The Influence of Regional Economic Levels on Catamaran Market Demand and Pricing: A Random Forest Regression Model

There are several main reasons for this phenomenon. Firstly, Catamarans are relatively more expensive compared to Monohulled Sailboats, requiring larger investments and maintenance costs. Therefore, in regions with relatively weak economic levels, people may be more inclined to purchase cheaper Monohulled Sailboats, which will affect the market demand for Catamarans.

Secondly, Catamarans have obvious advantages in terms of sailing performance and adaptability to different sea conditions, and are therefore more commonly used in luxury yachts, sailboat rentals, and other high-end tourism industries, all of which are relatively high consumption fields. However, when the local economic level is lower, the market demand for these high-end tourism industries may be restricted, which will also affect the development of Catamarans in the local market.

Thirdly, shipbuilding is one of the industries related to Catamarans, and shipbuilding is a capital-intensive and high-tech industry. For regions with less developed economies, it requires a large amount of capital, technology, and manpower input. Therefore, the local economic level will directly affect the development level of the local shipbuilding industry, thereby affecting the production and sales of Catamarans.

In summary, this paper distinguishes regions based on different economic levels and uses the random forest method to construct a model to fit the regression model of sailboat listing prices and influencing factors. The study has found significant regional differences, and the regional effects of different sailboat variants are also different. Because Catamarans tend to be leisure sailboats, the prices of luxury goods such as leisure sailboats are correlated with regional price levels. High-income individuals who purchase sailboats will not pay too much attention to the parameters of the sailboat, so the price factors of Catamarans are highly correlated with regional price levels.

To discuss how the modeling of a given geographical region works in the Hong Kong (SAR) market, this paper uses the random forest regression model and adds Hong Kong's economic indicators to consider the impact of the region on sailboat prices. The data from Hong Kong is standardized and normalized before subsequent modeling. Taking the Bavaria 38 Cruiser produced in 2005 as an example, the year, loa, lwl, beam, draft, displacement, and sail area are normalized, combined with Hong Kong's economic data, and the resulting vector is entered into the random forest regression fitting function to obtain the normalized ListingPrice (USD), which can be used as the basis for sailboat pricing in Hong Kong. In Monohulled Sailboats, we take the Beneteau 56, Dufour 40, Hanse 385, and Bavaria 38 Cruiser (2005) as examples, and in Catamarans, we take the Leopard 47, Catana 50, and Nautitech 47 as examples. Their normalized ListingPrice (USD) is used for random forest regression prediction in Hong Kong, and the predicted pricing and original regional pricing for different models of ships in Hong Kong are as follows: