Lordship, CT
Lordship wildfire risk explained
USFS scores Lordship at the 35th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 1,568 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Lordship at the 27th national percentile — 8 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS classifies 99.3% of Lordship's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 0.7% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Lordship against the rest of the country
Lordship's 61st-percentile standing inside Connecticut outpaces its 35th 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, Lordship ranks 20,506 for wildfire risk (1 is highest) and 8,894 by building count (1 is largest). Within Connecticut alone, it ranks 84 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
Lordship's moderate rating (35th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Hardening a home in Lordship
Even with 99.3% of Lordship outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Lordship's figures come from
The methodology guide shows exactly how USFS turned 1,568 counted buildings into the percentiles shown above for Lordship. The exposure-zones guide covers what Lordship's dominant minimal exposure actually means, with real examples from across the dataset.