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Although rental housing has historically maintained a peripheral position within the community-building sphere, the current economic volatility is evidence of how imbalanced housing policy can impact overall stability, particularly among low-income people within low-income communities. Economic and other macro-environmental shifts will have lasting and poignant impacts on low-income geographies; therefore, the state of rental housing within the context of urban neighborhoods will continue to be a critical policy matter. This research explores whether the low-income housing tax credit (LIHTC) program encourages the development of housing with the physical and operational attributes that strengthen low-income neighborhoods. Given the program's growing dominance, this study analyzes whether specific characteristics associated with neighborhood revitalization are prevalent in LIHTC properties located within qualified census tracts. Also examined are the methodologies among nonprofit developers and for-profit developers relative to these development characteristics.

The findings indicate that properties under 50 units are more likely to be located within suburban qualified census tracts. Within the urban core, the results reveal that qualified census tract LIHTC developments are more often serving extremely low and low-income families. The research outcomes also show that nonprofit developers are more likely to serve lower incomes and utilize certified property management agents for these properties. Given the unique needs of urban and suburban low-income neighborhoods and a national environment that portents a growing dependence upon the LIHTC, the findings suggest that both enhanced coordination between state, regional, and local interests and innovation in resource allocation policy are critical to erasing the neighborhood divide that marginalizes low-income people in low-income communities.

Models of race-based segregation establish that individual characteristics or housing market attributes are complementary causes of the observed level of races’ concentration inside an urban space. The goal of this work is to establish which variables, and in which order of magnitude, among individual characteristics, housing features, and local amenities correlate with immigrants’ segregation, in the case of consistent within-city immigrants’ mobility. We capture the degree of segregation for different immigration groups by a local concentration statistics that is directly obtained from segregation curves, and we use data on the Verona Municipality as a case study. We find strong evidence in favor of the role of the housing market and housing ownership distribution across city areas.

The neighborhoods north and northwest of downtown St Louis are blighted by their abundance of substandard, abandoned, and demolished housing. Crime, poverty, and unemployment are high while family stability, educational achievement, and health outcomes are low. These conditions are not unique to St Louis, but can be found in neighborhoods in every city in America. How did this happen? What factors led to the demise of these neighborhoods? This chapter examines the history of St Louis along with theories of neighborhood succession to identify possible explanations for the city's collapse.

The pattern of racial segregation in U.S. elementary and secondary schools has changed significantly over the last 25 years. This chapter examines the relationship between the racial composition of schools and the choices white parents make concerning the schools their children attend. Restricted access files at the Bureau of the Census allow us to identify each household's Census block of residence and, in turn, suburban public school districts and urban public school attendance areas. We find that the racial composition of schools and neighborhoods are very important in the school and location decisions of white families.

Airport noise is an undesirable consequence of arriving and departing flights. Much research effort has focused on how such noise affects the prices of houses located nearby and consistently finds that more noise is associated with lower housing prices.¹ On the other hand, few studies have examined the determinants of airport noise.

Purpose — In this paper we describe a total design data collection method (expanding the definition of the usual “total design” terminology used in typical household travel surveys) to emphasize the need to describe individual and group behaviors embedded within their spatial, temporal, and social contexts.

Methodology/approach — We first offer an overview of recently developed modeling and simulation applications predominantly in North America followed by a summary of the data needs in typical modeling and simulation modules for statewide and regional travel demand forecasting. We then proceed to describe an ideal data collection scheme with core and satellite survey components that can inform current and future model building. Mention is also made to the currently implemented California Household Travel Survey that brings together multiple agencies, modeling goals, and data collection component surveys.

Findings — The preparation of this paper involved reviewing emerging transportation modeling approaches and paradigms, policy questions, and behavioral issues and considerations that are important in the multimodal transportation planning context. It was found that many of the questions being asked of policy makers in the transportation domain require a deep understanding of the interactions and constraints under which individuals make activity-travel choices, the learning processes at play, and the attitudes and perceptions that shape ways in which people adjust their travel behavior in response to policy interventions. Based on the work, it was found that many of the traditional travel survey designs are not able to provide the comprehensive data needed to estimate activity-based model systems that truly capture the full range of behavioral considerations and phenomena of importance.

Originality/value of paper — This paper offers a review of the emerging transportation modeling approaches and behavioral paradigms of importance in activity-based travel demand forecasting. The paper discusses how traditional travel survey designs are inadequate to meet the data needs of emerging modeling approaches. Based on a review of all of the data needs and new data collection methods that are making it possible to observe a full range of human behaviors, the paper offers a total survey data collection design that brings together many different surveys and data collection protocols. The core household travel survey is augmented by a full slate of special purpose surveys that together yield a rich behavioral database for activity-based microsimulation modeling. The paper is a valuable reference for transportation planners and modelers interested in developing data collection enterprises that will feed the next generation of transportation models.

The effect of trade on poverty is an open question. Although trade may create opportunities in the form of new markets, producers must be able to switch their production and access these markets to reap the benefits from trade. Those producers that cannot change may be stuck trying to sell products in a market with increased competition from imports. In this chapter, we consider which Mexican farmers have been able to adapt to market changes afforded by North American Free Trade Agreement (NAFTA). We find that although some farmers, particularly those with access to outside information through education or technical assistance, have moved out of corn production, a number of both subsistence and market producers have increased the fraction of their land in corn after NAFTA. We also find that market producers respond quite differently from subsistence farmers to agricultural and other infrastructural factors.