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Prediction Using Artificial Neural Network of Turkey's Housing Sales Value

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  • Burcu Yaman Selci

    (Pamukkale Universitesi, Denizli Sosyal Bilimler Meslek Yuksekokulu Dis Ticaret Bolumu, Denizli, Turkiye)

Abstract

In order to keep the supply and demand in the real estate sector in balance, it is very important to make accurate estimates of house sales with an analysis method that can make strong predictions. However, it is noteworthy that the number of studies focusing on house sales estimates in the literature is quite low and the number of studies that make predictions with artificial neural networks from new generation techniques is remarkable. Therefore the aim of this study is to contribute to the prediction and forecasting of sales literature houses in Turkey performing with artificial neural networks. In the study, housing-price index, new housing-price index, non-new housing-price index, house sales to foreigners, interest rates opened to housing loans over TL, consumer price index and exchange rate were selected as independent variables and residential sales were used as dependent variables. A model has been developed in neural networks. The data were taken monthly to cover the periods of 2013: 01-2019: 12 and the analyzes were carried out in the MATLAB R2013a program. Using the NARX network for prediction and forecasting analysis, the prediction of 2013: 01- 2019: 12 period and the prediction of 2020: 01 period was obtained. MSE was used as a performance criterion. As a result of the analysis, it has been determined that the predicted values produced by artificial neural networks and the predictive value of 2020: 01 are quite close to real values and artificial neural networks can detect seasonal effects. The smallness of the MSE value also proved the success of forecasting and forecasting. This confirms that artificial neural networks produce strong statistical results in predicting and predicting residential sales.

Suggested Citation

  • Burcu Yaman Selci, 2021. "Prediction Using Artificial Neural Network of Turkey's Housing Sales Value," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(35), pages 19-32, December.
    • Handle: RePEc:ist:ekoist:v:0:y:2021:i:35:p:19-32
    DOI: 10.26650/ekoist.2021.35.180033
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    References listed on IDEAS

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    1. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
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