Winona, MS
Winona wildfire risk explained
Winona sits at the 69th 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 2,801 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Winona at the 71st national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Winona's buildings actually sit
Of Winona's 2,801 counted buildings, 56% 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.
Where Winona ranks
There's little gap between Winona's 69th national percentile and its 55th percentile inside Mississippi, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Winona ranks 9,644 for wildfire risk (1 is highest) and 5,763 by building count (1 is largest). Within Mississippi alone, it ranks 190 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
Shopping for coverage in Winona
At the 69th percentile nationally, Winona 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
Because Direct exposure dominates in Winona (56%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Winona's figures come from
Every one of the two percentiles behind Winona's 9,644-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Winona's dominant direct exposure actually means, with real examples from across the dataset.