Good Hope, GA
Good Hope wildfire risk explained
Out of every US place USFS scores, Good Hope lands at the 62nd percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 238 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Good Hope at the 66th national percentile — 3 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS classifies 97.5% of Good Hope's buildings as Direct exposure, higher than its 2.5% Indirect share and far above its 0% Minimal share — a profile where 232 structures sit close enough to vegetation that lot clearing matters most.
Where Good Hope ranks
Good Hope's 62nd national percentile looks worse in isolation than its 28th ranking inside Georgia does — this place is on the milder end for its own state, by 34 points. Among the 31,521 US communities USFS scores, Good Hope ranks 11,846 for wildfire risk (1 is highest) and 23,521 by building count (1 is largest). Within Georgia alone, it ranks 479 of 669 places by risk. See the full county-by-county picture for Georgia on its state page.
Shopping for coverage in Good Hope
At the 62nd percentile nationally, Good Hope 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.
Lowering exposure, not just insuring around it
Good Hope's 97.5% 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 Good Hope's figures come from
The methodology guide shows exactly how USFS turned 238 counted buildings into the percentiles shown above for Good Hope. The exposure-zones guide covers what Good Hope's dominant direct exposure actually means, with real examples from across the dataset.