Analyzing Citizens’ Perception and Attitudes Towards Housing Development in Norwalk Through SEM and Machine Learning Methods - Hamid Mostofi; Brian Bidolli; Thomas Livingston (Urban Science)
This study analyzed citizens’ subjective perceptions and attitudes toward housing development in Norwalk, Connecticut, employing structural equation modeling (SEM) and machine learning techniques. A survey of 580 residents was conducted to study the relationship between the willingness to buy or rent from housing projects in Norwalk and perceptions of risks, benefits, and sustainability beliefs. SEM identified latent variables influencing attitudes, including perceived risks (e.g., overcrowding and the loss of historical character), benefits (e.g., enhanced infrastructure, and job creation), and sustainability beliefs. The results revealed that perceived benefits have a stronger positive impact on citizens’ attitudes than risks, mediated by sustainability beliefs in this city. Machine learning via random forest highlighted key predictors of willingness to buy or rent, such as affordability, walkability, and public infrastructure improvements, with affordability being the most influential factor. These findings emphasize the importance of framing housing projects to align with citizens’ priorities, especially through campaigns emphasizing economic and environmental benefits. The integration of SEM and machine learning offers a robust approach to understanding citizen preferences, ensuring greater support and citizens’ acceptance for sustainable housing initiatives. These findings provide actionable insights for urban planners to understand the interrelations between concerns, perceived benefits, and beliefs to foster the social acceptance of sustainable urban policies.