A Mixed Spatially Correlated Logit Model: Formulation and Application to Residential Choice Modeling
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In recent years, there have been important developments in the simulation analysis of the mixed multinomial logit (MMNL) model as well as in the formulation of increasingly flexible closedform models belonging to the Generalized Extreme Value (GEV) class. In this paper, we bring these developments together to propose a mixed spatially correlated logit (MSCL) model for location-related choices. The MSCL model represents a powerful approach to capture both random taste variations as well as spatial correlation in location choice analysis. The MSCL model is applied to an analysis of residential location choice using data drawn from the 1996 Dallas-Fort Worth household survey. The empirical results underscore the need to capture unobserved taste variations and spatial correlation, both for improved data fit and the realistic assessment of the effect of sociodemographic, transportation system, and land-use changes on residential location choice.