Kamaili, HI
Kamaili wildfire risk explained
USFS scores Kamaili at the 87th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 206 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Kamaili at the 85th percentile, close to its 87th-percentile risk score.
What "at risk" means for the buildings here
206 buildings are counted in Kamaili, and 98.5% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
Kamaili against the rest of the country
Kamaili ranks lower within Hawaii (27th percentile statewide) than its 87th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Kamaili ranks 4,162 for wildfire risk (1 is highest) and 24,634 by building count (1 is largest). Within Hawaii alone, it ranks 120 of 163 places by risk. See the full county-by-county picture for Hawaii on its state page.
Kamaili and the insurance market
Kamaili's 87th-percentile, very high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
What would actually reduce this score
Because Direct exposure dominates in Kamaili (98.5%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Kamaili's figures come from
Every one of the two percentiles behind Kamaili's 4,162-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Kamaili's dominant direct exposure actually means, with real examples from across the dataset.