Gantt, AL
Gantt, AL's wildfire risk, in USFS's own numbers
Gantt sits at the 67th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 187 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 Gantt at the 69th percentile, close to its 67th-percentile risk score.
What "at risk" means for the buildings here
USFS classifies 78.6% of Gantt's buildings as Direct exposure, higher than its 20.9% Indirect share and far above its 0.5% Minimal share — a profile where 147 structures sit close enough to vegetation that lot clearing matters most.
How Gantt compares
Gantt's 67th national percentile looks worse in isolation than its 39th ranking inside Alabama does — this place is on the milder end for its own state, by 28 points. Among the 31,521 US communities USFS scores, Gantt ranks 10,482 for wildfire risk (1 is highest) and 25,340 by building count (1 is largest). Within Alabama alone, it ranks 362 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
Gantt's 67th-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Gantt (78.6%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Gantt's figures come from
Gantt's 67th-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 Gantt's dominant direct exposure actually means, with real examples from across the dataset.