This study develops a new way to analyze the evolving dynamics of wealth concentration in the Global South, where indigenous norms and institutions play a crucial role in accumulating wealth. The study particularly focuses on the unique development of the Korean housing finance system: its development path can be characterized as the history of challenges, which refer to forces that have hindered the accumulation of wealth, and responses, which refer to counteractions that seek alternative modes of wealth accumulation. On the basis of such structural dissonance, this study situates how the reconstitution of housing capital discriminatorily reconstructs households’ resilience against economic and social insecurity to maintain and/or pursue homeownership. These findings explicitly contradict contemporary theories of financial intermediation that financial market reform that accompanies new products and services, lower interest rates, and greater liquidity substantially motivates households to purchase real estate. Rather, financial globalization increases inequalities in wealth that are set in motion through the tension between formal and informal housing capital by creating junctions that transfer risk and uncertainty in the financial market onto individuals.

Care robots have the potential to address the challenge of aging societies, such as labor shortages or the aging workforce. While previous studies have focused mainly on the productivity or workability of care robots, there has been an increasing need to understand the social value of care robots. This study attempted to identify the social values of care robots by conducting focus group interviews (FGIs) with twenty-four care recipients and caregivers and by using analytic hierarchy processes (AHPs) with thirteen individuals with expertise in the care service and care robot industries. Our results show that the labor- and health-related benefits, the technology innovation, and the provision of essential care work have the highest importance among the criteria of care robots’ social values. The criteria that receive lowest priority are cost, the autonomy and needs of the care recipients, and the organizational innovation. Our study suggests that along with the private benefits and costs of care robots, their social values also need to be considered to improve the quality of care and to unlock the potential of the care robot industries.

In this study, we examine the prediction accuracy of machine learning methods to estimate commercial real estate transaction prices. Using machine learning methods, including Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Deep Neural Networks (DNN), we estimate the commercial real estate transaction price by comparing relative prediction accuracy. Data consist of 19,640 transaction-based office properties provided by Costar corresponding to the 2004–2017 period for 10 major U.S. CMSA (Consolidated Metropolitan Statistical Area). We conduct each machine learning method and compare the performance to identify a critical determinant model for each office market. Furthermore, we depict a partial dependence plot (PD) to verify the impact of research variables on predicted commercial office property value. In general, we expect that results from machine learning will provide a set of critical determinants to commercial office price with more predictive power overcoming the limitation of the traditional valuation model. The result for 10 CMSA will provide critical implications for the out-of-state investors to understand regional commercial real estate market.