Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

House Price Index YoY in the United States decreased to 2.80 percent in May from 3.20 percent in April of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Housing Index in Hong Kong increased to 138.84 points in July 27 from 137.76 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

New housing price index (NHPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (201612=100).

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

Total Housing Inventory in the United States decreased to 1530 Thousands in June from 1540 Thousands in May of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.

The house price for Ontario is forecast to decrease by eight percent in 2023, followed by a minor increase of one percent in 2024. From roughly 932,000 Canadian dollars, the average house price in Canada's second most expensive province for housing is expected to fall to 861,000 Canadian dollars in 2024. After British Columbia, Ontario is Canada's most expensive province for housing. Ontario Ontario is the most populated province in Canada, located on the eastern-central side of the country. It is an English speaking province. To the south, it borders American states Minnesota, Michigan, Ohio, Pennsylvania, and New York. Its provincial capital and largest city is Toronto. It is also home to Canada’s national capital, Ottawa. Furthermore, a large part of Ontario’s economy comes from manufacturing, as it is the leading manufacturing province in Canada. The population of Ontario has been steadily increasing since 2000. The population in 2018 was an estimated 14.3 million people. The median total family income in 2016 came to 83,160 Canadian dollars. Ontario housing market The number of housing units sold in Ontario is projected to rise until 2024. Additionally, the average home prices in Ontario have significantly increased since 2007.

Vietnam Consumer Price Index (CPI): Housing, and House Maintenance data was reported at 204.330 2000=100 in Oct 2009. This records an increase from the previous number of 203.210 2000=100 for Sep 2009. Vietnam Consumer Price Index (CPI): Housing, and House Maintenance data is updated monthly, averaging 132.300 2000=100 from Jul 2001 (Median) to Oct 2009, with 99 observations. The data reached an all-time high of 207.870 2000=100 in Apr 2008 and a record low of 102.400 2000=100 in Aug 2001. Vietnam Consumer Price Index (CPI): Housing, and House Maintenance data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.T015:Table VN.I015: Consumer Price Index: 2000=100. Rebased from 2005=100 to 2009=100. Replacement series ID: 228444402

Graph and download economic data for Monthly Supply of New Houses in the United States (MSACSR) from Jan 1963 to Jun 2025 about supplies, new, housing, and USA.

Housing Index in the United Kingdom decreased to 511.60 points in June from 511.80 points in May of 2025. This dataset provides - United Kingdom House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table shows the average purchase price that has been paid in the reporting period for existing own homes purchased by a private individual. The average purchase price of existing own homes may differ from the price index of existing own homes. The average purchase price is no indicator for price developments of owner-occupied residential property. The average purchase price reflects the average price of dwellings sold in a particular period. The fact that de dwellings sold differs from one period to another is not taken into account. The following instance explains which problems are entailed by the continually changing of the quality of the dwellings sold. Suppose in February of a particular year mainly big houses with extensive gardens beautifully situated alongside canals are sold, whereas in March many small terraced houses are sold. In that case the average purchase price in February will be higher than in March but this does not mean that house prices are increased. See note 3 for a link to the article 'Why the average purchase price is not an indicator'.

Data available from: 1995

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The average purchasing prices of existing owner-occupied sold homes can be calculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

Changes as of 17 February 2025: Added average purchase prices of the municipalities for the year 2024.

When will new figures be published? New figures are published approximately one to three months after the period under review.

Graph and download economic data for Real Residential Property Prices for Canada (QCAR628BIS) from Q1 1970 to Q1 2025 about Canada, residential, HPI, housing, real, price index, indexes, and price.

US Census 2000 source data for Rhode Island excerpted from Summary File 3 (SF3) of Population, Housing & Economic information including sex, race, age, employment, transportation, education, income, household, family, housing unit, place of birth and language information to the Census Block Group level. SF3 data is based on a sample population but totals have been extrapolated to coincide with whole population totals. The spatial base for this data set was derived from Census TIGER line data based on RIGIS 1:5000 scale mapping accepted and updated by the US Census in 2005. It was spatially georeferenced into the Rhode Island State Plane Coordinate System by RIGIS in 2006 Tabular and Geographic US Census 2000 Summary File 3(SF3) information for general GIS use. The intention of this dataset was to provide an overview of data from summary file 3 (SF3). It does not include all data from SF3 only an overview of significant items. Users with more detailed needs may access additional source information, available from SF3, obtainable directly from the U.S. Census.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. The first wave of results was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in these particular RGIS Clearinghouse tables are for San Juan County and all census block groups within San Juan County. There are two data tables. One provides total counts of housing units, ocupied housing units and vacant housing units, while the other provides counts of total housing uings along with proportions of occupied and vacant housing units. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

No of Housing Unit: Georgia data was reported at 4,282,106.000 Unit in 2017. This records an increase from the previous number of 4,236,284.000 Unit for 2016. No of Housing Unit: Georgia data is updated yearly, averaging 4,049,890.000 Unit from Jun 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 4,282,106.000 Unit in 2017 and a record low of 3,305,925.000 Unit in 2000. No of Housing Unit: Georgia data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.EB012: Number of Housing Units: By States.

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. The first wave of results was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in this particular RGIS Clearinghouse table are for each block in Sierra County and the county as a whole. The data table provides total counts of housing units, ocupied housing units and vacant housing units. This file, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

South Africa House Price Index: Medium Sized Houses data was reported at 544.475 2000=100 in Nov 2016. This records an increase from the previous number of 539.711 2000=100 for Oct 2016. South Africa House Price Index: Medium Sized Houses data is updated monthly, averaging 49.860 2000=100 from Jan 1966 (Median) to Nov 2016, with 611 observations. The data reached an all-time high of 544.475 2000=100 in Nov 2016 and a record low of 3.621 2000=100 in Jan 1966. South Africa House Price Index: Medium Sized Houses data remains active status in CEIC and is reported by Absa Group Limited South Africa. The data is categorized under Global Database’s South Africa – Table ZA.EB001: House Price Index: 2000=100.