Household mobility dynamics are a complex phenomenon due to the multi-faceted human-environment interactions involved. These dynamics are affected by a variety of variables, spanning economic, structural, political and environmental. With respect to this latter aspect, much research has explored the relationship between the development of urban green areas, the increase of price and the formation of gentrification. Within this context, the present work explores housing mobility and gentrification dynamics proposing a mixed-method approach that combines a spatially explicit agent-based model (ABM), a Multicriteria Decision Analysis (MCDA) and a Hedonic Price (HP). The case study of the transformation project of Victoria Park in Brisbane (Australia) is used to verify the applicability of this integrated approach. In the methodological framework proposed, the ABM simulates the housing mobility of the residents through an individual decision-making process, where the likelihood of mobility is based on various empirical endogenous factors (i.e., household socio-economic characteristics) and exogenous ones (i.e., market variation). The MCDA supports the identification of the relative importance of these factors using a panel of local experts and stakeholders. The Spatial HP explores the impact of urban parks on real estate prices. The current study found the abilities of MCDA and HP to reduce the assumptions of ABM by generating quali-quantitative information of citizens' behaviors and estimating price increase. Moreover, the model highlights the importance of considering both supply and demand sides when analyzing gentrification. The specific results of the case study show the influence of the park in the formation of green gentrification.

Combining an agent-based model, hedonic pricing and multicriteria analysis to model green gentrification dynamics / Caprioli, C.; Bottero, M.; De Angelis, E.. - In: COMPUTERS, ENVIRONMENT AND URBAN SYSTEMS. - ISSN 0198-9715. - ELETTRONICO. - 102:101955(2023). [10.1016/j.compenvurbsys.2023.101955]

Combining an agent-based model, hedonic pricing and multicriteria analysis to model green gentrification dynamics

Caprioli C.;Bottero M.;De Angelis E.
2023

Abstract

Household mobility dynamics are a complex phenomenon due to the multi-faceted human-environment interactions involved. These dynamics are affected by a variety of variables, spanning economic, structural, political and environmental. With respect to this latter aspect, much research has explored the relationship between the development of urban green areas, the increase of price and the formation of gentrification. Within this context, the present work explores housing mobility and gentrification dynamics proposing a mixed-method approach that combines a spatially explicit agent-based model (ABM), a Multicriteria Decision Analysis (MCDA) and a Hedonic Price (HP). The case study of the transformation project of Victoria Park in Brisbane (Australia) is used to verify the applicability of this integrated approach. In the methodological framework proposed, the ABM simulates the housing mobility of the residents through an individual decision-making process, where the likelihood of mobility is based on various empirical endogenous factors (i.e., household socio-economic characteristics) and exogenous ones (i.e., market variation). The MCDA supports the identification of the relative importance of these factors using a panel of local experts and stakeholders. The Spatial HP explores the impact of urban parks on real estate prices. The current study found the abilities of MCDA and HP to reduce the assumptions of ABM by generating quali-quantitative information of citizens' behaviors and estimating price increase. Moreover, the model highlights the importance of considering both supply and demand sides when analyzing gentrification. The specific results of the case study show the influence of the park in the formation of green gentrification.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2978172