Abstract
Urban growth processes are notoriously complex, depending on vastly different demographic, socio-cultural and economic factors. The analysis is even more complex in the metropolitan areas, since they are the result of ancient agglomeration processes in a phase of intensive development of settlement and, more recently, of the formation of urban polycentrism. Investigation requires collection, analysis and processing of useful information at homogeneous territorial units, based on already consolidated models or through new validating protocols.
The present paper analyzes urban growth based on a micro-scale approach, identifying homogeneous local districts for localization features that can affect real estate market value for residential use. These features include the location of the housing unit compared with the city center, the level of infrastructure, the presence of community facilities and shops, but also the external environment quality in terms of availability of green public and air pollution degree. Geographic Information System applications are used to process the available dataset to identify, at first, the demographic evolution of Naples as a functional urban region according to the life cycle model proposed by Van den Berg and, then, the real estate dynamics of the metropolitan in the light of income flows which each asset is capable of producing.
Understanding the spatio-temporal evolution of real estate property values can be useful to explain the intimate mechanism of urban growth at the metropolitan scale.
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This paper is to be attributed in equal parts to the four authors.
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Bencardino, M., Granata, M.F., Nesticò, A., Salvati, L. (2016). Urban Growth and Real Estate Income. A Comparison of Analytical Models. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9788. Springer, Cham. https://doi.org/10.1007/978-3-319-42111-7_13
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