The Use of Artificial Neural Networks (ANN) in Forecasting Housing Prices in Ankara, Turkey

• Autorzy: Olgun Kitapci , Ömür Tosun , Murat Fatih Tuna , Tarik Turk
• Języki publikacji: EN

Abstrakty

EN

The purpose of this paper is to forecast housing prices in Ankara, Turkey using the artificial neural networks (ANN) approach. The data set was collected from one of the biggest real estate web pages during April 2013. A three-layer (input layer - one hidden layer - output layer) neural network is designed with 15 different inputs to forecast the future housing prices. The proposed model has a success rate of 78%. The results of this paper would help property investors and real estate agents in developing more effective property pricing management in Ankara. We believe that the artificial neural networks (ANN) proposed here will serve as a reference for countries that develop artificial neural networks (ANN) method-based housing price determination in future. Applying the artificial neural networks (ANN) approach for estimation of housing prices is relatively new in the field of housing economics. Moreover, this is the first study that uses the artificial neural networks (ANN) approach for analyzing the housing market in Ankara/Turkey. (original abstract)

Słowa kluczowe

EN

Housing; Forecasting; Artificial neural networks (ANN); Prices

PL

Mieszkalnictwo; Prognozowanie; Sztuczne sieci neuronowe (SSN); Ceny

• Czasopismo: Journal of Marketing and Consumer Behaviour in Emerging Markets
• Rocznik: 2017
• Numer: nr 1(5)
• Strony: 4--14

Twórcy

autor

Olgun Kitapci

• Akdeniz University, Turkey

autor

Ömür Tosun

• Akdeniz University, Turkey

autor

Murat Fatih Tuna

• Cumhuriyet University, Turkey

autor

Tarik Turk

• Cumhuriyet University, Turkey

Bibliografia

• Azadeh, A., Ziaei, B. and Moghaddam, M. (2012). 'A hybrid fuzzy regression-fuzzy cognitive map algorithm for forecasting and optimization of housing market fluctuations', Expert Systems with Applications, Vol. 39, No. 1, pp. 298-315.
• Brown. N.J. and Harvey, R.S. (1982). 'On the estimation of structural hedonic price model', Econometrica, Vol. 50, No. 5, pp. 765-768.
• Chen, Z., Cho, S.-H., Poudyal, N. and Roberts, R. K. (2007). 'Forecasting Housing Prices Under Different Submarket Assumptions', American Agricultural Economics Association Annual Meeting, Portland, OR, July 29-August 1.
• Coskun, Y. and Ertugrul, H.M. (2016). 'House price return volatility patterns in Turkey, İstanbul, Ankara and İzmir'. Journal of European Real Estate Research, Vol. 9, No. 1, pp. 26-51.
• García, N., Gámez, M. and Alfaro, E. (2008). 'ANN+GIS: An automated system for property valuation', Neurocomputing, Vol. 71, No. 4-6, pp. 733-742.
• He, C., Wang, Z., Guo, H., Sheng, H., Zhou, R. and Yang, Y. (2010). 'Driving Forces Analysis for Residential Housing Price in Beijing', Procedia Environmental Sciences, 2, 925-936.
• Jha, G. K. (2007). 'Artificial Neural Networks', Indian Agricultural Research Institute, pp. 1-10.
• Kaya, A. and Atan, M. (2014). 'Determination of the factors that affect house prices in Turkey by using hedonic pricing model', Journal of Business, Economics and Finance, Vol. 3, No. 3, pp. 313-327.
• Liang, L. and Wu, D. (2005). 'An application of pattern recognition on scoring Chinese corporations financial conditions based on back propagation neural network', Computers and Operational Research, Vol. 32, No. 5, pp. 1115-1129.
• Özdemir, D. and Temur, G. T. (2009). 'DEA ANN approach in supplier evaluation system', WASET, Vol. 54, pp. 343-348.
• Park, B. and Bae, J.K. (2015). 'Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data', Expert Systems with Applications, Vol. 42, No. 6, pp. 2928-2934.
• Pendharkar, P. C. and Rodgre, J. A. (2003). 'Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption', Decision Support Systems, Vol. 36, No. 1, pp. 117-136.
• Piazzesi, M., Schneider and M., Tuzel, S. (2007). 'Housing, Consumption and Asset Pricing', Journal of Financial Economics, Vol. 83, pp. 531-569.
• Rabiega, W.A., Lin, T.-W. and Robinson, L.M. (1984). 'The property value impacts of public housing projects in low and moderate density residential neighborhoods', Land Economics, Vol. 60, No. 2, pp. 174-179.
• Rao, C.P. and Ali, J. (2002). 'Neural network model for database marketing in the new global economy', Marketing Intelligence & Planning, Vol. 20, No. 1, pp. 35-43.
• Selim, H. (2009). 'Determinants of house prices in Turkey: hedonic regression versus artificial neural network', Expert Systems with Applications, Vol. 36, No. 2, pp. 2843-2852.
• Shimizu, C., Nishimura, K.G. and Karato, K. (2014). 'Nonlinearity of housing price structure: Assessment of three approaches to nonlinearity in the previously owned condominium market of Tokyo', International Journal of Housing Markets and Analysis, Vol. 7, No. 4, pp. 459-488.
• Stevenson, S. (2004). 'New Empirical Evidence on Heteroscedasticity in Hedonic Housing Models', Journal of Housing Economics, Vol. 23, No. 3, pp. 136-153.
• Tosun, Ö. (2012). 'Using data envelopment analysis-neural network model to evaluate hospital efficiency', International Journal Productivity and Quality Management, Vol. 9, No. 2, pp. 245-257.
• Tayyebia, A., Pijanowskia, B. C. and Tayyebi, A. H. (2011). 'An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran', Landscape and Urban Planning, Vol. 100, No. 1-2, pp. 35-44.
• Turkish Real Estate Market Report (2014), Deloitte Turkey.
• Wu, D., Yang, Z. and Liang, L. (2006). 'Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank', Expert Systems with Applications, Vol. 31, No. 1, pp. 108-111.
• Yan, Y., Xu, W., Bu, H., Song, Y, Zhang, W., Yuan, H. and Wang, S.-Y. (2007). 'Method for Housing Price Forecasting based on Methodology', Systems Engineering - Theory & Practice, Vol. 27, No. 7, pp. 1-9.
• Yuksel, A. (2016). 'The relationship between stock and real estate prices in Turkey: Evidence around the global financial crisis', Central Bank Review, Vol. 16, No. 1, pp. 33-40.

Identyfikatory

DOI

10.7172/2449-6634.jmcbem.2017.1.1