Hedonic Prices for Multicomponent Products

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The price of a smartphone likely reflects its functional features that consumers value, such as its storage capacity, camera resolution, battery life, display size, and display resolution, among others. However, a nontrivial portion of the price of a smartphone might also be attributable to the brand name and the perceived social value that the brand name imparts, separate from the smartphone’s functionality. By regressing a multicomponent product’s retail price on the product’s individual features, hedonic price analysis enables one to determine how much consumers are willing to pay for each observable feature of a multicomponent product, including a product’s brand name. In this article, we conduct hedonic price analysis of smartphones available in the United States as of December 2018 to test whether a smartphone’s brand name possesses statistically significant explanatory power for a smartphone’s price that is unrelated to the technical functionality of the smartphone, and we find that it does.

As of April 2019, this article is the first publicly reported hedonic price analysis of the demand for features of smartphones in the United States that uses the “least absolute shrinkage and selector operator” (LASSO) regression—an objective variable selection method based on a machine learning algorithm—to identify the functional features that are the best predictors of a smartphone’s price. In contrast, the existing empirical literature has relied on a set of explanatory variables that were chosen either arbitrarily or on the basis of heuristics used by marketing firms. Even after accounting for the objectively selected set of functional features that best predict a smartphone’s retail price, we find that a smartphone’s brand name possesses statistically significant explanatory power for the smartphone’s retail price separate from those functional features. To the extent that hedonic price analysis is used in litigation or arbitration, one advantage of this approach is that it does not rely on any confidential business information, which in turn contributes to its replicability.

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Cite as

J. Gregory Sidak & Jeremy O. Skog, Hedonic Prices for Multicomponent Products, 4 Criterion J. on Innovation 301 (2019).