Data Cleaning for Real Estate MLS Exports, CRM Contact Databases, Transaction Management Systems, Agent Rosters and Property Management Platforms
By Email ExtractorPublished 7 min read
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Why Real Estate Data Gets Dirty
Real estate professionals work across multiple systems that rarely sync cleanly: the MLS (Multiple Listing Service), one or more CRMs, transaction management systems, marketing platforms, lead generation sources, property management software and accounting systems. Each system has its own contact records, property records and transaction records, creating duplication, inconsistency and data decay:
Agent information changes when agents switch brokerages; sold data may not reflect actual closing price or terms; property data (square footage, lot size, room count) varies by who entered it; MLS-to-MLS data feeds for multi-board agents introduce duplicates
CRM (Follow Up Boss, kvCORE, Chime, BoomTown, LionDesk, Wise Agent)
Contacts: leads, clients, past clients, sphere of influence, agents
Duplicate contacts from multiple lead sources (Zillow, Realtor.com, website, open house sign-in, referral); incomplete records (lead with phone but no email, or email but no name); stale contacts (moved, changed phone/email, deceased)
Party names may not match CRM records; email addresses entered by transaction coordinators may differ from CRM; duplicate transactions from amendments or extensions
Lead generation platforms (Zillow Premier Agent, Realtor.com, BoldLeads, Ylopo)
Tenant records accumulate (former tenants not archived); owner contact info changes; property addresses have formatting inconsistencies; vendor records duplicate with accounting system
Contacts imported from CRM but not synced back; unsubscribes in marketing platform not reflected in CRM; segmentation based on stale data
Cleaning Workflows
CRM contact deduplication
Step
Action
Details
1. Export all contacts
Export from CRM: first name, last name, email, phone, address, source, tags, last activity date, pipeline stage
Full contact export; include all fields that help identify duplicates
2. Identify duplicates by email
Upload contact email export (CSV) to Email Extractor to identify duplicate email addresses across your database
Exact email match = definite duplicate; same person, multiple records
3. Identify duplicates by phone
Sort by phone number; identify records with same phone but different emails or names
Same phone, different email = likely same person with multiple email addresses; merge records
4. Identify duplicates by name + address
Sort by last name + ZIP code; scan for name variations (Robert/Bob, Elizabeth/Liz, Katherine/Kate/Katie) at same address
Fuzzy name matching catches duplicates that email and phone matching miss
5. Merge duplicates
For each duplicate set: designate the primary record (most complete, most recent activity); merge contact information, tags, notes and activity history from secondary records into primary; delete secondary records
Unified contact record with complete history
6. Verify surviving records
Run merged email list through email verification service; identify invalid addresses; update or remove
Clean, deduplicated, verified contact database
MLS data export cleaning
Data field
Common problem
Cleaning approach
Property address
Inconsistent formatting (123 Main St vs 123 Main Street vs 123 Main St.); unit/suite numbers in wrong field; directional prefixes/suffixes (N, S, E, W) inconsistent
Standardise to USPS format; split unit numbers to separate field; consistent directional formatting
Agent name
Name changes (marriage, divorce); nickname vs. legal name; suffixes (Jr., III) inconsistently included
Standardise to legal name as registered with state real estate commission; store display name separately
Agent email
Changes when agent switches brokerage; personal email vs. brokerage email used inconsistently
Track both personal and brokerage email; update brokerage email when agent moves; flag records with bounced emails
Price fields
List price, sale price, original list price may not reflect actual transaction; concessions not always captured; price changes not always reflected
Verify sale price against public records (county recorder); note concessions; track price history
Status
Active, pending, sold, withdrawn, expired, cancelled -- timing of status changes varies by MLS; back-on-market may or may not create new record
Standardise status definitions; track status change dates; link back-on-market to original listing
Square footage
Source varies (tax records, builder plans, agent measurement, appraiser); above-grade vs. total discrepancy
Note source of square footage; flag discrepancies exceeding 10% between sources
Room count
Bedrooms and bathrooms counted differently (half bath vs. three-quarter bath; basement bedroom vs. conforming bedroom)
Use MLS definitions; note non-conforming rooms separately
Agent roster maintenance (for brokerages)
Action
Frequency
Method
Purpose
Verify agent licence status
Monthly
Cross-reference roster against state real estate commission database; flag expired, inactive or suspended licences
Compliance (agents must have active licence to practice); advertising compliance (only active agents in marketing)
Update agent contact information
Quarterly
Request email and phone verification from each agent; or send a verification email that agents confirm
Current contact info for company communications, lead routing, compliance notifications
Reconcile MLS membership
Monthly
Compare brokerage roster with MLS membership records; identify agents on roster but not in MLS (or vice versa)
MLS membership is required to access MLS; agents on roster but not in MLS cannot list or search
Clean departed agent records
Within 30 days of departure
Transfer listings to new agent; update CRM records (reassign leads and clients); remove from marketing; notify clients of new point of contact; deactivate email and system access
Compliance; client continuity; prevent departed agents from accessing client data
When compiling agent contact data from state real estate commission databases (HTML, CSV), MLS rosters (CSV), national association directories (HTML) and brokerage websites (HTML), upload all files to Email Extractor to extract and deduplicate email addresses. Agents hold licences in multiple states, belong to multiple MLS systems and appear on brokerage websites, creating significant duplication.
Lead Source Reconciliation
Lead source
Data quality issue
Cleaning approach
Zillow Premier Agent
Leads may include consumers who inquired on properties outside your service area; duplicate leads (same consumer, multiple properties); some leads use temporary phone numbers
Filter by geography; deduplicate by phone number; verify email before adding to CRM long-term nurture
Realtor.com
Similar to Zillow; additional issue of leads intended for listing agent being routed to buyer's agent (or vice versa)
Verify lead intent (buyer vs. seller); deduplicate against existing CRM contacts
Open house sign-in (digital or paper)
Handwritten names are often illegible; phone numbers may be incomplete or fake; email addresses have high typo rate
Digital sign-in (Spacio, Curb Hero, Open Home Pro) reduces errors; verify emails within 24 hours; follow up by phone to confirm information
Website registration
Bot submissions; competitors scouting; tyre-kickers using fake info
CAPTCHA; email verification on registration; score by engagement (property views, saved searches) before routing to agent
Social media ads (Facebook, Instagram)
Pre-filled forms sometimes pull outdated contact info from social profile; lead intent may be weak (clicked ad but not serious)
Verify email and phone; score by engagement before routing; expect lower conversion rate vs. portal leads
Referral (agent-to-agent or client referral)
Generally highest quality; but handoff may lose information; referral source expects updates
Verify contact info during handoff; track referral source for commission; send referral source updates per agreement
Metrics
Metric
Problem threshold
Healthy threshold
How to measure
CRM duplicate rate
Over 15%
Under 5%
Run deduplication; count duplicates / total records
Lead source bounce rate (email)
Over 10%
Under 3%
Bounced emails / total emails sent per lead source
Agent roster accuracy
Over 5% of agents with outdated contact info
Under 2%
Bounced agent communications / total agent communications
MLS data match rate (to public records)
Under 85%
Over 95%
Sale price in MLS matches county recorder / total records checked
Contact completeness (email)
Under 60% of contacts have email
Over 80% of contacts have email
Contacts with email / total contacts
Contact completeness (phone)
Under 70% of contacts have phone
Over 85% of contacts have phone
Contacts with phone / total contacts
Lead-to-CRM deduplication rate
Over 20% of new leads are duplicates of existing contacts
Under 10%
Duplicate leads identified at import / total leads imported
