Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methodsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Purpose The purpose of this paper is to contribute to the literature on seasoned equity offerings (SEOs) by examining the underpricing of European real estate corporations and identifying determinants explaining the phenomenon of setting the offer price at a discount at SEOs. Design/methodology/approach With a sample of 470 SEOs of European real estate investment trusts (REITs) and real estate operating companies (REOCs) from 2004 to 2018, multivariate regression models are applied to test for theories on the pricing of SEOs. This paper furthermore tests for differences in underpricing for REITs and REOCs as well as specialized and diversified property companies. Findings Significant underpricing of 3.06 percent is found, with REITs (1.90 percent) being statistically less underpriced than REOCs (5.08 percent). The findings support the market timing theory by showing that managers trying to time the equity market gain from lower underpricing. Furthermore, underwritten offerings are more underpriced to reduce the risk of the arranging bank, but top-tier underwriters are able to reduce offer price discounts by being more successful in attracting investors. The results cannot support the value uncertainty hypothesis, but they are in line with placement cost stories. In addition, specialized property companies are subject to lower underpricing. Practical implications An optimal issuance strategy taking into account timing, relative offer size and the choice of the underwriter can minimize the amount of “money left on the table” and therefore contribute to the lower cost of raising capital. Originality/value This is the first study to investigate SEO underpricing for European real estate corporations, pricing differences of REITs and REOCs in seasoned offerings and the effect of market timing on the pricing of SEOs.

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