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

Laura Gabrielli, Aurora Greta Ruggeri and Massimiliano Scarpa

ERES from European Real Estate Society (ERES)

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.

Keywords: Artificial Neural Network; Energy class; Market Value; Property Valuation (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2024-01-01
New Economics Papers: this item is included in nep-big, nep-ene, nep-env, nep-mac and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2024-198

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