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, 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 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

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

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.

This dataset includes Point-in-Time (PIT) data collected in Cambridge between 2012 and 2024. 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 2012-2024. The dataset contains four 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.

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

In 2023, the around 11.1 percent of the population was living below the national poverty line in the United States. Poverty in the United StatesAs shown in the statistic above, the poverty rate among all people living in the United States has shifted within the last 15 years. The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines poverty as follows: “Absolute poverty measures poverty in relation to the amount of money necessary to meet basic needs such as food, clothing, and shelter. The concept of absolute poverty is not concerned with broader quality of life issues or with the overall level of inequality in society.” The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the most people living in poverty in 2022, with about 25 percent of the population earning an income below the poverty line. In comparison to that, only 8.6 percent of the White (non-Hispanic) population and the Asian population were living below the poverty line in 2022. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2022. Child poverty peaked in 1993 with 22.7 percent of children living in poverty in that year in the United States. Between 2000 and 2010, the child poverty rate in the United States was increasing every year; however,this rate was down to 15 percent in 2022. The number of people living in poverty in the U.S. varies from state to state. Compared to California, where about 4.44 million people were living in poverty in 2022, the state of Minnesota had about 429,000 people living in poverty.

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.

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

This dataset provides data on youth who sought DYCD Runaway & Homeless Youth services and then were unable to access an RHY bed or subsequently refused services. 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.

The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the "An Assessment of San Francisco’s Vulnerability to Flooding & Extreme Storms" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdfData Dictionary: (see attachment here also: https://data.sfgov.org/Health-and-Social-Services/San-Francisco-Flood-Health-Vulnerability/cne3-h93g)

Field Name Data Type Definition Notes (optional)

Census Blockgroup Text San Francisco Census Block Groups

Children Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014.

Chidlren_wNULLvalues Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elderly Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014.

Elderly_wNULLvalues Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

NonWhite Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014.

NonWhite_wNULLvalues Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Poverty Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014.

Poverty_wNULLvalues Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Education Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014.

Education_wNULLvalues Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

English Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014.

English_wNULLvalues Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elevation Numeric Minimum elevation in feet. United States Geologic Survey 2011.

SeaLevelRise Numeric Percent of land area in the 100-year flood plain with 36-inches of sea level rise. San Francisco Sea Level Rise Committee, AECOM 77inch flood inundation layer, 2014.

Precipitation Numeric Percent of land area with over 6-inches of projected precipitation-related flood inundation during an 100-year storm. San Francisco Public Utilities Commission, AECOM, 2015.

Diabetes Numeric Age-adjusted hospitalization rate due to diabetes; adults 18+. California Office of Statewide Health Planning and Development, 2004-2015.

MentalHealth Numeric Age-adjusted hospitalization rate due to schizophrenia and other psychotic disorders. California Office of Statewide Health Planning and Development, 2004-2015.

Asthma Numeric Age-adjusted hospitalization rate due to asthma; adults 18+. California Office of Statewide Health Planning and Development, 2004 - 2015.

Disability Numeric Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014.

Disability_wNULLvalues

Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

HousingQuality Numeric Annual housing violations, per 1000 residents. San Francisco Department of Public Health, San Francisco Department of Building Inspections, San Francisco Fire Department, 2010 - 2012.

Homeless Numeric Homeless population, per 1000 residents. San Francisco Homeless Count 2015.

LivAlone Numeric Households with a householder living alone. American Community Surevey 2009 - 2014.

LivAlone_wNULLvalues Numeric Households with a householder living alone. American Community Surevey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

FloodHealthIndex Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood Vulnerability Assessment Methodology Section.

FloodHealthIndex_Quintiles Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood

Students in temporary housing (STH) are defined as students experiencing housing instability at any point, for any length of time, during the school year (from the first day of school to 7/2). This includes students and families that are "doubled up" (sharing the housing of others due to economic hardship), living in shelter (including NYC Department of Homeless Services family shelters or Human Resources Administration domestic violence shelters), or living in some other unstable, temporary housing. There were approximately 87,000 New York City district school students who resided in temporary housing in the 2020-21 school year, with about two thirds of them residing in doubled up living arrangements. Approximately 9,500 of those 87,000 students were residing in the DHS shelter system on any given night. The DOE works in close partnership with the Department of Homeless Services to provide streamlined support for students in shelter throughout each day.