Econometric analysis on factors affecting HDB and private property resale prices through hedonic pricing models

• Main Authors: Ong, Michelle Hui Fang, Toh, Ding Xiang, Lim, Vaneson Dun
• Other Authors: Bao Te
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Social sciences::Economic development
• Online Access: https://hdl.handle.net/10356/147773
• Institution: Nanyang Technological University

This study aims to examine and compare the effect of housing factors on resale prices of Housing and Development Board (HDB) flats and Private Property (PP) in Singapore through the use of hedonic pricing models. Data for resale transactions of HDB flats and PP were obtained from Singapore Open Data and the Real Estate Information System (REALIS) database respectively. In addition, locational data were obtained from mapping services. To account for inflation and other time-related influences, the resale prices were normalised by their respective price indices. Ordinary Least Square regression was then conducted on the models to analyse the relationship between the housing factors and resale prices. Factors such as floor area, floor level and years of tenure left were found to have a significant and positive relationship with resale prices in both markets. On the other hand, factors such as distance to the nearest Mass Rapid Transit (MRT) station and distance to the Central Business District (CBD) were found to have a significant and negative relationship. To further analyse the relative importance of factors to buyers in each market, the absolute values of standardised regression coefficients were used to rank the factors. For HDB resale flat buyers, the most important factors are larger flat types and floor area, indicating the high emphasis that they place on comfort. As for PP buyers, the most important factors are floor area, distance to the CBD and District 14, indicating the high emphasis that they place on comfort, convenience and neighbourhood prestige. The results may help urban planners, developers and buyers to make more informed decisions with regards to urban planning, development projects and housing choices.