Lily Shen

Associate Professor

Department of Finance

College of Business

Clemson University

145 Business Building, Clemson, SC 29634

Email: yannan at berkeley dot edu

About Me

Current Affiliations

Associate Professor, Clemson University, Department of Finance

Visiting Scholar, Federal Reserve Bank of Atlanta

Education

Ph.D. The Pennsylvania State University, University Park

B.A. University of California, Berkeley

RA for Robert Edelstein, Haas College of Business

Research Interests

Fintech, Machine Learning, Household Finance, Mortgages, Mortgage Backed Securities, Real Estate

Selected Work

Journal Publications

Double Trigger for Mortgage Default: the Fracking Boom, with Chris Cunningham and Kristopher Gerardi, 2021 (Management Science).

– 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 endogenity 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 nor sell their homes.
Information in Financial Contracts: Evidence from Securitization Agreements, with Ambrose, Han, and Korgaonkar 2022 (Accepted, Journal of Financial and Quantitative Analysis).– 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.

Information Value of Property Descriptions: A Machine Learning Approach, with Stephen Ross, 2021 (Journal of Urban Economics).

–This paper employs unsupervised machine learning to quantify the uniqueness of housing units based on real estate property descriptions. Results indicate that textual data disseminate information that traditional, quantitative hedonic attributes cannot capture. A one standard deviation increase in uniqueness compared to neighboring properties leads to a 15% increase in property sale prices and a 4 day delay in the number of days on the market before sale. Further, annual house price indices ignoring unobserved quality uniqueness lead to an 11% to 16% overstatement of housing price appreciation during the recovery period after the Great Recession.

Past Experiences and Investment Decisions: Evidence from Real Estate Markets, with Brent Ambrose, 2021 (Journal of Real Estate Finance and Economics).

– 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 explorations affects home–buyers’ willingness to pay for houses near fracking wells.

Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rental, with Sean Wilkoff, 2022 (Journal of Regional Science).

– 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. 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.

Economies of Scale and the Operating Efficiency of REITs: A Revisit, with Michael Highfield and Thomas Springer, 2019 (Journal of Real Estate Finance and Economics).

– 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.

The Odd One Out? The Impact of Property Uniqueness on Selling Time and Selling Price, with Thomas Springer, 2012 (Forthcoming at the Journal of Housing Research).

Under Review and Working Papers

Landlord Rights, Evictions, and Rent Affordability, with Edward Coulson and Thao Le, 2022 (Under Review at the Journal of Urban Economics)

–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 while a one-unit increase in the Tenant-Right Index reduces eviction rate by 8.9 percent, rental housing is 6.1 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. Thus the welfare effects of tenant rights depend on the presumably large benefits for those who avoid eviction versus a loss of consumer surplus for other housing consumers.

The Good, the Bad and the Ordinary: Estimating Agency Value-Added using Real Estate Transactions, with Chris Cunningham and Kristopher Gerardi, 2022

–We recover measures of real estate agents' value added in the housing transaction process using a detailed Multiple Listing Services (MLS) property transactions database covering three large metro areas. 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. Furthermore, we find 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, we document that homes sold via a "flat-fee" agency on the MLS, obtain prices that are 1% to 5% higher compared to those obtained by a traditional listing agent and take no longer to sell.

Impact of High-Skill Jobs On Commercial Real Estate, with Sumit Agarwal and Brent Ambrose, 2022

– Job vacancies and labor demand are among the most critical drivers behind the value of local commercial properties. Using 4.8 million job advertisements from more than 40,000 career websites from 2010 to 2020, we measure labor demand trends within metro areas and assess their impact on CRE rent levels.

The Role Agent Connectedness in Real Estate, with Xiaojin Sun, 2022

–We document a non-linear effect of agent connectedness on residential real estate transaction outcomes for price and advertising duration.