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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorAydogdu, B.
dc.contributor.authorMawed, Danya
dc.date.accessioned2023-08-11T00:02:26Z
dc.date.available2023-08-11T00:02:26Z
dc.date.issued2023
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/44633
dc.description.abstractIn recent years, sustainable buildings that promote energy efficiency have become a major priority in the development and construction industry, due to their potential environmental, economic, and social benefits. Energy labels, which range from A (highly efficient) to G (highly inefficient), are used to assess the energy efficiency of buildings. This study focuses on residential properties in the Netherlands and aims to investigate the impact of energy labels on the sale price of buildings. By exploring this impact we seek to understand the implications of energy label upgrades on the market value of the houses. Public data provided by the Central Bureau of Statistics in the Netherlands, along with dwellings data from an online real estate listings platform, were utilized for this study. Our findings demonstrate that a house's energy label influences its sell price, indicating that energy label upgrades have the potential to increase property value. The research provides evidence of how changes in the energy label rating impact house prices. Understanding this influence offers valuable insights for homeowners, real estate professionals, investors, and policymakers. This information enables stakeholders to make better decisions in the real estate and investing markets
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectwe aim to investigate the impact of energy labels on the sale price of buildings. By exploring this impact we seek to understand the implications of energy label upgrades on the market value of the houses.
dc.titleExploring the Impact of Energy Labels on Residential Properties Prices: A Data-Driven Analysis
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.courseuuApplied Data Science
dc.thesis.id21636


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