Session 16: Housing and Urban Economics

What explains the principal patterns and changes in racial segregation in American cities? Where does change occur and how shall this shape the way that we think about neighborhood racial tipping? Here we develop a discrete choice approach to neighborhood selection and racial segregation, but crucially relax the common assumption of zero spillovers of racial tastes across locations. Our approach allows us to nest Schelling’s (1971) bounded neighborhood and spatial proximity models in general equilibrium. In simulations, we investigate the equilibrium properties of our model and derive hypotheses that we explore empirically using US census data from 1970 to 2000. We show that patterns in the data support the presence of spatial racial spillovers and show that these are key to understanding where neighborhood tipping, which we define as drastic racial change, is taking place. Our results on the locus of racial change conflict strongly with those of Card et al. (2008). For this reason, we revisit their work. We show that a combination of modern regression discontinuity methods plus theory-consistent restrictions lead their tipping results, which they define as dramatic white exit at critical thresholds of the minority share, to become precisely-measured zeros. Instead of tipping driving their results, we provide evidence that biased White suburbanization, closer to the ideas of Boustan (2010), explains the patterns they identify. In sum, our approach provides a distinct way of interpreting neighborhood tipping, as well as a promising path for understanding both the cross section and dynamics of spatial racial patterns in the data.

We build a dynamic model of an urban area which combines key features of quantitative spatial models and macro-housing models. It integrates a large number of locations, forward-looking households, commuting, costly migration, uninsurable income risk, and housing tenure choice. We cast the model in continuous time, while shocks arrive and some choices are taken at discrete time intervals. This “mixed time” approach allows us to efficiently compute both steady state equilibrium and transition dynamics, even when there are thousands of location pairs. Using a quantitative model of the San Francisco Bay Area, we show how accounting for forward-looking behavior, spatial frictions, and transition dynamics changes the estimated effects of spatially heterogeneous shocks and policies that have traditionally been studied with static models.

This paper estimates the causal effects of childhood residential location on the adult income of native-born Israeli children and the children of immigrants from the former Soviet Union and studies the consequences of location effect heterogeneity on the design and effectiveness of neighborhood recommendation policies. The causal effects of childhood location contribute substantial variability to the adult earnings of Israeli children. While the place effects of high-income immigrants and high-income natives are strongly correlated, location effects for low-income immigrants are uncorrelated with location effects for low-income natives. Guided by these findings, we develop a policy targeting framework aiming to recommend the top locations in Israel while incorporating the constraint that the policymaker cannot make ethnicity-dependent location recommendations. Using empirical Bayes tools, we find that targeting policies based on pooled population-wide averages yield inferior outcomes for immigrants. Robust targeting strategies designed to perform well against the least favorable sorting patterns reveal a set of 5 cities that are likely to benefit children of both groups.

High-skilled workers and high-productivity firms co-locate in large cities. In this paper, I study how the two-sided sorting of workers and firms affects spatial earnings inequality, efficiency of the allocation of workers and firms across cities, and the welfare consequences of place-based policies. I build a general equilibrium model in which heterogeneous workers and firms sort across cities and match within cities. I estimate the model using Canadian matched employer-employee data and decompose the urban earnings premium, finding that worker and firm sorting account for 67% and 27% of this premium, respectively. The decentralized equilibrium is inefficient as low-productivity firms overvalue locating in high-skilled cities. The optimal spatial policy would incentivize high-skilled workers and high-productivity firms to co-locate to a greater extent while redistributing income towards low-earning cities, leading to a 6% increase in social welfare. Model counterfactuals underscore the importance of two-sided sorting when evaluating distributional and aggregate outcomes of place-based policies.

I study the impact of the recent rise in remote work on households’ consumption, wealth and housing decisions, examining both short-run and long-run effects. Using detailed UK property-level housing data and a heterogeneous agent model with endogenous housing tenure and city geography, I show that remote work shifts households’ housing demand by increasing the demand for space and reducing the commuting costs. It affects where people live in the city and their housing wealth accumulation. The effects vary by access to remote work, income, and wealth. The rise in work-from-home can be compared to a suburb-wide gentrification shock as wealthy telecommuters opt for larger suburban homes, dis- placing marginal owners who turn to renting. In the long-run, work-from-home leads to the rise of a tele-premium and consumption inequality increases.

The social cost of evictions is increasingly well-understood. Less understood is the scope for policy to reduceevictions. We use lease-level ledger data from high-eviction rental markets to study which evictions can be averted, for how long, and with which policies. To do so, we characterize several determinants of the landlord decision to evict: the persistence of shocks to tenant default risk, landlords’ information about these shocks, and landlords’ costs in the eviction process. We find nonpayment is common, but so is recovery; landlords frequently forbear nonpayment; and eviction decisions are consistent with landlords learning over time about tenants’ evolving nonpayment risk. Based on these patterns, we develop and estimate a dynamic discrete choice model of the eviction decision with which we analyze tenant-protection policy. The estimated model shows that taxes on eviction or subsidies for delinquent tenants are relatively cost-effective and preserve matches for tenants with higher future ability-to-pay. In contrast, in-kind legal aid more effectively targets tenants in the greatest financial distress.

A rent guarantee insurance (RGI) policy makes a limited number of rent payments to the landlord on behalf of an insured tenant unable to pay rent due to a negative income or health expenditure shock. We introduce RGI in a rich quantitative equilibrium model of housing insecurity and show it increases welfare by improving risk sharing across idiosyncratic and aggregate states of the world, reducing the need for a large security deposits, and reducing homelessness which imposes large costs on society. While unrestricted access to RGI is not financially viable for either private or public insurance providers due to moral hazard and adverse selection, restricting access can restore viability. Private insurers must target better off renters to break even, while public insurers focus on households most at-risk of homelessness. Stronger tenant protections increase the effectiveness of RGI.

This paper demonstrates that unpaid rent risk makes landlords reluctant to supply housing services to fragile tenants; and that insuring owners against it improves the access of renters to high-opportunity neighborhoods. We study the implementation of Visale, a publicly funded rent guarantee insurance policy in France, free of charge to eligible tenants and landlords. We exploit exhaustive registry information on all French households, data on the universe of Visale beneficiaries and claim payouts, and quasi- experimental eligibility variation across renters. We demonstrate that the non payment guarantee increased access to private-sector rental housing for eligible tenants. The effects are stronger for immigrants and those with low or volatile incomes, who often do not satisfy standard screening criteria for landlords. The scheme eased the spatial mobility of low-income renters towards higher-wage, higher-rent locations. It led to new household formation and some reallocation of the vacant housing stock, but may have displaced ineligible households in tighter housing markets.

Growing cities in low- and middle-income countries offer increased market access, yet this requires that residents explore their surroundings. This is not always the case. In a sample of 800 casual workers in Nairobi, the median person commutes 7.8 km but has never been to half the neighborhoods at most 75 minutes from where they live. We offer short-term employment to these workers and experimentally induce familiarity by training participants in either familiar or unfamiliar locations. We measure willingness to work in different locations across the city. Participants need to be paid more to work in a neighborhood that is unfamiliar at baseline. The premium is equivalent to 3.5 km of distance or to 113 Ksh (23% of the median daily wage), and this is fully offset after one visit. Participant beliefs about labor market opportunities and safety in unfamiliar neighborhoods are initially worse on average, but converge after one visit. We consider two additional potential barriers to exploration: forecasting errors and the attentional salience of familiar neighborhoods.

We use detailed location information from the Longitudinal Employer‐Household Dynamics (LEHD) database to develop new evidence on the effects of spatial mismatch on the relative earnings of Black workers in large US cities. We classify workplaces by the size of the pay premiums they offer in a two‐way fixed effects model, providing a simple metric for defining “good” jobs. We show that: (a) Black workers earn nearly the same average wage premiums as whites; (b) in most cities Black workers live closer to jobs, and closer to good jobs, than do whites; (c) Black workers typically commute shorter distances than whites; and (d) people who commute further earn higher average pay premiums, but the elasticity with respect to distance traveled is slightly lower for Black workers. We conclude that geographic proximity to good jobs is unlikely to be a major source of the racial earnings gaps in major U.S. cities today.

Since the Great Recession, the rise of single-family rental companies has changed the investor ownership landscape in the U.S. Using housing transaction data, we document the rise of Long Term Rental (LTR) companies, defined as inclusive of single-family rental, rent-to-own, and real estate private equity firms, by constructing a panel of national single-family housing portfolios between 2010 and 2022. We show that LTR growth outstripped all other investor types, such as builders, iBuyers, and small investors, over the last decade. These companies geographically concentrate their holdings in select census tracts and expand their local market shares over time. To estimate LTRs’ impacts on local housing markets, we construct a novel instrument predicting LTR entry, which we name the “suitability index.” In the cross-section, this instrument leverages differential revealed preferences in product characteristics across landlord types. In the time-series, we interact these differential product preferences with a proxy for falling property management costs over time. In the first stage, more suitable locations for LTRs experience higher growth in LTR shares: a one-standard-deviation increase in the instrument implies a 54.4% higher annual growth in LTR share relative to the baseline mean. We use this instrument for LTR market entry to estimate the causal impact of LTR market share on local house prices. We find that a one-standard-deviation above the mean increase in LTR share growth leads to an annual additional house price growth of 1.24pp and additional rent growth of 2.51pp. Finally, we discuss how the reallocation of homeownership across small and large landlords, as well as owner-occupants and investors, contribute to these price increases.

Why is it so expensive to build? Foerster et al. (2022) find that the American economy for-goes 0.75 percentage points in GDP growth every year because of sluggish productivity growth in construction, which implies a $1 trillion loss every five years. We formalize and evaluate the hypothesis that land-use regulation limits the size of building projects, which reduces the size of construction companies, which limits scale economies and the incentives to invest in innovation, which limits productivity growth. We document a construction productivity Kuznets curve in 20th century America, where homes built per construction worker remained stagnant between 1900 and 1940, boomed after World War II, and then plummeted after 1970, perhaps because, as the country became wealthier, it also regulated land use more. Around those years, innovation in construction also started to persistently lag behind other goods-producing sec-tors. The US now today has higher production costs than comparably developed countries, and these costs are higher in more regulated cities. Residential construction firms are small, relative to other industries like manufacturing, and smaller firms are less productive. More regulated metropolitan areas have smaller and less productive firms. Under the assumption that one half of the observed link between size and productivity is causal, America’s residential construction firms would be approximately 50 percent more productive if their size distribution matched that of manufacturing.

Manufactured housing is the largest source of unsubsidized affordable housing in the US, but the production of manufactured homes has fallen from more than fifty percent of single‐family housing starts in the mid‐70s to under ten percent today. Using publicly claimed security interests and the movement reported on oversize trip permits, I follow each home in Texas from factory to dealership to buyer, as it transforms from finished goods to wholesale collateral to consumer collateral, to show that this restriction in supply is consistent with market fore‐closure. Upstream manufacturers extend “floor plan” financing to downstream retailers buying homes for their lots, and restrict output in the downstream market. Floor plan financing acts as the vertical restraint a manufacturer needs, during two decades of a growing housing shortage, to distort competition closer to the inefficient monopoly outcome.

Voter support for anti-development policies contributes to America’s acute housing short-age. Prevailing theories of support for anti-development policies emphasize NIMBYism (op- position to housing nearby) and homeowners’ self-interest. We offer an additional explanation drawing on symbolic politics theory. This theory argues that voters have positive or negative affect towards various symbols, often developed early in life; later, they evaluate policies based on their affect towards relevant symbols. In line with the theory, we show that affect towards salient symbols powerfully explain anti-development preferences. First, homeowner- renter gaps in support for increasing density disappear when accounting for affect towards cities. Next, experiments show that affect towards developers, government entities, and new housing’s residents also explain anti-development preferences when policies make these symbols visible; homeownership and NIMBYism often fail to predict patterns we find. Finally, consistent with a role for socialization, historical public opinion data suggests that birth cohort helps explain affect towards cities.