The need for accurate and unbiased assessment of residential real property has always been important not only to financial institutions lending on or holding such assets but also to municipalities that rely on property taxes as their critical source of revenue. The common methodology for predicting residential property sale price is based on traditional multiple regression in spite of known issues. Machine learning methods have been proposed as an alternative approach but the results are far from satisfactory. A review of existing studies and relevant issues can help researchers better assess the pros and cons of the approaches in this important stream of research and move the field forward. This article provides such a review. In our review, we have noticed that common to both the regression-based methods and machine learning methods are the use of batch-mode learning. Thus in addition to providing a review of recent research on batch-based residential property prediction models, this article also explores a new approach to constructing residential property price prediction models by treating past sale records as an evolving data stream. The results of our study show that the data stream approach outperforms the traditional regression method and demonstrate the potential of data stream methods in improving prediction models for residential property prices.

中文翻译：

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预测房屋销售价格：现有方法的回顾和数据流方法的说明以提高性能

对住宅不动产进行准确和公正评估的需求一直很重要，不仅对借出或持有此类资产的金融机构，而且对依赖财产税作为主要收入来源的市政当局也很重要。尽管存在已知问题，但预测住宅物业销售价格的常用方法仍基于传统的多元回归。机器学习方法已被提出作为替代方法，但结果远不能令人满意。对现有研究和相关问题的回顾可以帮助研究人员更好地评估这一重要研究流中方法的利弊，并推动该领域向前发展。这篇文章提供了这样的评论。在我们的评论中，我们注意到基于回归的方法和机器学习方法的共同点是使用批处理模式学习。因此，除了回顾最近关于基于批处理的住宅物业预测模型的研究外，本文还探索了一种通过将过去的销售记录视为不断发展的数据流来构建住宅物业价格预测模型的新方法。我们的研究结果表明，数据流方法优于传统的回归方法，并展示了数据流方法在改进住宅物业价格预测模型方面的潜力。本文还探讨了一种通过将过去的销售记录视为不断发展的数据流来构建住宅物业价格预测模型的新方法。我们的研究结果表明，数据流方法优于传统的回归方法，并展示了数据流方法在改进住宅物业价格预测模型方面的潜力。本文还探讨了一种通过将过去的销售记录视为不断发展的数据流来构建住宅物业价格预测模型的新方法。我们的研究结果表明，数据流方法优于传统的回归方法，并展示了数据流方法在改进住宅物业价格预测模型方面的潜力。