Indian Field, CT
Indian Field wildfire risk explained
Indian Field sits at the 24th 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 588 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Indian Field's burn probability — fire likelihood with no building count factored in — sits at the 25th percentile nationally.
Indian Field's building exposure, zone by zone
USFS classifies 52% of Indian Field's buildings as Direct exposure, higher than its 0% Indirect share and far above its 48% Minimal share — a profile where 306 structures sit close enough to vegetation that lot clearing matters most.
How Indian Field compares
Indian Field's risk sits at a similar level relative to Connecticut (22nd percentile statewide) as it does nationally (24th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Indian Field ranks 23,850 for wildfire risk (1 is highest) and 15,987 by building count (1 is largest). Within Connecticut alone, it ranks 167 of 214 places by risk. See the full county-by-county picture for Connecticut on its state page.
What this risk score means for insurance
Indian Field's moderate rating (24th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Indian Field (52%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Indian Field's figures come from
The methodology guide shows exactly how USFS turned 588 counted buildings into the percentiles shown above for Indian Field. The exposure-zones guide covers what Indian Field's dominant direct exposure actually means, with real examples from across the dataset.