IDEAS home Printed from https://ideas.repec.org/a/ire/issued/v26n042023p565-581.html
   My bibliography  Save this article

A Real Estate Appraisal Model with Artificial Neural Networks and Fuzzy Logic: A Local Case Study of Samsun City

Author

Listed:
  • Mehmet Emin Tabar

    (Bitlis Eren University)

  • Aziz Sisman

    (Ondokuz Mayis University)

  • Yasemin Sisman

    (Ondokuz Mayis University)

Abstract

There have been great innovations in the field of real estate appraisal which have replaced the classical methods by recently established modern methods that involve computer technologies known as artificial intelligence. here are several artificial intelligent methods like fuzzy logic and artificial neural networks. In this study, two different real estate appraisal applications that are based on the artificial neural network and fuzzy logic methods are compared. An actual data set is taken from real estate agencies to develop a real estate appraisal model in Samsun city in Turkey. All of the real estate data belong to a certain time interval in May 2020. The selected parameters are scored by a valuation expert and the scores are then subjected to normalization. The obtained values are inputted into a fuzzy logic and artificial neural networks editor in MATLAB software and the valuation model is created. The values we obtain from the artificial neural networks and fuzzy logic are compared with actual sales data. It is observed that using these methods in real estate appraisal provides advantages in terms of time, cost and tangibility, as well as the means to create large database for real estate appraisal in a certain region.

Suggested Citation

  • Mehmet Emin Tabar & Aziz Sisman & Yasemin Sisman, 2023. "A Real Estate Appraisal Model with Artificial Neural Networks and Fuzzy Logic: A Local Case Study of Samsun City," International Real Estate Review, Global Social Science Institute, vol. 26(4), pages 565-581.
    • Handle: RePEc:ire:issued:v:26:n:04:2023:p:565-581
    as

    Download full text from publisher

    File URL: http://www.gssinst.org/irer/wp-content/uploads/2024/01/vol26-no4_5-Appraisal-Model-with-Artificial-Neural-Networks-Tabar.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Limsombunchai, Visit, 2004. "House Price Prediction: Hedonic Price Model vs. Artificial Neural Network," 2004 Conference, June 25-26, 2004, Blenheim, New Zealand 97781, New Zealand Agricultural and Resource Economics Society.
    2. Young-Lung Lee & Chang Jung & Yih Yeh Kuang, 2003. "Fair Evaluation of real Estate Value in Urban Area via Fuzzy Theory," ERES eres2003_198, European Real Estate Society (ERES).
    3. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
    4. Nghiep Nguyen & Al Cripps, 2001. "Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, American Real Estate Society, vol. 22(3), pages 313-336.
    5. Bhattacharjee, Natalia V. & Tollner, Ernest W., 2016. "Improving management of windrow composting systems by modeling runoff water quality dynamics using recurrent neural network," Ecological Modelling, Elsevier, vol. 339(C), pages 68-76.
    6. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    7. 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)

    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. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    2. 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.
    3. Tien Foo Sing & Jesse Jingye Yang & Shi Ming Yu, 2022. "Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)," The Journal of Real Estate Finance and Economics, Springer, vol. 65(4), pages 649-674, November.
    4. Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, vol. 42(3), pages 987-1009, June.
    5. Camilo Serrano & Martin Hoesli, 2010. "Are Securitized Real Estate Returns more Predictable than Stock Returns?," The Journal of Real Estate Finance and Economics, Springer, vol. 41(2), pages 170-192, August.
    6. Jun Kang & Hyun Jun Lee & Seung Hwan Jeong & Hee Soo Lee & Kyong Joo Oh, 2020. "Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    7. 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.
    8. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
    9. William Cheung & Lewen Guo & Yuichiro Kawaguchi, 2021. "Automated valuation model for residential rental markets: evidence from Japan," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-34, December.
    10. Elena B. Pokryshevskaya & Evgeny A. Antipov, 2011. "Applying a CART-based approach for the diagnostics of mass appraisal models," Economics Bulletin, AccessEcon, vol. 31(3), pages 2521-2528.
    11. Jasmina Ćetković & Slobodan Lakić & Marijana Lazarevska & Miloš Žarković & Saša Vujošević & Jelena Cvijović & Mladen Gogić, 2018. "Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    12. 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.
    13. Núñez Tabales, Julia M. & Caridad y Ocerin, José María & Rey Carmona, Francisco J., 2013. "Artificial Neural Networks for Predicting Real Estate Prices || Redes neuronales artificiales para la predicción de precios inmobiliarios," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 29-44, June.
    14. Maurizio d’Amato, 2007. "Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies," International Real Estate Review, Global Social Science Institute, vol. 10(2), pages 42-65.
    15. Mahdieh Yazdani & Maziar Raissi, 2023. "Real Estate Property Valuation using Self-Supervised Vision Transformers," Papers 2302.00117, arXiv.org.
    16. Craig Ellis & Patrick J. Wilson & Ralf Zurbruegg, 2007. "Real Estate ‘Value’ Stocks and International Diversification," Journal of Property Research, Taylor & Francis Journals, vol. 24(3), pages 265-287, September.
    17. Gang-Zhi Fan & Seow Eng Ong & Hian Chye Koh, 2006. "Determinants of House Price: A Decision Tree Approach," Urban Studies, Urban Studies Journal Limited, vol. 43(12), pages 2301-2315, November.
    18. Áron Horváth & Blanka Imre & Zoltán Sápi, 2016. "The International Practice of Statistical Property Valuation Methods and the Possibilities of Introducing Automated Valuation Models in Hungary," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 15(4), pages 45-64.
    19. Moreno-Izquierdo, Luis & Egorova, Galina & Peretó-Rovira, Alexandre & Más-Ferrando , Adrián, 2018. "Exploring the use of artificial intelligence in price maximisation in the tourism sector: its application in the case of Airbnb in the Valencian Community," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 42, pages 113-128.
    20. Renigier-Biłozor Małgorzata & Wiśniewski Radosław, 2012. "The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe," Folia Oeconomica Stetinensia, Sciendo, vol. 12(2), pages 103-125, December.

    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:ire:issued:v:26:n:04:2023:p:565-581. 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: IRER Graduate Assistant/Webmaster (email available below). General contact details of provider: https://www.gssinst.org/gssinst/index.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.