Sleepy Eye, MN
How exposed is Sleepy Eye to wildfire?
Out of every US place USFS scores, Sleepy Eye lands at the 6th percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 2,120 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Sleepy Eye at the 5th percentile, close to its 6th-percentile risk score.
Where Sleepy Eye's buildings actually sit
USFS classifies 96.9% of Sleepy Eye's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 3.1% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
How Sleepy Eye compares
There's little gap between Sleepy Eye's 6th national percentile and its 10th percentile inside Minnesota, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Sleepy Eye ranks 29,510 for wildfire risk (1 is highest) and 7,144 by building count (1 is largest). Within Minnesota alone, it ranks 825 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
At the 6th national percentile, Sleepy Eye rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
With 96.9% of buildings rated Minimal exposure, Sleepy Eye gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Sleepy Eye's figures come from
Sleepy Eye's 6th-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 Sleepy Eye's dominant minimal exposure actually means, with real examples from across the dataset.