Housing and Urban Development Health, Economic,

and Residential Stability (HUD HEARS) Study

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

DISCLAIMER

The contents of this report are the views of the author and do not necessarily reflect the views or policies of the U.S. Department of Housing and Urban Development or the U.S. Government.

ii

Housing and Urban Development

Health, Economic, and Residential Stability

(HUD HEARS) Study

Final Report

Prepared for:

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

Alastair Matheson

Danny Colombara

Megan Suter

Annie Pennucci

Tyler Shannon

Andy Chan

Public Health—Seattle & King County

in partnership with

King County Housing Authority

Seattle Housing Authority

December 2022

iii

Acknowledgments

The authors of this report gratefully acknowledge their colleagues at Public Health—Seattle & King County, King County Housing Authority (KCHA) and Seattle Housing Authority (SHA). We would particularly like to thank Niki Petrakos, Zichen Liu, Hantong Hu, and Taylor Keating, who led the analysis of housing outcomes following exit as part of the biostatistics capstone project at the University of Washington. We extend our appreciation to University of Washington Professors Patrick Heagerty and Lloyd Mancl for lending their support and expertise for the housing outcomes following exit analysis. We also want to acknowledge Amy Laurent’s leadership in the housing and health work and for her assistance in conceiving of this project.

Thank you to Jacquie Bachand at U.S. Department of Housing and Urban Development Office of Policy Development and Research for your guidance throughout the project and for your review of the final report.

Finally, a special thank you to the residents of KCHA and SHA who contributed the data that made this report possible. The authors hope that the knowledge gained from this work can be used to improve wellbeing for you and future residents.

iv

Foreword

Significant turnover occurs in U.S. Department of Housing and Urban Development (HUD) housing assistance, with a current median length of subsidy receipt of about 5 years for families with children. However, to date, little systematic research exists on the short- or long-term outcomes of households who leave HUD-assisted housing or on positive and negative exits for most HUD programs.

In 2020, HUD’s Office of Policy Development and Research announced funding for research to help us learn more about how households that exit HUD-assisted housing fare after exit, and to help HUD and housing providers identify ways to support positive exits and improve long-term outcomes. To conduct this research, HUD asked research grant applicants to leverage HUD administrative data on tenants by linking it with other secondary longitudinal data sources.

This report, Housing and Urban Development Health, Economic, and Residential Stability (HUD HEARS) Study, is one of two exploratory studies supported by those research grants. The research team linked HUD

administrative data on assisted tenants with eight secondary data sources for households in the Seattle-King County, Washington, area to examine three research questions: 1) What constitutes a positive or negative exit from HUD-assisted housing? 2) What factors are associated with each exit type? 3) Is a positive exit associated with better post-exit outcomes than a negative exit?

The study leveraged Data Cross Sectors for Housing and Health, a successful 5-year housing and health collaborative between Public Health—Seattle & King County, King County Housing Authority, and Seattle Housing Authority designed to provide public housing authorities with information about healthcare utilization for the residents they serve. The study included a sample of 8,266 households who exited HUD-assisted housing programs.

The research team classified 13.5 percent of those exits as “positive,” 31.6 percent as “negative,” and 54.9

percent as “neutral.” Positive exits involved “increased income, attaining homeownership, and moving to nonsubsidized rentals,” and negative exits involved “eviction, lease violations, criminal activity, or abandoning the property.” Other exits, such as health- or family-related factors, were classified as neutral because they could not be clearly identified as positive or negative.

The high rate of negative exits—31.6 percent—is particularly troubling because the study showed that one-in-four of those households with negative exits experienced homelessness within 1 year of exit, compared with 3

percent of those with a positive exit. Those with positive exits were much less likely to have an emergency department visit in the year following exit and were also associated with much higher incomes before and after exit.

This study explored why people exit assisted housing and what happens to them following that exit. Given the high rate of negative exits and the high rate of homelessness associated with them, it is important that we continue to work with our housing partners to find best practices for reducing negative exits and expanding positive exits and outcomes for HUD-assisted households.

Solomon Greene

Principal Deputy Assistant Secretary for Policy Development and Research

U.S. Department of Housing and Urban Development

v

Contents

Acknowledgments ..................................................................................................................................................... iv

Foreword .................................................................................................................................................................... v

Contents .................................................................................................................................................................... vi

List of Exhibits .......................................................................................................................................................... viii

Executive Summary .................................................................................................................................................... x

Introduction ........................................................................................................................................................ x

Literature Review ............................................................................................................................................... x

Data Sources and Linkage ................................................................................................................................... x

Exits and Types .................................................................................................................................................. xi

Factors Associated with Exits from Housing Assistance .................................................................................... xi

Outcomes Following Exit ................................................................................................................................... xi

Conclusion ....................................................................................................................................................... xiii

Chapter 1 : Introduction .............................................................................................................................................1

Chapter 2 : Literature Review Summary ....................................................................................................................3

Introduction ............................................................................................................................................................3

Results ....................................................................................................................................................................3

Exit Types ............................................................................................................................................................4

Factors Associated with Exits .............................................................................................................................5

Outcomes Following Exits...................................................................................................................................6

Conclusions .............................................................................................................................................................7

Chapter 3 : Data Sources and Linkage ........................................................................................................................8

Chapter 4 : Exits and Exit Types ............................................................................................................................... 10

Chapter 5 : Who Exits From Housing Assistance? ................................................................................................... 15

Chapter 6 : Outcomes Following Exit: Residential Stability..................................................................................... 24

Chapter 7 : Outcomes Following Exit: Physical Health ............................................................................................ 26

Chapter 8 : Outcomes Following Exit: Behavioral Health ........................................................................................ 29

Chapter 9 : Outcomes Following Exit: Economic ..................................................................................................... 31

Chapter 10 : Conclusion........................................................................................................................................... 34

Policy and Program Implications ..................................................................................................................... 34

Reproducibility and Sustainability ................................................................................................................... 35

Recommendations for Future Work ................................................................................................................ 36

Appendix A : Acronyms ........................................................................................................................................... 37

Appendix B : Literature Review ............................................................................................................................... 38

Appendix C : Data Sources and Linkage .................................................................................................................. 50

vi

Appendix D : Exit Definitions ................................................................................................................................... 52

Appendix E : Factors Associated With Exit .............................................................................................................. 55

Appendix F : Housing Outcomes Following Exit ...................................................................................................... 65

Appendix G : Physical Health Outcomes Following Exit .......................................................................................... 68

Appendix H : Behavioral Health Outcomes Following Exit ...................................................................................... 77

Appendix I : Wage Outcomes Following Exit ........................................................................................................... 80

Appendix J : References ........................................................................................................................................... 89

vii

List of Exhibits

Exhibit 1: Key Outcomes ........................................................................................................................................... xii

Exhibit 2-1: Literature Review Search Results ............................................................................................................4

Exhibit 3-1: Number of People With Exits During the Study Period...........................................................................9

Exhibit 4-1: Top 10 Reasons for Exits From King County Housing Authority .......................................................... 10

Exhibit 4-2: Top 10 Reasons for Exits From Seattle Housing Authority................................................................... 11

Exhibit 4-3: Number of Exits by Public Housing Authority and Year ....................................................................... 12

Exhibit 4-4: Exit Categories by Public Housing Authority and Year (All Exits) ......................................................... 13

Exhibit 4-5: Exit Categories by Public Housing Authority and Year (Excluding Deaths) .......................................... 14

Exhibit 5-1: Demographics of Heads of Household Who Exited Versus Controls Who Did Not and by Exit Type .. 16

Exhibit 5-2: Regression Output for Heads of Household Who Exited Versus Controls Who Did Not ..................... 19

Exhibit 5-3: Regression Output for Heads of Household by Exit Type .................................................................... 21

Exhibit 6-1: Kaplan-Meier Curves of Time to Homelessness by Exit Type .............................................................. 25

Exhibit 7-1: Regression Results for Health Outcomes by Exit Type ......................................................................... 27

Exhibit 7-2: Regression Results for Health Outcomes Comparing Exit Types With People Remaining in Housing

Assistance ................................................................................................................................................................ 28

Exhibit 8-1: Behavioral Health Crisis Events, by Exit Type ....................................................................................... 30

Exhibit 9-1: Observed Quarterly Wages for Those Who Exited Seattle and King County Public Housing Authority

Programs Between January 1, 2016, and January 1, 2018 ...................................................................................... 32

Exhibit 9-2: Predicted Quarterly Wages by Exit Type Show Faster Wage Growth for Positive Exits Before Exiting

and Faster Wage Growth for Negative Exits After Exiting ...................................................................................... 33

Exhibit B-1: Summary of Relevant Literature .......................................................................................................... 40

Exhibit C-1: Data Sources Used for HUD HEARS ...................................................................................................... 50

Exhibit C-2: Identity Linkage Between HUD HEARS Data Sources ........................................................................... 51

Exhibit D-1: Exit Reasons and Categories ................................................................................................................ 52

Exhibit E-1: Demographics of Heads of Households Who Exited Versus Those Who Did Not, by Medicaid

Enrollment Status .................................................................................................................................................... 57

Exhibit E-2: Demographics of All Those Who Exited, by Exit Type (Individual Level).............................................. 59

Exhibit E-3: Regression Output for Heads of Household Who Exited Versus Controls Who Did Not (Medicaid

Population) .............................................................................................................................................................. 61

Exhibit E-4: Regression Output for Heads of Household by Exit Type (Medicaid Population) ............................... 63

Exhibit F-1: Sensitivity Analysis of Time to Homeless, by Exit Reason, Negative Versus Neutral ........................... 66

Exhibit F-2: Sensitivity Analysis of Time to Homeless, by Exit Reason, Positive Versus Neutral ............................. 67

Exhibit G-1: Demographics of Those Included in the Analysis of Physical Health Outcomes ................................. 69

Exhibit G-2: Regression Output from the Physical Health Outcomes Model, by Exit Type .................................... 71

Exhibit G-3: Regression Output from the Physical Health Outcomes Model, by Exit Type Versus Remaining ....... 74

Exhibit H-1: Adjusted Odds Ratios for the Association Between Exit Type and Behavioral Health Crisis Events for

All Types of Housing Assistance............................................................................................................................... 78

Exhibit I-1: Demographics During the Quarter of Exit for Those Who Exited Seattle and King County Public

Housing Authority Programs Between January 1, 2016, and January 1, 2018 ....................................................... 82

Exhibit I-2: Regression Fixed Effect Coefficients Describing the Relationship Between Exit Type and Wages for

Those Who Exited Seattle and King County Public Housing Authority Programs Between January 1, 2016, and

January 1, 2018 ........................................................................................................................................................ 84

Exhibit I-3: Mean Predicted Wages Are Similar to Mean Observed Wages for Each Exit Type and Quarter, Seattle

and King County Public Housing Authority Programs Between January 1, 2016, and January 1, 2018 ................. 85

viii

Exhibit I-4: Regression Fixed Effect Coefficients Describing the Relationship Between Exit Type and Percentage of

Area Median Income for Those Who Exited Seattle and King County Public Housing Authority Programs Between

January 1, 2016, and January 1, 2018 ..................................................................................................................... 86

Exhibit I-5: Residual Plot of Model Estimates Over Time Shows No Evidence of Autocorrelation ......................... 87

Exhibit I-6: Mean Predictions of Percentage of Area Median Income for Those Who Exited Seattle and King

County Public Housing Authority Programs Between January 1, 2016, and January 1, 2018 ................................ 88

ix

Executive Summary

Introduction

In 2019, the U.S. Department of Housing and Urban Development (HUD) set a goal of increasing the proportion of households that exit HUD-supported housing for positive reasons (e.g., homeownership) (HUD, 2019).

However, little is known about factors related to different types of exits from housing assistance, and outcomes that follow from exiting are even less well understood. To address that gap, this research sought to answer three key questions:

1. What constitutes a positive or negative exit from HUD-assisted housing?

2. What factors are associated with categories of exits (positive, neutral, negative)?

3. Is a positive exit from housing assistance associated with better post-exit outcomes than for residents who left for negative reasons?

The project was a collaboration between Public Health—Seattle & King County (PHSKC), King County Housing Authority (KCHA), and Seattle Housing Authority (SHA). The Washington State Institutional Review Board approved the research for this report.

Literature Review

A systematic literature review was conducted to examine what was already known about the key questions.

After reviewing more than 7,000 titles and abstracts, only 26 documents were deemed relevant to the topic.

Younger age, male gender, White race, smaller household size, and economic and rental market conditions are all associated with exiting housing assistance. However, very few studies looked at the relationship between demographic or economic factors and positive and negative exits. Receiving housing assistance during childhood is associated with positive outcomes later in life (Andersson et al., 2016; Aratani, 2010; Chetty, Hendren, and Katz, 2016; Newman and Harkness, 2002). People who exit housing for any reason tend to be in a more precarious position in terms of residential stability and income (Gubits, Khadduri, and Turnham, 2009; Kang, 2020; McInnis, Buron, and Popkin, 2007; Richter et al., 2021; Smith et al., 2014) Positive exits are associated with improved health and better housing situations (Smith et al., 2014). Full details of the literature review are in Chapter 2 and Appendix B.

Data Sources and Linkage

Several different administrative datasets were drawn on to examine outcomes following exit across multiple domains:

• PHA demographic data from data HUD Form 50058 Moving to Work, which collects data on households and individuals receiving federal housing assistance.

• Exit reasons collected on a separate form and stored by PHAs in a different data system.

• Behavioral Health and Recovery Division (BHRD) service data that include mental health and substance use claims.

• Employment Security Department (ESD) wage data.

• Healthcare for the Homeless Network (HCHN) data.

• Homeless Management Information System (HMIS) data.

• Medicaid claims data.

The authors used an existing multisector data system to link the data sources. The King County Integrated Data Hub (IDH) combines identities across several datasets, including BHRD, HCHN, HMIS, and Medicaid. The IDH uses a mix of probabilistic and deterministic methods to match individuals across data systems via a proprietary tool, Informatica. PHA data (HUD Form 50058 and exit data from both KCHA and SHA) were probabilistically linked on x

name, Social Security number, date of birth, and gender. IDH, ESD, and PHA data were then linked using the same probabilistic approach.

After applying restrictions such as excluding exits due to death or transfers between PHA programs, the basis for most analyses was 8,266 heads of household (1,118 [13.5 percent] positive, 4,538 [54.9 percent] neutral, and 2,610 [31.6 percent] negative) and 16,301 individuals (17.8 percent positive, 49.0 percent neutral, 33.2 percent negative). Additional details are in Chapter 3 and Appendix C.

Exits and Types

Exit reasons and categories were standardized in consultation with the PHAs. Positive exits consisted of reasons that were perceived as likely to be associated with self-sufficiency—for example, increased income, attaining homeownership, and moving to nonsubsidized rentals. Negative exits—such as eviction, lease violations, criminal activity, or abandoning the property—were those expected to be associated with adverse life events and poorer outcomes. Several exit reasons could not be clearly identified as positive or negative and were classified as neutral; for example, exit for health reasons or moving in with friends and family could be associated with a positive or negative trajectory, depending on the circumstances. A full list of exit reasons and their categories is in Appendix D. Deaths, voucher expiration, and moving to nonsubsidized rentals were among the top causes of exit for both PHAs. Most other common exit reasons fell into the neutral category for both PHAs, although KCHA also had two positive reasons—being over income and attaining homeownership—in its top 10.

Factors Associated with Exits from Housing Assistance

After adjusting for other factors, male gender, receiving a project-based voucher, homelessness within the previous 3 years, and having a behavioral health crisis event or emergency department (ED) visit were all associated with increased odds of exits of any type. Being older than age 25, increased time in assisted housing (6 or more years), having a large household size, having a single-caregiver household, and having a disability or chronic condition were associated with decreased odds of exit. Race or ethnicity and experiencing a hospitalization were not associated with exiting.

Among those who exited, some commonality existed between positive and negative exits compared with neutral exits. Male gender and longer time in assisted housing were positively associated with both positive and negative exits, whereas senior age (62 years or more) and receiving PBV assistance were negatively associated with both positive and negative exits.

Substantial differences also existed in factors associated with positive and negative exits. American Indian/Alaskan Native, Black, or Latinx people were more likely to have a negative exit compared with White individuals, and Asians were less likely to have a negative exit than White individuals. Heads of household who were single caregivers, had a disability, experienced a behavioral health crisis event, or had a recent ED visit were more likely to have a negative exit and less likely to have a positive exit than a neutral exit. Those who had recently been homeless were less likely to have a positive exit, but no difference existed between negative and neutral exits. Full details can be found in Chapter 5 and Appendix E.

Outcomes Following Exit

Four primary outcomes, occurring within 1 year following exit from housing assistance, were examined: xi

Exhibit 1: Key Outcomes

Outcome

Main Findings

Residential stability (becoming homeless or unstably One in four people with negative exits experienced housed, referred to as homelessness in this report)

homelessness within 1 year of exit, compared with 3

percent of those with a positive exit.

Physical health (ED visits, hospitalizations, and well-

Positive exits led to lower levels of ED visits

child checkups)

compared with negative exits or staying in housing

assistance.

Behavioral health (experiencing an acute crisis event) The biggest predictor of a post-exit behavioral health crisis was a pre-exit crisis. Even after adjusting for

prior crises, negative exits were associated with

double the risk of a post-exit crisis.

Wage income

Households with positive exits had $2,000 to $2,500

higher quarterly wages both before and after exit.

ED = emergency department.

Residential Stability

Among all 16,666 people who exited housing assistance, 2,682 (16.1 percent) experienced homelessness within 1 year of leaving, with a mean time to homelessness of 321 days. The risk of homelessness was not spread evenly across exit types; only 3.1 percent of people with positive exits had a homelessness event, compared with 14.5 percent for neutral exits and 25.4 percent for negative exits. After adjustment, people with positive exits were 82 percent less likely to experience homelessness than those with neutral exits, whereas people with negative exits were 74 percent more likely than those with neutral exits.

Physical Health

After adjustment, those with positive exits had 26-percent-lower odds of having one or more ED visits in the year following exit than those with negative exits. Neither positive exits nor neutral exits were significantly different from negative exits in terms of hospitalizations. No significant differences in well-child checkups were observed when comparing positive versus negative or neutral versus negative exits.

When the exit types were compared with people who continued receiving housing assistance, positive exits were again associated with 20-percent-lower odds of ED visits but were no different in terms of hospitalizations or well-child visits. Children exiting for neutral reasons had approximately 35-percent-lower odds of having a well-child checkup than children who remained. No significant differences in ED visits or hospitalizations existed between neutral exits and people remaining in assisted housing. Finally, people with negative exits had slightly higher but nonsignificant odds of one or more ED visits, were 26 percent more likely to be hospitalized, and were about 38 percent less likely to have a well-child visit than people who continued to receive housing assistance.

Behavioral Health

The proportion of people having one or more behavioral health crisis events in the 12 months following exit was 0.8 percent, 2.8 percent, and 3.5 percent for those with positive, neutral, and negative exits, respectively.

Among all study participants, a negative exit was associated with 110-percent-higher odds of a behavioral health crisis event in the year following exit compared with those with a neutral exit. However, no significant difference existed in the odds of a behavioral health crisis event between those with neutral and those with positive exits.

A similar trend was seen in the Medicaid subpopulation, in which, relative to those with neutral exits, those with xii

negative exits had 61-percent-higher odds of behavioral health crisis events in the year following exit, and no significant difference existed in the odds of a behavioral health crisis occurring among those with positive exits.

Wage Income

The relationship between exit type (positive or negative) and wages for the four quarters after the exit quarter was described. Wages for the four quarters before the exit quarter and during the exit quarter were also assessed to account for preexisting trends.

Wages at all time points varied substantially. Mean wages among positive exits were higher than those among negative exits across all time points. During the quarter of exit, those with positive exits had higher median wage earnings than those with negative exits ($7,763 versus $4,823), higher median work hours (480 versus 406), and higher median hourly wages ($18/hour versus $16/hour). Four quarters post exit, the mean wages among positive and negative exits were $8,495 and $6,146, respectively.

A model was fit predicting wages four quarters before exit, during the quarter of exit, and four quarters after exit. The model showed that, in the period before exit, wage increases were greater among positive exits, whereas after exiting, wage increases were greater among negative exits.

Conclusion

The results from the Housing and Urban Development Health, Economic, and Residential Stability (HUD HEARS) study show that realizing the goal of increased exits from housing assistance due to self-sufficiency has a way to go; positive exits made up only 13.5 percent of all nondeath exits in the study. The findings also reinforce the idea that the goal is a worthy one because negative and neutral exits were associated with worse outcomes than positive exits.

Linking data across sectors offers a way to comprehensively describe the experience of people receiving housing assistance. It also enables PHAs and HUD to understand the trajectories of the people they serve, all the way from the circumstances under which a person enters housing assistance through to their outcomes following exit from housing assistance. Results show that those circumstances are intertwined; previous homelessness, ED

visits, and behavioral health crises are associated with negative exits and are also more likely to occur after negative exits, even after adjusting for baseline events. The exact direction of causation is unclear and may be circular in nature. Holistic interventions that encompass health, economic, and housing elements will require collaborations between PHAs and social service and economic organizations that have mutual interests in the well-being of the populations served by PHAs.

Although the confluence of datasets used in this analysis is unique to the King County setting, the component datasets are either used nationally or have equivalents in other states. All MTW PHAs use the 50058 MTW form, HUD sets data standards for HMIS, and Medicaid claims look similar across states. Other jurisdictions will likely have wage and behavioral health service data that could be linked for an equivalent initiative. Data from other sectors, such as education and social services, would add to the completeness of data on the experience of a person receiving housing assistance.

Finally, future HUD work on exits and exit types should focus on the following:

• Consider how to build a standardized, comprehensive process for collecting exit information.

• Collect qualitative information about exit circumstances.

• Engage with current PHA housing recipients on linked data.

xiii

Chapter 1: Introduction

Housing affordability continues to be a significant challenge facing many American households. Nearly one-half of all renters are housing cost burdened, defined as spending 30 percent or more of income on housing costs (Martinez and Mather, 2022). Federal housing assistance, primarily in the form of housing choice vouchers (HCVs) or public housing (PH), reaches only 20 to 25 percent of eligible low-income households, leaving many people struggling to afford stable housing (Turner and Kingsley, 2008). One possible approach to ensuring that as many people as possible get assistance is to create pathways for people receiving housing assistance to become economically self-sufficient and no longer require housing support. To that end, in 2019, the U.S.

Department of Housing and Urban Development (HUD) set a goal of increasing the proportion of households that exit HUD-supported housing for positive reasons (e.g., homeownership) (HUD, 2019).

Understanding which tenants are likely to leave for positive or negative reasons can inform policies and programs that aim to encourage positive exits. Ensuring that a positive exit is likely to be beneficial to those exiting is also imperative. In addition, a full understanding of the consequences of exiting allows for the identification of interventions that might mitigate the negative impacts.

However, little is known about factors related to different types of exits from housing assistance, and outcomes that follow from exiting are even less well understood. In response to funding opportunity FR-6400-N-58

( Examining Long-Term Outcomes Following Exit From HUD-Assisted Housing), the Housing and Urban Development Health, Economic, and Residential Stability (HUD HEARS) Study sought to answer three key questions:

1. What constitutes a positive or negative exit from HUD-assisted housing?

2. What factors are associated with categories of exits (positive, neutral, negative)?

3. Is a positive exit from housing assistance associated with better post exit outcomes than for residents who left for negative reasons?

The project was a collaboration between Public Health—Seattle & King County (PHSKC), King County Housing Authority (KCHA), and Seattle Housing Authority (SHA). All three agencies have worked together for several years to bring housing and health data together to better understand the needs of housing assistance recipients in King County. Both SHA and KCHA are Moving to Work (MTW) public housing authorities (PHAs) that serve clients predominantly living in an urban or suburban setting, although King County also encompasses a large rural area. 1 Seattle and the surrounding area have experienced a huge increase in population over the past decade, growing at 2 to 3 times the national average of 7.4 percent from 2010 to 2020 (Office of Planning & Community Development, 2021). The population boom has been accompanied by a large increase in wealth, with the median income increasing from $60,000 in 2010 to $102,000 in 2019 in Seattle and from $66,000 to $102,000 in King County as a whole (not adjusted for inflation) (Public Health—Seattle & King County, 2022).

Both population and income changes have put pressure on the housing market, leading to average rent prices increasing by 43 percent from 2012 to 2017 (Regional Affordable Housing Task Force, 2019) This report documents the findings from the research and is organized in line with the study questions. Chapter

2 summarizes the literature to date on the topic of exits from housing assistance. Data sources and linkage methods used to answer the research questions are discussed in Chapter 3. Chapter 4 addresses the first 1 MTW PHAs have greater flexibility than other PHAs in how they use federal funding, with the idea that they will generate innovative ideas and programs that can be rolled out nationally.

1

question of how to place each exit reason into positive, neutral, and negative categories. The factors associated with exiting from housing and with each exit type are described in Chapter 5. Chapters 6–9.each focus on a different outcome following exit, covering homelessness, physical and behavioral health, and wages. Finally, the research and next steps for this work are summarized in Chapter 10. More details for each research question are provided in a series of appendixes.

2

Chapter 2: Literature Review Summary

Introduction

An exploratory review of the literature in response to funding opportunity FR-6400-N-58 revealed that no established consensus exists on factors related to exiting housing assistance and subsequent outcomes. The aim is to systematically summarize the existing literature relevant to housing exits and identify the gaps in knowledge that the Housing and Urban Development Health, Employment, and Residential Stability (HUD

HEARS) study could fill. Specifically, the review addressed the following questions:

1. What constitutes a positive or negative exit from housing?

2. What factors are associated with positive or negative exits?

3. What health, economic, or housing outcomes are associated with exiting housing assistance (for positive or negative reasons)?

Due to the nature of the topic, it was anticipated that relevant information on housing exits would be contained in the gray literature, including reports from housing authorities and presentations. This review, therefore, relied on searches in both published and gray literature. A full description of the methods used is in Appendix B.

Results

The searches in April 2021, across all sources, yielded 9,117 articles and reports, of which 1,936 were duplicates.

After screening titles and abstracts and adding in references found during a full-text review, 57 documents were selected for full-text review. Of those documents, 26 were deemed relevant to the HUD HEARS study questions.

A summary of the selected documents is in Appendix B.

3

Exhibit 2-1: Literature Review Search Results

9,117 documents

identified

1,936 duplicates

removed

7,181 documents

reviewed by title

and abstract

7,129 articles

deemed not

relevant

5 documents

57 documents

identified from

reviewed by full text

reference review

31 articles deemed

not relevant

26 documents

selected

Exit Types

Only seven studies described exit types, and just three attempted to categorize exits as positive or negative.

Several studies noted limitations in national databases regarding reasons for exits, which presents an opportunity for improved data collection efforts.

The literature did not reveal consistency in what was considered a positive exit; McInnis, Buron, and Popkin (2007) suggested marriage or higher income, Smith et al. (2014) used homeownership or higher income, and Rohe, Webb, and Frescoln (2016) defined a positive exit as moving to private-market housing. Similarly, negative exits were defined slightly differently. McInnis, Buron, and Popkin (2007) used the broadest definition and included breaking program rules, being evicted, being relocated from public housing and unable to move back, and rent and utility costs that were too high. Smith et al. (2014) included lease violations, evictions, and inability to lease up during the period in their definition of negative exits, whereas Rohe, Webb, and Frescoln (2016) defined them as failure to pay rent, violating lease terms, or moving without notice.

In their two studies of the HUD Veterans Affairs Supportive Housing (HUD-VASH) program, Montgomery and Cusack (2017) and Montgomery et al. (2017) listed several reasons why veterans had left the program, including accomplishing goals, being evicted, no longer interested in the program, and death, although those reasons were not explicitly categorized as positive or negative. In his evaluation of a Family Self-Sufficiency (FSS) 4

program, Anthony (2005) noted that people who completed the FSS program (and exited) had higher incomes than the comparison group, but he did not classify that as a positive reason for exit.

In their evaluation of the Welfare to Work program, Gubits, Khadduri, and Turnham (2009) noted that people who relinquished their voucher often did so inadvertently due to difficulty navigating the housing authority processes and rules, but the authors did not quantify the proportion who cited that reason. Studies that identified a breakdown of positive versus negative exits were not consistent in the proportion of each type.

Smith et al. (2014) found that approximately 53 percent of leavers did so for positive reasons, whereas McInnis, Buron, and Popkin (2007) noted only that around 20 percent have positive reasons. Montgomery and Cusack (2017) and Montgomery et al. (2017) found a proportion in between (33 to 42 percent had met the VASH

program goals). However, the difference in approach between Smith et al. and McInnis, Buron, and Popkin’s classifications is important to note. Smith et al. used a hierarchy of data sources to assign all leavers to a positive or negative reason, whereas McInnis, Buron, and Popkin allowed for unclassified exits. For that reason, both articles found a similar proportion of people who had a negative exit (46 percent for McInnis, Buron, and Popkin; 47 percent for Smith et al.)

Summary: Few studies have explicitly classified exit types or quantified the proportion who exit for positive or negative reasons. Where classifications have been made, slightly less than one-half of people receiving housing assistance exit for negative reasons, although assigning exits as positive or negative can be difficult.

Factors Associated with Exits

A majority (18) of the articles and reports examined factors associated with exits from housing. Several studies used panel data or HUD data systems, such as the Multifamily Tenant Characteristics System (MTCS) and Tenant Rental Assistance Certification System (TRACS), databases to explore the topic, typically using survival analysis methods (Ambrose, 2005; Cortes, Lam, and Fein, 2008; Dantzler and Rivera, 2019; Freeman, 2005; Geyer, Dastrup, and Finkel, 2019; Hungerford, 1996; Lubell, Shroder, and Steffen, 2003; McClure, 2018; Olsen et al., 2005). Those studies generally agreed that increased age, being female, being non-White, being disabled, and tighter rental markets were all associated with a lower likelihood of exiting from housing. Larger households were generally found to be more likely to exit, but evidence on the presence of children was mixed; Ambrose (2005) found increased exits for larger households but only for project-based vouchers, whereas Cortes, Lam, and Fein (2008) found decreased exits, especially if younger children were present. Geyer, Dastrup, and Finkel (2019) found that the introduction of small-area Fair Market Rents increased the probability of exit and shortened the median time to exit. Among VASH participants, women were more likely to still be housed after one year than men (Kasprow et al., 2000), but having a service-connected disability was associated with exiting (Montgomery et al., 2017).

Two studies used evaluations of FSS programs to look at exits. Anthony (2005) found that younger adults, single participants, those without children, those with a high school diploma, and those who acquired more skills during the training were more likely to succeed at the FSS program and exit housing assistance. Rohe, Webb, and Frescoln (2016) found that completion of the program had a small effect on positive exits. However, the sample sizes in both evaluations were small, and the specific nature of the FSS programs in question limits generalizability to the wider population receiving housing assistance.

5

Another group of studies examined who was at risk of eviction or lease violations. Among residents of a large affordable housing organization (Mercy Housing), increased age, being Asian (versus White), and living in senior or supported housing (versus family housing) were associated with a reduced risk of lease violation, whereas being female, Black, or other race (versus White), having a larger household, and increased income were associated with increased risk of a lease violation (Brisson and Covert, 2015). Due to the counterintuitive nature of the finding regarding income, Brisson and Covert (2015) conducted further analyses and found that an increase in stable benefits was associated with decreased risk of a lease violation, but increases in work income, variable benefits income, and other income were related to a slightly higher likelihood of experiencing a lease violation. Richter et al. (2021) explored who received an eviction order compared with an eviction filing.

Although most of their findings were for all landlords combined, public housing and nonprofit organizations were the landlords in more than a quarter of all filings, and an unknown number of residents with private landlords would have been receiving an HCV. The authors found that being male, being White (versus Black), having more children, and having had an eviction filing in the past were associated with an eviction order versus just a filing. Having an eviction filing by a public housing entity or nonprofit organization carried a lower risk of getting an eviction order, relative to a filing by a private entity. Among VASH participants, being male, older, having alcohol or drug disorders, and having used acute care were all associated with increased levels of eviction (Montgomery and Cusack, 2017).

Only one study examined differences between people with positive and negative exits (Smith et al., 2014). No difference existed between positive and negative exits in terms of age, gender, and household size. Those with positive exits were more likely to be married at the end of the study, have ever been married, and be Hispanic, and those with negative exits were more likely to be non-Hispanic Black. Those with positive exits were less likely to have ever been homeless, less likely to live in overcrowded housing, and less likely to have a high housing cost burden.

Summary: Certain demographic categories (younger age, male gender, White race, smaller household size) and economic and rental market conditions are associated with exiting housing assistance. However, very few studies looked at the relationship between demographic and economic factors and positive and negative exits.

Outcomes Following Exits

One-half of the selected studies examined outcomes in some way, although most compared the impact of housing assistance in general rather than the impact of positive or negative exits. Four studies considered the impact of receiving housing assistance as a child on future outcomes (Andersson et al., 2016; Aratani, 2010; Chetty, Hendren, and Katz, 2016; Newman and Harkness, 2002). More years receiving housing assistance as a child is associated with an increased likelihood of working, increased income, and reduced incarceration.

Evidence on high school completion and college attendance was more mixed, with Aratani (2010) finding no effect and Chetty, Hendren, and Katz (2016) finding a positive association only among younger children.

Findings were mixed for people who leave housing for any reason. They tended to have increased mobility and were more likely to experience homelessness than those who remain in housing assistance (Gubits, Khadduri, and Turnham, 2009; Kang, 2020; McInnis, Buron, and Popkin, 2007). Some studies found higher earnings among leavers but a more precarious financial position, possibly due to reduced levels of public assistance (Gubits, 6

Khadduri, and Turnham, 2009; McInnis, Buron, and Popkin, 2007). People who completed FSS programs tended to have higher incomes and reduced use of public assistance compared with those who did not complete the programs (Anthony, 2005; Rohe and Kleit, 1997).

Among the VASH population, most (more than 90 percent) did not return to U.S. Department of Veterans Affairs (VA) homeless programs in the observation period (Montgomery et al., 2017), but one study found that more than 40 percent experienced 1 or more days of homelessness within 4.5 years of being housed (O’Connell, Kasprow, and Rosenheck, 2008). The difference between those two outcomes may be explained by the first study’s lack of access to other data related to homelessness (e.g., the local Homeless Management Information System).

Evictions were associated with increased mobility, shelter use, school absenteeism, and reduced blood lead testing (Richter et al., 2021). People with negative exits in general were slightly more likely to feel safe in their neighborhood and less likely to say that the neighborhood had alcohol problems; loitering problems; or trash, graffiti, and abandoned buildings, compared with those who continued to receive housing assistance (Smith et al., 2014). Positive exits were associated with living in better housing and neighborhoods, better self-reported health, and reduced use of welfare (Smith et al., 2014).

Summary: Receiving housing assistance during childhood is associated with positive outcomes later in life.

People who exit housing for any reason tend to be in a more precarious position in terms of residential stability and income. Negative exits are associated with worse residential stability and health outcomes than positive exits and generally compare poorly to those who continue receiving housing assistance. Positive exits are associated with improved health and better housing situations.

Conclusions

The literature provides limited evidence regarding positive and negative exits from housing assistance. Very few studies have tried to define exit types, although several noted the need for improved data collection on the topic. Only one study comprehensively looked at exit types; that study was within the context of the Moving to Opportunity experiment, so it may not be generalizable to the wider population. The literature contains almost no information regarding the health of individuals following housing exits of any kind and limited data on the sorts of neighborhoods people move to and economic outcomes. Although several studies looked at outcomes following time in housing, not many comprehensively examined the short-term impact of leaving on health, economic factors, and residential stability.

7

Chapter 3: Data Sources and Linkage

Several administrative datasets were used to examine outcomes following exit across multiple domains:

• Public housing authority (PHA) demographic data primarily came from data collected on HUD Form 50058 Moving to Work, which collects data on households and individuals receiving federal housing assistance.

• Exit reasons were collected on a separate form and stored in a different data system but linked using the methods described below.

• Behavioral Health and Recovery Division (BHRD) service data that include mental health and substance use claims.

• Employment Security Department (ESD) wage data.

• Healthcare for the Homeless Network (HCHN) data.

• Homeless Management Information System (HMIS).

• Medicaid claims data.

Linking administrative data from other sectors leads to a better understanding of complex individual needs, provides insight into circumstances before exit, and offers an opportunity to assess outcomes after people exit housing assistance. Because administrative data are routinely collected, this approach has the potential to be more sustainable than one-off or project-driven data collection. Successful cross-sector data linkages related to housing have previously examined physical health, behavioral health, crime, and income (Actionable Intelligence for Social Policy, 2015; Albertson et al., 2020; Chetty, Hendren, and Katz, 2016; Ellen, Dragan, and Glied, 2020; Laurent et al., 2020). However, most examples are limited in that they only linked across one nonhousing sector or were one-off linkages of administrative data.

For this study, individuals were linked across datasets through a series of probabilistic and deterministic matches using a combination of Informatica and the RecordLinkage package in R. Full details for each data source and the linkage process are in Appendix C .

Of the 19,411 exit events recorded by the King County Housing Authority (KCHA) and Seattle Housing Authority (SHA), 19,008 (97.9 percent) were matched to HUD Form 50058 data for 36,170 individuals (Exhibit 3-1). KCHA exit reason data were incomplete before 2016, so KCHA exits were restricted to 2016 through 2018, whereas for SHA, exits from 2012 through 2018 were included. Most analyses were restricted to the study period, exits that led to a person leaving PHA support (as opposed to “false exits,” when a person transferred programs, joined a different household that was receiving support, or otherwise remained in the housing data within 12 months of the exit date), the most recent exit per person, nondeath exits, and complete demographics (Exhibit 3-1). After applying those restrictions, the basis for many analyses was 8,266 heads of households (1,118 [13.5 percent]

positive, 4,538 [54.9 percent] neutral, and 2,610 [31.6 percent] negative) and 16,301 individuals (17.8 percent positive, 49.0 percent neutral, 33.2 percent negative). Exceptions to those restrictions are noted in each chapter.

8

Exhibit 3-1: Number of People With Exits During the Study Period

KCHA = King County Housing Authority. SHA = Seattle Housing Authority.

Source: KCHA and SHA data

9

Chapter 4: Exits and Exit Types

The King County Housing Authority (KCHA) and Seattle Housing Authority (SHA) had already classified their exit reasons into positive, neutral, and negative categories. In consultation with both public housing authorities (PHAs), exit reasons were standardized minor modifications made to the categories. Positive exits consisted of reasons that were perceived as likely to be associated with self-sufficiency—for example, increased income, attaining homeownership, and moving to nonsubsidized rentals. Negative exits—such as eviction, lease violations, criminal activity, or abandoning the property—were those expected to be associated with adverse life events and poorer outcomes. Several exit reasons could not be clearly identified as positive or negative and were classified as neutral; for example, exit for health reasons or moving in with friends and family could be associated with a positive or negative trajectory, depending on the circumstances. A full list of exit reasons and their categories is in Appendix D. To gain a fuller sense of exit time trends, data presented in this chapter are based on all available years of data (2016 through 2020 for KCHA, 2012 through 2020 for SHA).

Deaths, voucher expiration, and moving to nonsubsidized rentals were among the top causes of exit for both PHAs (Exhibit 4-1 and Exhibit 4-2). Most other common exit reasons fell into the neutral category for both PHAs, although KCHA also had two positive reasons—being over income and homeownership—in its top 10.

Exhibit 4-1: Top 10 Reasons for Exits From King County Housing Authority

Exit Reason

Exit Category

N

Deceased

Neutral

467

Moved in w/Family/Friends

Neutral

372

Voucher Expired

Negative

322

Section 8 Over Income

Positive

192

Landlord Eviction

Negative

166

Moved to Nonsubsidized Rental

Positive

133

Section 8 Incoming Portability Move Out

Neutral

131

Client Would not Disclose Reason

Neutral

113

Client Location Unknown/Abandoned Unit

Negative

107

Homeownership

Positive

72

Source: King County Housing Authority data

10

Exhibit 4-2: Top 10 Reasons for Exits From Seattle Housing Authority

Exit Reason

Exit Category

N

Project-Based/Mod Rehab: Moved Out, Location Unknown

Neutral

1,746

Deceased

Neutral

1,485

Voluntary Self-Termination

Neutral

444

Health

Neutral

406

Project-Based/Mod Rehab: Moved to Hospital/Assisted Living

Neutral

316

Moved to Nonsubsidized Rental

Positive

286

Project-Based/Mod Rehab: Moved to Nontime-Limited Subsidized

Neutral

251

Housing

Voucher Expired

Negative

243

Other

Neutral

231

Client Would Not Disclose Reason

Neutral

167

Source: Seattle Housing Authority data

In any given year, approximately 4 to 5 percent of each PHA’s residents exited, although the proportion was lower in 2020 due to the COVID-19 pandemic (Exhibit 4-3). At KCHA, the proportion of exits for positive reasons increased over time, and the proportion for negative reasons decreased, regardless of whether or not deaths (neutral) were included (Exhibit 4-4 and Exhibit 4-5). For SHA, the proportion of positive exits increased slightly over time, but the proportion of negative exits did not change.

11

Exhibit 4-3: Number of Exits by Public Housing Authority and Year

KCHA = King County Housing Authority. SHA = Seattle Housing Authority.

Note: KCHA exit data are incomplete before October 2015.

Source: KCHA and SHA data

12

Exhibit 4-4: Exit Categories by Public Housing Authority and Year (All Exits)

KCHA = King County Housing Authority. SHA = Seattle Housing Authority.

Note: KCHA exit data are incomplete before October 2015.

Source: KCHA and SHA data

13

Exhibit 4-5: Exit Categories by Public Housing Authority and Year (Excluding Deaths)

KCHA = King County Housing Authority. SHA = Seattle Housing Authority.

Note: KCHA exit data are incomplete before October 2015.

Source: KCHA and SHA data

14

Chapter 5: Who Exits From Housing Assistance?

The linked data described in the previous chapters was used to determine the factors associated with exiting from housing assistance in general and each exit type. Exits from King County Housing Authority (KCHA) (2016

through 2018) and Seattle Housing Authority (SHA) (2012 through 2018), were examined, using heads of household as the unit of analysis. For the analysis of exiting versus remaining, each exiting person was randomly matched to four controls who remained in housing for at least 12 months past the exit date, and a binomial logistic regression model was run. For the exit type analysis, neutral exits were set as the reference category as part of a multinomial logistic regression. A subanalysis of Medicaid enrollees was conducted to look at the relationship between health events and exiting. Full details of the data variables and methods are in Appendix E.

After applying the inclusion criteria noted in Chapter 3 and limiting to heads of household, 8,266 exits (2,610

negative, 4,538 neutral, and 1,118 positive) and 25,162 nonexiting controls were analyzed in the regression analysis. Demographics for each group are in Exhibit 5-1. The secondary analysis of public housing authority (PHA) recipients who also had full, nondual Medicaid coverage before exit was limited to 3,001 households. A comparison of demographics for people who are included in the Medicaid analysis versus not is in Appendix E

(Exhibit E-1) (this table is not restricted to those younger than age 62 to allow for age group comparisons).

15

Exhibit 5-1: Demographics of Heads of Household Who Exited Versus Controls Who Did Not and by Exit Type Demographic

Remained

Exited

Neutral Exit Positive Exit Negative Exit

(N=25,162)

(N=8,266) (N=4,538)

(N=1,118)

(N=2,610)

Age

Mean (years)

52.4

50.7

53.2

48.9

47.2

Median (years)

52

49

52

47

45

Senior (age 62+)

29.9%

26.9%

33.3%

21.0%

18.3%

Gender

Another Gender

353 (1.4%)

97 (1.2%)

48 (1.1%)

17 (1.5%)

32 (1.2%)

Female

16,117 (64.1%)

4,869 (58.9%) 2,628 (57.9%)

678 (60.6%)

1,563 (59.9%)

Male

8,692 (34.5%)

3,300 (39.9%) 1,862 (41%)

423 (37.8%)

1,015 (38.9%)

Race/Ethnicity

AI/AN

329 (1.3%)

158 (1.9%)

81 (1.8%)

<20

65 (2.5%)

Asian

2,464 (9.8%)

689 (8.3%)

421 (9.3%)

118 (10.6%)

150 (5.7%)

Black

8,558 (34%)

2,866 (34.7%) 1,413 (31.1%)

437 (39.1%)

1,016 (38.9%)

Latinx

1,684 (6.7%)

561 (6.8%)

299 (6.6%)

72 (6.4%)

190 (7.3%)

Multiple

2,530 (10.1%)

737 (8.9%)

367 (8.1%)

114 (10.2%)

256 (9.8%)

NH/PI

203 (0.8%)

67 (0.8%)

34 (0.7%)

<10

25 (1%)

White

9,394 (37.3%)

3,188 (38.6%) 1,923 (42.4%)

357 (31.9%)

908 (34.8%)

Time in Housing

Mean Time (years)

5.9

5

4.5

6.2

5.6

Median Time (years)

5.5

3.7

3

5.6

4.5

Household Characteristics

Head of Household w/Disability

44.3%

42.0%

45.4%

25.2%

43.3%

Mean Household Size

2.2

2

1.7

2.6

2.1

Median Household Size

1

1

1

2

1

Single Caregiver

19.0%

17.3%

15.0%

14.5%

22.6%

Program Type

PBV

4,672 (18.6%)

3,586 (43.4%) 2,761 (60.8%)

308 (27.5%)

517 (19.8%)

PH

7,118 (28.3%)

1,840 (22.3%) 912 (20.1%)

331 (29.6%)

597 (22.9%)

TBV

13,372 (53.1%)

2,840 (34.4%) 865 (19.1%)

479 (42.8%)

1,496 (57.3%)

16

Demographic

Remained

Exited

Neutral Exit Positive Exit Negative Exit

(N=25,162)

(N=8,266) (N=4,538)

(N=1,118)

(N=2,610)

Health and Homelessness Events

Experienced Recent Homelessness

5,726 (22.8%)

3,256 (39.4%) 1,972 (43.5%)

226 (20.2%)

1,058 (40.5%)

Experienced 1+ Behavioral Health Crisis

Events in Year Before Exit (excl. Medicaid 408 (1.6%)

570 (6.9%)

339 (7.5%)

18 (1.6%)

213 (8.2%)

ED visits)

Experienced 1+ Behavioral Health Crisis

Events in Year Before Exit (inc. ED visits)1

313 (0.9%)

240 (2.8%)

122 (8.0%)

<10

82 (7.2%)

Average # ED Visits in Year Before Exit1

0.8

1

2

0.8

2.1

Experienced 1+ ED Visits in Year Before

Exit1

13,435 (36.6%)

3,381 (40.0%) 862 (56.6%)

118 (34.7%)

689 (60.5%)

Average # Hospitalizations in Year Before

Exit (per 100 people)1

6.1

7.8

17.5

6.8

15.4

Experienced 1+ Hospitalizations in Year

Before Exit1

1,657 (4.5%)

440 (5.2%)

175 (11.5%)

19 (5.6%)

107 (9.4%)

Average # of Chronic Conditions1

1

0.9

1.8

1.5

2

AI/AN = American Indian/Alaska Native. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

1 Note: Some health event data available for those age <62 enrolled in Medicaid (Remained: N=36,737; Exited: N=8,448; Neutral: N=1,522; Positive: N=340; Negative: N=1,139).

Source: NO HARMS integrated study dataset

17

Descriptive Statistics

Heads of household who exited for any reason were more likely than those who remained in housing to have the following attributes (Exhibit 5-1):

• Male (39.9 percent versus 34.5 percent).

• Shorter average tenure in housing assistance (median of 3.7 versus 5.5 years).

• Receiving project-based voucher (PBV) assistance (43.4 percent versus 18.6 percent).

• Experienced recent homelessness (39.4 percent versus 22.8 percent).

• Have had a recent behavioral health crisis (6.9 percent versus 1.6 percent).

Race, household size, having a single caregiver, or having a head of household with a disability did not substantially vary between those exiting and those remaining in housing. In the secondary analysis of Medicaid recipients, people exiting had greater healthcare use in the year before exit for both emergency department (ED) visits (55.6 percent had one or more visits versus 46.9 percent for people remaining) and hospitalizations (10.0 percent versus 8.8 percent).

When comparing exits by type, those with a neutral exit tended to be older than those with positive or negative exits (median age 52 years compared with 47 and 45 years, respectively), were slightly more likely to be male (41.0 percent versus 37.8 and 38.9 percent), were more likely to be White (42.4 percent versus 31.9 and 34.8

percent), and had shorter average tenure in housing assistance (median of 3 years versus 5.6 and 4.5 years) (Exhibit 5-1). Those with a positive exit had larger average household sizes (mean of 2.6 versus 1.7 and 2.1 for neutral and negative exits, respectively), were more likely to be living in public housing (29.6 percent versus 20.1

and 22.9 percent) and were less likely to have experienced recent homelessness (20.2 percent versus 43.5 and 40.5 percent) or a behavioral crisis (1.6 percent versus 7.5 and 8.2 percent). Among Medicaid recipients, those with positive exits had lower levels of recent ED visits (34.7 percent versus 56.6 and 60.5 percent), hospitalizations (5.6 percent versus 11.5 and 9.4 percent), and chronic conditions at the time of exit (average of 1.5 versus 1.8 and 2.0)

Regression Results

After adjusting for other factors, male gender, receiving a project-based voucher, homelessness within the previous 3 years, and having a behavioral health crisis event were all associated with increased odds of exits of any type (Exhibit 5-2). Being older than age 25, increased time in housing (6+ years), larger household size, having a single-caregiver household, and having a disability were all associated with decreased odds of exit.

Race/ethnicity was not associated with exiting. For the secondary analysis of housing recipients who also had 7+

months of full Medicaid coverage in the year before exit, experiencing one or more ED visits in the year before exit was positively associated with exit (adjusted odds ratio [aOR]: 1.27, 95-percent confidence interval [CI]: 1.16–1.40, p< 0.001), experiencing a hospitalization in the same timeframe was not associated with exit, and having two or more chronic conditions was negatively associated with exits (0.75, 95-percent CI: 0.68–0.83, p<0.001) and Exhibit E-3).

18

Exhibit 5-2: Regression Output for Heads of Household Who Exited Versus Controls Who Did Not Demographic

Odds Ratio

95% CI

Age

<25

Ref

—

25–44

0.67***

0.58–0.78

45–61

0.48***

0.41–0.55

62+

0.50***

0.43–0.58

Gender

Female

Ref

—

Male

1.08**

1.02–1.15

Multiple

0.96

0.76–1.21

Race/Ethnicity

White

Ref

—

AI/AN

1.25*

1.01–1.53

Asian

0.92

0.83–1.01

Black

1.06

1.00–1.13

Latinx

0.97

0.87–1.09

Multiple

1.00

0.90–1.10

NH/PI

1.10

0.81–1.47

Time in Housing

<3

Ref

—

3–5.99

1.15***

1.07–1.23

6–9.99

0.95

0.89–1.03

10+

1.16***

1.07–1.26

Household Characteristics

Head of Household w/Disability

0.70***

0.66–0.75

Household Size

0.90***

0.89–0.92

Single Caregiver

0.76***

0.70–0.82

Program Type

TBV

Ref

—

PBV

2.94***

2.75–3.14

PH

1.20***

1.12–1.29

19

Demographic

Odds Ratio

95% CI

Health and Homelessness Events

Experienced Recent Homelessness

1.41***

1.32–1.51

Experienced 1+ Behavioral Health Crisis Event in Year Before Exit (excl. ED visits)

2.91***

2.53–3.35

Experienced 1+ Behavioral Health Crisis Event in Year Before Exit (incl. ED visits)a

2.12***

1.69–2.66

Experienced 1+ ED Visit in Year Before Exita

1.27***

1.16–1.40

Experienced 1+ Hospitalization in Year Before Exita

0.96

0.82–1.12

2+ Chronic Conditionsa

0.75***

0.68–0.83

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

AI/AN = American Indian/Alaska Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander.

PBV = project-based voucher. PH = public housing. Ref = Reference group. TBV = tenant-based voucher.

a Some health event data available only for those age <62 enrolled in Medicaid (N = 9,234 for controls, 3,001 for exits).

Source: NO HARMS integrated study dataset

20

Exhibit 5-3: Regression Output for Heads of Household by Exit Type

Negative/Positive Exits vs. Neutral Exits

(neutral N=4,538)

Negative Exits (N=2,610)

Positive Exits (N=1,118)

Demographic

Odds

Ratio

95% CI

Odds Ratio

95% CI

Age

<25

ref

—

Ref

—

25–44

1.02

0.78–1.33

1.43

0.95–2.16

45–61

0.87

0.66–1.15

1.43

0.94–2.17

62+

0.43***

0.32–0.58

0.59*

0.38–0.91

Gender

Female

ref

—

Ref

—

Male

1.33***

1.18–1.51

1.34***

1.14–1.56

Multiple

1.00

0.61–1.64

1.16

0.64–2.11

Race/Ethnicity

White

ref

—

Ref

—

AI/AN

1.86**

1.26–2.74

0.92

0.49–1.76

Asian

0.80

0.64–1.01

0.99

0.77–1.27

Black

1.25***

1.10–1.43

1.20*

1.01–1.43

Latinx

1.30*

1.03–1.63

1.13

0.84–1.52

Multiple

1.10

0.90–1.35

1.14

0.87–1.48

NH/PI

1.27

0.69–2.32

0.85

0.37–1.94

Time in Housing (years)

<3

ref

—

Ref

—

3–5.99

1.18*

1.01–1.37

1.28*

1.05–1.56

6–9.99

1.14

0.97–1.34

1.36**

1.11–1.68

10+

1.20*

1.00–1.43

1.54***

1.24–1.92

Household Characteristics

Head of Household w/Disability

1.03

0.90–1.17

0.53***

0.45–0.63

Household Size

0.98

0.94–1.02

1.11***

1.06–1.16

Single Caregiver

1.33***

1.12–1.57

0.62***

0.50–0.77

21

Negative/Positive Exits vs. Neutral Exits

(neutral N=4,538)

Negative Exits (N=2,610)

Positive Exits (N=1,118)

Demographic

Odds

Ratio

95% CI

Odds Ratio

95% CI

Program Type

TBV

ref

—

Ref

—

PBV

0.07***

0.06–0.09

0.31***

0.26–0.38

PH

0.45***

0.39–0.52

0.86

0.71–1.03

Health and Homelessness Events

Experienced Recent Homelessness

1.76***

1.53–2.03

0.63***

0.52–0.76

Experienced 1+ Behavioral Health Crisis

Event in Year Before Exit (excl. ED visits)

1.68***

1.36–2.08

0.43***

0.26–0.71

Experienced 1+ Behavioral Health Crisis

Event in Year Before Exit (incl. ED visits)a

1.50*

1.06–2.12

0.70

0.31–1.56

Experienced 1+ ED Visit in Year Before Exita 1.30**

1.08–1.58

0.62***

0.47–0.82

Experienced 1+ Hospitalization in Year

Before Exita

0.79

0.59–1.06

0.74

0.44–1.26

2+ Chronic Conditionsa

0.91

0.75–1.11

0.96

0.72–1.29

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

AI/AN = American Indian/Alaska Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander.

PBV = project-based voucher. PH = public housing. Ref = Reference group. TBV = tenant-based voucher.

aHealth event data available only for those age <62 enrolled in Medicaid (N = 1,522/1,139/340 for neutral/negative/positive exits).

Source: NO HARMS integrated study dataset

Among those who exited, some commonality was present between positive and negative exits as compared with neutral exits. Male gender and longer time in housing were positively associated with both positive and negative exits, whereas senior age (62+) and receiving PBV assistance were negatively associated with both positive and negative exits (Exhibit 5-3). Why those factors have similar associations for both positive and negative exits is unclear, and a deeper analysis of specific exit reasons may yield a better understanding of this finding.

Substantial differences emerged in factors associated with positive and negative exits. American Indian/Alaska Natives, Black, or Latinx were more likely to have a negative exit compared with Whites, and Asians were less likely to have a negative exit. The reasons for differences by race/ethnicity are unclear. The study does not assess the systemic factors disproportionately affecting certain racial and ethnic groups to determine sufficient causation links. Race/ethnicity may be a proxy for additional factors that were not included in the model. In the literature review (Chapter 2), one 2014 study (Smith et al.) found disproportionate negative exits for non-Hispanic Black households, with a greater share of these participants reporting problems with landlords as the 22

reason for exit. Heads of household in single-caregiver households and heads of household who had a disability, experienced a behavioral health crisis event, or had a recent ED visit were more likely to have a negative exit and less likely to have a positive exit when compared with neutral exits. Those associations suggest that single caregivers or those with health problems face barriers to working and may experience other obstacles to stable housing. Those with recent homelessness were less likely to have a positive exit, but no difference arose between negative and neutral exits.

23

Chapter 6: Outcomes Following Exit: Residential Stability

Residential stability was measured by the time to experiencing homelessness or unstable housing in the year following exit from housing assistance. Because administrative data sources do not always perfectly capture dates of events, individuals with a date of homelessness within 30 days before the exit date from housing assistance were counted as having a time to homelessness of zero days.

This analysis built upon existing work at King County that uses multiple sources in the Integrated Data Hub (IDH) to reduce undercounting of housing instability (Johnson, McHugh, and Reimal, 2021), using data from the Homeless Management Information System (HMIS), King County Behavioral Health and Recovery Division (BHRD), King County Healthcare for the Homeless Network (HCHN), and people in the Medicaid data who listed their address as “homeless.”

To compare exit types, a Cox proportional hazards model was fit to the data, with time to homelessness within 1

year of exiting housing as the outcome and exit type (positive, negative, or neutral) as the independent variable.

The model was adjusted for several demographic variables using propensity scores, which is a method used to balance comparison groups. To identify the exit factors with the greatest influence on the results, a leave-one-out analyses was conducted in which each exit factor with at least 100 exits was removed in turn and the model was rerun. Full details are in Appendix F.

Among all 16,666 people who exited housing assistance, 2,682 (16.1 percent) experienced homelessness within 1 year of leaving, with a mean time to homelessness of 321 days (Exhibit 6-1). The risk of homelessness was not spread evenly across exit types; only 3.1 percent of people with positive exits had a homelessness event, compared with 14.5 percent for neutral exits and 25.4 percent for negative exits. After adjustment for demographic variables, people with positive exits were 82 percent less likely to experience homelessness than those with neutral exits, and people with negative exits were 74 percent more likely than those with neutral exits.

In the assessment of influential exit reasons, landlord evictions appeared to be the most influential form of negative exit; when this reason was removed, negative and neutral exits looked more similar. Conversely, when

“PB/MR [project-based/Mod Rehab] moved out, location unknown,” which was classified as neutral, was removed, the hazard ratio between negative and neutral increased from 1.74 to 3.24, indicating that this reason is actually negative (Exhibit F-1). When “PB/MR moved out, location unknown” was removed from the positive versus neutral comparison, the two groups looked more similar, reinforcing the idea that this category is negative. The most influential positive reason was “PB/MR moved to nontime-limited market rate,” although none of the positive reasons significantly altered the overall result (Exhibit F-2).

24

Exhibit 6-1: Kaplan-Meier Curves of Time to Homelessness by Exit Type

Source: NO HARMS integrated study dataset

25

Chapter 7: Outcomes Following Exit: Physical Health

Medicaid data was used to look at three health outcomes in the year following exit from housing assistance: (1) all-cause emergency department (ED) visits, (2) all-cause hospitalizations, and (3) well-child checkups. Positive exits were expected to be associated with lower levels of ED visits and hospitalizations and greater likelihood of well-child checkups compared with both neutral and negative exits. For well-child checkups, it was hypothesized that a history of previous preventive visits would mitigate some of the impacts of a negative exit. To examine this theory, results were separated by 1+ well-child visits in the year before exit versus no visits.

In addition to the inclusion criteria noted in Chapter 3, the following restrictions were added:

• Medicaid coverage (enrolled in a program that offers full benefits, nondual [i.e., not also enrolled in Medicare], and not concurrently enrolled in other health insurance programs) for at least 7 of the 12

months before and following exit. The 7-month threshold ensures that healthcare events in the claims data would be detected (Washington State Health Care Authority, 2022).

• For ED visits and hospitalizations, the analysis was restricted to ages <62 because this is the cut point for senior housing at the public housing authorities (PHAs), and most people older than 65 are also enrolled in Medicare. Therefore, this would not provide a complete picture of their healthcare use.

• The well-child analysis was restricted to children ages <6 years because this is the age at which at least one visit per year is recommended (Washington State Health Care Authority, 2020).

To account for confounding, models were adjusted for the following variables: gender, age, race/ethnicity, head of household with a self-reported disability, length of time in housing, housing assistance type, household size, and single caregiver (one adult and one or more children in the household). The ED visit and hospitalization analyses were also adjusted for baseline health, as measured by 1+ ED visits or hospitalizations in the year before exit and 2+ chronic conditions. Details of the groups used for each variable are in Appendix G.

For all models, a multinomial logistic regression, with negative exits as the reference group and generalized estimating equations to account for clustering at the household level was used. To evaluate whether the act of moving was detrimental to health, the analysis was repeated, comparing each exit type to randomly selected controls who remained in housing for 12 months following the matched exit date (and met all other criteria).

After applying the Medicaid inclusion requirements to the 16,301 exits in Exhibit 3-1, there were 5,550 exits (2,205 negative, 2,346 neutral, and 999 positive). The secondary analysis included 34,039 nonexiting controls, and the analysis of well-child outcomes included 316 negative exits, 408 neutral exits, 150 positive exits, and 5,823 nonexiting controls.

After adjustment, those with positive exits had 26-percent lower odds (95-percent confidence interval [CI]: 6–

39-percent lower, p < 0.01) of having one or more ED visits in the year following exit than those with negative exits (Exhibit 7-1). Neither positive exits nor neutral exits were significantly different from negative exits in terms of hospitalizations. No significant differences were observed in well-child checkups when comparing positive versus negative or neutral versus negative exits across either stratum of previous visit history.

When exit types were compared with those who remained receiving housing assistance, positive exits were again associated with lower odds of ED visits (adjusted odds ratio [aOR]: 0.80, 95-percent CI: 0.69–0.94, p < 0.01) but were no different in terms of hospitalizations or well-child visits (Exhibit 7-2). Children exiting for neutral reasons had approximately 35-percent lower odds of having a well-child checkup than children who remained, regardless of whether they had completed a well-child checkup in the previous year. No significant differences 26

appeared in ED visits or hospitalizations between neutral exits and people remaining. Finally, people with negative exits had slightly higher but nonsignificant odds of one or more ED visits (aOR: 1.10, 95-percent CI: 1.00–1.21, p = 0.054) and were more likely to be hospitalized (aOR: 1.26, 95-percent CI: 1.03–1.55, p < 0.05) than people who continued to receive housing assistance. Both those with and without previous well-child visits had 33-percent and 43-percent lower odds, respectively, of having a well-child visit following exit than those continuing to receive housing assistance (95-percent CI: 10–51-percent lower odds, p < 0.01 and 95-percent CI: 13–62-percent lower odds, p < 0.01, respectively).

Exhibit 7-1: Regression Results for Health Outcomes by Exit Type

ED = emergency department.

Source: NO HARMS integrated study dataset

27

Exhibit 7-2: Regression Results for Health Outcomes Comparing Exit Types With People Remaining in Housing Assistance ED = emergency department.

Source: NO HARMS integrated study dataset

28

Chapter 8: Outcomes Following Exit: Behavioral Health

This analysis examined whether the nature of public housing authority (PHA) exits is associated with acute behavioral health crisis events in the year following exit, using linked data described in Chapter 3, Medicaid data described in Chapter 7, and service delivery data from the King County Behavioral Health and Recovery Division (BHRD). It was hypothesized that, relative to neutral exits, positive exits would be associated with a lower risk of behavioral health crisis events in the year following exit, whereas negative exits would be associated with a higher risk of behavioral health crisis events.

All individuals who exited housing assistance with King County Housing Authority (KCHA) from 2016 through 2018 and Seattle Housing Authority (SHA) from 2012 through 2018 and who had all available covariate information were included. Exit type was categorized as neutral, positive, or negative, as described in Chapter 4.

Behavioral health crisis events included acute behavioral health services provided by the Department of Community and Human Services (DCHS) via the King County BHRD and court-ordered mental health treatment required by the Washington State Involuntary Treatment Act. In a secondary analysis, the study population was further limited to individuals <62 years of age and who had full Medicaid coverage for 7 of the 12 calendar months before and 7 of the 12 calendar months after the date of exit from housing. In the Medicaid subset, the outcomes described above were examined, with the addition of emergency department visits due to behavioral health events. See Appendix H for more information.

Confounding variables were selected a priori and reflected participant characteristics at the time of exit. They included gender (male, female, both genders reported at different points in time), age at exit, 1+ behavioral health crisis events in the 12 months before exit, time in housing, single-caregiver household (single adult with 1+ children), household size, race/ethnicity (American Indian/Alaska Native, Asian, Black, Latino, multiple races, Native Hawaiian/Pacific Islander, and White), type of assistance (project-based vouchers, public housing, or tenant-based vouchers), and head of household with a disability. In the Medicaid subset, there was also adjusted for history of treatment for behavioral health conditions (listed in Appendix H). Treatment for behavioral health conditions was based on algorithms applied to the Chronic Conditions Data Warehouse, which uses diagnoses in claims data to identify chronic health conditions (Centers for Medicare and Medicaid Services, 2022).

Summary statistics by exit type for all participants and those in the Medicaid subset were calculated. Next, multivariable logistic regression models, with neutral exit as the referent category, were used. Analyses were repeated for the Medicaid subset. Generalized estimating equations were used to account for clustering at the household level.

The sample included 16,301 participants for whom full covariate data were available; full demographic characteristics, by exit type, can be found in Exhibit E-2. The proportion having one or more behavioral health crisis events in the 12 months following exit was 0.8 percent, 2.8 percent, and 3.5 percent for those with positive, neutral, and negative exits, respectively (Exhibit 8-1). The Medicaid subset included 5,550 participants, of whom 5.0 percent, 13.9 percent, and 15.2 percent had at least one crisis event among those with positive, neutral, and negative exits, respectively.

Results of logistic regression models are shown in Exhibit H-1. Among all study participants, a negative exit was associated with 110-percent higher odds (95-percent confidence interval [CI]: 1.64–2.69, p<0.001) of a behavioral health crisis event in the year following exit, compared with those with a neutral exit type. However, no significant difference existed in the odds of a behavioral health crisis event between those with neutral and positive exits 29

(adjusted odds ratio [aOR]: 0.95, 95-percent CI: 0.60–1.49). A similar trend was seen in the Medicaid subset, in which, relative to those with neutral exits, those with negative exits had 61-percent higher odds (95-percent CI: 1.29–2.00) of behavioral health crisis events in the year following exit, and no significant difference was present in the odds of a behavioral health crisis among those with positive exits (aOR=0.90, 95-percent CI: 0.62–1.30).

Exhibit 8-1: Behavioral Health Crisis Events, by Exit Type

Category

Positive Neutral

Negative

Crisis Events

n

2,902

7,984

5,415

Proportion With 1+ Crisis Event

0.8%

2.8%

3.5%

Mean Number Crisis Events (Per 100)

3.2

7

9.3

Median Number Events

0

0

0

Range of Crisis Event Numbers

0–32

0–30

0–27

Crisis Events (Medicaid Subpopulation)a

n

999

2,346

2,205

Proportion With 1+ Crisis Event

5.0%

13.9%

15.2%

Mean Number Crisis Events (Per 100)

14.9

59.6

54.7

Median Number Events

0

0

0

Range of Crisis Event Numbers

0–32

0–63

0–49

aIncludes behavioral-health-related emergency department visits not captured in the full analysis.

Source: NO HARMS integrated study dataset

30

Chapter 9: Outcomes Following Exit: Economic

The relationship between exit type (positive or negative) and wages for the four quarters after the exit quarter was described. Wages four quarters before the exit quarter and during the exit quarter were also assessed to account for preexisting trends. The data were limited to exits between January 1, 2016, and January 1, 2018, to households with wage earners between 18 and 61 years of age at the time of exit and to households with more than 1 year of tenure in housing assistance.

Summary statistics are reported with statistical significance defined by a p-value less than 0.05 for a Kruskal-Wallis (continuous variables) or chi-square (categorical variables) test. A multilevel/hierarchical regression model was created for the relationship of exit type with wage earnings over time. A model of the relationship of exit type and percentage of Area Median Income (AMI) over time was created as a secondary analysis.

The analysis included 1,355 individuals (positive = 680, negative = 675) in 954 households. When the proportions of positive and negative exits were compared, Asians (11.9 percent versus 7.3 percent) were overrepresented, and Black individuals (43.4 percent versus 49.2 percent) were underrepresented among positive exits (Exhibit

I-1). During the quarter of exit, those with positive exits had higher median wage earnings ($7,763 versus $4,823), higher median work hours (480 versus 406), and higher median hourly wages ($18/hour versus $16/hour). Positive exits were more likely to occur in the spring and summer and to have received housing assistance for more years (mean 9 years versus 7 years). Positive exits were less likely to have a head of household with a disability (10.4 percent versus 16.6 percent) and to live in single-caregiver households (9.0

percent versus 26.1 percent). Positive exits also had a higher mean percent AMI (34 percent versus 29 percent).

Finally, regarding program type, tenant-based vouchers (TBVs) were more common among negative exits (73.4

percent versus 65.6 percent), whereas project-based vouchers (PBVs) (17.5 percent versus 13.6 percent) and public housing (PH) (16.9 percent versus 12.9 percent) were more common among positive exits.

Substantial variance in wages was present at all points in time, and the mean wages among positive exits were higher than those among negative exits four quarters before exit, during the quarter of exit, and four quarters post exit (Exhibit 9-1). Four quarters post exit, the mean wages among positive and negative exits were $8,495

and $6,146, respectively.

31

Exhibit 9-1: Observed Quarterly Wages for Those Who Exited Seattle and King County Public Housing Authority Programs Between January 1, 2016, and January 1, 2018

Note: The black points and error bars are the mean and 95-percent confidence interval, respectively.

Source: NO HARMS integrated study dataset

A model was created predicting wages four quarters before exit, during the quarter of exit, and four quarters after exit (Exhibit I-2). It performed well based on a scatterplot of the observed versus predicted wages (not shown), a plot of residuals over time (Exhibit I-5), and a comparison of the mean quarterly observed values to the mean quarterly predicted values (Exhibit I-3). A plot of the mean predicted values by quarter and exit type shows that, in the period before exit, wage increases were greater among positive exits, whereas after exiting, wage increases were greater among negative exits (Exhibit 9-2). The secondary model of exit type and percentage of AMI demonstrated a similar pattern (Exhibit I-4 and Exhibit I-6).

32

Exhibit 9-2: Predicted Quarterly Wages by Exit Type Show Faster Wage Growth for Positive Exits Before Exiting and Faster Wage Growth for Negative Exits After Exiting

Source: NO HARMS integrated study dataset

33

Chapter 10: Conclusion

Linking data across sectors offers a way to comprehensively describe the experience of people receiving housing assistance. It also enables public housing authorities (PHAs) and HUD to understand the trajectories of the people they serve all the way from the circumstances under which they enter housing assistance through to their outcomes following exit from housing.

The Housing and Urban Development Health, Economic, and Residential Stability (HUD HEARS) study has shown that who exits from housing assistance was not random. Males, those on project-based vouchers (PBVs), the recently homeless, and people who experienced a behavioral health crisis event or emergency department (ED) visit were more likely to exit. The type of exit is also strongly associated with a range of factors, only some of which are readily identifiable in PHA data. Heads of household in single-caregiver households, heads of household who had a disability, and heads of household who had experienced a behavioral health crisis event or a recent ED visit were more likely to have a negative exit and less likely to have a positive exit, when compared with neutral exits. Conversely, larger household size was associated with positive exits but not negative exits.

The type of exit from housing assistance matters:

• About one in four people who exit for negative reasons experience homelessness or unstable housing in the year following exit, compared with 1 in 32 for positive exits.

• People with positive exits are less likely to have an ED visit than those with negative or neutral exits.

• Those with negative exits are 74 percent more likely to experience a behavioral health crisis than those with neutral exits.

• Positive exits are associated with higher household income, although the gap between positive and negative exits narrows following exit from housing assistance.

Policy and Program Implications

The results from HUD HEARS show that realizing the goal of increased exits from housing assistance due to self-sufficiency has a way to go; positive exits made up only 13.5 percent of all nondeath exits in the study. The findings also reinforce the idea that the goal is a worthy one because negative and neutral exits were associated with worse outcomes than positive exits.

For PHAs that are working to increase positive exits while minimizing negative exits, the findings present some challenges. First, some factors such as gender and type of housing assistance had the same associations for both positive and negative exit types (compared with neutral). Second, some factors are generally fixed (e.g., date of birth, gender, race/ethnicity), and targeting services based solely on those factors would likely violate fair housing and related civil rights laws. Further investigation into why those characteristics are associated with exits and exit types could help services to be adapted accordingly.

Where a more specific association between factors and exit type emerged, policy and program implications are clearer. Although not statistically significant, the relationship between being of working age and positive exits suggests that an emphasis on workforce training and other self-sufficiency programs may be warranted. A longer tenure in housing assistance was associated with increased odds of a positive exit, which suggests that a stabilization period is required before households can get themselves to a position where positive exits are more possible. PHAs may wish to investigate what characteristics of the early years of housing assistance are not conducive to positive exits and determine what can be done to help households through the transition period.

34

Knowing that recent homelessness is a risk factor for negative exits suggests that efforts to support people transitioning from homelessness into housing are crucial. Indeed, the PHAs in this study are already participants in federal initiatives for specific voucher types such as Emergency Housing Vouchers and Veterans Affairs Supportive Housing vouchers that pair housing with supportive services. They also fund supportive initiatives through their own programming, contract with community-based organizations and local government, and maintain referral partnerships with local providers. Similarly, when negative exits occur, wraparound services or warm handoffs to other social support agencies may help prevent future homelessness and should be studied further. However, not all PHAs are in a position to do so; King County Housing Authority (KCHA) and Seattle Housing Authority (SHA) can undertake those programs through grant funding and because their Moving to Work (MTW) authority offers flexibility in how funds are used. Other PHAs without MTW authority are less able to resource those kinds of supports.

The associations between ED visits and crisis events with negative exits highlight that housing is interconnected with other aspects of a person’s life. ED visits and crisis events were associated with the increased likelihood of a negative exit, and a negative exit was associated with an increased likelihood of subsequent ED visits and crisis events, even after controlling for baseline events. The exact direction of causation is unclear and may be circular in nature (healthcare events trigger a series of events that increase the chance of a negative exit, and the reason for exiting has flow-on effects for future health needs). Holistic interventions that encompass health and housing elements will require collaborations between PHAs and healthcare organizations that have mutual interests in avoiding ED visits, behavioral health crises, and negative exits.

Finally, even though those with positive exits had higher postexit median wage earnings than those with negative exits (annualized wages of $33,980 versus $24,584), the amount is still far less than what is required to afford to live in the Seattle/King County area, where 80 percent of the Area Median Income is $95,300 for a family of four (King County Housing Authority, 2022). That finding suggests that, even after exit, households will continue to require safety net services, and positive exits should not be assumed to equate to economic self-sufficiency.

Reproducibility and Sustainability

Although the confluence of datasets used in this analysis is unique to the King County setting, the component datasets are either used nationally or have equivalents in other states. All MTW PHAs use the 50058 MTW form, HUD sets data standards for the Homeless Management Information System (HMIS), and Medicaid claims look similar across states. Other jurisdictions are likely to have wage and behavioral health service data that could be linked for an equivalent initiative. Data from other sectors, such as education and social services, would add to the completeness of data on the experience of a person receiving housing assistance.

As noted in Chapter 3, some datasets were already being regularly linked, whereas others were brought together specifically for the HUD HEARS project. The project work focused on exit and postexit factors, but the linked data have vast potential for population assessments, program evaluations, and informing policies. The goal of the partners involved in HUD HEARS is to provide regular updates on the results presented in this report and make them available to interested parties, in a manner to the existing Health and Housing Data Dashboard:

https://kingcounty.gov/depts/health/data/health-housing.aspx. To that end, PHA data is in the process of being added to the integrated data hub, which will facilitate routine analyses of linked data.

35

Recommendations for Future Work

The findings from this project have specific implications for PHAs as they consider programs and policies that might affect exit type. However, HUD HEARS is not the final word on work related to exits from assisted housing, and several areas remain for future work:

Consider how to build a standardized, comprehensive process for collecting exit information. Consistency around when and how PHAs gather data on exits from housing assistance would allow for comparisons both across PHAs and over time, though lists of exit reasons should be flexible enough to address specific PHA needs.

A standard way of mapping exit reasons to categories may be an appropriate middle ground. In addition, collecting information on when and why nonheads of household exit may yield additional insights about how to increase opportunities for positive exits.

Collect qualitative information about exit circumstances. The scope of the HUD HEARS project did not allow for engaging with those who have exited from housing assistance. Gathering stories and other qualitative information from people exiting would add valuable context to the statistics and should be prioritized in future work.

Engage with current PHA housing recipients on linked data. The consent process used by KCHA and SHA allows for the sort of work undertaken for HUD HEARS, and an institutional ethics review board approved the project.

However, meaningful engagement with current housing recipients around data linkage and use offers several benefits. It provides a path to truly informed consent about how a person’s data are collected, linked, and used.

Adding community voices and sharing power around the decision making process is an important element of increasing equity. Finally, the people who use the various services that collect their data are best placed to offer ideas for how the data could best be used to improve well-being.

36

Appendix A: Acronyms

Acronym

Definition

AI/AN

American Indian/Alaska Native

AMI

area median income

aOR

adjusted odds ratio

BHRD

Behavioral Health and Recovery Division

CI

95% confidence interval

DCHS

Department of Community and Human Services

ED

emergency department

ESD

Washington Employment Security Department

FSS

Family Self-Sufficiency

HCHN

Healthcare for the Homeless Network

HCV

housing choice voucher

HMIS

Homeless Management Information System

HUD

U.S. Department of Housing and Urban Development

HUD HEARS

Housing and Urban Development Health, Economic, and Residential Stability

Study

IDH

integrated data hub

KCHA

King County Housing Authority

MTCS

Multifamily Tenant Characteristics System

MTW

Moving to Work

NH/PI

Native Hawaiian/Pacific Islander

OR

odds ratio

PB/MR

project-based/Mod Rehab

PBV

project-based voucher

PH

public housing

PHA

public housing authority

PHSKC

Public Health—Seattle & King County

S8

Section 8

SHA

Seattle Housing Authority

TBV

tenant-based voucher

TRACS

Tenant Rental Assistance Certification System

VASH

Veterans Affairs Supportive Housing

37

Appendix B: Literature Review

Detailed Methodology

Inclusion Criteria

The following inclusion criteria were applied to both the published and gray literature searches:

• Published in English.

• A central focus on populations receiving federal housing assistance in the United States. For the purposes of this review, federal housing assistance refers to living in public housing, receiving a housing choice voucher (HCV), or receiving a project-based subsidy. Other forms of housing assistance (e.g., permanent supportive housing) were not included as they are not directly relevant to the larger Housing and Urban Development Health, Economic, and Residential Stability (HUD HEARS)project.

• Reports on special populations (e.g., veterans, elderly) were included, but limitations on generalizability were noted.

• Reports since 1990.

• All study types, including descriptive analyses of exits and subsequent outcomes. Quantitative and qualitative approaches were included.

Search Terms

The following search terms were used:

• “HUD” OR “Housing and Urban Development” OR “housing assistance” OR “housing program” OR

“public housing” OR “housing voucher” OR “tenant-based voucher” OR “housing choice voucher” OR

“Section 8” OR “subsidized housing.”

AND

• “leave” OR “leaver” OR “exit” OR “exiting” OR “termination” OR “terminate” OR “completion” OR

“complete.”

Published Literature Strategy

The following databases were used for the published literature search (number of results is also shown):

• Campbell Collaboration (n = 5).

• EconLit (n = 31).

• Google Scholar (first 15 pages) (n = 150).

• PubMed (n = 33).

• ScienceDirect (note, due to limitations on search terms, the following search string was used for ScienceDirect: (“Housing and Urban Development” OR “housing assistance” OR “public housing” OR

“housing voucher” OR “subsidized housing”) AND (“leave” OR “exit” OR “terminate” OR “completion”) (n = 5,060).

• Web of Science (n = 109).

Gray Literature Strategy

The following search engines for gray literature databases were used to search for publications, reports, or other relevant documents (number of results are also shown):

• Google (first 10 pages) (n = 100).

• National Bureau of Economic Research Working Papers (https://www.nber.org/papers.html).

38

o Results were restricted to papers under the following topics: “Health, Education, and Welfare”

and “Poverty and Well-being” (n = 687).

• PAIS Index (n = 1,968).

Specific Websites

The following websites for relevant publications (number of results are also shown) were searched:

• HUD Office of Policy Development and Research (the first 250 results under a search for “exit” were reviewed).

• Urban Institute (the 399 papers under the Federal Programs and Policies subject, under the Housing and Housing Finance category were searched).

• HousingIs.org (n = 9).

• National Low Income Housing Coalition (n = 43).

• Center on Budget and Policy Priorities (n = 273).

Relevant References

The references cited in articles that were selected for analysis to identify other relevant articles.

Processing Results

First, the titles, abstracts, or executive summaries of documents were reviewed to screen for relevancy. The full-text versions of documents that were initially deemed relevant were reviewed for a deeper assessment. No quantitative meta-analysis was conducted. Relevant documents were summarized across the domains below, and a qualitative synthesis was conducted:

• Year of study/report and authors.

• Years of data included.

• Population included (location, demographics, housing and voucher types).

• Number of people included.

• Question to which the results pertain.

• Domain of any results that focused on outcomes following exit (physical health, mental health, economic, residential stability, crime, etc.).

• Comparison groups.

• Primary findings.

• Any major limitations.

39

Full List of Papers Examined

Exhibit B-1: Summary of Relevant Literature

Authors

Years

Locations and

Assistance

No. of People

Research Q

Primary Findings

Limitations

Examined Special

Type

Included and

(outcome

Populations

Comparison

category)

Groups

Ambrose,

1994–2002

National

PH, HCV, and 25,336

Factors

-

Across all assistance types, having a head of

B.W. (2005)

project-

households

associated

household who was elderly, female, Black, Asian,

based

with exit

Hispanic, or disabled was associated with decreased

vouchers

None. The study

exits from housing support.

used a survival

-

Having children in the household was associated with

analysis with

increased exits, but only for project-based vouchers.

several

-

Larger households were associated with increased

covariates.

exits among those in public housing, decreased exits

among those with project-based vouchers, and no

association among those with tenant-based

vouchers.

-

An increased percentage of people in the census

tract who were linguistically isolated (a proxy for

proportion with recent immigration) was strongly

associated with decreased exits among all assistance

types.

-

Households are more likely to leave assisted housing

during periods of economic expansion and less likely

to leave during periods of economic uncertainty.

Households residing in public housing units are

significantly less sensitive to changes in local

economic conditions than households receiving

tenant-based housing assistance.

Andersson, F.

Baseline

Non-MTW

PH and HCV

~1.172m

Outcomes

Each additional year spent in subsidized housing is

Some censoring of time

et al. (2016)

was 2000,

counties

Time spent in

following exits associated with increased earnings at age 26 and reduced spent in housing (only followup

subsidized

incarceration. The effects are greatest for non-Hispanic

used 1997–2005) but

was 2010

Youth ages 13–

housing.

Black and Hispanic individuals.

used imputation to

18 in 2000 living

Did not live in

correct.

with 1+ sibling

subsidized

(Income,

housing.

incarceration)

Anthony, J

1994–2003

Rockford, IL

PH and HCV

135 (69 who

Exit type

Completion of the FSS program was associated with

Small sample size, the

(2005)

graduated from

higher income at program exit (median of $22,938 vs.

Rockford HA FSS

People who

FSS and 66 who

$13,964)

program may not be

signed up for FSS

did not)

Factors

-

Young adults (25–40 years) were 3.6 times as likely

generalizable to other

associated

to complete FSS as mature adults (>40 years old).

areas.

Graduated from

with exit

-

Unmarried participants with or without children

FSS vs. did not

were almost three times as likely to succeed as those

40

who were married or divorced (almost all the

participants were female).

-

Participants who did not have a high school diploma

were only 27 percent as likely to succeed as those

who did.

-

Compared with those who acquired three or more

skills in the program, those who acquired one or two

skills or no skills had virtually no chance of success.

Outcomes

Fifty-seven of the successful participants became

following exits homeowners within 2 years of graduation; 36 of the

homeowners were living in their own homes in 2003,

(Employment; several years after acquiring them.

Residential

stability)

Aratani, Y

Baseline

National

PH only

200–400 (varied

Outcomes

-

No significant differences in high school graduation

-

Only considered

(2010)

was 1979–

by outcome)

following exits

or college attendance.

living in PH as of

1981,

Age 19 or

(Educational

-

Marginally more likely to be receiving a housing

1981 but people

followup

younger in 1981

Lived in PH vs.

attainment;

subsidy in the short term (by 1987) but no difference

could have lived in

was 1987

did not

Economic self-

in the longer term (by 1997).

PH in the past

and

sufficiency;

-

No significant differences in receiving other welfare,

(29% non-PH

1997/1998

Wealth)

being employed, owning an automobile, or owning a

people had).

car.

-

Multiple testing

problem (looked

at 12 outcomes by

total and then

White and Black).

-

Propensity score

matching might

have missed

important

confounders.

Brisson, D.,

2010–2012

18 states

HCV, project-

15,328

Factors

-

Increased age, being Asian (vs. White), and living in

No adjustment for

and Covert, J.

based, LIHTC households

associated

senior or supported housing (vs. family housing)

length of time in

(2015)

Mercy Housing

with exit

were associated with reduced risk of a lease

housing.

residents

Those evicted vs.

violation.

those not

-

Being female; Black or other race (vs. White); having

a larger household; or increased income were

associated with increased risk of a lease violation.

-

Increases in work income, variable benefits income,

and other income are related to a slightly higher

likelihood of experiencing a lease violation. An

increase in stable benefits is related to a slightly

lower likelihood of experiencing a lease violation.

Chetty, R.,

MTO was

Baltimore,

PH and HCV

7,340

Outcomes

-

Median income was $1,624 higher for the

MTO took place in five

Hendren, N.,

1994–1998, Boston, Chicago,

following exits

intervention group compared with the control group larger cities, so findings

41

and Katz, L.F.

followup

Los Angeles, New

Offered a

(Educational

among the younger age groups (statistically

may not be

(2016)

ranged

York City

voucher and

attainment;

significant). Income was $1,109 higher when

generalizable to other

from 2000

required to move Income;

comparing the HCV-only group to control group, but

settings.

to 2014

Age 21 by 2012

to a low-income

Marriage and

this was not significant. Among the older age group,

(divided into

area; offered a

fertility)

the intervention group and HCV-only group had

groups >13 at

voucher; and not

lower median income than controls, but this was not

random

offered a

significant.

assignment and

voucher (but

-

Among the younger age group, children in the

13-18 at random

could remain in

intervention group were 2.5 percentage points more

assignment)

PH)

likely to attend college than the control group (19%

vs. 16.5%). There was a smaller, nonsignificant

increase for the HCV-only group. Among the older

age group, children in the intervention group were

4.3 percentage points less likely to attend college

than the control group (11.3% vs. 15.6%). Similarly,

the HCV-only group were significantly less likely to

attend college.

-

Among younger children, those in the experimental

group were more likely to be married (5.3% vs. 3.4%)

and more likely to have the father listed on the birth

certificate (50.9% vs. 44.1%) than those in the

control group. Among older children, there was no

significant difference in the percent married and

fathers were less likely to be listed on the birth

certificate (38.4% vs. 46.7%).

Cortes, A.,

1997 to

National

HCV only

759,557

Factors

-

Non-Whites, females, and households with children

Lam, K., and

2005

household

associated

(especially younger children) were less likely to exit

Fein, D. (2008)

records

with exit

housing support.

-

Households that exited had lower median income.

Nonelderly

The average vacancy rate was higher for exiters, and

heads of

the average poverty rate was slightly lower.

household with

children;

nonelderly,

disabled heads of

household with

children; and

nonelderly heads

of household

with at least one

disabled child

Dantzler, P.A., Those who

National

PH only

3,066

Factors

-

An expectation of moving, being married, having

The paper was framed

and Rivera,

entered PH

associated

some college education, having a disability, and living as examining an

J.D. (2019)

after 1986

Those who

with exit

in an area with a higher unemployment rate were

intention to move, but

through to

expressed an

positively associated with exiting public housing.

the question asks more

2013

expectation of

about an expectation

42

moving in the 2

-

Increased tenure in housing and being older were

of moving, which could

years

negatively associated with exiting public housing.

be for positive or

subsequent to

negative reasons.

being

It was not clear if

interviewed vs.

people who moved out

those who did

of PH were supported

not

by an HCV or not.

Freeman, L.

1995–2002

National

PH and HCV

~7.5m

Factors

-

Non-Whites, older people, females, people with a

(2005)

associated

disability, those with children, those receiving HCV

None. The study

with exit

support (vs. PH), and those living in the Northeast

used a survival

were less likely to exit housing assistance.

analysis with

-

A higher local vacancy rate was strongly associated

several

with exiting housing assistance.

covariates.

-

The pattern for tenure in housing assistance was not

clear.

Geyer, J.,

1995–2017

145 PHAs

HCV only

~1m households

Factors

Introduction of SAFMR increased the probability of exit by

Dastrup, S.,

associated

27% and shortened the median time to exit.

and Finkel, M.

7 Small-Area Fair with exit

(2019)

Market Rent

PHAs vs. 138

comparison PHAs

using

metropolitan-

area fair market

rents

Gubits, D.,

Baseline

CA (Los Angeles

Welfare to

3,167

Exit type

“Those who relinquish vouchers may lose them

Khadduri, J.,

was 2000,

and Fresno), GA

Work

inadvertently through inability to navigate housing

and Turnham, followup

(Atlanta and

voucher

People who

authority rules and the housing market, or they may have

J. (2009)

was 2004

Augusta), TX

holders only

leased up but

comparatively high earnings and desire to let others take

(Houston), and

relinquished

advantage of the voucher.”

WA (Spokane)

their voucher,

Factors

-

Families are more likely to relinquish the voucher

people who

associated

and also are more likely to have relatively older

leased up and

with exit

children (the youngest member of household was

continued to use

age 6–17 when the voucher was issued), are more

their voucher,

likely to be White or Hispanic, have had a driver’s

and people who

license at baseline, and have been receiving

did not lease up

Medicaid at baseline.

-

Families are less likely to relinquish the voucher and

also are more likely to have a high reservation wage

($13–15), more likely to have been enrolled in a

training program at baseline, more likely to have

been living in public or assisted housing at baseline,

and more likely to have received TANF at baseline.

Outcomes

“Compared to those who still hold vouchers, those who

following exits relinquished a voucher report that they: have more

(Income;

earnings, receive less TANF and Food Stamps, have larger

43

Residential

households, live in similar neighborhoods (slightly

stability;

poorer), are more likely to have experienced

Welfare)

homelessness in the past year, are more likely to be in

poverty when both cash and near cash income are

considered and have less monthly food per person. Even

though relinquishers have more earnings than those who

still hold vouchers, they seem to be somewhat worse off

at the point of follow-up. Based on comments from the

in-depth interviews, families value being able to live

independently from their extended family. Therefore, we

interpret the larger households of relinquishers as less

desirable than the smaller households of voucher

holders.”

Hungerford,

1986–1989

National

PH and HCV

1,226

Factors

-

Females and elderly were more likely to remain with

T.L. (1996)

households

associated

housing support.

with exit

-

When removing households with left censoring,

Exited housing

females, Black, and elderly were more likely to

vs. did not

remain in public housing, and greater education was

associated with leaving. Those with a disability were

more likely to continue to receive an HCV.

Kang, S.

1999–2009

National

PH and HCV

3,751

Outcomes

-

PH leavers are approximately 5.2x as likely to

(2020)

following exits

experience housing instability compared with those

Left housing

(Residential

who remain in public housing.

assistance vs. did stability)

-

HCV leavers are approximately 5.8x as likely to

not

experience housing instability compared with those

who remain in public housing.

Kasprow, W.J., 1991–1999

National

HCV only

1,649

Factors

Women were significantly more likely than men to still be

Rosenheck,

associated

housed after 1 year (OR=2.49, CI=1.81 to 3.18).

R.A., Frisman,

VASH

Still in housing

with exit

L., DiLella, and

after one year vs.

D. (2000)

not

Lubell, Jeffrey

1999–2000

National

PH and HCV

92,397 PH and

Factors

-

Among both PH and HCV recipients, those with an

Only a descriptive

M; Shroder,

131,467 HCV

associated

elderly head of household had longer lengths of stay

study. No testing was

Mark; Steffen,

with exit

than other groups. Households with children also

done to examine

Barry (2003)

Household type

had longer lengths of stay.

statistical significance

(elderly,

of differences.

disabled,

nonelderly and

nondisabled with

children,

nonelderly and

nondisabled

without children

McClure, K.

1995–2015

National

PH, HCV, and ~81m records

Factors

-

Length of stay has increased over time, more so for

(2018)

project-

associated

non-White households.

with exit

44

based

None. Survival

-

Households that exited assisted housing had similar

vouchers

analyses by

median income compared with households that

covariates

remained in assisted housing.

-

Income was negatively correlated with length of stay.

-

Higher area poverty levels and vacancy rates were

associated with shorter lengths of stay.

-

Higher area rent levels were associated with longer

lengths of stay.

McInnis, D. et

2001–2005

Atlantic City,

PH and HCV

715 households

Exit type

“About one in five of the other unassisted renters cited a

al. (2007)

Chicago,

‘positive reason’ such as marriage or higher incomes as

Durham,

Those who were

the reason they were no longer eligible to receive

Richmond,

no longer

assistance. But far more—nearly half (46 percent) of

Washington, D.C.

receiving

unassisted renters—cited a negative reason for why they

assistance and

no longer received assistance, including breaking program

HOPE VI

those who were

rules, being evicted, being relocated from public housing

households

and unable to move back, and rent and utility costs that

were too high.”

Outcomes

-

Roughly 23 percent of unassisted renters reported

following exits

that they have moved three or more times since

(Residential

2001, compared with 8.7 percent of voucher holders

stability;

and 1.9 percent of other public housing residents.

Economic

-

Unassisted renters and voucher holders had similar

attainment)

levels of being late paying utilities (43–44%), but the

proportion was much lower among PH residents.

-

Unassisted renters were much more likely to report

being late paying their rent and most likely to report

being evicted for nonpayment of rent.

Montgomery,

2011–2014

National

HCV only

7,383

Exit type

-

Almost one-half (42.5%) of leased-up exiters did so

-

Exit from VASH did

A.E. et al.

because they had accomplished their goals. Other

not equate to

(2017)

VASH

Exited VASH vs.

main reasons were being evicted (9.1%), death

exiting subsidized

stayed in the

(8.7%), and finding other housing (8.1%).

housing; 1/3

program

-

One in five (21.9%) nonleased-up exiters were no

continued

longer interested in participating in VASH, 16.6%

receiving housing

could not be located, 14.2% had found other

support.

housing, and 10.1% had noncompliance with VASH

-

Veterans may

case management.

have accessed

Factors

-

Among those who had leased up, having a service-

other community-

associated

connected disability was associated with exiting.

based

with exit

-

Among exiters, having PTSD was positively

homelessness

associated with not being leased up.

assistance

Outcomes

-

Almost 93 percent of leased-up exiters and 90

programs to which

following exits

percent of nonleased-up exiters did not return to VA

the research team

(Residential

homeless programs during the observation period.

did not have

stability)

-

Having a service-connected disability and being

access (e.g., local

female were associated with reduced homelessness

HMIS)

45

after exit. Having a drug use disorder was associated

with increased homelessness.

Montgomery,

2008–2016

National

HCV only

20,146

Exit type

Veterans who exited HUD-VASH during the observation

A.E., and

period and had either been evicted (N = 4684; 10.2%) or

Cusack, M.

Those who had

Exited VASH due

left the program because they had accomplished their

(2017)

exited VASH

to eviction vs.

case management goals (N = 15,462; 33.7%). The leading

exited due to

reasons for exiting for the remaining 25,688 veterans who

accomplishing

were excluded from the study were finding other housing

goals

(N = 4,641; 10.1%) and no longer being financially eligible

(N = 3,741; 8.2%) or interested (N = 2,878; 6.3%); a

further 3,795 (8.3%) veterans died while in HUD-VASH

housing.

Factors

-

Males were about 50% more likely to be evicted than

associated

females, and younger veterans were somewhat more

with exit

likely to be evicted than older veterans.

-

Veterans receiving compensation for a service-

connected disability and veterans with chronic

medical conditions had lower odds of eviction,

whereas those with psychosis, history of self-injury,

and alcohol use disorders were more than 50% more

likely to be evicted.

-

Drug use disorders raised the odds of eviction by

about 150%.

-

Use of acute care was generally associated with

eviction with the largest effects observed in acute

care related to substance use.

-

Primary care and outpatient medical care were

largely protective.

Newman, S.J.,

Baseline

National

PH only

1,183

Outcomes

-

Every year of public housing residence between ages

and Harkness, was 1968–

following exits

10 and 16 is estimated to increase the probability of

J.M. (2002)

1982,

Youth ages 10–

Public housing

(Income;

working between ages 25 and 27 by 7 percentage

followup

16 at baseline

during youth vs.

Welfare

points.

was at ages

unassisted

receipt)

-

Less significant, but still notable, every year of public

20–27

housing residence is also estimated to reduce years

(1978–

of welfare dependence between ages 20 and 27 by

1993)

0.71 of a year and to increase annual earnings

between ages 25 and 27 by $1,861

O'Connell,

Baseline

Cleveland, New

HCV only

392

Outcomes

Approximately 40% of the VASH group experienced 1+

M.J., Kasprow, was 1992–

Orleans, San

following exits day of homelessness within 4.5 years of being housed.

W., and

1995,

Diego, San

VASH vs.

(Residential

Rosenheck,

followup

Francisco

intensive case

stability)

R.A. (2008)

was for up

management vs.

to 5 years

VASH

standard care

Olsen, E. et al. 1992–2002

National

HCV only

~1.1m

Factors

-

Disabled, elderly, Black, and White heads of

-

Assumes that

(2005)

households

associated

household (compared with non-Black, non-White)

participants only

with exit

were less likely to exit.

leave the HCV

46

None. Survival

-

Increased family size was associated with increased

program when

analyses with

likelihood of exiting.

there is a net

covariates

-

A $100-per-month decrease in the local payment

benefit to them.

standard was associated with a 3% increase in the

-

Used the most

rate of program exit, and an increase of $100 per

recent

month in the minimum tenant contribution to rent

certification data,

was associated with a 12.6% increase in program

not any EOP data

attrition.

(because it is not

checked), but that

data may

inaccurately state

income levels if

people left for an

income-based

reason.

Richter, F.G-C

2011–2017, Cleveland

PH and

19,748

Factors

-

Among all those with an eviction filing (not just those -

Could only identify

et al. (2021)

evictions

possibly HCV

associated

in PH), being White (vs. Black), being male, having

PH landlord, not

between

People who

with exit

more children, and having had a filing in the past

HCV recipients.

2013 and

received an

year were associated with receiving an eviction

-

Most analyses

2016

eviction order vs.

order.

were for all

people who had

-

Having an eviction filing by a public housing entity or

landlords

an eviction filing

nonprofit organization carries a lower risk of getting

combined,

but no eviction

an eviction order, relative to a filing by a private

although PH and

order

entity.

nonprofit

Outcomes

-

Among all those in the study, receiving an eviction

landlords made up

following exits

order was associated with increased mobility in the

only 28% of the

(Residential

three quarters following eviction compared with

total.

stability;

those who received an eviction filing but no eviction

School

order.

attendance;

-

Households in public housing who are not evicted do

Health)

not have an increase in shelter use relative to the

baseline year. However, those who are evicted from

public housing increase shelter use by 3.3 days in the

following year and by almost 2 days (1.97) the

subsequent year.

-

In the school year of the eviction filing, children in

7th to 12th grade in households with an eviction

move-out order have a share of absent days 2.3

percentage points higher relative to those in

households without an eviction move-out order. For

kindergarten to 6th grade, there is no significant

difference in the share of absent days for children of

households with an eviction order relative to those

without an eviction move-out order.

-

Children in households with an eviction filing had

lower rates of lead testing compared with the

47

Cleveland average, and the rate for children in

households with an eviction order was lower than

that of households with a filing but no order. The

proportion of children with elevated blood lead

levels was higher for children in households with an

eviction filing than for Cleveland overall, but there

was not a great difference between children in

households with an eviction order and those with

only a filing.

Rohe, W.M.,

1989–1995

Charlotte, NC

PH only

224

Outcomes

-

All groups had a higher monthly mean wage

-

Small sample size,

and Kleit, R.G.

following exits

compared to baseline, but graduates had the largest

the Gateway FSS

(1997)

People who

People who

(Income;

increase ($792 compared with $660 for dropouts and

program may not

applied for the

participated in

Welfare)

$245 for the comparison group).

be generalizable

FSS program

the FSS program

-

All groups experienced decreases in the proportions

to other areas.

vs. those who

receiving Aid to Families with Dependent Children

-

People dropped

applied but did

benefits, but graduates had the largest decrease

out of the

not complete the

(23% points compared with 21 for dropouts and 3 for

program for

application

the comparison group).

different reasons

process or

-

All groups experienced decreases in the proportions

so are a

declined once

receiving food stamps, but graduates had the largest

heterogeneous

accepted

decrease (26% points compared with 8 for dropouts

group.

and 9 for the comparison group).

-

Graduates were more likely to own their own home

at followup.

Rohe, W.M.,

2011–2014

Charlotte, NC

PH only

550

Exit type

Positive move-outs were defined as moving to private-

Webb, M.D.,

market housing. Negative move-outs (i.e., evictions) were

and Frescoln,

Work

defined by failure to pay rent, violating lease terms, or

K.P. (2016)

requirement

moving without notice.

sites with a

Factors

Some evidence indicates that work requirements

history of FSS

associated

increased positive move outs, but the numbers were very

programs, work

with exit

small.

requirement

sites with newly

introduced case

managers, and

nonwork

requirement

sites

Smith, R.E. et

MTO was

Baltimore,

HCV only

1,149

Exit type

-

Positive exits were defined as homeownership or

al. (2014)

1994–1998, Boston, Chicago,

households

exceeding the income limit. Negative exits included

followup

Los Angeles, New

lease violations, evictions, or inability to lease up

ranged

York City

- Receiving

during the period.

from 2008–

housing

-

After using a hierarchy of information sources to fill

2011

assistance vs. not

in gaps (a reason for exit was provided by only 40.6%

at final followup

of leavers), 53% were classified as having a positive

exit and 47% as having a negative exit.

48

- Positive vs.

Factors

-

No difference was seen between positive and

negative exits

associated

negative exits in terms of age, gender, or household

with exit

size.

-

Those with positive exits were more likely to be

married at the end of the study, had ever been

married, and were Hispanic. Those with negative

exits were more likely to be non-Hispanic Black.

-

Those with positive exits were less likely to have ever

been homeless, less likely to live in overcrowded

housing, and less likely to have a high housing cost

burden. Median income at study end was

substantially higher, but given that income formed

part of the definition of a positive exit, this finding is

not surprising.

-

Those with positive exits had similar demographics

to those who remained receiving housing assistance.

Outcomes

-

Those with positive exits were more likely to rate

following exits

their housing as excellent or good, have a higher

(Neighborhoo

neighborhood satisfaction rating, and feel safe in

d

their neighborhood than people with negative exits

characteristics

and those still receiving assistance. Those with

;

negative exits were similar to those receiving

Health)

assistance but were slightly more likely to feel safe in

their neighborhood and less likely to say it had

alcohol problems; loitering problems; or trash,

graffiti, and abandoned buildings.

-

Those with positive exits were also more likely to

rate their health as good or better and less likely to

take medicines for blood pressure or have

depression than those with negative exits and those

still receiving assistance.

-

Those with positive exits were less likely to be

receiving other forms of welfare than those still

receiving assistance. Those with negative exits also

were less likely to receive other forms of welfare,

despite having a similar median income to those still

receiving assistance.

-

Perhaps as a consequence, those with a negative exit

were more likely to report food insecurity.

EOP = end of participation. FSS = Family Self-Sufficiency. HA = Housing Authority. HCV = housing choice voucher. LIHTC = Low-Income Housing Tax Credit. MTO = Moving to Opportunity. PH = public housing. PTSD = post-traumatic stress disorder. SAFMR = Small-Area Fair Market Rent. TANF = Temporary Assistance for Needy Families.

49

Appendix C: Data Sources and Linkage

Exhibit C-1: Data Sources Used for HUD HEARS

Data Source

Years Used

Existing Linkages

BHRD

2012–2019

HCHN, HMIS, and Medicaid

ESD

2012–2019

HCHN

BHRD, HMIS, and Medicaid

HMIS

2012–2019

BHRD, HCHN, and Medicaid

Medicaid Claims Data

2012–2019

• 50058 Data

• HMIS and BHRD

PHA Administrative Data (including 50058)

2012–2019

Medicaid

PHA Exit Data

2012–2019

BHRD = Behavioral Health and Recovery Division. ESD = Employment Security Department. HCHN = Healthcare for the Homeless Network. HMIS = Homeless Management Information System. PHA = public housing authority.

An existing multisector data system was used to link data sources. The King County Integrated Data Hub (IDH) combines identities across several datasets, including Behavioral Health and Recovery Division (BHRD), Healthcare for the Homeless Network (HCHN), Homeless Management Information System (HMIS), and Medicaid. The IDH uses a mix of probabilistic and deterministic methods to match individuals across data systems via a proprietary tool (Informatica, Redwood City, CA). Public housing authority (PHA) data (50058 and exit data from King County Housing Authority [KCHA] and Seattle Housing Authority [SHA]) were probabilistically linked on name, Social Security number, date of birth, and gender using the RecordLinkage package in R v4.2.0 and RStudio v2022.2.3.492 (R Core Team, 2022; RStudio Team, 2022; Sariyar and Borg, 2020). IDH, Employment Security Department (ESD), and PHA data were then linked using the same RecordLinkage approach (Exhibit C-2).

50

Exhibit C-2: Identity Linkage Between HUD HEARS Data Sources

SHA 50058

KCHA

Exits

BHRD

50058

PHA

HCHN

IDH

HMIS

HUD

ESD

HEARS

Study ID

Medicaid

BHRD = Behavioral Health and Recovery Division. ESD = Employment Security Department. HCHN = Healthcare for the Homeless Network. HMIS = Homeless Management Information System. HUD HEARS = Housing and Urban Development Health, Economic, and Residential Stability. IDH = Integrated Data Hub. KCHA = King County Housing Authority. PHA = public housing authority. SHA = Seattle Housing Authority.

51

Appendix D: Exit Definitions

Exhibit D-1: Exit Reasons and Categories

PHA Exit Reason

Category

KCHA Section 8 Cross Absorption

Neutral

KCHA Section 8 Absorption

Neutral

KCHA Homeownership

Positive

KCHA Moved to Nonsubsidized Rental

Positive

KCHA Section 8 Over Income

Positive

KCHA Moved—Needed a Higher Level of Services

Neutral

KCHA Transitional Housing Graduate to KCHA Managed Units

Neutral

KCHA Transitional Housing Graduate to Any Section 8 Voucher Neutral

KCHA Transitional Housing Graduate to KCHA PBA

Neutral

KCHA Transitional Housing Graduate to Nonsubsidized Rental

Positive

KCHA Transitional Housing Graduate to Other Subsidized Rental Neutral

KCHA Transitional Housing Nongraduate Early Program Exit

Neutral

KCHA Deceased

Neutral

KCHA Moved—Changed Subsidy Program Type

Neutral

KCHA Section 8 Incoming Portability Move Out

Neutral

KCHA Moved In w/Family or Friends

Neutral

KCHA Subsidy in Jeopardy Client Choice

Negative

KCHA Landlord Eviction

Negative

KCHA Noncompliance—Paperwork Violation

Negative

KCHA Inspection/Damages

Negative

KCHA Fraud—Household Income

Negative

KCHA Noncompliance—Criminal Activity

Negative

KCHA Fraud—Household Composition

Negative

KCHA Client Location Unknown/Abandoned Unit

Negative

KCHA Absence—Incarceration

Negative

KCHA Absence Treatment/Hospital

Negative

KCHA Port Out Termination

Neutral

KCHA Nonpayment of Rent

Negative

KCHA Section 8 PB Failed Social Services Program

Negative

KCHA Expired—Term Limit Program

Neutral

KCHA Moved to Non-KCHA Subsidized Rental

Neutral

KCHA Voucher Expired

Negative

KCHA Expired—Ported Out

Negative

KCHA Nonpayment of Retroactive Rent

Negative

KCHA Section 8 Sponsor-Based Provider-Based Move Out

Neutral

KCHA Client Would Not Disclose Reason

Neutral

KCHA Failed to Provide Information

Negative

52

KCHA PM Move to KCHA Section 8 Voucher

Neutral

SHA

180 Days $50 or Less HAP

Positive

SHA

180 Days Away from Assisted Unit

Negative

SHA

180 Days Zero HAP

Positive

SHA

Client Location Unknown/Abandoned Unit

Negative

SHA

Absence—Extended Leave

Negative

SHA

Absence—Incarceration

Negative

SHA

Absence—Treatment/Hospital

Negative

SHA

Criminal Activity

Negative

SHA

Deceased

Neutral

SHA

Client Would Not Disclose Reason

Neutral

SHA

Domestic Violence

Negative

SHA

Eviction—Abandonment

Negative

SHA

Eviction—Nonpayment

Negative

SHA

Eviction—Judgment/Physical

Negative

SHA

Eviction—Judgment/Physical - Criminal

Negative

SHA

Eviction—Judgment/Physical - Other

Negative

SHA

Expired—Ported Out

Neutral

SHA

Expired—Term Limit Program

Neutral

SHA

Voucher Expired

Negative

SHA

Failure to Complete HQS Inspection

Negative

SHA

Failure to Complete Reexamination

Negative

SHA

Failed to Provide Information

Negative

SHA

Fraud—Household Composition

Negative

SHA

Fraud—Household Income

Negative

SHA

Fraud—Other

Negative

SHA

FUP Youth 18-Month Expiration

Neutral

SHA

Graduated—180 Days $50 or Less HAP

Positive

SHA

Health

Neutral

SHA

HQS Breach

Negative

SHA

Ineligible—Citizenship/Immigration

Neutral

SHA

Lease Enforcement

Negative

SHA

Lease Violation—Criminal

Negative

SHA

Landlord Eviction

Negative

SHA

Lease Violation—Noncriminal

Negative

SHA

Location

Negative

SHA

More than 60 Days Absent from the Unit

Negative

SHA

Moved—Changed Subsidy Program Type

Neutral

SHA

Homeownership

Positive

SHA

Moved—Needed a Higher Level of Services

Neutral

SHA

Moved to Nonsubsidized Rental

Positive

53

SHA

Moved—Shelter

Negative

SHA

Moved—Transitional Housing Program

Negative

SHA

Moved in w/Family or Friends

Neutral

SHA

Mutual Termination

Neutral

SHA

Neighborhood Quality

Negative

SHA

No Longer Used as of 9/14/16 (Other)

Neutral

SHA

Noncompliance—Citizenship/Immigration

Negative

SHA

Noncompliance—Criminal Activity

Negative

SHA

Noncompliance—HQS

Negative

SHA

Noncompliance—Paperwork Violation

Negative

SHA

Noncompliance—Payment Plan/Debt to SHA

Negative

SHA

Noncompliance—Program Partnership

Negative

SHA

Other

Neutral

SHA

Other Subsidized Housing/HCV

Neutral

SHA

Other Violation of Participant Obligations

Negative

SHA

Noncompliance—Payment Plan/Debt to SHA

Negative

SHA

PB/MR Moved Due to Incarceration

Negative

SHA

PB/MR Moved Out, Location Unknown

Neutral

SHA

PB/MR Moved to Hospital/Assisted Living

Neutral

SHA

PB/MR Moved to Nontime-Limited Market-Rate Home

Positive

SHA

PB/MR Moved to Nontime-Limited Subsidized Housing

Neutral

SHA

PB/MR Moved to Shelter

Negative

SHA

PB/MR Moved to Temporary Housing (family, friends)

Neutral

SHA

PB/MR Moved to Transitional Housing Program

Neutral

SHA

Homeownership

Positive

SHA

Rent Too High

Negative

SHA

Moved to Nonsubsidized Rental

Positive

SHA

Serious/Repeated Lease Violations (Criminal)

Negative

SHA

Serious/Repeated Lease Violations (Noncriminal)

Negative

SHA

Unit/Property Quality

Negative

SHA

Client Would Not Disclose Reason

Neutral

SHA

Port Out Termination

Neutral

SHA

PB/MR Moved Out, Location Unknown

Neutral

SHA

Voluntary Self-Termination

Neutral

SHA

Voucher Expired

Negative

FUP = Family Unification Program. HAP = Housing Assistance Payments. HCV = housing choice voucher. HQS = Housing Quality Standards. KCHA = King County Housing Authority. Mod Rehab = moderate rehabilitation. MR = Mod Rehab. PB = project-based.

PBA = project-based assistance. PHA = public housing authority. PM = private market. SHA = Seattle Housing Authority.

Sources: KCHA and SHA

54

Appendix E: Factors Associated With Exit

Detailed Methodology

Data Sources and Variables

The following variables from HUD Form 50058 Moving to Work (MTW) data were used in the exit analyses: (1) head of household demographics: gender (male, female, or both male and female reported over time, which was termed multiple), age (<25, 25–44, 45–61, 62+ [senior housing eligibility begins at age 62]), race/ethnicity (American Indian/Alaska Native, Asian, Black, Latinx, multiple races, Native Hawaiian/Pacific Islander, White), self-reported disability, length of time in housing; and (2) household characteristics: household size, single caregiver (one adult and one or more children in the household), and assistance type (project-based vouchers [PBVs], public housing [PH], or tenant-based vouchers

[TBVs]).

Exits were restricted to those with at least a 12-month gap between the exit date and any subsequent housing (termed “true exits”) and nondeath exits. If a head of household had multiple exits during the study period, the most recent exit was used. If multiple exit categories were recorded for a single event, the reason that belonged to the smallest group (positive, then negative, then neutral) was prioritized.

On the basis of existing literature and public housing authority (PHA) expertise, it was hypothesized that health status and previous housing instability would influence exits from housing and exit type. In addition to the demographic factors listed above, Behavioral Health and Recovery Division (BHRD) data was used to identify people who had experienced an acute behavioral health crisis event in the 12

months before housing exit. Homelessness was defined as one or more of the following circumstances in the 3 years before exit: appearing in Homeless Management Information System (HMIS) or Healthcare for the Homeless Network (HCHN) data, having a housing status in BHRD data that indicated housing instability, or having an address listed as “Homeless” in the Medicaid data (Johnson, McHugh, and Reimal, 2021). Medicaid data was used to identify those who had experienced emergency department (ED) visits or hospitalizations for any reason in the 12 months before housing exit and those with one or more chronic conditions, as defined by the Chronic Condition Warehouse (Centers for Medicare and Medicaid Services, 2022). An enhanced definition of behavioral health crisis event that added behavioral health-related ED visits from Medicaid to the BHRD data was created. Collectively, the Medicaid-derived all-cause ED visit, hospitalization, and chronic condition measures are a proxy for a person’s health status.

Statistical Analysis

The primary analysis aimed to answer two questions: (1) What factors are associated with exiting from housing assistance? and (2) What factors are associated with each exit type? For both analyses, the unit of analysis was the head of household. Although some exit reasons may apply to the entire household, others focus on the individual, and other household members may continue to receive housing assistance.

To look at the first question, four controls (heads of household who remained in housing) were randomly matched for each exit without replacement and assigned the controls a pseudo-exit date that matched the exit date to assess the demographic and other variables noted above. A 4:1 ratio was used because greater ratios yield minimal gain in power to detect differences, and a limited number of controls were available for matching (Breslow, 2005). Controls were eligible to be matched if they 55

remained in housing for at least 12 months following the case exit date. Controls were not matched on any other characteristics to allow examination of how each variable was associated with exits. If they had been matched on a factor (e.g., age), the distribution of that factor would have been artificially balanced between those who exited and controls, meaning that no relationship between the factor and exiting would be found.

First, descriptive statistics were examined for programmatically meaningful differences in characteristics, as defined by subject matter experts who work with the PHA population. A binomial logistic regression was used to evaluate the relationship between each variable and exiting from housing. To examine factors associated with exit type, a multinomial logistic regression with neutral exits as the reference category was used. The DHARMa R package was used to conduct model checking (Hartig, 2022).

Secondary Analysis

Healthcare use data (ED visits, hospitalizations, and diagnosed chronic conditions) were available only for those who were enrolled in Medicaid before exiting. Therefore, a secondary analysis with the subset of participants (both those exiting and controls) who had full, non-dual (i.e., they were not also enrolled in Medicare) Medicaid coverage for at least 7 of the 12 months before the exit or pseudo-exit date. That minimum coverage requirement ensured that if a person visited the ED, was hospitalized, or was diagnosed with a chronic condition, the event would likely be detected in the claims data (Washington State Health Care Authority, 2022). Because Medicaid members with dual Medicare coverage were excluded, secondary analyses were also restricted to those ages <62; most older Medicaid recipients also have Medicare and Medicaid claims may be incomplete.

Detailed Results

For both those who remained and those who exited, people with 7 or more months of full Medicaid coverage in the year before exit were younger (median of 44/41 years for remained/exited and had Medicaid versus 59/56 years for those without Medicaid), more likely to be female (70.2 percent/64.6

percent versus 60.0 percent/55.3 percent), be Black (43.8 percent/43.5 percent versus 27.6

percent/29.1 percent), have a larger household (mean 2.8/2.4 versus 1.8/1.7), and have a single caregiver (30.1 percent/28.4 percent versus 11.8 percent /10.4 percent), but less likely to have a disability (35.4 percent/37.0 percent versus 50.1 percent/45.2 percent) (Exhibit E-1). Among those with Medicaid coverage, those exiting were more likely to be receiving a project-based voucher (PBV) than those who remained (49.4 percent versus 22.2 percent).

Although analyses were at the head-of-household level, a demographic profile of all those who exited is in Exhibit E-2. The pattern of differences between each exit type was largely the same as for heads of households (shown in Exhibit 5-1).

56

Exhibit E-1: Demographics of Heads of Households Who Exited Versus Those Who Did Not, by Medicaid Enrollment Status Remained, No

Remained,

Exited, No

Exited,

Demographic

Medicaid

Medicaid

Medicaid

Medicaid

(N=15,214)

(N=9,948)

(N=5,083)

(N=3,183)

Age

Mean (years)

58

44

56.5

41.4

Median (years)

59

44

56

41

Senior (age 62+)

44.8%

7.2%

40.1%

5.7%

Gender

Another Gender

208 (1.4%)

145 (1.5%)

61 (1.2%)

36 (1.1%)

Female

9,131 (60.0%)

6,986 (70.2%)

2,813 (55.3%)

2,056 (64.6%)

Male

5,875 (38.6%)

2,817 (28.3%)

2,209 (43.5%)

1,091 (34.3%)

Race/Ethnicity

AI/AN

171 (1.1%)

158 (1.6%)

75 (1.5%)

83 (2.6%)

Asian

1,763 (11.6%)

701 (7.0%)

522 (10.3%)

167 (5.2%)

Black

4,202 (27.6%)

4,356 (43.8%)

1,481 (29.1%)

1,385 (43.5%)

Latinx

1,011 (6.6%)

673 (6.8%)

339 (6.7%)

222 (7.0%)

Multiple

1,539 (10.1%)

991 (10.0%)

468 (9.2%)

269 (8.5%)

NH/PI

119 (0.8%)

84 (0.8%)

45 (0.9%)

22 (0.7%)

White

6,409 (42.1%)

2,985 (30.0%)

2,153 (42.4%)

1,035 (32.5%)

Time in Housing

Mean Time (years)

6.2

5.5

5.4

4.4

Median Time (years)

6.2

4.5

4.3

3.0

Household Characteristics

Head of Household w/ a Disability

50.1%

35.4%

45.2%

37.0%

Mean Household Size

1.8

2.8

1.7

2.4

Median Household Size

1

2

1

2

Single Caregiver

11.8%

30.1%

10.4%

28.4%

Program Type

PBV

2,462 (16.2%)

2,210 (22.2%)

2,013 (39.6%)

1,573 (49.4%)

PH

4,985 (32.8%)

2,133 (21.4%)

1,330 (26.2%)

510 (16.0%)

TBV

7,767 (51.1%)

5,605 (56.3%)

1,740 (34.2%)

1,100 (34.6%)

57

Remained, No

Remained,

Exited, No

Exited,

Demographic

Medicaid

Medicaid

Medicaid

Medicaid

(N=15,214)

(N=9,948)

(N=5,083)

(N=3,183)

Health and Homelessness Events

Experienced Recent Homelessness

2,373 (15.6%)

3,353 (33.7%)

1,448 (28.5%)

1,808 (56.8%)

Experienced 1+ Behavioral Health

Crisis Events in Year Before Exit (excl. 220 (1.4%)

188 (1.9%)

343 (6.7%)

227 (7.1%)

Medicaid ED visits)

AI/AN = American Indian/Alaska Native. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

Source: NO HARMS integrated study dataset

58

Exhibit E-2: Demographics of All Those Who Exited, by Exit Type (Individual Level) Demographic

All Exits

Neutral Exit Positive Exit Negative Exit

(N=16,301)

(N=7,984)

(N=2,902)

(N=5,415)

Age

Mean (years)

33.9

37.1

31.1

30.8

Median (years)

31

35

27

27

Senior (age 62+)

14.8%

20.3%

9.2%

9.7%

Gender

Another Gender

221 (1.4%)

99 (1.2%)

41 (1.4%)

81 (1.5%)

Female

8,793 (53.9%)

4,293 (53.8%)

1,572 (54.2%)

2,928 (54.1%)

Male

7,287 (44.7%)

3,592 (45.0%)

1,289 (44.4%)

2,406 (44.4%)

Race/Ethnicity

AI/AN

262 (1.6%)

120 (1.5%)

20 (0.7%)

122 (2.3%)

Asian

1,422 (8.7%)

782 (9.8%)

317 (10.9%)

323 (6.0%)

Black

6,983 (42.8%)

3,245 (40.6%)

1,348 (46.5%)

2,390 (44.1%)

Latinx

1,303 (8.0%)

583 (7.3%)

188 (6.5%)

532 (9.8%)

Multiple

1,341 (8.2%)

585 (7.3%)

265 (9.1%)

491 (9.1%)

NH/PI

227 (1.4%)

103 (1.3%)

36 (1.2%)

88 (1.6%)

White

4,763 (29.2%)

2,566 (32.1%)

728 (25.1%)

1,469 (27.1%)

Time in Housing

Mean Time (years)

5.5

4.7

7.0

5.9

Median Time (years)

4.4

3.2

7.1

5.0

Household Characteristics

Head of Household Disability

27.3%

30.9%

14.7%

28.7%

Mean Household Size

3.2

2.9

3.9

3.2

Median Household Size

3

2

4

3

Single Caregiver

25.7%

24.7%

15.7%

32.6%

Program type

PBV

6,152 (37.7%)

4,436 (55.6%)

755 (26.0%)

961 (17.7%)

PH

3,239 (19.9%)

1,418 (17.8%)

743 (25.6%)

1,078 (19.9%)

TBV

6,910 (42.4%)

2,130 (26.7%)

1,404 (48.4%)

3,376 (62.3%)

Health and Homelessness Events

Experienced Recent Homelessness

5,015 (30.8%)

2,857 (35.8%)

401 (13.8%)

1,757 (32.4%)

Experienced 1+ Behavioral Health Crisis Events

in Year Before Exit (excl. Medicaid ED visits)

608 (3.7%)

356 (4.5%)

23 (0.8%)

229 (4.2%)

59

Demographic

All Exits

Neutral Exit Positive Exit Negative Exit

(N=16,301)

(N=7,984)

(N=2,902)

(N=5,415)

Experienced 1+ Behavioral Health Crisis Events

in Year Before Exit (inc. ED visits)a

173 (3.1%)

97 (4.1%)

<10

70 (3.2%)

Average # ED Visits in Year Before Exita

1.1

1.2

0.5

1.1

Experienced 1+ ED Visits in Year Before Exita

2,265 (40.8%)

1,048 (44.7%)

286 (28.6%)

931 (42.2%)

Average # Hospitalizations in Year Before Exit

(per 100 people)a

7.5

8.7

3.9

7.9

Experienced 1+ Hospitalizations in Year Before

Exita

287 (5.2%)

148 (6.3%)

30 (3.0%)

109 (4.9%)

Average # of Chronic Conditionsa

1

1

0.7

1.1

AI/AN = American Indian/Alaska Native. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

a Health event data available for those age <62 enrolled in Medicaid (All exits N=5,550; Negative N=2,205; Neutral N=2,346; Positive N=999).

Source: NO HARMS integrated study dataset

60

Exhibit E-3: Regression Output for Heads of Household Who Exited Versus Controls Who Did Not (Medicaid Population) Demographic

Odds Ratio

95% CI

Age

<25

ref

—

25–44

0.67***

0.56–0.81

45–61

0.50***

0.41–0.61

Gender

Female

ref

—

Male

1.05

0.94–1.17

Multiple

0.97

0.65–1.43

Race/Ethnicity

White

ref

—

AI/AN

1.23

0.90–1.67

Asian

0.94

0.77–1.15

Black

1.03

0.93–1.15

Latinx

0.92

0.76–1.10

Multiple

0.90

0.76–1.07

NH/PI

0.89

0.52–1.45

Time in Housing

<3

ref

—

3–5.99

1.18**

1.05–1.32

6–9.99

1.16*

1.01–1.32

10+

1.22**

1.05–1.42

Household Characteristics

Head of Household w/Disability

0.81***

0.72–0.90

Household Size

0.93***

0.90–0.96

Single Caregiver

0.82***

0.73–0.92

Program Type

TBV

ref

—

PBV

2.80***

2.52–3.11

PH

1.26***

1.11–1.43

Health and Homelessness Events

Experienced Recent Homelessness

1.74***

1.57–1.94

Experienced 1+ Behavioral Health Crisis Event in Year Before exit (incl. ED visits)a

2.12***

1.69–2.66

61

Demographic

Odds Ratio

95% CI

Experienced 1+ ED Visit in Year Before Exita

1.27***

1.16–1.40

Experienced 1+ Hospitalization in Year Before Exita

0.96

0.82–1.12

2+ Chronic Conditionsa

0.75***

0.68–0.83

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

AI/AN = American Indian/Alaska Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. Ref = Reference group. TBV = tenant-based voucher.

a Health event data available for those age <62 enrolled in Medicaid (N = 9,234 for controls, 3,001 for exits).

Source: NO HARMS integrated study dataset

62

Exhibit E-4: Regression Output for Heads of Household by Exit Type (Medicaid Population)

Negative/Positive Exits Versus Neutral Exits

(neutral N=1,522)

Negative Exits

Positive Exits

(N=1,139)

(N=340)

Demographic

Odds Ratio

95% CI Odds Ratio 95% CI

Age

<25

ref

—

ref

—

25–44

0.97

0.70–1.35

0.99

0.58–1.67

45–61

0.86

0.60–1.22

0.92

0.53–1.61

Gender

Female

ref

—

ref

—

Male

1.08

0.88–1.34

1.21

0.90–1.63

Multiple

0.77

0.33–1.82

2.14

0.85–5.37

Race/Ethnicity

White

ref

—

ref

—

AI/AN

1.67

0.98–2.85

0.56

0.17–1.92

Asian

0.85

0.54–1.33

1.35

0.82–2.22

Black

1.14

0.93–1.40

1.00

0.74–1.36

Latinx

1.24

0.87–1.79

1.26

0.74–2.14

Multiple

0.95

0.68–1.33

0.93

0.57–1.54

NH/PI

2.58

0.90–7.36

1.19

0.23–6.12

Time in Housing (years)

<3

ref

—

ref

—

3–5.99

1.41**

1.12–1.78

1.36

0.96–1.93

6–9.99

1.55**

1.18–2.02

1.61*

1.10–2.36

10+

1.85***

1.35–2.53

2.49***

1.63–3.82

Household Characteristics

Head of Household w/Disability

0.90

0.72–1.13

0.48***

0.33–0.68

Household Size

0.93*

0.88–0.99

1.10*

1.02–1.18

Single Caregiver

1.12

0.89–1.40

0.56***

0.40–0.78

Program Type

TBV

ref

—

ref

—

PBV

0.11***

0.09–0.14

0.59**

0.42–0.82

63

Negative/Positive Exits Versus Neutral Exits

(neutral N=1,522)

Negative Exits

Positive Exits

(N=1,139)

(N=340)

Demographic

Odds Ratio

95% CI Odds Ratio 95% CI

PH

0.82

0.63–1.07

2.08***

1.45–2.98

Health and Homelessness Events

Experienced Recent Homelessness

2.12***

1.69–2.65

0.87

0.63–1.20

Experienced 1+ Behavioral Health Crisis Event in Year Before

Exit (incl. ED visits)

1.50*

1.06–2.12

0.70

0.31–1.56

Experienced 1+ ED Visit in Year Before Exit

1.30**

1.08–1.58

0.62***

0.47–0.82

Experienced 1+ Hospitalization in Year Before Exit

0.79

0.59–1.06

0.74

0.44–1.26

2+ Chronic Conditions

0.91

0.75–1.11

0.96

0.72–1.29

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

AI/AN = American Indian/Alaska Native. CI = confidence interval. ED = emergency department. HCV = Housing Choice Voucher.

NH/PI = Native Hawaiian/Pacific Islander. PH = public housing. Ref = reference group.

Source: NO HARMS integrated study dataset

Code

https://github.com/PHSKC-APDE/hud_hears/tree/main/analyses/exit_factors

64

Appendix F: Housing Outcomes Following Exit

Detailed Methodology

To control for factors that might distort the relationship between exit type and subsequent homelessness, the model was adjusted for individual-level confounders (age at exit date, gender, race, and homelessness within 3 years before the exit date) and household-level confounders (agency (Seattle Housing Authority [SHA] or King County Housing Authority [KCHA]), assistance program type (grouped into major categories of public housing, project-based vouchers, or tenant-based vouchers), length of time in housing (years from entrance to exit date), household size, an indicator for the head of household having a disability, and an indicator for the household having a single caregiver). Propensity scores were calculated for each exit type using a multinomial regression model that contained the confounding variables above and accounted for household clustering.

Inverse probability treatment weighting (IPTW) was used to weight the observations in the Cox proportional hazards model using the propensity scores. Household clustering was accounted by using sandwich estimators.

For the leave-one-out analyses, the primary analysis was re-run with each exit factor with at least 100

exits omitted in turn. The resulting distributions were visualized with forest plots comparing them with the hazard ratio estimates from the primary analysis. The exit reasons that resulted in the estimate changing the most when omitted were considered the most influential exit reasons in the primary analysis.

Detailed Results

The two figures below show the results of the leave-one-out analyses, first focusing on the negative-versus-neutral comparison (Exhibit F-1) and then positive versus neutral (Exhibit F-2).

65

Exhibit F-1: Sensitivity Analysis of Time to Homeless, by Exit Reason, Negative Versus Neutral HCV = housing choice voucher. HQS = Housing Quality Standards. KCHA = King County Housing Authority. MR = Mod Rehab. PB

= project-based. SHA = Seattle Housing Authority.

Source: NO HARMS integrated study dataset

66

Exhibit F-2: Sensitivity Analysis of Time to Homeless, by Exit Reason, Positive Versus Neutral Source: NO HARMS integrated study dataset

Code

https://github.com/PHSKC-APDE/hud_hears/tree/main/analyses/capstone

67

Appendix G: Physical Health Outcomes Following Exit

Detailed Methodology

Models were adjusted for the following variables:

 Gender (male, female, or another gender).

 Age (<25, 25–44, 45–62).

 Race/ethnicity (American Indian/Alaska Native, Asian, Black, Latinx, multiple races, Native Hawaiian/Pacific Islander, White).

 Head of household with a self-reported disability.

 Length of time in housing (<3, 3–<6, 6–<10, 10+ years).

 Housing assistance type (housing choice voucher or public housing).

 Household size.

 Single caregiver (one adult and one or more children in the household).

 1+ emergency department (ED) visit/hospitalization in the year before exit.

 2+ chronic conditions (as defined by the Chronic Condition Warehouse [Centers for Medicare and Medicaid Services, 2022]).

Detailed Results

Among exits, those who exited for positive reasons were more likely to be Asian and less likely to be Latinx or multiple races (Exhibit G-1). At the household level and compared with other exit types, those with positive exits tended to have received housing assistance longer, were in larger households, were less likely to have or be single caregivers, were less likely to have a head of household with a disability, and were more likely to live in public housing. People with positive exits also tended to be healthier, with fewer chronic conditions, ED visits, and hospitalizations in both the year before and the year after exit. Among ages <6, those with positive exits were more likely to have well-child checkups before and following exit.

When compared with people who continued to receive housing assistance, those exiting for any reason were similar in terms of age, gender, and race/ethnicity but tended to have shorter times in housing assistance, have smaller households, be more likely to have a head of household with a disability, and be less likely to live in public housing (Exhibit G-1). Those exiting also were slightly more likely to have an ED

visit or hospitalization in the year before and after exit but less likely to have a well-child visit after exit.

68

Exhibit G-1: Demographics of Those Included in the Analysis of Physical Health Outcomes Demographic

Remained

Exited

Negative Exit Neutral Exit Positive Exit

(N=34,039)

(N=5,550) (N=2,205)

(N=2,346)

(N=999)

Age

Mean (years)

21.7

22.2

22.2

23.0

20.0

Median (years)

15

16

16

17

15

Gender

Another Gender

603 (1.8%)

75 (1.4%)

30 (1.4%)

33 (1.4%)

12 (1.2%)

Female

18,952 (55.7%)

3,051 (55.0%) 1,277 (57.9%)

1,235 (52.6%)

539 (54.0%)

Male

14,484 (42.6%)

2,424 (43.7%) 898 (40.7%)

1,078 (46.0%)

448 (44.8%)

Race/Ethnicity

AI/AN

396 (1.2%)

111 (2.0%)

63 (2.9%)

41 (1.7%)

<10

Asian

2,307 (6.8%)

384 (6.9%)

92 (4.2%)

172 (7.3%)

120 (12.0%)

Black

17,743 (52.1%)

2,792 (50.3%) 1,096 (49.7%)

1,184 (50.5%)

512 (51.3%)

Latinx

2,798 (8.2%)

497 (9.0%)

254 (11.5%)

176 (7.5%)

67 (6.7%)

Multiple

3,087 (9.1%)

431 (7.8%)

194 (8.8%)

179 (7.6%)

58 (5.8%)

NH/PI

495 (1.5%)

91 (1.6%)

41 (1.9%)

39 (1.7%)

<20

White

7,213 (21.2%)

1,244 (22.4%) 465 (21.1%)

555 (23.7%)

224 (22.4%)

Time in Housinga

Mean Time (years)

5.8

4.9

5.5

3.8

6.7

Median Time (years)

5.2

3.5

4.4

2.3

6.4

Household Characteristicsa

Mean Household Size

4.3

2.8

2.8

2.5

3.8

Median Household Size

4

2

2

2

4

Single Caregiver

35.8%

30.9%

36.2%

29.7%

20.8%

Head of Household w/Disability 19.4%

31.4%

31.2%

37.3%

14.1%

Program Typea

PBV

6,299 (18.7%)

1,245 (44.3%) 255 (22.8%)

865 (67.9%)

125 (29.8%)

PH

6,788 (20.1%)

430 (15.3%)

210 (18.8%)

120 (9.4%)

100 (23.9%)

TBV

20,650 (61.2%)

1,135 (40.4%) 652 (58.4%)

289 (22.7%)

194 (46.3%)

69

Demographic

Remained

Exited

Negative Exit Neutral Exit Positive Exit

(N=34,039)

(N=5,550) (N=2,205)

(N=2,346)

(N=999)

Health and Homelessness Events

Average # of Chronic Conditions 1.0

1.0

1.1

1.0

0.7

Average # ED Visits in Year Before

Exit

0.8

1.1

1.1

1.2

0.5

Average # Hospitalizations in

Year Before Exit (per 100 people) 6.0

7.5

7.9

8.7

3.9

Experienced 1+ ED Visits in Year

Before Exit

12,529 (36.8%)

2,265 (40.8%) 931 (42.2%)

1,048 (44.7%)

286 (28.6%)

Experienced 1+ Hospitalizations

in Year Before Exit

1,516 (4.5%)

287 (5.2%)

109 (4.9%)

148 (6.3%)

30 (3.0%)

Completed 1+ Well-Child Visits in

the Year Before Exit (ages <6)

4,285 (73.6%)

614 (70.3%)

215 (68.0%)

287 (70.3%)

112 (74.7%)

Average # ED Visits in Year After

Exit

0.7

1.2

1.2

1.4

0.5

Average # Hospitalizations in

Year After Exit (per 100 people) 5.2

6.9

8.2

7.4

3

Experienced 1+ ED Visits in Year

After Exit

12,116 (35.6%)

2,149 (38.7%) 920 (41.7%)

964 (41.1%)

265 (26.5%)

Experienced 1+ Hospitalizations

in Year After Exit

1,271 (3.7%)

260 (4.7%)

115 (5.2%)

121 (5.2%)

24 (2.4%)

Completed 1+ Well-Child Visits in

the Year After Exit (ages <6)b

3,836 (65.9%)

486 (55.6%)

168 (53.2%)

228 (55.9%)

90 (60.0%)

AI/AN = American Indian/Alaska Native. ED = emergency department. HCV = housing choice voucher. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

a At household level (Remained N=33,737; Exited N=2,810; Negative N=1,117; Neutral N=1,274; Positive N=419).

b Ages <6 (Remained N=5,823; Exited N=874; Negative N=316; Neutral N=408; Positive N=150).

Source: NO HARMS integrated study dataset

70

Exhibit G-2: Regression Output from the Physical Health Outcomes Model, by Exit Type Category

Well-Child

Well-Child

ED Visits

Hospitalizations

Checkups

Checkups

(with previous

(without

visit)

previous visit)

Odds

Ratio 95% CI Odds Ratio

95% CI

Odds

Ratio 95% CI Odds

Ratio 95% CI

Exit Category

Negative

ref

—

ref

—

ref

—

ref

—

Positive

0.74**

0.61–

0.89

0.71

0.44–1.15 1.27

0.74–

2.16

1.57

0.67–

3.67

Neutral

0.87

0.75–

1.00

0.91

0.65–1.26 0.82

0.54–

1.26

1.12

0.60–

2.09

Age

<25

ref

—

ref

—

—

—

—

—

25–44

1.26**

1.07–

1.49

2.75***

1.89–3.99 —

—

—

—

45–<62

0.94

0.75–

1.17

1.84*

1.15–2.95 —

—

—

—

Age at Exit (years)

—

—

—

—

0.82***

0.74–

0.92

1.03

0.83–

1.27

Gendera

Female

ref

—

ref

—

ref

—

ref

—

Male

0.88*

0.77–

0.99

0.53***

0.39–0.71 0.93

0.66–

1.30

0.97

0.59–

1.61

Multiple

1.21

0.74–

1.99

1.21

0.46–3.17 —

—

—

—

Race/Ethnicity

White

ref

—

ref

—

ref

—

ref

—

AI/AN

1.87*

1.15–

3.05

1.26

0.58–2.75 10.50*

1.24–

89.05

0.00***

0.00–

0.00

Asian

0.56***

0.42–

0.74

0.58

0.27–1.24 1.94

0.78–

4.85

0.70

0.18–

2.76

Black

0.99

0.85–

1.16

0.91

0.67–1.24 1.15

0.68–

1.93

0.63

0.29–

1.36

Latinx

1.08

0.85–

1.36

0.58

0.31–1.07 0.95

0.45–

1.98

0.68

0.26–

1.82

Multiple

1.16

0.91–

1.48

0.89

0.53–1.49 0.85

0.35–

2.04

0.93

0.29–

3.03

71

Category

Well-Child

Well-Child

ED Visits

Hospitalizations

Checkups

Checkups

(with previous

(without

visit)

previous visit)

Odds

Ratio 95% CI Odds Ratio

95% CI

Odds

Ratio 95% CI Odds

Ratio 95% CI

NH/PI

1.35

0.83–

2.19

2.17

0.91–5.19 0.17

0.03–

1.06

0.26

0.03–

2.12

Time in Housing (years)

<3

ref

—

ref

—

ref

—

ref

—

3–5.99

0.92

0.78–

1.08

0.68*

0.47–0.98 0.71

0.47–

1.09

0.69

0.35–

1.35

6–9.99

0.88

0.74–

1.06

0.74

0.49–1.11 1.00

0.56–

1.78

0.63

0.30–

1.35

10+

0.80*

0.66–

0.97

0.76

0.49–1.19 2.52*

1.22–

5.19

0.81

0.33–

1.96

72

Category

Well-Child

Well-Child

ED Visits

Hospitalizations

Checkups

Checkups

(with previous

(without

visit)

previous visit)

Odds

Ratio 95% CI Odds Ratio

95% CI

Odds

Ratio 95% CI Odds

Ratio 95% CI

Household Characteristics

Household Size

0.91***

0.88–

0.95

0.92

0.84–1.01 0.87*

0.76–

0.99

1.05

0.89–

1.24

Single Caregiver

0.93

0.81–

1.07

0.89

0.64–1.24 0.91

0.59–

1.40

0.97

0.52–

1.82

Head of Household

Disability

1.01

0.85–

1.20

1.27

0.93–1.74 1.09

0.56–

2.13

1.91

0.75–

4.86

Program Type

HCV

ref

—

ref

—

ref

—

ref

—

PH

0.74**

0.61–

0.90

0.89

0.56–1.42 0.66

0.40–

1.08

0.81

0.34–

1.89

TBV

0.87

0.74–

1.02

1.07

0.74–1.54 0.73

0.47–

1.15

1.04

0.56–

1.95

Health

No. ED Visits in Year

Before Exit

1.53***

1.44–

1.62

—

—

—

—

—

—

No. Hospitalizations in

Year Before Exit

—

—

2.05***

1.68–2.50 —

—

—

—

2+ Chronic Conditions 2.28*** 1.91–

2.72

2.47***

1.78–3.43 —

—

—

—

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

AI/AN = American Indian/Alaska Native. CI = confidence interval. ED = emergency department. HCV = Housing Choice Voucher.

NH/PI = Native Hawaiian/Pacific Islander. PH = public housing. Ref = reference group. TBV = tenant-based voucher.

a Too few with multiple gender to include in model for well-child checkups.

Source: NO HARMS integrated study dataset

73

Exhibit G-3: Regression Output from the Physical Health Outcomes Model, by Exit Type Versus Remaining Well-Child

Well-Child

ED Visits

Hospitalizations

Checks

Checks

Category

(with previous

(without

visit)

previous visit)

Odds

Ratio

95% CI Odds Ratio

95% CI

Odds

Ratio

95% CI Odds

Ratio

95% CI

Exit Category

Remained

ref

—

ref

—

ref

—

ref

—

Positive

0.80**

0.69–

0.94

0.82

0.54–1.23 0.76

0.51–

1.14

0.85

0.43–

1.69

Neutral

1.06

0.96–

1.16

1.16

0.93–1.44 0.57***

0.44–

0.75

0.69

0.46–

1.01

Negative

1.10

1.00–

1.21

1.26*

1.03–1.55 0.67**

0.49–

0.90

0.57**

0.38–

0.87

Age

<25

ref

—

ref

—

—

—

—

—

25–44

1.24***

1.17–

1.32

2.67***

2.29–3.11 —

—

—

—

45–<62

0.88**

0.81–

0.95

1.64***

1.35–2.00 —

—

—

—

Age at Exit (years)

—

—

—

—

0.74***

0.71–

0.77

1.00

0.92–

1.08

Gendera

Female

ref

—

ref

—

ref

—

ref

—

Male

0.89***

0.85–

0.93

0.51***

0.45–0.58 0.94

0.83–

1.07

1.17

0.97–

1.42

Multiple

1.11

0.94–

1.30

0.92

0.62–1.38 —

—

—

—

Race/Ethnicity

White

ref

—

ref

—

ref

—

ref

—

AI/AN

1.11

0.90–

1.36

0.98

0.64–1.52 0.93

0.46–

1.85

0.91

0.37–

2.24

Asian

0.55***

0.50–

0.62

0.75*

0.57–0.98 1.46*

1.03–

2.07

0.69

0.39–

1.20

Black

1.12***

1.05–

1.18

1.08

0.95–1.24 1.18

0.98–

1.44

1.04

0.78–

1.38

Latinx

1.09

1.00–

1.20

0.91

0.72–1.15 1.10

0.84–

1.44

0.96

0.64–

1.44

74

Well-Child

Well-Child

ED Visits

Hospitalizations

Checks

Checks

Category

(with previous

(without

visit)

previous visit)

Odds

Ratio

95% CI Odds Ratio

95% CI

Odds

Ratio

95% CI Odds

Ratio

95% CI

Multiple

1.02

0.94–

1.12

0.90

0.73–1.11 0.98

0.73–

1.33

0.91

0.59–

1.40

NH/PI

1.09

0.91–

1.32

1.58*

1.05–2.39 0.79

0.49–

1.27

0.43*

0.23–

0.83

Time in Housing (years)

<3

ref

—

ref

—

ref

—

ref

—

3–5.99

1.00

0.94–

1.07

0.82**

0.71–0.95 1.00

0.85–

1.17

1.05

0.81–

1.35

6–9.99

0.97

0.92–

1.04

0.77***

0.66–0.89 0.99

0.84–

1.18

0.85

0.65–

1.11

10+

0.90***

0.84–

0.96

0.66***

0.56–0.77 0.95

0.77–

1.18

0.63**

0.46–

0.87

75

Well-Child

Well-Child

ED Visits

Hospitalizations

Checks

Checks

Category

(with previous

(without

visit)

previous visit)

Odds

Ratio

95% CI Odds Ratio

95% CI

Odds

Ratio

95% CI Odds

Ratio

95% CI

Household Characteristics

Household Size

0.96***

0.94–

0.97

0.95**

0.92–0.98 0.93***

0.90–

0.97

1.03

0.97–

1.09

Single Caregiver

1.02

0.97–

1.08

0.83**

0.73–0.95 0.78**

0.67–

0.90

0.86

0.68–

1.08

Head of Household

w/Disability

1.08*

1.02–

1.15

1.03

0.90–1.18 1.05

0.84–

1.32

1.28

0.92–

1.80

Program Type

HCV

ref

—

ref

—

ref

—

ref

—

PH

0.98

0.91–

1.06

0.89

0.75–1.06 0.94

0.77–

1.15

1.60**

1.16–

2.22

TBV

1.02

0.96–

1.08

1.02

0.89–1.17 0.82*

0.69–

0.97

1.22

0.95–

1.57

Health

No. ED Visits in Year

Before Exit

1.69***

1.65–

1.73

—

—

—

—

—

—

No. Hospitalizations in

Year Before Exit

—

—

2.13***

1.93–2.34 —

—

—

—

2+ Chronic Conditions 1.86***

1.74–

1.99

2.54***

2.22–2.92 —

—

—

—

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

AI/AN = American Indian/Alaska Native. CI = confidence interval. ED = emergency department. NH/PI = Native Hawaiian/Pacific Islander. PH = public housing. TBV = tenant-based voucher.

a Too few with multiple gender to include in model for well-child checks.

Source: NO HARMS integrated study dataset

Code

https://github.com/PHSKC-APDE/hud_hears/tree/main/analyses/health

76

Appendix H: Behavioral Health Outcomes Following Exit

Behavioral health conditions identified in Medicaid claims data based on algorithms provided by the Chronic Conditions Data Warehouse:

1. Attention deficit hyperactivity disorder (ADHD).

2. Adjustment disorders.

3. Alcohol use disorders.

4. Anxiety disorder.

5. Cannabis use disorder.

6. Cocaine use disorder.

7. Depression.

8. Disruption/impulse/conduct disorders.

9. Mania/bipolar disorder.

10. Opioid use disorders.

11. Other stimulant use disorders.

12. Other substance use disorders.

13. Psychotic disorder.

14. Sedative use disorder.

77

Exhibit H-1: Adjusted Odds Ratios for the Association Between Exit Type and Behavioral Health Crisis Events for All Types of Housing Assistance

All Exits

Medicaid Subset

Category

Odds Ratio

95% CI

Odds Ratio

95% CI

Exit Category

Neutral

ref

—

ref

—

Negative

2.10***

1.64–2.69

1.61***

1.29–2.00

Positive

0.95

0.60–1.49

0.90

0.62–1.30

Age

Age at Exit (years)

0.99***

0.98–0.99

1.03***

1.02–1.03

Gender

Female

ref

—

ref

—

Male

0.84

0.68–1.04

0.91

0.74–1.12

Multiple

0.71

0.23–2.17

1.24

0.54–2.83

Race/Ethnicity

White

ref

—

ref

—

AI/AN

0.92

0.44–1.95

1.67

0.91–3.08

Asian

0.77

0.44–1.35

0.37**

0.20–0.70

Black

0.86

0.66–1.10

0.82

0.65–1.04

Latinx

1.28

0.86–1.92

0.76

0.52–1.11

Multiple

1.21

0.84–1.73

1.36

0.97–1.91

NH/PI

1.25

0.46–3.38

0.68

0.18–2.53

Time in Housing

Years in Housing

0.95**

0.92–0.98

0.97*

0.94–0.99

Household Characteristics

Household Size

0.61***

0.53–0.71

0.89**

0.83–0.96

Single Caregiver

0.72

0.49–1.07

1.01

0.78–1.29

Head of Household w/Disability

1.86***

1.43–2.41

1.43**

1.13–1.80

Program Type

TBV

ref

—

ref

—

PBV

1.77***

1.31–2.39

1.49**

1.17–1.90

PH

1.12

0.78–1.60

0.79

0.57–1.10

78

All Exits

Medicaid Subset

Category

Odds Ratio

95% CI

Odds Ratio

95% CI

Existing Behavioral Health

Previous Crisis Events

9.53***

7.39–12.28

8.45***

6.81–10.49

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

AI/AN = American Indian/Alaskan Native. CI = confidence interval. NH/PI = Native Hawaiian/Pacific Islander. PBV = project-based voucher. PH = public housing. Ref = reference group. TBV = tenant-based voucher.

Source: NO HARMS integrated study dataset

Code

https://github.com/PHSKC-APDE/hud_hears/tree/main/analyses/behavioral

79

Appendix I: Wage Outcomes Following Exit

Study Population

King County Housing Authority (KCHA, 2016–2018) and Seattle Housing Authority (SHA, 2012–2018) clients constituted the cohort. The cohort was limited to those who exited federally supported housing between 2016 and 2018 to prevent the introduction of temporal biases. The cohort was further limited KCHA and SHA clients with a final exit on record who did not reenter public housing within 1 year (i.e.,

“true exits”) and to those with a recorded positive or negative exit, as defined in Chapter 4 . Those who were public housing authority (PHA) clients for less than 1 year were also excluded. Finally, observations were limited to wage earners between 18 and 61 years of age and excluded households with a wage earner 62 years old or older because senior housing and pension eligibility begin at age 62.

Data Sources and Variables

Foundational demographic data (age, gender, race/ethnicity, single-caregiver household, and head of household with a disability) were extracted from HUD Form 50058. Wage data was obtained from the Washington State Employment Security Department (ESD). Wage data are available for most Washington State employees except independent contractors and specific exempt employees (Employment Security Department, Washington State, n.d.). Wages greater than three standard deviations from the mean wage were excluded, as were hourly wages below the legal minimum wage (King County Procurement and Payables Section, 2021; Washington State Department of Labor and Industries, n.d.). The quarter of exit was defined as quarter zero. The quarters before were coded as -4, -

3, -2, -1 and the quarters after exit as 1, 2, 3, 4.

HUD Neighborhood Stabilization Program tables provided data for calculation of the percentage of Area Median Income (AMI), which was limited to households in Washington State with fewer than nine members (HUD PD&R, n.d.).

Analytic individual-level characteristics included client age, gender (female, male, or multiple [those with records indicating both male and female at different times]), race (with Hispanic as a race), quarterly wage earnings, quarterly hours worked, and quarterly hourly wages. Household-level characteristics included exit year (2016, 2017, or 2018), exit season (winter, spring, summer, or fall), number of years receiving housing assistance (continuous), head of household with a disability (binary), single-caregiver household (binary), housing agency (KCHA or SHA), and PHA program type (tenant-based voucher [TBV], project-based voucher [PBV], and public housing [PH]).

Data Linkage

The foundational data linkage process was described in Chapter 3. In addition, wage data were linked to housing data by Social Security number.

Statistical Analysis

Chi-square (categorical variables) and Kruskal-Wallis (continuous variables) tests were used to assess statistically significant differences in client characteristics by exit type. Variables that were associated with the exit type in univariate analyses were designated as potential confounders. When potential confounders were also associated with quarterly wages (assessed using Kruskal-Wallis or Spearman’s Rank Correlation tests), they were designated as confounders and were included in the final model.

80

The relationship between exit type and quarterly wages was modeled using linear regression, with random effects to account for repeated measures (persons and households) and nesting (persons within households) (equation 1). Time (quarters -4 to 4) was modeled as cubic spline with a knot at the time of exit (quarter 0) and included an interaction with exit type. A likelihood ratio test was used to determine whether to keep the interaction term. All previously identified confounders were included in the model without data transformations. Model quality was assessed by creating plots of observed versus predicted wages and plots of residuals over time.

Equation 1.

quarterly.wage = β0 + β1*exit_type + β2*spline(time) + β3*exit_type*spline(time) +

β4*confounder_1 + β5 *confounder_2+ … + βn+3* confounder_n + e + u, where …

e is the random intercept for the individual

u is the random intercept and slope for the household

The mean predicted quarterly wage was calculated by averaging 10,000 samples from the normal distribution defined by the estimate and standard error predicted for each row of the original dataset. A counterfactual was created by ascribing the mean quarterly change in wages among negative exits to the starting positive exit mean quarterly wage. Quarterly positive, negative, and counterfactual predicted wages were plotted for descriptive analyses.

Statistical significance was based on a two-sided p-value of < 0.05 and regression uncertainty was expressed as 95-percent confidence intervals (CIs). R and Rstudio were used for all analyses, with the lmerTest package for regression and the marginaleffects package for predictions (Arel-Bundock, 2022; Kuznetsova, Brockhoff, and Christensen, 2017; R Core Team, 2022; RStudio Team, 2022).

Secondary Analysis

In a secondary analysis, wages were replaced with the percentage of AMI. Percentage of AMI was of interest because it accounts for overall household wages and household size and is the metric used to define eligibility for federally subsidized housing.

81

Detailed Results

Exhibit I-1: Demographics During the Quarter of Exit for Those Who Exited Seattle and King County Public Housing Authority Programs Between January 1, 2016, and January 1, 2018

Demographic

Negative (N=675) Positive (N=680)

Total (N=1,355)

p-value

Age

0.293

Mean (SD)

34 (11)

35 (13)

35 (12)

Gender

0.076

Female

449 (66.5%)

412 (60.6%)

861 (63.5%)

Male

220 (32.6%)

261 (38.4%)

481 (35.5%)

Race/Ethnicity

0.006

AI/AN

15 (2.2%)

<10

21 (1.5%)

Asian

49 (7.3%)

81 (11.9%)

130 (9.6%)

Black

332 (49.2%)

295 (43.4%)

627 (46.3%)

Latinx

50 (7.4%)

54 (7.9%)

104 (7.7%)

Multiple

56 (8.3%)

67 (9.9%)

123 (9.1%)

NH/PI

16 (2.4%)

<10

24 (1.8%)

White

157 (23.3%)

169 (24.9%)

326 (24.1%)

Wages

< 0.001

Mean (SD)

5,568 (4,425)

8,048 (5,059)

6,812 (4,911)

Median

4,823

7,673

6,356

Hours

< 0.001

Mean (SD)

363 (210)

448 (186)

408 (202)

Median

406

480

452

Missinga

225

160

385

Wages Hourly

< 0.001

Mean (SD)

18 (8)

20 (8)

19 (8)

Median

16

18

17

Missing

225

160

385

Exit Year

< 0.001

2016

189 (28.0%)

206 (30.3%)

395 (29.2%)

2017

267 (39.6%)

199 (29.3%)

466 (34.4%)

2018

219 (32.4%)

275 (40.4%)

494 (36.5%)

Season

0.012

Winter

149 (22.1%)

121 (17.8%)

270 (19.9%)

Spring

183 (27.1%)

212 (31.2%)

395 (29.2%)

Summer

160 (23.7%)

194 (28.5%)

354 (26.1%)

Fall

183 (27.1%)

153 (22.5%)

336 (24.8%)

82

Years in Public Housing

< 0.001

Mean (SD)

7 (4)

9 (4)

8 (4)

Household Characteristics

Head of Household

w/Disability

112 (16.6%)

71 (10.4%)

183 (13.5%)

< 0.001

Single-Caregiver Household 176 (26.1%)

61 (9.0%)

237 (17.5%)

< 0.001

Percentage of AMI

< 0.001

Mean (SD)

37 (29)

66 (34)

51 (35)

Missing

7

17

24

Agency

0.675

KCHA

450 (66.7%)

446 (65.6%)

896 (66.1%)

SHA

225 (33.3%)

234 (34.4%)

459 (33.9%)

Program Type

0.007

TBV

92 (13.6%)

119 (17.5%)

211 (15.6%)

PBV

87 (12.9%)

115 (16.9%)

202 (14.9%)

PH

495 (73.4%)

446 (65.6%)

941 (69.5%)

Missing

1

0

1

AI/AN = American Indian/Alaska Native. AMI = Area Median Income. KCHA = King County Housing Authority. NH/PI = Native Hawaiian/ Pacific Islander. PBV = project-based voucher. PH = public housing. SD = standard deviation. SHA = Seattle Housing Authority. TBV = tenant-based voucher.

a When “Missing” is not shown, there are no missing values for the given variable.

Source: NO HARMS integrated study dataset

83

Exhibit I-2: Regression Fixed Effect Coefficients Describing the Relationship Between Exit Type and Wages for Those Who Exited Seattle and King County Public Housing Authority Programs Between January 1, 2016, and January 1, 2018

Term

Estimate (95% CI)

p-value

(Intercept)

$4,873 ($4,184, $5,563)

<0.001

Positive exit

$1,589 ($1,067, $2,111)

<0.001

spline(time, knots = c(0))1

$349 (– $43, $740)

0.081

spline(time, knots = c(0))2

$733 ($300, $1,166)

0.001

spline(time, knots = c(0))3

$921 ($559, $1,283)

<0.001

spline(time, knots = c(0))4

$1,233 ($1,000, $1,466)

<0.001

Exit year: 2016

Referent

Exit year: 2017

– $797 (– $1,379, – $216)

0.007

Exit year: 2018

$29 (– $545, $603)

0.922

Head of household with disability

– $1,087 (– $1,756, – $418) 0.001

Project type

TBV

Referent

PBV

– $874 (– $1,547, – $201)

0.011

PH

$23 (– $646, $692)

0.947

Years in public housing

$90 ($33, $147)

0.002

exit:spline(time, knots = c(0))1

– $179 (– $732, $374)

0.526

exit:spline(time, knots = c(0))2

$1,017 ($407, $1,628)

0.001

exit:spline(time, knots = c(0))3

$684 ($173, $1,194)

0.009

exit:spline(time, knots = c(0))4

$537 ($208, $866)

0.001

CI = confidence interval. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

Source: NO HARMS integrated study dataset

84

Exhibit I-3: Mean Predicted Wages Are Similar to Mean Observed Wages for Each Exit Type and Quarter, Seattle and King County Public Housing Authority Programs Between January 1, 2016, and January 1, 2018

Quarter

Exit Type Predicted Observed

– 4

Positive

$6,706

$6,701

– 3

Positive

$6,933

$6,933

– 2

Positive

$7,294

$7,337

– 1

Positive

$7,691

$7,621

Exit

Positive

$8,024

$8,048

1

Positive

$8,223

$8,217

2

Positive

$8,322

$8,380

3

Positive

$8,386

$8,322

4

Positive

$8,475

$8,495

– 4

Negative

$4,927

$4,934

– 3

Negative

$5,161

$5,139

– 2

Negative

$5,346

$5,369

– 1

Negative

$5,493

$5,500

Exit

Negative

$5,611

$5,570

1

Negative

$5,714

$5,771

2

Negative

$5,822

$5,772

3

Negative

$5,963

$5,988

4

Negative

$6,160

$6,155

Source: NO HARMS integrated study dataset

85

Exhibit I-4: Regression Fixed Effect Coefficients Describing the Relationship Between Exit Type and Percentage of Area Median Income for Those Who Exited Seattle and King County Public Housing Authority Programs Between January 1, 2016, and January 1, 2018

Term

Estimate (95% CI)

p-value

(Intercept)

21% (13%, 29%)

<0.001

Positive exit

24% (16%, 32%)

<0.001

spline(time, knots = c(0))1

4% (– 1%, 9%)

0.101

spline(time, knots = c(0))2

2% (– 3%, 8%)

0.396

spline(time, knots = c(0))3

5% (0%, 10%)

0.03

spline(time, knots = c(0))4

5% (2%, 8%)

<0.001

Exit year: 2016

Referent

Exit year: 2017

– 2% (– 9%, 5%)

0.563

Exit year: 2018

– 4% (– 12%, 3%)

0.264

Head of Household with disability

– 3% (– 12%, 7%)

0.59

Project type

TBV

Referent

PBV

– 2% (– 11%, 8%)

0.741

PH

– 8% (– 17%, 0%)

0.06

Years in public housing

2% (1%, 3%)

<0.001

exit:spline(time, knots = c(0))1

– 7% (– 16%, 2%)

0.152

exit:spline(time, knots = c(0))2

12% (2%, 22%)

0.02

exit:spline(time, knots = c(0))3

– 1% (– 10%, 7%)

0.728

exit:spline(time, knots = c(0))4

8% (2%, 13%)

0.005

CI = confidence interval. PBV = project-based voucher. PH = public housing. TBV = tenant-based voucher.

Source: NO HARMS integrated study dataset

86

Exhibit I-5: Residual Plot of Model Estimates Over Time Shows No Evidence of Autocorrelation Source: NO HARMS integrated study dataset

87

Exhibit I-6: Mean Predictions of Percentage of Area Median Income for Those Who Exited Seattle and King County Public Housing Authority Programs Between January 1, 2016, and January 1, 2018

AMI = Area Median Income.

Source: NO HARMS integrated study dataset

Code

https://github.com/PHSKC-APDE/hud_hears/tree/main/analyses/wages/final_report

88

Appendix J: References

Actionable Intelligence for Social Policy. 2015. Project 50: Ending Chronic Homelessness with Permanent Supportive Housing and Integrated Data Systems. https://aisp.upenn.edu/resource-article/project-

50-ending-chronic-homelessness-with-permanent-supportive-housing-and-integrated-data-

systems/.

Albertson, Elaine Michelle, Roxana Chen, Alastair Matheson, Maria Guillermina Ursua, Mike Dolan Fliss, and Stephanie Farquhar. 2020. "Effect of Public Housing Redevelopment on Reported and Perceived Crime in a Seattle Neighborhood," Crime Prevention and Community Safety 22: 381–398.

https://doi.org/10.1057/s41300-020-00096-5.

Ambrose, Brent W. 2005. "A Hazard Rate Analysis of Leavers and Stayers in Assisted Housing Programs,"

Cityscape 8 (2): 69–93. http://www.jstor.org/stable/20868593.

Andersson, Fredrik, John C. Haltiwanger, Mark J. Kutzbach, Giordano Palloni, Henry O. Pollakowski, and Daniel H. Weinberg. 2016. Childhood Housing and Adult Earnings: A Between-Siblings Analysis of Housing Vouchers and Public Housing. Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy Development and Research.

https://www.huduser.gov/portal/publications/Childhood-Housing-Adult-Earnings.html.

Anthony, Jerry. 2005. "Family Self-Sufficiency Programs: An Evaluation of Program Benefits and Factors Affecting Participants’ Success," Urban Affairs Review 41 (1): 65–92.

https://doi.org/10.1177/1078087405277883.

Aratani, Yumiko. 2010. "Public Housing Revisited: Racial Differences, Housing Assistance, and Socioeconomic Attainment Among Low-Income Families," Social Science Research 39 (6): 1108–

1125. https://doi.org/https://doi.org/10.1016/j.ssresearch.2010.04.007.

Arel-Bundock, Vincent. 2022. marginaleffects: Marginal Effects, Marginal Means, Predictions, and Contrasts. https://cran.r-project.org/package=marginaleffects.

Breslow, Norman E. 2005. "Case-Control Studies." In Handbook of Epidemiology, edited by Wolfgang Ahrens and Iris Pigeot. Springer: Berlin/Heidelberg. https://doi.org/10.1007/978-3-540-26577-1_7.

Brisson, Daniel, and Jennifer Covert. 2015. "Housing Instability Risk Among Subsidized Housing Recipients: Characteristics Associated with Late or Nonpayment of Rent," Social Work Research 39

(2): 119–128. https://doi.org/10.1093/swr/svv009.

Centers for Medicare and Medicaid Services. 2022. "Chronic Conditions Data Warehouse."

https://www2.ccwdata.org/web/guest/condition-categories-chronic.

Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. "The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment,"

American Economic Review 106 (4): 855–902. https://doi.org/10.1257/aer.20150572.

Cortes, Alvaro, Ken Lam, and David Fein. 2008. "Household Life Cycle and Length of Stay in Housing Assistance Programs," Cityscape 10 (1): 117–156. http://www.jstor.org/stable/20868645.

Dantzler, Prentiss A., and Jason D. Rivera. 2019. "Settling In or Moving Out? Exploring the Effect of Mobility Intentions on Public Housing Exits," Housing Studies 34 (4): 715–733.

https://doi.org/10.1080/02673037.2018.1470229.

89

Ellen, Ingrid Gould, Kacie L. Dragan, and Sherry Glied. 2020. "Renovating Subsidized Housing: The Impact on Tenants’ Health," Health Affairs 39 (2): 224–232,232A–232E.

https://doi.org/http://dx.doi.org/10.1377/hlthaff.2019.00767.

Employment Security Department, Washington State. n.d. Occupations Exempted from Unemployment Insurance Coverage.

https://esdorchardstorage.blob.core.windows.net/esdwa/Default/ESDWAGOV/employer-

Taxes/ESD-exempt-professions-chart.pdf.

Freeman, Lance. 2005. "Does Housing Assistance Lead to Dependency? Evidence From HUD

Administrative Data," Cityscape 8 (2): 115–133.

Geyer, Judy, Samuel Dastrup, and Meryl Finkel. 2019. "Impact of Expanded Choice on Attrition in the Housing Voucher Program," Cityscape 21 (3): 71–88.

https://www.huduser.gov/portal/periodicals/cityscpe/vol21num3/Cityscape-Nov_2019.pdf.

Gubits, Daniel, Jill Khadduri, and Jennifer Turnham. 2009. Housing Patterns of Low-Income Families With Children: Further Analysis of Data From the Study of the Effects of Housing Vouchers on Welfare Families. Cambridge, MA: Joint Center for Housing Studies of Harvard University.

https://www.jchs.harvard.edu/research-areas/working-papers/housing-patterns-low-income-

families-children-further-analysis-data.

Hartig, Florian. 2022. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. https://cran.r-project.org/package=DHARMa.

Hungerford, Thomas L. 1996. "The Dynamics of Housing Assistance Spells," Journal of Urban Economics 39 (2): 193–208. https://doi.org/10.1006/juec.1996.0010.

Johnson, Carolina, Christina McHugh, and Emily Reimal. 2021. Integrating Data to Better Measure Homelessness. Seattle, WA: King County Department of Community and Human Services.

https://kingcounty.gov/~/media/depts/community-human-

services/department/documents/KC_DCHS_Cross_Systems_Homelessness_Analysis_Brief_12_16_

2021_FINAL.ashx.

Kang, Seungbeom. 2020. "To Whom Do Housing Policies Provide Stable Housing? Examining Housing Assistance Recipients and Leavers," Urban Affairs Review 57 (5).

https://doi.org/10.1177/1078087420906203.

Kasprow, Wesley J., Robert A. Rosenheck, Linda Frisman, and Diane DiLella. 2000. "Referral and Housing Processes in a Long-Term Supported Housing Program for Homeless Veterans." Psychiatric Services 51 (8): 1017–1023. https://doi.org/10.1176/appi.ps.51.8.1017.

King County Housing Authority. 2022. "Subsidized Housing Eligibility."

https://www.kcha.org/housing/subsidized/eligibility.

King County Procurement and Payables Section. 2021. "Living Wage Ordinance—King County."

https://kingcounty.gov/depts/finance-business-operations/procurement/about-us/Living-

Wage.aspx.

Kuznetsova, Alexandra, Per B. Brockhoff, and Rune H. B. Christensen. 2017. "lmerTest Package: Tests in Linear Mixed Effects Models," Journal of Statistical Software 82 (13): 1–26.

https://doi.org/10.18637/jss.v082.i13.

90

Laurent, Amy A., Alastair Matheson, Katie Escudero, and Andria Lazaga. 2020. "Linking Health and Housing Data to Create a Sustainable Cross-Sector Partnership," American Journal of Public Health 110 (S2): S222–S224. https://doi.org/10.2105/AJPH.2020.305693.

Lubell, Jeffrey M., Mark Shroder, and Barry Steffen. 2003. "Work Participation and Length of Stay in HUD-Assisted Housing," Cityscape 6 (2): 207–223.

Martinez, Matthew, and Mark Mather. 2022. U.S. Housing Cost Burden Declines Among Homeowners but Remains High for Renters. Washington, DC: Population Reference Bureau.

https://www.prb.org/articles/u-s-housing-cost-burden-declines-among-homeowners-but-remains-

high-for-renters/.

McClure, Kirk. 2018. "Length of Stay in Assisted Housing," Cityscape 20 (1): 11–38.

McInnis, Debi, Larry Buron, and Susan J. Popkin. 2007. Are HOPE VI Families at Greater Risk for Homelessness? Brief no.7. Washington, DC: Urban Institute.

Montgomery, Ann Elizabeth, and Meagan Cusack. 2017. HUD-VASH Exit Study—Final Report.

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

Montgomery, Ann Elizabeth, Meagan Cusack, M., Dorota Szymkowiak, Jamison Fargo, and Thomas O’Toole. 2017. "Factors Contributing to Eviction From Permanent Supportive Housing: Lessons From HUD-VASH," Evaluation and Program Planning 61: 55–63.

https://doi.org/10.1016/j.evalprogplan.2016.11.014.

Newman, Sandra J., and Joseph M. Harkness. 2002. "The Long-Term Effects of Public Housing on Self-Sufficiency," Journal of Policy Analysis and Management 21 (1): 21–43.

O’Connell, Maria J., Wesley Kasprow, and Robert A. Rosenheck. 2008. "Rates and Risk Factors for Homelessness After Successful Housing in a Sample of Formerly Homeless Veterans," Psychiatric Services 59 (3): 268–275.

Office of Planning & Community Development. 2021. "About Seattle."

https://www.seattle.gov/opcd/population-and-demographics/about-seattle.

Olsen, Edgar O., Catherine A. Tyler, Jonathan W. King, and Paul E. Carrillo. 2005. "The Effects of Different Types of Housing Assistance on Earnings and Employment," Cityscape 8 (2): 163–187.

Public Health—Seattle & King County. 2022. "Community Health Indicators: American Community Survey." https://kingcounty.gov/depts/health/data/community-health-indicators/american-

community-survey.aspx?shortname=Median household income.

R Core Team. 2022. R: A Language and Environment for Statistical Computing. https://www.r-

project.org/.

Regional Affordable Housing Task Force. 2019. Final Report and Recommendations for King County, WA.

King County, WA.

https://kingcounty.gov/~/media/initiatives/affordablehousing/documents/report/RAH-Report-

Summary-7-17-19.ashx?la=en.

Richter, Francisca Garcia-Cobián, Claudia Coulton, April Urban, and Stephen Steh. 2021. "An Integrated Data System Lens Into Evictions and Their Effects," Housing Policy Debate 31 (3–5): 762–784.

https://doi.org/10.1080/10511482.2021.1879201.

91

Rohe, William M., and Rachel Garshick Kleit. 1997. "From Dependency to Self-Sufficiency: An Appraisal of the Gateway Transitional Families Program," Housing Policy Debate 8 (1): 75–108.

https://doi.org/10.1080/10511482.1997.9521248.

Rohe, William M., Michael D. Webb, and Kirsten P. Frescoln. 2016. "Work Requirements in Public Housing: Impacts on Tenant Employment and Evictions," Housing Policy Debate 26 (6): 909–927.

https://doi.org/10.1080/10511482.2015.1137967.

RStudio Team. 2022. RStudio: Integrated Development Environment for R. https://posit.co/.

Sariyar, Murat, and Andreas Borg. 2020. RecordLinkage: Record Linkage Functions for Linking and Deduplicating Data Sets. https://cran.r-project.org/package=RecordLinkage.

Smith, Robin E., Susan J. Popkin, Taz George, and Jennifer Comey. 2014. What Happens to Housing Assistance Leavers? Low-Income Working Families Paper 27. Washington, DC: Urban Institute.

https://search.proquest.com/books/what-happens-housing-assistance-

leavers/docview/1680143054/se-2?accountid=14784.

Turner, Margery Austin, and G. Thomas Kingsley. 2008. Federal Programs for Addressing Low-Income Housing Needs. Washington, DC: Urban Institute.

U.S. Department of Housing and Urban Development (HUD), Office of Policy Development & Research (PD&R). n.d. "Neighborhood Stabilization Program Data."

https://www.huduser.gov/portal/datasets/NSP.html.

———. 2019. Strategic Plan 2018–2022. Washington, DC: U.S. Department of Housing and Urban Development. https://www.hud.gov/sites/dfiles/SPM/documents/HUDSTRATEGICPLAN2018-

2022.pdf.

Washington State Department of Labor & Industries. n.d. "History of Washington State’s Minimum Wage." https://lni.wa.gov/workers-rights/wages/minimum-wage/history-of-washington-states-

minimum-wage.

Washington State Health Care Authority. 2020. EPSDT [Early Periodic Screening, Diagnosis, and Treatment] Well-Child Checkups for Your Child or Teen. https://www.hca.wa.gov/assets/free-or-

low-cost/19-0056-epsdt-well-child-checkups.pdf.

———. 2022. Delivery System Reform Incentive Payment (DSRIP) Measurement Guide.

https://www.hca.wa.gov/assets/program/mtp-measurement-guide.pdf.

92

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Washington, DC 20410-6000

January 2024

Document Outline

Housing and Urban Development Health, Economic, and Residential Stability (HUD HEARS) Study

DISCLAIMER

Acknowledgments

Foreword

Contents

List of Exhibits

Executive Summary

Introduction

Literature Review

Data Sources and Linkage

Exits and Types

Factors Associated with Exits from Housing Assistance

Outcomes Following Exit

Residential Stability

Physical Health

Behavioral Health

Wage Income

Conclusion

Chapter 1: Introduction

Chapter 2: Literature Review Summary

Introduction

Results

Exit Types

Factors Associated with Exits

Outcomes Following Exits

Conclusions

Chapter 3: Data Sources and Linkage

Chapter 4: Exits and Exit Types

Chapter 5: Who Exits From Housing Assistance?

Descriptive Statistics

Regression Results

Chapter 6: Outcomes Following Exit: Residential Stability

Chapter 7: Outcomes Following Exit: Physical Health

Chapter 8: Outcomes Following Exit: Behavioral Health

Chapter 9: Outcomes Following Exit: Economic

Chapter 10: Conclusion

Policy and Program Implications

Reproducibility and Sustainability

Recommendations for Future Work

Appendix A: Acronyms

Appendix B: Literature Review

Detailed Methodology

Inclusion Criteria

Search Terms

Published Literature Strategy

Gray Literature Strategy

Specific Websites

Relevant References

Processing Results

Full List of Papers Examined

Appendix C: Data Sources and Linkage

Appendix D: Exit Definitions

Appendix E: Factors Associated With Exit

Detailed Methodology

Data Sources and Variables

Statistical Analysis

Secondary Analysis

Detailed Results

Appendix F: Housing Outcomes Following Exit

Detailed Methodology

Detailed Results

Appendix G: Physical Health Outcomes Following Exit

Detailed Methodology

Detailed Results

Appendix H: Behavioral Health Outcomes Following Exit

Appendix I: Wage Outcomes Following Exit

Study Population

Data Sources and Variables

Data Linkage

Statistical Analysis

Secondary Analysis

Detailed Results

Appendix J: References