Discrete choice and microsimulation methods for agent-based land use modeling

This thesis describes methods to model the land use component of an urban system. Specifi- cally, it proposes methods to model and simulate the location choice of agents (households or firms) and the formation of prices for real estate goods in a city. These methods are based on the application of two main tools: discrete choice models and microsimulation. Modeling urban systems is extremely relevant for project evaluation and policy making, due to the expensive, large and often irreversible nature of interventions at the urban scale. However, this is a complex task because it involves several sub-systems (land use, transport and energy among others) together with a large number of heterogeneous, interacting agents. Location choice and price models are a fundamental component of land use models because they describe the dynamics of the city and the spatial distribution of agents and activities. These models are complex because of the large nature of the problem, the presence of nonlinearities due to location externalities and the quasi-unique nature of the traded goods (locations or dwellings). In general, land use models are often hard to implement due to the large amount of data required to model each and every sub-system, even more if the modeling approach is agent-based or disaggregated. This thesis contributes to the field of land use modeling in four aspects. First, an analysis of the formation of the choice set in problems with a large number of alternatives. The analysis is focused on comparing methods to model the availability of each alternative with explicit choice set formation models. Results show that availability-modeling heuristics are useful when dealing with large choice sets, but may significantly deviate from the explicit model. Second, a model for simultaneous estimation of location choice and real estate price is pro- posed. The model considers that each good in the market is traded in a latent auction, where the potential willingness to pay of all agents determines the transaction price. The proposed approach has the advantage of explaining prices as a function of the willingness to pay of the agents, therefore not being determined by the market conditions of the estimation period, as it happens with hedonic price models. Another advantage comes from the latent nature of the auction, which allows to estimate the model even when detailed data on transaction prices is not available. The model is estimated for the city of Brussels using a double mea- surement equation approach, allowing to estimate the location choice and the price model simultaneously. The proposed approach is compared with other methods, showing better results, especially when available price data is aggregated. Third, a market clearing method for agent-based models is proposed. The approach takes into account the expectations of bidding agents as they observe (and react to) the real estate market conditions. The model assumes that, after adjustment of their expectations, agents bid simultaneously for the available locations. This produces a higher level market clearing that determines the real estate price. The proposed method does not require to solve a fixed point problem to find an equilibrium (or market clearing prices) and thus does not require to group agents in clusters. This makes possible to calculate the expectation adjustment at an individual level, therefore making the approach suitable for application in a microsimulation framework. The proposed model is implemented for the city of Brussels and a simulation is performed for the 2001-2008 period. Results show that the model is able to reproduce the observed trends of spatial distribution of agents and real estate prices. Finally, a case study of a full, integrated land use and transport microsimulation model is presented. The model, implemented in the urban simulation platform UrbanSim and the traffic microsimulator MATSim, is estimated and applied to the city of Brussels. The analysis of the case study is focused on the requirements and difficulties of implementing a full scale land use microsimulator, with a special focus on data collection, data processing, model estimation and calibration of the system. An analysis of the trade-off between level of details, implementation costs and quality of the results is also provided, identifying the major difficulties when implementing large scale urban microsimulation models.