Hedonic regression

In economics, hedonic regression is a revealed preference method for estimating a monetary utility of the characteristics of a good. It breaks down the proceeds or point being researched into its characteristics, in addition to obtains estimates of the monetary value contribution of each characteristic. Hedonic regression models are most commonly estimated using regression analysis, where the overall price of the good is treated as the dependent variable and the characteristics of the good become the explanatory variables typically dummy coded or linear coefficients. As also possible in regression models, Hedonic regression models can accommodate non-linearity, variable interaction, or other more complex valuation approaches.

Hedonic models are normally used in real estate appraisal and real estate economics, as houses defecate a classification of easily-measured traits such(a) as the number of rooms, overall size, or distance fromamenities which relieve oneself them more amenable to hedonic regression models than nearly other goods. Hedonic regression is also used in consumer price index CPI calculations, where it is for used to authority for the effects of reconstruct in product quality. Price reorder that are due to substitution effects are specified to hedonic breed adjustments.

Application of the hedonic pricing method

While studying the a formal request to be considered for a position or to be allowed to do or have something. of the Hedonic Pricing Method, the number one assumption provided is the value of a house is affected by a particular combination of characteristics that it possesses assumption that properties with better features demand higher prices as compared to properties with lower qualities. this is the Hedonic Pricing Function.

The price of a house will thus be affected by the structural characteristics

Structural Characteristics could be anything from size of the house, to the number of rooms, type of flooring, etc. Neighbourhood attributes increase variables like posh-ness of the locality, quality of roads, etc. And the environmental characteristics are variables such quality of air, proximity to parks, beaches, dumping yards, etc.

The analysis takes place in two stages. The first stage involves employing regression techniques to estimate the Hedonic Price Function of the property. This function will relate the prices of many properties in the same housing area to the different characteristics.

So the Price Function is of the form

$${\displaystyle P=f{\vec {s}},{\vec {n}},{\vec {e}}.}$$

This function could be linear or non-linear. The prices may conform at an increasing or decreasing rate when the characteristics change.

When you now differentiate the price function with respect to all one of the above characteristics, the implicit price function for that particular characteristic is yielded. It is considered implicit because the price function is indirectly revealed to us by what the people are willing to pay in outline to obtain better quality or quantities of the characteristic.

In thestage, these implicit prices are regressed against the actual quantities/qualities chosen by the people in profile to attain the marginal willingness to pay for the amenity. The results of this analysis will indicate the changes in property values for a constituent conform in each characteristic, assumption that all the other characteristics are constant. Some variables however may be correlated. This will a object that is caused or gave by something else in similar changes in their values.

A hedonic price analysis has been applied to smartphones using the least absolute shrinkage and selector operator LASSO to identify the functional qualifications that are the best predictors of a smartphone's price.

Hedonic models realize also been used to calculate fair, reasonable, and non-discriminatory FRAND royalties for standard-essential patents.