scholarly journals 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

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Nurul Fazira Sa’at ◽  
Nurul Hana Adi Maimun ◽  
Nurul Hazrina Idris
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural Network Model ◽  
House Price ◽  
Predictive Performance ◽  
Hedonic Price Model ◽  
Price Forecasting ◽  
Hedonic Price ◽  
Price Model ◽  
Forecasting Performance

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.

THE ARTIFICIAL NEURAL NETWORK MODEL (ANN) FOR MALAYSIAN HOUSING MARKET ANALYSIS

2019 ◽  
Vol 17 (9) ◽  
Author(s):  
Siti Norasyikin Abd. Rahman ◽  
Nurul Hana Adi Maimun ◽  
Muhammad Najib Mohamed Razali ◽  
Suriatini Ismail
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Goodness Of Fit ◽  
House Prices ◽  
Hedonic Model ◽  
Forecasting Performance ◽  
Artificial Neural

The Hedonic Model, a traditional method for forecasting house prices has been criticised due to nonlinearity, multicollinearity and heteroskedasticity problems, which were argued to affect estimation accuracy. Unlike the Hedonic Model, the Artificial Neural Network Model (ANN), permits nonlinear relationships and thus avoids the problems plaguing the Hedonic Model resulting in superior forecasting performance. Despite these advantages, attempts to model house prices using ANN are limited in geography and data thus besetting the usefulness of the results. To address the research gap, this paper aims to establish such a new model using ANN in forecasting house prices. A sample of double-storey terraced houses transacted in Johor Bahru are analysed using ANN. The findings illustrate a superior forecasting performance for ANN through high values of goodness of fit and low values of errors. This paper adds to the house price modelling literature and provides new knowledge to both academics and practitioners.

scholarly journals Determinants of Real Estate Prices in the Statutory City of Brno

2018 ◽  
Vol 66 (4) ◽  
pp. 991-999
Author(s):  
Vlastimil Reichel ◽  
Petr Zimčík
Keyword(s):  
Real Estate ◽  
House Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Least Square ◽  
Hedonic Price Model ◽  
Market Demand ◽  
Hedonic Price ◽  
Price Model ◽  
Real Estate Prices

The real estate market demand for both‑houses and flats has been growing recently. This trend is one of the factors, which are able to influence the price of real estate. Our paper introduces determinants of housing price and their influence on actual real estate prices in the statutory city of Brno. The aim of this paper is to present key determinants of house price and find the hedonic price model, which describes, how determinants affect house price best. Realized prices were analyzed for housing units located in different districts of Brno. Key determinants in this research are a year of sale, an area of flat in square meters, number of rooms, location in districts of Brno, type of masonry and reconstruction. The dataset covers the time period between years 2012 and 2015. Hedonic price model is estimated by the method of ordinary least square. Besides main aim, four assumptions were verified, which should determine the influence on the price of individual determinants in the statutory city of Brno.

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