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

In 2023, about 3.6 percent of the estimated number of homeless veterans in the United States were Native American. In comparison, 57 percent were white and 31.3 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.

Quarterly Technical Performance Report (TPR) from HVRP grant recipients. This includes demographic and employment characteristics of HVRP participants aggregated at the grant recipient-level. The TPR is an Excel-based data collection and reporting tool for grant recipients to enter participant and project information. The workbook allows VETS’ Grant Officer’s Technical Representatives (GOTR) to monitor performance and enables the aggregation and analysis of grant recipient data to assess the effectiveness of grant programs and submit reports to Congress. Grant recipients must submit the TPR and its accompanying TPN for a specific grant award for all twelve quarters in the grant Period of Performance (PoP). The TPR Excel workbook is comprised of six worksheets: 1. Planned Goals – Recipient input required 2. Tech Perf Report – Report / minimal recipient input required 3. New Enrollment Entry – Recipient input optional 4. Participant Info – Recipient input required 5. Demographics Summary – Report / No input required 6. Goals v. Actual – Report / No input required

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.

This brief uses data from the 2009 and 2010 Annual Homeless Assessment Reports (AHAR) to Congress. The reports were sponsored by the Department of Housing and Urban Development and the Department of Veterans Affairs.

Policy Map Point-in-time estimates of homeless veterans population in January 2020, HUD, Delaware Statewide CoC, DE (Continuum of Care)

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 are separate files but are now combined.

Information updated as of 2/06/2025.

Financial overview and grant giving statistics of National Coalition for Homeless Veterans

ObjectiveWhile Veteran homelessness has steadily declined over the last decade, those who continue to be unhoused have complex health and social concerns. Housing instability interferes with access to healthcare, social services, and treatment adherence. Preventing unwanted housing transitions is a public health priority. This study is the first phase of a larger research agenda that aims to test the acceptability and feasibility of smartphone-enabled data collection with veterans experiencing homelessness. In preparation for the development of the smartphone data collection application, we utilized ethnographic methods guided by user-centered design principles to inform survey content, approach to recruitment and enrollment, and design decisions.MethodsWe used a case study design, selecting a small sample (n = 10) of veterans representing a range of homelessness experiences based on risk and length of time. Participants were interviewed up to 14 times over a 4-week period, using a combination of qualitative methods. Additionally, 2 focus group discussions were conducted. Interviews were audio-recorded and transcribed. Data were synthesized and triangulated through use of rapid analysis techniques.ResultsAll participants had experience using smartphones and all but one owned one at the time of enrollment. Participants described their smartphones as “lifelines” to social network members, healthcare, and social service providers. Social relationships, physical and mental health, substance use, income, and housing environment were identified as being directly and indirectly related to transitions in housing. Over the course of ~30 days of engagement with participants, the research team observed dynamic fluctuations in emotional states, relationships, and utilization of services. These fluctuations could set off a chain of events that were observed to both help participants transition into more stable housing or lead to setbacks and further increase vulnerability and instability. In addition to informing the content of survey questions that will be programmed into the smartphone app, participants also provided a broad range of recommendations for how to approach recruitment and enrollment in the future study and design features that are important to consider for veterans with a range of physical abilities, concerns with trust and privacy, and vulnerability to loss or damage of smartphones.ConclusionThe ethnographic approach guided by a user-centered design framework provided valuable data to inform our future smartphone data collection effort. Data were critical to understanding aspects of day-to-day life that important to content development, app design, and approach to data collection.

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.

Financial overview and grant giving statistics of 4th District Riders for Homeless Veteran Inc.

Quarterly Technical Performance Report from Homeless Veterans' Reintegration Program (HVRP) grantees. The reports have information on demographic characteristics, and employment and earnings outcomes for the clients served by grantees. The report also has information at the individual-level and aggregated to the grantee-level.

These reports are a series of Excel spreadsheets collected from 150+ grantees from around the US through different sharepoint sites.

Context

This database contains the data reported in the Annual Homeless Assessment Report to Congress (AHAR). It represents a point-In-time count (PIT) of homeless individuals, as well as a housing inventory count (HIC) conducted annually.

The data represent the most comprehensive national-level assessment of homelessness in America, including PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

These data can be trended over time and correlated with other metrics of housing availability and affordability, in order to better understand the particular type of housing resources that may be needed from a social determinants of health perspective.

HUD captures these data annually through the Continuum of Care (CoC) program. CoC-level reporting data have been crosswalked to county levels for purposes of analysis of this dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_hud_pit_homelessness

Sample Query

What has been the change in the number of homeless veterans in the state of New York’s CoC Regions since 2012? Determine how the patterns of homeless veterans have changes across the state of New York

homeless_2018 AS ( SELECT Homeless_Veterans AS Vet18, CoC_Name
FROM bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc WHERE SUBSTR(CoC_Number,0,2) = "NY" AND Count_Year = 2018 ),

veterans_change AS ( SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange FROM homeless_2018 JOIN homeless_2012 ON homeless_2018.CoC_Name = homeless_2012.CoC_Name )

SELECT * FROM veterans_change

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"

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/

Financial overview and grant giving statistics of Midwest Shelter for Homeless Veterans Inc.

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 Homeless Veterans Services of Dallas Inc.

This dataset comes from Pierce County's Homeless Management Information System (HMIS). HMIS is a local information technology system used to collect client-level data and data on the provision of housing and services to homeless individuals and families and persons at risk of homelessness.

Federal and State funders require any Continuum of Care receiving federal and state homeless funds use a locally-administered data system to record and analyze homeless information. To comply with this requirement Pierce County has contracted with Bowman Systems L.L.C. for the use of the ServicePoint HMIS database.