Purpose

The equipment resale price plays an important role in calculating the optimum time for equipment replacement. Some of the existing models that predict the equipment resale price do not take many of the influencing factors on the resale price into account. Other models consider more factors that influence equipment resale price, but they still with low accuracy because of the modeling techniques that were used. An easy tool is required to help in forecasting the resale price and support efficient decisions for equipment replacement. This research presents a machine learning (ML) computer model helping in forecasting accurately the equipment resale price.

Design/methodology/approach

A measuring method for the influencing factors that have impacts on the equipment resale price was determined. The values of those factors were measured for 1,700 pieces of equipment and their corresponding resale price. The data were used to develop a ML model that covers three types of equipment (loaders, excavators and bulldozers). The methodology used to develop the model applied three ML algorithms: the random forest regressor, extra trees regressor and decision tree regressor, to find an accurate model for the equipment resale price. The three algorithms were verified and tested with data of 340 pieces of equipment.

Findings

Using a large number of data to train the ML model resulted in a high-accuracy predicting model. The accuracy of the extra trees regressor algorithm was the highest among the three used algorithms to develop the ML model. The accuracy of the model is 98%. A computer interface is designed to make the use of the model easier.

Originality/value

The proposed model is accurate and makes it easy to predict the equipment resale price. The predicted resale price can be used to calculate equipment elements that are essential for developing a dependable equipment replacement plan. The proposed model was developed based on the most influencing factors on the equipment resale price and evaluation of those factors was done using reliable methods. The technique used to develop the model is the ML that proved its accuracy in modeling. The accuracy of the model, which is 98%, enhances the value of the model.

中文翻译：

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预测建筑设备转售价格：机器学习模型

目的

设备转售价格对于计算设备更换的最佳时间起着重要作用。现有的一些预测设备转售价格的模型没有考虑到很多对转售价格的影响因素。其他模型考虑了更多影响设备转售价格的因素，但由于所使用的建模技术，它们的准确性仍然较低。需要一个简单的工具来帮助预测转售价格并支持设备更换的有效决策。这项研究提出了一种机器学习 (ML) 计算机模型，有助于准确预测设备转售价格。

设计/方法论/途径

确定了影响设备转售价格的影响因素的衡量方法。这些因素的价值是针对 1,700 件设备及其相应的转售价格进行测量的。这些数据用于开发涵盖三种类型设备（装载机、挖掘机和推土机）的机器学习模型。用于开发模型的方法应用了三种机器学习算法：随机森林回归器、额外树回归器和决策树回归器，以找到设备转售价格的准确模型。三种算法均通过340台设备的数据进行了验证和测试。

发现

使用大量数据来训练机器学习模型，得到了高精度的预测模型。额外树回归算法的准确性是用于开发 ML 模型的三种算法中最高的。该模型的准确率为 98%。计算机界面的设计是为了使模型的使用更加容易。

原创性/价值

所提出的模型是准确的，并且可以轻松预测设备转售价格。预测转售价格可用于计算对于制定可靠的设备更换计划至关重要的设备要素。所提出的模型是根据对设备转售价格影响最大的因素开发的，并使用可靠的方法对这些因素进行了评估。用于开发模型的技术是 ML，它证明了其建模的准确性。模型的准确率达到98%，提升了模型的价值。