Seville, CA
How exposed is Seville to wildfire?
USFS's Wildfire Risk to Communities model puts Seville at the 85th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 126 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 Seville at the 87th percentile, close to its 85th-percentile risk score.
Where Seville's buildings actually sit
Indirect exposure is dominant in Seville (55.6% of 126 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 44.4% sit in the Direct zone.
Where Seville ranks
Seville's 85th national percentile looks worse in isolation than its 49th ranking inside California does — this place is on the milder end for its own state, by 36 points. Among the 31,521 US communities USFS scores, Seville ranks 4,881 for wildfire risk (1 is highest) and 27,881 by building count (1 is largest). Within California alone, it ranks 805 of 1,570 places by risk. See the full county-by-county picture for California on its state page.
Seville and the insurance market
Seville sits in California, one of two states that legally require a wildfire-risk disclosure at sale. California's FAIR Plan, the state's insurer of last resort, ended 2025 with 668,609 residential policies after adding 21,859 in Q4 alone — the kind of market shift a 85th-percentile score like Seville's can end up mattering for. More on the disclosure law.
Hardening a home in Seville
Because 55.6% of Seville'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 Seville's figures come from
The methodology guide shows exactly how USFS turned 126 counted buildings into the percentiles shown above for Seville. The exposure-zones guide covers what Seville's dominant indirect exposure actually means, with real examples from across the dataset.