Two Rivers, WI
Two Rivers wildfire risk explained
Two Rivers's 6,626 buildings earn a 19th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Two Rivers at the 20th percentile, close to its 19th-percentile risk score.
Where Two Rivers's buildings actually sit
6,626 buildings are counted in Two Rivers, and 70.3% of them are Indirect exposure — ember-driven risk rather than the 6.2% in Direct exposure or the 23.5% rated Minimal. At 6,626 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Two Rivers against the rest of the country
Two Rivers scores 19th nationally and 26th within Wisconsin — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Two Rivers ranks 25,705 for wildfire risk (1 is highest) and 2,614 by building count (1 is largest). Within Wisconsin alone, it ranks 600 of 808 places by risk. See the full county-by-county picture for Wisconsin on its state page.
Two Rivers and the insurance market
Two Rivers's low wildfire rating (19th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Lowering exposure, not just insuring around it
Because 70.3% of Two Rivers'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 Two Rivers's figures come from
The methodology guide shows exactly how USFS turned 6,626 counted buildings into the percentiles shown above for Two Rivers. The exposure-zones guide covers what Two Rivers's dominant indirect exposure actually means, with real examples from across the dataset.