Ideal, GA
Ideal wildfire risk explained
USFS's Wildfire Risk to Communities model puts Ideal at the 66th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 256 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 Ideal at the 67th percentile, close to its 66th-percentile risk score.
Ideal's building exposure, zone by zone
USFS classifies 78.1% of Ideal's buildings as Direct exposure, higher than its 21.9% Indirect share and far above its 0% Minimal share — a profile where 200 structures sit close enough to vegetation that lot clearing matters most.
Where Ideal ranks
Inside Georgia, Ideal sits at just the 48th percentile even though it scores 66th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Ideal ranks 10,739 for wildfire risk (1 is highest) and 22,934 by building count (1 is largest). Within Georgia alone, it ranks 350 of 669 places by risk. See the full county-by-county picture for Georgia on its state page.
Shopping for coverage in Ideal
Ideal's 66th-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 Ideal
Ideal's 78.1% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Ideal's figures come from
The methodology guide shows exactly how USFS turned 256 counted buildings into the percentiles shown above for Ideal. The exposure-zones guide covers what Ideal's dominant direct exposure actually means, with real examples from across the dataset.