Applying dynamic Bayesian tree in property sales price estimation

Abstract

Accurate prediction of Residential Property Sale Price is very important and significant in the operation of the real estate market. Property sellers and buyers/Investors wish to know a fair value for their properties in particular at the time of the sales transaction. The main reason to build an Automated Valuation Model is to be accurate enough to replace human. To select a most suitable model for the property sale price prediction, this paper examined seven Tree-based machine learning models including Dynamic Bayesian Tree (online learning method), Random Forest, Stochastic Gradient Boosting, CART, Bagged CART, Tree Bagged Ensembles and Boosted Tree (batch learning methods) by comparing their RMSE and MAE performances. The performance of these models are tested on 1967 records of unit sales from 19 suburbs of Sydney, Australia. The main purpose of this study is to compare the performance of batch models with the online model. The results demonstrated that Dynamic Bayesian Tree as an online model stands in the middle of batch models based on the root mean square error (RMSE) and mean absolute error (MAE). It shows using online model for estimating the property sale price is reasonable for real world application.