Academic versus Industry Research: Reflections by Jacques Gordon (MIT & LaSalle)
In recently moving from a position as “Head of Global Research and Strategy” at LaSalle Investment Management to a faculty member at MIT’s Center for Real Estate, I am sometimes asked about the transition from industry to academic research.
There is a backstory to this question. At conferences where academics and industry folks mingle, there is plenty of banter whenever a university-based researcher moves “to the dark side”, as I did 35 years ago. The supposedly short attention spans of the consumers of industry research are contrasted with the slow and careful approach of “The Forces of Truth and Light” -- who must meet the higher standard of a peer-review to get published in academic journals.
In truth, the two types of research were never that far apart. At events hosted by the Real Estate Research Institute (US), Homer Hoyt (US), The Society of Property Researchers (UK), INREV (Europe), ANREV (Asia-Pacific), NCREIF (US), and PREA (US), academics and industry analysts learn from each other and create a richer environment for both types of research to thrive. Moreover, some of the best real estate conferences and symposia are hosted by universities, where practitioners and academics interact in settings that encourage an open discussion of trends and an exchange of ideas.
Throughout history, many of the most innovative technologies and the building blocks of financial economics were introduced at universities and quickly migrated to industrial applications. Today is no exception: Artificial Intelligence, Large Language Models, Machine Learning, and Quantum Computing all started in a university environment before being quickly adopted by industry. Likewise, many of the most challenging events that shook up real estate markets (the dot.com bubble, the Global Financial Crisis, Brexit, and Covid) have been studied rigorously by university-based experts in financial economics, political science, urban studies, virology, and immunology to help us all understand the underlying forces behind these shocks, and how long the shock waves last.
As I participate in the seminars sponsored by various ‘laboratories’ at MIT, I am struck by several fundamental facts:
- First, the scientific method takes time. Research must be replicable and data sets verified and shared in order for researchers to confirm the work of others. That said, the pace of the research is more rapid than I could have imagined. Multi-disciplinary teams are tackling the global challenges of climate change, carbon capture, electrification, heat stress, housing affordability, and sustainable building technologies. In economics, finance, and urban studies, the Social Science Research Network helps speed up the process of previewing work, while authors wait for publication of their research in the top journals.
- Second, academic teams are partnering up with corporations and industry associations to understand what can be learned from real-time data generated by building users and managers. Academics from different universities are invited to each other’s seminars to contribute and critique the latest studies. The connection between MIT and Maastricht University is especially strong with post-docs and faculty interacting regularly at places like the Sustainable Urbanization Lab at MIT or this MCRE-hosted real estate blog. The collaboration between the largest Dutch pension funds and MCRE to create the Global Real Estate Sustainability Benchmark (GRESB) 15 years ago is the most well-known example of industry-academic collaboration in the sustainability space. Yet, the academic-industry divide continues to narrow when corporate partners share data and ideas with the academics. The top laboratories at MIT and the MCRE invite corporate researchers to join and share their experiences and explore together what datasets could shed light on trenchant problems.
- My third and final point: Amazing, path-breaking research occurs right in the classroom. Today’s students have access to more research tools and computing power than any previous generation. They have data management skills that far exceed anything I anticipated. The results show up in class presentations, problem sets and group projects that are part of the curriculum of a Master’s degree in data science, finance, or real estate. I have been awed by the creative approaches to using new techniques for traditional data sources (e.g. building photos, satellite images, or attitudinal survey research) or new data sets found in obscure corners of the internet (e.g. Airbnb bookings or mobile-phone tracking data). Master’s students have a lot in common with teams of industry researchers--they are both using open-source statistical software and they have access to micro-data sets that were unheard of ten or twenty years ago in real estate. It’s not uncommon to see student projects that rival cutting-edge industry research or financial analysis. The most pleasant surprise of my return to academia is that when standing in front of a group of smart, hard-working masters’ students, the learning vibes go both ways! The students have as much to teach me, as I do them. In a very tangible way, we are all students of the asset class.