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

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 ******* 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 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.

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.

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.

In 2023, there were about 10,173 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 4,468.

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.

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.

In the United States, California had the highest amount of homeless children and youths enrolled in their schools for the school year 2019-20 at 246,350 students. North Carolina had 28,877 recorded homeless students for the same year. A homeless child or youth is someone who lacks a fixed residence or is forced to share housing due to economic hardship.

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.

Context

This database contains the data reported in the Annual Homeless Assessment Report to Congress (AHAR). It represents a point-In-time count (PIT) of homeless individuals, as well as a housing inventory count (HIC) conducted annually.

The data represent the most comprehensive national-level assessment of homelessness in America, including PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

These data can be trended over time and correlated with other metrics of housing availability and affordability, in order to better understand the particular type of housing resources that may be needed from a social determinants of health perspective.

HUD captures these data annually through the Continuum of Care (CoC) program. CoC-level reporting data have been crosswalked to county levels for purposes of analysis of this dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_hud_pit_homelessness

Sample Query

What has been the change in the number of homeless veterans in the state of New York’s CoC Regions since 2012? Determine how the patterns of homeless veterans have changes across the state of New York

homeless_2018 AS ( SELECT Homeless_Veterans AS Vet18, CoC_Name
FROM bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc WHERE SUBSTR(CoC_Number,0,2) = "NY" AND Count_Year = 2018 ),

veterans_change AS ( SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange FROM homeless_2018 JOIN homeless_2012 ON homeless_2018.CoC_Name = homeless_2012.CoC_Name )

SELECT * FROM veterans_change

In 2022, about ****** veterans living in California were homeless, the most out of all U.S. states.

BackgroundOpioid use disorder (OUD) is a growing public health crisis, with opioids involved in an overwhelming majority of drug overdose deaths in the United States in recent years. While medications for opioid use disorder (MOUD) effectively reduce overdose mortality, only a minority of patients are able to access MOUD; additionally, those with unstable housing receive MOUD at even lower rates.ObjectiveBecause MOUD access is a multifactorial issue, we leverage machine learning techniques to assess and rank the variables most important in predicting whether any individual receives MOUD. We also seek to explain why persons experiencing homelessness have lower MOUD access and identify potential targets for action.MethodsWe utilize a gradient boosted decision tree algorithm (specifically, XGBoost) to train our model on SAMHSA’s Treatment Episode Data Set-Admissions, using anonymized demographic and clinical information for over half a million opioid admissions to treatment facilities across the United States. We use Shapley values to quantify and interpret the predictive power and influencing direction of individual features (i.e., variables).ResultsOur model is effective in predicting access to MOUD with an accuracy of 85.97% and area under the ROC curve of 0.9411. Notably, roughly half of the model’s predictive power emerges from facility type (23.34%) and geographic location (18.71%); other influential factors include referral source (6.74%), history of prior treatment (4.41%), and frequency of opioid use (3.44%). We also find that unhoused patients go to facilities that overall have lower MOUD treatment rates; furthermore, relative to housed (i.e., independent living) patients at these facilities, unhoused patients receive MOUD at even lower rates. However, we hypothesize that if unhoused patients instead went to the facilities that housed patients enter at an equal percent (but still received MOUD at the lower unhoused rates), 89.50% of the disparity in MOUD access would be eliminated.ConclusionThis study demonstrates the utility of a model that predicts MOUD access and both ranks the influencing variables and compares their individual positive or negative contribution to access. Furthermore, we examine the lack of MOUD treatment among persons with unstable housing and consider approaches for improving access.

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

In the United States in 2023, 89.2 percent of the homeless population living in El Dorado County, California were unsheltered.

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

This poll, fielded January 6-8, 1992, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked to give their opinions of President George H.W. Bush and his handling of the presidency, foreign policy, and the economy. Respondents were asked to list the most important problem facing the country, which candidate they would vote for if the election for president were being held that day, and whether they were likely to vote in the Democratic or Republican presidential primary or caucus. Several questions asked for respondents' opinions of the Democratic and Republican presidential nominees, which candidates they would like to see win the nominations for president, and what issues they would like to see the candidates emphasize in their campaigns. Opinions were collected on how much George H.W. Bush cared about the general public, whether he distributed his time properly between foreign policy problems and problems at home, and whether his visits to countries in Asia would increase the number of jobs in the United States. A series of questions addressed the causes of homelessness, whether it was something the government could do a lot about, and whether respondents had personally seen a lot of homeless people in their community. Additional questions asked respondents to rate the condition of the national economy, whether they would be better off financially if George H.W. Bush was re-elected president, whether recession was something a president could do a lot about, and whether George H.W. Bush was healthy enough to be an effective president for a second term. Demographic variables include sex, race, age, household income, education level, political party affiliation, political philosophy, and voter registration status.