44 datasets found
  1. 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.

  2. 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.

  3. 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.

  4. Rate of homeless individuals by metro area in the U.S. 2017

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Rate of homeless individuals by metro area in the U.S. 2017 [Dataset]. https://www.statista.com/statistics/1007757/rate-homeless-individuals-metro-area-us/
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2017
    Area covered
    United States
    Description

    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 ****.

  5. d

    Directory Of Unsheltered Street Homeless To General Population Ratio 2012

    • catalog.data.gov
    • data.cityofnewyork.us
    • +3more
    Updated Sep 2, 2023
    + more versions
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    data.cityofnewyork.us (2023). Directory Of Unsheltered Street Homeless To General Population Ratio 2012 [Dataset]. https://catalog.data.gov/dataset/directory-of-unsheltered-street-homeless-to-general-population-ratio-2012
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    Dataset updated
    Sep 2, 2023
    Dataset provided by
    data.cityofnewyork.us
    Description

    "Ratio of Homeless Population to General Population in major US Cities in 2012. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. Unsheltered estimates are from 2011 except for Seattle and New York City (2012) and Chicago (2009). All General Population figures are from the 2010 U.S. Census enumeration."

  6. d

    Annual point-in-time (PIT) estimates of homelessness reveal stark...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Baginski, Pamela (2023). Annual point-in-time (PIT) estimates of homelessness reveal stark differences among San Francisco Bay Area counties [Dataset]. http://doi.org/10.7910/DVN/YQZCNK
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Baginski, Pamela
    Area covered
    San Francisco Bay Area
    Description

    INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

  7. 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.

  8. Number of homeless youth U.S. 2023, by state

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Number of homeless youth U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/727835/number-of-homeless-young-people-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

    In 2023, there were about ****** homeless youth living in California, the most out of any U.S. state. New York had the second-highest number of homeless youth in that year, at *****.

  9. e

    Homelessness (Survey of Homeless) - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Jul 5, 2023
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    (2023). Homelessness (Survey of Homeless) - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/86195dfd-f8fb-5f0b-855f-c976e15da1cc
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    Dataset updated
    Jul 5, 2023
    Description

    The social situation of the homeless in a Cologne suburb. Topics: Most important problems in the settlement; problems in the relationship between the settlement and surroundings; plans to leave; length of residence in the settlement and year of first utilization of a city shelter; reason for admission into a city shelter; type of quarters on first admission and before admission; frequency of moving into such accomodations and settlements; perceived deterioration from the move; number of rooms; possession of durable economic goods; defects in residence; number of children and schools attended or kindergarten; attitude to establishment of a special school in the part of town; perceived discrimination of one´s children in school; regular pocket-money for the children; place of leisure time of one´s children; contacts of one´s children outside of the settlement; person raising the children; perceived discrimination of the homeless; exercise of an honorary activity in the settlement; attitude to a self-help committee in the settlement; interest in participation in such a committee; assumed effectiveness of a community of interests of the homeless; most important tasks of such a community of interests; most important institutions as contact to improve the situation of the homeless; location of place of work; frequency of change of job; change of occupation; satisfaction with place of work; shopping place; possession of savings; manager of family income; decision-maker for expenditures; debts; eating main meal together; leisure activities in the settlement; contact persons in leisure time; leisure contacts outside the settlement; neighborhood contacts in the settlement; contacts with non-homeless; establishing these contacts on leisure time or through work; identification as Cologne resident or resident of the part of town; desire to move to another part of town; favorite part of town in Cologne; intensity of contact with the population in the part of town; contacts with residents of another settlement; participation in meetings of the Poll Buergerverein; assumed representation of interests of the homeless through this organization; most influencial personalities in the part of town; persons making a particular effort for the homeless; most important differences between the residents of one´s own settlement and another settlement in the part of town; knowledge of press reports and television reports about the homeless and judgement on validity; most important reasons for homelessness; most important measures to prevent homelessness; perceived differences between the homeless; filing a complaint against the city to obtain better housing; experiences with contacts with authorities; satisfaction with the manager of the settlement; most important task of a manager; anomy (scale); comparison of personal housing situation with that of parents; social origins; social mobility compared with father and father-in-law; contacts with relatives; judgement of relatives about living in this settlement; relatives likewise living in emergency shelters; personal condition of health; number of sick family members and type of illnesses; recommendations on dealing with the homeless; society or the individual as responsible for one´s own homelessness; desire for integration in a normal residential area; personal extent of commiting crimes and conviction; type of offenses; perceived improvement in living conditions in the emergency shelter; comparison of the situation between the settlement and a temporary shelter; place of birth; length of residence in Cologne; re-married; religiousness; club memberships; extent of club activity; party preference; assumed effectiveness of this survey on the situation of the homeless. Interviewer rating: name sign on door; description of residential furnishings regarding family pictures, other pictures, knick-knacks, religious figures and possession of books; condition of windows, wallpaper and furniture; length of interview; number of persons present during interview; carrying out house work by the person interviewed during the interview; conduct of other persons present during the conversation; willingness of respondent to cooperate. Die soziale Situation von Obdachlosen in einem Kölner Vorort. Themen: Wichtigste Probleme in der Siedlung; Probleme im Verhältnis zwischen Siedlung und Umgebung; Auszugspläne; Wohndauer in der Siedlung und Jahr der ersten Inanspruchnahme einer städtischen Unterkunft; Grund für die Einweisung in eine städtische Unterkunft; Unterkunftstyp bei der ersten Einweisung und vor der Einweisung; Umzugshäufigkeit in solchen Unterkünften und Siedlungen; empfundene Verschlechterung durch den Umzug; Wohnraumzahl; Besitz langlebiger Wirtschaftsgüter; Schäden in der Wohnung; Kinderzahl und besuchte Schulen bzw. Kindergärten; Einstellung zur Einrichtung einer Sonderschule im Stadtteil; empfundene Diskriminierung der Kinder in der Schule; regelmäßiges Taschengeld für die Kinder; Freizeitort der Kinder; Kontakte der Kinder außerhalb der Siedlung; Erziehungsperson für die Kinder; empfundene Diskriminierung der Obdachlosen; Ausüben einer ehrenamtlichen Tätigkeit in der Siedlung; Einstellung zu einem Selbsthilfekomitee in der Siedlung; Interesse an der Beteiligung in einem solchen Komitee; vermutete Wirksamkeit einer Interessengemeinschaft der Obdachlosen; wichtigste Aufgaben einer solchen Interessengemeinschaft; wichtigste Institutionen als Ansprechpartner zur Verbesserung der Situation der Obdachlosen; Ortslage der Arbeitsstätte; Häufigkeit von Arbeitsplatzwechsel; Berufswechsel; Zufriedenheit mit der Arbeitsstelle; Einkaufsort; Besitz von Ersparnissen; Verwalter des Familieneinkommens; Entscheider über Ausgaben; Schulden; gemeinsame Einnahme der Hauptmahlzeit; Freizeitaktivitäten in der Siedlung; Kontaktpersonen in der Freizeit; Freizeitkontakte außerhalb der Siedlung; Nachbarschaftskontakte in der Siedlung; Kontakte zu Nichtobdachlosen; Aufnahme dieser Kontakte in der Freizeit oder durch die Arbeit; Identifikation als Kölner oder Bewohner des Stadtteils; Umzugswunsch in einen anderen Stadtteil; beliebtester Stadtteil in Köln; Intensität des Kontaktes zur Bevölkerung im Stadtteil; Kontakte zu Bewohnern einer anderen Siedlung; Beteiligung an Versammlungen des Poller Bürgervereins; vermutete Interessenvertretung der Obdachlosen durch diesen Verein; einflußreichste Persönlichkeiten im Stadtteil; Personen, die sich besonders für die Obdachlosen einsetzen; wichtigste Unterschiede zwischen den Bewohnern der eigenen Siedlung und einer weiteren Siedlung im Stadtteil; Kenntnis von Presseberichten und Fernsehberichten über die Obdachlosen und Beurteilung des Wahrheitsgehaltes; wichtigste Gründe für Obdachlosigkeit; wichtigste Vorbeugungsmaßnahmen zur Verhinderung von Obdachlosigkeit; perzipierte Unterschiede zwischen Obdachlosen; Beschwerdeführung gegen die Stadt zur Bereitstellung einer besseren Wohnung; Erfahrungen mit Behördenkontakten; Zufriedenheit mit dem Verwalter der Siedlung; wichtigste Aufgabe eines Verwalters; Anomie (Skala); Vergleich der eigenen Wohnsituation mit der der Eltern; soziale Herkunft; soziale Mobilität gegenüber dem Vater und dem Schwiegervater; Verwandtschaftskontakte; Urteil der Verwandtschaft über das Wohnen in dieser Siedlung; Verwandte, die ebenfalls in Notunterkünften leben; eigener Gesundheitszustand; Zahl der erkrankten Familienmitglieder und Art der Krankheiten; Vorschläge zur Behandlung von Obdachlosen; Gesellschaft oder Individuum als Verantwortlicher für die eigene Obdachlosigkeit; Wunsch nach Integration in eine normale Wohngegend; eigene Straffälligkeit und Verurteilung; Art der Delikte; empfundene Verbesserung der Lebensbedingungen in der Notunterkunft; Vergleich der Situation zwischen der Siedlung und einem Übergangshaus; Geburtsort; Wohndauer in Köln; wiederverheiratet; Religiosität; Vereinsmitgliedschaften; Umfang der Vereinstätigkeit; Parteipräferenz; vermutete Wirksamkeit dieser Befragung auf die Situation der Obdachlosen. Demographie: Alter; Geschlecht; Familienstand; Kirchgangshäufigkeit; Schulbildung; Berufstätigkeit; Einkommen. Interviewerrating: Namensschild an der Tür; Beschreibung der Wohnungseinrichtung bezüglich Familienbilder, sonstiger Bilder, Nippfiguren, religiöser Figuren und Bücherbesitz; Zustand der Fenster, Tapeten und Möbel; Interviewdauer; Anzahl der anwesenden Personen beim Interview; Erledigung von Haushaltsarbeiten der befragten Person während des Interviews; Verhalten der übrigen Anwesenden während des Gesprächs; Kooperationsbereitschaft des Befragten.

  10. a

    Homelessness Risk Assessment App

    • uds-open-data-centerforgis.hub.arcgis.com
    Updated Jun 1, 2023
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    University of Louisville Center for GIS (2023). Homelessness Risk Assessment App [Dataset]. https://uds-open-data-centerforgis.hub.arcgis.com/items/e2f84306df3044268cbf920f7bed817b
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    Dataset updated
    Jun 1, 2023
    Dataset authored and provided by
    University of Louisville Center for GIS
    Description

    Homelessness has been a consistent problem for the city of Louisville for decades now. Despite efforts from the city government and local nonprofits, homelessness increased 139% last year alone. The Covid-19 pandemic significantly worsened the crisis, but the risk factors that contribute to homelessness are still endemic across the city: lack of affordable housing, lack of access to physical and mental healthcare, stagnant wages, etc. Homelessness has negative effects on mortality, personal health of the homeless, and public health in general (also see here, no paywall). When I recently attended a strategy meeting for the Louisville Downtown Partnership, one of the top issues voted by attendees was the rise of homelessness downtown. This could come from genuine care or that many Americans associate homeless people with crime. Everyone benefits when the issues that cause homelessness are addressed effectively, and a vital part of that is knowing what areas are most at-risk.The app above was made to map certain risk factors across Jefferson County. The risk factors include percent of households with 50%+ income going to rent, persons without health insurance coverage, percent of households at or below the poverty line, percent of households using public assistance, percent of persons reporting extensive physical and mental distress, unemployment, along with other economic and health-based factors. This doesn’t include every possible factor that could cause homelessness, but many that have strong effects. A dummy census tract was made with all the worst possible outcomes for risk factors, which was then used to rank the similarity of every census tract in Jefferson County; the lower the rank, the more at-risk the tract is. The app allows you to click through every tract in the county and see the ten most at-risk ones.The most at-risk places tend to line up with the west end and areas of the city that were historically redlined. These areas also saw mass amounts of “urban renewal” in the 60s and 70s. They also tend to line up with areas of the city that face the highest eviction rates (thanks to Ryan Massey for pointing this out).

  11. d

    Data from: Homeless Shelters.

    • datadiscoverystudio.org
    • data.wu.ac.at
    csv, json, rdf, xml
    Updated Feb 3, 2018
    + more versions
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    (2018). Homeless Shelters. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/158d5f3bd2d2412fb41f40979779951c/html
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    rdf, xml, json, csvAvailable download formats
    Dataset updated
    Feb 3, 2018
    Description

    description: This data set shows the location of Baltimore City's Tansitional and Emergency "Homeless" Shelter Facilities. However, this is not a complete list. It is the most recent update (2008), and is subjected to change. The purpose of this data set is to aid Baltimore City organizations to best identify facilities to aid the homeless population. The data is broken down into two categories: Emergency Shelter and Transitional Housing. Please find the two definitions below. The first is simply _ _ _shelter _ and the second is a more involved program that is typically a longer stay. Emergency Shelter: Any facility with overnight sleeping accommodations, the primary purpose of which is to provide temporary shelter for the homeless in general or for specific populations of homeless persons. The length of stay can range from one night up to as much as six months. Transitional Housing: a project that is designed to provide housing and appropriate support services to homeless persons to facilitate movement to independent living within 24 months. These data set was provided by Greg Sileo, Director of the Mayor's Office of Baltimore Homeless Services.; abstract: This data set shows the location of Baltimore City's Tansitional and Emergency "Homeless" Shelter Facilities. However, this is not a complete list. It is the most recent update (2008), and is subjected to change. The purpose of this data set is to aid Baltimore City organizations to best identify facilities to aid the homeless population. The data is broken down into two categories: Emergency Shelter and Transitional Housing. Please find the two definitions below. The first is simply _ _ _shelter _ and the second is a more involved program that is typically a longer stay. Emergency Shelter: Any facility with overnight sleeping accommodations, the primary purpose of which is to provide temporary shelter for the homeless in general or for specific populations of homeless persons. The length of stay can range from one night up to as much as six months. Transitional Housing: a project that is designed to provide housing and appropriate support services to homeless persons to facilitate movement to independent living within 24 months. These data set was provided by Greg Sileo, Director of the Mayor's Office of Baltimore Homeless Services.

  12. e

    Homelessness (Investigation in Part of Town) - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Apr 25, 2023
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    (2023). Homelessness (Investigation in Part of Town) - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/ec6f42c2-77e8-5914-a011-68ae50da068a
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    Dataset updated
    Apr 25, 2023
    Description

    Community integration of homeless in a Cologne suburb. Topics: Characterization of the suburb Poll; closeness with the suburb or with the city of Cologne; length of residence in the suburb; previous place of residence and moving frequency; rent costs; size of household and number of rooms; possession of durable economic goods; year of construction of building; satisfaction with residence; moving plans; possible destination of moving; particular advantages of the residential area in Poll; favorite part of town of Cologne; familial relations in the part of town or in the entire city; frequency of contact with parents, grandparents, children, siblings and the rest of the relatives; distribution of circle of friends about the part of town and the other parts of the city; contacts with neighbors and colleagues; location of place of work; frequency of change of place of work; occupational mobility; desire for remaining in the part of town given a change of occupation; shopping habits; frequency of trips downtown; leisure activities and place of these leisure activities; club membership; time extent of club activity; participation in activities of the Poll Buergerverein; significance of this organization; judgement on the moving of schools; most influencial personalities in the suburb; most important integration factors in the part of town; influence of the part of town on the entire city; anomy (scale); evaluation of despicability of selected crimes; most important reasons for development of so-called Rocker groups; most effective measures to reduce crime; perceived differences in the old and new part of town; identification of areas that belong together in the part of town and assignment of different social groups to the parts of town; assignment of social groups to the homeless settlement; significance of the homeless problem and preferred measures to eliminate it; measures to prevent homelessness; attitude to differential treatment of the homeless and the rest of the population; recommendations on treatment of the homeless; judgement on the proportion of homeless in the part of town; personal contacts with the homeless; intensity of contacts; fear of contact and social distance to the homeless; preferred measures in view of the two homeless settlements in Poll; perceived differences among the homeless; typical characteristics with which one can recognize the homeless; judgement on a media report about the homeless in Poll; judgement on the municipal facilities in the part of town; personal importance of the existence of such facilities; religiousness. Interviewer rating: residential building size and willingness of respondent to cooperate. Gemeindliche Integration von Obdachlosen in einem Kölner Vorort. Themen: Charakterisierung des Vororts Poll; Verbundenheit mit dem Vorort oder mit der Stadt Köln; Wohndauer im Vorort; vorheriger Wohnort und Umzugshäufigkeit; Mietkosten; Haushaltsgröße und Anzahl der Räume; Besitz langlebiger Wirtschaftsgüter; Baujahr des Hauses; Zufriedenheit mit der Wohnung; Umzugspläne; mögliches Umzugsziel; besondere Vorzüge der Wohnlage in Poll; beliebtester Stadtteil von Köln; verwandtschaftliche Beziehungen im Stadtteil bzw. in der gesamten Stadt; Kontakthäufigkeit mit den Eltern, Großeltern, Kindern, Geschwistern und den übrigen Verwandten; Verteilung des Bekanntenkreises über den Stadtteil und die übrigen Teile der Stadt; Kontakte zu Nachbarn und Arbeitskollegen; Ortslage der Arbeitsstätte; Häufigkeit des Wechselns der Arbeitsstätte; berufliche Mobilität; Wunsch nach Verbleiben im Stadtteil bei Berufswechsel; Einkaufsgewohnheiten; Besuchshäufigkeit in der City; Freizeitaktivitäten und Ort dieser Freizeitaktivitäten; Vereinsmitgliedschaft; zeitlicher Umfang von Vereinstätigkeit; Teilnahme an Aktivitäten des Poller Bürgervereins; Bedeutung dieses Vereins; Beurteilung der Verlegung von Schulen; einflußreichste Persönlichkeiten im Vorort; wichtigste Integrationsfaktoren im Stadtteil; Einfluß des Stadtteils auf die ganze Stadt; Anomie (Skala); Bewertung der Verwerflichkeit von ausgewählten Straftaten; wichtigste Ursachen für das Entstehen sogenannter Rockergruppen; wirksamste Maßnahmen zur Reduzierung der Kriminalität; perzipierte Unterschiede im alten und neuen Stadtteil; Identifizierung zusammengehörender Gebiete im Stadtteil und Zuordnung unterschiedlicher sozialer Gruppen zu den Stadtteilen; Zuordnung sozialer Gruppen zur Obdachlosensiedlung; Bedeutung des Obdachlosenproblems und präferierte Maßnahmen zur Beseitigung; vorbeugende Maßnahmen zur Verhinderung von Obdachlosigkeit; Einstellung zur differenzierten Behandlung von Obdachlosen und der übrigen Bevölkerung; Vorschläge zur Behandlung von Obdachlosen; Beurteilung des Obdachlosenanteils im Stadtteil; eigene Kontakte zu Obdachlosen; Intensität der Kontakte; Berührungsängste und soziale Distanz zu Obdachlosen; präferierte Maßnahmen im Hinblick auf die beiden Obdachlosensiedlungen in Poll; perzipierte Unterschiede bei den Obdachlosen; charakteristische Merkmale, an denen man Obdachlose erkennen kann; Beurteilung eines Medienberichts über die Obdachlosen in Poll; Beurteilung der kommunalen Einrichtungen im Stadtteil; persönliche Wichtigkeit der Existenz solcher Einrichtungen; Religiosität. Demographie: Alter; Familienstand; Kinderzahl; Kirchgangshäufigkeit; Schulbildung; Berufstätigkeit; Einkommen; Haushaltsgröße. Interviewerrating: Wohnhausgröße und Kooperationsbereitschaft des Befragten.

  13. d

    Number of People Experiencing Homelessness

    • data.ore.dc.gov
    Updated Aug 20, 2024
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    City of Washington, DC (2024). Number of People Experiencing Homelessness [Dataset]. https://data.ore.dc.gov/datasets/number-of-people-experiencing-homelessness
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    Dataset updated
    Aug 20, 2024
    Dataset authored and provided by
    City of Washington, DC
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The most recent rate of homelessness is calculated using ACS population estimates from the previous year, unless otherwise noted.

    Data Source: HUD's Annual Homeless Assessment Report (AHAR) Point-in-Time (PIT) Estimates by State and American Community Survey (ACS) 1-Year Estimates

    Why this MattersSafe, adequate, and stable housing is a human right and essential for the health and well-being of individuals, families, and communities.People who experience homelessness also struggle to maintain access to healthcare, employment, education, healthy relationships, and other basic necessities in life, according to the DC Interagency Council on Homelessness Strategic Plan.BIPOC populations are disproportionately affected by homelessness due to housing discrimination, mass incarceration, and other policies that have limited socioeconomic opportunities for Black, Latino, and other people of color.

    The District's Response Strategic investments in proven strategies for driving down homelessness, including the Career Mobility Action Plan (Career MAP) program, operation of non-congregate housing, and expansion of the District’s shelter capacity.Homelessness prevention programs for at-risk individuals and families, such as emergency rental assistance, targeted affordable housing, and permanent supporting housing.Programs and services to enhance resident’s economic and employment security and ensure access to affordable housing.

  14. Community Housing & Homeless Shelters in the US - Market Research Report...

    • ibisworld.com
    Updated Dec 15, 2024
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    IBISWorld (2024). Community Housing & Homeless Shelters in the US - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-states/market-research-reports/community-housing-homeless-shelters-industry/
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    Dataset updated
    Dec 15, 2024
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2014 - 2029
    Description

    Community housing and homeless shelters, mostly small nonprofits, heavily depend on government and charitable funding. According to the Annual Homelessness Assessment Report (AHAR 2023), out % of 653,100 individuals experiencing homelessness, 60.7% were sheltered, while 39.3% remained unsheltered, highlighting a significant underserved market. The pandemic increased unemployment, housing costs and poverty levels, raising demand for shelter services, with government support aiding many establishments. As a result, industry revenue grew at a compound annual growth rate (CAGR) of 5.0%, reaching $21.9 billion by 2024, with a 2.0% climb in 2024 alone. Notably, industry profit rose to 7.0%, with most profit reinvested into operations, as 96.0% of shelters are nonprofits and 98.0% of community housing providers are federally tax-exempt. Individual service needs vary widely. About one-third of shelter services cater to emergency housing. Six out of ten people experiencing homelessness are in urban areas, explaining the concentration of shelters in cities. Also, three out of ten people experiencing homelessness come from a family with children. Catering to a diverse demographic (families, youths, adults, veterans) can restrict economies of scale, but specialized services can attract targeted charitable contributions. Urban shelters face higher rents and costs because of competitive pressures. However, they can gain from group purchasing, network development for better rates and spreading positive information to boost donations. Service provision is expected to remain fragmented, with shelters competing intensely for grants. Donations will fluctuate depending on the economy, increasing during booms and decreasing in downturns. Shelters integrating telehealth, training and security measures may attract a broader group, reducing unsheltered homelessness and increasing revenue for service and infrastructure improvements. Despite favorable economic trends, such as decreasing poverty and unemployment rates and slower housing price growth, revenue will strengthen at a CAGR of only 0.2%, reaching $22.0 billion by 2029.

  15. d

    Homelessness (Investigation in Part of Town)

    • da-ra.de
    • dbk.gesis.org
    Updated 1995
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    Fritz Sack; Peter Höhmann (1995). Homelessness (Investigation in Part of Town) [Dataset]. http://doi.org/10.4232/1.2578
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    Dataset updated
    1995
    Dataset provided by
    GESIS Data Archive
    da|ra
    Authors
    Fritz Sack; Peter Höhmann
    Time period covered
    1969 - 1970
    Description

    A survey of the homeless in two settlements of the part of town is archived under ZA Study No. 2579.

  16. Tables on homelessness

    • gov.uk
    Updated Jul 22, 2025
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    Ministry of Housing, Communities and Local Government (2025). Tables on homelessness [Dataset]. https://www.gov.uk/government/statistical-data-sets/live-tables-on-homelessness
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    Dataset updated
    Jul 22, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Ministry of Housing, Communities and Local Government
    Description

    Statutory homelessness live tables

    Statutory homelessness England Level Time Series

    https://assets.publishing.service.gov.uk/media/687a5fc49b1337e9a7726bb4/StatHomeless_202503.ods">Statutory homelessness England level time series "live tables" (ODS, 314 KB)

    Detailed local authority-level tables

    For quarterly local authority-level tables prior to the latest financial year, see the Statutory homelessness release pages.

    https://assets.publishing.service.gov.uk/media/687e211892957f2ec567c5c6/Detailed_LA_202503.ods">Statutory homelessness in England: January to March 2025

     <p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute"><abbr title="OpenDocument Spreadsheet" class="gem-c-attachment_abbr">ODS</abbr></span>, <span class="gem-c-attachment_attribute">1.2 MB</span></p>
    
    
    
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       This file is in an <a href="https://www.gov.uk/guidance/using-open-document-formats-odf-in-your-organisation" target="_self" class="govuk-link">OpenDocument</a> format
    

    This file may not be suitable for users of assistive technology.

    Request an accessible format.

      If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email <a href="mailto:alternativeformats@communities.gov.uk" target="_blank" class="govuk-link">alternativeformats@communities.gov.uk</a>. Please tell us what format you need. It will help us if you say what assistive technology you use.
    

    <a class="govuk-link" target="_self" data

  17. e

    Causes of Homelessness among Older People in Four Cities in England, and...

    • b2find.eudat.eu
    Updated Oct 22, 2023
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    (2023). Causes of Homelessness among Older People in Four Cities in England, and Boston, Massachusetts, 2001-2003 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/23f4f6d5-c163-5644-9970-3e36bd06590e
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    Dataset updated
    Oct 22, 2023
    Area covered
    Massachusetts, Boston, England
    Description

    Abstract copyright UK Data Service and data collection copyright owner. A comparative study of the causes of new episodes of homelessness among people aged 50 or more years was undertaken in Boston, Massachusetts (USA), Melbourne, Australia, and four English cities. The aims were to make a substantial contribution to the predominantly American debate on the causes of homelessness, and to make practice recommendations for the improvement of prevention. The study had several objectives. It aimed to collect information about the antecedents, triggers and risk factors for becoming homeless in later life and about the national and local policy and service contexts. Furthermore, the researchers aimed to analyse and interpret the findings with reference to an integrated model of the causes of homelessness that represented structural and policy factors, including housing, health and social service organisation and delivery factors, and personal circumstances, events, problems and dysfunctions. The aim was to do this collaboratively, by drawing on the project partners' experience and knowledge. Finally, it was hoped to develop recommendations for housing, primary health care and social welfare organisations for the prevention of homelessness. This was to be done by identifying the common sequences and interactions of events that precede homelessness and their markers (or 'early warning' indicators) and by holding workshops in England with practitioners and their representative organisations on new ways of working. By the study of contrasting welfare and philanthropic regimes in a relatively homogeneous category of homeless incidence (i.e. recent cases among late middle-aged and older people), it was hoped that valuable insights into the relative contributions of the policy, service and personal factors would be obtained. The study focused on older people who had recently become homeless, purposely to gather detailed and reliable information about the prior and contextual circumstances. To have included people who had been homeless for several years would have reduced the quality of the data because of 'recall' problems. Users should note that data from the Australian sample for the study are not included in this dataset. Main Topics: The data file includes information about the English respondents and those from Boston. It was compiled in two stages. The first stage involved each project partner entering the pre-coded responses into the file. All partners then identified themes and created codes for the open-ended responses, and the resulting variables were added. Data quality-control procedures included blind checks of the data coding and keying. The first 200 variables pertain to information collected from the respondents. They comprise descriptive variables of the circumstances prior to homelessness, including housing tenure during the three years prior to the survey, previous homelessness, employment history, income, health and addiction problems, and contacts with family, friends and formal services. The respondents were asked to rate whether specific factors were implicated in becoming homeless, and where appropriate, a following open-ended question sought elaboration. The remaining variables comprise information collected from the respondents' 'key workers' about their understanding of the events and states that led to their clients becoming homeless. No sampling frame was available. The sample profiles have been compared with those of all homeless people (not just the recently homeless) in the study locations, most effectively in London and Boston. No gross biases were revealed. The samples represent a large percentage of the clients who presented to the collaborating organisations during the study period and who gave their informed consent to participate. Agreed definitions of homelessness were: sleeping on the streets or in temporary accommodation such as shelters; being without accommodation following eviction or discharge from prison or hospital; living temporarily with relatives or friends because the person has no accommodation, but only if the stay had not exceeded six months, and the person did not pay rent and was required to leave. People who had been previously homeless were included in the survey if they had been housed for at least 12 months prior to the current episode of homelessness. Face-to-face interview Self-completion the 'key workers' (case managers) completed questionnaires about their assessments of the respondents’ problems and of the events and states that led to homelessness. Further clarifications and checks were made by telephone.

  18. Number of homeless people in London 2025, by borough

    • statista.com
    Updated Jun 30, 2025
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    Statista (2025). Number of homeless people in London 2025, by borough [Dataset]. https://www.statista.com/statistics/381365/london-homelessness-rough-sleepers-by-london-borough/
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    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 1, 2024 - Mar 31, 2025
    Area covered
    United Kingdom (England), London
    Description

    In 2024/25, there were ***** rough sleepers reported in Westminster, making it the London borough with the highest number of rough sleepers in that year. Other boroughs which also had a high number of homeless people included, Camden, Ealing, and Southwark.

  19. g

    Obdachlosigkeit (Obdachlosenbefragung)

    • search.gesis.org
    • pollux-fid.de
    • +1more
    Updated Apr 13, 2010
    + more versions
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    Sack, Fritz; Höhmann, Peter (2010). Obdachlosigkeit (Obdachlosenbefragung) [Dataset]. http://doi.org/10.4232/1.2579
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    Dataset updated
    Apr 13, 2010
    Dataset provided by
    GESIS search
    GESIS Data Archive
    Authors
    Sack, Fritz; Höhmann, Peter
    License

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

    Description

    The social situation of the homeless in a Cologne suburb. Topics: Most important problems in the settlement; problems in the relationship between the settlement and surroundings; plans to leave; length of residence in the settlement and year of first utilization of a city shelter; reason for admission into a city shelter; type of quarters on first admission and before admission; frequency of moving into such accomodations and settlements; perceived deterioration from the move; number of rooms; possession of durable economic goods; defects in residence; number of children and schools attended or kindergarten; attitude to establishment of a special school in the part of town; perceived discrimination of one´s children in school; regular pocket-money for the children; place of leisure time of one´s children; contacts of one´s children outside of the settlement; person raising the children; perceived discrimination of the homeless; exercise of an honorary activity in the settlement; attitude to a self-help committee in the settlement; interest in participation in such a committee; assumed effectiveness of a community of interests of the homeless; most important tasks of such a community of interests; most important institutions as contact to improve the situation of the homeless; location of place of work; frequency of change of job; change of occupation; satisfaction with place of work; shopping place; possession of savings; manager of family income; decision-maker for expenditures; debts; eating main meal together; leisure activities in the settlement; contact persons in leisure time; leisure contacts outside the settlement; neighborhood contacts in the settlement; contacts with non-homeless; establishing these contacts on leisure time or through work; identification as Cologne resident or resident of the part of town; desire to move to another part of town; favorite part of town in Cologne; intensity of contact with the population in the part of town; contacts with residents of another settlement; participation in meetings of the Poll Buergerverein; assumed representation of interests of the homeless through this organization; most influencial personalities in the part of town; persons making a particular effort for the homeless; most important differences between the residents of one´s own settlement and another settlement in the part of town; knowledge of press reports and television reports about the homeless and judgement on validity; most important reasons for homelessness; most important measures to prevent homelessness; perceived differences between the homeless; filing a complaint against the city to obtain better housing; experiences with contacts with authorities; satisfaction with the manager of the settlement; most important task of a manager; anomy (scale); comparison of personal housing situation with that of parents; social origins; social mobility compared with father and father-in-law; contacts with relatives; judgement of relatives about living in this settlement; relatives likewise living in emergency shelters; personal condition of health; number of sick family members and type of illnesses; recommendations on dealing with the homeless; society or the individual as responsible for one´s own homelessness; desire for integration in a normal residential area; personal extent of commiting crimes and conviction; type of offenses; perceived improvement in living conditions in the emergency shelter; comparison of the situation between the settlement and a temporary shelter; place of birth; length of residence in Cologne; re-married; religiousness; club memberships; extent of club activity; party preference; assumed effectiveness of this survey on the situation of the homeless. Interviewer rating: name sign on door; description of residential furnishings regarding family pictures, other pictures, knick-knacks, religious figures and possession of books; condition of windows, wallpaper and furniture; length of interview; number of persons present during interview; carrying out house work by the person interviewed during the interview; conduct of other persons present during the conversation; willingness of respondent to cooperate.

  20. d

    Compendium – LBOI section 2: Housing and homelessness

    • digital.nhs.uk
    xlsx
    Updated Sep 22, 2015
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    (2015). Compendium – LBOI section 2: Housing and homelessness [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/compendium-local-basket-of-inequality-indicators-lboi/current/section-2-housing-and-homelessness
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    xlsx(356.0 kB)Available download formats
    Dataset updated
    Sep 22, 2015
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Time period covered
    Jun 1, 2003 - Dec 31, 2014
    Area covered
    England
    Description

    DCLG collects information on the number of households with or expecting dependent children, who are, at the end of each quarter, in any of the following types of temporary accommodation: • Bed and Breakfast (B&B) - typically involves the use of privately managed hotels where households share at least some facilities and meals are provided; • Annexe accommodation - is also generally paid on a nightly basis, privately managed but may not be part of a B&B hotel and may not involve shared facilities. A distinction is made on the basis of whether at least some facilities are shared or there is exclusive use of all facilities; • Hostel accommodation - hostels assumes shared accommodation, owned or leased and managed by either a local authority, housing association or non-profit making organisation; includes reception centres and emergency units; • Private sector accommodation - dwellings may be leased from the private sector, either directly, or by a local authority or a Registered Social Landlord; • Other - includes mobile homes, such as caravans, ‘demountables’, ‘portacabins’ and ‘transposables.’ The last 20 years have seen a rapid increase in homelessness, with the numbers of officially homeless families peaking in the early 1990s. In 1997 102,000 were statutory homeless, i.e. they met the definition of homelessness laid down in the 1977 Housing (Homeless Persons) Act. Other homeless people included rough sleepers - those without any accommodation at all - and hostel users. In 1997, fifty eight per cent of statutory homeless households had dependent children, and a further 10 per cent had a pregnant household member, compared to 51% and 10% respectively in 2003. Poor housing environments contribute to ill health through poor amenities, shared facilities and overcrowding, inadequate heating or energy inefficiency. The highest risks to health in housing are attached to cold, damp and mouldy conditions. In addition, those in very poor housing, such as homeless hostels and bedsits, are more likely to suffer from poor mental and physical health than those whose housing is of higher quality. People living in temporary accommodation of the bed and breakfast kind have high rates of some infections and skin conditions and children have high rates of accidents. Living in such conditions engenders stress in the parents and impairs normal child development through lack of space for safe play and exploration. Whilst cause and effect are hard to determine, at the very least homelessness prevents the resolution of associated health problems. Legacy unique identifier: P01088

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

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6 scholarly articles cite this dataset (View in Google Scholar)
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.

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