Tilden, NE
Tilden wildfire risk explained
USFS scores Tilden at the 66th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 625 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 Tilden at the 55th percentile, lower than its 66th-percentile risk score, because so much is actually built here.
Where Tilden's buildings actually sit
Tilden rates 89% Minimal exposure against just 11% Direct and 0% Indirect — of 625 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
How Tilden compares
Tilden's risk sits at a similar level relative to Nebraska (60th 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, Tilden ranks 10,815 for wildfire risk (1 is highest) and 15,507 by building count (1 is largest). Within Nebraska alone, it ranks 238 of 583 places by risk. See the full county-by-county picture for Nebraska on its state page.
What this risk score means for insurance
Tilden's 66th-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.
What would actually reduce this score
With 89% of buildings rated Minimal exposure, Tilden gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Tilden's figures come from
Every one of the two percentiles behind Tilden's 10,815-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Tilden's dominant minimal exposure actually means, with real examples from across the dataset.