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.
Highlights
Matching supply and demand (SAD) directly affects the success of online trading
Leads to this discrepancy between the rental housing market’s SAD, which seriously hinders the efficiency of rental housing service platforms. erefore, can the creation of an effective matching method for online rental housing platforms solve the operational inefficiency of the rental housing market but can promote the rapid development of the rental market
The matching model of online-listed rental housing (ORH) is mainly a balanced search model, whose purpose is to reduce search costs [8, 9]. ere are a few models to measure the quality of supply and demand matching based on the similarities between SAD [10]. ere are models to establish a double-sided matching theory to solve the problem of public rental housing [11]
Summary
Matching supply and demand (SAD) directly affects the success of online trading. regarding rental housing, efficiency in matching SAD is not high [1]; many countries around the world have rental housing markets characterized by mismatches in SAD [2]. Is shows that traditional rental housing supply structures are incapable of matching personalized demands, and the mismatch between the quality and quantity of ORH leads to this discrepancy between the rental housing market’s SAD, which seriously hinders the efficiency of rental housing service platforms. Erefore, can the creation of an effective matching method for online rental housing platforms solve the operational inefficiency of the rental housing market but can promote the rapid development of the rental market. To promote the effective matching of rental housing online, some scholars have made various attempts to study the SAD matching of ORH. With the acceleration of China’s urbanization process—a powerful means of regulating urban real estate, retaining the working population, and attracting new talents—the rental housing market has become an important part of deepening the reform of the housing system and an important way to achieve the goal of improving urban residents’ quality of life. Since 2011, Shanghai’s permanent migrant population has remained above 9.6 million per year, which accounts for more than 40% of the total resident population, as shown in Figure 4. is huge population size has resulted in an increase in living demands. erefore, the development of Shanghai’s rental housing market requires urgent attention
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