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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.

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.

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 ...

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.

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

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.

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 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.

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 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.

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.

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.

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.

The U.S. housing market has slowed, after ** consecutive years of rising home prices. In 2021, house prices surged by an unprecedented ** percent, marking the highest increase on record. However, the market has since cooled, with the Freddie Mac House Price Index showing more modest growth between 2022 and 2024. In 2024, home prices increased by *** percent. That was lower than the long-term average of *** percent since 1990. Impact of mortgage rates on homebuying The recent cooling in the housing market can be partly attributed to rising mortgage rates. After reaching a record low of **** percent in 2021, the average annual rate on a 30-year fixed-rate mortgage more than doubled in 2023. This significant increase has made homeownership less affordable for many potential buyers, contributing to a substantial decline in home sales. Despite these challenges, forecasts suggest a potential recovery in the coming years. How much does it cost to buy a house in the U.S.? In 2023, the median sales price of an existing single-family home reached a record high of over ******* U.S. dollars. Newly built homes were even pricier, despite a slight decline in the median sales price in 2023. Naturally, home prices continue to vary significantly across the country, with West Virginia being the most affordable state for homebuyers.

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: Alice, TX data was reported at 65.000 USD th in Jun 2020. This records a decrease from the previous number of 193.000 USD th for Apr 2020. Median Home Sale Price: All Residential: Alice, TX data is updated monthly, averaging 126.500 USD th from Jun 2013 (Median) to Jun 2020, with 14 observations. The data reached an all-time high of 276.000 USD th in Nov 2018 and a record low of 20.000 USD th in Sep 2017. Median Home Sale Price: All Residential: Alice, 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.

This is the unadjusted median house priced for residential property sales (transactions) in the area for a 12 month period with April in the middle (year-ending September). These figures have been produced by the ONS (Office for National Statistics) using the Land Registry (LR) Price Paid data on residential dwelling transactions.

The LR Price Paid data are comprehensive in that they capture changes of ownership for individual residential properties which have sold for full market value and covers both cash sales and those involving a mortgage.

The median is the value determined by putting all the house sales for a given year, area and type in order of price and then selecting the price of the house sale which falls in the middle. The median is less susceptible to distortion by the presence of extreme values than is the mean. It is the most appropriate average to use because it best takes account of the skewed distribution of house prices.

Note that a transaction occurs when a change of freeholder or leaseholder takes place regardless of the amount of money involved and a property can transact more than once in the time period.

The LR records the actual price for which the property changed hands. This will usually be an accurate reflection of the market value for the individual property, but it is not always the case. In order to generate statistics that more accurately reflect market values, the LR has excluded records of houses that were not sold at market value from the dataset. The remaining data are considered a good reflection of market values at the time of the transaction. For full details of exclusions and more information on the methodology used to produce these statistics please see http://www.ons.gov.uk/peoplepopulationandcommunity/housing/qmis/housepricestatisticsforsmallareasqmi

The LR Price Paid data are not adjusted to reflect the mix of houses in a given area. Fluctuations in the types of house that are sold in that area can cause differences between the median transactional value of houses and the overall market value of houses. Therefore these statistics differ to the new UK House Price Index (HPI) which reports mix-adjusted average house prices and house price indices.

If, for a given year, for house type and area there were fewer than 5 sales records in the LR Price Paid data, the house price statistics are not reported. Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.