Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples
1
Department of Industrial Engineering, University of Naples “Federico II”, 80138 Napoli, Italy
2
Department of Architecture and Industrial Design, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
3
School of Engineering, University of Basilicata, 85100 Potenza, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2017, 9(11), 2138; https://doi.org/10.3390/su9112138
Received: 15 August 2017 / Revised: 12 November 2017 / Accepted: 15 November 2017 / Published: 21 November 2017
(This article belongs to the Special Issue Real Estate Economics, Management and Investments)
Abstract
This paper experiments an artificial neural networks model with Bayesian approach on a small real estate sample. The output distribution has been calculated operating a numerical integration on the weights space with the Markov Chain Hybrid Monte Carlo Method (MCHMCM). On the same real estate sample, MCHMCM has been compared with a neural networks model (NNs), traditional multiple regression analysis (MRA) and the Penalized Spline Semiparametric Method (PSSM). All four methods have been developed for testing the forecasting capacity and reliability of MCHMCM in the real estate field. The Markov Chain Hybrid Monte Carlo Method has proved to be the best model with an absolute average percentage error of 6.61%. View Full-TextKeywords:
real estate appraisals; hedonic price model; artificial neural networks; Bayesian approach; Markov Chain Hybrid Monte Carlo Method; multiple regression analysis; Penalized Spline Semiparametric Method
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Del Giudice, V.; De Paola, P.; Forte, F.; Manganelli, B. Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples. Sustainability 2017, 9, 2138.
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