<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The similarity between premises, statutorily defined as the comparability of the compiled objects, is, in practice, identified by the statement of identity or the proximity of evaluation of the selected features describing the complied objects. Independent of the nature of these features (qualitative or quantitative), as well as of the method of identifying the proximity of their prices, the quest process for similar premises ends in the compilation of these premises into a group, which continues to remain inconsistent to some extent. The inevitable heterogeneity of the prices of premises summarized in this way leads to the suspicion that other features which had not been taken into account during the current stages of analysis exist. The identification of these features can significantly improve the quality of the valuation process based on the selected premises.</p><p>The presented article discusses a method of identifying the plausible number of indiscernible factors influencing the differentiation of unitary prices in an analyzed set of premises, in the event when the collected information on these premises does not provide sufficient knowledge on the reasons behind such a differentiation. The result of the performed research can, in practice, be applied to program the procedures for the data search, and hence reduce the costs related to the acquisition of information on the premises (e.g. from the real estate market).</p></abstract>

The similarity between premises, statutorily defined as the comparability of the compiled objects, is, in practice, identified by the statement of identity or the proximity of evaluation of the selected features describing the complied objects. Independent of the nature of these features (qualitative or quantitative), as well as of the method of identifying the proximity of their prices, the quest process for similar premises ends in the compilation of these premises into a group, which continues to remain inconsistent to some extent. The inevitable heterogeneity of the prices of premises summarized in this way leads to the suspicion that other features which had not been taken into account during the current stages of analysis exist. The identification of these features can significantly improve the quality of the valuation process based on the selected premises.The presented article discusses a method of identifying the plausible number of indiscernible factors influencing the differentiation of unitary prices in an analyzed set of premises, in the event when the collected information on these premises does not provide sufficient knowledge on the reasons behind such a differentiation. The result of the performed research can, in practice, be applied to program the procedures for the data search, and hence reduce the costs related to the acquisition of information on the premises (e.g. from the real estate market).

Search For Dissimilarity Factors For Nominally Indiscernible Facilities

Faculty of Engineering and Architecture, Lublin University of Technology

Real Estate Management and Valuation

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Search For Dissimilarity Factors For Nominally Indiscernible Facilities"}

The similarity between premises, statutorily defined as the comparability of the compiled objects, is, in practice, identified by the statement of identity or...

Search For Dissimilarity Factors For Nominally Indiscernible Facilities

Published Online: Oct 05, 2015

Page range: 65 - 72

DOI: https://doi.org/10.1515/remav-2015-0026

Keywords
similarity, data models, data selection

© Jacek Zyga

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Search For Dissimilarity Factors For Nominally Indiscernible Facilities

Published Online: Oct 05, 2015

Page range: 65 - 72

DOI: https://doi.org/10.1515/remav-2015-0026

Keywordssimilarity, data models, data selection

© Jacek Zyga

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
similarity, data models, data selection