The Prediction of Affordable Housing Prices in Petaling Jaya District Using R Statistical Computing Environment


  • Suresh Nodeson Faculty of Business and Finance, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia
  • Kesavan Krishnan Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia.
  • Sathis Krishnan Faculty of Computer Science and Information Technology, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia.



Affordable housing price, Linear regression, Random forest and gradient boosting


Affordable housing especially in a city environment is recognized as one of citizen needs among the mid-dle-income groups. This research paper intended to explore the possibilities to use data mining algorithms: Linear Regression, Random Forest and Gradient Boosting algorithms for predicting and analyzing the housing affordability price for middle-income earners in Petaling Jaya district, Malaysia. The dataset from Malaysia House Index by Petaling Jaya district used to evaluate based on the proposed algorithms. The dataset extracted based on residential property sub-sectors with three attributes. Based on the prediction models, as results, the Gradient Boosting algorithm shows higher accuracy of 74% for predicting affordable housing price in Petaling Jaya district, Malaysia compared to other prediction techniques.


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How to Cite

Nodeson, S., Krishnan, K., & Krishnan, S. (2023). The Prediction of Affordable Housing Prices in Petaling Jaya District Using R Statistical Computing Environment. Journal of Contemporary Issues and Thought, 13(1), 35–40.