Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and pracels nationally.

Over 250M parcels, updated daily.

Access detailed property and tax assessment records with our extensive nationwide database. This robust dataset provides comprehensive information about residential and commercial properties, including detailed ownership, valuation, and transaction history. Core Data Elements:

Complete property identification (APNs, Tax IDs) Full property addresses with geocoding Precise latitude/longitude coordinates FIPS codes and Census tract information School district assignments

Property Characteristics:

Detailed lot dimensions and size Building square footage breakdowns Living area measurements Basement and attic specifications Garage and parking information Year built and effective year Number of bedrooms and bathrooms Room counts and configurations Building class and condition codes Construction details and materials Property amenities and features

Valuation Information:

Current AVM (Automated Valuation Model) values Confidence scores and value ranges Market valuations with dates Assessed values (land and improvements) Tax amounts and years Tax rate codes and districts Various tax exemption statuses

Transaction History:

Current and previous sale details Recording dates and document numbers Sale prices and price codes Buyer and seller information Multiple mortgage records including:

Loan amounts and terms Lender information Recording dates Interest rates Due dates Loan types and positions

Ownership Details:

Current owner information Corporate ownership indicators Owner-occupied status Mailing addresses Care of names Foreign address indicators

Legal Information:

Complete legal descriptions Subdivision details Lot and block numbers Zoning information Land use codes HOA information and fees

Property Status Indicators:

Vacancy flags Pre-foreclosure status Current listing status Price ranges Market position

Perfect For:

Real Estate Professionals

Property researchers Title companies Real estate attorneys Appraisers Market analysts

Financial Services

Mortgage lenders Insurance companies Investment firms Risk assessment teams Portfolio managers

Government & Planning

Urban planners Tax assessors Economic developers Policy researchers Municipal agencies

Data Analytics

Market researchers Data scientists Economic analysts GIS specialists Demographics experts

Data Delivery Features:

Multiple format options Regular updates Bulk download capability Custom field selection Geographic filtering API access available Standardized formatting Quality assured data

Quality Assurance:

Verified against public records Regular updates Standardized formatting Address verification Geocoding validation Duplicate removal Data normalization Quality control processes

This comprehensive property database provides unprecedented access to detailed property information, perfect for industry professionals requiring in-depth property data for analysis, research, or business development. Our data undergoes rigorous quality control processes to ensure accuracy and completeness, making it an invaluable resource for real estate professionals, financial institutions, and government agencies. Updated continuously from authoritative sources, this dataset offers the most current and accurate property information available in the market. Custom data extracts and specific geographic coverage options are available to meet your exact needs.

Weekly/Quarterly/Annual and One-time options are available for sale.

See our sample

Explore property insights effortlessly with APISCRAPY's services – Realtor Property Data, Realtor Data, and Realtor API. Access publicly available property listings and Property Owner Data seamlessly. Our platform is easy to integrate, making property data access simple and efficient.

Gain access to 100% of U.S. homeowners, with this real-time residential property characteristic API with key property info including:

Address Standardization Current owner Last purchase date Purchase amount Year built Property use Bed / Baths Pool Garage type Square footage Zoning School district Tax amount

The ProspectNow Data API delivers all the data and metadata you need for residential and commercial properties across the U.S.

It is designed to provide flexibility, as well as qualified, up-to-date data from a dependable source, so you can focus on providing great customer experiences.

Whether you want to enrich existing datasets, improve your own customer-facing application, or integrate our data into your tech stack, we have everything you need in our REST API, including:

Property Ownership Building Characteristics Valuation Mortgage Information Foreclosure/Preforeclosures Property Tax Info Market Data Properties Predicted to Sell Properties Predicted To Refinance +more

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

Global intelligence on professional short-term rental and vacation rental operators. The Property Management Company Dataset provides a comprehensive view of the professional supply side of the short-term rental and vacation rental industry. Covering thousands of management companies worldwide, it includes verified company identifiers, portfolio size, distribution footprint, performance KPIs, and geographic concentration — enabling benchmarking, market sizing, and investment analysis across destinations. Sourced directly from connected property management systems and verified OTA listings, this dataset captures a uniquely accurate picture of the professional management landscape. It highlights operational scale, market penetration, and performance metrics such as average occupancy, ADR, RevPAR, and revenue growth across managed portfolios. Key Highlights: Global Coverage: Includes professional property management companies across North America, Europe, Asia-Pacific, Latin America, and the Middle East.

Comprehensive Company Profiles: Features company name, portfolio size, property count, markets served, and OTA distribution footprint.

Performance Attributes: Tracks average occupancy, ADR, RevPAR, length of stay, and booking pace at the company and market levels.

Market Dynamics: Understand consolidation trends, brand penetration, and operational scale within the professional management sector.

Flexible Delivery: Available through API or dataset downloads with customizable coverage and update frequency.

Ideal For: Investors & M&A Analysts: Identify emerging operators, assess consolidation activity, and benchmark management performance.

Tourism Boards & Destination Analysts: Quantify professional short-term rental activity and its contribution to local lodging supply.

Hospitality Tech Platforms: Target high-value management partners and evaluate integration opportunities.

Researchers & Policy Experts: Analyze industry structure, professionalization, and global distribution of managed supply.

Use It To: Map and benchmark professional management presence across markets.

Assess company-level performance and scalability trends.

Identify acquisition targets or partnership opportunities in the professional rental ecosystem.

Support tourism policy, regulatory, and market analysis with verified operator data.

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.

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.

Main Dataset

I scrapped data from 99acres using their (kind of) hidden API. I scrapped almost 10,000+ data using my scrapper app see here.

DESCRIPTION OF THE DATA

• Contains details of properties of Gurgaon, Hyderabad, Mumbai, Kolkata cities of India.
• All datasets of different cities contains almost 10K properties.
• In some datasets, some columns are not available. Sorry!!
• Target column: PRICE

Data Usage

This dataset can be used for various real estate-related tasks, including:

• Property price prediction.
• Market analysis to identify trends and patterns.
• Identifying popular property types and locations.
• Evaluating the impact of property attributes on price.

EXPLANATION OF EACH COLUMNS

NOTE: Not all the columns are important for you so first try to understand your problem statement and then filter this dataset accordingly.

• AGE: The age of the property in years.
• ALT_TAG: An alternative tag or description.
• AMENITIES: Describes the amenities available with the property.
• AREA: The area of the property.
• BALCONY_NUM: The number of balconies in the property.
• BATHROOM_NUM: The number of bathrooms in the property.
• BEDROOM_NUM: The number of bedrooms in the property.
• BROKERAGE: Information about the brokerage or agency associated with the property listing.
• BUILDING_ID: An integer identifier for the building.
• BUILDING_NAME: The name of the building.
• BUILTUP_SQFT: The total built-up area of the property in square feet.
• CARPET_SQFT: The total carpet area of the property in square feet.
• CITY_ID: An identifier for the city in which the property is located.
• CITY: The city where the property is located.
• CLASS_HEADING: A heading for the property class.
• CLASS_LABEL: A label representing the property class.
• CLASS: A classification label for the property.
• COMMON_FURNISHING_ATTRIBUTES: Attributes related to the furnishings and amenities commonly found in the property.
• CONTACT_COMPANY_NAME: The name of the company or agency responsible for the property listing.
• CONTACT_NAME: The name of the contact person associated with the property listing.
• DEALER_PHOTO_URL: URL to a photo or image associated with the property dealer.
• DESCRIPTION: A description of the property listing.
• EXPIRY_DATE: The date when the listing expires.
• FACING: Indicates the direction the property is facing.
• FEATURES: Describes the features of the property.
• FLOOR_NUM: The floor number of the property.
• FORMATTED_LANDMARK_DETAILS: Details of nearby landmarks.
• FORMATTED: Formatted information related to the property.
• FSL_Data: Data related to the property, possibly specific to a particular real estate agency.
• FURNISH: Indicates whether the property is furnished.
• FURNISHING_ATTRIBUTES: Attributes describing the level of furnishing in the property.
• GROUP_NAME: The name of the group or organization to which the property may belong.
• LISTING: Information about the property listing, possibly including its status and other details.
• LOCALITY_WO_CITY: The locality name without the city information.
• LOCALITY: The specific locality or neighborhood where the property is situated.
• location: Additional location information.
• MAP_DETAILS: Contains latitude and longitude information.
• MAX_AREA_SQFT: The maximum area of the property in square feet.
• MAX_PRICE: The maximum price of the property.
• MEDIUM_PHOTO_URL: URL to a medium-sized photo or image of the property.
• metadata: Additional metadata or information about the dataset.
• MIN_AREA_SQFT: The minimum area of the property in square feet.
• MIN_PRICE: The minimum price of the property.
• OWNTYPE: An integer representing the ownership type.
• PD_URL: URL to additional property details.
• PHOTO_URL: URL to photos or images associated with the property.
• POSTING_DATE: The date when the property listing was posted.
• PREFERENCE: Indicates the preference type for the property listing (e.g., "S" for sale).
• PRICE_PER_UNIT_AREA: The price per unit area of the property.
• PRICE_SQFT: The price per square foot of the property.
• PRICE: The price of the property. This is target column for ML.
• PRIMARY_TAGS: Primary tags or labels.
• PRODUCT_TYPE: The type of product listing.
• profile: Profile information related to the property or listing.
• PROJ_ID: An integer identifier for the project.
• PROP_DETAILS_URL: URL to detailed property information.
• PROP_HEADING: A heading or title for the property.
• PROP_ID: A ...

Please read all metadata before accessing the dataset. Note that records shown here are updated at different frequencies from data in products from MDP and SDAT. Please see the full documentation at https://opendata.maryland.gov/api/views/ed4q-f8tm/files/WtRzMltUzm25OasOCYtu7PgOGUfrplWsZTalSH4Iukg?download=true&filename=Real%20Property%20Records%20Documentation.pdf and review the dedicated metadata site (https://opendata.maryland.gov/dataset/Beta-Maryland-Statewide-Real-Property-Assessments-/ed4q-f8tm/about).

Given a known property address (input) BatchData Property Data Lookup API instantly returns information on the property, ownership, property listings data, and transactional history.

• Property Address Information
• Assessment Details
• Building Characteristics
• Demographics
• Foreclosure
• Occupancy/Vacancy
• Involuntary Liens
• MLS & Agent Arrays
• Owner Names & Mailing Address
• Property Owner Profiles
• Current & Prior Sales
• Tax Information
• Valuation & Equity

BatchData's robust data science team curates over a dozen primary and secondary tier 1 data sources to offer unparalleled database depth, accuracy, and completeness.

BatchData is used by lead generation, product, operations, and acquisitions teams to power websites, fuel applications, build lists, enrich data, and improve data governance. A suite of APIs and self-service list building platforms provide access to 150M+ residential properties.

Residential Real Estate Data includes: - Property Address Information - Assessment Details - Building Characteristics - Demographics - Foreclosure - Occupancy/Vacancy - Involuntary Liens - MLS & Agent Arrays - Owner Names & Mailing Address - Property Owner Profiles - Current & Prior Sales - Tax Information - Valuation & Equity

Real Estate Data APIs include: - Residential Property Search - Residential Property Lookup - Residential Address Verification - Residential Property Skip Trace - Geocoding

BatchData's robust data science team curates over a dozen primary and secondary tier 1 data sources to offer unparalleled database depth, accuracy, and completeness.

This City Owned Property data has been compiled from deeds, maps, assessor records, and other public records on file in the City of Hartford. The intent of this data layer is to depict a graphical representation of real property information relative to the planimetric features for the City of Hartford and is subject to change as a more accurate survey may disclose.

Do not use this dataset. It does not actually provide Property Data in its current form. We are working on improvements to the dataset to more accurately reflect its title and original intent.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Real Property parcel characteristics for Allegheny County, PA. Includes information pertaining to land, values, sales, abatements, and building characteristics (if residential) by parcel. Disclaimer: Parcel information is provided from the Office of Property Assessments in Allegheny County. Content and availability are subject to change. Please review the Data Dictionary for details on included fields before each use. Property characteristics and values change due to a variety of factors such as court rulings, municipality permit processing and subdivision plans. Consequently the assessment system parcel data is continually changing. Please take the dynamic nature of this information into consideration before using it. Excludes name and contact information for property owners, as required by Ordinance 3478-07.

Orientation

The first two items listed below are slightly different versions of the most current property-assessments records. The first is optimized for faster download but has 1) a few fields (including PROPERTY_ZIP and MUNICODE) as integers instead of strings and 2) the date columns in two different formats. The second item downloads more slowly, is optimized for API queries, and has all dates in a standard YYYY-MM-DD format. Further down you can find useful links, documentation, and then archived versions of property assessments files.

This dataset is comprised of the final assessment rolls submitted to the New York State Department of Taxation and Finance – Office of Real Property Tax Services by 996 local governments. Together, the assessment rolls provide the details of the more than 4.7 million parcels in New York State.

The dataset includes assessment rolls for all cities and towns, except New York City. (For New York City assessment roll data, see NYC Open Data [https://opendata.cityofnewyork.us])

For each property, the dataset includes assessed value, full market value, property size, owners, exemption information, and other fields.

Tip: For a unique identifier for every property in New York State, combine the SWIS code and print key fields.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Data for all Commercial Building Permits issued since 2000, including status and work performed.

Update Frequency: Daily

GSA Auctions offers Federal personal property assets ranging from common place items (such as office equipment and furniture) to more select products like scientific equipment, heavy machinery, airplanes, vessels and vehicles. GSA Auctions’ online capabilities allow GSA to offer assets located across the country to any interested buyer, regardless of location. Build your own tools using our API to access GSA Auctions listings. The Auctions API is a GET API which has currently one operation. The operation will retrieve GSA Auctions data. The output data will be in XML and JSON format. These files are downloadable. The data in the API output file is live data.