Purpose of Bias Explanation
This article explains the structural biases that arise in residential listing datasets used for Lagos-related content. Its purpose is to clarify how data structure, sourcing, and publication mechanics shape what becomes visible, without attempting to correct, quantify, or model those biases.
Platform-Centered Bias
Residential listing datasets are inherently platform-centered. They reflect the practices, reach, and participation levels of selected listing platforms rather than the full residential environment of Lagos. Properties that are more likely to be advertised through formal platforms are structurally more visible than those that are not.
Contributor and Submission Bias
Listings depend on voluntary submission by contributors such as agents or property representatives. This introduces bias based on contributor incentives, professional practices, and familiarity with platforms. Residential assets that are marketed informally or through non-digital channels are structurally excluded.
Categorization and Labeling Bias
Platform-defined categories, location labels, and property attributes shape how residential assets are represented. Misalignment between lived geography and platform taxonomies can result in overrepresentation of certain labels and underrepresentation of others, without indicating actual residential distribution.
Visibility and Rotation Effects
Listing visibility is affected by rotation, duplication, removal, and re-publication practices. These mechanisms can amplify or suppress apparent presence within datasets, creating patterns that reflect listing lifecycle behavior rather than residential conditions.
Interpretive Boundaries
These structural biases do not permit adjustment-based interpretation or inference. The dataset supports descriptive reference to visible listings only and cannot be treated as balanced, neutral, or representative of Lagos residential realities.