Kitzmiller, MD
How exposed is Kitzmiller to wildfire?
USFS's Wildfire Risk to Communities model puts Kitzmiller at the 56th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 199 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Kitzmiller at the 54th national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
Kitzmiller's building exposure, zone by zone
Direct exposure dominates in Kitzmiller: 67.3% of its 199 buildings, versus 32.7% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Kitzmiller against the rest of the country
Compare Kitzmiller's two percentiles: 79th within Maryland, only 56th nationally — a gap of 23 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Kitzmiller ranks 13,866 for wildfire risk (1 is highest) and 24,887 by building count (1 is largest). Within Maryland alone, it ranks 112 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Kitzmiller and the insurance market
At the 56th national percentile, Kitzmiller rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Kitzmiller's 67.3% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Kitzmiller's figures come from
Every one of the two percentiles behind Kitzmiller's 13,866-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Kitzmiller's dominant direct exposure actually means, with real examples from across the dataset.