Melbourne, IA
Melbourne wildfire risk explained
Melbourne sits at the 21st percentile nationally for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — per USFS's Wildfire Risk to Communities model, built from its 500 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Melbourne at the 21st national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Only 18.6% of Melbourne's 500 buildings carry Direct exposure and 0% carry Indirect; the remaining 81.4% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.
How Melbourne compares
Melbourne's risk sits at a similar level relative to Iowa (34th percentile statewide) as it does nationally (21st) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Melbourne ranks 24,991 for wildfire risk (1 is highest) and 17,352 by building count (1 is largest). Within Iowa alone, it ranks 677 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.
Melbourne and the insurance market
Melbourne's moderate wildfire rating (21st percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Hardening a home in Melbourne
Melbourne's 81.4% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Melbourne's figures come from
Melbourne's 21st-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 Melbourne's dominant minimal exposure actually means, with real examples from across the dataset.