A Zonal RUM Model to Value Recreation Sites with Aggregate Visitation Data

Abstract

This paper develops a zonal random utility maximization (RUM) model of recreation demand from visitor counts and census data. In contrast, traditional RUM models of recreation demand use individual trip data collected through surveys. After demonstrating proof-of-concept with a Monte Carlo analysis, we apply the zonal RUM model to data on hunting and fishing trips. Our results confirm that the zonal model produces preference parameters and willingness-to-pay estimates close to those from the traditional model. The zonal RUM model provides a practical substitute to the traditional model in applications that lack access to individual data.