Mineola, IA
How exposed is Mineola to wildfire?
Mineola sits at the 45th 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 114 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Mineola at the 43rd national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
Mineola's building exposure, zone by zone
USFS classifies 86.8% of Mineola's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 13.2% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Mineola against the rest of the country
Mineola's 61st-percentile standing inside Iowa outpaces its 45th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Mineola ranks 17,329 for wildfire risk (1 is highest) and 28,450 by building count (1 is largest). Within Iowa alone, it ranks 394 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.
What this risk score means for insurance
At the 45th national percentile, Mineola rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in Mineola
With 86.8% of buildings rated Minimal exposure, Mineola gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Mineola's figures come from
Mineola's 45th-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 Mineola's dominant minimal exposure actually means, with real examples from across the dataset.