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 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.

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

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.

This dataset contains estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth provided by The U.S. Department of Housing and Urban Development. The estimates cover the period of years 2007-2017 and are at national, state and Continuums of Care (CoC) Point-In-Time (PIT) level.

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.

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.

Homelessness is a social crisis in the United States of America. According to McKinney–Vento Homeless Assistance Act, homeless people are those who lack a fixed, regular and adequate nighttime residence. "Ratio of Homeless Population to General Population in major US Cities in 2012.

Table of homeless population by Year (for years 2009 through 2012)

This report outlines the key findings of the annual Point-In-Time (PIT) count and Housing Inventory Count (HIC) conducted in January of each year. Specifically, this report provides estimates of homelessness self-reported, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

Current link at the time of dataset creation: https://www.hudexchange.info/resource/4832/2015-ahar-part-1-pit-estimates-of-homelessness/

Number of Businesses statistics on the Community Housing & Homeless Shelters industry in United States

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 ****.

In 2021, **** percent of homeless people lived in housing before entering a shelter in the United States. In that same year, *** percent of homeless people were living in institutional settings before entering a shelter.

This report displays the data communities reported to HUD about the nature of and amount of persons who are homeless as part of HUD's Point-in-Time (PIT) Count. This data is self-reported by communities to HUD as part of its competitive Continuum of Care application process. The website allows users to select PIT data from 2005 to present. Users can use filter by CoC, states, or the entire nation.

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

In 2023, *** percent of the estimated number of homeless individuals in the United States were between the ages of 18 and 24, while *** percent were under 18.

This dataset includes Point-in-Time (PIT) data collected in Cambridge between 2012 and 2024. The PIT count is a count of sheltered and unsheltered homeless persons on a single night in January. The U.S. Department of Housing and Urban Development (HUD) requires that communities receiving funding through the Continuum of Care (CoC) Program conduct an annual count of homeless persons on a single night in the last 10 days of January, and these data contribute to national estimates of homelessness reported in the Annual Homeless Assessment Report to the U.S. Congress. This dataset is comprised of data submitted to, and stored in, HUD’s Homelessness Data Exchange (HDX).

This dataset includes basic counts and demographic information of persons experiencing homelessness on each PIT date from 2012-2024. The dataset contains four rows for each year, including one row for each housing type: Emergency Shelter, Transitional Housing, or Unsheltered. The dataset also includes housing inventory counts of the number of shelter and transitional housing units available on each of the PIT count dates.

Information about persons staying in emergency shelters and transitional housing units is exported from the Homeless Management Information System (HMIS), which is the primary database for recording client-level service records. Information about persons in unsheltered situations is compiled by first conducting an overnight street count of persons observed sleeping outdoors on the PIT night to establish the total number of unsheltered persons. Demographic information for unsheltered persons is then extrapolated by utilizing assessment data collected by street outreach workers during the 7 days following the PIT count.

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.