Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples

Vincenzo Del Giudice, Pierfrancesco De Paola, Fabiana Forte and Benedetto Manganelli
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Vincenzo Del Giudice: Department of Industrial Engineering, University of Naples “Federico II”, 80138 Napoli, Italy
Pierfrancesco De Paola: Department of Industrial Engineering, University of Naples “Federico II”, 80138 Napoli, Italy
Fabiana Forte: Department of Architecture and Industrial Design, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
Benedetto Manganelli: School of Engineering, University of Basilicata, 85100 Potenza, Italy

Sustainability, 2017, vol. 9, issue 11, 1-17

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%.

Keywords: real estate appraisals; hedonic price model; artificial neural networks; Bayesian approach; Markov Chain Hybrid Monte Carlo Method; multiple regression analysis; Penalized Spline Semiparametric Method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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