Mineral Point, MO
Mineral Point wildfire risk explained
USFS scores Mineral Point at the 66th national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 146 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Mineral Point at the 68th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Mineral Point's buildings actually sit
60.3% of Mineral Point's 146 buildings sit in USFS's Direct exposure zone, roughly 88 structures close enough to burnable vegetation for flame contact, not just embers — 39.7% fall in the Indirect, ember-only zone and 0% are Minimal.
Mineral Point against the rest of the country
There's little gap between Mineral Point's 66th national percentile and its 73rd percentile inside Missouri, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Mineral Point ranks 10,624 for wildfire risk (1 is highest) and 27,028 by building count (1 is largest). Within Missouri alone, it ranks 292 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.
Shopping for coverage in Mineral Point
Mineral Point's high rating (66th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Mineral Point
Because Direct exposure dominates in Mineral Point (60.3%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Mineral Point's figures come from
The methodology guide shows exactly how USFS turned 146 counted buildings into the percentiles shown above for Mineral Point. The exposure-zones guide covers what Mineral Point's dominant direct exposure actually means, with real examples from across the dataset.