The online residential home sale listings industry is experiencing significant changes in its dynamics because of the increased number of homes for sale. The growth in listings is because of various factors, including a climb in the number of homeowners choosing to sell, the easing of the mortgage rate lock-in effect, and economic concerns driving the sale of investment properties. These conditions and the shift from a seller's market towards a more balanced, or even a buyer's market, translate into increased traffic and engagement on home sale platforms. This presents an opportunity for these online platforms to enhance their user experience, refine search tools and offer data analytics to help buyers navigate the increased options. By the end of 2025, industry revenue has climbed at a CAGR of 3.0% and is expected to total $2.2 billion in 2025. In 2025, revenue is expected to strengthen by an estimated 4.2%. Despite enjoying growth, the industry faces challenges with the elevated mortgage rates reducing demand for home purchases, leading to a market freeze. Despite the gain in home listings, actual transaction volumes have remained subdued, creating a challenging environment for the online residential home sale listing platforms. To stay competitive, these platforms are pivoting to offer enhanced tools for price comparisons, real-time mortgage calculators and in-depth educational content to help buyers understand the increased cost of borrowing and also navigate the high inventory but low turnover market. Industry profit has climbed as revenue has outpaced wage growth through the end of 2025. Through the end of 2030, online platforms must position themselves for demographic shifts and changing consumer preferences. Gen Z and younger millennials, who are entering homebuying age, are demanding a more tech-driven, seamless and mobile-first experience. The industry will also continue to see online platforms transform into comprehensive, one-stop digital destinations offering integrated services for every stage of the housing journey. Embracing changes such as artificial intelligence and data analytics to enhance user experience, streamlining listings uploads and offering real-time communication between buyers, sellers, and agents will be crucial for future success. Platforms that offer user-friendly, one-stop experiences and are equipped to provide advanced, feature-rich mobile experiences are set to capture greater market share. Overall, industry revenue will gain at a CAGR of 3.3% through 2030 to total $2.6 billion.

House sales shows changes in the number or value of purchased newly built and existing dwellings bought by households. Available data units in this dataset are number, quarterly index, quarterly rate of change, annual rate of change and percentage in the year. Data is not available for all countries. No aggregates.

Home sales activity in San Diego, California, slowed down in 2022, with the number of residential transactions declining by more than 10,000. In 2022, the volume of home sales amounted to ******, down from ****** in the previous year.

Quarterly dataset of the France New Home Sales, including historical data, latest releases, and long-term trends from 2015-09-30 to 2025-06-30. Available for free download in CSV format.

In 2024, the existing home sales index for single-family homes in Japan stood at ***** index points, up by **** percent compared to the previous year.The existing home sales index measures the development of the second-hand housing market based on the number of ownership transfers due to the sale and purchase of buildings. It includes data for detached houses and condominiums.

Monthly dataset of the Ireland New Home Sales, including historical data, latest releases, and long-term trends from 2015-08-31 to 2025-09-30. Available for free download in CSV format.

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Same dataset as "House Sales in King County, USA", but with treated content and with a split version (train-test) allowing direct use in machine learning models.

We have 14 columns in the dataset, as it follows:

• date: Date of the home sale
• price: Price of each home sold
• bedrooms: Number of bedrooms
• bathrooms: Number of bathrooms
• living_in_m2: Square meters of the apartments interior living space
• nice_view: A flag that indicates the view's quality of a property
• perfect_condition: A flag that indicates the maximum index of the apartment condition
• grade: An index from 1 to 5, where 1 falls short of quality level and 5 have a high quality level of construction and design
• has_basement: A flag indicating whether or not a property has a basement
• renovated: A flag if the property was renovated
• has_lavatory: Check for the presence of these incomplete/secondary bathrooms (bathtub, sink, toilet)
• single_floor: A flag indicating whether the property had only one floor
• month: The month of the home sale
• quartile_zone: A quartile distribution index of the most expensive zip codes, where 1 means less expansive and 4 most expansive.

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.

In 2024, the existing home sales index for condominiums in Japan stood at ***** index points, while the existing home sale index for detached houses stood at ***** index points.The existing home sales index measures the development of the second-hand housing market based on the number of ownership transfers due to the sale and purchase of buildings. It includes data for detached houses and condominiums.

This dataset contains a listing of results from the sale of New York State surplus real estate.

Quarterly dataset of the Portugal New Home Sales, including historical data, latest releases, and long-term trends from 2015-09-30 to 2025-06-30. Available for free download in CSV format.

New Home Sales in the United States increased to 800 Thousand units in August from 664 Thousand units in July 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.

Monthly dataset of the Norway - Home Sales, including historical data, latest releases, and long-term trends from 2015-09-30 to 2025-10-31. Available for free download in CSV format.

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.

Content

The dataset consists of Price of Houses in King County , Washington from sales between May 2014 and May 2015. Along with house price it consists of information on 18 house features, date of sale and ID of sale.

Attribute information

1. id - Unique id for each home sold
2. date - Date of the home saled
3. price - Price of each home sold
4. bedrooms - Number of bedrooms
5. bathrooms - Number of bathrooms
6. sqft _ living - Square footage of the apartments interior living space
7. sqft _ lot - Square footage of the land space
8. floors - Number of floors
9. waterfront - A dummy variable for whether the apartment was overlooking the waterfront or not
10. view - An index from 0 to 4 of how good the view of the property was
11. condition - an index from 1 to 5 on the condition of the apartment
12. grade - An index from 1 to 13 , where 1-3falls short of building construction and design, 7 has an average level of construction and design , and 11-13 have a high quality level of construction and design
13. sqft _ above - the square footage of the interior housing space that is above ground level
14. sqft _ basement - the square footage of the inerior housing space that is below ground level
15. yr _ built - The year of the house was initially built
16. yr _ renovated - The year of the house's last renovation
17. zipcode - What zipcode area the house is in
18. lat - Lattitude
19. long - Longitude
20. sqft _ living15 - The square footage of inerior housing living space for the nearest nearest 15 neighbours
21. sqft _ lot15 - the square footage of the land lots of the nearest 15 neighbours

Context

The purpose of this kernel is to predict the price of a house that a realtor can charge, or a customer can invest to buy a house by considering multiple input factors. Also, to classify the houses into Good and Excellent category based on the input variables by using best machine learning classification and regression algorithms with more efficiency.

Content

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. The dataset is pretty unbalanced with wide range of houses information that are built and renovated from the year 1990 to 2015. The dataset has total 21 variables including price,price, condition, number of bedrooms, bathrooms and other features of house.

Inspiration

I was inspired by the House sales dataset in King County, USA (https://www.kaggle.com/harlfoxem/housesalesprediction) and House Sales in Ontario (https://www.kaggle.com/mnabaee/ontarioproperties) datasets and the predictions and classifiers used.

Sale of Houses can go high and low depending on the market and multiple factors like location, number of bedrooms, year built etc. All these factors help in deriving the sale price of the house and grading of the house. Millions of houses information can be stored with all the details and factors in the historical timelines. Using machine learning techniques, we can analyze the data and predict the price of new houses and also classify the houses and fix a price value by calculating all the factors that directly or indirectly impact on the overall sale of house.

In 2024, the existing home sales index in Tokyo Prefecture in Japan stood at *** index points.The existing home sales index measures the development of the second-hand housing market based on the number of ownership transfers due to the sale and purchase of buildings. It includes data for detached houses and condominiums.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Graph and download economic data for Existing Home Sales (EXHOSLUSM495S) from Oct 2024 to Oct 2025 about headline figure, sales, housing, and USA.

Median sales prices for single family homes in Atherton, Belmont, Menlo Park, Redwood City, San Carlos, and San Mateo from 2006 - 2015. This data was provided by MLS Listings and the Silicon Valley Association of REALTORS (SILVAR).