Ellerslie, MD
Ellerslie wildfire risk explained
Out of every US place USFS scores, Ellerslie lands at the 60th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 413 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Ellerslie's burn probability — fire likelihood with no building count factored in — sits at the 59th percentile nationally.
Where Ellerslie's buildings actually sit
413 buildings are counted in Ellerslie, and 64.2% of them are Indirect exposure — ember-driven risk rather than the 35.8% in Direct exposure or the 0% rated Minimal.
How Ellerslie compares
Ellerslie'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, Ellerslie ranks 12,542 for wildfire risk (1 is highest) and 18,968 by building count (1 is largest). Within Maryland alone, it ranks 74 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Shopping for coverage in Ellerslie
Ellerslie's 60th-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Hardening a home in Ellerslie
With ember exposure the dominant pattern in Ellerslie (64.2% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Ellerslie's figures come from
Ellerslie'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 Ellerslie's dominant indirect exposure actually means, with real examples from across the dataset.