Zhu, BingBingZhuFüss, RolandRolandFüssRottke, NicoNicoRottke2023-04-132023-04-132011-05-01https://www.alexandria.unisg.ch/handle/20.500.14171/9420010.1007/s11146-009-9209-8This paper develops a method to capture anisotropic spatial autocorrelation in the context of the simultaneous autoregressive model. Standard isotropic models assume that spatial correlation is a homogeneous function of distance. This assumption, however, is oversimplified if spatial dependence changes with direction. We thus propose a local anisotropic approach based on non-linear scale-space image processing. We illustrate the methodology by using data on single-family house transactions in Lucas County, Ohio. The empirical results suggest that the anisotropic modeling technique can reduce both in-sample and out-of-sample forecast errors. Moreover, it can easily be applied to other spatial econometric functional and kernel forms.enSpatial regression - Hedonic price model - Anisotropic spatial correlation - Simultaneous autoregressive model - Housing marketjournal article