various values of z. The basic idea behind hedonic techniques is that one can use a hedonic equation to calculate the expected price of a particular variety—which may not in fact be offered for sale in the period being considered—based on its observable characteristics. Then, as long as the set of observable characteristics includes all characteristics that matter to consumers and the equation is properly specified, these results can be used to correct for product quality change.
To estimate hedonic equations, variation (either cross-sectional or longitudinal, depending on model specification) in the measurable quantity of an attribute is needed to produce coefficient estimates. Categories of goods for which quality change is frequent but incremental, and for which characteristic changes are easy to measure, are considered the best candidates for hedonic analysis. Most obviously, products must have characteristics that are clearly identifiable as valued by consumers. For computers, a relatively easy case, these might include processing speed, hard drive space, memory, and monitor size. However, in most instances, quality characteristics are more difficult to identify, let alone quantify. For example, measuring the performance of cars is highly subjective, as is quantification of their handling, comfort, or safety. Apparel is even more difficult, since consumers’ valuations may change over time with fashions. Identifying the characteristics of services that consumers’ value can also be very difficult.
The successful use of hedonic methods rests on a modeler’s ability to identify and measure quality-determining characteristics and specify an equation that effectively links them to the prices of different models or varieties. It also depends on the availability of good data. In order to produce meaningful results, one generally needs data on more product models than are represented in a typical price index’s sample of items. In addition, the reliability of regression coefficients depends directly on the amount of variation (both in terms of presence of indicator variables and magnitude of continuous variables) in the set of characteristics specified in the equation.
In theory, quality adjustments to observed model prices (or of a product-specific index covering all models) can be estimated directly from the hedonic regression. In practice, the critical question is whether one can reliably estimate functions that capture the relationship between market price and characteristics that confront individual consumers. Here, the issue of consumer heterogeneity (see Chapter 8) arises again in a way that affects the index’s distributional properties. First, without heterogeneity there would be no hedonic surface in the first place, since identical individuals would choose the same variety (bundle of characteristics) and pay the same price. But because individuals value product characteristics differently at the margin, quality adjustments can alter the relevance of an index as a representation of price changes faced by specific groups or individuals. For instance, people who do not use cell phones do not care about their characteristics, and even the preferences among those who do use them vary greatly. Thus, when prices of cell phones are adjusted to compensate for quality change associated with model turnover, the overall index only becomes more