The Impact of the Training Set Size on the Classification of Real Estate with an Increased Fiscal Burden

• Autorzy: Sebastian Gnat
• Języki publikacji: EN

Abstrakty

EN

The introduction of an ad valorem tax can lead to an increase in the tax burden on real estate. There are concerns that this increase will be large and widespread. Before undertaking any actual actions related to the real estate tax reform, pilot studies and statistical analyses need to be conducted in order to verify the validity of those concerns and other aspects regarding the replacement of a real estate tax, agricultural tax and forest tax with an ad valorem tax. The article presents results of research on the effectiveness of the classification of real estate into a group at risk of an increase of tax burden with the use of the k-nearest neighbors method. The main focus was to determine the size of a real estate set (training data set) on the basis of which classification is conducted, as well as on the efficiency of that classification, depending on the size of such data set.(original abstract)

Słowa kluczowe

EN

Ad valorem tax system; Ad valorem tax; Real estate valuation; Tax burdens

PL

System podatkowy ad valorem; Podatek katastralny; Wycena nieruchomości; Obciążenia podatkowe

• Czasopismo: Real Estate Management and Valuation
• Rocznik: 2019
• Numer: vol. 27, iss. 2
• Strony: 53--62

Twórcy

autor

Sebastian Gnat

• University of Szczecin, Poland

Bibliografia

• BAO Y., ISHII N., 2002, Combining Multiple K-Nearest Neighbor Classifiers for Text Classification by Reducts, In: Lange S., Satoh K., Smith C.H. (eds) Discovery Science. DS 2002. Lecture Notes in Computer Science, vol 2534. Springer, Berlin, Heidelberg.
• BOSCHETTI A., MASSARON L., 2017, Python Data Science Essentials in Polish: Python, Podstawy nauki o danych, Helion, Gliwice.
• CZAJA J., 2001, Methods of appraising real property market and cadastral value in Polish: Metody szacowania wartości rynkowej i katastralnej nieruchomości, Komp-system, Kraków.
• ENSTRÖM-ÖST C., SÖDERBERG B., WILHELMSSON M., 2017, Homeownership rates of financially constrained households, Journal of European Real Estate Research, Vol. 10 Issue: 2, pp.111-123.
• ETEL L., DOWGIER R., 2013, Local taxes and charges - time for a change in Polish: Podatki i opłaty lokalne - czas na zmiany, Białystok: Temida 2.
• FELDMAN D., GROSS S., 2005, Mortgage Default: Classification Trees Analysis, The Journal of Real Estate Finance and Economics, Volume 30, Issue 4, pp. 369-396.
• GŁUSZAK M., 2015, Multinomial Logit Model of Housing Demand in Poland, Real Estate Management and Valuation, Vol. 23, No. 1, pp. 84-89.
• GNAT S., 2010, Analysis of the Effects of Replacing Current Property Tax with ad Valorem Property Tax in a Sample Municipality, Folia Oeconomica Stetinensia, 8 16, pp. 82-98.
• GNAT S., 2018, Analysis of Communes' Potential Fall in Revenue Following Introduction of ad Valorem Property Tax, Real Estate Management and Valuation, vol. 26, no. 1, pp. 63-72.
• GNAT S., SKOTARCZAK M., 2006, Analysis of local tax rates distributions in the West Pomeranian Voivodeship municipalities in the period of 2002-2004 in Polish: Analiza rozkładów stawek podatków lokalnych w gminach województwa zachodniopomorskiego w latach 2002-2004, in: J. Hozer red., Economic situation vs. real estate market in Polish: Koniunktura gospodarcza a rynek nieruchomości, Szczecin: Uniwersytet Szczeciński, Instytut Analiz, Diagnoz i Prognoz Gospodarczych, pp. 74-82.
• HASTIE T., TIBSHIRANI R., FRIEDMAN J., 2009, The elements of statistical learning, Springer, New York.
• HOZER J., FORYŚ I., ZWOLANKOWSKA M., KOKOT S., KUŹMIŃSKI W., 1999, Econometric algorithm of land real estate mass appraisal in Polish: Ekonometryczny algorytm masowej wyceny nieruchomości gruntowych, Uniwersytet Szczeciński, Stowarzyszenie Pomoc i Rozwój, Szczecin.
• LAGERHOLM M., PETERSON C., BRACCINI G., EDHENBRANDT L., SORNMO L., 2000, Clustering ECG Complexes Using Hermite Functions and Self-Organizing Maps, IEEE Trans. Biomed. Eng., vol. 47, No. 7, pp. 838-848.
• PACE P.K., 1996, Relative performance of the grid, nearest neighbor, and OLS estimators, The Journal of Real Estate Finance and Economics, Volume 13, Issue 3, pp. 203-218.
• PEDREGOSA F., VAROQUAUX G., GRAMFORT A., MICHEL V., THIRION B., GRISEL O., BLONDEL M., PRETTENHOFER P., WEISS R., DUBOURG V., VANDERPLAS J., PASSOS A., COURNAPEAU D., BRUCHER M., PERROT M., DUCHESNAY É., 2011, Scikit-learn: Machine Learning in Python, JMLR 12, pp 2825-2830.
• PLUMMER E., 2014, The Effects of Property Tax Protests on the Assessment Uniformity of Residential Properties, Real Estate Economics, Volume 4, Issue 4, pp. 900-937.
• RASCHKA S., 2018, Python. Machine learning in Polish: Python. Uczenie maszynowe, Wydawnictwo Helion, Gliwice.
• SAWIŁOW E., 2009, The application of methods of multi-dimensional comparative analysis for the purpose of cadastral value determination in Polish: Zastosowanie metod wielowymiarowej analizy porównawczej dla potrzeb ustalania wartości katastralnych, Studia i Materiały Towarzystwa Naukowego Nieruchomości, Vol. 17, no. 1.
• Song Xin-Ping, Hu Zhi-Hua, Du Jian-Guo, Sheng Zhao-Han, 2014, Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China, Journal of Forecasting, Volume 33, Issue 8, pp. 611-626.
• TROJANEK M., KISIAŁA W., 2016, The Diversification of Communes' Revenue from Real Estate Across Provinces, Real Estate Management and Valuation, Vol. 24, No. 2, pp. 36-49.
• WÓJTOWICZ K., 2006, Analysis of potential effects of real estate tax system reform in Poland in Polish: Analiza potencjalnych skutków reformy systemu opodatkowania nieruchomości w Polsce. Public finances. Lublin: Wyd. UMCS.

Identyfikatory

DOI

10.2478/remav-2019-0015