📋 Multiple Listing Service Standards

MLS Data Explained: RETS vs RESO Web API & Scraping

An architectural breakdown of MLS data feeds, IDX standards, RETS sunsetting, RESO Web APIs, and custom web scraping extraction methods.

Published: August 2026 • 8 Min Read • By WebScrapingHub Engineering

What is MLS Data?

An MLS (Multiple Listing Service) is a centralized suite of private databases established by real estate brokers to share property listing details and co-broke commission agreements. Over 600 regional MLS boards operate across North America, powering over 90% of residential real estate transactions.

Evolution of MLS Data Protocols

1. RETS (Real Estate Transaction Standard)

RETS is an XML-based legacy HTTP protocol launched in 1999 for querying real estate databases. While RETS has been officially sunset by RESO (Real Estate Standards Organization), many legacy MLS portals still maintain RETS authentication endpoints.

2. RESO Web API (RESTful Open Data)

RESO Web API is the modern web standard, delivering normalized JSON data via standardized OData / RESTful web endpoints. It defines uniform field names (e.g. `ListPrice`, `BedroomsTotal`, `StandardStatus`).

3. IDX (Internet Data Exchange) & VOW Feeds

IDX feeds allow participating brokers to display each other's active listings on public agent websites, subject to local MLS compliance rules.

RETS vs RESO Web API vs Managed Web Scraping

Feature RETS (Legacy) RESO Web API Managed MLS Scraper
Format XML / COMPACT-DECODED JSON / OData JSON / CSV / SQL
Licensing Barrier Requires MLS Brokerage Approval Requires MLS Brokerage Approval No Broker License Lock Required
Setup Speed Weeks/Months Weeks 24-48 Hours On-Demand

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WebScrapingHub extracts structured MLS property listings and agent contacts directly into custom database feeds.

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Key MLS Data Fields & Property Attributes

Understanding the field structure of MLS data is essential before building any scraping or API integration. Under the RESO Data Dictionary standard, MLS records contain standardized fields across four core categories:

📋 Listing Identification

  • ListingKey (Unique MLS ID)
  • ListingId (Human-readable MLS#)
  • StandardStatus (Active, Pending, Closed)
  • ListingContractDate & ExpirationDate

💵 Financial Fields

  • ListPrice & OriginalListPrice
  • ClosePrice (Sold Comps)
  • AssociationFee (HOA)
  • TaxAnnualAmount & TaxYear

🏠 Physical Specifications

  • BedroomsTotal & BathroomsTotalInteger
  • LivingArea (SqFt)
  • LotSizeSquareFeet
  • YearBuilt & PropertyType

👤 Agent & Office Data

  • ListAgentFullName & ListAgentEmail
  • ListAgentDirectPhone
  • ListOfficeName & ListOfficeKey
  • CoListAgentKey (Co-listing Agent)

How Managed MLS Scraping Works Without a Broker License

Traditional MLS data access requires membership in a licensed real estate brokerage, NAR membership, and local MLS board agreements — a process that takes months and costs thousands of dollars annually. Managed MLS web scraping bypasses this barrier entirely by extracting data from consumer-facing portals like Zillow, Realtor.com, Redfin, and Homes.com, which syndicate MLS data via IDX under their own brokerage licenses.

WebScrapingHub's data pipeline processes MLS information through five structured stages:

  1. Target Configuration: Define geography (cities, ZIP codes, counties), property types, and refresh frequency.
  2. Anti-Bot Bypass Layer: Residential IP rotation, browser fingerprint spoofing, and CAPTCHA-solving middleware ensure uninterrupted access.
  3. Structured Parsing: Raw HTML, JSON API responses, and GraphQL payloads are normalized into a consistent RESO-aligned field schema.
  4. Deduplication & Validation: Cross-portal deduplication removes redundant listings. Addresses are standardized against USPS postal records.
  5. Delivery: Clean data is pushed to your destination — CSV, database, API, or cloud storage — on your defined schedule.

MLS Data Use Cases

🤖 Automated Valuation Models

PropTech companies and lenders feed MLS comps data — ClosePrice, DaysOnMarket, neighborhood price-per-sqft — into regression and machine learning valuation models.

📧 Realtor Lead Generation

Marketing firms extract listing agent names, emails, and direct phone numbers from MLS-syndicated portals to build B2B outreach campaigns for mortgage, title, and CRM services.

📊 Market Research & Reports

Research analysts compile median list price trends, inventory velocity, and days-on-market benchmarks across target metro areas for quarterly market intelligence reports.

🏘️ Investment Acquisition Screening

Fix-and-flip investors and iBuyers use MLS data to identify undervalued properties by cross-referencing OriginalListPrice with ClosePrice deviations and cumulative days on market.

Frequently Asked Questions: MLS Data

What is the difference between MLS data and public property records?

MLS data contains agent-submitted listing details (photos, descriptions, agent contacts) updated in near-real-time. Public property records (county assessor data) contain legal ownership, tax assessments, and deed transfers — typically updated quarterly or annually.

Can I access MLS data without a real estate license?

Yes. Managed web scraping services like WebScrapingHub extract publicly visible MLS data from IDX-syndicated consumer portals (Zillow, Realtor.com, Redfin) without requiring brokerage membership or MLS board agreements.

How often is MLS listing data updated?

MLS portals refresh listing statuses in near-real-time as agents update records. Our scraping pipelines can run daily, every 6 hours, or hourly to capture new listings, price changes, and status updates as they occur.

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