Cataula, GA
How exposed is Cataula to wildfire?
USFS's Wildfire Risk to Communities model puts Cataula at the 78th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 726 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Cataula at the 81st national percentile — 3 points above its risk-to-structures score, a gap driven by how much is actually built there.
Cataula's building exposure, zone by zone
Direct exposure dominates in Cataula: 98.4% of its 726 buildings, versus 1.7% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
How Cataula compares
There's little gap between Cataula's 78th national percentile and its 87th percentile inside Georgia, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Cataula ranks 7,056 for wildfire risk (1 is highest) and 14,379 by building count (1 is largest). Within Georgia alone, it ranks 86 of 669 places by risk. See the full county-by-county picture for Georgia on its state page.
Cataula and the insurance market
At the 78th percentile nationally, Cataula carries the 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 98.4% of Cataula 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 Cataula's figures come from
Cataula's 78th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Cataula's dominant direct exposure actually means, with real examples from across the dataset.