WildfireRiskFinder

Byhalia, MS

Byhalia wildfire risk explained

High
80thpercentile nationally

Byhalia sits at the 80th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 763 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Byhalia at the 83rd percentile, close to its 80th-percentile risk score.

Byhalia's building exposure, zone by zone

763Total buildings
58.6%Direct exposure
41.4%Indirect exposure
0%Minimal exposure

763 buildings are counted in Byhalia, and 58.6% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.

Where Byhalia ranks

Byhalia scores 80th nationally and 80th 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, Byhalia ranks 6,428 for wildfire risk (1 is highest) and 13,986 by building count (1 is largest). Within Mississippi alone, it ranks 87 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.

Shopping for coverage in Byhalia

Byhalia's high rating (80th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.

Hardening a home in Byhalia

Because Direct exposure dominates in Byhalia (58.6%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.

Where Byhalia's figures come from

Byhalia's 80th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Byhalia's dominant direct exposure actually means, with real examples from across the dataset.