Potosi, MO
Potosi wildfire risk explained
USFS's Wildfire Risk to Communities model puts Potosi at the 67th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 1,343 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Potosi at the 68th national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
Potosi's building exposure, zone by zone
Indirect exposure is dominant in Potosi (82.2% of 1,343 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 17.8% sit in the Direct zone.
Potosi against the rest of the country
Potosi's risk sits at a similar level relative to Missouri (74th percentile statewide) as it does nationally (67th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Potosi ranks 10,431 for wildfire risk (1 is highest) and 9,863 by building count (1 is largest). Within Missouri alone, it ranks 274 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
Potosi's 67th-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
Because 82.2% of Potosi's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Potosi's figures come from
Potosi's 67th-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 Potosi's dominant indirect exposure actually means, with real examples from across the dataset.