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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.

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.

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.

Graph and download economic data for Median Sales Price of Existing Homes (HOSMEDUSM052N) from Sep 2024 to Sep 2025 about sales, median, housing, and USA.

This dataset provides values for AVERAGE HOUSE PRICES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Home prices in the U.S. reach new heights The American housing market continues to show remarkable resilience, with the S&P/Case Shiller U.S. National Home Price Index reaching an all-time high of 331.69 in June 2025. This figure represents a significant increase from the index value of 166.23 recorded in January 2015, highlighting the substantial growth in home prices over the past decade. The S&P Case Shiller National Home Price Index is based on the prices of single-family homes and is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The S&P Case Shiller National Home Price Index series also includes S&P/Case Shiller 20-City Composite Home Price Index and S&P/Case Shiller 10-City Composite Home Price Index – measuring the home price changes in the major U.S. metropolitan areas, as well as twenty composite indices for the leading U.S. cities. Market fluctuations and recovery Despite the overall upward trend, the housing market has experienced some fluctuations in recent years. During the housing boom in 2021, the number of existing home sales reached the highest level since 2006. However, transaction volumes quickly plummeted, as the soaring interest rates and out-of-reach prices led to housing sentiment deteriorating. Factors influencing home prices Several factors have contributed to the rise in home prices, including a chronic supply shortage, the gradual decline in interest rates, and the spike in demand during the COVID-19 pandemic. During the subprime mortgage crisis (2007-2010), the construction of new homes declined dramatically. Although it has gradually increased since then, the number of new building permits, home starts, and completions are still shy from the levels before the crisis. With demand outweighing supply, competition for homes can be fierce, leading to bidding wars and soaring prices. The supply of existing homes is further constrained, as homeowners are less likely to sell and move homes due to the worsened lending conditions.

The median home sales price is the middle value of the prices for which homes are sold (both market and private transactions) within a calendar year. The median value is used as opposed to the average so that both extremely high and extremely low prices do not distort the prices for which homes are sold. This measure does not take into account the assessed value of a property.Source: First American Real Estate Solutions (FARES) and RBIntel (2022-forward)Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2022, 2023

Average House Prices in Canada increased to 688800 CAD in October from 687600 CAD in September of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

The S&P Case Shiller Portland Home Price Index has increased steadily in recent years. The index measures changes in the prices of existing single-family homes. The index value was equal to 100 as of January 2000, so if the index value is equal to *** in a given month, for example, it means that the house prices have increased by ** percent since 2000. The value of the S&P Case Shiller Portland Home Price Index amounted to ***** in August 2024. That was higher the national average.

This table shows the price development of existing own homes. Aside from the price indices, Statistics Netherlands also publishes figures on the number of sold dwellings, the average purchase price, and the total sum of the purchase prices of the sold dwellings. The House Price Index of existing own homes is based on a complete registration of sales of dwellings by the Dutch Land Registry Office (Kadaster) and the (WOZ) value of all dwellings in the Netherlands. Indices can fluctuate, for example when a limited number of dwellings of a certain type is sold. In such cases we recommend using the long-term figures. The average purchase price of existing own homes may differ from the price index of existing own homes. The change in the average purchase price, however, is not an indicator for price developments of existing own homes.

Data available from: January 1995 till December 2023

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The numbers of existing owner-occupied sold homes can be recalculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

From reporting month January 2024, the base year of the House Price Index for Existing Dwellings (PBK) will be adjusted from 2015 to 2020. In February 2024, the first figures of this new series will be released. These figures will be available in a new StatLine table. The old series (base year = 2015) can still be consulted via StatLine, but will no longer be updated

Changes as of 11 March 2024: This table has been discontinued. This table is followed by Existing own homes; purchase prices, price indices 2020=100. See paragraph 3.

Key information about House Prices Growth

• Denmark house prices grew 12.3% YoY in Jan 2022, following an increase of 9.2% YoY in the previous month.
• YoY growth data is updated monthly, available from Jan 2007 to Jan 2022, with an average growth rate of 0.0%.

CEIC calculates monthly House Prices Growth from Property Price Index. Statistics Denmark used to provide Property Price Index of One Family Houses with base 2006=100. House Prices Growth covers single family houses only.

Key information about House Prices Growth

• Japan house prices grew 3.4% YoY in Aug 2025, following an increase of 4.7% YoY in the previous month.
• YoY growth data is updated monthly, available from Apr 2009 to Aug 2025, with an average growth rate of 1.3%.
• House price data reached an all-time high of 10.2% in Apr 2022 and a record low of -9.4% in Apr 2009.

CEIC calculates House Prices Growth from monthly Residential Property Price Index. The Ministry of Land, Infrastructure, Transport and Tourism provides Residential Property Price Index with base 2010=100.

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.

This series does not include apartment prices. 2015 Figure changed on the 27/6/16 as revised data received from the Local authority Measured in €

Graph and download economic data for All-Transactions House Price Index for Idaho (IDSTHPI) from Q1 1975 to Q3 2025 about ID, appraisers, HPI, housing, price index, indexes, price, and USA.

Key information about House Prices Growth

• Canada house prices dropped 1.8% YoY in Oct 2025, following a decrease of 1.8% YoY in the previous month.
• YoY growth data is updated monthly, available from Jan 1982 to Oct 2025, with an average growth rate of 5.1%.
• House price data reached an all-time high of 16.5% in Mar 1989 and a record low of -9.7% in Apr 1991.

CEIC calculates House Prices Growth from monthly House Price Index. Statistics Canada provides House Price Index with base December 2016=100. House Price Index covers New Housing only.

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q2 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

India's residential house prices - quarterly and annual changes in house prices across cities, expert analysis and comparison with global peers.

This dataset gives the house prices of 50 European cities, plus other features (like local GDP per capita, population density, ...). This can be used either for data analysis or for linear regression.

The FHFA House Price Index (FHFA HPI®) is a comprehensive collection of public, freely available house price indexes that measure changes in single-family home values based on data from all 50 states and over 400 American cities that extend back to the mid-1970s.