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.

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.

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.

In 2023, the estimated number of homeless people in the United States was highest in California, with about ******* homeless people living in California in that year.

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.

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.

"Ratio of Homeless Population to General Population in major US Cities in 2012. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. Unsheltered estimates are from 2011 except for Seattle and New York City (2012) and Chicago (2009). All General Population figures are from the 2010 U.S. Census enumeration."

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

This statistic depicts the rate of homeless individuals in the United States in 2017, by metropolitan area. In 2017, the rate of homelessness per 10,000 individuals was highest in New York City, at ****.

Community housing and homeless shelters, mostly small nonprofits, heavily depend on government and charitable funding. According to the Annual Homelessness Assessment Report (AHAR 2023), out % of 653,100 individuals experiencing homelessness, 60.7% were sheltered, while 39.3% remained unsheltered, highlighting a significant underserved market. The pandemic increased unemployment, housing costs and poverty levels, raising demand for shelter services, with government support aiding many establishments. As a result, industry revenue grew at a compound annual growth rate (CAGR) of 5.0%, reaching $21.9 billion by 2024, with a 2.0% climb in 2024 alone. Notably, industry profit rose to 7.0%, with most profit reinvested into operations, as 96.0% of shelters are nonprofits and 98.0% of community housing providers are federally tax-exempt. Individual service needs vary widely. About one-third of shelter services cater to emergency housing. Six out of ten people experiencing homelessness are in urban areas, explaining the concentration of shelters in cities. Also, three out of ten people experiencing homelessness come from a family with children. Catering to a diverse demographic (families, youths, adults, veterans) can restrict economies of scale, but specialized services can attract targeted charitable contributions. Urban shelters face higher rents and costs because of competitive pressures. However, they can gain from group purchasing, network development for better rates and spreading positive information to boost donations. Service provision is expected to remain fragmented, with shelters competing intensely for grants. Donations will fluctuate depending on the economy, increasing during booms and decreasing in downturns. Shelters integrating telehealth, training and security measures may attract a broader group, reducing unsheltered homelessness and increasing revenue for service and infrastructure improvements. Despite favorable economic trends, such as decreasing poverty and unemployment rates and slower housing price growth, revenue will strengthen at a CAGR of only 0.2%, reaching $22.0 billion by 2029.

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

This map shows the percent of population who are veterans. This pattern is shown by states, counties, and tracts. The data is from the most current American Community Survey (ACS) data from the U.S. Census Bureau. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty.The pop-up highlights the breakdown of veterans by gender.Zoom to any area in the country to see a local or regional pattern, or use one of the bookmarks to see distinct patterns of poverty through the US. Data is available for the 50 states plus Washington D.C. and Puerto Rico.The data comes from this ArcGIS Living Atlas of the World layer, which is part of a wider collection of layers that contain the most up-to-date ACS data from the Census. The layers are updated annually when the ACS releases their most current 5-year estimates. Visit the layer for more information about the data source, vintage, and download date for the data.

These are the measures adopted by the Heading Home Ramsey Governing Board for tracking top level changes in homeless population and outcomes in the homeless services system. This data is used in the Heading Home Ramsey Community Dashboard Measures https://data.ramseycounty.us/stories/s/wwpp-7i2j. Except for the total county population and housing bed counts, all of the data are derived from the homeless management information system, known as HMIS. Not all homeless persons are served by agencies that use HMIS and therefore these measures do not necessarily cover all homeless persons.

Many of the measures separate housing project types (shelter, rapid rehousing, permanent supportive housing) because of the different lengths of service and types of interventions. For definitions of standard project types for homeless services and other terms, see these descriptions at https://www.unitedtoendhomelessness.org/blog/types-of-housing-support-for-the-homeless .

For more information about other homeless data and special reports visit Heading Home Ramsey's visit https://www.headinghomeramsey.org/stats-data.

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.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

In 2023, there were about ****** homeless youth living in California, the most out of any U.S. state. New York had the second-highest number of homeless youth in that year, at *****.

Abstract copyright UK Data Service and data collection copyright owner. A comparative study of the causes of new episodes of homelessness among people aged 50 or more years was undertaken in Boston, Massachusetts (USA), Melbourne, Australia, and four English cities. The aims were to make a substantial contribution to the predominantly American debate on the causes of homelessness, and to make practice recommendations for the improvement of prevention. The study had several objectives. It aimed to collect information about the antecedents, triggers and risk factors for becoming homeless in later life and about the national and local policy and service contexts. Furthermore, the researchers aimed to analyse and interpret the findings with reference to an integrated model of the causes of homelessness that represented structural and policy factors, including housing, health and social service organisation and delivery factors, and personal circumstances, events, problems and dysfunctions. The aim was to do this collaboratively, by drawing on the project partners' experience and knowledge. Finally, it was hoped to develop recommendations for housing, primary health care and social welfare organisations for the prevention of homelessness. This was to be done by identifying the common sequences and interactions of events that precede homelessness and their markers (or 'early warning' indicators) and by holding workshops in England with practitioners and their representative organisations on new ways of working. By the study of contrasting welfare and philanthropic regimes in a relatively homogeneous category of homeless incidence (i.e. recent cases among late middle-aged and older people), it was hoped that valuable insights into the relative contributions of the policy, service and personal factors would be obtained. The study focused on older people who had recently become homeless, purposely to gather detailed and reliable information about the prior and contextual circumstances. To have included people who had been homeless for several years would have reduced the quality of the data because of 'recall' problems. Users should note that data from the Australian sample for the study are not included in this dataset. Main Topics: The data file includes information about the English respondents and those from Boston. It was compiled in two stages. The first stage involved each project partner entering the pre-coded responses into the file. All partners then identified themes and created codes for the open-ended responses, and the resulting variables were added. Data quality-control procedures included blind checks of the data coding and keying. The first 200 variables pertain to information collected from the respondents. They comprise descriptive variables of the circumstances prior to homelessness, including housing tenure during the three years prior to the survey, previous homelessness, employment history, income, health and addiction problems, and contacts with family, friends and formal services. The respondents were asked to rate whether specific factors were implicated in becoming homeless, and where appropriate, a following open-ended question sought elaboration. The remaining variables comprise information collected from the respondents' 'key workers' about their understanding of the events and states that led to their clients becoming homeless. No sampling frame was available. The sample profiles have been compared with those of all homeless people (not just the recently homeless) in the study locations, most effectively in London and Boston. No gross biases were revealed. The samples represent a large percentage of the clients who presented to the collaborating organisations during the study period and who gave their informed consent to participate. Agreed definitions of homelessness were: sleeping on the streets or in temporary accommodation such as shelters; being without accommodation following eviction or discharge from prison or hospital; living temporarily with relatives or friends because the person has no accommodation, but only if the stay had not exceeded six months, and the person did not pay rent and was required to leave. People who had been previously homeless were included in the survey if they had been housed for at least 12 months prior to the current episode of homelessness. Face-to-face interview Self-completion the 'key workers' (case managers) completed questionnaires about their assessments of the respondents’ problems and of the events and states that led to homelessness. Further clarifications and checks were made by telephone.

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_