Purchases of dwellings in housing companies 2010-2017 by year, average square meter price, region and size of dwelling

Housing Index in Spain increased to 2033 EUR/SQ. METRE in the first quarter of 2025 from 1972.10 EUR/SQ. METRE in the fourth quarter of 2024. This dataset provides the latest reported value for - Spain House Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Index in Montenegro increased to 2158 EUR/SQ. METRE in the first quarter of 2025 from 1936 EUR/SQ. METRE in the fourth quarter of 2024. This dataset provides - Montenegro Average Prices of Dwellings in New Residential Buildings- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains an history of nearly all of the real estate transactions concerning a single house/appartment in France from 2014 to today. Some variables likely to have an impact on the price of real estate are also provided as time series: the households income levels per city, the average debt level of french peoples, the average amount of savings of french people, the interest rates of loans, the price of the rent per city, the number of houses and number of vacant houses per city.

This dataset is provided under a permissive licence, and is free to use for commercial uses. It has a vocation of helping research concerning the dynamics of real estate prices.

The dataset consist in extraction from several openly available datasets put together in a practical format: The DVF+ database of real estate transactions, the IRCOM dataset of household incomes and income taxes, average interest rates of real estate loans from the banque de france website, the LOVAC dataset of number of vacant and occupied housings per city, the OECD dataset of financial assets per capita, the "carte des loyers" dataset of 2018 and 2022 which list the average price of the rent per square meter, the Indice de Référence des Loyers (IRL) time series which is an index defining the maximum rent increase that can be applied to an already rented housing and is calculated every 3 months as the inflation adjusted buying power of 100€ in 1998, the TEC00104 eurostat dataset of debt levels.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

Housing Index in Philippines increased to 12668.86 PHP/SQ. METRE in May from 12224.02 PHP/SQ. METRE in April of 2025. This dataset provides - Philippines Housing Sales - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This layer shows household size by tenure (owner or renter). This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the average household size as well as the count of all housing units. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25009, B25010, B19019Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, 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 level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Property Price: YTD Avg: Beijing data was reported at 28,360.916 RMB/sq m in Mar 2025. This records a decrease from the previous number of 36,835.882 RMB/sq m for Feb 2025. Property Price: YTD Avg: Beijing data is updated monthly, averaging 19,466.029 RMB/sq m from Jan 2003 (Median) to Mar 2025, with 267 observations. The data reached an all-time high of 42,343.603 RMB/sq m in Jun 2021 and a record low of 4,515.769 RMB/sq m in Feb 2004. Property Price: YTD Avg: Beijing data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Price – Table CN.PD: NBS: Property Price: Monthly.

China Property Price: YTD Avg: Overall data was reported at 9,510.153 RMB/sq m in Mar 2025. This records a decrease from the previous number of 9,547.228 RMB/sq m for Feb 2025. China Property Price: YTD Avg: Overall data is updated monthly, averaging 5,157.474 RMB/sq m from Dec 1995 (Median) to Mar 2025, with 352 observations. The data reached an all-time high of 11,029.538 RMB/sq m in Feb 2021 and a record low of 599.276 RMB/sq m in Feb 1996. China Property Price: YTD Avg: Overall data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Price – Table CN.PD: NBS: Property Price: Monthly.

Ukraine Average Apartment Prices: Kyiv: per 1 Square Metre data was reported at 1.930 USD th in Apr 2025. This stayed constant from the previous number of 1.930 USD th for Mar 2025. Ukraine Average Apartment Prices: Kyiv: per 1 Square Metre data is updated monthly, averaging 1.890 USD th from Apr 2000 (Median) to Apr 2025, with 300 observations. The data reached an all-time high of 4.040 USD th in Oct 2008 and a record low of 0.350 USD th in Apr 2001. Ukraine Average Apartment Prices: Kyiv: per 1 Square Metre data remains active status in CEIC and is reported by Blagovist Real Estate Agency. The data is categorized under Global Database’s Ukraine – Table UA.EB002: Average Apartment Price.

Property Price: YTD Avg: Shanghai data was reported at 39,575.041 RMB/sq m in Mar 2025. This records an increase from the previous number of 38,438.579 RMB/sq m for Feb 2025. Property Price: YTD Avg: Shanghai data is updated monthly, averaging 16,245.712 RMB/sq m from Jan 2003 (Median) to Mar 2025, with 267 observations. The data reached an all-time high of 49,301.406 RMB/sq m in Feb 2021 and a record low of 3,659.000 RMB/sq m in Feb 2003. Property Price: YTD Avg: Shanghai data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Price – Table CN.PD: NBS: Property Price: Monthly.