The effect of SARS on the price of re-entrants in multi-storey apartment buildings

Abstract

Natural lighting and ventilation have long been a primary consideration in building design, particularly for those high-rise and densely packed apartment blocks where mechanical ventilation is normally secondary. In Hong Kong, there are prescriptive legal requirements governing the provision of natural lighting and ventilation in private buildings. This, coupled with developers’ profit-maximizing incentives, often gives rise to re-entrant designs commonly found in apartment buildings in Hong Kong. This paper aims to study the economic impacts of the disposition of re-entrants on property prices with reference to the revelation of the chimney effect of re-entrants after the occurrence of the mass community outbreak of Severe Acute Respiratory Disease (SARS) in Amoy Gardens in 2003. In this study, we use a hedonic pricing model to examine whether flats designed with a re-entrant is sold at a different price than those without a re-entrant. We draw a sample of property transactions from a popular single residential development with variations in re-entrant designs. A total of 357 transactions were collected, of which 90 were transacted after the SARS event. The model is capable to monitor any significance changes in the premium of re-entrant and its relationship with floor level before and after SARS. Based on the hedonic pricing analysis, we found that the preference for re-entrants is floor-dependent. Before SARS, homebuyers were not fond of re-entrants on low floor levels, but they were willing to pay more for re-entrants as the floor level increases. Yet, the outbreak of SARS did not significantly change their preferences for re-entrants. The market is capable to capitalize the building design into property prices. Developers and designs should improve their building design to meet the ever changing needs of the market. This paper provides an empirical framework to examine how homebuyers price a particular design feature using property transaction data.