In finance, the term hedonic regression denotes a process used to find the actual value of goods, services and real estate.
In the hedonic regression process, the attributes of an asset are broken down into individual components. Each of these components is valued individually. The individual values of components are then combined to determine the asset’s actual value.
This method of determining value is widely used in real estate, due to the infinite number of variables involved (location, access to amenities, distance to schools and workplaces, environment, construction quality, materials, design, aesthetics, historical or emotional value).
A number of different hedonic regression models are used, each with its own set of components and variables. The Box-Cox, linear and semi-log models are among the most widely used.
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