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 an estimated 324,854 white homeless people in the United States, the most out of any ethnicity. In comparison, there were around 243,624 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 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 about 653,104 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 647,258. 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 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.

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 52.1 percent within this time period.

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 the United States in 2023, 89.2 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 88.7.

In the period from 2012 to 2013, discoveries in shale oil and advances in drilling techniques created an oil boom in North Dakota. Migrant workers from across the continent flocked to the rural prairie state in search of plentiful and well-paying jobs. The state now boasts high economic indexes across the board, including the lowest unemployment rate in the country. But the boom has put a strain on North Dakota's infrastructure. As some cities nearly double their populations, housing has been unable to keep pace with the growth. Employed and healthy individuals are forced to brave the frigid northern conditions in cars and tents.The three maps in this web application paint a picture of the homeless problem in North Dakota by showing how the state's homeless counts, percentages, and change compare to the rest of the United States. While North Dakota's total homeless population is relatively low, the population is high for its size and growing at a tremendous rate.

In 2023, about 68.4 percent of the estimated number of homeless individuals in the United States were male, compared to 30 percent who were female.

Estimated Percent of People of All Ages in Poverty for United States was 12.50% in January of 2023, according to the United States Federal Reserve. Historically, Estimated Percent of People of All Ages in Poverty for United States reached a record high of 15.90 in January of 2011 and a record low of 11.30 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimated Percent of People of All Ages in Poverty for United States - last updated from the United States Federal Reserve on June of 2025.

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.

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 51,785 chronically homeless people living in California.

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_

Homelessness is a costly and traumatic condition that affects hundreds of thousands of people each year in the U.S. alone. Most homeless programs focus on assisting people experiencing homelessness, but research has shown that predicting and preventing homelessness can be a more cost-effective solution. Of the few studies focused on predicting homelessness, most focus on people already seeking assistance; however, these methods necessarily cannot identify those not actively seeking assistance. Providing aid before conditions become dire may better prevent homelessness. Few methods exist to predict homelessness on the general population, and these methods use health and criminal history information, much of which may not be available or timely. We hypothesize that recent financial health information based on utility payment history is useful in predicting homelessness. In particular, we demonstrate the value of utility customer billing records to predict homelessness using logistic regression models based on this data. The performance of these models is comparable to other studies, suggesting such an approach could be productionalized due to the ubiquity and timeliness of this type of data. Our results suggest that utility billing records would have value for screening a broad section of the general population to identify those at risk of homelessness.

The Street Needs Assessment (SNA) is a survey and point-in-time count of people experiencing homelessness in Toronto on April 26, 2018. The results provide a snapshot of the scope and profile of the City's homeless population. The results also give people experiencing homelessness a voice in the services they need to find and keep housing. The 2018 SNA is the City's fourth homeless count and survey and was part of a coordinated point-in-time count conducted by communities across Canada and Ontario. The results of the 2018 Street Needs Assessment were summarized in a report and key highlights slide deck. During the course of the night, a 23 core question survey was completed with 2,019 individuals experiencing homelessness staying in shelters (including provincially-administered Violence Against Women shelters), 24-hour respite sites (including 24-hour women's drop-ins and the Out of the Cold overnight program open on April 26, 2018), and outdoors. The SNA includes individuals experiencing absolute homelessness but does not capture hidden homelessness (i.e., people couch surfing or staying temporarily with others who do not have the means to secure permanent housing). This dataset includes the SNA survey results; it does not include the count of people experiencing homelessness in Toronto. The SNA employs a point-in-time methodology for enumerating homelessness that is now the standard for most major US and Canadian urban centres. While a consistent methodology and approach has been used each year in Toronto, changes were made in 2018, in part, as a result of participation in the national and provincial coordinated point-in-time count. As a result, caution should be made in comparing these results to previous SNA survey results. Key changes included: administering the survey in a representative sample (rather than census) of shelters; administering the survey in all 24-hour respite sites and a sample of refugee motel programs added to the homelessness service system since the 2013 SNA; and a standard set of core survey questions that communities were required to follow to ensure comparability. In addition, in 2018, surveys were not conducted in provincially-administered health and treatment facilities and correctional facilities as was done in 2013. The 2018 survey results provide a valuable source of information about the service needs of people experiencing homelessness in Toronto. This information is used to improve the housing and homelessness programs provided by the City of Toronto and its partners to better serve our clients and more effectively address homelessness. Visit https://www.toronto.calcity-government/data-research-maps/research-reports/housing-and-homelessness-research-and-reports/

Homelessness is a costly and traumatic condition that affects hundreds of thousands of people each year in the U.S. alone. Most homeless programs focus on assisting people experiencing homelessness, but research has shown that predicting and preventing homelessness can be a more cost-effective solution. Of the few studies focused on predicting homelessness, most focus on people already seeking assistance; however, these methods necessarily cannot identify those not actively seeking assistance. Providing aid before conditions become dire may better prevent homelessness. Few methods exist to predict homelessness on the general population, and these methods use health and criminal history information, much of which may not be available or timely. We hypothesize that recent financial health information based on utility payment history is useful in predicting homelessness. In particular, we demonstrate the value of utility customer billing records to predict homelessness using logistic regression models based on this data. The performance of these models is comparable to other studies, suggesting such an approach could be productionalized due to the ubiquity and timeliness of this type of data. Our results suggest that utility billing records would have value for screening a broad section of the general population to identify those at risk of homelessness.