House price prediction using a machine learning model: A survey of literature

• Published in: International Journal of Modern Education and Computer Science
• Main Author: Zulkifley N.H.; Rahman S.A.; Ubaidullah N.H.; Ibrahim I.
• Format: Article
• Language: English
• Published: Modern Education and Computer Science Press 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096969881&doi=10.5815%2fijmecs.2020.06.04&partnerID=40&md5=a764babd25df8ca20ed2612200594f0b

Data mining is now commonly applicable to the real estate market. Data mining's ability to extract relevant knowledge from irrelevant data makes it very useful to predict house price, important house attributes, and many more. A research has stated that fluctuation of house prices has often been a concern for house owners and real estate market. A survey of literature is carried out to analyze the relevance attributes to forecast house price and the most efficient models to predict the house price. The findings of this analysis verified the usage of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to other models. Moreover, our findings also suggest that locational attributes and structural attributes were a prominent factor in house price prediction. This study will give a tremendous benefits especially towards house developers and researchers in order to determine the most significant attributes in determining house price and in order to acknowledge the best machine learning model that should be used to conduct study in this field. © 2020 MECS.