Papaikou, HI
Papaikou wildfire risk explained
Papaikou sits at the 75th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 584 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Papaikou at the 70th national percentile — 4 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
53.1% of Papaikou's 584 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 46.8% Direct and 0.2% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Where Papaikou ranks
Papaikou ranks lower within Hawaii (6th percentile statewide) than its 75th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Papaikou ranks 7,969 for wildfire risk (1 is highest) and 16,043 by building count (1 is largest). Within Hawaii alone, it ranks 154 of 163 places by risk. See the full county-by-county picture for Hawaii on its state page.
Papaikou and the insurance market
Papaikou's 75th-percentile, 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.
Lowering exposure, not just insuring around it
Because 53.1% of Papaikou's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Papaikou's figures come from
The methodology guide shows exactly how USFS turned 584 counted buildings into the percentiles shown above for Papaikou. The exposure-zones guide covers what Papaikou's dominant indirect exposure actually means, with real examples from across the dataset.