WildfireRiskFinder

Shaw, MS

Shaw, MS's wildfire risk, in USFS's own numbers

Low
3rdpercentile nationally

Shaw's 797 buildings earn a 3rd-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Shaw at the 3rd national percentile — 0 points below its risk-to-structures score, a gap driven by how much is actually built there.

Where Shaw's buildings actually sit

797Total buildings
15.4%Direct exposure
0%Indirect exposure
84.6%Minimal exposure

84.6% of Shaw's 797 buildings sit in USFS's Minimal exposure zone, with only 15.4% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.

Where Shaw ranks

There's little gap between Shaw's 3rd national percentile and its 1st percentile inside Mississippi, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Shaw ranks 30,673 for wildfire risk (1 is highest) and 13,661 by building count (1 is largest). Within Mississippi alone, it ranks 418 of 420 places by risk. See the full county-by-county picture for Mississippi on its state page.

Shopping for coverage in Shaw

Shaw's low rating (3rd percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

What would actually reduce this score

With 84.6% of buildings rated Minimal exposure, Shaw gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.

Where Shaw's figures come from

Every one of the two percentiles behind Shaw's 30,673-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Shaw's dominant minimal exposure actually means, with real examples from across the dataset.