Measurement of entropy in the assessment of homogeneity of areas valued with the Szczecin Algorithm of Real Estate Mass Appraisal

• Authors: Gnat Sebastian
• Languages of publication: EN

Abstracts

EN

Aim/purpose–General real estate taxation is a process regulated, inter alia, by the Real Estate Management Act. It is intended to establish a tax base for real estate in the event of a change in real estate tax base. General taxation is one of several applications of mass valuation of real estate, which enables valuation of many properties at the same time and with a uniform approach. One of the methods of mass valuation of real estate already applied in practice is the Szczecin Algorithm of Real Estate of Mass Appraisal (SAREMA). One of the immanent features of general taxation and the algorithm itself is the division of a selected area into possibly homogeneous areas called taxing zones with-in the general taxation terminology and, more broadly, elementary areas, according to the nomenclature used in the SAREMA. The paper presents the results of the studies on the measurement of elementary areas homogeneity on the example of land plots located in Szczecin. It is important to assess whether the designated sub-areas ofvaluation cover properties similar to each other in terms of their specific characteristics. If so, it will help to obtain more accurate mass valuation results.Design/methodology/approach–The paper proposes to use a modified entropy measure to establish whether the designated areas are homogeneous in terms of the specified properties of real estate. The database of real estate includes more than 1.5 thousand urbanised land plots located in Szczecin. The measurement of entropy will be preceded by the specification of elementary areas. The available methods include the application of an expert approach, under which land boundaries will be indicated by property valuers. Findings –The main conclusion of the study is that a modified measure of entropy en-sures a better indication of the degree of indefiniteness of valued sub-areas and thus it offers a better way of supporting the delimitation of these sub-areas in comparison to the classical measure of entropy.Research implications/limitations–The delimitation of valuation sub-areas constitutes an important element of mass valuation. Proper execution of this process enables obtain-ing much more precise valuations. An objective measure of homogeneity gives a chance to compare different approaches to the creation of the above-mentioned sub-areas and to choose the best of them.Originality/value/contribution–The main achievement of the study is a proposal to modify the classical entropy measure, thanks to which it better reflects the specificity of the assessment of homogeneity of the areas valued in terms of property market analysis.

Keywords

EN

Entropy; Property market analysis; Property mass valuation

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Journal of Economics and Management
• Year: 2019
• Volume: 38
• Pages: 89-106

Contributors

author

Gnat Sebastian

• Institute of Econometrics and Statistics. Faculty of Economics and Management. University of Szczecin, Szczecin, Poland

References

• Adair, A. S., Berry, J. N., & McGreal, W. S. (1996). Hedonic modelling, housing submarkets and residential valuation. Journal of Property Research, 13(1), 67-83. https://doi.org/10.1080/095999196368899
• Bird, M., & Slack, E. (2002). Land and property taxation around the world: A review. Journal of Property Tax Assessment and Administration, 7(3), 31-80.
• Boeing, G. (2019). Urban spatial order: street network orientation, configuration, and entropy (Preprint). Retrieved from https://arxiv.org/pdf/1808.00600
• Bourassa, S. C., Hamelink, F., Hoesli, M., & MacGregor, B. D. (1999). Defining housing submarkets. Journal of Housing Economics, 8(2), 160-183. https://doi.org/10.1006/jhec.1999.0246
• Brown, P. K., & Hepworth, M. A. (2001). Study of European land tax systems (Working Papers). Cambridge, MA: Lincoln Institute of Land Policy.
• Cabral, P., Augusto, G., Tewolde, M., & Araya, Y. (2013). Entropy in urban systems. Entropy, 15(12), 5223-5236. https://doi.org/10.3390/e15125223
• Cellmer, R., & Kuryj, J. (2011). Określanie stref o podobnej cenności gruntów z wykorzystaniem metod geostatystycznych [Designation of the zones of similar land value with the use of geostatistical methods]. Studia i Materiały Towarzystwa Naukowego Nieruchomości, 19(3), 7-18.
• Cymerman, R., Fiedorowicz, E., Grabowski, R., Gwiaździńska, M., & Kurowska, K. (2000). Wartość planistyczna jako wskaźnik w planowaniu przestrzennym [Planning value as an indicator in spatial planning]. In Materiały konferencyjne VIII Konferencji Naukowej Towarzystwa Naukowego Nieruchomości, „Koncepcje wartości w teorii i praktyce wyceny nieruchomości” (pp. 101-108). Olsztyn: Towarzystwo Naukowe Nieruchomości.
• Czyż, T., & Hauke, J. (2015). Entropy in regional analysis. Quaestiones Geographicae, 34(4), 69-78. https://doi.org/10.1515/quageo-2015-0037
• Dedkova, O., & Polyakova, I. (2018). Development of mass valuation in Republic of Belarus. Geomatics and Environmental Engineering, 12(3), 29-39. https://doi.org/10.7494/geom.2018.12.3.29
• Doszyń, M. (2008). Statystyczno-ekonometryczna analiza skłonności ludzkich [Statistical and econometric analysis of human propensities]. Szczecin: Wydawnictwo Naukowe Uniwersytetu Szczecińskiego.
• Galster, C. (1996). William Grigsby and the analysis of housing submarkets and filtering.Urban Studies, 33(10), 1797-1805. https://doi.org/10.1080/0042098966376
• Ge, X. J., & Du, Y. (2007). Main variables influencing residential property values using the entropy method – the case of Auckland (Paper presented at the proceedings of the 5th International Structural Engineering and Construction Conference. Shunan, Japan). Retrieved from https://www.asres2007.um.edu.mo/papers/041%20-%20PAPER.pdf
• Hozer, J., Kokot, S., & Kuźmiński, W. (2002). Metody analizy statystycznej rynku w wycenie nieruchomości [Methods of statistical analysis of the market in real estate appraisal]. Warszawa: PFSRM.
• Hozer, J., Kokot, S., Foryś, I., Zwolankowska, M., & Kuźmiski, W. (1999). Ekonometryczny algorytm masowej wyceny nieruchomości gruntowych [Econometric algorithm of mass land real estate appraisal]. Szczecin: Uniwersytet Szczeciński, Stowarzyszenie „Pomoc i Rozwój”.
• Jahanshiri, E., Buyong, T., & Shariff, A. R. M. (2011), A review of property mass valuation models. Pertanika Journal of Science & Technology, 19, 23-30.
• Keskin, B., & Watkins, C. (2016). Defining spatial housing submarkets: Exploring the case for expert delineated boundaries. Urban Studies, 54(6), 1446-1462. https://doi.org/10.1177/0042098015620351
• Kotkowski, P. (1999). Propozycja nowej klasyfikacji terenów miejskich [Proposition of a new classification of urban areas]. Acta Universitatis Lodziensis, Folia Geographica Socio-Oeconomica, 2, 115-124.
• Kopyściańska, K. (2016). Koncepcja podatku katastralnego w Polsce na tle doświadczeń wybranych krajów [Concept of a cadastral tax in Poland against the experience of selected countries]. Wrocław: Prawnicza i Ekonomiczna Biblioteka Cyfrowa.
• Kuryj, J. (2007). Metodyka wyceny masowej nieruchomości na bazie aktualnych przepisów prawnych [Methodology of mass valuation of real estate on the basis of current legal regulations]. Wycena – Wartość – Obrót – Zarządzanie Nieruchomościami, 4(81), 50-58.
• Mika, M. (2016). Implementacja europejskich trendów podatku katastralnego w warunkach polskich [Implementation of European trends in cadastral taxation in Polish conditions]. Acta Scientiarum Polonorum Administratio Locorum, 15(4), 99-110.
• Manganelli, B., Pontrandolfi, P., Azzato, A., & Murgante, B. (2014). Using geographically weighted regression for housing market segmentation. International Journal of Business Intelligence and Data Mining, 9(2), 161-177. https://doi.org/10.1504/IJBIDM.2014.065100
• Medvedkov, Y. V. (1967). The concept of entropy in settlement pattern analysis. Regional Science Association Papers, 18(1), 165-168.
• Ordinance of the Council of Ministers dated 29 June 2005 on general real property taxation. Journal of Laws, 131, item 1092.
• Peng, L., & Thibodeau, T. (2013). Risk segmentation of American homes: The case of Denver. Real Estate Economics, 41(3), 569-599. https://doi.org/10.1111/reec.12005
• Pryce, G. (2013). Housing submarkets and the lattice of substitution. Urban Studies, 50(13), 2682-2699. https://doi.org/10.1177/0042098013482502
• Purvis, B., Mao, Y., & Robinson, D. (2019). Entropy and its application to urban systems. Entropy, 21(1), 56. https://doi.org/10.3390/e21010056
• Rae, A. (2015). Online housing search and the geography of submarkets. Housing Studies, 30(3), 453-472. https:/doi.org/10.1080/02673037.2014.974142
• Raschka, S., & Mirjalili, V. (2017). Python machine learning. Birmingham-Mumbai: Packt Publishing.
• Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34-55. https:/doi.org/10.1086/260169
• Telega, T., Bojar, Z., & Adamczewski, Z. (2002). Wytyczne przeprowadzenia powszechnej taksacji nieruchomości [Guidelines for conducting universal real estate taxation]. Przegląd Geodezyjny, 6, 6-11.
• Sawiłow, E. (2009). Analiza wybranych metod modelowania wartości katastralnych nieruchomości [Analysis of selected methods of real estate cadastral value modelling]. Acta Scientiarum Polonorum Geodesia et Descriptio Terrarum, 8(2), 27-38.
• Surowiec, G., & Malczewska, A. (2001). Automatyzacja procesu wyodrębniania stref izowartościowych nieruchomości w oparciu o zdjęcia lotnicze [Automation of the process of isolating isovalue real estate zones based o aerial photographs]. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 11, 1-29–1-35.
• Wilson, A. G. (1970). Entropy in urban and regional modelling. London: Pion Press.
• Wu, Ch., & Sharma, R. (2012). Housing submarket classification: The role of spatial contiguity. Applied Geography, 32(2), 746-756. https:/doi.org/10.1016/j.apgeog.2011.08.011
• Wu, Y., Wei, Y. D., & Li, H. (2019, May). Analyzing spatial heterogeneity of housing prices using large datasets. Applied Spatial Analysis & Policy, 1-34. https:/doi.org/10.1007/s12061-019-09301-x

Identifiers

ISSN

1732-1948