Quitman, MS
Quitman wildfire risk explained
Quitman sits at the 59th percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 1,410 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 Quitman at the 62nd percentile, close to its 59th-percentile risk score.
What "at risk" means for the buildings here
50.5% of Quitman's 1,410 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 49.4% Direct and 0.1% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Quitman against the rest of the country
Inside Mississippi, Quitman sits at just the 33rd percentile even though it scores 59th 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, Quitman ranks 12,800 for wildfire risk (1 is highest) and 9,588 by building count (1 is largest). Within Mississippi alone, it ranks 281 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
What this risk score means for insurance
At the 59th national percentile, Quitman rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Quitman (50.5% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Quitman's figures come from
Every one of the two percentiles behind Quitman's 12,800-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Quitman's dominant indirect exposure actually means, with real examples from across the dataset.