The quarterly Nationwide house price index for all houses in the United Kingdom (UK) exceeded 14,000 index points in the second quarter of 2025. The index shows the development of housing prices, with 1952 used as a baseline year. An index value of 14,425.6 implies a price increase of 14,000 percent between 1952 and 2025.

The Nationwide average UK house price increased during the period under observation, reaching a value of over 272,751 British pounds as of the second quarter of 2025. During the same quarter in 2015, the average house price stood at 194,258 British pounds.

The Nationwide index for newly built houses increased overall during the period under observation, reaching 14,869.7 index points as of the second quarter of 2025. The baseline year for the index was 1952 when the index value was set to 100. According to the source, the average newly built house in 1952 cost 2,107 British pounds.

House Price Index YoY in the United States decreased to 1.70 percent in September from 2.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

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.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q3 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

The KinderData Property Dataset delivers comprehensive nationwide coverage of U.S. residential and commercial real estate, unifying assessor, recorder, parcel, ownership, and valuation intelligence into a single, analytics-ready resource. Updated continuously from verified county, state, and federal sources, KinderData provides the depth and accuracy required for real estate investment, lending, underwriting, marketing, and large-scale data enrichment.

At the core of our dataset are 160M+ parcel-level property records, each standardized and linked across assessor and recorder files to reflect the most current ownership, transfer, and structural information available. Every record includes rich property attributes—lot specifications, land use classifications, building details, tax assessments, zoning, and improvement histories—allowing users to build precise filters, models, and segmentation strategies.

KinderData differentiates itself with Tier-1 owner contact intelligence, offering industry-leading match accuracy to individuals, households, and corporate owners. Each property may include linked owner names, mailing addresses, corporate structures, and phone/email append options, enabling seamless activation for marketing, home services outreach, acquisition campaigns, and identity resolution workflows.

Transaction data includes full historical sales chains, price points, deed types, document recordings, and transfer timestamps. This is complemented by current and historical valuations, assessment data, tax records, and localized market signals that support underwriting models, AVM enhancement, forecasting, and regional trend analysis.

Delivered in flexible formats and compatible with modern data warehouses, KinderData ensures fast integration for PropTech providers, financial institutions, home-improvement operators, and analytics teams. Whether powering property-search platforms, enriching CRMs, driving lead-generation campaigns, or supporting multi-market investment decisions, KinderData provides a unified, trusted foundation for property-level intelligence at national scale.

The U.S. housing market has slowed, after ** consecutive years of rising home prices. In 2021, house prices surged by an unprecedented ** percent, marking the highest increase on record. However, the market has since cooled, with the Freddie Mac House Price Index showing more modest growth between 2022 and 2024. In 2024, home prices increased by *** percent. That was lower than the long-term average of *** percent since 1990. Impact of mortgage rates on homebuying The recent cooling in the housing market can be partly attributed to rising mortgage rates. After reaching a record low of **** percent in 2021, the average annual rate on a 30-year fixed-rate mortgage more than doubled in 2023. This significant increase has made homeownership less affordable for many potential buyers, contributing to a substantial decline in home sales. Despite these challenges, forecasts suggest a potential recovery in the coming years. How much does it cost to buy a house in the U.S.? In 2023, the median sales price of an existing single-family home reached a record high of over ******* U.S. dollars. Newly built homes were even pricier, despite a slight decline in the median sales price in 2023. Naturally, home prices continue to vary significantly across the country, with West Virginia being the most affordable state for homebuyers.

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

Single Family Home Prices in the United States increased to 415200 USD in October from 412300 USD in September of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This is quarterly yield data for medium and large commercial buildings from the commercial real estate rental trend survey provided by the Korea Real Estate Board (formerly Korea Appraisal Board).

Context

Zillow operates an industry-leading economics and analytics bureau led by Zillow’s Chief Economist, Dr. Stan Humphries. At Zillow, Dr. Humphries and his team of economists and data analysts produce extensive housing data and analysis covering more than 500 markets nationwide. Zillow Research produces various real estate, rental and mortgage-related metrics and publishes unique analyses on current topics and trends affecting the housing market.

At Zillow’s core is our living database of more than 100 million U.S. homes, featuring both public and user-generated information including number of bedrooms and bathrooms, tax assessments, home sales and listing data of homes for sale and for rent. This data allows us to calculate, among other indicators, the Zestimate, a highly accurate, automated, estimated value of almost every home in the country as well as the Zillow Home Value Index and Zillow Rent Index, leading measures of median home values and rents.

Content

The Zillow Rent Index is the median estimated monthly rental price for a given area, and covers multifamily, single family, condominium, and cooperative homes in Zillow’s database, regardless of whether they are currently listed for rent. It is expressed in dollars and is seasonally adjusted. The Zillow Rent Index is published at the national, state, metro, county, city, neighborhood, and zip code levels.

Zillow produces rent estimates (Rent Zestimates) based on proprietary statistical and machine learning models. Within each county or state, the models observe recent rental listings and learn the relative contribution of various home attributes in predicting prevailing rents. These home attributes include physical facts about the home, prior sale transactions, tax assessment information and geographic location as well as the estimated market value of the home (Zestimate). Based on the patterns learned, these models estimate rental prices on all homes, including those not presently for rent. Because of the availability of Zillow rental listing data used to train the models, Rent Zestimates are only available back to November 2010; therefore, each ZRI time series starts on the same date.

Acknowledgements

The rent index data was calculated from Zillow's proprietary Rent Zestimates and published on its website.

Inspiration

What city has the highest and lowest rental prices in the country? Which metropolitan area is the most expensive to live in? Where have rental prices increased in the past five years and where have they remained the same? What city or state has the lowest cost per square foot?

This dataset contains the results of the estimate of the outflow of elderly people from the housing market in four scenarios derived from the WLO cahier 'Regional developments and urbanization' (CPB & PBL 2015). These are single people over the age of 65 who die, move to institutional housing or start living together, leaving a home behind. The table contains the absolute number of outflowing households for the 40 COROP regions for 2015 and the reference years 2030 and 2050 for the four High-base and Low-base scenarios and the additional High-spread and Low-concentration scenarios. The shares of the outflow are also included in the total available supply, as are the shares of rental and owner-occupied homes in the outflow. The outflow of older households has been calculated on the basis of the calculations for WLO regional developments and urbanisation. The relative weight of this outflow in the total available supply on the housing market (in addition to regular flow and new construction) has also been mapped out. An additional model was used to estimate the ratio between rental and owner-occupied homes in the outflow. For further explanation, see Eskinasi, M. & J. Ritsema van Eck (2018), Outflow of the elderly from the housing market. The Hague: PBL. Use the WLO Package Leaflet for correct use of the reference scenarios and usage restrictions http://www.wlo2015.nl/rapporten-wlo/bijsluiter The package leaflet discusses: - Use of the reference scenarios - Use of the uncertainty assessments in addition to the reference scenarios - Role of international and national policy in the WLO scenarios - Erosion of the starting points of scenarios due to new insights, developments and policy - WLO scenarios and transition - Availability and level of detail of data Use the following source reference: Eskinasi, M. & J. Ritsema van Eck (2018) Outflow of elderly people from the housing market: a nationwide estimate based on the WLO. The Hague: PBL.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller thelevel of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock· Population: Total Population (B01003)· Households: Total number of households (B25003)· OwnHH: Total number of owner-occupied households (B25003)· RentHH: Total number of renter-occupied households (B25003)· TotalU: Total number of housing units (B25001)· VacantU: Total number of vacant units (B25004)· SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)· ForSale: Total number of units currently for sale (B25004)· ForRent: Total number of units currently for rent (B25004)· MedianHI: Median Household Income (B25119)· OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· Poverty: Population below poverty level. (B17001)· PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)· MYearBuilt: Median structure year of construction. (B25035)

The average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

Existing Home Sales in the United States increased to 4100 Thousand in October from 4050 Thousand in September 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.

The urban land price index for Taiwan had a value of ****** in September 2022. By the end of September 2013, Taiwan's nationwide urban land price had an index value of **. Within two years, the index value increased by ** points to almost *** by September of 2015. Since then, the price of urban land in Taiwan had stabilized slightly below that level. The stabilization of prices was the result of measures from the Taiwanese government to halt speculative activity. Among those measures were selective credit controls and property tax reforms.