Winona, MO
Winona wildfire risk explained
Winona's 811 buildings earn a 77th-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 Winona at the 80th national percentile — 3 points above its risk-to-structures score, a gap driven by how much is actually built there.
Winona's building exposure, zone by zone
Direct exposure dominates in Winona: 71.8% of its 811 buildings, versus 28.2% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
How Winona compares
Winona's 94th-percentile standing inside Missouri outpaces its 77th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Winona ranks 7,183 for wildfire risk (1 is highest) and 13,545 by building count (1 is largest). Within Missouri alone, it ranks 67 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.
What this risk score means for insurance
Winona's 77th-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 71.8% of Winona in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Winona's figures come from
The methodology guide shows exactly how USFS turned 811 counted buildings into the percentiles shown above for Winona. The exposure-zones guide covers what Winona's dominant direct exposure actually means, with real examples from across the dataset.