WildfireRiskFinder

Alma, NE

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

High
66thpercentile nationally

USFS's Wildfire Risk to Communities model puts Alma at the 66th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 915 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 Alma at the 61st percentile, close to its 66th-percentile risk score.

Where Alma's buildings actually sit

915Total buildings
4.7%Direct exposure
95.3%Indirect exposure
0%Minimal exposure

915 buildings are counted in Alma, and 95.3% of them are Indirect exposure — ember-driven risk rather than the 4.7% in Direct exposure or the 0% rated Minimal.

Alma against the rest of the country

Alma's risk sits at a similar level relative to Nebraska (61st percentile statewide) as it does nationally (66th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Alma ranks 10,657 for wildfire risk (1 is highest) and 12,593 by building count (1 is largest). Within Nebraska alone, it ranks 226 of 583 places by risk. See the full county-by-county picture for Nebraska on its state page.

Alma and the insurance market

Alma's high rating (66th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.

Hardening a home in Alma

Alma's 95.3% 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 Alma's figures come from

Every one of the two percentiles behind Alma's 10,657-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Alma's dominant indirect exposure actually means, with real examples from across the dataset.