Hoback, WY
Hoback wildfire risk explained
USFS scores Hoback at the 92nd national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 849 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Hoback's burn probability — fire likelihood with no building count factored in — sits at the 90th percentile nationally.
Hoback's building exposure, zone by zone
USFS classifies 94.5% of Hoback's buildings as Direct exposure, higher than its 5.5% Indirect share and far above its 0% Minimal share — a profile where 802 structures sit close enough to vegetation that lot clearing matters most.
Where Hoback ranks
Hoback scores 92nd nationally and 96th within Wyoming — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Hoback ranks 2,695 for wildfire risk (1 is highest) and 13,173 by building count (1 is largest). Within Wyoming alone, it ranks 8 of 192 places by risk. See the full county-by-county picture for Wyoming on its state page.
Hoback and the insurance market
Hoback's 92nd-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
With 94.5% of Hoback in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Hoback's figures come from
Every one of the two percentiles behind Hoback's 2,695-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Hoback's dominant direct exposure actually means, with real examples from across the dataset.