Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q4 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

Title

Colorado Real Estate Market 2026

Subtitle

8,300+ active properties for spatial EDA, pricing regression, and data imputation.

Dataset Overview

Welcome to the Colorado Real Estate 2026 dataset! This collection provides a massive cross-sectional snapshot of the current housing and rental market across the state of Colorado. From $45 million luxury ski-in/ski-out mountain estates in Aspen to accessible suburban townhomes in Denver, this dataset captures the extreme price variance and geographic diversity of one of the most dynamic real estate markets in the US.

Data Science Applications

This dataset was intentionally preserved with its raw feature structures and natural missing values, making it an exceptional playground for data cleaning, imputation, and predictive modeling:

• Advanced Data Imputation: Use machine learning techniques (like KNN) to predict and fill in the missing sqft, beds, and stories data based on surrounding property features.
• Feature Selection & Multicollinearity: The dataset includes redundant bathroom metrics (baths, baths_full, baths_full_calc). Build correlation matrices to decide which features provide the strongest signal for pricing models.
• Pricing Regression: Train models (XGBoost, Random Forest) to predict the active listPrice based on sqft, garage space, and property type.
• NLP & Text Extraction: Use the text column to parse out premium marketing keywords (e.g., "Mountain View", "Renovated") to see how they impact property valuations.

Column Descriptors

Provenance: Sources & Methodology

• Sources: This dataset aggregates active 2026 property listings from major US real estate platforms, primarily sourcing from Realtor.com, Zillow, and Redfin to ensure a comprehensive, multi-platform market snapshot.
• Extraction Methodology: The raw data was extracted utilizing targeted Apify actors. The extraction tools were configured to automatically handle site rate-limiting, respect server loads, and adhere to robots.txt protocols while navigating dynamic real estate maps.
• Data Processing: The raw scrape was processed in Python/Pandas. Fatal errors (such as completely empty columns, exact duplicates, and listings missing a listPrice) were rigorously purged to ensure the dataset is machine-learning ready. However, secondary features (like sqft and beds) were intentionally left with their natural missing values to provide the data science community with authentic data imputation challenges.
• Disclaimer: This data is strictly intended for educational data science, EDA, and research purposes only.

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

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.

South Carolina Real Estate Dataset 2025

Dataset Overview

This comprehensive real estate dataset contains over 5,000 property listings from South Carolina, collected in 2025 from Realtor.com using apify api. The dataset captures diverse property types including single-family homes, condominiums, land parcels, townhomes, and other residential properties. This dataset provides a rich snapshot of South Carolina's real estate market suitable for predictive modeling, market analysis, and investment research.

Data Science Applications

• Price Prediction Models: Build regression models (Random Forest, XGBoost, Neural Networks) to predict property values based on size, location, bedrooms, and age
• Property Type Classification: Develop multi-class classifiers to categorize properties based on physical characteristics
• Market Segmentation: Apply clustering algorithms (K-means, DBSCAN) to identify distinct property segments and price brackets
• Time Series Analysis: Analyze construction trends and property age distributions to forecast future development patterns
• Investment Opportunity Detection: Create anomaly detection models to identify undervalued properties or outliers
• Feature Engineering: Generate derived features like price per square foot, bathroom-to-bedroom ratios for enhanced model performance

Column Descriptors

• type: Primary property category (single_family, condos, land, townhomes, multi_family, farm)
• sub_type: Detailed property classification (condo, townhouse, co_op)
• sqft: Property size in square feet
• baths: Number of bathrooms (decimal values indicate half baths)
• beds: Number of bedrooms
• stories: Number of floors/stories in the property
• year_built: Construction year of the property
• listPrice: Property listing price in USD

Ethically Obtained Data

This dataset was ethically scraped from publicly available listings on Realtor.com and is provided strictly for educational and learning purposes only. The data collection complied with ethical web scraping practices and contains only publicly accessible information. Users should utilize this dataset exclusively for academic research, educational projects, and learning data science techniques. Any commercial use is strictly prohibited.

REdistribute modernizes real estate data accessibility by providing access to fresh, reliable listings from trusted MLS sources.

For Market Insights & Analytics, this standardized bulk dataset enables: - Macro and micro-level housing market trend analysis - Competitive benchmarking and regional performance tracking - Consumer demand forecasting grounded in verified transaction activity

Key features: • Flexible Delivery: Available via a bulk data API or directly through Snowflake • Residential or Multi-Class: Choose a residential-only dataset or full MLS coverage across all property types, including residential, multi-family, land, commercial, rentals, farm and more • Comprehensive Field Access: Explore 800+ fields providing a complete view of both residential and non-residential property data • Fast & Fresh: Stay current with daily updates sourced directly from trusted MLSs partners

The sample data covers one listing in JSON format. For access to a broader set of sample listings (10,000+), reach out to the REdistribute sales contact.

ABOUT REDISTRIBUTE

REdistribute aims to modernize real estate data accessibility, fostering innovation and transparency through direct access to the most reliable MLS data. Our commitment to data integrity and direct MLS involvement guarantees the freshest, most accurate insights, empowering businesses across industries to drive innovation and make informed decisions.

Abstract

Title: Cotality Smart Data Platform (SDP): Owner Transfer and Mortgage

The Owner Transfer and Mortgage data covers over 450 million properties, and includes over 50 years of sales history. The tables were generated in June 2024, and cover all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C.

Formerly known as CoreLogic Smart Data Platform: Owner Transfer & Mortgage.

Methodology

In the United States, parcel data is public record information that describes a division of land (also referred to as "property" or "real estate"). Each parcel is given a unique identifier called an Assessor’s Parcel Number or APN. The two principal types of records maintained by county government agencies for each parcel of land are deed and property tax records. When a real estate transaction takes place (e.g. a change in ownership), a property deed must be signed by both the buyer and seller. The deed will then be filed with the County Recorder’s offices, sometimes called the County Clerk-Recorder or other similar title. Property tax records are maintained by County Tax Assessor’s offices; they show the amount of taxes assessed on a parcel and include a detailed description of any structures or buildings on the parcel, including year built, square footages, building type, amenities like a pool, etc. There is not a uniform format for storing parcel data across the thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, Cotality collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. Cotality augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from Cotality's parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see Cotality 2024 GitLab.

Usage

The Owner Transfer and Mortgage data covers over 450 million properties, and includes over 50 years of sales history. The tables were generated in June 2024, and cover all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C. The Owner Transfer data provides historical information about property sales and ownership-related transactions, including full, nominal, and quitclaim transactions (involving a change in title/ownership). It contains comprehensive property and transaction information, such as property characteristics, current ownership, transaction history, title company, cash purchase/foreclosure/resale/short sale indicators, and buyer information.

The Mortgage data provides historical information at the mortgage level, including purchase, refinance, equity, as well as details associated with each transaction, such as lender, loan amount, loan date, interest rate, etc. Mortgage details include mortgage amount, type of loan (conventional, FHA, VHA), mortgage rate type, mortgage purpose (cash out first, consolidation, standalone subordinate), mortgage ARM features, and mortgage indicators such as fixed-rate, conforming loan, construction loan, and private party. The Mortgage data also includes subordinate mortgage types, rate details, and lender details (NMLS ID, Loan Company, Loan Officers).

The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.

Mortgage records can be linked to a transaction using the MORTGAGE_COMPOSITE_TRANSACTION_ID.

For more information about included variables, please see:

• cotality_sdp_owner_transfer_data_dictionary_2024.txt
• cotality_sdp_mortgage_data_dictionary_2024.txt
• Mortgage_v3.xlsx
• Owner Transfer_v3.xlsx

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For a count of records per FIPS code, please see cotality_sdp_owner_transfer_counts_2024.txt and cotality_sdp_mortgage_counts_2024.txt.

For more information about how the Cotality Smart Data Platform: Owner Transfer and Mortgage data compares to legacy data, please see 2025_Legacy_Content_Mapping.pdf.

Bulk Data Access

Data access is required to view this section.

Build your application on bedrock, not sand.

In the world of PropTech, data accuracy isn't just a feature—it's the product. Yet, most developers struggle with the chaos of raw county assessor data: inconsistent zoning codes, missing square footage, and fragmented tax records. BatchData’s Core Property & Land Use dataset solves this by ingesting, cleaning, and normalizing data from thousands of municipal sources into a single, developer-ready schema.

The "Source of Truth" for US Real Estate Covering over 155 million residential and commercial properties (99.8% of the US population), this dataset provides a high-definition digital twin of every parcel. We go beyond basic "beds and baths." Our proprietary ingestion engine standardizes granular building details—from roof types and foundation styles to heating systems and pool presence—allowing you to build precise filters and valuation models.

Why BatchData Property Intelligence?

Standardized Zoning & Land Use: We map thousands of local zoning codes (e.g., "R-1" vs "Res-Low") into a unified taxonomy, enabling you to programmatically identify "Single Family Residential" or "Vacant Land" across state lines without writing custom logic for every county.

Deep Building Characterization: Access over 75 distinct attributes related to the physical structure, essential for insurance underwriting, renovation estimation, and solar feasibility studies.

Historical Assessment Data: We track assessed values and tax amounts over time, giving you a longitudinal view of a property's holding costs and tax appreciation.

Key Statistics Coverage: 155,000,000+ Properties (Nationwide)

Attributes: 200+ Standardized Data Points

Update Frequency: Daily (Continuous Ingestion)

Property Types: Residential, Commercial, Industrial, Agricultural, Vacant Land

Geocoding: Roof-top precision Lat/Long coordinates

Detailed Data Schema & Attributes This dataset is organized into six logical clusters to support diverse use cases:

1. Building Characteristics (76+ Data Points)

Physical Structure: Living Area (Sq Ft), Building Area, Gross Area, Number of Stories, Year Built, Effective Year Built.

Room Counts: Bedrooms, Bathrooms (Full/Half/Quarter), Total Rooms.

Construction Details: Foundation Type (Slab/Basement/Pier), Roof Cover (Shingle/Tile/Metal), Roof Shape, Exterior Wall Type (Stucco/Brick/Siding), Frame Type.

Amenities & Systems: Pool (Yes/No), Pool Area, Fireplace (Count/Type), Heating/Cooling System Type, Garage Type/Size, Parking Spaces.

1. Land, Lot & Zoning (9+ Data Points)

Lot Geometry: Lot Size (Acres), Lot Size (Sq Ft), Lot Frontage, Lot Depth.

Land Use: Standardized Land Use Codes (e.g., SFR, Condo, Duplex, Vacant), County Land Use Descriptions.

Zoning: Standardized Zoning Codes, Zoning Descriptions, Topography, Site Influence (e.g., Corner Lot, Cul-de-sac).

1. Tax & Assessment (8+ Data Points)

Valuation: Total Assessed Value, Assessed Land Value, Assessed Improvement Value, Market Value.

Taxation: Total Tax Amount, Tax Year, Tax Area/Code, Exemption Status (e.g., Homestead).

History: Historical assessment data available for trend analysis.

1. Location & Geocoding (10+ Data Points)

Address: Standardized USPS Street Address, City, State, Zip, Zip+4.

Coordinates: Latitude, Longitude (Rooftop Precision).

Geo-Political: Census Tract, Block Group, Carrier Route.

1. Legal & Identification (20+ Data Points)

Identifiers: APN (Assessor Parcel Number), FIPS Code (State/County), Composite ID (BatchData Universal ID).

Legal Description: Full Legal Description, Subdivision Name, Tract Number, Block/Lot Number.

Deed Pointers: Last Sale Date, Last Sale Price (correlated with our Deed/Sales dataset).

Expanded Use Cases by Industry 1. PropTech & Search Portals

Search Experience: Power advanced filters like "Homes with a pool on >0.5 acres" or "Properties with basements built after 1990."

Listing Enrichment: Auto-populate listing pages with deep property details (Roof Type, Year Built, Tax History) to improve SEO and user engagement.

AVM Development: Train Automated Valuation Models using our granular "Improvement Value" and "Condition" data to predict market prices with higher accuracy.

1. Home Services & Energy (Solar/HVAC/Roofing)

Solar Feasibility: Use "Roof Shape," "Roof Cover," and "Building Area" combined with "Latitude/Longitude" to remotely qualify homes for solar panel installation.

Roofing Leads: Target properties with "Roof Material: Asphalt Shingle" built 15-20 years ago to identify homeowners likely needing a roof replacement.

HVAC Sizing: accurate "Living Area" and "Story Count" data helps estimators calculate load requirements before sending a truck.

1. Real Estate Investment (REI)

"Buy Box" Automation: Programmatically screen entire zip codes to find properties that match strict criteria (e.g., "3+ Beds, 2+ Baths, Built > 1980, Zoning: SFR").

Development Potential: Identify "Vacant Land" parcels with specific zoning codes (e.g., R-2 or R-3) ...

Graph and download economic data for Total Revenue for Real Estate Property Managers, All Establishments, Employer Firms (REVEF53131ALLEST) from 2009 to 2022 about management, employer firms, accounting, revenue, real estate, establishments, services, and USA.

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 217 data sources in Washington, D.C.
3. 315,000+ properties in Washington, D.C.
4. 4 billion records (listed in the "data types" block above) in total
5. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

Property Data Graphs Market Outlook

According to our latest research, the Global Property Data Graphs market size was valued at $2.1 billion in 2024 and is projected to reach $8.7 billion by 2033, expanding at a CAGR of 16.8% during 2024–2033. The primary factor driving the rapid growth of the Property Data Graphs market is the increasing demand for advanced data analytics and visualization tools in the real estate sector. With the proliferation of big data and the need for actionable insights, property data graphs are becoming indispensable for stakeholders seeking to make informed decisions, optimize investments, and streamline operations. The integration of artificial intelligence and machine learning into property data graph solutions is further accelerating market expansion by enabling predictive analytics, risk assessment, and automated valuation models.

Regional Outlook

North America currently holds the largest share of the global Property Data Graphs market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region’s mature real estate industry, robust technological infrastructure, and early adoption of digital transformation initiatives. The United States, in particular, has witnessed significant investments in property technology (PropTech) platforms, supported by favorable policies and the presence of leading market players. Additionally, the high penetration of cloud-based solutions and the growing trend of smart cities in North America have further fueled the adoption of property data graphs, making it a benchmark for other regions in terms of innovation and market maturity.

Asia Pacific is emerging as the fastest-growing region in the Property Data Graphs market, with a projected CAGR of 20.4% from 2024 to 2033. The surge in urbanization, rising disposable incomes, and a booming real estate sector in countries like China, India, and Southeast Asia are key drivers behind this growth. Governments across the region are investing heavily in digital infrastructure and smart city projects, which necessitate sophisticated data analytics tools for property management, valuation, and investment analysis. Furthermore, the increasing adoption of cloud-based solutions and mobile technologies in Asia Pacific is enabling real estate stakeholders to leverage property data graphs for enhanced operational efficiency and strategic decision-making.

In emerging economies across Latin America, the Middle East, and Africa, the Property Data Graphs market is witnessing gradual adoption, primarily due to challenges such as limited digital infrastructure, regulatory complexities, and lower awareness levels among end-users. However, localized demand is rising as governments and financial institutions recognize the value of data-driven insights for property valuation, risk management, and urban planning. Policy reforms aimed at improving transparency and digitization in real estate transactions are expected to create new opportunities for market players. The need for tailored solutions that address regional nuances and compliance requirements will be critical for driving adoption and unlocking the full potential of property data graphs in these markets.

Report Scope

We used the open-access Zillow Inc. GetSearchResults API to sample house data for each ZPID in accordance with daily API call limits. For more information on the API see the official documentation page: https://www.zillow.com/howto/api/GetSearchResults.htm. We anonymized the property address and ZPID fields.

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

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.

Explore the expanding Property Management Software Platform market, projected to reach $3.61 billion by 2025 with a robust 6.4% CAGR. Discover key drivers, cloud adoption trends, and regional growth in this essential real estate technology sector.

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 real estate market analysis for Lake Forest, Illinois, covering sales trends, pricing, and market conditions.

Real Estate Assessment Property data. The Department of Finance values properties every year as one step in calculating property tax bills.

This data is provided as a one-off project and there are no plans to update it. The data is collected from the 3 main appraisal districts and users may go to them to obtain land records and appraisal data, or contact HPD staff for assistance. This layer contains land use, zoning, and appraisal data for the purposes of long-range planning and scenario modelling, current to October 2016, but based on a variety of sources with different capture dates. The land use information and parcel geography are based on a land use inventory. It also includes estimates of residential units based on building permit, appraisal data, aerials, and a variety of other sources. An ArcGIS lyr file is also provided to allow users to draw this GIS layer in ArcMap.

🏠 Real Estate Market Dataset

A comprehensive real estate dataset containing basic property information (location, size, price, age) and synthetic market analytics (ROI, demand, volatility, liquidity scores).

Essential Details:

• Dataset size (76,038 records)
• Column count (13 total)
• Data completeness (100%)

Column Explanations:

Property Info: bhk → Bedrooms + halls + kitchens count type → Property category (apartment, villa, etc.) locality → Neighborhood/area name area → Size in square feet price → Property price price_unit → Currency type region → Geographic location status → Construction stage age → How old the property is

Synthetic Market Data: expected_roi(%) → Investment return percentage demand_indicator → Market demand (1-10 scale) market_volatility_score → Risk level (1-10, lower=safer) property_liquidity_index → Ease of selling (1-10, higher=easier)

Note: Market analytics columns are synthetically generated for analysis purposes.