Pricing and uncertainty in the leasing of durable goods

Pricing is a critical issue in the strategic marketing of durable goods, both to companies selling goods and to consumers choosing among alternative products. The multi-dimensional pricing of durable goods is a complex problem. We are interested in the basic question of how profit maximizing firms set prices while considering the effect these prices have on customers\u27 demand. We develop a modeling framework for a class of problems in which there are two players (a decision-maker and a segment of customers), there are multiple prices (some of which are risky parameters), and there are unresolved uncertainties at the time the pricing decisions are made (which affect how the risky parameters contribute to the players\u27 gains and losses). We apply this framework to the problem of pricing leasing contracts. We first study the basic pricing question for an operating lease with a guaranteed buyback option, and consider several extensions on the consumers\u27 choice model. We then study the pricing of other types of lease (fair-market and open-end), that differ in the way the burden associated with the risk parameter is assigned among players. Among the findings of our extensive numerical analysis is the observation that, when the manufacturer sets prices to maximize his total expected revenue from leasing, he does so by trading increases in the revenues per lease with decreases in customer demand, and vice versa. This tradeoff is present in the manufacturer\u27s pricing decisions as we often find that higher interest rates are associated with lower prices, and vice versa. In general, the manufacturer sets prices in order to reduce salvage value risk, as long as the loss in market share is not significant. Customers with high willingness to pay are less concerned about the amount of risk associated with the type of lease contract, and are willing to accept higher lease payments, down payment or expected expenses. We also find that when setting the lease pricing variables, the region around the optimal solution is often very flat, making it possible for the manufacturer to offer a wide variety of non-optimal prices without significantly decreasing his expected revenue