Sterling, GA
Sterling, GA's wildfire risk, in USFS's own numbers
USFS scores Sterling at the 83rd national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 1,106 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Sterling's burn probability — fire likelihood with no building count factored in — sits at the 80th percentile nationally.
What "at risk" means for the buildings here
1,106 buildings are counted in Sterling, and 80.7% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
Where Sterling ranks
Sterling scores 83rd nationally and 94th within Georgia — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Sterling ranks 5,450 for wildfire risk (1 is highest) and 11,241 by building count (1 is largest). Within Georgia alone, it ranks 44 of 669 places by risk. See the full county-by-county picture for Georgia on its state page.
Shopping for coverage in Sterling
At the 83rd percentile nationally, Sterling carries the very high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
What would actually reduce this score
With 80.7% of Sterling 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 Sterling's figures come from
Every one of the two percentiles behind Sterling's 5,450-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.