What’s hedonic regression?
Hedonic regression uses a regression model to quantify variables’ impact on an item’s price or demand. In a hedonic regression model, the dependent variable is the product’s price (or demand). In contrast, the independent variables are features that are thought to affect value for the buyer or customer. The computed coefficients on the independent variables represent purchasers’ weights on the good’s attributes.
Knowledge of Hedonic Regression
Hedonic regression is prevalent in real estate, retail, and economics for pricing models. In economics and consumer science, hedonic pricing relies on revealed preferences to assess the relative impact of factors on price and demand for goods and services. Regression analysis can establish the relative relevance of variables affecting the price of a property, such as the number of bedrooms, bathrooms, and proximity to schools.
Hedonic pricing regression estimates how many factors impact the price of a product or real estate, such as a house, using conventional least squares or more complex regression methods. Regressing the price based on independent factors based on economic theory, intuition, or consumer research defines the price as the dependent variable. An inductive technique, like data mining, can screen and choose variables for the model. The product’s properties might be continuous or dummy variables.
Hedonic Regression Applications
Most often, the hedonic pricing method is used in the housing market, where the price of a building or piece of land is based on its size, appearance, features like solar panels or state-of-the-art faucet fixtures, and condition, as well as its surrounding environment.
Plugging a house’s features into the estimated hedonic regression equation predicts its price.
Hedonic regression controls for product quality variations in consumer price index (CPI) computations. Modeling the price of any product in the CPI basket as a function of a set of qualities allows for the projected impact of changing attributes. The hedonic quality adjustment reduces price differentials due to quality changes by adding or removing the anticipated value of the change from the item’s price.
Hedonics Origin
In 1974, University of Rochester and Harvard University professor Sherwin Rosen published “Hedonic Pricing and Implicit Markets: Product Differentiation in Pure Competition,” which introduced hedonic pricing. Rosen claims in the article that an item’s overall price is the sum of its homogenous qualities’ prices. Regressing an item’s pricing on these attributes shows how each factor affects it.
Conclusion
Hedonic regression uses regression analysis to assess how different factors affect a product’s price or demand.
Price is generally the dependent variable in a hedonic regression model. Buyer or customer utility qualities are the independent variables.
Real estate pricing and price index quality adjustment employ hedonic regression.
