Property data drives almost every real estate decision. Investors, lenders, agents, and proptech builders all need accurate records, valuations, and listings to do their work.
The numbers behind that data are huge. According to Redfin's 2024 market analysis, the combined value of U.S. homes gained $2.5 trillion in 2024 to reach $49.7 trillion in residential housing value. This guide breaks down the top real estate data providers, what they offer, and how to pick the right one.
What Is a Real Estate Data Provider?
A real estate data provider is a company that collects, cleans, and sells property-related data. That data includes property records, ownership details, valuations, listings, and mortgage or foreclosure information.
Providers pull this data from three main places: public county records, listing portals, and their own proprietary databases. They then package it as an API, a bulk file, or a searchable platform you can query.
The value comes from scale and structure. Raw records are messy and spread across thousands of sources, so providers do the heavy lifting of turning them into clean, usable data.
What Kinds of Data Do Real Estate Providers Offer?
Real estate data falls into a few core categories. Each type answers a different question about a property, from who owns it to what it is worth.
Most providers cover several of these categories, but few cover all of them well. Here is what the main data types include.
Property and Ownership Records
Property records are the foundation. They include deeds, parcel boundaries, owner names, contact details, and full sales history.
Most of this data starts as public county records. The scale is enormous: the parcel data aggregator Regrid estimates the U.S. has 150 to 160 million land parcels, assembled from more than 3,200 county records. That figure is a vendor estimate, not an official government count.
Valuations and AVMs
An AVM, or automated valuation model, is a computer model that estimates a property's value. It works by comparing recent sales of similar homes, called comps, along with property traits like size and location.
Valuation quality now carries legal weight. In August 2024, six federal agencies (the CFPB, OCC, Federal Reserve Board, FDIC, NCUA, and FHFA) published a 2024 interagency AVM rule that sets quality-control standards for automated valuation models, effective October 1, 2025. This makes accurate, well-sourced valuation data more important than ever.
Listings, Mortgage, and Foreclosure Data
Listing data shows what is on the market. Much of it comes from the MLS, or Multiple Listing Service, a regional database that agents use to share active listings, prices, and property details.
Providers also track mortgage and lien records, plus pre-foreclosure and foreclosure filings. This data splits into two markets: residential (homes) and commercial (offices, retail, and industrial property).
The Best Real Estate Data Providers in 2026
The providers below are the most-cited names across the market, grouped by what they do best. Some focus on U.S. data, while others offer global coverage, so match the provider to where your properties are.
Comparison at a Glance
| Provider | Best for | Coverage | Delivery | Notable |
|---|---|---|---|---|
| ATTOM | Proptech and lenders | U.S. | API, bulk, cloud | 158M+ properties, 9,000 attributes |
| CoreLogic (Cotality) | Institutions | U.S. | API, bulk | ~5.5B records over 50 years |
| CoStar Group | CRE brokers and investors | U.S. + global | Platform, API | Commercial real estate leader |
| PropStream | Individual investors | U.S. | Platform, self-serve | ~160M properties, lead gen |
| BatchData | Outreach-driven investors | U.S. | API-first | 150M+ properties, 600+ data points |
| Bright Data | Custom and global datasets | Global | API, datasets, scraping | Pulls from listing portals |
| Datarade | Comparing many vendors | Global | Marketplace | 100+ data providers |
| HouseCanary | Institutional valuation | U.S. | API, platform | AVM and forecasting focus |
ATTOM
ATTOM is known for the depth of its U.S. property data. It covers 158 million-plus properties, holds 70 billion rows of data, and tracks around 9,000 data attributes per record.
You can access it through an API, bulk files, or a cloud data warehouse. This flexibility makes ATTOM a strong fit for proptech companies and lenders building data-heavy products.
CoreLogic (Cotality)
CoreLogic, now branded Cotality, is a giant in enterprise property intelligence. It holds roughly 5.5 billion records collected over 50 years, all focused on the U.S. market.
Its strengths are valuations, mortgage data, and insurance risk analysis. CoreLogic is best suited to large institutions like banks and insurers that need deep, standardized data.
CoStar Group
CoStar is the clear leader in commercial real estate data. It specializes in lease comps, sales comps, and detailed market analytics for offices, retail, and industrial space.
Its data feeds serious decisions on large commercial deals. CoStar is best for commercial real estate brokers and investors who live in that market.
PropStream
PropStream focuses on self-serve data for everyday investors. It covers around 160 million properties and adds tools for lead generation and running comps.
The platform is built for individual investors and agents, not enterprise engineering teams. You search and filter properties directly in the app rather than through a raw data feed.
BatchData
BatchData takes an API-first approach to U.S. property and owner data. It covers 150 million-plus properties with more than 600 data points per record.
Its standout feature is contact enrichment and skip tracing, which help you find owner phone numbers and addresses. That makes BatchData a good fit for outreach-driven investors who cold-call or mail leads.
Bright Data, Datarade, and HouseCanary (Marketplaces and Specialists)
These three cover the rest of the market cleanly. Each solves a different sourcing problem.
- Bright Data: Offers global property datasets and tools to scrape listings directly from portals like Zillow, Zoopla, and Redfin.
- Datarade: Runs a marketplace of 100-plus data providers, so you can compare vendors in one place.
- HouseCanary: Specializes in institutional-grade AVMs and valuation forecasting.
Bright Data sits between two worlds: buying packaged data and building your own by scraping. That bridge leads straight into the next question.
How to Choose a Real Estate Data Provider
Picking a provider comes down to a few practical criteria. Weigh each one against your use case and budget.
- Key point: Coverage and geography. Confirm the provider actually covers your target regions and property types, since many are U.S.-only.
- Key point: Freshness. Data ages fast, so check how often records update. Volume backs this up: per NAR's January 2025 release, a total of 4.06 million previously owned homes were sold in 2024, the lowest number since 1995, and every sale changes the underlying records.
- Key point: Data types. Make sure the provider offers the specific fields you need, whether that is ownership, valuations, or foreclosure signals.
- Key point: Delivery method. Decide whether you need a live API, bulk files, or a searchable platform, because this shapes how you use the data.
- Key point: Compliance. Check how the provider handles personal data and privacy laws like GDPR and CCPA.
- Key point: Cost. Compare pricing models, since some charge flat monthly fees and others charge per record or per query.
Buy Packaged Data or Build Your Own? (The Overlooked Option)
Most guides stop at picking a vendor. But there are really two paths: license a packaged dataset from a provider, or collect the data yourself from listing portals with an API.
The right choice depends on how much control you need and how standardized your data must be. Here is a side-by-side look at the tradeoffs.
| Factor | Buy packaged data | Build your own |
|---|---|---|
| Speed to start | Fast, data is ready | Slower, you set up the pipeline |
| Control over fields | Fixed by the vendor | You define the schema |
| Freshness | Vendor's update cycle | You choose when to refresh |
| Coverage | Standardized nationwide | Any portal you target |
| Pricing | Often quote-based | Per-request, transparent |
| Compliance | Handled by vendor | Your responsibility |
When Buying Packaged Data Makes Sense
Buying is the fastest path to usable data. The provider has already cleaned, normalized, and organized nationwide records, and often handles compliance for you.
This works well when you need standardized coverage across the whole country and don't want to build or maintain infrastructure. The tradeoffs are cost and lock-in, since many enterprise providers hide their pricing behind quote-based contracts.
When Building Your Own Dataset Wins
Building your own dataset gives you full control. You choose the exact fields, the update frequency, and the schema, and you pull directly from sources like Zillow, Redfin, Realtor.com, and Zoopla. If you want a starting point, here is how to scrape Zillow listings at scale.
This path needs bot-resistant infrastructure to work reliably. According to Imperva's 2025 Bad Bot Report, automated traffic was 51% of all web traffic in 2024, so listing sites use strong defenses that require JavaScript rendering and residential proxies to get past cleanly.
How a Web Data API Fits In
A web data API turns any listing URL into clean, structured JSON. It handles the JavaScript rendering and proxy rotation for you, and it can gather data across a whole site and scale through batch jobs.
Olostep is one example of this approach. Its web scraping API extracts property fields as structured JSON. You can also crawl an entire portal even when the site has no sitemap.
The scale is built for large jobs: Olostep can process around 100,000 pages in 5 to 7 minutes and roughly 1 million requests in about 15 minutes. As a product proof point, its own real estate market analyzer pulls Zillow and Redfin data to show valuations and trends.
Cost is a key part of the build-vs-buy math. A build-your-own approach uses transparent per-request pricing instead of hidden enterprise quotes, and you only pay for the records you collect. When evaluating a data-collection tool, it helps to compare web scraping APIs on JS rendering, proxies, and batch scale.
Teams then use that extracted data for web scraping for market research, from tracking prices to spotting new listings early.
Frequently Asked Questions
What is the best real estate data provider?
There is no single best provider, because the right one depends on your coverage needs, use case, and budget. ATTOM and CoreLogic lead on depth, PropStream and BatchData suit individual investors, and building your own pipeline wins when you need full control.
How much does real estate data cost?
Self-serve tools run from about $30 to $200 per month, while enterprise contracts are often undisclosed and quote-based. API or per-record pricing can be cheaper when you only need targeted data.
Can I get real estate data for free?
Some public records and listing-portal samples are free to access. But coverage and freshness are limited, so serious use usually requires a paid provider or your own data pipeline.
What data do real estate investors actually use?
Investors rely on ownership and contact info, valuations, comps, distressed or foreclosure signals, and broader market trends. Buyer behavior matters too: per Redfin's 2024 all-cash data, 32.6% of U.S. home purchases were all-cash in 2024, down from 35.1% in 2023.
Is scraping real estate websites legal?
Publicly available data can often be collected, but terms of service, personal data rules, and regional laws still apply. Use compliant tools and consult legal counsel before you build a pipeline.
Conclusion and Getting Started
The best real estate data provider is the one that matches your coverage, use case, and budget. ATTOM and CoreLogic serve institutions, PropStream and BatchData serve investors, and CoStar owns commercial data.
But buying a packaged dataset is not your only option. When you need full control over fields, freshness, and cost, building your own dataset with a web data API is often the smarter path. Start small, extract clean JSON from the portals you care about, and scale up from there.
