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The Spatial neural network model with disruptive technology for property appraisal in real estate industry

机译：房地产业财产鉴定中断技术的空间神经网络模型

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摘要

Property valuation is a complex issue that has always been the focal point for the real estate industry. The traditional valuation models used for appraisals cannot meet real-world demand anymore due to the improper processing of correlated information of nearby facilities. In this study, we propose a Spatial Neural Network (SNN) model, called Property Appraisal 4.0, that uses disruptive technology to forecast property values and discover hidden neighbourhood features of real estate information in the satellite embedding vectors. The latest deep learning technologies are also employed, such as knowledge distillation, incremental learning, and DeepAutomated Optical Inspection. Class Activation Mapping is also adapted to reinforce the proposed spatial neural network in the model. Experimental results show that our approach's performance is better than that of previous mainstream models, such as the Hedonic Pricing Model and Support Vector Machines.
机译：物业估值是一个复杂的问题，这一直是房地产业的焦点。 由于附近设施相关信息的相关信息不当，可用于评估的传统估值模型不能满足真实的需求。 在这项研究中，我们提出了一种被称为财产评估4.0的空间神经网络（SNN）模型，该模型使用中断技术来预测房地产值，并发现卫星嵌入向量中的房地产信息隐藏的邻居特征。 还采用了最新的深度学习技术，例如知识蒸馏，增量学习和深仿光学检查。 类激活映射也适于在模型中加强所提出的空间神经网络。 实验结果表明，我们的方法的性能优于以前的主流模型，例如储层定价模型和支持向量机。

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