Sterling, UT
Sterling wildfire risk explained
USFS's Wildfire Risk to Communities model puts Sterling at the 79th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 190 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 Sterling at the 77th percentile, close to its 79th-percentile risk score.
What "at risk" means for the buildings here
Direct exposure dominates in Sterling: 56.8% of its 190 buildings, versus 43.2% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Where Sterling ranks
Sterling's 79th national percentile looks worse in isolation than its 38th ranking inside Utah does — this place is on the milder end for its own state, by 41 points. Among the 31,521 US communities USFS scores, Sterling ranks 6,697 for wildfire risk (1 is highest) and 25,262 by building count (1 is largest). Within Utah alone, it ranks 204 of 326 places by risk. See the full county-by-county picture for Utah on its state page.
Shopping for coverage in Sterling
Sterling's 79th-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 Direct exposure dominates in Sterling (56.8%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Sterling's figures come from
Every one of the two percentiles behind Sterling's 6,697-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Sterling's dominant direct exposure actually means, with real examples from across the dataset.