Abstract This paper studies the impact of zoning and land use policies on urban development and welfare. It exploits an ambitious policy experiment led by Mayor Bloomberg in New York City during 2002-2013. The reform relaxed zoning regulations and increased the floorspace allowance, equivalent to 5% of the city's total housing stock. Using a granular panel data and the spatial discontinuity design, I find the quantity of supply responded slowly to the reform. The policy-induced new development generated positive spillover effects in surrounding areas but were delayed by the slow supply response. To quantify the welfare implications, I develop a dynamic spatial model, featuring the interaction between the lumpy adjustment of housing supply and the spatial reallocation of heterogeneous workers. For estimation, the paper uses Euler's method combined with a key property from the land market equilibrium to reduce the computation burden. Counterfactual analysis suggests that the reform increases the total housing supply by 0.7% in the long run and delivers welfare benefits for both high- and low-skilled workers. The reform reduces the regulatory constraints on housing, but it is not enough to help supply overcome high adjustment costs in this market. These costs erode 67% of the policy's potential impact on new development. The findings highlight the persistence of supply frictions thwarting the growth of productive cities.

Abstract We contribute novel estimates of scale effects in platforms. Our research design overcomes challenges including (i) the non-rivalry of data which makes it difficult to observe the direct benefits when platforms add more data and (ii) the endogeneity of scaling decisions since platforms tend to expand in high-growth markets. We study the 2013 merger of three Multiple Listings Service (MLS) in Florida, the primary platforms where properties are transacted. We use MLS listings data from 2009 to 2019 to trace out the effects of the merger on housing transactions and revenues of brokerage firms. The merger sharply increased the platform scale for (treated) brokerages whose platforms were not dominant in a local market prior to the merger. This elevenfold increase in platform scale for non-dominant brokerages exposed their listings to more buying agents and also expanded their access to more listings. Differences-indifferences estimates indicate that non-dominant brokerages are more likely to have inter-platform matches and their sales are also faster by two weeks, consistent with the merger reducing search costs. From the average firm’s perspective, however, the revenue benefits from scaling platforms is small. This is consistent with the localized nature of intermediation in real estate and the need for in-person services. We see remarkable heterogeneity with the elasticities for the largest firms in the top tercile being twice as large as elasticities for the medium tercile firms, consistent with the limited capacity of small firms to scale geographically.

Abstract Business closure policies have been widely implemented to slow down the transmission of COVID-19 while maintaining the functions of key industries. However, it is difficult to measure the contribution of each business type to virus transmission. In this paper, we combine a large data that tracks individual consumption patterns with a model of industry-specific consumption trip decisions to assess the effectiveness of different business closure policies. This framework allows us to estimate the impact of alternative closure policies on people’s mobility decisions and their subsequent impact on neighborhood-level COVID transmissions. We find that early reopening led to a prolonged pandemic and a large case surge in the second wave during 2020, even though the reopening allowed the city to regain its economic function as a consumption hub. An alternative policy that extends lockdown is more cost-effective as it makes future traveling safer and prevents the economy from relapsing into a more stringent policy regime.

Abstract Developing country cities, in their rapid urbanization processes, are often accompanied by the formation of spatially concentrated low-income neighborhoods, leaving concerns for prolonged poverty traps and inequality. Recent evidence from U.S. housing programs shows moving families out of disadvantageous locations has large and positive impact on intergenerational mobility and human capital accumulation (Chetty et al., 2015). However, there has been relatively scant empirical evidence on how effective these programs are in developing countries due to the lack of data. We examine the impact of Chile’s national housing subsidy program, the largest housing program in Latin America that has provided subsidized homeownership to a total of 0.3 million beneficiaries during 2002-2009. We link administrative data on housing subsidy applicants from the Ministry of Housing with labor income history data to track individuals over a long horizon. To establish the causal impact of receiving the subsidy, we employ a dynamic regression discontinuity design that centers around the poverty cutoff score of actual and almost recipients. Next, we examine two mechanisms through which the subsidy affects the household’s long-term return on the labor market: (1) improved access to the labor market through residential mobility; (2) tenure security that provides sufficient insurance against unemployment shock.

Abstract The rent price has been on a remarkable uptrend since the financial crisis of 2008. Meanwhile, new construction has been slow to catch up, raising concerns about missing affordable rental housing. However, the new construction is not the only source of rental supply within a city. Homes can be converted between rental and owner-occupied markets to accommodate the change in the relative demand. However, little is known about this supply process and how the market friction may hinder the conversion. The entry of large buy-to-rent (B2R) investors across major US cities during the financial crisis provides a unique lens to study this supply process. Using CoreLogic Housing Transaction data, the paper documents the acquisition process by these large investors in the Atlanta Metropolitan area between 2006-2016. It finds that these large buyers’ decisions for Where and How Much to invest in different neighborhoods are guided by forward-looking beliefs about future rent growth. When conducting such large-scale investments, they use spatial diversification strategies to mitigate the cost of demand uncertainty. Based on these empirical findings, the paper develops and estimates a model that combines the discrete choice model with a model of portfolio construction to help rationalize both the extensive margin and the intensive margin of their entry decisions across neighborhoods. Finally, the paper performs counterfactual simulations to quantify the impact of B2R entry on the distribution of rental supply.