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.

The graph displays the estimated number of homeless people in the United States from 2007 to 2024. The x-axis represents the years, ranging from 2007 to 2023, while the y-axis indicates the number of homeless individuals. The estimated homeless population varies over this period, ranging from a low of 57,645 in 2014 to a high of 771,000 in 2024. From 2007 to 2013, there is a general decline in numbers from 647,258 to 590,364. In 2014, the number drops significantly to 57,645, followed by an increase to 564,708 in 2015. The data shows fluctuations in subsequent years, with another notable low of 55,283 in 2018. From 2019 onwards, the estimated number of homeless people generally increases, reaching its peak in 2024. This data highlights fluctuations in homelessness estimates over the years, with a recent upward trend in the homeless population.

"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."

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.

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.

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 data package has the purpose to offer data for socio-economic indicators and to cover as much as possible the entire this indicator category with regard to the indicator type and to the geographic level. The major sources of the data are the U.S. Census Bureau and the U.S. Bureau for Labor Statistics. Another used sources of data are the U.S. Department of Housing and Urban Development and the U.S. Department of Housing and the U.S. Department Of Agriculture (Economic Research Service).

This report displays the data communities reported to HUD about the nature of and amount of persons who are homeless as part of HUD's Point-in-Time (PIT) Count. This data is self-reported by communities to HUD as part of its competitive Continuum of Care application process. The website allows users to select PIT data from 2005 to present. Users can use filter by CoC, states, or the entire nation.

The DC Metropolitan Area Drug Study (DCMADS) was
conducted in 1991, and included special analyses of homeless and
transient populations and of women delivering live births in the DC
hospitals. DCMADS was undertaken to assess the full extent of the
drug problem in one metropolitan area. The study was comprised of 16
separate studies that focused on different sub-groups, many of which
are typically not included or are underrepresented in household
surveys. The Homeless and Transient Population
study examines the prevalence of illicit drug, alcohol, and tobacco
use among members of the homeless and transient population aged 12 and
older in the Washington, DC, Metropolitan Statistical Area (DC
MSA). The sample frame included respondents from shelters, soup
kitchens and food banks, major cluster encampments, and literally
homeless people. Data from the questionnaires include history of
homelessness, living arrangements and population movement, tobacco,
drug, and alcohol use, consequences of use, treatment history, illegal
behavior and arrest, emergency room treatment and hospital stays,
physical and mental health, pregnancy, insurance, employment and
finances, and demographics. Drug specific data include age at first
use, route of administration, needle use, withdrawal symptoms,
polysubstance use, and perceived risk.This study has 1 Data Set.

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

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

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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

This data tracks the number of beds available for runaway and homeless youth and young adults as well as the number and percent vacant. Data include Crisis Shelters, Crisis Shelters HYA (Homeless Young Adults), Transitional Independent Living, and Transitional Independent Living HYA. For more information about programs, visit https://www1.nyc.gov/site/dycd/services/services.page and https://discoverdycd.dycdconnect.nyc/home. For the RHY Data Collection, please follow this link.

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.

The data provides annual capacity statistics for homeless shelters in Canada. The numbers of permanent beds are reported for each emergency homeless shelter and transitional housing facility in cities across all provinces and territories. Data on shelters are obtained from the National Service Provider List, which is a comprehensive listing of homeless shelters in Canada. It is compiled by the Community Development and Homelessness Partnerships Directorate as part of the National Homelessness Information System (NHIS), a data development initiative that focuses on the collection and analysis of homeless shelter data in Canada.

According to U.S. Department of Housing and Urban Development's definition, homelessness includes individuals and families who lack a fixed, regular, and adequate nighttime residence. A homeless count provides a "snapshot in time" to quantify the size of the homeless population at a specific point during the year. Regardless of how successful outreach efforts are, an undercount of people experiencing homelessness is possible. Counts includes persons experiencing unsheltered and sheltered homelessness. Greater Los Angeles Homeless Count occurred in the nights of February 22, 23 and 24, 2022. Glendale's count occurred in the morning and evening of February 25, 2022. Long Beach's count occurred in the early morning of February 24, 2022. Pasadena's count occurred in the evening of February 22, 2022 and morning of February 23, 2022. Data not available for Los Angeles City neighborhoods and unincorporated Los Angeles County; LAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels.Housing affordability is a major concern for many Los Angeles County residents. Housing burden can increase the risk for homelessness. Individuals experiencing homelessness experience disproportionately higher rates of certain health conditions, such as tuberculosis, HIV infection, alcohol and drug abuse, and mental illness. Barriers to accessing care and limited access to resources contribute greatly to these observed disparities.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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

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.

The purpose of the San Mateo County Homeless Census and Survey is to gather and analyze information to help us understand who is homeless in our community, why they are homeless and what interventions they need to end their homelessness. This data forms the basis for effective planning to solve this complex and longstanding problem. The San Mateo County Human Services Agency’s Center on Homelessness the San Mateo County Continuum of Care Steering Committee were responsible for overseeing this data collection effort, with assistance from a broad group of community partners, including non-profit social service providers, city and town governments, and homeless and formerly homeless individuals. The Census and Survey was designed to meet two related sets of data needs. The first is the requirement of the U.S. Department of Housing and Urban Development (HUD) that communities applying for McKinney-Vento Homelessness Assistance funds (also known as Continuum of Care or “CoC” funds) must conduct a point-in-time count of homeless people a minimum of every two years. These counts are required to take place in the last ten days of January.

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).