Gaylordsville, CT
Gaylordsville, CT's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Gaylordsville lands at the 42nd percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 706 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 Gaylordsville at the 41st percentile, close to its 42nd-percentile risk score.
Where Gaylordsville's buildings actually sit
Of Gaylordsville's 706 counted buildings, 94.5% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
How Gaylordsville compares
Compare Gaylordsville's two percentiles: 87th within Connecticut, only 42nd nationally — a gap of 46 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Gaylordsville ranks 18,364 for wildfire risk (1 is highest) and 14,592 by building count (1 is largest). Within Connecticut alone, it ranks 28 of 214 places by risk. See the full county-by-county picture for Connecticut on its state page.
What this risk score means for insurance
Gaylordsville's elevated rating (42nd percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
Gaylordsville's 94.5% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Gaylordsville's figures come from
The methodology guide shows exactly how USFS turned 706 counted buildings into the percentiles shown above for Gaylordsville. The exposure-zones guide covers what Gaylordsville's dominant direct exposure actually means, with real examples from across the dataset.