Data Typologies in Urban Housing Research: A Systematic Review of the Literature

Abstract

The increasing digitalisation of housing markets has expanded the types and sources of data available for research. However, there is limited understanding of how these diverse data types are used across different themes in urban housing studies and which analytical approaches are applied. This study addresses these questions through a systematic review of 71 peer-reviewed studies published between 2010 and 2021, following PRISMA guidelines. The review identifies five dominant research themes: housing market analysis, rental market analysis, housing policy evaluation, housing affordability, and housing inequality. It also classifies five main data sources: official statistics, non-official statistics, surveys and qualitative data, big data, and social media. A cross-examination of themes and data types shows that official statistics remain the most frequently used across the themes, while emerging data sources such as big data and social media are underutilised—especially in research on informal housing and demand-side dynamics. Regression analysis and hedonic modelling are the most commonly applied analytical methods, with the choice of method largely shaped by research objectives and data types. By developing a cross-typology framework linking research themes, data sources, and methods, this study provides an evidence base for inclusive, responsive, and data-informed strategies that support socially and economically sustainable urban housing systems.