Single Family Home Prices in the United States increased to 422800 USD in May from 414000 USD in April 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 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.

Existing Home Sales in the United States increased to 4030 Thousand in May from 4000 Thousand in April 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 Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q1 2025 about sales, housing, and USA.

House Price Index YoY in the United States decreased to 3 percent in April from 3.90 percent in March 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 Q1 2025 about sales, median, housing, and USA.

New Home Sales in the United States decreased to 623 Thousand units in May from 722 Thousand units in April 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.

Average House Prices in the United States increased to 522200 USD in May from 511200 USD in April of 2025. This dataset includes a chart with historical data for the United States New Home Average Sales Price.

Just as in many other countries, the housing market in the UK grew substantially during the coronavirus pandemic, fueled by robust demand and low borrowing costs. Nevertheless, high inflation and the increase in mortgage rates has led to house price growth slowing down. According to the forecast, 2024 is expected to see house prices decrease by ***** percent. Between 2024 and 2028, the average house price growth is projected at *** percent. A contraction after a period of continuous growth In June 2022, the UK's house price index exceeded *** index points, meaning that since 2015 which was the base year for the index, house prices had increased by ** percent. In just two years, between 2020 and 2022, the index surged by ** index points. As the market stood in December 2023, the average price for a home stood at approximately ******* British pounds. Rents are expected to continue to grow According to another forecast, the prime residential market is also expected to see rental prices grow in the next years. Growth is forecast to be stronger in 2024 and slow down in the period between 2025 and 2028. The rental market in London is expected to follow a similar trend, with Central London slightly outperforming Greater London.

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).

Context

I have previously shared a classification based dataset to classify the gender which is liked by those who are new to machine learning as it give a pretty good accuracy, which encouraged me to create a regression dataset to predict continues values. I have tried many real world datasets for regression problems which are predicting with lower accuracy and high error rate. As a beginner, I have struggled and worried why and how the dataset performs poorly. This is another main reason why I created this dataset. Although this is a made up dataset, I have considered all the features when deciding the price of the property. If you are a beginner, you would love to try this as the results are stunning..

Content

Since this is a populated data, I will straightaway explain the features and the label. FEATURES 1. land_size_sqm - This the total size of the land in square meters. 2. house_size_sqm - This is the area in which house is located within the land. This is measured in square meters. 3. no_of_rooms - This indicates the number of rooms available in the house. 4. no_of_bathrooms - This shows the number of total bathrooms made in the house. 5. large_living_room - This indicates whether the house includes a larger living room or not. The assumption is that all the houses contain a living room. This feature attempts to classify whether it's large or small where '1' means large and '0' means small. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 6. parking_space - This indicates whether there is a parking space or not. '1' represents the parking available while '0' represents no parking space available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 7. front_garden - This shows whether there is a garden available in front of the house. '1' means the garden available and '0' means no garden available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 8. swimming_pool - This shows the availability of the swimming pool at the house. 1 represents the availability of the swimming pool while 0 represents the non availability of the same. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 9. distance_to_school_km - This shows the distance from the house to the nearest school in Kilometers. 10. wall_fence - This shows whether there is a wall fence or not. '1' mean there is wall fence and '0' means no wall fence. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 11. **house_age_or_renovated **- This is either the age of the house in years or the period from the date of renovation. 12. water_front - this indicates whether the house is located in front of the water or not. 1 means waterfront and 0 means its not located near the water. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 13. distance_to_supermarket_km - what is the distance to the nearest supermarket in kilometers.

LABEL property_value - This is the price of the property

Following features are only available in the "house price dataset original v2 cleaned" and "house price dataset original v2 with categorical features" data only. 14. crime_rate - its in float and falls between 0 and 7. lesser the better 15. room_size - As the name suggests, it explains the size of the room. 0 is being 'small', 1 is being 'medium', 2 is 'large' and 3 is being 'Extra large'. However in the categorical dataset, these values are categorical and self explanatory.

Acknowledgements

I spent around 3 hours creating this dataset. Enjoy..

Inspiration

Share your notebooks to see which algorithm predicts the house price precisely.

Avg Housing Price: Free Market: Less than 5 Years Old: Badajoz data was reported at 1,155.800 EUR/sq m in Mar 2018. This records an increase from the previous number of 1,138.600 EUR/sq m for Mar 2016. Avg Housing Price: Free Market: Less than 5 Years Old: Badajoz data is updated quarterly, averaging 1,385.700 EUR/sq m from Mar 2010 (Median) to Mar 2018, with 19 observations. The data reached an all-time high of 1,617.400 EUR/sq m in Jun 2010 and a record low of 1,097.500 EUR/sq m in Dec 2013. Avg Housing Price: Free Market: Less than 5 Years Old: Badajoz data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.

Avg Housing Price: Free Market: More than 5 Years Old data was reported at 1,559.400 EUR/sq m in Mar 2018. This records an increase from the previous number of 1,550.700 EUR/sq m for Dec 2017. Avg Housing Price: Free Market: More than 5 Years Old data is updated quarterly, averaging 1,517.500 EUR/sq m from Mar 2010 (Median) to Mar 2018, with 33 observations. The data reached an all-time high of 1,835.500 EUR/sq m in Mar 2010 and a record low of 1,445.100 EUR/sq m in Sep 2014. Avg Housing Price: Free Market: More than 5 Years Old data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.

Graph and download economic data for Residential Property Prices for Japan (QJPN628BIS) from Q1 1955 to Q4 2024 about Japan, residential, HPI, housing, price index, indexes, and price.

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

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.

Avg Housing Price: Free Market: Less than 5 Years Old: Pamplona data was reported at 1,945.100 EUR/sq m in Dec 2017. This records a decrease from the previous number of 1,978.000 EUR/sq m for Sep 2017. Avg Housing Price: Free Market: Less than 5 Years Old: Pamplona data is updated quarterly, averaging 2,295.000 EUR/sq m from Mar 2010 (Median) to Dec 2017, with 15 observations. The data reached an all-time high of 3,110.800 EUR/sq m in Jun 2012 and a record low of 1,831.700 EUR/sq m in Sep 2016. Avg Housing Price: Free Market: Less than 5 Years Old: Pamplona data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.

Avg Housing Price: Free Market: More than 5 Years Old: Ciudad Real data was reported at 951.800 EUR/sq m in Mar 2018. This records a decrease from the previous number of 953.500 EUR/sq m for Dec 2017. Avg Housing Price: Free Market: More than 5 Years Old: Ciudad Real data is updated quarterly, averaging 1,041.900 EUR/sq m from Mar 2010 (Median) to Mar 2018, with 33 observations. The data reached an all-time high of 1,730.100 EUR/sq m in Dec 2011 and a record low of 947.400 EUR/sq m in Sep 2014. Avg Housing Price: Free Market: More than 5 Years Old: Ciudad Real data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.

Total Housing Inventory in the United States increased to 1540 Thousands in May from 1450 Thousands in April of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.