Gloster, MS
Gloster, MS's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Gloster at the 78th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 758 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 Gloster at the 79th percentile, close to its 78th-percentile risk score.
Where Gloster's buildings actually sit
USFS classifies 61.7% of Gloster's buildings as Direct exposure, higher than its 38.3% Indirect share and far above its 0% Minimal share — a profile where 468 structures sit close enough to vegetation that lot clearing matters most.
Where Gloster ranks
Gloster's risk sits at a similar level relative to Mississippi (76th percentile statewide) as it does nationally (78th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Gloster ranks 7,087 for wildfire risk (1 is highest) and 14,039 by building count (1 is largest). Within Mississippi alone, it ranks 100 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
Gloster's 78th-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
Because Direct exposure dominates in Gloster (61.7%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Gloster's figures come from
Every one of the two percentiles behind Gloster's 7,087-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Gloster's dominant direct exposure actually means, with real examples from across the dataset.