WildfireRiskFinder

Gorman, MD

Gorman wildfire risk explained

Elevated
59thpercentile nationally

Out of every US place USFS scores, Gorman lands at the 59th percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 104 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Gorman at the 57th percentile, close to its 59th-percentile risk score.

Gorman's building exposure, zone by zone

104Total buildings
100%Direct exposure
0%Indirect exposure
0%Minimal exposure

104 buildings are counted in Gorman, and 100% 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% rated Minimal.

Where Gorman ranks

Gorman's 84th-percentile standing inside Maryland outpaces its 59th 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, Gorman ranks 13,016 for wildfire risk (1 is highest) and 28,849 by building count (1 is largest). Within Maryland alone, it ranks 85 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

Gorman's elevated rating (59th 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

Because Direct exposure dominates in Gorman (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.

Where Gorman's figures come from

The methodology guide shows exactly how USFS turned 104 counted buildings into the percentiles shown above for Gorman. The exposure-zones guide covers what Gorman's dominant direct exposure actually means, with real examples from across the dataset.