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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 Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

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.

Description:

This dataset contains various features of residential properties along with their corresponding prices. It is suitable for exploring and analyzing factors influencing housing prices and for building predictive models to estimate the price of a property based on its attributes.

Usage:

• This dataset can be used for exploratory data analysis to understand the relationships between different housing features and prices.
• It can also be used to build machine learning models for predicting housing prices based on the given features.

License: This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Dataset

This dataset was created by 173050055

Released under Other (specified in description)

Contents

Displacement risk indicator showing how many households within the specified groups are facing either housing cost burden (contributing more than 30% of monthly income toward housing costs) or severe housing cost burden (contributing more than 50% of monthly income toward housing costs).

The year-end value of the S&P Case Shiller National Home Price Index amounted to 321.45 in 2024. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given year, for example, it means that the house prices increased by 30 percent since 2000. S&P/Case Shiller U.S. home indices – additional informationThe S&P Case Shiller National Home Price Index is calculated on a monthly basis and is based on the prices of single-family homes in nine U.S. Census divisions: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain and Pacific. The index is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The index illustrates the trend of home prices and can be helpful during house purchase decisions. When house prices are rising, a house buyer might want to speed up the house purchase decision as the transaction costs can be much higher in the future. The S&P Case Shiller National Home Price Index has been on the rise since 2011.The S&P Case Shiller National Home Price Index is one of the indices included in the S&P/Case-Shiller Home Price Index Series. Other indices are the S&P/Case Shiller 20-City Composite Home Price Index, the S&P/Case Shiller 10-City Composite Home Price Index and twenty city composite indices.

Task Description: Real Estate Price Prediction

This task involves predicting the price of real estate properties based on various features that influence the value of a property. The dataset contains several attributes of real estate properties such as square footage, the number of bedrooms, bathrooms, floors, the year the property was built, whether the property has a garden or pool, the size of the garage, the location score, and the distance from the city center.

The goal is to build a regression model that can predict the Price of a property based on the provided features.

Dataset Columns:

ID: A unique identifier for each property.

Square_Feet: The area of the property in square meters.

Num_Bedrooms: The number of bedrooms in the property.

Num_Bathrooms: The number of bathrooms in the property.

Num_Floors: The number of floors in the property.

Year_Built: The year the property was built.

Has_Garden: Indicates whether the property has a garden (1 for yes, 0 for no).

Has_Pool: Indicates whether the property has a pool (1 for yes, 0 for no).

Garage_Size: The size of the garage in square meters.

Location_Score: A score from 0 to 10 indicating the quality of the neighborhood (higher scores indicate better neighborhoods).

Distance_to_Center: The distance from the property to the city center in kilometers.

Price: The target variable that represents the price of the property. This is the value we aim to predict.

Objective: The goal of this task is to develop a regression model that predicts the Price of a real estate property using the other features as inputs. The model should be able to learn the relationship between these features and the price, providing an accurate prediction for unseen data.

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.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

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The NYC Housing dataset contains information about the New York City Housing and Preservation Department's (HPD) affordable housing development projects. It includes data on building characteristics, affordability levels, location, and ownership information for all properties in the dataset.

The dataset consists of several files, including Building Data, Project Data, and HPD Contacts. The Building Data file contains information on individual buildings, such as the building's address, number of units, and building type. The Project Data file contains information on the development projects that contain these buildings, including information on the funding programs used to develop the projects and the affordability levels of the units. The HPD Contacts file contains contact information for HPD employees responsible for the management of each project.

The NYC Housing dataset is a valuable resource for researchers, policymakers, and developers interested in affordable housing in New York City. It can be used to analyze trends in affordable housing development, identify neighborhoods with high levels of affordable housing, and evaluate the effectiveness of various affordable housing programs.

Percentage of the population living in a household where total housing costs (net of housing allowances) represent more than 40% of the total disposable household income (net of housing allowances).

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.

Danish residential house prices (1992-2024)

About the dataset (cleaned data)

The dataset (parquet file) contains approximately 1,5 million residential household sales from Denmark during the periode from 1992 to 2024. All cleaned data is merged into one parquet file here on Kaggle. Note some cleaning might still be nessesary, see notebook under code.

Also, added a random sample (100k) of the dataset as a csv file.

Done in Python version: 2.6.3.

Raw data

Raw data and more info is avaible on Github repositary: https://github.com/MartinSamFred/Danish-residential-housingPrices-1992-2024.git

The dataset has been scraped and cleaned (to some extent). Cleaned files are located in: \Housing_data_cleaned \ named DKHousingprices_1 and 2. Saved in parquet format (and saved as two files due to size).

Cleaning from raw files to above cleaned files is outlined in BoligsalgConcatCleanigGit.ipynb. (done in Python version: 2.6.3)

Webscraping script: Webscrape_script.ipynb (done in Python version: 2.6.3)

Provided you want to clean raw files from scratch yourself:

Uncleaned scraped files (81 in total) are located in \Housing_data_raw \ Housing_data_batch1 and 2. Saved in .csv format and compressed as 7-zip files.

Additional files added/appended to the Cleaned files are located in \Addtional_data and named DK_inflation_rates, DK_interest_rates, DK_morgage_rates and DK_regions_zip_codes. Saved in .xlsx format.

Content

Each row in the dataset contains a residential household sale during the period 1992 - 2024.

“Cleaned files” columns:

0 'date': is the transaction date

1 'quarter': is the quarter based on a standard calendar year

2 'house_id': unique house id (could be dropped)

3 'house_type': can be 'Villa', 'Farm', 'Summerhouse', 'Apartment', 'Townhouse'

4 'sales_type': can be 'regular_sale', 'family_sale', 'other_sale', 'auction', '-' (“-“ could be dropped)

5 'year_build': range 1000 to 2024 (could be narrowed more)

6 'purchase_price': is purchase price in DKK

7 '%_change_between_offer_and_purchase': could differ negatively, be zero or positive

8 'no_rooms': number of rooms

9 'sqm': number of square meters

10 'sqm_price': 'purchase_price' divided by 'sqm_price'

11 'address': is the address

12 'zip_code': is the zip code

13 'city': is the city

14 'area': 'East & mid jutland', 'North jutland', 'Other islands', 'Capital, Copenhagen', 'South jutland', 'North Zealand', 'Fyn & islands', 'Bornholm'

15 'region': 'Jutland', 'Zealand', 'Fyn & islands', 'Bornholm'

16 'nom_interest_rate%': Danish nominal interest rate show pr. quarter however actual rate is not converted from annualized to quarterly

17 'dk_ann_infl_rate%': Danish annual inflation rate show pr. quarter however actual rate is not converted from annualized to quarterly

18 'yield_on_mortgage_credit_bonds%': 30 year mortgage bond rate (without spread)

Uses

Various (statistical) analysis, visualisation and I assume machine learning as well.

Practice exercises etc.

Uncleaned scraped files are great to practice cleaning, especially string cleaning. I’m not an expect as seen in the coding ;-).

Disclaimer

The data and information in the data set provided here are intended to be used primarily for educational purposes only. I do not own any data, and all rights are reserved to the respective owners as outlined in “Acknowledgements/sources”. The accuracy of the dataset is not guaranteed accordingly any analysis and/or conclusions is solely at the user's own responsibly and accountability.

Acknowledgements/sources

All data is publicly available on:

Boliga: https://www.boliga.dk/

Finans Danmark: https://finansdanmark.dk/

Danmarks Statistik: https://www.dst.dk/da

Statistikbanken: https://statistikbanken.dk/statbank5a/default.asp?w=2560

Macrotrends: https://www.macrotrends.net/

PostNord: https://www.postnord.dk/

World Data: https://www.worlddata.info/

Dataset picture / cover photo: Nick Karvounis (https://unsplash.com/)

Have fun… :-)

Dataset on Housing Prices in the Philippines, scraped from from Lamudi on May 2023.

Median price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

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.

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 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