Warianty tytułu
Języki publikacji
Abstrakty
Research background: Mass valuation is a process in which many properties are valued simultaneously with a uniform approach. An example of a procedure used for mass real estate valuation is the Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA), which can be developed into a multiple regression model. The algorithm is based on a set of drawn representative properties. This set determines, inter alia, the quality of obtained valuations. Purpose: The objective of the study is to verify the hypothesis whether changing the method of sampling representative properties from the originally used simple random sampling to stratified sampling improves the results of the SAREMA econometric variant. Research methodology: The article presents a study that uses two methods of representative properties sampling - simple random sampling and stratified sampling. Errors of the models of valuation created taking into account both methods of sampling and different number of representative properties are compared. A key aspect of the survey is the choice of a better sampling method. Results: The study has shown that stratified sampling improves valuation results and, more specifically, allows for lower root mean square errors. Stratified sampling yielded better results in the initial phase of the study with more observations, but reducing the percentage of strata participating in the draws, despite the increase in RMSE, guaranteed lower errors than the corresponding results based on simple sampling in all variants of the study Novelty: The article confirms the possibility of improving the results of mass property valuation by changing the scheme of representative properties sampling. The results allowed for the conclusion that stratified sampling is a better way of creating a set of representative properties. (original abstract)
Czasopismo
Rocznik
Numer
Strony
152-167
Opis fizyczny
Twórcy
autor
- University of Szczecin, Poland
Bibliografia
- Antipov, E.A., Pokryshevskaya, E.B. (2012). Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications, 39 (2), 1772-1778. DOI: 10.1016/j.eswa.2011.08.077.
- Benjamin, J.D., Randall, S., Guttery, R.S., Sirmans, C.F. (2004). Mass Appraisal: An Introduction to Multiple Regression Analysis for Real Estate Valuation. Journal of Real Estate Practice and Education, 7 (1), 65-77. DOI: 10.1080/10835547.2004.12091602.
- Burgard, J.P., Münnich, R., Zimmermann, T. (2014). The Impact of Sampling Designs on Small Area Estimates for Business Data. Journal of Official Statistics, 30 (4). 749-771. DOI: 10.2478/jos-2014-0046.
- Chun & Griffith (2013). Spatial statistics and geostatistics, SAGE: Los Angeles.
- Ćetković, J., Slobodan, L., Lazarevska, L., Žarković, M., Vujošević, S., Cvijović, J., Gogić, M. (2018). Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application. Complexity 2018. DOI: 10.1155/2018/1472957.
- Dell, G. (2017). Regression, Critical Thinking, and the Valuation Problem Today. Appraisal Journal, 85 (3), 217-230.
- Dmytrów, K., Gnat, S. (2019). Application of AHP Method in Assessment of the Influence of Attributes on Value in the Process of Real Estate Valuation, Real Estate Management and Valuation, 27 (4), 15-26. DOI: 10.2478/remav-2019-0032.
- Doszyń, M., Gnat, S., Bas, M. (2017). The Econometric Procedures of Specific Transactions Identification. Folia Oeconomica Stetinensia, 17 (1), 20-30. DOI: 10.1515/foli-2017- 0002.
- Doszyń, M. (2012). Ekonometryczna wycena nieruchomości. Studia i Prace Wydziału Nauk Ekonomicznych i Zarządzania Uniwersytetu Szczecińskiego, 26, 41-52.
- Doszyń, M. (ed.) (2020). System kalibracji macierzy wpływu atrybutów w szczecińskim algorytmie masowej wyceny nieruchomości. Szczecin: Wydawnictwo Naukowe Uniwersytetu Szczecińskiego.
- Hozer, J., Gnat, S., Kokot, S., Kuźmiński ,W. (2019). The Problem of Designating Elementary Terrains for the Purpose of Szczecin Algorithm of Real Estate Mass Appraisal. Real Estate Management and Valuation, 27 (3), 42-58. DOI: 10.2478/remav-2019-0024.
- Etikan, I., Musa, S.A., Alkassim, R.S. (2016). Comparison of Convenience Sampling and Purposive Sampling. American Journal of Theoretical and Applied Statistics, 5 (1), 1-4. DOI: 10.11648/j.ajtas.20160501.11.
- Hozer, J., Kokot, S., Kuźmiński, W. (2002), Methods of statistical market analysis in real estate valuation. Warsaw: Polish Federation of Valuers' Associations.
- Isakson, H.R. (1998). The Review of Real Estate Appraisals Using Multiple Regression Analysis. Journal of Real Estate Research, 15 (2), 177-190.
- Jahanshiri, E., Buyong, T., Shariff, A.R.M. (2011). A review of Property Mass Valuation Models. Pertanika Journal of Science & Technology, 19, 23-30.
- Kauko, T., d'Amato, M. (2008). Mass Appraisal Methods: An International Perspective for Property Valuers. Blackwell Publishing Ltd.
- Kilpatrick, J. (2011). Expert Systems and Mass Appraisal. Journal of Property Investment & Finance, 29 (4/5), 529-550. DOI: 10.1108/14635781111150385.
- Lahiri, P. (2003). On the Impact of Bootstrap in Survey Sampling and Small-Area Estimation. Statistical Science, 18 (2), 199-210. DOI: 10.1214/ss/1063994975.
- McCluskey, W.J., Daud, D.Z., Kamarudin, N. (2014). Boosted regression trees: An application for the mass appraisal of residential property in Malaysia. Journal of Financial Manage********ment of Property and Construction, 19 (2), 152-167. DOI: 10.1108/JFMPC-06-2013-0022.
- Rao, J.N.K. (2011). Impact of Frequentist and Bayesian Methods on Survey Sampling Practice: A Selective Appraisal. Statistical Science, 26 (2), 240-256. DOI: 10.1214/10-STS346.
- Schreuder, H.T., Gregoire, T.G., Weyer, J.P. (2001). For what applications can probability and non-probability sampling be used? Environmental Monitoring and Assessment, 66, 281-291. DOI: 10.1023/A:1006316418865.
- Wang, X., Wen, J., Zhang, Y., Wang, Y. (2014). Real estate price forecasting based on SVM optimized by PSO. Optik, 125 (3), 1439-1443. DOI: 10.1016/j.ijleo.2013.09.017.
- Zurada, J., Levitan, A.S., Guan, J. (2011). A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context. Journal of Real Estate Research, 33 (3), 349-387. DOI: 10.1080/10835547.2011.12091311
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171613785