<![CDATA[House prices play a crucial role in shaping the economic and social dynamics of society, influenced by environmental conditions, accessibility, and socio-economic factors. This study develops an adaptive model for predicting house listing prices by integrating statistical and geospatial analysis techniques. Data were collected through web scraping using the Rumah123.com API and cover property prices across subdistrict and district levels in Bogor. Satellite imagery and environmental indicators—such as the Normalized Difference Built-Up Index (NDBI) and Normalized Difference Water Index (NDWI)—were analyzed to uncover market trends. The findings reveal that urban density has negative spillover effects on surrounding areas, while green spaces and high-moisture zones positively influence property values. Digital infrastructure and air quality also affect housing preferences. The Spatial Durbin Error Model (SDEM) accounts for complex spatial dependencies, resulting in more realistic price estimates. These insights contribute to improved decision-making for housing investments by various stakeholders.]]>
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