New Franklin, MO
New Franklin wildfire risk explained
New Franklin sits at the 53rd percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 574 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 New Franklin at the 54th percentile, close to its 53rd-percentile risk score.
What "at risk" means for the buildings here
Indirect exposure is dominant in New Franklin (76.7% of 574 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 23.3% sit in the Direct zone.
New Franklin against the rest of the country
New Franklin scores 53rd nationally and 39th within Missouri — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, New Franklin ranks 14,879 for wildfire risk (1 is highest) and 16,183 by building count (1 is largest). Within Missouri alone, it ranks 654 of 1,062 places by risk. See the full county-by-county picture for Missouri on its state page.
Shopping for coverage in New Franklin
At the 53rd national percentile, New Franklin rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
Because 76.7% of New Franklin's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where New Franklin's figures come from
New Franklin's 53rd-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 New Franklin's dominant indirect exposure actually means, with real examples from across the dataset.