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 2023, the estimated number of homeless people in the United States was highest in California, with about ******* homeless people living in California in that year.

In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

Between 2022 and 2023, New Hampshire had the highest positive percentage change in the estimated number of homeless people in the United States, with the number of homeless people living in New Hampshire increasing by **** percent within this time period.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

In 2023, there were about ****** homeless youth living in California, the most out of any U.S. state. New York had the second-highest number of homeless youth in that year, at *****.

In the United States, California had the highest amount of homeless children and youths enrolled in their schools for the school year 2019-20 at ******* students. North Carolina had ****** recorded homeless students for the same year. A homeless child or youth is someone who lacks a fixed residence or is forced to share housing due to economic hardship.

This study examines the spatial patterns of homelessness and resources for the homeless population in Louisville, KY with the goal of identifying where homeless populations are located in relation to resources. Working with census data and some of the resources for the homeless, this study uncovers the realities that the homeless face in different parts of the city. This research research was made as a senior thesis for the University of Louisville's department of Geographic and Environmental Sciences. Table 1. Income and Poverty between the United States and Louisville/Jefferson County metro government, Kentucky in 2019 (United States Census Bureau 2021)Homeless people are thought of as less than full citizens. Whether the rest of the city's people agree or disagree, they are citizens, and should have rights to the city as much as everyone else. The opioid crisis, unmanaged mental illnesses, lack of employment, and other issues like limitations on affordable housing have increased the population of homeless people in Louisville in recent years (Reed 2021). More than 1.5 million children experience homelessness in the United States (Poverty USA 2019). The poverty rate in Louisville, Kentucky is 15.9%, and 1 in 10 renters were facing eviction as of 2019. The 2019 Point In Time Count shows that on a randomly picked night in Louisville, 1071 of the city's people are experiencing homelessness, which is an increase of 15% from the 2018 count (Coalition for the Homeless 2019). The previous data compared to the count for 2020 of 1102 people, shows a trend in increasing homeless population (Coalition for the Homeless 2020).

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

In 2022, about ******* homeless people lived outside of homeless shelters in California, the most out of all U.S. states.

Novel and comprehensive cross-sectional datasets were developed to document and measure city level homeless policies across issue area. The dataset is comprised of the 100 largest cities in the United States, including FIPS identifiers and matched Continuum of Care (CoC) level identifiers by CoC number associated with the city. The datasets include city-level homeless policies in the 100 largest cities across the issues of homeless plans; housing plans with mentions of homelessness; homeless outreach teams; and the role of sanitation in homeless policy. Each dataset includes sub-codes to evaluate the governance structure and policy goals of each type of city-level homeless policy. Primary data were collected in 2021 and 2022. Please see the attached publications for complete methodology and data collection procedures for each policy type.

Abstract copyright UK Data Service and data collection copyright owner. A comparative study of the causes of new episodes of homelessness among people aged 50 or more years was undertaken in Boston, Massachusetts (USA), Melbourne, Australia, and four English cities. The aims were to make a substantial contribution to the predominantly American debate on the causes of homelessness, and to make practice recommendations for the improvement of prevention. The study had several objectives. It aimed to collect information about the antecedents, triggers and risk factors for becoming homeless in later life and about the national and local policy and service contexts. Furthermore, the researchers aimed to analyse and interpret the findings with reference to an integrated model of the causes of homelessness that represented structural and policy factors, including housing, health and social service organisation and delivery factors, and personal circumstances, events, problems and dysfunctions. The aim was to do this collaboratively, by drawing on the project partners' experience and knowledge. Finally, it was hoped to develop recommendations for housing, primary health care and social welfare organisations for the prevention of homelessness. This was to be done by identifying the common sequences and interactions of events that precede homelessness and their markers (or 'early warning' indicators) and by holding workshops in England with practitioners and their representative organisations on new ways of working. By the study of contrasting welfare and philanthropic regimes in a relatively homogeneous category of homeless incidence (i.e. recent cases among late middle-aged and older people), it was hoped that valuable insights into the relative contributions of the policy, service and personal factors would be obtained. The study focused on older people who had recently become homeless, purposely to gather detailed and reliable information about the prior and contextual circumstances. To have included people who had been homeless for several years would have reduced the quality of the data because of 'recall' problems. Users should note that data from the Australian sample for the study are not included in this dataset. Main Topics: The data file includes information about the English respondents and those from Boston. It was compiled in two stages. The first stage involved each project partner entering the pre-coded responses into the file. All partners then identified themes and created codes for the open-ended responses, and the resulting variables were added. Data quality-control procedures included blind checks of the data coding and keying. The first 200 variables pertain to information collected from the respondents. They comprise descriptive variables of the circumstances prior to homelessness, including housing tenure during the three years prior to the survey, previous homelessness, employment history, income, health and addiction problems, and contacts with family, friends and formal services. The respondents were asked to rate whether specific factors were implicated in becoming homeless, and where appropriate, a following open-ended question sought elaboration. The remaining variables comprise information collected from the respondents' 'key workers' about their understanding of the events and states that led to their clients becoming homeless. No sampling frame was available. The sample profiles have been compared with those of all homeless people (not just the recently homeless) in the study locations, most effectively in London and Boston. No gross biases were revealed. The samples represent a large percentage of the clients who presented to the collaborating organisations during the study period and who gave their informed consent to participate. Agreed definitions of homelessness were: sleeping on the streets or in temporary accommodation such as shelters; being without accommodation following eviction or discharge from prison or hospital; living temporarily with relatives or friends because the person has no accommodation, but only if the stay had not exceeded six months, and the person did not pay rent and was required to leave. People who had been previously homeless were included in the survey if they had been housed for at least 12 months prior to the current episode of homelessness. Face-to-face interview Self-completion the 'key workers' (case managers) completed questionnaires about their assessments of the respondents’ problems and of the events and states that led to homelessness. Further clarifications and checks were made by telephone.

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

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. Table Name Indicator Name Universe Timeframe Source Race Notes Source URL

homeownership_pct % Homeownership Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

renters_pct % Renters Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

mean_home_value Mean Home Value Households 2021 Public Use Microdata Sample (PUMS) All races are Non-Hispanic LA County eGIS-Demography

accepted_mortgage_pct Accepted Mortgate Rate Mortgage Applications 2021 Home Mortgage Disclosure Act HMDA categories - https://files.consumerfinance.gov/f/documents/cfpb_reportable-hmda-data_regulatory-and-reporting-overview-reference-chart-2019.pdf https://ffiec.cfpb.gov/data-browser/data/2021

rent_burden_pct Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

rent_burden_severe_pct Severely Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

eviction_per_100_hh Eviction Rate Renter Households 2014-2017 The Eviction Lab at Princeton University

https://data-downloads.evictionlab.org/#data-for-analysis/

homeless_count Homeless Count Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_homeless_pct % Homeless Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_county_pct % County Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

unable_pay_mortgage_rent% Delayed or Were Unable to Pay Mortgage or Rent in the past 2 Years Households 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

homeless_ever% Who Reported Ever Being Homeless or Not Having Their Own Place to Live or Sleep in the past Five Years Adults 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

Homeless and battered women's shelters compiled from Reference USA. Reference USA is an internet-based reference service from the Government Division of InfoGroup. This site was designed as a reference to government agencies. ReferenceUSAGov database contains more than 57 million US businesses, 320 million residents, and 855,000 healthcare providers. InfoGroup compiles information from public sources, including yellow pages and business white pages telephone directories, annual reports, federal government data, leading business magazines trade newsletters, major newspapers, industry and specialty directories, and postal service information. Over 350 database specialists make phone calls to verify information on business and healthcare providers in the database, placing in excess of 24 million phone calls annually.

This dataset provides information on individuals experiencing sheltered homelessness in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year. "Sheltered" homelessness refers to individuals residing in emergency shelter, safe haven, or transitional housing project types. This measure overlaps, but is different from, the Point in Time (PIT) Count (SD23 Measure EOA.E.1a), which is a snapshot of both sheltered and unsheltered homelessness on one night in January.

Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/2ejn-hrh2

Content

Included in this data set are data elements that will help the public identify agencies that are certified to operate programs for runaway and homeless youth. These programs are available to assist runaway and homeless youth in emergency situation and provide independent living skills for youth in transition. Data elements include the agency name, agency business address, phone number, website and type of program offered.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Zac Ong on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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

There are several forms, regulations and data associated with the Emergency Assistance (EA) Family Shelter Program for our business partners and constituents.

The California System Performance Measures (CA SPMs) are a series of metrics developed by the California Interagency Council on Homelessness (Cal ICH), pursuant to Health and Safety Code §50220.7, that help the state and local jurisdictions assess their progress toward preventing, reducing, and ending homelessness. All measures except for Measure 1b are generated using data from the state’s Homelessness Data Integration System. Measure 1b and Point in Time (PIT) Count data are sourced from each Continuum of Care’s PIT Count. Measure 1b and PIT Count data are not shown for 2021 because of irregularities in that year’s counts. For more information about the measures and how they are calculated, please see the California System Performance Measures Guide and Glossary: https://www.bcsh.ca.gov/calich/documents/california_system_performance_measures_guide.pdf

For more information about Measure 1b and PIT Count data, please see the Department of Housing and Urban Development’s website: https://www.hudexchange.info/programs/hdx/pit-hic.