Durant, MS
Durant wildfire risk explained
Durant sits at the 74th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 1,267 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Durant at the 77th percentile, close to its 74th-percentile risk score.
Where Durant's buildings actually sit
1,267 buildings are counted in Durant, and 59.8% of them are Indirect exposure — ember-driven risk rather than the 40.2% in Direct exposure or the 0% rated Minimal.
How Durant compares
There's little gap between Durant's 74th national percentile and its 70th percentile inside Mississippi, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Durant ranks 8,099 for wildfire risk (1 is highest) and 10,263 by building count (1 is largest). Within Mississippi alone, it ranks 127 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
Durant and the insurance market
Durant's 74th-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
With ember exposure the dominant pattern in Durant (59.8% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Durant's figures come from
Every one of the two percentiles behind Durant's 8,099-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Durant's dominant indirect exposure actually means, with real examples from across the dataset.