A Journal of Policy

Development and Research

The housing-healTh ConneCTion

Volume 20, number 2 • 2018

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

Managing Editor: Mark D. Shroder

Associate Editor: Michelle P. Matuga

Advisory Board

Dolores Acevedo-Garcia

Brandeis University 

Ira Goldstein 

The Reinvestment Fund 

Richard K. Green 

University of Southern California 

Mark Joseph 

Case Western Reserve University 

Matthew E. Kahn 

University of California, Los Angeles 

C. Theodore Koebel 

Virginia Tech 

Jens Ludwig 

University of Chicago 

Mary Pattillo 

Northwestern University 

Carolina Reid 

University of California 

Patrick Sharkey 

New York University

Cityscape

A Journal of Policy 

Development and Research

The housing-healTh ConneCTion

Volume 20, number 2 • 2018

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

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

ethnography, finance, geography, law, planning, political science, public policy, regional science, sociology, statistics, and urban studies.

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

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

huduser.gov/periodicals/cityscape.html.

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

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

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

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

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

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

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

Contents

Symposium

The Housing-Health Connection ................................................................................................... 1 

Guest Editors: Veronica Helms Garrison and Craig Evan Pollack

Guest Editors’ Introduction 

The Health-Housing Nexus: New Answers to Key Questions ................................................... 3

The Impact of the Vermont Support and Services at Home Program on Healthcare 

Expenditures ................................................................................................................................ 7 

by Amy Kandilov, Vince Keyes, Martijn van Hasselt, Alisha Sanders, Noëlle Siegfried,  

Robyn Stone, Patrick Edwards, Aubrey Collins, and Jenna Brophy

A Pilot Community Health Worker Program in Subsidized Housing: The Health +  

Housing Project ......................................................................................................................... 19 

by Amy L. Freeman, Tianying Li, Sue A. Kaplan, Ingrid Gould Ellen, Ashley Young, Diane  

Rubin, Marc N. Gourevitch, and Kelly M. Doran

Work Requirements and Well-Being in Public Housing .......................................................... 39 

by Kirstin Frescoln, Mai Thi Nguyen, William M. Rohe, and Michael D. Webb

Innovative Approaches to Providing Rental Assistance: States and Localities Seek To  

Support Health and Human Services Goals ............................................................................. 53 

by Anna Bailey, Peggy Bailey, and Douglas Rice

Embedding Health in Affordable Housing Development: Results of the Health Action  

Plan Pilot Project ....................................................................................................................... 73 

by Stephany De Scisciolo, Krista Egger, and Mary Ayala

Connecting Fragmented Systems: Public Housing Authority Partnerships With the  

Health Sector ............................................................................................................................. 85 

by Stephen Lucas

‘Then I Found Housing and Everything Changed’: Transitions to Rent-Assisted Housing  

and Diabetes Self-Management ............................................................................................... 107 

by Danya E. Keene, Mariana Henry, Carina Gormley, and Chima Ndumele

Homelessness During Infancy: Associations With Infant and Maternal Health and  

Hardship Outcomes ................................................................................................................. 119 

by Diana B. Cutts, Allison Bovell-Ammon, Stephanie Ettinger de Cuba, Richard Sheward,  

Meg Shaefer, Cassie Huang, Maureen M. Black, Patrick H. Casey, Sharon Coleman, Megan  

Sandel, and Deborah A. Frank

Cityscape iii

Contents

Impact of Rental Assistance on Modifiable Health Risk Factors and Behaviors  

in Adults .................................................................................................................................. 133 

by Cathy L. Antonakos and Natalie Colabianchi

The Geography of Vacant Housing and Neighborhood Health Disparities After the  

U.S. Foreclosure Crisis ........................................................................................................... 145 

by Kyungsoon Wang and Dan Immergluck

Refereed Papers .................................................................................................................... 171

Employment and Earnings Trajectories During Two Decades Among Adults in  

New York City Homeless Shelters .......................................................................................... 173 

by Stephen Metraux, Jamison D. Fargo, Nicholas Eng, and Dennis P. Culhane

Exploring Patterns of Tax Increment Financing Use and Structural Explanations in  

Missouri’s Major Metropolitan Regions ................................................................................... 203 

by Susan G. Mason and Kenneth P. Thomas

Departments .......................................................................................................................... 233

Data Shop 

The Long-Term Dynamics of Affordable Rental Housing: Creating and Using a  

New Database .......................................................................................................................... 235 

by John C. Weicher, Frederick J. Eggers, and Fouad Moumen

Foreign Exchange 

Continuous Repayment Structures in Japanese Housing Finance for Elderly People: 

Applications To Mitigate Counterparty Risk Through U.S. Reverse Mortgage Design ........ 245 

by Christopher Feather

Creating Permanent Housing Affordability: Lessons From German Cooperative  

Housing Models ....................................................................................................................... 263 

by Kathryn Reynolds

SpAM 

Understanding and Enhancing the U.S. Department of Housing  and Urban  

Development’s  ZIP Code Crosswalk Files ............................................................................. 277 

by Ron Wilson and Alexander Din

Referees 2017–18 .................................................................................................................... 295 

iv Volume 20 Number 2

Symposium

The Housing-Health Connection 

Guest Editors: Veronica Helms Garrison and Craig Evan Pollack

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape

1

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

2 The Housing-Health Connection

Guest Editors’ Introduction

The Health-Housing Nexus: 

New Answers to Key Questions

Veronica Helms Garrison

U.S. Department of Housing and Urban Development 

Craig Evan Pollack 

Johns Hopkins University, Bloomberg School of Public Health 

The views expressed in this introduction are those of the guest editors and do not represent the official positions or policies of the Office of Policy Development and Research, the U.S. Department of Housing and Urban Development, or the U.S. government.

The link between housing and health is far reaching and complex. Historically, the housing-health 

nexus has been primarily associated with physical exposures and dilapidated housing; however, 

recent studies suggest that adverse health outcomes are also linked to housing rental assistance 

status, housing insecurity, a lack of affordable housing, and neighborhood quality.

Substandard, unaffordable housing and stalled community development represent important 

public health challenges facing millions of American families, and disentangling the complex 

relationships between housing and health is crucial for policymakers. This Cityscape symposium 

adds to the current body of evidence highlighting the relationship between health and housing. 

Throughout this issue, the authors examine innovative approaches to addressing cross-sector poli-

cies and programs that promote the utilization of housing as a platform to improve quality of life. 

The following 10 articles begin to answer key questions pertinent to housing and health. The first three articles examine the health impact of interventions targeting specific aspects of housing. The next three articles highlight state and local efforts designed to bridge the divide between housing and health. The last four articles provide new qualitative and quantitative evidence exploring the link between housing and health. Specifically, the articles explore the following topics and questions.

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape

3

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

Garrison and Pollack

Evaluations of the Health Impact of Housing-Based 

Interventions

1. Does providing supportive services in housing developments reduce Medicare spending?  

Kandilov et al. (2018) used Medicare claims data and a difference-in-difference study design 

to investigate Vermont’s Supportive and Services at Home program. They found that, although 

the program was not associated with lower healthcare spending overall, Medicare expenditures 

declined in a subset of developments.

2. Does the placement of community health workers in assisted housing developments 

promote positive health outcomes?  By studying the implementation of community health 

workers in two subsidized housing developments, Freeman et al. (2018) found that a large 

majority of residents reported meeting their goals, expressed improved overall well-being, and 

stated satisfaction with the program.

3. Can work requirements promote self-sufficiency? What are the health consequences of 

these requirements?  Using a mixed-methods approach, Frescoln et al. (2018) explored the 

use of work requirements in public housing and its impact on residents’ overall well-being. The 

authors suggest that, when work requirements are implemented alongside case management, 

these initiatives can increase residents’ employment, albeit with potential negative impacts on 

receipt of food assistance and Medicaid enrollment. 

State and Local Efforts To Bridge the Divide Between 

Housing and Health 

4. How do state and local housing programs address the relationship between housing 

assistance and health?  Bailey, Bailey, and Rice (2018) provided an indepth analysis regarding the development of 19 state- and locally funded rental assistance programs in two cities and 

eight states, showing how these programs promote housing affordability and address the 

complex housing and health relationship. 

5. How can housing developers incorporate public health throughout the design process?   

De Scisciolo, Egger, and Ayala (2018) detailed a pilot study in which five community 

development corporations partnered with public health professionals to create Health Action 

Plans. Results indicate the potential for these plans to help prioritize health in the development process. 

6. To what extent are public housing authorities currently partnering to support the 

health and well-being of U.S. Department of Housing and Urban Development-assisted 

individuals and families?  Through a survey of housing authorities, Lucas (2018) explored how large public housing authorities promote resident and community health for assisted tenants. 

Housing authorities reported high engagement with public health entities and community-

based social and human service providers, but housing authorities also reported limitations in 

funding, staffing, and data sharing. 

4 The Housing-Health Connection

The Health-Housing Nexus: New Answers to Key Questions

New Qualitative and Quantitative Evidence Examining the 

Link Between Housing and Health

7. What are the mechanisms through which subsidized housing might impact chronic 

disease management?  Using qualitative interview data to examine the transition into assisted housing, Keene et al. (2018) found that receipt of rental assistance was accompanied by 

improvements in diabetes self-management. 

8. How does homelessness during infancy impact maternal and child health?  Cutts et al. 

(2018) found that homelessness during infancy is a risk for adverse infant and maternal health 

and hardship. The authors identified that homelessness during infancy was significantly 

associated with higher developmental risk and, for mothers, worse overall health and depressive 

symptoms. 

9. Does receipt of housing assistance impact health behaviors?  Through the analysis of panel survey data, Antonakos and Colabianchi (2018) found that moving to assisted housing was 

linked with higher rates of smoking initially but not at long-term followup. No association was 

observed for several other health behaviors.

10. How does housing vacancy impact population health?  Wang and Immergluck (2018) 

observed that higher numbers of vacant homes is significantly associated with adverse health 

outcomes in the neighborhood. This link between housing vacancy and neighborhood health 

varied according to whether the metropolitan area was experiencing strong or weak growth. 

We hope that the research presented in this symposium will stimulate innovative ideas about how 

the housing and health sectors can partner at the national, state, and local levels to promote secure housing and foster individual and population health.

Guest Editors

Veronica Helms Garrison is a social science analyst at the U.S. Department of Housing and Urban 

Development, Office of Policy Development and Research, and a doctoral student at the Johns 

Hopkins Bloomberg School of Public Health.

Craig Evan Pollack is an associate professor of medicine, epidemiology, and health policy and 

management at the Johns Hopkins School of Medicine and the Bloomberg School of Public Health.

References

Antonakos, Cathy L., and Natalie Colabianchi. 2018. “Impact of Rental Assistance on Modifiable 

Health Risk Factors and Behaviors in Adults,”  Cityscape 20 (2): 133–143. 

Bailey, Anna, Peggy Bailey, and Douglas Rice. 2018. “Innovative Approaches to Providing Rental 

Assistance: States and Localities Seek To Support Health and Human Services Goals,”  Cityscape 20 

(2): 53–71. 

Cityscape

5

Garrison and Pollack

Cutts, Diana B., Allison Bovell-Ammon, Stephanie Ettinger de Cuba, Richard Sheward, Meg Shae-

fer, Cassie Huang, Maureen Black, Patrick H. Casey, Sharon Coleman, Megan Sandel, and Deborah 

A. Frank. 2018. “Homelessness During Infancy: Associations With Infant and Maternal Health and 

Hardship Outcomes,”  Cityscape 20 (2): 119–132.

De Scisciolo, Stephany, Krista Egger, and Mary Ayala. 2018. “Embedding Health in Affordable 

Housing Development: Results of the Health Action Plan Pilot Project,”  Cityscape 20 (2): 73–84. 

Freeman, Amy L., Tianying Li, Sue A. Kaplan, Ingrid Gould Ellen, Ashley Young, Diane Rubin, 

Marc N. Gourevitch, and Kelly M. Doran. 2018. “A Pilot Community Health Worker Program in 

Subsidized Housing: The Health + Housing Project,”  Cityscape 20 (2): 19–37. 

Frescoln, Kirstin, Mai Thi Nguyen, William M. Rohe, and Michael D. Webb. 2018. “Work Require-

ments and Well-Being in Public Housing,”  Cityscape 20 (2): 39–52. 

Kandilov, Amy, Vince Keyes, Martijn van Hasselt, Alisha Sanders, Noëlle Siegfried, Robyn Stone, 

Patrick Edwards, Aubrey Collins, and Jenna Brophy. 2018. “The Impact of the Vermont Support 

and Services at Home Program on Healthcare Expenditures,”  Cityscape 20 (2): 7–18.

Keene, Danya E., Mariana Henry, Carina Gormley, and Chima Ndumele. 2018. “‘Then I Found 

Housing and Everything Changed’: Transitions to Rent-Assisted Housing and Diabetes Self-

Management,”  Cityscape 20 (2): 107–118. 

Lucas, Stephen. 2018. “Connecting Fragmented Systems: Public Housing Authority Partnerships 

With the Health Sector,”  Cityscape 20 (2): 85–106. 

Wang, Kyungsoon, and Dan Immergluck. 2018. “The Geography of Vacant Housing and Neighbor-

hood Health Disparities After the U.S. Foreclosure Crisis,”  Cityscape 20 (2): 145–170.

6 The Housing-Health Connection

The Impact of the Vermont Support 

and Services at Home Program on 

Healthcare Expenditures

Amy Kandilov

Vince Keyes

RTI International 

Martijn van Hasselt 

University of North Carolina at Greensboro 

Alisha Sanders 

LeadingAge Center for Applied Research

Noëlle Siegfried 

RTI International

Robyn Stone 

LeadingAge Center for Applied Research

Patrick Edwards 

Aubrey Collins 

Jenna Brophy 

RTI International 

Abstract

•  Objective: The Support and Services at Home (SASH) program in Vermont aims 

to coordinate care and assist participants in accessing the health care and support 

services they need to maintain their health and age comfortably and safely in their 

homes. Most program participants are residents of U.S. Department of Housing and 

Urban Development (HUD)-assisted properties or Low-Income Housing Tax Credit 

(LIHTC) properties. Our objective is to estimate the impact of the first 5 1/2 years of 

the SASH program on the Medicare expenditures of these participants.

•  Methods: We use a difference-in-differences model, comparing the change in the 

expenditures among the SASH participants with the change in the expenditures for a 

comparison group of Medicare beneficiaries in HUD-assisted or LIHTC properties that 

did not host the SASH program. 

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape

7

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

Kandilov, Keyes, van Hasselt, Sanders, Siegfried, Stone, Edwards, Collins, and Brophy Abstract (continued)

•  Results: Our findings indicate that participants—particularly dual-eligible participants—

in SASH panels that are overseen by the Cathedral Square Corporation, and in the subset 

of those panels that are in an urban county, experience slower growth in total Medicare 

expenditures and expenditures for hospital care, emergency department visits, and 

specialist physician visits relative to the comparison group.

•  Conclusions: Although we do not find that the SASH program has a significant impact on 

Medicare expenditures for all participants in our sample, the favorable results among a 

subset of panels, containing nearly one-half of the SASH participants in HUD-assisted or 

LIHTC properties, provides evidence that a housing-plus-services model has the potential 

to slow the growth of healthcare costs.

Introduction

Older adults prefer to live independently in their own homes for as long as possible (Oswald 

et al., 2010). Advancing age, however, increases the likelihood of chronic illness, frailty, and 

disability and consequently places some older adults in greater need of health and long-term 

services and supports (LTSS; see AARP, 2009; Redford and Cook, 2001). To continue to live 

independently, older adults may need access to in-home support services and housing that 

can be adapted to their growing needs (Lawton, 1976). 

Living independently as they age may be particularly difficult for lower-income older adults, 

with fewer resources for support and greater healthcare needs. A recent analysis has shown 

that tenants assisted by U.S. Department of Housing and Urban Development (HUD) pro-

grams have more health problems than unassisted low-income adults, including significantly 

greater odds of poor general health, disability, and several chronic conditions (Helms, Sper-

ling, and Steffen, 2017).

Taking advantage of the colocation of large numbers of older adults in publicly assisted, 

multiunit rental properties could help these low-income adults better address their health and 

functional challenges. Organizing a system of health and LTSS around this type of housing 

has many potential benefits. Economies of scale can be achieved in organizing, delivering, 

and purchasing services. Publicly assisted, service-enriched housing also enables onsite 

staff to observe and respond to residents’ health and supportive services needs as they arise. 

Recent research shows that older residents living in housing with onsite service coordinators 

had significantly lower odds of being hospitalized than residents in housing without service 

coordinators (Sanders et al., 2014). Incorporating the surrounding community into service-

enriched housing strategies may result in improved health and lower healthcare and LTSS 

costs among residents. 

8 The Housing-Health Connection

The Impact of the Vermont Support and Services at Home Program on Healthcare Expenditures The Support and Services at Home (SASH) program in Vermont is designed to promote 

greater care coordination for a high-cost population of older adults and individuals with dis-

abilities living in affordable housing properties. The program’s unique contribution is its use of teams embedded in properties as a platform to connect residents to health services and social 

supports in the community. A full-time SASH coordinator and a quarter-time SASH wellness 

nurse together serve each panel of approximately 100 participants. The SASH program 

launched in July 2011 and by December 2016 had expanded into 54 panels in nonprofit 

affordable housing properties throughout Vermont. 

When a participant first enrolls in SASH, the SASH coordinator and wellness nurse complete 

a comprehensive assessment of health conditions, medications, and support services currently 

used or needed. This assessment, updated annually, helps the SASH coordinator to identify 

the health and service needs of the individual and to target group programming toward com-

mon needs across the panel. To connect participants to resources in the community, the SASH 

coordinator and wellness nurse partner with local service provider organizations, such as 

home health agencies, councils on aging, and community mental health organizations. SASH 

participants consent to share their healthcare information with the SASH staff, community 

partners, and healthcare providers, which allows the SASH staff to work with the participants’ 

healthcare providers to ensure proper medication usage, successful hospital discharges, and 

overall coordination and continuity of care. 

The SASH program is a statewide initiative coordinated at the state, regional, and local levels. 

The nonprofit housing provider Cathedral Square Corporation (CSC) developed the SASH 

program and oversees the program at the state level, coordinating program expansion and 

training. At the regional level, six Designated Regional Housing Organizations (DRHOs) 

are responsible for overseeing the SASH program in their geographic regions. The program 

is delivered at the community level through the SASH panels, which are operated by more 

than 20 affordable housing organizations in their properties, called  SASH sites.1 HUD, the Low-Income Housing Tax Credit (LIHTC) program, the U.S. Department of Agriculture Rural 

Development, the State of Vermont, or a combination of agencies fund the participating hous-

ing properties. 

The SASH program is available to all residents in the SASH sites—we refer to residents who 

enroll as  site-based participants. SASH is also available to any Medicare beneficiary living in the surrounding community; participants who are not living in a SASH site are called  community 

participants. Most SASH panels have both site-based and community participants. Panels with more site-based participants are called  site-based panels. Those with more community participants are referred to as  mixed panels. Our analysis includes only the site-based participants from site-based or mixed panels, who constitute 55 percent of all SASH participants. Using 

a comparison group of similar Medicare beneficiaries, we estimate the impact of the SASH 

program on multiple categories of Medicare expenditures. 

1 A complete list of housing organizations participating in SASH is available at http://sashvt.org/admin/.

Cityscape

9

Kandilov, Keyes, van Hasselt, Sanders, Siegfried, Stone, Edwards, Collins, and Brophy Data and Methodology

As of December 2016, 5,386 individuals had at least one quarter of participation in SASH. 

We linked the CSC list of SASH participants to the housing assistance databases from HUD to 

identify the participants who were living in the SASH sites. The housing records come from 

three separate HUD databases and cover the years from 2012 to 2016. The Tenant Rental 

Assistance Certification System is the database for all properties in programs run by HUD’s 

Office of Multifamily Housing (Section 202, Section 236, Section 8, and so on); the Public 

and Indian Housing Information Center (PIC) is the database for public housing and Housing 

Choice Vouchers; and LIHTC is the database for low-income housing developed through tax 

credits. We further linked the participant data with Medicare fee-for-service (FFS) enrollment 

data. The 2,986 SASH participants found in both Medicare FFS enrollment and HUD housing 

databases are included in this analysis. 

For the comparison group, we identified Medicare beneficiaries who were not participating 

in the SASH program and cross-referenced them with HUD housing records to identify 

beneficiaries who were living in HUD-assisted or LIHTC housing properties that did not host 

the SASH program. Only nonprofit properties could host the SASH program; also, non-SASH 

properties had fewer senior residents than the SASH properties (Kandilov et al., 2017). Those 

who met the comparison group criteria were 3,437 beneficiaries.

Exhibit 1 shows the demographic and health characteristics of the SASH participants and the 

comparison group, as well as their average monthly Medicare expenditures. Some significant 

differences in characteristics emerge between the SASH participants and the comparison 

group. The SASH participants are, on average, 8.5 years older than the comparison group and 

have smaller households and lower household incomes. A larger proportion of comparison 

group beneficiaries originally qualified for Medicare due to disability, and a larger proportion 

are living in LIHTC properties. Because these characteristics may be correlated with healthcare 

expenditures, we use propensity score matching techniques to balance these covariates 

between the SASH participants and the comparison group and to increase the comparability 

of the two groups. 

We estimate a propensity score using a logistic regression model to predict the probability 

of participating in the SASH program, where SASH participation is the dependent vari-

able and beneficiary characteristics prior to SASH enrollment—age, race, gender, income, 

household size, property type, qualified for Medicare due to disability, Medicaid enrollment 

(dual eligible), hierarchical condition categories (HCC) risk score, and Charlson index—are 

the independent variables. The HCC risk score is interpreted as the predicted healthcare 

costs relative to the average Medicare FFS beneficiary. The Charlson comorbidity index is a 

mortality predictor that sums across a list of 18 chronic conditions; a higher Charlson score 

indicates the presence of more chronic conditions. Both the HCC risk score and the Charlson 

score are created using diagnosis codes on claims in the year before the start of the SASH 

program. Each SASH participant is matched to up to five comparison beneficiaries with the 

closest propensity scores. We match with replacement and adjust the weights for comparison 

10 The Housing-Health Connection

The Impact of the Vermont Support and Services at Home Program on Healthcare Expenditures Exhibit 1

Characteristics of SASH Participants and Comparison Group

Demographic and Health Status Characteristics

SASH Participants

Comparison Group

Total beneficiaries (n)

2,986

3,437

Demographics

Mean age (years)

69.2

60.7

Age ≤ 64 (%)

32.9

54.4

Age 65–74 (%)

29.8

20.6

Age ≥75 (%)

37.3

24.9

White (%)

97

95

Female (%)

68

65

Originally qualified for Medicare due to disability (%)

42

57

Mean household income ($)

16,435

17,369

Mean household size (n)

1.14

1.43

Medicaid eligible (%)

53

53

Property type

LIHTC only (%)

19.02

52.17

PIC or TRACS

73.04

47.83

Unknown

7.94

0.00

Mean monthly Medicare expenditures ($)

Total Medicare

812.60 

766.75 

Acute care

285.15 

254.32 

Postacute care

92.32 

78.92 

Emergency room 

41.14 

38.16 

Hospital outpatient department

143.40 

156.32 

Primary care physician

25.35 

28.68 

Specialty physician

51.24 

55.12 

Hospice

8.19 

7.22 

LIHTC = Low-Income Housing Tax Credit. PIC = Public and Indian Housing Information Center. SASH = Support and Services at Home. TRACS = Tenant Rental Assistance Certification System.

Notes: Beneficiary characteristics determined in the year prior to the start of the SASH program (June 2010 through July 2011). Monthly Medicare expenditures averaged across all data used in the analysis, January 2006 through December 2016.

beneficiaries who are matched to multiple SASH participants. Further, the weights for both 

the comparison group and the SASH participants (who start with a weight of 1) are adjusted 

to account for their Medicare eligibility within the quarter.

SASH enrollment took place on a rolling basis, with most participants joining the program 

months or even years after the official start of SASH in July of 2011. To account for this rolling entry, we separated SASH participants into cohorts based on the calendar quarter when they 

started receiving services. To mimic this rolling entry among the comparison group, control 

beneficiaries were assigned to a quarter’s cohort only if they were alive and Medicare eligible 

at that point in time. Unlike in the treatment group, in which cohorts are based on enrollment 

and are mutually exclusive, comparison beneficiaries can be assigned to multiple cohorts 

based on their longevity and continued Medicare eligibility. This difference is necessary to 

mimic the trajectory of the treatment group, whose staggered enrollment guarantees a certain 

longevity after the official start of the program and prevents us from comparing the costs of 

SASH participants who joined the program in 2014 with those comparison group beneficia-

ries who died in 2012 or 2013.

This analysis uses a differences-in-differences model to estimate the impact of the SASH 

program on per-beneficiary-per-month (PBPM) Medicare expenditures. PBPM expenditures 

Cityscape 11

Kandilov, Keyes, van Hasselt, Sanders, Siegfried, Stone, Edwards, Collins, and Brophy are calculated by dividing quarterly expenditures for each beneficiary by 3, which reduces the 

proportion of the observations that are zeros compared with actual PBPM expenditures. The 

calculated monthly amount is therefore preferable to actual monthly expenditures for a linear 

regression analysis. We estimate the following equation using ordinary least squares (OLS)—

Y  =  α  +  α  + α  +  β X  +  β Blueprint  +  β MAPCP  +  β  Att  +  γ  Cohort +  γ2Cohort …     (1) it

0

t

p

1 it

2

i

3

i

4

it

1

1i

2i

γ Cohort  +  β SASH  +  β Demo  +  ε .

C

Ci

5

i

6

it

it

In the previous equation, the subscript  i identifies each beneficiary and the subscript  t identifies the time period (quarter). The dependent variable,  Y , denotes the outcome for the it

i th beneficiary in quarter  t. The intercept is  α . We include two sets of fixed effects;  α  ( t = 

0

t

1,2,… T) are quarterly fixed effects that control for average trends in outcomes across time for all beneficiaries, and  α  ( p = 1,2,… P) are property fixed effects that control for property char-p

acteristics that do not change across time but could be correlated to healthcare expenditures 

(such as distance to the nearest hospital). Beneficiary-level demographic characteristics prior 

to SASH enrollment—age, race, gender, income, household size, and property type—and 

beneficiary-level healthcare characteristics prior to SASH enrollment—qualified for Medicare 

due to disability, Medicaid eligibility (dual eligible), HCC risk score, and Charlson comorbidity 

score—are included in  X . The error term is denoted  ε .

it

it

The state of Vermont initiated a medical home program for primary care practices, called 

Blueprint for Health, which later expanded as part of the Multi-Payer Advanced Primary Care 

Practice (MAPCP) Demonstration. Primary care practice participation in these initiatives could 

affect healthcare expenditures, and so we include time-invariant indicator variables  Blueprint  i and  MAPCP  to denote beneficiaries whose recent primary care practice (based on frequency of i

visits) participated in these programs. The variable  Att  (= 0,1) is an indicator that equals 1 start-it

ing in the quarter that a beneficiary was first assigned to a MAPCP practice.  MAPCP ,  Blueprint , i

i

and  Att  are independent of SASH participation (and often unknown to the participant). Indica-it

tors for the cohorts (described previously) are represented by the variables  Cohort ,  Cohort … 

1

2

Cohort , where  C equals the total number of cohorts in the analysis to date.

C

The variable  SASH  is equal to 1 in all time periods for SASH participants and is equal to 0 in i

all time periods for the comparison beneficiaries. The variable  Demo  denotes quarters after it

SASH participants joined the program and its coefficient ( β ) is the outcome of interest, the 6

estimate of the impact of the SASH program on Medicare expenditures. This coefficient is 

interpreted as the difference between SASH and comparison beneficiaries with respect to their 

average change in outcomes between before and after periods. A  negative value corresponds 

to a  slower rate of change in expenditures among SASH participants relative to comparison 

beneficiaries. Among this population of older and disabled adults, healthcare costs generally 

are increasing over time, so a negative coefficient typically indicates that the average outcomes 

increased among both groups but at a slower rate among SASH participants. Conversely, a 

positive value corresponds to a  faster rate of change in expenditures among SASH participants relative to comparison beneficiaries, which typically indicates that average outcomes increased 

among both groups but at a slower rate among comparison beneficiaries. A negative coef-

ficient indicates that the SASH program had a favorable impact on Medicare expenditures.

12 The Housing-Health Connection

The Impact of the Vermont Support and Services at Home Program on Healthcare Expenditures Results

The results reported in exhibit 2 are denominated at the PBPM level. In the first column, 

we see that the SASH program does not have a significant impact on the entire sample of 

site-based SASH participants. The ability of the SASH program to have a significant impact 

on Medicare expenditure growth, however, may be enhanced or hindered by certain char-

acteristics of the individual SASH panels. For example, panels with a larger proportion of 

community participants may be less effective at slowing the growth of Medicare expenditures 

because the coordinator and wellness nurse may not have the same level of interaction with 

participants who are not living in the SASH site, and the need to travel to many community 

participants may further reduce the limited number of wellness nurse hours (10 hours per 

week). We hypothesized that certain types of SASH panels would have participants who 

demonstrated more favorable or less favorable outcomes. The panel characteristics we tested 

were as follows—

1. Site-based panels versus mixed panels—for site-based panels, more than 50 percent of SASH 

participants are living in a HUD-assisted or LIHTC housing property that is hosting the SASH 

program, whereas for the mixed panels more than 50 percent of participants are living in the 

community. Site-based panels may perform better because most of the participants have easy 

and more regular in-person access to the SASH staff in the host property. Note, even in the 

mixed panels, the SASH participants included in the analysis are the ones living in the HUD-

assisted or LIHTC host property, not the participants living in the community.

2. CSC DRHO panels versus other DRHO panels—the statewide administrator for the SASH 

program, CSC, also serves as one of the six DRHOs that oversee the SASH program. A 

substantial portion (48 percent) of SASH participants in our sample are in panels in the CSC 

DRHO. Most CSC DRHO panels were established in the first year of the SASH program and 

thus have more experience. These panels have fewer community participants, and most are in 

urban areas where travel time is less of a constraint on the SASH staff. CSC panels also benefited earlier than other panels from an additional level of support provided by SASH team leaders.

3. Urban panels versus rural panels—the urban panels are those in Chittenden County, whereas 

rural panels are in all other counties. Urban areas tend to have more community resources that 

can be tapped to aid the SASH participants. Also, rural panels require that SASH staff travel long distances to reach their participants. Note that the urban panels are a subset of the CSC DRHO 

panels.

Our results in exhibit 2 indicate that the SASH program has no significant impact, on site-

based panels or on mixed panels, for total Medicare expenditures or for any of the seven 

subcategories of Medicare expenditures. Despite our hypothesis that a larger proportion of 

community participants in a panel could reduce the impact of the SASH program, we find no 

significant impact of the SASH program on the site-based participants in the site-based panels.

In the fourth and fifth columns of exhibit 2, we consider separately the impact of the SASH 

program among site-based participants in CSC DRHO panels and in other DRHO panels. 

Among CSC DRHO panels, the SASH program is associated with $91.59 PBPM lower cost 

Cityscape 13

Kandilov, Keyes, van Hasselt, Sanders, Siegfried, Stone, Edwards, Collins, and Brophy Exhibit 2

Difference-in-Differences Per-Beneficiary-Per-Month Estimates for Eight Categories 

of Medicare Expenditures, Comparing SASH Program Participants to Non-SASH 

Comparison Beneficiaries

All SASH 

Site-

Other 

Mixed  CSC DRHO 

Urban 

Rural  

Partici-

Based 

DRHO 

Panels

Panels

Panels

Panels

pants

Panels

Panels

(n = 2,845) (n = 2,166) (n = 679) (n = 1,374) (n = 1,471) (n = 1,122) (n = 1,723)

Total Medicare

12.72

– 17.32

22.31

– 91.59*

59.95

– 122.24*

63.35

(42.56)

(46.61)

(75.16)

(46.37)

(50.49)

(48.74)

(47.51)

Acute care

– 2.82

– 23.01

15.08

– 56.63*

21.82

– 70.64**

21.40

(22.06)

(23.56)

(47.89)

(23.60)

(28.20)

(24.91)

(26.27)

Postacute care

16.79

12.07

17.43

– 8.78

31.71*

– 16.81

32.10*

(12.34)

(13.78)

(20.54)

(13.29)

(14.88)

(13.81)

(13.92)

Emergency room

– 3.67

– 5.04

– 3.93

– 9.84*

 – 0.87

– 12.48**

0.03

(3.70)

(4.04)

(5.14)

(4.32)

(4.39)

(4.37)

(4.22)

Hospital outpatient 

– 8.68

– 5.77

– 20.90

– 8.51

– 9.97

– 7.71

– 9.96

department

(7.85)

 (8.45)

(13.30)

(10.09)

(9.71)

(11.43)

(8.94)

Primary care physician

2.29

2.10

1.75

1.65

2.14

1.54

2.27

(1.48)

(1.73)

(2.38)

(1.36)

(2.41)

(1.51)

(2.14)

Specialist physician

– 3.34

– 4.48

– 3.66

– 6.83*

– 1.92

– 6.70*

– 2.55

(2.24)

(2.48)

(3.69)

(2.79)

(2.67)

(2.99)

(2.60)

Hospice care

– 0.25

– 0.90

– 2.24

4.19

– 6.50

4.29

– 4.84

(3.61)

(3.75)

(6.21)

(4.56)

(4.37)

(4.64)

(4.23)

* p < .05. ** p < .01.

CSC = Cathedral Square Corporation. DRHO = Designated Regional Housing Organization. SASH = Support and Services at Home.

Notes: Standard errors are in parentheses. Medicare Part A and Part B claims data from January 2006 through December 2016. The SASH program began in July 2011. Baseline data for each beneficiary looks back to January 2006, if available.

growth. Most of this lower cost growth is driven by the $56.63 PBPM in lower acute care cost 

growth. The SASH program also has statistically significant favorable impacts on Medicare 

expenditures for emergency departments (-$9.84 PBPM) and specialist physicians (-$6.83 

PBPM). No significant favorable impacts are on the Medicare expenditures of participants in 

the other DRHO panels.

In the sixth and seventh columns of exhibit 2, we divide the panels into urban and rural pan-

els and consider the impact of SASH on those two types of panels separately. Among urban 

panels, the SASH program is associated with $122.24 PBPM lower growth in total Medicare 

expenditures. Most of this lower cost growth is driven by the $70.64 PBPM lower growth 

in acute care costs. The SASH program also has statistically significant favorable impacts on 

Medicare expenditures for emergency departments (-$12.48 PBPM) and specialist physicians 

(-$6.70 PBPM). No significant favorable impacts were found on the Medicare expenditures of 

participants in the rural panels.

In addition to our primary analysis presented in exhibit 2, we also conducted two robust-

ness checks and two subgroup analyses to better highlight the populations where the SASH 

program has a favorable impact. In the first row of exhibit 3, we estimate the impact of the 

SASH program as in exhibit 2, but we topcode expenditures (that is, reassign the highest 

expenditure values) at the 99th percentile, to reduce the impact of outliers on the results. We 

14 The Housing-Health Connection

The Impact of the Vermont Support and Services at Home Program on Healthcare Expenditures Exhibit 3

Difference-in-Differences Per-Beneficiary-Per-Month Estimates for Total Medicare 

Expenditures, Comparing SASH Program Participants to Non-SASH Comparison 

Beneficiaries, Robustness Checks and Subpopulations

All SASH 

Other 

Site-Based  Mixed  CSC DRHO 

Rural  

Partici-

DRHO  Urban Panels

Panels

Panels

Panels

Panels

pants

Panels

(n = 2,845) (n = 2,166) (n = 679) (n = 1,374) (n = 1,471) (n = 1,122) (n = 1,723)

Outliers trimmed at 

9.76

– 11.35

8.12

 – 88.47**

61.95 – 110.88 ***

60.20

99th percentile

(37.32)

(41.81)

(55.45)

(38.66)

(43.72)

(40.83)

(41.19)

General linearized 

39.09

– 0.38

76.70

– 60.33

106.67

– 82.80

108.38

model estimation

(48.20)

(52.53)

(83.84)

(47.71)

(60.32)

(48.59)

(57.41)

(n = 1,345) (n = 1,053) (n = 292) (n = 747)

(n = 598) (n = 621)

(n = 724)

Medicare-only 

12.20

– 8.57 – 11.89

– 69.63

63.90 – 108.19

80.15

beneficiaries

(56.74)

(61.27)

(87.86)

(65.25)

(71.51)

(68.95)

(65.14)

(n = 1,500) (n = 1,1613) (n = 387) (n = 627)

(n = 873) (n = 501)

(n = 999)

Dually eligible 

17.83

– 22.56

50.81 – 105.66*

60.92 – 127.65 **

55.15

Medicare/ Medicaid 

(50.06)

(51.83) (104.65)

(54.76)

(61.53)

(56.67)

(58.91)

beneficiaries

* p < .05. ** p < .01. *** p < .001.

CSC = Cathedral Square Corporation. DRHO = Designated Regional Housing Organization. SASH = Support and Services at Home.

Notes: Standard errors are in parentheses. Medicare Part A and Part B claims data from January 2006 through December 2016. The SASH program began in July 2011. Baseline data for each beneficiary looks back to January 2006, if available.

continue to find that the SASH program has a significant favorable impact on total Medicare 

expenditures for CSC DRHO panels and for urban panels. Another way to reduce the impact 

of outlier expenditure values is to estimate a general linearized model (GLM) instead of the 

OLS model employed elsewhere. In this specification (second row of exhibit 3), we find no 

significant impact of the SASH program. In the third row of exhibit 3, we consider the SASH 

participants who have only Medicare, whereas in the fourth row of exhibit 3, we report the 

results for dual-eligible SASH participants. We do not find a significant impact of the SASH 

program on the Medicare-only participants, but we do see significantly slower Medicare cost 

growth among the dual eligible in CSC DRHO and urban panels.

Limitations

Our methodological approach sought to limit the impact of selection bias by focusing on par-

ticipants who lived in the housing properties where the SASH program was implemented and 

removing from the sample the community participants who enrolled in SASH on their own or 

were referred by their healthcare providers due to their healthcare needs. Some selection bias 

may remain, however, because not all residents of the SASH sites chose to participate in the 

program, and those who did sign up were older and had more comorbidities than those who 

did not participate (Kandilov et al., 2017).

We were unable to include Medicaid claims data; also, Medicare claims data for substance 

abuse disorders were redacted from research-identifiable files during the analysis period. To 

Cityscape 15

Kandilov, Keyes, van Hasselt, Sanders, Siegfried, Stone, Edwards, Collins, and Brophy the extent that SASH participants and the comparison group had similar levels of Medicaid 

expenditures, or similar levels of substance abuse disorder claims, these omissions should 

have little impact on our results. If the SASH program had a favorable effect on Medicaid 

expenditures or on expenditures for substance abuse disorder claims, then our results will 

understate the true effect of SASH of healthcare expenditures.

Finally, our analysis is limited to beneficiaries enrolled in Medicare FFS, excluding those 

with Medicare Advantage. Only 8 percent of Vermont’s Medicare beneficiaries have Medicare 

Advantage (Jacobson et al., 2017).

Discussion

When we examine all SASH panels, and when we examine site-based panels and mixed 

panels separately, we find no evidence that the SASH program has a significant impact on any 

of the eight Medicare expenditure outcomes that we considered. Note that more than one-half 

of the SASH participants in the mixed panels are community participants excluded from the 

analysis.

We do find favorable impacts of the SASH program among CSC DRHO panels and among 

urban panels, which make up 48 and 39 percent of the sample, respectively. Note that all the 

urban panels, which are the panels in Chittenden County, are panels in the CSC DRHO but 

not all these CSC DRHO panels are in Chittenden County, which makes it difficult for us to 

separate the influence of these two characteristics. These results are robust to trimming the 

outliers, suggesting that the effectiveness estimates are not driven by only SASH participants 

with the highest expenditures. The results from the GLM do not provide evidence that the 

SASH program was able to reduce growth in Medicare expenditures, although the estimates 

do have the same sign and are of similar magnitude to the OLS estimates, but with larger stan-

dard errors. A possible reason for this lack of precision is that the gamma distribution used 

in the GLM specification might not fit the healthcare expenditures in our sample particularly 

well.

Why might CSC DRHO panels, and particularly those in Chittenden County, be more suc-

cessful in reining in Medicare costs? In the site visits that our evaluation team conducted, we 

identified a few possible differences that could help to explain these results. The CSC panels 

benefited earlier from having an additional level of support and management, called SASH 

team leaders. The SASH team leader role was created to support the SASH coordinators and 

remove some of their administrative burden, enabling them to focus more time on SASH 

participants’ care coordination and healthcare needs. SASH team leaders help organize events 

and programs, assist with resident issues, and aid with documentation. Although this team 

leader role has now been implemented throughout the SASH program, during much of the 

analysis period only the CSC panels benefited from this additional support-management role. 

Also, our site-visit interviews revealed that the urban panels in Vermont tended to have access 

to more healthcare and social support services in their communities than those in rural areas. 

Chittenden County had multiple initiatives to coordinate and integrate care across service 

providers, which could contribute to the favorable impact of SASH found among these panels.

16 The Housing-Health Connection

The Impact of the Vermont Support and Services at Home Program on Healthcare Expenditures Finally, the significant impact of the SASH program on expenditures among dual-eligible 

participants but not among Medicare-only participants suggests that additional research on 

the program impact for the Medicaid population is warranted.

Conclusion

In the evaluation of the SASH program, our team explored whether coordinated services 

provided in an affordable housing setting affected healthcare costs for older adults and 

individuals with disabilities by examining total Medicare costs and many subcategories of 

Medicare costs to determine any shifts in healthcare utilization. In addition to these analyses, 

our evaluation incorporated a survey of SASH participants and interviews with SASH staff, 

stakeholders, and community partners.

When we compared all SASH participants living in properties hosting the SASH program to 

a comparison group of similar Medicare beneficiaries living in other HUD-assisted or LIHTC 

housing, we found no evidence that the SASH program had an impact on the growth in Medi-

care costs. However, for a subset of the SASH panels, namely those under the direction of CSC 

and those in Chittenden County, Medicare cost growth was significantly slower relative to the 

comparison group. Favorable impacts of the SASH program were particularly pronounced for 

dual-eligible participants. 

Although slowed growth in Medicare costs is an important goal of the SASH program, the 

program also has other potential benefits. For example, SASH participants surveyed had 

higher self-reported physical function scores and less difficulty with medication management 

(Kandilov et al., 2017). Future work will examine the impact of the SASH program on Medic-

aid expenditures, because Medicare expenditures do not capture total health care costs for the 

dual eligible. 

We have identified promising features of the SASH program that could provide baseline 

knowledge for establishing future housing-plus-services programs; we have also identified 

populations that experience favorable outcomes from this type of program. Using multidisci-

plinary teams embedded in affordable housing properties to link residents to healthcare and 

social service supports available in the community has the potential to help control expanding 

medical costs. Continuing research efforts should delve further into identifying the character-

istics of care coordination initiatives in an affordable housing setting that are associated with 

achieving favorable outcomes for participants.

Acknowledgments

The U.S. Department of Housing and Urban Development and the U.S. Department of Health 

and Human Services, Assistant Secretary for Planning and Evaluation and Administration on 

Aging, supported the research in this article. The authors thank the Vermont Support and 

Services at Home team members who participated in the interviews and shared their experi-

ences with the study team.

Cityscape 17

Kandilov, Keyes, van Hasselt, Sanders, Siegfried, Stone, Edwards, Collins, and Brophy Authors

Amy Kandilov is a senior research economist at RTI International.

Vince Keyes is a research systems analyst at RTI International.

Martijn van Hasselt is an assistant professor at the University of North Carolina at Greensboro. 

Alisha Sanders is the Director of Housing and Services Policy Research at LeadingAge Center 

for Applied Research.

Noëlle Siegfried is a research public health analyst at RTI International.

Robyn Stone is the Senior Vice President of Research at LeadingAge Center for Applied Research.

Patrick Edwards is an economist at RTI International.

Aubrey Collins is an economist at RTI International.

Jenna Brophy is an economist at RTI International.

References

AARP. 2009.  Beyond 50.09 Chronic Care: A Call to Action for Health Care Reform. Washington, DC: AARP Public Policy Institute.

Helms, Veronica E., Jon Sperling, and Barry L. Steffen. 2017.  A Health Picture of HUD-Assisted Adults, 2006–2012: HUD Administrative Data Linked With the National Health Interview Survey. 

Washington, DC: U.S. Department of Housing and Urban Development. 

Jacobson, Gretchen, Anthony Damico, and Tricia Neuman. 2017.  Medicare Advantage 2017 

Spotlight: Enrollment Market Update. San Francisco: Kaiser Family Foundation.

Kandilov, Amy, Vincent Keyes, Noelle Siegfried, Kevin Smith, Patrick Edwards, Jenna Brophy, 

Ann Larsen, Martijn van Hasselt, Alisha Sanders, and Robyn Stone. 2017.  Support and Services 

at Home (SASH) Evaluations: Evaluation of the First Four Years. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation.

Lawton, M. Powell. 1976. “The Relative Impact of Congregate and Traditional Housing on 

Elderly Tenants,”  Gerontologist 16: 237–242.

Oswald, Frank, Daniela Jopp, Christoph Rott, and Hans-Werner Wahl. 2010. “Is Aging in 

Place a Resource for or Risk to Life Satisfaction?”  Gerontologist 51: 238–250.

Redford, Linda, and David Cook. 2001. “Rural Health Care in Transition: The Role of 

Technology. The Public Policy and Aging Report, National Academy on an Aging Society,” 

Gerontological Society of America 12 (1): 1–5.

Sanders, Alisha, Kimberly Smathers, Taryn Patterson, Robyn Stone, Jordan Kahn, Jaclyn 

Marshall, and Lisa Alecxih. 2014.  Affordable Senior Housing Plus Services: What’s the Value?  

Washington, DC: LeadingAge Center for Housing Plus Services.

18 The Housing-Health Connection

A Pilot Community Health Worker 

Program in Subsidized Housing: 

The Health + Housing Project

Amy L. Freeman

Tianying Li

Sue A. Kaplan

New York University School of Medicine

Ingrid Gould Ellen  

New York University

Ashley Young 

Diane Rubin 

Henry Street Settlement

Marc N. Gourevitch

Kelly M. Doran 

New York University School of Medicine

Abstract

•  Objectives: We examine the implementation of a community health worker (CHW) 

program in subsidized housing, describe needs identified and priorities set by 

residents, and summarize participant-reported outcomes.

•  Methods: Partnering with a local community-based organization, four bilingual 

CHWs recruited adult residents in one public housing building and one Section 8 

building to participate in a 15-month intervention. Residents set health-related 

and life-improvement goals and developed an action plan for achieving them. 

CHWs used a motivational interviewing framework to help residents achieve 

their goals and connect them to case management, healthcare services, and other 

community resources. Prior to the intervention, surveyors approached every unit in 

both buildings for a baseline survey; 390 of an estimated 819 residents responded 

(47.6 percent). Of the 226 who completed an intake assessment with a CHW, 149 

completed the program assessment questionnaire (65.9 percent).

•  Results: Residents reported high levels of chronic disease, mental health issues, 

and low satisfaction with social relationships. 226 residents (61.3 percent female, 

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 19

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

Freeman, Li, Kaplan, Ellen, Young, Rubin, Gourevitch, and Doran

Abstract (continued)

29.7 percent age 65 years or older, and 68.6 percent Hispanic or Latino) completed an 

intake assessment with a CHW and received an average of 11 in-person visits. Most 

program assessment respondents reported partially or completely achieving their most 

important goal (82.0 percent). They also reported high levels of satisfaction with the 

CHW program (96.6 percent) and improved overall well-being (78.6 percent).

•  Conclusions: CHWs based in subsidized housing buildings encountered high levels of 

medical and social needs among residents. Improvements in self-reported well-being and 

high levels of satisfaction with the program suggest that such place-based initiatives may 

be effective in addressing health and its determinants.

Introduction

More than 4 million people live in public housing or project-based Section 8 housing subsidized 

by the U.S. Department of Housing and Urban Development (HUD). Residents of these buildings 

are disproportionately racial or ethnic minorities, and more than 70 percent are extremely low 

income (HUD, 2016).1 These factors are associated with significant health disparities and needs 

(Bor, Cohen, and Galea, 2017; Liao et al., 2011). Although recent research demonstrates that 

housing subsidies deliver health and economic benefits to low-income adults in the United States 

(Andersson et al., 2016; Fenelon et al., 2017; Simon et al., 2017), subsidized housing residents 

represent a relatively vulnerable group that experiences high rates of chronic disease (Digenis-Bury et al., 2008; Feinberg et al., 2015; Helms, Sperling, and Steffen, 2017). Some evidence shows that many residents are already sick when they enter subsidized housing (Ruel et al., 2010), and the 

environmental conditions in aging public housing buildings have been shown to increase the risk 

of illness, such as pediatric asthma (Northridge et al., 2010). The clustering of significant need in subsidized housing presents a potential opportunity to provide efficient place-based interventions to improve health, as having a high volume of concentrated need can facilitate targeted interventions. 

A growing body of literature suggests the effectiveness of community health worker (CHW) models 

in improving health outcomes among vulnerable populations (Cosgrove et al., 2014; Islam et al., 

2014a; Kangovi et al., 2017a; Margellos-Anast, Gutierrez, and Whitman, 2012). CHWs share cul-

tural, linguistic, or other key characteristics with the communities they serve (Love, Gardner, and Legion, 1997). Interventions using CHWs or other lay health workers, such as community health 

advocates and peer navigators, have been successfully implemented in community and clinic 

settings alike (Islam et al., 2017; Kangovi et al., 2017a; Kim et al., 2016). However, few CHW 

programs have been implemented in subsidized housing, and those that exist have focused primar-

ily on specific medical conditions or health behaviors (Brooks et al. 2017; Gutierrez Kapheim et al., 1 Defined as families whose incomes do not exceed the higher of the federal poverty level or 30 percent of Area Median Income (AMI).

20 The Housing-Health Connection

A Pilot Community Health Worker Program in Subsidized Housing:  

The Health + Housing Project

2015; Levy et al., 2006; Lopez et al., 2017; Quintiliani et al., 2014; Rorie et al., 2011; Scammell et al., 2011; Sikkema et al., 2000; Slater et al., 1998; Zhu et al., 2002). Community-based CHWs 

are well positioned to help residents with a broader range of issues, especially given that social and economic factors, outside the healthcare system or a narrow disease-focused framework, drive a 

large share of overall health outcomes (Woolf and Braveman, 2011). For vulnerable low-income 

populations who experience cultural and communication barriers in accessing healthcare and 

social services, trusted community-based CHWs can act as a crucial bridge to these resources 

(Islam et al., 2017) and provide a better understanding of their clients’ residential environments, including aspects that might shape health. 

To our knowledge, no studies to date have examined a CHW intervention that is co-located in sub-

sidized housing, is open to all residents regardless of health status, and addresses broadly defined resident health-related needs rather than specific diseases or behaviors. The Health + Housing 

Project was a pilot CHW program in subsidized housing that aimed to improve resident health by 

providing access to and information about medical care, addressing social determinants of health, 

and connecting residents to needed community resources. In this article, we answer the following 

research questions: (1) What were the health-related needs and priorities of subsidized housing 

residents? (2) Were residents willing to engage with a CHW and set health-related goals? (3) Did 

residents find the CHW intervention acceptable and helpful for meeting their health-related goals 

and improving their well-being? (4) What lessons learned can inform implementation of other 

CHW programs in subsidized housing? 

Methods

The Health + Housing CHW intervention was conducted in two subsidized apartment buildings in 

the Lower East Side neighborhood of Manhattan, New York City (NYC). Community and govern-

mental stakeholders and local housing providers assisted in selecting the buildings. One building 

is owned and operated by the city public housing authority and the other is a privately owned 

Section 8 building.2 Together, the buildings comprise 450 apartment units (200 units in one 

building; 250 in the other) with an estimated 819 adult residents. All residents 18 years and older were eligible to participate in the CHW program and invited to complete baseline and postprogram 

surveys if they spoke English, Spanish, or Chinese (Mandarin or Cantonese). The Institutional 

Review Board at the New York University (NYU) School of Medicine approved the study. 

Baseline Survey

Eight bilingual surveyors conducted baseline surveys between December 2015 and March 2016. 

Surveys were completed in person during daytime, evening, and weekend hours, and residents 

were offered a $5 incentive. Surveyors made multiple attempts to recruit residents in each apart-

ment and kept tracking logs of recruitment attempts to ensure that each apartment was approached 

2 Eligibility requirements (household earnings less than 80 percent of AMI) are generally the same for both types of building; therefore the residents are similar in terms of income and demographics. However, Section 8 buildings often receive larger operating subsidies from the government, especially in high-cost areas. They also are privately managed and, unlike public housing, have the ability to leverage private capital. For this reason, Section 8 buildings may be better maintained than public housing, although it depends on the quality of the management company.

Cityscape 21

Freeman, Li, Kaplan, Ellen, Young, Rubin, Gourevitch, and Doran

at least six times at varying hours and days. Additionally, fliers describing the study and inviting residents to call the project director were placed under every apartment door and posted next to 

elevator banks. Surveyors also recruited residents in front of the intervention buildings and held survey workshops, providing food, to encourage residents to participate. All adults living in each unit were eligible to participate. Surveyors used secure cellular-enabled tablets, entering responses directly into REDCap (Harris et al., 2009) to minimize data entry errors. 

The baseline survey consisted of 149 questions on demographics, general health status, chronic 

disease, healthcare access and utilization, housing conditions, social service needs, social support, and food security. Questions were drawn from commonly used and validated questionnaires when 

possible, including PROMIS-10 (general health, satisfaction with social activities, pain rating; Cella et al., 2010); NHANES (chronic disease, insurance coverage, visits to primary care, overall diet; 

CDC, 2014); PHQ-2 and GAD-2 (depression and anxiety screening; Kroenke, Spitzer, and Williams, 

2003; Skapinakis, 2007); and U.S. Department of Agriculture (food security; Gundersen et al., 2017). 

Surveys were translated into Spanish and Chinese and back translated to check for accuracy.

Health + Housing CHW Program Description

Health + Housing partnered with a local community-based organization—Henry Street Settlement 

(HSS)—to assist with the hiring and supervision of field staff and to serve as a primary referral site for case management, health enrollment, and parent support services. Prior to program launch, 

the study team solicited input about program design and outreach strategies from stakeholders, 

including local community-based organizations, building residents and management, city agencies, 

and other academic colleagues. 

The program attempted to hire bilingual (Spanish/English and Chinese/English) CHWs from the 

same neighborhood as the intervention buildings. Although only one of the four that were hired 

was a local resident, all CHWs shared linguistic and cultural characteristics with building residents. 

CHWs completed a 35-hour training that focused on core competencies that included CHW 

identity and roles, social determinants of health, models of behavioral change, and communication 

skills. CHWs subsequently received additional training on study protocol, chronic disease manage-

ment, motivational interviewing, mental health first aid, and smoking cessation. 

CHWs attempted to recruit residents from all apartments, and each CHW was initially assigned 90 

to 100 individuals who had completed the baseline survey prior to the start of the program. Resi-

dents who were “frequent users” of health care (defined as three or more self-reported emergency 

department visits, or one or more hospitalizations, in the past year) were prioritized for recruitment. Subsequent waves of recruitment were conducted for residents who had not completed 

the baseline survey, until all 450 apartments had been attempted. Residents were not offered an 

incentive to participate in the program beyond the services offered by the CHW.

Once a building resident agreed to participate in the program, the CHW worked with him or her 

in sequential visits to complete a baseline survey (if not already done), an intake assessment, a 

goal-setting exercise, and an action plan that outlined steps for working on each goal (Islam et al., 2014b; Kangovi et al., 2017b). The intake assessment included demographic information, history 

of physical and mental health issues, medications, and primary health-related concerns. For the 

22 The Housing-Health Connection

A Pilot Community Health Worker Program in Subsidized Housing:  

The Health + Housing Project

goal-setting activity, residents selected up to 5 goals from a pre-established list of 23 suggested goals (residents could also write in their own goals). The list was developed by reviewing goal-setting forms used by other programs and anticipating a range of social determinants of health 

that might be important to residents’ well-being. After ranking their goals in order of importance, participants rated their motivation level for completing each goal and then, with CHW guidance, 

developed an action plan for reaching their goals. 

Following completion of this process, CHWs met with residents as frequently as needed and used 

motivational interviewing3 to help residents achieve their goals. Activities included connecting 

residents to case management at HSS (for example, benefits screening and enrollment), assisting 

with care coordination, and linking residents to services in the community. CHWs also offered 

health education related to chronic disease management, nutrition, physical activity, smoking 

cessation, and stress reduction (NYU CSAAH and NYU-CUNY Prevention Research Center, 2015). 

CHWs met weekly with the supervisor at HSS, a licensed clinical social worker, and the study 

project director to troubleshoot cases; and weekly with the project director and data manager to 

review progress and complete data entry. The CHW intervention period ran for 15 months (April 

2016 through June 2017). 

Program Assessment

After the program ended, residents who reported working with a CHW were asked to complete 

a 20-item program assessment questionnaire to assess change in overall wellness, connection to 

community resources, their experience working with the CHW, satisfaction with the program, 

and whether they felt they had achieved their primary goal. Only respondents who reported their 

most important goal in a prior question were asked if they felt they had achieved that goal. The 

questionnaire was translated into Spanish and Chinese and administered by six bilingual surveyors 

using the same protocol that was used for the baseline survey. New surveyors were hired (instead 

of using CHWs) to avoid desirability bias in participants’ responses. After one month of data col-

lection, the financial incentive to participate was increased from $5 to $20 in an effort to improve response rates. 

Data Analysis

Univariate analyses of baseline survey variables were performed to describe characteristics of CHW 

program participants and nonparticipants. For analytical purposes, participants were defined as 

residents who completed an intake assessment with a CHW. Differences between participants and 

nonparticipants were examined using  t-tests for continuous variables and χ2 or Fisher’s exact tests for categorical variables. Post-hoc pairwise comparisons were performed and Bonferroni’s correc-tion used to adjust for multiple comparisons. The program assessment questionnaire was analyzed 

using descriptive statistics. 

3 “Motivational interviewing is a directive, client-centred counselling style for eliciting behaviour change by helping clients to explore and resolve ambivalence. Compared with nondirective counselling, it is more focused and goal-directed” 

(Rollnick and Miller, 1995: 326).

Cityscape 23

Freeman, Li, Kaplan, Ellen, Young, Rubin, Gourevitch, and Doran

Results

In this study, we looked at data collected prior to and as part of the CHW intervention to assess 

the acceptability and potential impact of the Health + Housing Project. We analyzed results of 

the baseline survey, intake assessments, participant goal setting data, and the program assessment questionnaire.

Health Needs of Residents

Of the 819 adults estimated to be living in the two intervention buildings, valid baseline surveys were conducted with 390 residents (47.6 percent response rate; see exhibit 1). At least one resident completed the baseline survey in 266 of the 450 apartment units (59.1 percent unit response rate). 

Based on available data provided by building management, survey respondents appeared similar to 

building residents overall in age, gender, and race/ethnicity. Exhibit 2 shows that most respondents were female (62.5 percent) and born in the United States (59.9 percent). Nearly 57 percent had 

a household income of less than $20,000 per year. Nearly all (94.6 percent) said they had health 

insurance, and 86.0 percent had seen a primary care physician in the past 6 months. In terms of 

health in the past 12 months, 9.0 percent reported that they had visited an emergency department 

three or more times and 14.9 percent reported one or more hospitalizations. Slightly more than 40 

percent reported their general health status as fair or poor. Chronic diseases, anxiety, and depression were common. More than one-third (37.7 percent) rated their diet as fair or poor, and 47.4 

percent reported food insecurity. Nearly a one-fourth (22.5 percent) rated their satisfaction with social activities as fair or poor. 

A total of 226 residents of the 390 baseline survey takers (57.9 percent) completed an intake as-

sessment with a CHW. Compared with nonparticipants (n = 164), CHW program participants were 

significantly older and had higher levels of social disadvantage and health-related needs (exhibit 2). 

Participants were more likely than nonparticipants to have a household income less than $20,000 

(62.7 versus 47.2 percent) and to be unemployed or unable to work (32.0 versus 17.6 percent). 

A higher percentage of participants had three or more visits to the emergency department (13.3 

versus 3.1 percent), and one or more hospitalizations (18.7 versus 9.8 percent) in the past year. 

Participants were more likely to report their general health as fair or poor (44.9 versus 34.2 percent), and to screen positive for depression (22.5 versus 8.3 percent) and anxiety (21.1 versus 7.0 

percent). They were also more likely to report their diet as fair or poor (44.3 versus 28.7 percent) and to suffer from food insecurity (52.4 versus 40.3 percent). A larger percentage reported their 

satisfaction with social activities and relationships as fair or poor (27.7 versus 15.4 percent).

24 The Housing-Health Connection

A Pilot Community Health Worker Program in Subsidized Housing:  

The Health + Housing Project

Exhibit 1

Health + Housing Project Flow Diagram

Estimated  eligible  adults  in  

two  intervention  buildings  

(n=819)

Took  baseline  survey  

(n=418)

Excluded  (n=28):

·∙    Duplicates  (n=3)

·∙    Non-‐residents  (n=22)

·∙    Underage  (n=1)

·∙    Dropouts  (n=2)

Reached  out  by  CHWs  for  

CHW  program  recruitment*  

(n=390)

Excluded  (n=164):

·∙    Declined  to  participate/

     Unable  to  be  reached  

Completed  program  intake  

Completed  goal  setting  

Completed  action  plan  

form  (n=226)

form  (n=211)

(n=191)

·∙   Decided  to  discontinue  (n=7)

·∙    Decided  to  discontinue  (n=6)

·∙   Could  not  be  reached  (n=8)

·∙    Moved  away  (n=3)

·∙    Could  not  be  reached  (n=11)

Did  not  complete  program  

assessment  questionnaire  (n=77):

·∙    Refused  (n=41)

·∙    Moved  away  (n=10)

·∙    Died  (n=3)

·∙    Missing  data  (n=23)

Completed  program  

assessment  questionnaire  

(n=149)  

CHW = community health worker.

*While  most  baseline  surveys  were  completed  prior  to  CHW  recruitment  attempts,  some  were  completed  by  CHWs  for  

* Although most baseline surveys were completed prior to CHW recruitment attempts, some were completed by CHWs for residents  who  agreed  to  participate  in  the  CHW  program,  but  had  not  completed  a  baseline  survey  in  the  pre-‐program  period.

residents who agreed to participate in the CHW program but had not completed a baseline survey in the preprogram period.

Cityscape 25

Freeman, Li, Kaplan, Ellen, Young, Rubin, Gourevitch, and Doran

Exhibit 2

Baseline Characteristics of Health + Housing Project Program Participants Versus 

Nonparticipants (1 of 2)

Total

Participantsa   Nonparticipants 

(N = 390)

(n = 226)

(n = 164)

Characteristic

n (%)

n (%)

n (%)

p

Demographics

Female

243 (62.5)

138 (61.3)

105 (64.0)

0.59

Age, years

< 0.001

18–44

159 (41.3)

72 (32.4)

87 (53.4)

45–64

126 (32.7)

84 (37.8)

42 (25.8)

65+

100 (26.0)

66 (29.7)

34 (20.9)

Country of birth

< 0.05

United States—50 states

182 (46.8)

98 (43.4)

84 (51.5)

United States—Puerto Rico

51 (13.1)

39 (17.3)

12 (7.4)

Dominican Republic

75 (19.3)

41 (18.1)

34 (20.9)

China

56 (14.4)

35 (15.5)

21 (12.9)

Race/ethnicity

0.42

Hispanic or Latino

252 (64.8)

155 (68.6)

97 (59.5)

Asian

84 (21.6)

45 (19.9)

39 (23.9)

Black or African-American

35 (9.0)

17 (7.5)

18 (11.0)

White

9 (2.3)

4 (1.8)

5 (3.1)

Education

0.94

Less than HS degree

164 (42.4)

97 (43.3)

67 (41.1)

HS degree or equivalent

81 (20.9)

47 (21.0)

34 (20.9)

Some college education

94 (24.3)

54 (24.1)

40 (24.5)

College degree or higher

48 (12.4)

26 (11.6)

22 (13.5)

Annual household income

< 0.05

< $20,000

155 (56.6)

104 (62.7)

51 (47.2)

$20,000–$39,999

65 (23.7)

33 (19.9)

32 (29.6)

$40,000+

54 (19.7)

29 (17.5)

25 (23.2)

Current work situation

< 0.001

Employed

154 (40.0)

69 (30.7)

85 (53.1)

Unemployed

53 (13.8)

39 (17.3)

14 (8.8)

Homemaker, student

38 (9.9)

17 (7.6)

21 (13.1)

Retired

93 (24.2)

67 (29.8)

26 (16.3)

Unable to work

47 (12.2)

33 (14.7)

14 (8.8)

Physical and mental health

Hypertension

131 (33.7)

84 (37.3)

47 (28.7)

0.07

Diabetes

60 (15.4)

40 (17.7)

20 (12.2)

0.14

Asthma

82 (21.2)

55 (24.4)

27 (16.7)

0.06

Positive depression screening

63 (16.6)

50 (22.5)

13 (8.3)

< 0.001

Positive anxiety screening

57 (15.2)

46 (21.1)

11 (7.0)

< 0.001

Severe painb

79 (20.6)

54 (24.2)

25 (15.5)

< 0.05

General health (fair or poor)

157 (40.4)

101 (44.9)

56 (34.2)

< 0.05

Poor health self-efficacyc

61 (15.6)

40 (17.7)

21 (12.8)

0.19

Overall diet (fair or poor)

147 (37.7)

100 (44.3)

47 (28.7)

< 0.01

Insurance and healthcare utilization

Currently insured

366 (94.6)

211 (94.2)

155 (95.1)

0.70

Time without insuranced 

42 (11.5)

24 (11.4)

18 (11.7)

0.93

Visited primary care providerd

270 (86.0)

168 (89.4)

102 (81.0)

< 0.05

Needed medical care but didn’t 

32 (8.3)

26 (11.6)

6 (3.7)

< 0.01

get itd

3+ ED visits in past 12 months

35 (9.0)

30 (13.3)

5 (3.1)

< 0.001

1+ hospitalization in past 12 

58 (14.9)

42 (18.7)

16 (9.8)

< 0.05

months

26 The Housing-Health Connection

A Pilot Community Health Worker Program in Subsidized Housing:  

The Health + Housing Project

Exhibit 2

Baseline Characteristics of Health + Housing Project Program Participants Versus 

Nonparticipants (2 of 2)

Total

Participantsa   Nonparticipants 

(N = 390)

(n = 226)

(n = 164)

Characteristic

n (%)

n (%)

n (%)

p

Social needs and social satisfaction

Food insecurityd 

182 (47.4)

118 (52.4)

64 (40.3)

< 0.05

Unmet social service needse 

200 (52.4)

127 (57.0)

73 (45.9)

< 0.05

Unable to pay rent on timed 

69 (18.2)

47 (21.4)

22 (13.8)

0.06

Satisfaction w/ social activities  

87 (22.5)

62 (27.7)

25 (15.4)

< 0.01

(fair/poor)  

ED = emergency department. HS = high school. 

a Participant defined as a resident who completed an intake assessment with a community health worker.

b Defined as rating of ≥ 7 on scale of 0–10 to question, “How would you rate your pain on average (where 0 is no pain and 10 

is the worst pain imaginable)?”

c Defined as “a little” or “not at all” confident in ability to take good care of health.

d Within past 6 months.

e Defined as responding yes to 1 or more out of 10 social, financial, or other services needed but not received during the past 6 months.

Resident Engagement and Goal Setting

During the course of the 15-month intervention, CHWs recorded more than 2,400 in-person visits 

with participants, averaging 11 visits per participant (median = 8, range = 1–81). Residents 65 

years and older had a higher mean number of visits than residents 18 to 64 years (16 versus 9). 

CHWs managed a caseload at any given time of 40 to 53 residents. Residents worked with CHWs 

for 8 months on average and left the caseload when they decided they no longer wanted to receive 

visits or when the overall intervention period ended.

CHWs engaged in a wide range of activities with residents depending on their needs and goals. 

Of the 226 residents who completed a CHW program intake assessment, 211 (93.4 percent) 

completed the goal-setting activity, and 191 (84.5 percent) completed an action plan (see exhibit 1). 

Exhibit 3 shows the goals on which residents chose to focus by frequency and self-rated order of 

importance. “Be physically active” or “exercise regularly” were the most frequent goals set, and “get my illness under control” or “take my medicine” were the goals most frequently ranked as most important. CHW activities included communicating with healthcare providers and family members; 

making and attending medical appointments; assisting with transportation; completing benefits 

applications; enrolling in Health Homes, Meals-on-Wheels, and low-cost fitness classes; and con-

tacting or following up with housing management for repairs or complaints. In addition, CHWs 

referred residents to HSS for case management services (for example, assistance with Medicaid or 

food stamp applications), legal assistance for eviction or rent arrears, and workforce development or English as a Second Language classes. CHWs recorded 428 referrals for participants, nearly 

one-half of which were to HSS (48.0 percent). The remaining referrals were made to services such 

as medical, eye, or dentist visits; senior centers; exercise classes or gyms; and food pantries.

Cityscape 27

Freeman, Li, Kaplan, Ellen, Young, Rubin, Gourevitch, and Doran

Exhibit 3

Health + Housing Project Participant Goals

Goal Ranked #1 in  

Goals Set by Participantsa

Count

%

Count

%

Importance by Participants

Be physically active/exercise 

130

17

Get my illness under control/take 

46

22

regularly

my medicine

Find or change job/job readiness skills

96

12

Find/change job/job readiness skills

38

18

Eat a healthy diet

87

11

Access to healthcare/mental 

17

8

healthcare

Get my illness under control/take my 

83

11

Access—other (benefits, financial 

16

8

medicine

services, other)

Lose weight

47

6

Housing

16

8

Housing

45

6

Eat a healthy diet

14

7

Access to healthcare/mental 

45

6

Family goal/get help for family 

12

6

healthcare

member

Family goal/get help for family 

44

6

Be physically active/exercise 

10

5

member

regularly

Access—other (benefits, financial 

41

5

Resolve legal problem

9

4

services, other)

Cope with stress

39

5

Cope with stress

8

4

Cut down/quit alcohol/smoking

38

5

Access to food

6

3

Access to food

27

3

Cut down/quit alcohol/smoking

5

2

Resolve legal problem

21

3

Lose weight

5

2

Minor/major apartment repairs

18

2

Minor/major apartment repairs

4

2

Education goal (not job related)

14

2

Other

3

1

Other

11

1

Education goal (not job related)

2

1

Total goals set

786

100

Total

211

100

Note: n = 211, because 15 people who completed an intake assessment (n = 226) chose not to set goals.

a Participants could select up to five goals. Goals are clustered into similar categories for the exhibit (see appendix A for full list of goals). Percentages on the left panel represent the number of goals selected by participants in each category divided by the total number of all goals set (N = 786).

Participant Assessment of the CHW Program and Self-Reported Outcomes

At the conclusion of the intervention, 149 residents out of 226 who completed an intake assess-

ment with a CHW (65.9 percent) completed the program assessment questionnaire (exhibit 4). 

Residents reported high levels of satisfaction with the CHW program. More than three-fourths said 

they were “very comfortable” or “extremely comfortable” speaking with their CHW about their 

issues (76.5 percent), and nearly all were “satisfied” or “very satisfied” with their individual CHW 

(96.6 percent) and the CHW program overall (96.6 percent).

As a result of their participation in the CHW program, participants reported improvement in their 

overall well-being and achievement of their most important goal. When asked to what degree things 

had gotten better for them since working with their CHW, 78.6 percent responded “moderate,” 

“high,” or “very high” degree. Most said the program met their needs “quite a bit” or “completely” 

(59.6 percent). Most (80.6 percent) reported that they would “definitely” or “probably” take part in the program again if given the opportunity. In terms of goal achievement, 82.0 percent responded 

that they either “partially” or “completely” achieved their most important goal, and 91.5 percent 

said that setting goals and creating an action plan was “somewhat” or “extremely” helpful. Of the 77 

respondents who said that they had been referred to HSS to see a case manager, 46 (60.5 percent) 

said they had followed up and met with a case manager there. Most (70.5 percent) said they felt more connected to services in the community because of the work they did with their CHW. 

28 The Housing-Health Connection

A Pilot Community Health Worker Program in Subsidized Housing:  

The Health + Housing Project

Exhibit 4

Health + Housing Project Program Assessment Questionnaire Results

Questions

n (%)

How comfortable felt speaking with CHW about issues 

Very/extremely comfortable

114 (76.5)

Somewhat comfortable

31 (20.8)

Not very/not at all comfortable

4 (2.7)

Overall was satisfied or very satisfied with CHW 

142 (96.6)

Overall was satisfied or very satisfied with CHW program as a whole 

142 (96.6)

To what degree have things gotten better since started working with CHW 

Moderate/high/very high degree

114 (78.6)

Small degree/not at all

31 (21.4)

To what degree did program meet needs

Quite a bit/completely

87 (59.6)

Somewhat

27 (18.5)

A little/not at all

32 (21.9)

Would choose to participate in program again

Probably/definitely

120 (80.6)

Maybe

20 (13.4)

Probably not/definitely not

9 (6.0)

If set goals, success in completing most important one 

Partially/completely achieved the goal

105 (82.0)

Made no progress on the goal

16 (12.5)

Did not try to achieve the goal

7 (5.5)

Setting goals and making an action plan was somewhat or extremely helpful to improving 

118 (91.5)

overall wellness 

Was referred by CHW to case manager at Henry Street Settlement

77 (55.0)

Met with case manager at Henry Street Settlement 

46 (60.5)

Experience working with the case manager at Henry Street Settlement was good, very 

40 (87.0)

good, or excellent

Felt more connected to services in community because of work with CHW 

105 (70.5)

Frequency of CHW meetings was about right

125 (84.5)

CHW explained what program was about clearly or very clearly

128 (86.5)

How well CHW helped with issues 

Very/extremely well 

104 (70.8)

Somewhat well

35 (23.8)

Not very well/not at all well

8 (5.4)

CHW = community health worker.

Notes: N = 149, because 77 people who completed an intake assessment (n = 226) did not complete the program assessment. The denominator is less than 149 for some questions because of branching logic and/or missing data. 

Discussion

The Health + Housing Project was a place-based pilot project aimed at testing the feasibility, 

acceptability, and potential impact of a CHW intervention co-located in subsidized housing. Prior 

housing-based CHW or CHW-like interventions have focused on breast cancer screening (Slater 

et al., 1998; Zhu et al., 2002); pediatric asthma (Gutierrez Kapheim et al., 2015; Levy et al., 2006; Scammell et al., 2011); diabetes, hypertension, and asthma (Lopez et al., 2017); chronic disease 

screening and follow-up (Rorie et al., 2011); HIV prevention (Sikkema et al., 2000); smoking 

cessation (Brooks et al., 2017); and obesity (Quintiliani et al., 2014). This study differed in its emphasis on enabling residents to determine on which aspects of their broadly defined health to 

focus for improvement. 

Cityscape 29

Freeman, Li, Kaplan, Ellen, Young, Rubin, Gourevitch, and Doran

Similar to residents living in subsidized buildings elsewhere in the United States, the program 

found that residents living in the two intervention buildings had a high prevalence of physical 

and mental illness (Feinberg et al., 2015; Helms, Sperling, and Steffen, 2017) and also expressed 

high rates of food insecurity and other needs. For example, residents had higher rates of diabetes, hypertension, asthma, depression, and self-reported fair or poor general health than the overall 

population in NYC (NYC DOHMH, 2017). This high concentration of multiple health needs 

among building residents suggests that subsidized low-income buildings may indeed be good 

targets for CHW programs. This finding may be all the more relevant in contexts such as NYC, 

where rapid development of market-rate housing is juxtaposed with pockets of concentrated need 

(such as those found in subsidized housing) that may be lost within apparent improvements in 

overall neighborhood health when using data aggregated at the neighborhood level or higher. 

CHW programs targeted at a building level may be one way to respond to increased income and 

health disparities in local communities.

Across several measures, the Health + Housing program found that residents who participated 

in the CHW program were at higher risk and had greater needs than residents who did not. 

Program participants were not only less well-off financially and more likely to be unemployed than nonparticipants, but they also reported significantly greater mental health problems and healthcare utilization, greater food insecurity, and less satisfaction with their social activities. This finding, particularly regarding the higher healthcare use of participants, can be partially explained by the fact that CHWs made additional attempts to recruit residents identified as “frequent users” of acute healthcare services. The differences observed between participants and nonparticipants, however, 

may also have illustrated some degree of “self-selection” into the CHW program by residents who 

needed it most. In sum, our findings indicate that a place-based CHW program in subsidized 

housing will find a population in significant need and willing to engage. 

The protocol developed for the Health + Housing Project pilot was designed to be participant led; 

participants identified and prioritized their health-related goals with support and motivation from the CHWs. CHWs were able to encourage most participants to set goals and establish a related 

action plan to accomplish them. This type of client-centered protocol has been effective in helping patients make behavior changes (MacGregor et al., 2005) and has been successfully used in other 

CHW studies (Islam et al., 2014b; Kangovi et al., 2017b). Of the respondents who reported setting 

goals, most said they partially or completely achieved their most important goal. CHWs were 

also successful at making appropriate referrals as needed, especially to the program partner’s case management services (at HSS). It appears that residents, with guidance from a CHW, will follow 

through on referrals to neighborhood resources and make progress in achieving health-related 

goals. 

Lessons Learned

Although subsidized housing buildings present unique opportunities for CHW programs, they 

also pose specific challenges. For example, compared with conducting CHW programs in health 

clinics with a “captive audience,” Health + Housing surveyors and CHWs attempted to engage 

people as they went about their daily lives. Having surveyors and CHWs available at varied hours 

and days of the week was important. The program also found that word of mouth became a useful 

30 The Housing-Health Connection

A Pilot Community Health Worker Program in Subsidized Housing:  

The Health + Housing Project

recruitment tool. When residents worked with CHWs and found them helpful, they often told 

their neighbors, who subsequently engaged with the program. Partnering with a community-based 

organization that offers a range of services in the languages spoken by residents was also crucial. 

In addition to the high percentage of referrals that CHWs made to HSS, the partnership provided 

additional legitimacy to the project, licensed clinical supervision of CHWs, and a space for team 

meetings. 

The experience working in the two intervention buildings challenged the assumption that all 

subsidized housing residents are stably housed. A surprising number of residents set goals related to housing, as many adult children struggled to find independent housing in NYC’s tight housing 

market. Although Health + Housing CHWs were adept at assisting with housing applications, 

because more than 400,000 families are on waiting lists for public or Section 8 housing in NYC 

(NYCHA, 2017), the prospects of securing affordable housing are low. As a result, CHWs assisted 

a handful of residents experiencing acute family conflict with placement in homeless shelters. 

Subsidized housing-based CHW programs in high-cost cities should anticipate such requests and 

consider incorporating a housing specialist into the project.

Finally, the high rates of mental health issues reported among residents point to an area in particular need of further attention in subsidized housing. Although the program anticipated significant 

mental health needs among residents, and CHWs attended an 8-hour mental health first aid 

training course offered by the NYC Health Department, the shortage of high-quality mental health 

resources with open availability limited the ability of CHWs to successfully navigate participants to care. Future programs would benefit from establishing direct relationships with mental health 

providers to facilitate access to treatment. Additionally, the high percentage of program participants who rated their satisfaction with social activities and relationships as fair or poor may be connected to their poor physical and mental health (Thoits, 2011) and points to the potential for CHWs to 

have a positive impact on residents’ lives by providing meaningful social interaction.

Limitations

Although participant-reported outcomes were overwhelmingly positive, this pilot study had 

limitations. First, the program was conducted in only two subsidized housing buildings on the 

Lower East Side of NYC and may not be generalizable to other communities. Second, the baseline 

survey sample size of 390 and program assessment questionnaire sample size of 149 are relatively 

small and limit our ability to show statistical significance for some outcomes or to perform 

subgroup analyses. The baseline survey response rate was lower than anticipated (47.6 percent 

of individuals, 59.1 percent of apartment units), but slightly higher than response rates from 

other surveys conducted in NYC public housing (Feinberg et al., 2015). We do not know how 

survey respondents differed from nonrespondents; however, based on available data, respondents 

appeared similar to building residents overall in age, gender, and race/ethnicity. Third, selection bias may have been present specifically for the residents who agreed to complete the program assessment questionnaire, limiting the ability to fully evaluate the effectiveness of the CHW program. 

Fourth, the program analyzed only self-reported outcomes, which may have been affected by 

reporting bias. Lastly, although the program monitored CHW performance regularly and attempted 

to address noticeable differences in resident engagement, the relatively open-ended nature of the 

Cityscape 31

Freeman, Li, Kaplan, Ellen, Young, Rubin, Gourevitch, and Doran

intervention protocol made it difficult to standardize CHW activities, leading to variability among the four CHWs. Program participants may, therefore, have had variable experiences depending on 

the CHW with whom they worked, which is likely true for CHW programs in general; the sample 

size did not allow for results to be stratified by individual CHW. 

Conclusion 

Our article contributes to the literature on place-based CHW programs by demonstrating that 

subsidized housing buildings are promising sites for CHW interventions. We found that building 

residents in general and program participants in particular had very high levels of medical, mental health, and health-related social needs. Further, residents responded positively to a program that enabled them to determine what types of health-related goals to set, expressing a high rate of 

satisfaction with the CHW program and reporting improvements in overall well-being.

Appendix A: List of 23 Goals From Which Participants Chose

Choose up to five goals from the list below that you would like to work on (ST = short-term; LT = 

long-term).

Disease Management 

Diet/Exercise  

Smoking/Alcohol  

Access  

Goals

Goals

Goals

Goals

̆ Take my medicine 

̆ Eat a healthy diet 

̆ Cut down on 

̆ Access to health 

(ST)

(LT)

smoking (ST)

care (ST)

̆ Get my illness under   

̆ Lose weight (LT)

̆ Cut down on 

̆ Access to food (ST)

control (LT)

̆ Be physically 

alcohol (ST)

̆ Access—other (ST)

active (ST)

̆ Quit smoking (LT)

̆ Exercise regularly 

̆ Quit drinking 

(LT)

alcohol (LT)

Family Goals

Employment Goals

Apartment Goals

Other Goals

̆ Get help for a family   

̆ Job readiness 

̆ Minor apartment 

̆ Cope with stress 

member (ST/LT)

skills (ST)

repairs (ST)

(ST) 

̆ Family goal—other 

̆ Find/change job 

̆ Major apartment 

̆ Resolve legal 

(ST/LT)

(LT) 

repairs (LT)

problem (LT)

̆ Apartment goal—

̆ Other (ST/LT)

other (ST/LT)

If any checked are “Other,” specify here:_________________________________

Acknowledgments

The authors gratefully acknowledge funding for this project from the Robin Hood Foundation and 

NYU Langone Health community health improvement funding as part of its Community Service 

32 The Housing-Health Connection

A Pilot Community Health Worker Program in Subsidized Housing:  

The Health + Housing Project

Plan. They thank the survey staff who collected data, the residents and resident associations who 

took part in the project, and the dedicated community health workers (Laureen Cuprill, Stephanie 

Liu, Rosalie Roman, and Daniel Tsai) who worked with residents and helped them to improve their 

lives. The authors also thank their community partner, Henry Street Settlement, and the building 

management for their supportive partnerships.

Authors

Amy L. Freeman is a program manager in the Department of Population Health at the NYU School 

of Medicine.

Tianying Li is a data analyst in the Department of Population Health at the NYU School of Medicine.

Sue A. Kaplan is a research associate professor in the Department of Population Health at the NYU 

School of Medicine.

Ingrid Gould Ellen, the Paulette Goddard Professor of Urban Policy and Planning, is the Director 

of the Urban Planning Program at the NYU Wagner Graduate School of Public Service and is the 

Faculty Director of the Furman Center for Real Estate and Urban Policy.

Ashley Young is program director of the Neighborhood Resource Center at Henry Street Settlement.

Diane Rubin is chief program officer at Henry Street Settlement.

Marc N. Gourevitch, the Muriel and George Singer Professor of Population Health, is Chair of the 

Department of Population Health.

Kelly M. Doran is an assistant professor in the Ronald O. Perelman Department of Emergency 

Medicine and the Department of Population Health at the NYU School of Medicine. 

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Cityscape 37

38 The Housing-Health Connection

Work Requirements and  

Well-Being in Public Housing

Kirstin Frescoln 

Children and Family Futures

Mai Thi Nguyen

William M. Rohe

Michael D. Webb 

University of North Carolina at Chapel Hill

Abstract

•  Objectives: Work requirements in public housing are highly controversial, and 

little is known about their impacts. We examined how implementation of a work 

requirement paired with supportive services by Charlotte Housing Authority has 

impacted residents’ overall well-being. Although the policy might improve well-being 

by increasing household income, it might also engender stress through greater housing 

precarity. 

•  Methods: This mixed-methods study analyzes data from 126 resident surveys 

conducted before and after work requirement implementation, interviews with 48 

residents, and household-level administrative data. Survey and administrative data 

capture changes in income and health between 2010 and 2014. Interviews provide 

qualitative insights on changes in health, household income, and overall well-being.

•  Results: We find that residents want to work and report both positive and negative 

effects associated with the work requirement. Resident interviews suggest increases 

in household income led to a reduction in overall stressors. Negative impacts include 

cuts in or elimination of Medicaid and Supplemental Nutrition Assistance Program 

benefits (more commonly known as food stamps). Self-rated health did not improve. 

•  Conclusions: We find work requirements—when implemented with case management 

and opportunities to complete work-related activities in lieu of employment—are 

associated with both positive and negative impacts. We urge public housing agencies 

implementing similar policies to carefully monitor and evaluate not only changes in 

household income and evictions but also welfare supports and the health and well-

being of all residents in households affected by the policy.

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 39

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

Frescoln, Nguyen, Rohe, and Webb

Introduction

Poverty and minority status can exacerbate individuals’ risk for physical and mental disabilities 

(Adler and Rehkopf, 2008). Public housing residents, therefore, 64 percent of whom are classified 

as very low-income and 45 percent of whom are African-American, are at especially high risk for 

poor health. Furthermore, many public housing residents live in negative social environments (for 

example, high crime and socially isolated) that can further lower well-being (Bennett, Smith, and 

Wright, 2006). One study found that public housing residents were three times more likely to 

report fair or poor health and twice as likely to be diagnosed with chronic health conditions com-

pared with Black women nationally.1 In addition, 29 percent of public housing residents reported 

poor mental health (Manjarrez, Popkin, and Guernsey, 2007). 

This article addresses the question: What are the impacts on the well-being of residents when pub-

lic housing agencies (PHAs) require work-able (that is, nonelderly2 and nondisabled) residents to 

have a job in order to remain in their housing? In theory, a work requirement could improve well-

being by increasing household income (Danna and Griffin, 1999; Stronks et al., 1997). However, 

work requirements could also reduce well-being by increasing stress and housing precarity (Bowie 

and Dopwell, 2013; Hasenfeld, Ghose, and Larson, 2004; Starkey et al., 2012).

Currently, only eight PHAs—all participants in the U.S. Department of Housing and Urban 

Development (HUD) Moving to Work (MTW) demonstration program—have implemented work 

requirements for some or all of their work-able residents (Webb, Frescoln, and Rohe, 2014). As of 

March 2018, requirements range from 20 hours per week of work for a single household member 

to 30 hours for all work-able adults in the household (HUD, 2017). Although all policies have 

been implemented with case management supports and protections for residents who find compli-

ance difficult, failure to comply results in eventual eviction in all but one agency (HUD, 2017).

The MTW demonstration encourages participating PHAs to implement innovative policies and 

programs to help residents achieve self-sufficiency, among other statutory goals.3 To do so, MTW 

agencies can seek waivers from standard HUD regulations and to combine various funding streams 

into a single, flexible account. MTW agencies have the funding and regulatory flexibility to implement not only policies to structure resident behaviors, such as work requirements, but also policies to support those behaviors, including case management, transportation, childcare assistance, and 

tuition reimbursement. Together, these supports could increase well-being by connecting public 

housing residents to employment, education, healthcare, and other resources. 

Well-being is a multidimensional concept that encompasses physical, mental, emotional and 

social functioning (ODPHP, n.d.). To achieve a state of well-being, one must be sufficiently healthy and resourced to meet the basic needs of the family, while also being able to engage with family, 

friends, and community. This article seeks to expand our understanding of public housing work 

requirements by examining how well-being, examined through changes in household income and 

1 The Manjarrez, Popkin, and Guernsey (2007) study compares outcomes of African-American women, as 88 percent of adults in the HOPE VI Panel Study sample were women and 90 percent were Black.

2 The U.S. Department of Housing and Urban Development defines  elderly as people age 62 and older. 

3 These other goals include increasing housing options and achieving cost efficiencies.

40 The Housing-Health Connection

Work Requirements and Well-Being in Public Housing

self-rated health, is affected by Charlotte Housing Authority’s (CHA’s) work policy. To date, work requirements in public housing have attracted little research attention; these studies have primarily focused on the economic impacts on public housing residents and have not addressed the impacts 

on broader indicators of well-being. For example, an evaluation of CHA’s work requirement found a 

statistically significant and positive impact on residents’ employment status without a corresponding increase in evictions (Rohe, Webb, and Frescoln, 2016). Placing these findings in the broader 

context of resident well-being is crucial to understand the impact of work requirements.

Background 

This article examines the effects of a work requirement implemented by CHA on the well-being of 

affected residents. CHA manages roughly 3,300 public housing units and 8,500 housing choice 

vouchers. Admitted to MTW in 2007, their program comprises several major initiatives including 

rent reforms, expanding its housing portfolio, and a work requirement, which only applies to 

work-able residents in 5 of its 15 public housing developments.

CHA’s work requirement mandates that all work-able households—those households with at 

least one nondisabled adult age 18 to 61—maintain 15 hours per week of employment. In lieu of 

employment, residents may complete preapproved work-related activities for up to 12 months. 

Beginning in fall 2011, CHA has provided voluntary case management to residents at all 5 work-

requirement sites. CHA notified affected residents in fall 2013 that work requirement enforcement 

would begin in January 2014.4

Case managers work with property managers to monitor compliance. Sanctions for noncompliance 

include—

•  Initially, noncompliant residents are placed on a 2-month improvement plan that requires them 

to meet with case managers and either obtain employment or complete “work-related activities.” 

Approved work-related activities include documented job searches, training or licensure 

programs, and some educational activities. 

•  Continued noncompliance results in loss of one-half the household’s subsidy. In most cases, this loss means that rent would be reassessed from the minimum $75 to between $360 and $450 

per month for a two- to three-bedroom unit. 

•  If noncompliance continues for more than 6 months, the household loses its entire subsidy.

•  Households are evicted if they cannot pay the higher rents or if they remain noncompliant for 

1 year.

Prior evaluations of CHA’s work requirement indicate broad resident support for, and compliance 

with, the policy (Rohe et al., 2013; Rohe, Webb, and Frescoln, 2016). Most residents believe the 

work requirement is fair. Among work-able respondents to a 2012 survey, 87 percent of those in 

the work requirement sites and 80 percent of those in the non-work-requirement sites thought so 

4 Enforcement of the policy was delayed due to high unemployment resulting from the 2010 recession.

Cityscape 41

Frescoln, Nguyen, Rohe, and Webb

(Rohe et al., 2013).5 Administrative data suggest that the policy has not increased evictions; since enforcement began in May 2014 and September 2016, only two households have been evicted 

(Rohe, Webb, and Frescoln, 2017).

Although case management alone was not successful at increasing employment, when paired with 

a work requirement, residents’ employment increased significantly (Rohe, Webb, and Frescoln, 

2016). Analysis of administrative data in December 2012 (15 months after case management be-

gan) indicated 51 percent of residents were employed. One year later, slightly prior to enforcement of the work requirement, 58 percent were employed, and a year after enforcement began, wage 

employment rose to 88 percent. After another year, the number of residents employed was 93.9 

percent (Rohe, Webb, and Frescoln, 2016). 

Methods

CHA implemented the work requirement in 5 of its 15 family public housing sites, thereby allow-

ing for a natural quasi-experimental design. Residents living in the 5 work requirement sites are 

the treatment group, and the comparison group is composed of work-able residents living in the 

other 10 family public-housing sites. 

Given how little we know about public housing work requirements, we used mixed-methods and 

convergent analysis (Creswell, 2015)—which involves separate analysis of each type of data, then 

comparison of the results to seek areas of convergence or divergence in the findings—to investigate the impacts of the policy on well-being. This approach improves our understanding by leveraging 

the advantages of both quantitative and qualitative methods (Caracelli and Greene, 1997; London, 

Schwartz, and Scott, 2007). 

Data for this study include survey responses, CHA administrative data, and longitudinal interviews with residents. Surveys were mailed to all heads of household in CHA’s 15 nonelderly public housing sites in July 2010 (response rate 75 percent) and September 2014 (response rate 53 percent). 

Survey data include responses to questions about physical and mental health and food security. 

Quantitative analysis is limited to those work-able respondents who responded to both the 2010 

and 2014 surveys (25 treatment and 101 comparison cases). Administrative data for each house-

hold include the head of household’s age, the number of children, disability and elderly status, and household income.6

Semi-structured interviews were conducted with work-able residents subject to the work requirement in January 2014, September 2014, and November 2015 and with residents in the non-work-requirement 

(comparison) sites in September 2016. Interviewees in the treatment group were randomly selected 

from residents living in the five work requirement sites in January 2014 (n = 15). The interview 

5 Mail-out/mail-back surveys were sent to the head of household in all CHA public housing units. Among those living in the work-requirement sites, the response rate was 57.5 percent, and it was 52.8 percent among residents in the comparison sites.

6 The income variable used is the Total Household Income in HUD’s Multifamily Tenant Characteristics System. Work-able households living in CHA housing have their incomes verified biennially, unless they are a minimum renter, in which case the household is required to report an increase in income within 30 days. Due to these policies, the Total Household Income may not be the household’s actual income at the time the variable was taken in June 2011 and December 2014.

42 The Housing-Health Connection

Work Requirements and Well-Being in Public Housing

samples for September 2014 (n = 14) and November 2015 (n = 15) include this original sample, 

as well as additional randomly selected individuals. Fifteen heads of household were interviewed 

more than once across the three periods. Residents placed on improvement plans or a subsidy 

sanction were oversampled to ensure that we heard their perspectives. 

The comparison interviews were drawn from work-able residents who had been living in the 

comparison family public housing sites in December 2013 and who were still living in CHA public 

housing in September 2016. The sample was matched with work-able residents living in the 

work-requirement sites in December 2013 based on age, whether dependent children were living 

in the home, household income, and whether the head of household had a high school diploma or 

general educational development (GED) certificate. 

We asked all interviewees about their household composition; health; employment experiences; 

educational background; participation in education, job training, or life skills programs; and 

receipt of welfare benefits such as Medicaid and the Supplemental Nutrition Assistance Program, or SNAP (hereafter, food stamps). We also asked how working outside the home affects their families, 

their views on the work requirement and, if applicable, experiences with case managers and sanc-

tions. Interviews were recorded, transcribed, and analyzed using deductive coding.

Results 

Due to limitations related to sample size, results are suggestive of trends rather than causal inferences. Summary statistics are presented for all work-able survey respondents living in public 

housing without the work requirement (comparison) and in the five work requirement sites (treat-

ment). As exhibit 1 shows, the sample is largely composed of African-American females. Work 

requirement respondents were slightly younger (average 42 versus 47 years), were more likely to 

have children (80 versus 61 percent), and were better resourced—56 percent had reliable access to 

a car, and 84 percent had a high school diploma or GED. 

Median household income increased between 2010 and 2014 for both groups (exhibit 2). Median 

income among comparison group members was lower than that of the treatment group in 2011 

($1,812 versus $7,540) and in 2014 after the work requirements were implemented ($3,204 

versus $10,826). The resulting change in median income from 2011 to 2014 was $1,392 for the 

comparison group versus $3,286 for the treatment group.  

The 2014 survey of households living in the work requirement sites asked what, if anything, the head of household had done in response to the work requirement. Of respondents, 52 percent indicated 

they had looked for a new job, 37 percent said they had found a new job, and 22 percent said they 

were working more hours. The Total Household Income data within the Multifamily Tenant Charac-

teristics System data are reflective of these survey results. Reported wage employment increased from 48 to 60 percent among residents in the work requirement sites between 2011 and 2014.

Although household income increased, food insecurity rose among households subject to the work 

requirement from 60 percent in 2010 to 76 percent in 2014. The increase is consistent with what 

heads of household said during interviews—that their food stamps were cut as household income 

rose. 

Cityscape 43

Frescoln, Nguyen, Rohe, and Webb

Exhibit 1

Demographic Characteristics of Treatment and Comparison Groups

Public Housing  

Work Requirement

2014

Comparison (N = 101)

Treatment (N = 25)

Mean

Std. Dev.

Mean

Std. Dev.

% Femalea

98

—

96

—

% Black or African-Americana

98

—

100

—

Age head of householda

46.99

9.89

42.04

9.18

% Dependent child in homea

61

—

80

—

Total number children in homea

1.38

1.50

1.84

1.68

% Reliable access to carb

42

—

56

—

% High school diploma or GEDb

52

—

84

—

GED = general educational development certificate. 

Sources: aCharlotte Housing Authority (CHA) Tenant Directories (December 2014); b2014 survey of CHA public housing residents Exhibit 2

Income Characteristics of Treatment and Comparison Groups

Public Housing—Comparison (N = 101)

Work Requirement—Treatment (N = 25)

2011

2014

2011

2014

Std.  

Std.   Change

Std.  

Std.   Change

Median

Median

Median

Median

Dev.

Dev.

Dev.

Dev.

Median  

$1,812 $5,183  $3,204 $6,011  $1,392 $7,540 $6,187 $10,826 $8,016 $3,286

household 

incomea 

%

%

Change 

%

%

Change 

Households 

20

36

16

48

60

12

with wage 

any incomea 

Households 

9

17

8

28

40

12

with total 

income  

≥ $1,000/

montha

Households 

69

50

– 19

36

24

– 12

minimum 

rentera

Food ran out 

67

65

– 2

60

76

16

sometimes  

or oftenb 

Sources: aJune 2011 and December 2014 Multifamily Tenant Characteristics System reports; b2010 and 2014 surveys of Charlotte Housing Authority public housing residents

Low-income and minority populations have worse health outcomes than the general population, 

as our survey results confirm (exhibit 3). On average, our respondents estimated their health to 

be “good” (1 is poor, 3 is good, and 5 is excellent), but overall self-rated health worsened slightly within both the comparison (3.21 to 2.91) and treatment (3.84 to 3.04) groups between 2010 and 

2014. In the comparison and treatment groups, 45 and 40 percent of respondents, respectively, 

reported having at least two chronic diseases in 2014, including asthma, hypertension, diabetes, or an autoimmune or inflammatory disease such as lupus, fibromyalgia, or arthritis.  

44 The Housing-Health Connection

Work Requirements and Well-Being in Public Housing

Exhibit 3

Health Characteristics of Treatment and Comparison Groups

Public Housing—Comparison (N = 101)

Work Requirement—Treatment (N = 25)

2010

2014

2010

2014

Std.  

Std.   Change

Std.  

Std.   Change

Median

Median

Median

Median

Dev.

Dev.

Dev.

Dev.

Self-rated 

3.21

1.19

2.91

1.14

– 0.30

3.84

0.90

3.04

1.14

– 0.80

health 

CES-D (score 

9.13

6.22

10.28

6.63

1.15

8.60

5.85

10.44

5.99

1.84

≥ 10 is  

depressed)

BMI (≥ 30 

32.78

9.01

33.99

9.35

1.21

32.95

10.09

35.18

9.67

2.23

obese in  

AA women)a

%

%

Change

%

%

Change

Two or more 

20

45

25

12

40

28

chronic 

diseases 

Physical 

52

54

2

40

64

24

health limited 

accomplish-

ments

Physical 

46

44

– 2

28

44

16

health limited 

activities 

Emotional 

49

46

– 3

32

48

16

health limited 

accomplish-

ments 

Anxiety  

42

39

– 3

24

40

16

affected 

activities 

AA = African-American. BMI = body mass index. CES-D = Center for Epidemiologic Studies Depression. 

aNew studies account for different ranges of healthy and unhealthy BMI in non-White populations (Wagner and Heyward, 2000).

Source: 2010 and 2014 surveys of Charlotte Housing Authority public housing residents

Survey data indicate that respondents’ poor health impacts their daily living in multiple ways and most measures of health worsened for those in both the treatment and comparison groups. Self-rated health, depression, body mass index, and the number of chronic diseases heads of household 

reported all increased. Although not statistically significant, reported anxiety increased more 

among the treatment than the comparison group between 2010 and 2014. 

Work Requirements and Income Changes

Based on resident surveys and interviews, CHA work requirement impacted household income 

in four key ways. First, for some, the policy provided the impetus and support to secure wage 

employment. Although the perception that public housing residents do not want to work is 

Cityscape 45

Frescoln, Nguyen, Rohe, and Webb

common (Falk, McCarty, and Aussenberg, 2014; Mead, 1986), nearly every resident we spoke 

with in the treatment and comparison groups wanted to work and believed that other work-able 

residents should as well. One woman living in a work requirement site shared, “[I think the work 

policy is fair, because] it pushes me to go out and do things I should already do, things I should’ve already done … job research, working on resumes, etc.” Despite this support, many also expressed 

fear about what would happen to them if they were unable to find work, “For the people who are 

looking—it’s hard. You can’t make people hire you. You can’t make people give you a chance.”

Many interviewees in the treatment sample told us that case managers provided the support 

needed to obtain wage employment. A mother of two who was unemployed when she moved into 

a work-requirement site explained, “I think it is a good stepping stone if you do what you are actually supposed to do. ‘Cause they have a lot of resources for you to accomplish the things you are 

trying to accomplish. …I would say my best experience is my case worker.” Another praised her 

case manager for encouraging her to complete Certified Nursing Assistant (CNA) training. She now 

has a job she enjoys that pays $14 per hour. 

Second, the work requirement seems to have affected education, which in turn affects income, in 

two very different ways. Some residents said the case management helped them return to school to 

obtain certifications, such as a CNA, enabling them to earn more. Others, however, stated that the policy was impeding their educations (particularly completion of GED). Although less commonly 

expressed, frustration with case managers was mostly related to enforcement of the policy, such as requiring parents with dependent children and inadequate childcare to work in low-wage, dead-end jobs. A mother of three without a GED who had been sanctioned for not meeting the work 

requirement said she did not find the case manager helpful, because “they pushing you to find any 

kind of job. You know what I mean? It’s not fair, because they’re not thinking about it long term. I mean working at McDonald’s is not a long-term job.”

Third, households that transitioned from unemployment or underemployment talked about having 

their welfare supports reduced. Many in the work requirement sites told us that their welfare 

benefits had been reduced as their wage incomes increased. Several expressed frustrations similar 

to this one: “The more you make, the more they take.” One resident explained, “I have a temp job 

making $8 per hour. They cut my food stamps from $333 down to $65 so that’s not incentive to 

work.” 

Reductions in welfare benefits are especially acute for most interviewees, as their jobs did not pay enough for families to live independent of welfare. An interviewee living in a work-requirement 

site shared a common sentiment. When asked about her ability to become economically self-

sufficient, she said, “The biggest problem with me, is trying to find a decent paying, permanent 

job… because you can’t survive off of eight, nine, even ten dollars.” Another work requirement 

interviewee stated, “I’m doing way better than I was before but that’s just because I’m working hard to get what I want. Other than that, no, I still feel like I’m at the bottom. I’m not getting anywhere.” 

Finally, parents pointed to the tradeoffs of increased employment on their family—more income 

but also less family time. Nearly all interviewees discussed working in the context of their parenting role. Many in the work-requirement sites told us that the increased income resulting from 

changes in employment had improved the overall well-being of their families, because they felt 

46 The Housing-Health Connection

Work Requirements and Well-Being in Public Housing

less financial pressure and could afford to do a little bit more than meet basic needs: “I feel like it’s a stress when I’m not working, because I do not like bill collectors and then I don’t like them to turn my stuff off. Then my kids… when they [are] going on field trips, ‘Momma, we just need 

five dollars… OK, I can give you five dollars to go on the field trip.’ But when I’m not working, it makes me feel bad.” 

All but three of our interviewees were single mothers, and each addressed the challenge of 

balancing work and parenting. The lack of affordable and reliable childcare directly impacted their employment. One told us, “My biggest challenge is being a single mom. I mean, I have so much 

on me. If… one of the kids gets sick, I’m the only one to go get them.” Another recounted turning 

down a fast food management position because she did not have affordable childcare. 

Interviewees across all sites told us the security provided by living in public housing had a positive impact on their overall well-being. Not only did it provide shelter, but it also helped them stabilize financially. One shared, “If it wasn’t for the rent being adjustable I know I probably would have 

been evicted a long time ago.” Another articulated the role that public housing played in her ability to plan for self-sufficiency goals: “Being a single mother of four and just going to school, the housing authority is very beneficial to me. I need to establish a career and work on it until I’m able to move out on my own.” 

Work Requirements and Health Outcomes

As both survey results and resident interviews evidenced, the baseline physical and mental health 

of CHA’s work-able public housing residents is very poor. Although health measures generally 

worsened within both groups, individuals living in the work-requirement sites reported dimin-

ished health in all measures. 

Both groups reported high rates of depressive symptomatology; depression negatively impacts 

physical health and reduces capacity for employment (Ross and Mirowsky, 1995; Wells, Stewart, 

and Hays, 1989). Interviews with work-requirement residents suggest much of this condition is sit-

uational—the demands of being a single mother and struggling financially—but it was exacerbated 

by trauma events, poor health histories, or the inability to afford healthcare. A 35-year-old mother of three who did not have health insurance and was relocated to one of the work-requirement 

sites after a serious domestic violence incident, told us, “They said I got high blood-pressure. I’m anemic. I know I’m depressed, so I guess I’d just say ‘good.’ When I’m not at work I sleep a lot. I’m sad, I mean, I just feel like down, depression, sad. I don’t want to be bothered.”

The way a work requirement interacts with other welfare benefits, such as food stamps and access 

to Medicaid, is of significant concern given the health challenges of public housing residents. For instance, North Carolina Basic Medicaid Eligibility stipulates a mother with a single dependent 

cannot make more than $434 monthly to be eligible for full Medicaid benefits. If she has two 

children, she cannot make more than $569, and with three children, no more than $667 monthly 

(NCDHHS, n.d.). A household of a single parent with one child who lives in a CHA work-

requirement development would make  one dollar too much to qualify for full Medicaid insurance if working only the required 15 hours per week at minimum wage (15 x $7.25 x 4 = $435). As 

Cityscape 47

Frescoln, Nguyen, Rohe, and Webb

such, the work requirement policy of only 15 hours a week and the qualifications for Medicaid 

are incongruent. Complying with the requirements for one program disqualifies a household from 

receiving the benefits of the other, thereby creating hardships.

Without full medical insurance for routine care, interviewees described going to the emergency 

room to access medication and healthcare. One woman subject to the work requirement shared, 

“I had to go to the ER. My blood pressure was through the roof and my head was hurting so bad. 

They were going to admit me if it didn’t come down. I just want to be happy about something. I’m 

glad to be alive. I’m thankful for my health and my strength and my children and my family so I 

am happy about some things. But I am always depressed and sad about stuff.”

Despite the challenges of reduced benefits and balancing parenting and employment, those subject 

to the work requirement reported feeling less stressed as a result of increased wage income. At 

least part of this decrease in stress resulted from feeling that they were better able to care for their families, “Me being the only parent, it’s important for [my son] to see me going to work and working to get what we need.”

Discussion

This article seeks to understand how a public housing work requirement paired with case manage-

ment affects well-being as measured by changes in household income and self-rated health. Survey 

and interview data suggest CHA’s public housing residents support enforcement of a 15-hour-

per-week work requirement for work-able residents when implemented with case management 

support and opportunities for compliance through work-related activities. Despite overall support 

for the policy and increases in household income, interviewees reported frustrations with obtaining living wage employment, reductions in welfare benefits following marginal increases in income, a 

perceived PHA focus on employment over education, and deteriorations in self-reported health. 

During interviews, those residents in the work-requirement sites reported that the policy had pro-

vided motivation to find work and that the case managers had offered support and encouragement. 

Work-requirement interviewees, who had increased their wage employment, indicated that increased 

wages had reduced some stressors by providing the household with a little extra money that could be used to pay down bills or purchase small “treats” such as a meal out or school pictures. 

Although both employment and hours worked increased, few households reached the threshold of 

$1,000 monthly that Sullivan and DeCoster (2001) found would improve mental health and depres-

sion; only 17 percent of comparison households and 40 percent of treatment households met that 

threshold in 2014. One reason households likely did not meet this threshold was low educational 

attainment; only 52 percent of comparison and 84 percent of treatment heads of household within 

the survey sample had either a high school diploma or a GED. Several interviewees argued that the 

policies would be more effective if residents without a GED or high school diplomas were given a 

choice between obtaining a GED or wage employment.7 Households were particularly frustrated that 

increases in income resulted in reductions in their food stamp benefits and Medicaid coverage. 

7 The 2016 revision of CHA’s work policy requires 20 hours of work but also includes a provision for the head of household to attend educational programing and work just 15 hours.

48 The Housing-Health Connection

Work Requirements and Well-Being in Public Housing

Access to sufficient, healthy food and healthcare is of critical importance, as responses to the 2010 

and 2014 surveys suggested that self-rated health worsened within both treatment and comparison 

groups. Reasons for this decline may include the baseline health status of the residents and the 

normal effects of aging. 

Most of the literature suggests that wage employment improves health (Faragher, Cass, and 

Cooper, 2005; Ross and Mirowsky, 1995; Stronks et al., 1997), and interviewees in the work-

requirement sites reported some reductions in financial stressors; however, the survey data revealed reductions rather than improvements in measures of health. The relatively brief time of the intervention and the sample size may have contributed to these results. Also, research on welfare reform indicates that low-wage employment might not be sufficient to generate health improvements 

(Cancian et al., 1999; Corcoran et al., 2000; Meyer and Sullivan, 2006; Scott et al., 2004). 

Limitations

Our study has several limitations we hope to address in future research. First, it was dependent 

on a small survey sample of 126 work-able households and an interview sample of 48 individuals. 

Second, we would like to examine changes during a longer period following work-requirement 

enforcement. Finally, household income and education within the treatment and comparison 

groups were significantly different at baseline. Despite these limitations, this study contributes to our limited knowledge of the effects of work requirements on public housing residents. 

Policy Recommendations

Only MTW agencies currently have the authority to impose work requirements. Given the limita-

tions of this study and lack of other studies examining work requirements in public housing, we 

caution against expanding such policies without additional study. HUD should require any PHA 

proposing a work requirement to collect additional data, including changes in health and well-

being for all those living in the household. 

CHA’s policy reflects recognition of barriers many work-able public housing residents encounter 

in finding and maintaining employment. Other PHAs should consider a similar policy that begins 

with a low employment threshold (15 to 20 hours), case management for all affected households 

or those that are noncompliant, and provision for engagement in “work-related activities” in lieu 

of wage employment. These policies have provided a safety net for residents who make good faith 

efforts to find employment and protection from immediate eviction for noncompliance. 

One of the most significant barriers to employment was low education levels and a lack of job 

skills. PHAs considering implementing a work requirement should allow those subject to the 

policy to meet it through completion of education and job training. Partnerships with community 

agencies, including Workforce Investment Boards, could help residents develop employment skills 

and obtain marketable licensures. 

PHAs should work with local public health organizations to provide additional support for 

improving the health of public housing residents. The health needs of this population have been 

Cityscape 49

Frescoln, Nguyen, Rohe, and Webb

well documented, and our study provides additional evidence of the need for expanded services. 

Federal and state policymakers should carefully consider the way work requirements may impact 

access to healthcare and food. 

Overall, CHA’s work requirement, including case management and other services, was associated 

with increases in wage employment without increasing evictions. Parents described wanting to 

work to provide financially for their families and to serve as a role model for their children. They reported feeling less stressed when they had a little extra money to treat their families and pay 

their bills. Despite this, household income was not sufficient to support a move out of public 

housing, physical and mental health needs remained severe, and few residents made progress 

toward improving their educational levels. PHAs could improve the effectiveness of public housing 

interventions to increase family self-sufficiency and well-being by providing more job training and educational opportunities, additional health interventions, and access to case management services. 

Acknowledgments

Funding for this research came from Charlotte Housing Authority and a grant from the University 

of North Carolina Participatory Research Program. Although both received interim reports 

throughout the research period, neither attempted to influence these findings or policy recommen-

dations. The authors also thank Sydney Corn, research assistant at the University of North Carolina Center for Urban and Regional Studies, for her work on this study and thank the many student 

assistants who have supported this research over the years. 

Authors

Kirstin Frescoln is a senior program associate with Children and Family Futures.

Mai Thi Nguyen is an associate professor and associate chair at the University of North Carolina at Chapel Hill, Department of City and Regional Studies.

William M. Rohe is the Director of the Center for Urban and Regional Studies and the Cary C. 

Boshamer Distinguished Professor in the Department of City and Regional Studies at the University 

of North Carolina at Chapel Hill.

Michael D. Webb is a senior research associate at the University of North Carolina at Chapel Hill, Center for Urban and Regional Studies.

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Economic Research. http://ideas.repec.org/p/nbr/nberwo/11976.html.

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North Carolina Department of Health and Human Services (NCDHHS). n.d. “Medicaid Income 

Limits.” https://dma.ncdhhs.gov/medicaid/get-started/eligibility-for-medicaid-or-health-choice/

health-choice-income-and-resources-requirements/medicaid-income-limits.

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and Well-Being.” https://www.healthypeople.gov/2020/about/foundation-health-measures/Health-

Related-Quality-of-Life-and-Well-Being.

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Authority’s Moving Forward Program: Interim Assessment Report. Chapel Hill: The University of North Carolina at Chapel Hill, Center for Urban and Regional Studies.

Rohe, William, Michael Webb, and Kirstin Frescoln. 2017.  Innovations in Housing: Third Interim Moving Forward Assessment. Chapel Hill: The University of North Carolina at Chapel Hill, Center for Urban and Regional Studies.

———. 2016. “Work Requirements in Public Housing: Impacts on Tenant Employment and Evic-

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Work, Unstable Income: Implications for Family Well-Being in the Era of Time-Limited Welfare,” 

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Interrelationship Between Income, Health and Employment Status,”  International Journal of Epidemiology 26 (3): 592–600. https://doi.org/10.1093/ije/26.3.592.

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52 The Housing-Health Connection

Innovative Approaches to 

Providing Rental Assistance: 

States and Localities Seek  

To Support Health and  

Human Services Goals

Anna Bailey

Peggy Bailey

Douglas Rice

Center on Budget and Policy Priorities

Abstract

•  Objectives: We sought to learn more about how state- and locally funded rental 

assistance programs were created, how they are structured, whom they serve, and 

how they are funded.

•  Methods: We conducted qualitative research about 19 state- and locally funded 

rental assistance programs in eight states and two cities using phone interviews, 

online surveys, and email exchanges with officials and providers familiar with the 

programs, and we conducted online research to gather additional information about 

the programs.

•  Results: Although the rental assistance programs varied, key themes emerged, 

including (1) most programs, recognizing the impact of housing stability on health 

outcomes, targeted populations served by state or local health and human services 

programs; (2) most programs served a growing number of households over time; (3) 

funding generally increased over time and most of it came from general revenue; 

and (4) programs involved collaboration between the housing and health and human 

services agencies to ensure clients’ needs were comprehensively met. 

•  Conclusions: Although state and local rental assistance alone cannot fill the gap 

between the need for and supply of affordable housing, it can play a critical role in 

helping states and localities achieve better outcomes for special populations in which 

states and localities are deeply invested. 

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

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

Bailey, Bailey, and Rice

Background

State and local policymakers increasingly recognize that a lack of stable, affordable housing not 

only causes homelessness but can worsen people’s health and raise state and local costs (Hostetter and Klein, 2014). High housing costs affect people’s access to doctors, healthy food, and medications and can increase their risk of being forced into institutional care such as nursing homes or mental health facilities (Braveman et al., 2011). People with significant health challenges who 

struggle to pay for housing often use costly health and social services that can strain government budgets, including emergency shelters, mental health and substance use treatment, and institutional health care (Bodurtha et al., 2017). Insufficient affordable housing also constrains policymakers’ 

ability to implement community-based best practices. For example, states and localities have a 

legal obligation under the Supreme Court’s  Olmstead decision to provide services that help people with disabilities live independently in the community.1 Adhering to this obligation is difficult when the available affordable housing is inadequate. 

Federal affordable housing resources are scarce. Federal programs such as the Housing Choice 

Voucher (HCV), public housing, and Section 8 Project-Based Rental Assistance programs provide 

the vast majority of rental aid, but three in four eligible households receive no aid because of funding limitations (CBPP, 2017a, 2017b). Most state and local housing agencies that administer federal rental assistance have closed their waiting lists due to high demand, and those on waiting lists 

often wait years for assistance (Mazzara, 2017; PAHRC, 2017). Only a small fraction of agencies 

prioritize households facing homelessness, families with young children, or people with disabilities (HUD, 2017). 

Some states and localities have filled part of the gap by funding their own rental aid programs 

(Bergquist et al., 2014; NLIHC, 2017). This article reviews findings from our qualitative study about how 19 such state- or locally funded programs across eight states (Connecticut, Illinois, Iowa, Maryland, Minnesota, Nebraska, New Jersey, and Oregon) and two cities (District of Columbia and San 

Francisco) were created and funded, how they operate, and whom they serve. It also discusses key 

observations for other states and localities interested in initiating or expanding rental assistance. 

Methods

The National Low Income Housing Coalition (NLIHC) created and periodically updates a database 

of state- and locally funded affordable housing programs (NLIHC, 2017).2 Using the NLIHC data-

base as a starting point, a 2014 Technical Assistance Collaborative (TAC) report created summary 

profiles of more than 70 such programs (Bergquist et al., 2014). TAC’s review found that these 

programs tend to target specific populations, are often intended as a bridge to federal assistance, do not require engagement in services, and use tenant-based models. 

1 In 1999, the Supreme Court ruled in  Olmstead v.  L.C.  that the Americans with Disabilities Act requires states to serve people with disabilities in the least restrictive environment possible, which has led the U.S. Department of Justice to investigate, and sometimes sue, states suspected of noncompliance. See http://www.bazelon.org/the-olmstead-case/.

2 Note that we first referenced NLIHC ’s database in 2016, and the database may have been updated since then.

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Innovative Approaches to Providing Rental Assistance:  

States and Localities Seek To Support Health and Human Services Goals

Building on these resources, we sought to learn more about these programs to identify lessons that states and communities might find useful. Excluding one-time or very short-term aid programs, 

our review of the NLIHC and TAC surveys identified 66 active state- or locally funded rental assistance programs, most of which are state programs, in 32 states serving about 120,000 households. 

Out of this universe, we explored programs in a small number of states and cities. 

We were particularly interested in programs that target people with health challenges and link 

housing aid to health and social service supports. For example, we selected Iowa and Maryland 

because their housing subsidies targeted people served by Medicaid programs that help people 

with disabilities live in the community instead of institutions. To learn from a cross-section of communities and programs, however, we also considered other criteria to ensure diversity in program 

location, age, and size. We included a mix of states and cities because both levels of government 

likely have meaningful lessons to share. We also included a mix of tenant-based and project-based 

approaches. We excluded some states, including New York, because the amount of resources 

invested could be difficult to replicate. Ultimately, we interviewed agency officials and advocates about 19 programs in 10 locations (8 states and 2 cities). 

We began by emailing and speaking with a subset of state and local officials and advocates from 

selected communities (chiefly in New Jersey and Illinois). We interviewed officials managing the 

programs and advocates familiar with the programs’ history—people we either already knew or 

identified through contact information available online or referrals. These strategies enabled us 

to speak with people about every program we targeted. We used the preliminary conversations to 

verify the accuracy of the NLIHC and TAC survey information, test potential survey questions, and 

identify people with more detailed information to offer, such as the programs’ creation, structure, changes over time, and funding. 

Based on those conversations, we developed a simple online survey—with questions about a pro-

gram’s size, eligibility criteria, and funding—to use with the remaining locations (see appendix B). 

We followed up with semi-structured phone interviews with officials and advocates who completed 

surveys to learn more about each program, including its origin and changes over time. We asked 

them roughly the same questions as the preliminary group but grouped the more straightforward 

questions into the survey and reserved more complex questions for phone conversations. We re-

ceived survey responses for each program for which we requested them, although one survey was 

completed after the phone conversation. In some cases, we asked follow-up questions or sought 

clarification via email. We also used information published on local and state government websites to supplement what we learned from surveys, emails, and phone conversations.3 

Limitations

Our results reflect a relatively small share of programs that do not constitute a strictly representative sample. Also, we gathered information during the period 2016 to 2017, and some information 

3 See appendix A for an overview of key characteristics of each program, including the kind of agency that oversees it, the populations served, the services offered, whether subsidies are tenant or project based, and the approximate size of each program. Much of the information in appendix A was derived from the survey (see appendix B for substantive survey questions), although some was also obtained from phone conversations with advocates and officials.

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could now be outdated. Although we received answers to most of our questions for each program, 

not all the officials and advocates we contacted could answer all our questions. For some 

programs, we were unable to delineate the program’s origins, the reasoning behind certain policy 

decisions (such as shifting program management to a different agency), or initial program funding 

levels. 

Results

We identified several common features among the 19 programs that may be of interest to policy-

makers and stakeholders—

•  To further the state’s or locality’s health and human services goals in addition to affordable 

housing goals, most of the programs target special populations, particularly people with 

significant physical or mental health needs, and connect them to a set of services.

•  Programs use a mixture of tenant- and project-based rental assistance, with many using both. 

•  Programs are often designed as a bridge to federal rental assistance.

•  Coordination between housing agencies and health and human services agencies is common for 

programs that serve special populations. 

•  Program funding tends to grow over time to serve more households.

•  Most programs rely on general revenue, not dedicated sources such as special taxes or fees.

Program Features

States and localities have many options in designing rental assistance. Although the programs we 

analyzed had a variety of designs, many shared common features (see appendix A for details on 

each program). 

Policy Goals

Providing affordable housing resources is a central purpose of the rental assistance programs. For instance, New Jersey’s State Rental Assistance Program was created in part as a temporary stopgap 

at a time when funding for federal vouchers had stalled.4 Similarly, advocates in Washington, D.C., stated that sequestration’s effect on federal rental assistance funding contributed to the growth of the Local Rent Supplement Program. However, many programs target goals beyond housing affordability. In addition, although many programs were created or expanded to meet state or local policy needs, federal mandates or initiatives sometimes played a role. 

Deinstitutionalization—the movement away from institutionalizing people with disabilities and 

toward integrating them into the community—is one of the most common policy goals, often tied 

to compliance with the Supreme Court’s  Olmstead decision. Studies routinely show that lack of 4 To learn more information about sequestration’s impact on federal rental assitance, see https://www.cbpp.org/research/

housing/sequestrations-rising-toll-100000-fewer-low-income-families-have-housing-vouchers.

56 The Housing-Health Connection

Innovative Approaches to Providing Rental Assistance:  

States and Localities Seek To Support Health and Human Services Goals

affordable housing is a significant barrier to moving people out of nursing homes or other health 

facilities (Irvin et al., 2017); targeted rental assistance can remove this barrier for people who could otherwise live in the community with the right supports. Before the  Olmstead decision, Minnesota’s Bridges Program was created to assist deinstitutionalization. U.S. Department of Justice (DOJ) 

enforcement of  Olmstead directly led to the creation of Illinois’ Bridges Program through a 2011 

settlement agreement, whereas Nebraska officials said that DOJ’s  Olmstead enforcement contributed to the creation of the Rental Assistance Program for People with Mental Illness in 2006, although 

no lawsuit or investigation was pending. States also often cite  Olmstead as a reason for expanding programs. 

Another common program goal is addressing homelessness, including stabilizing or improving 

the health of people with serious physical or mental health conditions who are experiencing 

homelessness. Homelessness can destabilize people’s health by exacerbating mental illness, expos-

ing people to the elements or unsanitary conditions, or contributing to or exacerbating substance 

use disorders. In addition, homelessness can make it more difficult for people to obtain healthcare and social support services. Officials and advocates noted the hope that targeting rental assistance to people who might otherwise rely on costly shelters or frequently utilize expensive emergency 

services like hospital emergency departments may also reduce state and local costs in these areas. 

Several programs were created specifically to address homelessness. Advocates in Washington, 

D.C., noted that the Permanent Supportive Housing Program was created after the closing of a 

local homeless shelter made homelessness more visible. San Francisco created the Local Operating 

Subsidy Program in 2004 as part of a 10-year plan to end homelessness, only a year after the 

federal government started promoting such plans.5 Similar plans to end homelessness, including in 

Minnesota and Connecticut, contributed to the growth of other rental assistance programs. 

Target Populations 

Every program we analyzed limits eligibility to people with incomes of less than the Area Median 

Income (AMI).6 About one-half of them explicitly require participants to have incomes at or below 

30 percent of AMI; a few serve higher-income people, including 50 or 60 percent of AMI. Several 

programs lack income eligibility requirements but have population-specific eligibility criteria—

such as homelessness or participation in particular services—that likely mean they serve people 

with incomes well below AMI. 

Primarily for the reasons cited previously, every program we studied targets aid to people who 

have mental illness, experience homelessness, or have other qualifying characteristics (for example, families with children). Even the programs that do not  exclusively serve special populations set aside some rental assistance for such groups or prioritize them in practice. 

5 To learn more about the federal government’s role in encouraging states to adopt 10-year plans to end homelessness, see 

http://nlihc.org/sites/default/files/Sec7.08_Ten-Year-Plan_2015.pdf. 

6 The U.S. Department of Housing and Urban Development (HUD) uses AMI as a benchmark to determine eligibility for federal rental assistance programs, and 11 of the 19 state and local rental assistance programs we analyzed also use AMI in their eligibility criteria. Of the 11 rental assistance programs that use AMI, 8 are currently operated at least in part by a housing agency. 

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Bailey, Bailey, and Rice

Leaders in Creating New Programs

Each program we studied had champions who were instrumental in creating it. In some cases, 

advocates cultivated champions within agencies or legislatures; in others, agency officials appear to have proposed new programs without major outside advocacy. Regardless, legislative advocacy has 

been key to increasing funding and protecting programs from cuts.

Agency-Led Efforts

Health and human services officials, rather than legislators or housing advocates, were often the 

primary champions for creating new programs. Indeed, in some cases, housing advocates were 

unaware of rental assistance programs long after their creation. In these cases, the health agencies determined that lack of affordable housing interfered with their ability to effectively serve their clients and achieve the policy goals discussed previously. 

Advocate-Led Efforts 

Several programs were enacted after advocates—typically housing and homelessness advocates—

organized legislative advocacy campaigns for more rental assistance resources. For instance, 

homelessness advocates were key to creating Minnesota’s Transitional Housing Program in the 

1980s; they later joined affordable housing advocates to push for the Minnesota Housing Trust 

Fund, which eventually became an additional funding source for rental assistance. On the whole, 

successful campaigns to create, expand, or protect rental assistance programs have received cross-

partisan support. 

Services

Nearly all of the programs we studied are connected to a package of services, largely state- or 

locally funded health services. These programs include community-based mental health services 

such as case management and outpatient therapy and medication management, which help people 

manage symptoms while living in the community, and are typically broadly available to members 

of the community—not only people receiving rental assistance. The most commonly offered 

services are supportive housing services, which help people maintain stable housing after exiting 

homelessness, such as by helping them pay rent on time, understand their rights as tenants, and 

manage their health to prevent hospitalization or institutionalization (Dohler et al., 2016). 

Most of the services offered alongside rental assistance are optional for participants (Bergquist et al., 2014; NLIHC, 2017). However, a small number of programs require participants to receive 

certain basic services, such as monthly case management visits, as long as they receive rental 

assistance. Also, some only serve people who already receive specific services; for example, Iowa’s Home and Community Based Services (HCBS) Rental Assistance Program provides rental assistance 

only to people who receive home- and community-based services.7 

7 Home- and community-based services are Medicaid services that help seniors or people with disabilities receive services in their homes and communities rather than in nursing homes or other healthcare facilities. See https://www.cms.gov/

Outreach-and-Education/American-Indian-Alaska-Native/AIAN/LTSS-TA-Center/info/hcbs.html for a list of the types of services states can provide through their HCBS programs. 

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Subsidy Structure

More than one-half of the programs we studied cover the difference between rent and 30 percent of 

the tenant’s income, with the subsidy often capped at Fair Market Rent (FMR) or a similar measure, similar to federal rental assistance (CBPP, 2017a).8 However, two programs have additional subsidy caps: Oregon Health Authority’s State Rental Assistance program’s monthly $500 cap and the 

Nebraska Rental Assistance Program’s $6,000 annual cap (with some exceptions). 

Two other programs provide less generous income-based subsidies: Connecticut’s Rental Assistance 

Program requires most tenants to contribute 40 percent of their income (30 percent for elderly 

tenants and tenants with disabilities), and San Francisco’s Direct Access to Housing requires a 50- 

percent contribution. Washington, D.C.’s Housing First Subsidy Program uses a flat rent not based 

on income, and Illinois’ Rental Housing Support uses two flat rents, one for people with incomes 

of less than 15 percent of AMI and one for those with incomes between 15 and 30 percent of AMI. 

Size

Most of the programs we studied grew significantly over time. They now serve an average of 1,600 

households (ranging from fewer than 100 to more than 5,400); only two serve fewer than 700. 

The two smallest programs were the most narrowly targeted, serving recipients of specific Medicaid services: Iowa’s HCBS Rental Assistance Program and Maryland’s Money Follows the Person 

subsidy. The two largest programs were among the least targeted: Connecticut’s Rental Assistance 

Program and New Jersey’s State Rental Assistance Program, both of which set aside some rental 

assistance for special populations but otherwise base eligibility on income. 

Tenant- and Project-Based Subsidies

Rental assistance can be either tenant based (meaning the household uses the subsidy to rent a home of its choice on the private market) or project based (meaning the subsidy is tied to a specific building or unit). Some of the programs we analyzed are strictly project based, some are strictly tenant based, and about one-half use both models. For instance, Connecticut’s Rental Assistance Program largely 

uses a tenant-based model but also funds permanent supportive housing units with project-based subsidies. Programs serving special populations are just as likely to use tenant-based as project-based rental assistance, and many—like Washington, D.C.’s Permanent Supportive Housing Program—use both. 

Bridge to Federal Rental Assistance

Nearly one-half of the programs we analyzed help households afford rent while waiting for federal 

rental assistance. They either (1) were explicitly intended to function as a bridge to federal rental assistance or (2) required participants to apply for federal rental assistance as a condition of eligibility. Once participants obtain federal assistance, the state or local funds are redirected to serve other households waiting for federal assistance. 

8 Subsidies in HUD’s HCV program are based on a HUD-determined FMR, which typically reflects the cost of rent (including utilities) of units at the 40th percentile in a metropolitan area or county. The goal is to ensure that the subsidy can help low-income people afford adequate yet modest housing while enabling the program to assist as many households as possible. See huduser.gov/portal/datasets/fmr.html.

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Bailey, Bailey, and Rice

Most of these bridge or bridge-like programs either have no time limits or have exceptions to time limits, allowing people to keep receiving aid while waiting for federal subsidies. This aid helps 

vulnerable individuals such as those experiencing homelessness or at risk of institutionalization get safe, stable housing more quickly, potentially preventing further decline in their health while they wait for federal assistance. 

Generally, households may receive state or local rental assistance for several years before federal assistance becomes available. To ensure a timely transition into federal assistance, Maryland’s 

Money Follows the Person Bridge Subsidy Demonstration enters into memoranda of understanding 

with public housing agencies (PHAs), under which the PHAs prioritize bridge subsidy recipients to 

receive federal housing choice vouchers within 3 years of receiving the state subsidy.

Cross-Agency Coordination 

The programs we examined are chiefly administered by state or local housing agencies or the 

health and human services agencies. Four are jointly administered by the housing  and the health and human services agencies, typically with the former administering the rental assistance and the latter managing services. The rest are nearly evenly split between those administered by a housing agency and those administered by a health and human services agency. 

In some cases, particularly programs created by a health and human services agency, the agency 

that originally administered the program no longer does so. Four such programs moved from a 

health and human services agency to the corresponding housing agency or are now jointly admin-

istered in collaboration with the housing agency. For instance, the Minnesota Bridges Program, 

which serves people with mental illness, was created within the state human services agency but 

later moved to the state housing agency. 

Administering agencies sometimes contract with nonprofit organizations, PHAs, or local Contin-

uums of Care (the planning bodies that coordinate federal homelessness resources) to implement 

the rental subsidy or services. For example, both of Connecticut’s programs contract with PHAs to 

manage the rental subsidies, including managing a waitlist alongside the HCV program waitlist. 

Some programs in Minnesota (the Transitional Housing Program and Housing Trust Fund) and 

New Jersey (the supportive housing portion of the State Rental Assistance Program) utilize their 

local Continuums of Care to organize and prioritize referrals. Several programs that serve people 

with mental illness or other health needs rely heavily on healthcare providers for referrals.

Funding Trends 

Generally, funding for the programs we examined have grown significantly over time (along with 

growth in the number of households), and funding doubled for at least one-half of them.9 For in-

stance, New Jersey’s State Rental Assistance Program started with $10 million annually in the early 9 Figures on funding levels do not take inflation into account. Also, we were unable to obtain information on initial program funding for about one-half of the programs we examined.

60 The Housing-Health Connection

Innovative Approaches to Providing Rental Assistance:  

States and Localities Seek To Support Health and Human Services Goals

2000s and now receives more than $40 million annually.10 Iowa’s HCBS Rent Subsidy program 

started with about $70,000 in the mid-1990s and now receives more than $650,000 in general 

revenue funds. 

States and localities typically fund rental assistance in one of two ways: (1) general revenue appropriated each legislative session, or (2) dedicated revenue sources (typically fees, taxes, or interest). 

Of the 19 programs we studied, 13 rely solely on appropriations, 1 relies on dedicated funds, and 

the remaining 5 have used both. Nearly all the programs that rely solely on appropriated funds 

grew substantially over time, despite some occasional dips. The 5 programs that have used both 

kinds of funding also saw growth. In contrast, the only program relying solely on dedicated fund-

ing (Illinois’ Rental Housing Support program) saw about a 20-percent decline in funding since its creation a decade ago. 

Discussion

States and localities used at least portions of their rental assistance programs to address homelessness, physical and mental health problems, and other social problems, with most programs ex-

plicitly targeting people experiencing such problems. States and localities facing similar challenges could use lessons from existing programs to think creatively about how to use rental assistance to help address them. The programs we analyzed largely lack meaningful outcome measurements, 

meaning judgment about which models are best must be reserved for future research. Although 

no single way exists to advocate for or structure rental assistance programs, our research suggests that the following can guide stakeholders engaged in early conversations, strategic planning, and 

program implementation. 

Target Rental Assistance to Special Populations

Targeting rental assistance to people with particular needs can help programs gain and maintain 

support—and use scarce resources effectively. Agency officials, legislators, and advocates all view rental assistance as an important tool for addressing other state or local priorities. Given the wide range of goals that rental assistance can support, such as ending homelessness and improving 

health care, champions for creating rental assistance can be found in many places: in legislatures, government agencies, and community organizations invested in serving vulnerable populations. 

Moreover, because rental assistance would likely remain scarce even with robust state and local 

efforts, states and localities should focus their modest resources on the groups facing the direst circumstances. People who are experiencing homelessness or have mental health conditions were the 

most common target populations of the programs we analyzed. Another potential target is high-

cost users of the health care system, such as people with frequent emergency room or inpatient 

10 The New Jersey State Rental Assistance Program was originally—and for most of its duration—funded by general revenues. New Jersey shifted the program onto the state’s housing trust fund during the Great Recession. The trust fund typically funds affordable housing development, and many hope that the rental assistance will be transferred back to general revenue funding in the near future, allowing the trust fund to return to funding development. 

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hospital visits; often they are unstably housed, and programs that couple affordable housing with 

services (like supportive housing) have been shown to reduce state and local costs (Dohler et al., 2016). 

Some populations not included in our sample might also be worth targeting. Two examples are 

youth aging out of the foster care system and people who were formerly incarcerated. Both groups 

often struggle to afford housing, and the resulting instability can contribute to homelessness, 

involvement in the justice system, and other adverse outcomes (Annie E. Casey Foundation, 2014; 

Metraux, Roman, and Cho, 2007; NHCHC, 2013). 

Engage Cross-Agency Partners Early and Often

Cross-sector partnerships are key to effectively serving special populations with both housing and service needs. Partnering with housing experts can reduce administrative duplication, improve efficiency, and avoid challenges and missteps. Of the programs we studied, 12 were formed in a health 

and human services agency, with little or no help from the housing agency; 4 of them later had at 

least the rental assistance part of the program moved to housing agencies, presumably because of 

difficulties administering rental assistance. 

Although health and human services officials understand how rental assistance benefits the people 

they serve, housing agencies typically have much deeper expertise and capacity to design and 

manage rental assistance. To administer rental assistance effectively, agencies require financial 

and practical mechanisms to help people locate housing that meets their needs, perform property 

inspections, and make timely rent payments. Agencies also need to recruit and retain landlords 

willing to work with the agency, accept housing subsidies for payment, and rent to people with 

poor rental and credit histories. In addition, programs that require recipients to be on the waiting list for federal housing assistance should connect with the housing agency to ensure participants 

eventually receive federal assistance. Finally, division of labor between housing and health and 

human service agencies enables the latter to focus on addressing peoples’ needs  beyond housing unaffordability. 

Think Carefully and Creatively About Funding Opportunities 

Rental assistance programs can grow over time, enabling them to serve more households, particu-

larly when legislators understand how rental assistance supports other health and human service 

goals. The  kind of program funding used is an important design consideration. Although our investigation did not find that one funding source (general revenue versus dedicated funding) is 

best, the following factors can help when weighing funding options. 

The choice of funding source depends greatly on the local political climate. If the strongest 

support for creating new rental assistance comes from within a government agency, it might be 

easier for the agency to request a modest amount of general funds to start a new program than to 

convince legislators to create a new dedicated funding source or divert funds from an existing one. 

Dedicated funding sources can also face significant opposition from stakeholders with an interest 

in maintaining access to an existing fee or tax or who oppose the creation of a new one. Among 

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advocacy campaigns that sought a dedicated funding source, like the trust fund in Minnesota, non-

profit housing developers were important stakeholders because trust funds often fund affordable 

housing construction or rehabilitation. 

The funding source’s sustainability is another key consideration. Without sustainable funding, a 

program may not keep up with rising rents and may have to terminate rental assistance for some 

households. Funding that keeps up with rising rents is particularly important for rental assistance with no time limits (as in the vast majority of programs we analyzed). 

Dedicated funding may seem more sustainable because the revenue is systematically collected 

from a fee or tax. In contrast, appropriated funds require legislators to allocate funds for the 

program, meaning the rental assistance competes directly with other legislative priorities every 

year. However, the only program we examined that relies exclusively on dedicated funds—Illinois’ 

Rental Housing Support program—was also the only program to see a significant funding  decline 

since its creation.11 Dedicated funds can be vulnerable during periods of budget shortfalls and can also see fluctuation or stagnation in the fees or taxes that fund them. For instance, dedicated funding sources often include part of the mortgage recording fees paid on the purchase or sale of real estate; if fewer people buy homes, this revenue falls. Moreover, most of the programs relying on 

appropriated funds have experienced significant growth. That said, 5 of the programs we examined 

have at some point relied on both kinds of funding, and advocates indicated they have been able 

to convince legislators to use general revenues to offset declining funding or scale up programs if a dedicated funding source becomes insufficient. Thus, although stakeholders attempting to create a 

new program should think creatively about funding structures, sustaining and growing programs 

depends largely on building a strong base of political support for the program’s goals. 

Track and Share Meaningful Outcomes

Most of the programs we analyzed track basic output measures, like how many households they 

serve and for how long. More outcome monitoring and formal evaluations are needed, however, to 

determine how the programs affect longer-term housing outcomes and impact other systems, such 

as by reducing health spending, improving health outcomes, reducing the use of emergency home-

less shelters, improving recovery from substance abuse, and reducing recidivism among people 

involved in the criminal justice system. Data in these areas would bolster arguments for creating 

or expanding programs while identifying areas needing improvement. Philanthropic organizations 

could invest in building government and provider capacity to measure client outcomes and imple-

ment new initiatives when outcome goals are not met. 

Additional Considerations 

States, localities, and other stakeholders should consider other issues in designing rental assistance. 

For instance, a growing body of research finds that growing up in low-poverty neighborhoods 

with quality schools can significantly improve low-income children’s educational achievement 

and later economic success (Chetty, Hendren, and Katz, 2016). Rental assistance can be designed 

11 Note that Illinois faced serious state budget challenges between 2015 and 2017 that may have affected its rental assistance program. See https://www.nytimes.com/2017/07/06/us/illinois-budget-shutdown-states-rauner.html.

Cityscape 63

Bailey, Bailey, and Rice

to expand families’ access to such neighborhoods. More broadly, programs should always protect 

tenants’ basic rights, including their rights under the Fair Housing Act. Other considerations can be critical for particular populations; for example, programs that connect people to services should track outcomes to ensure the services offered are sufficient to help participants stay stably housed, avoiding evictions or institutionalization. 

Conclusion

State and local rental assistance programs appear to benefit from enduring support from policy-

makers and advocates, particularly when the rental subsidy helps the government achieve health 

or other social welfare goals. The many lessons learned from existing programs could inform states or localities seeking to build new programs or improve existing ones, particularly during a time 

when funding for federal rental assistance has been stagnant. We also hope this research opens the door for more comprehensive and systematic study of state and local rental assistance. 

Appendix A

Exhibit A-1, on the following pages, lists key features of state and local rental assistance programs based on information collected during the period 2016 to 2017.

64 The Housing-Health Connection

Innovative Approaches to Providing Rental Assistance:  

States and Localities Seek To Support Health and Human Services Goals

Units

1,344

5,473

2,500

1,400

360

Number of 

ype 

opriations

opriations

opriations

opriations

opriations and/or 

$2,300,000

$52,300,000

$18,000,000

Dedicated

$13,354,200

Dedicated

Dedicated Funds)

$658,000 Annual 

appr

(Appr

Annual appr

Annual appr

Funding Amount and T

and annual appr

oject 

oject 

Based

Both

oject based

oject based

Pr

Pr

Tenant based

Tenant or Pr

Tenant based; pr

based for setasides

ed 

ed basic 

None

None

Services

services

Requir

services

management, 

optional other 

None for general 

Requir

include permanent 

population; setasides 

supportive housing

mental health case 

participation in HCBS 

setasides 

ences allowed 

People with 

with disabilities

General; mental 

General (local 

efer

Mental illness

disabilities (on 

HCBS waiver)

Target Populations

Elderly and people 

illness and homeless 

pr

including homeless)

 a

ent Agency:

agency

es (1 of 4)

Curr

Housing or Health 

and Human Services

State housing agency

State housing agency

State housing agency

State human services 

State housing agency

ogram Featur

ogram NamePr

ogram

ogram

ogram

ogram

Assistance 

Pr

Pr

Support

Supportive Housing 

Bridge Subsidy 

Pr

Pr

Exhibit A-1

Key Pr

States

Connecticut

Elderly Rental 

Rental Assistance 

Illinois

Rental Housing 

Permanent 

Iowa

HCBS Rent Subsidy 

Cityscape 65

Bailey, Bailey, and Rice

Units

770

1,200

1,993

875

Number of 

89 (estimated 

over 5 years)

ype 

opriations 

opriations 

opriations

opriations 

opriations

opriations and/or 

$2,500,000 

$4,300,000

$3,184,000

$2,600,000

and dedicated

$10,276,000

and dedicated

dedicated and 

Dedicated Funds)

(Appr

one-time appr

annual appr

annual appr

annual appr

annual appr

Funding Amount and T

drawn down over 5 years

oject 

based

Based

Both

oject based

Tenant based

Tenant based

Pr

Tenant or Pr

Both; mostly tenant 

ogram

ed case 

Services

services

services 

ed behavioral 

mental health 

management

Must participate 

Requir

health services

in MFP home and 

community-based 

services pr

Some voluntary 

Optional community 

Requir

ong 

ence for 

People with 

efer

homeless

mental illness)

Mental illness 

homelessness

pr

Mental illness 

or substance 

dependence

Target Populations

disabilities (including 

Homeless or at risk of 

General with str

 a

ent Agency:

agency

agency

es (2 of 4)

Curr

State health and 

human services 

Housing or Health 

and Human Services

State health agency

State housing agency

State human services 

State housing agency

ogram

ogram Featur

ogram NamePr

ogram

ogram for 

Demonstration

Trust Fund

Pr

Adults with SMI 

and Substance 

Dependence

Exhibit A-1

Key Pr

Maryland

MFP Bridge Subsidy 

Minnesota

Bridges Pr

Transitional Housing 

Pr

Minnesota Housing 

Nebraska

Rental Assistance 

66 The Housing-Health Connection

Innovative Approaches to Providing Rental Assistance:  

States and Localities Seek To Support Health and Human Services Goals

b

Units

3,883

2,500

1,700

950

3,400

1,355

Number of 

ype 

opriations)

opriations

opriations

opriations

opriations

opriations

ecently shifted 

opriations and/or 

$41,300,000

$18,600,000

$20,000,000 

$52,000,000

$9,2000,000

$45,400,000

Dedicated Funds)

(Appr

annual appr

annual appr

annual appr

annual appr

om annual appr

biennium appr

Funding Amount and T

dedicated (r

fr

oject 

Based

based

Both

Both

Both

Both

Tenant based

Tenant or Pr

Both; mostly tenant 

ed monthly 

Services

permanent 

available

services

Some optional 

mental health 

services, including 

supportive housing

housing services

support services

Optional supportive 

Tenancy supports 

and peer support 

Some permanent 

supportive housing 

services available 

Requir

services home visit 

and optional tenancy 

Voluntary permanent 

supportive housing 

oblem 

der

der

ences for 

en set aside

disor

disor

homeless

institution

General with 

General with 

efer

Mental illness or 

substance use 

substance use 

pr

Mental illness, 

and homeless or 

Target Populations

childr

Mental illness and 

Mental illness and 

substance use, or 

leaving institution

homeless, disabilities, 

homeless or leaving 

other health pr

 a

Both

Both

Both

Both

ent Agency:

es (3 of 4)

Curr

Housing or Health 

and Human Services

State housing agency

State health agency

ogram Featur

ogram NamePr

ogram 

ogram

ogram

ogram

Assistance 

Pr

egon

egon Health 

Authority State 

Rental Assistance

Supplement 

Pr

Pr

Supportive Housing 

Pr

Exhibit A-1

Key Pr

New Jersey

State Rental 

Supportive Housing

Or

Or

Cities

District of Columbia

Local Rent 

Home First Subsidy 

DC Permanent 

Cityscape 67

Bailey, Bailey, and Rice

d

e

Units

1,638

1,459

2,449

Number of 

y of the Local Rent 

ype 

opriations

opriations

opriations

opriations and/or 

$23,426,608

$12,359,887

$23,321,866

Dedicated Funds)

(Appr

annual appr

annual appr

annual appr

Funding Amount and T

oject 

ve people experiencing homelessness, which used to be managed 

Based

oject based

oject based

oject based

Pr

Pr

Pr

Tenant or Pr

some

Services

e services 

available

to manage programs that ser

car

onsible for managing the program rather than the agency in which the program was created. Some ortive housing and 540 units for the new targeted affordable housing subcategor

Supportive housing 

and onsite health 

Supportive housing 

services available for 

Supportive housing 

services available

.

.

condition

seniors)

Homeless

.org/

Homeless with 

Target Populations

serious physical or 

behavioral health 

Homeless (families, 

youth, single adults, 

 a

http://hsh.sfgov

c

vices. LOSP = Local Operating Subsidy Program. MFP = Money Follows the Person. SMI = serious mental illness.

. See 

ent Agency:

agency

agency

Specialized 

Specialized 

homelessness 

homelessness 

es (4 of 4)

Curr

and specialized 

Housing or Health 

homeless agency)

and Human Services

Both (housing agency 

vices agency

ect 

ogram Featur

ogram NamePr

ogram

Access to Housing

Housing First 

Pr

Exhibit A-1

Key Pr

San Francisco

San Francisco Dir

San Francisco LOSP

San Francisco 

HCBS = Home and Community Based Ser

 “Current Agency” refers to the state or local agency or agencies that are currently resp

 This total includes about 1,600 project-based units that overlap significantly with supp

 San Francisco recently created a Department of Homelessness and Supportive Housing 

 Some overlap with Direct Access to Housing and other supportive housing exists.

 Some overlap with LOSP exists. 

a

programs were created in one agency but later transferred to another agency b

Supplement Program. c

by the health and human ser d

e

68 The Housing-Health Connection

Innovative Approaches to Providing Rental Assistance:  

States and Localities Seek To Support Health and Human Services Goals

Appendix B: Key Survey Questions

1.  How many households are served by the program?

2.  How many households were served when the program began?

3.  Who is eligible for the program? 

4.  Are there preferences or set-asides within the program for certain groups?

5.  Do tenants have to be on a federal rental assistance waitlist to receive assistance through this program?

6.  How much do tenants pay toward rent?

7.  Is there a maximum benefit tenants can receive?

8.  Is there a time limit? If so how long?

9.  Is the program project-based or tenant-based?

10.  If you chose “other,” how would you describe the program? 

11.  How much funding does the program receive annually?

12.  What is the funding source for the program? 

13.  What agencies administer the program on the ground? 

14.  Does the program track any outcomes? Please list any outcomes you track. 

15.  Are tenants offered any services through the program? Please indicate whether these services 

are required.

Acknowledgments 

Grant funding from The Kresge Foundation supported this research. The authors also thank Ehren 

Dohler and Anna Swanson for their contributions to this research, and the officials and advocates 

who took the time to complete our survey and speak with us. 

Authors

Anna Bailey is a policy analyst for the Center on Budget and Policy Priorities’ health integration project.

Peggy Bailey is the director of health integration for the Center on Budget and Policy Priorities.

Douglas Rice is a senior policy analyst for the Center on Budget and Policy Priorities’ housing team. 

Cityscape 69

Bailey, Bailey, and Rice

References

Annie E. Casey Foundation. 2014. “From Foster Home to Homeless: Strategies To Prevent 

Homelessness for Youth Transitioning From Foster Care.” http://www.aecf.org/resources/from-

foster-home-to-homeless/.

Bergquist, Rachel, Emily Cooper, Kevin Martone, and Melany Mondello. 2014. “State Funded 

Housing Assistance Programs.” Technical Assistance Collaborative. http://www.tacinc.org/me-

dia/43566/State Funded Housing Assistance Report.pdf.

Bodurtha, Peter, Renee Van Siclen, Erik Erickson, Mark Legler, Lisa Thornquist, Kate Diaz Vickery, Tyler Winkelman, Courtney Hougham, and Ross Owen. 2017. “Cross-Sector Service Use and Costs 

Among Medicaid Expansion Enrollees in Minnesota’s Hennepin County.” Center for Health Care 

Strategies. http://www.chcs.org/media/Hennepin-Research-Brief_103017.pdf.

Braveman, Paula, Mercedes Dekker, Susan Egerter, Tabashir Sadegh-Nobari, and Craig Pollack. 

2011. “Housing and Health.” Robert Wood Johnson Foundation. https://www.rwjf.org/content/

dam/farm/reports/issue_briefs/2011/rwjf70451.

Center on Budget and Policy Priorities (CBPP). 2017a. “Policy Basics: Federal Rental Assistance.” 

https://www.cbpp.org/research/housing/policy-basics-federal-rental-assistance.

———. 2017b. “Three Out of Four Low-Income At-Risk Renters Do Not Receive Federal Rental 

Assistance.” https://www.cbpp.org/three-out-of-four-low-income-at-risk-renters-do-not-receive-

federal-rental-assistance.

Chetty, Raj, Nathaniel Hendren, and Lawrence Katz. 2016. “The Effects of Exposure to Better 

Neighborhoods on Children: New Evidence From the Moving to Opportunity Project,”  American 

Economic Review 106 (4): 855–902. https://scholar.harvard.edu/hendren/publications/effects-

exposure-better-neighborhoods-children-new-evidence-moving-opportunity.

Dohler, Ehren, Peggy Bailey, Douglas Rice, and Hannah Katch. 2016. “Supportive Housing Helps 

Vulnerable People Live and Thrive in the Community.” Center on Budget and Policy Priorities. 

https://www.cbpp.org/research/housing/supportive-housing-helps-vulnerable-people-live-and-

thrive-in-the-community.

Hostetter, Martha, and Sarah Klein. 2014. “In Focus: Using Housing To Improve Health and 

Reduce the Costs of Caring for the Homeless.” Commonwealth Fund. http://www.commonwealth-

fund.org/publications/newsletters/quality-matters/2014/october-november/in-focus. 

U.S. Department of Housing and Urban Development (HUD). 2017. “Study of PHAs’ Efforts To Serve 

People Experiencing Homelessness.” https://www.huduser.gov/portal/pha_homelessness.html.

Irvin, Carol V., Alex Bohl, Kate Stewart, Susan R. Williams, Allison Steiner, Noelle Denny-Brown, 

Andrea Wysocki, Rebecca Coughlin, Jason Smoot, and Victoria Peebles. 2017. “Money Follows the 

Person 2015 Annual Evaluation Report.” Mathematica Policy Research. https://www.mathematica-

mpr.com/our-publications-and-findings/publications/money-follows-the-person-2015-annual-

evaluation-report.

70 The Housing-Health Connection

Innovative Approaches to Providing Rental Assistance:  

States and Localities Seek To Support Health and Human Services Goals

Mazzara, Alicia. 2017. “Housing Vouchers Work: Huge Demand, Insufficient Funding for Housing 

Vouchers Means Long Waits.” Center on Budget and Policy Priorities. https://www.cbpp.org/blog/

housing-vouchers-work-huge-demand-insufficient-funding-for-housing-vouchers-means-long-

waits.

Metraux, Stephen, Caterina G. Roman, and Richard S. Cho. 2007. “Incarceration and Homeless-

ness.” 2007 National Symposium on Homelessness Research. huduser.gov/publications/pdf/p9.pdf.

National Health Care for the Homeless Council (NHCHC). 2013. “Incarceration & Homelessness: 

A Revolving Door of Risk,”  In Focus: A Quarterly Research Review of the National HCH Council 2 (2): 

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National Low Income Housing Coalition (NLIHC). 2017. “State and City Funded Rental Housing 

Programs.” http://nlihc.org/RHP.

Public and Affordable Housing Research Corporation (PAHRC). 2017. “Housing Agency Wait-

ing Lists and Demand for Rental Assistance.” https://www.housingcenter.com/wp-content/

uploads/2017/11/waiting-list-spotlight-media-kit.pdf.

Cityscape 71

72 The Housing-Health Connection

Embedding Health in Affordable 

Housing Development: Results of 

the Health Action Plan Pilot Project

Stephany De Scisciolo

Krista Egger

Mary Ayala

Enterprise Community Partners

Abstract

•  Objectives: Although affordable housing holds great potential for improving the 

health of its residents, the optimal way to incorporate health into the affordable hous-

ing planning and design process remains unknown. Working with five community 

development corporations (CDCs), we performed a pilot study of their approach to 

developing Health Action Plans, a structured process that formalizes collaboration 

between CDCs and public health professionals. 

•  Methods: Five CDCs were selected through a competitive process to receive finan-

cial and technical assistance to develop Health Action Plans. The evaluation used 

a mixed-methods approach. Data were collected through monthly Community of 

Practice calls, structured interviews conducted twice during the project, and prepilot 

and postpilot surveys to assess the CDCs’ ability to implement the Health Action Plan 

framework in the future. 

•  Results: Four CDCs developed Health Action Plans specific to their projects. The 

plans varied in the health issues addressed and the health-promoting strategies con-

sidered. A fifth CDC developed generic guidelines. All CDCs gained a deeper under-

standing of how the built environment can impact health and found that engaging 

residents and understanding local health needs improved the development process. 

They were likely to engage public health professionals in the future and consider how 

their development decisions affect resident health. 

•  Conclusions: The Health Action Plan may be an important framework to guide CDCs 

to look at development as a mechanism to promote resident health outcomes. Work 

remains to be done before the creation of a Health Action Plan becomes routine, in-

cluding the incorporation of additional tools and resources that bolster cross-sector 

collaboration. 

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 73

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

De Scisciolo, Egger, and Ayala

Background 

Place matters. This simple statement reflects our growing understanding of how the social and 

physical environment in which people live can influence both individual and population health 

outcomes. Housing is one of the most important places we inhabit and has the potential to signifi-

cantly impact resident health in numerous ways—from site selection to the building materials used 

to operations and maintenance procedures. 

Architects, planners, and developers play roles in ensuring that the built environment is health 

promoting. Factors such as walkability, access to services, healthy food, transportation, and safety all translate to better health (Berke and Vernez-Moudon, 2014). Similarly, quality design and construction, coupled with regular building maintenance, can help to prevent illness and contribute to improved physical and mental wellbeing (Robert Wood Johnson Foundation, 2011). 

Enterprise Green Communities Criteria

Enterprise Community Partners (hereafter, Enterprise) launched its Green Communities Initiative in 2004. The central element of the initiative is the Green Communities Criteria (hereafter, the Criteria), which is the nation’s leading green building standard for affordable housing. The Criteria have been used to certify properties in 43 states, ensuring healthy design and building practices across the affordable housing field. Protecting human health by reducing greenhouse gas emissions and by 

promoting proven healthy housing design and operations practices has been an imperative of the 

Criteria since its inception. During its most recent update, Enterprise sought to amplify this emphasis on health, and the current version of the Criteria includes best practices in active design, health-related criteria inspired by the Health Impact Assessment (HIA) process and new standards for indoor air quality. More specifically, the 2015 update includes two process-based criteria that interweave the integrative design process from the green building sector with the HIA process from the public health sector. By so doing, the Criteria provide affordable housing developers an actionable path to considering and prioritizing resident health-promoting outcomes through design. One of these criteria is mandatory for all projects pursuing Enterprise Green Communities certification (Criterion 1.2a,  Resident Health and Well-Being: Design for Health) and one is optional (Criterion 1.2b,  Resident Health and Well-Being: Health Action Plans; Enterprise, 2015). Although both these criteria ask developers to consider resident health in their project designs, Criterion 1.2b requires a more rigorous association with a public health professional and community stakeholders, as well as more robust followup action. These criteria were developed through a partnership involving Enterprise, the Health Impact Project (a collaboration between the Robert Wood Johnson Foundation and The Pew Charitable Trusts), and the U.S. Green Building Council—all organizations poised to promote the comprehensive and 

systematic consideration of health in housing through green building certification programs.

The Health Action Plan Process

Although many affordable housing developers include health-promoting design features in their 

buildings, these design decisions are often made without regard to the specific health needs of a 

building’s current or future residents. Development of a Health Action Plan (through Criterion 

1.2b) calls for housing developers to collaborate with public health professionals to assess, identify, 74 The Housing-Health Connection

Embedding Health in Affordable Housing Development:  

Results of the Health Action Plan Pilot Project

implement, and monitor achievable actions to enhance the health-promoting features of their proj-

ect and to minimize features that could present health risks. The Health Action Plan framework 

identifies five Resident Health Campaigns that encompass many of the health issues that dispro-

portionately affect low-income communities—injury and accessibility, asthma and respiratory 

health, cardiovascular diseases, Type 2 diabetes and obesity, cancer and health outcomes related to toxin exposure, and mental health.

Based on a review of local public health data and input from residents and other community 

stakeholders, community development corporations (CDCs) and their public health partners create 

a Health Action Plan that focuses on one or more of the Resident Health Campaigns or identifies 

additional community concerns. Developers then work with their public health partners to design 

changes to the built environment that will address these concerns. Exhibit 1, taken from the 

Criteria, offers an example of a Health Action Plan.

Exhibit 1

Example of a Health Action Plan

Was This  If Selected,  

Key Health  

Rationale for  

Strategy   Indicate How 

Issue and  

Potential 

Examples of 

Selecting or  

Elected?  This Strategy 

Population  Intervention

Strategies

Rejecting This  

(Yes or 

Will Be  

Group

Example Strategy

No)

Implemented

High  

Eliminate  

Prioritize the 

Yes

Specification   High-impact strategy in terms 

incidence of  or reduce use  specification 

of linoleum   of addressing health issue; also 

childhood 

of potential  of hard surface 

for kitchens; 

a flooring choice that reduces 

asthma

asthmagens

flooring

cork flooring  

ongoing maintenance and 

for  

replacement costs. Given the 

bedrooms

disparities in asthma rates by 

race, ethnicity and income in our 

community, this strategy will also 

help to address health equity.

Above- 

Prioritize   Street infrastruc-

No

NA

Our project team does not have 

average 

features  

ture improve-

the capacity to affect local trans-

prevalence 

that  

ments to safely 

portation infrastructure.

of childhood 

promote 

accommodate 

obesity

physical 

users of all ages, 

activity

abilities and 

transportation 

modes

Above- 

Prioritize  

Playground

Yes

We will be 

This feature will provide a local, 

average 

features  

including a  safe space for the families living 

prevalence 

that  

100-square- in our development to play and 

of childhood 

promote 

foot play-

socialize. Otherwise, closest 

obesity

physical 

ground as   playground is 2 miles from proj-

activity

part of the  ect, not easily accessible. Given 

project.

the disparities in childhood 

obesity rates by race, ethnicity 

and income in our community, 

this strategy will also help to 

address health equity.

NA = not applicable.

Cityscape 75

De Scisciolo, Egger, and Ayala

Developing the Health Action Plan is only one step of the process. The accompanying Implementa-

tion and Monitoring Plan ensures that the strategies adopted during the design phase perform as 

expected and positively contribute to resident health. The Implementation and Monitoring Plan 

identifies design, operations, and health metrics for each strategy adopted in the Health Action Plan. 

The Health Action Plan Pilot Project

Enterprise and the Health Impact Project were interested in evaluating the ways in which CDCs 

implemented the Health Action Plan framework and the capacities needed to achieve a develop-

ment perspective anchored by promoting resident health outcomes. Funded by the Health Impact 

Project, Enterprise conducted a pilot project, along with a formative evaluation, involving five 

CDCs between July and December 2016. The purpose of the pilot was to observe and support 

the ways in which the affordable housing developers used public health data and forged the key 

partnerships necessary to create a Health Action Plan and a Monitoring and Implementation Plan. 

A secondary goal of the pilot was to build the organizational capacity of these developers to ensure their ability to implement the Health Action Plan framework in future projects. 

Participating organizations were selected through a competitive process, with a request for proposal issued in June 2016. Twelve organizations applied and were ranked based on the following factors.

•  Development project schedule.

•  Thoroughness and comprehensive nature of the proposal.

•  Demonstrated experience in developing affordable housing.

•  Commitment to addressing resident health outcomes through housing solutions.

The five organizations selected were Grant Housing and Economic Development Corporation 

(California), Gulf Coast Housing Partnership (Louisiana), LUCHA (Illinois), Mercy Housing South-

east (Georgia), and SKA Marin (New York). 

In addition to their geographic diversity, the development projects represented a mix of construc-

tion types (single-family, multifamily lowrise, and multifamily highrise) and resident populations (families and seniors). Projects were in various stages of the predevelopment process, with construction beginning after the end of the pilot. Each CDC received a $10,000 grant to facilitate their partnership with a local public health professional, technical assistance from national experts, and connection to a Community of Practice peer network. Participants were expected to benefit from 

deep technical support on a single housing project and apply the knowledge they gained going 

forward.

Methods

The formative evaluation took a mixed-methods approach to understand how implementing the 

Health Action Plan framework influenced decision making and the kinds of assistance necessary to 

scale adoption of the framework across the industry. More specifically, the evaluation was designed to answer the following questions.

76 The Housing-Health Connection

Embedding Health in Affordable Housing Development:  

Results of the Health Action Plan Pilot Project

•  How did the developers identify and use local health data and resources? What evidence 

informed the creation of their Health Action Plans?

•  To what extent did the developers partner with local health providers and public health 

professionals?

•  Did the developers engage community stakeholders? Who were the most relevant voices at the 

table?

•  How did the developers staff this activity and delegate roles and responsibilities?

•  What amount of resources did the developers use?

•  What factors in the process influenced decision making?

The pilot project also sought to increase the developers’ capacity in four key areas: (1) organizational commitment to embedding health in future site design and operations decisions;  

(2) development of partnerships with public health professionals; (3) data collection, analysis, and interpretation; and (4) stakeholder engagement around resident and community health issues.

Data were collected throughout the pilot using both formal and informal methods. Monthly Commu-

nity of Practice calls were a rich source of information on the challenges the developers faced in locating and contracting with a public health professional, engaging with community stakeholders, and 

interpreting the requirements of the Health Action Plan process. The open-ended agendas for these 

calls enabled each group to share their successes and challenges and to learn from the experiences of their colleagues. The calls, which were recorded, were well attended by the pilot participants, and staff from Enterprise and the Health Impact Project joined to provide technical assistance. 

The monthly calls enabled the evaluators to stay abreast of changes in the development process 

and gauge their impact on completing the steps of the Health Action Plan. For example, one 

developer had to delay their search for a public health professional to secure project funding when a planned-for source fell through. Real-world circumstances such as this offered important insights into how developers might implement the Health Action Plan process while responding to the 

inherent uncertainties of affordable housing construction.

Individual structured interviews were also held with each CDC at the midpoint and end of the 

pilot project. Each hour-long interview involved all members of the developer team, as well as the primary evaluator and Enterprise project director. A set of common questions were used, with time 

allowed at the end of each interview for unstructured discussion. Interview notes were transcribed and coded to discern cross-cutting themes.

To determine the influence of the pilot project on developer capacity, a brief assessment survey was administered via an online survey at project start and, with some modifications, re-administered at project end. The evaluator created the survey and other members of the Enterprise team and staff 

from the Health Impact Project reviewed it. A convenience sample was used to pilot test the survey instrument. A link to the survey was sent via email to each organization. This procedure was used 

for both the prepilot and postpilot surveys. 

Cityscape 77

De Scisciolo, Egger, and Ayala

One person from each CDC completed the survey on behalf of their organization; in all but one case, the same person completed both surveys. Using the Organizational Capacity Matrix (exhibit 2), which was developed for the purposes of this evaluation as a guide, the prepilot and postpilot survey responses were evaluated to determine the organizations’ movement along the awareness continuum 

for each dimension. Data on resource expenditures, including staff and consultant time spent on 

the Health Action Plan process, were also collected.

Exhibit 2

Organizational Capacity Matrix

Organizational  

Data Collection and 

Stakeholder  

Outcome

Partnerships

Capacity

Analysis

Engagement

Consistently 

Deep organiza-

Ongoing partner-

Consistently uses  Consistently engages 

apply

tional commitment 

ship with public 

health-related data 

with stakeholders 

to health outcomes  health professionals.  to inform decision  around health issues.

as evidenced by a 

Partnership resulted 

making.

formal policy and 

in health-enhancing 

dedicated staff and 

design features or 

other resources at 

programming in at 

organization level.

least one project.

Act

Demonstrated orga-

Partnered with a 

Has used health- 

Engages with stake-

nizational commit-

public health profes- related data but does  holders on issues, 

ment to health out- sional on at least one  not do so consis-

but lacks experience 

comes as evidenced  project. Partnership 

tently.

engaging around 

by dedicated staff or  resulted in health-

health issues.

project resources at 

enhancing design 

project level.

features or program-

ming in at least one 

project.

Intention or 

Interested in  

Interested in  

Interested in using  Interested in engag-

willingness to 

increasing health 

partnering with a 

health-related data 

ing stakeholders 

act

focus in future.

public health  

but is unsure how   around health issues.

professional.

to do so.

Awareness

Somewhat aware of 

Does not intend to 

Identified barriers to 

Does not regularly 

connection between  partner with a public  using health-related  engage stakeholders.

health and housing.

health professional.

data.

Results

The experiences of the five participating CDCs provided a candid look at the process by which 

affordable housing developers implement the Health Action Plan framework. Each organization 

approached the key implementation steps differently, and all but one successfully created a 

Health Action Plan as outlined in the Criteria. The Implementation and Monitoring Plan proved 

problematic for several groups, in part because of their lack of experience in monitoring resident outcomes related to building design. Despite these challenges, four of the five developers either 

had fully developed monitoring plans or had the basics of a plan that could be solidified by the 

time final building design decisions were made. The products these four developers created would 

qualify for the full points available in the Green Communities certification process. One developer 78 The Housing-Health Connection

Embedding Health in Affordable Housing Development:  

Results of the Health Action Plan Pilot Project

began the Health Action Plan process after final construction drawings were complete and missed 

the opportunity to create a project-specific Health Action Plan. However, their participation in the pilot resulted in design guidelines that will be valuable in the future. 

Enterprise and the Health Impact Project were also interested in whether participating in the pilot had increased the CDCs’ ability to implement the Health Action Plan framework in the future. As 

a result of participating in the pilot, each group saw an increase in their organizational capacity in at least one dimension (exhibit 2). Every group gained a deeper appreciation of how the built 

environment can influence resident health outcomes. Most gained the ability to partner with a 

health professional in the future, and several expected to continue the partnership they had formed during the pilot. The types of partners they selected varied and included healthcare providers, 

nonprofit public health institutes, private consultants, and in one case, a public-health-oriented architect. 

Those groups that engaged community members found the experience to be a critical complement 

to published health data and believed that they had gained the skills necessary to conduct commu-

nity meetings in the future. One developer conducted key stakeholder interviews, while another 

held two community focus groups. A third sought to leverage a local health fair as a way to engage the community, although this plan did not happen within the timeframe of the pilot project.

The one dimension along which most groups did not substantially increase their capacity was data 

collection and analysis. The developers relied on their public health partners for data collection and analysis, and most expressed that they would continue to rely on these partners for data 

analysis. Data used in the development of the Health Action Plans were drawn from publicly avail-

able, secondary data sources, including the American Community Survey, local health atlases, and 

neighborhood plans. Some groups committed to conducting an annual resident survey as part of 

their Implementation and Monitoring Plans but expected to engage a third party to analyze their 

results. 

Exhibits 3 and 4 provide the results of the evaluation. Exhibit 3 offers information about each 

CDC, including details about their projects, the public health professionals they partnered with, 

the data sources used, and the ways in which they engaged key stakeholders. Exhibit 4 summarizes 

the findings in terms of the evaluation questions posed at the start of the pilot.

In addition to these results, several key lessons emerged that have broader implications for scaling this work. For each of these lessons, a participant quote provides additional context and meaning. 

•  Participating in the pilot broadened the developers’ understanding of the relationships 

between health and housing.  “As developers, we are not service providers, so we don’t think about what kind of a room could be best for delivering services or how a space can welcome 

people. Before this pilot, we didn’t realize that place and design can intersect to increase the 

health and wellness of our residents.”

•  Community engagement is an essential part of the process and revealed unexpected 

insights.  “The community members provided a lot of input on mental health issues, 

perspectives on safety issues, and knowledge of who in the community was providing health 

assets. The residents are the experts on what they’re experiencing.” 

Cityscape 79

De Scisciolo, Egger, and Ayala

Exhibit 3

Findings by Participating Community Development Corporations

Development 

Community  

Developer

Public Health Partner

Data Sources

Type

Engagement

Grant HED  

76 units 

Raimi + Associates

LA City Health Atlas; 

Community  

(Los Angeles, CA)

 (multifamily);

Plan for a Healthy Los 

health fair 

13,000 square 

Angeles; California 

(planned to  

feet commercial 

Health Interview 

occur after pilot)

retail; supportive 

Survey

services for  

formerly  

incarcerated 

residents

Gulf Coast Housing 

40 units  

Andrew Ryan, MPH

Local hospitals and 

Neighborhood 

Partnership 

(single-family  

state health officials; 

stakeholder 

(New Orleans, LA)

detached homes)

local crime data

interviews

LUCHA 

40 units  

Illinois Public  

U.S. Census;  

Community 

(Chicago, IL)

(single-family  

Health Institute

American Community   focus groups

detached homes)

Survey; Chicago 

Health Atlas; Illinois 

Hospital Association 

COMP data; Illinois 

Department of Public 

Health, Mortality Files 

Mercy Housing 

77 units  

Matt Finn, American 

Annual Resident 

Resident and 

Southeast 

(housing for  

Institute of Architects, 

Survey

other  

(Atlanta, GA)

seniors) 

National Council  

stakeholder 

paired with 

of Architectural 

interviews

40,000-square-

Registration Boards, 

foot healthcare 

Leadership in Energy  

facility

& Environmental 

Design Accredited 

Professional

Cognitive Design, LLC

SKA Marin 

152 units  

New York City Health 

2015 East Harlem  

Community 

(New York, NY)

(multifamily)

+ Hospitals  

Health Profile

leaders, local 

Corporation;  

elected officials, 

New York  

local established 

Academy of  

institutions, 

Medicine

public health 

professionals 

(completed prior 

to pilot)

•  Partnering with a public health professional is important, but it takes time to find the 

right fit.  “Our organization realized that we needed somebody who knew more about health, so we reached out to the NY Academy of Medicine who had just released a report about the health 

of the community in East Harlem. They were happy to work with us.” 

80 The Housing-Health Connection

Embedding Health in Affordable Housing Development:  

Results of the Health Action Plan Pilot Project

Exhibit 4

Findings by Evaluation Questions

Evaluation Question

Result

How did developers identify and use existing 

In all but one case, the public health professionals 

local health data and resources? What evidence 

reviewed and interpreted public health data for the 

informed the creation of their Health Action 

developers. Two developers sought community 

Plans?

feedback to ground truth these data and to 

prioritize the health issues identified.

Did the developers partner with local health 

Each developer team did partner with a public health 

providers and public health professionals? 

professional; in one case, this individual was a 

health-focused architect. The process of locating a 

public health professional and negotiating a scope 

of work took more time than anticipated.

Did the developers engage community 

Three of the five developer teams met with 

stakeholders? Who were the most relevant 

community stakeholders, including residents, 

voices at the table?

service providers, and health experts. The others 

had either engaged stakeholders prior to the pilot 

project or had a scheduled event delayed beyond 

the timeframe of the pilot.

How did the developers staff this activity and 

The lead staff person for the pilot project varied 

delegate the roles and responsibilities required?

among the developer teams and included an 

executive director, project manager, and regional 

director. The responsibility for drafting the Health 

Action Plan and Implementation and Monitoring 

Plans fell to the public health professional engaged 

by the community development corporation.

What amount of resources did developers use?

Total costs incurred by the organizations ranged 

from $10,000 to $15,000. These costs were mainly 

attributed to staff time and partnering with the 

public health professional. The costs associated 

with partnering with a public health professional 

ranged from $5,100 to $9,500.

What factors in the process most influenced 

Two factors were most important in influencing 

decision making?

decision making—working with public health 

professionals and engaging community 

stakeholders.

•  Health data can inform design decisions and should be considered early in the process.  

“We will use health data to make design decisions when planning for a particular type of 

community, such as senior housing or permanent supporting housing.” 

•  Development of the Monitoring and Implementation Plan and the need for continued 

monitoring posed the greatest challenge for the pilot participants.  “How can we give 

ourselves some indication down the road of how this went? Thinking about design impacts 

has an implication for monitoring—how can we get a sense that what we did had a meaningful 

impact, particularly as we think about what to include in future projects.” 

•  To ensure success, implementing the criterion should be a seamless addition to the typical 

development process, rather than another requirement.  “Success is tied to the development 

cycle of particular projects. This effort must be institutionalized as part of the organization’s 

mission so that this process is part of all projects from the beginning.” 

Cityscape 81

De Scisciolo, Egger, and Ayala

Limitations

This formative evaluation had several limitations. The projects selected for the pilot were all new construction projects; thus, we were unable to observe how the Health Action Plan framework 

might be implemented by developers rehabilitating an existing property. The timeframe for the 

pilot did not reflect the affordable housing development cycle. The pilot project ran for 5 months; the predevelopment stage of an affordable housing project can run much longer and is subject to 

changes in financing, staffing, and so on. This artificially compressed timeframe did not enable 

the CDCs to implement the framework fully. Lastly, the $10,000 grant provided each organization 

the funds necessary to engage a public health professional, which alleviated the burden on the 

developer of finding the necessary resources within their project budget. 

Qualitative data collection and analysis are subject to additional limitations. The small sample size limits the generalizability of the evaluation results. Although extensive notes were taken at each individual interview, these sessions were not recorded, and important observations could have 

been omitted from the analysis. The evaluator chose to manually code interview notes and, by 

doing so, may have introduced bias in assessing common themes and lessons learned. In addition, 

the unstructured agenda of the Community of Practice calls could have prevented key issues from 

being surfaced. To address these threats to validity, both quantitative and qualitative data collection methods and sources were used to increase confidence in the evaluation results. 

Discussion

Four of the five participating CDCs successfully completed the pilot and developed a Health Action 

Plan (online appendix 1, available at huduser.gov/portal/periodicals/cityscpe/vol20num2/appendix1.

html). In lieu of creating a Health Action Plan, one CDC developed common space design guidelines, which they plan to implement in all future housing projects for seniors. Each plan was uniquely tailored to the specific needs of the local community and varied in the number of health 

issues addressed and health-promoting strategies considered. Ultimately, the strategies selected 

depended on the project resources available. In one case, the developer noted that creating the 

Health Action Plan had allowed them to seek additional funding from local philanthropy to build a 

rock-climbing wall requested by the youth living within their affordable housing communities. 

Considering public health data was an important part of the process, ground-truthing that data 

with local stakeholders proved equally important. Community members may prioritize health 

issues differently than the data would suggest. In Louisiana, the CDC found that the greatest 

health concern to the community was the potential for children to drown in a canal that was 

near the property. That concern was not captured by any publicly available data source and was 

only uncovered by talking with people living near the site. In response, the developer proposed a 

natural barrier consistent with the site design to make it difficult for children to access the water. In Chicago, community members were most concerned about depression and anxiety and identified 

a lack of fellowship with neighbors as a contributing cause. This finding led the developer to consider strategies to increase opportunities for neighbors to interact, including enhancing common 

areas and installing bulletin boards to advertise community events.

82 The Housing-Health Connection

Embedding Health in Affordable Housing Development:  

Results of the Health Action Plan Pilot Project

A review of public health data may also raise issues about which residents are unaware or uncon-

cerned. For example, the Louisiana CDC found that local data indicated high crime rates near 

their project site. The developer team expected to hear from the community their concerns about 

safety. Instead, community members did not identify crime as a problem in the area. This apparent 

disconnect between the data and resident perception led the developer team to interview the local 

police chief who better explained the data and provided additional context. Working with the 

police department, the developer chose to install security cameras and lights to enhance resident 

safety.

As these examples indicate, an essential component of the Health Action Plan framework is the 

need to supplement public health data with the lived experiences of community stakeholders. 

Doing so provides a deeper and more holistic understanding of the needs and priorities of the 

community. This knowledge will enable a developer to select those health-promoting strategies that will lead to the greatest gains in resident health outcomes. 

Conclusions 

As part of their final interviews, each CDC was asked how the Health Action Plan framework 

could be scaled across the industry. Responses fell into three broad categories: (1) create additional tools (for example, list of public health professionals or a reference library of approved Health 

Action Plans and Monitoring and Implementation Plans); (2) consider changes to the criterion to 

provide flexibility in creating and implementing Health Action Plans encompassing both the built 

environment and programs aimed at improving resident outcomes (for example, exercise classes, 

computer labs, and so on); and (3) drive systems change to embed health considerations in the 

regulatory framework governing affordable housing. Regarding this latter suggestion, one key 

means of changing industry practice is through the Qualified Allocation Plan (QAP) process, which 

awards low-income housing tax credits to development projects meeting specific selection criteria 

within each state. Since 1986, most of the affordable housing in this country has been developed 

and maintained using these credits, and 22 states currently include the Criteria in their QAPs. To increase the competitiveness of their housing tax credit applications, developers are incentivized to follow the Criteria, which now includes the Health Action Plan framework. Consequently, QAPs 

are an important element of the Health Action Plan scaling strategy.

Enterprise continues to look for opportunities to partner in this work and has recently launched a project involving two CDCs in the Boston area that will implement the Health Action Plan framework within the context of a property retrofit. Additional tools and templates are being created to accelerate adoption of the Health Action Plan framework, including a list of public health professionals to assist developers in finding a suitable partner and a set of standardized health outcome metrics to relieve the need for developers to create their own. This latter effort responds directly to the challenges that the CDCs experienced in developing the Implementation and Monitoring Plan. 

Planning for the next version of the Criteria (to be issued in 2019) will consider additional strategies to expand adoption. Although implementing the Health Action Plan framework is relatively 

inexpensive (especially when considered relative to an overall project budget), developers often 

operate on razor-thin margins, and each additional cost must have demonstrated value. 

Cityscape 83

De Scisciolo, Egger, and Ayala

The Health Action Plan Pilot Project was successful in demonstrating the ability of CDCs to 

approach development in ways that promote resident health outcomes. The need for widespread 

adoption of health as a design consideration in affordable housing was reiterated through the 

findings of this pilot. Creating a Health Action Plan provides developers with a keen understanding of the health needs of their residents and enables them to address those needs through thoughtful 

and intentional design and development practices. Improving resident health outcomes through 

the built environment is a relatively new priority for the affordable housing industry. The Criteria, and particularly the Health Action Plan framework, offers developers the tools necessary to achieve this goal.

Acknowledgments 

This work was supported by the Health Impact Project, a collaboration of the Robert Wood John-

son Foundation and The Pew Charitable Trusts (grant number 00029331). The views expressed 

are those of the authors and do not necessarily reflect the views of the Health Impact Project, The Pew Charitable Trusts, or the Robert Wood Johnson Foundation.

Authors 

Stephany De Scisciolo is Senior Director of Knowledge, Impact and Strategy at Enterprise Com-

munity Partners.

Krista Egger is Senior Director of National Initiatives at Enterprise Community Partners.

Mary Ayala is a program officer in National Initiatives at Enterprise Community Partners.

References

Berke, Ethan, and Anne Vernez-Moudon. 2014. “A Framework To Support Health-Enhancing Be-

havior Through Environmental Policy and Health Research,”  Journal of Epidemiology and Community Health 68 (6): 586–590.

Enterprise Green Communities. 2015.  2015 Enterprise Green Communities Criteria. New York. 

Robert Wood Johnson Foundation. 2011. “Housing and Health.” Part 7 of 12,  Exploring the Social Determinants of Health. Princeton, NJ.

84 The Housing-Health Connection

Connecting Fragmented 

Systems: Public Housing 

Authority Partnerships  

With the Health Sector

Stephen Lucas

Council of Large Public Housing Authorities

Abstract

•  Objectives: The primary objective was to learn about the types of health partnerships 

and priorities large public housing authorities (PHAs) have developed to improve 

resident and community health. 

•  Methods: The Council for Large Public Housing Authorities (CLPHA) developed 

a survey that catalogued PHAs’ partnerships with the health sector to gain insight 

into health-related initiatives for residents. CLPHA conducted 15 indepth interviews 

to develop a survey instrument that was administered online in 2017. Participants 

included 39 PHAs (57-percent response rate) that collectively serve 24 percent of the 

3.5 million U.S. Department of Housing and Urban Development-assisted public 

housing and housing choice voucher households nationally (n = 847,908). 

•  Results: Large PHAs report high engagement with public health entities and 

community-based social service providers. Respondents also report working with 

healthcare service providers, including behavioral health providers and federally 

qualified health centers. The most common health-related activities in which PHAs 

are engaged include healthcare service coordination (87 percent) and improving 

healthy community resources (67 percent). Perceived barriers to establishing health-

housing partnerships and health-related programming or alignment include concerns 

about privacy or liability and lack of resources or capacity. 

•  Conclusions: Large PHAs’ level of engagement with the health sector vary widely by 

agency as does the depth and breadth of established health partnerships. 

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 85

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

Lucas

Background

Stakeholders across sectors serving low-income Americans increasingly recognize that breaking 

down silos can produce more positive life outcomes and promote effective service delivery. 

Specifically, intersections between housing and health have recently gained attention as the hous-

ing sector has embraced “health in all policies,” and the health sector has increasingly sought to address social determinants of health like housing (Bostic et al., 2012; HHS, 2010; HUD, 2014). 

Underscoring the impetus for these initiatives is the high medical need of the population served by housing assistance programs. National estimates suggest that, although low-income adults receiving U.S. Department of Housing and Urban Development (HUD) rental assistance are far more 

likely to have health insurance coverage and report higher rates of healthcare service utilization compared with those who do not receive housing assistance, they do not necessarily have better 

health outcomes and often report lower health status (Fenelon et al., 2017; Helms, Sperling, and 

Steffen, 2017; Simon et al., 2017). 

The extent to which existing systems-level partnerships and alignment efforts encourage col-

laboration between public housing and healthcare institutions that serve this population remains 

largely unknown. A survey by the Council of Large Public Housing Authorities (CLPHA) sought to 

learn about the prevalence and types of health partnerships that have been developed to improve 

resident and community health, including with which health entities public housing authorities 

(PHAs) most often partner. Secondarily, CLPHA sought to understand what health-related priori-

ties PHAs set, either alone or in concert with partners, and any target subpopulations of public 

housing residents.

The study focused on PHAs as they act as the chief provider of housing to low-income Americans 

at the state and local level. In this context, PHAs may play a critical role in multisector solutions to address complex challenges associated with poverty and health for millions of low-income Americans. CLPHA focuses on “large” PHAs as defined by HUD as managing 1,250 units or more. As of 

March 2018, CLPHA’s large PHA membership manages 40 percent of the nation’s public housing 

program, administer 26 percent of the Housing Choice Voucher (HCV) program, and operate a 

wide array of other housing programs. Large PHAs like these may have greater resources, scale, or 

both to effectively create partnerships around resident health. 

Methods

CLPHA developed the health and housing partnership survey, spanning a wide range of topics, 

primarily through key informant interviews with public housing authorities. 

Overview

This project represents a survey of members of CLPHA that was administered from August 2017 to 

November 2017. CLPHA is “a national non-profit organization that works to preserve and improve 

public and affordable housing through advocacy, research, policy analysis, and public education” 

(CLPHA, 2018).

86 The Housing-Health Connection

Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector Survey Development

CLPHA developed a draft survey instrument designed to collect information on a wide range of 

PHA partnerships with the health sector, including type of common health partners, health priori-

ties, type of health-related initiatives, extent to which health partnerships were formalized, source or sources of funding for health programming, engagement with data sharing with health partners, 

and incorporation of resident health into strategic planning efforts. 

The survey instrument was modified based on key informant interviews with 15 PHAs conducted 

from April 2017 to July 2017. Each PHA executive director contacted for these interviews was 

asked to invite all staff (for example, directors or coordinators) knowledgeable about the agency’s current activities and future planning of health-related initiatives, and each call consisted of between one and five PHA staff members and one CLPHA staff member who acted as the interviewer. 

Interviews ranged in length from 30 to 90 minutes, and each agency was asked to share their orga-

nization’s health-related activities and priorities, with standard followup questions to elicit greater detail. Agencies described existing partnership activities, services, or referrals, examples of their strongest health partnerships, and short- and long-term goals. Some PHAs were asked to review 

items from the survey instrument for clarity and comprehensiveness. The final survey instrument 

included 21 core survey questions, including affirmative “choose all that apply” statements, about respondents’ health partnerships. Appendix A reproduces the survey questions. 

Survey Distribution

The survey was distributed online to all 68 CLPHA members. One email announcement of the 

survey was sent to PHA executive directors. Information about the survey was subsequently 

included in three editions of the CLPHA e-newsletter and followed up with individual emails to 

PHA executive directors from nonrespondent PHAs on two occasions, 1 month prior to closing the 

survey and 1 week prior to closing. PHA executives, senior-level staff, or both were encouraged to complete the survey on behalf of each agency, drawing on other staff or external partners as needed to accurately answer all survey questions. No incentives were offered to respondents to complete 

the survey. Duplicative survey responses from the same housing authority were clarified with fol-

lowup emails or phone calls.

Results

The survey elicited responses from 39 PHAs (57 percent of all CLPHA members) that collectively 

serve 24 percent (847,908) of the 3.5 million HUD-assisted public housing and HCV households 

nationally. The study sample includes PHAs from 20 different states and represents 80 percent of 

the 1.1 million public housing and HCV units managed by CLPHA members. When compared 

with nonrespondents, survey respondents represented larger portfolios on average, with the mean 

number of HUD-assisted, occupied units among respondents at 8,737 compared with 2,499 

among nonrespondents. Slightly more representation was from states in the West (33.3 percent 

of respondents versus 18.5 percent of nonrespondents) and slightly less from states in the South 

(28.2 versus 40.7 percent). For a more detailed comparison of survey respondents and nonrespon-

dents see exhibit 1. 

Cityscape 87

Lucas

Exhibit 1

Characteristics of Public Housing Authority Respondents

Respondents

Nonrespondents

(n = 39)

(n = 27)

Characteristic

Mean (Median)

Number of assisted, occupied units

8,737 (1,511)

2,499 (1,922)

Percent elderly

13.5 (11.9)

12.9 (12.1)

Percent disabled age 62 or less

15.1 (13.7)

14.4 (15.4)

Percent below 80% AMI

96.9 (98.4)

96.8 (98.6)

Number of children

15,443 (7,049)

8,210 (8,170)

Region

n (%)

Northeast

7 (18.0)

6 (22.2)

Midwest

7 (18.0)

4 (14.8)

South

11 (28.2)

11 (40.7)

West

13 (33.3)

5 (18.5)

U.S. Virgin Islands or Puerto Rico

1 (2.56)

1 (3.70)

AMI = Area Median Income.

Internal Planning, Goal-Setting, and Staff Responsibilities 

A key objective of the survey was to gain a sense of motivation by large PHAs to expand health-

related programming and systems alignment efforts and to better understand the nature of their 

goals. For example, in response to the following multiple-choice question, “In general, our housing authority would like to _____ our work at the intersection of health and housing,” 69 percent of 

respondents answered “expand,” 31 percent answered “maintain/improve,” and no respondents 

said “reduce.” Despite interest in expanding or maintaining current efforts, PHAs are often without resources dedicated to health initiatives, and they draw on a variety of funding sources to support their health programming and alignment efforts. Most respondents indicate that they appropriate 

internal PHA funds (62 percent) and resources provided by nonprofit partners (56 percent). 

One-third or fewer PHAs report drawing funding from other sources such as foundations, hospitals 

and other healthcare service providers, private-sector partners, and community development cor-

porations or organizations. One-half of Moving to Work (MTW) demonstration program agencies 

that responded (6 of 12) report using funding flexibility under the MTW program to fund health-

related programming and alignment efforts. Five agencies (13 percent) report using the Rental 

Assistance Demonstration (RAD) program to enhance the built environment to encourage healthier 

behaviors and improve accessibility. Fewer than 10 percent report using local and federal govern-

ment grants and social impact, or “pay for success,” bonds to help fund health-related initiatives. 

Dedicated Health Staff

Only 5 PHAs (13 percent) report having dedicated housing-health staff members. The staff most 

commonly involved with health-housing initiatives are resident services directors and staff (74 

percent), service coordinators (67 percent), Family Self-Sufficiency Program coordinators (51 

percent), property management personnel (41 percent), and executive leadership members (33 

percent). Fewer than one-fourth of respondents (21 percent) report having staff hired in temporary, grant-funded capacities related to health activities. 

88 The Housing-Health Connection

Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector Types of Services

PHAs’ current health-related initiatives and interests in future activities varied. Respondents were asked to indicate in which of 10 activities they were engaged and those that interest their institutions (exhibit 2). Most PHAs report working with residents around health priorities (79 percent), 

providing or contracting for supportive housing and other wraparound services (62 percent), 

implementing health interventions for seniors and people with disabilities (62 percent), facilitating onsite or visiting health service delivery (59 percent), and conducting strategic planning focused on setting health-related goals (54 percent). 

Exhibit 2

Current Public Housing Authority Health Activities and Interests in Future Activities

Engaging residents about health 

79% 

21% 

Seniors and people with disabilities 

62% 

38% 

Supportive housing 

62% 

28% 

Onsite or visiting health services 

59% 

38% 

Conducting health strategic planning 

54% 

44% 

Pursuing health investments in housing 

49% 

51% 

Colocation of health services 

46% 

49% 

Participating in research 

38% 

59% 

Health impact assessments 

28% 

64% 

Local and regional foundations 

28% 

72% 

0%  10%  20%  30%  40%  50%  60%  70%  80%  90%  100% 

Actively engaged 

Interested but not active 

Not interested 

Note: N = 39.

Health Impact Assessments

Despite high rates of engagement with residents and health-related strategic planning, only 28 

percent of PHAs report having conducted more formalized, comprehensive health impact assess-

ments (HIAs), which “[use] an array of data sources and analytic methods and [consider] input 

from stakeholders to determine the potential effects of a proposed policy, plan, program, or project on the health of a population and the distribution of those effects within the population” (NRC, 

2011: 5). PHAs are interested in these efforts; 64 percent say they are interested in conducting 

HIAs to address new projects’ impacts on resident health (exhibit 2). Other health activities PHAs are included in but not currently pursuing include raising funds from local or regional foundations to support health programs (72 percent), participating in health-housing research projects and 

interventions (59 percent), and securing investments in affordable housing from health sector 

partners (51 percent). Few PHAs indicate a lack of interest in any of the 10 activity options. 

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Priority Health Conditions

PHAs were asked whether or not they had internal programs, work with external health partners, 

or both, that specifically address 22 health condition or behavior categories spanning medical and behavioral health (exhibit 3). PHAs are most commonly engaged with substance use disorders 

(SUDs), with 72 percent of respondents indicating they work with SUDs and addiction broadly or 

alcohol dependence and tobacco use or addiction specifically. About two-thirds (64 percent) work 

on at least one specific medical health condition, such as diabetes (46 percent), heart disease (46 

percent), and asthma (38 percent), and nearly one-half of all PHAs (44 percent) report working on 

two or more of the conditions in this category. 

One-half of PHAs (51 percent) work on preventive health efforts, such as prenatal care (36 percent) and sexually transmitted infections and diseases (31 percent) and dental care (28 percent), with 

36 percent working on two or more. Slightly less than one-half (46 percent) report focusing on 

behavioral health conditions. A third of respondents (33 percent) have efforts directed at physical disabilities. An identical number of PHAs (33 percent) report focusing on general wellness (for 

example, stress reduction, physical activity, and nutritious food preparation and eating) rather than Exhibit 3

Health Conditions of Interest for PHAs

Health Conditions

n

%

Behavioral health conditionsa

18

46

Anxiety and stress

16

41

Bipolar Disorder

13

33

Depression

15

38

Post-traumatic stress disorder

12

31

Schizophrenia

10

26

Medical health conditionsb

25

64

Asthma

15

38

Chronic obstructive pulmonary disease

8

21

Diabetes

18

46

Dementia

9

23

Heart disease

18

46

Obesity

13

33

Physical disabilities

13

33

Preventive healthc

20

51

Dental care

11

28

Infant mortality

9

23

Prenatal care and pregnancy

14

36

Sexually transmitted infections and diseases

12

31

Vision care

10

26

Substance use disordersd

28

72

Addiction, general

22

56

Alcohol dependence

21

54

Substance use disorders

18

46

Tobacco use

25

64

Wellness, no specific condition

13

33

PHA = public housing authority. 

a Twelve PHAs (31 percent) have initiatives addressing two or more conditions in this category.

b Seventeen PHAs (44 percent) have initiatives addressing two or more conditions in this category.

c Fourteen PHAs (36 percent) have initiatives addressing two or more conditions in this category.

d Twenty PHAs (51 percent) have initiatives addressing two or more conditions in this category.

Note: N = 39.

90 The Housing-Health Connection

Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector or in addition to programming or services addressing specific conditions. It should be noted that  

8 of the 13 PHAs choosing “wellness” as an option in this category also indicate they also work 

on at least one specific health condition.

Subpopulations of Interest

Although PHAs may focus on certain priority health conditions and behaviors, they may also focus 

their programming and partnership efforts on specific segments of their resident population. The 

leading subpopulations of interest (exhibit 4) are older adults and elderly people (74 percent); infants, toddlers, children, and families (67 percent); adult individuals (59 percent); and people with disabilities (49 percent). A smaller proportion of PHAs report a focus on people connected to other systems, such as the criminal justice system or Medicare-Medicaid dual eligibles (41 percent); 

veterans (33 percent); people living with HIV/AIDS (28 percent); and people identifying as lesbian, gay, bisexual, or transgender (13 percent).

Exhibit 4

Resident Subpopulations Targeted With Public Housing Authority Initiatives

People who identify as LGBT (n=5) 

13% 

People living with HIV-AIDS (n=11) 

28% 

Veterans (n=13) 

33% 

Connected to other systems (n=16) 

41% 

People with disabilities (n=19) 

49% 

Adult individuals (n=23) 

59% 

Infants, children, adolescents, and families (n=26) 

67% 

Older adults and the elderly (n=29) 

74% 

0%  10%  20%  30%  40%  50%  60%  70%  80%  90%  100% 

HIV-AIDS = human immunodeficiency virus and acquired immune deficiency syndrome. LGBT = lesbian, gay, bisexual, or transgender.

General Direction of Health Initiatives

Most PHAs (87 percent) report supporting healthcare service coordination activities. Other com-

mon focuses of programming include increasing healthy community resources like community 

gardens, healthy retail options, and bike-sharing services (67 percent), improving the built 

environment (46 percent), and varied offerings dictated by funders or partners (36 percent). 

Slightly fewer than one-third of PHAs help provide preventive health for children and adolescents 

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(30 percent), offer harm reduction resources like prevention and treatment of sexually transmitted infections and diseases or drug use (30 percent), and support medication management support (20 

percent).

Location of Programming and Service Provision

When asked to identify the location of their programming, most PHAs indicate that their health 

programming focused on residents in specific buildings (72 percent). Slightly more than one-half 

of respondents (51 percent) report having initiatives that bring health services or education to 

residents’ doorsteps (including visiting services), and slightly less than one-half of respondents (44 

percent) take a community “hub”-based approach with institutions like schools, hospitals, com-

munity centers, libraries, and churches. Slightly less than one-fourth of respondents (23 percent) report having other forms of decentralized health programming (not focused on specific buildings) 

that serve voucher families.

Types of Health Partners

The survey asked PHAs to indicate which of 34 types of organizations they work with across 

public health, healthcare service providers, community-based health and social service providers, 

community resources and development, and advocacy/funding/research. PHAs most commonly 

work with community-based social and human service providers and public health entities, with 

85 percent of respondents (n = 33) working with at least one health partner in each of those cat-

egories (exhibit 5). PHAs report often working with multiple organizations within these categories: 56 percent work with three or more community-based social or human service providers such 

as Aging and Disability Resource Centers and Area Agencies on Aging (56 percent) and homeless 

continuums of care (51 percent), and 36 percent report working with three or more public health 

entities such as local and state health departments (64 percent and 62 percent respectively), 

violence prevention organizations (33 percent), and organizations serving veterans (33 percent). 

In addition to community-based service providers and public health, most PHAs also work with 

healthcare service providers (79 percent); advocacy, funding, and research entities (72 percent); 

and community resources and development organizations (54 percent). One-half of respondents 

(49 percent) work with three or more types of healthcare service providers. Some of the most 

common partners in this category include behavioral health providers (51 percent), fitness facilities and providers (41 percent), federally qualified health centers (38 percent), hospitals (33 percent), and dental providers (33 percent). Within the advocacy, funding, and research category, PHAs 

most commonly work with universities and research centers (46 percent) and advocacy organiza-

tions (44 percent). The most prevalent community resource or development partners for PHAs are 

parks and recreation (36 percent), community development corporations (31 percent), and law 

enforcement (31 percent).

92 The Housing-Health Connection

Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector Exhibit 5

Most Common Health Partners for PHAs

Health Partners

n

%

Community resources and developmenta

21

54

Affinity groups (for example, walking, running, weight loss, etc.)

7

18

Community development corporations

12

31

Environmental health

4

10

Law enforcement

12

31

Parks and recreation agencies and organizations

14

36

Urban planners

3

8

Healthcare service providersb

31

79

Behavioral health providers

20

51

Dental

13

33

Emergency departments

5

13

Family planning and sexual health providers

5

13

Federally Qualified Health Centers

15

38

Fitness providers/facilities

16

41

Hospitals

13

33

State Medicaid agencies

4

10

Medicaid Managed Care Organizations

6

15

Pharmacists

6

15

Public healthc

33

85

Local health departments

25

64

Nutrition organizations

24

62

Schools and school-based providers

8

21

State health departments

5

13

Veterans organizations 

13

33

Violence prevention organizations

13

33

Community-based human and social service providersd

33

85

Aging & Disability Resource Centers and Area Agencies on Aging

22

56

Assisted living

11

28

Child and adolescent health and welfare

13

33

Home health agencies

11

28

Homeless continuums of care

20

51

Social service providers and charities, general

23

59

Supportive housing services

14

36

Advocacy, funding & researche

28

72

Advocacy organizations

17

44

Data sharing entities

5

13

Foundations/funders

9

23

Think tanks

2

5

University/research centers

18

46

PHA = public housing authorities.

a Seven PHAs (18 percent) work with three or more types of organizations in this category.

b Nineteen PHAs (49 percent) work with three or more types of organizations in this category.

c Fourteen PHAs (36 percent) work with three or more types of organizations in this category.

d Twenty-two PHAs (56 percent) work with three or more types of organizations in this category.

e Six PHAs (15 percent) work with three or more types of organizations in this category.

Note: N = 39.

Information Sharing 

Most respondents have formal memoranda of understanding (MOUs) with health partners (69 

percent) and refer residents to healthcare service providers (64 percent), one-half of respondents report sharing data with health partners (49 percent), and slightly under one-fourth of PHAs share or exchange financial resources with health partners (23 percent) and share staffing resources (21 

percent). 

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To characterize the reciprocal nature of data sharing activities for health initiatives, respondents could indicate the following, choosing all that applied—11 percent characterize the data sharing as unidirectional (share data with partners or partners share data with the agency without reciprocation), 11 percent as multidirectional (share data with and receive data from health partners), 39 

percent as both or depending on the partner (engaged in both unidirectional and multidirectional 

data sharing), 6 percent as supported by centralized or third-party data system or repository, or 

17 percent as conducted without a formal process (data shared on informal basis). One-third of 

respondents (33 percent) report not currently sharing or receiving data from health partners.

Limitations

This survey received responses from 39 of the 125 large PHAs (managing 1,250 or more units) 

across the country. Although the 39 survey respondents represent 24 percent of all public hous-

ing and HCV households served nationally, this sample is self-selected (that is, those who chose 

to complete the survey) rather than a representative sample of large PHAs or PHAs of any size. 

Although the demographics of nonrespondents are comparable with those of respondents (see ex-

hibit 2), it is unknown whether the PHAs that did not complete the survey have a higher, similar, 

or lower level of engagement with health partners. 

Additionally, the survey only includes data from PHAs that were members of CLPHA as of August 

2017. The capacity, activity level, and interest in health-housing partnerships among smaller 

housing authorities (fewer than 1,250 available units) cannot necessarily be inferred based on the results of this survey. CLPHA is currently fielding a brief survey to more than 3,000 PHAs of various sizes, with the support of the Public and Affordable Housing Research Corporation (PAHRC).

Lastly, the survey did not provide detailed data about PHAs’ individual partnerships and programs, instead providing a more high-level summary snapshot. Additional surveys of and interviews with 

PHAs could explore the specific elements of successful, fully reciprocal partnerships, as well as 

barriers to cross-sector collaboration. 

Discussion

This survey of large PHAs presents findings relevant to the future of housing and health partner-

ships involving PHAs. First, all PHAs want to either expand or improve on their work at the 

intersection of housing and public health, with 92 percent already engaged in at least some health partnerships. Second, despite engaging in a variety of program and systems alignment efforts 

(targeting specific subpopulations and conditions), partnership opportunities between PHAs 

and certain health entities that have clear overlap in populations and needs served are seemingly 

underexplored. Third, PHAs must overcome challenges like limited funding and regulatory hurdles 

(for example, Health Insurance Portability and Accountability Act1 [HIPAA] privacy concerns) to 

expand internal capacity and deepen cross-sector engagement—challenges that health partners can 

1 Pub. L. 104–191, 110 Stat. 1936. August 21, 1996.

94 The Housing-Health Connection

Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector help PHAs resolve. The findings of this survey form an important baseline regarding PHA health 

system partnerships and underscore the desirability of PHAs as key housing partners for all facets of the health sector serving vulnerable populations.

Engaging Underutilized Health Entities

The survey suggests that PHAs are engaged with a wide and varying array of health partners across 

components of health systems, but certain types of health partners could partner with PHAs given 

demographic information about resident or patient populations and utilization patterns. For 

example, the following two emerged as key areas of opportunity.

1. Medicaid entities: Although 85 percent of PHAs work with at least one type of healthcare service provider, most PHAs do not work with large Medicaid partners, such as state Medicaid agencies 

(10 percent) or Medicaid managed care organizations, or MCOs (15 percent). An estimated 

75 percent of adults in HUD-assisted households have public health insurance (for example, 

Medicaid, Medicare), suggesting more opportunities for better alignment between PHAs and 

Medicaid systems exist (Fenelon et al., 2017; Helms, Sperling, and Steffen, 2017; Simon et al., 

2017). Many Medicaid MCOs have started to invest more resources in social determinants of 

health such as housing, with promising opportunities to serve residents’ often-complex health 

needs (Scally, Waxman, and Gourevitch, 2017; SAHF, 2017). 

2. Acute or emergency care: Fewer than one-half of PHAs work with key safety-net healthcare 

service providers like hospitals (33 percent) and emergency rooms (13 percent). Residents in 

HUD-assisted households use emergency services at a higher rate than the general population. 

According to a recent report, “Nearly one-fourth (22.9 percent) of HUD-assisted adults reported 

two or more ER visits during the past 12 months. This rate is greater than the 17.8 percent rate 

among unassisted low-income renters, and more than twice the 8.6 percent rate among adults 

in the general population” (HUD, 2017: 13). Many PHAs indicate having standard operating 

procedures for staff to connect residents with emergency health services (38 percent) and 

nonemergency health services (49 percent), but only 18 percent report systematically tracking 

these referrals. Interventions seeking to decrease nonurgent emergency department visits such as 

case management, care planning, information sharing, and diversion strategies have been shown 

to be effective at reducing unneeded visits (Moe et al., 2017; Raven et al., 2016).

Formalizing Partnerships

Most CLPHA members report establishing formal MOUs with health partners and referring resi-

dents to healthcare service providers. More than one-half of respondents report sharing data with 

health partners, although it is unclear from this survey how much of PHAs’ data-sharing informs 

decision making and the sophistication level of the data being collected, tracked, and analyzed. 

PHAs express concern about violating HIPAA requirements, with several PHAs identifying this as a 

barrier to confidently engage health partners around health data. The level of formality with which PHAs and their partners conduct business is less critical to evaluate than the degree to which PHAs and their partners engage in collaborative goal setting, decisionmaking, and accountability tracking. Formalized agreements can help facilitate such partnerships, as well as help PHAs manage 

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long-term partnerships based on stated, shared commitments. Separate from this survey, CLPHA 

works with membership to identify examples of MOUs with health sector partners that have ef-

fectively established or strengthened partnerships, or both.

Funding Partnerships and Capacity Building

Currently, large PHAs are primarily funding their health-related activities with limited internal 

resources and nonprofit partners, often adding health partnership activities to resident service 

leadership and staff without resources for dedicated staff. Smaller numbers of PHAs—around one-

fourth or fewer—fund these efforts with the support of foundations, healthcare service providers, 

private-sector partners, and community development corporations or organizations. Even fewer 

are leveraging alternative financing mechanisms for health-related services such as social impact 

bonds, despite large PHAs’ general familiarity with innovative, mixed-finance deals from affordable housing development. To increase capacity, PHAs could explore innovative financing mechanisms 

for health-related activities and prioritize financial arrangements and health partnerships that 

provide financial resources long-term and as needed.

Partnership Quality and Effectiveness

Although the survey provides greater insight into PHAs’ existing health partnerships—with whom 

they work and in what capacity—it did not collect enough data to assess how far-reaching these 

partnerships are or the quality of these partnerships more generally. To foster better collaboration between PHAs and the health sector, more information about these health-related activities and 

priorities is needed, as well as the partnerships that make them possible and sustainable. Future 

surveys should seek to identify success factors and barriers specific to PHAs and health sector 

partners to eventually assess the quality of these partnerships in their interconnectedness, impact on outcomes, and cost-effectiveness. 

Future Research

Results suggest merit in this survey and support expanding administration to a larger, nationally 

representative sample of PHAs to provide greater insight into these critical partnerships. The CL-

PHA released another version of this survey in February 2018 to more than 3,000 PHAs of various 

sizes, with the support of the PAHRC.

Conclusion

CLPHA’s survey results provide a compelling snapshot and baseline concerning the number and 

breadth of PHA partnerships with the health sector, including types of partners, health conditions of interest, subpopulations targeted, types of collaborative activities, and service or educational offerings for residents of HUD-assisted housing, which all constitute key ingredients in successful cross-sector collaborations or partnerships aimed at improving health (Towe et al., 2016). Additionally, this project identified gaps in the existing partnership and health intervention landscapes pertinent to housing agencies. Large PHAs’ level of engagement with the health sector and health-related priorities vary widely by agency, as do the goals of their established health partnerships. 

96 The Housing-Health Connection

Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector Future inquiry could explore other ingredients of successful cross-sector collaboration, such as 

the quality of these PHA health partnerships individually and collectively, the depth and impact of cross-sector investments related to these partnerships, and the effectiveness of policies developed and implemented to support collaboration across sectors.

Housing providers pursue a wide range of health partnerships and nearly uniformly seek to 

expand and refine cross-sector efforts to improve resident health. This desire should be paired with resources to help PHAs build greater capacity to partner with the health sector and, in the process, learn more about what successful partnerships can achieve for individuals and families in assisted housing.

Appendix A: Survey Instrument (40 total questions, 21 

survey questions)

Introductory Text: 

The Council of Large Public Housing Authorities (CLPHA) is committed to working with public 

housing authorities (PHAs) and health sector partners to develop resources, trainings, and conven-

ings that promote housing as a critical social determinant of health and wellbeing.

This survey will help establish a baseline of cross-system partnerships between housing and health providers. Results from this survey will provide a clear sense of how larger PHAs are working with health system partners, what lessons can be learned from successes to date, and what needs PHAs 

have as they work to improve resident/community health and wellbeing.

General Information Questions (4 total)

•  Housing Authority Name

•  State/Province

•  Who is Completing the Survey on Behalf of the Agency?

•  Point of Contact for Health-Related Activities

Core Survey Questions (16 total)

1. In general, our housing authority would like to ______ our work at the intersection of health 

and housing. 

§ Expand

§ Maintain/Improve

§ Reduce

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2. We are interested in or currently engaged in the following activities (not interested, interested but not active, active)

§ Conducting strategic planning focused on setting health-related goals

§ Engaging resident to guide efforts to improve community health

§ Health impact assessments to assess new projects’ impacts on resident health

§ Securing investments in affordable housing from health sector partners

§ Raising funds from local or regional foundations to support health programs

§ Colocation of affordable housing and health service providers

§ On-site and/or visiting health service delivery for residents

§ Participating in health-housing research projects and interventions

§ Implementing health interventions for seniors and/or people with disabilities

§ Providing and/or contracting for supportive housing services and/or other wraparound health 

and human services

3. We have internal programs and/or work with external partners to specifically address the 

following conditions in our resident population. (Check all that apply.)

§ Addiction

§ Alcohol dependence

§ Asthma

§ Anxiety/stress

§ Bipolar Disorder

§ COPD

§ Dental

§ Depression

§ Diabetes

§ Dementia

§ Disabilities (physical)

§ Heart disease / high blood pressure

§ Infant mortality

§ Post-traumatic stress disorder (PTSD)

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Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector

§ Prenatal care and pregnancy

§ Obesity

§ Schizophrenia

§ Sexually-transmitted infections and diseases (STI/D)

§ Substance use disorders

§ Tobacco use

§ Vision

§ We do not focus on specific conditions. Instead, we focus on general wellness and healthier 

behaviors (e.g. stress reduction, physical activity, nutritious food preparation and eating, 

etc.).

§ We do not currently have targeted health-related programming.

4. We have specific health programs, interventions, and/or partnerships targeting the following 

groups of residents:

§ Families (“whole family” interventions)

§ Adult individuals

§ Adolescents

§ Children

§ Infants/toddlers

§ Expectant and new mothers/parents

§ Seniors

§ People living with physical disabilities

§ People living with psychiatric disabilities

§ People living with HIV/AIDS

§ Formerly incarcerated (or other "justice-involved") individuals/families

§ People who identify as lesbian, gay, bisexual, and/or transgender (LGBT)

§ Those receiving other forms of public assistance besides HUD assistance (e.g. Medicaid, 

TANF, SSI/SSDI)

§ Dual-eligibles (i.e. people enrolled in both Medicare and Medicaid)

§ Veterans

§ N/A or None of the Above

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5. Our health-housing programming and alignment efforts can best be characterized as: (Check all 

that apply)

§ Centralized (i.e. focused on residents in specific buildings)

§ Decentralized (i.e. focused on voucher families)

§ Visiting (i.e. bringing health interventions to residents’ doorsteps)

§ “Hub”-based (e.g. schools, hospitals, community centers, libraries, churches)

6. Our health-housing programming and alignment efforts include: (Check all that apply)

§ Built environment (e.g. building rehabilitation, removing environmental health hazards, 

improving walkability of neighborhoods, increasing use of stairs)

§ Increasing healthy community resources (e.g. urban gardens, farmers markets, bike sharing 

services, “healthy” retail options)

§ Healthcare service coordination (i.e. working with health service providers to make referrals, 

provide on-site or visiting services, etc.)

§ Medication management and other compliance-related interventions

§ Preventative health for children and adolescents (e.g. vaccinations)

§ Harm reduction (e.g. STI/D prevention and treatment, drug use)

§ Focused on groups dictated by funders/partners (i.e. those funding/supporting the 

intervention decide who to target within our resident population)

§ None of the Above

§ Other (please specify)

7. Our health and housing interventions are targeted at residents with the following forms of HUD 

assistance. (Check all that apply)

§ Public housing

§ Project-based vouchers

§ Tenant-based vouchers

§ Special-purpose vouchers (e.g. HUD-VASH, FUP, NED)

8. Our health and housing work leverages the following programs supported by HUD: (Check all 

that apply)

§ LIHTC-financed or RAD developments

§ Continuums of Care (CoCs)

§ Choice Neighborhoods

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Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector

§ Hope IV Revitalization

§ Lead Safe Housing

§ Section 202 – Supportive Housing for Elderly

§ Section 811 – Supportive Housing for Persons with Disabilities

§ Self-Sufficiency Program

§ Smoke-Free Initiative

§ None of the Above

§ N/A

§ Other (please specify)

9. Which of the following partners directly support or provide health-related programming and/or 

help guide your efforts to improve resident and community health? (Check all that apply)

§ Advocacy organizations

§ Aging and Disability Resource Centers (ADRCs) / Area Agencies on Aging (AAAs)

§ Affinity-based community groups (e.g. walking/running/weight loss support groups)

§ Assisted living providers

§ Behavioral health providers

§ Bike-share programs

§ Child and adolescent health and welfare

§ Community development corporations/organizations

§ Continuums of Care (CoC)

§ Data sharing organizations (warehouses, repositories, nonprofit conveners, etc.)

§ Dental care providers

§ Environmental health organizations

§ Emergency/urgent care departments

§ Family planning and sexual health providers

§ Federally-Qualified Health Centers (FQHCs)

§ Fitness providers or facilities (i.e. gyms, YMCA/YWCAs)

§ Funders for health programming

§ Home health agencies / home care

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§ Hospitals – nonprofit

§ Hospitals – private

§ Law enforcement

§ Local health department

§ Medicaid – Managed Care Organizations (MCOs)

§ Medicaid – State Agencies

§ Medicare – Special Needs Plans (including Dual-Eligible SNPs)

§ Nutrition (including food shopping/preparation)

§ Parks and recreation agencies/organizations/foundations

§ Pharmacists

§ Physician practices (separate from physicians affiliated with health systems)

§ Private-sector health clinics

§ Schools and school-based providers and clinics

§ Social service providers / charities (e.g. Catholic Charities)

§ State health department

§ Supportive housing service providers

§ Think tanks

§ Transitional care providers

§ Universities/research centers

§ Urban planners

§ Veterans organizations and/or Veterans Administration

§ Violence prevention organizations

10. Choose “yes” if at least one health partnership satisfies each statement. (Check all that apply)

§ We have Memorandums of Understanding (MOUs) with our health partners.

§ We share data with our partners for health-related goals.

§ We refer patients/residents to each other when appropriate.

§ We share/exchange financial resources when appropriate.

§ We share staffing resources (e.g. co-hire FTEs, loan/receive staffing support) when 

appropriate.

102 The Housing-Health Connection

Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector 11. Sources of funding for health-related initiatives: (Check all that apply)

§ PHA Funds (e.g. Section 8 admin fees, MTW-related savings, ROSS Program)

§ Foundations

§ Private-sector partners (e.g. Medicaid managed care)

§ Nonprofit partners

§ Community development corporations/organizations

§ Hospitals and other healthcare service providers

§ Social impact bonds or other non-traditional financing

§ N/A

§ Other (please specify)

12.  Which of the following statements are true for your agency, if any? (Check all that apply)

§ We leverage funding flexibility from the Moving to Work (MTW) program to fund health-

related programming and alignment efforts.

§ We have used RAD conversions as a vehicle for public health-informed changes (e.g. making 

buildings more accessible and tailored to residents’ health and human service needs) to 

residents’ built environments.

§ We do NOT use MTW flexibility or RAD specifically for health-related programming or 

systems alignment.

13.  We have (or have ready access to) data about resident health in the following areas: (Check all that apply)

§ Chronic health conditions (e.g. diabetes, asthma, depression, COPD, HIV/AIDS)

§ History of emergency healthcare referrals (i.e. PHA staff referrals to services)

§ Health-related behaviors (e.g. physical activity, tobacco use)

§ Attendance at and/or satisfaction with health education programming

§ Health insurance coverage status

§ Healthcare service utilization (e.g. visits to primary care)

§ Other (please specify)

14. Our data sharing with healthcare partners is: (Check all that apply)

§ Unidirectional (i.e. we share data with them or they share data with us without reciprocation)

§ Multi-directional (i.e. we share data with and receive data from health sector partners)

Cityscape 103

Lucas

§ Depends on the partner; we are engaged in both unidirectional and multidirectional data 

sharing

§ Supported by centralized and/or third-party data systems or repositories

§ Conducted without a formal process (i.e. on an informal basis)

§ Not Sure

§ We do not currently share data with healthcare partners

15.  Which of the following are true for your agency?

§ We have an established process for staff to follow to connect residents with emergency health 

services.

§ We have an established process for staff to follow to connect residents with non-emergency 

healthcare services.

§ We track referrals to health services by resident/household.

16.  Which of the following staff members are involved with your health-housing initiatives? (Check all that apply; there might be overlap since specific titles vary by PHA)

§ Service coordinators

§ Resident services

§ Family Self-Sufficiency coordinators

§ Dedicated health-housing staff

§ Executive leadership (e.g. ED/CEO, COO, etc.)

§ Property management

§ Temporary/grant-funded staff

§ Other (please specify)

Partnership Inventory Questions (5 total)

Please complete the following fields to provide an inventory of your health-housing partners.

1. Partner 1 (name of partner, nature of partnership and any relevant notes)

2. Partner 2 (name of partner, nature of partnership and any relevant notes)

3. Partner 3 (name of partner, nature of partnership and any relevant notes)

4. Partner 4 (name of partner, nature of partnership and any relevant notes)

5. Partner 5 (name of partner, nature of partnership and any relevant notes)

104 The Housing-Health Connection

Connecting Fragmented Systems: Public Housing Authority Partnerships With the Health Sector CLPHA Programming-Specific Questions (5 total)

•  What would you hope to learn more about from other housing authorities and experts focused 

on the intersection of health and housing? (open-ended)

•  What resources would you like to see come from peer PHAs and generally from CLPHA’s 

Housing Is Initiative? (open-ended)

•  What activities would you and your staff like to participate in to improve your organization’s 

health and housing planning and programming? (open-ended)

•  Would you be interested in attending an online health-housing strategic planning training in 

early 2018? (yes. no, maybe)

•  Would you be willing to participate in a Health-Housing workgroup or webinar series?

Author

Stephen Lucas is Health Research and Policy Manager at the Council of Large Public Housing 

Authorities.

References 

Bostic, Raphael W., Rachel L.J. Thornton, Elizabeth C. Rudd, and Michelle J. Sternthal. 2012. 

“Health in All Policies: The Role of the US Department of Housing and Urban Development and 

Present and Future Challenges,”  Health Affairs 31 (9): 2130–2137. 

Council of Large Public Housing Authorities (CLPHA). 2018. “About CLPHA.” http://clpha.org/about. 

Fenelon, Andrew, Patrick Mayne, Alan E. Simon, Lauren M. Rossen, Veronica Helms, Patricia 

Lloyd, Jon Sperling, and Barry L. Steffen. 2017. “Housing Assistance Programs and Adult Health in 

the United States,”  American Journal of Public Health 107 (4): 571–578.

Helms, Veronica E., Jon Sperling, and Barry L. Steffen. 2017.  A Health Picture of HUD-Assisted Adults, 2006–2012: HUD Administrative Data Linked With the National Health Interview Survey. 

Washington, DC: U.S. Department of Housing and Urban Development. huduser.gov/portal/sites/

default/files/pdf/Health-Picture-of-HUD.pdf. 

U.S. Department of Health and Human Services (HHS). 2010. “Healthy People 2020: An Opportu-

nity To Address Societal Determinants of Health in the United States.” https://www.healthypeople.

gov/2010/hp2020/advisory/SocietalDeterminantsHealth.htm.

U.S. Department of Housing and Urban Development (HUD). 2014.  Strategic Plan 2014–2018. 

Washington, DC. huduser.gov/portal/publications/pdf/HUD-564.pdf.

Moe, Jessica, Scott W. Kirkland, Erin Rawe, Maria B. Ospina, Ben Vandermeer, Sandy Campbell, 

and Brian H. Rowe. 2017. “Effectiveness of Interventions To Decrease Emergency Department 

Visits by Adult Frequent Users: A Systematic Review,”  Academic Emergency Medicine 24 (1): 40–52.

Cityscape 105

Lucas

National Research Council (NRC). 2011.  Improving Health in the United States: The Role of Health Impact Assessment. Washington, DC: The National Academies Press. https://doi.org/10.17226/13229.

Raven, Maria C., Margot Kushel, Michelle J. Ko, Joanne Penko, and Andrew B. Bindman. 2016. 

“The Effectiveness of Emergency Department Visit Reduction Programs: A Systematic Review,”  An-

nals of Emergency Medicine 68 (4): 467–483. https://doi.org/10.1016/j.annemergmed.2016.04.015. 

Stewards of Affordable Housing for the Future (SAHF). 2017.  The Path to Partnership: Lessons 

Learned in the Pursuit of Joint Initiatives Between Affordable Housing Providers and Medicaid Managed Care Programs. Stewards of Affordable Housing for the Future. Columbus, OH. https://www.sahfnet.

org/sites/default/files/uploads/sahf.hma_matchmaking_lessons_learned_final_4.20.17.pdf.

Scally, Corianne Payton, Elaine Waxman, and Ruth Gourevitch. 2017.  A National Insurer Goes Local: Emerging Strategies for Integrating Health and Housing. Washington, DC: Urban Institute. https://

www.urban.org/sites/default/files/publication/91966/2001419_uhc_case_study_3.pdf.

Simon, Alan E., Andrew Fenelon, Veronica Helms, Patricia C. Lloyd, and Lauren M. Rossen. 2017. 

“HUD Housing Assistance Associated With Lower Uninsurance Rates and Unmet Medical Need,” 

Health Affairs 36 (6): 1016–1023.

Towe, Vivian L., Laura Leviton, Anita Chandra, Jennifer C. Sloan, Margaret Tait, and Tracy Orleans. 

2016. “Cross-Sector Collaborations and Partnerships: Essential Ingredients To Help Shape Health 

and Well-Being,”  Health Affairs 35 (11): 1964–1969.

Additional Reading

Cunningham, Mary, and Graham MacDonald. 2012.  Housing as a Platform for Improving Outcomes 

Among Low-Income Children. Washington, DC: Urban Institute.

National Center for Health in Public Housing (NCHPH). 2018a. “Health Center Sites in or Im-

mediately Accessible to Public Housing Developments.” https://nchph.org/about/public-housing-

primary-care-grantees/. 

———. 2018b.  Housing and Health: Building Partnerships To Support Public Housing Residents. 

Alexandria, VA: National Center for Health in Public Housing. https://nchph.org/wp-content/

uploads/2018/02/Housing-and-Health.pdf. 

Pew Charitable Trusts. 2018. “About Health Impact Assessment.” http://www.pewtrusts.org/en/

projects/health-impact-project/health-impact-assessment.

Sturtevant, Lisa, and Janet Viveiros. 2016. “How Investing in Housing Can Save on Health Care.” 

https://www.tn.gov/content/dam/tn/health/program-areas/NHC_Invest_Housing_Save_Health_

Care_2016.pdf.

U.S. Department of Housing and Urban Development. 2016. “Picture of Subsidized Housing.” 

huduser.gov/portal/datasets/assthsg.html.

106 The Housing-Health Connection

‘Then I Found Housing and 

Everything Changed’: Transitions  

to Rent-Assisted Housing and 

Diabetes Self-Management

Danya E. Keene

Mariana Henry

Carina Gormley

Chima Ndumele

Yale University

Abstract

•  Objective: This study draws on qualitative interview data to examine transitions into 

rent-assisted housing as they relate to diabetes self-management behaviors. 

•  Methods: We conducted qualitative interviews with low-income residents of New 

Haven, Connecticut, who had a diagnosis of type 2 diabetes. To examine experiences 

of transition into rent-assisted housing, we drew on interviews with those participants 

who were living in rent-assisted housing at the baseline interview (n = 18) and 

participants (n = 5) who transitioned into rent-assisted housing between baseline 

and a 9-month followup. Interviews probed participants’ housing and diabetes 

experiences. Analysis followed an inductive grounded theory approach. 

•  Results: Our data suggest that improvements in diabetes self-management 

accompanied the receipt of rental assistance. By providing housing access to those 

participants who previously had no place of their own, rental assistance facilitated 

environmental control that supported diabetes routines. By making housing 

more affordable, rental assistance also improved some participants’ ability to 

afford diabetes-related expenses and mitigated health-demoting financial stress. 

Additionally, for some participants, rental assistance provided residential stability 

that facilitated access to health-promoting local social support.

•  Conclusions: Although more research is needed, these data suggest that expanded 

access to rental assistance could both improve population health and reduce 

healthcare spending associated with preventable diabetes-related complications.

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 107

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

Keene, Henry, Gormley, and Ndumele

Introduction

The demand for rental assistance in the United States—in the form of rental vouchers, public 

housing units, and other project-based subsidies—continues to outpace supply (Fischer and Sard, 

2017). The U.S. Department of Housing and Urban Development (HUD) currently provides rental 

assistance to approximately 5 million low-income households (HUD, 2016), representing less 

than one-fourth of households eligible to receive a subsidy (Alexander et al., 2014). These rental subsidies are a primary source of affordable housing for low-income Americans given an increasingly unaffordable rental market in which most poor renting households spend more than one-half 

of their income on housing costs (Aurand et al., 2018; Desmond, 2016). 

Emerging research suggests that the existing shortage of rental assistance may have health 

consequences. In particular, using a unique data linkage that combines HUD data with nationally 

representative health surveys, Fenelon et al. (2017) found that adults living in public housing 

report better self-rated health and less psychological distress than those who moved into public 

housing up to 2 years later—the average length of HUD waiting lists for rental assistance. In other work using the same linked data, Simon et al. (2017) found that, relative to future recipients, current recipients of rental assistance are less likely to report unmet needs for health care due to cost. 

These studies suggest potential health benefits of rental assistance and also health consequences 

of unmet need for this resource. More research is needed, however, to examine both the causal 

relationship between rental assistance and health and the processes through which these effects 

may operate. Additionally, research is needed to examine how rental assistance may affect self-

management behaviors and disease trajectories among those living with chronic health conditions. 

Type 2 diabetes is one prevalent and costly chronic condition that housing access may affect. In 

2015, approximately 22 million American adults had received a diagnosis of type 2 diabetes, rep-

resenting approximately 9 percent of the adult population (CDC, 2017). Low-income individuals, 

who are more likely to be struggling with affordable housing problems, experience both a higher 

prevalence of type 2 diabetes and also disproportionately higher rates of morbidity and mortality 

associated with diabetes-related complications (CDC, 2017; Lutfey and Freese, 2005; Stevens et al., 2014). To minimize the risk of complications such as kidney, eye, and vascular disease, individuals living with diabetes must carefully manage their blood glucose levels through medication, diet, 

exercise, glucose monitoring, and healthcare visits (Glasgow, Toobert, and Gillette, 2001). Given 

these complex and resource-intensive behavioral demands, social conditions play an important 

role in facilitating or constraining adherence to recommended diabetes self-management routines 

(Chaufan, Constantino, and Davis, 2012; Lutfey and Freese, 2005; Weaver et al., 2014). Further-

more, given the strong association between daily glucose control and diabetes complications, social conditions that affect self-management behaviors are likely to affect long-term outcomes (Lutfey 

and Freese, 2005). As such, investments in social resources, such as housing that can improve self-management capacity, may ultimately reduce disease burdens and narrow disparities.

As an important source of affordable housing for low-income households, rental assistance may 

represent an opportunity to improve both housing access and chronic disease self-management. 

Rental assistance may affect diabetes management through several mechanisms. First, this as-

sistance can provide a “home” (Dupuis and Thorns, 1998) to individuals who do not have one 

108 The Housing-Health Connection

‘Then I Found Housing and Everything Changed’:  

Transitions to Rent-Assisted Housing and Diabetes Self-Management

because they are homeless or are living in places that they cannot call home. Access to a home can provide autonomy and control over one’s environment, which, as prior research indicates, can enable the establishment and maintenance of consistent health routines (Aidala et al., 2005; Padgett, 2007). Second, by making housing affordable, rental assistance may enable individuals to invest 

more in their diabetes care. Indeed, existing research finds that individuals living in more affordable housing are less likely to miss medications and appointments (Pollack, Griffin, and Lynch, 

2010) and that housing cost burdens can make it difficult for individuals to prioritize their health needs (Keene, Guo, and Murrillo, 2018). Finally, by making housing affordable, rental assistance 

may prevent forced moves that can have negative consequences for health (Desmond and Kimbro, 

2015), and enable residential stability that fosters health-promoting social support (Keene, Bader, and Ailshire, 2013). 

Despite some research demonstrating the potential importance of housing for individuals with dia-

betes (Berkowitz et al., 2018; Keene, Guo, and Murillo, 2018; Ludwig et al., 2011; Vijayaraghavan 

et al., 2011), little is known about the processes through which rental assistance may affect diabetes self-management. In this article, we draw on qualitative interviews to examine how individuals experience transitions from waiting lists into rent-assisted housing and how these experiences may shape participants’ diabetes-related behaviors. 

Methods

The interviews presented here are part of a qualitative study designed to examine the intersection of housing and diabetes management among low-income individuals. This study took place in 

New Haven, Connecticut, a city with approximately 130,000 residents that, like many U.S. cities, 

faces a shortage of both affordable market-rate and rent-assisted units (McDonald and Pething, 

2014). In New Haven, 55 percent of renters spend more than 30 percent of their income on rent, 

and 80 percent of renters in the lowest income quintile are severely cost burdened, spending more 

than 50 percent of their income on rent (JCHS, 2017). Given these high rents, rental assistance 

is an important component of New Haven’s affordable housing landscape. In 2016, 9,153 New 

Haven households and 19,221 individuals received HUD-funded rental assistance in the form 

of Housing Choice Vouchers (HCVs; 5,737 units), traditional public housing (1,840 units), and 

project-based Section 8 (2,536 units) (HUD, 2016). In 2016, 692 New Haven households also 

received state-funded long-term rental vouchers through Connecticut’s Rental Assistance Program 

(RAP). The demand for this assistance exceeds supply. Nearly 7,000 households are on waiting lists for public housing in New Haven, and 2,500 households are on waiting lists for vouchers that are 

currently closed to new applicants (Elm City Communities, 2017). 

After obtaining approval from the Yale University Institutional Review Board, we used flyers and 

snowball sampling to recruit 40 participants who were over age 24, diagnosed with type 2 diabe-

tes, and income eligible for rental assistance. All participants who were either screened or enrolled in the study earned less than 50 percent of the Area Median Income. We selected interview 

participants following established purposive sampling procedures that aim to diversify the sample 

according to theoretically relevant factors. Specifically, we constructed our interview sample to include a range of housing experiences (subsidized, unsubsidized, homeless) and treatment regimes. 

Cityscape 109

Keene, Henry, Gormley, and Ndumele

The interviews followed a semistructured format, relying on an interview guide that included 

broad and open-ended questions with followup probes. This format ensured that certain topics of 

interest were covered, but also enabled the interviewer to probe unanticipated themes and to adapt the interview throughout the research process (Corbin and Strauss, 2014; Weiss, 1994). Exhibit 1 

describes the primary questions in our interview guide. Approximately 9 months after the baseline 

interview, 26 members of the sample participated in followup interviews. All data were collected 

between July 2016 and August 2017. Interviews were audio recorded and transcribed verbatim. 

They each lasted 45 to 120 minutes, and participants were compensated $50.00 per interview.

To examine experiences of transition into rent-assisted housing, this article draws on interviews 

with participants who were living in rent-assisted housing at baseline (n = 18) and participants 

who transitioned into rent-assisted housing between baseline and followup (n = 5). Exhibit 2 

provides demographic information for these 23 participants. 

Following a grounded theory approach (Corbin and Strauss, 2014), our analysis was an ongoing, 

iterative process that co-occurred with data collection. We wrote thematic summaries after each 

interview and memos about developing concepts. We used memos, summaries, and group discus-

sion to iteratively and collaboratively develop a codebook. Using Dedoose software, two coders 

independently applied the codebook to one-half of the transcripts and resolved inconsistencies 

through discussion. A single coder then coded the remaining transcripts. For this article, we ex-

tracted and reviewed coded data related to (1) moving into subsidized housing, (2) housing afford-

ability, and (3) place-related barriers and place-related facilitators of diabetes self-management. We also reviewed full transcripts to contextualize these excerpts within participants’ broader narratives. 

Exhibit 1

Semistructured Interview Guide: Primary Questions

Topic

Question

Diabetes care

Can you tell me about when you were first diagnosed with diabetes?

What are the things that you do to manage your diabetes?

Are there things that make it harder for you to manage your diabetes? Or things 

that make it easier?

Can you tell me a little bit about the people who are involved in your diabetes 

care?

Diabetes and place

Are there places where it is harder for you to manage your diabetes? Or places 

where it is easier?

Tell me a little bit about where you live. (We follow this with a series of probes 

that capture affordability, stability and quality of current housing and prior 

housing experiences.)

What is/was it like to manage your diabetes while living (insert each housing 

situation)?

While living (insert housing situation) was there ever a time that it was hard to 

keep your blood sugar under control? Can you tell me about that time?

110 The Housing-Health Connection

‘Then I Found Housing and Everything Changed’:  

Transitions to Rent-Assisted Housing and Diabetes Self-Management

Exhibit 2

Participant Baseline Characteristics 

Characteristic

n

Age (years, mean) 

53

Race

Black and/or African American

17

White and/or Caucasian

2

Hispanic and/or Latino(a)

1

Multiracial and/or other

3

Gender

Male

12

Female

11

Ever homeless

Yes

15

No

8

Rental assistance at baseline

Yes

18

Noa

5

Type of rental assistance

Public housing

12

Housing Choice Voucher

5

Rental Assistance Program voucher

5

Permanent Supportive Housing

1

Using insulin

Yes

12

No

11

a At baseline, two of these participants were living in subsidized transitional housing programs that provided shared living arrangements. Both moved into long-term and independent rent-assisted housing between baseline and followup.

Note: N = 23 total participants.

Results

The following sections draw on interview data to describe how transitions to rent-assisted housing may support diabetes self-management in a number of ways, including providing participants with 

improved control over their housing environment, reducing financial strain, and facilitating hous-

ing stability. In presenting these data we use pseudonyms to protect participants’ anonymity.

A Home of My Own

The receipt of rental assistance enabled some participants to secure a home of their own, often 

after years of living with friends, with family members, on the streets, or in homeless shelters. 

When Regina (age 46) moved into a public housing unit a year before the baseline interview, she 

received her first set of house keys to a place she could finally call her own. She described her 

new apartment as a dream come true and as a change that dramatically affected her diabetes self-

management. She explained, “Then I found housing. I got housing, everything, my numbers, as far 

as my health, got back on track. I’m insulin-dependent. I have many medications that I take on a 

daily basis, but since I’ve had housing, my diabetes changed. It went from up here to being down 

here in the right place.”

Regina described her new apartment as providing autonomy and control over her environment 

that helped her to establish and maintain a diabetes routine. In her new apartment, she could keep Cityscape 111

Keene, Henry, Gormley, and Ndumele

her medications in the same place next to her bed and could sit the same chair when she took 

them each morning. Having her own place also provided her with better control over her diet. She 

explained, “Not being in my own place, it was hard to try to fight my diabetes and stay healthy and stuff because when you’re living with somebody else, it’s almost like you have to eat whatever’s being made.” This changed when she moved into her own apartment. As she noted, “But it’s a whole 

lot different ’cause now, when you in your own place, you can cook that healthy food now, you can 

have that healthy stuff.”

When Monte (age 59) obtained his own public housing unit between baseline and followup, 

improved control over his environment enabled him to better manage his diabetes. At baseline, 

Monte lived in a transitional housing building, where he had his own room but shared common 

spaces. He described how his eating habits changed in his new apartment where he had his own 

kitchen, noting, “I had better access to a kitchen to prepare my own meals so I wasn’t eating meals prepared by other people. So I was a lot more aware of what was going into my meals and how I 

was preparing them. So that made a big difference.”

Monte also described how his new apartment gave him better control over his sleep schedule, 

enabling him to focus on his diabetes management. He explained, “Being able to sleep and not be 

stressed is a definite mind-changer. Changes the outlook and attitude. Gives me the opportunity 

to reflect in my own way and my own time to get things done the way I wanna get them done, the 

way I feel comfortable doing things. Makes a big difference, makes a big difference.” After Monte 

moved into his own apartment, his Hemoglobin A1C, a measure of average blood glucose, fell so 

dramatically that his doctor, not believing the result, repeated the blood test. 

Homeless participants, whose receipt of rental assistance enabled them to move off the streets or 

out of emergency shelters, described particularly dramatic improvements in their diabetes manage-

ment associated with these moves. For example, Tory (age 39) lived in shelters and on the streets 

before receiving an RAP voucher from the state of Connecticut and moving into his own apartment 

4 months before the baseline interview. He noted, “It was harder when I was homeless because like 

I said kitchens, the shelters, they feed you pasta. And if you out there all day and didn’t eat nothing, you eat whatever they give you. … Like I said, once I got the apartment I was like, ‘Okay.’”

In addition to dietary improvements, formerly homeless participants noted that the receipt of 

rental assistance improved their medication adherence. Participants described many challenges 

of storing and taking medication in emergency homeless shelters. For example, Myron (age 57), 

who spent many months on the street and in homeless shelters, explained, “I had a problem with 

my medicine. I kept it in storage because I didn’t trust the shelter. People steal stuff all the time in there.” While living in a homeless shelter, Mike (age 60) often stored his medications with shelter staff but could not always retrieve them as needed. He also recounted an instance where staff lost his medications and another time when his medications were confiscated on entering the shelter. 

Mike’s control over his medication improved dramatically when he moved to his own subsidized 

permanent supportive housing unit. He explained, “But now it’s nice I’ve got all my [medications] 

now on my dresser drawer, everything that I need and I just take with me what I need for the day.” 

Not all participants associated the receipt of rental assistance with improvements in their diabetes management. Some, who moved from other stable housing arrangements, did not perceive a change. 

112 The Housing-Health Connection

‘Then I Found Housing and Everything Changed’:  

Transitions to Rent-Assisted Housing and Diabetes Self-Management

One participant even noted a loss of social support associated with her move from her mother’s apartment to her own public housing unit. Participants whose prior housing did not provide autonomy, 

however, nearly universally described positive health impacts of rental assistance receipt. 

An Affordable Place To Live

Participants also described how rental assistance improved housing affordability with positive 

implications for their diabetes self-management. For example, prior to receiving a state-funded 

rental assistance voucher, Melvin struggled to pay the rent, sometimes at the expense of eating 

well. Melvin explained, “It was hard because, number one, we wanted to make sure the rent was 

paid. Now it don’t make no difference having all this food and all this stuff when you ain’t got a place to stay.” In his new rent-assisted apartment, Melvin was very careful about his diet, prepared his own meals, and ate recommended proteins such as salmon. When asked about his diet before 

receiving the rental assistance voucher, he explained, “Oh no, ain’t eat no salmon. Tuna fish in a can… Oodles of noodles, chop hot dogs up in them, or something like that.” In addition to providing more room in his budget for nutritious food, Melvin described a reduction in financial stress 

that he perceived to positively affect his diabetes. He noted, “Shoot, I think it [the voucher] made my diabetes better.… Less stress.” 

Janet (age 57) also described a reduction in financial stress associated with the HCV that she had been receiving for 8 years. She explained, “But I’m not stressed on finances because of my Section 8, my rent is like really—this apartment is $1,300 a month. My portion is like maybe 260 which is 

a blessing.” Janet, who was a student at a local community college, also discussed how financial 

stress could interfere with her medication adherence. For example, the day before the interview 

she missed her medication dose amidst stress related to her financial aid. She explained, “then I 

find out that I don’t got no books because I don’t got financial aid, so my stress level went here. 

And everything that I needed to do for Janet went out the door. I got so overwhelmed.”

Although participants like Janet and Melvin experienced rent decreases when they received rent as-

sistance, for others rent was a new expense. For example, prior to moving into her subsidized unit, Jamie was living with her daughter, where she did not pay rent. Although Myron also did not pay 

rent in the shelter where he previously lived, he nonetheless attributed budgetary improvements to his public housing unit. When he was homeless, he paid for a monthly storage locker, a car, and 

frequent meals out. In his new apartment, he prepared his own meals, dramatically reducing his 

food budget, and gave the car to his ex-wife, who took over the payments. His downtown public 

housing building enabled him to walk or take the bus everywhere. He also described better control 

over his finances in general, noting, “Since I moved in, it’s just a little better organized in my head so I can budget better.”

Although Myron still struggled to make ends meet, occasionally running out of food at the end of 

the month or owing the pharmacy for his medication copayment, he was able to invest more in his 

diabetes care. For example, he shopped for protein-rich foods and bought a used bicycle that he 

rode for exercise. In general, Myron noted the importance of housing that is both stable and affordable, stating, “Again, it’s the affordable part of affordable housing that’s a blessing. Not being able to afford where you live is a nightmare, without having that stable place where you know you can get 

rest and get yourself together.”

Cityscape 113

Keene, Henry, Gormley, and Ndumele

A Stable Place To Live

Increased affordability can prevent evictions as well as stress related to the threat of a forced 

move, both of which can be disruptive to diabetes routines. Indeed, some participants described 

feeling safe from eviction in their rent-assisted housing. For example, Bella (age 55) noted that she was “totally secure” in her HCV-supported unit, in which she had lived for 5 years. Regina also 

described this sense of security and rootedness in her public housing unit. She explained, “This 

is my last stop home. This is my retirement home. This is gonna be the last place. I’m not going 

anywhere else because why would I move and give up the luxury of having to pay only $59 and 

everything is included?… I feel safe here. I don’t wanna move anywhere else. So for the next  

5 years, if we stay in contact, this is where you’ll be coming to see me ’cause this is where I’ll be.”

In addition to supporting consistent diabetes routines, this residential stability may also enable individuals to establish local social support networks that are health promoting. For example, 

Marcus (age 62), who had lived in his public housing unit for 1 year, described drawing on sup-

port from Myron, who lived in the same building. This support became particularly valuable after 

Marcus had a heart attack between the first and second interview. He explained, “between my 

brother and Myron, they took care of everything that had to be done.… Like if I needed something 

to eat, they wouldn’t let me eat nothing out of the way. I had to eat salads… and I just considered myself really blessed that I had people around me that took care of me.” Marcus described Myron 

as a source of support for other residents as well, noting, “...he looks out for everybody. He’s a real good guy.”

Although rental assistance could provide health-promoting stability, the initial receipt of assistance was nearly always associated with a move. For some participants, this move temporarily disrupted 

self-management routines. For example, Michelle (age 47) moved to a public housing unit between 

baseline and followup, from a room that she was renting from a friend. Directly after moving to her new unit, Michelle was admitted to the hospital with very high blood sugar. She described being tired from the move, not taking her medications, and turning away the visiting nurse who helped her daily. 

When Michelle moved to her new place, she also no longer had support from family members living 

nearby. By the second interview, however, Michelle seemed to have adjusted to her new apartment 

and had developed sources of support in her new building, including from a neighbor who drove 

her to the interview. She also described an overall improvement in her diabetes self-management. 

Although she had been in and out of the hospital prior to moving, during the 8 months spent in 

her new apartment, her only admission was the one directly following her move. 

Discussion 

Participants’ narratives suggest that the receipt of rental assistance can support improvements 

in diabetes self-management. In some cases, rental assistance provided housing access to those 

participants who previously had no place of their own, in turn providing autonomy and environ-

mental control that facilitated dietary and medication regimes. It also improved some participants’ 

finances, enabling them to invest more in their diabetes-related expenses and mitigating financial 114 The Housing-Health Connection

‘Then I Found Housing and Everything Changed’:  

Transitions to Rent-Assisted Housing and Diabetes Self-Management

stress. Finally, although moves associated with the receipt of rental assistance could temporarily disrupt diabetes routines, participants’ narratives suggest that, in the long run, this assistance may facilitate residential stability and associated access to health-promoting social support. 

Our data also point to important variation in participants’ experiences of rental assistance receipt. 

In particular, this experience, and its implications for diabetes self-management, seemed to be 

shaped by participants’ previous housing situation. Not surprisingly, individuals whose rent 

assistance enabled them to move off the streets or out of emergency homeless shelters described 

particularly dramatic changes in dietary and medication regimes. Many participants, however, who 

were not previously homeless but were struggling with inadequate or unaffordable housing, also 

described significant benefits of rental assistance for their diabetes management. A few participants described a loss of social support or experienced disruptions in their routine in the period immediately following a move into rent-assisted housing. In this sense, our data suggest the potential utility of developing programs that can provide extra support to new recipients of rental assistance, particularly those who have chronic health conditions, during the potentially vulnerable period 

surrounding a move. 

The implications of rental assistance for diabetes self-management may also vary depending on 

individuals’ access to affordable medical care. Many participants in our study had virtually no 

healthcare expenses as a result of their Medicaid coverage, which was accessible to many because 

of Connecticut’s expansion of Medicaid through the Affordable Care Act. For individuals living in 

states with more limited access to Medicaid coverage, the challenges of managing diabetes without 

rental assistance may be greater. Access to stable and affordable housing may also better position individuals to benefit from the medical care that they receive, and more research is needed to better understand this intersection.

Although our findings suggest benefits of rental assistance for type 2 diabetes management, our 

qualitative study was designed to examine processes rather than to estimate causal effects. As with most qualitative studies, our sample size and design limits our ability to make population level 

generalizations. Furthermore, our small sample limits our ability to fully examine variations in 

the experience of rental assistance receipt across personal characteristics. It is also unclear to what extent the experiences of this sample of New Haven residents would be transferable to other settings or to other low-income renters within New Haven who may not be represented in our study. 

Additionally, our study is limited by drawing primarily on retrospective accounts of transition from waiting lists to rent-assisted housing. Although many of these retrospective accounts provided rich detail about this transition, future longitudinal studies that can prospectively observe the receipt of rental assistance will be an important addition to the literature. Finally, future research that moves beyond self-report to assess objective measures of diabetes self-management and control is needed 

to estimate and quantify the potential population health impact of rental assistance for individuals living with type 2 diabetes and other behaviorally managed chronic conditions. 

Nonetheless, the qualitative data presented here illustrate numerous plausible pathways through 

which rental assistance may operate to improve diabetes self-management and ultimately long-term 

diabetes outcomes. Although more research is needed, these data suggest that expanded access 

to rental assistance could both improve population health and reduce the large existing costs 

Cityscape 115

Keene, Henry, Gormley, and Ndumele

preventable diabetes-related complications (Yang et al., 2013). The cost savings associated with 

preventing large and unnecessary downstream healthcare costs may offset costs associated with 

expanding rental assistance programs (Sandel and Desmond, 2017).

Acknowledgments 

The authors thank the participants in this study for sharing their experiences. They also thank 

Penelope Schlesinger, Melanie Vaughn, three anonymous reviewers, and the editors of this 

symposium for their comments and contributions. This publication was supported by funding 

from Yale Clinical and Translational Science Award grant UL1TR000142 from the National Center 

for Advancing Translational Science at the National Institutes of Health (NIH). This work was also supported by funding from the National Institute of Diabetes and Digestive and Kidney Diseases at 

NIH through grant P30DK045735 to the Yale Diabetes Research Center. 

Authors

Danya E. Keene is an assistant professor in the Department of Social Behavioral Sciences at the Yale School of Public Health.

Mariana Henry is a recent MPH graduate from the Department of Chronic Diseases at the Yale 

School of Public Health.

Carina Gormley is an undergraduate student at Yale College and a research assistant at the Yale 

School of Public Health.

Chima Ndumele is an assistant professor in the Department of Health Policy and Management at 

the Yale School of Public Health.

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118 The Housing-Health Connection

Homelessness During Infancy: 

Associations With Infant  

and Maternal Health  

and Hardship Outcomes

Diana B. Cutts

Hennepin County Medical Center

Allison Bovell-Ammon 

Boston Medical Center

Stephanie Ettinger de Cuba 

Boston University

Richard Sheward 

Boston Medical Center

Meg Shaefer 

The Everett Clinic

Cassie Huang 

Boston University

Maureen M. Black 

University of Maryland

Patrick H. Casey 

University of Arkansas for Medical Sciences

Sharon Coleman 

Boston University

Megan Sandel 

Deborah A. Frank 

Boston Medical Center

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 119

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

Cutts, Bovell-Ammon, Ettinger de Cuba, Sheward, Shaefer, Huang, Black, Casey, Coleman, Sandel, and Frank

Abstract

•  Objective: Homelessness among children is correlated with developmental delays, 

fair or poor health, and high healthcare utilization (AAP, 2013). Associations of 

homelessness specifically among infants younger than 12 months, however, are 

unknown. This study evaluates homelessness during infancy as a risk for adverse 

infant and maternal health and hardship. 

•  Methods: From May 2009 to December 2015, 9,980 mothers of infants younger than 

12 months were surveyed at emergency departments and primary care clinics in five 

U.S. cities. Infants were classified as having a history of homelessness if they were 

homeless at any point versus being consistently housed during their first year. Infant 

health outcomes included caregiver report of fair or poor health, developmental risk, 

and hospitalizations. Maternal health outcomes included self-report of fair or poor 

health and positive screen for depressive symptoms. Hardships included household 

and child food insecurity and foregone medical care or prescriptions due to cost.

•  Results: After adjusting for potential confounders, homelessness during infancy 

was associated with higher adjusted odds of fair or poor infant health (adjusted 

odds ratio [AOR] 1.71; 95-percent confidence interval [CI] 1.18, 2.47;  p  < 0.01) 

and developmental risk (AOR 1.62; 95-percent CI 1.04, 2.53;  p  = 0.03), but not 

hospitalizations. Compared with consistently housed mothers, mothers with a history 

of homelessness had higher adjusted odds of fair or poor health and depressive 

symptoms. History of homelessness was associated with higher adjusted odds of 

household and child food insecurity and foregone health care for family members 

other than the infant.

•  Conclusions: Homelessness in infancy is associated with adverse outcomes for infants 

and mothers. Interventions providing housing and other health-related resources to 

homeless families with infants may improve health and family hardship.

Introduction

In the United States, approximately 1.27 million children younger than 6 are homeless in a given 

year. (HHS Administration for Children and Families, 2016). In 2015, the U.S. Department of 

Housing and Urban Development found nearly one-half of families experiencing homelessness had 

a child under the age of 6, and 10.4 percent of homeless families had an infant under 12 months of age (HUD, 2015). According to HHS Administration for Children and Families (2016), infancy is 

the period of life when a person is most likely to live in a homeless shelter.

Stable housing is a foundation for children’s healthy growth and development (Sandel et al., 2018), particularly from conception through the first 3 years of life, when the brain and body are growing and developing most rapidly. Prenatal homelessness is associated with higher adjusted odds of low 

120 The Housing-Health Connection

Homelessness During Infancy:  

Associations With Infant and Maternal Health and Hardship Outcomes

birth weight and preterm delivery (Cutts et al., 2015; Little et al., 2005). Deprivation during the most rapid period of brain development can fundamentally impact the architecture of the brain 

leading to long-term impacts on cognitive, socioemotional, and motor ability (Berkman, 2009; 

Shonkoff and Garner, 2012; Shonkoff and Phillips, 2000).

It is well-documented that children under age 18 who experience homelessness, as compared with 

stably housed children, manifest increased risk for multiple adverse health conditions, including 

a greater likelihood of chronic and acute illness, developmental delay, behavioral problems, early substance use, high-risk sexual behaviors, and poor school performance, in addition to more 

limited access to health care (Bassuk, 1991; Noell et al., 2001; Rubin et al., 1996; Weinreb et al., 1998). Throughout early childhood, not specifically during infancy, homelessness is associated 

with developmental delays and psychiatric disorders (Grant et al., 2007). 

Maternal well-being is also necessary for optimal infant development (Casey et al., 2004; Cum-

mings and Davies, 1994). Previous research indicates that maternal mental health, particularly 

depression, is a mediating factor for child health outcomes (Ashiabi and O’Neal, 2007). Homeless 

mothers are more likely to suffer worse physical and mental health than the general population and have higher incidence of acute and chronic conditions including asthma, chronic anemia, chronic 

ulcers, and unmet dental needs (Rog and Buckner, 2007; Weinreb et al., 2006; Zima, Wells, and 

Benjamin, 1996). 

For most families, homelessness is a consequence of limited financial resources to afford stable 

housing (Grant et al., 2013). Families with children who are homeless may also concurrently 

experience other economic hardships, including food insecurity and lack of access to affordable 

health care (Gundersen et al., 2003; Miller and Lin, 1988). Each hardship alone is associated with poor health outcomes in children and adults (Ayanian, Weissman, and Schneider, 2000). Even 

after a family is no longer homeless, economic hardships associated with limited financial resources persist (Gubits et al., 2016). Cumulatively, these hardships exert additive short- and long-term 

negative effects on family health and well-being (Frank et al., 2010; Grant et al., 2013).

To our knowledge, no information can be found about the association of homelessness with health 

outcomes of infants and their mothers and the risk of other concurrent economic hardships. The goal of the current study is to assess whether homelessness in infancy was associated with adverse health and hardship outcomes for mothers and infants during this particularly vulnerable year of life. 

Methods

Children’s HealthWatch (http://www.childrenshealthwatch.org) is an ongoing collaborative research study monitoring the health and well-being of young children and their families in the United 

States since 1998. Children’s HealthWatch collects data in Baltimore, Boston, Little Rock, Min-

neapolis, and Philadelphia. This network of researchers examines associations of family economic 

hardships and participation in public assistance programs on young child and caregiver health. 

This analysis used data from the cross-sectional Children’s HealthWatch study. As described previ-

ously (Rose-Jacobs et al., 2016), trained research assistants elicited caregivers’ verbal responses to a 20-to-30 minute survey of household demographics, maternal and child health, and household 

Cityscape 121

Cutts, Bovell-Ammon, Ettinger de Cuba, Sheward, Shaefer, Huang, Black, Casey, Coleman, Sandel, and Frank

housing status and economic hardships. Participants were caregivers seeking medical care for their child younger than 48 months of age in emergency departments or primary care clinics in hospitals 

in the five cities. Eligibility included English, Spanish, and (in Minneapolis only) Somali speakers, state residency, and knowledge of the child’s health and household. For this analysis, the sample was restricted to families of children less than 12 months old. Caregivers of critically ill or injured children were not approached. Each site obtained institutional review board approval prior to data collection, which was renewed annually. All research was conducted in accord with prevailing ethical principles. 

Of the 32,610 caregivers of children younger than 4 approached between May 1, 2009, and 

December 31, 2015, 3,607 (11.1 percent) were ineligible for the study and 2,158 (7.4 percent) 

refused or were unable to complete the interview. Children younger than 12 months comprised 

12,989 (48.4 percent) of the completed interviews. To better ensure that participants had relatively similar economic backgrounds, exclusion criteria included private insurance and homeownership, 

which excluded 2,267 (17.4 percent) of families with children younger than 12 months. Addition-

ally, given previously published associations between prenatal homelessness and health outcomes, 

742 infants (2.3 percent) whose mothers were homeless during pregnancy were also excluded. The 

final analytic sample was 9,980.

Measures

Demographics.  Variables collected included study site and clinical setting, mother’s country of birth (United States or other); age; race and/or ethnicity; marital status; education and employment; infant’s sex, age, insurance status, birth weight, and breastfeeding history; and number of 

children in household.

Homeless during the infant’s lifetime.  Participants were asked “Since your child was born has s/he ever been homeless or lived in a shelter?” Homelessness was defined as living in a shelter, 

motel, and other transitional living situations or not having a consistent place to sleep at night. 

Those participants who reported currently living in a shelter, motel, car, or no consistent place to sleep at night were included in the homeless group. Participants were classified into two categories: (1) “consistently housed”—no history of homelessness since the infant’s birth, and (2) “history of homelessness”—homeless for any period of time since the infant’s birth. 

Child health.  Infant health status was measured using the RAND Corporation health status question asking caregivers to rate their infant’s overall health at the point in time of the interview as excellent, good, fair, or poor (National Center for Health Statistics, 1998). The outcome variable for this study combined response options into two categories: fair or poor compared with excellent or good child health. Fair or poor reported health status is highly predictive of increased health services utilization and higher healthcare costs (O’Hara and Caswell, 2013). Caregivers were also 

asked if the infant had been hospitalized other than at birth (“lifetime hospitalizations”).

Developmental risk.  Caregiver concerns about the infant’s developmental status for infants four months or older (n = 5,336, 53.5 percent of the sample) were ascertained using the Parents’ 

Evaluation of Developmental Status (PEDS), a validated screening tool (Glascoe, 2000). Parents 

described concerns about their child’s development at the time of the interview. Developmental 

risk was defined by endorsement of two or more concerns on the PEDS. 

122 The Housing-Health Connection

Homelessness During Infancy:  

Associations With Infant and Maternal Health and Hardship Outcomes

Maternal health.  Maternal health status was measured by self-report of health status as excellent, good, fair, or poor (CDC, 1994). The outcome variable for this study combined response options into two categories: fair or poor compared with excellent or good caregiver health. Maternal depressive symptoms were detected using the Kemper scale (Kemper and Babonis, 1992). This scale consists 

of three validated questions: (1) “How many times in the past week has this statement been true for you? I felt depressed (affirmed feeling depressed for one or more days in the past week)”; (2) “In the past year, have you had 2 weeks or more during which you felt sad, blue or lost pleasure in things that you usually cared about or enjoyed?”; and (3) “Have you had 2 or more years in your life when you felt sad most days, even if you felt okay sometimes?” Caregivers who responded affirmatively to two or more questions were categorized as positive for depressive symptoms.

Food insecurity.  The U.S. Food Security Survey Module, or FSSM, is an 18-question scale developed by the U.S. Department of Agriculture and is considered the “gold standard” in assessment of 

household food security (Bickel et al., 2000; USDA Economic Research Service, 2017). The assess-

ment reflects the previous 12 months for each question. Households categorized as household food 

insecure (HFI) had at least three affirmative responses to nonchild-specific questions. Households categorized as child food insecure (CFI) gave affirmative responses to at least two of the eight child-specific questions in addition to at least three affirmative responses on nonchild-specific questions.

Foregone care.  Participants were asked whether at any time (1) the index infant or (2) another family member had foregone needed health care (prescriptions and/or medical care) due to the 

family’s inability to afford care. 

Statistical Techniques

Multiple logistic regression analyses were performed, controlling for confounders including study 

site, mother’s race or ethnicity, educational attainment, employment, marital status, breastfeeding history, child’s age, and number of children in the household. All analyses were conducted using 

two-sided tests and a significance level of  α = 0.05. Statistical analyses were performed using SAS 

software (version 9.3; SAS Institute, Cary, NC). 

Results

Between May 2009 and December 2015, 9,980 caregivers with infants were interviewed. Of those 

interviewed, 300 (3.0 percent) families had experienced homelessness since the birth of their 

infant, and 9,680 families (97.0 percent) were consistently housed. The mean age of homeless 

infants was slightly older than those consistently housed, 5.9 months (standard deviation [sd] 

= 3.6) compared with 4.8 months (sd = 3.6;  p < 0.01). No significant difference was found in maternal age; overall mean age was 25.7 years (sd = 5.7). Also, no significant difference resulted for low birth weight status; overall, 13.7 percent of infants were born with low birth weights. 

Compared with consistently housed caregivers, those with a history of homelessness had higher 

rates of unemployment and of not having a partner (67.0 versus 81.0 percent and 62.8 versus 

76.2 percent, respectively). Those with a history of homelessness had lower rates of high school or higher education completion (69.9 versus 58.3 percent). Consistently housed families on average 

had more children than those who experienced homelessness (2.4 versus 2.0; exhibit 1). 

Cityscape 123

Cutts, Bovell-Ammon, Ettinger de Cuba, Sheward, Shaefer, Huang, Black, Casey, Coleman, Sandel, and Frank

Exhibit 1

Sample Description According to Homelessness Status of Infants Younger Than 12 

Months

Consistently 

Homelessness 

Overall

Variable

Housed 

During Infancy

p-Value

n (%)

n (%)

 n (%)

Total participants

9,980 (100)

9,680 (97)

300 (3)

Site

< 0.01

Baltimore

2,010 (20)

1,983 (21)

27 (9)

Boston

1,752 (18)

1,615 (17)

137 (46)

Little Rock

1,903 (19)

1,884 (20)

19 (6)

Minneapolis

2,342 (23)

2,266 (23)

76 (25)

Philadelphia

1,973 (20)

1,932 (20)

41 (14)

Caregiver’s place of birth

0.38

U.S. born (vs. foreign 

7,248 (73)

7,037 (73)

211 (71)

born)

Child gender

0.66

Female

4,615 (46)

4,480 (46)

135 (45)

Age of child (months)

< 0.01

Mean (std. dev.)

4.9 (3.6)

4.8 (3.6)

5.9 (3.6)

Child breastfed

0.08

Yes

6,533 (66)

6,323 (65)

210 (70)

Child’s health insurance

0.23

Public (vs. none)

9,360 (94)

9,073 (94)

287 (96)

Caregiver’s ethnicity

< 0.01

Hispanic

3,330 (34)

3,197 (33)

133 (45)

Black non-Hispanic

5,007 (51)

4,888 (51)

119 (40)

White non-Hispanic

1,246 (13)

1,211 (13)

35 (12)

Other

314 (3)

304 (3)

10 (3)

Marital status

< 0.01

Married/partnered

3,663 (37)

3,592 (37)

71 (24)

Caregiver’s education 

< 0.01

attainment

Less than high school 

3,037(31)

2,913 (30)

124 (42)

diploma

High school

4,035 (40)

3,924 (41)

111 (37)

More than high school

2,879 (29)

2,816 (29)

63 (21)

Caregiver’s age

0.50

Mean (std. dev.)

25.7 (5.7)

25.7 (5.7)

25.5 (5.8)

Caregiver’s employment

< 0.01

Employed

3,249 (33)

3,192 (33)

57 (19)

WIC 

0.54

Yes

9,005 (91)

8,738 (91)

267 (90)

SNAP

< 0.01

Yes

6,023 (61)

5,805 (61)

218 (73)

Subsidized housing

< 0.01

Yes

1,883 (19)

1,865 (20)

18 (6)

Low birthweight

0.26

Less than 2,500g

1,357 (14)

1,323(14)

34 (12) 

Number of children in 

< 0.01

household

Mean (std. dev.)

2.3 (1.3)

2.4 (1.3)

2.0 (1.4)

SNAP = Supplemental Nutrition Assistance Program. std. dev. = standard deviation. WIC = Special Supplemental Program for Women, Infants, and Children program.

Notes: Chi-square testing was utilized for categorical variables, and a  t -test was utilized for continuous variables. Exclusion criteria included private health insurance, homeownership, index child >12 months of age, and prenatal homelessness. 

124 The Housing-Health Connection

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Associations With Infant and Maternal Health and Hardship Outcomes

Unadjusted Outcomes

Infants with a history of homelessness had higher unadjusted rates of hospitalizations, fair or poor health, and developmental concerns than consistently housed infants (22 versus 18 percent, 12 

versus 8 percent, and 14 versus 7 percent, respectively). Caregivers who experienced homelessness 

in the first 12 months of their infant’s life were more frequently reported fair or poor health and depressive symptoms than caregivers who were consistently housed (31 versus 20 percent and 39 

versus 18 percent, respectively). Of families who experienced homelessness, 44 percent reported 

household food insecurity compared with 25 percent of consistently housed families. Child food 

insecurity rates were also higher among those with a history of homelessness compared with 

consistently housed families (19 versus 11 percent). Families with a history of homelessness were 

more likely to forego needed medical care for any household member other than the index child 

than consistently housed families (21 versus 16 percent). Rates of foregone care for the index child did not differ between groups (exhibit 2).

Exhibit 2

Unadjusted Outcomes by Homelessness: Infant Health, Maternal Health, and 

Household-Level Hardships Among Infants Younger Than 12 Months

Consistently 

Homelessness 

Overall

Variable

Housed 

During Infancy

p-Value

n (%)

n (%)

 n (%)

Total participants

9,980 (100)

9,680 (97)

300 (3)

Child health 

Lifetime hospitalizations

1,808 (18)

1,742 (18)

66 (22)

0.08

Child health fair or poor 

777 (8)

740 (8)

37 (12)

< 0.01

Developmental risk (PEDS 

368 (7)

340 (7)

28 (14)

< 0.01

two or more concerns) 

Maternal health

Maternal health fair or 

1,979 (20)

1,887 (20)

92 (31)

< 0.01

poor

Depression screen

1,770 (19)

1,656 (18)

114 (39)

< 0.01

Household-level hardships

Household food insecurity

2,513 (25)

2,380 (25)

133 (44)

< 0.01

Child food insecurity

1,124 (11)

1,066 (11)

58 (19)

< 0.01

Household foregone care

1,634 (17)

1,570 (16)

64 (21)

0.02

Child foregone care

331 (3)

319 (3)

12 (4)

0.51

PEDS = Parents’ Evaluation of Developmental Status.

Notes: Chi-square testing was utilized for categorical variables, and a  t -test was utilized for continuous variables. Exclusion criteria included private health insurance, homeownership, index child >12 months of age, and prenatal homelessness.

Infant Health Outcomes

Compared with infants who were consistently housed, a history of homelessness during infancy 

was associated with higher adjusted odds of fair or poor child health (adjusted odds ratio [AOR] 

1.71; 95-percent confidence interval [CI] 1.18, 2.47;  p < 0.01) and developmental risk (AOR 1.62; 95-percent CI 1.04, 2.53;  p = 0.03). Risk of lifetime hospitalizations (exhibit 3) did not differ between the two groups (AOR 1.17; 95-percent CI 0.87,1.58;  p = 0.30).

Cityscape 125

Cutts, Bovell-Ammon, Ettinger de Cuba, Sheward, Shaefer, Huang, Black, Casey, Coleman, Sandel, and Frank

Exhibit 3

Adjusted Outcomes by Homelessness: Infant Health, Maternal Health, and 

Household-Level Hardships Among Infants Younger Than 12 Months

Homelessness 

Consistently 

Variable

During Infancy

p-Value

Housed 

AOR (95% CI)

Lifetime hospitalizations 

1.00

1.17 (0.87, 1.58)

0.30

Child health

Child health fair or poor 

1.00

1.71(1.18, 2.47)

< 0.01

Developmental risk (PEDS two or more 

1.00

1.62 (1.04, 2.53)

0.03

concerns) 

Maternal health

Maternal health fair or poor

1.00

1.87 (1.44, 2.43)

< 0.01

Depression screen

1.00

2.98 (2.30, 3.86)

< 0.01

Household-level hardships

Household food insecurity

1.00

2.07 (1.62, 2.65)

< 0.01

Child food insecurity

1.00

1.59 (1.16, 2.17)

< 0.01

Household foregone care

1.00

1.74 (1.29, 2.35)

< 0.01

Child foregone care

1.00

0.78 (0.41, 1.46)

0.44

AOR = adjusted odds ratio. CI = confidence interval. PEDS = Parents’ Evaluation of Developmental Status.

Notes: Adjusted for site, mother’s race/ethnicity, educational attainment, employment status, marital status, child’s age, breastfeeding history, and number of children in the household. Child’s age not in maternal health outcome models.

Maternal Health Outcomes

Compared with mothers of infants who had been consistently housed since the birth of their 

child, mothers in the history of homelessness group had higher adjusted odds of being in fair or 

poor health (AOR 1.87; 95-percent CI 1.44, 2.43;  p < 0.01) and screening positive for depressive symptoms (AOR 2.98; 95-percent CI 2.30, 3.86;  p < 0.01; exhibit 3). 

Household-Level Hardships

Compared with those consistently housed, families who had been homeless since the child’s birth 

had higher adjusted odds of HFI (AOR 2.07; 95-percent CI 1.62, 2.65;  p < 0.01), CFI (AOR 1.59; 95-percent CI 1.16, 2.17;  p < 0.01), and foregone health care for family members other than the index child (AOR 1.74; 95-percent CI 1.29, 2.35;  p < 0.01). Adjusted odds of foregone health care for the index child did not differ between the two groups (AOR 0.78; 95-percent CI 0.41, 1.46;  p = 

0.44; exhibit 3).

Limitations

This study has several limitations. First, significant findings in this cross-sectional study design reflect associations, not causation. Second, the study did not consider duration, whether the 

family was homeless once or over multiple periods of time, type or quality of alternative living 

arrangements for homeless families, or the housing quality for those who reported they were 

consistently housed. Quality of living arrangements may vary greatly within each group. Third, 

we did not collect information on other known risk factors of poor health outcomes that are 

more prevalent among homeless families compared with consistently housed families, including 

history of physical, emotional, or sexual abuse; exposure to interpersonal violence and community 

violence; substance use history; or mental health diagnoses (Bassuk and Rosenberg, 1988; Rog 

126 The Housing-Health Connection

Homelessness During Infancy:  

Associations With Infant and Maternal Health and Hardship Outcomes

and Buckner, 2007). These and other unmeasured covariates may contribute to adverse health 

outcomes described in this research. Finally, although other researchers independently validated 

the questions used in this study, respondents may have overreported or underreported negative 

child or maternal health outcomes. Previous research on the health status questions utilized in this study, however, show significant associations between reports of fair or poor health and higher 

healthcare utilization and costs (O’Hara and Caswell, 2013). Despite these limitations, the findings of this study provide evidence of the association between homelessness during infancy and adverse 

infant and maternal health outcomes as well as family material hardships, which have potential 

public policy implications.

Discussion

After controlling for confounders, we found significantly increased adjusted odds of poor infant 

and maternal health outcomes in families with infants who had experienced homelessness. These 

infants were at increased risk for fair or poor health and developmental risk, but not hospitalizations. Furthermore, their mothers were more likely to report their own health as fair or poor and 

more likely to report maternal depressive symptoms.

Families who experienced homelessness since the birth of the infant were more likely than con-

sistently housed families to report an inability to afford enough food. Caregivers will often forego basic needs, including food, to buffer their children from the lack of family resources (Edin et al., 2013). In extreme instances, however, families are forced to decrease the quality or quantity of 

food for children, known as child food insecurity.

Families of infants who experienced homelessness were also more likely to report foregoing needed 

health care for other family members other than the index child due to family inability to afford the care. 

Given previous research on the associations between homelessness during the prenatal period on 

birth outcomes, the findings of this article, and the prevalence of infants with histories of homelessness both prenatally and postnatally, more research is needed to address the potential cumula-

tive impact of prenatal and postnatal homelessness on infant and early childhood health outcomes. 

Additionally, research on specific diagnoses and health indicators, such as those documented by 

health providers in medical records, beyond the reported outcomes of overall general health and 

development discussed in this article are necessary to understand the severity of health impacts 

homelessness during infancy has on child health.

The findings of this study demonstrate the potential toxicity of homelessness in the first twelve 

months of life as well as its potential effect on the health of caregivers. These data suggest that children who experience homelessness early in life may be at greater disadvantage than their 

peers as they grow and learn, but more research is necessary to identify the longitudinal impacts 

of homelessness during infancy on health and educational outcomes. Early exposures to adverse 

conditions and poor health are linked to negative health outcomes over the life course, especially when hardships persist (Shonkoff and Garner, 2012). Family homelessness also impacts parental 

physical and mental health, both of which are linked to negative health and developmental out-

comes in children (Casey et al., 2004). Further, the impacts of homelessness may be compounded 

Cityscape 127

Cutts, Bovell-Ammon, Ettinger de Cuba, Sheward, Shaefer, Huang, Black, Casey, Coleman, Sandel, and Frank

by other material hardships. Inability to afford food and access health care are independently as-

sociated with poor health outcomes, but, as our previous work has shown, hardships experienced 

simultaneously increase risks of adverse child and maternal health outcomes (Frank et al., 2010). 

This study adds to the extensive body of research on the negative child and family outcomes asso-

ciated with lack of stable housing (Gubits et al., 2016). Previous Children’s HealthWatch research demonstrates that more subtle housing instability short of homelessness significantly increases 

the risk of poor caregiver and child health as well as economic hardships (Sandel et al., 2018). 

Policies and programs that prevent homelessness and provide services that respond holistically to 

the combined needs of parents and children, also known two-generation strategies, for alleviating 

family hardships may improve health and child development. Evidence-based policies that help 

move families toward stable housing and increase the availability of affordable housing (Bailey et al., 2015) should be implemented widely, robustly funded, and expanded.

Conclusion 

Homelessness during infancy is associated with early harm to children’s health and development, 

poor caregiver health and maternal mental health, and additional material hardships for families. 

Each of these outcomes has long-term impacts on health, educational attainment, and workforce 

participation later in life. Together, the cumulative force of these impacts may exert lifelong negative effects on a child’s future. Efforts to prevent homelessness and coordinate services for families experiencing homelessness, especially for families with young children, are urgently needed. 

Partnerships between healthcare systems, housing providers, early education providers, and social 

service agencies offer promising results for coordinating solutions that respond to family needs holistically. Adequate and stable federal, state, and local funding across sectors is critical for maximiz-ing the impact of these innovative strategies. Additionally, increasing federal funding for programs that support the housing, health, early education and care, and nutrition needs for families can 

improve our national health and well-being and strengthen our country’s future.

Acknowledgments

This study was funded by The John D. and Catherine T. MacArthur Foundation.

Authors

Diana B. Cutts is the Director of the Children’s Growth and Nutrition Program at Hennepin County 

Medical Center, associate professor of pediatrics at the University of Minnesota, and principal 

investigator for Children’s HealthWatch.

Allison Bovell-Ammon is the Deputy Director of Policy Strategy for Children’s HealthWatch at 

Boston Medical Center.

Stephanie Ettinger de Cuba is the Executive Director for Children’s HealthWatch at the Boston 

University School of Medicine.

128 The Housing-Health Connection

Homelessness During Infancy:  

Associations With Infant and Maternal Health and Hardship Outcomes

Richard Sheward is the Deputy Director of Innovative Partnerships for Children’s HealthWatch at 

Boston Medical Center.

Meg Shaefer is a pediatrician at The Everett Clinic.

Cassie Huang was a research assistant at the Boston University School of Public Health and a 

research and policy intern at Children’s HealthWatch. 

Maureen M. Black is the John A. Scholl Professor of Pediatrics and Director of the Growth and 

Nutrition Clinic at the University of Maryland School of Medicine, a distinguished fellow at RTI 

International, and a principal investigator for Children’s HealthWatch.

Patrick H. Casey was a professor of pediatrics in the College of Medicine at the University of 

Arkansas for Medical Sciences and a principal investigator for Children’s HealthWatch.

Sharon Coleman is a research manager at the Boston University School of Public Health, Biostatis-

tics and Epidemiology Data Analytics Center.

Megan Sandel is the Associate Director of the Grow Clinic for Children at Boston Medical Center, 

an associate professor of pediatrics at the Boston University Schools of Medicine and Public Health, and a principal investigator for Children’s HealthWatch.

Deborah A. Frank is the Director of the Grow Clinic for Children at Boston Medical Center, profes-

sor of child health and well-being at the Boston University School of Medicine, and a principal 

investigator for Children’s HealthWatch.

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132 The Housing-Health Connection

Impact of Rental Assistance on 

Modifiable Health Risk Factors 

and Behaviors in Adults

Cathy L. Antonakos

Natalie Colabianchi

University of Michigan

Abstract

•  Objectives: Housing may influence health through various mechanisms and is 

recognized as a social determinant of health. This study investigated the influence 

of rental assistance on modifiable health risk factors and behaviors using data from 

the Panel Study of Income Dynamics. Participants receiving rental assistance were 

compared with participants not receiving rental assistance on body mass index (BMI), 

obesity, smoking, alcohol use, and physical activity.

•  Methods: Participants (N = 1,374) were ages 18 to 62, heads of household, and 

had not received rental assistance for 4 years prior to baseline. Treatment group 

participants (n = 116) received rental assistance between baseline and the 2-year 

followup. Control group participants (n = 1,258) were eligible for rental assistance 

2 years after baseline but did not receive assistance. Models estimated the average 

treatment effect on the treated for each health indicator in each followup year. 

Participants were matched on age, race and ethnicity, gender, education, disability 

status, employment, household income, and number of children in the family unit. 

•  Results: At the 2-year followup, smoking was significantly higher among treatment-

group participants. A sensitivity analysis excluding permanently disabled 

participants showed significantly higher obesity in the treatment group 2 years after 

baseline. No significant differences were found 4 or 6 years after baseline on any 

outcome. 

•  Conclusions: Rental assistance was associated with increased smoking and obesity 

2 years after baseline but did not influence BMI, alcohol consumption, or physical 

activity. Interventions to reduce smoking and obesity may improve the health of 

individuals who receive rental assistance.

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 133

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

Antonakos and Colabianchi

Introduction

Rental assistance has been recognized as a mechanism for improving the lives of individuals 

through the provision of better-quality and more-affordable housing (Shaw, 2004). However, 

few studies have examined the influence of rental assistance on physical health risk factors and 

behaviors, and findings have shown both and positive and negative influences of various forms of 

rental assistance on health (Fauth, Leventhal, and Brooks-Gunn, 2004; Fenelon et al., 2017; Fertig and Reingold, 2007). 

Fauth, Leventhal, and Brooks-Gunn (2004) studied Black and Latino adults in high-poverty areas 

in Yonkers, New York, using data from the Yonkers Project. Adults randomly assigned by lottery to 

move to newly built public housing facilities were compared with other adults, who stayed in high-

poverty areas, on measures of well-being, including physical health and alcohol abuse symptoms. 

Adults who moved to new public housing facilities were found to have fewer reported health 

problems, such as diabetes and asthma, and were less likely to report alcohol abuse symptoms 

approximately 2 years after moving. Fenelon et al. (2017) linked National Health Interview Survey 

data and U.S. Department of Housing and Urban Development (HUD) data to study the influence 

of rental assistance on adults’ physical and mental health. Study participants living in public 

housing, and those in multifamily housing, had lower odds of fair or poor reported health status 

as compared with future public housing residents, controlling for demographic characteristics 

and neighborhood factors. Fertig and Reingold (2007) investigated the effect of living in a public housing project (self-reported data) on health among mothers in the Fragile Families and Child 

Wellbeing Study, using a baseline measure from survey data obtained after the birth of a child and followup data 1 and 3 years later. By contrast with Fenelon et al. (2017), the study found overall health status to be worse among mothers who reported moving into a public housing project between baseline and the 1-year interview, and mothers in public housing projects were more likely 

to be overweight at the 3-year interview. 

These studies differ in design, definition of rental assistance, method for assigning residents to treatment or control group, measured outcomes, and analytic methods. Two of the studies used 

samples from nationally representative databases (Fenelon et al., 2017; Fertig and Reingold, 2007) and one studied residents in a local rental assistance program (Fauth, Leventhal, and Brooks-Gunn, 2004). Although all the studies were longitudinal, the study by Fauth, Leventhal, and Brooks-Gunn (2004) lacked baseline data. However, all the studies tested associations between rental as-

sistance and one or more physical health indicators, and they all compared residents who received 

rental assistance with similar residents who did not receive assistance.

This study focuses on the effects of rental assistance on modifiable health risk factors and behaviors among adult participants in the Panel Study of Income Dynamics (PSID), which is a nationally representative panel study of individuals in the United States. The aim of the study was to determine whether rental assistance influenced health as evidenced by changes in body mass index (BMI), 

obesity, alcohol consumption, smoking, and physical activity from baseline to a subsequent wave 

2, 4, or 6 years following baseline. 

134 The Housing-Health Connection

Impact of Rental Assistance on Modifiable Health Risk Factors and Behaviors in Adults Methods

Several data sources were used to construct a pooled analysis dataset. This dataset included three baseline years (1999, 2001, and 2003) and 2-, 4- and 6-year followup waves for each baseline year. 

Data sources included PSID survey data, geospatial data, data on rental assistance, and HUD in-

come limit data. The PSID survey data, geospatial data, and data on rental assistance were merged 

with HUD income limit data to determine study eligibility. We used a pooled cross-sectional design with propensity score matching to estimate the influence of rental assistance on each health indicator 2, 4, and 6 years after baseline. The study was approved by our institutional review board.

Sample

PSID participants included in this study (N = 1,374) were between 18 and 62 years of age at 

baseline and were identified as the same head of household from 2 years prior to baseline through 

the 2-year followup point. The baseline age limit of 62 years was used to exclude participants 

who might become eligible for housing for seniors at age 62. PSID participants included in the 

treatment group were receiving rental assistance 2 years after baseline but did not receive rental assistance from 4 years prior to baseline through the baseline year. The control group included 

PSID participants who were eligible for rental assistance 2 years after baseline but did not receive rental assistance from 4 years prior to baseline through 6 years after baseline. 

The PSID Assisted Housing Database (AHD) was used in part to determine whether a participant 

met criteria for inclusion in the treatment or control group (PSID, 2014). The PSID AHD was 

originally constructed by matching the addresses of PSID families with the street addresses of 

subsidized housing units, including Section 8 and voucher programs. The AHD includes the 

PSID family identifier and study year as well as the type of rental assistance, using HUD classifications. The AHD data for 1995 and later years classify four categories of rental assistance: public housing; other project-based housing, including low-income housing tax credits; tenant-based 

housing (primarily vouchers); and Farmers Home, state-assisted housing (HUD, 2017, 2002). We 

combined all four of the assisted housing categories to create a rental assistance indicator, coded 1 if a participant was receiving any type of rental assistance in a given year and 0 if the participant was not receiving rental assistance. We used PSID family identifiers and study year in the AHD to 

link the rental assistance indicator to other PSID data on families and individuals (McGonagle and Sastry, 2016; Newman and Schnare, 1997). Each year of PSID data was linked to each year of AHD 

data from the first prebaseline year through the final 6-year followup. The match was restricted 

to participants identified as the same head of household from prebaseline through 2 years after 

baseline for participants in the treatment group and from prebaseline through the 6-year followup 

for participants in the control group. These constraints enabled us to match family data on receipt of rental assistance to the head of household across multiple years, as appropriate for each group. 

To maintain an adequate sample, data on rental assistance at 4 and 6 years after baseline were not used to define the treatment group. 

Control group participants were determined to be eligible for rental assistance at the 2-year 

followup wave based on PSID total household income, number of people in the family unit, and 

Cityscape 135

Antonakos and Colabianchi

HUD income limit data.1 We used the 80 percent of Area Median Income limit to determine 

eligibility for rental assistance (HUD, 2001). About one-half of the participants in the control 

group met criteria for inclusion in more than one of the samples (baseline years 1999, 2001, and 

2003). These participants were the same head of household during multiple years and were eligible 

for rental assistance in more than one baseline year but did not receive rental assistance during 

any prebaseline or followup year. These participants were randomly assigned to one of the three 

subsamples to balance the number of observations across time prior to merging (exhibit 1). 

The resulting sample included 1,405 adult PSID participants eligible for the treatment or control 

group. The 1,374 PSID participants included in the analysis sample had complete data on all 

baseline covariates, with 116 participants in the treatment group and 1,258 participants in the 

control group.

Exhibit 1

Longitudinal Samples

2-Year  

4-Year  

6-Year  

Treatment   Control 

Sample

Prebaseline Baseline

Followup

Followup

Followup

n

n

1

1995–1997

1999

2001

2003

2005

22

431

2

1997–1999

2001

2003

2005

2007

34

413

3

1999–2001

2003

2005

2007

2009

60

414

Measures

Data on participants’ demographic characteristics and health status were obtained from PSID. 

Demographic variables measured at baseline included age, sex, race and ethnicity, education, per-

manent disability, employment status, hours worked in the previous year, total household income, 

and number of children in the family unit (exhibit 2). Race/ethnicity was determined from two 

separate questionnaire items indicating race and ethnicity and was coded as non-Hispanic Black, 

non-Hispanic White, Hispanic, or non-Hispanic other. The questionnaire item on ethnicity was 

asked for the first time in 2005; thus it was extrapolated to earlier baseline years and combined 

with data on race to create the race/ethnicity variable.

Data on modifiable health risk factors and behaviors were obtained from PSID for baseline 

and the followup waves. These health-related variables, used as outcomes in separate models, 

included BMI, obesity, smoking (any number of cigarettes), alcohol consumption (any alcohol 

and number of drinks per day), light physical activity (frequency per week), and heavy physical 

activity (frequency per week). BMI was calculated from self-reported height and weight measured 

in pounds and inches using the Centers for Disease Control and Prevention formula for adults: 

weight (pounds) / [height (inches)]2 x 703 (CDC, 2017). Obesity was defined as a BMI of 30 or 

higher (CDC, 2017). Smoking and alcohol consumption were determined from the PSID survey 

questions— 

1 PSID 2010 geospatial data and PSID public data (PSID, 2017a, 2017b) obtained for this study were matched with HUD 

income limit data (HUD, 2005, 2003, 2001) for assisted housing programs for years 2001, 2003, and 2005 separately using state, county, and metropolitan statistical area geocodes. Nearly all the PSID locations were matched with HUD data (2001, 97.8 percent; 2003, 98.4 percent; 2005, 99.0 percent). These data were then merged with PSID AHD data using a family identifier for each year individually. 

136 The Housing-Health Connection

Impact of Rental Assistance on Modifiable Health Risk Factors and Behaviors in Adults Exhibit 2

Demographic Characteristics at Baseline

Treatment

Control

n = 116

n = 1,258

t /  χ2

Age (years)

39.6 (10.7)

43.1 (10.7)

3.4***

Male gender (%)

44.8

67.3

22.8***

Race/ethnicity (%)

33.7***

Non-Hispanic Black

61.2

39.3

Non-Hispanic White

25.9

53.7

Hispanic

7.8

4.5

Non-Hispanic other

5.2

2.5

Education (%)

22.7***

Less than high school

37.9

21.4

High school diploma

38.8

37.3

Some college

19.0

37.1

Missing

4.3

4.2

Permanently disabled (%)

12.1

5.9

6.8**

Employed (%)

66.4

76.8

6.3*

Hours worked previous year 

1,474 (969)

1,772 (943)

3.2**

Household income ($) 

27,360 (22,785)

41,553 (42,986)

3.5***

Number of children in family unit

1.6 (1.6)

1.0 (1.2)

– 4.8***

*  p  < .05. ** p  < .01. ***  p  < .001.

Notes: Numbers in parentheses are standard deviations. Comparing the treatment and control groups,  t  is for means and χ 2 

is for percentages.

•  “Do you smoke cigarettes?”

•  “Do you ever drink any alcoholic beverages such as beer, wine, or liquor?”

•  “On average, do you have less than one drink a day, one or two drinks a day, three to four 

drinks a day, or five or more drinks a day?” 

The number of alcoholic drinks per day was coded as none (0), less than one (1), one to two (2), 

three to four (3), and five or more (4). Physical activity, coded as the number of times per week, was determined from survey items on light and heavy physical activity— 

•  “How often do you participate in light physical activity such as walking, dancing, gardening, 

golfing, bowling, etc.?”

•  “How often do you participate in vigorous physical activity or sports—such as heavy 

housework, aerobics, running, swimming, or bicycling?”

Analysis

Propensity score matching was used to estimate the effect of rental assistance on each health-

related outcome in separate models predicting outcomes at 2, 4, and 6 years following baseline. 

Participants included in each model had complete data across all waves for the health indicator 

being tested and complete data on baseline covariates. The propensity score for a given model in-

cluded baseline covariates and the appropriate baseline health indicator (for example, baseline BMI for the models predicting BMI following baseline). The propensity score is an estimate of the probability of treatment based on a set of observed covariates, obtained from a logit model, with scores ranging from 0 to 1. Matching is achieved by pairing similar subjects in the treatment and control Cityscape 137

Antonakos and Colabianchi

groups based on their propensity scores. The average treatment effect on the treated (ATET) is es-

timated by finding matches for participants in the treatment group from participants in the control group. For each matched case, and for each health indicator separately, the observed outcome for 

a matched participant in the control group was imputed for the treatment group participant. The 

ATET is estimated as the average of the differences between the observed and imputed outcomes 

of participants in the treatment group; it indicates the average effect of receiving rental assistance on the health of individuals in the treatment group at a given time point.2 An assumption is made 

that matching on the propensity score, which is constructed from a set of covariates, is adequate to remove the influence of systematic differences between the nonrandomized treatment and control 

groups (Rosenbaum and Rubin, 1983). We used one-to-one matching for all analyses.

The ATET coefficients for the 2-, 4-, and 6-year outcomes were estimated for each of the de-

pendent variables using Stata’s -teffects psmatch- command (Garrido et al., 2014; Social Science 

Computing Cooperative, 2015; StataCorp, 2015). Participants in the control group were matched 

with participants in the treatment group on a set of baseline covariates including baseline health measure, age, sex, race/ethnicity, education, employment status, number of hours worked in the 

previous year, permanent disability status, total household income, and number of children in the 

family unit. Stata v. 15.0 was used for all analyses (StataCorp, 2017).

A sensitivity analysis was conducted using data for participants who were not permanently 

disabled (n = 1,286; treatment group n = 102 and control group n = 1,184), because disabled 

individuals may receive benefits not available to nondisabled individuals and may have restrictions on physical mobility that can influence health.

Results

The treatment and control groups differed significantly on all the demographic characteristics at 

baseline (exhibit 2). Participants in the treatment group were younger and were more likely to be 

female, Black, permanently disabled, less educated, and unemployed. They also had lower house-

hold incomes and more children on average as compared with participants in the control group.

Unadjusted descriptive statistics for the health indicators for participants included in propensity score matching are shown in exhibit 3. The ATET coefficients for each model are shown in exhibit 4. 

Smoking was significantly higher among participants in the treatment group at the 2-year followup 

as compared with matched control group participants. At the 2-year followup, BMI and obesity 

were moderately but not statistically significantly higher among the treatment group relative to 

the matched control group participants. None of the differences for smoking, BMI, or obesity were 

significant at the 4- or 6-year followup points. Alcohol consumption and physical activity did not differ between the treatment group and matched controls in any of the models estimated.

2 ATET is estimated as τ= E[ μ̅ (1, p( X))-  μ̅ (0, p( X))[ W=1], Where τ is the treatment effect on the treated,  p( X) is the propensity score,  μ̅ (1,  p( X)) is the conditional mean under exposure to the treatment,  μ̅ (0,  p( X)) is the conditional mean under no exposure to the treatment, and  W = 1 indicates treatment group (Abadie and Imbens, 2016). 

138 The Housing-Health Connection

Impact of Rental Assistance on Modifiable Health Risk Factors and Behaviors in Adults Exhibit 3

Average Health Measures by Treatment Group

2-Year  

4-Year  

6-Year  

Baseline

Followup

Followup

Followup

Variable

Group

n

M (SD)

M (SD)

M (SD)

M (SD)

BMI 

Treatment

95

29.0 (6.3)

30.4 (7.0)

30.3 (6.3)

30.3 (6.9)

Control

1,162

28.0 (5.7)

28.2 (5.6)

28.6 (6.0)

29.0 (6.2)

Obesity (%)

Treatment

95

36.8 (4.8)

47.4 (5.0)

50.5 (5.0)

42.1 (5.0)

Control

1,162

29.1 (4.5)

31.9 (4.7)

33.0 (4.7)

34.9 (4.8)

Smoker (%)

Treatment

101

33.7 (4.7)

35.6 (4.8)

32.7 (4.7)

31.7 (4.7)

Control

1,212

31.9 (4.7)

30.3 (4.6)

28.5 (4.5)

27.8 (4.5)

Alcohol—any (%)

Treatment

102

58.8 (4.9)

54.9 (5.0)

50.0 (5.0)

55.9 (5.0)

Control

1,210

60.1 (4.9)

60.6 (4.9)

59.6 (4.9)

57.9 (4.9)

Alcohol—drinks/day

Treatment

100

0.8 (0.9)

1.1 (1.2)

1.1 (1.3)

1.4 (1.4)

Control

1,193

0.8 (0.9)

1.0 (1.1)

1.2 (1.2)

1.4 (1.3)

Light physical activity  Treatment

99

3.7 (4.3)

4.3 (4.7)

3.5 (4.1)

3.1 (4.1)

(times/week)

Control

1,170

5.1 (6.7)

4.5 (6.0)

3.9 (4.9)

4.1 (5.9)

Heavy physical activity  Treatment

102

2.0 (6.1)

1.5 (2.2)

2.0 (3.7)

2.2 (5.5)

(times/week)

Control

1,185

2.1 (4.2)

2.1 (5.2)

1.9 (3.2)

2.2 (3.2)

BMI = body mass index. M = mean. SD = standard deviation.

Note: Summary statistics are unadjusted for baseline covariates.

Exhibit 4

Average Treatment Effect on the Treated

2-Year Followup

4-Year Followup

6-Year Followup

Coef. (SE)

p

Coef. (SE)

p

Coef. (SE)

p

BMI

1.03 (0.53)

.05

0.62 (0.66)

.35

0.27 (0.72)

.71

Obesity

0.11 (0.06)

.07

0.07 (0.06)

.23

0.02 (0.06)

.72

Smoking

0.11 (0.05)

.04

0.09 (0.05)

.06

0.04 (0.05)

.46

Alcohol—any

– 0.05 (0.07)

.45

– 0.02 (0.07)

.79

– 0.01 (0.07)

.88

Alcohol—drinks/day

– 0.08 (0.14)

.55

– 0.07 (0.16)

.66

0.12 (0.18)

.51

Light physical activity

0.06 (1.16)

.96

– 0.45 (0.58)

.43

– 1.23 (0.95)

.20

Heavy physical activity

0.18 (0.33)

.59

0.45 (0.63)

.47

– 0.02 (0.66)

.97

BMI = body mass index. SE = standard error.

Note: 2-year followup  p -value for BMI = .051.

Results of the sensitivity analysis (not shown) estimating the ATET for the subgroup of individuals who were not permanently disabled revealed significantly higher likelihood of obesity among 

treatment group participants at the 2-year followup (Coef. = 0.14, SE = 0.07,  p = .04). Differences in obesity between the groups were not statistically significant at the 4- or 6-year followup points. 

Results for smoking, alcohol consumption, and physical activity showed no significant differences 

between the treatment group and matched controls in any of the models estimated.

Discussion

Results of this study reveal significant treatment effects 2 years after baseline on smoking and on obesity in a sensitivity analysis that excluded permanently disabled individuals. In contrast to the finding of no influence on smoking behavior in Fertig and Reingold (2007) at 1 or 3 years after 

Cityscape 139

Antonakos and Colabianchi

receiving rental assistance, our study did find a significant increase in smoking in the treatment group. In our study, the control group differed in many ways from the treatment group at baseline. 

Despite matching on propensity scores, the control group in our study may represent a different 

segment of the population than the control group in the study by Fertig and Reingold (2007) 

because all participants in that sample were drawn from the Fragile Families and Child Wellbeing 

Study. Also, in contrast to our finding of no differences in alcohol use, Fauth, Leventhal, and 

Brooks-Gunn (2004) found a reduction in symptoms of alcohol abuse among participants who 

received rental assistance; the measures of alcohol use and alcohol abuse are dissimilar, which 

could explain this difference in part. However, the control sample in Fauth, Leventhal, and Brooks-Gunn (2004) was drawn entirely from a population of adults living in a high-poverty area, whereas 

our sample was not restricted in that way. 

In the sensitivity analysis, we found that obesity increased in the treatment group. This finding 

is similar to the finding in Fertig and Reingold (2007) of an increase in overweight participants 

3 years after receiving rental assistance. Measures of overall health status, such as the number of health symptoms in Fauth, Leventhal, and Brooks-Gunn (2004) and general reported health status 

in Fenelon et al. (2017), are not directly comparable with the more specific health measures in our study. Both of those studies found improved health status among adults receiving rental assistance as compared with similar adults who did not receive rental assistance.

The PSID AHD data were used to determine whether a participant received rental assistance at 

any time from 2 years prior to the baseline year through a 6-year followup. We did not determine 

whether individuals received rental assistance more than 4 years prior to the baseline year or 

whether treatment group participants received assistance after the 2-year followup. A more restrictive sampling method would have resulted in excessive data loss in the treatment group. Thus 

results may include the influence of rental assistance beyond the 2-year followup wave. Samples 

were combined across years in order to obtain a sufficient number of observations, but combining 

data across years could bias the results of the study. However, combining data across years could 

provide a more robust sample, as time-dependent influences on outcomes that are omitted from 

the model may be attenuated by this method. The number of observations in the treatment group, 

even after combining the samples, ranged from 95 to 102 for the models estimated, limiting the 

power of the tests. In addition, PSID AHD data were available only through 2009, which limited 

the waves of data we included in the analysis. A set of baseline covariates controlled for in the 

analysis included a range of measures meant to capture potential confounding influences on 

outcomes, but unobserved covariates may also have influenced the findings. Multiple statistical 

tests were conducted, increasing the possibility of a false positive finding. The use of self-reported height and weight to determine BMI may have introduced error in the analysis to the extent that 

participants’ self-reported height and weight data were inaccurate. The use of a variable indicating any smoking, rather than frequency or number of cigarettes per day, combines individuals who 

smoke less with those who smoke more. This measure, however, captures the increased likelihood 

of smoking and provides some indication of increased exposure of smokers and nonsmokers in a 

housing unit to second-hand smoke, a known carcinogen.

140 The Housing-Health Connection

Impact of Rental Assistance on Modifiable Health Risk Factors and Behaviors in Adults The results of this study suggest that interventions to reduce smoking may be needed for the 

population of individuals receiving rental assistance, because the proportion of people smoking 

increased within 2 years after rental assistance began. The findings support HUD’s final rule on 

smoke-free housing, which became effective in 2017 and will be fully implemented in 2018.3 

Among individuals who receive rental assistance and who are not disabled, results of the sensitivity analysis suggest a need for targeted interventions to reduce obesity. Results of this study showed effects of rental assistance within a short time after assistance was received, but not in a longer timeframe, at 4 to 6 years after baseline. Thus, interventions might be most effective if they occur soon after individuals receive rental assistance. Environmental factors that may affect health were not examined in this study but might contribute to health risk factors and behaviors and should be investigated in further work.

Conclusion

In a sample of individuals from PSID, a propensity score analysis showed increased likelihood of 

smoking among individuals who received rental assistance between baseline and a 2-year followup. 

BMI, obesity, alcohol consumption, and light and heavy physical activity were not significantly 

different in this matched case analysis. A sensitivity analysis showed increased obesity among nondisabled individuals. Results of the study suggest that interventions to reduce smoking and obesity may benefit individuals who receive rental assistance.

Acknowledgments

Research reported in this article was funded (in part) by the National Cancer Institute of the 

National Institutes of Health under award number R03CA184478 to Natalie Colabianchi. The 

collection of data used in this study was partly supported by the National Institutes of Health 

under grant numbers R01 HD069609 and R01 AG040213, and the National Science Foundation 

under award numbers SES 1157698 and 1623684. The content is solely the responsibility of the 

authors and does not necessarily represent the official views of the National Institutes of Health. 

Any opinions, findings, and conclusions or recommendations expressed in this material are those 

of the authors and do not necessarily reflect the views of the National Science Foundation. Nancy 

E. Hood contributed to the early development of this work.

Authors

Cathy L. Antonakos is a research area specialist senior at the University of Michigan School of 

Kinesiology.

Natalie Colabianchi is an associate professor in the School of Kinesiology and research associate 

professor at the Institute for Social Research at the University of Michigan.

3 “Instituting Smoke-Free Public Housing; Final Rule,” 24 CFR Parts 965–966.  Federal Register 81 (233). December 5, 2016.

Cityscape 141

Antonakos and Colabianchi

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Cityscape 143

144 The Housing-Health Connection

The Geography of Vacant 

Housing and Neighborhood 

Health Disparities After the 

U.S. Foreclosure Crisis

Kyungsoon Wang

Georgia Institute of Technology

Dan Immergluck 

Georgia State University

Abstract

•  Objectives: We examined the impact of long-term (6 months or more) vacant housing 

and various durations of vacancy on a variety of health outcomes at the neighbor-

hood level across three types of U.S. metropolitan areas (metros): (1) those that have 

experienced consistently strong growth, (2) those that have undergone weak growth, 

and (3) those hit hardest by the foreclosure crisis.

•  Methods: We used hierarchical linear modeling with long-term vacant housing data 

derived from the U.S. Postal Service as well as data for health outcomes obtained 

from the Centers for Disease Control and Prevention to examine the health effects of 

residents who resided in 19,243 neighborhoods (census tracts) in the 50 largest met-

ropolitan areas during the housing recovery.

•  Results: Neighborhood long-term vacancy is significantly associated with neighborhood 

health problems in adults, but the association between vacant housing and neighbor-

hood health outcomes varies based on the growth trajectory of the metropolitan area. 

For most health outcome measures, long-term vacancies are more strongly associated 

with poor outcomes in strong-growth and hard-hit metros than in weak-growth metros, 

but the reverse is true for asthma and mental health. Our findings also suggest that very 

long-term (more than 3 years) vacant housing increased significantly after the housing 

crisis and was significantly associated with health problems in all three types of metros. 

•  Conclusions: The differences in the relationship between neighborhood-level long-

term housing vacancy and health outcomes across the three types of metros should 

be considered when addressing community development strategies for decreasing va-

cancy rates aimed at improving health outcomes.

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 145

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

Wang and Immergluck

Introduction

The Great Recession, the most severe housing market crisis in the United States since the Great 

Depression, saw mortgage foreclosure of more than 5.5 million homes by the end of 2014 (Carlyle, 

2015). In areas with high foreclosure rates, the resulting accumulation of vacant properties generated negative effects on neighborhoods, including decreased property values and increased crime rates 

(Apgar and Duda, 2005; Immergluck and Smith, 2006; Mallach, 2008; Raleigh and Galster, 2014; 

Schuetz, Been, and Ellen, 2008). High levels of neighborhood housing vacancies—especially lasting 

more than several months—have long been a concern to community developers and policymakers 

(Hollander, 2011; Sternlieb and Indik, 1969). 

Although a number of researchers have examined the health effects of foreclosures, few have 

studied the relationship between vacant housing and health outcomes at the neighborhood (census 

tract) level within the context of metropolitan areas (metros). Studies of vacant housing and health outcomes are limited to case studies in particular cities or sets of cities (Cohen et al., 2003; Cohen et al., 2000). A need exists to study connections between vacant housing and health that allow for some generalization, yet recognize that these relationships may vary across different types of metros and housing markets. The effects of high levels of neighborhood vacancy on health outcomes may 

differ across types of metros. For example, vacancies created by new construction may be common 

in “strong-growth” metros, those induced by population loss may be common in “weak-growth” 

metros, and those resulting from foreclosure or eviction are prevalent in boom-bust “hard hit” 

metros affected by the foreclosure crisis of the late 2000s. 

This study examines the trajectory of long-term vacant housing from 2011 to 20141 and investi-

gates the association between long-term (6 months or more) vacant housing and neighborhood 

health outcomes in 2014 across three types of U.S. metros. Our definition of long-term vacancy 

follows Immergluck (2016), who defines it as a property vacant for 6 months or longer and so 

avoids most transitional vacancies for rent or sale. In this study, we sought to answer two research questions: First, is long-term vacant housing associated with neighborhood health outcomes, and 

if so does this association vary across different types of metropolitan areas? Second, does the length of vacancy, ranging from relatively shorter duration (6 to 12 months) to very long duration (3 years or longer) matter? That is, do vacancies of different lengths have different effects on health outcomes at the neighborhood level? An examination of the determinants of health outcomes in deteriorated 

physical environments such as those with boarded-up housing across different cities may help 

policymakers and planners design effective tools for improving neighborhood health outcomes and 

decreasing the health inequality associated with vacant housing. 

Background

After the foreclosure crisis, the U.S. housing market exhibited disparities in market recovery and neighborhood health outcomes. The national housing market recovery, examined by trajectories 

of national vacancy rates and housing values, exhibited geographic disparities, with some metros 

1 We examined the trajectory of long-term vacancy after 2011 because the discontinuity in the data source was in 2011. We also selected 2014, when both vacancy and health data were publicly available across the United States, which enabled us to construct a cross-sectional design for regression analyses. Details about the data will be discussed in the data section.

146 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

recovering relatively slowly or worsening whereas others recovered quickly and improved (Immer-

gluck, 2016; Wang, 2016). Moreover, low-income and minority groups tended to experience more 

stress from debt and foreclosure, which worsened health disparities (Houle, 2014; Libman, Fields, 

and Saegert, 2012; Saegert, Fields, and Libman, 2011). 

Uneven housing recovery and health disparities can be explained partly by a process of cumulative 

causation. Myrdal (1957) asserted that the process of cumulative causation with capital and labor 

flowing from lagging regions into developed regions tends to generate unbalanced regional growth 

and disparities. Likewise, the rise in the number of foreclosures led to a great number of vacant 

properties, which depressed the construction industry and businesses dependent on local con-

sumer spending. As a result of high concentrations of vacant homes in neighborhoods, residents 

may move to other neighborhoods in pursuit of higher-quality services, schools, infrastructure, and jobs. At the same time, amenities and tax bases can deteriorate, leading to further disinvestment in these areas. This vicious cycle of special polarization can lead to greater health inequalities. Given the possibilities of cumulative pressures toward distress, external intervention may be critical to addressing housing and health disparities in some cities. 

Neighborhood physical and socioeconomic conditions might lead to negative health outcomes by 

influencing health behaviors in various ways. For example, although the availability of affordable housing and convenient transportation may improve neighborhood health outcomes, physically 

deteriorated neighborhoods with substandard housing may erode residential health and well-being 

(Cohen et al., 2003; Cohen et al., 2000; Krieger and Higgins, 2002; Miles, Coutts, and Mohamadi, 

2011; Ross and Mirowsky, 2010). Substandard housing including dampness and mold, deteriorat-

ing insulation, lead paint, the presence of rodents, and toxic chemicals can increase the incidence of allergies, headaches, vomiting, asthma, and other respiratory diseases; lung cancer; and mental health problems (Dales et al., 1991; Jacobs et al., 2002; Peat, Dickerson, and Li, 1998; Phipatanakul et al., 2000). In addition, neighborhood socioeconomic conditions are also associated with health outcomes. In general, residents with lower incomes, lower levels of education, and fewer 

economic opportunities are more likely to live in substandard homes and deteriorated neighbor-

hoods, which result in multiple health problems that contribute to cumulative health disparities 

(Houle, 2014; Rugh, Albright, and Massey, 2015; Libman, Fields, and Saegert, 2012; Saegert, 

Fields, and Libman, 2011).

As many foreclosed homes became vacant in the late 2000s, even in areas that had not previously experienced vacancy problems, and because studies focusing on the relationship between vacant hous-

ing and health are scarce, we reviewed studies that explored the relationship between foreclosures and health. The studies provide evidence that a rise in the number of foreclosures has negative 

effects on residents and neighborhood conditions, including effects on home values, social capital, neighborhood stability, and crime rates (Ellen, Lacoe, and Sharygin, 2013; Immergluck and Smith, 

2006; Li and Morrow-Jones, 2010; Ross and Squires, 2011; Schuetz, Been, and Ellen, 2008). 

Consequently, residents who experienced defaults and foreclosures during the Great Recession 

also experienced serious physical and mental health degradation (Cannuscio et al., 2012; Libman, 

Fields, and Saegert, 2012; Pollack and Lynch, 2009), and living in neighborhoods with high levels 

of such properties is associated with weight gain, hospital visits, and mental health problems such as depression and suicide (Arcaya et al., 2013; Currie and Tekin, 2015; Houle and Light, 2014). 

Cityscape 147

Wang and Immergluck

The focus of this study is to examine the relationship between long-term vacant housing and 

neighborhood health outcomes. Similar to foreclosures, vacant properties are associated with 

decreases in home values and increases in crime, and the longer a home remains vacant in a neigh-

borhood, the stronger are such effects (Cui and Walsh, 2015; Han, 2014). Although two studies 

found negative effects of vacant housing on health outcomes (Cohen et al., 2003; Cohen et al., 

2000), none have revealed the effects of longer durations of vacancy on health outcomes, nor have 

any examined the relationship between vacant housing and neighborhood health problems across 

the United States following the Great Recession. 

Based on the literature concerning vacant and foreclosed homes, we hypothesize that the associa-

tion between long-term vacant housing and health outcomes will be amplified in neighborhoods 

with longer durations of vacant housing. During the mortgage crisis, the accumulation of 

foreclosed properties varied across different types of metros: traditionally weak markets had persistently higher levels of foreclosed properties, and boom-bust markets with initially lower levels of foreclosed homes experienced large declines in home values and large increases in foreclosed properties (Immergluck, 2010). Thus, we hypothesize that, in the aftermath of the foreclosure crisis, a change of vacancy that largely stemmed from foreclosures may affect health outcomes differently 

across different types of metros. 

Data and Methods

We used long-term vacant housing data collected by the U.S. Postal Service (USPS) and aggregated 

quarterly to the census tract level by the U.S. Department of Housing and Urban Development 

(HUD), which provide information on residential vacancies with durations from 3 to 36 months 

or longer (HUD, 2016).2 Health data were obtained from the Centers for Disease Control and 

Prevention (CDC) for the 500 largest U.S. cities. The data consisted of 2014 estimates on health 

outcomes among adults at the census tract level: overall mental health, overall physical health, 

cancer, coronary heart disease (CHD), diabetes, asthma, arthritis, high blood pressure, stroke, 

high cholesterol, chronic obstructive pulmonary disease, chronic kidney disease, and missing 

all teeth. CDC released its 2014 health indicator data for the 500 largest cities, containing about 28,000 census tracts, in December 2016 through the CDC Chronic Data Portal. The primary data 

source was the CDC 2014 Behavioral Risk Factor Surveillance System, which surveyed adults older 

than 18 years (CDC, 2017). We pooled the health estimations only for the year 2014.3 The CDC 

500-city health indicator datasets and HUD-USPS periodic vacancy datasets enabled us to carry 

out comparative analyses across the United States. To examine associations between long-term 

2 The USPS identifies a vacant address as one to which mail has not been delivered for more than 3 months (GAO, 2011). 

HUD-USPS data, so named throughout the article, provide counts of “no stat” addresses that are viewed as long-term vacancies but not classified as vacant because they are not habitable. As HUD staff recommended, we excluded no-stat addresses because they could generate significant measurement error.

3 The dataset includes 2013 and 2014 model-based small area estimates for 27 measures that are categorized as 5 unhealthy behaviors, 13 health outcomes, and 9 prevention practices. Data sources used in measurements of adults older than 18 include the Behavioral Risk Surveillance System data, U.S. Census Bureau 2010 data, and the American Community Survey (ACS) 2009–2013 and 2010–2014 5-year data (CDC, 2017).

148 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

vacant housing and neighborhood health outcomes in regression analyses, we used the data from 

the CDC and the HUD-USPS for the year 2014, when both vacancy and health data were available 

across the United States. 

Exhibit 1 illustrates long-term vacancy rates in the second quarter of 2014 for the 50 largest metros and the geographical locations of the CDC 500-city boundaries. We merged the two datasets at the 

tract level: 2014 HUD-USPS vacancy data for the 50 largest U.S. metros and 2014 CDC health data 

for 500 cities. As a result, we were able to construct a dataset consisting of 19,243 tracts in 295 

U.S. cities in the 50 largest metropolitan areas. Tracts for 205 U.S. cities were excluded from the dataset because they were not in the 50 largest metropolitan areas. 

Exhibit 1

The Long-Term Vacancy Rate, 500-City Boundaries, and Three Types of MSAs in 

the 50 Largest MSAs

CDC = Centers for Disease Control and Prevention. HUD-USPS = data from U.S. Department of Housing and Urban Development and the U.S. Postal Service. MSA = metropolitan statistical area.

Note: As of the second quarter of 2014.

A Simple Typology of the 50 Largest Metropolitan Areas

To measure the relationship between vacant homes and health outcomes across the different 

types of metros, we constructed a simple typology of large metropolitan areas. We used cluster 

analysis to categorize the three types of metros representing metropolitan growth and economic 

development during the recent housing crisis. The four clustering variables included changes in 

Cityscape 149

Wang and Immergluck

population, Gross Domestic Product (GDP), and home values from 2005 to 2014. The fourth vari-

able was changes in the population for the short term, from 2011 to 2014, to assign more weight 

to population growth because shrinking cities are generally defined as those experiencing popula-

tion decline over a relatively short term.4 Using this approach, we classified the 50 largest metros into “strong-growth,” “hard-hit,” or “weak-growth” metros. Then we classified tracts according to 

the type of metro and found 7,552 tracts in strong-growth metros, 4,017 tracts in hard-hit metros, and 7,405 tracts in weak-growth metros. 

Exhibit 1 presents three types of metropolitan areas. Strong-growth metros exhibit high population growth (a mean of 21 percent), strong economic growth (a mean of 9.85 percent in GDP per 

capita), and moderate levels of home appreciation (a mean of 6.5 percent) from 2005 to 2014, and 

high short-term population growth (a mean of 6.5 percent) from 2011 to 2014. These metros had 

lower neighborhood long-term vacancy rates (about 2.3 percent) in the second quarter of 2014. 

They are in the West (including San Francisco, California; Seattle, Washington; and Portland, Or-

egon); in the South (including Houston, Austin, and Dallas, Texas); and in the East (including New York City; Columbus, Ohio; Nashville, Tennessee; and Raleigh, North Carolina). Hard-hit metros 

include metros with moderate growth in population (16 percent), a decline in GDP per capita  

(9 percent), and a large deflation of home values (18 percent) from 2005 to 2014, and a moderate 

short-term growth of population (4.6 percent) from 2011 to 2014. These metros were the hardest 

hit by the most recent economic shock and had higher vacancy rates (about 2.8 percent) in 2014. 

They include most California and Florida metros, including Riverside, Sacramento, and San Diego, 

California, and Miami and Jacksonville, Florida. These hard-hit metros experienced relatively 

high levels of foreclosures during the mortgage crisis. Weak-growth metros typically experienced 

low levels of long-term population growth (5.4 percent), modest growth in GDP per capita (2.1 

percent), and moderate home value increases (10.3 percent) from 2005 to 2014. They either saw 

no short-term population growth or lost population. These metros had the highest vacancy rates 

(about 3.4 percent) in the second quarter of 2014. Weak-growth metros are generally traditional 

Rust Belt metros, including Baltimore, Maryland; Buffalo, New York; Chicago, Illinois; Cleveland, 

Ohio; Detroit, Michigan; St. Louis, Missouri; and Philadelphia and Pittsburgh, Pennsylvania.

Long-Term Vacancy in 295 Cities Within the 50 Metropolitan Areas

We constructed variables measuring the number of long-term vacant residential units by various 

durations of vacancy, including 6 months to 1 year, 1 to 2 years, 2 to 3 years, and 3 years or more, and by year for 295 U.S. cities within the 50 largest metropolitan areas. We break down vacant 

units further into the three types of metros in appendix A. 

Exhibit 2 illustrates the long-term vacant housing trajectories in 295 cities in the three types of metros after the foreclosure crisis. The top panel of exhibit 2 shows that cities experienced gradual decreases in long-term vacancy rates (0.6 percentage point in strong-growth, 1.2 in hard-hit, 

and 0.6 in weak-growth metros) from 2011 to 2014. The bottom panel of exhibit 2 shows that, 

4 Data for the cluster analysis were obtained from ACS 2005 (1-year estimates), ACS 2011 (1-year estimates), ACS 2014  

(1-year estimates), ACS 2010–2014 (5-year estimates), and the Bureau of Economic Analysis. The clusters are distinctive groups that show that the value of the silhouette measure of cohesion and separation is more than 0.5 and that analysis of variance results for the three clusters have significantly different means among the four clustering variables (Norusis, 2012).

150 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

Exhibit 2

Changes in Vacancy From the Second Quarter of 2011 to the Second Quarter of 

2014 in 295 U.S. Cities 

Note: Denominators of both long-term (6 months or more) and very long-term (more than 3 years) are all residential addresses in each tract in the second quarter of each year.

although the number of housing units vacant for less than 3 years gradually declined from 2011 to 

2014, vacancies of more than 3 years markedly increased in all three types of metros. The number 

of properties vacant for longer than 3 years increased by 94.3 percent (61,706 to 119,908 units) in hard-hit metros, by 66.1 percent (180,888 to 300,535 units) in weak-growth metros, and by 64.9 

percent (106,877 to 176,197 units) in strong-growth metros. Cities in hard-hit metros exhibited 

a greater reduction in vacancies over 6 months but did not have higher rates during the 2011-to-

2014 period. Cities in weak-growth metros showed the small reductions in vacancies during the 

housing recovery period, but they consistently had the highest vacancy rates and larger increases in vacancies lasting more than 3 years.

Multivariate Analysis

We examined the association of long-term vacancies with neighborhood health outcomes in the three 

types of metropolitan areas using hierarchical linear modeling (HLM), which is commonly used to 

examine neighborhood characteristics and health outcomes (LeClere, Rogers, and Peters, 1998). 

Cityscape 151

Wang and Immergluck

Although ordinary least squares regression assumes that all observations are not correlated, HLM 

allows for correlated observations when lower-level observations are clustered within higher-level groups. In this study, census tracts are used as proxies for neighborhoods clustered within metro areas. 

We ran separate metro-tract HLM models in each of the three types of metropolitan areas. Our 

dependent variables included 13 health indicators. A key predictor variable in each model is the 

percentage of units that are long-term vacant (that is, those units vacant for 6 months or longer in 2014). This variable is the sum of long-term vacancies divided by the number of residential units 

in each census tract. Because of the positively skewed nature of long-term vacancy rates, we transformed these variables to logarithms. We also took the logarithm of the health indicators because 

this log-log form generated a good fit for these models. As all variables are in logarithmic form, the coefficients of the log-log models represent the elasticities of health indicators with respect to long-term vacancy (Wooldridge, 2009). That is, the coefficients represent the expected percentage 

change in the health outcome variable for each 1-percent increase in the vacancy rate.

We ran separate models using various durations of long-term vacancy in 2014, including 6 months 

to 1 year, 1 to 2 years, 2 to 3 years, and 3 years or more. Census tract-level neighborhood control variables, which were selected based on factors identified in previous research, include percent African-American, percent Hispanic, percent Asian, percent married households, median age, percent 

families below poverty, percent persons with less than a high school diploma, percent uninsured 

households, percent commuting more than 30 minutes, and Median Family Income (Cohen et al., 

2003; Cohen et al., 2000; Houle, 2014; Pollack and Lynch, 2009; Ross and Mirowsky, 2001, 2010). 

Metropolitan-level control variables include changes in population and unemployment rates for the 

past decade (Cohen et al., 2003; Houle, 2014). To control for location affordability, we include HUD 

housing and transportation affordability indices of poverty-level households (HUD, 2017). 

Results and Discussion

Exhibit 3 provides descriptive statistics for the three types of metros. Generally, health problems in cities in weak-growth metros exhibit the highest means (the exceptions were cancer and chronic 

kidney disease), and the means of hard-hit and strong-growth metros were similar. In all three 

types of metros, the health problem that has the highest percentage of adults living in cities is high cholesterol, followed by high blood pressure and arthritis. Vacancy rates in the second quarter of 2014 were consistently high in cities in weak-growth metros (4.6 percent), followed by those in 

hard-hit metros (3.0 percent), and then those in strong-growth metros (2.1 percent) metros. On 

average, weak-growth metros, which often include cities with declining populations and older 

industries, contain a higher share of African-Americans than other metros. They also consist of the most disadvantaged populations with higher poverty rates and lower educational attainment, due 

in part to racial discrimination and segregation. 

The results of regressing 13 adult health outcome variables (in logged form) on long-term vacancy 

rates (in logged form) and the control variables show that the results for the control variables 

are generally consistent with prior research across the three types of metros (see appendix B). 

Neighborhoods with lower-income households, more African-Americans, and less–educated, 

higher-poverty populations are disproportionately exposed to health problems.

152 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

Exhibit 3

Descriptive Statistics (1 of 2)

Strong Growth

Hard Hit

Weak Growth

Variable Description

Mean

SD

Mean

SD

Mean

SD

Neighborhood-level dependent variable

Health outcome (adults aged ≥18 years)a

MHLTH

% Mental health not 

11.909

3.400

12.530

3.445

13.484

3.893

good for days ≥14 

days

PHLTH

% Physical health not  12.141

4.194

12.509

4.247

13.912

4.866

good for ≥14 days 

CANCER

% Cancer (excluding 

5.133

1.566

5.692

2.248

5.367

1.654

skin cancer) 

CHD

% Coronary heart 

5.281

1.759

5.758

2.294

6.085

2.095

disease 

DIABETES

% Diagnosed diabetes  10.307

3.846

10.092

3.678

11.744

4.728

CASTHMA

% Current asthma 

9.357

1.840

9.227

1.535

10.296

2.404

ARTHRITIS

% Arthritis 

20.860

5.181

21.892

6.084

23.838

7.087

BPHIGH

% High blood pressure  29.678

6.732

29.369

7.358

32.048

9.194

STROKE

% Stroke 

2.821

1.185

2.940

1.275

3.421

1.700

TEETHLOST

% All teeth lost 

15.074

7.835

13.944

7.144

17.645

9.511

HIGHCHOL

% High cholesterol 

35.804

4.429

36.324

5.279

36.495

4.598

COPD

% Chronic obstructive 

5.734

2.151

6.224

2.242

6.767

2.833

pulmonary disease 

KIDNEY

% Chronic kidney 

2.556

0.697

2.945

0.907

2.853

0.889

disease 

Neighborhood-level independent variables

Demographic characteristicsb

BLACK

% Black

17.410

23.063

15.483

22.521

27.909

33.571

ASIAN

% Asian

11.160

14.962

6.012

7.845

7.143

10.299

HISPANIC

% Hispanic

26.752

23.738

28.736

24.831

22.885

26.625

MARRIED

% Married households 42.223

16.719

42.975

17.007

36.936

17.727

AGE

Median age

35.911

6.236

37.013

8.563

35.759

6.675

Socioeconomic characteristicsb

POVERTY

% Families below 

14.429

12.599

14.472

12.777

18.206

15.034

poverty level

INCOME

Median family income 

7.417

4.145

6.968

3.719

6.384

3.739

($10,000)

LOW_EDU

% Less than high 

16.912

13.524

15.092

12.764

18.387

13.914

school education

UNINSURED

% Uninsured 

15.405

10.040

15.866

9.609

14.722

8.884

households

COMMUTE

% Workers commuting  47.133

19.041

37.567

12.282

41.014

15.001

> 30 minutes

Vacant housingc

VACANCY_6MPLUS_14 % Vacancy (6 months 

2.126

3.400

2.965

3.591

4.571

6.620

+) in 2Q 2014

VACANCY_6M_1Y

% Vacancy (6 

0.216

0.653

0.351

0.634

0.421

0.903

months–1 year) in 2Q 

2014

VACANCY_1Y_2Y 

% Vacancy (1–2 years)  0.335

0.584

0.634

1.006

0.653

1.214

in 2Q 2014

VACANCY_2Y_3Y

% Vacancy (2–3 years)  0.263

0.567

0.500

1.160

0.626

1.151

in 2Q 2014

VACANCY_3YPLUS

% Vacancy (3 years +) 

1.327

2.619

1.488

2.157

2.882

4.716

in 2Q 2014

Cityscape 153

Wang and Immergluck

Exhibit 3

Descriptive Statistics (2 of 2)

Strong Growth

Hard Hit

Weak Growth

Variable Description

Mean

SD

Mean

SD

Mean

SD

Metropolitan-level independent variables

Macro characteristicsd

POP_CH

% Change in 

13.954

5.936

12.635

2.644

3.615

3.329

population (2005–

2014)

UNEMP_CH

Change in 

22.616

9.479

40.447

10.100

30.071

14.177

unemployment rate 

(2005–2014)

HCOST

Housing costs as a 

115.380

20.821 105.773

13.052 106.728

15.859

percentage of income 

(type2-poverty level)

TCOST

Transportation costs 

50.425

5.137

55.262

4.082

51.193

4.351

as a percentage 

of income (type2-

poverty level)

N

7,552

4,017

7,405

2Q = second quarter. SD = standard deviation. 

Sources: a CDC (2014); b American Community Survey (ACS) 2011–2015; c 2014 U.S. Department of Housing and Urban Development (HUD)-U.S. Postal Service vacancy data; d HUD Location Affordability Portal, ACS 2005–2009, ACS 2010–2014, Bureau of Economic Analysis

Long-Term (6 Months or More) Vacant Housing and Neighborhood Health 

Outcomes 

Exhibit 4 summarizes key results by reporting the coefficients for long-term vacancy. The signifi-

cance and magnitude of long-term vacancy varies across the metro types. Long-term (6 months or 

more) vacancies are significantly and positively associated with 13 adult health problems in strong-growth metros, 13 in hard-hit metros, and 12 in weak-growth metros. Heart-related diseases such 

as CHD and stroke were most prevalent in adults living in neighborhoods with high vacancy across 

three types of metros after the recent foreclosure crisis. Among health outcomes, CHD is the health problem most strongly associated with high vacancy rates in strong-growth and hard-hit metros. 

For example, every 1-percent increase in long-term vacancy rate was associated with 0.0318- and 

0.0225-percent increases in the proportion of residents who had CHD in strong-growth and hard-

hit metros, respectively. In general, the magnitudes of the associations were high in strong-growth and hard-hit metros and lowest in weak-growth metros (the exceptions being mental health and 

asthma outcomes). For example, in our models, all else being equal, every 1-percent increase in 

the long-term vacancy rate was associated with 0.0136- and 0.0109-percent increases in the pro-

portion of residents who had overall physical health problems in strong-growth and hard-hit met-

ros, respectively, but only a 0.0082-percent increase in weak-growth metros. In addition, all else being equal, every 1-percent increase in the long-term vacancy rate was associated with 0.0171- 

and 0.0163-percent increases in the proportion of residents who had cancer in strong-growth and 

hard-hit metros, respectively, but it had no significant association in weak-growth metros. 

At first glance, these results may seem counterintuitive. We might have expected that high levels 

of long-term vacancies would have stronger effects on health outcomes in weak-growth metros. 

154 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

Exhibit 4

HLM for Long-Term Vacancy (6 Months or More) and Health in 295 U.S. Cities 

Independent 

Dependent 

Strong Growth

Hard Hit

Weak Growth

Variables: log  

Variable: log (%  (% Long-

Health  

Estimated

Estimated

Estimated

Term  

t-Value

t-Value

t-Value

Outcome)

Coefficient

Coefficient

Coefficient

Vacancy)

% Mental  

% Vacancy  

0.0034

2.330**

0.0050

3.310***

0.0053

2.410**

health

(6 months 

+), 2Q 2014

% Physical 

% Vacancy  

0.0136

5.620***

0.0109

4.190***

0.0082

3.270***

health

(6 months 

+), 2Q 2014

% Cancer

% Vacancy  

0.0171

4.120***

0.0163

3.920***

0.0039

1.060

(6 months 

+), 2Q 2014

% Coronary 

% Vacancy 

0.0318

5.830***

0.0225

8.090***

0.0127

3.250***

heart disease 

 (6 months 

(CHD)

+), 2Q 2014

% Diagnosed  % Vacancy  

0.0197

5.610***

0.0147

3.890***

0.0110

4.930***

diabetes 

(6 months 

+), 2Q 2014

% Current 

% Vacancy  

0.0027

2.610***

0.0027

3.580***

0.0031

3.020***

asthma 

(6 months 

+), 2Q 2014

% Arthritis 

% Vacancy  

0.0160

4.930***

0.0141

3.400***

0.0088

5.270***

(6 months 

+), 2Q 2014

% High blood  % Vacancy  

0.0149

6.410***

0.0117

5.010***

0.0082

4.090***

pressure 

(6 months 

+), 2Q 2014

% Stroke 

% Vacancy  

0.0284

5.740***

0.0213

8.440***

0.0138

4.140***

(6 months 

+), 2Q 2014

% All teeth lost  % Vacancy  

0.0189

5.100***

0.0139

7.660***

0.0093

2.300**

(6 months 

+), 2Q 2014

% High 

% Vacancy  

0.0107

5.280***

0.0074

3.860***

0.0059

5.900***

cholesterol 

(6 months 

+), 2Q 2014

% Chronic 

% Vacancy  

0.0197

5.800***

0.0169

8.200***

0.0098

2.840***

obstructive 

(6 months 

pulmonary 

+), 2Q 2014

disease

% Chronic 

% Vacancy  

0.0163

5.720***

0.0127

7.500***

0.0078

3.630***

kidney  

(6 months 

disease

+), 2Q 2014

2Q = second quarter. HLM = hierarchical linear modeling.

**  p  < 0.05. ***  p  < 0.01. 

However, these metros already had higher levels of vacancy before the foreclosure crisis and 

have other regional stressors that may be more influential on health outcomes, such as higher 

unemployment rates. These results suggest that neighborhoods in metros with historically lower 

vacancy rates (strong-growth and hard-hit metros) may be more sensitive to vacancy shocks at the 

neighborhood level, at least in terms of most of the health outcome measured here.

Cityscape 155

Wang and Immergluck

Except for asthma and mental health, in weak-growth metros the association between neighbor-

hoods with high levels of vacancy and health outcomes were weaker. Of course, asthma and mental 

health are not trivial health problems. Asthma, in particular, has been the most prevalent chronic disease in residents living in poor housing and deteriorated neighborhoods, currently affecting 

more than 24 million Americans (NCHS, 2015a, 2015b). In our models, all else being equal, every 

1-percent increase in the long-term vacancy rate was associated with a 0.0031-percent increase in 

the proportion of residents who had asthma in weak-growth metros, but only 0.0027-percent in-

creases in strong-growth and hard-hit metros. In addition, every 1-percent increase in the long-term vacancy rate was associated with a 0.0053-percent increase in the proportion of residents who had 

overall mental health problems in weak-growth metros, but 0.0034- and 0.0050-percent increases 

in strong-growth and hard-hit metros, respectively. One explanation for this finding is the possible cumulative factors affecting asthma and mental health that are present in weak-growth metros and 

that may interact with vacancies. These effects are generally small. Nonetheless it appears that long-term neighborhood vacancy in weak-growth metros is modestly associated with asthma and mental 

health problems. 

Hard-hit metros in exhibit 4 also exhibited a significant association between long-term vacant housing and health outcomes. Vacancy is associated with health outcomes in a way that is somewhat 

similar to that in strong-growth metros, but the coefficients are smaller in magnitude. Two distinct economic variables are associated with neighborhood health outcomes—housing affordability and 

changes in the unemployment rate at the metropolitan level (see appendix B). Generally, increases 

in housing costs as a percentage of income at the metro level were negatively associated with 

neighborhood health problems, indicating that spending more on housing may improve housing 

conditions and create a healthier environment, particularly in hard-hit metros after the foreclosure crisis. However, extremely low-income households (that is, those below the poverty level) have 

little to spend on housing, which can have negative spillover effects on health outcomes. This finding provides further support for the notion that housing cost burdens can have negative spillover 

effects onto health outcomes. Another economic condition, rising unemployment at the metropoli-

tan level, is associated with neighborhood health problems, particularly in hard-hit metros.

Various Durations of Vacant Housing and Neighborhood Health Outcomes

Exhibit 5 provides estimation results for the relationship between various durations of long-term 

vacancy rates and health outcomes across the three types of metropolitan areas. Overall, our results show that very long-term vacant housing (more than 3 years) is significantly associated with health problems across all three types of metros. The coefficients of the very long-term vacancy rate are much larger than for any of the shorter durations, indicating that very long durations of vacancy have a particularly strong association with health problems. Generally, although health outcomes are 

associated with both shorter and longer durations of vacancy rates in strong-growth metros, health outcomes are also associated with very long durations of vacancy in weak-growth metros. In hard-hit metros, health problems more often occurred in the mid-duration of vacancy (from 1 to 2 years) 

and/or after very long durations. Because most vacancies in these metros were from recently fore-

closed homes, foreclosure processes (that is, foreclosure notice, auction, and redemption or eviction) that lasted 1 to 2 years might have affected neighborhood health. Thus, when properties lie vacant for very long periods, they are strongly associated with health problems across all three types of metros. 

156 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

Exhibit 5

HLM for Long-Term Vacant Housing in Various Duration and Health in Three Types of 

Top 50 Metropolitan Areas (1 of 4)

Independent 

Strong Growth

Hard Hit

Weak Growth

Dependent  Variables: log  

Variable: log  (% Long-

(% Health 

Estimated

Estimated

Estimated

Term  

t-Value

t-Value

t-Value

Outcome)

Coefficient

Coefficient

Coefficient

Vacancy)

% Mental 

% Vacancy 

0.0022

2.220**

0.0009

0.720

0.0023

2.190**

health

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0014

1.170

0.0034

2.990***

0.0012

1.200

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0026

2.280**

0.0015

1.130

0.0006

0.500

(2–3 yrs.), 

2Q 2014 

% Vacancy (>  – 0.0002

– 0.120

0.0012

0.740

0.0031

1.720*

3 yrs.), 2Q 

2014

% Physical 

% Vacancy 

0.0049

2.630***

0.0020

1.550

0.0026

1.850*

health

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0025

1.750*

0.0054

3.320***

0.0018

1.700*

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0049

2.840***

0.0016

0.900

0.0014

0.990

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0076

3.400***

0.0058

1.730*

0.0067

3.420***

3 yrs.), 2Q 

2014

% Cancer

% Vacancy 

0.0027

1.620

0.0043

2.110**

0.0023

1.430

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0002

0.090

0.0059

1.850*

0.0016

0.670

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0024

1.720*

0.0042

1.010

0.0012

0.540

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0016

1.430

0.0076

1.380

0.0029

1.590

3 yrs.), 2Q 

2014

% Coronary  % Vacancy 

0.0092

2.520**

0.0043

1.890*

0.0029

1.570

heart disease  (1/2–1 yr.), 

(CHD)

2Q 2014 

% Vacancy 

0.0051

2.580***

0.0082

2.370**

0.0023

1.100

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0110

2.730***

0.0041

0.990

0.0025

0.970

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0181

3.710***

0.0099

1.310

0.0102

3.670***

3 yrs.), 2Q 

2014

Cityscape 157

Wang and Immergluck

Exhibit 5

HLM for Long-Term Vacant Housing in Various Duration and Health in Three Types of 

Top 50 Metropolitan Areas (2 of 4)

Independent 

Strong Growth

Hard Hit

Weak Growth

Dependent  Variables: log  

Variable: log  (% Long-

(% Health 

Estimated

Estimated

Estimated

Term  

t-Value

t-Value

t-Value

Outcome)

Coefficient

Coefficient

Coefficient

Vacancy)

% Diagnosed  % Vacancy 

0.0056

2.300**

0.0033

1.950*

0.0023

1.890*

diabetes

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0019

0.920

0.0043

1.530

0.0001

0.040

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0081

2.520**

0.0016

0.630

0.0022

1.170

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0132

3.740***

0.0099

2.050**

0.0094

5.370***

3 yrs.), 2Q 

2014

% Current 

% Vacancy 

0.0018

2.320**

0.0003

0.410

0.0012

1.900*

asthma

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0007

0.690

0.0011

1.400

0.0009

1.450

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0008

0.910

0.0006

0.820

0.0004

0.610

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0011

0.960

0.0017

2.100**

0.0015

1.820*

3 yrs.), 2Q 

2014

% Arthritis

% Vacancy 

0.0074

2.670***

0.0031

1.900*

0.0027

2.110**

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0038

2.120**

0.0061

2.240**

0.0034

2.580***

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0061

2.120**

0.0036

1.090

0.0017

0.910

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0078

2.190**

0.0070

1.290

0.0043

2.840***

3 yrs.), 2Q 

2014

% High blood  % Vacancy 

0.0036

1.730*

0.0021

1.470

0.0017

1.740*

pressure

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0020

1.370

0.0038

1.550

0.0015

1.210

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0049

2.720***

0.0005

0.250

0.0018

1.050

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0104

4.480***

0.0078

2.130**

0.0059

3.460***

3 yrs.), 2Q 

2014

158 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

Exhibit 5

HLM for Long-Term Vacant Housing in Various Duration and Health in Three Types of 

Top 50 Metropolitan Areas (3 of 4)

Independent 

Strong Growth

Hard Hit

Weak Growth

Dependent  Variables: log  

Variable: log  (% Long-

(% Health 

Estimated

Estimated

Estimated

Term  

t-Value

t-Value

t-Value

Outcome)

Coefficient

Coefficient

Coefficient

Vacancy)

% Stroke

% Vacancy 

0.0086

2.730***

0.0042

1.840*

0.0026

1.370

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0045

2.070**

0.0058

1.910*

0.0024

1.410

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0091

2.620***

0.0031

1.010

0.0017

0.780

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0171

3.860***

0.0148

3.420***

0.0111

4.210***

3 yrs.), 2Q 

2014

% All teeth 

% Vacancy 

0.0051

3.000***

0.0038

2.470**

0.0028

1.470

lost

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0028

1.720*

0.0055

2.690***

0.0009

0.460

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0059

2.390**

0.0019

0.930

0.0007

0.360

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0099

3.330***

0.0064

2.960***

0.0079

2.340**

3 yrs.), 2Q 

2014

% High 

% Vacancy 

0.0028

1.870*

0.0013

1.450

0.0009

1.180

cholesterol

(1/2–1 yr.), 

2Q 2014 

% Vacancy 

0.0023

2.350**

0.0018

1.140

0.0008

0.960

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0043

2.480**

0.0019

0.950

0.0004

0.320

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0056

2.560**

0.0046

1.600

0.0046

5.170***

3 yrs.), 2Q 

2014

% Chronic 

% Vacancy 

0.0077

2.700***

0.0034

1.860*

0.0044

2.480**

obstructive 

(1/2–1 yr.), 

pulmonary 

2Q 2014 

disease

% Vacancy 

0.0050

3.250***

0.0088

3.790***

0.0037

1.990**

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0072

2.820***

0.0033

1.070

0.0018

0.890

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0093

2.880***

0.0068

1.490

0.0060

2.320**

3 yrs.), 2Q 

2014

Cityscape 159

Wang and Immergluck

Exhibit 5

HLM for Long-Term Vacant Housing in Various Duration and Health in Three Types of 

Top 50 Metropolitan Areas (4 of 4)

Independent 

Strong Growth

Hard Hit

Weak Growth

Dependent  Variables: log  

Variable: log  (% Long-

(% Health 

Estimated

Estimated

Estimated

Term  

t-Value

t-Value

t-Value

Outcome)

Coefficient

Coefficient

Coefficient

Vacancy)

% Chronic 

% Vacancy 

0.0056

2.740***

0.0026

1.860*

0.0014

1.320

kidney 

(1/2–1 yr.), 

disease

2Q 2014 

% Vacancy 

0.0015

1.250

0.0032

1.550

0.0005

0.440

(1–2 yrs.), 

2Q 2014 

% Vacancy 

0.0062

2.970***

0.0015

0.750

0.0013

0.850

(2–3 yrs.), 

2Q 2014 

% Vacancy (> 

0.0097

3.890***

0.0087

2.640***

0.0070

4.120***

3 yrs.), 2Q 

2014

2Q = second quarter. HLM = hierarchical linear modeling.

*  p  < 0.1. **  p  < 0.05. ***  p  < 0.01.

Limitations

Despite its contribution of providing evidence of an association between long-term vacancies 

and public health across housing markets, this study contains limitations that call for additional research. Because it relies on a cross-sectional design, we cannot conclude that long-term housing vacancy causes these health outcomes. Our study simply indicates an association between 

long-term vacancy and certain health conditions, controlling for important neighborhood and 

metropolitan characteristics. Further research should utilize expanded longitudinal data and causal inference methods.

A second limitation of this study is the use of census tracts as proxies for neighborhoods, which 

might generate biased results because smaller units of neighborhoods, such as block groups, pro-

vide more socioeconomically homogeneous data (McKenzie, 2013; Shuler et al., 1992). However, 

we used census tracts with about 4,000 residents because they are the smallest units in our datasets and because scholars generally agree that census tracts reflect reliable socioeconomic and housing data that are publicly available (Sawichi and Flynn, 1996). 

Another limitation of this study is the possibility of omitted variable bias. Although our independent variables are generally guided by the existing literature, our access to data is limited. For example, we lack details on changes in the quality of the housing stock, which may have deteriorated more in strong-growth metros than in weak-growth metros. It may be that vacancy is not the 

proximate driver of the relationships found here, but rather something associated with vacancy 

that is not accounted for by the various control variables.

160 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

Conclusion and Implications

In this study, we examined how living in areas with high levels of long-term vacant housing is 

associated with neighborhoods’ health outcomes during the housing market recovery period across 

metropolitan areas (metros), and how it has disproportionately impacted some metros. For our 

first research question, our findings suggest that city neighborhoods with high long-term vacancy 

rates are significantly associated with adult health problems across the cities, but the relationship varies according to the growth trajectories of the metropolitan areas. Although neighborhoods in 

strong-growth and hard-hit metros are strongly associated with more health problems, those in 

weak-growth metros have a weaker association with health outcomes, except for mental health and 

asthma. A change in the vacancy rate in neighborhoods with initially lower levels of vacancies in 

strong and hard-hit metros may have experienced more shock and stress resulting in more health 

problems; however, neighborhoods with historically higher vacancy rates in weak-growth metros 

may have cumulative factors that contribute to asthma and mental health issues that interact with 

vacancy.

Our findings with regard to the second question suggest that very long-term (more than 3 years) 

vacant housing is more strongly associated with health problems across all types of metros. 

Although long-term (6 months or more) vacancy in strong-growth metros is associated with health 

problems, only the very long-term vacancy is associated with a broad set of health problems in 

weak-growth regions. 

These findings suggest several implications for planners and policymakers attempting to cope with 

highly concentrated vacant properties in neighborhoods. Generally, cities in weak-growth metros 

had the highest levels of neighborhood vacancy from 2011 through 2014, but long-term vacancy 

in these cities, compared with that in cities in strong-growth and hard-hit neighborhoods, had a 

weaker association with most health problems. (Again, the important exceptions were asthma and 

mental health.) This finding indicates that historically high levels of vacancies in neighborhoods may be less-significant determinants of poor health and that health outcomes in these metros 

may be more strongly associated with other regional or neighborhood factors, such as regional 

economic conditions, neighborhood environmental conditions, housing quality, and other latent 

variables that may be more salient drivers of health outcomes in weak-growth metros.

At the same time, when looking only at very long-term vacant units, these properties were strongly associated with negative health outcomes in cities in all three types of metros. Moreover, the relationships between these very long-term vacancies and health outcomes were much stronger than 

for vacancies between 6 and 36 months.

From a public health perspective, these findings suggest that, in weak-growth metros, efforts to 

reduce vacant properties should focus on those units that have been vacant for more than 3 years. 

However, in strong-growth and hard-hit metros, it is with good reason that one can expect signifi-

cant public health benefits from addressing vacancies of between 6 and 36 months. Although other 

reasons to reduce vacant units of shorter durations may be valid in cities in weak- growth metros, the health effects are not likely to be significant, except on asthma and mental health outcomes.

Cityscape 161

Wang and Immergluck

The second key implication for policy and planning is that very long-term vacancies have the greatest negative impacts on health outcomes across all types of metros, so, from a public health per-

spective, addressing these sorts of vacancies should be prioritized. Shorter-duration vacant housing may be more easily purchased and reused by investors or homeowners, whereas very long-term 

vacancy is a more challenging issue. However, health-focused efforts should generally aim first to reduce the number of very long-term vacancies. When the property may not be salvageable, this 

focus may entail targeted demolition. At the federal level, HUD and the U.S. Department of Health 

and Human Services should consider efforts to address these very long-term vacancies. Moreover, 

local and state health planners should consider community development strategies for decreasing 

long-term vacancy rates to improve health outcomes.

Appendix A

Exhibit A-1

Vacant Housing Units and Vacancy Duration in 295 U.S. Cities in Three Types of 

Metropolitan Areas (1 of 2)

Metro-

Residen- Vacancy  Vacancy  Vacancy  Vacancy  Vacancy  Vacancy 

politan 

Residential 

Year

tial 

< 3  

3–6 

6–12 

1–2  

2–3  

3 + 

Area 

Address

Vacancy Months Months Months

Years

Years

Years 

Type

Strong  2Q, 2011

13,899,145

390,470

22,311

37,574

54,284 121,380

48,044 106,877

growth  

(2.81%) (0.16%) (0.27%) (0.39%) (0.87%) (0.35%) (0.77%)

2Q, 2012

14,634,556

357,218

28,817

26,011

30,791

63,462

82,041 126,096

(2.44%) (0.20%) (0.18%) (0.21%) (0.43%) (0.56%) (0.86%)

2Q, 2013

14,729,662

364,170

15,296

23,057

47,117

56,969

46,355 175,376

(2.47%) (0.10%) (0.16%) (0.32%) (0.39%) (0.31%) (1.19%)

2Q, 2014

14,873,955

327,538

18,126

18,927

29,901

48,637

35,750 176,197

(2.20%) (0.12%) (0.13%) (0.20%) (0.33%) (0.24%) (1.18%)

 % change 

7.0% – 16.1% – 18.8% – 49.6% – 44.9% – 59.9% – 25.6%

64.9%

(2Q 

2011– 

2Q 2014) 

Hard hit 2Q, 2011

7,804,613

356,760

31,283

45,512

66,888 112,063

39,308

61,706

(4.57%) (0.40%) (0.58%) (0.86%) (1.44%) (0.50%) (0.79%)

2Q, 2012

8,248,904

318,794

34,413

31,547

40,310

68,178

68,940

75,406

(3.86%) (0.42%) (0.38%) (0.49%) (0.83%) (0.84%) (0.91%)

2Q, 2013

8,281,888

316,347

18,876

30,722

46,097

62,273

45,430 112,949

(3.82%) (0.23%) (0.37%) (0.56%) (0.75%) (0.55%) (1.36%)

2Q, 2014

8,333,812

277,465

14,900

24,797

28,042

51,074

38,744 119,908

(3.33%) (0.18%) (0.30%) (0.34%) (0.61%) (0.46%) (1.44%)

 % change 

6.8% – 22.2% – 52.4% – 45.5% – 58.1% – 54.4%

– 1.4%

94.3%

(2Q 

2011– 

2Q 2014) 

162 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

Exhibit A-1

Vacant Housing Units and Vacancy Duration in 295 U.S. Cities in Three Types of 

Metropolitan Areas (2 of 2)

Metro-

Residen- Vacancy  Vacancy  Vacancy  Vacancy  Vacancy  Vacancy 

politan 

Residential 

Year

tial 

< 3  

3–6 

6–12 

1–2  

2–3  

3 + 

Area 

Address

Vacancy Months Months Months

Years

Years

Years 

Type

Weak 

2Q, 2011

11,718,195

572,529

31,504

46,551

81,443 170,896

61,247 180,888

growth  

(4.89%) (0.27%) (0.40%) (0.70%) (1.46%) (0.52%) (1.54%)

2Q, 2012

12,343,906

548,121

34,722

39,358

50,814

98,003 123,098 202,126

(4.44%) (0.28%) (0.32%) (0.41%) (0.79%) (1.00%) (1.64%)

2Q, 2013

12,388,877

558,141

18,582

33,094

59,543

91,705

73,716 281,501

(4.51%) (0.15%) (0.27%) (0.48%) (0.74%) (0.60%) (2.27%)

2Q, 2014

12,453,654

531,138

20,817

27,270

45,375

71,191

65,950 300,535

(4.26%) (0.17%) (0.22%) (0.36%) (0.57%) (0.53%) (2.41%)

 Percent 

change

(2Q 2011– 

6.3%

– 7.2% – 33.9% – 41.4% – 44.3% – 58.3%

7.7%

66.1%

2Q 2014) 

2Q = second quarter. 

Appendix B

The tables on the following pages present the results of regressing 13 adult health outcome 

variables on long-term vacancy rates in cities in the three types of large U.S. metropolitan areas—

strong growth (exhibit B-1), hard hit (exhibit B-2), and weak growth (exhibit B-3).

Cityscape 163

Wang and Immergluck

*** *** *** *** *** *** *** ***

*** ***

*

*** **

eas

15

7,552

KIDNEY

0.8482 0.0044

0.0011 0.0019 0.0239 0.0037

0.0059

0.0163

0.0146 0.0163

– 0.0045

– 0.0135

– 0.0006 – 0.0008

– 0.0145 – 0.0029 – 0.0041 – 0.0033

– 10,150 – 10,129

* *** *** *** *** *** *** *** *** *** ** ***

**

*

*** **

 15

opolitan Ar

COPD

1.8152 0.0010

0.0031 0.0194 0.0071

0.0127 0.0027

0.0197

0.0050

0.0023

0.0234 0.0154

7,552

– 0.0088 – 0.0040

– 0.0370

– 0.0005

– 0.0218

– 0.0065

– 6,558 – 6,538

*** *** *** ** *** *** *** ** ***

** ***

***

*** **

*** **

 15

owth Metr

5.0162 0.0005

0.0004 0.0018 0.0163 0.0024

0.0018 0.0005

0.0107

0.0045

0.0062 0.0069

7,552

HIGHCHOL

– 0.0018

– 0.0028

– 0.0005

– 0.0188

– 0.0075 – 0.0210

– 16,573 – 16,553

-

* *** ***

*** *** *** ***

***

*

**

*** ***

ong-Gr

15

7,552

TEETH LOST

2.4066 0.0053

0.0004

0.0057

0.0164 0.0036 0.0002 0.0189

0.0075

0.0234

0.0191 0.0313

– 0.0032 – 0.0004

– 0.0002

– 0.0478

– 0.0218

– 0.0092

– 8,063 – 8,043

eas, Str

* *** *** *** *** *** *** *** ***

*** ***

*** * **

*** **

 15

7,552

STROKE

2.0102 0.0074

0.0022 0.0315 0.0070

0.0112 0.0008

0.0284

0.0116

0.0320 0.0173

– 0.0065 – 0.0010

– 0.0257

– 0.0011

– 0.0313

– 0.0106 – 0.0179

– 4,233 – 4,213

*** *** *** *** *** *** *** *** ***

* ***

**

**

*** **

opolitan Ar

15

7,552

BPHIGH

3.9024 0.0052

0.0002 0.0021 0.0228 0.0041

0.0029 0.0004

0.0149

0.0020

0.0127 0.0169

– 0.0029

– 0.0062

– 0.0007

– 0.0208

– 0.0084 – 0.0065

– 11,160 – 11,140

*** *** *** *** *** *** *** *** ***

***

*** ** ***

*** **

 15

THRITIS

4.2534 0.0024

0.0038 0.0279 0.0036

0.0047 0.0007

0.0160

0.0123

0.0202 0.0096

7,552

– 7,687 – 7,667

op 50 U.S. Metr

AR

– 0.0067 – 0.0014

– 0.0179

– 0.0004

– 0.0360

– 0.0132 – 0.0106

*** *** *** ***

*** *** *** ***

* ***

***

**

*** ***

 15

al linear modeling.

1.6414 0.0024

0.0002

0.0026

0.0050 0.0004 0.0003 0.0027

0.0001

0.0184

0.0025 0.0042

7,552

CASTHMA

– 0.0037 – 0.0020

– 0.0027

– 0.0102

– 0.0159

– 0.0007

– 23,442 – 23,422

** *** *** *** *** *** *** *** *** *

***

**

**

*** **

 15

3.4456 0.0081

0.0030 0.0036 0.0284 0.0054

0.0093 0.0015

0.0197

0.0076

0.0207 0.0223

7,552

DIABETES

– 0.0027

– 0.0203

– 0.0002

– 0.0268

– 0.0108 – 0.0226

– 7,453 – 7,432

** *** *** * *** *** *** *** ***

*** ***

***

**

*** **

 15

CHD

3.008 0.002

0.003 0.037 0.006

0.009 0.001

0.032

0.013

7,552

– 0.008 – 0.001

– 0.023

– 0.002

– 0.035

– 0.012 – 0.021

0.0378 0.0301

– 2,958 – 2,938

** ** *** *** *** ***

***

*** *** ***

**

*

*** ***

 15

7,552

CANCER

2.7506

0.0029 0.0340 0.0006

0.0171

0.0063

0.0281 0.0217

– 0.0009 – 0.0071 – 0.0024

– 0.0087 – 0.0004 – 0.0022 – 0.0011

– 0.0241

– 0.0067 – 0.0225

– 5,213 – 5,192

** *** ***

*** *** *** *** *** ***

***

***

*** **

TH

15

acant Housing and Health in 295 Cities in T

PHL

1.5159 0.0027

0.0005 0.0021 0.0121 0.0065

0.0116 0.0027 0.0002 0.0136

0.0043

0.0174

0.0107 0.0065

7,552

– 0.0047

– 0.0272

ea – 0.0241

– 0.0033

– 12,456 – 12,435

* *** *** *** *** *** *** *** *** *** *** **

***

***

*** ***

p  < 0.01.

 is housing that is vacant for more than 6 months. All dependent variables are in natural log form.

erm V

TH

15

MHL

1.1097 0.0014

0.0005

0.0037

0.0087 0.0025 0.0008 0.0034

0.0021 0.0001 0.0316

0.0054 0.0066

7,552

– 0.0039 – 0.0011

– 0.0059

– 0.0237

– 0.0165

fects

– 17,652 – 17,632

-

p  < 0.05. *** 

fects

TY

ea

cept)

ANIC

cept

opoli

Long-term vacant

Exhibit B-1

Variables

HLM for Long-T

Fixed ef  Level 1: census tract (Inter BLACK ASIAN HISP MARRIED AGE POVER INCOME LOW-EDU UNINSURED COMMUTE VACANCY_

6MPLUS  Level 2: metropolitan ar  POP_CH UNEMP_CH HCOST TCOST Random Ef Level 1 Level 2  inter Model fit AIC BIC Number of observations Level 1: census tract Level 2:  metr tan ar p  < 0.1. ** 

AIC = Akaike information criterion. BIC = Baysian information criterion. HLM = hierarchic

* 

Notes: 

164 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

*** *** *** ** *** *** *** *** **

***

*** ***

*** **

 12

eas

4,017

KIDNEY

0.3986 0.0038

0.0016 0.0011 0.0213 0.0031

0.0064

0.0000 0.0127

0.0149

0.0018

0.0150 0.0083

– 0.0047

– 0.0123

– 0.0017

– 0.0035

– 0.0094

– 5,165 – 5,151

*** *** *** *** *** *** *** ***

***

*** *

*** **

 12

COPD

4,017

opolitan Ar

1.2358 0.0009

0.0019 0.0169 0.0070

0.0122 0.0022 0.0003 0.0169

0.0224

0.0027

0.0220 0.0339

– 0.0085 – 0.0047

– 0.0334

– 0.0122

– 0.0095

– 3,612 – 3,599

***

***

*** *** ***

***

***

** *

*** **

 12

3.2004 0.0002

0.0011 0.0138 0.0024 0.0002 0.0020

0.0003 0.0074

0.0098

0.0009

0.0065 0.0115

4,017

– 8,480 – 8,466

d-Hit Metr

HIGHCHOL

– 0.0021 – 0.0001

– 0.0001

– 0.0151

– 0.0047

-

*** *** *** *

*** *** *** ***

***

* **

*** **

 12

4,017

TEETH LOST

2.8613 0.0045

0.0001 0.0005 0.0052

0.0167 0.0048

0.0139

0.0170 0.0082

0.0169 0.0150

eas, Har

– 0.0040 – 0.0010

– 0.0487

– 0.0005

– 0.0089 – 0.0004

– 4,682 – 4,669

*** *** *** * *** *** *** ***

***

**

*** **

 12

4,017

STROKE

0.0066

0.0011 0.0278 0.0063

0.0114

0.0213

0.0163

0.0165

0.0334 0.0304

– 0.9168

– 0.0072 – 0.0012

– 0.0242

– 0.0007 – 0.0003

– 0.0195

– 0.0057

– 1,968 – 1,954

opolitan Ar

*** *** ***

** *** *** * ***

***

** *** *** *

***

 12

4,017

BPHIGH

2.0676 0.0048

0.0000 0.0010 0.0205 0.0040

0.0032

0.0001 0.0117

0.0134

0.0156

0.0132 0.0039

– 0.0033

– 0.0029

– 0.0004

– 0.0298

– 0.0063

– 5,705 – 5,691

* ** *** *** *** *** *** *** ***

* ***

*** **

*** **

 12

THRITIS

4,017

op 50 U.S. Metr

2.0309 0.0016

0.0023 0.0240 0.0035

0.0050

0.0007 0.0141

0.0187

0.0076

0.0214 0.0197

AR

– 0.0065 – 0.0019

– 0.0132

– 0.0003

– 0.0193

– 0.0083

– 3,709 – 3,695

*** *** *** ***

*** *** *** *** **

***

*** **

 12

al linear modeling.

1.8702 0.0024

0.0027

0.0046 0.0005 0.0002 0.0027

0.0030 0.0031

0.0048

0.0018 0.0047

4,017

CASTHMA

– 0.0030 – 0.0017 – 0.0001 – 0.0027

– 0.0084

– 0.0003

– 13,580 – 13,566

***

*** *** *** *** *** ***

***

*** *

*** **

 12

0.8605 0.0076

0.0038 0.0021 0.0248 0.0051

0.0084 0.0007 0.0007 0.0147

0.0162

0.0073

0.0218 0.0170

4,017

DIABETES

– 0.0023

– 0.0164

– 0.0229

– 0.0068

– 3,648 – 3,634

*** *** ** *** *** *** ***

***

** *

*** *

 12

CHD

0.3078 0.0012

0.0017 0.0328 0.0054

0.0094

0.0225

0.0207

0.0112

4,017

– 0.0090 – 0.0013

– 0.0191

– 0.0007 – 0.0003

– 0.0311

– 0.0092

0.0404 0.0423

– 1,204 – 1,191

** *** *** *** ***

**

***

***

***

*** *

 12

4,017

CANCER

0.3193

0.0017 0.0291 0.0002

0.0003

0.0000 0.0163

0.0171

0.0125

0.0318 0.0128

– 0.0019 – 0.0079 – 0.0031

– 0.0058

– 0.0034

– 0.0274

– 0.0055

– 2,179 – 2,165

*** ***

*** *** *** *** *** ***

***

*** *

*** **

TH

12

acant Housing and Health in 295 Cities in T

PHL

1.3496 0.0026

0.0000 0.0014 0.0105 0.0064

0.0111 0.0029 0.0005 0.0109

0.0174

0.0119

0.0105 0.0206

4,017

– 0.0046

– 0.0256

– 6,572 – 6,558

ea – 0.0143

– 0.0063

erm V

*** *** *** ***

*** *** *** *** *** ** ***

***

*** **

p  < 0.01.

 is housing that is vacant for more than 6 months. All dependent variables are in natural log form.

TH

12

MHL

2.5717 0.0016

0.0001

0.0038

0.0081 0.0033 0.0005 0.0050

0.0113

0.0008

0.0055 0.0043

4,017

– 0.0031 – 0.0014

– 0.0069

– 0.0199

– 9,202 – 9,187

– 0.0103

– 0.0026

fects

-

p  < 0.05. *** 

fects

TY

ea

cept)

ANIC

cept

opoli

Long-term vacant

Exhibit B-2

Variables

HLM for Long-T

Fixed ef  Level 1: census tract (Inter BLACK ASIAN HISP MARRIED AGE POVER INCOME LOW-EDU UNINSURED COMMUTE VACANCY_

6MPLUS  Level 2: metropolitan ar  POP_CH UNEMP_CH HCOST TCOST Random Ef Level 1 Level 2  inter Model fit AIC BIC Number of observations Level 1: census tract Level 2:  metr tan ar p  < 0.1. ** 

AIC = Akaike information criterion. BIC = Baysian information criterion. HLM = hierarchic

* 

Notes: 

Cityscape 165

Wang and Immergluck

*** *** *** *** *** *** *** *** ***

***

***

*** **

 22

eas

7,405

KIDNEY

0.0046

0.0015 0.0015 0.0200 0.0033

0.0067

0.0078

0.0002 0.0193

0.0127 0.0115

– 0.8639

– 0.0028

– 0.0134

– 0.0021 – 0.0001

– 0.0353 – 0.0009

– 10,808 – 10,776

*** *** *** *** *** *** *** ***

***

**

*** **

 22

opolitan Ar

COPD

0.0009

0.0028 0.0156 0.0066

0.0137 0.0010 0.0004 0.0098

0.0027 0.0275

0.0206 0.0176

7,405

– 0.2949

– 0.0078 – 0.0043

– 0.0388

– 0.0370 – 0.0044

– 7,274 – 7,242

*** *** ***

*** *** *** ** ***

***

**

*** ***

 22

owth Metr

3.0563 0.0003

0.0000 0.0017 0.0127 0.0023

0.0023

0.0003 0.0059

0.0008

0.0060

0.0059 0.0107

7,405

HIGHCHOL

– 0.0017

– 0.0033

– 0.0006

– 0.0225

– 0.0031

– 16,438 – 16,407

-

* *** *** **

*** *** *** ***

**

*

*** ***

eak-Gr

22

7,405

TEETH LOST

1.8408 0.0040

0.0002 0.0043

0.0161 0.0029

0.0093

0.0041

0.0025 0.0145

0.0186 0.0146

– 0.0022 – 0.0011 – 0.0001

– 0.0500

– 0.0004

– 0.0086

– 8,014 – 7,984

eas, W

*** *** *** *** *** *** *** *** *

***

***

*** ***

 22

7,405

STROKE

0.0067

0.0016 0.0262 0.0064

0.0117

0.0138

0.0025

0.0093

0.0285 0.0120

– 0.0363

– 0.0035 – 0.0014

– 0.0271

– 0.0013 – 0.0001

– 0.0422

– 0.0049

– 4,948 – 4,917

** *** ***

*** *** *** *** ***

***

***

*** ***

opolitan Ar

22

7,405

BPHIGH

2.0924 0.0049

0.0002 0.0019 0.0181 0.0036

0.0030

0.0082

0.0177

0.0110 0.0124

– 0.0017

– 0.0066

– 0.0005 – 0.0004

– 0.0357 – 0.0001 – 0.0033

– 11,915 – 11,883

*** *** *** *** *** *** *** ***

***

**

*** ***

 22

THRITIS

1.2970 0.0022

0.0037 0.0219 0.0035

0.0056

0.0006 0.0088

0.0264

0.0167 0.0217

7,405

AR

– 8,801 – 8,771

op 50 U.S. Metr

– 0.0066 – 0.0017

– 0.0169

– 0.0011

– 0.0400 – 0.0032 – 0.0025

** *** *** ***

*** *** *** ***

***

*** ***

 22

al linear modeling.

2.2641 0.0024

0.0024

0.0053 0.0003 0.0000 0.0031

0.0050

0.0031

0.0021 0.0109

7,405

CASTHMA

– 0.0029 – 0.0017 – 0.0001 – 0.0026

– 0.0092

– 0.0035

– 0.0029

– 24,246 – 24,215

***

*** *** *** *** *** ***

***

**

*** ***

 22

1.1275 0.0077

0.0031 0.0028 0.0234 0.0050

0.0088 0.0000 0.0002 0.0110

0.0117

0.0190 0.0217

7,405

DIABETES

– 0.0012

– 0.0213

– 0.0377 – 0.0016 – 0.0053

– 7,821 – 7,789

*** *** *** *** *** *** *** *** **

***

***

*** **

 22

CHD

0.3059 0.0016

0.0029 0.0297 0.0052

0.0103

0.0002 0.0127

0.0177

0.0320 0.0129

7,405

– 0.0063 – 0.0018

– 0.0230

– 0.0019

– 0.0528 – 0.0005 – 0.0051

– 4,064 – 4,032

*** *** *** *** *** ** *** *** ***

**

*** **

 22

7,405

CANCER

0.0210

0.0036 0.0274 0.0009

0.0013

0.0004 0.0039

0.0018

0.0193

0.0240 0.0236

– 0.0010 – 0.0071 – 0.0035

– 0.0084

– 0.0034

– 0.0407

– 0.0019

– 6,138 – 6,107

*** *** ***

*** *** *** *** *** *** * ***

**

*** **

TH

22

acant Housing and Health in 295 Cities in T

PHL

2.4662 0.0026

0.0002 0.0017 0.0103 0.0059

0.0115 0.0017 0.0006 0.0082

0.0107 0.0061

7,405

– 0.0046

– 0.0309

ea – 0.0189 – 0.0002 – 0.0040 – 0.0013

– 12,070 – 12,038

*** *** *** ***

*** *** *** *** *** ** **

*** ***

p  < 0.01.

 is housing that is vacant for more than 6 months. All dependent variables are in natural log form.

erm V

TH

22

MHL

2.4380 0.0012

0.0002

0.0034

0.0090 0.0025 0.0006 0.0053

0.0015 0.0021

0.0057 0.0048

7,405

– 0.0039 – 0.0013

– 0.0053

– 0.0250

– 0.0061 – 0.0009

fects

– 16,759 – 16,729

-

p  < 0.05. *** 

fects

TY

ea

cept)

ANIC

cept

opoli

Long-term vacant

Exhibit B-3

Variables

HLM for Long-T

Fixed ef  Level 1: census tract (Inter BLACK ASIAN HISP MARRIED AGE POVER INCOME LOW-EDU UNINSURED COMMUTE VACANCY_

6MPLUS  Level 2: metropolitan ar  POP_CH UNEMP_CH HCOST TCOST Random Ef Level 1 Level 2  inter Model fit AIC BIC Number of observations Level 1: census tract Level 2:  metr tan ar p  < 0.1. ** 

AIC = Akaike information criterion. BIC = Baysian information criterion. HLM = hierarchic

* 

Notes: 

166 The Housing-Health Connection

The Geography of Vacant Housing and Neighborhood  

Health Disparities After the U.S. Foreclosure Crisis

Acknowledgments

The authors thank the editors and five anonymous reviewers for their valuable comments and 

suggestions. 

Authors

Kyungsoon Wang is a researcher graduated from the School of City and Regional Planning at the 

Georgia Institute of Technology.

Dan Immergluck is a professor in the Urban Studies Institute at Georgia State University.

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170 The Housing-Health Connection

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 20 Number 2 • 2018

Cityscape 171

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

172 Refereed Papers

Employment and Earnings 

Trajectories During Two Decades 

Among Adults in New York City 

Homeless Shelters

Stephen Metraux

University of Delaware

Jamison D. Fargo  

Utah State University

Nicholas Eng

University of Chicago

Dennis P. Culhane 

University of Pennsylvania

Introduction

Few portrayals exist of homeless persons as wage earners. Instead, common images of this popula-

tion manifest stereotypes of “drunk, stoned, crazy and sick” single adults (Snow, Anderson, and 

Koegel, 1994: 461; Wright, 1989) and of families headed by single parents beset by trauma and 

lacking human capital (Bassuk, 2007; ICPH, 2013; Rog and Buckner, 2008). Behind these negative 

portrayals lie more fundamental questions related to the relevance of work in a setting of extreme poverty. 

In this study, we take up questions related to the role of employment and earnings in entries into and exits from homelessness, events related to broader dynamics of homelessness. The preponder-ance of research on homelessness remains focused on associations between individual character-

istics and outcomes related to becoming or remaining homeless, although such associations are 

overstated (Draine et al., 2002) and facilitate the stigma that accompanies homelessness (Phelan et al., 1997). Employment, insofar as it has a bearing on homelessness, is more ephemeral than are 

the relatively static individual traits. Specifically, the vagaries of losing and gaining employment can lead to becoming homeless and, alternately, offer a means of exiting homelessness. 

Such employment dynamics are consistent with a stochastic model of homelessness. A precipitat-

ing shock, reflecting a sudden and transitory change in circumstances, is prerequisite to becoming 

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Metraux, Fargo, Eng, and Culhane

homeless, and the magnitude of the shock needed to induce homelessness is inversely proportional 

to the degree of vulnerability an individual or family has to homelessness due to household 

(individual or family) social and economic factors (Goodman, Messeri, and O’Flaherty, 2016; 

O’Flaherty, 2012, 2009). In other words, adverse life events are instrumental for pushing a house-

hold into homelessness (Curtis et al., 2013). Job and earnings loss, as a commonly occurring eco-

nomic shock (Couch, Daly, and Gardiner, 2011), is the event most often associated with falling into poverty, while regained work and earnings is the most frequent event that again lifts a household 

out of poverty (Bane and Ellwood, 1986; Cellini, McKernan, and Ratcliffe, 2008; Morduch and 

Siwicki, 2017). In a similar fashion, we investigate whether change in job status and earnings act as a catalyst for both subsequent homelessness (in the wake of a job-related shock) and for exits from homelessness (following regained work and earnings) in a large population of sheltered adults. 

Research on employment and earnings among the homeless population has not attracted attention 

commensurate to the value that popular and policy discourse gives it (Long, Rio, and Rosen, 

2007). One reason for this imbalance is that researchers have had much more difficulty accessing 

administrative records related to employment than records related to health and disability. Both 

types of data are considered highly sensitive and have considerable privacy safeguards. However, 

researchers examining the nature and extent of disability among the homeless routinely access 

health records, which are appropriately protected by confidentiality restrictions that include 

provisions of the 1996 Health Insurance Portability and Accessibility Act, or HIPAA (HHS, 2003). 

Meanwhile, administrative records on employment and income, from such sources as state 

employment agencies, the Internal Revenue Service, and the Social Security Administration (SSA), 

have been largely closed to researchers. 

In this study, we provide one of the most expansive and systematic views to date of the role of 

employment and earnings in a large, sheltered homeless population consisting of both individual 

and family households. Using matched and aggregated administrative data from SSA and the New 

York City (NYC) Department of Homeless Services (DHS), we juxtapose aggregated earnings and 

shelter-use data for 160,525 sheltered adults during two decades of followup. If employment rep-

resents a shock of sufficient magnitude to precipitate homelessness, then these data should show 

associations between declines in employment and earnings and onset of shelter use. Furthermore, 

a correspondence between exits from homelessness and increases in employment levels and earn-

ings would further underscore the ties between employment and homelessness. 

We frame this investigation on three research questions. First, and basically, what is the extent of employment and earnings in a homeless population, before, during and after shelter use? Second, 

are changes in employment and earnings related to entering and exiting shelter? Finally, how do 

these dynamics between employment and homelessness differ among adults who are homeless as 

part of family households and those who are homeless as individuals?

Homelessness and Employment—A Review

How prevalent is employment among people who are homeless? Rossi’s (1989) monograph, based 

on results from Rossi, Fisher, and Willis (1986), provided the first comprehensive look at this 

question. Rossi portrayed homelessness as “the most aggravated state of a more prevalent problem, 

174 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

extreme poverty” (Rossi, 1989: 8), with median monthly income for the average Chicago homeless person less than $168 ($378 in 2017 dollars). Thirty-two percent of the survey respondents reported receiving earnings in the month prior to being interviewed. Based on overall income levels, Rossi posited that this employment was typically low paying, intermittent, unsteady, and unskilled. 

Findings on earnings and labor force participation from subsequent major surveys of homeless 

populations have been consistent with Rossi’s conclusions (Burt and Cohen, 1989; Zuvekas and 

Hill, 2000). In the most recent major survey of the national homeless population that assessed 

income and earnings, the National Survey of Homeless Assistance Providers and Clients under-

taken by the U.S. Census Bureau, 44 percent of respondents reported income from work in the 30 

days prior to taking the survey. For about one-half of these respondents, these earnings came from temporary positions, day labor, or informal jobs (Burt, 2001; Burt et al., 1999). 

This 44 percent employment figure provides the benchmark for our first research question related 

to the prevalence of employment among the homeless population. This finding, simplified to the 

assertion that 44 percent of homeless people work, has become the most widely disseminated 

statistic about homelessness and employment (Jacobson, 2013; Shaheen and Rio, 2007; SAMHSA, 

2013). Although this estimate lacks precision and now is dated, it does retain a symbolic balance 

in which, despite high unemployment rates among the homeless population, homeless persons 

nonetheless work more than is commonly assumed (Hartwell, 2000). Furthermore, having a 

substantial proportion of the homeless population in receipt of earnings underscores how the 

low wages and the sporadic, temporary, and irregular nature of their employment translates into 

insufficient income for exiting homelessness (Bartley and Roberts, 2006; Bogard et al., 2001; Shier, Jones, and Graham, 2012; Theodore, 2003). In this article, we empirically reassess this figure with a more updated homeless population.

Our second question, whether changes in employment and earnings are related to entering and 

exiting shelter, has a scant literature. Homeless persons, when asked the reasons for becoming 

homeless, will frequently invoke job loss as a precipitating event (Burt, 2001; Levin, McKean, 

and Raphael, 2004; Metraux et al., 2017). Furthermore, being homeless creates substantially 

increased barriers to locating and maintaining regular wage labor. These barriers include the stigma associated with being homeless, lack of dependable access to secure storage for one’s belongings, 

difficulty maintaining personal hygiene, and reconciling work hours with shelter schedules. These 

difficulties, in addition to other impediments to employment that homeless persons frequently 

have, contribute to a trajectory of attenuated attachment to the work force and a process in which informal work (for example, recycling, panhandling, illicit activities, childcare) progressively replaces wage labor as an income source (Gowan, 2010; Liebow, 1993; Snow and Anderson, 1993).

Three studies assessed employment and job loss insofar as they affected homelessness. Two stud-

ies followed adults in at-risk families in NYC—one (Smith et al., 2005) found that employment 

did not act as a protective factor for homelessness, but losing employment increased the risk of 

entering shelter; the other (Shinn et al., 2013) found that having employment was associated with 

families avoiding a shelter entry. In the third, Swami (2017), in an exception to this tendency, uses Journeys Home, an Australian study panel dataset of households who were homeless or at risk for 

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Metraux, Fargo, Eng, and Culhane

homelessness, to examine how homelessness affects employment transitions. This study finds a 

negative association between homelessness and employment entry but finds that individual traits, 

instead of homelessness, explain most of this association. 

The third question takes into account that key differences exist in the circumstances around em-

ployment and wage income, and in responses to job-related shocks, between those who are home-

less as individuals (that is, single adults) compared with adults who are homeless as part of families with children. Among the single adult homeless, the overall aging of this group has progressively 

eroded their workforce attachment since the 1980s (Culhane et al., 2013a, 2013b). Furthermore, 

high rates of mental disorder, substance abuse, and criminal history hampers steady employment 

(Shaheen and Rio, 2007; Zlotnick, Robertson, and Tam, 2002; Zuvekas and Hill, 2000). The 

homeless who stand to be most detached from the workforce are the roughly 20 percent who are 

deemed chronically homeless, who have been homeless for an extended period of time, and who 

have a disabling condition (Caton et al., 2005; Caton, Wilkins, and Anderson, 2008). Persons in 

this subgroup require extensive support for securing and maintaining stable employment but are 

typically difficult to engage in standard employment support programs (Shaheen and Rio, 2007).

A different set of dynamics prevails among adults who are homeless with families. Adults in 

families are predominantly in their twenties and female, single-parent providers for one or more 

preschool-age children (Rog, Holupka, and Patton, 2007; HUD, 2012). Adults in homeless families 

have lower rates of employment than their single adult counterparts (Burt et al., 1999, Zlotnick, 

Robertson, and Tam, 2002). However, three independent studies of sheltered families in NYC 

found that substantial proportions of adults in sheltered families have ties to the work force. A 

Vera Institute of Justice study found that 79 percent of a sample of sheltered families contained 

adults who had worked in the 5-year period before they entered shelter, with 69 percent becoming 

unemployed during the 5-year period before they entered shelter (Smith et al., 2005). Shinn and 

colleagues (2013) found that 44 percent of families entering shelter after applying for prevention services were working prior to shelter admission. Finally, a survey by the Institute for Children, Poverty, and Homelessness finds that 31 percent of adults in homeless families in NYC shelters 

were working either part or full time, and another 57 percent of this group was unemployed with 

previous work history (ICPH, 2013). 

The ICPH report also found substantial barriers to employment among these families that included 

childcare needs, lack of education and work history, and mental health issues related to depression and experiences of trauma. These findings on high unemployment levels and substantial barriers to 

work among homeless heads of families are consistent with previous research (Brooks and Buckner, 

1996; Rog and Buckner, 2008). Two studies compare employment dynamics among sheltered 

and housed heads of families. Shinn et al. (1998) found that among adults in families receiving 

Temporary Assistance for Needy Families, or TANF, a lower prevalence of work history occurs for 

those who were in shelters (38 percent) compared with their housed counterparts (49 percent). 

Lehmann et al. (2007) found that adults in newly homeless families, again compared with housed 

counterparts, were more likely to have stopped working in the year prior to interviewing for the 

study (47 and 11 percent respectively). 

176 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

In a manner similar to single adult homeless, a minority of homeless families remains homeless for an extended period. However, the chronic single adult homeless also had high rates of disabilities and appear to be less employable, whereas homeless families with long stays are no more likely 

than other homeless families to be unemployed and may exhibit a resilience that facilitates the 

ability to endure the long wait that usually precedes obtaining subsidized housing (Culhane et al., 2007; Weinreb, Rog, and Henderson, 2010). For many of these long-term homeless families, the 

extended period that they spend in shelter occurs in transitional housing arrangements, which 

often provide structured vocational programming. As such, extended stays provide support for 

developing vocational skills and locating employment, with the ultimate goal of regained housing 

self-sufficiency (ICPH, 2013). 

Given substantial demographic and contextual differences between homeless adults in individual and family households, we assess them separately and expect to find different work trajectories in each subpopulation. Research on job-related shocks found that in general low-income households are 

able to recover more quickly from earnings shocks than higher-income households (Guvenen et al., 

2015), although they are also more susceptible to lasting economic “scarring” effects if the earnings shock extends into long-term unemployment (Guvenen et al., 2017). Additionally, the magnitude 

of this recovery varies by age, with workers in their early years much better positioned to recover economically from an earnings shock than their older counterparts (Karahan and Ozkan, 2013). 

Given younger age and childcare obligations, we expect relatively low workforce participation among the family adult subpopulation prior to their homelessness, but they will be better positioned to make long-term vocational recoveries. In contrast, among the individual adult subpopulation, older age and disability will make vocational recovery more difficult following homelessness. 

The results of prior research collectively provide some guideposts to the present study, in which we examine work and earnings for a large group of sheltered adults during an extended period that 

includes the times before, during, and after homelessness. On a basic level, the previous bench-

mark of 44 percent employment provides a comparison with the prevalence of employment in this 

study group, and we add data on earnings to supplement the information provided by employment 

rates. Although we expect this study to find a temporal association between job loss and onset of 

homelessness that is consistent with findings from previous studies, the extent to which homeless 

households recover from the shock of job and earnings loss is, as far as we can tell, an unanswered question. If the dynamics of this recovery process among homeless households are consistent with 

dynamics among households more generally, than the recovery trajectories for adults in families 

should differ from single adults. 

Additionally, the presence of two factors particular to homelessness—extended shelter stays and 

exits to stable housing—should also be associated with differential degrees of recovery from em-

ployment and earnings shocks. First, the difficulty in maintaining employment while homeless will 

have longer-term effects for those with extended shelter stays and will correspond with diminished ties between work and shelter exit. Conversely, exits from homelessness to stable housing will be 

associated with more positive outcomes, as employment will facilitate establishing stable housing, and vice versa. Although these two expected outcomes follow from the known with respect to 

the relationship between homelessness and employment, we do not have data that are capable of 

establishing a directional association between changes in employment and movement in and out of 

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Metraux, Fargo, Eng, and Culhane

homelessness, and either one can conceivably lay the groundwork for the other. Given the paucity 

of findings on this topic, however, confirming that a relationship exists and providing insight into the nature of these relationships should advance the current understanding of the manner in which 

employment and homelessness interact. 

Methods

The research is a retrospective observational study based on matching two large administrative da-

tasets to assess shelter use and employment during the course of more than 2 decades for 160,525 

sheltered adults in New York City. 

Sample and Data

Data used in this study were administrative records from two sources—DHS records on shelter 

use and earnings records from SSA. DHS operates or funds separate shelter networks for unac-

companied (that is, single) adults and families. Combined, these two shelter networks include 

approximately 85 percent of all general homeless shelter beds in NYC. DHS collected demographic 

and shelter-use information from these shelters in two administrative databases (one covering fam-

ily shelters and the other covering single adult shelters) since the late 1980s. 

DHS sent records for 175,524 persons, the universe of records for persons who had initial stays 

in DHS shelters (either family or single adult) between 1990 and 2002, to SSA, where they were 

matched with earnings records for the 10 years prior to and up to 10 years following onset of 

DHS shelter use. SSA provided these earnings records through the time period of 1980 through 

2007, so a full 10 years of earnings was not available for all persons. SSA maintains comprehensive records of individual earnings for all individuals who receive wages that are subject to payroll tax deductions and who are, thereby, accruing eligibility for future SSA retirement benefits. Identifiers from DHS records (name, social security number, date of birth, and sex) were first verified through SSA records, using probabilistic and deterministic matching methods, and then matched with 

individual SSA records. Due to strict confidentiality policies surrounding individual SSA records, SSA personnel performed the data match.

The resulting dataset, which was aggregated and deidentified, became the basis for this study. SSA was able to unduplicate and validate 160,525 (91 percent) of these records.1 These records were 

then aggregated so that the matched records were grouped in a deidentified, aggregated (frequency 

table) format, consisting of finely grained cells containing all available combinations of nine 

criteria. The nine criteria that formed the basis for subdividing the aggregated earnings information (annual earnings, number of persons receiving SSA wage income) into smaller cells included— 

•  Year of earnings.  Divided into each of the 28 years for which earnings were examined for this study (1980–2007). 

•  Shelter status.  Two categories, whether or not a person had a record of shelter use in each given year. 

1 See Metraux et al. (2011) for more details.

178 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

•  Year of first shelter use.  Divided into 13 cohorts, based on year of first recorded shelter use, 1990–2002. 

•  Shelter type.  Two categories, based on whether the adult in question stayed in shelters primarily as an individual (that is, single adult) or as part of a family.

•  Pattern of shelter use.  Every adult was assigned one of three categories according to their pattern of shelter use in the 2-year period following their initial entry into shelter. These patterns of shelter use were assigned through cluster analysis methods and were based on configurations 

of total discrete stays and total days spent in shelter. The “transitional” designation signifies a pattern of a small number of days (typically less than 90) spent in shelter during a small number of stays (typically one or two). “Episodic” and “long-term” shelter use designations typically involved substantially longer stays consumed during the course of either few stays (chronic) or 

numerous stays (episodic). Detailed information on this cluster typology is available for singles 

(Kuhn and Culhane, 1998) and families (Culhane et al., 2007). 

•  Exit from shelter.  Two types of housing associated with last shelter exit, permanent or nonpermanent (A small number of persons did not have exit outcomes, because they did not exit 

by the end of the study period.). This information was abstracted from numerous disposition 

categories noted on the person’s latest shelter record. Any records not indicating an exit to per-

manent-housing placement we considered as nonpermanent exits.

•  Age.  Calculated at point of initial homelessness and grouped into eight categories. The first group included those ages 18 to 25; we classified persons ages 25 to 55 into six groups by 

5-year increments; and the final category included persons older than 55. Also, we included a 

category for missing age. 

•  Race or ethnicity.  Five categories—White, Black, Hispanic, other, and unknown. 

•  Sex.  Three categories—male, female, and unknown.

Due to confidentiality safeguards, data on earnings (and thus, employment) were only provided if 

the number of persons in a particular cell who received any earnings was five or more, thus cells 

with fewer than five persons remained empty in the dataset generated for this study. Each of the 

cells contained data on total income amount (sum for all cases in the cell), the standard deviation of the mean income per person, the number of individuals earning income, and the total number 

of individuals in the cell. Earnings for all years were indexed for inflation to 2008 U.S. dollars (U.S. Bureau of Labor Statistics, 2012). From these data, this study uses three earnings-related 

outcomes—employment rate (number of income earners divided by the total number of individu-

als in the cell), annual average income for income earners only (total income for the cell divided by the number of income earners in the cell), and annual average income for the cell (total income for the cell divided by number of individuals in the cell). For illustrative purposes, data from several sample aggregated cells are presented in appendix exhibit A-1.

The aggregated dataset returned from SSA consisted of 67,409 different cells representing 

3,049,708 persons-years of observation. However, we removed cells from the data for the following 

reasons: (1) Data went beyond 10 years before or after the first year of homelessness (4,477 cells); Cityscape 179

Metraux, Fargo, Eng, and Culhane

(2) Fewer than five people had earnings (10,324 cells); (3) Average earnings were improbably high 

(annual income exceeding, on average, $70,000 for earners only or $40,000 for all cases, as these 

17 cells were likely data anomalies). As a result, the final dataset consisted of 52,591 data cells representing 2,859,576 person years—1,098,258 (38 percent) from adults in families and 1,761,318 

(62 percent) from single adults. Although the discarded 10,324 underpopulated cells represented 

15 percent of the total cells, they contained only 6 percent of the total person years.

Aggregating these data limits possible analyses; our strategy to mitigate this limitation was to 

create aggregate cells that were specific as possible, given the available data. Using the nine criteria to create these granular cells led to 1,822,500 possible aggregate cell combinations (multiplying 

all combinations of the nine categories and taking into account time constraints), and the avail-

able 52,591 cells represented 3 percent of the possible cells. Appendix exhibit A-2 provides a 

further breakdown of this cell distribution. Many cells were unpopulated, for which no data were 

returned. As an example of this difference between limited population of possible cells, in the criterion “sex,” the possible cell combinations in the unknown category overwhelm the small number 

of unknown values. Furthermore, men in the family shelter data and women in the single shelter 

data are relatively sparse, leading to more unpopulated cells. 

Because of the omission of some of the data cells, the number of persons represented in the data 

varied from year to year. “Year 0,” the year in which persons experienced their first shelter episode, contained data from 152,323 people—63,289 (42 percent) adults in families and 89,034 (58 

percent) single adults.

Statistical Analysis

We conducted all analyses separately for adults who sought shelter as part of a family (that is, 

adults with families) and for adults who were homeless as individuals (that is, single adults). We first analyzed data descriptively, creating summaries of demographic, homelessness, and economic 

variables. 

The three earnings outcomes (employment, overall earnings, earnings among wage earners) were 

then modeled longitudinally as dependent variables using weighted linear mixed-effects regression 

models. We modeled employment as the percent of individuals employed during a given year for 

each aggregated group in our sample. Earnings data were modeled as mean U.S. dollars during a 

given year per aggregated group. The multivariate analyses used logarithmic transformations of 

the earnings data. Performing this transformation helped to normalize the distribution of earnings. 

A preliminary visual inspection of the economic outcomes over time indicated a sharp change 

in trend for each of the dependent variables at the point of first occurrence of homelessness for 

nearly all subpopulations (decreasing trend rapidly changed into an increasing trend). Therefore, 

a piecewise (that is, segmented or spline) statistical modeling strategy was employed whereby two 

slopes or segments for time were specified (that is, a single knot at the time of the initial incidence of homelessness) (Draper and Smith, 1998). The change was so sharp that a global quadratic effect 

for time would not accurately model the observed effect. The first segment contained data for 

the 10-year period preceding the first recorded shelter stay, and the second segment spanned the 

180 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

10-year period following this onset of first shelter use. The visual inspection also suggested a high degree of nonlinearity in the observed economic outcomes over time. Given the expected nonlinearity, we tested polynomial (or power) transformations for segments one and two in our models to 

improve fit (quadratic and cubic).

We selected mixed-effects models, because three levels of analysis (or clusters) were possible 

due to the aggregated and nested nature of the data: (1) Up to 20 repeated measurements over 

time of economic outcomes nested within aggregated groups (falling between 1980 and 2007);  

(2) Based on demographic and homelessness characteristics, 954 aggregated groups nested within 

year of first shelter-use cohorts; (3) Thirteen years of first shelter-use cohorts (years 1990–2002). 

Mixed-effects models allow for such clustering and correctly estimate the standard errors of model parameters, thus relaxing assumptions of independence of observations. In these models, allowing 

slopes for time (and their polynomial transformations) and intercepts to vary randomly at the 

aggregated group and cohort-levels can account for such clustering. The mixed-effects regression 

models included the following independent variables from the available data as fixed effects—sex, 

race or ethnicity, age group (treated as an ordinal variable), shelter status, exit housing type, and shelter-use pattern. Additionally, interactions between sex and time segments were included in all mixed-effects models to capture potential differences between men and women in all outcomes 

over time. Therefore, the mixed-effects models had the following form (random effects in italics)—

% employed or ln(earnings) ~ [level 1: repeated measures over time:  year before 1st shelter 

(1)

use + ( year before 1st shelter use)2 +  year after 1st shelter use + ( year after 1st shelter use)2] + 

shelter status + pattern of shelter use + exit housing type + age + race/ethnicity + sex + 

sex*[each of the following:  year before 1st shelter use + ( year before 1st shelter use)2 +  year after 1st shelter use + ( year after 1st shelter use)2] + [level 2:  demographic and homelessness cluster id] + [level 3:  year of 1st shelter use cohort].     

To model the obtained frequency table data, all analyses were weighted by cell frequency (Venables and Ripley, 2002). We conducted all analyses using the R environment for statistical computing (R 

Development Core Team, 2016), with the  lme4 package for mixed-effects models (Bates, Maechler, and Bolker, 2010) and the  lattice package for trellis graphics (Sarkar, 2008).

These regression models will be limited in their interpretability due to uncertainties in temporal sequencing among the covariates of interest. Specifically, precise times for such events as com-mencement of employment, shelter exits, and housing acquisition are unknown, and the associa-

tions in many of these relationships are potentially bidirectional. For example, exiting a shelter to housing can facilitate gaining employment as readily as gaining employment could facilitate a 

shelter exit. Such simultaneity bias precludes making inferences beyond the existence of an as-

sociation. As no previous research has been conducted on whether or not associations between the 

covariates of interest exist, we feel the value is in assessing these associations despite the substantial circumscriptions around interpretation. 

Cityscape 181

Metraux, Fargo, Eng, and Culhane

Results

Exhibit 1 summarizes the demographic and shelter-use characteristics for the adults in the family 

and single adult groups. The majority of the sample was single adults (62 percent) who were 

overwhelmingly male (80 percent). In contrast, the adults who were homeless as part of families 

(38 percent) were nearly exclusively female (93 percent). The former group was also substantially 

older when compared with the adults in the family households. Among the racial and ethnic group 

categories, a majority of both household types were of Black (non-Hispanic) race. About three-

fourths of the single adults had short-term transitional shelter-use patterns, although only about one-half of the adults in families had such shelter-use patterns. A majority (63 percent) of the 

adults in families exited shelter to stable living situations, although only 20 percent of single adults were recorded as doing so.

Exhibit 1

Demographic Characteristics for Adults With Initial Shelter Use in New York City 

Between 1990 and 2002, Stratified by Family and Single Household Types

Family

Single

Persons 

63,289

89,034

Sex (%)

  Female

93

20

  Male

7

80

Age at time of first shelter stay (%)

  18–24

49

15

  25–29

18

15

  30–34

13

18

  35–39

8

16

  40–44

4

11

  45–49

1

7

  50 or more

0

7

  Missing

7

11

Race or ethnicity (%)

  Black (non-Hispanic)

55

56

  Hispanic (any race)

30

24

  White (non-Hispanic)

1

13

  Other or missing

14

7

Year of initial shelter entry (%)

  1990

10

11

  1991–1993

26

26

  1994–1996

22

23

  1997–1999

16

21

  2000–2002

25

18

Shelter-use pattern (%)

  Transitional

51

77

  Episodic

2

11

  Long term

47

12

Exit to a stable living situation (%)

63

20

182 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

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Exhibit 2 presents employment rates and mean earnings for the time periods before, during, and after shelter use. Results are reported separately for adults in family and single households and are further stratified by homelessness type and housing type on shelter exit. The proportions in exhibit 2 for employment reflect the weighted average annual rate for those receiving SSA-recorded earnings. 

For example, the participation rate for the entire sample (not shown on table) prior to the first 

instance of shelter use was 49 percent. This percentage means that, in an average year prior to 

the onset of shelter use, nearly one-half of the entire sample had earnings. The earnings similarly reflect average annual SSA earnings amounts (in 2008 dollars) during the course of each of the 

three periods. To illustrate this observation, we can again consider the entire sample (results not shown on table), where the weighted average annual earnings in the period before shelter use was 

$5,697, when both earners and nonearners are included, and $11,612 per year when only earners 

were included. 

Employment rates and earnings showed different trajectories among adults in families and single 

adults. For example, adults in families had an average annual employment rate of 43 percent prior 

to the onset of shelter use that fell to 38 percent during the years of shelter use and then increased to 58 percent in the postshelter years. Looking at the subgroups defined by shelter-use measures, 

the 2 percent of adults in families who showed episodic patterns of shelter use (from exhibit 1) 

had worse outcomes, and virtually no differences in employment appeared between temporary and 

long-term subgroups or among those exiting to permanent-housing arrangements and those with 

exits to other arrangements. Average annual earnings also increased substantially after shelter exit, both for the total group and for the working subgroup. Average annual earnings among workers 

dropped from $8,483 (preshelter) to $7,342 (shelter onset) and then rose to $13,531 after shelter 

exit. Overall, this finding represents a net 60 percent increase during the total course of the study period, despite the presumed setback of shelter use. This combined increase in both employment 

and in the amount of earnings means that, for the overall group (including nonworkers), the 

average annual amount of earnings more than doubled from the preshelter to the postshelter 

period (from $3,677 to $7,783). However, even when only considering the 58 percent of adults in 

families who had earnings, the average annual earnings amount ($13,531) still was less than the 

poverty guidelines for a family of two ($14,000 in 2008).

For single adults, a more mixed trend emerged. An average overall employment rate of 52 percent 

in the years preceding initial shelter use dropped to 45 percent during years with shelter use and dropped further to 42 percent in the years following shelter use. This decline was not uniform, 

however. For instance, those with long-term shelter-use patterns and those exiting shelter to 

permanent-housing arrangements had rates that rebounded slightly after exiting shelter. Despite 

the overall decline in employment among sheltered single adults over time, overall annual average 

earnings rebounded after shelter exit. When looking at the average annual earnings for work-

ers, the 38-percent decrease in earnings (from $12,965 to $8,029) associated with the onset of 

shelter use was followed by annual postshelter earnings that averaged $15,291, amounting to an 

18-percent increase in average annual earnings during the entire study period. Furthermore, 42 

percent of single adults who were in the workforce during the postshelter period earned enough, 

on average, to exceed the poverty guideline for a one-person household ($10,400 in 2008). Again, 

persons with long-term shelter stay patterns and persons exiting to permanent housing had higher 

Cityscape 183

Metraux, Fargo, Eng, and Culhane

($)

6,746 9,104 6,565 6,496 8,603 6,372 3,585 4,590 3,082 3,555 4,709 3,401 6,487 9,775 5,312 6,376 9,590 5,931

Earnings (All) 

ork City Between

($)

8,029 9,204 7,053 8,063 8,686 7,893

Earnings 

12,965 16,327 11,647 12,718 14,844 12,533

15,291 19,058 13,264 15,152 17,505 14,750

Single Adults

(Workers Only) 

(%)

52 56 56 51 58 51 45 50 44 44 54 43 42 51 40 42 55 40

Employment 

ds of Shelter Use in New Y

3,677 3,925 1,915 3,459 3,811 3,422 2,767 2,965 1,054 2,606 2,729 2,850 7,783 8,056 4,999 7,630 8,114 7,209

Earnings 

(All) ($)

($) 8,483 9,207 4,291 7,856 8,792 7,900 7,342 7,947 4,115 6,711 7,423 7,178

9,660

Earnings 

13,531 13,709

13,466 13,840 12,965

ypes and Shelter Stay Characteristics

(Workers Only) 

Adults in Families

(%)

43 43 45 44 43 43 38 37 26 39 37 40 58 59 52 57 59 56

Employment 

ned Income Amounts for Adults With Recor

Level

Long term Episodic Transitional Permanent Other

Long term Episodic Transitional Permanent Other

Long term Episodic Transitional Permanent Other

Variable

Total  Homeless type

Exit type

Total  Homeless type

Exit type

Total  Homeless type

Exit type  

verage Annual Employment Rates and Ear

Time Period

Exhibit 2

A

1990 and 2002, Stratified by Family and Single Household T

om shelter 

Prior

to initial

shelter stay

During initial

shelter stay

After exit

fr

184 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

average annual earnings when compared with the other subgroups. The annual earnings averages 

for all single adults dropped from $6,746 to $3,585 (a 47-percent decline) with the onset of shelter use and increased again to $6,487 in the years following shelter use to roughly regain the lost earnings. This relative parity reflects the offsetting trends of declining participation rate and rebounding earnings amounts. 

Exhibits 3 and 4 illustrate, by year and household type, the annual proportions of employment, 

and exhibits 5 and 6 illustrate average earnings amounts during a 2-decade period. For each of the two pairs of exhibits, exhibits 3 and 4 show results for the whole of the single adults and adults in families groups, and exhibits 5 and 6 compare each of these groups by sex. The exhibits provide a 

more temporal context for the overall annual trends for adults in families and single adults sum-

marized in exhibit 2.

For adults in families, both employment and earnings dropped in conjunction with the onset of shelter use and subsequently recovered to levels higher than those preceding year 0 (exhibits 3 and 5). 

These trajectories differed among men and women heads of household. Employment (exhibit 4) 

among men recovered to about the rate prior to homelessness (roughly 60 percent). Women, who 

had a substantially lower participation rate in the preshelter period (40 to 45 percent), increased in the postshelter period to rates comparable with those of men (roughly 60 percent). For earnings (exhibit 6), male workers had, on average, more income than their female counterparts, but female 

workers made larger gains in earnings income from the preshelter to the postshelter time periods. 

Among the single adults, although trends for the individual cohorts varied somewhat, the ag-

gregated trend for employment showed a steady decline that did not appear affected by the onset 

of shelter use (exhibit 3). The decline was more pronounced over time for the men compared with 

women in the single adults group (exhibit 4). Looking at earnings (exhibit 5), workers realized 

a sharp drop in the years immediately preceding the onset of shelter use, and average annual 

earnings bottomed out in year 0 before regaining levels realized in the years preceding shelter use. 

Exhibit 3

Employment Before and After First Instance of Homelessness for Families and Singles 

Notes: Thick lines represent the trend for all 13 cohorts collapsed, and data from individual cohorts selected at 3-year intervals are presented for clarity. Year 0 indicates year of initial shelter entry.

Cityscape 185

Metraux, Fargo, Eng, and Culhane

Exhibit 4

Employment Before and After First Instance of Homelessness for Male and Female 

Single and Family Households 

Notes: Thick lines represent the trend for all 13 cohorts collapsed, and data from individual cohorts selected at 3-year intervals are presented for clarity. Year 0 indicates year of shelter entry.

The earnings for all persons, reflecting the combined participation and worker-earnings trends, 

show an overall drop coinciding with the onset of shelter use and a much more modest recovery 

in the subsequent years. Although men on average received higher levels of earnings income than 

women, the earnings trends over time are similar for men and women (exhibit 6). 

The regression results for employment and earnings for adults in families (exhibit 7) and for single adults (exhibit 8) were largely consistent with the descriptive results. Random effects are not presented, as fixed effects are of the most interest. Due to the large sample size, nearly all effects were statistically significant, so the focus in reporting the results will be on direction of the coefficient (that is, positive or negative association) and the corresponding magnitude of the effect of the 

estimators. Although the results for the interaction terms are reported in the tables, they were used as control measures (primarily to account for sex differences) and do not assist with interpreting the results. 

186 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

Exhibit 5

Earned Income Before and After First Instance of Homelessness for Families and 

Singles

Notes: Thick lines represent the trend for all 13 cohorts collapsed, and data from individual cohorts selected at 3-year intervals are presented for clarity. Year 0 indicates year of shelter entry.

Exhibit 6

Earned Income Before and After First Instance of Homelessness for Male and Female 

Single and Family Households 

Notes: Two sets of results are presented in each part of the exhibit, for earners only and for all cases. Thick lines represent the trend for all 13 cohorts collapsed, and data from individual cohorts selected at 3-year intervals are presented for clarity. Year 0 

indicates year of shelter entry.

Cityscape 187

Metraux, Fargo, Eng, and Culhane

—

—

—

t

393.20

54.00 92.00

38.00 41.50 29.30 66.20

11.10

– 11.50

– 80.80

– 26.90 – 92.30 – 16.10 – 21.60

– 27.20

– 17.60

—

—

—

SE

1.93 0.74 0.36 0.38 0.77 0.31 0.30 0.10 0.74 1.39 0.34 0.45 0.06

0.04

0.37

0.04

Earnings (All)

—

—

—

b

2.86

0.39

758.10 – 8.48 19.78 34.55

11.81 12.64

48.55

– 62.48

– 37.41 – 31.40 – 7.22 – 1.26

– 0.95

– 6.56

, adjusted for other effects in the model 

t

—

—

—

7.90

5.40

2.40

0.60

765.80

47.90 39.80

13.80 94.60 14.70

– 16.20

– 55.40

– 27.30 – 21.60

– 25.70

– 30.10

ned Income Over Time for Men and

—

—

—

SE

1.11 0.46 0.30 0.44 0.87 0.37 0.37 0.11 0.70 1.42 0.45 0.45 0.04

0.02

0.30

0.03

val). Earnings are in dollar units with a natural logarithmic 

Earnings

(Earners Only)

—

—

—

b

2.93 5.06

2.43

0.72

0.02

846.11 – 7.51 14.14 17.60

10.68 10.29

– 48.02

– 38.93 – 9.69

– 1.00

– 0.68

t

101.79

10.62 57.57

58.45 62.14

92.54

18.19

– 16.75

– 53.43

– 6.32

– 1.42

– 4.76

– 216.93

– 16.40 – 141.37

– 36.83

– 82.30

– 12.35 – 30.56

earnings per a one-unit increase in the value of each predictor

ts can be interpreted in terms of the percent change in employment per a one-unit increase in 

SE

0.45 0.19 0.19 0.07 0.14 0.06 0.06 0.02 0.15 0.28 0.07 0.10 0.02

0.00

0.02 0.08 0.10 0.01 0.01

ession Models for Employment and Ear

ork City Between 1990 and 2002

Employment

b

46.02

2.06 4.11

3.64 3.77

0.19

– 3.19

– 7.71

– 4.10 13.78 – 4.60 – 9.32 – 0.60 – 0.75

– 0.05

– 0.02 – 0.99 – 3.14 – 0.04

fects Regr

val, earnings—among earners only—increases 10.68 percent). 

. t = t-value. 

ds of Shelter Use in New Y

ed ed

eceding shelter)

eceding shelter) 

Independent Variables

, adjusted for other effects in the model (for example, employment drops 4.1 percent per increase in age inter cept

ed

ed

omen Who Had Recor

Exhibit 7

Adults in Families—Results of Mixed-Ef

W

Inter Year (segment 1, 10 years pr Year (segment 2, 10 years following shelter) Not in shelter Episodic shelter use (versus long term) Transitional shelter use (versus long term) Exit to stable housing Age Male White (versus Black) Hispanic (versus Black) Other (versus Black) Year (segment 1, 10 years pr squar Year (segment 2, 10 years following shelter) 

squar Male x year (segment 1) Male x year (segment 2) Male x year (segment 1) squar Male x year (segment 2) squar Male x year (segment 1) cubed b = coefficient value. SE = standard error

Notes: Employment rates are in percent units (for example, 45 percent), and coefficien

each predictor

transformation and, therefore, can also be interpreted in terms of the percent change in 

(for example, for each increase in the age inter

188 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

t

35.40 82.40

91.90

97.10

3.30

9.80 15.90

omen

776.20 – 30.10

– 44.20 – 25.20

– 65.80

– 10.20 – 32.10 – 2.00 – 23.70 – 22.20

– 27.50

SE

1.00 0.51 0.44 0.42 0.64 0.48 0.40 0.09 0.51 0.55 0.48 0.60 0.04 0.04 0.23 0.24 0.02 0.02

Earnings (All)

b

0.74

0.15 0.33

778.61

15.52 34.35

36.59

49.44

– 15.22

– 28.14 – 12.21

– 5.99

– 5.65 – 15.55 – 1.17 – 1.04 – 0.95

– 6.50

, adjusted for other effects in the model 

t

2.60 2.80

985.10

50.30 96.90

38.80 75.10 43.10

– 22.70

– 40.60 – 5.00

– 9.50 – 7.10 – 1.60 – 24.30 – 31.70 – 4.90 – 5.20

val). Earnings are in dollar units with a natural logarithmic 

ned Income Over Time for Men and W

SE

0.85 0.43 0.28 0.38 0.58 0.44 0.36 0.08 0.51 0.51 0.43 0.55 0.03 0.03 0.23 0.21 0.02 0.02

Dependent Variables

Earnings (Earners Only)

b

6.37

0.05 0.05

833.45 – 9.67 13.86 36.35

13.91

22.01

– 23.30 – 2.22

– 4.85 – 3.05 – 0.88 – 0.84 – 0.83 – 1.14 – 1.08

t

—

—

253.30

24.10

– 27.60 – 0.30 – 0.50 – 34.90 – 14.10 113.00

135.20

– 2.10 – 3.30

– 6.00

– 340.80

– 29.30 – 61.80

– 36.40

earnings per a one-unit increase in the value of each predictor

ts can be interpreted in terms of the percent change in employment per a one-unit increase in 

—

—

SE

0.23 0.05 0.11 0.08 0.13 0.10 0.08 0.02 0.10 0.11 0.10 0.12 0.01

0.02 0.05

0.01

Employment

b

—

—

ession Models for Employment and Ear

57.67

9.02

0.11

– 1.28 – 0.03 – 0.04 – 4.39 – 1.36

– 5.87 13.22 – 3.15 – 5.88 – 0.26 – 0.04

– 0.13 – 1.89

ork City Between 1990 and 2002

fects Regr

val, earnings—among earners only—increases 6.37 percent).

. t = t-value. 

ed ed

eceding shelter)

ed ed

ds of Shelter Use in New Y

Independent Variables

, adjusted for other effects in the model (for example, employment drops 5.87 percent per increase in age inter cept

Exhibit 8

Single Adults—Results of Mixed-Ef

Who Had Recor

Inter Year (segment 1, 10 years pr Year (segment 2, 10 years following shelter) Not in shelter Episodic shelter use (versus long term) Transitional shelter use (versus long term) Exit to stable housing Age Male White (versus Black) Hispanic (versus Black) Other (versus Black) Year (segment 1) squar Year (segment 2) squar Male x year (segment 1) Male x year (segment 2) Male x year (segment 1) squar Male x year (segment 1) squar b = coefficient value. SE = standard error

Notes: Employment rates are in percent units (for example, 45 percent), and coefficien

each predictor

transformation and, therefore, can also be interpreted in terms of the percent change in 

(for example, for each increase in the age inter

Cityscape 189

Metraux, Fargo, Eng, and Culhane

The results presented in exhibit 7 show how, for adults in families, the time around the onset of 

shelter use reflects an economic bottoming out. Across all three models, the contrasting effects for the 2-year variables indicate a progressive decline in employment and earnings in the decade leading up to shelter onset, followed by a reversal in coefficient value for the decade following shelter onset indicating a recovery from the losses of the prior decade. The positive coefficient values 

associated with the variable “year” in the time segment following initial shelter use were in addition to the strongly positive coefficient values associated with not being in shelter. Additionally, adults in families with episodic stay patterns had worse employment and earnings outcomes, and adults 

in families with temporary stay patterns had only modestly better, albeit significant, outcomes in these areas when compared with those with long-term shelter stay patterns. Exit to stable housing 

was associated with better outcomes in all three models for families. Finally, looking at the demographic covariates, increasing age was associated with declines in employment and with increases 

in earnings, with the earnings coefficient remaining positive in the earnings model for the complete group. Male heads of households in families clearly did better than women, especially with respect to employment. All racial and ethnic groups had worse outcomes in comparison with those of 

Black race. 

Based on the results for the single adults in exhibit 8, a steady decline in employment was associated with the “year” covariate for the time period preceding the onset of shelter use and then a 

nonsignificant association in the subsequent period. Juxtaposing these findings means that single 

adults who did work, earnings also declined with time in the preshelter period but rebounded in 

the 10-year period following the initial shelter episode. This earnings rebound was strong enough 

so that it maintained its overall positive association with earnings in the years following shelter onset in the third model, which included all persons. Not being in shelter also had no effect on 

employment but had a strong, positive association with earnings. Compared with those in the 

long-term cluster, those single adults with both episodic and temporary stay patterns fared worse 

across all models, the former substantially so and the latter to a more modest degree. Stable exit was also associated with more positive participation and earnings outcomes for single adults. 

Among the demographic variables for single adults, increased age was associated with decreased 

participation and increased earnings, men had higher rates of participation and amounts of earn-

ings compared with women, and the White and Hispanic groups had worse outcomes compared 

with the Black reference group. 

Discussion

At the most basic level, the results of this study are consistent with the literature on employment among homeless adults—even when sheltered, 38 percent of adults in families and 45 percent 

of single adults received wage income. Beyond that, wage income bottomed out, and employ-

ment rates declined for both groups in the period only prior to the onset of homelessness. This 

finding supports an association between job-related shocks and homelessness that are frequently 

overlooked in research on homelessness, with its predominant focus on more static behavioral and 

physical health-related determinants. 

190 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

Following the onset of homelessness, the vocational fortunes of adults in families and single adults diverge after job loss and homelessness. In what Ellwood (1982) described as the difference between “blemishes” and “scars” (Ruhm, 1991), adults in families, as a group, were more blemished 

in that, following their homelessness, posthomeless levels of employment and earnings recovered 

and exceeded prehomeless levels. For single adults, however, the job-related shock and homeless-

ness were more scarring; although wages recovered, employment continued a steady decline. The 

divergences in these trends among the two subpopulations likely have bases in gender and age 

differences among the two sheltered subpopulations, shown in exhibit 1, and the differences in 

disability, employability, and family composition that stem from these demographic differences. 

Separate and more detailed explanations for these trends adults in families and single adults will be forthcoming shortly. 

The aggregate levels of wages and employment prior to homelessness challenge stereotypes of 

homeless adults as unemployable and extremely low-income people. Among single adults, in an 

average year prior to becoming homeless, slightly more than one-half worked. Among those who 

did work, average earnings of nearly $13,000 suggest that income could be on either side of the 

poverty income guidelines, depending on household size. For adults in families, average employ-

ment (43 percent) and average annual earnings of workers ($8,483) were lower but still substantial considering that many of the adults in this group were single mothers with preschool-age children. 

As only aggregated data were available for this study, we are unable to lay out the individual 

dynamics between work, earnings, and homelessness. However, these findings support conclusions 

that, in the aggregate, the onset of homelessness is sensitive to loss of employment, regardless of whether this sequence is direct or mediated by factors such as physical or mental health crises that, sui generis, may also contribute to becoming homeless. 

This conclusion applies to those adults in the study group who are homeless both as individuals 

and with their families. After the onset of homelessness, however, the employment trajectories for each of these subgroups diverge. 

Families

The effect of homelessness on employment for adults in families more resembles a time-limited 

setback than a protracted decline. Many homeless adults in families started, resumed, or continued employment following shelter entry, as employment among this group dropped from 43 percent in 

the overall preshelter period to 38 percent during the time they were sheltered but then rebounded to 58 percent during the overall postshelter period. Among wage earners, average annual income 

from wages increased following shelter use to nearly $16,000. This amount, depending on house-

hold size, hovers around poverty income guidelines. 

Various factors may have contributed to this recovery. Disproportionately, families in shelters are homeless when their children are of preschool age (Culhane and Metraux, 1999). As the children 

age, options for childcare (including school enrollment) increase, and logistical barriers to working ease. Employment and earnings were also higher for adults in families who were sheltered later 

in the study period, a trend that likely reflects greater economic prosperity and increased job 

opportunities in the late 1990s and early 2000s but may also have been facilitated by the greater 

Cityscape 191

Metraux, Fargo, Eng, and Culhane

emphasis on work for welfare recipients that was part of the Personal Responsibility and Work 

Opportunity Reconciliation Act (NASW, 1996), colloquially known as “welfare reform” that was 

enacted in 1996. 

Also, homeless-specific factors were associated with employment and earnings. Longer shelter 

stays did not facilitate increased employment or earnings, which supports Culhane et al.’s (2007) 

skepticism about the benefit that homeless families receive from extended stays in shelter-based 

transitional housing programs. In contrast, exiting from shelter to stable housing was associated 

with higher employment and earnings. It is unclear from these data whether work facilitated 

housing stability or vice versa, or whether the relationships were bidirectional and mutually 

reinforcing (Swami, 2017). In addition, the qualities that enhanced the ability of persons to secure and maintain work may also have facilitated their making stable living arrangements on leaving 

shelter. Although more research is needed to understand the nature of this relationship, on a 

practical level, measures to increase opportunities for one domain (employment or housing) stand 

to facilitate improved outcomes in the other.

Single Adults

Like adults in families, those single adults who did work after their initial bout of shelter use 

realized aggregate earnings levels that exceeded preshelter earnings levels within a decade. Unlike adults in families, the levels of employment continued to decline (at a reduced level) after shelter use. This latter trend is consistent with the literature reviewed previously, in which homelessness typically occurred after a process of progressive detachment from the labor force. This fits the previously described narrative in which common barriers such as disability, substance abuse, criminal justice involvement, and lack of job skills all become more acute with increasing age. 

Despite this trend, roughly 40 percent of the single adults did maintain at least some attachment 

to the work force. Judging by the average annual earnings (and assuming these earnings were 

sustained to some degree), this work generated enough income to facilitate lasting exits from 

homelessness for a substantial proportion of these wage earners. The positive association found 

between exits to stable housing and both higher earnings and employment supports this outcome. 

However, we also found an association between higher employment and earnings and long-term 

shelter stay patterns in both the descriptive and multivariate results, and both prior to and fol-

lowing onset of shelter. This finding is counterintuitive, as long-term, “chronic” stay patterns are typically associated with age and disability (Kuhn and Culhane, 1998; HUD, 2007) and should 

be tied to worse employment outcomes. The findings in this article suggest that a substantial con-

stituency exists among single adults with long-term shelter-use patterns that would benefit from 

employment and vocational assistance (Gale and Rio, 2006). This association would indicate the 

need for a policy shift to counterbalance the disproportionate focus on sustained disability-related needs among this group. 

Demographic Factors

Among demographic factors, the difference in participation rates, and in earnings, between men 

and women is the most prominent finding. This gender disparity mirrors that which is found in 

the general workforce. In this context, it disproportionately affects sheltered families, who are 

192 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

overwhelmingly headed by single women, and affects the wellbeing of sheltered children and their 

prospects of regaining housing. Among the other demographic characteristics, increasing age, as 

expected, was associated with declining employment but also to higher earnings for those remain-

ing in the workforce. Black race was associated with better participation and earnings outcomes 

when compared with the other racial and ethnic categories in this study, perhaps because more 

persons of Black race were homeless primarily for economic reasons (that is, with less disability 

and other vocational impairments) and were thus at an advantage, among the homeless milieu, in 

the labor market. 

Limitations

Finally, we need to point out limitations to this study. This study, with its focus on Social Security Administration earnings, underreported total income received by homeless households in two 

ways. First, any under-the-table work (that is, work not reported to SSA) and income received 

from working in the informal economy were not represented in these data. An undetermined but 

substantial amount of income that extremely low-income people receive comes from such informal 

labor (Edin and Lein, 1997), which includes (but is not limited to) illicit activities, odd jobs, 

panhandling, and scavenging. Such labor is often more tenuous and less amenable to supporting 

efforts to gain and maintain stable housing (Gowan, 2010; Snow and Anderson, 1993).

A second way that these earnings data underreported total income was in their failure to include 

any income assistance received from benefit programs. This omission includes income from 

benefits for families, such as TANF, and for people with disabilities, such as the SSA’s Supplemental Security Income. Although these income assistance programs and others like them often do not 

move a household above the poverty guidelines, they can represent a steady income source and, 

when coupled with other benefits such as Supplemental Nutrition Assistance Program, or SNAP 

(that is, food stamps), and subsidized housing, can lead to sustained exits from homelessness. 

Furthermore, many recipients of benefits from programs such as TANF and Supplemental Security 

Income are out of the workforce in that they are not actively searching for work and do not con-

sider them employable. Thus, the employment rate reported in this article is lower than if one were only to consider those who are engaged in working or seeking work. 

This study examined homelessness insofar as persons using shelter in Department of Homeless 

Service’s administrative records appear. Thus, we did not include the undetermined number of 

persons not making use of shelter services. The size of this homeless subgroup is notoriously 

difficult to assess, but a general agreement is that in services-rich areas, such as New York City, the large majority of homeless persons come into at least some contact with the shelter system. In NYC, DHS administers or supports most shelters, approximately 85 percent, and they report into 

the DHS database. 

This study examined a sheltered homeless population in an atypical U.S. city. Evidence suggests 

that, other than the scale of homelessness in NYC, the characteristics of its population are not that different than that of other U.S. cities (Metraux et al., 2001). However, we in no way maintain that the population examined was representative of other homeless populations. Nonetheless, the range 

of this study, with 160,525 sheltered persons and 2.9 million person years, renders this study 

group an important part of the homeless population to study in its own right. 

Cityscape 193

Metraux, Fargo, Eng, and Culhane

The data for this study were only available in aggregated form, and although this aggregating 

ensured the confidentiality of personal data on employment and earnings, it created limitations on drawing conclusions. Population-level participation rates and earnings amounts could be tracked 

over time, but individual earnings could not be. Thus, no way of discerning individual employ-

ment trajectories was available that could be used to gain insights on key topics such as stability of earnings among individuals over time. Moreover, beyond information on shelter-use dynamics 

and basic demographics, no collateral information on individual or contextual factors exist that 

could also affect employment and earnings. This deficiency limits the parameters of this study to 

reporting basic employment trends among sheltered adults and leaves many unanswered questions 

for further research.

Finally, although we document trajectories and identify associations between such dynamics as 

employment and regained housing stability, or chronic homelessness and workforce participation, 

we reiterate that the data do not support making inferences about the directions of these associa-

tions, nor do these findings have any predictive value. Nonetheless, only identifying these associations provides clear directions for future research and policy initiatives.

Conclusion

This study represents, to our knowledge, the first to make use of administrative data on employ-

ment and earnings to systematically track a large homeless population during an extended period 

of time. A set of insights on employment among homeless adults emerges that is consistent with 

findings of previous research, and it also shows employment to play a larger role with descending 

into and recovering from a sheltered homeless episode than previously documented. This conclu-

sion has implications not only for this population but also for those in the more general population of working low-income people. As a significant proportion of this population has a work history, 

similarly the precarious nature of low-wage employment leaves a broader segment of the working 

low-income people facing the very real risk of homelessness. 

The levels of employment and wage earnings suggest that the homeless, as a population, struggle 

in the labor market. At the same time, the associations between employment and housing and 

the progressive gains adults in families made in the labor market following shelter stays show the promise of targeting employment as a means to prevent and ameliorate homelessness. Enhancing 

opportunities for and rewards from employment for the homeless population enjoys nearly univer-

sal support as a policy goal. In contrast, employment among the homeless population has been a 

lightly tread on area of research and hopefully additional research in this area, and more generally on the economic correlates of homelessness, will follow. 

Appendix 

The exhibits on the following two pages present further detail on the data cited in this article.

194 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

, 

onic

onic

onic

Pattern 

Episodic Chr Episodic

Chr

Chr

. However

Shelter Stay 

Temporary Temporary

Temporary

Permanent

Permanent Permanent

Permanent

Exit Type

Nonpermanent

Nonpermanent Nonpermanent

Nonpermanent

White Black

Other

Black White Black

Race or 

Ethnicity

Hispanic

Hispanic

 before, during, or after the first shelter stay

Age

35–40 35–40 18–25 25–30 45–50 30–35 18–25 45–50

F

F

F F

Sex

M

M M

M

Shelter 

Type

Single Single Single Single Family Family Family Family

ear of Homelessness columns.

1990 1994 1990 1990 1990 1998 1998 1990

First Year 

in Shelter

9,793.89

6,406.28

4,769.60

5,862.01

Earnings 

(SD) ($)

16,968.66

11,180.92

14,517.00

10,159.53

ear with that in the first Y

Earnings 

(Sum) ($)

263,409.21 92,159.48 92,901.24 136,229.14 47,061.78 123,301.79 59,132.08 111,394.35

N

18 17 25 17 20 13 12 22

Total 

3 10 11 9 15 7 3 15

Number 

With No 

Earnings

7

8 5 6 9 7

With 

15

14

Number 

Earnings 

Year

1980 1987 1986 1997 1981 1994 2001 1999

Earnings 

egated Dataset Observations—Eight Sample Cells

o limit table size, table does not show data for time period of data, that is, whether the data for the aggregated cluster were 1 2 3 4 5 6 7 8

Exhibit A-1

Aggr

Cell 

F = female. M = male. SD = standard deviation.

Notes: T

such data can be inferred by comparing the data included in the Earnings Y

Cityscape 195

Metraux, Fargo, Eng, and Culhane

Exhibit A-2

Distributions of Aggregate Cells by Component Criteria (1 of 2)

Percent of 

Criteria and Categories

Number of Cells

Possible Cells

Percent of Total

Possible

Total cells

52,591

1,822,500

100.0

2.9

Age

     18–24

6,475

202,500

12.3

3.2

     25–29

6,852

202,500

13.0

3.4

     30–34

7,339

202,500

14.0

3.6

     35–39

6,885

202,500

13.1

3.4

     40–44

5,234

202,500

10.0

2.6

     45–49

3,433

202,500

6.5

1.7

     50–54

1,718

202,500

3.3

0.8

     55 or more

1,394

202,500

2.7

0.7

     Unknown

13,261

202,500

25.2

6.5

Sex

     Male

28,167

607,500

44.7

3.9

     Female

23,490

607,500

53.6

4.6

     Unknown

934

607,500

1.8

0.2

Race or ethnicity

     Black

24,430

364,500

46.5

6.7

     Hispanic

14,611

364,500

27.8

4.0

     White

6,657

364,500

12.7

1.8

     Other

4,523

364,500

8.6

1.2

     Unknown

2,370

364,500

4.5

0.7

Shelter type

     Single

17,818

911,250

33.9

2.0

     Family

34,773

911,250

66.1

3.8

First year of shelter stay

     1990

4,354

145,800

8.3

3.0

     1991

4,336

145,800

8.2

3.0

     1992

4,254

145,800

8.1

2.9

     1993

4,126

145,800

7.8

2.8

     1994

4,311

145,800

8.2

3.0

     1995

4,376

145,800

8.3

3.0

     1996

4,325

145,800

8.2

3.0

     1997

4,302

145,800

8.2

3.0

     1998

4,249

145,800

8.1

2.9

     1999

3,821

138,510

7.3

2.8

     2000

3,880

131,220

7.4

3.0

     2001

4,129

123,930

7.9

3.3

     2002

2,128

116,640

4.0

1.8

Shelter use pattern

     Chronic

16,330

607,500

31.1

2.7

     Episodic

7,302

607,500

13.9

1.2

     Transitional

28,959

607,500

55.1

4.8

Exit type

     To permanent housing 

22,910

607,500

43.6

3.8

     To nonpermanent housing

29,272

607,500

55.7

4.8

     Unknown

409

607,500

0.8

0.1

196 Refereed Papers

Employment and Earnings Trajectories During Two Decades 

Among Adults in New York City Homeless Shelters

Exhibit A-2

Distributions of Aggregate Cells by Component Criteria (2 of 2)

Percent of 

Criteria and Categories

Number of Cells

Possible Cells

Percent of Total

Possible

Timing of earnings year

     Before shelter

24,699

663,390

47.0

3.7

     During shelter

10,313

568,620

19.6

1.8

     After shelter

17,579

537,030

33.4

3.3

Earnings year

     1980

183

2,430

0.3

7.5

     1981

363

4,860

0.7

7.5

     1982

517

7,290

1.0

7.1

     1983

701

9,720

1.3

7.2

     1984

905

12,150

1.7

7.4

     1985

1,125

14,580

2.1

7.7

     1986

1,344

17,010

2.6

7.9

     1987

1,557

19,440

3.0

8.0

     1988

1,790

21,870

3.4

8.2

     1989

1,984

24,300

3.8

8.2

     1990

2,158

53,460

4.1

4.0

     1991

2,278

87,480

4.3

2.6

     1992

2,359

94,770

4.5

2.5

     1993

2,413

94,770

4.6

2.5

     1994

2,494

94,770

4.7

2.6

     1995

2,546

94,770

4.8

2.7

     1996

2,659

94,770

5.1

2.8

     1997

2,744

94,770

5.2

2.9

     1998

2,848

94,770

5.4

3.0

     1999

2,964

94,770

5.6

3.1

     2000

3,051

94,770

5.8

3.2

     2001

2,795

87,480

5.3

3.2

     2002

2,520

53,460

4.8

4.7

     2003

2,234

48,600

4.2

4.6

     2004

1,978

43,740

3.8

4.5

     2005

1,677

38,880

3.2

4.3

     2006

1,315

34,020

2.5

3.9

     2007

1,089

29,160

2.1

3.7

Note: The 1,822,500 total cells represent the maximum combinations of cells for all criteria except for “Earnings Year” and 

“Timing,” which have lower numbers of maximum combinations (1,462,860 and 1,769,040 respectively) due to logistical impossibilities related to some of the combinations (for example, not all earnings years can be timed as “after” onset of shelter use, as earnings years are tracked starting in 1980, and the earliest onset of shelter use was 1990).

Authors

Stephen Metraux is an associate professor at the University of Delaware in the School of Public 

Policy and Administration.

Jamison D. Fargo is a professor at Utah State University in the Department of Psychology.

Nicholas Eng is a senior data scientist at LinkedIn.

Dennis P. Culhane is the Dana and Andrew Stone Professor in Social Policy at University of Penn-

sylvania in the School of Social Policy and Practice.

Cityscape 197

Metraux, Fargo, Eng, and Culhane

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202 Refereed Papers

Exploring Patterns of Tax Increment 

Financing Use and Structural 

Explanations in Missouri’s Major 

Metropolitan Regions

Susan G. Mason    

Boise State University

Kenneth P. Thomas  

University of Missouri-St. Louis

Abstract

This article examines tax increment financing (TIF) in Kansas City and St. Louis, two 

heavy users of the tool under the same statutory authority. Based on a complete database 

of TIF projects through 2013 (2012 for Kansas City) and numerous interviews with 

local government officials in both metropolitan areas, we explore the TIF use of these 

two cities, which have different structural aspects and have gone through sharp policy 

changes, to examine if central cities that use different strategies beget different outcomes 

in their suburban areas. We document distinctly different patterns of use in the two 

central cities. When St. Louis dramatically increased its TIF use under Mayor Francis 

Slay, the number of projects per year in the suburbs increased. Kansas City suburbs 

appeared to fill the gap in TIF use when the city sharply decreased its use of TIF under 

Mayor Mark Funkhouser. More research is needed to determine the factors that drive 

these mixed effects and if they hold true by context and in other metropolitan areas.

Introduction

Tax increment financing (TIF) can be defined as “a geographically targeted tax, expenditure, and 

regulatory inducement to a specific location” that works by using “taxes derived from the increases in assessed values (the tax increment) resulting from new development... to pay for infrastructure needs and development expenditures in the TIF district” (Man, 2001: 1). This incentive is different from a tax abatement, which eliminates the need to pay taxes on any portion of the improvements 

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 203

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

Mason and Thomas

to the property for a specific period of time, for example, 10 years. Why is this difference 

important? The proposed benefit of TIF is that development or redevelopment can be funded with 

future revenue as opposed to current revenue or foregoing revenue. At the time a locality uses TIF, the locality frequently sells a bond and pays it back from future revenue typically via increased 

property taxes generated from the development. In theory, this program provides an incentive for 

development that might otherwise not occur. 

Critics argue that in practice TIF subsidizes development in a region that would have occurred 

anyway, but not necessarily in that particular location, were it not for the TIF. For example, real estate consultants have adopted and promoted TIF as an urban development strategy and at times 

at the expense of other economic development opportunities (Weber and O’Neill-Kohl, 2013). 

A bigger concern of critics is that TIF also has the potential to take revenue from districts, such as schools and library districts, which would have otherwise been gained in the form of property 

tax increases from the development. The one time that would not be the case is when a region 

shares a state border and the movement of development across the state border would actually 

produce a net loss for a particular state but not the region. Another criticism is that TIF projects are not evaluated on serving regional land-use needs. This evaluation would require a regionwide 

perspective to consider whether the location of a particular project is the best use of the land and whether that project would be better allocated elsewhere to utilize land within the region in a more appropriate manner (Luce, 2003: 4–5). 

As municipalities look for ways to move ahead after the recession of 2007–2009, it is important 

that their limited resources work in ways that generate the best outcomes overall. This consid-

eration raises the possibility that how a central city uses TIF may have a negative influence at 

the regional level if it draws investment from suburbs that might be a better location for certain projects than in the central city. However, little is known about the effect of central city TIF strategies on economic development outcomes in the suburbs. Given that previous studies have shown 

the existence of competitive dynamics in TIF use, it is plausible that the TIF use of a region’s largest political and economic entity could have an outsized effect on the surrounding suburbs. Moreover, 

if central cities pursue different strategies, those strategies could obviously lead to different effects in the suburbs. Teasing out these relationships can advance our knowledge of how to advance the 

economic well-being of regions and combat the worst aspects of competitive behavior, such as a 

competition described by Tiebout (1956), in which cities chase revenue opportunities while trying 

to push out land uses they deem less desirable such as low-income housing. 

No evidence of significant research exists on whether large central cities’ TIF use has an effect on the way suburbs of those cities respond and use TIF, although it seems likely core cities could be influencing the use of TIF regionwide. As such, this research explores whether the activities of 

central city might shape the behavior of the suburbs when it comes to the use of TIF. Specifically, this research examines the policies and practices of Kansas City and St. Louis and the way their TIF 

usage influences the outcomes of their suburban areas’ TIF use. We aim to answer the question of 

whether or not central-city TIF use affects the use of TIF in suburban cities in the same metropolitan statistical areas (MSAs) within the same state. 

204 Refereed Papers

Exploring Patterns of Tax Increment Financing Use and Structural  

Explanations in Missouri’s Major Metropolitan Regions

Utilizing the nearly comprehensive data on TIF generated by the  TIF Annual Reports (2010–2014) of the Missouri Department of Economic Development, we were able to map and chart TIF use and 

search for patterns in the adoption of this economic development tool between 1988 and 2013. In 

addition, we used extensive interviews to confirm our understanding of the usage patterns in each 

region and to reach some tentative explanations for the reasons cities adopted certain patterns. 

Our findings were mixed but suggestive. Kansas City and St. Louis, both in Missouri, are definitely pursuing different TIF strategies, with larger projects in Kansas City and more numerous projects 

in St. Louis. As Kansas City reduced its use of TIF, the Kansas City suburbs stepped in to fill an economic development vacuum that Kansas City created by lessening its use of TIF. In St. Louis, 

something else is uniquely happening in the region, as the increase in TIF in the core city did not result in a reduction in the suburbs. Finally, our interviews show that many aspects of TIF remain controversial 30 years after the state legislature originally authorized TIF. 

This article begins with a review of the literature on the strategies of cities to use TIF in general and the way TIF projects in particular fit into the overall regional economic development schema. 

An emphasis is on qualitative studies of major uses of cities. We then discuss the procedures and 

methods we used in the study and present an analysis of the results. In the conclusion, we consider policy implications and directions for further research.

Literature Review

California was the creator of the first TIF statute in 1952 in response to the decline of federal 

funding for slum clearance and redevelopment provided in statutes in the 1930s and 1940s 

(O’Toole, 2011: 7; Van Fossen, 2010: 749). When Congress repealed Title I of the 1949 Housing 

Act in 1974, eight other states provided local governments the authority to use TIF. Nearly all the other states were using TIF by 2000 (Johnson and Man, 2001).

The effect of TIF use has been studied from the perspective of outcomes for jurisdictions. Some 

authors analyzed the effect of TIF projects on spatial inequality (Anderson and Wassmer, 1995; 

Dye and Merriman, 2000). Sands, Reese, and Trudeau (2007: 68–69) find the balance of needs, 

both citywide and for neighborhoods, is important to prevent taxing jurisdictions of “have” and 

“have not” neighborhoods, where some neighborhoods are excluded from growth in their library 

or school tax base for years due to the constraints of TIF funds paying off a bond, for example. 

In this case, additional potential revenue that may have gone to a school district or library is 

diverted to the TIF until the loan is paid, while neighboring non-TIF districts in the region may see property tax revenues rise and realize those benefits during the life of the TIF. Similarly, Merriman, Skidmore, and Kashian (2011) find that significant property value reallocation occurs with TIF use rather than absolute property value increases. As fruitful future research, Merriman, Skidmore, and Kashian (2011: 243) also recommended careful consideration of who benefits from TIF use and 

who does not. To this point, Pacewicz (2013: 415) found that “the degree to which cities use TIF is puzzling, because urban leaders believe that their own use of TIF is fiscally unsustainable and yet continue to create TIF-backed securities at ever-higher rates.”

Cityscape 205

Mason and Thomas

The literature is unclear about the relationship between central cities and suburbs. Some scholars contend suburbs are no longer dependent on cities and that suburbs compete for economic 

activity, although other scholars note suburbs and cities are interconnected, complementing 

one another’s economies (Ihlanfeldt, 1995). Pelissero and Fasenfest (1989: 303–305) found 

the type of suburb is a predictor of the way the suburb behaves within a region. The suburbs’ 

policy orientation drives their stance from reactive to aggressive with regard to their approach to economic development issues.

The literature also reveals differences in outcomes due to forms of governance and leadership. 

Feiock, Jeong, and Kim (2003) noted that mayor-council governments and council manager 

governments bring with them different styles and motivations that result in action that is more 

long term and less risky from council—manager forms of government to more high risk behavior 

that can lead to short term political benefits, which is behavior more likely associated with 

mayor council governments. DeSoto, Tajalli, and Opheim (2006) found that regardless of the 

type of government (that is, council managers or mayor-council), mayors’ capacity to govern 

has strengthened over time, enhancing both their authority and management strategies. This 

enhancement provided mayors with more latitude to address issues in their cities. Even though 

relationships between cities and suburbs, in regard to their regional economies, is still debated, more evidence exists that mayors, regardless of form of government, can play a formidable role in 

economic policy, orientation, and outcomes of their cities. 

A previous examination of the spatial patterns of municipal TIF use (Mason and Thomas, 2010) 

found that being geographically nearer or adjacent to a city that uses TIF increases the likelihood of the use of TIF on other cities. This relationship has implications for metropolitan regions as a whole. For example, if what Mason and Thomas (2010) found applies to a region and not only to 

cities in general, then cities that compete more by using TIF would potentially do so regardless of the size of the city with which they compete due to being adjacent cities. In turn, larger central cities might find the cities nearest them use TIF more often than cities farther away. Betz et al. (2012) found a proximity argument that supports metropolitan factors are at play and identified that not 

only counties with more Republican voters increase the number of economic development activi-

ties undertaken but also their proximity to a metropolitan region. Felix and Hines (2013) found 

two features of communities offering incentives for development include cities that are closer to 

state borders and those that are poorer, but not necessarily the poorest communities, were the 

cities more likely to use tax incentives. Additionally, they found at the county level the economic conditions of the area are inversely associated with implementing economic development policies. 

Another way regional differences may affect outcomes could be due to regions within a state having unique fiscal governance systems and, as such, this similarity has an effect on both the degree and way TIF is used in a region, as Weber, Hendrick, and Thompson (2008) found. Pacewicz (2016: 

265–266) found additional evidence that regional factors may be at play and concluded that the 

ability of city leaders to maximize TIF outcomes for their jurisdiction relies on local considerations such as geographical, fiscal, and regulatory constraints, which in turn created different outcomes by place. Taken together, spatial proximity and regional differences may have their own unique 

effects, such as fostering competition in one situation and pleading no contest between areas in 

another region. However, consideration of the way large cities within a metropolitan region use TIF 

as part of a larger economic development strategy has been given scant attention. 

206 Refereed Papers

Exploring Patterns of Tax Increment Financing Use and Structural  

Explanations in Missouri’s Major Metropolitan Regions

Research Design and Methodology

The St. Louis and Kansas City metropolitan regions dominate Missouri’s use of TIF. They provide 

two interesting cases to explore, because their central cities have the most TIF projects by far. 

These two cities have different structural aspects, as explained in the following paragraphs. 

Moreover, the two cities underwent sharp policy changes, which gives us the opportunity to 

explore whether central cities that use different strategies beget different outcomes in their suburbs. 

This article examines three questions. The first is, what patterns can be seen in the use of TIF in the St. Louis, MO-IL and Kansas City, MO-KS MSAs? Second, what role, if any, does competition 

from other communities within their MSA in Missouri or adjacent neighboring states (that is, 

Kansas and Illinois) play that are part of the greater MSA? In relation to the second question, 

several explanations may be found, for example, the city and the suburbs are complements, so if 

a core city engages in the use of TIFs, the suburbs are less likely to do so. Alternatively, it could be that if the core city does not use TIF, the suburbs will secure their own development using 

TIF. It is equally likely that when a core city uses TIF, the suburbs behave as competitors and 

aggressively seek development using TIF as well, which was previously demonstrated in Missouri 

suburbs among the adjacent cities in suburbs and more rural areas (Mason and Thomas, 2010). 

Alternatively, it is possible that if the core city does not use TIF, the suburbs also do not pursue TIF 

to enhance their development prospects. The third question explores the causes that might account 

for the patterns found? Could leadership, geography, structural aspects of government, or path 

dependency play a role?

Kansas City and the city of St. Louis are the two largest cities in Missouri in terms of geography, population, and economic impact. Kansas City is the largest city, with a population of 459,787 (as of 2010), but resides in the second largest MSA, with a population of 2,087,471. St. Louis is the 

second largest city in the state of Missouri, with population of 319,294 (as of 2010), but resides in the most populous MSA in the state of Missouri, with 2,811,588 people (U.S. Census Bureau, 

2016). The geography of Kansas City is much larger, encompassing 319 square miles compared 

with only 66 square miles for St. Louis, yet the geography of the MSAs are the reverse, with Kansas City being smaller at 7,856 square miles and the St. Louis MSA being larger at 9,391 square miles 

(Geolytics, 2000). The Gross Metropolitan product of the Kansas City MSA within Missouri is 

$41.68 billion (as of 2004), which is 20.5 percent of the Missouri Gross State Product compared 

with the city of St. Louis MSA within Missouri at $80.94 billion, which is 39.8 percent of the 

state’s Gross State Product (Global Insight, 2006: 59). Both cities have mayor-council governments, and the MSAs each have their own Metropolitan Planning Organizations that are also councils of 

governments. The Metropolitan Planning Organization in St. Louis documented incentives used 

to support hundreds of projects in eight counties across the region that raise concerns about the 

regional economic value of TIF (EWGCOG, 2011).

Using the nearly comprehensive data on TIF generated by the  TIF Annual Reports (2010–2014) of the Missouri Department of Economic Development, we tabulated the data by city and suburb for 

the two regions and searched for patterns in the adoption of this economic development tool. In 

addition, we conducted extensive semi-structured interviews to confirm an understanding of the 

usage patterns in each region and to reach some tentative explanations for why cities adopted the 

patterns they did.

Cityscape 207

Mason and Thomas

For this study, we interviewed people in a variety of positions, including four current and former economic development officers, five city officials, four school board members, three TIF commissioners, and one other person from the core cities and their in-state suburbs. We conducted 17 interviews 

from 2012 to 2014. We identified initial interviewees from state and local economic development 

agencies and used the snowball technique to increase the variety and number of interviewee 

perspectives. We sampled a range to begin to identify at least some of the mechanics through 

which city and suburbs argue for or against TIF use in each of the regions. We continued interviews until we were learning little if anything new from the additional interviews. We assured interviewees confidentiality and, therefore, are not referenced individually. This method provided a wealth of 

inductive qualitative evidence derived from the participants’ perspectives as opposed to the researcher. 

All interviewees were from areas that used TIF, because we were interested in understanding 

more about TIF use. We do not attempt to say our findings are generalizable. Our goal is to ask 

the question and explore the relationships between cities and suburban TIF use, if one exists. 

Adhering to Small’s (2009: 10, 25) advice for studying processes that are not known, we used 

qualitative research, which is superior for this type of work and illuminates more information than other types of research when asking questions about how and why processes work. Additionally, 

we place in juxtaposition a rare case and period involving the mayors of Kansas City and St. 

Louis for comparison in the same approximate timeframe. These more recent mayors of the core 

cities of St. Louis and Kansas City took very different approaches to the use of TIF. The city of St. 

Louis dramatically increased its TIF usage after the election of Mayor Francis Slay (2001–2016), 

whereas Kansas City substantially decreased its TIF usage during the administration of Mayor 

Mark Funkhouser (2007–2011). These differences may help to further illuminate the relationship 

between central cities and suburbs. In this way, we follow Small’s unique or deviant case emphasis to understand more about what is going on in general; for example, “when X occurs, whether 

Y will follow depends on Z” (Small, 2009: 21, 23). The deviant case can provide emergent 

knowledge from the cases, although not populations of similar cases (Small, 2009: 20), to reveal 

knowledge and perhaps develop hypotheses about a broader picture of the relationships between 

cities and suburbs and their TIF use. Finally, the creation of tables and graphs by year permits 

an examination of TIF use by both regions to examine if any regional effects were due to the 

differences in TIF use by the central cities of the metropolitan areas. The maps provide information on the spatial proximity of TIF use within the region and in relation to their proximity to the 

central cities. 

Economic Development Strategies

In Missouri, three means occur to justify the use of TIF for redevelopment—blighted, conservation, or economic development areas. The definition of each follows.

•  Blighted area is an area that by reason of the predominance of defective or inadequate 

street layout, unsanitary or unsafe conditions, deterioration of site improvements, improper 

subdivision or obsolete platting, or the existence of conditions that endanger life or property 

by fire and other causes, or any combination of such factors, retards the provision of housing 

accommodations or constitutes an economic or societal liability or a menace to the public 

health, safety, morals, or welfare in its present condition and use.

208 Refereed Papers

Exploring Patterns of Tax Increment Financing Use and Structural  

Explanations in Missouri’s Major Metropolitan Regions

•  Conservation area is any improved area within the boundaries of a redevelopment area in the 

territorial limits of a municipality, in which 50 percent or more of the structures are aged 35 

years or more.

•  Economic development area is any area or portion of an area within the territorial limits of a 

municipality that does not meet the requirements of blighted or conservation areas, respectively, 

and in which the governing body of the municipality finds that redevelopment will not be solely 

used for development of commercial businesses that unfairly compete in the local economy and 

is in the public interest, because it will—

§ Discourage commerce, industry, or manufacturing from moving their operations to another 

state.

§ Result in increased employment in the municipality.

§ Result in preservation or enhancement of the tax base of the municipality.

The definition of blight is particularly vague, which, coupled with permitted uses of TIF for 

economic development, make the tool ripe for any project (Kelsay, 2007: 14–15). Between 2005 

and 2012, TIF bond sales by state reveal that Missouri ranked fourth at $722 million in TIF bonds 

of all the states that have adopted TIF in the United States, behind California, Colorado, and 

Connecticut (O’Toole, 2011). Additionally, Missouri is 1 of only 18 states to allow for economic 

activity taxes that permit taxes for earnings, profits, utilities, and sales taxes in addition to the standard use of property tax increases for reimbursement of a development plan (Briffault, 

2010). In Missouri, 50 percent of the economic activity tax increment can be used to help fund 

the development plan. Missouri even provides for a Super TIF, which makes available the entire 

economic activity tax increment to the developer (Kelsay, 2007: 2).

In one influential early study, The Brookings Institution found that the Kansas City region had six cities with TIF districts, and the vast majority of the districts were in the central core of the city (Luce, 2003: v). The author found that TIF was more likely to be used in stressed communities 

than in nonstressed communities in Kansas City compared with St. Louis (Luce, 2003: 13). The 

suburbs closest to Kansas City were also greater users of TIF than the more far-flung areas (Luce, 2003: 8). As we detail in the following sections, these patterns have changed since the study’s 

publication.

Kansas City Economic Development Strategy

One striking feature of Kansas City TIF projects is the large number of huge projects, with a TIF 

value of more than $100 million. Of the 100 Kansas City TIF projects listed in various issues of the Missouri Department of Economic Development’s  TIF Annual Reports (2010–2014), 9 have values of more than $100 million (exhibit 1). 

More than one-half of interviewees suggested that the most likely reason for the numerous large 

projects is that Kansas City officials try to make the city conducive for businesses and developers to stay in the state of Missouri, instead of the Kansas side of the region. The large TIF projects are a result of the intensity of the competition Missouri has with the state of Kansas as a way to keep jobs and businesses in the Missouri side of the region. 

Cityscape 209

Mason and Thomas

Exhibit 1

Kansas City TIF Projects With Reimbursable Costs of More Than $100 Million

Name of Project

Date

TIF Value ($)

Total Project Cost ($)

Aid Intensitya (%)

Briarcliff West

1990

116,567,038

547,896,964

21

Hickman (Aventis)

1992

230,104,500

655,199,600

35

Santa Fe

1993

166,931,257

575,791,682

29

Shoal Creek

1994

130,718,310

186,246,912

70

Riverfront

1999

225,527,306

582,558,906

39

Pershing Road

2000

314,434,599

589,057,605

53

Three Trails

2002

186,144,576

949,355,059

20

Kansas City Live

2004

167,948,209

371,135,195

45

H&R Block

2004

292,317,824

308,399,088

95

TIF = tax increment financing.

a Aid Intensity is TIF reimbursable costs divided by total project costs.

Source: Missouri Department of Economic Development (2011)

Illustrating this point, one interviewee explained that developers, who demonstrate they cannot 

make the project finances work without the incentive, initiate most uses of TIF. The interviewee 

went on to explain—

We have a few proactive TIFs where we take an area considered blighted and make a 

conscious decision to stimulate redevelopment. There are a number of TIFs used to stimulate 

redevelopment for our neighborhood. Our metro area is split by a state line with affluent 

suburban communities in Kansas and we have aggressive battles on business retention. 

Business leaders have been trying to get Kansas and Missouri states to stop it. Recently, 

Kansas got AMC headquarters for 47 million dollars to move 5 miles across the state line.1  

Although a common use of TIF for development in Kansas City is for large projects, five TIF 

districts in Kansas City use TIF for home repair projects. Individual projects can receive up to 

$20,000; the program has resulted in “$3.8 million in public and private reinvestments in 400 

neighborhood homes as of 2008” (PeopleTrust, 2011: 14). Looking at the years 1988 through 

2006, prior to the election of Mayor Funkhouser, Kansas City approved 118 TIF projects. From 

2007 through 2011, only 5 TIF projects were approved. 

St. Louis Economic Development Strategy

TIF usage in St. Louis differs in important respects from that in Kansas City. Only 3 of 132 TIF 

projects in St Louis have a subsidy value more than $100 million, as listed in exhibit 2 (Missouri Department of Economic Development, 2014, 2013, 2012, 2011, 2010).

The average size of TIF projects and the average  aid intensity 2 in St. Louis are lower than in Kansas City.3 Only 10 St. Louis projects have a TIF value of more than $20 million. By contrast, in Kansas City, 28 projects have a TIF value of more than $20 million. It should be no surprise that St. Louis has a low aid intensity because the city’s policy is to adhere to a 15-percent limit for each project, except under special conditions (PFM, 2016: 17).

1 For more on this move, see Hawley (2011).

2 This term is borrowed from the European Union, where it is defined as subsidy/investment. This standardized measure enables comparison of the size of subsidies given to projects of different magnitudes.

3 Calculated from Missouri Department of Economic Development (2015, 2014, 2013, 2012, 2011).

210 Refereed Papers

Exploring Patterns of Tax Increment Financing Use and Structural  

Explanations in Missouri’s Major Metropolitan Regions

Exhibit 2

St. Louis TIF Projects With Reimbursable Costs of More Than $100 Million

Name of Project

Date

TIF Value ($)

Total Project Cost ($)

Aid Intensitya (%)

Grand Center

2002

104,679,000

531,316,000

20

Northside 

2009

390,648,325

3,634,000,000

11b

Cortex

2012

158,200,000

2,200,000,000

7

TIF = tax increment financing.

a Aid Intensity is TIF reimbursable costs divided by total project costs.

b This figure does not include state tax credits the project received, making the true aid intensity higher.

Sources: Good Jobs First (2017); Missouri Department of Economic Development (2011); Volkmann (2009) Since Luce’s (2003) report was published, the city of St. Louis began to use TIF exponentially more frequently. This greater intensity in usage resulted from the election of Mayor Slay (2001–2017), 

who inaugurated the widespread use of the tool in St. Louis. Prior to 2002, the city of St. Louis 

had approved a total of 11 TIF projects, but 121 projects were approved from 2002 through 2013. 

The average aid intensity for the entire period was 36 percent in Kansas City versus 9 percent in St. 

Louis, as exhibits 3 and 4 show.

Exhibit 3

Kansas City—Number and Amount of TIF Projects, by Year

Number 

Total TIF 

Total Project  

Average Aid  

Year

Approved

Reimbursement ($)

Cost ($)

Intensity (%)

1988

2

36,902,828

85,632,052

43

1989

0

0

0

NA

1990

1

547,896,964

547,896,964

100

1991

4

32,769,144

276,760,144

12

1992

5

339,019,319

947,470,993

36

1993

4

179,944,707

691,863,307

26

1994

15

339,528,180

789,067,022

43

1995

5

61,026,473

134,459,599

45

1996

4

59,584,585

376,033,522

16

1997

6

64,889,767

165,207,382

39

1998

6

36,123,575

156,142,172

23

1999

19

370,668,283

975,724,443

38

2000

15

440,791,216

889,601,960

50

2001

0

0

0

NA

2002

3

236,465,494

1,130,470,381

21

2003

4

56,706,803

730,240,373

8

2004

13

485,385,007

949,838,897

51

2005

4

73,276,366

238,888,067

31

2006

7

166,441,551

786,704,789

21

1988–2006

117

3,5127,420,262

9,872,002,067

21

2007

1

1,720,000

15,022,034

11

2008

3

65,437,277

194,591,696

34

2009

1

4,233,145

18,046,801

23

2010

0

0

0

NA

2011

0

0

0

NA

2007–2011

5

71,390,422

227,660,531

32

2012

1

2,621,500

44,203,654

6

Total

123

3,601,432,184

10,143,866,252

36

NA = not applicable. TIF = tax increment financing.

Source: Missouri Department of Economic Development  TIF Annual Reports  (2010–2014)

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Exhibit 4

St. Louis—Number and Amount of TIF Projects, by Year

Number 

Total TIF 

Total Project  

Average Aid  

Year

Approved

Reimbursement ($)

Cost ($)

Intensity (%)

1988

0

0

0

NA

1989

0

0

0

NA

1990

1

14,365,000

53,312,932

27

1991

1

2,728,919

44,860,000

6

1992

0

0

0

NA

1993

0

0

0

NA

1994

0

0

0

NA

1995

0

0

0

NA

1996

0

0

0

NA

1997

1

300,000

3,518,000

9

1998

1

14,500,000

14,500,000

100

1999

3

50,140,000

244,510,258

21

2000

1

400,000

3,850,000

10

2001

3

12,600,000

21,320,000

59

1988–2001

11

95,033,919

385,871,190

25

2002

10

119,986,332

737,381,811

16

2003

11

49,382,458

222,783,871

22

2004

23

110,376,919

637,738,179

17

2005

15

39,275,000

278,809,316

14

2006

17

142,896,000

820,244,372

17

2007

10

36,410,220

215,557,174

17

2008

15

59,113,361

421,147,134

14

2009

8

406,203,325

8,233,920,390

5

2010

3

30,517,640

125,859,866

24

2011

2

5,670,000

21,901,000

26

2012

4

9,550,000

58,253,830

16

2013

3

45,950,000

544,975,000

8

2002–2013

121

1,055,331,255

12,318,571,943

9

Total

132

1,150,365,174

12,704,443,133

9

NA = not applicable. TIF = tax increment financing.

Source: Missouri Department of Economic Development  TIF Annual Reports  (2010–2014)

According to one local official, an important reason for the lower number of large TIF subsidies in St. Louis is that, unlike Kansas City, very few were district-type TIF projects. Indeed, this official said the city had only three such projects—Lafayette Square, Grand Center, and Cortex.4 Instead, 

a large number of St. Louis’ tax increment financing projects consisted of the renovation of a 

single historic downtown building. Additionally, according to one official, Kansas City has more 

Greenfield TIF projects than St. Louis does. 

According to one local official, the perception and reality of the city’s decline drove St. Louis’ 

economic development strategy. The city’s population was 856,796 in 1950 and fell to 319,294 

in the 2010 census, a drop of 63 percent (Moore, 2011). Moreover, as the city is not part of any 

county, it does not have the option to annex adjacent areas to spur growth. “Our challenge was to 

give people confidence the city had a future,” said this interviewee.

St. Louis had been an early adopter of TIF in 1990 with the St. Louis Marketplace project, 

but it struggled to keep tenants (Tucci, 1996). The city was saddled with paying off general 

4 A fourth, Northside, was in legal limbo at the time of the interview, although the Missouri Supreme Court later approved it.

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obligation bonds due to the poor performance of the project, rather than having shifted the risk to bondholders with revenue bonds. Until Mayor Slay came into office, the city largely avoided the 

use of TIF as a result of that bad experience. Under Mayor Slay, St. Louis approved scores of new 

TIF projects, many of which were to rehabilitate a single building.5 Except for one other project, the city avoided the use of general obligation bonds.

Patterns of Tax Increment Financing Use in the Kansas City and St. Louis 

Suburbs

Because scores of suburbs surround both Kansas City and St. Louis and the competitive dynamics 

of the core cities in regions are different, it makes no sense to say an overall strategy of use was present in the suburbs. However, it is possible to identify clear patterns of use that changed over time. Interviewees underscored this point when asked to describe the strategy or pattern of TIF use and competition with suburbs, cities, and states. One interviewee indicated the strategy was “willy nilly.” Another interviewee from Kansas City pointed out that in terms of competition with the 

suburbs, “not much with Missouri suburbs because the state is not involved in those situations.” 

A St. Louis interviewee noted: “Municipalities are a mixed bag; some are very competitive. But 

generally, there has been cooperation with the Council and three [undisclosed] suburban cities.”

In Kansas City, suburban TIF use increased in amount and number after 2001 (exhibits 5 through 8), when a number of TIF projects were approved at the fringes of the Kansas City metropolitan 

area, in contrast to the findings of Luce (2003). From 1988 through 2006, 69 TIF projects were 

approved whereas from 2007 to 2011, 26 TIF projects were approved.6 In general, the use of TIF 

in Kansas City supersedes that of the suburbs by wide margins, however, prior to 2007, the use of 

TIF spiked in both Kansas City and its suburbs. 

The number of TIF projects increased around 2002, but the actual amount of reimbursable TIF in 

the suburbs did not surge until slightly prior to 2007. After 2007 and during the recession, the use of TIF subsided substantially in both the city and suburbs, although TIF use was slightly greater by suburban cities than Kansas City during this period. Furthermore, the use of TIF was considered 

destructive in Kansas City when dealing with competition from another state. One economic 

developer noted—

There is very intense inter-state competition, a race to the bottom it is sometimes called, 

that makes taxes move to another area giving the business/developer a huge incentive, 

but we have the same number of jobs with a new building and remodeled location but 

the other state may gain tax revenues.

Another interviewee noted the effect of cross state competition in Kansas City as—

… impossible to describe how intense and destructive not just that it is not helpful 

but it hurts the state of MO and [the funds] could be used for development but it is 

squandered. 

5 See project descriptions in Missouri Department of Economic Development (2011).

6 See exhibits 3 and 13 for overall use of TIF in the Missouri part of the Kansas City metropolitan area.

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Exhibit 5

Kansas City and Suburbs—Number of TIF Projects, 1988–2006

Legend

Kansas City TIFs

117

Suburban TIFs

 69

¯

State of Missouri

Figure 5. Kans

TIF = tax increment financing. as City a

Note: Striped area represents the study area.  nd Suburbs Number of TIFs 1988-2006

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Exhibit 6

Kansas City and Suburbs—Number of TIF Projects, 2007–2011

Legend

Kansas City TIFs

5

Suburban TIFs

26

State of Missouri

¯

Figure 9. Kan

TIF = tax increment financing.  sas City

Note: Striped area represents the study area.  and Suburbs Number of TIFs 2007-2011

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Exhibit 7

Kansas City and Suburbs—Total Reimbursable TIF, 1988–2013

TIF = tax increment financing.

Exhibit 8

Kansas City and Suburbs—Number of Approved TIF Projects, 1988–2013

TIF = tax increment financing.

Another interviewee noted the competition or strategy of TIF use is—

Varied depends on what portion of the city you are talking about. It varies [even more] 

in the suburbs and seems to be used to stimulate growth and economic development 

quicker and I think to attract consumers to the area and this has proven to be the case 

that you will see in an area in our school district.

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As one measure of this intensity, the Hall Family Foundation in Kansas City documented $217 

million in state subsidies through the Promoting Employment Across Kansas Program, or PEAK, in 

Kansas and the Missouri Works Program in Missouri that were used during 5 years to lure compa-

nies across the state line within the Kansas City MSA. Ultimately, 3,289 jobs moved from Missouri 

to Kansas, and 2,824 jobs moved in the other direction, a net of 465 in Kansas’ favor ( The Economist, 2014). This outcome works out to $466,667 per net job in subsidies, and even those 465 jobs are not new to the metropolitan area. Before this article was completed, on November 5, 2017, the 

border war got a typical John Oliver sendup on his show “Last Week Tonight” (Campbell, 2017).

From 2014 to 2016, momentum was strong for tamping down this job piracy. In 2014, the 

Missouri General Assembly passed a law that would ban the use of Missouri Works Program 

subsidies for relocations within the Kansas City metropolitan area. This ban would come into effect if the state of Kansas were to pass a corresponding law by August 2016 (Eulitt, 2016). However, 

just when it seemed that a truce was in reach, Kansas Governor Sam Brownback proposed 

legislation for a truce in 2016 that included exceptions for creating net new jobs or constructing a new building costing $10 million or more. Missouri leaders did not approve of these changes, and 

the agreement collapsed (Eveld and Stafford, 2016).

In the St. Louis suburbs from 1988 through 2001, 42 TIF projects were approved, and 48 projects 

approved from 2002 through 2013. TIF was not used in a significant way in the suburbs until the 

mid-1990s. The first big uptick in TIF use for the city of St. Louis is not until 2001 (exhibits 9, 10, 11, and 12).7 

The pattern of TIF usage in the St. Louis suburbs appears similar throughout the time period after the mid-1990s. Additionally, the overall average aid intensity of St. Louis’ suburbs was 20 percent compared with 27 percent in the Kansas City suburbs (exhibits 13 and 14). However, a marked 

difference prevails in the average aid intensity for Kansas City (36 percent) compared with the city of St. Louis (9 percent).8  

Summing up the kind and spatial allocation of TIF in the Kansas City and St. Louis regions, we 

found the TIF projects were larger in Kansas City and mainly in the downtown area, although in 

the Kansas City suburbs, TIF projects are nearer the city and airport and typically involve retail or commercial development with a small percentage of total TIF dollars going to housing. In St. 

Louis, we noted TIF projects are smaller and primarily involve single-building developments 

and retail, with a noteworthy exception of one large retail and commercial TIF that performed so 

poorly it stymied TIF use for years, prior to Mayor Slay taking office. In the St. Louis suburbs, 

TIF was occasionally used in areas adjacent to the city of St. Louis. However, these suburban St. 

Louis TIF projects were much more likely to be farther from the city of St. Louis than in the Kansas City region. Throughout St. Louis and its suburbs, retail was the predominant use of TIF. In both 

regions, the primary use of TIF did not seem to be in areas that were particularly blighted nor 

greenfields, but rather near neighborhoods and often for retail, and in the case of Kansas City, for more stressed areas in general.

7 See exhibits 4 and 14 for overall TIF use in the Missouri part of the St. Louis metropolitan area.

8 See exhibits 3 and 4.

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Exhibit 9

St. Louis City and Suburbs—Number of TIF Projects, 1988–2006

State of Missouri

Legend

St. Louis TIFs

11

Suburban TIFs

42

¯

Figure 6. St. Lo

TIF = tax increment financing.  uis City

Note: Striped area represents the study area.  and Suburbs Number of TIFs 1988-2001

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Exhibit 10

St. Louis City and Suburbs—Number of TIF Projects, 2002–2013

State of Missouri

Legend

St. Louis TIFs

121

Suburban TIFs

48

¯

Figure 10. St. Lo

TIF = tax increment financing.  uis City

Note: Striped area represents the study area.  and Suburbs Number of TIFs 2002- 2013

Cityscape 219

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Exhibit 11

St. Louis and Suburbs—Total TIF Reimbursement, 1988–2013

TIF = tax increment financing.

Exhibit 12

St. Louis and Suburbs—Number of Approved TIF Projects, 1988–2013

TIF = tax increment financing.

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Exhibit 13

Kansas City Suburbs—Number and Amount of TIF Projects Approved, by Year

Number 

Total TIF 

Total Project  

Average Aid  

Year

Approved

Reimbursement ($)

Cost ($)

Intensity (%)

1988

1

33,922,324

33,922,324

100

1989

3

12,514,891

37,255,078

34

1990

1

12,408,045

13,732,580

90

1991

0

0

0

NA

1992

0

0

0

NA

1993

1

3,073,176

13,054,813

24

1994

1

8,380,910

80,810,850

10

1995

0

0

0

NA

1996

3

100,815,450

254,606,578

40

1997

3

24,268,198

68,491,196

35

1998

3

22,955,374

120,173,275

19

1999

4

5,872,880

48,544,000

12

2000

7

160,766,568

577,083,932

28

2001

2

28,127,186

68,294,794

41

2002

11

140,747,476

471,634,760

30

2003

5

38,786,713

273,535,623

14

2004

3

123,634,098

479,428,624

26

2005

12

111,805,341

508,923,957

22

2006

9

141,966,146

574,841,327

25

1988–2006

69

970,044,776

3,624,333,711

27

2007

12

251,235,442

1,018,294,757

25

2008

5

14,612,855

68,406,345

22

2009

5

92,186,351

438,256,312

21

2010

3

33,818,560

93,584,163

36

2011

1

1,850,000

6,030,216

31

2007–2011

26

393,703,208

1,624,571,793

24

2012

3

66,557,088

136,598,024

49

2013

1

6,296,249

22,203,161

28

Total

99

1,436,601,321

5,407,706,689

27

NA = not applicable. TIF = tax increment financing.

Source: Missouri Department of Economic Development  TIF Annual Reports  (2010–2014)

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Exhibit 14

St. Louis Suburbs—Number and Amount of TIF Projects, by Year

Number 

Total TIF 

Total Project  

Average Aid  

Year

Approved

Reimbursement ($)

Cost ($)

Intensity (%)

1988

1

14,366,800

49,428,000

29

1989

3

3,177,280

3,177,280

100

1990

2

2,845,620

2,845,620

100

1991

3

14,416,380

46,416,380

31

1992

1

2,096,000

36,771,000

6

1993

0

0

0

NA

1994

1

15,430,000

57,000,000

27

1995

2

33,680,000

142,080,000

24

1996

6

56,730,000

556,735,421

10

1997

8

82,102,000

684,349,000

12

1998

6

69,046,000

370,141,000

19

1999

6

104,699,421

278,980,033

38

2000

1

19,600,000

133,683,000

15

2001

2

19,985,000

64,645,000

31

1988–2001

42

438,174,501

2,426,251,734

18

2002

5

25,750,000

90,500,000

28

2003

6

63,152,194

172,793,000

37

2004

4

79,381,530

202,809,855

39

2005

6

113,150,000

825,346,562

14

2006

8

98,112,748

729,481,038

13

2007

8

171,954,890

888,439,743

19

2008

0

0

0

NA

2009

1

26,750,000

26,750,000

100

2010

4

48,520,000

93,120,000

52

2011

1

4,002,000

23,552,000

17

2012

4

55,085,750

148,335,975

37

2013

1

15,000,000

46,199,000

32

2002–2013

48

700,859,112

3,247,327,173

22

Total

90

1,139,033,613

5,673,578,907

20

NA = not applicable. TIF = tax increment financing.

Source: Missouri Department of Economic Development  TIF Annual Reports  (2010–2014)

Findings

The processes of employing TIF in St. Louis and Kansas City reveal several significant differences. 

First, the outlooks of the mayors may have an influence on TIF usage. In Kansas City, Mayor 

Funkhouser is on record opposing TIF use. Moreover, Kansas City has term limits for the mayor, 

whereas St. Louis does not. Mayor Slay, who vastly increased TIF use in St. Louis, was reelected to his fourth term in 2013. Second, Kansas City faces much more competition from cities in Kansas 

than St. Louis faces from cities in Illinois. In the judgment of several interviewees, relatively little competition occurs between Missouri cities in the Kansas City metropolitan area. By contrast, in 

the St. Louis metropolitan area, a relatively high level of competition exists among cities within the region, especially in regard to retail projects.9 The third difference is that the TIF Commission in Kansas City consists of 11 persons, with 6 appointed by the city and 5 by the school board and 

9 Some competition is found between the city of St. Louis and Clayton, an upscale suburb that is the county seat of St. Louis County, for office and headquarters projects. Thus, St. Louis recently gave a $7 million TIF to Laclede Gas Company for a new headquarters after it threatened to move to Clayton (Weiderman, 2013).

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other affected taxing districts. St. Louis, by contrast, has only 9 people on the TIF Commission,10 

with six appointed by the city, two representing the school board and one representing all other 

taxing districts. This six-to-three city-appointed majority makes it easier for proposed TIF projects to receive commission approval in St. Louis than in Kansas City, whereas in Kansas City the majority is six to five. According to one official, the St. Louis TIF Commission has never rejected a proposal brought by the St. Louis Development Corporation. A fourth factor interviewees mentioned is that 

Kansas City has an advisory panel to the TIF Commission and St. Louis does not. 

One interviewee in St. Louis noted, “The city didn’t use TIF much [before Slay’s election] but did use lots of tax abatements. Now that they use TIF, it’s for small projects with the occasional large one.” Another local official in St. Louis pointed out the tension between the city and the school 

board, which perfectly overlap in area, in regard to both TIF and tax abatement. Indeed, the school board sued the city over a tax abatement in 1992 ( St. Louis Post-Dispatch, 1992).

In Kansas City and its suburbs, a symbiotic relationship is present, about which one interviewee in Kansas City indicated— 

It has enhanced development in terms of bricks and mortar…there are negative and 

positive impacts both [for cities and suburbs] if you talk to the superintendent of 

[undisclosed] suburb they have big box retail abated at 75 percent but they felt nothing 

was going to happen without doing it. In my district [in the central city] TIF enhances 

the quality of life for residents but if we get additional students that erodes our district 

revenue. 

He went on to note that their school district crosses into both city and suburban boundaries. This overlap also happens further out in the suburbs as well, where one community’s school district has a large number of students from a neighboring city. The upshot is Kansas City or the hinterland 

city may have a TIF that is increasing revenue from sales tax from a “big box retail,” yet a school district with an overlapping boundary has property tax abated, making the school district suffer 

while serving two communities—one with the development and one that does not realize the sales 

tax revenue. In essence, a similar phenomenon plays out in the central city, first-ring suburbs, and communities further out because of overlapping taxing jurisdictions with communities that use 

TIF and those that do not. Another interviewee provided a more explicit example of the way this 

scenario has played out.

The [undisclosed] TIF has caused a tremendous amount of growth in our district. This 

growth has been both on the side of retail development and residential. Parents looking 

for a quality school system and easy access to the Interstate, International Airport, and 

quality shopping now have many subdivisions from which to choose on the one side 

of our school district. There are many positive aspects of this growth, but there are also 

many challenges that this growth has caused, ranging from overcrowded schools to traffic 

congestion. It has also caused some political tension with the City of [Undisclosed], since 

the taxes are collected by Kansas City, but much of the expense of educating the new 

students falls on the businesses and homeowners in [undisclosed] city.

10 This difference derives from the state TIF Statute.

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Another Kansas City interviewee noted when “cities and suburbs share profits, it affords them 

regional benefits. When working together, they can get benefits of a stadium or amenities. But 

when you compete, they spend it on corporate welfare. It is a cancer dragging down the whole 

region.” 

In terms of the quantity of TIF developments in the two periods, the total number of suburban 

TIF projects approved dropped from 69 through 2006 to 26 during 2007 to 2011. However, the 

number of TIF projects averaged per year through 2006 was 3.5 compared with an average of 5.2 

TIF projects per year in the Kansas City suburbs. In the St. Louis suburbs, TIF projects increased both in number and on average from 42 prior to and through 2001, an average of 3.2 TIF projects 

per year, to 48 after during 2002 to 2013, or on average 4 TIF projects per year. After 2001, a 

greater frequency of use prevailed in the suburbs, and those communities using TIF were nearer 

the central city than previously. Once again, much of the city’s change in TIF use was tied to the mayor in office during the time. Mayor Funkhouser had a reputation of retrenching from the use of 

TIF, whereas Mayor Slay advocated it as a way for the city of St. Louis to move forward. The actions of the central city have an impact on the suburbs but in different ways. In the case of Kansas City, the suburbs appear to use the tool more when the central city does not. In the case of the city of St. Louis, the suburbs increased their use slightly as the city increased its frequency of TIF use considerably. In both cases, suburban use continued and also declined most precipitously during 

the recession years. 

Discussion and Conclusions

Although more research is clearly needed in regard to the central cities, the lack of mayoral term limits may play a role in St. Louis by making it easier for the mayor to put into place a desired 

economic development strategy. In Kansas City, the mayor has limited opportunity (two 4-year 

terms) and must be strategic in leveraging the support of stakeholders who helped the mayor 

get elected or may be in a position to help in future elections. Additionally, St. Louis faces little competition from Illinois on economic development projects. Most competition comes from within 

the region on the Missouri side of the state. In the case of Kansas City, the state of Kansas began to leverage state dollars that make competition for economic development with the city of Kansas 

City, Missouri fierce in a way that more often leads to a bidding war between the state and the city. 

One interviewee specifically spoke to this issue noting—

Have to have both states say no to playing the game but no one wants to be the first to 

say no because they don’t want the development to go to the other side. Kansas tax cuts 

and tax incentives that Governor Brownback supports. In KC it is with TIF, but not in 

Kansas state. It is definitely a brutal situation for KC as it is on the border. This won’t be 

as big of deal in St. Louis because the Illinois side is not as attractive.

Moreover, in St. Louis mayoral appointees enjoy a six-to-three majority on the TIF Commission 

compared with a six-to-five majority for Kansas City on its TIF Commission, making it easier 

for St. Louis to pass TIF proposals than Kansas City. Indeed, the St. Louis TIF Commission has 

never rejected a proposed TIF, whereas the Kansas City TIF Commission rejected at least three. 

In addition, the data suggest that central city subsidy choices cast a shadow over the economic 

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Explanations in Missouri’s Major Metropolitan Regions

development decisions of their suburbs, at least in the Kansas City area, which faces strong 

neighboring-state competition with potentially substantial effects. Finally, our interviews show that many aspects of TIF remain controversial 35 years after the state legislature originally authorized TIF. The most significant issue remains the ability of a municipality to use the property tax 

revenues that would have gone to other taxing districts as part of the funding for a TIF subsidy in Missouri. In a number of other states, the school board and other taxing districts can opt out or 

even have veto power over TIF proposals.

In particular, the 2001 election of Mayor Slay in St. Louis, who served four terms through 2017, 

led to a drastic increase in the use of TIF in the city. In Kansas City, by contrast, the city saw far fewer TIF projects approved when Funkhouser was mayor from 2007 to 2011.

The findings are suggestive. Kansas City and St. Louis definitely are pursuing different TIF 

strategies, with larger projects in Kansas City. The onslaught of new developments in St. Louis 

(more than 100 since Mayor Slay was elected compared with about 6 before his election) may 

contribute to an increasingly competitive atmosphere in the region. The two periods that saw 

annual increases in TIF projects in the St. Louis suburbs were the pre-Slay years (1988–2000) with 46 TIF projects and the Slay years (2001–2013) with 53 TIF projects. In the Kansas City suburbs, 

the number of projects decreased from 73 during 1988 through 2006 to 26 during 2007 through 

2011. With the exception of 2007, the suburbs of Kansas City, Missouri also appeared to reduce 

their use of TIF. Perhaps this reduction was due to the economic recession, or perhaps they were 

following the lead of the central city and not competing as assertively for development. In the St. 

Louis region, the increase in TIF in the core city is also witnessed in the suburbs but nowhere near the degree as the central city. These findings suggest that to a small extent, suburbs may take cues from their central cities, but the outcomes are somewhat different. In the Kansas City region, the suburbs picked up the use of TIF when the core city was not using TIF as much. This result may 

corroborate Felix and Hines’ (2013) findings that interstate competition breeds more tax incentive use than intrastate competition. In short, in the case of Kansas, this outcome may be due to the 

large size of TIF projects the core city pursued to keep the state of Kansas from moving business 

out of the state but remain within the region. In St. Louis, TIF use in the suburbs is more or less similar, perhaps because competition of TIF use is with adjacent cities, regardless of whether that city is a core city. This situation may be because the city of St. Louis is less of a threat than Kansas City is in terms of being a magnet for development opportunities or because St. Louis faces less 

interstate competition in their region than the Kansas City region. 

In light of these findings, a review of the Missouri TIF statute is necessary to investigate changes that would further regional cooperation instead of intraregional competition, such as all 

communities sharing in the cost and benefits of the TIF, such as the effect on taxing resources for all the jurisdictions that benefit from the economic development. The Missouri General Assembly 

in 2008 created county TIF Commissions in St. Louis, St. Charles, and Jefferson Counties, which 

replaced individual municipal TIF Commissions.11 However, more broad-based actions, as the 

11 The county TIF Commissions can recommend against local TIF projects, but a two-thirds majority of the local city council can override the county TIF Commission recommendations, leading one local official to describe the county TIF Commissions as “toothless” and suggest that their decisions be made final; that is, not subject to override by the municipality.

Cityscape 225

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one suggested here, are needed in the region and statewide. One reason cooperation may not be 

attractive, as one interviewee noted, is “Nice word ‘Truce’ if you are winning why would you quit 

if you were Kansas why let you catch up?” Another driver that might prevent more cooperation 

is the reverse; if you are behind, why not try to use every advantage or tool you have to gain jobs or development? Some interviewees underscored the need for reform, including this person who 

indicated—

KC and perhaps St. Louis are similar in that they are difficult in terms of helping you 

understand [TIF use] as the players and the plan, the vast number of players, they ways 

[TIF uses] are set-up and the way TIF is being used…. Having said that, there is no one 

in taxing jurisdictions that is not frustrated by TIF. 

Ultimately, our findings suggest the way central cities use TIF may also influence the way suburban cities use TIF as well. To some degree, the core city’s behavior may bolster suburbs, where the 

outlying cities replicate the similar behavior, in the case of St. Louis, but may not be competing explicitly with the city of St. Louis for development, as other studies have shown cities often do in adjacent city competition. In the case of Kansas City, suburban cities may take the lead when the 

core city does not use the tool as much. This reversal of roles has tremendous implications for the way our regions grow, especially regions that cross state lines and may have another competing 

source for development. Driving the size and aid intensity of TIF, not only in the city but also 

regionwide, may be the fact that Kansas City, Kansas and other Kansas-side suburbs compete for 

jobs and development so fiercely. 

In the case of Kansas City, one might say that the city behaves in a risk-averse fashion, as the state of Kansas’s grab for development threatens the city’s investment and tax base. Later, when Kansas 

City declines to engage in the bidding with the state of Kansas, this tactic sends a signal to its suburbs that they may want to engage in risky decisions to avoid a loss in regional development. 

It also appears in contrast to Pacewicz’s (2013) findings that Kansas City, under the direction of Mayor Funkhouser, was behaving in a less puzzling manner in terms of financial sustainability, 

as Funkhouser curbed the use of TIF. In the case of St. Louis, the behavior of using TIF may be 

perceived more as a gain compared with the previously poor experience with TIF that made the 

city reluctant to use it, in addition to the cost of the perception and reality of the city’s decline. 

For the city of St. Louis, obtaining development at some cost, or a small loss, warrants some level of TIF use for the city to improve its image, especially when they do not face much competition 

from the neighboring state of Illinois. Also, St. Louis may have acted sustainably, using their 

tax incentives to focus on smaller TIF projects, although more numerous than the Funkhouser-

administration period. The suburbs of St. Louis probably do not see the city as a competitor, but 

rather hope that the city’s demise will be stymied and not spread. This strategy would subdue the 

competition from the suburbs and make it not as reactive to the behavior in the central city, as seen in Kansas City. In a one-state study, the end effect on a bistate region, if any, is hard to determine. 

However, other taxing jurisdictions, such as libraries and school districts, suffer from less revenue and potential tax value loss, which should return in 23 years, except in places like St. Louis where the city and the county share the same geographic boundaries. Furthermore, more often than not, 

by the time properties again generate tax revenue, the loss in property value that is taxed negates the gain. Some interviewees in the Kansas City area have even called for amendments to the TIF 

226 Refereed Papers

Exploring Patterns of Tax Increment Financing Use and Structural  

Explanations in Missouri’s Major Metropolitan Regions

length. One interviewee suggested the length of the TIF should end when the development is 

complete or simply made more definitive. Another interviewee noted that the current 23-year 

period could be expanded by 10 more years when the TIF boundaries are expanded and that TIF 

redevelopment plans can be amended many times. 

In St. Louis, where virtually no competition for jobs and development occurs regionally with the 

neighboring state of Illinois, TIF use in the central city (St. Louis, Missouri) is concentrated on smaller projects. Although project size has increased in the suburbs in both regions, the findings reveal that TIF size and aid intensity are not as large in the St. Louis metropolitan area as in Kansas City. It is possible that the communities’ perceptions of their situations influence the views of 

their TIF actions. For example, Kansas City facing real losses to Kansas and St. Louis facing no 

threat from Illinois may help explain two divergent patterns of TIF use in these central cities but complementary ones with their suburbs. One solution for cities and taxing districts under siege 

or wishing to improve their state, whether their anchor is one of loss or gain, would be to share 

in both the benefits and costs of the TIF projects regionwide. In the case of Kansas City, the city could become a formidable threat to the State of Kansas in a bidding war when it comes to jobs 

and growth. In the case of St. Louis, the allocation of costs and benefits could be more evenly 

distributed and enrich the entire community, as opposed to abating only momentarily a further 

decline. Much like Sands, Reese, and Trudeau (2007) note, this strategy could help balance 

regionwide both city and neighborhood needs. Finally, our interviews show that many aspects of 

TIF remain controversial 30 years after the state legislature originally authorized TIF. 

More research is needed, specifically on additional comparisons of central cities and their suburbs. 

Consideration to municipalities’ anchoring points (of loss or gain) along with the more structural factors—such as mayoral term limits, commission majorities, regional competition, barriers to 

cooperation, and historical use, as well as amount and size of TIF projects—would be helpful to 

determine driving factors and further explain existing patterns of TIF use. 

In light of these findings, changes to the Missouri TIF statute are probably necessary to promote 

regional cooperation more than intraregional competition. Although it is possible, cities might not want to forgo their competitive nature for fear of losing a chance at development or giving another area an edge, surrendering tax dollars to development that might be just as well suited in another part of the region, may need to be considered. An equity based approach to using development 

incentives regionally may return better outcomes for all jurisdictions than competing with tax 

dollars that may affect the future of services such as education. In 2007, the Missouri General 

Assembly established county TIF Commissions in St. Louis, St. Charles, and Jefferson Counties, 

where municipalities have only 3 of the 12 votes, and negative decisions by the Commission 

require the cities to obtain a two-thirds city council supermajority to approve a TIF (Butler, 2012: 65). The city of Ellisville did override the county TIF Commission in 2012 adopting an $11 

million TIF for a Wal-Mart store (Deere, 2012), and several St. Louis interviewees suggested that 

these county TIF Commissions should be strengthened to make it even more difficult for a city to 

adopt a TIF proposal that the commission has ruled against. In 2016, the state legislature did just that, restricting TIF in those cases to expenditures on land clearance and building demolition only (Schlinkmann, 2016). The situations that several Kansas City interviewees described suggest that 

extending the law statewide could be a valuable reform. In fairness to both the communities and 

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Mason and Thomas

the regions, it may be that now is the time to take a regional approach to economic development. If the spoils and costs are shared more evenly across a region, not unlike the case of Minneapolis-St. 

Paul’s regionwide economic development revenue sharing and joint development of wastewater 

infrastructure, some unintended consequences may be averted.

Acknowledgments

The authors thank Paul Morrow and Charles Herrick for their excellent research assistance and 

everyone who generously agreed to an interview for this project. We also appreciate the helpful 

comments from our reviewers. We received comments on previous versions of this article from 

David Stokes of the Show-Me Institute, as well as participants at the Midwest Political Science Association conference in 2012 and the Urban Affairs Association conference in 2013.

Authors

Susan G. Mason is Emeritus professor at Boise State University, School of Public Service.

Kenneth P. Thomas is professor of political science at the University of Missouri-St. Louis.

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232 Refereed Papers

Departments

In this issue—

• Data Shop

• Foreign Exchange

• SpAM

• Referees 2017–18

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 233

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

234 Departments

Data Shop

Data Shop, a department of  Cityscape , presents short articles or notes on the uses 

of data in housing and urban research. Through this department, the Office of Policy 

Development and Research introduces readers to new and overlooked data sources and to 

improved techniques in using well-known data. The emphasis is on sources and methods 

that 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. 

The Long-Term Dynamics of 

Affordable Rental Housing: 

Creating and Using 

a New Database

John C. Weicher  

Hudson Institute

Frederick J. Eggers

Fouad Moumen

Econometrica, Inc.

Abstract

In a study recently released by the Hudson Institute, the authors reported on the 

dynamics of affordable rental housing during the period between 1985 and 2013. 

In this article, we describe the data used in that analysis, present some of the most 

important findings, and explain how to obtain the data and the documentation, as well 

as the study.

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 235

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

Weicher, Eggers, and Moumen

Introduction

Housing assistance for low-income households has been a public policy objective since the 1930s, 

and housing has long been recognized as part of the social safety net. Assistance is not, however, an entitlement, and a large share of the housing occupied by low-income families is provided 

privately, both with and without federal support. As a result, little is known about the affordable rental stock, particularly how it has changed over time. Taking advantage of the longitudinal nature of the American Housing Survey (AHS) between 1985 and 2013, we have created a longitudinal 

database covering the whole period and analyzed the changes in the affordable rental stock during 

those years (Weicher, Eggers, and Moumen, 2017). This article briefly describes the creation of the database and summarizes the empirical results of the analysis.

The study used all 15 national AHSs derived from the sample drawn in 1985 and augmented 

through 2013. We classify rental units, both occupied and vacant, as affordable, moderate, or high rentals by comparing gross rent to local family median income with adjustment for the number of 

bedrooms. Once units enter the AHS sample, their status can vary across surveys. Possible statuses are rental (differentiated by affordability), owner stock, use as seasonal or second homes, temporarily out of the stock, or permanently out of the stock. We observed both stability and substantial 

variation with respect to status in the paths followed by units across surveys. A number of issues had to be resolved before analyzing the data. A fundamental issue was how to identify those rental units that are assisted; we used two approaches to solve this problem. The next section discusses 

these issues and describes how we resolved them. 

We looked at the dynamics of affordable rental housing in three ways. First, we tracked the career paths of all units from either 1985 or the time they entered the housing stock until either 2013 or when they left the housing stock permanently. Second, we report on what happened by 2013 to 

those units that were affordable rentals in 1985 and also on where those units came from that were affordable rentals in 2013. Finally, we aggregated affordable rental housing across all 15 surveys and identified the types of units that provided that housing. In the third section, we report some of the more interesting findings from these analyses.

The concluding section explains how to obtain a copy of the report. It also describes the datasets available for use by others and indicates how to acquire them. 

Data, Definitions, and Issues

We use a sample of 65,540 housing units obtained from the AHS that the U.S. Department of 

Housing and Urban Development (HUD) sponsors and the U.S. Census Bureau conducts. The 

sample was drawn in 1985; 66 percent of our units come from that original 1985 sample. HUD 

and the Census Bureau have added to the 1985 AHS sample with each survey to represent units 

that have entered the housing stock through new construction or by other means. The remainder 

of our sample comes from these additions through 2013, the end year of our analysis and the final 

year of data collection for this AHS sample. 

236 Data Shop

The Long-Term Dynamics of Affordable Rental Housing: Creating and Using a New Database Once a unit is in the AHS sample, the Census Bureau surveys the household occupying that same 

unit every 2 years, enabling us to observe changes in both housing units and their occupants over 

time. The sample is large and was carefully designed to represent the housing stock nationwide, 

and the information on both the housing units and its occupants is detailed and consistently 

reported. Like the Census Bureau, we weight each observation so that the sample of 65,540 

observations can represent the 156 million housing units that were in the housing inventory for all or some of the years between 1985 and 2013. In order to adjust for some problems with the AHS 

sample, we have had to modify the pure weight assigned to each unit by the Census Bureau. 

Our definition of affordable rental housing is— 

A rental unit is affordable if the sum of rent, utilities, and related costs, adjusted for 

the number of bedrooms, is less than or equal to 30 percent of 50 percent of local area 

median income. 

This definition takes both costs and income into account. Affordability improves (or worsens) as 

either housing costs decrease (or increase) or household incomes increase (or decrease). “Fifty 

percent of local area median income” is the HUD definition of very low income and is also the 

standard for eligibility for assisted housing. “Thirty percent” is the required contribution of income from tenants of assisted housing and is supposed to represent a reasonable boundary between 

what a family should spend for housing and what it should spend on other goods. The bedroom 

adjustment recognizes that an affordable rent for a two-bedroom unit would not be the same as an 

affordable rent for a one-bedroom unit.

Resolving Data Issues

In the course of the study, we identified several special issues. We addressed them in ways that we considered appropriate for this study.

•  Missing data: Not all units in the survey provided complete interviews. We filled in missing 

values for various questions using the status of the unit in an adjoining survey—taking the re-

sponse from the nearest survey. If two surveys were equally near with different responses, we 

chose the previous survey if the unit’s control number was odd and the next survey if the con-

trol number was even. We had to allocate responses in 1 year for 12,041 units and in 2 or more 

years for 8,882. 

•  Sample reduction: For the 2007 and 2009 surveys, about 5,000 units were dropped from the 

sample for cost considerations. These units returned to the survey in 2011. We employed our 

allocation procedures for missing data to fill in responses for the two surveys.

•  Median income adjustments: Through 2005, HUD calculated median incomes for metropoli-

tan areas or nonmetropolitan counties using the most recent decennial census. In 2007, HUD 

changed its methodology, using the American Community Survey (ACS). HUD observed that 

the income data from the ACS tended to be lower than the income data from the decennial cen-

sus. We used the data from the 2005 and 2007 ACS surveys to make the 2007 and later local 

median incomes consistent with the 2005 and earlier local median incomes.

Cityscape 237

Weicher, Eggers, and Moumen

•  Variation in bedroom counts: For nearly one-half of our sample units, respondents provided the 

same count of bedrooms in each survey; slightly more than one-half (51.4 percent) had more 

than one count of the number of bedrooms. To see how serious this variation might be, we did 

an alternative analysis in which we limited the variation in bedroom counts.

•  Units not included in all surveys: From time to time, the AHS for a given year contains information for some units that is not available in other years. A special mobile home sample was added for 

only the 2005 survey, for example, and units were added in 2011 and retained in 2013 to enhance 

the sample. We eliminated these units from our database, because we did not have information 

about them for each survey and could not use them as part of our longitudinal analysis. 

Assisted Housing

Identifying assisted housing has been a difficult problem in the AHS. Until 2011 identification 

of assisted units had to be based on the household interviewee’s response to questions about as-

sistance. Unfortunately, these responses have not been a satisfactory method for identifying units receiving assistance. Many more households have reported receiving housing assistance in the 

AHS than were actually receiving assistance, according to HUD program data.1 The problems with 

the AHS in this regard have been recognized for many years. HUD’s efforts to address them have 

included several revisions of the questionnaire, most recently in 1997.2  

We have addressed these problems in two ways. First, we used the consistency of AHS responses 

to identify assisted units. We classified units that were always or nearly always identified as “assisted” by the responding occupant as being assisted through the entire period. Units that were 

infrequently identified as “assisted” were classified as not being assisted. Although the data are imprecise in any one survey, we considered this to be the best available method of identifying 

households that received assistance that was tied to the units. Second, we made use of program 

information on assistance status that has been provided only for the 2011 and 2013 surveys. In 

those surveys, HUD-assisted units were identified by matching the address of the sample unit to 

the administrative records for HUD programs. Units that were either public housing or privately 

owned assisted projects in 2011 or 2013 were identified as assisted back to 1985 or to the first 

year in which the unit was part of the AHS sample. 

Using both approaches, we find that public housing and privately owned assisted housing projects 

were an important source, but never the predominant source, of affordable rental housing. Assisted housing projects accounted for 21 percent of the affordable rental inventory in 1985 and 16 

percent in 2013. We believe that the decline represents the shift of federal housing assistance from project-based assistance to housing vouchers.3

1 Units assisted in Farmers Home Administration (FmHA) multifamily programs are not identified in the AHS but cannot account for the overreporting of assistance by households in the sample. The Section 514 and 515 programs have 434,000 

assisted units, according to FmHA program data that have recently been made available (Scally and Lipsetz, 2017).

2 The 1997 revisions are described in HUD (2000: A-22–A-25) and ICF (2001: 43).

3 HUD data show that project-based assistance reached a plateau around 1984 at about 3.1 million units and peaked in 1995 at about 3.2 million; by 2010, only 2.6 million units were assisted in this manner. No units received housing vouchers or certificates until 1976; by 1988, more than 1 million units were assisted, and by 2002, over 2 million were assisted (Weicher, 2012: Table 4-5).

238 Data Shop

The Long-Term Dynamics of Affordable Rental Housing: Creating and Using a New Database Overall, the quality of the affordable rental stock improved steadily from 1985 to 2013, using both the standard measure of inadequacy reported in the AHS since the late 1980s and a more detailed measure developed for this analysis. This improvement in quality was also the case for moderate rent and high rent units, and for the owner stock. Not surprisingly, affordable rental units were found to be of lower quality than other rental units or owner units but, for the most part, affordable rental units were of acceptable quality. Assisted housing units were generally of higher quality than unassisted affordable rental but the quality differential had virtually disappeared by 2013 as the assisted stock aged.

Key Findings 

Most of our key findings come from either a comparative static analysis that explained the changes in affordable rental housing between 1985 and 2013 or an aggregate analysis, which we termed 

“unit-years,” that examined the sources of affordable rental housing over all 15 surveys. 

Counting Affordable Units

To track the “career path” of a housing unit, we classify it as being in one of eight statuses at the time of each of the 15 AHS surveys from 1985 and through 2013. The eight statuses and the 

numbers we assign to them4 are—

0  Not yet in the sample

1  Affordable rental unit

2  Moderate rental unit

3  High rental unit

4  Owner unit

5  Seasonal unit or second home

7  Temporarily out of the housing stock

8  Permanent loss from the housing stock

A moderate rent unit is defined in a similar manner to an affordable unit; the sum of rent, utilities, and other costs must be “greater than 30 percent of 50 percent of local area median income, and 

less than or equal to 30 percent of 80 percent of local area median income.” A high-rent unit has 

housing costs “greater than 80 percent of local area median income.” We include vacant units 

in categories 1 through 3 if they are vacant for rent, vacant for rent or sale, or rented but not yet occupied, and in category 4 if they are vacant for sale only or sold but not yet occupied. All other vacancies, such as seasonal use only, are put into category 5. Temporary losses include units used for nonresidential purposes or units needing repairs to be habitable. By 2013, 7,447 of our sample units had become permanent losses.

4 In the first stage of our analysis, we assigned a value of “6” to units with missing information on status in a given year. In subsequent stages, we assigned a status value for that year using the procedure described previously.

Cityscape 239

Weicher, Eggers, and Moumen

Using these statuses, we identify 25,642 unique paths that our sample units took during the course of the 15 surveys. The most common path is “always owner-occupied” from 1985 through 2013; 

12,279 sample units take this path, 18.7 percent of the sample. The second most common is al-

ways affordable rental housing from 1985 through 2013; the 1,388 sample units that take this path 

constitute 2.1 percent of the sample. The next 14 most common paths are units that entered the 

sample after 1985 and were always owner-occupied, once they did so—units entering the sample 

in 1987, for example.

At the other end of the distribution, 22,488 units take unique paths; only one unit in the sample 

follows that path. These unique paths constitute 34.3 percent of the sample, more than the 

number of units that were always owner occupied once they entered the sample. Only two units 

each follow another 1,456 paths. Put another way, only one unit follows 87.7 percent of the paths, and only two units each follow another 5.7 percent. The career paths followed by housing units are many and diverse. 

Comparative Statics: Looking Backward and Forward

Affordable rental housing has constituted a remarkably stable share of the total stock. The proportion was 14.8 percent in both 1985 and 2013. During the period, the share never rose above 16.4 

percent or fell below 14.5 percent. With the growth of the housing stock, the number of affordable rental units rose from 15.0 million in 1985 to 19.7 million in 2013. These additional units came 

from three sources (as exhibit 1 shows).

•  Rental units that were not affordable in 1985 but became so by 2013—4.6 million units.

•  Housing units that were not rental in 1985; they were part of the owner stock or were seasonal 

or second homes—3.8 million owner units (owner-occupied or available for sale) and 0.5 

million whose occupants had a usual residence elsewhere (URE), such as seasonal housing units 

or second homes.

Exhibit 1

Backward-Looking Analysis: Where Did the 2013 Affordable Rental Stock Come From, 

Vis-à-Vis 1985?

Percent of 2013 Affordable 

Status in 1985

Frequency

Rental Stock

Affordable

6,243,000

31.7

Were in higher rent categories

4,615,000

23.4

Were owner stock 

3,845,000

19.5

Were seasonal/URE stock

463,000

2.3

Were temporarily out of the housing stock

200,000

1.0

Were additions to stock

4,337,000

22.0

New construction

2,557,000

13.0

Other additions

1,780,000

9.0

Total

19,702,000

100.0

URE = usual residence elsewhere.

240 Data Shop

The Long-Term Dynamics of Affordable Rental Housing: Creating and Using a New Database

•  Units that did not exist or did not provide housing in 1985—4.5 million, of which 2.5 million 

were new construction; 1.8 million were added to the stock by merging two or more units into 

a single unit, splitting one unit into two or more, or conversion from other purposes; and 0.2 

million were temporarily out of the housing stock in 1985.

Looking forward from 1985, about 6.2 million affordable rental units were also affordable rentals 

in 2013. The other 8.8 million were no longer affordable, or no longer rental, or no longer pro-

vided housing (as exhibit 2 shows).

•  Affordable rental units in 1985 that were still rental in 2013 but not affordable—1.7 million 

units.

•  Units that were no longer rental—2.7 million, of which 1.9 million were in the owner stock 

and 0.8 million were no longer primary residences but had become UREs, such as seasonal or 

second homes.

•  Units that had been lost to the housing stock—4.3 million, of which 4.0 million were 

permanent losses and 0.3 million were temporary losses that could be reversible. 

Thus the most common reason for losses from the affordable rental stock between 1985 and 2013 

was that the unit was no longer providing housing—it was permanently lost. During the same 

period, the most common source of additional affordable rental housing was the higher rent stock. 

Exhibit 2

Forward-Looking Analysis: What Happened to the 1985 Affordable Rental Stock by 

2013?

Percent of 1985 Affordable 

Status in 2013

Frequency

Rental Stock

Still affordable 

6,243,000

41.7

Gentrified

1,722,000

11.5

Owner stock

1,884,000

12.6

Seasonal, URE, or similar

791,000

5.3

Temporary, reversible loss

276,000

1.8

Permanent loss to housing stock

4,053,000

27.1

Total

14,969,000

100.0

URE = usual residence elsewhere.

Comparative Statics: Looking Both Ways

Exhibit 3 puts the gains and losses together to account for the increase in the number of affordable rental units. Of the additional 4.7 million affordable rental units, only 0.2 million came from net additions to the housing stock; the losses nearly matched the number of new units. The other 

4.5 million came as a result of changes within the 1985 existing housing stock; 2.9 million from 

changes in the rent levels of the 1985 rental stock, units that filtered down exceeding those that gentrified; and 1.6 million from tenure shifts between owned or URE units and rental housing. 

The net increase from filtering is nearly twice as important as the net increase from tenure shifts, and far more important than the net increase from new construction and demolitions (and other 

sources of additions or removals). The gross increases are very similar—4.6 million from filtering, Cityscape 241

Weicher, Eggers, and Moumen

Exhibit 3

Accounting for the Net Change Between 1985 and 2013 

Frequency

1985 affordable rental stock

14,971,000

New construction or other additions

4,537,000

Demolitions or other losses

4,330,000

Net effect

207,000

Filtering—becoming affordable

4,615,000

Gentrifying—rising rents

1,722,000

Net effect

2,892,000

Tenure shift—owned or URE/seasonal to affordable rental

4,307,000

Tenure shift—affordable rental to owned or URE/seasonal 

2,675,000

Net effect

1,632,000

Net addition

4,732,000

2013 affordable rental stock

19,702,000

URE = usual residence elsewhere.

4.5 million from new construction and other additions, 4.3 million from the nonrental hous-

ing—but fewer units are lost from gentrification than from tenure shifts and far fewer than from 

demolitions and other losses.

Sources of Affordable Housing During 1985–2013

To look at affordable rental housing  throughout the entire period, we developed the concept of unit-years of housing. If 100 units furnish affordable rental housing for 10 years, we record this activity as 1,000  unit-years of affordable rental housing. Similarly, 100 units furnishing 10 years of owner-occupied housing would be said to provide 1,000 unit-years of owner stock. Of course, the 

same unit can provide unit-years of different types of housing, for example, 8 unit-years of affordable rental, 4 unit-years of seasonal housing, and 10 unit-years in the owner stock. Units following a variety of paths can provide unit-years of affordable rental housing.

During the entire period, about 535 million years of affordable rental housing existed—about 17.9 

million units each year on average. The largest contributors to affordable housing during these 

years are diverse and quite surprising. 

•  The largest contributors to affordable rental housing throughout the years were the 44 million 

units that were most often part of the owner stock but were affordable rentals for less than one-

half of their time in the housing stock. These units accounted for 24.4 percent of all affordable 

rental housing. On average, they were affordable rentals for 3 years out of the 30. 

•  Another 5.8 million units served both the owner and renter sectors but were affordable rentals 

for one-half or more of their time in the housing stock. These units provided nearly 18 years of 

affordable rental housing on average and accounted for 19.1 percent of all affordable rental housing. 

•  Taken together, units that were both rental and owner (or seasonal) accounted for 43.5 percent 

of all affordable rental housing.

242 Data Shop

The Long-Term Dynamics of Affordable Rental Housing: Creating and Using a New Database

•  The 3.5 million assisted units accounted for 17.8 percent of all affordable rental housing.

•  The 20.8 million units that were permanently lost by 2013 accounted for 12.1 percent of all 

affordable rental housing. 

•  The 2.8 million units that were always rental and affordable for one-half or more of their time in the housing stock accounted for another 10.3 percent. 

•  The 6.1 million units that were always rental but affordable for less than one-half of their time in the housing stock accounted for another 6.3 percent. These last two categories encompass 

most units that were filtered or gentrified. 

•  Often filtration is thought of as a smooth process in which units move from high rent to 

moderate rent to affordable. We found only 1.1 million units that followed this path, and 

they accounted for 2.6 percent of all affordable rental housing. Further analysis shows that 80 

percent of these units were never high rent during the period studied.

•  Similarly, gentrification is often thought of as a smooth process from affordable to moderate rent or high rent. We found only 0.4 million units that followed this path, accounting for 0.7 percent 

of all affordable rental housing.

•  Private units that were always affordable, and units that were always affordable except for one survey, accounted for the remaining 6.8 percent of all affordable rental housing.

•  Finally, 65.7 million units were never rental and another 3.3 million units were always either 

moderate or high rental.

Looked at another way, close to one-third of all affordable rental housing during the period was 

provided by units that were affordable rentals for less than one-half their time in the housing stock.

Conclusion: Accessing the Databases and Our Analysis

In the course of our research, we created two databases and documented the construction of each 

database and the special variables they contain. The databases, documentation, and the full study, 

“The Long-Term Dynamics of Affordable Rental Housing,” are available on the Hudson Institute 

website at https://www.hudson.org/research/13340-data-for-the-long-term-dynamics-of-

affordable-rental-housing. 

The first database,  hudson_institute_file_1, contains 95,444 observations and 2,164 variables. This file merges all 15 AHS national surveys based on the sample drawn in 1985 and extracts key AHS 

variables. New variables were created for use in the merging and cleaning of the individual AHS 

files for each survey. We also appended relevant variables from HUD’s Housing Affordability Data 

System and the Census Bureau’s Where Did They Go file, which contains historic information on 

all units that are part of the national sample. Finally, we eliminated units that were part of supplemental samples used in some but not all the AHS surveys.

The second database,  hudson_institute_file_2, contains 65,540 observations and 3,728 variables. 

The second file modifies the first by eliminating cases that were absent from too many AHS 

Cityscape 243

Weicher, Eggers, and Moumen

surveys, cases with too much missing information on affordability, and cases that were defective in other respects. It also standardizes descriptive variables based on 2013 values or the most recent values if 2013 data are missing. Further, the file includes a new bedroom variable that reduces 

survey-to-survey variation in the bedroom count, a variable identifying assisted project-based 

housing, the weights used in our analyses, and several special variables created for our analysis.

Hudson_institute_file_1 is designed for researchers who want to conduct different analyses than ours but also want to avoid the work of merging the files and extracting the data.  Hudson_institute_file_2 

is available to researchers who want to conduct longitudinal analyses of housing policy issues and take advantage of some of the special features developed in the course of our study.

Acknowledgments

The authors thank The John D. and Catherine T. MacArthur Foundation for supporting this 

interesting and important research. Mijo Vodopic, Senior Program Officer at the Foundation, 

provided encouragement throughout the second stage of the study, and we are grateful for his 

assistance. We particularly thank Michael A. Stegman, the former Director of Policy and Housing at the Foundation, for suggesting the topic and for recognizing the untapped policy analysis potential of the American Housing Survey’s longitudinal structure. We also appreciate the help provided 

by Arthur R. Cresce and Tamara A. Cole of the Census Bureau and Shawn J. Bucholtz, David A. 

Vandenbroucke, and George R. Carter of HUD.

Authors

John C. Weicher is Director of the Center for Housing and Financial Markets at Hudson Institute.

Frederick J. Eggers is senior economist at Econometrica, Inc.

Fouad Moumen is senior statistician at Econometrica, Inc.

References

ICF Consulting (ICF). 2001. “Documentation of Changes in the 1997 American Housing Survey.” 

huduser.org/portal/datasets/ahs/docchg1997.pdf.

Scally, Corianne Payton, and David Lipsetz. 2017. “New Public Data Available on USDA Rural 

Housing Service’s Single-Family and Multifamily Programs,”  Cityscape 19 (1): 295–304.

U.S. Department of Housing and Urban Development (HUD). 2000.  Rental Housing Assistance 

– The Worsening Crisis: A Report to Congress on Worst Case Housing Needs. Washington, DC: Government Publishing Office. 

Weicher, John C. 2012.  Housing Policy at a Crossroads: The Why, How, and Who of Assistance Programs. Washington, DC: AEI Press.

Weicher, John C., Frederick J. Eggers, and Fouad Moumen. 2017.  The Long-Term Dynamics of Af-

fordable Rental Housing. Washington, DC: Hudson Institute. 

244 Data Shop

Foreign Exchange

Foreign Exchange, a department of  Cityscape , reports on what the U.S. Department of 

Housing and Urban Development’s International and Philanthropic Engagement Division 

has learned about new departures in housing and development policy in cities and suburbs 

throughout the world that might have value if applied in U.S. communities. If you have 

a recent research report or article of fewer than 2,000 words to share in a forthcoming 

issue of  Cityscape , please send a one-paragraph abstract to katrina.a.durbak@hud.gov.

Continuous Repayment Structures 

in Japanese Housing Finance for 

Elderly People: Applications To 

Mitigate Counterparty Risk Through 

U.S. Reverse Mortgage Design

Christopher Feather    

Kalamu Consulting

Abstract

What innovations can improve the risk management of the Home Equity Conversion 

Mortgage (HECM) and HECM mortgage-backed securities (HMBS) programs? The 

Japanese housing finance sector has relevant insights for reverse mortgage design in 

the United States. Through the Japan Housing Finance Agency, or JHF, the Special 

Repayment System for the Elderly program can inform the strengthening of U.S. efforts 

for senior citizens. As HECM and HMBS counterparties confront challenges with the 

financial sustainability of their business operations, recurring repayment structures, 

like those embedded in Japanese housing finance products, can be considered to help 

alleviate strains on lenders and, more particularly, issuers and servicers. 

This article assesses the alternative approach of continuous payments from Japanese 

loan design for elderly people within the American reverse mortgage context. The 

conclusion is that broad-based collaboration and mutual awareness are required to 

manage cash flow timing risks and advanced servicing liabilities with stakeholders 

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 245

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

Feather

Abstract (continued)

toward strategically advancing HECM and HMBS market development. Collective action to 

mitigate counterparty risk can ensure the option is preserved—if not bolstered in a responsible manner—for aged homeowners seeking to financially supplement their income at affordable 

terms while continuing to live in their homes. 

Introduction

Reverse mortgages can support the economic security of senior homeowners who lack adequate 

financial resources to maintain their livelihoods. The benefits of Home Equity Conversion 

Mortgage (HECM) products are evident, as they enable elderly borrowers to monetize their home 

equity while concurrently aging-in-place at their pledged residence. Aside from these advantages, 

the pioneering accrual-based structure has an appeal to HECM borrowers.1 Mortgagors liquidate 

a proportion of their home equity and do not make continuous interest rate payments throughout 

the term of the loan. In lieu of these recurring borrower payments, borrowers repay the entire 

principal, as well as the full interest amount accumulated at maturity since the reverse mortgage’s origination. 

Despite borrower attraction to deferred payments, lenders, issuers, and servicers participating 

in the HECM and HECM mortgage-backed securities (HMBS) programs will incur business 

operation costs. Issuers and servicers must have sufficient capital resources on two fronts.2 First, the programs mandate counterparties to provide intermediate funding to borrower draws prior 

to being sold for securitization into HMBS as participations.3,4 Furthermore, once HECMs reach 

maturity—occurring when the unpaid principal balance attains 98 percent of the Maximum Claim 

Amount (MCA)5—counterparties must buy out the loan with associated costs and await reimburse-

ment from the Federal Housing Administration (FHA).6 The result is distinctive financial exposure 

for reverse mortgage counterparties for significant periods of time, accentuated with delayed FHA 

insurance claims, especially when influxes occur in maturing HECM volume.

1 The accrual-based structure refers to the manner in which reverse mortgages accumulate owed capital without an immediate cash transaction. Eventually, a disbursement is made repaying the lent money and interest to the lender.

2 Issuers are business entities in the Ginnie Mae mortgage-backed securities program that aggregate collateral and sell securities to fund their operations. Servicers are commercial organizations that administer and process loan transactions.

3 Counterparties are defined in this article as the operational stakeholders—consisting of lenders, issuers, and servicers—

participating in the FHA mortgage insurance and Ginnie Mae mortgage-backed securities programs.

4 Ginnie Mae’s HMBS program allows for issuers and servicers to include components of the HECM loan beyond principal draws as participations, such as monthly insurance premiums, servicing fees, and guaranty fees (Ginnie Mae, 2017). This mechanism is an important divergence from forward mortgages where pools consist of collateralized principal.

5 The MCA is the arbitrary amount these reverse mortgages can accumulate prior to FHA buy out as policy determines.

6 These instances in which the issuer is responsible to use “their own funds” for repurchase “to ensure that security holders receive outstanding principal and interest” is commonly referred to as Mandatory Purchase Events (Ginnie Mae, 2017: 35-4).

246 Foreign Exchange

Continuous Repayment Structures in Japanese Housing Finance for Elderly People: Applications To Mitigate Counterparty Risk Through U.S. Reverse Mortgage Design

The nature of deferred servicing fees and interest rate margins has been a notable barrier to entry for new counterparties. The delayed borrower remittance structure has to some extent limited 

the deepening of reverse mortgage activities. The concentration of reverse mortgage lending and 

securitization issuance is disproportionate within a relatively small segment of counterparties for both programs.7 The consequence is systemic risk that can affect the continued provision of reverse mortgage products at accessible and affordable terms for senior homeowners. The hazard is valid, 

as reduced programmatic participation has resulted in unrealized potential, as well as reduced 

stability and constrained growth, which is inherent in the design and administration of reverse 

mortgages and their securities. 

Alternatively, Japan has been actively experimenting with housing finance approaches as their society substantially ages (Feather, 2018). The recent Special Repayment System for the Elderly program— 

through the Japan Housing Finance Agency (JHF)—is one facet of their efforts in expanding financial means for senior homeowners.8 The program is different than the American reverse mortgage 

counterpart as elderly borrowers are required to repay interest rates continuously each month, similar to the payment structure of forward mortgages in both countries. Japanese borrowers likewise do not pay loan principal until maturity, analogous to the product design of the HECM and HMBS. 

The following assesses the merits of this Japanese feature, focusing on how continuous repayment 

could deepen development of the HECM and HMBS programs. Specifically, the recurring payment 

structure present in the Japanese program can be a means to alleviate a dimension of financial 

strain imposed on U.S. counterparties with advanced servicing liabilities9 and cash flow timing10 

in both funding intermediate borrower HECM draws, as well as the mandatory repurchase at FHA 

assignment at 98 percent of the MCA.

Although benefits for counterparties exist, so do drawbacks. Adapting the HECM and HMBS 

design in allowing for continuous payment features, similar to those of the Japanese program, can 

directly affect end-user appeal in the United States, especially as one in five Americans will be age 62 and older in 2019 and beyond (Census, 2017). Additionally, such a change in loan product 

design can alter investment channeled through capital markets and impact liquidity levels in the 

HMBS market.11  

7 Although HECMs are a small share of the mortgage portfolio at FHA, these reverse mortgages make up more than one-half of housing loans by dollar volume assigned to the U.S. Department of Housing and Urban Development’s (HUD’s) balance sheet (FHA, 2016). 

8 The JHF program is dually referred to as the “Special Repayment System for the Elderly” and the “Special Repayment Rules for the Elderly” depending on the translation from Japanese to English. 

9 This article defines advanced servicing liability as the mortgage insurance premium and Ginnie Mae guaranty payments that counterparties must advance to the U.S. government. 

10 Likewise deferred cash flow timing—for the purposes of this article—is defined particularly for the programmatic mandate that counterparties must repurchase HECM loans and related HMBS participations that reach 98 percent of the MCA. The assignment of these reverse mortgages to FHA means counterparties must advance interest rate payments to investors and await reimbursement from their filing of FHA mortgage insurance claims for these HECMs. Deferred cash flow can also include the intermediate time between counterparty funding of borrower draws and securitization through the sale of participations to the capital markets. However, for ease of reference, the article uses this definition.

11 The full faith and credit guarantee of timely principal and interest payments that the U.S. government assures on Ginnie Mae MBS, including HMBS, provides a “high quality bond alternative” in the fixed income space, including U.S. Treasuries (Irving and Schmitt, 2013: 1). 

Cityscape 247

Feather

The ensuing sections analyze the strengths and weaknesses of continuous repayment structures for 

reverse mortgages. Reverse mortgage design can be a tool to mitigate counterparty risk.12 It is fundamental, however, for stakeholders to understand the largely overlooked contribution counterpar-

ties have in enabling the operations of the reverse mortgage programs in the United States. More 

important than the novel structure of recurring borrower payments for reverse mortgages is the 

further development of attentive counterparty risk management and inclusive collaboration toward 

strengthening the HECM and HMBS programs among consumers, government, and industry. 

Special Repayment System for Elderly People in Japan

Japan is popularly referred to as the oldest country in the world for having the largest concentration of elderly persons. As Japanese society experiences accelerated aging, public and private sector entities are exploring innovative approaches to successfully meet the socioeconomic needs of 

senior citizens among this unprecedented demographic change. Fundamental to these efforts is the 

need to expand adequate housing supply configured for elderly people. 

JHF has several programs to promote access to residences designed and serviced for elderly people 

(Kobayashi, Konishi and Takeishi, 2017).13 The Special Repayment System for the Elderly is a 

housing finance program that provides funds enabling senior homeowners, age 60 and older, 

to renovate their residences for the purpose of actualizing age-friendly design features (Kojima, 

2013).14 The Special Repayment System, as a component of JHF’s urban development lending, also 

allows for elderly borrowers to purchase reconstructed condominium housing as their residence.15 

Whether for renovation or urban development loan purposes, lenders provide borrowers with 

upfront principal in the form of a lump sum principal advance. Borrowers are only required to 

make continuous interest rate payments during the term of the loan. Specifically, the program eases repayment burden on the borrower, as it provides a “grace period” whereby principal is repaid only when the borrower dies (JHF, 2014: 37). On death, lenders and servicers collect the remaining 

outstanding loan balance from the borrower’s estate.16 

12 Counterparty risk is defined here as the hazard in which FHA lenders and Ginnie Mae issuers and servicers fail in financial and operational terms, resulting in the inhibited provision of HECM and HMBS, as well as substantial costs to the U.S. government.

13 JHF provides multifamily loans to developers as well. The purpose of this program is to construct rental housing with nursing services for elderly people (JHF, 2016, 2014). The United States has correspondingly made significant investments in housing with assisted living and nursing homes (Manda, 2015). In addition, JHF launched a new rental insurance program to provide lessors with guaranteed lease payments to overcome rental discrimination for elderly people in 2017 

(Kobayashi, 2017).

14 Besides age-friendly housing renovations, the Special Repayment System for the Elderly program began focusing on anti-seismic earthquake modifications (JHF, 2014). The program began in the aftermath of the 2011 Great East Japan Earthquake and resulted in 1,658 loans between 2012 and 2014 (JHF, 2014).

15 JHF consults with management associations and developers seeking to adapt condominium units for purchase by elderly people through the urban development lending component of the program (JHF, 2016). 

16 In 2017, this mechanism was extended to include loan modifications for elderly homeowners age 70 and older who are delinquent and facing payment difficulties (Kobayashi, 2017).

248 Foreign Exchange

Continuous Repayment Structures in Japanese Housing Finance for Elderly People: Applications To Mitigate Counterparty Risk Through U.S. Reverse Mortgage Design

The open-ended housing finance mechanism for this program is unique for the country’s broader 

sector. Elderly borrowers in Japan often encounter mortgage restrictions when applying for residential loans. Particularly, senior citizen borrowers are required to make all loan repayments by the time they attain 80 years of age (Kojima, 2013). The outcome is that the Special Repayment System provides a means for elderly Japanese households to access financing in an otherwise exclusive market.17 

Although no home equity liquidation occurs, the Special Repayment System for the Elderly has 

relevant operations for reverse mortgage financing in the American context. Equivalent to the 

FHA’s provision of mortgage insurance of HECM, JHF provides insurance contracts on Special 

Repayment System loans from small- and medium-sized financial institutions (JHF, 2014).18 

The continuous interest payment structures that Japanese senior borrowers must pay is among the 

differences between the Japanese and American programs. The program requires monthly interest 

payments on the loan. Some critics argue this is a “not ideal” feature for borrowers, as the loan 

obligation imposes a financial burden on participating elderly households (Kojima, 2013: 9).19 

Nonetheless, a relatively low-interest rate environment—in which Japanese elders accumulate 

substantial cash deposits—has made such a concern relatively negligible.

HECM and HMBS Counterparties in the United States

Irrespective of their role as lender, issuer, or servicer, HECM and HMBS counterparties make the 

underlying legal framework and programmatic policies work in delivering financing to senior 

borrowers.20 Undoubtedly, the unprecedented scale of FHA endorsements and Ginnie Mae securi-

tizations—in the historical global development of reverse mortgages—validates the fundamentals 

of reverse mortgage design and the employed public-private partnerships model in the United 

States.21 Notwithstanding, the HECM program has encountered challenges in achieving durable 

fiscal soundness for FHA’s financial health (Szymanoski, Lam, and Feather, 2017).22,23 The financial 17 The Special Repayment System for the Elderly program enables borrowers to withdraw ¥10 million (approximately $89,380) or less (Kojima, 2013). The Japanese loan is recourse-based, with the borrower retaining the title of the property until obligations are settled. The U.S. HECM loan is nonrecourse for senior homeowners.

18 JHF seeks to expand the provision of reverse mortgages through the provision of mortgage insurance similar to FHA’s HECM insurance program (JHF, 2016).

19 Another key difference for the Special Repayment loans is the aforementioned recourse-based characteristic (Kobayashi, Konishi, and Takeishi, 2017). 

20 Ginnie Mae issuers are often the servicers as well for their mortgage pools (HUD, 2011). Ginnie Mae enables issuers to enter into servicing agreements with subservicers as well. However, subservicers must also be Ginnie Mae-approved issuers (Ginnie Mae, 2007). 

21 FHA endorsements refer to reverse mortgages approved for mortgage insurance to lenders.

22 For example, in fiscal year 2016, FHA’s Mutual Mortgage Insurance (MMI) Fund suffered economic value losses valued at $7.7 billion. This cash outflow from the MMI Fund was greater than those annually incurred following the global financial crisis from 2008–2009 (FHA, 2016; Szymanoski, Lam, and Feather, 2017).

23 Critics cite recent modifications, beginning in 2011, as the cause for Mutual Mortgage Insurance Fund “volatility” 

for the HECM Insurance Program (FHA, 2016: 50). The critics contend the dual purpose of the modification—for consumer protection and fiscal soundness—have limited borrower demand and negated business incentives for deepened counterparty participation.

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issues FHA confronts with HECMs relate to an array of credit, interest rate, policy, and other 

economic risks.24 Principal among risks for HUD and U.S. taxpayers is the threat of counterparty 

insolvency. 

The risk among counterparties is disproportional in both programs. In 2017, the top eight HECM 

lenders composed 63.2 percent of the primary reverse mortgage market (RMI, 2017). The second-

ary market is concentrated to an even greater extent. During the same year, the six leading HMBS 

issuers furnished more than four in five of total Ginnie Mae guaranteed reverse mortgage securities. 

Moreover, for all intents and purposes, one entity services most of the issuers in these programs. 

The condensed nature of both primary and secondary market actors poses systemic risks to the 

future provision of reverse mortgages. 

As observed in exhibit 1, overlap is common with several counterparties in the reverse mortgage 

industry. For instance, American Advisors Group, Reverse Mortgage Funding and Finance of 

America Reverse are the top three market leaders for both lending HECMs and issuing and 

servicing HMBS.25 If several counterparties default, especially those from the HMBS program, then 

Exhibit 1

Comparing HECM and HMBS Counterparty Production, January–December 2017

AAG = American Advisors Group. FoAR = Finance of America Reverse. HECM = Home Equity Conversion Mortgage. HMBS 

= HECM mortgage-backed security. Longbridge = Longbridge Financial LLC. LWF = Live Well Financial. Nationstar = Nationstar Mortgage. Ocwen = Ocwen Loan Servicing. RMF = Reverse Mortgage Funding. RMS = Reverse Mortgage Solutions. 

Sources: RMI, 2017; Ginnie Mae disclosure data

24 Recent FHA changes in mortgage insurance premium rates and Principal Limit Factors underscore the ongoing policy efforts to “sustain the HECM program as a viable financial resource” (FHA, 2017: 2).

25 Besides their dominant status, these three counterparties are nonbank financial institutions. The prominence of such nondepository institutions in the housing finance sector is a recent market change since the 2008–2009 global financial crisis (Ginnie Mae, 2016, 2014). FHA also stated this change to smaller nonbank lending partners “increases counterparty risk exposure” (FHA, 2016: 50). Accordingly, less than 1 percent of HMBS issuers were banking institutions in 2017. For HECM counterparties, approximately less than 10 percent are deposit-based entities in 2017.

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operations could potentially cripple reverse mortgage operations. In such a scenario, the outcome 

would be ominous, especially given the U.S. government obligations through the provision of 

mortgage insurance and the full-faith and credit guarantee by FHA and Ginnie Mae respectively.

Should economic market conditions falter, unsavory lending practices and mismanagement grow 

or unintended consequences from policy modifications occur, FHA and Ginnie Mae could be 

unexpectedly called on to rescue the HECM and HMBS program.26 In such a scenario, large-scale 

servicer and issuer default likely would necessitate that the U.S. government engage and support 

the proper sale and transfer of portfolios, loan pools, and mortgage servicing rights.27 

Were confidence inhibited, and few interested parties sought reverse mortgage portfolio acquisi-

tion, the government could possibly take over management and administration, acting as a 

counterparty of last resort, due to outstanding obligations with senior borrowers and investors.28 

This active government role with HECMs, HMBS, or both products would likely result in capital 

infusions of taxpayer dollars. The bailout, depending on the political climate,29 could attract undue attention and potentially jeopardize efforts to return the HECM and its securitization to their former status or anything closely resembling it.30 In such scenarios, the consequence likely would be inhibited access to reverse mortgages for senior citizens. Reduced access would mean few options 

for senior citizens seeking to bolster their retirement security and age in place.

Strategic approaches to counterparty risk management are essential to prevent and mitigate pos-

sible counterparty failures. U.S. government processes already exist to strengthen controls and 

avoid such losses for forward mortgages. FHA evaluates these lenders across specific default and 

delinquency metrics. In instances of “excessive default and claim rates compared to peers,” FHA 

monitors—and can limit, if not terminate—any counterparty’s Lender Insurance (LI) authority in 

originating and underwriting reverse mortgages with mortgage insurance (FHA, 2014a: 1; 2014b). 

Correspondingly, Ginnie Mae launched the Issuer Operational Performance Profile (IOPP) to 

measure operational and default performance in early 2015 (Ginnie Mae, 2017a).31 Similar to LI 

authority at FHA, the IOPP helps inform the amount of commitment authority Ginnie Mae ap-

proves to issuers forming the underlying collateral pools for HMBS securities (Ginnie Mae, 2017a). 

26 HMBS issuers can encounter mismanagement issues special to HECM and HMBS. One example is the assignment of HECMs to FHA once the outstanding balance crosses more than 98 percent of the MCA. Improper operational management by counterparties can result in unrecoverable claims from FHA once loans exceed the 98-percent benchmark. 

27 Ginnie Mae has used its authority to seize mortgage pools from defaulted issuers in several cases to ensure investors received timely principal and interest payments (Whalen, 2017). The defaulted portfolios have been auctioned to the highest bidder to resume administrative processes and servicing. 

28 Ginnie Mae seeks to counter issuer default risk through “Master SubServicer,” or MSS, arrangements. Through MSS, Ginnie Mae-contracted agents provide full servicing support to defaulted issuer pools.  

29 Some critics have argued the “subsidize[d] risks” of “taxpayer funds” merit limiting the government’s role in the HECM 

insurance program (Shadab, 2012: 1). 

30 In such a catastrophic scenario, where interest or confidence in the programs is severely limited, immediate financial relief could immediately come from FHA’s Mutual Mortgage Insurance Fund or Ginnie Mae’s collection of guarantee fees. 

In instances where these resources are not enough, additional capital infusions could come from the U.S. Department of the Treasury, which would probably need authorization and appropriation from the U.S. Congress.

31 Ginnie Mae monitors issuer financial risk for liquidity and corporate credit and default risk based on delinquency ratios for securitized loans (Ginnie Mae, 2016a; GAO, 2011). 

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Counterparty Financial Burdens With Deferred Payments

Beyond extending operational performance assessments to HECM lending as is done with HMBS 

and issuer activity, reverse mortgage design is another tool that can mitigate counterparty risk. 

Compared with forward mortgages and mortgage-backed securities, the structures of HECM and 

HMBS have distinctive liabilities for the involved counterparties. The exceptional accrual-based 

structure presents, in part, certain financial burdens for those actors delivering financing to 

borrowing senior homeowners. Realignment of borrower payment schedules and counterparty 

advances can mitigate risks that limit issuer and servicer participation in these reverse mortgage programs.

Like forward mortgages, cash flow for reverse mortgages consists of lent capital, transaction costs, and accumulated interest. Different is the gradual buildup of interest and embedded costs during 

the duration of the loan. Although servicers regularly submit monthly statements to the borrowers, the costs are, in some sense, imperceptible to the mortgagor, as minimal financial burden is realized during the life of the borrower. 

The borrower pays the aggregated loan amount on the reverse mortgage’s maturity. Besides volun-

tary prepayment or the mortgagor moving out of the pledged residence, the loan regularly matures 

when the borrower passes away. In instances of mortality, the borrowers’ heirs have the option of 

paying the accrued obligation amount from another source of funds or selling the home to use 

proceeds to compensate counterparties.32 As such, the senior borrower routinely does not witness 

the settling transaction that concludes the life of the reverse mortgage.

Despite the somewhat discrete nature of reverse mortgage costs borrowers realize, counterparties 

are keenly aware of the cash flow schedule, because it largely determines the financial viability 

of their business. For the average senior borrower, costs usually represent a significant, albeit 

relatively small component of their borrowing. For lenders, issuers, and servicers, these costs 

determine financial viability of their businesses and the accessibility of HECMs for prospective 

senior homeowners.

Alone the sums of interest rate payments and fees are smaller components than principal, as modeled in exhibit 2, for the typical cash flow of a HECM loan and related HMBS costs. However, accrued 

interest for the average reverse mortgage, during the typical American life expectancy, is commonly 34 percent of the total loan amount value.33 Fees and closing costs are less, accounting for 6 percent. 

Upfront and annual mortgage insurance premiums to FHA are between 8 and 9 percent.34

32 FHA rules for HECM lenders ensure heirs will not have to pay more than the full loan balance or 95 percent of the appraised value, whichever is less. In situations where the loan balance is worth more than the home, heirs will not have to pay the excess amount (FHA, 1994). 

33 The average loan amount has approximately a principal balance of $300,000 with an interest rate of 5 percent in 2016 

(FHA, 2016). This calculation assumes all costs are financed into the HECM for a single lump sum disbursement.

34 The HECM mortgage insurance premiums reflect the change in the upfront premium structure from 2 percent of the maximum claim amount and the annual premium structure from 0.5 percent of the outstanding reverse mortgage balance in 2017 (FHA, 2017). The increased annual premiums for the outstanding HECM balance rather than the principal limit means greater financial resources are required for issuers to advance these payments to FHA. Additionally, the updated Principal Limit Factor is applied October 2, 2017, and forward.

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Exhibit 2

HECM and HMBS Cash Flow Model for Typical Senior Borrower With Lump Sum 

Disbursementa

HECM = Home Equity Conversion Mortgage. HMBS = HECM mortgage-backed security. MIP = mortgage insurance premium.

Note: “Closing Costs” include fees related to appraisal, HECM counseling, credit report, flood certification, escrow settlement and closing, document preparation, recording, courier, pest inspection, survey, and title insurance. 

a Besides the detailed interest rate amounts, fees, and closing costs, senior HECM borrowers must also make property tax and flood and hazard insurance payments, otherwise the program deems such delinquencies as loans in default. The costs for flood and homeowners insurance are on average $700 and $964 per year. Property taxes are typically 1 to 1.5 percent of home value, approximately $6,250 for the average home annually. 

Together, the approximate 49 percent sum constitutes a substantial component of the total loan 

amount. Although interest rate index and margin are one-half of these costs, they represent the 

largest component of the overall HECM amount after the principal the borrower receives. These 

interest payment amounts are critical, however, as they support the operating costs and earnings 

for reverse mortgage stakeholders, including counterparties and the U.S. government.

Both HECM and HMBS counterparties rely on interest rate margins as profit to grow their business. 

However, reverse mortgage counterparties—particularly issuers and servicers—have longer term 

revenue collection timelines, often times years after origination.35 Issuers and servicers often recoup cash flow from securitization, after funding borrower draws. 

Once the unpaid principal balance of HECMs reaches 98 percent of the MCA, counterparties must 

also buy out the loans and the substituent HMBS participations providing principal and interest 

rate payments to investors. The result is more capital-intensive for reverse mortgage counterparties funding borrower draws, as well as HECM assignments to FHA. In this respect, the accrual-based 

structure poses greater counterparty risk than the continuous payment schedule of forward mort-

gages with interest rate payments paid monthly.

Borrowers typically pay few out-of-pocket costs when their reverse mortgage is issued. Should the 

senior borrowers not have the funds immediately on hand to pay closing costs, origination fees and 35 According to publicly available financial filings from issuers, the average loan life for HECM is often fewer than 5 years.  

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the upfront mortgage insurance premium, lenders provide the option for payment to come from 

the principal drawn from the HECM. In these cases, lenders receive payments from the borrower’s 

principal drawn at issuance. As a result, HECM lenders receive many of their costs at issuance 

at the borrower’s expense, either in direct transfer or from financing through the loan’s principal drawn.36 

Once the HECM is issued, the nature of the cash flow substantively changes; issuer and servicer 

revenue sources start accruing. Interest rate payments accumulate, and the borrower pays them 

only when the HECM becomes due and payable. Without intermediate funding through the secu-

ritization of HECM participations, servicing fees are similarly collected and transferred to servicers only when the reverse mortgage matures.

Alternatively, FHA’s annual mortgage insurance premiums (MIPs) and Ginnie Mae’s guaranty fees 

are paid to the U.S. government each and every month. The fees are calculated based on the out-

standing principal balance on the HECM loan and its securitized participations. Although the U.S. 

government receives these payments regularly throughout the life of the loan, these fees do not 

transfer from the borrower to the servicer. Instead the administrating counterparty must advance 

monthly the insurance premium and guaranty fee to the U.S. government from the issuer and 

servicer’s own corporate funds. 

The components of HECM and HMBS revenues are either paid to the program stakeholder or 

deferred, as exhibit 3 summarizes. At issuance, lenders and the FHA receive their fees, costs, and upfront MPI either directly from the borrower or from the principal drawn through the HECM.37 

After issuance, counterparties pay FHA and Ginnie Mae monthly their annual MIPs and guaranty 

fees. These payments, however, are advanced from issuer and servicer funds. FHA and Ginnie Mae 

insurance premiums and guaranty fees are subordinate to issuer, servicer, and investor interest rate payments and servicing fees.

The servicing and administration obligations for HECM and HMBS counterparties have significant 

liabilities due to the nature of which revenues are disbursed. Per exhibit 4—sans securitization and Exhibit 3

HECM and HMBS Stakeholder Revenue Sources

Stakeholder

Revenue Item

Are Payments Deferred?

Lender

Closing costs

No, can be financed into HECM and paid from principal.

Lender

Origination fees

No, can be financed into HECM and paid from principal.

FHA

Upfront MIP

No, can be financed into HECM and paid from principal. 

FHA

Monthly MIP

Yes, but counterparty must advance monthly.

Ginnie Mae

Guaranty fee

Yes, but counterparty must advance monthly.

Servicer

Servicing fee

Yes, obtained at maturity.

Issuer and investors

Interest rate index 

Yes, both components disbursed at maturity.

and margin

FHA = Federal Housing Administration. HECM = Home Equity Conversion Mortgage. HMBS = HECM mortgage-backed security. MIP = mortgage insurance premium.

36 Depending on the HECM lender’s evaluation of borrower risk, the mortgagee can waive or discount origination fees and omit certain closing costs. 

37 Without the upfront MIP payment, “FHA cannot endorse the mortgage” (FHA, 1994: 7–3, 4).

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Exhibit 4

Estimating HECM and HMBS Counterparty Revenue Liabilities per Whole Loana

HECM = Home Equity Conversion Mortgage. HMBS = HECM mortgage-backed security. MIP = mortgage insurance premium.  

a The estimates are made for typical lump sum disbursement for HECM collateral home value of $500,000 at an adjustable rate, increasing from a 4.65 percent interest rate during the average life expectancy of a senior borrower of 72 years of age in 2016.  

b Counterparty revenues include costs for servicing and interest rate index obligations to lenders. As such, counterparty profits are likely less than these figures, especially when considering business expenditures.

purchase of participations from the capital markets—counterparties must advance 12.2 percent of 

the revenues they will receive to FHA and Ginnie Mae in the form of the annual MIP and 

guaranty

fee. This amount can be around $10,989 per HECM loan serviced.

Inevitably, reverse mortgage counterparties—retaining the whole loans on their portfolio—will 

earn revenues from the substantially larger deferred interest rate payments as well as servicing fees. 

For the typical loan during an average American life expectancy of 81 years, revenues can amount 

to $79,230. The issuer and servicer only receive these revenues when the HECM and HMBS 

become due and payable, typically years later following origination and securitization. Minus the 

costs, the profit per reverse mortgage loan is estimated to be equivalent to or less than advanced servicing liabilities that are reimbursed to the counterparty on HECM maturity. 

The revenues are substantial for counterparties, which must already have substantial cash available to participate in HECM and HMBS for these advanced payments. For counterparties without the 

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requisite cash reserves, they likely will explore participating in other businesses. The forward 

mortgage market could be more appealing as these products have recurring interest rate payments, 

lower net worth requirements, and fewer obligations delaying cash flow.38  

Counterparties typically receive financial cash flow relief through HMBS securitization as exhibit 5 

illustrates. Many counterparties sell the HECM participation securities to support the funding 

of borrower draws. However, the counterparty must buy out the loan and its accrued costs once 

the unpaid balance of a HECM reaches mandatory 98 percent of the MCA. The buy out provides 

interim funding and thereby gives investors a more definitive timeline when their accrued interest and principal payments, as well as reimbursement for their purchase of participations consisting 

of MIPs and guaranty fees. The consequence, however, is that counterparties must reassume these 

costs—an estimated cost of $104,901 for interest rate margin payments per loan, as exhibit 5 

indicates—and wait for FHA to offset these costs and revenues.39 

Exhibit 5

Modeling HECM and HMBS Issuer-Servicer Cash Flow Advances and Deferred 

Reimbursement From Investors and FHA per Multiple Disbursements

FHA = Federal Housing Administration. HECM = Home Equity Conversion Mortgage. HMBS = HECM mortgage-backed security. 

MCA = maximum claim amount. 

a This model has multiple draws and participations to illustrate the issuer-servicer cash flow process on HECMs with additional disbursements. For this reason, the resulting sum of interest rate payments is less than equivalent lump sum disbursements, as exhibits 2 and 4 showed.

38 In 2015, Ginnie Mae raised net worth requirements requiring HMBS issuers to have $5 million (Ginnie Mae, 2017b). 

HMBS issuers have a higher net worth requirement than single-family and multifamily mortgage-backed securities counterparts for Ginnie Mae securities at $2.5 and $1 million respectively (Ginnie Mae, 2017b).

39 Already counterparties incur costs from defaulted HECM loans, approximately one in five of which are defaulted and therefore ineligible to be assigned to FHA.

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The reverse mortgage industry is concerned that HMBS counterparties must “wait” because FHA 

cannot process HECM insurance claims fast enough (NRMLA, 2016: 3). Specifically, the process-

ing times can be lengthy for the assigned HECMs at 98 percent of the MCA. Delayed FHA claim 

processing can become even more deferred, as mandatory purchase events have been predicted 

to affect 81 percent of the active pool count by 2020 (Ginnie Mae, 2015). In 2018 alone, nearly 

34,000 HECMs are expected to be assigned from counterparties to FHA (Ginnie Mae, 2015). 

Further delays in FHA insurance claim processing can severely constrain the financial viability of counterparties and threaten the HECM and HMBS programs.40 

Regardless of the supporting cash flow to counterparties from investors, the deferred cash flow, 

particularly the mandatory repurchase event at 98 percent of MCA, constitutes a significant barrier to entry for counterparties and underscores the capital-intensive nature of reverse mortgages compared with their forward analogs. Such barriers to entry for both new and expanded counterparties 

merit stakeholder consideration on whether to modify HECM, and thereby HMBS programmatic 

policies. Permitting the option for continuous repayment structures would have various implica-

tions for each participant beyond solely reverse mortgage counterparties.

Policy Implications in Permitting Recurring Interest Rate 

Structures

Continuous repayment structures, like those utilized in Japan’s Special Repayment System for the 

Elderly program, can help toward mitigating financial burdens on HECM and HMBS counterpar-

ties, especially with the advanced servicing liabilities and deferred cash flow for issuers and servicers. Deciding whether to permit recurring interest rate payment structures in reverse mortgages has significant policy implications for each stakeholder. In considering the inclusion of payment 

structures with this option, consumers, industry, investors, and policymakers must thoroughly 

understand and opine on the ramifications of such policy change.

For senior citizens, continuous interest rate payments can add a significant financial burden and 

affect their participation in the HECM program. Traditionally, senior borrowers have sought reverse mortgages in instances when they are cash-poor homeowners. If recurring interest rate payment 

structures were to be permitted into the HECM program, borrowers would need to have either the 

needed money on hand or reduced principal draws allocated for monthly interest rate payment 

amounts. 

Recent HECM program changes in the reverse mortgage have sought to reposition the product into 

a financial planning tool rather than a product of last resort. For these borrowers, seeking to bolster their retirement security, adding the aforementioned 9- to 10-percent interest rate payment options of the total loan value may not jeopardize the financial wellbeing for this intended group of senior households. Moreover, such a change would be less impactful for borrowers in a low-interest 

40 Aside from reverse mortgage design through recurring interest rate payment structures, Ginnie Mae could consider monitoring the issuance of head and tail HMBS participation typologies. Specifically, tracking tail participations—the subsequent uncertificated portions of the HECM—could help assess the financial burden on issuers and servicers who may rely on securitization of guaranty fees and annual MIPs to derive temporary cash flow from investors through their purchase of HMBS pools.

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rate market environment. Should counterparties be able to verify that continuous interest rate 

payments would lower business costs and thereby provide better financing terms on HECMs, 

consumers and advocate groups may be more receptive in considering the potential allowance of 

these structures.

Lenders, especially those specialized in HECM origination, may not be affected by the allowance of continuous interest rate payment structures. The business model of many lenders primarily derives 

revenue from originating fees and closing costs paid at or before issuance of the reverse mortgage. 

Further the borrower already pays these fees and costs either out of pocket or through principal draws.

Conversely, HMBS issuers and servicers likely would benefit from allowing recurring interest 

payment structures in the HECM. The current obligations of advanced servicing liabilities and 

delayed cash flow add an extra burden on secondary mortgage market actors. Issuers need to fund 

all HECM borrower draws, prior to securitization, and finance repurchase when FHA assignment 

occurs in addition to property disposition for loan defaults. Certainly, many counterparties have 

overcome these challenges and achieved success, as indicated by the super majority HMBS market 

share that the three largest businesses amassed. Regardless, continuous interest rate payments may help improve appeal and minimize financial barriers to entry for new operators. 

The participating U.S. government entities, FHA and Ginnie Mae, require their MIPs and guaranty 

fees to be paid continuously throughout the duration of the HECM until maturity. The require-

ment is logical given the government’s role in enabling the reverse mortgage market. The payment 

precedence of annual MIPs and guaranty fees also reinforces the primacy of the American taxpayer 

in supporting the reverse mortgage market more than issuer and servicing interest rate payments 

and servicing fees. 

FHA’s provision of mortgage insurance alleviates credit risk concern for lenders and investors while expanding access to such financing for senior homeowners. Ginnie Mae concurrently improves 

investment into HMBS with the full-faith and credit sovereign guaranty from the U.S. government 

for the timely principal and interest rate payments to bondholders. The channeling of investment 

into HMBS improves liquidity in the secondary reverse mortgage market and provides more afford-

able terms for the financing of HECMs for senior borrowers.

The proposed continuous interest rate structure can affect investment into HECM securities and 

liquidity into the HMBS market. At present, investors are encouraged to buy HMBS pools since 

interest rate payments can be 30 to 50 basis points higher than the yields on conventional collat-

eralized mortgage obligations or CMOs. The recurring structure, however, likely would transition 

interest rate payments to a monthly cash flow schedule for HMBS investors. The change may also 

affect the prepayment rate on such securities. 

If borrowers are aware of the financial burden recurring interest rate payments present, then end-

users may be more likely to voluntarily repay reverse mortgage obligations to reduce prospective 

cost liabilities. For this reason, such a change may increase prepayments and shorten overall 

HECM and HMBS pool durations. The recent shift to faster prepayment speeds for the HECM dur-

ing the 30-year Ginnie II Single-Family MBS, as exhibit 6 illustrates, may counter specific investor concern of increased prepayment speeds. Oppositely, the new structure could also result in even 

faster rates on HMBS pools.

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Exhibit 6

HECM to G2SF (30-Year) Prepayment Spread, January 2013–March 2018

10%

5%

0%

1-month conditional 

prepayment rate

–5%

–10%

–15%

–20%

G2SF = Ginnie II Single-Family Mortgage-Backed Securities. HECM = Home Equity Conversion Mortgage. 

Sources: NVA, 2018; Ginnie Mae Disclosure Data

Conclusion

Disproportionate risks exist in the HECM and HMBS programs despite the relatively small share of 

reverse mortgages within FHA’s insured and Ginnie Mae’s guaranteed portfolios.41 Primary among 

hazards is counterparty risk that can imperil the future provision of affordable reverse mortgage 

products in primary and secondary markets, where HECM and HMBS are largely the only vehicle 

for the consideration of senior homeowners.42 In the event of a major counterparty or sectorwide 

default, few issuers and servicers may be able to take on additional risks, forcing the U.S. government to directly act.

The present concentration in the reverse mortgage market, in terms of HECM lending and securi-

tization, merits consideration of innovative approaches in limiting potential risks that contribute to sweeping failures with counterparties. As such, it is in the interest of the U.S. government to strengthen the HECM and HMBS programs. Beyond government contingency plans and oversight 

through monitoring the performance and operations of HECM and HMBS counterparties, reverse 

mortgage product design is a complementary tool for consideration in mitigating counterparty 

risk. Among approaches, it may be prudent to strategically reduce barriers to entry and overcome 

operational deficiencies in both programs to increased counterparty participation. 

41 In 2016, HECMs constituted 3.9 percent of FHA’s single-family insured mortgages (FHA, 2016). Correspondingly, HMBS 

were around 3.2 percent of Ginnie Mae’s outstanding guaranteed MBS (Ginnie Mae, 2016b).

42 No mainstream reverse mortgage products alternatively exist; similarly, neither private-label entities nor the government-sponsored enterprises securitize reverse mortgages.

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The accrual-based HECM and HMBS structure is certainly one of the many innovative features of 

the reverse mortgage in the United States.43 However, issuers and servicers have made a largely 

unrecognized contribution to certain deficiencies embedded in this reverse mortgage cash flow 

structure. Moreover, the constant efforts of issuers and servicers in financially adapting cash flow between funding ongoing borrower HECM draws, selling pooled participation securities to the 

capital markets and repurchasing HMBS pools, as they are assigned to FHA, deserves recognition. 

In addition, alternative payment structures need to be explored with regard to reverse mortgages 

in the United States. The Japanese approach, as evidenced through the Special Repayment System 

for the Elderly, can provide a prospective solution for consideration to alleviate certain financial burdens placed on counterparties. 

Recurring payment structures can improve management of cash flow timing risks and assuage 

advanced servicing liabilities unique to the design of HECM and HMBS products. However, 

permitting continuous interest rate payments into HECMs and HMBS structures has profound 

ramifications. Recurring interest rate payments may provide more certainty into counterparty 

business operations but may also significantly affect other stakeholders, particularly borrowers and investors. 

The exploration of recurring payment structures necessitates close collaboration and validation 

with stakeholders. Continuous interest rate structures have the potential to deepen counterparty 

participation and reduce the business risks and costs associated with reverse mortgages. The out-

come can be more affordable terms for senior homeowners seeking to strengthen their retirement 

security. 

Acknowledgments

The author thanks the anonymous reviewers for their constructive comments as well as John 

Getchis from Ginnie Mae for his technical counsel on various aspects of the secondary mortgage 

market and capital markets. The author also thanks Masahiro Kobayashi and Osamu Takahashi 

from the Japan Housing Finance Agency for their work that inspired this research. The author rec-

ognizes Alven Lam for enabling this study through the U.S.-Japan Housing and Innovation Forum 

on Urban Housing and Financial Innovation facilitated through the U.S. Department of Housing 

and Urban Development and Ginnie Mae.

Author

Christopher Feather is the Executive Director of Kalamu Consulting, a firm specializing in financial sector development. 

43 The U.S. government, through HUD, pioneered the innovative approaches. “FHA was the first organization to insure reverse mortgages on a national scale” (FHA, 2016: 17). Likewise Ginnie Mae executed the first HECM securitization through the HMBS program.

260 Foreign Exchange

Continuous Repayment Structures in Japanese Housing Finance for Elderly People: Applications To Mitigate Counterparty Risk Through U.S. Reverse Mortgage Design

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Offering Quality and Yield.” https://www.fidelity.com/bin-public/060_www_fidelity_com/documents/

gnma-mortgage-backed-securities-a-treasury-alternative-offering-quality-and-yield-retail.pdf. 

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———. 2014.  JHF Disclosure Booklet 2014. Tokyo, Japan: JHF. www.jhf.go.jp/files/300196956.pdf. 

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Kobayashi, Masahiro, Shoichiro Konishi, and Toshihiko Takeishi. 2017. “The Reverse Mortgage 

Market in Japan and Its Challenges,”  Cityscape 19 (1): 99–118. huduser.gov/portal/periodicals/

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262 Foreign Exchange

Creating Permanent Housing 

Affordability: Lessons From  

German Cooperative  

Housing Models

Kathryn Reynolds    

Urban Institute

Introduction

The United States and Germany, as major economic and world powers and, respectively, the first 

and second largest destination for immigrants worldwide, are each faced with their own unique 

challenges in creating economic opportunity for their most vulnerable residents. Moreover, in both Germany and the United States, the location, quality, and quantity of affordable housing is one of the most significant factors in creating greater equality of opportunity (Katz, Noring, and Garrelts, 2016). This article explores Germany’s use of cooperative housing as a platform for long-term 

affordable housing and better economic outcomes for low- and moderate-income persons. In the 

United States, shared equity housing models, which typically take the form of community land 

trusts and cooperative housing, have become increasingly popular in the past few years, in part, as a local response to increases in inequality. Germany has a robust market for cooperative housing 

with around 2,000 cooperative projects offering approximately 2.2 million units (Bundesminis-

terium, 2017). Lessons from Germany’s experience with cooperative housing can inform recent 

efforts in the United States.

What Is Cooperative Housing?

The term “cooperative housing” describes many different forms of housing, ranging from for-profit 

cooperative owner-occupied housing to affordable cooperative housing to cohousing. This article 

will focus on the type of affordable cooperative housing that is most prevalent in the United States, namely limited-equity cooperative housing, which has an equivalent in the German context. 

However, cohousing, community land trusts and similar “shared equity” living forms will also be 

discussed. In each case, the type of housing will be identified and defined.

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 263

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

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Affordable Housing: A Comparison of German and U.S. 

Markets 

In both Germany and the United States, the location, quality, and quantity of affordable housing 

are significant factors in creating greater equality of opportunity. Stanford researchers Chetty and Hendren’s (2015) work shows that, in the United States, the neighborhood in which a person 

resides affects nearly every other aspect of their life, whether it is availability of jobs, quality of schools and medical services, availability of other social infrastructure (parks, childcare, and cultural amenities), and transportation mobility options. According to this research, racial and income neighborhood segregation in the United States is associated with less upward economic mobility 

from childhood into adulthood and the persistence of poverty through multiple generations 

(Chetty and Hendren, 2015). In a 2009-to-2012 German study, researchers found a correlation 

between the quality of the built environment, particularly that of the neighborhood, with that of 

employment, education, and income levels of the inhabitants (BBSR, 2012). In fact, many similari-

ties to the housing and urban development policy environments exist between both countries, 

most significantly, their similar structures for financing urban development activities at the federal level. The following section will briefly explore the two countries’ affordable housing sector and current trends.

Affordable Housing in Germany

In Germany, the bulk of affordable housing construction began during reconstruction after World 

War II. Between 1950 and 2000, the government subsidized approximately 24 million units, 

of which 9 million were dedicated for low-income residents, called  soziale Wohnungen or social housing (Harlander, 2017). In the early 1970s, the construction of new housing units decreased as 

historic preservation, revitalization efforts, and modernizing projects took a larger role. After reunification of East and West Germany in the1990s, Germany saw a renewed, if smaller, demand for 

affordable housing development due to the poor state of the housing stock in former East Germany 

and an influx of asylum seekers from the Balkans (Harlander, 2017). 

Several important housing policy shifts began in the 1990s, including the privatization of formerly government-owned social housing stock and, in 2007, a shift in responsibility for affordable housing production from the federal government to the 16 states or  Länder (BPB, 2009). The federal government now supplements state finances for new affordable housing production with funding 

called  soziale Wohnraumförderung. This funding is distributed from the federal government to the 16 states and then to eligible localities, with each level of government providing one-third of the funding. States and localities use this resource to finance a number of affordable housing activities, including subsidizing private developers who create affordable units. Due to an increase in demand for affordable housing, the federal portion of this funding has increased in recent years from €518.2 

million annually in 2015 to €1.5 billion in both 2016 and 2017 (Bundesministerium, 2017). 

Affordable housing is also supported through vouchers, which provide direct payments to residents 

from the federal government ( Wohngeld) and from the local governments (for example,  Kosten der Unterkunft). This voucher system is a means-tested program in which all qualifying households receive housing assistance. In 2016, Germany provided  Wohngeld to 631,000 households (DeStatis, 264 Foreign Exchange

Creating Permanent Housing Affordability: Lessons From German Cooperative Housing Models 2017). In addition to  Wohngeld, housing allowances are provided under the social welfare system. 

Each year Germany spends about €17 billion providing housing vouchers to a total of 5 million 

households (Housing Europe, 2017). Although the constitution gives states the responsibility for 

the provision of affordable housing, most devolved this responsibility to their larger cities, and as such, the type and mix of affordable housing varies significantly at the local level. 

Beginning in 2004, the German federal government elevated the importance of cooperative 

housing to that of ownership and renting (Bundesministerium, 2018). Because each member of a 

housing cooperative is part owner, with a life-long right to occupancy, the government views the 

benefits of this housing type as providing the means for self-governance, self-help, and the financial development of residents. Further, this housing is considered important to retaining long-term affordability in the face of gentrification. Today, approximately 2.2 million cooperative housing 

units are in existence, which equate to 6 percent of all housing units in Germany. Five million 

citizens currently live in one specific type of housing cooperative— Genossenschaft (Bundesministerium, 2018). This statistic does not include the number of persons residing in cohousing and other shared equity housing models throughout the country.

Affordable Housing in the United States

The U.S. housing market has been defined by several phases. One of the most significant was the 

growth of the single-family housing market in the suburbs after World War II. For the first time in U.S. history, bank loans became available to many households due to the development of new financial products. Before the Fair Housing Act of 1968 made housing discrimination illegal, the Federal Housing Administration (FHA) made home loans available mainly to White families in new suburban single-housing divisions (Rose, 2010). Further, many homeowners placed restrictive covenants 

in their deeds, prohibiting the sale of their home to people of color (Khadduri, 2015). The result, by the beginning of the 1960s, was a highly unequal housing distribution with middle- and upper-class White persons living in the suburbs or more desirable urban neighborhoods and people of color 

largely relegated to the less attractive neighborhoods and older housing stock in urban areas. The repercussions of these policies continue today and affect more than only the quality of housing. 

The federal homeownership mortgage interest tax deduction allows for households to accumulate 

significant savings and, over time, wealth. The lack of homeownership opportunity historically for people of color has contributed significantly to the lack of wealth accumulation for this group and the increasing inequality between White people and people of color in the United States. 

Today, the Low-Income Housing Tax Credit (LIHTC) program, funded by the U.S. Department of 

Treasury and distributed by states, is responsible for the largest share of new affordable housing construction in the United States. Through this program, states and local bodies issue up to $8 

billion in tax credits for the preservation, rehabilitation, and creation of new affordable housing units by private developers (HUD, 2017). The U.S. Department of Housing and Urban Development (HUD) also provides direct transfers to landlords in a program similar to  Wohngeld called the Housing Choice Voucher (HCV) program, or Section 8. With this subsidy, households are 

responsible for paying one-third of their income toward rent, and the HUD subsidy provides the 

remaining amount, up to a set threshold. Approximately 2.2 million households participate in 

this program. The existing pool of public housing units that the federal government built between 

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the1930s and the 1970s serves another 1.1 million households (Gould Ellen and Yager, 2015). 

Only approximately one of every four households that qualify for housing assistance in the United 

States receive that assistance from the federal government (CBO, 2015).

Cooperative and shared equity housing models are less prevalent in the United States than in Ger-

many; however, they have recently seen a resurgence in popularity in the United States as a tool for preserving affordability in increasingly expensive cities and regions (GSN, 2016a). The Grounded 

Solutions Network, the largest association of shared equity housing providers in the United States, defines  shared equity as any housing model that creates or preserves housing with lasting affordability (GSN, 2016b). In the United States, prevalent models include community land trusts and 

limited equity cooperatives. Community land trusts are nonprofit, community-based organizations 

whose mission is to provide affordable housing by owning land and leasing it to those who live 

in houses built on that land, creating permanent affordability (Greenstein and Sungu-Eryilmaz, 

2005). Limited equity cooperative housing is housing eligible to low-income members who pur-

chase shares at below-market prices and are subject to limitations on the amount of profit they can receive on the resale of their units (Mallin, 2018). 

Comparison of Affordable Housing: United States and Germany

The United States and Germany have remarkably similar funding instruments for housing and the 

urban development programs that support affordable housing. For example, the HCV program 

is comparable with the  Wohngeld program. Each program provides that the tenant receives a 

governmental grant to finance the rental costs, with the subsidy going to the landlord in the case of HCV and, typically, the tenant in the  Wohngeld program. The  soziale Wohnraumförderung program can be compared with the LIHTC program. Although not set up with the sole purpose for incentivizing private developers,  soziale Wohnraumförderung allows for this use and many localities provide guaranteed payments to developers who produce new affordable housing units in tight housing 

markets. Both countries provide block grants to localities for urban development activities through similar programs called  Städtebauförderung and Community Development Block Grant, respectively. 

For all the similarities, the scale of the German programs outpace those in the United States. When evaluated as a percentage of Gross Domestic Product, or GDP, Germany provides significantly more 

public funding than the United States toward affordable housing preservation and production. 

Second, only 14 percent of low-income renters in Germany face housing cost overburden (spend 

40 percent or more of disposable income on housing costs), although 59 percent of comparable 

renters do in the United States (DeStatis, 2018; OECD, 2017a). An analysis of all U.S. funding 

sources shows that only 31 units of affordable housing are available for every 100 low-income 

families who would otherwise qualify (NLIHC, 2016).

German and U.S. housing markets differ in other ways as well. Germans tend to rent their homes, 

with an average of 55 percent of households renting, whereas U.S. citizens are more likely to be 

homeowners—only 35 percent are renters (OECD, 2017b). In German cities, the percentage of 

renters can be even higher. For instance, in the city of Leipzig, the share of renters is 87 percent (Bild, 2017). As such, a significant political base in Germany advocates to keep rents affordable and tenants protected. In the United States on the other hand, renters are, as a percentage, smaller and tend to have lower incomes than homeowners; therefore, they are less politically potent. 

266 Foreign Exchange

Creating Permanent Housing Affordability: Lessons From German Cooperative Housing Models Despite these differences, both the United States and Germany are grappling with two similar 

trends in their housing markets. The first trend is a growing issue of gentrification, marked by 

increased migration into urban centers largely by young professionals and, therefore, an increase in demand for existing housing stock. Another trend is increasing unaffordability of goods, including rent, due to sluggish growth in wages during the past 15 years (HUD, 2016). A third trend is the 

increase in single-person headed households (Zabel, 2016). Due to high demand for housing, 

particularly in urban areas, and rising inequality, pressure is increasing on affordable housing for people in need and historically marginalized groups. 

Why Cooperative Housing? 

One of the most unique aspects of Germany’s affordable housing market is the use of cooperative 

housing models; typically developed and supported at the local level. As in the United States, this sector of the housing market is growing—both in terms of resurgence of traditional cooperative 

housing forms ( Genossenschaften) and new shared housing models. Although cooperative housing is also present in the United States, it comprises a small share of the affordable housing mix. The Urban Homesteading Assistance Board estimates that of an original 425,000 units of affordable cooperative housing, approximately 167,000 units remain. In Germany, approximately 2.2 million cooperative 

housing units serve 4.6 million residents (COOP, 2018). This housing is considered important, 

particularly in retaining long-term affordability in the face of gentrification (Gerhardt, 2017). 

In Germany, the cooperative housing field is quite broad, encompassing many legal and organiza-

tional structures, project sponsors, and goal. In general, two main types of cooperative housing exist in Germany—that provided by large and well-established cooperative housing organizations and 

the smaller project-cooperatives. The latter are characterized by interest in a group of people in the renovation or new construction of housing units for long-term self-use. Most projects in Germany 

are owner occupied, multifamily buildings, in which each household has their own living space 

(Ache and Fedrowitz, 2012). The legal and operating structures are quite similar to those used in the United States. Typically, private firms, small organizations (similar to U.S. nonprofit organizations), or self-formed groups of people establish a legally recognized organization and bylaws that govern the operation of the entity. The organization or group takes on low-interest financing to purchase, build, and renovate an existing building and, sometimes, contribute to the renovation directly through 

sweat equity. In the end, each tenant has their own living space and access to shared amenities, such as terraces, outdoor space, laundry facilities, and shared kitchen areas. As in the United States, one can create a cooperative housing model, which places no limit on the equity that can be raised by 

the “shareholders” or tenants, or create a limited-equity cooperative, in which shareholder values are capped based on the bylaws of the organization, thus preserving affordability for generations. 

In Germany, the only subsidy available to build cooperative housing is through government-spon-

sored low-interest mortgages (COOP, 2018). The government-owned development bank, KfW, of-

fers attractive financing with low-interest rates for the new construction, purchase, and renovation of housing for self-use (KfW, 2018a). Individuals may also qualify for a loan to purchase a share in a cooperative housing organization. Interest rates are quite low on these products, even lower than comparable HUD low-interest mortgages. However, compared with the United States, the amount 

of capital that is available to an individual borrower is limited. For example, KfW’s products for Cityscape 267

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home or cooperative share purchase are capped at a maximum of €50,000 (KfW, 2018b). Although 

these borrowing amounts may increase when coupled with regional bank products that each state 

offer and with special incentives for the use of energy efficiency building techniques, individuals still need significant savings to invest. With some regions seeing sharp increases in land value, cooperative living is now out of reach for some moderate- and low-income families (Gerhardt, 2017).

Cooperative housing models meet many societal goals in Germany, including creating multigenera-

tional living, in which older residents age in place with younger residents, mixed-functional living quarters, in which persons with disabilities can live independently and in an integrated setting, 

as well as long-term or permanently affordable housing in quality neighborhoods (Selbstnutzer, 

2017). These models also have several indirect benefits, which include (1) revitalization of empty or underused building stock, (2) provision of ownership of housing to groups often underserved 

by traditional financial markets, and (3) improvement in the stability and quality of affordable 

housing because residents own a share in the building and must be committed to the project to see 

it to fruition. Although cooperative housing has many benefits, disadvantages must also be consid-

ered that continue to pose challenges within the German context. First, as previously discussed, 

the startup costs for residents in terms of finances and also investment of time and energy can be high. Second, such living forms could have the potential to aggravate segregation in the affordable housing sector if the initial tenants choose new residents based on their similarity to existing 

members. In the United States, however, this concern can be mitigated by enforcement of antidis-

crimination and fair housing laws, and many times these projects are completed with a goal for a 

better socioeconomic or social mix. For example, a study of cooperative and cohousing efforts in 

the state North Rhine Westphalia frequently showed a mix of residents with different backgrounds, 

such as old and young, families and single persons, different nationalities, and different income 

groups (Ache and Fedrowitz, 2012). 

Due to Germany’s long history with cooperative housing and relatively large number of projects, 

innovations are found that may be useful to consider in the United States. The following examples 

exemplify innovations in the German market and further illustrate potential benefits to cooperative housing.

City of Munich: Cooperative Housing as an Antidote to Gentrification 

The city of Munich has seen the greatest demand for housing and increase in housing prices in 

the country, due in large part to migration into the city of young professionals and E.U. citizens seeking jobs in this economic powerhouse. To protect its citizens from increasing living costs, 

specifically housing costs, the city released a 4-year housing strategy that was laid out in their 2012-to-2016 “Housing Offensive” and their 2017-to-2021 Housing in Munich VI strategy (Landeshauptstadt, n.d., 2012). As part of both strategies, Munich stated that of the city-owned land 

set aside for new housing, 20 to 40 percent would be provided at reduced prices to cooperative 

housing groups (Landeshauptstadt, n.d., 2012). The city of Munich stated that it viewed coopera-

tive housing groups as important partners due to their interest in creating permanently affordable housing that fit into the fabric of existing neighborhoods. In addition to inexpensive land, cooperative housing projects that enumerated goals in line with the overall Munich strategy could receive additional benefits such as reduced borrowing costs. 

268 Foreign Exchange

Creating Permanent Housing Affordability: Lessons From German Cooperative Housing Models City of Leipzig: Cooperative Housing as a Tool for City Revitalization 

As part of former East Germany, much of the city of Leipzig’s downtown housing stock was 

neglected between World War II and reunification. After reunification in the 1990s, most of the 

population was attracted to new housing stock being built on the outskirts of the city or to West 

Germany in search of new employment (Harlander, 2017). The building stock left in the urban 

core was largely blighted and uninhabitable. Since the 1990s, the city of Leipzig has slowly revitalized, beginning with the historic city center and moving outward into surrounding neighborhoods. 

This revitalization occurred mainly due to private investments, which benefited from tax reduction for this purpose. However, about one-third of buildings remained difficult to refurbish for various reasons including locations along main traffic roads and in neglected areas with low qualities of 

public and green spaces. 

A key part of this revitalization strategy has been the use of new, smaller, and project-oriented cooperative housing models, used to incentivize private investment in city housing stock (Gerhardt, 

2017). In this climate, several nonprofit organizations formed to work in partnership with the 

city to help groups of interested residents obtain clear titles to properties or contact abandoned building owners in the hopes of obtaining agreements for their reuse. Today, much of Leipzig has 

revitalized successfully, and it is one of the fastest growing cities in Germany (Stadt Leipzig, 2017). 

Due to high demand for housing and land, the city and all organizations involved in the reuse 

of blighted housing stock have formed one umbrella organization— Netzwerk Leipziger Freiheit 

(2018)—to bring investors to potential cooperative projects quickly and advise promising projects 

with the goal to obtain as much financing as possible. 

Citywide Incubators

Most large cities in Germany offer city-run incubators for the creation of cooperative and shared 

housing models. For example, in Leipzig, the  Netzwerk Leipziger Freiheit provides free consultation, connection with similar projects, access to and information about available financial grants, and financial consulting for persons interested in such a project. Incubators can also be used to promote cooperative housing to a specific segment of the population, specifically one that is vulnerable or in need of more affordable housing options. For example, the city of Berlin provides a consulting 

service for families and seniors who are interested in setting up multigenerational cohousing for 

the purposes of aging in place called  Netzwerkagentur Generationen Wohnen (2018), or Network Agency for Cross-generational Living. The service provides access to peer learning from other such projects and facilitation in the founding of the projects. In 2010, the Faculty of Spatial Planning at Dortmund University found that 26 municipalities across Germany supported cohousing projects, 

with support ranging from a website to more comprehensive approaches with the provision of 

special funding or city-owned land (Ache and Fedrowitz, 2012).

Sharehaus Refugio, Berlin: Using a Cooperative Living Model To Provide 

Transitional Housing for Refugees

The Sharehaus Refugio project in Berlin provides an example of an innovative cooperative housing 

model. Refugio provides temporary living space for up to 18 months to as many as 40 residents 

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who apply and meet the criteria to live in a shared setting and bring a particular skill to the 

community. In 2017, one-half of the residents were recent refugees seeking political asylum in 

Germany. Of the other one-half, some were German nationals and others voluntary newcomers to 

Berlin. In all, 10 nationalities were represented in the house (Harutyunyan, 2017). The residents of the house can offset their monthly “rent” costs by running a popular café on the ground floor, hosting events in a resident-refurbished banquet room, and providing city tours, which are provided 

from the perspective of recent refugees and formerly homeless individuals—all on a volunteer 

basis. German residents provide help with language skills and job searching for refugee residents, and refugee and non-German residents organize events that introduce German residents to their 

cultures. In 2017, Sharehaus planned to open a new building using the same model but offered 

exclusively to families (Refugio Berlin, 2017).

Barriers to Cooperative Housing in the U.S. Market

For all the potential positive outcomes of cooperative housing, why does the United States not 

produce more? In fact, research has shown very low default rates for U.S. mortgages originated for cooperative housing as compared with traditional housing for low-income persons (HUD, 2012). 

Therefore, cooperative housing could be a low-risk and low-public subsidy method for providing 

home ownership to persons typically not qualified to enter traditional financial markets. Despite 

this advantage, several reasons exist for the lack of cooperative housing to date in the U.S. market. 

1. The U.S. housing market as an investment vehicle.  Wealth in the United States is mostly undiversified and invested in the housing market as opposed to cash, bonds, and mutual funds 

(Salzman and Zwinkels, 2017). Housing is not the most efficient investment vehicle; however, 

economists have shown that consumer biases, such as overconfidence in the perceived future 

value of homes and fear of not making a beneficial investment decision (that is, not buying a 

home), lead to less efficient consumer behavior. For this reason, even low-income households 

that might benefit from the stability of a limited-equity cooperative home aim to purchase at 

market rate to take a chance at a high return. In Germany, where housing is not the dominant 

savings and investment mechanism and where healthcare, retirement, and other social costs are 

largely borne by the government, greater consumer interest is found in the stability of a limited-

equity cooperative housing model. 

2. Available financial products.  In Germany, each state provides a range of tools conducive to creating cooperative housing, including low-interest mortgages and down-payment assistance. 

In the United States, cooperative housing loans are costly to originate, as they tend to be unique, provide financing for small amounts, and require extra legal work. Since the sunsetting of 

previous FHA-insured loan products and decreases in other government funding for affordable 

housing during the past 10 years, financing these housing models in the United States has 

become more difficult (Ortiz, 2017). 

HUD offers insurance through the FHA for mortgage loans for nonprofit cooperative housing 

corporations (Section 213 of the National Housing Act of 1961). However, the volume of deals is 

quite low. In 2015, HUD insured one project with 77 units for $13.5 million. Cooperative housing 

projects can sometimes use Shared Appreciation Mortgage products—or second mortgages in 

which a nonprofit organization or local borrower offers a no-payment mortgage that is paid back 

270 Foreign Exchange

Creating Permanent Housing Affordability: Lessons From German Cooperative Housing Models at the time of sale with an agreed-on amount of appreciation. These funds are typically reinvested in the property to make homeownership affordable to another low-income buyer (HUD, 2012). 

However, such products are often financed at the local level by the federal HOME Investment 

Partnerships Program, a program for which Congress has decreased funding in recent years. 

3. The local tax structure.  For cooperative housing to remain affordable in the long term, particularly amid gentrification of the surrounding neighborhood, it is helpful for localities to 

provide property tax relief or conveyance of undervalued land (publicly owned land, publicly 

owned housing units, decommissioned public buildings, and so on) for this purpose (Davis 

and Jacobus, 2008). However, most U.S. localities rely on property tax income as their largest 

revenue source. Therefore, incentives are not aligned for localities to favor such incentives. 

Localities in Germany are financed primarily by transfers from higher levels of government and, 

thus, are less sensitive to fluctuations in property value.

4. Awareness and education.  Traditional lenders and affordable housing developers are 

largely unaware of this housing model, the benefits of this housing type, how to analyze risk, 

underwrite, and create the correct loan products for its creation. More education is needed 

particularly for traditional mortgage lenders and the LIHTC industry. LIHTC is the financing 

vehicle for most new affordable housing in the United States. 

Greater awareness is required in the public sector as well. Greater awareness could lead to 

municipal policies that encourage limited-equity cooperative homeownership, for example, 

rights of first refusal. With available financing products and incentives, this policy allows for 

tenants’ associations to convert their existing multifamily buildings into cooperative owner-

occupied housing, preserving affordability.

5. Development patterns and preferences.  In Germany, the focus of housing development 

has been on retaining compact, dense, transit-oriented living. Certainly, suburban and rural 

villages and towns exist throughout the country; however, a higher percentage of the population 

already lives in multifamily housing than in the United States (Carliner and Marya, 2016). The 

community-oriented nature of this model may not suit the tastes of many Americans who are 

used to living in close proximity to neighbors or prefer rural and suburban, single-family living.

Despite these barriers, recent trends in the United States, including sluggish wage growth 

compared with housing prices, the rise of single-family households, greater interest in a sharing 

economy, and demand for housing in major metropolitan areas and corresponding unaffordability 

of home ownership, have led to an increased interest in shared equity housing models at both 

local and federal levels. Many localities, including Baltimore, Detroit, and Washington D.C. among others, have recently announced either community land trust or cooperative housing models 

to improve long-term affordability in target neighborhoods (Semuels, 2015). In addition, the 

Federal Housing Finance Agency (FHFA) and the conservator of Fannie Mae and Freddie Mac (the 

government-sponsored enterprises, or GSEs) recently announced plans to increase liquidity and 

awareness in this market.

Cityscape 271

Reynolds

As a requirement of the Housing and Economic Recovery Act of 2008, FHFA was instructed to 

develop strategies to direct more financing to underserved markets. In 2016, FHFA released its 

Enterprise Duty to Serve Underserved Markets Final Rule.1 With this rule, FHFA directed the GSEs 

to increase the amount of investment capital available to support shared equity financing for af-

fordable homeownership, including reducing the burden of underwriting loans for cooperative and 

other shared equity housing models. In December 2017, FHFA released the Underserved Market 

Plans for Fannie Mae and Freddie Mac. In its plan, Fannie Mae committed to improve liquidity 

in the mortgage market for shared equity housing models, such as cooperative housing, by 

purchasing an additional 1,100 to 1,300 loans from this market during the next 3 years. Freddie 

Mac, for its part, will conduct pilots to align financial product offerings with the needs in the field and explicitly mention shared equity models in its Seller/Servicer Guide. Both GSEs will conduct 

outreach and education to traditional lenders to promote more shared equity loan originations 

(Abraham, 2017; FHFA, 2018). This new injection of capital may alleviate one of the largest barri-

ers to production of cooperative housing in the United States—access to financial products.

Conclusion

Germany and the United States can learn a great deal from one another. Both countries have 

diverse residents and communities, are destinations for immigrants, have similar urban develop-

ment and housing policy environments, face similar issues regarding affordable housing—such as 

recent movement into and preference for housing stock in urban cores, tight housing markets, and 

wage stagnation since the great recession—and share similar potential mitigating factors to address these challenges. Both countries seek policy interventions that alleviate income inequality, provide chances for greater diversity in neighborhoods, and accommodate an aging population. Cooperative housing is one housing policy ripe for transatlantic exchange. This exchange is particularly 

relevant now as the U.S. housing finance environment is focused on producing liquidity in the 

shared equity market and as local governments in both countries signal a willingness to implement 

new housing models to counter increasingly high prices in their housing markets. 

In Germany, cooperative housing is seen as a preferred method of providing affordable housing, 

because it is self-created, self-governed, stable, and requires lesser government subsidy. Such 

housing can also produce other social benefits, such as the ability of older generations to live with young families into old age, integrated housing for people with disabilities, an alternative to shelter for newly arrived residents, and an ownership opportunity for those normally excluded from traditional financial markets. The opportunities are varied and, with recent changes in housing finance and increasing local action, the United States is poised to benefit.

Acknowledgments

The author thanks Oliver Weigel, Susanne Glöckner, Tilman Buchholz of the Bundesministerium 

des Innern, für Bau und Heimat, Stefan Heinig and Karolin Pannike, the city of Leipzig, and Jens 

Gerhardt of Netzwerk Leipziger Freiheit for their input and support. 

1 Enterprise Duty To Serve Underserved Markets Final Rule 12 CFR Part 1282.  Federal Register 81 (250) December 29, 

2016. https://www.gpo.gov/fdsys/pkg/FR-2016-12-29/html/2016-30284.htm.

272 Foreign Exchange

Creating Permanent Housing Affordability: Lessons From German Cooperative Housing Models Author

Kathryn Reynolds is a policy program manager with the Research to Action Lab at the Urban Institute. 

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276 Foreign Exchange

SpAM

SpAM (Spatial Analysis and Methods) presents short articles on the use of spatial 

statistical techniques for housing or urban development research. Through this depart-

ment of  Cityscape , the Office of Policy Development and Research introduces readers to the use of emerging spatial data analysis methods or techniques for measuring geographic 

relationships in research data. Researchers increasingly use these new techniques to 

enhance their understanding of urban patterns but often do not have access to short 

demonstration articles for applied guidance. If you have an idea for an article of no more 

than 3,000 words presenting an applied spatial data analysis method or technique, please 

send a one-paragraph abstract to rwilson@umbc.edu  for review.

Understanding and Enhancing 

the U.S. Department of Housing 

and Urban Development’s 

ZIP Code Crosswalk Files

Ron Wilson

University of Maryland, Baltimore County 

Alexander Din 

Maryland Department of Housing and Community Development

Abstract

ZIP Codes1 are commonly used for mapping, spatial analyses, creating tables, or 

other reporting products. Used for these tasks, the results from using these geographies 

often are distorted because of adverse statistical properties inherent with ZIP Codes. 

Summarizing ZIP Code data to other large geographies (for example, county, Core Based 

Statistical Area, state) associates them with these other geographies to create aggregate 

counts so that metropolitan or county rankings can be reported. This process requires 

ZIP Codes to be properly allocated to these other geographies to accurately associate a 

record with that area. Although some companies or government organizations already 

provide a crosswalk to these geographies, the allocation method used is unclear, leaving 

it indiscernible as to the accuracy of the assignment of ZIP Codes. In this article, we 

demonstrate how to use the U.S. Department of Housing and Urban Development 

1 ZIP Code is the acronym for Zoning Improvement Plan Code.

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 277

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

Wilson and Din

Abstract (continued)

(HUD) United States Postal Service ZIP Code Crosswalk Files to more directly control the 

ZIP Code allocation process of records to alternative geographies. In meeting this objective, 

we also provide results of an analysis using the HUD Crosswalk File in associating a ZIP 

Code with U.S. counties. 

ZIP Codes Are Problem Geographies

Organizations use ZIP Codes for many analytical tasks, such as to verify addresses, allocate resources, or create analytical products (for example, maps, tables, or conduct reports). Although ZIP Codes 

have legitimate—but limited—use in analysis, they have adverse effects on the results. ZIP Codes are problematic, because their boundaries are not created for analytical purposes like other geographies. 

ZIP Codes were designed to more efficiently deliver mail, not as geographies to be used for analysis. 

Because of their nature, the boundaries vary in size and shape that amplifies a common, adverse 

statistical problem when used for analysis. This effect, known as the Modifiable Areal Unit Problem (MAUP), is ever present in analyses that use geography. It is well documented in several studies on how ZIP Codes are notorious for distorting policy-related analyses (Beyer, Schultz, and Rushton, 

2007; Cudnick et al., 2012; Dai, 2010; Grubesic and Matisziw, 2006; Hipp, 2007; Krieger et al., 

2002; Montalvo and Reynal-Querol, 2017; Wilson, 2015). Further, when mapping ZIP Code data, 

the choice of the thematic mapping method used to display the data can further exacerbate the 

misrepresentation of results through erroneous patterns depicted in the map (Wilson, 2011). A final deficiency in the use of ZIP Codes for analysis is that they typically do not contain any social, demographic, or economic data that can be used to create contextualized statistics of ratios, percentages, rates, or densities from the record counts contained within. When data are provided, those values are distorted from the same aggregation problems mentioned in the following paragraphs.

First, grouping characteristics by an area boundary that is too large, oddly shaped, or a combina-

tion of both leads to summary statistics that may not be representative of the population within 

the boundary. A second adverse effect is one that can hide a pattern of extreme values on either 

end of the characteristic values (Wilson, 2013). In this instance, extreme differences that exist 

for a characteristic are canceled out because the summary statistic represents the norm and does 

not reveal either extreme. A third adverse effect, and related to the second,2 is the reversal of 

2 For both the second and third adverse effects of ZIP Codes use, the phenomenon is known as Simpson’s Paradox that occurs from aggregation bias. Simpson’s Paradox occurs when data are aggregated to groups and prevents any analysis from detecting underlying patterns between cases that share a commonality. This problem occurs because statistical techniques treat combined observations as a single group, in which the observations are assumed to be indistinguishable from one another and not have any shared commonalities that might affect the results. However, cases often do share commonalities that may have converse relationships between an outcome and the explanatory factors when analyzed as a subgroup. The repartitioning of the underlying data from smaller to larger geographic units can cancel out or reverse patterns in smaller units. The paradox is a consequence of the MAUP, in which statistical results are affected by modifications to the geographic unit’s boundary size or shape or both. Aggregated data are uniquely partitioned by their geography, and when geographic units are changed, the new boundary sizes and shapes are repartitioned.

278 SpAM

Understanding and Enhancing the U.S. Department of  

Housing and Urban Development’s ZIP Code Crosswalk Files

a relationship if two characteristics are examined together (Hipp, 2007; Montalvo and Reynal-

Querol, 2017; Wilson, 2015). Here, a positive or negative relationship between two characteristics is reversed from what it would be if a more appropriate area were used in the analysis because the characteristics would have been assigned to differing areas. 

About Allocating ZIP Code Data to Other Geographies

Several private companies obtain address data from the U.S. Postal Service (USPS) or ZIP Code 

geographies from the U.S. Census Bureau to enhance them and sell the information. Companies in 

the private industry add value to these products, making them more robust by adding geographic 

information or the creation of boundaries for mapping.3 The boundaries created are estimated or 

modified by delineating areas using topographical point or line landmarks following each organiza-

tion’s proprietary method, including comparisons with the ZIP Code Tabulation Areas (ZCTAs) 

provided by the Census Bureau. Each company has their own undisclosed method of creating 

these boundaries, with each claiming theirs is the most accurate.

ZIP Codes typically overlap with other geographies and cannot always be completely associated 

with areas in another geography. Exhibit 1 demonstrates how a decision must be made in allocat-

ing address counts from ZIP Codes to the census tract geography. The map shows a ZIP Code (light 

gray outline) crosscutting three census tracts (dark gray outline) and the geographic distribution of addresses within each tract. For many analyses, address counts must be associated with only one 

other geography, lest the addresses be counted multiple times and adversely affect statistical results. 

The ZIP Code in exhibit 1 has 2,860 addresses, with 1,962 (65.8 percent) in tract C, 872 (30.5 

percent) in tract B, and 26 (1.0 percent) in tract C. If the address locations are available, then those proportion counts get assigned to each tract.

However, without knowing the location of the address, a decision must be made to either assign 

all the addresses within a ZIP Code to one of the other geographies or proportionally divide the 

addresses to each of the three tracts. 

If all 2,860 of the addresses in the ZIP Code are assigned to one of the tracts without knowing 

where the actual addresses are, the counts will be assigned by using one of two general methods 

that assigns the ZIP Code addresses to other geographies based on the geometric properties of the 

areas. The first method uses the center location (large gray points) of the ZIP Code—that is, the 

centroid—and assigns the full count of addresses to the geography in which that center is located. 

In the previous example, the ZIP Code will be assigned to tract B, because the center is slightly 

inside the boundary next to tract A. One variant of this approach is the simple assigning of address counts to the geography, with the largest proportion of an area that the ZIP Code crosscuts. In the previous example, the ZIP Code will be assigned to tract C, because it has the largest portion of its area in that county. The second approach assigns a proportion of address counts that is commensurate with proportion of tract areas inside the ZIP Code. In the previous example, tract C will get 3 The USPS has never made a ZIP Code boundary file available to the public.

Cityscape 279

Wilson and Din

Exhibit 1

ZIP Code Boundary Crosscut With Census Tracts

54 percent of the addresses, 33 percent of address will go to tract B, and the remaining 13 percent will go to tract A. Regardless of method, allocating from either of these methods has varying risks to numerical accuracy of the allocation. 

Other ZIP Code products provide crosswalks to other geographies that use a similar geocoding 

method as HUD. An examination of ZIP Code Download, ZIP Boundary, and the HUD data sets 

shows that the difference between identified counties and Core Based Statistical Areas (CBSAs) is 

less than 1 percent of each other in regard to which geographies the ZIP Codes become associated. 

Therefore, the differences between them are negligible. The HUD product, however, is the only one 

of the three that crosswalks ZIP Codes to census tracts, making it valuable for micro-analyses.

Geocoding Address Data and Identifying Related 

Geographies

HUD can overcome the aforementioned analytical problems, because the agency has a Geocode 

Service Center (GSC) that provides a high level of accuracy in assigning corresponding geographies 280 SpAM

Understanding and Enhancing the U.S. Department of  

Housing and Urban Development’s ZIP Code Crosswalk Files

to the addresses received from USPS. On a quarterly basis, HUD receives domestic address data 

from USPS that are sent to the GSC to match, standardize, and return geographic information that 

staff can use in HUD products. This additional information offers a wide selection of geographic 

information to meet the many varied analytical questions the agency asks from its data systems. 

Diagnostics are also returned from the GSC helping to ensure the data are correctly geocoded, en-

abling staff to evaluate the quality of address matching and geographic positioning of the location.

Having the actual locations of the addresses facilitates HUD’s capabilities in accurately analyzing applicant data with mapping and spatial analysis. With the longitude (X) and latitude (Y) that the GSC produces, mapping and spatial analysis can be conducted directly on the locations, eliminating error when examining groups. 

The GSC also returns the names and codes of geographies in which the addresses fall so that other 

types of analyses can be done that will not require the locations. With location geocoding of ad-

dresses, an address can be directly associated to other geographies to which they are contextually associated. For example, an address can be associated to the census block, block group, tract, 

county, and CBSA they are within or other non-Census geographies that are available. These 

geographies can facilitate the (1) simple tabulating of counts by geography, (2) mapping or spatial analysis of socioeconomic data, or (3) creation of geographic data for advanced statistical modeling. Exhibit 2 demonstrates this spatial overlay process.

Exhibit 2

Geocoding Process of Addresses by ZIP Code to Overlapping Geographies

Cityscape 281

Wilson and Din

HUD cannot release the geocoded USPS address data at the location level, but the agency can release a file that takes advantage of this geocoding to produce a file that can crosswalk ZIP Codes to several other geographies so that other agencies may use those geographies instead of using ZIP Codes as 

analytical units. The value of HUD’s USPS ZIP Code Crosswalk Files is that they enable users to 

escape the adverse effects of using ZIP Codes by allocating the data to more appropriate geographies for analysis.

HUD USPS ZIP Code Crosswalk Files

HUD produces USPS ZIP Code Crosswalk Files that correspond to four Census Bureau geogra-

phies, which are (1) census tracts, (2) counties and county equivalents, (3) CBSAs, and (4) con-

gressional districts. Each file contains two general pieces of information.4 The first is the ZIP Code and geographic identification of the corresponding geography. The second is the ratios of address 

type in the geography the ZIP Code overlaps. Each crosswalk file contains the following address 

type ratios: (1) residential, (2) business, or (3) other. Exhibit 3 contains the full descriptions of the each element in each of the crosswalk files.5

Because many ZIP Codes overlap the boundaries of the other geographies, duplicate ZIP Code 

records will exist, requiring the user to make a decision about which geography to associate the 

ZIP Code. Exhibit 4 shows the structure of the HUD ZIP Code to County Crosswalk File. As 

an example, in Mt. Airy, Maryland, four records for ZIP Code 21771 are highlighted, with each 

recording the proportion of residential addresses in that ZIP Code. That ZIP Code overlaps four 

counties, with residential ratios distributed at 0.4652 (47 percent), 0.4143 (41 percent), 0.1088 

(11 percent), and 0.0117 (1 percent), all adding up to 1.0 (100 percent). 

Exhibit 3

Data Elements in the HUD USPS ZIP Code Crosswalk Files

Element

Description

ZIP

5  digit  USPS  ZIP  code

11  digit  unique  2000  or  2010  Census  tract  GEOID  consisting  of  state  FIPS  +  county  FIPS  +  tract  code.  

TRACT

The  decimal  is  implied  and  leading  and  trailing  zeros  have  been  preserved.

COUNTY

5  digit  unique  2000  or  2010  Census  county  GEOID  consisting  of  state  FIPS  +  county  FIPS.

5  digit  CBSA  code  for  Micropolitan  and  Metropolitan  Areas  as  defined  by  OMB  in  February  of  2013.  

CBSA

ZIP  codes  with  a  CBSA  code  of  ‘99999’  are  not  located  within  a  CBSA.  In  Metropolitan  Areas  that  are  

broken  out  into  Divisions,  the  code  reported  is  the  CBSA  Metropolitan  Division  code.

The  ratio  of  residential  addresses  in  the  ZIP  –  Tract,  County,  or  CBSA  part  to  the  total  number  of  

RES_RATIO

residential  addresses  in  the  entire  ZIP.

The  ratio  of  business  addresses  in  the  ZIP  –  Tract,  County,  or  CBSA  part  to  the  total  number  of  

BUS_RATIO

business  addresses  in  the  entire  ZIP.

The  ratio  of  other  addresses  in  the  ZIP  –  Tract,  County,  or  CBSA  part  to  the  total  number  of  other  

OTH_RATIO

addresses  in  the  entire  ZIP.

The  ratio  of  all  addresses  in  the  ZIP  –  Tract,  County,  or  CBSA  part  to  the  total  number  of  all  types  of  

TOT_RATIO

addresses  in  the  entire  ZIP.

CBSA = Core Based Statistical Area. FIPS = Federal Information Processing Standards. GEOID = geographic identification. 

HUD = U.S. Department of Housing and Urban Development. OMB = Office of Management and Budget. USPS = United States Postal Service.

4 huduser.gov/portal/datasets/usps_crosswalk.html.

5 More details can be found on the HUD USPS ZIP Code Crosswalk Files website at huduser.gov/portal/datasets/usps_

crosswalk.html - codebook. 

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Exhibit 4

Example of the ZIP Code to County Crosswalk File

The res_ratio, bus_ratio, and oth_ratio columns show the proportions of the corresponding ad-

dress type within each county. The tot_ratio column is the total number of all address types within each county. Again, for ZIP Code 21771 and rounding up at the fourth decimal place, from top to 

bottom, the tot_ratio column is 0.4922 + 0.3931 + 0.1027 + 0.011 = 1.0.

The ratios can be used to help decide which county to assign to the ZIP Code. For ZIP Codes with 

ratios of 1.0, no decision needs to be made, because all the addresses are contained within a single county. For ZIP Codes with ratio proportions, a decision can be made to assign the addresses to a 

county based on one of two approaches. The first approach is to assign all addresses to the county with the largest ratio. With probability theory, any address with that ZIP Code has a greater chance of being in the county with the largest proportion of addresses. 

A second approach is to proportionally assign the addresses to each county through geoprocessing 

in a geographic information system or cross-tabulating in statistical or general database software. 

This approach can allocate addresses in one of two ways. The first approach is to randomly assign 

each address to a county by proportion if only counts, rates, and densities are all that are needed for analysis. In this instance, it would not matter if an address was assigned to the wrong county, because only the aggregate value from its assignment is used and not a characteristic such as sex, ethnicity, or age. The second approach is to use an additional geography file, such as a street 

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network, to determine if the street of the address was primarily in one county or a neighboring 

county. This file is used if the desire is to have more accurate counts or rates for characteristics such as sex, ethnicity, or age.

Examining Geographic Overlap in the HUD USPS ZIP Code 

Crosswalk Files

The number of overlaps between a ZIP Code and another geography is a function of scale. The 

smaller the overlapping geography is, greater is the overlap of the ZIP Codes. Exhibit 5 shows 

the total number of ZIP Codes overlaps with the CBSA, county, and census tract boundaries. For 

the ZIP Codes that overlap multiple geographic boundaries, tracts have the smallest number of 

no overlaps—about 36 percent, 74 percent for counties, and 83 percent for CBSAs. These data 

translate into the chance an address is not in the boundary (error) being 61 percent for tracts, 26 

percent for counties, and 17 percent for CBSAs. For CBSAs and counties, the opportunity for error 

is even lower if the proportion of addresses in one of the overlaps is examined.

For counties, the distribution of residential ratios also shows a low potential for error in misassociation of county to ZIP Code. Of the 26 percent of residential address that are in multiple counties, 16 percent of residences are 90 to 99 percent in one area, four percent are 80 to 89 percent in one area, leaving 6 percent of residences 30 to 79 percent in one or more counties. The crosswalking of ZIP Codes to census tracts are of particular concern when assigning a one-to-one relationship.

Exhibit 5

ZIP Code Overlap Results Across Three Geographies

ZIP  Code  Overlap  Counts,  by  Geography

Tract

County

CBSA

Cross-‐cuts

Count  

Percent

Count

Percent

Count

Percent

None

14,106

35.89

29,107

73.8 32,908

83.4

2

5,730

14.58

7,772

19.7

5,658

14.3

3

4,313

10.97

2,141

5.4

829

2.1

4

3,090

7.86

399

1.0

66

0.2

5

2,218

5.64

36

0.1

1

0.0

6

1,550

3.94

6

0.0

0

0.0

7

1,226

3.12

0

0.0

0

0.0

8

1,059

2.69

0

0.0

0

0.0

9

930

2.37

0

0.0

0

0.0

10

816

2.08

1

0.0

0

0.0

11  to  68

4,263

10.86

0

0.0

0

0.0

Total

39,301

100 39,462

100.0 39,462

100.0

CBSA = Core Based Statistical Area. 

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Exhibit 6 shows the residential ratio distribution across overlapping ZIP Codes.6,7 The exhibit gives an indication of the chance an address will be misassociated with a particular geography when a 

ZIP Code overlap occurs. For CBSAs and counties, the potential for error is minimal, with most of 

the residential ratios being largely in one area more than the others.

Of the 17 percent of residential addresses that are in multiple CBSAs, about 10 percent of residences are 90 to 99 percent in one CBSA, 3 percent are 80 to 89 percent in one, leaving only 4 percent 

of residences 50 to 79 percent in one or more CBSAs—with the possibility of being outside of a 

CBSA, because they do not completely cover the United States. 

Exhibit 6

Nonduplicate ZIP Code Results Across Three Geographies

Residential  Ratio  Distribution  of  Zip  Code  Overlap,  by  Geography

Tract

County

CBSA

Res  Ratio

Count  

Percent

Count

Percent

Count

Percent

 0  (Outside)

4,897

12.5

4,938

12.5

4,938

12.5

0.01  to  0.04

0

0.0

0

0.0

0

0.0

0.05  to  0.09

124

0.3

0

0.0

0

0.0

0.10  to  0.14

1,003

2.6

0

0.0

0

0.0

0.15  to  0.19

1,778

4.5

0

0.0

0

0.0

0.20  to  0.29

3,183

8.1

1

0.0

0

0.0

0.30  to  0.39

2,327

5.9

20

0.1

6

0.0

0.40  to  0.49

2,059

5.2

91

0.2

32

0.1

0.50  to  0.59

2,688

6.8

633

1.6

378

1.0

0.60  to  0.69

2,217

5.6

741

1.9

433

1.1

0.70  to  0.79

2,216

5.6

1,002

2.5

607

1.5

0.80  to  0.84

1,272

3.2

695

1.8

435

1.1

0.85  to  0.89

1,378

3.5

900

2.3

574

1.5

0.90  to  0.94

1,547

3.9

1,409

3.6

926

2.4

0.95  to  0.99

2,971

7.6

4,724

12.0

3,110

7.9

1  (Inside)        

9,641

24.5 24,308

61.6

28,023

71.0

Total

39,301

100 39,462

100.0 39,462

100.0

Total  Whole:  14,538

39.3

29,246

74.1

32,961

83.5

Total  Part:

24,763

60.7

10,216

25.9

6,501

16.5

CBSA = Core Based Statistical Area. 

6 Duplicate ZIP Code records were removed by keeping only the ZIP Code with the highest residential ratio in a geography, the geography to which all the ZIP Codes would get assigned.

7 The ZIP Codes are those that are outside of a county or census tract, or are in some other geographic boundary other than a county or its equivalent in U.S. territories or military bases. 

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The distribution in exhibit 6 shows that of the 61 percent of residential address that are in multiple tracts, only about 12 percent of residences are 90 to 99 percent in one area, 7 percent are 80 to 

89 percent in one area, but 45 percent of residences are in one tract or another. These percentages translate into approximately of one-third of ZIP Codes having the greatest chance of an address 

being assigned to the wrong census tract if a one-to-one approach is used in associating ZIP Codes to a tract using the highest residential proportion of addresses. 

Even more precarious is that some ZIP Codes are split among numerous tracts, leaving the highest 

residential ratio less than 50 percent, with the other ratios similar to the other tracts. Exhibit 7 

shows the records for ZIP Code 36067 in Autauga County, Alabama, which overlaps 13 census 

tracts. Exhibit 8 shows the size and shape disparity among the geographies.

Exhibit 7 shows the highest tract residential ratio to be in tract 0100102082 at only approximately 23 percent, followed by four tracts with 10 to 14 percent. The remaining eight tracts have between 1 and 7 percent of residential addresses. This even distribution makes the assignment of a ZIP 

Code to any of these tracts dubious, because 77 percent of the remaining residential population is spread over a large enough area to have equal representation of that ZIP Code.

However, unlike assigning a ZIP Code to county, CBSA, or congressional district geographies, 

where each will get a ZIP Code assignment, this approach has an analytical consequence when us-

ing census tracts. Because tracts are often smaller than ZIP Code coverage, a problem of incomplete coverage occurs—that is, geographic holes form. Exhibit 9 shows the census tracts in the Mid-Atlantic states after assigning the ZIP Codes to the tract with the highest residential ratio. The dark gray polygons are the tracts assigned ZIP Codes because they had the highest residential ratio, with the light gray areas being the holes in which a tract was not associated with a ZIP Code.

The geographic holes occur because the ZIP Codes in those areas are larger than the tracts, as ex-

hibit 6 indicates of the more even distribution of residential ratios. When a ZIP Code is assigned to Exhibit 7

Census Tract Address Distribution for ZIP Code 36067, Autauga County, Alabama

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Exhibit 8

Census Tract Overlap With ZIP Code 36067, Autauga County, Alabama

Exhibit 9

Remaining Census Tracts After ZIP Code Assignment of Highest Residential Ratio

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one tract, all the adjacent tracts are omitted from an analysis, because they have no associated ZIP 

Code. As seen, this omission primarily occurs in urbanized areas. Unless the analytical objective is to only know which tract has the highest residential ratio, a proportion assignment solution must 

be found to assign a ZIP Code to all overlapping tracts with a one-to-many approach.

A potential way to approach the use of the ZIP Code to tract Crosswalk File is to consider less the idea of a one-to-one match with a census tract, but instead take a many-to-one approach that connects the ZIP Code to an associated place name, and assign it instead. This approach changes the 

geographic relationship from an assignment to a single overlapping geography to one of proximity 

to a recognized place with a name preferred by USPS and the Census Bureau. The tracts and any 

related socioeconomic data and all ZIP Codes associated with the place can be used in a more 

representative way. With that relationship, the data can be linked to the Census Bureau place-name geography and mapped, tabulated, or spatially analyzed with distance-based statistics. 

Further, if the ZIP Code to census tract file is not reduced to a single one-to-one match to a tract, then the file can be linked to the ZIP Code Tabulation Area or other ZIP Code boundary file and 

geoprocessed to integrate the two layers and provide the proportions of addresses in each tract 

across all the overlapping ZIP Codes. Then, the tract Federal Information Processing Standards 

(FIPS) or geographic identification can be summarized to create aggregate residential ratios to the tracts, thereby transferring the residential ratios—and other ratios—to each tract. 

Enhancing and Evaluating the HUD USPS ZIP Code 

Crosswalk Files

The Crosswalk Files contain only the most basic information about the geographies the ZIP Codes 

overlap, which are Census Bureau FIPS identification. Without the geography names, it is difficult to know to which of the corresponding parts of the United States the data refer. However, with 

a geographic identification, the Crosswalk Files can be linked to a number of other data sources 

to create a file that contains more robust and contextual information about the ZIP Code. As 

mentioned, several other data sources are available that provide additional information that can be matched to the Crosswalk Files. Companies, such as ZIP Boundary8 and ZIP Code Database Org9 

provide information that contains additional characteristics associated for each ZIP Code. In addition, SAS® software users have access to ZIP Code data that are provided in the SAS Help library, 

which SAS purchases from ZIP Code Download.10 These additional sources of information can 

enhance and evaluate the HUD USPS ZIP Code Crosswalk Files analysis. 

Enhancing a Crosswalk File

The SAS11 ZIP Code data are used to demonstrate enhancing the HUD Crosswalk File with U.S. 

counties and equivalents. ZIP Code data from the companies mentioned previously provide similar 

8 http://www.zipboundary.com/. 

9 https://www.unitedstateszipcodes.org/zip-code-database/.  

10 https://zipcodedownload.com/.

11 SAS ZIP Codes are in the SAS Help library, to which the data originate from ZIP Code Download https://zipcodedownload.com/. 

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variables to create a geographic context for the HUD ZIP Code Crosswalk Files. Exhibit 10 lists the additional information the SAS data provide. These enhancements offer the opportunity to geographically contextualize the HUD Crosswalk File, as well as facilitate a comparison of the accuracy with other crosswalk files. ZIP Codes and their geographies change frequently, and each company 

processes the updates differently, which means similar analyses will yield slightly different results. 

A number of variables in the SAS file are valuable when examining the HUD United States Postal 

Service cross-walk files. First is the COUNTYNM variable that contains the name of the county that corresponds with county code in the HUD cross-walk file. Note that the Metropolitan Statistical 

Area (MSA) is contained in the SAS data, but not the Core Based Statistical Areas information. Us-

ing the county code, this data can be linked with the CBSA listing from the Census Bureau to add 

that information. 

Exhibit 10

Additional ZIP Code Information From the ZIP Code Database

SAS  ZIP  Code  Table  Variables

#

Variable

Type

1 ZIP

The  5-‐digit  ZIP  Code

2 X

Longitude  (degrees)  of  the  center  (centroid)  of  ZIP  Code.

3 Y

Latitude  (degrees)  of  the  center  (centroid)  of  ZIP  Code.

4 ZIP_CLASS

ZIP  Code  Classification:P=PO  Box  U=Unique  zip  used  for  large  organizations

5 CITY

Name  of  city/org

6 STATE

Two-‐digit  number  (FIPS  code)  for  state/territory

7 STATECODE

Two-‐letter  abbrev.  for  state  name.

8 STATENAME

Full  name  of  state/territory

9 COUNTY

FIPS  county  code.

10 COUNTYNM

Name  of  county/parish.

11 MSA

Metro  Statistical  Area  code  by  common  pop-‐pre  2003;  no  MSA  for  rural

12 AREACODE

Single  Area  Code  for  ZIP  Code.

13 AREACODES

Multiple  Area  Codes  for  ZIP  Code.

14 TIMEZONE

Time  Zone  for  ZIP  Code.

15 GMTOFFSET

Diff  (hrs)  between  GMT  and  time  zone  for  ZIP  Code

16 DST

ZIP  Code  obeys  Daylight  Savings:  Y-‐Yes  N-‐No

17 PONAME

USPS  Post  Office  Name:  same  as  City

18 ALIAS_CITY

USPS  -‐  alternate  names  of  city  separated  by  ||

19 ALIAS_CITYN

Local  -‐  alternate  names  of  city  separated  by  ||

20 CITY2

Clean  CITY  name  for  geocoding

21 STATENAME2

Clean  STATENAME  for  geocoding

FIPS = Federal Information Processing Standards. MSA = metropolitan statistical area. USPS = United States Postal Service.

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The ZIP CLASS variable identifies if a ZIP Code is (1) military,12 (2) post office box,13 (3) standard, or (4) unique.14 These distinctions provide insight into understanding the types of ZIP Codes contained in the Crosswalk Files that help to understand why some ZIP Codes do not match up to a ZIP 

Code boundary or location, such as the ZCTA or the boundary files from private companies. Linking 

the HUD ZIP Code to County Crosswalk File to the SAS ZIP Code file shows that 22 percent (9,201) 

ZIP Codes are Post office boxes, 73 percent (29,783) are standard, and 5 percent (2,090) are unique.

ZIP Codes are often used to represent place names and linking the HUD Crosswalk File to the 

SAS data can help identify those places. ZIP Codes can have multiple names associated with it 

that reflect successful mail deliver to and address with one of several cities, towns, or subdivisions (places) within its boundaries. These names are in the CITY, PONAME, ALIAS CITY, and ALIAS 

CITYN variables. However, a ZIP Code will always have exactly one place that is the primary (de-

fault) city, town, or subdivision that is captured in the CITY variable. These names can be used in identifying all the places associated with a ZIP Code, including using one of them as an alternative to assigning a ZIP Code to a census tract as discussed previously.

In a similar manner, if the SAS ZIP Code information is linked to the ZIP Code to County Crosswalk File, a summarization by county that includes a listing of the contained places can be conducted. 

Additional analyses can be done with socioeconomic data linked to the places. Additional geograph-

ic information is provided that indicates the telephone area codes covered, time-related factors, and the Cartesian X and Y coordinates that represent the geometric center of the ZIP Code area.

Evaluating A Crosswalk File

As described previously, HUD directly geocodes address data to the overlapping geographies that 

contain them, allowing for a specific cross-referencing between a ZIP Code and other geographies. 

As well, other companies do not provide information about how ZIP Codes are  crosswalked to another geography. With the processed ZIP Code to County Crosswalk Files on the largest residential 

ratio, a difference comparison can be conducted between the county designations in the HUD 

Crosswalk Files and the SAS data. The results show that about 3 percent (909) of the ZIP Codes 

differ in county assignment. Exhibit 11 shows the ZIP Code areas and locations that mismatch 

between the two sources. 

ZIP Codes overlapping multiple counties are highlighted with a black outline. The black and white 

points are ZIP Code locations that are either associated with another county or is a nondeliverable ZIP Code. The large ZIP Code in the center that cuts across four counties is identified in the HUD 

12 A military ZIP Code is a single entity code that represents an armed forces base or other property in which all mail is sent to a central location and distributed to the addresses within that property.

13 A post office box ZIP Code represents multiple locations for same address, that is, delivery to same organization in multiple properties or building.  

14 Unique ZIP Codes represent a single high-volume address, for example, large organizations (government, academic, company, nonprofit). Mail delivery is to the organization that is in one large building across multiple properties or buildings or both. Some large organizations have multiple receipt centers to make mail management more efficient by delivering and collecting from multiple stops across the organization. At other times, the post office box ZIP Code is at a post office that is in another county and that has a different ZIP Code. Further, branch offices for large organization can be physically located in another county outside the ZIP Code to which it belongs.

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Exhibit 11

County Assignment Mismatches With HUD ZIP Code Crosswalk Files

Crosswalk Files as belonging to Montgomery County, Maryland, which is the county with the 

smallest overlap, instead of Fredrick County, Maryland. However, about 47 percent of the resi-

dences are in that very small area, making it the county with which the ZIP Code becomes associ-

ated when using the highest residential ratio in the HUD Crosswalk Files. The SAS data, however, 

show that the ZIP Code belongs to Frederick County, which is the county that has the west section 

of the ZIP Code area. According to the HUD file, that part of the ZIP Code (in Frederick County) 

has only about 41 percent of the residential addresses, with Carroll and Howard Counties having 

about 11 and 1 percent, respectively. 

Similarly in exhibit 11, ZIP Code locations (white points) in Montgomery County and in Prince 

George’s County, Maryland are both federal government agencies and are associated with a Wash-

ington, D.C. ZIP Code. The other locations in the exhibit are locations that were simply in another county according to the HUD file by comparison to designation in the SAS data.

Data Limitations of the HUD USPS ZIP Crosswalk Files

Four limitations of the HUD Crosswalk data are of note. First, the Crosswalk Files do not contain 

the ZIP Codes for armed forces (military) and for the U.S. territories of American Samoa and the 

North Mariana Islands (Saipan). However, the territories of Guam, Puerto Rico, and the U.S. Virgin Islands are included.

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Second, aside from the missing ZIP Codes noted previously, a small number—less than 1 per-

cent—of addresses HUD receives from USPS cannot be geocoded due to errors in the addresses or 

the base maps. This means that some five-digit ZIP codes may not be in the Crosswalk Files.

Third, several other ZIP Codes across the United States are unavailable in the HUD Crosswalk Files. 

In some instances, this means that an undeliverable ZIP Code will be active but does not show up on the USPS list of ZIP Codes. A ZIP Code may be active, but it will not be found in the HUD Crosswalk Files, because that ZIP Code has been associated with its parent instead. In exhibit 12, more than 1,629 ZIP Code locations from the SAS dataset are shown that are not in the HUD Crosswalk Files.

Not all the unmatched ZIP Codes are post office boxes. Of these nonmatches, 48 (3 percent) 

are standard, 889 (55 percent) are post office boxes, and 692 (42 percent) are unique. About 54 

percent of the nonmatches are for large private, government, or academic organizations with their 

own ZIP Codes that have mail delivered or collected from a central location within the organiza-

tion. The remaining 46 percent have no name associated with the code and likely do not have mail 

directly delivered to that location, but from another mail-receiving ZIP Code. 

Finally, HUD has not yet tabulated a crosswalk for CBSAs that solely use the CBSA code for every 

metropolitan area. This limitation means that in the ZIP Code to CBSA Crosswalk File, the metro-

politan division code is used in place of the actual CBSA code, which does not always correspond 

with the Census Bureau-assigned CBSA code. To remedy, a separate file matching the Census 

Exhibit 12

ZIP Codes Not in HUD ZIP Code Crosswalk Files

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Bureau CBSA delineation files and the CBSA Crosswalk Files must be made on the metropolitan 

division codes to obtain the correct CBSA codes. However, at the time of this article, HUD is cor-

recting the previously discussed limitation. By the fourth quarter of fiscal year 2017, HUD will split the ZIP Code-CBSA Crosswalk File into two products. One file will contain the CBSA codes, and 

the second file will contain metropolitan division codes. Users who need the CBSA Crosswalk Files 

prior to the fourth quarter of 2017 must use the Census Bureau CBSA delineation files to identify 

the CBSAs. After the fourth quarter of 2017, users can select the appropriate Crosswalk Files for 

their needs.

Summary

The HUD Crosswalk Files are one valuable piece of the geographic context puzzle when making 

use of ZIP Codes. The files contain no geographic information other than ZIP Codes and the corre-

sponding identifications of the geographies that they cross-reference. This relationship enables the files to easily expand an address dataset to another geography that can connect to a wealth of other geographic information and socioeconomic data. Because the Crosswalk Files contain the ratios of 

address types, the most important aspect of the data is that users are empowered in making their 

own decisions about assigning ZIP Codes to other geographies. 

Authors

Ron Wilson is an adjunct faculty member of the Geographic Information Systems Program at the 

University of Maryland, Baltimore County.

Alexander Din is a housing research and GIS analyst with the Maryland Department of Housing 

and Community Development.

References 

Beyer, Kirsten M.M., Alan F. Schultz, and Gerard Rushton. 2007. “Using ZIP® Codes as Geocodes 

in Cancer Research.” In  Geocoding Health Data: The Use of Geographic Codes in Cancer Prevention and Control, Research, and Practice, edited by Gerard Rushton, Marc Armstrong, Josephine Gittler, Barry Greene, Claire Pavlik, Michele West, and Dale Zimmerman. New York: CRC Press: 37–67.

Cudnik, Michael T., Jing Yao, Dana Zive, Craig Newgard, and Alan T. Murray. 2012. “Surrogate 

Markers of Transport Distance for Out-of-Hospital Cardiac Arrest Patients,”  Prehospital Emergency Care 16 (2): 266–272.

Dai, Dajun. 2010. “Black Residential Segregation, Disparities in Spatial Access to Health Care 

Facilities, and Late-Stage Breast Cancer Diagnosis in Metropolitan Detroit,”  Health & Place 16 (5): 1038–1052.

Grubesic, Tony H., and Timothy C. Matisziw. 2006. “On the Use of ZIP Codes and ZIP Code 

Tabulation Areas (ZCTAs) for the Spatial Analysis of Epidemiological Data,”  International Journal of Health Geographics 5: 58.

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Hipp, John R. 2007. “Block, Tract, and Levels of Aggregation: Neighborhood Structure and Crime 

and Disorder as a Case in Point,”  American Sociological Review 79: 659–680.

Krieger, N., P. Waterman, J.T. Chen, M.J. Soobader, S.V. Subramanian, and R. Carson. 2002. “ZIP 

Code Caveat: Bias due to Spatiotemporal Mismatches between ZIP Codes and US Census-defined 

Geographic Areas—The Public Health Disparities Geocoding Project,”  American Journal of Public 

Health 92 (7): 1100–1102. 

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Evidence. Working paper. Barcelona Graduate School of Economics.

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———. 2013 “Changing Geographic Units and the Analytical Consequences: An Example of 

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———. 2011. “Visualizing Racial Segregation Differently—Exploring the Changing Patterns From 

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294 SpAM

Referees 2017–18

The Office of Policy Development and Research gratefully acknowledges the contributions 

of the following referees and their assistance in making  Cityscape  worth reading.

Jason Amirhadji

Michael Eriksen

John Renne

Mindy Ault

Anne Fletcher

Eric Roberts

Cara Berton

Matt Freedman

Elizabeth Rudd

Stephen Billings

Philip M.E. Garboden

Galen Savidge-Wilkins

Charles Branas

Regina Gray

Corianne Payton Scally

Paul Byrne

Daniel Hindman

Jenny Schuetz

Kathryn A. Carroll

Michael K. Hollar

Heather Schwartz

George R. Carter III

Paul Joice

Alan Simon

Elizabeth Cochran

Kevin Kane

Teresa Souza

Dennis Culhane

Bree Lang

Philip Tegeler

Andrew L. Dannenberg

Eric Larson

Rachel Thornton

Ryan Devlin

Sabriya Linton

Chris Trent

Shawn Du

Kirk McClure

Maya Venkataramani

Mark Dynarski

Edgar O. Olsen

Nicole Elsasser Watson

Renia Ehrenfeucht

Josh Pacewicz

Richard Wilson

Ethan Elkind

Danilo Pelletiere

Kamillah Wood

Ingrid Gould Ellen

Jagruti Rekhi

Cityscape: A Journal of Policy Development and Research • Volume 20 Number 2 • 2018

Cityscape 295

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

296 Departments

Contents

Symposium 

The Housing-Health Connection ..................................................................1

Guest Editors: Veronica Helms Garrison and Craig Evan Pollack

Guest Editors’ Introduction 

The Health-Housing Nexus: New Answers to Key Questions ............................................................ 3

The Impact of the Vermont Support and Services at Home Program on Healthcare  

Expenditures  by Amy Kandilov, Vince Keyes, Martijn van Hasselt, Alisha Sanders, Noëlle  

Siegfried, Robyn Stone, Patrick Edwards, Aubrey Collins, and Jenna Brophy ................................. 7

A Pilot Community Health Worker Program in Subsidized Housing: The Health + Housing  

Project  by Amy L. Freeman, Tianying Li, Sue A. Kaplan, Ingrid Gould Ellen, Ashley Young,  

Diane Rubin, Marc N. Gourevitch, and Kelly M. Doran .............................................................. 19

Work Requirements and Well-Being in Public Housing  by Kirstin Frescoln, Mai Thi Nguyen,  

William M. Rohe, and Michael D. Webb .................................................................................. .39

Innovative Approaches to Providing Rental Assistance: States and Localities Seek To Support  

Health and Human Services Goals  by Anna Bailey, Peggy Bailey, and Douglas Rice ........................ 53

Embedding Health in Affordable Housing Development: Results of the Health Action Plan  

Pilot Project  by Stephany De Scisciolo, Krista Egger, and Mary Ayala .......................................... 73

Connecting Fragmented Systems: Public Housing Authority Partnerships With the  

Health Sector  by Stephen Lucas .................................................................................................. 85

‘Then I Found Housing and Everything Changed’: Transitions to Rent-Assisted Housing and  

Diabetes Self-Management  by Danya E. Keene, Mariana Henry, Carina Gormley, and  

Chima Ndumele ...................................................................................................................... 107

Homelessness During Infancy: Associations With Infant and Maternal Health and Hardship  

Outcomes  by Diana B. Cutts, Allison Bovell-Ammon, Stephanie Ettinger de Cuba, Richard  

Sheward, Meg Shaefer, Cassie Huang, Maureen M. Black, Patrick H. Casey, Sharon  

Coleman, Megan Sandel, and Deborah A. Frank ..................................................................... 119

Impact of Rental Assistance on Modifiable Health Risk Factors and Behaviors in Adults  

by Cathy L. Antonakos and Natalie Colabianchi ...................................................................... 133

The Geography of Vacant Housing and Neighborhood Health Disparities After the U.S.  

Foreclosure Crisis  by Kyungsoon Wang and Dan Immergluck .................................................... 145

Refereed Papers ............................................................................................171

Employment and Earnings Trajectories During Two Decades Among Adults in New York City  

Homeless Shelters  by Stephen Metraux, Jamison D. Fargo, Nicholas Eng, and  

Dennis P. Culhane ................................................................................................................... 173

Exploring Patterns of Tax Increment Financing Use and Structural Explanations in Missouri’s  

Major Metropolitan Regions  by Susan G. Mason and Kenneth P. Thomas ................................... 203

Departments ...................................................................................................233

Data Shop

The Long-Term Dynamics of Affordable Rental Housing: Creating and Using a New  

Database  by John C. Weicher, Frederick J. Eggers, and Fouad Moumen ................................... 235

Foreign Exchange

Continuous Repayment Structures in Japanese Housing Finance for Elderly People: Applications  

To Mitigate Counterparty Risk Through U.S. Reverse Mortgage Design  by Christopher Feather ...... 245

Creating Permanent Housing Affordability: Lessons From German Cooperative Housing Models   

by Kathryn Reynolds ................................................................................................................ 263

SpAM

Understanding and Enhancing the U.S. Department of Housing  and Urban Development’s   

ZIP Code Crosswalk Files  by Ron Wilson and Alexander Din ...................................................... 277

Referees 2017–18 .......................................................................................................295

Cityscape

U.S. Department of Housing

and Urban Development

Volume 20 number 1 

Office of Policy Development and Research

• 2018

Document Outline

Cover

Title Page

Contents

Symposium 

Guest Editors’ Introduction 

The Impact of the Vermont Support and Services at Home Program on Healthcare Expenditures

A Pilot Community Health Worker Program in Subsidized Housing: The Health + Housing Project

Work Requirements and  Well-Being in Public Housing

Innovative Approaches to Providing Rental Assistance: States and Localities Seek  To Support Health 

Embedding Health in Affordable Housing Development: Results of the Health Action Plan Pilot Project

Connecting Fragmented Systems: Public Housing Authority Partnerships  With the Health Sector

‘Then I Found Housing and Everything Changed’: Transitions  to Rent-Assisted Housing and Diabetes Se

Homelessness During Infancy: Associations With Infant  and Maternal Health  and Hardship Outcomes

Impact of Rental Assistance on Modifiable Health Risk Factors and Behaviors in Adults

The Geography of Vacant Housing and Neighborhood Health Disparities After the U.S. Foreclosure Crisi

Refereed Papers 

Employment and Earnings Trajectories During Two Decades Among Adults in New York City Homeless Shelt

Exploring Patterns of Tax Increment Financing Use and Structural Explanations in Missouri’s Major Me

Departments

The Long-Term Dynamics of Affordable Rental Housing: Creating and Using  a New Database

Continuous Repayment Structures in Japanese Housing Finance for Elderly People: Applications To Miti

Creating Permanent Housing Affordability: Lessons From  German Cooperative  Housing Models

Understanding and Enhancing  the U.S. Department of Housing  and Urban Development’s  ZIP Code Cross

Referees 2017-18