WildfireRiskFinder

Denio, NV

Denio wildfire risk explained

Very High
97thpercentile nationally

Out of every US place USFS scores, Denio lands at the 97th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 68 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Denio at the 97th national percentile — 0 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

68Total buildings
92.7%Direct exposure
7.4%Indirect exposure
0%Minimal exposure

68 buildings are counted in Denio, and 92.7% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.

Denio against the rest of the country

Denio's 97th national percentile looks worse in isolation than its 61st ranking inside Nevada does — this place is on the milder end for its own state, by 36 points. Among the 31,521 US communities USFS scores, Denio ranks 1,041 for wildfire risk (1 is highest) and 30,360 by building count (1 is largest). Within Nevada alone, it ranks 48 of 123 places by risk. See the full county-by-county picture for Nevada on its state page.

What this risk score means for insurance

Denio's very high rating (97th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.

Hardening a home in Denio

Because Direct exposure dominates in Denio (92.7%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.

Where Denio's figures come from

Denio's 97th-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 Denio's dominant direct exposure actually means, with real examples from across the dataset.