Unravelling Market Shifts in the Pricing of Composite Goods Using Hedonic Price Index Methods

In the Spring 2017 edition of BRG Review, John Davis provides an in-depth analysis of how hedonic models can be used to estimate price indexes for composite goods that include many product characteristics. He describes four methods that each address differently that the importance of product characteristics to consumers may change over time. He applies each method to data for home sales and home characteristics in Oakland, California, showing that a hybrid model provides the best statistical fit of the data. (paper starts on page 14)

From the abstract:

This paper presents techniques for estimating hedonic price regressions and hedonic price indices. These methods are suited to an analysis of the impact that product characteristics have on the prices of composite goods, such as smartphones and single-family homes. An application of the methods described in this paper uncovers the presence of a market shift in both price-product characteristic relationships and the rate of price growth. The analysis finds that a price bubble occurred in Oakland, California, home sales in the mid-2000s that was associated with price growth in historically higher-minority (and higher-poverty) neighborhoods.