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A Flexible Neuro-Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non-Linear Environments

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  • Ali Azadeh
  • Mohammad Sheikhalishahi
  • Ali Boostani

Abstract

Changes in housing price affect both individuals and government since they have substantial influence on the socio-economic conditions. Valuations of housing are necessary in order to assess the benefits and liabilities in housing sector. This study presents a flexible meta-modelling approach for improvement of housing price estimation in ambiguous and complex environments. It is composed of artificial neural network (ANN) and fuzzy linear regression (FLR). Seven FLR models are considered to cover latest approaches and viewpoints. Also, ANN is applied to data sets. The preferred FLR model is selected via mean absolute percentage of error (MAPE) for further considerations, and then the preferred FLR model and the best structure of ANN are applied to the data set. Finally, the preferred model is selected based on MAPE. The intelligent approach of this study is applied for estimation and forecasting housing price in Iran. The housing price in Iran mainly is based on eight economic indices including currency, oil income, general index, house service pricing index, rate of informal market, gross domestic production in basic price, added value of oil group and construction materials price. FLR is identified as the preferred model with lowest MAPE for housing price forecasting in Iran. This shows that the housing market of Iran is associated with severe environmental fuzziness and ambiguity. This is the first study that introduces a flexible neuro-fuzzy approach for improved estimation and forecasting of housing price in noisy, complex and uncertain environments.

Suggested Citation

  • Ali Azadeh & Mohammad Sheikhalishahi & Ali Boostani, 2014. "A Flexible Neuro-Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non-Linear Environments," South African Journal of Economics, Economic Society of South Africa, vol. 82(4), pages 567-582, December.
    • Handle: RePEc:bla:sajeco:v:82:y:2014:i:4:p:567-582
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    References listed on IDEAS

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    1. Allan Din & Martin Hoesli & Andre Bender, 2001. "Environmental Variables and Real Estate Prices," Urban Studies, Urban Studies Journal Limited, vol. 38(11), pages 1989-2000, October.
    2. Chris Leishman & Colin Jones & Will Fraser, 2000. "The influence of uncertainty on house builder behaviour and residential land values," Journal of Property Research, Taylor & Francis Journals, vol. 17(2), pages 147-168, January.
    3. Hojati, Mehran & Bector, C. R. & Smimou, Kamal, 2005. "A simple method for computation of fuzzy linear regression," European Journal of Operational Research, Elsevier, vol. 166(1), pages 172-184, October.
    4. Wang, Hsiao-Fan & Tsaur, Ruey-Chyn, 2000. "Resolution of fuzzy regression model," European Journal of Operational Research, Elsevier, vol. 126(3), pages 637-650, November.
    5. Tanaka, Hideo & Hayashi, Isao & Watada, Junzo, 1989. "Possibilistic linear regression analysis for fuzzy data," European Journal of Operational Research, Elsevier, vol. 40(3), pages 389-396, June.
    6. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods Of Forecasting House Prices In Six Metropolitan Areas Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(2), pages 298-313, June.
    7. J. David Cummins & Richard Derrig, 1997. "Fuzzy Financial Pricing of Property-Liability Insurance," North American Actuarial Journal, Taylor & Francis Journals, vol. 1(4), pages 21-40.
    8. Elaine M. Worzala & Margarita Lenk & Ana Silva, 1995. "An Exploration of Neural Networks and Its Application to Real Estate Valuation," Journal of Real Estate Research, American Real Estate Society, vol. 10(2), pages 185-202.
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    2. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.

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