I.J. Modern Education and Computer Science, 2020, 6, 46-54 Published Online December 2020 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2020.06.04 Copyright © 2020 MECS I.J. Modern Education and Computer Science, 2020, 6, 46-54 House Price Prediction using a Machine Learning Model: A Survey of Literature Nor Hamizah Zulkifley Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Email: mizalee10@gmail.com Shuzlina Abdul Rahman Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Email: shuzlina@fskm.uitm.edu.my Nor Hasbiah Ubaidullah Faculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tg. Malim Perak. Email: hasbiah@fskik.upsi.edu.my Ismail Ibrahim Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Email: ismailibrahim@uitm.edu.my Received: 15 July 2020; Accepted: 22 October 2020; Published: 08 December 2020 Abstract: 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. Index Terms: House Price Prediction, Machine Learning Model, Support Vector Regression, Artificial Neural Network, XGBoost 1. Introduction House is one of human life's most essential needs, along with other fundamental needs such as food, water, and much more. Demand for houses grew rapidly over the years as people's living standards improved. While there are people who make their house as an investment and property, yet most people around the world are buying a house as their shelter or as their livelihood. According to [1], housing markets have a positive impact on a country's currency, which is an important national economy scale. Homeowners will purchase goods such as furniture and household equipment for their home, and homebuilders or contractors will purchase raw material to build houses to satisfy house demand, which is an indication of the economic wave effect created by the new house supply. Besides that, consumers have capital to make a large investment, and the construction industry is in good condition can be seen through a country's high level of house supply. According to [2], numerous international organizations and human rights have emphasized house importance. House is profoundly rooted in the economic, financial, and political structure of each country. Nevertheless, [3] reported that the fluctuation of house prices has always been an issue for house owners, buildings and real estate, besides [4] stated that house has become unaffordable as there is substantial price growth in several countries in the