Hilltown, VA
How exposed is Hilltown to wildfire?
Out of every US place USFS scores, Hilltown lands at the 67th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 166 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Hilltown's burn probability — fire likelihood with no building count factored in — sits at the 70th percentile nationally.
What "at risk" means for the buildings here
USFS classifies 100% of Hilltown's buildings as Direct exposure, higher than its 0% Indirect share and far above its 0% Minimal share — a profile where 166 structures sit close enough to vegetation that lot clearing matters most.
Where Hilltown ranks
Hilltown scores 67th nationally and 79th within Virginia — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Hilltown ranks 10,446 for wildfire risk (1 is highest) and 26,194 by building count (1 is largest). Within Virginia alone, it ranks 146 of 681 places by risk. See the full county-by-county picture for Virginia on its state page.
What this risk score means for insurance
Hilltown's 67th-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.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Hilltown (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Hilltown's figures come from
Every one of the two percentiles behind Hilltown's 10,446-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Hilltown's dominant direct exposure actually means, with real examples from across the dataset.