On the Relationship between Market Sentiment and Commercial Real Estate Performance—A Textual Analysis Examination

Beracha, Eli; Lang, Marcel; Hausler, Jochen

doi:10.22300/0896-5803.41.4.605

Cited by 27 publications

(16 citation statements)

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“…Ruscheinsky et al. (2018) reveal a leading relationship of media expressed sentiment to future REIT market movements; while, Beracha et al. (2019) show that news-based sentiment has predictive power for the direct commercial real estate market in the US.…”

Section: Previous Literature and Hypothesis Developmentmentioning

confidence: 98%

“…By analyzing earnings conference calls for a REIT sample, Doran et al (2012) and Price et al (2017) show that sentiment impacts initial reaction-window abnormal returns. Ruscheinsky et al (2018) reveal a leading relationship of media expressed sentiment to future REIT market movements; while, Beracha et al (2019) show that newsbased sentiment has predictive power for the direct commercial real estate market in the US. Hausler et al (2018) confirms that news-based sentiment leads both the direct as well as the securitized real estate market in the US.…”

Section: Underpricing Of Us Reit Iposmentioning

confidence: 99%

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Can textual analysis solve the underpricing puzzle? A US REIT study

Paulus

Koelbl

Schaefers

2021

JPIF

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PurposeAlthough 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.Design/methodology/approachThis 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.FindingsContrary 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.Practical implicationsThe 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.Originality/valueThis is the first study which applies textual analysis to corporate disclosures of US REITs in order to explain IPO underpricing.

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Section: Previous Literature and Hypothesis Developmentmentioning

confidence: 98%

Section: Underpricing Of Us Reit Iposmentioning

confidence: 99%

Can textual analysis solve the underpricing puzzle? A US REIT study

Paulus

Koelbl

Schaefers

2021

JPIF

View full text Add to dashboard Cite

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“…Hausler et al (2018) examined the relationship between news-based sentiment, captured through a machine learning approach, and the US securitised and direct commercial real estate markets. Beracha et al (2019) captured news-based market sentiment for real estate and its extent through textual analysis. Taking Guangzhou as an example, Gao and Zhao (2018) used text mining method to extract media influence from media texts, and constructed the buyer confidence index, and used it as a proxy variable for home-buyers' sentiment.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

Wang

Liu

2022

International Journal of Strategic Property Management

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Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarters from 2016 to 2020, the spatial effect of market sentiment on housing price is empirically analyzed by calculating direct and indirect effects. The results show that market sentiment had a significant positive effect on housing prices in the local and neighboring cities over the research period. However, the impact of market sentiment on housing price was heterogeneous in terms of geographical region; the direct effect was stronger in the eastern region than in the central and western regions, and the indirect effect was significant only in the eastern region. These findings can provide references for government to formulate housing market regulation policies and measures based on market sentiment.

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“…More recently, new forms of sentiment measures have also emerged. The widely used online search engines provided by Google Trends (Dietzel et al , 2014) as well as textual analysis (Beracha et al , 2019) can be a rich source of information for constructing this type of sentiment measure. Be it rational or irrational, sentiment-driven behaviour can augment pricing, which does not necessarily reflect the fundamentals of, or alterations to, investment opportunities (Edelen et al ., 2010).…”

Section: A the Ardl Model And Bounds Testsmentioning

confidence: 99%

The effect of sentiment on commercial real estate returns: investor and occupier perspectives

Cheung

Lee

2021

JPIF

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PurposeReal estate is an asset that is traded in highly segmented, illiquid and informationally inefficient local markets. A short sale in real estate is almost infeasible and therefore impedes informed rational arbitrageurs to trade against mispricing. Thus, real estate returns are prone to sentiment-driven behaviours. Will the impacts on asset returns be identical for different types of sentiment?Design/methodology/approachThis study argues that not all sentiment effects are created equal. Using the bounds test of the autoregressive distributed lag (ARDL) models, this paper examines how occupier sentiment versus investor sentiment contributes to the short-run and long-run dynamics of commercial real estate returns in Australia.FindingsThe empirical evidence suggests that investor sentiment and occupier sentiment influence return asymmetrically after macroeconomic conditions are controlled for.Practical implicationsThe sectoral analysis further reveals that sector-specific sentiment plays a significant role in explaining commercial real estate returns. Furthermore, notable improvement is found in producing more accurate prediction in returns, given that measures of occupier and investor sentiment are appropriately specified in the forecast.Originality/valueThis study is novel in the sense that it acknowledges the impacts of occupiers' and investors' sentiment may be fundamentally different. The unique innovation and contribution of this study to behavioural finance literature are based on a new dataset from the Royal Institute of Chartered Surveyors which includes a survey-based measure of investor sentiment and occupier sentiment.

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On the Relationship between Market Sentiment and Commercial Real Estate Performance—A Textual Analysis Examination

Cited by 27 publications

References 0 publications

Can textual analysis solve the underpricing puzzle? A US REIT study

Can textual analysis solve the underpricing puzzle? A US REIT study

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

The effect of sentiment on commercial real estate returns: investor and occupier perspectives

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