Island Pond, VT
Island Pond wildfire risk explained
Out of every US place USFS scores, Island Pond lands at the 8th percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 652 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Island Pond at the 8th national percentile — 0 points below its risk-to-structures score, a gap driven by how much is actually built there.
Island Pond's building exposure, zone by zone
61.2% of Island Pond's 652 buildings sit in USFS's Direct exposure zone, roughly 399 structures close enough to burnable vegetation for flame contact, not just embers — 36% fall in the Indirect, ember-only zone and 2.8% are Minimal.
How Island Pond compares
There's little gap between Island Pond's 8th national percentile and its 2nd percentile inside Vermont, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Island Pond ranks 29,074 for wildfire risk (1 is highest) and 15,185 by building count (1 is largest). Within Vermont alone, it ranks 176 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
Island Pond's low wildfire rating (8th 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.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Island Pond (61.2%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Island Pond's figures come from
Island Pond's 8th-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 Island Pond's dominant direct exposure actually means, with real examples from across the dataset.