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.

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

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 were separate files but are now combined.

Information updated as of 11/13/2025.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_homeless_population. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

Tempe relies on data to inform and support decision making for the city’s Homeless Solutions strategy. This comprehensive effort ensures that the city has the most up-to-date information to meet needs, identify emerging trends and create solutions. In this hub site, you’ll find data related to:Outreach and engagementReporting homeless encampmentsVerifying and resolving encampmentsAnnual Point-in-Time homeless countSite is Google Translate enabled. DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this page, please visit https://tempegov.hub.arcgis.com:/overview/edit.

National Statistics on Homelessness. Data on households found to be homeless. Contains most useful or most popular data, presented by type and other variables, including by geographical area or as a time series.

BackgroundAddressing Citizen’s perspectives on homelessness is crucial for the design of effective and durable policy responses, and available research in Europe is not yet substantive. We aim to explore citizens’ opinions about homelessness and to explain the differences in attitudes within the general population of eight European countries: France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, and Sweden.MethodsA nationally representative telephone survey of European citizens was conducted in 2017. Three domains were investigated: Knowledge, Attitudes, and Practices about homelessness. Based on a multiple correspondence analysis (MCA), a generalized linear model for clustered and weighted samples was used to probe the associations between groups with opposing attitudes.ResultsResponse rates ranged from 30.4% to 33.5% (N = 5,295). Most respondents (57%) had poor knowledge about homelessness. Respondents who thought the government spent too much on homelessness, people who are homeless should be responsible for housing, people remain homeless by choice, or homelessness keeps capabilities/empowerment intact (regarding meals, family contact, and access to work) clustered together (negative attitudes, 30%). Respondents who were willing to pay taxes, welcomed a shelter, or acknowledged people who are homeless may lack some capabilities (i.e. agreed on discrimination in hiring) made another cluster (positive attitudes, 58%). Respondents living in semi-urban or urban areas (ORs 1.33 and 1.34) and those engaged in practices to support people who are homeless (ORs > 1.4; p<0.005) were more likely to report positive attitudes, whereas those from France and Poland (p<0.001) were less likely to report positive attitudes.ConclusionThe majority of European citizens hold positive attitudes towards people who are homeless, however there remain significant differences between and within countries. Although it is clear that there is strong support for increased government action and more effective solutions for Europe’s growing homelessness crisis, there also remain public opinion barriers rooted in enduring negative perceptions.

This dataset provides information on individuals who exit homelessness to permanent housing destinations and then return to homelessness within 2 years from their exit in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year. 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/cutp-y8m4

The term "Homelessness" is often considered to apply only to people "sleeping rough". However, most of our statistics on homelessness relate to the statutorily homeless i.e. those households which meet specific criteria of priority need set out in legislation, and to whom a homelessness duty has been accepted by a local authority. Such households are rarely homeless in the literal sense of being without a roof over their heads, but are more likely to be threatened with the loss of, or are unable to continue with, their current accommodation. A "main homelessness duty" is owed where the authority is satisfied that the applicant is eligible for assistance, unintentionally homeless and falls within a specified priority need group. Such statutorily homeless households are referred to as "acceptances". This dataset provides statistics on the numbers of households accepted as statutorily homeless and presented in terms of acceptances per 1000 households in each local authority area. The total number of acceptances is broken down further according to ethnicity in the related dataset, Homelessness Acceptances. The numbers are presented in terms of households, not individuals. A household is defined as: one person living alone, or a group of people living at the same address who share common housekeeping or a living room. Values of less than five households have been suppressed. In addition, some values of five or greater have been suppressed to prevent other suppressed values being calculated This data is also available in Table 784a, available for download as an Excel spreadsheet.

Decisions on whether a household is homeless and in priority need. The term "Homelessness" is often considered to apply only to people "sleeping rough". However, most of our statistics on homelessness relate to the statutorily homeless i.e. those households which meet specific criteria of priority need set out in legislation, and to whom a homelessness duty has been accepted by a local authority. Such households are rarely homeless in the literal sense of being without a roof over their heads, but are more likely to be threatened with the loss of, or are unable to continue with, their current accommodation. All households that apply for assistance under the Housing and Homelessness Acts are referred to as "decisions". However, these do not include households found to be ineligible for assistance (some persons from abroad are ineligible for assistance). This dataset provides statistics on the numbers of decisions made on applications for assistance. The data is broken down by local authority and according to the outcome of the decision: either rejected, together with reason for rejection, or accepted. The numbers are presented in terms of households, not individuals. A household is defined as: one person living alone, or a group of people living at the same address who share common housekeeping or a living room. Values of less than five households have been suppressed. In addition, some values of five or greater have been suppressed to prevent other suppressed values being calculated This data is also available in Table 784a, available for download as an Excel spreadsheet.

This dataset provides information on individuals experiencing sheltered or unsheltered homelessness in the Austin/Travis County Continuum of Care (CoC) on a single night in January when the Point in Time (PIT) Count occurs. "Sheltered" homelessness refers to individuals residing in emergency shelter, safe haven, or transitional housing project types. Unsheltered homelessness refers to individuals with a primary nighttime residence that is a public or private place not designed for or ordinarily used as a regular sleeping accommodation for human beings, including a car, park, abandoned building, bus or train station, airport, or camping ground on the night designated for the count. This measure overlaps, but is different from, the annual count of sheltered homelessness in HMIS (SD23 Measure EOA.E.1b). 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/hjiv-t2tm Last Updated December 2020 with data for 2020 PIT Count.

This data is no longer being actively updated. The dataset is deprecated and will be removed from the Portal within the next three months. If you have any questions, please reach out to the Open Data team by filling out the following Contact Us form: https://louisvilleky.wufoo.com/forms/open-data-contact-form/ The Community Services division encompasses the client-based services including Neighborhood Place, Community Action Partnership, Self-Sufficiency Services, and Outreach & Advocacy.VisualData Dictionary:Index - Numeric identifier for each list item.Concern - High level issue related to homelessness in Louisville summarized briefly.Impacted population - Group most impacted by the listed concern.GAP in Local Services - Missing process / structure in the local system which leaves the concern unaddressed.Barriers to Progress - What issue/problem would need to be overcome to advance solutions to the listed concern.Possible FY22 Initiatives - What actions could be taken during FY22 to work toward fixing the listed concern.Post Fiscal Year Impact - Expected medium/long term result of proposed initiative.Financial Requirements - Brief statement about finances. i.e., what level of funding would be needed to accomplish the proposed initiative, identify potential source, explain use of funds, etc.Structural Changes Needed? - Y/N column indicating if capital changes (construction, renovation, purchase of land/structures, etc.) will be required to address concern.Contact:Ethan Lambertethan.lambert@louisvilleky.gov

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

People experiencing homelessness have historically had high mortality rates compared to housed individuals in Canada, a trend believed to have become exacerbated during the COVID-19 pandemic. In this matched cohort study conducted in Toronto, Canada, we investigated all-cause mortality over a one-year period by following a random sample of people experiencing homelessness (n = 640) alongside matched housed (n = 6,400) and low-income housed (n = 6,400) individuals. Matching criteria included age, sex-assigned-at-birth, and Charlson comorbidity index. Data were sourced from the Ku-gaa-gii pimitizi-win cohort study and administrative databases from ICES. People experiencing homelessness had 2.7 deaths/100 person-years, compared to 0.7/100 person-years in both matched unexposed groups, representing an all-cause mortality unadjusted hazard ratio (uHR) of 3.7 (95% CI, 2.1–6.5). Younger homeless individuals had much higher uHRs than older groups (ages 25–44 years uHR 16.8 [95% CI 4.0–70.2]; ages 45–64 uHR 6.8 [95% CI 3.0–15.1]; ages 65+ uHR 0.35 [95% CI 0.1–2.6]). Homeless participants who died were, on average, 17 years younger than unexposed individuals. After adjusting for number of comorbidities and presence of mental health or substance use disorder, people experiencing homelessness still had more than twice the hazard of death (aHR 2.2 [95% CI 1.2–4.0]). Homelessness is an important risk factor for mortality; interventions to address this health disparity, such as increased focus on homelessness prevention, are urgently needed.

This data set shows the location of Baltimore City's Tansitional and Emergency "Homeless" Shelter Facilities. However, this is not a complete list. It is the most recent update (2008), and is subjected to change. The purpose of this data set is to aid Baltimore City organizations to best identify facilities to aid the homeless population. The data is broken down into two categories: Emergency Shelter and Transitional Housing. Please find the two definitions below. The first is simply ��_��_��_shelter��_�� and the second is a more involved program that is typically a longer stay. Emergency Shelter: Any facility with overnight sleeping accommodations, the primary purpose of which is to provide temporary shelter for the homeless in general or for specific populations of homeless persons. The length of stay can range from one night up to as much as six months. Transitional Housing: a project that is designed to provide housing and appropriate support services to homeless persons to facilitate movement to independent living within 24 months. These data set was provided by Greg Sileo, Director of the Mayor's Office of Baltimore Homeless Services.

The Street Needs Assessment (SNA) is a survey and point-in-time count of people experiencing homelessness in Toronto on April 26, 2018. The results provide a snapshot of the scope and profile of the City's homeless population. The results also give people experiencing homelessness a voice in the services they need to find and keep housing. The 2018 SNA is the City's fourth homeless count and survey and was part of a coordinated point-in-time count conducted by communities across Canada and Ontario. The results of the 2018 Street Needs Assessment were summarized in a report and key highlights slide deck. During the course of the night, a 23 core question survey was completed with 2,019 individuals experiencing homelessness staying in shelters (including provincially-administered Violence Against Women shelters), 24-hour respite sites (including 24-hour women's drop-ins and the Out of the Cold overnight program open on April 26, 2018), and outdoors. The SNA includes individuals experiencing absolute homelessness but does not capture hidden homelessness (i.e., people couch surfing or staying temporarily with others who do not have the means to secure permanent housing). This dataset includes the SNA survey results; it does not include the count of people experiencing homelessness in Toronto. The SNA employs a point-in-time methodology for enumerating homelessness that is now the standard for most major US and Canadian urban centres. While a consistent methodology and approach has been used each year in Toronto, changes were made in 2018, in part, as a result of participation in the national and provincial coordinated point-in-time count. As a result, caution should be made in comparing these results to previous SNA survey results. Key changes included: administering the survey in a representative sample (rather than census) of shelters; administering the survey in all 24-hour respite sites and a sample of refugee motel programs added to the homelessness service system since the 2013 SNA; and a standard set of core survey questions that communities were required to follow to ensure comparability. In addition, in 2018, surveys were not conducted in provincially-administered health and treatment facilities and correctional facilities as was done in 2013. The 2018 survey results provide a valuable source of information about the service needs of people experiencing homelessness in Toronto. This information is used to improve the housing and homelessness programs provided by the City of Toronto and its partners to better serve our clients and more effectively address homelessness. Visit https://www.toronto.calcity-government/data-research-maps/research-reports/housing-and-homelessness-research-and-reports/

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 contains estimates of the prevalence of homelessness on Census night 2016, derived from the Census of Population and Housing using the Australian Bureau of Statistics (ABS) definition of homelessness. Prevalence is an estimate of how many people experienced homelessness at a particular point-in-time. The ABS uses six homeless operational groups to present the estimates of homelessness. Estimates are also presented for selected groups of people who may be marginally housed and whose living arrangements are close to the statistical boundary of homelessness and who may be at risk of homelessness. Data is by SA4 2016 boundaries. Periodicity: 5 yearly. For more information visit the Australian Bureau of Statistics.

Homeless Students in Arkansas (2024–25): What the data says

TL;DR: 10.9k Arkansas students experienced homelessness in 2024–25 (0.8% of enrollment). Most are “Doubled Up”, sharing housing because of loss of housing or economic hardship. Geography matters: large, fast-growing counties report the highest counts even when they aren’t the poorest, and poverty explains much but not all of variation in homelessness.

Data & Method

Sources: Arkansas Department of Education 2024–2025; NIH poverty estimates (see workbook notes).

Unit of analysis: county-level counts of students

Tools: Tableau Public dashboard + worksheets; regression overlay on county scatter.

What to look at in the dashboard

County Map – Homeless students by county. Use the map to spot hotspots, hover for counts and enrollment context.

Housing Type Breakdown – Statewide composition: Doubled-Up 89.3%, Awaiting Foster Care 4.9%, Hotels/Motels 3.9%, Unsheltered 1.9%. Hidden homelessness dominates the lived experience of students.

Poverty vs. Homeless Students (Scatter) – A clear positive relationship (R² ≈ 0.59, p < 0.0001) indicates poverty is a strong driver, but not the whole story—some populous counties sit above/below the line.

County Comparison Bars – For larger counties (e.g., Benton, Pulaski, Washington), most identified students are Doubled-Up, and that share typically ranges 80–92%, underscoring the need for family-stability interventions.

Key findings

Scale: ~10,872 students (≈0.8% of 1.46M enrollment) were identified as experiencing homelessness statewide.

Geography ≠ poverty alone: Benton County reports the highest count despite not being among the highest poverty counties, reflecting population growth and housing pressure.

Mechanism: “Doubled Up” is the dominant pathway into homelessness for students. It's far more common than shelters, motels, or unsheltered situations. Supports that keep families stably housed (rent/utility assistance, eviction prevention, rapid re-housing) are likely to reach the largest group.

How analysts can use this

Targeting: Combine county counts with local enrollment to compute rates and flag counties that are high count and high rate for prioritization.

Program design: Given the 89% Doubled Up share, expect needs around transportation, documentation, and quick stabilization rather than shelter capacity alone.

Further work: Add rental vacancy, eviction filings, and new construction permits to the model to explain outliers.

Caveats

Counts reflect identification, not true prevalence; under identification is common for Doubled Up students.

County differences may reflect district identification practices and local resources.

Exploration tips: Use the dashboard’s tooltips, legend toggles (to isolate housing types), and the regression line on the scatter to compare counties to the statewide trend.