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

## TL;DR

This paper proposes a semi-supervised cross-city property appraisal framework that transfers features and fine-tunes location data, reducing data collection needs and training time while maintaining high accuracy.

## Contribution

It introduces a novel HFT+HLF framework that enables effective transfer learning across cities with different location features, improving scalability and efficiency.

## Key findings

- Achieves comparable or better accuracy than fully supervised models.
- Reduces data collection and training time for multi-city systems.
- Effective for cities with limited data.

## 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.