WildfireRiskFinder

Cornish, ME

Cornish wildfire risk explained

Low
17thpercentile nationally

Cornish sits at the 17th 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 502 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Cornish's burn probability — fire likelihood with no building count factored in — sits at the 18th percentile nationally.

What "at risk" means for the buildings here

502Total buildings
64.1%Direct exposure
35.9%Indirect exposure
0%Minimal exposure

502 buildings are counted in Cornish, and 64.1% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.

How Cornish compares

Within Maine, Cornish ranks higher (80th percentile) than it does nationally (17th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Cornish ranks 26,094 for wildfire risk (1 is highest) and 17,312 by building count (1 is largest). Within Maine alone, it ranks 32 of 155 places by risk. See the full county-by-county picture for Maine on its state page.

What this risk score means for insurance

Cornish's low wildfire rating (17th 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.

What would actually reduce this score

Because Direct exposure dominates in Cornish (64.1%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.

Where Cornish's figures come from

The methodology guide shows exactly how USFS turned 502 counted buildings into the percentiles shown above for Cornish. The exposure-zones guide covers what Cornish's dominant direct exposure actually means, with real examples from across the dataset.