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

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...

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Bibliographic Details
Published in:Sustainability 2017-11, Vol.9 (11), p.2138
Main Authors: Del Giudice, Vincenzo, De Paola, Pierfrancesco, Forte, Fabiana, Manganelli, Benedetto
Format: Article
Language:English
Subjects:
Appraisals
Bayesian analysis
Computer simulation
Markov analysis
Markov chains
Monte Carlo simulation
Multiple regression analysis
Neural networks
Numerical integration
Real estate
Real estate appraisal
Regression analysis
Sustainability
Urban areas
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Summary: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%.
ISSN:2071-1050
2071-1050
DOI:10.3390/su9112138