Hedonic pricing evaluates the benefit of a non-market characteristic (e.g., pollution, fatality risk) on market prices. It is most commonly applied to variations in residential prices reflecting the value of local environmental attributes.
The basic premise of the hedonic pricing method is that the price of a marketed good is related to its characteristics, or the services it provides. For example, the price of a car reflects the characteristics of that car—transportation, comfort, style, luxury, safety features, fuel economy, etc. The individual characteristics of a car or other good can be valued by looking at how its price people changes when controlling for other characteristics.
Hedonic pricing is a convenient method for estimating transportation-related benefits and disbenefits affecting residential property values. These can be negative benefits of transportation facilities such as freeway noise, or positive benefits such as improved access to activities.
The first step is to collect data on residential property sales in the region for a specific time period (usually one year). The required data include:
Data on housing prices and characteristics are available from municipal offices, multiple listing services, and other sources.
Once the data are collected and compiled, the next step is to statistically estimate a function that relates property values to property characteristics. Regression analysis is typically used to estimate the influence of various property characteristics.
A model for a set of factors determining house prices could be:
P = f (D, S, V, E, H, T)
P = Price
D = Distance from the nearest central business district
S = Size of house
V = Rating of view
E = School quality
H = Proximity to highway
T = Proximity to transit
This is called a hedonic price function. The regression typically uses the logarithms of the values for the various factors. A statistical analysis package such as the Regression function in Microsoft Excel or SPSS can be used for the computations of the following type of equation:
ln (P) = ln β0 + β1 ln (D) + β2 ln (S) + β3 ln (V) + β4 ln (E) + β5 ln (H) + β6 ln (T) + e
The β values represent the role that each factor plays in the value of the residence. For example β5 is the value of each unit of proximity to the highway.
The town of Southold, Long Island, New York has coastlines on both the Peconic Bay and Long Island Sound. Compared to the rest of Long Island, it is a relatively rural area, with a large amount of farmland. However, population and housing density are rapidly increasing in the town, resulting in development pressures on farmland and other types of open space.
The Peconic Estuary Program is considering various management actions for the Estuary and surrounding land areas. In order to assess some of the values that may result from these management actions, a hedonic valuation study was conducted, using 1996 housing transactions.
The study found that the following variables that are relevant for local environmental management had significant effects on property values in Southold:
Based on the results of this study, managers could, for example, calculate the value of preserving a parcel of open space, by calculating the effects on property values adjacent to the parcel. For a hypothetical simple case, the value of preserving a 10 acre parcel of open space, surrounded by 15 "average" properties, was calculated as $410,907.
Boardman, A., D. Greenberg, A. Vining, and D. Weimer. Cost-Benefit Analysis: Concepts and Practice. pp. 339-344.
King, D.M. and M. Mazzotta, Ecosystem Evaluation (website). Available at: http://www.ecosystemvaluation.org/hedonic_pricing.htm.
Uyeno, D., S. Hamilton, and A. Biggs, "Density of Residential Land Use and the Impact of Airport Noise." Journal of Transport Economics and Policy, 27, no. 1, 1993.