Tree based Machine Learning in Predicting the Price of Green Building

Abstract

Researchers and industry players acknowledged that machine learning application is useful in assisting human for solving many kinds of real life problems, including in real estate and property industry. In this paper, we present the empirical steps for implementing machine learning approaches in the prediction of green building price. Green building conserve natural resources and reduce the negative impact of the building development. This paper provides a report from the data collection method, preliminary data analysis with statistical method, and the experimental implementation of the machine learning models from training, validating to testing. The results show that the tree based machine learning produced better performances on the green building properties, which further tested with another five hold-out data. The testing results show that the machine learning with tree based scheme was able to predict the green building price higher than the observed price for the eight out of the ten cases within the acceptable valuation ranges.