Cope, CO
Cope, CO's wildfire risk, in USFS's own numbers
Cope sits at the 65th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 128 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Cope at the 61st national percentile — 4 points below its risk-to-structures score, a gap driven by how much is actually built there.
Cope's building exposure, zone by zone
50.8% of Cope's 128 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 49.2% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Where Cope ranks
Cope's 65th national percentile looks worse in isolation than its 32nd ranking inside Colorado does — this place is on the milder end for its own state, by 33 points. Among the 31,521 US communities USFS scores, Cope ranks 11,178 for wildfire risk (1 is highest) and 27,784 by building count (1 is largest). Within Colorado alone, it ranks 323 of 472 places by risk. See the full county-by-county picture for Colorado on its state page.
Cope and the insurance market
At the 65th percentile nationally, Cope carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
What would actually reduce this score
Because 50.8% of Cope'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 Cope's figures come from
Every one of the two percentiles behind Cope's 11,178-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Cope's dominant indirect exposure actually means, with real examples from across the dataset.