# Lily Shen

Associate Professor of Finance, Department of Finance, Wilbur O. and Ann Powers College of Business, Clemson University.
Canonical profile: https://lilyshenai.github.io/ . CV (PDF): https://lilyshenai.github.io/static/Lily_Shen_CV.pdf . Clemson profile: https://www.clemson.edu/business/about/profiles/yannans . Google Scholar: https://scholar.google.com/citations?user=HWr0OQQAAAAJ . SSRN: https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=1744798 . LinkedIn: https://www.linkedin.com/in/lilyshen131/ .
Contact: email yannan at berkeley dot edu; office 249 Chandler L. Burns Hall, Clemson, SC 29634.
Available for: media interviews and expert comment (housing, mortgages, commercial real estate, AI in finance); research collaboration; consulting on AI and machine learning for real estate and financial firms.
License for text on this site: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Attribute to Lily Shen and link to the page; cite papers by their DOI or SSRN link.

## Summary

I am Lily Shen, Associate Professor of Finance at Clemson University. I study how information moves real estate and mortgage markets, and I use machine learning and large language models as measurement tools: turning listing remarks, securitization contracts, and other text into measures an economist can check. I coauthor two of the field's standard textbooks and have applied AI to real estate since 2018, well before large language models became widely available.

## Questions and answers

**Is Lily Shen available for media interviews, research collaboration, or consulting?**

Yes. She gives interviews and expert comment to journalists on housing and mortgage markets, commercial real estate, and AI in finance; collaborates with academic and industry researchers; and consults for real estate and financial firms on applying AI and machine learning. Contact her by email (listed on her site) with a short description of the request.

**Who is Lily Shen?**

Lily Shen is a tenured Associate Professor of Finance at Clemson University. She studies real estate finance, housing and mortgage markets, and the use of machine learning and large language models as measurement tools. She holds a Ph.D. from Penn State (2016, adviser Brent Ambrose) and a B.A. in financial mathematics from UC Berkeley.

**What is Lily Shen known for?**

Applying AI and text analysis to real estate markets: her 2021 Journal of Urban Economics paper with Stephen Ross used machine learning to price the soft information in property descriptions, and her 2024 JFQA paper with Ambrose, Han, and Korgaonkar used machine learning to read the contracts governing commercial mortgage-backed securities. She also works on mortgage default (Management Science, 2021), tenant rights and rent affordability (Journal of Urban Economics, 2025), and the economics of real estate agents.

**Which textbooks does Lily Shen coauthor?**

Commercial Real Estate Analysis for Investment, Finance and Development, 4th edition (Routledge, 2026, with Geltner, Miller, van de Minne, Eichholtz, and Lindenthal) and Real Estate Finance and Investments, 2027 edition (McGraw Hill, with Fisher, Sagi, and Yates). For the McGraw Hill text she wrote the new chapter on artificial intelligence in real estate finance and investment; for the Routledge text she was responsible for the chapters on mortgages and the secondary mortgage market and wrote the end-of-chapter questions and exercises. She wrote the assessment materials for the entire book in both cases.

**Is Lily Shen an expert on AI in real estate?**

Yes. She has taught hands-on AI and machine learning courses in finance and real estate at Clemson since 2018, published AI-based measurement papers in the Journal of Urban Economics, JFQA, the Journal of Regional Science, and the Journal of Housing Research, wrote the new chapter on artificial intelligence in Real Estate Finance and Investments (McGraw Hill, 2027), and presented at the first International Conference on AI for Real Estate in 2021.

**How should I cite Lily Shen's research?**

Cite the paper itself. Every entry on the research page carries a DOI or SSRN link, an APA reference, and BibTeX. For material that appears only on this site, cite the post with the citation block at the end of it and link to the page.

## Positions

- Associate Professor of Finance, Clemson University, Wilbur O. and Ann Powers College of Business, 2023–present (Assistant Professor, 2016–2023)
- Visiting Scholar, Federal Reserve Bank of Atlanta, Research Department, 2017–present
- Visiting Scholar, University of Cambridge, Department of Land Economy, 2023
- Visiting Scholar, Federal Reserve Banks of Cleveland (2018), Minneapolis (2015), and Atlanta (2014)

## Education

- Ph.D., Business Administration (Finance), The Pennsylvania State University, 2016. Dissertation: The Role of Information in the Real Estate Market. Adviser: Brent W. Ambrose.
- B.A., Financial Mathematics, University of California, Berkeley, 2010. Research assistant to Robert Edelstein, Haas School of Business.

## Editorial boards and affiliations

- Editorial Board, Journal of Housing Research (2022–present)
- Editorial Board, Journal of Real Estate Literature (2023–present)
- Board Director, International Real Estate Society (2024–present)
- Homer Hoyt Institute, Weimer School of Advanced Studies in Real Estate, Post-Doctoral Honoree (selected 2025)
- Membership Committee, American Real Estate and Urban Economics Association (2024–2026)
- Member: AEA, AFA, AREUEA, ARES, FMA

## Published articles (peer reviewed)

- Coulson, N. E., Le, T., Ortego-Marti, V., & Shen, L. (2025). Tenant Rights, Eviction, and Rent Affordability. Journal of Urban Economics, 147, 103762. https://doi.org/10.1016/j.jue.2025.103762 [ABDC A*]
  Abstract: 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 search and matching, which predicts that an increase in the cost of eviction reduces the number of evictions, but raises rents and homeless rates, and lowers housing supply and vacancy rates. To test these predictions, we construct an annual index to measure the level of the legal protection of tenant rights in each state. Our instrumental variable results indicate that while a one-unit increase in the Tenant-Right Index reduces the eviction rate by 0.32 percentage points, rental housing is 1.8 percent more expensive in areas where tenants have more protections against landlords. A higher Tenant-Right Index is also associated with a decrease in housing supply and an increase in the homeless rate. Taken together, our findings highlight a significant trade-off between tenant protections and rent affordability.
- Shen, L., & Sun, X. (2025). The More, the Merrier: Agent Connectedness in Real Estate Trading Networks. Journal of Real Estate Research, 47(2), 149-169. https://doi.org/10.1080/08965803.2025.2466957 [ABDC B] American Real Estate Society Best Article Award.
  Abstract: This study applies network theory to examine how real estate agents' connections influence transaction outcomes, using data from over 40,000 sales in Atlanta, GA. It finds that agents with stronger networks achieve higher sales prices, with a one standard deviation increase in connectedness leading to a 2.61% price boost. These well-connected agents sell unique properties faster and more accurately price them, suggesting that their networks enhance market efficiency through improved information flow.
- Ambrose, B. W., Han, Y., Korgaonkar, S., & Shen, L. (2024). Information in Financial Contracts: Evidence from Securitization Agreements. Journal of Financial and Quantitative Analysis, 59(4), 1692-1725. https://doi.org/10.1017/S0022109023000509 [ABDC A*]
  Abstract: 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 boilerplate text, we document substantial variation across PSAs, both within- and across-underwriters and over time. A part of this variation is driven by differences in loan collateral across deals. Additionally, we find that differences in PSAs are correlated with ex-post loan and bond performance. Collectively, our analysis suggests the importance of examining the entire governing document, rather than specific components, when analyzing complex financial securities.
- Ambrose, B. W., & Shen, L. (2023). Past Experiences and Investment Decisions: Evidence from Real Estate Markets. Journal of Real Estate Finance and Economics, 66(2), 300-326. https://doi.org/10.1007/s11146-021-09844-2 [ABDC A]
  Abstract: This paper investigates how market participants form their risk perspectives about the real estate market through a sequence of information shocks. Guided by a theoretical Bayesian learning model, we exploit a natural experiment afforded by the fracking boom in Pennsylvania in the late 2000s. We empirically examine whether familiarity with conventional gas exploration affects home-buyers' willingness to pay for houses near fracking wells.
- Shen, L., & Wilkoff, S. (2022). Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals. Journal of Regional Science, 62(3), 799-829. https://doi.org/10.1111/jors.12581 [ABDC A] Featured by The Economist, December 2020.
  Abstract: 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 cleanliness dictionary to detect whether an Airbnb unit is clean. We use a difference-in-difference specification to value the change in income related to reviewer-perceived cleanliness during the COVID-19 pandemic. We find the following results: First, available listings declined by 25% once the pandemic hit and those that remained lost 22% of their income and had occupancy decrease by 20%. Second, properties that were perceived to be clean increased their income by 17.5% and their occupancy by 16.5%, mitigating the negative shock due to COVID-19. Third, rental prices for clean Airbnb listings did not increase after COVID-19. In addition, we study the interaction of Airbnb supply on the long-term rental market during a market decline.
- Shen, L., & Springer, T. M. (2022). The Odd One Out? The Impact of Property Uniqueness on Selling Time and Selling Price. Journal of Housing Research, 31(2), 220-240. https://doi.org/10.1080/10527001.2022.2078530 [ABDC B] Best Paper Award, Journal of Housing Research, 2022 volume.
  Abstract: We employ machine learning to develop measures of residential real estate uniqueness from written advertisements. These measures are exogenous from sales prices. We distinguish the effect of market uniqueness (compared to houses for sale at the same time) from the effect of universal uniqueness (compared to houses in the same sub-market) on sale prices and time-on-the-market (TOM). The hedonic models show that a one standard deviation increase in market uniqueness leads to a 13% ($48,490) increase in sale prices at the cost of delaying the transaction for 1.7 days, whereas a one standard deviation increase in universal uniqueness only leads to an 11% ($41,030) increase in sale prices at the cost of delaying the transaction for 3 days. We validated the impact of uniqueness on TOM using two hazard models. Our results highlight the importance of uniqueness and market timing in real estate.
- Shen, L., & Ross, S. L. (2021). Information Value of Property Description: A Machine Learning Approach. Journal of Urban Economics, 121, 103299. https://doi.org/10.1016/j.jue.2020.103299 [ABDC A*]
  Abstract: 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 capture. A one standard deviation increase in the uniqueness of a property based on this "soft" information leads to a 15% increase in property sale price in a hedonic price model and a 10% increase in a repeat sales price model. An annual hedonic price index ignoring our measure of unobserved quality overstates real estate prices by between 10% to 23% and mistimes the stabilization of housing prices following the Great Recession. Similar, but smaller effects, are observed for the repeat sales price index.
- Cunningham, C., Gerardi, K., & Shen, L. (2021). The Double Trigger for Mortgage Default: Evidence from the Fracking Boom. Management Science, 67(6), 3943-3964. https://doi.org/10.1287/mnsc.2020.3647 [ABDC A*]
  Abstract: 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 endogeneity in the location of drilling activity. The fracking measures most associated with the labor-intensive act of creating wells have the largest net effects. Furthermore, we show that fracking activity significantly increased employment growth at the county level. These results are consistent with the double-trigger theory of mortgage default, which contends that liquidity shocks in addition to negative equity drive borrowers to default due to their inability to afford payments or sell their homes.
- Highfield, M. J., Shen, L., & Springer, T. M. (2021). Economies of Scale and the Operating Efficiency of REITs: A Revisit. Journal of Real Estate Finance and Economics, 62(1), 108-138. https://doi.org/10.1007/s11146-019-09741-9 [ABDC A]
  Abstract: Building on past research in the economies of scale debate, and recognizing the substantial changes in this industry since the turn of the millennium, we use a time-varying stochastic frontier approach to monitor changes in REIT efficiency over the 2001-2015 period. While the stochastic frontier model suggests that the overall level of operating efficiency for US equity REITs is stable, using a series of linear models we document evidence that economies of scale still exist in this industry.

## Papers under revision

- Cunningham, C., Gerardi, K., & Shen, L. (2024). The Good, the Bad and the Ordinary: Estimating Agent Value-Added using Real Estate Transactions. Revise and resubmit, Management Science. https://www.atlantafed.org/research/publications/wp/2022/09/29/11--estimating-value-added-by-real-estate-agents
  Abstract: We recover measures of real estate agents' value added in the housing transaction process using a detailed Multiple Listing Service (MLS) property transactions database covering three large metro areas. Controlling for property and location characteristics, we find considerable heterogeneity in the sale prices obtained by realtors as well as the time that it takes to complete a sale. Focusing on a sample of repeat sales, we estimate an inter-quartile price range for both listing and buying agents to be approximately 4 to 5 percent. The inter-quartile range for days-on-the-market is between 15 and 25 days. Listing agents that tend to sell for a premium do not take any longer to sell, indicating that agents do not have fixed price posting/acceptance strategies. Agents that sell homes at a premium also tend to pay more when acting as a buyer's agent, which is inconsistent with realtors being good negotiators. Finally, homes sold via a "flat-fee" agency on the MLS obtain prices that are 1 to 5 percent higher compared to those obtained by a traditional listing agent and take no longer to sell.

## Working papers

- Shen, L. (2026). Cheap Talk or Priced Information? Listing Language and Housing Transaction Outcomes. Working paper, SSRN, June 2026. https://papers.ssrn.com/abstract=7097938
  Abstract: 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 language into auditable property and transaction categories. Within search-area-year markets and conditional on a saturated hedonic specification, a one-standard-deviation increase in residual information is associated with 7.7 percent higher sale prices, 7.6 percent higher list prices, and 2.5 fewer days on market. Adverse disclosure is associated with 17.4 percent lower sale prices, 16.7 percent lower list prices, and a 0.7 percent lower sale-to-list ratio. Urgency pressure is associated with 4.7 percent lower sale prices and 4.4 percent lower list prices, but not with a distinct sale-to-list adjustment or shorter marketing time. Market-stress language rises during the housing crisis and is more prevalent when local prices are lower, marketing time is longer, and discounts are larger. Repeat-sale and listing-agent specifications imply more conservative but economically meaningful price differences, and residual information and adverse disclosure retain pricing content after LASSO-selected token controls. The findings show that remarks reveal priced property and transaction information beyond structured MLS fields while identifying the economic categories in which that information appears.

## Textbooks

- David M. Geltner, Norman G. Miller, Alex van de Minne, Piet Eichholtz, Thies Lindenthal, Lily Shen (2026). Commercial Real Estate Analysis for Investment, Finance and Development, 4th edition. Routledge. 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. The book's website, CREBook.net, carries chapter overviews, slides, exercises, and spreadsheet models; the podcast CRE Explained walks through it chapter by chapter. https://crebook.net/
- Jeffrey D. Fisher, Jacob Sagi, Stephanie R. Yates, Lily Shen (2027). Real Estate Finance and Investments, 2027 edition. McGraw Hill. The widely adopted foundational text in real estate finance, developed by William Brueggeman and Jeffrey Fisher. For the 2027 edition Lily Shen wrote the new chapter, Artificial Intelligence in Real Estate Finance and Investment, the first AI chapter in the book's history, and the assessment materials for the entire book.

## Teaching

- AI in Finance (graduate, M.S. in Finance), Clemson University, new course, Spring 2027. Large language models as analytical tools for finance and real estate.
- FIN 4170 Real Estate Finance, Clemson University, 2016–present. Sections of 45–80; semester-long home-financing project on a real property; industry speaker series.
- FIN 3070 Real Estate Principles, Clemson University, 2016–present. In person and online; the online course is certified by Quality Matters (2024).
- FIN 4980 Creative Inquiry: Machine Learning and AI in Finance, Clemson University, 2018–present. Clemson's first hands-on AI courses in finance and real estate; an undergraduate research lab whose work has been published and featured in The Economist. Lab site: https://eisheim6.wixsite.com/aiinfinance
- Contemporary Issues in Real Estate; Real Estate Finance, The Pennsylvania State University, 2012–2015. Instructor and teaching assistant.

## Podcast: Commercial Real Estate Explained (CRE Explained)

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 matters to investors and lenders, and the one idea to take away. Hosted by Lily Shen and produced with her Clemson Creative Inquiry student team. Free, no sign-up, on Spotify.
https://open.spotify.com/show/5OICcnUJdBqfuUWDEOv7tk

- 2026-10-22: Chapter 11 Introduction: The Discount Rate, How Investors Measure Risk and Return (https://open.spotify.com/episode/1rPYSFzTkWQTBeg8RITpmW)

## Leadership and service

- Board Director, International Real Estate Society (2024–present)
- Editorial boards, Journal of Housing Research (2022–) and Journal of Real Estate Literature (2023–)
- Membership Committee, AREUEA (2024–2026); program committees for AREUEA 2024 and the inaugural 2027 W. P. Carey Real Estate Research Conference
- Chair, Clemson real estate faculty searches (2023, 2024); AI Task Force; Research Impact Academy, inaugural cohort (2026–27)
- Proposed Clemson's real estate study-abroad program (London and Cambridge) and a signature experiential-learning program in real estate (2026)
- Designed and taught 'Your First AI Agent,' a hands-on AI workshop for College of Business faculty and staff (Sep. 2026)
- More than 1,700 students taught since 2016; Spring 2026 Real Estate Finance sections above university, college, and department benchmarks on every evaluation item.
- Clemson College of Business Dean's Award for Student Engagement, 2019–20 (single annual recipient).
- Founded Clemson's undergraduate real estate speaker series (2021): bank executives, brokerage CEOs, and Federal Reserve economists in the classroom.
- Former faculty adviser, Clemson Real Estate Club; mentees have gone to Amazon, Deloitte, Truist, and Cisco.
- Program building: recruiting and curriculum committees for Clemson's M.S. in Finance; proposals for a real estate study-abroad program in London and Cambridge and a signature experiential-learning program in real estate.
- Host of CRE Explained, the companion podcast to Commercial Real Estate Analysis; free teaching materials at CREBook.net.

## Honors, awards, and grants

- 2026: American Real Estate Society Kinnard Young Scholar Award (cumulative research contribution)
- 2026: James R. Webb ARES Foundation Global Ambassador Grant
- 2026: Clemson College of Business Senior Scholar Research Excellence Award (2025–26)
- 2025: ARES Best Article Award, Journal of Real Estate Research (agent connectedness paper, with Xiaojin Sun)
- 2025: Homer Hoyt Institute / Weimer School Post-Doctoral Honoree
- 2025: Red Pen Award (best referee), Journal of Real Estate Literature
- 2024: Cocoziello Institute Seed Grant, $85,000 (reshoring and real estate markets, with Penn State coauthors)
- 2024: Clemson University Mid-Career Faculty Grant; featured faculty member at the Clemson Board of Trustees meeting
- 2022: Best Paper Award, Journal of Housing Research (2022 volume); Best Paper Award (3rd place), AREUEA International Conference, Dublin
- 2021: Real Estate Research Institute (RERI) Research Grant
- 2020: Clemson College of Business Dean's Award for Student Engagement
- 2019: American Real Estate Society Manuscript Prize in Real Estate Valuation (single author)
- 2018: AEA CSWEP Economics Fellowship (also 2015, 2014)

## Press coverage

- 2024, South Carolina Public Radio: A Commercial Real Estate Collapse Might be Looming. So Why is South Carolina Not Worried? (https://www.southcarolinapublicradio.org/sc-news/2024-07-16/a-commercial-real-estate-collapse-might-be-looming-so-why-is-south-carolina-not-worried)
- 2024, WBTW (CBS): How AI is Revolutionizing Home Buyers' Access to Housing Market Insights
- 2023, Clemson News: A Unique Home Might Take Longer to Sell, but the Payoff Could be Worth a Few Extra Days on the Market (https://news.clemson.edu/clemson-university-researchers-prove-that-a-unique-home-might-take-longer-to-sell-but-the-payoff-could-be-worth-a-few-extra-days-on-the-market/)
- 2022, The Washington Post: Owners vs. Agents: Who Sells More (on the agent value-added paper) (https://www.washingtonpost.com/business/2022/12/02/best-states-to-live/)
- 2022, Federal Reserve Bank of Atlanta, Policy Hub: Estimating Value Added by Real Estate Agents (https://www.atlantafed.org/research/publications/wp/2022/09/29/11--estimating-value-added-by-real-estate-agents)
- 2020, The Economist: Airbnb Guests Seek Out Cleaner Properties in the Pandemic (https://www.economist.com/graphic-detail/2020/12/08/airbnb-guests-seek-out-cleaner-properties-in-the-pandemic)
- 2020, Federal Reserve Bank of Atlanta, macroblog: COVID-19 Mortgage Relief: The Role of Income Support (cites the double-trigger paper) (https://www.atlantafed.org/blogs/macroblog/2020/05/27/covid-19-mortgage-relief-the-role-of-income-support)
- 2020, Clemson News / ABC News 4: Tight Landlord Regulations May Result in Homelessness Rise, Clemson Study Says (https://abcnews4.com/news/local/clemson-study-shows-strict-landlord-regulation-may-increase-homelessness)
- 2020, Clemson News: Clemson Study Employs AI to Decipher 'Soft' Features of a Home's Value (https://news.clemson.edu/clemson-study-employs-ai-to-decipher-soft-features-of-a-homes-value/)
- 2016, Pittsburgh Business Times: Fracking Region Mortgage Defaults Below Pennsylvania Average (https://www.bizjournals.com/pittsburgh/news/2016/09/21/fracking-region-mortgage-defaults-below-pa-average.html)

## Selected talks

- 2027: AREUEA/ASSA Annual Meeting, Washington, DC (two papers, scheduled)
- 2026: 4th ODU Real Estate Research Symposium, Old Dominion University (scheduled, Oct.)
- 2026: Cambridge | HKU Real Estate Finance and Investment Symposium, University of Cambridge
- 2026: Homer Hoyt Institute Weimer School session, West Palm Beach; ARES Annual Meeting, Destin; JRER/UCF Current Issues Symposium, Orlando
- 2025: University of Kentucky, Gatton College; Federal Reserve Bank of Richmond, Real Estate Commissions and Housing Market Frictions Conference
- 2024: Florida State University; Urban Economics Association North American Meeting, Georgetown; University of Hawaii at Manoa; The Murphy Institute housing conference, New Orleans
- 2023: University of Georgia; Baruch College, Zicklin School; University of Cincinnati, Lindner College; Zillow (invited research talk); Penn State; Old Dominion University symposium
- 2022: Singapore Management University; University of Texas at Arlington; Cambridge | Florida | Geneva | NUS Real Estate Finance and Investment Symposium; University of Utah housing affordability summit; Federal Reserve Bank of Atlanta; AREUEA International, Dublin (best paper award)
- 2021: MIT Center for Real Estate; 1st International Conference on AI for Real Estate (SDSU and Santa Clara); Financial Management Association; Regional Science Association
- 2019: University of Connecticut; Federal Reserve Bank of Philadelphia; Renmin University, Beijing; Cambridge | Florida | NUS Symposium; Federal Reserve Bank of St. Louis Rental Housing Conference

## Service

- Program committees: 2027 W. P. Carey Real Estate Research Conference (ASU, inaugural); 2024 AREUEA National Conference; 2022 Midwest Finance Association; 2021 FMA; 2016 SFA.
- Session chair at AREUEA (2022, 2024, 2025) and the UNC Institute for Private Capital real estate symposium (2020).
- Referee for Real Estate Economics, Management Science, Journal of Urban Economics, Journal of Regional Science, Regional Science and Urban Economics, Journal of Real Estate Finance and Economics, Financial Management, and others.
- Clemson: chair of the real estate faculty searches (2023, 2024); AI Task Force; Research Impact Academy (inaugural cohort, 2026–27); college research working group; finance curriculum, recruiting, and seminar committees.
- Designed and delivered 'Your First AI Agent,' a hands-on workshop on AI research agents for College of Business faculty and staff (Sep. 2026).

## Research notes on this site

- AI in real estate finance: what this site covers and how to cite it (https://lilyshenai.github.io/posts/ai-in-real-estate-finance-what-this-site-covers/): An introduction to this site: who writes it, the three research strands it covers (text as priced information in housing markets, machine learning on financial contracts, and AI and the people who intermediate the market), and how to cite the posts and the papers behind them.
- Cleanliness is next to income: a machine-learning cleanliness measure and Airbnb outcomes during COVID-19 (Journal of Regional Science, 2022) (https://lilyshenai.github.io/posts/airbnb-cleanliness-covid-machine-learning/): Summary of Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals, with Sean Wilkoff. In Austin, Texas, Airbnb listings fell 25 percent at the onset of COVID-19 and surviving listings lost 22 percent of income, but units reviewers perceived as clean, measured with a machine-learning cleanliness dictionary, gained 17.5 percent in income and 16.5 percent in occupancy.
- How to build auditable AI text measures for economic research: a two-step method (https://lilyshenai.github.io/posts/auditable-ai-text-measures-two-step-method/): A practical method for using large language models as measurement tools in economics and finance. The model discovers the vocabulary for each economic category across the full corpus; a fixed, published vocabulary then scores every document, so results are reproducible, traceable phrase by phrase, and testable out of sample.
- Cheap talk or priced information? What listing remarks do to prices, bargaining, and marketing time (https://lilyshenai.github.io/posts/cheap-talk-or-priced-information-listing-remarks/): Summary of my working paper Cheap Talk or Priced Information? In 63,067 Atlanta MLS transactions, residual listing information is associated with 7.7 percent higher sale prices and 2.5 fewer days on market, adverse disclosure with 17.4 percent lower sale prices, and urgency language with 4.7 percent lower sale prices, showing that remarks carry priced information beyond structured MLS fields.
- Property descriptions carry priced information that hedonic models miss (Journal of Urban Economics, 2021) (https://lilyshenai.github.io/posts/information-value-property-descriptions-machine-learning/): Summary of Information Value of Property Description: A Machine Learning Approach, with Stephen L. Ross. A machine-learning measure of the soft information in listing text raises sale prices by 15 percent per standard deviation in a hedonic model and 10 percent in a repeat-sales model, and price indices that ignore it overstate prices by 10 to 23 percent and mistime the post-recession stabilization.
- Are securitization contracts boilerplate? Machine learning on CMBS pooling and servicing agreements (JFQA, 2024) (https://lilyshenai.github.io/posts/machine-learning-cmbs-pooling-servicing-agreements/): Summary of Information in Financial Contracts: Evidence from Securitization Agreements, with Brent W. Ambrose, Yiqiang Han, and Sanket Korgaonkar. A machine-learning comparison of the pooling and servicing agreements behind commercial mortgage-backed securities shows substantial variation across deals, underwriters, and time, partly explained by collateral, and correlated with later loan and bond performance.
- The odd one out: unique homes sell for more and take only slightly longer (Journal of Housing Research, 2022) (https://lilyshenai.github.io/posts/property-uniqueness-selling-time-and-price/): Summary of The Odd One Out? The Impact of Property Uniqueness on Selling Time and Selling Price, with Thomas M. Springer. Machine-learning measures of uniqueness from listing text show that a one-standard-deviation increase in market uniqueness raises sale prices by 13 percent ($48,490) while delaying sale by 1.7 days; universal uniqueness raises prices 11 percent ($41,030) at a 3-day delay.
- Well-connected real estate agents sell for more: network theory on 40,000 Atlanta transactions (Journal of Real Estate Research, 2025) (https://lilyshenai.github.io/posts/real-estate-agent-networks-connectedness/): Summary of The More, the Merrier: Agent Connectedness in Real Estate Trading Networks, with Xiaojin Sun. A one-standard-deviation increase in an agent's network connectedness is associated with 2.61 percent higher sale prices, and connected agents sell unique properties faster and price them more accurately, consistent with networks improving information flow.
- What is soft information in housing markets, and how can it be measured? (https://lilyshenai.github.io/posts/what-is-soft-information-in-housing-markets/): Soft information is what a listing says that structured fields cannot: condition, character, seller circumstances, local demand. This explainer defines the term, contrasts it with hard information, and summarizes what my research finds when listing text is measured with machine learning: it is priced, it changes marketing time, and it moves before price indices do.

## AI Briefs: short posts on AI

- Notes from my workshop on AI agents (https://lilyshenai.github.io/blog/notes-from-my-workshop-on-ai-agents/): What I covered in a workshop for faculty and staff in our college on setting up an AI agent: how it differs from the chat window, the commands that work in both the app and the terminal, how to set up a folder and a short rules file for a task you repeat, a literature folder as the example, and how to check the output.
- Why I write about AI (https://lilyshenai.github.io/blog/why-i-write-about-ai/): AI Briefs are short posts on what is actually useful in AI for finance, real estate, teaching, and research: one tool, one method, or one mistake, from someone who has taught and published with these tools since 2018.

Skills and data: Python, R, Stata, SAS, Matlab, ArcGIS, LaTeX; large-scale MLS, mortgage, CMBS, and commercial property data.