WildfireRiskFinder

Kramer, ND

How exposed is Kramer to wildfire?

Moderate
25thpercentile nationally

Kramer's 90 buildings earn a 25th-percentile wildfire-risk score nationally under USFS's model — close to the middle of USFS's national wildfire-risk range. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Kramer at the 23rd percentile, close to its 25th-percentile risk score.

What "at risk" means for the buildings here

90Total buildings
33.3%Direct exposure
0%Indirect exposure
66.7%Minimal exposure

Only 33.3% of Kramer's 90 buildings carry Direct exposure and 0% carry Indirect; the remaining 66.7% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.

Where Kramer ranks

Kramer's 25th national percentile looks worse in isolation than its 9th ranking inside North Dakota does — this place is on the milder end for its own state, by 16 points. Among the 31,521 US communities USFS scores, Kramer ranks 23,614 for wildfire risk (1 is highest) and 29,503 by building count (1 is largest). Within North Dakota alone, it ranks 367 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

Kramer's moderate wildfire rating (25th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.

Hardening a home in Kramer

Kramer's 66.7% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.

Where Kramer's figures come from

Kramer's 25th-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 Kramer's dominant minimal exposure actually means, with real examples from across the dataset.