InsuranceQuoteToolkit

How we calculate estimates

Every calculator on InsuranceQuoteToolkit is built on a transparent formula. Below is the exact math each tool uses.

Auto Insurance Estimator

annual = state_base × age_factor × vehicle_factor × history_factor × tier_factor / 1.05
  • state_base — average annual auto premium for the state (from NAIC + state DOI averages)
  • age_factor — 2.10 (under 21), 1.60 (21–24), 1.20 (25–29), 1.00 (30–64), 1.08 (65–74), 1.25 (75+)
  • vehicle_factor — sedan 1.00, SUV 1.08, truck 1.05, EV 1.18, luxury 1.35, sports 1.45
  • history_factor — clean 1.00, minor 1.25, major 1.65, DUI 2.10
  • tier_factor — minimum 0.75, standard 1.00, full 1.45

Life Insurance Estimator

coverage = (income × earning_years + debt) × goal_multiplier
premium_mid = (coverage / 1000) × (0.60 + max(0, age − 25) × 0.05)
  • earning_years — 10 + 2 per dependent (5 if no dependents)
  • goal_multiplier — income-replacement 1.0, debt-payoff 0.6, legacy 1.4, final-expenses 0.25
  • Premium range published as 0.75× (low) to 1.40× (high) of midpoint to reflect carrier variation

Home Insurance Estimator

value_factor = max(0.5, value / 300000)
sqft_factor = max(0.6, sqft / 1800)
annual = state_base × value_factor × sqft_factor × tier_factor × (0.85 + (risk − 1) × 0.6)
  • state_base — state average homeowners premium
  • risk — regional risk factor (0.88–1.25) reflecting catastrophe exposure
  • tier_factor — minimum (ACV) 0.75, standard 1.00, extended replacement 1.45

SR-22 Calculator

surcharge_pct = violation_factor × history_factor
new_annual = state_base × (1 + surcharge_pct)
  • violation_factor — DUI 0.95, reckless 0.70, no-insurance 0.40, repeat 1.20, license-suspension 0.55
  • history_factor — clean 1.00, minor 1.10, major 1.25, prior DUI 1.40
  • Filing fee added from state-specific schedule ($0–$50)

Important caveats

These are educational estimates, not quotes. Actual premiums depend on carrier-specific underwriting (credit score where allowed, vehicle VIN, prior insurance history, claim history, occupation, marital status, ZIP-level loss data, and dozens of other factors) that we intentionally do not collect.