This study investigates how artificial intelligence (AI) can reduce information asymmetry and perceived risks in the social housing market, thereby supporting homeownership among urban residents in Ho Chi Minh City.
Grounded in the theory of planned behavior, the study employs partial least squares structural equation modeling to test the hypothesized relationships using survey data collected from 209 potential social housing homebuyers in Ho Chi Minh City, Vietnam.
The results indicate that information asymmetry negatively affects purchase intention while increasing perceived performance risk and perceived financial risk. These perceived risks, in turn, weaken purchase intention. Furthermore, AI-supported boundary-spanning activities significantly moderate the relationship between purchase intention and actual purchase behavior, strengthening the translation of intention into real purchases.
The study relies on survey data from a single urban context, which may limit the generalizability of the findings. Future research could examine longitudinal effects, incorporate objective behavioral data or explore other housing markets and regulatory environments.
The findings suggest that policymakers, project developers and regulatory authorities can leverage AI-enabled information disclosure and communication mechanisms to reduce perceived risks, enhance transparency and build trust in the social housing markets, thereby improving market efficiency and purchase outcomes.
By mitigating information gaps and perceived risks, AI-supported mechanisms can facilitate access to social housing, promote equitable homeownership opportunities and contribute to sustainable urban development.
The study extends the literature on social housing and AI by empirically demonstrating how AI-supported boundary-spanning activities help bridge information gaps and convert purchase intentions into actual housing decisions within an emerging-market context.
