Macy, NE
Macy, NE's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Macy lands at the 88th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 300 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Macy at the 88th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS classifies 49.3% of Macy's buildings as Direct exposure, higher than its 49.3% Indirect share and far above its 1.3% Minimal share — a profile where 148 structures sit close enough to vegetation that lot clearing matters most.
How Macy compares
Macy's risk sits at a similar level relative to Nebraska (96th percentile statewide) as it does nationally (88th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Macy ranks 3,908 for wildfire risk (1 is highest) and 21,639 by building count (1 is largest). Within Nebraska alone, it ranks 24 of 583 places by risk. See the full county-by-county picture for Nebraska on its state page.
Macy and the insurance market
Macy's 88th-percentile, very 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 Macy
Because Direct exposure dominates in Macy (49.3%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Macy's figures come from
Every one of the two percentiles behind Macy's 3,908-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Macy's dominant direct exposure actually means, with real examples from across the dataset.