What Is Hedonic Regression?

What Is Hedonic Regression?

Let me explain hedonic regression directly: it's the use of a regression model to estimate how various factors influence the price of a good, or sometimes the demand for it. In this model, the dependent variable is the price or demand, and the independent variables are the attributes of the good that you believe affect utility for the buyer or consumer. The coefficients you get from this estimation show the weights that buyers assign to those qualities.

Key Takeaways

You should know that hedonic regression applies regression analysis to gauge the impact of factors on a good's price or demand. Typically, price serves as the dependent variable, with attributes providing utility to the buyer as independent variables. This approach is standard in real estate pricing and in adjusting quality for price indexes.

Understanding Hedonic Regression

Hedonic regression fits into hedonic pricing models and you'll see it in real estate, retail, and economics. It's a revealed-preference method in economics and consumer science to figure out the relative importance of variables affecting price or demand for a good or service. For instance, if a house's price depends on characteristics like bedrooms, bathrooms, and school proximity, you can use regression to determine each variable's importance.

This regression often uses ordinary least squares or more advanced techniques to estimate how factors affect the price of a product or real estate. You define price as the dependent variable and regress it on independent variables thought to influence it, based on economic theory, your intuition, or consumer research. You could also use an inductive approach like data mining to select variables. Attributes can be continuous or dummy variables.

Applications of Hedonic Regression

The classic application is in the housing market, where a building or land's price comes from its own characteristics—such as size, appearance, features like solar panels or modern fixtures, and condition—plus environmental factors like neighborhood crime rate, access to schools and downtown, pollution levels, or nearby home values.

You can predict any house's price by inputting its attributes into the hedonic regression equation—that's a fast fact to remember.

Hedonic regression also controls for quality changes in consumer price index (CPI) calculations. Model a good's price as a function of attributes, and if attributes change, calculate the price impact. The hedonic quality adjustment subtracts or adds the estimated value of that change from the item's price to remove quality-related differentials.

Origin of Hedonics

In 1974, Sherwin Rosen presented the theory of hedonic pricing in his paper 'Hedonic Pricing and Implicit Markets: Product Differentiation in Pure Competition,' linked to the University of Rochester and Harvard University. Rosen argues that an item's total price is the sum of its homogeneous attributes' prices. You can regress the item's price on these characteristics to find each one's effect.