South English, IA
South English, IA's wildfire risk, in USFS's own numbers
South English's 173 buildings earn a 54th-percentile wildfire-risk score nationally under USFS's model — modestly above the national average for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks South English at the 55th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
South English's building exposure, zone by zone
Only 19.1% of South English's 173 buildings carry Direct exposure and 0% carry Indirect; the remaining 80.9% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.
Where South English ranks
Compare South English's two percentiles: 74th within Iowa, only 54th nationally — a gap of 20 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, South English ranks 14,558 for wildfire risk (1 is highest) and 25,894 by building count (1 is largest). Within Iowa alone, it ranks 267 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
At the 54th national percentile, South English rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Even with 80.9% of South English 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 South English's figures come from
South English's 54th-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 South English's dominant minimal exposure actually means, with real examples from across the dataset.