Decorah, IA
Decorah wildfire risk explained
USFS's Wildfire Risk to Communities model puts Decorah at the 39th national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 3,221 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 Decorah at the 41st percentile, close to its 39th-percentile risk score.
What "at risk" means for the buildings here
USFS classifies 67.5% of Decorah's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 11.7% Direct and 20.8% Indirect — a landscape-level risk rather than a building-by-building one.
How Decorah compares
Within Iowa, Decorah ranks higher (54th percentile) than it does nationally (39th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Decorah ranks 19,374 for wildfire risk (1 is highest) and 5,143 by building count (1 is largest). Within Iowa alone, it ranks 472 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.
What this risk score means for insurance
Decorah's moderate wildfire rating (39th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
What would actually reduce this score
Even with 67.5% of Decorah 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 Decorah's figures come from
Every one of the two percentiles behind Decorah's 19,374-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Decorah's dominant minimal exposure actually means, with real examples from across the dataset.