The Department of Housing and Urban Development (HUD) found homelessness in the U.S. grew 3 years in a row (2017-2019). Rising homelessness in metropolitan areas drove the increases.
We found HUD’s count likely underestimated the homeless population. Organizations across the U.S. provide data for this inherently difficult count. HUD could improve its instructions to them, which in turn could improve data quality.
In addition, our statistical analysis found median rent increases of $100 a month were associated with a 9% increase in homelessness in the areas we examined.
We recommended ways for HUD to improve measurement of homelessness.
Unsheltered Homelessness in San Francisco, California
Street corner with blankets and furniture
What GAO Found
Data collected through the Point-in-Time (PIT) count—a count of people experiencing homelessness on a single night—have limitations for measuring homelessness. The PIT count is conducted each January by Continuums of Care (CoC)—local homelessness planning bodies that apply for grants from the Department of Housing and Urban Development (HUD) and coordinate homelessness services. The 2019 PIT count estimated that nearly 568,000 people (0.2 percent of the U.S. population) were homeless, a decline from the 2012 count of about 621,500 but a slight increase over the period's low of about 550,000 in 2016. While HUD has taken steps to improve data quality, the data likely underestimate the size of the homeless population because identifying people experiencing homelessness is inherently difficult. Some CoCs' total and unsheltered PIT counts have large year-over-year fluctuations, which raise questions about data accuracy. GAO found that HUD does not closely examine CoCs' methodologies for collecting data to ensure they meet HUD's standards. HUD's instructions to CoCs on probability sampling techniques to estimate homelessness were incomplete. Some CoC representatives also said that the assistance HUD provides on data collection does not always meet their needs. By strengthening its oversight and guidance in these areas, HUD could further improve the quality of homelessness data.
To understand factors associated with homelessness in recent years, GAO used PIT count data to conduct an econometric analysis, which found that rental prices were associated with homelessness. To mitigate data limitations, GAO used data from years with improved data quality and took other analytical steps to increase confidence in the results. CoC representatives GAO interviewed also identified rental prices and other factors such as job loss as contributing to homelessness.
Estimated Homelessness Rates and Household Median Rent in the 20 Largest Continuums of Care (CoC), 2018
Note: This map shows the 20 largest Point-in-Time counts by CoC in 2018. GAO estimated 2018 homelessness rates because the U.S. Census Bureau data used to calculate these rates were available up to 2018 at the time of analysis. GAO used 2017 median rents (in 2018 dollars) across all unit sizes and types.
Why GAO Did This Study
Policymakers have raised concerns about the extent to which recent increases in homelessness are associated with the availability of affordable housing. Moreover, counting the homeless population is a longstanding challenge. GAO was asked to review the current state of homelessness in the United States. This report examines (1) efforts to measure homelessness and HUD's oversight of these efforts and (2) factors associated with recent changes in homelessness.
GAO analyzed three HUD data sources on homelessness and developed an econometric model of the factors influencing changes in homelessness. GAO also conducted structured interviews with 12 researchers and representatives of 21 CoCs and four focus groups with a total of 34 CoC representatives responsible for collecting and maintaining homelessness data. CoCs were selected for interviews and focus groups to achieve diversity in size and geography. GAO also visited three major cities that experienced recent increases in homelessness.
GAO recommends that HUD (1) conduct quality checks on CoCs' data-collection methodologies, (2) improve its instructions for using probability sampling techniques to estimate homelessness, and (3) assess and enhance the assistance it provides to CoCs on data collection. HUD concurred with the recommendations.
Recommendations for Executive Action
|Department of Housing and Urban Development||1. HUD's Office of Special Needs Assistance Programs should conduct quality assurance checks on the PIT count methodology data it requires CoCs to submit and take actions as appropriate to ensure that HUD's standards for conducting valid and reliable PIT counts are met. (Recommendation 1)|
|Department of Housing and Urban Development||2. HUD's Office of Special Needs Assistance Programs should provide more detailed instructions on using probability sampling techniques to complete the PIT count, such as by updating its <i>Point-in-Time Count Methodology Guide</i> to instruct CoCs on reporting measures of error and bias in PIT count results. (Recommendation 2)|
|Department of Housing and Urban Development||3. HUD's Office of Special Needs Assistance Programs should assess and enhance the usefulness of its assistance to CoCs' data collection efforts. (Recommendation 3)|