Koshkonong, MO
How exposed is Koshkonong to wildfire?
USFS scores Koshkonong at the 79th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 145 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Koshkonong's burn probability — fire likelihood with no building count factored in — sits at the 83rd percentile nationally.
What "at risk" means for the buildings here
Indirect exposure is dominant in Koshkonong (80.7% of 145 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 19.3% sit in the Direct zone.
Where Koshkonong ranks
Koshkonong's 96th-percentile standing inside Missouri outpaces its 79th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Koshkonong ranks 6,524 for wildfire risk (1 is highest) and 27,082 by building count (1 is largest). Within Missouri alone, it ranks 46 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.
Koshkonong and the insurance market
At the 79th percentile nationally, Koshkonong carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Lowering exposure, not just insuring around it
Koshkonong's 80.7% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Koshkonong's figures come from
Koshkonong's 79th-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 Koshkonong's dominant indirect exposure actually means, with real examples from across the dataset.