Title

Days on Market: Measuring Liquidity in Real Estate Markets

Abstract

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.

Meeting Name

22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 (2016: Aug. 13-17, San Francisco, CA)

Department(s)

Computer Science

Comments

This work was partially supported by grants from National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the Natural Science Foundation of China (NSFC, Grant No.s 71329201, 61403358, 61572032, 71571093), and the Youth Innovation Promotion Association of CAS.

Keywords and Phrases

Commerce; Houses; Learning systems; Multiple factors; Multitask learning; Potential buyers; Real estate; Real estate industries; Real estate market; Residential communities; Social information; Data mining; Days on market; Multi-task learning

International Standard Book Number (ISBN)

978-1-4503-4232-2

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Association for Computing Machinery (ACM), All rights reserved.

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