Single Family Home Prices in the United States increased to 398400 USD in February from 393400 USD in January of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Sep 2024, following an increase of 6.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2024, with an average growth rate of 5.5%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

What Makes Our Data Unique?

Inmuebles24’s Mexico Real Estate Listings Data offers an unparalleled level of detail and accuracy in the real estate sector. With over 100,000 meticulously curated property listings, this dataset is designed to provide users with the most comprehensive view of the Mexican real estate market. Each listing includes detailed metadata such as property type, location, pricing, and contact information, along with additional attributes like the number of bedrooms, bathrooms, and available amenities. Our data is enriched with precise geolocation coordinates, allowing for advanced spatial analysis and mapping applications.

Our dataset stands out for its up-to-date nature, with listings scraped and refreshed regularly to ensure that buyers and analysts always have access to the latest market trends. This dynamic approach to data curation means that users can trust the data for making informed decisions, whether they are monitoring market trends, conducting investment research, or developing real estate strategies.

How Is the Data Generally Sourced?

The data is sourced directly from Inmuebles24, one of Mexico's leading real estate marketplaces. We employ a robust web scraping infrastructure that captures the full breadth of listings available on the platform. Our scraping technology is designed to extract data efficiently, ensuring that we capture every relevant detail from the listings, including images, descriptions, pricing, and metadata. Each entry is validated and cleaned to remove any duplicates or outdated information, ensuring that the dataset is both comprehensive and reliable.

Primary Use-Cases and Verticals

This Data Product is particularly valuable across several key verticals:

Real Estate Investment Analysis: Investors can leverage this dataset to identify lucrative opportunities by analyzing property prices, location attributes, and market trends.

Market Research and Trends: Researchers can use the data to track the evolution of the real estate market in Mexico, identifying shifts in pricing, demand, and supply across various regions.

Property Development: Developers can assess the market landscape, understanding where new developments might meet the most demand based on the attributes and locations of current listings.

Urban Planning: Government and city planners can utilize the geolocation data to analyze urban sprawl, housing density, and other critical metrics for sustainable development.

Real Estate Marketing: Marketers and real estate agents can tailor their strategies based on detailed insights into the types of properties available, pricing trends, and consumer preferences.

How Does This Data Product Fit into Our Broader Data Offering?

This Mexico Real Estate Listings Data Product is part of our broader commitment to providing high-quality, actionable data across various sectors and geographies. Inmuebles24’s real estate data complements our extensive portfolio of data products that cater to industries such as financial services, marketing, and location-based services. By integrating this dataset with other data offerings, users can derive even deeper insights. For example, combining real estate data with consumer behavior data could unlock new dimensions of market research, enabling a more holistic approach to understanding market dynamics.

Our broader data offering is built around the principle of providing end-to-end data solutions that empower businesses to make data-driven decisions with confidence. Whether you’re a real estate investor, a market researcher, or a developer, our data products are designed to meet your needs with precision and reliability

House Price Index YoY in the United States remained unchanged at 4.80 percent in January. 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 Q4 2024 about sales, median, housing, and USA.

Nahb Housing Market Index in the United States decreased to 39 points in March from 42 points in February of 2025. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

New Home Sales in the United States increased to 676 Thousand units in February from 664 Thousand units in January of 2025. This dataset provides the latest reported value for - United States New 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 Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q3 2024 about residential, HPI, housing, real, price index, indexes, price, and USA.

VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

Inflation-adjusted data are presented to illustrate how home prices 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.

Context

Zillow has a lot of data about housing prices in America.

Content

Data about housing prices and rental prices broken down according to city and state and number of bedrooms. More detail can be found at https://www.zillow.com/research/data/ and at https://www.zillow.com/research/home-sales-methodology-7733/.

Acknowledgements

The data was downloaded from https://www.zillow.com/research/data/. Banner photo from Ian Keefe on Unsplash. Dataset license described at https://www.zillow.com/research/data/.

Median price paid for residential property in England and Wales, for all property types by lower layer super output area. Annual data..

Analysis of ‘Boston House Prices-Advanced Regression Techniques’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fedesoriano/the-boston-houseprice-data on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Similar Datasets

• Gender Pay Gap Dataset: LINK
• California Housing Prices Data (5 new features!): LINK
• Company Bankruptcy Prediction: LINK

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town 2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft. 3) INDUS: proportion of non-retail business acres per town 4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M] 6) RM: average number of rooms per dwelling 7) AGE: proportion of owner-occupied units built prior to 1940 8) DIS: weighted distances to five Boston employment centres 9) RAD: index of accessibility to radial highways 10) TAX: full-value property-tax rate per $10,000 [$/10k] 11) PTRATIO: pupil-teacher ratio by town 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

Output variable: 1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

--- Original source retains full ownership of the source dataset ---

Existing Home Sales in the United States increased to 4260 Thousand in February from 4090 Thousand in January 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 All-Transactions House Price Index for Detroit-Dearborn-Livonia, MI (MSAD) (ATNHPIUS19804Q) from Q2 1976 to Q4 2024 about Detroit, MI, appraisers, HPI, housing, price index, indexes, price, and USA.

Context

The dataset consists of lists of unique objects of popular portals for the sale of real estate in Russia. More than 540 thousand objects. The dataset contains 540000 real estate objects in Russia.

Content

The Russian real estate market has a relatively short history. In the Soviet era, all properties were state-owned; people only had the right to use them with apartments allocated based on one's place of work. As a result, options for moving were fairly limited. However, after the fall of the Soviet Union, the Russian real estate market emerged and Muscovites could privatize and subsequently sell and buy properties for the first time. Today, Russian real estate is booming. It offers many exciting opportunities and high returns for lifestyle and investment. The real estate market has been in a growth phase for several years, which means that you can still find properties at very attractive prices, but with good chances of increasing their value in the future.

Dataset

The dataset has 13 fields. - date - date of publication of the announcement; - time - the time when the ad was published; - geo_lat - Latitude - geo_lon - Longitude - region - Region of Russia. There are 85 subjects in the country in total. - building_type - Facade type. 0 - Other. 1 - Panel. 2 - Monolithic. 3 - Brick. 4 - Blocky. 5 - Wooden - object_type - Apartment type. 1 - Secondary real estate market; 2 - New building; - level - Apartment floor - levels - Number of storeys - rooms - the number of living rooms. If the value is "-1", then it means "studio apartment" - area - the total area of ​​the apartment - kitchen_area - Kitchen area - price - Price. in rubles

Attention.

The dataset may contain erroneous data due to input errors on services, as well as outliers, and so on.

:)

Using this dataset, we offer Kagglers algorithms that use a wide range of functions to predict real estate prices. Competitors will rely on a vast dataset that includes housing data and macroeconomic models. An accurate forecasting model provides more confidence to its clients in a volatile economy.

This dataset uses data provided from Washington State’s Housing Market, a publication of the Washington Center for Real Estate Research (WCRER) at the University of Washington.

Median sales prices represent that price at which half the sales in a county (or the state) took place at higher prices, and half at lower prices. Since WCRER does not receive sales data on individual transactions (only aggregated statistics), the median is determined by the proportion of sales in a given range of prices required to reach the midway point in the distribution. While average prices are not reported, they tend to be 15-20 percent above the median.

Movements in sales prices should not be interpreted as appreciation rates. Prices are influenced by changes in cost and changes in the characteristics of homes actually sold. The table on prices by number of bedrooms provides a better measure of appreciation of types of homes than the overall median, but it is still subject to composition issues (such as square footage of home, quality of finishes and size of lot, among others).

There is a degree of seasonal variation in reported selling prices. Prices tend to hit a seasonal peak in summer, then decline through the winter before turning upward again, but home sales prices are not seasonally adjusted. Users are encouraged to limit price comparisons to the same time period in previous years.

Graph and download economic data for All-Transactions House Price Index for Los Angeles County, CA (ATNHPIUS06037A) from 1975 to 2024 about Los Angeles County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.

The Housing Affordability Index value in the United States plummeted in 2022, surpassing the historical record of 107.1 index points in 2006. In 2024, the housing affordability index measured 98.1 index points, making it the second-worst year for homebuyers since the start of the observation period. What does the Housing Affordability Index mean? The Housing Affordability Index uses data provided by the National Association of Realtors (NAR). It measures whether a family earning the national median income can afford the monthly mortgage payments on a median-priced existing single-family home. An index value of 100 means that a family has exactly enough income to qualify for a mortgage on a home. The higher the index value, the more affordable a house is to a family. Key factors that drive the real estate market Income, house prices, and mortgage rates are some of the most important factors influencing homebuyer sentiment. When incomes increase, consumer power also increases. The median household income in the United States declined in 2022, affecting affordability. Additionally, mortgage interest rates have soared, adding to the financial burden of homebuyers. The sales price of existing single-family homes in the U.S. has increased year-on-year since 2011 and reached 389,000 U.S. dollars in 2023.

Snapshot img

Real Estate Market Size 2025-2029

The real estate market size is forecast to increase by USD 1,258.6 billion at a CAGR of 5.6% between 2024 and 2029.

The market is experiencing significant shifts and innovations, with both residential and commercial sectors adapting to new trends and challenges. In the commercial realm, e-commerce growth is driving the demand for logistics and distribution centers, while virtual reality technology is revolutionizing property viewings. Europe's commercial real estate sector is witnessing a rise in smart city development, incorporating LED lighting and data centers to enhance sustainability and efficiency. In the residential sector, wellness real estate is gaining popularity, focusing on health and well-being. Real estate software and advertising services are essential tools for asset management, streamlining operations, and reaching potential buyers. Regulatory uncertainty remains a challenge, but innovation in construction technologies, such as generators and renewable energy solutions, is helping mitigate risks.

What will be the Size of the Real Estate Market During the Forecast Period?

Request Free Sample

The market continues to exhibit strong activity, driven by rising population growth and increasing demand for personal household space. Both residential and commercial sectors have experienced a rebound in home sales and leasing activity. The trend towards live-streaming rooms and remote work has further fueled demand for housing and commercial real estate. Economic conditions and local market dynamics influence the direction of the market, with interest rates playing a significant role in investment decisions. Fully furnished, semi-furnished, and unfurnished properties, as well as rental properties, remain popular options for buyers and tenants. Offline transactions continue to dominate, but online transactions are gaining traction.
The market encompasses a diverse range of assets, including land, improvements, buildings, fixtures, roads, structures, utility systems, and undeveloped property. Vacant land and undeveloped property present opportunities for investors, while the construction and development of new housing and commercial projects contribute to the market's overall growth.

How is this Real Estate Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Residential
  Commercial
  Industrial


Business Segment

  Rental
  Sales


Manufacturing Type

  New construction
  Renovation and redevelopment
  Land development


Geography

  APAC

    China
    India
    Japan
    South Korea


  North America

    Canada
    US


  Europe

    Germany
    UK


  South America

    Brazil


  Middle East and Africa

By Type Insights

The residential segment is estimated to witness significant growth during the forecast period.

The market encompasses the buying and selling of properties designed for dwelling purposes, including buildings, single-family homes, apartments, townhouses, and more. Factors fueling growth in this sector include the increasing homeownership rate among millennials and urbanization trends. The Asia Pacific region, specifically China, dominates the market due to escalating homeownership rates. In India, the demand for affordable housing is a major driver, with initiatives like Pradhan Mantri Awas Yojana (PMAY) spurring the development of affordable housing projects catering to the needs of lower and middle-income groups. The commercial real estate segment, consisting of office buildings, shopping malls, hotels, and other commercial properties, is also experiencing growth.

Furthermore, economic and local market conditions, interest rates, and investment opportunities in fully furnished, semi-furnished, unfurnished properties, and rental properties influence the market dynamics. Technological integration, infrastructure development, and construction projects further shape the real estate landscape. Key sectors like transportation, logistics, agriculture, and the e-commerce sector also impact the market.

Get a glance at the market report of share of various segments Request Free Sample

The Residential segment was valued at USD 1440.30 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 64% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The Asia Pacific region holds the largest share of The market, dr

Housing Index in China decreased by 4.80 percent in February from -5 percent in January of 2025. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.