Courtland, MS
Courtland, MS's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Courtland at the 74th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 280 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 Courtland at the 77th percentile, close to its 74th-percentile risk score.
What "at risk" means for the buildings here
Direct exposure dominates in Courtland: 89.3% of its 280 buildings, versus 10.7% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
How Courtland compares
Courtland's risk sits at a similar level relative to Mississippi (70th percentile statewide) as it does nationally (74th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Courtland ranks 8,098 for wildfire risk (1 is highest) and 22,188 by building count (1 is largest). Within Mississippi alone, it ranks 126 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 74th percentile nationally, Courtland 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.
Hardening a home in Courtland
With 89.3% of Courtland 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 Courtland's figures come from
The methodology guide shows exactly how USFS turned 280 counted buildings into the percentiles shown above for Courtland. The exposure-zones guide covers what Courtland's dominant direct exposure actually means, with real examples from across the dataset.