Cape St. Claire, MD
Cape St. Claire wildfire risk explained
Out of every US place USFS scores, Cape St. Claire lands at the 32nd percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 3,000 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Cape St. Claire at the 31st national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Cape St. Claire's building exposure, zone by zone
3,000 buildings are counted in Cape St. Claire, and 51.3% of them are Indirect exposure — ember-driven risk rather than the 46.7% in Direct exposure or the 1.9% rated Minimal.
How Cape St. Claire compares
Within Maryland, Cape St. Claire ranks higher (49th percentile) than it does nationally (32nd) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Cape St. Claire ranks 21,487 for wildfire risk (1 is highest) and 5,434 by building count (1 is largest). Within Maryland alone, it ranks 269 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Cape St. Claire and the insurance market
At the 32nd national percentile, Cape St. Claire rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
Because 51.3% of Cape St. Claire'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 Cape St. Claire's figures come from
The methodology guide shows exactly how USFS turned 3,000 counted buildings into the percentiles shown above for Cape St. Claire. The exposure-zones guide covers what Cape St. Claire's dominant indirect exposure actually means, with real examples from across the dataset.