Clifford, ND
How exposed is Clifford to wildfire?
Out of every US place USFS scores, Clifford lands at the 39th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 78 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Clifford at the 36th national percentile — 3 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Clifford's buildings actually sit
Clifford rates 69.2% Minimal exposure against just 30.8% Direct and 0% Indirect — of 78 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Clifford against the rest of the country
Clifford ranks lower within North Dakota (22nd percentile statewide) than its 39th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Clifford ranks 19,383 for wildfire risk (1 is highest) and 29,978 by building count (1 is largest). Within North Dakota alone, it ranks 311 of 402 places by risk. See the full county-by-county picture for North Dakota on its state page.
What this risk score means for insurance
At the 39th national percentile, Clifford rates moderate 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
Even with 69.2% of Clifford outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Clifford's figures come from
Clifford's 39th-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 Clifford's dominant minimal exposure actually means, with real examples from across the dataset.