Saranap, CA
How exposed is Saranap to wildfire?
USFS scores Saranap at the 71st national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 1,967 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Saranap at the 72nd national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Saranap's buildings actually sit
Saranap rates 42.5% Minimal exposure against just 25.8% Direct and 31.8% Indirect — of 1,967 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
How Saranap compares
Saranap's 71st national percentile looks worse in isolation than its 28th ranking inside California does — this place is on the milder end for its own state, by 42 points. Among the 31,521 US communities USFS scores, Saranap ranks 9,223 for wildfire risk (1 is highest) and 7,568 by building count (1 is largest). Within California alone, it ranks 1,126 of 1,570 places by risk. See the full county-by-county picture for California on its state page.
What this risk score means for insurance
Because Saranap is in California, a wildfire-hazard disclosure is legally required before a sale closes here — unusual nationally, since only these two states mandate it. The state's FAIR Plan alone carried 668,609 policies by the end of 2025. Full detail in the disclosure-law guide.
Hardening a home in Saranap
With 42.5% of buildings rated Minimal exposure, Saranap gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Saranap's figures come from
Every one of the two percentiles behind Saranap's 9,223-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Saranap's dominant minimal exposure actually means, with real examples from across the dataset.