Research

What explains the significant dispersion in the rent-price ratio of rental housing units across and within US cities? Conventional urban models attribute this heterogeneity to differences in rent growth expectations, although it could also reflect a dispersion in returns earned by investors. In this paper, I construct local average ratios using administrative data on rental properties that reflect the investment decision of household real estate investors in a broad cross section of US cities and neighborhoods. A strong negative covariance between ratios and prices, along with a novel application of the Campbell-Shiller decomposition, can be used to reject the null that investors' rent growth expectations plausibly account for this dispersion. To quantify the relative importance of returns, I decompose past rent-price ratio dispersion into realized dispersion in rent growth and returns. I find that 80% of within-city and 90% of cross-city rent-price dispersion is explained by differential returns, implying large risk differentials, or barriers to arbitrage. These findings underscore the importance of integrating financial mechanisms in understanding the geography of housing markets and household finance.

The Interstate Highway System (IHS) has profoundly influenced American cities. While highways have facilitated accessibility and spurred development, their impacts on local communities have prompted debates about their broader legacy. This paper examines how the availability of land mediates the IHS’s effects on the housing market, including housing and land prices. I find that highways drive population growth where land is abundant, and lower prices where it is scarce. Using an AMM-style model the paper quantifies the welfare and population effects of the IHS’s housing market channel, finding large, though heterogeneous, gains. These findings highlight the potential housing benefits of infrastructure projects when subsequent development is possible.

What explains the significant dispersion in rent-price ratios units across and within US cities? Conventional urban models attribute this heterogeneity to differences in rent growth expectations. Using a Campbell-Shiller decomposition I find that since 1980 rent growth only accounts for 10-30% of cross-sectional dispersion, leaving 70-90% to be explained by differential returns. To rationalize persistent differences in returns I build a theoretical framework that embeds an asset pricing model within an urban setting. In the model, landlords invest only in properties that are nearby, an assumption supported by the data. Geographic segmentation among landlords and inelastic housing supply cause spatial dispersion in rental returns in response to local heterogeneity in wealth levels and inequality, as well as rent volatility. Model-implied comparative statics hold in the data both within and across cities: returns fall and prices rise with average wealth and the share of investors, and vice-versa with the share of renters. Calibration of the model suggests that equalizing investor wealth and risk across space would reduce rent-price dispersion by 30% and 70%, respectively. My analysis suggests that the geography of wealth and rent growth volatility are essential in understanding spatial differences in housing prices.

This paper leverages new measurement of neighborhood consumption amenities to demonstrate that housing prices and rents in U.S. cities are likely determined as much by access to amenities as by access to employment. We extend the Alonso-Muth-Mills model, allowing residents to derive utility from within-city trips to amenities. The model delivers standard estimable log-linear pricing equations as well as new measures of amenities based on a destination’s popularity during leisure hours. We find our amenity measures add substantial explanatory power, have large effects in magnitude, and as much as halve naive estimates of commute costs such that employment and amenity access are similarly important. The findings hold using a wide variety of alternative measures and are neither driven by density nor fully explained by the locations of business establishments. These results suggest the potential robustness of cities to changes in employment locations.