Rienzi, MS
How exposed is Rienzi to wildfire?
Rienzi sits at the 68th 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 231 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Rienzi's burn probability — fire likelihood with no building count factored in — sits at the 71st percentile nationally.
Where Rienzi's buildings actually sit
84.4% of Rienzi's 231 buildings sit in USFS's Direct exposure zone, roughly 195 structures close enough to burnable vegetation for flame contact, not just embers — 15.6% fall in the Indirect, ember-only zone and 0% are Minimal.
How Rienzi compares
Rienzi ranks lower within Mississippi (52nd percentile statewide) than its 68th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Rienzi ranks 9,958 for wildfire risk (1 is highest) and 23,766 by building count (1 is largest). Within Mississippi alone, it ranks 202 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
Shopping for coverage in Rienzi
Rienzi's 68th-percentile, 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.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Rienzi (84.4%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Rienzi's figures come from
Rienzi's 68th-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 Rienzi's dominant direct exposure actually means, with real examples from across the dataset.