# RECORD 321664826269788c8435ead0315a2539fb9837f3
# TITLE: ENHANCING THE ACCURACY OF MALAYSIAN HOUSE PRICE FORECASTING: A COMPARATIVE ANALYSIS ON THE FORECASTING PERFORMANCE BETWEEN THE HEDONIC PRICE MODEL AND ARTIFICIAL NEURAL NETWORK MODEL
# YEAR: 2021.0  VENUE: PLANNING MALAYSIA  DOI: 10.21837/pm.v19i17.1003
# AUTHORS: Nurul Fazira Sa’at; N. H. Adi Maimun; N. H. Idris
# PAGES: 11  CANDIDATE TABLE PAGES (1-based): []
# ABSTRACT: The Hedonic Price Model (HPM), a prominent model used in real estate appraisal and economics, has been argued to be marred with nonlinearity, multicollinearity and heteroscedasticity problems that affect the accuracy of price predictions. An alternative method called Artificial Neural Network Model (ANN) was identified as capable of addressing the shortcomings of HPM and produces superior predictive performance. Hence, this study aims to evaluate the forecasting performance between HPM and ANN using Malaysian housing transaction data from the period between 2009 to 2018, sourced from the Valuation and Property Service Department, Johor Bahru. The models’ performance was evaluated and compared based on their statistical and predictive performance. Results showed that ANN outperformed HPM in both statistical and predictive performance. This study benefits the expansion of academic and practical knowledge in enhancing the accuracy of house price forecasting.


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1 Research Assistant at Universiti Teknologi Malaysia. Email: nurulfazirasaat@gmail.com 
PLANNING MALAYSIA: 
Journal of the Malaysian Institute of Planners 
VOLUME 19 ISSUE 3 (2021), Page 249 – 259 
ENHANCING THE ACCURACY OF MALAYSIAN HOUSE PRICE 
FORECASTING: A COMPARATIVE ANALYSIS ON THE 
FORECASTING PERFORMANCE BETWEEN THE HEDONIC PRICE 
MODEL AND ARTIFICIAL NEURAL NETWORK MODEL 
 
Nurul Fazira Sa’at1, Nurul Hana Adi Maimun2 & Nurul Hazrina Idris3 
 
1,2Centre for Real Estate Studies, Institute for Smart Infrastructure and 
Innovative Construction, 
UNIVERSITI TEKNOLOGI MALAYSIA 
3Geoscience and Digital Earth Centre, Research Institute for Sustainable 
Environment (RISE), 
UNIVERSITI TEKNOLOGI MALAYSIA 
 
 
Abstract 
 
The Hedonic Price Model (HPM), a prominent model used in real estate appraisal 
and economics, has been argued to be marred with nonlinearity, multicollinearity 
and heteroscedasticity problems that affect the accuracy of price predictions. An 
alternative method called Artificial Neural Network Model (ANN) was identified 
as capable of addressing the shortcomings of HPM and produces superior 
predictive performance. Hence, this study aims to evaluate the forecasting 
performance between HPM and ANN using Malaysian housing transaction data 
from the period between 2009 to 2018, sourced from the Valuation and Property 
Service Department, Johor Bahru. The models’ performance was evaluated and 
compared based on their statistical and predictive performance. Results showed 
that ANN outperformed HPM in both statistical and predictive performance. This 
study benefits the expansion of academic and practical knowledge in enhancing 
the accuracy of house price forecasting. 
 
 
Keywords: Property forecasting, property valuation, predictive accuracy, hedonic 
price model, artificial neural network