Kaiminani, HI
How exposed is Kaiminani to wildfire?
Kaiminani's 4,367 buildings earn a 81st-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Kaiminani's burn probability — fire likelihood with no building count factored in — sits at the 76th percentile nationally.
Kaiminani's building exposure, zone by zone
Most of Kaiminani's buildings (65%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
Kaiminani against the rest of the country
Inside Hawaii, Kaiminani sits at just the 11th percentile even though it scores 81st nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Kaiminani ranks 6,082 for wildfire risk (1 is highest) and 3,911 by building count (1 is largest). Within Hawaii alone, it ranks 146 of 163 places by risk. See the full county-by-county picture for Hawaii on its state page.
Shopping for coverage in Kaiminani
At the 81st percentile nationally, Kaiminani 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.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Kaiminani (65% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Kaiminani's figures come from
Kaiminani's 81st-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Kaiminani's dominant indirect exposure actually means, with real examples from across the dataset.