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46match
elevatedco-living.com
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0 co-citations5 shared tech1 shared categoryhouses
40match
elena-ivanova.com
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0 co-citations2 shared tech2 shared categoryhouses
40match
jfkdevelopment.com
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0 co-citations6 shared tech1 shared categoryhouses
32match
advantagepropertyads.com
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0 co-citations4 shared tech0 shared categoryreal-estate
32match
georgialuxe.com
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0 co-citations4 shared tech0 shared categoryreal-estate-buying-and-selling
32match
maison-montserrat.com
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0 co-citations2 shared tech1 shared categoryhouses
32match
3clovergroup.com
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0 co-citations2 shared tech1 shared categoryhouses
32match
bannahomes.com
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0 co-citations2 shared tech1 shared categoryhouses
32match
copyaliving.com
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0 co-citations2 shared tech1 shared categoryhouses
32match
costaricacoffeefarm.com
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0 co-citations2 shared tech1 shared categoryland-and-farms
32match
diehofe.com
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0 co-citations2 shared tech1 shared categoryhouses
31match
hoffmanrealestateok.com
0 co-citations5 shared tech1 shared categoryhouses
31match
greenwoodsyndicate.com
Greenwood Syndicate
0 co-citations5 shared tech1 shared categoryhouses
31match
bakerlands.com
0 co-citations5 shared tech1 shared categoryreal-estate-buying-and-selling
31match
kanana-jakarta.com
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0 co-citations2 shared tech1 shared categorydevelopmental-sites
31match
hayanaif.com
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0 co-citations2 shared tech1 shared categoryhouses
31match
aerosailinterglobe.com
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0 co-citations2 shared tech1 shared categoryhouses
31match
24homeoffer.com
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0 co-citations2 shared tech1 shared categoryhouses

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.