100+ datasets found
  1. Homeownership rate in Europe 2023, by country

    • statista.com
    Updated Sep 5, 2024
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    Statista (2024). Homeownership rate in Europe 2023, by country [Dataset]. https://www.statista.com/statistics/246355/home-ownership-rate-in-europe/
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    Dataset updated
    Sep 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Europe
    Description

    In the presented European countries, the homeownership rate extended from 42 percent in Switzerland to as much as 96 percent in Albania. Countries with more mature rental markets, such as France, Germany, the UK and Switzerland, tended to have a lower homeownership rate compared to the frontier countries, such as Lithuania or Slovakia. The share of house owners among the population of all 27 European countries has remained relatively stable over the past few years. Average cost of housing Countries with lower homeownership rates tend to have higher house prices. In 2023, the average transaction price for a house was notably higher in Western and Northern Europe than in Eastern and Southern Europe. In Austria - one of the most expensive European countries to buy a new dwelling in - the average price was three times higher than in Greece. Looking at house price growth, however, the most expensive markets recorded slower house price growth compared to the mid-priced markets. Housing supply With population numbers rising across Europe, the need for affordable housing continues. In 2023, European countries completed between one and six housing units per 1,000 citizens, with Ireland, Poland, and Denmark responsible heading the ranking. One of the major challenges for supplying the market with more affordable homes is the rising construction costs. In 2021 and 2022, housing construction costs escalated dramatically due to soaring inflation, which has had a significant effect on new supply.

  2. Data from: Residential housing segregation and urban tree canopy in 37 US...

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    • portal.edirepository.org
    Updated Dec 16, 2020
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    Dexter H Locke (2020). Residential housing segregation and urban tree canopy in 37 US Cities; data in support of Locke et al 2021 in npj Urban Sustainability [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F5008%2F2
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    Dataset updated
    Dec 16, 2020
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Dexter H Locke
    Time period covered
    Jan 1, 1930 - Dec 31, 2018
    Area covered
    Variables measured
    city, Can_P, holc_grade
    Description

    Our goal in this paper is to examine whether there are similar patterns in the distribution of tree canopy by Home Owners’ Loan Corporation (HOLC) graded neighborhoods across 37 cities. A pre-print of the paper can be found here: https://osf.io/preprints/socarxiv/97zcs This data packages contains: 1. City-specific file geodatabases with features classes of the HOLC polygons obtained from the Mapping Inequality Project https://dsl.richmond.edu/panorama/redlining/ , and tables summarizing tree canopy, and in some cases other land cover classes. 2. An *.R script that replicates all of the analyses, graphs, and tables in the paper. Other double checks, exploratory, and miscellaneous outputs are created by the script too as a bonus. Everything in the paper can be done with the script; additional work outputs are also created. 3. A *.csv file containing city, the HOLC grade, and the percent tree canopy cover. This can be used to create the main findings of the paper and this flat file is provided as an alternative to running the R script to extract information from the geodatabases, combine, and analyze them. The intention is that this file is more widely accessible; the underlying information is the same. Redlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remains unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect, and may improve quality of life in cities. In our prior research in Baltimore, MD, we discovered that redlining policy influenced the location and allocation of trees and parks. Our analysis of 37 metropolitan areas here shows that areas formerly graded D, which were mostly inhabited by racial and ethnic minorities, have on average ~23% tree canopy cover today. Areas formerly graded A, characterized by U.S.-born white populations living in newer housing stock, had nearly twice as much tree canopy (~43%). Results are consistent across small and large metropolitan regions. The ranking system used by Home Owners’ Loan Corporation to assess loan risk in the 1930s parallels the rank order of average percent tree canopy cover today.

  3. Cities with the highest number of free housing transactions in Spain 2023

    • statista.com
    Updated Jan 30, 2025
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    Statista (2025). Cities with the highest number of free housing transactions in Spain 2023 [Dataset]. https://www.statista.com/statistics/765368/transactions-from-living-place-free-by-municipalities-ranking-spain/
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    Dataset updated
    Jan 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Spain
    Description

    In 2023, Madrid led the ranking of municipalities with the highest number of free housing transactions in Spain with almost 40,000 free property purchases. Barcelona ranked second on the list, with a total amount that stood at approximately 16,000 housing transactions. Free housing transactions refers to purchases of real estate raised to public deed before a notary and not subject to any public protection regime.

  4. USA 2020 Census Housing Characteristics - Place Geographies

    • hub.arcgis.com
    • data-isdh.opendata.arcgis.com
    • +1more
    Updated Jun 6, 2023
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    Esri (2023). USA 2020 Census Housing Characteristics - Place Geographies [Dataset]. https://hub.arcgis.com/maps/304613e0a1704c7a9e99574792a86383
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    Dataset updated
    Jun 6, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows housing units by tenure (owner or renter), and vacancy status data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H2, H3, H4, H4B, H4C, H4D, H4E, H4F, H4G, H4H, H4I, H5, H9, H12, H12B, H12C, H12D, H12E, H12F, H12G, H12H, H12I, H13, H13B, H13C, H13D, H13E, H13F, H13G, H13H, H13I, H15, HCT2 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

  5. Data from: Neighborhood Socioeconomic and demographic changes in Baltimore's...

    • search.dataone.org
    • portal.edirepository.org
    • +1more
    Updated Oct 11, 2022
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    Dexter H Locke (2022). Neighborhood Socioeconomic and demographic changes in Baltimore's (BES) Neighborhoods: 1930 to 2010 [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F5000%2F1
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    Dataset updated
    Oct 11, 2022
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Dexter H Locke
    Time period covered
    Jan 1, 1930 - Jan 1, 2017
    Area covered
    Variables measured
    Name, p_own, p_black, p_eduHS, p_white, time_yr, Comments, neigh_yr, p_eduCOL, p_vacant, and 5 more
    Description

    This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015.

  6. d

    Affordable Rental Housing Developments

    • catalog.data.gov
    • data.cityofchicago.org
    • +1more
    Updated Jan 3, 2025
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    data.cityofchicago.org (2025). Affordable Rental Housing Developments [Dataset]. https://catalog.data.gov/dataset/affordable-rental-housing-developments
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    Dataset updated
    Jan 3, 2025
    Dataset provided by
    data.cityofchicago.org
    Description

    The rental housing developments listed below are among the thousands of affordable units that are supported by City of Chicago programs to maintain affordability in local neighborhoods. The list is updated periodically when construction is completed for new projects or when the compliance period for older projects expire, typically after 30 years. The list is provided as a courtesy to the public. It does not include every City-assisted affordable housing unit that may be available for rent, nor does it include the hundreds of thousands of naturally occurring affordable housing units located throughout Chicago without City subsidies. For information on rents, income requirements and availability for the projects listed, contact each property directly. For information on other affordable rental properties in Chicago and Illinois, call (877) 428-8844, or visit www.ILHousingSearch.org.

  7. Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade

    • gis-for-racialequity.hub.arcgis.com
    Updated Jul 23, 2020
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    Urban Observatory by Esri (2020). Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade [Dataset]. https://gis-for-racialequity.hub.arcgis.com/maps/063cdb28dd3a449b92bc04f904256f62
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    Dataset updated
    Jul 23, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. NOTE: This map has been updated as of 1/16/24 to use a newer version of the data layer which contains more cities than it previously did. As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. Map opens in Sacramento, CA. Use bookmarks or the search bar to get to other cities.Cities included in this mapAlabama: Birmingham, Mobile, MontgomeryArizona: PhoenixArkansas: Arkadelphia, Batesville, Camden, Conway, El Dorado, Fort Smith, Little Rock, Russellville, TexarkanaCalifornia: Fresno, Los Angeles, Oakland, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: Boulder, Colorado Springs, Denver, Fort Collins, Fort Morgan, Grand Junction, Greeley, Longmont, PuebloConnecticut: Bridgeport and Fairfield; Hartford; New Britain; New Haven; Stamford, Darien, and New Canaan; WaterburyFlorida: Crestview, Daytona Beach, DeFuniak Springs, DeLand, Jacksonville, Miami, New Smyrna, Orlando, Pensacola, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Columbus, Macon, SavannahIowa: Boone, Cedar Rapids, Council Bluffs, Davenport, Des Moines, Dubuque, Sioux City, WaterlooIllinois: Aurora, Chicago, Decatur, East St. Louis, Joliet, Peoria, Rockford, SpringfieldIndiana: Evansville, Fort Wayne, Indianapolis, Lake County Gary, Muncie, South Bend, Terre HauteKansas: Atchison, Greater Kansas City, Junction City, Topeka, WichitaKentucky: Covington, Lexington, LouisvilleLouisiana: New Orleans, ShreveportMaine: Augusta, Boothbay, Portland, Sanford, WatervilleMaryland: BaltimoreMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Fall River, Fitchburg, Haverhill, Holyoke Chicopee, Lawrence, Lexington, Lowell, Lynn, Malden, Medford, Melrose, Milton, Needham, New Bedford, Newton, Pittsfield, Quincy, Revere, Salem, Saugus, Somerville, Springfield, Waltham, Watertown, Winchester, Winthrop, WorcesterMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Jackson, Kalamazoo, Lansing, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Austin, Duluth, Mankato, Minneapolis, Rochester, Staples, St. Cloud, St. PaulMississippi: JacksonMissouri: Cape Girardeau, Carthage, Greater Kansas City, Joplin, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Elizabeth City, Fayetteville, Goldsboro, Greensboro, Hendersonville, High Point, New Bern, Rocky Mount, Statesville, Winston-SalemNorth Dakota: Fargo, Grand Forks, Minot, WillistonNebraska: Lincoln, OmahaNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen County, Camden, Essex County, Monmouth, Passaic County, Perth Amboy, Trenton, Union CountyNew York: Albany, Binghamton/Johnson City, Bronx, Brooklyn, Buffalo, Elmira, Jamestown, Lower Westchester County, Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Schenectady, Staten Island, Syracuse, Troy, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorain, Portsmouth, Springfield, Toledo, Warren, YoungstownOklahoma: Ada, Alva, Enid, Miami Ottawa County, Muskogee, Norman, Oklahoma City, South McAlester, TulsaOregon: PortlandPennsylvania: Allentown, Altoona, Bethlehem, Chester, Erie, Harrisburg, Johnstown, Lancaster, McKeesport, New Castle, Philadelphia, Pittsburgh, Wilkes-Barre, YorkRhode Island: Pawtucket & Central Falls, Providence, WoonsocketSouth Carolina: Aiken, Charleston, Columbia, Greater Anderson, Greater Greensville, Orangeburg, Rock Hill, Spartanburg, SumterSouth Dakota: Aberdeen, Huron, Milbank, Mitchell, Rapid City, Sioux Falls, Vermillion, WatertownTennessee: Chattanooga, Elizabethton, Erwin, Greenville, Johnson City, Knoxville, Memphis, NashvilleTexas: Amarillo, Austin, Beaumont, Dallas, El Paso, Forth Worth, Galveston, Houston, Port Arthur, San Antonio, Waco, Wichita FallsUtah: Ogden, Salt Lake CityVirginia: Bristol, Danville, Harrisonburg, Lynchburg, Newport News, Norfolk, Petersburg, Phoebus, Richmond, Roanoke, StauntonVermont: Bennington, Brattleboro, Burlington, Montpelier, Newport City, Poultney, Rutland, Springfield, St. Albans, St. Johnsbury, WindsorWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Madison, Milwaukee County, Oshkosh, RacineWest Virginia: Charleston, Huntington, WheelingAn example of a map produced by the HOLC of Philadelphia:

  8. Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade

    • sal-urichmond.hub.arcgis.com
    • vaccine-confidence-program-cdcvax.hub.arcgis.com
    • +3more
    Updated Jun 24, 2020
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    Urban Observatory by Esri (2020). Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade [Dataset]. https://sal-urichmond.hub.arcgis.com/datasets/UrbanObservatory::home-owners-loan-corporation-holc-neighborhood-redlining-grade
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    Dataset updated
    Jun 24, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    There is a newer and more authoritative version of this layer here! It is owned by the University of Richmond's Digital Scholarship Lab and contains data on many more cities.The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. This layer includes 7,148 neighborhoods spanning 143 cities across the continental United States. NOTE: As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. More cities are available in this feature layer from University of Richmond.Cities included in this layerAlabama: Birmingham, Mobile, MontgomeryCalifornia: Fresno, Los Angeles, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: DenverConnecticut: East Hartford, New Britain, New Haven, StamfordFlorida: Jacksonville, Miami, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Chattanooga, Columbus, MaconIllinois: Aurora, Chicago, Decatur, Joliet, GaryIndiana: Evansville, Fort Wayne, Indianapolis, Gary, Muncie, South Bend, Terre HauteKansas: Greater Kansas City, WichitaKentucky: Lexington, LouisvilleLouisiana: New OrleansMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Haverhill, Holyoke Chicopee, Lexington, Malden, Medford, Melrose, Milton, Needham, Newton, Quincy, Revere, Saugus, Somerville, Waltham, Watertown, Winchester, WinthropMaryland: BaltimoreMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Kalamazoo, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Duluth, MinneapolisMissouri: Greater Kansas City, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Greensboro, Winston SalemNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen Co., Camden, Essex County, Hudson County, TrentonNew York: Bronx, Brooklyn, Buffalo, Elmira, Binghamton/Johnson City, Lower Westchester Co., Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Staten Island, Syracuse, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorrain, Portsmouth, Springfield, Toledo, Warren, YoungstownOregon: PortlandPennsylvania: Altoona, Erie, Johnstown, New Castle, Philadelphia, PittsburghSouth Carolina: AugustaTennessee: Chattanooga, KnoxvilleTexas: DallasVirginia: Lynchburg, Norfolk, Richmond, RoanokeWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Milwaukee, Oshkosh, RacineWest Virginia: Charleston, WheelingAn example of a map produced by the HOLC of Philadelphia:

  9. b

    Median Number of Days on the Market - City

    • data.baltimorecity.gov
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +1more
    Updated Mar 24, 2020
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    Baltimore Neighborhood Indicators Alliance (2020). Median Number of Days on the Market - City [Dataset]. https://data.baltimorecity.gov/datasets/bniajfi::median-number-of-days-on-the-market-1?layer=1
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    Dataset updated
    Mar 24, 2020
    Dataset authored and provided by
    Baltimore Neighborhood Indicators Alliance
    Area covered
    Description

    The median number of days that homes listed for sale sit on the public market in a given area. This time period is from the date it is listed for sale till the day the contract of sale is signed. Private (non-listed) home sale transactions are not included in this indicator. The median days on market is used as opposed to the average so that both extremely high and extremely low days on the market do not distort the length of time for which homes are listed on the market. Source: RBIntel Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022

  10. Transaction price for the purchase of new housing units in German cities...

    • statista.com
    Updated Sep 4, 2024
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    Statista (2024). Transaction price for the purchase of new housing units in German cities 2016-2023 [Dataset]. https://www.statista.com/statistics/739472/new-dwellings-transaction-price-by-city-germany-europe/
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    Dataset updated
    Sep 4, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Germany
    Description

    The average transaction price for the purchase of new residential properties increased in the main cities in Germany between 2016 and 2021. Afterward, prices they stabilized and slightly decreased in 2023. By 2023, Munich had the highest average transaction price for new dwellings of approximately 10,900 euros per square meter. Frankfurt ranked second with an average transaction price of 7,700 euros per square meter, followed by Hamburg and Berlin.

  11. Residential construction costs in the U.S. Q4 2024, by city

    • flwrdeptvarieties.store
    • statista.com
    Updated Mar 22, 2025
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    Fernando de Querol Cumbrera (2025). Residential construction costs in the U.S. Q4 2024, by city [Dataset]. https://flwrdeptvarieties.store/?_=%2Fstudy%2F59103%2Fsingle-family-homes-in-the-united-states%2F%23zUpilBfjadnL7vc%2F8wIHANZKd8oHtis%3D
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    Dataset updated
    Mar 22, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Fernando de Querol Cumbrera
    Area covered
    United States
    Description

    In the last quarter of 2024, San Francisco, New York, and Honolulu were some of the U.S. cities with the highest housing construction costs. Meanwhile, Phoenix had one of the lowest construction costs for high-end multifamily homes at 280 U.S. dollars per square foot and Las Vegas for single-family homes between 235 and 470 U.S. dollars per square foot. Construction cost disparities As seen here, the construction cost for a high-end multi-family home in San Francisco in the first quarter of 2024 was over twice more expensive than in Phoenix. Meanwhile, there were also great differences in the cost of building a single-family house in New York and in Portland or Seattle. Some factors that may cause these disparities are the construction materials, installation, and composite costs, differing land values, wages, etc. For example, although the price of construction materials in the U.S. was rising at a slower level than in 2022 and 2023, several materials that are essential in most construction projects had growth rates of over five percent in 2024. Growing industry revenue Despite the economic uncertainty and other challenges, the size of the private construction market in the U.S. rose during the past years. It is important to consider that supply and demand for housing influences the revenue of this segment of the construction market. On the supply side, single-family home construction fell in 2023, but it is expected to rise in 2024 and 2025. On the demand side, some of the U.S. metropolitan areas with the highest sale prices of single-family homes were located in California, with San Jose-Sunnyvale-Santa Clara at the top of the ranking.

  12. M

    Vital Signs: List Rents – by property

    • open-data-demo.mtc.ca.gov
    • data.bayareametro.gov
    application/rdfxml +5
    Updated Dec 8, 2016
    + more versions
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    real Answers (2016). Vital Signs: List Rents – by property [Dataset]. https://open-data-demo.mtc.ca.gov/dataset/Vital-Signs-List-Rents-by-property/wfp9-cb9q/about
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    application/rssxml, csv, tsv, xml, json, application/rdfxmlAvailable download formats
    Dataset updated
    Dec 8, 2016
    Dataset authored and provided by
    real Answers
    Description

    VITAL SIGNS INDICATOR List Rents (EC9)

    FULL MEASURE NAME List Rents

    LAST UPDATED October 2016

    DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

    DATA SOURCE real Answers (1994 – 2015) no link

    Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

    CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

    METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

    Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

    Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

    Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

  13. C

    City of Chicago Affordable Housing

    • data.cityofchicago.org
    application/rdfxml +5
    Updated Dec 30, 2024
    + more versions
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    City of Chicago (2024). City of Chicago Affordable Housing [Dataset]. https://data.cityofchicago.org/Community-Economic-Development/City-of-Chicago-Affordable-Housing/94ys-kvyv
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    tsv, csv, application/rssxml, json, application/rdfxml, xmlAvailable download formats
    Dataset updated
    Dec 30, 2024
    Authors
    City of Chicago
    Area covered
    Chicago
    Description

    The affordable rental housing developments listed below are supported by the City of Chicago to maintain affordability standards. For information on rents, income requirements and availability, contact each property directly. For information on other affordable rental properties in Chicago and Illinois, call (877) 428-8844, or visit www.ILHousingSearch.org.

  14. U.S. housing - cities with the highest vacancy rates 2012

    • statista.com
    Updated Jul 31, 2012
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    Statista (2012). U.S. housing - cities with the highest vacancy rates 2012 [Dataset]. https://www.statista.com/statistics/247168/us-cities-with-the-highest-vacancy-rates/
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    Dataset updated
    Jul 31, 2012
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The statistic represents the cities with the highest vacancy rates in the United States in 2012, sorted by city. In Memphis, the vacancy rate for rental housing units stood at approximately 15 percent.

  15. f

    Statistical summary of housing prices and household numbers in 20 major...

    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Linzi Zheng; Lu Zhang; Ke Chen; Qingsong He (2023). Statistical summary of housing prices and household numbers in 20 major cities. [Dataset]. http://doi.org/10.1371/journal.pone.0263577.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Linzi Zheng; Lu Zhang; Ke Chen; Qingsong He
    License

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

    Description

    Statistical summary of housing prices and household numbers in 20 major cities.

  16. Consolidated City

    • data-isdh.opendata.arcgis.com
    • hub.arcgis.com
    Updated Jun 21, 2023
    + more versions
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    Esri (2023). Consolidated City [Dataset]. https://data-isdh.opendata.arcgis.com/maps/esri::consolidated-city-2
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    Dataset updated
    Jun 21, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows age and sex data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P12, P12B, P12C, P12D, P12E, P12F, P12G, P12H, P12I, P13, P13B, P13C, P13D, P13E, P13F, P13G, P13H, P13I, PCT8, PCT11 Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

  17. N

    Housing

    • data.cityofnewyork.us
    • data.wu.ac.at
    application/rdfxml +5
    Updated Mar 10, 2025
    + more versions
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    Department of Citywide Administrative Services (DCAS) (2025). Housing [Dataset]. https://data.cityofnewyork.us/City-Government/Housing/6pv2-jwg7
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    json, tsv, csv, application/rdfxml, xml, application/rssxmlAvailable download formats
    Dataset updated
    Mar 10, 2025
    Authors
    Department of Citywide Administrative Services (DCAS)
    Description

    A Civil Service List consists of all candidates who passed an exam, ranked in score order. An established list is considered active for no less than one year and no more than four years from the date of establishment. For more information visit DCAS’ “Work for the City” webpage at: http://www.nyc.gov/html/dcas/html/work/work.shtml

  18. C

    Low Income Senior Housing

    • data.cityofchicago.org
    Updated Dec 30, 2024
    + more versions
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    City of Chicago (2024). Low Income Senior Housing [Dataset]. https://data.cityofchicago.org/Community-Economic-Development/Low-Income-Senior-Housing/et54-beih
    Explore at:
    csv, tsv, application/rssxml, application/rdfxml, xml, application/geo+json, kmz, kmlAvailable download formats
    Dataset updated
    Dec 30, 2024
    Authors
    City of Chicago
    Description

    The affordable rental housing developments listed below are supported by the City of Chicago to maintain affordability standards. For information on rents, income requirements and availability, contact each property directly. For information on other affordable rental properties in Chicago and Illinois, call (877) 428-8844, or visit www.ILHousingSearch.org.

  19. 2020 American Community Survey: B08128 | MEANS OF TRANSPORTATION TO WORK BY...

    • data.census.gov
    + more versions
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    ACS, 2020 American Community Survey: B08128 | MEANS OF TRANSPORTATION TO WORK BY CLASS OF WORKER (ACS 5-Year Estimates Detailed Tables) [Dataset]. https://data.census.gov/table/ACSDT5Y2020.B08128
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

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

    Time period covered
    2020
    Description

    Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Workers include members of the Armed Forces and civilians who were at work last week..2019 ACS data products include updates to several categories of the existing means of transportation question. For more information, see: Change to Means of Transportation..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

  20. Number of housing units built in Mexico 2023, by city

    • statista.com
    Updated Dec 16, 2024
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    Statista (2024). Number of housing units built in Mexico 2023, by city [Dataset]. https://www.statista.com/statistics/1449589/number-of-housing-units-built-in-mexico-2023-by-city/
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    Dataset updated
    Dec 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Mexico
    Description

    Juárez in the state of Nuevo León was the Mexican city with the highest number of homes built in 2023. Also in Nuevo León, García was the second city in the ranking with 3,840 housing units built. Aguascalientes and Benito Juárez (Quintana Roo) were the next cities in the list. Nuevo León was indeed the Mexican state with most home completions.

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Statista (2024). Homeownership rate in Europe 2023, by country [Dataset]. https://www.statista.com/statistics/246355/home-ownership-rate-in-europe/
Organization logo

Homeownership rate in Europe 2023, by country

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47 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 5, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2023
Area covered
Europe
Description

In the presented European countries, the homeownership rate extended from 42 percent in Switzerland to as much as 96 percent in Albania. Countries with more mature rental markets, such as France, Germany, the UK and Switzerland, tended to have a lower homeownership rate compared to the frontier countries, such as Lithuania or Slovakia. The share of house owners among the population of all 27 European countries has remained relatively stable over the past few years. Average cost of housing Countries with lower homeownership rates tend to have higher house prices. In 2023, the average transaction price for a house was notably higher in Western and Northern Europe than in Eastern and Southern Europe. In Austria - one of the most expensive European countries to buy a new dwelling in - the average price was three times higher than in Greece. Looking at house price growth, however, the most expensive markets recorded slower house price growth compared to the mid-priced markets. Housing supply With population numbers rising across Europe, the need for affordable housing continues. In 2023, European countries completed between one and six housing units per 1,000 citizens, with Ireland, Poland, and Denmark responsible heading the ranking. One of the major challenges for supplying the market with more affordable homes is the rising construction costs. In 2021 and 2022, housing construction costs escalated dramatically due to soaring inflation, which has had a significant effect on new supply.

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