Prentiss, MS
Prentiss, MS's wildfire risk, in USFS's own numbers
Prentiss's 812 buildings earn a 80th-percentile wildfire-risk score nationally under USFS's model — well above the national norm for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Prentiss at the 82nd national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Prentiss's buildings actually sit
Most of Prentiss's buildings (58%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
Prentiss against the rest of the country
Prentiss scores 80th nationally and 80th 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, Prentiss ranks 6,301 for wildfire risk (1 is highest) and 13,537 by building count (1 is largest). Within Mississippi alone, it ranks 84 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
Shopping for coverage in Prentiss
At the 80th percentile nationally, Prentiss carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Prentiss (58% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Prentiss's figures come from
Prentiss's 80th-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 Prentiss's dominant indirect exposure actually means, with real examples from across the dataset.