The US Office Real Estate Market Report is Segmented by Building Grade (Grade A, Grade B, and More), by Transaction Type (Rental and Sales), by End Use (Information Technology (IT & ITES), BFSI (Banking, Financial Services and Insurance), and More) and by States (Texas, California, Florida and More). The Report Offers Market Size and Forecasts in Value (USD) for all the Above Segments.

Redfin is a real estate brokerage and publishes the US housing market data on a regular basis. Using this dataset, you can analyze and visualize housing market data for US cities. Timeline: Starting from February 2012 until the present time (Data is refreshed and updated on a monthly basis)

The dataset has the following columns: - period_begin - period_end - period_duration
- region_type
- region_type_id - table_id - is_seasonally_adjusted. (indicates if prices are seasonally adjusted; f represents False) - region - city - state - state_code - property_type - property_type_id - median_sale_price
- median_sale_price_mom (median sale price changes month over month) - median_sale_price_yoy (median sale price changes year over year) - median_list_price
- median_list_price_mom (median list price changes month over month) - median_list_price_yoy (median list price changes year over year) - median_ppsf (median sale price per square foot) - median_ppsf_mom (median sale price per square foot changes month over month) - median_ppsf_yoy (median sale price per square foot changes year over year) - median_list_ppsf (median list price per square foot) - median_list_ppsf_mom (median list price per square foot changes month over month) - median_list_ppsf_yoy. (median list price per square foot changes year over year) - homes_sold (number of homes sold) - homes_sold_mom (number of homes sold month over month) - homes_sold_yoy (number of homes sold year over year) - pending_sales
- pending_sales_mom
- pending_sales_yoy
- new_listings - new_listings_mom
- new_listings_yoy
- inventory - inventory_mom
- inventory_yoy
- months_of_supply
- months_of_supply_mom - months_of_supply_yoy
- median_dom (median days on market until property is sold) - median_dom_mom (median days on market changes month over month) - median_dom_yoy (median days on market changes year over year) - avg_sale_to_list (average sale price to list price ratio) - avg_sale_to_list_mom (average sale price to list price ratio changes month over month) - avg_sale_to_list_yoy (average sale price to list price ratio changes year over year) - sold_above_list
- sold_above_list_mom - sold_above_list_yoy - price_drops - price_drops_mom - price_drops_yoy - off_market_in_two_weeks (number of properties that will be taken off the market within 2 weeks) - off_market_in_two_weeks_mom (changes in number of properties that will be taken off the market within 2 weeks, month over month) - off_market_in_two_weeks_yoy (changes in number of properties that will be taken off the market within 2 weeks, year over year) - parent_metro_region - parent_metro_region_metro_code - last_updated

Filetype: gzip (gz) Support for gzip files in Python: https://docs.python.org/3/library/gzip.html

Data Source & Credit: Redfin.com

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

The Artificial Intelligence (AI) in Real Estate market is rapidly transforming the landscape of property buying, selling, and management, utilizing advanced algorithms and data analytics to enhance decision-making processes and customer experiences. As of 2023, the estimated market size for AI in real estate stands

The Artificial Intelligence (AI) market in the real estate industry is rapidly evolving, signifying a transformative shift in how properties are bought, sold, and managed. With a current market size reflecting substantial investment and interest, AI applications in real estate range from predictive analytics and vir

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

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)

Global Artificial Intelligence in Real Estate Market is segmented by Application (Real Estate Agents_ Property Managers_ Investors_ Homeowners), Type (Property Valuation_ Predictive Analytics_ Customer Relationship Management (CRM)_ Property Management_ Smart Homes), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

This dataset is part of our Data Structures (Machine Learning) course project at the French University in Armenia (UFAR) under the supervision of PhD Varazdat Avetisyan. The dataset was collected through web scraping and contains valuable insights into the Armenian real estate market, covering apartments, houses, and commercial properties.

👥 Contributors: • Vahe Mirzoyan • Arsen Martirosyan • Arman Nagdalyan

📌 Data Collection Process: • Scraping Tools Used: Selenium & BeautifulSoup in Google Colab • Source: Real estate website (Armenia) • Storage: Data was structured and stored in Google Sheets & CSV format

📊 Dataset Features:

The dataset includes the following columns: • ID – Unique identifier for each property • Address – Property location • Floors – Total number of floors • Rooms – Number of rooms • Area (sq.m) – Total square meters of the property • Bathrooms – Number of bathrooms • Building Type – Old or new construction • Price (USD) – Listed price of the property

Check Market Research Intellect's Artificial Intelligence (AI) In Real Estate Market Report, pegged at USD 2.5 billion in 2024 and projected to reach USD 10.2 billion by 2033, advancing with a CAGR of 22.5% (2026-2033).Explore factors such as rising applications, technological shifts, and industry leaders.

The Information Technology (IT) sector in the real estate market has rapidly transformed how industry stakeholders conduct business, making it a critical area of focus for professionals and investors alike. IT solutions have streamlined property management, enhanced customer experience, and facilitated a more effici

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Context:

My dataset is a valuable collection of real estate information sourced from REALTING.com, an international affiliate sales system known for facilitating safe and convenient property transactions worldwide. REALTING.com has a strong foundation, with its founders boasting approximately 20 years of experience in creating information technologies for the real estate market. This dataset offers insights into various properties across the globe, making it a valuable resource for real estate market analysis, property valuation, and trend prediction.

Content:

The dataset contains information on a diverse range of properties, each represented by a row of data. Here are the key columns and their contents:

• Title: A brief description or name of the property listing.
• Country: The country where the property is located.
• Location: The specific address or location of the property within the country.
• Building Construction Year: The year in which the building was constructed.
• Building Total Floors: The total number of floors or stories in the building.
• Apartment Floor: The floor on which the apartment is situated within the building.
• Apartment Rooms: The total number of rooms in the apartment.
• Apartment Bedrooms: The number of bedrooms in the apartment.
• Apartment Bathrooms: The number of bathrooms in the apartment.
• Apartment Total Area: The total area of the apartment in square meters.
• Apartment Living Area: The living area of the apartment in square meters.
• Price in USD: The price of the property listed in United States Dollars (USD).
• Image: References or links to images associated with the property listing.
• URL: Web links to the full property listing or more detailed information.

This dataset is rich in real estate-related information, making it suitable for various analytical tasks such as market research, property comparison, geographical analysis, and more. The dataset's global scope and diverse property attributes provide a comprehensive view of the international real estate market, offering ample opportunities for data-driven insights and decision-making.

global luxury real estate market size was worth around USD 276 billion in 2024 and is predicted to grow to around USD 538 billion by 2034.(CAGR) of roughly 6.9%

The Real Estate market plays a pivotal role in the global economy, serving as a fundamental component of wealth generation and investment for individuals and businesses alike. Defined broadly, real estate encompasses residential, commercial, and industrial properties, each segment contributing uniquely to economic h

Global Artificial Intelligence in Real Estate Market is segmented by Application (Real estate industry_ Technology industry_ Finance industry_ Construction industry_ Urban planning), Type (Technology_ Real estate_ AI_ Machine learning_ Property technology), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

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).

Real Estate Market size was valued at USD 79.7 Trillion in 2024 and is projected to reach USD 103.6 Trillion by 2031, growing at a CAGR of 5.1% during the forecasted period 2024 to 2031

Global Real Estate Market Drivers

Population Growth and Urbanization: In order to meet the demands of businesses, housing needs, and infrastructure development, there is a constant need for residential and commercial properties as populations and urban areas rise.

Low Interest Rates: By making borrowing more accessible, low interest rates encourage both individuals and businesses to make real estate investments. Reduced borrowing costs result in reduced mortgage rates, opening up homeownership and encouraging real estate investments and purchases.

Economic Growth: A thriving real estate market is a result of positive economic growth indicators like GDP growth, rising incomes, and low unemployment rates. Robust economies establish advantageous circumstances for real estate investment, growth, and customer assurance in the housing sector. Job growth and income increases: As more people look for rental or purchase close to their places of employment, housing demand is influenced by these factors. The housing market is driven by employment opportunities and rising salaries, which in turn drive home buying, renting, and property investment activity. Infrastructure Development: The demand and property values in the surrounding areas can be greatly impacted by investments made in infrastructure projects such as public facilities, utilities, and transportation networks. Accessibility, convenience, and beauty are all improved by improved infrastructure, which encourages real estate development and investment.

Government Policies and Incentives: Tax breaks, subsidies, and first-time homebuyer programs are a few examples of government policies and incentives that can boost the real estate market and homeownership. Market stability and growth are facilitated by regulatory actions that promote affordable housing, urban redevelopment, and real estate development.

Foreign Investment: Foreign capital can be used to stimulate demand, diversify property portfolios, and pump capital into the real estate market through direct property purchases or real estate investment funds. Foreign investors are drawn to the local real estate markets by favorable exchange rates, stable political environments, and appealing returns.

Demographic Trends: Shifting demographic trends affect housing preferences and demand for various property kinds. These trends include aging populations, household formation rates, and migration patterns. It is easier for real estate developers and investors to match supply with changing market demand when they are aware of demographic fluctuations.

Technological Innovations: New technologies that are revolutionizing the marketing, transactions, and management of properties include digital platforms, data analytics, and virtual reality applications. In the real estate industry, technology adoption increases market reach, boosts customer experiences, and increases operational efficiency.

Environmental Sustainability: Decisions about real estate development and investment are influenced by the growing knowledge of environmental sustainability and green building techniques. Market activity in environmentally aware real estate categories is driven by demand for eco-friendly neighborhoods, sustainable design elements, and energy-efficient buildings.

The real estate market attained a value of USD 4295.21 Billion in 2024. The market is expected to grow at a CAGR of 6.40% during the forecast period of 2025-2034. By 2034, the market is expected to reach USD 7987.31 Billion.

Smart city initiatives are integrating technology into urban planning to improve livability, efficiency, and sustainability. Real estate is at the core of these transformations, with developments featuring smart utilities, real-time traffic monitoring, and digitally managed public services. In March 2025, Gopalan Enterprises invested ₹500 crore to launch 3,000 AI-powered smart homes by 2025, marking its tech-driven expansion. These innovations attract both residents and businesses seeking convenience, safety, and lower operating costs.

Shifts in lifestyle and work habits are redefining the real estate market dynamics. Post-pandemic preferences include larger homes, outdoor spaces, and home offices. In commercial real estate, flexible layouts, wellness-focused designs, and high-speed internet are essential. Tenants seek amenity-rich environments with co-working zones, fitness centers, and green spaces. The rise of remote and hybrid work has further made suburban and exurban areas more attractive. Customization, comfort, and connectivity are now key selling points across both residential and commercial segments.

The Housing Data Extracted from Homes.com (USA) dataset is a comprehensive collection of 2 million real estate listings sourced from Homes.com, one of the leading real estate platforms in the United States. This dataset offers detailed insights into the U.S. housing market, making it an invaluable resource for real estate professionals, investors, researchers, and analysts.

The dataset contains extensive property details, including location, price, property type (single-family homes, condos, apartments), number of bedrooms and bathrooms, square footage, lot size, year built, and availability status. Organized in CSV format, it provides users with easy access to structured data for analyzing trends, developing investment strategies, or building real estate applications.

Key Features:

• Record Count: 2 million housing listings from across the USA.
• Data Fields: Property address, price, property type, bedrooms, bathrooms, square footage, lot size, year built, and availability.
• Format: CSV format for easy integration with data analysis platforms, machine learning models, and real estate tools.
• Source: Directly sourced from Homes.com’s USA real estate listings.
• Geographical Focus: Comprehensive coverage of properties across all regions of the United States.

Use Cases:

• Real Estate Market Research: Analyze property prices, market trends, and housing demand in various U.S. regions.
• Investment Analysis: Use data to identify high-potential properties and regions for real estate investments.
• Property Comparison: Compare listings by price, location, and features to evaluate market conditions across different cities and states.
• Machine Learning Models: Build predictive models for price forecasting, property valuation, and real estate recommendation systems.
• Content Creation: Create real estate-related content, reports, and insights for the U.S. housing market using up-to-date data.

Comprehensive dataset of Albanian real estate market intelligence including official government reference prices, market trends, and investment insights