Niangua, MO
Niangua, MO's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Niangua at the 75th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 282 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Niangua at the 78th percentile, close to its 75th-percentile risk score.
What "at risk" means for the buildings here
282 buildings are counted in Niangua, and 54.3% of them are Indirect exposure — ember-driven risk rather than the 45.7% in Direct exposure or the 0% rated Minimal.
Where Niangua ranks
Within Missouri, Niangua ranks higher (92nd percentile) than it does nationally (75th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Niangua ranks 7,758 for wildfire risk (1 is highest) and 22,122 by building count (1 is largest). Within Missouri alone, it ranks 90 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.
Niangua and the insurance market
Niangua's 75th-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.
Hardening a home in Niangua
Because 54.3% of Niangua'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 Niangua's figures come from
Every one of the two percentiles behind Niangua's 7,758-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Niangua's dominant indirect exposure actually means, with real examples from across the dataset.