Year: 2024
Type: article
Abstract: Sailboat pricing is a nuanced field, significantly influenced by vessel age, geographical location, and brand reputation. To address this complexity, we leveraged the XGBoost algorithm, a powerful machine learning tool, and refined it through Bayesian optimization to create a highly accurate model for predicting sailboat prices. Our analysis revealed significant regional disparities, with sailboats from Europe, the USA, and the Caribbean commanding varying market valuations. When applying this optimized model to the Hong Kong used sailboat market, we were able to generate forecasts with improved accuracy, though still with room for enhancement. Notably, the Lagoon brand stood out as a consistent high performer, underscoring the importance of brand reputation in sailboat pricing. To provide actionable intelligence, we have synthesized a comprehensive analysis that integrates our research findings and predictive strategies. This analysis presents clear visualizations alongside concise interpretations, thereby constituting a pivotal resource for informed decision-making within the local market context.
Authors: Ningjing He, Jiaqi Wang
Institution: Zhejiang University of Finance and Economics
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403376208