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.

This statistic shows the estimated number of chronically homeless people in the United States in 2020, by state. In 2020, there were about ****** chronically homeless people living in California.

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

This statistic shows the percentage of homeless people in the United States in 2020, by household type. In 2020, about **** percent of the homeless population were unsheltered individuals.

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.

Compares demographic characteristics of COVID respite and hotel shelters operating March 2020 to June 2022 with all single adults in shelters and total homeless population from the point-in-time count on 1/23/2020 and total county population from the US Census American Community Survey for 2018. Multiracial American Indians are counted in "American Indian" for the respite and hotel shelters and singles shelters whereas all multiracial persons in other data sets are counted in the category "Multiracial".

In the United States in 2023, **** percent of the homeless population living in El Dorado County, California were unsheltered.

INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

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 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.

Data in PolicyMap COVID-19 Quick Maps includes:Severe COVID-19 Health Risk Index, created by PolicyMap for the New York Times.COVID-19 Daily Cases and Deaths (counts, rates and weekly averages) as reported by the New York Times.COVID-19 Testing Rates as reported by the COVID Tracking ProjectSocial Vulnerability from the Centers for Disease Control. This includes an overall index created by the CDC, as well as the underlying four categories of indicators used by the CDC in the creation of this index: socioeconomic status, household composition and disability status, minority status and language and, housing and transportation.Underlying Health Conditions, such as asthma and COPD, as estimated by PolicyMap using CDC’s Behavioral Risk Factor Surveillance System.Basic demographics including age, race and incomes from the Census’ American Community Survey.Homeless Population counts from the Department of Housing and Urban Development.Computer and Internet Access from the Census’ American Community Survey.ICU Beds as reported by Kaiser Health News.Hospital Capacity and Federally Qualified Health Centers from the Health Resources and Services Administration.Insured and Uninsured Populations from the Census’ American Community Survey.See also - https://www.policymap.com/2020/05/policymap-covid19-quick-maps/

This study examines the spatial patterns of homelessness and resources for the homeless population in Louisville, KY with the goal of identifying where homeless populations are located in relation to resources. Working with census data and some of the resources for the homeless, this study uncovers the realities that the homeless face in different parts of the city. This research research was made as a senior thesis for the University of Louisville's department of Geographic and Environmental Sciences. Table 1. Income and Poverty between the United States and Louisville/Jefferson County metro government, Kentucky in 2019 (United States Census Bureau 2021)Homeless people are thought of as less than full citizens. Whether the rest of the city's people agree or disagree, they are citizens, and should have rights to the city as much as everyone else. The opioid crisis, unmanaged mental illnesses, lack of employment, and other issues like limitations on affordable housing have increased the population of homeless people in Louisville in recent years (Reed 2021). More than 1.5 million children experience homelessness in the United States (Poverty USA 2019). The poverty rate in Louisville, Kentucky is 15.9%, and 1 in 10 renters were facing eviction as of 2019. The 2019 Point In Time Count shows that on a randomly picked night in Louisville, 1071 of the city's people are experiencing homelessness, which is an increase of 15% from the 2018 count (Coalition for the Homeless 2019). The previous data compared to the count for 2020 of 1102 people, shows a trend in increasing homeless population (Coalition for the Homeless 2020).

This statistic shows the estimated number of chronically homeless people in the United States from 2011 to 2020, sorted by family status. In 2020, about ***** of the chronically homeless people in the U.S. had a family.

In 2023, about **** percent of the estimated number of homeless individuals in the United States were male, compared to ** percent who were female.

Between 2022 and 2023, New Hampshire had the highest positive percentage change in the estimated number of homeless people in the United States, with the number of homeless people living in New Hampshire increasing by **** percent within this time period.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. Table Name Indicator Name Universe Timeframe Source Race Notes Source URL

homeownership_pct % Homeownership Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

renters_pct % Renters Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

mean_home_value Mean Home Value Households 2021 Public Use Microdata Sample (PUMS) All races are Non-Hispanic LA County eGIS-Demography

accepted_mortgage_pct Accepted Mortgate Rate Mortgage Applications 2021 Home Mortgage Disclosure Act HMDA categories - https://files.consumerfinance.gov/f/documents/cfpb_reportable-hmda-data_regulatory-and-reporting-overview-reference-chart-2019.pdf https://ffiec.cfpb.gov/data-browser/data/2021

rent_burden_pct Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

rent_burden_severe_pct Severely Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

eviction_per_100_hh Eviction Rate Renter Households 2014-2017 The Eviction Lab at Princeton University

https://data-downloads.evictionlab.org/#data-for-analysis/

homeless_count Homeless Count Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_homeless_pct % Homeless Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_county_pct % County Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

unable_pay_mortgage_rent% Delayed or Were Unable to Pay Mortgage or Rent in the past 2 Years Households 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

homeless_ever% Who Reported Ever Being Homeless or Not Having Their Own Place to Live or Sleep in the past Five Years Adults 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

Data for pop-up reports in the DRP Equity App.Field Descriptions:

FieldDescriptionSourceSource Year geoidCensus block group geoidUS Census2020 tract_nameCensus tract nameUS Census2020 csaCountywide Statistical AreaeGIS2024 sdSupervisorial DistricteGIS2021 total_popPopulationUS Census ACS 5-year, table b010012023 pop_under_10Population under 10US Census ACS 5-year, table b010012023 pop_over_65Population over 65US Census ACS 5-year, table b010012023 pop_pocPeople of Color PopulationUS Census ACS 5-year, table b030022023 pop_nh_whiteNon-Hispanic White PopulationUS Census ACS 5-year, table b030022023 pop_nh_blackNon-Hispanic Black PopulationUS Census ACS 5-year, table b030022023 pop_nh_aianNon-Hispanic American Indian and Alaska Native PopulationUS Census ACS 5-year, table b030022023 pop_nh_asianNon-Hispanic Asian PopulationUS Census ACS 5-year, table b030022023 pop_nh_nhpiNon-Hispanic Native Hawaiian and Pacific Islander PopulationUS Census ACS 5-year, table b030022023 pop_nh_otherNon-Hispanic some other race PopulationUS Census ACS 5-year, table b030022023 pop_nh_twoormoreNon-Hispanic two or more races PopulationUS Census ACS 5-year, table b030022023 pop_latinxHispanic/Latino PopulationUS Census ACS 5-year, table b030022023 language_universe_tractUniverse (denominator) for language indicators (tract level)US Census ACS 5-year, table c160012023 language_spanish_tractSpeak Spanish and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_french_tractSpeak French and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_german_tractSpeak German and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_slavic_tractSpeak Slavic and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_other_european_tractSpeak other Indo-European language and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_korean_tractSpeak Korean and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_chinese_tractSpeak Chinese (including Mandarin) and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_vietnamese_tractSpeak Vietnamese and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_tagalog_tractSpeak Tagalog and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_other_asian_tractSpeak other Asian language and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_arabic_tractSpeak Arabic and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_other_tractSpeak some other language and speak English less than very well (tract level)US Census ACS 5-year, table c160012023 language_english_tractSpeak English very wellUS Census ACS 5-year, table c160012023 education_universeUniverse (denominator) for education indicatorsUS Census ACS 5-year, table b150032023 less_than_9thLess than 9th gradeUS Census ACS 5-year, table b150032023 hs_no_degreeSome high school (no degree)US Census ACS 5-year, table b150032023 hs_gradHigh school graduateUS Census ACS 5-year, table b150032023 gedGED or high school equivalentUS Census ACS 5-year, table b150032023 some_collegeSome college (no degree)US Census ACS 5-year, table b150032023 associatesAssociates degreeUS Census ACS 5-year, table b150032023 bachelorsBachelors degreeUS Census ACS 5-year, table b150032023 graduate_professionalGraduate or Professional degreeUS Census ACS 5-year, table b150032023 renters_universeUniverse (denominator) of renter householdsUS Census ACS 5-year, table b250702023 renters_burdenedHousing burdened households (renters)US Census ACS 5-year, table b250702023 owners_universeUniverse (denominator) of owner householdsUS Census ACS 5-year, table b250912023 owners_burdenedHousing burdened households (owners)US Census ACS 5-year, table b250912023 med_incomeMedian incomeUS Census ACS 5-year, table b190132023 unsheltered_tractUnsheltered homeless population (tract level)LAHSA Homeless Count2022 sheltered_tractSheltered homeless population (tract level)LAHSA Homeless Count2022 polburdp_tractPollution Burden percentileCalEnviroScreen 4.02021 labor_forcePopulation in labor forceUS Census ACS 5-year, table b230252023 employedPopulation in labor force that is employedUS Census ACS 5-year, table b230252023 ctcac_ed_domn_tractCTCAC school qualityCTCAC Opportunity Map2023 ctcac_index_tractCTCAC High segregation and povertyCTCAC Opportunity Map2023 overcrowd_universeUniverse (denominator) for overcrowding indicatorUS Census ACS 5-year, table b250142023 overcrowdOvercrowded householdsUS Census ACS 5-year, table b250142023 novehicle_universeUniverse (denominator) for no vehicle indicatorUS Census ACS 5-year, table b250442023 novehicleHouseholds with no vehicleUS Census ACS 5-year, table b250442023 nointernet_universeUniverse (denominator) for no internet indicatorUS Census ACS 5-year, table b280112023 nointernetHouseholds with no internet accessUS Census ACS 5-year, table b280112023 med_yrbuiltmed_yrbuilt_ownermed_yrbuilt_renterMedian year residential structure built (by tenure)US Census ACS 5-year, table b250372023 yrbuilt_

In 2023, there were an estimated ****** severely mentally ill homeless people living outside of a shelter in the United States.