Commercially Minded | The hidden language of charts

Imagine you are reviewing a fund prospectus. There is a chart showing five years of returns, and it looks impressive. A steep upward curve, the kind that commands attention. Now consider this: by adjusting a few parameters, that same data could look completely flat. 

The commercial real estate sector generates an enormous volume of data - yields, vacancy rates, capital flows - and, by and large, is good at producing and analysing it. What receives far less scrutiny is how that data is then visualised, and what that means for the conclusions people reach.

How data visualisation determines what property professionals understand

For most people, a chart can be the only point of contact with the underlying information, for example when reading a report or sitting in a pitch meeting. This means having limited access to the raw data, and forming a view from one or two visuals. If that visual is not doing its job, the insight does not land, regardless of the quality of the analysis behind it.

A chart is never neutral

Every visual is a set of decisions: what has been included, how it has been structured, what has been emphasised. Even a chart that looks default or objective is still guiding the reader, those choices are simply unexamined rather than deliberate. As Alan Smith, Head of Visual and Data Journalism at the Financial Times, has argued, there is no such thing as a neutral chart.

The spectrum from mistake to manipulation

Not all misleading charts are the product of bad intent. Common design failures include over-labelling, competing colours that obscure the main pattern, and the use of a map or pie chart where a simpler form such as a bar chart would communicate more clearly.

There are also then instances of deliberate manipulation. For example, adjusting axis scaling can make a modest change look dramatic, and dual-axis charts placing two data series on the same visual can imply a relationship that may not exist. This is not inherently dishonest - the same approach can legitimately surface a subtle but important trend. In different hands, however, it becomes a way to steer a reader towards a conclusion the data does not support.

Timeframe selection is equally important, and particularly live in the current geopolitical climate. A key example is the impact of the COVID-19 pandemic; a chart starting in 2019 can tell a very different story from one starting in 2021.

What readers should look for

Rather than asking what a chart shows, ask how it is guiding what you take away. Where does the scale start? What has been excluded? What is the timeframe, and why does it begin where it does? Those choices often reveal as much about the intended message as the data itself. A well-designed chart makes the pattern obvious without requiring the reader to work for it.  

The responsibility of those who produce and consume real estate data visualisations

For those producing charts, attempting neutrality by doing less is not the same as being unbiased. A chart stripped of deliberate design choices is not objective, it is shaped by defaults rather than decisions, and those defaults still influence what the reader takes away. A chart that fails to make the pattern clear has not communicated the insight, however strong the underlying analysis might be.

For those reading them, the questions worth asking are less about the data itself and more about the decisions surrounding it. Go beyond asking what the chart shows - ask how it is guiding what you take away. Once you start reading charts through that lens, the hidden language behind them becomes a good deal harder to ignore.

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