Implementing Approaches

to Address Unsheltered

Homelessness

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Implementing Approaches to Address

Unsheltered Homelessness

FINAL RESEARCH REPORT

Prepared by:

Hiren Nisar

Natalie Morrissey

Cory Carr

Charles Horseman

Dennis Okyere

2M Research

December 21, 2020

ACKNOWLEDGMENTS

The Study Team would like to thank subject matter expert Barbara Poppe for her support in planning and developing the case studies included in this project. We would also like to thank the following Continuums of Care for their participation and support : Homeward in Richmond, Virginia; Montgomery County Department of Health and Human Services; and San Diego Regional Task Force for the Homeless.

MacKenzie Regier, Gail Clark, Cindy Romero, and Joshua Townley provided invaluable editing and formatting support throughout the study. The Study Team would also like to acknowledge our partners at the U.S. Department of Housing and Urban Development’s (HUD’s) Office of Policy Development and Research, with specific assistance from Galen Savidge-Wilkins, Nicole Watson, and Ransford Osafo-Danso. We also thank Brett Esders and William Snow from HUD’s Office of Special Needs Assistance Programs.

Disclaimer

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

FINAL RESEARCH REPORT

FOREWORD

Even before the COVID-19 pandemic placed an unprecedented strain on the homeless assistance system, unsheltered homelessness—people sleeping on the streets, in cars, or in other places not meant for human habitation—was a growing concern for HUD policy makers. After declining steadily for more than a decade, HUD’s Point-in-Time count measuring homelessness nationally has risen each year since 2017. This trend was driven by an increase in the number of people experiencing unsheltered homelessness, which has increased each year since 2015 even as the number of people in emergency shelters has decreased.

The research community has produced valuable insights into what causes people to experience homelessness. We understand from this research that the number of people who end up unsheltered, in aggregate, depends on housing market conditions and what resources are available when people need to access the homelessness assistance system. The analysis of market trends in this report finds that communities with tighter housing markets have higher rates of unsheltered homelessness, consistent with previous research like HUD’s Market Predictors of Homelessness (2019). The analysis of available homelessness assistance resources provides a descriptive look at shelter and permanent housing capacity across communities with different trends in homelessness.

The report’s three case studies—on Richmond, Virginia; Montgomery County, Maryland; and San Diego, California—il ustrate how local policy and market contexts shape communities’ responses to unsheltered homelessness. The diversity of approaches helps inform key policy questions:

• How can the existing tools of the homeless assistance system better serve people living in unsheltered situations? Data from Richmond showed positive outcomes for an effort to target Rapid Re-Housing (RRH) to people experiencing unsheltered homelessness and those with high levels of vulnerability.

• What is the best way to deploy new resources? Montgomery County demonstrated the value of additional housing resources when paired with a more targeted vulnerability assessment.

• How can programs launched during times of crisis provide lessons for the future? San Diego launched two emergency shelter options in response to the COVID-19 pandemic: non-congregate shelter in hotels and congregate shelter at the city’s convention center. Although formerly unsheltered residents of the convention center had comparable rates of returning to homelessness as other shelter residents, people staying in the hotels did substantially better than both those in the convention center and in the mainstream shelter system overall.

These case studies are a valuable first look at complex programs, but more work is needed, particularly around efforts to address homelessness during and after the COVID-19 pandemic. I hope this report provides a starting point for future research into the causes of and solutions to unsheltered homelessness.

Todd Richardson

Acting Assistant Secretary for Policy Development and Research

U.S. Department of Housing and Urban Development

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TABLE OF CONTENTS

Acknowledgments .................................................................................................................................. ii

Executive Summary ............................................................................................................................... iv

Key Findings from the Quantitative Study .................................................................................................... iv

Key Findings from the Qualitative Study ....................................................................................................... vi

1. Introduction ..................................................................................................................................... 1

Study Objectives ............................................................................................................................................ 3

Overview of the Report ................................................................................................................................. 4

2. Methodology and Data Sources........................................................................................................ 5

Approach to the Quantitative Study .............................................................................................................. 5

Approach to the Qualitative Study ................................................................................................................ 9

3. Overview of Housing Factors Associated with Community-Level Unsheltered Homelessness........ 12

Trends in Unsheltered Homelessness .......................................................................................................... 12

Level of Resources Available in Homeless Assistance System ...................................................................... 16

Performance of Homeless Assistance Systems ............................................................................................ 19

Housing Market Characteristics ................................................................................................................... 21

4. Rapid Re-housing Program in Richmond, Virginia .......................................................................... 24

Context ........................................................................................................................................................ 25

Rapid Re-Housing Program Overview .......................................................................................................... 26

Key Policies Affecting Implementation ........................................................................................................ 28

Greater Richmond Continuum of Care Implementation of Rapid Re-housing .............................................. 29

Key Partnerships Supporting Program Activities .......................................................................................... 35

Sustainability ............................................................................................................................................... 36

Successes, Challenges, and Lessons Learned ................................................................................................ 37

Conclusion ................................................................................................................................................... 40

5. Montgomery County’s Systems Approach ...................................................................................... 41

Context ........................................................................................................................................................ 42

Program Overview ....................................................................................................................................... 45

Key Policies Affecting Implementation ........................................................................................................ 46

Montgomery County CoC’s Implementation of a Systems Approach ........................................................... 47

Key Partnerships Supporting Program Activities .......................................................................................... 53

Sustainability ............................................................................................................................................... 54

Successes, Challenges, and Lessons Learned ................................................................................................ 55

Conclusion ................................................................................................................................................... 57

6. San Diego’s COVID-19 Emergency Shelters ..................................................................................... 58

Context ........................................................................................................................................................ 59

Program Overview ....................................................................................................................................... 62

Key Policies Affecting Implementation ........................................................................................................ 63

Outreach to Individuals Experiencing Unsheltered Homelessness ............................................................... 64

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Temporary Lodging Program ....................................................................................................................... 66

Operation Shelter to Home: The Convention Center ................................................................................... 68

Housing Navigation ..................................................................................................................................... 71

Key Partnerships Supporting Program Activities .......................................................................................... 75

Sustainability ............................................................................................................................................... 76

Successes, Challenges, and Lessons Learned ................................................................................................ 77

Conclusion ................................................................................................................................................... 79

7. Conclusion ...................................................................................................................................... 81

Findings from the Quantitative Study .......................................................................................................... 81

Findings from the Qualitative Study ............................................................................................................ 82

Appendix A. Additional Variable Considerations .................................................................................. 84

Appendix B. Constructing the Continuum of Care-Level Dataset .......................................................... 85

Appendix C. Detailed Information on the Quantitative Study and Additional Tables ......................... 101

Appendix D. Master Interview Guides ................................................................................................ 108

Greater Richmond Continuum of Care ....................................................................................................... 108

Montgomery County Continuum of Care ................................................................................................... 118

San Diego’s COVID-19 Emergency Shelters ................................................................................................ 128

Appendix E. Lessons Learned from Interviews with Individuals with Lived Experience ...................... 140

Recruitment and Logistics .......................................................................................................................... 140

Interview Guide Considerations................................................................................................................. 141

Interviewing Reflections ............................................................................................................................ 142

Appendix F. SUMMARY STATISTICS FOR PROGRAM-LEVEL DATA FOR THE THREE CASE STUDIES ...... 143

Greater Richmond Continuum of Care ....................................................................................................... 143

Montgomery County Continuum of Care ................................................................................................... 147

San Diego’s COVID-19 Emergency Shelters ................................................................................................ 150

References .......................................................................................................................................... 153

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EXECUTIVE SUMMARY

On a single night in January 2019, 567,715 people—including individuals and people in families—were experiencing homelessness in the United States based on the U.S. Department of Housing and Urban Development’s (HUD’s) Point-in-Time (PIT) count. Slightly more than one-third (37 percent; 211,293) of those people were experiencing unsheltered homelessness, meaning they slept on the streets, in cars, in parks, or in other places not designated for or used as regular sleeping accommodations (Henry et al., 2020). Following nearly a decade of decline, the number of people experiencing unsheltered homelessness has increased each year since 2015, when an estimated 173,268 people experienced unsheltered homelessness in the United States (Henry et al., 2020). Individuals experiencing unsheltered homelessness1 are especially vulnerable to the negative health, economic, and social effects of homelessness. Communities across the United States are implementing strategies to address unsheltered homelessness—and with the recent emergence of the Coronavirus Disease (COVID-19), there is an urgent need for enhanced cooperation between public health authorities, homeless service systems, and other partners. This study used a mixed-methods approach to (1) create a quantitative profile of housing market characteristics and homeless services systems elements across communities with different trends in their unsheltered homeless population (Quantitative Study), and (2) create three qualitative profiles, supplemented with available program data, of Continuums of Care (CoCs)2

implementing programs or initiatives to address unsheltered homelessness in their communities (Qualitative Study).

KEY FINDINGS FROM THE QUANTITATIVE STUDY

The quantitative study builds on previous analyses of the relationships between homelessness and housing, economic characteristics, and demographic characteristics. Previous research identified housing market factors as one of the most important factors associated with unsheltered homelessness at the community level (Glynn and Fox, 2017; Nisar et al., 2019). Those studies largely grouped CoCs together nationally or in limited groups on the basis of geography, market characteristics, or available data. The current study uses more recent housing market characteristics data (2014–2018) and offers a new perspective on these data by instead grouping CoCs on the basis of their underlying trend in unsheltered homelessness. Among the CoCs with available data, slightly more than 54 percent experienced an increase in their counts of individuals experiencing unsheltered homelessness from 2015

through 2019, 45 percent of CoCs experienced a decrease in counts, and 1 percent of CoCs experienced no change in their counts. To add nuance to the analysis, the study team subdivided the “increasing”

and “decreasing” groups of CoCs on the basis of whether the change in the unsheltered population has been continuous or the trend has shown some variation. This distinction enables the study team to focus separately on communities where the trend is unambiguously positive or negative and on 1 This report uses the term “individual” to refer to a person who is not part of a family with children during an episode of homelessness. Individuals may be homeless as single adults or unaccompanied youth, or they may be in multiple-adult or multiple-child households.

2 This report uses the term “Continuum of Care” to refer to a group of stakeholders within a specific geographic area that are organized to develop and implement a plan to prevent and end homelessness in that geographic area.

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communities with a mixed track record over the past few years in their analysis. The study team classified the 336 CoCs into four groups:

1. “Steady increasing” CoCs that had two increasing intermediate3 unsheltered homeless counts.

2. “Fluctuating increasing” CoCs that had a larger 2019 count of individuals experiencing unsheltered homelessness than in 2015 but varying intermediate trends.

3. “Fluctuating decreasing” CoCs that had a smaller 2019 count of individuals experiencing unsheltered homelessness than in 2015 and varying intermediate trends.

4. “Steady decreasing” CoCs that had decreasing intermediate trends.

The prominent housing market differences among the groups were higher home values, median rental prices, and fair market rents in CoCs with “increasing” counts relative to those with “decreasing”

unsheltered homelessness counts. Whereas only small differences exist across other housing market characteristics, the combination of higher rents and the unavailability of rental units suggests that

“increasing” CoCs more often operate in tight rental markets compared with “decreasing” CoCs. On the housing supply side, the rate of new housing permits was consistently higher in communities with

“increasing” counts. That finding, along with larger increases in median home values over time for the same groups possibly indicates newer built housing units in tighter markets may still be unaffordable to people at risk of homelessness. The “steady decreasing” group of CoCs was associated with lower shares of renter-occupied housing units, higher rental vacancy rates, and low incidences of overcrowding.

Within the “increasing” groups, CoCs with “fluctuating increasing” trends tended to have higher home values and more available resources in their homeless assistance systems. Taken together, these results suggest a tight rental market and an unaffordable housing market for the “increasing” groups of CoCs, with “steady increasing” CoCs in a worse position to respond. These findings support the conclusion of earlier research that housing market dynamics and the availability of affordable housing are closely tied to the rates of unsheltered homelessness at the community level.

Resources available for CoCs in each of the four groups were proportionally highest for CoCs with fluctuating trends across all programs analyzed. Across all years, the “fluctuating increasing” and

“fluctuating decreasing” CoCs had the highest and second highest mean bed counts per capita. “Steady increasing” CoCs had the lowest permanent supportive housing (PSH)/other supportive housing bed counts per capita in each year and had little change in emergency shelter bed counts per capita. “Steady decreasing” CoCs had low numbers of CoCs reporting at least one rapid re-housing (RRH) bed and low counts of RRH beds per capita in 2015 but experienced high growth in terms of both those measures by 2019. Taken together, these data suggest that higher levels and growth in shelter and permanent housing resources among the two “fluctuating” CoC groups may be influencing the trend in unsheltered homelessness, whereas the comparably lower levels of resources in the two “steady” groups could imply that market factors end up playing a larger role in their overall trends both positively and negatively.

System performance measures—key metrics for HUD policymakers—reveal differences between

“increasing” and “decreasing” groups of CoCs in terms of durations of homelessness, bed coverage, and programmatic outcomes. Each of the four measures capturing the duration of homelessness suggests that, on average, the two “increasing” CoC groups are associated with longer periods of homelessness.

The “steady increasing” CoCs had the lowest average percentages of successful street outreach 3 Intermediate trends are the changes in unsheltered homelessness population between 2015 and 2017 or 2017 and 2019.

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outcomes and successful exits in the most recent year, whereas the “steady decreasing” CoC group had the highest average percentages for those measures. 4 These findings suggest that market factors need to be considered when assessing elements of program performance and efficacy.

KEY FINDINGS FROM THE QUALITATIVE STUDY

Communities across the United States are implementing strategies to tackle increasing unsheltered homelessness. The qualitative phase of this study presents case studies of three communities that are adopting practices specifically aimed at addressing unsheltered homelessness, including one effort in response to the COVID-19 pandemic. The practices highlighted in this study are from the following CoCs:

 Greater Richmond CoC: Richmond, Virginia—a “steady increasing” CoC. 5

 Montgomery County CoC: Montgomery County, Maryland—a “fluctuating decreasing” CoC.

 San Diego City and County CoC: San Diego County, California. 6

Facing limited PSH availability, 7 the Greater Richmond CoC specifically targets RRH—short- to medium-term rental assistance—to single adults, with an emphasis on those experiencing unsheltered homelessness. 8 The Greater Richmond CoC pairs the RRH program, which is considered a “lighter touch”

housing program, with a variety of services its partners offer to stabilize clients who may otherwise have been eligible for more intensive PSH. The Greater Richmond CoC reported that only 17 percent of clients who came from unsheltered settings returned to homelessness within a 12-month period after exit. This finding is notable because it is consistent with the outcomes of other RRH programs, despite those programs not necessarily targeting the most vulnerable population in their communities. 9 The Greater Richmond CoC demonstrated that, if implemented in this fashion, RRH paired with services can be a cost-effective alternative to PSH in certain housing markets and an effective bridge to other ongoing forms of assistance, such as Veterans Affairs Supportive Housing (VASH) for clients who are veterans.

The Montgomery County CoC implements a systems approach to house people with the highest vulnerability scores in the county, many of whom are individuals experiencing unsheltered homelessness. The Montgomery County CoC coordinates outreach at the county level and implements an unnamed client policy, which allows outreach providers and shelter operators to track people experiencing homelessness who do not provide a name. The Montgomery County CoC added a custom 9-point vulnerability scale accounting for a person’s veteran status, if they are currently unsheltered, if they are vulnerable to exploitation, and other vulnerability criteria, to the standard Vulnerability Index-4 The measure for street outreach includes only CoCs that reported on street outreach (value greater than 0).

5 Greater Richmond CoC noted that the impact of weather shelters not being open on the day of the PIT count affected the accuracy of the unsheltered count in 2019.

6 San Diego County was not categorized because the CoC changed their PIT count methodology between 2015 and 2019.

7 PSH is an intervention that combines affordable housing assistance with case management and voluntary support services to address the needs of people who experience chronic homelessness. For more details, please see

https://endhomelessness.org/ending-homelessness/solutions/permanent-supportive-housing/.

8 RRH provides short-term rental assistance and services to help people obtain housing quickly, increase self-sufficiency, and stay housed. For more details, see https://endhomelessness.org/ending-homelessness/solutions/rapid-re-housing/.

9 Recent literature shows returns to homelessness in other RRH evaluations compared with the benchmark of 15 percent set by the National Al iance to End Homelessness. Greater Richmond CoC specifically targets single adults experiencing homelessness and has a return-to-homelessness rate of 17 percent within 1 year of program exit.

https://www.huduser.gov/portal/sites/default/files/pdf/Systematic-Review-of-Rapid-Re-housing.pdf

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Service Prioritization Decision Assistance Tool to better prioritize vulnerable clients. The Montgomery County CoC primarily houses clients through PSH—which combines housing with voluntary support services and case management—and tweaked the standard program model by offering a high- or low-intensity service approach. Montgomery County also has significantly expanded county funding for PSH

since 2017, adding 300 beds in 3 years, and substantially reduced the number of people experiencing unsheltered homeless; that number dropped from 131 people in 2017 to 75 people in 2019—a nearly 43-percent decrease.

The Regional Task Force on the Homeless (RTFH) serves as the CoC lead for San Diego County and helped coordinate a COVID-19 pandemic response for people experiencing homelessness in the county.

The response included modified outreach methods and the opening of temporary shelters at the San Diego Convention Center and three hotels in the county that offered non-congregate shelter. 10 Both of those shelters temporarily expanded their capacity starting in April through an anticipated closure in December 2020. The efforts successfully engaged people experiencing homelessness who had never engaged in services or shelter; 26 percent of the clients served through the projects had never been reached by the mainstream homeless services system. RTFH worked with key partners—including service providers, the San Diego Housing Commission, the city of San Diego, and the county of San Diego—to implement the temporary emergency shelters, to attempt to limit the spread of COVID-19, and to provide strategic housing navigation from multiple housing programs, including PSH, VASH, RRH, and other programs. The programs were relatively successful in temporarily sheltering people experiencing unsheltered homelessness, increasing shelter capacity, reducing the risk of catching COVID-19, and connecting clients to services including housing such as RRH and project- or tenant-based PSH programs. The Temporary Lodging Program showed particular promise; formerly unsheltered residents sheltering in hotels had considerably lower rates of returning to unsheltered homelessness relative to those served in the convention center and those served by existing shelter options.

10 San Diego County’s Temporary Lodging Program is similar to the model for California State’s Project Roomkey; however, the county’s program was initiated before the state’s program and uses local Coronavirus Aid, Relief, and Economic Security (CARES) Act funding, not funding from the state. The county submitted numbers for the non-congregate hotels to the California Department of Social Services that are posted on the Project Roomkey website. The county has used state Project Roomkey funding to provide six trailers to support COVID-19 testing efforts.

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1. INTRODUCTION

On a single night in January 2019, 567,715 people—including individuals and people in families—were experiencing homelessness in the United States based on the U.S. Department of Housing and Urban Development’s (HUD’s) Point-in-Time (PIT) count. Slightly more than one-third (37 percent; 211,293) of those people were experiencing unsheltered homelessness, meaning they slept on the streets, in cars, in parks, or in other places not designated for or used as regular sleeping accommodations (Henry et al., 2020). 11 Following nearly a decade of decline, the number of people experiencing unsheltered homelessness has increased each year since 2015, when an estimated 173,268 people experienced unsheltered homelessness in the United States; however, that trend varies across geography and markets; unsheltered homelessness is more prevalent in large metropolitan areas and along the West Coast (Henry et al., 2018; Nisar et al., 2019). Given these trends, many communities have implemented programs and initiatives to address unsheltered homelessness.

Homelessness is associated with myriad negative issues related to economic status, physical health and mental conditions, and social and emotional outcomes. These negative effects manifest in different ways for people who are experiencing sheltered or unsheltered homeless. Evidence indicates that individuals12 who are experiencing unsheltered homelessness are more vulnerable than people in shelters and deal with higher rates of issues related to physical and mental health and substance abuse (NASEM, 2018; Nisar et al., 2019; Rountree, Hess, and Lyke, 2019). Those health complications can affect the delivery and implementation of programs to provide health care and other assistance to individuals who are unsheltered. For example, a reliance on emergency medical care may mean that individuals do not receive proper, regular treatment for ongoing health conditions, which can cause strain and complications related to accessing other resources, including housing.

The demographics of the population experiencing unsheltered homelessness differ substantially from the population in sheltered settings. Compared with the overall homeless population, single adults make up nearly all (93 percent) of the unsheltered population, and people who are unsheltered are mostly male (69 percent). The racial composition of the unsheltered homeless population indicates disproportionate representation by people of color compared with the general population, although less so than for the overall homeless population. For example, in 2019, 57 percent of people experiencing unsheltered homelessness were White (compared with 77 percent of the overall population and 48

percent of the overall homeless population), and 27 percent of people experiencing unsheltered homelessness were African-American (compared with 13 percent of the overall population and 40

percent of the overall homeless population) (Census, 2018b; Henry et al., 2020). Unsheltered homelessness increased across all racial groups from 2018 to 2019, with the largest absolute increases among Whites (an increase of 5,592; 5 percent) and African-Americans (an increase of 5,288; 10

11 Despite increases in the number of individuals experiencing homelessness each year since 2015, the total population of individuals experiencing unsheltered homelesnesss has decreased since 2007.

12 This report uses the term “individual” to refer to a person who is not part of a family—that is, a household with at least one adult and at least one child—during an episode of homelessness. Homeless individuals may be single adults or unaccompanied youth, or individuals may be in multiple-adult or multiple-child households.

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percent). The number of Native Americans experiencing unsheltered homelessness increased by 2,200

(28 percent).

The demographics of the population experiencing unsheltered homelessness differ substantially from the population in sheltered settings. Compared with the overall homeless population, single adults compose nearly all (93 percent) of the unsheltered population, and people who are unsheltered are mostly male (69 percent). Rates of chronic homelessness—having a disabling condition while experiencing long or repeated durations of homelessness—are substantial y higher among the population experiencing unsheltered homelessness (30 percent) than among the population in sheltered settings (12 percent). 13 Those differences between the unsheltered and sheltered homeless populations may justify the need for programs tailored to specific populations (Henry et al., 2020).

Previous research indicates that housing market characteristics—including rental costs, evictions, and prevalence of overcrowded housing units—are associated with higher rates of unsheltered homelessness (Glynn and Fox, 2017; Nisar et al., 2019). Those housing market factors reflect the shortage of housing units that are affordable for individuals who are presently or at risk of experiencing homelessness.

A wide range of housing and social services has been developed to assist people experiencing unsheltered homelessness and to address their needs. Many studies have examined these policies and programs, with a robust body of literature focused on permanent supportive housing (PSH). PSH is general y used to support the most vulnerable people experiencing homelessness, including the chronically homeless14 and people with disabilities. PSH provides indefinite rent subsidies for housing, along with wraparound supportive services, with the goal of recipients maintaining stable housing. 15

Evidence suggests that PSH programs improve health outcomes, increase the use of health services, and increase housing stability among participants (NASEM, 2018).

Other recent studies have focused on local policies and programs that seek to address unsheltered homelessness. For example, the City of Philadelphia’s Encampment Resolution Pilot was an initiative to shut down two homeless encampments in the Kensington neighborhood of the city. Evidence from an evaluation of this pilot program indicated that the coordination of services and support systems is instrumental to effectively support closing encampments, which displaces many individuals who are experiencing chronic homelessness (Metraux et al., 2019). In another study, the Economic Roundtable produced a meta-analysis of 26 PIT count datasets to provide a detailed description of the population experiencing homelessness in Los Angeles, California (Flaming, Burns, and Carlen, 2018). That research provides insights into policy prescriptions that may affect the unsheltered homeless population in particular. For example, the study highlights the need for targeted interventions that use system-wide engagement to integrate housing assistance with social services, health care, and employment assistance. Specifically, the study indicates that approximately one-third of individuals who are 13 This information is based on PIT count information CoCs provided to HUD.

https://files.hudexchange.info/reports/published/CoC_PopSub_NatlTerrDC_2019.pdf.

14 Twenty-nine percent of individuals experiencing unsheltered homelessness were chronically homeless in 2019.

https://files.hudexchange.info/reports/published/CoC_PopSub_NatlTerrDC_2019.pdf.]

15 According to the 2019 Annual Homelessness Assessment Report (Henry et al., 2020), 60,941 individuals were chronical y homeless and unsheltered.

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experiencing unsheltered homelessness live in their vehicles, indicating that policies should be specifically tailored to the needs of a variety of populations.

The recent emergence of the coronavirus disease (COVID-19) highlights the need for comprehensive services and enhanced cooperation between Continuums of Care (CoCs), public health authorities, state and local homeless service systems, and other partners. 16 Although how the health and economic effects of the COVID-19 pandemic will affect the unsheltered homeless population is still unclear, communities are responding with substantial efforts to address COVID-19 among homeless populations. 17 For example, in California, which has both the largest share of its homelessness population who are unsheltered and the largest unsheltered homeless population overall, the state government launched Project Roomkey, which secured 15,000 hotel rooms for people experiencing homelessness statewide. The state structured the program to give preference to people considered high risk, such as older adults, people displaying COVID-19 symptoms, and people who have tested positive for COVID-19 (Bedayn and Simpson, 2020).

Unsheltered homelessness has always been a priority for HUD and communities across the country, but recent increases in unsheltered homelessness and the COVID-19 pandemic have underscored the need for better information on efforts to address the issue. Various individual and community factors are associated with unsheltered homelessness, and communities across the United States are implementing strategies to address unsheltered homelessness. Deepening knowledge of these factors and initiatives will enable policymakers to develop and implement effective programs and tools to reduce and eliminate homelessness among unsheltered populations.

STUDY OBJECTIVES

This study analyzes conditions in the housing markets in communities and how those conditions are associated with unsheltered homelessness in those communities. The study also describes local responses to unsheltered homelessness. The study team used a mixed-methods approach to address the study objectives:

Quantitative Study. Create community-level profiles of market characteristics and homeless services system elements across areas with decreasing or increasing levels of unsheltered homelessness. Conduct a quantitative analysis of data on local housing market characteristics in communities with increasing and decreasing levels of unsheltered homeless population and of the program data for the programs or initiatives selected in the qualitative study.

Qualitative Study. Create qualitative local profiles of challenges and successful approaches in the administration of programs or initiatives addressing unsheltered homelessness,

supplementing with program- or community-level data where available.

16 CoCs administer and support various community-wide programs with the goals of ending homelessness and increasing self-sufficiency among those individuals in populations who are experiencing homelessness.

https://www.hudexchange.info/programs/coc/.

17 The U.S. Centers for Disease Control and Prevention provides “Interim Guidance for Homeless Service Providers To Plan and Respond to COVID-19” : https://www.cdc.gov/coronavirus/2019-ncov/community/homeless-shelters/plan-prepare-

respond.html.

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This report describes additional insights into the community-level factors associated with unsheltered homelessness and insights into the programs that communities are using to address unsheltered homelessness at the local level.

OVERVIEW OF THE REPORT

This report is organized into seven chapters. Following this introduction, Chapter 2 provides an overview of the methodology and a summary of the data used in the study. Chapter 2 is divided into two main sections, outlining the study team’s approach to the quantitative and qualitative studies. Key considerations addressed in those sections are the research approach, data types and sources, data collection procedures, and analysis techniques. Chapter 3 presents results for the quantitative phase of the study, including an overall analysis of the housing market factors associated with changes in unsheltered homelessness and a comparison of CoC types by housing market characteristics, available resources, and system performance. Chapters 4 through 6 present the results of the qualitative case studies for the Greater Richmond CoC (Chapter 4), Montgomery County CoC (Chapter 5), and San Diego County CoC (Chapter 6). The authors present their conclusions in Chapter 7.

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2. METHODOLOGY AND DATA SOURCES

This chapter discusses the overall approach used to address the research objective and the accompanying research questions (RQs). More specifically, this chapter focuses on the strategies to generate and analyze the data necessary to answer each RQ. This chapter is organized according to the two main phases of the research: the quantitative study and the qualitative study.

APPROACH TO THE QUANTITATIVE STUDY

The quantitative phase of this study reveals a comprehensive quantitative profile of communities according to those groups of communities in which unsheltered homelessness is increasing or decreasing. The quantitative study builds on previous analyses of the relationships between homelessness and housing, economic, and demographic characteristics. Prior research identified housing market factors as one of the most important factors associated with unsheltered homelessness at the community level (Nisar et al., 2019). Data from Nisar et al. (2019) provide a valuable starting point for addressing the major RQs for the quantitative phase of the study. 18 By using more recent data, the current study expands on the existing data sources regarding factors associated with homelessness rates across communities. The analysis in this study differs from the approach taken in the Nisar et al. (2019) study in two ways: this study does not include non-housing variables, and it examines Point-in-Time (PIT) counts from 2015 to the most recent year (2019).

The following are the major RQs for the quantitative study:

1. What is different in housing markets where unsheltered homelessness has increased and in markets where unsheltered homelessness has decreased?

2. What is different between the level of resources available in the homeless assistance system in both temporary and permanent housing communities where unsheltered homelessness has

increased and in places where unsheltered homelessness has decreased?

3. What is different about the performance of homeless assistance systems in communities where unsheltered homelessness has increased and in communities where unsheltered homelessness has decreased?

4. What are the characteristics of communities with high rates of unsheltered homelessness that then saw a decrease in recent years?

Before addressing the RQs above, this report presents a broad picture of housing market factors associated with community-level changes in unsheltered homelessness, which involved the study team examining trends in PIT count data reported by Continuums of Care (CoCs) and identifying additional factors that provide a more nuanced understanding of unsheltered homelessness trends. The study team computed descriptive statistics at the CoC level for all primary outcome variables (overall rate of homelessness, sheltered homelessness rate, and unsheltered homelessness rate) and all housing market 18 Whereas Nisar et al. (2019) examined community-level factors across several domains (housing market, economic conditions, safety net, demographic composition, and climate conditions), the current study focuses solely on community-level housing market data combined with HUD-provided data on homelessness.

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variables associated with homelessness from Nisar et al. (2019). The Study Team also conducted an analysis of the primary variables of interest over time, especial y when correlations between those variables were high. The report presents only the relevant and interesting data analysis based on the average homelessness data within groupings (communities where unsheltered homelessness is increasing vs. communities where it is decreasing).

After assembling a broad picture of local housing market conditions associated with unsheltered homelessness, the Study Team grouped CoCs on the basis of their overarching trends in unsheltered homelessness, using the PIT count data. The list below outlines the CoC groupings on the basis of their absolute increases and decreases in unsheltered homelessness from 2015 to 2019. 19

1. CoCs with a net increase in unsheltered homeless counts between 2015 and 2019, with increases between 2015 and 2017 and with increases between 2017 and 2019.

2. CoCs with a net increase in unsheltered homeless counts between 2015 and 2019 but a smaller decrease or no change between 2015 and 2017 or between 2017 and 2019.

3. CoCs with a net decrease in unsheltered homeless counts between 2015 and 2019, with decreases between 2015 and 2017 and with decreases between 2017 and 2019.

4. CoCs with a net decrease in unsheltered homeless counts between 2015 and 2019 but a smaller increase or no change between 2015 and 2017 or between 2017 and 2019.

DATA SOURCES

The Study Team used publicly available data on levels of sheltered and unsheltered homelessness (PIT

count); local housing market conditions (such as Fair Market Rent [FMR], newly permitted housing units, and vacancy rate); resources for individuals experiencing homelessness (Housing Inventory Count [HIC]); and designated performance metrics (System Performance Measures [SPMs]). 20 The Study Team worked with HUD to ensure that this analysis included a comprehensive list of data sources that covers all the factors identified. Exhibit 2.1 and Exhibit 2.2 outline the primary outcome variables and housing market variables (associated with unsheltered homelessness), respectively. As the primary outcome variables are measured at the CoC level, the Study Team created a dataset in which the unit of measurement is the CoC. For details on how the final dataset was constructed at the CoC level, please see Appendix B.

Appendix C presents detailed information on the variable creation. The results from the analysis of these data sources are presented in Chapter 3.

19 Four CoCs had no change in the unsheltered homeless counts between 2015 and 2019. The study team worked with HUD to include those four CoCs in one of the four categories mentioned. In addition, 25 CoCs are missing data; the study team did not categorize those CoCs unless HUD provided a category for them.

20 Data from the Homelessness Management Information System (HMIS) for each of the programs or initiatives selected for the qualitative study were examined to provide contextual information for the case studies.

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Exhibit 2.1 | CoC-Level Variables

Factors of

Variables

Data Source

Years

Scaling Variable

Homelessness

Rate of individuals experiencing

HUD PIT; Census

2019; 2017;

Estimated total

unsheltered homelessness (per 10,000

Bureau PEP (for

Levels of Sheltered

2015

population

population)

scaling)

and Unsheltered

HUD PIT; Census

Homelessness

Rate of individuals experiencing

2019; 2017;

Estimated total

Bureau PEP (for

homelessness (per 10,000 population)

2015

population

scaling)

2019; 2017;

Estimated total

Total year-round ES beds

HUD HIC

2015

population

2019; 2017;

Estimated total

Available Homeless

Total year-round TH beds

HUD HIC

2015

population

Assistance

Resources

2019; 2017;

Estimated total

Total year-round RRH beds

HUD HIC

2015

population

2019; 2017;

Estimated total

Total year-round PSH/OPH beds

HUD HIC

2015

population

Average length of homelessness in

HUD SPM

2017; 2015

-

days (ES, SH)*

Median length of homelessness in

HUD SPM

2017; 2015

-

days (ES, SH)*

Average length of homelessness in

HUD SPM

2017; 2015

-

days (ES, SH, TH)*

Median length of homelessness in

HUD SPM

2017; 2015

-

days (ES, SH, TH)*

Bed coverage rate (ES, TH)

HUD SPM

2017; 2015

-

Performance of

Percentage returns in 6 months (ES,

HUD SPM

2017; 2015

-

Homeless

TH, SH, SO, PSH)

Assistance System

Percentage returns in 12 months (ES,

HUD SPM

2017; 2015

-

PSH, TH, SH, SO)

Percentage returns in 24 months (ES,

HUD SPM

2017; 2015

-

PSH, SO, SH, TH)

Percentage with successful SO

HUD SPM

2017; 2015

outcome

Percentage with successful ES, PSH-

HUD SPM

2017; 2015

-

RRH, SH, TH exit

Percentage with successful PSH

HUD SPM

2017; 2015

-

retention or exit

Urbanicity

Urbanicity category

HUD

-

-

Notes: Variables with asterisks use weighted average to adjust for CoC mergers since the reported 2015 values. The collapsing of these four variables used the total HMIS count from SPM data as weights.

Acronyms: ES = emergency shelter; HIC = Housing Inventory Count; HMIS = Homeless Management Information System; PEP

= Population Estimates Program; PIT = Point-in-Time; PSH = permanent supportive housing; RRH = rapid re-housing; SH =

safe haven; SO = street outreach; SPM = System Performance Measure; TH = transitional housing.

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Exhibit 2.2 | Community-Level Housing Market Variables

CoC Weighting

Variables

Data Sources

Years

Geographies

Scaling Variable

Variable

Average House

2018; 2016;

County; Place

Owner-occupied

ACS 5-year Estimates

-

Values

2014

in County

housing units

Zil ow; ACS 5-year

County;

2018; 2016;

Owner-occupied

Median House Values

Estimates (for

Place; County

-

2014

housing units

weighting)

Subdivision

Percentage of

2018; 2016;

County; Place

Owner-occupied

Homeowners with

ACS 5-Year Estimates

-

2014

in County

housing units

Cost Burden

Average Contract

2018; 2016;

County; Place

ACS 5-Year Estimates

-

-

Rent

2014

in County

2018; 2016;

County;

Renter-occupied

Median Contract Rent ACS 5-Year Estimates

-

2014

Place; Tract

housing units

Average Rental Utility

2018; 2016;

County;

Renter-occupied

ACS 5-Year Estimates

-

Cost

2014

Place; Tract

housing units

Percentage of Renters

2018; 2016;

County; Place

Renter-occupied

ACS 5-Year Estimates

-

with Cost Burden

2014

in County

housing units

Share of Renter-

2018; 2016;

County; Place

Total occupied

ACS 5-Year Estimates

-

Occupied Units

2014

in County

housing units

2018; 2016;

County; Place

Rental Vacancy Rate

ACS 5-Year Estimates

-

Total rental units

2014

in County

ACS 5-Year Estimates;

Census Bureau, 2010

2018; 2016;

County; Place

Housing Density

-

Square miles

Decennial Census (for

2014

in County

scaling)

Eviction Lab; ACS 5-

County;

Eviction Rate

Year Estimates (for

2016, 2014

-

Total rental units

Place; Tract

scaling)

Eviction Lab; ACS 5-

County;

Eviction Filing Rate

Year Estimates (for

2016, 2014

-

Total rental units

Place; Tract

scaling)

Share of

2018; 2016;

County; Place

Occupied housing

Overcrowded

ACS 5-Year Estimates

-

2014

in County

units

Housing Units

HUD FMR;a ACS 5-

County;

FMRs (by number of

2018; 2016;

Renter-occupied

Year Estimates (for

County

-

bedrooms)

2014

housing units

weighting)

Subdivision

Census Bureau

(2018a), Housing

Newly Permitted

2018; 2016;

Permits Survey; ACS

County; Place

-

Total housing units

Housing Units

2014

5-Year Estimates (for

scaling)

Census Bureau

Average Value of

(2018a), Housing

2018; 2016;

Newly Permitted

Permits Survey, ACS

County; Place

-

Total housing units

2014

Housing Units

5-Year Estimates (for

scaling)

a Although HUD published FMR data, the ACS 5-year estimates are the underlying data source for FMR estimates.

ACS = American Community Survey; FMR = Fair Market Rent.

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APPROACH TO THE QUALITATIVE STUDY

The qualitative phase comprises three case studies of programs or initiatives designed to specifically address unsheltered homelessness. Major RQs for the qualitative study include the following: 1. What are the key designs and components of the program or initiative?

2. How does the specific program or initiative fit within the larger community’s response to homelessness and other community-level responses to homelessness?

3. What are the challenges associated with the implementation of this program or initiative?

4. Do relevant stakeholders believe this program has been successful in reducing unsheltered homelessness?

5. What kinds of planned or desired changes to the program model or initiative structure would increase success?

6. Did the program or initiative require homeless service providers to collaborate with nontraditional partners, and what could have improved those partnerships?

7. Is this program or initiative suitable for wide adoption on a greater scale, either within the community in question or in other communities?

8. Is this program or initiative sustainable, and how have the communities planned for stepped-down levels of support from different parts of government?

This section describes the study team’s approach to selecting and recruiting the study sites, data collection procedures, and techniques for analyzing the qualitative data.

SITE SELECTION AND RECRUITMENT

The study team worked with HUD to identify and select three CoC programs or initiatives to participate in the case studies, with the goal of investigating and describing programs or initiatives that are addressing unsheltered homelessness. 21 The sample included three HUD-recommended programs or initiatives with interesting approaches to addressing unsheltered homelessness, either in response to the COVID-19 pandemic or before the outbreak in the United States. Those programs include the following:

 Greater Richmond CoC (GRCoC)—Richmond, Virginia—a “steady increasing” CoC22

o The case study focuses on GRCoC’s use of rapid re-housing (RRH) vouchers, specifically for the unsheltered population.

 Montgomery County CoC (MCCoC)—Montgomery County, Maryland—a “fluctuating

decreasing” CoC

o The MCCoC consists of private and public groups aiming to prevent and end

homelessness in Montgomery County, Maryland. The case study focuses on

21 Seattle/King County CoC operates throughout the City of Seattle and King County region. The case study was intended to focus on Seattle/King County’s response to COVID-19, specifical y on the rapid deployment of an isolation and quarantine approach. The site was dropped from the study due to concerns of duplication with a University of Washington and Gates Foundation study investigating the isolation and quarantine efforts.

22 GRCoC noted that the impact of weather shelters not being open on the day of the PIT count affected the accuracy of the unsheltered count in 2019.

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Montgomery County’s system design and how the CoC has achieved major reductions in

unsheltered homelessness.

 San Diego County CoC—San Diego County, California23

o The Regional Task Force on the Homeless (RTFH) serves as the CoC lead for San Diego County. The case study focused on the CoC’s response to COVID-19 among the

unsheltered homeless population. Specifically, the study team examined the CoC’s

implementation of non-congregate shelter for individuals experiencing homelessness, a temporary emergency shelter at the San Diego Convention Center, and revised outreach protocols.

After selecting the CoCs, the study team scheduled a 60-minute introduction telephone call with the point of contact, other designated program staff, or both at each CoC to identify and collect contact information from no more than 12 program stakeholders (including 3 alternates) to participate in the telephone interviews. Stakeholder types included staff of local CoCs, nonprofit partners, local government staff, public officials, or individuals with lived experience with the initiative or program. 24

PRIMARY DATA COLLECTION PROCESS

Following HUD’s selection of sites, the study team used information gathered from HUD’s subject matter experts (SMEs), the introduction calls, and the program documents to develop a master interview guide for each CoC program or initiative.

The study team’s SME, Barbara Poppe, provided input

Interview Guide Domains

on the content of each master guide, which was



Context and Policies Affecting

organized into standard domains on the basis of the

Implementation

RQs and included questions specific to the unique and



Program Implementation

innovative aspects of the programming, as well as



Supporting Partnerships

questions relevant to the types of stakeholders



Successes and Chal enges



Recommendations for Improvement,

interviewed for each site. Appendix D includes the

Expansion, and Sustainability

master interview guide for each site. The study team

scheduled 60-minute telephone interviews with up to

nine program stakeholder interviews per site between September 9 and October 30, 2020. For individuals with lived experience with the program or initiative, the study team sought assistance from service provider staff (that is, case managers) to schedule and organize the interviews (see Appendix E

for additional details on the recruitment of individuals with lived experience). The study team understood that, given the ongoing COVID-19 response in many cities, CoCs and specific respondents might be unable to participate in the study or might have time limitations, which required limiting the duration of some interviews, such as with government staff and public health officials. Thus, the study team optimized interview guides to focus on the most pertinent questions on the basis of the 23 San Diego County was not categorized because it has changed the PIT count methodology since 2015.

https://www.sandiego.gov/sites/default/files/statement_rtfh_pitc.pdf.

24 Learning from individuals with lived experience with the initiative was key to understanding the nuances of the CoC’s approach to addressing unsheltered homelessness. The study team worked with CoC staff to ensure they had the necessary resources to participate in the virtual site visit. For that purpose, the study team also provided those individuals with incentive ($100 gift cards) to participate in the interview.

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respondents’ roles, thereby limiting the interviews’ duration. The study team worked with CoC staff to replace respondents if they were unresponsive or not available during the data collection period.

Local-Level Program and Initiatives Data

To supplement the interview data, the study team obtained local-level program data from the sampled study sites. The local-level data comprised demographic and programmatic information for people who were experiencing unsheltered homelessness and who had been in contact with the homeless services system of the sampled CoCs. Discussion items for the data calls included the nature of the local-level program data that the study sites track and how much of the data could be shared with the study team, as well as any associated data use agreements (DUAs) that both parties may be required to sign. Each CoC required a DUA signed by both parties that specified the data elements to be shared, the uses of the data, and how the data would be transferred, stored, and safeguarded. The period for the requested data varied based on the focus of each case study. For the GRCoC and MCCoC case studies, the study team requested program-level data on people who have been in contact with the homeless services system from January 1, 2018, through December 31, 2019. Project types on which the data were reported included street outreach, RRH, and shelter (ES, SH, and TH); as well as data from the Shelter and Housing Coordination project in the Homeless Management Information System (HMIS). For the San Diego case study, which focused on the CoC’s response to COVID-19 among the unsheltered homeless population, the data included people who have been in contact with the homeless services system from February 1 through August 31, 2020. Project types included street outreach (SO), temporary lodging program, homeless-dedicated non-congregate emergency shelter, and congregate shelter settings (convention center and other shelters).

QUALITATIVE ANALYSIS—INTERVIEWS AND PROGRAM DATA

The study team developed a high-level codebook to code the interview data by site, and those data were further tailored to the site on the basis of key components. The study team grouped interview transcripts by CoC, and one study team member coded data from each CoC to build familiarity with the nuances of the program or initiative. Once the data were coded, the study team completed a within-site (that is, not cross-case) thematic analysis of the data to create a profile of each CoC’s program or initiative. The study team developed the first profile, GRCoC, in close consultation with the study team’s SME, Barbara Poppe, to ensure satisfactory and appropriate interpretation and presentation of findings.

In addition, the study team provided a draft of the first profile to HUD SMEs for their review and substantive input on the profile’s structure and content, which informed the development of the remaining profiles: San Diego and Montgomery County CoCs. The study team also provided the individual case studies to the respective CoCs for their review. In addition, the study team supplemented the qualitative interviews with local-level program data. After receiving data from the CoCs, the study team cleaned and transformed each data file into a standardized, common format such that all files had a common set of variable names, formats, lengths, and so forth, and specified the value(s) used to indicate missing data. Then, we conducted a descriptive analysis to help us understand the characteristics of program participants, intensity of program participation, primary outcomes of the program, and extent to which participating in the programs leads to placements in permanent housing.

Findings from the analysis of the local-level program data are incorporated in the case studies and presented in Appendix F.

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3. OVERVIEW OF HOUSING FACTORS

ASSOCIATED WITH COMMUNITY-LEVEL

UNSHELTERED HOMELESSNESS

Although the overall homeless population has declined since 2011, in more recent years, the unsheltered homeless population has increased substantially. Unsheltered homelessness in major cities and Continuums of Care (CoCs) covering mostly urban areas25 rose by nearly 25 percent—from 87,345 in 2015 to 109,252 in 2017. By contrast, between 2015 and 2017, suburban CoCs experienced a very smal increase in unsheltered homelessness, and rural CoCs experienced a decline in unsheltered homelessness (Nisar et al., 2019). National- or even state-level data can paint a confusing picture regarding trends in homelessness. Rates of increase in people experiencing homelessness on the west coast in particular has drawn national focus recently, but relatively less attention has been paid to communities that are improving or that have experienced mixed results in addressing homelessness. To address that issue, the study team analyzed publicly available data on housing market conditions, Housing Inventory Count (HIC) data, Point-in-Time (PIT) count data, and System Performance Measures (SPMs) to examine market characteristics and homeless system elements across communities with increasing and decreasing levels of unsheltered homelessness and thereby obtain a more nuanced picture of housing markets and conditions in communities with similar overall trends. This chapter provides an overview of the results of the quantitative analysis. The chapter presents a broad overview of housing market factors associated with community-level changes in unsheltered homelessness. Next, the chapter addresses each research question associated with the quantitative phase by comparing the CoC groups’ characteristics with respect to housing markets, available resources, and system performance.

TRENDS IN UNSHELTERED HOMELESSNESS

The study team analyzed trends in homeless counts for CoCs that provided consistent and complete PIT

counts across the period of analysis. 26 Of the potential 391 CoCs that submitted PIT counts in 2019, 20

CoCs had zero or missing values for individuals who were experiencing unsheltered homelessness in the years of interest, and another 35 CoCs had made methodological changes to their PIT counts, according 25 The term “Continuum of Care” means a group of stakeholders within a specific geographic area that are organized to develop and implement a plan to prevent and end homelessness in that geographic area.

(https://www.hudexchange.info/programs/coc/).

26 The PIT count data noted and adjusted for all CoC mergers from 2015 to 2019. Only two instances of irregular service area changes warranted revisions. Irregular service area changes were remedied through the combination of data for the CoCs in question. The two cases involved Roseville, Rocklin/Placer County CoC (CA-515), and Nevada County CoC (CA-531); and Bridgeport, Stamford, Norwalk, Danbury/Fairfield County CoC (CT-503), and Connecticut Balance of State CoC (CT-505). In the case of the California CoCs, the service area of CA-515 during 2017 and 2015 PIT counts included the county that was covered by the newly established CA-531 in 2019. The two CoCs in Connecticut experienced a swap in coverage areas that resulted in CT-503 having a larger service area in 2019 PIT counts than in previous years, whereas CT-505 had a shrinking coverage area.

The study team also removed four records of CoCs from U.S. Territories because supplementary housing market data on those areas was unavailable.

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to HUD. 27 The exclusion of those incomplete or methodologically varying CoCs allows for a more complete and organic trend analysis of PIT counts over time. Among the 336 CoCs meeting the criteria for analysis, about 54 percent experienced an increase in their counts of individuals who were experiencing unsheltered homelessness from 2015 through 2019, with slightly more than 45 percent of CoCs experiencing a decrease in counts and 1 percent of CoCs reporting no change in counts. The study team classified the 336 CoCs into 4 groups:

1. “Steady increasing” CoCs that had two increasing intermediate28 unsheltered homeless counts (25 percent).

2. “Fluctuating increasing” CoCs that had a larger 2019 count of individuals experiencing unsheltered homelessness than in 2015 but varying intermediate trends (29.17 percent).

3. “Fluctuating decreasing” CoCs that had a smaller 2019 count of individuals who were experiencing unsheltered homelessness than in 2015 and varying intermediate trends (25.89

percent).

4. “Steady decreasing” CoCs that had decreasing intermediate trends (19.94 percent). 29

Exhibit 3.1 displays the count of CoCs within each group and the overall share of each CoC group. An examination of the magnitude of those trends indicates that the two groups with increasing populations of individuals who were unsheltered from 2015 through 2019 experienced larger differences in terms of proportional change (see Exhibit 3.2 and

Exhibit 3.1 | CoC Groupings

Appendix C). 30 Those CoCs in the “steady

increasing” group experienced a 123.8-percent

increase in unsheltered homelessness compared

with a 38.4-percent increase for the “fluctuating

increasing” group. For CoCs with decreasing

populations of unsheltered individuals, CoCs in

the “steady decreasing” group experienced a

46.6-percent decrease in unsheltered

homelessness compared with a 26.8-percent

decrease for CoCs in the “fluctuating decreasing”

group.

Notes: A complete list of CoCs by grouping is provided in

Exhibit B.3 in Appendix B. Exhibit B.4 provides the map of the

CoCs by grouping.

27 The value of 391 includes the 2 study team-induced mergers and excludes CoCs in U.S. Territories.

28 Intermediate PIT count trends are the trends between the 2015 and 2017 PIT counts or between the 2017 and 2019 PIT

counts.

29 The four CoCs that reported no change in unsheltered homeless counts in 2015 and 2019 were classified on the basis of their most recent intermediate trend. Of those four CoCs, two reported increases in the number of individuals who were experiencing unsheltered homelessness between 2017 and 2019 and were classified as “fluctuating increasing,” whereas the remaining two experienced decreases and were classified as “fluctuating decreasing.”

30 Although this statement may sound discouraging, the distribution of change values with the two “decreasing” groups should theoretically be smaller than the positive values of the “increasing” groups because negative percentage changes have a ceiling value of 100 percent. In other words, individual counts can grow by more than double the initial count value, but an end count value is at least zero, which would be equal to a 100-percent change.

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Exhibit 3.2 | Number of Individuals Experiencing Unsheltered Homelessness Over Time, by CoC Grouping Steady Increasing

Steady Decreasing

1500

1257.6

500

389.8

993.5

400

1000

737.4

267.2

ount

ount 300

208.2

ean C

200

500

ean C

M

M 100

0

0

2015

2017

2019

2015

2017

2019

Year

Year

Fluctuating Increasing

Fluctuating Decreasing

367.2

600

485.8

400

339.8

435.5

500

394.3

284.8

300

400

ount

ount

300

200

ean C 200

ean C

M

100

100

M

0

0

2015

2017

2019

2015

2017

2019

Year

Year

Note: *CoCs with fewer than 10 individuals who were experiencing unsheltered homelessness in any of the 3 years of PIT counts are excluded from these results because those CoCs may produce misleading percentage change values.

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Higher mean values for the “steady increasing” CoC group, coupled with less extreme quartile values in 2015 (as seen in Exhibit C.1 in Appendix C), suggest that this group has a right skew, driven by a set of very high outliers, compared with the other three CoC groups. Conversely, the upper and lower quartile values for the “steady decreasing” group suggest that that group had higher starting counts in 2015 than the other CoC groups. This fact seems to indicate that CoCs in the “steady decreasing” group may be implementing programs or may have had other factors that led to a reduction in unsheltered homelessness. Regarding the percentage change in unsheltered homelessness from 2015 to 2019, the median value for the percentage change in the “steady increasing” CoC group suggests that almost one-half of those areas experienced a doubling of individuals who were experiencing unsheltered homelessness in 4 years. The CoCs within the two groups associated with fluctuating intermediate trends experienced similar size reductions or increases, as measured by 50th-percentile values. Finally, the “steady decreasing” group of CoCs that commonly had large starting counts experienced an average decrease of 46.6 percent.

To compare trends across CoCs, it is important to scale PIT counts as a share of the overall population (represented as the number of individuals who are experiencing unsheltered homelessness per 10,000

population). Scaling PIT counts to the overall population in the area reveals distinctive patterns among the four CoC groups (Exhibit 3.3). In rates of both unsheltered and overall homelessness, a clear ascending pattern emerges in mean values from “steady increasing” to “decreasing groups” in 2015.

That is, CoCs in the “steady increasing” group had lower rates of unsheltered and overall homelessness in 2015, with progressively higher rates for each group (“fluctuating increasing,” “fluctuating decreasing,” and “steady decreasing”). Similarly, a clear descending pattern in mean values across groups occured in 2019, apart from the mean unsheltered rate for “fluctuating decreasing” CoCs. The added nuance of population provides a clearer picture of how raw PIT count trends correspond to population growth and lends credence to trend categorization. CoCs classified as “steady increasing” can be described as CoCs starting with proportionally low rates of individuals experiencing unsheltered and total homelessness that experienced major increases in recent years. Conversely, “steady decreasing”

CoCs that reported decreases in the number of people experiencing unsheltered homelessness started with markedly high rates of unsheltered and total homelessness. Areas with fluctuating increasing or decreasing numbers of people experiencing homelessness were between CoCs with steady positive or negative trends of homelessness and more often reported larger decreases than increases in intermediate trends of rates of overall homelessness, as shown by the negative percentage change from 2015 and 2019 in Exhibit 3.3.

Exhibit 3.3 | Rates of Overall and Unsheltered Homeless Persons, by CoC Grouping

Mean Values

% Change from

Variable

CoC Grouping

2015 to 2019

2015

2017

2019

Steady Increasing

4.86

6.67

9.22

211.8%

Rate of Unsheltered Homeless

Fluctuating Increasing

5.14

5.30

6.50

55.0%

Individuals

Fluctuating Decreasing

5.84

5.33

3.92

-33.2%

(per 10,000 Population)

Steady Decreasing

8.92

5.77

4.42

-51.4%

Steady Increasing

16.21

16.85

19.15

13.9%

Overall Rate of Homeless

Fluctuating Increasing

18.86

17.53

18.68

-4.1%

Individuals

Fluctuating Decreasing

19.23

17.59

16.51

-14.0%

(per 10,000 Population)

Steady Decreasing

19.50

15.20

13.80

-24.1%

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To better understand how the CoCs’ distribution varies by geography, the study team categorized each CoC into four urbanicity groups31 following the categorization used in Henry et. al (2020). Exhibit 3.4

shows that three of four CoCs that operate in major cities have experienced an overall increase in unsheltered homeless counts between 2015 and 2019. CoCs that operate in areas described as non-major cities have a similar distribution, with about one in five CoCs reporting an increase in unsheltered homelessness from 2015 to 2017 and 2017 to 2019. That the distribution of CoC types by geography is close to evenly distributed across trend groups is notable on its own. TSome have a perception that increasing homelessness is a problem in cities, but as seen in Exhibit 3.4, more than one-half of “steady increasing” CoCs are not in major cities. In addition, one-fourth of the communities with “decreasing”

trends are in major cities.

Exhibit 3.4 | Urbanicity by CoC Group

Other Largely

Major City

Largely Suburban

Largely Rural

Urban

CoC Grouping

% Within

% Within

% Within

% Within

N

N

N

N

Group

Group

Group

Group

Steady Increasing

19

44.19

12

22.22

33

22.00

20

22.99

Fluctuating Increasing

13

30.23

11

20.37

52

34.67

21

24.14

Fluctuating Decreasing

10

23.26

18

33.33

29

19.33

29

33.33

Steady Decreasing

1

2.33

13

24.07

36

24.00

17

19.54

CoC = Continuum of Care.

Note: Urbanicity classification for the two cases of artificial mergers were not determined and are excluded from this table.

In the following sections, the study team compares the characteristics of communities that experienced increases and decreases in unsheltered homelessness in terms of (1) resources available in the homeless assistance system, both temporary and permanent housing; (2) performance of homeless assistance systems; and (3) housing markets in an effort to examine what influences changes in unsheltered homeless counts.

LEVEL OF RESOURCES AVAILABLE IN HOMELESS ASSISTANCE SYSTEM

Groups with fluctuating trends typically reported more homeless assistance resources, as measured by proportionally higher bed counts, than CoCs with steady increases or decreases in people experiencing unsheltered homelessness. To appropriately analyze resource levels across CoCs, the study team used the population rate and scaling formula employed for PIT counts to bed counts as well. Programmatic trends across all groups show that CoCs have been increasing emergency shelter (ES), rapid re-housing (RRH), and permanent supportive housing and other permanent housing (PSH/OPH) bed counts per capita since 2015, and the per capita means of transitional housing (TH) have been steadily decreasing (see Exhibit 3.5). In addition to decreased TH beds, the decrease in the CoCs reporting at least one TH

bed since 2015 suggests that fewer CoCs are providing year-round TH beds. The comparison of per capita means within groups shows the largest programmatic expansion occurred in the RRH program, 31 The study applies the same urbanicity categorization as used in Henry et al. (2020). The Annual Homeless Assessment Report, or AHAR, report uses the geographic data the U.S. Department of Education’s National Center for Education Statistics published to determine the urbanicity categories for all CoCs.

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FINAL RESEARCH REPORT

for which the percentage change in per capita bed rates varied between 50.3 percent and 82.7 percent across the four CoC groupings. 32 Conversely, the prevalence of CoCs reporting at least one RRH bed has increased from 2015 to 2019 from as little as 20.24 percentage points in the “steady increasing” CoCs to 26.87 percentage points in the “steady decreasing” group.

Across all years and programs, both of the fluctuating CoC groups provided the highest per capita counts of year-round beds. CoCs in the “steady increasing” group can be distinguished from those in the other groups by lower per capita counts of PSH/OPH beds, low growth in per capita ES beds, and a smaller reduction in TH beds. On the opposite side, the “steady decreasing” group of CoCs is characterized as having the lowest per capita counts of ES beds and dramatic decreases in per capita TH beds, and they often are later adopters of RRH but with a high percentage growth in RRH beds since 2015. Although CoCs with fluctuating trends provide the highest means across all programs, programmatic growth differs between those of overall increasing and decreasing unsheltered counts. CoCs classified as

“fluctuating increasing” provided the highest per capita bed resource counts in 2015 and 2019, prioritized growth in PSH/OPH (as shown by the large difference in means), and had consistently increasing or decreasing means for all programs between 2015 and 2017 and between 2017 and 2019.

The mirroring “fluctuating decreasing” group produced the largest difference in means between terminus years for both ES and RRH but had the smallest growth in means for PSH/OPH. “Steady decreasing” and “fluctuating decreasing” CoCs overall also featured increases in the means of RRH and PSH/OPH beds per capita in 2017 but saw a decrease in PSH/OPH beds per capita in 2019. To a lesser extent, this same up-and-down trend happened with “fluctuating decreasing” means for RRH, whereas the mean beds per capita for “steady decreasing” CoCs continued to rise into 2019. Without a deeper dive into each CoC, those results may reflect programs coming online or going offline or CoCs running out of funds for RRH or PSH.

32 The “fluctuating increasing” group of CoCs experienced the smallest mean increase of 50.3 percent (calculated as [4.97-3.31]/3.31), and the “fluctuating decreasing” group had the largest increase, of 82.7 percent.

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Exhibit 3.5 | Housing Inventory Count Rates by Types of Beds

ES Beds

TH Beds

16.0

16.0

14.0

14.0

000

12.0

9.0

12.0

000

10.0

7.6

8.5

7.8

9.1

7.8

9.1

8.2 8.3

6.8 6.5 7.1

10.0

8.0

ion

8.0

4.9

5.9

5.8

5.1

6.0

ion 6.0

3.9

4.6

4.9

3.3

4.1

3.8

3.8 3.1

4.0

ulat

4.0

2.0

ulat 2.0

ate per 10,

0.0

ate per 10,

Pop

0.0

Pop

Steady

Fluctuating

Fluctuating

Steady

Steady

Fluctuating

Fluctuating

Steady

ean R

Increasing

Increasing

Decreasing

Decreasing

ean R

Increasing

Increasing

Decreasing

Decreasing

M

M

2015

2017

2019

2015

2017

2019

RRH Beds

PSH/OPH Beds

16.0

16.0

14.0 14.7

14.2 13.4

13.5

14.0

12.5

14.0

11.9 12.0

12.1

12.2

000

12.0

000

12.0

10.4

10.8

10.0

10.0

ion 8.0

ion 8.0

6.0

4.4 5.0

6.0

ulat

3.0 3.8

3.3

4.1 4.1

3.3

4.0

2.2

2.2

1.9 2.6

ulat

ate per 10,

4.0

2.0

ate per 10,

Pop

2.0

Pop

0.0

0.0

ean R

Steady

Fluctuating

Fluctuating

Steady

ean R

Steady

Fluctuating

Fluctuating

Steady

M

M

Increasing

Increasing

Decreasing

Decreasing

Increasing

Increasing

Decreasing

Decreasing

2015

2017

2019

2015

2017

2019

ES = emergency shelter; PSH/OPH = permanent supportive housing/other permanent housing; RRH = rapid re-housing; TH = transitional housing.

Note: Reported values of zero are excluded from mean calculations.

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FINAL RESEARCH REPORT

PERFORMANCE OF HOMELESS ASSISTANCE SYSTEMS

Relating trends in unsheltered homelessness to selected SPMs is complicated by the variability of reported values of SPM within CoC groups and the limited time frame of data available (2015 and 2017).

After appropriate data cleaning was performed, 33 the study team performed statistical tests of yearly differences within each group. In 44 tests of mean differences between groups, only three statistically significant differences were found at the 95-percent confidence level (see Exhibit 3.6).

The “steady increasing” and “fluctuating increasing” CoC group means for the measure of successful PSH

outcomes (SPM 7) increased from 2015 to 2017 by 1.74 and 1.42 percentage points, respectively. These two results may seem counterintuitive but potentially signify expanded local efforts to curb increasing homelessness by ensuring positive results. Finally, a 12.4-percentage-point decrease in successful street outreach (SO) outcomes for “steady increasing” CoCs was found to be statistically significant. Whereas increased rates of positive SO outcomes ideally parallel decreasing rates of unsheltered homelessness, increasing rates of homelessness may explain a significant decrease in positive SO outcomes because resources may become stretched.

Cross-sectional means of the selected measures indicate that sheltered individuals experience longer stays in CoCs classified as one of the two “increasing” groups, and other marginal divisions may exist in bed coverage rates and programmatic outcomes. The most notable difference between the overall

“increasing” and “decreasing” CoC groups is captured by central measures of homelessness duration.

The smallest value for the median duration of homelessness within ES-Safe Haven (SH) (35.88 days) across the two “increasing” groups for both years is marginally greater than the largest median duration of homelessness of 35.19 days for the two “decreasing” CoCs. The average duration of homelessness for measures including ES-SH and ES-SH-TH also show a clear division between “increasing” and

“decreasing” groups of CoCs in each year.

Unlike measures of duration of homelessness, mean bed coverage rates across the CoC groups did not noticeably differ in 2015 but showed an ascending pattern, from 75.75 percent in “steady increasing”

CoCs to 86.11 percent in “steady decreasing” CoCs, for 2017. The “steady increasing” CoCs also held the lowest average percentage of successful SO outcomes, at 40.54 percent, and the lowest percentage of successful ES-PSH-RRH-SH-TH exits, at 42.21 percent, in 2017, whereas the “steady decreasing” CoCs had the highest average values for those measures.

33 The study team mediated the reporting issues by removing observations in the 1st and 99th percentile for each SPM.

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FINAL RESEARCH REPORT

Exhibit 3.6 | System Performance Measures, by CoC Grouping

Mean Value

Variable

CoC Grouping

Difference

2015

2017

Steady Increasing

79.91

78.33

-1.57

SPM 1: Average Duration of

Fluctuating Increasing

85.72

72.05

-13.67

Homelessness, in Days

(ES, SH)

Fluctuating Decreasing

71.36

61.47

-9.89

Steady Decreasing

75.03

65.18

-9.85

Steady Increasing

40.91

35.88

-5.02

SPM 1: Median Duration of

Fluctuating Increasing

44.90

36.31

-8.59

Homelessness, in Days

(ES, SH)

Fluctuating Decreasing

35.19

30.95

-4.24

Steady Decreasing

34.94

35.04

0.11

Steady Increasing

131.13

110.39

-20.73

SPM 1: Average Duration of

Fluctuating Increasing

133.49

117.77

-15.73

Homelessness, in Days

Fluctuating Decreasing

115.24

102.76

-12.48

(ES, SH, TH)

Steady Decreasing

120.11

101.85

-18.26

Steady Increasing

59.20

48.97

-10.22

SPM 1: Median Duration of

Fluctuating Increasing

65.13

55.35

-9.78

Homelessness, in Days

Fluctuating Decreasing

50.72

46.11

-4.61

(ES, SH, TH)

Steady Decreasing

54.12

47.92

-6.20

Steady Increasing

78.32

75.75

-2.57

Bed Coverage Percentage

Fluctuating Increasing

79.83

81.72

1.89

(ES, TH)

Fluctuating Decreasing

79.73

83.62

3.88

Steady Decreasing

79.16

86.11

6.95*

Steady Increasing

8.50

8.51

0.01

SPM2: Percentage Returns in 6

Fluctuating Increasing

7.01

7.38

0.37

Months

(ES, PSH, SH, SO, TH)

Fluctuating Decreasing

8.90

8.61

-0.29

Steady Decreasing

8.12

7.29

-0.83

Steady Increasing

13.07

13.00

-0.07

SPM2: Percentage Returns in 12

Fluctuating Increasing

10.88

11.04

0.16

Months

(ES, PSH, SH, SO, TH)

Fluctuating Decreasing

13.58

13.07

-0.51

Steady Decreasing

11.49

11.37

-0.13

Steady Increasing

17.97

18.30

0.33

SPM2: Percentage Returns in 24

Fluctuating Increasing

15.60

16.16

0.56

Months

Fluctuating Decreasing

19.03

18.23

-0.80

(ES, PSH, SH, SO, TH)

Steady Decreasing

16.19

16.13

-0.06

Steady Increasing

52.95

40.54

-12.41**

SPM 7: Percentage with

Fluctuating Increasing

56.57

49.03

-7.54*

Successful SO Outcome

Fluctuating Decreasing

46.17

48.48

2.31

Steady Decreasing

59.06

54.65

-4.41

Steady Increasing

44.98

42.21

-2.76

SPM 7: Percentage with

Fluctuating Increasing

44.40

44.67

0.27

Successful ES, PSH, RRH, SH, TH

Exit

Fluctuating Decreasing

45.44

46.37

0.93

Steady Decreasing

48.90

48.20

-0.70

Steady Increasing

91.48

93.26

1.78**

SPM 7: Percentage with

Fluctuating Increasing

92.43

93.86

1.42**

Successful PSH Retention or Exit

Fluctuating Decreasing

92.04

93.18

1.14

Steady Decreasing

92.84

93.10

0.27

CoC = Continuum of Care. ES = emergency shelter. PSH = permanent supportive housing. RRH = rapid re-housing. SH = safe haven. SPM = system performance measures. TH = transitional housing.

Note: Asterisks indicate statistically significant findings at the fol owing levels * p < .05, ** p < .01, and *** p < .001

from t-tests of mean differences.

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FINAL RESEARCH REPORT

HOUSING MARKET CHARACTERISTICS

Numerous studies have found significant relationships between homelessness and housing market factors primarily related to housing affordability and availability. As much of the housing market variables in this report are sourced from American Community Survey (ACS) 5-year estimates, differences in years across CoC groups are not appropriate; however, cross-sectional differences within the same year can be used to make inferences. 34,35 The analyses in this section describe housing market conditions for the year before the PIT count data used in the above section, followed by an examination of time-series variables that hypothetically parallel changes in PIT counts.

Differences between the broader increasing and decreasing groups of CoCs can be observed in several variables that relate to rental prices and, to a lesser extent, rental vacancy rates and the prevalence of overcrowded units (see Exhibit 3.736 and Exhibit C.3). The CoCs within the “steady increasing” and

“fluctuating increasing” groups have the highest mean rents in all measures of rent. This persistent theme of high rental costs in the “fluctuating increasing” CoCs could be well understood locally and could potentially even be predictable, whereas the increase in rental costs for the “steady increasing”

CoCs could act as a shock event. A growing gap in mean rents between the two “increasing” groups and the two “decreasing” groups is further highlighted by the significant increases in fair market rent (FMR) for all numbers of bedrooms. Similarly, the mean rates of overcrowded housing units descend in value from “steady increasing” to “steady decreasing” groups of CoCs. Another pattern in occupancy is the slight differences in rental vacancy rates between “increasing” and “decreasing” CoCs. Although only small differences exist, the presence of both higher rents and unavailability of rental units suggests that

“steady increasing” CoCs more often operate in tighter rental markets compared with “steady decreasing” CoCs. Finally, housing density—the number of housing units per square mile—appears to be dramatically higher among the two “fluctuating” groups than among the groups with steady trends.

34 Yearly time points include some degree of crossover in the underlying survey sample. Although correlating changes in housing market conditions to growing levels of unsheltered homelessness may be inappropriate across years due to crossover, one can draw cross-sectional differences between groups within each year. Conversely, variables from sources without crossover or with dampened crossover influence can be examined both cross-sectionally and over time.

35 The study used ACS 5-year estimates to scale data that were otherwise free of crossover.

36 The information provided is from the ACS 5-year estimates, so calculating a percentage change from 2014 to 2018 is not appropriate due to crossover sample. The information from 2014 is presented in Exhibit C.3.

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FINAL RESEARCH REPORT

Exhibit 3.4 | Housing Market Characteristics in 2018, by CoC Grouping

Variable

CoC Grouping

N

Mean

Steady Increasing

84

$299.80

Fluctuating Increasing

98

$339.26

Average House Value (in $1,000s)

Fluctuating Decreasing

87

$252.65

Steady Decreasing

67

$276.24

Steady Increasing

84

23.34%

Percentage of Homeowners with Cost

Fluctuating Increasing

98

24.25%

Burden

Fluctuating Decreasing

87

22.74%

Steady Decreasing

67

23.04%

Steady Increasing

84

$887.48

Fluctuating Increasing

98

$954.73

Median Contract Rent

Fluctuating Decreasing

87

$814.43

Steady Decreasing

67

$850.63

Steady Increasing

84

$898.82

Fluctuating Increasing

98

$959.91

Average Contract Rent

Fluctuating Decreasing

87

$817.35

Steady Decreasing

67

$858.76

Steady Increasing

84

$151.27

Fluctuating Increasing

98

$147.61

Average Rental Utility Cost

Fluctuating Decreasing

87

$151.00

Steady Decreasing

67

$153.13

Steady Increasing

84

49.26%

Percentage of Renters with Cost

Fluctuating Increasing

98

49.88%

Burden

Fluctuating Decreasing

87

50.03%

Steady Decreasing

67

49.83%

Steady Increasing

84

36.56%

Fluctuating Increasing

98

35.45%

Share of Renter-Occupied Units

Fluctuating Decreasing

87

35.47%

Steady Decreasing

67

33.05%

Steady Increasing

84

5.86%

Fluctuating Increasing

98

5.69%

Rental Vacancy Rates

Fluctuating Decreasing

87

6.64%

Steady Decreasing

67

7.22%

Steady Increasing

84

432.50

Fluctuating Increasing

98

758.39

Housing Density

Fluctuating Decreasing

87

520.09

Steady Decreasing

67

425.63

Steady Increasing

84

1.02%

Fluctuating Increasing

98

0.87%

Share of Overcrowded Housing Units

Fluctuating Decreasing

87

0.78%

Steady Decreasing

67

0.65%

Steady Increasing

78

$736.96

Fluctuating Increasing

86

$782.34

FMR, 0 Bedroom

Fluctuating Decreasing

77

$681.67

Steady Decreasing

63

$702.48

Steady Increasing

78

$850.89

Fluctuating Increasing

86

$897.75

FMR, 1 Bedroom

Fluctuating Decreasing

77

$773.44

Steady Decreasing

63

$798.65

Steady Increasing

78

$1,062.75

Fluctuating Increasing

86

$1,119.97

FMR, 2 Bedrooms

Fluctuating Decreasing

77

$964.29

Steady Decreasing

63

$996.43

CoC = Continuum of Care. FMR = fair market rent.

Note: See Appendix C for more details.

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FINAL RESEARCH REPORT

Changes in housing market characteristics since 2014 indicate that CoCs within the two “increasing”

groups are associated with high rates of newly permitted housing units and have larger increases in median house values. The average yearly value of newly permitted housing units is consistently high for both “increasing” groups compared with “decreasing” CoCs (see Exhibit 3.8 and Exhibit C.4). The two

“increasing” groups also experienced higher growth in the rate of newly permitted housing units over time. Finally, the pattern of higher house values for “increasing” groups shown in the cross-sectional table is confirmed by medians over time. High rental costs and rental vacancy rates shown in Exhibit 3.7, coupled with a high and increasing number of permitted housing units in the two “increasing” groups, is indicative of tighter rental markets. Although growth in rental vacancies and rents was not explicitly examined, growth in housing permits suggests that both are more likely to be increasing over time because developers tend to be attracted to build in areas with high rental unit demand and high rental costs.

Exhibit 3.5 | Housing Market Trend Analysis, by CoC Group, 2014 Through 2018

Average %

Variable

CoC Grouping

N

2014

2016

2018

Change, 2014

to 2018*

Steady Increasing

84

$227.36

$251.04

$285.55

23.48%

Median House Values

Fluctuating Increasing

97

$260.28

$286.49

$319.67

21.73%

($1,000s)

Fluctuating Decreasing

87

$188.73

$203.18

$226.14

19.51%

Steady Decreasing

66

$201.87

$223.19

$246.31

19.04%

Steady Increasing

84

0.82

0.97

1.00

30.66%

Rate of Newly Permitted

Fluctuating Increasing

97

0.80

0.92

0.98

32.31%

Housing Units

Fluctuating Decreasing

87

0.67

0.77

0.79

18.73%

Steady Decreasing

67

0.68

0.74

0.84

30.40%

Steady Increasing

84

$192.67

$204.53

$210.11

11.62%

Average Value of Newly

Fluctuating Increasing

97

$190.61

$194.26

$198.52

8.09%

Permitted Housing Units

Fluctuating Decreasing

87

$180.70

$188.10

$195.37

9.30%

($1,000s)

Steady Decreasing

67

$206.46

$219.69

$222.83

10.39%

*Average was estimated after calculating the percentage change for each CoC.

CoC = Continuum of Care.

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FINAL RESEARCH REPORT

4. RAPID RE-HOUSING PROGRAM IN

RICHMOND, VIRGINIA

SUMMARY: Facing limited permanent supportive housing (PSH) availability, the Greater Richmond CoC

specifically targets rapid re-housing (RRH)—short- to medium-term rental assistance—to single adults, with an emphasis on those experiencing unsheltered homelessness. The Greater Richmond CoC pairs the RRH program, which is considered a “lighter touch” housing program, with a variety of services its partners offer to stabilize clients who may otherwise have been eligible for more intensive PSH. The Greater Richmond CoC reported that only 17 percent of clients who came from unsheltered settings returned to homelessness within a 12-month period after exit, which is notable because it is consistent with outcomes of other RRH programs, despite those programs not necessarily targeting the most vulnerable population in their communities. The Greater Richmond CoC demonstrated that, if implemented in this fashion, RRH

paired with services can be a cost-effective alternative to PSH in certain housing markets and an effective bridge to other ongoing forms of assistance, such as Veterans Affairs Supportive Housing (VASH) for clients who are veterans.

Homeward leads Greater

Exhibit 4.1. Map of GRCoC’s Jurisdiction

Richmond’s Continuum of Care

(GRCoC), a CoC spanning a

large geographic area including

the city of Richmond and seven

other counties (see Exhibit

4.1). In 2009, GRCoC

implemented the Rapid Re-

Housing (RRH) program to

provide housing to people

experiencing homelessness

and, since 2015, has

emphasized serving individuals

experiencing unsheltered

homelessness. From January 1,

2018, to December 31, 2019,

39.9 percent of single adults

GRCoC = Greater Richmond’s Continuum of Care.

who participated in RRH came

Source: GRCoC: About Us.

from an unsheltered setting. 37

To understand the GRCoC’s implementation of the RRH program, the study team worked with GRCoC

37 This information was derived from the program data provided by the CoC to the study team.

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FINAL RESEARCH REPORT

staff to identify and recruit eight stakeholders to participate in telephone interviews as part of a

“virtual” site visit. The study team completed the interview using a tailored interview guide (see Appendix D). Respondents included a program participant, CoC staff, housing providers, and outreach staff. Responses were collectively analyzed in NVivo qualitative data software and synthesized to address the study research questions. Additional data compiled under the quantitative study, program data, and program materials provided by stakeholders were integrated to support qualitative findings.

The following sections describe the local context; program partnerships; program components; sustainability; and overall successes, challenges, and lessons learned from implementation.

CONTEXT

Since 2015, the Greater Richmond region’s overal homeless population has decreased in size from 818

people in 2015 to 497 people in 2019, according to the annual HUD Point-In-Time (PIT) count (HUD, 2020); however, the unsheltered homeless population grew over that period, with an increase from 86

people reported in 2015 to 165 in 2019 (City of Richmond, 2020; HUD, 2015, 2019). 38 Consequently, the rate of unsheltered homelessness grew from 0.81 people to 1.54 people per 10,000 population. Exhibit 4.2 shows the changes in the rates of homelessness from 2015 to 2019. The unsheltered population in the Greater Richmond region consists of mostly adults (aged 25 to 44 years) and is made up of more single adults than families (HUD, 2020). The lack of housing that is affordable for low-income households, and Permanent Supportive Housing (PSH) in particular, in the Greater Richmond area requires GRCoC to use alternative programs to house individuals experiencing homelessness.

Exhibit 4.2. Rates of Homelessness and Housing Inventory in the Greater Richmond Area 38 GRCoC noted that the effect of weather shelters not being open on the night of the 2019 PIT count likely affected the accuracy of the unsheltered count in 2019.

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FINAL RESEARCH REPORT

Changes in the rental market across the Richmond area have pushed lower income households out of centrally located neighborhoods to farther flung suburbs. Specifically, affordable one-bedroom units appropriate for single adults are in short supply; the average gross rent for a one-bedroom unit has increased from $857 in 2015 to $975 in 2019, an increase of 13.8 percent (similar to the national average gross rent for a one-bedroom unit, which has increased from $810 in 2015 to $953 in 2019—an increase of 17.7 percent) (U.S. Census Bureau, 2020b). Those market dynamics undoubtedly strain many low-income households, but rent in the area remains low relative to other major cities, which has likely contributed to Richmond’s downward trend in overall homelessness in recent years. For many vulnerable individuals exiting homelessness, even the lowest priced housing is often not affordable without some form of ongoing housing assistance. Exhibit 4.3 shows additional housing market factors for the Richmond area in 2018 (U.S. Census Bureau, 2020a).

Exhibit 4.3. Housing Market Factors in the Greater Richmond Area in 2018

PSH, which provides ongoing rent subsidy with supportive services for people with disabilities experiencing homelessness, is also in limited supply in Richmond. GRCoC has slightly more than 1,000

PSH units, but unit turnovers are nominal. Roughly 35 PSH units become available annually, and the program had nearly 92-percent bed utilization in 2019 (Greater Richmond Continuum of Care, 2020a).

Service providers considered the waitlists to be stagnant and reported examples of people waiting on prioritization lists for years before being housed. To address the immediate and growing need, GRCoC

set out to use an alternative housing resource that was more available to serve people experiencing homelessness: RRH.

RAPID RE-HOUSING PROGRAM OVERVIEW

GRCoC chose to target its RRH resources to house people with the highest vulnerability scores, many of whom experience unsheltered homelessness and would otherwise have been appropriate candidates to receive PSH. GRCoC staff consider their agency an early adopter of RRH; staff began researching the program in 2007 and formally adopted the program in 2009 to better serve families experiencing homelessness. In 2015, GRCoC began focusing specifically on single adults experiencing homelessness.

The RRH program is primarily funded through HUD Continuum of Care program dollars, followed by the Virginia State Housing Solutions program, which combines HUD Emergency Solutions Grants (ESG) and state general funds. Private funders also provide significant financial support, which facilitates the provision of additional services such as move-in assistance, groceries, and cell phones.

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FINAL RESEARCH REPORT

We have relatively large numbers of people experiencing chronic homelessness. We decided not to wait for permanent supportive housing to become available, so we referred those people to rapid re-housing because we have good, high-quality providers and more capacity.

The RRH program is a time-limited housing intervention (generally up to 24 months) designed to help individuals quickly exit homelessness and return to permanent housing in the community. RRH focuses on moving households into housing in the private rental market quickly and is often delivered using the Housing First model, offering assistance without preconditions such as employment, income, absence of a criminal record, or sobriety. GRCoC operates RRH programs for families and individuals, with most serving single adults. The program prioritizes serving people with the highest vulnerability score and longest current episode of homelessness, many of whom are experiencing unsheltered homelessness.

Nationally, the RRH program is considered a “lighter touch” intervention for households, meaning it is general y offered to people who required less intensive wraparound services and lower levels of rental assistance to exit homelessness and maintain stable housing. GRCoC leverages partners’ services alongside the RRH rental assistance subsidies, however, to create a tailored program for households that have higher service needs. Doing so allows GRCoC to provide RRH for individuals who score high enough on vulnerability assessments to warrant PSH but who otherwise would be in the shelter system or on the street.

In 2018 and 2019, 68 percent of the single adults who came from unsheltered settings and entered RRH

were older than age 45, and 78 percent noted a disability. Many of the older adults were not yet eligible for Social Security benefits because they were younger than age 65. RRH clients who came from unsheltered settings identified as African-American (67.5 percent vs. 73.1 percent in the overall homeless population) or White (31.8 percent vs. 26.5 percent in the overall homeless population), and a surprisingly high percentage (53.5 percent) were veterans. The program serves single adults with longer histories of homelessness and barriers to housing, including physical and mental disabilities and illnesses, limited capacity for employment due to lack of work experience or skill, loss of key documentation such as identification, and criminal history. 39 Some individuals may hesitate to engage in the homelessness response systems, including shelters, because of past experiences with the system that left them with trauma or unsatisfactory outcomes.

GRCoC has found that using the three components of RRH—assistance finding housing, move-in assistance and rental assistance, and RRH case management and services—enables most people to have their housing needs adequately met and enables exits from homelessness (see Exhibit 4.5 for details on the amount of assistance received). Clients are not required to engage in all three program elements, and assistance and services are tailored to each individual’s needs. Over time, the program reduces housing subsidies to promote financial independence and supports clients obtaining income through jobs and mainstream benefits. GRCoC implements a flexible approach to ensure that the individual’s needs are met by offering comprehensive services, supplementing housing with other programs, and, if needed, extending housing subsidies from a typical stay, which can range from 6 to 24 months through 39 The program can help clients obtain the documents necessary to become housed, such as state identification and Social Security cards. This is often referred to as being document ready.

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RRH. GRCoC pairs housing with partner and community services to simultaneously address clients’

housing and service needs so that when clients exit the program, they remain housed.

KEY POLICIES AFFECTING IMPLEMENTATION

GRCoC’s homelessness response system specifically targets single adults through a network of partnerships and key policies driving prioritization within the Coordinated Entry System. In deciding which partners to engage in the CoC response, GRCoC considers partners that provide the most services to single adults to ensure that the goals of the specific partners align with the goals of the response system. GRCoC’s coordinated entry process places all people experiencing homelessness who are interested in housing or shelter on a centralized by-name list, then prioritizes the list based on the longest duration of homelessness and highest vulnerability assessment score; this process functionally prioritizes the unsheltered population because they often have more vulnerabilities (for example, being older, being unsheltered, and having significant health issues). 40

Previously, the process to prioritize people for RRH focused exclusively on individuals coming from an unsheltered setting. Although that focus is beneficial for those experiencing unsheltered homelessness, GRCoC found that this prioritization caused transitions from shelters to RRH to “grind to a halt,”

meaning that a population with similar vulnerabilities ended up being stuck in the shelter system.

Recognizing the need for a more equitable prioritization process, GRCoC increased data collection from outreach providers, such as how they record contacts in the Homeless Management Information System (HMIS) to better document the duration of homelessness and vulnerabilities, so the data are more relevant for the matching and prioritization process. This method allows staff to more appropriately match clients with resources and service providers.

GRCoC and its partners leverage multiple sources of funding to offer a cohesive program that focuses on reducing barriers to housing. HUD provides the largest contribution to the RRH fund, at approximately $1 million in 2020. The state of Virginia provided roughly $700,000 in 2020 to GRCoC by combining federal non-entitlement ESG Program funds and state general funds through a program known as the Virginia State Housing Solutions Program to support emergency shelter operations, RRH, and coordinated entry. Additional entitlement ESG funds of up to $220,000 annually support RRH within the City of Richmond and Henrico County jurisdictions. GRCoC and its partners “braid” private, state, and federal funding to offer RRH and other key supportive services, leveraging flexibilities of certain funding sources to fill in key needs that may not be allowable under another funding source so that the program feels seamless to clients.

Stakeholders noted the significance of local, private funds to “gap fill” services, such as gas and transportation assistance and groceries, for people exiting homelessness that may be unallowable through state and federal grants or programs. Stakeholders continue to work with private donors aligned with the objectives of local, state, and federal funds to increase the comprehensiveness of the program and reduce barriers to housing.

40 GrCoC uses the Vulnerability Index-Service Prioritization Decision Assistance Tool to prioritize clients for housing options. For additional information, see https://www.orgcode.com/tools_you_can_use.

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GREATER RICHMOND CONTINUUM of CARE IMPLEMENTATION OF

RAPID RE-HOUSING

GRCoC implements a coordinated approach to RRH in the Greater Richmond area. Nationally, RRH

programs tend to serve families who are housed after a stay in the shelter system, but almost 40

percent of people GRCoC houses through RRH are single adults experiencing unsheltered homelessness, although families and sheltered individuals can also receive RRH. Individuals experiencing unsheltered homelessness are either engaged through street outreach or may call a CoC hotline, which (a) diverts those who can resolve their housing crisis that led to their homelessness with minimal supports from the homeless response system and (b) conducts intake for others who may require more assistance to resolve their housing crises. Individuals matched to RRH receive rental assistance subsidies for up to 24

months, and service providers work with clients to ensure that once the subsidy ends, clients can remain housed. Exhibit 4.4 shows an overview of the program, and the following sections describe the procedures in each step of the program.

Exhibit 4.4. GRCoC’s Rapid Re-Housing Program Overview and Services

VI-SPDAT = Vulnerability Index—Service Prioritization Decision Assistance Tool.

Note: Clients may also exit services if they stop participation, return to homelessness, or no longer contact program staff.

ENGAGING INDIVIDUALS EXPERIENCING UNSHELTERED HOMELESSNESS

Individuals experiencing unsheltered homelessness access the homeless response system through one of two ways: they may call into a CoC-led homeless crisis hotline or staff from CoC partner agencies connect to individuals through street outreach. Most individuals experiencing unsheltered homelessness are engaged through outreach conducted by Projects for Assistance in Transition from Homelessness (PATH) staff or RRH provider staff. Receiving approximately 5,500 calls per month, 41 the homeless crisis line is staffed with multiple diversion specialists who work with clients who are experiencing homelessness or are at risk of homelessness. A specialist encourages the client to self-resolve or explores alternatives to emergency shelter, such as staying with family or friends. If an individual is unable to resolve his or her housing crisis through these options, the specialist enters the individual’s 41 Homeward. Shelter Referral. PDF file provided by GRCoC. Accessed October 16, 2020.

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information into the HMIS to be added to the centralized by-name list for emergency or transitional shelter or a permanent housing program, such as RRH.

Staff from more than five agencies provide street outreach across the entire GRCoC region. A HUD-funded outreach coordinator facilitates communication and coordination of staff across the Greater Richmond area. Outreach staff conduct street patrols at least 4 days a week; record an average of 197

entries per month in HMIS; and provide survival kits, information on local mental health treatment and substance use disorder treatment, and information about the nearest food sites and other services that may be available. The HMIS contains documentation of all interactions with individuals experiencing unsheltered homelessness and is open to all HMIS users so that outreach staff may see previous program history. 42 In the past few years, to ensure that high-quality and pertinent data are collected, GRCoC has led improvements to HMIS data collection procedures, such as the vulnerability assessment, to help match individuals to appropriate housing resources. The police department in the city of Richmond also supports outreach through a dedicated team that specifically responds to calls related to homelessness. For example, if Richmond businesses call the police department regarding individuals loitering, a special team within the department will respond, either through a move-along request or by bringing an outreach provider to engage the individual. Police officers attend committee meetings and coordinate with partner staff on outreach strategies, with the goal of reducing citations for individuals experiencing homelessness and connecting them with the appropriate resources that they may need.

PRIORITIZING INDIVIDUALS FOR RAPID RE-HOUSING

Connecting individuals experiencing homelessness to housing within GRCoC has evolved over time to reduce the subjectivity of the prioritization process. Before 2015, respondents stated that much of the success of whether a person would have access to RRH depended on the extent to which the case manager advocated for the client. GRCoC implemented a revised coordinated entry policy to facilitate a fairer and more equitable system in which the most vulnerable individuals are prioritized. 43 GRCoC now relies on vulnerability scores and duration of homelessness as the key factors driving prioritization of individuals matched to RRH. Weekly, GRCoC staff export HMIS data and case conference with housing providers and outreach staff to discuss solutions for matching individuals on the by-name list for RRH.

Outreach staff may interact with a client multiple times to

build rapport before conducting any type of assessment.

[The housing stability plans are]

Once an individual expresses interest in housing, the

supposed to be strengths-based

outreach staff conduct the vulnerability assessment and

housing barrier assessments where

begin developing a housing stability plan. The housing

[clients] real y pul from their inner

stability plan is client driven and based on the client’s goals

strengths to help figure out what it is

of obtaining education or skills, obtaining a steady income

that they can bring in order to

through a job or mainstream benefits, or achieving other

overcome all the barriers that they

personal goals that will help the client remain stably housed

may have and accomplish their goals

when he or she exits the RRH program. Throughout clients’

to remain stable in housing.

time in the RRH program, housing providers work with them

42 The sharing of client data is governed by client consent to sharing (the release of information); those who do not consent wil not have their information shared.

43 In April 2015, the GRCoC Coordinated Access Committee voted to begin implementation of VI-SPDAT throughout GRCoC for emergency shelters. More information can be obtained at http://endhomelessnessrva.org/service-providers/vi-spdat.

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to connect them to local resources that help meet those goals. For the substantial portion of clients who are veterans, VA resources such as VASH and Supportive Services for Veteran Families can provide additional resources and assistance after they exit the RRH program. The housing stability plan coincides with the length of the individuals’ RRH subsidy such that once clients successfully complete goals, they exit the program and should be able to maintain housing without financial assistance. If an individual does not meet their goals during participation in the RRH program, GRCoC may identify private funding to continue to support the individual’s housing to avoid exiting the individual to homelessness.

SUPPORTS PREPARING CLIENTS FOR HOUSING

While clients wait to be matched to RRH, mobile outreach staff provide housing-focused case management. During that time, the client may remain unsheltered or may be matched to emergency housing—if the client is interested and space is available. An individual with lived experience discussed how they kept in touch with the case manager by phone because in-person appointments were difficult to coordinate before housing. Most clients are housed within 30 to 45 days of intake from outreach, which an individual with lived experience believed was reasonable (Greater Richmond Continuum of Care, 2020b). Outreach staff provide services at locations convenient for the client. In some instances, case managers search for documentation in records at local hospitals, jails, or the Department of Motor Vehicles, which becomes more difficult to do when a client has minimal records due to being new to the area or having a minimal history in the area. Outreach staff may also provide referrals to mainstream benefits or services providers on the basis of client needs and interests.

Once matched to the RRH program, outreach staff verify the person’s interest within 3 to 4 days. Clients then begin receiving services from housing stabilization case managers at the RRH providers, who offer varying levels of support based on the clients’ needs. Case managers average a caseload of 18 RRH

clients at a time because “these folks require a lot [of help], so it takes a lot of support for the case managers.” Case managers assist clients to meet other needs, such as providing mental health skill-building, health care, or referrals to substance use disorder treatment or other services if clients have not yet been referred. Housing providers may also offer cell phones, supported by local funds, and teach clients how to use them so that providers can stay in touch with the clients while they are in the program.

LOCATING HOUSING

Once an individual is matched to the RRH program, either from shelters or from an unsheltered situation, staff identify housing options with the client, help the client select his or her unit, and, if needed, pay for application fees. RRH providers make connections to a range of housing types—

including individual apartments, rooming houses, and shared housing—and community settings, scattered-site, master-leased, and independent apartments. Stakeholders believed that community-based housing, versus single-site housing, reduces clients’ concerns about being housed with only people who previously experienced homelessness, which

can be a barrier to keeping clients housed. For individuals

I was…looking for help getting my

with high barriers to housing, however—such as those with

own place or…a room or anything like

significant criminal and eviction records or those who have

that…anything that would keep me

limited to no income—the client may be connected to

safe. I wanted to be somewhere.

rooming houses, which are more accepting of those

histories, according to stakeholders. Rooming houses rent

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for lower rates than apartments or other one-bedroom units. In rooming houses, clients live with one or more roommates, who may not be RRH clients, which limits housing providers’ ability to ensure a comfortable, shared environment, compared with private, housing-organization-owned units. Some stakeholders noted concern about rooming houses because some roommates may have substance use or other issues that may not be conducive to the client achieving his or her goals, such as sobriety.

Alternatively, clients may also access shared housing with RRH clients from the same housing provider.

When possible, the housing provider negotiates one master lease for a unit and executes individual leases to the clients for each room, which stakeholders noted was more cost efficient than individual units.

On an ongoing basis, housing provider staff engage landlords to increase participant access to and selection of housing. GRCoC tracks landlords’ vacancies and unit characteristics to expedite housing identification. RRH provider staff engage and build relationships with local landlords so that they are more wil ing to rent to clients exiting homelessness. Staff are trained on housing identification, landlord-tenant rights and responsibilities, and other core competencies supporting housing assistance. RRH

providers leverage local and private funds to pay for security deposits or back rent or utilities that may be preventing a client from securing a lease and provide financial guarantees for any excessive property damages made by the clients, which has facilitated landlords’ willingness to rent to RRH clients. On average, clients who previously experienced unsheltered homelessness locate housing in just 1 month.

MOVING INTO AND ADJUSTING TO HOUSING

RRH providers support clients by meeting basic needs at

move-in, including security deposits, and by providing

I like that [the service provider] had

services after clients have moved into housing. Providers use

different resources…and one of [the

private funds to pay for moving costs, furniture, mattresses,

individuals there] helps you manage

bedding, and food assistance once clients have moved into

with the budgeting of rent.

housing. In the state of Virginia, individuals with significant

criminal histories are not eligible to receive Supplemental

Nutrition Assistance Program (SNAP) benefits, which drives the demand for private funds to support food assistance. Case managers may also offer assistance to clients as they adjust to housing, including assisting with monthly budgeting, teaching clients how to plan a grocery list, and providing transportation. An individual with lived experience with the program suggested that more assistance with budgeting was needed for long-term success in housing, particularly as the subsidy decreased.

Clients may also be referred to peer counselors for substance use treatment or assistance adjusting to the social aspect of housing. Services may be office based, such as those focusing on mental health and benefits assistance, or home based. Case managers can continue their services as long as someone is enrolled in the RRH program.

RAPID RE-HOUSING SUBSIDY

Clients may receive subsidized rent for up to 24 months over

a 36-month period through the RRH program, although such

We never, ever, ever say, “You’re

an extended period is less frequent. Al RRH providers pay

going to get 6 months of RRH. You’re

the clients’ security deposit and the first month’s rent. Most

going to get a year of RRH.” It’s

housing providers begin with a base of 2 to 3 months of

always a month to month.

subsidy; however, GRCoC does not implement a “one size

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fits all” approach to the length of subsidy, which varies on an individual basis according to the needs of each client. The average duration of rental subsidy is 7 months. GRCoC leadership and partners understand that the clients are high need by the system design, and providers accordingly implement a flexible approach assessing individuals’ service needs and income. If clients have income, they contribute 30 percent of their income to monthly rent; if clients have no income, they contribute no more than $50 toward monthly rent until they secure an income.

Housing provider staff take a progressive approach to determine how and when to increase clients’

portion of the rent to promote financial independence in anticipation of the subsidy ending. Depending on the clients’ housing stability plan and goals, employment navigators work with clients to identify jobs or higher paying jobs or help them obtain their high school diploma or GED to increase future earning potential. For example, an individual participating in the program described receiving help identifying job opportunities that pay higher wages. Other clients may be referred to SSI, SSDI, Social Security (retirement), or other mainstream benefits to obtain a steady source of income. From 2018 to 2019, 56.7 percent of clients’ main source of income was from disability insurance, 31.7 percent from earned income, and 7.7 percent from retirement or pensions. In 2019, 10 percent of clients increased their income through a job and 13 percent through another source, such as mainstream benefits (Greater Richmond Continuum of Care, 2020b). On a monthly basis, housing providers assess the clients’ income and needs and determine whether a rent adjustment is warranted and when the subsidy will end. Using this step-down strategy, GRCoC helped 87 percent of all RRH clients remain in permanent housing at program exit in 2019 (Greater Richmond Continuum of Care, 2020b). Most stakeholders agreed that the program has been successful in helping individuals experiencing unsheltered homelessness in the Greater Richmond area and addressing their needs; without RRH, the numbers of that population may have increased further. GRCoC stakeholders noted that the program is serving the intended population, primarily individuals who were experiencing unsheltered homelessness, which stakeholders view as a success of their system.

We, as a program, have decided that we wil hold onto [clients] a little longer, and we do tend to hold longer than other programs in their area. But that’s a commitment, we have to try and to get more and more services and give people more of an opportunity to accommodate to what it means to be in housing, and not only having four walls around you, but responsibilities around you to help be able to sustain that.

Clients who are able to pay for housing costs and clients who are no longer attempting housing stabilization (for example, no longer seeking sources of income to help pay for rent) are exited from the program. If an individual is identified as at risk of losing housing without the subsidy, GRCoC may identify other local resources to support the clients’ rent for a few additional months, although that situation is rare. Stakeholders noted that few clients returned to homelessness after the program, with only 17 percent of RRH clients who were original y unsheltered returning to homelessness within 1 year (Greater Richmond Continuum of Care, 2020b). Stakeholders noted that those individuals likely would benefit from more intensive supports offered through PSH to resolve their barriers to maintaining

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housing. A formal bridge program from RRH to limited mainstream PSH does not currently exist, 44

although 22 percent of participants end up in supportive housing funded through the Veterans Affairs Supportive Housing (VASH) program (53.5 percent of RRH clients who came from an unsheltered setting were veterans), as noted in Exhibit 4.5. At program exit, 5.2 percent of individuals move into housing with a family member or friend, 1.3 percent move to a hotel or motel paid for using an emergency shelter subsidy voucher, and 1.3 percent move away without further information provided (unknown).

Individuals unsuccessfully exiting the RRH program—that is, stopping participation, returning to homelessness, or no longer contacting program staff—is uncommon, per the CoC stakeholders.

Unsuccessful exiting may present challenges for clients looking to become housed again, depending on the landlord and housing requirements. For example, although prioritization is not affected by evictions, evictions could limit the ability for that person to become housed due to landlord decisions not to rent to people with histories of eviction. Individuals receive, on average, $280 per month in one-time moving services and $847 per month in rental assistance.

Exhibit 4.5. Outcome Measures for RRH Clients from Unsheltered Situations, 2018 to 2019

Length of Stay in Project (N=174*)

Average time from project start date45 to move-in

36 days

Average length of stay from move-in date

181 days

Destination At Project Exit (N=155**)

Rental by client, with no subsidy

33.6%

Rental by client, with RRH or equivalent subsidy46

25.8%

Rental by client, with VASH housing subsidy

21.9%

Staying or living with family/friends

5.2%

Rental by client, with other ongoing housing subsidy

1.9%

Jail, prison, juvenile detention facility

3.2%

Deceased

1.3%

No exit interview completed

1.3%

Hotel/motel paid for with emergency shelter voucher

1.3%

Substance abuse treatment facility

0.7%

Permanent housing for formerly homeless persons

0.7%

Other

1.9%

Unknown

1.3%

Average Amount of Assistance (N=174*)

One-time Move-in Assistance

$279.90

Rental Assistance per Month

$846.74

RRH Performance Measures

44 GRCoC is in the process of developing a formal bridge program from RRH to PSH.

45 “Project start date” refers to the date the client was admitted into the project. To be admitted, the following terms must be met: information provided by the client or from the referral indicates that they meet the criteria for admission; the client has indicated that he or she wants to be housed in this project; and the client is able to access services and housing through the project.

46 GRCoC was asked for clarification of what “equivalent subsidy” means, but the program was not able to clarify. The study team suspect that it means Supportive Services for Veteran Families (SSVF) housing subsidy or continuation of subsidy similar to RRH through local funds.

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17%

Returns to Homelessness Within 1 Year of Exit to Permanent Destination

*Information includes clients who are stil in the program. **Information was available only for those clients who exited the program by December 31, 2019. See Exhibit F.2 in Appendix F for more details.

RRH = rapid re-housing. VASH = Veteran Affairs Supportive Housing.

Note: RRH Performance Measures from Greater Richmond Continuum of Care, 2020.

Source: GRCoC’s Homeward Community Information System, January 1, 2018, to December 31, 2019.

KEY PARTNERSHIPS SUPPORTING PROGRAM ACTIVITIES

GRCoC partners include housing and service providers, outreach staff, local nonprofit organizations, and other community stakeholders who work to address unsheltered homelessness. Exhibit 4.6 shows the primary service providers supporting outreach, housing, and other services related to RRH that are offered to individuals experiencing unsheltered homelessness. This list includes those who service single adults, but other service providers exist within GRCoC who serve a broader range of clients. Outreach staff provide access points into the region’s network of homeless services and actively locate unsheltered groups to engage them in the system. Housing and service providers help clients secure and maintain housing tailored to their needs. Other service partners support programs using one-on-one services, such as employment navigation, health care, or peer counseling. Other stakeholders in the community—such as the police departments, public libraries, and recovery services—assist in providing additional support and connection points to people experiencing unsheltered homelessness.

Exhibit 4.6. Primary GRCoC Providers Supporting RRH

Outreach/Crisis Hotline

Other Service Providers

RRH Providers

 Area Congregations Together in

 Community Services Boards

 HomeAgain

Service

 Daily Planet

 St. Joseph’s Villa

 Commonwealth Catholic Charities

 Local Nonprofit Organizations

 Virginia Supportive

 Daily Planet Health Services

 Municipal Police Departments

Housing

 HomeAgain

 Richmond Behavioral Health

 Homeward

Authority

 Richmond Behavioral Health

 Richmond Department of

Authority

Social Services

 Richmond Department of Social

Services

 Senior Connections

 St. Joseph’s Villa

GRCoC intentionally selects service partners to fund through

a rigorous application process led by a ranking and review

We really invested in our quality

committee. To award federal and state funding, GRCoC

providers and worked hard as a

reviews local applications against the performance

continuum to make sure that people

measures and RRH performance and the demonstrated

were doing Rapid Re-Housing well.

capacity and outcomes of the programs the organization

offers. GRCoC weights partners that serve single individuals

to align with their system’s focus and selects partners with the capacity to provide quality services.

Stakeholders described the system’s focus on serving the highly vulnerable, often unsheltered

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population as a type of “collective intentionality” starting with selecting invested partners. Throughout the funding year, GRCoC assesses partners’ effectiveness through three performance benchmarks:

 Reduce the length of time an individual experiences homelessness: individuals should move into permanent housing in an average of 30 days or less.

 Exit clients to permanent housing: at least 80 percent of clients should exit RRH to permanent housing.

 Limit returns to homelessness within 1 year of program exit: at least 85 percent of clients who exit to permanent housing should not return to homelessness within 1 year.

GRCoC also uses committees and the CoC board to connect providers and resources in the region. The CoC committees discuss the coordinated entry system, bylaws and committee charters, and policies and procedures, often troubleshooting any issues that arise within the system. Importantly, the committees include public input and service provider perspectives to ground and diversify input. The GRCoC board is also leveraged to promote ownership of the CoC systems. The board is representative of the local partners, agencies, and other groups with a vested interest in homeless services, which has helped build connections across community partners and has bolstered community buy-in and political will.

Most stakeholders agree that GRCoC is effectively leveraging

available resources and coordinating efforts within the

When we have problems with policies,

community for individuals experiencing unsheltered

when things aren’t flowing, when

homelessness. The CoC prides itself on its ability to identify

there are glitches, or when we find

and overcome challenges by adapting processes and

gaps, we’re able to report those things

procedures; however, in this process, stakeholders

and people [who] begin to work on

suggested that enhancing communication related to RRH

those [issues] see what we can do to

processes and the state of subsidized housing in general

help fil in the gap and overcome the

would be beneficial. Stakeholders suggested that

barrier, whatever it is...

partnerships may be further improved by clarifying the

stakeholders’ overall understanding of the interventions

offered and agency roles within the process, particularly for referrals and hand-offs between agencies.

Clearly defining roles and services is important to ensure that resources, staff and otherwise, are used efficiently.

SUSTAINABILITY

GRCoC stakeholders discussed a promising future for the RRH program involving expansion, discussed innovations of adding a new CoC-level position, and provided reflections on how the program may be adopted by other communities with increasing rates of unsheltered homelessness.

FUTURE PLANS FOR RAPID RE-HOUSING IN RICHMOND

Moving forward, GRCoC plans to streamline efforts by developing a bridge process between RRH and other housing resources, creating a process for followups, and hiring a CoC-level housing coordinator.

GRCoC recognizes the issues related to individuals who unsuccessfully exit the RRH program and intends to develop a procedure facilitating the transfer of clients to higher intensity services offered through PSH, although staff anticipate that PSH will remain extremely limited for clients who are not veterans.

Using private funds, GRCoC plans to add a 3-month followup to check in with clients who have exited

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the program to determine whether any additional resources are needed to keep clients housed. GRCoC

also plans to begin working more closely with the local public housing authorities to bridge clients to voucher programs, which has not been common to date. Finally, GRCoC plans to hire a CoC-level housing coordinator to centralize and facilitate landlord engagement, decreasing the burden placed on partners.

ADOPTING RAPID RE-HOUSING

GRCoC believes that the successful addressing of

unsheltered homelessness through RRH in the Greater

If people aren’t rapidly housing

Richmond area is easily translatable to other communities.

unsheltered people, they need to get

Stakeholders believed that, at a minimum, the program

on it. Because otherwise, people are

affords clients housing for a period of time, with lasting

going to die. At a certain point, what

impacts on the individuals who remain housed after the

are you waiting for?

subsidy ends. Stakeholders believed the program is

especial y effective because it focuses on the region’s most

vulnerable individuals experiencing unsheltered homelessness who have significant histories of homelessness. Stakeholders advise communities interested in leveraging RRH in a similar manner to dive in, build in feedback loops, listen to partners, and be ready to learn from the community’s experiences to refine the approach to meet clients’ unique needs based on their available resources.

SUCCESSES, CHALLENGES, AND LESSONS LEARNED

Stakeholders reflected on their experiences implementing RRH over the past decade and identified successes, challenges, and lessons learned throughout the program’s implementation. The sections below detail key points described by stakeholders.

SUCCESSES

GRCoC implements a flexible program to ensure that the clients’ needs are adequately met before transitioning clients off subsidies. Beginning at enrollment, GRCoC and its partners leverage private funds to “gap fill” services, which may not be allowed through state or federal funding. Once participants are housed, GRCoC provides flexibility in the length of the RRH subsidy for up to 24 months.

The flexibility helps individuals become stably housed and to access other benefits (such as mainstream benefits or health care), which improve their ability to maintain housing. An individual with lived experience noted that those services were crucial to their success not only in maintaining housing but also in locating job opportunities and ultimately becoming employed. If a participant requires subsidies for longer than 24 months, GRCoC works with its partners to identify other sources of support, such as private funding through donations, so that the individual can maintain housing even after he or she reaches the limits of the RRH program.

GRCoC leverages a data-driven approach with continual quality improvement built into their review and assessment process. GRCoC intentionally selects the organizations it funds on the basis of their history serving single adults experiencing unsheltered homelessness. GRCoC staff actively review HMIS

records to ensure data quality and completeness. Accurate data enable GRCoC to match individuals with appropriate resources and services and to prioritize the by-name list. GRCoC holds service providers accountable through three performance benchmarks. The CoC board reviews the benchmarking data

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and more to identify areas for improvement and to ensure that the program targets the intended populations.

Coordinated systems and processes effectively leverage limited resources. GRCoC takes a systems approach to addressing homelessness. The homeless crisis hotline diverts individuals at risk of homelessness and facilitates self-resolution as possible, which decreases pressures on already strained resources. Once an individual is experiencing homelessness, GRCoC leverages centralized intake processes through the homeless crisis hotline and street outreach and by collecting data through HMIS

to coordinate a holistic response to the individual’s needs. One individual with lived experience noted the importance of being able to call the homeless crisis hotline multiple times to get answers to all their questions and, ultimately, to get connected to a case manager. The RRH program’s success is founded on these connected, centralized systems, which enable limited resources to be effectively al ocated within the response system. In 2019, RRH offered a cost-effective solution to address homelessness at the rate of $4,769 per household served per year, compared with $11,500 per household served per year through PSH (Greater Richmond Continuum of Care, 2020b).

Local buy-in and program champions provide a strong backbone to the program and facilitate flexible funding pools. The RRH program is championed by the CoC lead agency and its partners that provide and maintain the quality of services offered through the program. The GRCoC lead agency actively provides guidance to partners and relies on quality housing providers to implement RRH. Local, private funders align with the program’s goals and provide nearly one-fourth of the program’s funds, facilitating flexible spending on items such as cell phones and groceries, which supports clients adjusting to housing. The CoC board represents a system-level committee comprising service providers and community organizations. This representation facilitates the organizations’ ownership of the program and the clients’ outcomes so that members and organizations feel that they are working together to collectively address unsheltered homelessness in the Greater Richmond area.

CHALLENGES

Some clients with higher needs may be unsuccessful in maintaining housing after the subsidy ends because they require longer term rental assistance and the high-intensity services offered through PSH. GRCoC’s RRH program has documented that most participants will succeed in the program and maintain housing after the subsidy ends; however, that may not be the case for individuals with significant barriers and limited income and who require more time than is available to have their needs resolved, in which case the client may exit the program before the subsidy’s end. Stakeholders believed that no patterns were present to determine which clients would or would not succeed in the RRH

program. For those who do not succeed, GRCoC does not have a program or process to bridge individuals from RRH to PSH. As noted earlier, 17 percent of participants return to homelessness after the RRH subsidy ends. Some of them may also acquire an eviction or other monetary fees, which act as barriers to obtaining housing in the future (Greater Richmond Continuum of Care, 2020b; Gubits et al., 2018). 47

Coordination of outreach providers may be improved by clarifying roles, responsibilities, and processes. In general, stakeholders noted confusion on the hand-off from the crisis hotline to outreach 47 GRCoC is starting a process to bridge people from RRH to PSH.

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or from outreach to RRH providers and on coordination between police and outreach teams. Some stakeholders believed that confusion related to that coordination delayed housing and occasionally led to individuals becoming disengaged while providers attempted to clarify roles. Other stakeholders believed that the limited hours of outreach (Monday through Friday, 8:00 a.m. to 4:00 p.m.) were not appropriate for the population.

Service providers may experience exacerbated challenges because the RRH program targets the most vulnerable individuals experiencing unsheltered homelessness. Program participants may have limited work experience or skil s, minimal experience with technologies commonly used in the workplace, and significant criminal records, which act as barriers to finding a job and securing a steady income needed to afford rent once the RRH subsidies end. Providers work with the higher needs clients, who often require more time to be stably housed compared with clients with lower vulnerability scores or individuals with shorter histories of homelessness. Focusing on higher needs clients requires lower caseloads and dedication of more resources before individuals can successfully exit the RRH program.

Stakeholders note the challenges associated with housing clients with higher needs in the RRH program; however, due to the lack of PSH, GRCoC has intentionally targeted the most vulnerable population, and its partners accept those challenges as the means for housing as many individuals as possible. Although for higher vulnerability individuals to successfully transition to a permanent housing solution may take longer, stakeholders note that even when some of those individuals return to homelessness, the time they spent away from a situation of unsheltered homelessness can be positive for them High rent prices act as barriers when clients transition off RRH subsidies. RRH providers may help participants access the most affordable housing available. For some, however, the price of rent may be financial y unsustainable once the subsidy ends, with one-bedroom gross rents averaging $975—that is, the price of rent may be the lowest available but not low enough to fit within a participant’s budget once the subsidy ends, forcing some clients to move into another unit or return to homelessness.

LESSON LEARNED

Building in feedback loops and processes for stakeholder input strengthens the homeless response system and supports proactive adjustments to CoC processes. Stakeholders underscored how bodies such as the GRCoC board and other committees are essential to their process, which actively engages partner organizations and other community stakeholders. Feedback obtained through those committees informs the CoC’s overall efforts to address homelessness and accounts for multiple perspectives, including the perspectives of staff who work directly with clients and system-level partners.

The prioritization process required refinement to ensure that the RRH program did not exclusively serve individuals experiencing unsheltered homelessness. In 2015, when GRCoC began focusing more specifically on single adults experiencing homelessness, GRCoC found that prioritizing the unsheltered population meant that RRH no longer had the capacity to serve the sheltered population. GRCoC

adjusted its coordinated entry system to prioritize individuals with the highest vulnerability assessment score and the longest duration of homelessness, regardless of whether the individual was sheltered or unsheltered. The current system enables the program to serve the most vulnerable, who are primarily individuals coming from unsheltered settings. From January 1, 2018, to December 31, 2019, 39.9

percent of single adults who participated in RRH came from an unsheltered setting.

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Matching individuals experiencing unsheltered homelessness directly from the streets to housing required coordination between RRH providers and outreach staff. The GRCoC lead agency worked with RRH providers and outreach staff to coordinate handoffs from outreach staff to housing providers once a client was matched to the program. Previously, housing providers had worked directly with shelter providers, so the change in procedure required training so that individuals were easily referred from one partner to another.

CONCLUSION

Facing increasing levels of unsheltered homelessness and a high need for housing, GRCoC decided to test RRH as a solution to a severe shortage of PSH openings. The PSH inventory was not at a scale sufficient to meet the increasing needs, nor was it expanding at a rate sufficient to meet the population’s needs. GRCoC believed RRH to be the best option for vulnerable people experiencing homelessness despite RRH not traditionally being used for high-need individuals. To implement this shift, GRCoC prioritized communication and cooperation not only between the CoC and service providers but also with landlords and private funders. Those partnerships helped RRH become a successful option for many people who were experiencing unsheltered homelessness.

Modifying RRH to serve a higher need population, with a focus on unsheltered homelessness, started out with some challenges. GRCoC initially prioritized individuals experiencing unsheltered homelessness over people in sheltered settings to mitigate the hazards for people living in places not meant for human habitation. The prioritization of people experiencing unsheltered homelessness created a bottleneck in the shelter system, in which shelter exits for vulnerable individuals effectively stopped completely. As that unintended consequence was identified through communication with partners and other community stakeholders, GRCoC refocused coordinated entry to prioritize on the basis of vulnerability and enhanced data collection efforts to ensure that participants’ vulnerabilities were being comprehensively assessed. That change allowed a more comprehensive view of individuals in both sheltered and unsheltered situations, providing more equity in housing solutions. A significant majority of single adults accessing RRH are unsheltered, and all admitted to RRH are the most vulnerable.

To implement RRH in that fashion, GRCoC relies on RRH providers to support clients and on other providers for wrap-around supportive services. To serve more highly vulnerable clients, RRH providers had to provide more intensive services and work more closely with a broader array of community partners. Additional private funding was also required to fill gaps for services that were not covered through government funding. To ensure a range of housing options for this vulnerable population, GRCoC and providers increased efforts to educate landlords on the benefits of RRH and to reduce admission barriers. Rooming houses and shared housing situations were also used to expand the number of housing options. GRCoC’s relationships with its partners have been crucial to the overall success of the program. The success rates for clients remaining in permanent housing at exit and low rates of return to homelessness within 1 year of program exit from RRH are indicators of the success of those collaborations. GRCoC’s program demonstrates that RRH is a cost-effective, short-term alternative to PSH for some individuals and may help transition clients to other subsidies, such as VASH, as they become available.

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5. MONTGOMERY COUNTY’S SYSTEMS

APPROACH

SUMMARY: The Montgomery County CoC implements a systems approach to house people with the highest vulnerability scores in the county, many of whom are individuals experiencing unsheltered homelessness. The Montgomery County CoC coordinates outreach at the county level and implements an unnamed client policy, which al ows outreach providers and shelter operators to track people experiencing homelessness who do not provide a name. The Montgomery County CoC added a custom 9-point vulnerability scale—accounting for a person’s veteran status, if they are currently unsheltered, if they are vulnerable to exploitation, and other vulnerability criteria—to the standard Vulnerability Index-Service Prioritization Decision Assistance Tool to better prioritize vulnerable clients. The Montgomery County CoC primarily houses clients through PSH, which combines housing with voluntary support services and case management and tweaked the standard program model by offering a high- or low-intensity service approach. Montgomery County has also significantly expanded county funding for PSH

since 2017, adding 300 beds in 3 years, and substantial y reduced the number of people experiencing unsheltered homeless from 131 people in 2017 to 75 people in 2019—a nearly 43-percent decrease.

The Montgomery County

Exhibit 5.1. Map of Montgomery County’s Jurisdiction

Department of Health and Human

Services leads the Montgomery

County Continuum of Care (MCCoC),

a CoC serving the Maryland cities of

Rockville, Gaithersburg, and Takoma

Park and other unincorporated areas,

including Bethesda and Silver Spring

(Exhibit 5.1). In 2019, MCCoC,

partner agencies, and other

stakeholders established a strategic

plan to end all homelessness in

Montgomery County by the end of

2023. The plan’s systems design and

approach emphasize the importance

of collaboration (U.S. Department of

Health and Human Services, 2019).

Source: HUD Exchange, MD-601—Montgomery County CoC

Note: The city of Gaithersburg is shown as a separate jurisdiction

To conduct the case study, the study

because it receives a separate CDBG allocation from Montgomery County,

team worked with MCCoC staff to

but homelessness and human services coordination takes place at the

identify and recruit eight

county level.

stakeholders to participate in telephone interviews as part of a “virtual” site visit. The study team completed the interviews using a tailored interview guide (see Appendix D). Respondents included CoC

staff, a program participant, housing providers, and outreach staff. Responses were collectively analyzed

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in NVivo qualitative data software and synthesized to address the study’s qualitative research questions.

Additional data compiled under quantitative study, program HMIS data, and program materials provided by stakeholders were integrated to support the qualitative findings. The following sections describe the local context; program partnerships; program components; sustainability; and overall successes, challenges, and lessons learned from implementation.

CONTEXT

Since 2015, Montgomery County’s overal homeless population has decreased in size from 1,100 people to 647 in 2019 (HUD, 2015). The unsheltered homeless population in Montgomery County consists of mostly single adults older than age 45 (70.5 percent) who are predominantly African-American (54.2

percent) and mostly identify as male (70.4 percent). In 2019, 89.2 percent of people experiencing unsheltered homelessness reported a disability. Most (76 percent) of the unsheltered homeless population had endured homelessness for more than 12 months, with 70.4 percent reporting chronic homelessness. Exhibit 5.2 shows the changes in rates of homelessness from 2015 to 2019. The HUD

Point-in-Time count data show a decline in the unsheltered population from 2015 to 2019: 103 people in 2015, 96 people in 2016, 131 people in 2017, 133 people in 2018, and 75 people in 2019 (HUD, 2015).

The decline from 2018 to 2019 required continued support within the systems design of Montgomery County, including increasing permanent housing supply; leveraging partnerships; and engaging in a client-centered, Housing First model.

Exhibit 5.2. Rates of Homelessness and Housing Inventory in Montgomery County

Rates of Homelessness, 2015 to 2019

5,019

27.58

PSH Beds*

26.65

24.27

16.92

12.67

13.41

3,930

2015

2017

2019

Shelter Beds**

Rate of Homelessness (per 10,000 Population)

Rate of Unsheltered Homelessness (per 10,000 Population)

2019 Housing Resources

* Data represent permanent supportive housing and other permanent housing beds.

** Data represent transitional housing, safe haven, and emergency shelter.

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MCCoC has made significant progress addressing homelessness in Montgomery County and focuses on making homelessness rare, brief, and one time only. From 2016 to 2019, MCCoC saw decreases in the number of people experiencing homelessness for the first time, decreases in the duration of homelessness, and increases in the number of exits to permanent housing, overall (U.S. Department of Health and Human Services, 2019). In 2016, 1,606 people experienced homelessness for the first time, dropping to 1,404 people in 2018 (U.S. Department of Health and Human Services, 2019). MCCoC has seen an increase in the number of exits from the homeless system to permanent housing, going from 32

percent in 2017 to 62 percent in 2020 (U.S. Department of Health and Human Services, 2019). The agency also achieved a functional end to homelessness among veterans in 2015. MCCoC and its partners have collaborated to house more than 156 veterans since June 2015 and reported an average of six veterans experiencing chronic homelessness in January 2020. Similarly, MCCoC made significant strides in ending homelessness for individuals with disabilities and reported fewer than 11 people with disabilities experiencing homelessness in January 2020 (U.S. Department of Health and Human Services, 2019).

Although MCCoC has been successful at addressing unsheltered homelessness and overall homelessness, the agency recognizes continued gaps: a dearth of affordable housing for low-income households, limited PSH resources for individuals who become chronically homeless, and lack of employment services and job opportunities for people exiting homelessness (Montgomery County Continuum of Care, 2020). Montgomery County renters experience high rent burdens, with more than one-half of renters paying 30 percent or more of their income toward rent. Overall gross rent for a one-bedroom dwelling increased from $1,383 in 2015 to $1,592 in 2019 (much higher than the national average gross rent of $953 for a one-bedroom dwelling in 2019) (U.S. Census Bureau, 2020b), with average contract rent totaling $1,588 in 2018, as shown in Exhibit 5.3 (U.S. Census Bureau, 2020a).

Exhibit 5.3. Housing Market Factors in Montgomery County in 2018

MCCoC reports an average of 37 people newly entering homelessness and 23 exits from the system each month and projects nearly 2,200 single adults experiencing homelessness in Montgomery County by 2023 (U.S. Department of Health and Human Services, 2019). Stakeholders described that many of the people currently experiencing unsheltered homelessness experience mental illness, specifically symptoms of paranoia, as well as diabetes and other health conditions; substance use disorders; and unclear immigration status. The drug K2 has become an issue among some segments of the unsheltered population and can compound users’ mental il ness. Stakeholders noted more visibility of the homeless

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population in the downtown areas of

Exhibit 5.4. Change in Housing Inventory, 2015 to 2019

the county, such as Silver Spring, where

resources are more readily available.

Permanent Housing Resources, 2015 to 2019

Other people may reside in secluded

encampments in the wooded areas of

the cities. Stakeholders reported that

many of the individuals experiencing

1,924

2,030

2,227

unsheltered homelessness are the most

eds

medically vulnerable.

f B

After achieving low rates of chronic

r o

homelessness, MCCoC rethought its

prioritization for the PSH program.

umbeN

Since 2015, MCCoC has increased its

75

180

283

PSH capacity from 1,924 beds in 2015

to 2,227 beds in 2019 and made smaller

investments in RRH (see Exhibit 5.4).

2015

2017

2019

MCCoC had been working for years

RRH

PSH*

under the assumption that people

experiencing chronic homelessness

* Data represent permanent supportive housing and other permanent

housing beds.

were the most vulnerable in the

community; however, after review of client assessment data from 2016 through 2019 (Exhibit 5.5), MCCoC found that the chronically homeless population did not have uniformly high scores on the vulnerability assessment. The CoC noted that some people experiencing chronic homelessness who were matched to PSH received a higher intensity of services than needed. The CoC believed that to be an inefficient model that contradicted progressive engagement principles (Montgomery County Continuum of Care, 2020). MCCoC believes that by targeting people experiencing homelessness who have the highest scores on vulnerability assessment who have not yet had extended histories of homelessness, fewer people wil become chronically homeless. Based on that belief, MCCoC decided to alter its prioritization process for PSH to ensure

that the housing served the people with

Exhibit 5.5. Assessment Scores of Chronically

disabilities who had the highest vulnerability

Homeless Housed From 2016 Through 2019

assessment scores, including people experiencing

unsheltered homelessness, with the duration of

homelessness as a tiebreaker when needed.

Exhibit 5.5 shows the assessments of the

chronically homeless population housed from

2016 through 2019 ( n = 425), measured by the

Vulnerability Index-Service Prioritization Decision

Assistance Tool (VI-SPDAT) or full SPDAT; those

data show that only 9 percent of the chronically

homeless population scored as “very high” acuity,

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with the largest share scoring as “low” acuity. 48 Of these individuals, 87 percent were matched to PSH, and 3 percent were matched to RRH.

PROGRAM OVERVIEW

Over the past decade, MCCoC and its partners designed the Montgomery County systems approach to target the most vulnerable people experiencing homelessness, many of whom are unsheltered, by aligning funding and policies. First, the CoC focused on a Housing First approach that aims to reduce the time people spend experiencing homelessness and aims to provide people with stable housing without compliance requirements for services. MCCoC implemented policies that allow for more comprehensive assessments of people’s needs and opportunities to track unengaged individuals experiencing unsheltered homelessness in their Homeless Management Information System (HMIS). The CoC used that information and the coordinated entry system to prioritize the most vulnerable people for housing, as they saw a misalignment between the duration of homelessness and high needs. Many people with the longest history of homelessness in Montgomery County were not the individuals with the highest needs; therefore, the high level of services offered by PSH might not be necessary. MCCoC began categorizing PSH-eligible clients into two groups and developed reimbursement mechanisms that created levels of intensity of supportive services offered through PSH that correlate with the client’s level of need for services (based on the vulnerability assessment) to create an efficient, person-centric, and cost-effective method of implementing PSH.

We come from this idea that people don’t fail out of housing, except [when] the system fails them. We want to make sure that we have an adequate continuum to be able to provide [the] most [appropriate]

housing for everyone in the continuum. So that’s sort of what we mean by person-centric, that it really is based on what do they need and how can we help bring those services together.

48 The VI-SPDAT captures client history related to history of housing and homelessness; risks related to health and criminal history; socialization and daily function; and overal wel ness. Additional information can be found at http://pehgc.org/wp-

content/uploads/2016/09/VI-SPDAT-v2.01-Single-US-Fillable.pdf.

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KEY POLICIES AFFECTING IMPLEMENTATION

COORDINATED ENTRY POLICIES

In February 2019, the Montgomery County

Interagency Council on Homelessness (ICH), the CoC’s

The VI-SPDAT is a good tool in helping us

governing board, agreed to focus PSH on persons who

identify broad categories of what’s the right

met a more comprehensive standard of vulnerability,

housing intervention, but it isn’t as detailed

many of whom were unsheltered, rather than on

as we would like it to be, and hard for us to

simply targeting the chronically homeless. ICH also

really distinguish what’s the difference

revised coordinated entry system policies to reflect

between a 14 and a 16. There’s really not

the change in focus. To supplement the VI-SPDAT,

that much difference . . . we came up this

MCCoC created an additional vulnerability assessment

whole idea of the low intensity versus the

consisting of nine metrics that measure a person’s

high intensity.

acuity in terms of mental and physical health,

engagement, veteran status, or other criteria (as

shown in Exhibit 5.6). The VI-SPDAT and the acuity score make up the final vulnerability assessment score.

Individuals who score an 8 or higher on the VI-SPDAT are matched to PSH using a two-step process.

First, MCCoC categorizes two groups on the basis of their VI-SPDAT score. Clients with scores higher than 13 are prioritized for high-intensity PSH, whereas those with scores between 8 and 12 are prioritized for low-intensity PSH. Then, within each group, MCCoC prioritizes clients on the basis of the number of additional vulnerabilities reported in the supplemental assessment. For example, individuals with the highest number of vulnerabilities and with VI-SPDAT scores higher than 13 are prioritized for high-intensity PSH. To maintain functional zero of veteran homelessness, MCCoC prioritizes any veterans over non-veterans for appropriate housing resources on the basis of vulnerability scores. Clients who do not qualify for PSH are matched to RRH or other housing resources through the coordinated entry system.

Exhibit 5.6. Additional Vulnerability Assessment Criteria

Criteria Description

1. Poor Engagement with Services: How willing is the person to accept housing and services?

2. Currently Unsheltered.

3. Poor Management of Activities of Daily Living (ADLs): Is the person able to manage ADLs such as cooking and cleaning without assistance?

4. Veteran Status.

5. Risk or History of Exploitation: Is the person vulnerable to sexual, financial, or other types of exploitation due to gender identity, ethnicity, developmental disabilities, and the like?

6. Mental Health, as defined by the Montgomery County Housing Support Services Acuity Scale.

7. Substance Use, as defined by the Montgomery County Housing Support Services Acuity Scale.

8. Cognitive Deficits, as defined by the Montgomery County Housing Support Services Acuity Scale.

9. Medical Conditions, as defined by the Montgomery County Housing Support Services Acuity Scale.

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In tandem with the additional vulnerability assessment, MCCoC implemented initial and continuing education training support for staff conducting the acuity scoring to ensure consistency and to avoid any potential biases that may affect a person’s prioritization for housing. MCCoC also created a specific CoC

Outreach Coordinator position to assist with overall outreach strategies and to help standardize assessment scoring through partner collaboration. The additional training for case managers and staff ensures that staff accurately score clients and do not introduce biases that would result in a higher score than appropriate.

In 2018, MCCoC established an unnamed client policy

that allows outreach staff to input details about a

I think [the unnamed client policy is] really

person experiencing homelessness who may not yet

important when we’re dealing with people

be engaged or comfortable providing their name to

with acute mental il ness, when you’re

the outreach staff. MCCoC implements an open HMIS

dealing with people who have a lengthy

approach that allows all providers and shelter

history in maybe drug dealing and drug use,

operators to see clients’ past engagements. The policy

as wel as questionable immigration

allows outreach providers to input characteristics of a

background. None of those individuals want

person, such as that they wear a red hat or that they

to be asked a lot of questions.

park in a specific location, in HMIS. Once clients are in

HMIS, they may be matched to housing that may be

of interest to them. Stakeholders reported that some clients are interested in housing but not in services; therefore, when offered housing, they may engage and accept the housing. Engaging and documenting interactions with individuals, even without knowing their name, al ows the CoC and partners to monitor how often the individuals are being seen, which is important in tracking a person’s time experiencing homelessness and establishing their residence in the county, as well as knowledge sharing among various providers who may encounter the person.

MONTGOMERY COUNTY COC’S IMPLEMENTATION OF A SYSTEMS

APPROACH

MCCoC implements a coordinated approach to address unsheltered homelessness in Montgomery County consisting of coordinated outreach, housing-focused supports, and housing resources, primarily PSH and RRH. MCCoC and its partners leverage multiple sources of funding to offer a cohesive program that focuses on reducing barriers to housing. Montgomery County is one of the few counties in the country to fund its own PSH program, called the Housing Initiative Program (HIP). HIP uses allocated recordation tax resources and property taxes to supplement HUD CoC program funds for the county’s PSH program. As shown in Exhibit 5.4, Montgomery County’s PSH offered through HIP grew by more than 300 beds from 2015 to 2019.

ENGAGING INDIVIDUALS EXPERIENCING UNSHELTERED HOMELESSNESS

Individuals experiencing unsheltered homelessness access

housing options a number of ways. Individuals may cal into

[I heard about the program by]

various agencies for assistance, or they may hear about

gathering information, like bulletins

housing options through street outreach or day shelter

that they [put] up…

facilities. Several nonprofit organizations provide outreach

in specific jurisdictions of the county; Bethesda Cares

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covers outreach in down-county, and Every Mind conducts outreach in Rockville and up-county. Projects for Assistance in Transition from Homelessness (PATH) workers provide outreach to individuals with mental health conditions and substance use disorders across the county on a referral basis. Pathways to Housing has an integrated behavioral-health street outreach team that serves the entire county; this program is funded through a grant with the Substance Abuse and Mental Health Services Administration. In 2018, MCCoC added a hospital outreach worker in one of the emergency rooms to identify people experiencing homelessness who frequent the hospital for services.

The MCCoC Outreach Coordinator works with street outreach providers across the system to discuss policies, ongoing issues, gaps in procedures, and more. MCCoC conducts quarterly “blitz counts” of observations on the number and location of people experiencing unsheltered homelessness; that information is compiled into a “hot spot” map that is disseminated to the outreach providers and furnishes the location of outreach staff. In general, stakeholders noted the scarcity of outreach workers to cover the entire county; outreach workers may more likely be called to areas of the county, such as Silver Spring, where the homeless population is more visible to businesses and the community and where people experiencing homelessness may receive trespassing charges. In those areas, outreach workers direct people to shelters and public restrooms and may also provide education to businesses on individuals’ rights to choose where they stay—to build understanding and reduce calls to the police.

The coordinated entry system is advertised throughout

Exhibit 5.7. MCCoC’s Outreach Flyer

the county and to people experiencing homelessness

through partner organizations and outreach providers.

Exhibit 5.7 represents an outreach flyer from MCCoC

that is canvassed throughout the county. One

individual with lived experience noted receiving

contact information for homeless services on a flyer in

the community, similar to Exhibit 5.7, and heard of the

services through word of mouth. MCCoC also

collaborates with partners to identify and engage

people experiencing unsheltered homelessness.

Locations such as corrections facilities, hospitals, crisis

centers, emergency shelters, feeding programs, and

day shelter facilities are all locations where people

experiencing homelessness can call or visit to get

information and assistance. MCCoC recently engaged

nontraditional partners—such as employees from jails,

hospitals, and libraries—by including them as

committee members on CoC boards and by educating

them on how to recognize and refer people

experiencing homelessness to resources in the

community. Engagement with both traditional and

Source: MCCoC Outreach Services

nontraditional partners is essential because they act as

access points for individuals experiencing unsheltered homelessness.

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PREPARING CLIENTS FOR HOUSING

Outreach workers provide engaged people experiencing unsheltered homelessness with housing-focused outreach—in other words, they work with clients to obtain needed services such as health care, and they help clients secure documents, such as state IDs or birth certificates, needed for housing. 49 One challenge outreach staff face is related to proof of residence in the past 9 months, which is required by the CoC for people to be eligible for housing and outreach services. If those people do not provide such proof, they are referred back to the jurisdiction where they lost their housing, or they remain unsheltered until they have 9 months of residence shown within HMIS. That requirement is an issue because of the Washington, D.C., region’s particular overlay of state and county jurisdictional boundaries within the larger housing market. Those individuals may, however, receive donations or participate in meal programs while they wait to establish residency.

Once a client is engaged, they complete the VI-SPDAT or the full SPDAT and the 9-point acuity scale to record client needs and vulnerabilities so that outreach workers can determine what type of housing resources are appropriate. Outreach workers primarily use the VI-SPDAT to assess clients; however, if the outreach staff determine that the individual is unable to provide accurate responses, the full SPDAT

may be implemented. The vulnerability assessments (that is, the 9-point scale and the SPDAT) are entered into HMIS, and clients are assigned to by-name lists for the respective programs. PSH houses most individuals exiting chronic homelessness (90.5 percent) in MCCoC; 61.9 percent in the portion of PSH units are county-funded and 28.6 percent are HUD-funded PSH (Montgomery County Continuum of Care, n.d.). The primary housing programs are described below:

 PSH: The PSH program, known as HIP, offers a rental voucher to clients and matches individuals to nonprofit service providers, who provide housing navigation, case management, and care coordination. The program is further divided into two levels of intensity:

o High-intensity PSH: Either scattered-site units or single-site complexes with onsite case management or 24/7 services; serves people experiencing homelessness with a VI-SPDAT score of 13 or higher.

o Low-intensity PSH: Scattered-site units and case management; serves people experiencing homelessness with a VI-SPDAT score of 8 to 13.

 RRH: Short- to medium-term rental assistance designed to house clients for 3 to 6 months of rental subsidy and light case management; serves single adults experiencing homelessness with VI-SPDAT scores of 4 to 7, with a total vulnerability assessment score of 7 to 15 and the longest histories of homelessness, who are expected to sustain housing after barriers to housing are addressed through case management.

Clients are connected to a nonprofit agency case manager once their information is entered into HMIS.

Case managers determine the level of care needed while the client waits for housing based on the client’s vulnerability assessments. Case managers gather identification and other important information, such as existing conditions, substance abuse, or alcohol abuse. Staff encourage individuals experiencing unsheltered homelessness to check into shelters while waiting to be housed, but it is not a requirement 49 This is also referred to as being document ready, which means the client has the necessary documentation to obtain housing.

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to be housed. Outreach teams may refer a client for Health Care for the Homeless services for healthcare services.

HOUSING PROGRAMS SERVING PEOPLE EXITING UNSHELTERED HOMELESSNESS

Once a person experiencing homelessness is prioritized for a housing program, the client must be located within 5 days to confirm interest and proceed with housing location. Once confirmed, housing navigators from housing providers work with the client to locate housing. Some shelters implement a

“rapid exit” strategy that assigns clients a predetermined length of stay at the shelter (30, 60, or 90

days) at admission based on acuity. Although it is not implemented consistently among shelter operators, stakeholders reported that this strategy has reduced duration of stay and increased exits to permanent housing for clients because the strategy is housing-oriented and places a time limit on the length of the clients’ stay at the shelter. This process enables housing providers to develop a timeline and prioritize for services and housing needs on the basis of a client’s vulnerability. If someone is unable to secure housing during this time, they are able to stay in shelter for additional time.

With the increase in housing resources available through PSH or RRH, 516 people experiencing either chronic homelessness (n=301) or unsheltered homelessness (n=215) were housed through the programs from January 1, 2018, to December 31, 2019. Exhibit 5.8 shows additional details on destination at exit and returns to homelessness.

Exhibit 5.8. Destination at Exit and Returns to Homelessness for Individuals Experiencing Homelessness, in Percentages

Individuals Experiencing

Individuals Experiencing

Chronic Homelessness

Unsheltered Homelessness

( n=301)

( n=215)

PSH

89.7

97.0

RRH

9.0

2.8

Return to Homelessness*

6.3

7.9

PSH = permanent supportive housing. RRH = rapid re-housing.

Notes: * represents returns to places not meant for human habitation or to an emergency shelter. The two categories are mutually exclusive.

Data source: MCCoC HMIS Data, January 1, 2018, to December 31, 2019

Permanent Supportive Housing

If a client is matched to PSH, they are connected to a nonprofit service provider who offers the care coordination, case management, and housing location services (if needed) components of the program.

Service provider staff use vulnerability scores, motivational interviewing, and observations to understand the needs of the client in preparation for housing. A small number of clients are matched to one of three single-site or “project-based” PSH units operated by nonprofit providers in facilities specifically dedicated to PSH clients. Progress Place offers 21 units staffed 24/7, and each client has their own efficiency unit on one floor of a building. These programs are reserved for the highest acuity clients.

If the client is matched to a program that offers scattered-site PSH, then a case manager or care coordinator is assigned. Service providers col aborate within CoC committees to assign client matches to the programs on the basis of the services and practices of the providers and client needs. For example, some service providers are better at using harm-reduction practices, and some providers can offer

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roommate situations, in which they have a master

lease for a unit, and the organization sublets to the

[I was offered options where I wanted to

individual clients. Some service providers target

live.] I was offered different cities, like Silver

clients with the highest medical needs; the provider

Spring, Clarksburg, Gaithersburg, Shady

assigns care coordinators to coordinate medical

Grove, Rockville. I told them I real y didn’t

care, which is reimbursed through a Medicaid state

care. I just want to get off the streets.

waiver. Another PSH provider offers units in

scattered-site properties that they renovate and

manage. Specific service providers may require less documentation, which may make people with immigration documents more comfortable.

If needed, the service provider staff gather input on preferred housing location and types of housing, such as shared living or single apartments. An individual with lived experience noted that the housing provider offered several cities to choose from in the county. Most service providers assist clients in scheduling tours of units, completing leases, requesting reasonable accommodations, and establishing communication with the landlords. Once a client is approved for a unit, housing provider staff complete an inspection of the unit. Various service providers can provide a representative payee program, surety bonds instead of security deposits, and application fees as needed.

Rapid Re-Housing

Clients who are not eligible for PSH may be matched to RRH providers. Providers assign housing navigators to assist in identifying units, engaging landlords, and filing the application with the client.

Previously, RRH had an income requirement that limited access to the program for many people experiencing unsheltered homelessness who did not have an income. In recent years, MCCoC

restructured the program so that income was not required for someone to initially participate. RRH

offers up to 24 months of subsidy, with most participants receiving 3 to 12 months of assistance. Clients are required to pay a portion of rent under RRH based on their income. Only 2.8 percent of chronically homeless individuals are housed through RRH.

Rapid re-housing has been a great housing tool that’s getting a massive amount of people into housing. Because . . .getting prioritized to PSH, it takes time. It’s vulnerability, right? So many people that are on the street, they may not have four or five hospitalizations in the past six months, but they may be on the street for a long time . ... So, they may not really prioritize up at the level of PSH, but they can get rapid re-housing. So, we were able to capture those individuals and house them through rapid re-housing.

ENGAGING LANDLORDS

MCCoC employs a county housing coordinator who works to establish and foster relationships with new and existing landlords across the county. The county conducts lease-up events and actively recruits new landlords to participate in its programming. Landlord relationships have increased the number of housing options available for clients and decreased the burden associated with housing navigation. Case managers can maintain open communication with landlords over time and have release-of-information

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documents that allow for direct communication between the service provider and landlords so that case managers can mitigate any challenges as they arise during the clients’ tenancy.

Furthermore, stakeholders noted that in many cases, landlords are very receptive to the HIP rental assistance because it is paid directly to the landlord, which ensures timely and full payments.

Stakeholders noted relatively few challenges identifying housing; however, providers may have to advocate on behalf of the client to maintain housing quality. Specific clients may encounter more challenges when looking for housing. Respondents noted that clients with significant criminal records in particular are more challenging to lease up. Many clients have numerous petty charges and only a few convictions. Clients with arson charges are often the hardest to house and may be served by a PSH

program that does its own property management.

MOVE-IN AND ONGOING ASSISTANCE

Nonprofit service providers support clients during

move-in by meeting basic needs. Providers assist with

So, you just move somebody into an

reasonable accommodation requests, such as grab

apartment, but that doesn’t mean anything

bars, low pile carpet for wheelchairs, and ramps.

because what is it? It’s like a box with

Providers also assist with household goods, such as

nothing in it and it’s dark at night. So, we

furniture, bedding, kitchen supplies, and other

had to come up with what is the minimum

essentials. Previously, no minimum standard existed

standard [of furnishings] required . . ..

for what was required for clients when they moved in.

Providers recently implemented a minimum standard,

which was developed on the basis of federal refugee resettlement guidelines.

Once the client is housed, the nonprofit service provider supports onsite case management services. The level of case management is determined on the basis of the clients’ initial vulnerability assessments and whether they are assigned to the high- or low-intensity PSH. Stakeholders underscored the importance of helping clients transition into housing from living outside because people exiting unsheltered homelessness may experience more difficulty than people who have been sheltered. Case managers often check in with clients daily—either in person or by phone—when clients first move in and then decrease the frequency of check-ins over time. Case managers work with clients to get to know the neighborhood and where to find local transportation, grocery stores, and laundry facilities. If a client needs more hands-on assistance, case managers may teach clients basic cleaning and cooking.

Stakeholders noted that many people coming from unsheltered situations may present different vulnerabilities over time, such as diabetes, as the stress associated with living unsheltered subsides.

Case managers and care coordinators stay in close communication with individuals to identify their needs over time.

PSH vouchers are considered ongoing housing assistance, meaning that they do not have a specified end date. If clients leave their housing, they may be reassigned to a new unit, but they do not necessarily lose case management or ongoing rental assistance. Over time, as clients stabilize, they may be moved between programs, in accordance with a progressive engagement service model. In 2017, MCCoC

implemented a policy known as Move Up, which allows PSH clients to change levels of intensity; for example, if someone no longer requires high-intensity services, then they may be moved to a lower intensity PSH. Clients may also be moved to a voucher program or between PSH service providers. PSH

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clients are reassessed no more than once a year to document changes in needs over time and to inform changes in the level of PSH.

KEY PARTNERSHIPS SUPPORTING PROGRAM ACTIVITIES

MCCoC partners include housing and service providers, outreach staff, local nonprofit organizations, and other community stakeholders who have a role in addressing homelessness. Exhibit 5.9 shows key service providers supporting outreach, housing, and other services related to MCCoC’s systems approach. Outreach staff provide access points into the county system of homeless services and actively locate persons experiencing unsheltered homelessness to engage them in the system. Housing and service providers help clients secure and maintain housing tailored to their needs. Other service partners support programs using one-on-one services, such as employment navigation, health care, or peer counseling. Other stakeholders in the community—such as the police departments, public libraries, and recovery services—assist in providing additional support and connection points to those experiencing unsheltered homelessness.

Exhibit 5.9. Key Providers Supporting MCCoC’s Systems Approach

Outreach Providers

Other Service Providers

Housing Providers

 Bethesda Cares

 Catholic Charities

 Bethesda Cares

 City of Gaithersburg

 City of Gaithersburg

 Catholic Charities

 EveryMind

 Community Reach of

 City of Gaithersburg

 Interfaith Works

Montgomery County

 Cornerstone Montgomery

 PATH program

 Cornerstone Montgomery

 EveryMind

 Pathways to Housing

 EveryMind

 Family Services, Inc.

 Municipalities

 People Encouraging People

 Housing Opportunities

 Shepherds Table

Commission of Montgomery

 Rainbow Place

County

 Interfaith Works

 Montgomery County Coalition

for the Homeless

 Pathways to Housing

 The Coordinating Center

MCCoC intentionally selects partners that share the system’s goals of Housing First and person-centered programming. The CoC uses a structured committee to solicit input from various service providers and stakeholders in the community and to encourage communication across agencies. Committees steer the development and revision of key protocols and procedures. Exhibit 5.10 shows the different committees and their respective roles and responsibilities within the MCCoC.

Exhibit 5.10. MCCoC Interagency Commission on Homelessness Committees

Committee Names

Roles and Responsibilities

Strategy and Planning

Long-term vision and strategy

Communication

Public education and messaging

People’s

Ensures people-centered efforts

Outcomes and Improvement

Harnesses data for improvement

Partnerships and Funding

Mobilizes resources

Systems Coordination

Cross-sector program and policy implementation

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Source: Montgomery County Interagency Commission on Homelessness, Bi-monthly Meeting. March 4, 2020.

(https://www.montgomerycountymd.gov/Homelessness/Resources/Files/Meetings/2020/20-ICHFullMtgPresentationMar4-

2020.pdf)

Notably, the MCCoC includes a People’s Committee that consists of individuals who have lived experience with homelessness. The committee represents MCCoC’s commitment to integrating multiple perspectives into the governance of their CoC and its programming. The Partnerships and Funding Committee forges partnerships and establishes priorities to enhance the strategic plan of the CoC. The committee also provides a cohesive effort to fundraise that ensures a systems-wide approach to funding nonprofits within the CoC and reduces nonprofits’ burden of having to fundraise on their own. Other committees within MCCoC include a Systems Coordination Committee, which coordinates the implementation of a housing and service system across the entire CoC—not only the homelessness continuum. The Communication Committee focuses on public engagement and education, including managing the website and developing campaigns, and the Outcomes and Improvement Committee monitors performance targets and identifies best practices within the system.

SUSTAINABILITY

MCCoC stakeholders discussed a promising future for their systems approach, with the implementation of several revisions already planned; however, stakeholders shared their uncertainty related to their continued reduction of homelessness in Montgomery County. Stakeholders stated that they anticipate that the number of people experiencing homelessness will increase by 40 to 45 percent due to the COVID-19 pandemic and its effects on the community and economy.

FUTURE PLANS

Moving forward, MCCoC plans to create an intergovernmental advisory group to streamline coordination between government partners involved in the Montgomery County homeless response system. MCCoC aims to continue enhancing partnerships with locations that people experiencing homelessness are likely to frequent, such as transportation facilities and libraries, to enable greater outreach and better outcomes. MCCoC is also working to create a landlord mitigation fund to provide financial guarantees if a unit is damaged by a client. Other future plans include reconsidering the definition of “residence” and creating a response to the COVID-19 pandemic.

Stakeholders noted areas for improvement related to partnerships, training, and implementing specific outreach protocols. Stakeholders suggested increasing coordination and training between providers for wraparound services to enhance the support of care in RRH. Stakeholders also suggested revising outreach contracts to specify hours for outreach, adding nighttime and weekend shifts, appropriate caseloads, and other coordination protocols.

ADOPTING MONTGOMERY COUNTY’S APPROACH

MCCoC stakeholders believe that the systems approach to addressing unsheltered homelessness is translatable to other communities. Multiple stakeholders believed that partnerships and strong policies facilitating person-centered supports are necessary to implement a systems approach. In fact, stakeholders identified the MCCoC Coordinated Entry policies as the “single most important” thing that MCCoC has done to address homelessness and believed it was appropriate for other communities.

Stakeholders believed that their coordinated entry system set their program apart because it considered

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a more holistic assessment of individuals’ needs, measured through vulnerability indices. Adoption of additional indices is a significant and easily adoptable approach to vulnerability assessments, which requires additional buy-in and support from system partners.

SUCCESSES, CHALLENGES, AND LESSONS LEARNED

Stakeholders reflected on their experiences implementing the systems approach over the past decade and identified successes, challenges, and lessons learned throughout the program’s implementation.

The sections that follow detail key points described by stakeholders.

SUCCESSES

MCCoC comprehensively assesses clients’ needs and uses them to prioritize housing resources within the coordinated entry system. MCCoC developed a vulnerability assessment that includes additional weights for client characteristics, such as if they are unsheltered, a veteran, or vulnerable to exploitation. MCCoC enters that information in the coordinated entry system and uses the information to understand which housing resources best fit the client.

MCCoC implements a person-centered approach to services and housing in which clients’ needs are considered at every stage of the housing process. Beginning with outreach, clients may engage outreach workers as little or as much as they like. During the housing process, MCCoC matches higher acuity clients to one of two tiers of PSH, and clients have input on the type and location of the housing.

If clients are unsuccessful in a program, they can be transferred between PSH service providers if another service provider can better meet their needs. Case managers use motivational interviewing to identify client goals and tailor the supports provided to clients on the basis of their needs over time.

MCCoC implements an outreach policy allowing for clients to be identified and prioritized for housing without their having to share a name. The use of the unnamed client policy in Montgomery County empowers outreach providers to track and engage individuals who might not feel comfortable providing their name. Outreach staff log a description of the individual and the location where they were met or were observed. Sharing information throughout the HMIS allows for other outreach staff who may come across the individual to access information that may be pertinent to scoring vulnerability and subsequently housing them.

CHALLENGES

People experiencing unsheltered homelessness are likely to remain unsheltered as they wait to be matched to housing because the emergency shelter is undesirable or has high barriers. Partner agencies note that clients may be deterred from using the emergency shelter in Montgomery County due to its large size and limitations in what clients can bring with them. Clients experiencing unsheltered homelessness may have personal items, such as shopping carts and pets, that are prohibited in the shelter. Partner agencies also noted that those coming from an unsheltered situation may fear the size of the shelter or the other people who reside in the shelter. Clients may also be deterred from entering a shelter due to their drug use and dependency.

Due to limited behavioral health and substance use treatment services, individuals with higher needs may be less likely to remain stably housed over time. Stakeholders reported a shortage of treatment services and peer supports for individuals experiencing co-occurring mental health disorders and

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substance use disorders. Individuals experiencing such co-occurring disorders may be less likely to remain housed over time and may be evicted four to five times in PSH programs because they are unable to stabilize without treatment.

Residency requirements limit many people experiencing unsheltered homelessness from accessing services and housing. In the Washington, D.C., metro area, the respondents stated that the cities feel continuous to many people, and crossing CoC jurisdictions is easy, including for people experiencing homelessness. If people lose housing in a city outside Montgomery County, they are ineligible for housing and services beyond donations and day centers until they have proof of at least 9 months of residency in the CoC. People typically reside unsheltered as they wait to establish residence, or they are referred back to the county where they lost housing. The CoC is currently reconsidering how they define

“residence,” particularly for people experiencing homelessness. 50

LESSONS LEARNED

Successful implementation of homeless strategies is dependent on partner and community buy-in and CoC leadership. MCCoC has leveraged partnerships with housing and service providers, community stakeholders, and individuals with lived experience to enhance the systems approach to addressing unsheltered homelessness. The CoC is enhancing work between government partners within the community to expand outreach and presence for those experiencing homelessness. Engaging with stakeholders and educating the community on homelessness is crucial to effectively operating the systems approach among partners.

Driven by data, MCCoC adjusted its coordinated entry system processes to prioritize the most vulnerable individuals in the county after achieving low rates of chronic homelessness. MCCoC

previously assumed that individuals with the longest history of homelessness were the most vulnerable; however, after reviewing client assessment data and housing utilization, MCCoC found minimal overlap between individuals with a disability experiencing homelessness and individuals with the longest duration of homelessness. Once MCCoC achieved low rates of chronic homelessness, the agency based its coordinated entry system prioritization on the highest overall vulnerability, factoring in individuals coming from unsheltered situations.

In an effort to keep PSH targeted to the most vulnerable, MCCoC implemented the policies and protocols facilitating PSH-to-PSH transfer and PSH-to-voucher transfers, allowing clients to transfer housing programs on the basis of needs. Clients who stabilize over time may be moved out of PSH, which provides additional openings in the program. Other clients may transfer from various PSH service providers because another service provider better meets their needs. In both cases, housing and services are continuous and allow resources to be allocated efficiently.

50 MCCoC participates in the Metropolitan Washington Council of Governments (COG), a regional organization of the Washington area’s 24 major local governments and their governing officials, plus area members of the Maryland and Virginia legislatures and the U.S. Senate and House of Representatives. The nine participating CoCs are collaborating across jurisdictions to ensure that the experience of homelessness in metropolitan Washington is brief, rare, and non-recurring, and they are working to address challenges such as residence requirements.

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CONCLUSION

MCCoC implements a Housing First, person-centered approach to addressing unsheltered homelessness in its county. Although MCCoC has challenges related to proof-of-residency requirements, the unnamed client policy facilitates engagement with people who are reluctant or are uninterested in engaging in outreach and prioritizes them for housing before they provide a name. MCCoC’s coordinated entry system details all client engagements and allows open access for shelter operators, outreach workers, and other staff involved in clients’ care, which provides accurate client histories for service providers and helps to establish residency for certain clients. The CoC implements a comprehensive vulnerability assessment tool including both VI-SPDAT scores and supplemental measures and uses that information to identify the most appropriate housing options for clients.

The CoC relies heavily on its PSH inventory and, to a much smaller degree, RRH to house people exiting unsheltered homelessness. In support of that effort, the county has expanded its PSH inventory through the launch of the HIP program using local tax revenue. MCCoC uses vulnerability indices to assign clients to a high- or low-intensity PSH, in line with progressive engagement and appropriate and effective utilization of resources within the CoC. The PSH program provides rental vouchers for clients and matches the client to service providers on the basis of client needs and openings from the service providers. MCCoC allows for transfers between PSH service providers if another provider would better meet a client’s needs and between PSH and other voucher programs if a client no longer requires the same intensity of services. For RRH, MCCoC increased access to the program by eliminating income requirements to participate.

Overall, MCCoC implements a person-centered approach that includes client input at multiple stages and consistently considers individual client needs. MCCoC does not require clients to be in a shelter for them to be matched to housing, nor do they have to provide a name. Clients have input on the type of housing they receive and the location of the housing within the county. Service provider staff tailor their own case management or care coordination on the basis of the clients’ unique needs and preferences to promote a smooth transition into housing and stability over time. Using those person-centered practices, MCCoC reported relatively few returns to homelessness after a client was placed in permanent housing.

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6. SAN DIEGO’S COVID-19 EMERGENCY

SHELTERS

SUMMARY: The Regional Task Force on the Homeless (RTFH) serves as the CoC lead for San Diego County and helped coordinate a COVID-19 pandemic response for people experiencing homelessness in the county. The response included modified outreach and the opening of temporary shelters at the San Diego Convention Center and three hotels within the county that offered non-congregate shelter, both of which temporarily expanded shelter capacity starting in April through an anticipated closure in December 2020. The efforts successfully engaged people experiencing homelessness who had never engaged in services or shelter; 26 percent of the clients served through the projects had never been reached by the mainstream homeless services system. RTFH worked with key partners, including service providers, the San Diego Housing Commission, the city of San Diego, and the county of San Diego to implement the temporary emergency shelters to attempt to limit spread of COVID-19 and provide strategic housing navigation from multiple housing programs, including PSH, VASH, RRH, and other programs. The programs were relatively successful in temporarily sheltering people experiencing unsheltered homelessness, increasing shelter capacity, reducing the risk of catching COVID-19, and connecting clients to services, including housing such as RRH and project- or tenant-based PSH

programs. The Temporary Lodging Program showed particular promise; formerly unsheltered residents sheltering in hotels had considerably lower rates of returning to unsheltered homelessness relative to those served in the convention center and by existing shelter options.

This case study focuses specifically on the efforts related to outreach, the convention center, and select non-congregate hotels to shelter individuals and prevent

the spread of COVID-19 in the San Diego County

Continuum of Care, primarily within the city limits of San

Diego. RTFH is the lead agency for San Diego County’s CoC,

which includes 18 municipalities and unincorporated areas

of the county. Exhibit 6.1 shows the share of the homeless

population by region across San Diego County according to

the 2020 Point-in-Time (PIT) count. In April 2020, RTFH

managed hotels to be used as non-congregate shelters51

and the San Diego Convention Center (known as

Operation Shelter to Home) to provide safe, temporary

shelter and services to individuals experiencing

homelessness who could not otherwise safely shelter in

place during the COVID-19 pandemic and to try to connect

them to permanent housing. To conduct the case study,

Exhibit 6.1. Locations of Homeless

the study team worked with RTFH staff to identify and recruit

Population in San Diego County

nine stakeholders to participate in telephone interviews as

Source: San Diego Regional Task Force on the Homeless

51 The hotels discussed in the case studies were locally known as the “Temporary Lodging Program.” They were opened before the California state’s Project Roomkey program and did not leverage state Project Roomkey funding.

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part of a “virtual” site visit. The study team completed the interviews using a tailored interview guide (see Appendix D). Respondents included two program participants, CoC staff, housing and service providers, public officials, and outreach staff; important to note is that the study is limited to nine interviews, which represent a limited scope on the project discussed. 52 Responses were collectively analyzed in NVivo qualitative data software and synthesized to address the study’s qualitative research questions. Additional data compiled under quantitative study, program data, and program materials provided by stakeholders were integrated to support qualitative findings. The program-level data were analyzed before the completion of the project activities, which concluded in late 2020 and, therefore, have significant limitations regarding the overall outcomes. The following sections describe the local context; program partnerships; program components; sustainability; and overall successes, challenges, and lessons learned from implementation.

CONTEXT

In recent years, the San Diego region has made significant strides in addressing homelessness. In fact, San Diego was the only major county in the state of California that saw a decrease in the homeless population in 2019 (City of San Diego, 2020b). 53 According to the January 2020 PIT count data, the population experiencing unsheltered homelessness decreased by 11 percent from 2019 to 2020, although the sheltered population increased slightly, by 2 percent; San Diego saw an overall reduction in the total homeless population of 6 percent (San Diego Regional Task Force on the Homeless, 2020c). 54 In 2019, the number of people experiencing unsheltered homelessness totaled 3,971. Exhibit 6.1 shows that the majority of the unsheltered population resides in the city of San Diego (65 percent) and, more specifically, in the downtown area (58 percent) (San Diego Regional Task Force on the Homeless, 2020c).

Exhibit 6.2 shows rates of homelessness over time and the housing resources available in 2019.

52 The County of San Diego served as the lead agency for non-congregate hotels and was not available to participate in an interview for this study but provided some quick feedback on the case study.

53 Although the San Diego CoC changed its methodology for reporting the PIT count, the City of San Diego provided evidence that its unsheltered population had decreased.

54 The 2020 PIT count implemented a new methodology; for additional details see

https://www.sandiegouniontribune.com/news/homelessness/story/2019-04-29/new-counting-techniques-finds-fewer-

homeless-in-county.

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Exhibit 6.2 | Rates of Homelessness and Housing Inventory in San Diego County

* Data represent permanent supportive housing and other permanent housing beds.

** Data represent transitional housing, safe haven, and emergency shelter.

Many stakeholders attribute the high rates of homelessness to high costs of living, limited stock of single-room occupancy units, and rising rent costs. Between 2015 and 2019, gross rent for a one-bedroom unit increased from $1,111 to $1,436—a nearly 30-percent increase (much higher than the national average gross rent for a one-bedroom unit; that rent increased from $810 in 2015 to $953 in 2019—an increase of 17.7 percent) (U.S. Census Bureau, 2020b). In 2018, 57 percent of renters paid more than 30 percent of their monthly income toward rent (compared with 50 percent nationally), and average contract rent totaled $1,506 per month (compared with $945 nationally) (U.S. Census Bureau, 2020a), as seen in Exhibit 6.3.

Exhibit 6.3. Housing Market Factors in San Diego County in 2018

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Shelter beds and permanent supportive housing (PSH) also remain in high demand in the region. Level of resources available in the homeless assistance system showed a 92-percent use of emergency shelter beds and an 81-percent use of PSH beds on the night of the January 2020 PIT count (San Diego Regional Task Force on the Homeless, 2020c).

Stakeholders reported strong political will to address homelessness in San Diego, guiding the implementation of policies for city and county service providers and increasing funding for services. For instance, the 2019 City of San Diego Community Action Plan on Homelessness set the goal of reducing unsheltered homelessness in the city by 50 percent in 3 years and establishing timelines for ending veteran and youth homelessness in the city. In early 2020, the CoC Board approved the Policy Guidelines

for Regional Response for Addressing Unsheltered Homelessness and Encampments Throughout San

Diego County (Regional Response for Addressing Unsheltered Homelessness), which developed policy guidance for outreach practices that promoted a housing-focused orientation, shifted the role of law enforcement, and encouraged diverse stakeholders to follow standard practices rooted in Housing First philosophies and trauma-informed care.

In March 2020, the state of California issued a mandatory shelter-in-place order due to the outbreak of COVID-19 in California and the United States. The shelter-in-place order immediately decreased the workforce supporting homeless services by an estimated 30 to 50 percent, as staff were told to stay home or elected to stay home in response to safety concerns about the virus and its spread (City of San Diego, 2020e). To allow for social distancing, existing shelters reduced capacity to 70 percent of their normal occupancy. Due to the closures and reduced operations, many people enduring homelessness in the region experienced unmet needs within the first few months of the pandemic. As stated by a person with lived experience, the downtown district became a “ghost town”; homeless services became scarce, and access to key government offices became nearly nonexistent. Although emergency services remained available, some services deemed nonessential—such as the Department of Motor Vehicles or the Social Security office—closed, meaning that if people needed an ID to access services or housing, they had to wait for those offices to reopen. Respondents with lived experience with homelessness believed that the isolation induced by the COVID-19 pandemic worsened symptoms of depression and resulted in increases in drug use, which they believed people residing in unsheltered situations experience more often.

On March 18, 2020, the state of California offered a one-time funding opportunity to support local isolation capacity, emergency shelter operations and capacity, street outreach, staffing, and transportation to help Californians experiencing homelessness and reduce the spread of COVID-19

(Office of Governor Gavin Newsom, 2020). The city of San Diego, county of San Diego, and RTFH

received approximately $7.1 million (San Diego Regional Task Force on the Homeless, 2020a). In addition to the emergency COVID funding, the state had already offered existing one-time block grant funding through the Homeless Emergency Aid Program (HEAP) and the Homeless Housing Assistance and Prevention Program to support a variety of housing assistance and service supports for people, including families and youth experiencing homelessness (San Diego Regional Task Force on the Homeless, 2020a). In an unprecedented partnership, the city of San Diego, county of San Diego, RTFH, and the San Diego Housing Commission leveraged federal grants (primarily Coronavirus Aid, Relief, and Economic Security [CARES] Act funding) and Federal Emergency Management Agency reimbursements—

and state funds—to bolster a coordinated approach to temporarily shelter or house and support people experiencing homelessness during the COVID-19 pandemic.

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PROGRAM OVERVIEW

The region opened a new congregate shelter at the convention center with more than 1,200 beds and operated non-congregate hotel rooms. They offered 165 rooms to individuals or families experiencing homelessness, known locally as the Temporary Lodging Program. The new emergency shelter capacity compensated for a 30-percent reduction in existing shelters in the city of San Diego, which decreased existing shelter capacity from roughly 1,500 to 1,000 beds. 55 The overall shelter capacity in the city, specifically, increased by at least 900 beds on net despite the reduced capacity of some existing shelter providers. Homeless Management Information System (HMIS) data from February to August of 2020

show that 26 percent of the clients served through either the convention center or the Temporary Lodging Program had never received shelter or services before their enrollment in the programs. The two programs not only expanded capacity but also serviced clients who had not historically engaged in services or shelter.

OPERATION SHELTER TO HOME: THE CONVENTION CENTER CONGREGATE SHELTER

On April 1, 2020, the city of San Diego launched Operation

The convention center offered

Shelter to Home in partnership with the County of San

check-ups with a public health

Diego, the San Diego Housing Commission, RTFH, and the

nurse, security, meals, showers and

San Diego Convention Center. The program temporarily

restrooms, laundry, case

repurposed the San Diego Convention Center as a shelter for

management, and housing

single adults experiencing homelessness in the city of San

navigation. If needed, additional

Diego. Nonprofit organizations—including Alpha Project,

services included onsite access to

Father Joe’s Vil ages, and Veterans Village of San Diego—

behavioral health and primary care

were responsible for specific wings of the convention

services.

center. The convention center opened in a phased

approach. In the first phase, 765 clients were moved from

other shelters, including four bridge shelter programs and

one interim housing program, into the convention center due to reduced capacity or to keep the individuals safe during the pandemic. In the second phase, outreach teams established posts within the city to identify and recruit interested individuals experiencing unsheltered homelessness. On the basis of client preference and medical history, clients were matched to the convention center or another shelter that was in a different location or that offered more medical care. All clients received COVID-19

symptom screenings and general health and wellness checks by county public health nurses.

55 Additional increases in shelter capacity occurred across the county of San Diego through the opening of additional congregate and non-congregate shelters.

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NON-CONGREGATE HOTELS: TEMPORARY LODGING PROGRAM

Beginning in April 2020, several hotels in San Diego County

Temporary Lodging Program

began operating as temporary non-congregate shelters for

services varied by service provider

households experiencing homelessness in the county. Three

supporting each hotel. At a

RTFH hotels included in this case study served adults who

minimum, services included daily

were older than age 65, had a chronic medical condition, or

meals, laundry, room cleaning, daily

were previously homeless but were able to maintain

safety and cleaning checks, case

activities of daily living. A total of 165 rooms were available

management, and housing

to single adults or families without children in the three

navigation.

hotels. Interfaith Community Services and the Downtown

San Diego Partnership provided services to clients in those

hotels and coordinated intakes.

KEY POLICIES AFFECTING IMPLEMENTATION

HOMELESSNESS POLICIES

Throughout 2019, RTFH led the development of the Policy Guidelines for Regional Response for Addressing Unsheltered Homelessness and Encampments Throughout San Diego County, which was adopted in January 2020 (San Diego Regional Task Force on the Homeless, 2020b). That policy laid the groundwork for municipalities, government agencies, and other service providers involved in the regional response to unsheltered homelessness to establish best practices and consideration for homeless outreach and encampment abatement. The policy underscored the importance of regional collaboration, client-centered approaches, decriminalization of homelessness, Housing First principles, and other practices aimed at enhancing and standardizing outreach practices. Stakeholders reported varying degrees to which those policies affected the implementation of the convention center and Temporary Lodging Program. Many stakeholders noted that additional education around the regional policy was planned; however, due to COVID-19, the implementation of some of the policy guidelines was put on hold.

The City of San Diego Community Action Plan on Homelessness was adopted by the San Diego City Council in October 2019. The willingness of organizations across the community to engage in the COVID-19 response for people experiencing unsheltered homelessness was consistent with the action plan. The Action Plan on Homelessness also promoted support for pilot programs across the city, such as neighborhood-based outreach. The action plan prioritized and expanded programs that served as the basis of the COVID-19 response, which aimed to use the expanded capacity to shelter many of the people experiencing homelessness in San Diego.

COORDINATED ENTRY POLICIES AND EMERGENCY PRIORITIZATION FOR THE CONVENTION CENTER

Before the COVID-19 pandemic, the San Diego CoC—under the direction of RTFH—prioritized households experiencing homelessness on the basis of their length of time being homeless and the vulnerabilities they face while unhoused. The measurements created through San Diego’s community scoring tools, including the Vulnerability Index-Service Prioritization Decision Assistance Tool (VI-SPDAT), aided in the creation of a by-name list. To ensure client flow from the emergency shelters (including the convention center and Temporary Lodging Program) and timely matches, RTFH enacted emergency protocols to standardize and streamline assessment and to prioritize convention center and Temporary

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Lodging Program clients. The assessment process included client preferences, a brief medical history, COVID-19 screenings, and consideration of other client-centered needs, such as mobility issues. First, clients were prioritized into three cohorts:

 Green status: Clients were engaged in case management or services, interested in being housed, and document ready. 56

 Yellow status: Clients were engaged in case management and interested in housing but may not have had documents available to obtain housing.

 Red status: Clients were not engaged in case management and not interested in housing.

Within each group, clients were prioritized on the basis of their duration of homelessness, vulnerabilities, age, and health conditions. Stakeholders noted that in some ways, that stratification prioritized lower needs individuals because they may be more likely to be “green” status, or readily engaged and document ready. The system was designed to generate flow between the emergency shelters (including the convention center and Temporary Lodging Program) and housing resources, in line with the mayoral promise to house individuals experiencing homelessness during the COVID-19

pandemic.

OUTREACH TO INDIVIDUALS EXPERIENCING UNSHELTERED

HOMELESSNESS

Outreach across San Diego County is provided by neighborhood, municipal, regional, and county organizations, composing a network of outreach providers. In the central region of San Diego, organizations—including Projects for Assistance in Transition from Homelessness (PATH), Alpha Project, Father Joe’s Vil ages, Downtown San Diego Partnership, and Veterans Village of San Diego—provide street outreach services. In other parts of the county, organizations such as Interfaith Community Services, McCallister Institute, and HomeStart provide outreach support. In addition, across the region are various police-led Homeless Outreach Teams (HOT), which may include mental health specialists, social workers, housing staff, and others who serve homeless populations, although the teams vary by city. RTFH supports outreach workers, who are distributed across the county on the basis of the proportional distribution of the homeless population through HEAP funding from the state. The City of San Diego and the County of San Diego also dedicate significant amounts of funding to be used for street outreach in their geographic jurisdictions. Historically, the network of outreach providers met in a quarterly county-level meeting to discuss outreach but had little additional coordination; many stakeholders indicated that the system often produced fractured results and left a significant portion (about 20 percent) of the unsheltered population unengaged due to portions of the county with no outreach.

56 “Document ready” refers to clients who have the appropriate documents required to become housed, such as a driver’s license, state identification, or a Social Security card.

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Before the outbreak of COVID-19, RTFH and other

partners were working with a consultant to enhance

We’re [working to align our outreach]

community standards for outreach practices and

efforts to do something, to make sure

coordination based on a person-centered approach and

that our efforts and our standards,

the incorporation of best practices. At the time of the

our outreach efforts, are more

interviews, the standards had not yet been finalized; 57

standardized and that we’re working

however, many stakeholders reported implementing

in concert with one another versus

practices consistent with the standards before they were

kind of just arbitrarily continuing to

fully implemented in the fall of 2020. For example, the

cover a vast area of our community.

outreach teams began implementing an app to track and

coordinate outreach across the county; the technology

was used previously for the PIT count data collection and adapted to coordinate outreach geographically. Other principles in the standards included prioritizing meal programs and ensuring access to basic services.

IDENTIFYING INITIAL EMERGENCY SHELTER CLIENTS THROUGH OUTREACH

The convention center was opened to individuals experiencing unsheltered homelessness after the existing City of San Diego shelter clients were consolidated from other shelters; Temporary Lodging Program rooms were filled through the service providers referring clients. For the convention center, the neighborhood HOTs established posts in neighborhoods early in the program to identify and recruit individuals experiencing unsheltered homelessness. Once someone indicated interest, HOT vans transported people from the posts to the convention center for centralized intake. On an ongoing basis, outreach workers from a nonprofit organization referred clients to Temporary Lodging Program rooms with Interfaith Community Services and the Downtown San Diego Partnership, which provided the supportive services at the hotels.

In the city of San Diego, stakeholders disagreed on the appropriateness of the HOT posts used to initially fill the convention center; the HOTs existed before the COVID-19 pandemic, but they were typically neighborhood based. During the pandemic, HOTs were deployed across the city in areas where they had not necessarily worked previously. Some stakeholders believed that the approach reduced bias because the HOTs may not have had a history with the people experiencing homelessness in the area.

Stakeholders also believed that the approach created a fairer system and may have increased potential clients’ receptivity to the services because they did not have past experiences with the staff working the post. Other stakeholders, however, compared the recruitment process to a “cold call” precisely because the HOTs operating the posts had no history or relationships with the people in the area who may have been engaged with an outreach provider previously. Stakeholders thought that building on established relationships may have been more beneficial rather than starting fresh.

ONGOING OUTREACH DURING COVID-19

At the start of the COVID-19 pandemic, outreach and other homeless services initially diminished due to safety precautions and staffing shortages. Once the community had a better understanding of how to safely conduct outreach during a pandemic, outreach staff altered their approach and began providing 57 The standards were finalized and adopted for use in November 2020 after the data collection effort for this study had ended.

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meals, water, face masks, hygiene kits, and other survival items, in accordance with guidance from the Centers for Disease Control and Prevention. The county also supported public health nurses joining HOTs in regular outreach. Outreach teams prioritized areas of the county that had significant decreases in resources available to the homeless population—what stakeholders called “resource deserts”—due to COVID-19-related business closures. In general, outreach during the COVID-19 pandemic, particularly in the first few months, remained limited due to staff shortages.

Many people experiencing unsheltered homelessness were initially hesitant to seek shelter because they were worried about the unhindered spread of COVID-19 in the congregate settings. To address that concern, outreach teams began educating the homeless population on the importance of handwashing and social distancing. The teams also provided general information about COVID-19 and safety measures put in place at the convention center if the clients were interested in shelter.

[We thought] everybody [in the shelter was] going to get sick. And some guys were saying like, “That’s what they want, they want to get rid of the homeless people, and by putting them all together, [if]

somebody gets sick, we’l all get sick and we’ll all get died [sic], and that’s the way they’ll get rid of us.”

And I’m watching them [run the convention center], that’s not the case, they’re trying to help us, man.

Outreach workers also began using meals offered through a new program to engage individuals experiencing unsheltered homelessness. A significant philanthropic donation and a strengthened collaboration between the Lucky Duck Foundation, RTFH, and the San Diego Sheriff’s Department resulted in meals being available to outreach workers to provide to individuals residing outdoors. The program provided meals three times a week to as many as 25 outreach teams; the program initially offered 400 meals per day but—because of significant demand—grew to feed nearly 1,000 people per day. The outreach staff used the meals to facilitate engagement with potential clients who may have been otherwise unreceptive to engagement; those meals were especially needed given the dearth of resources, including meal programs, during the COVID-19

pandemic and gave outreach teams an opportunity to

[They] use those meals as an effective

build trust and rapport and to educate clients about

tool in outreach workers’ tool belt to

COVID-19-related precautions. The outreach staff used the

ultimately build trust and rapport and

mobile app to track where meals were being provided and

that trusting relationship to help the

strategize team location to ensure that areas of the city

homeless in their homelessness.

were not missed.

TEMPORARY LODGING PROGRAM

The County of San Diego partnered with RTFH to help provide support to the three hotels in the county to provide temporary housing to asymptomatic individuals with underlying health conditions who were experiencing unsheltered homelessness. RTFH helped with coordination, and Interfaith Community Services and the Downtown San Diego Partnership provided direct services to individuals residing in those hotels. Between April and October 2020, the program served 209 people who were previously

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experiencing unsheltered homeless—representing 28 percent of the overall population served. 58 The average age of those clients was 58 years old, with roughly 30 percent of clients older than age 65.

Clients in the Temporary Lodging program were generally a more vulnerable population than the overall unsheltered population. Nearly all (85.4 percent) clients reported a disability, including mental health conditions (71.4 percent), physical disabilities (54.3 percent), or other chronic health conditions (65.7

percent), and 21.6 percent of the unsheltered population reported experiencing chronic homelessness. 59 The program offered private hotel rooms as temporary shelter, housing navigation, optional case management, and additional onsite and offsite services. Clients either left the program when they became housed, chose to leave, or were unable to comply with public health orders or program regulations.

HOTEL MANAGEMENT, LOCATION, AND AMENITIES

RTFH managed the relations with the hoteliers, provided data management support, funded case management services, and coordinated with the county for daily meal delivery, and the service providers coordinated onsite and offsite services and case management. Interfaith Community Services provided services to individuals in the two North County hotels; and Downtown Partnership provided services to individuals in a downtown area hotel. Stakeholders were unaware of how the county selected hotels and noted that in some cases, the hotels were in undesirable neighborhoods next to strip clubs, liquor stores, or a military base, which had little greenspace and limited access to services.

Some stakeholders noted a misalignment between the location of the hotels and the areas with the highest incidence of COVID-19 cases.

Stakeholders shared challenges related to the selection of hotel based on quality, location, and amenities. The hotels had issues related to mold, structure, electrical systems, plumbing, bed bugs, and rats. Hotels included very few ADA-accessible rooms, which were a key need of the client population.

Stakeholders reported that hoteliers invoiced for numerous damage charges, which stakeholders believed to be unreasonable and caused tensions with hoteliers. Furthermore, stakeholders described the hotels as “stripped down” in many ways; hotels offered limited laundry facilities, toiletries, cleaning, and trash disposal, and many rooms did not include typical amenities such as refrigerators. In addition, hotel staff were inexperienced with the population and were often the only staff onsite to handle issues and complaints.

TEMPORARY LODGING PROGRAM FOR INDIVIDUALS EXPERIENCING UNSHELTERED HOMELESSNESS

Service providers arranged for a variety of onsite and offsite services, including medical care, pharmacy deliveries, referrals to mainstream benefits, homeless court, and document readiness services.

Stakeholders reported that individuals who were able to manage activities of daily living with health conditions often stabilized (for example, decreased stress levels) in the program once they connected to those services. Other individuals with substance use disorders and serious health conditions impeding 58 Data represent preliminary data through October 3, 2020 rather than the final outcomes of the project, which at the time of writing is scheduled to conclude in December 2020 .

59 This information is based on PIT count data provided by CoC as part of their CoC Program application process, per the Notice of Funding Availability (NOFA) for the Fiscal Year 2019 Continuum of Care Program Competition.

https://files.hudexchange.info/reports/published/CoC_PopSub_CoC_CA-601-2019_CA_2019.pdf.

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their ability to manage activities related to daily living required higher levels of support than providers were able to offer and were exited to other shelters that could meet those needs.

Both hotel service providers serving single adults started by identifying the highest need individuals with underlying health conditions; however, one service provider realized it could not provide the supports necessary for the highest needs clients, and they shifted to sheltering individuals who were more engaged and receptive to services and housing support. The other service provider continued to intake individuals with the highest needs but faced significant staffing shortages due to the shelter-in-place order, staff absences related to COVID-19, and existing shortages. Ultimately, even that service provider consolidated the two hotels it operated into one hotel and focused on fewer clients with significant barriers compared with the other service provider.

Service providers required clients to abide by specific rules to maintain their room in the hotel. Service providers conducted daily room checks to monitor for cleanliness and asked clients not to disturb one another and to abide by public health rules. Clients were unable to have guests, weapons, or pets on site. Providers noted difficulty getting clients to follow shelter-in-place guidelines, which they felt undermined the intent of the isolation and quarantine program.

Stakeholders firmly believed that sheltering people in hotel rooms enabled individuals to have privacy and dignity while they were being matched to permanent housing resources. Stakeholders noted that few opportunities existed for private shelter and believed that the Temporary Lodging Program enabled the clients to reveal needs that may not be captured in an assessment or may not present while a client is in a shared housing situation. The stabilization provided opportunities for case managers to advocate for their clients on the basis of those needs and more accurately identify long-term housing opportunities.

OPERATION SHELTER TO HOME: THE CONVENTION CENTER

In an unprecedented partnership, the city of San Diego, RTFH, San Diego Housing Commission, County of San Diego, the San Diego Convention Center, and homeless services providers (including Alpha Project, Father Joe’s Vil ages, and Veterans Village of San Diego) opened the convention center to compensate for the reduced shelter capacity and to serve single adults experiencing unsheltered homelessness in the city of San Diego during the COVID-19 pandemic. The convention center, or Operation Shelter to Home, offered three socially distanced congregate areas with cots, shower facilities, hot meals, 24/7 security, laundry, handwashing stations, and more. The program sheltered roughly 1,200 people per day, 69

percent of whom came from an unsheltered setting. From April to October 2020, the convention center provided shelter to 1,869 people who formerly had been experiencing unsheltered homelessness. The program was open to single adults experiencing unsheltered homelessness who were referred by outreach staff or, less commonly, to walk-ins. Clients in need of more medical care or who were not able For me it was great because I had three meals a day, they had bathrooms, they had showers.

If you didn’t have a phone you could use their phone. The people were really nice, really helpful.

If you needed anything you just had to talk to somebody, and they would go out of their way to try to get it for you.

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to manage ADLs were referred to other shelters, such as the Paul Mirabile Center, where additional medical care was available. Clients exited the program for the same reasons as those in the Temporary Lodging Program (such as when they became housed, they chose to leave, or they were unable to comply with public health orders or program requirements).

Convention center clients who came from unsheltered settings tended to be similar to those in the entire shelter system except in terms of having multiple types of disability; however, the convention center clients substantially differed from those in the Temporary Lodging Program (see Exhibit 6.4). The clients in the convention center tended to be younger than Temporary Lodging Program clients (47

years old compared with 58 years old). Convention center clients who came from unsheltered settings were less likely to report a disability compared with Temporary Lodging Program clients: 62.7 percent of convention center clients reported a disability, including mental health conditions (68.3 percent), physical disabilities (41.2 percent), or other chronic health conditions (48.1 percent). The convention center also served fewer women who were experiencing unsheltered homelessness (25.1 percent) than did the Temporary Lodging Program (33.3 percent).

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Exhibit 6.4 Demographic Characteristics of Convention Center and Temporary Lodging Program Clients from Unsheltered Situations, 2019

Existing Temporary COVID-19 Shelter Resources

Shelter

Demographic Characteristics

System

Convention

Temporary Lodging

Center

Program

(n=

( n=1,869)

( n=209)

3,192)

Age (Mean)

Average age at time of project start (in years)

48.4

46.5

57.8

Gender

Male (%)

75.7

74.5

65.2

Female (%)

23.9

25.1

33.3

Race

African-American (%)

26.0

27.0

16.8

Asian-American (%)

1.9

1.9

0.0

White (%)

64.7

63.6

75.8

Native American (%)

2.5

2.6

1.1

Pacific Islander (%)

1.0

1.1

0.0

Multiracial (%)

3.9

3.8

6.3

Household Type

Single adult (%)

49.2

50.7

49.8

Ethnicity

Hispanic (%)

22.6

23.6

14.1

Veteran Status

Yes (%)

18.3

15.8

12.6

Disability Status

Yes (%)

62.9

62.7

85.4

Type of Disability†

Mental health problem (%)

40.6

68.3

71.4

Physical (%)

28.5

41.2

54.3

Chronic health condition (%)

32.8

48.1

65.7

Developmental (%)

6.7

11.3

14.3

Income Amount

Mean monthly income (in dollars)

954.8

938.0

1,115.6

Note: See Exhibit F.5 in Appendix F for more details. These are distinct clients; thus, the n’s for Convention Center and Temporary Lodging Program wil not sum up to this total.

† Participants reported multiple categories; percentages will not sum to 100.

Source: San Diego Regional Task Force on the Homeless (RTFH), February 1 to August 31, 2020.

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CONVENTION CENTER AMENITIES

With support from the County of San Diego, RTFH, and the San Diego Housing Commission, clients were offered general healthcare, behavioral healthcare, and housing navigation services on site. To monitor for COVID-19, clients completed daily written health questionnaires and check-ups with county public health nurses, including temperature checks. All areas of the Convention Center implemented social distancing so that when individuals were waiting in food lines or for a case manager, they were no less than 6 feet apart. Additional precautions—such as mandatory handwashing upon entry, face mask requirements, and microbial films on high-touch surfaces—were put in place to prevent the spread of COVID-19 in the convention center. Clients underwent biweekly COVID-19 testing. Line safety officers patrolled the convention center and “gently” reminded clients of precautions, if needed. If a client presented with symptoms consistent with COVID-19, staff placed the client in an onsite isolation area to begin mitigation precautions quickly. Stakeholders reported satisfaction with the comprehensiveness of the protocols and noted extremely low rates of COVID-19 among convention center clients and staff. 60

The convention center offered large projectors and Wi-Fi access for clients to have entertainment and connectivity for work, school, or other needs. Individuals with lived experience with the program were general y satisfied with the amenities; however, one person noted that the experience was typical of a shelter environment, where pests are not uncommon. Another expressed dissatisfaction with the personal hygiene of other clients.

Individuals who resided in the convention center believed the rules of the convention center to be reasonable. In general, clients were not allowed to have weapons on the premises, have guests, get in fights with other clients, or miss curfew (8:00 p.m.) more than three unexcused times. If clients had a documented exception, such as a nighttime job, they were allowed to return to the convention center after curfew. Over time, individuals grew tired of the early curfew, which they felt interrupted time they had to spend with friends and family outside the shelter. If clients had pets, the animals were al owed to stay outside in a dog run and kennel, but they were not allowed inside the building.

HOUSING NAVIGATION

Within 10 days of opening the convention center, the San Diego Housing Commission established a Housing Navigation Team of 11 staff members to work with clients placed in the convention center.

Housing navigators worked with convention center clients in cohorts, derived from the emergency And it’s not just working directly with the client, it’s working with [multiple] entities, and helping all of those entities understand what the next steps are also, like, “Here’s what we need from you to get this moving forward, and here’s what the person needs from you, and the Housing Authority still needs this. The VA needs this.” It’s helping almost translate or interpret across all of these different entities so that we can get things moving forward.

60 Following data col ection for this study, in December 2020, the city of San Diego reported 55 positive cases from the convention center as COVID-19 cases surged in California and across the United States. For additional information, visit

https://www.10news.com/news/local-news/san-diego-news/55-test-positive-for-coronavirus-at-san-diego-convention-

center-shelter.

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prioritization process. Housing navigation was also provided by the respective service providers at each of the Temporary Lodging Program hotels to support successful housing placements.

The housing navigators were tasked with addressing barriers to housing, such as missing identification, and working strategically within the coordinated entry system to find housing resources appropriate for clients. The housing navigators engaged clients on a weekly basis to assess challenges, identify opportunities to help the client find housing, and implement strategies for exiting to housing. Housing navigators helped clients complete paperwork and eligibility verifications for housing programs (such as RRH or PSH). The main exit strategies included prioritizing clients with mainstream benefits, such as Supplemental Security Income (SSI) and Social Security Disability Insurance (SSDI); using flexible funds to support arrears, security deposits, and other move-in expenses; and expanding the San Diego Family Reunification Program. 61

DESTINATION AT PROJECT EXIT FOR CLIENTS FROM UNSHELTERED SETTINGS

As of August 2020, among clients who came from unsheltered settings, the most common destination for a client after project exit was returning to a place not meant for habitation or an unsheltered setting; more clients from the convention center were exited to homelessness compared with those from the Temporary Lodging Program (48.2 percent and 17.3 percent, respectively). 62 Stakeholders noted that many of those exits were due to behavior, drug use, or client preference; individuals with lived experience noted that many people who left the convention center preferred to live outdoors rather than in a shelter. The rates of returns to homelessness may be related to the fact that 26 percent of the clients sheltered through the program had not received shelter or services in the past.

The second most common destination at exit for clients who came from unsheltered settings and were served through either program was family reunification (3.0 percent), which was more common for convention center clients than for those served by the Temporary Lodging Program (3.3 percent compared with 1.2 percent). Another common exit destination for those clients was RRH; nearly 6

percent of Temporary Lodging Program clients received housing through the RRH program, which was significantly higher than for clients who used any shelter (1.6 percent). A total of 1.5 percent of clients who were previously unsheltered and were served through the COVID-19 projects received housing through PSH, in part because two PSH developments opened in August, which created opportunities for matches for the most vulnerable clients. Across both COVID-19 programs, 0.8 percent of clients obtained housing through tenant-based voucher programs. Overal , 5.8 percent of previously unsheltered clients served through the convention center and 18.6 percent of Temporary Lodging Program clients exited to permanent housing. 63 Clients received assistance with security deposits and application fees but minimal assistance with household items such as mattresses, sheets, and other common goods. Exhibit 6.5 shows the most common destinations at exit for the unsheltered homeless population served from February 1 to August 31, 2020, through (1) any safe haven, transitional shelter, 61 For more information, see https://downtownsandiego.org/family-reunification-program/.

62 Data analyses included preliminary data from February 1 to August 31, 2020; as of the latter date, both projects were still in operation.

63 Destinations include places not meant for habitation; rental by client with no subsidy; rental by client, with RRH or equivalent subsidy; rental by client, with VASH housing subsidy; rental by client, with HCV voucher (tenant or project based); rental by client, with other ongoing housing subsidy; rental by client in a public housing unit; owned by client, with ongoing housing subsidy; permanent housing for formerly homeless persons; and long-term care facility or nursing home.

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or emergency shelter exclusive of temporary COVID response shelters; (2) the convention center; and (3) the Temporary Lodging Program, which are presented separately. 64

Exhibit 6.5. Destination at Exit by Project Type for Individuals Experiencing Unsheltered Homelessness, in Percentages

Temporary COVID

Existing

Shelter Resources

Shelter

Destination at Exit

Temporary

System*

Convention

Lodging

( n=3,192)

Center

Program

( n=1,869)

( n=209)

Emergency shelter—Homeless

22.2

5.7

29.5

Place not meant for habitation—Homeless

37.1

48.2

17.3

Staying with family or friend

4.2

3.3

1.2

Rental by client, with RRH or equivalent subsidy

1.6

0.9

5.8

Rental by client, with VASH housing subsidy

0.8

0.8

0.6

Rental by client, with HCV (tenant- or project-based)

0.5

0.7

1.7

Rental by client with no subsidy

2.4

1.2

2.3

PSH for formerly homeless persons

2.6

1.5

1.2

Unknown

1.3

0.6

18.5

No exit interview completed

21.8

33.9

6.4

* Includes any single adult who used any other transitional housing, safe havens, or emergency shelter from February 1 to August 31, 2020.

** Includes any single adult who used emergency shelter offered at the convention center or through the Temporary Lodging Program from February 1 to August 31, 2020.

HCV = Housing Choice Voucher. RRH = rapid re-housing. VASH = Veterans Affairs Supportive Housing.

The study team interviewed two individuals with lived experience of homelessness who reported that some units offered through housing programs were undesirable. Ultimately, those two individuals found suitable housing: one client received an individual unit with 6 months of subsidy, and the other client selected a unit with a shared bathroom and kitchen and a private room with 2 months of subsidy.

LANDLORD ENGAGEMENT AND SYSTEM-LEVEL NAVIGATION

The Landlord Engagement and Assistance Program (LEAP),

run by the San Diego Housing Commission, supported

[Through LEAP], we’re really trying to

housing navigation by engaging new landlords and

just be as malleable and flexible as we

incentivizing their participation in housing programs, such as

can be to really just do whatever it is

by using tenant-based voucher programs. LEAP offers

that needs to be done to get things

landlords security deposit guarantees, covers application

moving along and to help people

fees, provides bonuses for first-time landlords, and pays

moving into their next home.

vacancy losses for units that remain vacant between

tenants. In some cases, that meant working across public

64 Data represent preliminary data because the programs were ongoing at the time of the data reporting period.

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housing authorities serving the region, which required more communication and collaboration between organizations.

When one of the housing navigators identified a policy-related barrier to housing a client, the navigator presented the issue to a policy team made up of leaders from the city and county for problem solving.

For example, the policy team found that many clients were missing income documents and thus were unable to verify income due to closed Social Security offices; to address that challenge, RTFH requested a HUD waiver for clients to be able to self-certify income from SSI or SSDI, which facilitated their match to PSH. 65 Through another HUD waiver, clients were able to self-certify unit inspections before move-in, verifying that units met mandatory health quality standards. The waiver reduced delays in completing in-person inspections and potential COVID-19 exposure for clients and housing contractors. 66

Data are only numbers, but the ability to network, hold those county calls, and really dive into some of the needs or the gaps, the gaps that were not being filled, and how do we address it, and bringing the community together, I can’t tell you how much I would have been in the dark if I wouldn’t have been able to be on those tele-briefings and hear what was really going on.

Stakeholders felt that the housing navigators and policy team became “system navigators” as they worked together, across agencies and funders, to identify how processes could be streamlined or revised to house clients from the convention center. The group conducted collaborative case conferencing, reviewing the lists of individuals who had yet to be housed and conducting better problem solving on how to connect them to housing through partnerships and referrals. The policy team also carefully reviewed outcomes data related to the housing matches to understand where people were going once they were housed.

In one notable example, stakeholders collaborated to expedite matching shelter clients to the VASH

program, which had been underutilized in previous years. Before the COVID-19 pandemic, the average time to receive housing through VASH was 8 to 9 months. Within the first 8 weeks of the convention center opening, more than 160 veterans were identified, and as of September 2020, more than 75

percent of those clients were in the intake process for the program, which normally would have taken several months longer. The San Diego Housing Commission worked with the U.S. Department of Veterans Affairs (VA), RTFH, and shelter operators to expedite the process and shift intake responsibilities to the housing navigators who were on site at the convention center. Stakeholders attributed the success to a “deep willingness of the partners to have tough conversations... and streamline processes.”

65 The study did not col ect information on why the income verification and eligibility was required for PSH beds and for which agency in HUD the waiver was obtained. One possibility is that the San Diego CoC relies on the Housing Choice Voucher (HCV) Program for the PSH beds.

66 Both waivers were included in HUD’s Flexibilities/Waivers Granted by the CARES Act + Mega Waiver and Guidance; HUD.

2020. Flexibilities/Waivers Granted by the CARES Act + Mega Waiver and Guidance.

https://www.hud.gov/sites/dfiles/CPD/documents/Flexibilities_Waivers_Guidance_for_CARE_Act_CPD_Funds_062320.pdf.

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KEY PARTNERSHIPS SUPPORTING PROGRAM ACTIVITIES

Numerous agencies within the region partnered to implement outreach, the convention center shelter, the Temporary Lodging Program, and housing navigation for clients during the COVID-19 pandemic.

Exhibit 6.6 includes many of the partners supporting the various elements of the program. The following sections describe key partnerships supporting each element of the program.

Exhibit 6.6. Partners Supporting the COVID-19 Response Activities

Outreach

Temporary Lodging

Program

Convention Center

Housing Navigation

 County of San Diego

 County of San Diego  Alpha Project

 Behavioral Health

 Downtown San Diego  Downtown San Diego  City of San Diego

Services

Partnership

Partnership

 Father Joe’s Villages

 Other Homeless and

 HOTs

 Hoteliers

 RTFH

Housing Providers

 Interfaith Community  Interfaith Community  San Diego County

 Public Housing

Services

Services

 San Diego Housing

Authorities

 Lucky Duck

 Medical providers

Commission

 RTFH

Foundation

 RTFH

 Veterans Vil age of San  San Diego Housing

 Other outreach

Diego

Commission

programs

 VA

 PATH

 RTFH

HOT = Homeless Outreach Team. PATH = Projects for Assistance in Transition from Homelessness. RTFH

= Regional Task Force on the Homeless.

OUTREACH PARTNERS

Organizations involved in outreach began putting the core elements of the Regional Response for Addressing Unsheltered Homelessness into practice during the COVID-19 pandemic. As previously noted, outreach providers began using the mobile app to geographically coordinate outreach, prioritizing certain areas and ensuring the comprehensiveness of coverage. The Lucky Duck Foundation and RTFH led the coordination of the mobile meal program adopted during the COVID-19 pandemic and connected the nonprofit organization supplying the meals to the outreach workers providing those meals for individuals experiencing unsheltered homelessness.

TEMPORARY LODGING PROGRAM PARTNERS

The Temporary Lodging Program, in particular, caused RTFH to lean into its role as a CoC lead agency and regional planning body and draw on the expertise of regional service providers. As the CoC lead agency, RTFH is not a service provider and, therefore, relied on service partners to provide direct services to Temporary Lodging Program clients. RTFH and its partners intentionally wrote contracts with broad parameters for case management organizations and “deferred to the expertise in the field”—its partners—to offer effective services. RTFH also remained open to revisiting contracts and resources and communicating with service providers to support clients in the hotels.

RTFH established new relationships with the three Temporary Lodging Program hotels. Stakeholders agreed that the relationships with the hoteliers could have been improved by first ensuring that all parties were “on the same page” at the start of the program. Stakeholders believed that orienting

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hoteliers to the program, its goals, and its population would have created a “shared understanding” and would have eased tensions with clients. Stakeholders noted that relationships with hoteliers suffered due to conflicting financial interests, in which hotels charged damage costs and negotiated nightly rates that were thought to be excessive.

CONVENTION CENTER LEADERSHIP

Housing navigators informed a larger policy team that led much of the system navigation and that facilitated housing matches out of the convention center. The policy team included representatives from the City of San Diego, RTFH, San Diego Housing Commission, County of San Diego, and other city staff and reported to a larger Incident Command Team overseeing the entire homeless crisis response at the convention center. Service providers and the San Diego Housing Commission (the housing navigation team) informed the policy team of trends and issues based on their on-the-ground experiences with clients and mitigated issues. The policy team worked with the incident command team to implement solutions across agencies, including streamlined referrals, changes in responsibility, and identification of opportunities for HUD waiver requests.

SUSTAINABILITY

At the onset of the programs, stakeholders involved in the programs wanted to leverage the convention center and the Temporary Lodging Program as a way to connect people experiencing homelessness to housing resources unlike ever before. The Temporary Lodging Program and the convention center were meant to be short-term programs but aimed to have long-term impacts on addressing homelessness in San Diego. The programs taught system-level organizations—such as RTFH, the San Diego Housing Commission, the County of San Diego, and others—to better problem-solve within their own system and with the resources available to them; however, stakeholders knew that the resources would be finite and wanted to avoid exiting individuals back to the streets if possible. On July 16, 2020, California Governor Gavin Newsom announced Project Homekey, which offered funding to purchase hotels and motels and provide an opportunity to rehabilitate housing—including hotels, motels, vacant apartment buildings, and other buildings—and convert such facilities into interim or permanent long-term housing (California Department of Housing and Community Development, 2020).

With the support of California’s Project Homekey, San Diego stakeholders were very interested in significantly expanding their housing inventory, particularly with single-occupancy rooms (California Department of Housing and Community Development, 2020). Stakeholders noted challenges related to the quick turnaround of the proposals and coordination of procurement. On October 13, 2020, however, the San Diego City Council approved the procurement of 2 hotels with 332 rooms to house more than 400 people experiencing homelessness; San Diego County plans to support $5.4 million in behavioral care and services over 2 years (City of San Diego, 2020a).

In October 2020, the San Diego City Council also approved the continued funding of the convention center shelter through approximately $4.8 million of federal CARES Act funding, $4.3 million in Coronavirus Relief Funds, and $2.3 mil ion from existing shelter budgets. The program is set tol continue through December 2020 and will align with the opening of the two Project Homekey hotels (City of San Diego, 2020c).

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ADOPTING SAN DIEGO’S APPROACH

Stakeholders believed that the model, particularly the coordination at the systems level, was suitable for other communities experiencing pandemics or natural disasters. Stakeholders believed that for such programs to operate successfully, organizational leaders should be open to collaborating, rethinking processes, and dismantling bureaucratic barriers to streamline and expedite housing matches for clients, specifically for underutilized programs such as VASH in San Diego. Stakeholders recommended clear lines of communication at the organizational level and ensuring that service providers also have input on system-level decisions because they have an indepth understanding of the target population.

SUCCESSES, CHALLENGES, AND LESSONS LEARNED

Stakeholders reflected on their experiences conducting outreach and implementing the emergency shelters offered at the convention center and the Temporary Lodging Program during the COVID-19

pandemic. The sections below detail what stakeholders believed to be the key successes, challenges, and lessons learned throughout the implementation.

SUCCESSES

COVID-19 infection rates at the congregate shelter were low in the early stages of implementation but have risen recently. At the beginning of the initiative, stakeholders admitted to being uncertain about how well they would be able to manage COVID-19 prevalence in a congregate shelter setting, so they implemented extensive precautions—including interval COVID-19 testing, temperature checks, and social distancing measures—to help keep spread to a minimums. Stakeholders felt that these measures were successful early on, and only 23 of 8,563 COVID-19 tests (0.2 percent) returned positive from April 1 to October 31, 2020 (City of San Diego, 2020d). Stakeholders attributed that early success to the extensive and comprehensive procedures in place; however, after data collection in December 2020, an additional 55 people associated with the convention center—both clients and staff—tested positive.

Even with that recent spike, the number of positive cases (0.6 percent of all staff and clients served) is relatively low compared with the number of positive cases in similar cities on the West Coast. 67, 68

The convention center offered significant shelter capacity and consolidated limited resources within the city. Stakeholders believed that the convention center was an efficient model to shelter individuals experiencing unsheltered homelessness, particularly in the context of a pandemic requiring social distancing and significant reductions in staffing and existing shelter capacity due to COVID-19. The program engaged a significant number of people experiencing homelessness who had not received shelter or services in the past, approximately 26 percent of all clients from the emergency shelters (convention center or the Temporary Lodging Program). In addition, the onsite delivery of services 67 For additional information, visit https://www.10news.com/news/local-news/san-diego-news/55-test-positive-for-

coronavirus-at-san-diego-convention-center-shelter.

68 “By April, more than 200 of an estimated 8,000 homeless people in San Francisco have tested positive for the virus, and half came from an outbreak at a homeless shelter. In King County, which includes Seattle, more than 400 of an estimated 12,000

homeless residents have been diagnosed. In Los Angeles County, more than 1,200 of an estimated 66,000 homeless people have been diagnosed. It’s slightly higher in Maricopa County, which includes Phoenix, where nearly 500 of an estimated 7,400

homeless people have tested positive, including nine who died.” For additional information, visit

https://www.seattletimes.com/seattle-news/health/coronavirus-hasnt-devastated-the-homeless-as-many-feared/.

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decreased many previous barriers to services caused by providers needing to locate the client to engage them. The convention center pooled resources from multiple agencies to effectively support clients in the shelter, provided replacement capacity for existing shelters, and expanded the number of shelter beds available. The convention center uniquely offered 24/7 access for clients compared with many other San Diego shelters, which were open only as night shelters.

Increased collaboration between longstanding partners resulted in streamlined processes, which led to higher rates of housing matches. More than 650 clients experiencing homelessness from the convention center were housed from April to October of 2020, which is nearly double the number of housing matches compared with the same time frame in 2019. Many stakeholders believed that the housing match rates from the Temporary Lodging Program and the convention center would not have been possible without collaboration between organizations such as RTFH, the San Diego Housing Commission, and other San Diego County agencies. Committed leaders drove collaboration between organizations and prompted partners to reconsider their normal processes and seek ways to eliminate bureaucratic challenges or, in some instances, apply for HUD waivers. Various administrative and service provider organizations participated in the policy team, which helped to pool resources and creatively solve clients’ barriers to housing. Service partners increased case conferencing in partnership with coordinated entry to understand and address individual needs. Stakeholders reported that the collaborative effort aligned partners under a common goal of housing participants.

CHALLENGES

Stakeholders noted challenges for housing program participants and feared that high-needs clients may have exited to homelessness. 69 San Diego stakeholders intended to match individuals residing in the emergency shelters to housing; however, as of September 2020, approximately 35 clients remained in Temporary Lodging Program hotels and had not yet been matched to permanent housing resources.

Stakeholders shared that those clients may require more time to address barriers to housing, such as stabilization of severe mental health conditions, and that additional housing (including dramatic increases in PSH and RRH) and wraparound resources are needed to facilitate the clients being housed.

The establishment of the emergency shelters diverted staffing and housing resources from regular support operations and those already in queue to be matched to housing. Stakeholders identified staffing as a universal challenge in implementing San Diego’s approach to addressing homelessness during the COVID-19 pandemic. Due to the pandemic, many organizations operated with limited staffing because staff from homeless reservice organizations and other nonessential city employees were diverted from their regular duties to support Operation Shelter to Home. Outreach became limited for people who remained unsheltered, and service providers operated in a limited capacity. In addition, housing resources were prioritized for clients of the convention center shelter and Temporary Lodging Program, which left few housing resources open for individuals residing in other shelters or individuals who were already on the by-name list.

69 As of the closing of the hotels in October 2020, according to the CoC, the majority of people exited successfully to permanent housing, and all others were assisted with finding temporary housing through existing shelter programs. No one from the Temporary Lodging Program returned to an unsheltered setting.

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LESSONS LEARNED

Stakeholders believed that the training of hotel staff, physical inspections of hotels, and careful consideration of hotel locations would have improved the experience for program participants.

Stakeholders reported general difficulty engaging hotels to operate as shelters. Stakeholders felt that hotel staff were inexperienced with the client population and suggested that the lack of training may have caused escalated tension in interactions with clients or, in general, limited their understanding of the population’s needs. Stakeholders noted that physical inspections of buildings would have documented the condition before client move-in and facilitated better shelter conditions for the clients.

In addition, stakeholders suggested considering the hotel location relative to other amenities or services that the clients might require so that the hotel was more convenient and suggested working with service provider staff who have an understanding of clients’ needs to determine the location of hotels.

Stakeholders suggested that contracts with hotels should include clear language on the reasonableness of costs. Stakeholders noted that hotels may be more sustainable if the nightly costs were negotiated at a lower rate. In one example, the general public was able to book a hotel room at $30 per night, whereas the county paid a significantly higher nightly rate in the same hotel. In addition, hotel contracts should include clear language as to what is included in routine maintenance versus what is considered to be damage repair: stakeholders reported significant issues related to costs associated with plumbing, such as clogged drains, and other maintenance they considered to be routine, which resulted in unanticipated costs for the program.

Stakeholders underscored the importance of onsite case management in addition to housing case management. Due to finite resources, the Temporary Lodging Program did not fund case management, which stakeholders believed was an unmet need for program participants. Stakeholders emphasized the importance of considering onsite, residential case management to address clients’ high needs before expanding the program to ensure comprehensiveness of services.

Stakeholders learned more about the unsheltered homeless population and their needs through the programs, which they hope to integrate in future homeless responses. County organizations learned about the individual needs of the clients in the convention center through consistent and frequent interactions and the high volume of clients in a short amount of time. Stakeholders gained clarity on where clients go after shelter stays and which clients are more likely to go with which housing resources.

Stakeholders hope to integrate this in-depth knowledge related to the individuals, not just the population, in future coordinated housing initiatives.

CONCLUSION

When the COVID-19 pandemic began, stakeholders were already working more collaboratively and strategically with the recent launch of a new City of San Diego Homeless Action Plan and new policy guidelines to address unsheltered homelessness across the region. Organizations—including the City of San Diego, RTFH, San Diego Housing Commission, and County of San Diego—came together to try to keep people experiencing homelessness safe from COVID-19 and ensure that shelter and services were provided. Armed with new federal and state resources, the COVID-19 response focused on adding new temporary shelter with services to expand shelter capacity during the pandemic. The efforts included the Temporary Lodging Program (non-congregate hotel shelter for people considered at high risk for COVID-19) and Operation Shelter to Home at the San Diego Convention Center (congregate shelter).

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Housing navigation and comprehensive services were hallmarks of both approaches. Street outreach to individuals experiencing unsheltered homelessness was also updated to be better coordinated, and organizations collaborated to ensure that those living outside had access to basic necessities, such as regular meals.

Stakeholders had varying perspectives on the successes, challenges, and effects of the implementation of those strategies. They agreed that progress has been made to improve the components and continue to refine system policies over the course of the pandemic. The programs successfully engaged a significant number (26 percent) of people experiencing homelessness who had never received shelter or services in the past. The programs also were general y successful at improving access to getting basic needs met and advancing public health practices.

As of August 31, 2020, of the people who came from unsheltered settings, 17 percent of people leaving the Temporary Lodging Program hotels exit to unsheltered settings, whereas 48 percent of Operation Shelter to Home at the convention center exited to unsheltered homelessness. When including exits to any homeless situation (sheltered or unsheltered), participants in the Temporary Lodging Program still had better outcomes, but the difference was smaller. Overall, 5.8 percent of previously unsheltered clients served through the convention center exited to permanent housing, whereas 18.6 percent of Temporary Lodging Program clients exited to permanent housing. Those findings are notable given that the Temporary Lodging Program served an older, more vulnerable subset of the unsheltered population.

More research is needed to fully examine the cost effectiveness of those programs. Future research could also explore the efficacy of the programs on the homeless population overal rather than analyzing the formerly unsheltered population specifical y. Notably, as of December 1, 2020, more than 1,000

clients of the convention center had been housed through permanent housing, and roughly 400 of those were expected to move into Project Homekey facilities at the end of 2020 (Warth, 2020). The lessons learned during this ambitious effort are being applied to strengthen the overall community response to homelessness. The relationships that were created and strengthened during this pandemic response will bolster the cross-sector partnerships necessary to reduce unsheltered homelessness in San Diego County.

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7. CONCLUSION

The number of people experiencing homelessness who are unsheltered—meaning they are sleeping outside or in other places not meant for habitation—has increased each year since 2015. Those individuals are especially vulnerable to the negative health, economic, and social effects of homelessness. Communities across the United States are implementing strategies to address the problem of unsheltered homelessness, and the recent emergence of the coronavirus disease (COVID-19) highlights the urgent need for enhanced cooperation between public health authorities, homeless service systems, and other partners to address this problem. This study analyzes the conditions in the housing markets and homeless services systems within communities that have increasing and decreasing levels of unsheltered homelessness and identifies three local responses to address unsheltered homelessness. The study is intended to provide HUD with additional insights into the community-level factors associated with unsheltered homelessness, as wel as insights into the programs communities use to address unsheltered homelessness at the local level. Deepening knowledge of those factors and initiatives will enable policymakers to develop and implement effective programs and tools to reduce and eliminate unsheltered homelessness. This chapter reviews the pertinent findings associated with the main phases of the study and identifies limitations and future research directions.

FINDINGS FROM THE QUANTITATIVE STUDY

Using the trends in unsheltered homeless counts for Continuums of Care (CoCs), the study team classified 336 CoCs into four groups:

1. “Steady increasing” CoCs that had two increasing intermediate unsheltered homeless counts.

2. “Fluctuating increasing” CoCs that had a larger 2019 count of people experiencing unsheltered homelessness than in 2015 but with varying intermediate trends.

3. “Fluctuating decreasing” CoCs that had a smaller 2019 count of people experiencing unsheltered homelessness than in 2015 but with varying intermediate trends.

4. “Steady decreasing” CoCs that had decreasing intermediate trends.

The prominent housing market differences among the groups were higher home values, median rental prices, and fair market rents in CoCs with “increasing” counts relative to those with “decreasing”

unsheltered homelessness counts. Whereas only small differences exist across other housing market characteristics, the combination of higher rents and unavailability of rental units suggests that

“increasing” CoCs more often operate in tight rental markets compared with “decreasing” CoCs. On the housing supply side, the rate of new housing permits was consistently higher in communities with

“increasing” counts. That finding, along with larger increases in median home values over time for the same groups, possibly indicates that newer built housing units in tighter markets may still be unaffordable to people at risk of homelessness. The “steady decreasing” group of CoCs was associated with lower shares of renter-occupied housing units, higher rental vacancy rates, and low incidences of overcrowding. Within the “increasing” groups, CoCs with “fluctuating increasing” trends tended to have higher home values and more available resources in their homeless assistance systems. Taken together, these results suggest a tight rental market and an unaffordable housing market for the “increasing”

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groups of CoCs, with “steady increasing” CoCs in a worse position to respond, which supports earlier research’s conclusion that housing market dynamics and the availability of affordable housing are closely tied to rates of unsheltered homelessness at the community level.

Resources available for CoCs in each of the four groups were proportionally highest for CoCs with fluctuating trends across all programs analyzed. Across all years, the “fluctuating increasing” and

“fluctuating decreasing” CoCs had the highest and second highest mean bed counts per capita. “Steady increasing” CoCs had the lowest permanent supportive housing (PSH)/other supportive housing bed counts per capita in each year and had little change in emergency shelter bed counts per capita. “Steady decreasing” CoCs had low numbers of CoCs reporting at least one rapid re-housing (RRH) bed and low counts of RRH beds per capita in 2015 but experienced high growth in terms of both those measures by 2019. Taken together, those data suggest that higher levels and growth in shelter and permanent housing resources among the two “fluctuating” CoC groups may be influencing the trend in unsheltered homelessness, whereas the comparably lower levels of resources in the two “steady” groups could imply that market factors end up playing a larger role in their overall trends, both positively and negatively.

System Performance Measures, key metrics for HUD policymakers, reveal differences between

“increasing” and “decreasing” groups of CoCs in terms of durations of homelessness, bed coverage, and programmatic outcomes. Each of the four measures capturing the duration of homelessness suggests that, on average, the two “increasing” CoC groups are associated with longer periods of homelessness.

The “steady increasing” CoCs had the lowest average percentages of successful street outreach outcomes and successful exits in the most recent year, whereas the “steady decreasing” CoC group had the highest average percentages for these same measures. These findings suggest that market factors must be considered when assessing elements of program performance and efficacy.

Communities across the United States are implementing strategies to tackle increasing unsheltered homelessness. The qualitative phase of this study highlights case studies of three communities adopting practices specifically aimed at addressing unsheltered homelessness, including one effort in response to the COVID-19 pandemic.

FINDINGS FROM THE QUALITATIVE STUDY

Each of the three case studies demonstrated unique programs and initiatives addressing unsheltered homelessness in their respective communities.

Facing limited PSH availability, the Greater Richmond CoC specifically targets RRH—short- to medium-term rental assistance—to single adults, with an emphasis on those experiencing unsheltered homelessness. The Greater Richmond CoC pairs the RRH program, which is considered a “ lighter touch”

housing program, with a variety of services offered by its partners to stabilize clients who may otherwise have been eligible for more intensive PSH. The Greater Richmond CoC reported that only 17 percent of clients who came from unsheltered settings returned to homelessness within a 12-month period after exit, which is notable because it is consistent with outcomes of other RRH programs, despite those programs not necessarily targeting the most vulnerable population in their communities. The Greater Richmond CoC demonstrated that, if implemented in this fashion, RRH paired with services can be a cost-effective alternative to PSH in certain housing markets and an effective bridge to other ongoing forms of assistance, such as Veterans Affairs Supportive Housing (VASH) for clients who are veterans.

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The Montgomery County CoC implements a systems approach to house people with the highest vulnerability scores in the county, many of whom are individuals experiencing unsheltered homelessness. The Montgomery County CoC coordinates outreach at the county level and implements an unnamed client policy, which allows outreach providers and shelter operators to track people experiencing homelessness who do not provide a name. The Montgomery County CoC added a custom 9-point vulnerability scale—accounting for a person’s veteran status, if they are currently unsheltered, if they are vulnerable to exploitation, and other vulnerability criteria—to the standard Vulnerability Index-Service Prioritization Decision Assistance Tool to better prioritize vulnerable clients. The Montgomery County CoC primarily houses clients through PSH, which combines housing with voluntary support services and case management, and tweaked the standard program model by offering a high- or low-intensity service approach. Montgomery County has also significantly expanded county funding for PSH

since 2017, adding 300 beds in 3 years, and substantially reduced the number of people experiencing unsheltered homeless from 131 people in 2017 to 75 people in 2019—a nearly 43-percent decrease.

The Regional Task Force on the Homeless (RTFH) serves as the CoC lead for San Diego County and helped coordinate a COVID-19 pandemic response for people experiencing homelessness in the county.

The response included modified outreach and the opening of temporary shelters at the San Diego Convention Center and three hotels within the county that offered non-congregate shelter, both of which temporarily expanded shelter capacity starting in April through an anticipated closure in December 2020. The efforts successfully engaged people experiencing homelessness who had never engaged in services or shelter; 26 percent of the clients served through the projects had never been reached by the mainstream homeless services system. RTFH worked with key partners—including service providers, the San Diego Housing Commission, the City of San Diego, and the County of San Diego—to implement the temporary emergency shelters to attempt to limit the spread of COVID-19.

RTFH and stakeholders also provide strategic housing navigation from multiple housing programs, including PSH, VASH, RRH, and other programs. The programs were relatively successful in temporarily sheltering people experiencing unsheltered homelessness, increasing shelter capacity, reducing the risk of catching COVID-19, and connecting clients to services, including housing, such as RRH and project- or tenant-based PSH programs. The Temporary Lodging Program showed particular promise; formerly unsheltered residents sheltering in hotels had considerably lower rates of returning to unsheltered homelessness relative to those served in the convention center and by existing shelter options.

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APPENDIX A. ADDITIONAL VARIABLE

CONSIDERATIONS

Whereas the initial list of housing market variables was largely influenced by Nisar et al. (2019), the analysis in this study provides additional Continuum of Care (CoC)-level variables without the need for weighted averaging, selects more geographic granularity beyond the county level, and includes a revised approach to assessing data quality. As some American Community Survey (ACS)-sourced variables include precalculated median values, the aggregation of smaller geographies to generate CoC-level values warrants a weighting procedure to appropriately combine values. This averaging of precalculated summary statistics may seem to be less intuitive or overprocessed. To address those potential concerns, the study team generated additional variables of straightforward average values based on aggregates divided by the appropriate counts of housing unit types. Those average values also provide additional insights into underlying distributions for house values, contract rent, and rental utility costs within CoCs.

Although most CoC boundaries conform to county or county-equivalent boundaries, those CoCs that do not conform were merged to create artificial CoC conglomerates in Nisar et al. (2019). The study team avoided merging those nonconforming CoCs in this analysis by instead using separate county- and community-level datasets to obtain the needed CoC-level estimates. That difference, built on data sparseness concerns posed in Nisar et al. (2019), imposed a strict coverage threshold to ensure reliability in CoC-level estimates. 70 External data sources may not provide data for all geographic units that make up a CoC or provide flag variables suggesting methodologically different values. To assess the extent of reliable coverage, the study team estimated two separate versions of each scaling denominator when collapsing to the CoC level. 71 Before that collapse, values for one of the scaling denominators were set to missing if the county-level numerator was either missing or flagged, whereas the other version of the denominator included non-missing values for all geographic units within a CoC.

Collapsing to the CoC level would then generate a sum for each of those two denominator values, and the rate of coverage was calculated by dividing the non-missing version by the altered version. If the rate of coverage within a CoC was less than 50 percent, the specific CoC-level value for that variable was set to missing. To provide a hypothetical example, one can assume that a CoC contains five counties but eviction counts were available for only two of those counties. If the three counties missing eviction data contained more than 50 percent of the rental housing units (the denominator for eviction rate variable) within the CoC, the CoC-level estimate would be excluded due to a low coverage rate.

70 Sparseness was not of concern for variables solely based on ACS 5-year estimates.

71 Variables with potential geographic coverage issues were scaled with ACS 5-year estimates in which the issue would be present in the CoC-level numerator.

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APPENDIX B. CONSTRUCTING THE

CONTINUUM OF CARE-LEVEL DATASET

The study team compiled raw data at various geographic levels to generate a Continuum of Care (CoC)-

level dataset. Most CoCs serve an area that is bounded by county or county-equivalent lines. Less commonly, CoCs may also serve more localized areas, such as a single city, multiple cities, or parts of counties not covered by city-level CoCs. The four possible underlying CoC service areas are listed below in order of frequency:72

Case 1. A CoC service area aligns with county or county-equivalent boundaries.

Case 2. A CoC service area aligns with city or city-equivalent boundaries.

Case 3. A CoC service area covers the remainder of a county area for a county containing city-level CoCs.

Case 4. A CoC service area can be described as both cases 1 and 3 or cases 2 and 3.

Because most CoCs have either complete or partial relationships to county boundaries, the construction of the CoC-level dataset was primarily built on county-level data. City-level data were also used for case 2 CoCs and to generate “subtrahend” and “addend” datasets to adjust the county-level data to calculate cases 3 and 4 CoC estimates. Most instances of cases 2 through 4 were localized and sparse across the country, apart from 11 CoCs in eastern Massachusetts.

In the first possible case, county-level data were obtained and collapsed to the CoC level. If a CoC served multiple counties and the data source did not contain raw dividends and divisors components, the CoC-level collapse used a weighting scheme based on either renter- or owner-occupied housing units. For the cases of city-level CoCs, the study team used either census place or county subdivision-level raw data, depending on the source data, to produce CoC-level estimates. 73 With the exception of one CoC, weighting to combine precalculated rates was not needed to produce CoC-level estimates. 74

Generating CoC-level data for a third service area description involved either simple subtraction from county-level estimates using the “subtrahend” dataset or a more complicated weighting procedure with smaller geographic units. Raw data sourced entirely from ACS estimates were available at the “County within Place” summary level that could be subtracted from county-level estimates to obtain the needed partial county-level values. Those units provide separate county-specific estimates for parts of cities that cross county lines or reflect census place estimates in single-county cities. In addition to providing separate county estimates, some non-ACS data sources provided estimates that could be easily subtracted from county-level data, even in the case of multi-county cities. For example, Census Housing Permit Survey data aggregated place estimates to a single county for all multi-county cities, leaving 72 A complete list of CoCs by service area types is listed in Exhibit B.3.

73 Apart from CoCs in New England states, Fair Market Rent (FMR) variables for city-level CoCs were manually assigned their overarching county values.

74 Somerville CoC (MA-517) in Massachusetts combined raw data from the town of Arlington and city of Somerville.

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some counties with the desired partial county estimate. 75 Both Zillow data and Eviction Lab data required specialized collapses to obtain partial-county data.

The few CoCs classified as service area case 4 were constructed using adjustments to the county-level dataset described in the paragraph above or using addends records for city-level data. Four CoCs that act as state balances serve multiple counties, including partial counties, described above. The only difference between those cases is the mixture of partial and complete county data in the eventual collapse to the CoC level. Final y, three CoCs in Massachusetts can be broadly described as including partial county areas with additional city- or town-level service areas. In the county-level dataset, the study team generated values for those three areas by first using the subtrahend dataset to create partial-county estimates and then adding the few city-level estimates to the partial counties using the addend dataset.

The following sections of this appendix describe the geographic area for CoCs that relate to cases 2

through 4 and provide a full description of the processes for obtaining CoC-level estimates by variable.

Exhibit B.1 provides a summary of each CoC classified as cases 2 through 4, followed by a more indepth description of the 11 CoCs in Massachusetts.

Exhibit B.1 Descriptions of Geographic Cases 2, 3, and 4

County Areas

CoC Name (CoC Number)

Description

Los Angeles City and County CoC

Los Angeles

(CA-600)

Los Angeles County contains service areas for four CoCs.

City-level CoCs include Long Beach, Pasadena, and

County,

Long Beach CoC (CA-606)

Glendale. The county area outside these three cities is

California

Pasadena CoC (CA-607)

serviced by Los Angeles City and County CoC.

Glendale CoC (CA-612)

Fulton County CoC (GA-502)

Atlanta, which operates a city-level CoC service area, lies

Fulton County,

DeKalb County CoC (GA-508)

within Fulton County and DeKalb County. The county

Georgia; DeKalb

areas outside Atlanta are serviced by Fulton County CoC

County, Georgia Atlanta CoC (GA-500)

and DeKalb County CoC.

Cook County CoC (IL-511)

Cook County contains service areas for two CoCs.

Chicago CoC includes the city of Chicago, and the county

Cook County,

area outside of Chicago is serviced by Cook County CoC.

Illinois

Chicago CoC (IL-510)

Although the city of Chicago technical y has land area in

DuPage County, this smal portion includes only O’Hare

International Airport and does not contain housing units.

Bristol County,

Boston CoC (MA-500)

Massachusetts; Lynn CoC (MA-502)

Essex County,

New Bedford CoC (MA-505)

Eastern Massachusetts contains 11 CoCs that include

Massachusetts; Lowel CoC (MA-508)

several city-level CoCs as well as cases 3 and 4 CoCs. A

Middlesex

Cambridge (MA-509)

complete description of these CoCs is provided following

County,

Gloucester, Haverhill,

this table.

Massachusetts; Salem/Essex County CoC (MA-

Norfolk County, 510)

75 For example, housing permit data for the city of Atlanta, which lies in Fulton County (served by GA-502) and DeKalb County (GA-508), were added to the Fulton County record. In this case, DeKalb County did not require adjustment for the city-based Atlanta CoC.

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County Areas

CoC Name (CoC Number)

Description

Massachusetts; Quincy, Brockton, Weymouth,

Plymouth

Plymouth City and County CoC

County,

(MA-511)

Massachusetts; Fall River CoC (MA-515)

Suffolk County,

Massachusetts Balance of State

Massachusetts

(MA-516)

Somerville CoC (MA-517)

Attleboro, Taunton/Bristol

County CoC (MA-519)

Detroit CoC (MI-501)

Wayne County contains service areas for two CoCs.

Wayne County,

Dearborn, Dearborn Heights,

Detroit CoC includes the city of Detroit, and the county

Michigan

Westland/Wayne County CoC

area outside Detroit is serviced by Dearborn, Dearborn

(MI-502)

Heights, Westland/Wayne County CoC.

Nebraska Balance of State CoC

Lancaster County contains the entire service area for

(NE-500)

Lincoln CoC and part of the Nebraska Balance of State

Lancaster

CoC service area. Lincoln CoC includes the city of Lincoln,

County,

and the county area outside Lincoln is serviced by

Nebraska

Lincoln CoC (NE-502)

Nebraska Balance of State CoC. The Balance of State CoC

service area also includes other counties.

Manchester CoC (NH-501)

Hillsborough County contains service areas for two CoCs.

Hillsborough

Manchester CoC includes the city of Manchester, and

County, New

Nashua/Hillsborough County CoC the county area outside Manchester is serviced by

Hampshire

(NH-502)

Nashua/Hillsborough County CoC.

Albuquerque CoC (NM-500)

Bernalillo County contains the entire service area for

Albuquerque CoC and part of New Mexico Balance of

Bernalillo

State CoC service area. Albuquerque CoC includes the

County, New

New Mexico Balance of State CoC city of Albuquerque, and the county area outside

Mexico

(NM-501)

Albuquerque is serviced by New Mexico Balance of State

CoC. The Balance of State CoC service area also includes

other counties.

Oklahoma City CoC (OK-502)

Oklahoma City, which operates a city-level CoC service

Oklahoma Balance of State CoC

area, lies within parts of Canadian, Cleveland, and

(OK-503)

Oklahoma Counties. The county area outside Oklahoma

Canadian

City within Cleveland County is serviced by

County,

Norman/Cleveland County CoC. The county area outside

Oklahoma;

Oklahoma City within Canadian and Oklahoma Counties

Cleveland

is serviced by Oklahoma Balance of State CoC. The

County,

Balance of State CoC service area also includes other

Oklahoma;

Norman/Cleveland County CoC

counties. Oklahoma City technically extends into

Oklahoma

(OK-504)

Pottawatomie County as wel , but the 2010 Decennial

County,

Census reported that this portion included a total of only

Oklahoma

64 persons. Pottawatomie County is served by the

Balance of State CoC, and no adjustments for Oklahoma

City were performed.

Texas Balance of State CoC (TX-

Amarillo, which operates a city-level CoC service area,

Potter County,

607)

lies within parts of Potter County and Randall County.

Texas; Randall

The county areas outside Amarillo within both counties

County, Texas

Amarillo CoC (TX-611)

are serviced by Texas Balance of State CoC. The Balance

of State CoC service area also includes other counties.

CoC = Continuum of Care.

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FINAL RESEARCH REPORT

Eastern Massachusetts contains several city-level CoCs, as well as cases 3 and 4 CoCs. City-level CoCs include Boston (Suffolk County), Lynn (Essex), New Bedford (Bristol), Lowell (Middlesex), Cambridge (Middlesex), Fall River (Bristol), and Somerville (which includes Somervil e and the town of Arlington, both in Middlesex). Four other CoC cases of mixed geographic units required the additional use of the subtrahend and addend datasets to adjust county-level data. The Gloucester, Haverhill, Salem/Essex County CoC covers most of Essex County and the town of Wilmington in Middlesex County but does not cover the Essex County cities of Lynn (city-level CoC) and Lawrence (served by MA-516). The Quincy, Brockton, Weymouth, Plymouth City and County CoC covers all of Plymouth County and the cities of Quincy and Weymouth in Norfolk County. The Massachusetts Balance of State CoC covers large portions of three counties, including Middlesex County (with the exception of Arlington, Cambridge, Lowel , Somerville, and Wilmington), Norfolk County less the cities of Quincy and Weymouth, and Suffolk County less Boston, and includes the city of Lawrence in Essex County. Lastly, the Attleboro, Taunton/Bristol County CoC includes most of Bristol County but does not include the cities of New Bedford and Fall River. Exhibit B.2 provides an accounting of all adjustments made to county-level data for CoCs with mixed geographies in Massachusetts.

Exhibit B.2 Summary of Adjustments to Massachusetts County-Level Data

Assigned

Added Estimates

CoC Name (CoC Number)

Subtracted Estimates (Reason)

County

(Actual County)

Gloucester, Haverhill,

Wilmington

Lynn (city-level CoC)

Salem/Essex County CoC

Essex

(Middlesex)

(MA-510)

Lawrence (served by MA-516)

Quincy, Brockton,

Quincy (Norfolk)

Weymouth, Plymouth

Plymouth

City and County CoC (MA-

Weymouth (Norfolk)

511)

Arlington (city-level CoC)

Lawrence (Essex)

Cambridge (city-level CoC)

Middlesex

Lowell (city-level CoC)

Massachusetts Balance of

Somerville (city-level CoC)

State CoC (MA-516)

Wilmington (served by MA-510)

Quincy (served by MA-511)

Norfolk

Weymouth (served by MA-511)

Suffolk

Boston (city-level CoC)

Attleboro,

New Bedford (city-level CoC)

Taunton/Bristol County

Bristol

Fall River (city-level CoC)

CoC (MA-519)

CoC = Continuum of Care.

UNWEIGHTED VARIABLES FROM ACS 5-YEAR ESTIMATES

The most straightforward procedures to obtain CoC-level estimates were from variables completely sourced from ACS 5-year estimates except for those that required weighting. These variables included—

 Average House Value.

 Percentage of Homeowners with Cost Burden.

 Average Contract Rent.

 Average Rental Utility Cost.

 Percentage of Renters with Cost Burden.

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FINAL RESEARCH REPORT

 Share of Renter-Occupied Units.

 Rental Vacancy Rates.

 Share of Overcrowded Housing Units.

City-level CoC estimates were obtained using data at “Place in County” geographic units that were also used in the subtrahends dataset to adjust county-level data to obtain the desired partial-county estimates and used in the addends dataset for the three cases in Massachusetts.

MEDIAN HOUSE VALUE

The Zillow Home Value Index (ZHVI) was used to obtain median house value data. Unlike county-level data used for case 1 CoCs, the “city” data did not contain Federal Information Processing Standards (FIPS) codes. The study team merged ACS data for weighting on the names of cities using either place or county subdivision geographical units. County subdivision data were needed for areas in Massachusetts, Michigan, and New Hampshire because place estimates are not provided for all geographic entities in those states (such as towns and townships). As those data were already at the city level, case 2 CoCs did not require weighting. 76 County-level data for cases 3 and 4 CoCs were obtained by weighting the cities within each county using owner-occupied housing unit estimates.

MEDIAN CONTRACT RENT

Median contract rent was obtained using ACS 5-year estimates and required census tract-level data to produce estimates for CoCs that included remainder county areas (case 3). Although tracts align to county and county-equivalent lines, they are not necessarily bounded by Census Places. To ensure that only relevant tracts were obtained, the study team developed a specialized crosswalk of tracts using 2010 Decennial Census Summary File 1 data. The crosswalk was developed by first filtering the data to include records of only two geographic units, “State-County-Census Tract” (summary level-140) and

“State-Place-County-Census Tract” (summary level-158). The latter of those two units contains data for the parts of a tract within a place and was filtered to include only records for places that make up the subtrahend and addend datasets. To determine which tracts are needed and appropriate to capture partial estimates, the study team generated percentages of the tract population, area, and housing units that fall within a place. If more than one-half of a tract’s percentage in two of the three categories (population, area, or housing units) was within the selected places, the study team removed those tract records because they mostly captured a segment of a city record. The remaining tracts reflected partial-county tracts that were either outside the boundaries of city-level CoCs or had only slight crossover into city-level CoCs’ service areas. The crosswalk was then matched to tract-level data of median contract rents, and a weighted average by renter-occupied units was calculated to generate partial-county estimates.

HOUSING DENSITY

The process for obtaining housing density followed the same procedure as unweighted ACS variables.

City-level CoC estimates were obtained using data at “place in county” geographic units that were also used in the subtrahends dataset to adjust county-level data to obtain the desired partial-county 76 The only exception to this statement was again Somerville CoC (MA-517), which combines the town of Arlington and city of Somervil e.

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FINAL RESEARCH REPORT

estimates and used in the addends dataset for the three cases in Massachusetts. “Place in county”

estimates of land area were obtained from the Decennial Census Summary File 1.

EVICTION AND EVICTION FILING RATE

The Eviction Lab data used the tract crosswalk described above for partial-county estimates but only for cases where the conflicting city-level CoC crossed county lines. Those cases included the counties associated with Atlanta and Oklahoma City but not Amaril o CoC, as data were not available for the two counties associated with this CoC. All city-level CoC estimates for the two eviction variables were obtained using place-level data from Eviction Lab, divided by rental housing units obtained from CoCs.

Partial-county estimates were obtained using the subtrahend and addend datasets, with the only exceptions being the two inter-county cities mentioned above.

FAIR MARKET RENTS

With the exception of New England states, al FMRs are determined at the county level; thus, city-level CoCs were manually assigned their overarching county values. FMRs for counties associated with multi-county CoC cities did not differ across counties. New England states produce FMRs at the county subdivision level. To produce CoC-level estimates for those records, a weighted average was calculated using ACS data on renter-occupied housing units.

NEWLY PERMITTED HOUSING UNITS

The U.S. Census Bureau produces housing permit data at the place and county levels for all areas that mandate permits. County-level estimates are produced from place-level data, which are not necessarily bounded to county or county-equivalent lines; thus, the places that extend into multiple counties are attributed to only one county in the county-level data. Subtracting partial-city estimates was not needed for DeKalb County, Georgia; Canadian County, Oklahoma; and Cleveland County, Oklahoma. 77

77 Amarillo, Texas, data were missing, so subtrahend adjustment was not needed for Randal County or Potter County.

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FINAL RESEARCH REPORT

Exhibit B.3 List of CoCs by Geographic Case Number

Case

CoC

CoC Name

Grouping

Numbers Number

1

AK-500

Anchorage CoC

Steady Decreasing

1

AK-501

Alaska Balance of State CoC

Steady Increasing

1

AL-500

Birmingham/Jefferson, St. Clair, Shelby Counties CoC*

1

AL-501

Mobile City & County/Baldwin County CoC

Fluctuating Decreasing

1

AL-502

Florence/Northwest Alabama CoC*

1

AL-503

Huntsvil e/North Alabama CoC

Fluctuating Increasing

1

AL-504

Montgomery City & County CoC

Steady Decreasing

1

AL-506

Tuscaloosa City & County CoC*

1

AL-507

Alabama Balance of State CoC

Fluctuating Increasing

1

AR-500

Little Rock/Central Arkansas CoC

Steady Increasing

1

AR-501

Fayetteville/Northwest Arkansas CoC

Steady Increasing

1

AR-503

Arkansas Balance of State CoC

Steady Increasing

1

AR-504

Delta Hills CoC*

1

AR-505

Southeast Arkansas CoC*

1

AZ-500

Arizona Balance of State CoC

Fluctuating Decreasing

1

AZ-501

Tucson/Pima County CoC

Fluctuating Decreasing

1

AZ-502

Phoenix, Mesa/Maricopa County CoC

Steady Increasing

1

CA-500

San Jose/Santa Clara City & County CoC

Steady Increasing

1

CA-501

San Francisco CoC

Fluctuating Increasing

1

CA-502

Oakland, Berkeley/Alameda County CoC

Steady Increasing

1

CA-503

Sacramento City & County CoC

Steady Increasing

1

CA-504

Santa Rosa, Petaluma/Sonoma County CoC*

1

CA-505

Richmond/Contra Costa County CoC

Fluctuating Increasing

1

CA-506

Salinas/Monterey, San Benito Counties CoC

Fluctuating Decreasing

1

CA-507

Marin County CoC

Steady Decreasing

1

CA-508

Watsonville/Santa Cruz City & County CoC

Fluctuating Increasing

1

CA-509

Mendocino County CoC

Fluctuating Decreasing

1

CA-510

Turlock, Modesto/Stanislaus County CoC

Steady Increasing

1

CA-511

Stockton/San Joaquin County CoC

Steady Increasing

1

CA-512

Daly/San Mateo County CoC

Fluctuating Increasing

1

CA-513

Visalia/Kings, Tulare Counties CoC

Steady Increasing

1

CA-514

Fresno City & County/Madera County CoC

Steady Increasing

Rosevil e, Rocklin/Placer County CoC (CA-515) &

1

Fluctuating Increasing

Nevada County CoC Combined (CA-531)

Redding/Shasta, Siskiyou, Lassen, Plumas, Del Norte,

1

CA-516

Steady Increasing

Modoc, Sierra Counties CoC

1

CA-517

Napa City & County CoC

Steady Increasing

1

CA-518

Vallejo/Solano County CoC

Steady Increasing

1

CA-519

Chico, Paradise/Butte County CoC*

1

CA-520

Merced City & County CoC

Steady Decreasing

1

CA-521

Davis, Woodland/Yolo County CoC

Steady Increasing

1

CA-522

Humboldt County CoC

Fluctuating Increasing

1

CA-523

Colusa, Glenn, Trinity Counties CoC

Steady Increasing

1

CA-524

Yuba City & County/Sutter County CoC

Fluctuating Increasing

1

CA-525

El Dorado County CoC

Fluctuating Increasing

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FINAL RESEARCH REPORT

Case

CoC

CoC Name

Grouping

Numbers Number

Amador, Calaveras, Mariposa, Tuolumne Counties

1

CA-526

CoC*

1

CA-527

Tehama County CoC*

1

CA-529

Lake County CoC

Steady Increasing

1

CA-530

Alpine, Inyo, Mono Counties CoC

Steady Increasing

1

CA-601

San Diego City and County CoC*

1

CA-602

Santa Ana, Anaheim/Orange County CoC*

1

CA-603

Santa Maria/Santa Barbara County CoC*

1

CA-604

Bakersfield/Kern County CoC*

1

CA-608

Riverside City & County CoC*

1

CA-609

San Bernardino City & County CoC

Fluctuating Increasing

1

CA-611

Oxnard, San Buenaventura/Ventura County CoC

Fluctuating Increasing

1

CA-613

Imperial County CoC*

1

CA-614

San Luis Obispo County CoC

Fluctuating Increasing

1

CO-500

Colorado Balance of State CoC

Fluctuating Decreasing

1

CO-503

Metropolitan Denver CoC

Steady Increasing

1

CO-504

Colorado Springs/El Paso County CoC

Fluctuating Increasing

Norwalk, Danbury/Fairfield County CoC (CT-503) &

1

Fluctuating Decreasing

Connecticut Balance of State CoC (CT-505)

1

DC-500

District of Columbia CoC

Fluctuating Increasing

1

DE-500

Delaware Statewide CoC

Steady Increasing

1

FL-500

Sarasota, Bradenton/Manatee, Sarasota Counties CoC Fluctuating Decreasing

1

FL-501

Tampa/Hil sborough County CoC

Fluctuating Decreasing

1

FL-502

St. Petersburg, Clearwater, Largo/Pinellas County CoC Steady Decreasing

1

FL-503

Lakeland, Winterhaven/Polk County CoC*

1

FL-504

Deltona, Daytona Beach/Volusia, Flagler Counties CoC Fluctuating Decreasing

1

FL-505

Fort Walton Beach/Okaloosa, Walton Counties CoC

Steady Decreasing

1

FL-506

Tallahassee/Leon County CoC

Steady Decreasing

1

FL-507

Orlando/Orange, Osceola, Seminole Counties CoC

Fluctuating Increasing

1

FL-508

Gainesvil e/Alachua, Putnam Counties CoC

Fluctuating Decreasing

Fort Pierce/St. Lucie, Indian River, Martin Counties

1

FL-509

Steady Decreasing

CoC

1

FL-510

Jacksonvil e-Duval, Clay Counties CoC

Steady Increasing

1

FL-511

Pensacola/Escambia, Santa Rosa Counties CoC

Steady Decreasing

1

FL-512

St. Johns County CoC

Steady Decreasing

1

FL-513

Palm Bay, Melbourne/Brevard County CoC

Fluctuating Increasing

1

FL-514

Ocala/Marion County CoC

Steady Decreasing

1

FL-515

Panama City/Bay, Jackson Counties CoC*

1

FL-517

Hendry, Hardee, Highlands Counties CoC

Steady Decreasing

Columbia, Hamilton, Lafayette, Suwannee Counties

1

FL-518

Fluctuating Decreasing

CoC

1

FL-519

Pasco County CoC*

1

FL-520

Citrus, Hernando, Lake, Sumter Counties CoC

Steady Decreasing

1

FL-600

Miami-Dade County CoC

Fluctuating Increasing

1

FL-601

Ft Lauderdale/Broward County CoC*

1

FL-602

Punta Gorda/Charlotte County CoC

Steady Decreasing

1

FL-603

Ft Myers, Cape Coral/Lee County CoC

Steady Decreasing

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FINAL RESEARCH REPORT

Case

CoC

CoC Name

Grouping

Numbers Number

1

FL-604

Monroe County CoC

Steady Decreasing

1

FL-605

West Palm Beach/Palm Beach County CoC

Fluctuating Increasing

1

FL-606

Naples/Collier County CoC

Fluctuating Increasing

1

GA-501

Georgia Balance of State CoC*

1

GA-503

Athens-Clarke County CoC

Fluctuating Decreasing

1

GA-504

Augusta-Richmond County CoC

Fluctuating Increasing

1

GA-505

Columbus-Muscogee CoC

Steady Decreasing

1

GA-506

Marietta/Cobb County CoC

Steady Increasing

1

GA-507

Savannah/Chatham County CoC

Steady Decreasing

1

HI-500

Hawaii Balance of State CoC

Steady Decreasing

1

HI-501

Honolulu City and County CoC

Steady Increasing

1

IA-500

Sioux City/Dakota, Woodbury Counties CoC

Fluctuating Decreasing

1

IA-501

Iowa Balance of State CoC

Fluctuating Decreasing

1

IA-502

Des Moines/Polk County CoC

Fluctuating Decreasing

1

ID-500

Boise/Ada County CoC

Fluctuating Decreasing

1

ID-501

Idaho Balance of State CoC

Steady Increasing

1

IL-500

McHenry County CoC

Steady Decreasing

1

IL-501

Rockford/DeKalb, Winnebago, Boone Counties CoC

Steady Increasing

1

IL-502

Waukegan, North Chicago/Lake County CoC

Steady Increasing

1

IL-503

Champaign, Urbana, Rantoul/Champaign County CoC

Fluctuating Decreasing

1

IL-504

Madison County CoC

Fluctuating Increasing

1

IL-506

Joliet, Bolingbrook/Wil County CoC

Steady Increasing

Peoria, Pekin/Fulton, Tazewel , Peoria, Woodford

1

IL-507

Steady Decreasing

Counties CoC

1

IL-508

East St. Louis, Belleville/St. Clair County CoC

Steady Decreasing

1

IL-512

Bloomington/Central Illinois CoC

Fluctuating Increasing

1

IL-513

Springfield/Sangamon County CoC

Fluctuating Increasing

1

IL-514

DuPage County CoC

Steady Decreasing

1

IL-515

South Central Il inois CoC

Steady Increasing

1

IL-516

Decatur/Macon County CoC

Steady Decreasing

1

IL-517

Aurora, Elgin/Kane County CoC

Steady Increasing

1

IL-518

Rock Island, Moline/Northwestern Illinois CoC

Fluctuating Increasing

1

IL-519

West Central Illinois CoC*

1

IL-520

Southern Illinois CoC

Fluctuating Increasing

1

IN-502

Indiana Balance of State CoC

Fluctuating Increasing

1

IN-503

Indianapolis CoC

Fluctuating Decreasing

1

KS-502

Wichita/Sedgwick County CoC

Fluctuating Decreasing

1

KS-503

Topeka/Shawnee County CoC

Steady Increasing

1

KS-505

Overland Park, Shawnee/Johnson County CoC

Fluctuating Increasing

1

KS-507

Kansas Balance of State CoC

Steady Increasing

1

KY-500

Kentucky Balance of State CoC

Fluctuating Increasing

1

KY-501

Louisville-Jefferson County CoC

Fluctuating Increasing

1

KY-502

Lexington-Fayette County CoC

Fluctuating Decreasing

1

LA-500

Lafayette/Acadiana CoC

Fluctuating Increasing

1

LA-502

Shreveport, Bossier/Northwest Louisiana CoC

Fluctuating Decreasing

1

LA-503

New Orleans/Jefferson Parish CoC

Steady Decreasing

1

LA-505

Monroe/Northeast Louisiana CoC

Fluctuating Decreasing

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FINAL RESEARCH REPORT

Case

CoC

CoC Name

Grouping

Numbers Number

1

LA-506

Slidell/Southeast Louisiana CoC

Fluctuating Increasing

1

LA-507

Alexandria/Central Louisiana CoC

Fluctuating Decreasing

1

LA-509

Louisiana Balance of State CoC

Steady Increasing

1

MA-503

Cape Cod Islands CoC

Steady Decreasing

1

MA-504

Springfield/Hampden County CoC

Fluctuating Increasing

1

MA-506

Worcester City & County CoC

Steady Increasing

Pittsfield/Berkshire, Franklin, Hampshire Counties

1

MA-507

Fluctuating Increasing

CoC

1

MD-500

Cumberland/Al egany County CoC

Steady Increasing

1

MD-501

Baltimore CoC

Fluctuating Increasing

1

MD-502

Harford County CoC

Fluctuating Increasing

1

MD-503

Annapolis/Anne Arundel County CoC

Fluctuating Increasing

1

MD-504

Howard County CoC

Steady Increasing

1

MD-505

Baltimore County CoC

Fluctuating Decreasing

1

MD-506

Carroll County CoC

Steady Increasing

1

MD-507

Cecil County CoC

Fluctuating Decreasing

1

MD-508

Charles, Calvert, St. Mary’s Counties CoC

Steady Decreasing

1

MD-509

Frederick City & County CoC

Fluctuating Increasing

1

MD-510

Garrett County CoC*

1

MD-511

Mid-Shore Regional CoC

Fluctuating Decreasing

1

MD-512

Hagerstown/Washington County CoC

Fluctuating Increasing

1

MD-513

Wicomico, Somerset, Worcester Counties CoC

Steady Decreasing

1

MD-600

Prince George’s County CoC

Steady Decreasing

1

MD-601

Montgomery County CoC

Fluctuating Decreasing

1

ME-500

Maine Statewide CoC

Fluctuating Increasing

1

MI-500

Michigan Balance of State CoC

Fluctuating Decreasing

1

MI-503

St. Clair Shores, Warren/Macomb County CoC

Steady Decreasing

1

MI-504

Pontiac, Royal Oak/Oakland County CoC

Steady Decreasing

1

MI-505

Flint/Genesee County CoC

Steady Decreasing

1

MI-506

Grand Rapids, Wyoming/Kent County CoC

Fluctuating Decreasing

1

MI-507

Portage, Kalamazoo City & County CoC

Steady Increasing

1

MI-508

Lansing, East Lansing/Ingham County CoC

Steady Decreasing

1

MI-509

Washtenaw County CoC

Steady Decreasing

1

MI-510

Saginaw City & County CoC

Steady Decreasing

1

MI-511

Lenawee County CoC*

1

MI-512

Grand Traverse, Antrim, Leelanau Counties CoC

Fluctuating Decreasing

1

MI-513

Marquette, Alger Counties CoC*

1

MI-514

Battle Creek/Calhoun County CoC

Fluctuating Decreasing

1

MI-515

Monroe City & County CoC*

1

MI-516

Norton Shores, Muskegon City & County CoC

Fluctuating Increasing

1

MI-517

Jackson City & County CoC

Fluctuating Increasing

1

MI-518

Livingston County CoC*

1

MI-519

Holland/Ottawa County CoC

Steady Increasing

1

MI-523

Eaton County CoC*

1

MN-500

Minneapolis/Hennepin County CoC

Steady Increasing

1

MN-501

Saint Paul/Ramsey County CoC*

1

MN-502

Rochester/Southeast Minnesota CoC

Steady Increasing

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FINAL RESEARCH REPORT

Case

CoC

CoC Name

Grouping

Numbers Number

1

MN-503

Dakota, Anoka, Washington, Scott, Carver Counties

Fluctuating Increasing

1

MN-504

Northeast Minnesota CoC

Fluctuating Increasing

1

MN-505

St. Cloud/Central Minnesota CoC

Fluctuating Increasing

1

MN-506

Northwest Minnesota CoC

Fluctuating Decreasing

1

MN-508

Moorhead/West Central Minnesota CoC

Steady Decreasing

1

MN-509

Duluth/St. Louis County CoC

Fluctuating Increasing

1

MN-511

Southwest Minnesota CoC

Steady Decreasing

1

MO-500

St. Louis County CoC

Steady Decreasing

1

MO-501

St. Louis City CoC

Fluctuating Decreasing

St. Charles City & County, Lincoln, Warren Counties

1

MO-503

Steady Decreasing

CoC

1

MO-600

Springfield/Greene, Christian, Webster Counties CoC*

1

MO-602

Joplin/Jasper, Newton Counties CoC

Steady Increasing

1

MO-603

St. Joseph/Andrew, Buchanan, DeKalb Counties CoC

Steady Increasing

Kansas City, Independence, Lee’s Summit/Jackson,

1

MO-604

Steady Increasing

Wyandotte Counties, MO & KS

1

MO-606

Missouri Balance of State CoC

Fluctuating Increasing

1

MS-500

Jackson/Rankin, Madison Counties CoC*

1

MS-501

Mississippi Balance of State CoC

Fluctuating Decreasing

1

MS-503

Gulf Port/Gulf Coast Regional CoC

Steady Decreasing

1

MT-500

Montana Statewide CoC

Steady Decreasing

1

NC-500

Winston-Salem/Forsyth County CoC

Fluctuating Increasing

1

NC-501

Asheville/Buncombe County CoC

Fluctuating Increasing

1

NC-502

Durham City & County CoC

Steady Increasing

1

NC-503

North Carolina Balance of State CoC*

1

NC-504

Greensboro, High Point CoC

Steady Decreasing

1

NC-505

Charlotte/Mecklenberg CoC

Fluctuating Increasing

Wilmington/Brunswick, New Hanover, Pender

1

NC-506

Fluctuating Increasing

Counties CoC

1

NC-507

Raleigh/Wake County CoC

Steady Increasing

1

NC-509

Gastonia/Cleveland, Gaston, Lincoln Counties CoC

Fluctuating Increasing

1

NC-511

Fayetteville/Cumberland County CoC

Steady Decreasing

1

NC-513

Chapel Hil /Orange County CoC

Fluctuating Increasing

1

NC-516

Northwest North Carolina CoC

Steady Decreasing

1

ND-500

North Dakota Statewide CoC*

1

NE-501

Omaha, Council Bluffs CoC

Fluctuating Increasing

1

NH-500

New Hampshire Balance of State CoC

Fluctuating Increasing

1

NJ-500

Atlantic City & County CoC

Fluctuating Increasing

1

NJ-501

Bergen County CoC

Steady Decreasing

1

NJ-502

Burlington County CoC

Fluctuating Increasing

Camden City & County/Gloucester, Cape May,

1

NJ-503

Fluctuating Increasing

Cumberland Counties CoC

1

NJ-504

Newark/Essex County CoC

Fluctuating Increasing

1

NJ-506

Jersey City, Bayonne/Hudson County CoC

Steady Increasing

1

NJ-507

New Brunswick/Middlesex County CoC

Steady Increasing

1

NJ-508

Monmouth County CoC

Steady Increasing

1

NJ-509

Morris County CoC

Fluctuating Increasing

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FINAL RESEARCH REPORT

Case

CoC

CoC Name

Grouping

Numbers Number

1

NJ-510

Lakewood Township/Ocean County CoC

Fluctuating Increasing

1

NJ-511

Paterson/Passaic County CoC

Steady Increasing

1

NJ-512

Salem County CoC*

1

NJ-513

Somerset County CoC

Fluctuating Increasing

1

NJ-514

Trenton/Mercer County CoC

Steady Decreasing

1

NJ-515

Elizabeth/Union County CoC

Steady Decreasing

1

NJ-516

Warren, Sussex, Hunterdon Counties CoC

Fluctuating Increasing

1

NV-500

Las Vegas/Clark County CoC

Fluctuating Decreasing

1

NV-501

Reno, Sparks/Washoe County CoC

Fluctuating Increasing

1

NV-502

Nevada Balance of State CoC

Fluctuating Increasing

1

NY-500

Rochester, Irondequoit, Greece/Monroe County CoC

Fluctuating Decreasing

Elmira/Steuben, Allegany, Livingston, Chemung,

1

NY-501

Schuyler Counties CoC*

1

NY-503

Albany City & County CoC

Steady Increasing

1

NY-504

Cattaragus County CoC*

Syracuse, Auburn/Onondaga, Oswego, Cayuga

1

NY-505

Fluctuating Decreasing

Counties CoC

1

NY-507

Schenectady City & County CoC

Steady Decreasing

Buffalo, Niagara Falls/Erie, Niagara, Orleans,

1

NY-508

Fluctuating Decreasing

Genesee, Wyoming Counties CoC

1

NY-510

Ithaca/Tompkins County CoC*

Binghamton, Union Town/Broome, Otsego,

1

NY-511

Fluctuating Decreasing

Chenango, Delaware, Cortland, Tioga Counties CoC

1

NY-512

Troy/Rensselaer County CoC

Steady Decreasing

1

NY-513

Wayne, Ontario, Seneca, Yates Counties CoC*

1

NY-514

Jamestown, Dunkirk/Chautauqua County CoC*

1

NY-516

Clinton County CoC

Steady Decreasing

1

NY-518

Utica, Rome/Oneida, Madison Counties CoC

Steady Decreasing

1

NY-519

Columbia, Greene Counties CoC

Steady Increasing

1

NY-520

Franklin, Essex Counties CoC*

1

NY-522

Jefferson, Lewis, St. Lawrence Counties CoC

Fluctuating Decreasing

Glens Falls, Saratoga Springs/Saratoga, Washington,

1

NY-523

Warren, Hamilton Counties CoC*

1

NY-525

New York Balance of State CoC*

1

NY-600

New York City CoC

Fluctuating Increasing

1

NY-601

Poughkeepsie/Dutchess County CoC

Fluctuating Decreasing

1

NY-602

Newburgh, Middletown/Orange County CoC

Fluctuating Decreasing

1

NY-603

Nassau, Suffolk Counties CoC

Steady Decreasing

1

NY-604

Yonkers, Mount Vernon/Westchester County CoC

Fluctuating Increasing

1

NY-606

Rockland County CoC

Fluctuating Increasing

1

NY-607

Sullivan County CoC*

1

NY-608

Kingston/Ulster County CoC

Fluctuating Decreasing

1

OH-500

Cincinnati/Hamilton County CoC

Fluctuating Decreasing

1

OH-501

Toledo/Lucas County CoC

Steady Decreasing

1

OH-502

Cleveland/Cuyahoga County CoC

Steady Increasing

1

OH-503

Columbus/Franklin County CoC

Steady Increasing

1

OH-504

Youngstown/Mahoning County CoC

Fluctuating Decreasing

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FINAL RESEARCH REPORT

Case

CoC

CoC Name

Grouping

Numbers Number

1

OH-505

Dayton, Kettering/Montgomery County CoC

Fluctuating Increasing

1

OH-506

Akron, Barberton/Summit County CoC

Fluctuating Decreasing

1

OH-507

Ohio Balance of State CoC*

1

OH-508

Canton, Massillon, Al iance/Stark County CoC

Steady Decreasing

1

OK-500

North Central Oklahoma CoC

Fluctuating Decreasing

1

OK-501

Tulsa City & County CoC

Steady Increasing

1

OK-505

Northeast Oklahoma CoC

Steady Increasing

1

OK-506

Southwest Oklahoma Regional CoC

Fluctuating Decreasing

1

OK-507

Southeastern Oklahoma Regional CoC*

1

OR-500

Eugene, Springfield/Lane County CoC

Steady Increasing

1

OR-501

Portland, Gresham/Multnomah County CoC

Fluctuating Increasing

1

OR-502

Medford, Ashland/Jackson County CoC

Fluctuating Decreasing

1

OR-503

Central Oregon CoC

Steady Increasing

1

OR-505

Oregon Balance of State CoC

Steady Increasing

1

OR-506

Hillsboro, Beaverton/Washington County CoC

Steady Decreasing

1

OR-507

Clackamas County CoC

Fluctuating Decreasing

1

PA-500

Philadelphia CoC

Steady Increasing

1

PA-501

Harrisburg/Dauphin County CoC

Fluctuating Increasing

Upper Darby, Chester, Haverford/Delaware County

1

PA-502

Fluctuating Decreasing

CoC

1

PA-503

Wilkes-Barre, Hazleton/Luzerne County CoC

Fluctuating Decreasing

Lower Merion, Norristown, Abington/Montgomery

1

PA-504

Fluctuating Increasing

County CoC

1

PA-505

Chester County CoC

Fluctuating Increasing

1

PA-506

Reading/Berks County CoC

Fluctuating Increasing

1

PA-508

Scranton/Lackawanna County CoC

Fluctuating Decreasing

1

PA-509

Eastern Pennsylvania CoC

Steady Decreasing

1

PA-510

Lancaster City & County CoC

Fluctuating Increasing

1

PA-511

Bristol, Bensalem/Bucks County CoC

Fluctuating Decreasing

1

PA-512

York City & County CoC

Fluctuating Decreasing

Pittsburgh, McKeesport, Penn Hills/Al egheny County

1

PA-600

Fluctuating Increasing

CoC

1

PA-601

Western Pennsylvania CoC

Steady Decreasing

1

PA-603

Beaver County CoC

Fluctuating Decreasing

1

PA-605

Erie City & County CoC

Steady Decreasing

1

RI-500

Rhode Island Statewide CoC

Steady Increasing

1

SC-500

Charleston/Low Country CoC

Fluctuating Decreasing

1

SC-501

Greenville, Anderson, Spartanburg/Upstate CoC*

1

SC-502

Columbia/Midlands CoC

Steady Decreasing

1

SC-503

Myrtle Beach, Sumter City & County CoC

Fluctuating Decreasing

1

SD-500

South Dakota Statewide CoC

Steady Increasing

1

TN-500

Chattanooga/Southeast Tennessee CoC

Fluctuating Increasing

1

TN-501

Memphis/Shelby County CoC

Fluctuating Decreasing

1

TN-502

Knoxville/Knox County CoC

Steady Increasing

1

TN-503

Central Tennessee CoC

Fluctuating Decreasing

1

TN-504

Nashville-Davidson County CoC

Fluctuating Increasing

1

TN-506

Upper Cumberland CoC

Steady Decreasing

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FINAL RESEARCH REPORT

Case

CoC

CoC Name

Grouping

Numbers Number

1

TN-507

Jackson/West Tennessee CoC

Steady Decreasing

1

TN-509

Appalachian Regional CoC

Fluctuating Decreasing

1

TN-510

Murfreesboro/Rutherford County CoC

Fluctuating Increasing

Morristown/Blount, Sevier, Campbel , Cocke Counties

1

TN-512

Steady Decreasing

CoC

1

TX-500

San Antonio/Bexar County CoC

Fluctuating Increasing

1

TX-503

Austin/Travis County CoC

Steady Increasing

1

TX-600

Dallas City & County, Irving CoC

Steady Increasing

1

TX-601

Fort Worth, Arlington/Tarrant County CoC

Steady Increasing

1

TX-603

El Paso City & County CoC

Steady Increasing

1

TX-604

Waco/McLennan County CoC

Fluctuating Increasing

Wichita Falls/Wise, Palo Pinto, Wichita, Archer

1

TX-624

Steady Increasing

Counties CoC

Houston, Pasadena, Conroe/Harris, Ft. Bend,

1

TX-700

Montgomery, Counties CoC*

1

TX-701

Bryan, College Station/Brazos Valley CoC

Fluctuating Increasing

1

UT-500

Salt Lake City & County CoC

Steady Increasing

1

UT-503

Utah Balance of State CoC

Fluctuating Increasing

1

UT-504

Provo/Mountainland CoC

Steady Increasing

Richmond/Henrico, Chesterfield, Hanover Counties

1

VA-500

Steady Increasing

CoC

Norfolk, Chesapeake, Suffolk/Isle of Wight,

1

VA-501

Steady Increasing

Southampton Counties CoC

1

VA-502

Roanoke City & County, Salem CoC

Fluctuating Increasing

1

VA-503

Virginia Beach CoC

Fluctuating Decreasing

1

VA-504

Charlottesville CoC

Fluctuating Increasing

1

VA-505

Newport News, Hampton/Virginia Peninsula CoC

Fluctuating Decreasing

1

VA-507

Portsmouth CoC

Fluctuating Decreasing

1

VA-508

Lynchburg CoC

Fluctuating Increasing

1

VA-513

Harrisburg, Winchester/Western Virginia CoC

Steady Increasing

1

VA-514

Fredericksburg/Spotsylvania, Stafford Counties CoC

Fluctuating Decreasing

1

VA-521

Virginia Balance of State CoC

Fluctuating Decreasing

1

VA-600

Arlington County CoC

Fluctuating Decreasing

1

VA-601

Fairfax County CoC

Fluctuating Increasing

1

VA-602

Loudoun County CoC

Fluctuating Increasing

1

VA-603

Alexandria CoC

Steady Decreasing

1

VA-604

Prince William County CoC

Fluctuating Decreasing

1

VT-500

Vermont Balance of State CoC

Fluctuating Decreasing

1

VT-501

Burlington/Chittenden County CoC

Fluctuating Decreasing

1

WA-500

Seattle/King County CoC

Fluctuating Increasing

1

WA-501

Washington Balance of State CoC

Fluctuating Decreasing

1

WA-502

Spokane City & County CoC*

1

WA-503

Tacoma, Lakewood/Pierce County CoC

Steady Increasing

1

WA-504

Everett/Snohomish County CoC

Steady Increasing

1

WA-508

Vancouver/Clark County CoC*

1

WI-500

Wisconsin Balance of State CoC

Steady Decreasing

1

WI-501

Milwaukee City & County CoC

Fluctuating Decreasing

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FINAL RESEARCH REPORT

Case

CoC

CoC Name

Grouping

Numbers Number

1

WI-502

Racine City & County CoC

Fluctuating Increasing

1

WI-503

Madison/Dane County CoC

Fluctuating Decreasing

1

WV-500

Wheeling, Weirton Area CoC

Fluctuating Decreasing

1

WV-501

Huntington/Cabell, Wayne Counties CoC

Steady Increasing

Charleston/Kanawha, Putnam, Boone, Clay Counties

1

WV-503

Fluctuating Decreasing

CoC

1

WV-508

West Virginia Balance of State CoC

Fluctuating Decreasing

1

WY-500

Wyoming Statewide CoC

Fluctuating Decreasing

2

CA-606

Long Beach CoC

Fluctuating Decreasing

2

CA-607

Pasadena CoC

Steady Decreasing

2

CA-612

Glendale CoC

Fluctuating Increasing

2

GA-500

Atlanta CoC

Fluctuating Decreasing

2

IL-510

Chicago CoC*

2

MA-500

Boston CoC

Fluctuating Decreasing

2

MA-502

Lynn CoC

Fluctuating Decreasing

2

MA-505

New Bedford CoC

Fluctuating Increasing

2

MA-508

Lowel CoC

Fluctuating Decreasing

2

MA-509

Cambridge CoC

Fluctuating Increasing

2

MA-515

Fall River CoC*

2

MA-517

Somerville CoC

Fluctuating Increasing

2

MI-501

Detroit CoC*

2

NE-502

Lincoln CoC

Steady Decreasing

2

NH-501

Manchester CoC

Fluctuating Increasing

2

NM-500

Albuquerque CoC*

2

OK-502

Oklahoma City CoC

Steady Increasing

2

TX-611

Amarillo CoC

Steady Increasing

3

CA-600

Los Angeles City & County CoC

Steady Increasing

3

GA-502

Fulton County CoC

Fluctuating Increasing

3

GA-508

DeKalb County CoC

Fluctuating Increasing

3

IL-511

Cook County CoC

Fluctuating Decreasing

Dearborn, Dearborn Heights, Westland/Wayne

3

MI-502

Steady Increasing

County CoC

3

NH-502

Nashua/Hillsborough County CoC

Fluctuating Decreasing

3

OK-504

Norman/Cleveland County CoC

Steady Increasing

4

MA-510

Gloucester, Haverhill, Salem/Essex County CoC

Steady Increasing

Quincy, Brockton, Weymouth, Plymouth City and

4

MA-511

Fluctuating Decreasing

County CoC

4

MA-516

Massachusetts Balance of State CoC

Steady Increasing

3

MA-519

Attleboro, Taunton/Bristol County CoC

Steady Decreasing

4

NE-500

Nebraska Balance of State CoC

Fluctuating Decreasing

4

NM-501

New Mexico Balance of State CoC

Steady Increasing

4

OK-503

Oklahoma Balance of State CoC

Fluctuating Increasing

4

TX-607

Texas Balance of State CoC*

CoC = Continuum of Care.

Notes: Asterisk indicates that CoC was excluded from component 1 analysis due to incomplete Point in Time (PIT) counts or changes in PIT count methodology since 2015.

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Exhibit B.4 Map of CoCs by Grouping

CoC = Continuum of Care. Notes: CoCs indicated by geographic service area cases 2 and 3, as well as CoCs with incomplete Point in Time (PIT) counts or changes in PIT count methodology since 2015, are excluded in the map.

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APPENDIX C. DETAILED INFORMATION ON

THE QUANTITATIVE STUDY AND

ADDITIONAL TABLES

The quantitative analysis provides insight into each of the Continuum of Care (CoC) groupings discussed in this report through a three-step approach.

Step 1. The study team began by analyzing the Point-in-Time (PIT) count data to identify CoCs for each group. The increases or decreases in unsheltered homeless counts between the first year (2015) and the most recent year (2019) distinguished CoCs by their net, or “absolute,”

change between the time periods. The study team then subdivided the groups of CoCs with absolute increases and decreases by their intermediate trends associated with 2017 unsheltered homeless counts. This process resulted in the classification of all CoCs into the four substantive groups (as outlined in Chapter 2). The CoCs were further classified in percentile-rank classes on the basis of, first, their percentage change in unsheltered homelessness from 2015 to 2019 and, second, their absolute levels of unsheltered homelessness in 2019. Certain CoCs were excluded or included from their original y identified groupings, especially when the study team believed that a community may have changed its methodology or experienced some other anomalous event that may have contributed to a spike or drop in the number of individuals experiencing unsheltered homelessness. In such cases, those CoCs’ PIT counts did not reflect a true increase or decrease in unsheltered homelessness. 78

Step 2. Following the determination of CoC composition of each group, the study team cleaned and merged the data sources. Examining the results from the Nisar et al. (2019) study, the study team, in consultation with HUD, determined the local housing market characteristics that are consistently and theoretically associated with changes in numbers of unsheltered homelessness.

The team examined several variables, including data on housing markets, resources available in the homeless assistance system, and performance of homeless assistance systems (see the section on data sources in Chapter 2).

Step 3. In the final step, the study team compared the characteristics of the CoC groups to determine whether the groups were different with respect to housing markets (RQ 1), available resources (RQ 2), and system performance (RQ 3). 79

The sections that follow describe in detail the full set of variables, along with their associated data sources and years of measurement.

78 HUD identified 35 CoCs that experienced methodological changes in PIT counts and were excluded from the identified groupings.

79 The study team’s analysis of group 3 (CoCs with large increases, or high rates, that then experienced a decrease in unsheltered homelessness) cuts across RQs 1 through 3. The analysis examines the characteristics of group 3 in terms of housing markets, available resources, and system performance.

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PRIMARY OUTCOME VARIABLES (COC-LEVEL VARIABLES)

The CoC-level data were obtained from Point-in-Time (PIT) count, Housing Inventory Count (HIC), and System Performance Measure (SPM) data for 3 years: 2015, 2017, and 2019. 80 The primary variables of interest—the number of individuals experiencing unsheltered homelessness and the overall count of homeless individuals—were scaled into rates per 10,000 population (Byrne et al., 2012; Fargo et al., 2013; Nisar et al., 2019). Homeless assistance resources were measured by Emergency Shelter (ES); Transitional Housing (TH); rapid re-housing (RRH); and Permanent Supportive Housing and Other Permanent Housing (PSH/OPH) bed counts captured in HIC reports. 81,82 Finally, the study team compared trends in unsheltered homelessness with significant changes in reported SPM measures and urbanicity characteristics of trend groupings. Exhibit 2.1 provides a summary of CoC-level data elements, such as the conceptual domain, specific variable, data source, availability of recent data, and denominator used in scaling.

HOUSING MARKET VARIABLES (COMMUNITY-LEVEL VARIABLES)

Variables capturing community-level housing market characteristics include several measures of rental market factors and facets of homeownership costs, housing density, the prevalence of overcrowded housing units, and construction of new housing units. As in Nisar et al. (2019), most housing variables are sourced from American Community Survey (ACS) 5-year estimates and relate to rental affordability and occupancy characteristics of the local housing market. The following variables were used:

 Variables concerning rental affordability include average and median contract rent, average monthly utility cost, percentage of renters experiencing cost burden, and Fair Market Rents (FMRs) tabulated by HUD. 83, 84

 Other rental market variables include ACS estimates of the share of renter-occupied units and rental vacancy rates, and eviction rates and eviction filing rates from the Eviction Lab at Princeton University. 85

 Variables capturing facets of homeownership costs include ACS estimates of average house values and the percentage of homeowners experiencing cost burden. Median house values were obtained from Zil ow, an online real estate database. 86

80 SPM data for 2019 were not publicly available before the submission of this report.

81 Although this information was reported separately, HUD suggested that bed counts for OPH may have been misclassified as PSH in earlier HIC counts. To mediate that issue, the study team combined the bed counts for PSH and OPH categories.

82 Safe Haven (SH) bed counts were excluded because many CoCs do not participate in this program. Although reported separately, PSH/OPH were combined due to reporting practices.

83 Average monthly utility costs include the costs of utilities and fuels (U.S. Census Bureau, 2018a).

84 “Renters experiencing cost burden” is defined as the share of renters with monthly gross rent costs greater than or equal to 30 percent of household income. Gross rent includes contract rent plus the cost of utilities and fuels (U.S. Census Bureau, 2018a).

85 The eviction rate considers the number of households that received an eviction judgment that ordered renters to leave, whereas the eviction filing rate includes the total number of eviction filings in an area. The filing rate differs from the eviction rate in that the filing rate can have multiple records for the same address, whereas the eviction rate includes only one record per address each year (Desmond et al., 2018)

86 “Homeowners experiencing cost burden” is defined as the share of homeowners with monthly owner costs greater than or equal to 30 percent of household income. Monthly owner costs include debts on the property (such as mortgage payments, home equity loans); property insurance; utilities; and fuels. Further information or clarification can be obtained from yearly ACS subject definition documents (U.S. Census Bureau, 2018a).

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 Finally, the study team examined four housing market variables unrelated to tenant classification. Those variables include ACS estimates of housing density and the share of overcrowded housing units and the rate of newly permitted housing units and the associated average value of the units from the U.S. Census Bureau’s Housing Permit Survey. 87

Although the initial list of housing market variables was largely influenced by Nisar et al. (2019), additional data considerations on weighting, geographic granularity, and data quality were implemented and are further outlined in Appendix A. Exhibit 2.2 provides a summary of housing market variables, including the underlying data sources, availability of recent data, geographic units for raw variables, variable used for weighting, and the denominator used in scaling.

87 An “overcrowded housing unit” is defined as a unit with a people-per-room ratio greater than or equal to 1.5.

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ADDITIONAL TABLES

Exhibit C.1 | Number of Unsheltered Individuals Over Time, by CoC Grouping

Percentile

CoC Grouping

Variable

Mean

N*

25th

50th

75th

Unsheltered Individuals, 2015

737.4

36

114.5

388

Steady

Unsheltered Individuals, 2019

1,257.6

72

225.5

692

78

Increasing

Difference in Unsheltered Individuals from 2015 to 2019

520.1

36

101

303

Percentage Change from 2015 to 2019

123.8% 56.4%

92.0% 138.1%

Unsheltered Individuals, 2015

394.3

38.5

114.5

362.5

Fluctuating

Unsheltered Individuals, 2019

485.8

54.0

153

462

84

Increasing

Difference in Unsheltered Individuals from 2015 to 2019

91.4

8.5

27

55.5

Percentage Change from 2015 to 2019

38.4%

9.6%

25.9%

50.9%

Unsheltered Individuals, 2015

367.2

50

99

347

Fluctuating

Unsheltered Individuals, 2019

284.8

36

75

249

71

Decreasing

Difference in Unsheltered Individuals from 2015 to 2019

-82.4

-77

-20

-12

Percentage Change from 2015 to 2019

-26.9% -39.6% -24.7% -11.1%

Unsheltered Individuals, 2015

389.8

69

217

522

Steady

Unsheltered Individuals, 2019

208.2

39

94

253

57

Decreasing

Difference in Unsheltered Individuals from 2015 to 2019

-181.6

-291

-113

-44

Percentage Change from 2015 to 2019

-46.6% -61.2% -46.5% -28.7%

CoC = Continuum of Care.

Note: *CoCs with fewer than 10 unsheltered individuals in any of the 3 years of PIT counts are excluded from this table because those CoCs may produce misleading percentage change values.

Exhibit C.2 | HIC Rates, by Group

2015

2017

2019

Average %

Change

Variable

CoC Grouping

% CoC

% CoC

% CoC

Mean

Mean

Mean

from 2015

Reporting

Reporting

Reporting

to 2019

Rate of ES

Steady Increasing

7.64

100.00

7.76

98.81

7.84

100.00

2.62%

Beds (per

Fluctuating Increasing

8.52

100.00

9.05

100.00

9.12

100.00

7.04%

10,000

Fluctuating Decreasing

8.16

100.00

8.28

100.00

9.03

100.00

10.66%

Population)

Steady Decreasing

6.80

100.00

6.51

100.00

7.07

100.00

3.97%

Rate of TH

Steady Increasing

4.90

98.81

3.85

96.43

3.34

96.43

-31.84%

Beds (per

Fluctuating Increasing

5.90

100.00

4.57

96.94

4.05

92.86

-31.36%

10,000

Fluctuating Decreasing

5.77

100.00

4.89

98.85

3.79

96.55

-34.32%

Population)

Steady Decreasing

5.12

98.51

3.83

98.51

3.06

98.51

-40.23%

Rate of RRH

Steady Increasing

2.19

72.62

3.03

90.48

3.83

92.86

74.89%

Beds (per

Fluctuating Increasing

3.31

71.43

4.39

84.69

4.97

96.94

50.15%

10,000

Fluctuating Decreasing

2.24

75.86

4.12

94.25

4.08

96.55

82.14%

Population)

Steady Decreasing

1.88

67.16

2.60

92.54

3.32

94.03

76.60%

Rate of PSH/

Steady Increasing

10.42

96.43

11.94

96.43

11.95

96.43

14.68%

OPH Beds

Fluctuating Increasing

12.45

96.94

14.01

97.96

14.66

98.98

17.75%

(per 10,000

Fluctuating Decreasing

12.14

100.00

14.16

100.00

13.35

100.00

9.97%

Population)

Steady Decreasing

10.80

100.00

13.51

100.00

12.23

100.00

13.24%

ES = emergency shelter; HIC = Housing Inventory Count; OPH = other permanent housing; PSH = permanent supportive housing; RRH = rapid re-housing; TH = transitional housing.

Note: Reported values of zero are excluded from mean calculations.

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Exhibit C.3 | Housing Market Characteristics, by CoC Grouping

2014

2018

Variable

CoC Grouping

Standard

Standard

Mean

N

Mean

N

Deviation

Deviation

Steady Increasing

255.72

124.62

84

299.80

167.79

84

Average House Value Fluctuating Increasing

292.05

168.09

98

339.26

221.12

98

(in $1,000s)

Fluctuating Decreasing

225.31

102.27

87

252.65

121.87

87

Steady Decreasing

244.96

152.21

67

276.24

185.79

67

Steady Increasing

27.93

6.69

84

23.34

5.49

84

Percentage of

Fluctuating Increasing

28.86

7.06

98

24.25

6.11

98

Homeowners with

Fluctuating Decreasing

26.74

6.26

87

22.74

5.11

87

Cost Burden

Steady Decreasing

27.63

6.20

67

23.04

5.20

67

Steady Increasing

783.84

227.33

84

887.48

285.36

84

Median Contract

Fluctuating Increasing

843.47

288.08

98

954.73

349.04

98

Rent ($)

Fluctuating Decreasing

732.96

233.39

87

814.43

260.70

87

Steady Decreasing

763.61

255.39

67

850.63

305.76

67

Steady Increasing

796.54

235.61

84

898.82

289.12

84

Average Contract

Fluctuating Increasing

850.91

294.56

98

959.91

348.59

98

Rent ($)

Fluctuating Decreasing

736.57

249.90

87

817.35

273.93

87

Steady Decreasing

770.65

262.27

67

858.76

305.04

67

Steady Increasing

146.80

23.63

84

151.27

23.95

84

Average Rental

Fluctuating Increasing

144.29

24.73

98

147.61

23.35

98

Utility Cost ($)

Fluctuating Decreasing

147.60

24.68

87

151.00

24.49

87

Steady Decreasing

150.38

25.19

67

153.13

24.91

67

Steady Increasing

51.59

4.56

84

49.26

4.40

84

Percentage of

Fluctuating Increasing

52.00

5.17

98

49.88

5.41

98

Renters with Cost

Fluctuating Decreasing

51.84

4.73

87

50.03

4.80

87

Burden

Steady Decreasing

52.49

4.53

67

49.83

4.55

67

Steady Increasing

36.15

7.84

84

36.56

7.83

84

Share of Renter-

Fluctuating Increasing

35.05

10.64

98

35.45

10.75

98

Occupied Units (%)

Fluctuating Decreasing

35.04

9.41

87

35.47

9.67

87

Steady Decreasing

32.67

8.32

67

33.05

8.55

67

Steady Increasing

6.58

2.23

84

5.86

2.83

84

Rental Vacancy Rates Fluctuating Increasing

6.50

2.33

98

5.69

1.98

98

(%)

Fluctuating Decreasing

7.15

2.90

87

6.64

3.05

87

Steady Decreasing

8.07

4.23

67

7.22

3.61

67

Steady Increasing

421.55

851.40

84

432.50

869.65

84

Housing Density

Fluctuating Increasing

736.99

1748.04

98

758.39

1,807.14

98

(number of people/

Fluctuating Decreasing

510.04

991.21

87

520.09

1,018.72

87

square mile)

Steady Decreasing

416.42

746.95

67

425.63

769.01

67

Steady Increasing

0.98

0.96

84

1.02

0.86

84

Share of

Fluctuating Increasing

0.77

0.65

98

0.87

0.72

98

Overcrowded

Housing Units (%)

Fluctuating Decreasing

0.65

0.68

87

0.78

0.81

87

Steady Decreasing

0.62

0.66

67

0.65

0.57

67

Steady Increasing

621.36

173.46

73

736.96

249.26

78

Fluctuating Increasing

674.88

230.62

87

782.34

313.46

86

FMR, 0 Bedroom ($)

Fluctuating Decreasing

609.81

193.26

72

681.67

182.73

77

Steady Decreasing

636.09

206.78

67

702.48

239.88

63

Steady Increasing

722.26

199.93

73

850.89

290.85

78

Fluctuating Increasing

782.72

259.51

87

897.75

375.08

86

FMR, 1 Bedroom ($)

Fluctuating Decreasing

694.58

202.95

72

773.44

206.63

77

Steady Decreasing

731.59

225.08

67

798.65

298.32

63

Steady Increasing

910.25

244.43

73

1,062.75

364.36

78

FMR, 2 Bedrooms ($)

Fluctuating Increasing

977.05

315.23

87

1,119.97

462.50

86

Fluctuating Decreasing

872.40

243.31

72

964.29

252.26

77

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2014

2018

Variable

CoC Grouping

Standard

Standard

Mean

N

Mean

N

Deviation

Deviation

Steady Decreasing

913.43

270.23

67

996.43

369.69

63

Steady Increasing

1,244.40

333.75

73

1,450.66

506.19

78

Fluctuating Increasing

1,307.50

421.50

87

1,497.25

609.00

86

FMR, 3 Bedrooms ($)

Fluctuating Decreasing

1,158.37

311.95

72

1,285.22

331.27

77

Steady Decreasing

1,220.00

361.95

67

1,325.64

481.74

63

Steady Increasing

1,442.54

392.46

73

1,699.36

594.3

78

Fluctuating Increasing

1,509.65

527.85

87

1,734.69

675.8

86

FMR, 4 Bedrooms ($)

Fluctuating Decreasing

1,322.37

384.20

72

1,471.97

376.8

77

Steady Decreasing

1,407.14

448.16

67

1,535.73

544

63

CoC = Continuum of Care. FMR = fair market rent.

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Exhibit C.4 | Housing Market Trend Analysis, by CoC Group

2014

2016

2018

Variable

CoC Grouping

Mean

N

Mean

N

Mean

N

Steady Increasing

227.36

84

251.04

84

285.55 84

Median House Values

Fluctuating Increasing

260.28

97

286.49

97

319.67 98

($1,000s)

Fluctuating Decreasing

188.73

87

203.18

87

226.14 87

Steady Decreasing

201.87

66

223.19

67

246.31 67

Steady Increasing

9.44

84

23.46

84

Median House Value

Fluctuating Increasing

8.70

97

21.73

97

(Average % Change

Fluctuating Decreasing

7.50

87

19.51

87

Since 2014)

Steady Decreasing

7.55

66

19.04

66

Steady Increasing

0.82

84

0.97

84

1.00

84

Rate of Newly

Fluctuating Increasing

0.80

97

0.92

98

0.98

98

Permitted Housing

Fluctuating Decreasing

0.67

87

0.77

87

0.79

87

Units (%)

Steady Decreasing

0.68

67

0.74

67

0.84

67

Rate of Newly

Steady Increasing

27.15

84

30.66

84

Permitted Housing

Fluctuating Increasing

20.38

97

32.31

97

Units (Average %

Fluctuating Decreasing

18.20

87

18.73

87

Change Since 2014)

Steady Decreasing

16.41

67

30.40

67

Average Value of

Steady Increasing

192.67

84

204.53

84

210.11 84

Newly Permitted

Fluctuating Increasing

190.61

97

194.26

98

198.52 98

Housing Units

Fluctuating Decreasing

180.70

87

188.10

87

195.37 87

($1,000s)*

Steady Decreasing

206.46

67

219.69

67

222.83 67

Average Value of

Steady Increasing

7.91

84

11.62

84

Newly Permitted

Fluctuating Increasing

5.05

97

8.09

97

Housing Units

Fluctuating Decreasing

6.89

87

9.30

87

(Average % Change

Since 2014)*

Steady Decreasing

8.05

67

10.39

67

Steady Increasing

3.14

33

3.37

35

Fluctuating Increasing

2.70

48

2.48

48

Eviction Rate

Fluctuating Decreasing

3.36

39

3.83

41

Steady Decreasing

2.90

30

3.02

25

Steady Increasing

-14.82

27

Eviction Rate

Fluctuating Increasing

6.34

38

(Average % Change

Fluctuating Decreasing

15.05

31

Since 2014)

Steady Decreasing

0.70

20

Steady Increasing

6.84

38

7.69

40

Fluctuating Increasing

8.82

48

7.54

48

Eviction Filing Rate (%) Fluctuating Decreasing

9.07

39

8.59

41

Steady Decreasing

10.58

31

11.19

26

Steady Increasing

2.45

32

Eviction Filing Data

Fluctuating Increasing

-9.49

38

(Average % Change

Fluctuating Decreasing

-0.25

31

Since 2014)

Steady Decreasing

-1.22

21

CoC = Continuum of Care.

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APPENDIX D. MASTER INTERVIEW GUIDES

GREATER RICHMOND CONTINUUM OF CARE

MASTER INTERVIEW GUIDE

MASTER SEMI-STRUCTURED INTERVIEW GUIDE AND QUESTIONNAIRE:

Implementing Approaches to Address Unsheltered Homelessness

The U.S. Department of Housing and Urban Development (HUD) has contracted our company, 2M

Research, to conduct a study on the approaches that communities are implementing to address unsheltered homelessness. For our purposes, a person experiencing unsheltered homelessness is someone residing in a place not meant for human habitation, such as the street or cars, parks, sidewalks, or abandoned buildings. The study’s goal is to better understand the key components of Richmond’s approach, its supporting partnerships, its impacts on addressing unsheltered homelessness, and other factors affecting sustainability and scalability. For the purpose of this interview, we want to focus on your efforts before COVID and specifically on your use of rapid re-housing (RRH) vouchers.

As part of this study, we are conducting interviews with key stakeholders to understand various perspectives on approaches to addressing unsheltered homelessness. For this interview, we are interested in the role you play within the overall effort to address unsheltered homelessness, specifical y the use of RRH vouchers for the unsheltered population. [If needed: Depending on how your agency is organized, different people may need to participate and answer different sections of the interview. ] The interview is scheduled to last up to 60 minutes. [CoC and Partner Staff: Responses to this interview will be used for research purposes only and will NOT be used for compliance monitoring. ] Do you have any questions about the study that we can answer for you?

Consent to Participate

We would like to interview you as a key informant who has been involved in supporting and/or implementing the Greater Richmond Continuum of Care’s Rapid Rehousing Program. We hope that you will feel comfortable being open in our conversation today, as the information you give us is crucial to improving efforts that provide support to individuals and families experiencing unsheltered homelessness. While we will make every effort to protect your privacy, HUD staff will read our report; therefore, we cannot guarantee complete anonymity, given your role in the efforts and the study’s specific focus. However, we wil not use your name in reporting what we have learned during this interview, and we will combine your responses with the responses of others who are participating in the study interviews. Would you still like to participate in the study?

Individuals with Lived Experience Only—Consent to Participate

We would like to interview you as an individual who has been part of the Greater Richmond Continuum of Care to learn about your experiences, both good and bad, with the program. We hope that you will feel comfortable being open in your thoughts about your interactions with the program, as the

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information you provide is important to improving community programs for people in need. As such, we will make every effort to protect your privacy. Your name and other identifying information will not be used when we report what we have learned during this interview, and we will combine your responses with the responses of others participating in interviews as part of this study. However, because program staff referred you to participate in this discussion, staff may be able to identify your experiences described in the report.

To thank you for your time today, we will send you a $50 gift card. We will get your information to send that gift card once we have completed the interview.

Would you still like to participate in the interview?

Permission to Record

We will take notes during the interview. Also, we would like to audio record the conversation so that we can make sure our notes are right. We wil transcribe the recording and remove your name from the transcript. We will not share the recording outside of the study team, and we will destroy it at the end of the study. Is it okay that we take notes and record this interview?

 If interviewee agrees to be recorded:

o Thanks. Let’s get started. Now, we are going to turn on the recorder (BEGIN

RECORDING).

 If interviewee declines:

o Okay, that is not a problem.

Section 1. Introduction

I’d first like to understand a bit about how you’ve been involved in the community’s efforts to address homelessness, specifically for unsheltered individuals.

1. Would you please start by telling us a little bit about yourself in relation to your involvement with Rapid Re-housing for unsheltered homelessness? What is your role in supporting Rapid Rehousing?

[Probe for: how long have you been in this role, which services do you support]

Section 2. Market Challenges, Policies, and Local Context

Next, I’d like to go into a bit more detail about your local unsheltered population’s needs, policies that affect efforts to address homelessness, and your perceptions of public attitudes toward homelessness.

2. What are the key needs of people experiencing unsheltered homelessness in your community?

[Probe for: needs of unsheltered vs. sheltered, types of services, types of housing (Permanent Supportive Housing [would those served do better in PSH], affordable housing, shelter), effects of the market on needs or services available]

a. [If not covered in Q1] How does your organization address these needs?

b. What other resources are available in your community to address these needs?

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c. What do you think are the unmet needs? In other words, what needs are not being adequately addressed through services/resources offered by your organization or your partners?

CoC Staff and Housing and Service Providers

3. What state or local policies have been most influential on the design or implementation of your efforts? How?

a. How have the policies facilitated a coordinated response within your community and across partners?

Probe for: tracking individuals, Coordinated Entry

b. How have the policies negatively impacted your program?

Probe for: policing practices (e.g., punitive policies against encampments, move along), HUD

CoC resource restrictions

Community-Based Stakeholders (e.g., law enforcement)

4. In general, how has the public’s attitude toward homelessness impacted your community’s efforts to address unsheltered homelessness? What makes you think this?

a. What is the political attitude toward unsheltered homelessness? How has it impacted your community’s efforts?

Section 3. Program Implementation

We would like to ask you a few questions to help us understand more about your community’s efforts to reduce unsheltered homelessness.

CoC Staff

5. How do you use Coordinated Entry to match people experiencing unsheltered homelessness to housing providers?

[Probe for: access points, assessment/screenings, shelter, housing, support services]

a. How does your CoC use Coordinated Entry to assess and match individuals to RRH?

6. Are all of individuals/families experiencing unsheltered homelessness matched to housing? If no, why not?

Probe for: Most vulnerable matched, appropriate resources matched

7. How is your CoC organized to provide services and housing to the unsheltered population?

a. How are outreach providers coordinated across partners/efforts?

b. How do you collaborate with your partners to coordinate services and referrals for unsheltered individuals?

8. How does the CoC determine allocation of resources to address unsheltered homelessness?

[Probe for: staffing, procedures to amend funding initiatives, local vs. federal resources]

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a. [If organization is minimally influential in determining allocation] How is this determined?

What would your organization like to see differently if you did have more influence?

9. What were the key factors considered when deciding what investments to make and where those investments would be deployed/implemented?

a. Why was RRH selected as the main program to house unsheltered individuals? What were the benefits? What were the drawbacks?

10. What CoC policies or practices has your organization changed or implemented to support efforts to address unsheltered homelessness?

a. [If needed] What were the reasons for the changes?

b. How, if at all, are these policies and practices different for unsheltered individuals vs.

sheltered?

c. How have these changes affected the implementation of your efforts?

11. How has your CoC’s Coordinated Entry system changed to support efforts to address unsheltered homelessness?

a. What were the reasons for the changes?

b. How, if at all, is this different for unsheltered individuals vs. sheltered?

c. How have these changes affected the implementation of your efforts?

12. How does your CoC connect Rapid Re-housing clients to long-term housing after their subsidy ends? Do these clients need support like Housing Choice Vouchers or other long-term subsidized housing programs such as Low-Income Housing Tax Credit units to stay stably housed?

[Probe for: Where do people end up, is it based on affordability when assistance ends]

13. How do HMIS data inform your activities aimed at reducing unsheltered homelessness?

[Probe for: key metrics used, why these metrics were chosen]

a. How are these data used to track RRH program’s outcomes?

b. What other data sources inform your understanding of the unsheltered population and its needs? What have these data shown?

Housing and Service Providers

14. What supports or services does your organization offer to individuals experiencing unsheltered homelessness?

[Probe for: disability documentation, chronic homelessness documentation, transportation, other case management, financial assistance, basic needs, employment, benefits, health care and treatment]

15. How do you identify clients for each of the services you offer?

a. [If applicable] How are outreach services to unsheltered individuals organized and administered? [Probe for: regular outreach vs. outreach in response to complaint/concern]

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b. How have your processes to identify or recruit unsheltered individuals changed since your efforts began? Why?

16. How are individuals experiencing unsheltered homelessness screened/assessed (i.e., ViSPDAT)?

[Probe for: vulnerability tools, other processes/tools]

a. How has this changed since your efforts began? When? Why?

17. [Housing Providers Only] How are unsheltered individuals/families connected to Coordinated Entry and housing?

a. What are the steps in matching individuals to RRH? Where are these individuals coming from before they are matched to RRH (e.g., emergency shelters or unsheltered situations)?

b. How does your CoC use the ViSPDAT to prioritize clients?

c. What services do clients receive to identify/locate housing (e.g., assistance with landlords, resources, other services)?

d. What kinds of housing do clients receive through RRH?

[Probe for: types of housing (shared [if so, what type], studio/efficiency), specific neighborhoods/locations]

e. How is tenant share of rent structured?

18. [Service Providers Only] How are people experiencing unsheltered homelessness connected to the RRH program?

a. What documents are required for these opportunities? What happens if those experiencing unsheltered homelessness do not have these documents?

a. What approaches or processes has your agency used to ensure critical needs are met?

19. How/when do RRH clients stop receiving services (i.e., How do they exit? What is available after?)?

20. How does your organization support participants who came from unsheltered situations staying housed (e.g., policies or supports)?

a. [Housing Providers Only] What type of supports does your organization offer to address challenges with landlords (e.g., assistance engaging or recruiting landlords, educating landlords, offering financial incentives or securities)?

b. [Housing Providers Only] What support is available for RRH participants after they exit the program?

[Probe for: types of support (i.e., mental health, substance use, SSI/SNAP income support)]

c. [Housing Providers Only] What types of vouchers/housing resources (e.g., PSH, transitional, scattered site, project based) are available to clients? Of these resources, which one is prioritized and for whom?

d. [Housing Providers Only] Do participants typically stay in the same housing when RRH

subsidies end? If no, why not?

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21. [Housing Providers Only] If housing is provided outside of the Coordinated Entry process, please describe how this process works and the types of housing available.

Law Enforcement

22. How is your department organized to respond to calls related to homelessness?

a. Is there a specific team or set of officers that most often interact with individuals experiencing unsheltered homelessness?

[Probe for: role of engagement team]

b. When was the team or set created and why?

23. What are the local policies and practices related to how law enforcement interacts with individuals experiencing unsheltered homelessness?

a. What, if any, policies promote alternatives to arrests, citations, or other punitive responses by law enforcement responding to individuals experiencing homelessness?

b. What training do the team/officers receive to interact with individuals experiencing homelessness?

24. How common is it for encampments to be closed by law enforcement?

a. Has there been any change in this practice? If yes, how so and for what reason? When did this practice change?

b. Are there areas where encampments are more of an issue?

25. Have you seen any major changes in the past few years in the number of people experiencing unsheltered homelessness in encampments or otherwise?

a. If so, what do you think were the reasons for these changes?

26. How do law enforcement and other first responders collaborate with homeless providers and other service providers?

a. What role does law enforcement play in referring individuals experiencing unsheltered homelessness to housing programs or other services?

All Respondents

27. In your opinion, how successful have the approaches we’ve discussed today been in reducing unsheltered homelessness in your community?

a. What are the most successful components of your approach to reduce unsheltered homelessness?

b. What makes these activities successful?

28. What would make the approaches more successful? What recommendations do you have for improvements?

a. What would be needed to make these changes?

29. What have been the biggest challenges in offering the approaches we’ve discussed today?

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[Probe for: coordination, resources, policies, political will or buy-in, staffing, other, changing circumstances or guidance, engaging target population]

a. How have you worked to overcome these challenges?

30. What barriers do you still face in trying to house unsheltered individuals?

[Probe for: gaps in services]

31. How do your community’s efforts build on your baseline services or on previous initiatives, such as Ending Veteran Homelessness?

[Probe for: events, collaborations, processes]

Section 4. Partnerships (CoC Staff, Partners, Providers Only)

CoC Staff

32. Has Richmond established or leveraged committees or other organized bodies to coordinate efforts to address homelessness? Please describe.

a. [If yes] When and why was [committee/body name] established?

b. [If yes] Who was involved in [committee/body name]?

c. [If yes] What was the role of [committee/body name]?

d. [If yes] How has [committee/body name] influenced your efforts to reduce unsheltered homelessness?

33. [Cross-reference information already provided; cycle through probes for each partner organization] Which partners do you consider to be your primary partners in addressing unsheltered homelessness? Why?

a. [If not already addressed] What services does [partner name] support?

b. How long has your organization partnered with [partner name]?

c. [If new] What was the primary reason for establishing the partnership with [partner name]?

d. Why is your partnership with [partner name] important to your efforts to reduce unsheltered homelessness?

e. How could your partnership with [partner name] be improved?

Housing and Service Providers/Law Enforcement Agency

34. Please describe your partnership with Greater Richmond’s Continuum of Care.

a. How do you collaborate and communicate with Greater Richmond’s Continuum of Care?

b. What are the primary activities that you work on with Greater Richmond’s Continuum of Care?

35. What works well in your partnership with Greater Richmond’s Continuum of Care?

36. How could your partnership with Greater Richmond’s Continuum of Care be improved?

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Section 5. Lessons Learned and Sustainability (CoC Staff and Housing and Service Providers Only) 37. How has [organization name] used the data to determine the impact of your community’s efforts to reduce unsheltered homelessness?

[Probe for: specific metrics used, assessing or tracking reductions in unsheltered populations (tailor based on data calls under Component 1)]

38. How has your organization worked to step services down in terms of preparing participants to remain housed after the RRH subsidy ends?

a. What services are specifically offered with this goal in mind (e.g., employment assistance)?

39. Do you believe the efforts we’ve discussed today would be appropriate for another community?

Why or why not?

a. What considerations should be made before another community adapts the approach(es)?

40. What have you learned from implementing these approach(es)?

a. What recommendations or suggestions would you make to another community working to address unsheltered homelessness?

41. To what extent could the RRH program be expanded?

a. What resources would be required to expand the program?

b. [If appropriate] What would make it more sustainable (e.g., partnerships, finances)?

c. Should RRH be expanded to meet the needs of your local unsheltered population?

Section 6. Closing

42. Is there any other important information that will help us understand your communities’ efforts to address unsheltered homelessness?

43. Those are all the questions we have. Is there anything else you’d like to share or reiterate for us to highlight in the case study?

We would like to thank you for taking time from your busy schedule(s) to speak with us today. Your answers have provided us with valuable insight into approaches that communities are implementing to address unsheltered homelessness. Should you have any additional thoughts that you would like to share, please feel free to contact us.

____________________________________________________________________________

Individuals with Lived Experiences

1. We understand that you’ve received services from [organization name]. Could you tell us a little about what they’ve done to help you (e.g., case management, treatment, transportation, budgeting, documentation required, housing assistance in terms of finding and keeping)?

2. What other organizations have helped you with services? What were those services like?

3. How did you hear about these programs/organizations?

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[Probe for: recruitment/referral source—person, agency, location]

4. What services/help have you received from the program (e.g., case management, treatment)?

Now, I’d like to ask you a little more about what you thought of the organizations/programs/

services you got.

For each service:

a. Where do/did you receive services?

b. How easy are the services for you to get to?

5. What, if anything, makes it hard for you to receive services?

[Probe for: missing needed documents, not knowing what’s available or who to ask, health issues (behavioral, mental, physical), requirements (eligibility) for program, such as sobriety or criminal history, times services were offered]

6. What’s your current housing like? Would you change anything about your current housing?

7. [If needed; skip to probe if not needed] How has the program helped you with permanent housing?

[Probe for: assistance with paperwork, locating housing]

a. [If housed through program] What type of housing do/did you live in?

b. [If housed through program] How did you choose your place?

c. [If housed through program] How long did/can you live there?

d. [If housed through program] What kinds of help did you receive when moving in?

e. [If no longer housed through program] Why did you decide to leave the housing?

f. [If never housed through program, probe for why]

8. Since you began the program with [organization], have you returned to a homeless situation?

a. What led to that?

9. What services did you need/want that you did not receive?

10. What did you like about the overall program? What was/has been the most helpful about the program?

11. What did you not like about the overall program? What would have been helpful for you to

[get/stay in] the housing?

We would like to thank you for taking time to speak with us today. Your answers have provided us with valuable insights. We would now like to ask for your email or mailing address to be able to send your $50 gift card. Would you like the gift card to be sent by mail or email?

[If mail] What is the address where you would like us to send the gift card?

[If email] What is the email address where you would like us to send the gift card?

We can send a $50 gift card from Walmart, Walgreens, Target, CVS, or Kroger. Where would you like your gift card from?

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How may we follow up if we have any questions when sending the gift card?

If you have any questions or if you have any additional thoughts that you would like to share, please feel free to contact us by email at ccarr@2mresearch.com or by phone at 817-856-0898. You should expect your gift card within 1 to 2 weeks. Thank you again!

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MONTGOMERY COUNTY CONTINUUM OF CARE

MASTER INTERVIEW GUIDE

MASTER SEMI-STRUCTURED INTERVIEW GUIDE AND QUESTIONNAIRE:

Implementing Approaches to Address Unsheltered Homelessness

The U.S. Department of Housing and Urban Development (HUD) has contracted our company, 2M

Research, to conduct a study on the approaches that communities are implementing to address unsheltered homelessness. For our purposes, someone experiencing unsheltered homelessness is a person residing in a place not meant for human habitation, such as the street or cars, parks, sidewalks, or abandoned buildings. The study’s goal is to better understand the key components of Montgomery County’s approach, its supporting partnerships, its impacts on addressing unsheltered homelessness, and other factors affecting sustainability and scalability. For the purpose of this interview, we want to focus on your efforts before COVID and specifically on the use of Montgomery County’s system approach to reduce unsheltered homelessness.

As part of this study, we are conducting interviews with key stakeholders to understand the approach to addressing unsheltered homelessness from various perspectives. Over the years, Montgomery County has implemented plans and models to address homelessness, such as their 10-year plan to end homelessness, and a Housing First model that has shifted to provide a range of housing support services to persons experiencing or at risk of homelessness. For this interview in particular, we are interested in your role within the overall effort of the Montgomery County Continuum of Care’s system approach to address unsheltered homelessness. The interview is scheduled to last up to 60 minutes. [CoC and Partner Staff: Responses to this interview will be used for research purposes only and will NOT be used for compliance monitoring. ] Do you have any questions about the study before we get started?

Consent to Participate

Next, I would like to go through the consent to participate. We would like to interview you as a key informant who has been involved in supporting and/or implementing Montgomery County’s system approach to address unsheltered homelessness. We hope that you will be open in our conversation today. The information you give us is crucial to improving efforts that provide support to individuals and families experiencing unsheltered homelessness. While we wil make every effort to protect your privacy, HUD staff will read our report; therefore, we cannot guarantee complete anonymity, given your role in the CoC efforts and the specific focus of the study. However, we will not use your name in reporting what we have learned during this interview, and we will combine your responses with the responses of others participating in interviews as part of this study. Would you still like to participate in the study?

Individuals with Lived Experience Only—Consent to Participate

We would like to interview you as an individual who has been part of the Montgomery County CoC to learn about your experiences, both good and the bad, with the program. We hope that you will be open about your thoughts about the interactions with the Montgomery County Continuum of Care. The information you provide is important to improving community programs for those in need. We will

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make every effort to protect your privacy. Your name or other identifying information will not be used in reporting what we have learned during this interview, and we will combine your responses with the responses of others participating in interviews as part of this study. However, because program staff referred you to participate in this discussion, it is possible that staff may be able to identify your experiences described in the report.

To thank you for your time today, we will send you a $50 gift card; once we have completed the interview, we will get your information to send you the gift card. Would you still like to participate in the interview?

Permission to Record

We wil take notes during the interview. We would like to record the conversation so that we can make sure our notes are right. We will transcribe the recording and will remove your name from the transcript. We will not share the recording outside of our 2M study team, and we will destroy it at the end of the study. Is it okay that we take notes and record this interview?

 If interviewee agrees to be recorded:

o Thanks. Let’s get started. Now, we are going to turn on the recorder (BEGIN

RECORDING). Can you please confirm you have agreed to participate?

 If interviewee declines:

o Okay, that is not a problem.

Section 1. Introduction

I’d first like to understand a bit about how you’ve been involved in the community’s efforts to address homelessness, specifically for unsheltered people.

44. Would you please start by telling us a little bit about your involvement with Montgomery County’s system approach to addressing unsheltered homelessness? What is your role in supporting [this effort]?

[Probe for: how long have you been in this role, which services do you support]

Section 2. Market Challenges, Policies, and Local Context

Next, I’d like to go into a bit more detail about your local unsheltered population and their needs, policies impacting efforts addressing homelessness, and your perceptions on public attitudes toward homelessness.

45. What are the key needs of people experiencing unsheltered homelessness in Montgomery County (i.e., vulnerabilities of population)?

[Probe for: needs of unsheltered vs. sheltered, types of services, types of housing (Permanent Supportive Housing, affordable housing, shelter), effects of the market on needs or services available]

a. [If not covered in Q1] How does [your organization] address these needs?

b. What other resources are available in Montgomery County to address these needs?

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c. What do you think are the unmet needs of the individuals experiencing unsheltered homelessness in Montgomery County—that is, needs that are not currently being adequately addressed through services/resources offered by your organization or your partners?

CoC Staff and Housing and Service Providers

46. What state or local policies have been most influential on the design or implementation of your efforts? How?

c. How have the policies facilitated a coordinated response within Montgomery County and across partners?

d. How have the policies negatively impacted your initiative?

[Probe for: policing practices (e.g., punitive policies against encampments, move along), HUD CoC resource restrictions]

Community-Based Stakeholders (e.g., law enforcement)

47. In general, how has the public’s attitude toward unsheltered homelessness impacted your community’s efforts to address unsheltered homelessness in Montgomery County? What makes you think that?

a. What is the political attitude toward unsheltered homelessness in Montgomery County? How has that attitude impacted your community’s efforts?

Section 3. Program Implementation

We would like to ask you a few questions to help us understand more about your community’s efforts to reduce unsheltered homelessness.

CoC Staff

48. How do you use Coordinated Entry to match people experiencing unsheltered homelessness to housing providers?

[Probe for: access points, assessment/screenings, shelter, housing, support services, geographic differences (i.e. rural-suburban-urban)]

49. Are all of the individuals/families experiencing unsheltered homelessness matched to housing? If no, why not?

50. How is your CoC organized to provide services and housing to the unsheltered population?

a. How are outreach providers coordinated across partners/efforts?

b. How do you collaborate with your partners to coordinate services and referrals for unsheltered individuals?

51. How does the CoC determine al ocation of resources for people experiencing unsheltered homelessness within Montgomery County?

[Probe for: staffing, procedures to amend funding initiatives, local vs. federal resources]

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a. [If organization is minimally influential in determining allocation] How is this determined?

What additional resources would your organization like to see if you did have more

influence?

52. What were the key factors considered when deciding what investments to make and where those investments would be deployed/implemented?

53. What CoC policies or practices has your organization changed or implemented to support efforts to address unsheltered homelessness?

d. [If needed] What were the reasons for the changes?

e. How, if at all, are these policies and practices different for unsheltered individuals vs.

sheltered?

f. How have these changes affected the implementation of your efforts?

54. How has your CoC’s Coordinated Entry system changed to support efforts to address unsheltered homelessness?

a. What were the reasons for the changes?

b. How, if at all, is Coordinated Entry different for unsheltered individuals vs. sheltered?

c. How have these changes affected the implementation of your efforts?

55. How does your CoC connect clients who may not have appropriate documentation of chronic homelessness, for example, to housing? One example is housing these individuals through CoC-funded apartments.

56. How do HMIS (Homeless Management Information System) data inform your activities aimed at reducing unsheltered homelessness?

[Probe for: key metrics used, why these were chosen]

a. What other data sources inform your understanding of the unsheltered population and its needs? What have these data shown?

Housing and Service Providers

57. What supports or services does [your organization] offer to people experiencing unsheltered homelessness?

[Probe for: disability documentation, chronic homelessness documentation, transportation, other case management, financial assistance, basic needs, employment, benefits, health care and treatment]

58. How do you identify clients for each of the services you offer?

c. [If applicable] How are outreach services to people experiencing unsheltered homelessness organized and administered? [Probe for: regular outreach vs. in response to

complaint/concern]

d. How have your processes to identify or recruit individuals experiencing unsheltered homelessness changed since your efforts began? Why?

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59. How are individuals experiencing unsheltered homelessness screened/assessed (i.e., ViSPADT)?

[Probe for: vulnerability tools, other processes/tools]

a. How have screening and assessment changed since your efforts began? When? Why?

60. [Housing Providers Only] How do people experiencing unsheltered homelessness access emergency housing?

[Probe for: coercive strategies, outreach strategies, specific priority populations]

a. How long can they use emergency housing (e.g., other limitations, such as abstaining from drugs)?

b. How are they connected to more permanent housing resources (e.g., any support services to find permanent housing)?

c. What are the reasons people experiencing unsheltered homelessness do not use emergency housing? What are the primary barriers?

d. What exit strategies does your organization provide to support individuals as they leave emergency housing?

[Probe for: who provides these exit strategies, what services are included, what are the long-term strategies to keep individuals from returning to homelessness]

61. [Housing Providers Only] How are people experiencing unsheltered homelessness connected to Coordinated Entry and housing?

a. How does your CoC prioritize clients (e.g., vulnerability tools)?

[Probe for: Where do people on lower or middle end of scale end up?]

b. What services do clients receive to identify/locate housing (e.g., assistance with landlords, resources, other services)?

c. What types of vouchers/housing resources (e.g., PSH, transitional, scattered site, project based) are available to clients? Of these resources, which one is prioritized and for whom?

[Probe for: RRH used for unsheltered?]

d. What kinds of housing do clients receive (e.g., PSH, transitional, scattered site, project based)?

[Probe for: types of housing (shared, studio/efficiency), specific neighborhoods/locations]

e. How is tenant share of rent structured?

62. [Housing Providers Only] If housing is provided outside of the Coordinated Entry process, please describe how this process works for people experiencing unsheltered homelessness and the types of housing available.

63. [Service Providers Only] What opportunities do people experiencing unsheltered homelessness have to obtain housing?

a. What documents are required for these opportunities? What happens if they do not have these documents?

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b. What approaches or processes has your agency used to ensure critical needs are met?

64. How/when do participants stop receiving [housing/services] (i.e., time limits, financial eligibility)?

How often do you see this (i.e., how do they exit? what is available after?)?

65. How does your organization help participants who come from unsheltered situations stay housed (e.g., policies or supports)?

a. [Housing Providers Only] What types of support does your organization offer to address challenges with landlords (e.g., assistance engaging or recruiting landlords, educating landlords, offering financial incentives or securities]?

b. [Richmond: Housing Providers Only] What support is available for Rapid Re-Housing participants after they exit the program?

c. [Housing Providers Only] Do participants typically stay in the same housing when subsidies end? If no, why not?

66. [Housing Providers Only] If housing is provided outside of the Coordinated Entry process, please describe how this process works and the types of housing available.

Law Enforcement

67. How is your department organized to respond to calls related to homelessness?

a. Is there a specific team or set of officers that most often interact with people experiencing unsheltered homelessness?

b. When was it created and why?

68. What are the local policies and practices related to how law enforcement interacts with people experiencing unsheltered homelessness?

[Probe for: Variation by jurisdiction in county]

a. What, if any, policies promote alternatives to arrests, citations, or other punitive responses by law enforcement responding to individuals experiencing homelessness?

b. What training do the team/officers receive to interact with individuals experiencing homelessness?

69. How common is it for encampments to be closed by law enforcement?

a. Has there been any change in this practice? If yes, how so and for what reason? When did this practice change?

b. Are there areas where encampments are more of an issue?

70. Have you seen any major changes in the past few years in the number of people experiencing unsheltered homelessness in encampments or otherwise?

a. If so, what do you think were the reasons for these changes?

71. What collaborations do law enforcement and other first responders have with homelessness and other service providers?

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a. What role does law enforcement play in referring individuals experiencing unsheltered homelessness to housing programs or other services?

All Respondents

72. In your opinion, how successful have the approaches we’ve discussed today been in reducing unsheltered homelessness in Montgomery County?

a. What are the most successful components of your approach to reduce unsheltered homelessness?

b. What makes these activities successful?

73. What would make the approaches more successful in your community? What recommendations do you have for improvements?

a. What would be needed to make these changes?

74. What have been the biggest challenges in offering the approaches we’ve discussed today?

[Probe for: coordination, resources, policies, political will or buy-in, staffing, other, changing circumstances or guidance, engaging target population]

a. How have you worked to overcome these challenges?

75. What barriers do you still face in trying to house people experiencing unsheltered homelessness?

[Probe for: gaps in services]

76. How do your community’s efforts build on your baseline services or on previous initiatives, such as Ending Veteran Homelessness?

[Probe for: events, collaborations, processes]

Section 4. Partnerships (CoC Staff, Partners, Providers Only)

Next, I’d like to discuss the role of partnerships in supporting your approaches to reduce unsheltered homelessness.

CoC Staff

77. Has Montgomery County established or leveraged committees or other organized bodies to coordinate efforts to address homelessness? Please describe.

a. [If yes] When and why was the committee established?

b. [If yes] Who was involved in these committees/bodies?

c. [If yes] What was the role of the committee/body?

d. [If yes] How has the committee/body influenced your efforts to reduce unsheltered homelessness?

78. [Cross-reference information already provided; cycle through probes for each partner organization] Which partners do you consider to be your primary partners in addressing unsheltered homelessness in Montgomery County? Why?

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a. [If not already addressed] What services do they support?

b. How long has your organization partnered with [organization name]?

c. [If new] What was the primary reason for establishing this partnership?

d. Why is your partnership with [organization name] important to your efforts to reduce unsheltered homelessness?

e. How could your partnership be improved?

Housing and Service Providers/Law Enforcement Agency

79. Please describe your partnership with Montgomery County CoC.

a. How do you collaborate and communicate?

b. What are the primary activities that you work on together?

80. What works well in your partnership?

81. How could your partnership with Montgomery County CoC be improved?

Section 5. Lessons Learned and Sustainability (CoC Staff and Housing and Service Providers Only) I would now like to discuss how efforts made within your community to combat unsheltered homelessness could be replicated in other communities, and whether this approach is sustainable on a larger scale.

82. How has [organization name] used the data to determine the impact of your community’s efforts to reduce unsheltered homelessness?

[Probe for: specific metrics used, assessing or tracking reductions in unsheltered homelessness (tailor based on data calls under Component 1)]

83. How, if at all, does your CoC step down services in terms of transitioning participants to other service programs or long-term housing (i.e., for PSH, this can be Move-On)?

[Probe for: What is the main tool/program used? PSH? Any use of Rapid Re-housing for unsheltered homelessness?]

84. Do you believe the efforts to address unsheltered homelessness in Montgomery County would be appropriate for another community? Why or why not?

a. What considerations should be made before another community adapts the approach(es)?

85. What have you learned from implementing these approach(es) in Montgomery County?

a. What recommendations or suggestions would you make to another community working to address unsheltered homelessness?

86. To what extent do you believe your CoC wil be able to sustain the low rates of unsheltered homelessness, particularly in a declining economy? Please describe.

[Probe for: Vulnerability if state/local funding is reduced]

87. To what extent could your current approach be expanded?

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[Probe for: Is every provider using Housing First model, Rapid Rehousing]

a. What resources would be required to expand the approach?

b. [If appropriate] What would make the approach more sustainable (e.g., partnerships, finances)?

Section 6. Closing

88. Is there any other important information that will help us understand about your communities’

efforts to address unsheltered homelessness?

89. Those are all the questions we have. Is there anything else you’d like to share or reiterate for us to highlight in the case study?

We would like to thank you for taking time from your busy schedule(s) to speak with us today. Your answers have provided us with valuable insight into approaches that communities are implementing to address unsheltered homelessness. Should you have any additional thoughts that you would like to share, please feel free to contact us.

____________________________________________________________________________

Individuals with Lived Experiences

12. We understand that you’ve received services from [organization name]. Could you tell us a little about what they’ve done to help you (e.g., case management, treatment, transportation, budgeting, documentation required, housing assistance in terms of finding and keeping)?

13. What other organizations have helped you with services? What were those services like?

14. How did you hear about these programs/organizations?

[Probe for: recruitment/referral source – person, agency, location]

15. What services/help have you received from the program (e.g., case management, treatment)?

Now, I’d like to ask you a little more about what you thought of the organizations/programs/

services you got.

For each service:

a. Where do/did you receive services?

b. How easy are the services for you to get to?

16. What, if anything, makes it hard for you to receive services?

[Probe for: missing needed documents, not knowing what’s available or who to ask, health issues (behavioral, mental, physical), requirements (eligibility) for program, such as sobriety or criminal history, times services were offered]

17. What’s your current housing like? Would you change anything about your current housing?

18. [If needed; skip to probe if not needed] How has the program helped you with permanent housing?

[Probe for: assistance with paperwork, locating housing]

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a. [If housed through program] What type of housing do/did you live in?

b. [If housed through program] How did you choose your place?

c. [If housed through program] How long did/can you live there?

d. [If housed through program] What kinds of help did you receive when moving in?

e. [If no longer housed through program] Why did you decide to leave the housing?

f. [If never housed through program, probe for why]

19. Since you began the program with [organization], have you had to sleep outside?

a. What led to that?

20. What services did you need/want that you did not receive?

21. What did you like about the overall program? What was/has been the most helpful about the program?

22. What did you not like about the overall program? What would have been helpful for you to

[get/stay in] the housing?

We would like to thank you for taking time to speak with us today. Your answers have provided us with valuable insights. We would now like to ask for your email or mailing address to be able to send your $50 gift card. Would you like the gift card to be sent by mail or email?

[If mail] What is the address where you would like us to send the gift card?

[If email] What is the email address where you would like us to send the gift card?

We can send a $50 gift card from Walmart, Walgreens, Target, CVS, or Kroger. Where would you like your gift card to be from?

How may we follow up if we have any questions when sending the gift card?

If you have any questions or if you have any additional thoughts that you would like to share, please feel free to contact us by email at nmorrissey@2mresearch.com or by phone at 817-856-0898. You should expect your gift card within 1 to 2 weeks. Thank you again!

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SAN DIEGO’S COVID-19 EMERGENCY SHELTERS

MASTER INTERVIEW GUIDE

MASTER SEMI-STRUCTURED INTERVIEW GUIDE AND QUESTIONNAIRE:

Implementing Approaches to Address Unsheltered Homelessness

The U.S. Department of Housing and Urban Development (HUD) has contracted our company, 2M

Research, to conduct a series of studies on approaches that communities are implementing to address unsheltered homelessness. For our purposes, a person experiencing unsheltered homelessness is someone residing in a place not meant for human habitation, such as on the street or in cars, parks, sidewalks, or abandoned buildings. The study’s goal is to better understand the key components of your community’s approaches to addressing homelessness, the supporting partnerships involved, and other factors affecting sustainability and scalability.

In San Diego, we are specifically looking at shelter offered through Project Roomkey (now a part of Project Homekey), the increased street outreach, and the shelter offered at the convention center. For each of these efforts, we are specifically interested in how these efforts have impacted people experiencing unsheltered homelessness.

As part of this study, we are conducting interviews with key stakeholders to understand approaches to addressing unsheltered homelessness from various perspectives. For this interview, we are interested in the role your organization plays in your community’s efforts to address unsheltered homelessness, specifically since the onset of the COVID-19 pandemic. [If multiple respondents: Depending on how your agency is organized, different people may need to participate and answer different sections of the interview. ] The interview is scheduled to last up to 60 minutes. [CoC and Partner Staff: Responses to this interview wil be used for research purposes only and will NOT be used for compliance monitoring in any way. ] Do you have any questions for us about the study?

Consent to Participate

We would like to interview you as a key informant who has been involved in supporting and/or implementing Project Roomkey (now a part of Project Homekey), the increased street outreach, and the shelter offered at the convention center. We hope that you will be open in our conversation today. The information you give us is crucial to improving support provided to individuals and families experiencing unsheltered homelessness. While we will make every effort to protect your privacy, HUD staff will read our report; therefore, we cannot guarantee complete anonymity, given your role in the CoC efforts and the specific focus of the study. However, we wil not use your name in reporting what we have learned during this interview, and we will combine your responses with the responses of others participating in interviews as part of this study. Would you still like to participate in the study?

Individuals with Lived Experience Only—Consent to Participate

We would like to interview you as an individual who has been a part of the [description of program/partnership name] to learn about your experiences, both good and bad, with the program.

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We hope that you will be open with your thoughts about the program because the information you provide is important to improving community programs for those in need. We will make every effort to protect your privacy: Your name or other identifying information wil not be used in reporting what we have learned during this interview. However, because staff referred you to participate in this discussion, they may be able to identify your experiences as they are described in the report. We will not use your name in reporting what we have learned during this interview, and we will combine your responses with the responses of others who are participating in interviews as part this study.

To thank you for your time today, we will send you a $50 gift card; once we complete the interview, we wil ask for your information to send you the gift card. Would you still like to participate in the interview?

Permission to Record

We wil take notes during the interview. We would like to record the conversation so that we can make sure our notes are correct. We will transcribe the recording and wil remove your name from the transcript. We will not share the recording outside of our 2M study team, and we will destroy it at the end of the study. Is it okay that we take notes and record this interview?

 If interviewee agrees to be recorded:

o Thanks. Let’s get started. Now, we are going to turn on the recorder (BEGIN

RECORDING). Now that we have the recording started, can you please confirm that you would like to participate?

 If interviewee declines:

o Okay, that is not a problem.

Section 1. Introduction

I’d first like to understand how you’ve been involved in the community’s efforts to address homelessness, specifically for people experiencing unsheltered homelessness.

90. Would you please start by telling us a little bit about yourself as it relates to your involvement with

[program component]? What is your role in supporting [program component]?

a. How has your role changed as a result of COVID?

[Probe for: which services do you support]

Section 2. Market Challenges, Policies, and Local Context

Next, I’d like to go into a bit more detail about your local unsheltered population and their needs, policies that impact efforts to address homelessness, and your perceptions of public attitudes toward homelessness.

91. What changes (e.g., size, location) have you seen in the unsheltered homeless population since the onset of COVID? What were the reasons for these changes?

a. How have these changes influenced your organization’s approaches to serving the population?

92. We understand that affordable housing is in short supply. We’re interested in hearing some about the other needs, or vulnerabilities, of your local unsheltered homeless population. What do you

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see as the key needs of people experiencing unsheltered homelessness in San Diego since the onset of COVID? How are these needs different than before COVID? How are those needs different than the needs of the sheltered population?

93. What resources are available to meet the needs of people experiencing unsheltered homelessness during COVID?

a. Since COVID began, how has your organization changed the resources/services offered to people experiencing unsheltered homelessness?

b. How do these resources build on the resources/services that were available before COVID?

Housing and Service Providers

94. What state or local policies have influenced the implementation of your services targeting the unsheltered homeless population? How have they influenced implementation?

a. What policies indirectly influenced services available to the unsheltered population? That is, what policies were not specifically intended for individuals experiencing homelessness but have benefited them in some way?

95. How have policing practices influenced the implementation of your efforts to reduce unsheltered homelessness?

[Probe for: punitive policies against encampments, move along, changes to sweeps due to COVID]

CoC Staff

96. How did the regional unsheltered policy set the stage for your organization’s work to address unsheltered homelessness during COVID?

a. How is the policy driving different responses within San Diego County?

b. How did the policy influence the resources available to your efforts?

c. How did the policy influence the organizations who were involved?

97. How did San Diego’s 2019 Community Action Plan on Homelessness influence your efforts to reduce homelessness during COVID?

a. How did the policy influence the resources available to your efforts?

b. How did the policy influence the organizations that were involved?

98. What state-level policies have been most influential for your community’s response to COVID-related unsheltered homelessness? These policies may include those that were not intended specifically for the homeless population but have had an effect on the homeless population.

a. How have these policies affected your design and/or implementation?

Community-Based Stakeholders (e.g., law enforcement)

99. In general, how did you approach the issue of unsheltered homelessness prior to COVID? How has this changed over time?

100. In general, what was the public’s attitude toward unsheltered homelessness prior to COVID?

How has this attitude changed over time?

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101. How has the public attitude toward homelessness affected your community’s efforts to address unsheltered homelessness during the COVID pandemic?

Section 3. Program Implementation

We would like to ask you a few questions to help us understand more about your community’s efforts to reduce unsheltered homelessness since the onset of COVID, starting with some background on your existing resources.

Outreach Staff

102. How did your community coordinate and leverage resources that were available prior to COVID

to implement your response to COVID?

a. How do your current (during COVID) outreach practices build on the practices used before COVID?

b. How has your organization had to change outreach strategies due to COVID? Why?

103. What are the primary outreach strategies targeting people experiencing unsheltered homelessness?

a. Which strategies have worked well? Why?

b. What have been the challenges associated with these strategies during COVID?

104. What is the process like for referring individuals experiencing unsheltered homelessness to Project Roomkey?

a. Who is referred?

b. What is required for the referral?

c. What are the challenges in making these referrals?

d. What makes this process easy?

105. What is the process for referring individuals experiencing unsheltered homelessness to the temporary shelter?

a. Who is referred?

b. What is required for the referral?

c. What are the challenges to making these referrals?

d. What makes this process easy?

Project Roomkey and Temporary Shelter

106. How is the existing (pre-COVID) shelter system operating with Project Roomkey and the convention center offering shelter?

a. Why was the existing shelter system not suitable for the COVID response?

b. What was the capacity of the existing shelter system like before COVID?

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c. What is the capacity now? What are the impacts of diverting resources (staff and services) to the COVID initiatives (Project Roomkey and convention center)?

d. How are the COVID initiatives (Project Roomkey and convention center) impacting the existing shelters?

Temporary Shelter (Convention Center)

107. How did your community coordinate and leverage resources that were available prior to COVID

to implement your response to COVID?

a. How was your shelter system reorganized to address unsheltered homelessness during COVID?

b. How was your existing shelter system (prior to COVID) impacted by COVID?

108. How do people experiencing unsheltered homelessness access the temporary shelter?

a. How are individuals prioritized for the shelter? Are individuals experiencing unsheltered homelessness prioritized?

[Probe for: encampment situation risks (i.e., density, location, hygiene)]

b. What is required for them to stay in the shelter?

c. How long can they stay in the shelter?

d. What has made it easy for individuals experiencing unsheltered homelessness to access the shelter?

e. What has made it hard for individuals experiencing unsheltered homelessness to access the shelter?

109. What is the intake process like?

a. How has this been streamlined/modified compared to the process before COVID?

b. What is working well?

c. What has been more challenging in this process?

110. How did your community implement CDC and local health department guidelines in the temporary shelters?

[Probe for: social distancing, education on COVID, sanitizers, cleaning routines, laundry and bedding changes, monitoring of movements during isolation]

a. What challenges have you encountered when implementing safety protocols?

111. What services are available at the temporary shelter?

[Probe for: co-location vs. offsite services, disability documentation, chronic homelessness documentation, transportation, other case management, financial assistance, basic needs, employment, benefits, health care and treatment]

a. What services are missing? Why are these services important?

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b. What services have been key to meeting the needs of people who came from unsheltered situations?

c. Why has it been important to offer these services on site?

d. What services are available to people when they leave?

112. How have coordination of services and referral processes for people experiencing unsheltered homelessness changed as a result of COVID?

113. How are people who came from unsheltered situations connected to long-term housing opportunities?

a. What types of housing programs are available after they leave the shelter?

[Probe for: voucher-based programs, project-based programs, master lease programs like PSH, Veterans Affairs Supportive Housing (VASH), Housing Authority (HCV)]

b. What kinds of housing do they receive?

c. How is the rent structured?

d. What types of support do clients receive to stay housed once they are housed? What exit strategies does your organization provide to support individuals as they leave the temporary shelter?

e. How successful have you been in your efforts to house these individuals?

f. What has made these efforts successful?

g. What has been challenging when trying to house these individuals?

Project Roomkey

114. How are people experiencing unsheltered homelessness referred to Project Roomkey (hotels)?

a. How are people prioritized? Are individuals coming from unsheltered situations prioritized?

b. What are the requirements for individuals to be able to stay in the facilities?

c. How long can individuals stay in the facilities?

d. What are the barriers to accessing the facilities?

e. What has made it easy for individuals experiencing unsheltered homelessness to access the facilities?

115. What is the intake process like for Project Roomkey?

a. What is working well?

b. What has been more challenging in this process?

116. What services are offered in the facilities?

[Probe for: co-location vs. offsite services, disability documentation, chronic homelessness documentation, transportation, other case management, financial assistance, basic needs, employment, benefits, health care and treatment]

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a. What services are missing? Why are these services important?

b. What services have been key to meeting the needs of those who have formerly experienced unsheltered homelessness?

c. Why has it been important to offer these services on site?

d. What services are available to people when they leave?

117. How are people who came from unsheltered situations connected to long-term housing opportunities?

a. How are they connected to Coordinated Entry?

b. What types of housing programs are available after they leave the facility?

[Probe for: voucher-based programs, project-based programs, master lease programs like PSH, VASH, Housing Authority (HCV)]

c. What kinds of housing do they receive?

d. How is the rent structured?

e. What types of support do clients receive to stay housed once they are housed? What exit strategies does your organization provide to support individuals as they leave the hotel facilities?

f. How successful have you been in your efforts to house these individuals?

g. What has made these efforts successful?

h. What has been challenging when trying to house these individuals?

CoC Staff

118. How did your organization determine what investments to make and where those investments would be deployed/implemented?

a. What were the funding sources of these investments? Why?

119. How has your CoC planned to transition individuals/families out of the temporary shelter and hotel (Project Roomkey) into more permanent shelters?

a. What services are available to support this?

b. What types of housing programs are available to people experiencing unsheltered homelessness after they exit the emergency shelter or hotel (Project Roomkey)?

[Probe for: voucher-based programs, project-based programs, master lease programs like PSH, VASH, Housing Authority (HCV)]

c. How successful have you been in your efforts to house these individuals? What has made these efforts successful?

d. What has been challenging in these efforts?

120. What is the process for matching unsheltered individuals and families to permanent housing once they’ve been housed in the temporary shelter or the hotel (Project Roomkey)?

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[Probe for: role of Coordinated Entry, access points, assessment/screenings, general availability]

a. What is the difference in services offered to sheltered vs. unsheltered?

121. What specific policies and practices have you changed or implemented to support your efforts to address unsheltered homelessness during COVID? How have these policies and practices affected your efforts related to the non-congregate shelter?

[Probe for: streamlined intake, reduced documentation, data sharing

a. [If needed] What were the reasons for the changes?

b. How, if at all, are these policies and practices different for people sleeping in sheltered vs.

unsheltered situations?

Law Enforcement

122. How is your department organized to respond to calls related to homelessness?

a. Is there a specific team or set of officers that most often interacts with people experiencing homelessness?

[Probe for: role of engagement team

b. How have your department’s response protocols changed as a result of COVID?

123. What are the local policies and practices related to how law enforcement interacts with people experiencing unsheltered homelessness?

a. What, if any, policies promote alternatives to arrests, citations, or other punitive responses by law enforcement responding to individuals experiencing homelessness?

b. How has this changed as a result of COVID?

124. How common is it for encampments to be closed by law enforcement?

a. Has this practice changed since the onset of COVID? If yes, how so and why?

125. Have you seen any major changes in the number of people experiencing unsheltered homelessness in encampments or otherwise since the onset of COVID?

a. If so, what do you think were the reasons for the changes? [Probe for: individuals moving locations, changes in resources, changes in access to shelters]

126. What type of training has your team/officers received to interact with people experiencing homelessness?

a. When did you receive this training?

b. What was the impact on your practices?

c. What training has been provided since the onset of COVID?

127. What collaborations do law enforcement and other first responders have with homeless and related service providers?

a. Have you had any new collaborations since the onset of COVID?

All Respondents

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128. In your opinion, how successful have these approaches been at reducing or helping unsheltered homelessness in San Diego County during COVID?

129. What are the most successful aspects of [San Diego’s/your organization’s] approach to reduce unsheltered homelessness?

130. What has made these activities successful?

131. What would make the approaches more successful? What recommendations do you have for improving the activities?

a. What would be needed to make these changes?

132. What have been the biggest challenges to the approaches we’ve discussed today?

[Probe for: coordination, resources, policies, political will or buy-in, staffing, changing circumstances or guidance, engaging target population]

a. What barriers does your organization still face in trying to house unsheltered individuals during COVID? How, if at all, are you working to address these barriers?

[Probe for: gaps in services, future plans/modifications]

133. [If needed] How do your community’s efforts leverage your experience or practices used in initiatives, such as Ending Chronic or Veteran Homelessness?

[Probe for: pre-COVID activities, events, collaborations, processes]

Section 4. Partnerships (CoC Staff, Partners, Providers Only)

134. Has your organization established or leveraged committees (or other organized bodies) to coordinate efforts to address homelessness in response to COVID?

[Probe for: any new partners during COVID and why]

a. [If yes] When and why were they established?

b. [If yes] Who was involved in these committees/bodies?

c. [If yes] What was the role of the committee/body?

d. [If yes] How have they influenced your efforts to reduce unsheltered homelessness?

CoC Staff

135. [Cross-reference information already provided; cycle through probes for each partner organization] Which partners do you consider to be your primary partners in implementing your community’s response to COVID? Why?

[Probe for: relationship with VA, public health authority]

a. [If not already addressed] What services does [partner name] support?

b. How long has your organization partnered with [partner name]?

a. [If new] What was the primary reason for establishing the partnership with [partner name]?

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b. [If existing] How did your response change your relationship/collaboration with

[organization name]?

c. Why is your partnership with [organization name] important to your efforts to reduce unsheltered homelessness?

d. How could your partnership with [partner name] be improved?

Housing and Service Providers

136. Please describe your partnership with San Diego Housing and Community Development (HCDS).

a. How do you collaborate and communicate with HCDS?

b. What are the primary activities that you work together on?

137. What works well in your partnership with HCDS?

138. How could your partnership with HCDS be improved?

Section 5. Lessons Learned and Sustainability

139. How has [organization name] used the data on unsheltered homelessness to inform program activities?

[Probe for: HMIS, assessing or tracking reductions in unsheltered, decreasing/scaling activities, adding services; [tailor based on data calls under Component 1]]

a. What were the key metrics used? What have they shown?

140. Do you believe the efforts we’ve discussed today would be appropriate to expand? Why?

a. What would be needed to expand the housing/services/approaches?

141. Do you believe the efforts we’ve discussed today would be appropriate for another community?

Why or why not?

b. What considerations should be made before another community adapts the approach(es)?

142. What have you learned from implementing these approaches? What do you wish you knew when you started planning these approaches?

a. What recommendations or suggestions do you have for another community working to address unsheltered homelessness?

143. How has your agency been able to sustain these activities through the COVID pandemic so far?

144. To what extent do you think the efforts we’ve discussed today will be sustained throughout the COVID pandemic?

145. To what extent do you think the efforts we’ve discussed today are sustainable after the COVID

pandemic has ended? Why?

[Probe for: specific efforts (i.e., Project Homekey), facility procurement, staffing/partnership models, role of zoning laws]

a. [If no] What could be changed to make these efforts sustainable?

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b. [If yes] How wil these efforts be sustained over time?

Section 6. Closing

146. Is there any other important information that will help us understand your community’s efforts to address unsheltered homelessness?

147. Those are all the questions we have. Is there anything else you want to share or reiterate?

We would like to thank you for taking time from your busy schedule(s) to speak with us today. Your answers have provided us with valuable insight into approaches that communities are implementing to address unsheltered homelessness. Should you have any additional thoughts that you would like to share, please feel free to contact us by email at nmorrissey@2mresearch.com.

Individuals with Lived Experiences

23. We understand that you received services while you were at [shelter name]. Could you tell us a little about what they’ve done to help you? (e.g., case management, treatment, transportation, budgeting, documentation required, housing assistance in terms of finding and keeping) 24. What other organizations have helped you with services? What were those services like?

25. How did you hear about these programs/organizations? [Probe for: recruitment/referral source—person, agency, location]

26. Now, I’d like to ask you a little more about what you thought of the

organizations/programs/services you got. [For each service:]

a. Where do/did you receive services?

b. How easy is it for you to get to the services?

27. What, if anything, makes it hard for you to receive services?

[Probe for: missing needed documents, not knowing what’s available or who to ask, health issues (behavioral, mental, physical), requirements (eligibility) for program—such as sobriety or criminal history, times services were offered]

28. What was your experience like in the [temporary shelter or Project Roomkey]?

a. What made you decide to seek shelter at [temporary shelter or Project Roomkey]?

b. What did you like about it?

c. What didn’t you like about it?

d. What would you change about it? What services did you need/want that you did not receive?

e. How do you think the program could be improved? [Probe for: services, coordination of services, communication]

29. How has the program helped you with permanent housing?

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[Probe for: assistance with paperwork, locating housing]

a. [If housed through program] What type of housing do/did you live in?

b. [If housed through program] How did you choose your place?

c. [If housed through program] How long did/can you live there?

d. [If housed through program] What kinds of help did you receive when moving in?

e. [If no longer housed through program] Why did you decide to leave the housing?

f. [If never housed through program, probe for why]

30. Since you began the program with [organization], have you been in a situation where you experienced homelessness?

a. What led to that?

31. What services did you need/want that you did not receive?

32. What did you like about the overall program? What was/has been most helpful about the program?

33. What didn’t you like about the overall program? What would have been helpful for you to

[get/stay in] the housing?

34. How do you think the program could be improved? [Probe for: services, coordination of services, communication]

35. How do you think COVID (Coronavirus) has influenced your life?

We would like to thank you for taking time to speak with us today. Your answers have provided us with valuable insights. We would now like to ask for your email or mailing address, so we can send your $50 gift card. Would you like the gift card to be sent by mail or email?

[If mail] What is the address where you would like us to send the gift card?

[If email] What is the email address where you would like us to send the gift card?

We can send a $50 gift card from Walmart, Walgreens, Target, CVS, or Kroger. Where would you like your gift card to be from?

How may we follow up if we have any questions when sending the gift card?

If you have any questions or if you have any additional thoughts that you would like to share, please feel free to contact us by email at nmorrissey@2mresearch.com or by phone at 817-856-0898. You should expect your gift card to arrive within 1 to 2 weeks. Thank you, again!

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APPENDIX E. LESSONS LEARNED FROM

INTERVIEWS WITH INDIVIDUALS WITH

LIVED EXPERIENCE

The study team conducted four interviews with individuals with lived experience with homelessness from the three programs. In this section, we outline the process used to engage the individuals, key considerations, and other lessons learned from the recruiting and interviewing experience. These reflections may inform future research conducted remotely (not in person) with the people experiencing homelessness or people who recently exited homelessness. These findings are based on a very smal ( n=4) convenience sample and may not be appropriate for all research conducted with individuals with lived experience with homelessness.

RECRUITMENT AND LOGISTICS

The study team initially requested the CoC point of contact to nominate one or more service providers that could connect the study team to an individual with lived experience in the program of interest. The study team specified that this individual should have unsheltered experience and may or may not be currently housed. The study team then reached out to the selected service provider staff; in general, service provider staff were receptive to the request and requested specific details related to the interview, such as duration, mode of delivery, and specific topics to provide to the client. The staff then contacted specific case managers of clients they thought would be interested in participating.

Initially, the study team received minimal responses about the inquiry from individuals with lived experience. The study team opted to increase the incentive from $50 to $100. With that increase, the study team noticed more engagement from individuals with lived experiences and more support from their case managers who helped in recruitment. The study team attempted to recruit six individuals with lived experience; ultimately four completed interviews, one person remained unresponsive, and one person refused. The individual who refused to participate was initial y contacted and given information about the study by his or her case manager. The study team then contacted the individual via phone.

Upon answering, the individual was doubtful regarding the study team’s intentions and feared the study was not legitimate. Although the study team attempted to alleviate any concerns with further information and context, the individual declined to participate but noted that he or she might reconsider after speaking with the case manager. The study team worked with the case manager to help resolve any concerns of the individual with lived experience but was unable to connect again. That experience may indicate the importance of oversampling respondents and involvement of case managers in the recruitment process because some people may refuse or be hesitant to engage in interviews without additional support.

With the four completed interviews, the case managers were successful in bridging the communication between the study team and the respondent. The study team found that case managers engaged more after completing their interview for the study (if applicable). In some instances, the initial point of contact (case manager) followed up with the study team after discussing with colleagues which client

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may be wil ing to participate or interested in participating. That caused delays in receiving the contact information for the potential client respondents. The study team began recruitment on August 31, 2020; completed the first interview with a person with lived experience on September 23, 2020; and completed the fourth and final interview on October 30, 2020; for reference, the CoC and partner interviews included in the study concluded on October 21, 2020. Due to COVID-19, the case managers had less frequent in-person interactions with clients than was typical practice before COVID-19, which may have affected the recruitment timeline. Overall, that shows that recruitment, including working with case managers, requires more time and effort than recruitment of other types of stakeholders.

The study team reached out to clients by email, text, and phone call; one client preferred to communicate by text, one by phone, and two by email. One participant had recently lost his phone on a bus and sporadically communicated by email, which resulted in delays in conducting the interview.

Ultimately, the case manager visited the client in person for a reason unrelated to the study, and the study team was able to conduct an impromptu telephone interview.

For the other three interviews, the study team scheduled a meeting with each of the individuals with lived experience at a time convenient for the client. The study team did not send a meeting invitation or call-in information; rather, the team called the individual at the selected time to avoid any potential issues with the call-in information associated with a telephone meeting. The interviews were scheduled within 1 to 3 days of making the initial contact with the client; the study team believed that would reduce the potential for the respondent to forget the appointment. None of those interviews had to be rescheduled.

The interviews lasted from 25 to 50 minutes on the basis of the level of detail provided by the individual.

The sample included two men and two women, who were assigned the same point of contact from the study team to conduct the recruitment and interview. To mitigate any potential issues related to gender dynamics, female respondents were assigned a female interviewer.

At the end of the interview, the interviewer offered five options for gift cards to national retailers that could be delivered by mail or by email. The study team offered options to ensure that people who were experiencing unsheltered homelessness at the time of the interview could select a store that was conveniently located for them, such as a CVS in a downtown district, and would be able to receive gift cards if a mailing address was not accessible; however, all respondents included in this sample were housed at the time of the interview. The study team provided gift cards to the participants by emailing a digital version to one participant and by mailing physical cards to the home addresses of the other three participants.

INTERVIEW GUIDE CONSIDERATIONS

The interview guide used with individuals with lived experience was constructed to be appropriate for all reading and comprehension levels, feel conversational for the respondent, avoid unnecessary formalities, and promote the establishment of trust between the interviewer and the respondent. First, the study team streamlined the consent to focus on the study’s goals, how the information will be used, and other statements required for subject ethical protections. The study team provided much of that information during the recruitment phase to ensure that the respondent was aware of and comfortable with the information covered in the consent before the scheduled meeting. The study team used plain

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language in the consent and conducted a readability test to determine the accessibility of the language included.

The interview guide began with foundational questions about the services received, including what services were received, what organizations offered them, and when respondents received them and then moved into more specific questions about the respondent’s level of satisfaction, which could be more controversial if a respondent were dissatisfied with services. The guide was purposefully developed to go from high-level questions to more sensitive questions related to satisfaction with services and current housing situation in anticipation of the respondent potentially being unsheltered at the time of the interview. The study team believed that the progression of questions would facilitate the building of rapport before the interviewer asked potentially sensitive questions. Al language included in the scripted questions read at grade-level 4 to ensure comprehension. Based on the initial success of the guide (described below), the guide did not require ongoing revisions, which would have been appropriate if questions were not well received by respondents, particularly because this guide was not pilot tested.

INTERVIEWING REFLECTIONS

Overall, the study team incorporated procedures to be sensitive to respondents’ potential concerns or barriers to participating in the interviews to ensure the success of the data collection. Interviewers clearly stated expectations for the interview in advance, including having access to a phone and a quiet place to do the interview; asking advance permission for a notetaker to be on the call; asking permission for recording during the call; and remaining sensitive to the length of the interview time. Respondents were forthcoming in sharing their experiences and stories that contributed to their homelessness, including job loss, drug use, other financial issues, symptoms of mental health conditions, and other barriers to maintaining housing; respondents seemed to consider sharing this context important before proceeding to answer questions about their reflections on the services received. We speculate that the presence of the case manager for one respondent, whom we were not able to reach without the assistance of the case manager, may have influenced respondent’s answers. During the interview, the case manager listened to the questions, and the study team overheard some prompting when the respondent needed help recalling the names of programs and the amount of financial assistance provided.

The study team supplemented scripted questions with probes, as needed, to collect detailed data; some respondents required more probing, whereas others responded to questions and probes and covered most topics with little direction. Opening, foundational questions seemed to be effective in setting expectations for the content covered in the conversation; very few instances of redirection (that is, getting the respondent back on topic) were required, which was slightly unexpected on the basis of the study team’s previous experiences with individuals with lived experiences with homelessness and community stakeholders. Respondents did not seem apprehensive about any questions asked and answered all questions openly (that is, no refusals). Overall, our approach was not unlike how we typically interview community stakeholders; interviewers maintained a respectful, empathetic, and conversational tone throughout the interview to build rapport with respondents and allowed them to share thoughts they believed to be relevant to the questions asked.

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APPENDIX F. SUMMARY STATISTICS FOR

PROGRAM-LEVEL DATA FOR THE THREE

CASE STUDIES

GREATER RICHMOND CONTINUUM OF CARE

SUMMARY STATISTICS OF PROGRAM-LEVEL DATA

Exhibit F.1. Demographic Characteristics of RRH Single Adult Clients from Unsheltered Situations, 2018 to 2019

Demographic Characteristics

Age (Mean)

Average age at time of project start (in years)

50.3

Total number of responses

174

Age (Range)

18–24 years (%)

4.6

25–34 years (%)

11.5

35–44 years (%)

16.1

45–54 years (%)

20.7

55–64 years (%)

33.9

65+ years (%)

13.2

Total number of responses

174

Gender

Male (%)

82.8

Female (%)

17.2

Total number of responses

174

Race

African-American (%)

67.8

White (%)

31.6

Native American (%)

0.6

Total number of responses

174

Ethnicity

Hispanic (%)

4.6

Non-Hispanic (%)

95.4

Total number of responses

174

Veteran Status

Yes (%)

53.5

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Demographic Characteristics

No (%)

46.5

Total number of responses

174

Disability Status

Yes (%)

78.7

No (%)

21.3

Total number of responses

174

Type of Disability

Mental health problem (%)

38.7

Alcohol and drug abuse (%)

24.1

Physical/medical (%)

18.9

Chronic health condition (%)

11.7

Developmental (%)

4.4

HIV/AIDS (%)

2.2

Total number of responses

137

Client Receives Cash Income

Yes (%)

66.1

No (%)

33.9

Total number of responses

174

Income Amount

Mean monthly income (in dollars)

966.5

Total number of responses

113

Source of Income†

Alimony or other spousal support (%)

0.9

Child support (%)

1.8

Earned income (%)

34.5

Retirement/pension income from another job (%)

1.8

Veterans Affairs (VA) service-connected disability compensation (%)

17.7

VA non-service-connected disability pension (%)

8.0

Worker’s compensation (%)

0.9

Retirement from social security (%)

7.1

Supplemental Security Income (SSI) (%)

25.7

Social Security Disability Insurance (SSDI) (%)

18.6

Nonfinancial resources

5.3

Other (%)

3.5

Total number of responses

113

Client Receives Non-Cash Benefits

Yes (%)

40.8

No (%)

59.2

Total number of responses

174

Source of Non-Cash Benefits

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Demographic Characteristics

Supplemental Nutrition Assistance Program (%)

100.0

Total number of responses

71

† Participants reported multiple categories; percentages will not sum to 100.

Source: Greater Richmond Continuum of Care’s Homeward Community Information System, January 1, 2018, to December 31, 2019.

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Exhibit F.2. Outcome Statistics of RRH Single Adult Clients from Unsheltered Situations, 2018 to 2019

Project Outcome Characteristics

Length of Stay in Project from Project Start Date (in Days)

Minimum

2

Median

168

Mean

217

Maximum

1,327

Total number of responses

174

Length of Stay in Project from Move-In Date (in Days)

Minimum

0

Median

107

Mean

181

Maximum

1,454

Total number of responses

174

Destination After Project Exit†

Rental by client with no subsidy (%)

33.6

Rental by client, with RRH or equivalent subsidy (%)

25.8

Rental by client, with VASH housing subsidy (%)

21.9

Staying or living with family/friend (%)

5.2

Rental by client, with other ongoing housing subsidy (%)

1.9

Jail, prison, juvenile detention facility (%)

3.2

Deceased (%)

1.3

No exit interview completed (%)

1.3

Hotel/motel paid for with ES voucher (%)

1.3

Substance abuse treatment facility (%)

0.7

Permanent housing for formerly homeless persons (%)

0.7

Other (%)

1.9

Unknown (%)

1.3

Total number of responses

155

ES = emergency shelter; RRH = rapid re-housing; VASH = Veterans Affairs Supportive Housing.

† Percentages are based on clients that had exited the programs as of December 31, 2019.

Source: Greater Richmond Continuum of Care’s Homeward Community Information System, January 1, 2018, to December 31, 2019.

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MONTGOMERY COUNTY CONTINUUM OF CARE

SUMMARY STATISTICS OF PROGRAM-LEVEL DATA

Exhibit F.3. Demographic Characteristics of Permanent Housing Clients from Unsheltered Situations, 2018 to 2019

Demographic Characteristics

Age (Mean)

Average age at time of project start (in years)

50.1

Total number of responses

301

Age (Range)

18–24 years (%)

3.0

25–34 years (%)

11.6

35–44 years (%)

15.0

45–54 years (%)

29.2

55–64 years (%)

30.0

65+ years (%)

11.3

Total number of responses

301

Gender

Male (%)

70.4

Female (%)

29.2

Transgender (%)

0.3

Total number of responses

301

Race

African-American (%)

54.2

Asian (%)

2.7

White (%)

42.5

Native American (%)

0.3

Pacific Islander (%)

0.3

Total number of responses

301

Ethnicity

Hispanic (%)

12.7

Non-Hispanic (%)

87.3

Total number of responses

300

Veteran Status

Yes (%)

6.1

No (%)

93.9

Total number of responses

295

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Demographic Characteristics

Percent

Disability Status

Yes (%)

89.2

No (%)

10.8

Total number of responses

297

Total number of months homeless in the past 3 years

1–3 months (%)

13.5

4–6 months (%)

4.9

7–9 months (%)

1.5

10–12 months (%)

4.1

12+ months (%)

76.0

Total number of responses

267

Chronic Homelessness

Yes (%)

71.4

No (%)

28.6

Total number of responses

297

Source: Montgomery County Continuum of Care, January 1, 2018, to December 31, 2019.

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Exhibit F.4. Outcome Statistics of Permanent Housing Clients from Unsheltered Situations, 2018 to 2019

Project Characteristics

n=301

Project Type

PH–Housing with services (%)

1.3

PH–Permanent supportive housing (%)

89.7

PH–Rapid re-housing (RRH) (%)

9

Number of responses

301

Total Length of Stay in Project (in Days)

Minimum

10

Median

698

Mean

867

Maximum

6994

Number of responses

301

Destination After Project Exit†

Rental by client with no subsidy (%)

15.6

Deceased (%)

15.6

Jail, prison, juvenile detention facility (%)

10.9

Staying or living with family/friend (%)

9.4

Permanent housing for formerly homeless persons (%)

9.3

Rental by client, with other ongoing housing subsidy (%)

7.8

No exit interview completed (%)

6.3

Other (%)

6.3

Place not meant for habitation (%)

4.7

Long-term care facility or nursing home (%)

3.1

Rental by client, with RRH or equivalent subsidy (%)

1.6

Emergency shelter (%)

1.6

Hospital or other residential non-psychiatric facility (%)

1.6

Substance abuse treatment facility (%)

1.6

Unknown (%)

4.7

Total number of responses

64

† Percentages are based on clients that had exited the programs as of December 31, 2019.

Source: Montgomery County Continuum of Care, January 1, 2018, to August 31, 2019.

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SAN DIEGO’S COVID-19 EMERGENCY SHELTERS

SUMMARY STATISTICS OF PROGRAM-LEVEL DATA

Exhibit F.5. Demographic Characteristics of Convention Center and Temporary Lodging Program Clients from Unsheltered Situations, 2019

Existin

Temporary COVID-19 Shelter

g

Overall

Resources

Shelter

1

Demographic Characteristics

Convention

Temporary Lodging

System

( n=

Center

Program

(n=

2,045)

( n=1,869)

( n=209)

3,192)

Age (Mean)

Average age at time of project start (in years)

48.4

47.5

46.5

57.8

Total number of responses

3,190

2,044

1,869

208

Age (Range)

18–24 years (%)

4.7

4.9

5.3

1.0

25–34 years (%)

15.2

15.9

16.8

5.3

35–44 years (%)

17.3

18.9

20.1

8.7

45–54 year (%)

23.3

24.7

25.7

15.4

55–64 years (%)

29.0

26.8

25.5

39.9

65+ years (%)

10.6

9.0

6.6

29.8

Total number of responses

3,190

2,044

1,869

208

Gender

Male (%)

75.7

73.6

74.5

65.2

Female (%)

23.9

25.9

25.1

33.3

Transgender (%)

0.4

0.4

0.3

1.0

Gender nonconforming (%)

0.1

0.1

0.1

0.5

Total number of responses

3,190

2,043

1,869

207

Race

African-American (%)

26.0

26.3

27

16.8

Asian (%)

1.9

1.7

1.9

0.0

White (%)

64.7

64.6

63.6

75.8

Native American (%)

2.5

2.4

2.6

1.1

Pacific Islander (%)

1.0

1.0

1.1

0.0

Multiracial (%)

3.9

4.0

3.8

6.3

Total number of responses

3,169

2,026

1,869

190

Household Type

Single adult (%)

49.2

50.6

50.7

49.8

Indeterminable household (%)

50.8

49.4

49.3

50.2

Total number of responses

3,192

2,045

1,869

209

Ethnicity

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FINAL RESEARCH REPORT

Hispanic (%)

22.6

22.8

23.6

14.1

Non-Hispanic (%)

77.4

77.2

76.4

85.9

Total number of responses

3,174

2,027

1,869

191

Veteran Status

Yes (%)

18.3

15.5

15.8

12.6

No (%)

81.7

84.5

84.2

87.4

Total number of responses

3,168

2,024

1,867

190

Disability Status

Yes (%)

62.9

63.1

62.7

85.4

No (%)

37.1

36.9

37.3

14.6

Total number of responses

3,032

1,889

1,851

41

Type of Disability†

Mental health problem (%)

40.6

68.4

68.3

71.4

Physical (%)

28.5

41.6

41.2

54.3

Chronic health condition (%)

32.8

48.7

48.1

65.7

Developmental (%)

6.7

11.4

11.3

14.3

Total number of responses

3,026

1,192

1,160

35

Income Amount

1,115.

Mean monthly income (in dollars)

954.8

944.3

938.0

6

Total number of responses

1,155

673

650

25

Source of Income†

Earned income (%)

10.8

7.1

7.1

8.0

General Assistance (%)

11.8

12.0

12.5

0.0

Unemployment income (%)

3.2

3.0

2.9

4.0

Temporary Assistance for Needy Families (%)

0.3

0.3

0.3

0.0

Retirement/pension income from another job

1.9

1.6

1.5

4.0

(%)

VA service-connected disability compensation

9.6

7.3

7.2

8.0

(%)

VA non-service-connected disability pension

3.1

2.7

2.8

0.0

(%)

Worker’s compensation (%)

0.2

0.2

0.2

0.0

Private disability insurance (%)

0.1

0.2

0.2

0.0

Retirement from social security (%)

5.4

4.8

4.5

12.0

SSI (%)

41.8

47.9

47.4

64.0

SSDI (%)

18.5

20.7

20.6

20.0

Other (%)

2.3

2.4

2.5

0.0

Total number of responses

1,152

673

650

25

1 These are distinct clients; thus, the n’s for Convention Center and Temporary Lodging Program will not sum to this total.

† Participants reported multiple categories; percentages will not sum to 100.

Source: San Diego Regional Task Force on the Homeless (RTFH), February 1 to August 31, 2020.

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Exhibit F.6 Outcome Statistics of Convention Center and Temporary Lodging Program Clients from Unsheltered Situations, 2019

Temporary COVID-19 Shelter Resources

Overall1

Project Characteristics

(n=

Convention

Temporary Lodging

2,045)

Center

Program

(n=1,869)

(n=209)

Length of Stay in Project from Project Start Date (in Days)

Minimum

0

0

1

Mean

44

43

53

Maximum

165

152

165

Total number of responses

2,045

1,869

209

Destination After Project Exit†

Place not meant for habitation (%)

45.8

48.2

17.3

Rental by client with no subsidy (%)

1.3

1.2

2.3

Rental by client, with RRH or equivalent subsidy (%)

1.4

0.9

5.8

Rental by client, with VASH housing subsidy (%)

0.7

0.8

0.6

Rental by client, with HCV voucher (%)

0.8

0.7

1.7

Rental by client, with other ongoing housing subsidy (%)

1.0

0.5

5.8

Rental by client in a public housing unit (%)

0.1

0.1

0.0

Owned by client, with ongoing housing subsidy (%)

0.1

0.0

0.6

Staying or living with family/friend (%)

3.0

3.3

1.2

Jail, prison, juvenile detention facility (%)

0.2

0.2

0.0

Foster care home or foster care group home (%)

0.1

0.0

0.6

Hospital or other residential non-psychiatric facility (%)

0.9

0.7

2.3

Psychiatric hospital or other psychiatric facility

0.4

0.3

0.6

Transitional housing for homeless persons (%)

1.7

1.8

0.6

Emergency shelter (%)

5.7

3.9

28.9

No exit interview completed (%)

31.3

33.9

6.4

Hotel/motel paid for without ES voucher (%)

0.9

0.7

2.9

Long-term care facility or nursing home (%)

0.2

0.1

0.6

Substance abuse treatment facility (%)

0.5

0.5

0.0

Permanent housing for formerly homeless persons (%)

1.5

1.5

1.2

Safe Haven (%)

0.2

0.1

1.7

Other (%)

0.1

0.1

0.6

Unknown (%)

2.1

0.6

18.5

Total number of responses

1,612

1,472

173

ES = emergency shelter; HCV = Housing Choice Voucher; RRH = rapid re-housing; VASH = Veterans Affairs Supportive Housing.

1 These are distinct clients; thus, the n’s for Convention Center and Temporary Lodging Program will not sum to this total.

† Percentages are based on clients that had exited the programs as of August 31, 2020.

Source: San Diego Regional Task Force on the Homeless (RTFH), February 1 to August 31, 2020.

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U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Washington, DC 20410-6000

July 2021

Document Outline

Implementing Approaches to Address Unsheltered Homelessness

ACKNOWLEDGMENTS

FOREWORD

TABLE OF CONTENTS

EXECUTIVE SUMMARY

KEY FINDINGS FROM THE QUANTITATIVE STUDY

KEY FINDINGS FROM THE QUALITATIVE STUDY

1. INTRODUCTION

STUDY OBJECTIVES

OVERVIEW OF THE REPORT

2. METHODOLOGY AND DATA SOURCES

APPROACH TO THE QUANTITATIVE STUDY

DATA SOURCES

APPROACH TO THE QUALITATIVE STUDY

SITE SELECTION AND RECRUITMENT

PRIMARY DATA COLLECTION PROCESS

QUALITATIVE ANALYSIS—INTERVIEWS AND PROGRAM DATA

3. OVERVIEW OF HOUSING FACTORS ASSOCIATED WITH COMMUNITY-LEVEL UNSHELTERED HOMELESSNESS

TRENDS IN UNSHELTERED HOMELESSNESS

LEVEL OF RESOURCES AVAILABLE IN HOMELESS ASSISTANCE SYSTEM

PERFORMANCE OF HOMELESS ASSISTANCE SYSTEMS

HOUSING MARKET CHARACTERISTICS

4. RAPID RE-HOUSING PROGRAM IN RICHMOND, VIRGINIA

CONTEXT

RAPID RE-HOUSING PROGRAM OVERVIEW

KEY POLICIES AFFECTING IMPLEMENTATION

GREATER RICHMOND CONTINUUM of CARE IMPLEMENTATION OF RAPID RE-HOUSING

ENGAGING INDIVIDUALS EXPERIENCING UNSHELTERED HOMELESSNESS

PRIORITIZING INDIVIDUALS FOR RAPID RE-HOUSING

SUPPORTS PREPARING CLIENTS FOR HOUSING

LOCATING HOUSING

MOVING INTO AND ADJUSTING TO HOUSING

RAPID RE-HOUSING SUBSIDY

KEY PARTNERSHIPS SUPPORTING PROGRAM ACTIVITIES

SUSTAINABILITY

FUTURE PLANS FOR RAPID RE-HOUSING IN RICHMOND

ADOPTING RAPID RE-HOUSING

SUCCESSES, CHALLENGES, AND LESSONS LEARNED

SUCCESSES

CHALLENGES

LESSON LEARNED

CONCLUSION

5. MONTGOMERY COUNTY’S SYSTEMS APPROACH

CONTEXT

PROGRAM OVERVIEW

KEY POLICIES AFFECTING IMPLEMENTATION

COORDINATED ENTRY POLICIES

MONTGOMERY COUNTY COC’S IMPLEMENTATION OF A SYSTEMS APPROACH

ENGAGING INDIVIDUALS EXPERIENCING UNSHELTERED HOMELESSNESS

PREPARING CLIENTS FOR HOUSING

HOUSING PROGRAMS SERVING PEOPLE EXITING UNSHELTERED HOMELESSNESS

ENGAGING LANDLORDS

MOVE-IN AND ONGOING ASSISTANCE

KEY PARTNERSHIPS SUPPORTING PROGRAM ACTIVITIES

SUSTAINABILITY

FUTURE PLANS

ADOPTING MONTGOMERY COUNTY’S APPROACH

SUCCESSES, CHALLENGES, AND LESSONS LEARNED

SUCCESSES

CHALLENGES

LESSONS LEARNED

CONCLUSION

6. SAN DIEGO’S COVID-19 EMERGENCY SHELTERS

CONTEXT

PROGRAM OVERVIEW

OPERATION SHELTER TO HOME: THE CONVENTION CENTER CONGREGATE SHELTER

NON-CONGREGATE HOTELS: TEMPORARY LODGING PROGRAM

KEY POLICIES AFFECTING IMPLEMENTATION

HOMELESSNESS POLICIES

COORDINATED ENTRY POLICIES AND EMERGENCY PRIORITIZATION FOR THE CONVENTION CENTER

OUTREACH TO INDIVIDUALS EXPERIENCING UNSHELTERED HOMELESSNESS

IDENTIFYING INITIAL EMERGENCY SHELTER CLIENTS THROUGH OUTREACH

ONGOING OUTREACH DURING COVID-19

TEMPORARY LODGING PROGRAM

HOTEL MANAGEMENT, LOCATION, AND AMENITIES

TEMPORARY LODGING PROGRAM FOR INDIVIDUALS EXPERIENCING UNSHELTERED HOMELESSNESS

OPERATION SHELTER TO HOME: THE CONVENTION CENTER

CONVENTION CENTER AMENITIES

HOUSING NAVIGATION

DESTINATION AT PROJECT EXIT FOR CLIENTS FROM UNSHELTERED SETTINGS

LANDLORD ENGAGEMENT AND SYSTEM-LEVEL NAVIGATION

KEY PARTNERSHIPS SUPPORTING PROGRAM ACTIVITIES

OUTREACH PARTNERS

TEMPORARY LODGING PROGRAM PARTNERS

CONVENTION CENTER LEADERSHIP

SUSTAINABILITY

ADOPTING SAN DIEGO’S APPROACH

SUCCESSES, CHALLENGES, AND LESSONS LEARNED

SUCCESSES

CHALLENGES

LESSONS LEARNED

CONCLUSION

7. CONCLUSION

FINDINGS FROM THE QUANTITATIVE STUDY

FINDINGS FROM THE QUALITATIVE STUDY

APPENDIX A. ADDITIONAL VARIABLE CONSIDERATIONS

APPENDIX B. CONSTRUCTING THE CONTINUUM OF CARE-LEVEL DATASET

UNWEIGHTED VARIABLES FROM ACS 5-YEAR ESTIMATES

MEDIAN HOUSE VALUE

MEDIAN CONTRACT RENT

HOUSING DENSITY

EVICTION AND EVICTION FILING RATE

FAIR MARKET RENTS

NEWLY PERMITTED HOUSING UNITS

APPENDIX C. DETAILED INFORMATION ON THE QUANTITATIVE STUDY AND ADDITIONAL TABLES

PRIMARY OUTCOME VARIABLES (COC-LEVEL VARIABLES)

HOUSING MARKET VARIABLES (COMMUNITY-LEVEL VARIABLES)

ADDITIONAL TABLES

APPENDIX D. MASTER INTERVIEW GUIDES

GREATER RICHMOND CONTINUUM OF CARE

MASTER INTERVIEW GUIDE

MONTGOMERY COUNTY CONTINUUM OF CARE

MASTER INTERVIEW GUIDE

SAN DIEGO’S COVID-19 EMERGENCY SHELTERS

MASTER INTERVIEW GUIDE

APPENDIX E. LESSONS LEARNED FROM INTERVIEWS WITH INDIVIDUALS WITH LIVED EXPERIENCE

RECRUITMENT AND LOGISTICS

INTERVIEW GUIDE CONSIDERATIONS

INTERVIEWING REFLECTIONS

APPENDIX F. SUMMARY STATISTICS FOR PROGRAM-LEVEL DATA FOR THE THREE CASE STUDIES

GREATER RICHMOND CONTINUUM OF CARE

SUMMARY STATISTICS OF PROGRAM-LEVEL DATA

MONTGOMERY COUNTY CONTINUUM OF CARE

SUMMARY STATISTICS OF PROGRAM-LEVEL DATA

SAN DIEGO’S COVID-19 EMERGENCY SHELTERS

SUMMARY STATISTICS OF PROGRAM-LEVEL DATA

REFERENCES