Real estate price prediction takes an important role in the economy that can drive up and down the stock prices and even generate disruptive economic events. Many researchers have tried to understand the pricing mechanism with machine learning techniques such as support vector machine, neural network, random forest, and AdaBoost. The boundary problem, on the other hand, makes the pricing scheme more complicated, and this trend is accelerated especially in the situation of population decline in Japan. In this paper, we discuss how we could approach the boundary problem in real estate prediction. We propose a new comprehensive inference model extending and adapting XGBoost to the domain that has the boundary conditions problem by utilizing the distance between the instances in the domain data set to make the layers of bumpy boundaries smooth for more accurate predictions and robustness against the domain data set. The experiments result showed our proposed method performed well on both hypothetical data sets and actual real estate price data. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

---

房地产价格预测中具有边界条件的新 XGBoost 推理

房地产价格预测在经济中发挥着重要作用，可以推动股价上涨和下跌，甚至产生破坏性的经济事件。许多研究人员试图通过支持向量机、神经网络、随机森林和 AdaBoost 等机器学习技术来理解定价机制。另一方面，边界问题使定价方案更加复杂，而且这种趋势在日本人口减少的情况下加速。在本文中，我们讨论了如何处理房地产预测中的边界问题。我们提出了一种新的综合推理模型，通过利用域数据集中实例之间的距离，将 XGBoost 扩展到存在边界条件问题的域，使凹凸不平的边界层平滑，从而对域数据进行更准确的预测和鲁棒性放。实验结果表明，我们提出的方法在假设数据集和实际房地产价格数据上均表现良好。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。