Watervliet, MI
Watervliet, MI's wildfire risk, in USFS's own numbers
USFS scores Watervliet at the 10th national percentile for wildfire risk to structures (among the lower wildfire-risk places nationally), a figure built from 801 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Watervliet at the 10th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
75.3% of Watervliet's 801 buildings sit in USFS's Minimal exposure zone, with only 24.7% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
Where Watervliet ranks
Within Michigan, Watervliet ranks higher (31st percentile) than it does nationally (10th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Watervliet ranks 28,526 for wildfire risk (1 is highest) and 13,619 by building count (1 is largest). Within Michigan alone, it ranks 515 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.
Watervliet and the insurance market
Watervliet's low rating (10th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Lowering exposure, not just insuring around it
With 75.3% of buildings rated Minimal exposure, Watervliet gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Watervliet's figures come from
Watervliet's 10th-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 Watervliet's dominant minimal exposure actually means, with real examples from across the dataset.