Rethinking PropTech: Drawing insights about the real estate technology industry through technical experimentation

Over the past ten years, the real estate technology industry (also known as PropTech) has undergone a massive expansion. This rapid expansion, fueled by a >140x growth in venture capital funding over the past decade, has propelled PropTech startups to actively shape the ways in which we engage with the built environment; physically, digitally, financially, and otherwise.

This rapid change calls for rigorous inquiries into the nature and mechanics of PropTech. Prior work that attempts to

understand PropTech has focused on establishing broad definitions of the industry, occasionally scoping down to highlight a few issues at play at a technical level. While these approaches have been incredibly useful in defining an otherwise nebulous industry, they do not examine how the technical issues of PropTech relate to and inform the structures at play within industry, an important perspective to take on an industry that is fundamentally driven by technology.

This thesis draws insights about PropTech by taking a decidedly different approach; one which uses technical

experimentation, data analysis, and industry immersion as a basis of examining the broader industry from the inside. Specifically, this thesis discusses the connection between business models and data distribution algorithms, highlights the necessity to account for macroeconomics in data and algorithms, presents an approach to addressing data scarcity in real estate by parsing unstructured data, investigates Zillow’s automated valuation model, analyzes the geospatial specificity of its predictive accuracy,

discusses the methods of communicating this accuracy, probes the issue of geographic specificity of value, and reflects upon hands-on experience within industry to illustrate the vastly different considerations at play within different PropTech companies.

In taking this sociotechnical approach, new insights emerge and a different mode of thought arises - one which is

hierarchical, contextualizing technical issues within areas of industry and highlighting their relationship to the much broader phenomena of macroeconomics, regulation and politics, culture and society, and the physical environment.