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Using big data to relate fluctuations in real estate prices with the Green Homes Directive: a case study encompassing the Italian territory

Author

Listed:
  • Laura Gabrielli
  • Aurora Greta Ruggeri
  • Massimiliano Scarpa

Abstract

The energy performance of buildings has emerged as a critical factor in the real estate sector, intertwining environmental sustainability with market pricing. Therefore, this study aims to explore the relationship between a building's energy performance, as indicated by its energy class, and its market value. Leveraging a web-parsing automated procedure, the authors gathered approximately 200,000 observations of properties currently listed for sale across Italy, capturing both asking prices and energy class specifications. Through the analysis of this extensive dataset, an Artificial Neural Network was trained to develop a predictive tool for estimating property market values based on various building characteristics, with particular emphasis on understanding the impact of energy class on market prices. In conclusion, this research opens the debate on the significance of energy class in evaluating the market value of buildings, especially within the context of the European Green Homes Directive.

Suggested Citation

  • Laura Gabrielli & Aurora Greta Ruggeri & Massimiliano Scarpa, 2024. "Using big data to relate fluctuations in real estate prices with the Green Homes Directive: a case study encompassing the Italian territory," ERES eres2024-198, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2024-198
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    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2024-198
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    More about this item

    Keywords

    Artificial Neural Network; Energy class; Market Value; Property Valuation;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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