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Providing adequate public rental housing (PRH) of a decent quality at a desirable location is a major challenge in many cities. Often, a prominent opponent of PRH development is its host community, driven by a belief that PRH depreciates nearby property values. While this is a persistent issue in many cities around the world, this study proposed a new approach to assessing the impact of PRH on nearby property value. This study utilized a machine learning technique called long short-term memory (LSTM) to construct a set of housing price prediction models based on 547,740 apartment transaction records from the city of Busan, South Korea. A set of apartment characteristics and proximity measures to PRH were included in the modeling process. Four geographic boundaries were analyzed: The entire region of Busan, all neighborhoods of PRH, the neighborhoods of PRH in the “favorable,” and the “less favorable” local housing market. The study produced accurate and reliable price predictions, which indicated that the proximity to PRH has a meaningful impact on nearby housing prices both at the city and the neighborhood level. The approach taken by the study can facilitate improved decision making for future PRH policies and programs.

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The increasing energy burden on vulnerable households is critical in modern cities, it is crucial to understand how cities can characterize energy vulnerability and its relationship with the environment. This study modeled relationships between energy consumption and built environmental factors to compare determinants in average and energy-vulnerable households. While the conventional approach of identifying energy vulnerability often relies on household income, this study suggested a new approach by considering the energy-vulnerable group as a low-income class with high energy expenditure. A traditional regression model (semi-log regression) and advanced machine learning algorithm (ensemble gradient boosting, XGboost) were employed to maximize the performance of the modeling processes. The results indicated that the overall modeling performance was superior with regard to the machine learning algorithm, producing the r-squared value of 0.92 for the energy-vulnerable households, compared to the 0.34 of the semi-log regression model. While the direction of the association of the determinants was similar in the average and energy-vulnerable households, the level of association exhibited a clear difference, especially for the effect of income (comparing 0.30 to 0.03) and housing type (comparing -0.45 to -0.63). The study identified several implications regarding urban energy management and policy based on the findings.