Gaylesville, AL
Gaylesville wildfire risk explained
USFS scores Gaylesville at the 84th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 180 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Gaylesville at the 86th national percentile — 3 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 86.1% of Gaylesville's buildings as Direct exposure, higher than its 13.9% Indirect share and far above its 0% Minimal share — a profile where 155 structures sit close enough to vegetation that lot clearing matters most.
Gaylesville against the rest of the country
Gaylesville's risk sits at a similar level relative to Alabama (85th percentile statewide) as it does nationally (84th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Gaylesville ranks 5,188 for wildfire risk (1 is highest) and 25,607 by building count (1 is largest). Within Alabama alone, it ranks 89 of 592 places by risk. See the full county-by-county picture for Alabama on its state page.
Gaylesville and the insurance market
Gaylesville's very high rating (84th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Gaylesville
Because Direct exposure dominates in Gaylesville (86.1%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Gaylesville's figures come from
Every one of the two percentiles behind Gaylesville's 5,188-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Gaylesville's dominant direct exposure actually means, with real examples from across the dataset.