There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based...

<title>Abstract</title> Spatial filtering in various forms has become a popular way to address spatial dependence in statistical models (Griffith, 2003; Tiefelsdorf & Griffith, 2007). However, spatial filtering faces computational challenges for large <italic>n</italic> as the current method requires order of n-super-3 operations....

Standard spatial autoregressive models rely on spatial weight structures constructed to model dependence among "n" regions. Ways of parsimoniously modeling the connectivity among the sample of <formula format="inline"><simplemath>"N"&equals;"n"-super-2</simplemath></formula> origin-destination (OD) pairs that arise in a closed system of interregional flows has...

Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known...

The vast majority of market valuations employ either some formal estimator such as ordinary least squares (OLS) or rely upon an informal set of rules defining the grid adjustment estimator. The success of the grid adjustment estimator suggests the data do not obey the ideal assumptions...

We examine the distribution of sales for a retail chain in the Houston market using a spatial gravity model. Unlike previous empirical studies, our approach models spatial dependencies among both consumers and retailers. The results show that both forms of spatial dependence exert statistically...

Straightforward application of OLS to statistical mass appraisal models does not always produce defensible estimates due to debilitating levels of multicollinearity in the data as appraisers attempt to improve the predictive ability of their models through the inclusion of a long list of...