【学术研讨】机会区计划对住宅房地产的影响

Classroom:    Room306, Tongji Building A

ABSTRACT:

Opportunity zones (OZs) are designated census tracts in which real estate investments can gain tax benefits. Introduced by the U.S. Tax Cuts and Jobs Act of 2017, the goal of the OZ program is to foster economic development in distressed neighborhoods. In this paper, we investigate and optimize the OZ selection process, and examine the impact of OZs by exploiting two datasets: a proprietary real estate dataset that includes 36.1 million residential transactions spanning all 50 U.S. states, and census-tract demographics data between 2010 and 2019. We show that census tracts with higher poverty and unemployment rates were more likely to be selected; counter intuitively, tracts with a higher average real estate price were also more likely to be selected. We then apply difference-in-differences, synthetic control, and matching techniques to rigorously assess the impact of the OZ program on two key real estate metrics, price and transaction volume. We find that the OZ program increased real estate prices by 4.03%-6.13% but do not observe a significant effect on the transaction volume. We also find that investors primarily targeted the high-end real estate market, namely exhibiting a cherry-picking behavior. To better fulfill its intended societal and economic goals, we propose an optimization framework with fairness considerations for OZ assignment decisions. We show that the OZs assigned from fairness-aware optimization can better serve distressed communities and mitigate investors' cherry-picking behavior. Our paper underscores the importance of incorporating fairness in OZ designation to achieve a desirable real estate market reaction. Our large-scale empirical analysis provides a comprehensive assessment of the current government OZ assignment, and our fairness-aware optimization framework provides concrete recommendations for policy makers.

GUEST BIO

Dr. Liu is currently an Assistant Professor in the Department of Information Systems and Analytics (ISA) at the Leavey School of Business, Santa Clara University. She received her Ph.D. in Operations, Technology, and Information Management from the Samuel Curtis Johnson Graduate School of Management, Cornell University in 2021. She also holds a Bachelor's degree in Logistics Engineering from Tianjin University and a Master's degree in Management from Cornell University.

Dr. Liu's research focuses on the design and operation of online markets, innovative business models, and the intersection of operations and finance. Her research also involves other methodologies such as game theory and economic modeling. She applies data-driven methods, including statistical learning and machine learning, to support and guide business decision-making in digital platforms. She also aims to design recommendation systems for online B2B platforms through predictive analytics and field experiments. Her work has been recognized, and she was a finalist for the 2021 INFORMS Innovative Applications in Analytics Award. She has published articles in top academic journals in the field of management.