Evolution of urban built space Markets and decisions.

摘要

To understand the factors that influence the spatio-temporal distribution of built space, and thus population in an urban area, play an extremely important role in our greater understanding of urban travel behaviour. Existing location of activity centres, especially home and work, strongly influences the short-term individual-level decisions such as mode of transportation, and long-term household-level decisions such as change in job and residential location. Conditions in the built space market also affect households' and firms' location and relocation decisions, and hence influence the general travel patterns in an urban area. This research addresses two very important, but at the same time, not very widely investigated dimensions that play a key role in the evolution of built space and population distribution: Markets and Decisions. A disequilibrium based microsimulation modelling framework is developed for the built space markets. This framework is then used to operationalize the Greater Toronto and Hamilton Area's owner-occupied housing market within Integrated Land Use Transportation and Environment (ILUTE) modelling system. Simulation results captured heterogeneity in the transaction prices, due to type of dwellings and different market conditions, in a very disaggregate fashion. On the decisions side, this research first developed a generic multidimensional modelling framework that captures the behaviour of builders in terms of the supply of new built space. The where, when, how much, and what type of supply decisions were incorporated within a single framework. This modelling framework was then applied to estimate a model for the supply of new office space in the Greater Toronto Area (GTA). Estimation results indicated a risk taker behaviour on the builders' part, while market conditions and supply of resources (labour, construction cost etc.) were also found to be important factors in decision making. In addition to that, this research also developed a comprehensive hedonic analysis for the asking rent of office space in the Greater Toronto Area. The effects of accessibility, quality, location, and market conditions on rent were explored. Data indicated a high degree of spatial heterogeneity and clustering effects. Spatial analysis techniques were incorporated within the hedonic framework to capture these effects. Estimation results indicated that access to transport infrastructure, distance from CBD, and vacancy rate were significant in explaining the variation in the rent.