Glassmanor, MD
Glassmanor wildfire risk explained
USFS scores Glassmanor at the 28th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 2,953 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Glassmanor's burn probability — fire likelihood with no building count factored in — sits at the 28th percentile nationally.
What "at risk" means for the buildings here
Glassmanor rates 82.8% Minimal exposure against just 17.2% Direct and 0% Indirect — of 2,953 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Glassmanor against the rest of the country
Glassmanor's 44th-percentile standing inside Maryland outpaces its 28th 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, Glassmanor ranks 22,533 for wildfire risk (1 is highest) and 5,508 by building count (1 is largest). Within Maryland alone, it ranks 296 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Glassmanor and the insurance market
Glassmanor's moderate wildfire rating (28th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
What would actually reduce this score
Even with 82.8% of Glassmanor 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 Glassmanor's figures come from
Glassmanor's 28th-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 Glassmanor's dominant minimal exposure actually means, with real examples from across the dataset.