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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.

Global house prices experienced a significant shift in 2022, with advanced economies seeing a notable decline after a prolonged period of growth. The real house price index (adjusted for inflation) for advanced economies peaked at nearly *** index points in early 2022 before falling to around ***** points by the second quarter of 2023. In the second quarter of 2025, the index reached ***** points. This represents a reversal of the upward trend that had characterized the housing market for roughly a decade. Likewise, real house prices in emerging economies declined after reaching a high of ***** points in the third quarter of 2021. What is behind the slowdown? Inflation and slow economic growth have been the primary drivers for the cooling of the housing market. Secondly, the growing gap between incomes and house prices since 2012 has decreased the affordability of homeownership. Last but not least, homebuyers in 2024 faced dramatically higher mortgage interest rates, further contributing to worsening sentiment and declining transactions. Some markets continue to grow While many countries witnessed a deceleration in house price growth in 2022, some markets continued to see substantial increases. Turkey, in particular, stood out with a nominal increase in house prices of over ** percent in the first quarter of 2025. Other countries that recorded a two-digit growth include North Macedonia and Russia. When accounting for inflation, the three countries with the fastest growing residential prices in early 2025 were North Macedonia, Portugal, and Bulgaria.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Column Name	Description
Country	The country where the housing market data is recorded 🌍
Year	The year of observation 📅
Average House Price ($)	The average price of houses in USD 💰
Median Rental Price ($)	The median monthly rent for properties in USD 🏠
Mortgage Interest Rate (%)	The average mortgage interest rate percentage 📉
Household Income ($)	The average annual household income in USD 🏡
Population Growth (%)	The percentage increase in population over the year 👥
Urbanization Rate (%)	Percentage of the population living in urban areas 🏙️
Homeownership Rate (%)	The percentage of people who own their homes 🔑
GDP Growth Rate (%)	The annual GDP growth percentage 📈
Unemployment Rate (%)	The percentage of unemployed individuals in the labor force 💼

In 2025, India was the country with the highest increase in house prices since 2010 among the Asia-Pacific (APAC) countries under observation. In the second quarter of the year, the nominal house price index in India reached over 359 index points. This suggests an increase of 259 percent since 2010, the baseline year when the index value was set to 100. It is important to note that the nominal index does not account for the effects of inflation, meaning when adjusted for inflation, price growth in real terms was slower.

Key information about House Prices Growth

• US house prices grew 3.3% YoY in Sep 2025, following an increase of 4.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2025, with an average growth rate of -12.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

CEIC calculates House Prices Growth from quarterly House Price Index. Federal Housing Finance Agency provides House Price Index with base January 1991=100.

In the second quarter of 2025, North Macedonia, Portugal, and Bulgaria registered the highest house price increase in real terms (adjusted for inflation). In North Macedonia, house prices outgrew inflation by nearly ** percent. When comparing the nominal price change, which does not take inflation into consideration, the average house price growth was even higher.

Meanwhile, many countries experienced declining prices, with Hong Kong recording the biggest decline, at ***** percent. That has to do with a broader trend of a slowing global housing market.

Trends in House price index. The latest data for over 100 countries around the world.

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.

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.

CEIC calculates quarterly House Prices Growth from quarterly Residential Property Price Index. Sinyi Realty Incorporation provides Residential Property Price Index with base March 2016=100.

Turkey experienced the highest annual change in house prices in 2025, followed by North Macedonia and Portugal. In the second quarter of the year, the nominal house price in Turkey grew by **** percent, while in North Macedonia and Portugal, the increase was **** and **** percent, respectively. Meanwhile, some countries saw prices fall throughout the year. That has to do with an overall cooling of the global housing market that started in 2022. When accounting for inflation, house price growth was slower, and even more countries saw the market shrink.

Key information about House Prices Growth

• India house prices grew 2.5% YoY in Jun 2025, following an increase of 5.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2011 to Jun 2025, with an average growth rate of 5.1%.
• House price data reached an all-time high of 30.6% in Mar 2011 and a record low of -11.4% in Sep 2020.

CEIC calculates House Prices Growth from quarterly House Price Index. National Housing Bank provides House Price Index with base 2017-2018=100. House Prices Growth covers Mumbai only. House Prices Growth prior to Q2 2014 is calculated from House Price Index with base 2007=100.

Global House Standardised Price-Income Ratio by Country, 2023 Discover more data with ReportLinker!

How have house prices evolved over the long run? This paper presents annual house prices for 14 advanced economies since 1870. We show that real house prices stayed constant from the nineteenth to the mid-twentieth century, but rose strongly and with substantial cross-country variation in the second half of the twentieth century. Land prices, not replacement costs, are the key to understanding the trajectory of house prices. Rising land prices explain about 80 percent of the global house price boom that has taken place since World War II. Our findings have implications for the evolution of wealth-to-income ratios, the growth effects of agglomeration, and the price elasticity of housing supply.

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 Real Residential Property Prices for Germany (QDER628BIS) from Q1 1970 to Q2 2025 about Germany, residential, HPI, housing, real, price index, indexes, and price.

Graph and download economic data for Real Residential Property Prices for Hong Kong SAR (QHKR628BIS) from Q4 1979 to Q2 2025 about Hong Kong, residential, HPI, housing, real, price index, indexes, and price.

Graph and download economic data for Residential Property Prices for Brazil (QBRN628BIS) from Q1 2001 to Q2 2025 about Brazil, residential, HPI, housing, price index, indexes, and price.

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.

United States House Price Index: FHFA: New York data was reported at 679.080 Mar1980=100 in Jun 2018. This records an increase from the previous number of 670.010 Mar1980=100 for Mar 2018. United States House Price Index: FHFA: New York data is updated quarterly, averaging 288.040 Mar1980=100 from Mar 1975 (Median) to Jun 2018, with 174 observations. The data reached an all-time high of 679.080 Mar1980=100 in Jun 2018 and a record low of 73.490 Mar1980=100 in Mar 1976. United States House Price Index: FHFA: New York data remains active status in CEIC and is reported by Federal Housing Finance Agency. The data is categorized under Global Database’s United States – Table US.EB014: House Price Index.

Graph and download economic data for Real Residential Property Prices for Australia (QAUR628BIS) from Q1 1970 to Q2 2025 about Australia, residential, HPI, housing, real, price index, indexes, and price.