Halawa, HI
Halawa wildfire risk explained
Out of every US place USFS scores, Halawa lands at the 96th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 3,409 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 Halawa at the 94th percentile, close to its 96th-percentile risk score.
Halawa's building exposure, zone by zone
66% of Halawa's 3,409 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 0.8% Direct and 33.2% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Where Halawa ranks
Halawa's 96th national percentile looks worse in isolation than its 80th ranking inside Hawaii does — this place is on the milder end for its own state, by 16 points. Among the 31,521 US communities USFS scores, Halawa ranks 1,354 for wildfire risk (1 is highest) and 4,904 by building count (1 is largest). Within Hawaii alone, it ranks 32 of 163 places by risk. See the full county-by-county picture for Hawaii on its state page.
What this risk score means for insurance
At the 96th percentile nationally, Halawa carries the very high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
What would actually reduce this score
With ember exposure the dominant pattern in Halawa (66% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Halawa's figures come from
Every one of the two percentiles behind Halawa's 1,354-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Halawa's dominant indirect exposure actually means, with real examples from across the dataset.