Indian Hills, NV
Indian Hills, NV's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Indian Hills at the 97th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 2,951 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Indian Hills at the 96th percentile, close to its 97th-percentile risk score.
What "at risk" means for the buildings here
2,951 buildings are counted in Indian Hills, and 66.4% of them are Indirect exposure — ember-driven risk rather than the 33.5% in Direct exposure or the 0.1% rated Minimal.
Where Indian Hills ranks
Indian Hills's 97th national percentile looks worse in isolation than its 63rd ranking inside Nevada does — this place is on the milder end for its own state, by 34 points. Among the 31,521 US communities USFS scores, Indian Hills ranks 973 for wildfire risk (1 is highest) and 5,510 by building count (1 is largest). Within Nevada alone, it ranks 46 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
At the 97th percentile nationally, Indian Hills carries the very high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Lowering exposure, not just insuring around it
Because 66.4% of Indian Hills'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 Indian Hills's figures come from
Every one of the two percentiles behind Indian Hills's 973-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Indian Hills's dominant indirect exposure actually means, with real examples from across the dataset.