Eva, AL
Eva wildfire risk explained
USFS scores Eva at the 70th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 472 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Eva at the 74th national percentile — 4 points above its risk-to-structures score, a gap driven by how much is actually built there.
Eva's building exposure, zone by zone
Direct exposure dominates in Eva: 91.5% of its 472 buildings, versus 8.5% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Where Eva ranks
Inside Alabama, Eva sits at just the 46th percentile even though it scores 70th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Eva ranks 9,505 for wildfire risk (1 is highest) and 17,834 by building count (1 is largest). Within Alabama alone, it ranks 321 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
Eva's 70th-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
Eva's 91.5% 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 Eva's figures come from
Every one of the two percentiles behind Eva's 9,505-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Eva's dominant direct exposure actually means, with real examples from across the dataset.