Kellogg Point, CT
Kellogg Point wildfire risk explained
Out of every US place USFS scores, Kellogg Point lands at the 35th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 26 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Kellogg Point at the 34th percentile, close to its 35th-percentile risk score.
What "at risk" means for the buildings here
26 buildings are counted in Kellogg Point, and 100% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
How Kellogg Point compares
Kellogg Point's 63rd-percentile standing inside Connecticut outpaces its 35th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Kellogg Point ranks 20,349 for wildfire risk (1 is highest) and 31,400 by building count (1 is largest). Within Connecticut alone, it ranks 80 of 214 places by risk. See the full county-by-county picture for Connecticut on its state page.
Kellogg Point and the insurance market
Kellogg Point's moderate wildfire rating (35th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Hardening a home in Kellogg Point
Because Direct exposure dominates in Kellogg Point (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Kellogg Point's figures come from
Kellogg Point's 35th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Kellogg Point's dominant direct exposure actually means, with real examples from across the dataset.