Argyle, WI
Argyle, WI's wildfire risk, in USFS's own numbers
USFS scores Argyle at the 31st national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 574 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Argyle at the 32nd national percentile — 1 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
Most of Argyle's buildings (84.3%) 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.
Where Argyle ranks
Within Wisconsin, Argyle ranks higher (68th percentile) than it does nationally (31st) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Argyle ranks 21,700 for wildfire risk (1 is highest) and 16,189 by building count (1 is largest). Within Wisconsin alone, it ranks 257 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 31st national percentile, Argyle rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in Argyle
Because 84.3% of Argyle'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 Argyle's figures come from
Every one of the two percentiles behind Argyle's 21,700-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Argyle's dominant indirect exposure actually means, with real examples from across the dataset.