Exploring a multilevel approach with spatial effects to model housing price in San José, Costa Rica

Abstract

A multilevel model of the housing market for San Jose Metropolitan Region (Costa Rica) was developed, including spatial effects. Hierarchical relations (lower level units nested into higher level) were modeled by specifying multilevel models with random intercepts and a conditional autoregressive (CAR) term to include spatial effects from neighboring units at the higher level (districts). The random intercepts and conditional autoregressive models presented the best fit to the data. Variation at the higher level accounted for 16% of variance in the random intercepts model and 28% in the CAR model. The sign and magnitude of regression coefficients proved remarkably stable across model specifications. Multilevel and CAR models represented an important improvement in modeling housing price, despite most of the variation still occurring at the lower level, by improving the overall model fit and expanding the interpretation of model results. However, the CAR specification only represented a limited advance over the random intercepts formulation.