Market Predictors of Homelessness:

How Housing and Community

Factors Shape Homelessness

Rates Within Continuums of Care

Multidisciplinary Research Team

March 26, 2019

Prepared by:

Hiren Nisar

Mal ory Vachon

Charles Horseman

Jim Murdoch

2M Research

ACKNOWLEDGMENTS

The study team would like to thank Dal as Elgin for assistance with the literature review and Madison Davis for assistance with data col ection and cleaning. Gail Clark, MacKenzie Regier, Joshua Townley, and Cindy Romero 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) Office of Policy Development and Research, with specific assistance from Galen Savidge-Wilkins, Nicole Watson, Ransford Osafo-Danso, Lydia Taghavi, and Benjamin Houck. We also thank Wil iam Snow, Norman Suchar, and Harper Sutherland 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 HUD or the U.S. Government.

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FOREWORD

Reducing homelessness is a key objective within HUD’s FY 2018–2022 Strategic Framework. Recent changes in how homelessness manifests within communities, most visibly in west coast communities where unsheltered homelessness has increased in recent years, is shaping HUD’s approach to meeting this objective. Though total homelessness has general y been declining national y since 2011, unsheltered homelessness rose by nearly 25 percent in major cities and largely urban Continuums of Care (CoCs) between 2015 and 2017.

While the causes of homelessness are complex and interconnected, community-level factors can be strong predictors of homelessness rates. The national data set developed for this study helps to fil this gap by disentangling which factors matter most in different types of communities and by offering some clues about places where more targeted analysis is needed.

By assessing the relative effects of housing, economic, safety net, demographic, and climate factors on homelessness rates, the study’s findings support the idea that improving the availability and affordability of rental housing is often a community’s best line of defense against homelessness. Across the country, housing market factors more consistently predicted rates of total homelessness than other economic factors. This finding is consistent with what many communities have experienced—increases in homelessness where rents are high. Finding opportunities to relax restrictions on producing a greater supply of housing that is affordable in these places may provide relief. High median rents, overcrowding, and evictions were particularly strong predictors of total homelessness rates in urban areas and tight, high-cost housing markets. Holding these factors constant, the study finds that increased housing density is protective against homelessness. HUD’s work to reduce regulatory barriers to affordable housing production, including an increase in the supply of unsubsidized middle-market housing for workers, and to encourage private landlord participation in subsidized housing programs is, therefore, especially timely and directly relevant to reducing the incidence of homelessness nationally. As Chair of the White House Council on Eliminating Regulatory Barriers to Affordable Housing, Secretary Ben Carson is leading efforts to boost the supply of housing that is affordable by identifying policies, regulations, and administrative obstacles to cost-effective development. This initiative serves related goals—to open avenues for increasing both the quantity and density of affordable homes where regulatory barriers are currently prohibitive. HUD’s task force to increase private landlord participation in subsidized housing programs is similarly intended to increase the efficiency of rental subsidies in the private market by making existing stock more accessible to vulnerable populations.

Demographics also play a role, particularly in ways that reflect strain on a community’s housing supply.

The study finds higher levels of net in-migration and more one-person households correspond with higher rates of homelessness across several market types, suggesting that housing production goals should account for both population growth and adequate unit types to reduce homelessness risk.

Consistent with this study, HUD’s biennial Worst Case Housing Needs Report to Congress shows that people living alone are often vulnerable to severe housing problems as rents rise. More efficiently tapping existing housing stock by increasing opportunities to share housing—both on the private market and within subsidized housing programs—is an important companion to constructing more affordable homes as we work to end homelessness national y. Creating opportunities for vulnerable households to

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better withstand rent increases may also provide relief in challenging markets. For example, HUD has proposed changes to its rental assistance programs to encourage income gains by relaxing rent penalties and by eliminating barriers that can discourage household formation such as the current annual rent certification structure.

HUD has long pushed for communities to bring their own public, private, and philanthropic resources to the table to address homelessness. Indeed, there are communities around the country making major progress on ending homelessness by bringing these often disconnected actors together. This shows up in HUD’s data in different ways. For example, a local church group offering space for emergency shelter could mean fewer people get counted as unsheltered but are still counted as homeless. Decisions around resources are often just as complex and interconnected as the factors in this study, so while it’s helpful to have breakouts of rates of total homelessness, readers should be cautious about interpreting differences between factors related to unsheltered and sheltered homelessness.

Other studies of community-level factors tend to focus on a specific slice of the larger picture of what causes homelessness. This study is intended to be a wider, exploratory look at as many predictive influences on homelessness as possible to see which rise to the top, which certain housing market factors did. It is important to view these findings as a valuable foundation upon which further research can build, which may provide conclusions about the causal relationship between these factors and homelessness.

Seth D. Appleton

Assistant Secretary for Policy Development and Research

U.S. Department of Housing and Urban Development

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TABLE OF CONTENTS

Acknowledgments ............................................................................................... i

Foreword ............................................................................................................ ii

Executive Summary ............................................................................................ 1

Introduction ........................................................................................................ 6

Understanding the Problem ....................................................................................................................... 6

Literature Review on Homelessness ........................................................................................................... 8

Data .................................................................................................................. 15

Dependent Variables: HUD Point-in-Time Counts ...................................................................................... 15

Independent Variables: Predictors of Homelessness ................................................................................. 17

Empirical Strategy ............................................................................................. 23

Descriptive Analysis .................................................................................................................................. 23

Variable Selection ..................................................................................................................................... 30

Statistical Model and Analysis ................................................................................................................... 30

National Model of Homelessness ...................................................................... 32

Subgroup Analysis............................................................................................. 38

Urbanicity ................................................................................................................................................. 38

Tight, High-Cost Rental Markets ................................................................................................................ 49

Unsheltered Homelessness on the West Coast .......................................................................................... 57

Conclusion ........................................................................................................ 61

Appendix A: Research Questions ...................................................................... 67

Appendix B: Other Data Sources Considered ..................................................... 68

Appendix C: Creation of Continuum of Care-to-County Crosswalk .................... 71

Appendix D: Additional Tables .......................................................................... 74

Appendix E: Variable Selection Process Results ............................................... 100

Appendix F: Summary Statistics for Tight, High-Cost Rental Markets .............. 107

Appendix G: Summary Statistics for West Coast Continuums of Care .............. 110

Appendix H: Local Policy Scans........................................................................ 113

References ...................................................................................................... 117

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EXECUTIVE SUMMARY

More than 550,000 people in communities across the United States experienced homelessness on a single night in 2018—about 17 in every 10,000 people. The myriad of negative economic, health, and social outcomes linked to homelessness requires an understanding of its root causes so policymakers can develop and implement effective strategies to prevent and curb the incidences and duration of homelessness. This study provides insight into our understanding of the underlying community-level factors that may be associated with homelessness in the United States.

A key motivation for this study is the desire to understand the larger economic forces influencing the homelessness policy landscape; and, therefore, the work related to ending and preventing homelessness across communities. While both sheltered and unsheltered homeless populations have declined since 2011, in more recent years, the unsheltered homeless population has increased substantially. Unsheltered homelessness in major cities and mostly urban Continuums of Care (CoCs)1

rose by nearly 25 percent, from 87,345 in 2015 to 109,252 in 2017. In contrast, between 2015 and 2017, suburban CoCs experienced a very small increase in unsheltered homelessness, and rural CoCs experienced a decline in unsheltered homelessness.

To continue progressing toward the goals of ending and preventing homelessness, we must further our knowledge of the basic community-level determinants of homelessness. The primary objectives of this study are to (1) identify market factors that have established effects on homelessness, (2) construct and evaluate empirical models of community-level homelessness, (3) use these models to identify and analyze relationships within subgroup populations of local markets, and (4) assess the feasibility of conducting future research to support local communities’ efforts to prevent and end homelessness.

Exhibit ES-1 and our descriptive analyses show differences in homelessness rates across communities in the United States. This map il ustrates the concentration of homelessness in predominantly urban areas and the west coast. While the average rate of total homelessness across all communities was around 17

homeless people per population of 10,000, this number varied from 9.4 in rural communities to 28.4 in urban communities. Accordingly, urban communities had large sheltered and unsheltered homeless population rates relative to rates for suburban and rural communities. The reasons for this difference might be many and beyond the scope of this study. We provide some exploratory analysis to evaluate this phenomenon. Overal , housing variables show a tight and overcrowded housing market in urban communities, with housing costs that are much higher compared with rural communities. Economic conditions vary by urbanicity as well, with suburban communities having the highest median income, lower poverty, and lower participation rates in safety net programs than urban and rural communities.

With respect to demographic characteristics, urban communities are more diverse in terms of race and ethnicity and have a higher share of residents with a bachelor’s degree or higher compared with rural communities. Also, urban areas tend to have more housing assistance resources al ocated to support the homeless. These inherent differences show that varying factors may contribute to homelessness 1 Continuums of Care (CoCs) are organizations that administer and support various community-wide programs with the goals of ending homelessness and increasing self-sufficiency among populations experiencing homelessness

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

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within communities. As a result, we expand the analysis to subgroups, including communities with tight, high-cost rental markets and communities on the west coast (in California, Oregon, and Washington).

Exhibit ES-1 | Map of Total Homelessness Rates (Per 10,000 Population) Across CoCs Sources: U.S. Census’s (Census) intercensal population estimates; U.S. Department of Housing and Urban Development (HUD) Point-in-Time (PIT) count data

These subgroup analyses shed light on the differences in factors associated with homelessness across communities. The average rate of total homelessness was around 37 homeless people per 10,000

population in tight, high-cost rental markets and 11.4 homeless people in other rental markets.

Communities in tight, high-cost rental markets also had higher rates of sheltered and unsheltered homelessness due to reduced availability and affordability of housing units. Economic conditions also varied, with higher median incomes, lower unemployment rates, and lower poverty rates in tight, high-cost rental markets. With respect to safety net programs, there are no clear trends between tight, high-cost rental markets and other rental markets. For demographic characteristics, tight, high-cost markets are more diverse in terms of race and ethnicity, with populations having higher levels of education compared with other markets.

Distinct differences also exist between communities on the west coast (California, Oregon, and Washington) and communities in other regions of the United States. In west coast communities, the average rate of total homelessness was around 33 homeless people per 10,000 population in 2017, compared with 14 for communities in other regions; west coast communities also had higher proportions of unsheltered homelessness (see exhibit ES-2). Housing characteristics vary greatly across regions, with higher home values, median rents, and shares of renter-occupied units in west coast communities. Economic and safety net conditions across regions indicate that west coast communities

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have higher median incomes and unemployment rates, with safety net participation varying across the west coast and other regions. In terms of demographic characteristics, west coast communities have a lower percentage of White people and a higher percentage of Hispanic and Asian people than communities in other regions. Net-migration rates are also much higher in west coast communities. Our descriptive analyses illustrate a possible relationship between community-level housing market dynamics, the availability of affordable housing, and homelessness.

Exhibit ES-2 | Map of Unsheltered Homelessness as a Percentage of Total Homelessness in 2017

Sources: Census’s intercensal population estimates; HUD PIT count data

This study combined several datasets to identify and generate community-level factors across five broad domains—housing market, economic conditions, safety net, demographic composition, and climate conditions—that the literature hypothesizes are associated with homelessness. Our analytical approach involved the estimation of several regression specifications—(1) national-level, (2) urbanicity subgroups, (3) tight, high-cost rental market subgroup, and (4) unsheltered homelessness in the west coast subgroup—with rates of total, sheltered, and unsheltered homelessness as the dependent variables for the first three specifications.

Results of our regression analysis indicate that a wide variety of factors are associated with rates of homelessness and that different factors contribute to the type of homelessness (total, sheltered, and unsheltered) across various communities. Housing factors are among the most consistent determinants of community-level homelessness, confirming previous studies’ findings that housing market factors, particularly housing costs and housing market tightness, matter. Furthermore, issues of housing affordability are inherently related to factors in the economic and safety net domains, as economically

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disadvantaged populations may struggle to afford housing in areas with high housing costs. Across the various specifications, the significant independent variables associated with rates of homelessness differ based on the outcome of interest and subgroup category.

Housing affordability is a central issue in examining the factors that are associated with homelessness across communities. Our estimates indicate that communities with higher median rents have increased rates of total homelessness. In line with concerns regarding affordability (or lack thereof), our estimates indicate that the share of renters with a housing cost burden greater than 30 percent of their income is also positively associated with rates of total homelessness, mainly in rural areas and tight, high-cost rental markets. Higher density of housing units is associated with lower rates of total homelessness, particularly in the tight, high-cost rental markets. Furthermore, overcrowded housing units also indicate a lack of housing availability and are a potential y important factor that is positively associated with rates of total homelessness, especially in the national model and in tight, high-cost rental markets. We find fewer significant estimates and contradictory results for economic and safety net factors in determining community-level total homelessness. We find that higher unemployment rates are associated with increased rates of total homelessness in communities with tight, high-cost rental markets; however, the relationship is negative in urban areas, and higher poverty rates are negatively associated with total homelessness in rural areas. The number of households receiving cash assistance is positively correlated with homelessness in urban areas, and the share of HUD-assisted units is positively correlated with homelessness in suburban areas as well as in the national model. The findings on demographic characteristics indicate variation in the factors that are related to rates of total homelessness across the nation and subgroup categories. We find few significant relationships between total homelessness and race and ethnicity categories. In the national model, in rural areas, and in communities with tight, high-cost rental markets, the share of the Hispanic population is associated with lower rates of total homelessness. Finally, across specifications, there is no clear relationship between rates of total homelessness and climate conditions. These findings indicate that, other than the measures in the housing domain, the important factors in the other domains that matter depend on the subgroup of interest.

The factors associated with rates of sheltered homelessness2 may be different from those associated with total and unsheltered homelessness. Counts of sheltered homeless people are necessarily constrained by the resources allocated to create shelter options. Our estimates in the national model indicate that communities with higher median rents have increased rates of sheltered homelessness.

Communities with high housing density have lower rates of sheltered homelessness in the national model, as wel as in urban areas and communities in tight, high-cost rental markets. High eviction rates are related to high rates of sheltered homelessness, mainly in tight, high-cost rental markets. Changes in eviction rates are positively associated with rates of sheltered homelessness in urban areas. Eviction may displace individuals and families from their rental homes, forcing them to seek shelter in areas that may not have available or affordable housing options. We find that high unemployment rates are 2 Sheltered homelessness refers to people who are staying in emergency shelters, transitional housing programs, or safe havens. Emergency shelters provide temporary shelter, and transitional housing programs provide a place to stay and supportive services for up to 24 months. Safe havens, a much less common program model, provide temporary shelter and services for hard-to-serve individuals, whereas transitional housing programs provide a place to stay and supportive services for up to 24 months. People in safe havens are included in the PIT counts but not included in the yearly shelter count in Part 2

of the Annual Homeless Assessment Report to Congress (Henry et al., 2018a). Sheltered homelessness is also dependent on the amount of funding a community receives.

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associated with higher rates of sheltered homelessness in communities with tight, high-cost rental markets. These estimates illustrate the important relationship between housing affordability and rates of sheltered homelessness. Furthermore, across several specifications, we find no consistently significant relationships between rates of sheltered homelessness and poverty (other than positive association in the national model). This finding potential y indicates that poverty rates affect sheltered homelessness in communities in different ways. For example, housing cost burden may be a more important factor than considering poverty rate alone. There are fewer consistently significant relationships between rates of sheltered homelessness and demographic and climate characteristics.

For unsheltered homelessness, 3 across several specifications and subgroups, our estimates suggest that housing market characteristics vary in magnitude and significance in terms of their association with rates of unsheltered homelessness. We consistently find, however, that the share of overcrowded housing units is positively associated with rates of unsheltered homelessness. Furthermore, the association of unsheltered homelessness with economic and safety net variables also depends on the subgroup, with a negative association between unemployment rates and unsheltered homelessness in urban CoCs but a positive association in suburban and west coast CoCs. Similarly, in the safety net domain, our results show that high shares of HUD-assisted units are associated with lower rates of unsheltered homelessness in urban areas and communities with tight, high-cost rental markets, while high shares of HUD-assisted units are associated with higher rates of unsheltered homelessness in suburban areas and communities on the west coast. In addition, the share of houses built prior to 1940, used as a proxy for federal funding, 4 is associated with lower rates of unsheltered homelessness in suburban CoCs; such results suggest that CoC funding may reduce rates of unsheltered homelessness.

For the demographic domain, net-migration and household composition are consistent positive significant factors associated with rates of unsheltered homelessness across different specifications.

Regarding education, a high share of the population with less than a col ege degree is associated with increased rates of unsheltered homelessness in suburban and rural areas.

The findings from this study provide key insights into the various factors that contribute to the rates of total, sheltered, and unsheltered homelessness in various communities across the United States. This study identifies new factors that provide insights into predicting homelessness across different areas.

The study found that the significant factors associated with rates of homelessness varied by outcome and subgroup, suggesting more research is needed to understand these factors as policymakers work to prevent and end homelessness at the national and local levels.

3 Unsheltered homeless individuals have a primary nighttime location that is not typically designated for sleeping accommodations (Henry et al., 2017).

4 Fol owing the literature, we also use an instrument variable strategy and find similar association between federal funding and unsheltered homelessness.

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INTRODUCTION

Understanding the Problem

More than 550,000 people in communities across the United States experienced homelessness5 on a single night in 2018—that is about 17 in every 10,000 people in the United States (Henry et al., 2018b).

Over the past decade, the homeless assistance world has evolved to implement evidence-based practices, such as rapid rehousing, coordinated entry, and Housing First, that led to more effective and long-term solutions for persons experiencing homelessness. As communities have incorporated these practices, there has been a steady decline in homelessness nationally. In the last 2 years, however, homelessness has increased, albeit modestly. The myriad negative issues linked to homelessness, such as economic status, health and mental state, and social and emotional outcomes, require an understanding of the root causes of homelessness so that policymakers can develop and implement effective policies to prevent the incidence and reduce the duration of homelessness. Zeroing in on the root causes of homelessness involves understanding the underlying community-level factors that are associated with homelessness. The need for such research is also reflected in the U.S. Department of Housing and Urban Development’s (HUD) Strategic Plan 2018–2022 (HUD, 2018a), which seeks to “end homelessness by preventing it whenever possible and quickly helping Americans who experience a housing crisis to ensure such experiences are brief and non-recurring,” to “enhance and reform our rental assistance programs by providing sustainable models to empower communities to address local affordable housing needs,” and to work with “local partners to identify and tear down regulatory barriers that are stifling the development of affordable homes.” This research is intended to inform strategies for reducing the number of people experiencing sheltered and unsheltered homelessness measured by the Point-in-Time (PIT) by each Continuum of Care (CoC). 6

Following a decade of decline, between 2017 and 2018, the number of people experiencing unsheltered homelessness increased again by 3 percent, following an 8-percent increase in 2017, as measured in a single night. This trend, however, varies across geography and markets. Recent increases in homelessness have been attributed to increases in homelessness in some of the largest metropolitan areas (Henry et al., 2018a). Exhibit 1-1 provides a visual depiction of CoCs with an increase in total homelessness from 2015 to 2017. The emergence and persistence of homelessness has been noted specifically in large rental markets with low vacancy rates in the west; high and rising rents; and low construction of new housing, including in areas such as Los Angeles, San Diego, Portland, and Oakland.

By contrast, rental burdens are lower in some cities that have similar conditions (such as Seattle and the San Francisco Bay area), 7 perhaps due to their expanding employment and income opportunities.

Unsustainably high rent burdens are becoming the norm in these cities, locking people into constant 5 HUD terminology defines a homeless person as a person who lacks a fixed, regular, and adequate nighttime residence.

6 CoCs administer and support various community-wide programs with the goals of ending homelessness and increasing self-sufficiency among populations experiencing homelessness (https://www.hudexchange.info/programs/coc/).

7 Mansur et al. (2002) suggested that the number of homeless population is sensitive to the income distribution and changes in housing costs in cities. Glynn, Byrne, and Culhane (2018) provided empirical evidence that homelessness in a community sharply increases once the median rental costs exceeds 32 percent of median income (close to the federal definition of affordable housing).

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financial and personal crises related to finding and maintaining housing. Greater knowledge of these underlying structural determinants requires further research on the complex ways that levels of homelessness are affected by conditions in local housing and labor markets, the demographic characteristics of the population, and local policies. This study solicits research that will accomplish the following—

1. Identify, through a comprehensive literature review, market factors with established and robust effects on homelessness.

2. Construct and evaluate empirical models to analyze recent trends in homelessness and how those trends relate to community-level factors such as housing and economic markets and demographic variations.

3. Use these models to analyze relationships within subpopulations of local markets.

4. Assess, after control ing for market and economic forces, the feasibility of conducting future research on the effects that regulations and land use policies may have on levels of homelessness.

By using more recent estimates of homelessness within communities, this study expands the existing literature regarding factors that are associated with rates of homelessness across communities.

Quantifying this association between community factors and homelessness provides HUD with opportunities to sharpen focus on the factors within the community and to help those at risk of homelessness. This study also provides HUD insights that may inform and guide policies for tackling homelessness in areas experiencing increases in their unsheltered homeless populations.

Exhibit 1-1 | Map of Percentage Increase in Total Homelessness from 2015 to 2017

Notes: Exhibit D-2 in appendix D provides the top five CoCs with the largest changes in rates of homelessness from 2015 to 2017. Exhibit D-3 in appendix D maps the percentage increase in the rates of total homelessness from 2015 to 2017.

Source: HUD PIT count data

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Literature Review on Homelessness

Homelessness research over the past two and a half decades has been shaped by a col ection of notable developments in approaches to examining the factors associated with homelessness. Chief among these changes is increased recognition of the need to integrate individual and structural perspectives when studying homelessness (Fargo et al., 2013; Lee, Price-Spratlen, and Kanan, 2003). These multidimensional models acknowledge the role of adverse events (for example, mental illness and domestic violence) in shaping the likelihood that individuals become homeless, but the models also recognize that this likelihood is contingent on community-level factors such as rent prices and other economic factors.

The increasingly important role of multidimensional models that combine aggregate individual- and community-level factors is supplemented by research highlighting the considerable differences between homelessness within rural and urban communities (Fitchen, 1992; Lawrence, 1995). In contrast to their urban counterparts, rural communities are less likely to have large, visible homeless populations.

Despite evidence of a higher proportionate incidence of homelessness than in urban communities (Lawrence, 1995), and according to field interviews conducted by researchers in upstate New York, the rural homeless population is more likely to “double up” or accept grossly inadequate housing, as opposed to the urban image of literal homelessness meaning that people will be sleeping in public view (Fitchen, 1992). The associated effect is a notable undercounting of the homeless within rural communities, 8 thereby underscoring the need to account for the degree of “rurality” within community-level analyses of homelessness. In addition, innate theoretical differences seem to exist in the causes of homelessness in rural and urban communities. Subgroup analysis can help flesh out the different factors associated with homelessness in rural communities and suggest new directions for policies and programs that target rural homelessness.

A third development pertains to improvements in the data used to study homelessness over the past two decades. Byrne et al. (2012) criticized prior research for relying on methodologically flawed estimates of the size of the homeless population. Notably, previous measures of homelessness suffered from methodological limitations in the form of sole reliance on informant interviews, the use of shelter bed capacity as a proxy for homelessness, and the use of inconsistent protocols by communities counting the homeless population. In response to these chal enges, HUD provided extensive technical assistance to communities to improve the enumeration of their homeless populations and developed the HUD PIT counts of unsheltered and sheltered homeless people (Dunton, 2014). The PIT count methodology provides a systematic approach used by the CoCs to conduct accurate counts of sheltered and unsheltered homelessness during the last 10 days of January. This methodology has been shown to 8 PIT counts are not designed to include households who are doubled up or living in poor-quality housing. PIT counts are designed to count people living in homeless shelters and people living in unsheltered locations not meant for human habitation, such as in cars or on the street. PIT count data are col ected by homelessness service providers and by street count volunteers. The U.S. Department of Education has different measures, like doubling up, because data are col ected at schools.

Household-level surveys like the American Housing Survey (AHS) identify poor-quality housing. In short, different data sources would need to be combined and deduplicated in order to capture all types of homelessness described previously. HUD

considers doubling up and poor-quality housing to be an indicator of housing insecurity among housed people but does not consider these factors to be indicators of homelessness. HUD is currently developing new AHS measures to better capture these issues (see Watson and Carter, 2018).

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provide more reliable estimates of homelessness throughout the country (Byrne et al., 2014) and has been credited with providing an improved ability to examine the effects of public policies on homelessness (O’Flaherty, 2018). 9 Researchers have used different measures of the homelessness rate in a community. Most recent studies have used the number of total homeless, sheltered homeless, or unsheltered homeless adults per 10,000 adults in the general population or per 10,000 adults living in poverty (Byrne et al., 2012; Glynn and Fox, 2017; Hanratty, 2017; Lee, Price-Spratlen, and Kanan, 2003; Lucas, 2017).

EXAMINING FACTORS ASSOCIATED WITH HOMELESSNESS

Over the past several decades, a growing body of empirical research confirms that key market and community conditions affect levels of homelessness (Byrne et al., 2012; Glynn and Fox, 2017; Hanratty, 2017; Lee, Price-Spratlen and Kanan, 2003; Quigley, Raphael, and Smolensky, 2001). These studies have commonly modeled homelessness in a geographic area as a function of various community-level factors—

 Housing market conditions.

 Economic market conditions.

 Local policy factors and the available safety net.

 Demographic composition.

 Climate conditions.

The literature reviews in Byrne et al. (2012) and O’Flaherty (2018) provided a starting point for the current study. In the sections below, we provide summaries of the pertinent findings across the five factors identified previously.

Relationship Between Housing Market Conditions and Homelessness

An array of studies found consistent evidence of a significant positive relationship between a community’s housing market conditions and the size of its homeless population. Economic principles governing the availability and pricing of housing and the growth in demand for the lowest quality housing explain a vast portion of the variation in homelessness across metropolitan housing markets (Quigley, Raphael, and Smolensky, 2001). Similarly, Lee, Price-Spratlen, and Kanan (2003) found that the median rent level exerts a dominant effect on metropolitan homelessness rates and that higher priced areas have higher rates of homelessness. Byrne et al. (2012) used HUD’s PIT count data to model the community-level variation in homelessness across the United States. Their estimates suggest that rent level, the prevalence of single-person households, and the presence of households that had recently moved to the area were all important factors in determining homelessness. In a study of homeless families and single adults, Fargo et al. (2013) found that housing market factors explained a greater proportion of variance in homelessness rates than were explained by demographic, behavioral, public health, and safety net factors. Notably, the findings confirmed results from previous studies that showed housing market factors and housing costs are the strongest predictors of homelessness.

9 Homeless Management Information System data can be used to provide an unduplicated count of people who are experiencing sheltered homelessness and have client-level information and service use patterns. In recent years, HUD has provided technical assistance in clarifying some of the data col ection procedures within communities to improve sheltered homelessness estimates. This study is focused on homelessness measures from HUD’s PIT count. Other studies may wish to investigate opportunities to use housing inventory count and System Performance Measures data to assess availability of shelter beds or outcomes for sheltered homeless people.

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More recently, a study of increases in the Zillow Rent Index and homeless counts in the 25 largest U.S.

Metropolitan Statistical Areas found strong, significant, and positive relationships between rental costs and homelessness in some areas (New York; Los Angeles; Washington, DC; and Seattle) but insignificant relationships elsewhere (Glynn and Fox, 2017). Glynn, Byrne, and Culhane (2018) provide empirical evidence that homelessness in a community sharply increases once the median rental costs exceed 32

percent of median income (close to the federal definition of affordable housing). In right-to-shelter localities, homelessness increased with higher median rent, demonstrating that median rents remain an important determinant of area homelessness rates even after control ing for community-level, time-invariant differences via a fixed effects model (Hanratty, 2017). Collectively, the strong relationship between housing market conditions and homelessness has notable implications for efforts to address homelessness across communities. Research suggests that modest improvements in the affordability and availability of rental housing can have significant effects on the incidence of homelessness (Quigley, Raphael, and Smolensky, 2001) and that policies that expand access to affordable housing may be an important component of the tools used to reduce homelessness (Hanratty, 2017).

In short, rental markets are tightening nationwide with high housing costs, forcing even people with higher incomes to rent instead of purchasing affordable homes and leaving fewer affordable rental units to low-income households (Galvez et al., 2017). As a result, finding an affordable house to rent might not be possible for this vulnerable population.

Relationship Between Economic Market Conditions and Homelessness

The relationship between local economic conditions and homelessness has been the focus of several previous studies. Most studies generally find a strong association between homelessness, income, and unemployment rates. Individual-level studies have found homelessness occurs in families who tend to occupy the lower end of the income spectrum. Curtis et al. (2013) examined the relationship between individual-level shocks to family incomes, such as the birth of children with severe health conditions, and homelessness. The study found that shocks to family incomes were a key factor affecting the likelihood that families will become homeless.

As a result, community-level studies have included both these factors—average household income and unemployment rate—while modeling homelessness. The magnitude and significance of estimates have varied greatly, however, based on the other variables included in the regression model (Byrne et al., 2012). Although the individual-level studies suggest some amount of association between income and homelessness, more recent reviews of the extant literature have suggested that, on an aggregate level, changes in the unemployment rate cannot be expected to trigger significant changes in community homelessness rates (O’Flaherty, 2018).

Previous studies have identified household income as a significant economic factor, with the percentage of households with low income positively associated with community homelessness rates (Byrne et al., 2012; Lee, Price-Spratlen, and Kanan, 2003). Across families and single adults residing within metropolitan areas, measures of household discretionary income were found to be the most important factors in determining community homelessness rates (Fargo et al., 2013). The association of unemployment and poverty rates on homelessness with 1 year of cross-sectional data might not provide enough statistical power to estimate the relationship. As a result, studies have found mixed results of unemployment rates on homelessness within a community. For example, Quigley, Raphael, and

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Smolensky (2001) found the coefficient on unemployment rate was negative and insignificant, whereas Byrne et al. (2012) found a positive and significant association in non-metropolitan areas.

In summary, economic conditions, in contrast to housing market conditions, are anticipated to have a more secondary relationship with community levels of homelessness. While the magnitude and significance of variables measuring economic conditions have varied across studies (Hanratty, 2017), these variables play a theoretically and methodologically important role in the development of robust models for examining the factors associated with community levels of homelessness.

Relationship Between Local Policy Conditions/Safety Net and Homelessness

A wide range of studies has examined the extent to which various state and local policy factors may influence homelessness. Such studies have typically examined policies impacting the general social safety net and those specifical y targeting housing and homelessness. The former category includes state supplements to Temporary Assistance for Needy Families (TANF) and Supplemental Security Income, as wel as availability of treatment programs for people with mental illnesses, disabilities, and substance abuse disorders. The latter category includes right-to-shelter, bus-out, and permanent supportive housing policies, among others (Byrne et al., 2012). Khadduri (2008) described the interconnection between homelessness and various policy factors, explaining that housing subsidies (such as the Housing Choice Voucher program) can prevent homelessness by fil ing gaps in the availability of affordable housing. That study also explains how housing subsidies may differentially benefit certain populations, such as single mothers and TANF recipients, suggesting other potential interactions10 between various factors that influence homelessness.

Previous studies have shown that safety net policies focusing on providing some form of income or other support for individuals experiencing adverse life events have had mixed effects. At the end of the 20th century, many experts viewed the magnitude of homelessness as a function of the size of the safety net, which had contracted in size during the previous decade (Lee, Price-Spratlen, and Kanan, 2003). Subsequent empirical research, however, has raised questions about the veracity of this assumption. Notably, Lee, Price-Spratlen, and Kanan (2003) found that the availability of public assistance was indeed negatively associated with homelessness rates, suggesting that the programs were effective in keeping impoverished families and individuals from slipping into homelessness. At the same time, the study found that expenditures from Aid to Families with Dependent Children (AFDC), the precursor to TANF, had a positive association with homelessness rates in the community. The authors suggested that the size of these expenditures may have had a “magnet” effect so that higher AFDC

payments attracted disadvantaged families from other communities. Popov (2016) and Lucas (2017) presented some preliminary evidence that homeless families migrate to communities with more generous programs. Meanwhile, Fargo et al. (2013) found safety net measures to be among the poorest predictors of homelessness rates. Few safety net variables were significantly associated with homelessness rates in metropolitan communities, while higher levels of TANF expenditures were associated with higher levels of homelessness in nonmetropolitan communities.

10 Potential interactions of individual characteristics have been suggested in the literature. For community-level studies, however, interactions of percentages of single mothers and TANF recipients achieves little without including the percentage of single mothers who are TANF recipients. Due to the data limitations and the degrees of freedom in the number of covariates that can be included in the model, this study has only included a few pertinent and available interaction terms in the model.

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In contrast, policies specifical y targeting housing and homelessness within communities were found to have higher levels of empirical support. Previous research by Mansur et al. (2002) found that a substantial portion of homelessness can be eliminated through increased reliance on housing subsidies.

Similarly, O’Flaherty (2012) found that housing subsidies provide an optimal homelessness prevention program. Meanwhile, Burt, Pearson, and Montgomery (2007) found evidence that a combination of prevention strategies, rapid exits from shelter strategies, and community support strategies provided the most effective and efficient approaches to reducing homelessness rates. Moulton (2013) found that a combination of long-term housing and services to homeless individuals with disabilities can reduce chronic homelessness. Meanwhile, increased investment in permanent supportive housing, which has emerged as a preferred policy intervention for addressing chronic homelessness in recent years, was found to have modest negative associations with chronic homelessness at the community level in earlier studies (Byrne et al., 2014). A study using more recent data, however, may find an even stronger negative relationship as the policy matures.

The findings that were shown previously demonstrate empirical support for a variety of policy interventions focused on targeting housing and homelessness, but questions persist about which policies may be optimal. A recent study by Gubits et al. (2018) provides critical insight into which policies may be most effective in reducing homelessness. This large, randomized controlled trial of 2,282

homeless families compared housing stability for families receiving long-term rent subsidies, short-term rent subsidies, and transitional housing programs with supportive services against a common control group of usual care. The findings conclusively demonstrate that long-term rent subsidies reduced individual-level homelessness, while the other interventions had little effect. Accordingly, the study provides strong support for the view that long-term housing subsidies effectively resolve the enduring economic problems that most homeless families face while demonstrating a notable lack of support for the view that policies must require homeless families to address psychosocial problems in order to maintain housing.

Relationship Between Demographic Composition and Homelessness

Throughout the literature, previous studies have often found strong relationships between homelessness and various demographic characteristics, including age, gender, family status, and race.

Byrne et al. (2012), however, have highlighted a lack of consensus among previous studies on the specific demographic factors that are consistently associated with homelessness. The lack of significant relationships between demographic factors may be attributed to the diversity of demographic variables included within previous studies, and the likelihood of interactions between demographic variables may be attributed to other economic and housing market factors (Shinn et al., 1998). For example, in areas with high rents and tight markets, single-parent families may find it more difficult to make ends meet due to lack of a second income, leading to higher rates of homelessness. Avenues for future research and avenues of particular interest in this study include the interaction of demographic factors with other factors that may influence homelessness. Further on, we review pertinent categories of demographic variables including key subpopulations, household compositions and personal networks, and cultural factors.

Several key subpopulations have been identified as significantly more likely to comprise homeless populations. Baby boomers (individuals born between 1946 and 1964) have been shown to make up a highly disproportionate share of the homeless population and to be positively associated with the size of a community’s homeless population (Byrne et al., 2012; Culhane et al., 2013). Similarly, veterans have

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been found to have higher levels of risk for homelessness and to be significantly overrepresented within the homeless population (Fargo et al., 2012). The size of the Hispanic population was also found to be positively associated with homelessness rates (Byrne et al., 2012). Notably, this finding was inconsistent with previous research that showed Hispanics were more likely to be underrepresented, given a greater propensity for using informal housing arrangements to avoid homelessness (Rosenheck, Bassuk, and Salomon, 1999). Accordingly, Byrne et al. (2012) identified a need for future research to further examine the associations between Hispanic ethnicity and homelessness.

Variables pertaining to the size and composition of households (Bohanon, 1991; Burt, 1993) and personal networks (Fitchen, 1992) present another important category of demographic variables. Single-person households, which are more economical y vulnerable and intensify the demand for housing, exert a notable influence on homelessness rates (Lee, Price-Spratlen, and Kanan, 2003). Household composition also exerts significant influence, with the proportion of female-headed households associated with increased homelessness (Burt, 1993; Elliott and Krivo, 1991). The positive associations between single-person and female-headed households may be attributed, in part, to social networks.

Notably, previous research has found that in both urban and rural communities, personal networks serve as the first line of defense against literal homelessness (Fitchen, 1992). Relatives and friends, given their inherent abilities to provide social and housing assistance, are argued to be instrumental to the recovery of families and individuals that are on the brink of homelessness.

Numerous behavioral and public health factors, which may vary considerably across communities, have also been shown to affect a community’s level of homelessness. Early studies found violent crime rates have a significant positive relationship to homelessness (Grimes and Chressanthis, 1997). More recently, notable public health outcomes differences between metropolitan and nonmetropolitan communities have been shown to influence rates (Fargo et al., 2013). Within metropolitan regions, community aggregate levels of alcohol consumption and individuals reporting no social support were uniquely associated with family homelessness, while community-level aggregate drug use and homicide rates were uniquely associated with individuals experiencing homelessness. In contrast, life expectancy, religious adherence, and crime were cultural factors uniquely associated with homelessness within nonmetropolitan communities. Final y, previous individual-level studies have also demonstrated a significant relationship between experiencing family abuse and neglect and subsequent homelessness within rural and urban communities (Thrane et al., 2006).

Relationship Between Climate Conditions and Homelessness

A final category of variables focuses on the relationship between climate conditions and homelessness rates. Previous research has long theorized that a community’s climate, including its average summer and winter temperatures and levels of precipitation, affects its rate of homelessness. Empirical evidence has indeed suggested that communities with warm climates have higher rates of unsheltered homelessness, on average, than communities with colder climates (Corinth and Lucas, 2018). This relationship, however, appears to be more tenuous among homelessness studies that control for an array of factors within their explanatory models. O’Flaherty (2018) suggests including the interaction between population characteristics such as poverty rates and conditions that affect the entire population (like temperature) in the model. 11 Across metropolitan contexts, average temperature 11 This study considered using interactions, but due to the limitations of using cross-sectional data with 376 observations and the number of independent variables, we leave this for future studies with better data.

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ranges were unassociated with homelessness rates, while areas with heavy precipitation had a significant negative association (Lee, Price-Spratlen, and Kanan, 2003). To more ful y examine the relationship between climate and homelessness, Corinth and Lucas (2018) analyzed the relationship between the distribution of temperatures on CoC homelessness rates. CoCs with cold climates were found to have uniformly low rates of unsheltered homelessness, while warmer CoCs demonstrated substantial variation in the size of the unsheltered population. These findings seem to be clearly tied to the fact that the underlying data—namely PIT counts—are collected in January. The authors noted the need for future research to careful y account for climate factors when examining the determinants of homelessness.

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DATA

Through a comprehensive data assessment that included a literature review and a descriptive data analysis, the study team constructed a dataset of factors believed to influence homelessness across communities. The study team started with a list of predictors suggested by HUD and identified additional variables during the literature review. 12 All potential variables were then examined for any issues related to data quality and availability. This assessment of potential variables involved exploratory descriptive analysis of independent variables available at the county, metropolitan statistical area, or state levels from a number of data sources within the same domains that have been identified in prior research associated with homelessness (housing, economic, safety net, demographic, and climate factors). Variables were excluded from the analysis for several reasons—

 Geographic incompatibility. 13

 Sparseness or data availability.

 Higher quality data available from another source.

 High degree of correlation with other dependent variables in the same domain.

Exhibit 2-2 outlines the primary outcome variables and predictors of homelessness used in this study. 14

The final dataset consists of various housing, economic, safety net, demographic, and climate factors sourced largely from HUD and U.S. Census Bureau (Census) databases. The sections below describe in detail the full set of variables, along with their associated data sources and years of measurement.

Dependent Variables: HUD Point-in-Time Counts

Each year since 2007, HUD has estimated the number of people experiencing homelessness—in both sheltered and unsheltered situations—on a single night in the last week of January in approximately 400

12 The initial set of variables suggested by HUD were renter share, median rents, and rental vacancy rates; housing density; per unit (or square foot) land cost; per unit (or square foot) construction cost; prevalence of overcrowded housing; median incomes, poverty, and unemployment; available homeless assistance resources, including supportive housing beds; the number of HUD-subsidized units within a community; population change and racial and ethnic population characteristics; prevalence of single-parent households; prevalence of opioid disorders or deaths; right-to-shelter policies; bus-out policies; Public Housing Authority homeless preference policies; weather conditions; and ratio of unassisted extremely low-income (ELI) renters to number of turnover-assisted units annually.

13 The section entitled “Constructing the Dataset at the CoC Level” provides an overview of how the dataset was constructed, including the geographic level of aggregation.

14 The study primarily relied on publicly available data. As a result, some data sources were not available for the study. appendix B presents the rationale for their exclusion.

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Continuums of Care (CoCs)15 across all 50 states, the District of Columbia, and U.S. territories. 16 These estimates form HUD’s Point-in-Time (PIT) count data. CoCs are local planning bodies responsible for coordinating a full range of homelessness services in a geographic area, which may cover a city, county, group of counties, or an entire state. 17 The data reported for each CoC typically include the number of sheltered and

One in every four persons experiencing

unsheltered homeless people and subgroup counts of

homelessness in 2017 were in New York

veterans, children and youth, the chronical y homeless,

City or Los Angeles (Henry et al., 2017).

individuals, and families. 18 For the primary outcomes of this study, we constructed three rates of homelessness for each

CoC using the PIT estimates—

1. Total number of homeless people per 10,000 population. 19

2. Total number of sheltered homeless people per 10,000 population. 20

3. Total number of unsheltered homeless people per 10,000 population.

The overall population in each CoC, by which the PIT counts are scaled, 21 is obtained from the Census’s intercensal population estimates. 22

For approximately 70 CoCs, PIT count data col ection occurs biennial y in January of odd years, while the remainder conduct counts annual y. Due to this nuance in the reporting requirements of CoCs’ PIT count data and due to data availability of the independent variables discussed later, the study team used the 15 There are currently around 400 CoCs operating across all 50 states, the District of Columbia, and U.S. territories. CoCs administer and support various community-wide programs with the goals of ending homelessness and increasing self-sufficiency among populations experiencing homelessness (https://www.hudexchange.info/programs/coc/).

16 In this study, we focus on outcomes for all 50 states and the District of Columbia. Puerto Rico and all other U.S. territories were excluded due to concerns of external validity, as wel as availability of data on independent variables. The factors related to homelessness in Puerto Rico and other U.S. territories may be different from those on the mainland. For example, Puerto Rico is grappling with its worst financial crisis and defaulting on its debt. Puerto Rico’s economy has been in a recession for more than a decade, with median income around $16,000 and a high unemployment rate (around 10 percent) and decreasing labor force due to drastic net-migration out of Puerto Rico. In addition, more than half of the population of Puerto Rico are living in poverty. (For more details, see

https://www.census.gov/quickfacts/fact/table/puertorealcomunidadpuertorico/IPE120217).

17 The CoC definition is specified in more detail in the Annual Homeless Assessment Report (Henry et al., 2017).

18 The PIT count of unsheltered homeless people in each CoC relies on volunteers who spent the night gathering information.

Many of the volunteers are homelessness researchers, but some may be amateurs. As a result, the unsheltered homeless count may have some measurement error that might not be just noise and may be correlated to the characteristics of the CoC

(see O’Flaherty, 2018 for more details). The PIT count data, however, remain the best available national unduplicated count of homeless people.

19 This definition of homelessness using the PIT counts fol ows the literature (Byrne et al., 2012; Fargo et al., 2013).

20 Counts of sheltered homelessness should not be viewed as a pure reflection of the demand for temporary crisis housing.

Rather, the number of people counted is a function of available beds, which is necessarily constrained by the resources al ocated to create shelter options in a CoC. Furthermore, CoCs can and do choose to allocate scarce funding to permanent housing programs, which are used to house homeless individuals and families who wil not show up in sheltered homeless counts.

21 CoCs in large metro areas would have large homeless populations. To provide a reasonable measure of the issue of homelessness in a community, we fol ow the literature in scaling the PIT counts by the community population.

22 Throughout the report, we will reference two distinct sources of Census data. The intercensal estimates come from the Population Estimates Program that publishes annual categorical counts of persons and housing units. The other frequently mentioned data source is the ACS 5-year estimates that feature similar population estimates but are subject to availability constraints. For the remainder of the report, we refer to intercensal population estimates or use of them as “intercensal population” and refer to ACS data as “ACS” or “ACS 5-year.”

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PIT count data from 2017 for regression analysis. 23 The county-level data from one of our data sources, American Community Survey (ACS), are only available as 5-year estimates. 24,25 Exhibit 2-1 presents the total homeless populations and rates of homelessness from 2011 through 2017. 26 While levels and rates of total homelessness have decreased during this period, the count of unsheltered homeless people increased by nearly 20,000 between 2015 and 2017, as depicted by the lower dashed line. Throughout our discussions of data and results, any years discussed for the outcome of interest reflect the year the PIT count was collected and reported. All independent variables, however, apart from January temperature and precipitation, represent the year prior to the PIT data collection. For example, rates of homelessness from the 2017 PIT count data are associated with 2016 measures of the dependent variable.

Exhibit 2-1 | Total Homelessness and Rates of Homelessness, 2011 through 2017

700,000

25

600,000

20

500,000

ess

ess

15

400,000

0)

elessn 00

elessn

om

om 300,000

10 f H (Per 10,

Total H 200,000

Rates o

5

100,000

0

0

2011

2013

2015

2017

Total Homeless

Total Sheltered

Total Unsheltered

Homeless Rate

Sheltered Rate

Unsheltered Rate

Sources: Census’s intercensal population estimates; HUD PIT count data

Independent Variables: Predictors of Homelessness

In consultation with HUD, the study team used the literature review to find data sources, with the measures highlighted in the Introduction chapter having clear evidence and theoretical underpinnings to 23 Years correspond to the years HUD PIT counts were col ected. As PIT counts are col ected in January, right-hand side variables correspond to data from the previous year. For example, 2017 PIT counts are merged with 2016 Census data.

24 ACS 1-year estimates are available for approximately 800 out of more than 3,100 counties. Future studies can consider using ACS 1-year estimates and concentrating their analyses on this smaller set of counties and associated CoCs.

25 Earlier ACS 5-year estimates from 2007 through 2011 contains housing market data that capture a major part of the recession/foreclosure crisis and might create potential issues in explaining homelessness in the community.

26 See appendix F for more details on homelessness in specific cities.

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make them important factors associated with homelessness. Data on factors were gathered from a wide variety of sources. Exhibit 2-2 presents the independent variables collected across the five domains that are included in the dataset: housing, economic, safety net, demographic, and climate.

HOUSING MARKET VARIABLES

In accordance with the literature review, the study includes measures of housing market factors—

mainly tightness of the housing markets and availability of affordable housing that are associated with homelessness as independent variables. Most of these housing market variables are derived from ACS

and include those that affect homeowners (median home values and cost burden of homeowners)27 and renters (median rent, median rental utility costs, 28 cost burden of renters, percent of renter-occupied housing units in a community, vacancy rate of rental units); rate of overcrowding; 29 and housing density30 in the community. While other measures of housing costs (namely home values) may be theoretically important in models of homelessness, rents reflect the type of housing costs that are most likely to matter in determining homelessness at the community level. Due to the high correlation between median home values and median rent (0.93) in the community and in order not to hinder our ability to interpret the results, we only include median rent in our regressions, even though we present the home values in exhibit 2-2. Other sources of data were exploited to capture information on evictions and home price growth. We extracted data on eviction rates from the Eviction Lab at Princeton University and scaled the data by the number of renter-occupied housing units (Desmond et al., 2018b). 31 Finally, the data include a house price index representing percentage growth in housing prices since 2009, obtained from the Federal Housing Finance Agency (FHFA) (Bogin, Doerner, and Larson, 2016). Finally, we also included a categorical urbanicity variable. 32

ECONOMIC VARIABLES

Our set of economic variables includes labor market characteristics obtained from the Bureau of Labor Statistics (BLS), in addition to labor and income figures from the Census. Our broadest measure of unemployment is sourced from the BLS Local Area Unemployment Statistics and is calculated by dividing the total unemployed population by the labor force population (BLS, 2018). We also included in our dataset local median household income and poverty rate measures from the Census’s Small Area Income and Poverty Estimates (SAIPE). As explained previously, the median income, median house values, and median rent represent whether individuals in the community can afford housing in that community. Due to the high correlation between median income and median rent (0.82) in the community and so as not to hinder our ability to interpret the results, we only include median rent in 27 Cost burden for homeowners and renters is defined as the share of homeowners (or renters) with housing costs greater than 30 percent of their income.

28 Cost of utilities was estimated by taking the difference of median contract rent and median gross rent.

29 The rate of overcrowding in a housing unit is defined as the share of units with more than 1.5 people per room.

30 We use a dummy variable that is equal to 1 when density is greater than or equal to the 75th percentile (408 units per square mile) value in 2017.

31 Eviction Lab uses estimates of renter-occupied households from the 2000 and 2010 Census and ESRI Business Analyst 2016.

There are cases of states—Alaska, Arkansas, South Dakota and North Dakota—and counties in states that do not have consistent data coverage. The estimates from these states were achieved by using an adjustment from state-reported county statistics. See the Methodology report (Desmond et al., 2018a) for more detail.

32 The four CoC urbanicity categories in the data include Major City, Largely Urban, Largely Suburban, and Largely Rural. Our analysis combines Major City CoCs with Largely Urban CoCs. We refer to this new category as “Urban” throughout the text and results.

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our regressions, even though we present the median income in exhibit 2-2. Estimates of income inequality included a Gini coefficient sourced from ACS 5-year estimates. 33 The Gini coefficient is a representation of an area’s income distribution. Gini coefficients can range from 0 to 100 percent, with 100 percent reflecting maximum income inequality (in other words, a large share of income or wealth in the hands of a smal number of individuals) and a value of 0 representing perfect equality.

SAFETY NET VARIABLES

The measures for participation in social safety net programs include the Earned Income Tax Credit (EITC), Social Security Disability Insurance (SSDI), and Supplemental Security Income (SSI). Using data from the Internal Revenue Service (IRS) (Internal Revenue Service, 2018), we calculated the share of households benefiting from EITC by dividing the number of federal income tax returns receiving EITC by the total number of tax returns, which are comparable to the number of households in a given area. The study team extracted numerators for the participation rates in SSDI and SSI from Social Security Administration (SSA) reports (SSA, 2017a, 2017b). We estimated participation rates in both SSDI and SSI by scaling participation by intercensal population. The final rate modeled was the proportion of households receiving cash assistance income to all families. 34 Cash assistance includes general assistance and Temporary Assistance for Needy Families (TANF). Housing market variables also include programmatic data on HUD-assisted households from the Picture of Subsidized Households (HUD, 2018b) and federal funding reports for each CoC. 35 The measures of occupancy rates36 of HUD-assisted units were extracted from the Picture of Subsidized Households. The study team calculated an additional variable to capture the proportion of HUD-assisted units by dividing the total number of HUD-assisted units in the Picture of Subsidized Households by the total number of housing units in that area (obtained from Census intercensal housing unit estimate). 37 Programmatic information on a measure (percentage of houses built before 1940) related to awarded federal CoC funding but not related to homelessness was included as an instrument to measure if communities with high funding affect homelessness in the community (fol owing some of the recent literature).

DEMOGRAPHIC VARIABLES

We include broad intercensal population estimates by race and ethnicity, gender, age distributions, and net migration. The study team divided each of these estimates by the overall intercensal population to create proportions of individuals in each group. We included specific measures from the ACS 5-year 33 Census describes the Gini coefficient in the 2017 ACS subject definition as “a measure of how much a distribution varies from a proportionate distribution.” Further information can be found at https://www2.census.gov/programs-

surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf (U.S. Census Bureau, 2017).

34 Public assistance income includes general assistance and TANF. Separate payments received for hospital or other medical care (vendor payments) are excluded. This does not include SSI or noncash benefits such as Food Stamps.

35 HUD’s Picture of Subsidized Households is publicly available at https://www.huduser.gov/portal/datasets/assthsg.html, while HUD provided federal CoC funding data for 2010 through 2017 to the study team.

36 Occupancy rates measured in HUD administrative data are not necessarily a true measure of assistance utilization. For example, a unit may be assigned but unoccupied during the application and inspection process (artificially reducing the numerator). In addition, the number of subsidized units available are the number of units that HUD has financially paid for and can financially pay for in rental assistance, which depends on the actual per-unit cost of assistance. For the housing choice voucher program, however, some units may have costs that are higher than the anticipated per-unit cost (artificial y inflated denominator).

37 HUD’s Worst Case Housing Needs (WCN) report (Watson et al., 2017) uses an alternate measure of HUD assistance: the income-eligible population that receives housing assistance. The WCN report can be accessed at

https://www.huduser.gov/portal/sites/default/files/pdf/Worst-Case-Housing-Needs.pdf.

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estimates to control for family composition in terms of single-parent households, the prevalence of single-adult households, educational attainment, and veteran status. Our variable for the share of veterans is scaled by the total adult population 25 years and over, and the proportion of children living in single-parent households is scaled by the overall number of children under age 18. Educational attainment is measured by the share of the population with at least a bachelor’s degree compared with the overall population.

Measures of health and access to health care are considered important determinants of homelessness in a community. We include healthcare costs, availability of mental health providers, and alcohol consumption as proxies for the measures of health. As a potential source of individual-level income shocks that may cause low-income households to be homeless, healthcare costs are represented through the average Medicare parts A and B reimbursements per enrollee. 38 The study team obtained precalculated reimbursement figures from the Dartmouth Atlas of Health Care (2018). We measure the availability of mental health treatment through counts of mental health providers, which are scaled by the overall intercensal population. These counts were provided by the County Health Rankings (CHR) program from The University of Wisconsin Population Health Institute (2018). Alcohol consumption is factored through measures of mortality rates and the prevalence of excessive drinking in adults, which is also obtained from CHR. The Institute for Health Metrics and Evaluation (IMHE) at the University of Washington, which supplied mortality rate data, and CHR precalculated these proportions (IMHE, 2018).

CLIMATE VARIABLES

We included variables accounting for climate conditions that may explain PIT counts, especially because the data are collected in January, and some of the homelessness measures may be sensitive to weather.

More specifically, we extracted measures of the average temperature for the month of January, average summer temperatures, total precipitation for the month of January, and total annual precipitation. We obtained these county-level measurements from the National Oceanic and Atmospheric Administration (NOAA) Climate Divisional Database (nCLIMDIV) (Vose et al., 2014). 39

CONSTRUCTING THE DATASET AT THE CONTINUUM OF CARE LEVEL

As our primary outcome is measured at the CoC level, the study team created a dataset in which the unit of measurement is the CoC. An overarching chal enge of constructing the dataset at the CoC level is twofold. First, CoC boundaries change over time. 40 Second, there are discrepancies between CoC

boundaries and the geographic boundaries across the other datasets (most datasets are available at the county level). CoCs typically serve areas representing a city, individual county, group of counties, or entire state. Given these issues, there are four possible relationships between county and CoC

boundaries—

1. A single county is served by a single CoC.

2. Multiple counties are served by a single CoC.

3. Multiple CoCs serve areas within a single county.

38 Medicare enrol ees are not the actual population of interest, but the costs faced by the recipients represent the healthcare costs in the community.

39 County-level data were not available for Alaska, District of Columbia, and Hawaii. All areas within Alaska and Hawaii were assigned city-level records from Anchorage and Honolulu, respectively. Climate data for District of Columbia were measured at Ronald Reagan Washington National Airport in Virginia (NOAA, 2019).

40 The exact number of CoCs vary due to the creation, dissolution, and merging of CoCs between 2011 and 2017. In 2011, there were 432 CoCs, whereas in 2017, there were 399 CoCs.

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4. Combination of 2 and 3: A county is served by multiple CoCs that also serve other counties.

In the first possible case, no changes are made to CoCs or counties. In the second case, the data for all counties served by a single CoC are aggregated to the CoC level by taking the sum or a population-weighted average of the county-level measures. In the third case, all the PIT estimates are summed up to create a combined CoC-level estimate. The fourth possible case involves a county served by multiple CoCs that also serve other counties. In these cases, we sum up the PIT estimates, as well as the county-level estimates, to more accurately reflect the data for the areas served by those CoCs. 41 Ultimately, our dataset includes 376 individual CoCs or combined CoC areas serving 3,117 counties (more than 99

percent of the total counties in the United States). 42

Exhibit 2-2 | Potential Variables to Be Included in Model of Homelessness

Predictors of Homelessness

Data Source (Years)

Geography

Available

Scaling Variable

Dependent Variables

Rate of Homelessness

HUD PIT (2017)

CoC

Estimated total population

Rate of Sheltered Homelessness

HUD PIT (2017)

CoC

Estimated total population

Rate of Unsheltered Homelessness

HUD PIT (2017)

CoC

Estimated total population

Housing Domain

Median House Values

ACS 5-Year (2016)

County

–

House Price Growth Index

FHFA (2016)

County

–

Percentage of Homeowners with Cost

Burden

ACS 5-Year (2016)

County

Owner-occupied housing

units

Median Rent

ACS 5-Year (2016)

County

–

Median Rental Utility Cost

ACS 5-Year (2016)

County

–

Percentage of Renters with Cost Burden

ACS 5-Year (2016)

County

Rental-occupied housing

units

Share of Renter-Occupied Units

ACS 5-Year (2016)

County

Occupied housing units

Rental Vacancy Rates

ACS 5-Year (2016)

County

Rental housing units

Housing Density

ACS 5-Year (2016)

County

Total housing units

Eviction Rate

Eviction Lab (2016)

County

Renter-occupied households

Share of Overcrowded Housing Units

ACS 5-Year (2016)

County

Occupied housing units

Urbanicity

HUD (2016)

CoC

–

Economic Domain

Median Household Income

SAIPE (2016)

County

–

Civilian noninstitutionalized

Unemployment Rate

BLS (2016)

County

labor force 16 years and

older

Gini Coefficient of Income Inequality

ACS 5-Year (2016)

County

–

Poverty Rate

SAIPE (2016)

County

–

Safety Net Domain

Share of Families Receiving Cash Assistance

ACS 5-Year (2016)

County

Total households

Share of Households Eligible for EITC

IRS (2016)

County

Income tax returns

SSDI Participation Rate

SSA (2016)

County

Estimated total population

SSI Participation Rate

SSA (2016)

County

Estimated total population

Share of HUD-Assisted Units

HUD Picture of Subsidized

Households (2016)

County

Total estimated housing units

HUD-Assisted Unit Occupancy Rate

HUD Picture of Subsidized

Housing (2016)

County

HUD-supported housing units

(occupied and unoccupied)

41 We updated a HUD-provided CoC-county crosswalk to aggregate county estimates to the CoC level and overcome this mismatch of boundaries across datasets. Future studies can either use this crosswalk or handle the aggregations differently if granular Census data are available.

42 See appendix C for a more detailed description of how we constructed the final CoC-level dataset.

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Predictors of Homelessness

Data Source (Years)

Geography

Available

Scaling Variable

Federal CoC Funding+

HUD Administrative Data

(2016)

CoC

Percentage of Houses Built Before 1940

ACS (2016)

County

-

Demographic Domain

White Percentagea

Census (2016)

County

Estimated total population

Black Percentagea

Census (2016)

County

Estimated total population

Asian Percentagea

Census (2016)

County

Estimated total population

Hispanic Percentagea

Census (2016)

County

Estimated total population

Other Race Percentagea

Census (2016)

County

Estimated total population

Proportion of Childrenb

Census (2016)

County

Estimated total population

Proportion of Adultsb

Census (2016)

County

Estimated total population

Proportion of Seniorsb

Census (2016)

County

Estimated total population

Proportion of Females

Census (2016)

County

Estimated total population

Net Migration Rate

Census (2016)

County

Estimated total population

Share of Single-Adult Households

ACS 5-Year (2016)

County

Total households

Share of Children in Single-Parent

Households

ACS 5-Year (2016)

County

Population under 18 years

Share of Veterans

ACS 5-Year (2016)

County

Civilian population 25 years

and older

Percentage with Bachelor’s Degree or

Higher

ACS 5-Year (2016)

County

Population 25 through 64

years

Percentage with Some Col ege

ACS 5-Year (2016)

County

Population 25 through 64

years

Percentage with High School Diploma

ACS 5-Year (2016)

County

Population 25 through 64

years

Percentage with Less than High School

ACS 5-Year (2016)

County

Population 25 through 64

years

Healthcare Cost per Enrollee

Dartmouthc (2016)

County

Medicare enrol ees

Robert Wood Johnson

Ratio of Mental Health Providers

Foundation (RWJF) CHRd

County

Estimated total population

(2017)

Alcohol Mortality Rate (Per 100,000)

IMHEd (2014)

County

Unstated population

Proportion of Excess Drinkers

RWJF CHRc (2017)

County

Unstated population

Climate Domain

Average January Temperature

NOAA nCLIMDIV (2017)

County

-

Average Summer Temperaturee

NOAA nCLIMDIV (2016)

County

-

Precipitation in January

NOAA nCLIMDIV (2017)

County

-

Total Annual Precipitation

NOAA nCLIMDIV (2016)

County

-

+ Fol owing Lucas (2017) and Popov (2016), we use the percentage of houses built before 1940 as an instrument or proxy for the Federal CoC funding to estimate the association between funding in the community and homelessness.

a Race and ethnicity comprise an exclusive category for which Hispanic or Latino is treated as a separate race. The estimated population categories for Native American, Hawaiian, or Pacific Island or two-or-more-race persons is combined with the

“Other race” category.

b Age comprises an exclusive category that classifies children as younger than 20 years old and seniors as older than 64 years.

c Healthcare cost and mental health provider data are obtained from the Dartmouth Atlas of Health Care and the University of Wisconsin-CHR, respectively. The underlying source of the data is the Centers for Medicare & Medicaid Services (The Dartmouth Institute for Health Policy and Clinical Practice, 2018; University of Wisconsin Population Health Institute, 2018).

d Alcohol mortality rate and excess drinking are obtained from the Institute for Health Metrics and the University of Wisconsin-CHR, respectively. The underlying source of the data is the Centers for Disease Control and Prevention (University of Wisconsin Population Health Institute, 2018).

e Average summer temperature reflects the average of monthly temperatures for June, July, and August.

Notes: Denominator variables “Estimated total population” and “Total estimated housing units” are from Census’s intercensal population estimate.

| 22

EMPIRICAL STRATEGY

On a single night in January 2017, nearly 550,000 people were living in homelessness in the United States, representing nearly 17 of every 10,000 individuals. More than 350,000 of these homeless people were sheltered, while nearly 200,000 were unsheltered. Our descriptive analysis in the first section sheds light on the variation in sheltered and unsheltered homelessness among urban, suburban, and rural areas. The second section presents our systematic way of identifying the relevant independent variables to include in the model across the five domains: housing, economics, safety net, demographics, and climate. The third section details our model and analysis approach to estimating the relationship between rates of homelessness and community-level factors across these domains.

Descriptive Analysis

The study team computed descriptive statistics for all three primary outcome variables—the overall rate of homelessness, sheltered homelessness rate, and unsheltered homelessness rate—and more than 60

independent variables. We compared their means and distributions across three categories of urbanicity:43 largely urban Continuums of Care (CoCs) ( N = 104), largely suburban CoCs ( N = 160), and largely rural CoCs ( N = 110). 44

Exhibit 3-1 presents the Point-in-Time (PIT) counts of total

homeless, sheltered homeless, and unsheltered homeless

The increase in number of people

people from 2011 through 2017 for al CoCs by urbanicity

experiencing homelessness in largely

category. The overall estimates for all CoCs show that

urban areas from 2015 to 2017 is

homelessness decreased from 2011 through 2017 (Panel A in entirely driven by unsheltered exhibit D-1 in appendix D). The reduction between 2015 and homelessness.

2017, however, was mitigated by a relatively sizeable

increase in the unsheltered homeless population. The

population of unsheltered homeless people increased by nearly 20,000, from 168,726 in 2015 to 188,643 in 2017. Exhibit 3-1 shows that aggregate estimates for the total homeless population for largely suburban and largely rural CoCs show a reduction in overall homeless population estimates, whereas largely urban CoCs experienced an increase from 308,185 in 2011 to 323,186 in 2017. In addition, this increase in the overall homeless population in major city CoCs and largely urban CoCs is entirely driven by an increase in unsheltered homelessness in those CoCs (an increase of 22,000—from 87,345 in 2015 to 109,252 in 2017). Similarly, largely suburban CoCs also saw an increase (albeit small) in their unsheltered homeless population.

43 A CoC is defined as urban, rural, or suburban based on whether the population resides more in one type of area than the others, based on data from the Census Bureau. For example, a rural CoC has more people residing in the rural areas than in the suburban or urban areas. It is common for CoCs to have people living in urban, suburban, or rural areas; however, this urbanicity designation reflects the aggregate data about the types of geographies where people in the CoC reside.

44 Major city and largely urban CoC urbanicity categories were combined for the descriptive analyses. A comprehensive list of CoCs by urbanicity is provided in exhibit D-4 in appendix D.

| 23

Exhibit 3-1 | Point-in-Time Counts of Total Homelessness in 2011 Through 2017, by Urbanicity Notes: These totals are for the populations of all 50 states plus the District of Columbia. All U.S. territories are excluded from this analysis.

Exhibit 3-2 presents, by urbanicity category, the CoC-level summary statistics for the primary outcomes of this study in 2017. While the average rate of homelessness across all CoCs was around 17 homeless people per population of 10,000, this number varied from 9.4 in largely rural CoCs to 28.4 in largely urban CoCs. Similarly, largely urban CoCs had large sheltered and unsheltered homeless populations relative to homeless populations in largely suburban and largely rural CoCs. For largely urban CoCs, the average sheltered and unsheltered population rates were 18.8 and 9.6 per 10,000, respectively. In contrast, sheltered homelessness rates for largely suburban and largely rural CoCs were 8.2 and 5.8 per 10,000, respectively, and the unsheltered homelessness rates were around 4 in 10,000 for both types of CoCs. In general, these numbers show that the largely urban CoCs tend to have large homeless populations.

Exhibit 3-2 | Average Point-in-Time Counts by Urbanicity in 2017

Major City

Largely

Largely

PIT Counts Per CoC

All CoCs

and Largely

Suburban

Urban CoCs

CoCs

Rural CoCs

Mean

SD

Mean

SD

Mean SD Mean SD

Average Homeless (per 10,000 population)

17.0

18.4

28.4

24.2

12.2

9.8

9.4

9.4

Average Sheltered Homeless (per 10,000 population)

11.1

14.2

18.8

21.0

8.2

5.5

5.8

3.7

Average Unsheltered Homeless (per 10,000 population)

5.8

9.8

9.6

12.7

4.1

7.2

3.6

7.0

Observations

374

104

160

110

Notes: These estimates represent the weighted average, based on the overall population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2017. All U.S. territories are excluded from this analysis.

Exhibit 3-3 presents the 2016 summary statistics for variables in the housing domain by urbanicity.

Overal , the housing variables show a tight and overcrowded housing market in the largely urban CoCs.

As expected, the housing costs are much higher in urban and suburban CoCs, with median home values more than $100,000 greater and median rents more than $300 greater per month compared with rural

| 24

CoCs. In 2016, median home values in urban and suburban CoCs were around $286,000 and $261,000, respectively, and median rents were around $950 in both urbanicity categories. In contrast, home values in rural CoCs were around $146,000 in 2016, and median rents were around $600 per month.

Furthermore, urban CoCs have a larger share of renter-occupied units (45.3 percent) compared with suburban (33.3 percent) and rural CoCs (30.4 percent). Rental vacancy rates, however, are greater in rural CoCs (6.9 percent), compared with slightly under 6 percent in urban and suburban CoCs. The cost burden for homeowners and renters is also slightly higher in urban and suburban CoCs compared with rural CoCs, with about half the renters spending more than 30 percent of their income on rent. Urban CoCs also have more overcrowded housing units (5 percent) compared with suburban and rural CoCs with slightly higher eviction rates (2.3 percent compared with 2 percent).

Exhibit 3-3 | Summary Statistics for Continuum of Care-Level Housing Variables, 2016

Major City and

Largely

Largely Rural

Variables in Housing Domain

All CoCs

Largely Urban

CoCs

Suburban CoCs

CoCs

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Median Home Value ($1,000s)

232.2 141.6 285.8 167.9 260.7 135.4 145.9

43.1

House Price Index

11.1

15.6

19.5

16.1

9.5

16.4

3.5

8.5

Percentage of Homeowners with Cost Burden (%)

25.7

6.4

28.1

6.4

27.7

6.3

21.1

3.4

Median Rent ($100s)

8.3

2.8

9.4

2.6

9.5

2.8

6.0

1.1

Median Rental Utility Cost ($10s)

14.7

3.0

13.5

3.2

14.7

2.5

16.0

2.7

Percentage of Renters with Cost Burden (%)

50.5

4.9

52.0

4.9

52.2

4.8

47.5

3.6

Percentage of Renter-Occupied Units (%)

36.6

9.6

45.3

8.7

33.3

7.5

30.4

3.4

Rental Vacancy Rate (%)

6.2

2.2

5.7

2.4

5.9

2.0

6.9

2.0

Percentage of CoCs with High Housing Density (%)

37.3

48.4

67.7

47

42

49.5

0.0

0.0

Eviction Rate (%)

2.1

1.8

2.3

1.9

2.0

2.1

2.0

1.4

Change in Eviction Rate (%) a

-0.4

1.4

-0.4

1.4

-0.4

1.6

-0.3

1.0

Percentage of Overcrowded Housing Units (%)

3.5

2.6

5.0

3.1

2.9

2.2

2.5

1.5

a Change in Eviction Rate represents the difference between 2016 and 2012 values.

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis. Due to the high correlation with median rent, median home value is excluded from the regression analysis.

Exhibit 3-4 shows that suburban CoCs operate in economically better areas with higher median income, lower unemployment, and lower poverty rates. Areas where suburban CoCs operate have the highest median income at $68,000, followed by the median income of $62,000 for areas where urban CoCs operate and about $52,000 for rural CoCs. In addition, poverty rates are higher in urban CoCs (14.9

percent) and rural CoCs (14.6 percent) compared with suburban CoCs (11.4 percent). Estimates of income inequality, as measured by the Gini coefficient, show slightly higher dispersions in urban CoCs than suburban and rural CoCs.

| 25

Exhibit 3-4 | Summary Statistics for Continuum of Care-Level Economic Variables, 2016

Major City and

Largely Suburban

Largely Rural

Variables in Economic Domain

All CoCs

Largely Urban

CoCs

CoCs

CoCs

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Median Income ($1,000s)

60.4

14.5

61.6

13.6

67.6

17.0

52.1

6.1

Unemployment Rate (%)

4.9

1.2

4.9

1.3

4.8

1.2

5.1

1.0

Gini Coefficient of Income Inequality (%)

46.1

2.9

48.2

2.4

45.5

3.0

44.3

1.5

Poverty Rate (%)

13.7

3.8

14.9

3.8

11.4

3.5

14.6

3.0

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis. Due to the high correlation with median rent, median income is excluded from the regression analysis.

Exhibit 3-5 presents the 2016 summary statistics for variables in the safety net domain. Urban and rural CoCs have higher rates of participation in safety net programs than suburban CoCs have. While rates of cash assistance do not vary greatly, household Earned Income Tax Credit (EITC) eligibility is higher in urban CoCs (19.6 percent) and rural CoCs (19.9 percent) compared with suburban CoCs (16.1 percent).

Similarly, Supplemental Security Income (SSI) participation is also higher in urban CoCs (3 percent) and rural CoCs (2.5 percent) compared with suburban CoCs (2.1 percent). Rural CoCs have the largest Social Security Disability Insurance (SSDI) participation rates, with 3.4 percent of the population qualifying for the program, compared with 2.2 percent in urban CoCs and 2.5 percent in suburban CoCs. Final y, urban CoCs receive more than five times as much federal CoC funding ($35.5 million) as suburban CoCs ($6.7

million) and rural CoCs ($6.6 million). As federal CoC funding is high in CoCs servicing places with higher homelessness rates, the study uses an instrument for the percentage of houses built before 1940, which determines 25 percent of the federal CoC funding formula. HUD-assisted housing is more prevalent in urban CoCs, with HUD-assisted units representing 4.6 percent of all units compared with 3.2 percent of units in suburban CoCs and 2.8 percent of units in rural CoCs. 45

Exhibit 3-5 | Summary Statistics for Continuum of Care-Level Safety Net Variables, 2016

Major City and

Largely

Largely Rural

Variables in Safety Net Domain

All CoCs

Largely Urban

CoCs

Suburban CoCs

CoCs

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Percentage of Households Receiving Cash

Assistance (%)

2.7

1.2

3.0

1.5

2.6

1.2

2.5

0.8

Percentage of Households Eligible for EITC (%)

18.6

5.5

19.6

5.5

16.1

5.1

19.9

4.9

SSDI Participation Rate (%)

2.7

1.0

2.2

0.7

2.5

0.9

3.4

0.9

SSI Participation Rate (%)

2.5

1.1

3.0

1.3

2.1

0.9

2.5

0.9

Share of HUD-Assisted Units (%)

3.6

2.1

4.6

2.7

3.2

1.9

2.8

0.7

Occupancy Rate of HUD-Assisted Units (%)

92.6

3.6

93.3

3.1

92.8

3.9

91.5

3.6

Percentage of Houses Built Before 1940 (%)

12.6

11.0

14.2

13.5

11.1

10.0

12.5

8.4

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis. Due to high correlation with poverty rates, EITC eligibility and SSI participation are excluded from the regression analysis. SSDI participation is excluded from regression analysis due to the high correlation with median rent.

45 Another way to think about this is that the share of the income-eligible population that receives housing assistance is the approach HUD reports in the Worst-Case Housing Needs (WCN) report. By the WCN measure, income-eligible households in nonmetro areas are more likely to receive housing assistance than similar households in cities or suburbs. See exhibits 2-1 and 2-2 at https://www.huduser.gov/portal/sites/default/files/pdf/Worst-Case-Housing-Needs.pdf.

| 26

Exhibit 3-6 presents the 2016 summary statistics for variables in the demographic domain. Urban CoCs operate in more diverse regions in terms of race and ethnicity. Urban CoCs are 46.5 percent White, 16

percent Black, 26 percent Hispanic, and 8.6 percent Asian. By comparison, suburban CoCs and rural CoCs are 62.9 percent and 75.2 percent White, respectively. Urban CoCs also appear to be slightly younger, with a lower share of residents older than age 65 (13.1 percent) compared with suburban CoCs (16

percent) and rural CoCs (16.7 percent).

There appears to be little variation across geographies by household type. Single-person households comprise 28.7 percent of al households in urban CoCs, 26.4 percent in suburban CoCs, and 26.8 percent in rural CoCs. In addition, urban CoCs have a slightly larger share of children (0 through 18 years) living in single-parent households (28.6 percent) compared with suburban (24.5 percent) and rural CoCs (24.6

percent). Finally, rural CoCs have a larger share of veterans (10.7 percent) compared with urban (7.5

percent) and suburban CoCs (9 percent).

The education distribution varies greatly by urbanicity. Rural CoCs have a much lower share of residents with bachelor’s degrees (23.8 percent) compared with urban (35.8 percent) and suburban CoCs (34.8

percent). While urban CoCs have the largest share of residents with a bachelor’s degree or higher, they also have the largest share of residents with less than a high school education.

Among health categories, healthcare costs are slightly higher in urban CoCs than in suburban and rural CoCs. Alcohol mortality is slightly higher in major city and urban CoCs, while rates of excessive drinking do not appear to vary across geographies.

Exhibit 3-7 shows climate conditions across the nine Census geographic divisions. For the climate conditions observed at the time of PIT counts, CoCs within the South region experienced warmer and wetter conditions in the month of January. Similarly, South region CoCs were warmer during the previous summer months and experienced more precipitation in 2016. The summary statistics also show that CoCs within the Pacific region experienced notably higher precipitation totals in January 2017 (9.7

inches) than the second highest region (6.0 in the East South Central region). 46 Additional summary statistics for climate conditions by urbanicity categories are included in exhibit D-5 in appendix D.

Exhibit 3-6 | Summary Statistics for Continuum of Care-Level Demographic Characteristics, 2016

Major City and

Largely Suburban

Largely Rural

Variables in Demographic Domain

All CoCs

Largely Urban

CoCs

CoCs

CoCs

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Total Population (1,000s)

862.8

1,257.0 1,094.9 1574.3

644.9

523.6

960.3

1,592.0

Percentage White (%)

61.1

20.3

46.5

16.8

62.9

16.9

75.2

15.6

Percentage African-American (%)

12.5

11.2

16.0

12.3

12.7

10.5

8.4

9.2

Percentage Hispanic (%)

17.9

15.6

26.0

17.1

15.7

12.9

11.2

12.4

Percentage Asian (%)

5.5

6.2

8.6

6.8

6.0

6.4

1.8

1.7

Percentage Children (Age 0 through 19)

(%)

25.4

2.5

25.6

2.5

25.0

2.8

25.6

2.0

46 Mean January precipitation values reflect inch totals from January 2017 and may seem intuitively higher than expected for the Pacific Census region. A further review revealed exceptionally higher values for California CoCs, which are further explained by this article from the National Oceanic and Atmospheric Administration: https://www.climate.gov/news-

features/event-tracker/soaking-rains-and-massive-snows-pile-california-january-2017 (Di Liberto, 2017).

| 27

Major City and

Largely Suburban

Largely Rural

Variables in Demographic Domain

All CoCs

Largely Urban

CoCs

CoCs

CoCs

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Percentage Adult (Age 20 through 64)

(%)

59.4

2.5

61.2

2.2

59.0

2.5

57.7

1.1

Percentage Senior (Age 65 and Older)

(%)

15.2

3.2

13.1

1.7

16.0

4.1

16.7

2.1

Percentage Female (%)

50.8

0.8

51.0

0.9

51.0

0.7

50.3

0.7

Net-Migration Rate (%)

0.4

0.8

0.3

0.7

0.5

0.9

0.3

0.6

Change in Net-Migration Rate a

0.0

0.6

-0.2

0.5

0.1

0.6

0.2

0.6

Percentage of One-Person Households

(%)

27.4

3.9

28.7

4.3

26.4

4.4

26.8

2.2

Percentage of Under 18 Population in

Single-Parent Households (%)

26.0

6.3

28.6

6.8

24.5

6.5

24.6

4.6

Percentage Veteran (25 and Older) (%)

9.0

2.9

7.5

3.3

9.0

2.8

10.7

1.2

Percentage with Bachelor’s Degree or

Higher (%)

31.5

9.6

35.8

9.0

34.8

9.7

23.8

3.9

Percentage with Some Col ege (%)

30.6

4.6

28.7

4.7

30.3

4.7

32.8

3.3

Percentage with High School Diploma

(%)

26.2

6.1

22.3

4.3

25.0

5.4

31.7

4.0

Percentage with Less than High School

(%)

11.7

4.5

13.3

4.7

9.9

4.0

11.7

3.9

Healthcare Costs ($1,000s)

10.4

1.5

11.1

1.8

10.5

1.3

9.5

0.8

Mental Health Providers as Share of

Population (%)

0.1

0.2

0.0

0.0

0.1

0.1

0.3

0.2

Alcohol Mortality (Per 100,000)

2.9

1.3

3.1

1.4

2.6

1.0

2.9

1.4

Excessive Drinking Rate (%)

18.6

2.6

19.2

2.5

18.6

2.2

18.0

2.8

a Change in Net-Migration Rate represents the difference between 2016 and 2012 values.

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis. For the regression analysis, percentage White is the omitted race/ethnicity category; percentage other (two or more races, Native American, Pacific Islander) is excluded from the analysis; and percentage adult is the omitted age category.

| 28

Exhibit 3-7 | Summary Statistics for Continuum of Care-Level Climate Conditions, by Census Division Northeast Region

Midwest Region

South Region

West Region

East North

West North

South

East South

West South

Variables in Climate

New England

Middle

Atlantic

Central

Central

Atlantic

Central

Central

Mountain

Pacific

Domain

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Average January

Temperature (°F)

30.8

3.7

32.8

3.9

30.1

3.8

25.8

9.2

49.1

11.0

47.9

5.1

48.9

7.2

30.9

10.9

41.8

11.2

Average June, July, and

August Temperature (°F)

70.1

2.2

72.4

3.2

73.0

2.5

73.8

4.7

79.3

3.5

79.6

1.9

82.0

1.6

73.2

7.9

70.5

7.5

Total January Precipitation

(Inches)

3.7

1.0

3.5

0.7

3.0

0.9

1.3

0.7

3.8

2.2

6.0

2.7

3.7

2.0

2.6

1.6

9.7

6.1

Total Annual Precipitation

(Inches)

37.5

2.3

38.2

3.5

37.4

3.5

36.4

5.7

46.2

9.0

47.5

7.1

44.9

17.0

14.9

5.1

35.7

24.1

Notes: These estimates represent the weighted average temperature and precipitation for each CoC for January 2017 and the average summer and annual precipitation from 2016. Estimates represent the population of all 50 states plus the District of Columbia. All U.S. territories are excluded from this analysis. Summer temperature represents the average temperature for June, July, and August. 47 Further information on Census regions can be found at https://www2.census.gov/programs-surveys/sahie/reference-

maps/2015/us-regdiv.pdf

47 These 3 months were selected for average summer temperatures according to sources such as the fol owing: https://www.washingtonpost.com/news/capital-weather-

gang/wp/2018/09/06/hot-nights-summer-low-temperatures-were-warmest-on-record-in-lower-48/?utm_term=.e5b411b3b8eb.

| 29

Variable Selection

The data assessment outlined in the Data chapter resulted in the identification of more than 60

independent variables across the five domains: housing, economic, safety net, demographic, and climate conditions. Preliminary regression analysis of the relationship between these independent variables and the outcomes of interest—total homeless, sheltered homeless, and unsheltered homeless people—

suggested potential overfitting and a large degree of multicollinearity. 48 These factors may make the relationships between these collinear regressors and the dependent variables of interest difficult to interpret. The study team fol owed a two-step data selection approach to deal with these issues of multicollinearity and model overfit—

1. Qualitative analysis.

2. Backward stepwise selection.

First, the study team used qualitative assessment to identify the initial list of variables to include in the model. This assessment relied on theoretical context from the literature, as wel as the examination of correlation among specific pairs of independent variables. Through this process, the study team dropped several potential independent variables that were highly correlated with each other (median home values and median income were correlated with median rent in the community) and other modeling issues into our regression specifications. Variables dropped during the qualitative assessment are further outlined in appendix B.

Following this qualitative assessment, the study team applied a systematic variable selection process using statistical and econometric methodologies to select a set of independent variables that maximizes the explanatory power of the model. We used the vselect module in Stata49 to run a stepwise variable selection process that reduced the set of explanatory variables based on overall model fit, thereby al owing the study team to select those independent variables that maximize the adjusted R-squared (Lindsey, 2014; Lindsey and Sheather, 2010). Variables dropped in this process because they did not add any explanatory power for the outcome of interest. Additional exhibits in appendix D provide regression analysis results for models with the same variables to provide easier comparison across models. The results using the same set of variables show findings like the regression results in this chapter and the next. A detailed description of the stepwise elimination process and the overal results of the variable selection process are presented in appendix E.

Statistical Model and Analysis

Previous studies have typical y modeled homelessness as a function of various geographic-level factors, including local housing and economic conditions, safety net program participation, demographic composition, and climate conditions. This framework provided the study team with a useful starting point as we considered potential models and estimation strategies. To examine this relationship and further our understanding of the CoC-level factors that are associated with rates of homelessness, the 48 Post-estimation analysis involving the assessment of the variance inflation factor of the independent variables suggested a large degree of multicol inearity.

49 Further information on vselect dynamics can be found at https://www.stata-journal.com/sjpdf.html?articlenum=st0213.

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study team used ordinary least squares (OLS) to estimate the national-level model presented in equation (1).

The study team modeled rates of homelessness in the following manner:

𝐻𝐻𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑀𝑀𝑖𝑖 + 𝛾𝛾𝐸𝐸𝑖𝑖 + 𝛿𝛿𝑆𝑆𝑖𝑖 + 𝜃𝜃𝐷𝐷𝑖𝑖 + 𝜌𝜌𝑊𝑊𝑖𝑖 + 𝜀𝜀 (1) The dependent variable 𝐻𝐻 is the rate of total, sheltered, or unsheltered homelessness per 10,000

population in CoC 𝑖𝑖 in 2017. The right-hand side of equation (1) consists of five vectors representing each domain: 𝑀𝑀 includes variables in the housing market domain, 𝐸𝐸 includes variables in the economic domain, 𝑆𝑆 includes variables in the safety net domain, 𝐷𝐷 includes variables in the demographic domain, and 𝑊𝑊 includes variables in the climate conditions domain. The variables included in each vector are determined by the variable selection process outlined in the previous section. Coefficient estimates 𝛽𝛽, 𝛾𝛾,

𝛿𝛿, 𝜃𝜃, and 𝜌𝜌 provide insight into the factors associated with rates of homelessness at the CoC level.

Regression models include controls for state effects as necessary. Standard errors are clustered at the state level for the OLS regression.

Our overal estimation approach was shaped, in part, by the data availability constraints outlined in the Data chapter. Because some variables, especially the American Community Survey (ACS) 5-year estimates, exhibit little variation from 2012 through 2017, we present cross-sectional estimates of equation (1). 50 Our cross-sectional models al ow for the inclusion of the most robust set of independent variables but clearly hamper our ability to exploit variations in rates of homelessness over time. Future studies, particularly those interested in large population areas, could also use the ACS 1-year data instead of the ACS 5-year estimates used in this study. Finally, in addition to estimates of the national-level model, the study team examined the factors that are associated with rates of homelessness among various categories of urbanicity, on the west coast and in tight, high-cost markets with large shares of renters.

50 With respect to ACS 5-year estimates, it should be noted that during the period between 2011 and 2016, the economy was also recovering from the Great Recession.

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NATIONAL MODEL OF HOMELESSNESS

This chapter presents the estimates of our national-level model for the three outcomes of interest: rates of total, sheltered, and unsheltered homelessness per 10,000 population. The dependent variable is the rate of homelessness and the set of independent variables are those that were chosen through the variable selection process described in the “Variable Selection” section of the Empirical Strategy chapter. 51

Exhibit 4-1 presents the regression estimates of the relationship between Continuum of Care-(CoC) level factors and rates of total, sheltered, and unsheltered homelessness per 10,000 population as estimated by the Point-in-Time (PIT) counts in 2017. 52 These estimates show that many variables across the five domains are significantly associated with rates of homelessness. Across all three specifications, R-squared estimates (0.82 for total homeless, 0.81 for sheltered homeless, and 0.68 for unsheltered homeless people) indicate that the variables in our models account for a high degree of variation in rates of homelessness across CoCs. These estimates do show some differences in the magnitude and significance of estimates across the outcomes of interest.

TOTAL HOMELESSNESS

Estimates in the housing domain indicate that rates of total homelessness may be related to factors that crowd individuals out of the formal housing market. The effects for median rent, the presence of overcrowded housing units, and living in a CoC with high housing density were particularly salient in the national model of total homelessness. 53 While both median rent and the share of overcrowded units are positively associated with rates of total homelessness, housing density is negatively associated with rates of total homelessness. Our estimates indicate that a 10-percent increase in median rent is associated with a 1.92-person increase in the rate of total homelessness per 10,000, and a 1-percentage-point increase in the share of overcrowded housing units is associated with a 5.44-person increase in the rate of total homelessness per 10,000. In addition, CoCs going from a low housing density to high housing density is associated with a 3.37 per 10,000 population reduction in the rate of total homelessness. Given that the mean rate of total homelessness across all communities is around 17 per 10,000 population, these estimates show substantively relevant relationships between total homelessness and median rents, the presence of overcrowded housing units, and housing density.

51 As mentioned previously, all dependent variables presented in this report reflect the year the PIT count was col ected. All independent variables, however, except for January temperature and precipitation, represent the year prior to the PIT data col ection. For example, rates of homelessness from the 2017 PIT count data are associated with 2016 dependent variables, including the American Community Survey (ACS) 5-year estimates that correspond to the 2012 through 2016 ACS.

52 The regression models presented in the National Model of Homelessness chapter and the Subgroup Analysis Chapter are weighted by the population in each CoC. appendix D presents alternative specifications and sensitivity tests that il ustrate our estimates are generally robust to weighting and the inclusion of additional independent variables.

53 As mentioned in the Data chapter, housing density is a dummy variable that is equal to 1 when density is greater than or equal to the 75th percentile (408 units per square mile) value in 2017.

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High median rents may prevent low-income or otherwise

economical y disadvantaged individuals from affording

New York City CoC

adequate shelter. 54 Similarly, an increased prevalence of

Rate of Total Homelessness: 88.8 per 10,000

overcrowded rental housing units in a community

Median Rent: $1,220

indicates tighter housing markets or lack of availability of Share of Overcrowded Units: 9.2 percent affordable rentals, contributing to increased rates of total

homelessness. Furthermore, low housing density in a CoC San Francisco CoC

Rate of Total Homelessness: 78.3 per 10,000

is associated with higher rates of total homelessness. Low Median Rent: $1,573

housing density could be an indication of low housing

Share of Overcrowded Units: 6.2 percent

availability and vice versa; therefore, areas with low

availability may have higher rates of total homelessness. In Anchorage CoC

practice, the picture is more complex. The New York City Rate of Total Homelessness: 37.9 per 10,000

and San Francisco CoCs have the two highest rates of

Median Rent: $1,146

housing density (11,466 and 8,397 units per square mile, Share of Overcrowded Units: 4.1 percent respectively), accompanied by high median rents and rates

of overcrowding. They also have some of the highest rates of total homelessness. Of all CoCs, the New York City CoC’s rate of total homelessness per 10,000 population is the 3rd highest, the rents are the 32nd highest, and the rate of overcrowding is 4th highest. In the San Francisco CoC, the rate of total homelessness is 6th highest, the median rents are 6th highest, and the rate of overcrowding is 11th highest. These major cities and their high nominal homelessness counts often receive substantial y more focus in media and public discussion, but they may be extreme examples when viewing the relationship between density and homelessness, all else being equal. For example, Anchorage CoC has a relatively low housing density (68 units per square mile) with high rates of total homelessness (37.9 per 10,000) and high median rents and thus may be more reflective of the national environment.

Within the economic domain, the Gini coefficient has a negative relationship with total homelessness rates within that community. We interpret this association as an increase in the dispersion of incomes, which may be associated with lower total homelessness rates, and vice versa. While income is not included in the model, estimates of the relationship between the Gini coefficient and total homelessness may illuminate the association between income and total homelessness. Although this result may seem counterintuitive, variation in the measure of income inequality may suggest that higher incomes among top earners relative to low-income individuals are associated with lower rates of overall homelessness.

Income inequality rises when the incomes of top earners are higher relative to the rest of the population, thereby increasing overal incomes (or median incomes). Areas with high inequality and many high-income earners may also use their tax systems to fund various policies that reduce homelessness, or these areas could have such high housing costs that lower-income households at higher risk of falling into homelessness may move to other communities. The summary statistics in exhibit 3-3 suggest that the variability and influence of suburban CoCs may possibly be driving these 54 High home values make homeownership unaffordable. This barrier may increase the demand for rental housing even for some high-income individuals who may prefer to own a home and may lead to crowding many low-income individuals out of the housing market altogether. While measures of housing costs (such as home values) may be theoretically important in models of homelessness, rents reflect the costs of temporary rather than permanent housing solutions that are likely to matter in determining homelessness at the community level. Median home value and median income are excluded from all regressions due to high correlation with median rent and to enable easier interpretation of regression estimates, as stated in the Empirical Strategy chapter.

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results. For example, the Everett/Snohomish County CoC is

a suburban CoC in Washington that serves as an example

In 2017, the Everett/Snohomish County CoC

of this negative relationship between the Gini coefficient

in Washington had a total homelessness rate

and total homelessness. The Everett/Snohomish County

higher than the national median (13.5 per

CoC has a relatively high rate of total homelessness (above 10,000) with the 22nd lowest Gini coefficient the median value for all CoCs) and a low Gini coefficient of (41), with median rent and a share of income inequality.

overcrowded households near the top 25

percent of al CoCs.

In the safety net domain, the share of HUD-assisted units

in a CoC is positively associated with rates of total homelessness. A high share of HUD-assisted units may indicate high need among hard-to-house or otherwise economical y disadvantaged individuals. These estimates for safety net variables should not be interpreted as causal, and this relationship is likely a reflection of the housing options and needs of the low-income population in a given CoC. 55

Estimates for the demographic domain il ustrate a few significant relationships between these factors and rates of total homelessness. The share of a CoC’s population who are Hispanic and the share who are children are both negatively associated with rates of total homelessness. Khadduri et al. (2018) emphasized that Hispanic populations have lower measures of housing instability compared with African-American populations. In addition, families with children may receive other forms of assistance that prevent them from becoming homeless. These estimates also suggest that CoCs with high net-migration rates have higher rates of total homelessness. This result may indicate that an increase in population during this timeframe and an increase in housing demand from new migrants can crowd others out of the housing market. Finally, there are no significant relationships between climate variables and rates of total homelessness.

SHELTERED HOMELESSNESS

As with total homelessness, estimates in the housing domain indicate that rates of sheltered homelessness are associated with factors that crowd individuals out of the formal homeownership and rental markets. As with total homelessness, these results indicate that high median rents, a large share of overcrowded units, and low housing density are associated with increased rates of sheltered homelessness. Each of these estimates indicates that the lack of available, affordable housing plays an important role in sheltered homelessness.

Within the economic domain, our estimates suggest that

inequality is associated with lower levels of sheltered

In 2017, the Overland

homelessness. Increased income inequality, as measured

Park/Shawnee/Johnson County CoC in

by the Gini coefficient, is also associated with lower rates

Kansas had the fifth lowest rate of sheltered

of sheltered homelessness. As outlined in the total

homelessness (1.9 per 10,000), with a

homelessness section, high inequality may indicate a large poverty rate around 5.5 percent (seventh high-income population that is less likely to experience

lowest), inequality around the median for all

homelessness. In line with these results, our estimates also CoCs, and a share of HUD-assisted units around 1.5 percent (bottom 10 percent).

suggest that poverty rates are positively associated with

55 HUD’s Worst Case Housing Needs (WCN) report (Watson et al., 2017) uses an alternate measure of HUD assistance: the income-eligible population that receives housing assistance. The WCN report can be accessed from

https://www.huduser.gov/portal/sites/default/files/pdf/Worst-Case-Housing-Needs.pdf.

| 34

rates of sheltered homelessness. Areas with high poverty rates may also have lower income and employment, meaning that a large share of the population is unable to afford suitable housing. Within the safety net domain, our estimates indicate that the share of HUD-assisted units in an area is positively associated with rates of sheltered homelessness. The relationship between sheltered homelessness and HUD-assisted units should not be interpreted as causal.

Race is significantly associated with rates of sheltered homelessness. These estimates indicate that areas with a larger share of Hispanic or Asian residents compared with the share of White residents in the community have lower rates of sheltered homelessness. In addition, high net-migration rates are associated with increased rates of sheltered homelessness. As outlined previously, net-migration rates may be associated with other local factors that increase the demand for housing and crowd vulnerable populations out of the housing market, leading to higher rates of sheltered homelessness. Furthermore, two health-related variables, alcohol mortality rate and excessive drinking rate, are positively associated with rates of sheltered homelessness. These variables serve as proxies for other health and addiction conditions that may be related to sheltered homelessness. Individuals suffering from addiction or other health issues may be unable to participate in the labor market and find affordable housing. Finally, precipitation (January and annual) are both associated with sheltered homelessness. While the relationship between January precipitation and sheltered homelessness is negative, the coefficient on annual precipitation is positive. Estimates in the climate domain may be driven by regional trends in weather, as illustrated in exhibit 3-7.

UNSHELTERED HOMELESSNESS

For rates of unsheltered homelessness, the estimates from our national model indicate few significant factors. The estimates suggest that the prevalence of overcrowded rental units is associated with higher rates of unsheltered homelessness. Overcrowded rental housing units in a community may indicate tighter housing markets or unavailability of affordable rents, contributing to increased rates of unsheltered homelessness. No variables in the economic and safety net domains are associated with unsheltered homelessness.

Within the demographic domain, our estimates suggest that unsheltered homelessness is associated with age, net migration, household status, and veteran status in a CoC. Estimates indicate that the share of seniors is negatively associated with rates of unsheltered homelessness. Like children, seniors represent a vulnerable population who may receive other forms of assistance, such as pensions or social security, that prevent unsheltered homelessness. In addition, these estimates suggest that CoCs with large increases in the net-migration rates between

2012 and 2016 have higher rates of unsheltered

Honolulu CoC

homelessness. The share of one-person households is Rate of Unsheltered Homelessness: 23.4 per 10,000

positively associated with rates of unsheltered

Share of Overcrowded Units: 9.2 percent

homelessness. One-person households may be at

Share of Veterans: 11.7 percent

increased risk for homelessness, especially in the case

of unforeseeable events, particularly within regions

San Antonio/Bexar County CoC

that lack affordable housing. At the community level, Rate of Unsheltered Homelessness: 5.7 per 10,000

the prevalence of one-person households may also

Share of Overcrowded Units: 4.5 percent

represent a lower incidence of shared housing

Share of Veterans: 12.9 percent

arrangements, such as having a roommate, that

| 35

provide affordable rental options in otherwise expensive markets. Furthermore, the share of veterans is positively associated with rates of unsheltered homelessness. This finding aligns with findings in Fargo et al. (2012) that veterans are more likely to be homeless than people in the overal population. The Honolulu CoC in Hawaii and the San Antonio/Bexar County CoC in Texas have relatively high rates of unsheltered homelessness, high shares of overcrowded housing units, and high shares of veterans. For the Honolulu CoC, the share of unsheltered homelessness is near the highest 5 percent, the share of overcrowded units near the highest 1 percent, and the share of veterans in the population is in the highest 25 percent. Similarly, in the San Antonio/Bexar County CoC, the rate of unsheltered homelessness is in the highest 25 percent, the share of overcrowded units is near the highest 10

percent, and the share of veterans in the population is near the highest 10 percent.

Finally, CoCs with high average January precipitation have higher rates of unsheltered homelessness. As with sheltered homelessness, these estimates may be driven by the regional weather patterns illustrated in exhibit 3-7. The Census’s Pacific division, which includes Alaska, California, Hawaii, Oregon, and Washington, had the highest January precipitation and some of the highest rates of unsheltered homelessness. The Subgroup Analysis chapter presents additional insights into unsheltered homelessness in the west.

Exhibit 4-1 | Regression Estimates of National-Level Model

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.042

(0.034)

Percentage of Homeowners with Cost

Burden

-0.14

(0.25)

-0.38*

(0.20)

0.30*

(0.16)

Natural Logarithm of Median Rent ($100s)

19.2**

(8.14)

28.8***

(8.44)

-14.3

(9.90)

Median Rental Utility Cost ($10s)

0.40

(0.30)

-0.31

(0.29)

Percentage of Renters with Cost Burden

0.039

(0.22)

-0.28

(0.19)

0.29*

(0.15)

Percentage of Renter-Occupied Units

0.15

(0.24)

0.39

(0.23)

-0.18

(0.13)

Rental Vacancy Rate

-0.011

(0.29)

0.066

(0.23)

0.035

(0.24)

High Housing Density CoC

-3.37**

(1.49)

-2.84**

(1.34)

-1.55

(0.94)

Eviction Rate

0.47

(0.31)

0.72*

(0.37)

Change in Eviction Rate

-0.33

(0.26)

Percentage of Overcrowded Housing Units

5.44***

(0.82)

2.43***

(0.82)

3.12***

(0.90)

Urban CoC

1.80

(2.37)

0.59

(2.08)

1.10

(1.01)

Suburban CoC

-2.60

(1.93)

-1.70

(1.38)

-0.90

(1.02)

Economic Domain

Unemployment Rate

0.12

(0.91)

-0.34

(0.46)

0.44

(0.67)

Gini Coefficient of Income Inequality

-0.70**

(0.30)

-0.89**

(0.42)

Poverty Rate

0.047

(0.71)

1.07***

(0.39)

-0.84

(0.56)

Safety Net Domain

Share of HUD-Assisted Units

2.99***

(0.70)

3.90***

(0.70)

-0.70*

(0.37)

Occupancy Rate of HUD-Assisted Units

-0.11

(0.18)

0.056

(0.090)

-0.18

(0.16)

Percentage of Houses Built Before 1940

0.15

(0.11)

0.11

(0.11)

Demographic Domain

Percentage African-American

-0.12

(0.13)

-0.031

(0.11)

-0.023

(0.040)

Percentage Hispanic

-0.30***

(0.10)

-0.25***

(0.080)

-0.020

(0.059)

Percentage Asian

-0.24

(0.26)

-0.45**

(0.20)

0.20

(0.17)

Percentage Children (Age 0 through 19)

-0.91**

(0.43)

-0.29

(0.54)

-0.65*

(0.34)

Percentage Senior (Age 65 and Older)

-0.049

(0.39)

0.72

(0.51)

-0.60**

(0.28)

Percentage Female

1.82*

(1.01)

-1.14

(0.99)

Net-Migration Rate

3.20**

(1.26)

2.24**

(0.97)

| 36

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Change in Net-Migration Rate

2.36*

(1.33)

2.78**

(1.11)

Percentage of One-Person Households

1.24*

(0.63)

1.18***

(0.39)

Percent of Under 18 Population in Single-

Parent Households

-0.22

(0.36)

-0.51

(0.31)

Percentage Veteran (Age 25 and Older)

-0.50

(0.35)

0.66**

(0.28)

Percentage without a Bachelor's Degree

-0.17

(0.16)

Healthcare Costs ($1,000s)

0.54

(0.66)

Alcohol Mortality (Per 100,000)

0.89

(0.68)

1.31**

(0.52)

Excessive Drinking Rate

0.48**

(0.22)

-0.47*

(0.26)

Climate Domain

Average January Temperature (°F)

-0.071

(0.15)

-0.16

(0.12)

0.095

(0.058)

Average June, July, and August Temperature

(°F)

0.19

(0.29)

0.32

(0.20)

-0.19

(0.13)

Total January Precipitation (Inches)

0.49

(0.32)

-0.39**

(0.19)

0.81***

(0.17)

Total Annual Precipitation (Inches)

0.088

(0.067)

0.12**

(0.049)

Intercept

-33.3

(31.8)

-151.6***

(49.6)

107.5

(74.8)

Observations

374

374

374

R-Squared

0.82

0.81

0.68

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the variables selected for the subgroup and outcome of interest using the procedures outlined in the Empirical Strategy chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS variables and ACS 5-year estimates for 2016

correspond to the 2012 through 2016 ACS. Rural CoC is the reference urbanicity category; percentage White is the reference race/ethnicity category; and percentage adult is the reference age category.

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SUBGROUP ANALYSIS

Past research has highlighted the considerable differences between homelessness within rural and urban communities (Fitchen, 1992; Lawrence, 1995). As our literature review shows, innate theoretical differences exist in the causes of homelessness in rural and urban communities. In the first subsection, we first examine the different factors that affect homelessness in urban, suburban, and rural communities. Numerous studies place the primary cause of homelessness on the lack of available affordable housing, which is common in tight, high-cost rental markets. These markets are characterized by high housing/rental cost burden and low rental vacancy rates in a high relative share of rental units.

In the second subsection, we explore the role of community-level factors in these tight, high-cost markets with large shares of renters. Finally, nearly one-third of homeless people in the United States live in California, Oregon, and Washington state, despite the west coast comprising only 16 percent of the population. Not only is homelessness more common on the west coast, but it is also more visible because a larger proportion of homeless people are unsheltered. Nationwide, 35 percent of homeless people sleep outside, in vehicles, or other locations not intended for human habitation. In California, however, 68 percent of homeless people are unsheltered, and 57 percent are unsheltered in Oregon. In the final section of this chapter, we examine the factors that are related to unsheltered homelessness on the west coast.

The “descriptive analysis” section in the Empirical Strategy chapter displayed descriptive statistics for the urbanicity categories. In Appendices F and G, we provide descriptive statistics for the other two subgroup analyses. Below we list the number of Continuums of Care (CoCs) in each subgroup that we examine—

1. Urbanicity: urban CoCs ( N = 104), suburban CoCs ( N = 160), and rural CoCs ( N = 110). 56

2. CoCs in tight, high-cost markets with large shares of renters ( N = 59).

3. West coast CoCs in California, Oregon, and Washington state ( N = 51). 57

The estimates do show some differences in the magnitude and significance of estimates across the outcomes of interest and for each subgroup. These models, however, are exploratory in nature, designed to provide insight into the interactions between various subgroups and into factors associated with homelessness, and the estimates should not be interpreted as identifying causal relationships. As a result, the study team only emphasizes associations with factors for which the coefficients are significant, at least at a 5-percent level.

Urbanicity

This section presents the estimates of our model stratified by CoC urbanicity categories: urban CoCs, suburban CoCs, and rural CoCs (mapped in exhibit 5-1). Exhibits 5-2, 5-3, and 5-4 present regression 56 As discussed in the Empirical Strategy chapter, urbanicity is determined by the type of area (urban, suburban, and rural) in which most of the population in a CoC resides.

57 Several CoCs in California were combined to reflect mergers and county geographies. See appendix C for details on the construction of the dataset. appendix D also presents regression analysis of the entire west Census region subgroup (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming [ N =

73]).

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estimates by urbanicity for rates of total, sheltered, and unsheltered homelessness per 10,000

population, respectively. These subgroup estimates il ustrate a high degree of variation in magnitude and statistical significance across urbanicity categories. A major caveat of these models, however, is that they have slightly more than 100 CoC observations each; their results should be considered exploratory and interpreted with some caution.

Exhibit 5-1 | Map of Continuums of Care by Urbanicity Category

Source: HUD urbanicity data

LARGELY URBAN COCS

Exhibit 5-2 presents the regression estimates, for urban CoCs, of the relationship between CoC-level factors and total homeless people per 10,000 population. Across all three specifications, R-squared estimates (0.87 for total homeless people; 0.90 for sheltered homeless people; 0.88 for unsheltered homeless people) indicate that the variables in our models account for a high degree of variation in rates of homelessness across CoCs.

Total Homelessness

For the housing domain, these estimates indicate that the availability of affordable housing may be a significant factor associated with rates of total homelessness in urban CoCs. High median rents and a high share of renter-occupied units are both associated with increased rates of total homelessness in urban CoCs. These estimates align with those from the national model. High median rents may price many people out of the rental market; similarly, a high share of renters can indicate high costs of home ownership, with economical y disadvantaged individuals crowded out of the rental market and more likely to face homelessness. In the economic domain, our estimates suggest that high rates of unemployment are associated with lower rates of total homelessness. Low unemployment rates may

| 39

indicate a high-income area with high median rents, where individuals are more likely to be homeless due to a lack of affordable housing despite attachment to the labor market. The share of households receiving cash assistance is positively associated with rates of total homelessness, suggesting that poorer urban areas have higher rates of total homelessness, perhaps due to inability to afford suitable housing. Estimates in the demographic and climate domains suggest that these characteristics are not significant factors associated with rates of total homelessness.

The Nashvil e/Davidson County CoC in

Tennessee and the Oakland/Alameda County Nashvil e/Davidson County CoC

CoC in California represent urban CoCs with

Rate of Total Homelessness: 33.9 per 10,000

high rates of total homelessness, median

Median Rent: $756

rents, shares of rental units, and rates of cash Share of Rental Units: 46 percent assistance, as well as low unemployment

Unemployment Rate: 3.6 Percent

rates. These areas have booming regional

Share of Households Receiving Cash Assistance: 4.6 percent

economies (as represented by low

Oakland/Alameda County CoC

unemployment rates) and high poverty rates Rate of Total Homelessness: 34.1 per 10,000

(as represented by the share of people

Median Rent: $1,344

receiving cash assistance), and these

Share of Rental Units: 47.4 percent

dynamics contribute to local housing

Unemployment Rate: 4.3 Percent

affordability crises. For the

Share of Households Receiving Cash Assistance: 3.6 percent

Nashvil e/Davidson County CoC, the rate of

total homelessness is in the highest 25 percent, median rent is in the highest 50 percent, the share of rental units is near the highest 25 percent, unemployment is in the lowest 10 percent, and the share receiving cash assistance is in the highest 25 percent, compared with all other urban CoCs. Similarly, for the Oakland/Alameda County CoC, the rate of total homelessness is in the highest 25 percent, median rent is in the highest 10 percent, the share of rental units is in the highest 25 percent, unemployment is in the lowest 50 percent, and the share of households receiving cash assistance is in the highest 25

percent for all urban CoCs.

Sheltered Homelessness

For urban CoCs, many variables in the housing domain demonstrate significant relationships with rates of sheltered homelessness and align with total homelessness in the urban CoCs. High housing costs are associated with increased rates of sheltered homelessness. Growth in home prices since 2009, as measured by the house price index, is positively associated with rates of sheltered homelessness in urban CoCs. 58 While the coefficient on median rent is positive, it is only marginally significant for sheltered homelessness in urban CoCs. The association between the share of cost-burdened renters and the rate of sheltered homelessness in urban CoCs is negative; therefore, areas with a low share of renters with a cost burden greater than 30 percent of their income have high rates of sheltered homelessness. Such estimates could be the result of local economic dynamics, especially if the low burden is driven by high-income renters (spending a relatively low share of income on rent) who crowd out lower-income renters from the market, leading to higher rates of sheltered homelessness. In this specification for sheltered homelessness in urban CoCs, median rent is marginal y significant, as so many 58 The house price index has a base year of 2009. We calculate the percentage growth in home prices since 2009 using this index.

| 40

variables explain variation in sheltered homelessness, and many significant factors associated with housing costs may be picked up by the house price index, rather than median rents. 59 High shares of renter-occupied units and being in a low-density urban area with high eviction rates and overcrowded units are both associated with increased rates of sheltered homelessness. These findings reinforce the narrative that lower-income individuals may be crowded out of the market due to the lack of available and affordable housing options. Within this

general framework, urban localities exhibit

variation. For example, the Baltimore City CoC Baltimore City CoC

Rate of Sheltered Homelessness: 34.4 per 10,000

in Maryland is an example of a high housing

Share of Renters with Cost Burden: 53.7 percent

density CoC with a rate of sheltered

Share of HUD-Assisted Units: 11.9 percent

homelessness in the highest 5 percent, a

share of cost-burdened renters in the highest

25 percent, and a share of HUD-assisted units in the highest of all urban CoCs.

We find no significant association between economic factors and sheltered homelessness in urban areas. For urban CoCs, estimates in the safety net domain indicate that areas with low-income populations that need housing assistance (as indicated by higher shares of HUD-assisted units) are associated with higher rates of sheltered homelessness. In the demographic domain, we find a negative relationship between the share of Asians in a community and the rate of sheltered homelessness in urban areas. In addition, estimates indicate that the share of women in the community is positively associated with rates of sheltered homelessness. This result may reflect that sheltering programs may be more likely to serve women or homeless families, especially single mothers with children. Finally, in the climate domain, the average January temperature is negatively associated with rates of sheltered homelessness, while total annual precipitation is positively associated with rates of sheltered homelessness. Higher January temperatures may be associated with lower rates of sheltered homelessness due to regional weather patterns and policies regarding homelessness such as “Right to Shelter” Laws. For example, warm weather areas on the west coast tend to have relatively high rates of unsheltered homelessness, while colder weather areas like New York City and Boston have relatively high rates of sheltered homelessness. As mentioned previously, regional climate patterns illustrated in exhibit 3-7 shed more light on these relationships.

Unsheltered Homelessness

For urban CoCs, several variables in the housing

domain are significantly associated with rates of San Jose/Santa Clara City & County CoC

unsheltered homelessness. Higher shares of

Rate of Unsheltered Homelessness: 28.2 per 10,000

overcrowded housing units are associated with Share of Homeowners with Cost Burden: 30.6 percent Share of Renters with Cost Burden: 48 percent

higher rates of unsheltered homelessness in

Share of Overcrowded Housing Units: 7.8 percent

urban CoCs. Similarly, a large share of

Unemployment Rate: 3.8 percent

homeowners who have a cost burden greater

than 30 percent of their income and high rental vacancy rates are associated with increased rates of unsheltered homelessness in urban CoCs. In the economic domain, estimates indicate that a low 59 In an alternate specification not included in this report, we exclude the house price index from the urban CoC regression of sheltered homelessness and find that the coefficient on median rent is positive and statistically significant, while the share of renters with cost burden is no longer significant. These results il ustrate the importance of housing cost variables as factors associated with rates of sheltered homelessness, especial y in urban CoCs.

| 41

unemployment rate is associated with high rates of unsheltered homelessness in urban CoCs. As mentioned previously, low unemployment may signal a strong market in which economically disadvantaged individuals are more likely to be homeless due to the lack of affordable housing.

Estimates in the safety net domain indicate that high shares of HUD-assisted units are associated with lower rates of unsheltered homelessness. These estimates could indicate that the prevalence and use of HUD programs may curb rates of unsheltered homelessness in urban CoCs. Among urban CoCs, the San Jose/Santa Clara City and County CoC in California has the third highest rate of unsheltered homelessness, a share of homeowners with cost burden in the highest 25 percent, a share of renters with cost burden in the lowest 25 percent, a share of overcrowded housing units in the highest 5

percent, and an unemployment rate in the lowest 25 percent.

Exhibit 5-2 | Regression Estimates of Model for Largely Urban Continuums of Care Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.22**

(0.10)

Percentage of Homeowners

with Cost Burden

-0.76

(0.72)

1.10**

(0.41)

Natural Logarithm of Median

Rent ($100s)

23.83**

(11.13)

29.62*

(14.72)

-13.63

(10.24)

Percentage of Renters with

Cost Burden

-0.77*

(0.42)

-0.85***

(0.29)

-0.39

(0.28)

Percentage of Renter-

Occupied Units

1.71***

(0.36)

1.25***

(0.39)

-0.78***

(0.23)

Rental Vacancy Rate

0.17

(0.58)

0.01

(0.59)

0.77**

(0.32)

High Housing Density CoC

-8.37*

(4.60)

-10.38***

(3.34)

Change in Eviction Rate

1.19*

(0.65)

1.39**

(0.56)

Percentage of Overcrowded

Housing Units

1.57**

(0.73)

4.18***

(0.96)

Economic Domain

Unemployment Rate

-4.56**

(2.13)

0.17

(1.20)

-2.61***

(0.96)

Gini Coefficient of Income

Inequality

1.32

(1.06)

-0.62

(0.41)

Poverty Rate

0.07

(0.83)

0.51

(0.56)

0.44

(0.44)

Safety Net Domain

Percentage of Households

Receiving Cash Assistance

3.53**

(1.56)

0.79

(0.61)

Share of HUD-Assisted Units

0.58

(1.15)

2.84***

(0.93)

-1.32**

(0.50)

Occupancy Rate of HUD-

Assisted Units

0.22

(0.31)

0.33

(0.27)

0.40*

(0.20)

Demographic Domain

Percentage African-American

-0.07

(0.16)

0.07

(0.11)

Percentage Hispanic

-0.05

(0.09)

-0.12

(0.10)

Percentage Asian

-0.75**

(0.28)

0.25

(0.21)

Percentage Children

(Age 0 through 19)

-1.60

(1.11)

0.26

(1.02)

-1.64*

(0.88)

Percentage Senior

(Age 65 and Older)

-0.10

(1.35)

1.21

(1.13)

-1.18

(0.84)

Percentage Female

5.05***

(1.49)

-1.99*

(1.04)

Net-Migration Rate

1.42

(1.22)

Percentage of One-Person

Households

1.53***

(0.37)

Percentage Veteran

(25 and Older)

0.59

(0.41)

| 42

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Percentage without a

Bachelor’s Degree

1.06

(0.81)

-0.56

(0.58)

Alcohol Mortality

(Per 100,000)

-0.64*

(0.33)

Excessive Drinking Rate

-0.07

(0.16)

0.07

(0.11)

Climate Domain

Average January Temperature

(°F)

-0.27

(0.24)

-0.37**

(0.16)

0.29

(0.20)

Average June, July, and

August Temperature (°F)

-0.79**

(0.33)

Average January Precipitation

(Inches)

0.81

(0.51)

0.59*

(0.31)

Total Annual Precipitation

(Inches)

0.09

(0.15)

0.27***

(0.07)

-0.15**

(0.06)

Intercept

-122.94*

(66.02)

-362.57***

(85.88)

221.62***

(66.84)

Observations

104

104

104

R-Squared

0.87

0.90

0.88

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the variables selected for the subgroup and outcome of interest using the procedures outlined in the Empirical Strategy chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. American Community Survey (ACS) variables and ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Percentage White is the omitted race/ethnicity category, and percentage adult is the omitted age category. A list of CoCs by urbanicity is listed in appendix D.

In the demographic domain, only the share of one-person households is associated with rates of unsheltered homelessness in urban CoCs. Areas with a higher share of one-person households have higher rates of unsheltered homelessness. This result could indicate that single-income households struggle to afford suitable housing accommodations. In addition, as mentioned previously, because housing assistance is often targeted at more vulnerable populations, one-person households may not be a high priority for shelters, leading to higher rates of unsheltered homelessness. In the climate domain, estimates indicate that average summer temperature and total annual precipitation are both negatively associated with rates of unsheltered homelessness.

LARGELY SUBURBAN CONTINUUMS OF CARE

Exhibit 5-3 presents the regression estimates, for suburban CoCs, of the relationship between CoC-level factors and total homeless people per 10,000 population. Across all three specifications, R-squared estimates (0.61 for total homeless people, 0.50 for sheltered homeless people, and 0.59 for unsheltered homeless people) indicate that the variables in our models account for a relatively high degree of variation in rates of homelessness.

Total Homelessness

For the housing domain, these estimates indicate

that housing costs are significant factors associated Naples/Collier County CoC

with rates of total homelessness in suburban CoCs. Rate of Total Homelessness: 17 per 10,000

High median rents are associated with increased

Median Rent: $975

rates of total homelessness, suggesting that rental Change in the Net Migration Rate: +1 percent housing affordability (or lack of affordability) may

contribute to rates of total homelessness in suburban CoCs. We find that areas with a low share of

| 43

homeowners who have a cost burden greater than 30 percent of their income have high rates of total homelessness. Such estimates could be the result of local economic dynamics, especially if the low burden is driven by high-income homeowners (spending a relatively low share of income on housing) who crowd out lower-income individuals from the market, leading to higher rates of total homelessness.

While there are no significant factors in the economic domain, for the safety net domain, the existence of a high share of HUD-assisted units is associated with higher rates of total homelessness in suburban CoCs. The share of HUD-assisted units in an area may reflect overall economic conditions and a low-income population that may be more vulnerable to homelessness. In the demographic domain, the change in the net migration rate is positively associated with rates of total homelessness, and high alcohol mortality rates are also related to increased rates of total homelessness. In addition, a high share of the population under age 18 living in single-parent households is associated with lower rates of total homelessness in suburban CoCs. Previous discussions indicate that families with children are targets of other social and housing assistance that may prevent homelessness. Finally, estimates in the climate domain indicate that higher average January temperatures are associated with higher rates of total homelessness in suburban CoCs. The Naples/Collier County CoC in Florida has rates of total homelessness in the highest 25 percent, with median rents near the highest 25 percent and a change in the net migration rate in the highest 10 percent among al suburban CoCs.

Sheltered Homelessness

For suburban CoCs, few variables across all domains are significantly related to rates of sheltered homelessness. In the housing domain, estimates indicate that a high share of cost-burdened renters is associated with higher rates of sheltered homelessness in suburban CoCs. A higher share of renters with a cost burden greater than 30 percent of their income may signal a tight, high-cost rental market in which economical y disadvantaged individuals may be crowded out of affordable rental housing in suburban areas. Certain individuals may be priced out of the market and are not finding other long-term subsidized housing, contributing to rates of sheltered homelessness. Estimates in the safety net domain suggest that lack of availability of affordable housing contribute to increased rates of sheltered homelessness. We find that a high share of HUD-assisted units is positively associated with rates of sheltered homelessness. In the demographic domain, alcohol mortality rates are positively associated with higher rates of sheltered homelessness. Finally, in the climate domain, high average January temperatures are associated with higher rates of sheltered homelessness in suburban CoCs, while higher average summer temperatures are associated with lower rates of sheltered homelessness in suburban CoCs.

The Atlantic City and County CoC in New Jersey and the

Springfield CoC in Massachusetts are suburban CoCs

Atlantic City & County CoC

with high rates of sheltered homelessness with high

Rate of Sheltered Homelessness: 11.6 per 10,000

shares of cost-burdened renters and high shares of

Share of Renters with Cost Burden: 61.6 percent

HUD-assisted units. For the Atlantic City CoC, the rate Share of HUD-Assisted Units: 5.7 percent of sheltered homelessness is in the highest 25 percent,

the share of renters with high-cost burden is near the Springfield CoC (Massachusetts)

Rate of Sheltered Homelessness: 48.1 per 10,000

highest 1 percent, and the share of HUD-assisted units Share of Renters with Cost Burden: 55.9 percent is in the highest 10 percent of all suburban CoCs. The

Share of HUD-Assisted Units: 10.5 percent

Springfield CoC has the highest rate of sheltered

homelessness among all CoCs, the share of renters with

| 44

high-cost burden is in the highest 25 percent, and the share of HUD-assisted units is in the highest 1

percent of all suburban CoCs. The high rate of sheltered homelessness in the Springfield CoC could be related to the fact that Massachusetts is a right-to-shelter state for families.

Exhibit 5-3 | Regression Estimates of Model for Largely Suburban CoCs

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

Percentage of Homeowners with Cost Burden

-0.45**

(0.17)

-0.21

(0.15)

-0.21**

(0.10)

Natural Logarithm of Median Rent ($100s)

10.82**

(4.39)

0.62

(2.97)

7.46**

(3.17)

Percentage of Renters with Cost Burden

0.65*

(0.33)

0.40**

(0.17)

0.21

(0.18)

Percentage of Renter-Occupied Units

0.19

(0.17)

0.02

(0.19)

0.15

(0.09)

Rental Vacancy Rate

0.11

(0.33)

0.41*

(0.21)

-0.22

(0.16)

High Housing Density CoC

-2.69

(2.08)

-1.63

(1.36)

Economic Domain

Unemployment Rate

2.00*

(1.13)

-0.60

(0.80)

2.09***

(0.64)

Gini Coefficient of Income Inequality

-0.19

(0.17)

Poverty Rate

-1.23*

(0.62)

-0.50

(0.34)

-0.67**

(0.25)

Safety Net Domain

Percentage of Households Receiving Cash

Assistance

0.73

(0.77)

Share of HUD-Assisted Units

2.54**

(1.00)

2.12**

(0.94)

1.17**

(0.51)

Occupancy Rate of HUD-Assisted Units

-0.07

(0.16)

-0.02

(0.11)

-0.01

(0.10)

Percentage of Houses Built Before 1940

-0.15**

(0.06)

Demographic Domain

Percentage African-American

-0.09*

(0.05)

Percentage Hispanic

0.02

(0.11)

Percentage Asian

-0.10

(0.12)

Net-Migration Rate

-3.05*

(1.77)

-1.04

(0.62)

Change in Net-Migration Rate

4.00***

(1.21)

2.46***

(0.70)

Percentage of One-Person Households

0.64**

(0.29)

0.69***

(0.23)

Percentage of Under-18 Population in

Single-Parent Households

-0.58**

(0.28)

-0.54**

(0.20)

Percentage without a Bachelor’s Degree

0.16*

(0.09)

0.15**

(0.05)

Healthcare Costs ($1,000s)

-1.34

(0.98)

-0.70

(0.49)

Alcohol Mortality (per 100,000)

2.72***

(0.87)

1.06**

(0.40)

Climate Domain

Average January Temperature (°F)

0.54***

(0.11)

0.22***

(0.06)

0.26***

(0.06)

Average June, July, and August Temperature (°F)

-0.36

(0.27)

-0.31**

(0.13)

Total January Precipitation (Inches)

0.50

(0.32)

0.57***

(0.11)

Total Annual Precipitation (Inches)

-0.09

(0.09)

-0.03

(0.04)

-0.05

(0.03)

Intercept

-35.43

(38.33)

15.98

(18.52)

-45.04**

(21.24)

Observations

160

160

160

R-Squared

0.61

0.50

0.59

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the variables selected for the subgroup and outcome of interest using the procedures outlined in the Empirical Strategy chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS variables and ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Percentage White is the omitted race/ethnicity category, and percentage adult is the omitted age category. A list of CoCs by urbanicity is listed in appendix D.

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Unsheltered Homelessness

Variables across all domains are significantly associated with rates of unsheltered homelessness in suburban CoCs. In the housing domain, high median rents are associated with high rates of unsheltered homelessness in suburban CoCs. The percentage of homeowners with cost burden greater than 30

percent of their income is negatively related to rates of unsheltered homelessness in suburban CoCs. In the economic domain, high unemployment rates are associated with increased rates of unsheltered homelessness, while high poverty rates are associated with lower rates of unsheltered homelessness.

High-poverty populations may be eligible for other forms of housing and shelter assistance that can prevent them from becoming part of the unsheltered homeless population. Estimates in the safety net domain indicate that high shares of HUD-assisted units are associated with increased rates of unsheltered homelessness. The share of houses built prior to 1940, which serves as a proxy for CoC

funding, is associated with lower rates of unsheltered homelessness. Because CoC funding supports shelters, these estimates suggest that funding may prevent unsheltered homelessness in suburban CoCs.

In the demographic domain, migration, household composition, and education variables are most significant in suburban CoCs. An increase in the net migration rate is associated with higher rates of unsheltered homelessness. As described previously, migration can increase the demand for housing, crowding vulnerable individuals out of the rental market and increasing unsheltered homelessness in suburban CoCs. While a high share of the under-18 population living in single-parent households is associated with lower rates of unsheltered homelessness, a high share of one-person households is associated with increased rates of unsheltered homelessness in suburban CoCs. A high share of the population who lack a col ege degree is associated with increased rates of unsheltered homelessness.

Finally, in the climate domain, high January precipitation and temperatures are associated with higher rates of unsheltered homelessness in suburban CoCs. The Riverside City and County CoC in California is a suburban CoC with a rate of unsheltered

homelessness in the highest 25 percent, median Riverside City & County CoC

rents in the highest 25 percent, unemployment

Rate of Unsheltered Homelessness: 6.9 per 10,000

rates in the highest 10 percent, and a share of the Median Rent: $1,062

population without a col ege degree near the

Unemployment Rate: 6.1 percent

highest 5 percent of all suburban CoCs.

Share with Less than a Bachelor’s Degree: 79.5 percent

LARGELY RURAL CONTINUUMS OF CARE

Exhibit 5-4 presents the regression estimates, for rural CoCs, of the relationship between CoC-level factors and the total number of homeless people per 10,000 population. Across all three specifications, R-squared estimates (0.82 for total homeless people, 0.78 for sheltered homeless people, and 0.72 for unsheltered homeless people) indicate that the variables in our models account for a high degree of variation in rates of homelessness across CoCs.

Total Homelessness

For the housing domain, these estimates indicate that several factors are associated with rates of total homelessness in rural CoCs. High shares of homeowners experiencing cost burden, a high house price index, a high share of renter-occupied units, and a high share of renters with housing costs greater than 30 percent of their income are all associated with higher rates of total homelessness in rural CoCs. The share of renters and share of individuals with a housing cost burden are both positively associated with

| 46

increased rates of total homelessness. These findings indicate that the housing market is an important and dominant determinant of rates of total homelessness in rural areas.

Estimates in the economic domain indicate that

areas with high poverty may have lower rates of Central Oregon CoC

total homelessness. As described previously,

Rate of Total Homelessness: 34.4 per 10,000

individuals living in poverty may qualify for

Share of Homeowners with Cost Burden: 29.2 percent

housing or social assistance that prevents

Share of Renters with Cost Burden: 52.8 percent

homelessness. From estimates of unsheltered

Poverty Rate: 11.6 percent

homelessness in rural areas described later, the

coefficient on poverty in the model of total homelessness may be driven by the results for the unsheltered homeless population. The Central Oregon CoC is a rural CoC with rates of total homelessness in the highest 10 percent, the share of homeowners with high cost burden near the highest 10 percent, the share of renters with high cost burden in the highest 25 percent, and a poverty rate in the lowest 10 percent of all rural CoCs. In the safety net domain, the HUD-assisted housing variables are insignificant, possibly because U.S. Department of Agriculture (USDA) rural development subsidized housing may be more prevalent than HUD-subsidized housing in rural areas. The data on USDA housing programs are not included in this study.

For the demographic domain, high shares of Hispanic people and high shares of veterans are associated with reduced rates of total homelessness in rural CoCs. In contrast, high shares of seniors and high shares of the population without a col ege degree are associated with higher rates of total homelessness. Regarding health, high alcohol mortality rates are associated with higher rates of total homelessness. Final y, higher January temperatures and higher precipitation are both associated with increased rates of total homelessness in rural CoCs.

Sheltered Homelessness

In the housing domain, a high share of homeowners

and renters with housing costs greater than 30

Monroe County CoC

percent of their income are both associated with

Rate of Sheltered Homelessness: 43.9 per 10,000

high rates of sheltered homelessness in rural CoCs. Share of Homeowners with Cost Burden: 37.6 percent There are no significant factors associated with

Share of Renters with Cost Burden: 61.5 percent

sheltered homelessness in the economic or safety

Share of One-Person Households: 32.5 percent

net domains. Estimates in the demographic domain

indicate that race, age, and a high share of one-person households are significantly associated with sheltered homelessness. High shares of African-American and Hispanic populations are both associated with lower rates of sheltered homelessness in rural CoCs. 60 In the climate domain, no variables are significantly associated with rates of sheltered homelessness in rural CoCs. Among all rural CoCs, the Monroe County CoC in Florida has the highest rates of sheltered homelessness and homeowners with high-cost burden, the second highest rate of renters with high-cost burden, and a share of one-person households near the highest 5 percent.

60 Regarding the share of African-American people in a population, these estimates may seem counterintuitive, especially because African-Americans make up a relatively large share of the homeless population (Henry et al., 2017). The independent variable, however, is the share of African-Americans in the overall population, which may not necessarily be related to the share of African-Americans experiencing sheltered homelessness or the overal rate of sheltered homelessness in rural CoCs.

| 47

Unsheltered Homelessness

For rural CoCs, several variables in the housing domain are significantly associated with rates of unsheltered homelessness. As with total homelessness above, high shares of homeowners experiencing cost burden, a high house price index, a high share of renter-occupied units, and a high share of renters with housing costs greater than 30 percent of their income are all associated with higher rates of unsheltered homelessness in rural CoCs. In the economic domain, our estimates suggest that rural CoCs with high poverty have lower rates of unsheltered homelessness. For rural CoCs, in the safety net domain, we find no factors significantly associated with rates of unsheltered homelessness. In rural CoCs, higher shares of African-Americans and veterans are associated with lower rates of unsheltered homelessness. In contrast, high shares of the population without a college degree and high alcohol mortality rates are associated with higher rates of unsheltered homelessness in rural CoCs. Finally, in the climate domain, estimates indicate that high January precipitation rates are associated with high rates of unsheltered homelessness. As mentioned previously, these estimates may be driven by regional weather patterns illustrated in exhibit 3-7.

The North Dakota Statewide CoC is a rural CoC

with a rate of unsheltered homelessness in the

North Dakota Statewide CoC

highest 25 percent, the highest growth in home

Rate of Unsheltered Homelessness: 4.4 per 10,000

prices, a share of renters in the highest 10

House Price Index: 40.5 percent

percent, and poverty rates in the lowest 5 percent Share of Renters: 36.7 percent of rural CoCs. The high growth in home prices, as Poverty Rate: 10.2 percent measured by the house price index, is likely

related to the oil boom in North Dakota that increased the demand for housing.

Exhibit 5-4 | Regression Estimates of Model for Largely Rural Continuums of Care Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.26***

(0.06)

0.22***

(0.06)

Percentage of Homeowners with Cost Burden

0.86***

(0.18)

0.33**

(0.15)

0.37**

(0.16)

Natural Logarithm of Median Rent ($100s)

0.13

(9.55)

5.28

(3.52)

-8.99

(6.14)

Median Rental Utility Cost ($10s)

-0.55

(0.34)

-0.17

(0.15)

-0.44

(0.42)

Percentage of Renters with Cost Burden

0.78***

(0.27)

0.26**

(0.10)

0.52**

(0.24)

Percentage of Renter-Occupied Units

0.92***

(0.17)

0.13

(0.10)

0.82***

(0.14)

Rental Vacancy Rate

0.30

(0.39)

0.13

(0.11)

0.27

(0.29)

Percentage of Overcrowded Housing Units

0.72*

(0.36)

Economic Domain

Unemployment Rate

-0.40

(0.72)

-0.06

(0.32)

-0.69

(0.68)

Poverty Rate

-1.24***

(0.33)

-0.29

(0.20)

-0.92***

(0.29)

Safety Net Domain

Percentage of Households Receiving Cash

Assistance

0.39

(0.38)

Share of HUD-Assisted Units

-0.50

(0.78)

0.30

(0.50)

-0.64

(0.66)

Occupancy Rate of HUD-Assisted Units

-0.04

(0.15)

-0.03

(0.06)

-0.05

(0.13)

Demographic Domain

Percentage African-American

-0.30*

(0.15)

-0.14**

(0.06)

-0.18***

(0.07)

Percentage Hispanic

-0.19**

(0.08)

-0.10***

(0.04)

-0.05

(0.06)

Percentage Asian

0.14

(0.34)

-0.20

(0.26)

0.35

(0.28)

| 48

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Percentage Children (Age 0 through 19)

1.69*

(0.88)

0.63**

(0.28)

0.47

(0.60)

Percentage Senior (Age 65 and Older)

1.09**

(0.52)

0.14

(0.17)

0.92*

(0.54)

Percentage of One-Person Households

1.46*

(0.80)

0.92***

(0.18)

Percentage of Under-18 Population in

Single-Parent Households

-0.26

(0.43)

-0.06

(0.17)

Percentage Veteran (25 and Older)

-1.34***

(0.34)

-1.13***

(0.38)

Percentage without a Bachelor’s Degree

0.68**

(0.27)

0.51***

(0.17)

Alcohol Mortality (Per 100,000)

1.66***

(0.54)

1.44***

(0.35)

Climate Domain

Average January Temperature (°F)

0.51*

(0.26)

0.03

(0.04)

0.30*

(0.15)

Average June, July, and August Temperature (°F)

-0.57**

(0.26)

-0.36

(0.24)

Average January Precipitation (Inches)

0.41*

(0.23)

0.69***

(0.21)

Intercept

-

153.62***

(53.35)

-61.91***

(17.97)

-53.98*

(31.50)

Observations

110

110

110

R-Squared

0.82

0.78

0.72

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the variables selected for the subgroup and outcome of interest using the procedures outlined in the Empirical Strategy chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS variables and ACS 5-year estimates for 2016

correspond to the 2012 through 2016 ACS. Percentage White is the omitted race/ethnicity category, and percentage adult is the omitted age category. A list of CoCs by urbanicity is listed in appendix D.

Tight, High-Cost Rental Markets

The study team defined a CoC as existing within a tight, high-cost rental market if three out of the following four criteria were met—

 Tight rental market with rental vacancy rate less than or equal to 5 percent in 2017.

 High-cost rental market with median rents greater than or equal to the 75th percentile value in 2017 ($901.80 per month).

 High-cost housing market with median home values greater than or equal to the 75th percentile value in 2017 ($246,800).

 Market with renter share greater than or equal to 75th percentile value in 2017 (39.68 percent).

Exhibit 5-5 displays the tight, high-cost rental markets across the country, with most of the CoCs along the east and west coasts.

| 49

Exhibit 5-5 | Map of Tight, High-Cost Rental Market Continuums of Care

Source: Census ACS 5-Year Estimates

Exhibit 5-6 presents the total, sheltered, and unsheltered homeless populations and rates of homelessness from 2011 through 2017 for the subgroup of CoCs in tight, high-cost rental markets.

Overall, there was little change in the rates of total, sheltered, and unsheltered homelessness during this period. As depicted by the solid bars, however, total homelessness in CoCs with tight, high-cost rental markets increased by nearly 68,000 between 2015 and 2017, mainly driven by the increase in the unsheltered population. In addition, rates of unsheltered homelessness increased from around 13

percent in 2015 to 15 percent in 2017, as il ustrated by the lowest dashed line.

| 50

Exhibit 5-6| Homelessness in Continuums of Care with Tight, High-Cost Rental Markets, 2011

through 2017

300,000

45

40

250,000

35

200,000

30

essness

essness

25 el 000)

el 150,000

om

om

20 f H (Per 10,

100,000

15

Total H

Rates o

10

50,000

5

-

0

2011

2013

2015

2017

Total Homeless

Total Sheltered

Total Unsheltered

Homeless Rate

Sheltered Rate

Unsheltered Rate

Sources: Census’s intercensal population estimates; HUD Point-in-Time (PIT) count data Exhibit 5-7 presents the regression estimates of the relationship between CoC-level factors and rates of total, sheltered, and unsheltered homelessness per 10,000 population for CoCs in tight, high-cost rental markets and other CoCs. These estimates show that many variables across the five domains are significantly associated with rates of homelessness. This exhibit presents three separate specifications, where the independent variables are interacted with a dummy variable representing the subgroup.

Therefore, the group of columns under each outcome of interest are the estimates from a single specification. Across all three specifications, R-squared estimates (0.90 for total homeless people; 0.92

for sheltered homeless people; 0.79 for unsheltered homeless people) indicate that the variables in our models account for a high degree of variation in rates of homelessness across CoCs. The independent variables in these specifications are the same variables as the national model in the National Model of Homelessness chapter, with the variables associated with the subgroup determination removed (in other words, rental vacancy rates, median rent, and renter share).

TOTAL HOMELESSNESS

For the housing domain, these estimates indicate that total homelessness is negatively associated with housing density in CoCs in tight, high-cost rental markets. A greater number of housing units per square mile is associated with lower rates of total homelessness for CoCs in tight, high-cost rental markets compared with CoCs in other rental markets. This estimate could mean that greater housing availability prevents low-income individuals from being crowded out of the market. For all CoCs, regardless of rental market status, the results show that larger percentages of renters with high-cost burdens are associated with increased rates of total homelessness. In contrast, for CoCs in tight, high-cost rental markets, estimates indicate that a large share of homeowners with a high-cost burden is associated with lower rates of total homelessness. Furthermore, high eviction rates and high shares of overcrowded units are both associated with higher rates of total homelessness in CoCs with tight, high-cost rental

| 51

markets. Perhaps unsurprisingly, these estimates suggest that lack of housing availability is an important factor driving homelessness in tight, high-cost rental markets.

In the economic domain, for CoCs in tight, high-cost rental markets, labor market conditions appear to be strongly related to rates of total homelessness. The estimates suggest that higher unemployment rates are associated with increased rates of total homelessness in CoCs in tight, high-cost markets compared with other CoCs. If a higher population of people in a CoC are unemployed and not able to afford housing costs, there may be higher rates of total homelessness, especially in tight, high-cost rental market CoCs. Complementary to this point, we also observe that a high rate of overcrowded housing is associated with higher rates of total homelessness. In other rental markets, these estimates indicate that high-income inequality, as measured by the Gini coefficient, is associated with lower rates of total homelessness. Because these other markets are not tight and high-cost rental markets, even in areas where income dispersion favors the wealthy, low-income individuals are able to afford housing, leading to lower rates of total homelessness.

In the safety net domain, for tight, high-cost rental markets the estimates suggest that high occupancy rates in HUD-assisted units are related to lower rates of total homelessness, especially when compared with CoCs in other housing markets. 61 High occupancy rates of HUD-assisted units may reflect the overall market conditions for CoCs in tight, high-cost rental markets. These estimates could indicate that HUD-assisted units are fil ing a need in these markets, providing housing to those who may be crowded out of housing in tight, high-cost rental markets. For CoCs within tight, high-cost rental markets, the share of houses built prior to 1940 (a proxy for CoC funding) is positively associated with rates of total homelessness. 62 In addition, a higher share of HUD-assisted units is associated with lower rates of total homelessness for CoCs within tight, high-cost rental markets.

Estimates for the demographic domain il ustrate a broad range of significant factors that are associated with rates of total homelessness for CoCs in tight, high-cost rental markets. For race and ethnicity, these estimates suggest that the share of Hispanic residents is negatively related to rates of total homelessness for CoCs in tight, high-cost rental markets relative to those in other market CoCs.

Khadduri et al. (2018) point out that, on average, Hispanic populations have lower measures of housing instability and are less likely to be homeless than African-Americans, even though the two groups have similar rates of poverty. The study illustrates that some of these results may be related to lower mobility (in other words, Hispanic populations move less frequently than their African-American or White peers), so Hispanic populations may not face the negative effects of tight, high-cost rental markets and may have support structures to prevent homelessness.

Other demographic factors that show significance in tight, high-cost rental markets include the change in net-migration rates, age distribution, and health conditions. The results suggest that an increasing year-over-year migration rate is associated with lower rates of total homelessness in tight, high-cost rental markets. This estimate could reflect areas experiencing sudden economic growth or recovery that 61 As explained in the “independent variables: predictors of homelessness” section in the Data chapter, estimates of the occupancy rate of HUD-assisted units from the Picture of Subsidized Households may underestimate the true occupancy rate.

62 These estimates for CoC funding should not be interpreted as causal as CoC funding wil be targeted toward areas with high rates of homelessness. Estimates from alternative specification estimates for tight, high-cost rental markets in appendix D

indicate that the coefficient estimates of the share of houses built prior to 1940 are sensitive to the inclusion of additional independent variables, and the proxy for CoC funding is only significant (and negative) for unsheltered homelessness in other rental market CoCs.

| 52

may coincide with promising labor market

conditions. In contrast, CoCs that are not within

Austin/Travis County CoC

tight, high-cost rental markets tend to experience

Rate of Total Homelessness: 16.9 per 10,000

higher rates of homelessness with an increase in net Unemployment Rate: 3.2 percent migration rate. The estimates suggest that tight

Occupancy Rate of HUD-Assisted Units: 93 percent

rental market CoCs with higher percentages of

Percent Hispanic: 33.9 percent

seniors are also associated with higher rates of

homelessness, while a high percentage of children is associated with lower rates of total homelessness for CoCs in tight, high-cost rental markets. Furthermore, these estimates indicate that a high share of one-person households is associated with higher rates of total homelessness in other market CoCs.

Health factors also differ in their association with homelessness. Alcohol mortality is associated with higher rates of total homelessness in CoCs that are not in tight, high-cost rental markets. Finally, in the climate domain, for CoCs in tight, high-cost rental markets, higher total annual precipitation is associated with higher rates of homelessness compared with CoCs in other rental markets. Of CoCs in tight, high-cost rental markets, the Austin/Travis County CoC has total homelessness above the median, unemployment rates in the lowest 25 percent, an occupancy rate of HUD-assisted units at the median, and a share of Hispanic residents in the highest 25 percent.

SHELTERED HOMELESSNESS

For the housing domain, results indicate that high eviction rates are associated with higher rates of sheltered homelessness in CoCs with tight, high-cost rental markets. Eviction rates provide insights into the displacement of renters, especial y when comparing CoCs in tight, high-cost rental markets to other CoCs. As with total homelessness in tight, high-cost rental market CoCs, we find that housing density is negatively associated with rates of sheltered homelessness. There is a positive association between sheltered homelessness and the share of renters with cost burden in other market CoCs but not for CoCs in tight, high-cost rental markets. Being an urban CoC and having a high share of overcrowded housing units are both associated with positive rates of sheltered homelessness for all CoCs.

Within the economic domain, our estimates suggest that high unemployment rates are associated with higher levels of sheltered homelessness for CoCs in tight, high-cost rental markets relative to other CoCs. For CoCs in tight, high-cost rental markets, no safety net factors appear significantly associated with rates of sheltered homelessness. For other market CoCs, however, our estimates indicate that the share of houses built prior to 1940, the proxy for CoC funding, is associated with higher rates of sheltered homelessness. As mentioned previously, these estimates should not be interpreted as causal because CoC funding is target toward areas with a higher need for shelter beds.

For the demographic domain, these estimates suggest that a broad group of factors is associated with rates of sheltered homelessness. For race and ethnicity, we find that the share of African-American residents is positively related to rates of total

homelessness for CoCs in tight, high-cost rental

markets, while there is a negative relationship between District of Columbia CoC

the share of Asian residents and the rate of sheltered

Rate of Sheltered Homelessness: 96.1 per 10,000

homelessness. The share of seniors aged 65 and older is Eviction Rate: 2.6 percent associated with higher rates of sheltered homelessness Under-18 Population in Single-Parent Households: in other CoCs relative to those in tight, high-cost rental 47.7 percent

| 53

markets. In addition, for CoCs in tight, high-cost rental markets, the share of veterans is negatively associated with rates of sheltered homelessness compared with CoCs in other markets. Alcohol mortality rates are positively associated with rates of sheltered homelessness, especially for CoCs in tight, high-cost rental markets. Finally, our estimates indicate that total annual precipitation is associated with high rates of sheltered homelessness in CoCs in tight, high-cost rental markets. Of all CoCs in tight, high-cost rental markets, the District of Columbia CoC in Washington, DC has the highest rate of sheltered homelessness, an eviction rate in the highest 10 percent, and the highest share of children under 18 living in single-parent households.

UNSHELTERED HOMELESSNESS

In the housing domain, the house price index and share of homeowners with a cost burden greater than 30 percent of their income are positively associated with rates of unsheltered homelessness, especially for other CoCs compared with CoCs in tight, high-cost rental markets. In addition, a high share of overcrowded units is associated with high rates of unsheltered homelessness for CoCs in tight, high-cost rental markets compared with other CoCs. There is a negative relationship between the change in eviction rates and rates of unsheltered homelessness for CoCs in tight, high-cost rental markets. These estimates indicate that CoCs with lower changes in eviction rates may have higher rates of unsheltered homelessness. Some areas with tight high-cost rental markets may have policies in place that protect renters, thus leading to low eviction rates (or changes in eviction rates) while still maintaining high rates of unsheltered homelessness.

For all CoCs, there are no significant relationships between unsheltered homelessness and factors in the economic domain. In the safety net domain, a high share of HUD-assisted units is associated with lower rates of unsheltered homelessness for CoCs in tight, high-cost rental markets. This result could indicate that low-income populations may be using HUD assistance, resulting in lower rates of sheltered homelessness. Estimates from the demographic domain indicate that veteran status is positively associated with rates of unsheltered homelessness in CoCs in tight, high-cost rental markets. As for the other rental market status, higher percentages of Asian residents and a large increase in the net-migration rate are associated with higher rates of unsheltered homeless. High shares of veterans are associated with increased rates of unsheltered homelessness in CoCs with tight, high-cost rental markets compared with CoCs with other markets. Veterans populations may be a low-priority for capacity-constrained shelters because these populations are more likely to consist of older males (Fargo et al., 2012).

Higher shares of the population without a bachelor’s

degree is associated with lower unsheltered

Val ejo/Solano County CoC

homelessness rates in tight, high-cost rental markets. Rate of Unsheltered Homelessness: 20.8 per 10,000

Although tight, high-cost markets may be

Share of Overcrowded Housing Units: 4.6 percent

Percentage Veteran: 11.6 percent

experiencing an economic boom, they are typical y

comprised of low shares of persons without a bachelor’s degree while also experiencing noticeably higher unsheltered homeless rates. Lastly, higher January precipitation totals in CoCs with higher January precipitation totals are associated with higher rates unsheltered homelessness rates in tight, high-cost CoCs. Of al CoCs in tight, high-cost rental markets, the Vallejo/Solano County CoC in California has a rate of unsheltered homelessness in the highest 25 percent, shares of overcrowded housing units in the highest 50 percent, and a share of veterans in the highest 10 percent.

| 54

Exhibit 5-7 | Regression Estimates of Model for Continuums of Care with Tight, High-Cost Rental Markets 2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Independent Variables

CoCs in Tight,

Other CoCs

CoCs in Tight, High-

Cost Rental Markets

Other CoCs

CoCs in Tight, High-

Cost Rental Markets

Other CoCs

High-Cost Rental

Markets

Housing Domain

House Price Index

0.10**

(0.04)

-0.03

(0.05)

Percentage of Homeowners with Cost

Burden

0.19

(0.15)

-0.90***

(0.32)

-0.06

(0.11)

-0.55*

(0.28)

0.22***

(0.07)

0.34

(0.31)

Median Rental Utility Cost ($10s)

-0.03

(0.15)

0.07

(0.77)

-0.07

(0.16)

1.79

(1.52)

Percentage of Renters with Cost Burden

0.36**

(0.15)

0.73***

(0.24)

0.23**

(0.11)

-0.16

(0.64)

0.06

(0.06)

0.51

(0.52)

High Housing Density CoC

-2.19*

(1.13)

-11.95***

(3.39)

-0.58

(0.75)

-11.55***

(3.82)

-1.42*

(0.76)

-4.29

(4.65)

Eviction Rate

-0.40

(0.24)

3.62**

(1.49)

-0.23

(0.16)

7.44***

(1.54)

Change in Eviction Rate

-0.03

(0.15)

-6.31**

(2.89)

Percentage of Overcrowded Housing Units

1.01

(0.82)

7.97***

(1.51)

0.98**

(0.41)

5.04**

(2.18)

-0.01

(0.64)

6.31***

(1.85)

Urban CoC

1.20

(1.57)

14.21

(10.34)

2.38***

(0.84)

16.13**

(7.10)

-0.04

(0.76)

-11.59

(7.54)

Suburban CoC

-1.27

(1.36)

0.81

(13.13)

0.58

(0.78)

10.31

(8.64)

-0.65

(0.68)

-17.46*

(10.14)

Economic Domain

Unemployment Rate

0.28

(1.06)

6.92***

(1.61)

-0.66

(0.47)

5.55***

(1.19)

0.77

(0.70)

3.74

(2.45)

Gini Coefficient of Income Inequality

-0.55**

(0.22)

1.47

(0.92)

0.13

(0.20)

-0.11

(1.02)

Poverty Rate

-0.32

(0.42)

-0.69

(0.96)

-0.31

(0.28)

-1.21

(1.61)

-0.10

(0.14)

-0.18

(1.03)

Safety Net Domain

Share of HUD-Assisted Units

1.88***

(0.67)

-3.21*

(1.63)

0.87*

(0.52)

3.30*

(1.78)

0.41

(0.30)

-1.87**

(0.78)

Occupancy Rate of HUD-Assisted Units

0.04

(0.09)

-1.20***

(0.35)

-0.05

(0.06)

-0.30

(0.41)

-0.02

(0.06)

-0.12

(0.30)

Percentage of Houses Built Before 1940

-0.05

(0.10)

0.54**

(0.22)

0.15**

(0.06)

-0.12

(0.28)

Demographic Domain

Percentage African-American

-0.12

(0.12)

0.51

(0.40)

-0.09

(0.07)

0.56**

(0.23)

-0.01

(0.04)

0.19

(0.29)

Percentage Hispanic

-0.04

(0.09)

-0.90***

(0.28)

-0.06

(0.04)

-0.77*

(0.39)

0.02

(0.05)

-0.44*

(0.22)

Percentage Asian

0.50

(0.35)

-0.22

(0.26)

0.04

(0.14)

-0.62***

(0.19)

0.57**

(0.26)

0.18

(0.26)

Percentage Children (Age 0 through 19)

0.25

(0.39)

-2.65**

(1.16)

-0.49*

(0.25)

-2.46

(1.54)

-0.11

(0.28)

-1.75

(1.08)

Percentage Senior (Age 65 and Older)

-0.11

(0.27)

2.16**

(0.95)

-0.47***

(0.15)

1.44

(1.13)

0.01

(0.13)

-0.01

(1.26)

Percentage Female

0.38

(0.45)

0.79

(2.52)

-1.61*

(0.90)

-1.88

(2.39)

Net-Migration Rate

3.40***

(1.25)

-0.10

(3.11)

1.56**

(0.64)

-3.06

(4.91)

Change in Net-Migration Rate

0.47

(1.15)

-12.46**

(4.70)

2.74***

(0.97)

2.66

(5.65)

Percentage of One-Person Households

1.04***

(0.36)

0.18

(1.16)

0.37*

(0.19)

0.98

(0.91)

Percentage of Under-18 Population in

Single-Parent Households

0.11

(0.36)

0.46

(0.53)

0.34

(0.22)

-0.88

(0.77)

Percentage Veteran (25 and Older)

0.21*

(0.12)

-2.61***

(0.61)

0.23

(0.17)

1.97**

(0.94)

Percentage without a Bachelor’s Degree

0.00

(0.07)

-0.81**

(0.37)

Healthcare Costs ($1,000s)

-0.69

(0.53)

1.09

(1.07)

Alcohol Mortality (Per 100,000)

1.33**

(0.54)

1.51

(1.15)

1.00***

(0.26)

4.12***

(1.35)

Excessive Drinking Rate

-0.02

(0.11)

-0.51

(0.69)

-0.24*

(0.13)

0.36

(0.92)

| 55

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Independent Variables

CoCs in Tight,

Other CoCs

CoCs in Tight, High-

Cost Rental Markets

Other CoCs

CoCs in Tight, High-

Cost Rental Markets

Other CoCs

High-Cost Rental

Markets

Climate Domain

Average January Temperature (°F)

0.04

(0.16)

-0.21

(0.30)

-0.07

(0.08)

0.66

(0.40)

0.06

(0.08)

-0.57

(0.81)

Average June, July, and August

Temperature (°F)

-0.16

(0.26)

0.63

(0.74)

-0.04

(0.13)

0.65

(0.80)

-0.02

(0.21)

0.18

(0.87)

Total January Precipitation (Inches)

0.31

(0.20)

0.58

(0.64)

0.09

(0.09)

-1.05

(0.77)

0.17

(0.15)

2.04**

(0.96)

Total Annual Precipitation (Inches)

-0.03

(0.06)

0.53***

(0.12)

-0.02

(0.03)

0.39***

(0.09)

Intercept

-23.99

(30.23)

-7.97

(24.10)

72.95

(47.74)

Observations

374

374

374

R-Squared

0.90

0.92

0.80

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤

0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the variables included in the national model for the outcome of interest (total, sheltered, or unsheltered homelessness) in the National Model of Homelessness chapter. Change variables are calculated by subtracting the 2012 value from the 2016

value for each CoC. ACS variables and ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category.

| 56

Unsheltered Homelessness on the West Coast

Exhibit 5-8 presents the total, sheltered, and unsheltered homeless populations and rates of homelessness from 2011 through 2017 for the west coast CoCs in California, Oregon, and Washington state. The levels and rates of total homelessness decreased from 2011 through 2015 but increased by nearly 20,000 between 2015 and 2017. This increase can be attributed entirely to the increase in unsheltered homelessness. From 2015 to 2017, unsheltered homelessness increased by nearly 20,000

people, and the rate of unsheltered homelessness increased from 18 homeless people per 10,000 in 2015 to 21 homeless people per 10,000 in 2017. In 2017, west coast CoCs accounted for nearly 60

percent of the unsheltered homeless population nationwide. As a result, in this section, the study team focuses solely on the unsheltered homeless population on the west coast.

Exhibit 5-8 | Homelessness in West Coast Continuums of Care, 2011 through 2017

180,000

40

160,000

35

140,000

30

120,000

25 essness

essness 100,000

el 000)

el

20 om

om 80,000

f H

15

(Per 10,

60,000

Total H

Rates o

10

40,000

5

20,000

0

0

2011

2013

2015

2017

Total Homeless

Total Sheltered

Total Unsheltered

Homeless Rate

Sheltered Rate

Unsheltered Rate

Sources: Census’s intercensal population estimates; HUD PIT count data

Exhibit 5-9 presents the regression estimates of the relationship between CoC-level factors and rates of unsheltered homelessness per 10,000 population for west coast CoCs and CoCs in other regions. These estimates show that many variables across the five domains are significantly associated with rates of homelessness. This exhibit presents one regression specification, in which the independent variables are interacted with a dummy variable representing the west coast subgroup, and the coefficient estimates for each subgroup (other region and west coast) are presented side-by-side. For this model, the R-squared estimate of 0.87 indicates that the variables in our models account for a high degree of variation in rates of unsheltered homelessness across west coast CoCs. The independent variables in these specifications are the same variables used for the national model in the National Model of Homelessness chapter.

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For rates of unsheltered homelessness, the model illustrates more significant factors compared with the national model presented in the National Model of Homelessness chapter. In the housing domain, high median rent is associated with higher rates of unsheltered homelessness for CoCs in other regions.

Notably, the coefficient on median rent is negative but only marginally significant for west coast CoCs, which may be surprising given the substantial increases in both rent and the unsheltered population in west coast communities in recent years. Because so many housing domain variables explain variations in unsheltered homelessness and many are significant in this specification for unsheltered homelessness in west coast CoCs, factors associated with housing costs may be picked up by the other independent variables, rather than median rents. Furthermore, because this analysis uses a nominal rent level rather than a measure of change, this coefficient may be capturing the differences between larger, higher income communities with high rents who may have a large total unsheltered population, but lower rates than smaller, poorer communities with higher rates of unsheltered homelessness. Additional research may be needed to fully explore the relationships between changes in the relative and absolute rents in communities and changes in their unsheltered population.

Focusing on other elements of the housing domain, a high share of homeowners with cost burden is associated with high rates of unsheltered homelessness in west coast CoCs, a high share of renters with cost burden is associated with lower rates of unsheltered homelessness in west coast CoCs. Large increases in eviction rates are associated with lower rates of unsheltered homelessness in west coast CoCs. This association might be due to local policies that support households that have been evicted.

We examine some of these policies in appendix H. Furthermore, high housing density, which may indicate the availability of housing, is associated with lower rates of unsheltered homelessness for west coast CoCs as compared with CoCs in other regions. In addition, for west coast CoCs, both urban and suburban CoC statuses are associated with lower rates of unsheltered homelessness compared with rural CoCs on the west coast. In the economic domain, a high unemployment rate is associated with high rates of unsheltered homelessness in west coast CoCs compared with CoCs in other regions.

For west coast CoCs, housing-related safety net

variables are significant factors associated with

Imperial County CoC

rates of unsheltered homelessness in the safety net Rate of Unsheltered Homelessness: 56.7 per 10,000

domain. For the housing-related safety net

Median Rent: $641

Share of Homeowners with Cost Burden: 29.9 percent

variables, a high share of HUD-assisted units in west Rental Vacancy Rate: 4.7 percent coast CoCs is associated with higher rates of

Share of Overcrowded Units: 10.4 percent

unsheltered homelessness. In addition, the

occupancy rate of HUD-assisted units is negatively

Watsonvil e/Santa Cruz City and County CoC

related to rates of unsheltered homelessness. High Rate of Unsheltered Homelessness: 65.4 per 10,000

occupancy rates of HUD-assisted units may signal

Median Rent: $1,385

that a smal but important segment of low-income

Share of Homeowners with Cost Burden: 35.2 percent

housing needs are met through HUD programs,

Rental Vacancy Rate: 1.9 percent

possibly contributing to lower rates of unsheltered Share of Overcrowded Units: 6.7 percent homelessness in west coast CoCs.

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The Imperial County and Watsonvil e/Santa Cruz City and County CoCs in California are both suburban CoCs with higher rates of unsheltered homelessness than two urban CoCs, Los Angeles City and County and Seattle/King County, even though these smaller communities have different median rents and rental vacancy rates. The Imperial County CoC has the third highest rate of unsheltered homelessness, median rents in the lowest 25 percent, a share of homeowners with cost burden around the median, a rental vacancy rate around the highest 25 percent, the third highest share of overcrowded units, and a share of HUD-assisted units in the highest 25 percent of all west coast CoCs. The Watsonvil e/Santa Cruz City and County CoC has the second highest rate of unsheltered homelessness, median rents in the highest 25 percent, a share of homeowners with cost burden near the highest 5 percent, the lowest rental vacancy rate, a share of overcrowded units near the highest 25 percent, and the highest share of HUD-assisted units of all west coast CoCs. The Los Angeles CoC has the largest unsheltered homeless population of any community in the country at 42,828 in 2017. 63 In addition, the Los Angeles City and County CoC has a rate of unsheltered homelessness in the highest 10 percent, median rents near the highest 25 percent, the highest share of homeowners with cost burden, a rental vacancy rate in the lowest 50 percent, and the second highest share of overcrowded units of all west coast CoCs. The Seattle/King County CoC has the third largest

unsheltered homeless population among al

Los Angeles City & County CoC

communities in the country. Furthermore, the

Rate of Unsheltered Homelessness: 42.2 per 10,000

Seattle/King County CoC has a rate of

Median Rent: $1,167

unsheltered homelessness near the highest 25

Share of Homeowners with Cost Burden: 38.3 percent

percent, median rents near the highest 25

Rental Vacancy Rate: 3.3 percent

percent, a share of homeowners with cost

Share of Overcrowded Units: 11.8 percent

burden in the lowest 25 percent, a rental

vacancy rate in the lowest 25 percent, and a

Seattle/King County CoC

share of overcrowded units near the lowest 25

Rate of Unsheltered Homelessness: 25.4 per 10,000

Median Rent: $1,149

percent of all west coast CoCs. More context

Share of Homeowners with Cost Burden: 27.6 percent

around local policy approaches in Seattle is

Rental Vacancy Rate: 3.1 percent

available in appendix H.

Share of Overcrowded Units: 3.2 percent

Estimates from the demographic domain

indicate that several factors are significantly

associated with rates of unsheltered homelessness in west coast CoCs. The share of Asian residents in CoCs in other regions is associated with higher rates of unsheltered homelessness. The shares of children and seniors are both negatively associated with rates of unsheltered homelessness; these are vulnerable populations that could be eligible to receive other forms of housing assistance that prevent unsheltered homelessness. In contrast, the share of women is positively associated with rates of unsheltered homelessness in west coast CoCs. Similarly, high increases in the net-migration rate are associated with high rates of unsheltered homelessness in west coast CoCs. High shares of one-person households are related to high rates of unsheltered homelessness in all CoCs; however, the coefficient estimate is larger for west coast CoCs. One-person households may be a low priority for shelters, or these households may feel like shelters will not meet their needs and, as a result, they may be more 63 The total count of unsheltered homelessness represents the total unsheltered homeless population in al four CoCs within Los Angeles County. The Los Angeles City and County CoC was combined with the Glendale, Long Beach, and Pasadena CoCs as all are within Los Angeles County. See appendix C for further details on CoC aggregation assumptions.

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likely to become part of the unsheltered homeless population if opportunities for shared housing arrangements are scarce. Furthermore, high healthcare costs and high excessive drinking rates are associated with lower rates of homelessness in west coast CoCs. Final y, for the climate domain, high January temperatures, summer temperatures, and January precipitations are related to increased rates of unsheltered homelessness in west coast CoCs. In contrast, high January precipitation is associated with lower rates of unsheltered homelessness for CoCs in other regions.

Exhibit 5-9 | Regression Estimates of Model for Unsheltered Homelessness in West Coast Continuums of Care (CoCs) Compared with Other Region CoCs

Independent Variables

2017 Unsheltered Homelessness Rate (Per 10,000 Population)

Other Region CoCs

West Coast CoCs

Housing Domain

House Price Index

0.04

(0.03)

0.17

(0.15)

Percentage of Homeowners with Cost Burden

0.12

(0.09)

2.27***

(0.38)

Natural Logarithm of Median Rent ($100s)

5.45**

(2.27)

-10.14*

(5.76)

Median Rental Utility Cost ($10s)

0.03

(0.10)

0.56

(1.09)

Percentage of Renters with Cost Burden

-0.01

(0.07)

-2.20***

(0.22)

Percentage of Renter-Occupied Units

-0.03

(0.09)

-0.41

(0.31)

Rental Vacancy Rates

0.47***

(0.15)

-2.49***

(0.77)

High Housing Density CoC

-1.11*

(0.63)

-14.41**

(5.73)

Change in Eviction Rate

-0.23*

(0.13)

-3.00**

(1.22)

Percentage of Overcrowded Housing Units

0.11

(0.41)

5.64***

(0.98)

Urban CoC

0.21

(0.72)

-2.51**

(1.18)

Suburban CoC

-0.42

(0.40)

-9.79***

(1.39)

Economic Domain

Unemployment Rate

0.07

(0.35)

1.18**

(0.49)

Poverty Rate

-0.01

(0.12)

-0.59*

(0.32)

Safety Net Domain

Share of HUD-Assisted Units

0.29*

(0.15)

5.06***

(1.38)

Occupancy Rate of HUD-Assisted Units

0.05

(0.04)

-0.40***

(0.14)

Demographic Domain

Percentage African-American

-0.03

(0.03)

-0.91*

(0.50)

Percentage Hispanic

-0.05

(0.04)

-0.63

(0.41)

Percentage Asian

0.31***

(0.12)

-0.84*

(0.44)

Percentage Children (Age 0 through 19)

0.01

(0.18)

-2.86***

(0.62)

Percentage Senior (Age 65 and Older)

-0.14

(0.12)

-2.89**

(1.23)

Percentage Female

-0.80

(0.54)

3.41***

(0.42)

Change in Net-Migration Rate

1.71**

(0.71)

7.44**

(3.48)

Percentage of One-Person Households

0.41**

(0.17)

2.82***

(0.26)

Percentage Veteran (25 and Older)

0.10

(0.10)

0.02

(0.88)

Percentage without a Bachelor's Degree

0.13**

(0.05)

-0.18

(0.43)

Healthcare Costs ($1,000s)

-0.51

(0.37)

-3.41***

(0.64)

Excessive Drinking Rate

-0.08

(0.08)

-4.65***

(0.35)

Climate Domain

Average January Temperature (°F)

0.12

(0.08)

0.47***

(0.10)

Average June, July, and August Temperature (°F)

-0.05

(0.17)

1.50***

(0.30)

Total January Precipitation (Inches)

-0.45***

(0.16)

0.93***

(0.26)

Intercept

9.83

(27.47)

Observations

374

R-Squared

0.87

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the variables included in the national model for the outcome of interest (total, sheltered, or unsheltered homelessness) in the National Model of Homelessness chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS variables and ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS.

Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category. States in the west coast region include California, Oregon, and Washington.

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CONCLUSION

This study provides new insights into the factors associated with homelessness in communities across the United States. While homelessness is an acknowledged problem in many areas, its causes are myriad and may vary based on the characteristics of respective communities. This report furthers the col ective understanding of the heterogeneity in community-level rates of total, sheltered, and unsheltered homelessness.

Results from our regression analysis indicate that a wide variety of factors across the five domains (housing market, economic conditions, safety net, demographic characteristics, and climate conditions) are associated with rates of homelessness and that these factors vary by type of homelessness (total, sheltered, and unsheltered). In general, the regression specifications presented in this study improve on the explanatory power of models presented in previous studies. Our national models explain between 68 and 82 percent of the variation in the outcome of interest, compared with 58 percent in the Byrne et al. (2012) study.

Notably, the model specifications presented in this study also expand on the sets of covariates included in previous studies. This expansion of the independent variables represents a methodological chal enge, in that including the full set of covariates in each specification would result in overfitted models plagued by multicollinearity muddling model interpretation. To address this chal enge, the current study also contributes important methodology to the literature, including the use of systematic variable selection algorithms. The rich set of covariates allows for individual model specifications and the inclusion of different variables within and across domains that, in turn, allow us to illustrate nuances in the relationships between variables in each domain and the outcomes of interest.

We find that factors within the housing domain are most consistently associated with community-level homelessness, confirming the findings from previous studies. 64 Specifically, our estimates for variables in the housing domain echo the finding in Byrne et al. (2012) that housing market dynamics and the availability of affordable housing are closely tied to homelessness at the Continuum of Care (CoC) level.

Furthermore, issues of housing affordability are inherently related to factors in the economic and safety net domains because economical y disadvantaged populations may struggle to afford housing in areas with high costs. Our findings demonstrate that the ability to obtain affordable housing is related to factors across domains. Across the various specifications presented in the National Model of Homelessness chapter and the Subgroup Analysis chapter, however, the significant independent variables associated with rates of homelessness differ based on the outcome of interest and subgroup category.

64 These findings corroborate results in previous studies that find housing market factors are strong predictors of homelessness (Byrne et al., 2012; Fargo et al., 2013; Lee, Price-Spratlen, and Kanan, 2003).

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TOTAL HOMELESSNESS

With regard to rates of total homelessness and consistent with previous studies, we find a positive association between median rents and rates of total homelessness, except in rural CoCs. 65 In line with concerns regarding affordability, our estimates do indicate that the share of renters with housing cost burden greater than 30 percent of their income is also positively associated with rates of total homelessness in rural CoCs and communities with tight, high-cost rental markets. 66 The positive association of overcrowded housing units and total homelessness, in both the national model and in communities in tight, high-cost rental markets, also supports housing availability being an important factor. If the existing units have more people per room, fewer units may be available to low-income renters. Alternatively, larger homes may not be affordable for low-income renters.

Estimates in the economic domain indicate that total homelessness and the affordability of housing are tied to local economic and labor market characteristics. Our estimates regarding the relationship between unemployment rates and total homelessness are inconclusive. Specifically, we find that higher unemployment rates are associated with increased rates of total homelessness in CoCs with tight, high-cost rental markets; however, the association is negative in urban CoCs. In the safety net domain, we find that housing-related assistance factors are more strongly associated with total homelessness. In the national model and for suburban CoCs, we find a high share of HUD-assisted units is associated with higher rates of total homelessness in the community. High shares of HUD-assisted units may suggest that the area has a large population that needs housing assistance and is more likely to be homeless. For communities with tight, high-cost rental markets, we find that a higher occupancy rate of HUD-assisted units is associated with lower rates of total homelessness. This finding suggests that access to affordable units through HUD assistance is reducing total homelessness. There is no clear relationship between rates of total homelessness and participation in other social safety net programs as measured by the share of the population receiving cash assistance, mainly in urban areas. Earlier literature found a positive relationship between participation in social safety net programs and individual-level homelessness. Our estimates indicate that measuring the effect of other social safety net programs at the community level may be difficult. 67

Our findings on demographic characteristics indicate variation in the factors that affect rates of total homelessness across the national and subgroup analysis. We find few significant relationships between homelessness and race and ethnicity categories. Across several specifications, however, CoCs with higher shares of the Hispanic population are associated with lower rates of total homelessness, like the findings in Byrne et al. (2012). Khadduri et al. (2018) provided a plausible explanation, suggesting that Hispanic populations have lower measures of housing instability. 68 Net-migration rates are positively associated with rates of total homelessness in the national model, while changes in net-migration rates 65 While most studies that include median rent find a positive coefficient, Glynn and Fox (2017) found a significant and positive association between median rents and rates of homelessness in New York, Los Angeles, Washington, DC, and Seattle, but insignificant estimates in other urban areas.

66 Fargo et al. (2013) also found that a high share of households with rental cost burden of greater than 30 percent of the household income is associated with higher rates of homelessness among both families and single adults.

67 These results mirror the finding in Fargo et al. (2013) that, in general, safety net variables do not serve as strong predictors of homelessness.

68 While Khadduri et al. (2018) pointed out that, on average, Hispanic populations have lower measures of housing instability than African-Americans, Byrne et al. (2012) found a positive relationship between the share of Hispanic residents and rates of homelessness.

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are positively associated with total homelessness in suburban CoCs. For communities with tight, high-cost rental markets, however, we find a negative relationship between changes in the net-migration rate and total homelessness. These results may provide further evidence that housing market dynamics and availability are associated with rates of total homelessness. In some cases, an influx of population may crowd out vulnerable renters and increase rates of homelessness. 69 In addition, community-level age distribution appears to be associated with rates of total homelessness with a negative coefficient on the share of children in the national model and for CoCs in tight, high-cost rental markets and a positive coefficient on the share of senior adults in rural areas and for CoCs in tight, high-cost rental markets.

Such estimates indicate that the shares of vulnerable populations are associated with rates of total homelessness. The effect varies across different models, suggesting that the other demographic characteristics may not be robustly associated with total homelessness and could represent proxies for other omitted constructs, as already highlighted in the literature. 70 Finally, the relationship between climate conditions and rates of total homelessness appears inconclusive.

SHELTERED HOMELESSNESS

Sheltered homeless people are those in emergency shelters, transitional housing programs, or safe haven temporary shelters (Henry et al., 2017). Because shelters are often geared toward providing accommodations for the most vulnerable, hard-to-house populations, the factors associated with rates of sheltered homelessness may be different from those associated with total and unsheltered homelessness. Housing affordability is stil of paramount importance, however, in terms of factors related to rates of sheltered homelessness in communities across the country. Our national model estimates indicate that CoCs with higher median rents have increased rates of sheltered homelessness.

We find no significant association, however, between median rents and rates of sheltered homelessness in other specifications (in urban CoCs, the association is positive but marginal y statistically significant, possibly due to the sample size issue). In addition, our findings suggest that the share of homeowners with a housing cost burden greater than 30 percent of their income is also positively associated with rates of sheltered homelessness, especially in suburban and rural CoCs. Estimates for renters with cost burden vary in sign and significance across specifications. Together, these findings suggest that areas with families and individuals who have a large cost burden due to owning expensive homes or renting high-cost apartments, relative to their income, may be more susceptible to higher rates of sheltered homelessness. Results for CoCs in tight, high-cost rental markets show that high eviction rates are related to high rates of sheltered homelessness, possibly driven by displaced families who tend to seek shelter instead of living on the streets, especially as these areas lack affordable housing options.

For rates of sheltered homelessness, estimates in the economic domain indicate various significant factors across model specifications. Notably, we find that high unemployment rates are associated with higher rates of sheltered homelessness in CoCs in tight, high-cost rental markets. This finding illustrates the relationship between housing affordability and rates of sheltered homelessness because high unemployment rates indicate that fewer residents can access affordable housing units due to lack of 69 Lee, Price-Spratlen, and Kanan (2003) found that rates of migration are positively associated with rates of homelessness, particularly in metropolitan areas. The authors indicated that higher demand for housing may increase the exposure of vulnerable populations to homelessness and subpar housing accommodations.

70 Byrne et al. (2012) highlighted a lack of consensus in the previous literature regarding the specific demographic characteristics that affect homelessness.

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employment opportunities. In the national model, we find that high-income inequality is associated with lower rates of total homelessness, while high poverty is associated with higher rates of total homelessness. Across other specifications, however, we find no significant relationships between rates of sheltered homelessness and poverty. 71 We do find a strong positive association between the share of HUD-assisted units in a community and sheltered homelessness in the national model, as well as urban and suburban areas.

Estimates of the relationship between rates of sheltered homelessness and demographic characteristics indicate that race and ethnicity, migration, household composition, and health conditions are important factors. Across the nation and rural CoCs, there is a strong negative relationship between the share of the Hispanic population and rates of sheltered homelessness. In addition, a high share of Asian residents is associated with lower rates of sheltered homelessness in the national model, as wel as urban CoCs and communities with tight, high-cost rental markets. Estimates of the relationship between net-migration rates indicate that the coefficient is positive and significant in the national model but negative and insignificant in other models. As explained previously, such dynamics may crowd out vulnerable populations from the housing market. Household composition and the share of one-person households appear to be positively associated with rates of sheltered homelessness in rural CoCs. 72 Finally, high alcohol mortality and excessive drinking rates, which function as measures of health and wellbeing in the community, are associated with high rates of sheltered homelessness, mainly in the national model.

Finally, the relationship between climate conditions and rates of sheltered homelessness appears inconclusive with only the total annual precipitation having consistent positive association across the national, urban and tight, high-cost rental markets.

UNSHELTERED HOMELESSNESS

Unsheltered homeless individuals have a primary nighttime location that is not typically designated for sleeping accommodations (Henry et al., 2017). Given that unsheltered homeless people lack a formal attachment to housing and rental markets, factors associated with rates of total and sheltered homelessness may not have the same relationship with unsheltered homelessness rates. Within the housing domain, estimates for independent variables vary in direction and significance across specifications, indicating that the factors associated with unsheltered homelessness depend on local market within the subgroup of interest. For example, the coefficient on median rents is positive and significant for suburban CoCs but negative and insignificant across al other specifications. The share of homeowners with cost-burden greater than 30 percent of their income tends to increase rates of unsheltered homelessness in urban, rural, and west coast CoCs but decreases unsheltered homelessness in suburban communities. The share of renter-occupied units is associated with lower rates of unsheltered homelessness in urban CoCs, while the association is positive in rural CoCs. 73 Across several specifications, our estimates suggest that the share of overcrowded housing units is positively associated with rates of unsheltered homelessness.

71 Hanratty (2017) il ustrated the sign and significance of poverty coefficients from various studies. While most previous studies found no significant relationship, Quigley, Raphael, and Smolensky (2001) found a positive and significant relationship.

72 Shinn et al. (1998) indicated that shelters may state whether to accept men. If shelter rules vary by CoC, these factors could affect estimates of the relationship between one-person households and rates of sheltered homelessness.

73 Fargo et al. (2013) found a positive and significant association between homelessness and the share of renter-occupied housing.

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Rates of unsheltered homelessness are associated with few factors in the economic domain. Our estimates indicate that high unemployment rates are associated with low rates of unsheltered homelessness in urban CoCs but high rates of unsheltered homelessness in suburban CoCs and west coast CoCs. In the safety net domain, our results show that factors related to housing assistance are associated with rates of unsheltered homelessness, with the direction of the relationships varying across specifications. The share of HUD-assisted units is negatively associated with unsheltered homelessness in urban CoCs and CoCs in tight, high-cost rental markets, while the coefficient is positive in suburban and west coast CoCs. A high occupancy rate is associated with lower rates of unsheltered homelessness in west coast CoCs but not significant in other specifications. In addition, the share of houses built prior to 1940, used as a proxy for federal CoC funding, is associated with lower rates of unsheltered homelessness in suburban CoCs. 74 Such results suggest that CoC funding may curb rates of unsheltered homelessness.

For the demographic domain, net-migration and household composition are consistently significant factors associated with rates of unsheltered homelessness. Increases in net-migration rates are associated with higher rates of unsheltered homelessness in the national model and suburban and west coast CoCs. The share of one-person households is positively associated with rates of unsheltered homelessness across most specifications. Finally, the relationship between climate conditions and rates of unsheltered homelessness show that average January temperature and precipitation have consistent positive association across subgroup analysis other than communities in tight, high-cost rental markets.

LIMITATIONS OF THE STUDY

We would like to caution the reader, as there are certain limitations on the estimates that should be taken into consideration when interpreting the results. The limitations largely stem from data availability that restricts the methodological options available. As such, we focus on rates of homelessness from a single year (2017) as presented in various specifications outlined in the previous two chapters. In addition, the estimates presented in this study do not imply causality. For example, increases in estimates for an independent variable should not be interpreted as a causal relationship between homelessness and that variable but rather as a factor that is associated with rates of homelessness. Further understanding of these relationships is crucial to our understanding of potential policy remedies for preventing homelessness.

NEXT STEPS AND PLANS FOR FUTURE RESEARCH

While this study provides new estimates of the factors associated with homelessness in communities across the United States, future research should further expand the subgroup analysis and delve deeper into the factors that may affect homelessness in specific areas, such as rural communities or cities with tight, high-cost rental markets. 75 Additional models examining the differences between urban and rural areas would broadly increase our understanding of the factors that contribute to homelessness. Future studies should explore the possibility of using data from the Annual Homelessness Assessment Report, Part II (Henry et al., 2018a), system performance measures, and housing inventory count available in the 74 Lucas (2017) used the share of houses built prior to 1940 as an instrument for federal CoC funding and found a positive but statistical y insignificant relationship between funding and rates of unsheltered homelessness.

75 See appendix H for an assessment of the local policies regarding homelessness in New York, San Francisco, and Seattle.

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Homeless Management Information System to perform a more in-depth analysis of those experiencing sheltered homelessness.

CONCLUDING REMARKS

The findings from this study provide key insights into the various factors that contribute to rates of total, sheltered, and unsheltered homelessness in communities across the nation. In addition, this study identifies new factors that provide insights into predicting homelessness across different regions. The study found that the significant factors associated with rates of homelessness varied by outcome and subgroup, suggesting a need for more research to increase understanding of these factors and targeted policy interventions to further prevent and end homelessness nationally and locally.

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APPENDIX A: RESEARCH QUESTIONS

The 10 research questions (RQs) are listed. Exhibit A-1 presents our proposed RQs for each project phase.

1. What data are available to model market predictors of homelessness?

2. Which local and regional housing market and economic factors best explain recent homelessness trends?

3. Which local and regional demographic trends and population dynamics help explain recent homelessness trends?

4. What control variables are important to include in models of homelessness trends?

5. Which factors heighten or mitigate the risk of homelessness across metropolitan areas and regions?

6. Which community-level fixed effects, demographic effects, or policy-driven interaction effects should the models account for?

7. How do communities that implemented a suite of local homelessness policies (such as preferences, shelter rights, or bus-out policies) relate to comparable locations?

8. Among tight, high-cost housing markets with large renter shares, are there particular conditions that exacerbate or protect against homelessness?

9. Which factors are driving homelessness levels in the west?

10. What data are available for a potential future study on how regulations and land-use policies may be contributing to these problems?

Exhibit A-1 | Mapping of Research Phases and Research Questions

Phases of Research Project

RQs

Phase 1: Build a robust dataset of factors with known influence on homelessness.

RQs 1 through 6

Phase 2: Produce a national model of market predictors of homelessness,

accounting for appropriate explanatory and control variables.

RQs 2 through 8

Phase 3: Conduct subgroup analysis at urbanicity-level, for CoCs in tight, high-cost housing markets and CoCs in the west.

RQ 9

Phase 4: Assess the feasibility of a future study on how regulations and land-use policies may be contributing to these problems after controlling for exogenous

RQs 7 through 10

market forces.

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APPENDIX B: OTHER DATA SOURCES CONSIDERED

Exhibit B-1 presents the variables and data sources not included in the model. These variables had significant limitations that are outlined in the

“Explanation” column in the exhibit.

Exhibit B-1 | Data Sources Not Included in Model

Predictors of Homelessness

Data Source

Geography

Available

Years Available

Explanation

Housing Domain

Ratio of unassisted extremely

low-income renters to number HUD Picture of Subsidized

The study team was unable to obtain

of turnovers of assisted units

Households (PSH); HUD

County

Unknown

administrative data related to the number of

annually

administrative data

turnovers.

46

Per unit (or square foot)

Metropolitan

construction cost

Lincoln Institute

Statistical Areas

1984 through 2016

This data source represents 46 MSAs. In

(MSAs)

addition, house values are included as a proxy

for cost.

Per unit (or square foot) land

cost

Lincoln Institute

46 MSAs

1984 through 2016

This variable was dropped during the qualitative

review. Originally intended to proxy for housing

Median year home built

ACS

County

2009 through 2017

quality stock, the addition of “Percent of Houses

Built Before 1940” raised concerns over

redundancy.

This variable was dropped during the qualitative

review. The eviction filling rate counts the total

number of filed evictions in which multiple

Eviction filing rate

Eviction Lab

County

2000 through 2016

evictions can be filed to a single address per

year. Thus, formulating a rate by dividing by

population or housing units would result in

double counting.

This variable was dropped during the qualitative

Rate of new unit construction

Census Building Permits

review. New construction is likely correlated to

Survey

County

2009 through 2017

other housing market and economic variables

and may not hold theoretical value.

Economic Domain

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Predictors of Homelessness

Data Source

Geography

Available

Years Available

Explanation

Employment rate for low,

This variable was dropped during the qualitative

middle and high skilled

ACS

County

2010 through 2017

review in favor of separate unemployment rate

workers

and educational attainment variables.

Safety Net Domain

This variable was dropped during the qualitative

review. This measure of over-housing would

Percent of HUD-assisted

serve as a proxy for low-income housing

households with more

HUD Picture of Subsidized

disequilibrium. The variable was omitted as HUD

bedrooms than people

Housing (PSH)

County

2009 through 2018

suggested the values are largely driven by

programmatic requirements and do not reflect

the overall population.

This variable was dropped during the qualitative

People per unit in

review in favor of an ACS measure of

HUD-assisted households

HUD PSH

County

2009 through 2018

overcrowded housing. This measure was specific

to HUD households rather than the overall

population.

Percent of HUD-assisted

This variable was dropped during the qualitative

households with income

review. The variable was omitted as HUD

below 30 percent of local

HUD PSH

County

2009 through 2018

suggested this would like driven by

median family income

programmatic requirements and does not reflect

the overall population.

This variable was dropped during the qualitative

review as it may introduce endogeneity through

Permanent supportive

HUD Housing Inventory

simultaneity. While the number of beds

housing bedsa

Counts (HIC)

CoC

2005 through 2018

contributes directly to sheltered homeless

counts, CoCs experiencing high levels of

homelessness may mediate this issue by

increasing shelter capacity.

This variable was dropped during the qualitative

review as it may introduce endogeneity through

simultaneity. While the number of beds

Temporary housing bedsb

HUD HIC

CoC

2005 through 2018

contributes directly to sheltered homeless

counts, CoCs experiencing high levels of

homelessness may mediate this issue by

increasing shelter capacity.

Local Demographic Domain

| 69

Predictors of Homelessness

Data Source

Geography

Available

Years Available

Explanation

This variable was dropped during the qualitative

Population density

Census

County

1980 through 2018

review as it is presumably correlated to CoC

urbanicity.

Domestic violence

Centers for Disease Control

and Prevention (CDC)

State

2012

Data are not available at county level.

Sparse data are available at the county level.

CDC

County

1999 through 2016

Excess alcohol consumption and alcohol

Drug and alcohol abuse

(CDC)

mortality rates are included in the model.

(drug overdose deaths)

Substance Abuse and

Data are not available at the county level.

Mental Health Services

State

2002 through 2017

Interstate CoC would complicate the use of

Administration (SAMHSA)

(SAMHSA)

state-level data.

Data are not available at the county level. Data

Incarceration rate

Bureau of Justice Statistics

Jurisdiction

1970 through 2015

reported at the jurisdiction level may include

multicounty jurisdictions that are incompatible

with a Continuum of Care-level dataset.

Prevalence of opioid disorders

Data are not available at the county level.

or deaths

CDC

County

1999 through 2016

Alcohol consumption and alcohol death rates are

included in the model.

Additional Policies Recommended by HUD

These data represent policies in the largest 25

Bus-out policies

The Guardian

25 largest

U.S. cities as of mid-2016, so bus-out policies

U.S. cities

2011 through 2016

may potential y not be included in the model for

all counties.

Public Housing Authority

HUD data on PHA

The study team did not include these data due

(PHA) homelessness

homelessness preference

PHA

2012

to issues of recency and mapping PHA coverage

preference policies

policies

areas.

A study by the National Law Center on

Homelessness and Poverty provides an overview

Right-to-shelter policies

National Law Center on

of municipal policies regarding homelessness.

Homelessness and Poverty

Municipal

2014

The study team's concerns over the consistency

of estimation led us to exclude these data. They

are discussed, however, in appendix H.

a Permanent supportive housing (PSH) beds also include Rapid Re-Housing (RRH), and Other Permanent Housing (OPH).

b Temporary housing beds include Emergency Shelter (ES), Transition Housing (TH), and Safe Haven (SH).

ACS = American Community Survey. CDC = Centers for Disease Control and Prevention. HIC = housing inventory counts. MSA = metropolitan statistical area. PHA = public housing authority. PSH = picture of subsidized housing. SAMHSA = Substance Abuse and Mental Health Services Administration.

| 70

APPENDIX C: CREATION OF CONTINUUM

OF CARE-TO-COUNTY CROSSWALK

HUD provided the study team with three datasets that served as the sources for our updated Continuum of Care (CoC)-to-county crosswalk—

1. Point-in-Time (PIT) data that defined CoC mergers over time.

2. County-to-CoC crosswalk.

3. Urbanicity file that defined CoCs as one of four categories—major city, largely urban largely suburban and largely rural.

The study team used the HUD-provided data to create a unique list of CoCs to be compared with the HUD-provided CoC-to-county crosswalk. This process revealed several inconsistencies between CoC and county geographies, which are outlined in the Data chapter. This section provides a specific account of the relationships between CoCs and counties in the HUD-provided data.

CONTINUUM OF CARE MISSING FROM HUD-PROVIDED CROSSWALK

The first issue the study team encountered was that the HUD-provided crosswalk did not include some CoCs that were included in the other HUD-provided data sources. Several city-based CoCs were absent from the crosswalk, with only the county or balance of state CoC appearing. The following list provides an accounting of the states, counties, and 19 CoCs missing from the HUD-provided crosswalk:

 Arkansas—Sebastian County: Old Fort Homeless Coalition CoC.

 California—Los Angeles County: Pasadena CoC, Long Beach CoC, Glendale CoC.

 Georgia—Fulton County: Atlanta CoC.

 Illinois—Cook County: Chicago CoC.

 Massachusetts—Bristol County: New Bedford CoC, Fall River CoC; Essex County: Lynn CoC; Middlesex County: Somerville CoC, Cambridge CoC, Lowel CoC; Suffolk County: Boston CoC.

 Michigan—Wayne County: Detroit CoC.

 New Hampshire—Hil sborough County: Manchester CoC.

 Nebraska—Lancaster County: Lincoln CoC.

 New Mexico—Bernalil o County: Albuquerque CoC.

 Texas—Potter County and Randal County: Amaril o CoC.

 Oklahoma—Oklahoma County: Oklahoma City CoC.

Each of the 19 CoCs was handled on a case-by-case basis. After identifying the respective counties of each CoC, the study team scrutinized the coverage area of the CoC reported in the crosswalk.

CONTINUUM OF CARE AND COUNTY AGGREGATION ASSUMPTIONS

Exhibit C-1 displays our mapping of CoCs to county data in cases for which multiple CoCs were aggregated due to overlapping geographies and CoC coverage. 76 We also observed several alignment 76CoC dashboard reports, which display some county coverage, can be found at

https://www.hudexchange.info/programs/coc/coc-dashboard-reports/?filter_Year=&filter_State=AR&filter_CoC=AR-

503&program=CoC&group=Dash.

| 71

issues for CoCs within Massachusetts. The HUD-provided crosswalk indicated that Middlesex County was jointly covered by a CoC from the Essex County CoC and Massachusetts Balance of State. In addition, several CoCs within counties also existed. Exhibit C-1 outlines our approach to matching these Massachusetts counties to combined CoCs.

Exhibit C-1 | Mapping of Multiple Continuums of Care That Were Combined to County State

County

CoC Name

Combined CoC Name

Arkansas

Sebastian

AR-508 Old Fort Homeless Coalition

AR-503 Arkansas Balance of State CoC

AR-503 Arkansas Balance of State CoC

CA-600 Los Angeles City and County CoC

California

Los Angeles

CA-606 Long Beach CoC

CA-600 Los Angeles City and County CoC

CA-607 Pasadena CoC

CA-612 Glendale CoC

GA-500 Atlanta/Roswel /DeKalb, Fulton

Counties CoC (2010–2012)a

Georgia

DeKalb and

GA-502 Fulton County CoC (2013–2017)a

GA-500 Atlanta/Roswel /DeKalb, Fulton

Fulton

GA-508 DeKalb County CoC (2013–

Counties CoC

2017)a

GA-500 Atlanta CoC (2013–2017)a

Il inois

Cook

IL-511 Cook County CoC

IL-511 Cook County CoC

IL-510 Chicago CoC

MA-519 Attleboro/Taunton/Bristol

Bristol

County CoC

MA-519 Attleboro/Taunton/Bristol

MA-509 New Bedford CoC

County CoC

MA-515 Fal River CoC

MA-502 Lynn CoC

Essex

MA-510 Gloucester, Haverhil ,

Salem/Essex County CoC

Massachusetts

MA-508 Lowel CoC

MA-509 Cambridge CoC

MA-510 Gloucester, Haverhil ,

MA-516 Massachusetts Balance of State

Middlesex

Salem/Essex County CoC

CoC

MA-516 Massachusetts Balance of State

MA-517 Somervil e CoC

Norfolk

MA-516 Massachusetts Balance of State

Suffolk

MA-500 Boston CoC

MA-516 Massachusetts Balance of State

MI-502 Dearborn/Dearborn

Michigan

Wayne

Heights/Westland/Wayne County CoC

MI-502 Dearborn/Dearborn

Heights/Westland/Wayne County CoC

MI-501 Detroit CoC

NH-502 Nashua/Hil sborough County

New Hampshire

Hil sborough

CoC

NH-502 Nashua/Hil sborough County

CoC

NH-501 Manchester CoC

a The provided PIT data included separate records of “GA-500 Atlanta/Roswel /DeKalb, Fulton Counties” CoC, “GA-500

Atlanta CoC,” “GA-502 Fulton County CoC,” and “GA-508 DeKalb County CoC,” in which the latter three CoCs did not have data for years 2010 through 2012. For consistency across years, we aggregated al CoC and county-level data together.

In other cases, city CoCs served counties that were also served by the balance of state CoC, and the county data were disaggregated from the balance of state to preserve the CoC observation (see exhibit C-2). The study team determined that these cities would have substantial influence in their respective county-level data and combining them with substantively different counties could result in lost precision.

| 72

Exhibit C-2 | Counties Reassigned from Balance of State Continuums of Care

State

County

CoC Name

Crosswalk CoC

City Share of

County Population

Nebraska

Lancaster

NE-502 Lincoln CoC

NE-500 Nebraska Balance of State

90.53%

New Mexico

Bernalil o

NM-500 Albuquerque CoC

NM-501 New Mexico Balance of State

82.38%

Oklahoma

Oklahomaa

OK-502 Oklahoma City CoCa

OK-503 Oklahoma Balance of State

65.63%

Texas

Potter

TX-611 Amaril o CoC

TX-607 Texas Balance of State

87.13%

Randall

TX-611 Amaril o CoC

TX-607 Texas Balance of State

70.58%

a Like Amaril o, Texas, the city of Oklahoma City covers portions within Cleveland, Canadian, and Pottawatomie Counties.

Notes: The city share of the county population was derived from the 2010 Decennial Census. These values were obtained from https://www.census.gov/quickfacts/fact/table/US/PST045218 and

http://statsamerica.org/CityCountyFinder/Default.aspx.

OTHER GEOGRAPHIC ASSUMPTIONS

When aggregating CoCs with different urbanicity profiles, we assigned the resulting areas the highest level of urbanicity. For example, Chicago CoC (major city) and Cook County CoC (largely suburban) wil take on the value of “Major City CoC” when combined.

In addition to missing CoCs, there were also issues regarding CoCs that cross state lines. For most of these cases, we assign the CoC to the state represented in its CoC number. At the request of HUD, we differentiated CoC and county data for “Kansas City, Independence, Lee’s Summit/Jackson, Wyandotte Counties CoC” into Missouri and Kansas records.

The HUD-provided PIT data included areas that split and presented counts for the newly formed CoCs.

To al ow for consistent measurement of the same geographic areas over time, we aggregated areas that experienced splits. This was the case with CA-523 Colusa, Glenn, Trinity Counties CoC that previously contained areas covered by CA-527 Tehama County CoC and CA-529 Lake County CoC.

The HUD-provided PIT data also included a CoC (CA-528) without a CoC title. On further review, we found that this CoC may have served as “Del Norte CoC” but had been combined with “CA-516

Redding/Shasta, Siskiyou, Lassen, Plumas, Del Norte, Modoc, Sierra Counties CoC” during the period of interest from 2010 through 2017. Assuming that CA-528 merged into CA-516, we added all CoC data tied to the former to the latter.

Some areas were intentional y dropped from our dataset under the assumption that they are not serviced by the CoC program. The HUD-provided crosswalk included counties identified as “unassigned”

and the crosswalk assigned a CoC ID of “ZZ-999.” Exhibit C-3 displays counties that are currently not covered by a CoC.

Exhibit C-3 | Counties That are Not Covered by a Continuum of Care

State

County

Rationale or Assumption

Baker

Listed as unclaimed in crosswalk

Florida

Dixie

Listed as unclaimed in crosswalk

Union

Listed as unclaimed in crosswalk

Herkimer

Listed as unclaimed in crosswalk

New York

Putnam

Listed as unclaimed in crosswalk

Oklahoma

Washita

Listed as unclaimed in crosswalk

| 73

APPENDIX D: ADDITIONAL TABLES

ADDITIONAL SUMMARY STATISTICS AND DESCRIPTIVE ANALYSIS

Exhibit D-1 | Population and Point-in-Time (PIT) Counts for Al Years, 2010 through 2017

Total Population

Homeless

Sheltered Homeless

Unsheltered Homeless

Panel A: All Continuums of Care (CoCs)

2010

306,427,728

630,806

401,865

228,941

2011

309,103,669

618,611

390,924

227,687

2012

311,409,685

616,556

388,516

228,040

2013

313,759,219

584,483

393,049

191,434

2014

316,000,844

570,514

399,434

171,080

2015

318,389,398

558,573

389,847

168,726

2016

320,806,928

544,084

372,049

172,035

2017

323,173,216

548,312

359,669

188,643

Panel B: Major City and Largely Urban CoCs

2010

106,871,294

316,864

224,274

92,590

2011

106,762,106

308,185

215,863

92,322

2012

107,977,167

307,302

214,884

92,418

2013

109,269,974

307,892

221,648

86,244

2014

110,438,575

309,888

229,868

80,020

2015

111,629,394

313,474

226,129

87,345

2016

112,826,797

311,551

219,405

92,146

2017

113,865,635

323,186

213,934

109,252

Panel C: Largely Suburban CoCs

2010

97,514,157

177,141

104,626

72,515

2011

98,642,943

177,194

105,518

71,676

2012

99,365,764

173,649

103,050

70,599

2013

100,093,358

155,994

101,915

54,079

2014

100,841,426

149,348

100,209

49,139

2015

101,611,382

138,449

97,091

41,358

2016

102,392,379

129,497

90,074

39,423

2017

103,190,766

126,206

84,397

41,809

Panel D: Largely Rural CoCs

2010

102,042,277

136,801

72,965

63,836

2011

103,698,620

133,232

69,543

63,689

2012

104,066,754

135,605

70,582

65,023

2013

104,395,887

120,597

69,486

51,111

2014

104,720,843

111,278

69,357

41,921

2015

105,148,622

106,650

66,627

40,023

2016

105,587,752

103,036

62,570

40,466

2017

106,116,815

98,920

61,338

37,582

Notes: The totals are for the population of all 50 states plus the District of Columbia. All U.S. territories are excluded from this analysis.

Sources: Census’s intercensal population estimates; HUD PIT count data

| 74

Exhibit D-2 | CoCs with Largest Changes in Rates of Homelessness From 2015 to 2017

CoCs with the Largest Increase in Rates of Homelessness (Per 10,000) from 2015 to 2017

Total Homelessness

Sheltered Homelessness

Unsheltered Homelessness

Stat

e

CoC Name

2015

Rate

Increase Stat

e

CoC Name

2015

Rate

Increase

Stat

e

CoC Name

2015

Rate

Increase

Redding/Shasta, Siskiyou, Lassen,

1

CA

Mendocino

County

108.4

32.9

CA

Plumas, Del Norte, Modoc, Sierra

7.8

9.2

CA

Imperial County

20.4

36.3

Counties

2

CA

Imperial County

31.0

32.8

CA

Chico/Paradise/Butte County

12.8

7.1

CA

Mendocino County

91.0

32.1

3

FL

Pasco County

21.1

29.7

FL

Tallahassee/Leon County

14.8

6.8

FL

Pasco County

17.8

29.5

4

CA

Chico/Paradise

/Butte County

25.5

27.2

OR

Portland-Gresham-Multnomah

County

24.6

6.7

CA

Inyo, Mono, Alpine

Counties

13.4

21.5

5

CA

Inyo, Mono,

Alpine Counties

15.8

20.6

NY

Albany City & County

18.7

6.4

CA

Chico/Paradise/Butte

County

12.7

20.1

CoCs with the Largest Decrease in Rates of Homelessness (Per 10,000) from 2015 to 2017

Total Homelessness

Sheltered Homelessness

Unsheltered Homelessness

Stat

Decreas

Stat

Decreas

e

CoC Name

2015

Rate

e

e

CoC Name

2015

Rate

Decrease Stat

e

CoC Name

2015

Rate

e

Punta

5

FL

Gorda/Charlotte

33.4

-20.9

NY

Elmira/Steuben, Allegany, Livingston,

County

Chemung, Schuyler Counties

15.1

-9.0

CA

Merced City & County

28.0

-16.9

Hendry, Hardee,

4

FL

Highlands

48.3

-24.8

MA

Quincy/Brockton/Weymouth/Plymo

Counties

uth City and County

35.0

-14.4

FL

Hendry, Hardee,

Highlands Counties

37.3

-17.3

3

CA

Humboldt

County

87.7

-32.1

MA

Springfield

63.7

-15.6

FL

St. Johns County

44.2

-32.9

2

FL

St. Johns County

53.3

-34.3

NC

Durham City & County

26.0

-16.5

CA

Humboldt County

69.5

-37.7

Columbia,

Hamilton,

Columbia, Hamilton,

1

FL

Lafayette,

82.9

-46.0

NJ

Burlington County

29.7

-17.2

FL

Lafayette, Suwannee

78.0

-48.3

Suwannee

Counties

Counties

Notes: The "2015 Rate" is the rate of homelessness for each type in 2015 per 10,000 population. The "Increase” and "Decrease" represent the change (per 10,000) from 2015

to 2017.

Sources: Census’s intercensal population estimates; HUD PIT count data

| 75

Exhibit D-3 | Map of Percentage Increase in Total Homelessness Rate from 2015 to 2017

Notes: The "Increase” represents the change (per 10,000) from 2015 to 2017.

Sources: Census’s intercensal population estimates; HUD PIT count data

| 76

Exhibit D-4 | List of CoCs by Urbanicity Category

CoC Number

CoC Name

Largely Rural CoCs

AK-501

Alaska Balance of State CoC

AL-502

Florence/Northwest Alabama CoC

AL-505

Gadsden/Northeast Alabama CoC

AL-507

Alabama Balance of State CoC

AR-503

Arkansas Balance of State CoC a

AR-504

Delta Hil s CoC

AR-505

Southeast Arkansas

AR-512

Boone, Baxter, Marion, Newton Counties CoC

AZ-500

Arizona Balance of State CoC

CA-506

Salinas/Monterey, San Benito Counties CoC

CA-509

Mendocino County CoC

CA-516

Redding/Shasta, Siskiyou, Lassen, Plumas, Del Norte, Modoc, Sierra Counties CoC

CA-519

Chico/Paradise/Butte County CoC

CA-522

Humboldt County CoC

CA-523

Lake County CoC

CA-526

Amador, Calaveras, Tuolumne and Mariposa Counties CoC

CA-530

Inyo, Mono, Alpine Counties CoC

CO-500

Colorado Balance of State CoC

FL-508

Gainesvil e/Alachua, Putnam Counties CoC

FL-515

Panama City/Bay, Jackson Counties CoC

FL-517

Hendry, Hardee, Highlands Counties CoC

FL-518

Columbia, Hamilton, Lafayette, Suwannee Counties CoC

FL-604

Monroe County CoC

GA-501

Georgia Balance of State CoC

HI-500

Hawaii Balance of State CoC

IA-501

Iowa Balance of State CoC

ID-501

Idaho Balance of State

IL-512

Bloomington/Central Illinois CoC

IL-515

South Central Illinois CoC

IL-518

Rock Island/Moline/Northwestern Illinois CoC

IL-519

West Central Il inois CoC

IL-520

Southern Illinois CoC

IN-502

Indiana Balance of State CoC

KS-507

Kansas Balance of State CoC

KY-500

Kentucky Balance of State CoC

LA-500

Lafayette/Acadiana CoC

LA-505

Monroe/Northeast Louisiana CoC

LA-507

Alexandria/Central Louisiana CoC

MD-510

Garrett County CoC

MD-511

Mid-Shore Regional CoC

MD-513

Wicomico/Somerset/Worcester County CoC

ME-500

Maine Balance of State CoC

MI-500

Michigan Balance of State CoC

MI-511

Lenawee County CoC

MI-512

Grand Traverse, Antrim, Leelanau Counties CoC

MI-513

Marquette, Alger Counties CoC

MI-517

Jackson City & County CoC

MN-502

Rochester/Southeast Minnesota CoC

MN-504

Northeast Minnesota CoC

MN-505

St. Cloud/Central Minnesota CoC

| 77

CoC Number

CoC Name

MN-506

Northwest Minnesota CoC

MN-508

Moorhead/West Central Minnesota CoC

MN-509

Duluth/St. Louis County CoC

MN-511

Southwest Minnesota CoC

MO-602

Joplin/Jasper, Newton Counties CoC

MO-606

Missouri Balance of State CoC

MS-501

Mississippi Balance of State CoC

MS-503

Gulf Port/Gulf Coast Regional CoC

MT-500

Montana Statewide CoC

NC-503

North Carolina Balance of State CoC

NC-516

Northwest North Carolina CoC

ND-500

North Dakota Statewide CoC

NE-500

Nebraska Balance of State CoC

NH-500

New Hampshire Balance of State CoC

NM-501

New Mexico Balance of State CoC

NV-502

Nevada Balance of State CoC

NY-501

Elmira/Steuben, Allegany, Livingston, Chemung, Schuyler Counties CoC

NY-504

Cattaraugus County CoC

NY-506

Fulton, Montgomery, Schoharie Counties CoC

NY-510

Ithaca/Tompkins County CoC

NY-511

Binghamton, Union/Broome, Otsego, Chenango, Delaware, Cortland, Tioga Counties CoC

NY-513

Wayne, Ontario, Seneca, Yates Counties CoC

NY-514

Jamestown/Dunkirk/Chautauqua County CoC

NY-516

Clinton County CoC

NY-518

Utica/Rome/Oneida, Madison Counties CoC

NY-519

Columbia/Greene County CoC

NY-520

Franklin County CoC

NY-522

Jefferson/Lewis/St. Lawrence Counties CoC

NY-607

Sul ivan County CoC

NY-608

Kingston/Ulster County CoC

OH-507

Ohio Balance of State CoC

OK-500

North Central Oklahoma CoC

OK-503

Oklahoma Balance of State CoC

OK-505

Northeast Oklahoma CoC

OK-506

Southwest Oklahoma Regional CoC

OK-507

Southeastern Oklahoma Regional CoC

OR-503

Central Oregon CoC

OR-505

Oregon Balance of State CoC

PA-509

Eastern Pennsylvania CoC

PA-601

Western Pennsylvania CoC

SC-503

Myrtle Beach/Sumter City & County CoC

SD-500

South Dakota Statewide CoC

TN-500

Chattanooga/Southeast Tennessee CoC

TN-503

Central Tennessee CoC

TN-506

Oak Ridge/Upper Cumberland CoC

TN-507

Jackson/West Tennessee CoC

TN-509

Appalachian Regional CoC

TN-512

Morristown/Blount, Sevier, Campbel , Cocke Counties CoC

TX-604

Waco/McLennan County CoC

TX-607

Texas Balance of State (BoS) CoC

TX-624

Wichita Fal s/Wise, Palo Pinto, Wichita, Archer Counties CoC

VA-504

Charlottesvil e CoC

VA-508

Lynchburg CoC

VA-513

Harrisburg, Winchester/Western Virginia CoC

| 78

CoC Number

CoC Name

VA-521

Virginia Balance of State (BoS) CoC

VT-500

Vermont Balance of State CoC

WA-501

Washington Balance of State CoC

WI-500

Wisconsin Balance of State CoC

WV-500

Wheeling/Weirton Area CoC

WV-501

Huntington/Cabel , Wayne Counties CoC

WV-508

West Virginia Balance of State CoC

WY-500

Wyoming Statewide CoC

Largely Suburban CoCs

AL-500

Birmingham/Jefferson, St. Clair, Shelby Counties CoC

CA-504

Santa Rosa/Petaluma/Sonoma County CoC

CA-505

Richmond/Contra Costa County CoC

CA-507

Marin County CoC

CA-508

Watsonvil e/Santa Cruz City & County CoC

CA-510

Turlock/Modesto/Stanislaus County CoC

CA-512

Daly/San Mateo County CoC

CA-515

Rosevil e/Rocklin/Placer, Nevada Counties CoC

CA-520

Merced City & County CoC

CA-521

Davis/Woodland/Yolo County CoC

CA-524

Yuba City & County/Sutter County CoC

CA-525

El Dorado County CoC

CA-602

Santa Ana/Anaheim/Orange County CoC

CA-603

Santa Maria/Santa Barbara County CoC

CA-608

Riverside City & County CoC

CA-609

San Bernardino City & County CoC

CA-613

Imperial County CoC

CA-614

San Luis Obispo County CoC

CT-505

Connecticut Balance of State CoC

DE-500

Delaware Statewide CoC

FL-500

Sarasota/Bradenton/Manatee, Sarasota Counties CoC

FL-501

Tampa/Hil sborough County CoC

FL-502

St. Petersburg/Clearwater/Largo/Pinel as County CoC

FL-503

Lakeland/Winter Haven/Polk County CoC

FL-504

Daytona Beach/Daytona/Volusia, Flagler Counties CoC

FL-505

Fort Walton Beach/Okaloosa, Walton Counties CoC

FL-507

Orlando/Orange, Osceola, Seminole Counties CoC

FL-509

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

FL-511

Pensacola/Escambia/Santa Rosa County CoC

FL-512

St. Johns County CoC

FL-513

Palm Bay/Melbourne/Brevard County CoC

FL-514

Ocala/Marion County CoC

FL-519

Pasco County CoC

FL-520

Citrus, Hernando, Lake, Sumter Counties CoC

FL-601

Ft Lauderdale/Broward County CoC

FL-602

Punta Gorda/Charlotte County CoC

FL-603

Ft Myers/Cape Coral/Lee County CoC

FL-605

West Palm Beach/Palm Beach County CoC

FL-606

Naples/Col ier County CoC

GA-506

Marietta/Cobb County CoC

HI-501

Honolulu CoC

IL-500

McHenry County CoC

IL-501

Rockford/Winnebago, Boone Counties CoC

IL-502

Waukegan/North Chicago/Lake County CoC

| 79

CoC Number

CoC Name

IL-504

Madison County CoC

IL-506

Joliet/Bolingbrook/Wil County CoC

IL-507

Peoria/Perkin/Fulton, Peoria, Tazewel , Woodford CoC

IL-508

East Saint Louis/Bel evil e/Saint Clair County CoC

IL-509

Dekalb City & County CoC

IL-514

DuPage County CoC

IL-517

Aurora/Elgin/Kane County CoC

KS-505

Overland Park/Shawnee/Johnson County CoC

LA-506

Slidel /Southeast Louisiana CoC

LA-509

Houma-Terrebonne, Thibodaux CoC

MA-503

Cape Cod/Islands CoC

MA-504

Springfield CoC

MA-506

Worcester City & County CoC

MA-507

Pittsfield/Berkshire County CoC

MA-511

Quincy/Brockton/Weymouth/Plymouth City and County CoC

MA-519

Attleboro/Taunton/Bristol County CoC a

MD-500

Cumberland/Allegany County CoC

MD-502

Harford County CoC

MD-503

Annapolis/Anne Arundel County CoC

MD-504

Howard County CoC

MD-505

Baltimore County CoC

MD-506

Carrol County CoC

MD-507

Cecil County CoC

MD-508

Charles, Calvert, St. Mary's Counties CoC

MD-509

Frederick City & County CoC

MD-512

Hagerstown/Washington County CoC

MD-600

Prince Georgès County/Maryland CoC

MD-601

Montgomery County CoC

MI-503

St. Clair Shores/Warren/Macomb County CoC

MI-504

Pontiac/Royal Oak/Oakland County CoC

MI-505

Flint/Genesee County CoC

MI-509

Ann Arbor/Washtenaw County CoC

MI-510

Saginaw City & County CoC

MI-515

Monroe City & County CoC

MI-516

Norton Shores/Muskegon City & County CoC

MI-518

Livingston County CoC

MI-519

Hol and/Ottawa County CoC

MI-523

Eaton County CoC

MN-503

Dakota, Anoka, Washington, Scott, Carver Counties CoC

MO-500

St. Louis County CoC

MO-503

St. Charles, Lincoln, Warren Counties CoC

MS-500

Jackson/Rankin, Madison Counties CoC

NC-501

Ashevil e/Buncombe County CoC

NC-506

Wilmington/Brunswick, New Hanover, Pender Counties CoC

NC-509

Gastonia/Cleveland, Gaston, Lincoln Counties CoC

NJ-500

Atlantic City & County CoC

NJ-501

Bergen County CoC

NJ-502

Burlington County CoC

NJ-503

Camden City/Camden, Cumberland, Gloucester, Cape May Counties CoC

NJ-504

Newark/Essex County CoC

NJ-506

Jersey City/Bayonne/Hudson County CoC

NJ-507

New Brunswick/Middlesex County CoC

NJ-508

Monmouth County CoC

NJ-509

Morris County CoC

| 80

CoC Number

CoC Name

NJ-510

Lakewood Township/Ocean County CoC

NJ-511

Paterson/Passaic County CoC

NJ-512

Salem County CoC

NJ-513

Somerset County CoC

NJ-514

Trenton/Mercer County CoC

NJ-515

Elizabeth/Union County CoC

NJ-516

Warren, Sussex, Hunterdon Counties CoC

NY-500

Rochester/Irondequoit/Greece/Monroe County CoC

NY-503

Albany City & County CoC

NY-505

Syracuse, Auburn/Onondaga, Oswego, Cayuga Counties CoC

NY-507

Schenectady City & County CoC

NY-508

Buffalo, Niagara Falls/Erie, Niagara, Orleans, Genesee, Wyoming Counties CoC

NY-512

Troy/Rensselaer County CoC

NY-523

Glens Falls/Saratoga Springs/Saratoga, Washington, Warren, Hamilton Counties CoC

NY-601

Poughkeepsie/Dutchess County CoC

NY-602

Newburgh/Middletown/Orange County CoC

NY-603

Nassau, Suffolk Counties/Babylon/Islip/ Huntington CoC

NY-604

Yonkers/Mount Vernon/New Rochelle/Westchester CoC

NY-606

Rockland County CoC

OH-500

Cincinnati/Hamilton County CoC

OH-502

Cleveland/Cuyahoga County CoC

OH-505

Dayton/Kettering/Montgomery County CoC

OH-506

Akron/Barberton/Summit County CoC

OH-508

Canton/Massil on/Alliance/Stark County CoC

OK-504

Norman/Cleveland County CoC

OR-502

Medford/Ashland/Jackson County CoC

OR-506

Hil sboro/Beaverton/Washington County CoC

OR-507

Clackamas County CoC

PA-501

Harrisburg/Dauphin County CoC

PA-502

Upper Darby/Chester/Haverford/Delaware County CoC

PA-503

Wilkes-Barre/Hazleton/Luzerne County CoC

PA-504

Lower Marion/Norristown/Abington/Montgomery County CoC

PA-505

Chester County CoC

PA-506

Reading/Berks County CoC

PA-508

Scranton/Lackawanna County CoC

PA-510

Lancaster City & County CoC

PA-511

Bristol/Bensalem/Bucks County CoC

PA-512

York City & County CoC

PA-600

Pittsburgh/McKeesport/Penn Hil s/Allegheny County CoC

PA-603

Beaver County CoC

PA-605

Erie City & County CoC

PR-502

Puerto Rico Balance of Commonwealth CoC

PR-503

South/Southeast Puerto Rico CoC

RI-500

Rhode Island Statewide CoC

SC-500

Charleston/Low Country CoC

SC-501

Greenvil e/Anderson/Spartanburg Upstate CoC

SC-502

Columbia/Midlands CoC

TN-502

Knoxvil e/Knox County CoC

TN-510

Murfreesboro/Rutherford County CoC

UT-500

Salt Lake City & County CoC

UT-503

Utah Balance of State CoC

UT-504

Provo/Mountainland CoC

VA-500

Richmond/Henrico, Chesterfield, Hanover Counties CoC

VA-501

Norfolk/Chesapeake/Suffolk/Isle of Wight, Southampton Counties CoC

| 81

CoC Number

CoC Name

VA-502

Roanoke City & County/Salem CoC

VA-514

Fredericksburg/Spotsylvania, Stafford Counties CoC

VA-601

Fairfax County CoC

VA-602

Loudoun County CoC

VA-604

Prince Wil iam County CoC

WA-503

Tacoma/Lakewood/Pierce County CoC

WA-504

Everett/Snohomish County CoC

WA-508

Vancouver/Clark County CoC

WI-502

Racine City & County CoC

WV-503

Charleston/Kanawha, Putnam, Boone, Clay Counties CoC

Largely Urban and Major City CoCs

AK-500

Anchorage CoC

AL-501

Mobile City & County/Baldwin County CoC

AL-503

Huntsvil e/North Alabama CoC

AL-504

Montgomery City & County CoC

AL-506

Tuscaloosa City & County CoC

AR-500

Little Rock/Central Arkansas CoC

AR-501

Fayettevil e/Northwest Arkansas CoC

AZ-501

Tucson/Pima County CoC

AZ-502

Phoenix/Mesa/Maricopa County Regional CoC

CA-500

San Jose/Santa Clara City & County CoC

CA-501

San Francisco CoC

CA-502

Oakland/Alameda County CoC

CA-503

Sacramento City & County CoC

CA-511

Stockton/San Joaquin County CoC

CA-513

Visalia, Kings, Tulare Counties CoC

CA-514

Fresno/Madera County CoC

CA-517

Napa City & County CoC

CA-518

Vallejo/Solano County CoC

CA-600

Los Angeles City & County CoC a

CA-601

San Diego City and County CoC

CA-604

Bakersfield/Kern County CoC

CA-611

Oxnard/San Buenaventura/Ventura County CoC

CO-503

Metropolitan Denver Homeless Initiative CoC

CO-504

Colorado Springs/El Paso County CoC

CT-503

Bridgeport/Norwalk /Stamford/Fairfield County CoC

DC-500

District of Columbia CoC

FL-506

Tallahassee/Leon County CoC

FL-510

Jacksonvil e-Duval, Clay Counties CoC

FL-600

Miami/Dade County CoC

GA-500

Atlanta/Roswel /DeKalb, Fulton Counties CoC a

GA-503

Athens/Clarke County CoC

GA-504

Augusta CoC

GA-505

Columbus-Muscogee/Russel County CoC

GA-507

Savannah/Chatham County CoC

IA-500

Sioux City/Dakota, Woodbury Counties CoC

IA-502

Des Moines/Polk County CoC

ID-500

Boise/Ada County CoC

IL-503

Champaign/Urbana/Rantoul/Champaign County CoC

IL-511

Cook County CoC a

IL-513

Springfield/Sangamon County CoC

IL-516

Decatur/Macon County CoC

IN-503

Indianapolis CoC

KS-502

Wichita/Sedgwick County CoC

| 82

CoC Number

CoC Name

KS-503

Topeka/Shawnee County CoC

KY-501

Louisvil e/Jefferson County CoC

KY-502

Lexington/Fayette County CoC

LA-502

Shreveport/Bossier/Northwest CoC

LA-503

New Orleans/Jefferson Parish CoC

MA-516

Massachusetts Balance of State CoC a

MD-501

Baltimore City CoC

MI-502

Dearborn/Dearborn Heights/Westland/Wayne County CoC a

MI-506

Grand Rapids/Wyoming/Kent County CoC

MI-507

Portage/Kalamazoo City & County CoC

MI-508

Lansing/East Lansing/Ingham County CoC

MI-514

Battle Creek/Calhoun County CoC

MN-500

Minneapolis/Hennepin County CoC

MN-501

Saint Paul/Ramsey County CoC

MO-501

St. Louis City CoC

MO-600

Springfield/Greene, Christian, Webster Counties CoC

MO-603

St. Joseph/Andrew, Buchanan, DeKalb Counties CoC

MO-604K

Kansas City/Wyandotte County CoC (Kansas)

MO-604M

Kansas City/Independence/Lee's Summit/Jackson County CoC (Missouri)

NC-500

Winston Salem/Forsyth County CoC

NC-502

Durham City & County CoC

NC-504

Greensboro/High Point CoC

NC-505

Charlotte/Mecklenberg CoC

NC-507

Raleigh/Wake County CoC

NC-511

Fayettevil e/Cumberland County CoC

NC-513

Chapel Hil /Orange County CoC

NE-501

Omaha/Council Bluffs CoC

NE-502

Lincoln CoC

NH-502

Nashua/Hil sborough County CoC a

NM-500

Albuquerque CoC

NV-500

Las Vegas/Clark County CoC

NV-501

Reno/Sparks/Washoe County CoC

NY-600

New York City CoC

OH-501

Toledo/Lucas County CoC

OH-503

Columbus/Franklin County CoC

OH-504

Youngstown/Mahoning County CoC

OK-501

Tulsa City & County/Broken Arrow CoC

OK-502

Oklahoma City CoC

OR-500

Eugene/Springfield/Lane County CoC

OR-501

Portland-Gresham-Multnomah County CoC

PA-500

Philadelphia CoC

TN-501

Memphis/Shelby County CoC

TN-504

Nashvil e/Davidson County CoC

TX-500

San Antonio/Bexar County CoC

TX-503

Austin/Travis County CoC

TX-600

Dallas City & County/Irving CoC

TX-601

Fort Worth/Arlington/Tarrant County CoC

TX-603

El Paso City & County CoC

TX-611

Amaril o CoC

TX-700

Houston, Pasadena, Conroe/Harris, Ft. Bend, Montgomery, Counties CoC

TX-701

Bryan/Col ege Station/Brazos Valley CoC

VA-503

Virginia Beach CoC

VA-505

Newport News/Hampton/Virginia Peninsula CoC

VA-507

Portsmouth CoC

| 83

CoC Number

CoC Name

VA-600

Arlington County CoC

VA-603

City of Alexandria CoC

VT-501

Burlington/Chittenden County CoC

WA-500

Seattle/King County CoC

WA-502

Spokane City & County CoC

WI-501

Milwaukee City & County CoC

WI-503

Madison/Dane County CoC

a Aggregated CoCs described in appendix C

Exhibit D-5 | Summary Statistics for CoC-Level Climate Variables, by Urbanicity Major City and

All CoCs

Largely Urban

Largely

Largely Rural

Variables in Climate Domain

CoCs

Suburban CoCs

CoCs

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Average January Temperature (°F)

39.1

12.1

40.4

10.8

40.3

11.8

36.2

13.1

Average June, July, and August Temperature (°F)

75.2

5.9

76.6

5.8

75.2

5.5

73.8

6.2

Total January Precipitation (Inches)

4.4

3.7

4.4

3.3

4.5

3.8

4.2

3.8

Total Annual Precipitation (Inches)

39.4

13.7

37.4

15.2

40.4

13.0

39.9

13.0

Notes: These estimates represent the weighted average temperature and precipitation for each CoC for January 2017 and the average summer and annual precipitation from 2016. Estimates represent the population of all 50 states plus the District of Columbia. All U.S. territories are excluded from this analysis. Summer temperature represents the average temperature for June, July, and August.

| 84

NATIONAL MODEL ADDITIONAL REGRESSION TABLES

Exhibit D-6 presents regression estimates of factors associated with total, sheltered, and unsheltered homelessness, where the independent variables are the same across all specifications and represent the union of al variables included in the model in the National model of Homelessness chapter. These estimates show that the national models of total, sheltered, and unsheltered homelessness are robust to the inclusion of additional independent variables. Notably, there are more significant variables associated with unsheltered homelessness, with the share of HUD-assisted units and the share of children in the population significant in the current model.

Exhibit D-6 | Regression Estimates of National-Level Model Using a Consistent Set of Independent Variables

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.03

(0.06)

-0.01

(0.05)

0.04

(0.03)

Percentage of Homeowners with Cost

Burden

-0.09

(0.28)

-0.41*

(0.21)

0.32

(0.23)

Natural Logarithm of Median Rent ($100s)

17.81*

(9.87)

32.04***

(9.78)

-14.23

(10.39)

Median Rental Utility Cost ($10s)

0.12

(0.32)

0.44

(0.29)

-0.32

(0.34)

Percentage of Renters with Cost Burden

0.02

(0.24)

-0.28

(0.20)

0.30

(0.18)

Percentage of Renter-Occupied Units

0.15

(0.25)

0.35

(0.25)

-0.20

(0.13)

Rental Vacancy Rate

0.05

(0.28)

0.02

(0.24)

0.03

(0.24)

High Housing Density CoC

-3.81**

(1.71)

-2.75*

(1.62)

-1.06

(0.98)

Eviction Rate

0.46

(0.31)

0.66*

(0.38)

-0.20

(0.29)

Change in Eviction Rate

-0.01

(0.36)

0.28

(0.43)

-0.29

(0.30)

Percentage of Overcrowded Housing Units

5.52***

(0.88)

2.31***

(0.71)

3.21***

(1.04)

Urban CoC

1.70

(2.30)

0.72

(1.99)

0.97

(1.03)

Suburban CoC

-2.81

(2.12)

-1.62

(1.42)

-1.19

(1.14)

Economic Domain

Unemployment Rate

0.17

(1.00)

-0.34

(0.50)

0.51

(0.74)

Gini Coefficient of Income Inequality

-0.82*

(0.42)

-0.78**

(0.34)

-0.04

(0.35)

Poverty Rate

0.07

(0.78)

1.02**

(0.41)

-0.95*

(0.54)

Safety Net Domain

Share of HUD-Assisted Units

2.92***

(0.70)

3.90***

(0.65)

-0.98**

(0.45)

Occupancy Rate of HUD-Assisted Units

-0.11

(0.20)

0.06

(0.09)

-0.18

(0.17)

Percentage of Houses Built Before 1940

0.15

(0.11)

0.10

(0.11)

0.05

(0.09)

Demographic Domain

Percentage African-American

-0.13

(0.17)

-0.03

(0.13)

-0.10

(0.08)

Percentage Hispanic

-0.30***

(0.11)

-0.24***

(0.08)

-0.05

(0.05)

Percentage Asian

-0.26

(0.32)

-0.42**

(0.21)

0.16

(0.17)

Percentage Children (Age 0 through 19)

-0.92*

(0.54)

-0.33

(0.55)

-0.59**

(0.24)

Percentage Senior (Age 65 and Older)

-0.07

(0.39)

0.55

(0.47)

-0.62***

(0.21)

Percentage Female

0.56

(1.83)

1.95*

(1.10)

-1.39

(1.20)

Net-Migration Rate

2.86**

(1.33)

2.20**

(1.00)

0.66

(0.78)

Change in Net-Migration Rate

2.58*

(1.42)

0.16

(0.94)

2.43**

(0.95)

Percentage of One-Person Households

1.27*

(0.68)

0.21

(0.40)

1.07**

(0.44)

Percentage of Under 18 Population in

Single-Parent Households

-0.22

(0.50)

-0.58

(0.35)

0.36

(0.28)

Percentage Veteran (25 and Older)

0.03

(0.35)

-0.61*

(0.36)

0.64**

(0.27)

Percentage without a Bachelor's Degree

-0.07

(0.24)

0.14

(0.15)

-0.21

(0.24)

Healthcare Costs ($1,000s)

0.14

(0.93)

-0.24

(0.73)

0.39

(0.57)

Alcohol Mortality (Per 100,000)

0.94

(0.85)

1.25*

(0.64)

-0.32

(0.45)

Excessive Drinking Rate

-0.07

(0.41)

0.46*

(0.24)

-0.52

(0.36)

| 85

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Climate Domain

Average January Temperature (°F)

-0.07

(0.14)

-0.16

(0.12)

0.09

(0.07)

Average June, July, and August Temperature

(°F)

0.14

(0.29)

0.34*

(0.20)

-0.20

(0.22)

Total January Precipitation (Inches)

0.47

(0.34)

-0.39**

(0.18)

0.85***

(0.22)

Total Annual Precipitation (Inches)

0.09

(0.07)

0.11**

(0.04)

-0.02

(0.05)

Intercept

-48.59

(102.10) -174.34***

(59.49)

125.75

(92.36)

Observations

374

374

374

R-Squared

0.82

0.81

0.68

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the union of variables across all outcomes of interest (total, sheltered, or unsheltered homelessness) included in the national model in the National Model of Homelessness chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS

variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category.

Exhibit D-7 presents regression estimates where the share of homes built prior to 1940 is used as an instrument for federal CoC funding, fol owing the approach used in Lucas (2017) and Popov (2016). As mentioned previously, the relationship between CoC funding and rates of total, sheltered, and unsheltered homelessness is endogenous because CoC funding may directly impact homelessness. The share of homes built prior to 1940 is used as an instrument because it is a part of the CoC funding formula but is otherwise unrelated to rates of homelessness. The independent variables included in these specifications are the same as those included in the national model in the National Model of Homelessness chapter. These results indicate using the share of homes built prior to 1940 as an instrument for CoC funding does not change the interpretation of the coefficients. In particular, federal CoC funding is not a significant factor associated with rates of total, sheltered, and unsheltered homelessness in the national model.

Exhibit D-7 | Instrumental Variables Regression Estimates of National-Level Model Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.07**

(0.03)

Percentage of Homeowners with Cost

Burden

-0.17

(0.23)

-0.42**

(0.20)

0.34*

(0.18)

Natural Logarithm of Median Rent ($100s)

17.77**

(7.31)

28.20***

(8.66)

-13.07*

(7.37)

Median Rental Utility Cost ($10s)

0.43

(0.30)

-0.31

(0.26)

Percentage of Renters with Cost Burden

0.13

(0.21)

-0.22

(0.19)

0.34**

(0.16)

Percentage of Renter-Occupied Units

0.24

(0.22)

0.46**

(0.20)

-0.20

(0.13)

Rental Vacancy Rate

0.06

(0.30)

0.09

(0.21)

0.09

(0.22)

High Housing Density CoC

-4.03**

(1.60)

-2.93**

(1.25)

-1.34

(1.02)

Eviction Rate

0.19

(0.34)

0.57

(0.36)

Change in Eviction Rate

-0.38

(0.26)

Percentage of Overcrowded Housing Units

3.45**

(1.52)

1.04

(1.18)

2.21**

(0.97)

Urban CoC

-0.04

(2.59)

-0.46

(1.72)

0.53

(1.35)

Suburban CoC

-1.91

(1.64)

-0.91

(1.35)

-0.09

(0.86)

Economic Domain

Unemployment Rate

0.40

(1.16)

-0.28

(0.54)

0.59

(0.83)

Gini Coefficient of Income Inequality

-0.94***

(0.23)

-0.98***

(0.36)

Poverty Rate

0.02

(0.59)

1.05***

(0.35)

-0.79*

(0.44)

| 86

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Safety Net Domain

Share of HUD-Assisted Units

3.21***

(0.61)

4.18***

(0.65)

-0.64*

(0.36)

Occupancy Rate of HUD-Assisted Units

-0.12

(0.17)

0.05

(0.08)

-0.20

(0.17)

Federal CoC Funding ($100,000s)

0.02

(0.01)

0.01

(0.01)

0.01

(0.01)

Demographic Domain

Percentage African-American

-0.12

(0.12)

-0.01

(0.11)

-0.04

(0.04)

Percentage Hispanic

-0.24**

(0.10)

-0.21***

(0.08)

-0.00

(0.06)

Percentage Asian

-0.09

(0.23)

-0.35*

(0.20)

0.24*

(0.13)

Percentage Children (Age 0 through 19)

-0.12

(0.78)

0.23

(0.59)

-0.34

(0.45)

Percentage Senior (Age 65 and Older)

0.20

(0.39)

0.97*

(0.51)

-0.55***

(0.21)

Percentage Female

0.98

(0.74)

-1.77

(1.59)

Net-Migration Rate

3.35***

(1.16)

1.92**

(0.97)

Change in Net-Migration Rate

1.25

(1.26)

2.61***

(0.92)

Percentage of One-Person Households

1.34**

(0.56)

1.18***

(0.35)

Percent of Under 18 Population in

Single-Parent Households

-0.31

(0.36)

-0.55*

(0.31)

Percentage Veteran (25 and Older)

-0.50

(0.37)

0.69***

(0.26)

Percentage without a Bachelor's Degree

-0.15

(0.12)

Healthcare Costs ($1,000s)

-0.22

(0.79)

Alcohol Mortality (Per 100,000)

1.52*

(0.91)

1.68**

(0.68)

Excessive Drinking Rate

0.58***

(0.22)

-0.51**

(0.25)

Climate Domain

Average January Temperature (°F)

0.07

(0.16)

-0.07

(0.14)

0.14

(0.09)

Average June, July, and August Temperature

(°F)

-0.03

(0.25)

0.20

(0.19)

-0.20

(0.13)

Total January Precipitation (Inches)

0.45

(0.32)

-0.40**

(0.19)

0.80***

(0.18)

Total Annual Precipitation (Inches)

0.06

(0.06)

0.10**

(0.05)

-

Intercept

-36.06

(35.53)

118.55**

(38.68)

132.29

(91.85)

*

Observations

374

374

374

R-Squared

0.82

0.81

0.69

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the variables selected for the subgroup and outcome of interest using the procedures outlined in the Empirical Strategy chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS variables, ACS 5-year estimates for 2016

correspond to the 2012 through 2016 ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category.

Exhibit D-8 presents unweighted regression estimates of the national model for the set of independent variables used in the National Model of Homelessness chapter. These results are quantitatively and qualitatively like those in that chapter; however, the significance of some variables differs from those in the main body of the report. Notably, median rent is not significant in these specifications.

| 87

Exhibit D-8 | Unweighted Regression Estimates of National-Level Model

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.02

(0.06)

Percentage of Homeowners with Cost

Burden

0.02

(0.28)

-0.20

(0.16)

0.24

(0.20)

Natural Logarithm of Median Rent ($100s)

14.80

(9.51)

13.13*

(7.04)

5.93

(6.03)

Median Rental Utility Cost ($10s)

0.16

(0.21)

-0.28

(0.34)

Percentage of Renters with Cost Burden

0.18

(0.22)

0.01

(0.19)

0.09

(0.13)

Percentage of Renter-Occupied Units

0.06

(0.25)

0.02

(0.19)

0.09

(0.09)

Rental Vacancy Rate

-0.03

(0.28)

0.22

(0.15)

-0.18

(0.25)

High Housing Density CoC

-4.21***

(1.51)

-2.31**

(1.08)

-0.72

(0.84)

Eviction Rate

0.33

(0.33)

0.14

(0.24)

Change in Eviction Rate

-0.32

(0.30)

Percentage of Overcrowded Housing Units

3.00***

(1.00)

1.71**

(0.78)

0.98*

(0.54)

Urban CoC

-1.92

(3.44)

1.60

(1.62)

-2.85

(2.21)

Suburban CoC

-4.80**

(2.12)

-0.98

(1.06)

-3.26**

(1.60)

Economic Domain

Unemployment Rate

1.31

(0.98)

-0.25

(0.45)

1.49**

(0.66)

Gini Coefficient of Income Inequality

-0.11

(0.33)

0.17

(0.25)

Poverty Rate

-0.74

(0.53)

-0.02

(0.30)

-0.60

(0.44)

Safety Net Domain

Share of HUD-Assisted Units

2.17***

(0.72)

2.01***

(0.60)

0.04

(0.26)

Occupancy Rate of HUD-Assisted Units

-0.20

(0.21)

0.02

(0.06)

-0.21

(0.24)

Percentage of Houses Built Before 1940

0.17

(0.11)

0.19***

(0.07)

Demographic Domain

Percentage African-American

-0.21

(0.16)

-0.08

(0.07)

-0.17**

(0.08)

Percentage Hispanic

-0.13

(0.12)

-0.14**

(0.06)

-0.06

(0.06)

Percentage Asian

-0.13

(0.23)

-0.22

(0.17)

0.02

(0.14)

Percentage Children (Age 0 through 19)

0.07

(0.73)

-0.46

(0.42)

0.76*

(0.38)

Percentage Senior (Age 65 and Older)

-0.21

(0.33)

-0.08

(0.23)

0.06

(0.21)

Percentage Female

0.29

(0.53)

-1.27*

(0.72)

Net-Migration Rate

1.65

(1.40)

1.67

(1.05)

Change in Net-Migration Rate

2.26**

(1.06)

2.09***

(0.77)

Percentage of One-Person Households

1.44*

(0.81)

1.18*

(0.62)

Percent of Under 18 Population in

Single-Parent Households

-0.11

(0.39)

0.12

(0.19)

Percentage Veteran (25 and Older)

-0.10

(0.21)

0.07

(0.22)

Percentage without a Bachelor's Degree

0.09

(0.07)

Healthcare Costs ($1,000s)

-0.56

(0.40)

Alcohol Mortality (Per 100,000)

2.15***

(0.74)

1.34***

(0.35)

Excessive Drinking Rate

0.40

(0.25)

-0.04

(0.26)

Climate Domain

Average January Temperature (°F)

0.27

(0.17)

-0.10

(0.12)

0.36***

(0.12)

Average June, July, and August Temperature

(°F)

-0.00

(0.31)

0.20

(0.21)

-0.19

(0.15)

Total January Precipitation (Inches)

0.86***

(0.23)

0.05

(0.11)

0.82***

(0.16)

Total Annual Precipitation (Inches)

0.00

(0.07)

-0.01

(0.04)

Intercept

-65.28*

(35.47)

-61.78*

(35.21)

15.24

(38.49)

Observations

374

374

374

R-Squared

0.58

0.56

0.53

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are unweighted. Independent variables represent the variables selected for the subgroup and outcome of interest

| 88

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

using the procedures outlined in the Empirical Strategy chapter. Change variables are calculated by subtracting the 2012

value from the 2016 value for each CoC. ACS variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016

ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category.

Exhibit D-9 presents unweighted regression estimates of factors associated with total, sheltered, and unsheltered homelessness, where the independent variables are the same across all specifications and represent the union of all variables included in the models in the National Model of Homelessness chapter. These estimates are in line with those presented in that chapter.

Exhibit D-9 | Unweighted Regression Estimates of National-Level Model Using a Consistent Set of Independent Variables

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.04

(0.09)

0.04

(0.05)

0.00

(0.06)

Percentage of Homeowners with Cost

Burden

0.06

(0.26)

-0.18

(0.12)

0.23

(0.22)

Natural Logarithm of Median Rent ($100s)

20.57**

(9.87)

18.39**

(7.13)

2.18

(6.27)

Median Rental Utility Cost ($10s)

-0.06

(0.37)

0.16

(0.19)

-0.22

(0.35)

Percentage of Renters with Cost Burden

0.08

(0.27)

-0.05

(0.20)

0.13

(0.13)

Percentage of Renter-Occupied Units

0.10

(0.29)

-0.05

(0.23)

0.15

(0.10)

Rental Vacancy Rate

-0.05

(0.30)

0.20

(0.17)

-0.25

(0.23)

High Housing Density CoC

-4.21***

(1.51)

-2.31**

(1.08)

-0.72

(0.84)

Eviction Rate

0.45

(0.39)

0.14

(0.26)

0.31

(0.32)

Change in Eviction Rate

-0.38

(0.45)

-0.07

(0.25)

-0.32

(0.32)

Percentage of Overcrowded Housing Units

2.31**

(1.07)

1.52**

(0.68)

0.80

(0.73)

Urban CoC

-1.57

(3.44)

1.95

(1.51)

-3.52

(2.34)

Suburban CoC

-4.26*

(2.18)

-0.55

(1.00)

-3.70**

(1.78)

Economic Domain

Unemployment Rate

1.27

(0.99)

-0.23

(0.46)

1.50**

(0.66)

Gini Coefficient of Income Inequality

0.07

(0.45)

0.38

(0.26)

-0.32

(0.37)

Poverty Rate

-0.66

(0.70)

0.00

(0.34)

-0.66

(0.46)

Safety Net Domain

Share of HUD-Assisted Units

2.50***

(0.79)

2.00***

(0.62)

0.50

(0.40)

Occupancy Rate of HUD-Assisted Units

-0.21

(0.23)

0.01

(0.06)

-0.22

(0.22)

Percentage of Houses Built Before 1940

0.13

(0.11)

0.20***

(0.07)

-0.07

(0.06)

Demographic Domain

Percentage African-American

-0.20

(0.18)

-0.09

(0.08)

-0.11

(0.13)

Percentage Hispanic

-0.18

(0.11)

-0.15**

(0.07)

-0.03

(0.06)

Percentage Asian

-0.12

(0.27)

-0.19

(0.18)

0.08

(0.15)

Percentage Children (Age 0 through 19)

0.25

(0.77)

-0.58

(0.46)

0.83**

(0.41)

Percentage Senior (Age 65 and Older)

-0.03

(0.33)

-0.33

(0.28)

0.30*

(0.15)

Percentage Female

-0.93

(1.24)

0.48

(0.70)

-1.42*

(0.75)

Net-Migration Rate

1.28

(1.33)

1.10

(1.00)

0.19

(0.70)

Change in Net-Migration Rate

2.46**

(1.04)

0.82

(0.67)

1.63**

(0.69)

Percentage of One-Person Households

1.34

(0.81)

0.16

(0.29)

1.18*

(0.65)

Percent of Under 18 Population in

Single-Parent Households

-0.14

(0.40)

0.05

(0.23)

-0.18

(0.30)

Percentage Veteran (25 and Older)

-0.32

(0.32)

-0.14

(0.21)

-0.18

(0.18)

Percentage without a Bachelor's Degree

0.21

(0.17)

0.18*

(0.09)

0.03

(0.14)

Healthcare Costs ($1,000s)

-0.81

(1.02)

-0.66

(0.82)

-0.15

(0.46)

| 89

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Alcohol Mortality (Per 100,000)

2.05***

(0.70)

1.26***

(0.37)

0.79*

(0.43)

Excessive Drinking Rate

0.39

(0.45)

0.33

(0.25)

0.06

(0.30)

Climate Domain

Average January Temperature (°F)

0.28*

(0.16)

-0.13

(0.12)

0.40***

(0.10)

Average June, July, and August Temperature

(°F)

0.09

(0.28)

0.30

(0.20)

-0.21

(0.16)

Total January Precipitation (Inches)

0.83***

(0.23)

0.01

(0.10)

0.82***

(0.16)

Total Annual Precipitation (Inches)

0.00

(0.08)

0.00

(0.04)

0.00

(0.05)

Intercept

-55.59

(59.80)

-92.94**

(46.27)

37.35

(51.07)

Observations

374

374

374

R-Squared

0.59

0.57

0.55

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are unweighted. Independent variables represent the union of variables across all outcomes of interest (total, sheltered, or unsheltered homelessness) included in the national model in the National Model of Homelessness chapter.

Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category.

Exhibit D-10 presents unweighted regression estimates where the share of homes built prior to 1940 is used as an instrument for federal CoC funding. The independent variables in these specifications are the same as those in the models in the National Model of Homelessness chapter. These results indicate using the share of homes built prior to 1940 as an instrument for CoC funding does not change the interpretation of the coefficients. Median rent, however, is insignificant across all specifications, as in the unweighted national model presented in exhibit D-8. In this specification, federal CoC funding is a significant factor associated with rates of sheltered homelessness in the national model but as expected, is not significant for rates of total or unsheltered homelessness. The results indicate that higher rates of CoC funding are associated with high rates of sheltered homelessness.

Exhibit D-10 | Unweighted Instrumental Variables Regression Estimates of National-Level Model

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.03

(0.07)

Percentage of Homeowners with Cost

Burden

-0.03

(0.21)

-0.29

(0.18)

0.21

(0.26)

Natural Logarithm of Median Rent ($100s)

12.53

(8.39)

11.40

(7.01)

4.69

(7.48)

Median Rental Utility Cost ($10s)

0.16

(0.24)

-0.28

(0.36)

Percentage of Renters with Cost Burden

0.34*

(0.20)

0.19

(0.15)

-0.04

(0.23)

Percentage of Renter-Occupied Units

0.22

(0.21)

0.23

(0.17)

0.08

(0.13)

Rental Vacancy Rate

0.06

(0.30)

0.27*

(0.15)

-0.22

(0.24)

High Housing Density CoC

-5.78***

(2.17)

-3.61***

(1.07)

-0.25

(1.06)

Eviction Rate

0.15

(0.34)

0.02

(0.23)

Change in Eviction Rate

-0.13

(0.31)

Percentage of Overcrowded Housing Units

1.38

(1.16)

-0.18

(0.86)

1.94

(1.27)

Urban CoC

-3.72

(3.47)

0.19

(1.72)

-2.38

(2.23)

Suburban CoC

-4.42**

(2.15)

-0.19

(1.08)

-4.47***

(1.63)

Economic Domain

Unemployment Rate

1.66

(1.20)

0.05

(0.64)

1.10*

(0.61)

Gini Coefficient of Income Inequality

-0.42

(0.26)

-0.06

(0.19)

| 90

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Poverty Rate

-0.98**

(0.48)

-0.32

(0.25)

-0.45

(0.49)

Safety Net Domain

Share of HUD-Assisted Units

2.19***

(0.71)

2.11***

(0.56)

0.16

(0.27)

Occupancy Rate of HUD-Assisted Units

-0.28

(0.22)

-0.08

(0.07)

-0.13

(0.23)

Federal CoC Funding ($100,000s)

0.05

(0.03)

0.05**

(0.02)

-0.04

(0.03)

Demographic Domain

Percentage African-American

-0.25*

(0.15)

-0.11

(0.07)

-0.13

(0.08)

Percentage Hispanic

-0.12

(0.13)

-0.14*

(0.08)

-0.05

(0.06)

Percentage Asian

-0.18

(0.22)

-0.28*

(0.16)

0.09

(0.17)

Percentage Children (Age 0 through 19)

0.75

(0.83)

0.25

(0.51)

0.27

(0.58)

Percentage Senior (Age 65 and Older)

0.02

(0.31)

0.29

(0.32)

-0.03

(0.29)

Percentage Female

-0.73

(0.48)

-0.25

(1.27)

Net-Migration Rate

0.92

(1.47)

0.79

(0.97)

Change in Net-Migration Rate

2.05**

(0.95)

2.22***

(0.72)

Percentage of One-Person Households

1.58**

(0.68)

1.07*

(0.63)

Percent of Under 18 Population in

Single-Parent Households

-0.20

(0.42)

0.10

(0.20)

Percentage Veteran (25 and Older)

-0.17

(0.23)

0.08

(0.24)

Percentage without a Bachelor's Degree

0.08

(0.09)

Healthcare Costs ($1,000s)

0.85

(1.29)

Alcohol Mortality (Per 100,000)

2.50***

(0.82)

1.67***

(0.40)

Excessive Drinking Rate

0.37

(0.27)

0.14

(0.32)

Climate Domain

Average January Temperature (°F)

0.38**

(0.16)

0.01

(0.08)

0.31***

(0.11)

Average June, July, and August Temperature

(°F)

-0.16

(0.21)

0.06

(0.13)

-0.23

(0.18)

Total January Precipitation (Inches)

0.86***

(0.24)

0.06

(0.11)

0.76***

(0.13)

Total Annual Precipitation (Inches)

-0.03

(0.07)

-0.04

(0.04)

Intercept

-60.01

(38.66)

-8.47

(27.49)

-30.29

(67.48)

Observations

374

374

374

R-Squared

0.56

0.42

0.42

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are unweighted. Independent variables represent the variables selected for the subgroup and outcome of interest using the procedures outlined in the Empirical Strategy chapter. Change variables are calculated by subtracting the 2012

value from the 2016 value for each CoC. ACS variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016

ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category.

URBANICITY SUBGROUP ADDITIONAL REGRESSION TABLES

Exhibit D-11 presents regression estimates for urban CoCs, where the independent variables are the same across all specifications and represent the union of all variables included in the models in the National Model of Homelessness chapter. These results indicate that the inclusion of additional independent variables does not tend to change the interpretation of most coefficients. In the model of total homelessness, however, median rent and unemployment lose significance, and the share of HUD-assisted units becomes significant.

| 91

Exhibit D-11 | Regression Estimates of Model for Urban CoCs Using a Consistent Set of Independent Variables

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

-0.07

(0.14)

-0.02

(0.11)

-0.05

(0.08)

Percentage of Homeowners with Cost

Burden

-0.39

(0.86)

-1.28**

(0.58)

0.90*

(0.45)

Natural Logarithm of Median Rent ($100s)

32.74*

(19.32)

39.95***

(14.43)

-7.21

(10.84)

Percentage of Renters with Cost Burden

-0.89**

(0.37)

-0.49*

(0.28)

-0.40

(0.29)

Percentage of Renter-Occupied Units

0.15

(0.38)

0.89**

(0.38)

-0.74***

(0.22)

Rental Vacancy Rate

0.10

(0.50)

-0.52

(0.53)

0.63**

(0.30)

High Housing Density CoC

-7.10***

(2.43)

-8.57***

(2.62)

1.47

(1.95)

Change in Eviction Rate

0.70

(0.65)

1.24**

(0.55)

-0.54

(0.49)

Percentage of Overcrowded Housing Units

6.42***

(1.01)

2.28***

(0.80)

4.14***

(0.92)

Economic Domain

Unemployment Rate

-1.65

(1.77)

0.82

(1.14)

-2.48**

(1.11)

Gini Coefficient of Income Inequality

0.47

(0.67)

0.98*

(0.49)

-0.51

(0.48)

Poverty Rate

0.93

(0.77)

0.48

(0.64)

0.45

(0.47)

Safety Net Domain

Percentage of Households Receiving Cash

Assistance

0.79

(1.07)

0.10

(1.20)

0.69

(0.68)

Share of HUD-Assisted Units

2.32**

(0.96)

3.50***

(0.89)

-1.18**

(0.48)

Occupancy Rate of HUD-Assisted Units

0.16

(0.26)

-0.15

(0.30)

0.30

(0.19)

Demographic Domain

Percentage African-American

0.04

(0.21)

0.02

(0.20)

0.02

(0.13)

Percentage Hispanic

-0.28

(0.17)

-0.12

(0.11)

-0.15

(0.12)

Percentage Asian

-0.13

(0.40)

-0.34

(0.28)

0.20

(0.25)

Percentage Children (Age 0 through 19)

-3.11**

(1.32)

-1.40

(1.42)

-1.71*

(1.01)

Percentage Senior (Age 65 and Older)

-1.28

(1.25)

-0.07

(1.55)

-1.21

(1.02)

Percentage Female

1.69

(1.54)

3.71***

(1.32)

-2.01*

(1.05)

Net-Migration Rate

7.35***

(1.96)

5.68***

(1.83)

1.68

(1.34)

Percentage of One-Person Households

0.72

(0.83)

-0.77

(0.69)

1.49***

(0.38)

Percentage without a Bachelor's Degree

0.59**

(0.24)

0.44

(0.29)

0.15

(0.19)

Alcohol Mortality (Per 100,000)

1.33

(1.12)

1.86**

(0.83)

-0.54

(0.63)

Excessive Drinking Rate

-0.72

(0.69)

-0.04

(0.55)

-0.68

(0.41)

Climate Domain

Average January Temperature (°F)

-0.20

(0.23)

-0.53**

(0.20)

0.33

(0.21)

Average June, July, and August Temperature

(°F)

-0.43

(0.43)

0.40

(0.32)

-0.82**

(0.32)

Total January Precipitation (Inches)

-0.43

(0.45)

-0.93**

(0.37)

0.50

(0.32)

Total Annual Precipitation (Inches)

0.12

(0.10)

0.28***

(0.07)

-0.16**

(0.07)

Intercept

-69.54

(84.72)

-283.43***

(71.49)

213.89**

*

(70.77)

Observations

104

104

104

R-Squared

0.93

0.93

0.88

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the union of variables across all outcomes of interest (total, sheltered, or unsheltered homelessness) included in the national model in the National Model of Homelessness chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS

variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Percentage White is the omitted race/ethnicity category and percentage adult is the omitted age category. A list of CoCs by urbanicity is listed in appendix D.

| 92

Exhibit D-12 presents regression estimates for suburban CoCs, where the independent variables are the same across all specifications and represent the union of all variables included in the models in the National Model of Homelessness chapter. These results indicate that the inclusion of additional independent variables does not change the interpretation of the regression coefficients.

Exhibit D-12 | Regression Estimates of Model for Suburban CoCs Using a Consistent Set of Independent Variables

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

Percentage of Homeowners with Cost Burden

-0.36**

(0.17)

-0.15

(0.17)

-0.21

(0.12)

Natural Logarithm of Median Rent ($100s)

8.80

(7.53)

2.79

(5.18)

6.01

(3.82)

Percentage of Renters with Cost Burden

0.61*

(0.34)

0.37*

(0.20)

0.24

(0.17)

Percentage of Renter-Occupied Units

0.17

(0.24)

-0.01

(0.18)

0.19

(0.12)

Rental Vacancy Rate

0.21

(0.32)

0.32

(0.22)

-0.11

(0.18)

High Housing Density CoC

-2.12

(1.92)

-0.09

(1.15)

-2.03

(1.27)

Economic Domain

Unemployment Rate

1.78

(1.32)

-0.65

(0.80)

2.44***

(0.65)

Gini Coefficient of Income Inequality

-0.25

(0.53)

-0.11

(0.34)

-0.14

(0.35)

Poverty Rate

-1.18*

(0.60)

-0.52

(0.37)

-0.66**

(0.29)

Safety Net Domain

Percentage of Households Receiving Cash

Assistance

0.31

(0.93)

-0.03

(0.53)

0.35

(0.66)

Share of HUD-Assisted Units

2.70**

(1.26)

1.87*

(0.95)

0.83

(0.55)

Occupancy Rate of HUD-Assisted Units

-0.10

(0.14)

-0.03

(0.10)

-0.06

(0.08)

Percentage of Houses Built Before 1940

-0.04

(0.12)

0.08

(0.08)

-0.12

(0.08)

Demographic Domain

Percentage African-American

-0.04

(0.15)

-0.09

(0.09)

0.04

(0.14)

Percentage Hispanic

0.04

(0.13)

0.04

(0.09)

-0.01

(0.07)

Percentage Asian

-0.11

(0.30)

-0.08

(0.11)

-0.04

(0.25)

Net-Migration Rate

-3.29

(1.99)

-1.58*

(0.92)

-1.71

(1.53)

Change in Net-Migration Rate

4.11***

(1.40)

0.90

(0.89)

3.21***

(0.92)

Percentage of One-Person Households

0.69*

(0.36)

0.07

(0.16)

0.62**

(0.26)

Percent of Under 18 Population in

Single-Parent Households

-0.55

(0.47)

0.02

(0.22)

-0.57

(0.40)

Percentage without a Bachelor's Degree

0.08

(0.19)

0.04

(0.13)

0.04

(0.12)

Healthcare Costs ($1,000s)

-1.47

(1.00)

-0.89*

(0.45)

-0.59

(0.63)

Alcohol Mortality (Per 100,000)

2.49**

(1.09)

1.06**

(0.41)

1.43

(0.94)

Climate Domain

Average January Temperature (°F)

0.59***

(0.15)

0.21**

(0.09)

0.38***

(0.12)

Average June, July, and August Temperature

(°F)

-0.42*

(0.21)

-0.21

(0.16)

-0.21*

(0.12)

Total January Precipitation (Inches)

0.53*

(0.27)

0.05

(0.10)

0.48**

(0.18)

Total Annual Precipitation (Inches)

-0.10

(0.06)

-0.03

(0.04)

-0.07**

(0.03)

Intercept

-9.20

(43.59)

7.57

(31.86)

-16.77

(25.32)

Observations

160

160

160

R-Squared

0.61

0.52

0.61

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the union of variables across all outcomes of interest (total, sheltered, or unsheltered homelessness) included in the national model in the National Model of Homelessness chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS

variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Percentage White is the omitted race/ethnicity category and percentage adult is the omitted age category. A list of CoCs by urbanicity is listed in appendix D.

| 93

Exhibit D-13 presents regression estimates for rural CoCs, where the independent variables are the same across all specifications and represent the union of all variables included in the models in the National Model of Homelessness chapter. These results indicate that the inclusion of additional independent variables does not change the interpretation of the regression coefficients.

Exhibit D-13 | Regression Estimates of Model for Rural CoCs Using a Consistent Set of Independent Variables

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Housing Domain

House Price Index

0.23***

(0.08)

0.02

(0.04)

0.21**

(0.09)

Percentage of Homeowners with

Cost Burden

0.77***

(0.21)

0.38**

(0.17)

0.40**

(0.15)

Natural Logarithm of Median Rent

($100s)

-0.66

(9.64)

6.67*

(3.71)

-7.32

(8.61)

Median Rental Utility Cost ($10s)

-0.52

(0.35)

-0.13

(0.15)

-0.39

(0.38)

Percentage of Renters with Cost

Burden

0.78***

(0.29)

0.24**

(0.10)

0.54**

(0.25)

Percentage of Renter-Occupied

Units

0.91***

(0.21)

0.17*

(0.10)

0.74***

(0.18)

Rental Vacancy Rate

0.32

(0.40)

0.12

(0.11)

0.20

(0.38)

Percentage of Overcrowded

Housing Units

0.56

(0.93)

0.37

(0.40)

0.19

(0.91)

Economic Domain

Unemployment Rate

-0.62

(0.80)

0.08

(0.38)

-0.70

(0.76)

Poverty Rate

-1.24***

(0.34)

-0.33

(0.22)

-0.90***

(0.32)

Safety Net Domain

Percentage of Households Receiving

Cash Assistance

0.30

(0.76)

0.42

(0.38)

-0.12

(0.65)

Share of HUD-Assisted Units

-0.57

(0.92)

0.13

(0.62)

-0.70

(0.80)

Occupancy Rate of HUD-Assisted

Units

-0.06

(0.15)

-0.01

(0.06)

-0.04

(0.14)

Demographic Domain

Percentage African-American

-0.30**

(0.15)

-0.15**

(0.07)

-0.15

(0.14)

Percentage Hispanic

-0.19*

(0.10)

-0.12***

(0.03)

-0.07

(0.10)

Percentage Asian

0.03

(0.40)

-0.25

(0.22)

0.29

(0.42)

Percentage Children (Age 0 through

19)

1.51*

(0.85)

0.82**

(0.34)

0.69

(0.85)

Percentage Senior (Age 65 and

Older)

1.06*

(0.56)

0.17

(0.19)

0.89*

(0.52)

Percentage of One-Person

Households

1.41*

(0.82)

1.04***

(0.21)

0.38

(0.81)

Percent of Under 18 Population in

Single-Parent Households

-0.22

(0.45)

-0.07

(0.21)

-0.15

(0.37)

Percentage Veteran (25 and Older)

-1.34***

(0.37)

-0.25

(0.30)

-1.09***

(0.33)

Percentage without a Bachelor's

Degree

0.61**

(0.28)

0.08

(0.14)

0.53**

(0.20)

Alcohol Mortality (Per 100,000)

1.51**

(0.63)

0.07

(0.39)

1.44**

(0.56)

Climate Domain

Average January Temperature (°F)

0.48*

(0.26)

0.12

(0.07)

0.36

(0.23)

Average June, July, and August

Temperature (°F)

-0.53*

(0.27)

-0.14

(0.14)

-0.39

(0.27)

Total January Precipitation (Inches)

0.50*

(0.29)

-0.17

(0.15)

0.67**

(0.25)

Intercept

-139.57***

(51.74)

-69.68***

(21.40)

-69.90

(46.97)

| 94

Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Observations

110

110

110

R-Squared

0.82

0.79

0.72

Notes: Regression models include an intercept and state dummy variables. Robust standard errors in parentheses are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. For ACS

variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS, and estimates for 2012 correspond to the 2007 through 2011 ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; percentage adult is the omitted age category; and percentage with bachelor’s degree or higher is the omitted education category.

TIGHT, HIGH-COST RENTAL MARKET SUBGROUP ADDITIONAL REGRESSION TABLES

Exhibit D-14 presents regression estimates for CoCs in tight, high-cost rental markets, where the independent variables are the same across all specifications and represent the union of all variables included in the models in the National Model of Homelessness chapter. These results indicate that the inclusion of additional independent variables does not change the interpretation of the regression coefficients. There are more significant variables, however, in the economic and safety net domains related to unsheltered homelessness.

| 95

Exhibit D-14 | Regression Estimates of Model for CoCs in Tight, High-Cost Rental Markets Using a Consistent Set of Independent Variables

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Independent Variables

CoCs in Tight,

CoCs in Tight,

CoCs in Tight,

Other CoCs

High-Cost Rental

Other CoCs

High-Cost Rental

Other CoCs

High-Cost Rental

Markets

Markets

Markets

Housing Domain

House Price Index

0.07

(0.06)

-0.08

(0.10)

0.00

(0.04)

-0.04

(0.10)

0.07

(0.04)

-0.04

(0.09)

Percentage of Homeowners with Cost

Burden

0.25

(0.16)

-1.47**

(0.56)

0.04

(0.10)

-0.09

(0.21)

0.21**

(0.10)

-1.38***

(0.38)

Median Rental Utility Cost ($10s)

0.08

(0.23)

0.26

(0.78)

0.04

(0.16)

-0.02

(0.45)

0.03

(0.16)

0.28

(0.74)

Percentage of Renters with Cost Burden

0.36***

(0.13)

1.19

(0.81)

0.27**

(0.11)

0.29

(0.52)

0.09

(0.06)

0.90

(0.61)

High Housing Density CoC

-1.99

(1.36)

-12.98**

(6.04)

-0.82

(0.87)

-5.09

(3.38)

-1.16

(0.92)

-7.90**

(3.11)

Eviction Rate

-0.37

(0.26)

4.69***

(1.70)

-0.18

(0.18)

5.34***

(1.24)

-0.19

(0.18)

-0.65

(0.68)

Change in Eviction Rate

-0.04

(0.26)

1.89

(1.45)

-0.16

(0.18)

8.80***

(2.62)

0.12

(0.17)

-6.92***

(2.16)

Percentage of Overcrowded Housing Units

0.68

(0.97)

6.87**

(2.71)

1.11**

(0.50)

2.80

(2.86)

-0.44

(0.66)

4.07

(2.47)

Urban CoC

1.37

(1.45)

13.01

(9.19)

1.99**

(0.88)

10.08**

(4.38)

-0.62

(0.84)

2.93

(6.25)

Suburban CoC

-0.98

(1.51)

2.92

(12.67)

0.27

(0.84)

5.06

(5.39)

-1.25

(0.93)

-2.14

(7.76)

Economic Domain

Unemployment Rate

0.30

(1.17)

9.56***

(2.22)

-0.53

(0.45)

5.72***

(1.00)

0.83

(0.80)

3.84**

(1.83)

Gini Coefficient of Income Inequality

-0.46

(0.30)

2.30**

(0.94)

-0.15

(0.26)

0.34

(0.67)

-0.32

(0.23)

1.96**

(0.74)

Poverty Rate

-0.30

(0.45)

-1.01

(1.90)

-0.31

(0.32)

-1.25

(1.55)

0.01

(0.19)

0.23

(0.76)

Safety Net Domain

Share of HUD-Assisted Units

1.93**

(0.73)

-2.99

(2.05)

0.93*

(0.55)

0.62

(0.97)

1.00**

(0.41)

-3.61**

(1.38)

Occupancy Rate of HUD-Assisted Units

0.01

(0.09)

-1.31***

(0.46)

-0.03

(0.06)

-0.38

(0.49)

0.04

(0.06)

-0.93***

(0.29)

Percentage of Houses Built Before 1940

-0.00

(0.09)

0.51**

(0.20)

0.13**

(0.06)

0.00

(0.14)

-0.13***

(0.04)

0.51***

(0.16)

Demographic Domain

Percentage African-American

-0.11

(0.15)

0.22

(0.47)

-0.13*

(0.08)

-0.03

(0.37)

0.02

(0.10)

0.24*

(0.14)

Percentage Hispanic

-0.03

(0.08)

-0.67

(0.40)

-0.06

(0.04)

-0.57

(0.37)

0.04

(0.06)

-0.09

(0.23)

Percentage Asian

0.58

(0.37)

0.01

(0.33)

-0.06

(0.16)

-0.36

(0.29)

0.64**

(0.27)

0.36*

(0.21)

Percentage Children (Age 0 through 19)

0.10

(0.41)

-2.37

(1.57)

-0.09

(0.33)

-0.87

(0.95)

0.19

(0.27)

-1.50

(1.28)

Percentage Senior (Age 65 and Older)

-0.12

(0.25)

2.17*

(1.16)

-0.34

(0.20)

1.71***

(0.52)

0.21

(0.16)

0.46

(1.11)

Percentage Female

-1.14

(1.09)

-2.55

(3.68)

0.07

(0.41)

-0.70

(2.05)

-1.21

(0.98)

-1.85

(2.77)

Net-Migration Rate

2.87**

(1.09)

0.93

(5.93)

1.81**

(0.73)

7.41**

(3.21)

1.06

(0.69)

-6.47

(6.07)

Change in Net-Migration Rate

1.11

(1.18)

-

13.66***

(4.91)

-0.41

(0.67)

-14.70***

(3.46)

1.52*

(0.84)

1.03

(4.63)

Percentage of One-Person Households

0.89**

(0.37)

0.69

(1.33)

0.39

(0.24)

0.45

(0.86)

0.50*

(0.26)

0.24

(0.65)

Percentage of Under-18 Population in

Single-Parent Households

0.21

(0.49)

0.66

(0.65)

0.34

(0.25)

0.63

(0.49)

-0.13

(0.37)

0.03

(0.56)

Percentage Veteran (25 and Older)

0.17

(0.22)

1.69

(1.26)

0.13

(0.11)

-1.31

(0.94)

0.05

(0.18)

3.00***

(0.52)

Percentage without a Bachelor's Degree

-0.06

(0.15)

-0.39

(0.30)

-0.08

(0.10)

0.11

(0.14)

0.03

(0.12)

-0.49*

(0.27)

Healthcare Costs ($1,000s)

-0.42

(0.84)

3.72**

(1.39)

-0.16

(0.39)

0.77

(1.52)

-0.26

(0.60)

2.95**

(1.38)

Alcohol Mortality (Per 100,000)

1.20*

(0.61)

2.26

(1.36)

0.76***

(0.27)

2.35**

(0.98)

0.44

(0.46)

-0.08

(1.11)

| 96

2017 Homelessness Rate (Per 10,000 Population)

Total

Sheltered

Unsheltered

Independent Variables

CoCs in Tight,

CoCs in Tight,

CoCs in Tight,

Other CoCs

High-Cost Rental

Other CoCs

High-Cost Rental

Other CoCs

High-Cost Rental

Markets

Markets

Markets

Excessive Drinking Rate

-0.14

(0.24)

-1.02

(0.77)

-0.08

(0.11)

-0.84

(0.56)

-0.06

(0.17)

-0.17

(0.50)

Climate Domain

Average January Temperature (°F)

0.01

(0.18)

-0.07

(0.85)

-0.04

(0.08)

0.15

(0.35)

0.05

(0.12)

-0.23

(0.80)

Average June, July, and August

Temperature (°F)

-0.11

(0.28)

0.30

(1.34)

-0.06

(0.12)

0.12

(0.85)

-0.05

(0.22)

0.18

(1.06)

Total January Precipitation (Inches)

0.28

(0.21)

0.22

(1.32)

0.10

(0.08)

-1.03

(0.89)

0.18

(0.15)

1.24

(1.03)

Total Annual Precipitation (Inches)

-0.02

(0.07)

0.55***

(0.14)

-0.02

(0.03)

0.16**

(0.07)

-0.01

(0.05)

0.39***

(0.11)

Intercept

41.80

(53.44)

0.63

(25.21)

41.18

(58.43)

Observations

374

374

374

R-Squared

0.91

0.94

0.83

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤

0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the union of variables across al outcomes of interest (total, sheltered, or unsheltered homelessness) included in the national model in the National Model of Homelessness chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category.

| 97

WEST CENSUS REGION SUBGROUP REGRESSION TABLES

Exhibit D-15 presents regression estimates of unsheltered homelessness for CoCs in the West Census region, where the independent variables are the same as those included in the models in the National Model of Homelessness chapter. States in the West Census region include Alaska, Arizona, California, Colorado, Hawai , Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

This model is an alternative to the specification presented in the “unsheltered homelessness on the west coast” section of the Subgroup Analysis chapter and sheds light on factors associated with unsheltered homelessness in a broader group of states in the west. These results are quantitatively and qualitatively like those presented in that section, however, the significance of factors varies. Specifically, median rent, poverty, and race and ethnicity are all significant in West Census region CoCs. In contrast, rental vacancy, housing density, unemployment, occupancy of HUD-assisted units, age, the share of women in the population, migration, one-person households, healthcare costs, and January temperatures are only significant in the model of west coast CoCs.

Exhibit D-15 | Regression Estimates of Model for West Census Region CoCs

2017 Unsheltered Homelessness Rate (Per 10,000 Population)

Independent Variables

Other Region CoCs

West Census Region CoCs

Housing Domain

House Price Index

-0.01

(0.02)

0.62***

(0.01)

Percentage of Homeowners with Cost Burden

0.02

(0.10)

3.74***

(0.20)

Natural Logarithm of Median Rent ($100s)

3.51*

(1.98)

3.39

(14.68)

Median Rental Utility Cost ($10s)

0.03

(0.12)

-0.53

(0.55)

Percentage of Renters with Cost Burden

0.01

(0.06)

-4.73***

(0.44)

Percentage of Renter-Occupied Units

-0.01

(0.09)

-1.17**

(0.49)

Rental Vacancy Rates

0.45***

(0.16)

-4.15***

(0.77)

High Housing Density CoC

-0.58

(0.66)

-13.30***

(2.68)

Change in Eviction Rate

-0.12

(0.13)

-3.40***

(1.00)

Percentage of Overcrowded Housing Units

-0.20

(0.39)

7.07***

(0.57)

Urban CoC

0.01

(0.74)

-5.51

(4.43)

Suburban CoC

-0.37

(0.55)

-14.23***

(4.58)

Economic Domain

Unemployment Rate

-0.09

(0.35)

-0.77**

(0.29)

Poverty Rate

0.03

(0.13)

5.40***

(1.00)

Safety Net Domain

Share of HUD-Assisted Units

0.70***

(0.24)

8.62***

(0.70)

Percent of Houses Built Before 1940

-0.07**

(0.03)

-0.93***

(0.18)

Demographic Domain

Percentage African-American

-0.01

(0.03)

-1.29***

(0.28)

Percentage Hispanic

-0.05**

(0.02)

-0.36**

(0.17)

Percentage Asian

0.05

(0.06)

-0.56***

(0.18)

Percentage Children (Age 0 through 19)

-0.07

(0.19)

-0.54

(0.40)

Percentage Senior (Age 65 and Older)

0.01

(0.11)

-1.61

(1.03)

Percentage Female

-0.85

(0.59)

0.15

(0.83)

Change in Net-Migration Rate

1.07

(0.67)

1.83

(2.77)

Percentage of One-Person Households

0.32

(0.19)

1.66*

(0.89)

Percentage Veteran (25 and Older)

0.09

(0.08)

0.85

(0.60)

Percentage without a Bachelor's Degree

0.02

(0.04)

-0.47**

(0.21)

Healthcare Costs ($1,000s)

-0.22

(0.27)

-0.11

(1.19)

Excessive Drinking Rate

0.01

(0.06)

-3.25***

(0.47)

Climate Domain

Average January Temperature (°F)

0.13***

(0.05)

-0.07

(0.14)

Average June, July, and August Temperature (°F)

0.05

(0.12)

0.69***

(0.24)

Total January Precipitation (Inches)

-0.39***

(0.12)

0.70***

(0.23)

Intercept

21.87

(33.60)

| 98

2017 Unsheltered Homelessness Rate (Per 10,000 Population)

Independent Variables

Other Region CoCs

West Census Region CoCs

Observations

374

R-Squared

0.87

Notes: Each regression model includes an intercept, and robust standard errors (in parentheses) are clustered at the state level. The symbols ***, **, and * represent significance values of p ≤ 0.01, p ≤ 0.05, and p ≤ 0.1, respectively. Regression models are weighted by the population in each CoC. Independent variables represent the variables included in the national model for the outcome of interest (total, sheltered, or unsheltered homelessness) in the National Model of Homelessness chapter. Change variables are calculated by subtracting the 2012 value from the 2016 value for each CoC. ACS variables, ACS 5-year estimates for 2016 correspond to the 2012 through 2016 ACS. Rural CoC is the omitted urbanicity category; percentage White is the omitted race/ethnicity category; and percentage adult is the omitted age category. States in the West Census region include Alaska, Arizona, California, Colorado, Hawai , Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

| 99

APPENDIX E: VARIABLE SELECTION

PROCESS RESULTS

The second step in our variable selection process was used to eliminate redundant variables in terms of overal model fit. The backward vselect selection process functioned by regressing on potential explanatory variables and iteratively eliminating until the selected information criteria (adjusted R-squared) no longer improves. This process works in stages where the initial stage contains al specified explanatory variables and the subsequent stages contain a list of remaining variables from the iterative elimination. At each stage, regressions omitting a single predictor from the list of remaining variables are run. The variable omitted in the highest performing model—in terms of the adjusted R-squared—

wil then be eliminated. This process continues until the adjusted R-squared is no longer improved by the omission of any remaining variables. To ensure that key variables would not be omitted from the final models, we fixed the regressions of each stage to include 10 variables. When the resulting vselect variable list included partial sets of categorical variables, we added the eliminated categorical variables into the final models for interpretability.

As the number of observations substantially differ between models, we ran different stepwise elimination processes for national and urbanicity-specific models. For each of the three national models (national model, tight high-cost rental markets, and west coast), we ran the elimination process across all variables. Because the urbanicity models contained far fewer observations, a stricter elimination process was needed to preserve degrees of freedom. The models for urbanicity fol owed a two-step process in which the first stepwise elimination was conducted within each domain. The resulting mix of variables for each domain was then run through another elimination process across al the remaining variables. We did not run additional stepwise elimination processes for tight, high-cost rental markets and west coast CoCs analyses. These models included the resulting variables identified in the corresponding national model selection. As the tight, high-cost rental market indicator was constructed from median home value, median rent, rental vacancy rate, and rental share values, we excluded these from the regressions. The final model variables are outlined in exhibit E-1 through exhibit E-3.

| 100

Exhibit E-1 | Variable Selection Results for Total Homelessness (Per 10,000 population)

Major City and Largely

Urban CoCs

Largely Suburban CoCs

Largely Rural CoCs

Step 2.

Step 3.

Step 2.

Step 3.

Step 2.

Step 3.

Tight

National

Domain-

Across-

Domain-

Across-

Domain-

Across-

Rental

Independent Variables

Qualitative

Specific

Domain

Specific

Domain

Specific

Domain

Markets

Review

Variable

Variable

Variable

Variable

Variable

Variable

Selection

Selection

Selection

Selection

Selection

Selection

Housing Domain

Natural Logarithm of Median Home Value ($1,000s)

House Price Index







Percentage of Homeowners with Cost Burden















Natural Logarithm of Median Rent ($100s)

















Change in Median Rent ($100s)

Median Rental Utility Cost ($10s)











Percentage of Renters with Cost Burden



















Median Year Home Built

Percentage of Renter-Occupied Units

















Rental Vacancy Rate

















High Housing Density CoC















Eviction Rate











Change in Eviction Rate







Eviction Filing Rate

Percentage of Overcrowded Housing Units







Permitted Units as a Share of Total Units

Percentage of Urban CoCs







Percentage of Suburban CoCs







Economic Domain

Natural Logarithm of Median Income ($1,000s)

Change in Median Income ($10,000s)

Unemployment Rate



















Employment Rate for Middle-Skil ed Workers

Gini Coefficient of Income Inequality















Poverty Rate



















Safety Net Domain

Percentage of Households Receiving Cash

Assistance











Percentage of Households Eligible for EITC

SSDI Participation Rate

SSI Participation Rate

Share of HUD-Assisted Units



















Occupancy Rate of HUD-Assisted Units



















Percentage of HUD-Assisted Households with More

Bedrooms than People

| 101

Major City and Largely

Urban CoCs

Largely Suburban CoCs

Largely Rural CoCs

Step 2.

Step 3.

Step 2.

Step 3.

Step 2.

Step 3.

Tight

National

Domain-

Across-

Domain-

Across-

Domain-

Across-

Rental

Independent Variables

Qualitative

Specific

Domain

Specific

Domain

Specific

Domain

Markets

Review

Variable

Variable

Variable

Variable

Variable

Variable

Selection

Selection

Selection

Selection

Selection

Selection

People Per Unit in HUD-Assisted Households

Percentage of HUD-Assisted Households with

Income Below 30% of Local Median Family Income

Percentage of Houses Built Before 1940











Federal CoC Funding ($100,000s)



Permanent Supportive Housing Beds

Temporary Housing Beds

Demographic Domain

Population Density

Percentage White

Percentage African-American











Percentage Hispanic













Percentage Asian















Percentage Other Race

Percentage Children (Age 0 through 19)

















Percentage Adult (Age 20 through 64)

Percentage Senior (Age 65 and Older)















Percentage Female





Net-Migration Rate













Change in Net-Migration Rate











Percentage of One-Person Households















Change in Percentage of One-Person Households

Percentage of Under-18 Population in Single-Parent

Households

















Percentage Veteran (25 and Older)









Percentage without a Bachelor's Degree



Healthcare Costs ($1,000s)







Mental Health Providers as Share of Population



Alcohol Mortality (Per 100,000)



Excessive Drinking Rate











Climate Domain

Average January Temperature (°F)



















Average June, July, and August Temperature (°F)

















Total January Precipitation (Inches)



















Total Annual Precipitation (Inches)

















| 102

Exhibit E-2 | Variable Selection Results for Sheltered Homelessness

Major City and Largely

Urban CoCs

Largely Suburban CoCs

Largely Rural CoCs

Qualitative

Step 2.

Step 3.

Step 2.

Step 3.

Step 2.

Step 3.

Tight

Review

National

Domain-

Across-

Domain-

Across-

Domain-

Across-

Rental

Independent Variables

Specific

Domain

Specific

Domain

Specific

Domain

Markets

Variable

Variable

Variable

Variable

Variable

Variable

Selection

Selection

Selection

Selection

Selection

Selection

Housing Domain

Natural Logarithm of Median Home Value

($1,000s)

House Price Index









Percentage of Homeowners with Cost Burden



















Natural Logarithm of Median Rent ($100s)

















Change in Median Rent ($100s)

Median Rental Utility Cost ($10s)













Percentage of Renters with Cost Burden



















Median Year Home Built

Percentage of Renter-Occupied Units

















Rental Vacancy Rate

















High Housing Density CoC











Eviction Rate











Change in Eviction Rate









Eviction Filing Rate

Percentage of Overcrowded Housing Units

















Permitted Units as a Share of Total Units

Percentage of Urban CoCs







Percentage of Suburban CoCs







Economic Domain

Natural Logarithm of Median Income ($1,000s)

Change in Median Income ($1,000s)

Unemployment Rate



















Employment Rate for Middle-Skil ed Workers

Gini Coefficient of Income Inequality















Poverty Rate



















Safety Net Domain

Percentage of Households Receiving Cash

Assistance







Percentage of Households Eligible for EITC

SSDI Participation Rate

SSI Participation Rate

Share of HUD-Assisted Units



















Occupancy Rate of HUD-Assisted Units



















People Per Unit in HUD-Assisted Households

| 103

Major City and Largely

Urban CoCs

Largely Suburban CoCs

Largely Rural CoCs

Qualitative

Step 2.

Step 3.

Step 2.

Step 3.

Step 2.

Step 3.

Tight

Review

National

Domain-

Across-

Domain-

Across-

Domain-

Across-

Rental

Independent Variables

Specific

Domain

Specific

Domain

Specific

Domain

Markets

Variable

Variable

Variable

Variable

Variable

Variable

Selection

Selection

Selection

Selection

Selection

Selection

Percentage of HUD-Assisted Households with

Income Below 30 Percent of Local Median Family

Income

Percentage of Houses Built Before 1940













Federal CoC Funding ($100,000s)



Permanent Supportive Housing Beds

Temporary Housing Beds

Demographic Domain

Population Density

Percentage White

Percentage African-American

















Percentage Hispanic

















Percentage Asian



















Percentage Other Race

Percentage Children (Age 0 through 19)

















Percentage Adult (Age 20 through 64)

Percentage Senior (Age 65 and Older)















Percentage Female











Net-Migration Rate











Change in Net-Migration Rate





Percentage of One-Person Households







Change in Percentage of One-Person Households

Percentage of Under-18 Population in

Single-Parent Households















Percentage Veteran (25 and Older)







Percentage without a Bachelor's Degree



Healthcare Costs ($1,000s)





Mental Health Providers as Share of Population





Alcohol Mortality (Per 100,000)

















Excessive Drinking Rate







Climate Domain

Average January Temperature (°F)



















Average June, July, and August Temperature (°F)













Total January Precipitation (Inches)











Total Annual Precipitation (Inches)















| 104

Exhibit E-3 | Variable Selection Results for Unsheltered Homelessness

Major City and Largely

Urban CoCs

Largely Suburban CoCs

Largely Rural CoCs

Qualitative

Step 2.

Step 3.

Step 2.

Step 3.

Step 2.

Step 3.

Tight

Review

National

Domain-

Across-

Domain-

Across-

Domain-

Across-

Rental

Independent Variables

Specific

Domain

Specific

Domain

Specific

Domain

Markets

Variable

Variable

Variable

Variable

Variable

Variable

Selection

Selection

Selection

Selection

Selection

Selection

Housing Domain

Natural Logarithm of Median Home Value

($1,000s)

House Price Index















Percentage of Homeowners with Cost Burden



















Natural Logarithm of Median Rent ($100s)

















Change in Median Rent ($100s)

Median Rental Utility Cost ($10s)













Percentage of Renters with Cost Burden



















Median Year Home Built

Percentage of Renter-Occupied Units

















Rental Vacancy Rate

















High Housing Density CoC











Eviction Rate





Change in Eviction Rate









Eviction Filing Rate

Percentage of Overcrowded Housing Units













Permitted Units as a Share of Total Units

Percentage of Urban CoCs







Percentage of Suburban CoCs







Economic Domain

Natural Logarithm of Median Income ($10,000s)

Change in Median Income ($10,000s)

Unemployment Rate



















Employment Rate for Middle-Skil ed Workers

Gini Coefficient of Income Inequality











Poverty Rate



















Safety Net Domain

Percentage of Households Receiving Cash

Assistance













Percentage of Households Eligible for EITC

SSDI Participation Rate

SSI Participation Rate

Share of HUD-Assisted Units



















Occupancy Rate of HUD-Assisted Units



















| 105

Major City and Largely

Urban CoCs

Largely Suburban CoCs

Largely Rural CoCs

Qualitative

Step 2.

Step 3.

Step 2.

Step 3.

Step 2.

Step 3.

Tight

Review

National

Domain-

Across-

Domain-

Across-

Domain-

Across-

Rental

Independent Variables

Specific

Domain

Specific

Domain

Specific

Domain

Markets

Variable

Variable

Variable

Variable

Variable

Variable

Selection

Selection

Selection

Selection

Selection

Selection

Percentage of HUD-Assisted Households with

More Bedrooms than People

People Per Unit in HUD-Assisted Households

Percentage of HUD-Assisted Households with

Income Below 30 Percent of Local Median Family Income

Percentage of Houses Built Before 1940











Federal CoC Funding ($100,000s)



Permanent Supportive Housing Beds

Temporary Housing Beds

Demographic Domain

Population Density

Percentage White

Percentage African-American

















Percentage Hispanic















Percentage Asian

















Percentage Other Race

Percentage Children (Age 0 through 19)















Percentage Adult (Age 20 through 64)

Percentage Senior (Age 65 and Older)

















Percentage Female











Net-Migration Rate









Change in Net-Migration Rate













Percentage of One-Person Households

















Change in Percentage of One-Person Households

Percentage of Under-18 Population in

Single-Parent Households









Percentage Veteran (25 and Older)













Percentage without a Bachelor's Degree



Healthcare Costs ($1,000s)











Mental Health Providers as Share of Population





Alcohol Mortality (Per 100,000)











Excessive Drinking Rate















Climate Domain

Average January Temperature (°F)



















Average June, July, and August Temperature (°F)

















Total January Precipitation (Inches)



















Total Annual Precipitation (Inches)













| 106

APPENDIX F: SUMMARY STATISTICS FOR

TIGHT, HIGH-COST RENTAL MARKETS

Exhibit F-1 | Average PIT Counts by Tight, High-Cost Rental Market Status, 2017

CoCs in Tight, High-

PIT Counts Per CoC

Cost Rental Markets

Al Other CoCs

Mean

SD

Mean

SD

Average Homeless (Per 10,000 population)

37.1

28.5

11.4

8.0

Average Sheltered Homeless (Per 10,000 population)

22.5

25.7

8.0

5.5

Average Unsheltered Homeless (Per 10,000 population)

14.6

16.1

3.4

5.1

Observations

59

315

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2017. All U.S. territories are excluded from this analysis.

Exhibit F-2 | Summary Statistics for Housing Variables by Tight, High-Cost Rental Market Status, 2016

CoCs in Tight, High-Cost

Variables in Housing Domain

Rental Markets

Al Other CoCs

Mean

Standard

Deviation

Mean

Standard

Deviation

Median Home Value ($1,000s)

451.9

134.7

170.9

58.6

House Price Index

25.6

15.8

7.0

13.0

Percentage of Homeowners with Cost Burden (%)

33.2

5.3

23.6

5.0

Median Rent ($100s)

12.4

2.0

7.2

1.7

Median Rental Utility Cost ($10s)

11.9

2.7

15.5

2.6

Percentage of Renters with Cost Burden (%)

54.0

4.9

49.6

4.5

Percentage of Renter-Occupied Units (%)

45.6

12.7

34.1

6.6

Rental Vacancy Rate (%)

3.8

1.1

6.8

1.9

Percentage of CoCs with High Housing Density (%)

75.9

43.1

26.6

44.3

Eviction Rate (%)

1.0

1.1

2.4

1.9

Change in Eviction Rate (%) a

-0.1

1.1

-0.5

1.4

Percentage of Overcrowded Housing Units (%)

6.4

3.4

2.7

1.6

Percentage of Urban CoCs (%)

61.0

49.2

28.2

45.0

Percentage of Suburban CoCs (%)

37.7

48.9

30.4

46.1

Percentage of Rural CoCs (%)

1.3

11.4

41.5

49.3

a Change in Eviction Rate represents the difference between 2016 and 2012 values.

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis.

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Exhibit F-3 | Summary Statistics for Economic Variables by Tight, High-Cost Rental Market Status, 2016

CoCs in Tight, High-Cost Rental

Variables in Economic Domain

Markets

Al Other CoCs

Mean

Standard Deviation

Mean

Standard Deviation

Median Income ($1,000s)

75.9

16.9

56.1

10.2

Unemployment Rate

4.5

1.0

5.0

1.2

Gini Coefficient of Income Inequality

47.3

3.2

45.7

2.7

Poverty Rate

12.2

4.1

14.1

3.6

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis.

Exhibit F-4 | Summary Statistics for Safety Net Variables by Tight, High-Cost Rental Market Status, 2016

CoCs in Tight, High-

Variables in Safety Net Domain

Cost Rental Markets

Al Other CoCs

Mean

Standard

Deviation

Mean

Standard

Deviation

Percentage of Households Receiving Cash Assistance (%)

3.1

1.2

2.6

1.2

Percentage of Households Eligible for EITC (%)

15.5

5.4

19.4

5.2

SSDI Participation Rate (%)

1.7

0.5

2.9

0.9

SSI Participation Rate (%)

2.7

1.3

2.5

1.0

Share of HUD-Assisted Units (%)

4.8

3.1

3.2

1.6

Occupancy Rate of HUD-Assisted Units (%)

92.6

3.6

92.5

3.6

Percentage of Houses Built Before 1940 (%)

16.0

13.8

11.7

9.9

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis.

Exhibit F-5 | Summary Statistics for Demographic Variables by Tight, High-Cost Rental Market Status, 2016

CoCs in Tight, High-

Variables in Demographic Domain

Cost Rental Markets

Al Other CoCs

Mean

Standard

Deviation

Mean

Standard

Deviation

Total Population (1,000s)

1,188.6

1,782.7

801.8

1,125.3

Percentage White (%)

45.4

16.6

65.5

19.0

Percentage African-American (%)

9.7

7.9

13.2

11.9

Percentage Hispanic (%)

27.8

13.5

15.1

15.1

Percentage Asian (%)

13.8

8.2

3.2

2.5

Percentage Children (Age 0 through 19) (%)

24.6

2.2

25.6

2.5

Percentage Adult (Age 20 through 64) (%)

61.8

2.4

58.7

2.1

Percentage Senior (Age 65 and Older) (%)

13.7

1.9

15.7

3.4

Percentage Female (%)

50.8

0.9

50.8

0.8

Net-Migration Rate (%)

0.1

0.6

0.4

0.8

Change in Net-Migration Rate a

-0.3

0.4

0.1

0.6

Percentage of One-Person Households (%)

26.1

4.5

27.7

3.7

Percentage of Under-18 Population in Single-Parent Households (%)

23.5

6.2

26.6

6.2

Percentage Veteran (25 and Older) (%)

6.3

2.6

9.8

2.5

Percentage with Bachelor’s Degree or Higher (%)

40.2

10.0

29.2

8.1

Percentage with Some Col ege (%)

26.7

5.0

31.6

3.9

Percentage with High School Diploma (%)

20.1

4.2

27.9

5.4

Percentage with Less than High School (%)

13.0

5.3

11.3

4.1

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CoCs in Tight, High-

Variables in Demographic Domain

Cost Rental Markets

Al Other CoCs

Mean

Standard

Deviation

Mean

Standard

Deviation

Healthcare Costs ($1,000s)

11.7

1.8

10.0

1.2

Mental Health Providers as Share of Population (%)

0.0

0.1

0.1

0.2

Alcohol Mortality (Per 100,000)

2.7

1.3

2.9

1.3

Excessive Drinking Rate (%)

19.0

2.3

18.5

2.6

a Change in Net-Migration Rate represents the difference between 2016 and 2012 values.

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis. For the regression analysis, percentage White is the omitted from the race/ethnicity category; while percentage other (two or more races, Native American, Pacific Islander) is excluded from the analysis; and percentage adult is the omitted age category.

Exhibit F-6 | Summary Statistics for Climate Variables by Tight, High-Cost Rental Market Status

CoCs in Tight, High-

Variables in Climate Domain

Cost Rental Markets

Al Other CoCs

Mean

Standard

Deviation

Mean

Standard

Deviation

Average January Temperature (°F)

41.0

9.7

38.8

12.5

Average June, July, and August Temperature (°F)

72.5

6.2

75.7

5.7

Total January Precipitation (Inches)

6.5

5.1

4.0

3.2

Total Annual Precipitation (Inches)

36.4

18.3

40.0

12.6

Notes: These estimates represent the weighted average temperature and precipitation for each CoC for January 2017 and the average summer and annual precipitation from 2016. Estimates represent the population of all 50 states plus the District of Columbia. All U.S. territories are excluded from this analysis. Summer temperature represents the average temperature for June, July, and August.

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APPENDIX G: SUMMARY STATISTICS FOR

WEST COAST CONTINUUMS OF CARE

Exhibit G-1 | Average PIT Counts by West Coast CoC Status, 2017

West Coast

All Other

PIT Counts Per CoC

CoCs

Region CoCs

California CoCs

Oregon CoCs

Washington

CoCs

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Average Homeless (Per 10,000

population)

33.4

19.9

13.9

16.4

34.2

20.8

34.1

15.8

29.0

18.0

Average Sheltered Homeless

(Per 10,000 population)

12.1

6.7

11.0

15.2

10.8

5.4

14.7

10.1

17.2

8.7

Average Unsheltered Homeless

(Per 10,000 population)

21.4

15.6

3.0

4.1

23.3

16.5

19.5

8.3

11.8

9.7

Observations

51

323

38

7

6

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2017. All U.S. territories are excluded from this analysis. Some CoCs in California were merged; please see appendix C for further detail on this process.

Exhibit G-2 | Summary Statistics for Housing Variables by West Coast Status, 2016

Variables in Housing Domain

West Coast CoCs

Al Other Region CoCs

Mean

SD

Mean

SD

Median Home Value ($1,000s)

403.9

171.9

200.0

108.8

House Price Index

30.1

14.8

7.5

13.0

Percentage of Homeowners with Cost Burden (%)

32.5

4.3

24.4

5.9

Median Rent ($100s)

11.3

2.8

7.8

2.4

Median Rental Utility Cost ($10s)

11.7

2.7

15.2

2.7

Percentage of Renters with Cost Burden (%)

55.3

4.5

49.7

4.5

Percentage of Renter-Occupied Units (%)

44.1

7.4

35.2

9.3

Rental Vacancy Rate (%)

3.9

1.1

6.6

2.1

Percentage of CoCs with High Housing Density (%)

47.7

50.4

35.4

47.9

Eviction Rate (%)

0.8

0.6

2.4

1.9

Change in Eviction Rate (%) a

-0.3

0.7

-0.4

1.5

Percentage of Overcrowded Housing Units (%)

7.2

3.2

2.8

1.8

Percentage of Urban CoCs (%)

55.4

50.2

31.5

46.5

Percentage of Suburban CoCs (%)

33.0

47.5

31.8

46.6

Percentage of Rural CoCs (%)

11.6

32.3

36.7

48.3

a Change in Eviction Rate represents the difference between 2016 and 2012 values.

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis.

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Exhibit G-3 | Summary Statistics for Economic Variables by West Coast Status, 2016

West Coast CoCs

Al Other Region CoCs

Variables in Economic Domain

Mean

Standard

Deviation

Mean

Standard

Deviation

Median Income ($1,000s)

68.6

16.4

58.9

13.6

Unemployment Rate

5.5

1.9

4.8

0.9

Gini Coefficient of Income Inequality

46.7

2.5

46.0

2.9

Poverty Rate

13.6

3.8

13.7

3.8

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis.

Exhibit G-4 | Summary Statistics for Safety Net Variables by West Coast Status, 2016

West Coast CoCs

Al Other Region CoCs

Variables in Safety Net Domain

Mean

Standard

Deviation

Mean

Standard

Deviation

Percentage of Households Receiving Cash Assistance (%)

3.8

1.5

2.5

1.0

Percentage of Households Eligible for EITC (%)

16.9

5.5

18.9

5.4

SSDI Participation Rate (%)

1.9

0.7

2.8

1.0

SSI Participation Rate (%)

3.0

1.0

2.5

1.1

Share of HUD-Assisted Units (%)

3.3

1.1

3.6

2.2

Occupancy Rate of HUD-Assisted Units (%)

93.3

3.2

92.4

3.6

Percentage of Houses Built Before 1940 (%)

9.5

7.6

13.2

11.4

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis.

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Exhibit G-5 | Summary Statistics for Demographic Variables by West Coast Status, 2016

West Coast CoCs

Al Other Region CoCs

Variables in Demographic Domain

Mean

Standard

Deviation

Mean

Standard

Deviation

Total Population (1,000s)

993.4

1,534.2

842.2

1,209.0

Percentage White (%)

45.3

18.4

64.1

19.3

Percentage African-American (%)

5.1

3.1

13.8

11.7

Percentage Hispanic (%)

33.0

16.1

15.1

13.8

Percentage Asian (%)

12.7

8.8

4.2

4.5

Percentage Children (Age 0 through 19) (%)

25.5

2.9

25.4

2.4

Percentage Adult (Age 20 through 64) (%)

60.6

2.8

59.2

2.4

Percentage Senior (Age 65 and Older) (%)

14.0

2.4

15.5

3.3

Percentage Female (%)

50.3

0.6

50.9

0.8

Net-Migration Rate (%)

0.4

0.7

0.3

0.8

Change in Net-Migration Rate a

0.1

0.6

0.0

0.6

Percentage of One-Person Households (%)

24.6

3.6

27.9

3.8

Percentage of Under-18 Population in Single-Parent Households (%)

22.3

3.2

26.6

6.5

Percentage Veteran (25 and Older) (%)

7.7

3.1

9.3

2.8

Percentage with Bachelor’s Degree or Higher (%)

32.4

10.8

31.4

9.4

Percentage with Some Col ege (%)

31.3

5.0

30.4

4.5

Percentage with High School Diploma (%)

20.9

4.4

27.2

5.8

Percentage with Less than High School (%)

15.5

5.9

11.0

3.7

Healthcare Costs ($1,000s)

11.3

1.9

10.2

1.4

Mental Health Providers as Share of Population (%)

0.1

0.2

0.1

0.2

Alcohol Mortality (Per 100,000)

2.8

1.4

2.9

1.3

Excessive Drinking Rate (%)

18.5

1.7

18.6

2.7

a Change in Net-Migration Rate represents the difference between 2016 and 2012 values.

Notes: These estimates represent the weighted average, based on the overal population of each CoC. Estimates represent the population of all 50 states plus the District of Columbia for 2016. All U.S. territories are excluded from this analysis. For the regression analysis, percentage white is the omitted race/ethnicity category; while percentage other (two or more races, Native American, Pacific Islander) is excluded from the analysis; and percentage adult is the omitted age category.

Exhibit G-6 | Summary Statistics for Climate Variables by West Coast Status

West Coast CoCs

Al Other Region CoCs

Variables in Climate Domain

Mean

Standard

Deviation

Mean

Standard

Deviation

Average January Temperature (°F)

41.6

8.0

38.7

12.6

Average June, July, and August Temperature (°F)

70.5

7.3

75.9

5.3

Total January Precipitation (Inches)

10.4

5.8

3.4

1.9

Total Annual Precipitation (Inches)

37.3

24.3

39.8

11.2

Notes: These estimates represent the weighted average temperature and precipitation for each CoC for January 2017 and the average summer and annual precipitation from 2016. Estimates represent the population of all 50 states plus the District of Columbia. All U.S. territories are excluded from this analysis. Summer temperature represents the average temperature for June, July, and August.

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APPENDIX H: LOCAL POLICY SCANS

The study team, in coordination with the U.S. Department of Housing and Urban Development, identified three locations (New York City, Seattle, and San Francisco) as potential sites for further analysis of local policies that may alleviate or exacerbate homelessness in each area. Collectively, this group of cities ranks in the top 10 of total and unsheltered homelessness and represents diverse factors and settings associated with the issue of homelessness in each city. Each area has tried to solve its growing homelessness crisis through varied responses and approaches. While we focus on three cities in this appendix, recommendations for future feasible comparison cities, based on policy, geography, and weather factors, include Washington, DC (New York City); Los Angeles or Oakland, CA (San Francisco); and Portland, OR (Seattle). The feasibility of a deep-dive study in each of these cities is contingent on data availability and cooperation from various stakeholders at the local level. For each city of interest, the study team has identified several organizations and potential data sources that will be useful for further exploration into why homelessness is so prevalent in these cities and how the cities have responded to its growing crisis.

HOMELESSNESS AND LOCAL POLICIES IN NEW YORK CITY, SAN FRANCISCO, AND

SEATTLE

This section presents a preliminary overview of homelessness and the policy landscape in New York City, San Francisco, and Seattle. Exhibit H-1 presents counts, rates, and rankings of the total, sheltered, and unsheltered homelessness in New York City, San Francisco, and Seattle in 2017. All three cities have among the highest counts and rates of homelessness in the United States, with al three ranking in the top 10 cities in terms of the total homeless population. Exhibit H-2 presents a more detailed overview of various policies and regulations related to homelessness and housing. 77 Given the serious issue of homelessness in these cities, understanding the relationship between homelessness and the policy and regulatory landscape is an important step toward the goal of ending homelessness.

Exhibit H-1 | Homelessness in New York City, San Francisco, and Seattle in 2017

Continuum of Care (CoC) Name

Count (Ranking)

Rate Per 10,000 Population

(Ranking)

Total Homelessness

New York City CoC

76,501

(1)

88.8

(3)

San Francisco CoC

6,858

(9)

78.3

(6)

Seattle/King County CoC

11,643

(3)

54.0

(12)

Sheltered Homelessness

New York City CoC

72,565

(1)

84.2

(2)

San Francisco CoC

2,505

(23)

28.6

(11)

Seattle/King County CoC

6,158

(5)

28.6

(12)

Unsheltered Homelessness

New York City CoC

3,936

(7)

4.6

(105)

San Francisco CoC

4,353

(5)

49.7

(5)

Seattle/King County CoC

5,485

(3)

25.4

(22)

77 Exhibit H-2 is intended to provide a comprehensive but not conclusive list of homelessness and housing policies and regulations in New York City, San Francisco, and Seattle.

| 113

New York City has the largest homeless population in the United States (76,501 in 2017) and the third highest rate of total homelessness (88.8 homeless people per 10,000 population in 2017). The city has implemented several strategies to help its homeless population, as such a large share of homeless people can create a strain on social and public resources, such as emergency response, public safety, and other health resources. New York is a “right-to-shelter” city, meaning that the city government holds an obligation to provide shelter to all residents (individuals and families) who have no alternative shelter accommodations (New York City Department of Homeless Services). The right-to-shelter policy contributes to New York City’s low unsheltered homeless population relative to the overall homeless population. While New York City’s 2017 unsheltered homeless population ranked 7th of all CoCs in the United States, its rate of unsheltered homelessness was ranked 105th. Previous research in New York City has shown that lack of affordable housing, triggered by various factors such as eviction, domestic violence, job loss, or dangerous housing conditions, is one of the leading causes of homelessness, especially for families. The Preventive Assistance and Temporary Housing program developed under former Mayor Bloomberg is a basis for finding alternative housing for those who seek shelter. Since taking office in 2014, Mayor de Blasio has tried a three-pronged approach to tackle the increasing homeless population in New York City (De Blasio, Palacio, and Banks, 2017). The first plan he implemented sought to stem the number of homeless people seeking shelter by aggressively expanding anti-eviction measures to provide legal and emergency rental assistance to keep families in their homes and out of the shelter system. The second plan aims to streamline some of the city’s preventive services by reorganizing the homeless-services bureaucracy. The third strategy aims to build 90 new shelters in the next 5 years, ending the city’s reliance on expensive commercial hotels and private apartments to shelter its homeless people. In addition, New York City has a combination of mandatory and voluntary inclusionary zoning policies that require a certain share of new construction to be affordable to those with lower incomes (New York City Department of City Planning).

While policies like right-to-shelter and inclusionary zoning are aimed at reducing the unsheltered and total homeless populations, respectively, New York City has several other policies and programs that may exacerbate the problem. For example, New York has building codes that place height restrictions on residential buildings, thereby reducing the number of high-rises and affordable housing units that can be built (Bui, Chaban, and White, 2016). In this regard, Mayor de Blasio aims to build or preserve 200,000

units of affordable housing. Washington, DC, has been identified as an appropriate analog due to its physical proximity to New York City, as well as similar weather and climate conditions. In addition, Washington, DC, is also a “right-to-shelter” city, with potential parallels in existing strategies and policies.

San Francisco ranked fifth in the count (4,543) and rate (49.7 homeless people per 10,000 population) of unsheltered homelessness in 2017 in the United States. In 2016, Mayor Lee launched the Department of Homelessness and Supportive Housing to address the decade-long stagnation of the number of homeless people in San Francisco. The new data-driven, evidence-based Homelessness Response System coordinates and aligns all the interventions in San Francisco that provide support and services including prevention, Housing First—a system focused on permanent supportive housing and a rapid rehousing model, client-focused services of care, and short-term rental subsidies and support services to help people exit homelessness (City and County of San Francisco Department of Homelessness and Supportive Housing, 2017). During the implementation of the Housing First approach, however, the number of shelter beds and daytime hours has been reduced during the same time frame, which some

| 114

believe is related to an increase in the number of homeless people on the streets. Recently, the city has started to build new shelters, called Navigation Centers, to tackle this particular issue.

San Francisco, like New York City, has an inclusionary zoning program designed to address growing housing affordability (City and County of San Francisco Mayor’s Office of Housing and Community Development, 2018). This program is designed to incentivize and/or fund the construction of affordable housing units. Most residents are also covered by rent control efforts on behalf of the city, limiting the legal amount that a landlord may increase tenants’ rents annual y (San Francisco Tenants Union, 2018).

Such policies work to reduce the number of San Franciscans on the street without appropriate shelter.

The city’s current vagrancy and “quality of life” laws, however, are designed to prevent homelessness through more punitive interventions that may do little to reduce the total and unsheltered homeless populations (City and County of San Francisco Board of Supervisors, 2016). In recent years, San Francisco has worked to break up large, long-standing tent encampments of unsheltered homeless people within the city (Davila, 2018). These efforts are believed to reduce street homelessness while improving the quality of life of all San Franciscans.

Seattle has seen increasing issues related to the availability of affordable housing, and the subsequent impact on the city’s homeless population is evident. In particular, Seattle has the third highest total (11,643) and unsheltered (5,485) homeless populations in the United States. The City of Seattle has outlined a three-pronged approach for addressing homelessness: (1) programs to prevent people from falling into homelessness, (2) affordable housing programs geared toward moving people from shelters, and (3) emergency shelter programs that provide safety to unsheltered homeless people while they search for housing accommodations (City of Seattle Homelessness Response Blog, 2018). Specifically, regarding the third approach, Seattle is supporting the use of “tiny house” emergency service villages that provide safe accommodations in tiny homes for those living in tent encampments. This model creates smal , permanent housing units for people who would otherwise be homeless, providing these individuals a chance to look for employment or to address underlying health issues. This form of intervention seeks to more directly affect the number of unsheltered homeless people on the street.

Housing affordability and availability are major issues in Seattle. The city’s Multifamily Tax Exemption Program is a zoning policy that offers an incentive to developers to set aside 20 to 25 percent of the units on their property as income- and rent-restricted, creating another opportunity for lower income individuals to avoid becoming homeless (City of Seattle Office of Housing, n.d.). More recently, Seattle has relaxed policies regarding mandatory parking requirements in new multifamily construction projects, which may reduce the costs of developing additional lower-income housing options (Waller, 2018). Currently, the Seattle City Council is in the process of finalizing a suite of reforms related to mandatory housing affordability and designed to relax stringent zoning standards, al owing for the construction of denser housing and more multifamily units (Lloyd, 2019).

The policies and regulations outlined in this section (and exhibit H-2) represent a comprehensive but not conclusive list. Our research, however, indicates that a future mixed-methods study on homelessness in New York City, San Francisco, and Seattle would shed additional light on the relationships between policy and homelessness at the local level. A high-level research plan for such a study is outlined in the following section.

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Exhibit H-2 | The Presence of Local Policies and Regulations Regarding Homelessness and Housing in New York City, San Francisco, and Seattle

Local Policies and Regulations

New York City San Francisco

Seattle

Policies Regulating the Presence and Conduct of Homeless People

Bus-out





Encampment zone/tent city





Local homelessness ordinances (for example, sit/lie restrictions)







Right-to-shelter



Safe parking accommodations for homeless people living in automobiles



Development and Zoning Regulations

Development impact fees



Height restrictions on development







Inclusionary zoning policies





Local employment and wage requirements for construction



Minimum unit set-asides for low income



Parking requirements for developing multifamily housing







Percentage of land zoned for multifamily housing







Requirements for energy efficient construction, such as LEED







Stormwater runoff requirements





Tiny home zoning allowances





Transit-oriented development policies







Units per developable acre limitations





Funding and Taxation

Local property tax policies for rental units







Local tax on land sales



Per capita annual funding for local affordable housing trust funds



Regulations Associated with Housing Costs

Local rent controls/ordinances







Requirements for hazard/flood/earthquake insurance

FUTURE STUDY ON THE RELATIONSHIP BETWEEN LOCAL POLICIES AND

HOMELESSNESS

This section outlines the plans for a future mixed-methods study examining the relationship between homelessness and local policies and regulations in New York City, San Francisco, and Seattle. The proposed study would minimally include the following components—

1. Review of local data sources and quantitative data analysis.

2. Interviews with local stakeholders and qualitative data analysis.

A rigorous qualitative component would complement the findings of any quantitative data analysis with insights from people with boots-on-the-ground experience with homelessness. For each of the cities, the study should continue to review and assess the availability of local and regional data sources overviewed in exhibit H-2. The process to identify potential data sources, however, may include preliminary data analysis, additional review of literature, and a public records search.

The proposed qualitative study would involve a series of interviews with local stakeholders to examine (1) their perspectives on the factors contributing to homelessness in their area, (2) the activities and policies implemented, and (3) the estimated effectiveness of these activities and lessons learned. Such interviews will provide important information about how local stakeholders view the factors associated with homelessness, as well as the policies designed to alleviate it. These interviews can provide insights into promising practices that could be adopted more widely to reduce homelessness in other areas.

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Document Outline

- Acknowledgments

- Foreword

- Executive Summary

- Introduction

- Understanding the Problem

- Literature Review on Homelessness

- Examining Factors Associated with Homelessness

- Relationship Between Housing Market Conditions and Homelessness

- Relationship Between Economic Market Conditions and Homelessness

- Relationship Between Local Policy Conditions/Safety Net and Homelessness

- Relationship Between Demographic Composition and Homelessness

- Relationship Between Climate Conditions and Homelessness

- Data

- Dependent Variables: HUD Point-in-Time Counts

- Independent Variables: Predictors of Homelessness

- Housing Market Variables

- Economic Variables

- Safety Net Variables

- Demographic Variables

- Climate Variables

- Constructing the Dataset at the Continuum of Care Level

- Empirical Strategy

- Descriptive Analysis

- Variable Selection

- Statistical Model and Analysis

- National Model of Homelessness

- Total Homelessness

- Sheltered Homelessness

- Unsheltered Homelessness

- Subgroup Analysis

- Urbanicity

- Largely Urban CoCs

- Total Homelessness

- Sheltered Homelessness

- Unsheltered Homelessness

- Largely Suburban Continuums of Care

- Total Homelessness

- Sheltered Homelessness

- Unsheltered Homelessness

- Largely Rural Continuums of Care

- Total Homelessness

- Sheltered Homelessness

- Unsheltered Homelessness

- Tight, High-Cost Rental Markets

- Total Homelessness

- Sheltered Homelessness

- Unsheltered Homelessness

- Unsheltered Homelessness on the West Coast

- Conclusion

- Total Homelessness

- Sheltered Homelessness

- Unsheltered Homelessness

- Limitations Of The Study

- Next Steps and Plans for Future Research

- Concluding Remarks

- Appendix A: Research Questions

- Appendix B: Other Data Sources Considered

- Appendix C: Creation of Continuum of Care-to-County Crosswalk

- Continuum of Care Missing from HUD-Provided Crosswalk

- Continuum of Care and County Aggregation Assumptions

- Other Geographic Assumptions

- Appendix D: Additional Tables

- Additional Summary Statistics and Descriptive Analysis

- National Model Additional Regression Tables

- Urbanicity Subgroup Additional Regression Tables

- Tight, High-Cost Rental Market Subgroup Additional Regression Tables

- West Census Region Subgroup Regression Tables

- Appendix E: Variable Selection Process Results

- Appendix F: Summary Statistics for Tight, High-Cost Rental Markets

- Appendix G: Summary Statistics for West Coast Continuums of Care

- Appendix H: Local Policy Scans

- Homelessness and Local Policies in New York City, San Francisco, and Seattle

- Future Study on the Relationship Between Local Policies and Homelessness

- References

Table of Contents

Acknowledgments

Foreword

Executive Summary

Introduction

Understanding the Problem

Literature Review on Homelessness

Examining Factors Associated with Homelessness

Relationship Between Housing Market Conditions and Homelessness

Relationship Between Economic Market Conditions and Homelessness

Relationship Between Local Policy Conditions/Safety Net and Homelessness

Relationship Between Demographic Composition and Homelessness

Relationship Between Climate Conditions and Homelessness

Data

Dependent Variables: HUD Point-in-Time Counts

Independent Variables: Predictors of Homelessness

Housing Market Variables

Economic Variables

Safety Net Variables

Demographic Variables

Climate Variables

Constructing the Dataset at the Continuum of Care Level

Empirical Strategy

Descriptive Analysis

Variable Selection

Statistical Model and Analysis

National Model of Homelessness

Total Homelessness

Sheltered Homelessness

Unsheltered Homelessness

Subgroup Analysis

Urbanicity

Largely Urban CoCs

Total Homelessness

Sheltered Homelessness

Unsheltered Homelessness

Largely Suburban Continuums of Care

Total Homelessness

Sheltered Homelessness

Unsheltered Homelessness

Largely Rural Continuums of Care

Total Homelessness

Sheltered Homelessness

Unsheltered Homelessness

Tight, High-Cost Rental Markets

Total Homelessness

Sheltered Homelessness

Unsheltered Homelessness

Unsheltered Homelessness on the West Coast

Conclusion

Total Homelessness

Sheltered Homelessness

Unsheltered Homelessness

Limitations Of The Study

Next Steps and Plans for Future Research

Concluding Remarks

Appendix A: Research Questions

Appendix B: Other Data Sources Considered

Appendix C: Creation of Continuum of Care-to-County Crosswalk

Continuum of Care Missing from HUD-Provided Crosswalk

Continuum of Care and County Aggregation Assumptions

Other Geographic Assumptions

Appendix D: Additional Tables

Additional Summary Statistics and Descriptive Analysis

National Model Additional Regression Tables

Urbanicity Subgroup Additional Regression Tables

Tight, High-Cost Rental Market Subgroup Additional Regression Tables

West Census Region Subgroup Regression Tables

Appendix E: Variable Selection Process Results

Appendix F: Summary Statistics for Tight, High-Cost Rental Markets

Appendix G: Summary Statistics for West Coast Continuums of Care

Appendix H: Local Policy Scans

Homelessness and Local Policies in New York City, San Francisco, and Seattle

Future Study on the Relationship Between Local Policies and Homelessness

References