This study investigates the factors shaping real estate values in Thu Duc City, located in Ho Chi Minh City, Vietnam, over the past five years (2019–2024), focusing on a comparative analysis of econometric models to identify the most suitable framework for the data and research assumptions. Drawing on data from various online real estate platforms in Thu Duc, the analysis employs spatial autoregressive models (SAR, SEM, SDM) alongside multiple linear regression (MLR) to explore the interplay between transportation accessibility, central location, and property prices. Findings reveal that spatial autoregressive models significantly outperform MLR, which displays spatial autocorrelation in its residuals. Among these, the SEM model with the Queen matrix emerges as the most effective, demonstrating that property prices rise when closer to public transport routes, decline when farther from commercial hubs, and are adversely impacted by proximity to train stations. Highlighting the importance of spatial models, this study emphasizes their role in reducing biases and achieving more accurate insights in urban real estate analysis, particularly in rapidly developing areas like Thu Duc.