Location-Centered House Price Prediction: A Multi-Task Learning Approach
Nanjing University of Science and Technology（南京理工大学）；RMIT University（皇家墨尔本理工大学）；Nanjing University of Finance and Economics（南京财经大学）；Swinburne University of Technology（斯威本科技大学）
Guangliang Gao, Zhifeng Bao, Jie Cao, A. K. Qin, Timos Sellis, Zhiang Wu
Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.
Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University（北京航空航天大学大数据与脑机智能高精尖创新中心）；School of Computing, University of Leeds（利兹大学计算学院）；UrBrain Technology；Department of Computer Science, University of Illinois at Chicago（伊利诺伊大学芝加哥分校计算机科学系）；Department of Computer Science and Engineering, Lehigh University（理海大学计算机科学与工程系）
