IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v27y2019i2p33-43n3.html
   My bibliography  Save this article

Principles and Criteria for using Statistical Parametric Models and Conditional Models for Valuation of Multi-Component Real Estate

Author

Listed:
  • Adamczyk Tomasz

    (AGH University of Science and Technology, Department of Geomatics)

  • Bieda Agnieszka

    (AGH University of Science and Technology, Department of Geomatics)

  • Parzych Piotr

    (AGH University of Science and Technology, Department of Geomatics)

Abstract

The complexity of multi-component real properties results from the possibility of identifying various components in legal, physical or functional terms. The possibility of distinguishing various functional elements of real properties, combined with the specificity resulting from their market properties, is problematic when applying the comparative approach to real estate valuation. In this case, the valuation procedure can be implemented using statistical models: the parametric model or the conditional one.This research paper demonstrates the construction of the parametric and conditional models taking into account the geometric and pricing attributes of multi-component real estate. The authors paid attention to adjusting the models to the available market data. They also specified the conditions for the use of statistical models in the real estate valuation process. Based on the analytical and accounting considerations, the estimation criteria for the parametric model and the conditional model were defined, which allow the correct application of these models at the stages of the real estate market analysis and the real estate valuation process.

Suggested Citation

  • Adamczyk Tomasz & Bieda Agnieszka & Parzych Piotr, 2019. "Principles and Criteria for using Statistical Parametric Models and Conditional Models for Valuation of Multi-Component Real Estate," Real Estate Management and Valuation, Sciendo, vol. 27(2), pages 33-43, June.
    • Handle: RePEc:vrs:remava:v:27:y:2019:i:2:p:33-43:n:3
    DOI: 10.2478/remav-2019-0013
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/remav-2019-0013
    Download Restriction: no
    File URL: https://libkey.io/10.2478/remav-2019-0013?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. Zhangcheng Chen & Yueming Hu & Chen Jason Zhang & Yilun Liu, 2017. "An Optimal Rubrics-Based Approach to Real Estate Appraisal," Sustainability, MDPI, vol. 9(6), pages 1-19, May.
    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. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    2. Bieda Agnieszka, 2018. "Conditional Model of Real Estate Valuation for Land Located in Different Land Use Zones," Real Estate Management and Valuation, Sciendo, vol. 26(1), pages 122-130, March.
    3. Renigier-Biłozor, Małgorzata & Źróbek, Sabina & Walacik, Marek & Borst, Richard & Grover, Richard & d’Amato, Maurizio, 2022. "International acceptance of automated modern tools use must-have for sustainable real estate market development," Land Use Policy, Elsevier, vol. 113(C).

    More about this item

    Keywords

    conditional model; parametric model; statistical models; real estate valuation; statistical analysis of the market; multi-component real estate;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:27:y:2019:i:2:p:33-43:n:3. 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.