I am an Economist at LinkedIn.
I graduated with a PhD in Economics from Stanford GSB in Jan 2022. In my research, I combined causal inference and machine learning methods to study inefficiencies and riskiness in the US housing market.
Here is my LinkedIn.
We build a rich panel dataset tracking two measures of housing market liquidity: time-on-market and price dispersion. The two measures co-vary closely at seasonal and business-cycle frequencies, but there is substantial independent variation in the cross-section of counties. This suggests that the two measures reflect different dimensions of market liquidity. Using a housing search model, we show that time-on-market and price dispersion can be thought of as equilibrium outcomes from a supply and demand system for liquidity. Consistent with the model’s predictions, proxies for liquidity supply are negatively correlated with both measures, whereas a proxy for liquidity demand is negatively correlated with time-on-market, but positively correlated with price dispersion.
This paper studies how the quality of goods traded in a market evolves in the presence of predictable cyclical changes in market conditions. We document a set of novel stylized facts about the US housing market. We show that housing markets of better neighborhoods are more seasonal and higher quality houses are more likely to be traded in summer when housing markets are hot. We then consider a choice problem of a seller who owns an asset with perfectly observable quality who must decide whether to enter the market now or wait for the market conditions to improve. Market participation is costly, and the cost depends on the seller's type. In the model, the quality of sellers who enter the market fluctuates in response to cyclical changes in market conditions. We characterize conditions under which each seller type strategically chooses to delay entering the market until market conditions improve.
I train a recurrent neural network to predict individuals’ race using information contained in their name and location of residence. I introduce a novel data source that contains millions of race/name/zipcode triplets and covers the entire US. I train my baseline LSTM model using only personal name data. The baseline model attains overall accuracy of 0.87, which is a non-trivial improvement over the existing benchmarks in the literature. However, because personal names of Non-Hispanic Whites and Non-Hispanic Blacks follow similar naming conventions, the baseline model struggles at accurately classifying Non-Hispanic Blacks. Using location data in addition to personal name improves classification accuracy of the model for Non-Hispanic Blacks substantially, and allows the best model to achieve 0.89 accuracy on the test set.
Industrial Coagglomerations and Risk Concentration
In this paper, I argue that industrial concentration increases the exposure of local areas to industry-specific risk. Industrial concentration, therefore, creates risk concentration through amplified pass-through of industry-specific productivity shocks into local wages, local employment, and prices of local assets. Empirically, I show that industrial concentration increases year-to-year volatility of local wages, employment, and house prices. Next, I document that more specialized MSAs are more likely to experience large positive or negative shocks at decadal frequencies. Finally, I conduct case studies of persistent industry-specific shocks to further demonstrate the large pass-through of these shocks into house prices.
Work in Progress
Can Market Forces Bridge the Racial Gaps in the US Housing Market? with Cody Cook