Milford Square, PA
Milford Square wildfire risk explained
USFS's Wildfire Risk to Communities model puts Milford Square at the 14th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 420 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Milford Square's burn probability — fire likelihood with no building count factored in — sits at the 13th percentile nationally.
What "at risk" means for the buildings here
USFS classifies 50.7% of Milford Square's buildings as Direct exposure, higher than its 21.4% Indirect share and far above its 27.9% Minimal share — a profile where 213 structures sit close enough to vegetation that lot clearing matters most.
Where Milford Square ranks
Milford Square scores 14th nationally and 13th within Pennsylvania — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Milford Square ranks 27,212 for wildfire risk (1 is highest) and 18,825 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,737 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Milford Square and the insurance market
Milford Square'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
Because Direct exposure dominates in Milford Square (50.7%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Milford Square's figures come from
Milford Square's 14th-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 Milford Square's dominant direct exposure actually means, with real examples from across the dataset.