Applegate, MI
Applegate wildfire risk explained
Applegate sits at the 5th percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 167 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Applegate at the 5th national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.
Applegate's building exposure, zone by zone
167 buildings are counted in Applegate, and 61.1% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 38.9% rated Minimal.
How Applegate compares
There's little gap between Applegate's 5th national percentile and its 12th percentile inside Michigan, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Applegate ranks 29,951 for wildfire risk (1 is highest) and 26,135 by building count (1 is largest). Within Michigan alone, it ranks 659 of 745 places by risk. See the full county-by-county picture for Michigan on its state page.
Shopping for coverage in Applegate
At the 5th national percentile, Applegate rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
With 61.1% of Applegate in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Applegate's figures come from
Applegate's 5th-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 Applegate's dominant direct exposure actually means, with real examples from across the dataset.