Abstract: Why do storefronts remain empty for more than a year in some of the world’s highest-rent retail districts? Landlords with vacancies derive option value from two sources of uncertainty. First, increasing downstream retail demand may drive up market rents tomorrow. Second, different tenants may have different willingness to pay for the same space, creating an incentive for landlords to wait for a particularly high rent offer. High move-in costs, search frictions, and high contract dissolution costs for landlords amplify this option value. We estimate the model parameters by matching quarterly vacancy rates, lease-up rates, and tenant exit rates from a comprehensive, high-frequency storefront tracking service, combined with micro data on commercial leases. In a counterfactual exercise, we find that reducing the variance of the match quality distribution by 50% reduces long-run vacancy rates by 33% on average, while reducing the variance of the aggregate state variable has almost no effect. Finally, we use the estimated model to quantify the impact of a retail vacancy tax on long-run vacancy rates, average rents, and social welfare. Vacancies would have to generate negative externalities of $18.72 per square foot per quarter (about 30% of average rents) to justify a 1% vacancy tax on assessed property values.
Abstract: We document the rise of storefront vacancies in prime retail locations, a phenomenon we refer to as high-rent blight, in America’s largest and most expensive urban retail market: Manhattan. We identify a little-known contracting feature between retail landlord and their bankers that generates vacancies in the downstream market for retail space. Specifically, widespread covenants in commercial mortgage agreements impose rent floors for any new leases landlords may sign with tenants, short-circuiting the price mechanism in times of low demand for retail space. Quasi-experimental estimates suggest that binding rent floors imposed by mortgage covenants substantially reduce the probability of occupancy, and we show in counterfactual exercises that covenants may have increased vacancy rates by as much as 14% over the 2016 to 2020 period.
“Gentrification and Retail Churn: Theory and Evidence” (with Edward L. Glaeser and Michael Luca), Regional Science and Urban Economics, 100 (2023). [accepted version] [NBER Working Paper 28271]
Abstract: How does gentrification transform neighborhood retail amenities? This paper presents a model in which gentrification harms incumbent residents by increasing rental costs and by eliminating distinctive local stores. While rising rents can be offset with targeted transfers, the destruction of neighborhood character can – in principle – reduce overall social surplus. Empirically we find that gentrifying neighborhoods experience faster growth in both the number of retail establishments and business closure rates than their non-gentrifying counterparts. However, we see little evidence that gentrification is associated with changes in retail mix or prices – suggesting limited welfare losses.
“DSGE Forecasts of the Lost Recovery” (with Michael Cai, Marco Del Negro, Marc Giannoni, Abhi Gupta, and Pearl Li), International Journal of Forecasting, 35(4) (2019): 1770-1789. [staff report] [ssrn]
Abstract: The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but instead generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters, and notably better for output growth than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.