This study investigates and quantifies the effect of different specifications of the spatial weights matrix (��) on estimates and inferences in the context of a regression model using the lattice perspective with polygon-type data. The study also investigates an alternative to the specification of �� by estimating a spatial variance-covariance matrix based on known features of the spatial data. Previous literature has addressed the a priori construction of �� and selection criteria but assumes point-type data. This study’s primary contribution is the setup of a true and known benchmark that allows the comparison of the different specifications of ��. This is accomplished by using a disaggregate point-type data generating process which is then aggregated into polygon-type data. Monte Carlo simulations show that current specifications of �� used in maximum likelihood estimation for the spatial error model perform poorly. Additionally, the estimated spatial variance-covariance matrix outperforms the traditional specifications of ��.

Hedonic pricing methods and GIS (Geographic Information Systems) were used to evaluate relationships between sale price of single family homes and landscape fragmentation and natural land cover. Spatial regression analyses found that sale prices increase as landscapes become less fragmented and the amount of natural land cover around a home increases. The projected growth in population and employment in the Richmond, Virginia region and subsequent increases in land development and landscape fragmentation presents a challenge to sustaining intact healthy ecosystems in the Richmond region. Spatial regression analyses helped illuminate how land cover patterns influence sale prices and landscape patterns that are economically and ecologically advantageous.