Ford Cliff, PA
Ford Cliff wildfire risk explained
USFS scores Ford Cliff at the 34th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 229 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Ford Cliff at the 36th national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
Ford Cliff's building exposure, zone by zone
Indirect exposure is dominant in Ford Cliff (96.9% of 229 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 3.1% sit in the Direct zone.
How Ford Cliff compares
There's little gap between Ford Cliff's 34th national percentile and its 47th percentile inside Pennsylvania, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Ford Cliff ranks 20,842 for wildfire risk (1 is highest) and 23,834 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,055 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Ford Cliff and the insurance market
Ford Cliff's moderate rating (34th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
Ford Cliff's 96.9% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Ford Cliff's figures come from
The methodology guide shows exactly how USFS turned 229 counted buildings into the percentiles shown above for Ford Cliff. The exposure-zones guide covers what Ford Cliff's dominant indirect exposure actually means, with real examples from across the dataset.