Onawa, IA
How exposed is Onawa to wildfire?
Onawa sits at the 64th 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 1,934 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Onawa's burn probability — fire likelihood with no building count factored in — sits at the 61st percentile nationally.
Onawa's building exposure, zone by zone
Onawa rates 90.9% Minimal exposure against just 9.1% Direct and 0% Indirect — of 1,934 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
How Onawa compares
Compare Onawa's two percentiles: 88th within Iowa, only 64th nationally — a gap of 24 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Onawa ranks 11,244 for wildfire risk (1 is highest) and 7,667 by building count (1 is largest). Within Iowa alone, it ranks 120 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.
Shopping for coverage in Onawa
Onawa's 64th-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
Even with 90.9% of Onawa outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Onawa's figures come from
Every one of the two percentiles behind Onawa's 11,244-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Onawa's dominant minimal exposure actually means, with real examples from across the dataset.