Hedonic regression is a type of regression analysis that is used to measure the effect of various factors on the price of a good or service. The dependent variable is usually the price and the independent variables are the attributes of the good or service that are assumed to provide utility to the consumer. Examples of independent variables can include product features, characteristics, or the quality of a good or service.

Hedonic regression models can be used in real estate pricing, particularly when market demand is affected by a variety of different features. For instance, the value or cost of a property may depend on its size, area amenities, proximity to desirable locations such as parks or amusement centres, or local schools. Hedonic regression analysis enables economists to determine the estimated dollar value of these features, adjusting the overall cost of the property.

In addition, hedonic regression models can also be used to adjust quality when computing price indexes, such as for consumer durables or cars. This type of regression analysis is useful for controlling the quality of goods in price indexes, with control variables like horsepower, fuel efficiency, and other features important to car buyers. By controlling for these factors in the regression, researchers can provide reliable and meaningful estimates of consumer prices over time.

All in all, hedonic regression is a powerful and versatile econometric tool that can be effectively used to help determine the impact of different attributes of a good or service on its market value or its inclusion in a price index. This is particularly helpful when dealing with complex markets, where the estimation of prices or price changes cannot be done by simply looking at overall averages. Hedonic regression is a useful way to measure the contribution that each of the factors has on the price, giving economists a more accurate understanding of the reasons behind surges or drops in price in some markets.