11.522 UIS Research Seminar (Fall 2015) Discussion notes

Monday, October 5, 2015, Part 1

Examining the Spatial Effects of Government Housing Policy: A Case Study of Singapore

Discussion Leader: Jingsi Shaw

Policy makers and modelers have long recognized the importance of modeling urban travel and land use in an integrated fashion.  During the last few decades, efforts have been made to improve integrated urban models in such a way that the behavior of “agents” (such as households), including their residential location choices and workplace location choices, can be better modeled. Improvement in modeling residential mobility will allow planners and policy makers to gain more insights into the spatial patterns of neighborhood development and determine demand for public infrastructure and services, including the transportation system. However, few studies examine the long-term effect of government housing regulations on the spatial distribution of residents. This is important for policy makers and planners to examine unintended consequences of government regulation on housing markets after several cycles of changes of economic conditions and demographic shifts. My study uses Singapore as a case to investigate to what extend government intervention in public housing might affect the spatial pattern of different types of households.

I will introduce three papers that focus on two closely interconnected research areas: residential mobility and agent-based models for residential relocation choice. The first paper, written by Feijten and Mulder (2002), starts with the development of the theories on the relationship between household trajectories and moving behavior.  Along this line, they adopt a longitudinal perspective and try to answer the question, how does the timing of family events (such as marriage and births) affect the timing of moving. The second study (Wu and Birkin, 2013) uses microsimulation modeling to demonstrate the effects of adding demographic and life course events into the microsimulation. The third paper (Shaw and Ferreira, 2015) describes my current work on examining the long-term spatio-demographic consequence of government housing regulation in Singapore. By examining the recent census data, we can observe an interesting spatial pattern: young children are disproportionately clustered in the peripheral area whereas the elderly are concentrated in the central region. Evidence shows that this phenomenon could be a mixed effect of market and government forces. The goal of our further study is to see the extent to which government housing policies could accelerate (or slow down) the formation of demographic clusters and the persistency of such spatio-demographic phenomenon.  Toward this end, I will demonstrate how to use a type of cohort-survival model to examine the long-term spatial and demographic effects of government housing regulation. For people who want to have a deeper understanding of the interplay between life cycle events and housing choices, please refer to the additional reading (by Kulu and Steele).

Reading

Feijten, Peteke and Clara Mulder. 2010. The Timing of Household Events and Housing Events in the Netherlands: A Longitudinal Perspective. Housing Studies. 17(5): 733-792.

Wu, Belinda and M. Birkin. 2013. Moses: A Dynamic Spatial Microsimulation Model for Demographic Planning. Spatial Microsimulation: A Reference Guide for Users, Chapter 11 in Understanding Population Trends and Processes (Eds, R. Tanton and K.L. Edwards). Springer.

Shaw, Jingsi and J. Ferreira. 2015. Examining the Long-term Effect of Government Housing Regulations on Spatial Distribution of Residents: A Case Study of Singapore. Proceeding of the14^th International Conference on Computers in Urban Planning and Urban Management (CUPUM). July 7-10, Cambridge, USA.

Optional Additional Reading

Kulu, Hill and F. Steele. Interrelationships Between Childbearing and Housing Transitions in the Family Life Course. Demography. 50(5): 1687-1714.

Discussion Questions

1. Given the interrelationships between household events and households’ housing choices, what do you think as a modeler and/or planner we could consider doing to improve our understanding of the ways in which housing regulations impact such relationships?

2. Traditional land use and transportation interaction models take the population as exogenously specified input.  They do not explicitly consider family formation, birth rates, and other changes to the demographic process. For what kind of planning decision-making do you think it would be especially important to pay more attention to the demographic change process?

3.  One way to account for lifecycle and demographic changes within microsimulation models is to add historical information to the categories into which families are placed.  Of course it is easy to add so much history that we explode the number of different household categories.  What minimal amount of differentiation would be most useful when simulating the timing and residential relocation choices of households?