North Tunica, MS
North Tunica wildfire risk explained
USFS scores North Tunica at the 23rd national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 374 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks North Tunica at the 24th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where North Tunica's buildings actually sit
USFS classifies 96.5% of North Tunica's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 3.5% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Where North Tunica ranks
North Tunica's 23rd national percentile looks worse in isolation than its 7th ranking inside Mississippi does — this place is on the milder end for its own state, by 16 points. Among the 31,521 US communities USFS scores, North Tunica ranks 24,334 for wildfire risk (1 is highest) and 19,783 by building count (1 is largest). Within Mississippi alone, it ranks 392 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.
What this risk score means for insurance
North Tunica's moderate rating (23rd percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Hardening a home in North Tunica
With 96.5% of buildings rated Minimal exposure, North Tunica gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where North Tunica's figures come from
North Tunica's 23rd-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 North Tunica's dominant minimal exposure actually means, with real examples from across the dataset.