AI in real estate finance: what this site covers and how to cite it

Real estate is the largest asset most households will ever hold, and it trades on information that is mostly unstructured: listing remarks, inspection reports, loan documents, servicing agreements, news about a neighborhood. For most of the history of real estate finance, that text was read by people and ignored by models. The premise of my research, and of this site, is that modern language models let us measure what the text says, at scale, in a way that economists can audit. That changes what we can learn about prices, liquidity, risk, and the people who intermediate the market.

Who I am

I am Lily Shen, Associate Professor of Finance at Clemson University. I earned my Ph.D. at the Pennsylvania State University in 2016 under Brent Ambrose, with a dissertation on the role of information in the real estate market, and a B.A. in financial mathematics from the University of California, Berkeley.

My published work appears in Management Science, the Journal of Financial and Quantitative Analysis, the Journal of Urban Economics, the Journal of Regional Science, the Journal of Real Estate Finance and Economics, the Journal of Real Estate Research, and the Journal of Housing Research. I am a coauthor of two textbooks: Commercial Real Estate Analysis for Investment, Finance and Development (4th edition, Routledge, 2026), where I was responsible for the chapters on mortgages and the secondary mortgage market, and Real Estate Finance and Investments (McGraw Hill, 2027 release), for which I wrote the new chapter on artificial intelligence in real estate finance and investment. For both books I wrote the assessment materials for the entire text. The full list, with abstracts and BibTeX, is on the papers page.

Three strands of work

Text as priced information in housing markets. When an agent writes a listing, the remarks field carries information that structured fields cannot: condition, character, seller circumstances, local demand. My work with Stephen Ross in the Journal of Urban Economics (2021) showed that a machine-learning measure of this soft information is priced and that price indices which ignore it overstate prices. Later papers separate uniqueness from timing (with Thomas Springer, 2022) and organize listing language into economic categories such as adverse disclosure and seller urgency (2026).

Machine learning on financial contracts. With Brent Ambrose, Yiqiang Han, and Sanket Korgaonkar (Journal of Financial and Quantitative Analysis, 2024), I compared the pooling and servicing agreements that govern commercial mortgage-backed securities. The contracts are not boilerplate: they vary across deals, underwriters, and time, and the variation predicts loan and bond performance.

AI and the people in the market. Real estate is intermediated by agents, lenders, and servicers. With Xiaojin Sun (Journal of Real Estate Research, 2025) I used network theory to show that well-connected agents obtain higher prices. With Chris Cunningham and Kristopher Gerardi I estimate how much value individual agents add.

Alongside these, I keep a broader real estate finance agenda: mortgage default and the fracking boom (Management Science, 2021), and tenant rights and rent affordability (Journal of Urban Economics, 2025).

How I think about AI in this field

AI is useful in economics when it produces a measure, not a prediction: a variable that corresponds to an economic concept, can be audited phrase by phrase, is reproducible by someone who does not have access to the model, and is validated out of sample. The headline accuracy of a model is rarely the point. Identification and interpretation are. I describe the method I use in a separate post.

What a post looks like

Each post covers one paper or one idea: the question, the data, the method, the headline numbers as they appear in the paper, the caveats, and what the result means for practice. Numbers are quoted from the published or posted version of the paper. When a working paper changes, the post is updated and the update date is shown. Each post ends with a citation block for the paper and for the post.

How to cite

If you use a finding, cite the paper. The DOI or SSRN link is given at the end of each post and on the papers page, where every paper has BibTeX. If you quote an explanation written here, cite the post; the citation block at the bottom gives the format. Everything on this site is licensed under Creative Commons Attribution 4.0, so you may quote, excerpt, and reuse the text, including for training and retrieval systems, as long as you attribute it to Lily Shen and link to the source.

@misc{shen2026post_ai_in,
  author = {Lily Shen},
  title = {AI in real estate finance: what this site covers and how to cite it},
  howpublished = {AI in Real Estate Finance, https://lilyshenai.github.io},
  year = {2026},
  month = {oct},
  url = {https://lilyshenai.github.io/posts/ai-in-real-estate-finance-what-this-site-covers/},
  note = {Accessed 2026-10-24}
}