Lonaconing, MD
Lonaconing, MD's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Lonaconing at the 58th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 488 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Lonaconing's burn probability — fire likelihood with no building count factored in — sits at the 55th percentile nationally.
Lonaconing's building exposure, zone by zone
67.4% of Lonaconing's 488 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 32.6% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
How Lonaconing compares
Lonaconing's 83rd-percentile standing inside Maryland outpaces its 58th 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, Lonaconing ranks 13,145 for wildfire risk (1 is highest) and 17,570 by building count (1 is largest). Within Maryland alone, it ranks 91 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Lonaconing and the insurance market
At the 58th national percentile, Lonaconing rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Because 67.4% of Lonaconing'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 Lonaconing's figures come from
Every one of the two percentiles behind Lonaconing's 13,145-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Lonaconing's dominant indirect exposure actually means, with real examples from across the dataset.