<![CDATA[This study crafts a machine learning framework that systematically integrates multi-hazard disaster risk assessments into automated property valuation for the Jakarta Metropolitan Area. The framework addresses 25–30% MAPE typically observed in disaster-prone regions, providing more reliable valuation results. We made 114 prediction features from 42 input variables by using 14,284 property data from Indonesian markets, physical risk data from the Think Hazard platform, and socio-economic data from Central Bureau of Statistics. Elastic Net model performed superior compared to other models which had R² = 0.7922 and a MAPE of 28.27%. We found that some disaster risks had unexpected beneficial effects on property prices. We expected that risks related to the earth (+40.5%) and water (+19.2%) would have positive effects, while risks related to the weather (-66.9%) would have negative effects. These conflicting results suggest that in complex urban markets, the quality of infrastructure, location premiums, and differences in risk perception may outweigh simple risk penalties. The idea gives realistic ideas for property valuation that takes risks into account, but it also points out big problems with how the market judges how likely a disaster is to happen.]]>
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