Editing Hedonic regression (section)

==Hedonic models and real estate valuation==
In real estate economics, Hedonic regression is used to adjust for the issues associated with researching a good that is as [[heterogeneous]], such as buildings. Because individual buildings are so different, it is difficult to estimate the demand for buildings generically. Instead, it is assumed that a house can be decomposed into characteristics such as its amount of bedrooms, the size of its lot, or its distance from the city center. A hedonic regression equation treats these attributes (or bundles of attributes) separately, and estimates prices (in the case of an additive model) or elasticity (in the case of a log model) for each of them. This information can be used to construct a price index that can be used  to compare the price of housing in different cities or to do time series analysis. As with CPI calculations, Hedonic pricing can be used to:  

* Correct for quality changes in constructing a housing price index. 
* Assess the value of a property, in the absence of specific market transaction data.
* To analyze the demand for various housing characteristics, as well as housing demand in general. 

Due to the macro-oriented nature of hedonic models, with regard to their more general approach to assessment when compared to the more exacting and specific (albeit less contextualized) approach of individual assessment, when used for mass appraisal, the [[Uniform Standards of Professional Appraisal Practice]], or USPAP, has established mass appraisal standards to govern the use of hedonic regressions and other automated valuation models when used for [[real estate appraisal]].<ref>Hedonic Regression Models

Ben J. Sopranzetti

Rutgers, The State University of New Jersey

Article · August 2015

DOI: 10.1007/978-1-4614-7750-1_78</ref>