Hedonic regression

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Hedonic regression, or more generally hedonic demand theory, in economics is a method of estimating demand or prices. It decomposes the item being researched into its constituent characteristics, and obtains estimates of the value of each characteristic. In essence it assumes that there is a separate market for each characteristic. It may be estimated using ordinary least squares (OLS) regression analysis. Often an attribute vector (or dummy variable) is assigned to each characteristic or group of characteristics. Each characteristic within a vector is either included in the regression or not, by multiplying it by either 1 or 0.

For example, a refrigerator today costs more than a refrigerator 20-30 years ago. But the refrigerators of today are better than a refrigerator from 20 years ago. Whereas the older refrigerators required manual defrosting, ice trays placed in the freezer to make ice, and pitchers in the refrigerator to make cold water, the modern refrigerator is frost-free and usually comes with an icemaker and water dispenser that can be connected to a water line. These improved features are what is called hedonicism or utility. So instead of just looking at the mere price increase of refrigerators, economists look at the price increases caused by the addition of new refrigerator features. Using this method, the price of some items today are adjusted downwards for consumer price index CPI purposes to compensate for the rise in features. Hedonic calculations apply to items like computers, vehicles, or TV's.

It is commonly used in real estate economics and consumer price index (CPI) calculations. In consumer price index calculations hedonic regression is used to control the effect of changes in product quality. Price changes that are due to substitution effects are subject to hedonic quality adjustments.

Some economists, have criticized the US government's use of hedonic regression in computing its CPI, fearing it can be used to mask the "true" inflation rate and thus lower the interest it must pay on Treasury Inflation-Protected Securities (TIPS) and Social Security cost of living adjustments.

In real estate economics, it is used to adjust for the problems associated with researching a good that is as heterogeneous as buildings. Because buildings are so different, it is difficult to estimate the demand for buildings generically. Instead, it is assumed that a house can be decomposed into characteristics such as number of bedrooms, size of plot, or distance to the city center. A hedonic regression equation treats these attributes (or bundles of attributes) separately, and estimates prices (in the case of an additive model) or elasticity (in the case of a log model) for each of them. This information can be used to construct a price index that can be used to compare the price of housing in different cities, or to do time series analysis. As with Consumer price index (CPI) calculations, hedonic pricing can be used to correct for quality changes in constructing a housing price index. It can also be used to assess the value of a property, in the absence of specific market transaction data. It can also be used to analyse the demand for various housing characteristics, and housing demand in general. It has also been used to test assumptions in spatial economics.

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