100+ datasets found
  1. b

    Real Estate Dataset

    • brightdata.com
    .json, .csv, .xlsx
    Updated Aug 1, 2025
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    Bright Data (2025). Real Estate Dataset [Dataset]. https://brightdata.com/products/datasets/real-estate
    Explore at:
    .json, .csv, .xlsxAvailable download formats
    Dataset updated
    Aug 1, 2025
    Dataset authored and provided by
    Bright Data
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    Real estate datasets from various websites cover all major real estate data points including: property type, size, location, price, bedrooms, baths, address, history, images, and much more. Popular use cases include: forecast housing demand, analyze price fluctuations, improve customer satisfaction, see past prices to monitor market trends, and more.

  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. d

    Zillow Real Estate Data Extraction | Real-time Real Estate Market Data | No...

    • datarade.ai
    Updated Nov 7, 2023
    + more versions
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    APISCRAPY (2023). Zillow Real Estate Data Extraction | Real-time Real Estate Market Data | No Infra Cost | Pre-built AI & Automation | 50% Cost Saving | Free Sample [Dataset]. https://datarade.ai/data-products/zillow-real-estate-data-extraction-real-time-real-estate-ma-apiscrapy
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    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Nov 7, 2023
    Dataset authored and provided by
    APISCRAPY
    Area covered
    Iceland, Portugal, Croatia, Canada, Spain, Albania, Bulgaria, Liechtenstein, Belgium, Isle of Man
    Description

    Note:- Only publicly available data can be worked upon

    APISCRAPY collects and organizes data from Zillow's massive database, whether it's property characteristics, market trends, pricing histories, or more. Because of APISCRAPY's first-rate data extraction services, tracking property values, examining neighborhood trends, and monitoring housing market variations become a straightforward and efficient process.

    APISCRAPY's Zillow real estate data scraping service offers numerous advantages for individuals and businesses seeking valuable insights into the real estate market. Here are key benefits associated with their advanced data extraction technology:

    1. Real-time Zillow Real Estate Data: Users can access real-time data from Zillow, providing timely updates on property listings, market dynamics, and other critical factors. This real-time information is invaluable for making informed decisions in a fast-paced real estate environment.

    2. Data Customization: APISCRAPY allows users to customize the data extraction process, tailoring it to their specific needs. This flexibility ensures that the extracted Zillow real estate data aligns precisely with the user's requirements.

    3. Precision and Accuracy: The advanced algorithms utilized by APISCRAPY enhance the precision and accuracy of the extracted Zillow real estate data. This reliability is crucial for making well-informed decisions related to property investments and market trends.

    4. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can access the desired Zillow real estate data without unnecessary delays.

    5. User-friendly Interface: APISCRAPY provides a user-friendly interface, making it accessible for individuals and businesses to navigate and utilize the Zillow real estate data scraping service with ease.

    APISCRAPY provides real-time real estate market data drawn from Zillow, ensuring that consumers have access to the most up-to-date and comprehensive real estate insights available. Our real-time real estate market data services aren't simply a game changer in today's dynamic real estate landscape; they're an absolute requirement.

    Our dedication to offering high-quality real estate data extraction services is based on the utilization of Zillow Real Estate Data. APISCRAPY's integration of Zillow Real Estate Data sets it different from the competition, whether you're a seasoned real estate professional or a homeowner wanting to sell, buy, or invest.

    APISCRAPY's data extraction is a key element, and it is an automated and smooth procedure that is at the heart of the platform's operation. Our platform gathers Zillow real estate data quickly and offers it in an easily consumable format with the click of a button.

    [Tags;- Zillow real estate scraper, Zillow data, Zillow API, Zillow scraper, Zillow web scraping tool, Zillow data extraction, Zillow Real estate data, Zillow scraper, Zillow scraping API, Zillow real estate da extraction, Extract Real estate Data, Property Listing Data, Real estate Data, Real estate Data sets, Real estate market data, Real estate data extraction, real estate web scraping, real estate api, real estate data api, real estate web scraping, web scraping real estate data, scraping real estate data, real estate scraper, best real, estate api, web scraping real estate, api real estate, Zillow scraping software ]

  4. c

    Real Estate DataSet

    • cubig.ai
    Updated May 28, 2025
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    CUBIG (2025). Real Estate DataSet [Dataset]. https://cubig.ai/store/products/317/real-estate-dataset
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    Dataset updated
    May 28, 2025
    Dataset authored and provided by
    CUBIG
    License

    https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service

    Measurement technique
    Synthetic data generation using AI techniques for model training, Privacy-preserving data transformation via differential privacy
    Description

    1) Data Introduction • The Real Estate DataSet consists of 506 examples, including home prices in the Boston suburbs and various residential and environmental characteristics.

    2) Data Utilization (1) Real Estate DataSet has characteristics that: • The dataset provides 13 continuous variables and one binary variable, including crime rate, house size, environmental pollution, accessibility, tax rate, and population characteristics. (2) Real Estate DataSet can be used to: • House Price Forecast: It can be used to develop a regression model that predicts the median price (MEDV) of a house based on various residential and environmental factors. • Analysis of Urban Planning and Policy: It can be used for urban development and policy making by analyzing the impact of residential environmental factors such as crime rates, environmental pollution, and educational environment on housing values.

  5. R

    Residential Real Estate Market in the United States Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 7, 2025
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    Data Insights Market (2025). Residential Real Estate Market in the United States Report [Dataset]. https://www.datainsightsmarket.com/reports/residential-real-estate-market-in-the-united-states-17275
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    doc, pdf, pptAvailable download formats
    Dataset updated
    Mar 7, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    United States, Global
    Variables measured
    Market Size
    Description

    The US residential real estate market, a cornerstone of the American economy, is projected to experience steady growth over the next decade. While the provided CAGR of 2.04% is a modest figure, it reflects a market maturing after a period of significant expansion. This sustained growth is driven by several key factors. Firstly, population growth and urbanization continue to fuel demand for housing, particularly in densely populated areas and emerging suburban markets. Secondly, low interest rates (historically, though this can fluctuate) have made mortgages more accessible, stimulating buyer activity. Thirdly, a robust construction sector, though facing challenges in material costs and labor shortages, is gradually increasing the housing supply, mitigating some of the upward pressure on prices. However, challenges remain. Rising inflation and potential interest rate hikes pose a risk to affordability, potentially dampening demand. Furthermore, the ongoing evolution of remote work is reshaping residential preferences, with a shift toward larger homes in suburban or exurban locations. This trend impacts the relative demand for various property types, potentially increasing the appeal of landed houses and villas compared to apartments and condominiums in certain regions. The segmentation of the market into apartments/condominiums and landed houses/villas provides crucial insights into consumer preferences and investment strategies. High-density urban areas will continue to see strong demand for apartments and condos, while suburban and rural areas are likely to experience a greater increase in landed property sales. Major players like Simon Property Group, Mill Creek Residential, and others are strategically adapting to these trends, focusing on both development and management across various property types and geographic locations. Analyzing regional data within the US (e.g., comparing growth in the Northeast versus the Southwest) will highlight market nuances and potential investment opportunities. While the global data provided is valuable for understanding broader market forces, focusing the analysis on the US market allows for a more granular understanding of the specific drivers, trends, and challenges within this significant segment of the real estate sector. The forecast period (2025-2033) suggests continued, albeit measured, expansion. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Key drivers for this market are: Investment Plan Towards Urban Rail Development. Potential restraints include: Italy’s Fragmented Approach to Tenders. Notable trends are: Existing Home Sales Witnessing Strong Growth.

  6. d

    Real Estate Property Tax Rates

    • catalog.data.gov
    • datasets.ai
    Updated Jul 26, 2025
    + more versions
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    Arlington County (2025). Real Estate Property Tax Rates [Dataset]. https://catalog.data.gov/dataset/real-estate-property-tax-rates
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    Dataset updated
    Jul 26, 2025
    Dataset provided by
    Arlington County
    Description

    This dataset includes the historical Arlington real estate property tax rates, including rate value and tax rate type description.

  7. Forecast growth rate of leading residential real estate markets worldwide...

    • statista.com
    Updated Jun 20, 2025
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    Statista (2025). Forecast growth rate of leading residential real estate markets worldwide until 2028 [Dataset]. https://www.statista.com/forecasts/1427645/residential-real-estate-transactions-growth-worldwide-by-country
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    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Russia
    Description

    Turkey, Russia, and Mexico were the countries with the highest forecast annualized growth of residential real estate transactions until 2028. According to a 2023 forecast by Statista Market Insights, the value of residential sales in Russia will rise by about ** percent annually between 2023 and 2028. In the U.S. - the largest housing market - the growth rate is expected to be much lower at **** percent.

  8. h

    Real-Estate-Price-Prediction

    • huggingface.co
    Updated Mar 7, 2025
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    Globose Technology Solutions (2025). Real-Estate-Price-Prediction [Dataset]. https://huggingface.co/datasets/globosetechnology12/Real-Estate-Price-Prediction
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    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.

  9. T

    Sweden Real Estate Price Index

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Feb 1, 2002
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    TRADING ECONOMICS (2002). Sweden Real Estate Price Index [Dataset]. https://tradingeconomics.com/sweden/housing-index
    Explore at:
    xml, json, excel, csvAvailable download formats
    Dataset updated
    Feb 1, 2002
    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
    Dec 31, 1975 - Mar 31, 2025
    Area covered
    Sweden
    Description

    Housing Index in Sweden decreased to 936 points in the first quarter of 2025 from 937 points in the fourth quarter of 2024. This dataset provides - Sweden House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  10. d

    Real Estate Sales 2001-2022 GL

    • catalog.data.gov
    • data.ct.gov
    Updated Dec 20, 2024
    + more versions
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    data.ct.gov (2024). Real Estate Sales 2001-2022 GL [Dataset]. https://catalog.data.gov/dataset/real-estate-sales-2001-2018
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    Dataset updated
    Dec 20, 2024
    Dataset provided by
    data.ct.gov
    Description

    The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

  11. F

    Interest Rates and Price Indexes; Multi-Family Real Estate Apartment Price...

    • fred.stlouisfed.org
    json
    Updated Jun 12, 2025
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    (2025). Interest Rates and Price Indexes; Multi-Family Real Estate Apartment Price Index, Level [Dataset]. https://fred.stlouisfed.org/series/BOGZ1FL075035403A
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 12, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Interest Rates and Price Indexes; Multi-Family Real Estate Apartment Price Index, Level (BOGZ1FL075035403A) from 1985 to 2024 about multifamily, real estate, family, interest rate, interest, rate, price index, indexes, price, and USA.

  12. Real Estate Market Analysis APAC, North America, Europe, South America,...

    • technavio.com
    Updated Feb 24, 2025
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    Technavio (2025). Real Estate Market Analysis APAC, North America, Europe, South America, Middle East and Africa - US, China, Japan, India, South Korea, Australia, Canada, UK, Germany, Brazil - Size and Forecast 2025-2029 [Dataset]. https://www.technavio.com/report/real-estate-market-analysis
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    Dataset updated
    Feb 24, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global, United Kingdom, Canada, United States
    Description

    Snapshot img

    Real Estate Market Size 2025-2029

    The real estate market size is forecast to increase by USD 1,258.6 billion at a CAGR of 5.6% between 2024 and 2029.

    The market is experiencing significant shifts and innovations, with both residential and commercial sectors adapting to new trends and challenges. In the commercial realm, e-commerce growth is driving the demand for logistics and distribution centers, while virtual reality technology is revolutionizing property viewings. Europe's commercial real estate sector is witnessing a rise in smart city development, incorporating LED lighting and data centers to enhance sustainability and efficiency. In the residential sector, wellness real estate is gaining popularity, focusing on health and well-being. Real estate software and advertising services are essential tools for asset management, streamlining operations, and reaching potential buyers. Regulatory uncertainty remains a challenge, but innovation in construction technologies, such as generators and renewable energy solutions, is helping mitigate risks.
    

    What will be the Size of the Real Estate Market During the Forecast Period?

    Request Free Sample

    The market continues to exhibit strong activity, driven by rising population growth and increasing demand for personal household space. Both residential and commercial sectors have experienced a rebound in home sales and leasing activity. The trend towards live-streaming rooms and remote work has further fueled demand for housing and commercial real estate. Economic conditions and local market dynamics influence the direction of the market, with interest rates playing a significant role in investment decisions. Fully furnished, semi-furnished, and unfurnished properties, as well as rental properties, remain popular options for buyers and tenants. Offline transactions continue to dominate, but online transactions are gaining traction.
    The market encompasses a diverse range of assets, including land, improvements, buildings, fixtures, roads, structures, utility systems, and undeveloped property. Vacant land and undeveloped property present opportunities for investors, while the construction and development of new housing and commercial projects contribute to the market's overall growth.
    

    How is this Real Estate Industry segmented and which is the largest segment?

    The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

    Type
    
      Residential
      Commercial
      Industrial
    
    
    Business Segment
    
      Rental
      Sales
    
    
    Manufacturing Type
    
      New construction
      Renovation and redevelopment
      Land development
    
    
    Geography
    
      APAC
    
        China
        India
        Japan
        South Korea
    
    
      North America
    
        Canada
        US
    
    
      Europe
    
        Germany
        UK
    
    
      South America
    
        Brazil
    
    
      Middle East and Africa
    

    By Type Insights

    The residential segment is estimated to witness significant growth during the forecast period.
    

    The market encompasses the buying and selling of properties designed for dwelling purposes, including buildings, single-family homes, apartments, townhouses, and more. Factors fueling growth in this sector include the increasing homeownership rate among millennials and urbanization trends. The Asia Pacific region, specifically China, dominates the market due to escalating homeownership rates. In India, the demand for affordable housing is a major driver, with initiatives like Pradhan Mantri Awas Yojana (PMAY) spurring the development of affordable housing projects catering to the needs of lower and middle-income groups. The commercial real estate segment, consisting of office buildings, shopping malls, hotels, and other commercial properties, is also experiencing growth.

    Furthermore, economic and local market conditions, interest rates, and investment opportunities in fully furnished, semi-furnished, unfurnished properties, and rental properties influence the market dynamics. Technological integration, infrastructure development, and construction projects further shape the real estate landscape. Key sectors like transportation, logistics, agriculture, and the e-commerce sector also impact the market.

    Get a glance at the market report of share of various segments Request Free Sample

    The Residential segment was valued at USD 1440.30 billion in 2019 and showed a gradual increase during the forecast period.

    Regional Analysis

    APAC is estimated to contribute 64% to the growth of the global market during the forecast period.
    

    Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

    For more insights on the market share of various regions, Request Free Sample

    The Asia Pacific region holds the largest share of The market, dr

  13. T

    Saudi Arabia Real Estate Price Index

    • tradingeconomics.com
    • es.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, Saudi Arabia Real Estate Price Index [Dataset]. https://tradingeconomics.com/saudi-arabia/housing-index
    Explore at:
    xml, excel, csv, jsonAvailable 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
    Mar 31, 2014 - Mar 31, 2025
    Area covered
    Saudi Arabia
    Description

    Housing Index in Saudi Arabia increased to 104.90 points in the first quarter of 2025 from 104.20 points in the fourth quarter of 2024. This dataset provides - Saudi Arabia Housing Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

  14. Commercial real estate cap rates in the U.S. 2012-2023 with a forecast until...

    • statista.com
    Updated Jun 20, 2025
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    Statista (2025). Commercial real estate cap rates in the U.S. 2012-2023 with a forecast until 2026 [Dataset]. https://www.statista.com/statistics/245008/us-commercial-property-cap-rates/
    Explore at:
    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    Retail properties had the highest capitalization rates in the United States in 2023, followed by offices. The cap rate for office real estate was **** percent in the fourth quarter of the year and was forecast to rise further to **** percent in 2024. Cap rates measure the expected rate of return on investment, and show the net operating income of a property as a percentage share of the current asset value. While a higher cap rate indicates a higher rate of return, it also suggests a higher risk. Why have cap rates increased? The increase in cap rates is a consequence of a repricing in the commercial real estate sector. According to the National NCREIF Property Return Index, prices for commercial real estate declined across all property types in 2023. Rental growth was slow during the same period, resulting in a negative annual return. The increase in cap rates reflects the increased risk in the investment environment. Pricing uncertainty in the commercial real estate sector Between 2014 and 2021, commercial property prices in the U.S. enjoyed steady growth. Access to credit with low interest rates facilitated economic growth and real estate investment. As inflation surged in the following two years, lending policy tightened. That had a significant effect on the sector. First, it worsened sentiment among occupiers. Second, it led to a decline in demand for commercial spaces and commercial real estate investment volumes. Uncertainty about the future development of interest rates and occupier demand further contributed to the repricing of real estate assets.

  15. T

    United States House Price Index YoY

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 27, 2025
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    TRADING ECONOMICS (2025). United States House Price Index YoY [Dataset]. https://tradingeconomics.com/united-states/house-price-index-yoy
    Explore at:
    json, excel, xml, csvAvailable download formats
    Dataset updated
    May 27, 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, 1992 - May 31, 2025
    Area covered
    United States
    Description

    House Price Index YoY in the United States decreased to 2.80 percent in May from 3.20 percent in April of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

  16. d

    Residential Real Estate Data | USA Coverage | 74% Right Party Contact Rate |...

    • datarade.ai
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    BatchData, Residential Real Estate Data | USA Coverage | 74% Right Party Contact Rate | BatchData [Dataset]. https://datarade.ai/data-products/batchservice-real-estate-data-150-million-us-property-records-batchservice
    Explore at:
    .json, .csv, .xml, .xls, .sql, .txtAvailable download formats
    Dataset authored and provided by
    BatchData
    Area covered
    United States of America
    Description

    BatchData provides access to 150+ million residential and commercial properties and property owners, covering 99+% of the us population. Enrich records, build lists, or power real estate websites and application based on:

    • Property Type
    • Property Owner Info
    • Building Characteristics
    • MLS Listing Details
    • Foreclosure Information
    • Distress Factors
    • Mortgage Details
    • Household Demographics
    • Ownership/Vacancy Status
    • Home Equity
    • Real Estate Valuation
    • Property Liens
    • Transfer of Sale, Probate, Inherited
    • Much more!

    BatchData is both a data and technology company, offering multiple self-service platforms, APIs and professional services solutions to meet your specific data needs. Whether you're looking for residential real estate data, commercial real estate data, property listing and transaction data, we've got you covered!

    BatchData is the most comprehensive aggregator of US property and homeowner information, known for accuracy and completeness of records. BatchService can also provides homeowner and agency contact information for residential and commercial properties, including cell phone number and emails.

  17. 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

  18. Forecast: Real Estate Activities Gross Value Added Growth Rate in the US...

    • reportlinker.com
    Updated Apr 7, 2024
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    ReportLinker (2024). Forecast: Real Estate Activities Gross Value Added Growth Rate in the US 2022 - 2026 [Dataset]. https://www.reportlinker.com/dataset/82d5b3972335094beedac5c5c3236343cd21559e
    Explore at:
    Dataset updated
    Apr 7, 2024
    Dataset provided by
    Reportlinker
    Authors
    ReportLinker
    License

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

    Area covered
    United States
    Description

    Forecast: Real Estate Activities Gross Value Added Growth Rate in the US 2022 - 2026 Discover more data with ReportLinker!

  19. m

    US Residential Real Estate Market Analysis | Trends, Forecast, Size &...

    • mordorintelligence.com
    pdf,excel,csv,ppt
    Updated Jun 21, 2025
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    Mordor Intelligence (2025). US Residential Real Estate Market Analysis | Trends, Forecast, Size & Industry Growth Report 2030 [Dataset]. https://www.mordorintelligence.com/industry-reports/residential-real-estate-market-in-usa
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Jun 21, 2025
    Dataset authored and provided by
    Mordor Intelligence
    License

    https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy

    Time period covered
    2019 - 2030
    Area covered
    United States
    Description

    The United States Residential Real Estate Market is Segmented by Property Type (Apartments and Condominiums, and Villas and Landed Houses), by Price Band (Affordable, Mid-Market and Luxury), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Region (Northeast, Midwest, Southeast, West and Southwest). The Market Forecasts are Provided in Terms of Value (USD)

  20. United States Rental Rate for Residential Real Estate Market Size, Share and...

    • expertmarketresearch.com
    Updated May 1, 2025
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    Claight Corporation (Expert Market Research) (2025). United States Rental Rate for Residential Real Estate Market Size, Share and Growth Analysis Report: Forecast Trends and Outlook 2025-2034 [Dataset]. https://www.expertmarketresearch.com/reports/united-states-rental-rate-for-residential-real-estate-market
    Explore at:
    pdf, excel, csv, pptAvailable download formats
    Dataset updated
    May 1, 2025
    Dataset provided by
    Authors
    Claight Corporation (Expert Market Research)
    License

    https://www.expertmarketresearch.com/privacy-policyhttps://www.expertmarketresearch.com/privacy-policy

    Time period covered
    2025 - 2034
    Area covered
    United States
    Variables measured
    CAGR, Forecast Market Value, Historical Market Value
    Measurement technique
    Secondary market research, data modeling, expert interviews
    Dataset funded by

    Description

    The United States rental rate for residential real estate market was volumed at 1.32 per unit per month in 2024. The industry is expected to grow at a CAGR of 5.00% during the forecast period of 2025-2034 to attain a volume of 2.15 per unit per month by 2034.

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Link copied
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Bright Data (2025). Real Estate Dataset [Dataset]. https://brightdata.com/products/datasets/real-estate

Real Estate Dataset

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
.json, .csv, .xlsxAvailable download formats
Dataset updated
Aug 1, 2025
Dataset authored and provided by
Bright Data
License

https://brightdata.com/licensehttps://brightdata.com/license

Area covered
Worldwide
Description

Real estate datasets from various websites cover all major real estate data points including: property type, size, location, price, bedrooms, baths, address, history, images, and much more. Popular use cases include: forecast housing demand, analyze price fluctuations, improve customer satisfaction, see past prices to monitor market trends, and more.

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