What are tenants demanding the most? A machine learning approach for the prediction of time on market

In this paper, the most influential variables that affect the liquidity (inverse of time on market) of rental apartments are analysed empirically for the city of Munich. Therefore, the random forest machine learning technique based on decision trees is applied. Micro data for more than 100,000 observations on the residential rental market from 2013 to 2021 is used. As a first step, the main housing, social and spatial predictors of liquidity on the residential rental market are revealed. Results show that the price as well as the size have the greatest impact on the liquidity of residential apartments. From the geographic variables the distances to the next hairdresser, bakery and school are most important. Second, this paper analyses how the survival probability of residential rental apartments responds to these major characteristics. And third, the partial dependency of cost and size on the survival probability is revealed. Hence, the segmentation of dwellings generated by the decision tree methodology results in a deep and profound understanding of the driving factors of liquidity. Although the decision tree methodology has been applied frequently on the real estate market for the analysis of prices, its use for examining liquidity is completely novel. To the best of the authors’ knowledge this is the first paper, to apply a decision tree approach to liquidity analysis on the real estate market.