房地产
计算机科学
人工智能
情态动词
政府(语言学)
数据库事务
机器学习
数据挖掘
算法
财务
经济
数据库
语言学
哲学
化学
高分子化学
作者
Sutong Wang,Jiacheng Zhu,Yunqiang Yin,Dujuan Wang,Tao Cheng,Yanzhang Wang
标识
DOI:10.1109/tmm.2021.3126153
摘要
With the development of online real estate trading platforms, multi-modal housing trading data, including structural information, location, and interior image data, are being accumulated. The accurate appraisal of real estate makes sense for government officials, urban policymakers, real estate sellers, and personal purchasers. In this study, we propose an interpretable multi-modal stacking-based ensemble learning (IMSEL) method that deals with various modalities for real estate appraisals. We crawl the structural and image data of real estate in Chengdu city, China from the nation's largest real estate transaction platform with the location information, including public services, within 2 km of the real estate using Baidu map. We then compare the predictive results from IMSEL with those from previous state-of-art methods in the literature in terms of the root mean square error, mean absolute percentage error, mean absolute error, and coefficient of determination (R 2 ). The comparison results show that IMSEL outperformed the other methods. We verified the improvement of introducing a data transformation strategy and deep visual features through a 10-fold cross-validation. We also discuss the managerial implications of our research findings.
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