Jacinto, MS
Jacinto, MS's wildfire risk, in USFS's own numbers
Jacinto sits at the 73rd 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 66 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Jacinto's burn probability — fire likelihood with no building count factored in — sits at the 75th percentile nationally.
Where Jacinto's buildings actually sit
Of Jacinto's 66 counted buildings, 100% 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.
Jacinto against the rest of the country
There's little gap between Jacinto's 73rd national percentile and its 66th percentile inside Mississippi, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Jacinto ranks 8,545 for wildfire risk (1 is highest) and 30,425 by building count (1 is largest). Within Mississippi alone, it ranks 143 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
Jacinto and the insurance market
Jacinto's high rating (73rd 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 100% of Jacinto 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 Jacinto's figures come from
The methodology guide shows exactly how USFS turned 66 counted buildings into the percentiles shown above for Jacinto. The exposure-zones guide covers what Jacinto's dominant direct exposure actually means, with real examples from across the dataset.