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KeyHomeSale - Desiree Carroll Team | Desiree Carroll | Keller Williams Real Estate — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Real Estate Buying And Selling →

DomainMatchTitleCountry/LangCategoryAI filesContactAI-protection
nancymatt.com 84 match
2 shared topics
Nancy Matt Team | Keller Williams Real Estate United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
thebansalteam.com 82 match
2 shared topics
The Bansal Team | Keller Williams Realty United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
doubleboldt.net 81 match
2 shared topics
Double Boldt Real Estate | Keller Williams Realty en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
deissteam.com 81 match
2 shared topics
Deiss Team Real Estate | Ryan Deiss​ | Keller Williams Revolution United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
delcohomesforsale.com 81 match
2 shared topics
Krystyna Latsios | Keller Williams Real Estate -Media Pa en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
the808team.com 81 match
2 shared topics
The 808 Team | Keller Williams Realty Maui | PLACE en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
thejaywhiteteam.com 81 match
2 shared topics
Jay White Team | Keller Williams Realty United States~ en real-estate robotsllmsaihumans emailphone none
thedustinweaverteam.com 81 match
2 shared topics
The Dustin Weaver Team | Keller Williams Realty United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
montgomeryht.com 80 match
2 shared topics
Montgomery Home Team | Keller Williams Realty | PLACE United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
debonishomes.com 80 match
2 shared topics
DeBonis Real Estate Team — Keller Williams Redlands United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
thedaleyteam.com 80 match
2 shared topics
The Daley Team | Ryan Daley | Keller Williams Realty Lancaster United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
dematteiteam.com 80 match
2 shared topics
DeMattei Real Estate Team | Chris DeMattei | Keller Williams Realty Roseville | PLACE United States~ en real-estate robotsllmsaihumans emailphone partial · 10
gileshometeamaz.com 80 match
2 shared topics
Giles Home Team | Keller Williams Integrity First Realty United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
thebrittsteam.com 80 match
2 shared topics
The Britts Team | Keller Williams Realty of Delmarva | PLACE United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
giffordteam.com 80 match
2 shared topics
The Gifford Team | Denise Gifford | Keller Williams Advisors United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
myhomesearchfl.com 80 match
2 shared topics
DB Team | Donnie Brookman | Keller Williams Advantage III Realty United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
thebradleyrealestategroup.com 80 match
2 shared topics
The Bradley Real Estate Group | Jasmin Bradley | Keller Williams Realty United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10
thebarrottteam.com 79 match
2 shared topics
Sarah Barrott Team | Keller Williams | PLACE United States~ en real-estate-buying-and-selling robotsllmsaihumans emailphone partial · 10

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.