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.

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

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.

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.

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/

This report outlines the key findings of the 2014 Point-In-Time (PIT) and Housing Inventory (HIC) counts conducted in January 2014. Specifically, this report provides 2014 national, state, and CoC-level PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

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.

🇺🇸 United States

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 link provides access to Department of Veterans Affairs, Veterans Benefits Administration Media and Publications Fact Sheets, electronic brochures, videos and other publications.

This data represents all emergency medical services calls related to possible opioid abuse.

Locations represented are approximated and not the actual location of the incident. A computer generated randomized distance adjustment is applied to each incident location provided in the spreadsheet on the Open Data site to ensure data are anonymous. This approximated location data is also shown on the dashboard .

Opioid Abuse Probable

A call may be coded as “opioid abuse probable” for many reasons, such as

• Are there are any medical symptoms indicative of opioid abuse?
• Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)?
• Are there witnesses or patient statements made that point to opioid abuse?
• Is there any other evidence that opioid abuse is probable with the patient?

“Opioid abuse probable” is determined by Tempe Fire Medical Rescue Department’s Emergency medical technicians and paramedics on scene at the time of the incident.

Narcan/Naloxone Given

“Narcan/Naloxone Given” refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses.

Groups

“Groups” are used to determine if there are specific populations that have an increase in opioid abuse.

• The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well.
• The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempe’s Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well.
• The “unknown” group includes incidents where a patient is unable to answer or refuses to answer the demographic questions.

Gender

Patient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine.

Data Set History

Data sets were evolving in 2017 due to software upgrades and identifying new parameters to focus data collection on. The 2018 data will be a more comprehensive set of data that includes all the fields identified throughout 2017. Data sets may continue to evolve based on the needs of the community and healthcare trends.

Information about the data can be found at Data Documentation

The WSC program, funded through the Workforce Investment Act (WIA); National Emergency Grant (NEG) and other competitive grants, provides employment training and placement services for adults and dislocated workers, with a special emphasis on vulnerable populations, including persons with disabilities, veterans, homeless, English-language learners and older workers. Annual goals are established by California Employment Development Department and the US Department of Labor.