Minneola, KS
Minneola wildfire risk explained
Minneola sits at the 44th 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 482 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Minneola at the 41st national percentile — 3 points below its risk-to-structures score, a gap driven by how much is actually built there.
Minneola's building exposure, zone by zone
USFS classifies 89.6% of Minneola's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 10.4% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Where Minneola ranks
Inside Kansas, Minneola sits at just the 21st percentile even though it scores 44th 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, Minneola ranks 17,577 for wildfire risk (1 is highest) and 17,666 by building count (1 is largest). Within Kansas alone, it ranks 573 of 721 places by risk. See the full county-by-county picture for Kansas on its state page.
Minneola and the insurance market
At the 44th national percentile, Minneola rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
With 89.6% of buildings rated Minimal exposure, Minneola gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Minneola's figures come from
Every one of the two percentiles behind Minneola's 17,577-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Minneola's dominant minimal exposure actually means, with real examples from across the dataset.