Cecilton, MD
Cecilton, MD's wildfire risk, in USFS's own numbers
USFS scores Cecilton at the 26th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 362 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Cecilton at the 25th percentile, close to its 26th-percentile risk score.
Cecilton's building exposure, zone by zone
362 buildings are counted in Cecilton, and 54.7% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 6.9% rated Minimal.
Cecilton against the rest of the country
There's little gap between Cecilton's 26th national percentile and its 36th percentile inside Maryland, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Cecilton ranks 23,482 for wildfire risk (1 is highest) and 20,018 by building count (1 is largest). Within Maryland alone, it ranks 337 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Shopping for coverage in Cecilton
Cecilton's moderate rating (26th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
Cecilton's 54.7% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Cecilton's figures come from
Every one of the two percentiles behind Cecilton's 23,482-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Cecilton's dominant direct exposure actually means, with real examples from across the dataset.