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.

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 supports measure S.D.4.c of SD23.

The Downtown Austin Community Court (DACC) was established to address quality of life and public order offenses occurring in the downtown Austin area utilizing a restorative justice court model. DACC’s priority population consists of individuals experiencing homelessness and the program’s main goal is to permanently stabilize individuals experiencing homelessness. To effectively serve these individuals, DACC created an Intensive Case Management (ICM) Program, which uses a client-centered and housing-focused approach. The ICM Program focuses on rehabilitating and stabilizing individuals using an evidenced-based model of wraparound interventions to help them achieve long-term stability. Because individuals participating in case management are literally homeless, case managers must actively seek their clients in the community through outreach activities and often times work on behalf of the client via collateral engagement with other social service and housing providers. This measure highlights case management activities accomplished via outreach and collateral engagement.

View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/65cb-wtrs

Data source: manually tracked internally on a monthly checkbox report

Calculation: Numerator: number of clients served through outreach Denominator: total number of cases filed that are homeless

this dataset on the portal covers an annual range based on the city's fiscal year.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This measure calculates how long it takes to adjudicate a case from the time when the case was filed.

The Downtown Austin Community Court (DACC) is dedicated to processing cases efficiently and in alignment with nationally established time standards to reduce delay and ensure timely justice, but cases related to individuals experiencing homelessness typically take longer than 180 days to adjudicate due to the case management activities associated with these cases. Case management activities include but are not limited to acquiring birth certificates, Social Security cards, accessing substance use, mental health and medical services and acquiring permanent housing. Cases related to non-homeless individuals are typically adjudicated within 30-180 days. DACC monitors the length of time it takes to process cases and makes necessary adjustments to ensure compliance with time standards.

The dataset for court cases adjudicated within case processing time standards covers a time period of Fiscal years 2016-first quarter of Fiscal year 2020.

Data source: court's electronic case management system Calculation: Numerator-case disposition date Denominator- the date the case was filed. Measure Time Period: Quarterly (Fiscal Year)
Automated: no

Date of last description update: 4/1/2020

The Downtown Austin Community Court (DACC) was established to address quality of life and public order offenses occurring in the downtown Austin area utilizing a restorative justice court model. DACC’s priority population consists of individuals experiencing homelessness and the program’s main goal is to permanently stabilize individuals experiencing homelessness. To effectively serve these individuals, DACC created an Intensive Case Management (ICM) Program, which uses a client-centered and housing-focused approach. The ICM Program focuses on rehabilitating and stabilizing individuals using an evidenced-based model of wraparound interventions to help them achieve long-term stability. Because individuals participating in case management are literally homeless, case managers must actively seek their clients in the community through outreach activities and often times work on behalf of the client via collateral engagement with other social service and housing providers. This measure highlights case management activities accomplished via outreach and collateral engagement.

The Downtown Austin Community Court (DACC) is dedicated to processing cases efficiently and in alignment with nationally established time standards to reduce delay and ensure timely justice, but cases related to individuals experiencing homelessness typically take longer than 180 days to adjudicate due to the case management activities associated with these cases. Case management activities include but are not limited to acquiring birth certificates, Social Security cards, accessing substance use, mental health and medical services and acquiring permanent housing. Cases related to non-homeless individuals are typically adjudicated within 30-180 days. DACC monitors the length of time it takes to process cases and makes necessary adjustments to ensure compliance with time standards.

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