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 Deep-Automated 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.

物业估值是一个复杂的问题，一直是房地产行业的焦点。由于对附近设施的相关信息处理不当，用于评估的传统估值模型已不能满足现实世界的需求。在这项研究中，我们提出了一种称为 Property Appraisal 4.0 的空间神经网络 (SNN) 模型，该模型使用破坏性技术来预测财产价值并发现卫星嵌入向量中房地产信息的隐藏邻域特征。还采用了最新的深度学习技术，如知识蒸馏、增量学习和深度自动化光学检测。类激活映射也适用于加强模型中提出的空间神经网络。实验结果表明，我们的方法'