Using Econometric Forecasting of Individual Landowner Decisions to Identify Parcels for Ecosystem Restoration, Land Preservation or Transportation Design

As urban areas encroach on sensitive rural lands, balancing economic development with environmental concerns becomes more difficult. In rapidly growing Florida, Comprehensive Land Use Plans attempt to address this issue by designating allowable future land use and zoning; however, these plans don't always align with an individual landowner's objectives. Given this discrepancy, land use can be patterned, and therefore predicted by aggregating all parcel-specific decisions of individual landowners over time. This methodology may assist practitioners when policy decisions such as transportation design, ecosystem restoration, and land preservation require a parcel level approach. The aggregation of parcel-specific decisions by individual landowners requires econometric models which incorporate GIS analysis; these models help predict development at the parcel level through analysis of spatially explicit attributes and other indicators. This paper analyzes 22 square miles of the environmentally sensitive Wekiva River Basin (WRB) near metropolitan Orlando, Florida. This area was recently approved for a bisecting roadway which is expected to increase the conversion of rural to developed land. For the WRB area, a specified probit regression model predicted with 91% accuracy which parcels would remain in rural use, and which would develop by the end of a five year period. The model's output was then overlaid with current data to forecast future parcel-specific land use decisions over a five year horizon. Impacts for conservation and land purchases, impaired waterways, and right-of-way acquisition are forecasted over that horizon and discussed later in the paper. This paper is organized as follows: first, a brief review of the theory underlying the model is presented; next, the model and data specification; the results; and finally, applications of the model which use predicted outcomes in several resource-related scenarios.