Estimating Warehouse Rental Price using Machine Learning Techniques

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.

Real estate rental market: a 10-year bibliometric-based review

“…Canas et al (2015) developed a multi-criteria decision support system to calculate the residential rents. Ma et al (2018) used the machine learning techniques to estimate the rental price of warehouses and found that the warehouses' location and land price have a pivotal impact on its rent. Benefield et al (2019) estimated a simultaneous systems model by using virtual tours as a proxy to observe the agent effort.…”

Benchmarking logistics facilities: a rating model to assess building quality and functionality

“…On a similar scope, Ma et al (2018) applied machine learning algorithms (linear regression, regression tree, random forest regression and gradient boosting regression trees) to estimate warehouse rental prices in China, within the Beijing area. The data collected mostly referred to information about location (e.g.…”

“…This lack of a comprehensive model that can provide a structured and holistic assessment of logistics facilities is significant, since the value of a building is not merely linked to financial metrics or its individual features (e.g. size or the clear building height), and it must take into consideration multiple elements simultaneously, together with its operating requirements (Ma et al, 2018).…”

Multi-Lane Short-Term Traffic Forecasting With Convolutional LSTM Network

“…Machine learning algorithms build empirical functions by learning from data. Provided with large amount of training data, these models usually show advanced prediction capacity compared to classical statistical models [15], [16]. Many machine learning techniques have been employed for short-term traffic prediction, such as support vector regression (SVR) [17], [18], k-nearest neighbor algorithm (KNN) [19], fuzzy logic [20], [21], and artificial neural network (ANN) models [14], [22], [23].…”