WildfireRiskFinder

Coggon, IA

How exposed is Coggon to wildfire?

Low
18thpercentile nationally

USFS's Wildfire Risk to Communities model puts Coggon at the 18th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 407 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 Coggon at the 17th percentile, close to its 18th-percentile risk score.

Where Coggon's buildings actually sit

407Total buildings
23.8%Direct exposure
0%Indirect exposure
76.2%Minimal exposure

Only 23.8% of Coggon's 407 buildings carry Direct exposure and 0% carry Indirect; the remaining 76.2% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.

Coggon against the rest of the country

Coggon scores 18th nationally and 28th within Iowa — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Coggon ranks 25,964 for wildfire risk (1 is highest) and 19,082 by building count (1 is largest). Within Iowa alone, it ranks 728 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.

Shopping for coverage in Coggon

At the 18th national percentile, Coggon rates low 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

Coggon's 76.2% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.

Where Coggon's figures come from

Coggon's 18th-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 Coggon's dominant minimal exposure actually means, with real examples from across the dataset.