New Hebron, MS
New Hebron, MS's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts New Hebron at the 80th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 325 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, New Hebron's burn probability — fire likelihood with no building count factored in — sits at the 83rd percentile nationally.
What "at risk" means for the buildings here
56.9% of New Hebron's 325 buildings sit in USFS's Direct exposure zone, roughly 185 structures close enough to burnable vegetation for flame contact, not just embers — 43.1% fall in the Indirect, ember-only zone and 0% are Minimal.
New Hebron against the rest of the country
New Hebron scores 80th nationally and 81st within Mississippi — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, New Hebron ranks 6,270 for wildfire risk (1 is highest) and 20,959 by building count (1 is largest). Within Mississippi alone, it ranks 83 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
New Hebron and the insurance market
New Hebron's 80th-percentile, very 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
Because Direct exposure dominates in New Hebron (56.9%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where New Hebron's figures come from
The methodology guide shows exactly how USFS turned 325 counted buildings into the percentiles shown above for New Hebron. The exposure-zones guide covers what New Hebron's dominant direct exposure actually means, with real examples from across the dataset.