Pala, CA
Pala wildfire risk explained
USFS's Wildfire Risk to Communities model puts Pala at the 100th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 677 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 Pala at the 100th percentile, close to its 100th-percentile risk score.
Pala's building exposure, zone by zone
Direct exposure dominates in Pala: 81.1% of its 677 buildings, versus 18.9% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Pala against the rest of the country
Pala scores 100th nationally and 96th within California — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Pala ranks 127 for wildfire risk (1 is highest) and 14,896 by building count (1 is largest). Within California alone, it ranks 84 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
Pala's 100th-percentile score lands in California, the only two states requiring a wildfire disclosure at sale. California's surplus-lines homeowners market passed 300,000 policies for the first time in 2025, driven by carriers pulling back from wildfire-exposed areas statewide. The disclosure-law guide covers what it requires.
Hardening a home in Pala
Pala's 81.1% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Pala's figures come from
Every one of the two percentiles behind Pala's 127-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Pala's dominant direct exposure actually means, with real examples from across the dataset.