New Iberia, LA
New Iberia wildfire risk explained
New Iberia's 14,018 buildings earn a 19th-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 Iberia's burn probability — fire likelihood with no building count factored in — sits at the 21st percentile nationally.
Where New Iberia's buildings actually sit
82.8% of New Iberia's 14,018 buildings sit in USFS's Minimal exposure zone, with only 3.9% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself. At 14,018 buildings, this is a substantial built environment, so the shares below describe a real population of structures, not a handful of edge cases.
How New Iberia compares
There's little gap between New Iberia's 19th national percentile and its 18th percentile inside Louisiana, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, New Iberia ranks 25,403 for wildfire risk (1 is highest) and 1,071 by building count (1 is largest). Within Louisiana alone, it ranks 400 of 488 places by risk. See the full county-by-county picture for Louisiana on its state page.
Shopping for coverage in New Iberia
New Iberia's low rating (19th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
New Iberia's 82.8% 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 Iberia's figures come from
The methodology guide shows exactly how USFS turned 14,018 counted buildings into the percentiles shown above for New Iberia. The exposure-zones guide covers what New Iberia's dominant minimal exposure actually means, with real examples from across the dataset.