Estimating Warehouse Rental Price using Machine Learning Techniques

IAENG Internaitonal journal of computer science

Yixuan Ma¹  Zhenji Zhang²  Alexander Ihler³  Baoxiang Pan³ 

[1] 1. Beijing Jiao Tong University2. University of California Irvine;Beijing Jiao Tong University;University of California Irvine

关键词: sharing warehousing;    price estimation;    machine learning;   

DOI  :  10.15837/ijccc.2018.2.3034

学科分类：计算机科学（综合）

来源: International Association of Engineers

【 摘 要 】

Boosted by the growing logistics industry and digital transformation, the sharing warehouse market is undergoing a rapid development. Both supply and demand sides in the warehouse rental business are faced with market perturbations brought by unprecedented peer competitions and information transparency. A key question faced by the participants is how to price warehouses in the open market. To understand the pricing mechanism, we built a real world warehouse dataset using data collected from the classified advertisements websites. Based on the dataset, we applied machine learning techniques to relate warehouse price with its relevant features, such as warehouse size, location and nearby real estate price. Four candidate models are used here: Linear Regression, Regression Tree, Random Forest Regression and Gradient Boosting Regression Trees. The case study in the Beijing area shows that warehouse rent is closely related to its location and land price. Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation. Additionally, tree models have better performance than the linear model, with the best model (Random Forest) achieving correlation coefficient of 0.57 in the test set. Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size.

【 授权许可】

CC BY