McDonald, NC
McDonald, NC's wildfire risk, in USFS's own numbers
USFS scores McDonald at the 71st national percentile for wildfire risk to structures (well above the national norm for wildfire risk), a figure built from 91 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks McDonald at the 70th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where McDonald's buildings actually sit
91 buildings are counted in McDonald, and 62.6% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
How McDonald compares
There's little gap between McDonald's 71st national percentile and its 71st percentile inside North Carolina, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, McDonald ranks 9,177 for wildfire risk (1 is highest) and 29,460 by building count (1 is largest). Within North Carolina alone, it ranks 222 of 772 places by risk. See the full county-by-county picture for North Carolina on its state page.
McDonald and the insurance market
At the 71st percentile nationally, McDonald carries the high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Lowering exposure, not just insuring around it
McDonald's 62.6% 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 McDonald's figures come from
The methodology guide shows exactly how USFS turned 91 counted buildings into the percentiles shown above for McDonald. The exposure-zones guide covers what McDonald's dominant direct exposure actually means, with real examples from across the dataset.