Predicting Condominium Prices in Malaysia: A Comparative Analysis of Machine Learning Models - Easwaramoorthy, Sathishkumar Veerappampalayam; Majid, Shamsul Bin; Cheng Zhi, Clement Chew; Fadzillah Suhaimi, Putera Aiman Idris Bin (Information Research Communications, 2025)
This study seeks to predict condominium prices in Malaysia by tackling the challenge of accurately assessing property values based on a range of features. The objective of this research is to create a model that can aid people in making well-informed decisions by identifying key variables influencing their prices by predicting the prices of condominium in Malaysia. A variety of techniques used in machine learning, such as linear regression and Gradient Boosting are used to construct predictive models. The findings reveal that Gradient Boosting (XGBoost) outperforms the others, it has the lowest error in matrixes such as Mean Squared Error, as well as the highest R-squared value showcasing the relevance of the features. The research concludes that the developed model is capable of accurately estimating property values, providing significant insights for the Malaysian real estate sector.
Keywords: Condominium Prices; Malaysia; Predictive Modeling; Machine Learning; Real Estate Analysis.