IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v26y2018i1p5-15n1.html
   My bibliography  Save this article

Application of Passive Methods of Robust Estimation: Baarda's and Pope's in Real Estate Market Analysis

Author

Listed:
  • Śpiewak Beata

    (Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology)

Abstract

The article presents the results of research into the appraisal of the usefulness and effectiveness of passive methods of robust estimation in real estate market analysis. Two methods are considered, i.e.: Baarda's and Pope's. The research is based on a database of flats sold in Kraków in 2015.It has been proved that algorithms of robust estimation allow for eliminating outlying transactions from the collected database, causing the set of observations to become coherent and free of observations which might be fraught with gross error caused by the human factor.

Suggested Citation

  • Śpiewak Beata, 2018. "Application of Passive Methods of Robust Estimation: Baarda's and Pope's in Real Estate Market Analysis," Real Estate Management and Valuation, Sciendo, vol. 26(1), pages 5-15, March.
    • Handle: RePEc:vrs:remava:v:26:y:2018:i:1:p:5-15:n:1
    DOI: 10.2478/remav-2018-0001
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/remav-2018-0001
    Download Restriction: no
    File URL: https://libkey.io/10.2478/remav-2018-0001?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. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
    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. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    2. Damian Przekop, 2020. "Feature Engineering for Anti-Fraud Models Based on Anomaly Detection," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(3), pages 301-316, September.
    3. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    4. Gaucher, Solenne & Klopp, Olga & Robin, Geneviève, 2021. "Outlier detection in networks with missing links," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    5. Andrzej Chmielowiec, 2021. "Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data," Computational Statistics, Springer, vol. 36(4), pages 2813-2840, December.
    6. Marc Chataigner & Stéphane Crépey & Jiang Pu, 2020. "Nowcasting Networks," Post-Print hal-03910123, HAL.
    7. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2019. "Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators," European Journal of Operational Research, Elsevier, vol. 278(3), pages 942-960.
    8. David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
    9. Stéphane Crépey & Lehdili Noureddine & Nisrine Madhar & Maud Thomas, 2022. "Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks," Working Papers hal-03777995, HAL.
    10. Zhongqiu Wang & Guan Yuan & Haoran Pei & Yanmei Zhang & Xiao Liu, 2020. "Unsupervised learning trajectory anomaly detection algorithm based on deep representation," International Journal of Distributed Sensor Networks, , vol. 16(12), pages 15501477209, December.
    11. Arata, Linda & Fabrizi, Enrico & Sckokai, Paolo, 2020. "A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data," Economic Modelling, Elsevier, vol. 90(C), pages 190-208.
    12. Wentao Yang & Huaxi He & Dongsheng Wei & Hao Chen, 2022. "Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space," Journal of Geographical Systems, Springer, vol. 24(2), pages 261-279, April.
    13. Fournier, Nicholas PhD & Farid, Yashar Zeinali PhD & Patire, Anthony David PhD, 2021. "Potential Erroneous Degradation of High Occupancy Vehicle (HOV) Facilities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3z76r7tj, Institute of Transportation Studies, UC Berkeley.
    14. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    15. Tommaso Barbariol & Enrico Feltresi & Gian Antonio Susto, 2020. "Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection," Energies, MDPI, vol. 13(12), pages 1-24, June.
    16. Puteri Paramita & Zuduo Zheng & Md Mazharul Haque & Simon Washington & Paul Hyland, 2018. "User satisfaction with train fares: A comparative analysis in five Australian cities," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-26, June.
    17. Liqun Diao & Grace Y. Yi, 2023. "Classification Trees with Mismeasured Responses," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 168-191, April.
    18. Durgesh Samariya & Amit Thakkar, 2023. "A Comprehensive Survey of Anomaly Detection Algorithms," Annals of Data Science, Springer, vol. 10(3), pages 829-850, June.
    19. 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).
    20. Gasser, Patrick, 2020. "A review on energy security indices to compare country performances," Energy Policy, Elsevier, vol. 139(C).

    More about this item

    Keywords

    robust estimation; real estate market analysis; residential properties; Baarda's method; Pope's method;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • R39 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other

    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:26:y:2018:i:1: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.