Terramuggus, CT
Terramuggus, CT's wildfire risk, in USFS's own numbers
Terramuggus sits at the 47th percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 545 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Terramuggus at the 45th national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
Terramuggus's building exposure, zone by zone
545 buildings are counted in Terramuggus, and 86.8% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0.7% rated Minimal.
How Terramuggus compares
Within Connecticut, Terramuggus ranks higher (99th percentile) than it does nationally (47th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Terramuggus ranks 16,725 for wildfire risk (1 is highest) and 16,628 by building count (1 is largest). Within Connecticut alone, it ranks 4 of 214 places by risk. See the full county-by-county picture for Connecticut on its state page.
What this risk score means for insurance
Terramuggus's elevated rating (47th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
Because Direct exposure dominates in Terramuggus (86.8%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Terramuggus's figures come from
Every one of the two percentiles behind Terramuggus's 16,725-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Terramuggus's dominant direct exposure actually means, with real examples from across the dataset.