What is Hedonic Regression?
Hedonic regression leverages a regression model to assess the influence that various factors exert on the price of a good, or occasionally its demand. In this model, the dependent variable is the price (or demand) of the good, and the independent variables are the traits believed to sway the buyer or consumer’s utility, like number of bedrooms in a house or the quality of school districts nearby. The estimated coefficients on these variables highlight the relative importance buyers place on each attribute.
Key Takeaways
- Hedonic regression applies regression analysis to estimate how different factors impact the price or demand for a good.
- Typically, price serves as the dependent variable while attributes offering utility to buyers are considered independent variables.
- This technique is widely used in real estate pricing and for adjusting quality in price indexes.
Understanding Hedonic Regression
Hedonic regression is foundational in creating hedonic pricing models, chiefly utilized in real estate, retail, and economic analysis. By deciphering the impact of characteristics like the number of rooms or vicinity to amenities, regression analysis helps estimate their relative importance.
These pricing regressions use methods like ordinary least squares or more advanced techniques to quantify the impact of various factors on pricing. The attributes can be continuous or categorical variables, selected based on theory, intuition, or consumer research. Even data mining approaches can help determine the variables to include.
Applications of Hedonic Regression
Real Estate:
The housing market frequently sees hedonic regression applied to estimate prices based on property attributes and environmental factors. Component features that might be analyzed include property size, condition, nearby amenities, security, and overall neighborhood reputation. By inputting the attributes into a hedonic regression equation, the price of properties can be precisely forecasted.
Consumer Price Index (CPI):
Hedonic regression is instrumental in CPI calculations when accommodating changes in product quality. With product prices modeled based on various attributes, any quality change’s effect on price can be isolated and neutralized, ensuring accurate pricing through the hedonic quality adjustment method.
Origin of Hedonics
In 1974, Sherwin Rosen introduced a pioneering theory of hedonic pricing in his paper “Hedonic Pricing and Implicit Markets: Product Differentiation in Pure Competition.” This work posited that a product’s total price can be decomposed into the sum of prices of its distinct characteristics. By dissecting these unique features, their individual price impacts can be quantitatively determined.
Related Terms: regression analysis, hedonic pricing, consumer price index, revealed-preference, data mining.
References
- U.S. Bureau Labor of Statistics. “Frequently Asked Questions about Hedonic Quality Adjustment in the CPI”.
- New York University. “Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition”.
