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With the popularity of circular economy around the world, transactions in the second-hand sailboat market are extremely active. Determining pricing strategies and exploring their regional effects is a blank area of existing research and has important practical and statistical significance. Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data. On this basis, we also constructed a regional effect multi-level model (RE-MLM) from three levels: geography, economy and country to clarify the impact of geographical areas on sailboat prices. The research results show that, first of all, the price of second-hand sailboats fluctuates greatly, and the predicted value better reflects the overall average price level. Secondly, there are significant regional differences in price levels across regions, economies and ethnic groups. Therefore, the price of second-hand sailboats is affected by many factors and has obvious regional effects. In addition, the model evaluation results show that the model constructed in this study has good accuracy, validity, portability and versatility, and can be extended to price simulation and regional analysis of different markets in different regions.随着循环经济在全球范围内的普及，二手帆船市场的交易活跃度极高。探究定价策略及其区域效应，是现有研究的空白领域，兼具重要的实践价值与统计学意义。为此，本文采用随机森林（Random Forest）模型与XGBoost算法识别核心价格指标，并创新性地引入滚动式非线性自回归（Nonlinear AutoRegressive，NAR）动态神经网络模型，对二手帆船价格数据开展模拟与预测工作。在此基础上，本文还从地理、经济与国家三个维度构建了区域效应多层级模型（Regional Effect Multi-Level Model，缩写RE-MLM），以厘清地理区域对帆船价格的影响机制。研究结果表明：其一，二手帆船价格波动幅度较大，模型预测值能够较好地反映整体均价水平；其二，不同区域、经济体与族群的价格水平存在显著的区域差异。由此可见，二手帆船价格受多重因素影响，且具有显著的区域效应。此外，模型评估结果显示，本文构建的模型具备良好的准确性、有效性、可移植性与通用性，可推广应用于不同区域不同市场的价格模拟与区域分析工作。