Ames, IA
Ames wildfire risk explained
Ames's 16,314 buildings earn a 28th-percentile wildfire-risk score nationally under USFS's model — close to the middle of USFS's national wildfire-risk range. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Ames at the 29th national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Ames's buildings actually sit
Ames rates 87% Minimal exposure against just 13% Direct and 0% Indirect — of 16,314 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually. At 16,314 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Ames against the rest of the country
There's little gap between Ames's 28th national percentile and its 43rd percentile inside Iowa, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Ames ranks 22,557 for wildfire risk (1 is highest) and 888 by building count (1 is largest). Within Iowa alone, it ranks 581 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.
Shopping for coverage in Ames
Ames's moderate wildfire rating (28th 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.
Hardening a home in Ames
Even with 87% of Ames 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 Ames's figures come from
The methodology guide shows exactly how USFS turned 16,314 counted buildings into the percentiles shown above for Ames. The exposure-zones guide covers what Ames's dominant minimal exposure actually means, with real examples from across the dataset.