In 2022, about ****** veterans living in California were homeless, the most out of all U.S. states.

In 2023, about *** percent of the estimated number of homeless veterans in the United States were Native American. In comparison, ** percent were white and **** percent were Black or African American.

In 2023, about **** percent of the estimated number of homeless veterans in the United States were male, compared to **** percent who were female.

In 2023, about ****** homeless veterans in the United States were estimated to be living outside a homeless shelter. In comparison, ****** homeless veterans were estimated to be living inside a homeless shelter in that year.

The Veterans' Employment and Training Service (VETS) tracks HVRP participant outcomes using data collected from grant recipients. VETS shares HVRP outcomes with the public. These data show the national level targets and outcomes for eleven (11) measures by Program Year (PY), including breakouts by sex, ethnicity, race, age, and grant population. The 11 measures are: Number of Participants Served Percentage of Total Participants Served Number of Exiters Percentage of Total Number of Exiters Number of Participants Co-Enrolled at American Job Centers (AJCs) Average Hourly Wage at Placement Placement Rate (exit-based) Placement Rate – Episodically Homeless (exit-based) Employment Rate 2nd Quarter After Exit Employment Rate 4th Quarter After Exit Median Earnings 2nd Quarter After Exit"

Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, gender, veteran status, and disability status.

This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives.

The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity were separate files but are now combined.

Information updated as of 7/29/2025.

Financial overview and grant giving statistics of Homeless Military Veterans

The Veterans' Employment and Training Service (VETS) tracks participant outcome measures for the HVRP program. Programmatic performance outcomes are collected from grant recipients through the Technical Performance Report (TPR) form. VETS shares HVRP outcomes with the public. These data show the national level targets and outcomes for eleven (11) measures by Program Year (PY), including breakouts by subpopulation, gender, ethnicity, race, and age. The 11 measures are: 1. Number of Participants Served 2. Percentage of Total Participants Served 3. Number of Exiters 4. Percentage of Total Number of Exiters 5. Number of Participants Co-Enrolled at American Job Centers (AJCs) 6. Average Hourly Wage at Placement 7. Placement Rate (exit-based) 8. Placement Rate – Episodically Homeless (exit-based) 9. Employment Rate 2nd Quarter After Exit 10. Employment Rate 4th Quarter After Exit 11. Median Earnings 2nd Quarter After Exit

This map shows the percent of population who are veterans. This pattern is shown by states, counties, and tracts. The data is from the most current American Community Survey (ACS) data from the U.S. Census Bureau. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty.The pop-up highlights the breakdown of veterans by gender.Zoom to any area in the country to see a local or regional pattern, or use one of the bookmarks to see distinct patterns of poverty through the US. Data is available for the 50 states plus Washington D.C. and Puerto Rico.The data comes from this ArcGIS Living Atlas of the World layer, which is part of a wider collection of layers that contain the most up-to-date ACS data from the Census. The layers are updated annually when the ACS releases their most current 5-year estimates. Visit the layer for more information about the data source, vintage, and download date for the data.

This dataset contains estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth provided by The U.S. Department of Housing and Urban Development. The estimates cover the period of years 2007-2017 and are at national, state and Continuums of Care (CoC) Point-In-Time (PIT) level.

Financial overview and grant giving statistics of Maine Homeless Veterans Alliance

Homelessness impacts entire families, with potential intergenerational consequences. Addressing family homelessness provides both immediate relief and long-term societal benefits. While various programs exist to mitigate homelessness, the United States (US) Department of Veterans Affairs’ (VA) Supportive Services for Veteran Families (SSVF) program offers a distinctive model for combating homelessness among veterans by supporting their families as well. We analyzed VA SSVF administrative data from 2014 to 2022, covering over 800,000 program entries from all SSVF beneficiaries in the US, to describe the sociodemographic profiles of SSVF veteran families—including children and adult family members of veterans. Families receiving SSVF assistance faced substantial economic and health-related challenges, including high unemployment, single-income dependency, and service-related disabilities. Children in these families represent a particularly vulnerable population, underscoring the need for targeted interventions to prevent long-term adverse outcomes. Our findings point to the role of SSVF in providing essential support for homeless veterans by also offering important services to their families. This broader approach offers lessons that may extend beyond the veteran community to address homelessness in individuals nationwide. Expanding coordinated, multi-agency approaches that build upon and modify the SSVF model could strengthen national efforts to reduce homelessness.

IntroductionMeasuring long-term housing outcomes is important for evaluating the impacts of services for individuals with homeless experience. However, assessing long-term housing status using traditional methods is challenging. The Veterans Affairs (VA) Electronic Health Record (EHR) provides detailed data for a large population of patients with homeless experiences and contains several indicators of housing instability, including structured data elements (e.g., diagnosis codes) and free-text clinical narratives. However, the validity of each of these data elements for measuring housing stability over time is not well-studied.MethodsWe compared VA EHR indicators of housing instability, including information extracted from clinical notes using natural language processing (NLP), with patient-reported housing outcomes in a cohort of homeless-experienced Veterans.ResultsNLP achieved higher sensitivity and specificity than standard diagnosis codes for detecting episodes of unstable housing. Other structured data elements in the VA EHR showed promising performance, particularly when combined with NLP.DiscussionEvaluation efforts and research studies assessing longitudinal housing outcomes should incorporate multiple data sources of documentation to achieve optimal performance.

This layer contains detailed Point in Time counts of homeless populations from 2019. This layer is modeled after a similar layer that contains data for 2018, 2013, and 2008.Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. Original source is the 2019 sheet within the "2007 - 2019 PIT Counts by CoCs.xlsx" (downloaded on 3/10/2020) file. Key fields were kept and joined to the CoC boundaries available from HUD's Open Data site.Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) 2019 counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

The County of Sonoma conducts an annual homeless count for the entire county. The survey data is derived from a sample of about 600 homeless persons countywide per year. The resulting information is statistically reliable only for the county as a whole, not for individual locations. The exception is the City of Santa Rosa, where the sample taken within the city is large enough to be predictive of the overall homeless population in that city.

This report outlines the key findings of the annual Point-In-Time (PIT) count and Housing Inventory Count (HIC) conducted in January of each year. Specifically, this report provides estimates of homelessness self-reported, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

Current link at the time of dataset creation: https://www.hudexchange.info/resource/4832/2015-ahar-part-1-pit-estimates-of-homelessness/

When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

In 2020, surveys conducted among people experiencing homelessness in King County, Washington found that 55 percent of those who were veterans suffered from post-traumatic stress disorder (PTSD), compared to 39 percent of those who were not veterans. This statistic shows the percentage of veteran and non-veteran homeless persons in King County, Washington who stated they had select health conditions as of 2020.

This map shows Point in Time counts of the overall homeless populations from 2019. Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. Original source is the 2019 sheet within the "2007 - 2019 PIT Counts by CoCs.xlsx" (downloaded on 3/10/2020) file. Key fields were kept and joined to the CoC boundaries available from HUD's Open Data site.Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) 2019 counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

Financial overview and grant giving statistics of Georgia Homeless Veteran Resources Incorporation