Land Use Change with Spatially Explicit Data: A Dynamic Approach

Most of the economic literature that uses spatially-explicit data to estimate the determinants of land-use change is limited to static models and cross-sectional data sets. Recent attempts to move to a more dynamic analysis include using panel data sets and survival analysis. In this study, we use a discrete choice dynamic model of land-use where the agent's choices are regarded as the solution to a dynamic optimization problem. The irreversibility of some decisions, expectations about future prices, and forward-looking behavior of the land operator can all be accounted for. Our results show that a model specification that incorporates some of the complexities of the decision process improves upon results found in the existing literature. First, prediction accuracy of land use change is superior to any of the existing models. Second, we demonstrate that models that do not account for transactions costs tend to overestimate the effects of changes in transportation costs.