Norton Center, MA
Norton Center, MA's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Norton Center lands at the 15th percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 587 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Norton Center's burn probability — fire likelihood with no building count factored in — sits at the 14th percentile nationally.
Norton Center's building exposure, zone by zone
USFS classifies 69.5% of Norton Center's buildings as Direct exposure, higher than its 28.8% Indirect share and far above its 1.7% Minimal share — a profile where 408 structures sit close enough to vegetation that lot clearing matters most.
Where Norton Center ranks
Norton Center scores 15th nationally and 7th within Massachusetts — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Norton Center ranks 26,724 for wildfire risk (1 is highest) and 16,002 by building count (1 is largest). Within Massachusetts alone, it ranks 231 of 248 places by risk. See the full county-by-county picture for Massachusetts on its state page.
Norton Center and the insurance market
Norton Center's low wildfire rating (15th 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 Norton Center (69.5%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Norton Center's figures come from
Norton Center's 15th-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 Norton Center's dominant direct exposure actually means, with real examples from across the dataset.