Ph.D.
Financial Economist: Contracts, Valuation, Real Estate, Risk Management
I study how contracts, information, and networks shape outcomes in financial markets, with a focus on real estate and credit markets.
I study how contracts, information, and networks shape outcomes in financial markets, with a focus on real estate and credit markets.
Visiting Assistant Professor
Massachusetts Institute of Technology
The Center for Real Estate
"The Winner's Curse in Housing Markets" (link) ↗"Pricing Government Contract Risk Premia: Evidence from the 2025 Federal Lease Terminations" (link) ↗ Revised Version on SSRN
"The Winner's Curse in Housing Markets"
2025 Housing Research Manuscript Award
American Real Estate Society (ARES)
SFS Cavalcade (UVA Darden)
AREUEA-National
Pre-WFA Summer Real Estate Research Symposium
European Finance Association
Behavioral Science & Policy Association (Harvard Kennedy School)
I am an Assistant Professor of Real Estate Finance at the Saunders College of Business,
Rochester Institute of Technology. I am also a Visiting Assistant Professor at
Massachusetts Institute of Technology Center for Real Estate.
My research examines real estate and credit markets,
with a focus on housing market frictions, mortgage default, commercial lease risk, and
the role of networks and information in shaping transaction outcomes.
Recent projects study the pricing of government contract risk in commercial real estate, winner's curse effects in residential bidding wars, and how professional networks affect career longevity in the real estate industry. I also have strong interests in asset pricing, particularly in price bubbles.
I regularly write public-facing commentary on housing, mortgage policy, and commercial real estate, and have discussed these issues on radio programs, podcasts, and other media outlets.
My curriculum vitae is available as a PDF.
Are government contracts a safe investment? We investigate this question using unanticipated Department of Government Efficiency (DOGE) cancellations of federal leases as a shock to commercial mortgage default risk. Offices with DOGE-notified leases experience a persistent 24% net operating income decline, with large, negative spillovers to CMBS prices and rental cash flows tied to nearby private-tenant leases in Washington, D.C. Spillovers are driven by increased vacancy from tenants with high exposure to procurement contracts involving disrupted federal agencies. Simulations of office property value losses from early lease terminations indicate substantial market-wide repricing of government contract risk.
The figure tracks junior tranche CMBS bond prices around the emergence of DOGE-related federal lease termination risk. Treated bonds are tied to properties with GSA leases exposed to early termination during the 2025-2029 presidential term, while comparison bonds become eligible later. The figure asks whether prices diverged around January 2025, when the first DOGE termination notices in the Washington, D.C. area appeared.
Recipient of the 2025 ARES Housing Research Manuscript Award.
This paper tests for a winner's curse in housing markets by examining the subsequent performance of bidding war transactions relative to non-bidding war transactions. We develop a model in which homebuyers who purchase their house in a bidding war experience lower annualized returns and a higher likelihood of mortgage default. Consistent with the model, we find that homebuyers in bidding wars experience average total unlevered returns that are 10.5 percentage points lower, equivalent to an overpayment of 8.2 percent, and are 1.9 percentage points more likely to default. These winner's curse effects are more pronounced among socioeconomically vulnerable homebuyers.
This figure illustrates how bidding wars can push prices above a home's underlying value. As more buyers compete, the winning bid can reflect overpayment rather than only fundamentals.
Working paper.
Identifying the causal effect of networks on career success is challenging due to endogenous network formation. Leveraging quasi-random matching among residential real estate agents, we show that transacting with a well-connected counterparty agent increases a new agent's short-term probability of survival, transaction volume, and career length. A one-standard-deviation increase in initial counterparty centrality increases a new agent's probability of surviving one year by more than 4 percentage points and lengthens average career duration by 37%. A dynamic Bayesian network model formalizes the underlying mechanism, demonstrating how initial network capital shapes long-run career trajectories.
The figure illustrates how real estate agent networks are built from transaction data. It aggregates repeated transactions between the same agents, preserving the direction and strength of relationships while making the network easier to analyze.
Work in progress.
International Journal of Theoretical and Applied Finance, 25(3), 2250013, 1–25.
LinkedIn, July 2025.
Department of Finance & Accounting
Saunders College of Business
Rochester Institute of Technology
107 Lomb Memorial Drive
Bldg. 12 (LOW), Room 2072
Rochester, NY 14623
Prospective research students and collaborators are warmly invited to reach out with a brief note describing their interests.