Comparative Statistical Analysis of the Main Approaches to Property Valuation
Abstract
This paper is oriented to provide comparative statistical analysis of real estate valuation data received on the basis of classical and computerized valuation approaches. The paper main objective is to compare accuracy of valuation results which could be expected with utilization of classical valuation approach and those from automated valuation models. The analysis provided is based on initial data from US market, MSCI investment and analytical agency global data and results received from 2 developed combined cluster-neural models application. To assure grounded comparison coefficient of data scattering to measure range of valuation results distribution for different approaches was used. For classical analysis minimum required volume of samples or number of comparable needed are established on the basis of statistical theory of small samplings. Results achieved for Ukrainian real estate market with less reliable basic statistical data demonstrated further remarkable increase of scattering coefficient. Statistical calculations performed on the basis of developed 2 cluster-neural network models resulted in scattering coefficients for the same accuracy level equal to 2.56 and 2.84, correspondingly. Based on broad statistical information analyzed paper demonstrates and compares accuracy expected from application of classical and computerized approaches of residential assets valuation. Small sampling statistical theory application for assessing number of comparable necessary for classical valuation market approach is demonstrated.
References
[2] Chartfield, C., and Collins, A. 1980. Introduction to Multivariate Data Analysis. Chapman and Hall.
[3] D’Amato, M., and Kauko, T. 2017. Advances in Automated Valuation Modelling. Springer.
[4] Damodoran Online. 2017. Available at: http://people.stern.nyu.edu/adamodar/New_Home_Page/datacurrent.html#corpgov
[5] European AVM Alliance. 2017. Available at: https://www.europeanavmalliance.org/european-avm-standards.html
[6] European Central Bank. 2014. Available at: https://www.ecb.europa.eu/paym/coll/risk/riskcontrol/html/index.en.html
[7] Gmurman, V.E. 2003. Theory of Probability and Mathematical Statistics. Vyshaya shkola.
[8] International Valuation Standards. 2017. Available at: https://www.ivsc.org/about
[9] Kilpatrick, J. 2011. Expert systems and mass appraisal. Journal of Property Investment & Finance, 2(4/5): 529-550.
[10] Kucharska-Stasyak, E. 2018. Statistics in the Context of Economic Theory and the Limits of Automated Valuation Models in the Valuation of Individual Properties. University of Lodz. Available at: https://www.tegova.org/data/bin/a5aeb259a7f740_Ewa_Kucharska-Stasiak_Paper.pdf
[11] Kummerov, M. 2003.Theory for Real Estate Valuation: An Alternative Way to Teach Real Estate Price Estimation Methods. Available at: https://www.researchgate.net/publication/228819822
[12] Lehmann, E.L. 1999. ‘Student’ and Small-Sample Theory. Statistical Science, 1(4): 418-426.
[13] Lenk, M.M., Worzala, E.M. and Silva, A. 1997. High-tech valuation: should artificial neural networks bypass the human valuer. Journal of Property Valuation and Investment, 15(1): 8-26.
[14] Limpscomb, C.A. 2017. Valuation: the Next Generation of AVMs, Fair & Equitable, the International Association of Assessing Officers. Springer.
[15] Limsombunchao, V. 2004. House price prediction hedonic price model vs. artificial neural network. Paper presented at the New Zealand Agricultural and Resource Economics Society Conference, June25-26, in Blenheim, New Zealand.
[16] Matysiak, G.M. 2017. Automated Valuation Models (AVMs): Here to Say, TEGoVA. Available at: https://www.tegova.org/data/bin/a5af569104b30b_George_Andrew_Matysiak_Paper.pdf
[17] McCluskey, W.J., et al. 2013. Prediction accuracy in mass appraisal: a comparison of modern approaches. Journal of Property Research, 30(4): 239-265.
[18] Penny, K.I. 1996. Appropriate critical values when testing for a single multivariable outlier by using the mahalanobis distance. Applied Statistics 45(1): 73-81.
[19] Peterson, S., and Flanagan, A. 2009. Neural network hedonic pricing models in mass real estate appraisal. Journal of Real Estate Research, 31(2): 147-164.
[20] Reid, B. 2017. Private Real Estate: Valuation Comparison and Sale Price Comparison. Morgan Stanley Capital International. Available at: https://www.msci.com/documents/10199/a7327c73-2ddf-4bdb-b635-0325e4c4a9a1
[21] Robson, G., and Downie, M.L. 2008. Automated Valuation Models: An International Perspective. Council of Mortgage Lenders.
[22] TEGoVA. 2016. European Valuation Standards EVS-2016. Available at: https://www.tegova.org/data/bin/a5738793c0c61b_EVS_2016.pdf
[23] TEGoVA. 2017. European Valuation Standard EVS 6 ‘Automated Valuation Models (AVMs)’. Available at: https://www.tegova.org/data/bin/a59fb29ed4040a_EVS_6_Automated_Valuation_Models_%28AVMs%29.pdf
[24] Tretton, D. 2007. Where is the world of property valuation for taxation purposes going? Journal of Property Investment & Finance, 25(5): 482-514.
[25] Uniform Residential Appraisal Report. 2018. Available at: https://www.fanniemae.com/content/guide_form/1004.pdf
[26] Ventolo, W.L., and Williams, M.R. 2012. Fundamentals of Real Estate Appraisal. Dearborn Trade Publishing.
[27] Yakubovsky, V.V., Bychkov, O.S. and Scherba, A.O. 2017. Combined neural network model for real estate market range value estimation. Paper presented at the 4th International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2017), June4-7, in Lodz, Poland.
[28] Zurada, J., Levitan, A. and Guan, J. 2011. A Comparison of regression and artificial intelligence methods in a mass appraisal context. Journal of Real Estate Research, 33(3): 349-388.
The Copyright Transfer Form to ASERS Publishing (The Publisher)
This form refers to the manuscript, which an author(s) was accepted for publication and was signed by all the authors.
The undersigned Author(s) of the above-mentioned Paper here transfer any and all copyright-rights in and to The Paper to The Publisher. The Author(s) warrants that The Paper is based on their original work and that the undersigned has the power and authority to make and execute this assignment. It is the author's responsibility to obtain written permission to quote material that has been previously published in any form. The Publisher recognizes the retained rights noted below and grants to the above authors and employers for whom the work performed royalty-free permission to reuse their materials below. Authors may reuse all or portions of the above Paper in other works, excepting the publication of the paper in the same form. Authors may reproduce or authorize others to reproduce the above Paper for the Author's personal use or for internal company use, provided that the source and The Publisher copyright notice are mentioned, that the copies are not used in any way that implies The Publisher endorsement of a product or service of an employer, and that the copies are not offered for sale as such. Authors are permitted to grant third party requests for reprinting, republishing or other types of reuse. The Authors may make limited distribution of all or portions of the above Paper prior to publication if they inform The Publisher of the nature and extent of such limited distribution prior there to. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in The Paper. This agreement becomes null and void if and only if the above paper is not accepted and published by The Publisher, or is with drawn by the author(s) before acceptance by the Publisher.