Colton, NY
How exposed is Colton to wildfire?
Colton sits at the 12th percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 245 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Colton at the 12th percentile, close to its 12th-percentile risk score.
What "at risk" means for the buildings here
USFS classifies 93.1% of Colton's buildings as Direct exposure, higher than its 6.9% Indirect share and far above its 0% Minimal share — a profile where 228 structures sit close enough to vegetation that lot clearing matters most.
Colton against the rest of the country
Colton's 30th-percentile standing inside New York outpaces its 12th 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, Colton ranks 27,676 for wildfire risk (1 is highest) and 23,305 by building count (1 is largest). Within New York alone, it ranks 905 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
What this risk score means for insurance
Colton's low wildfire rating (12th 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.
What would actually reduce this score
Because Direct exposure dominates in Colton (93.1%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Colton's figures come from
Every one of the two percentiles behind Colton's 27,676-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Colton's dominant direct exposure actually means, with real examples from across the dataset.