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Homogeneous Feature Transfer and Heterogeneous Location Fine-Tuning for Cross-City Property Appraisal Framework

  • Yihan GuoEmail author
  • Shan Lin
  • Xiao Ma
  • Jay Bal
  • Chang-tsun Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)

Abstract

Most existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model. As different cities usually contain a different set of location features (district names, apartment names), most existing mass appraisal methods have to train a new model from scratch for different cities or regions. As a result, these approaches require massive data collection for each city and the total training time for a multi-city property appraisal system will be extremely long. Besides, some small cities may not have enough data for training a robust appraisal model. To overcome these limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By transferring partial neural network learning from a source city and fine-tuning on the small amount of location information of a target city, our semi-supervised model can achieve similar or even superior performance compared to a fully supervised Artificial neural network (ANN) method.

Keywords

Property valuation Transfer Learning Mass appraisal 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yihan Guo
    • 1
    Email author
  • Shan Lin
    • 1
  • Xiao Ma
    • 1
  • Jay Bal
    • 1
  • Chang-tsun Li
    • 1
    • 2
  1. 1.University of WarwickCoventryUK
  2. 2.Charles Sturt UniversityWagga WaggaAustralia

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