Shiloh, GA
Shiloh, GA's wildfire risk, in USFS's own numbers
USFS scores Shiloh at the 78th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 291 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Shiloh'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
291 buildings are counted in Shiloh, and 86.3% 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.
Shiloh against the rest of the country
There's little gap between Shiloh's 78th national percentile and its 88th percentile inside Georgia, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Shiloh ranks 7,024 for wildfire risk (1 is highest) and 21,874 by building count (1 is largest). Within Georgia alone, it ranks 85 of 669 places by risk. See the full county-by-county picture for Georgia on its state page.
What this risk score means for insurance
Shiloh's 78th-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.
Hardening a home in Shiloh
With 86.3% of Shiloh 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 Shiloh's figures come from
The methodology guide shows exactly how USFS turned 291 counted buildings into the percentiles shown above for Shiloh. The exposure-zones guide covers what Shiloh's dominant direct exposure actually means, with real examples from across the dataset.