Moosup, CT
How exposed is Moosup to wildfire?
Moosup sits at the 28th 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 1,384 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Moosup at the 24th percentile, close to its 28th-percentile risk score.
Where Moosup's buildings actually sit
57.2% of Moosup's 1,384 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 42.9% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
How Moosup compares
Moosup scores 28th nationally and 34th within Connecticut — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Moosup ranks 22,747 for wildfire risk (1 is highest) and 9,693 by building count (1 is largest). Within Connecticut alone, it ranks 143 of 214 places by risk. See the full county-by-county picture for Connecticut on its state page.
Shopping for coverage in Moosup
Moosup's moderate wildfire rating (28th 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.
What would actually reduce this score
Moosup's 57.2% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Moosup's figures come from
Every one of the two percentiles behind Moosup's 22,747-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Moosup's dominant indirect exposure actually means, with real examples from across the dataset.