IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v32y2024i4p12-27n1002.html
   My bibliography  Save this article

Identifying the Current Status of Real Estate Appraisal Methods

Author

Listed:
  • Numan Jamal A. A.

    (Universiti Sains Malaysia (USM), Main Campus, Penang, Malaysia)

  • Yusoff Izham Mohamad

    (Universiti Sains Malaysia (USM), Main Campus, Penang, Malaysia)

Abstract

Real estate appraisal, also known as property valuation, plays a crucial role in numerous economic activities and financial decisions, such as taxation assessment, bank lending, and insurance, among others. However, the current methods used in real estate appraisal face several challenges related to fundamental aspects such as accuracy, interpretation, data availability, and evaluation metrics. Therefore, the purpose of this research is to identify the current status of real estate appraisal methods, highlighting challenges and providing guidance for scholars to undertake further research in addressing them. The methodology retrieves the most recent papers published in the Scopus database over the past five years, covering the period from 2019 to the end of 2023, with an emphasis on empirical studies. These retrieved papers serve as references to capture the current status of real estate appraisal methods. The research findings confirm a clear trend towards increased utilization of artificial intelligence techniques, especially machine learning, but with unfinished work regarding related challenges. Artificial intelligence techniques enhance the accuracy of real estate appraisal, paving the way for improved decision support systems in business, financial, and economic sectors.

Suggested Citation

  • Numan Jamal A. A. & Yusoff Izham Mohamad, 2024. "Identifying the Current Status of Real Estate Appraisal Methods," Real Estate Management and Valuation, Sciendo, vol. 32(4), pages 12-27.
    • Handle: RePEc:vrs:remava:v:32:y:2024:i:4:p:12-27:n:1002
    DOI: 10.2478/remav-2024-0032
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/remav-2024-0032
    Download Restriction: no
    File URL: https://libkey.io/10.2478/remav-2024-0032?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
    ---><---

    References listed on IDEAS

    as
    1. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.
    2. Anders Hjort & Johan Pensar & Ida Scheel & Dag Einar Sommervoll, 2022. "House price prediction with gradient boosted trees under different loss functions," Journal of Property Research, Taylor & Francis Journals, vol. 39(4), pages 338-364, October.
    3. B.V. Binoy & M.A. Naseer & P.P. Anil Kumar, 2022. "Spatial variation of the determinants affecting urban land value in Thiruvananthapuram, India," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 17(3), pages 611-656, December.
    4. Winky K.O. Ho & Bo-Sin Tang & Siu Wai Wong, 2021. "Predicting property prices with machine learning algorithms," Journal of Property Research, Taylor & Francis Journals, vol. 38(1), pages 48-70, January.
    5. Miriam Steurer & Robert J. Hill & Norbert Pfeifer, 2021. "Metrics for evaluating the performance of machine learning based automated valuation models," Journal of Property Research, Taylor & Francis Journals, vol. 38(2), pages 99-129, April.
    6. Gaetano Lisi, 2019. "Sales comparison approach, multiple regression analysis and the implicit prices of housing," Journal of Property Research, Taylor & Francis Journals, vol. 36(3), pages 272-290, July.
    7. Alexander N. Bogin & Jessica Shui, 2020. "Correction to: Appraisal Accuracy and Automated Valuation Models in Rural Areas," The Journal of Real Estate Finance and Economics, Springer, vol. 61(4), pages 730-731, November.
    8. Michael Mayer & Steven C. Bourassa & Martin Hoesli & Donato Scognamiglio, 2022. "Machine Learning Applications to Land and Structure Valuation," JRFM, MDPI, vol. 15(5), pages 1-24, April.
    9. Bastian Krämer & Moritz Stang & Vanja Doskoč & Wolfgang Schäfers & Tobias Friedrich, 2023. "Automated valuation models: improving model performance by choosing the optimal spatial training level," Journal of Property Research, Taylor & Francis Journals, vol. 40(4), pages 365-390, October.
    10. Sukampon Chongwilaikasaem & Tanit Chalermyanont, 2022. "Flood hazards and housing prices: a spatial regression analysis for Hat Yai, Songkhla, Thailand," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 16(6), pages 1052-1070, July.
    11. Alexander N. Bogin & Jessica Shui, 2020. "Appraisal Accuracy and Automated Valuation Models in Rural Areas," The Journal of Real Estate Finance and Economics, Springer, vol. 60(1), pages 40-52, February.
    12. Bastian Krämer & Moritz Stang & Vanja Doskoc & Wolfgang Schäfers & Friedrich Tobias, 2023. "Automated Valuation Models: Improving Model Performance by Choosing the Optimal Spatial Training Level," ERES eres2023_120, European Real Estate Society (ERES).
    13. Tomić, Hrvoje & Ivić, Siniša Mastelić & Roić, Miodrag & Šiško, Josip, 2021. "Developing an efficient property valuation system using the LADM valuation information model: A Croatian case study," Land Use Policy, Elsevier, vol. 104(C).
    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. 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.
    2. Ela Ertunç & Zlatica Muchová & Hrvoje Tomić & Jaroslaw Janus, 2022. "Legal, Procedural and Social Aspects of Land Valuation in Land Consolidation: A Comparative Study for Selected Central and Eastern Europe Countries and Turkey," Land, MDPI, vol. 11(5), pages 1-22, April.
    3. Mari O. Mamre & Dag Einar Sommervoll, 2024. "Coming of Age: Renovation Premiums in Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 69(2), pages 307-342, August.
    4. 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.
    5. João A. Bastos & Jeanne Paquette, 2024. "On the uncertainty of real estate price predictions," Working Papers REM 2024/0314, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    6. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.
    7. Oğuz Mısır & Mehmet Akar, 2022. "Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    8. Jungsun Kim & Jaewoong Won & Hyeongsoon Kim & Joonghyeok Heo, 2021. "Machine-Learning-Based Prediction of Land Prices in Seoul, South Korea," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    9. Felix Lorenz & Jonas Willwersch & Marcelo Cajias & Franz Fuerst, 2023. "Interpretable machine learning for real estate market analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(5), pages 1178-1208, September.
    10. Mostofi Fatemeh & Toğan Vedat & Başağa Hasan Basri, 2022. "Real-estate price prediction with deep neural network and principal component analysis," Organization, Technology and Management in Construction, Sciendo, vol. 14(1), pages 2741-2759, January.
    11. Robert J. Hill & Norbert Pfeifer & Miriam Steurer & Radoslaw Trojanek, 2021. "Warning: Some Transaction Prices can be Detrimental to your House Price Index," Graz Economics Papers 2021-11, University of Graz, Department of Economics.
    12. Bas Marcin, 2024. "Impact of the Apartment’s Window Exposure to World Directions on Transaction Price," Real Estate Management and Valuation, Sciendo, vol. 32(4), pages 44-54.
    13. Robert J. Hill & Norbert Pfeifer & Miriam Steurer & Radoslaw Trojanek, 2024. "Warning: Some transaction prices can be detrimental to your house price index," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 70(2), pages 320-344, June.
    14. Munkhbaatar Buuveibaatar & Kangjae Lee & Wonhee Lee, 2023. "Developing an LADM Valuation Information Model for Mongolia," Land, MDPI, vol. 12(4), pages 1-21, April.
    15. Kara, Abdullah & Lemmen, Christiaan & van Oosterom, Peter & Kalogianni, Eftychia & Alattas, Abdullah & Indrajit, Agung, 2024. "Design of the new structure and capabilities of LADM edition II including 3D aspects," Land Use Policy, Elsevier, vol. 137(C).
    16. Sevgen Sibel Canaz & Tanrivermiş Yeşim, 2024. "Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 100-111.
    17. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    18. Parker, Kevin & Gaydon, Daniel J. & Fulmore, Anthony & Boyle, Douglas M., 2024. "Accounting Students’ perceptions of delivery modalities during and after the COVID-19 pandemic," Journal of Accounting Education, Elsevier, vol. 68(C).
    19. Radulović, Aleksandra & Sladić, Dubravka & Govedarica, Miro & Raičević, Dušan, 2023. "Using LADM and BIM for property and tourist taxation in the municipality of Bar, Montenegro," Land Use Policy, Elsevier, vol. 131(C).
    20. Hill, Robert J. & Trojanek, Radoslaw, 2022. "An evaluation of competing methods for constructing house price indexes: The case of Warsaw," Land Use Policy, Elsevier, vol. 120(C).

    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:vrs:remava:v:32:y:2024:i:4:p:12-27:n:1002. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.