What is Hedonic Regression Method?

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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 believed to influence utility for the buyer or consumer of the good. The resulting estimated coefficients on the independent variables can be interpreted as the weights that buyers place on the various qualities of the good.

Definition

The hedonic regression method involves using regression models to estimate the impact of various factors on the price or demand of a product. In a hedonic regression model, the dependent variable is the product's price or demand, while the independent variables are the product attributes believed to affect the buyer's or consumer's utility. The estimated coefficients of these independent variables can be interpreted as the buyer's valuation of different qualities of the product.

Origin

The hedonic regression method originated in the fields of economics and market research, initially used to analyze price differences in the real estate market. Economists began using this method in the 1960s to study the impact of different attributes on product prices.

Categories and Features

The hedonic regression method is mainly divided into linear and nonlinear regression. Linear regression assumes a linear relationship between independent and dependent variables, while nonlinear regression allows for more complex relationships. Linear regression is simple and easy to use, suitable for beginners, whereas nonlinear regression can more accurately capture complex market dynamics.

Case Studies

A typical case is the analysis of the real estate market. Researchers use the hedonic regression method to analyze the impact of different house characteristics (such as location, size, age) on house prices. Another case is the automobile market, analyzing the impact of different brands, models, and configurations on car prices.

Common Issues

Common issues investors face when using the hedonic regression method include model overfitting and improper variable selection. Overfitting can lead to poor model performance on new data, while improper variable selection may result in biased outcomes.

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