Assistant Professor at Zicklin School of Business, Baruch College, CUNY 2018 -
PhD Stanford University 2018 (advisors: Martin Schneider, Monika Piazzesi, Gregor Jarosch)
MA New Economic School 2011, MA Higher School of Economics 2010, BA Higher School of Economics 2008
137 East 22nd Street, Office 408, New York, NY 10010
I am an applied theorist working in Real Estate and Macro-Finance. My research studies the role of financial frictions, uncertainty, and heterogeneity in financial markets and in the real economy. In terms of tools, I use quantitative models with incomplete markets and heterogeneous agents and discipline these models with micro data.
I use panel data from the Survey of Consumer Finances to study the recent U.S. housing bust in a quantitative lifecycle model. In the model, households face two types of idiosyncratic shocks: income shocks and moving shocks. The income shocks produce the large and long-lasting impact of unemployment on future earnings documented in recent empirical work. The moving shocks are estimated from survey data on reasons for moving to match the characteristics of marginal buyers. Movers are younger, have lower wealth and less secure jobs, making them more sensitive to unemployment and credit conditions. Moving shocks amplify the quantitative importance of labor and credit channels that explain the observed decline in house prices and rise in foreclosures. The Home Affordable Modification Program helped stabilize prices and reduce foreclosures at a relatively small cost, working mainly as insurance against bad shocks to the most vulnerable households.
This paper studies a labor search model with agents who are averse to ambiguity (Knightian uncertainty). Shocks to confidence about future productivity are modeled as changes in ambiguity. Using the Survey of Professional Forecasters data, I find that confidence shocks help explain the equity premium and the stock market volatility, including their term structure. Ambiguity amplifies the response of the economy to productivity shocks, helping the model produce more realistic dynamics of unemployment, vacancies, tightness, stock prices, and returns. Returns in the model are predictable with price-dividend ratios; both returns and dividends are predictable with unemployment, like in the data. Two extensions consider shocks to bargaining power and to separation rate and find similar implications of ambiguity about these shocks.
The Tax Cuts and Jobs Act of 2017 (TCJA) reduced the incentive for households to claim itemized deductions that subsidize homeownership and simultaneously lowered income tax rates. We use an equilibrium model to quantify the effects of the TCJA on house prices, homeownership, and welfare. The reform removes the tax subsidy to owner-occupied housing for most households, who now choose to claim the standard deduction. However, over-consumption of tax-subsidized housing by the remaining wealthy itemizers persists. Our results highlight the critical, yet surprisingly understudied, role of the standard deduction in determining the tax-favored status of owner-occupied housing, and through this channel, its effects on homeownership, consumption, and mortgage debt accumulation across the wealth and income distributions
Why did the real interest rate decline and the equity premium increase over the last 30 years? This paper assesses the role of uncertainty and credit market frictions. We quantify a model with heterogeneous households using data on not only asset prices and macro aggregates, but also households' debt and equity positions. We find that compensation for both uncertainty and frictions is reflected in asset prices. Moreover, a secular increase in frictions is important to understand jointly the decline in the real rate, a higher equity premium, and the relative scarcity of debt. Modeling uncertainty as ambiguity allows for tractable characterization of asset premia and precautionary savings effects in steady state.
To match the observed patterns of trade policy over the business cycle, I study a menu-auction model of trade policy in which financial frictions give rise to unemployment. Firms face idiosyncratic liquidity shocks that force some of them into bankruptcy. Tariffs raise profits providing liquidity to overcome the shocks. Both equilibrium and optimal tariffs are hump-shaped in the size of the shocks: if shocks are small, tariffs rise (as compared to the no-shock case) to save employment; if shocks are large, tariffs fall to sweep away inefficient firms. The optimal policy encourages substitution by helping less affected industries create more jobs for the unemployed from other industries. The response to a crisis depends on the political regime: as compared to democracies, autocracies give more protection to industries that generate more profits and are at higher risk. The results also hold in a two-country extension of the model.
Work in progress
We study how rising tuition and student loan policies affect households' decisions about housing, consumption, and saving for retirement.
The Macroeconomic Effects of Working-from-Home (with Christos Makridis)
This paper quantitatively investigates the macroeconomic effects of working-from-home (WFH) over the business cycle.
Idiosyncratic, uninsurable labor income risk primarily in the form of unemployment risk plays a large role in macro models with asset pricing. We study a heterogeneous-agents labor search model with portfolio choice that links labor and asset markets. Unemployment risk gives rise to high equity premium mostly because the job finding rate is correlated with productivity. Additionally, there is feedback from asset pricing to job creation: high equity premium lowers incentives to post vacancies, which in turn lowers the job finding rate and gives rise to an even higher premium.
We develop a framework to jointly study the dynamics of financial and housing markets. Our baseline model features lifecycle choice, aggregate risk, and endogenous prices of stocks, bonds, and houses. Such models are currently missing from the literature because of the computational challenges that arise from the large state space and complex decision rules that are hard to approximate with standard tools. Approximate dynamic programming and reinforcement learning help address these challenges. We use samples to optimize performance when working with large state space. To deal with complex decision rules in large environments, we test a set of flexible function approximation techniques such as lasso regression, nearest neighbor estimator, random forest, and kernel estimators.
Migration and Growth
I develop a two-country model of idea flows based on Lucas (2009) and Lucas and Mall (2012). The countries differ in technology distribution, ability distribution, and population size. I study the dynamic consequences of voluntary migration between countries and show that migration does not necessarily help the convergence of GDP per capita or of growth rates (poverty traps and club convergence are possible). I consider three types of migration policies for the recipient country and find that (1) the optimal policy to maximize long-run growth is to invite immigrants whose ability is above the domestic average ability, and (2) the optimal policy to maximize GDP per capita is to invite the immigrants whose productivity is above the domestic average productivity.
I teach Real Estate Finance and Investment (RES 3200) and Real Estate Capital Markets (RES 3400)
Materials for Real Estate Capital Markets (RES 3400) Fall'23: Syllabus, Part 1: Introduction, HousePrices, Part2: Mortgage Basics, Part3: MBS Intro, Part4: Mortgage Pass-Through Securities, Part 4 Excel, Part5: CMO, P5Excel 1: Sequential Pay, Midterm1Review, P5Excel 2: Seq Pay with Z-Bond, P5Excel 3: CMO with Residual and Z-Bond, P5Excel 4: IO-PO, P5Excel 5: IO-PO IRR Calculations, Part6: CMBS, Midterm 2 Review, Grade Calculator
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When not solving hetero agents models, I enjoy hiking and all kinds of climbing in the US and abroad.