Kinnelon, NJ
How exposed is Kinnelon to wildfire?
Kinnelon sits at the 44th percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 3,781 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Kinnelon at the 42nd percentile, close to its 44th-percentile risk score.
What "at risk" means for the buildings here
Of Kinnelon's 3,781 counted buildings, 97.5% carry Direct exposure and only 0.2% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
How Kinnelon compares
Kinnelon scores 44th nationally and 48th within New Jersey — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Kinnelon ranks 17,748 for wildfire risk (1 is highest) and 4,443 by building count (1 is largest). Within New Jersey alone, it ranks 366 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 44th national percentile, Kinnelon rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
With 97.5% of Kinnelon 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 Kinnelon's figures come from
Kinnelon's 44th-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 Kinnelon's dominant direct exposure actually means, with real examples from across the dataset.