Rapid urbanization is a pressing issue for planners, policymakers, transportation engineers, air quality modelers and others. Due to significant environmental, traffic and other impacts, the process of land development highlights a need for land use models with behavioral foundations. Such models seek to anticipate future settlement and transport patterns, helping ensure effective public and private investment decisions and policymaking, to accommodate growth while mitigating environmental impacts and other concerns. A variety of land use models now exist, but a market-based model with sufficient spatial resolution and defensible behavioral foundations remains elusive. This dissertation addresses this goal by developing and applying such a model. Real estate markets involve numerous interactive agents and real estate with a great level of heterogeneity. In the absence of tractable theory for realistic real estate markets, this research takes a “bottom-up” approach and simulates the behavior of tens of thousands of individual agents based on actual data. Both the supply and demand sides of the market are modeled explicitly, with endogenously determined property prices and land use patterns (including distributions of households and firms). Notions of competition were used to simulate price adjustment, and market-clearing prices were obtained in an iterative fashion. When real estate markets reach equilibrium, each agent is aligned with a single, utility-maximizing location and each allocated location is occupied by the highest bidding agent(s). This approach helps ensure a form of local equilibrium (subject to imperfect information on the part of most agents) along with useroptimal land allocation patterns. The model system was applied to the City of Austin and its extraterritorial jurisdiction. Multiple scenarios reveal the strengths and limitations of the market simulation and available data sets. While equilibrium prices in forecast years are generally lower than observed or expected, the spatial distributions of property values, new development, and individual agents are reasonable. Longer-term forecasts were generated to test the performance the model system. The forecasted households and firm distributions in year 2020 are consistent with expectations, but property prices are forecasted to experience noticeable changes. The model dynamics may be much improved by more appropriate maximum bid prices for each property. More importantly, this work demonstrates that microsimulation of real estate markets and the spatial allocation of households and firms is a viable pursuit. Such approaches herald a new wave of land use forecasting opportunities, for more effective policymaking and planning. / text