IDEAS home Printed from https://ideas.repec.org/a/ire/issued/v24n032021p385-403.html
   My bibliography  Save this article

An Empirical Method for Decomposing the Contributions of Land and Building Values to Housing Value

Author

Listed:
  • Kuan-Lun Pan

    (National Taiwan University)

  • Hsiao Jung Teng

    (Anfu Co., Ltd)

  • Shih-Yuan Lin

    (National Chengchi University)

  • Yu En Cheng

    (Anfu Co., Ltd)

Abstract

This paper develops an empirical method that uses two separate housing related components to estimate housing value: land and building. The artificial neural network (ANN) technique is used to iteratively solve for two hedonic models simultaneously by minimizing the difference in the observed total value and the sum of the estimated land and building values. This method enables one to objectively separate housing value into land and building components. Using actual sales transaction data from Taipei City, we estimate the land value as a share of the total housing value. The results show that the land value accounts for a higher share with older properties. The share of the land value of low-rise buildings tends to be higher than that of high-rise buildings. The share of the land value can deviate by 20 percentage points between more or less expensive housing communities within Taipei City.

Suggested Citation

  • Kuan-Lun Pan & Hsiao Jung Teng & Shih-Yuan Lin & Yu En Cheng, 2021. "An Empirical Method for Decomposing the Contributions of Land and Building Values to Housing Value," International Real Estate Review, Global Social Science Institute, vol. 24(3), pages 385-403.
  • Handle: RePEc:ire:issued:v:24:n:03:2021:p:385-403
    as

    Download full text from publisher

    File URL: https://www.gssinst.org/irer/wp-content/uploads/2021/10/v24-no3-3_An-Empirical-Method-for-Decomposing-the-Contributions-of-Land-and-Building-Values-to-Housing-Value.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Kitova, Olga & Dyakonova, Ludmila & Savinova, Victoria, 2020. "Prediction of Socio-Economic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon”," MPRA Paper 104234, University Library of Munich, Germany, revised 19 Nov 2020.
    8. Narula, Subhash C. & Wellington, John F. & Lewis, Stephen A., 2012. "Valuating residential real estate using parametric programming," European Journal of Operational Research, Elsevier, vol. 217(1), pages 120-128.
    9. Kristoffer B. Birkeland & Allan D. D'Silva & Roland Füss & Are Oust, 2021. "The Predictability of House Prices: "Human Against Machine"," International Real Estate Review, Global Social Science Institute, vol. 24(2), pages 139-183.
    10. 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.
    11. 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.
    12. Ansgar Belke & Jonas Keil, 2018. "Fundamental Determinants of Real Estate Prices: A Panel Study of German Regions," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 24(1), pages 25-45, February.
    13. 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.
    14. Shawn L. Robey & Mark A McKnight & Misty R. Price & Rachel N. Coleman, 2019. "Considerations for a Regression-Based Real Estate Valuation and Appraisal Model: A Pilot Study," Accounting and Finance Research, Sciedu Press, vol. 8(2), pages 1-99, May.
    15. Mahdieh Yazdani & Maziar Raissi, 2023. "Real Estate Property Valuation using Self-Supervised Vision Transformers," Papers 2302.00117, arXiv.org.
    16. Koen Koning & Tatiana Filatova & Okmyung Bin, 2018. "Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 247-263, February.
    17. 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.
    18. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    19. Alla Koblyakova & Larisa Fleishman & Orly Furman, 2022. "Accuracy of Households’ Dwelling Valuations, Housing Demand and Mortgage Decisions: Israeli Case," The Journal of Real Estate Finance and Economics, Springer, vol. 65(1), pages 48-74, July.
    20. 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.

    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:24:n:03:2021:p:385-403. 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.