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 are separate files but are now combined.

Information updated as of 2/06/2025.

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.

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 po

Analysis of ‘COVID-19 Cases by Population Characteristics Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a3291d85-0076-43c5-a59c-df49480cdc6d on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change. Due to the changes, starting on January 22, 2022, the number of new cases reported daily will be higher than under the old system as cases that would have taken longer to process will be reported earlier.

A. SUMMARY This dataset shows San Francisco COVID-19 cases by population characteristics and by specimen collection date. Cases are included on the date the positive test was collected.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how cases have been distributed among different subgroups. This information can reveal trends and disparities among groups.

Data is lagged by five days, meaning the most recent specimen collection date included is 5 days prior to today. Tests take time to process and report, so more recent data is less reliable.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases and deaths are from: * Case interviews * Laboratories * Medical providers

These multiple streams of data are merged, deduplicated, and undergo data verification processes. This data may not be immediately available for recently reported cases because of the time needed to process tests and validate cases. Daily case totals on previous days may increase or decrease. Learn more.

Data are continually updated to maximize completeness of information and reporting on San Francisco residents with COVID-19.

Data notes on each population characteristic type is listed below.

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.

Sexual orientation * Sexual orientation data is collected from individuals who are 18 years old or older. These individuals can choose whether to provide this information during case interviews. Learn more about our data collection guidelines. * The City began asking for this information on April 28, 2020.

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

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

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.

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.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing

--- Original source retains full ownership of the source dataset ---

Analysis of ‘COVID-19 Deaths by Population Characteristics Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/60f5842f-a359-4b03-ad21-1bcfc3bf7fe6 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. Deaths are included on the date the individual died.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

Data is lagged by five days, meaning the most date included is 5 days prior to today. All data update daily as more information becomes available.

B. HOW THE DATASET IS CREATED COVID-19 deaths are suspected to be associated with COVID-19. This means COVID-19 is listed as a cause of death or significant condition on the death certificate.

Data on the population characteristics of COVID-19 deaths are from: * Case interviews * Laboratories * Medical providers

These multiple streams of data are merged, deduplicated, and undergo data verification processes. It takes time to process this data. Because of this, data is lagged by 5 days and death totals for previous days may increase or decrease. More recent data is less reliable.

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Sexual orientation * Sexual orientation data is collected from individuals who are 18 years old or older. These individuals can choose whether to provide this information during case interviews. Learn more about our data collection guidelines. * The City began asking for this information on April 28, 2020. Gender * The City collects information on gender identity using these guidelines.

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

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.

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.

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. * Facilities are mandated to report COVID-19 cases or deaths among their residents. The City follows up with these facilities to confirm.
* There may be differences between the City’s SNF data and the California Department of Public Health (CDPH) dashboard. The difference may be because the City and the State use dif

--- Original source retains full ownership of the source dataset ---

For each local authority, the number of households accepted as homeless, per 1000 households.

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.

This dataset contains the numbers of households accepted as homeless and in priority need, broken down by local authority and by ethnicity.

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 detailed explanation of the responsibilities of local authorities in this area is available from the DCLG website, here.

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. The data is broken down according to the ethnic group of the applicants and by local authority area.

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.

Abstract copyright UK Data Service and data collection copyright owner.

The Supporting People Client Records and Outcomes dataset comprises information about clients who entered and left housing support services that were in receipt of funding from the Department of Communities and Local Government's (DCLG) 'Supporting People' programme, which ran from April 2003 to April 2011. The data provide information about the routes by which Supporting People services were accessed, the personal characteristics of service users, and outcomes for clients matched against their identified needs. Client Records data were collected from the start of the Supporting People programme; the information formed part of the framework for assessing service quality and gathering management information, providing valuable data for service providers, local authorities and central government. The data covered client characteristics for the 21 different client groups who received Supporting People services. Outcomes data were collected for short-term Supporting People services (28 days or more, but less than two years) from 31 May 2007. In addition, Outcomes data for long-term Supporting People services (over two years) was collected from July 2007. The Outcomes Framework was based on the Department for Education and Skills' (DfES) 'Every Child Matters' framework which captures five high level outcomes, relating to economic well-being, enjoying and achieving (e.g. participating in training or education activities), being healthy, staying safe, and making a positive contribution. Information was also gathered on further underlying indicators. Data were collected by providers of Supporting People services. Completion was not mandatory, although a number of local authorities made it a condition of their contract with providers to submit this information. No estimates were made for missing returns.

In April 2011, the Department ceased collection of Supporting People Client Record and Outcome data, when the Supporting People programme funding rolled into Formula Grant (the blanket term given to the main sources of general Government funding for English local authorities. For further information, see the DCLG's Supporting People webpages.

End User Licence and Special Licence datasets
A more detailed version of the Supporting People Client Records and Outcomes dataset, containing finer-level geographic data and information on service providers, is available under SN 7020. However, users should note that it is subject to restrictive Special Licence Access conditions and is available to UK users only with the depositor's permission. If the End User Licence (EUL) version (SN 7005) is sufficient for analysis, it is recommended that version is used, as it is available to all registered users under standard access conditions.


Main Topics:
The Client Records dataset records the routes by which services are accessed, and Supporting People client characteristics, including: type of service received, age, gender, economic status, disability, religion, ethnicity, client group, homeless status, statutory services required, source of referral, previous accommodation. Client record data capture flow into services and were completed by service providers each time a client entered a housing support service in receipt of Supporting People funding.

The Short-Term Outcomes dataset records the characteristics and outcomes achieved by clients leaving short-term (28 days or more but less than 2 years in duration) Supporting People services, including: type of service received, age, gender, economic status, disability, religion, ethnicity, client group, support needs identified and outcomes achieved against these needs. Outcomes fall under the following five high level categories economic well-being, enjoying and achieving, being healthy, staying safe, and making a positive contribution. Outcomes for short-term services data capture flow out of services and were completed by service providers each time a client exited short-term housing support services in receipt of Supporting People funding. A pragmatic approach was taken to collecting this information for clients in receipt of a service for less than 28 days; this data wa not required but may have been submitted at local discretion.

The Long-Term Outcomes dataset records the characteristics and outcomes achieved by a sample of clients in receipt of long term (2 years or more in duration) Supporting People services, including: type of service received, age, gender, economic status, disability, religion, ethnicity, client group, support needs identified and outcomes achieved against these needs. Outcomes fall under the following five high level categories: economic well-being, enjoying and achieving, being healthy, staying safe, and making a positive contribution. Outcomes for long-term services data captured a snapshot of a...