IDEAS home Printed from https://ideas.repec.org/a/bla/sajeco/v82y2014i4p567-582.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/saje.12047
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Peter Wanke & Carlos Barros & Nkanga Pedro João Macanda, 2016. "Predicting Efficiency in Angolan Banks: A Two-Stage TOPSIS and Neural Networks Approach," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 461-483, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Azadeh, A. & Khakestani, M. & Saberi, M., 2009. "A flexible fuzzy regression algorithm for forecasting oil consumption estimation," Energy Policy, Elsevier, vol. 37(12), pages 5567-5579, December.
    2. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    3. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    4. de Andres-Sanchez, Jorge, 2007. "Claim reserving with fuzzy regression and Taylor's geometric separation method," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 145-163, January.
    5. Roldán López de Hierro, Antonio Francisco & Martínez-Moreno, Juan & Aguilar Peña, Concepción & Roldán López de Hierro, Concepción, 2016. "A fuzzy regression approach using Bernstein polynomials for the spreads: Computational aspects and applications to economic models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 13-25.
    6. Hao, Peng & Guo, Junpeng, 2017. "Constrained center and range joint model for interval-valued symbolic data regression," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 106-138.
    7. Jos魍ar𨁍ontero-Lorenzo & Beatriz Larraz-Iribas, 2012. "Space-time approach to commercial property prices valuation," Applied Economics, Taylor & Francis Journals, vol. 44(28), pages 3705-3715, October.
    8. Hyunsoo Kim & Youngwoo Kwon & Yeol Choi, 2020. "Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
    9. Jose Torres-Pruñonosa & Pablo García-Estévez & Camilo Prado-Román, 2021. "Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing," Mathematics, MDPI, vol. 9(7), pages 1-16, April.
    10. Vladimir Vargas-Calder'on & Jorge E. Camargo, 2020. "Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy," Papers 2012.09115, arXiv.org.
    11. AyÅŸe Tansu, 2022. "Fuzzy Regression Analysis with a proposed model," Technium, Technium Science, vol. 4(1), pages 250-273.
    12. Pavel Škrabánek & Jaroslav Marek & Alena Pozdílková, 2021. "Boscovich Fuzzy Regression Line," Mathematics, MDPI, vol. 9(6), pages 1-14, March.
    13. Chung, William, 2012. "Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 95(C), pages 45-49.
    14. Wu, Hsien-Chung, 2003. "Fuzzy estimates of regression parameters in linear regression models for imprecise input and output data," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 203-217, February.
    15. Shafaei Bajestani, Narges & Vahidian Kamyad, Ali & Nasli Esfahani, Ensieh & Zare, Assef, 2018. "Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model," European Journal of Operational Research, Elsevier, vol. 264(3), pages 859-869.
    16. Eduardo Conde, 2014. "A Minmax Regret Linear Regression Model Under Uncertainty in the Dependent Variable," Journal of Optimization Theory and Applications, Springer, vol. 160(2), pages 573-596, February.
    17. Helga Flavia Tothăzan & Adela Deaconu, 2020. "Neuronal Network Artificial Model for Real Estate Appraisal: Logic, controversies, and utility for the Romanian context," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1093-1100, December.
    18. Horvath, Sabine & Soot, Matthias & Zaddach, Sebastian & Neuner, Hans & Weitkamp, Alexandra, 2021. "Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis," Land Use Policy, Elsevier, vol. 107(C).
    19. Barros, C.P. & Emrouznejad, Ali, 2016. "Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banksAuthor-Name: Wanke, Peter," European Journal of Operational Research, Elsevier, vol. 249(1), pages 378-389.
    20. Pierluigi Morano & Paolo Rosato & Francesco Tajani & Benedetto Manganelli & Felicia Di Liddo, 2019. "Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach," Sustainability, MDPI, vol. 11(18), pages 1-28, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:sajeco:v:82:y:2014:i:4:p:567-582. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.