Newport, KY
Newport, KY's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Newport at the 10th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 5,702 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Newport's burn probability — fire likelihood with no building count factored in — sits at the 11th percentile nationally.
Newport's building exposure, zone by zone
Newport rates 92.3% Minimal exposure against just 7.7% Direct and 0% Indirect — of 5,702 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually. At 5,702 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
Where Newport ranks
Newport scores 10th nationally and 10th within Kentucky — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Newport ranks 28,235 for wildfire risk (1 is highest) and 3,032 by building count (1 is largest). Within Kentucky alone, it ranks 497 of 552 places by risk. See the full county-by-county picture for Kentucky on its state page.
Shopping for coverage in Newport
Newport's low wildfire rating (10th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Lowering exposure, not just insuring around it
Newport's 92.3% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Newport's figures come from
Newport's 10th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Newport's dominant minimal exposure actually means, with real examples from across the dataset.