Naylor, GA
How exposed is Naylor to wildfire?
USFS's Wildfire Risk to Communities model puts Naylor at the 80th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 122 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Naylor at the 80th national percentile — 0 points below its risk-to-structures score, a gap driven by how much is actually built there.
Naylor's building exposure, zone by zone
Direct exposure dominates in Naylor: 76.2% of its 122 buildings, versus 23.8% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
How Naylor compares
There's little gap between Naylor's 80th national percentile and its 92nd percentile inside Georgia, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Naylor ranks 6,209 for wildfire risk (1 is highest) and 28,052 by building count (1 is largest). Within Georgia alone, it ranks 58 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
Naylor's 80th-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.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Naylor (76.2%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Naylor's figures come from
The methodology guide shows exactly how USFS turned 122 counted buildings into the percentiles shown above for Naylor. The exposure-zones guide covers what Naylor's dominant direct exposure actually means, with real examples from across the dataset.