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

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Data Mining (AusDM 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 996))

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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.

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Authors and Affiliations

  1. University of Warwick, Coventry, UK

    Yihan Guo, Shan Lin, Xiao Ma, Jay Bal & Chang-tsun Li

  2. Charles Sturt University, Wagga Wagga, Australia

    Chang-tsun Li

Authors
  1. Yihan Guo
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  2. Shan Lin
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  3. Xiao Ma
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  4. Jay Bal
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  5. Chang-tsun Li
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Corresponding author

Correspondence to Yihan Guo .

Editor information

Editors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  2. University of Auckland, Auckland, New Zealand

    Yun Sing Koh

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. Data Science and Engineering, Australian Taxation Office, Canberra, Australia

    Graco Warwick

  5. Department of Information Technology, University of Wollongong, Wollongong, NSW, Australia

    David Stirling

  6. School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia

    Chang-Tsun Li

  7. School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia

    Zahidul Islam

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Guo, Y., Lin, S., Ma, X., Bal, J., Li, Ct. (2019). Homogeneous Feature Transfer and Heterogeneous Location Fine-Tuning for Cross-City Property Appraisal Framework. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_13

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  • DOI: https://doi.org/10.1007/978-981-13-6661-1_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6660-4

  • Online ISBN: 978-981-13-6661-1

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