Publications

  • Welcome to the Neighborhood: What can Regional Science Contribute to the Study of Neighborhoods?

    In this paper the authors argue that neighborhoods are highly relevant for the types of issues at the heart of regional science. First, residential and economic activity takes place in particular locations, and particular neighborhoods. Many attributes of those neighborhood environments matter for this activity, from the physical amenities, to the quality of the public and private services received. Second, those neighborhoods vary in their placement in the larger region and this broader arrangement of neighborhoods is particularly important for location choices, commuting behavior and travel patterns. Third, sorting across these neighborhoods by race and income may well matter for educational and labor market outcomes, important components of a region's overall economic activity. For each of these areas we suggest a series of unanswered questions that would benefit from more attention. Focused on neighborhood characteristics themselves, there are important gaps in our understanding of how neighborhoods change - the causes and the consequences. In terms of the overall pattern of neighborhoods and resulting commuting patterns, this connects directly to current concerns about environmental sustainability and there is much need for research relevant to policy makers. And in terms of segregation and sorting across neighborhoods, work is needed on better spatial measures. In addition, housing market causes and consequences for local economic activity are under researched. The authors expand on each of these, finishing with some suggestions on how newly available data, with improved spatial identifiers, may enable regional scientists to answer some of these research questions.

  • What Can We Learn about the Low Income Housing Tax Credit Program by Looking at the Tenants?

    Using tenant-level data from fifteen states that represent more than thirty percent of all Low Income Housing Tax Credit (LIHTC) units, this paper examines tenant incomes, rental assistance and rent burdens to shed light on key questions about our largest federal supply-side affordable housing program. Specifically, what are the incomes of the tenants, and does this program reach those with extremely low incomes? What rent burdens are experienced, and is economic diversity within developments achieved? We find that more than forty percent of tenants have extremely low incomes, and the overwhelming majority of such tenants also receive some form of rental assistance. Rent burdens are generally higher than for HUD housing programs, but vary greatly by income level and are lowered by the sizable share of owners who charge below maximum rents. Finally, we find evidence of both economically diverse developments and those with concentrations of households with extremely low incomes.

  • What Can We Learn about the Low-Income Housing Tax Credit Program by Looking at the Tenants?

    This policy brief examines LIHTC tenant income to assess the extent to which the program’s target demographic is served. The brief finds that forty percent of LIHTC units house extremely low-income (ELI) households. In addition, the report finds that of ELI households living in LIHTC units, more than 70 percent receive some form of rental assistance, which suggests that additional subsidies are crucial to the functionality of the program. In terms of rent burden, LIHTC tenants, particularly those without rental assistance, have higher rent burdens than HUD tenants. Since it was created in 1986, the LIHTC program has created over 2.2 million units of affordable housing and today it is the largest affordable housing program in the U.S. This study is the first rigorous, national analysis of the incomes of LIHTC tenants.

  • What do Business Improvement Districts do for Property Owners?

    The article explores on the impact of business improvement districts (BIDS) to property owners in New York City. The scheme is essential to private local governments through the businesses' pay fees to supplement the package of public services in their local area. By using difference-in-difference (DD) hedonic modeling approach, one can estimate changes in property values in BID areas compared to those non-BID areas.

  • What Do We Know About Housing Choice Vouchers?

    Four decades after its creation, the Housing Choice Voucher Program is the largest low-income housing subsidy program managed by the Department of Housing and Urban Development (HUD). This literature review covers what we know and don’t know about the Housing Choice Voucher Program. 

    Research shows that vouchers reduce the rent burdens of low-income households, allow them to live in less crowded homes, and help them to avoid homelessness. The program has been less successful, however, in getting recipients to better neighborhoods and schools, and perhaps the greatest disappointment of the program is its limited reach. Families wait for years in most places to receive a voucher, and only one in four households eligible for a voucher nationally receives any federal housing assistance. Further, a significant minority of households who receive vouchers never use them, in part because of the difficulty of finding willing landlords with acceptable units. Thus, as effective as the program is, there is still much to learn about its operation and how we might improve it.

  • What Do We Know About Housing Choice Vouchers?

    The Housing Choice Voucher Program provides assistance to approximately 2.2 million households each year, making it the largest low-income housing subsidy program managed by the U.S. Department of Housing and Urban Development (HUD). This paper reviews what we know about the program. In brief, experimental research shows that vouchers help to reduce the rent burdens of low-income households, allow them to live in less crowded homes, and minimize the risk of homelessness. Research also shows, however, that the program has been far less successful in getting recipients to better neighborhoods and schools. And perhaps the greatest disappointment of the program is its limited reach. Families typically wait for years to receive a voucher, and only one in four households eligible for a voucher nationally receives any federal rental housing assistance. Another issue is that a significant share of households who receive vouchers never use them, in part because of the difficulty of finding willing landlords with acceptable units. Thus, as effective as the program is, there is still room for improvement.

  • What Have We Learned from HUD’s Moving to Opportunity Program?

    “Choosing a Better Life?” is the first distillation of years of research on the MTO project, the largest rigorously designed social experiment to investigate the consequences of moving low-income public housing residents to low-poverty neighborhoods. In this book, leading social scientists and policy experts examine the legislative and political foundations of the project, analyze the effects of MTO on lives of the families involved, and explore lessons learned from this important piece of U.S. social policy.

  • What’s Happened to the Price of College? Quality Adjusted Net Price Indices for 4 Year College

    In this paper we estimate hedonic models of the (consumer) price of college to construct quality-adjusted net price indexes for U.S. four-year colleges, where the net price of college is defined as tuition and fees minus financial aid. For academic years 1990–91 to 1994–95, we find adjusting for financial aid leads to a 22 percent decline in the estimated price index for all four year colleges, while quality adjusting the results leads to a further, albeit smaller, decline. Nevertheless, public comprehensive colleges, perhaps an important gateway to college for students from low-income backgrounds, experienced the largest net price increases.

  • Why Do Higher Income Households Move Into Low Income Neighborhoods? Pioneering or Thrift?

    This paper offers several hypotheses about which US higher-income households choose to move into low-income neighbourhoods and why. It first explores whether the probability that a household moves into a relatively low-income neighbourhood (an RLIN move) varies with predicted household and metropolitan area characteristics. Secondly, it estimates a residential choice model to examine the housing and neighbourhood preferences of the households making such moves. Thirdly, it explores responses to survey questions about residential choices. Evidence is found that, in the US, households who place less value on neighbourhood services and those who face greater constraints on their choices are more likely to make an RLIN move. No evidence is found that households making RLIN moves are choosing neighbourhoods that are more accessible to employment. Rather, it is found that households making RLIN moves appear to place less weight on neighbourhood amenities than other households and more weight on housing costs.

  • Why Don’t Housing Voucher Recipients Live Near Better Schools? Insights from Big Data

    This paper by Ingrid Gould Ellen, Keren Mertens Horn, and Amy Ellen Schwartz, published in the Journal of Policy Analysis and Management, uses administrative data to explore why voucher households do not live near to better schools, as measured by school-level proficiency rates. It seeks to shed light on whether voucher households are more likely to move toward better schools when schools are most relevant, and how market conditions shape that response. The authors find that families with vouchers are more likely to move toward a better school in the year before their oldest child meets the eligibility cutoff for kindergarten. Further, the magnitude of the effect is larger in metropolitan areas with a relatively high share of affordable rental units located near high-performing schools and in neighborhoods in close proximity to higher-performing schools. Results suggest that, if given the appropriate information and opportunities, more voucher families would move to better schools when their children reach school age.