I am an Associate Professor at the Department of Economics, University of Hawaii Manoa. My research interests lie in the intersection of urban and housing economics, finance, and macroeconomics.
Broadly, my research agenda is aimed at understanding how over-the-counter markets for illiquid assets — such as housing, bonds, and loans — function and what are their macroeconomic implications.
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Using two and a half decades of microdata from the Survey of Construction, we document a robust presale price premium: houses sold before construction commences sell at an average of 2.8% more, with premiums reaching 15% for homes sold five or more months before construction starts. Houses sold post completion of construction also carry a modest 1.4% premium, creating a distinctive U-shaped pricing pattern. To explain this pricing pattern, we develop a search-theoretic model of the housing market in which developers face credit market frictions. We show that these credit frictions give rise to a novel channel that can rationalize the existence of both the presale and post completion premia. A calibration of our economy to the US housing market implies the credit frictions channel can explain about a third of the presale premium.
The corporate bond market provides a vital avenue for firms to cover their borrowing needs. Moreover, the ease with which corporate bonds can be (re)traded in secondary markets affects their liquidity and, effectively, the rate at which corporations can borrow. However, the literature has also pointed out that a well-functioning secondary market can depress money demand and hurt economic activity. We perform a careful quantitative analysis of the channels through which secondary market liquidity affects the real economy in the context of a New Monetarist model. We find that a deterioration in secondary market liquidity has a negative but modest impact on output and unemployment. This small net effect, however, conceals much larger underlying forces that operate in opposite directions and largely offset each other. We also show that the results of our decomposition exercise depend on the inflation rate. Our findings highlight the importance of studying investor portfolios together with asset prices to fully capture the interaction between financial markets and the real economy.
An increasing share of corporate loans, a critical source of firm credit, are sold off of banks' balance sheets and actively traded in a secondary over-the-counter market. We develop a microfounded equilibrium search-theoretic model with labor, credit, and financial markets to study the impact of this secondary loan market on the real economy. Our analysis highlights a policy-relevant trade-off: the market reduces the steady-state level of unemployment by 0.21pp, but it also amplifies unemployment's response to a 1% productivity drop by 0.07pp. Trading delays in the secondary market matter significantly: if trade were instantaneous, steady-state unemployment and its volatility would decline by 1.49pp and by 0.24pp, respectively.
A large literature in macroeconomics concludes that disruptions in financial markets have large negative effects on output and (un)employment. Though diverse, papers in this literature share a common characteristic: they all employ frameworks where money is not explicitly modeled. This paper argues that the omission of money may hinder a model's ability to evaluate the real effects of financial shocks, since it deprives agents of a payment instrument that they could have used to cope with the resulting liquidity disruption. In a carefully calibrated New-Monetarist model with frictional labor, product, and financial markets, we show that the existence of money dampens or even nearly eliminates the real impact of financial shocks, depending on the nature of the shock. We also show that the propagation of financial shocks to the real economy depends on the inflation level: high inflation regimes magnify the real effects of adverse financial shocks.
This paper studies the effects of financial frictions in construction on housing market dynamics. To this end, we build a search-theoretic model of the housing market in which there is endogenous entry of buyers and developers face credit constraints. We capture credit frictions by assuming that developers must search for financing before building a home à la Wasmer and Weil (2004). Our model explores a novel channel that links credit frictions faced by developers to the housing market. We calibrate the model to quantify the size of the credit channel during the 2012–2019 housing market recovery. Through a series of counterfactuals, our model predicts that the credit channel had a large impact on housing liquidity, construction, and the vacancy rate. Furthermore, it accounts for around half of the rise in prices during the 2012-2019 housing market recovery.
A salient feature of over-the-counter (OTC) markets is intermediation: dealers buy from and sell to customers as well as other dealers. Traditionally, the search-theoretic literature of OTC markets has rationalized this as a consequence of random meetings and ex post bargaining between investors. We show that neither of these are necessary conditions for intermediation. We build a model of a fully decentralized OTC market in which search is directed and sellers post prices ex ante. Intermediation arises naturally as an equilibrium outcome for a broad class of matching functions commonly used in the literature. We further explore, both analytically and numerically, how the extent of intermediation depends on the nature of frictions and model primitives. Our numerical exercises also contrast the model's equilibrium implications to those of a benchmark model with random meetings and ex post bargaining.
This paper develops a business cycle model of the housing market with search frictions and entry of both buyers and sellers. The housing market exhibits a well-established cyclical component, which features three stylized facts: prices move in the same direction as sales and the number of houses for sale, but opposite to the time it takes to sell a house. These stylized facts imply that in the data housing vacancies and the number of buyers are positively correlated, i.e. that the Beveridge Curve is upward sloping. A baseline search and matching model of the housing market is unable to match these stylized facts because it inherently generates a downward sloping Beveridge Curve. With free entry of both buyers and sellers, our model reproduces the positive correlation between prices, sales and vacancies, and matches the stylized facts qualitatively and quantitatively.
