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Mountain High HOA – Bend Oregon – Exclusive neighborhood in Bend — 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 →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
shadowmountaintulsa.com ↗	72 match 1 shared topics	Shadow Mountain HOA – A Neighborhood in Tulsa, OK	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
oldbend.org ↗	69 match 1 shared topics	Old Bend Neighborhood Association – At the heart of Bend, Oregon	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
oldbendneighborhood.org ↗	69 match 1 shared topics	Old Bend Neighborhood Bend OR – Old Bend, Bend Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
cedarbendneighborhood.org ↗	68 match 1 shared topics	Cedar Bend Neighborhood Morrisville NC – Cedar Bend, Morrisville Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
houndearsneighborhood.org ↗	68 match 1 shared topics	Hound Ears Neighborhood Boone NC – Hound Ears, Boone Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
meadowbrookbrighton.org ↗	68 match 1 shared topics	Meadowbrook – a great neighborhood in Brighton	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
milliehillneighborhood.org ↗	68 match 1 shared topics	Millie Hill Neighborhood Iron Mountain MI – Millie Hill, Iron Mountain Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
carthaysquare.org ↗	68 match 1 shared topics	Carthay Square Neighborhood Association	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
askvesta.com ↗	67 match 1 shared topics	Vesta AI - Neighborhood Insight	en	real-estate	robotsllmsaihumans	emailphone	none
hsbriverbend.com ↗	67 match 1 shared topics	River Bend HOA – Horseshoe Bend, ID	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
millersbayneighborhood.org ↗	67 match 1 shared topics	MILLERS BAY NEIGHBORHOOD - Home	en	real-estate Weebly	robotsllmsaihumans	emailphone	none
oakviewneighborhood.org ↗	67 match 1 shared topics	Oakview Neighborhood High Point NC – Oakview, High Point Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
oldhighland.org ↗	67 match 1 shared topics	Old Highland Neighborhood Association – A Small Town in a Big City	en	real-estate WordPress WooCommerce	robotsllmsaihumans	emailphone	none
carringtonneighborhood.org ↗	67 match 1 shared topics	Carrington Neighborhood Sanford NC – Carrington, Sanford Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
carolinatraceneighborhood.org ↗	67 match 1 shared topics	Carolina Trace Neighborhood Sanford NC – Carolina Trace, Sanford Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
oldrichmondneighborhood.org ↗	67 match 1 shared topics	Old Richmond Neighborhood Richmond IN – Old Richmond, Richmond Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
oldnorthsideneighborhood.org ↗	67 match 1 shared topics	Old Northside Neighborhood Indianapolis IN – Old Northside, Indianapolis Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none
huntneighborhood.org ↗	67 match 1 shared topics	Hunt Neighborhood Mount Vernon OH – Hunt, Mount Vernon Neighborhood	en	real-estate WordPress	robotsllmsaihumans	emailphone	none

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:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

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.