Madrid, NE
How exposed is Madrid to wildfire?
Madrid sits at the 38th percentile nationally for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — per USFS's Wildfire Risk to Communities model, built from its 256 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Madrid at the 36th national percentile — 3 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Madrid rates 78.1% Minimal exposure against just 21.9% Direct and 0% Indirect — of 256 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Madrid against the rest of the country
Madrid's 38th national percentile looks worse in isolation than its 22nd ranking inside Nebraska does — this place is on the milder end for its own state, by 16 points. Among the 31,521 US communities USFS scores, Madrid ranks 19,479 for wildfire risk (1 is highest) and 22,948 by building count (1 is largest). Within Nebraska alone, it ranks 454 of 583 places by risk. See the full county-by-county picture for Nebraska on its state page.
Shopping for coverage in Madrid
Madrid's moderate wildfire rating (38th 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.
Lowering exposure, not just insuring around it
Madrid's 78.1% 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 Madrid's figures come from
Every one of the two percentiles behind Madrid's 19,479-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Madrid's dominant minimal exposure actually means, with real examples from across the dataset.