Ruby, AK
Ruby wildfire risk explained
USFS's Wildfire Risk to Communities model puts Ruby at the 99th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 47 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Ruby at the 99th percentile, close to its 99th-percentile risk score.
What "at risk" means for the buildings here
Most of Ruby's buildings (53.2%) 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.
How Ruby compares
Ruby scores 99th nationally and 99th within Alaska — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Ruby ranks 238 for wildfire risk (1 is highest) and 30,991 by building count (1 is largest). Within Alaska alone, it ranks 4 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
Ruby's 99th-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.
Hardening a home in Ruby
Because 53.2% of Ruby'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 Ruby's figures come from
Ruby's 99th-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 Ruby's dominant indirect exposure actually means, with real examples from across the dataset.