This　study　employs　Multiple　Linear　Regression　methodology　to　examine　the　primary　determinants　that　impact　the　price　of　used　sailboats.　These　determinants　encompass　both　intrinsic　characteristics　of　the　sailboats　and　Regional　Effects.　Materials　and　Methods:　By　utilizing　data　acquired　from　online　sources,　we　can　construct　a　Multiple　Linear　Regression　model　for　the　purpose　of　forecasting　the　pricing　of　used　sailboats.　Following　the　data　cleaning　and　preparation　procedures,　the　application　of　Correlation　Analysis　enabled　the　identification　of　pivotal　factors.　Additionally,　the　utilization　of　K-Means　Clustering　facilitated　the　selection　of　a　subset　of　the　sample　data　specifically　pertaining　to　used　sailboats.　Subsequently,　the　potential　influences　of　regional　elements　on　sailboat　prices　were　hypothesized　and　elucidated　through　the　utilization　of　Multi-factor　Analysis　of　Variance.　The　model　is　optimized　based　on　a　regional　examination　of　Hong　Kong.　Results:　The　make,　length,　year,　beam,　and　draft　of　used　sailboats　are　just　a　few　factors　that　affect　their　price.　The　price　of　a　region　is　influenced　by　various　geographical　elements,　including　its　GDP,　water　area,　and　tax　rate.　The　results　of　the　Error　Analysis　indicated　that　the　model　had　a　significant　level　of　accuracy,　surpassing　0.85.　Conclusion:　The　results　of　this　study　indicate　that　the　optimized　Multiple　Linear　Regression　model　has　a　notable　level　of　accuracy,　minimal　bias,　and　a　satisfactory　fit　when　employed　for　the　purpose　of　predicting　the　pricing　of　used　sailboats.　©　2023　IEEE.