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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.

In December 2024, the average house price in England was pricier than in any other country. This considerable disparity in average house prices is in no small part down to the country's capital city, where the average asking price was more than double that of the UK’s average. Even in London, for those who can afford a mortgage, the savings made through buying over renting can be beneficial. What drives house prices? Average house prices are affected by several factors, including economic growth, unemployment, and interest rates. Housing supply also plays a considerable role, with a shortage of supply leading to increased competition and an upward push in prices. Conversely, an excess of housing means prices fall to stimulate buyers. House prices still set to grow The housing market in the UK is expected to continue to grow in the next years. By 2029,.the annual number of housing transactions is set to reach *** million. With transactions on the rise, the average house price is also set to rise.

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 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.

In 2025, Turkey had the highest inflation-adjusted house price index out of the ** European countries under observation, making it the country where house prices have increased the most since 2010. In Turkey, the house price index exceeded *** index points in the second quarter of 2025, showing an increase in real terms of *** percent since 2010, the baseline year for the index. Iceland and Hungary completed the top three, with an index value of *** and *** index points. In the past year, however, many European countries saw house prices decline in real terms. Where can I find other metrics on different housing markets in Europe? To assess the valuation in different housing markets, one can compare the house-price-to-income ratios of different countries worldwide. These ratios are calculated by dividing nominal house prices by nominal disposable income per head. There are also ratios that look at how residential property prices relate to domestic rents, such as the house-price-to-rent ratio for the United Kingdom. Unfortunately, these numbers are not available in a European overview. An overview of the price per square meter of an apartment in the EU-28 countries is available, however. One region, different markets An important trait of the European housing market is that there is not one market, but multiple. Property policy in Europe lies with the domestic governments, not with the European Union. This leads to significant differences between European countries, which shows in, for example, the homeownership rate (the share of owner-occupied dwellings of all homes). These differences also lead to another problem: the availability of data. Non-Europeans might be surprised to see that house price statistics vary in depth, as every country has their own methodology and no European body exists that tracks this data for the whole continent.

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.

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.

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.

Home prices fell by **** percent during the Great Recession of 2007 to 2009 in the United States. However, such a significant decrease in prices did not happen in the other four recessions which have occurred since 1980.

Most of the public concern about housing markets is based on claims that house prices have increased at historically anomalous rates and that house prices have outpaced incomes. The first claim is based on inaccurate historical data. The second is linked to relaxed credit constraints. House prices are likely to fall further, but not for the reasons usually proposed.

Key information about House Prices Growth

• Taiwan house prices grew 0.1% YoY in Sep 2025, following a decrease of 0.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2002 to Sep 2025, with an average growth rate of 15.1%.
• House price data reached an all-time high of 20.9% in Mar 2010 and a record low of -6.0% in Mar 2016.

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.

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

In 2024, the residential property price index in Seoul, the capital of South Korea, increased to around ***** percent year-on-year. 2022 had seen a significant drop in residential property prices in Seoul. How much is a house in Seoul? Housing prices in Seoul have experienced significant fluctuations in recent years. Auction bid price rate for apartments surged to reach over ** percent, rebounding from a decline in 2022. Similarly, the success rate of apartment auction bids showed dynamic trends, dropping to **** percent in late 2022 before recovering to over ** percent. Seoul boasted the highest mean purchase price for housing among all provinces of South Korea, with a gap of over *** million South Korean won between Seoul and Gyeonggi. Property prices in South Korea South Korea's real estate market demonstrates dynamic trends shaped by numerous factors. Economic growth, urbanization, government policies, interest rates, and foreign investment all contribute significantly to fluctuations in housing prices. Notably, the mean purchase price for apartments sharply declined in 2022 and 2023 following years of exponential growth before. While the housing transaction volume in Korea saw a significant decrease in 2022, it recovered slightly in 2024. Given the high housing prices, many citizens believe that property prices will continue to fall in the coming year.

The Halifax house price index, which was set at 100 in 1992, reached a value of more than 500 over the course of 2022. In December 2023, the index stood at 495.1, which was as slight increase from the same period in 2022. The average house price amounted to about 287,000 British pounds in December 2023. What drives house prices? Average house prices are affected by several factors: Economic growth, unemployment, interest rates and mortgage availability can all affect average prices. A shortage of supply means that the need for housing and, therefore competitive market created will push up house prices, whereas an excess of housing means prices fall to stimulate buyers. One of the main reasons for the decrease in house prices in the second half of 2022 was interest rates rising as a response to inflation. How many house sales occur per year? In the United Kingdom (UK), there are approximately one million residential property transactions annually. On a country level, England constitutes the majority of transactions made.

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 €

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.

Housing Index in Malta increased to 171.93 points in the second quarter of 2025 from 169.11 points in the first quarter of 2025. This dataset provides - Malta House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Average House Price

Dataset

This dataset was created by tianzhi li

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