Is preserving small,low-end rental housing feasible?

This paper uses multiple national datasets to examine the financial, structural, neighborhood, and tenant characteristics of 1–4 unit low-end rental properties, which house 44 percent of all poor renters in US cities. We investigate the feasibility of two strategies to stabilize these properties: (1) outsourcing property management, and (2) transferring bundles of properties to large owners to generate economies of scale, cash reserves, and lower financing costs. We find that approximately five percent of small affordable rental properties are stable, 65 percent are salvageable but at risk, and about 30 percent are not salvageable. For roughly 19 percent of the salvageable properties, a key problem is high vacancy rates, which could be addressed by professional tenant placement services. Bundling has greater potential, but requires purchases at below market prices, amounting to a subsidy.     

Comment on Kirk McClure's “Are low-income housing tax credit developments locating where there is a shortage of affordable units?”

Kirk McClure's article makes important contributions to our understanding of the way in which state allocating agencies are using the Low-Income Housing Tax Credit (LIHTC). However, one of the premises of his analysis – that allocating agencies should encourage the location housing developments in census tracts with a “surplus” of low-income renters – is mistaken. Census tracts are too small to be considered closed-system housing markets. Additionally, the LIHTC program does not exist in isolation, but instead as part of a combined national rental housing policy that includes both supply-side programs (LIHTC) and demand-side programs (housing vouchers). A final flaw in the notion that LIHTC units should be built in census tracts with a surplus of renter households in the 30% to 60% of AMI range compared with the units affordable to them is that increasing the amount of affordable housing in those tracts could have the effect of further concentrating households by income and race.     

Comment on Dennis P. Culhane et al.’s “Public shelter admission rates in Philadelphia and New York City: The implications of turnover for sheltered population counts”

Low-Income Housing Tax Credit (LIHTC) developments serve renter households with incomes between 30% and 60% of Area Median Family Income. Ideally, the program places units into neighborhoods where there is a shortage of units serving this cohort. LIHTC units are allocated to developers by state agencies through their Qualified Allocation Plans which should direct units to areas of need. Using a national database, this research examines where LIHTC developments were placed in service to determine whether these developments enter tracts experiencing shortages.

The LIHTC program is not directing units to those census tracts where there is a latent demand for units in this rent range. Rather, it is placing units into tracts that have surpluses. Equally, the program is not placing units in tracts with little or no affordable housing. This suggests that the program is not breaking down the income separation that exists in the nation's housing markets.     

How Do People-Based Housing Policies Affect People (and Place)?

Assisted housing programs in the United States aim to provide decent, safe, and affordable housing for low-income households. Increasingly, policymakers have also considered how assisted housing can provide access to lower poverty, income-diverse, and higher opportunity neighborhoods. The U.S. Department of Housing and Urban Development currently balances two strategies. First, place-based programs—immoveable subsidies linked to particular units—can both revitalize distressed neighborhoods and provide access to higher opportunity neighborhoods. Second, people-based assistance—housing vouchers for use on the private rental market—can facilitate moves out of high-poverty, low-opportunity neighborhoods. During this policy moment with fair housing priorities receiving national attention, understanding the efficacy of each approach is critically important. This article synthesizes past research on housing vouchers to identify the impact of people-based assistance on four outcomes: residents’ neighborhood attainment, education, economic outcomes, and health. I also review the scant literature examining how vouchers affect place rather than people. I conclude by identifying aspects of special voucher programs that promote positive outcomes that could potentially be scaled up.     

The Effects of a Housing Mobility Experiment on Participants’ Residential Environments

We used the Moving to Opportunity (MTO) housing experiment to inform how Housing Choice Vouchers and housing mobility policies can assist families living in high-poverty areas to make opportunity moves to higher quality neighborhoods, across a wide range of neighborhood attributes. We compared the neighborhood attainment of the three randomly assigned MTO treatment groups (low-poverty voucher, Section 8 voucher, control group) at 1997 and 2002 locations (4–7 years after baseline), using survey reports, and by linking residential histories to numerous different administrative and population-based data sets. Compared with controls, families in low-poverty and Section 8 groups experienced substantial improvements in neighborhood conditions across diverse measures, including economic conditions, social systems (e.g., collective efficacy), physical features of the environment (e.g., tree cover) and health outcomes. The low-poverty voucher group, moreover, achieved better neighborhood attainment compared with Section 8. Treatment effects were largest for New York, New York, and Los Angeles, California. We discuss the implications of our findings for expanding affordable housing policy.     

“Ragged urchins play on marquetry floors”: The discourse of filtering is reconstructed, 1920s–1950s

The idea of filtering has played a key role in our understanding of housing markets and in framing federal policy. The origins of the idea, however, and of the term itself, are poorly understood. Drawing loosely on the approach of discourse analysis, this article clarifies both issues, arguing that language shapes how we think about housing policy, and indeed policy itself. The concept of filtering emerged in Great Britain in the late nineteenth century where, by 1900, it informed arguments in favor of municipal (public) housing. It became influential in the United States in the 1920s but in 1938 was still referred to in different ways, notably as “hand-me-down housing.” Here, it was understood more narrowly, as an alternative to public housing. After 1939, the Federal Housing Administration, though not its leading consultant Homer Hoyt, popularized the term “filtering.” The neutral connotations of this metaphorical term suited the agency's goal of developing an apparently objective discourse of housing markets and market analysis. The term was normalized by the early 1960s.     

Why Do Housing Mobility Programs Fail in Moving Households to Better Neighborhoods?

This article conceptualizes the relationship between housing instability, residential mobility, and neighborhood quality. We summarize the existing literature about residential mobility and housing instability and examine their potential interactions along three dimensions: (a) the reasons for a move, including a variety of push and pull factors; (b) mobility outcomes in terms of whether moves result in residing in a better or worse neighborhood than that of the prior residence; and, especially important for low-income households, (c) the degree to which the current move and past experiences of moving have been discretionary or forced. Housing instability is a cumulative concept, with involuntary moves at its center. This synthetic model of housing instability's impact on mobility outcomes suggests that the more instability a household has experienced, the less likely mobility moves are to occur, or, if they do occur, to be long lasting. Policy implementation may underestimate the interaction between cumulative housing instability and residential mobility in housing mobility policies. Thus, these interactions have implications for mobility policies, pointing toward a path for future research that inform policies to move low-income households toward both greater housing stability and better neighborhood outcomes.