Predicting home sale prices: A review of existing methods and illustration of data stream methods for improved performance

Abstract

The need for accurate and unbiased assessment of residential real property has always been important not only to financial institutions lending on or holding such assets but also to municipalities that rely on property taxes as their critical source of revenue. The common methodology for predicting residential property sale price is based on traditional multiple regression in spite of known issues. Machine learning methods have been proposed as an alternative approach but the results are far from satisfactory. A review of existing studies and relevant issues can help researchers better assess the pros and cons of the approaches in this important stream of research and move the field forward. This article provides such a review. In our review, we have noticed that common to both the regression-based methods and machine learning methods are the use of batch-mode learning. Thus in addition to providing a review of recent research on batch-based residential property prediction models, this article also explores a new approach to constructing residential property price prediction models by treating past sale records as an evolving data stream. The results of our study show that the data stream approach outperforms the traditional regression method and demonstrate the potential of data stream methods in improving prediction models for residential property prices.