The nexus between transportation infrastructure and housing prices in metropolitan regions

This study investigates factors influencing housing prices in Tehran's metropolitan area and predicts their values. We analysed a sample of 32,162 housing units using Hedonic Model and Artificial Neural Networks for prediction and comparison. The Results indicate that distance to certain transportation facilities, such as airports, bus terminals, and road intersections, positively affects housing prices, potentially due to their capacity to attract traffic and environmental externalities. Conversely, distance to facilities like public bike stations and bus rapid transit stations negatively impacts prices, signalling their role in enhancing accessibility. Notably, ANN outperformed hedonic in predicting housing prices. These findings hold significant implications for policymakers, investors, and housing market stakeholders, shedding light on the intricate relationship between transportation infrastructure and housing prices. The study underscores the importance of employing diverse modelling methods to capture the non-linear dynamics of housing markets. These insights are crucial for evidence-based decision-making and can inform more effective policies and strategies in metropolitan Tehran and other similar cities. By aligning transportation policies with housing market needs, cities can enhance urban development and promote sustainable growth.