  This study develops a spatially varying coefficient model by extending the
random effects eigenvector spatial filtering model. The developed model has the
following properties: its coefficients are interpretable in terms of the Moran
coefficient; each of its coefficients can have a different degree of spatial
smoothness; and it yields a variant of a Bayesian spatially varying coefficient
model. Also, parameter estimation of the model can be executed with a
relatively small computationally burden. Results of a Monte Carlo simulation
reveal that our model outperforms a conventional eigenvector spatial filtering
(ESF) model and geographically weighted regression (GWR) models in terms of the
accuracy of the coefficient estimates and computational time. We empirically
apply our model to the hedonic land price analysis of flood risk in Japan.
