Real estate finance · Housing and mortgage markets · AI as measurement
Associate Professor of Finance, Clemson University
Associate Professor of Finance at Clemson University. I study housing and mortgage markets. Much of my recent work uses machine learning and language models to read the text that markets produce, property listings and securitization contracts, and measure what it tells us about prices and risk.
Real estate has been my field since 2009, and I still like it for the same reason: it is where finance meets actual places and the people who live and work in them.
For media inquiries, collaboration, and consulting, reach me at yannan at berkeley dot edu. I can comment on housing and mortgage markets, commercial real estate, and AI in finance.
Read my research CV (PDF) Google Scholar SSRN LinkedIn CREBook.net (Commercial Real Estate Analysis book site) Podcast
Nine peer-reviewed articles, including Management Science, the Journal of Financial and Quantitative Analysis, and two in the Journal of Urban Economics. Coauthor of two standard real estate textbooks, Commercial Real Estate Analysis (Routledge, 2026) and Real Estate Finance and Investments (McGraw Hill, 2027), and author of the new AI chapter in the latter. Started Clemson's first hands-on AI courses in finance and real estate in 2018.
Lily Shen, Stephen L. Ross
This paper employs machine learning to quantify the value of "soft" information contained in real estate property descriptions. Textual descriptions contain information that traditional hedonic attributes cannot captu...
Brent W. Ambrose, Yiqiang Han, Sanket Korgaonkar, Lily Shen
We introduce a novel application of machine learning to compare Pooling and Servicing Agreements (PSAs) that govern commercial mortgage-backed securities (CMBS). In contrast to the view that the PSA is largely boilerp...
Chris Cunningham, Kristopher Gerardi, Lily Shen
Our results show that homeowners exposed to fracking were significantly less likely to default on their loans. Instrumenting for fracking increases the magnitude of the effect appreciably, consistent with underlying e...
N. Edward Coulson, Thao Le, Victor Ortego-Marti, Lily Shen
We use state-level differences in the legal relationship between landlords and tenants to estimate the impact of these differences on housing markets. We construct a search-theoretic model of landlord and tenant searc...
Lily Shen
Using 63,067 Atlanta MLS transactions from 2007 to 2017, we examine what information listing remarks reveal and where it appears in housing transaction outcomes. An LLM scans the corpus and organizes broad listing lan...
Lily Shen, Sean Wilkoff
We use Airbnb data from Austin, Texas to empirically investigate the impact of the onset of COVID-19 on the short-term rental market. Specifically, we employ a machine learning algorithm to create an extensive cleanli...
David M. Geltner, Norman G. Miller, Alex van de Minne, Piet Eichholtz, Thies Lindenthal, Lily Shen
The standard advanced text in commercial real estate. Lily Shen is a coauthor of the fourth edition, responsible for the chapters on mortgages and the secondary mortgage market, and wrote the end-of-chapter questions, exercises, and assessment materials for the entire book.
Jeffrey D. Fisher, Jacob Sagi, Stephanie R. Yates, Lily Shen
The widely adopted foundational text in real estate finance. For the 2027 edition Lily Shen wrote the new chapter, Artificial Intelligence in Real Estate Finance and Investment, and the assessment materials for the entire book.
Hosted by Lily Shen
The companion podcast to Commercial Real Estate Analysis for Investment, Finance and Development (4th edition, Routledge, 2026). Each episode introduces one chapter of the book in plain language: what the chapter covers, why it...