[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v28y2020i2p52-62n5.html
   My bibliography  Save this article

Analytical Method for Correction Coefficient Determination for Applying Comparative Method for Real Estate Valuation

Author

Listed:
  • Gružauskas Valentas

    (Kaunas University of Technology, School of Economics and Business, Digitalization Scientific Group)

  • Kriščiūnas Andrius

    (Kaunas University of Technology, Faculty of Informatics, Department of Applied Informatics)

  • Čalnerytė Dalia

    (Kaunas University of Technology, Faculty of Informatics, Department of Applied Informatics)

  • Navickas Valentinas

    (Kaunas University of Technology, School of Economics and Business, Academic Center of Economics, Business and Management)

Abstract
Real estate valuation uses 3 main approaches: income, cost and comparative. When applying the comparative method, correction coefficients based on similar real estate transactions are determined. In practice, the coefficients and similar real estate objects are usually determined by using qualitative approach based on the valuators’ experience. The paper provides an analytical method for the determination of correction coefficient, which limits subjectivity when using the comparative method for valuation. The provided analytical approach also integrates macroeconomic indicators in the calculation process. It also addresses issues when available historical real estate transaction data is limited. A machine learning approach was applied to determine the average price of real estate in the region, with the possibility of using this information to obtain correction coefficients where historical data was unavailable. Alternative research usually focuses on final price estimation of the selected real estate object; however, the valuation standard of Tegova released in 2018 does not allow for applying analytically based approaches for individual real estate object evaluation; these approaches can be used only as a supportive tool for valuators.

Suggested Citation

  • Gružauskas Valentas & Kriščiūnas Andrius & Čalnerytė Dalia & Navickas Valentinas, 2020. "Analytical Method for Correction Coefficient Determination for Applying Comparative Method for Real Estate Valuation," Real Estate Management and Valuation, Sciendo, vol. 28(2), pages 52-62, June.
    • Handle: RePEc:vrs:remava:v:28:y:2020:i:2:p:52-62:n:5
    DOI: 10.1515/remav-2020-0015
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/remav-2020-0015
    Download Restriction: no
    File URL: https://libkey.io/10.1515/remav-2020-0015?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. Qingyun Du & Chao Wu & Xinyue Ye & Fu Ren & Yongjun Lin, 2018. "Evaluating the Effects of Landscape on Housing Prices in Urban China," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 109(4), pages 525-541, September.
    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. Alice Barreca, 2022. "Architectural Quality and the Housing Market: Values of the Late Twentieth Century Built Heritage," Sustainability, MDPI, vol. 14(5), pages 1-24, February.
    2. Guiwen Liu & Jiayue Zhao & Hongjuan Wu & Taozhi Zhuang, 2022. "Spatial Pattern of the Determinants for the Private Housing Rental Prices in Highly Dense Populated Chinese Cities—Case of Chongqing," Land, MDPI, vol. 11(12), pages 1-22, December.
    3. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    4. Kang, Yuhao & Zhang, Fan & Peng, Wenzhe & Gao, Song & Rao, Jinmeng & Duarte, Fabio & Ratti, Carlo, 2021. "Understanding house price appreciation using multi-source big geo-data and machine learning," Land Use Policy, Elsevier, vol. 111(C).
    5. Sheng Li & Yi Jiang & Shuisong Ke & Ke Nie & Chao Wu, 2021. "Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)," Land, MDPI, vol. 10(5), pages 1-15, May.
    6. Wu, Chao & Du, Yihao & Li, Sheng & Liu, Pengyu & Ye, Xinyue, 2022. "Does visual contact with green space impact housing pricesʔ An integrated approach of machine learning and hedonic modeling based on the perception of green space," Land Use Policy, Elsevier, vol. 115(C).

    More about this item

    Keywords

    Real estate valuation; Analytical models; Machine learning;
    All these keywords.

    JEL classification:

    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis
    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

    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:vrs:remava:v:28:y:2020:i:2:p:52-62:n: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: 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.