WildfireRiskFinder

Dexter, MN

How exposed is Dexter to wildfire?

Low
2ndpercentile nationally

USFS's Wildfire Risk to Communities model puts Dexter at the 2nd national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 263 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Dexter at the 2nd national percentile — 0 points below its risk-to-structures score, a gap driven by how much is actually built there.

Where Dexter's buildings actually sit

263Total buildings
25.5%Direct exposure
0%Indirect exposure
74.5%Minimal exposure

Most of Dexter's buildings (74.5% of 263) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.

Where Dexter ranks

There's little gap between Dexter's 2nd national percentile and its 2nd percentile inside Minnesota, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Dexter ranks 30,895 for wildfire risk (1 is highest) and 22,733 by building count (1 is largest). Within Minnesota alone, it ranks 895 of 914 places by risk. See the full county-by-county picture for Minnesota on its state page.

What this risk score means for insurance

Dexter's low rating (2nd percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Hardening a home in Dexter

With 74.5% of buildings rated Minimal exposure, Dexter gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.

Where Dexter's figures come from

The methodology guide shows exactly how USFS turned 263 counted buildings into the percentiles shown above for Dexter. The exposure-zones guide covers what Dexter's dominant minimal exposure actually means, with real examples from across the dataset.