Big Data for Housing and Their Interaction with Market Dynamics

Abstract This paper is the first to analyze the interactions between the keywords of online home listings and housing market dynamics. We consider the COVID-19 outbreak as a natural shock that brought a significant change to work modes and mobility and, in turn, consumer preference changes for home purchases. We link two types of big data: the universal transaction data of resale public housing and the database of more than 70,000 listings from the major online platform in Singapore. Using the Difference-in-Difference approach, we first find that housing units with a higher floor level and more rooms have experienced a significant increase in transaction prices while close proximity to public transportation and the central business district (CBD) led to a reduction in the price premium after COVID-19. Our text analysis results, using the natural language processing, suggest that the online listing keywords have consistently captured these trends and provide qualitative insights (e.g. view becoming increasingly popular) that could not be uncovered from the conventional database. Relevant keywords reveal trends earlier than transaction-based data, or at least in a timely manner.