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UNCORKED

Words are as revealing in property as elsewhere

by | Apr 28, 2021

Golden Oldie

Words are as revealing in property as elsewhere

by | Apr 28, 2021

This article was originally published in May 2020.

The words we choose are revealing. In an amusement park, small electrically powered cars with rubber fenders all round are driven in an enclosure. Some people call these cars “dodgems”, others call them “bumper cars”. Based on their choice of words, which group do you think will drive more recklessly?

According to the BBC, the Vikings “attacked Britain’s holy places, slaughtered the monks who lived there and carried away countless treasures”. In a Scandinavian museum, the narrative is more likely to be that the Vikings “established trading posts in Britain”. 

On 1 March 1815 Napoleon returned to France from exile on Elba. It took him three weeks to march to Paris from his landing point near Cannes. As Napoleon moved slowly north gathering supporters as he went, the French newspaper headlines evolved as follows:

  1. “The Corsican ogre has landed” 
  2. “The monster slept at Grenoble”
  3. “The usurper is directing his steps towards Dijon”
  4. “Bonaparte is only sixty leagues from the capital”
  5. “His Imperial and Royal Majesty arrived yesterday”

Choose your words carefully

Choice of words may be revealing in a business context too. For example, the words can reveal underlying concern about prospects. Take the text of the chairman’s statement in annual reports. Typically, it summarises events of the past financial year and concludes with a brief discussion of the firm’s prospects. It is a discretionary narrative and it tends to address issues of a strategic nature. 

The chairman’s letter has been researched by academics since the 1980s. For example, it has been used to identify future financial performance or bankruptcy risk. 

One study investigated managerial overconfidence. It analysed the Chairman’s statement in annual reports of 192 public listed UK firms over a decade. It found that two measures of overconfidence could be derived from those statements by applying natural language processing (NLP). NLP is a branch of artificial intelligence that helps computers understand and manipulate human language. NLP software can “slice and dice” words and phrases in different ways to extract value from a document. 

Watch your tone

One way to use NLP is to identify the tone or sentiment in the statement. Compare these phrases:

  • “[The company] is not immune to the widely reported challenges to physical retail in the UK” 
  • “A broader range of businesses than ever before are taking space at our campuses, at attractive rental levels”

The first phrase comes from the retailer JD Sports; its chairman’s statement has a slightly negative overall tone. The second one comes from the landlord British Land; its statement has a positive tone. 

There is no ‘I’ in team

NLP can identify overconfidence another way. It can examine usage of the first-person pronoun ‘I’. More frequent use of ‘I’ indicates the speaker’s intention to attribute good performance or news to themselves. Less frequent use of ‘I’ is a way to distance the speaker from bad performance or news. Searching for an upbeat tone and heavy use of ‘I’ are just two examples of text analysis that can be applied in a business context. 

Words count

In Didobi, we believe that unstructured data (that is, any data not arranged in rows and columns) is the property researcher’s friend. Unstructured data can support decision-making when used properly alongside structured data. With this in mind, we have built a dataset of over 300 chairman’s statements issued by UK REITs and listed property companies. Using NLP, we can interrogate the statements individually, in groups or by theme. The project is in its infancy but already some interesting patterns have emerged. 

About Stephen Ryan

About Stephen Ryan

Stephen Ryan is a research associate at Didobi (didobi.com), specialist advisers to the real estate industry. Stephen has worked in the real estate and wider financial services industry since 1988. Most recently he worked with INREV in Amsterdam, concentrating on real estate research, corporate governance and liquidity. Prior to INREV, Stephen was an investment consultant in Mercer where he advised institutional investors on real estate and on defined contribution investments.

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