Bond, MS
Bond, MS's wildfire risk, in USFS's own numbers
Bond sits at the 93rd percentile nationally for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — per USFS's Wildfire Risk to Communities model, built from its 312 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Bond at the 94th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Bond's building exposure, zone by zone
Of Bond's 312 counted buildings, 95.5% 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.
How Bond compares
There's little gap between Bond's 93rd national percentile and its 96th percentile inside Mississippi, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Bond ranks 2,234 for wildfire risk (1 is highest) and 21,319 by building count (1 is largest). Within Mississippi alone, it ranks 17 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
Bond and the insurance market
Bond's very high rating (93rd 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.
What would actually reduce this score
With 95.5% of Bond 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 Bond's figures come from
Every one of the two percentiles behind Bond's 2,234-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Bond's dominant direct exposure actually means, with real examples from across the dataset.