Pioche, NV
Pioche wildfire risk explained
Pioche's 614 buildings earn a 98th-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Pioche's burn probability — fire likelihood with no building count factored in — sits at the 95th percentile nationally.
Pioche's building exposure, zone by zone
Most of Pioche's buildings (57.7%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
How Pioche compares
Pioche ranks lower within Nevada (79th percentile statewide) than its 98th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Pioche ranks 632 for wildfire risk (1 is highest) and 15,640 by building count (1 is largest). Within Nevada alone, it ranks 25 of 123 places by risk. See the full county-by-county picture for Nevada on its state page.
Pioche and the insurance market
Pioche's 98th-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.
Lowering exposure, not just insuring around it
Because 57.7% of Pioche's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Pioche's figures come from
Every one of the two percentiles behind Pioche's 632-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Pioche's dominant indirect exposure actually means, with real examples from across the dataset.