What is hedonic regression?
Hedonic regression is the use of a regression model to estimate the influence that various factors have on the price of a good, or sometimes the demand for a good. In a hedonic regression model, the dependent variable is the price (or demand) of the good, and the independent variables are the attributes of the good that are believed to influence the utility to the buyer or consumer of the good. The estimated coefficients resulting from the independent variables can be interpreted as the weights that buyers give to the various qualities of the good.
- Hedonic regression is the application of regression analysis to estimate the impact that various factors have on the price or demand of a good.
- In a hedonic regression model, a price is usually the dependent variable and the attributes that are believed to provide utility to the buyer or consumer are the independent variables.
- Hedonic regression is commonly used in real estate pricing and quality adjustment for price indices.
Understanding hedonic regression
Hedonic regression is used in hedonic pricing models and is commonly applied in real estate, retail, and economics. Hedonic pricing is a revealed preference method used in economics and consumer science to determine the relative importance of variables that affect the price or demand for a good or service. For example, if the price of a house is determined by different characteristics, such as the number of bedrooms, the number of bathrooms, the proximity to schools, etc., regression analysis can be used to determine the relative importance of each variable.
Hedonic price regression uses ordinary least squares, or more advanced regression techniques, to estimate the extent to which various factors affect the price of a product or real estate, such as a house. Price is defined as the dependent variable and falls back on a set of independent variables believed to influence price, based on economic theory, researcher intuition, or consumer research. Alternatively, an inductive approach, such as data mining, can be used to filter and determine the variables to include in the model. The selected characteristics (called attributes) of the good can be represented as continuous or dummy variables.
Applications of hedonic regression
The most common example of the hedonic pricing method is the housing market, in which the price of a building or land is determined by the characteristics of the property itself (e.g. size, appearance, features such as solar panels or property status). faucet fixtures and conditions), as well as the characteristics of its surrounding environment (for example, if the neighborhood has a high crime rate and / or is accessible to schools and the city center, the level of water contamination and air, or the value of other nearby homes). The price of any given house can then be predicted by plugging the attributes of that house into the estimated equation.
Hedonic regression is also used in calculations of the consumer price index (CPI), to control for the effect of changes in product quality. The price of any good in the CPI basket can be modeled based on a set of attributes, and when one or more of these attributes change, its estimated impact on the price can be calculated. The hedonic quality adjustment method removes any price differential attributed to a change in quality by adding or subtracting the estimated value of that change from the item’s price.
Origin of hedonic
In 1974, Sherwin Rosen first presented a theory of hedonic prices in his article, “Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition,” affiliated with the University of Rochester and Harvard University. In the publication, Rosen argues that the total price of an item can be considered as the sum of the price of each of its homogeneous attributes. The price of an item can also be regressed on these unique characteristics to determine the effect of each characteristic on its price.