Martin's Additions, MD
Martin's Additions, MD's wildfire risk, in USFS's own numbers
Martin's Additions's 346 buildings earn a 15th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Martin's Additions at the 13th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Martin's Additions's buildings actually sit
Martin's Additions rates 65% Minimal exposure against just 35% Direct and 0% Indirect — of 346 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
How Martin's Additions compares
Martin's Additions's risk sits at a similar level relative to Maryland (14th percentile statewide) as it does nationally (15th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Martin's Additions ranks 26,885 for wildfire risk (1 is highest) and 20,425 by building count (1 is largest). Within Maryland alone, it ranks 453 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Martin's Additions and the insurance market
At the 15th national percentile, Martin's Additions 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 65% of buildings rated Minimal exposure, Martin's Additions gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Martin's Additions's figures come from
Martin's Additions's 15th-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 Martin's Additions's dominant minimal exposure actually means, with real examples from across the dataset.