TY - GEN
T1 - Days on market
T2 - 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
AU - Zhu, Hengshu
AU - Xiong, Hui
AU - Tang, Fangshuang
AU - Liu, Qi
AU - Ge, Yong
AU - Chen, Enhong
AU - Fu, Yanjie
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/8/13
Y1 - 2016/8/13
N2 - 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 extract key features from multiple types of heterogeneous real estate-related data, such as house profiles and geo-social information 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.
AB - 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 extract key features from multiple types of heterogeneous real estate-related data, such as house profiles and geo-social information 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.
KW - Days on market
KW - Multi-task learning
KW - Real estate
UR - http://www.scopus.com/inward/record.url?scp=84984996835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84984996835&partnerID=8YFLogxK
U2 - 10.1145/2939672.2939686
DO - 10.1145/2939672.2939686
M3 - Conference contribution
AN - SCOPUS:84984996835
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 393
EP - 402
BT - KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 13 August 2016 through 17 August 2016
ER -