Corinth, MS
How exposed is Corinth to wildfire?
Corinth's 7,459 buildings earn a 70th-percentile wildfire-risk score nationally under USFS's model — well above the national norm for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Corinth's burn probability — fire likelihood with no building count factored in — sits at the 73rd percentile nationally.
What "at risk" means for the buildings here
Of Corinth's 7,459 counted buildings, 61% carry Direct exposure and only 15.1% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first. At 7,459 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
How Corinth compares
Corinth's risk sits at a similar level relative to Mississippi (57th percentile statewide) as it does nationally (70th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Corinth ranks 9,520 for wildfire risk (1 is highest) and 2,291 by building count (1 is largest). Within Mississippi alone, it ranks 182 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
What this risk score means for insurance
At the 70th percentile nationally, Corinth 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
Because Direct exposure dominates in Corinth (61%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Corinth's figures come from
The methodology guide shows exactly how USFS turned 7,459 counted buildings into the percentiles shown above for Corinth. The exposure-zones guide covers what Corinth's dominant direct exposure actually means, with real examples from across the dataset.