Strum, WI
How exposed is Strum to wildfire?
Strum sits at the 24th percentile nationally for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — per USFS's Wildfire Risk to Communities model, built from its 584 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Strum at the 24th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Strum's buildings actually sit
Most of Strum's buildings (66.8%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
How Strum compares
Compare Strum's two percentiles: 44th within Wisconsin, only 24th nationally — a gap of 20 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Strum ranks 24,129 for wildfire risk (1 is highest) and 16,060 by building count (1 is largest). Within Wisconsin alone, it ranks 454 of 808 places by risk. See the full county-by-county picture for Wisconsin on its state page.
What this risk score means for insurance
At the 24th national percentile, Strum rates moderate 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
Because 66.8% of Strum's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Strum's figures come from
Strum's 24th-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 Strum's dominant indirect exposure actually means, with real examples from across the dataset.