IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v59y2022i2d10.1007_s10614-021-10099-5.html
   My bibliography  Save this article

Determining the Flat Sales Prices by Flat Characteristics Using Bayesian Network Models

Author

Listed:
  • Volkan Sevinç

    (Muğla Sıtkı Koçman University)

Abstract

There are various factors affecting flat sales prices. Various characteristics of a flat play an important role in determining its sales price. In this study, a machine learning based Bayesian network was built by a restrictive structural learning algorithm using the data collected from 24 randomly selected cities in Turkey. The data consist of the sales prices and various characteristics of a flat such as number of bedrooms, building age, availability of balcony, net area, heating type, mortgageability, number of bathrooms, seller type, presence in a housing estate area and floor location. After the model validity check, a sensitivity analysis was performed for the estimated Bayesian network model and related results were provided. Some of these results indicate that sales prices of flats mostly change depending on the number of bathrooms available. Additionally, number of bedrooms, net area and floor location are also determinative about the sales prices. The lack of significant difference among the sales prices of flats that are sold by construction companies, housing estate agents or property owners is another result obtained.

Suggested Citation

  • Volkan Sevinç, 2022. "Determining the Flat Sales Prices by Flat Characteristics Using Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 549-577, February.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:2:d:10.1007_s10614-021-10099-5
    DOI: 10.1007/s10614-021-10099-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-021-10099-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-021-10099-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Efthymiou, D. & Antoniou, C., 2013. "How do transport infrastructure and policies affect house prices and rents? Evidence from Athens, Greece," Transportation Research Part A: Policy and Practice, Elsevier, vol. 52(C), pages 1-22.
    2. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74, pages 132-132.
    3. G. Sirmans & Lynn MacDonald & David Macpherson & Emily Zietz, 2006. "The Value of Housing Characteristics: A Meta Analysis," The Journal of Real Estate Finance and Economics, Springer, vol. 33(3), pages 215-240, November.
    4. Sam K. Hui & Alvin Cheung & Jimmy Pang, 2010. "A Hierarchical Bayesian Approach for Residential Property Valuation:Application to Hong Kong Housing Market," International Real Estate Review, Global Social Science Institute, vol. 13(1), pages 1-29.
    5. Andres Jauregui & Diane Hite, 2010. "The impact of real estate agents on house prices near environmental disamenities," Housing Policy Debate, Taylor & Francis Journals, vol. 20(2), pages 295-316, March.
    6. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    7. Vincenzo Del Giudice & Pierfrancesco De Paola & Fabiana Forte & Benedetto Manganelli, 2017. "Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples," Sustainability, MDPI, vol. 9(11), pages 1-17, November.
    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. Nana Cui & Hengyu Gu & Tiyan Shen & Changchun Feng, 2018. "The Impact of Micro-Level Influencing Factors on Home Value: A Housing Price-Rent Comparison," Sustainability, MDPI, vol. 10(12), pages 1-23, November.
    2. Daikun Wang & Victor Jing Li & Huayi Yu, 2020. "Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area," Land, MDPI, vol. 9(5), pages 1-18, May.
    3. Haizhen Wen & Zaiyuan Gui & Chuanhao Tian & Yue Xiao & Li Fang, 2018. "Subway Opening, Traffic Accessibility, and Housing Prices: A Quantile Hedonic Analysis in Hangzhou, China," Sustainability, MDPI, vol. 10(7), pages 1-23, June.
    4. Trojanek, Radoslaw & Huderek-Glapska, Sonia, 2018. "Measuring the noise cost of aviation – The association between the Limited Use Area around Warsaw Chopin Airport and property values," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 103-114.
    5. Mai, Nhat Chi, 2018. "도이모이 이후 베트남의 주거 이동, 선택, 가격 결정요인 연구: 호치민시 사례 중심으로," OSF Preprints 6kdfy, Center for Open Science.
    6. Raul-Tomas Mora-Garcia & Maria-Francisca Cespedes-Lopez & V. Raul Perez-Sanchez & Pablo Marti & Juan-Carlos Perez-Sanchez, 2019. "Determinants of the Price of Housing in the Province of Alicante (Spain): Analysis Using Quantile Regression," Sustainability, MDPI, vol. 11(2), pages 1-33, January.
    7. Jay Mittal & Sweta Byahut, 2019. "Scenic landscapes, visual accessibility and premium values in a single family housing market: A spatial hedonic approach," Environment and Planning B, , vol. 46(1), pages 66-83, January.
    8. 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.
    9. Julie Bulteau & Thierry Feuillet & Rémy Le Boennec, 2018. "Spatial Heterogeneity of Sustainable Transportation Offer Values: A Comparative Analysis of Nantes Urban and Periurban/Rural Areas (France)," Post-Print halshs-01706911, HAL.
    10. Nicodemo, Catia & Raya, Josep Maria, 2012. "Change in the distribution of house prices across Spanish cities," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 739-748.
    11. Carlos Felipe Balcázar & Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2017. "Rent‐Imputation for Welfare Measurement: A Review of Methodologies and Empirical Findings," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 881-898, December.
    12. Cupal Martin & Sedlačík Marek & Michálek Jaroslav, 2019. "The Assessment of a Building’s insurable Value using Multivariate Statistics: The Case of the Czech Republic," Real Estate Management and Valuation, Sciendo, vol. 27(3), pages 81-96, September.
    13. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    14. Sofia Vale & Felipa de Mello-Sampayo, 2021. "Effect of Hierarchical Parish System on Portuguese Housing Rents," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    15. Todd H. Kuethe & Roman Keeney, 2012. "Environmental Externalities and Residential Property Values: Externalized Costs along the House Price Distribution," Land Economics, University of Wisconsin Press, vol. 88(2), pages 241-250.
    16. Bernd Fitzenberger & Benjamin Fuchs, 2017. "The Residency Discount for Rents in Germany and the Tenancy Law Reform Act 2001: Evidence from Quantile Regressions," German Economic Review, Verein für Socialpolitik, vol. 18(2), pages 212-236, May.
    17. Simon K.C. Cheung, 2017. "A Localized Model for Residential Property Valuation: Nearest Neighbor with Attribute Differences," International Real Estate Review, Global Social Science Institute, vol. 20(2), pages 221-250.
    18. Paliska, Dejan & Drobne, Samo, 2020. "Impact of new motorway on housing prices in rural North-East Slovenia," Journal of Transport Geography, Elsevier, vol. 88(C).
    19. Contreras, Víctor & Garay, Urbi & Santos, Miguel Angel & Betancourt, Cosme, 2014. "Expropriation risk and housing prices: Evidence from an emerging market," Journal of Business Research, Elsevier, vol. 67(5), pages 935-942.
    20. Biao Sun & Shan Yang, 2020. "Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities," IJERPH, MDPI, vol. 17(20), pages 1-23, October.

    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:kap:compec:v:59:y:2022:i:2:d:10.1007_s10614-021-10099-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.