Spalding, NE
Spalding, NE's wildfire risk, in USFS's own numbers
USFS scores Spalding at the 72nd national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 386 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Spalding at the 65th percentile, close to its 72nd-percentile risk score.
What "at risk" means for the buildings here
Indirect exposure is dominant in Spalding (97.4% of 386 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 2.6% sit in the Direct zone.
Where Spalding ranks
There's little gap between Spalding's 72nd national percentile and its 72nd percentile inside Nebraska, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Spalding ranks 8,860 for wildfire risk (1 is highest) and 19,497 by building count (1 is largest). Within Nebraska alone, it ranks 164 of 583 places by risk. See the full county-by-county picture for Nebraska on its state page.
Shopping for coverage in Spalding
Spalding's 72nd-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Hardening a home in Spalding
Because 97.4% of Spalding's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Spalding's figures come from
The methodology guide shows exactly how USFS turned 386 counted buildings into the percentiles shown above for Spalding. The exposure-zones guide covers what Spalding's dominant indirect exposure actually means, with real examples from across the dataset.