WildfireRiskFinder

Slaughters, KY

Slaughters wildfire risk explained

Moderate
29thpercentile nationally

Slaughters's 171 buildings earn a 29th-percentile wildfire-risk score nationally under USFS's model — close to the middle of USFS's national wildfire-risk range. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Slaughters at the 31st national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.

Where Slaughters's buildings actually sit

171Total buildings
60.8%Direct exposure
39.2%Indirect exposure
0%Minimal exposure

171 buildings are counted in Slaughters, and 60.8% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.

How Slaughters compares

Within Kentucky, Slaughters ranks higher (56th percentile) than it does nationally (29th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Slaughters ranks 22,374 for wildfire risk (1 is highest) and 25,985 by building count (1 is largest). Within Kentucky alone, it ranks 247 of 552 places by risk. See the full county-by-county picture for Kentucky on its state page.

Slaughters and the insurance market

Slaughters's moderate rating (29th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Hardening a home in Slaughters

Because Direct exposure dominates in Slaughters (60.8%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.

Where Slaughters's figures come from

Every one of the two percentiles behind Slaughters's 22,374-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Slaughters's dominant direct exposure actually means, with real examples from across the dataset.