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Non-Residential Real Estate Prices And Machine Learning: The How And The Why

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  • Raffaella Barone

Abstract

This paper examines the relationship between non-residential property prices and various social, economic, and environmental indicators within the provinces where these properties are located. We focus on indicators from the Eni Enrico Mattei Foundation and SDSN Italia that track the 17 sustainable development goals, as well as additional factors like crime rates, per capita GDP, and sales frequency. Using a machine learning algorithm, we predicted property sale prices and applied SHapley Additive exPlanations to assess the importance of each variable. Our findings highlight the strong influence of categorical variables and SDG indicators on prices. Finally, we used causal inference to explore how policy interventions might affect property prices.

Suggested Citation

  • Raffaella Barone, 2025. "Non-Residential Real Estate Prices And Machine Learning: The How And The Why," BAFFI CAREFIN Working Papers 25238, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    • Handle: RePEc:baf:cbafwp:cbafwp25238
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    More about this item

    Keywords

    Machine Learning; Real estate market; Financial Stability; Sustainability; Crimes;
    All these keywords.

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G01 - Financial Economics - - General - - - Financial Crises
    • R33 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Nonagricultural and Nonresidential Real Estate Markets

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