Valuation of Energy Efficiency in the DutchMarket for Commercial Real Estate; a Quantitative Approach using Robust Linear Modelling

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dc.contributor.advisorVeer van der, Koen
dc.contributor.authorVos, Thomas
dc.date.issued2023-08-29
dc.description.abstractWhile transition risk and the adoption of energy labelling are well studied in the context of the Residential Real Estate (RRE) market, this remains relatively under-studied for the Commercial Real Estate (CRE) market. By using the unique data set on the Dutch market of CRE provided by Stichting Vastgoeddata (StiVAD), this study shows differences in pricing energy efficiency between the different sub-markets of commercial real estate. In contrast to earlier studies on the CRE market, this study goes beyond focusing on a single submarket by including a larger spectrum of sub-markets. By including these markets, the research shows that the heterogeneous nature of the CRE market is reflected in the adoption of energy efficiency in the valuation of properties. While the study finds stable significant premia for energy efficiency, the coefficients are not robust across different model specifications. By studying the adoption of energy efficiency in the prices, the research could help to inform policy markers to better guide the transition towards a carbon-neutral economy. Moreover, the study contributes to the current literature by exploring different robust analysismethods within the real estate market. Keywords— Commercial Real Estate, Energy Efficiency, Transition Risk,MinimumEnergy Efficiency Standard
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/15140
dc.language.isoen
dc.thesis.facultyFaculteit der Managementwetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Managementwetenschappen::Master Economics::Financial Economics
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Managementwetenschappen::Master Economics
dc.thesis.typeMaster
dc.titleValuation of Energy Efficiency in the DutchMarket for Commercial Real Estate; a Quantitative Approach using Robust Linear Modelling

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