WildfireRiskFinder

Seabrook, MA

Seabrook, MA's wildfire risk, in USFS's own numbers

Moderate
29thpercentile nationally

Seabrook's 289 buildings earn a 29th-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.)

Fire likelihood alone (USFS's burn-probability figure) ranks Seabrook at the 21st national percentile — 8 points below its risk-to-structures score, a gap driven by how much is actually built there.

What "at risk" means for the buildings here

289Total buildings
39.5%Direct exposure
60.6%Indirect exposure
0%Minimal exposure

289 buildings are counted in Seabrook, and 60.6% of them are Indirect exposure — ember-driven risk rather than the 39.5% in Direct exposure or the 0% rated Minimal.

Where Seabrook ranks

Compare Seabrook's two percentiles: 72nd within Massachusetts, only 29th nationally — a gap of 43 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Seabrook ranks 22,342 for wildfire risk (1 is highest) and 21,943 by building count (1 is largest). Within Massachusetts alone, it ranks 71 of 248 places by risk. See the full county-by-county picture for Massachusetts on its state page.

Seabrook and the insurance market

Seabrook's moderate rating (29th 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 Seabrook

Because 60.6% of Seabrook's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.

Where Seabrook's figures come from

Seabrook's 29th-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 Seabrook's dominant indirect exposure actually means, with real examples from across the dataset.