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.

Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, gender, veteran status, and disability status.

This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives.

The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity were separate files but are now combined.

Information updated as of 7/29/2025.

This statistic displays the changes that occurred in the total number of homeless people in France from 2017 to 2024. We can observe that the number of homeless people stagnated at 143,000 until 2020, but reached 350,000 in 2024.

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.

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.

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.

SCC_HomelessPop_2015

Visualization of areas described in Vancouver Municipal Code 8.22.040, sections A through D. These areas include:LibrariesCity-owned parking lots and buildingsAreas within 1000' of Safe Stay locationsDeveloped City park landsLand used to operate a public water station, wastewater, or stormwater facilityFire impacted lands specified in 8.22.040.B.4Land within 200' of major water bodies

MOUD treatment rates by service setting and living arrangement.

Age-specific mortality rates for the Santa Clara County (SCC) unhoused population and the SCC general population (n = 974 deaths; 88,658 deaths respectively) and age-specific mortality rate ratios in 2019.

The number of homeless people in Portugal continuously increased from 2018 to 2023, though the number of unhoused persons contracted in 2021. In 2023, there were ****** homeless individuals in the country. Unsheltered individuals outnumbered the unhoused by more than ***** homeless persons.

Financial overview and grant giving statistics of Unhoused Humanity Inc.

Categories 1–6 are primary categories that were identified from CDC WONDER Underlying Causes of Death database. Category 7 is constructed based on the deaths of despair framework.

This dataset consists of survey responses from 40 older adults experiencing homelessness in Phoenix, Arizona (USA), assessing the perceptions of environmental hazards—specifically heat and air pollution—and attitudes toward coping resources and behaviors. The survey includes 51 questions co-created with community members across five categories: demographics and behavior, movement/transportation, climate perceptions, resource availability, and local knowledge mapping. Surveys were conducted indoors at a local service provider over two days in June 2024, when outdoor temperatures reached 42 degrees C and 45 degrees C. The dataset offers insights into potential public service reforms to mitigate heat and air pollution risks among Arizona’s unhoused population. The survey was approved by the Institutional Review Board of Arizona State University (IRB approval number: STUDY00018399).

Visualization of areas described in Vancouver Municipal Code 8.22.040, sections A through D. These areas include:LibrariesCity-owned parking lots and buildingsAreas within 1000' of Safe Stay locationsDeveloped City park landsLand used to operate a public water station, wastewater, or stormwater facilityFire impacted lands specified in 8.22.040.B.4Land within 200' of major water bodies

Visualization of areas described in Vancouver Municipal Code 8.22.040, sections A through D. These areas include:LibrariesCity-owned parking lots and buildingsAreas within 1000' of Safe Stay locationsDeveloped City park landsLand used to operate a public water station, wastewater, or stormwater facilityFire impacted lands specified in 8.22.040.B.4Land within 200' of major water bodies

Note: This Dataset is updated nightly and contains all downloadable Medical Examiner-Coroner records, January 1, 2018 to current, related to deaths that occurred in the County of Santa Clara under the Medical Examiner-Coroner’s jurisdiction and those deaths reportable to the Medical Examiner-Coroner (non-jurisdictional cases/NJA) but in which the office did not assume jurisdiction.

The Santa Clara County Medical Examiner- Coroner’s Office determines cause and manner of death for those deaths that fall under the jurisdiction of the Medical Examiner-Coroner, as defined by California Government code 27491.

The Medical Examiner-Coroner will not be responsible for data verification, interpretation or misinformation once data has been downloaded and manipulated from the dashboard.

Refer to the following document to know more of which deaths are reportable: https://medicalexaminer.sccgov.org/sites/g/files/exjcpb986/files/Reportable%20Death%20Chart%202018.pdf.

PurposeTo identify experiences of boredom and associations with psychosocial well-being during and following homelessness.MethodsUsing a convergent, mixed-methods explanatory design, we conducted quantitative interviews with 164 participants) (n = 102 unhoused; n = 62 housed following homelessness) using a 92-item protocol involving demographic components and seven standardized measures of psychosocial well-being. A sub-sample (n = 32) was approached to participate in qualitative interviews. Data were analyzed by group (unhoused; housed). Quantitative data were analyzed using descriptive statistics designed to generate insights into boredom, meaningful activity engagement, and their associations with psychosocial well-being during and following homelessness. Qualitative data were analyzed using thematic analysis. Quantitative and qualitative findings were integrated at the stage of discussion.ResultsQuantitative analyses revealed small to moderate correlations between boredom and increased hopelessness (rs = .376, p < .01), increased drug use (rs = .194, p < .05), and lowered mental well-being (rs = -.366, p < .01). There were no statistically significant differences between unhoused and housed participants on any standardized measures. Hierarchical regression analyses revealed that housing status was not a significant predictor of boredom or meaningful activity engagement (p>.05). Qualitative interviews revealed profound boredom during and following homelessness imposing negative influences on mental well-being and driving substance use.ConclusionsBoredom and meaningful activity are important outcomes that require focused attention in services designed to support individuals during and following homelessness. Attention to this construct in future research, practice, and policy has the potential to support the well-being of individuals who experience homelessness, and to contribute to efforts aimed at homelessness prevention.

Demographic and visit characteristics of emergency department patients by housing status, January 2013 - December 2021.

Houselessness affects over a million US students annually. Previous scholarship has detailed the experiences of unhoused students, as well as some steps schools can take to better support them. Still, little is known about the impacts of houselessness on learning outcomes, or the ways in which locale, different temporary housing accommodations, and race/ethnicity may complicate this relationship. Using data from the U.S. Department of Education (ED), the Stanford Education Data Archive (SEDA), and the Research Alliance for New York City Schools (RANYCS), this dissertation quantitatively explores the relationship between student houselessness and learning outcomes, and explores potential sources of variation in the magnitude of this relationship. Analyses include descriptive statistics and multi-variate linear regression models. This dissertation consists of two studies: the first is a national study, with analyses conducted at the district level, and the second is student level study in New York City (NYC). Results from Study #1 demonstrate a statistically significant relationship between rates of student houselessness and average standardized test performance at the district level. Furthermore, they demonstrate significant variation in the magnitude of this relationship across different levels of district urbanicity. Results from Study #2 in NYC confirm that houselessness is significantly associated with student’s learning outcomes at the individual level. They also demonstrate significant variation in the magnitude of this relationship across students’ temporary housing accommodations and racial/ethnic groups. Lastly, I find that student houselessness in NYC is also associated with the number of Office Disciplinary Referrals that a student receives and a student's likelihood of being chronically absent.