Used Sailboat Price Prediction Based on GA-BP Neural Wetwork
DOI:
https://doi.org/10.54097/hbem.v18i.12784Keywords:
Used Sailboat, Price Evaluation, GA-BP Neural Network, Multiple Linear Regression.Abstract
The paper presents a comprehensive study on sailboat pricing, considering various factors affecting listing price prediction. Through correlation analysis, eight key indicators influencing sailboat prices are identified. These indicators form the basis of a prediction model using the GA-BP neural network, resulting in a high accuracy rate of 0.87 in price estimation. The research also explores the impact of regions on sailboat listing prices by defining price level variables and selecting five region-representative indicators. The regional effect analysis model, based on ANOVA multiple regression, shows that the region significantly affects the markup, with factors like GDP and waterfront area playing a crucial role. The model's regression coefficients effectively explain the degree of impact. Comparing regional impact factor models using Hong Kong and Croatia data, the study finds that the Hong Kong sailboat price prediction model is accurate, providing valuable insights for the local market. Differences in coastal area and per capita GDP explain the regional price variations between Hong Kong and Croatia for both monohulled and catamaran sailboats. In conclusion, the research contributes to understanding sailboat pricing dynamics and emphasizes the significance of regional factors in price prediction. The findings offer valuable guidance for sailboat buyers, sellers, and market analysts.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
