The mass appraisal of the real estate by computational intelligence

Applied Soft Computing Journal

Volumn 11, Issue 1, 2011, Pages 443-448

  Kontrimas, Vilius ^a   Verikas, Antanas ^a,b  

^a KAUNAS UNIVERSITY OF TECHNOLOGY   (Lithuania)

^b HALMSTAD UNIVERSITY   (Sweden)

Author keywords

Committee; Mass appraisal of real estate; Multilayer perceptron; Ordinary least squares regression; Self organizing map; Support vector regression

Indexed keywords

COMMITTEE; MULTILAYER PERCEPTRON; ORDINARY LEAST SQUARES REGRESSIONS; REAL ESTATE; SUPPORT VECTOR REGRESSION;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL METHODS; CONFORMAL MAPPING; MULTILAYERS; TAXATION;

REGRESSION ANALYSIS;

EID: 77957891329     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2009.12.003     Document Type: Article

References (50)

1. A. Evans, H. James, and A. Collins Artificial neural networks: an application to residential valuation in the UK Journal of Property Valuation & Investment 11 1992 195 203
2. A. Krogh, and J. Vedelsby Neural network ensembles, cross validation, and active learning G. Tesauro, D.S. Touretzky, T.K. Leen, Advances in Neural Information Processing Systems vol. 7 1995 MIT Press 231 238
3. A. Verikas, and A. Lipnickas Fusing neural networks through space partitioning and fuzzy integration Neural Processing Letters 16 2002 53 65
4. A. Verikas, A. Lipnickas, K. Malmqvist, M. Bacauskiene, and A. Gelzinis Soft combination of neural classifiers: a comparative study Pattern Recognition Letters 20 1999 429 444
5. A. Verikas, A. Lipnickas, and K. Malmqvist Selecting neural networks for a committee decision International Journal of Neural Systems 12 2002 351 361
6. A. Verikas, M. Signahl, K. Malmqvist, and M. Bacauskiene Fuzzy committee of experts for segmentation of colour images Proceedings of 5th European Congress on Intelligent Techniques and Soft Computing, vol. 3 Aachen, Germany 1997 1902 1906
7. C.M. Bishop Neural Networks for Pattern Recognition 1995 Clarendon Press Oxford
8. C.L. Liu Classifier combination based on confidence transformation Pattern Recognition 38 2005 11 28
9. Ch.Ch. Chang, Ch.J. Lin, LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm.
10. C.J. Merz, and M.J. Pazzani Combining neural network regression estimates with regularised linear weights M.C. Mozer, M.I. Jordan, T. Petsche, Advances in Neural Information Processing Systems 1997 MIT Press 564 570
11. D.P.H. Tay, and D.K.K. Ho Artificial intelligence and the mass appraisal of residential apartments Journal of Property Valuation & Investment 10 1991 525 540
12. E. Worzala, M. Lenk, and A. Silva An exploration of neural networks and its application to real estate valuation Journal of Real Estate Research 10 2 1995
13. F.D. Foresee, and M.T. Hagan Gauss-Newton approximation to Bayesian learning Proceedings of the IEEE International Joint Conference on Neural Networks 1997 1930 1935
14. H. Zhang, and G. Sun Feature selection using tabu search method Pattern Recognition 35 2002 701 711
15. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Machine Learning 46 2002 389 422
16. I. Wilson, S. Paris, A. Ware, and D. Jenkins Residential price forecasting at national and regional levels FIG XXII International Congress in Washington 2002
17. International Valuation Standards Seventh edition, Appraisal Inst, 2005.
18. J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas On combining classifiers IEEE Trans Pattern Analysis and Machine Intelligence 20 1998 226 239
19. J. Neter, M. Kutner, and W. Wasserman Christopher Nachtsheim, Applied Linear Statistical Models 1996 McGraw-Hill Irwin
20. J. Shawe-Taylor, and N. Cristianini Kernel Methods for Pattern Analysis 2004 Cambridge University Press Cambridge, UK
21. J.X. Ge, G. Runeson, and K.C. Lam Forecasting Hong Kong housing prices: an artificial neural network approach Methodologies in Housing Research Conference 2003
22. J.M. Steppe, and K.W. Bauer Improved feature screening in feedforward neural networks Neurocomputing 13 1996 47 58
23. J.O. Rawlings, G.S. Pantula, and D.A. Dickey Applied Regression Analysis: A Research Tool 1998 Springer-Verlag New York
24. K.L. Priddy, S.K. Rogers, D.W. Ruck, G.L. Tarr, and M. Kabrisky Bayesian selection of important features for feedforward neural networks Neurocomputing 5 1993 91 103
25. K.Z. Mao Orthogonal forward selection and backward elimination algorithms for feature subset selection IEEE Transactions on Systems, Man, & Cybernetics Part B: Cybernetics 34 2004 629 634
26. Lithuania: Resolution of the Lithuanian government on the confirmation of the real estate valuation rules. Valstybes Zinios 117 (2005) 2005-09-29 (in Lithuanian).
27. Lithuania: The law amending the 8 Subsection of the law on valuation basics of property and bussinnes No. IX-1428. Valstybes Zinios 38 (2003) 2003-04-03 (in Lithuanian).
28. Lithuania: The law on valuation basics of property and bussinness No. VIII-1202. Valstybes Zinios 52 (1999) 1999-05-25 (in Lithuanian).
29. M. Taniguchi, and V. Tresp Averaging regularized estimators Neural Computation 9 1997 1163 1178
30. M.R. Linne, S.M. Kane, G. Dell, A Guide to Appraisal Valuation Modeling, 2000.
31. N. Nguyen, and A. Cripps Predicting housing value: a comparison of multiple regression analysis and artificial neural networks Journal of Real Estate Research 22 3 2001
32. P. Rossini Improving the results of artificial neural network models for residential valuation Proceedings of Fourth annual Pacific Rim Real Estate Society Conference 1998
33. P. Rossini, and P. Kershaw Using neural networks to estimate constant quality house price indices Proceedings of Fifth annual Pacific Rim Real Estate Society Conference 1999
34. S. Sollich, and A. Krogh Learning with ensembles: how over-fitting can be useful D.S. Touretzky, M.C. Mozer, H.E. Hasselmo, Advances in Neural Information Processing Systems vol. 8 1996 MIT Press 190 197
35. P.D. Gader, M.A. Mohamed, and J.M. Keller Fusion of handwritten word classifiers Pattern Recognition Letters 17 1996 577 584
36. P.W. Munro, and B. Parmanto Competition among networks improves committee performance M.C. Mozer, M.I. Jordan, T. Petsche, Advances in Neural Information Processing Systems vol. 9 1997 MIT Press 592 598
37. Q. Do, and D. Grudnitski A neural network approach to residential property appraisal Real Estate Appraiser 1992 38 45
38. R.A. Borst Artificial Neural Networks: The Next Modeling/Calibration Technology for the Assessment Community Artificial Neural Networks 10 1992 69 94
39. R. Amabile, and P. Rosato The use of neural networks in the spatial analysis of property values Proceedings of the Sixth Joint Conference on Agriculture, Food, and the Environment Minneapolis, Minnesota, August 31-September 2 1998
40. R.J. Gloudemans, Mass appraisal of real property, 1999.
41. S. Yu, S.G. Backer, and P. Scheunders Genetic feature selection combined with composite fuzzy nearest neighbor classiers for hyperspectral satellite imagery Pattern Recognition Letters 23 2002 183 190
42. S.P. Kim, J.C. Sanchez, D. Erdogmus, Y.N. Rao, J. Wessberg, J.C. Principe, and M. Nicolelis Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models Neural Networks 16 2003 865 871
43. S.B. Cho, and J.H. Kim Combining multiple neural networks by fuzzy integral for robust classification IEEE Transactions on Systems Man and Cybernetics 25 1995 380 384
44. T. Heskes Balancing between bagging and bumping M.C. Mozer, M.I. Jordan, T. Petsche, Advances in Neural Information Processing Systems vol. 9 1997 MIT Press 466 472
45. T. Kohonen Self-organizing Maps 3rd ed. 2001 Springer-Verlag Berlin
46. V. Kontrimas, and A. Verikas Neural networks based screening of real estate transactions Neural Network World 16 1 2007 17 30
47. V. Kontrimas, and A. Verikas Tracking of doubtful real estate transactions by outlier detection methods: a comparative study Information Technology and Control 35 2 2006 94 105
48. V. Kontrimas An application of hybrid computational methods to real estate markets Proceedings of Information technologies 2005 Technologija, Kaunas 2005 141 144
49. V. Tresp, and M. Taniguchi Combining estimators using non-constant weighting functions G. Tesauro, D.S. Touretzky, T.K. Leen, Advances in Neural Information Processing Systems vol. 7 1995 MIT Press
50. V. Vapnik The Nature of Statistical Learning 1995 Springer New York