Bulls Gap, TN
How exposed is Bulls Gap to wildfire?
USFS scores Bulls Gap at the 74th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 532 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 Bulls Gap at the 79th percentile, close to its 74th-percentile risk score.
Bulls Gap's building exposure, zone by zone
Direct exposure dominates in Bulls Gap: 62.2% of its 532 buildings, versus 37.8% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Bulls Gap against the rest of the country
There's little gap between Bulls Gap's 74th national percentile and its 74th percentile inside Tennessee, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Bulls Gap ranks 8,169 for wildfire risk (1 is highest) and 16,839 by building count (1 is largest). Within Tennessee alone, it ranks 129 of 502 places by risk. See the full county-by-county picture for Tennessee on its state page.
Shopping for coverage in Bulls Gap
Bulls Gap's 74th-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
What would actually reduce this score
Because Direct exposure dominates in Bulls Gap (62.2%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Bulls Gap's figures come from
The methodology guide shows exactly how USFS turned 532 counted buildings into the percentiles shown above for Bulls Gap. The exposure-zones guide covers what Bulls Gap's dominant direct exposure actually means, with real examples from across the dataset.