House Price Prediction using a Machine Learning Model: A Survey of Literature

• Published in: International journal of modern education and computer science Vol. 12; no. 6; pp. 46 - 54
• Main Authors: Hamizah Zulkifley, Nor, Abdul Rahman, Shuzlina, Ubaidullah, Nor Hasbiah, Ibrahim, Ismail
• Format: Journal Article
• Language: English
• Published: Hong Kong Modern Education and Computer Science Press 08.12.2020
• Subjects: Artificial Intelligence; Artificial neural networks; Computer science; Data mining; Education; Houses; Housing; Housing prices; International organizations; Machine learning; Neighborhoods; Neural networks; Price increases; Pricing; Purchasing; Real estate; Shopping; Support vector machines
• ISSN: 2075-0161; 2075-017X
• DOI: 10.5815/ijmecs.2020.06.04

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.