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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 Housing Inventory: Price Reduced Count in the United States (PRIREDCOUUS) from Jul 2016 to Oct 2025 about reduced count, price, and USA.

Graph and download economic data for Housing Inventory: Price Reduced Count in Miami-Dade County, FL (PRIREDCOU12086) from Jul 2016 to Oct 2025 about Miami-Dade County, FL; reduced count; Miami; FL; price; and USA.

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 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.

About Dataset

All the following text is copied directly from the original dataset used: https://www.kaggle.com/datasets/fedesoriano/the-boston-houseprice-data
The only difference is that features 12 and 13 have been removed for simplicity. See original link for a version with those features in place.

Similar Datasets

Gender Pay Gap Dataset: https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset
California Housing Prices Data (5 new features!): https://www.kaggle.com/fedesoriano/california-housing-prices-data-extra-features
Company Bankruptcy Prediction: https://www.kaggle.com/fedesoriano/company-bankruptcy-prediction
Spanish Wine Quality Dataset: https://www.kaggle.com/datasets/fedesoriano/spanish-wine-quality-dataset

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town
2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
3) INDUS: proportion of non-retail business acres per town
4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise)
5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M]
6) RM: average number of rooms per dwelling
7) AGE: proportion of owner-occupied units built prior to 1940
8) DIS: weighted distances to five Boston employment centres
9) RAD: index of accessibility to radial highways
10) TAX: full-value property-tax rate per $10,000 [$/10k]
11) PTRATIO: pupil-teacher ratio by town
[Original features 12 and 13 have been deliberately removed from this version of the dataset]

Output variable:
1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. https://www.researchgate.net/profile/Daniel-Rubinfeld/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air/links/5c38ce85458515a4c71e3a64/Hedonic-housing-prices-and-the-demand-for-clean-air.pdf

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley https://www.wiley.com/en-us/Regression+Diagnostics%3A+Identifying+Influential+Data+and+Sources+of+Collinearity-p-9780471691174

House prices grew year-on-year in most states in the U.S. in the first quarter of 2025. Hawaii was the only exception, with a decline of **** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Rhode Island—the state where homes appreciated the most—the increase was ******percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2025, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2025.

Housing Index in China remained unchanged at -2.20 percent in October. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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 Housing Inventory: Price Reduced Count in New Jersey (PRIREDCOUNJ) from Jul 2016 to Oct 2025 about reduced count, NJ, price, and USA.

House Price Index MoM in the United States decreased to 0 percent in September from 0.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index MoM.

The average resale house price in Canada was forecast to reach nearly ******* Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach *** million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was *** million Canadian dollars in 2024.

House prices in the UK rose dramatically during the coronavirus pandemic, with growth slowing down in 2022 and turning negative in 2023. The year-on-year annual house price change peaked at 14 percent in July 2022. In April 2025, house prices increased by 3.5 percent. As of late 2024, the average house price was close to 290,000 British pounds. Correction in housing prices: a European phenomenon The trend of a growing residential real estate market was not exclusive to the UK during the pandemic. Likewise, many European countries experienced falling prices in 2023. When comparing residential property RHPI (price index in real terms, e.g. corrected for inflation), countries such as Germany, France, Italy, and Spain also saw prices decline. Sweden, one of the countries with the fastest growing residential markets, saw one of the largest declines in prices. How has demand for UK housing changed since the outbreak of the coronavirus? The easing of the lockdown was followed by a dramatic increase in home sales. In November 2020, the number of mortgage approvals reached an all-time high of over 107,000. One of the reasons for the housing boom were the low mortgage rates, allowing home buyers to take out a loan with an interest rate as low as 2.5 percent. That changed as the Bank of England started to raise the base lending rate, resulting in higher borrowing costs and a decline in homebuyer sentiment.

After a period of rapid increase, house price growth in the UK has moderated. In 2025, house prices are forecast to increase by ****percent. Between 2025 and 2029, the average house price growth is projected at *** percent. According to the source, home building is expected to increase slightly in this period, fueling home buying. On the other hand, higher borrowing costs despite recent easing of mortgage rates and affordability challenges may continue to suppress transaction activity. Historical house price growth in the UK House prices rose steadily between 2015 and 2020, despite minor fluctuations. In the following two years, prices soared, leading to the house price index jumping by about 20 percent. As the market stood in April 2025, the average price for a home stood at approximately ******* British pounds. Rents are expected to continue to grow According to another forecast, the prime residential market is also expected to see rental prices grow in the next five years. Growth is forecast to be stronger in 2025 and slow slightly until 2029. The rental market in London is expected to follow a similar trend, with Outer London slightly outperforming Central London.

In 2022, house price growth in the UK slowed, after a period of decade-long increase. Nevertheless, in June 2025, prices reached a new peak, with the average home costing ******* British pounds. This figure refers to all property types, including detached, semi-detached, terraced houses, and flats and maisonettes. Compared to other European countries, the UK had some of the highest house prices. How have UK house prices increased over the last 10 years? Property prices have risen dramatically over the past decade. According to the UK house price index, the average house price has grown by over ** percent since 2015. This price development has led to the gap between the cost of buying and renting a property to close. In 2023, buying a three-bedroom house in the UK was no longer more affordable than renting one. Consequently, Brits have become more likely to rent longer and push off making a house purchase until they have saved up enough for a down payment and achieved the financial stability required to make the step. What caused the recent fluctuations in house prices? House prices are affected by multiple factors, such as mortgage rates, supply, and demand on the market. For nearly a decade, the UK experienced uninterrupted house price growth as a result of strong demand and a chronic undersupply. Homebuyers who purchased a property at the peak of the housing boom in July 2022 paid ** percent more compared to what they would have paid a year before. Additionally, 2022 saw the most dramatic increase in mortgage rates in recent history. Between December 2021 and December 2022, the **-year fixed mortgage rate doubled, adding further strain to prospective homebuyers. As a result, the market cooled, leading to a correction in pricing.

Housing Inventory: Price Reduced Count in Berkeley County, WV was 184.00000 Level in October of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Price Reduced Count in Berkeley County, WV reached a record high of 190.00000 in October of 2019 and a record low of 16.00000 in February of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Price Reduced Count in Berkeley County, WV - last updated from the United States Federal Reserve on November of 2025.

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.

Housing Inventory: Price Reduced Count Year-Over-Year in Caldwell County, NC was 67.50% in September of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Price Reduced Count Year-Over-Year in Caldwell County, NC reached a record high of 275.00 in January of 2023 and a record low of -64.71 in March of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Price Reduced Count Year-Over-Year in Caldwell County, NC - last updated from the United States Federal Reserve on October of 2025.

Graph and download economic data for Housing Inventory: Price Reduced Count in Dallas-Fort Worth-Arlington, TX (CBSA) (PRIREDCOU19100) from Jul 2016 to Oct 2025 about reduced count, Dallas, TX, price, and USA.

Housing Inventory: Price Reduced Count Month-Over-Month in Columbia, MO (CBSA) was -1.92% in September of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Price Reduced Count Month-Over-Month in Columbia, MO (CBSA) reached a record high of 122.22 in May of 2022 and a record low of -61.76 in December of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Price Reduced Count Month-Over-Month in Columbia, MO (CBSA) - last updated from the United States Federal Reserve on November of 2025.