Multiattribute Supply and Demand Matching Decision Model for Online-Listed Rental Housing: An Empirical Study Based on Shanghai

Abstract

The mismatch between the supply and demand of online-listed rental housing (ORH) is an important factor restricting the operational efficiency of online rental service platforms. However, extant literature pays little attention to this problem. This study proposes an ORH multiattribute supply and demand matching decision model based on the perceived utility of matching both sides of this market. The model considers the multiattribute information of ORH, such as area, transportation, rent, room, and interior decoration, and quantifies their perceived utility values based on the theory of disappointment. Thereafter, we construct the matching decision model and verify it for feasibility by applying it to Shanghai’s ORH supply and demand information—our empirical case. The results show that this method can be applied to online rental housing platforms and meet the supply and demand matching requirements to the greatest extent. The constructed model takes into account the perceptions of both supply and demand parties, may promote the effective matching of ORH supply and demand, and bears theoretical implications for the improvement of rental housing matching in ORH platforms.