Short Hills, NJ
Short Hills, NJ's wildfire risk, in USFS's own numbers
USFS scores Short Hills at the 33rd national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 4,795 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Short Hills's burn probability — fire likelihood with no building count factored in — sits at the 33rd percentile nationally.
Short Hills's building exposure, zone by zone
USFS classifies 70.6% of Short Hills's buildings as Direct exposure, higher than its 0% Indirect share and far above its 29.4% Minimal share — a profile where 3,384 structures sit close enough to vegetation that lot clearing matters most.
How Short Hills compares
There's little gap between Short Hills's 33rd national percentile and its 32nd percentile inside New Jersey, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Short Hills ranks 20,991 for wildfire risk (1 is highest) and 3,559 by building count (1 is largest). Within New Jersey alone, it ranks 476 of 700 places by risk. See the full county-by-county picture for New Jersey on its state page.
Short Hills and the insurance market
At the 33rd national percentile, Short Hills rates moderate 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
Short Hills's 70.6% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Short Hills's figures come from
The methodology guide shows exactly how USFS turned 4,795 counted buildings into the percentiles shown above for Short Hills. The exposure-zones guide covers what Short Hills's dominant direct exposure actually means, with real examples from across the dataset.