Estimating Warehouse Rental Price using Machine Learning Techniques

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.

[1]  Mark R. Segal,et al.  Machine Learning Benchmarks and Random Forest Regression , 2004 .

[2]  Zuyi Li,et al.  Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets , 2004 .

[3]  Evgeny A. Antipov,et al.  Mass Appraisal of Residential Apartments: An Application of Random Forest for Valuation and a CART-Based Approach for Model Diagnostics , 2010, Expert Syst. Appl..

[4]  Quan Pan,et al.  Price Recommendation on Vacation Rental Websites , 2017, SDM.

[5]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[6]  Philipp Herrmann,et al.  Sharing Means Caring? Hosts' Price Reaction to Rating Visibility , 2015, ECIS.

[7]  V. Limsombunchai.  House Price Prediction: Hedonic Price Model vs. Artificial Neural Network , 2004 .

[8]  Dan Ventura,et al.  Real-time Automatic Price Prediction for eBay Online Trading , 2009, IAAI.

[9]  D. Zhang,et al.  High-speed Train Control System Big Data Analysis Based on the Fuzzy RDF model and Uncertain Reasoning , 2017, Int. J. Comput. Commun. Control.

[10]  Rayid Ghani,et al.  Price prediction and insurance for online auctions , 2005, KDD '05.

[11]  Osman Aytekin,et al.  The use of fuzzy logic in predicting house selling price , 2010, Expert Syst. Appl..

[12]  Jun Li,et al.  Agent Behavior in the Sharing Economy : Evidence from Airbnb , 2015 .

[13]  Sahil Shah,et al.  Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..

[14]  D. Gracanin,et al.  Parking Search Optimization in Urban Area , 2017 .

[15]  J. Nicolau,et al.  Price determinants of sharing economy based accommodation rental : a study of listings from 33 cities on Airbnb.com , 2017 .

[16]  Kit Po Wong,et al.  Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping , 2012, IEEE Transactions on Power Systems.

[17]  Yunzhan Gong,et al.  LARGE SCALE SOFTWARE TEST DATA GENERATION BASED ON COLLECTIVE CONSTRAINT AND WEIGHTED COMBINATION METHOD , 2017 .

[18]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[19]  Jian-Guo Liu,et al.  Application of Fuzzy Neural Network for Real Estate Prediction , 2006, ISNN.

[20]  B. Ogrizek,et al.  Product Lifecycle Forecasting Using System's Indicators , 2017 .

[21]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .