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One number from 0 to 100 that tells you, at a glance, how good a lease is by the 1% rule, payment vs MSRP. So you stop guessing and choose with confidence.
Leases and loans are deliberately confusing: money factor, residual, cap cost. It is nearly impossible to tell if a price is good. The Hunter Score collapses all of it into one honest number: the higher it is, the better the deal. No US lease service does this.
Top-tier: payment near or below 1% of MSRP per month. Wholesale, not retail. Lock it.
A genuinely good deal: payment around the 1% rule, a healthy payment-to-MSRP ratio.
Around the typical payment-to-MSRP for this car. Fine, not a standout. Compare with higher-scored cars.
Payment is high relative to MSRP for this car. Keep looking.
LCR = monthly payment as a percent of MSRP. The rule of thumb: a great lease is about 1% of MSRP per month, and lower is better. A $40,000 car near $400/mo (1.0%) is the ideal; around $320/mo (0.8%) scores 100, around 2.4% scores 0. It is an absolute yardstick that does not depend on anyone's opinion of "the market".
It is an absolute measure: the same car earns the same score anywhere, with no "market" assumptions.
Finance uses a different rule: the score is the discount off MSRP plus the APR against the average for your credit tier (Experian data).
An ideal deal (95-100, "Hunter Verified Steal") is a payment near 0.8% of MSRP per month, with no markups. Example: a $40,000 car around $320/mo. That is wholesale, not retail.
The payment-to-MSRP ratio is mapped to 0-100.
LCR% = monthly_payment / MSRP * 100 // the 1% rule Hunter Score = round( clamp( (2.4 - LCR%) / (2.4 - 0.8) * 100 , 0..100 ) ) Bands: >=85 Steal | 65-84 Strong | 50-64 Fair | <50 Above Market
If data is incomplete or the payment is implausibly low (below 0.6% of MSRP, almost always a data error), we show "Pending" instead of a made-up number. Better nothing than a fake 100.
Today the score measures price against the market and MSRP. Next we add anti-markup factors: the dealer’s money-factor markup, the share of available incentives captured, and fee cleanliness (doc fee within California’s cap, no junk add-ons). The more data, the harder it is to game.