[2O4-OS-21a] 不動産とAI

[2O4-OS-21a-03] Predicting the vacancy duration of Japanese rental apartments

〇Patrik Andersson¹ (1. GA technologies Co., Ltd.)

キーワード：vacancy duration, hierarchical Bayes, survival analysis

One important task for a rental apartment management company is the assessment and setting of the rent of vacant apartments. Not only do the features of the apartment need to be taken into account, but also the duration of the vacancy. Setting a high rent may result in an unacceptably long vacancy while setting a low rent will negatively affect the cash flow. It therefore becomes necessary to jointly consider rent and vacancy duration.
We use both vacancy listings from public websites and proprietary data from one of Japan's largest management companies of one-room rental apartments to estimate the relationship between vacancy duration and rent. We use a combination of a LightGBM model and a Bayesian hierarchical survival data model to predict the vacancy duration using the characteristics and advertised rent of the apartment.
The results show that the vacancy duration can be predicted with good precision. The evaluation metrics are as good as can be expected from this type of data, and the predictions are well-calibrated. We also see that the vacancy duration varies depending on, for example, region and season.