While critical geographical research on smart cities, automated urban governance and short-term holiday rentals (vis-à-vis Airbnb) is prolific, real estate more broadly defined has not been a major focus of the critical digital studies agenda. This is surprising, given that a digitally driven reconfiguration of the real estate industry and their practices is well underway. This paper therefore scopes out some of the key issues in digital real estate. We first consider ‘platforms and infrastructures’, from phone and tablet apps to global data analytics and mobile cloud computing. Second, we outline how ‘people and labour’ are implicated in digital real estate, including the role of private sales and rental agents; residential property managers; individual investors; and holiday and rental tenants. Third, we consider how ‘digital commodification’ and ‘digital capital circulation’ are changing the way people advertise, buy, sell, rent, manage, think about and secure loans for real estate in the digital era. Fourth, we examine ‘orders and borders’, and how rules and scales relate to digital real estate, including human-technology relations (e.g., cyborg fantasies); geographical scales (e.g., local/global); spatial metaphors (e.g., physical/virtual); institutional scales (e.g., small to large companies); and data categories (e.g., small/big, data as volume).

Market value of a house is dependent on many factors. The hedonic house price model is a well-established method for identification of those factors. However, with the development of new technologies, new methods of modelling have emerged. This paper employs deep learning algorithms for the purpose of house price modelling, specifically to examine the impact of ‘view’ as an aesthetic feature on house prices. As a new technique, deep learning algorithms which learn directly from observed data are useful for modelling house prices. This method is particularly appropriate for examining complex real estate markets as they do not follow rational behaviour and portray nonlinear relationships, which are possible to model using deep leaning algorithms. Modelling of house prices requires information about structural variables associated with houses. However, information about these characteristics are not always available. Hence the study extracts most of the structural and locational attributes for the model using spatial analysis methods. The geography covered in the study is part of Illawarra region (Australia) where sea and beach views are attractive.

Housing affordability has reached crisis levels in many of the world’s major cities. Conventional economic models assume the issue to be primarily tied to supply and demand, or 'what the market can bear'. Yet price rises seem to be wholly incommensurate with changes in income and employment. This has led scholars to question whether economic theory alone can explain unaffordability in global cities. This paper presents a number of alternative theories on affordability from a geographical perspective. Drawing upon various literatures including neoclassical economics, the financialisation of real estate, cultural economies, and value theory, it provides a series of potential explanations to 'the million dollar question', setting an agenda for future research that is integrative and unorthodox.