South Milford, IN
How exposed is South Milford to wildfire?
Out of every US place USFS scores, South Milford lands at the 7th percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 180 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks South Milford at the 7th national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
51.7% of South Milford's 180 buildings sit in USFS's Minimal exposure zone, with only 48.3% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
South Milford against the rest of the country
Within Indiana, South Milford ranks higher (46th percentile) than it does nationally (7th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, South Milford ranks 29,304 for wildfire risk (1 is highest) and 25,621 by building count (1 is largest). Within Indiana alone, it ranks 524 of 967 places by risk. See the full county-by-county picture for Indiana on its state page.
Shopping for coverage in South Milford
At the 7th national percentile, South Milford rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in South Milford
With 51.7% of buildings rated Minimal exposure, South Milford gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where South Milford's figures come from
The methodology guide shows exactly how USFS turned 180 counted buildings into the percentiles shown above for South Milford. The exposure-zones guide covers what South Milford's dominant minimal exposure actually means, with real examples from across the dataset.