Application of The Hedonic Pricing Method
While studying the application of the Hedonic Pricing Method, the first assumption made is the value of a house is affected by a particular combination of characteristics that it possesses given that properties with better qualities 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 (s1, s2, s3...) of the house itself, characteristics of the locality/neighbourhood (n1, n2, n3...), and environmental characteristics (e1, e2, e3...)
Structural Characteristics could be anything from size of the house, to the number of rooms, type of flooring, etc. Neighbourhood attributes include 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 Price Function (P) = f (s1, s2, s3...sj; n1, n2, n3,...nj; e1, e2, e3,...ej) This function could be linear or non-linear. The prices may change at an increasing or decreasing rate when the characteristics change.
When you now differentiate the price function with respect to any 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 order to obtain better quality or quantities of the characteristic.
In the second stage, these implicit prices are regressed against the actual quantities/qualities chosen by the people in order to attain the marginal willingness to pay for the amenity. The results of this analysis will indicate the changes in property values for a unit change in each characteristic, given that all the other characteristics are constant. Some variables however may be correlated. This will result in similar changes in their values.
Read more about this topic: Hedonic Regression
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