The hedonic price model applied in this paper uses data on housing prices along with observable characteristics of the house and the environment to estimate the marginal implicit price of each characteristic. The marginal implicit price of individual characteristics can be estimated using a multiple regression model with housing price as the dependent variable and various characteristics as explanatory variables (see Freeman, 2003 for a complete description of the hedonic pricing model). Under the assumptions that the housing market is in equilibrium and that the area studied lies within a sin- gle housing market, the estimated marginal implicit prices derived from regression coefficients represent the price an individual would be willing to pay for an additional unit of a particular characteristic holding all other characteristics constant. So, for example, the esti- mated value of proximity to open space could be derived from the coefficient on proximity to open space in the regression model.
We use ordinary least squares regression analysis to estimate the hedonic pricing model to relate home sale price to the parcel, structural, neighborhood, and environmental characteristics of each property. This model may be written as