Abstract

When a slumping housing market pushes a national economy towards recession, policy makers, investors and homeowners tend to focus their attention on federal regulation of housing finance. However, they have all but ignored the impacts of local and state regulations on the production of housing itself. This is surprising, since recent evidence suggests local and state land use regulations may play an important role in housing market efficiency (Mayer and Soerville, 2000; Glaser, Gyourko, and Saks, 2005). Furthermore, scholars have failed to reconcile opposing theories of land use regulations and housing supply, so consistent definitions of regulation and efficiency remain elusive. This dissertation will help reconcile the opposing theories of urban economics, political economy, and regional planning with the question: How do land use regulations effect housing markets? Do their impacts vary by scale? While these theoretical models yield radically different answers, most conclude that other regulatory approaches result in housing market inefficiencies. But with several perspectives and viewpoints, what are the fundamentals of various models? How well do models and theories portray real world markets? Which models should policy makers follow? This dissertation uses a three-paper approach to address these questions. The first paper, an integrative analysis, intimately examines the idea that land use regulations may have played a role in the emergence of the 2007 recession. Results financial deregulation and decentralization of land use in the 1980s set the stage for a large housing bubble and subsequent crash. Second, an empirical analysis examines local government regulation, competition, and housing construction in Southern California. Findings indicate that as cities permit more multifamily units, their neighbors permit less, suggesting that local regulations and intercity competition may inefficiently restrict certain housing types. The third paper analyzes the impacts of state regulation on housing growth in Maryland, and finds that it may increase multifamily housing in urban areas, but decrease in suburban and exurban areas. This suggests that cities in non-urban areas might view state regulatory incentives as a source of inefficient growth or public expenditures, and that “smart growth” programs have limited effectiveness.

Abstract

A spectrum of traffic engineering and modern transportation planning problems requires the knowledge of the underlying trip pattern, commonly represented by dynamic Origin- Destination (OD) trip tables. In view of the fact that direct survey of trip pattern is technically problematic and economically infeasible, there have been a great number of methods proposed in the literature for updating the existing OD tables from traffic counts and/or other data sources. Unfortunately, there remain several common theoretical and practical aspects which impact the estimation accuracy and limit the use of these methods from most real-world applications. This dissertation itemizes and examines these critical issues. Then, the dissertation presents the developments, evaluations, and applications of two new frameworks intended to be used with the current and near-future data, respectively.

The first framework offers a systematic and practical procedure for preparing dynamic demand inputs for microscopic traffic simulation under planning applications with an estimation module based solely on traffic counts. Under this framework, the traditional planning model is augmented with a filter traffic simulation step, which captures important spatial-temporal characteristics of route and traffic patterns within a large surrounding network, to improve the flow estimates entering and leaving the final microscopic simulation network. A new bounded dynamic OD estimation model and a solution algorithm for solving a large problem are also proposed.

The second framework utilizes additional information from small probe samples collected over multiple days. There are two steps under this framework. The first step includes a suite of empirical and hierarchical Bayesian models used in estimating time dependent travel time distributions, destination fractions, and route fractions from probe data. These models provide multi-level posterior parameters and tend to moderate extreme estimates toward the overall mean with the magnitude depending on their precision, thus overcoming several problems due to non-uniform (over time and space) small sampling rates. The second step involves a construction of initial OD tables, an estimation of route-link fractions via a Monte Carlo simulation, and an updating procedure using a new dynamic OD estimation formulation which can also take into account the stochastic properties of the assignment matrix.