Rawls Springs, MS
Rawls Springs, MS's wildfire risk, in USFS's own numbers
Rawls Springs's 590 buildings earn a 85th-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Rawls Springs's burn probability — fire likelihood with no building count factored in — sits at the 87th percentile nationally.
What "at risk" means for the buildings here
Of Rawls Springs's 590 counted buildings, 86.6% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Rawls Springs against the rest of the country
There's little gap between Rawls Springs's 85th national percentile and its 87th percentile inside Mississippi, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Rawls Springs ranks 4,762 for wildfire risk (1 is highest) and 15,964 by building count (1 is largest). Within Mississippi alone, it ranks 54 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
Rawls Springs and the insurance market
Rawls Springs's 85th-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.
Hardening a home in Rawls Springs
Because Direct exposure dominates in Rawls Springs (86.6%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Rawls Springs's figures come from
The methodology guide shows exactly how USFS turned 590 counted buildings into the percentiles shown above for Rawls Springs. The exposure-zones guide covers what Rawls Springs's dominant direct exposure actually means, with real examples from across the dataset.