This study analyzes the determinants of apartment sale prices in Busan, South Korea, by applying spatial econometric models to distinguish between urban and non-urban areas. In the era of rapidly expanding digital real estate platforms, understanding localized price dynamics has direct implications for enhancing platform services such as market forecasting, valuation tools, and decision-support analytics. These platforms increasingly serve as the primary interface for buyers, sellers, and policymakers, yet most currently provide descriptive information and basic trend summaries rather than analytical insights into the structural drivers of housing prices. By incorporating spatial modeling results, platforms can move beyond simple listings to deliver advanced predictive functions, personalized valuation estimates, and neighborhood-level risk assessments. Using property and demographic variables such as exclusive area, floor level, year of construction, and local population density, the analysis confirms significant spatial autocorrelation, indicating clustering in apartment prices. Moran’s I statistic validates the non-random spatial distribution, supporting the use of spatial econometric approaches. To capture spatial dependence, three models are applied: the Spatial Lag Model (SLM), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The results show that while all models identify consistent effects of apartment characteristics and neighborhood population, the SDM provides the best fit and effectively captures both direct and spillover effects. These findings highlight that the determinants of apartment prices differ across urban and non-urban contexts, and they demonstrate how integrating spatial modeling insights into digital platforms can improve the accuracy of automated price estimations, provide more nuanced market intelligence, and support data-driven urban planning and housing policy.