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

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Vincenzo Del Giudice & Pierfrancesco De Paola & Fabiana Forte & Benedetto Manganelli, 2017. "Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples," Sustainability, MDPI, vol. 9(11), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2138-:d:119680
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    Citations

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    Cited by:

    1. Chien-Ming Yu & Pei-Fen Chen, 2018. "House Prices, Mortgage Rate, and Policy: Megadata Analysis in Taipei," Sustainability, MDPI, vol. 10(4), pages 1-23, March.
    2. Chmielewska Aneta & Adamiczka Jerzy & Romanowski Michał, 2020. "Genetic Algorithm as Automated Valuation Model Component in Real Estate Investment Decisions System," Real Estate Management and Valuation, Sciendo, vol. 28(4), pages 1-14, December.
    3. Chica-Olmo, Jorge & Cano-Guervos, Rafael, 2020. "Does my house have a premium or discount in relation to my neighbors? A regression-kriging approach," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    4. Vincenzo Del Giudice & Francesca Salvo & Pierfrancesco De Paola, 2018. "Resampling Techniques for Real Estate Appraisals: Testing the Bootstrap Approach," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    5. Sebastian Gnat & Mariusz Doszyn, 2020. "Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1230-1245.
    6. Daikun Wang & Victor Jing Li & Huayi Yu, 2020. "Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area," Land, MDPI, vol. 9(5), pages 1-18, May.
    7. Jorge Chica-Olmo & Rafael Cano-Guervos & Mario Chica-Rivas, 2019. "Estimation of Housing Price Variations Using Spatio-Temporal Data," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    8. Vincenzo Del Giudice & Pierfrancesco De Paola & Torrieri Francesca & Peter J. Nijkamp & Aviad Shapira, 2019. "Real Estate Investment Choices and Decision Support Systems," Sustainability, MDPI, vol. 11(11), pages 1-18, June.
    9. Volkan Sevinç, 2022. "Determining the Flat Sales Prices by Flat Characteristics Using Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 549-577, February.
    10. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.
    11. Fabiana Forte & Valentina Antoniucci & Pierfrancesco De Paola, 2018. "Immigration and the Housing Market: The Case of Castel Volturno, in Campania Region, Italy," Sustainability, MDPI, vol. 10(2), pages 1-17, January.

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