Hal Martin

This paper studies how design features influence the success of Housing Mobility Programs (HMPs) in reducing racial segregation. Targeting neighborhoods based on previous residents' outcomes does not allow for targeting race-specific outcomes, generates uncertainty when targeting income-specific outcomes, and generates bias in ranking neighborhoods' effects. Moreover, targeting opportunity bargains based on previous residents' outcomes selects tracts with large disagreements in current and previous residents' outcomes, with such disagreements predicted by sorting since 1990. HMP success is aided by the ability to port vouchers across jurisdictions, access to cars, and relaxing supply constraints, perhaps by targeting lower-ranked neighborhoods.

The Opportunity Atlas (OA) is an innovative data set that ranks neighborhoods according to children’s adult outcomes in several domains, including income. Conceptually, outcomes offer new evidence about neighborhood effects when measured in isolation from neighborhood sorting. This paper shows that neighborhood sorting contributes to OA estimates. We document cases in which small sample sizes and changes over time can explain disagreements between OA rankings and those based on contemporaneous variables. Our results suggest caution for interpretations of the OA data set at a granular level, particularly for predictions about the outcomes of black children in high-income neighborhoods.

There is currently interest in crafting public housing policy that combats, rather than contributes to, the residential segregation in American cities. One such policy is the Housing Mobility Program (HMP), which aims to help people move from disinvested neighborhoods to ones with more opportunities. This paper studies how design features influence the success of HMPs in reducing racial segregation. We find that the choice of neighborhood opportunity index used to define the opportunity areas to which participants are encouraged to move has limited influence on HMP success. In contrast, we find that three design features have large effects on HMP success: 1) whether the geographic scope is confined to the central city or is implemented as a metro-level partnership; 2) whether the eligibility criteria are race-based, race-conscious, or race-neutral; 3) whether tenant counseling, tenant search assistance, and landlord outreach are successful in relaxing rental housing supply constraints.

Landlords in high-opportunity neighborhoods screen out tenants using vouchers. In our correspondence experiment, signaling voucher status cuts landlord responses in half. This voucher penalty increases with posted rent and varies little with signals of tenant quality and race. We repeat the experiment after a policy change and test how landlords respond to raising voucher payment limits by $450 per month in high-rent neighborhoods. Most landlords do not change their screening behavior; those who do respond are few and operate at small scale. Our results suggest a successful, systematic policy of moving to opportunity would require more direct engagement with landlords.

Despite being eligible for use in any neighborhood, housing choice vouchers tend to be redeemed in low-opportunity neighborhoods. This paper investigates how landlords contribute to this outcome and how they respond to efforts to change it. We leverage a policy change in Washington, DC, that increased voucher rental payments only in high-rent neighborhoods. Using two waves of a correspondence experiment that bracket the policy change, we show that most opportunity landlords screen out prospective voucher tenants, and we detect no change in average screening behavior after a $450 per month increase in voucher payments. In lease-up data, however, enough landlords do respond to increased payments to equalize the flow of voucher tenants into high- vs. low-rent neighborhoods. Using tax data and listings from a website specializing in subsidized housing, we characterize a group of marginal opportunity landlords who respond to higher payments. Marginal opportunity landlords are relatively rare, list their units near market rates, operate on a small scale, and negatively select into the voucher program based on hard-to-observe differences in unit quality.

Despite being eligible for use in any neighborhood, housing choice vouchers tend to be redeemed in low-opportunity neighborhoods. This paper investigates whether landlord behavior contributes to this outcome by studying the recent expansion of neighborhood-based voucher limits in Washington, DC. We conduct two waves of a correspondence experiment: one before and one after the expansion. Landlords heavily penalize tenants who indicate a desire to pay by voucher. The voucher penalty is larger in high-rent neighborhoods, pushing voucher tenants to low-rent neighborhoods. We find no evidence that indexing rents to small areas affects landlord acceptance of voucher tenants. The data can reject the claim that increasing rent limits by less than $3,000 per month can eliminate the voucher penalty. Neighborhood rent limits do shift lease-up locations toward high-rent neighborhoods in the year after the policy change, an effect that is large relative to the number of voucher households that move but small relative to all voucher tenants.

This paper simulates changes to neighborhood home prices resulting from reforms to tax preferences in the recently passed Tax Cuts and Jobs Act (TCJA). The simulation uses federal tax data summarized at a fine geography to impute homeowner rents at the zip code level across six income classes. Employing a user cost framework, I model rents as a function of prices under the old tax law and under the TCJA. While the average price impact of the TCJA is found to be -5.7 percent, local effects range from 0 to -23 percent across zip codes. Variation across income class is also large. Simulations by income class suggest that the most severe declines in price occur for upper middle-income households ($100,000–$200,000). The paper also simulates partial versions of the TCJA that omit different features of the law that affect housing preference. I find that the higher standard deductions in the new law are the largest driver of price declines.

This paper provides evidence on the relationship between differential treatment of minority borrowers and their mortgage market outcomes. Using data from a field experiment that identifies differential treatment matched to real borrower transactions in the Home Mortgage Disclosure Act (HMDA) data, we estimate difference-in-difference models between African American and white borrowers across lending institutions that display varying degrees of differential treatment. Our results show that African Americans are more likely to be in a high-cost (subprime) loan when borrowing from lenders that are more responsive to them in the field experiment. We also show that net measures of differential treatment are not related to the probability of African American borrowers having a high-cost loan. Our results suggest that differential outcomes are related to within-institution factors, not just across-institution factors like institutional access, as previous studies find.

Using the results of a comprehensive in-person survey of properties in Cleveland, Ohio, we fit predictive models of vacancy and property conditions. We draw predictor variables from administrative data that is available in most jurisdictions such as deed recordings, tax assessor's property characteristics, and foreclosure filings. Using logistic regression and machine learning methods, we are able to make reasonably accurate out-of-sample predictions. Our findings indicate that housing professionals could use administrative data and predictive models to identify distressed properties between surveys or among nonsurveyed properties in an area subject to a random sample survey.

We simulate changes to metropolitan area home prices from reforming the Mortgage Interest Deduction (MID). Price simulations are based on an extended user cost model that incorporates two dimensions of behavioral change in home buyers: sensitivity of borrowing and the propensity to use tax deductions. We simulate prices with both inelastic and elastic supply. Our results show a wide range of price effects across metropolitan areas and prospective policies. Considering behavioral change and no supply elasticity, eliminating the MID results in average home price declines as steep as 13.5 percent in Washington, D.C., and as small as 3.5 percent in Miami-Fort Lauderdale, Florida. Converting the MID to a 15 percent refundable credit reduces prices by as much as 1.4 percent in San Jose, California, San Francisco, California, and Washington, D.C., and increases average prices in other metropolitan areas by as much as 12.1 percent (Miami-Fort Lauderdale). Accounting for market elasticities produces price estimates that are on average thirty-six percent as large as standard estimates.

Eviction filings have largely returned to their prepandemic levels in 2022 after a long period of being below trend. In this <em>Economic Commentary</em>, I describe the trends in eviction filings collected so far during the pandemic and pandemic-era policies aimed at mitigating the damage of the pandemic on housing stability. I show that restrictions on evictions, common early in the pandemic, are associated with lower levels of eviction filings; that recent rent-price growth is associated with higher levels of eviction filings; and that the timing of eviction trends varies in response to the federal emergency rental assistance program in a way that is consistent with the program’s design.

Temporary policies put in place to protect renters are beginning to expire. To understand how the crisis is affecting evictions, our researchers measured eviction filing activity in 44 cities and counties across the nation.

Evictions are a serious risk for households facing job loss and economic upheaval during the COVID-19 pandemic, and temporary policies put in place to protect renters are beginning to expire. To understand how the crisis is affecting evictions, we measured eviction filing activity across 44 cities and counties. As of July 7, 2020, eviction filings have almost returned to their prepandemic levels in places where local bans have expired or where they were never enacted. We find that eviction filings tend to surge after temporary policies expire much more in places that enacted both filing bans and hearing bans than those that enacted just hearing bans while allowing filings to continue. As federal stimulus supplements for the unemployed expire, evictions are likely to increase for households that have lost work because of the crisis unless there is material improvement in the economy (Mervosh 2020).

Increased digital services show that recent regulatory guidelines do not prevent bank branches from complying with the Community Reinvestment Act.