A　comprehensive　understanding　of　house　prices　and　their　factors　provide　insights　into　the　demand　for　housing　while　helping　policymakers　implement　measures　to　manage　the　housing　market.　Traditional　studies　either　focus　more　on　linear　relationships　and　ignore　complex,　non-linear　influences　or　consider　neighborhood　amenities　but　lose　sight　of　the　streetscape.　This　study　aims　to　enrich　the　literature　by　integrating　street-perception　characteristics　with　an　interpretable　machine-learning　technique　for　modeling　house　prices.　Specifically,　street-view　images　were　semantically　segmented　to　quantify　street-perception　characteristics　from　five　perspectives:　greenness,　openness,　enclosure,　walkability,　and　imageability.　By　combining　the　determinants　of　community　attributes　and　living　convenience,　17　explanatory　variables　were　fed　into　a　gradient-boosting　decision　tree　(GBDT)　model　to　estimate　housing　prices.　The　results　reveal　that　the　model　significantly　outperforms　the　linear　model　(R2　increased　by　47.87　%).　Additionally,　an　improvement　of　26.15　%　(R2)　was　observed　when　streetperception　characteristics　were　incorporated.　Moreover,　complicated　non-linear　relationships　and　interaction　effects　are　discussed　by　visualizing　partial　dependence　plots　(PDPs).　These　findings　offer　nuanced　guidance　for　improving　the　neighborhood　environment　to　promote　urban　equity　and　develop　a　sustainable　housing　market.