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

Regina Fang Ying Lin, Chiye Ou, Kuo Kun Tseng, Deng Bowen, K. L. Yung, W. H. Ip

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Article number121067
JournalTechnological Forecasting and Social Change
Publication statusPublished - Dec 2021


  • Class activation mapping
  • Deep-Automated Optical Inspection (AOI)
  • Disruptive technology
  • Real estate valuation
  • Spatial information
  • Spatial neural network

ASJC Scopus subject areas

  • Business and International Management
  • Applied Psychology
  • Management of Technology and Innovation


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