  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.
