Homogeneous feature transfer and heterogeneous location fine-tuning for cross-city property appraisal framework

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

    Research output: Book chapter/Published conference paperConference paperpeer-review


    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.

    Original languageEnglish
    Title of host publicationData Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers
    EditorsYanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Yun Sing Koh, Rafiqul Islam, Zahidul Islam
    PublisherSpringer-Verlag London Ltd.
    Number of pages14
    ISBN (Print)9789811366604
    Publication statusPublished - 16 Feb 2019
    Event16th Australasian Conference on Data Mining, AusDM 2018 - Charles Sturt University , Bathurst, Australia
    Duration: 28 Nov 201830 Nov 2018

    Publication series

    NameCommunications in Computer and Information Science
    ISSN (Print)1865-0929


    Conference16th Australasian Conference on Data Mining, AusDM 2018
    Internet address


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