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.

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

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 are separate files but are now combined.

Information updated as of 2/06/2025.

In 2023, about 3.6 percent of the estimated number of homeless veterans in the United States were Native American. In comparison, 57 percent were white and 31.3 percent were Black or African American.

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 73 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 653,104 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 243,000. 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.

Filtered view for primary race reporting and visualizations. Individuals receiving homeless-related services from a Maricopa County provider, during calendar year 2024 and whose last permanent city of residence prior to becoming homeless was Mesa.

In 2023, about 49 percent of the estimated number of unaccompanied homeless youth in the United States were white. In comparison, 35.8 percent of unaccompanied homeless youth were Black or African American in that year.

In 2022/23 the majority of rough sleepers reported in London during this period were white, accounting for 54.9 percent of all rough sleepers. During this same time period, 19.1 percent of rough sleepers seen in London were black, and 8.8 percent were Asian.

Filtered view for ethnicity reporting and visualizations. Individuals receiving homeless-related services from a Maricopa County provider, whose last permanent city of residence prior to becoming homeless was Mesa.

This data set contains calls for service at homeless shelters. Disclaimer - Race/Age/Gender/Ethnicity data is not captured for all records. Update Frequency: Daily

This dataset contains the numbers of households accepted as homeless and in priority need, broken down by local authority and by ethnicity.

The term "Homelessness" is often considered to apply only to people "sleeping rough". However, most of our statistics on homelessness relate to the statutorily homeless i.e. those households which meet specific criteria of priority need set out in legislation, and to whom a homelessness duty has been accepted by a local authority.

Such households are rarely homeless in the literal sense of being without a roof over their heads, but are more likely to be threatened with the loss of, or are unable to continue with, their current accommodation.

A detailed explanation of the responsibilities of local authorities in this area is available from the DCLG website, here.

A "main homelessness duty" is owed where the authority is satisfied that the applicant is eligible for assistance, unintentionally homeless and falls within a specified priority need group. Such statutorily homeless households are referred to as "acceptances".

This dataset provides statistics on the numbers of households accepted as statutorily homeless. The data is broken down according to the ethnic group of the applicants and by local authority area.

The numbers are presented in terms of households, not individuals. A household is defined as: one person living alone, or a group of people living at the same address who share common housekeeping or a living room.

Values of less than five households have been suppressed. In addition, some values of five or greater have been suppressed to prevent other suppressed values being calculated

This data is also available in Table 784a, available for download as an Excel spreadsheet.

According to a survey on regional homelessness conducted in September 2018, at around 43 percent, the majority of people living without a shelter in the Auckland region in New Zealand were of Māori ethnicity. In the same year, an estimated 16 percent of the entire population of the country were Māori.

Analysis of ‘COVID-19 Cases by Population Characteristics Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a3291d85-0076-43c5-a59c-df49480cdc6d on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change. Due to the changes, starting on January 22, 2022, the number of new cases reported daily will be higher than under the old system as cases that would have taken longer to process will be reported earlier.

A. SUMMARY This dataset shows San Francisco COVID-19 cases by population characteristics and by specimen collection date. Cases are included on the date the positive test was collected.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how cases have been distributed among different subgroups. This information can reveal trends and disparities among groups.

Data is lagged by five days, meaning the most recent specimen collection date included is 5 days prior to today. Tests take time to process and report, so more recent data is less reliable.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases and deaths are from: * Case interviews * Laboratories * Medical providers

These multiple streams of data are merged, deduplicated, and undergo data verification processes. This data may not be immediately available for recently reported cases because of the time needed to process tests and validate cases. Daily case totals on previous days may increase or decrease. Learn more.

Data are continually updated to maximize completeness of information and reporting on San Francisco residents with COVID-19.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Sexual orientation * Sexual orientation data is collected from individuals who are 18 years old or older. These individuals can choose whether to provide this information during case interviews. Learn more about our data collection guidelines. * The City began asking for this information on April 28, 2020.

Gender * The City collects information on gender identity using these guidelines.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Transmission type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation
* the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures.
These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing

--- Original source retains full ownership of the source dataset ---

This dataset shows the ethnicity of the lead applicant of the household currently in temporary accommodation to avoid homelessness according to the Homelessness Reduction Act 2017. The data includes both broad and detailed ethnicity.In England, local authorities have a statutory duty to prevent homelessness under the Homelessness Reduction Act 2017. This duty requires them to take reasonable steps to help individuals who are threatened with homelessness within 56 days to secure that accommodation does not cease to be available for their occupation. Small number suppression has been applied to those detailed ethnicities which are less than 10. All those individuals will be listed as a group called Data disclosure protection.

In 2022, around 32 percent of homeless people in Germany had Germany citizenship, while roughly six percent came from the European Union. The graph shows the distribution of homeless people in Germany by citizenship.

This shows the main housing duties granted to applicants who are eligible, have a priority need for accommodation and are not homeless intentionally (e.g., by choosing to leave suitable accommodation without a valid reason). Data includes ethnicity and year.Small number suppression has been applied to those detailed ethnicities which are less than 10. All those individuals will be listed as a group called Data disclosure protection.

BackgroundTuberculosis (TB) is a preventable and a curable disease. In Brazil, TB treatment outcomes are particularly worse among homeless populations who are either of black race, malnourished or living with HIV/AIDS and other comorbidities. This study therefore evaluated factors associated with unsuccessful TB treatment among homeless population (HP) compared to those with shelter.Methodology/Principal findingsThe study population was composed of 284,874 people diagnosed with TB in Brazil between 2015 and 2020 and reported in the Information System for Notifiable Diseases (SINAN), among which 7,749 (2.72%) were homeless and 277,125 (97.28%) were sheltered. Cox regression analysis was performed with both populations to identify factors associated with unsuccessful TB treatment, and significant predictors of TB treatment outcomes. Results show that HP are more susceptible to unfavorable outcomes when compared to sheltered people (Hazard Ratio (HR): 2.04, 95% CI 1.82–2.28). Among the HP, illicit drug use (HR: 1.38, 95% CI 1.09–1.74), mental disorders (HR: 2.12, 95% CI 1.08–4.15) and not receiving directed observed treatment (DOT) (HR: 18.37, 95% CI 12.23–27.58) are significant predictors of poor treatment outcomes. The use of illicit drugs (HR: 1.53, 95% CI 1.21–1.93) and lack of DOT (HR: 17.97, 95% CI 11.71–27.59) are associated with loss to follow-up, while lack of DOT (HR: 15.66, 95% CI 4.79–51.15) was associated with mortality among TB patients.Conclusion/significanceHomeless population living in Brazil are twice at risk of having an unsuccessful treatment, compared to those who are sheltered, with illicit drugs use, mental disorders and lack of DOT as risk factors for unsuccessful TB outcomes. Our findings reinforce the arguments for an intersectoral and integral approach to address these determinants of health among the vulnerable homeless populations.

A comparison of the race and ethnicity of highly capable students compared to the same demographic groups in the general student population. Comparisons are also included for the Low-Income, English Language Learners, Students with Disabilities, Section 504, Homeless, and Highly Mobile student groups. Data is aggregated by school, district, and state level.

The majority of the people found sleeping rough in London in 2022/23 were from the United Kingdom, at 4,265 people. People from Romania were the next most common nationality at 1,031 rough sleepers.

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.

This survey focuses on many issues including Mayor's race, voting intentions, rating of candidates, possible debates between candidates, homeless, and panhandlers.