# Hedonic Regression - Explained

What is a Hedonic Regression?

# What is a Hedonic Regression?

Hedonic regression refers to a revealed preference approach applied in consumer science and economics to determine the variables relative importance that affects the price of service or good. Regression analysis is the one used to determine the factors that affect the product or real estate price.

## How does a Hedonic Regression Work?

The theory of hedonic pricing was first presented in 1974 by Sherwin Rosen in his paper titled, Hedonic Pricing and Implicit Markets: Product Differentiation in Pure Competition, associated with the University of Harvard and Rochester University. In this publication, Rosen reasons that the total price of an item is the homogeneous attributes price. Also, the price of the item can be regressed on the unique characteristics to determine each of the characteristics of its price.

## Hedonic Regression Basics

You can use hedonic regression in hedonic pricing models. It is common among those individuals in real estate, economics, and retail. For instance, if people use individual characteristics such as the number of bathrooms or bedrooms to determine the price of a house, then regression analysis can be applied to weigh each variables relative importance.

## Example

The housing market is an example of the hedonic pricing method. Here the building price is determined by the propertys characteristics such as appearance, size, including the surrounding environments features such as proximity to school, crime rate, etc. Hedonic regression models usually regress one units commodity price on a function of the models characteristics as well as the time variable. The assumption is that a model prices sample can be collected either two time periods or more together with the associated models vector characteristics. You can also make use of hedonic regression to calculate the consumer price index, to control changes effects in the quality of a product. This method of hedonic adjustment usually removes differential in price that is attributed to quality change by either adding or subtracting the changes estimated value from the old items price.