DeLisle, MS
DeLisle wildfire risk explained
USFS's Wildfire Risk to Communities model puts DeLisle at the 96th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 1,020 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts DeLisle at the 96th percentile, close to its 96th-percentile risk score.
DeLisle's building exposure, zone by zone
Of DeLisle's 1,020 counted buildings, 96.4% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
DeLisle against the rest of the country
DeLisle scores 96th nationally and 100th within Mississippi — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, DeLisle ranks 1,259 for wildfire risk (1 is highest) and 11,799 by building count (1 is largest). Within Mississippi alone, it ranks 3 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
Shopping for coverage in DeLisle
DeLisle's 96th-percentile, very 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
DeLisle's 96.4% 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 DeLisle's figures come from
The methodology guide shows exactly how USFS turned 1,020 counted buildings into the percentiles shown above for DeLisle. The exposure-zones guide covers what DeLisle's dominant direct exposure actually means, with real examples from across the dataset.