In simple terms data is a collection of facts or information that can be analysed to generate useful insights. Traditional research environments distinguish between secondary and primary data. Secondary data refers to existing data, often generated for an unrelated purpose, that can be leveraged to address the particular research question. For example, living standards surveys used principally to enable a better understanding of living conditions and poverty levels often include questions on dwelling type and tenure. These can be used to quantify the rental market in terms of the number of households who rent and to characterise these households and their living conditions.
Primary data refers to data that is purposefully gathered to answer the research question because no secondary data exists. For example, it is typically not easy to find existing data sources that explore small scale landlord access to finance. To understand this in more detail might require a specific survey or a series of interviews (this was done in our research of small scale landlords in Uganda).
Data, whether primary or secondary, can be segmented into quantitative and qualitative data. Quantitative data – data that quantifies or monitors a phenomenon – is structured, numerical data historically gathered by closed-ended questionnaires or surveys. Increasingly, quantitative data sources include so-called ‘big data’; a term used to refer to high volume, complex data that cannot be manipulated using traditional data processing software. This can include administrative data including credit bureau data or municipal data, data ‘exhaust’ generated as a by-product of digital interactions, as well as sensor data (often referred to as the ‘internet of things’* ) and imagery. Much of this data can enrich the broader understanding of the number and quality of dwelling units and how this changes over time. However, it is less useful for a direct analysis of rental markets, which are principally concerned with a social arrangement which is not directly visible.
In contrast, qualitative data, sometimes referred to as ‘thick data’, is gathered using unstructured or semi-structed techniques such as in-depth interviews or focus group discussions. It typically seeks to explore motivations, attitudes and perceptions – providing insight on the ‘why’, as opposed to the ‘what’ questions.
Various entities generate useful data. Researchers, academics and consultants may explore specific aspects of housing or rental markets in some detail, while statistical agencies or global think tanks may conduct nationally representative household surveys that contain relevant data on household composition, location and other demographics, as well as dwelling form and housing tenure. In addition, statistical agencies might track rental levels and monitor rental escalations as a component of consumer price indexes.
Private sector market participants also generate useful data. Property market agents sometimes publish reports on the state of residential property and rental markets. In addition, data provided by online property and rental portals offers new opportunities to obtain rich, current data at a property level. Less data is typically publicly available on operating models and returns achieved by various developers, landlords or other sector participants (such as estate agents, management companies, developers and financiers).
Together, various data sources and typologies combine to create the data universe which underpins an analysis of housing markets. While these various data sources are different in scope and nature, they can be combined and overlaid to develop a rich characterisation of the housing sector.
See CAHF’s recent work which sets out a methodology for understanding and quantifying residential rental markets in Africa. The Methodology Focus Note summarises the approach. Four further Focus Notes set out data for rental markets in Tanzania, Uganda, Côte d’Ivoire and Senegal.
* Internet of Things (IoT) is a term used to describe physical devices that have been embedded with electronics, sensors, or other software that allows these things to connect and exchange data. In other words, physical devices that generate and exchange data with other devices. An example includes a wrist watch that tracks your steps, heart rate and sleep patterns and exchanges this data with a mobile phone app, also referred to as wearable technology.