Household moving and tenure behavior : translating retrospective "Recent Mover" surveys into prospective moving decisions

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 263-270).To assist policy makers with evaluating urban development policies and anticipating trends in the evolution of cities, researchers have significantly improved modem urban land-use-and-transportation (LUT) simulations. Despite extensive studies regarding the interdependency of household life cycle stages and moving decisions in demography, most existing LUT simulations do not address households changing life cycle stages when modeling residential relocation behavior. The reasons include 1) the data that capture households and housing transitions is hard to obtain, and 2) the analysis methods are mainly for cross-sectional datasets. This dissertation focuses on these issues and contributes to the literature in three respects: behavior exploration, methodology, and applications to housing and transportation policy analysis. The ultimate goal of this study is to have a better understanding of the relationship between household life cycle stages and their moving decisions when the housing market is heavily regulated with incentives based on age, family structure, and income. This research focuses on the housing market in Singapore as a case and utilizes a new dataset of recent movers. First, this study generates sampling weights both at the individual and household levels to correct sample bias. Then, this study uses discrete choice models to identify key household and housing factors that influence households' moving behavior at the household-level. In order to capture household characteristics at the time of decision-making, the household characteristics for those households that changed structure when moving had to be reconstructed. The results show that household moving decisions are mainly influenced by three sets of factors: life cycle stages, tenure choices and housing submarkets. Finally, this research adopts a Markov Chain Model (MCM) approach to estimate a set of forward-looking moving and tenure transition rates accounting for various issues, such as sample bias and "missing-move" problems. The final results improve the estimate of moving and tenure transition rates in several ways: adding more demographic factors, handling household structure changes, and relaxing the memoryless assumption to accommodate a special feature of the public housing sector in Singapore. I expect that this study will have important implications for LUT microsimulations as well as housing and transportation policymaking. It demonstrates a method to analyze a retrospective dataset of recent movers in order to obtain detailed forward-looking moving and tenure transition rates (which are required for microsimulations). It also demonstrates a way to model household structure changes at the household level without introducing a full set of demographic models at the individual level. This study shows that with detailed moving and tenure transition rates, researchers can better capture the critical interactions between households' moving decisions and government intervention on the housing market. This can improve the current LUT simulations in a way that they can be more sensitive to government housing regulation and support long-term policymaking regarding the spatial distribution of housing and transportation infrastructure.by Jingsi Xu Shaw.Ph. D