Snead, AL
Snead wildfire risk explained
USFS scores Snead at the 68th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 798 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 Snead at the 73rd percentile, close to its 68th-percentile risk score.
Snead's building exposure, zone by zone
Of Snead's 798 counted buildings, 69.4% carry Direct exposure and only 3.9% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
How Snead compares
Snead's 68th national percentile looks worse in isolation than its 41st ranking inside Alabama does — this place is on the milder end for its own state, by 27 points. Among the 31,521 US communities USFS scores, Snead ranks 10,231 for wildfire risk (1 is highest) and 13,647 by building count (1 is largest). Within Alabama alone, it ranks 352 of 592 places by risk. See the full county-by-county picture for Alabama on its state page.
Snead and the insurance market
Snead's 68th-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
Snead's 69.4% 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 Snead's figures come from
Snead's 68th-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 Snead's dominant direct exposure actually means, with real examples from across the dataset.