Agent-based joint model of residential location choice and real estate price for land use and transport model

Residential location choice (RLC) and real estate price (REP) models are traditional and key components of land use and transport model. In this study, an agent-based joint model of RLC and REP (RLC-REP model) was proposed for SelfSim, an agent-based dynamic evolution of land use and transport model. The RLC-REP model is capable of simulating the negotiation between the active household agents (buyers) and owner agents (sellers) using agent-based modeling. In particular, both utility maximization theory and prospect theory were used to develop a utility function to simulate the location choice behavior of active household agents. The utility function incorporates only two variables: house price and accessibility. The latter variable is calculated using MATSim, an activity-based model. The asking price behavior of owner agents is based on three specific rules. The residential location choices of household agents and house prices can be obtained by negotiation. Finally, genetic algorithm was used to estimate the parameters of the RLC-REP model. The calibrated model was tested in Baoding, a medium-sized city in China, and historical validation was performed to assess its performance. The results suggest that the forecasting ability of the RLC-REP model in terms of real estate price is satisfactory.