Great Meadows, NJ
Great Meadows, NJ's wildfire risk, in USFS's own numbers
Great Meadows's 194 buildings earn a 40th-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 Great Meadows at the 40th national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Direct exposure dominates in Great Meadows: 78.9% of its 194 buildings, versus 21.1% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
How Great Meadows compares
Great Meadows's risk sits at a similar level relative to New Jersey (44th percentile statewide) as it does nationally (40th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Great Meadows ranks 18,909 for wildfire risk (1 is highest) and 25,088 by building count (1 is largest). Within New Jersey alone, it ranks 396 of 700 places by risk. See the full county-by-county picture for New Jersey on its state page.
What this risk score means for insurance
At the 40th national percentile, Great Meadows rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
With 78.9% of Great Meadows 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 Great Meadows's figures come from
Great Meadows's 40th-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 Great Meadows's dominant direct exposure actually means, with real examples from across the dataset.