Matthews, GA
Matthews wildfire risk explained
USFS's Wildfire Risk to Communities model puts Matthews at the 65th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 164 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 Matthews at the 65th percentile, close to its 65th-percentile risk score.
Where Matthews's buildings actually sit
164 buildings are counted in Matthews, and 68.3% 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.
Where Matthews ranks
Matthews's 65th national percentile looks worse in isolation than its 40th ranking inside Georgia does — this place is on the milder end for its own state, by 25 points. Among the 31,521 US communities USFS scores, Matthews ranks 11,181 for wildfire risk (1 is highest) and 26,242 by building count (1 is largest). Within Georgia alone, it ranks 404 of 669 places by risk. See the full county-by-county picture for Georgia on its state page.
Shopping for coverage in Matthews
Matthews's 65th-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.
What would actually reduce this score
With 68.3% of Matthews 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 Matthews's figures come from
The methodology guide shows exactly how USFS turned 164 counted buildings into the percentiles shown above for Matthews. The exposure-zones guide covers what Matthews's dominant direct exposure actually means, with real examples from across the dataset.