Chi-Hong Tsai

Impact of bus rapid transit on housing price and accessibility changes in Sydney: A repeat sales approach

ABSTRACT New public transport infrastructure is expected to improve accessibility for local residents, and thus contribute to increased land value. The contribution that a bus rapid transit (BRT) system can make to increased land value is less certain than for rail-based systems, with the literature mostly containing bus-based examples from developing countries with extensive BRT networks. This article considers a BRT system named the Liverpool–Parramatta Transitway (LPT) that was implemented in southwestern Sydney in 2003 to improve public transport accessibility in the local area. A repeat sales model is constructed to investigate the impact of the LPT on residential housing prices and accessibility changes using repeat sales data from before and after the opening of the LPT. This identified little price difference between properties close to LPT stations and outside of the area that could be considered as affected by the LPT service coverage. This outcome is at variance with the theoretical underpinning of land value uplift and other empirical evidence relating to the LPT. Hedonic models using the same repeat sales data investigate the study area in more detail, stratifying the sample by housing type and by comparing separate before and after models. These research outcomes identify the extent to which the BRT system has an impact on local housing prices through accessibility improvements to the study area and provide a deeper understanding as to how the quantification of land value uplift from BRT represents one element of the wider economic benefits of a BRT system.

Examining Estimation Bias and Efficiency for Pseudopanel Data in Travel Demand Analysis

Pseudopanel data have been increasingly applied in travel demand analysis to investigate the long-run travel demand when genuine panel data are unavailable. However, conventional estimation techniques have typically been used without a careful consideration of some unique properties of pseudopanel data. This paper shows that ignoring these properties potentially leads to estimation bias or inefficiency not observed in genuine panel data. The method used is a Monte Carlo experiment with scenarios designed to generate various data possessing pseudopanel data characteristics under conditions of limited observations; the performance of various estimator is evaluated with the use of the simulation results. This research found that the large between-group variation of the exogenous variable and the variance of unobserved group effects in pseudopanel data are the primary causes of estimation bias and inefficiency. Other factors such as cohort sizes and nonspherical errors have a smaller effect on the estimators’ performance. An empirical application using Sydney Household Travel Survey data is also presented to illustrate the simulation findings.