New Milford, NJ
New Milford, NJ's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts New Milford at the 23rd national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 4,630 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, New Milford's burn probability — fire likelihood with no building count factored in — sits at the 24th percentile nationally.
New Milford's building exposure, zone by zone
USFS classifies 87.7% of New Milford's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 12.4% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
New Milford against the rest of the country
New Milford scores 23rd nationally and 9th 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, New Milford ranks 24,398 for wildfire risk (1 is highest) and 3,673 by building count (1 is largest). Within New Jersey alone, it ranks 635 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 23rd national percentile, New Milford 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
Even with 87.7% of New Milford outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where New Milford's figures come from
The methodology guide shows exactly how USFS turned 4,630 counted buildings into the percentiles shown above for New Milford. The exposure-zones guide covers what New Milford's dominant minimal exposure actually means, with real examples from across the dataset.