100+ datasets found
  1. House Price Prediction Dataset & Code

    • kaggle.com
    Updated Sep 19, 2023
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    Tushar Paul (2023). House Price Prediction Dataset & Code [Dataset]. http://doi.org/10.34740/kaggle/ds/3757184
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 19, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Tushar Paul
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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

  2. Real Estate Price Prediction Data

    • figshare.com
    txt
    Updated Aug 8, 2024
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    Mohammad Shbool; Rand Al-Dmour; Bashar Al-Shboul; Nibal Albashabsheh; Najat Almasarwah (2024). Real Estate Price Prediction Data [Dataset]. http://doi.org/10.6084/m9.figshare.26517325.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    Aug 8, 2024
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Mohammad Shbool; Rand Al-Dmour; Bashar Al-Shboul; Nibal Albashabsheh; Najat Almasarwah
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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].

  3. P

    Houses Dataset Dataset

    • paperswithcode.com
    Updated Feb 16, 2021
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    Eman Ahmed; Mohamed Moustafa (2016). Houses Dataset Dataset [Dataset]. https://paperswithcode.com/dataset/houses-dataset
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    Dataset updated
    Feb 16, 2021
    Authors
    Eman Ahmed; Mohamed Moustafa
    Description

    This dataset is used for predicting house prices from both images and textual information. It is composed of 535 sample houses from California, USA.

  4. House price prediction

    • kaggle.com
    Updated Aug 8, 2021
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    Sohaib Anwaar (2021). House price prediction [Dataset]. https://www.kaggle.com/datasets/sohaibanwaar1203/house-price-prediction
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 8, 2021
    Dataset provided by
    Kaggle
    Authors
    Sohaib Anwaar
    Description

    Dataset

    This dataset was created by Sohaib Anwaar

    Contents

  5. California Housing price prediction

    • kaggle.com
    Updated Mar 3, 2023
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    Siddartha Gandi (2023). California Housing price prediction [Dataset]. https://www.kaggle.com/datasets/siddarthagandi/california-housing-price-prediction
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 3, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Siddartha Gandi
    Area covered
    California
    Description

    The California Housing dataset is based on 1990 US census and is widely used for machine learning and statistics. It was published in 1990 by Pace, R. Kelley and Ronald Barry, and can be found in the UCI Machine Learning Repository. The California Data set gives the information about Economic and Geographic values of the Houses,and also the economic status of the people present in the California.

  6. Forecast house price growth in the UK 2024-2028

    • statista.com
    Updated Jun 20, 2025
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    Statista (2025). Forecast house price growth in the UK 2024-2028 [Dataset]. https://www.statista.com/statistics/376079/uk-house-prices-forecast/
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    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 2023
    Area covered
    United Kingdom
    Description

    Just as in many other countries, the housing market in the UK grew substantially during the coronavirus pandemic, fueled by robust demand and low borrowing costs. Nevertheless, high inflation and the increase in mortgage rates has led to house price growth slowing down. According to the forecast, 2024 is expected to see house prices decrease by ***** percent. Between 2024 and 2028, the average house price growth is projected at *** percent. A contraction after a period of continuous growth In June 2022, the UK's house price index exceeded *** index points, meaning that since 2015 which was the base year for the index, house prices had increased by ** percent. In just two years, between 2020 and 2022, the index surged by ** index points. As the market stood in December 2023, 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 years. Growth is forecast to be stronger in 2024 and slow down in the period between 2025 and 2028. The rental market in London is expected to follow a similar trend, with Central London slightly outperforming Greater London.

  7. T

    United States FHFA House Price Index

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States FHFA House Price Index [Dataset]. https://tradingeconomics.com/united-states/housing-index
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    xml, excel, json, csvAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1991 - Apr 30, 2025
    Area covered
    United States
    Description

    Housing Index in the United States decreased to 434.90 points in April from 436.70 points in March 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.

  8. h

    house-price

    • huggingface.co
    Updated May 15, 2024
    + more versions
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    Trang Dang (2024). house-price [Dataset]. https://huggingface.co/datasets/ttd22/house-price
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 15, 2024
    Authors
    Trang Dang
    Description

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

  9. f

    S1 Data. Analytical data set for the Housing_price and Machine learning...

    • figshare.com
    xlsx
    Updated Sep 8, 2024
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    Jaewon Han (2024). S1 Data. Analytical data set for the Housing_price and Machine learning study [Dataset]. http://doi.org/10.6084/m9.figshare.26965252.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Sep 8, 2024
    Dataset provided by
    figshare
    Authors
    Jaewon Han
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Hedonic Price Model, used in existing house price modeling, may not address the relationship between house prices and streetscapes perceived at the human eye level. Therefore, in this study, we analyzed the relationship between streetscapes perceived at eye level and single-family home prices in Seoul, Korea, using computer vision technology and machine learning algorithms. We used transaction data for 13,776 single-family housing sales between 2017 and 2019. To measure visually perceived streetscapes, this study used the Deeplab V3+ deep-learning model with 233,106 Google Street View panoramic images. Then, the best machine-learning model was selected by comparing the explanatory powers of the hedonic price model and all alternative machine-learning models. According to the results, the Gradient Boost model, a representative ensemble machine learning model, performed better than XGBoost, Random Forest, and Linear Regression models in predicting single-family house prices. In addition, this study used an interpretable machine learning model of the SHAP method to identify key features that affect single-family home price prediction. This solves the "black box" problem of machine learning models. Finally, by analyzing the nonlinear relationship and interaction effects between perceived streetscape characteristics and house prices, we easily and quickly identified the relationship between variables the hedonic price model partially considers.

  10. h

    Real-Estate-Price-Prediction

    • huggingface.co
    Updated Mar 7, 2025
    + more versions
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    Globose Technology Solutions (2025). Real-Estate-Price-Prediction [Dataset]. https://huggingface.co/datasets/globosetechnology12/Real-Estate-Price-Prediction
    Explore at:
    Dataset updated
    Mar 7, 2025
    Authors
    Globose Technology Solutions
    Description

    Problem Statement 👉 Download the case studies here Investors and buyers in the real estate market faced challenges in accurately assessing property values and market trends. Traditional valuation methods were time-consuming and lacked precision, making it difficult to make informed investment decisions. A real estate firm sought a predictive analytics solution to provide accurate property price forecasts and market insights. Challenge Developing a real estate price prediction system involved… See the full description on the dataset page: https://huggingface.co/datasets/globosetechnology12/Real-Estate-Price-Prediction.

  11. T

    United States Existing Home Sales Prices

    • tradingeconomics.com
    • zh.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 15, 2025
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    TRADING ECONOMICS (2025). United States Existing Home Sales Prices [Dataset]. https://tradingeconomics.com/united-states/single-family-home-prices
    Explore at:
    xml, excel, json, csvAvailable download formats
    Dataset updated
    May 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1968 - May 31, 2025
    Area covered
    United States
    Description

    Single Family Home Prices in the United States increased to 422800 USD in May from 414000 USD in April of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

  12. A

    ‘Jiffs house price prediction dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 13, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Jiffs house price prediction dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-jiffs-house-price-prediction-dataset-458f/1a7ff5ac/?iid=048-724&v=presentation
    Explore at:
    Dataset updated
    Feb 13, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Jiffs house price prediction dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/elakiricoder/jiffs-house-price-prediction-dataset on 13 February 2022.

    --- Dataset description provided by original source is as follows ---

    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.

    --- Original source retains full ownership of the source dataset ---

  13. UK House Price Index: data downloads June 2021

    • gov.uk
    • s3.amazonaws.com
    Updated Aug 18, 2021
    + more versions
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    HM Land Registry (2021). UK House Price Index: data downloads June 2021 [Dataset]. https://www.gov.uk/government/statistical-data-sets/uk-house-price-index-data-downloads-june-2021
    Explore at:
    Dataset updated
    Aug 18, 2021
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    HM Land Registry
    Area covered
    United Kingdom
    Description

    The UK House Price Index is a National Statistic.

    Create your report

    Download the full UK House Price Index data below, or use our tool to https://landregistry.data.gov.uk/app/ukhpi?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=tool&utm_term=9.30_18_08_21" class="govuk-link">create your own bespoke reports.

    Download the data

    Datasets are available as CSV files. Find out about republishing and making use of the data.

    Google Chrome is blocking downloads of our UK HPI data files (Chrome 88 onwards). Please use another internet browser while we resolve this issue. We apologise for any inconvenience caused.

    Full file

    This file includes a derived back series for the new UK HPI. Under the UK HPI, data is available from 1995 for England and Wales, 2004 for Scotland and 2005 for Northern Ireland. A longer back series has been derived by using the historic path of the Office for National Statistics HPI to construct a series back to 1968.

    Download the full UK HPI background file:

    Individual attributes files

    If you are interested in a specific attribute, we have separated them into these CSV files:

  14. T

    China Newly Built House Prices YoY Change

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 19, 2025
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    TRADING ECONOMICS (2025). China Newly Built House Prices YoY Change [Dataset]. https://tradingeconomics.com/china/housing-index
    Explore at:
    xml, excel, csv, jsonAvailable download formats
    Dataset updated
    May 19, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 2011 - Jun 30, 2025
    Area covered
    China
    Description

    Housing Index in China decreased by 3.20 percent in June from -3.50 percent in May of 2025. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  15. House price change forecast in Spain and Portugal 2023, with a forecast by...

    • statista.com
    Updated Feb 16, 2024
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    Statista (2024). House price change forecast in Spain and Portugal 2023, with a forecast by 2025 [Dataset]. https://www.statista.com/statistics/1165916/residential-real-estate-price-forecast-change-in-spain-and-portugal/
    Explore at:
    Dataset updated
    Feb 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2022
    Area covered
    Portugal, Spain
    Description

    House prices in Spain are forecast to fall in 2024, after increasing by 1.2 percent in 2023. Nevertheless, prices are expected to pick up in 2025, with an increase of one percent. The Portuguese housing market, on the other hand, grew by 5.5 percent in 2023, but was forecast to contract in the next two years.

  16. housing price index prediction project data

    • figshare.com
    txt
    Updated Mar 23, 2021
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    Sophia Zhou (2021). housing price index prediction project data [Dataset]. http://doi.org/10.6084/m9.figshare.14253278.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    Mar 23, 2021
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Sophia Zhou
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    S&P/Case-Shiller home price index and 12 demographic and macroeconomic factors in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco (SF) data were collected from the Federal Reserve Bank, FBI, and Freddie Mac. https://fred.stlouisfed.org; http://www.freddiemac.com/pmms/; https://www.philadelphiafed.org/surveys-and-data/community-development-data/consumer-credit-explorer; https://ucr.fbi.gov/crime-in-the-u.s/2005;

  17. T

    Spain House Prices

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Apr 15, 2025
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    TRADING ECONOMICS (2025). Spain House Prices [Dataset]. https://tradingeconomics.com/spain/housing-index
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset updated
    Apr 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 31, 1987 - Mar 31, 2025
    Area covered
    Spain
    Description

    Housing Index in Spain increased to 2033 EUR/SQ. METRE in the first quarter of 2025 from 1972.10 EUR/SQ. METRE in the fourth quarter of 2024. This dataset provides the latest reported value for - Spain House Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  18. A

    ‘Amsterdam House Price Prediction’ analyzed by Analyst-2

    • analyst-2.ai
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com), ‘Amsterdam House Price Prediction’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-amsterdam-house-price-prediction-c050/f17ebbed/?iid=002-225&v=presentation
    Explore at:
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Amsterdam
    Description

    Analysis of ‘Amsterdam House Price Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/thomasnibb/amsterdam-house-price-prediction on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    If you are like me, you might get overwhelmed when having to make big decisions such as buying a house. In such cases, I always like to go for a data driven approach, that will help me find an optimum solution. This involves two steps. First, we need to gather as much data as we can. Second, we need to define a metric for success.

    Gathering housing prices requires some effort. A caveat is that the asking prices are not the prices to which the houses were actually sold. Defining a metric for success is somewhat subjective. I consider a house to be a good option if the house price is cheap compared to other listings in the area.

    Content

    The housing prices have been obtained from Pararius.nl as a snapshot in August 2021. The original data provided features such as price, floor area and the number of rooms. The data has been further enhanced by utilising the Mapbox API to obtain the coordinates of each listing.

    Acknowledgements

    Thanks to Pararius

    --- Original source retains full ownership of the source dataset ---

  19. House Prices Prediction

    • kaggle.com
    Updated Nov 25, 2024
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    Richard Sikaonga (2024). House Prices Prediction [Dataset]. https://www.kaggle.com/datasets/richiesikaonga/house-prices-prediction/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 25, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Richard Sikaonga
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset

    This dataset was created by Richard Sikaonga

    Released under MIT

    Contents

  20. Residential real estate price forecast change in Norway 2022-2025

    • statista.com
    Updated Jul 11, 2025
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    Statista (2025). Residential real estate price forecast change in Norway 2022-2025 [Dataset]. https://www.statista.com/statistics/1174950/residential-real-estate-price-forecast-change-in-norway/
    Explore at:
    Dataset updated
    Jul 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    Norway
    Description

    House prices in Norway fell by *** percent and, according to the forecast, are expected to continue to fall until 2024. In 2023, properties were forecast to experience a decline in prices of ** percent. In 2025, growth is projected to recover, rising to **** percent.

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Tushar Paul (2023). House Price Prediction Dataset & Code [Dataset]. http://doi.org/10.34740/kaggle/ds/3757184
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House Price Prediction Dataset & Code

Predicting price of house in metropolitan cities (Dataset & Code)

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Sep 19, 2023
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Tushar Paul
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

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

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