New Lisbon, IN
New Lisbon wildfire risk explained
New Lisbon sits at the 2nd percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 146 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, New Lisbon's burn probability — fire likelihood with no building count factored in — sits at the 1st percentile nationally.
What "at risk" means for the buildings here
61% of New Lisbon's 146 buildings sit in USFS's Minimal exposure zone, with only 39% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
Where New Lisbon ranks
There's little gap between New Lisbon's 2nd national percentile and its 11th percentile inside Indiana, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, New Lisbon ranks 31,046 for wildfire risk (1 is highest) and 27,015 by building count (1 is largest). Within Indiana alone, it ranks 866 of 967 places by risk. See the full county-by-county picture for Indiana on its state page.
Shopping for coverage in New Lisbon
At the 2nd national percentile, New Lisbon rates low for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Even with 61% of New Lisbon outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where New Lisbon's figures come from
Every one of the two percentiles behind New Lisbon's 31,046-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what New Lisbon's dominant minimal exposure actually means, with real examples from across the dataset.