Understanding the Role of Trade-Ins in Durable Goods Markets: Theory and Evidence

T act of trading in a used car as partial payment for a new car resonates with practically all consumers. Such transactions are prevalent in many other durable goods markets, ranging from golf clubs to CT scanners. What roles do trade-ins play in these markets? What motivates the seller to set up a channel to facilitate trade-ins? Intuitively, accepting a trade-in would appear to stimulate demand for the producer’s product, but facilitating the resale of these used goods that substitute for new goods might also increase cannibalization. Although such transactions involve billions of dollars, we know relatively little about this practice from the extant research literature. This paper develops an analytical model that incorporates key features of real-world durable goods markets: (i) coexistence of new and used goods markets, (ii) consumer heterogeneity with respect to quality sensitivity, (iii) firms that anticipate the cannibalization problem arising from the coexistence of new and used goods, and (iv) lemon problems in resale markets, whereby sellers of used goods are better informed than buyers about the quality of their particular item. In our analysis, a trade-in policy amounts to an intervention by the firm in the used goods market, which reduces inefficiencies arising from the lemon problem. It motivates owners to purchase new goods and reduces their proclivity to hold on to purchased goods because of the low price the latter would fetch in a lemon market. We also show that trade-in programs are more valuable for less reliable products, as well as for products that deteriorate more slowly. Our analysis shows that producers in durable goods markets should consider trade-in programs as a matter of routine. Despite cannibalization concerns arising from a more active resale market, a producer’s profits will inevitably rise from introducing trade-ins, given pervasive lemon problems. We test the key predictions of the model about price and volume of trade by assembling a data set of transactions of U.S. automobile consumers and find broad support for our model.