Micanopy, FL
How exposed is Micanopy to wildfire?
Out of every US place USFS scores, Micanopy lands at the 91st percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 341 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Micanopy's burn probability — fire likelihood with no building count factored in — sits at the 90th percentile nationally.
Micanopy's building exposure, zone by zone
341 buildings are counted in Micanopy, and 99.1% 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.
Where Micanopy ranks
Micanopy's risk sits at a similar level relative to Florida (84th percentile statewide) as it does nationally (91st) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Micanopy ranks 2,731 for wildfire risk (1 is highest) and 20,541 by building count (1 is largest). Within Florida alone, it ranks 152 of 955 places by risk. See the full county-by-county picture for Florida on its state page.
What this risk score means for insurance
Micanopy's 91st-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.
What would actually reduce this score
With 99.1% of Micanopy in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Micanopy's figures come from
The methodology guide shows exactly how USFS turned 341 counted buildings into the percentiles shown above for Micanopy. The exposure-zones guide covers what Micanopy's dominant direct exposure actually means, with real examples from across the dataset.