31 datasets found
  1. Rate of homelessness in the U.S. 2023, by state

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Rate of homelessness in the U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/727847/homelessness-rate-in-the-us-by-state/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    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.

  2. c

    Top 15 States by Estimated Number of Homeless People in 2024

    • consumershield.com
    csv
    Updated Jun 9, 2025
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    ConsumerShield Research Team (2025). Top 15 States by Estimated Number of Homeless People in 2024 [Dataset]. https://www.consumershield.com/articles/how-many-homeless-us
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    csvAvailable download formats
    Dataset updated
    Jun 9, 2025
    Dataset authored and provided by
    ConsumerShield Research Team
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    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.

  3. Estimated number of homeless people in the U.S. 2007-2023

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Estimated number of homeless people in the U.S. 2007-2023 [Dataset]. https://www.statista.com/statistics/555795/estimated-number-of-homeless-people-in-the-us/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    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.

  4. Number of homeless people in the U.S. 2023, by race

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Number of homeless people in the U.S. 2023, by race [Dataset]. https://www.statista.com/statistics/555855/number-of-homeless-people-in-the-us-by-race/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    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.

  5. c

    Number of Homeless People in U.S. (2007-2024)

    • consumershield.com
    csv
    Updated Jun 9, 2025
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    ConsumerShield Research Team (2025). Number of Homeless People in U.S. (2007-2024) [Dataset]. https://www.consumershield.com/articles/how-many-homeless-us
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    csvAvailable download formats
    Dataset updated
    Jun 9, 2025
    Dataset authored and provided by
    ConsumerShield Research Team
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    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.

  6. Number of homeless people in Russia 2010-2021, by type of area

    • statista.com
    Updated Jan 23, 2023
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    Statista (2023). Number of homeless people in Russia 2010-2021, by type of area [Dataset]. https://www.statista.com/statistics/1360529/number-of-homeless-people-in-russia/
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    Dataset updated
    Jan 23, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Russia
    Description

    Nearly 11.3 thousand people in Russia were homeless, based on the population census data from 2021. The number of homeless residents decreased by 82 percent compared to 2010. The largest share of homeless people in the country lived in urban areas, at around 95 percent in 2021.

  7. Homeless people in Portugal 2018-2022, by type of homelessness

    • statista.com
    Updated Jun 19, 2025
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    Statista (2025). Homeless people in Portugal 2018-2022, by type of homelessness [Dataset]. https://www.statista.com/statistics/1535621/portugal-homeless-people-by-type-oh-homelessness/
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    Dataset updated
    Jun 19, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Portugal
    Description

    The number of homeless people in Portugal continuously increased from 2018 to 2022. In the latter year, there were ****** homeless individuals in the country. Unsheltered individuals outnumbered the unhoused by more than a thousand homeless persons.

  8. Special Eurobarometer 279: Poverty and exclusion

    • data.wu.ac.at
    zip
    Updated Sep 4, 2018
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    European Union Open Data Portal (2018). Special Eurobarometer 279: Poverty and exclusion [Dataset]. https://data.wu.ac.at/schema/www_europeandataportal_eu/NjUxOWZmNzgtNjFmNC00NTUwLWJjYjMtMDVjZDk4NWMwYjU3
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    zipAvailable download formats
    Dataset updated
    Sep 4, 2018
    Dataset provided by
    EU Open Data Portalhttp://data.europa.eu/
    European Union-
    Description

    The Directorate-General Employment of the European Commission commissioned a survey that examines public opinion about poverty and exclusion in the European Union. Between the 14th of February and the 18th of March 2007, TNS Opinion & Social, a consortium formed by TNS and EOS Gallup Europe interviewed 26,466 EU citizens aged 15 and over living in the 27 European Union Member States and 1,000 residents of Croatia. This report studies the following issues related to poverty and exclusion covered by the survey. ♦ First of all, we focus on the perceived existence of poverty in the European Union: to what extent are Europeans themselves affected by poverty and to what extent do they see poverty in the area in which they live? In this chapter we furthermore look at attitudes towards poverty: is it an inherited or acquired condition, what causes poverty and why do people live in need? ♦ The second part of the report focuses on one of the most extreme forms of exclusion, homelessness: why do people become homeless, what is the perceived risk of becoming homeless oneself and what do Europeans do to help homeless people? ♦ In the final part we examine what Europeans regard necessary in order to have a decent standard of living with regards to financial means, housing needs, ownership of durable goods, basic necessities and social integration. We also look specifically at people’s views concerning the requirements and the needs of children to live and develop well. We end the report with an examination of how people’s attitudes towards poverty relate to what they consider necessary for a decent standard of living. #####The results by volumes are distributed as follows: * Volume A: Countries * Volume AA: Groups of countries * Volume A' (AP): Trends * Volume AA' (AAP): Trends of groups of countries * Volume B: EU/socio-demographics * Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: http://www.gesis.org/en/home/

  9. Share of homeless population India 2011, by area

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Share of homeless population India 2011, by area [Dataset]. https://www.statista.com/statistics/1132046/india-share-of-homeless-population-by-area/
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2011
    Area covered
    India
    Description

    In 2011, about ** percent of the total population in India was homeless. Urban areas witnessed more homelessness in comparison to the rural areas of the country. Homelessness is a growing issue in India that leads to various other problems like violence and drug addiction among others.

  10. f

    Data_Sheet_1_EQ-5D-3L Health Status Among Homeless People in Stockholm,...

    • frontiersin.figshare.com
    pdf
    Updated May 31, 2023
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    Bo Burström; Robert Irestig; Kristina Burström (2023). Data_Sheet_1_EQ-5D-3L Health Status Among Homeless People in Stockholm, Sweden, 2006 and 2018.PDF [Dataset]. http://doi.org/10.3389/fpubh.2021.780753.s001
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    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers
    Authors
    Bo Burström; Robert Irestig; Kristina Burström
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Stockholm, Sweden
    Description

    Background: Homeless people are a socially excluded group whose health reflects exposures to intersecting social determinants of health. The aim of this study was to describe and compare the demographic composition, certain social determinants of health, and self-reported health among homeless people in Stockholm, Sweden, in 2006 and 2018.Methods: Analysis of data from face-to-face interviews with homeless people in Stockholm 2006 (n = 155) and 2018 (n = 148), based on a public health survey questionnaire adapted to the group, including the EQ-5D-3L instrument. The chi-squared test was employed to test for statistical significance between groups and the independent t-test for comparison of mean scores and values. Ordinary Least Squares (OLS) regression, with Robust Standard Errors (RSE) was performed on merged 2006 and 2018 data with mean observed EQ VAS score as outcome variable.Results: In 2018 more homeless people originated from countries outside Europe, had temporary social assistance than long-term social insurance, compared to in 2006. In 2018 more respondents reported lack of social support, exposure to violence, and refrained from seeking health care because of economic reasons. Daily smoking, binge drinking, and use of narcotic drugs was lower 2018 than 2006. In 2018 a higher proportion reported problems in the EQ-5D-3L dimensions, the mean TTO index value and the VAS index value was significantly lower than in 2006. In the regression analysis of merged data there was no significant difference between the years.Conclusions: Homeless people are an extremely disadvantaged group, have high rates of illness and disease and report poor health in all EQ-5D-3L dimensions. The EQ VAS score among the homeless people in 2018 is comparable to the score among persons aged 95–104 years in the general Swedish population 2017. The EQ-5D-3L instrument was easily administered to this group, its use allows comparison with larger population groups. Efforts are needed regarding housing, but also intensified collaboration by public authorities with responsibilities for homeless people's health and social welfare. Further studies should evaluate the impact of such efforts by health and social care services on the health and well-being of homeless people.

  11. a

    Where do Homeless Veterans live in the Dallas County

    • dallas-county-open-data-hub-dallascountygis.hub.arcgis.com
    Updated Apr 19, 2022
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    Dallas County GIS Information Technology (2022). Where do Homeless Veterans live in the Dallas County [Dataset]. https://dallas-county-open-data-hub-dallascountygis.hub.arcgis.com/maps/58333b56c9484a208a0181336515f48d
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    Dataset updated
    Apr 19, 2022
    Dataset authored and provided by
    Dallas County GIS Information Technology
    Area covered
    Description

    This map shows the percent of population who are veterans. This pattern is shown by states, counties, and tracts. The data is from the most current American Community Survey (ACS) data from the U.S. Census Bureau. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty.The pop-up highlights the breakdown of veterans by gender.Zoom to any area in the country to see a local or regional pattern, or use one of the bookmarks to see distinct patterns of poverty through the US. Data is available for the 50 states plus Washington D.C. and Puerto Rico.The data comes from this ArcGIS Living Atlas of the World layer, which is part of a wider collection of layers that contain the most up-to-date ACS data from the Census. The layers are updated annually when the ACS releases their most current 5-year estimates. Visit the layer for more information about the data source, vintage, and download date for the data.

  12. CBS News/New York Times Monthly Poll #1, January 1992

    • icpsr.umich.edu
    ascii, delimited, sas +2
    Updated Mar 11, 2008
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    Inter-university Consortium for Political and Social Research [distributor] (2008). CBS News/New York Times Monthly Poll #1, January 1992 [Dataset]. http://doi.org/10.3886/ICPSR04476.v1
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    stata, delimited, ascii, spss, sasAvailable download formats
    Dataset updated
    Mar 11, 2008
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/4476/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/4476/terms

    Time period covered
    Jan 1992
    Area covered
    United States
    Description

    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.

  13. Urban Employment Unemployment Survey 2012 - Ethiopia

    • datacatalog.ihsn.org
    Updated Oct 14, 2021
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    Central Statistical Agency (CSA) (2021). Urban Employment Unemployment Survey 2012 - Ethiopia [Dataset]. https://datacatalog.ihsn.org/catalog/9670
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    Dataset updated
    Oct 14, 2021
    Dataset provided by
    Central Statistical Agencyhttps://ess.gov.et/
    Authors
    Central Statistical Agency (CSA)
    Time period covered
    2012
    Area covered
    Ethiopia
    Description

    Abstract

    Statistical information on all aspects of the population is vital for the design, implementation, monitoring and evaluation of economic and social development plan and policy issues. Labor force survey is one of the most important sources of data for assessing the role of the population of the country in the economic and social development process. It is useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic wellbeing of the population. Statistics on the labor force further present the economic activity status and its relationship to other social and economic characteristics of the population. Seasonal and other variations as well as changes over time in the size, distribution, and characteristics of employed and unemployed population can be monitored using up-to-date information from labor force surveys. It serves as an input for assessing the achievements of the Millennium Development Goals (MDGs). Furthermore, labor force data is also useful as a springboard for monitoring and evaluation of the five years growth and transformation plan of the country.

    Geographic coverage

    The 2012 Urban Employment and Unemployment Survey (UEUS) covered all urban parts of the country except three zones of Afar, Six zones of Somali, where the residents are pastoralists.

    Analysis unit

    • Households
    • Individuals

    Universe

    This survey follows household approach and covers households residing in conventional households and thus, population residing in the collective quarters such as universities/colleges, hotel/hostel, monasteries, and homeless population etc., were not covered by this survey.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The list of households obtained from the 2007 population and housing census was used to select EAs. A fresh list of households from each EA was prepared at the beginning of the survey period. The list was then used as a frame to select 30 households from sample EAs.

    The country was divided into two broad categories - major urban centers and other urban center categories.

    Category I: In this category all regional capitals and five other major urban centers that have a high population size as compared to others were included. Each urban center in this category was considered as a reporting level. This category has a total of 16 reporting levels. To select the sample, a stratified two-stage cluster sample design was implemented. The primary sampling units were EAs of each reporting level.

    Category II: Urban centers other than those under category I were grouped into this category. A stratified three stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The survey questionnaire was organized into seven sections. Section 1 - Area identification of the selected household Section 2 - Particulars of household members Section 3 - Economic activity status during the last seven days Section 4 - Unemployment rate and characteristics of unemployed persons Section 5 - Economic activity status the population during the last six months Section 6 - Employment in the informal sector of Employment Section 7 - Economic activity of children aged 5-17 years

    A structured questionnaire was used to solicit the required data in the survey. The draft questionnaire was tested by undertaking a pretest in selected kebeles (lower administrative unit) in Addis Ababa. Based on the pretest, the content, logical flow, layout and presentation of the questionnaire was amended. The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre coded answers and column numbers were assigned for each question.

    Cleaning operations

    The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry by the subject matter experts.

    Using the computer edit specifications prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing, or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on frequency and tabulation results. This was done by senior programmers using CSPro software in collaboration with the senior subject experts from Manpower Statistics Team of the CSA.

    Response rate

    Response rate was 99.68%.

  14. Eurobarometer 67.1: Cultural Values, Poverty and Social Exclusion,...

    • icpsr.umich.edu
    ascii, delimited, sas +2
    Updated Jun 16, 2010
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    Papacostas, Antonis (2010). Eurobarometer 67.1: Cultural Values, Poverty and Social Exclusion, Developmental Aid, and Residential Mobility, February-March 2007 [Dataset]. http://doi.org/10.3886/ICPSR21522.v2
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    delimited, spss, sas, stata, asciiAvailable download formats
    Dataset updated
    Jun 16, 2010
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Papacostas, Antonis
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/21522/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/21522/terms

    Time period covered
    Feb 14, 2007 - Mar 25, 2007
    Area covered
    Romania, Finland, Cyprus, Slovakia, Netherlands, Lithuania, Poland, Malta, Global, Slovenia
    Description

    This round of Eurobarometer surveys diverged from the Standard Eurobarometer measures and queried respondents on the following topics: (1) cultural values, (2) poverty and social exclusion, (3) developmental aid, and (4) residential mobility. For the first major focus, cultural values, the survey asked respondents questions pertaining to the meaning and importance of culture, their interest and participation in cultural activities, and their national identity. The respondents were also asked to identify cultural values for Europe as well as other countries, about the importance and promotion of cultural exchange, and whether they would learn a foreign language. For the next major focus, respondents were asked to evaluate their personal financial situation and that of people dwelling in the vicinity of their homes, and to ascertain why people fall into poverty or are excluded from society. They were also asked why people become homeless, the likelihood that they, themselves, would become homeless, and whether they help the homeless. Respondents were further asked to evaluate their quality of life and to determine their needs in attaining decent living conditions for themselves and for children. For the third major focus, respondents were asked to evaluate their knowledge of developmental aid plans, the European Consensus on Development, and the Millennium Development Goals. Respondents were asked to identify the motivation of countries providing developmental aid, and the added value of the European Union (EU) in doing so. In addition, respondents shared their opinions as to which organizations should have the most influence on the priorities for developmental aid, and which countries and issues should be acknowledged as needing the most attention and assistance. The final major focus pertained to residential mobility. The survey queried respondents about their relocation history, reasons for moving or not moving, countries to which they intended to move, preparing for a move (including difficulties they may encounter), and the duration of their stay at a location. Demographic and other background information includes respondent's age, gender, nationality, origin of birth (personal and parental), marital status, left-to-right political self-placement, occupation, age when stopped full-time education, household composition, and ownership of a fixed or a mobile telephone and other durable goods. In addition, country-specific data include the type and size of locality, region of residence, and language of interview (select countries).

  15. i

    Census of Population and Housing of the Kyrgyz Republic 2009 - IPUMS Subset...

    • catalog.ihsn.org
    • microdata.worldbank.org
    Updated Mar 29, 2019
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    National Statistical Committee of the Kyrgyz Republic (2019). Census of Population and Housing of the Kyrgyz Republic 2009 - IPUMS Subset - Kyrgyz Republic [Dataset]. https://catalog.ihsn.org/catalog/5397
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    Dataset updated
    Mar 29, 2019
    Dataset provided by
    National Statistical Committee of the Kyrgyz Republic
    Minnesota Population Center
    Time period covered
    2009
    Area covered
    Kyrgyzstan
    Description

    Abstract

    IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

    The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

    Geographic coverage

    National coverage

    Analysis unit

    Individuals, households, and housing units

    UNITS IDENTIFIED: - Dwellings: Yes - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes - Special populations: Homeless people, temporarily absent persons, and temporary residents

    UNIT DESCRIPTIONS: - Dwellings: The totality of all living quarters, regardless of ownership and employment at the time of the census, including residential buildings, special houses (like hostels, houses for lonely and old people, children's homes, boarding houses for disabled, school hostels and boarding school), flats, service housings, holiday homes, hotels, other living accomodations in other buildings suited for living whether or not they are intended for living. - Households: A group of people sharing the same housing unit (or one person living alone), jointly keeping the house, i.e. fully or partially pooling their individual budgets for common expenditures for food and daily living needs or having a common budget who may or may not be related by kinship. - Group quarters: Groups of people living at the same institution (housing unit), sharing meals, without having individual budgets or common consumer expenditures, subject to the same general rules, and usually unrelated by kinship.

    Universe

    The entire population of the country, including private and institutional households, their accommodation and living conditions

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    MICRODATA SOURCE: National Statistical Committee of the Kyrgyz Republic

    SAMPLE DESIGN: 20% sample drawn by the country: systematic sample of every 5th household or every 5th individual in collective household 10% sample drawn by MPC from the 20% sample: systematic sample of every 2nd household

    SAMPLE UNIT: Households

    SAMPLE FRACTION: 10%

    SAMPLE SIZE (person records): 564,986

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Three census forms: List of Residents (Form 1), Census Questionnaire - Population (Form 2), and Census Questionnaire - Housing Fund (Form 3)

    Response rate

    COVERAGE: 100%

  16. u

    5th Sudan Population and Housing Census 2008 - IPUMS Subset - Sudan

    • microdata.unhcr.org
    • catalog.ihsn.org
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    Updated May 19, 2021
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    Central Bureau of Statistics (2021). 5th Sudan Population and Housing Census 2008 - IPUMS Subset - Sudan [Dataset]. https://microdata.unhcr.org/index.php/catalog/425
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    Dataset updated
    May 19, 2021
    Dataset provided by
    Central Bureau of Statistics
    Minnesota Population Center
    Time period covered
    2008
    Area covered
    Sudan
    Description

    Abstract

    IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

    The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

    Geographic coverage

    National coverage

    Analysis unit

    Household

    UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: No - Special populations: Yes (Homeless, refugees, camps)

    UNIT DESCRIPTIONS: - Dwellings: A building is an independent free-standing structure irrespective of its construction material, composed of one or more rooms. - Households: A household consists of a person or a group of persons who live together in the same housing unit or part of it and who consider themselves as one unit in terms of the provision of food and/or other essentials of living for the group. When most of the members of such a group are related by blood (i.e., biologically) the group shall be referred to as a Private Household for the purpose of the census. On the other hand when the group (i.e., household as defined earlier) consists of members who are not related by blood and they are more than 10, they will be considered as Non-Institutional Collective Household. Note that if the group consists of 10 or less members, it should be considered a private household. - Group quarters: An institution is usually a set of premises used to house a large number of people who are not related by blood or marriage but bound together by a common objective or personal interest (e.g., universities, boarding houses, hospitals, army barracks, camps, prisons, hotels, etc.)

    Universe

    Residents of Sudan

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    MICRODATA SOURCE: Central Bureau of Statistics

    SAMPLE DESIGN: Long form questionnaire for sedentary households (selected enumeration areas) and a sample of nomad households.

    SAMPLE UNIT: Household

    SAMPLE FRACTION: 16.6%

    SAMPLE SIZE (person records): 5,066,530

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Two forms: Long Questionnaire (for a sample of areas) and Short Questionnaire (for the rest of the country). The information used here is based on the long form questionnaire.

  17. Population and Housing Census 2018 - Wallis and Futuna

    • microdata.pacificdata.org
    Updated Apr 23, 2019
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    Institut national de la Statistique et des Etudes Economiques (INSEE) (2019). Population and Housing Census 2018 - Wallis and Futuna [Dataset]. https://microdata.pacificdata.org/index.php/catalog/203
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    Dataset updated
    Apr 23, 2019
    Dataset provided by
    The National Institute of Statistics and Economic Studieshttp://insee.fr/
    Service Territorial de la Statistique et des Etudes Economiques (STSEE)
    Time period covered
    2018
    Area covered
    Wallis and Futuna
    Description

    Abstract

    The census date was midnight, the 23rd of July 2018.

    The Census is the official count of population, household and dwellings in Wallis & Futuna and it gives a general overview of the country at one specific point in time: 23rd of July 2018. Since 1969 until 2003, Census has been taken once in every 7 or 6 years and every 5 years from 2003.

    The census can be the source of information for allocation of public funding, more particularly in areas such as health, education and social policy. The main users of the information provided by the Census are the government, education facilities (such as schools and tertiary organizations), local authorities, businesses, community organizations and the public in general.

    The objectives of Census changed over time shifting from earlier years where they were essentially household registrations and counts, to now where a national population census stands supreme as the most valuable single source of statistical data for Wallis & Futuna. This Census allowed to determine the legal population of Wallis and Futuna in all geographical aspects: Wallis island, Futuna island, the 3 "circonsriptions" (Alo, Sigave, Uvea) and 5 districts (Alo, Sigave, Hahake, Hihifo, Mua).

    Census data is now widely used to evaluate: - The availability of basic household needs in key sectors, to identify disadvantaged areas and help set priorities for action plans; - Benefits of development programmes in particular areas, such as literacy, employment and family planning;

    In addition, census data is useful to asses manpower resources, identify areas of social concern and for the improvement in the social and economic status of women by giving more information about this part of the population and formulating housing policies and programmes and investment of development funds.

    Geographic coverage

    National coverage.

    Analysis unit

    Households and Individuals.

    Universe

    The Census is covering all people alive on the reference date (23rd of July 2018), that are usually living in Wallis and Futuna - whichever nationality they are, for at least 12 months. The Census covered all household and communitiy members. Communities are considered to be: boarding schools, gendarmerie, retirement homes, religious communities, but also people living in mobile dwelling (e.g. boats) and homeless people.

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    Not applicable as it is a full coverage.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    There are two types of questionnaire for this Census:

    Individual sheet (Feuille de Logement or "FL"): describing the dwelling characteristics and enlisting all the individuals living in it; Individual form (Bulletin Individuel or "BI"): information on each individual that are usually living in the household.

    The questionnaires were distributed in French and are available in the "External Resources" section.

    Cleaning operations

    Data editing was done by SPC in collaboration with Wallis and Futuna NSO.

    Sampling error estimates

    Not applicable.

  18. Eurobarometer 74.1 (AUG-SEP 2010)

    • datacatalogue.cessda.eu
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    Updated Mar 14, 2023
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    Papacostas, Antonis (2023). Eurobarometer 74.1 (AUG-SEP 2010) [Dataset]. http://doi.org/10.4232/1.11625
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    Dataset updated
    Mar 14, 2023
    Dataset provided by
    European Commissionhttp://ec.europa.eu/
    Authors
    Papacostas, Antonis
    Time period covered
    Aug 26, 2010 - Sep 22, 2010
    Area covered
    Spain, Denmark, Hungary, Malta, Greece, Italy, Estonia, Romania, Ireland, Lithuania
    Measurement technique
    Face-to-face interviewCAPI (Computer Assisted Personal Interview) was used in those countries where this technique was available
    Description

    Since the early 1970s the European Commission´s Standard & Special Eurobarometer are regularly monitoring the public opinion in the European Union member countries. Principal investigators are the Directorate-General Communication and on occasion other departments of the European Commission or the European Parliament. Over time, candidate and accession countries were included in the Standard Eurobarometer Series. Selected questions or modules may not have been surveyed in each sample. Please consult the basic questionnaire for more information on country filter instructions or other questionnaire routing filters. In this study the following modules are included: 1. Poverty and social exclusion, 2. Mobile phone use in other EU countries, 3. Financial and economic crisis, 4. International trade.
    Topics: 1. Poverty and social exclusion: own life satisfaction (scale); satisfaction with family life, health, job satisfaction and satisfaction with standard of living (scale); personal definition of being poor; estimated spread of poverty in the own country; estimated proportion of poor in the total population; people who live in poverty in the own residential area; estimated increase of poverty: in the living area, in the own country, in the EU and in the world; reasons for poverty in general; social and individual causes of poverty; population group with the highest risk of poverty; absolutely neccessary long-lived assets for a minimum acceptable standard of living (heating facility, adequate housing, plenty of room to life and privacy, varied meals, repair or replacement of a refrigerator, an annual family vacation, medical care, dental care, access to banking services as well as to public transport, access to modern means of communication, to leisure and cultural events, electricity, gas and tap water); perceived impairments (deprivation) caused by poverty in the own country: access to decent housing, education, health care, regular meals, bank service, modern means of communication to the labor market, maintaining a network of friends and acquaintances, as well as the chance to start the own business; assessment of the financial situation and level of future generations compared to parents’ and grandparents’ generation; attitude towards poverty: the need for action by the government, too large income differences, duty of the government for the fair redistribution of wealth, more taxes for the rich, automatic reduction of poverty through economic growth, poverty will always exist, income inequality is necessary for economic development; perceived conflict groups: rich and poor, employers and workers, young and old, different racial and ethnic groups; general trust in people and trust in the parliament and the government (scale); trust in institutions in poverty reduction: EU, national government, local authorities, NGOs, religious institutions, private companies, citizens; causes of poverty in the own country: globalisation, low economic growth, profit motive, global financial system, politics, immigration, poor social system; primarily responsible for poverty reduction; importance of the role of the EU in combating poverty; prioritized policies of the state government to combat poverty; assessment of the effectiveness of public policies to reduce poverty; opinion on the extent of financial support for the poor; preference for state or private provision of jobs; attitude towards education fees; controlling for social spending; individual responsibility or responsibility of the government (welfare state) for the supply of citizens; attitude towards the minimum wage; optimistic about the future vs. personally perceived social exclusion; perceived difficulties to get access to financial services: bank account, bank card, credit card, consumer loans and a mortgage; personal risk of over-indebtedness; attitude towards loans: easy access to interest free loans for the poor, stronger verification of borrowers by credit institutions, easier access to start-up loans for unemployed, free financial advice for the poor, possibility for every individual to open a basic bank account; affordable housing in the residential environment; extent of homelessness in the residential environment and its recent change; reasonableness of the expenditure for the homeless by the national government and the local authorities; assumed reasons for homelessness: unemployment, no affordable housing, destruction of the living space by a natural disaster, indebtedness, illness, addiction to drugs or alcohol, family breakdown, loss of a close relative, mental health problems, lack of access to social services and support facilities, and lack of identification papers or free choice of this life; probability of own homelessness; personal charity actions to support poor people: monetary donations to charities, volunteer work in charities, help with recording in emergency shelters and with job search, giving clothes to poor people, buying...

  19. w

    Third General Census of Population and Housing 2005 - IPUMS Subset -...

    • microdata.worldbank.org
    • catalog.ihsn.org
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    Updated Apr 18, 2019
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    Bureau Central des Recensements et des Études de Population (2019). Third General Census of Population and Housing 2005 - IPUMS Subset - Cameroon [Dataset]. https://microdata.worldbank.org/index.php/catalog/1611
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    Dataset updated
    Apr 18, 2019
    Dataset provided by
    Bureau Central des Recensements et des Études de Population
    Minnesota Population Center
    Time period covered
    2005
    Area covered
    Cameroon
    Description

    Abstract

    IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

    The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

    Geographic coverage

    National coverage

    Analysis unit

    Household

    UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes - Special populations: Homeless; nomads

    UNIT DESCRIPTIONS: - Households: A standard household is a person or a group of people related or not, living in the same housing unit, often taking their meals together and working together on the other essential needs. This group generally recognizes the authority of one person who is called the Head of Household. - Group quarters: This is a group of people, who for non-family reasons which are mainly related to profession, health, school, denomination, or detention, live together in a specialized establishment or institution like a workers camp, military barracks, dormitories, a hospital with rooms, a convent, an orphanage, a prison.

    Universe

    All persons present in Cameroon at the time of the census, including visitors from other countries.

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    MICRODATA SOURCE: Central Bureau of Census and Population Studies

    SAMPLE DESIGN: Systematic sample of every 10th dwelling with a random start, drawn by MPC

    SAMPLE FRACTION: 10%

    SAMPLE UNIVERSE: Systematic sample of every 10th dwelling with a random start, drawn by MPC

    SAMPLE SIZE (person records): 1,772,359

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Four forms: Standard household questionnaire, communal household questionnaire, nomad questionnaire, and homeless questionnaire

    Response rate

    UNDERCOUNT: No available estimate

  20. g

    Eurobarometer 72.1 (Aug-Sep 2009)

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    • datacatalogue.cessda.eu
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    Updated Feb 3, 2012
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    Papacostas, Antonis (2012). Eurobarometer 72.1 (Aug-Sep 2009) [Dataset]. http://doi.org/10.4232/1.11136
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    application/x-spss-por(35094606), application/x-stata-dta(20143412), (2708), application/x-spss-sav(19252814)Available download formats
    Dataset updated
    Feb 3, 2012
    Dataset provided by
    GESIS search
    GESIS Data Archive
    Authors
    Papacostas, Antonis
    License

    https://www.gesis.org/en/institute/data-usage-termshttps://www.gesis.org/en/institute/data-usage-terms

    Time period covered
    Aug 28, 2009 - Sep 17, 2009
    Variables measured
    v441 - D10 GENDER, v14 - W5 WEIGHT EU6, v16 - W6 WEIGHT EU9, v18 - W7 WEIGHT EU10, v20 - W8 WEIGHT EU12, v442 - D11 AGE EXACT, v22 - W9 WEIGHT EU12+, v26 - W11 WEIGHT EU15, v30 - W14 WEIGHT EU25, v34 - W22 WEIGHT EU27, and 546 more
    Description

    Poverty and social exclusion, social services, climate change, and the national economic situation and statistics.

    Topics: 1. Poverty and social exclusion: own life satisfaction (scale); satisfaction with family life, health, job, and satisfaction with standard of living (scale); personal definition of poverty; incidence of poverty in the own country; estimated proportion of the poor in the total population; poor persons in the own residential area; estimated increase of poverty: in the residential area, in the own country, in the EU, and in the world; reasons for poverty in general; social and individual reasons for poverty; population group with the highest risk of poverty; things that are necessary to being able to afford to have a minimum acceptable standard of living (heating facility, adequate housing, a place to live with enough space and privacy, diversified meals, repairing or replacing a refrigerator or a washing machine, annual family holidays, medical care, dental care, access to banking services as well as to public transport, access to modern means of communication, to leisure and cultural activities, electricity, and running water); perceived deprivation through poverty in the own country regarding: access to decent housing, education, medical care, regular meals, bank services, modern means of communication, finding a job, starting up a business of one’s own, maintaining a network of friends and acquaintances; assessment of the financial situation of future generations and current generations compared to parent and grandparent generations; attitude towards poverty: necessity for the government to take action, too large income differences, national government should ensure the fair redistribution of wealth, higher taxes for the rich, economic growth reduces poverty automatically, poverty will always exist, income inequality is necessary for economic development; perceived tensions between population groups: rich and poor, management and workers, young and old, ethnic groups; general trust in people, in the national parliament, and the national government (scale); trust in institutions regarding poverty reduction: EU, national government, local authorities, NGOs, religious institutions, private companies, citizens; reasons for poverty in the own country: globalisation, low economic growth, pursuit of profit, global financial system, politics, immigration, inadequate national social protection system; primarily responsible body for poverty reduction; importance of the EU in the fight against poverty; prioritized policies of the national government to combat poverty; assessment of the effectiveness of public policies to reduce poverty; opinion on the amount of financial support for the poor; preference for governmental or private provision of jobs; attitude towards tuition fees; increase of taxes to support social spending; individual or governmental responsibility (welfare state) to ensure provision; attitude towards a minimum wage; optimism about the future; perceived own social exclusion; perceived difficulties to access to financial services: bank account, bank card, credit card, consumer loans, and mortgage; personal risk of over-indebtedness; attitude towards loans: interest free loans for the poor, stronger verification of borrowers by the credit institutions, easier access to start-up loans for the unemployed, free financial advice for the poor, possibility to open a basic bank account for everyone; affordable housing in the residential area; extent of homelessness in the residential area, and recent change; adequacy of the expenditures for the homeless by the national government, and the local authorities; assumed reasons for homelessness: unemployment, no affordable housing, destruction of the living space by a natural disaster, debt, illness, drug or alcohol addiction, family breakdown, loss of a close relative, mental health problems, lack of access to social services and support facilities, lack of identity papers, free choice of this life; probability to become homeless oneself; own support of homeless people: monetary donations to charities, volunteer work in a charity, help find access in emergency shelters and with job search, direct donations of clothes to homeless people, buying newspapers sold by homeless people, food donations; sufficient household income, or difficulties to make ends meet; ability to afford the heating costs, a week’s holiday once a year, and a meal with meat ever...

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Statista (2025). Rate of homelessness in the U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/727847/homelessness-rate-in-the-us-by-state/
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Rate of homelessness in the U.S. 2023, by state

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4 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 23, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2023
Area covered
United States
Description

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.

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