Pie charts

When to use a pie chart

Use a pie chart when:

• You want to show relative proportions (not exact comparisons)

• The total adds up to a meaningful whole (e.g. 100% of listings)

• The number of categories is limited and easy to understand

Avoid pie charts if you need to compare precise values—use a bar chart instead.

Configuration overview

A pie chart is configured using:

• Dimensions (what defines each slice)

• Metric (what is being measured)

Dimensions

The dimension defines how the data is grouped into slices.

Examples:

• Property type (apartment, house, villa)

• Buyer nationality

• Listing status (available, sold, reserved)

Each unique value becomes one slice of the pie.

Metric

The metric defines the value represented by each slice.

Examples:

• Number of properties

• Total transaction volume

• Average price (less common for pie charts)

Slice display options

You can control how values are displayed within each slice.

Show sum

Displays the total value of all slices combined in the centre of the pie.

Use this when you want to provide overall context, for example:

• Total number of listings: 120

• Total sales volume: €45M

Show value as slice value

Controls what is shown on each slice.

• Default: Percentage (e.g. 35%)

• Optional: Absolute value (e.g. 42 properties)

Best practice:

• Use percentages for high-level distribution insights

• Use absolute values when exact numbers matter to the reader

Group the rest in others

When there are many categories, the chart can become hard to read. This option simplifies it.

• If there are more than 10 slices, the 10th slice groups all remaining categories into “Others”

• This keeps the chart clean and readable

Example:

• Top 9 property types shown individually

• Remaining smaller categories grouped into “Others”

Footnote field

A footnote can be added below the chart to provide context or clarification.

Available formatting

• Bold text

• Links

What to include in a footnote

Use the footnote to:

• Explain the data source

• Clarify how values are calculated

• Add important context (e.g. time period)

Example:

• Data based on Engel & Völkers listings in 2025

• Includes only residential properties

Best practices

Do’s

• Use clear and meaningful dimensions (e.g. “Property type” instead of “Category”)

• Limit the number of slices for readability

• Use “Group the rest in others” when dealing with many small categories

• Add a footnote when context is not obvious

• Prefer percentages for quick insights

Don’ts

• Don’t use pie charts for more than 10–12 categories without grouping

• Don’t mix unrelated data in one chart

• Don’t rely on pie charts for precise comparisons

• Don’t omit context when the data could be misinterpreted

Example

Use case: Distribution of property types in listings

• Dimension: Property type

• Metric: Number of listings

• Slice display: Percentage (default)

• Grouping: Enabled, but this has no effect as there are only three property types.

• Footnote: “Based on active listings worldwide"

This setup gives a clear, high-level view of the market composition while keeping the chart easy to read.