Publications

  • Utility Allowances in Federally Subsidized Multifamily Housing

    This paper provides an analysis of the statutes, regulations, and guidance that govern the treatment of utility costs in the four largest federal subsidized housing programs—Public Housing, Project-Based Section 8, Housing Choice Vouchers, and Low-Income Housing Tax Credits—and the incentives these rules create for the consumption of utilities. It finds that many of these programs are structured such that tenants and owners are either indifferent about utility costs or are rewarded for overconsumption. This paper makes several recommendation for how these programs can be restructured to incentivize lower utility consumption, which can reduce the environmental footprint of subsidized housing, improve the financial viability of existing subsidized properties, and free resources that can be repurposed for other HUD goals.

  • Valuing Urban Land: Comparing the use of Teardown and Vacant Land Sales

    This study explores the use of “teardown” sales to estimate the value of urban land. When a buyer purchases a property intending to tear down the existing structure and rebuild, the value of land can potentially be estimated as the purchase price plus demolition costs. There has been little exploration of teardown sales in cities around the country, or any explicit comparisons between the estimates of land values derived from teardown sales and those derived through vacant land sales. This paper undertakes just such an explicit comparison, analyzing approximately 3800 teardown sales and 4900 vacant land sales occurring in New York City between 2003 and 2009. The two approaches yield surprisingly similar estimates of the value of both parcel attributes and locational amenities. However, vacant parcels are disproportionately located in very distressed neighborhoods and tend to be valued less highly than teardown parcels, even in the same neighborhood. Teardown parcels appear to be more representative of the city as a whole and may be a more useful approach to developing estimates of land prices, at least in the central cities of large urban areas where sample sizes are large enough.

  • Welcome Neighbors? New Evidence on the Possibility of Stable Racial Integration

    The conventional wisdom on racial integration in the United States is that there are three kinds of neighborhoods: the all-white neighborhood, the all-black neighborhood, and the exceedingly rare, highly unstable, racially mixed neighborhood. The only real disagreement is about why so few neighborhoods are successfully integrated. Some attribute it to white discrimination pure and simple: whites, that is, have consciously and determinedly excluded blacks from their communities. Others contend that it is a matter of minority choice. Like Norwegians in Brooklyn's Bay Ridge and Italians in Manhattan's Little Italy, African Americans, they explain, prefer to live among their own kind. Finally, others maintain that segregation is driven mainly by income differences across racial groups. But almost all agree that when African Americans do manage to gain a foothold in a previously all-white community, the whites move away in droves—a phenomenon well known as "white flight." Integration is no more than, in the words of Saul Alinsky, the "time between when the first black moves in and last white moves out."

  • 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?

    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.