✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Real estate platforms aggregate property listings from multiple MLS systems, brokerage IDX feeds, direct agent submissions, and public records. The same property frequently appears multiple times when it is listed on several MLS systems simultaneously, when it expires and is relisted with a new MLS number, or when both the listing and selling agents enter records. Address formatting inconsistencies -- '123 Main St' vs '123 Main Street, Apt 4B' -- compound the problem. dataclean.to matches property records by address normalization, parcel number, and listing details to produce a clean, non-redundant property database.
The Problem
Duplicate property listings are a persistent problem in real estate data. Home buyers searching on a portal see the same house listed three times from different feeds, creating confusion about whether these are separate units or the same property. Portal operators report inflated listing counts to advertisers, metrics that collapse under audit. Agents receive duplicate leads for the same property, wasting time on follow-ups for homes they have already reviewed. Property analytics that rely on listing counts overestimate inventory in a market, distorting days-on-market calculations and price trend analyses. Expired listings that reappear as new entries make it appear that a property just hit the market when it has been available for months. National Association of Realtors MLS policy
How to Fix It
1
Export property listing data
Pull listings from your MLS feeds, IDX connections, and direct submissions into CSV format. Include property address, city, state, zip, MLS number, listing price, listing date, bedrooms, bathrooms, square footage, and listing agent.
2
Upload to dataclean.to
Upload the CSV. The tool normalizes addresses and compares property details to identify listings that represent the same property from different data sources or listing periods.
3
Review duplicate property clusters
Examine flagged groups. Typical duplicates include the same property from two MLS systems with different MLS numbers, expired listings relisted with new numbers, and properties entered with address variations like 'Street' vs 'St' or missing unit numbers.
4
Consolidate into single property listings
Merge confirmed duplicates into one listing per property. Retain the most current listing status, the accurate asking price, the complete property details from the most detailed source, and all MLS numbers as cross-references.
5
Export the clean listing database
Download the deduplicated data for import into your portal or CRM. Clean listings give buyers an accurate picture of available inventory, provide agents with non-redundant leads, and support reliable market analytics.
Frequently Asked Questions
How does the tool handle properties relisted after expiration?
When a property is relisted with a new MLS number but the same address and similar details, both the expired and active listings are flagged as duplicates. You can merge them to show the full listing history, including original list date and any price changes.
Can it match addresses with different formatting?
Yes. The tool normalizes addresses by expanding abbreviations (St to Street, Apt to Apartment), standardizing directional prefixes, and removing inconsistent punctuation. '123 N Main St #4' and '123 North Main Street, Unit 4' are recognized as the same property.
What about multi-unit properties where individual units are also listed?
Individual unit listings (like condos) at the same street address are kept as separate entries because they have different unit numbers. A duplicate is flagged only when two entries share the same unit number at the same address.
Example: Input → Output
name
email
phone
city
status
Alice Johnson
alice@example.com
+1-555-0101
New York
active
alice johnson
ALICE@EXAMPLE.COM
5550101
new york
Active
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.