Kalifornsky, AK
Kalifornsky wildfire risk explained
Kalifornsky's 4,464 buildings earn a 85th-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, Kalifornsky's burn probability — fire likelihood with no building count factored in — sits at the 85th percentile nationally.
Kalifornsky's building exposure, zone by zone
88.9% of Kalifornsky's 4,464 buildings sit in USFS's Direct exposure zone, roughly 3,967 structures close enough to burnable vegetation for flame contact, not just embers — 9.5% fall in the Indirect, ember-only zone and 1.6% are Minimal.
How Kalifornsky compares
Inside Alaska, Kalifornsky sits at just the 70th percentile even though it scores 85th 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, Kalifornsky ranks 4,654 for wildfire risk (1 is highest) and 3,824 by building count (1 is largest). Within Alaska alone, it ranks 99 of 322 places by risk. See the full county-by-county picture for Alaska on its state page.
What this risk score means for insurance
Kalifornsky's 85th-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 Kalifornsky (88.9%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Kalifornsky's figures come from
Every one of the two percentiles behind Kalifornsky's 4,654-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Kalifornsky's dominant direct exposure actually means, with real examples from across the dataset.