Vineland, NJ
Vineland, NJ's wildfire risk, in USFS's own numbers
USFS scores Vineland at the 78th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 24,516 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Vineland at the 74th percentile, close to its 78th-percentile risk score.
Vineland's building exposure, zone by zone
Of Vineland's 24,516 counted buildings, 44.6% carry Direct exposure and only 37.8% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first. At 24,516 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Where Vineland ranks
Vineland scores 78th nationally and 84th 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, Vineland ranks 7,092 for wildfire risk (1 is highest) and 522 by building count (1 is largest). Within New Jersey alone, it ranks 115 of 700 places by risk. See the full county-by-county picture for New Jersey on its state page.
Shopping for coverage in Vineland
Vineland's high rating (78th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Vineland
Because Direct exposure dominates in Vineland (44.6%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Vineland's figures come from
The methodology guide shows exactly how USFS turned 24,516 counted buildings into the percentiles shown above for Vineland. The exposure-zones guide covers what Vineland's dominant direct exposure actually means, with real examples from across the dataset.