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A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?
Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.
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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.
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TwitterThe number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.
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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.
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Here's a short description of the dataset:
Serial Number: Is just a unique set of digits to identify each transaction
List year: This is the year that the particular property was put up for sale.
Date Recorded: Is the date that the transaction was completed. That is, the year the property was bought.
Town: The town where this property is located.
Address: The property's address.
Assessed Value: How much the property is generally considered to be worth.
Sale Amount: How much the property was actually sold for.
Sales Ratio: The ratio measures how close the selling price of the property is to it's assessed value.
Property Type: What kind of property it is.
Residential Type: If it is a residential property, what type is it.
Years until sold: Number of years before the property was finally sold
This dataset can be used for analysis and even machine learning projects. For those doing analysis, I invite you to try and answer these questions: * Average assessed value of properties from year to year? * Average sale amount of properties from year to year? * Average sales ratio of properties from year to year? * How long, on average, did it take for the different property types to get sold? * How long, on average, did it take for the different residential types to get sold? * Which towns saw the most property sales in 2021?
For those more interested in using this dataset in machine learning projects to forecast future property prices, I invite you also. Let's learn from your work.
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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.
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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.
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TwitterThe number of home sales in the United States peaked in 2021 at almost ************* after steadily rising since 2018. Nevertheless, the market contracted in the following year, with transaction volumes falling to ***********. Home sales remained muted in 2024, with a mild increase expected in 2025 and 2026. A major factor driving this trend is the unprecedented increase in mortgage interest rates due to high inflation. How have U.S. home prices developed over time? The average sales price of new homes has also been rising since 2011. Buyer confidence seems to have recovered after the property crash, which has increased demand for homes and also the prices sellers are demanding for homes. At the same time, the affordability of U.S. homes has decreased. Both the number of existing and newly built homes sold has declined since the housing market boom during the coronavirus pandemic. Challenges in housing supply The number of housing units in the U.S. rose steadily between 1975 and 2005 but has remained fairly stable since then. Construction increased notably in the 1990s and early 2000s, with the number of construction starts steadily rising, before plummeting amid the infamous housing market crash. Housing starts slowly started to pick up in 2011, mirroring the economic recovery. In 2022, the supply of newly built homes plummeted again, as supply chain challenges following the COVID-19 pandemic and tariffs on essential construction materials such as steel and lumber led to prices soaring.
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Graph and download economic data for Existing Home Sales (EXHOSLUSM495S) from Oct 2024 to Oct 2025 about headline figure, sales, housing, and USA.
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View monthly updates and historical trends for US Existing Home Sales. from United States. Source: National Association of Realtors. Track economic data w…
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This dataset comprises detailed real estate listings scraped from Realtor.com, providing a snapshot of various property types across Chicago. It includes 2,000 entries with information on property characteristics such as type, size, age, price, and features. This dataset was ethically collected using an API provided by Apify, ensuring all data scraping adhered to ethical standards.
This dataset is ideal for a variety of data science applications, including but not limited to: - Predictive Modeling: Forecast property prices based on various features like location, size, and age. - Market Analysis: Understand trends in real estate, including the types of properties being sold, pricing trends, and the influence of property features on market value. - Natural Language Processing: Analyze the textual descriptions provided for each listing to extract additional features or perform sentiment analysis. - Anomaly Detection: Identify unusual listings or potential outliers in the data, which could indicate errors in data collection or unique investment opportunities.
This dataset was responsibly and ethically mined, adhering to all legal standards of data collection. The use of Apify's API ensures that the data collection process respects privacy and the platform's terms of service.
We thank Realtor.com for maintaining a comprehensive and accessible database, and Apify for providing the tools necessary for ethical data scraping. Their contributions have been invaluable in the creation of this dataset. Credits to Dall E3 for thumbnail image.
This dataset is provided for non-commercial and educational purposes only. Users are encouraged to use this data to enhance learning, contribute to academic or personal projects, and develop skills in data science and real estate market analysis.
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View monthly updates and historical trends for US Existing Home Median Sales Price. from United States. Source: National Association of Realtors. Track ec…
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TwitterThe average home in the U.S. sold for several percent below its asking price in December 2022, as a result of the housing market slowing. Just a few months before that, In the second quarter of 2022, the so-called sale-to-list price ratio went above ***. This reflected the high housing demand and the need of prospective home buyers to bid above the asking price. Housing demand - as measured in pending home sales - went up, as mortgage rates were historically low and plummeted once rates were increased.
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TwitterThe Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.
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📝 Dataset Description: This synthetic dataset contains 3,000 residential property listings modeled after real U.S. house sales data (in a Zillow-style format). It is designed for use in real estate analysis, machine learning, data visualization, and web scraping practice.
Each row represents a unique property and includes 16 key features commonly used by real estate agents, investors, and analysts. The data spans multiple U.S. states and cities, with realistic values for price, square footage, bedroom/bathroom count, property type, and more.
✅ Included Fields: Price – Listing price (in USD)
Address, City, State, Zipcode – U.S. formatted property location
Bedrooms, Bathrooms, Area (Sqft) – Core home specs
Lot Size, Year Built, Days on Market
Property Type, MLS ID, Listing Agent, Status
Listing URL – Mock Zillow-style property link
⚙️ Use Cases: Exploratory data analysis (EDA)
Regression/classification model training
Feature engineering and preprocessing
Real estate dashboards and web app mockups
Practice with BeautifulSoup, Pandas, or Power BI
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Graph and download economic data for New One Family Houses Sold: United States (HSN1F) from Jan 1963 to Aug 2025 about 1-unit structures, headline figure, family, new, sales, housing, and USA.
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TwitterThe 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.
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This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.
| Field Name | Description | Type |
|---|---|---|
| PropertyID | A unique identifier for each property. | text |
| PropType | The type of property (e.g., Commercial or Residential). | text |
| taxkey | The tax key associated with the property. | text |
| Address | The address of the property. | text |
| CondoProject | Information about whether the property is part of a condominium | text |
| project (NaN indicates missing data). | ||
| District | The district number for the property. | text |
| nbhd | The neighborhood number for the property. | text |
| Style | The architectural style of the property. | text |
| Extwall | The type of exterior wall material used. | text |
| Stories | The number of stories in the building. | text |
| Year_Built | The year the property was built. | text |
| Rooms | The number of rooms in the property. | text |
| FinishedSqft | The total square footage of finished space in the property. | text |
| Units | The number of units in the property | text |
| (e.g., apartments in a multifamily building). | ||
| Bdrms | The number of bedrooms in the property. | text |
| Fbath | The number of full bathrooms in the property. | text |
| Hbath | The number of half bathrooms in the property. | text |
| Lotsize | The size of the lot associated with the property. | text |
| Sale_date | The date when the property was sold. | text |
| Sale_price | The sale price of the property. | text |
Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].
Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].
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This dataset provides values for NEW HOME SALES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.
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Monthly single-family home sales in Connecticut, 2001 through the present. Data updated monthly by the Connecticut Housing Finance Authority and tracked in the following dashboard: https://www.chfa.org/about-us/ct-monthly-housing-market-dashboard/.
CHFA has stopped maintaining the dashboard and associated datasets, and this dataset will no longer be updated as of 2022.
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https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg" alt="">
A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?
Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.