Philo, CA
Philo wildfire risk explained
Out of every US place USFS scores, Philo lands at the 86th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 296 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Philo at the 87th percentile, close to its 86th-percentile risk score.
Where Philo's buildings actually sit
USFS classifies 85.8% of Philo's buildings as Direct exposure, higher than its 14.2% Indirect share and far above its 0% Minimal share — a profile where 254 structures sit close enough to vegetation that lot clearing matters most.
Where Philo ranks
Philo's 86th national percentile looks worse in isolation than its 52nd ranking inside California does — this place is on the milder end for its own state, by 34 points. Among the 31,521 US communities USFS scores, Philo ranks 4,433 for wildfire risk (1 is highest) and 21,726 by building count (1 is largest). Within California alone, it ranks 753 of 1,570 places by risk. See the full county-by-county picture for California on its state page.
Shopping for coverage in Philo
Philo's 86th-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.
What would actually reduce this score
Philo's 85.8% 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 Philo's figures come from
The methodology guide shows exactly how USFS turned 296 counted buildings into the percentiles shown above for Philo. The exposure-zones guide covers what Philo's dominant direct exposure actually means, with real examples from across the dataset.