Automated Valuation Model based on fuzzy and rough set theory for real estate market with insufficient source data
Tài liệu tham khảo
American Bankers Association, 2010
Barańska, 2013, Real estate mass appraisal in selected countries – functioning systems and proposed solutions, Real Estate Manag. Valuat., 21, 35, 10.2478/remav-2013-0024
Bello, 2012, Rough sets in the soft computing environment, Inf. Sci., 212, 1, 10.1016/j.ins.2012.04.041
Bilozor, 2014, The application of geoinformation in the process of determining significance of real estate attributes, 14th GeoConference on Informatics. Geoiformatics and Remote Sensing. Photogrammetry and Remote Sensing Cartography and Gis. Section Cartography and Gis, 2014, vol. III, 941
Borst, 1992, Artificial neural networks: the next modelling/calibration technology for the assessment community, Prop. Tax J. IAAO, 10, 69
Borst, 2007, Comparative evaluation of the comparable sales method with geostatistical valuation models, Pacific Rim Prop. Res. J., 13, 106, 10.1080/14445921.2007.11104225
Chi, 2011, A hybrid approach of DEA. Rough set theory and random forests for credit rating, Int. J. Innov. Comput. Inf. Control, 7, 4885
d’Amato, 2004, A comparison between MRA and rough set theory for mass appraisal. A case in Bari, Int. J. Strateg. Prop. Manag., 8, 205, 10.3846/1648715X.2004.9637518
d’Amato, 2007, Comparing rough set theory with multiple regression analysis as automated valuation methodologies, Int. Real Estate Rev., 10, 42, 10.53383/100083
d’Amato, 2008, Rough set theory as property valuation methodology: the whole story, 220
2017
Downie, 2007, 10
Fischer, 2003, Multi-criteria analysis of ranking preferences on residential traits, 10th ERES Conference
Gonzalez, 2002, Explaining results in a neural-mass appraisal model
Guan, 2008, An adaptive neuro-fuzzy interface system based approach to real estate property assessment, J. Real Estate Res., 30, 395, 10.1080/10835547.2008.12091225
IAAO International Association of Assessing Officers, 2012
IAAO International Association of Assessing Officers, 2012, Ratio study practices in the United States and Canada: results of 2011 survey, The Technical Standards Committee, IAAO, J. Prop. Tax Assess. Admin., 9
IAAO International Association of Assessing Officers, 2013
IAAO International Association of Assessing Officers, 2018
Kaklauskas, 2012, Life cycle process model of a market-oriented and student centered higher education, Int. J. Strateg. Prop. Manag., 16, 414, 10.3846/1648715X.2012.750631
Kauko, 2002
Kauko, 2011
Komorowski, 1999, Rough sets: a tutorial, 3
Lentz, 1998, Residential appraisal and the lending process: a survey of issues, J. Real Estate Res., 15, 11, 10.1080/10835547.1998.12090912
McCluskey, 1997, Interactive application of computer assisted mass appraisal and geographic information systems, J. Prop. Valuat. Invest., 15, 448, 10.1108/14635789710189227
McCluskey, 2012, The potential of artificial neural networks in mass appraisal: the case revisited, J. Financ. Manag. Prop. Constr., 17, 274, 10.1108/13664381211274371
McCluskey, 2013, Prediction accuracy in mass appraisal: a comparison of modern approaches, J. Prop. Res., 30, 239, 10.1080/09599916.2013.781204
Pawlak, 1982, Rough sets, Int. J. Inf. Comput. Sci., 11, 10.1007/BF01001956
Pawlak, 1997
Quintos, 2013, Spatial weight matrices and their use as baseline values and location-adjustment factors in property assessment models, Cityspace, 15, 295
Renigier-Bilozor, 2008, Zastosowanie teorii zbiorów przybliżonych do masowej wyceny nieruchomości na małych rynkach (eng. Application of rough set theory to mass appraisal on small markets), Acta Scientiarum Polonorum: Administratio Locorum, 7, 35
Renigier-Bilozor, 2011, Analysis of real estate markets with the use of the rough set theory, J. Polish Real Estate Sci. Soc., 19
Renigier-Bilozor, 2014, Rating methodology for real estate markets – Poland case study, Pub. Int. J. Strateg. Prop. Manag., 18, 198, 10.3846/1648715X.2014.927401
Renigier-Bilozor, 2017, Real estate markets rating engineering as the condition of urban areas assessment, Land Use Policy, 61, 511, 10.1016/j.landusepol.2016.11.040
Renigier-Bilozor, 2019, Geoscience methods in real estate market analyses subjectivity decrease, Geosciences, 9, 130, 10.3390/geosciences9030130
RICS, 2012
RICS, 2013
Robinson, 2009, 3
Salvo, 2014, Property prices index numbers and derived indices, Prop. Manag., 32, 139
Simontti, 2015, Appraisal value and assessed value in Italy, Int. J. Econ. Stat., 3, 24
Stefanowski, 2000, Valued tolerance and decision rules
Wilhemsson, 2002, Spatial models in real estate economics, Hous. Theory Soc., 19, 92, 10.1080/140360902760385646
Worzala, 1995, An exploration of neural networks and its application to real estate valuation, J. Real Estate Res., 10, 185, 10.1080/10835547.1995.12090782
Wu, 2001, A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks, IEEE Trans. Fuzzy Syst., 9, 578, 10.1109/91.940970
Yakubovsky, 2018
Zavadskas, 2011, Multiple criteria decision making (MCDM) methods in economics: an overview, Technol. Econ. Dev. Econ., 17, 397, 10.3846/20294913.2011.593291
Zrobek, 2013, Modern challenges facing the valuation profession and allied university education in Poland, Real Estate Manag. Valuat., 21, 14, 10.2478/remav-2013-0002
