Lily Shen

Research

I study how information moves real estate and mortgage markets. Much of my recent work uses machine learning and large language models as measurement tools: turning listing remarks, securitization contracts, and other unstructured text into auditable economic measures, then asking what those measures reveal about prices, liquidity, risk, and the intermediaries who connect buyers, sellers, and lenders. Other work covers mortgage default, tenant rights and rent affordability, and the economics of real estate agents.

Bracketed ratings are from the 2025 ABDC Journal Quality List. Download all BibTeX · Plain-text version · Google Scholar

Published articles

Tenant Rights, Eviction, and Rent Affordability

N. Edward Coulson, Thao Le, Victor Ortego-Marti, Lily Shen · Journal of Urban Economics, 147, 103762 · 2025 · [ABDC A*]

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.

Publisher (DOI: 10.1016/j.jue.2025.103762)

Cite

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

@article{coulson2025tenant,
  author = {N. Edward Coulson and Thao Le and Victor Ortego-Marti and Lily Shen},
  title = {Tenant Rights, Eviction, and Rent Affordability},
  journal = {Journal of Urban Economics},
  year = {2025},
  volume = {147},
  pages = {103762},
  doi = {10.1016/j.jue.2025.103762}
}

The More, the Merrier: Agent Connectedness in Real Estate Trading Networks

Lily Shen, Xiaojin Sun · Journal of Real Estate Research, 47(2), 149-169 · 2025 · [ABDC B] · American Real Estate Society Best Article Award

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.

Publisher (DOI: 10.1080/08965803.2025.2466957)SSRNResearch note: Well-connected real estate agents sell for more: network theory on 40,000 Atlanta transactions (Journal of Real Estate Research, 2025)

Cite

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

@article{shen2025more,
  author = {Lily Shen and Xiaojin Sun},
  title = {The More, the Merrier: Agent Connectedness in Real Estate Trading Networks},
  journal = {Journal of Real Estate Research},
  year = {2025},
  volume = {47},
  number = {2},
  pages = {149--169},
  doi = {10.1080/08965803.2025.2466957}
}

Information in Financial Contracts: Evidence from Securitization Agreements

Brent W. Ambrose, Yiqiang Han, Sanket Korgaonkar, Lily Shen · Journal of Financial and Quantitative Analysis, 59(4), 1692-1725 · 2024 · [ABDC A*]

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.

Publisher (DOI: 10.1017/S0022109023000509)Online appendixResearch note: Are securitization contracts boilerplate? Machine learning on CMBS pooling and servicing agreements (JFQA, 2024)

Cite

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

@article{ambrose2024information,
  author = {Brent W. Ambrose and Yiqiang Han and Sanket Korgaonkar and Lily Shen},
  title = {Information in Financial Contracts: Evidence from Securitization Agreements},
  journal = {Journal of Financial and Quantitative Analysis},
  year = {2024},
  volume = {59},
  number = {4},
  pages = {1692--1725},
  doi = {10.1017/S0022109023000509}
}

Past Experiences and Investment Decisions: Evidence from Real Estate Markets

Brent W. Ambrose, Lily Shen · Journal of Real Estate Finance and Economics, 66(2), 300-326 · 2023 · [ABDC A]

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.

Publisher (DOI: 10.1007/s11146-021-09844-2)SSRN

Cite

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

@article{ambrose2023past,
  author = {Brent W. Ambrose and Lily Shen},
  title = {Past Experiences and Investment Decisions: Evidence from Real Estate Markets},
  journal = {Journal of Real Estate Finance and Economics},
  year = {2023},
  volume = {66},
  number = {2},
  pages = {300--326},
  doi = {10.1007/s11146-021-09844-2}
}

Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals

Lily Shen, Sean Wilkoff · Journal of Regional Science, 62(3), 799-829 · 2022 · [ABDC A] · Featured by The Economist, December 2020

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.

Publisher (DOI: 10.1111/jors.12581)Research note: Cleanliness is next to income: a machine-learning cleanliness measure and Airbnb outcomes during COVID-19 (Journal of Regional Science, 2022)

Cite

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

@article{shen2022cleanliness,
  author = {Lily Shen and Sean Wilkoff},
  title = {Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals},
  journal = {Journal of Regional Science},
  year = {2022},
  volume = {62},
  number = {3},
  pages = {799--829},
  doi = {10.1111/jors.12581}
}

The Odd One Out? The Impact of Property Uniqueness on Selling Time and Selling Price

Lily Shen, Thomas M. Springer · Journal of Housing Research, 31(2), 220-240 · 2022 · [ABDC B] · Best Paper Award, Journal of Housing Research, 2022 volume

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.

Publisher (DOI: 10.1080/10527001.2022.2078530)Research note: The odd one out: unique homes sell for more and take only slightly longer (Journal of Housing Research, 2022)Research note: What is soft information in housing markets, and how can it be measured?

Cite

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

@article{shen2022odd,
  author = {Lily Shen and Thomas M. Springer},
  title = {The Odd One Out? The Impact of Property Uniqueness on Selling Time and Selling Price},
  journal = {Journal of Housing Research},
  year = {2022},
  volume = {31},
  number = {2},
  pages = {220--240},
  doi = {10.1080/10527001.2022.2078530}
}

Information Value of Property Description: A Machine Learning Approach

Lily Shen, Stephen L. Ross · Journal of Urban Economics, 121, 103299 · 2021 · [ABDC A*]

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.

Publisher (DOI: 10.1016/j.jue.2020.103299)SSRNResearch note: Property descriptions carry priced information that hedonic models miss (Journal of Urban Economics, 2021)Research note: What is soft information in housing markets, and how can it be measured?

Cite

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

@article{shen2021information,
  author = {Lily Shen and Stephen L. Ross},
  title = {Information Value of Property Description: A Machine Learning Approach},
  journal = {Journal of Urban Economics},
  year = {2021},
  volume = {121},
  pages = {103299},
  doi = {10.1016/j.jue.2020.103299}
}

The Double Trigger for Mortgage Default: Evidence from the Fracking Boom

Chris Cunningham, Kristopher Gerardi, Lily Shen · Management Science, 67(6), 3943-3964 · 2021 · [ABDC A*]

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.

Publisher (DOI: 10.1287/mnsc.2020.3647)SSRN

Cite

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

@article{cunningham2021double,
  author = {Chris Cunningham and Kristopher Gerardi and Lily Shen},
  title = {The Double Trigger for Mortgage Default: Evidence from the Fracking Boom},
  journal = {Management Science},
  year = {2021},
  volume = {67},
  number = {6},
  pages = {3943--3964},
  doi = {10.1287/mnsc.2020.3647}
}

Economies of Scale and the Operating Efficiency of REITs: A Revisit

Michael J. Highfield, Lily Shen, Thomas M. Springer · Journal of Real Estate Finance and Economics, 62(1), 108-138 · 2021 · [ABDC A]

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.

Publisher (DOI: 10.1007/s11146-019-09741-9)

Cite

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

@article{highfield2021economies,
  author = {Michael J. Highfield and Lily Shen and Thomas M. Springer},
  title = {Economies of Scale and the Operating Efficiency of REITs: A Revisit},
  journal = {Journal of Real Estate Finance and Economics},
  year = {2021},
  volume = {62},
  number = {1},
  pages = {108--138},
  doi = {10.1007/s11146-019-09741-9}
}

Under revision

The Good, the Bad and the Ordinary: Estimating Agent Value-Added using Real Estate Transactions

Chris Cunningham, Kristopher Gerardi, Lily Shen · 2024 · Revise and resubmit, Management Science

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 paper

Cite

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

@unpublished{cunningham2024good,
  author = {Chris Cunningham and Kristopher Gerardi and Lily Shen},
  title = {The Good, the Bad and the Ordinary: Estimating Agent Value-Added using Real Estate Transactions},
  year = {2024},
  note = {Revise and resubmit, Management Science},
  url = {https://www.atlantafed.org/research/publications/wp/2022/09/29/11--estimating-value-added-by-real-estate-agents}
}

Working papers

Cheap Talk or Priced Information? Listing Language and Housing Transaction Outcomes

Lily Shen · 2026 · Working paper, SSRN, June 2026

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.

SSRNResearch note: How to build auditable AI text measures for economic research: a two-step methodResearch note: Cheap talk or priced information? What listing remarks do to prices, bargaining, and marketing timeResearch note: What is soft information in housing markets, and how can it be measured?

Cite

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

@unpublished{shen2026cheap,
  author = {Lily Shen},
  title = {Cheap Talk or Priced Information? Listing Language and Housing Transaction Outcomes},
  year = {2026},
  note = {Working paper, SSRN, June 2026},
  url = {https://papers.ssrn.com/abstract=7097938}
}

Grants and research awards

Copyright

"A Natural Language Processing Model for House Price Forecasting Using Text Analysis from House Description," Shen and Han, Clemson University technology disclosure 2018-034 (copyright 2018).