Menoken, ND
Menoken, ND's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Menoken at the 53rd national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 107 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Menoken at the 51st national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Menoken's buildings actually sit
Direct exposure dominates in Menoken: 92.5% of its 107 buildings, versus 7.5% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Menoken against the rest of the country
Menoken's risk sits at a similar level relative to North Dakota (59th percentile statewide) as it does nationally (53rd) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Menoken ranks 14,914 for wildfire risk (1 is highest) and 28,745 by building count (1 is largest). Within North Dakota alone, it ranks 167 of 402 places by risk. See the full county-by-county picture for North Dakota on its state page.
Menoken and the insurance market
At the 53rd national percentile, Menoken rates elevated 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 92.5% of Menoken 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 Menoken's figures come from
Menoken's 53rd-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 Menoken's dominant direct exposure actually means, with real examples from across the dataset.