected because they

were determined to be a potentially higher risk than even observably similar non-HEC group

members. This is consistent with the higher rates of counseling observed among the traditionally

riskier borrower characteristics in exhibit 3.

Although encouraging, these results, particularly those with respect to mortgage knowledge,

should be interpreted with caution for two reasons.

First, the outcomes on mortgage knowledge are self-reported, and it has been found that

consumers, in some instances, may overestimate their actual financial literacy (Lusardi and

Mitchell, 2011). For instance, counseling may make consumers more confident in their

knowledge, as opposed to actually improving it. Further research is needed linking receipt

of HEC to actual, rather than self-reported, knowledge. As noted earlier, early results by

DeMarco et al. (2016) of the ongoing HUD experiment indicate a modest positive effect

of HEC on respondents’ performance in a four-question financial literacy test.

Second, consumers may select into HEC along dimensions related to mortgage knowledge.

For instance, one plausible hypothesis is that borrowers who feel less confident in their

mortgage knowledge would be more likely to use HEC, as they stand to gain more from this

type of education. It is worth emphasizing that our results reject this hypothesis. Almost

all the coefficients for the relationship between HEC and self-reported mortgage knowledge

are positive, even if not all are statistically significant. As pointed out by Collins and O’Rourke (2011, 2010) at the time of their review, the literature was inconclusive as to which direction

the selection would operate. Our results point in the direction of positive selection.

Cityscape 71

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Conclusion

HEC is believed to improve the homebuying process for potential homebuyers. Although we

did not administer a mortgage literacy assessment, we do find evidence consistent with

improved familiarity and confidence with the mortgage process and related terminology.

Moreover, first-time homebuyers who reported receiving HEC had a higher level of satisfaction

with their mortgage. The results around loan performance, as measured by delinquencies,

are less conclusive. Subsequent analyses could continue to monitor mortgages reported in

the NMDB and NSMO data for patterns during different economic conditions.

A key limitation of this survey in identifying the effect of HEC on mortgage knowledge is that

we are unable to determine whether the responses on the survey reflect the postpurchase

(and thus post-HEC) state of knowledge or the prepurchase state of knowledge. Future

research should employ methods to distinguish between the pre-HEC and post-HEC states

of knowledge. By examining within-person changes in knowledge, one may be able to eliminate

most if not all concerns related to selection into HEC that may be related to mortgage knowledge.

An example of this approach is Carswell (2009): measures of financial distress were obtained

before and after counseling, with an aim of identifying the effect of HEC on within-person

changes in financial distress.

Acknowledgments

The authors thank Robert B. Avery, Xian Fang Bak, Ron Borzekowski, Daniel E. Coates, Thomas Daula, Samuel Frumkin, Ian H. Keith, Michael LaCour-Little, Gen Melford, David Sanchez, Susan Singer, Rebecca Sullivan, Cynthia Waldron, Stacey Walker, and Peter Zorn for providing comments and Julia Nguyen for assisting with data. Grace Li was a senior economist for Fannie Mae at the time this article was written.

Authors

Robert B. Argento is a quantitative analytics senior at Freddie Mac.

Lariece M. Brown is a quantitative analytics director at Freddie Mac.

Sergei Koulayev is an economist at the Consumer Financial Protection Bureau.

Grace Li is a statistical quantitative senior at SunTrust Investment Services.

Marina Myhre is a social science analyst at the U.S. Department of Housing and

Urban Development.

Forrest Pafenberg is a former supervisory economist at the Federal Housing Finance Agency.

Saty Patrabansh is the manager of the National Mortgage Database Program at the

Federal Housing Finance Agency.

72 National Survey of Mortgage Originations

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

References

Agarwal, Sumit, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet, and Douglas

D. Evanoff. 2014a. The Effectiveness of Mandatory Mortgage Counseling: Can One Dissuade

Borrowers from Choosing Risky Mortgages? NBER Working Paper No. 19920. Cambridge, MA:

National Bureau of Economic Research.

———. 2014b. “Predatory Lending and the Subprime Crisis.” Journal of Financial Economics 113

(1): 29–52.

———. 2010. “Learning to Cope: Voluntary Financial Education Programs and the Housing

Crisis,” American Economic Review: Papers and Proceedings 100 (2): 495–500.

Alexandrov, Alexei, and Sergei Koulayev. 2017. No Shopping in the U.S. Mortgage Market:

Direct and Strategic Effects of Providing Information. Working paper 2017-01. Washington, DC:

Consumer Financial Protection Bureau Office of Research.

Avila, Gabriela, Hoa Nguyen, and Peter Zorn. 2013. The Benefits of Pre-Purchase Homeownership

Counseling. Working paper (April). Tysons Corner, VA: Freddie Mac.

Brown, Scott R. 2016. “The Influence of Homebuyer Education on Default and Foreclosure Risk:

A Natural Experiment,” Journal of Policy Analysis and Management 35 (1): 145–172.

Carswell, Andrew T. 2009. “Does Housing Counseling Change Consumer Financial Behaviors?

Evidence from Philadelphia,” Journal of Family and Economic Issues 30 (4): 339–356.

Collins, J. Michael 2013. “The Impacts of Mandatory Financial Education: Evidence from a

Randomized Field Study,” Journal of Economic Behavior & Organization 95: 146–158.

Collins, J. Michael, and Collin M. O’Rourke. 2011. Homeownership Education and Counseling:

Do We Know What Works? Research Institute for Housing America Research Paper No. 1102.

———. 2010. “Financial Education and Counseling—Still Holding Promise,” Journal of Consumer

Affairs 44 (3): 483–498.

DeMarco, Donna, Nichole Fiore, Debbie Gruenstein Bocian, Shawn Moulton, Laura Peck, and

Abt Associates Inc. 2016. The First-Time Homebuyer Education and Counseling Demonstration: Early Insights. Washington, DC: U.S. Department of Housing and Urban Development Office of Policy Development and Research. https://www.huduser.gov/portal/sites/default/files/pdf/First-Time-

Home-Buyers.pdf.

Gerardi, Kristopher, Lorenz Goette, and Stephan Meier. 2010. Financial Literacy and Subprime

Mortgage Delinquency: Evidence from a Survey Matched to Administrative Data. Working paper

2010-10. Atlanta, GA: Federal Reserve Bank of Atlanta.

Li, Wei, Bing Bai, Laurie Goodman, and Jun Zhu. 2016. NeighborWorks America’s Homeownership

Education and Counseling: Who Receives It and Is It Effective? Research report. Washington, DC: Cityscape 73

Argento, Brown, Koulayev, Li, Myhre, Pafenberg, and Patrabansh

Urban Institute. http://www.urban.org/sites/default/files/publication/84476/2000950-

NeighborWorks-America’s-Homeownership-Education-and-Counseling-Who-Receives-It-and-

Is-It-Effective.pdf.

Lusardi, Annamaria, and Olivia S. Mitchell. 2014. “The Economic Importance of Financial Literacy:

Theory and Evidence,” Journal of Economic Literature, 52 (1): 5–44.

———. 2011. “Financial Literacy and Retirement Planning in the United States,” Journal of Pension Economics & Finance 10 (4): 509–525.

Mayer, Neil S., and Kenneth Temkin. 2016. “Prepurchase Counseling Effects on Mortgage

Performance: Empirical Analysis of NeighborWorks® America’s Experience,” Cityscape 18 (2): 73–98.

Moulton, Stephanie, J. Michael Collins, Cäzilia Loibl, and Anya Samak. 2015. “Effects

of Monitoring on Mortgage Delinquency: Evidence From a Randomized Field Study,”

Journal of Policy Analysis and Management 34 (1): 184–207.

Myhre, Marina L., and Nicole Elsasser Watson. 2017. Housing Counseling Works. Washington, DC: U.S. Department of Housing and Urban Development Office of Policy Development and Research.

https://www.huduser.gov/portal/sites/default/files/pdf/Housing-Counseling-Works.pdf.

Quercia, Roberto G., and Jonathan Spader. 2008. “Does Homeownership Counseling Affect the

Prepayment and Default Behavior of Affordable Mortgage Borrowers?” Journal of Policy Analysis

and Management 27 (2): 304–325.

Roll, Stephen, and Stephanie Moulton. 2016. The Impact of Credit Counseling on Consumer

Outcomes: Evidence from a National Demonstration Program. Washington, DC: Federal Deposit

Insurance Corporation. http://gflec.org/wp-content/uploads/2016/04/Roll-Stephen-and-

Moulton-Stephanie-The-Impact-of-Credit-Counseling-on-Consumer-Outcomes.pdf.

Smith, Marvin M., Daniel Hochberg, and William H. Greene. 2014. The Effectiveness of Pre-Purchase Homeownership Counseling and Financial Management Skills. Philadelphia, PA: Federal Reserve Bank of Philadelphia.

Spader, Jonathan, and Roberto G. Quercia. 2009. Pre-purchase Homeownership Counseling and

Mortgage Search. Working paper. Chapel Hill, NC: UNC Center for Community Capital.

Turnham, Jennifer, and Anna Jefferson. 2012. Pre-Purchase Counseling Outcome Study: Research

Brief Housing Counseling Outcome Evaluation. Research brief. Washington, DC: U.S. Department of Housing and Urban Development. https://www.huduser.gov/publications/pdf/pre_purchase_

counseling.pdf.

74 National Survey of Mortgage Originations

by Brian Bucks, Tim Critchfield, and Susan Singer

National Survey of Mortgage

Originations Survey Data on

Your Home Loan Toolkit

Brian Bucks

Tim Critchfield

Susan Singer

Consumer Financial Protection Bureau

Disclaimer

The views expressed in this article are those of the authors and are not necessarily those of the Consumer Financial Protection Bureau or the U.S. Government.

Abstract

The Real Estate Settlement Procedures Act (RESPA) requires that consumers who apply for a mortgage to purchase a home receive certain information on real estate settlement services and related costs.

Before 2015, the U.S. Department of Housing and Urban Development (HUD) published a booklet

titled Shopping for Your Home Loan: Settlement Cost Booklet (Booklet), which lenders provided to mortgage applicants. Starting in October 2015, coincident with the implementation of new mortgage loan estimate and closing disclosure forms, lenders began providing the Consumer Financial Protection Bureau’s substantially revised publication, the “Your Home Loan Toolkit” (Toolkit) (CFPB, n.d.1).

This article details the extent to which mortgage applicants recalled getting the Toolkit. Because the Toolkit is required only for purchase mortgages, the analysis considers purchase-mortgage borrowers’

recollection of receiving the Booklet or the Toolkit. We find that the percentage of homebuyers who remember receiving the information increased with the introduction of the new Toolkit. The estimated marginal effects are statistically significant and large compared with other factors

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 75

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

Bucks, Critchfield, and Singer

Introduction

Getting a mortgage is a complex financial decision and, for many consumers, it is the biggest and

most complex financial decision they will make. One of the primary federal regulations that relates to the acquisition of residential mortgage is the Real Estate Settlement Procedures Act (RESPA). RESPA requires that consumers who apply for a mortgage to purchase a home or to refinance an existing loan receive certain information on real estate settlement services and related costs. RESPA requires that purchase-mortgage borrowers get a “special information booklet” that informs them about real estate settlement services (12 U.S.C. 2601 et seq. and its implementing Regulation X, 12 CFR 1024.6).

From 1976 to 2015, HUD implemented this RESPA provision by requiring lenders to provide

purchase-mortgage applicants with an information booklet titled “Shopping for Your Home Loan:

HUD’s Settlement Cost Booklet” (Booklet). The Dodd-Frank Wall Street Reform and Consumer

Protection Act (Dodd-Frank Act) transferred the responsibility for the information booklet to the

Consumer Financial Protection Bureau (CFPB, n.d.1).1 On October 3, 2015, new RESPA rules were

implemented, including for the CFPB special information booklet. The CFPB booklet is titled “Your

Home Loan Toolkit: A Step-by-Step Guide” (Toolkit). RESPA requires that lenders and brokers

provide homebuyers a copy of the booklet within 3 days of submitting an application. RESPA

does not require the booklet to be given to consumers who are refinancing a residential mortgage.2

A comparison of both booklets shows clear differences in the content and format. The Booklet walks the applicant through the Good Faith Estimate (GFE) and HUD-1 Settlement Statement line by

line. The Toolkit does not walk the applicant through the Loan Estimate line by line3 and, although it provides detailed information on the Closing Disclosure, it does so in less detail than does the Booklet. Also, the Dodd-Frank Act listed specific topics to be described and explained in the CFPB

informational document.4 Some of those topics, such as an explanation of variable rate mortgages and home equity lines of credit, had been included in the Booklet. Further, the Dodd-Frank Act required that the information booklet include questions that a consumer should ask regarding the loan,

including, among other things, whether the consumer would have the ability to pay back the loan.

To fulfill that requirement, the Toolkit includes “The Talk” boxes throughout, which include

questions and factors that consumers should consider.

In this article, we detail the shares of mortgage applicants who recalled getting the CFPB Toolkit.

We compare those results with the shares of mortgage applicants who remembered getting the

earlier Booklet to understand how the recollection of receiving this information changed after

implementation of the new Toolkit. We look further at how changes in borrowers’ recall of those

documents varied by borrower and loan characteristics (for example, loan amount, borrower

age, and credit score). The analysis uses consumer responses from the National Survey of

1 Dodd-Frank Act §1450. The Booklet is available at https://www.hud.gov/sites/documents/HUD_SETTLE_COST.PDF,

and the Toolkit is available at https://files.consumerfinance.gov/f/201503_cfpb_your-home-loan-toolkit-web.pdf.

2 Lenders and mortgage brokers do not have to provide the booklet during refinancing transactions; closed-end loans, such as when the lender takes a subordinate lien; reverse mortgages; and any other federally related mortgage loan whose purpose is not the purchase of a one- to four-family residential property (12 CFR §1024.6(3)).

3 The switch from the Booklet to the Toolkit was done as part of the larger Truth in Lending Act (TILA)-RESPA Integrated Disclosures Rule, which, among other things, combined the GFE and the initial TILA disclosure and replaced them with a single Loan Estimate.

4 For a comprehensive list of topics, see Dodd-Frank Act §1450(2)(b).

76 National Survey of Mortgage Originations

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit Mortgage Originations (NSMO), a nationally representative survey that provides rich information

on the expectations, knowledge, experiences, and loan terms for consumers with a recent

mortgage origination.5

We find that the percentage of homebuyers who remember receiving information from their lender

increased with the introduction of the Toolkit. Specifically, the percentage of homebuyers who

remembered receiving the documents increased from 22 percent for the Booklet to 42 percent for

the Toolkit. In general, the increase in the share of homebuyers who remember the information

document tended to be larger for borrowers with lower credit scores, household income,

education, or experiences with mortgages. Using a multinomial logit model to better isolate the

role of the Toolkit in the increase in borrower recognition, we find that controlling for borrower and loan characteristics in NSMO does not meaningfully change the estimated effect of the

Toolkit on borrower recall. The estimated marginal effects of the Toolkit indicator are statistically significant and large compared with the other covariates in the model.

Literature Review

Choosing and closing on a mortgage is a complex process and one that most consumers do only

infrequently. That infrequency, in turn, may contribute to many consumers not understanding

the terms and conditions of their mortgage. Bucks and Pence (2008) explore whether borrowers

understand the terms and conditions of their adjustable rate mortgages and find that consumers

underestimate or do not understand the extent of possible rate increases from year to year or over the life of the loan. Lacko and Pappalardo (2010) found that many borrowers do not understand

the terms of their loans, including the total loan amount and whether their loan had restrictions, such as prepayment penalties. Borrowers’ lack of understanding of their mortgage rates and terms

could, in part, reflect asymmetric information and search costs. Mortgage professionals engage in

transactions on a frequent and regular basis but may find information costly to produce, and once

produced, it becomes a common good. Further, consumers may engage in only a limited search

across mortgage lenders because their perceived expected benefits from such a search are less than their expected costs.6

Various ways exist to increase knowledge, reduce asymmetric information, and encourage consumers

to search for the rates, terms, and conditions that meet their needs. Those ways include mandatory standardized disclosure and homeownership education and counseling (HEC). Numerous studies

have been conducted on prepurchase HEC programs.7 Those studies usually evaluate specific

programs that generally target consumers with low income or low credit scores and focus on whether HEC programs reduce mortgage default rates (see, for example, Agarwal et al. [2010]). Elsewhere in this volume, Argento and his colleagues (2018) evaluated NSMO survey respondents’ use of housing

counseling and found that those who reported receiving some form of HEC had better mortgage

knowledge and higher satisfaction with their mortgage terms and the process.

Standardized disclosures of loan terms and features may also increase consumer knowledge and

understanding of mortgage terms and conditions. Lacko and Pappalardo (2010) conducted a

5 NSMO and the National Mortgage Database® (NMDB) are described in the guest editors’ introduction of this Cityscape issue.

6 See Alexandrov and Koulayev (2017); Woodward and Hall (2010).

7 Collins and O’Rourke (2010), Moulton (2012), and Myhre and Watson (2017) provide a review of many of these studies.

Cityscape 77

Bucks, Critchfield, and Singer

randomized control experiment to test borrower understanding using either the mandatory TILA

and RESPA mortgage disclosures or prototype disclosures developed for the study. They found

that borrowers presented with the prototype disclosures had a higher level of understanding of the mortgage terms, but the borrowers could not fully describe important loan terms.8

Consumers have other ways to gain knowledge and be encouraged to search, including talking with

family and friends, engaging with websites on the homebuying process, and reading homebuying

handbooks provided by government agencies, such as the HUD and CFPB booklets.9

Virtually no studies focus on the effects of the various types of knowledge acquisition by consumers.

One exception, the CFPB’s Know Before You Owe Brief No. 3, found evidence of “learning by doing”

in the mortgage context. The study looked at changes in mortgage knowledge throughout the

homebuying process and found evidence that consumers became more knowledgeable through

the process, but it does not attribute this gain to any source in particular.10

Changes in Responses to Whether Borrower Remembers

Booklet or Toolkit

This article contributes to the literature by exploring whether purchase-mortgage borrowers were

more likely to recall the Booklet or the Toolkit and whether those documents led borrowers to ask

questions about their loans. These comparisons are intended as a simple test of whether the new

Toolkit is more salient and thus may be more effective in informing consumers. Further, we consider how the changes in borrowers’ ability to recall the booklet varied across groups based on borrower or loan characteristics.

Beginning in the third quarter of 2015, in anticipation of the replacement of the Booklet with the Toolkit, the NSMO questionnaire asked whether the respondent had received a copy of Shopping for Your Home Loan: HUD’s Settlement Cost Booklet (HUD, n.d.). A year later, when the survey sample was comprised mostly of mortgages taken out in early 2016 and some from late 2015, the question was

changed to ask whether the respondent remembered receiving a copy of Your Home Loan Toolkit: A Step-by-Step Guide (CFPB, n.d.).11 The analysis contrasts pooled responses to the four quarterly NSMO

surveys before the change to pooled responses for the subsequent five quarters, through the third

quarter of 2017.

The analysis is limited to mortgages for home purchase because lenders were only required to provide the Booklet or Toolkit for purchase transactions. The sample excludes refinance loans,

8 In integrating the TILA and RESPA mortgage disclosures, the CFPB used a User Center Design process with a qualitative and a quantitative phase (Kleimann, 2012; 2013).

9 The CFPB provides a webpage on mortgages that includes tools and resources for homebuyers (https://www.

consumerfinance.gov/consumer-tools/mortgages/). State and city governments also provide homebuyer guides, for example, Minnesota (https://www.ag.state.mn.us/consumer/handbooks/HmBuyers/default.asp), New York City

(https://cnycn.org/homebuyers-guide/), and Ohio (https://www.com.ohio.gov/documents/real_HomeBuyersGuide.pdf).

10 For the study analyzed in the CFPB’s Know Before You Owe Brief No. 3 (CFPB n.d.2), prospective homebuyers took an initial baseline survey and then were surveyed biweekly for up to 3 months or until they bought a home, whichever occurred first.

11 The questions differed slightly over time, as the earlier question asked whether the respondent had received the booklet (“Your lender may have given you a Shopping for Your Home Loan: HUD’s Settlement Cost Booklet, did you receive a copy?”), and the later question whether the borrower remembered receiving it (“Your lender may have given you a booklet, Your Home Loan Toolkit: A Step-by-Step Guide, do you remember receiving a copy?”).

78 National Survey of Mortgage Originations

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit loan modifications, and special-purpose mortgages (loans to add or remove a co-borrower, loans to

finance a construction loan, and new mortgages on mortgage-free properties).

Exhibit 1 shows that an average of 22 percent of NSMO respondents reportedly recalled receiving the Booklet.12 After the new Toolkit took effect, the share who recalled getting it jumped to 42 percent on average. The share of respondents who reported that they did not know whether they received the

document dropped from 38 percent to 27 percent.

Exhibit 1

Borrower Recall of Mortgage Booklet and Toolkit (Percent)

Don't Know

Yes

No

Booklet

38

22

39

Toolkit

27

42

30

Notes: The difference in the distribution of responses is statistically significant at a 0.1-percent level. The sample includes 3,083 purchase-mortgage borrowers in the 2015Q3–2016Q2 NSMO surveys and 2,722 purchase-mortgage borrowers in the 2016Q3–2017Q3 surveys.

The NSMO asked a followup question about whether the document led to the homebuyer asking

questions about his or her mortgage in hopes of determining how effective the documents were at

getting respondents to think about their mortgage. Exhibit 2 tabulates responses to this followup

question for respondents who recalled getting the document (top panel) and for all survey

respondents (bottom panel).

Exhibit 2

Share of Borrowers Who Asked Questions Based on Booklet or Toolkit (Percent)

Yes

No

Borrowers Who Recalled Booklet or Toolkit

Booklet

29

71

Toolkit

30

70

All Borrowers

Booklet

6

16

Toolkit

13

30

Notes: The sample includes 700 purchase-mortgage borrowers in the 2015Q3–2016Q2 NSMO surveys and 1148 purchase-mortgage borrowers in the 2016Q3–2017Q3 surveys. “Not applicable” responses for borrowers who said “No” or “Don’t Know” when asked whether they recalled the Booklet or Toolkit are not shown.

12 All statistics are weighted using nonresponse-adjusted analysis weights.

Cityscape 79

Bucks, Critchfield, and Singer

Overall, there was a statistically insignificant change in the likelihood borrowers asked questions, conditional on remembering the documents. The share who reportedly asked questions rose from

an average of 29 percent before the change to 30 percent after the Toolkit was introduced; roughly 70 percent of respondents did not ask additional questions in either time period. Because the share of borrowers who recalled the Booklet or Toolkit increased, the percentage of all borrowers who

asked questions based on these documents roughly doubled, from 6 percent for the Booklet to

13 percent for the Toolkit.

Exhibit 3 breaks down the changes in the estimates shown in Exhibit 1 after the Toolkit was

introduced for subgroups defined by borrower or loan characteristics. It shows, specifically, the

differences in the shares of respondents within subgroups defined by borrower or loan characteristics who said, “Don’t Know,” “Yes,” or “No.” In each row of the exhibit, the likelihood that respondents said they remember the new Toolkit increased by 15 percentage points or more.

Exhibit 3

Changes in Borrower Recall of Receiving Mortgage Disclosure, by Borrower and

Loan Characteristics

College Degree

x

No College Degree

x

Loan $250,000

x

$150,000 Loan <$250,000

x

Loan <$150,000

x

Household Income $100,000

x

Household Income <$100,000

x

Survey 6 Months After Orig’n

x

Survey >6 Months After Orig’n

x

Joint Borrowers

x

Single Borrower

x

55 or Older

x

34-54 Years Old

x

<35 Years Old

x

Lender

x

Broker

x

First-Time Borrower <55 y/o

x

Prior Mortgage or 55+ y/o

x

Score >780

x

Score 780

x

-10

0

10

20

Percentage Points

Don’t Know

Yes

x No

80 National Survey of Mortgage Originations

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit In general, the increases in the shares of “Yes” tended to be larger for groups of borrowers with

lower credit scores, household income, loan amount, education, or experience with mortgages.13

The increase in “Yes” responses was also greater for respondents who worked directly with a lender rather than a broker. There was little difference in the increases for joint and single borrowers and those who got the survey more than 6 months after they took out the mortgage. For all groups, the

percentages of “No” and “Don’t Know” responses both declined noticeably after the new Toolkit was

introduced, and in most cases the share of “Don’t Know” answers dropped by more than that of

“No” answers did.14

Multivariate Analysis

To better isolate the role of the Toolkit in the increase in borrower recollection shown in exhibits 1 and 3, we estimate a multinomial logit model of whether the respondent recalled the Booklet or

the Toolkit (“Don’t Know,” “Yes,” or “No”). The models include the covariates shown in exhibit 3,

a time trend, and controls for other factors that might be correlated with borrowers’ recall of the Toolkit or Booklet. These factors include debt-to-income ratio, loan-to-value, interest rate spread, loan type, the respondent’s labor force status, the respondent’s race and ethnicity, and household composition.15 In addition, the models incorporate information on borrowers’ knowledge and views

as they began the mortgage process, concern about qualifying, certainty about the type of mortgage they wanted, and knowledge about mortgage terms and the mortgage process.16 Together, these

controls serve to make sure that any measured change in borrower recall is not the result of shifts in risk or in borrower characteristics and attitudes.

Exhibit 4 shows the estimated marginal effects on the probability of each response for selected

covariates.17 Controlling for these additional factors does not meaningfully change the estimated

effect of the Toolkit on borrower recall. The Toolkit indicator leads to a significant estimated decline in the likelihood of “Don’t Know” (8 percentage points) and “No” (13 percentage points) and a

corresponding 22-percentage-point increase for “Yes.” These changes are similar to the unadjusted

differences of 11, 9, and 20 percentage points (see exhibit 1), respectively.

13 The score categories are based on the VantageScore® 3.0 for the survey respondent or his or her spouse or partner and the average of these scores when both are available. Income is self-reported household income. Age and education are those for the respondent and are self-reported values from the survey. First-time borrowers are people younger than 55

years of age who did not have any record of having a mortgage in the Experian data in the 7 years before the mortgage in consideration and who were buying a house in which they will primarily live. The category “Survey >6 months after orig’n” comprises loans with an origination date 180 days or more before the initial survey mailing for the respective survey wave.

14 Exhibit A.2 presents percentage point estimates for the differences shown in exhibit 3; the distributions of responses are significantly different before and after the introduction of the Toolkit, at a 0.1-percent level for all 20

groups shown.

15 See Exhibit A.1 for additional detail on the model covariates.

16 The NSMO also asks several questions about borrowers’ knowledge after they took out the mortgage; those responses are excluded from the models because that knowledge might have been affected by receipt of the Booklet or Toolkit.

17 Exhibit A.3 presents marginal effects and corresponding standard errors for all covariates.

Cityscape 81

Bucks, Critchfield, and Singer

Exhibit 4

Marginal Effects on Probability of Responses to Recall of Booklet or Toolkit,

Selected Covariates (Percentage Points)

Don't Know

Yes

No

Toolkit

– 8.1**

21.6***

– 13.4***

Credit Score

0.03

– 0.02

– 0.007

Age

– 0.3***

0.3***

0.07

Days Since Origination

0.008

– 0.002

– 0.006

Loan Amount <$50K

– 13.7**

5.8

7.9

Loan Amount $50K–$99.9K

– 4.1

0.9

3.2

Loan Amount $100K–$149.9K

– 2.8

2.2

0.6

Loan Amount $200K–$249.9K

2.3

– 1.0

– 1.3

Loan Amount $250K–$299.9K

1.4

– 3.9

2.5

Loan Amount $300K–$349.9K

– 1.6

– 2.3

3.9

Loan Amount $350K–$399.9K

4.2

– 1.0

– 3.1

Loan Amount $400K or More

0.5

2.8

– 3.3

Rate Spread

1.3

– 4.3***

3.0*

Loan-to-Value

– 0.08

0.2**

– 0.07

First-Time Borrower <55 Years Old

– 2.4

4.8*

– 2.4

Not Owner-Occupied

– 1.9

– 1.3

3.2

Broker

– 1.9

0.9

1.0

Joint Borrowers

– 0.5

0.9

– 0.4

Non-Hispanic White

2.8

– 6.9***

4.1*

Somewhat Concerned Qualified

6.9***

– 3.2

– 3.6

Not at All Concerned Qualified

4.5*

– 4.6*

0.07

Some Idea of Desired Mortgage

– 1.0

– 1.2

2.2

No Idea of Desired Mortgage

5.3

– 7.9**

2.6

* p<0.05, ** p<0.01, *** p<0.001

82 National Survey of Mortgage Originations

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit The Toolkit indicator is the only variable with a statistically significant effect on all three response outcomes, and those effects are generally larger than the estimated effects for other covariates.

The effects, for example, on the indicator for whether the respondent was a non-Hispanic White

are about one-third the magnitude of the effects for the Toolkit, and the marginal effects for the indicator for the respondent having no firm idea about the type of mortgage he or she wanted are

two-thirds to one-fifth. The estimated nearly 14-percentage-point reduction in the likelihood of

a “Don’t Know” response for borrowers with a loan amount of less than $50,000 compared with

a loan amount of $150,000–$199,999 (the omitted category) is the exception in having a larger

effect than the Toolkit. Qualitatively, the sign of the marginal effects for the Toolkit is comparable to the effects for age. In contrast, non-Hispanic White borrowers, those who were less concerned

about qualifying or had “no idea” about the desired mortgage, or those who had mortgage

loans with a larger rate spread were more likely to say they did not recall or didn’t know if they remembered getting the Booklet or Toolkit.

Conclusion

This article uses the rich information and quarterly data from the NSMO to examine whether

purchase-mortgage borrowers were more likely to recall the Toolkit than the Booklet, which the

Toolkit replaced in October 2015. The comparison of borrowers’ reported ability to recall and ask

questions based on this disclosure is intended as a simple test of whether the new Toolkit is more salient and thus may be more effective in informing consumers.

Borrowers were about 20 percentage points more likely to recall the Toolkit than they were the

Booklet. There was no meaningful difference in the likelihood that borrowers who remembered

the disclosures asked followup questions of their lender, but the increased share of borrowers who recall the Toolkit implies a significant increase in the overall share of borrowers prompted to ask questions as a result. The differences in recall hold within subsets of borrowers classified based on demographic, socioeconomic, and loan characteristics. Similarly, controlling for a variety of

borrower and loan characteristics does not meaningfully change the estimated effect of the Toolkit on borrower recall, and the marginal effect of the Toolkit flag is statistically significant and large compared with the estimated marginal effects for other covariates.

On the whole, these findings suggest that the design or content of the Toolkit made the

information more salient to borrowers. The large effect—both in absolute terms and compared

with other covariates—of the switch to the Toolkit on borrower recall suggests that testing of

informational and educational materials for consumer financial products may improve borrower

engagement with the content. Although the NSMO questions cannot shed light on which specific

aspects of information in the Toolkit were especially effective, more in-depth surveys or focus

groups may be able to distinguish these key features.

Cityscape 83

Bucks, Critchfield, and Singer

Appendix

Exhibit A.1

Sample Statistics (1 of 2)

Variable

Description

Mean

Min

Max

1.toolkit

Loan sampled in 2016Q3 or later

0.506

0

1

survey_wave

NSMO survey wave

10.5

7

15

Avg. credit score for respondent & spouse/

mnscore

731.1

502.5

839

partner (if applic.)

x74r

Respondent age

42.3

19

93

daystomail

Days from loan orig’n to 1st survey mailing

174.1

55

495

1.loan_amount_cat

Loan amt at orig’n <$50K

0.021

0

1

2.loan_amount_cat

Loan amt at orig’n $50K–$99.9K

0.129

0

1

3.loan_amount_cat

Loan amt at orig’n $100K–$149.9K

0.195

0

1

5.loan_amount_cat

Loan amt at orig’n $200K–$249.9K

0.142

0

1

6.loan_amount_cat

Loan amt at orig’n $250K–$299.9K

0.106

0

1

7.loan_amount_cat

Loan amt at orig’n $300K–$349.9K

0.069

0

1

8.loan_amount_cat

Loan amt at orig’n $350K–$399.9K

0.048

0

1

9.loan_amount_cat

Loan amt at orig’n $400K or more

0.120

0

1

rate_spread

Interest rate spread at origination

0.263

–1.5

1.5

ltv

Loan-to-value at origination

83.8

11

125

dtirecode

Debt-to-income at orig’n (missing set to 0)

35.5

0

100

1.missdti

Missing debt-to-income (DTI)

0.007

0

1

1.firsttime

Owner-occ,1st-time borrow <55yr

0.373

0

1

4.firsttime

Not owner-occupied

0.093

0

1

1.broker

Loan through broker (vs. lender or builder)

0.430

0

1

1.joint

Two or more borrowers

0.453

0

1

1.x83

Total household income less than $35K

0.059

0

1

2.x83

Total household income $35K–$49.9K

0.111

0

1

3.x83

Total household income $50K–$74.9K

0.202

0

1

5.x83

Total household income $100K–$174.9K

0.277

0

1

6.x83

Total household income $175K or more

0.160

0

1

2.x76rrecode

Respondent high school grad or less

0.104

0

1

3.x76rrecode

Respondent technical school

0.054

0

1

4.x76rrecode

Respondent some college

0.179

0

1

6.x76rrecode

Respondent postgraduate studies

0.280

0

1

1.rwhitenh

Respondent non-Hispanic White

0.765

0

1

2.loan_type

FHA insured

0.221

0

1

3.loan_type

VA guaranteed

0.081

0

1

4.loan_type

FSA/RHS insured

0.035

0

1

2.x05a

Somewhat familiar: interest rates available

0.390

0

1

84 National Survey of Mortgage Originations

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit Exhibit A.1

Sample Statistics (2 of 2)

Variable

Description

Mean

Min

Max

3.x05a

Not at all familiar: interest rates available

0.094

0

1

2.x05b

Somewhat familiar: types of mortgages avail.

0.423

0

1

3.x05b

Not at all familiar: types of mortgages avail.

0.152

0

1

2.x05c

Somewhat familiar: mortgage process

0.407

0

1

3.x05c

Not at all familiar: mortgage process

0.188

0

1

2.x05d

Somewhat familiar: downpayment to qualify

0.357

0

1

3.x05d

Not at all familiar: downpayment to qualify

0.085

0

1

2.x05e

Somewhat familiar: income needed to qualify

0.382

0

1

3.x05e

Not at all familiar: income needed to qualify

0.100

0

1

2.x05f

Somewhat familiar: own credit history, score

0.215

0

1

3.x05f

Not at all familiar: own credit history, score

0.030

0

1

2.x05g

Somewhat familiar: money needed at closing

0.388

0

1

3.x05g

Not at all familiar: money needed at closing

0.159

0

1

2.x06

Somewhat concerned about qualifying

0.339

0

1

3.x06

Not at all concerned about qualifying

0.470

0

1

2.x07

Some idea of mortgage wanted

0.393

0

1

3.x07

Little idea of mortgage wanted

0.096

0

1

1.rworkstatus

Respondent self-employed

0.096

0

1

3.rworkstatus

Respondent retired

0.084

0

1

4.rworkstatus

Respondent not working

0.045

0

1

1.x81a

(Grand)Children <18 in household

0.452

0

1

1.x81b

(Grand)Children 18–22 in household

0.067

0

1

1.x81c

(Grand)Children 23+ in household

0.047

0

1

1.x81def

Other relatives or non-relatives in household

0.089

0

1

Cityscape 85

Bucks, Critchfield, and Singer

Exhibit A.2

Changes in Borrower Recall of Receiving Mortgage Disclosure by

Borrower and Loan Characteristics (Percentage Points)

Don't

Know

Yes

No

Score ≤780

–12

22

–10

Score >780

– 8

15

– 7

Not First-Time or 55+

– 9

18

– 9

First-Time <55

– 15

24

– 9

Lender

– 11

18

– 7

Broker

– 11

22

– 11

<35 Years Old

– 13

22

– 9

35–54 Years Old

– 11

18

– 7

55 or Older

– 8

21

– 13

Single

– 11

20

– 9

Joint

– 11

20

– 9

Orig’n >6 Mos.

– 12

22

– 10

Orig’n ≤6 Mos.

– 10

19

– 9

Income <100K

– 9

21

– 13

Income ≥100K

– 14

19

– 5

Loan <$150K

– 10

23

– 13

<35 Years Old

– 13

22

– 9

35–54 Years Old

– 11

18

– 7

55 or Older

– 8

21

– 13

Less Than College

– 8

22

– 15

College or More

– 13

19

– 6

86 National Survey of Mortgage Originations

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit Exhibit A.3

Marginal Effects (ME) and Standard Errors (SE) from Multinomial Logit Model of Responses

to Recall of Booklet or Toolkit (Percentage Points)

Don't Know

Yes

No

Variable

ME

SE

ME

SE

ME

SE

1.toolkit

– 8.1

2.8**

21.6

2.7***

– 13.4

2.8***

survey_wave

– 0.8

0.6

– 0.5

0.6

1.2

0.6

mnscore

0.03

0.01

– 0.02

0.01

– 0.007

0.01

x74r

– 0.3

0.08***

0.3

0.07***

0.07

0.08

daystomail

0.008

0.01

– 0.002

0.01

– 0.006

0.01

1.loan_amount

– 13.7

4.5**

5.8

6

7.9

5.8

2.loan_amount

– 4.1

2.7

0.9

2.7

3.2

2.8

3.loan_amount

– 2.8

2.3

2.2

2.3

0.6

2.4

5.loan_amount

2.3

2.5

– 1

2.4

– 1.3

2.5

6.loan_amount

1.4

2.7

– 3.9

2.5

2.5

2.8

7.loan_amount

– 1.6

3.2

– 2.3

3

3.9

3.3

8.loan_amount

4.2

3.7

– 1

3.4

– 3.1

3.6

9.loan_amount

0.5

3

2.8

3

– 3.3

3

rate_spread

1.3

1.2

– 4.3

1.2***

3

1.2*

ltv

– 0.08

0.05

0.2

0.05**

– 0.07

0.05

dtirecode

– 0.09

0.07

0.05

0.06

0.04

0.07

1.missdti

– 16.4

6.1**

3.9

8.7

12.4

9.1

1.firsttime

– 2.4

2

4.8

2*

– 2.4

2.1

4.firsttime

– 1.9

2.7

– 1.3

2.6

3.2

2.8

1.broker

– 1.9

1.4

0.9

1.4

1

1.5

1.joint

– 0.5

1.5

0.9

1.4

– 0.4

1.5

1.x83

3.9

3.7

– 0.2

3.4

– 3.7

3.4

2.x83

0.3

2.9

– 0.4

2.7

0.2

2.8

3.x83

– 2.5

2.2

0.1

2.2

2.3

2.3

5.x83

– 0.9

2.1

0.9

2.1

– 0.03

2.1

6.x83

– 1.3

2.7

– 1.8

2.8

3.1

2.9

2.x76rrecode

2.1

2.6

– 2.7

2.4

0.6

2.5

3.x76rrecode

2.4

3.3

– 0.6

3.1

– 1.9

3.2

4.x76rrecode

2

2

– 1.1

2

– 1

2

6.x76rrecode

0.4

1.7

0.2

1.7

– 0.6

1.7

1.rwhitenh

2.8

1.7

– 6.9

1.7***

4.1

1.7*

2.loan_type

1.4

2.2

0.2

2.2

– 1.6

2.2

3.loan_type

– 3.9

2.7

– 3.9

2.6

7.8

3**

4.loan_type

0.9

4.4

0.1

4.2

– 1

4.3

2.x05a

0.7

1.9

1.4

1.9

– 2.1

1.9

3.x05a

1.4

3.6

– 0.8

3.5

– 0.6

3.5

Cityscape 87

Bucks, Critchfield, and Singer

Exhibit A.3

Marginal Effects (ME) and Standard Errors (SE) from Multinomial Logit Model of Responses

to Recall of Booklet or Toolkit (Percentage Points)

Don't Know

Yes

No

Variable

ME

SE

ME

SE

ME

SE

2.x05b

2.9

2

– 6.6

1.9***

3.7

1.9

3.x05b

0.4

3

– 7.9

3**

7.5

3.2*

2.x05c

– 1.7

2

– 1.7

1.9

3.4

2

3.x05c

– 2.3

3.1

– 1.5

3.1

3.8

3.2

2.x05d

1.7

2.2

– 1.2

2.1

– 0.5

2.2

3.x05d

– 7.6

3.4*

5.5

4

2.1

3.9

2.x05e

3.3

2.1

– 1

2.1

– 2.3

2.2

3.x05e

6.3

3.8

– 6.7

3.6

0.4

3.8

2.x05f

– 0.9

2

0.5

2

0.4

2

3.x05f

3.9

4.8

1.9

4.7

– 5.8

4

2.x05g

– 2.7

2

0.5

1.9

2.2

1.9

3.x05g

– 1

3

– 0.6

3

1.6

3

2.x06

6.9

2.1***

– 3.2

2.1

– 3.6

2.1

3.x06

4.5

2.2*

– 4.6

2.2*

0.07

2.3

2.x07

– 1

1.6

– 1.2

1.6

2.2

1.6

3.x07

5.3

3.1

– 7.9

2.8**

2.6

3

1.rworksta~s

0.7

2.4

– 1.8

2.4

1.1

2.5

3.rworksta~s

– 2.6

2.7

– 1.1

2.5

3.6

2.8

4.rworksta~s

1.6

3.2

– 2.3

3.1

0.7

3.3

1.x81a

0.05

1.5

– 3.3

1.5*

3.3

1.6*

1.x81b

– 2.5

2.6

2

2.6

0.5

2.7

1.x81c

– 3.8

3

1.5

3

2.3

3.1

1.x81def

– 2

2.4

– 3.3

2.4

5.3

2.6*

* p<0.05, ** p<0.01, *** p<0.001

88 National Survey of Mortgage Originations

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit Acknowledgments

The authors would like to thank Mary Bilinski, Julie Vore, Robert B. Avery, Ron Borzekowski, Misha Davies, and Leslie Parrish for their comments and assistance.

Authors

Brian Bucks is a supervisory economist with the Consumer Financial Protection Bureau.

Tim Critchfield is an interdisciplinary statistician in the Office of Research at the

Consumer Financial Protection Bureau.

Susan Singer is the deputy assistant director in the Office of Research with the

Consumer Financial Protection Bureau.

References

Agarwal, Sumit, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet, and Douglas D.

Evanoff. 2010. “Learning to Cope: Voluntary Financial Education and Loan Performance During

a Housing Crisis,” American Economic Review 100 (2): 495–500.

Alexandrov, Alexei, and Sergei Koulayev. 2017. No Shopping in the U.S. Mortgage Market: Direct

and Strategic Effects of Providing Information. CFPB Office of Research Working paper No. 2017-

01. Washington, DC: Consumer Financial Protection Bureau. https://papers.ssrn.com/sol3/papers.

cfm?abstract_id=2948491.

Argento, Robert B., Lariece M. Brown, Sergei Koulayev, Grace Li, Marina Myhre, Forrest Pafenberg,

and Saty Patrabansh. 2019. "First-Time Homebuyer Counseling and the Mortgage Selection

Experience in the United States: Evidence from the National Survey of Mortgage Originations,"

Cityscape 21 (2): 51–74.

Bucks, Brian, and Karen Pence. 2008. “Do Borrowers Know Their Mortgage Terms?” Journal of

Urban Economics 64 (2): 218–233.

Collins, J. Michael, and Collin M. O’Rourke. 2010. “Financial Education and Counseling—

Still Holding Promise,” Journal of Consumer Affairs 44 (3): 483–498.

Consumer Financial Protection Bureau (CFPB). n.d.1. Your Home Loan Toolkit: A Step-by-Step

Guide. Washington, DC: Consumer Financial Protection Bureau. https://files.consumerfinance.

gov/f/201503_cfpb_your-home-loan-toolkit-web.pdf.

———. 2018. n.d.2. What Do Prospective Homebuyers Know About Mortgages and Mortgage Shopping?

Know Before You Owe: Mortgage Shopping Study—Brief #3. Washington, DC: Consumer Financial

Protection Bureau. https://files.consumerfinance.gov/f/documents/bcfp_mortgages_shopping-

study_brief-3-knowledge.pdf.

Cityscape 89

Bucks, Critchfield, and Singer

Kleimann Communication Group, Inc. 2013. Know Before You Owe: Quantitative Study of the Current and Integrated TILA-RESPA Disclosures. Presented to the Consumer Financial Protection Bureau, November 20. Washington, DC: Kleimann.

———. 2012. Know Before You Owe: Evolution of the Integrated TILA-RESPA Disclosures. Presented to the Consumer Financial Protection Bureau, July 9. Washington, DC: Kleimann.

Lacko, James, M., and Janis K. Pappalardo. 2010. “The Failure and Promise of Mandated Consumer

Mortgage Disclosures: Evidence from Qualitative Interviews and a Controlled Experiment with

Mortgage Borrowers,” American Economic Review 100 (2): 516–521.

Moulton, Stephanie. 2012. “Pre-purchase Homebuyer Education and Counseling: Diverse Strategies

for Diverse Homebuyers.” In Consumer Knowledge and Financial Decisions, edited by Douglas J.

Lamdin. New York: Springer, 145–164.

Myhre, Marina, L., and Nicole Elsasser Watson. 2017. Housing Counseling Works. Washington, DC:

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

https://www.huduser.gov/portal/sites/default/files/pdf/Housing-Counseling-Works.pdf.

U.S. Department of Housing and Urban Development (HUD). n.d. Shopping for Your Home Loan.

HUD’s Settlement Cost Booklet. Washington, DC: HUD. https://www.hud.gov/sites/documents/HUD_

SETTLE_COST.PDF.

Woodward, Susan, E., and Robert E. Hall. 2010. “Consumer Confusion in the Mortgage Market:

Evidence of Less than a Perfectly Transparent and Competitive Market,” American Economic Review 100 (2): 511–515.

90 National Survey of Mortgage Originations

Refereed Papers

Refereed papers that appear in Cityscape have undergone a thorough and

timely double-blind review by highly qualified referees. The managing editor

reviews submitted manuscripts or outlines of proposed papers to determine

their suitability for inclusion in this section. To submit a manuscript or

outline, send an e-mail to cityscape@hud.gov.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 91

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

92 Refereed Papers

by Robynn Cox, Benjamin Henwood, Seva Rodnyansky,

Eric Rice, and Suzanne Wenzel

Road Map to a

Unified Measure of

Housing Insecurity

Robynn Cox

Benjamin Henwood

Seva Rodnyansky

Eric Rice

Suzanne Wenzel

University of Southern California

Abstract

This article discusses the evolution of the concept and measurement of housing insecurity. Our survey of the literature uncovers that multiple terms and definitions are used to describe housing insecurity.

Based on our analysis, we argue for one operational term, housing insecurity, and we put forth an operational definition that captures the various dimensions of this issue. We also argue for the development of an instrument that allows researchers to accurately measure the problem. We provide a road map for how this can be achieved based on the precedent set forth by the U.S. Food Security Survey Module.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 93

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

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Introduction

Housing insecurity is often a reality for individuals experiencing poverty and/or social

marginalization. Although it is multidimensional by nature, typically only a few of these dimensions are reliably measured and included in national estimates. Based on a thorough cross-field literature review of 106 quantitative, qualitative, and mixed-methods studies,1 we argue in this article that current measures of housing insecurity are incomplete, which may result in inadequate policy

responses and funding.

The importance of housing goes beyond the material infrastructure that serves as protection from

the elements (Shaw, 2004); housing is interrelated with physical, social, and psychological well-

being (Padgett, 2007). Research has primarily focused on either the health and education threats

associated with substandard housing and neighborhoods (Bashir, 2002; Katz, Kling, and Liebman,

2001; Leventhal and Brooks-Gunn, 2003; Ludwig et al., 2013; Marsh et al., 2000; Sanbonmatsu et

al., 2006) or the psychosocial benefits of housing as a home (Dupuis and Thorns, 1998; Low and

Lawrence-Zuniga, 2003; Padgett, 2007; Shaw, 2004; Somerville, 1992). Also a focus of research,

lack of housing altogether—or homelessness—brings into stark relief the fundamental importance

of housing security as a prerequisite for health, employment, and various other aspects of daily

functioning (Henwood et al., 2013).

In the United States, over half a million people experience homelessness on any given day (Henry et al., 2018). Efforts to address this extreme form of housing insecurity receive dedicated infrastructure and oversight. Congress provides direct funds to address homelessness through the McKinney-Vento Homeless Assistance Act, and communities across the United States are required to maintain

homeless management information systems to receive federal funding to address homelessness.

Each year, communities across the country also conduct a homeless count to monitor the scope of

the problem. An Annual Homeless Assessment Report (AHAR) is submitted to Congress each year

that includes monitoring from both the Homeless Management Information System (HMIS) and

homeless counts. This type of monitoring has helped direct resources and has enabled strategies that have reduced the overall number of chronically homeless adults and homeless veterans since the

inception of AHAR in 2007. Although some interventions to address homelessness promote housing

stability and security (Padgett, Henwood, and Tsemberis, 2015),2 the definition of what constitutes homelessness has been a moving target3 and is not considered to be part of a unified construct of

housing insecurity that can be measured on a continuum.

As compared with the extreme of homelessness or even housing affordability, there is much less

research exploring other facets of housing insecurity. This could partially be because individuals 1 See appendix B for a list of articles included in our literature review.

2 Housing First is an example of an effective intervention for homelessness, with research consistently demonstrating its impact on housing stability. Despite, or perhaps because of, overwhelmingly positive housing outcomes, there has been limited discussion of differences in the operationalization of housing stability and retention in studies of Housing First, which reflects inconsistencies in the definition of these concepts in the broader body of research on homelessness and housing (Byrne, Henwood, and Scriber, 2018).

3 Beginning in 2009, federal definitions of homelessness were expanded to permit inclusion of persons at “imminent risk of homelessness” to expand access to homelessness assistance benefits. Such persons include individuals or families whose residence will be lost within 14 days of application for homelessness assistance, for whom no subsequent residence has been identified, and for whom resources are lacking to obtain other permanent housing. Another group for which homeless assistance has been expanded is those individuals and families fleeing or attempting to flee domestic violence (HUD, 2012).

94 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

and families experiencing housing insecurity are less visible to the public. The limited focus on the more hidden aspects of housing insecurity, such as housing stability, housing quality, and behavioral responses to housing affordability (for example, trading housing safety, housing quality, neighborhood safety, and/or neighborhood quality for affordability), and the failure to take into consideration all the domains of housing insecurity (that is, housing affordability, housing stability, housing safety, housing quality, neighborhood safety, neighborhood quality, and homelessness) have resulted in much less

being known about its true prevalence and the actual costs it imposes on society. In addition, in

domains with more research focus, such as neighborhood effects, the link has not been adequately

made to the issue of housing insecurity. Developing a common language and uniform measurement

tool would help society mobilize resources, improve its understanding of the importance of this

invisible problem, and generate solutions.

In this article, we propose a uniform instrument to assess housing insecurity that can capture the multidimensional aspects of housing, such as access and quality. A comprehensive measure would

be able to encapsulate both access to and quality of housing and the behavioral tradeoffs made to

secure housing. In addition to examining how housing insecurity has been conceptualized across

the literature, we propose a road map for the creation of a new measure of housing insecurity, based on the development of the U.S. Food Security Survey Module that has been incorporated into the

Current Population Survey (CPS) annually since 1995 (National Research Council, 2006).

A Brief History of Housing Insecurity

The concept of adequate housing as a human right was adopted by the United Nations (U.N.)

General Assembly in Article 25 of the 1948 Universal Declaration of Human Rights and Article 11 of the International Covenant on Economic, Social and Cultural Rights in 1966 (U.N., 2014). The U.N.

Committee on Economic, Social and Cultural Rights did not define “adequate housing,” however,

until the Committee’s general comments in 1991 and 1997, at which point the U.N. characterized

adequate housing as meeting the following minimum criteria:

(a)

tenure security that guarantees legal protection against forced evictions,4 harassment,

and other threats;

(b)

availability of materials and infrastructure, such as safe drinking water; adequate

sanitation; energy for cooking, heating, and lighting; food storage; and refuse disposal;

(c)

affordability such that paying for housing does not compromise other human rights;

(d)

habitability that includes protection against the cold, damp, heat, rain, wind, other threats

to health, and structural hazards;

(e)

location that is not polluted or dangerous and that does not cut off access to employment

opportunities, healthcare services, schools, or other critical social institutions; and

(f)

accessibility that can meet the specific needs of disadvantaged and marginalized groups

and does not compromise the expression of cultural identity (U.N., 2014).

4 Note that forced evictions do not include evictions that are justifiable (for example, repeatedly not paying rent or damaging property without justifiable cause) and carried out in accordance with international human rights law (U.N., 2014).

Cityscape 95

Cox, Henwood, Rodnyansky, Rice, and Wenzel

The U.N. provides a framework to monitor human rights, including the right to housing, with several suggestions for housing indicators; those include the share of public expenditure on subsidized or public housing, reported cases of forced evictions, and rates of homelessness. Nonetheless, they do not operationalize a uniform measure of adequate housing or housing security (U.N., 2014).

Likewise, in 1949, the U.S. Congress passed the Housing Act of 1949. This controversial law5

…declare[d] that the general welfare and security of the Nation and the health and living

standards of its people require housing production and related community development

sufficient to remedy the serious housing shortage, the elimination of substandard and other

inadequate housing through the clearance of slums and blighted areas, and the realization

as soon as feasible of the goal of a decent home and suitable living environment for every

American family, thus contributing to the development and redevelopment of the

communities and to the advancement of the growth, wealth, and security of the Nation.6

The law explicitly recognizes the importance of affordable adequate housing and a “suitable living environment,” or neighborhood, not only for the health and well-being of the people (more than

40 years before the 1991 and 1997 U.N. criteria) but also for macroeconomic growth and stability.

In 1969, the U.S. Department of Health and Human Services (HHS) went a step further in explicitly

defining housing instability using five indicators:

(1)

exorbitant housing costs relative to income (greater than 50 percent);

(2)

inferior housing quality (for example, inadequate plumbing, heat, or electricity; leaks; holes; and so on);

(3)

neighborhood instability (for example, high rates of poverty, crime, and unemployment;

poor city services; litter; noise; pollution; and so on);

(4)

overcrowding; and,

(5)

at the extreme, the condition of homelessness (HHS, 1969).

The U.N. Housing Act of 1949 and HHS definitions take a broad view of what constitutes housing

security. The Housing Act of 1949 establishes the importance of housing to the welfare and

security of the people and the macroeconomic growth and stability of the nation, the U.N describes the minimum conditions necessary for housing security, and HHS provides a foundation for a

comprehensive housing insecurity definition. Although the definitions are, in general, mirror images of one another, the HHS definition is more narrowly defined and leaves out certain aspects of the

U.N. definition, such as expression of cultural identity, forced evictions, and accessibility to services required by disadvantage and marginalized groups. Nonetheless, the HHS definition incorporates

the dimension of housing affordability and is defined in a way that more readily permits the

operationalization of a housing insecurity measure: All five of the categories of housing insecurity in this definition are quantifiable. The HHS characterization of housing insecurity also illustrates that, while homelessness is a sufficient condition for housing insecurity, it is not a necessary one.

5 Critics of this law argue that it led to increases in housing insecurity by cultivating the slums and ghettos it sought to eliminate (von Hoffman, 2000).

6 U.S. Congress. 1949. Housing Act of 1949. Pub. L. 81-171. 63 Stat. 413-444. http://legisworks.org/congress/81/

publaw-171.pdf.

96 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

These definitions provide a good starting point to create a fully operational definition of housing insecurity.

In the next section, we conduct an exhaustive cross-field literature review, which will form the basis for the fully operational definition of housing insecurity that we set forth in the section after that.

Current Approaches to Housing Insecurity

Despite the fact that the HHS set forth a definition of housing insecurity as early as 1969, and both the U.N. and HHS depictions of this problem correctly outlined the concept, to date, working

definitions have not captured the multidimensional aspects of this issue. Most of the research has focused on only three dimensions (homelessness, stability, and affordability), giving a potentially incomplete view of the extent of housing insecurity. To better understand how housing insecurity

has been defined both conceptually and operationally, we conducted a thorough cross-field literature review to determine how housing insecurity is defined, measured, and used in research.

We sought out academic studies, government reports, and technical reports released from 1991

to 2017 on the topic of housing insecurity and its allied definitions. We operationalized this

review by gathering papers on the Google Scholar and Web of Science search engines for the

following terms: “housing security,” “housing insecurity,” “housing affordability,” “housing stability,”

“housing instability,” “homelessness,” “housing hardship,” “housing quality,” “housing safety,” and

“neighborhood safety.” Given the focus of this paper on the global concept of housing insecurity,

we restricted our search to the macrolevel terms listed above. Relevant papers (see appendix A,

exhibit A-1) either (1) included housing security as an explanatory variable measuring outcomes

on households, individuals, or populations; (2) sought to understand reasons for housing security

as a dependent variable; or (3) assessed the prevalence of housing insecurity. Our search focused

on the U.S. context; however, several articles from other developed countries were relevant to the development of a measure of housing insecurity. Our literature review yielded 106 quantitative,

qualitative, and mixed methods studies and reports from a variety of fields and outlets (see appendix B for a complete list of articles reviewed); 55 of those studies used some form of housing insecurity as an explanatory variable measuring a different individual-level or household-level outcome, 44 studies used housing insecurity as a dependent variable, seeking to understand its causes, and 17 studies

measured the prevalence of some form of housing insecurity. Of these studies, 7 were counted as

both explanatory and dependent variable studies, 2 were counted as both prevalence and dependent

variable, and 2 were counted as both prevalence and explanatory variable. Eleven studies were from an international context (see appendix table 1 of Cox et al., 2017a for a full list of all papers, with short descriptions).

Based on the 106 quantitative, qualitative, and mixed methods studies and reports reviewed, we find three major concerns with current approaches to measuring housing insecurity: (1) lack of a uniform definition, (2) underdeveloped concept, and (3) inconsistent measurement. Specifically, we find that the definition of what constitutes housing insecurity varies widely. In our scan of the literature, certain search terms—housing instability (43 percent), homelessness (34 percent), and housing insecurity

(16 percent)—had many more associated papers on this topic than did other key words7 (see

appendix A, exhibit A-2). Moreover, most papers did not refer to “housing (in)security” explicitly; 7 Certain papers came up under multiple search terms.

Cityscape 97

Cox, Henwood, Rodnyansky, Rice, and Wenzel

rather, they examined a particular subcategory, such as housing instability or affordability.

Definitions of what constitutes housing insecurity also varied widely. For example, Heflin, London, and Scott (2011) assessed how women in three disadvantaged Cleveland neighborhoods coped

with housing hardships, with the dependent variable—housing hardships—including housing

stability, housing quality, housing safety, and neighborhood safety. In contrast, Rourke et al. (2012) used a whole battery of housing-insecurity categories (stability, affordability, quality, neighborhood quality, and homelessness) in assessing physical and mental health outcomes in adults with HIV.

Even when definitions varied in consistency, measurements of the same condition often differed

in the literature (see appendix A, exhibit A-3). For example, 30 of the papers considered

housing (un)affordability as housing insecurity, but affordability was assessed using five different measurements: (1) “difficulty/inability to make payments on housing” (83 percent), (2) “housing

cost burden” (37 percent), (3) “foreclosure” (10 percent), (4) “legal housing issues” (7 percent), and (5) “having rental assistance” (3 percent), with some 30 percent of papers testing for multiple measurements but no studies testing for all five measures. There is variability even within

measures. Some researchers use a 30-percent housing burden cutoff (for example, Mimura, 2008),

while others use 50 percent (see Capps, 2001), and some use both (see Coulton et al., 2001).

Assessments of difficulty in making payments vary as well: most are solicited via survey responses, and questions are not standardized. The measurement of housing instability varies even more

than affordability. Eight different measures were employed in the 49 papers that used housing

instability as a measure of housing insecurity: (1) “multiple moves” (59 percent), (2) “doubling up”

(35 percent), (3) “eviction” (18 percent), (4) “overcrowding” (18 percent), (5) “duration of stay”

(14 percent), (6) “forced moves” (8 percent), (7) “living in unstable conditions” (2 percent), and (8) “living in multiple subsidized units over time” (2 percent). As with affordability, 39 percent of the papers used multiple definitions, although no study used all eight measures. There was also

considerable within-measure variability in instability, with overlapping definitions of overcrowding and doubling up, and overlapping definitions of multiple moves and duration of stay. For example,

studies differed on how many moves were multiple: in two studies of mothers and children, using

the same Fragile Families and Child Wellbeing Study, Ziol-Guest and McKenna (2014) defined it

as three moves over a 5-year timeframe, while Suglia, Duarte, and Sandel (2011) defined it as two

or more moves in 2 years.

Most of the papers reviewed define housing insecurity incompletely or narrowly. While some

papers we surveyed do focus only on a specific housing issue (such as homelessness), its

determinants, or its outcomes, many others claim to speak for housing insecurity in general.

Yet, it is rarely the case that all available dimensions of housing insecurity are combined into one indicator, even when more than one measure is available in a survey. Of the 106 surveyed papers,

none addresses all seven of our domains of housing insecurity, and only 8 studies address four or

more facets. Of the 73 papers with a focus on homelessness, 35 percent focused only on this one

subcategory of insecurity, and 38 percent focused on just two.

Data availability and specificity have also created challenges in studying the full continuum of

housing insecurity. In the reviewed literature, 98 of the 106 studies analyzed used data-driven

approaches to ascertain some dimension of housing insecurity. Twenty-nine studies collected

primary data; the rest relied on 38 different secondary sources. Among these secondary data

sources, the U.S. Center for Disease Control’s Behavioral Risk Factor Surveillance System survey;

98 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

the Children’s Health Watch survey; HUD’s National Survey of Homeless Assistance Providers

and Clients; the Worcester, MA Family Research Program; and Australia’s Journeys Home data

sets were used in three papers each; the Fragile Families and Child Wellbeing Study, the National

Survey of America’s Families, and the National Longitudinal Survey of Youth data sets were used

in two papers each. Such data set variation contributes to the different permutations in housing

insecurity measurement.

As a result of varying definitions, comparing housing insecurity across studies is difficult.

Moreover, many scholars define the measurement of their version of housing insecurity to a specific subpopulation of interest, making comparisons impossible. Such a narrow definition of housing

may also lead to undercounting and partial measurement. Take, for example, the affordability

dimension of housing insecurity. If we were only to define housing insecurity by affordability,

a household could be considered housing secure if their housing cost burden is below a certain

threshold (or if they do not report difficulty with making payments), even if they made tradeoffs

between affordability and housing quality such that they chose to live in a low-quality dwelling that poses threats to the household members’ health (containing lead paint, mold on the walls, and so

on) or safety (for example, active electrical wires sticking out, severely dilapidated stairs, living in a dangerous neighborhood) in order to obtain more affordable housing.

Toward a New Approach to Measuring Housing Insecurity

A comprehensive definition of housing insecurity, as set forth by HHS and the U.N. General

Committee, has yet to be put into use.8 Housing insecurity impacts many well-being outcomes for

adults and children, yet many aspects of housing insecurity are understudied, at least compared to homelessness and housing affordability (possibly because they are less visible to the public or more difficult to measure), and their prevalence and cost to society are not fully understood. Developing a uniform measurement tool would help society to mobilize resources, improve its understanding

of the importance of this problem, and generate solutions.

One possible definition (based on the overlapping descriptions set forth by HHS and the United

Nations) for housing security is as follows:

Availability of and access to stable, safe, adequate, and affordable housing and neighborhoods regardless of gender, race, ethnicity, or sexual orientation.

Likewise, housing insecurity would be defined as follows:

Limited or uncertain availability of stable, safe, adequate, and affordable housing and neighborhoods; limited or uncertain access to stable, safe, adequate, and affordable housing

and neighborhoods; or the inability to acquire stable, safe, adequate, and affordable housing

and neighborhoods in socially acceptable ways.

8 Note that the Office of Policy Development and Research within the U.S. Department of Housing and Urban Development (HUD), inspired by an earlier version of this paper first released as a working paper in 2016 (see Cox et al., 2017a), has developed and recently published a 60-day notice to pilot a housing insecurity module within the 2019

American Housing Survey. Published in the Federal Register as a notice on September 11, 2018. 83 Fed. Reg. 45,955-45,956. https://www.govinfo.gov/content/pkg/FR-2018-09-11/pdf/2018-19707.pdf.

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Cox, Henwood, Rodnyansky, Rice, and Wenzel

From this definition, it becomes clear that housing insecurity can be characterized by the

following dimensions:

(1) Housing Stability

(2) Housing Affordability

(3) Housing Quality

(4) Housing Safety

(5) Neighborhood Safety

(6) Neighborhood Quality

(7) Homelessness

Where housing stability is the ability of a household to stay in a housing unit of its choosing, for a duration of its choosing, without interruption or complication. Common manifestations of instability include living in overcrowded conditions, doubling up with relatives or friends, frequent moves,

forced moves such as eviction, and others. Housing affordability is a household being reasonably

able to pay for adequate housing, on time, within its budget; unaffordability includes having a high housing cost-to-income ratio (burden), the difficulty or inability to pay for the full costs of housing on time or fully, and legal issues related to paying for housing. Housing costs include not only the rent or mortgage but also relevant property taxes and utilities and routine maintenance bills.

Housing quality and safety deal with structural characteristics of a household’s housing unit. Unsafe housing poses direct health risks to inhabitants. Manifestations include open wires; lack of insulation or heating; holes in exteriors, roofs, or floors; lack of water access; and pests in the unit. Low-quality housing, while not directly harming the household, nevertheless shifts the members’ quality of life below an expected level; common manifestations include dilapidated exterior of building, peeling

paint in the interior of the unit, lack of access to (working) appliances, not having bathroom fixtures and/or flushing toilets, and having utilities that experience frequent breaking or stopping, including sewage, water, electricity, heat, and so on. In addition to the health concerns, it is important to include low-quality and unsafe housing in housing insecurity because of the tradeoffs individuals and households may choose to make between housing affordability, housing quality, and housing safety.

Neighborhood-level characteristics such as safety and quality are not often thought of as directly housing-related; however, they are certainly tied to households’ ability to live in a safe environment in particular and to its well-being in general. Therefore, we believe, neighborhood characteristics form part of a measure of housing insecurity. This is especially true if households are making tradeoffs in neighborhood quality or safety for greater housing affordability. For example, if a household can only afford to live in a high-crime neighborhood, there could be at least two possible policy interventions: give the household a subsidy to move elsewhere (an issue of housing affordability) or make the

current neighborhood safer (an issue of neighborhood safety). Lack of neighborhood safety entails

the presence of high crime, many abandoned buildings, the proximity of environmental hazards,

and excessive noise and traffic, among others. Low-quality neighborhoods include those with poor

services, poor infrastructure, low access to amenities, and others. Both neighborhood quality and

safety can be measured subjectively by self-reports or objectively, perhaps, through administrative data. For example, a neighborhood audit tool could be used as a more “objective” measure of

neighborhood quality, such as The Revised Residential Environment Assessment Tool (REAT 2.0),

which includes measures of neighborhood condition, natural surveillance, and natural elements

(Poortinga et al., 2017). Note that these tools may still suffer from observer bias. In terms

100 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

of neighborhood safety, these measures could be obtained by working with other government

agencies, such as the Federal Bureau of Investigation and the Environmental Protection Agency, that collect more objective administrative data on neighborhood safety. These measures might include

crime statistics from the FBI’s Uniform Crime Reports,9 neighborhood-level industrial hazard data

from EPA’s Toxic Release Inventory (for example, see Crowder and Downey, 2010), and EPA’s Safe

Drinking Water Information System Federal Reporting Services. One interesting implication of the

inclusion of neighborhood safety into a housing insecurity measure is that we could consider a

scale that might place more weight on variables that pose an immediate danger to the safety of the inhabitant. This could potentially lead to housing in areas with extreme violence being marked as

uninhabitable locations that need immediate assistance. The authors are not aware of a standard of neighborhood safety that would lead to this conclusion, but they note that epidemiological models of contagion might shed some light on such a threshold. These models could help to determine when

there might be an epidemic (in certain neighborhoods and communities), warranting the designation

of dwellings in those areas as uninhabitable.

Finally, homelessness can have multiple forms (HUD, 2012), such as literal homelessness (which

includes sleeping on the street), temporary housing (such as homeless shelters), and living in

dwellings not meant for human habitation (living in a vehicle, railroad car, abandoned building,

encampment, RV, and so on); while examples of more hidden forms of homelessness might include

couch surfing and doubling up.

This definition makes room for both a categorical and a continuous measure (both of which have

value) of housing insecurity. Such a measure would be a function of the number and depth of

housing issues along the aforementioned seven dimensions. While we have included homelessness as

part of our housing insecurity definition, as in the case of the condition of hunger in the food security module, it is conceivable that homelessness could be removed from the definition. As previously

mentioned, homelessness is a sufficient—but not necessary—condition for housing insecurity;

therefore, it could be modeled as the severest form of housing insecurity along a continuum, or

possibly as an outcome of housing insecurity. One argument for including homelessness within the

definition of housing insecurity is, unlike hunger, homelessness is experienced at the household

level; therefore, we have opted to keep it in our definition. What will prove to be more difficult when measuring homelessness in an index of housing insecurity is the ability to capture homeless

households that are doubling up with households that are not homeless.10

Developing a uniform definition does not mean that research or policy should not separately focus

on the different dimensions of housing insecurity when appropriate; rather, it suggests that to

precisely estimate the prevalence of this issue and accurately account for its social costs, we need a comprehensive definition so that we can develop an instrument that will jointly measure all of its dimensions. Specifically, a common definition of housing insecurity would facilitate the development of a validated national instrument of housing insecurity that can be assessed at the household level and, possibly, the individual level. To capture the multidimensionality of housing insecurity, this measure should be defined as a scale that can quantify housing security along a continuum, from the most housing secure to the most extreme cases of housing insecurity, as well as categorically.

9 Another source of crime data is the Centers for Disease Control and Prevention’s (CDC) National Vital Statistics System (NVSS), which has information on deaths by homicide, as well as the National Crime Victimization Survey (NCVS), which, although less objective, provides self-reports of crime victimization.

10 Specifically, some households may choose to double up, while others may do so because of the loss of housing.

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Cox, Henwood, Rodnyansky, Rice, and Wenzel

We provide several guidelines to aid in the development of such an instrument, to ensure that

this measure is useful in a variety of settings and contexts. A uniform multidimensional construct captures the presence of an underlying constellation of issues rather than just one problem, an

approach that may be appropriate for many studies, but not for all. Measurement frequency should

depend on the needs of the particular study, so survey question wording should be easily adaptable to different time lengths but will also have to take into consideration the burden of the survey on the respondent. Housing insecurity prevalence measures at national, state, regional, or local levels should be at frequent enough intervals (at least annually or biennially) to measure the evolution of housing insecurity. For example, similar to the U.S. Household Food Security Module, a housing security

module could survey respondents’ about their housing security over the past 12 months; and because this survey is collected biennially, to understand the housing characteristics of the U.S. population, population estimates of housing insecurity would be obtained every 2 years. In addition, the timeline for the baseline module questionnaire should cover the lifetime history of housing security of the household for the inaugural year in order to understand the past experience of housing insecurity

in American households.

Households should be the units of analysis for housing insecurity, unless study designs necessitate otherwise. While housing insecurity takes neighborhood-level characteristics into account, relevant outcomes are at the household level, recognizing that different processes may affect household-level and neighborhood-level insecurity. Measurement modules should be flexible to include local context.

For example, housing cost burden cutoffs may differ by metropolitan area, or regions in warm

climates do not need questions about the existence and performance of home heating equipment.

Similarly, rural contexts, Native American reservations, and other unique areas may require slight modifications. While this article approaches housing insecurity from a U.S. perspective, the measure should be easily transferrable to other developed countries and, with some modification, to the

developing world context.

Current Measures of Housing Insecurity

To date, no research has captured all the proposed seven domains of housing insecurity among a

nationally representative population. Tsui et al. (2011) have the most expansive measure of insecurity, including unaffordable or unsafe housing, unsafe neighborhoods, homelessness, experiencing

foreclosure, or having been in housing court. In contrast, Curtis and Geller (2010), Geller and

Franklin (2014),11 Goldrick-Rab et al. (2015), and Warren and Font (2015) view insecurity as a

combination of housing instability, unaffordability, and homelessness. Routhier (2018) restricts

her sample to renters and measures housing insecurity only among the dimensions of housing

affordability, housing stability, and poor physical unit conditions. Bailey et al. (2016) see insecurity as a housing stability and affordability issue. Buffardi et al. (2008) and Diette and Ribar (2018) see it as a combination of housing instability and homelessness. Others define insecurity as a mix of

homelessness and poor housing quality (Western, Braga, and Kohl, 2015), and yet others think of it as a mix of homelessness and unaffordability (Surratt et al., 2015). Campbell et al. (2014), Greder et al. (2008), Liu et al. (2014), and Stahre et al. (2015) equate housing insecurity with unaffordability only; Cutts et al. (2011) and Frank et al. (2010) associate it with only housing instability; and

Thurston et al. (2013) view it solely as unsafe housing.

11 Geller and Franklin (2014) also mentioned eviction as a component of housing insecurity.

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Although a comprehensive housing insecurity measure including all seven insecurity dimensions

is heretofore unavailable, some studies do document the prevalence of specific dimensions at

the national level for the United States. We next present the two dimensions that have national

prevalence statistics (homelessness and housing affordability), followed by a view of currently

existing multidimensional measures.

Homelessness

Of our seven housing insecurity dimensions, homelessness appears to have the most standardized

measurement in the United States, even though there is still disagreement on what constitutes

homelessness. Homelessness is typically categorized in one of three ways: being homeless at the

time of the survey, having been homeless at least one night throughout the survey year, or having

ever been homeless. Since 2007, the U.S. Department of Housing and Urban Development (HUD)

has conducted the Annual Homeless Assessment Report (AHAR) for Congress, which is an annual

point-in-time (PIT) homeless count, measuring the number of literal homeless at the time of

survey, throughout the country (Solari et al., 2016). The AHAR also documents homeless counts as

performed by homeless services agencies, shelters, and transition centers (Solari et al., 2016).

Exhibit 1 shows national estimates from AHAR and other sources prior to AHAR’s inception in 2007.

Exhibit 1

U.S. Homelessness Rate, Point-in-Time Count

0.35

0.30

0.30

0.27

0.25

0.21 0.21 0.21 0.21 0.20 0.20

0.20

0.19 0.18 0.18 0.17 0.17 0.17

0.15

0.13

0.10 0.10

0.10

cent of U.S. Population Homeless

Per

0.06

0.05

0.00

1984 1989 1990 1996 2004 2005 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AHAR

Other Sources

AHAR = Annual Homeless Assessment Report.

Sources: U.S. HUD AHAR 2017 Report, Exhibit 1.1; Honig and Filer (1993); Drake et al. (1989); Lee et al. (2010); National Law Center on Homelessness and Poverty (2004); Pearson, Montgomery, and Locke (2009)

Cityscape 103

Cox, Henwood, Rodnyansky, Rice, and Wenzel

The AHAR process standardized measurements and enabled adequate comparisons. This figure

is a great illustration of how the estimation of prevalence rates can improve once definitions are standardized. Prior to AHAR’s introduction, prevalence rates ranged from a high of roughly 0.3

percent to a low of about 0.06 percent. After the introduction of AHAR, however, prevalence rates

stabilized, ranging from roughly .22 percent to .2 percent from 2007 to 2012, with a continual

drop in the prevalence rates from 2013 to 2016 to about .17 percent. Prior to AHAR, it is likely

that differences in national estimates of homelessness were influenced by definitional differences versus actual differences in the prevalence of homelessness. Also, while some may argue that AHAR

only captures one type of homelessness, literal homelessness, it is clear from Exhibit 1 that AHAR

has helped to improve the estimation of homelessness over time.

The prevalence of homelessness can also vary by the time period over which it is defined and

the unit of measurement (that is, individual or household). For example, the point-in-time

prevalence rate will be vastly different from prevalence rates measuring homelessness over 12

months or one’s lifetime. Specifically, prevalence estimates of individuals homeless at least once during a given year were 1.08 percent, using the 1999 National Survey of Homeless Assistance

Providers and Clients (NSHAPC), and 0.85–1.19 percent in 2004, using estimates from the Urban

Institute and the Harvard Joint Center for Housing Studies (Kushel et al., 2006; National Law

Center for Homelessness and Policy, 2004). These studies occurred before AHAR’s inception, and

their definitions of homelessness may differ, possibly explaining the differences. Widening the

measurement time period further, Link et al. (1994) estimated the population of the United States

that has ever been homeless as approximately 7–14 percent. Thus, the measurement time period

is an important consideration in designing standardized measures.

Housing Affordability

Measures, and hence prevalence rates, vary among scholars documenting housing affordability

issues. The most commonly used are whether a household demonstrates difficulty paying for

housing-related expenses such as rent, mortgage, and utilities; whether rent or mortgage goes

unpaid over a given time period; and whether housing cost presents a burden, defined as spending

more than a certain percentage (usually between 30 and 50 percent) of household income per

month on housing. Other less common measures include experiencing legal issues related to

housing finance, foreclosure, or the receipt of housing assistance through a government program.

Exhibit 2 shows the national estimates for the three most frequently utilized housing affordability measures over time. Depending on the statistic used, recent estimates vary from 6 to 18 percent

of U.S. households that experience difficulties with housing affordability. Each statistic uses a

different nationally representative data set. Severe cost burden and difficulty paying for housing seem to trend closely in years when comparable data are available. On the other hand, the statistic for unpaid rent or mortgage, measured using the U.S. Census Bureau’s Survey of Income and

Program Participation (SIPP), show rates twice as low as severe housing cost burden for the same

years (Siebens, 2013).

12 Nationally reliable statistics on difficulty paying rent come from the National Survey of America’s Families, undertaken by the Urban Institute three times in 1997, 1999, and 2002 and not since.

104 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

Exhibit 2

Affordability-Related Housing Insecurity Estimates for U.S. Households 1997–2011

20

18

n

17

18

18

io 16

15

16

16

ulat 14

14

14

op

13

13

P 12

12

12

11

11

11

.S. 10

f U

8

e o

8

6

6

entag

4

ercP 2

0

00

01

02

03

04

05

06

07

09

10

11

1997

1999

20

20

20

20

20

20

20

20

20

20

20

Severe Cost Burden

Difficulty paying

Households with unpaid

(>50% of income spent on rent)

for housing

rent or mortgage

Sources: Joint Center for Housing Studies (2002, 2003, 2005, 2006, 2007, 2008, 2011, 2012, 2013); Urban Institute and Child Trends (NSAF) (1997, 1999, 2002); Siebens (SIPP) (2013)

National Multidimensional Measures of Housing Insecurity

Few studies mention the dimensions of housing safety or quality and neighborhood safety or quality, and national prevalence statistics for these are likewise difficult to find. Leopold et al. (2017) describe the difficulties in obtaining reliable and representative data on overcrowding, housing instability, housing quality, and neighborhood quality. They also point out the dearth of longitudinal data on

these topics (Leopold et al., 2017). Prevalence of neighborhood issues is often measured for only one neighborhood, city, or region, and definitions and measurements vary.

One alternative for housing quality and safety is the American Housing Survey (AHS), which

provides a variety of statistics that could be used to measure housing quality and safety (see Cox et al., 2017b). Since 1991 HUD has released a biennial report to Congress documenting the “Worst

Case Housing Needs” based on the biennial AHS data. Eligible U.S. households for this designation

are those who are renters, have incomes below 50 percent of area median income, and do not receive housing assistance. Of these, households are counted as having Worst Case Needs if they have severe rent burdens (paying more than 50 percent of income toward rent) or severely inadequate housing

quality in terms of heating, plumbing, electrical systems, or maintenance (HUD, 2017). Worst Case

Housing Needs at the national level have hovered between 4.5 and 6 percent for most years, rising

to 7.4 percent in 2011 in the aftermath of the Great Recession (exhibit 3). The Worst Case Housing Needs reports provide a start to measuring housing insecurity nationally, including the housing

Cityscape 105

Cox, Henwood, Rodnyansky, Rice, and Wenzel

affordability and quality dimensions. This measure leaves out multiple important dimensions of

housing insecurity, however, (such as neighborhood quality and safety, housing safety, instability, and homelessness) and is defined based on very low-income renters.

Exhibit 3

Percentage of U.S. Households with Worst Case Needs: 1978–2015

8.0

7.4

7.0

7.0

6.7

6.3

6.0

6.0

5.5

5.5 5.3

5.1

5.3 5.4

5.0 5.2

5.0

4.7 4.8 4.9

4.0

3.0

2.0

centage of U.S. Households

Per

1.0

0.0

1978 1983 1987 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

Notes: 1981, 1985, and 1989 linearly interpolated.

Sources: U.S. HUD Worst Case Needs Report 2017, Exhibit 1-4; U.S. HUD Worst Case Needs Report 2005; U.S. HUD Trends in Worst Case Needs for Housing, 1978–1999

Aside from HUD’s “Worst Case Housing Needs” reports, Siebens’ U.S. Census Bureau report (2013)

takes steps toward a national snapshot of housing insecurity prevalence based on 2011 data. The

author shows that 3.4 percent, 2.6 percent, and 6.7 percent of U.S. households had poor housing

quality, lived in unsafe housing, and lived in unsafe neighborhoods, respectively. Moreover, the author recommends summarizing a measure of hardship across nine indicators, the first five of which could qualify as housing-related: difficulty meeting essential expenses, not paying rent or mortgage, getting evicted, not paying utilities, having utilities cut off, having phone service cut, not seeing a doctor when needed, not seeing a dentist when needed, or not always having enough food (Siebens, 2013).

According to Siebens’ measure, in 2011, 78 percent of households faced zero hardships, 9 percent

faced one hardship, and 3 percent faced three or more hardships (Siebens, 2013). This measure

bears some similarity to our proposed measure of housing insecurity but focuses on overall material well-being instead of developing a comprehensive measure of housing insecurity, and while the report looks at various dimensions of housing insecurity that we have incorporated in our definition, the focus of the report is not solely on the development of a measure of housing insecurity. Therefore, Siebens’ scale captures both housing and nonhousing measures of well-being of U.S. households.

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A different approach, which looks at the effects of housing and neighborhood quality on mental

health, was undertaken by Wright and Kloos (2007). This approach measured three levels of housing

and environment variables among residents of supportive housing programs for the mentally ill across 34 housing sites in 10 cities in one U.S. state, for a total sample of 249 (Wright and Kloos, 2007).

Self-reported data on apartment quality, neighborhood quality, and neighborhood social climate

were obtained using the Housing Environment Survey. Housing data were validated by an observer

using the Housing Environment Rating Scale, and neighborhood-level data were supplemented by

census tract demographic and socioeconomic data from the 2000 census (Wright and Kloos, 2007).

Well-being was measured using four outcome variables: psychiatric distress, orientation to recovery, residential satisfaction, and adaptive functioning. The study finds that neighborhood-level variables explained more variance in well-being than either apartment- or census-tract-level variables. Wright and Kloos (2007) present an important finding and buttress the case for including neighborhood-level variables in a measure of housing security. The study, however, does not take into consideration housing affordability and stability, which are key components of housing insecurity present in many other studies. In addition, it is a local sample for a subpopulation, which may not readily generalize to the U.S. population as a whole.

As previously mentioned, data availability could pose a challenge to studying the full continuum

of housing insecurity. However, the AHS currently provides readily available data on many of the

housing insecurity dimensions for conducting a proof-of-concept analysis for a uniform instrument

based on the seven dimensions proposed in this paper (see Cox et al., 2017b for an analysis). As

previously mentioned, the AHS is administered biennially by HUD and contains questions pertinent

to housing affordability, quality, and safety, as well as neighborhood safety and quality. The AHS

also contains some information on housing instability, although measurement is incomplete and

inconsistent across survey waves for the variables that could capture housing instability. For example, overcrowding and eviction were added in 1997, while other variables related to housing instability, such as doubling up, foreclosure, and frequent moves, are only included in the 2013 survey. The AHS

is representative at a national level, for certain cities, and at the metropolitan area level, making it a useful tool for national, regional, and major metropolitan area studies. One drawback of the AHS is it follows the housing unit and not the household as the unit of analysis, which could potentially cause additional complications when constructing an externally valid instrument that measures population estimates of housing insecurity at the household level. Specifically, the AHS has a unique longitudinal study design, in which housing units sampled in a specific year are followed over time. This allows for stakeholders to obtain information on how housing stock and its occupants change over time but may require more thought about implementing a housing security module and, more importantly,

what such a module would measure in this type of survey design. This is especially true for capturing the housing insecurity domain of homelessness: AHS is not a reliable instrument to measure current homelessness because only individuals with housing are surveyed.

Discussion

Current working definitions of housing insecurity are inconsistent and incomplete, which may lead

to households that are actually experiencing housing insecurity to be counted as housing secure.

Different measures for the same phenomenon have significant potential to cause comparability

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Cox, Henwood, Rodnyansky, Rice, and Wenzel

issues and, potentially, omitted variable bias if certain facets within subcategories are omitted from measurement. Moreover, having multiple terms to describe a general social problem could lead to

confusion (not just among researchers), making it hard to mobilize resources and efforts to solve the problem. Therefore, based on the conceptual framework put forth by the U.N. General Committee

and the U.S. Department of Health and Human Services (HHS), we propose an operational definition

of housing insecurity that includes seven dimensions: housing stability, housing affordability, housing quality, housing safety, neighborhood safety, neighborhood quality, and homelessness.

This is not to say that research or policy should not individually focus on the different facets of housing insecurity when appropriate; rather, it suggests that to precisely estimate the prevalence of this issue and accurately account for its social costs, we need a comprehensive approach to measure the multiple dimensions of housing insecurity.

Housing insecurity measurement is a global problem that will require the deployment of resources

from not only the research community but also policymakers and practitioners to solve. Without

a comprehensive instrument, many aspects of housing insecurity will continue to be understudied,

and their prevalence and cost to society will not be fully understood. Consequently, it will be difficult to create policies that can impact the plethora of well-being outcomes for adults and children

associated with housing, which could lead to greater inefficiency and waste of resources. Given the potentially large social costs of housing insecurity, it is in the national interest of the United States to ensure a uniform, functional definition of housing insecurity that can capture all its dimensions. That means the government should mobilize and coordinate the resources needed for the development

of a national housing insecurity measure. Given its knowledge base and skill set, the research

community has a comparative advantage in creating, legitimizing, and validating any instrument

developed through this process.

A paradigm for the creation of a uniform housing insecurity instrument already exists with the U.S.

Food Security Survey Module, which has been incorporated into the U.S. Census Bureau’s Current

Population Survey (CPS) annually since 1995 (National Research Council, 2006). In 1984, the

President’s Task Force on Food Assistance determined that the lack of a valid indicator of hunger

constrained public policy concerning this issue. In October 1990, Congress ratified the National

Nutrition Monitoring and Related Research Act, authorizing the preparation and implementation of

a 10-year plan that incorporated as one of its goals to “establish and improve the quality of national nutritional and health status data and related databases and networks, and stimulate research

necessary to develop uniform indicators, standards, methodologies, technologies, and procedures for nutrition monitoring.”13 This led to the creation of the first federal food security instrument in 1995, which was included in the CPS.

The Food Security Module has now been included in the CPS for more than 20 years; as a result,

society’s understanding of food insecurity has grown tremendously. Specifically, we know more about the risk factors that cause food insecurity and whether food insecurity is a chronic or temporary

state. Additionally, food security is now considered a key indicator of the well-being of households 13 Nutrition Monitoring and Related Research Act of 1990. H.R. 1068, 101st Congress (1989–1990). https://www.

congress.gov/bill/101st-congress/house-bill/1608/text.

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Road Map to a Unified Measure of Housing Insecurity

and children, along with being considered an important outcome in the program evaluation of food

assistance programs. All these accomplishments stemmed from a transdisciplinary effort comprising

practitioners, policymakers, and academics devoted to developing a validated scale over 20 years

that has been used to understand and help solve one of society’s most intractable problems

(see Coleman-Jensen, 2015 and appendix exhibit A-4 for a full timeline of the creation of the

food security module). The time has now come to do the same for housing.

Acknowledgments

The authors would like to thank Christine Sabuni and Anna Sporrong for their assistance with this project.

Authors

Robynn Cox, is an assistant professor at the USC Suzanne Dworak-Peck School of

Social Work and can be emailed at robynnco@usc.edu.

Benjamin Henwood, is an associate professor at the USC Suzanne Dworak-Peck School of Social

Work and can be emailed at bhenwood@usc.edu.

Seva Rodnyansky, was a graduate student at the USC Sol Price School of Public Policy when

this article was written. He is currently a post-doctoral scholar at the Goldman School of Public

Policy at the University of California, Berkeley and can be emailed at seva@berkeley.edu.

Eric Rice, is an associate professor at the USC Suzanne Dworak-Peck School of

Social Work and can be emailed at ericr@usc.edu.

Suzanne Wenzel, is the Richard and Ann Thor Professorship in Urban Development and

professor of social work at the USC Suzanne Dworak-Peck School of Social Work and can be

emailed at swenzel@usc.edu.

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Cox, Henwood, Rodnyansky, Rice, and Wenzel

Appendix A

Exhibit A.1

Overall Motivation of Research on Housing Insecurity

Reason

Percent of Papers (N=106)

Explanatory Variable

52

Outcome

42

Prevalence

16

Outcome and Explanatory Variables

7

Outcome and Prevalence

2

Prevalence and Explanatory Variable

2

International Context

10

Exhibit A.2

Housing Insecurity Definition by Search Term, Based on Literature Review

Percent of Papers Mentioning

Search Term

Percent of Papers Mentioning

Search Term (N=102)

Search Term (Excluding

Homelessness (N=74)

Housing Insecurity

16.0

16.4

Housing Affordability

5.7

5.7

Housing Instability

42.5

37.0

Homelessness

34.0

46.6

Housing Quality

2.8

1.4

Housing Satisfaction

0.9

0

Housing Needs

0.9

0

Housing Hardship

4.7

6.8

Neighborhood Safety

1.9

2.7

Housing Safety

0.9

1.4

Exhibit A.3

Measures Used to Capture Housing Insecurity Within the Domains of Housing Affordability and

Housing Instability

Measure

Percent of Papers Using Measure

Housing Affordability

N=30

Difficulty/Inability to Make Payments

83

Housing Cost Burden

37

Foreclosure

10

Legal Housing Issues

7

Rental Assistance

3

Multiple Measures

30

Housing Instability

N=49

Multiple Moves

59

Doubling Up

35

Eviction

18

Overcrowding

18

Duration of Stay

14

Forced Moves

8

Living in Unstable Conditions

2

Living in Multiple Subsidized Units

2

Multiple Measures

39

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Road Map to a Unified Measure of Housing Insecurity

Exhibit A.4

Timeline of the Creation of a Food Security Module

A History of Food Security Measurement in the United States

January 1984

2003

The President’s Task Force on Food

At USDA’s request, an expert panel was

Assistance concluded that the absence of

convened by the Committee on National

any credible indicator of the extent of

Statistics of the National Academies to

hunger “contributes to a climate in which

conduct a thorough review of the food

policy discussions become unhelpfully

security measurement methods and the

heated, and unsubstantiated assertions are

language used to describe those conditions.

then substituted for hard information.”

November 2006

October 1990

USDA introduced new language to describe

The National Nutrition Monitoring and

ranges of severity of food insecurity in

Related Research Act of 1990 became law

response to recommendations by the

and included the Ten-Year Comprehensive

expert panel. “Food insecurity without

Plan to “Recommend a standardized

hunger” was renamed “low food security,”

mechanism and instrument(s) for defining

and “food insecurity with hunger” was

and obtaining data on the prevalence of

renamed “very low food security.”

“food insecurity” or “food insufficiency”

in the United States.

2011

1992

The food security survey questions were

added to the National Health Interview

USDA staff began reviewing existing

Survey, expanding the list of Federal

research on the conceptual basis for

surveys that had previously incorporated

measuring food insecurity and the

the questions, including the Panel Study

practical problems of developing a food

of Income Dynamics, National Health

security questionnaire for surveys.

and Nutrition Examination Survey, Early

Childhood Longitudinal Studies, Survey

January 1984

of Income and Program Participation,

and others.

USDA’s Food and Nutrition Service and the

U.S. Department of Health and Human

2014

Services’ National Center for Health Statistics

sponsored the National Conference on Food

The Food and Agriculture Organization of

Security Measurement and Research. The

the United Nations created and fielded the

conference identified the conceptual basis for

Food Insecurity Experience Scale (FIES) in

a national measure of food insecurity and for

200 languages in 150 countries as part of

implementing it in national surveys.

the Gallup World Poll. The FIES is based in

part on USDA’s food security measure.

April 1985

September 2015

The first Federal food security survey was

administered as part of the U.S. Census

ERS released the 20th year of annual food

Bureau’s annual Current Population Survey.

security survey data.

It has been fielded each year since.

1998

Source: USDA, Economic Research Service

ERS assumed sponsorship of the Census

Bureau’s annual food security survey, along

with responsibility for analyzing and

reporting the data and for coordinating

ongoing USDA research on food security.

Source: Reprinted from Coleman-Jensen (2015). https://www.ers.usda.gov/webdocs/charts/62517/oct15_feature_jensen_fig05.png?v=42276.

Cityscape 111

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Appendix B

Below is a list of readings not cited in the text but incorporated in our literature review. Together, the additional readings and the references comprise the 106 studies we consulted to generate our

literature review. These papers were selected by conducting literature searches in Google Scholar

and Web of Science using the following terms: “housing security,” “housing insecurity,” “housing

affordability,” “housing stability,” “housing instability,” “homelessness,” “housing hardship,” “housing quality,” “housing safety,” and “neighborhood safety.” Relevant papers either (1) included housing security as an explanatory variable measuring outcomes on households, individuals, or populations; (2) sought to understand reasons for housing security as a dependent variable; or (3) assessed the prevalence of housing insecurity. Our search focused on the U.S. context; however, several articles from other developed countries were relevant to the development of a measure of housing insecurity.

A table summarizing all of the articles can be found in Cox et al. (2017a).

Additional Reading

Allgood, S., and R.S. Warren, Jr. 2003. “The duration of homelessness: Evidence from a national

survey,” Journal of Housing Economics 12: 273–290

Aubry, T., F. Klodawsky, and D. Coulombe. 2011. “Comparing the housing trajectories of

different classes within a diverse homeless population,” American Journal of Community Psychology 49: 142–155. https://doi.org/10.1007/s10464-011-9444-z.

Baker, E., K. Mason, and R. Bentley. 2015. “Measuring housing affordability: A longitudinal

approach,” Urban Policy and Research 33: 275–290. https://www.tandfonline.com/doi/abs/

10.1080/08111146.2015.1034853.

Bassuk, E.L., J.C. Buckner, L.F. Weinreb, A. Browne, S.S. Bassuk, R. Dawson, and J.N. Perloff.

1997. “Homelessness in female-headed families: Childhood and adult risk and protective

factors,” American Journal of Public Health 87: 241–248. https://ajph.aphapublications.org/

doi/10.2105/AJPH.87.2.241.

Bassuk, E.L., J.N. Perloff, and R. Dawson. 2001. “Multiply homeless families: The insidious

impact of violence,” Housing Policy Debate 12: 299–320. https://www.tandfonline.com/doi/abs/

10.1080/10511482.2001.9521407.

Bebout, R.R., R.E. Drake, H. Xie, G. McHugo, and M. Harris. 1997. “Housing status among

formerly homeless dually diagnosed adults,” Psychiatric Services 48: 936–941.

https://ps.psychiatryonline.org/doi/abs/10.1176/ps.48.7.936.

Bennett, G.G., L.H. McNeill, K.Y. Wolin, D.T. Duncan, E. Puleo, and K.M. Emmons. 2007.

“Safe to walk? Neighborhood safety and physical activity among public housing residents,”

PLoS Med 4 (10): e306.

112 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

Berzin, S.C., A.M. Rhodes, and M.A. Curtis. 2011. “Housing experiences of former foster youth:

How do they fare in comparison to other youth?” Children and Youth Services Review 33: 2119–

2126. https://psycnet.apa.org/record/2011-14192-001.

Bossarte, R.M., J.R. Blosnich, R.I. Piegari, L.L. Hill, and V. Kane. 2013. “Housing instability

and mental distress among US veterans,” American Journal of Public Health 103: S213–S216.

https://ajph.aphapublications.org/doi/10.2105/AJPH.2013.301277.

Bradley, K.H., R.B. Oliver, N.C. Richardson, and E.M. Slayter. 2001. No Place Like Home:

Housing and the Ex-Prisoner. Issue brief. Boston, MA: Community Resources for Justice.

http://www.crj.org/cji/entry/publication_noplacelikehome.

Brisson, D., and J. Covert. 2015. “Housing instability risk among subsidized housing recipients:

Characteristics associated with late or nonpayment of rent,” Social Work Research 39: 119–128.

https://academic.oup.com/swr/article-abstract/39/2/119/1649409?redirectedFrom=fulltext.

Brueckner, M., M. Green, and S. Saggers. 2011. “The trappings of home: Young homeless

people’s transitions towards independent living,” Housing Studies 26: 1–16.

https://www.tandfonline.com/doi/abs/10.1080/02673037.2010.512751.

Buckner, J. C., E.L. Bassuk, and L.F. Weinreb. 2001. “Predictors of academic achievement

among homeless and low-income housed children,” Journal of School Psychology 39 (1): 45–69.

Burgard, S.A., K.S. Seefeldt, and S. Zelner. 2012. “Housing instability and health: Findings from

the Michigan Recession and Recovery Study,” Social Science & Medicine 75: 2215–2224.

https://www.ncbi.nlm.nih.gov/pubmed/22981839.

Burt, M.R. 1991. “Causes of the growth of homelessness during the 1980s,” Housing Policy

Debate 2: 901–936. https://www.tandfonline.com/doi/abs/10.1080/10511482.1991.9521077.

Cancian, M., and D.R. Meyer. 2004. “Alternative measures of economic success among TANF

participants: Avoiding poverty, hardship, and dependence on public assistance,” Journal of Policy Analysis and Management 23 (3): 531–548.

Carrion, B.V., V.A. Earnshaw, T. Kershaw, J.B. Lewis, E.C. Stasko, J.N. Tobin, and J.R. Ickovics.

2014. “Housing instability and birth weight among young urban mothers,” Journal of Urban

Health 92: 1–9. https://www.ncbi.nlm.nih.gov/pubmed/25344356.

Caton, C.L.M., B. Dominguez, B. Schanzer, D.S. Hasin, P.E. Shrout, A. Felix, E. Hsu,

et al. 2005. “Risk factors for long-term homelessness: Findings from a longitudinal study

of first-time homeless single adults,” American Journal of Public Health 95: 1753–1759.

https://ajph.aphapublications.org/doi/full/10.2105/AJPH.2005.063321.

Cityscape 113

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Clark, S.L. 2010. Housing instability: Toward a better understanding of frequent residential mobility among America’s urban poor. Washington, DC: Center for Housing Policy.

Cobb-Clark, D.A., N. Herault, R. Scutella, and Y-P. Tseng. 2015. “A journey home: What drives

how long people are homeless?” Journal of Urban Economics 91: 57–72.

Coley, R.L., T. Leventhal, A.D. Lynch, and M. Kull. 2013. “Relations between housing

characteristics and the well-being of low-income children and adolescents,” Developmental

Psychology 49: 1775–1789. https://www.ncbi.nlm.nih.gov/pubmed/23244408.

Cook-Craig, P., and L. Koehly. 2011. “Stability in the social support networks of homeless

families in shelter: Findings from a study of families in a faith-based shelter program,” Journal of Family Social Work 14: 191–207. https://www.tandfonline.com/doi/abs/10.1080/10522158.

2011.571553.

Curtis, M.A., H. Corman, K. Noonan, and N.E. Reichman. 2013. “Life shocks and homelessness,”

Demography 50: 2227–2253. https://www.ncbi.nlm.nih.gov/pubmed/23868747.

Dickson-Gomez, J., M. Convey, H. Hilario, A.M. Corbett, and M. Weeks. 2008. “Structural and

personal factors related to access to housing and housing stability among urban drug users in

Hartford, Connecticut,” Contemporary Drug Problems 35: 115–152. https://journals.sagepub.

com/doi/10.1177/009145090803500106.

Dornbusch, S.M. 1994. “Additional perspectives on homeless families,” American Behavioral Scientist

37: 404–411. https://journals.sagepub.com/doi/abs/10.1177/0002764294037003005.

Eastwood, E.A., and J.M. Birnbaum. 2007. “Physical and sexual abuse and unstable housing

among adolescents with HIV,” AIDS and Behavior 11: 116. https://link.springer.com/

article/10.1007/s10461-007-9236-5.

Fowler, P.J., D.B. Henry, and K.E. Marcal. 2015. “Family and housing instability: Longitudinal

impact on adolescent emotional and behavioral well-being,” Social Science Research 53: 364–374.

https://www.ncbi.nlm.nih.gov/pubmed/26188460.

Frederick, T.J., M. Chwalek, J. Hughes, J. Karabanow, and S. Kidd. 2014. “How stable is stable?

Defining and measuring housing stability,” Journal of Community Psychology 42: 964–979.

https://onlinelibrary.wiley.com/doi/abs/10.1002/jcop.21665.

German, D., M.A. Davey, and C.A. Latkin. 2007. “Residential transience and HIV risk

behaviors among injection drug users,” AIDS and Behavior 11: 21. https://link.springer.com/

article/10.1007/s10461-007-9238-3.

114 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

Glomm, G., and A. John. 2002. “Homelessness and labor markets,” Regional Science

and Urban Economics 32: 591–606. https://www.sciencedirect.com/science/article/pii/

S0166046201000977?via%3Dihub.

Gould, T.E., and A.R. Williams. 2010. “Family homelessness: An investigation of structural

effects,” Journal of Human Behavior in the Social Environment 20: 170–192. https://www.

tandfonline.com/doi/abs/10.1080/10911350903269765.

Greenberg, G.A., and R.A. Rosenheck. 2008a. “Homelessness in the state and federal prison

population,” Criminal Behaviour and Mental Health 18 (2): 88–103.

———. 2008b. “Jail incarceration, homelessness, and mental health: A national study,”

Psychiatric Services 59 (2): 170–177.

Harris, C., and D. Wagner. 2016. “The Navajo Nation accepted more than $1 billion for houses.

So, where did it go?” The Arizona Republic. https://www.azcentral.com/story/news/local/

arizona-investigations/2016/12/14/navajo-housing-federal-funds/94563354/.

Heflin, C., J. Sandberg, and P. Rafail. 2009. “The structure of material hardship in US

households: An examination of the coherence behind common measures of well-being,”

Social Problems 56 (4): 746–764.

Herman, D.B., E.S. Susser, E.L. Struening, and B.L. Link. 1997. “Adverse childhood experiences:

Are they risk factors for adult homelessness?” American Journal of Public Health 87: 249–255.

https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.87.2.249.

Hodgson, K.J., K.H. Shelton, M.B.M. van den Bree, and F.J. Los. 2013. “Psychopathology in

young people experiencing homelessness: A systematic review,” American Journal of Public Health 103: e24–e37. https://ajph.aphapublications.org/doi/10.2105/AJPH.2013.301318.

Hurlbut, M.S., R.L. Hough, and P.A. Wood. 1996. “Effects of substance abuse on housing stability

of homeless mentally ill persons in supported housing,” Psychiatric Services 47: 731–736.

https://ps.psychiatryonline.org/doi/10.1176/ps.47.7.731.

Jones, L. 2011. “The impact of transitional housing on the post-discharge functioning of former

foster youth,” Residential Treatment for Children & Youth 28: 17–38. https://www.tandfonline.

com/doi/abs/10.1080/0886571X.2011.541843.

Keall, M., M.G. Baker, P. Howden-Chapman, M. Cunningham, and D. Ormandy. 2010. “Assessing

housing quality and its impact on health, safety and sustainability,” Journal of Epidemiology & Community Health, 64 (9): 754–771.

Cityscape 115

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Khodyakov, D., A. Ochoa, B.L. Olivieri-Mui, C. Bouwmeester, B.J. Zarowitz, M. Patel, B.

Briesacher, et al. 2017. “Screening tool of older person’s prescriptions/screening tools to

alert doctors to right treatment medication criteria modified for U.S. nursing home setting,”

Journal of the American Geriatrics Society 65 (3): 586–591.

Kirkpatrick, S.I., and V. Tarasuk. 2011. “Housing circumstances are associated with household

food access among low-income urban families,” Journal of Urban Health 88: 284–296.

https://www.ncbi.nlm.nih.gov/pubmed/21286826.

Knopf-Amelung, Sarah. 2013. “Aging and housing instability: Homelessness among older and

elderly adults.” In Focus: A Quarterly Research Review of the National HCH Council 2 (1). Nashville, TN: National Health Care for the Homeless Council. https://www.nhchc.org/wp-content/

uploads/2011/09/infocus_september2013.pdf.

Koegel, P., E. Melamid, and M.A. Burnam. 1995. “Childhood risk factors for homelessness

among homeless adults,” American Journal of Public Health 85: 1642–1649.

https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.85.12.1642.

Landree, E., R. Silberglitt, B.G. Chow, L. Sherry, and M.S. Tseng. 2009. A delicate balance:

Portfolio analysis and management for intelligence information dissemination programs. Santa

Monica, CA: RAND Corporation. https://www.rand.org/pubs/monographs/MG939.html.

Los Angeles County Department of Public Health. 2015. Social determinants of health: Housing and health in Los Angeles County. Los Angeles, CA: Los Angeles County Department of Public Health.

Ma, C.T., Lauren Gee, and M.B. Kushel, M.D. 2008. “Associations between housing instability

and food insecurity with health care access in low-income children,” Ambulatory Pediatrics 8 (1): 50–57.

Margolis, A.D., R.J. MacGowan, O. Grinstead, J. Sosman, I. Kashif, T.P. Flanigan, and Project

START Study Group. 2006. “Unprotected sex with multiple partners: Implications for HIV

prevention among young men with a history of incarceration,” Sexually Transmitted Diseases 33

(3): 175–180.

Martinez, J., D. Bell, R. Camacho, L.M. Henry-Reid, M. Bell, C. Watson, and F. Rodriguez. 2000.

“Adherence to antiviral drug regimens in HIV-infected adolescent patients engaged in care in a

comprehensive adolescent and young adult clinic,” Journal of the National Medical Association 92: 55–61.

Mayberry, L.S., M. Shinn, J.G. Benton, and J. Wise. 2014. “Families experiencing housing

instability: The effects of housing programs on family routines and rituals,” American Journal of Orthopsychiatry 84: 95–109. https://www.ncbi.nlm.nih.gov/pubmed/24826832.

116 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

McVicar, D., J. Moschion, and J.C. van Ours. 2015. “From substance use to homelessness

or vice versa?” Social Science & Medicine 136–137: 89–98. https://www.ncbi.nlm.nih.gov/

pubmed/25989002.

Murphy, R.D., S.E. Zemore, and N. Mulia. 2013. “Housing instability and alcohol problems

during the 2007–2009 US recession: The moderating role of perceived family support,” Journal

of Urban Health 91: 17–32. https://link.springer.com/article/10.1007/s11524-013-9813-z.

Nakazato, N., U. Schimmack, and S. Oishi. 2010. “Effect of changes in living conditions on

well-being: A prospective top–down bottom–up model,” Social Indicators Research 100: 115–135.

https://link.springer.com/article/10.1007/s11205-010-9607-6.

Nemiroff, R., T. Aubry, and F. Klodawsky. 2010. “Factors contributing to becoming housed for

women who have experienced homelessness,” Canadian Journal of Urban Research 19: 23–45.

Obradovic´, J., J.D. Long, J.J. Cutuli, C.K. Chan, E. Hinz, D. Heistad, and A.S. Masten. 2009.

“Academic achievement of homeless and highly mobile children in an urban school district:

Longitudinal evidence on risk, growth, and resilience,” Development and Psychopathology 21

(2): 493–518.

Orwin, R.G., C.K. Scott, and C. Arieira. 2005. “Transitions through homelessness and factors

that predict them,” Journal of Substance Abuse Treatment 28: S23–S39.

https://www.journalofsubstanceabusetreatment.com/article/S0740-5472(04)00133-3/abstract.

Pavao, J., J. Alvarez, N. Baumrind, M. Induni, and R. Kimerling. 2007. “Intimate partner

violence and housing instability,” American Journal of Preventive Medicine 32: 143–146.

https://www.ajpmonline.org/article/S0749-3797(06)00434-X/abstract.

Phinney, R., S. Danziger, H.A. Pollack, and K. Seefeldt. 2007. “Housing instability among

current and former welfare recipients,” American Journal of Public Health 97: 832–837. ajph.

aphapublications.org/doi/10.2105/AJPH.2005.082677.

Piliavin, I., B.R. Entner Wright, R.D. Mare, and A.H. Westerfelt. 1996. “Exits from and

returns to homelessness,” Social Service Review 70: 33–57. https://journals.sagepub.com/doi/

abs/10.1177/0044118X11402851.

Pindus, N., T.G. Kingsley, J. Biess, D. Levy, J. Simington, and C. Hayes. 2017. Housing needs of American Indians and Alaska Natives in tribal areas: A report from the Assessment of American Indian, Alaska Native, and Native Hawaiian housing needs. Washington, DC: U.S. Department of Housing and Urban Development: Office of Policy Development and Research.

Cityscape 117

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Quigley, J.M., and S. Raphael. 2001. “The economics of homelessness: The evidence from North

America,” International Journal of Housing Policy 1: 323–336. https://www.tandfonline.com/doi/

abs/10.1080/14616710110091525.

Reid, K.W., E. Vittinghoff, and M.B. Kushel. 2008. “Association between the level of

housing instability, economic standing and health care access: A meta-regression,” Journal

of Health Care for the Poor and Underserved 19: 1212–1228. https://www.ncbi.nlm.nih.gov/

pubmed/19029747.

Rog, D.J., and J.C. Buckner. 2007. “Homeless families and children.” In Toward Understanding

Homelessness: The 2007 National Symposium on Homelessness Research 4: 1–33. Washington,

DC: U.S. Department of Housing and Urban Development. https://www.huduser.gov/portal/

publications/homeless/p5.html.

Rollins, C., N.E. Glass, N.A. Perrin, K.A. Billhardt, A. Clough, J. Barnes, G.C. Hanson, and T.L.

Bloom. 2012. “Housing instability is as strong a predictor of poor health outcomes as level of danger in an abusive relationship: Findings from the SHARE Study,” Journal of Interpersonal Violence 27: 623–643. https://journals.sagepub.com/doi/abs/10.1177/0886260511423241?journalCode=jiva.

Rosenthal, D., M.J. Rotheram-Borus, P. Batterham, S. Mallett, E. Rice, and N.G. Milburn. 2007.

“Housing stability over two years and HIV risk among newly homeless youth,” AIDS and

Behavior 11: 831–841. https://europepmc.org/abstract/med/17497219.

Rubin, D.H., C.J. Erickson, M. San Agustin, S.D. Cleary, J.K. Allen, and P. Cohen. 1996.

“Cognitive and academic functioning of homeless children compared with housed children,”

Pediatrics 97 (3): 289–294.

Shelton, K., P. Taylor, A. Bonner, and M. van den Bree. 2009. “Risk factors for homelessness:

Evidence from a population-based study,” Psychiatric Services 60: 465–472.

https://ps.psychiatryonline.org/doi/full/10.1176/ps.2009.60.4.465.

Stone, Kimberly E., Emmanuella M. Eastman, Andrea C. Gielen, Barbara Squires, Glenda Hicks,

Dana Kaplin, and Janet R. Serwint. 2007. “Home safety in inner cities: Prevalence and feasibility

of home safety-product use in inner-city housing,” Pediatrics 120 (2): e346–e353.

https://pediatrics.aappublications.org/content/120/2/e346..info.

Taylor, M.P., D.J. Pevalin, and J. Todd. 2007. “The psychological costs of unsustainable housing

commitments,” Psychological Medicine 37 (7): 1027–1036.

Toro, P.A., J.M.P. Rabideau, C.W. Bellavia, C.V. Daeschler, D.D. Wall, D.M. Thomas, and S.J. Smith.

1997. “Evaluating an intervention for homeless persons: Results of a field experiment,” Journal of Consulting and Clinical Psychology 65: 476–484. https://psycnet.apa.org/record/1997-06366-013.

118 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

Tsemberis, S., G. McHugo, V. Williams, P. Hanrahan, and A. Stefancic. 2007. “Measuring

homelessness and residential stability: The Residential Time-Line Follow-Back Inventory,” Journal of Community Psychology 35: 29–42. https://onlinelibrary.wiley.com/doi/abs/10.1002/jcop.20132.

U.S. Commission on Human Rights (USCHR). 2003. A quiet crisis: Federal funding and unmet

needs in Indian country. http://www.usccr.gov/pubs/na0703/na0204.pdf.

U.S. Department of Housing and Urban Development (HUD). 2016. Obama administration

announces nearly 50 percent decline in veteran homelessness. Press release.

U.S. General Accounting Office (GAO). 1994. Elementary school children: Many change schools

frequently, harming their education. http://archive.gao.gov/t2pbat4/150724.pdf.

U.S. Government Accountability Office (GAO). 2010. Native American housing: Tribes generally

view block grant program as effective, but tracking of infrastructure plans and investments needs improvement. http://www.gao.gov/assets/310/301157.pdf.

U.S. Interagency Council on Homelessness (USICH). 2011. Opening doors. Federal strategic plan

to prevent and end homelessness. Update 2011. https://www.usich.gov/resources/uploads/asset_

library/USICH_FSPUpdate_2012_12312.pdf.

Vega, W.A., and S.P. Wallace. 2016. “Affordable housing: A key lever to community health

for older Americans,” American Journal of Public Health 106: 635–636.

https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2015.303034.

Yoo, J.P., K.S. Slack, and J.L. Holl. 2009. “Material hardship and the physical health of school-

aged children in low-income households,” American Journal of Public Health 99 (5): 829–836.

Vijayaraghavan, M., E.A. Jacobs, H. Seligman, and A. Fernandez. 2011. “The association between

housing instability, food insecurity, and diabetes self-efficacy in low-income adults,” Journal of Health Care for the Poor and Underserved 22: 1279–1291. https://www.ncbi.nlm.nih.gov/

pubmed/22080709.

References

Bailey, Kathryn T., John T. Cook, Stephanie Ettinger de Cuba, Patrick H. Casey, Mariana Chilton,

Sharon M. Coleman, Diana Becker Cutts, Timothy C. Heeren, Ruth Rose-Jacobs, Maureen

M. Black, and Deborah A. Frank. 2016. “Development of an Index of Subsidized Housing

Availability and its Relationship to Housing Insecurity,” Housing Policy Debate 26 (1): 172–187.

https://www.tandfonline.com/doi/abs/10.1080/10511482.2015.1015042.

Cityscape 119

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Bashir, Samiya A. 2002. “Home is Where the Harm is: Inadequate Housing as a Public Health

Crisis,” American Journal of Public Health 92 (5): 733–738. DOI: 10.2105/ajph.92.5.733.

Buffardi, Anne L., Kathy K. Thomas, King K. Holmes, and Lisa E. Manhart. 2008. “Moving

Upstream: Ecosocial and Psychosocial Correlates of Sexually Transmitted Infections Among

Young Adults in the United States,” American Journal of Public Health 98 (6): 1128–1136. DOI:

10.2105/AJPH.2007.120451.

Byrne, Thomas, Benjamin F. Henwood, and Brynn Scriber. 2018. “Residential moves among

housing first participants,” Journal of Behavioral Health Services & Research 45 (1): 124–132.

DOI: 10.1007/s11414-016-9537-4.

Campbell, Iain, Sharon Parkinson, and Gavin Wood. 2014. Underemployment and housing insecurity: An empirical analysis of HILDA data. Melbourne, Australia: Australian Housing and Urban Research Institute.

Capps, Randy. 2001. Hardship Among Children of Immigrants: Findings from the 1999 National

Survey of American Families. Washington, DC: Urban Institute. http://webarchive.urban.org/

publications/310096.html.

Coleman-Jensen, Alisha. 2015. “Commemorating 20 Years of U.S. Food Security Measurement,”

Amber Waves, October 5. Washington, DC: U.S. Department of Agriculture Economic Research

Service. https://www.ers.usda.gov/amber-waves/2015/october/commemorating-20-years-of-us-

food-security-measurement/.

Coulton, Claudia, Cara J. Pasqualone, Toby Martin, Neil Bania, Nina Lalich, and Lisa Nelson. 2001.

Issues of Housing Affordability and Hardship Among Cuyahoga County Families Leaving Welfare: Quarter 4, 1998–Quarter 3, 1999, Special Topics in Welfare Reform Report No. 1. Cleveland, OH: Center on Urban Poverty and Social Change.

Cox, Robynn, Benjamin Henwood, Seva Rodnyansky, Suzanne Wenzel, and Eric Rice. 2017a.

Roadmap to a Unified Measure of Housing Insecurity. Working paper 07112017. Washington, DC:

Washington Center for Equitable Growth. https://equitablegrowth.org/working-papers/unified-

measure-of-housing-insecurity/.

Cox, Robynn, Seva Rodnyansky, Benjamin Henwood, and Suzanne Wenzel. 2017b. Measuring

Population Estimates of Housing Insecurity in the United States: A Comprehensive Approach.

Working paper 12192017. Washington, DC: Washington Center for Equitable Growth.

https://equitablegrowth.org/working-papers/measuring-housing-insecurity/.

Crowder, Kyle, and Liam Downey. 2010. “Inter-Neighborhood Migration, Race, and Environmental

Hazards: Modeling Micro-Level Processes of Environmental Inequality,” American Journal of

Sociology 115 (4): 1110–1149. DOI: 10.1086/649576.

120 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

Curtis, Marah A., and Amanda Geller. 2010. Housing Insecurity Among Urban Fathers.

Working paper no. 10-08. New York: Columbia Population Research Center. DOI:

10.7916/D8WH2W9T.

Cutts, Diana Becker, Alan F. Meyers, Maureen M. Black, Patrick H. Casey, Mariana Chilton,

John T. Cook, Joni Geppert, Stephanie Ettinger de Cuba, Timothy Heeren, Sharon Coleman,

Ruth Rose-Jacobs, and Deborah A. Frank. 2011. “US Housing Insecurity and the Health of

Very Young Children,” American Journal of Public Health 101 (8): 1508–1514.

DOI: 10.2105/AJPH.2011.300139.

Diette, Timothy M., and David C. Ribar. 2018. “A Longitudinal Analysis of Violence and Housing

Insecurity,” Economic Inquiry 56 (3): 1602–1621. DOI: 10.1111/ecin.12571.

Drake, Robert E., Michael A. Wallach, and J. Schuyler Hoffman. 1989. “Housing Instability and

Homelessness Among Aftercare Patients of an Urban State Hospital,” Psychiatric Services 40 (1): 46–51. DOI: 10.1176/ps.40.1.46.

Dupuis, Ann, and David C. Thorns. 1998. “Home, Home Ownership and the Search for

Ontological Security,” The Sociological Review 46 (1): 24–47. DOI: 10.1111/1467-954X.00088.

Dworsky, Amy. 2008. Educating Homeless Children in Chicago: A Case Study of Children in the

Family Regeneration Program. Chicago, IL: Chapin Hall at the University of Chicago. http://www.

niusileadscape.org/docs/FINAL_PRODUCTS/LearningCarousel/EducatingHomelessChicago.pdf.

Early, Dirk W. 2005. “An Empirical Investigation of the Determinants of Street Homelessness,”

Journal of Housing Economics 14 (1): 27–47. DOI: 10.1016/j.jhe.2005.03.001.

Fowler, Patrick J., Paul A. Toro, and Bart W. Miles. 2011. “Emerging Adulthood and Leaving

Foster Care: Settings Associated with Mental Health,” American Journal of Community Psychology

47 (3-4): 335–348. DOI: 10.1007/s10464-010-9401-2.

Frank, Deborah A., Patrick H. Casey, Maureen M. Black, Ruth Rose-Jacobs, Mariana Chilton,

Diana Cutts, Elizabeth March, Timothy Heeren, Sharon Coleman, Stephanie Ettinger de Cuba,

and John T. Cook. 2010. “Cumulative Hardship and Wellness of Low-Income, Young Children:

Multisite Surveillance Study,” Pediatrics 125 (5): e1115–e1123. DOI: 10.1542/peds.2009-1078.

Geller, Amanda, and Allyson Walker Franklin. 2014. “Paternal Incarceration and the

Housing Security of Urban Mothers,” Journal of Marriage and Family 76 (2): 411–427.

DOI: 10.1111/jomf.12098.

Goldrick-Rab, Sara, Katharine Broton, and Daniel Eisenberg. 2015. “Hungry to Learn: Addressing

Food and Housing Insecurity Among Undergraduates.” Madison, WI: Wisconsin Hope Lab. https://

postsecondary.gatesfoundation.org/wp-content/uploads/2016/11/Hungry-to-Learn.pdf.

Cityscape 121

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Greder, Kimberly, Christine Cook, Steven Garasky, and Lizmelia Ortiz. 2008. Latino Immigrants: Food and Housing Insecurity. Ames, IA: Iowa State University.

Heflin, Colleen, Andrew S. London, and Ellen K. Scott. 2011. "Mitigating Material Hardship:

The Strategies Low-Income Families Employ to Reduce the Consequences of Poverty,"

Sociological Inquiry 81 (2): 223–246. DOI: 10.1111/j.1475-682X.2011.00369.x.

Henry, Meghan, Anna Mahathey, Tyler Morrill, Anna Robinson, Azim Shivji, and Rian Watt.

2018. The 2018 Annual Homeless Assessment Report (AHAR) to Congress: Part 1: Point-in-

Time Estimates of Homelessness. Washington, DC: U.S. Department of Housing and Urban

Development.

Henwood, Benjamin F., Leopoldo J. Cabassa, Catherine M. Craig, and Deborah K. Padgett. 2013.

“Permanent Supportive Housing: Addressing Homelessness and Health Disparities?” American

Journal of Public Health 103 (S2): S188–S192. DOI: 10.2105/AJPH.2013.301490.

Honig, Marjorie, and Randall K. Filer. 1993. “Causes of Intercity Variation in Homelessness,”

The American Economic Review 83 (1): 248–255.

Joint Center for Housing Studies of Harvard University. 2013. The State of the Nation’s Housing 2013. Cambridge, MA. https://www.jchs.harvard.edu/sites/default/files/son2013_0.pdf.

———. 2012. The State of the Nation’s Housing 2012. Cambridge, MA. https://www.jchs.harvard.

edu/sites/default/files/son2012.pdf.

———. 2011. The State of the Nation’s Housing 2011. Cambridge, MA. https://www.jchs.harvard.

edu/sites/default/files/son2011.pdf.

———. 2008. The State of the Nation’s Housing 2008. Cambridge, MA. https://www.jchs.harvard.

edu/sites/default/files/son2008.pdf.

———. 2007. The State of the Nation’s Housing 2007. Cambridge, MA. https://www.jchs.harvard.

edu/sites/default/files/son2007.pdf.

———. 2006. The State of the Nation’s Housing 2006. Cambridge, MA. https://www.jchs.harvard.

edu/sites/default/files/son2006.pdf.

———. 2005. The State of the Nation’s Housing 2005. Cambridge, MA. https://www.jchs.harvard.

edu/sites/default/files/son2005.pdf.

———. 2003. The State of the Nation’s Housing 2003. Cambridge, MA. https://www.jchs.harvard.

edu/sites/default/files/son2003.pdf.

122 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

———. 2002. The State of the Nation’s Housing 2002. Cambridge, MA. https://www.jchs.harvard.

edu/sites/default/files/son2002.pdf.

Katz, Lawrence F., Jeffrey R. Kling, and Jeffrey B. Liebman. 2001. “Moving to Opportunity in

Boston: Early Results of a Randomized Mobility Experiment,” The Quarterly Journal of Economics

116 (2): 607–654. DOI: 10.1162/00335530151144113.

Kushel, Margot B., Reena Gupta, Lauren Gee, and Jennifer S. Haas. 2006. “Housing Instability

and Food Insecurity as Barriers to Health Care Among Low-Income Americans,” Journal of

General Internal Medicine 21 (1): 71–77. DOI: 10.1111/j.1525-1497.2005.00278.x.

Lee, Barrett A., Kimberly A. Tyler, and James D. Wright. 2010. “The New Homelessness

Revisited,” Annual Review of Sociology 36: 501–521. DOI: 10.1146/annurev-soc-070308-115940.

Leopold, Josh, Mary Cunningham, Lily Posey, and Tiffany Manuel. 2017. Improving measures

of housing insecurity: a path forward. Washington, D.C.: The Urban Institute.

Leventhal, Tama, and Jeanne Brooks-Gunn. 2003. “Moving to Opportunity: An Experimental

Study of Neighborhood Effects on Mental Health,” American Journal of Public Health 93 (9): 1576–1582.

Link, Bruce G., Ezra Susser, Ann Stueve, Joe Phelan, Robert E. Moore, and Elmer Struening.

1994. "Lifetime and Five-Year Prevalence of Homelessness in the United States," American Journal of Public Health 84 (12): 1907–1912.

Liu, Yong, Rashid S. Njai, Kurt J. Greenlund, Daniel P. Chapman, and Janet B. Croft. 2014.

“Relationships Between Housing and Food Insecurity, Frequent Mental Distress, and

Insufficient Sleep Among Adults in 12 US States, 2009,” Preventing Chronic Disease 11: E37.

DOI: 10.5888/pcd11.130334.

Low, Setha M., and Denise Lawrence-Zúñiga. 2003. The Anthropology of Space and Place: Locating Culture. Boston, MA: Wiley-Blackwell.

Ludwig, Jens, Greg J. Duncan, Lisa A. Gennetian, Lawrence F. Katz, Ronald C. Kessler, Jeffrey

R. Kling, and Lisa Sanbonmatsu. 2013. “Long-Term Neighborhood Effects on Low-Income

Families: Evidence from Moving to Opportunity,” The American Economic Review 103 (3):

226–231. DOI: 10.1257/aer.103.3.226.

Marsh, Alex, David Gordon, Pauline Heslop, and Christina Pantazis. 2000. “Housing

Deprivation and Health: A Longitudinal Analysis,” Housing Studies 15 (3), 411–428. DOI:

10.1080/02673030050009258.

Cityscape 123

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Mimura, Yoko. 2008. “Housing Cost Burden, Poverty Status, and Economic Hardship Among

Low-Income Families,” Journal of Family and Economic Issues 29 (1): 152–165.

National Law Center on Homelessness & Poverty (NLCHP). 2004. Homelessness in the United

States and the Human Right to Housing. Washington, DC: National Law Center on Homelessness

& Poverty. http://www.mplp.org/Resources/mplpresource.2006-06-13.0349156065/file0.

National Research Council. 2006. Food Insecurity and Hunger in the United States: An Assessment of the Measure. Washington, DC: The National Academies Press. http://www.nap.edu/

catalog/11578/food-insecurity-and-hunger-in-the-united-states-an-assessment.

Padgett, Deborah K. 2007. “There’s no place like (a) home: Ontological security among persons

with serious mental illness in the United States,” Social Science & Medicine 64: 1925–1936.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1934341/.

Padgett, Deborah K., Benjamin F. Henwood, and Sam J. Tsemberis. 2015. Housing First: Ending

Homelessness, Transforming Systems, and Changing Lives. Oxford, UK: Oxford University Press.

Pearson, Carol, Ann Elizabeth Montgomery, and Gretchen Locke. 2009. “Housing Stability

Among Homeless Individuals with Serious Mental Illness Participating in Housing First

Programs,” Journal of Community Psychology 37 (3): 404–417. DOI: 10.1002/jcop.20303.

Poortinga, Wouter, Tatiana Calve, Nikki Jones, Simon Lannon, Tabitha Rees, Sarah E. Rodgers,

Ronan A. Lyons, and Rhodri Johnson. 2017. “Neighborhood Quality and Attachment:

Validation of the Revised Residential Environment Assessment Tool,” Environment and Behavior

49 (3): 255–282. DOI: 10.1177/0013916516634403.

Rourke, Sean B., Tsegaye Bekele, Ruthann Tucker, Saara Greene, Michael Sobota, Jay Koornstra,

LaVerne Monette, Jean Bacon, Shafi Bhuiyan, Sergio Rueda, James Watson, Stephen W. Hwang,

James Dunn, and Keith Hambly. 2012. “Housing Characteristics and Their Influence on Health-

Related Quality of Life in Persons Living with HIV in Ontario, Canada: Results from the Positive

Spaces, Healthy Places Study,” AIDS and Behavior 16 (8): 2361–2373. DOI: 10.1007/s10461-

012-0284-0.

Routhier, Giselle. 2019. "Beyond Worst Case Needs: Measuring the Breadth and Severity of

Housing Insecurity Among Urban Renters," Housing Policy Debate 29 (2): 235–249. DOI:

10.1080/10511482.2018.1509228.

Sanbonmatsu, Lisa, Jeffrey R. Kling, Greg J. Duncan, and Jeanne Brooks-Gunn. 2006.

“Neighborhoods and Academic Achievement Results from the Moving to Opportunity

Experiment,” Journal of Human Resources 41 (4): 649–691. DOI: 10.3368/jhr.XLI.4.649.

124 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

Shaw, Mary. 2004. “Housing and Public Health,” Annual Review of Public Health 25: 397–418.

DOI: 10.1146/annurev.publhealth.25.101802.123036.

Shinn, Marybeth, Beth C. Weitzman, Daniela Stojanovic, James R. Knickman, Lucila Jiménez,

Lisa Duchon, Susan James, and David H. Krantz. 1998. “Predictors of Homelessness Among

Families in New York City: From Shelter Request to Housing Stability,” American Journal of Public Health 88 (11): 1651–1657. DOI: 10.2105/AJPH.88.11.1651.

Siebens, Julie. 2013. Extended Measures of Well-Being: Living Conditions in the United States: 2011

Report No. P70-136. Washington, DC: U.S. Census Bureau. https://www.census.gov/library/

publications/2013/demo/p70-136.html.

Solari, Claudia D., Sean Morris, Azim Shivji, and Tanya de Souza. 2016. The 2015 Annual

Homeless Assessment Report (AHAR) to Congress: PART 2: Estimates of Homelessness in the United States. Washington, DC: U.S. Department of Housing and Urban Development.

Somerville, Peter. 1992. “Homelessness and the Meaning of Home: Rooflessness or

Rootlessness?” International Journal of Urban and Regional Research 16 (4): 529–539. DOI:

10.1111/j.1468-2427.1992.tb00194.x.

Stahre, Mandy, Juliet VanEenwyk, Paul Siegel, and Rashid Njai. 2015. “Housing Insecurity and

the Association with Health Outcomes and Unhealthy Behaviors, Washington State, 2011,”

Preventing Chronic Disease 12: 140511. DOI: 10.5888/pcd12.140511.

Suglia, Shakira Franco, Cristiane S. Duarte, and Megan T. Sandel. 2011. “Housing Quality,

Housing Instability, and Maternal Mental Health,” Journal of Urban Health 88 (6): 1105–1116.

DOI: 10.1007/s11524-011-9587-0.

Surratt, Hilary L., Catherine L. O’Grady, Maria A. Levi-Minzi, and Steven P. Kurtz. 2015.

“Medication Adherence Challenges Among HIV Positive Substance Abusers: The Role of Food

and Housing Insecurity,” AIDS Care: Psychological and Socio-Medical Aspects of AIDS/HIV 27

(3): 307–314. DOI: 10.1080/09540121.2014.967656.

Thurston, Wilfreda E., Amrita Roy, Barbara Clow, David Este, Tess Gordey, Margaret Haworth-

Brockman, Liza McCoy, Rachel Rapaport Beck, Christine Saulnier, and Lesley Carruthers.

2013. “Pathways Into and Out of Homelessness: Domestic Violence and Housing Security

for Immigrant Women,” Journal of Immigrant & Refugee Studies 11 (3): 278–298. DOI:

10.1080/15562948.2013.801734.

Topp, Libby, Jenny Iversen, Eileen Baldry, and Lisa Maher. 2013. “Housing Instability Among

People Who Inject Drugs: Results from the Australian Needle and Syringe Program Survey,”

Journal of Urban Health 90 (4): 699–716. DOI: 10.1007/s11524-012-9730-6.

Cityscape 125

Cox, Henwood, Rodnyansky, Rice, and Wenzel

Tsui, Emma, Nicholas Freudenberg, Luis Manzo, Hollie Jones, Amy Kwan, and Monica Gagnon.

2011. Housing Instability at CUNY: Results from a Survey of CUNY Undergraduate Students.

New York: Healthy CUNY Initiative, City University of New York. https://www.gc.cuny.edu/

CUNY_GC/media/CUNY-Graduate-Center/PDF/Centers/Center%20for%20Human%20

Environments/cunyhousinginstability.pdf.

United Nations. 2014. The Right to Adequate Housing. Fact Sheet No. 21. http://www.ohchr.org/

Documents/Publications/FS21_rev_1_Housing_en.pdf.

U.S. Department of Health and Human Services (HHS). 1969. Ancillary Services to Support

Welfare to Work: Housing Instability. Washington, DC: U.S. Department of Health & Human Services, Office of the Assistant Secretary for Planning and Evaluation.

U.S. Department of Housing and Urban Development (HUD). 2018. The 2018 Annual Homeless

Assessment Report (AHAR) to Congress. Washington, DC: Office of Community Planning and

Development.

———. 2017. The 2017 Annual Homeless Assessment Report (AHAR) to Congress. Washington, DC:

Office of Community Planning and Development.

———. 2017. Worst Case Housing Needs: 2015 Report to Congress. Washington, DC: Office of

Policy Development and Research.

———. 2015. Worst Case Housing Needs: 2015 Report to Congress. Washington, DC: Office of

Policy Development and Research.

———. 2012. The 2012 Annual Homeless Assessment Report (AHAR) to Congress. Washington, DC:

Office of Community Planning and Development.

———. 2005. Worst Case Housing Needs: 2005 Report to Congress. Washington, DC: Office of

Policy Development and Research.

———. 2003. Trends in Worst Case Needs for Housing, 1978–1999. A Report to Congress on Worst

Case Housing Needs. Washington, DC: Office of Policy Development and Research.

U.S. Interagency Council on Homelessness (USICH). 2017. Mayors Challenge to End Homelessness.

https://www.usich.gov/solutions/collaborative-leadership/mayors-challenge.

Urban Institute, and Child Trends. 2002. National Survey of America’s Families (NSAF), 2002.

Ann Arbor, MI: Inter-University Consortium for Political and Social Research [distributor],

2007-10-03. https://www.researchconnections.org/childcare/studies/4582/version/1.

126 Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

———. 1999. National Survey of America’s Families (NSAF), 1999. Ann Arbor, MI:

Inter-University Consortium for Political and Social Research [distributor], 2007-10-03.

https://www.researchconnections.org/childcare/studies/3927/version/1.

———. 1997. National Survey of America’s Families (NSAF), 1997. Ann Arbor, MI:

Inter-University Consortium for Political and Social Research [distributor], 2007-10-04.

https://www.researchconnections.org/childcare/studies/4581/version/1.

von Hoffman, Alexander. 2000. “A Study in Contradictions: The Origins and Legacy of the

Housing Act of 1949,” Housing Policy Debate 11 (2): 299–326.

Warren, Emily J., and Sarah A. Font. 2015. “Housing Insecurity, Maternal Stress, and Child

Maltreatment: An Application of the Family Stress Model,” Social Service Review 89 (1): 9–39.

DOI: 10.1086/680043.

Western, Bruce, Anthony Braga, and Rhiana Kohl. 2015. The Boston Reentry Study: Housing

Insecurity After Prison. Cambridge, MA: Rappaport Institute for Greater Boston. http://scholar.

harvard.edu/files/brucewestern/files/housingvfinal.pdf?m=1441125268.

Wright, Patricia Ann, and Bret Kloos. 2007. "Housing Environment and Mental Health

Outcomes: A Levels of Analysis Perspective." Journal of Environmental Psychology 27 (1): 79–89.

DOI: 10.1016/j.jenvp.2006.12.001.

Ziol-Guest, Kathleen M., and Claire C. McKenna. 2014. “Early Childhood Housing Instability

and School Readiness,” Child Development 85 (1): 103–113. DOI: 10.1111/cdev.12105.

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128 Refereed Papers

The Effects of Flood Insurance

on Housing Markets

by Agustín Indaco, Francesc Ortega, and Süleyman Tas¸pınar

Agustín Indaco

Carnegie Mellon University in Qatar

Francesc Ortega

Süleyman Tas¸pınar

Queens College, City University of New York

Abstract

In this article, we analyze the role of flood insurance on the housing markets of coastal areas. To do so, we assembled a parcel-level dataset of the universe of residential sales for two coastal urban areas in the United States—Miami-Dade County (2008–15) and Virginia Beach (2000–16)—matched with their

Federal Emergency Management Agency (FEMA) flood maps, which characterize the flood-risk level for each property. First, we compare trends in housing values and sales activity among properties on the floodplain, as defined by the National Flood Insurance Program (NFIP), relative to properties located elsewhere within the same area. Despite the heightened flood risk in the past two decades, we do not find evidence of divergent trends. Second, we analyze the effects of the recent reforms to the NFIP.

In 2012 and 2014, Congress passed legislation announcing important increases in insurance premiums and flood map updates. We find robust evidence of large price reductions for properties that were drawn into the flood zone of the new FEMA flood maps. We estimate that, as a result of the mandatory insurance requirement in the flood zone, NFIP insurance costs for such properties in Virginia Beach will increase by an average of about $3,500 per year and lead to a reduction in housing values of about $64,000.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

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

Indaco, Ortega, and Tas¸pınar

Introduction

Rising sea levels have increased the risk of coastal flooding over the past few decades, becoming

the most economically damaging effect of climate change for many coastal locations (Buchanan,

Oppenheimer, and Kopp, 2017). The increasing flood risk has been shown to arise from increased

likelihood of tidal flooding in low-lying areas (Cooper et al., 2013; Sweet and Park, 2014; Sweet et al., 2014), increased frequency and severity of coastal flooding (Vitousek et al., 2017), and intensification of extreme flooding events (Hunter, 2010; Park et al., 2011; Tebaldi, Strauss, and Zervas, 2012).

At the same time, the population in shoreline areas in the United States has substantially increased (Hauer, Evans, and Mishra, 2016). One factor that may have contributed to this trend is the

availability of subsidized flood insurance through the National Flood Insurance Program (NFIP;

Conte and Kelly, 2017). The program, managed by the Federal Emergency Management Agency

(FEMA), was created through the 1968 National Flood Insurance Act and has since risen to more

than 5 million policyholders in 2016. Over the past two decades, NFIP has become burdened with

enormous amounts of debt following hurricanes Katrina (2005) and Sandy (2012), which prompted

a profound reform in 2012 and 2014, respectively. A major goal of the reform was

to increase insurance rates to reflect the increase in flood risk between now and the inception

of the program. As a result, flood insurance premiums increased substantially for many

floodplain properties.1

The main goal of this article is to examine whether flood insurance affects the housing markets of coastal areas. NFIP is the key program at the disposal of the U.S. Government to try to influence

individuals’ housing location decisions in areas close to bodies of water. In the context of rising sea levels, assessing whether the key elements of NFIP—that is, the new flood maps and the resulting

changed schedule of insurance premiums—are effective in shaping location decisions is important.

To carry out the analysis, we assembled a parcel-level dataset including the universe of residential sales for two coastal urban areas in the United States—Miami-Dade County (2008–15) and Virginia

Beach (2000–16)—matched with their FEMA flood maps, which characterize the flood-risk level for

each property.2 Parts of these areas are prone to flooding events, and flood insurance is a first-order policy issue in both.

The first question we ask is whether there has been any divergence in trends regarding housing

values and sales activity among properties under high risk of flooding, as defined by NFIP, relative to properties located elsewhere within the same area. Evidence of divergence in trends would suggest

that housing markets have already been incorporating the increases in coastal flooding risk. We find that these variables behaved very similarly in the floodplain of these coastal areas, relative to the rest of the respective areas, and mostly traced the economic cycle. The lack of evidence of a generalized 1 Flood insurance premiums vary as a function of flood risk and elevation. To understand the consequences of flood insurance reform on housing values in a simple manner, it is helpful to consider an example based on Kousky (2017).

Based on the 2012 reform, the new flood insurance rate corresponding to a single-family home within the 100-year floodplain and 4 feet below the base flood elevation increases from $2,644 to $10,263. Given that the prereform rates did not vary by elevation, however, some properties sitting above the base flood elevation may experience a decrease in premiums. Properties outside the 100-year floodplain pay much lower rates. As a result, the median premium across all single-family policies is $512.

2 Lacking a national sales registry, obtaining data from local sources and homogenizing individual sales data is a time-consuming endeavor.

130 Refereed Papers

The Effects of Flood Insurance on Housing Markets

differential trajectory for housing values on the floodplain of the coastal areas we analyze may be explained by the role of flood insurance. After all, we would not expect an increase in flood risk to have a large impact on housing values in a setting where properties are fully insured and insurance premiums remain constant over time.

Second, we focus on the recent reforms to NFIP in order to identify the effects of flood insurance on the housing market. In 2012 and 2014, Congress passed legislation to consider the updated (higher) estimated flood risk and ensure the financial viability of the flood insurance program.

We focus on two important features of the reform: the increase in flood insurance premiums

scheduled to take place from July 2012 onward and the release of updated flood maps, which

impose changes to a set of properties that are now subject to mandatory flood insurance

requirements. We hypothesize that the increases in premiums mandated by the reforms should

have negatively affected the values of properties located in the floodplain, many of which are subject to mandatory flood insurance requirements. Specifically, we ask whether the increase in premiums

affecting properties sold after July 2012 affected sales prices. We conduct a difference-in-difference analysis on the change in sales prices around the date of the reform. Our estimates do not reveal a differential effect from the enactment of the Biggert-Waters Act on previously established floodplain properties for the areas of Virginia Beach and Miami-Dade. In addition, we study the changes in the values of properties whose idiosyncratic flood risk has been updated in the new flood map. In the

case of Virginia Beach, where updated maps came into effect in 2015, we find clear evidence of

a large reduction in sales prices (of 25–28 log points) for properties that were drawn into the new flood maps. Hence, this finding is evidence that flood risk revisions affect housing values within 2 to 3 years, primarily through the effect on insurance premiums. As grandfathering of premium subsidies is phased out, these effects are likely to become more salient.

The rest of this article is organized as follows. The next section presents a brief review of the related literature. The following section provides background information on the history of flood insurance and recent policy changes. Then, we describe the main data sources and the merging process,

followed by a section with descriptive statistics on the main variables. Next, we study overall trends of housing prices in flood risk areas relative to the overall area. The next two sections present the main results, followed by our conclusion. Some details of the legislation are relegated to an appendix.

Literature

A large literature estimates the price discount on sales of houses within the 100-year floodplain

(Atreya, Ferreira, and Kriesel, 2013; Bin, Kruse, and Landry, 2008; Bin and Landry, 2013; Harrison, Bin, and Polasky, 2004; Smersh and Schwartz, 2001). Most studies use highly localized data and find evidence of a discount, but the magnitude ranges widely (from about 4 percent to 12 percent). In

recent studies, Zhang (2016) and Zhang and Leonard (2018) estimated spatial quantile regression

models using data for the Fargo-Moorhead metropolitan statistical area. They found evidence of a

3- to 6-percent floodplain penalty, larger among lower-priced homes. The authors argued that this

heterogeneous effect may reflect the existence of upper bounds on coverage limits. These studies also show that the penalty rose sharply following a large flooding event in 2009 but fell back to its normal Cityscape 131

Indaco, Ortega, and Tas¸pınar

value 2 years later. Compared with these studies, our dataset is much larger, and we focus on the role of flood insurance rather than the evolution of housing markets in the aftermath of large storms or flooding episodes.

Several recent papers also study flood risk and its implications. Gallagher (2014) studied how

economic agents update flood risk beliefs after floods occur, as measured by insurance take-up

rates. Using a national dataset with information on all flood insurance policies and whether a

community is hit by a flood, he found strong evidence of an immediate increase in the fraction

of homeowners covered by flood insurance in the affected communities, although the effect

vanishes after a few years. Conte and Kelly (2017) documented that the distribution of aggregate

hurricane damage in the United States is fat-tailed. They argued that this may be because the

distribution of coastal property is also fat-tailed; that is, storms tend to intersect geographic

areas with little or no property but also intersect areas with large amounts of property more

often than expected, given a normal distribution. The authors then went on to present a model

of homeowner behavior for buying insurance, which includes a disaster relief agency that

reimburses a fraction of household losses, and showed that moral hazard3 leads to an increase

in the number of households living on the coast and in the size (value) of these properties,

resulting in more damage during catastrophic storms.

Our analysis is particularly related to four recent studies. Bakkensen and Barrage (2017)

estimated that coastal housing prices exceed fundamentals4 by about 10 percent. Their analysis

suggests that residents of flood-prone areas may have below-average fear of flooding, which

might explain the sluggishness in the response of home prices in flood-prone areas to new

information of increased flood risk.

Our use of changes to the flood maps as a source of identification is reminiscent of Votsis and

Perrels (2016). These authors studied the effects of flood risk on housing values in Finland,

exploiting the introduction of flood maps following the European Union Water Directive.

These flood risk maps were made publicly available in 2006–07 for several flood-prone areas

in Finland. The authors employed a difference-in-difference approach to capture the price

differential of flood risk disclosure. They defined the treatment group as those dwellings located in the flood-prone area and the control group as nearby dwellings outside the flood-prone area.

They found a significant price drop after the flood risk maps were released, with values ranging

from 6 percent to 30 percent.

Recent work on the relationship between flood risk and housing values has been concentrated

on the aftermath of Hurricane Sandy in New York in 2012. Ortega and Tas¸pınar (2018) argued

that the hurricane triggered an increase in the perceived risk of living in the flood zone,

persistently lowering housing values. Relatedly, Gibson, Mullins, and Hill (2019) studied the

response of New York’s housing prices to what they considered three flood risk signals:

3 Moral hazard is defined as the behavior of increasing exposure to risk while insured, especially when the increased risk is taken because someone other than the policy holder bears the cost of those risks.

4 Here, “fundamentals” is understood as the basic quantitative and qualitative information that contributes to the financial valuation of a house—for example, number of rooms, year built, neighborhood location, and so on.

132 Refereed Papers

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(1) the Biggert-Waters Flood Insurance Reform Act; (2) Hurricane Sandy; and (3) revised FEMA

flood maps. This study shares with ours the goal of estimating the effects of the Biggert-Waters

Act on the housing market. Their analysis solely concerns New York City, however.5

Flood Insurance

Background: A Brief History of Flood Insurance

Congress created the National Flood Insurance Program (NFIP) in 1968, which is administered

by Federal Emergency Management Agency (FEMA), with the goal of providing affordable flood

insurance to homeowners. An integral part of the program is the Flood Insurance Rate Map (FIRM),

which establishes risk zones. These zones determine flood risk for each property, building code

requirements, and flood insurance requirements. Importantly, properties on the high-risk zone,

also known as the 100-year floodplain, are required by law to purchase flood insurance if they

have federally backed mortgages (or if they have received FEMA assistance in the past).

Largely because of Hurricane Katrina, NFIP accumulated a large amount of debt—more than

$25 billion. To make the program financially stable, Congress passed the Biggert-Waters Flood

Insurance Reform Act in 2012, which planned to eliminate subsidies to flood insurance rates and

phase out several exemptions. However, as a result of vigorous public opposition in affected areas, Congress passed the 2014 Homeowner Flood Insurance Affordability Act. This act repealed some

mandates of the 2012 act and slowed down some rate increases (as of April 1, 2015).6

Premium Increases

The 2012 Biggert-Waters Act stated that policies for properties purchased or newly insured after

July 2012 would have to pay full-risk rates. The act also mandated annual premium increases of

25 percent until rates reflect full risk for structures with severe repeated flood losses as well as for nonprimary residences and businesses (GAO, 2013). The 2014 act slowed down the premium

increases to 18 percent per year but did not repeal them.

New Flood Zone Maps

In addition to the aforementioned changes in flood insurance rates, revised FEMA flood maps were

commissioned by the 2012 act. The release of these maps differs across areas of the United States.

For instance, the new flood zone map for Virginia Beach went into effect on January 2015, whereas

the flood map for Miami-Dade has not yet been revised.7

Flood maps are created by FEMA in partnership with states and local communities. Hydrologic and

hydraulic studies determine ground elevations, as well as the depth of floodwaters, the amount of

water that will be carried during flood events, the width of floodplains, and obstruction of water flows. If FEMA determines that a flood map is required, it consults with state and local officials and 5 A previous version of our article (Indaco, Ortega, and Tas¸pınar, 2018) included analysis of the effects of NFIP reform on New York City’s housing market. We concluded that the close temporal overlap between Hurricane Sandy and the Biggert-Waters Act made it impossible to discern the effects of the two events. As a result, the current version of the article focuses solely on Virginia Beach and Miami-Dade, which were affected by the flood insurance reform but not by Hurricane Sandy. We were not aware of the study by Gibson, Mullins, and Hill (2019) at the time of conducting our analysis, and it seems that the authors of that study were not aware of our article either. Both studies seem to have been carried out simultaneously.

6 The appendix contains further details.

7 A new flood map for New York City was released in early 2013 but has not become effective as of early 2019.

Thus, political and affordability concerns can affect the timing of the adoption of revised flood maps.

Cityscape 133

Indaco, Ortega, and Tas¸pınar

issues a preliminary map. Local authorities can propose amendments or appeals that are considered

before issuing the definitive map and making it publicly available.

The Homeowner Flood Insurance Affordability Act of 2014 directs FEMA to notify members of

Congress when constituents in their district are affected by a flood mapping update. Typically,

members of Congress then disseminate the news to the homeowners in their districts. In addition,

in many states, sellers are required to disclose flood risk to potential buyers.

Data

Our dataset merges two different types of data. The first type of data is the universe of sales in the areas of Virginia Beach and Miami-Dade. These datasets have a similar structure for the two areas, but the coverage in terms of years and the actual property characteristics varies somewhat. The

second type of data that we use are the FIRMs produced by FEMA for each of these two areas.

These data contain shape files that allow us to overlay the tax parcel from the sales dataset onto the map of the flood risk zones.

The resulting dataset contains all sales that took place for these two areas, along with the flood risk class for each of the properties involved.

Housing Sales Data

We obtained the sales data for Virginia Beach from the Virginia Beach Real Estate Assessor’s Office.

The dataset contains the sales price and date for the last three transactions for every property in Virginia Beach. Approximately 160,000 properties included in this dataset have been sold at least

once. The data contain all transactions between January 2000 and September 2016. We merged the

sales data with another dataset provided by the area that contains the precise FEMA code for each

property both for the previous map of 2009 as well as the most recent map that came into effect in 2015. The merged dataset contains data on 72,289 properties and 120,551 transactions realized

after January 2000.

Our data for Miami-Dade County is based on the Florida Department of Revenue tax data files.

We obtained and merged the Sales-Data-Files (SDF), the Name-Address-Legal (NAL), and the

Name-Address-Personal (NAP) files. The merged dataset contains detailed information on parcels,

including land use code, sales price, sales year, sales month, and assessment value, as well as on the geographic characteristics of the parcels. Our sample corresponds to sales recorded from 2008 to

2015, with a total of 356,672 observations. The flood zone information for the parcels is based

on the FEMA flood insurance risk maps (effective since 2009).

Flood Insurance Rate Maps

We obtained the shape files for the FEMA flood insurance maps for our two urban areas of interest.

These maps partition the areas into three areas: the 100-year floodplain (also known as Special Flood Hazard Areas), the 500-year floodplain, and the rest of the area. Among these, the highest risk of flooding pertains to the 100-year floodplain.8 In the past few years, FEMA has produced revised

flood maps. The release of the revised maps has been staggered, and for some areas, the new maps

8 The flood maps include a finer partition of the set of properties, which can then be aggregated. As reported by FEMA (https://www.fema.gov/flood-zones), the 100-year floodplain is the union of the following categories: A, AO, AH, A1-A30, AE, A99, AR, AR/AE, AR/AO, AR/A1–A30, AR/A, V, VE, and V1–V30. The 500-year floodplain (not including the 100-year floodplain) is the union of category B properties and the X-shaded category. Finally, the minimal flood risk zone is the rest of the area, defined as the union of category C properties and the X-unshaded category.

134 Refereed Papers

The Effects of Flood Insurance on Housing Markets

are still under construction. As of early 2019, there exist revised maps for Virginia Beach but not for Miami-Dade. We provide more details on these maps in a later section.

Descriptive Statistics

In order to get rid of entries affected by typos in the sales price, and to eliminate sales of tiny parcels or other transactions that do not refer to proper housing units, we restrict to sales with sales prices above $10,000 and trim observations at the 99th percentile of the sales price. Exhibit 1 provides

information on the distribution of sales by housing type in each of our two areas. Our dataset for Virginia Beach (the data cover the 2000–16 period) contains 120,542 sales, of which 3.9 percent

correspond to properties located on the floodplain. We see a higher share of single-family and

two-family homes on the floodplain (80 percent) than in the rest of the area (74 percent). The data for Miami-Dade cover the 2008–15 period and contain 220,303 sales. In Miami-Dade, the floodplain

accounts for a much larger share of sales (51 percent) than for Virginia Beach, and the share of sales corresponding to single-family and two-family homes is similar on the floodplain (67 percent)

and in the rest of the area (68 percent).

Exhibit 1

Frequency of Sales by Type of Property (Land Use)

freq

freq

freq

rel. freq.

rel. freq.

rel. freq.

All

FP

Outside FP

All

FP

Outside FP

Virginia Beach 2000–2016

Single-Family to Two-Family

89,275

3,713

85,562

0.74

0.80

0.74

Multifamily (More Than 2)

173

12

161

<0.01

<0.01

<0.01

Other

31,094

917

30,177

0.26

0.20

0.26

Total

120,542

4,642

115,900

1.00

1.00

1.00

Miami-Dade 2008–2015

Single-Family to Two-Family

149,118

63,120

86,101

0.68

0.67

0.68

Multifamily (More Than 2)

14,172

6,063

8,126

0.06

0.06

0.06

Other

57,013

25,507

31,593

0.26

0.27

0.25

Total

220,303

94,690

125,820

1.00

1.00

1.00

FP = floodplain.

Notes: Floodplains are defined based on the FEMA flood insurance rate maps. The Virginia Beach and Miami-Dade flood maps are the 2009 versions.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office and the Florida Department of Revenue We now turn to the timing of sales, which is summarized in exhibit 2. This exhibit only reports sales pertaining to single-family homes in order to focus on a more homogeneous subsample that can be

better compared within and across areas. The first three columns refer to Virginia Beach. On average, 199 single-family homes on the floodplain sold, constituting 4 percent of the areawide sales, a rate that has fluctuated very little during the 2000–16 period. The last three columns of the exhibit refer to Miami-Dade. On average, 7,890 single-family homes located on the floodplain sold annually,

amounting to 42 percent of areawide sales.9 In sum, those data suggest that sales activity on the

floodplain has kept the same pace as in the rest of the respective areas during our period of analysis.

9 The frequency of sales for 2015 in Miami-Dade County in our dataset is roughly one-half the size of the number of sales in 2014. That is because the data are missing the sales corresponding to the last two quarters of 2015.

Note, however, that the share of sales corresponding to floodplain properties is 43 percent, in line with the share for the previous years.

Cityscape 135

Indaco, Ortega, and Tas¸pınar

Exhibit 2

Frequency of Sales by Year: Single-Family Homes Only

VB

VB

VB

MD

MD

MD

Year

Outside

FP Share

Outside

FP Share

FP

FP

(%)

FP

FP

(%)

2000

173

4,831

0.03

2001

213

6,210

0.03

2002

205

6,376

0.03

2003

201

5,754

0.03

2004

299

7,219

0.04

2005

256

6,304

0.04

2006

175

5,108

0.03

2007

204

4,888

0.04

2008

140

3,821

0.04

4,406

6,182

0.42

2009

174

4,132

0.04

5,917

8,330

0.42

2010

162

4,145

0.04

8,491

11,786

0.42

2011

188

4,059

0.04

7,544

10,333

0.42

2012

209

4,287

0.05

8,980

12,090

0.43

2013

218

4,716

0.04

11,000

14,792

0.43

2014

186

4,478

0.04

10,958

14,552

0.43

2015

224

4,843

0.04

5,824

8,036

0.42

2016

161

3,592

0.04

Avg.

199

4,986

0.04

7,890

10,763

0.42

FP = floodplain. MD = Miami-Dade. VB = Virginia Beach

Notes: Floodplains are defined based on the FEMA flood insurance rate maps. The Virginia Beach and Miami-Dade flood maps are the 2009 versions. Sales in 2016 for Virginia Beach do not include the data for the last quarter of that year. Likewise, sales for Miami-Dade in 2015 do not include data for the last quarter of that year.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office and the Florida Department of Revenue Let us now turn to the evolution of median sales prices. Exhibit 3 reports median sales prices

for single-family homes. The average sales price for a single-family home in Virginia Beach is

substantially higher on the floodplain than elsewhere in the area ($296,000 versus $229,000)

by about 29 percent. Column 3 shows that this ratio has also fluctuated substantially between

2000 and 2016 but without a clear trend. The last three columns in the exhibit report the data

for Miami-Dade County. In this case, median sales prices are very similar inside and outside the

floodplain ($186,000 versus $182,000). The floodplain differential has remained constant at

around 3 percent in the 2008–15 period. In sum, we do not observe a reduction in the floodplain

price differential associated with houses located on the floodplain taking place from 2013 onward.

Trends in Floodplain Premiums

Housing values in an area’s floodplain may be higher or lower than in the rest of the area. On one hand, proximity to the coast has an amenity value; on the other, it exposes those properties to risk of flooding. Regardless of whether the floodplain price differential is positive or negative, the increased 136 Refereed Papers

The Effects of Flood Insurance on Housing Markets

Exhibit 3

Median Sales Price by Year: Single-Family Homes Only

VB

VB

VB

MD

MD

MD

Year

Outside

FP/Outside

Outside

FP/Outside

FP

FP

(%)

FP

FP

(%)

2000

172,500

138,000

1.25

2001

148,000

145,000

1.02

2002

202,140

159,900

1.26

2003

225,000

175,500

1.28

2004

309,900

210,000

1.48

2005

381,750

265,000

1.44

2006

399,000

284,900

1.40

2007

355,950

285,000

1.25

2008

300,000

267,885

1.12

256,500

255,000

1.01

2009

257,000

250,000

1.03

165,000

165,000

1.00

2010

299,375

250,000

1.20

141,100

140,000

1.01

2011

290,041

240,000

1.21

149,250

145,200

1.03

2012

310,000

245,000

1.27

160,000

154,000

1.04

2013

295,000

245,000

1.20

185,000

180,000

1.03

2014

377,000

247,638

1.52

203,905

199,000

1.02

2015

341,000

250,000

1.36

224,250

220,000

1.02

2016

307,500

259,000

1.19

Avg.

296,346

229,347

1.29

185,626

182,275

1.02

FP = floodplain. MD = Miami-Dade. VB = Virginia Beach.

Notes: Floodplains are defined based on the FEMA flood insurance rate maps. The Virginia Beach and Miami-Dade flood maps are the 2009 versions. Sales in 2016 for Virginia Beach do not include the data for the last quarter of that year. Likewise, sales for Miami-Dade in 2015 do not include data for the last quarter of that year.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office and the Florida Department of Revenue flood risk associated with rising sea levels might have reduced the floodplain price premium if it had been incorporated into the beliefs of market agents. One needs to keep in mind, however, that flood insurance may dampen these effects. If insurance premiums remain unchanged, floodplain housing

prices may not respond to increased flooding risk. Therefore, whether the rise in risk of flooding has already affected housing markets is an empirical question.

Specifically, this section compares the trends in sales prices and sales activity for properties located on the floodplain of each of the two areas relative to properties located elsewhere within the same area. Importantly, in doing so, we will also account for individual-property or neighborhood-level heterogeneity.

Sales Activity

We begin by analyzing sales activity. One option would be to compare annual counts of sales in the floodplain to sales in the rest of the area. The same goal can be attained, however, while accounting for property-level heterogeneity. Specifically, we build a balanced property-level panel where each Cityscape 137

Indaco, Ortega, and Tas¸pınar

property i is observed in each year.10 Our data contain the universe of property sales in each of the areas. Hence, we can define an indicator Soldit for whether property i that takes a value of 1 in years when that property was sold and zero otherwise. Then we estimate a linear probability model:

Soldit = α i + λ t + β t FPi + ε it, (1)

where we are including property fixed-effects (α i), capturing all time-invariant characteristics of the property, in addition to year fixed-effects (λ t) that capture the evolution over time of the sales probability outside the floodplain. Time-varying β t captures the differential in the sales probability associated with being located in the 100-year floodplain (FP).11 The probability that a property is sold in a given year can be interpreted as a measure of selling activity based on the share of the stock of housing that is sold in any given year.

Exhibit 4a

Virginia Beach, Sales Probability: Areawide

.04

.02

0

-.02

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office 10 For each of the two areas, we obtained a complete roster of all residential properties, identified by their tax identifier.

We build a balanced panel at the annual level for all these properties. Because we know all sales over our sample period, we can then create a zero-one sales indicator for each property in each year.

11 In practice, β t FPi is shorthand notation for the sum over all periods of interactions between the floodplain indicator,

FPi, and the corresponding year dummy, Dt.

138 Refereed Papers

The Effects of Flood Insurance on Housing Markets

We begin with the results for Virginia Beach, shown in exhibit 4a and exhibit 4b. The former

figure shows an areawide large and monotonic drop in sales between 2004 and 2008, which

then plateaus between 2009 and 2011. Since 2011, sales have been slowly increasing but

are still far below the 2004 level. Given that data for 2016 are only through September, the

probability of sales for the year is somewhat inferior to that of previous years. This evolution

clearly reflects the economic cycle—see also column 1 in exhibit 5. Turning now to the

floodplain sales differential depicted in exhibit 4b, we observe an initial drop of the floodplain penalty in 2001 of 1 percentage point, relative to the reference year of 2000, that remains

below the reference year until 2012. The floodplain differential eventually oscillates around

its year-2000 value between 2012 and 2016.

Exhibit 4b

Virginia Beach, Sales Probability: Floodplain Differential

.02

0

-.02

-.04

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Notes: Point estimates from the event study at annual frequency on the sample of single-family homes. We are plotting the coefficients of the year dummies (top) and interactions with 100-year floodplain, or FP100 (bottom). Regressions include property fixed-effects, and standard errors are clustered at the block level, which allows for arbitrary correlations across parcels in the same block and over time.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office Cityscape 139

Indaco, Ortega, and Tas¸pınar

Exhibit 5

Estimation Floodplain Differential

Virginia Beach

Virginia Beach

Miami-Dade

Miami-Dade

1

2

3

4

Dep. Var.

Sold

Inp

Sold

Inp

FP x D2001

-0.01

-0.50***

[0.008]

[0.101]

FP x D2002

-0.02

-0.29***

[0.009]

[0.081]

FP x D2003

-0.03***

-0.23**

[0.009]

[0.090]

FP x D2004

-0.02***

-0.04

[0.007]

[0.069]

FP x D2005

-0.01

-0.01

[0.009]

[0.061]

FP x D2006

-0.01

-0.03

[0.008]

[0.057]

FP x D2007

-0.02***

-0.04

[0.007]

[0.061]

FP x D2008

-0.01

0.03

[0.007]

[0.061]

FP x D2009

-0.01

0.00

-0.000

0.052*

[0.007]

[0.061]

[0.004]

[0.027]

FP x D2010

-0.01

-0.04

-0.000

0.004

[0.008]

[0.060]

[0.003]

[0.023]

FP x D2011

-0.01

-0.02

0.002

0.020

[0.006]

[0.069]

[0.004]

[0.024]

FP x D2012

0.00

-0.02

0.005

0.025

[0.006]

[0.074]

[0.005]

[0.024]

FP x D2013

0.01

-0.08

0.004

0.013

[0.008]

[0.071]

[0.003]

[0.023]

FP x D2014

0.01

-0.10

0.008**

0.010

[0.008]

[0.082]

[0.004]

[0.023]

FP x D2015

-0.01

-0.01

0.001

0.025

[0.008]

[0.077]

[0.003]

[0.025]

FP x D2016

-0.00

0.00

[0.007]

[0.053]

Observations

1,063,928

89,275

815,104

131,230

Number of blocks

54,268

54,268

101,888

101,915

Fixed-effects

parcel

parcel

parcel

parcel

Clustering s.e.

block

block

block

block

Dep. Var. = dependent variable. FP = floodplain. Inp = log of sales price. s.e. = standard errors.

***p < 0.01, **p < 0.05, *p < 0.1

Notes: All models include property fixed-effects. Sample contains sales of single-family homes only. The sales probability models are estimated on a balanced panel, where each parcel appears in every year. The sample periods are 2000–16 for Virginia Beach and 2008–15 for Miami-Dade County. The coefficients referring to the first year in each area’s sample are dropped to avoid perfect collinearity. Standard errors clustered at the block level, which allows for arbitrary correlations across parcels in the same block and over time.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office and the Florida Department of Revenue 140 Refereed Papers

The Effects of Flood Insurance on Housing Markets

The graphs for Miami-Dade County also confirm this pattern. Sales activity clearly traces the

economic cycle (exhibit 6a), and sales probabilities for floodplain properties appear to increase

slightly during our sample period before going back to their initial value in the reference year

2008 (exhibit 6b).12

Exhibit 6a

Miami-Dade, Sales Probability: Areawide

.15

.10

.05

0

-.05

2009

2010

2011

2012

2013

2014

2015

Note: Exhibit plots the point estimates along with the corresponding 95-percent confidence interval.

Source: Authors’ calculations, using data from the Florida Department of Revenue

12 The estimates are collected in column 6 of exhibit 5. Note also the sharp drop in exhibit 6a corresponding to 2015.

This reflects the fact that our data for 2015 are missing the sales for the last two quarters. However, this seems to affect similarly properties on and off the floodplain. Thus, it probably does not bias the estimated floodplain differentials plotted in exhibit 6b.

Cityscape 141

Indaco, Ortega, and Tas¸pınar

Exhibit 6b

Miami-Dade, Sales Probability: Floodplain Differential

.015

.010

.005

0

-.005

-.010

2009

2010

2011

2012

2013

2014

2015

Notes: Point estimates from the event study at annual frequency on the sample of single-family homes. We are plotting the coefficients of the year dummies (top) and interactions with 100-year floodplain, or FP100 (bottom). Regressions include property fixed-effects, and standard errors are clustered at the block level, which allows for arbitrary correlations across parcels in the same block and over time.

Source: Authors’ calculations, using data from the Florida Department of Revenue

In conclusion, our analysis shows that sales in each area have been largely driven by the housing

cycle, and we do not find systematic evidence of a differential evolution of sales activity in the floodplain of the areas we have analyzed.

Sales Prices

Next, we turn to the analysis of sales prices. Let pit denote the sales price of property i in year t.

We assume the log of the sales price is given by

In pit = α i + λ t + β t FPi + ε it , (2)

where FPi is an indicator for being located on the 100-year floodplain (as defined at the beginning of the sample period). The point estimates for year dummies {λ t} trace out the average housing prices (for single-family homes) in the area, while {β t} captures the floodplain price differential. Clearly, 142 Refereed Papers

The Effects of Flood Insurance on Housing Markets

because of the property fixed-effects, properties that are sold only once do not contribute to the identification of the coefficients. In effect, our sample consists of properties that were sold repeatedly during our sample period.13

We report the time-varying coefficients graphically, beginning with the results for Virginia Beach. As shown in exhibit 7a, the housing cycle was also clearly noticeable in this area, with prices peaking in 2006, falling until 2011, and recovering since then. More relevant for our purposes, exhibit 7b suggests that the floodplain price differential remained stable around the 2000 level between 2004

and 2012, when the Biggert-Waters Act was implemented. The detailed estimates for {˄β t} can be found in column 2 in exhibit 5. Exhibit 8a illustrates the results for Miami-Dade. As seen in the

figure, sales prices outside the floodplain remained depressed between 2009 and 2011 and then

increased rapidly from 2012 onward. But, once again, we see no evidence of a differential trend in sales prices on the floodplain relative to that of the rest of the area in exhibit 8b. The estimates for

{˄β t} for Miami-Dade can be found in column 4 in exhibit 5.

Exhibit 7a

Virginia Beach, Sales Price: Areawide

.8

.6

.4

.2

0

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Note: Exhibit plots the point estimates along with the corresponding 95-percent confidence interval.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office 13 The point estimates are very similar if we use area-block fixed-effects in place of property fixed-effects. The reason is that houses in the same neighborhood (defined as an area or census block) tend to share many characteristics, such as construction year and size, because they were developed around the same time.

Cityscape 143

Indaco, Ortega, and Tas¸pınar

Exhibit 7b

Virginia Beach, Sales Price: Floodplain Differential

.2

0

-.2

-.4

-.6

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Notes: Point estimates from the event study at annual frequency on the sample of single-family homes. We are plotting the coefficients of the year dummies (top) and interactions with 100-year floodplain, or FP100 (bottom). Regressions include property fixed-effects, and standard errors are clustered at the block level, which allows for arbitrary correlations across parcels in the same block and over time.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office Summing up, our analysis in this section suggests that sales activity and housing prices have

been strongly influenced by the economic cycle. This has been true both for properties located

on the floodplain as elsewhere in the area. But, in general, we find very little evidence of

differential trends for the floodplain areas. Our interpretation of this finding, particularly for the years prior to the 2012–14 flood insurance reform, is that flood insurance has cushioned

the impact of increased flooding risk on housing values. The reason is that premiums remained

subsidized and largely unchanged during that period, despite the increased evidence of higher

risk.14 The trends analysis in this section can also be used to assess the assumption that causal

interpretation of difference-in-difference estimates requires the identifying assumption of a

lack of differential pretreatment trends for flood zone properties. Given that sales activities and sales prices evolved in a very similar fashion in and outside the flood zones in these areas, we

understand that a causal interpretation of the estimates for Virginia Beach and Miami-Dade

County is justified.

14 This interpretation is consistent with the findings in Bosker et al. (2018). Using detailed data for the Netherlands, where flood insurance is unavailable, these authors estimate a 1-percent price penalty for residential properties located in the flood zone. The availability of flood insurance in the United States may have been enough to shrink the flood zone price penalty down to almost zero.

144 Refereed Papers

The Effects of Flood Insurance on Housing Markets

Exhibit 8a

Miami-Dade, Sales Price: Areawide

.2

0

-.2

-.4

-.6

2009

2010

2011

2012

2013

2014

2015

Note: Exhibit plots the point estimates, along with the corresponding 95-percent confidence interval.

Source: Authors’ calculations, using data from the Florida Department of Revenue

Flood Insurance Reform: Premium Increases

Difference-in-Difference Estimation

As argued earlier, the 2012 flood reform ushered in increases in flood insurance premiums that

may have affected some sales that took place after July 2012. The 2014 act attenuated these

increases (to 18 percent per year) but did not eliminate them. As a result, it is natural to consider a difference-in-difference estimator around that date, comparing properties in the floodplain to

properties located elsewhere in the area. In order to more cleanly identify the effects of the policy change, we follow Hallstrom and Smith (2005) (who were interested in the effects of flood risk)

and focus our analysis on the repeat sales subsample that brackets July 2012.15 We hypothesize

that the price increases between two such sales for properties on the floodplain will be smaller than the price increase experienced by properties located elsewhere in the area. It is helpful to think of our treatment group as containing houses located on the floodplain that were sold before and after 15 For houses that were sold more than once after July 2012, we keep only the first sale and drop the later ones.

This corresponds to 241 sales for Virginia Beach and 6,178 for Miami-Dade.

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Exhibit 8b

Miami-Dade, Sales Price: Floodplain Differential

.15

.10

.05

0

-.05

2009

2010

2011

2012

2013

2014

2015

Notes: Point estimates from the event study at annual frequency on the sample of single-family homes. We are plotting the coefficients of the year dummies (top) and interactions with 100-year floodplain, or FP100 (bottom). Regressions include property fixed-effects, and standard errors are clustered at the block level, which allows for arbitrary correlations across parcels in the same block and over time. Exhibit plots the point estimates, along with the corresponding 95-percent confidence interval.

Source: Authors’ calculations, using data from the Florida Department of Revenue

July 2012. The control group includes repeat sales that happened before July 2012 and repeat sales that may straddle across 2012 but refer to properties located outside the floodplain.16

More specifically, we define a new indicator, PostBWt (for Biggert-Waters Act), for sales that took place after July 2012, and FPi denotes the floodplain dummy as before. We then pose that the log of the sales price for property i in year t is given by

In pit = α i + λ t + β t PostBWt x FPi + ε it , (3)

where coefficient β identifies the effects of the 2012 Biggert-Waters Act (effective from July 2012) on houses in the floodplain. We expect this coefficient to be negative because of the increase in

flood insurance for houses located on the floodplain and estimate the model separately for each

of our two coastal areas.

16 We can experiment with different variations of the control group, for example, restricting only to repeat sales that bracket July 2012. In that case, the control group is only made up of the repeat sales outside the floodplain.

146 Refereed Papers

The Effects of Flood Insurance on Housing Markets

As before, we restrict to the sample of single-family homes. The data for Virginia Beach contain

89,275 sales for single-family units between January 2000 and September 2016. Roughly 31

percent of those correspond to properties that have been sold only once in this period and are

thus dropped. In our sample period, roughly 40 percent of properties were sold exactly twice, and

roughly 29 percent were sold at least three times.17 For Miami-Dade, our data contain 131,099

sales for single-family homes from 2008 to 2015. Approximately 76 percent of the sales refer to

properties that were sold only once in this period. Among properties sold more than once, about

20 percent of the sales correspond to properties that were sold twice, and about 2.7 percent

were sold three times.18

Let us now turn to our estimation results. The key coefficient is the one associated with the

interaction between the post-reform and the floodplain indicators, denoted by β. This coefficient

is identified by the change in price for houses that were sold both before and after the reform

and are located on the floodplain, the net of the price changes between other pairs of sales.

Exhibit 9 collects the estimates. We begin with the results for Virginia Beach. In column 1,

the point estimate of the coefficient of interest is positive but not statistically different from zero.

In the case of Miami-Dade (column 2), it is negative, but, again, we are unable to reject the zero-null hypothesis. Taken together, these estimates suggest that the premium changes associated with

the 2012 act have not had, for the time being, any effect on sales prices for properties located on the floodplain on account of the data for the two areas in our sample.

Exhibit 9

Difference-in-Difference Estimator for the Log Sales Price Around July 2012, Repeat Sales Only

Virginia Beach

Miami-Dade

Dep. Var. Inp

1

2

FP x PostBWA

0.04

-0.008***

[0.023]

[0.020]

Observations

61,281

49,290

R-Squared

0.329

0.222

Number of Parcels

26,428

23,808

Year-Month Dummies

yes

yes

Sample

2000–16m9

2008–15

BWA = Biggert-Waters Act. FP = floodplain. Dep. Var. lnp = Dependent variable is log of sales price.

***p < 0.01, **p < 0.05, *p < 0.1

Notes: All models include property fixed-effects. PostBWA is an indicator taking a value of one for sales that took place after July 2012. Standard errors clustered at the block level, which allows for arbitrary correlations across parcels in the same block and over time.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office and the Florida Department of Revenue 17 Recall that the data provided to us by the Virginia Beach Government Real Estate Assessor’s Office include only the last three sales for each household.

18 The remaining 3 percent are units sold four to six times.

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Flood Insurance: New Flood Maps

The 2012 Biggert-Waters Act called for updating the flood maps across the country. This is a

staggered process. Whereas new maps for Virginia Beach were completed and put in effect in

2015, the revision of the flood map for Miami-Dade is still under construction, with an unknown

release date.

Interesting for our purposes, the new flood maps typically change the risk classification for

some properties. For instance, when the floodplain is expanded, some units that were located

outside the floodplain under the old map now find themselves located inside the new floodplain

because of the increased risk of flooding. This has potentially large consequences in terms of flood insurance. First, these properties are now subject to the mandatory flood insurance requirements.

In addition, the premiums these properties face are typically substantially higher, in line with

the new risk classification. It is important to keep in mind that the 2012 and 2014 reforms allow

for “grandfathering.” Namely, properties that were already insured can keep their old (lower)

premiums. When the property is sold (or the insurance policy lapses), however, the applicable

premiums will be those that correspond to the new risk classification.

It is also worth noting that property reclassifications may not always purely reflect an increase

in flood risk. Pralle (2019) analyzed the politics behind flood map revisions and documented

several instances where FEMA's revisions were likely influenced by local political concerns, rather than being solely driven by objective risk assessments. Nonetheless, a change in a property’s risk classification that draws that property in or out of a flood zone will typically have an impact on the flood insurance premium paid by the owner, which should be expected to capitalize in the market

value of the property.

Our goal in this section is to exploit the variation in the risk category of the properties that were reclassified in the new flood maps. We hypothesize that the prices of the houses subject to risk

reclassification may have been affected by the release of relevant information.

FEMA Flood Maps

FEMA flood maps for Virginia Beach were revised in both 2009 and 2015.19 The 2015 map

happens to contain fewer buildings in the flood zone than its predecessor.20 Exhibit 10 summarizes the changes in risk classification. In terms of sales, our data contain 520 cases of properties that were not part of the 100-year floodplain under the 2009 map but have been included in the newly

defined floodplain. At the same time, 683 sales correspond to properties that were previously in

the floodplain but are now excluded from it. The corresponding numbers for single-family homes

are 485 and 534, respectively.

19 The link lists the FEMA flood map revisions scheduled for 2015: https://www.servicelinknationalflood.com/Content/

pdfs/2015_Revision_List.pdf.

20 Individual properties or whole developments can request changes in risk classification to FEMA by providing their own technical assessments and by adopting protective measures.

148 Refereed Papers

The Effects of Flood Insurance on Housing Markets

Exhibit 10

Transition Matrix Flood Zones: Sales Counts: Virginia Beach

All Properties

All Properties

Single-Family

Single-Family

Dep. Var. Inp

Counts

Shares

Counts

Shares

Out-Out

115,389

0.957

85,077

0.953

FP-FP

3,959

0.033

3,179

0.036

Out-FP

520

0.004

485

0.005

FP-Out

683

0.006

534

0.006

Total

120,551

1.000

89,275

1.000

Dep. Var. lnp = Dependent variable is log of sales price. FP = floodplain.

Note: A property is in the (100-year) floodplain if it has risk classification A, AE, AH, or VE in the FEMA flood map. All these categories are subject to mandatory flood insurance requirements. The specific definitions for the flood zone designations are given in the appendix. The flood maps for Virginia Beach are dated 2009 and 2015.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office Thus, many individual homeowners received an update on their properties’ idiosyncratic flood

risk. Clearly, the properties that were not in the flood zone under the old map but were classified as belonging to the flood zone under the new map received the largest and most relevant amount

of information. We will exploit this in the next section.

Regression Analysis

In order to analyze the effects of a change in the flood risk zone designation, we restrict to sales of properties located in the floodplain under the old map or the new one (or both). Accordingly,

we build indicators for sales corresponding to properties located within the floodplain under

both maps ( InIn = 1), for sales of properties that were initially outside the floodplain but are within it under the new map ( OutIn = 1), and for sales of properties that were initially within the floodplain but are excluded from it under the new map ( InOut = 1). The resulting sample size (in terms of sales) is 5,162 (4,198 of which are single-family homes).

As before, we consider regression models for both sales activity and sales prices. The former

analysis is based on a balanced panel, and the dependent variable is an indicator for whether

a given property i was sold in year t:

Soldit = α i + λ t + β OIPostt x OutIni + β IOPostt x InOuti + ε it .

(4)

We note that property fixed-effects and year dummies are included in the regression, and in the

omitted category are properties that were located on the floodplain in both maps ( InIn = 1). The

"Post" indicator in the regression for Virginia Beach takes a value of 1 for 2015.

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In terms of sales prices, we consider the following (property) fixed-effects specification:

In pit = α i + λ t + β OIPostt x OutIni + β IOPostt x InOuti + ε it, (5)

where the dependent variable is the log of the sales price of property i and quarter-year t.

Our hypothesis is that β OI < 0 < β IO, indicating that properties that are being assigned a higher flood risk and are subject to the mandatory insurance requirement should experience a drop in

price, while properties whose risk is revised downward should experience a price increase. The

predictions in terms of the sales probability are less clear. An increase in the risk assessment could lead to either an increase or a decrease in the probability of being sold, depending on the speed

of adjustment of the property’s price.

The results are collected in exhibit 11. Column 1 reports the estimates regarding the sales

probability. The point estimates for both β OI and β IO are negative and statistically significant.

That is, the sales probability for houses experiencing a change in their risk assessment (in either direction) seems to have fallen relative to houses that were in the floodplain under both the old

and new maps. In terms of sales prices (columns 2 and 3), we find clear evidence of a large

reduction in sales prices (of 25–28 log points) for properties that were drawn into the new flood

maps, confirming our expectation. At the mean of our sample, this effect entails a $64,000

reduction in the value of a single-family home.21

Exhibit 11

Effects of Risk Reclassification: Virginia Beach

1

2

3

Dep. Var.

Sold

In p

In p

Post x Out-In

-0.01*

-0.25***

-0.28***

[0.006]

[0.076]

[0.078]

Post x In-Out

-0.01**

-0.07***

0.01

[0.004]

[0.085]

[0.074]

Observations

62,033

5,162

4,198

R-Squared

0.004

0.376

0.407

Number of Parcels

3,380

3,380

2,736

Building Class

All

All

Fam1

Dep. Var. = dependent variable. Fam1 = Single-family. ln p = log of sales price.

***p < 0.01, **p < 0.05, *p < 0.1

Notes: The “Post” indicator in the regression for Virginia Beach takes a value of 1 for 2016. Property fixed-effects are included in all models. The sample only includes sales referring to properties that are located on the floodplain under the old or new flood maps (or both). The omitted category in the regression models consists of sales of properties located on the floodplain under both the old and new maps. Recall that in column 1, the dataset is a balanced panel, with zero values for property-period cells if the property was not sold. All columns include year dummies. Standard errors are clustered at the area-block level in all columns.

Source: Authors’ calculations, using data from the Virginia Beach Real Estate Assessor's Office 150 Refereed Papers

The Effects of Flood Insurance on Housing Markets

Conclusion

The National Flood Insurance Program (NFIP) is the key program at the disposal of the U.S.

government to shape the actions of individuals regarding the decision to live in flood-prone

areas. In the current context of rising sea levels, it is important to assess the effectiveness of this policy tool. The mandatory insurance requirement derived from the FEMA flood maps and the

corresponding premiums are the elements of NFIP that more directly affect consumers through

their effect on housing values. This article analyzes the effectiveness of these two aspects of NFIP

based on the recent changes to the program, using data for two important urban coastal areas:

Virginia Beach and Miami-Dade County.

It is important to distinguish the effects of NFIP reforms from those of objective flood risk.

At some level, the flood maps and flood insurance premiums are connected to estimates of flood

risk at a high geographic granularity. Undoubtedly, however, other factors also play a role, such

as the decision to update flood maps or allow grandfathering rules, or mayors’ decisions to appeal a new flood map (Pralle, 2019). These factors can drive a wedge between objective flood risk

and market values. From this viewpoint, the usefulness of our analysis to identify the effects of

increased flood risk may be limited.

Our first finding is that property sales and sales prices have evolved similarly in the floodplains of Virginia Beach and Miami-Dade and elsewhere in these areas, largely tracing the economic cycle.

In a context of increasing flood risk, we interpret this parallel evolution as driven by the availability of flood insurance at constant and subsidized rates up until 2012, which may have cushioned the

impact of increased risk on housing values. First, homeowners also may not be fully aware of flood risk, homeowners less concerned about flooding risk may sort into the flood zone (Bakkensen and

Barrage, 2017), and the effective difference in flood insurance costs in and out of the flood zone may have been relatively small in the prereform period.

Second, we focus on two specific features of the 2012–14 flood insurance reform to try to

identify the effects of flood insurance on the housing market. First, we ask whether the scheduled increase in insurance premiums affecting properties sold after July 2012 affected sales prices. The estimates suggest that has not been the case. Because of the gradual implementation schedule of

the new rates, these effects may only materialize over a longer time span. The current high degree of heterogeneity in flood insurance premiums among properties within the flood zone may also

attenuate the estimates.

Third, the reform also mandated that FEMA update the flood maps of all areas, which is a

staggered process. New maps were publicly released and adopted for Virginia Beach in 2015.

The new maps provide an additional source of identification. Specifically, we focus on the

21 It is also interesting to quantify the increase in flood insurance costs for properties that are drawn into the flood zone. We obtained insurance quotes from The Flood Insurance Agency for a single-family house in Virginia Beach (flood zone A, AE, or AH) with a replacement value of $250,000, content coverage of $100,000, and a $5,000

deductible. The resulting annual premium is $3,506. Using a 5-percent interest rate, the present discounted value of such a payment amounts to $70,120. This is remarkably close to the $64,000 price reduction that we estimated.

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properties that have experienced an upward or downward revision in their idiosyncratic flood

risk classification. We found a large and robust negative effect on the prices of properties that have suffered an upward revision in flood risk in Virginia Beach (of 25–28 log points). We estimate that, as a result of the mandatory insurance requirement in the flood zone, NFIP insurance costs for

these properties will increase by an average of about $3,500 per year and lead to a reduction

in housing values of about $64,000.

These findings lead us to draw the following conclusions. First, we believe that the more gradual

pace of reform adopted in 2014 has delayed the effects of the more ambitious 2012 NFIP reform

on the housing market. We expect flood-zone housing values to reflect the increases in flood

insurance premiums over the medium term. However, our analysis has shown that the adoption

of new flood maps has a large and immediate effect on the values of properties that are drawn into the new flood zone, probably due to the mandatory flood insurance requirement now affecting

these properties.

All in all, our findings reveal that flood insurance reform has the potential to affect housing values and, hence, shape households’ location decisions. However, the implementation of the reforms has

been slowed down by political factors. At a national level, the Great Recession played an important role in the decision to adopt a more gradual increase in flood insurance premiums in 2014.

Likewise, areas like New York City appealed the new flood maps made public in 2013,

citing affordability concerns, delaying its implementation for several years.

Appendix

The Flood Insurance Reform Acts in Detail

The 2012 Biggert-Waters Act (BWA)

With BWA, the high-risk zones are expanded to reflect current risk levels. Regarding insurance

rates, there are increases for some properties (described in the following). Although rates will not change immediately for about 70 percent of the properties (because of grandfathering rules),

once those houses are sold (or experience massive flood damage), the new rates will kick in.

Specific implications:

• Policies will be written or renewed at full-risk rates for properties purchased or newly insured after July 5, 2012.

From October 1, 2013:

• Subsidized policies for (1) non-primary residences, (2) businesses, and (3) structures with

severe repeated flood losses will have annual premium increases of 25 percent until rates

reflect full risk.

152 Refereed Papers

The Effects of Flood Insurance on Housing Markets

• Primary residences in Special Flood Hazard Areas (SFHAs) can keep their subsidized rates

until (1) the property is sold, (2) the policy lapses, (3) the property suffers severe flood

losses (and the owner refuses an offer to mitigate), or (4) a new policy is purchased.

• Phasing out of grandfathered rates over 5 years.22

• Establishment of a Reserve Fund to help cover costs when claims exceed the annual

premiums collected by the National Flood Insurance Program (NFIP).

The 2014 Homeowner Flood Insurance Affordability Act (HFIAA)

This act repealed some mandates of the 2012 act, slowed down some rate increases, and

amended most provisions mandating that certain policies transition immediately to full-risk

rates.

Specific implications from April 1, 2015:

• Limits increases for individual premiums to 18 percent per year.

• Limits increases for average rate classes to 15 percent per year.

• Increases the Reserve Fund assessments required by BWA.

• Implements annual surcharges on all new and renewed policies. This is meant to

compensate for the loss in revenue from slowing down BWA rate increases. These surcharges

will be collected until all subsidies are eliminated. The surcharges are flat fees applied to all

policies regardless of the flood zone where the building is located. The annual surcharge for

primary residences is $25, and for non-primary residences or non-residential properties,

it is $250.

• Introduces cost-saving flood insurance coverage for properties newly mapped into

the SFHA. They will receive PRP (Preferred Risk Policy) rates for 1 year after the maps

become effective. The rates at renewal will increase no more than 18 percent per year.

• Resuscitates grandfathering. Grandfathering remains an option for policyholders when new

maps show their buildings in a higher risk area (for example, zone A to zone V or increase

in the Base Flood Elevation, or BFE). Available to property owners who (1) have flood

insurance policies in effect when the new flood maps become effective, or (2) have built-in

compliance with the Flood Insurance Rate Map (FIRM) in effect at the time of construction.

These policyholders have the option of using the flood zone on a previous FIRM that was in

effect when the building was originally constructed (for those built in compliance) or when

coverage was first obtained (for those with continuous coverage).

22 Prior to BWA, when revised maps showed higher risk zones, policyholders were permitted to grandfather and use the zone and elevation of an older map.

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Flood Zone Designations

Zone A is the flood insurance rate zone determined by approximate methods, as no BFEs are

available for these areas. Mandatory flood insurance purchase requirements apply, that is,

properties with a federally backed mortgage are required to purchase flood insurance.

Zone AE is the flood insurance rate zone that corresponds with flood depths greater than 3 feet.

Mandatory flood insurance purchase requirements apply.

Zone AH is the flood insurance rate zone that corresponds to areas of shallow flooding with

average depths between 1 and 3 feet. Mandatory flood insurance purchase requirements apply.

Zone VE is the flood insurance rate zone that corresponds to coastal areas that have additional

hazards associated with storm waves. Mandatory flood insurance requirements apply.

Zone X and Zone X-500 are flood insurance rate zones that are outside the floodplain or with

average flood depths of less than 1 foot. For these properties, it is not mandatory to purchase

flood insurance.

Acknowledgments

We thank PSC-CUNY (Award TRADA-47-538) for funding.

Authors

Agustín Indaco is an assistant teaching professor of economics at Carnegie Mellon University

in Qatar.

Francesc Ortega, corresponding author, is the Dina Axelard Perry Professor in Economics in the

Department of Economics at Queens College CUNY and can be reached at fortega@qc.cuny.edu.

Süleyman Tas¸pınar is an assistant professor in the Department of Economics at Queens

College CUNY.

References

Atreya, Ajita, Susana Ferreira, and Warren Kriesel. 2013. “Forgetting the Flood? An Analysis of the Flood Risk Discount over Time,” Land Economics 89 (4): 577–596.

Bakkensen, Laura A., and Lint Barrage. 2017. Flood Risk Belief Heterogeneity and Coastal Home

Price Dynamics: Going Under Water? NBER Working Paper No. 23854. Cambridge, MA: National

Bureau of Economic Research.

Bin, Okmyung, Jamie Brown Kruse, and Craig Landry. 2008. “Flood Hazards, Insurance Rates, and

Amenities: Evidence From the Coastal Housing Market,” Journal of Risk & Insurance 75 (1): 63–82.

154 Refereed Papers

The Effects of Flood Insurance on Housing Markets

Bin, Okmyung, and Craig Landry. 2013. “Changes in Implicit Flood Risk Premiums: Empirical

Evidence from the Housing Market,” Journal of Environmental Economics and Management 65

(3): 361–376.

Bin, Okmyung, and Stephen Polasky. 2004. “Effects of Flood Hazards on Property Values: Evidence

Before and After Hurricane Floyd,” Land Economics 80 (4): 490–500.

Bosker, Maarten, Harry Garretsen, Gerard Marlet, and Clemens van Woerkens. 2018. “Nether Lands:

Evidence on the Price and Perception of Rare Natural Disasters,” Journal of the European Economic Association 17 (2): 413–453.

Buchanan, Maya K., Michael Oppenheimer, and Robert E. Kopp. 2017. “Amplification of Flood

Frequencies with Local Sea Level Rise and Emerging Flood Regimes,” Environmental Research Letters 12 (6): 064009.

Conte, Marc N., and David L. Kelly. 2017. “An Imperfect Storm: Fat-Tailed Tropical Cyclone Damages, Insurance, and Climate Policy,” Journal of Environmental Economics and Management 92: 677–706.

Cooper, Hannah M., Charles H. Fletcher, Qi Chen, and Matthew M. Barbee. 2013. “Sea-Level Rise

Vulnerability Mapping for Adaptation Decisions Using LiDAR DEMs,” Progress in Physical Geography 37 (6): 745–766.

Gallagher, Justin. 2014. “Learning About an Infrequent Event: Evidence from Flood Insurance

Take-Up in the United States,” American Economic Journal: Applied Economics 6 (3): 206–233.

Gibson, Matthew, Jamie T. Mullins, and Alison Hill. 2019. Climate Risk and Beliefs: Evidence from

New York Floodplains. Working paper 2019-02. Williamstown, MA: Williams College, Department

of Economics.

Government Accountability Office (GAO). 2013. Flood Insurance: More Information Needed on

Subsidized Properties. Report to Congressional Committees 13-607. Washington, DC: United States Government Accountability Office.

Hallstrom, Daniel G., and V. Kerry Smith. 2005. “Market Responses to Hurricanes,” Journal of

Environmental Economics and Management 50 (3): 541–561.

Harrison, David M., Greg T. Smersh, and Arthur L. Schwartz, Jr. 2001. “Environmental Determinants

of Housing Prices: The Impact of Flood Zone Status,” Journal of Real Estate Research 21 (1/2): 3–20.

Hauer, Mathew E., Jason M. Evans, and Deepak R. Mishra. 2016. “Millions Projected To Be at Risk

From Sea-Level Rise in the Continental United States,” Nature Climate Change 6: 691–695.

Hunter, John. 2010. “Estimating Sea-Level Extremes Under Conditions of Uncertain Sea-Level Rise,”

Climatic Change 99 (3): 331–350.

Indaco, Agustín, Francesc Ortega, and Süleyman Tas¸pınar. 2018. The Effects of Flood Insurance

on Housing Markets. IZA discussion paper 11810. Bonn, Germany: Institute for the Study of Labor

(IZA).

Kousky, Carolyn. 2018. “Financing Flood Losses: A Discussion of the National Flood Insurance

Program,” Risk Management and Insurance Review 21 (1): 11–32.

Cityscape 155

Indaco, Ortega, and Tas¸pınar

Ortega, Francesc, and Süleyman Tas¸pınar. 2018. “Rising Sea Levels and Sinking Property Values:

Hurricane Sandy and New York’s Housing,” Journal of Urban Economics 106: 81–100.

Park, Joseph, Jayantha Obeysekera, Michelle Irizarry, Jenifer Barnes, Paul Trimble, and Winifred

Park-Said. 2011. “Storm Surge Projections and Implications for Water Management in South Florida,”

Climatic Change 107 (1): 109–128.

Pralle, Sarah. 2019. “Drawing Lines: FEMA and the Politics of Mapping Flood Zones,” Climatic Change 152 (2): 227–237.

Sweet, William V., and Joseph Park. 2014. “From the Extreme to the Mean: Acceleration and Tipping

Points of Coastal Inundation from Sea Level Rise,” Earth’s Future 2 (12): 579–600.

Sweet, William, Joseph Park, John Marra, Chris Zervas, and Stephen Gill. 2014. The Geography of Development: Evaluating Migration Restrictions and Coastal Flooding. NOAA Technical Report NOS

CO-OPS 073. Silver Spring, MD: National Oceanic and Atmospheric Administration.

Tebaldi, Claudia, Benjamin H. Strauss, and Chris E. Zervas. 2012. “Modelling Sea Level Rise Impacts on Storm Surges Along US Coasts,” Environmental Research Letters 7 (1): 014032.

Vitousek, Sean, Patrick L. Barnard, Charles H. Fletcher, Neil Frazer, Li Erikson, and Curt D. Storlazzi.

2017. “Doubling of Coastal Flooding Frequency Within Decades due to Sea-Level Rise,” Scientific Reports 7 (1): 1399.

Votsis, Athanasios, and Adriaan Perrels. 2016. “Housing Prices and the Public Disclosure of Flood

Risk: A Difference-in-Differences Analysis in Finland,” Journal of Real Estate Finance and Economics 53 (4): 450–471.

Zhang, Lei. 2016. “Flood Hazards Impact on Neighborhood House Prices: A Spatial Quantile

Regression Analysis,” Regional Science and Urban Economics 60 (Supplement C): 12–19.

Zhang, Lei, and Tammy Leonard. 2018. “Flood Hazards Impact on Neighborhood House Prices,”

Journal of Real Estate Finance and Economics: 1–19.

156 Refereed Papers

Departments

In this issue—

• Affordable Design

• Data Shop

• Graphic Detail

• Impact

• Policy Briefs

• SpAM

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 157

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

158 Departments

Affordable Design

The U.S. Department of Housing and Urban Development sponsors or cosponsors three annual

competitions for innovation in affordable design: The Innovation in Affordable Housing Student Design and Planning Competition; the American Institute of Architects – HUD Secretary’s

Housing Community Design Awards; and the HUD Secretary’s Opportunity & Empowerment

Award, co-sponsored with the American Planning Association. This Cityscape department

reports on the competitions and their winners. Each competition seeks to identify and develop

new, forward-looking planning and design solutions for expanding or preserving affordable

housing. Professional jurors determine the outcome of these competitions.

by Regina Gray

Annual HUD Secretary’s Opportunity

and Empowerment Award: Family

Scholar House, Louisville, Kentucky

Regina C. Gray

U.S. Department of Housing and Urban Development

For the past two decades, HUD’s Office of Policy Development and Research (PD&R) and the

American Planning Association (APA) have partnered to recognize planning development projects

that have truly transformed the community into one that is livable and resilient. The HUD Secretary’s Opportunity and Empowerment Award recognizes communities that have implemented creative and

effective strategies to improve the quality of life for low- and moderate-income residents. The award emphasizes how creative housing, economic development, and private investments can be used in or

with a comprehensive community development plan to build social equity and empower individuals

and families who reside in that community. Nominees must demonstrate how they respond

effectively to those challenges while simultaneously and effectively addressing community needs;

this is done by expanding affordable housing and transportation options and enhancing economic

competitiveness through sound and equitable development practices. PD&R recently completed a

review of the nominations for the annual HUD Secretary’s Opportunity and Empowerment Award.

The jury received six applications, deliberated in late fall, and recommended the Family Scholar

House (FSH) in Louisville, Kentucky, as this year’s award recipient.

FSH was selected, in large part, because the HUD-APA jury was particularly impressed with the

wide array of programs offered to area residents and their families to help lift them from poverty.

FSH is a nonprofit organization that offers programs that range from helping single mothers find

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 159

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

Gray

permanent employment to providing education and job opportunities for youth transitioning

from foster care. In the past year, 504 families have taken advantage of the various educational and career-oriented services that are offered by FSH, and of that number, 215 families currently reside in one of the five FSH multifamily properties.1

The Parkland Family Scholar House in Louisville, Kentucky.

Founded in 1995, the program has a broader mission to end the cycle of poverty and transform

the community by empowering families to achieve financial independence. The FSH model has

expanded to help participants locate low-cost housing, provide transportation and childcare

options, and offer job training and opportunities for the underemployed. The program has added a

variety of supportive services ranging from mental health assessments to food assistance and a host of financial services around credit repair and asset building.

The success story of the FSH centers on the continued commitment from the program’s public

and private partners. FSH and Marian Development established an impressively diverse funding

portfolio for the $10.5 million program, successfully leveraging low-income housing tax credits,

HOME investment funds, Community Development Block Grant funds, and foundational support.

An Affordable Housing Trust Fund, along with state and federal historical tax credits, were used to develop the five FSH multifamily housing developments in Louisville and one affiliate location in

Covington, Kentucky. The partnership implemented an ambitious funding campaign to raise an

1 According to the most recent annual report from FSH, supportive services are available for adults and children, particularly single parents, who are looking for opportunities to complete their education, seek better jobs, and programs that help stabilize housing conditions for themselves and their children. The annual report is accessible at the following link: https://familyscholarhouse.org/wp-content/uploads/2019/01/by-the-numbers.pdf.

160 Affordable Design

Annual HUD Secretary’s Opportunity and Empowerment Award:

Family Scholar House, Louisville, Kentucky

additional $1.2 million. The projects met several local planning goals, including the creation of

an additional 324 affordable housing units across all its satellite properties, a robust homelessness prevention initiative, and onsite supportive services for residents.

The FSH program’s success is further evidenced by the high percentage of participants who have

exited the program to stable housing and suitable employment. According to the organization’s

most recent statistics,2 for example, nearly 80 percent of participants have found permanent

employment, and all have located stable, affordable housing. A recent Washington Post article noted that more than 500 families have lived in one of the FSH satellite centers over the course of the past decade. The average tenure is about 3 years, and every family has found permanent, stable housing

(Kolodner, 2018).

The education center at the Louisville Family Scholar House location (pictured above) provides an array of educational and job training services.

Each of the five FSH developments uses an operating subsidy in the form of project-based Section

8 vouchers or Moving to Work housing choice vouchers. The jury found that these projects

met several local planning goals related to the creation of affordable housing, economic and

community development, homelessness prevention, and creation of specialized pre-residential

and residential services.

2 See the “By the Numbers” annual report released by FSH at the following link: https://familyscholarhouse.org/wp-

content/uploads/2019/01/by-the-numbers.pdf.

Cityscape 161

Gray

One of the newer FSH centers is housed at Riverport Landings, also located in Louisville, and is an “intergenerational living” community that offers senior housing and services, childcare services, and housing for single moms.

To learn more about the HUD Secretary’s Opportunity and Empowerment Award, go to

https://www.huduser.gov/portal/publications/pdf/hud_583_2015.pdf.

Author

Regina C. Gray is a social science analyst in the Affordable Housing Research and Technology

Division of the U.S. Department of Housing and Urban Development.

Reference

Kolodner, Meredith. 2018. “They Were Young. And Homeless. And Moms. Now, They

Have College Degrees,” The Washington Post, April 22. https://www.washingtonpost.

com/local/education/they-were-young-and-homeless-and-moms-now-they-have-

college-degrees/2018/04/22/7c9bb5ae-440f-11e8-bba2-0976a82b05a2_story. 

html?noredirect=on&utm_term=.4221df693aa2.

162 Affordable Design

Data Shop

Data Shop, a department of Cityscape, presents short articles or notes on the uses of data in

housing and urban research. Through this department, the Office of Policy Development and

Research introduces readers to new and overlooked data sources and to improved techniques in

using well-known data. The emphasis is on sources and methods that analysts can use in their

own work. Researchers often run into knotty data problems involving data interpretation or

manipulation that must be solved before a project can proceed, but they seldom get to focus in detail on the solutions to such problems. If you have an idea for an applied, data-centric note of no more than 3,000 words, please send a one-paragraph abstract to david.a.vandenbroucke@

hud.gov for consideration.

by Christos Makridis and Michael Ohlrogge

Tracking Individuals

Pre- and Post-Foreclosure

Christos Makridis

Massachusetts Institute of Technology (MIT) Sloan School of Management

Michael Ohlrogge

New York University School of Law

Disclaimer

All opinions expressed here are strictly those of the authors and do not reflect the views of affiliated organizations or the U.S. Government.

Abstract

In this article, the authors examine how it is possible to construct records that track 1.4 million households that experience foreclosure from their pre-foreclosure to post-foreclosure residences. These records were created by merging two powerful sets of data: county registrar of deeds records (licensed from CoreLogic, Inc.) and consumer mail marketing data (licensed from RefUSA). The article starts with a description of the county registrar of deeds data and how it can be used to create a dataset of mortgages and outcomes (including foreclosure). The authors proceed to describe the nature of the consumer marketing data from RefUSA, how it can be used to track households as they move locations, and then how that data can be merged with the mortgage records constructed from registrar of deeds data. The article also includes discussions of how those combined records can be merged with the Home Mortgage Disclosure Act (HMDA) database, using GIS software, to learn additional demographic information (income, race, and so on) about individuals with mortgages and foreclosures

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 163

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

Makridis and Ohlrogge

Introduction

Residential mortgages and their foreclosures played a pivotal role in the recent financial crisis.

A host of high-quality data sets exist with millions, billions, even trillions of observations on

residential mortgage origination, performance, delinquency, and foreclosure. Those data sets

have given rise to hundreds, if not thousands, of academic papers investigating which individuals

got residential mortgages, on what terms, how those mortgages performed, and the implications

of those findings for the broader economy. For all of this richness in investigation though, data

limitations have made it difficult or impossible for researchers to accurately track where specific individuals move after foreclosure.

More than 6 million households and 10 million individuals experienced mortgage foreclosures

from 2007 to 2013 (CoreLogic, Inc., 2017). Thus, understanding what happened to those people

is important from macro- and microeconomic perspectives and for documenting the human

impacts of one of the most significant financial events of the postwar period in the United States.

In our companion article (Makridis and Ohlrogge, 2018), we take on one such effort to improve

understanding of post-foreclosure outcomes by examining whether individuals move to areas with

better or worse labor market opportunities after foreclosures and what predicts the significant

heterogeneity among individuals in those outcomes.

In this article, we present details about our new method for constructing data that tracks more

than 1.4 million households that experienced mortgage foreclosure, identifying the precise

addresses at which they lived before and after the end of their mortgages. Our approach proceeds

through several steps.

First, we use parcel-level data, licensed from CoreLogic, Inc. and gathered from county registrars of deeds to identify the precise addresses and dates for which ownership of a piece of land changes hands due to a completed foreclosure action. Second, we use the street addresses (house number,

unit number [if applicable], street name, city, state, and ZIP Code) on these foreclosure records

to match them to a consumer mail marketing database licensed from RefUSA. Third, we use the

unique household identifiers obtained from RefUSA to track individuals to their next address

after a foreclosure. Finally, to obtain additional information about the individuals who experience foreclosure (such as their income at time of mortgage origination, race, and so forth), we match the foreclosure records with data from the Home Mortgage Disclosure Act (HMDA).

To our knowledge, this is the first and only data set to be used in academic research that identifies individuals who have lost their houses to foreclosure and then shows where they move after

that foreclosure. Furthermore, although our method depends on two types of data that must be

licensed for a fee, such licensing is available to any researcher with the requisite budget, and for both types of data, multiple sources are available from which to license the data, thus increasing the options, availability, and affordability of building such data.

The remainder of this article proceeds as follows. In the next section, we review existing mortgage data sets and their limitations with respect to identifying outcomes of individuals post-foreclosure.

We then describe how we constructed records of mortgage originations and outcomes from county

164 Data Shop

Tracking Individuals Pre- and Post-Foreclosure

registrar of deeds data. In the following section, we describe how we merged this mortgage data

with the RefUSA mail marketing database to identify where individuals move post-foreclosure. In

the next section, we discuss how we obtained additional information about the individuals subject

to foreclosure by merging our mortgage records with the HMDA database. In the final section, we

discuss alternative vendors for the two key types of proprietary data we use in our data construction.

Limitations of Existing Mortgage Data Sets

The reason that studying the post-foreclosure outcomes for households has been difficult lies

in the nature of how most mortgage data sets used for academic research are generated. Many

mortgage data sets, both public and proprietary, are ultimately generated by mortgage servicers and distributed to mortgage investors.1 After a mortgage foreclosure occurs, those who service or own

mortgages have relatively little reason to track where individuals move.2

Another type of mortgage data set comes from government records of mortgage originations. The

HMDA data set and the registrar of deeds data (described further herein) are examples; however,

HMDA does not track any information past a mortgage’s origination. Deed registries generally

provide the names of individuals who take out mortgages and own land. Then in theory it might

be possible to search such data by the names of individuals to discover where they move after

foreclosure. First-and-last name combinations are far from unique across the country; in any case, those records will be a small and unrepresentative subsample of the population who are able to

buy property again immediately following a mortgage foreclosure.

The only other data set similar to ours for tracking individuals post-foreclosure is that created

by the Federal Reserve Bank of New York (FRBNY), using Equifax credit data (see Brevoort and

Cooper, 2013; Lee and Van der Klaauw, 2010; Molloy and Shan, 2013). In addition to this data

being proprietary to Federal Reserve researchers, it has three significant limitations compared with the data we construct. First, this data set identifies only individuals who have begun the foreclosure process. Given that roughly one-half of foreclosure starts never result in a completed foreclosure (see, for example, Federal Housing Finance Agency, 2018), this introduces a substantial amount of

uncertainty to the data. Second, the FRBNY-Equifax data set identifies an individual’s location only when a credit report is requested for that person; thus, if an individual moves post-foreclosure but then does not have any credit reports run, that person will seem to have not moved at all. Finally, we track roughly 1.4 million households that experienced foreclosure between 2006 and 2011,

compared with only 330,000 households that experienced foreclosure between 2000 and 2009, as

tracked in the FRBNY-Equifax data (Brevoort and Cooper, 2013).3

1 Publicly available sets such as these include those from Fannie Mae, Freddie Mac, and the Columbia Collateral File.

Private ones that must be licensed for a fee include those from firms such as BlackBox and CoreLogic, Inc.

2 In particular, in a large number of states, antideficiency statutes explicitly prohibit mortgage owners from trying to collect from borrowers any amounts that remain unpaid on a mortgage loan after foreclosure of a house backing that loan. Even where those statutes do not apply, attempting to collect money beyond the value of the housing collateral from a foreclosed borrower is frequently not economically practicable.

3 That 330,000 figure is only for foreclosure starts, thus representing perhaps one-half that number of completed foreclosures.

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Makridis and Ohlrogge

Building Mortgage Records from Registrar of Deeds Data

Throughout the United States, property law generally requires that an interest in land, such

as an ownership or mortgage interest, be registered with the county in which the property

is located if that interest is to be legally enforceable. Those registries are then made publicly

available by counties so that, for instance, a person purchasing property can be assured

that the seller has the legal right to dispose of that asset. Commercial companies, such as

CoreLogic, Inc. and its predecessors, have in turn gathered this information from the roughly

3,000 U.S. counties, cleaned and standardized it, and licensed it for academic and other

research purposes.

County registrars of deeds create unique parcel identification numbers by which records on

individual properties can be traced over time.4 We thus start by identifying in our data when each new loan is taken out on a property, whether that loan is for the initial purchase of the property, a refinance of a purchase loan, or a home equity line of credit. For each such loan, we then observe the set of subsequent records tied to the property’s unique identifier.

If, for instance, we observe an initial purchase loan taken out on a property and no subsequent

records on that property until 6 years later, at which point we observe a new (non-foreclosure)

sale of that property from the prior owners to new owners, then we conclude that the initial loan

ended in a prepayment. If a loan is a purchase loan or a refinance of a purchase loan, and we

observe a subsequent loan of comparable size (adjusted for amortization) being taken out on the

same property, with no interceding changes of that property’s ownership, we likewise conclude that the loan was refinanced and thus ended in a prepayment. If, however, we observe a loan registered

on a property, and then we observe the property next changing hands through a foreclosure

transaction—with no intervening prepayment events—then we conclude that the given loan ended

in a foreclosure.

As a practical matter, if our only objective were to track people from before and after

foreclosure, our procedure here could be simpler. In particular, we would need to identify only

the dates and addresses at which foreclosures occur and then match those addresses to the

unique family IDs assigned to them by the RefUSA data, as described later in this article. We

also, however, wish to match those loan records to the HDMA data to gain more information

about the people whose homes are foreclosed on. We must therefore generate the more precise

information on the loans that are being foreclosed on, such as what type of loan (purchase,

refinance, home equity, and so on) it was, when the loan was originated, who the lender was,

and so forth.

Out of a starting set of roughly 500 million property-level records in the deed data we license, we derive approximately 96 million unique mortgage records through these procedures. In general,

multiple property-level records are associated with each mortgage (for instance, one or sometimes

4 Some of the work of companies such as CoreLogic, Inc. also involves ensuring the uniqueness and continuity of these identifiers.

166 Data Shop

Tracking Individuals Pre- and Post-Foreclosure

multiple records when the mortgage is taken out as well as when it is extinguished). We also

exclude records on commercial properties, loans for construction and development of subdivisions

and large groupings of residential properties, and other records not directly relevant for tracking residential mortgages to individual homeowners.5

Matching Deed Mortgage Records to Consumer

Marketing Data

Having created mortgage records and identified individuals whose properties were foreclosed

on, we next draw on the consumer mail marketing database licensed from RefUSA. RefUSA’s

primary clients are companies or charitable organizations that target individuals with

advertisements and solicitations. The foundation for RefUSA’s database comes from proprietary

data they license from the U.S. Postal Service. That database incorporates information from

individuals who fill out official “change of address” forms when they move, as well as a variety

of other government forms through which individuals disclose new addresses. RefUSA then

supplements that data through partnerships with credit card companies, magazines, utilities,

and similar entities to which people voluntarily report changes in address. Any person who

has moved and has subsequently seen catalogs and charitable solicitations follow them,

unprompted, to their new address will be familiar with the ability of firms like RefUSA to track

individuals, even without any deliberate actions by those individuals to disseminate their new

addresses. RefUSA has compiled this information into a yearly panel of roughly 130 million

U.S. households.

RefUSA assigns each household a unique identification number to track those households as they

move to new locations. Each yearly entry in the panel includes a full street address. Thus, one can readily identify households that move by filtering for those that change street addresses from one year to the next. The specific data we license from RefUSA spans years 2006 to 2012; thus, we are

able to identify households that move in years 2006 through 2011.

We match moving households in RefUSA with foreclosed households from the deed data on the

basis of the exact correspondence of house number (which includes unit number, if applicable),

street name, state, ZIP Code, and year of move or foreclosure. If a foreclosure occurs, for instance, in 2009, then depending on when a household’s address is updated in the RefUSA data, they may

not be recorded as moving until 2010. Thus, we allow a margin of 1 year when matching on the

year of move or foreclosure.

In the mortgage data constructed from deed records, we identify 4,088,248 foreclosures occurring

between 2006 and 2011. Of those, we are able to match 1,415,241 to records of households shown

as moving in the RefUSA data, giving us a match rate of 34.6 percent. Our best understanding of

the reason for this low match rate is that not all of the foreclosed households were initially in the 5 Overall, this construction of mortgage histories out of these public record filings is similar to the approach taken by Ferreira and Gyourko (2015). See also Diamond and McQuade (2016) and Ohlrogge (2018) for additional uses of such public records deed data.

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Makridis and Ohlrogge

RefUSA database, and for those that were, RefUSA may have experienced delays before identifying

and reporting them as having moved.6

A natural concern with a low match rate such as this is that the sample of foreclosures for which

we identify where the household moved post-foreclosure may not be representative of the full set

of foreclosures. We perform several tests to investigate whether this is likely a concern.7

First, we consider the initial mortgage amount (in dollars) for foreclosures that we match to the

RefUSA moving records and for those that we did not. We construct a quantile-quantile (Q-Q)

plot8 comparing the quantiles of the two distributions. Exhibit 1 presents the results, showing

nearly identical distributions between the two sets of foreclosures.9 Thus, no systematic bias seems to exist, whereby households with smaller mortgages (presumably, with lower incomes) are less

likely to be tracked to their post-foreclosure locations.

Next, we investigate whether geographic differences might be present, in which some areas are

more likely to have individuals whom we are able to successfully track via RefUSA. We compute

the fraction of total observations that each U.S. ZIP Code accounts for in the matched and total

sample, finding a correlation between the two samples of 0.92. Although a higher match rate

would undoubtedly be desirable, we are nevertheless able to identify a large number (1.4 million)

of foreclosures for which we can track the post-foreclosure residence of the household, and no

systematic differences seem to be present in either the individual or the geographic characteristics of the individuals for whom we are and are not able to match.

6 In Makridis and Ohlrogge (2017), we examined another data product from RefUSA that advertised information on the number of people employed at each U.S. business, with updates on a yearly basis. In that paper, we demonstrated that those employment counts are subject to substantial interpolation that may limit the number of applications for which they can be used. By contrast, determining the address of an individual is far easier than determining the number of people employed by a business. As such, we are less concerned about data errors in RefUSA’s consumer marketing database. Although such errors undoubtedly exist and unquestionably account for some portion of our failure to match foreclosure records, the tests we describe herein give us some confidence that match failures do not substantially reduce the usefulness of the data.

7 See also Makridis and Ohlrogge (2018) for additional validation tests and discussion.

8 A Q-Q plot is a graphical method for comparing two probability distributions by plotting their quantiles (for example deciles, quintiles, quartiles, medians) against each other.

9 In this plot, we restricted to mortgages less than $1 million in initial value. Only very sparse coverage exists for mortgages higher than that value, making comparisons of distributions much less meaningful.

168 Data Shop

Tracking Individuals Pre- and Post-Foreclosure

Exhibit 1

Q−Q Plot—Mortgage Amount All Foreclosures vs. RefUSA Matched Foreclosures

$1,000,000

es

$800,000

eclosur

$600,000

$400,000

Mortgage Amount: RefUSA Matched For

$200,000

$0

$0

$200,000

$400,000

$600,000

$800,000

$1,000,000

Mortgage Amount: All Foreclosures

Notes: This plot compares the distribution of the total mortgage amount among the full set of foreclosure records we constructed from the registrar of deeds data and the 34.6 percent of those records for which we were able to identify where the foreclosed household moved afterwards, based on matching the foreclosure records with the RefUSA consumer mail marketing database. If, for instance, more affluent individuals were more likely to be matched, that would be reflected in this plot. We view this Q-Q plot as a sufficient statistic for evaluating comparability of the matched and unmatched samples along the dimension of mortgage amount.

On the basis of those results, which suggest that the data reliability is still preserved given the matching process, we now present several summary results that demonstrate some of the investigations this

data set makes possible. First, exhibit 2 presents the distribution of distances moved by people post-foreclosure. The exhibit shows that the large majority moved between 10 and 100 miles to a new

location, with smaller numbers moving lesser and greater distances. Exhibit 3 presents specific empirical quantiles for the distribution of distances moved. The median distance moved is 34 miles—comparable, for instance, to a move from Oakland, California to San Jose, California. The 90th percentile for distance moved was 95 miles—comparable to a move from Philadelphia, Pennsylvania to New York, New York.

The 95th percentile for distance moved was 548.1 miles—comparable to a move from Memphis,

Tennessee to Atlanta, Georgia.

Cityscape 169

Makridis and Ohlrogge

Exhibit 2

Distance Moved After Foreclosures

400,000

300,000

200,000

Frequency

100,000

0

0

1

10

100

1,000

Distance Moved (Miles)

Notes: This graph depicts the distance (measured in miles) that individuals moved after foreclosure. As evidenced here, the vast majority of people moved within 10

and 100 miles, with smaller numbers moving less than 10 miles and a moderate number moving between 100 and 1,000 (or more) miles to a new location post-foreclosure. See exhibit 3 for specific quantiles of the distribution presented here.

170 Data Shop

Tracking Individuals Pre- and Post-Foreclosure

Exhibit 3

Distance Moved After Foreclosure—Percentiles

Quantile (%)

1

5

10

25

50

75

90

95

99

Miles Moved

3.8

8.8

12.7

21.5

34.0

49.1

95.0

548.1

1,744.9

Notes: This table presents several of the empirical percentiles for distance moved after foreclosure, corresponding to the distribution plotted in the histogram in exhibit 2.

Next, exhibit 4 focuses on one of the ZIP Codes (92336) in San Bernardino, California that was

at the epicenter of the foreclosure crisis. The figure plots the diaspora of people moving from ZIP

Code 92336 to a neighboring area.

Exhibit 4

Foreclosure Diaspora from ZIP Code 92336

N Household Moves

[ 0, 5)

[ 5, 10)

[ 10, 20)

[ 20, 50)

[ 50,100)

[100,200)

[200,277]

Origin ZIP Code

Notes: This exhibit presents ZIP Code 92336 (depicted here in orange), in San Bernardino, California, which is one of the ZIP Codes with the largest number of foreclosures in the United States. We observe 4,112 foreclosures in this ZIP code from 2006 through 2011 for which we can identify where the household moved after foreclosure. Of that number, 29.3 percent (1,205 households) remained in the given ZIP Code. Of the 2,907 that left the ZIP Code, 2,403 remained within a roughly 25-mile radius of the ZIP Code. This plot depicts how many individuals moved from ZIP Code 92336 to each other ZIP Code within that 25-mile radius.

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Makridis and Ohlrogge

Matching Deed Mortgage Records to HMDA

To gain additional information on foreclosed households, such as the income reported at the time

of taking out the mortgage as well as race and gender information on the borrowers, we also match

the mortgage records created from the registrars of deeds data with the HMDA data.10 Both sets of

data are extremely detailed, which makes accurate matching possible, although not without some

substantial challenges that must first be overcome. We match on the basis of agreement of census

tract, loan amount, year of loan origination, lender name, whether the loan is a purchase or a

refinance loan, and whether the loan is a conventional mortgage, FHA backed, or VA backed.

Census tracts are extremely small pieces of land, making them ideal for such a matching operation.

The U.S. Census Bureau targets between 2,000 and 8,000 people in each tract and re-adjusts

their boundaries after each decennial census to maintain their populations within that range. As

supplied by CoreLogic, Inc., the deed data comes with the census tracts as designated by the 2010

census. HMDA data reports 2010 census tracts starting in the year 2013.11 For loans originated

from 2003 to 2012, HMDA data uses the 2000 census tracts, and for loans from 1993 to 2002,

HMDA data uses the 1990 census tracts. The boundaries of census tracts can change over time.

Thus, for loans in the deed data originated before 2013, it is necessary to first compute the 2000 or 1990 census tract (depending on the loan’s origination date) before matching with HMDA data.

We use ArcGIS software and geographic shape files designating the boundaries of the census tracts to map the addresses in the deed records to census tracts from the appropriate census year.12 The shape files we use in this process are from the National Historical Geographic Information System (NHGIS).

A final challenge is that the names of lenders in the two databases are not precisely the same (for instance, one may contain “Bank of America,” whereas the other lists “Bank of America, NA”). We

therefore use an algorithm based on the Levenshtein distance between strings to pair lender names

that are not exact matches.

In total, we are able to match 68 percent of the mortgages in the deed data to HMDA data, for a

total of 65 million mortgage records, including both foreclosed and non-foreclosed mortgages.13

In Makridis and Ohlrogge (2018), we perform analyses similar to those discussed previously

to validate that the distributions of observable characteristics were very similar in the set of

foreclosures that we did and did not successfully match to HMDA data.

10 For 2006, we obtained HMDA data from the Inter-university Consortium for Political and Social Research:

https://doi.org/10.3886/ ICPSR24612.v2; for later years, we obtained it directly from the Federal Financial

Institutions Examination Council: https://www.ffiec.gov/hmda/hmdaproducts.htm; for earlier years, we obtained CDs from the National Archives.

11 For details on HMDA data reporting, see, for example, https://www.ffiec.gov/hmda/guide.htm.

12 In particular, we first found the latitude and longitude coordinates for each address. We then matched those coordinates to shape files for the census tracts.

13 This success rate closely matches that of other researchers who have performed similar such matching. See, for example, work by Nancy Wallace: https://bfi.uchicago.edu/sites/default/files/file_uploads/3_Wallace_MFM.pdf. In

calculating the percentage of deed records matched to HMDA data, we first removed certain deed records that would obviously not be in HMDA data, such as those for loans made to develop large tracks of land for new subdivisions, loans for commercial properties, and so forth.

172 Data Shop

Tracking Individuals Pre- and Post-Foreclosure

Alternative Data Sources

The comprehensive country registrar of deeds data that we use in this study was licensed

for a fee from CoreLogic, Inc.; however, other companies also sell comparable data, offering

researchers the potential to identify the best vendor for their purposes and to potentially

negotiate better prices. Attom Data Solutions14 is one such option. Some U.S. counties may well

also provide this information for free in a form accessible to researchers, although we have not

specifically investigated that possibility. Similarly, although we use consumer mail marketing

data licensed from RefUSA, other firms, such as the Experian credit rating agency, also offer

similar products for license.15

Acknowledgments

We would like to thank Brian Wang for diligent research assistance and Kris Kasianovitz for

assistance in acquiring the data used in this project. Michael thanks the Olin Foundation for Law

and Economics and the Stanford Advanced Financial Technologies (AFT) Lab for financial support.

Christos thanks the National Science Foundation Graduate Research Fellowship and the Stanford

Institute for Economic Policy Research Shultz Fellowship for financial support. All opinions

expressed here are strictly our own and not those of any institution with which we are affiliated.

Michael Ohlrogge was a Ph.D. candidate in Stanford University’s Department of Management

Science and Engineering at the time this article was written.

Authors

Michael Ohlrogge is an assistant professor at New York University School of Law.

Christos Makridis currently serves on the White House Council of Economic Advisers, is a Digital

Fellow at the MIT Sloan School of Management’s Initiative on the Digital Economy, a non-resident

fellow at the Harvard Kennedy School of Government’s Cyber Project, and Baylor University’s

Institute of Religious Studies.

References

Brevoort, Kenneth P., and Cheryl R. Cooper. 2013. “Foreclosure’s Wake: The Credit Experiences of

Individuals Following Foreclosure,” Real Estate Economics 41 (4): 747–792. DOI: 10.1111/1540-

6229.12021.

CoreLogic, Inc. 2017. United States Residential Foreclosure Crisis: Ten Years Later. Irvine, CA: CoreLogic, Inc. https://www.corelogic.com/research/foreclosure-report/national-foreclosure-

report-10-year.pdf.

14 The website for Attom Data Solutions is https://www.attomdata.com/data/property-data/recorder-data/.

15 Experian’s website is http://www.experian.com/small-business/mailing-lists.jsp.

Cityscape 173

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Diamond, Rebecca, and Timothy McQuade. 2016. Who Wants Affordable Housing in their

Backyard? An Equilibrium Analysis of Low Income Property Development. NBER Working Paper

No. w22204. Cambridge, MA: National Bureau of Economic Research.

Federal Housing Finance Agency (FHFA). 2018. Foreclosure Prevention Report. Washington, DC: Federal Housing Finance Agency. https://www.fhfa.gov/AboutUs/Reports/ReportDocuments/

FPR_1Q2018.pdf.

Ferreira, Fernando, and Joseph Gyourko. 2015. A New Look at the U.S. Foreclosure Crisis: Panel

Data Evidence of Prime and Subprime Borrowers from 1997 to 2012. NBER Working Paper No.

21261. Cambridge, MA: National Bureau of Economic Research.

Lee, Donghoon, and Wilbert Van der Klaauw. 2010. An Introduction to the FRBNY Consumer Credit

Panel, No. 479. New York: Federal Reserve Bank of New York.

Makridis, Christos, and Michael Ohlrogge. 2018. Moving to Opportunity? The Geography of the

Foreclosure Crisis & the Importance of Location. Working paper (April 14). Stanford, CA: Stanford University; Cambridge, MA: Massachusetts Institute of Technology. https://papers.ssrn.com/sol3/

papers.cfm?abstract_id=3162905.

———. 2017. “Validating RefUSA Micro-Data with the Longitudinal Employer-Household

Dynamics Data,” Economics Letters 152: 83–87. DOI: 10.1016/j.econlet.2017.01.001.

Molloy, Raven, and Hui Shan. 2013. “The Post-Foreclosure Experience of U.S. Households,” Real

Estate Economics 41 (2): 225–254. DOI: 10.1111/j.1540-6229.2012.00344x.

Ohlrogge, Michael. 2018. Bank Capital and Risk Taking: A Loan Level Analysis. Working paper.

Stanford, CA: Stanford University, Stanford Center for Financial and Risk Analytics. https://papers.

ssrn.com/sol3/papers.cfm?abstract_id= 3053240.

174 Data Shop

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 .

by Alexander Din

Visualizing and Comparing

Residential Permit Data Using

Lollipop Plots

Alexander Din

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 & Research, the U.S. Department of Housing and Urban Development, or the U.S. Government.

Residential permits are a common indicator of housing market activity. Residential permits indicate the demand for new homes, and by categorizing homes into different construction types, it is possible to understand what types of homes are in-demand in the market and the types of homes that the market

is producing. In this article, I use a cross between a scatter plot and a bar chart called a lollipop plot to visualize residential permits by year for single-family dwellings (SFDs) and townhomes in Montgomery County, Maryland. These data were obtained from dataMontgomery (2019), the open data portal for

the county. These data are for construction permits that were finalized between 2000 and 2018 for

SFDs and townhomes, as far back as data were available. Between 2000 and 2018, there were 14,831

and 6,322 permits for SFDs and townhomes, respectively.

The lollipop plot compares and offers a visualization of a pair of variables in a given record. In this demonstration, each year is a record, and the variables are the number of permits finalized for SFDs and townhomes. A pair of points is plotted on the graph and a line is established between each pair, demonstrating the difference between the two values. For example, as seen in exhibit 1, there were 649 SFD permits and 179 townhome permits in 2000; the lollipop plots a value at each data point

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 175

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

Din

and then connects the point via a line. Values for SFDs and townhomes are color-coded. In addition to being able to understand how many permits of each type were issued each year, the reader can also discern the difference between the numbers of permits of each type issued in a given year.

The first exhibit shows a standard lollipop plot. For each year, there are a pair of plotted points for the SFD and townhome values that are color-coded by category. The line between each pair

indicates the difference between the numbers of permits issued by each type. In the years prior

to the 2007–2008 financial crisis, SFD permits dwarfed townhome permits issuances. During

the recession years, from 2009 to 2013, SFD permits continued to be about twice the number of

townhome permits. In all years, permit issuances for SFDs were greater than those for townhomes.

Since 2013, permits for townhomes generally increased slightly, while permits for SFDs increased

considerably. In addition to showing the number of permits issued each year, the lollipop plot adds value by providing a visualization of the difference between the types of permits issued, allowing patterns to emerge and be more easily recognized.

Exhibit 1

Lollipop Plot for Single-Family Dwellings and Townhomes by Year

2018

Single-Family Dwellings

2017

Townhomes

2016

2015

2014

2013

2012

2011

2010

2009

Year

2008

2007

2006

2005

2004

2003

2002

2001

2000

200

400

600

800

1,000

1,200

1,400

1,600

Number of Permits

The second exhibit is a modification of the lollipop plot that, instead of comparing the difference between the types of construction, compares the median value for that type of construction

over the time period. Between 2000 and 2018, the median values for final SFD and townhome

permit issuances per year were 649 and 316, respectively. From 2001 to 2007, SFD permits were

generally far and above the median value and then steadily decreased, reaching a low point in 2011

and 2012. Since 2012, permits for SFDs have generally been similar to the median value for the

timeframe as a whole. Prior to the market crisis, permits for townhomes were more variable but in

176 Graphic Detail

Visualizing and Comparing Residential Permit Data Using Lollipop Plots

most years were above the median value. Similar to SFD permits, counts for townhome permits fell

during the recession years but rose again entering the middle decade and have remained above the

median value in most years since 2013. Using the median value as the mark for comparison allows

for values between years to be more directly comparable.

Exhibit 2

Lollipop Plot for Single-Family Dwellings and Townhomes by Year Compared to Median Value for

Each Permit Type

316

649

2018

Single-Family Dwellings

2017

Townhomes

2016

2015

2014

2013

2012

2011

2010

2009

Year

2008

2007

2006

2005

2004

2003

2002

2001

2000

200

400

600

800

1000

1200

1400

1600

Number of Permits

Acknowledgments

Alexander Din was a housing research and GIS analyst for the Maryland Department of Housing

and Community Development at the time this article was written.

Author

Alexander Din is a social science analyst at the U.S. Department of Housing and Urban

Development, Office of Policy Development & Research.

Reference

dataMontgomery. 2019. Residential Permits. https://data.montgomerycountymd.gov/Licenses-

Permits/Residential-Permit/m88u-pqki/data.

Cityscape 177

178 Departments

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 findings, application of economic principles, empirical investigation, and professional judgment

by Maria Chelo Manlagnit De Venecia

Acceptable Separation

Distance Standards for

Residential Propane Tanks

Maria Chelo Manlagnit De Venecia

U.S. Department of Housing and Urban Development

Summary of Analysis

Propane is the third most widely used fuel in the United States by number of households, after

electricity and natural gas. Residential households primarily use propane for space heating, water heating, and cooking (U.S. Energy Information Administration, 2015). The U.S. Department of

Housing and Urban Development’s (HUD’s) safety regulations establish standards for the acceptable

separation distance (ASD) between HUD-assisted projects and hazardous operations that include

the storage of substances that have the potential to cause an explosion or fire. Currently, the ASD

from an aboveground tank ranges from 125 to 139 feet. The proposed rule would reduce the ASD

of aboveground tanks to 10 feet if the capacity of the tank is 250 gallons or less and if the storage tank complies with National Fire Protection Association (NFPA) Code 58 concerning liquefied

petroleum gas (LP-gas) (NFPA, 2017).1

The benefits of the rule include reduction in costly mitigation measures, increased availability

of HUD-assisted projects, and improved administrative efficiencies. The reduction in cost

1 NFPA is a nonprofit organization that develops consensus codes and standards and provides research, training, and education to eliminate death, injury, property loss, and economic loss due to fire, electrical, and related hazards.

NFPA developed Code 58, which provides voluntary consensus standards used by the propane industry for the storage, handling, transportation, and use of LP-gas.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

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

De Venecia

from mitigation measures is estimated to be from $100,000 to $4 million per year and involve

approximately 20 projects per year. Given the quality of propane tanks, allowing a more proximate

placement of a propane tank to property will present an imperceptible increase of risk to that

property. Consequently, the savings from reducing mitigation costs is likely to outweigh the

reduced safety benefits.

Costs of the Proposed Rule

The costs of the rule stem from increased danger due to proximity. LP-gas is flammable at a wide

range of concentrations. Allowing greater proximity to a potential hazard will expose properties

and individuals to greater risk from explosions and fires. The reliability of propane tanks has

increased significantly, however, since HUD’s rule was originally promulgated more than 30 years

ago. HUD’s original regulation was based on studies from 1975 and 1982 and so, does not take into

consideration increased safety features of new technology in tank designs and updated fire safety

codes and standards, including NFPA Code 58.

More recent studies suggest that the evolution of industry safety practices has reduced the

probability of propane tank failure (Aherns, 2018, 2017; Flynn, 2010; Hall, 2014). NFPA, for

example, asserts that home structure fires in which LP-gas was the type of material first ignited

have fallen by 62 percent from 1980 to 2007 (Flynn, 2010), or an average annual decrease of 3.5

percent. Among the reasons for the safety improvement are improved construction techniques and

materials and strengthened fire codes (U.S. Fire Administration, 2017).

One study (Bullerdiek, 1986) revealed that particulate contamination; leaks from corroded or

damaged piping; nonprofessional installation, inspections, and maintenance; and failure to detect

odor or leakage were the major factors leading to appliance failure and subsequent injury. Later

studies examined the adequacy of ethyl mercaptan as an odorant to warn residents of potential

leaks (Arthur D. Little, Inc., 1987; U.S. Consumer Product Safety Commission, 1987). Many

research studies tested several odorants, but ethyl mercaptan remains the odorant of choice.2

Related to the issue on odorant fading, two new requirements have recently been added to the

2017 edition of NFPA Code 58 to facilitate tracking and recognition of LP-gas. Although the risk of tank failure cannot be totally eliminated through better design, significant progress has been made in safety standards to make HUD’s ASD overly cautious.

Benefits of the Proposed Rule

Reduced Costly Mitigation Measures

If a developer is not able to meet the minimum ASD requirement, there are acceptable ways of

mitigating the hazard. Those measures—including tank burial, building a blast wall to surround

the tank, or building a structure on a HUD-assisted property site to shield a proposed project

from the hazard—require an elaborate process involving costly construction procedures. The

2 This conclusion was based on series of tests and research studies published by the Joint Industry Task Force on Propane Odorization (see Nette, 2017). It is recognized, however, that no odorant will be completely effective as a warning agent in every circumstance. The most recent study is Roscioli et al. (2017).

180 Impact

Acceptable Separation Distance Standards for Residential Propane Tanks

labor and materials for burying a tank or constructing a blast wall can range from $5,000 to

$200,000 per unit.3 Specifically, the cost of burying a tank ranges from $20,000 to $100,000 per

unit, depending on several factors, such as local costs of labor and materials, volumetric capacity of the tank, location of the water table, frost level, and type of soil where the tank will be buried.

Additional costs include permit and design fees—especially in an urban environment—corrosion

protection, and tank vaulting. Blast walls, which usually are made of concrete block, mortar, or

rebar, can range from $5,000 to $200,000 per unit (a wider range than that for tank burial). Aside from the local costs of labor and materials, the type of materials used for the tank, proximity of the wall to the tank (the closer to the wall, the more robust the design of the wall should be), size of the footing of the wall (depending on the size and weight of the wall), and length of the wall are among the other factors that can affect the costs of constructing a blast wall.

The proposed rule would eliminate the need for mitigation measures such as tank burial or a blast

wall. Using the available information on the cost per mitigation measure, the construction cost

would be $5,000 to $200,000 per unit of mitigation measure. At least 20 mitigation measures per

year involve propane tanks of 250 gallons or less in HUD-assisted projects. The aggregate annual

reduction in cost would be $100,000 to $4 million (multiply $5,000 to $200,000 by 20). Over a

30-year structure life, the aggregate discounted savings range from $2.0 million to $80.8 million, assuming a 3-percent discount rate, and $1.3 million to $53.1 million, assuming a 7-percent

discount rate.

Increased Availability of HUD-Assisted Projects

With the proposed rule, HUD-assisted projects could be closer to propane tanks, increasing the

availability and choices for siting projects, especially in areas in need of additional housing units.

An estimated 30,890 housing projects with onsite propane tanks could be affected by the proposed

rule. Land is usually a significant cost of development and, depending on location, can be the

greatest cost. Reducing the amount of land needed for development will reduce a regulatory barrier to development and possibly increase the supply of housing.

Improved Administrative Efficiency

The proposed rule clarifies HUD’s regulations on propane containers and aligns them with

industry practices. Under NFPA Code 58, the minimum separation distance required for

containers between 125 and 500 gallons from a building or line of an adjoining property is 10

feet (NFPA, 2017).4 Having a regulation more consistent with standard practice makes siting

projects easier for HUD grantees.

3 Estimates include only costs of labor and materials. Other costs such as associated permits, design, and other administrative fees involved in burying a tank or building a blast wall are not included.

4 See NFPA 58, Table 6.4.1.1. The separation distances are based on the potential hazard of LP-gas; size and type of equipment used to contain it; possibility of leaks (which can ignite); and need for fuel in buildings—not on a worst-case scenario in which the LP-gas container fails catastrophically but as a minimum safe distance for radiant heat exposure to the containers and from the containers. Research has been done to evaluate the effects of radiant heat from fires to LP-gas containers. For example, see Raj (2005).

Cityscape 181

De Venecia

Conclusion

This proposed rule would reduce the regulatory burden on communities restricted in their ability

to site HUD-assisted projects—including low- and moderate-income housing—because of nearby

stationary aboveground propane storage tanks. The proposed exception will save HUD grantees

the cost of constructing mitigation measures to address residential propane tanks on properties

that do not meet the ASD. Based on current technology, developments in industry safety practices,

increased awareness to new safety standards, and improved quality of propane tanks, HUD does

not expect a noticeable increase in risk.

Acknowledgments

The author thanks Alastair McFarlane for encouraging the development of this article and

providing guidance in preparing the regulatory impact analysis on the proposed rule.

Author

Maria Chelo Manlagnit De Venecia is an economist in the Public Finance and Regulatory

Analysis Division, Office of Policy Development and Research, U.S. Department of Housing and

Urban Development.

References

Ahrens, Marty. 2018. Home Structure Fires in Which LP-Gas Was the Type of Material First Ignited and the Type of Material First Ignited in All Home Structure Fires. Quincy, MA: National Fire Protection

Association. https://www.nfpa.org/-/media/Files/News-and-Research/Fire-statistics-and-reports/

US-Fire-Problem/Fire-causes/osHomeStructureFiresLPGasFirstIgnited.ashx.

———. 2017. Home Structure Fires Involving Lightning and/or Natural Gas or LP-Gas. Quincy, MA: National Fire Protection Association.

Arthur D. Little, Inc. 1987. Characterization of LP-Gas Odorant Fade. Report prepared for the U.S.

Consumer Product Safety Commission. Washington, DC: Government Printing Office.

Bullerdiek, W.A. 1986. Engineering Hazard Analysis of Residential LP-Gas Fuel Usage. Frontier Technical Associates Report. Report prepared for the U.S. Consumer Product Safety Commission.

Washington, DC: U.S. Government Printing Office.

Flynn, Jennifer. 2010. Natural Gas and LP-Gas Home Structure Fires. Quincy, MA: National Fire Protection Association.

Hall, John. 2014. Fires Starting with Flammable Gas or Flammable or Combustible Liquid. Quincy, MA: National Fire Protection Association. https://www.nfpa.org/-/media/Files/News-and-Research/

Fire-statistics-and-reports/Hazardous-materials/osfiresstartinggas.pdf.

182 Impact

Acceptable Separation Distance Standards for Residential Propane Tanks

National Fire Protection Association (NFPA). 2017. NFPA 58, Liquified Petroleum Gas Code, 2017 ed.

Quincy, MA: National Fire Protection Association. https://www.nfpa.org/codes-and-standards/all-

codes-and-standards/list-of-codes-and-standards/detail?code=58.

Nette, Eric. 2017. NFPA 58 LP-Gas Code Handbook, 11th ed. Quincy, MA: National Fire

Protection Association.

Raj, Phani. 2005. “Exposure of a Liquified Gas Container to an External Fire,” Journal of Hazardous Materials 122 (1–2): 37–49.

Roscioli, Joseph R., Scott Herndon, David D. Nelson, and Tara Yacovitch. 2017. Methane, Ethane, and Propane Sensor for Real-Time Leak Detection and Diagnostics. Washington, DC: U.S. Department of Energy. https://www.osti.gov/servlets/purl/1347976.

U.S. Consumer Product Safety Commission. 1987. Information on LP-Gas (Propane) Odorant.

Memorandum. Washington, DC: U.S. Consumer Product Safety Commission.

U.S. Energy Information Administration. 2015. Residential Energy Consumption Survey (RECS).

Washington, DC: U.S. Energy Information Administration. https://www.eia.gov/consumption/

residential/data/2015/.

U.S. Fire Administration. 2017. Fire in the United States 2006–2015, 19th ed. Emmitsburg, MD: U.S.

Fire Administration. https://www.usfa.fema.gov/downloads/pdf/publications/fius19th.pdf.

Additional Reading

Arthur D. Little, Inc. 1975. Safety Consideration in Siting Housing Projects. Report prepared for the U.S. Department of Housing and Urban Development, Office of Policy Development and Research.

Washington, DC: U.S. Government Printing Office.

Rolf Jensen & Associates, Inc. 1982. Urban Development Siting with Respect to Hazardous Commercial/

Industrial Facilities. Report prepared for the U.S. Department of Housing and Urban Development, Office of Policy Development and Research. Washington, DC: U.S. Government Printing Office.

U.S. Department of Housing and Urban Development, Office of Community Planning and

Development. 2017. Memorandum for the Approval of Waiver for the State of Vermont of 24 CFR Part 51, Subpart C. Washington, DC: U.S. Department of Housing and Urban Development.

————. 2011. Acceptable Separation Distance Guidebook. Washington, DC: U.S. Department of

Housing and Urban Development.

————. 2008. Memorandum for the Approval of Waiver for the State of Mississippi of 24 CFR Part 51, Subpart C. Washington, DC: U.S. Department of Housing and Urban Development.

————. 1987. Siting of HUD-Assisted Projects Near Hazardous Facilities. Washington, DC: U.S.

Department of Housing and Urban Development.

Cityscape 183

184 Departments

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 .

by Taryn H. Gress, Mark L. Joseph,

and Seungjong Cho

Confirmations, New Insights, and

Future Implications for HOPE VI

Mixed-Income Redevelopment

Taryn H. Gress

Mark L. Joseph

Seungjong Cho

Jack, Joseph and Morton Mandel School of Applied Social Sciences

Case Western Reserve University

Abstract

As HUD advances its mission to create strong, sustainable, inclusive communities and high-quality, affordable homes for all, the promotion of mixed-income communities has become a core strategy. Across the United States, local governments and private developers are increasingly turning to mixed-income development as an approach to deconcentrate poverty and revitalize urban neighborhoods. With the Choice Neighborhoods Initiative, launched in 2010, the federal government extended its commitment to supporting the mixed-income approach to public housing transformation first implemented through the HOPE VI initiative in the mid-1990s. A comprehensive analysis of mixed-income units produced through the $6 billion HOPE VI program has not yet been undertaken, however. Using HOPE VI grantee quarterly report data from 1993–2014, we analyze the income and tenure mix of housing units that have been produced through the HOPE VI program as follows:

• Examine variations by factors such as age and size of development, region, and developer and describe the evolution of the program over time.

• Examine the factors associated with higher or lower reoccupancy of original residents.

• Examine funding leveraged through the HOPE VI and other grants.

• Review resident participation in community and supportive services.

• Undertake an analysis of the duration of various phases of the initiative, including relocation, demolition, construction, and occupancy.

Based on this descriptive analysis, we propose implications for future implementation and an evaluation of the Choice Neighborhoods Initiative.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 185

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

Gress, Joseph, and Cho

Introduction

Since the mid-1990s, mixed-income development has gained increased attention across the United

States as an urban revitalization strategy (Chaskin and Joseph, 2015; Joseph, Chaskin, and Webber, 2007). Mixed-income developments attempt to eliminate the isolation that challenges low-income

households living in neighborhoods with high concentrations of poverty (Wilson, 1987). The

term “mixed-income development” indicates the intentional financing, design, and construction

of a residential property with the goal of attracting individuals from a broad range of income and socioeconomic levels (Brophy and Smith, 1997). As the U.S. Department of Housing and Urban

Development (HUD) advances its mission to create strong, sustainable, inclusive communities and

quality, affordable homes for all, the promotion of mixed-income communities has become one of

the agency’s core strategies.

The HOPE VI program was launched in 1992 as a response to recommendations by the National

Commission on Severely Distressed Public Housing on the condition of the U.S. public housing

stock (Cisneros and Engdahl, 2009; Joice, 2017; Katz 2009; Vale, 2019). After 1995, the focus

of the program became the mixed-income transformation of deteriorating and isolated public

housing sites. To date, the HOPE VI program has been the largest and longest running federal

public housing transformation initiative, with 260 grants totaling more than $6 billion. The Obama Administration replaced the program with the Choice Neighborhoods Initiative in 2010 (Joice,

2017; Pendall et al., 2015; Pendall and Hendey, 2013).

In general, the HOPE VI program had a mixed record of accomplishment. The national HOPE VI

Panel Study by the Urban Institute found positive consequences of the program, such as improved

safety, better physical environments, and lower poverty rates (Buron, 2004; Comey, 2004; Popkin

et al., 2004; Popkin and Cunningham, 2009; Popkin et al., 2012). At the same time, the panel

study also reported that most of the original public housing residents did not return to benefit from the new housing developments. The program has also been criticized for reducing the number of

public housing units and for delays in constructing new units (Center for Community Change,

2003; National Housing Law Project, 2002).

Contribution of this Paper

Despite an extensive literature of evaluation reports and articles on various aspects of the HOPE

VI program, there has been no comprehensive outcomes analysis of the overall program. Most of

the research on the HOPE VI program has analyzed a single site or a selected subset of locations

(Buron et al., 2002; Chaskin and Joseph, 2015; Graves, 2011; Popkin et al., 2004; Popkin, Levy

and Buron, 2009; Tach, 2009; Vale, 2013; 2019). Addressing the gap in extant literature, this paper provides the results of the descriptive analysis of data extracted from quarterly progress reports from all 260 HOPE VI grants from 1993 through September 2014.1 The primary research question

that motivated our analysis was: What is the tenant income and tenure mix of housing units that

have been produced through the HOPE VI program?

1 Among 260 sites, one site did not produce any units. The site had been planned to produce 100 units but had not produced any as of the end of the reporting period. Thus, for all analysis using unit production information, 259 sites were analyzed, excluding that site.

186 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment Although not an evaluation of the program, the tenant income and tenure mix analysis here

provides insight into the nature of the mixed-income developments produced with HOPE VI

funding and, therefore, the potential influence that those developments had on residents and

communities through provision of a mix of subsidized and market-rate housing. Using the data

available in HOPE VI grantee reports, we analyze “income mix” based on the mix of unit types

(public housing replacement units, units subsidized through the Low-Income Housing Tax Credit

program, and market-rate units without a subsidy). Our focus is on HOPE VI as a place-based,

mixed-income development strategy, and we focus primarily on data available in the revitalization

grants. Our extraction and compilation of this data on all HOPE VI projects has enabled us to

revisit and reconsider some of the accumulated knowledge and conventional wisdom about the

program.2 Based on our analysis of this comprehensive data on unit production, we frame this

paper around three sets of findings. First, based on this new level of detail about HOPE VI grants, what have we been able to confirm that was known or surmised previously? Second, what new information have we been able to uncover with this data and where have we been able to shed new

light? Third, what are the implications of these confirmations and insights for future mixed-income research, policy, and practice?

With the Choice Neighborhoods Initiative (CNI), the federal government extended its commitment

to supporting the mixed-income approach to public housing transformation, and now more than

100 CNI planning and implementation grants are underway across the United States (HUD, 2015).

Although the Trump Administration has continued funding the Choice Neighborhoods Initiative, the

future direction of public housing policy is quite uncertain. This newly compiled dataset provides an opportunity to take stock of what was accomplished and learned from the country’s longest running

public housing transformation initiative. Based on this analysis of HOPE VI grant production, we

identify policy implications that may inform the implementation and evaluation of existing Choice

Neighborhoods Initiative projects and other future mixed-income development projects. We first

summarize some of the existing knowledge from the larger body of literature on the HOPE VI

program and then identify some of the key knowledge gaps that will be addressed in this paper.

Relocation, Demolition, and Construction

We begin our review of existing knowledge on the HOPE VI program with a look at the relocation of

public housing residents away from distressed public housing units, the demolition of those public housing units, and the construction of new public housing, affordable units, and market-rate units.

Demolition of public housing. In 1992, the National Commission on Severely Distressed Public Housing estimated that nationwide, approximately 86,000 (of 1.3 million, roughly 6.5 percent)

public housing units were “severely distressed” (NCSDPH, 1992). Initially, the HOPE VI program

focused on revitalization, rather than demolition, of the severely distressed public housing

units (Goetz, 2003). The program soon changed priorities, however, instead offering housing

vouchers to original residents for housing off-site and demolishing units without the one-for-one

replacement requirement (Katz, 2009). HUD shifted to an approach that promoted the economic

2 The data extraction and compilation were conducted in partnership with a research team at the Massachusetts Institute of Technology led by Dr. Lawrence Vale and Dr. Shomon Shamsuddin, now at Tufts University.

Cityscape 187

Gress, Joseph, and Cho

integration of former public housing sites by rebuilding public housing with units available for

higher-income residents to prevent the reconcentration of poverty (Turbov, 2006).

Replacement of public housing units. The one-for-one replacement requirement required

housing authorities to construct one new unit of affordable housing for every unit demolished.

With limited public funding for new construction, that requirement had been “the largest obstacle

to implementing HOPE VI” (Goetz, 2003: 59). In 1995, that requirement was removed, with

the intention to replace subsidized housing units with a combination of new construction or

rehabilitated public housing units and Section 8 tenant-based vouchers.

Some critics of this approach disagreed with the approach of replacing physical public housing

with some proportion of vouchers that facilitate a move to the private market. For instance, the

Center for Community Change (2003), in their report, A Hope Unseen: Voices From the Other Side of HOPE VI, argued that any loss of affordable housing was problematic and by privatizing public housing, the HOPE VI program was exacerbating a dire social problem. They stated that, by the

program’s close in 2012, 48,643 public housing units “will be gone, forever unavailable to low-

income families, particularly extremely low-income families” (Center for Community Change,

2003: 9). They also made the point that construction of other housing on redeveloped sites,

including those with Low-Income Housing Tax Credits (LIHTC), do not provide the same level of

affordability unless accompanied by other rental subsidies. The LIHTC program generally provides

affordability for households earning between 40 and 60 percent of the area median income,

whereas public housing typically serves families at or below 30 percent of area median income due

to the ongoing annual operating subsidy. In addition, LIHTC units have time limitations on their

affordability status, jeopardizing long-term access to affordable units (Khadduri et al., 2012).

Construction of market-rate units. Any review of HOPE VI has to acknowledge how the program developed and changed over the course of its two decades of operation. Although some of the first

HOPE VI grantees renovated all of the old buildings for reoccupancy by public housing families

exclusively, by 1995, HUD had more fully committed to a strategy of deconcentration together with

mixed-income and mixed-finance redevelopment across all sites. This dimension of the HOPE VI

program, which included the construction of market-rate housing units, also fit within a broader

move to elevate the role of public-private partnerships in the provision of affordable housing while expanding the role of public housing agencies in the mixed-finance realm. To make that possible,

in 1996, the HUD Office of Public Housing Investments published the mixed-finance rule, which

laid out the provisions for the formation of public-private partnerships to leverage public-sector funds with private-sector financing to produce mixed-income housing. Two years later, the Quality

Housing and Work Responsibility Act of 1998 (QHWRA) added an expanded mixed-finance

provision that allowed the use of public funds and operating subsidies for projects to be owned by private developers (Baron, 2009). Together, these steps created the opportunity to leverage private funds to design and implement HOPE VI developments that would be owned and operated by

private companies (Wexler, 2001). Mixed-finance development provided a way of generating the

capital needed to construct new buildings and renovate existing ones and the operating capital

to manage and sustain the brick-and-mortar investments. That move raised critiques among

affordable housing advocates that the privatization of public housing was more focused on

188 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment benefiting capital interests and higher income city residents than positive outcomes for low-income households (Fraser and Kick, 2007; Katz, 2009; Kleit, 2005, Lees, 2008; Polikoff, 2009).

Timing of relocation, demolition, and construction. Increased uncertainty and variation in construction timeframes was one consequence of the privatization of public housing, which meant

that redevelopment projects were more dependent on market conditions. Often, demolition and

construction had lengthy timeframes in the redevelopment process (Crowley, 2009). The long

periods between relocation, construction, and occupancy meant an extended waiting period for

residents who hoped to move back to the new developments (Popkin, Levy, and Buron, 2009).

Among other factors, such as resident preferences, selection criteria, eligibility, and physical and mental health needs, long construction timeframes likely had an influence on the low return rates

among original residents (Popkin, Cunningham, and Burt, 2005).

Funding

The trajectory of funding for the HOPE VI program changed considerably over time.

Decrease in funding over time. The HOPE VI program was affected by significant funding cuts beginning in 2000. And beginning in 2003, the Bush Administration tried to completely eliminate

the HOPE VI program each fiscal year (Polikoff, 2009). Although Congress successfully continued

the program, the funding was drastically cut and continued to be reduced through the final award

year in 2011. The Obama Administration closed down the program in 2010 and replaced it with

the Choice Neighborhoods Initiative, with lower overall levels of federal funding than the original HOPE VI program.

Income and Tenure Mix

The HOPE VI program made it feasible for public housing, affordable and market-rate housing,

and homeownership units to be within the same housing development and set a precedent for this

type of mixed-income redevelopment.

Income mix types. Although no one accepted definition of mixed-income housing exists, a

widely accepted definition focuses on the intentional financing and operations of a development

to facilitate a socioeconomic mix of residents (Brophy and Smith, 1997). Income mix in the public

housing transformation context is most often categorized into public housing, affordable housing,

and market-rate housing (Baron, 2009). Income mix can also be categorized by the percentage of

households in a certain income range as it relates to area median income (AMI), with conventional

categories being less than 50 percent AMI, 50 to 60 percent AMI, and more than 80 percent AMI.

Vale and Shamsuddin (2017: 59) identify four dimensions of income mix that can be considered

in planning, implementation, and assessment: “distribution of units by subsidy type (allocation);

spatial separation of income mix (proximity); distribution of homeownership versus rental units

(tenure); and time limits for subsidies that preserve the income mix (duration).”

Production of public housing homeownership units. The HOPE VI program also mixed rental

and homeownership units. Some of those homeownership units were set aside for former public

Cityscape 189

Gress, Joseph, and Cho

housing residents, as allowed by Section 32 of the U.S. Housing Act of 1937. With the opportunity

to transform public housing sites, some authorities and their development partners returned to this original aspiration and worked to include homeownership for relocated public housing residents

into the unit mix (Santiago and Galster, 2004; HUD, 2003). Redeveloped sites could also include

unsubsidized homeownership units, including in mixed-income, townhouse-style developments.

Reoccupancy

Although residents were given the opportunity to return to the redeveloped public housing within

new mixed-income developments, there were often significant barriers to their return.

Rates of return—challenges to returning. One of the major critiques of the HOPE VI program has been the low rates of return by original residents. Buron and his colleagues (2002) reported that across the country, rates of return to revitalized HOPE VI developments averaged 14 percent (see also Marquis and Ghosh, 2008). The HOPE VI grants included support for relocation services designed

to help residents move temporarily and then return to sites once the redevelopment was completed.

After redevelopment, however, mixed-income properties often implemented stricter requirements

and new screening criteria that included adherence to lease stipulations and other stipulations,

such as background checks, credit checks, and drug testing, which hampered reoccupancy efforts

(Center for Community Change, 2003; Joseph and Chaskin, 2012). Those requirements, in addition

to many other factors—such as limited time and information to make housing decisions, delays of

several years in the delivery of replacement units, mismatch of household size to new unit sizes, and apprehensions about the monitoring and social climate in the new developments—contributed to the

low return rates (Joseph and Chaskin, 2012; Popkin et al., 2004).

Community and Supportive Services

In addition to the redevelopment of deteriorating public housing, the HOPE VI program sought

to provide support for households as they transitioned from high-poverty housing to new

developments or alternative housing. To achieve that goal, HUD HOPE VI grants included funding

for Community and Supportive Services (CSS) that gave public housing residents the “tools to

enable them to improve their life skills and capacities and secure living wage jobs and, when they choose to do so, to relocate to a new neighborhood of their choice” (HUD, 2000: 1). Programming

provided through CSS varied greatly across sites; examples of programs included literacy training; job preparation, training, and retention; personal management skills; daycare; youth activities;

health services; community policing or security activities; and drug treatment (Naparstek et

al., 2000). Research on CSS programs shows that they have not been very effective in achieving

measurable outcomes (Center for Community Change, 2003; Holin et al, 2003; Levy and Woolley,

2007; Oakley, Fraser, and Bazuin, 2015; Popkin, Levy, and Buron, 2009).

Data and Methods

Data analyzed in this paper are from HUD’s HOPE VI quarterly grant progress reports from 1993

through the third quarter of 2014. These quarterly reports provide the most comprehensive look

190 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment at the unit production and financial information of HOPE VI developments available to date. The

quarterly project reporting was used by HUD and by the grantees as an administrative tracking

tool rather than a research tool, and the projection numbers were updated over time, as new

outcome targets were agreed to between HUD and grantees. The data include information on

actual and projected unit production, reoccupancy, funding, and timeframes of redevelopment

phases. Separate quarterly and national reports on Community Supportive Services were

extracted and compiled for analysis.

Descriptive and comparative analyses of the 260 revitalization grantee reports and Community

Supportive Services reports were completed using SPSS statistical software.3 The raw data were

checked for accuracy and discrepancies through extensive searches of electronic and hard-copy

reports and peer-reviewed articles on the HOPE VI program. Then data were extracted, cleaned,

and categorized by individual site level ( n=259), state level ( n=41), and award year ( n=19), for a comprehensive descriptive analysis.

Findings

We have organized this section as follows. We review major elements of the HOPE VI program:

relocation, demolition and construction, funding, return and relocation, and community and

supportive services. For each element, we present two types of findings: (1) existing knowledge

that we have been able to confirm with our analysis of the full grantee report data and (2) new

information and insights that we have been able to glean.

Demolition, Production, and Replacement Housing

We begin by confirming existing knowledge about the replacement of distressed public

housing units through the demolition and new construction of public housing, affordable

units, and market-rate units within HOPE VI developments. We then explore new knowledge

and insights about the income and tenure of units that were planned and built as part of the

HOPE VI program.

Existing Knowledge Confirmed

We confirmed that there was a net loss of public housing units replaced by public housing units,

affordable units, and market-rate units. Net loss of total housing assistance. Our analysis of redeveloped public housing sites includes consideration of the resulting mix of public housing

units and large numbers of units with shallower subsidies, mostly funded with Low-Income

Housing Tax Credits, with unsubsidized units (often referred to as “market rate” units, although

their affordability may vary). This analysis should be placed in the context of what seems to be

an overall net loss of rental assistance—available units that are most affordable to families at the lowest part of the income scale. About 155,000 public housing units were demolished through

3 Significance testing was not conducted because we analyzed the total population of 260 revitalization grants.

Cityscape 191

Gress, Joseph, and Cho

the HOPE VI program, including 56,755 units torn down through Demolition grants4 and 98,592

units demolished through Revitalization grants that included funding for demolition. Public

housing units that were demolished were to be replaced with a combination of new public housing

units (either rebuilt on site or at other sites), together with Section 8 Housing Choice Vouchers.

The program thus aimed to deconcentrate public housing through a combination of strategies.

A precise accounting of the apparent net loss of rental assistance is not possible in this paper based on the data available to the authors. A comparison of the approximately 155,000 total demolished

public housing with the total number of replacement units from both public housing and Section

8 vouchers based on the available data, however, suggests a significant shortfall in the net total of rental assistance that was provided as replacement housing assistance. The redeveloped sites

included 55,318 public housing units, including both new construction and rehabilitation units,

of which the latter would likely not be considered “replacement units” for demolished units.

Likewise, a precise number of total Section 8 Housing Choice Vouchers provided as replacement

units is not available to the authors. A review of existing research conducted by the Urban Institute in 2004 stated,

An estimated 63,000 to 70,000 of these supplemental vouchers were allocated to replace

emolished public housing units between 1995 and 2003. But it is not known how many were

for HOPE VI projects and how many were for other public housing demolition.5

Although further research on this topic would be needed for more accurate estimates of the total

replacement housing assistance provided, this overall context is important to keep in mind when

considering the remainder of this paper’s analysis of development, construction, and resulting unit types at the redeveloped sites.

Redevelopment: Public Housing, Low-Income Housing Tax Credits, and Market-Rate Units

According to grantee reports, 97,389 units have been produced through the HOPE VI program

(85,934 newly constructed units and 11,455 rehabbed units), including 49,949 replacement

4 As stated, HOPE VI had two types of grants: Revitalization grants and, later, Demolition grants. There were 285

Demolition grants. Demolition grants were added, in part, to provide funding for new statutorily required demolition requirements for large projects with long-term vacancies. See HUD “List of Demolition Grants” (October 2004) at

https://www.hud.gov/sites/documents/DOC_9890.PDF. Projects that received Demolition grants were eligible to apply for Revitalization grants to fund redevelopment. Among the grantees that received Demolition grants, 56 also received Revitalization grants to produce replacement units, and that unit production is thus captured in our analysis here. For statutory demolition requirements, see, for example, the mandatory demolition requirement enacted in 1996 through Section 202 of the Omnibus Consolidated Rescissions and Appropriations Act of 1996 (“to make a further downpayment toward a balanced budget”) (Public Law No: 104–134) at https://www.congress.gov/104/plaws/

publ134/PLAW-104publ134.pdf.

5 For estimates on numbers of replacement vouchers, see Popkin et al., 2004, at https://www.urban.org/sites/default/

files/alfresco/publication-pdfs/411002-A-Decade-of-HOPE-VI.PDF. Although assessing the reasons for the apparent shortfall of rental assistance units to replace demolished units is beyond the scope of this paper, one contributing factor may be an inconsistent and uneven implementation over time of HUD’s policy on whether to provide vouchers to replace demolished public housing that were vacant (as opposed to providing vouchers for families in need of relocation assistance). See, for example, HUD Notice PIH 2002-21, “Applications for Housing Choice Vouchers for Relocation or Replacement Housing Related to Demolition or Disposition,” which specifies that replacement vouchers would be provided as replacement for demolished vacant public units (see examples in Section 4 of the Notice). See

PIH 2002-21 at https://www.hud.gov/sites/documents/DOC_9093.PDF. Compare with other PIH Notices on the topic in previous and subsequent years.

192 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment public housing rental units and 5,369 public housing homeownership units.6 Of the total units,

55,318 units (56.8 percent) were public housing, 28,979 units (29.8 percent) were LIHTC

units (referred to in this paper as “affordable units”), and only 13,092 units (13.4 percent) were unsubsidized, market-rate units. Developments built in the first few years of the HOPE VI program

tended to include more rehabilitation of existing public housing units and also tended to replace

more of the demolished public housing units with new construction replacement public housing

units. As the program progressed, however, more affordable and market-rate units were also built

at the redeveloped public housing sites alongside new public housing units. Sites developed later in the HOPE VI program (post-1997) averaged 53.9 percent public housing, 34.9 percent affordable

units, and 11.2 percent market-rate units, whereas the sites developed in the earlier years of the program had a higher proportion of public housing units, averaging 67.5 percent public housing,

23.5 percent affordable units, and 9.0 percent market-rate units. Larger sites (≥ 319 units)7 tended to have a lower proportion of public housing and a higher proportion of market-rate housing than

did smaller sites (< 319 units). On average, the larger sites had 56.9 percent public housing units and 12.8 percent market-rate units, whereas the smaller sites had 64.4 percent public housing

units and 7.4 percent market-rate units.

Decrease in production over time. Starting in 2003, appropriations for the HOPE VI program were dramatically reduced, causing a decrease in demolition and construction of units (see exhibit 1). As a result, HOPE VI program grantees demolished and constructed more units in the first 10 years of

the program than in the remainder of the program. Likewise, grantee award cohorts after 2003 did

not produce as many units for rent or for sale as did those before 2003 (see exhibit 2).

6 Although new grants through the HOPE VI program were discontinued in 2010, many existing grantees are still completing the remaining construction and occupancy phases of their grants.

7 The median size of a HOPE VI development is 319 units.

Cityscape 193

Gress, Joseph, and Cho

Exhibit 1

Cumulative Redevelopment Progress by HOPE VI Grant Award Year (N=18)

100,000

90,000

80,000

70,000

60,000

50,000

40,000

Number of Units

30,000

20,000

10,000

0

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Award Year

Cumulative Construction

Cumulative Relocation

Cumulative Demolition

Note: In the award year information for 2011, only 12 units were constructed; that information was excluded.

194 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment Exhibit 2

Rental and Homeownership Unit Production by HOPE VI Grant Award Year (N=18)

8,000

7,000

6,000

5,000

4,000

Number of Units 3,000

2,000

1,000

0

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Award Year

For Rent

For Sale

New Information and Insights

Our descriptive analysis confirmed much of the knowledge on the HOPE VI program, and we

identified some new information and insights about the program that have implications for future

mixed-income housing development.

Most units were built as ultimately agreed to with HUD. Sites were generally able to meet the construction goals established in their most recent formal agreements with HUD. Because the

quarterly reports to which we had access were overwritten with the most recent projection data,

we were not able to analyze original site projections in this study. Overall, 88.3 percent of the

units projected in the most recent grantee agreements have been produced. Among the 259 sites

we analyzed, 66 (25.5 percent) produced fewer than their most recent projections and 193 (74.5

percent) produced the number of units planned.

Relatively more rental units were produced as planned. As described in exhibit 3, a higher percentage of the planned rental units (91.9 percent) was produced than homeownership units

Cityscape 195

Gress, Joseph, and Cho

(72.7 percent). Also, a higher percentage of the projected public housing units (94.7 percent) than affordable units (87.1 percent) and market-rate units (71.8 percent) units was produced.

Exhibit 3

Comparison of Projected and Actual Unit Production by HOPE VI Grantees

Projected*

Actual

Percent

Public Housing

53,226

49,949

93.8

Affordable

26,674

23,899

89.6

Rental

Market

9,786

8,530

87.2

Total

89,686

82,378

91.9

Public Housing

5,594

5,369

96.0

Affordable

6,607

5,080

76.9

Homeownership

Market

8,459

4,562

53.9

Total

20,660

15,011

72.7

Public Housing

58,820

55,318

94.0

Affordable

33,281

28,979

87.1

Grand Total

Market

18,245

13,092

71.8

Total

110,346

97,389

88.3

Notes: * “Projected” refers to the most recent projection numbers in the HOPE VI quarterly reports, which have been updated over time as agreements with HUD

have been finalized. Figures for public housing units include both new construction and rehabilitation.

Redevelopment activities included a mix of public housing, affordable (LIHTC) units, and

unsubsidized (market-rate) housing. Ultimately, the redevelopment side of the HOPE VI program focused on producing a mix of housing units with varying affordability levels, including

replacement public housing, with units subsidized by LIHTC with more moderate affordability,

and a relatively limited number of market-rate units. Although HOPE VI production resulted

in a decrease of physical public housing units, the redevelopments usually did not include

a large proportion of truly market-rate, unsubsidized housing; instead they mainly included

subsidized housing, replacing 85.5 percent of the original public housing with units that were

affordable for low- and moderate-income residents. An important outstanding question is, how

much affordability was provided by the non-public housing “affordable” units, and what was the

economic status of residents in those units? The data available in the quarterly reports did not

enable us to explore that question, and subsequent research would be needed to assess income

data, depending on availability, at redeveloped sites.

The distribution of mix types across the 259 HOPE VI developments demonstrates that affordable

housing was a priority across the sites (see exhibit 4). To compare the income mix of HOPE VI

projects, we adopted Vale and Shamsuddin’s 2014 four-category typology: Narrow Low-Income,

Polarized Bimodal, All But The Poorest, and Broad Continuum.8 We assigned each HOPE VI

development project to one of Vale and Shamsuddin’s income mix categories based on the

following criteria: Narrow Low-Income developments have no more than 15 percent market-rate

8 Vale and Shamsuddin revised this typology in their 2017 article, “All Mixed Up: Making Sense of Mixed-Income Housing Developments” published in the Journal of American Planning Association (Vale and Shamsuddin, 2017). Our results using their new typology are quite similar.

196 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment units, Polarized Bimodal developments have no more than 15 percent affordable units and thus

mainly have units at the high and low ends of the income mix, All But The Poorest developments

have no public housing units, and Broad Continuum developments have at least 15 percent of all

three unit types (public housing, affordable, and market rate). Most redevelopments had a Narrow

Low-Income mix ( n=179; 69.1 percent), followed by Broad Continuum ( n=66; 25.5 percent) and Polarized Bimodal ( n=13; 5.0 percent). Only one site had an All But The Poorest mix.9 Exhibit 5

shows the differences in average unit production and their proportion by mix type. On average,

the Narrow Low-Income developments had 97.8 percent public housing and affordable units, with

only 2.2 percent market-rate units. The average proportion of market-rate units was 26.3 percent in the Broad Continuum developments and 33.7 percent in the Polarized Bimodal developments.

Exhibit 4

Income Type Mix in 259 HOPE VI Developments

ginsu 100%

f Ho 90%

o 80%

esp 70%

60%

ree Ty 50%

Ths 40%

s 30%

ro

20%

x Ac 10%

e Mi 0% 1 5 9

m

13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

o

101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165 169 173 177 181 185 189 193 197 201 205 209 213 217 221 225 229 233 237 241 245 249 253 257

cIn

Public Housing

Affordable

Market Rate

Exhibit 5

Comparison of Housing Types by Mix Type in the HOPE VI Program (N=258)

Public Housing

Affordable

Market Rate

Total

n

M (SD)

%

M (SD)

%

M (SD)

%

M (SD)

%

Narrow Low-Income

179

217 (184)

67.8

101 (110)

30.0

8 (20)

2.2

326 (212) 100.0

Broad Continuum

66

195 (103)

42.1

158 (122)

31.6

140 (112)

26.3

492 (285) 100.0

Polarized Bimodal

13

283 (173)

60.9

37 (54)

5.4

179 (223)

33.7

500 (391) 100.0

Total

258

214 (167)

60.9

112 (115)

29.2

51 (99)

10.0

377 (255) 100.0

SD = standard deviation.

Notes: Only one site had an All But The Poorest mix type and was excluded. In all the tables, n refers to the sample of HOPE VI sites and M refers to the mean number of units at those sites.

Dominance of mixed-tenure developments. Although the HOPE VI program produced more rental units than homeownership units, a majority of sites ( n=184; 71.0 percent) were mixed tenure, with both rental and homeownership units. Mixed-tenure sites tended to be larger (M=402) than sites

9 Prospect Plaza in New York City included market-rate rental and affordable for-sale units and no public housing.

Cityscape 197

Gress, Joseph, and Cho

without homeownership units (M=312) and included a higher proportion of affordable and market-

rate units (see exhibit 6). Overall, 82,378 units (84.6 percent) of the HOPE VI units produced have been for rental housing, and 15,011 units (15.4 percent) have been designated for homeownership.

Exhibit 6

Comparison of Housing Types by Tenure Mix in the HOPE VI Program (N=259)

Public Housing

Affordable

Market Rate

Total*

n

M (SD)

%

M (SD)

%

M (SD)

%

M (SD)

%

Mixed Tenure

184

220 (178)

58.5

122 (118)

30.2

60 (111)

11.4

402 (272) 100.0

Rental Only

75

197 (137)

66.0

87 (104)

27.1

28 (56)

7.0

312 (196) 100.0

Total

259

214 (167)

60.6

112 (115)

29.3

51 (99)

10.1

376 (255) 100.0

SD = standard deviation.

Note: The total percentages in this chart may not add to 100 percent due to rounding.

Public housing homeownership units. The HOPE VI program had a greater focus on public housing homeownership than many might realize. About 10 percent of the replacement public

housing units were designated for homeownership (5,369 units). Among the 259 sites analyzed

here, a relatively high number ( n=105; 40.5 percent) produced public housing homeownership units. Among those, 34 sites produced 50 or more public housing homeownership units. It is an

untold dimension of the HOPE VI program that warrants closer investigation by researchers.

Variation in timeframes of redevelopment progress. Timeframes for relocation, demolition, construction, and occupancy ranged from weeks to years (see exhibit 7). We found that the

relocation phase took the longest amount of time on average (M=694 days), followed by

construction (M=667), demolition (M=516), and occupancy of the developments (M=260).10

Timeframes for redevelopment varied among sites. On average, as would be expected, larger sites

reported more days for each redevelopment phase (M relocation=829, M demolition=611, M construction=706, and M occupancy=293) than smaller sites (M relocation=539, M demolition=400, M construction=620, and M

occupancy=218). Among smaller sites, the longest phase was construction, but among larger sites, the longest phase was relocation. Longer timeframes for relocation, construction, and occupancy

directly influenced residents who hoped to return to the newly constructed development.

Exhibit 7

Average Duration of HOPE VI Relocation, Demolition, Construction, and Occupancy (days)

N

M (SD)

Median

Min if >0

Max

Relocation

234

694 (623)

466

33

3,643

Demolition

218

516 (677)

268

23

4.318

Construction

216

667 (336)

580

208

2,949

Occupancy

206

260 (367)

172

13

3,939

SD = standard deviation.

Note: Among the 259 sites, 234 sites provided relocation timeframes, 218 sites provided demolition timeframes, 215 sites provided construction timeframes and 206 sites provided occupancy timeframes.

10 Redevelopment phases are not necessarily sequential; one can start after another ends, but they can also be concurrent and overlapping.

198 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment Funding In our descriptive analysis, we confirmed information about HOPE VI program funding and provided insights on private funding leveraged from the HOPE VI program.

Existing Knowledge Confirmed

Funding allocated for the HOPE VI program was successfully expended and decreased over time.

Most budgeted funding was expended. Most (95.3 percent) of the budgeted HOPE VI grant funding was spent (M HOPE VI budgeted=$24.1M and M HOPE VI expended=$22.9M). HOPE VI grantees spent 85 percent of total budgeted funds for redevelopment, which includes both federal grants

(HOPE VI and other federal agency funds) and money from private sources (M total budgeted=$76.9M

and M total expended=$65.3M).

Decrease in funding over time. The annual expended HOPE VI funding ranged from $66.2M (in 2009) to $566.1M (in 1993), with an average of $332.7M and a median of $445.9M. Funding

expended in the HOPE VI program decreased over time (see exhibit 8). The total funding

expended was highest in the 2001 award year and dramatically decreased between 2003 and 2004.

The lowest funding was expended in 2009 and remained relatively low through 2010.

Exhibit 8

HOPE VI Total and Expended Funding by Award Year

$2,000,000,000

$1,800,000,000

$1,600,000,000

$1,400,000,000

$1,200,000,000

$1,000,000,000

$800,000,000

Funding in Dollars

$600,000,000

$400,000,000

$200,000,000

$0

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Award Year

Total Hope VI Funding

Expended HOPE VI Funding

Cityscape 199

Gress, Joseph, and Cho

New Information and Insights

Below, we explore the private funding that was leveraged by the HOPE VI program.

Leveraging redevelopment funding. HOPE VI funds were used to leverage significant amounts of other public and private funds. The approximately $6 billion of HOPE VI funds leveraged

approximately $11 billion of non-HOPE VI funds to complete redevelopment for a total of

approximately $17 billion expended. For every dollar of HOPE VI funds expended, about $1.80

was leveraged for the redevelopment projects.

Sites targeting an economic mix of residents consistently leveraged more funding (see exhibit

9). Among the four types of income mix, the Narrow Low-Income developments (M total

funding=$57.0M), which have only public housing and affordable units, generally had less funding and were not able to leverage as much in additional funds (Polarized Bimodal: M total funding=$78.6M; Broad Continuum: M total funding=$86.9M). Sites with a market-rate component were able to leverage more funds, and the Broad Continuum developments were able to leverage the most funds because

those developments consistently incorporated market-rate units.

Exhibit 9

Leverage Ratio of Funding by Mix Type in the HOPE VI Program (N=258)

Unit

HOPE VI

Total

Leverage

Production

Funding

Funding

Ratio

n

M (SD)

M (SD)

M (SD)

Narrow Low-Income

179

326 (212)

$22.5M (11.4M)

$57.0M (36.5M)

1:1.5

Broad Continuum

66

492 (285)

$23.9M (94.8M)

$86.9M (50.3M)

1:2.6

Polarized Bimodal

13

500 (391)

$27.7M (15.9M)

$78.6M (43.8M)

1:1.8

Total

258

377 (255)

$23.1M (11.2M)

$65.7M (42.8M)

1:1.8

SD = standard deviation.

The leverage ratio also varied among the regions designated by HUD (see exhibit 10). Compared

with other regions, the Rocky Mountain region showed the highest leverage ratio: About $3

was leveraged for every dollar of HOPE VI funding. This region, however, obtained the smallest

amount of HOPE VI funding and constructed the smallest number of units. On the other hand, the

Southeast-Caribbean region received the largest amounts of HOPE VI funding and produced the

largest number of units with leverage ratio of 1:1.7, close to the overall grantee average (1:1.8).

200 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment Exhibit 10

Leverage Ratio of HOPE VI Funding by HUD Region

Unit

HOPE VI

Total

Number of Grants

Name

Production

Funding

Funding

Leverage

Ratio

n

%

Sum

Sum

Sum

Rocky Mountain

5

1.9

1,740

$87.8M

$380.4M

1:3.3

Northwest

12

4.6

6,654

$323.9M

$1.3B

1:3.0

New England

15

5.8

4,060

$346.6M

$1.2B

1:2.5

New York-New Jersey

24

9.2

9,308

$586.7M

$1.9B

1:2.2

Pacific

18

6.9

5,665

$391.5M

$1.2B

1:2.1

Southeast-Caribbean

75

28.8

31,815

$1.6B

$4.3B

1:1.7

Mid-Atlantic

43

16.5

12,712

$903.4M

$2.5B

1:1.7

Midwest

41

15.8

14,389

$1.0B

$2.6B

1:1.6

Southwest

19

7.3

7,767

$488.2M

$1.2B

1:1.4

Great Plains

8

3.1

3,279

$179.1M

$420.8M

1:1.3

Total

260

100.0

97,389

$6.0B

$17.0B

1:1.8

Return and Relocation

We confirmed that many public housing residents did not return to the newly redeveloped housing,

and we now look more closely at the trends in return rates across the HOPE VI developments.

Existing Knowledge Confirmed

The rates of return were low across HOPE VI developments.

Low return rate. The grantee report data confirmed what has become conventional knowledge about the HOPE VI program: a relatively low percentage of all occupied units within HOPE VI

developments were occupied by the original residents returning to the developments. At least

two ways are used to explore how original residents have benefited from the redevelopments.

One method looks at redeveloped units as the denominator and the other method looks at the

original household population as the denominator. Of the 96,476 units that have been produced

and occupied, only 19,993 units (20.7 percent) were occupied by the original tenants at each development. That number can be considered the “proportion occupancy by original residents”

rate. The more familiar way to consider return rate (the “proportion of original residents that return”

rate) is to examine the percentage of original households that have returned to occupy new units

available after redevelopment. Of the households originally relocated, across sites an average of only 27.6 percent (with a median of 18.2 percent) returned to the new units.11 The relatively substantial difference between the average and the median is due to a skew in the distribution toward the lower range. Even among the units designated for public housing residents, a low percentage was occupied by the original residents. Of the 55,318 units that were designated as replacement units for original tenants, only 36.1 percent was occupied by residents of the original development.

11 This figure may continue to increase over time as remaining units are built in sites that are not yet complete and if more original residents return to those units.

Cityscape 201

Gress, Joseph, and Cho

New Information and Insights

We now discuss how the rates of return were low across HOPE VI developments despite the

variance in the income mix types in each development. We also found that these rates remained

low over time.

Consistently low return rates. Return rates do not change significantly when the 32 100-percent public housing replacement developments are removed from the analysis (the average becomes

26.8 percent and the median becomes 17.5 percent), suggesting, surprisingly, that return rates

were relatively low even when the developments did not mix incomes and instead replaced all of the public housing on site. Also, the return rates were almost identical for both larger (M=27.6

percent) and smaller (M=27.5 percent) sites, indicating that the return rates were very low

regardless of the size of developments.

Declining return rates over time. As indicated in exhibit 11, the average return rates by award year ranged from 6.3 percent (2009) to 44.7 percent (1993). Return rates mostly declined between

1993 and 2000, with spikes in 2001 and 2004 followed by a sharp drop-off in 2005 and another

relatively small peak in 2008. Newer sites have, thus far, experienced lower return rates (21.1

percent) than older sites (33.8 percent), which may be a function of time and a lag in construction and occupancy. The return rate at older sites, however, likely indicates the upper end of the average that can ultimately be expected.

Exhibit 11

Return Rates by HOPE VI Award Year

50%

45%

40%

35%

30%

n Rate 25%

Retur 20%

15%

10%

5%

0%

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

202 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment Community and Supportive Services

Some HOPE VI developments also received grants for Community and Support Services to

support residents through programs and services that focused on helping residents get jobs,

access education, improve their health and wellness, and even cover basic needs. We analyzed

grantee reports of CSS caseloads pre- and post-revitalization regarding resident enrollment in

and completion of services. We then conducted a comparative analysis of the following eight CSS

programs by tenure mix, size, and age: employment preparation/placement/ retention, job skills

training programs, high school or equivalent education, English as a Second Language (ESL)

courses, childcare, transportation assistance, counseling programs, and substance abuse programs.

We also compared broader enrollment and completion goals data for employment, economic

development, and homeownership at each development.

New Information and Insights

We share new information and insights on the use of CSS across HOPE VI developments by

residents who remained on site at a development during redevelopment and residents who were

relocated and then returned once redevelopment was complete. We compared the use of CSS

funds by developments with different types of income mixes. We found that most of the basic

goals outlined by developments receiving CSS funds were achieved, but broader goals of program

enrollment and completion were not.

Overall provision of CSS. CSS served both original public housing residents and new incoming residents between the ages of 19 and 64 who moved to redevelopment sites and original residents

who continued to live off site. Of the eight main CSS programs, 249 sites (95.7 percent) provided

at least one, and 93 sites (35.8 percent) provided all eight services. Those programs had an average caseload of 1,226 cases and a median of 767 cases.

Low proportion of remaining or returning individuals in CSS caseload. Among former public housing residents served in CSS programs ( N=54,453 cases), only 31.8 percent were served

while they remained on site ( n=5,869; 10.8 percent) or returned after redevelopment ( n=11,473; 21.1 percent). The other 68.2 percent who were served were relocated to other public housing

( n=13,680; 25.1 percent), to housing not receiving any HUD assistance ( n=5,423; 10.0 percent) or to housing using Section 8 vouchers ( n=18,008; 33.1 percent).

Comparative analysis of CSS. The number of available services varied by tenure mix and the size and age of the redevelopment. Mixed-tenure sites ( M = 1,301) had more residents enrolled in the CSS program than did rental-only sites ( M = 1,044). Among the 184 mixed-tenure sites, only 4 sites (2.2 percent) did not provide any of the eight CSS programs (9.3 percent in rental-only sites). Larger sites ( M = 1,626) tended to provide more CSS support to residents than did smaller sites ( M = 830).

Among the 130 larger sites, only 2 sites (1.5 percent) did not provide any of the eight programs

(7.0 percent in smaller sites). Finally, on average, older sites ( M = 1,415) have provided more CSS

support to residents than have newer sites ( M = 1,046); however, a slightly higher proportion of older (4.7 percent) than newer (3.8 percent) sites did not provide any of the eight programs.

Cityscape 203

Gress, Joseph, and Cho

Most program enrollment goals achieved. Overall, the HOPE VI sites achieved their goals for enrollment in the eight CSS programs (see exhibit 12). On average, employment preparation /

placement /retention had the largest number of enrollments, followed by transportation assistance, counseling, job skills training, and childcare. Still, a notable observation is that substance abuse programs, high school or equivalent education, and language courses enrolled the fewest number

of residents.

Exhibit 12

Enrollments of Caseloads Receiving HOPE VI CSS Programs

Enrollment

Goal

M*

Median

M

Median

Employment Preparation/

333

170

179

100

Placement/Retention

Transportation Assistance

291

110

136

70

Counseling Programs

239

87

146

444

Job Skills Training Programs

135

77

100

70

Childcare

116

61

97

60

High School or Equivalent

71

39

52

32

Education

Substance Abuse Programs

24

6

22

10

English as a Second Language

16

0

15

0

(ESL) Courses

Note: * Mean number of enrollees per HOPE VI site.

Broader enrollment and completion goals not met. In our comparison of the broader enrollment and completion goals for employment, economic development, and homeownership efforts, the

data revealed that some of the goals for those areas of focus were achieved but others were not.

In the area of employment, despite exceeding the number of projected total new job placements,

which includes all types of employment (full time, part time, seasonal, and temporary), the

number of participants currently employed and those employed for 6 months were far below their

enrollment goals. In economic development, entrepreneurship training exceeded the enrollment

goal but fell short on its completion goal. There were also fewer resident-owned businesses and

residents employed by those businesses than projected. For homeownership, the average number

enrolled in homeownership counseling exceeded the goal; however, the number of residents who

completed counseling was lower than the intended goal, and the number of participants who

purchased a home was also less than expected.

Discussion

Through a detailed descriptive analysis of production and financing data from 1993 to 2014,

this paper confirms previous expectations and findings regarding the HOPE VI program and

provides new information and insights. In addition to exploring the income and tenure mix of the

units produced, we answered questions about the nature of HOPE VI reoccupancy, timeframes

of production stages, financing, and the CSS programs available to residents of HOPE VI

developments. Our findings confirm many longstanding concerns about the program, such as the

reduction in the overall public housing stock and the low return rates of original residents. We also 204 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment uncovered some compelling insights about the program, such as the overall focus on low-income

housing production and the extent of the production of public housing homeownership units.

Overall, the main HOPE VI redevelopment housing product was replacement public housing units,

complemented by the inclusion of affordable housing units and, in lower proportions, market-rate

units. Almost one-half of the redevelopments included no market-rate housing. The vast majority

of production was rental, rather than homeownership, units. Even within the properties developed

to include homeownership, a substantial number of units were designated for public housing. Sites

with rental-only units tended to be smaller than mixed-tenure sites, and included fewer affordable and market-rate units.

Using Vale and Shamsuddin’s (2014) income mix typology, we found that well over two-thirds of

the sites have a Narrow Low-Income mix of public and affordable units and about one-fourth have

a Broad Continuum mix of public housing, affordable housing, and market-rate units. Our analysis

also shows that the majority of HOPE VI projects have focused primarily on producing housing

for low-income households, replacing more than one-half of the physical public housing units

demolished and building out another one-third of all new units as subsidized affordable units.

Looking at production over time, there was a greater focus on producing a more diverse income

mix at developments as the program progressed. The greatest demolition and construction of units

occurred in the first 10 years of the HOPE VI program. Starting in 2003, demolition, relocation,

and construction leveled off and production of homeownership units dropped sharply.

The data we received included only the most recent agreed-upon projections between the

grantees and HUD, thus we were not able to analyze original production projections against actual

production. In most cases, grantees met their most recent projected production goals. As would be

expected given the timing of the Great Recession and the associated housing market crash, where

production goals were not met, it was homeownership units that most often fell short, particularly market-rate homeownership; thus, the intended mix of incomes was not achieved at many sites.

This can be interpreted in at least two ways: for those who feared that HOPE VI mixed-income

projects would create environments in which public housing residents in particular and low-

income households in general were a significant minority, that has not come to pass; on the other

hand, to the extent that the success of the mixed-income strategy depended on a broader mix of

income and a critical mass of higher income residents, including homeowners, that objective was

not widely achieved through the HOPE VI program.

The number of phases and the time it took for each phase of development to begin and end

had a major impact on how the HOPE VI program affected residents. The long periods between

relocation, construction, and occupancy meant an extended waiting period for those residents who

hoped to move back to the new developments and likely contributed to the low return rates.

HOPE VI funds were used to leverage significant amounts of other public and private funding. The

$6 billion of expended HOPE VI funds leveraged $11 billion in nonfederal funds—$17 billion

expended from all sources. For every dollar of HOPE VI funds, about $1.80 was leveraged. Narrow

Low-Income developments, which have only public housing and affordable units, generally had

Cityscape 205

Gress, Joseph, and Cho

less funding and were not as able to leverage additional funds as were projects that included

market-rate or homeownership units.

The CSS programs provided services to original residents of the public housing developments

and to families who later moved into the revitalized site. We analyzed caseload output data, which included goals and outputs for each service. The services with the highest number of enrollments

were employment preparation/placement/retention, transportation assistance, counseling, job skills training, and childcare. Overall, CSS programs met or exceeded most enrollment goals, although,

on average, the sites fell short of completion goals for high school or equivalent education. The CSS

programs also fell short on employment retention and entrepreneurship support goals.

Study Limitations

This study had some important limitations. The findings are drawn from HOPE VI

administrative project reports from 1993 to 2014 and include quantitative data on production,

financing, and the CSS program and thus provide a numerical documentation of the program.

Given the limited information available in the reports, however, we were constrained to basic

descriptive and comparative statistical analyses. A text field for qualitative comments was

included in the CSS reports, but those data were of limited depth, consistency, and utility. Also, although the reports provide “projected” estimates regarding financing and unit production,

those estimates were updated over the course of the program and thus do not provide accurate

baseline projections for comparison.

Implications for Research, Policy, and Practice

HOPE VI produced predominantly public housing and subsidized rental housing, with less

production of market-rate rental and homeownership units than had been intended. Although it

produced higher quality replacement public housing in more mixed-income environments, the

HOPE VI program substantially decreased the stock of physical public housing units. Furthermore,

extended construction and occupancy timeframes and stringent reoccupancy requirements may

have prevented many residents from successfully returning to the revitalized housing. HOPE

VI funds successfully leveraged substantial levels of private funds for development. Due to the

decrease in federal funding over time, however, unit production levels were not sustained over

the life of the program. CSS programs generally achieved many of the stated enrollment goals but

were limited in the number of residents they could serve and fell short of the stated goals in several important areas, such as high school equivalency achievement. These findings suggest a number of

implications for policy and further research.

Implications for Research

Given the continued national and local investment in mixed-income redevelopment, a number

of research topics could be pursued, either from further data collection and analysis of HOPE

VI projects or from research on Choice Neighborhoods grantees and other emerging mixed-

income redevelopments.

206 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment 1. To fully understand the projected versus actual unit production through the HOPE VI

program—and thus inform future development efforts and negotiations—an analysis using

additional information about original projected goals may be useful.

2. A deeper understanding of the income levels of residents in each band of subsidy would

provide far better information about which households are benefiting from the housing and

what the income mix continuum looks like in various contexts.

3. Stronger performance measurement and reporting of resident outcomes, such as those that

follow, would provide a critical knowledge base on the upward mobility outcomes of residents

in diverse mixed-income contexts and on those who did not return:

a. The results of various levels and combinations of supportive services.

b. How the outcomes for residents compare across different levels of site income mix,

for example, Narrow Low-Income developments compared with Broad Continuum

developments.

c. How the outcomes for residents compare by level of physical integration among different

income groups within a development.

d. How outcomes for original residents who return compare with those of residents who do

not return.

e. How outcomes compare among original residents who return, newcomers to the public

housing replacement units, residents of the affordable units, and residents of the market-

rate units.

f. How resident outcomes vary with factors such as the size of the development and the

tenure mix at the development.

4. The nature, outcomes, and effect of public housing homeownership programs is an

underexamined area, including which residents qualified and participated and their outcomes.

5. The focus here, given the data available, was on the developments and not on the broader

neighborhood context. The surrounding neighborhoods, however, are an important dimension

to be included in future research both as a context that shapes the strategies and outcomes in

specific developments and as a unit of change brought about by the redevelopment. Analyzing

how site income mixes vary relative to the characteristics of the neighborhood context would

also be interesting.

Implications for Policy and Practice

Stepping back, we can draw from our findings to suggest some key areas for continued policy focus

and improvement.

Cityscape 207

Gress, Joseph, and Cho

1. Balancing dual priorities: ending segregation and concentrated poverty and increasing

affordable housing for the poor. This is an enduring strategic tension in this arena of

housing policy. There are legitimately conflicting policy imperatives here. It would be ideal

to be able to produce more high-quality public housing and facilitate access to more vibrant, socioeconomically diverse neighborhoods. No housing policy has yet fully resolved this

dual challenge and the major policy approaches—mixed-income redevelopment, housing

choice vouchers, and the Rental Assistance Demonstration program—each have operational

advantages and important downsides. Policymakers must continue to be as intentional

and comprehensive as possible in their efforts to use existing public resources to maximize

the provision of housing for the poor while leveraging private-sector resources to generate

investment in housing developments and their surrounding neighborhoods. Clarity about the

intended balance of these conflicting goals and vigilant accountability will be key.

2. Managing the market risks of privatization. The mixed-finance approach to HOPE VI and

the intentions of including market-rate rental and homeownership made the redevelopment

efforts extremely vulnerable to market conditions. The result—evident in the early phases

of the program and then exacerbated by the housing market crash—was extended delays in

unit production and, ultimately, substantial shortfalls in the production of market-rate units.

Policymakers should consider to what extent the market-rate shortfalls affected program

“success” in various local contexts and examine ways to offset the market risk; for example,

might smaller redevelopment phases, increased capacity building, and engagement of

nonprofit entities and community-based organizations be beneficial?

3. Increasing return rates. Clearly a major program shortcoming was the limited proportion of original residents who benefited from the new, higher quality housing and living

environments. Although some proportion of the nonreturners possibly used the relocation

opportunity for upward mobility to a low-poverty neighborhood, the literature suggests

mixed results for those who did not return and for voucher holders in general. The policy

implications include increased resources, attention to the relocation support process, and

strategies to make return easier, such as smaller redevelopment phases and phased relocation

on site or in close proximity.

4. Program enrollment is not sufficient: Providing the requisite support services for

positive outcomes. The CSS data analyzed here provide a severely limited purview into

the details of service provision and results. The program reports clearly show, however, that

although the HOPE VI grantees were able to exceed their enrollment goals in many cases—

indicating that they had achieved the objectives of linking with local partners and programs

that were offering the forms of support needed—evidently those enrollments generally did

not turn into sustained engagement or meaningful outcomes for participants. What is the

scope, quality, and duration of support needed to help residents affected by a mixed-income

redevelopment move toward self-sufficiency?

208 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment Acknowledgments

We thank Jenni Bartholomew for her editorial assistance. We thank our colleagues Dr. Claudia

Coulton and Dr. David Crampton for their advisory roles on this research study. We thank research

assistants Biwen Liu, Siyang Sun, and Michael Salwiesz for their data compilation work. We

thank our partners at the Resilient Cities and Housing Initiative at the Massachusetts Institute of Technology, led by Dr. Lawrence Vale and Dr. Shomon Shamsuddin, for their collaboration and

insights in extracting, compiling, and analyzing this data set. We thank HUD for the Research

Partnership grant that made this study possible and in particular Ron Ashford and Lars Gnissen for their help with our orientation to the data set, Madlyn Wohlman-Rodriguez for her encouragement

and responsiveness in supporting this research, and David Hardiman for his helpful comments

on an earlier version of the article. The work that provided the basis for this paper was supported by funding under a grant with the U.S. Department of Housing and Urban Development, Office

of Policy Development and Research (Instrument Number RP-14-OH-005). The substance

and findings of the work are dedicated to the public. The authors are solely responsible for the

accuracy of the statements and interpretations contained in this paper. Such interpretations do not necessarily reflect the views of the government.

References

Baron, Richard. 2009. “The Evolution of HOPE VI as a Development Program.” In From Despair

to Hope: HOPE VI and the New Promise of Public Housing in America’s Cities, edited by Henry G.

Cisneros and Lora Engdahl. New York: Brookings Institution: 31–47.

Brophy, Paul. C., and Rhonda N. Smith. 1997. “Mixed-Income Housing: Factors for Success,”

Cityscape 3 (2): 3–31.

Buron, Larry. 2004. An Improved Living Environment? Neighborhood Outcomes for HOPE VI Relocatees.

Metropolitan Housing and Communities: A Roof Over Their Heads Policy Series, Brief No. 3.

Washington, DC: Urban Institute.

Buron, Larry, Susan J. Popkin, Diane K. Levy, Laura G. Harris, and Jill Khadduri. 2002. The HOPE

VI Resident Tracking Study: A Snapshot of the Current Living Situation of Original Residents From Eight Sites. Washington, DC: Urban Institute.

Center for Community Change. 2003. A Hope Unseen: Voices From the Other Side of HOPE VI.

Washington, DC: ENPHRONT.

Chaskin, J. and Mark L. Joseph. 2015. Integrating the Inner City: The Promise and Perils of Mixed-Income Public Housing Transformation. Chicago, IL: University of Chicago Press.

Cisneros, Henry G., and Lora Engdahl, eds. 2009. From Despair to Hope: HOPE VI and the New

Promise of Public Housing in America’s Cities. New York: Brookings Institution.

Cityscape 209

Gress, Joseph, and Cho

Comey, Jennifer. 2004. An Improved Living Environment? Housing Quality Outcomes for HOPE VI

Relocatees. Metropolitan Housing and Communities: A Roof Over Their Heads Series, Policy Brief No. 2. Washington, DC: Urban Institute.

Crowley, Sheila. 2009. “HOPE VI: What Went Wrong.” In From Despair to Hope: HOPE VI and the

New Promise of Public Housing in America’s Cities, edited by Henry G. Cisneros and Lora Engdahl.

New York: Brookings Institution: 229–247.

Fraser, James C., and Edward L. Kick. 2007. “The Role of Public, Private, Non-Profit and

Community Sectors in Shaping Mixed-Income Housing Outcomes in the U.S.,” Urban Studies 44

(12): 2357–2377.

Goetz, Edward. G. 2003. Clearing the Way: Deconcentrating the Poor in Urban America. Washington, DC: Urban Institute Press.

Holin, Mary Joe, Larry Buron, Gretchen Locke, and Alvaro Cortes. 2003. I nterim Assessment of

the HOPE VI Program: Cross-Site Report. Washington, DC: U.S. Department of Housing and Urban Development.

Joice, Paul. 2017. “HOPE and Choice for HUD-Assisted Households,” Cityscape 19 (3): 449–473.

Joseph, Mark L., and Robert J. Chaskin. 2012. “Mixed-Income Developments and Low Rates of

Return: Insights From Relocated Public Housing Residents in Chicago,” Housing Policy Debate 22

(3): 377–406.

Joseph, Mark L., Robert J. Chaskin, and Henry S. Webber. 2007. “The Theoretical Basis for

Addressing Poverty Through Mixed-Income Development,” Urban Affairs Review 42 (3): 369–409.

Katz, Bruce. 2009. The Origins of HOPE VI. In From Despair to Hope: HOPE VI and the New Promise of Public Housing in America’s Cities, edited by H.G. Cisneros and L. Engdahl. New York: Brookings Institution: 15–29.

Khadduri, Jill, Carissa Climaco, Kimberly Burnett, Laurie Gould, and Louise Elving. 2012. What Happens to Low-Income Housing Tax Credit Properties at Year 15 and Beyond? Bethesda, MD: Abt Associates, Inc.

Kleit, Rachel G. 2005. “HOPE VI New Communities: Neighborhood Relationships in Mixed-

Income Housing,” Environment and Planning A: Economy and Space 37 (8): 1413–1441.

Lees, Loretta. 2008. “Gentrification and Social Mixing: Towards an Inclusive Urban Renaissance?”

Urban Studies 45 (12): 2449–2470.

Levy, Diane K., and Mark Woolley. 2007. Relocation is Not Enough: Employment Barriers Among

HOPE VI Families. HOPE VI: Where Do We Go from Here? Brief 6. Washington, DC: The Urban

Institute.

Marquis, Gerald P., and Soumen Ghosh. 2008. “Housing Opportunities for People Everywhere

(HOPE VI): Who Gets Back In?” The Social Science Journal 45 (3): 401–418.

210 Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment Naparstek, Arthur J., Susan R. Freis, G. Thomas Kingsley, Dennis Dooley, and Howard E. Lewis.

2000. HOPE VI: Community Building Makes a Difference. Washington, DC: U.S. Department of

Housing and Urban Development.

National Commission on Severely Distressed Public Housing (NCSDPH). 1992. Final Report

to the Congress and the Secretary of Housing and Urban Development. Washington, DC: U.S.

Government Printing Office.

National Housing Law Project. 2002. False Hope: A Critical Assessment of the HOPE VI Public Housing Redevelopment Program. Oakland, CA: National Housing Law Project.

Oakley, Deirdre, James Fraser, and Joshua Bazuin. 2015. “The Imagined Self-Sufficient

Communities of HOPE VI: Examining the Community and Social Support Component,” Urban

Affairs Review 51 (5): 726–746.

Pendall, Rolf, Martin D. Abravanel, Audra Brecher, Alex Curley, Elizabeth Davies, Megan Gallagher, Monica Getahun, David Greenberg, Taryn Gress, Chantal Hailey, Leah Hendey, Reed Jordan, Mark

Joseph, Amy Khare, G. Thomas Kingsley, Nancy Latham, Diane K. Levy, Brianna Losoya, Jen

McGraw, Hannah Melnicoe, Jamie Pfluecke, Kathryn L.S. Pettit, Susan J. Popkin, Aesha Rashid,

Hortencia Rodriguez, and Rachel Scheu. 2015. Developing Choice Neighborhoods: An Early Look at Implementation in Five Sites. Washington, DC: Urban Institute: 1–267.

Pendall, Rolf, and Leah Hendey. 2013. A Brief Look at the Early Implementation of Choice

Neighborhoods. Washington, DC: Urban Institute: 1–14.

Polikoff, Alexander 2009. “HOPE VI and the Deconcentration of Poverty,” In From Despair to Hope: HOPE VI and the New Promise of Public Housing in America’s Cities, edited by H.G. Cisneros and L.

Engdahl. New York: Brookings Institution: 65–81.

Popkin, Susan J., and Mary K. Cunningham. 2009. “Has HOPE VI Transformed Residents’ Lives?”

In From Despair to Hope: HOPE VI and the New Promise of Public Housing in America’s Cities, edited by H.G. Cisneros and L. Engdahl. New York: Brookings Institution: 191–203.

Popkin, Susan J., Mary K. Cunningham, and M. Burt. 2005. “Public Housing Transformation and

the Hard-to-House,” Housing Policy Debate 16 (1): 1–24.

Popkin, Susan J., Bruce Katz, Mary K. Cunningham, Karen D. Brown, Jeremy Gustafson, and

Margery A. Turner. 2004. A Decade of HOPE VI: Research Findings and Policy Challenges. Washington, DC: Urban Institute.

Popkin, Susan J., Diane K. Levy, and Larry Buron. 2009. “Has HOPE VI Transformed Residents’

Lives? New Evidence From the HOPE VI Panel Study,” Housing Studies 24 (4): 477–502.

Popkin, Susan J., Michael J. Rich, Leah Hendey, Chris Hayes, Joe Parilla, and G. Galster. 2012.

“Public Housing Transformation and Crime: Making the Case for Responsible Relocation,”

Cityscape 14 (3): 137–160.

Cityscape 211

Gress, Joseph, and Cho

Santiago, Anna Maria, and George C. Galster. 2004. “Moving From Public Housing to

Homeownership: Perceived Barriers to Program Participation and Success,” Journal of Urban Affairs 26 (3): 297–324.

Tach, Laura. 2009. “More Than Bricks and Mortar: Neighborhood Frames, Social Processes

and the Mixed-Income Redevelopment of a Public Housing Project,” City and Community 8 (3): 273–303.

Turbov, Mindy. 2006. “Public Housing Redevelopment as a Tool for Revitalizing Neighborhoods:

How and Why Did It Happen and What Have We Learned?” Northwestern Journal of Law and Social

Policy 1 (1): 167–201.

U.S. Department of Housing and Urban Development (HUD). 2015. Choice Neighborhoods

2015 Grantee Report. Washington, DC: HUD. https://www.hud.gov/sites/documents/

CNGRANTEEREPORT2015.PDF.

———. 2003. Guidance for PHAs Developing a Section 32 Homeownership Plan. Washington,

DC: HUD. https://www.hud.gov/sites/documents/DOC_8104.PDF.

———. 2000. Community and Supportive Services for Original Residents. General

Guidance for the HOPE VI Program (Draft). https://portal.hud.gov/hudportal/documents/

huddoc?id=DOC_9840.pdf.

Vale, Lawrence J. 2019. After the Projects: Public Housing Redevelopment and the Governance of the Poorest Americans. New York: Oxford University Press.

———. 2013. Purging the Poorest: Public Housing and the Design Politics of Twice-Cleared

Communities. Chicago, IL: University of Chicago Press.

Vale, Lawrence J., and Shomon Shamsuddin. 2017. “All Mixed Up: Making Sense of Mixed-Income

Housing Developments,” Journal of the American Planning Association 83 (1): 56-67.

———. 2014. All Mixed Up: Defining Mixed-Income in Public Housing Redevelopment. Working

paper. Cambridge, MA: Massachusetts Institute of Technology.

Wexler, Harry J. 2001. “HOPE VI: Market Means/Public Ends—The Goals, Strategies, and Midterm

Lessons of HUD’s Urban Revitalization Demonstration Program,” Journal of Affordable Housing and Community Development Law 10 (3): 195–233.

Wilson, William J. 1987. The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy.

Chicago, IL: University of Chicago Press.

212 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 department 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.

by Matthew J. Holian

Where is the City’s Center?

Five Measures of Central Location

Matthew J. Holian

San Jose State University

Abstract

In this article, the author describes and evaluates five databases that provide different measures of city center location. He identifies research that has used each database, calculates average distances between the locations, presents case studies for two cities, and provides suggestions for analysts searching for appropriate measures of city center location. Among the findings are that the location of a city’s city hall is a better proxy for the location of a city’s central business district (CBD) than are other measures in current use.

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 213

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

Holian

Introduction

Researchers across many disciplines need city center location data to identify such places as

central business districts (CBDs). The notion of a “city center”—as distinct from the “central

city” of a metropolitan statistical area (MSA)—has different meanings depending on the context.

One may envision the peak of a city skyline (often in financial centers); a historic transportation hub, such as Grand Central Station in New York City (Schelling, 1960); or a location defined on

objective criteria, such as population or employment density (Taubenböck et al., 2013).1 Other

meanings of the term city center could be possible. The concept of a CBD, however, is widely used in research.

In this article, I present five measures used in urban economics and related research to identify

city center locations. I describe strengths and weaknesses of the measures and present an analysis comparing the five databases, four of which cover U.S. MSA centers and a fifth that includes all

census geographies. In keeping with the applied focus of the SpAM department of this journal,

I also describe my methods so that others can adopt the techniques. I conclude the article with

suggestions for finding appropriate measures of the socioeconomic center of a city.

Five Measures of Central City Points

A point—that is, a location—is represented on a map by geographic coordinates, which give the

latitude and longitude of the “geocoded” point. At least five different measures have been used by researchers to identify city center locations. In this section, I describe those measures and outline their differences.

The first measure is from Central Business Districts: 1982 Census of Retail Trade (hereafter, 1982 Census), which was the last time the U.S. Census Bureau attempted to identify CBDs, and it

did so for 455 cities2 defined by existing census tract boundaries. Kneebone (2013: 3) notes that

“Though dated, the 1982 CBDs represent the last systematic identification of business districts at the national scale. Furthermore, the 1982 CBDs continue to exhibit significant overlap with the

densest job centers in the nation’s major metro areas.” Despite the strong conceptual appeal of the 1982 Census measure, one limitation for a researcher looking for geographic points is that those

tract-based definitions are actually areas.

Researchers working at the Federal Reserve have converted those areas to points and made the

data available on the Internet.3 Although I refer to this measure as the 1982 Census measure,

the conversion of areas to points was performed by Fee and Hartley (2013), who required

CBD points for 385 MSAs. They describe their methodology on pages 47 to 48 of their study:

1 A new and promising approach uses machine learning techniques along with both subjective assessment and objective criteria to determine CBD points (Brown et al., 2017).

2 This is my manual count from the PDF file available at the Internet archive: https://web.archive.org/

web/20070221191519/http://www.census.gov:80/geo/tiger/cbdct.pdf. This file lists census tracts identified by the 1982 study as a “central business district.” See Brown et al. (2017: 6) for more details on the 1982 Census delineation of CBDs.

3 These data can be accessed at http://www.danielaaronhartley.com/msas_with_central_city_cbds.csv.

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Where is the City’s Center? Five Measures of Central Location

We identify the latitude and longitude of the CBD by taking the collection of census

tracts listed in the 1982 Census of Retail Trade for the central city of the MSA (the city in the MSA with the largest population) and finding the centroid of that cluster of

census tracts. We identify the CBD latitude and longitude for 268 MSAs in this manner.

Note that because the 1982 Census measure is available for only 455 cities, Fee and Hartley

(2013) were able to merge these data to only an N=268 subset of their sample of contemporary

MSAs. Their solution to this problem, described by the authors on pages 47 to 48, takes us to

a second measure of central city location:

For the remaining 117 MSAs, whose central city was not listed in the 1982 Census

of Retail Trade, we use the latitude and longitude found by geocoding the MSA’s central

city found using the ArcGIS 10.0 North American Geocoding Service. ArcGIS returns

points that are, on average, very close to the CBDs from the Census of Retail Trade; for

the 268 cities for which we have both, the mean distance between the two is 0.39 miles.

One area of interest for Fee and Hartley (2013) was measuring the effect of so-called

agglomeration economies, which refers to greater invention, innovation, and enhanced

productivity because workers in the same industry live, work, or otherwise interact in close

proximity. Fee and Hartley (2013) found an average distance of 0.39 miles between the 1982

Census and the ArcGIS measures.4 The authors concluded that the measurement error was

likely small for most industries because Elvery and Sveikauskas (2010) suggest in their work

that distances of between 5 and 25 miles may be relevant for many industries. They discuss

the possibility, however, that relevant distances for agglomeration effects may be shorter in

industries with more highly skilled workers.5

A third measure, similar to the ArcGIS measure, was developed as part of the study by Holian and

Kahn (2015),6 who used Google Earth to geocode the principal city of each of 366 MSAs by recording the latitude and longitude returned—the location where the map view centered—from a city name

search. One of the applications in which Holian and Kahn (2015) were interested was whether

households living near the city center drive less on average than those who live farther away. Given that the average metropolitan U.S. household lived about 20 miles from their city’s downtown area, measurement error—if on the scale of that reported by Fee and Hartley (2013)—would not seem to

present a major threat to the internal validity of the analysis. Whether the Google Earth measure is close to the 1982 Census measure is unknown, but this topic is addressed it the next section.7

4 ArcGIS is a prominent Geographic Information System (GIS) software package by Environmental Systems Research Institute (ESRI). ESRI provides GIS data files for use in ArcGIS. Fee and Hartley (2013) used these data to create CBD

locations, but the authors do not describe how they determined the point location.

5 This possibility was raised by Fee and Hartley (2013) in endnote 2: 262.

6 These data are available to download on my blog: http://mattholian.blogspot.com/2013/05/central-business-district-

geocodes.html.

7 In their initial study, Holian and Kahn (2015) recognized the possibility of measurement error but noted that errors of up to a mile would likely not present a major challenge given the nature of the analysis, and that all points in a sample of points they visually inspected seemed to be reasonable approximations of CBD location.

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Like the ArcGIS measure, the Google Earth measure is also something of a “black box” in the

sense that the variables in the Google algorithm that determined the locations are unknown.

Nevertheless, this measure has been used or discussed in articles, including Anenberg and Kung

(2018) and Gardner and Hendrickson (2018); Ph.D. dissertations, including Molnar (2014),

Resseger (2014), and Su (2018); Federal Reserve Bank publications (Rappaport, 2017, 2016, and

2014); and working papers (Couture et al., 2018; Couture and Handbury, 2017; Hamilton and

Dourado, 2018).

A fourth measure was developed by Wilson and colleagues (2012), who provide geocodes based

on the address of each city’s city hall. These data are from a worksheet in the Chapter 1 spreadsheet file associated with their study.8 A principal advantage of these measures is that they are

conceptually clear, similar to the centers from the 1982 Census. Although the concept of a political center is distinct from that of a business center, one can reasonably expect these political centers to be near the CBDs. This city hall measure provides geocodes for the principal city in each of

368 MSAs.9 Examples of studies that have used or discussed location information from this source

include Dascher (2018); Hall, Palsson, and Price (2018); and Liu (2013).

Finally, a fifth measure of city center used in research is the U.S. Census Gazetteer.10 These center points are based on the interpolated latitude and longitude associated with the Census Bureau’s

TIGER/Line Shapefiles. The Gazetteer files provide population, land area, and “representative

latitude and longitude coordinates” for all census geographies, including tracts, counties, and cities.11 Documentation explaining how these points were selected or created is unavailable.12

This measure is similar to Google Earth and ArcGIS with regard to being a black box in creating

the central point; however, the Gazetteer is by far the most comprehensive measure I discuss here, which is its principal virtue and can be readily accessed for all census geographies. Holian and

Kahn (2015); James and Aadland (2011); and Nelson, Uwasu, and Polasky (2007) provide

examples of research use location information from this source.

Analysis

In the preceding section, I described conceptual strengths and weaknesses but posted several

questions about their accuracy in representing a city center. This section provides a quantitative assessment of the measures and offers some answers as to their accuracy.

To calculate the distance between two points, I use a formula called the spherical law of cosines.

This formula takes into account the spherical shape of the Earth and is accurate over distances

8 See https://www.census.gov/library/publications/2012/dec/c2010sr-01.html. The points can be found by clicking the link to the Excel file for Chapter 1.

9 The three measures, Wilson et al. (2012), Fee and Hartley (2013), and Holian and Kahn (2015), all provide geocodes for the principal city of all MSAs in the United States. The reason for the seeming discrepancy in sample sizes is that the MSA definitions change from year to year, and researchers naturally choose different year definitions. An additional complication of using MSAs as units of analysis is that the Census Bureau changes the definition of an MSA’s principal city in some years: the principal city is the most populous city in the MSA, so that designation will change when city populations grow or shrink in a way that changes the ranking of cities in the same MSA.

10 These data can be accessed at https://www.census.gov/geo/maps-data/data/gazetteer2010.html.

11 This quote is from “2017 U.S. Gazetteer Files” on the Census Bureau website: https://www.census.gov/geo/maps-data/

data/gazetteer2017.html.

12 These centers may be centroids (the geographic center of the city’s area, which may or may not be close to the economic center).

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Where is the City’s Center? Five Measures of Central Location

typical in intra-urban research. This formula yields what can be thought of as the distance between two points “as the crow flies.” I have created a spreadsheet file that implements this formula.13

I first calculated the average distance between each of the five measures, for a total of 10

measurements, shown in exhibit 1.

Exhibit 1

Matrix of Average (Mean) Distances (in km)

Arc GIS

Google Earth

City Hall

Gazetteer

1982 Census of Retail Trade

0.64

0.97

0.52

3.68

ArcGIS

0.98

0.48

3.36

Google Earth

0.94

3.50

City Hall

3.44

The results in exhibit 1 show the City Hall measure to be closest to the 1982 Census measure,

at 0.52 kilometers (km), and the Gazetteer measure is farthest from it, at 3.68 km. The mean

distance between the ArcGIS and 1982 Census measures used by Fee and Hartley (2013) is 0.64

kilometers, which is extremely close to the mean distance of 0.63 km (or 0.39 miles) that they

reported. Of note, I find nearly the same result as Fee and Hartley, even though my sample is

slightly different, with N=256 versus N=268. Sample sizes for each mean distance measure in

exhibit 1 are shown in exhibit 2.

I now offer a more detailed analysis of the measures. Exhibit 2 shows summary statistics for each

of the 10 distances. In addition to average distance, exhibit 2 reports the standard deviation of the distances. These data are useful to report because a measure of location that is farther from the CBD

on average but that has a smaller standard deviation may be more desirable for some purposes than

a measure that is closer on average but for which the possibility of large errors exists. In addition, exhibit 2 reports the number of cities (N) and the median, minimum, and maximum distances to

provide fuller insight into the characteristics of the distances between each CBD measure.14

13 This file and accompanying tutorial analysis can be downloaded from http://mattholian.blogspot.com/2017/03/

using-spreadsheet-to-calculate-distance.html. For more information on the formula, see http://www.movable-type.

co.uk/scripts/latlong.html. I have also written a blog article (Holian, 2017) that contains instructions for using that file, sample data, and a tutorial analysis. My “distance calculator” spreadsheet also contains a worksheet with additional instructions. Although I provide a lot of instructions, the spreadsheet is actually fairly easy to use. There are a few helpful practices that users would learn themselves after using it a few times, but I have tried to spell out all of them in the blog article so that others will not have to reinvent the wheel.

14 I did not include an estimate of the standard error (SE) of the means in repeated sampling, but that value can be readily calculated based on the formula SE=SD/√N. The test statistic for a test for which the mean distance is zero is then the mean divided by the SE. All of the mean distances are statistically significant at zero at conventional levels.

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Exhibit 2

Summary Statistics for 10 Distance Measures

N

Mean

Median

SD

Min

Max

1982Cen to ArcGIS

256

0.64

0.42

0.78

0.01

6.85

1982Cen to Google Earth

256

0.97

0.49

1.53

0.02

12.62

1982Cen to City Hall

256

0.52

0.36

0.77

0.02

6.93

1982Cen to Gazetteer

256

3.68

2.51

4.65

0.06

55.26

ArcGIS to Google Earth

361

0.98

0.50

1.60

0.00

13.07

ArcGIS to City Hall

361

0.48

0.30

0.65

0.01

5.72

ArcGIS to Gazetteer

361

3.36

2.37

4.39

0.12

54.14

City Hall to Google Earth

365

0.94

0.45

1.65

0.01

13.40

City Hall to Gazetteer

365

3.44

2.40

4.46

0.06

54.27

Google Earth to Gazetteer

366

3.50

2.35

4.95

0.07

54.14

1982Cen = 1982 Census of Retail Trade. SD = standard deviation.

The ArcGIS measure is 0.64 km from the 1982 Census measure on average, whereas the City

Hall measure is only 0.52 km away on average. That difference is a relatively small 0.12 km,

but it suggests, for example, that because the City Hall measure is closer to the 1982 Census

measure on average, studies such as Fee and Hartley (2013) and Holian and Kahn (2015) would

have been better off using the City Hall measure from Wilson et al. (2012) instead of the ArcGIS

or Google Earth measures. Is the difference large enough to be statistically significant? The value of the difference between means test statistic is 1.71 (p=0.087), meaning that the difference

is significant at the 10-percent level but not at the more conventional 5-percent level.15 The

standard deviation and maximum distances are very close for both the 1982 Census-to-City Hall

and the 1982 Census-to-ArcGIS distances. Finally, in addition to the Gazetteer measure having

the highest average distances to all other central location measures, the maximum distance is

much higher in the Gazetteer measure and in one case is more than 54 km away from the City

Hall measure, which is in the San Francisco MSA and is discussed in the next section.

15 See McDonald (2014) for a discussion about this test; the author of that article has also created a spreadsheet tool that implements this test, which can be downloaded from that page. This test is conceptually different from what is probably the more familiar difference-in-means hypothesis test.

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Where is the City’s Center? Five Measures of Central Location

Application

This section presents qualitative analysis for two MSAs as case studies, starting with New

Orleans. The Google Earth measure is not the spot that most people would identify as the

center of the city, but it is close to typical locations that people might cite. For example,

the Google Earth measure locates the city center at slightly less than 1 mile north of Jackson

Square, a central park in the city’s historic French Quarter. That difference may or may not

be a problem for some analyses, such as housing price change away from the city center,

but that distance could be a large enough error to be a problem for other applications,

such as in measuring the diffusion of ideas. For example, if workers on their lunch break

are not able to walk much more than a mile from their office, measurement error on this

scale could severely limit opportunities for informal exchange of ideas.

The New Orleans City Hall location is within the area that is known locally as the CBD,

but it is toward the outskirts of that district, near the Superdome and Interstate 10. The

ArcGIS measure is one large city block from the City Hall measure. Although these three

may be close for many applications, the 1982 Census measure is the best of the five because

it is firmly centered in the neighborhood identified by local residents as the CBD and is only

a regular city block from the French Quarter. Meanwhile, the Gazetteer measure is the worst

of the five; it locates the center in a wildlife refuge more than 10 miles away from the CBD

and the French Quarter. Exhibit 3 shows geocodes for all five measures, exhibit 4 presents

all five points, and exhibit 5 shows the four nearby points presented in greater detail.

Exhibit 3

Five Measures of Central Points for Two Cities

New Orleans

San Francisco

Latitude Longitude

Measure

Latitude Longitude

1

Google Earth

29.964722

– 90.070556

37.774930

– 122.419416

2

City Hall

29.952403

– 90.076487

37.778503

– 122.418307

3

Gazetteer

30.068636

– 89.939007

37.727239

– 123.032229

4

Census 1982

29.952499

– 90.070801

37.785702

– 122.408000

5

ArcGIS

29.953701

– 90.077751

37.777122

– 122.419639

Sources: 1: http://mattholian.blogspot.com/2013/05/central-business-district-geocodes.html ; 2: https://www.census.gov/library/publications/2012/dec/

c2010sr-01.html; 3: https://www.census.gov/geo/maps-data/data/gazetteer2010.html; 4 and 5: http://www.danielaaronhartley.com/msas_with_central_

city_cbds.csv

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Exhibit 4

Five Measures of Central Points for New Orleans

Notes: 1. Google Earth. 2. City Hall. 3. Gazetteer. 4. Census 1982. 5. ArcGIS. Plotted points are shown in Exhibit 3.

As another illustrative example with these measures, consider San Francisco, where the

Gazetteer point is on the Farallon Islands, which are in the Pacific Ocean and are visible from

the mainland only on the clearest of days. The City Hall, Google Earth, and ArcGIS measures,

however, are within one to four blocks of each other. Despite the near consensus reached by

those three measures, other contender definitions for the center of San Francisco—the historic

transportation center (the Ferry building), financial district (FiDi), or shopping district (Union Square)—are several kilometers away from city hall. The City Hall and Google Earth measures

are only a few kilometers away from each of those points, whereas the Gazetteer measure is

more than 50 km away. Finally, and as in the case of New Orleans, the 1982 Census measure

seems most appropriate for the majority of applications requiring a socioeconomic measure of

centrality: it is 2 blocks from the Union Square shopping district, which is between the Financial District, the Ferry Building, and city hall.

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Where is the City’s Center? Five Measures of Central Location

Exhibit 5

Four Measures of Central Location in New Orleans, Detail Area

Notes: 1. Google Earth. 2. City Hall. 4. Census 1982. 5. ArcGIS. Plotted points are shown in Exhibit 3.

Exhibit 6

Two Measures of Central Location in San Francisco

Notes: 3. Gazetteer. 4. Census 1982. Plotted points are shown in Exhibit 3.

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Exhibit 7

Four Measures of Central Location in San Francisco, Detail Area

Notes: 1. Google Earth. 2. City Hall. 4. Census 1982. 5. ArcGIS. Plotted points are shown in Exhibit 3.

Conclusion

Researchers who need access to a measure of city center locations have at least five databases at

their disposal. Depending on the nature of the researcher’s study, one may be more appropriate

than the others; however, in some situations, the choice between them would likely not matter

much, given the documented similarities across the measures. I conclude with the following

suggestions for using these measures:

• The 1982 Census points are probably the best measures of the CBD concept; however,

Fee and Hartley (2013) produced points for only 268 cities, and even the original survey

delineated CBDs for only 455 cities. This source alone will not cover all principal cities in

contemporary MSA samples.

• If the 1982 Census does not include the city being studied, use the City Hall database;

these points come closest on average to the 1982 Census points in this analysis. These

points are available for the principal cities of most of the currently defined MSAs.

• If a city is not available in either the 1982 Census or City Hall databases, the location

of the city hall could be geocoded using Google Earth or ArcGIS.

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Where is the City’s Center? Five Measures of Central Location

• If time constraints prohibit geocoding City Hall points, the Google Earth or ArcGIS geocoded

city points could be used; the ArcGIS points were closer to the 1982 Census points in this

analysis, suggesting that they are superior to the Google Earth measures for CBD purposes.

• The Gazetteer should be used only in cases in which large measurement errors are not

a problem, such as approximating airline travel distances between cities, or when they are

used to measure the center of small census geographies, such as tracts in metropolitan areas,

as in Holian and Kahn (2015). The Gazetteer measure is also the most readily accessible and

comprehensive in geographic scope of the five measures.

Time permitting, researchers can always complete their tasks using multiple measures and then

compare and contrast the results as a sensitivity analysis. I hope that continued research on city center location provides researchers with more and better data, and to that end I share the data

and analysis file I used in writing this article.

Acknowledgments

The author thanks Rich Taketa and an anonymous reviewer, who offered helpful comments during the drafting of this manuscript. Any errors are the author’s.

Author

Matthew J. Holian is a professor of economics at San Jose State University and can be reached

by email at matthew.holian@sjsu.edu.

References

Anenberg, Elliot, and Edward Kung. 2018. “Can More Housing Supply Solve the Affordability

Crisis? Evidence From a Neighborhood Choice Model,” Regional Science and Urban Economics.

https://doi.org/10.1016/j.regsciurbeco.2018.04.012.

Brown, Jason, Maeve Maloney, Jordan Rappaport, and Aaron Smalter Hall. 2017. How Centralized

is U.S. Metropolitan Employment? Working paper RWP 17-16 (November). Prepared for Kansas

City, MO: Federal Reserve Bank of Kansas City. https://dx.doi.org/10.18651/RWP2017-16.

Couture, Victor, Cecile Gaubert, Jessie Handbury, and Erik Hurst. 2018. “Income Growth and

the Distributional Effects of Urban Spatial Sorting,” Unpublished manuscript. https://economics.

stanford.edu/sites/g/files/sbiybj9386/f/welfare-implications-urban-current.pdf.

Couture, Victor, and Jessie Handbury. 2017. Urban Revival in America, 2000 to 2010. NBER

Working Paper No. 24084. Prepared for Cambridge, MA: National Bureau of Economic Research.

https://www.nber.org/papers/w24084.

Cityscape 223

Holian

Dascher, Kristof. 2018. “Function Follows Form,” Journal of Housing Economics. https://doi.

org/10.1016/j.jhe.2018.08.003.

Elvery, Joel A., and Leo Sveikauskas. 2010. “How Far Do Agglomeration Effects

Reach?” Unpublished paper. Cleveland State University. https://www.researchgate.net/

publication/228428788_How_far_do_agglomeration_effects_reach

Fee, Kyle, and Daniel Hartley. 2013. “The Relationship Between City Center Density and Urban

Growth or Decline.” In Revitalizing American Cities, edited by Susan M. Wachter and Kimberly A.

Zeuli. Philadelphia: University of Pennsylvania Press: 45–64.

Gardner, John, and Joshua R. Hendrickson. 2018. “If I Leave Here Tomorrow: An Option View

of Migration When Labor Market Quality Declines,” Southern Economic Journal 84 (3): 786–814.

https://doi.org/10.1002/soej.12249

Hall, Jonathan D., Craig Palsson, and Joseph Price. 2018. “Is Uber a Substitute or Complement

for Public Transit?” Journal of Urban Economics 108: 36–50. https://doi.org/10.1016/j.

jue.2018.09.003

Hamilton, Emily, and Eli Dourado. 2018. The Premium for Walkable Development Under

Land Use Regulations. Mercatus research paper C2010SR-01. Prepared for Arlington, VA:

Mercatus Center at George Mason University. https://www.mercatus.org/publications/walkable-

development-land-use-regulations.

Holian, Matthew J. 2017. “Using a spreadsheet to calculate the distance between two points.”

Matt Holian, Economist blog, March 4. http://mattholian.blogspot.com/2017/03/using-

spreadsheet-to-calculate-distance.html.

Holian, Matthew J., and Matthew E. Kahn. 2015. “Household Carbon Emissions From Driving

and Center City Quality of Life,” Ecological Economics 116: 362–368. https://doi.org/10.1016/j.

ecolecon.2015.05.012

James, Alex, and David Aadland. 2011. “The Curse of Natural Resources: An Empirical

Investigation of U.S. Counties,” Resource and Energy Economics 33 (2): 440–453. https://doi.

org/10.1016/j.reseneeco.2010.05.006

Kneebone, Elizabeth. 2013. Job Sprawl Stalls: The Great Recession and Metropolitan Employment

Location. Prepared for Washington, DC: Brookings Institution. https://www.brookings.edu/wp-

content/uploads/2016/06/Srvy_JobSprawl.pdf.

Liu, Haofei. 2013. What Affects the Number of Non-Work Stops Made During Commute Tours? A

Study Based on the 2009 U.S. National Household Travel Survey in Large Metropolitan Areas. Ph.D.

diss., University of California, Los Angeles.

McDonald, John. 2014. Handbook of Biological Statistics, 3rd ed. Baltimore, MD: Sparky

House Publishing: 126–130. http://www.biostathandbook.com/twosamplettest.html.

224 SpAM

Where is the City’s Center? Five Measures of Central Location

Molnar, Gabor. 2014. Three Empirical Essays on United States Broadband Internet Markets: Quality, Competition, and Welfare. Ph.D. diss., University of Colorado, Boulder.

Nelson, Erik, Michinori Uwasu, and Stephen Polasky. 2007. “Voting on Open Space: What

Explains the Appearance and Support of Municipal-Level Open Space Conservation Referenda

in the United States?” Ecological Economics 62 (3–4): 580–593. https://doi.org/10.1016/j.

ecolecon.2006.07.027

Rappaport, Jordan. 2017. “Crowdedness, Centralized Employment, and Multifamily Home

Construction,” Economic Review—Federal Reserve Bank of Kansas City Q (I): 41–83. https://www.

kansascityfed.org/~/media/files/publicat/econrev/econrevarchive/2017/1q17rappaport.pdf.

———. 2016. Productivity, Congested Commuting, and Metro Size. Research working paper RWP

16-03 (January). Prepared for Kansas City, MO: Federal Reserve Bank of Kansas City. https://

papers.ssrn.com/sol3/papers.cfm?abstract_id=2727441.

———. 2014. Monocentric City Redux. Research working paper RWP 14-09 (November).

Prepared for Kansas City, MO: Federal Reserve Bank of Kansas City. https://www.kansascityfed.

org/~/media/files/publicat/reswkpap/pdf/rwp14-09.pdf.

Resseger, Matthew George. 2014. Essays in Urban Economics. Ph.D. diss., Harvard University.

Schelling, Thomas C. 1960. The Strategy of Conflict. Cambridge: Harvard University Press.

Su, Yichen. 2018. The Rising Value of Time and the Origin of Urban Gentrification. Ph.D. diss., Stanford University.

Taubenböck, Hannes, Martin Klotz, Michael Wurm, J. Schmieder, B. Wagner, M. Wooster,

Thomas Esch, and Stefan Dech. 2013. “Delineation of Central Business Districts in Mega

City Regions Using Remotely Sensed Data,” Remote Sensing of Environment 136: 386–401.

https://doi.org/10.1016/j.rse.2013.05.019

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

Goworowska (with Darryl T. Cohen, Marc J. Perry, and Geoffrey W. Hatchard). 2012.

Patterns of Metropolitan and Micropolitan Population Change: 2000 to 2010. Prepared for the U.S.

Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau.

Washington, DC: U.S. Census Bureau. https://www.census.gov/library/publications/2012/dec/

c2010sr-01.html.

Cityscape 225

226 Departments

Racially Concentrated Areas of

Affluence: A Preliminary Investigation

Edward G. Goetz

Anthony Damiano

Rashad A. Williams

Correction

The volume 21, number 1 issue of Cityscape contained errors in exhibit 5 and on page 109.

The corrected discussion follows.

Income Concentration

Our analysis shows that 36 percent of metro areas had higher rates of concentrated affluence

compared with concentrated poverty. Overall, we find 16 percent more areas of concentrated

affluence compared to areas of concentrated poverty (2,297 tracts and 1,983 tracts respectively).

However, the distribution of concentrated affluence is skewed toward fewer metro areas leading

to higher average amounts of concentrated poverty compared to concentrated affluence in our

sample. Places with the highest rates of concentrated affluence include wealthy metro areas in

the West and Northeast such as San Jose, Washington, D.C., and San Francisco.

Exhibit 5

Average Metro Area Income

Share Tracts w/

Share Tracts w/

Concentrated Affluence

Concentrated Poverty

Region

N

mean

sd

mean

sd

Midwest

10

2.71

(1.41)

9.2

(4.14)

Northeast

7

4.95

(4.46)

5.13

(2.33)

South

22

4.27

(5.00)

5.33

(4.23)

West

11

7.00

(8.11)

2.27

(1.6)

Total

50

4.66

(5.39)

5.4

(4.14)

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 227

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

228 Departments

Referees 2018-19

Referees 2018–19

The Office of Policy Development and Research gratefully acknowledges the contributions of

the following referees and their assistance in making Cityscape worth reading.

Alex Abramovich

Matthew Desmond

Vincent Schiraldi

Laura Abrams

Anne Farrell

Jonathan Spader

Arthur Acolin

Phil Garboden

Scott Susin

Tim Aubry

Maurice N. Gattis

Halil Toros

Irina Barakova

Andrew Hanson

Greg Townley

Richard P. Barth

Judith Regina Havlicek

Mary Clair Turner

Tara Batista

Saahoon Hong

Kimberly Tyler

William Clark

B.K. Elizabeth Kim

Jamie Van Leeuwen

Anna Cororaton

Michelle Munson

Alex Wagaman

Susanna Curry

Nathanael Okpych

John Weicher

Richard Dembo

Rachel Ong

Lei Zhang

Cityscape: A Journal of Policy Development and Research • Volume 21, Number 2 • 2019

Cityscape 229

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

Symposium

National Survey of Mortgage Originations ...............................................1

Guest Editors: Robert B. Avery and Ron Borzekowski

Guest Editors' Introduction

National Survey of Mortgage Originations ....................................................................3

Mortgage Experiences of Rural Borrowers in the United States:

Insights from the National Survey of Mortgage Originations

by Tim Critchfield, Jaya Dey, Nuno Mota, and Saty Patrabansh .......................................9

Perceptions and Expectations of Mortgage Borrowers:

New Evidence from the National Survey of Mortgage Originations

by Chad R edmer .........................................................................................................33

First-Time Homebuyer Counseling and the Mortgage Selection Experience

in the United States: Evidence from the National Survey of Mortgage Originations

by Robert B. Argento, Lariece M. Brown, Sergei Koulayev, Grace Li, Marina Myhre,

Forrest Pafenberg, and Saty Patrabansh .......................................................................51

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit

by Brian Bucks, Tim Critchfield, and Susan Singer .........................................................75

Refereed Papers ...................................................................................................... 91

Road Map to a Unified Measure of Housing Insecurity

by Robynn Cox, Benjamin Henwood, Seva Rodnyansky,

Eric Rice, and Suzanne Wenzel ....................................................................................93

The Effects of Flood Insurance on Housing Markets

by Agustín Indaco, Francesc Ortega, and Süleyman Tas¸ pınar ...........................................129

Departments ............................................................................................................ 157

Affordable Design

Annual HUD Secretary’s Opportunity and Empowerment Award:

Family Scholar House, Louisville, Kentucky

by Regina C. Gray..................................................................................................... 159

Data Shop

Tracking Individuals Pre- and Post-Foreclosure

by Christos Makridis and Michael Ohlrogge ...............................................................163

Graphic Detail

Visualizing and Comparing Residential Permit Data Using Lollipop Plots

by Alexander Din ......................................................................................................175

Impact

Acceptable Separation Distance Standards for Residential Propane Tanks

by Maria Chelo Manlagnit De V enecia .......................................................................179

Policy Briefs

Confirmations, New Insights, and Future Implications for HOPE VI

Mixed-Income Redevelopment

by T aryn H. Gress, Mark L. Joseph, and Seungjong Cho ..............................................185

SpAM

Where is the City’s Center? Five Measures of Central Location

by Matthew J . Holian ................................................................................................213

Correction

Racially Concentrated Areas of Affluence: A Preliminary Investigation ........................227

Referees 2018–19 ................................................................................................229

Cityscape

VOLUME 21 NUMBER 2 • 2019

Office of Policy Development and Research

Document Outline

A Journal of PolicyDevelopment and Research

Cityscape

Contents

Symposium

National Survey of Mortgage Originations

Mortgage Experiences of Rural Borrowers in the United States: Insights from the National Survey of Mortgage Originations

Introduction

Literature

Definition of Urban and Rural Areas

Geographic Differences in Property, Loan, and Borrower Characteristics

Geographic Differences in Borrowers’ Experiences and Knowledge

Geographic Differences in Borrowers’ Lender and Mortgage Choice

Conclusions

Perceptions and Expectations of Mortgage Borrowers: New Evidence from the National Survey of Mortgage Originations

Introduction

Background

Borrower Backcasts of House Price Changes

Borrower Forecasts of House Prices

How Expectations of Future House Prices are Formed

Conclusion

First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations

Introduction

Literature

Analysis Sample and Outcomes

Empirical Strategy

Empirical Results

Conclusion

National Survey of Mortgage Originations Survey Data on Your Home Loan Toolkit

Introduction

Literature Review

Changes in Responses to Whether Borrower Remembers Booklet or Toolkit

Multivariate Analysis

Conclusion

Appendix

Refereed Papers

Road Map to a Unified Measure of Housing Insecurity

Introduction

Appendix A

Appendix B

The Effects of Flood Insurance on Housing Markets

Introduction

Flood Insurance

Flood Insurance Reform: Premium Increases

Flood Insurance: New Flood Maps

Conclusion

Appendix

Departments

Annual HUD Secretary’s Opportunity and Empowerment Award: Family Scholar House, Louisville, Kentucky

Tracking Individuals Pre- and Post-Foreclosure

Introduction

Limitations of Existing Mortgage Data Sets

Building Mortgage Records from Registrar of Deeds Data

Matching Deed Mortgage Records to Consumer Marketing Data

Matching Deed Mortgage Records to HMDA

Alternative Data Sources

Visualizing and Comparing Residential Permit Data Using Lollipop Plots

Acceptable Separation Distance Standards for Residential Propane Tanks

Summary of Analysis

Costs of the Proposed Rule

Benefits of the Proposed Rule

Conclusion

Confirmations, New Insights, and Future Implications for HOPE VI Mixed-Income Redevelopment

Introduction

Contribution of this Paper

Relocation, Demolition, and Construction

Funding

Income and Tenure Mix

Reoccupancy

Community and Supportive Services

Data and Methods

Findings

Demolition, Production, and Replacement Housing

Existing Knowledge Confirmed

New Information and Insights

Existing Knowledge Confirmed

New Information and Insights

Return and Relocation

Existing Knowledge Confirmed

New Information and Insights

Community and Supportive Services

New Information and Insights

Discussion

Study Limitations

Implications for Research, Policy, and Practice

Implications for Research

Implications for Policy and Practice

Where is the City’s Center? Five Measures of Central Location

Introduction

Five Measures of Central City Points

Analysis

Application

Conclusion

Racially Concentrated Areas of Affluence: A Preliminary Investigation

Correction