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Although many theories aim to explain initial public offering (IPO) underpricing, initial-day returns of US Real Estate Investment Trust (REIT) IPOs remain a “puzzle”. The literature on REIT IPOs has focused on indirect quantitative proxies for information asymmetries between REITs and investors to determine IPO underpricing. This study, however, proposes textual analysis to exploit the qualitative information, revealed through one of the most important documents during the IPO process – Form S-11 – as a direct measure of information asymmetries.

This study determines the level of uncertain language in the prospectus, as well as its similarity to recently filed registration statements, to assess whether textual features can solve the underpricing puzzle. It assumes that uncertain language makes it more difficult for potential investors to price the issue and thus increases underpricing. Furthermore, it is hypothesized that a higher similarity to previous filings indicates that the prospectus provides little useful information and thus does not resolve existing information asymmetries, leading to increased underpricing.

Contrary to expectations, this research does not find a statistically significant association between uncertain language in Form S-11 and initial-day returns. This result is interpreted as suggesting that uncertain language in the prospectus does not reflect the issuer's expectations about the company's future prospects, but rather is necessary because of forecasting difficulties and litigation risk. Analyzing disclosure similarity instead, this study finds a statistically and economically significant impact of qualitative information on initial-day returns. Thus, REIT managers may reduce underpricing by voluntarily providing more information to potential investors in Form S-11.

The results demonstrate that textual analysis can in fact help to explain underpricing of US REIT IPOs, as qualitative information in Forms S-11 decreases information asymmetries between US REIT managers and investors, thus reducing underpricing. Consequently, REIT managers are incentivized to provide as much information as possible to reduce underpricing, while investors could use textual analysis to identify offerings that promise the highest returns.

This is the first study which applies textual analysis to corporate disclosures of US REITs in order to explain IPO underpricing.

This study examines whether language disclosed in the Management Discussion and Analysis (MD&A) of US Real Estate Investment Trusts (REITs) provides signals regarding future firm performance and thus generates a market response.

This research conducts textual analysis on a sample of approximately 6,500 MD&As of US REITs filed by the SEC between 2003 and 2018. Specifically, the Loughran and Mcdonald (2011) financial dictionary, and a custom dictionary for the real estate industry created by Ruscheinsky et al. (2018), are employed to determine the inherent sentiment, that is, the level of pessimistic or optimistic language for each filing. Thereafter, a panel fixed-effects regression enables investigating the relationship between sentiment and future firm performance, as well as the markets’ reaction.

The empirical results suggest that higher levels of pessimistic (optimistic) language in the MD&A predict lower (higher) future firm performance. Hereby, the use of a domain-specific real estate dictionary, namely that developed by Ruscheinsky et al. (2018) leads to superior results. Corresponding to the notion that the human psyche is affected more strongly by negative than positive news (Rozin and Royzman, 2001), the market responds solely to pessimistic language in the MD&A.

The results suggest that the market can benefit from textual analysis, as investigating the language in the MD&A reduces information asymmetries between US REIT managers and investors.

This is the first study to analyze exclusively US REITs, whether language in the MD&A is predictive of future firm performance and whether the market responds to textual sentiment.