Ivyland, PA
Ivyland wildfire risk explained
USFS's Wildfire Risk to Communities model puts Ivyland at the 14th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 424 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Ivyland's burn probability — fire likelihood with no building count factored in — sits at the 16th percentile nationally.
What "at risk" means for the buildings here
Ivyland rates 80.4% Minimal exposure against just 19.6% Direct and 0% Indirect — of 424 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
How Ivyland compares
Ivyland's risk sits at a similar level relative to Pennsylvania (14th percentile statewide) as it does nationally (14th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Ivyland ranks 26,990 for wildfire risk (1 is highest) and 18,766 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,717 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
What this risk score means for insurance
Ivyland's low wildfire rating (14th 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
Even with 80.4% of Ivyland outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Ivyland's figures come from
Every one of the two percentiles behind Ivyland's 26,990-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Ivyland's dominant minimal exposure actually means, with real examples from across the dataset.