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An Overview of Real Estate Modelling Techniques for House Price Prediction

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Charting a Sustainable Future of ASEAN in Business and Social Sciences

Abstract

Housing price prediction in real estate industry is a very difficult task, and it has piqued the interest of many researchers over the past years in the quest to look for a suitable model to predict the price of property. For this reason, this paper aims to review the literature on the application of modelling technique that is usually being implemented to indicate the price prediction for properties. The modelling technique includes the Artificial neural network (ANN), Hedonic price model (HPM), Fuzzy logic system (FLS), Support vector machine (SVM), Linear regression (LR), Decision tree (DT), Random forest (RF), K-nearest neighbour (KNN), Partial least square (PLS), Naïve bayes (NB), Multiple regression analysis (MRA), Spatial analysis (SA), Gradient boosting (GB), Ridge regression, Lasso regression and Ensemble learning model (ELM). All these models were reviewed, and the explanation of advantages and disadvantages of each model was included. Hence, this paper reports the findings of reviews made on models which deal with regression and classification problem.

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Authors and Affiliations

  1. Universiti Teknologi MARA, Perak Branch, Seri Iskandar, Perak, Malaysia

    Thuraiya Mohd, Nur Syafiqah Jamil, Noraini Johari & Lizawati Abdullah

  2. Universiti Teknologi MARA, Perak Branch, Tapah, Perak, Malaysia

    Suraya Masrom

Authors
  1. Thuraiya Mohd
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  2. Nur Syafiqah Jamil
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  3. Noraini Johari
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  4. Lizawati Abdullah
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  5. Suraya Masrom
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Correspondence to Thuraiya Mohd .

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Editors and Affiliations

  1. Universiti Teknologi MARA, Perlis Branch, Malaysia

    Naginder Kaur

  2. Universiti Teknologi MARA, Perlis Branch, Malaysia

    Mahyudin Ahmad

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Mohd, T., Jamil, N.S., Johari, N., Abdullah, L., Masrom, S. (2020). An Overview of Real Estate Modelling Techniques for House Price Prediction. In: Kaur, N., Ahmad, M. (eds) Charting a Sustainable Future of ASEAN in Business and Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-15-3859-9_28

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