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60match
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1 co-citations5 shared tech2 shared categoryreal-estate-buying-and-selling
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52match
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didicinc.com
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52match
gentlykept.com
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0 co-citations4 shared tech2 shared categoryreal-estate-buying-and-selling
51match
gennawalsh.com
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1 co-citations3 shared tech2 shared categoryreal-estate-buying-and-selling
50match
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48match
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0 co-citations3 shared tech2 shared categoryreal-estate-buying-and-selling
48match
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0 co-citations3 shared tech2 shared categoryreal-estate
48match
ivanovichhomes.com
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0 co-citations3 shared tech2 shared categoryreal-estate
48match
hanafinfarm.com
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0 co-citations3 shared tech2 shared categoryreal-estate
48match
greenmarkproperties.com
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0 co-citations3 shared tech2 shared categoryreal-estate-buying-and-selling
48match
bagnallproperties.com
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48match
bowerscalifornia.com
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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.