Medical Lake, WA
Medical Lake wildfire risk explained
Out of every US place USFS scores, Medical Lake lands at the 87th percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 2,041 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Medical Lake at the 88th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Medical Lake's building exposure, zone by zone
2,041 buildings are counted in Medical Lake, and 88.5% of them are Indirect exposure — ember-driven risk rather than the 11.4% in Direct exposure or the 0.1% rated Minimal.
Where Medical Lake ranks
Medical Lake's risk sits at a similar level relative to Washington (78th percentile statewide) as it does nationally (87th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Medical Lake ranks 3,987 for wildfire risk (1 is highest) and 7,353 by building count (1 is largest). Within Washington alone, it ranks 138 of 628 places by risk. See the full county-by-county picture for Washington on its state page.
What this risk score means for insurance
Medical Lake's very high rating (87th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Lowering exposure, not just insuring around it
Because 88.5% of Medical Lake'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 Medical Lake's figures come from
Every one of the two percentiles behind Medical Lake's 3,987-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Medical Lake's dominant indirect exposure actually means, with real examples from across the dataset.