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Concept
Hedonic regression
Summary
In economics, hedonic regression, also sometimes called hedonic demand theory, is a revealed preference method for estimating demand or value. It decomposes the item being researched into its constituent characteristics, and obtains estimates of the contributory value for each. This requires that the composite good (the item being researched and valued) can be reduced to its constituent parts and that those resulting parts are in some way valued by the market. Hedonic models are most commonly estimated using regression analysis, although some more generalized models such as sales adjustment grids are special cases which do not.
An attribute vector, which may be a dummy or panel variable, is assigned to each characteristic or group of characteristics. Hedonic models can accommodate non-linearity, variable interaction, and other complex valuation situations.
Hedonic models are commonly used in real estate appraisal, real estate economics and Consumer Price Index (CPI) calculations. In CPI 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.
In real estate economics, Hedonic regression is used to adjust for the issues associated with researching a good that is as heterogeneous, such as buildings. Because individual 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 its amount of bedrooms, the size of its lot, or its distance from 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.
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