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Description:

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?

Acknowledgement:

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.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

ttd22/house-price dataset hosted on Hugging Face and contributed by the HF Datasets community

House Prices Dataset

Subtitle:

Detailed Real Estate Data for Predicting House Prices and Analyzing Market Trends

Description:

This dataset contains information on 21,613 properties, making it a comprehensive resource for exploring real estate market trends and building predictive models for house prices. The data includes various features capturing property details, location, and market conditions, providing ample opportunities for data exploration, visualization, and machine learning applications.

Key Features:

• General Information:

  • id: Unique identifier for each property.
  • date: Date of sale.

• Price Details:

  • price: Sale price of the house.

• Property Features:

  • bedrooms: Number of bedrooms.
  • bathrooms: Number of bathrooms (including partials as fractions).
  • sqft_living: Living space area in square feet.
  • sqft_lot: Lot size in square feet.
  • floors: Number of floors.
  • waterfront: Whether the property has a waterfront view.
  • view: Quality of the view rating.
  • condition: Overall condition of the house.
  • grade: Grade of construction and design (scale of 1–13).

• Additional Metrics:

  • sqft_above: Square footage of the property above ground.
  • sqft_basement: Basement area in square feet.
  • yr_built: Year the property was built.
  • yr_renovated: Year of last renovation.

• Location Coordinates:

  • zipcode: ZIP code of the property.
  • lat and long: Latitude and longitude coordinates.

• Neighbor Comparisons:

  • sqft_living15: Average living space of 15 nearest properties.
  • sqft_lot15: Average lot size of 15 nearest properties.

Use Cases:

• Predicting house prices using regression models.
• Identifying the impact of various features (e.g., number of bedrooms, location) on property prices.
• Analyzing market trends and spatial distribution of real estate prices.

This dataset is a valuable resource for anyone interested in real estate analytics, machine learning, or geographic data visualization.

Annual house price data based on a sub-sample of the Regulated Mortgage Survey.

Average House Prices in the United States increased to 534100 USD in August from 478200 USD in July of 2025. This dataset includes a chart with historical data for the United States New Home Average Sales Price.

Key information about House Prices Growth

• US house prices grew 3.3% YoY in Sep 2025, following an increase of 4.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2025, with an average growth rate of -12.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

The U.S. housing market has seen significant price growth since 2011, with the median sales price of existing single-family homes reaching a record high of ******* U.S. dollars in 2024. This represents a substantial increase of ******* over the past five years, highlighting the rapid appreciation of home values across the country. The trend of rising prices can also be observed in the new homes sold. Regional variations and housing shortage While the national median price provides a broad overview, regional differences in home prices are notable. The West remains the most expensive region, with prices twice higher than in the more affordable Midwest. This disparity persists despite efforts to increase housing supply. In 2024, approximately ******* building permits for single-family housing units were granted, showing a slight increase from previous years but still well below the 2005 peak of **** million permits. The ongoing housing shortage continues to drive prices upward across all regions. Market dynamics and future outlook The number of existing home sales has plummeted since 2020, reflecting the growing cost of homeownership. Factors such as high home prices, unfavorable economic conditions, and aggressive increases in mortgage rates have contributed to affordability challenges for many potential homebuyers. Despite these challenges, forecasts suggest a potential recovery in the housing market by 2025, though transaction volumes are expected to remain below long-term averages.

Median Home Sale Price: All Residential: Bridgeport, CT data was reported at 493.000 USD th in Jul 2020. This records an increase from the previous number of 485.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Bridgeport, CT data is updated monthly, averaging 385.000 USD th from Feb 2015 (Median) to Jul 2020, with 66 observations. The data reached an all-time high of 493.000 USD th in Jul 2020 and a record low of 336.000 USD th in Feb 2019. Median Home Sale Price: All Residential: Bridgeport, CT data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

The 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.

Median Home Sale Price: All Residential: Bonham, TX data was reported at 209.000 USD th in Jul 2020. This records an increase from the previous number of 167.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Bonham, TX data is updated monthly, averaging 115.000 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 209.000 USD th in Jul 2020 and a record low of 46.000 USD th in Jan 2013. Median Home Sale Price: All Residential: Bonham, TX data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

Median Home Sale Price: All Residential: Brenham, TX data was reported at 275.000 USD th in Jul 2020. This records an increase from the previous number of 231.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Brenham, TX data is updated monthly, averaging 211.000 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 319.000 USD th in Mar 2019 and a record low of 115.000 USD th in Feb 2013. Median Home Sale Price: All Residential: Brenham, TX data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

Median Sales Price of Houses Sold for the United States was 410800.00000 $ in April of 2025, according to the United States Federal Reserve. Historically, Median Sales Price of Houses Sold for the United States reached a record high of 442600.00000 in October of 2022 and a record low of 17800.00000 in January of 1963. Trading Economics provides the current actual value, an historical data chart and related indicators for Median Sales Price of Houses Sold for the United States - last updated from the United States Federal Reserve on December of 2025.

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.

Median Home Sale Price: All Residential: Bend, OR data was reported at 470.000 USD th in Jul 2020. This records an increase from the previous number of 430.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Bend, OR data is updated monthly, averaging 331.000 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 470.000 USD th in Jul 2020 and a record low of 170.000 USD th in Mar 2012. Median Home Sale Price: All Residential: Bend, OR data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

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.

This dataset contains 2,000 entries of house price data from all states in Malaysia, providing a comprehensive overview of the country’s real estate market for 2025. Sourced from Brickz, a trusted platform for property transaction insights, it includes detailed information such as property location, tenure, type, median prices, and transaction counts. This dataset is ideal for real estate market analysis, predictive modeling, and exploring trends across Malaysia’s diverse property market.

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Data Columns (Total 8 Columns):

1. Township: The specific township where the property is located (e.g., Cheras, Subang Jaya).
2. Area: The locality or broader area encompassing the township (e.g., Klang Valley, Penang Island).
3. State: The Malaysian state where the property is situated (e.g., Selangor, Johor, Penang).
4. Tenure: The property ownership type (e.g., Freehold, Leasehold).
5. Type: The category of property (e.g., Terrace, Condominium, Semi-Detached).
6. Median_Price: The median price (in MYR) for properties in the specified township or area.
7. Median_PSF: The median price per square foot (in MYR) for properties.
8. Transactions: The number of recorded property transactions.

Future Plans:

• Expanded Coverage: This dataset will be regularly updated with additional property data to make it even more versatile.
• Enhanced Features: Future updates may include rental prices, amenities, or property-specific details to offer deeper insights into Malaysia’s housing market.

Median Home Sale Price: All Residential: Columbus, OH data was reported at 255.000 USD th in Jul 2020. This records an increase from the previous number of 250.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Columbus, OH data is updated monthly, averaging 172.000 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 255.000 USD th in Jul 2020 and a record low of 122.000 USD th in Jan 2013. Median Home Sale Price: All Residential: Columbus, OH data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

Graph and download economic data for Median Sales Price of Existing Single-Family Homes (HSFMEDUSM052N) from Oct 2024 to Oct 2025 about 1-unit structures, family, sales, median, housing, price, and USA.