In apartment revenue management, rental rates for new and renewal leases are often optimized around a reference rent, which is defined as the “economic value” of an apartment unit. In practice, reference rents are usually estimated using some rules-based approaches. These rules are mostly intuitive to understand and easy to implement, but they suffer from the problems of being subjective, static, and lacking self-learning capability. In this study, we propose a reinforcement learning (RL) approach to estimating reference rents. Our intent is to find the optimal reference rent estimates via maximizing the average of RevPAUs over an infinite time horizon, where RevPAU (Revenue per Available Unit) is one of leading indicators that many apartments adapt. The proposed RL model is trained and tested against real-world datasets of reference rents that are estimated with the use of one rules-based approach by two leading apartment management companies. Empirical results show that this RL-based approach outperforms the rules-based approach with a 19% increase in RevPAU on average.

在公寓收入管理中，新租赁和续租的租金通常围绕参考租金进行优化，参考租金被定义为公寓单元的“经济价值”。实际上，参考租金通常使用一些基于规则的方法进行估算。这些规则大多数是直观易懂且易于实现的，但是它们存在主观，静态和缺乏自学能力的问题。在这项研究中，我们提出了一种强化学习（RL）的方法来估计参考租金。我们的目的是通过在无限时间范围内最大化RevPAU的平均值来找到最佳的参考租金估算值，其中RevPAU（每个可用单位的收入）是许多公寓适应的主要指标之一。拟议的RL模型针对参考租金的真实数据集进行了训练和测试，参考数据是由两家领先的公寓管理公司使用一种基于规则的方法进行估算的。实证结果表明，这种基于RL的方法优于基于规则的方法，RevPAU平均增加了19％。