Table of homeless population by Year (for years 2009 through 2012)

The graph displays the top 15 states by an estimated number of homeless people in the United States for the year 2025. The x-axis represents U.S. states, while the y-axis shows the number of homeless individuals in each state. California has the highest homeless population with 187,084 individuals, followed by New York with 158,019, while Hawaii places last in this dataset with 11,637. This bar graph highlights significant differences across states, with some states like California and New York showing notably higher counts compared to others, indicating regional disparities in homelessness levels across the country.

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.

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.

"Ratio of Homeless Population to General Population in major US Cities in 2012. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. Unsheltered estimates are from 2011 except for Seattle and New York City (2012) and Chicago (2009). All General Population figures are from the 2010 U.S. Census enumeration."

List of centers where homeless people are provided with hot meals, showers, medical help and a place to sleep

This dataset includes the daily number of families and individuals residing in the Department of Homeless Services (DHS) shelter system and the daily number of families applying to the DHS shelter system.

This dataset includes data starting from 01/03/2021. For older records, please refer to https://data.cityofnewyork.us/d/dwrg-kzni

Point in Time Count Numbers for 2007 to 2018 from HUD, which counts the number of people experiencing homelessness at the federal, state, and local level. https://www.hudexchange.info/resource/5783/2018-ahar-part-1-pit-estimates-of-homelessness-in-the-us/

Comprehensive dataset containing 3,632 verified Homeless shelter businesses in United States with complete contact information, ratings, reviews, and location data.

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

This dataset includes Point-in-Time (PIT) data collected in Cambridge between 2012 and 2025. The PIT count is a count of sheltered and unsheltered homeless persons on a single night in January. The U.S. Department of Housing and Urban Development (HUD) requires that communities receiving funding through the Continuum of Care (CoC) Program conduct an annual count of homeless persons on a single night in the last 10 days of January, and these data contribute to national estimates of homelessness reported in the Annual Homeless Assessment Report to the U.S. Congress. This dataset is comprised of data submitted to, and stored in, HUD’s Homelessness Data Exchange (HDX).

This dataset includes basic counts and demographic information of persons experiencing homelessness on each PIT date from 2012-2025. The dataset contains three rows for each year, including one row for each housing type: Emergency Shelter, Transitional Housing, or Unsheltered. The dataset also includes housing inventory counts of the number of shelter and transitional housing units available on each of the PIT count dates.

Information about persons staying in emergency shelters and transitional housing units is exported from the Homeless Management Information System (HMIS), which is the primary database for recording client-level service records. Information about persons in unsheltered situations is compiled by first conducting an overnight street count of persons observed sleeping outdoors on the PIT night to establish the total number of unsheltered persons. Demographic information for unsheltered persons is then extrapolated by utilizing assessment data collected by street outreach workers during the 7 days following the PIT count.

this graph was created in PowerBi,R and Loocker studio:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff21bb298c472dbc4bed21ef6dda71d5e%2Fgraph1.jpg?generation=1715375554075996&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fea25ef2b4f987b1c37d85ce0b24180ce%2Fgraph2.jpg?generation=1715375559925771&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F69022bdb532b6b315c2ac7261d211868%2Fgraph3.png?generation=1715375565218326&alt=media" alt="">

This topic page studies available data and empirical evidence on homelessness, focusing specifically on how it affects people in high-income countries. Homeless people are among the most vulnerable groups in high-income countries.

You can read our topic page on Extreme Poverty if you are interested in a broader perspective on economic deprivation and a perspective beyond high-income countries.

Homeless people in the US What data is available? One of the most common ways to measure homelessness is through so-called 'point-in-time' counts of people who are sleeping in shelters or on the streets. These are figures that are intended to reflect the number of people who are homeless 'on any given night'.

The main source of point-in-time estimates in the US is the Department of Housing and Urban Development, which releases the Annual Homeless Assessment Report to Congress (AHARC). They calculate 'point-in-time' estimates by counting homeless people in late January of each year.

The main underlying sources of data used to produce the figures published in the AHARC are (i) registries from shelters and (ii) counts and estimates of sheltered and unsheltered homeless persons provided by care organizations, as part of their applications for government funding.

The counts from the care organizations (called 'Continuums of Care' in the US) come from active counts that are undertaken at the community level, by walking around the streets, using pre-established methodologies.1

In these figures, 'Sheltered Homelessness' refers to people who are staying in emergency shelters, transitional housing programs, or safe havens. 'Unsheltered Homelessness', on the other hand, refers to people whose primary nighttime residence is a public or private place not designated for, or ordinarily used as, a regular sleeping accommodation for people – for example, the streets, vehicles, or parks.2

The most recent rate of homelessness is calculated using ACS population estimates from the previous year, unless otherwise noted.

Data Source: HUD's Annual Homeless Assessment Report (AHAR) Point-in-Time (PIT) Estimates by State and American Community Survey (ACS) 1-Year Estimates

Why this MattersSafe, adequate, and stable housing is a human right and essential for the health and well-being of individuals, families, and communities.People who experience homelessness also struggle to maintain access to healthcare, employment, education, healthy relationships, and other basic necessities in life, according to the DC Interagency Council on Homelessness Strategic Plan.BIPOC populations are disproportionately affected by homelessness due to housing discrimination, mass incarceration, and other policies that have limited socioeconomic opportunities for Black, Latino, and other people of color.

The District's Response Strategic investments in proven strategies for driving down homelessness, including the Career Mobility Action Plan (Career MAP) program, operation of non-congregate housing, and expansion of the District’s shelter capacity.Homelessness prevention programs for at-risk individuals and families, such as emergency rental assistance, targeted affordable housing, and permanent supporting housing.Programs and services to enhance resident’s economic and employment security and ensure access to affordable housing.

Statistics about homelessness for every local authority in England. This includes annual data covering 2009-10 to 2017-18 based on CLG live table 784, known as the P1E returns. There are also quarterly returns (live table 784a) which cover April to June; July to September, September to December and January to March, since April 2013 available on the CLG webpage (see links) Both are provided in excel and csv format. These data help us compare trends across the country for the decisions local authorities make when people apply to them as homeless and each district's use of temporary accommodation.

It is estimated that over a million young people run away or become homeless each year. These youth face increasingly complex issues, including substance abuse, physical and sexual abuse, and AIDS. The serious issues faced by these youths are coupled with funding constraints among almost all agencies providing services to this population. In 1974 the Family and Youth Services Bureau (FYSB) authorized funding to assist community based programs to serve youth who were not otherwise being served by traditional human service agencies. The funding was used for the operation of basic center programs which would provide support for runaway and homeless youth (RHY). The programs offered emergency shelter, crisis intervention services, and family reunification services. In 1988 the Transitional Living Program was introduced in order to provide services to older youth requiring assistance in becoming self-sufficient.While helping to support at-risk youth, the FYSB laws also mandate that certain data be regularly collected and reported. For example, FYSB supported agencies must report on the profile of the youth and families they serve, and provide an overview of the services which they deliver under their grant programs. In order to assist these grantees in their reporting responsibilities, FYSB funded the development of a Runaway and Homeless Youth Management Information System (RHY MIS). The RHY MIS was designed to provide comprehensive information on youth served, services provided, and programs which provide the services.The RHY MIS was designed, developed, and implemented by Information Technology International (ITI). Gradual implementation of the MIS began in 1992 with approximately 400 RHY grantee sites across the country. By 1995, virtually all existing grant programs had at least one staff member who had been trained and grantees were expected to use the MIS and submit data to FYSB on a quarterly basis.The fiscal year 1996 RHY MIS dataset contains data submitted during the federal fiscal year 1996. Data are included from participating agencies in 53 US States and Territories. The dataset includes three files. A demographics file contains 72540 observations and 153 variables. Two additional files contain 64100 observations and a combined 235 variables pertaining to youth problems and services provided.

Investigators: Papadopoulos, Helen & Diepenbrock, Elaine

This dataset displays demographics for the families and individuals residing in the Department of Homeless Services (DHS) shelter system.

Presents the number of individuals for each shelter facility type by borough and community district

This dataset includes the daily number of families and individuals residing in the Department of Homeless Services (DHS) shelter system and the daily number of families applying to the DHS shelter system prior to 3/1/2021. For the latest records, please refer to https://data.cityofnewyork.us/Social-Services/DHS-Daily-Report/k46n-sa2m

What is the Point-In-Time Count?

The U.S. Department of Housing and Urban Development (HUD) and Washington State Department of Commerce require communities to conduct a one-day Point-In-Time (PIT) Count to survey individuals experiencing homelessness. PIT Counts are one source of data among many that help us understand the magnitude and characteristics of people who are homeless in our community.

The Point-In-Time (PIT) Count is a one-day snapshot that captures the characteristics and situations of people living here without a home. The PIT Count includes both sheltered individuals (temporarily living in emergency shelters or transitional housing) and unsheltered individuals (those sleeping outside or living in places that are not meant for human habitation).

The annual PIT Count happens the last Friday in January, and is carried out by volunteers who interview people and asks where they slept the night before, where their last residence was located, what may have contributed to their loss of housing, and disabilities the individual may have. It also asks how long the individual has been homeless, age and demographics, and whether the person is a veteran and/or a survivor of domestic violence.

Like all surveys, the PIT Count has limitations. Results from the Count are influenced by the weather, by availability of overflow shelter beds, by the number of volunteers, and by the level of engagement of the people we are interviewing. Comparisons from year to year should be done with those limitations in mind.