Wautoma, WI
Wautoma, WI's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Wautoma lands at the 50th percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 1,153 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Wautoma at the 49th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Wautoma's building exposure, zone by zone
USFS puts 73.2% of Wautoma's 1,153 buildings in the Indirect exposure zone, versus 26.8% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
Wautoma against the rest of the country
Wautoma's 98th-percentile standing inside Wisconsin outpaces its 50th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Wautoma ranks 15,808 for wildfire risk (1 is highest) and 10,937 by building count (1 is largest). Within Wisconsin alone, it ranks 17 of 808 places by risk. See the full county-by-county picture for Wisconsin on its state page.
Wautoma and the insurance market
At the 50th national percentile, Wautoma rates elevated 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 73.2% of Wautoma'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 Wautoma's figures come from
Wautoma's 50th-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 Wautoma's dominant indirect exposure actually means, with real examples from across the dataset.