Measurement Models For Sailboats Price vs. Features And Regional Areas

TL;DR

This study uses machine learning to analyze how sailboat features and regional factors influence prices, revealing key specifications and regional price differences without GDP correlation.

Contribution

Introduces a machine learning approach to predict sailboat prices based on technical specs and regional data, highlighting the superior gradient descent model performance.

Findings

Monohulls are generally cheaper than catamarans.

Length, beam, displacement, and sail area positively correlate with price.

US has the highest average sailboat prices, GDP shows no correlation.

Abstract

In this study, we investigated the relationship between sailboat technical specifications and their prices, as well as regional pricing influences. Utilizing a dataset encompassing characteristics like length, beam, draft, displacement, sail area, and waterline, we applied multiple machine learning models to predict sailboat prices. The gradient descent model demonstrated superior performance, producing the lowest MSE and MAE. Our analysis revealed that monohulled boats are generally more affordable than catamarans, and that certain specifications such as length, beam, displacement, and sail area directly correlate with higher prices. Interestingly, lower draft was associated with higher listing prices. We also explored regional price determinants and found that the United States tops the list in average sailboat prices, followed by Europe, Hong Kong, and the Caribbean. Contrary to our initial hypothesis, a country's GDP showed no direct correlation with sailboat prices. Utilizing a 50% cross-validation method, our models yielded consistent results across test groups. Our research offers a machine learning-enhanced perspective on sailboat pricing, aiding prospective buyers in making informed decisions.