Estimating Warehouse Rental Price using Machine Learning Techniques
| Autor: | Alexander T. Ihler, Baoxiang Pan, Zhenji Zhang, Yixuan Ma |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
021103 operations research
Computer Networks and Communications Computer science business.industry 05 social sciences 0211 other engineering and technologies Linear model Real estate 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Warehouse Supply and demand Renting Computational Theory and Mathematics Beijing Open market operation 0502 economics and business Gradient boosting Artificial intelligence business computer 050203 business & management |
| Zdroj: | International Journal of Computers Communications & Control. 13:235-250 |
| ISSN: | 1841-9836 |
| DOI: | 10.15837/ijccc.2018.2.3034 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|