WildfireRiskFinder

Genoa, NE

Genoa, NE's wildfire risk, in USFS's own numbers

Elevated
54thpercentile nationally

USFS's Wildfire Risk to Communities model puts Genoa at the 54th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 636 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Genoa's burn probability — fire likelihood with no building count factored in — sits at the 50th percentile nationally.

What "at risk" means for the buildings here

636Total buildings
13.1%Direct exposure
29.3%Indirect exposure
57.7%Minimal exposure

Genoa rates 57.7% Minimal exposure against just 13.1% Direct and 29.3% Indirect — of 636 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.

Where Genoa ranks

Genoa scores 54th nationally and 41st within Nebraska — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Genoa ranks 14,378 for wildfire risk (1 is highest) and 15,393 by building count (1 is largest). Within Nebraska alone, it ranks 344 of 583 places by risk. See the full county-by-county picture for Nebraska on its state page.

Genoa and the insurance market

Genoa's elevated wildfire rating (54th 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

Genoa's 57.7% 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 Genoa's figures come from

Every one of the two percentiles behind Genoa's 14,378-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Genoa's dominant minimal exposure actually means, with real examples from across the dataset.