North Kensington, MD
North Kensington, MD's wildfire risk, in USFS's own numbers
North Kensington's 3,041 buildings earn a 8th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts North Kensington at the 8th percentile, close to its 8th-percentile risk score.
What "at risk" means for the buildings here
USFS classifies 64% of North Kensington's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 36% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
North Kensington against the rest of the country
North Kensington's risk sits at a similar level relative to Maryland (2nd percentile statewide) as it does nationally (8th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, North Kensington ranks 29,029 for wildfire risk (1 is highest) and 5,371 by building count (1 is largest). Within Maryland alone, it ranks 516 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
What this risk score means for insurance
North Kensington's low rating (8th 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
With 64% of buildings rated Minimal exposure, North Kensington gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where North Kensington's figures come from
Every one of the two percentiles behind North Kensington's 29,029-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what North Kensington's dominant minimal exposure actually means, with real examples from across the dataset.