IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v24y2016i3p5-15n1.html
   My bibliography  Save this article

Connection Between Similarity and Estimation Results of Property Values Obtained by Statistical Methods

Author

Listed:
  • Zyga Jacek

    (Faculty of Engineering and Architecture, Lublin University of Technology)

Abstract

The article discusses the topic of the application range of statistical methods in estimating property value on the grounds of a comparative approach. The analysis of application effects to estimate the unitary value of properties, respectively similar and dissimilar sets of market properties, by using the method of least squares and a linear price model. The prepared test set was developed from a priori assumed explanatory variable values as well as deterministically specified dependent variables (simulated prices) which were subjected to additional modification by a random factor. On the basis of the prepared set and series of accounting experiments, the estimation effects of any property out of a tested set were analyzed, understood as the determination of the value of the function of explanatory variables in the way of extrapolation or interpolation of values describing these variables. The experiments carried out show that the estimation of an explanatory variable for a random property out of a set of elements serving as the estimation base can be reliable only when it is related to the interpolation in the set of explanatory variables of this base. The application as an estimation base – a set in relation to which explanatory variables of the estimated property exceed the limits of corresponding variables, requires the completion of a basic set with records describing properties similar or close to the estimated property so that the values of explanatory variables for the estimated property are contained in the appropriate subsections of values of corresponding explanatory variables of the basic set.The paper refers to the issue of defining property market value indicating, by the prism of conducted experiments, that the estimation results obtained by means of statistical methods do not always meet the requirements of the statutory definition of market value, and hear rather in the direction of a result corresponding to the so-called “desk appraisal” result.

Suggested Citation

  • Zyga Jacek, 2016. "Connection Between Similarity and Estimation Results of Property Values Obtained by Statistical Methods," Real Estate Management and Valuation, Sciendo, vol. 24(3), pages 5-15, September.
    • Handle: RePEc:vrs:remava:v:24:y:2016:i:3:p:5-15:n:1
    DOI: 10.1515/remav-2016-0017
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/remav-2016-0017
    Download Restriction: no
    File URL: https://libkey.io/10.1515/remav-2016-0017?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. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    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. Prashant Das & Alan Ziobrowski, 2015. "The Relationship between Indian Realty Stocks and Online Searches," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 14(1), pages 1-19, April.
    2. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    3. Akshita Singh & Shailendra Kumar & Utkarsh Goel & Amar Johri, 2023. "Behavioural biases in real estate investment: a literature review and future research agenda," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    4. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
    5. Lianne Foti & Avis Devine, 2019. "High Involvement and Ethical Consumption: A Study of the Environmentally Certified Home Purchase Decision," Sustainability, MDPI, vol. 11(19), pages 1-11, September.
    6. Puhr, Harald & Müllner, Jakob, 2024. "Vox populi, vox dei: A concept and measure for grassroots socio-political risk using Google Trends," Journal of International Management, Elsevier, vol. 30(2).
    7. Camilo Acosta & Luis Baldomero-Quintana, 2024. "Quality of communications infrastructure, local structural transformation, and inequality," Journal of Economic Geography, Oxford University Press, vol. 24(1), pages 117-144.
    8. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.
    9. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    10. De Santis, Roberto A., 2020. "Impact of the Asset Purchase Programme on euro area government bond yields using market news," Economic Modelling, Elsevier, vol. 86(C), pages 192-209.
    11. Mirosław Bełej, 2022. "Does Google Trends Show the Strength of Social Interest as a Predictor of Housing Price Dynamics?," Sustainability, MDPI, vol. 14(9), pages 1-14, May.
    12. Renigier-Biłozor Małgorzata & Janowski Artur, 2024. "Human-Machine Synergy in Real Estate Similarity Concept," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 13-30.
    13. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    14. Damian S. Damianov & Xiangdong Wang & Cheng Yan, 2021. "Google Search Queries, Foreclosures, and House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 63(2), pages 177-209, August.
    15. Prashant Das & Alan Ziobrowski & N. Coulson, 2015. "Online Information Search, Market Fundamentals and Apartment Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 51(4), pages 480-502, November.
    16. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
    17. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    18. Theologos Dergiades & Eleni Mavragani & Bing Pan, 2017. "Arrivals of Tourists in Cyprus: Mind the Web Search Intensity," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 107, Hellenic Observatory, LSE.
    19. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    20. Vandenbergh, Joren, 2024. "Seller and search behavior in the Belgian housing market," Journal of Housing Economics, Elsevier, vol. 64(C).

    More about this item

    Keywords

    similarity; data models; data selection;
    All these keywords.

    JEL classification:

    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:24:y:2016:i:3:p:5-15:n:1. 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.