New Market, MD
New Market, MD's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts New Market at the 15th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 581 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts New Market at the 14th percentile, close to its 15th-percentile risk score.
Where New Market's buildings actually sit
52.5% of New Market's 581 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 47.5% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
New Market against the rest of the country
There's little gap between New Market's 15th national percentile and its 14th percentile inside Maryland, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, New Market ranks 26,821 for wildfire risk (1 is highest) and 16,094 by building count (1 is largest). Within Maryland alone, it ranks 451 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Shopping for coverage in New Market
At the 15th national percentile, New Market rates low 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
With ember exposure the dominant pattern in New Market (52.5% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where New Market's figures come from
Every one of the two percentiles behind New Market's 26,821-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what New Market's dominant indirect exposure actually means, with real examples from across the dataset.