Reeltown, AL
How exposed is Reeltown to wildfire?
USFS scores Reeltown at the 79th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 581 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Reeltown at the 82nd percentile, close to its 79th-percentile risk score.
What "at risk" means for the buildings here
98.1% of Reeltown's 581 buildings sit in USFS's Direct exposure zone, roughly 570 structures close enough to burnable vegetation for flame contact, not just embers — 1.9% fall in the Indirect, ember-only zone and 0% are Minimal.
How Reeltown compares
There's little gap between Reeltown's 79th national percentile and its 73rd percentile inside Alabama, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Reeltown ranks 6,542 for wildfire risk (1 is highest) and 16,088 by building count (1 is largest). Within Alabama alone, it ranks 162 of 592 places by risk. See the full county-by-county picture for Alabama on its state page.
Reeltown and the insurance market
Reeltown's 79th-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.
Hardening a home in Reeltown
Because Direct exposure dominates in Reeltown (98.1%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Reeltown's figures come from
Reeltown's 79th-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 Reeltown's dominant direct exposure actually means, with real examples from across the dataset.