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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.

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.

House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

Single Family Home Prices in the United States increased to 415200 USD in October from 412300 USD in September of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The 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.

View monthly updates and historical trends for US Existing Home Median Sales Price. from United States. Source: National Association of Realtors. Track ec…

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.

Redfin is a real estate brokerage and publishes the US housing market data on a regular basis. Using this dataset, you can analyze and visualize housing market data for US cities. Timeline: Starting from February 2012 until the present time (Data is refreshed and updated on a monthly basis)

The dataset has the following columns: - period_begin - period_end - period_duration
- region_type
- region_type_id - table_id - is_seasonally_adjusted. (indicates if prices are seasonally adjusted; f represents False) - region - city - state - state_code - property_type - property_type_id - median_sale_price
- median_sale_price_mom (median sale price changes month over month) - median_sale_price_yoy (median sale price changes year over year) - median_list_price
- median_list_price_mom (median list price changes month over month) - median_list_price_yoy (median list price changes year over year) - median_ppsf (median sale price per square foot) - median_ppsf_mom (median sale price per square foot changes month over month) - median_ppsf_yoy (median sale price per square foot changes year over year) - median_list_ppsf (median list price per square foot) - median_list_ppsf_mom (median list price per square foot changes month over month) - median_list_ppsf_yoy. (median list price per square foot changes year over year) - homes_sold (number of homes sold) - homes_sold_mom (number of homes sold month over month) - homes_sold_yoy (number of homes sold year over year) - pending_sales
- pending_sales_mom
- pending_sales_yoy
- new_listings - new_listings_mom
- new_listings_yoy
- inventory - inventory_mom
- inventory_yoy
- months_of_supply
- months_of_supply_mom - months_of_supply_yoy
- median_dom (median days on market until property is sold) - median_dom_mom (median days on market changes month over month) - median_dom_yoy (median days on market changes year over year) - avg_sale_to_list (average sale price to list price ratio) - avg_sale_to_list_mom (average sale price to list price ratio changes month over month) - avg_sale_to_list_yoy (average sale price to list price ratio changes year over year) - sold_above_list
- sold_above_list_mom - sold_above_list_yoy - price_drops - price_drops_mom - price_drops_yoy - off_market_in_two_weeks (number of properties that will be taken off the market within 2 weeks) - off_market_in_two_weeks_mom (changes in number of properties that will be taken off the market within 2 weeks, month over month) - off_market_in_two_weeks_yoy (changes in number of properties that will be taken off the market within 2 weeks, year over year) - parent_metro_region - parent_metro_region_metro_code - last_updated

Filetype: gzip (gz) Support for gzip files in Python: https://docs.python.org/3/library/gzip.html

Data Source & Credit: Redfin.com

The U.S. housing market continues to evolve, with the median price for existing homes forecast to fall to ******* U.S. dollars by 2027. This projection comes after a period of significant growth and recent fluctuations, reflecting the complex interplay of economic factors affecting the real estate sector. The rising costs have not only impacted home prices but also down payments, with the median down payment more than doubling since 2012. Regional variations in housing costs Home prices and down payments vary dramatically across the United States. While the national median down payment stood at approximately ****** U.S. dollars in early 2024, homebuyers in states like California, Massachusetts, and Hawaii faced down payments exceeding ****** U.S. dollars. This disparity highlights the challenges of homeownership in high-cost markets and underscores the importance of location in determining housing affordability. Market dynamics and future outlook The housing market has shown signs of cooling after years of rapid growth, with a modest price increase of *** percent in 2024. This slowdown can be attributed in part to rising mortgage rates, which have tempered demand. Despite these challenges, most states continued to see year-over-year price growth in 2025, with Rhode Island and West Virginia leading the packby home appreciation. As the market adjusts to new economic realities, potential homebuyers and investors alike will be watching closely for signs of stabilization or renewed growth in the coming years.

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.

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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.

Median Home Sale Price: All Residential: Crossville, TN data was reported at 201.000 USD th in Jul 2020. This records an increase from the previous number of 180.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Crossville, TN data is updated monthly, averaging 146.000 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 201.000 USD th in Jul 2020 and a record low of 97.000 USD th in Feb 2014. Median Home Sale Price: All Residential: Crossville, TN 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: 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.

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

About ** percent of home buyers in the United States in 2021 purchased homes that cost ******* U.S. dollars or more. The share of home buyers generally reduced as the house prices decreased. The largest share of purchases fell in the ******* to ******* and ******* to ******* price classes, which was below the national average sales price for existing homes.

Description:

This dataset contains prices of New York houses, providing valuable insights into the real estate market in the region. It includes information such as broker titles, house types, prices, number of bedrooms and bathrooms, property square footage, addresses, state, administrative and local areas, street names, and geographical coordinates.

Key Features:

• BROKERTITLE: Title of the broker
• TYPE: Type of the house
• PRICE: Price of the house
• BEDS: Number of bedrooms
• BATH: Number of bathrooms
• PROPERTYSQFT: Square footage of the property
• ADDRESS: Full address of the house
• STATE: State of the house
• MAIN_ADDRESS: Main address information
• ADMINISTRATIVE_AREA_LEVEL_2: Administrative area level 2 information
• LOCALITY: Locality information
• SUBLOCALITY: Sublocality information
• STREET_NAME: Street name
• LONG_NAME: Long name
• FORMATTED_ADDRESS: Formatted address
• LATITUDE: Latitude coordinate of the house
• LONGITUDE: Longitude coordinate of the house

Potential Use Cases:

• Price analysis: Analyze the distribution of house prices to understand market trends and identify potential investment opportunities.
• Property size analysis: Explore the relationship between property square footage and prices to assess the value of different-sized houses.
• Location-based analysis: Investigate geographical patterns to identify areas with higher or lower property prices.
• Bedroom and bathroom trends: Analyze the impact of the number of bedrooms and bathrooms on house prices.
• Broker performance analysis: Evaluate the influence of different brokers on the pricing of houses.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂 Thank you

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.

The median sales price of new homes sold in the United States increased steadily from 1965 to 2022, followed by two years of decline. In 2024, a newly built home cost approximately ******* U.S. dollars. That was a decline from the peak price of 434,500 U.S. dollars in 2022. Prices varied greatly across different regions in the country, with the most expensive housing found in the Northeast region.

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.