Remove Duplicates from Commercial Real Estate Listings
✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Commercial real estate data becomes riddled with duplicates when the same property is listed by multiple brokerages, appears in different MLS systems, and is tracked in internal deal management tools simultaneously. A 50,000 sq ft office building might appear as three separate listings because the landlord's broker, a tenant rep broker, and a sublease listing each created their own entry with slightly different square footage figures and asking rents. dataclean.to matches CRE records by normalizing addresses, comparing property attributes, and clustering listings that refer to the same physical space.
The Problem
Duplicate property listings in commercial real estate distort market analysis and waste broker time. Market reports that double-count available space overstate vacancy rates, misleading investors and developers. Brokers chasing what appear to be different listings may end up touring the same property twice. Tenant databases with duplicate contact records split leasing activity across profiles, making it impossible to see a tenant's full occupancy history. When the same property appears at different asking rents, it creates confusion about true market pricing and can undermine negotiation positions. RESO Data Dictionary for real estate data standards
How to Fix It
1
Compile property data from all sources
Gather listings from your CRE platform, MLS feeds, brokerage databases, and internal tracking spreadsheets. Include property address, suite or unit number, square footage, asking rent, property type, and listing broker.
2
Upload to dataclean.to
Upload the CSV. The tool standardizes addresses, normalizes suite and floor designations, and compares property attributes to identify listings that describe the same space.
3
Review duplicate property clusters
Examine groups of listings flagged as the same property. The tool highlights discrepancies such as different square footage measurements, conflicting asking rents, and varying property type classifications between sources.
4
Merge into authoritative property records
Consolidate confirmed duplicates into single listings. Select the most accurate square footage, retain the current asking rent, and preserve listing broker attribution from each source.
5
Export the clean property database
Download the deduplicated CSV and update your CRE platform. Market analysis, vacancy reports, and tenant prospecting now work from a dataset where each available space appears exactly once.
Frequently Asked Questions
How does the tool handle different suites in the same building?
Suites at the same address but with different unit numbers are treated as separate spaces. The tool only flags them as duplicates if the suite designations are variations of each other, such as 'Suite 200' and 'Ste 200' and 'Floor 2'.
What about properties listed for both lease and sale simultaneously?
The tool identifies that both listings refer to the same property and flags them for review. You can decide whether to merge them into one record with both options noted or keep them as separate listing types.
Can it reconcile different square footage figures from different sources?
The tool surfaces conflicting measurements so you can choose the most authoritative figure. It does not average or estimate square footage but flags the discrepancy for your manual resolution.
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.