MODELING HEDONIC PRICES IN SINGAPORE

This paper presents hedonic regressions for the Singaporean residential real-estate market. To these means, asking prices were collected from an online commercial property portal in February 2011. Transaction prices were collected from governmental data sets. These data sets are enhanced with locational data, such as vicinity to bus stops, MRT stations, supermarkets, (top) primary schools and other points of interests. Models were estimated with standard OLS, spatial auto regressive and geographically weighted regression methods for several sub-markets: private rental & buying and public (HDB) rental & buying. Floor area and distance to CBD are the most important drivers of house price. Dependent on the market, vicinity to public transport has a positive result. A higher floor level is considered positive as well. Furthermore, we find that spatial models function better than traditional OLS models and that using asking prices and transaction prices yields similar results despite the large difference between both types of prices.