Lisbon, WI
Lisbon wildfire risk explained
USFS scores Lisbon at the 34th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 5,142 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Lisbon's burn probability — fire likelihood with no building count factored in — sits at the 36th percentile nationally.
What "at risk" means for the buildings here
5,142 buildings are counted in Lisbon, and 70.7% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 26.9% rated Minimal. At 5,142 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Where Lisbon ranks
Compare Lisbon's two percentiles: 77th within Wisconsin, only 34th nationally — a gap of 44 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Lisbon ranks 20,879 for wildfire risk (1 is highest) and 3,356 by building count (1 is largest). Within Wisconsin alone, it ranks 179 of 808 places by risk. See the full county-by-county picture for Wisconsin on its state page.
Lisbon and the insurance market
At the 34th national percentile, Lisbon 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
Lisbon's 70.7% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Lisbon's figures come from
The methodology guide shows exactly how USFS turned 5,142 counted buildings into the percentiles shown above for Lisbon. The exposure-zones guide covers what Lisbon's dominant direct exposure actually means, with real examples from across the dataset.