Days on Market: Measuring Liquidity in Real Estate Markets
Hengshu Zhu*, Baidu Inc.; Hui Xiong, Rutgers; Fangshuang Tang, University of Science and Technology of China; Yong Ge, ; Qi Liu, University of Science and Technology of China; Enhong Chen, ; Yanjie Fu, Rutgers University
Days on Market (DOM) refers to the number of days a property is on the active market, which is an important measurement of market liquidity in real estate industry. Indeed, at the micro level, DOM is not only a special concern of house sellers, but also a useful indicator for potential buyers to evaluate the popularity of a house. At the macro level, DOM is an important indicator of real estate market status. However, it is very challenging to measure DOM, since there are a variety of factors which can impact on the DOM of a property. To this end, in this paper, we aim to measure real estate liquidity by examining multiple factors in a holistic manner. A special goal is to predict the DOM of a given property listing. Speciﬁcally, we ﬁrst ex-tract key features from multiple types of heterogeneous real estate-related data, such as house proﬁles and geo-social in-formation of residential communities. Then, based on these features, we develop a multi-task learning based regression approach for predicting the DOM of real estates. This approach can eﬀectively learn district-aware models for diﬀerent property listings by considering multiple factors. Finally, we conduct extensive experiments on real-world real estate data collected in Beijing and develop a prototype system for practical use. The experimental results clearly validate the eﬀectiveness of the proposed approach for measuring liquidity in real estate markets.
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