Urban green spaces and housing prices: the impact of metric choice on econometric models

It is widely recognized that green urban areas play a significant role in enhancing environmental quality, social and economic well-being within cities. In the context of urban planning and sustainable development, the study of their impact on housing prices can guide administrations more effectively in the design and allocation of green spaces. However, the choice of metrics for this purpose has the potential to influence the results. The present study aims to investigate how the choice of urban green space metrics influences the econometric analysis of real estate prices in a densely populated Italian city. The case study focuses on the city of Rome, which, being one of the European capitals with the largest extension of green urban surface, represents a particularly relevant context for investigating the effects on real estate values. The investigation employs a regression-based methodology, utilizing three green space metrics: total absolute surface, percentage coverage, and the presence or absence of green areas. The dependent variable in this study is housing prices, while the independent variables relate to socio-economic and environmental issues, including construction and social quality. The findings of this study underscore the significance of judicious selection of metrics in order to achieve reliable results. In contrast to the extant literature, which has hitherto concentrated exclusively on the presence of green spaces, this research examines how different metrics influence their estimated impact on property values. The findings offer valuable insights for urban planners and policymakers in optimizing the integration of green spaces into urban valuation models.