Linn Grove, IN
Linn Grove, IN's wildfire risk, in USFS's own numbers
USFS scores Linn Grove at the 1st national percentile for wildfire risk to structures (among the lower wildfire-risk places nationally), a figure built from 115 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Linn Grove at the 1st national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Only 12.2% of Linn Grove's 115 buildings carry Direct exposure and 0% carry Indirect; the remaining 87.8% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.
Where Linn Grove ranks
There's little gap between Linn Grove's 1st national percentile and its 4th percentile inside Indiana, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Linn Grove ranks 31,332 for wildfire risk (1 is highest) and 28,409 by building count (1 is largest). Within Indiana alone, it ranks 930 of 967 places by risk. See the full county-by-county picture for Indiana on its state page.
What this risk score means for insurance
Linn Grove's low rating (1st percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Hardening a home in Linn Grove
With 87.8% of buildings rated Minimal exposure, Linn Grove gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Linn Grove's figures come from
Every one of the two percentiles behind Linn Grove's 31,332-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Linn Grove's dominant minimal exposure actually means, with real examples from across the dataset.