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)

The United States real estate market was valued at USD 3.43 Trillion in 2024. The industry is expected to grow at a CAGR of 2.80% during the forecast period of 2025-2034 to reach a value of USD 4.52 Trillion by 2034. The market growth is mainly driven by the rising corporate investment, particularly in addressing the nation’s affordable housing shortage.

Major corporations are actively investing to integrate housing stability with social responsibility, supporting both new construction and the preservation of existing homes. In September 2024, UnitedHealth Group surpassed USD 1 billion in investments for affordable and mixed-income housing through direct capital and tax credits. These projects span 31 states and have delivered over 25,000 homes, simultaneously improved community health and providing secure housing for low- and moderate-income households.

Such corporate involvements are reshaping trends in United States real estate market by expanding the supply of affordable housing, reducing barriers for renters and homeowners, and stimulating development in high-demand urban and suburban areas. By aligning financial resources with strategic planning, corporations are enabling scalable solutions that meet social and economic objectives while enhancing overall market efficiency.

Overview

This dataset provides a comprehensive snapshot of the Texas real estate market as of 2024, featuring a curated selection of 500 property listings. It encompasses a wide array of properties, reflecting the diverse real estate landscape across Texas. This dataset serves as a foundational tool for understanding market dynamics, property valuations, and regional housing trends within the state.

Data Science Application of Dataset

Given its breadth and depth, this dataset is poised to facilitate a multitude of data science applications. Researchers and analysts can leverage this dataset for exploratory data analysis (EDA) to identify patterns, trends, and anomalies within the Texas real estate market. It is particularly suited for regression analyses to predict property prices based on various features, classification tasks to categorize properties into different market segments, and geographical data analysis to understand regional market dynamics. Despite the dataset's modest size, it offers a rich source for machine learning models aimed at providing insights into price determinants and market trends, ensuring practical applications remain within realistic and achievable bounds.

Full Column Descriptors

• url: Web address for the property listing on Realtor.com.
• status: Current status of the listing, indicating availability.
• id: Unique identifier for each property listing.
• listPrice: The asking price for the property.
• baths: Total number of bathrooms, including partials.
• baths_full: Number of full bathrooms.
• baths_full_calc: Calculated number of full bathrooms, for consistency.
• beds: Number of bedrooms in the property.
• sqft: Total square footage of the property.
• stories: Number of levels or floors in the property.
• sub_type: Specific sub-category of the property, if applicable.
• text: Descriptive narrative provided for the property listing.
• type: General category of the property (e.g., single-family, condo).
• year_built: Year the property was constructed.

Ethically Mined Publicly Available Data Only

This dataset has been meticulously compiled, adhering to ethical standards and ensuring all data is sourced from publicly available information. It respects privacy and copyright considerations, utilizing data that is openly accessible and intended for public consumption.

Acknowledgments

Gratitude is extended to Realtor.com for serving as an invaluable resource in the compilation of this dataset. The platform's commitment to providing comprehensive and accessible real estate data has significantly contributed to the depth and quality of this dataset.

Image Acknowledgment

The dataset thumbnail image is credited to Realtor.com, as featured on their official Facebook page. The image serves as a visual representation of the diverse and dynamic nature of the Texas real estate market, captured in this comprehensive dataset. View Image

Residential Real Estate Market is Segmented by Property Type (Apartments & Condominiums, and Landed Houses & Villas), by Price Band (Affordable, Mid-Market, and Luxury/Super-prime), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Region (North America, South America, Europe, Asia-Pacific, and Middle East & Africa). The Market Forecasts are Provided in Terms of Value (USD).

The property listings dataset contains information about real estate properties available for sale or rent in Brazil. It includes details such as property type (apartment, house, commercial property), location (city, neighborhood), size (square footage, number of rooms), price, amenities, and contact information for the property owner or real estate agent. This dataset can be used for market analysis, property valuation, and identifying trends in the real estate market.

Sales and Rental Prices Dataset: The sales and rental prices dataset provides information about the prices of real estate properties in Brazil. It includes data on property transactions, including sale prices and rental prices per square meter or per month. This dataset can be used to analyze price trends, compare property prices across different regions, and identify areas with high or low real estate market demand.

Property Characteristics Dataset: The property characteristics dataset contains detailed information about the features and attributes of real estate properties. It includes data such as the number of bedrooms, bathrooms, parking spaces, floor plan, construction year, building amenities, and property condition. This dataset can be used for property classification, identifying popular property features, and evaluating property quality.

Geographical Data: Geographical data includes information about the location and spatial features of real estate properties in Brazil. It can include data such as latitude and longitude coordinates, zoning information, proximity to amenities (schools, hospitals, parks), and neighborhood demographics. This dataset can be used for spatial analysis, identifying hotspots or desirable locations, and understanding the neighborhood characteristics.

Property Market Trends Dataset: The property market trends dataset provides information about market conditions and trends in the real estate sector in Brazil. It includes data such as the number of property listings, average time on the market, price fluctuations, mortgage interest rates, and economic indicators that impact the real estate market. This dataset can be used for market forecasting, understanding market dynamics, and making informed investment decisions.

Real Estate Regulatory Data: Real estate regulatory data includes information about legal and regulatory aspects of the real estate sector in Brazil. It can include data on property ownership, property taxes, zoning regulations, building permits, and legal restrictions on property transactions. This dataset can be used for legal compliance, understanding property ownership rights, and assessing the legal framework for real estate transactions.

Historical Data: Historical real estate data includes past records and trends of property prices, market conditions, and sales volumes in Brazil. This dataset can span several years and can be used to analyze long-term market trends, compare current market conditions with historical data, and assess the performance of the real estate market over time.

The India Real Estate Market Report is Segmented by Business Model (Sales and Rental), by Property Type (Residential and Commercial), by End-User (Individuals/Households, Corporates & SMEs and Others), and by City (Mumbai Metropolitan Region, Delhi NCR, Pune, Bengaluru, Hyderabad, Chennai, Kolkata, Ahmedabad, and the Rest of India). The Market Forecasts are Provided in Terms of Value (USD).

Our Bulk Automated Valuation Model (AVM) is a service that uses mathematical modeling to determine current market values. AVMs integrate vast amounts of data, including sales prices, market trends, and geographic information, to estimate real estate values with minimal human intervention – often referred to as “Desktop Valuations”. These models are designed to provide objective and uniform evaluations, helping to standardize property valuations across the board.

What Does Our AVM Offer?

Our Automated Valuation Model (AVM) leverages cutting-edge technologies, the most recent methodologies, and is supported by the foremost data provider with the largest datasets in the industry. This ensures a swift, exceptionally accurate AVM that delivers the comprehensive insights you need.

AVM Data Details:

• Property Address
• FIPS
• Property ID
• APN
• Final Value
• High Value
• Low Value
• Confidence Score
• Standard Deviation
• Valuation Date
• Recording Time Stamp
• Recording Type

The US Commercial Real Estate Market Report is Segmented by Property Type (Offices, Retail, Logistics, Others), by Business Model (Sales, Rental), by End-User (Individuals/Households, Corporates & SMEs, Others), and by Geography (Texas, California, Florida, New York, Illinois, Rest of US). The Market Forecasts are Provided in Terms of Value (USD).

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Oct 2025 about median and USA.

Description

Welcome to the "Real Estate Market Insights: Magic Bricks Web Scraped Dataset" available on Kaggle! This comprehensive dataset provides a wealth of information on real estate properties extracted from the popular real estate portal, Magic Bricks. With this dataset, you can explore and analyze the dynamic and ever-changing landscape of the real estate market.

Dataset Overview:

This dataset comprises meticulously scraped data from Magic Bricks, a prominent platform for buying, selling, and renting real estate properties in various regions. The dataset is regularly updated to ensure it reflects the most current market conditions and trends.

Key Features:

• Property Details: Gain access to a wide range of property details, including property type (apartment, house, commercial, etc.), location, size, and more.
• Price Information: Explore property prices, including listing price, area-based pricing, and price trends.
• Property Amenities: Discover the amenities and features associated with each property, from the number of bedrooms and bathrooms to parking availability and more.
• Property Status: Determine whether a property is available for sale, rent, or lease.

Use Cases:

• Market Analysis: Use this dataset to perform in-depth market analysis to understand price trends, property demand, and supply dynamics.
• Investment Opportunities: Identify potential investment opportunities in different regions based on price trends and property types.
• Location-Based Insights: Explore how property prices and amenities vary across different localities and cities.
• Real Estate Research: Use this dataset for academic research, business strategies, or data-driven decision-making.

Data Collection Method:

The dataset was collected using web scraping techniques, ensuring that it captures a wide array of properties listed on the Magic Bricks platform. Data integrity and accuracy are maintained through regular updates and quality checks.

Data Format:

The dataset is provided in a CSV format, making it easy to import and analyze using various data analysis tools and programming languages.

Disclaimer:

Please note that this dataset is for research and analytical purposes only. It is advisable to verify the data with Magic Bricks or other reliable sources before making any real estate transactions or investment decisions.

United Kingdom 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 (England, Scotland, Wales and Northern Ireland). The Market Forecasts are Provided in Terms of Value (USD)

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Discover the latest trends in the booming €1.95 trillion European residential real estate market. Explore growth forecasts (CAGR 4.5%), key drivers, regional breakdowns (UK, Germany, France), leading companies, and market challenges until 2033. Recent developments include: November 2023: DoorFeed, a Proptech company, raised EUR 12 million (USD 13.24 million) in seed funding, led by Motive Ventures and Stride and supported by renowned investors, including Seedcamp. Founded by veteran proptech entrepreneur and ex-Uber employee James Kirimi, DoorFeed aims to be the first choice for institutional investors seeking to invest in residential real estate. The company is looking to expand its footprint across Europe, with a focus on Spain, Germany, and the United Kingdom., October 2023: H.I.G, a global alternative investment firm with over USD 59 billion in assets under management, invested in the real estate development company, The Grounds Real Estate Development AG (“the Transaction”), which is listed on the alternative stock exchange. The proceeds of the transaction are expected to be utilized to fund the capital expenditures of the current projects of The Grounds. The Grounds, based in Berlin, specializes in the acquisition and development of German residential properties located in large metropolitan areas. In the transaction, the major shareholders of The Grounds, which currently hold 73% of the company’s shares, have agreed to grant H. I.G. the right to share in future rights issues.. Key drivers for this market are: Increasing Developments in the Residential Segment, Investments in the Senior Living Units. Potential restraints include: Limited Availability of Land Hindering the Market. Notable trends are: Student Housing to Gain Traction.

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

Dataset Overview

This dataset comprises detailed real estate listings scraped from Realtor.com, providing a snapshot of various property types across Chicago. It includes 2,000 entries with information on property characteristics such as type, size, age, price, and features. This dataset was ethically collected using an API provided by Apify, ensuring all data scraping adhered to ethical standards.

Data Science Applications

This dataset is ideal for a variety of data science applications, including but not limited to: - Predictive Modeling: Forecast property prices based on various features like location, size, and age. - Market Analysis: Understand trends in real estate, including the types of properties being sold, pricing trends, and the influence of property features on market value. - Natural Language Processing: Analyze the textual descriptions provided for each listing to extract additional features or perform sentiment analysis. - Anomaly Detection: Identify unusual listings or potential outliers in the data, which could indicate errors in data collection or unique investment opportunities.

Column Descriptors

1. type: The type of property (e.g., single-family home, condo).
2. text: A textual description of the property.
3. year_built: The year in which the property was constructed.
4. beds: The number of bedrooms.
5. baths: Total number of bathrooms (including full and half).
6. baths_full: Number of full bathrooms.
7. baths_half: Number of half bathrooms.
8. garage: Garage capacity (number of cars).
9. lot_sqft: Size of the lot in square feet.
10. sqft: Living area size in square feet.
11. stories: Number of stories/floors in the property.
12. lastSoldPrice: The price at which the property was last sold.
13. soldOn: The date on which the property was last sold.
14. listPrice: The listing price of the property at the time of data collection.
15. status: The current status of the listing (e.g., for sale, sold).

Ethically Mined Data

This dataset was responsibly and ethically mined, adhering to all legal standards of data collection. The use of Apify's API ensures that the data collection process respects privacy and the platform's terms of service.

Acknowledgements

We thank Realtor.com for maintaining a comprehensive and accessible database, and Apify for providing the tools necessary for ethical data scraping. Their contributions have been invaluable in the creation of this dataset. Credits to Dall E3 for thumbnail image.

Usage Policy

This dataset is provided for non-commercial and educational purposes only. Users are encouraged to use this data to enhance learning, contribute to academic or personal projects, and develop skills in data science and real estate market analysis.

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

The United Kingdom Real Estate Market Report is Segmented by Property Type (Residential and Commercial), by Business Model (Sales and Rental), by End User (Individuals/Households, Corporates & SMEs and Others), and by Country (England, Scotland, Wales, and Northern Ireland). The Report Offers Market Size and Forecasts in Value (USD) for all the Above Segments.

The U.S. Residential Real Estate report provides a detailed analysis of emerging investment pockets, highlighting current and future market trends. It offers strategic insights into capital flows and market shifts, guiding investors toward growth opportunities in key industry segments and regions.

The average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

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.