Barnet, VT
Barnet wildfire risk explained
Barnet sits at the 16th 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 106 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Barnet's burn probability — fire likelihood with no building count factored in — sits at the 16th percentile nationally.
Barnet's building exposure, zone by zone
USFS classifies 80.2% of Barnet's buildings as Direct exposure, higher than its 19.8% Indirect share and far above its 0% Minimal share — a profile where 85 structures sit close enough to vegetation that lot clearing matters most.
Barnet against the rest of the country
Within Vermont, Barnet ranks higher (56th percentile) than it does nationally (16th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Barnet ranks 26,485 for wildfire risk (1 is highest) and 28,788 by building count (1 is largest). Within Vermont alone, it ranks 76 of 179 places by risk. See the full county-by-county picture for Vermont on its state page.
What this risk score means for insurance
Barnet's low wildfire rating (16th 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 Barnet
With 80.2% of Barnet 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 Barnet's figures come from
Every one of the two percentiles behind Barnet's 26,485-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Barnet's dominant direct exposure actually means, with real examples from across the dataset.