This article proposes a non-parametric method for estimating spatial price functions. Space is divided into squares. The independent variables are barycentric coordinates that uniquely describe the location of observations in space. The regression coefficients are estimates of the height of the function directly over the vertices of the square spatial units. Within each square the function has hyperbolic iso-price curves and parabolic sections. The price function is continuous, but not differentiable, at the boundaries between contiguous squares. This method is applied to the problem of describing the price per front foot of land in the Chicago CBD. A rather complex price surface is revealed that would be difficult to estimate using other methodologies but was easily estimated by this simple method.
The Journal of Real Estate Finance and Economics – Springer Journals
Published: Oct 6, 2004
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