Cedar Grove, NM
Cedar Grove wildfire risk explained
Cedar Grove's 435 buildings earn a 90th-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Cedar Grove's burn probability — fire likelihood with no building count factored in — sits at the 86th percentile nationally.
Where Cedar Grove's buildings actually sit
100% of Cedar Grove's 435 buildings sit in USFS's Direct exposure zone, roughly 435 structures close enough to burnable vegetation for flame contact, not just embers — 0% fall in the Indirect, ember-only zone and 0% are Minimal.
Cedar Grove against the rest of the country
Cedar Grove's 90th national percentile looks worse in isolation than its 74th ranking inside New Mexico does — this place is on the milder end for its own state, by 16 points. Among the 31,521 US communities USFS scores, Cedar Grove ranks 3,247 for wildfire risk (1 is highest) and 18,552 by building count (1 is largest). Within New Mexico alone, it ranks 129 of 495 places by risk. See the full county-by-county picture for New Mexico on its state page.
What this risk score means for insurance
Cedar Grove's 90th-percentile, very high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Lowering exposure, not just insuring around it
Cedar Grove's 100% 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 Cedar Grove's figures come from
The methodology guide shows exactly how USFS turned 435 counted buildings into the percentiles shown above for Cedar Grove. The exposure-zones guide covers what Cedar Grove's dominant direct exposure actually means, with real examples from across the dataset.