Lone Rock, IA
Lone Rock wildfire risk explained
Lone Rock sits at the 4th percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 142 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Lone Rock at the 4th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Lone Rock's building exposure, zone by zone
USFS classifies 74.7% of Lone Rock's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 25.4% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
How Lone Rock compares
There's little gap between Lone Rock's 4th national percentile and its 9th percentile inside Iowa, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Lone Rock ranks 30,194 for wildfire risk (1 is highest) and 27,205 by building count (1 is largest). Within Iowa alone, it ranks 925 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
Lone Rock's low wildfire rating (4th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Hardening a home in Lone Rock
With 74.7% of buildings rated Minimal exposure, Lone Rock gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Lone Rock's figures come from
Lone Rock's 4th-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 Lone Rock's dominant minimal exposure actually means, with real examples from across the dataset.