Search 150M+ US property records with ownership, mortgage, equity, tax, lien, pre-foreclosure, and county assessor data in DealMachine.

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

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

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.

Comprehensive property data, parcel information, and ownership records for Park County, CO. Access real estate market insights and property details.

Context

This dataset contains Real Estate listings in the US broken by State and zip code.

Download

kaggle API Command !kaggle datasets download -d ahmedshahriarsakib/usa-real-estate-dataset

Content

The dataset has 1 CSV file with 10 columns -

1. realtor-data.csv (2,226,382 entries)
   • brokered by (categorically encoded agency/broker)
   • status (Housing status - a. ready for sale or b. ready to build)
   • price (Housing price, it is either the current listing price or recently sold price if the house is sold recently)
   • bed (# of beds)
   • bath (# of bathrooms)
   • acre_lot (Property / Land size in acres)
   • street (categorically encoded street address)
   • city (city name)
   • state (state name)
   • zip_code (postal code of the area)
   • house_size (house area/size/living space in square feet)
   • prev_sold_date (Previously sold date)

NB: 1. brokered by and street addresses were categorically encoded due to data privacy policy 2. acre_lot means the total land area, and house_size denotes the living space/building area

Acknowledgements

Data was collected from - - https://www.realtor.com/ - A real estate listing website operated by the News Corp subsidiary Move, Inc. and based in Santa Clara, California. It is the second most visited real estate listing website in the United States as of 2024, with over 100 million monthly active users.

Cover Image

Image by Mohamed Hassan from Pixabay

Disclaimer

The data and information in the data set provided here are intended to use for educational purposes only. I do not own any data, and all rights are reserved to the respective owners.

Inspiration

• Can we predict housing prices based on the features?
• How are housing price and location attributes correlated?
• What is the overall picture of the USA housing prices w.r.t. locations?
• Do house attributes (bedroom, bathroom count) strongly correlate with the price? Are there any hidden patterns?

ATTOM AVM delivers hyperlocal, statistically rigorous residential property valuations across the United States, providing Real-Time Real Estate Data that supports Real Estate Valuation Data, House Price Data, Residential Real Estate Data, and Property Data use cases. Built on ATTOM’s nationwide residential property and sales database, the model generates monthly valuation estimates for more than 97 million homes, with coverage spanning all 50 states and 3,143 counties.

Individual property value estimates are driven by ATTOM’s best-in-class neighborhood boundaries and recent sales transaction data, enabling the model to capture micro-location pricing differences within local markets. With limited exceptions for rural or low-transaction areas, valuation models rely on comparable sales occurring within 24 months of the valuation date to ensure relevance and accuracy.

ATTOM AVM applies multiple valuation approaches, including robust statistical models, market metrics derived from clusters of similar properties, and ensemble value-blending techniques. For properties eligible for more than one valuation method, a cascading model selection process automatically selects the approach proven to be most accurate within the surrounding geographic area.

The result is a transparent, scalable valuation framework that supports consistent home pricing analysis, market monitoring, and valuation-driven analytics across residential real estate markets nationwide.

Statewide property data, parcel information, and ownership records for Washington. Access real estate market insights across all counties.

Comprehensive property data, parcel information, and ownership records for Palm Beach County, FL. Access real estate market insights and property details.

Wholesale Real Estate property and owner data for Boone County, Missouri (FIPS 29019), sourced from Boone County parcel layer with full data lineage.

In 2023 and the first half of 2024, the largest property sale in the data center real estate market in Europe was DATA4 Paris-Saclay in Paris. In April 2023, Brookfield bought the ****** square meter property from AXA for an undisclosed price. The most expensive sale was Digital Frankfurt I. The valuation of the site was *** million U.S. dollars and Digital Core REIT obtained **** percent from Digital Realty.

ATTOM’s Vacancy Data delivers a nationwide view of long-term residential vacancy, identifying properties that have been vacant for at least 90 days across the United States. This dataset combines Property Owner Data, Rental Data, Real Estate Market Data, and Residential Real Estate Data to support ownership analysis, market insight, and opportunity identification.

Each record includes verified, standardized address-level information and detailed ownership data, allowing organizations to accurately locate vacant residential properties and understand ownership patterns at scale. Updated monthly, the dataset reflects current market conditions and captures vacancy trends across counties and ZIP codes nationwide.

By highlighting underutilized residential assets, Vacancy Data enables users to analyze vacancy dynamics, remove inactive addresses from outreach efforts, and identify properties that may represent strategic opportunities for investment, redevelopment, or targeted services.

Comprehensive property data, parcel information, and ownership records for San Diego County, CA. Access real estate market insights and property details.

Dataset SummaryPlease note: this data is live (updated nightly) to reflect the latest changes in the City's systems of record.About the Data:For a data dictionary / codebook, click here.This dataset is a polygon feature layer with the boundaries of all tax parcels owned by the City of Rochester. This includes all public parks, and municipal buildings, as well as vacant land and structures currently owned by the City. The data includes fields with features about each property including property type, date of sale, whether the property is expected to be sold, land value, dimensions, and more.To view a interactive map that features this layer, please visit:https://maps.cityofrochester.gov/portal/apps/webappviewer/index.html?id=eecba714701449c69054851c9ac57198About City Owned Properties:The City's real estate inventory is managed by the Division of Real Estate in the Department of Neighborhood and Business Development. Properties like municipal buildings and parks are expected to be in long term ownership of the City. Properties such as vacant land and vacant structures are ones the City is actively seeking to reposition for redevelopment to increase the City's tax base and economic activity. The City acquires many of these properties through the tax foreclosure auction process when no private entity bids the minimum bid. Some of these properties stay in the City's ownership for years, while others are quickly sold to development partners. For more information please visit the City's webpage for the Division of Real Estate: https://www.cityofrochester.gov/realestate/

DATASET IN BRIEF Sold transaction records for 7,805 Alabama residential properties across 6 property types and 421 ZIP codes. Includes list vs sold price, property specs, negotiation outcome (Above / At / Below Asking), sold-to-list ratio, price per square foot, property age, sale date features, and PII-redacted listing descriptions for 6,977 records.

APPLICATIONS - Sale price prediction: model lastSoldPrice from sqft, beds, baths, property type, garage count, property_age, and postal_code using regression. - Negotiation outcome classification: predict Above / At / Below Asking result using property features, type, and price bracket as inputs. - Market zone analysis: compare sold-to-list ratios and price_per_sqft by ZIP code to identify buyer's vs seller's market pockets across Alabama. - Seasonality research: use sold_month and sold_quarter to identify temporal patterns in sale prices and negotiation outcomes. - NLP / text mining: extract amenity keywords from 6,977 PII-redacted listing descriptions and correlate with price premium to score listing language value.

COLUMNS (VARIABLES) - type string Property type: single_family, condos, townhomes, multi_family, other, duplex_triplex - sub_type string Property sub-type where available (93% null for single-family records which carry no sub-type) - listPrice float Original asking price, USD (null where not publicly disclosed; ~10% of records) - lastSoldPrice float Final recorded sale price, USD (primary target variable; 100% populated) - soldOn string Date of sale, YYYY-MM-DD format - sqft float Interior square footage (100% populated) - stories float Number of storeys - beds float Number of bedrooms (100% populated) - baths float Total bath count - baths_full float Full bathrooms only - baths_full_calc float Calculated full bath count (from source data) - garage float Garage capacity in car spaces (null = no garage data; ~46% of records) - year_built float Year of original construction - postal_code string 5-digit ZIP code of the property - is_valid_al_zip bool True if postal_code is a confirmed Alabama ZIP (range 35004-36925) - sold_year float Year component extracted from soldOn - sold_month float Month component extracted from soldOn (1-12) - sold_quarter float Quarter component extracted from soldOn (1-4) - property_age float Years since construction (2026 minus year_built) - sold_to_list_ratio float lastSoldPrice / listPrice (null where listPrice not disclosed; 806 records) - price_premium_pct float Percentage premium or discount vs list price (positive = sold above asking, negative = below) - negotiation_outcome string Above Asking / At Asking / Below Asking / Unknown (derived from sold_to_list_ratio thresholds) - ratio_outlier_flag bool True if sold_to_list_ratio < 0.5 or > 1.5 (20 rows flagged; not removed) - price_per_sqft float lastSoldPrice / sqft, rounded to 2 decimal places (key comparables and valuation metric) - text_clean string Property listing description with PII redacted (available for 6,977 of 7,805 records) - pii_redacted_flag bool True if PII pattern-matching found and redacted content within text_clean (657 records)

IMAGE CREDITS Image generated using ChatGPT (OpenAI).

LICENSE CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike)

Records are derived from publicly listed property data with all PII removed. Published for research and educational use. Users should independently verify compliance with applicable listing platform data terms for any commercial application.

Property addresses appearing in the url field reflect publicly recorded sold transaction data available through US county assessor and MLS public records. pii_redacted_flag applies to the text_clean field, where inline addresses and contact details were removed. Public-record addresses in url are retained by design

Property addresses appearing in the url field reflect publicly recorded sold transaction data available through US county assessor and MLS public records. pii_redacted_flag applies to the text_clean field, where inline addresses and contact details were removed. Public-record addresses in url are retained by design

Monthly-refreshed real estate investment statistics for Gainesville, FL — median home price, rent, cap rate, appreciation, vacancy, and household income. Sourced from Zillow Research and U.S. Census ACS.

Property Valuation Software Market Outlook

As per our latest research, the global Property Valuation Software market size reached USD 4.2 billion in 2024, demonstrating robust momentum driven by digital transformation across real estate and financial sectors. The market is poised to expand at a CAGR of 11.6% from 2025 to 2033, projecting a value of USD 11.3 billion by 2033. This growth is primarily fueled by increased demand for data-driven property assessments, regulatory compliance requirements, and the rapid adoption of cloud-based solutions. The integration of artificial intelligence and advanced analytics is further accelerating the adoption of property valuation software worldwide, making it an indispensable tool for real estate professionals and financial institutions.

A significant growth factor for the Property Valuation Software market is the increasing digitization within the real estate sector. Real estate agencies, banks, and independent valuers are under mounting pressure to offer precise, quick, and transparent property valuations to meet evolving customer expectations and regulatory standards. The proliferation of big data, machine learning, and geospatial analytics has enabled property valuation software to deliver highly accurate and real-time assessments, reducing human error and enhancing decision-making. Furthermore, the shift from manual, paper-based valuation processes to automated digital solutions is unlocking new efficiencies, reducing operational costs, and minimizing the risk of compliance breaches. These advancements are vital in a market where accuracy, speed, and transparency are critical to maintaining competitiveness and customer trust.

Another key driver of market growth is the surge in real estate transactions globally, particularly in emerging economies. Rapid urbanization, infrastructural development, and a growing middle class are fueling the demand for residential, commercial, and industrial properties, which in turn is increasing the need for reliable property valuation tools. Property valuation software is also becoming essential for financial institutions, which rely on accurate property assessments for mortgage approvals, asset management, and risk mitigation. The software’s ability to integrate with multiple data sources, automate complex calculations, and ensure compliance with international valuation standards is making it a preferred choice for stakeholders across the real estate value chain. As property markets become more dynamic and interconnected, the adoption of advanced valuation solutions is expected to accelerate further.

The evolving regulatory landscape is also playing a crucial role in shaping the Property Valuation Software market. Governments and regulatory bodies are enforcing stringent guidelines on property appraisals, anti-money laundering (AML), and data privacy, compelling organizations to adopt robust digital solutions. Property valuation software platforms are increasingly incorporating compliance management features, audit trails, and reporting functionalities to help users adhere to regional and international standards. This regulatory push is particularly pronounced in regions like North America and Europe, where compliance requirements are stringent and penalties for non-compliance are severe. As regulations continue to evolve, the need for scalable, secure, and compliant valuation software will remain a significant growth catalyst for the market.

From a regional perspective, North America currently leads the global Property Valuation Software market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The dominance of North America can be attributed to the presence of major real estate firms, advanced IT infrastructure, and early adoption of digital technologies. Europe is witnessing steady growth, driven by regulatory harmonization and the modernization of property markets in countries like Germany, the UK, and France. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, propelled by rapid urban development, increasing property investments, and the digital transformation of real estate services in countries such as China, India, and Australia. Latin America and the Middle East & Africa are gradually catching up, with rising awareness about the benefits of property valuation software and ongoing investments in real estate and technology infrastructure.

Description

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

Dataset Overview:

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

Key Features:

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

Use Cases:

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

Data Collection Method:

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

Data Format:

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

Disclaimer:

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

Wholesale Real Estate property and owner data for Santa Cruz County, California (FIPS 06087), sourced from Santa Cruz County parcel layer with full data lineage.

Update Frequency: Annually

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.