Using a regression model to assess the influence of numerous factors on the price of a good, or occasionally the demand for a commodity, is known as hedonic regression. In this method, the good/service or asset is fractated into constituent parts or characteristics. This determines the contributory value of each characteristic separately through regression analysis.
The dependent variable in a hedonic regression model is the price (or demand) of the good. In contrast, the independent variables are the characteristics of the good that influence the value for the buyer or consumer of the commodity. The weights that purchasers place on the various qualities of the good might be understood as the estimated coefficients on the independent variables.
In Hedonic Pricing models, hedonic regression is utilized, and it is extensively used in Real Estate, retail, and Economics. Hedonic pricing is a revealed-preference method used in economics and consumer research to establish the relative relevance of the variables influencing a good's price or demand. Suppose the price of a home is impacted by various factors, such as the number of bedrooms, bathrooms, and proximity to schools. In that case, regression analysis can be used to establish the relative relevance of each variable.
The hedonic method estimates the extent to which many factors influence the price of a product or a piece of real estate, such as a house, using conventional least squares or more complex regression techniques. The dependent variable is the price, which is regressed against a collection of independent factors that influence the price, either on economic theory, the investigator's intuition, or consumer research. An inductive strategy, such as Data Mining, can also screen and choose the variables to include in the model. The selected features of the good (known as attributes) might be expressed as continuous or dummy variables.
Here is the hedonic regression function:
P = (loc, acc, nei, str, env)
The explanatory variables are the features that influence a property's price, such as:
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The housing Market is a common example of the hedonic pricing method, in which the characteristics of the property itself determine the price of a building or piece of Land, as well as the features of the surrounding environment, and is accessible to schools and a downtown area, the level of water annexed.
Hedonic regression is also employed in constructing the consumer price index (CPI) to account for changes in product quality. Any good in the CPI basket can be represented as a function of attributes, and the projected influence on the price can be determined when one (or more) of these attributes changes. By adding or deducting the projected value of that change from the item's worth, the hedonic quality adjustment approach eliminates any price discrepancy ascribed to a difference in quality.
The hedonic pricing mechanism is used in the housing market as an example. The property's attributes, such as appearance and size, and the surrounding environment's elements, such as proximity to school, crime rate, and so on, impact the building price. Hedonic regression models regress one unit's commodity price on a function of the model's attributes as well as the time variable in most cases.
The assumption is that a model pricing sample and the corresponding model vector attributes can be collected throughout two time periods or more. Hedonic regression can also determine the consumer price index and control the consequences of changes in a product's quality. By either adding or removing the changes estimated value from the old item's price, this approach of hedonic adjustment frequently removes price differentials ascribed to quality change.