Estimating Warehouse Rental Price using Machine Learning Techniques

Yixuan Ma · Zhenji Zhang · Alexander Ihler · Baoxiang Pan

55 citations0/0 files storedInternational Journal of Computers Communications & Control

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Housing Market and Economics Forecasting Techniques and Applications Data Mining and Machine Learning Applications

Abstract

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.

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References

2003Financial time series forecasting using support vector machines 2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques 2017Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com 2011Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics 2004House Price Prediction: Hedonic Price Model vs. Artificial Neural Network 2009The use of fuzzy logic in predicting house selling price 2006Application of Fuzzy Neural Network for Real Estate Prediction 2005Price prediction and insurance for online auctions

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