Evaluating the Impact of Urban Renewal on the Residential Real Estate Market: Artificial Neural Networks Versus Multiple Regression Analysis

Abstract

Urban regeneration interventions and strategies for the restoration of eco-systemic services, in addition to generating a range of community benefits, lead to an increase in property values in the neighbourhood. This study aims to investigate how redevelopment affects residential house prices. The aim of the paper is twofold. First the predictive potential of hedonic models is compared with that of Artificial Intelligence (AI) and Machine Learning (ML) approaches. Then, we characterise a novel Artificial Neural Network (ANN) model, which is still scarcely used to forecast how housing prices vary due to changes in the quality of the urban environment. The model includes among the variable inputs a series of environmental factors rarely considered in AI-ML approaches. The defined ANN is intended to support traditional approaches in order to provide planners and decision-makers with a more complete set of information on real estate trends. Applications to real case studies will allow to validate the model, as well as to identify the environmental variables that most significantly influence residential property values.