Sumiton, AL
Sumiton, AL's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Sumiton lands at the 76th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 1,728 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 Sumiton at the 79th percentile, close to its 76th-percentile risk score.
What "at risk" means for the buildings here
Direct exposure dominates in Sumiton: 64.5% of its 1,728 buildings, versus 35.5% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Where Sumiton ranks
Sumiton scores 76th nationally and 62nd within Alabama — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Sumiton ranks 7,615 for wildfire risk (1 is highest) and 8,310 by building count (1 is largest). Within Alabama alone, it ranks 226 of 592 places by risk. See the full county-by-county picture for Alabama on its state page.
What this risk score means for insurance
At the 76th percentile nationally, Sumiton 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.
Lowering exposure, not just insuring around it
With 64.5% of Sumiton in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Sumiton's figures come from
Sumiton's 76th-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 Sumiton's dominant direct exposure actually means, with real examples from across the dataset.