Lyndsey Rolheiser

We analyze the causal effect of air pollution (acute fine particulate matter) exposure on the commercial real estate (CRE) market. We instrument for air pollution using changes in local wind direction to find that an increase in fine particulate matter exposure leads to a contemporaneous decrease in CRE market values and (net) income as well as an increase in capital expenditures. Heterogeneous treatment analysis within a building-level fixed effects framework uncovers that the negative effect on market values is concentrated in the office sector, consistent with the notion that air pollution-induced decreases in CRE values are driven by a reduction in CRE assets’ productive capacity. Additionally, we document that the negative impact on (net) income is concentrated in the apartment sector, which is consistent with a broad set of local disamenity mechanisms identified in previous residential real estate literature.

Concerns over rising office vacancy rates and falling office building property values in many urban areas have increased the pressure on cities and developers to consider converting underused office space to residential use. To aid in current and future conversations surrounding the feasibility of conversion, we look to the recent past. In doing so, we provide an account of conversion and redevelopment activity in New York City over the past decade to uncover associated structural and locational characteristics. We find that office-to-residential conversions contributed the greatest share of residential rental units of all non-residential conversions from 2010 to 2020, with nearly 5900 units created. However, there is suggestive evidence that more recent obsolete office buildings generate significantly fewer units as compared to office conversions of the 1990s. We additionally model the probability of conversion and redevelopment. We find that hotels have the highest conversion probability, followed by loft, retail, industrial, and office. In general, relatively taller, narrower, older buildings with diminished value are more likely to be converted.

How have the longer journeys to work faced by Black commuters evolved in the United States over the last four decades? Black commuters spent 49 more minutes commuting per week in 1980 than White commuters; this difference declined to 22 minutes per week in 2019. Two factors account for the majority of the difference: Black workers are more likely to commute by transit, and Black workers make up a larger share of the population in cities with long average commutes. Increases in car commuting by Black workers account for nearly one quarter of the decline in the racialized difference in commute times between 1980 and 2019. Today, commute times have mostly converged (conditional on observables) for car commuters in small- and mid-sized cities. In contrast, differential job access today drives persistent differences of commute times, particularly in large, congested, and expensive cities.

Geographically and temporally granular housing price indexes are difficult to construct. Data sparseness, in particular, is a limiting factor in their construction. A novel application of a spatial dynamic factor model allows for the construction of census tract level indexes on a quarterly basis while accommodating sparse data. Specifically, we augment the repeat sales model with a spatial dynamic factor model where loadings on latent trends are allowed to follow a spatial random walk thus capturing useful information from similar neighboring markets. The resulting indexes display less noise than similarly constructed non-spatial indexes and replicate indexes from the traditional repeat sales model in tracts where sufficient numbers of repeat sales pairs are available. The granularity and frequency of our indexes is highly useful for policymakers, homeowners, banks and investors.