Kahaluu, HI
Kahaluu wildfire risk explained
USFS scores Kahaluu at the 96th national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 1,984 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Kahaluu at the 94th national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
Kahaluu's building exposure, zone by zone
58.9% of Kahaluu's 1,984 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 26.8% Direct and 14.3% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
How Kahaluu compares
There's little gap between Kahaluu's 96th national percentile and its 85th percentile inside Hawaii, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Kahaluu ranks 1,256 for wildfire risk (1 is highest) and 7,520 by building count (1 is largest). Within Hawaii alone, it ranks 25 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
Kahaluu's very high rating (96th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
What would actually reduce this score
Kahaluu's 58.9% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Kahaluu's figures come from
Every one of the two percentiles behind Kahaluu's 1,256-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Kahaluu's dominant indirect exposure actually means, with real examples from across the dataset.