House price prediction dataset

This dataset comprises housing data for various metropolitan cities of India. It includes: - Collection of prices of new and resale houses - The amenities provided for each house

This housing dataset is useful for a range of stakeholders, including real estate agents, property developers, buyers, renters, and researchers interested in analyzing housing markets and trends in metropolitan cities across India. It can be used for market analysis, price prediction, property recommendations, and various other real estate-related tasks.

Shape of dataset : (6207, 40)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11965067%2F75861c40e86a4d2d10c044be79542436%2FCapture.JPG?generation=1704918894425981&alt=media" alt="">

Github Link : https://github.com/TusharPaul01/House-Price-Prediction

For more such dataset & code check : https://www.kaggle.com/tusharpaul2001

Dataset Description:

This dataset has been meticulously pre-processed from the official UK government’s Price Paid Data, available for research purposes. The original dataset contains millions of rows spanning from 1995 to 2024, which posed significant challenges for machine learning operations due to its large size. For this project, we focused on house price predictions and filtered the data to only include transactions from 2015 to 2024. The final dataset contains 90,000 randomly sampled records, which should be ideal for training machine learning models efficiently. The goal of this dataset is to provide a well-structured, pre-processed dataset for students, researchers, and developers interested in creating house price prediction models using UK data. There are limited UK house price datasets available on Kaggle, so this contribution aims to fill that gap, offering a reliable dataset for dissertations, academic projects, or research purposes. This dataset is tailored for use in supervised learning models and has been cleaned, ensuring the removal of missing values and encoding of categorical variables. We hope this serves as a valuable resource for anyone studying house price prediction or real estate trends in the UK. In the future, I plan to provide an even larger dataset for more detailed and comprehensive predictions.

Features:

Feature Name - Description - Price - Sale price of the property (target variable). - Date - Date of the property transaction. Converted to datetime format for easier handling. - Postcode - Postcode of the property, offering location-based information. - property_type - Type of property (Detached, Semi-detached, Terraced, Flat, etc.). - new_build - Indicator whether the property was newly built at the time of sale (Yes or No). - freehold - Indicator whether the property was sold as freehold or leasehold (Freehold, Leasehold). - Street - Street name of the property location. - Locality - Locality of the property. - Town - Town or city where the property is located. - District - Administrative district of the property. - County - County where the property is located.

File Information:

The dataset is saved as a CSV file with 90,000 records, each representing a property transaction in the UK from 2015 to 2024. Feel free to explore this dataset and use it for any academic, research, or machine learning projects related to housing price predictions!

This synthetic dataset contains 10,000 samples of residential property data, generated to simulate real-world housing market behavior. Each row represents a single house and includes a mix of structural and locational features commonly used in regression-based price prediction models — such as house size, number of rooms, age, and distance from the city center. The target variable price is derived from these inputs using a realistic linear combination with added noise, making the dataset ideal for experimenting with linear regression, gradient descent, and other machine learning techniques in a controlled environment.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

This is a real dataset of house prices sold in Seattle, Washing, USA between August and December 2022. The task is to predict the house price in this area based on several features, which are described below.

Useful fact: * 1 acre = 43560 sqft

Context

I have previously shared a classification based dataset to classify the gender which is liked by those who are new to machine learning as it give a pretty good accuracy, which encouraged me to create a regression dataset to predict continues values. I have tried many real world datasets for regression problems which are predicting with lower accuracy and high error rate. As a beginner, I have struggled and worried why and how the dataset performs poorly. This is another main reason why I created this dataset. Although this is a made up dataset, I have considered all the features when deciding the price of the property. If you are a beginner, you would love to try this as the results are stunning..

Content

Since this is a populated data, I will straightaway explain the features and the label. FEATURES 1. land_size_sqm - This the total size of the land in square meters. 2. house_size_sqm - This is the area in which house is located within the land. This is measured in square meters. 3. no_of_rooms - This indicates the number of rooms available in the house. 4. no_of_bathrooms - This shows the number of total bathrooms made in the house. 5. large_living_room - This indicates whether the house includes a larger living room or not. The assumption is that all the houses contain a living room. This feature attempts to classify whether it's large or small where '1' means large and '0' means small. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 6. parking_space - This indicates whether there is a parking space or not. '1' represents the parking available while '0' represents no parking space available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 7. front_garden - This shows whether there is a garden available in front of the house. '1' means the garden available and '0' means no garden available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 8. swimming_pool - This shows the availability of the swimming pool at the house. 1 represents the availability of the swimming pool while 0 represents the non availability of the same. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 9. distance_to_school_km - This shows the distance from the house to the nearest school in Kilometers. 10. wall_fence - This shows whether there is a wall fence or not. '1' mean there is wall fence and '0' means no wall fence. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 11. **house_age_or_renovated **- This is either the age of the house in years or the period from the date of renovation. 12. water_front - this indicates whether the house is located in front of the water or not. 1 means waterfront and 0 means its not located near the water. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 13. distance_to_supermarket_km - what is the distance to the nearest supermarket in kilometers.

LABEL property_value - This is the price of the property

Following features are only available in the "house price dataset original v2 cleaned" and "house price dataset original v2 with categorical features" data only. 14. crime_rate - its in float and falls between 0 and 7. lesser the better 15. room_size - As the name suggests, it explains the size of the room. 0 is being 'small', 1 is being 'medium', 2 is 'large' and 3 is being 'Extra large'. However in the categorical dataset, these values are categorical and self explanatory.

Acknowledgements

I spent around 3 hours creating this dataset. Enjoy..

Inspiration

Share your notebooks to see which algorithm predicts the house price precisely.

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.

The quarterly pulse monitor expects the Dutch house prices to climb by *** percent in 2025 due to the decline in purchasing power, higher cost of borrowing and worsening economic conditions. The price of Dutch residential property in 2025 was approximately ******* euros. These developments came on top of other issues that were already prevalent in the Dutch housing market, such as the discussion about nitrogen and its effect on housing construction. The effects of nitrogen on the price of a house At the end of 2019, months before the coronavirus, there was already a lot of uncertainty whether their predictions would hold true. This had to do with the so-called “nitrogen decision” (in Dutch: stikstofbesluit) in May 2019. Simply put, a Dutch advisory body found that the domestic policy for nitrogen emission (formally known as Programmatische Aanpak Stikstof or Programmatic Approach Nitrogen) went against European rules. As of August 2019, a sizable share of the Dutch population was not familiar with this nitrogen policy. However, the advisory body’s decision led to an immediate stop to all construction in the country (amongst other things). By the end of 2019, this stop was still in place. For 2020, newly to be constructed houses have to comply to new rules regarding nitrogen emission. This puts new pressure on a housing market that already had to keep with increasing demand. How about the housing market in Amsterdam? In the year 2022, Amsterdam ranked as the most expensive city in the Netherlands to acquire an apartment, with an average price per square meter that was ***** euros more expensive than in Utrecht. Amsterdam was also well above the average rents found in other cities. A house in Amsterdam had a rent of approximately ** euros per square meter in 2023, whereas rents in Rotterdam cost roughly ** euros per square meter. It should be noted, however, that rent changes in the Dutch capital are significantly lower than those found in Rotterdam and especially Utrecht.

545 Indian residential property listings with 13 features including price, area, bedrooms, and amenities. Clean dataset with no missing values—ideal for house price prediction, regression modeling, and real estate ML projects.

Dataset

This dataset was created by ditsa pandey

Released under ODC Public Domain Dedication and Licence (PDDL)

Contents

Housing Index in the United States increased to 440.40 points in December from 439.70 points in November of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

ttd22/house-price dataset hosted on Hugging Face and contributed by the HF Datasets community

Annual house price data based on a sub-sample of the Regulated Mortgage Survey.

Context

This is a set of data created from imaginary data of house prices in an urban environment - Paris. I recommend using this dataset for educational purposes, for practice and to acquire the necessary knowledge. What I'm trying to do next is to create a classification dataset with same data from this dataset, I'll add a new column for class attribute ofc. Here is a classification dataset ---> classification dataset <---

Content

What's inside is more than just rows and columns. You can see house details listed as column names.

Description

All attributes are numeric variables and they are listed bellow:

• squareMeters
• numberOfRooms
• hasYard
• hasPool
• floors - number of floors
• cityCode - zip code
• cityPartRange - the higher the range, the more exclusive the neighbourhood is
• numPrevOwners - number of prevoious owners
• made - year
• isNewBuilt
• hasStormProtector
• basement - basement square meters
• attic - attic square meteres
• garage - garage size
• hasStorageRoom
• hasGuestRoom - number of guest rooms
• price - predicted value

Inspiration

Idea was to create dataset that is good for regression and that gives adequate results.

Housing Index in Slovakia increased to 208.52 points in the third quarter of 2025 from 198.85 points in the second quarter of 2025. This dataset provides - Slovakia House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Housing Index in Netherlands increased to 153.50 points in February from 153.30 points in January of 2026. This dataset provides - Netherlands House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The average Canadian house price declined slightly in 2023, after four years of consecutive growth. The average house price stood at ******* Canadian dollars in 2023 and was forecast to reach ******* Canadian dollars by 2026. Home sales on the rise The number of housing units sold is also set to increase over the two-year period. From ******* units sold, the annual number of home sales in the country is expected to rise to ******* in 2025. British Columbia and Ontario have traditionally been housing markets with prices above the Canadian average, and both are set to witness an increase in sales in 2025. How did Canadians feel about the future development of house prices? When it comes to consumer confidence in the performance of the real estate market in the next six months, Canadian consumers in 2024 mostly expected that the market would go up. A slightly lower share of the respondents believed real estate prices would remain the same.

The average price for a house in Newfoundland stood at approximately ******* Canadian dollars in 2024. According to the forecast, house prices in the province are set to continue rising in the next two years, reaching ******* Canadian dollars in 2026. Newfoundland was also the most affordable province for housing in Canada in 2024.Newfoundland Newfoundland and Labrador is the most easterly province in Canada. It’s an English-speaking province which borders French-speaking Quebec. The population of Newfoundland and Labrador has decreased since 2016, and stood most recently at ******* people. Its provincial capital and largest city is St. John’s. The economy of this province is heavily contingent on natural resources. The expansion of oil exportation has helped the economy grow, after it suffered during recent decades. Unfortunately, the population of Newfoundland and Labrador suffers one of the highest unemployment rates in Canada. Even though the low-income rate in Newfoundland and Labrador has decreased since 2000, ** percent of its population is still considered low income. Housing in Newfoundland Two-person households were the most common household size in Newfoundland and Labrador. Additionally, the number of single-detached housing starts per year in Newfoundland and Labrador has significantly decreased since 2012.

In 2022, housing prices in Belgium rose. According to the forecast, 2023 and 2024 will follow with a slight increase of two percent. Consumers signal much uncertainty on, for example, development of unemployment, which can hamper the housing market.

Belgium’s housing prices development

For years, house prices in Belgium followed a similar growth pattern to the country’s economy. Residential property prices grew when Belgium's economy performed well but stagnated when the economy slowed down. Since 2020, however, growth has accelerated. In 2022, the average house price exceeded 319,000 euros, up from 298,000 euros the year before.

The Belgian economy faces an uncertain future

Belgium’s real estate market is closely connected to the economic performance of the country. According to a 2022 forecast, the Belgian economy was predicted to grow by 2.1 percent in 2023. This prediction reflected inflation, supply chain disruptions impacting domestic demand, as well as (a lack of) international trade impacting Belgian growth.

Housing Index In the Euro Area increased to 156.19 points in the third quarter of 2025 from 153.73 points in the second quarter of 2025. This dataset provides the latest reported value for - Euro Area House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.