Abstract: This paper develops a model of land use in a growing community on the urban fringe and uses it to explore the spatial patterns and time path of development. The model is an agent-based model (ABM) of housing and land markets that includes as agents farmer/landowners, a developer who buys land and builds houses, and consumers who purchase housing. Housing is characterized by lot size and house size. As in all ABMs, macro-scale patterns emerge from many micro-scale interactions between individual agents, which are modeled computationally. In contrast to many other ABMs, however, the fundamentals of microeconomic decisionmaking are built into the model—consumers choose houses to maximize utility; farmers compare returns from agriculture to the expected value of their land in development; and developers purchase land and build houses so as to maximize profits. Model simulations reveal some aspects of sprawl such as “leapfrog” development, yet also confirm some results from traditional urban economic models, such as declining density and rent (land price) gradients. Sensitivity analyses on the utility function parameters, the distribution of agricultural productivity, and the travel costs highlight the importance of the economic features of the model.

Abstract: This paper uses an economic agent-based model of land use in a hypothetical urban fringe community to examine the effects of large-lot zoning on land conversion, land prices, and the spatial configuration and density of new development. The model incorporates the actions of heterogeneous housing consumers, developers, and farmer/landowners who make economic decisions in land and housing markets. The model allows for population growth and simulates the evolution of land use patterns and prices over a 20-year time period. Zoning regulations in the form of minimum lot size restrictions imposed in an outlying area are shown to have effects that vary with the stringency of the regulations: 2-acre minimum lot sizes have little effect on the spatial patterns of development, but they do increase land and housing prices and result in higher incomes in the region; 5-acre minimum lot sizes push development toward the city center, leaving agricultural land in the zoned region undeveloped until quite late in the simulation period. While house prices are higher with 5-acre zoning, land prices in the zoned region fall, highlighting the countervailing influences of lot size restrictions on land prices. The new modeling approach allows for the tracking of the transitional dynamics of development, both over space and time as the urban area grows.

Abstract: This paper suggests a cause of low density in urban development or urban sprawl that has not been given much attention in the literature. There have been a number of arguments put forward for market failures that may account for urban sprawl, including incomplete pricing of infrastructure, environmental externalities, and unpriced congestion. The problem analyzed here is that urban growth creates benefits for an entire urban area, but the costs of growth are borne by individual neighborhoods. An externality problem arises because existing residents perceive the costs associated with the new residents locating in their neighborhoods, but not the full benefits of new entrants which accrue to the city as a whole. The result is that existing residents have an incentive to block new residents to their neighborhoods, resulting in cities that are less dense than is optimal, or too spread out. The paper models several different types of urban growth, and examines the optimal and local choice outcomes under each type. In the first model, population growth is endogenous and the physical limits of the city are fixed. The second model examines the case in which population growth in the region is given, but the city boundary is allowed to vary. We show that in both cases the city will tend to be larger and less dense than is optimal. In each, we examine the sensitivity of the model to the number of neighborhoods and to the size of infrastructure and transportation costs. Finally, we examine optimal subsidies and see how they compare to current policies such as impact fees on new development.