Vina, AL
Vina, AL's wildfire risk, in USFS's own numbers
USFS scores Vina at the 65th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 258 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Vina's burn probability — fire likelihood with no building count factored in — sits at the 67th percentile nationally.
Where Vina's buildings actually sit
77.5% of Vina's 258 buildings sit in USFS's Direct exposure zone, roughly 200 structures close enough to burnable vegetation for flame contact, not just embers — 22.5% fall in the Indirect, ember-only zone and 0% are Minimal.
How Vina compares
Vina's 65th national percentile looks worse in isolation than its 35th ranking inside Alabama does — this place is on the milder end for its own state, by 30 points. Among the 31,521 US communities USFS scores, Vina ranks 11,146 for wildfire risk (1 is highest) and 22,876 by building count (1 is largest). Within Alabama alone, it ranks 388 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
Vina's 65th-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.
What would actually reduce this score
With 77.5% of Vina 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 Vina's figures come from
Vina's 65th-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 Vina's dominant direct exposure actually means, with real examples from across the dataset.