Nonparametric Assessment of the Effects of Neighborhood Land Uses on Residential House Values Abbreviated Title: Nonparametric Assessment of the Effects of Land Uses on House Values

The effects of land uses on residential property values are crucial when evaluating costs and benefits of land projects for the purpose of public policy prescription or business decision making. It is widely recognized that a nuclear plant or a prison, for example, may often have an adverse effect on the property values of the nearby houses, while a park, a museum or a university usually has a beneficial effect. The effect of a land use defined as a function of distance between the locations of the land use factor and a particular house is, however, inherently nonlinear (in an unknown form) and the use of a simple linear regression method could lead to a misleading conclusion. The purpose of this paper is to estimate the land use effect function by using the recently developed techniques of nonparametric regression method. There are three important features of our statistical model. First, it is a semiparametric model, which keeps a conventional linear form with respect to the dwelling attributes of the house just like in the popular hedonic model, but treats its location characteristics in a nonparametric fashion using the kernel method (Robinson 1988). Second, unlike the usual nonparametric regression, it keeps additive structure in the nonparametric component (Hastie and Tibshirani 1986, 1990), so that it retains much of the interpretative features of the linear models. Third, it uses the local linear smoother developed by Fan (1992, 1996), which is superior to other smoothers in terms of avoiding the boundary effect and other features. We estimate the effects of three land use factors: (1) golf courses, (2) a university, and (3) a nitrogen plant on the neighborhood home values in Lawrence, Kansas. The data on the sales price and other attributes of the house with 6,400 observations over the period from 1986 to 1995 are obtained from the Douglas County Appraisal Office and the data on distance to the three sites above are constructed using the Geographic Information System (GIS) ∗ The authors would like to thank three anonymous referees for their helpful comments. The work of the first author was supported by University of Kansas, General Research allocation 3442-20-0038-1001. The earlier version of the paper was presented at the American Real Estate and Urban Economics Association annual meeting in New York in January 1999. ∗ ∗ Corresponding author.