Hello my fellow data enthusiasts! I'm back!

My journey into the world of real estate data has been nothing short of exciting, and I’m thrilled to share the fruits of that adventure with you all. After spending a few weeks tinkering with APIs, parsing responses, and structuring data into something meaningful, I'm excited to present the CLEANEST Zillow Dataset you've every seen!

Analysts will be able to get actionable insights and a structured view into the fascinating world of property data.

Here’s the story behind the dataset: Zillow’s data provides a treasure trove of information, but raw responses can be messy with nested structures, and scattered details. So, I rolled up my sleeves and built a robust pipeline to extract key data points from each response. From property details to price history, every piece of information was carefully categorized and mapped into logical fields. My goal was to create a dataset that feels as polished and user-friendly as the apps we rely on daily.

What Makes This Dataset Special?

• Structured & Clean Data: Every property in the dataset has been meticulously processed, cleaned, and formatted to make analysis seamless.
• Comprehensive Coverage: Whether you're analyzing trends in property values or studying features that drive price differences, this dataset has you covered.
• Accessible Layout: Tables are structured logically, with relationships that make sense for analysts at every level. This dataset is more than just numbers – it’s a toolkit for anyone looking to dive into real estate analysis, build predictive models, or simply explore trends in the housing market.

If you have any questions, feedback, or just want to geek out about data, don’t hesitate to connect with me on LinkedIn or here on Kaggle. Let’s build something awesome together!

NOTES: I use Google's Cloud Composer to request this data and due to costs, I'm only grabbing data for properties that were recently put up for sale or sold within the day of execution. If you're looking for historical data, please reach out!

Disclaimer: This dataset is intended for non-commercial, academic purposes and does not infringe upon Zillow's intellectual property rights. For full details on Zillow's terms, please visit Zillow's Terms of Use.

Dive in, explore, and let me know what you think. Happy analyzing!

Other Datasets: - Spotify

Reference: https://www.zillow.com/research/zhvi-methodology/

Official Background

In setting out to create a new home price index, a major problem Zillow sought to overcome in existing indices was their inability to deal with the changing composition of properties sold in one time period versus another time period. Both a median sale price index and a repeat sales index are vulnerable to such biases (see the analysis here for an example of how influential the bias can be). For example, if expensive homes sell at a disproportionately higher rate than less expensive homes in one time period, a median sale price index will characterize this market as experiencing price appreciation relative to the prior period of time even if the true value of homes is unchanged between the two periods.

The ideal home price index would be based off sale prices for the same set of homes in each time period so there was never an issue of the sales mix being different across periods. This approach of using a constant basket of goods is widely used, common examples being a commodity price index and a consumer price index. Unfortunately, unlike commodities and consumer goods, for which we can observe prices in all time periods, we can’t observe prices on the same set of homes in all time periods because not all homes are sold in every time period.

The innovation that Zillow developed in 2005 was a way of approximating this ideal home price index by leveraging the valuations Zillow creates on all homes (called Zestimates). Instead of actual sale prices on every home, the index is created from estimated sale prices on every home. While there is some estimation error associated with each estimated sale price (which we report here), this error is just as likely to be above the actual sale price of a home as below (in statistical terms, this is referred to as minimal systematic error). Because of this fact, the distribution of actual sale prices for homes sold in a given time period looks very similar to the distribution of estimated sale prices for this same set of homes. But, importantly, Zillow has estimated sale prices not just for the homes that sold, but for all homes even if they didn’t sell in that time period. From this data, a comprehensive and robust benchmark of home value trends can be computed which is immune to the changing mix of properties that sell in different periods of time (see Dorsey et al. (2010) for another recent discussion of this approach).

For an in-depth comparison of the Zillow Home Value Index to the Case Shiller Home Price Index, please refer to the Zillow Home Value Index Comparison to Case-Shiller

Each Zillow Home Value Index (ZHVI) is a time series tracking the monthly median home value in a particular geographical region. In general, each ZHVI time series begins in April 1996. We generate the ZHVI at seven geographic levels: neighborhood, ZIP code, city, congressional district, county, metropolitan area, state and the nation.

Underlying Data

Estimated sale prices (Zestimates) are computed based on proprietary statistical and machine learning models. These models begin the estimation process by subdividing all of the homes in United States into micro-regions, or subsets of homes either near one another or similar in physical attributes to one another. Within each micro-region, the models observe recent sale transactions and learn the relative contribution of various home attributes in predicting the sale price. These home attributes include physical facts about the home and land, prior sale transactions, tax assessment information and geographic location. Based on the patterns learned, these models can then estimate sale prices on homes that have not yet sold.

The sale transactions from which the models learn patterns include all full-value, arms-length sales that are not foreclosure resales. The purpose of the Zestimate is to give consumers an indication of the fair value of a home under the assumption that it is sold as a conventional, non-foreclosure sale. Similarly, the purpose of the Zillow Home Value Index is to give consumers insight into the home value trends for homes that are not being sold out of foreclosure status. Zillow research indicates that homes sold as foreclosures have typical discounts relative to non-foreclosure sales of between 20 and 40 percent, depending on the foreclosure saturation of the market. This is not to say that the Zestimate is not influenced by foreclosure resales. Zestimates are, in fact, influenced by foreclosure sales, but the pathway of this influence is through the downward pressure foreclosure sales put on non-foreclosure sale prices. It is the price signal observed in the latter that we are attempting to measure and, in turn, predict with the Zestimate.

Market Segments Within each region, we calculate the ZHVI for various subsets of homes (or mar...

Here are a few use cases for this project:

1. Real Estate Property Analysis: The "Project Zillow" computer vision model can be used by real estate websites and agencies to automatically analyze and categorize property images, accurately identifying property features such as docks, swimming pools, and boats. This will help potential buyers find homes with their desired features more easily, improving user experience on real estate platforms.

2. Coastal City Planning: Urban planners and city governments in coastal areas can use "Project Zillow" to analyze satellite and aerial images of their cities, identifying structures and features like docks, boats, and solar panels. This information can be used for better infrastructure planning, coastal zone management, and tracking renewable energy adoption.

3. Environmental Impact Assessment: Environmental agencies and organizations can use "Project Zillow" to monitor the presence of docks, boats, and solar panels in ecological zones, sensitive water bodies, and coastal areas. This information will help assess the impact of human activities on the environment to promote sustainable development and marine conservation efforts.

4. Insurance and Risk Assessment: Insurance companies can use "Project Zillow" to automatically assess property features and associated risks for houses in their database. By identifying docks, swimming pools, boats, and solar panels, the model can help provide more accurate insurance premiums based on these property features and their impact on potential property damage or risk.

5. Tourism and Vacation Rental Industry: Vacation rental platforms can use "Project Zillow" to automatically identify and highlight key amenities like docks, swimming pools, boats, and solar panels in rental property listings. This will help guests quickly find the ideal rental property based on their preferences, ultimately increasing customer satisfaction and booking rates.

The online residential home sale listings industry is experiencing significant changes in its dynamics because of the increased number of homes for sale. The growth in listings is because of various factors, including a climb in the number of homeowners choosing to sell, the easing of the mortgage rate lock-in effect, and economic concerns driving the sale of investment properties. These conditions and the shift from a seller's market towards a more balanced, or even a buyer's market, translate into increased traffic and engagement on home sale platforms. This presents an opportunity for these online platforms to enhance their user experience, refine search tools and offer data analytics to help buyers navigate the increased options. By the end of 2025, industry revenue has climbed at a CAGR of 3.0% and is expected to total $2.2 billion in 2025. In 2025, revenue is expected to strengthen by an estimated 4.2%. Despite enjoying growth, the industry faces challenges with the elevated mortgage rates reducing demand for home purchases, leading to a market freeze. Despite the gain in home listings, actual transaction volumes have remained subdued, creating a challenging environment for the online residential home sale listing platforms. To stay competitive, these platforms are pivoting to offer enhanced tools for price comparisons, real-time mortgage calculators and in-depth educational content to help buyers understand the increased cost of borrowing and also navigate the high inventory but low turnover market. Industry profit has climbed as revenue has outpaced wage growth through the end of 2025. Through the end of 2030, online platforms must position themselves for demographic shifts and changing consumer preferences. Gen Z and younger millennials, who are entering homebuying age, are demanding a more tech-driven, seamless and mobile-first experience. The industry will also continue to see online platforms transform into comprehensive, one-stop digital destinations offering integrated services for every stage of the housing journey. Embracing changes such as artificial intelligence and data analytics to enhance user experience, streamlining listings uploads and offering real-time communication between buyers, sellers, and agents will be crucial for future success. Platforms that offer user-friendly, one-stop experiences and are equipped to provide advanced, feature-rich mobile experiences are set to capture greater market share. Overall, industry revenue will gain at a CAGR of 3.3% through 2030 to total $2.6 billion.

This data was procured in November 2022, from Zillow.com for the Austin, TX, USA area. This dataset can be used for data analysis, statistical multivariate regression analysis, clustering methods, ML regression analysis, and more.

Developed by the Atlanta Regional Commission's Research and Analytics Group, this feature class contains data about home sales prices and square footage from 2013, 2017 and 2018, for City of Atlanta Neighborhood Statistical Areas (NSAs — or neighborhood clusters designed for statistical analysis). Data obtained through Zillow's ZTRAX data. Attribution information: Zillow. 2018. “ZTRAX: Zillow Transaction and Assessor Dataset, 2018-Q4.” Zillow Group, Inc. http://www.zillow.com/ztrax/. Data processing and analysis for local geographies by the Atlanta Regional Commission's Research and Analytics Group.

This data was procured in November 2022, from Zillow.com for the Dallas, TX, USA area. This dataset can be used for data analysis, statistical multivariate regression analysis, clustering methods, ML regression analysis, and more. Please upvote this dataset, so I can determine future update frequency's for the data!

Thanks-Alex

Trulia is a prominent American online real estate marketplace and a subsidiary of Zillow, offering extensive property listings across the United States. Our Crawl Feeds team has successfully extracted over 100K real estate listings from Trulia, providing a comprehensive dataset that includes valuable insights into various property types, pricing trends, and neighborhood information. The last crawl was conducted on 1st September 2021, ensuring the data's relevancy and accuracy.

For businesses and analysts seeking tailored solutions, our Crawl Feeds team is equipped to customize the dataset according to your specific requirements. Whether you need format adjustments, changes in data frequency, or the inclusion or exclusion of specific fields, we can deliver a dataset that aligns perfectly with your needs. Our team can also integrate additional data points such as historical price trends, property amenities, and nearby schools to enhance your analysis.

Contact the Crawl Feeds team today to discuss your customization needs and leverage Trulia’s rich data to gain a competitive edge in the real estate market.

“Exploring Housing Affordability in Illinois: An In-Depth Study of the State’s Real Estate Market” focuses on the Illinois housing market from 2013 to 2022, mainly targeting housing affordability. Housing has been a cornerstone of stability in anyone’s life throughout history. Yet today, housing affordability has emerged as a critical societal issue impacting numerous individuals and families statewide. This study aims to get an overview of the trends of Illinois housing affordability over time across different counties in Illinois. It involves a comprehensive analysis of median home value and median incomes across Illinois counties, using data from two authoritative sources: the Census Bureau and Zillow. By providing insights, we can analyze and study the hidden factors that influence housing affordability over time and forecast future trends.

The global PropTech market is booming, projected to exceed $100 billion by 2033. Discover key trends, growth drivers, and regional insights in our comprehensive market analysis covering companies like Zillow, Airbnb, and WeWork. Explore the impact of AI, Big Data, and blockchain on real estate.

The Multiple Listing Service (MLS) Software market is poised for robust expansion, projected to reach a substantial market size of approximately $5,500 million by 2025, with a compelling Compound Annual Growth Rate (CAGR) of 12.5% anticipated throughout the forecast period from 2025 to 2033. This significant growth is fueled by the increasing digitization of real estate transactions and the rising demand for efficient property listing and management solutions among real estate professionals. The market is experiencing a strong surge driven by the need for streamlined workflows, enhanced data accuracy, and improved client engagement tools within the real estate industry. Cloud-based solutions are dominating the market due to their scalability, accessibility, and cost-effectiveness, offering a distinct advantage over traditional on-premises systems. The competitive landscape is characterized by the presence of key players like Zillow, Crexi, News Corp, and CoStar Group, who are continually innovating to offer comprehensive features such as advanced search functionalities, virtual tour integrations, and robust CRM capabilities. The market is segmented by application into Large Enterprises and Small and Medium-sized Enterprises (SMEs), with both segments demonstrating a growing appetite for advanced MLS software to gain a competitive edge. Geographically, North America, particularly the United States, remains a dominant region, while the Asia Pacific region is expected to witness the fastest growth due to its burgeoning real estate markets and increasing adoption of technology. Despite the positive outlook, challenges such as data security concerns and the initial cost of implementation for smaller entities could pose some restraints, but the overwhelming benefits of enhanced productivity and market reach are expected to outweigh these hurdles.

Discover the booming real estate & property management services market! This in-depth analysis reveals key trends, growth drivers, and challenges impacting the industry from 2019-2033, including insights on leading companies and regional performance. Learn about the lucrative opportunities and potential risks in this dynamic sector.

Context

Zillow has a lot of data about housing prices in America.

Content

Data about housing prices and rental prices broken down according to city and state and number of bedrooms. More detail can be found at https://www.zillow.com/research/data/ and at https://www.zillow.com/research/home-sales-methodology-7733/.

Acknowledgements

The data was downloaded from https://www.zillow.com/research/data/. Banner photo from Ian Keefe on Unsplash. Dataset license described at https://www.zillow.com/research/data/.

The global Property Technology (PropTech) market, valued at $27 billion in 2025, is projected to experience robust growth, driven by increasing urbanization, the rise of smart homes, and the growing adoption of digital technologies across the real estate sector. A Compound Annual Growth Rate (CAGR) of 4.6% from 2025 to 2033 suggests a significant expansion to approximately $38 billion by 2033. Key drivers include the increasing demand for efficient property management solutions, the need for enhanced customer experience through online platforms, and the integration of data analytics for better investment decisions. The market is segmented by application (hospitality, retail, manufacturing, construction, others) and property type (residential, commercial, others). The hospitality and residential segments currently dominate, fueled by platforms like Airbnb and Zillow, respectively. However, increasing technological advancements within the construction and manufacturing sectors promise significant future growth for these segments. Geographic distribution shows North America and Asia Pacific holding the largest market shares, driven by substantial investment in innovative PropTech startups and established players. The ongoing digital transformation within the real estate sector, combined with the continuous development of cutting-edge technologies like VR/AR for virtual property tours and AI-driven property valuations, positions the PropTech market for sustained expansion in the coming years. The competitive landscape is highly dynamic, with both established players (Zillow Group, Redfin, CoStar Group) and rapidly growing innovative startups (Airbnb, OYO, WeWork) vying for market share. Increased competition is likely to spur further technological innovation and drive down prices, making PropTech solutions more accessible to a wider range of consumers and businesses. While challenges exist, such as regulatory hurdles and data security concerns, the overall positive market outlook is largely driven by the undeniable benefits offered by PropTech solutions - enhanced transparency, streamlined processes, cost reduction, and improved efficiency across the real estate lifecycle. Future growth will depend on addressing these challenges effectively and adapting to the evolving technological landscape. Continued investment in research and development and strategic partnerships will be crucial for market participants to maintain competitiveness and capitalize on emerging opportunities.

Discover the booming market for Consumption Decision-Making Customized Services! This in-depth analysis reveals a $15 billion market in 2025, projected to reach $50 billion by 2033, driven by AI, big data, and consumer demand. Explore key trends, regional breakdowns, and leading companies shaping this dynamic landscape.

The size of the PropTech Agent Tool Market market was valued at USD 13.24 Billion in 2024 and is projected to reach USD 25.02 Billion by 2033, with an expected CAGR of 9.52% during the forecast period. Recent developments include: Recent developments in the PropTech Agent Tool Market have showcased significant advancements and shifts among key players such as Zillow, Redfin, and Opendoor. Zillow has been expanding its offerings, leveraging data analytics to enhance user experience while increasing its market share. In parallel, Redfin has reported a growth in home sales as it embraces technology to streamline real estate transactions. Opendoor continues to innovate its acquisition strategies, allowing for quicker sales processes, which is reshaping market dynamics. There have been notable mergers and acquisitions within the sector as well, with HomeLight acquiring certain assets from other startups, reinforcing its position. Moreover, Realtor.com and CoStar Group are collaborating on improved property listings, enhancing visibility for agents. Market valuations for these companies are on the rise, with firms like Compass and AppFolio experiencing increased investments, indicating growing confidence in the digital transformation of real estate processes. This influx of capital is impacting the overall competitive landscape, pushing companies to adopt more sophisticated technologies and offerings to meet evolving consumer demands and expectations within the industry.. Key drivers for this market are: AI-driven property management solutions, Enhanced virtual property tours; Integration with smart home technology; Data analytics for market insights; Automation in transaction processes. Potential restraints include: Technological advancements in real estate, Increasing demand for automation; Enhanced user experience expectations; Growing investment in PropTech; Rise of data analytics usage.

📝 Dataset Description: This synthetic dataset contains 3,000 residential property listings modeled after real U.S. house sales data (in a Zillow-style format). It is designed for use in real estate analysis, machine learning, data visualization, and web scraping practice.

Each row represents a unique property and includes 16 key features commonly used by real estate agents, investors, and analysts. The data spans multiple U.S. states and cities, with realistic values for price, square footage, bedroom/bathroom count, property type, and more.

✅ Included Fields: Price – Listing price (in USD)

Address, City, State, Zipcode – U.S. formatted property location

Bedrooms, Bathrooms, Area (Sqft) – Core home specs

Lot Size, Year Built, Days on Market

Property Type, MLS ID, Listing Agent, Status

Listing URL – Mock Zillow-style property link

⚙️ Use Cases: Exploratory data analysis (EDA)

Regression/classification model training

Feature engineering and preprocessing

Real estate dashboards and web app mockups

Practice with BeautifulSoup, Pandas, or Power BI

Discover the booming Consumption Decision-Making Customized Service market. This in-depth analysis reveals key trends, drivers, and restraints impacting this rapidly growing sector, projecting a CAGR exceeding 15% to 2033. Explore regional insights, segment breakdowns (enterprise vs. personal, cloud vs. on-premises), and leading companies shaping the future of personalized consumption.

The real estate lead generation software market is experiencing robust growth, driven by increasing adoption of digital marketing strategies by real estate agents and brokers. The market's value is estimated to be in the hundreds of millions of dollars in 2025, reflecting a significant expansion since 2019. This growth is fueled by several key factors: the rising preference for online property searches, the need for efficient lead management to improve conversion rates, and the increasing sophistication of CRM (Customer Relationship Management) systems integrated with lead generation tools. Competition is intense, with established players like Infusionsoft, Pardot, and Marketo vying for market share alongside niche players specializing in real estate like Real Geeks, BoomTown!, and Zillow Premier Agent. The market is segmented by software features (e.g., CRM, email marketing, social media integration), pricing models (subscription-based, one-time purchase), and deployment methods (cloud-based, on-premise). While the market shows strong potential, challenges remain, including the high cost of implementation for some solutions, the need for ongoing training and support, and the potential for data security breaches. The market is expected to maintain a healthy Compound Annual Growth Rate (CAGR) throughout the forecast period (2025-2033), propelled by ongoing technological advancements and the continued shift towards digital interactions in the real estate industry. Further growth will be influenced by evolving consumer preferences, regulatory changes impacting data privacy, and the emergence of innovative lead generation techniques such as AI-powered chatbots and personalized marketing campaigns. Companies are constantly innovating to offer more integrated solutions that streamline workflows and improve agent productivity. Geographic expansion, particularly in emerging markets with growing internet penetration, also presents a significant opportunity for market expansion. The competitive landscape will continue to evolve, with mergers and acquisitions likely as larger companies seek to consolidate their market presence. Success in this market will depend on offering robust, user-friendly software with strong customer support and a proven track record of generating qualified leads. The ability to integrate with other real estate platforms and provide insightful data analytics will be key differentiators for market leaders.

The global tenant background check service market is booming, driven by rising security concerns and digitalization. This comprehensive analysis reveals market size, growth trends, key players (TransUnion, Experian, Zillow, etc.), and regional breakdowns, offering valuable insights for investors and industry stakeholders. Explore the latest data and projections for 2025-2033.