Quantitative and Qualitative Analyses of Unsheltered Homelessness at the Community Level

Since 2015, the number of people experiencing unsheltered homelessness in the United States — a population especially vulnerable to the economic, housing, and social effects of homelessness — has risen continuously. Although rates of unsheltered homelessness have been increasing at the national level, at the local level, some communities are experiencing fluctuating and decreasing trends that require closer observation. To explain the housing conditions associated with the trends and local strategies to address them, HUD’s Office of Policy Development and Research conducted a study titled “Implementing Approaches to Address Unsheltered Homelessness.” The report was released in December 2020 and is divided into two parts: a quantitative study analyzing the relationship between homelessness and the housing market, and qualitative case studies of three communities — Richmond, Virginia; Montgomery County, Maryland; and San Diego County, California — and their strategies for addressing unsheltered homelessness.

Research Methods

To review housing conditions occurring alongside unsheltered homelessness, researchers completed the quantitative study in two steps. First, the researchers gathered HUD point-in-time (PIT) count data to analyze the trends in unsheltered homelessness levels for Continuums of Care (CoCs) with consistent reporting between 2015 and 2019 and categorized each CoC into one of four groups: steady increasing, fluctuating increasing, fluctuating decreasing, or steady decreasing. Next, the researchers determined the differences between CoC groups with increasing levels of unsheltered homelessness and those experiencing decreasing levels of homelessness using three types of data: Housing Inventory Count (HIC) data to determine the availability of resources, System Performance Measures (SPMs) to gauge the performance of homeless assistance systems, and data on local housing market conditions to describe housing market characteristics.

For the qualitative study, the researchers selected the Greater Richmond CoC in Virginia, Montgomery County’s CoC in Maryland, and San Diego County’s Regional Task Force on the Homeless (RTFH) in California as case studies implementing unique programs or initiatives to address unsheltered homelessness. They interviewed multiple stakeholders, including nonprofit partners, CoC staff, and participants from each program, to collect information across five domains: context and policies affecting implementation; program implementation; supporting partnerships; successes and challenges; and recommendations for improvement, expansion, and sustainability. Additional data included local-level demographic and programmatic information about people receiving services from the CoCs. The researchers synthesized these data with local client demographic data to create community-specific profiles detailing the characteristics of program participants; the intensity of participation; and primary program outcomes, including transitions to permanent housing.

Key Findings of the Quantitative Study

To compare the four CoC trend categories, the study looked at per capita rates of homelessness in each community using a measure of total people experiencing unsheltered homeless per 10,000 population. Of the 336 CoCs analyzed, approximately 54 percent experienced an increase in counts and 45 percent experienced a decrease. CoCs in the steady increasing group initially had lower PIT counts until they reported a major increase in unsheltered homelessness in 2019, whereas steady decreasing CoCs started with much higher rates of unsheltered homelessness before experiencing a decline. The study states that during intermediate counts, levels of decrease in the two fluctuating categories were larger than the levels of increase in counts. Across the increasing and fluctuating groups, the data indicate that areas experiencing increasing levels of unsheltered homelessness were outside of major cities, refuting the misconception that the rise in homelessness is primarily a function of urbanicity.

The study also compared the four categories’ local housing markets and level of resources as well as the performance of their homeless assistance systems. CoCs with increasing counts often were in areas with tight rental markets and higher home median values, median rental prices, and fair market rents than were CoCs with decreasing counts. Fluctuating CoCs had the highest level of available resources, which was measured in average bed count per capita. Finally, the study factored in street outreach, destination at exit, and length of stay in assistance as SPMs for each trend group. Steady increasing CoCs had lower numbers for successful outreach and worse levels of successful exits to permanent housing, whereas the steady decreasing CoCs performed better by these measures.

New Approaches to Conventional Homeless Assistance Produce Positive Outcomes

The CoCs from each case study selected for the qualitative analysis had a new approach to conventional housing assistance programs that resulted in sustainable outcomes that are potentially useful for other CoCs. The Montgomery County CoC’s and Greater Richmond CoC’s approaches focused on permanent housing as a solution to unsheltered homelessness. By implementing a Housing First, two-tiered permanent supportive housing (PSH) program model and perhaps more importantly, committing additional locally-funded PSH units to, Montgomery County’s CoC achieved a 43 percent decrease in its PIT count of unsheltered homelessness. Clients first underwent a vulnerability assessment that included supplemental factors along with the more standard Vulnerability Index-Service Prioritization Decision Assistance Tool (VI-SPDAT), which assigns a score for the risk and priority of individuals experiencing homelessness. Those with high vulnerability scores were placed in high-intensity PSH and those with lower vulnerability scores were placed in low-intensity PSH, a process that, compared with the previous model, more efficiently matched the most vulnerable clients with the most appropriate resources.

The Greater Richmond CoC was experiencing a shortage in PSH when it decided to switch to Rapid Re-Housing (RRH) as the primary housing assistance for people experiencing unsheltered homelessness. The flexible program offered a range of services tailored to clients’ specific needs and was often supplemented by private, and non-CoC funding sources to cover longer term subsidy after the first 24 months. These results suggest that tailoring the RRH program to target the most vulnerable was a cost-effective alternative to PSH in the Richmond housing market.

Amid the urgent need to limit the spread of COVID-19 amongst those experiencing unsheltered homelessness and declining shelter capacity as key components of the homeless assistance system implemented social distancing protocols, San Diego launched the Temporary Lodging program and deployed of temporary shelter capacity at the San Diego Convention Center. The Temporary Lodging Program utilized three hotels as non-congregate shelter for asymptomatic adults with underlying health conditions, and the Operation Shelter to Housing program, which consisted of three socially distanced congregate areas at the city convention center. Outreach workers first offered necessities and COVID-19 prevention kits that included face masks and hygiene supplies, which helped CoC staff establish new, trustworthy relationships with people experiencing unsheltered homelessness. Forty-eight percent of the congregate shelter clients returned to unsheltered homelessness, whereas only 17 percent of the non-congregate clients exited to unsheltered homelessness.

Policies to Prioritize Scarce Resources

Some stakeholders highlighted the value of proactive assessing how client vulnerability within the unsheltered population fit within the context of available outreach and permanent housing resources. Respondents in the study of Greater Richmond’s RRH Program believed RRH to be the best option for vulnerable people experiencing homelessness despite RRH not traditionally being used for high-need individuals. To implement this shift, Richmond prioritized communication and cooperation not only between the CoC and service providers but also with landlords and private funders. Montgomery County’s CoC previously assumed that chronic homelessness was positively correlated with vulnerability; however, a review of data from 2016 to 2019 revealed that the relationship was not uniform. Instead of prioritizing clients by length of homelessness, the CoC supplemented the VI-SPDAT with an acuity score derived from nine metrics examining a person’s physical and mental acuity, veteran status, and vulnerability to exploitation. The CoC placed individuals in a high-intensity PSH unit if their score was higher than 13 and a low intensity PSH unit if their score was between 8 and 12; if their score was less than 8, they were matched with non-PSH housing.

Before the pandemic, San Diego prioritized clients for housing and services using the VI-PSDAT score and degree of chronic homelessness. To ensure faster client flow, San Diego’s Temporary Lodging Program first placed individuals in tiered cohorts based on one’s engagement, willingness to accept resources, and possession of documentation such as identification and Social Security cards. The screening classified clients meeting all three criteria as “green status,” those who lacked documentation but were interested and engaged as “yellow status,” and those uninterested and unengaged as “red status.” The decision to deploy Homeless Outreach Teams in new areas, however, is what some stakeholders believe increased system fairness and client willingness to participate in housing assistance services.

Lessons Learned and Future Research

The report uses the quantitative study to emphasize the need for a more nuanced analysis of the relationship between homelessness and housing market conditions. Cities can often view unsheltered homelessness as fundamentally apart from sheltered homelessness, which can shape how communities respond overall, but the likely influence of market conditions and available resources suggest the opportunity for broader thinking. The three case studies reveal that flexibility in restructuring programs can benefit other communities in two ways. First, tiering programs based on a hierarchy of needs, while also iterating and customizing those assessment tools when appropriate, can ensure that people receive the appropriate level of care. Second, having more options available within a homeless assistance system, either to respond to different market conditions or public health considerations can offer clients an opportunity to access the most appropriate and effective resources.