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Using Artificial Neural Networks to Uncover Real Estate Market Transparency: The Market Value

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12954))

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Abstract

In real estate property valuation, the availability of comparables is crucial. The reliability of the valuation of the market value depends on the number and on the accuracy of data that a professional can rely on. International standards suggest using historical prices as comparable since they are real transactions of sale/rent of a property that actually happened in a specific market. However, in the Italian real estate market, historical transaction prices are not available for professionals, and they have to base their valuations, primarily, on the asking prices enclosed in the selling advertisements. Asking prices can change in the future as they are subject to negotiation. Besides, sell ads always contain incomplete data or even wrong information. In this research, we employ Artificial Neural Networks to estimate how much offer prices and selling advertisements are misleading in property valuation in Italy. We, in a way, assess the opacity of the Italian real estate market, and we designate the major sources of error. The present work is a first step towards developing a model fitted for estimating data accuracy used generally in real estate estimates, namely, asking prices.

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Authors and Affiliations

  1. University IUAV of Venice, Dorsoduro 2206, 30123, Venice, Italy

    Laura Gabrielli, Aurora Greta Ruggeri & Massimiliano Scarpa

Authors
  1. Laura Gabrielli
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  2. Aurora Greta Ruggeri
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  3. Massimiliano Scarpa
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Corresponding author

Correspondence to Laura Gabrielli .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Gabrielli, L., Ruggeri, A.G., Scarpa, M. (2021). Using Artificial Neural Networks to Uncover Real Estate Market Transparency: The Market Value. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12954. Springer, Cham. https://doi.org/10.1007/978-3-030-86979-3_14

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  • DOI: https://doi.org/10.1007/978-3-030-86979-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86978-6

  • Online ISBN: 978-3-030-86979-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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