WildfireRiskFinder

New Vienna, IA

New Vienna wildfire risk explained

Low
4thpercentile nationally

New Vienna's 271 buildings earn a 4th-percentile wildfire-risk score nationally under USFS's model — among the lower wildfire-risk places nationally. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, New Vienna's burn probability — fire likelihood with no building count factored in — sits at the 4th percentile nationally.

What "at risk" means for the buildings here

271Total buildings
11.4%Direct exposure
0%Indirect exposure
88.6%Minimal exposure

Only 11.4% of New Vienna's 271 buildings carry Direct exposure and 0% carry Indirect; the remaining 88.6% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.

Where New Vienna ranks

There's little gap between New Vienna's 4th national percentile and its 8th percentile inside Iowa, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, New Vienna ranks 30,290 for wildfire risk (1 is highest) and 22,466 by building count (1 is largest). Within Iowa alone, it ranks 941 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.

What this risk score means for insurance

At the 4th national percentile, New Vienna rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.

Lowering exposure, not just insuring around it

New Vienna's 88.6% 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 New Vienna's figures come from

New Vienna's 4th-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 New Vienna's dominant minimal exposure actually means, with real examples from across the dataset.