Minority Neighborhoods Research Articles

Race in relation to bank depositors and mortgage applications

AbstractData associated with the Home Mortgage Disclosure Act (HMDA) are often studied for evidence of discrimination in terms of minority applicants or for home location in minority neighborhoods, with mixed results. The present analysis utilizes a data set covering 2014–2016, combining characteristics of home location and applicants from the HMDA data with information on depositor characteristics from the Federal Insurance Deposit Corporation's Summary of Deposits data and the Census Bureau's American Community Survey. Consistent with predictions from the red‐lining literature, loan acceptance rates are adversely influenced by high minority locations, although the effect is larger for minority applicants. The relationship banking literature predicts that community banks will yield higher rates of loan acceptance, and the results support that hypothesis. That same literature suggests that familiarity in terms of similar race/ethnicity characteristics for depositors and home location or loan applicants will yield a loan acceptance advantage; and that hypothesis is not supported. Subsidiary analyses suggest that market competition improves loan acceptance rates for minority applicants, consistent with models of discrimination. Additionally, minority depositors are associated positively with loan acceptance rates, which may reflect higher levels of bank risk, and a risk premium, in those markets.

The Higher Cost of Being African-American or Latino: Subprime Home Mortgage Lending in New York City, 2004-2005

The recent turmoil in the financial markets caused by rising default rates on subprime residential home mortgages should not obscure that several studies have shown that African-Americans, Latinos, and residents of predominantly minority neighborhoods receive a disproportionately high percentage of subprime loans. The subprime lending crisis should also not obscure the fact that they have also received a disproportionately low percentage of all home mortgage loans. This report uses data made public pursuant to the Home Mortgage Disclosure Act (HMDA) to examine home mortgage lending in New York City in 2004 and 2005 to determine whether African-Americans, Latinos, and residents of predominantly minority neighborhoods received their share of home mortgage loans and whether they paid more for the loans they got. The report reaches several conclusions. The most important conclusion is that in New York City in 2005, African-Americans, Latinos, and residents of predominantly minority neighborhoods received significantly higher percentages of subprime HMDA and home purchase loans than whites and residents of predominantly white neighborhoods. Although the HMDA data that this report uses do not contain enough information about the creditworthiness of individual borrowers to determine whether these disparities are the result of illegal discrimination, the disparities in some cases are so stark that they beg for government enforcement agencies who have access to information about borrower creditworthiness to investigate individual lenders further and they invite private parties to commence litigation against lenders through which they can gain access to this information.

Associations between body mass index, physical activity and the built environment in disadvantaged, minority neighborhoods: Predictive validity of GigaPan® imagery

BackgroundThe built environment has been shown to influence health in studies of disadvantaged populations using different measurement methods. This study determined whether environmental exposures derived from GigaPan® images could serve as valid predictors of body mass index (BMI), walking and moderate to vigorous physical activity (MVPA) in a longitudinal study of low-income adults living in two primarily African American neighborhoods in Pittsburgh, Pennsylvania, USA. GigaPan® is a robotic system used to obtain high-resolution, panoramic images of environments. MethodsMicroscale environmental features along 481 streets were audited in 2015–2016 using an audit form. Environmental exposures were estimated for 731 adult participants, using a sample of street segments within a 0.4 km (0.25 mile) network distance from each participant's residential address. Summary environmental exposures were constructed using factor analysis. We tested associations between participant-level environmental exposures and objectively measured BMI, self-reported walking and objectively measured MVPA in regression models controlling for baseline health and demographic variables. ResultsThree factors representing participants’ environmental exposures were constructed: pedestrian bicycle-amenities; hilly-vacant-boarded; physical activity-recreation/low housing density. Environments with infrastructure and amenities supportive of walking and bicycling were associated with lower BMI (Coef. = ‒0.47, p = 0.02). Frequent walking was less likely in environments with more physical activity and recreation venues/low housing density (OR = 0.81, 95% CI [0.67, 0.96]). MVPA was not associated with any of the environmental measures and the hilly-vacant-boarded factor was not associated with any of the outcomes. ConclusionsPredictive validity was demonstrated for an environmental exposure factor that captured features supportive of walking and cycling in a model predicting BMI, using built environment audit data from GigaPan® imagery. A complementary analysis found lower odds of frequent walking in the neighborhood among participants with exposure to more physical activity and recreational features, but fewer types and lower density of housing.

The effects of devaluation and solvability on crime clearance

PurposeScholars suggest that clearance rates reflect (a) the solvability of cases (Gottfredson & Hindelang, 1979; Roberts, 2007), and/or (b) the populations that the police choose to prioritize (Black, 1976). But few studies consider the totality of contextual and situational characteristics that may explain clearance rates and contribute to important disparities among them. The current study presents a framework that considers the effect of various types of devaluation and solvability on clearance. MethodsLinear probability modeling is used to test the framework's utility and whether complaint, neighborhood, and police district characteristics affect the clearance of violent crimes in St. Louis, MO. ResultsThe findings suggest that while minority victims and neighborhoods may be devalued, specific crime features physically hinder crime-solving. Important interactions emerge between devaluation and solvability indicators, and crime types are found to have distinctive clearance predictors. The results suggest that witness and victim-offender relationship information might be particularly important in clearing crimes involving Black victims. ConclusionsOverall, the results highlight the importance of comprehensively studying crime-solving. Future research should continue to work toward developing a comprehensive conceptualization to explain police case clearance.

A geographically weighted regression approach to investigating local built-environment effects on home prices in the housing downturn, recovery, and subsequent increases

The 2007 financial crisis profoundly affected most American metropolitan areas. Over the past 10 years, Columbus, Ohio, has experienced a housing downturn, recovery, and subsequent increases. This allows to investigate the response of housing market in different periods of the recession. Ordinary and geographically-weighted regression (GWR) models were developed to examine global and local built-environment effects on home-price appreciations for the three periods while controlling for other physical and socioeconomic variables. The results found that home buyers showed an unchanged preference for residential privacy and amenity and avoided those features that might attract negative external effects from a period to another. The home-price appreciation rates showed different spatial patterns across the study region in the three periods. Nevertheless, the results suggested that suburban areas, particularly those in northern Columbus, better resisted, recovered from, and adapted to the recession. In the wake of the recession, a smaller house was preferred by home buyers. GWR models also provided some interesting findings. In the downturn, accessibility to a park or library helped sustain home prices in the northwest. Bus stop density had a positive effect in eastern Columbus in the recovery, most likely due to the high fuel price at that time. Neighborhoods with a higher income better retained their home value in the downturn, especially those in southern Columbus. Finally, this study found that the recession hit harder on minority neighborhoods in all three periods. This finding suggests that housing policies should focus on these neighborhoods with other social support.

Examine the effects of neighborhood equity on disaster situational awareness: Harness machine learning and geotagged Twitter data

A disaster-resilient city should address social inequity in all its forms. Complete, accurate, and up-to-the-minute situational awareness (SA) can help disaster relief organizations stabilize the dangers and prevent further losses in poor and ethnic minority neighborhoods. However, the lapse of months or years of survey data could lead to biases since retrospective studies are affected by the emotional status at the time of the survey. To address this, we aim to examine the effects of neighborhood equity on SA in a hurricane event using geotagged Twitter data. We adopted the sentiment analysis, convolutional neural network (CNN) model and latent Dirichlet allocation (LDA) model to reflect SA in Hurricane Florence. The multinomial logit regression model reveals that the tweet originated from a poor neighborhood has a higher probability of being more negative, compared to being neutral. Also, we surprisingly found that the sentiment of a tweet in a black neighborhood could be less likely to be negative. Another novel finding is that black neighborhoods could discuss the hurricane with a positive attitude while poor neighborhoods are more concerned about the work during the hurricane. This study shows that, by incorporating with aggregated sociodemographic data, geotagged Twitter data can also be used to understand disaster SA from the perspective of social equity.

Insurance status and head and neck radiotherapy interruption disparities in the Mid‐Southern United States

Relationships between health insurance coverage and radiotherapy (RT) interruption rates in patients with head and neck (H&N) cancer remain unclear. We performed a retrospective cohort study at our academic center. Days of RT interruption for individual patients were tabulated, analyzed for explanatory variables, and geographically mapped. 894 of 7526 (11.9%) scheduled treatment days were interrupted, impacting 149 of 216 (69%) patients. Medicaid/uninsured patients experienced a 7.3 day mean interruption (SD = 9.9) vs 3.4 days (SD = 5.2) for Medicare/private patients (P < .001). RT interruption was predicted by insurance status in multivariate analysis (P = .008). Higher RT interruption rates overlapped geospatially with low predicted median household income and racial minority neighborhoods. Unplanned treatment interruptions are a key source for H&N RT quality shortfalls in Medicaid/uninsured patients. This is the first geographic benchmarking of H&N RT delivery disparities across a complete metropolitan region, and will guide interventions studies to reduce interruption risk.

Neighborhood characteristics and effects on physical activity in an urban minority community – application of Health Belief Model to findings from Creighton University Center for Promoting Health and Health Equity (CPHHE-REACH) initiative

ABSTRACT Physical activity has been strongly associated with better physical and mental health outcomes. However, physical activity for better health outcomes depends on many factors, including the nature of the environment. In resource-challenged settings, the configurations of physical activities for salutogenic effects is rarely characterized. Using the Health Belief Model (HBM), this paper reviews and analyses factors that influence 150–300 minutes per week of Mild to Moderate Physical Activity (MMPA-150) among urban minority residents. Residents of a Racial and Ethnic Approach to Community Health (REACH)-targeted campaign funded by the U.S. Centers for Disease Control and Prevention (CDC), including schools, housing projects, community health center and faith-based organizations in minority neighborhoods in a mid-west city were surveyed with a REACH Health Activity Assessment questionnaire. Using HBM constructs, multinomial logistic regression analyses were applied to determine levels of factors that impact MMPA-150. Black/African Americans were significantly more likely than others to engage in MMPA-150. Perceived vulnerability also predicted MMPA-150 in the bivariate model. There is need for planning and implementation, based on differences in resource access/availability. It is essential to clearly identify salutogenic activities that are living style based, which may not be vulnerable to lack of ‘resources’, for their implementation. Targeting these living style-based activities with health promotion and education interventions may help move the needle forward regarding the salutogenic effects of physical activities, and engaging in them.

Racial/Ethnic Inequities in Access to High-Quality Dialysis Treatment in Chicago: Does Neighborhood Racial/Ethnic Composition Matter?

Blacks and Hispanics face a higher incidence rate of end-stage renal disease (ESRD) and tend to experience poorer access to quality health care compared with Whites. Income, education, and insurance coverage differentials are typically identified as risk factors, but neighborhood-level analyses may provide additional insights. We examine whether neighborhood racial composition contributes to racial/ethnic inequities in access to high-quality dialysis care in Chicago. Data are drawn from the United States Renal Data System merged to the ESRD Quality Incentive Program file and the American Community Survey (2005-2009) for facility and neighborhood characteristics (N = 2797). Outcomes included (1) spatial access (travel time to dialysis facilities) and (2) realized access (actual use of quality care). Neighborhood racial/ethnic composition was categorized into four types: predominantly White, Black, and Hispanic neighborhoods, and racially integrated neighborhoods. Blacks lived closer to a dialysis facility but traveled the same distance to their own dialysis compared with Whites. Hispanics had longer travel time to any dialysis than Whites, and the difference between Hispanics and Whites became no longer significant after adjusting for neighborhood racial/ethnic composition. Blacks and Hispanics had better access to a high-quality facility if they lived in integrated neighborhoods (OR = 1.85 and 3.77, respectively, p < 0.01) or in neighborhoods with higher concentrations of their own race/ethnicity (OR = 1.68 for Blacks in Black neighborhoods and 1.92 for Hispanics in Hispanic neighborhoods, p < 0.05) compared with Whites in predominantly White neighborhoods. Expanding opportunities for Blacks and Hispanics to gain access to racially integrated and minority neighborhoods may help alleviate racial/ethnic inequities in access to quality care among kidney disease patients.

How structural racism can kill cancer patients: Black patients with breast cancer and other malignancies face historical inequities that are ingrained but not inevitable. In this article, the second of a 2-part series, we explore the consequences of and potential solutions to racism and inequality in cancer care.

How structural racism can kill cancer patients: Black patients with breast cancer and other malignancies face historical inequities that are ingrained but not inevitable. In this article, the second of a 2-part series, we explore the consequences of and potential solutions to racism and inequality in cancer care.

COVID-19 and the Future of Us Fertility: What Can We Learn from Google?

We use data from Google Trends to predict the effect of the COVID-19 pandemic on future births in the United States. First, we show that periods of above-normal search volume for Google keywords relating to conception and pregnancy in US states are associated with higher numbers of births in the following months. Excess searches for unemployment keywords have the opposite effect. Second, by employing simple statistical learning techniques, we demonstrate that including information on keyword search volumes in prediction models significantly improves forecast accuracy over a number of cross-validation criteria. Third, we use data on Google searches during the COVID-19 pandemic to predict changes in aggregate fertility rates in the United States at the state level through February 2021. Our analysis suggests that between November 2020 and February 2021, monthly US births will drop sharply by approximately 15%. For context, this would be a 50% larger decline than that following the Great Recession of 2008-2009, and similar in magnitude to the declines following the Spanish Flu pandemic of 1918-1919 and the Great Depression. Finally, we find heterogeneous effects of the COVID-19 pandemic across different types of mothers. Women with less than a college education, as well as Black or African American women, are predicted to have larger declines in fertility due to COVID-19. This finding is consistent with elevated caseloads of COVID-19 in low-income and minority neighborhoods, as well as with evidence suggesting larger economic impacts of the crisis among such households.

Introduction

Introduction

Who Benefited from the Lending Boom to Poor and Minority Neighborhoods Leading to the Housing Crisis?

The late 1990s through the mid-2000s was a period of historic growth in mortgage lending and house prices and there is intense debate over whether lending growth was a cause or consequence of house price growth. I show that lending growth was strongest for lower-income and minority borrowers. However, I also show that there was pronounced lending growth for all borrowers, regardless of income or race, in lower-income and higher-minority neighborhoods. Importantly, each of these results hold after I control for the effects of growth, or expectations of growth, in house prices. This evidence is consistent with the view that the house price boom was caused by an expansion in the supply of mortgage credit, primarily benefiting borrowers and neighborhoods which previously had the most limited access to mortgage credit. In particular, the evidence can be explained by government housing policies which mandated increased lending to lower-income borrowers as well as borrowers in lower-income and higher-minority neighborhoods.

Do Advanced Information Technologies Produce Equitable Government Responses in Coproduction: An Examination of 311 Systems in 15 U.S. Cities

This article seeks to answer the following primary research question: Do governments respond differently to citizen service requests depending on where those requests originate in the city? This study is particularly salient in the wake of the Black Lives Matter protests in response to police violence or the gross neglect of infrastructure in Flint, MI. Although numerous studies have been able to demonstrate bias in policing, few (if any) have looked at biases that may be present in other types of general government services. Empirical evidence has supported the claims by some that some cities were responding slower to service requests made in poor and minority neighborhoods than they were in the richer, whiter neighborhoods, but these earlier works were from an era before 311. The article seeks to fill this gap in the modern coproduction literature to evaluate whether advanced information technologies enable equitable responses by governments. The results of our 15-city study of 311 systems (nonemergency service requests made by city residents) demonstrate no systematic differences in how the cities respond that would indicate a bias against minorities and poorer residents. Unsurprisingly, the effects are not consistent across all of our sample cities. Although some cities have statistically significant differences showing slower responses for these neighborhoods and others show quicker, the practical differences are so small as to be of little concern during our study period (2007–2016).

Dissecting Disorder Perceptions: Neighborhood Structure and the Moderating Role of Interethnic Contact and Xenophobic Attitudes

Although urban disorder has played a central role in neighborhood research, its impact may have been overstated in studies relying on the subjective perception of survey respondents only. Research on the “perception bias”—defined as the divergence between respondents’ subjective assessments and systematic observations of disorder—has revealed the ambiguous nature of disorder and opened a door to the analysis of the social construction of this environmental cognition. Using survey and observational data from 140 small neighborhoods in two German cities, we advance this research by focusing on the moderating role of residents’ interethnic contacts and attitudes. The results show that the effects of neighborhood minority concentration on the perception bias are conditional on the residents’ interethnic contacts and xenophobic attitudes. These findings highlight the subjectivity of disorder perceptions and caution against a naive understanding of Broken Windows theory.

The Place of Common Bond: Can Credit Unions Make Place for Solidarity Economy?

About 6,000 financial cooperatives, called credit unions, with more than 103 million members manage over $1 trillion in collective assets in the United States but are largely invisible and seen as inferior to private banks. In contrast to banks that generate profit for outside investors and do not give voice to customers, these not-for-profit institutions have a democratic governance structure and a mission to provide good services to their members. We use diverse economies and critical/feminist geographic information system (GIS) approaches to theorize them as noncapitalist alternatives to banks and possible sites of social transformation toward a solidarity economy. Using the case of cooperative finance in New York City, we analyze spatial patterns, characteristics, and placemaking practices of credit unions with different kinds of the common bond, a principle that unites a financial community, and in relation to urban geographies of class and race. We find that credit unions provide a historically proven mechanism for collective resistance to marginalization by racial capitalism and, depending on the common bond type, make place by (1) providing financial inclusion in poor and minority neighborhoods; (2) scaling up solidarity finance through participation of middle classes; and (3) diverting assets from capitalist investment into social reproduction and livelihoods. Credit unions express the racialized wealth of their communities, however, and create spatial exclusions that pose a challenge to postcapitalist movements such as solidarity economy. These findings are applicable to other places marked by segregation and call for further inquiry into possibilities and barriers to solidarity finance. Key Words: credit unions, financial geography, New York City, placemaking, solidarity economy.

Minority neighbourhoods and availability of green amenities: empirical findings from Seoul, South Korea

ABSTRACTAvailability of green amenities in cities often raises environmental justice issues. This study empirically examines Seoul, South Korea, where minority populations, such as foreigners, the poor, the old, the young, and the disabled, are generally increasing but the land area of parks and green spaces is gradually decreasing. Using ordinary least squares (OLS) and generalised least squares (GLS) regression models, this study finds that foreign population is mostly negatively associated with green amenities. The old and the young are positively associated with the green amenities in general but negatively with small and children’s parks. Neighbourhood parks do not seem to be associated with concentrations of any minority populations. Findings of this study argue for securing more lands for green amenities in cities and provide lessons to local planning practice.

Gentrification in the U.S. New South: Evidence from two types of African American communities in Charlotte

Neighborhood development in the U.S. New South offers a rich historical diversity reflecting different growth patterns during their initial development and a longer history of African American residential experiences. Previous studies indicate certain neighborhood racial characteristics cancel conditions known to encourage gentrification, such as proximity to downtown. While recent gentrification literature has offered contradictory findings about the importance of neighborhoods with a majority African American population, a key aspect missing from this debate is how U.S. New South cities are set apart from other regions by displaying processes of industrialization, segregation, and immigration that form low-density spatial patterns of urbanization. To that end, Charlotte, NC is used as a case study to reveal how two types of African American neighborhoods near new growth centers, rim villages and streetcar suburbs, become gentrified. Our research reveals that while dynamics of economic change restructure neighborhoods in Charlotte, they also destabilize infrastructure that supports economically and socially struggling African American communities. As mid-sized U.S. New South cities continue to grow, our viewpoint argues that more thorough and geographically sensitive studies are needed to address localized impacts of gentrification on minority neighborhoods to form site specific anti-gentrification strategies.

Impact of Bank Closings on Credit Extension to Businesses in Low-Income and Minority Neighborhoods

New research is surfacing since the last financial crisis, not only to help predict risks associated with bank failures but also to assess the impact of bank failures on the economy and local geographies. However, although bank failures occurred mostly among small (community) banks, much less is understood regarding how the closing of mission-oriented community banks, or minority-owned banks, affect traditionally underserved markets, areas such failed banks were designed to serve. We conduct an empirical investigation testing the effects of bank closings on local areas. We find that, as a result of bank closings, there are significant frictions with small businesses obtaining credit, which appear to be potent enough to cause cumulative declines in aggregate small business lending in neighborhoods, lasting up to 3 years. We also find evidence that such lending shocks have repercussions on small business growth. We find that the closing of large banks also has an impact on small business lending, consistent with previous research, which has shown that as small businesses lose credit from large banks, they are not able to switch easily to other banks, leading to a decline in aggregate lending in local areas. We find this to be true for low- or moderate-income (LMI) and minority businesses/neighborhoods. We also find that the failure of community development financial institutions (CDFIs) and minority depository institutions (MDIs) leaves a credit void that may not automatically be filled in LMI and minority neighborhoods.

NEIGHBORHOOD SOCIAL ENVIRONMENT AND PHYSICAL FUNCTION: EVIDENCE OF RACIAL AND ETHNIC DIFFERENCES

Empirical and theoretical scholarship suggest that as individuals age and their physical, cognitive, and social needs change, their neighborhood environment becomes increasingly important to their health and well-being. Despite recent advances in this area of research, a number of critical gaps remain. Namely, few studies examine the associations between neighborhood social environments and performance-based physical function. Furthermore, racial and ethnic differences are widely understudied. The objectives of this study are (1) to examine the association between neighborhood social cohesion and physical disorder on physical function in older adults, and (2) to identify potential racial/ethnic differences in these associations. Data come from round five (collected in 2015) of the National Health and Aging Trends Study (NHATS; N=5,619). A series of adjusted linear regression models were used to predict performance-based physical function based on characteristics of the neighborhood social environment (i.e., cohesion, disorder). Results showed that only neighborhood physical disorder was statistically significantly associated with poorer physical function (p < 0.05). Similarly, when stratified by race/ethnicity, only neighborhood physical disorder was associated with poorer physical function in Whites (p < 0.05). There was no significant association for either neighborhood social environment characteristic and physical function for Black or Hispanic older adults. Racial and ethnic differences warrant closer investigation in studies of neighborhood effects on health. Community-level interventions, policy makers, and researchers should consider the interactions between minority membership and neighborhood social environments when addressing issues of health and physical function.