Beardsley, MN
Beardsley, MN's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Beardsley lands at the 32nd percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 276 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Beardsley at the 27th national percentile — 5 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Beardsley's buildings actually sit
78.6% of Beardsley's 276 buildings sit in USFS's Minimal exposure zone, with only 21.4% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
How Beardsley compares
There's little gap between Beardsley's 32nd national percentile and its 39th percentile inside Minnesota, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Beardsley ranks 21,366 for wildfire risk (1 is highest) and 22,322 by building count (1 is largest). Within Minnesota alone, it ranks 558 of 914 places by risk. See the full county-by-county picture for Minnesota on its state page.
What this risk score means for insurance
Beardsley's moderate rating (32nd 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 Beardsley
Beardsley's 78.6% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Beardsley's figures come from
Every one of the two percentiles behind Beardsley's 21,366-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Beardsley's dominant minimal exposure actually means, with real examples from across the dataset.