Sterling, NE
Sterling, NE's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Sterling lands at the 64th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 406 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Sterling's burn probability — fire likelihood with no building count factored in — sits at the 57th percentile nationally.
Sterling's building exposure, zone by zone
Only 14.5% of Sterling's 406 buildings carry Direct exposure and 0% carry Indirect; the remaining 85.5% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.
Sterling against the rest of the country
Sterling's risk sits at a similar level relative to Nebraska (56th percentile statewide) as it does nationally (64th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Sterling ranks 11,317 for wildfire risk (1 is highest) and 19,099 by building count (1 is largest). Within Nebraska alone, it ranks 254 of 583 places by risk. See the full county-by-county picture for Nebraska on its state page.
Shopping for coverage in Sterling
At the 64th percentile nationally, Sterling 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
Sterling's 85.5% 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 Sterling's figures come from
Sterling's 64th-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 Sterling's dominant minimal exposure actually means, with real examples from across the dataset.