Dublin, PA
Dublin, PA's wildfire risk, in USFS's own numbers
Dublin sits at the 14th percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 578 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Dublin at the 12th percentile, close to its 14th-percentile risk score.
What "at risk" means for the buildings here
Of Dublin's 578 counted buildings, 45% carry Direct exposure and only 43.3% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Where Dublin ranks
There's little gap between Dublin's 14th national percentile and its 12th percentile inside Pennsylvania, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Dublin ranks 27,275 for wildfire risk (1 is highest) and 16,135 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,744 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
Dublin'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.
What would actually reduce this score
With 45% of Dublin in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Dublin's figures come from
The methodology guide shows exactly how USFS turned 578 counted buildings into the percentiles shown above for Dublin. The exposure-zones guide covers what Dublin's dominant direct exposure actually means, with real examples from across the dataset.