Crossnore, NC
How exposed is Crossnore to wildfire?
USFS's Wildfire Risk to Communities model puts Crossnore at the 73rd national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 147 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Crossnore at the 75th national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS classifies 76.9% of Crossnore's buildings as Direct exposure, higher than its 23.1% Indirect share and far above its 0% Minimal share — a profile where 113 structures sit close enough to vegetation that lot clearing matters most.
Where Crossnore ranks
Crossnore scores 73rd nationally and 74th within North Carolina — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Crossnore ranks 8,574 for wildfire risk (1 is highest) and 26,998 by building count (1 is largest). Within North Carolina alone, it ranks 202 of 772 places by risk. See the full county-by-county picture for North Carolina on its state page.
Crossnore and the insurance market
At the 73rd percentile nationally, Crossnore carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
What would actually reduce this score
Crossnore's 76.9% 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 Crossnore's figures come from
The methodology guide shows exactly how USFS turned 147 counted buildings into the percentiles shown above for Crossnore. The exposure-zones guide covers what Crossnore's dominant direct exposure actually means, with real examples from across the dataset.