Zillow reigns supreme in the U.S. real estate website landscape, attracting a staggering ***** million monthly visits in 2024. This figure dwarfs its closest competitor, Realtor.com, which garnered less than half of Zillow's traffic. Online platforms are extremely popular, with the majority of homebuyers using a mobile device during the buying process. The rise of Zillow Founded in 2006, the Seattle-headquartered proptech Zillow has steadily grown over the years, establishing itself as the most popular U.S. real estate website. In 2023, the listing platform recorded about *** million unique monthly users across its mobile applications and website. Despite holding an undisputed position as a market leader, Zillow's revenue has decreased since 2021. A probable cause for the decline is the plummeting of housing transactions and the negative housing sentiment. Performance and trends in the proptech market The proptech market has shown remarkable performance, with companies like Opendoor and Redfin experiencing significant stock price increase in 2023. This growth is particularly notable in the residential brokerage segment. Meanwhile, major players in proptech fundraising, such as Fifth Wall and Hidden Hill Capital, have raised billions in direct investment, further fueling the sector's development. As technology continues to reshape the real estate industry, online platforms like Zillow are likely to play an increasingly crucial role in how people search for and purchase homes. (1477916, 1251604)

Zillow.com was the most-visited real estate website worldwide in 2024, with an average of ************* visits per month during the measured period. Leboncoin.fr ranked second, with ***** million monthly visits, while Carigslist.org ranked third, with ***** million average accesses.

In 2024, U.S. homebuyers considered photos and the detailed information about a home listing as the most valuable features of real estate websites. Additionally, ** percent of respondents cited virtual listings as very useful, while ** percent listed flor plans.

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

Dataset Overview

This dataset contains real estate listings from Utah, comprising 4,440 entries and 14 columns. The data includes various attributes of properties such as type, description, year built, number of bedrooms and bathrooms, garage spaces, lot size, square footage, stories, listing price, and the date the property was last sold. The data was ethically mined and is to be used for educational and non-commercial purposes only.

Data Science Applications

Given the size of the dataset (4,440 entries) and the available columns, this dataset is well-suited for various data science applications, including but not limited to:

• Regression Analysis: Predict property listing prices based on features like square footage, number of bedrooms and bathrooms, year built, and lot size.
• Classification: Classify properties into different types or price ranges.
• Time Series Analysis: Analyze trends in property sales over time using the lastSoldOn column.
• Feature Engineering: Create new features such as price per square foot or age of the property at the time of sale to enhance predictive models.

Column Descriptors

• type: Type of property (e.g., single_family, land)
• text: Description of the property
• year_built: Year the property was built
• beds: Number of bedrooms
• baths: Total number of bathrooms
• baths_full: Number of full bathrooms
• baths_half: Number of half bathrooms
• garage: Number of garage spaces
• lot_sqft: Lot size in square feet
• sqft: Property size in square feet
• stories: Number of stories
• lastSoldOn: Date the property was last sold
• listPrice: Listing price of the property
• status: Current status of the property (e.g., for_sale)

Ethically Mined Data

This dataset was ethically mined from Realtor.com using an API provided by Apify. The data collection process ensured compliance with ethical standards and respect for the source of the information. The dataset is intended for educational and analytical purposes, promoting transparency and responsible data use.

Acknowledgements

• Apify: For providing the API used to mine the data.
• Realtor.com: For being the source of the data.
• DALL-E 3: For generating the thumbnail image for this dataset.

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.

1. Customers File (customers.csv)

• Description: This file contains information about clients involved in real estate transactions. It includes personal details such as name, surname, birth date, gender, and country, along with transaction-specific information like the purpose of the deal and the satisfaction level.
• Key Columns:
  • customerid: Unique identifier for the customer.
  • entity: Type of client, whether an individual or a company.
  • name and surname: First and last name of the customer.
  • birth_date: Customer's date of birth.
  • sex: Gender of the customer (Male/Female).
  • country and state: The country and state the customer is associated with.
  • purpose: Purpose of the transaction (e.g., Home purchase or Investment).
  • deal_satisfaction: Customer's satisfaction level with the transaction, ranging from 1 to 5.
  • mortgage: Indicates whether the transaction involved a mortgage (Yes/No).
  • source: How the customer was acquired (e.g., Website or Agency).

2. Properties File (properties.csv)

• Description: This file contains information about the properties sold, including building details, property type, area, price, and sale status.
• Key Columns:
  • id: Unique identifier for the property.
  • building: Number of the building where the property is located.
  • date_sale: The date when the property was sold.
  • type: Type of property (e.g., Apartment).
  • property#: The property number within the building.
  • area: Area of the property in square feet.
  • price: Sale price of the property.
  • status: Status of the sale (e.g., Sold).
  • customerid: The unique identifier of the customer associated with the property.

Suggested Analysis and Tasks

1 Customer Insights: - Customer Segmentation: Group customers based on demographics, purpose, or deal satisfaction to understand different customer profiles. - Satisfaction Analysis: Investigate what factors (e.g., property price, area, or mortgage involvement) influence customer satisfaction levels. - Source Effectiveness: Analyze which acquisition sources (e.g., website or agency) yield the highest deal satisfaction.

2 Property Market Analysis: - Price Trends: Analyze how property prices vary over time or by location to identify market trends. - Demand Analysis: Determine which types of properties (e.g., apartments vs. houses) are most popular based on sales data. - Area vs. Price: Explore the relationship between property area and price to develop pricing models or evaluate property value.

3 Predictive Modeling: - Price Prediction: Build models to predict property prices based on features like area, type, and location. - Satisfaction Prediction: Create models to predict customer satisfaction using transaction details and demographics. - Likelihood of Sale: Develop a model to predict the likelihood of a property being sold based on its attributes and market conditions.

4 Geographical Analysis: - Heatmaps: Create heatmaps to visualize property sales and identify high-demand areas. - Country and State Trends: Examine how real estate trends differ between countries and states.

5 Mortgage Impact Study: - Mortgage vs. Non-Mortgage Analysis: Compare transactions that involved a mortgage to those that didn’t to study the impact on price, satisfaction, and deal closure speed.

6 Time Series Analysis: - Sales Over Time: Analyze property sales over different periods to identify seasonal trends or patterns. - Customer Birth Date Analysis: Study any correlations between customers’ birth years and their purchasing behavior.

This dataset provides a comprehensive record of property listing price changes over time, including detailed property attributes, location information, and event types for each price change. It enables in-depth analysis of real estate market dynamics, pricing strategies, and property value trends across regions and property types.

Graph and download economic data for Housing Inventory: Active Listing Count in the United States (ACTLISCOUUS) from Jul 2016 to Oct 2025 about active listing, listing, and USA.

AI-Generated Real Estate Listing Market Outlook

According to our latest research, the global AI-Generated Real Estate Listing market size reached USD 1.42 billion in 2024, reflecting robust momentum in the adoption of artificial intelligence within the real estate sector. The market is projected to grow at a CAGR of 18.9% during the forecast period, with the value expected to reach USD 7.32 billion by 2033. This remarkable growth is primarily driven by the increasing demand for automation in property listing processes, enhanced customer experiences, and the need for efficient data-driven decision-making in real estate transactions. As per our comprehensive analysis, the market is witnessing rapid technological advancements and broader acceptance across various end-user segments, setting the stage for significant expansion over the next decade.

One of the primary growth factors fueling the AI-Generated Real Estate Listing market is the accelerating digital transformation within the real estate industry. Real estate agencies, property developers, and individual agents are increasingly leveraging artificial intelligence to automate the creation, curation, and management of property listings. AI-powered solutions can analyze vast amounts of data, generate compelling descriptions, and provide personalized recommendations, thereby reducing manual workload and enhancing the accuracy of listings. This automation not only improves operational efficiency but also allows real estate professionals to focus on higher-value activities such as client engagement and strategic decision-making. The integration of AI with existing real estate platforms is further streamlining workflows and offering a seamless experience for both sellers and buyers.

Another significant driver is the rising consumer expectation for personalized and immersive property search experiences. Modern homebuyers and renters demand highly detailed, visually appealing, and tailored property listings that go beyond traditional text-based descriptions. AI-generated listings can automatically incorporate high-quality images, virtual tours, and dynamic content based on user preferences and behavior analytics. This personalized approach increases user engagement, boosts conversion rates, and enhances overall satisfaction. Real estate platforms utilizing AI are able to match properties more effectively with potential buyers, minimize time-on-market, and optimize pricing strategies, thereby creating a competitive advantage for early adopters in the industry.

The proliferation of cloud computing and advancements in natural language processing (NLP) and computer vision technologies have also played a pivotal role in market growth. Cloud-based AI solutions offer scalability, flexibility, and cost-effectiveness, making them accessible to a broader range of real estate stakeholders, from large agencies to individual agents. Enhanced NLP algorithms enable the automatic generation of contextually relevant and grammatically accurate property descriptions, while computer vision assists in categorizing and enhancing property images. These technological innovations are not only improving the quality and consistency of listings but are also enabling the integration of real-time market insights and predictive analytics, further empowering users to make informed decisions.

From a regional perspective, North America remains the dominant market for AI-Generated Real Estate Listing solutions, accounting for the largest revenue share in 2024. The region's advanced technological infrastructure, high internet penetration, and strong presence of leading proptech companies have accelerated the adoption of AI-driven tools. Europe is also witnessing substantial growth, driven by increasing investments in digital real estate platforms and a rising focus on sustainability and smart city initiatives. The Asia Pacific region is expected to experience the fastest CAGR during the forecast period, fueled by rapid urbanization, growing real estate markets, and government initiatives supporting digital transformation. Latin America and the Middle East & Africa are gradually catching up as awareness and adoption of AI technologies expand across these regions.

The introduction of the AI-Powered Rental Price Index is a groundbreaking development in the real estate sector. This innov

Discover the booming MLS Listing Software market! This comprehensive analysis reveals a $5B market in 2025, projected to reach $14B by 2033, driven by cloud adoption and real estate tech advancements. Explore key trends, regional insights, and leading companies shaping this dynamic sector.

Discover the booming Multiple Listing Service (MLS) Software market! This in-depth analysis reveals a $876.7M market in 2025, projected to grow at a 6.2% CAGR through 2033. Explore key trends, drivers, and regional breakdowns impacting cloud-based and on-premises solutions for real estate professionals. Learn about leading companies and future growth opportunities.

In a constantly changing marketplace, access to timely property listings data is crucial. With this robust data set, you can gain a complete view of a single property, market trends, and even buyer behavior.

Discover the booming real estate listing brokerage software market! This comprehensive analysis reveals key trends, growth drivers, and leading companies shaping the future of real estate tech. Explore market size, CAGR, and regional breakdowns for informed decision-making.

The global House Rental Platforms market is poised for significant expansion, projected to reach an estimated value of $34,500 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 12.5% expected to propel it to $87,000 million by 2033. This impressive growth is primarily fueled by the increasing demand for flexible and convenient housing solutions, particularly among younger generations like millennials and Gen Z. The platform's ability to streamline the rental process, from property discovery and virtual tours to lease agreements and payment management, addresses key pain points for both renters and landlords. The burgeoning short-term rental market, driven by tourism and the rise of the gig economy, alongside the steady demand for long-term apartment and house rentals, are significant contributors to this upward trajectory. Technology advancements, including AI-powered search filters, virtual reality property viewings, and secure online payment systems, are further enhancing user experience and driving platform adoption. Key drivers for this market's ascent include urbanization, a growing preference for renting over homeownership, and the increasing adoption of digital tools for real estate transactions. While the market presents immense opportunities, certain restraints such as stringent regulatory frameworks in some regions, potential cybersecurity risks, and the intense competition among established and emerging players could pose challenges. However, the continuous innovation in platform features, the expansion into emerging markets, and strategic partnerships are expected to mitigate these concerns. The market encompasses a diverse range of property types, with apartments and houses dominating the landscape, catering to both long-term lease and short-term rental applications. Leading companies like HousingAnywhere, Rentberry, Spotahome, and Airbnb are at the forefront of shaping this dynamic industry, continuously introducing features to meet evolving consumer needs and solidify their market positions. This report provides a comprehensive analysis of the global house rental platform market, offering insights into its structure, dynamics, and future trajectory. We delve into the competitive landscape, product offerings, regional trends, and the key drivers and challenges shaping this rapidly evolving industry. The report leverages estimated user and transaction data in the millions to paint a clear picture of market scale and player influence.

Discover the booming Multiple Listing Service (MLS) Software market! Projected to reach $1.545 Billion by 2033 with a 6.5% CAGR, this report analyzes market trends, key players (Zillow, Realtor.com, Redfin), and regional growth. Get insights into driving forces, challenges, and future forecasts for the MLS software industry.

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

The Property Group is a leading real estate organization that provides expert guidance throughout the home buying and selling process. With a strong presence in Little Rock, Arkansas, the company has established itself as a trusted partner for individuals and families seeking to buy, sell, or rent properties. The Property Group's expert agents are well-versed in local market trends, ensuring that clients receive tailored solutions to their unique needs.

Through their user-friendly website, The Property Group offers a range of resources and tools for homebuyers, including exclusive property listings, neighborhood information, and real-time market reports. Whether buying or selling a home, clients can rely on the company's dedicated professionals to navigate the complex process with ease. With a focus on transparency, efficiency, and personalized attention, The Property Group has earned a reputation as a top choice for those seeking a seamless and stress-free real estate experience.

Our extensive database contains approximately 800,000 active rental property listings from across the United States. Updated daily, this comprehensive collection provides real estate professionals, investors, and property managers with valuable market intelligence and business opportunities. Database Contents

Property Addresses: Complete location data including street address, city, state, ZIP code Listing Dates: Original listing date and most recent update date Availability Status: Currently available, pending, or recently rented properties Geographic Coverage: Properties spanning all 50 states and major metropolitan areas

Applications & Uses

Market Analysis: Track rental pricing trends across different regions and property types Investment Research: Identify high-opportunity markets with favorable rental conditions Lead Generation: Connect with property owners potentially needing management services Competitive Intelligence: Monitor listing volumes, vacancy rates, and market saturation Business Development: Target specific neighborhoods or property categories for expansion

File Format & Delivery

Organized in easy-to-use CSV format for seamless integration with data analysis tools Accessible through secure download portal or API connection Daily updates ensure you're working with the most current market information Custom filtering options available to narrow results by location, date range, or other criteria

Data Quality

Rigorous validation processes to ensure address accuracy Duplicate listing detection and removal Regular verification of active status Standardized format for consistent analysis

Subscription Benefits

Access to historical listing archives for trend analysis Advanced search capabilities to target specific property characteristics Regular market reports summarizing key trends and opportunities Custom data exports tailored to your specific business needs

AK ~ 1,342 listings AL ~ 6,636 listings AR ~ 4,024 listings AZ ~ 25,782 listings CA ~ 102,833 listings CO ~ 14,333 listings CT ~ 10,515 listings DC ~ 1,988 listings DE ~ 1,528 listings FL ~ 152,258 listings GA ~ 28,248 listings HI ~ 3,447 listings IA ~ 4,557 listings ID ~ 3,426 listings IL ~ 42,642 listings IN ~ 8,634 listings KS ~ 3,263 listings KY ~ 5,166 listings LA ~ 11,522 listings MA ~ 53,624 listings MD ~ 12,124 listings ME ~ 1,754 listings MI ~ 12,040 listings MN ~ 7,242 listings MO ~ 10,766 listings MS ~ 2,633 listings MT ~ 1,953 listings NC ~ 22,708 listings ND ~ 1,268 listings NE ~ 1,847 listings NH ~ 2,672 listings NJ ~ 31,286 listings NM ~ 2,084 listings NV ~ 13,111 listings NY ~ 94,790 listings OH ~ 15,843 listings OK ~ 5,676 listings OR ~ 8,086 listings PA ~ 37,701 listings RI ~ 4,345 listings SC ~ 8,018 listings SD ~ 1,018 listings TN ~ 15,983 listings TX ~ 132,620 listings UT ~ 3,798 listings VA ~ 14,087 listings VT ~ 946 listings WA ~ 15,039 listings WI ~ 7,393 listings WV ~ 1,681 listings WY ~ 730 listings

Grand Total ~ 977,010 listings

Premiere Property Group is a real estate company that specializes in residential and commercial properties. The company's website provides information on a wide range of properties, including vacant land, single-family homes, and multi-unit dwellings. With a strong focus on customer satisfaction, Premiere Property Group's team of experienced agents and brokers work closely with clients to understand their unique needs and preferences.

Premiere Property Group's website offers a wealth of information for those looking to buy, sell, or rent properties. The company's extensive property listings are regularly updated to reflect the latest market trends and developments. By partnering with Premiere Property Group, clients can gain insights into the local real estate market, receive expert advice, and navigate the buying and selling process with confidence.