Estimating Warehouse Rental Price using Machine Learning Techniques
Keywords:sharing warehousing, price estimation, machine learning
AbstractBoosted 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.
Antipov, E. A.; Elena, B. P. (2012); Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics, Expert Systems with Applications, 39(2), 1772-1778, 2012. https://doi.org/10.1016/j.eswa.2011.08.077
Breiman, L. (1996); Bagging predictors, Machine Learning, 24(2), 123-140, 1996. https://doi.org/10.1007/BF00058655
Breiman, L. (2001); Random forests, Machine Learning, 45(1), 5-32, 2001. https://doi.org/10.1023/A:1010933404324
Chen, X.; Dong, Z. Y.; Meng, K.; Xu, Y.; Wong, K. P.; Ngan, H. W. (2012); Electricity price forecasting with extreme learning machine and bootstrapping, IEEE Transactions on Power Systems, 27(4), 2055-2062, 2012. https://doi.org/10.1109/TPWRS.2012.2190627
Elith, J.; Leathwick, J. R.; Hastie T. (2008); A working guide to boosted regression trees, Journal of Animal Ecology, 77(4), 802-813, 2008. https://doi.org/10.1111/j.1365-2656.2008.01390.x
Ghani, R. (2005); Price prediction and insurance for online auctions, Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 411-418, 2005. https://doi.org/10.1145/1081870.1081918
Gutt, D.; Herrmann, P. (2015); Sharing means caring? Hosts' price reaction to rating visibility ECIS, 2015.
Hastie, T.; Tibshirani, R.; Friedman, J. (2001); The Elements of Statistical Learning. Springer, 2001. https://doi.org/10.1007/978-0-387-21606-5
Kim, K. J. (2003); Financial time series forecasting using support vector machines, Neurocomputing 55(1), 307-319, 2003. https://doi.org/10.1016/S0925-2312(03)00372-2
Kusan, H.; Osman A.; Ilker O. (2010); The use of fuzzy logic in predicting house selling price, Expert systems with Applications, 37(3), 1808-1813, 2010. https://doi.org/10.1016/j.eswa.2009.07.031
Li, J.; Moreno, A.; Zhang, D. J. (2015); Agent behavior in the sharing economy: Evidence from Airbnb, Ross School of Business Working Paper Series, 1298, 2015.
Li, Y.; Wang, S.; Yang, Pan, Q.; Tang, J. (2017); Price recommendation on vacation rental websites, Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 399-407, 2017.
Limsombunchai, V. (2004); House price prediction: hedonic price model vs. artificial neural network, New Zealand Agricultural and Resource Economics Society Conference, 25-26, 2004.
Liu, J. G; Zhang, X. L.; Wu, W. P. (2006); Application of fuzzy neural network for real estate prediction, International Symposium on Neural Networks, 1187-1191, 2006.
Ma, Y. X.; Zhang, Z. J.; Pan, B. X. (2017); Reveal status quo of Beijing warehouse in open market, Logistics, Informatics and Service Sciences (LISS), 2017 International Conference on. IEEE, 2011-2017, 2017.
Maric, M.; Gracanin, D.; Zogovic, N.; Ruskic, N.; Ivanovic, B. (2017); Parking search optimization in urban area, International Journal of Simulation Modelling,16(2), 2017.
Patel, J.; Shah, S.; Thakkar, P.; Kotecha, K. (2015); Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques, Expert Systems with Applications, 42(1), 259-268, 2015. https://doi.org/10.1016/j.eswa.2014.07.040
Raykhel, I.; Ventura, D. (2008); Real-time automatic price prediction for eBay online trading, IAAI, 2009.
Rehar, T.; Ogrizek, B.; Leber, M.; Pisnik, A.; Buchmeister, B. (2017); Product lifecycle forecasting using system's indicators, International Journal of Simulation Modelling,16(1), 2017.
Segal, M. R. (2004); Machine learning benchmarks and random forest regression, Center for Bioinformatics Molecular Biostatistics, 2004.
Scrapy Community. (2015); Scrapy: A Fast and Powerful Scraping and Web Crawling Framework, http:// scrapy.org/doc/.
Wang, D.; Nicolau, J. L. (2017); Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb. com, International Journal of Hospitality Management, 62, 120-131, 2017. https://doi.org/10.1016/j.ijhm.2016.12.007
Yamin, H. Y.; Shahidehpour, S. M.; Li, Z. Y. (2004); Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets, International journal of electrical power & energy systems, 26(8), 571-581, 2004. https://doi.org/10.1016/j.ijepes.2004.04.005
Zhang, D. (2017); High-speed Train Control System Big Data Analysis Based on Fuzzy RDF Model and Uncertain Reasoning, International Journal of Computers, Communications & Control, 12(4), 2017. https://doi.org/10.15837/ijccc.2017.4.2914
Zhang, D.; Sui, J.; Gong, Y. (2017); Large scale software test data generation based on collective constraint and weighted combination method, Technical Gazette, 24(4), 1041- 1049, 2017.
[Online]. Available: www.expandedramblings.com/index.php/airbnb-statistics/, Accesed on 10 September 2017.
[Online]. Available: www.expandedramblings.com/index.php/uber-statistics/, Accesed on 10 September 2017.
[Online]. Available: www.dhl.com/, Accesed on 10 September 2017.
[Online]. Available: www.flexe.com/, Accesed on 10 September 2017.
[Online]. Available: www.flexe.com/Flexe-capacity-eco/, Accesed on 10 September 2017.
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