Point of Rocks, MD
How exposed is Point of Rocks to wildfire?
Point of Rocks sits at the 21st percentile nationally for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — per USFS's Wildfire Risk to Communities model, built from its 726 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Point of Rocks's burn probability — fire likelihood with no building count factored in — sits at the 19th percentile nationally.
Where Point of Rocks's buildings actually sit
58.1% of Point of Rocks's 726 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 41.9% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Point of Rocks against the rest of the country
Point of Rocks scores 21st nationally and 26th within Maryland — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Point of Rocks ranks 25,061 for wildfire risk (1 is highest) and 14,383 by building count (1 is largest). Within Maryland alone, it ranks 389 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Point of Rocks and the insurance market
Point of Rocks's moderate rating (21st 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 ember exposure the dominant pattern in Point of Rocks (58.1% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Point of Rocks's figures come from
The methodology guide shows exactly how USFS turned 726 counted buildings into the percentiles shown above for Point of Rocks. The exposure-zones guide covers what Point of Rocks's dominant indirect exposure actually means, with real examples from across the dataset.