Amory, MS
Amory wildfire risk explained
Amory sits at the 57th percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 4,162 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Amory's burn probability — fire likelihood with no building count factored in — sits at the 60th percentile nationally.
Amory's building exposure, zone by zone
Most of Amory's buildings (61.6%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
How Amory compares
Inside Mississippi, Amory sits at just the 28th percentile even though it scores 57th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Amory ranks 13,489 for wildfire risk (1 is highest) and 4,089 by building count (1 is largest). Within Mississippi alone, it ranks 303 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
Amory's elevated rating (57th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Amory (61.6% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Amory's figures come from
Every one of the two percentiles behind Amory's 13,489-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Amory's dominant indirect exposure actually means, with real examples from across the dataset.