McCoole, MD
McCoole, MD's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts McCoole at the 60th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 365 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks McCoole at the 58th national percentile — 2 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
Of McCoole's 365 counted buildings, 70.4% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Where McCoole ranks
McCoole's 86th-percentile standing inside Maryland outpaces its 60th 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, McCoole ranks 12,606 for wildfire risk (1 is highest) and 19,961 by building count (1 is largest). Within Maryland alone, it ranks 76 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
What this risk score means for insurance
McCoole's elevated rating (60th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Lowering exposure, not just insuring around it
With 70.4% of McCoole in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where McCoole's figures come from
McCoole's 60th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what McCoole's dominant direct exposure actually means, with real examples from across the dataset.