This document analyzes home mortgage lending patterns in the St. Louis metropolitan area, including the city of St. Louis and surrounding suburbs. It finds that while median family income best predicts lending levels across the region, the racial composition of neighborhoods is also a strong factor within the city - majority-white neighborhoods receive significantly more lending than majority-black neighborhoods. It also identifies several heavily segregated communities within the city and inner-ring suburbs with over 75% black populations that receive virtually no home purchase loans, even after controlling for income. This lack of access to credit perpetuates cycles of disinvestment and concentrated poverty in these neighborhoods.
This document provides a summary of a report for Birmingham City Council on barriers and aids to carers returning to or staying in work. It details the experiences of carers who were interviewed as part of the research. Key barriers identified include caring responsibilities, personal characteristics, financial factors, lack of formal/alternative care arrangements, and lack of support. The report provides pointers for developing a support pathway for carers, including improving access to general carer services, employment support programs, benefits advice, care services, and support networks. It emphasizes recognizing the carer role, building skills early on, and creating flexible pathways toward employment.
Umdoni Community Based Planning (CBP) Ward 4 Example Anthony Rippon
This document presents a community-based plan for Ward 4 in the Umdoni Local Municipality. It begins with an introduction and background to the project. It describes the objectives, scope, locality and ward boundaries. It then details the 5-stage process used to develop the plan, which included preparation, information gathering, analysis, reconciliation and planning. The plan presents a policy framework, situation analysis of demographics, services and facilities. It identifies economic activities and priority community needs. A strategic framework is outlined with a vision, goals and strategies. Finally, an implementation plan is provided with action tables to address priority projects and challenges.
This document provides a summary of a report on nonpartisan political websites in the 2000 US election. It finds that while these sites were initially hyped as engaging citizens in new ways, they ultimately failed to meet expectations due to low traffic. The report examines factors behind the sites' emergence and demise, including the tech boom and bust. It recommends a focus on local information, public-private partnerships, and a coherent plan for online political information to help define the role of nonpartisan sites going forward.
This document is a campaign manual for Libertarian candidates that provides guidance on effective campaign techniques. It covers preparing to run a campaign by setting goals and laying groundwork. It discusses organizing the campaign team by developing strategy, creating a campaign plan and timeline, managing budgets and staff. It offers tips for reaching voters through precinct walking, public appearances, phone banks, paid and earned media. It provides guidance on organizing petition drives and getting out the vote. The manual aims to help Libertarian candidates run effective, organized campaigns.
In presenting a clear picture of secondary privatization trends in Slovenia, the authors of this volume tried to evaluate the effectiveness of various privatization schemes in terms of their open-endedness (i.e., the degree to which they foster flexibility in adjustments of ownership structures) and in terms of achieving good corporate governance. Additionally, they formulate and examine hypotheses concerning the relationships between changes in the economic performance of enterprises and post-privatization changes in their ownership structures.
This report also includes a set of recommendations concerning necessary changes in the regulations and policies governing privatization and capital markets in Slovenia,designed to foster the development of privatized enterprises and to meet the requirements of the process of accession to the European Union.
Authored by: Andreja Bohm, Joze P. Damijan, Boris Majcen, Marko Rems, Matija Rojec, Marko Simoneti
This document evaluates the Strategic Decision Support Centers (SDSCs) implemented by the Chicago Police Department.
The SDSCs are real-time crime centers located in each police district that bring together staff, technologies, and data to support policing operations and strategic decision-making. The evaluation assessed SDSC operations, technologies, and the impact on crime rates.
The evaluation found that the SDSCs functioned as intended by facilitating communication and information sharing. Technologies like gunshot detection systems and video feeds provided timely data to police. Crime analysis supported strategic planning. However, opportunities for improvement were identified, such as better integrating technologies and standardizing processes across districts.
Statistical analysis found that monthly crime counts, including homic
Second Revision Syria Regional Response PlanJesse Budlong
This document is a revised regional response plan for Syria with sections on Jordan, Lebanon, Turkey, and Iraq. It provides an executive summary with tables of financial requirements by agency and sector. The regional overview discusses population figures, strategic objectives, planning assumptions, coordination efforts, and information management. Each country section details the context, needs, response activities, coordination, strategic objectives, and financial requirements by agency and sector.
The WalkUP Wake-Up Call: Atlanta The Poster Child of Sprawl Builds a Walkable...Jesse Budlong
Urban development in the second half of the 20th century gave rise to the sprawling geographies, connected by vast highway systems, that now characterize most U.S. metropolitan areas. Few metro areas are more sprawling than Atlanta, but things are changing quickly.
Leinberger and Austin's research has found a surprising and overwhelming recent reemergence of walkable urban development in metro Atlanta; in fact, it now accounts for the majority of the metro area's development.
In this report, the authors identify and rank Atlanta's established and up-and-coming WalkUPs on their economic performance and social equity. Their insights will help developers, real estate professionals, and urban planners determine the most productive places to invest capital and the initiatives most likely to stimulate walkable urban development.
This document provides a summary of a report for Birmingham City Council on barriers and aids to carers returning to or staying in work. It details the experiences of carers who were interviewed as part of the research. Key barriers identified include caring responsibilities, personal characteristics, financial factors, lack of formal/alternative care arrangements, and lack of support. The report provides pointers for developing a support pathway for carers, including improving access to general carer services, employment support programs, benefits advice, care services, and support networks. It emphasizes recognizing the carer role, building skills early on, and creating flexible pathways toward employment.
Umdoni Community Based Planning (CBP) Ward 4 Example Anthony Rippon
This document presents a community-based plan for Ward 4 in the Umdoni Local Municipality. It begins with an introduction and background to the project. It describes the objectives, scope, locality and ward boundaries. It then details the 5-stage process used to develop the plan, which included preparation, information gathering, analysis, reconciliation and planning. The plan presents a policy framework, situation analysis of demographics, services and facilities. It identifies economic activities and priority community needs. A strategic framework is outlined with a vision, goals and strategies. Finally, an implementation plan is provided with action tables to address priority projects and challenges.
This document provides a summary of a report on nonpartisan political websites in the 2000 US election. It finds that while these sites were initially hyped as engaging citizens in new ways, they ultimately failed to meet expectations due to low traffic. The report examines factors behind the sites' emergence and demise, including the tech boom and bust. It recommends a focus on local information, public-private partnerships, and a coherent plan for online political information to help define the role of nonpartisan sites going forward.
This document is a campaign manual for Libertarian candidates that provides guidance on effective campaign techniques. It covers preparing to run a campaign by setting goals and laying groundwork. It discusses organizing the campaign team by developing strategy, creating a campaign plan and timeline, managing budgets and staff. It offers tips for reaching voters through precinct walking, public appearances, phone banks, paid and earned media. It provides guidance on organizing petition drives and getting out the vote. The manual aims to help Libertarian candidates run effective, organized campaigns.
In presenting a clear picture of secondary privatization trends in Slovenia, the authors of this volume tried to evaluate the effectiveness of various privatization schemes in terms of their open-endedness (i.e., the degree to which they foster flexibility in adjustments of ownership structures) and in terms of achieving good corporate governance. Additionally, they formulate and examine hypotheses concerning the relationships between changes in the economic performance of enterprises and post-privatization changes in their ownership structures.
This report also includes a set of recommendations concerning necessary changes in the regulations and policies governing privatization and capital markets in Slovenia,designed to foster the development of privatized enterprises and to meet the requirements of the process of accession to the European Union.
Authored by: Andreja Bohm, Joze P. Damijan, Boris Majcen, Marko Rems, Matija Rojec, Marko Simoneti
This document evaluates the Strategic Decision Support Centers (SDSCs) implemented by the Chicago Police Department.
The SDSCs are real-time crime centers located in each police district that bring together staff, technologies, and data to support policing operations and strategic decision-making. The evaluation assessed SDSC operations, technologies, and the impact on crime rates.
The evaluation found that the SDSCs functioned as intended by facilitating communication and information sharing. Technologies like gunshot detection systems and video feeds provided timely data to police. Crime analysis supported strategic planning. However, opportunities for improvement were identified, such as better integrating technologies and standardizing processes across districts.
Statistical analysis found that monthly crime counts, including homic
Second Revision Syria Regional Response PlanJesse Budlong
This document is a revised regional response plan for Syria with sections on Jordan, Lebanon, Turkey, and Iraq. It provides an executive summary with tables of financial requirements by agency and sector. The regional overview discusses population figures, strategic objectives, planning assumptions, coordination efforts, and information management. Each country section details the context, needs, response activities, coordination, strategic objectives, and financial requirements by agency and sector.
The WalkUP Wake-Up Call: Atlanta The Poster Child of Sprawl Builds a Walkable...Jesse Budlong
Urban development in the second half of the 20th century gave rise to the sprawling geographies, connected by vast highway systems, that now characterize most U.S. metropolitan areas. Few metro areas are more sprawling than Atlanta, but things are changing quickly.
Leinberger and Austin's research has found a surprising and overwhelming recent reemergence of walkable urban development in metro Atlanta; in fact, it now accounts for the majority of the metro area's development.
In this report, the authors identify and rank Atlanta's established and up-and-coming WalkUPs on their economic performance and social equity. Their insights will help developers, real estate professionals, and urban planners determine the most productive places to invest capital and the initiatives most likely to stimulate walkable urban development.
This digital marketing plan proposes strategies to increase brand awareness, sales, and customer retention for Nevada Bobs, a golf retail company in Ireland. It includes research on the target audiences and development of four key persona groups. A multi-channel marketing strategy is outlined utilizing SEO, PPC, display advertising, social media (Facebook, Twitter, Instagram, Pinterest), email marketing, blogging, and Google My Business. Goals are set for each channel to drive reach, engagement, leads and conversions over 12 months. Performance will be measured through key metrics like website traffic, social media followers, click-through rates, dwell time and repeat visits. The plan aims to position Nevada Bobs as the premier retailer for quality customer service
Housing data for Q4 2010 showed a continued fragile recovery in the housing market. Single-family housing permits increased and starts remained steady, although completions fell. Multifamily permits, starts, and completions all fell slightly. Both new and existing home sales rose in Q4, while median prices for new homes remained steady and prices for existing homes fell slightly from a year ago. House price indices estimated a decline in prices in Q3 from Q2 and a year ago. Inventory of homes for sale decreased in Q4.
This document provides guidance for Knights of Columbus council leaders, including Grand Knights, District Deputies, and Financial Secretaries. It outlines organizational structure, award programs, duties of officers, protocols, and instructions for conducting activities. Key sections include qualifications for membership, duties of specific officer roles, instructions for conducting degree ceremonies and audits, and details on featured Supreme Council programs that councils can participate in for awards, including Refund Support Vocations Program, Habitat for Humanity, Special Olympics, and Food for Families. The document aims to equip leaders with practical information to grow membership, conduct charitable activities, and ensure compliance.
The document provides guidelines for formatting Water Research Foundation research reports. It outlines the required sections for reports, including the front matter (half title page, title page, disclaimer page, table of contents, etc.), text (introduction, methods, results, conclusions, recommendations chapters), and back matter (appendices, references, abbreviations). It provides direction on formatting aspects like page dimensions, typeface, headings, pagination, and placement of tables and figures. The document also addresses using copyrighted materials and publishing or presenting project results. Examples of each section are provided in appendices to demonstrate the proper formatting.
Rapport de la Banque Mondiale sur la Production et la Consommation du Charbon...Stanleylucas
This document summarizes a national assessment of charcoal production and consumption trends in Haiti. It was conducted by an interdisciplinary team led by the World Bank and included field research, interviews, and data collection on charcoal production and trade over three sampling periods. The results found that charcoal production and trade is highly concentrated around Port-au-Prince and varies significantly within days of the week and across regions of Haiti. Production in more remote areas is supplemented by "feeder roads" that transport additional charcoal into urban areas.
The series of currency crises which hit several developing countries in the 1990s did not leave the emerging market economies of Central and Eastern Europe unscathed. The roots of the crises in European Transition Economies were usually less sophisticated and easier to identify. Most crisis episodes in the former communist countries fit nicely with the ”first generation” canonical model elaborated in 1979 by Paul Krugman and developed in 1980s by other economists. In this model, fiscal imbalances are the main factor leading to depleting international reserves of the central bank and speculative attacks against national currencies.
Authored by: Rafal Antczak, Marek Dabrowski, Malgorzata Markiewicz, Artur Radziwill, Marcin Sasin
Published in 2001
This document provides a summary of the Editorial Toolbox Templates freeware which contains templates for newspapers and online editions. It includes templates for basic graphics and charts such as bar charts, pie charts, timelines, and flow charts. It also contains templates for financial and political graphics like currency symbols, flags, and trend arrows. The templates are for non-commercial use in programs like InDesign. The document provides instructions on using and modifying the templates effectively.
Global Employment Trends for Youth 2013 | A generation at risk the share of women in total (female and male) unemployment, which is primarily driven by differences in labour force participation rates. At the regional level, the female youth labour force participation rate in North Africa is the second lowest in 2012 – only 19.7 per cent of young females of the working-age population participate in the labour force while 46.8 per cent of young males participate. The gap between male and female youth participation is not expected to become much smaller in the medium term. It is projected that in 2017, 20.1 per cent of young women will be in the labour force. There is great heterogeneity between countries in terms of youth unemployment by sex. In 2011, the youth unemployment rates for males and females in Morocco were fairly close, with young men facing a slightly higher unemployment rate (18.1 per cent) than young women (17.4 per cent). In Algeria, on the other hand, young women were far more likely to be affected by unemployment than young men. The female youth unemployment rate in this country was 37.5 per cent in 2010, while the male youth unemployment rate stood at 18.7 per cent (ILO, 2011a). Skills mismatches are a structural labour market problem in North Africa, which can be illustrated using unemployment rates by educational attainment. The unemployment rates for persons with tertiary-level education are among the highest in the world, at 21.4 per cent, 18.9 per cent and 17.4 per cent in 2010 in Algeria, Egypt and Morocco, respectively. In Algeria and Egypt, they are higher than for persons with primary or secondary education, pointing at a mismatch between the supply of and demand for skills and education. In most advanced economies, persons with higher levels of education are less likely to be unemployed, but this does not seem to apply to North African economies, as prospects of finding jobs for those having completed tertiary education are grim. 2.2.8 Sub-Saharan Africa Although the regional youth unemployment rate in Sub-Saharan Africa is lower than in most other regions, it is significantly higher than the adult unemployment rate. Compared with an adult unemployment rate of 5.9 per cent in 2012, youth are twice as likely to be unemployed, with an estimated youth unemployment rate of 11.8 per cent in 2012. Youth unemployment rates much higher than the regional average are found in South Africa, where over half of young people in the labour force were unemployed in the first three quarters of 2012, and in Namibia (58.9 per cent in 2008), Réunion (58.6 per cent in 2011) and Lesotho (34.4 per cent in 2008; ILO, 2011a and 2013b). On current trends, the youth unemployment rate is projected to remain close to 11.7 per cent in the coming years. Similarly to South Asia, the relatively low regional youth unemployment rate in Sub- Saharan Africa is linked to the high levels of poverty. The region has by far the highest rate of working povert
This document summarizes the 2014 Matanuska-Susitna Borough Housing Needs Assessment. It analyzes population demographics, household characteristics, housing stock, housing costs, and affordability over time to determine if there is an affordable housing issue in the borough. The assessment finds that if current trends continue, housing may become unaffordable for median and lower-income households due to rising housing costs and stagnating incomes. It projects future housing needs based on past growth patterns to help identify potential risks to housing affordability. The goal is to determine the extent of any affordable housing issues and inform housing policy in the borough.
This document presents a five-year economic development strategy for Washington D.C. It was created through collaboration between local government, businesses, universities, and other organizations. The strategy aims to create 100,000 jobs and generate $1 billion in new tax revenue over five years. It identifies seven key sectors - federal government, professional services, technology, hospitality, retail, real estate, and education/healthcare. For each sector, the document analyzes strengths and opportunities. It then outlines strategic initiatives to support business growth and workforce development in order to diversify and strengthen the local economy. The strategy reflects the Mayor's vision of a prosperous and equitable D.C. for all residents.
The perceptions of the role of women and men in families have changed over the past few decades. Men are no longer perceived as the economic providers to families. The role of men in the family has undergone many “diverse demographic, socio-economic and cultural transformations” impacting the formation, stability and overall well-being of families. In light of this development, DESA’s Division for Social Policy and Development (DSPD) launched a new publication on “Men in Families and Family Policy in a Changing World” on 17 February focusing on the shifting roles and views of men in families.
The Situation of Adolescents and Youth in the Middle East and North Africa Re...Nicholas Cooper
This document provides a review of key trends related to youth in the Middle East and North Africa region based on available data. It covers 10 domains: demography, poverty, health, HIV/AIDS, education, economic participation, migration, civic engagement, child protection, and conflict/emergencies. While data availability is limited, key findings include relatively low poverty rates but high youth unemployment; health issues like injuries, mental health problems, and tobacco use; gains in education but challenges with quality and transition to work; and gaps in child protection. The review emphasizes the need for improved data collection on youth to inform policies that maximize their potential and enable a successful transition to adulthood.
- The document presents the findings of a baseline assessment of small-scale and artisanal gold mining conducted in Central and Eastern Equatoria States of South Sudan between February and May 2015.
- It finds that artisanal gold mining provides critical income to around 60,000 miners and indirectly benefits almost half a million people, but that current practices also have negative health, environmental, and social impacts.
- There is an inadequate legal framework and lack of government oversight of mining activities. As a result, artisanal miners and local communities have little awareness of rules or benefits meant for them.
This document is a mayoral campaign book written by Sam Liccardo outlining his vision and plans for San Jose if elected mayor. It discusses three main challenges facing San Jose - public safety, the city budget, and jobs. For public safety, Liccardo proposes hiring more police officers, leveraging technology like data analytics, emphasizing gang prevention, and restoring community policing. For the budget, he advocates for public-private partnerships, "Fresh Start" budgeting, and paying down debts. For jobs, he wants to help small businesses, use incentives to spur manufacturing, implement congestion pricing at the airport, and leverage libraries as job skills centers. The book concludes by laying out Liccardo's vision for San
This document provides a summary of key findings from a survey of living conditions in Anbar Province, Iraq in June 2008. The survey included interviews with 1,200 randomly selected households across Anbar. It collected demographic data on family size and composition. It also gathered information on employment status, household income levels, standards of living, access to services, and security conditions. The survey findings provide insight into how residents of Anbar were faring in terms of their social, economic and living conditions in mid-2008 after years of war and instability.
The document discusses various aspects of life in the British Isles. It covers the etymology, geography, flora and fauna, demographics, history, politics, culture, transportation and education systems of the region. Specific topics examined include the structure of the government and parliament, main political parties, local governance, healthcare system through the National Health Service and university system.
This document analyzes tourism in the proposed Kavango-Zambezi Transfrontier Conservation Area (TFCA), which would span parts of five countries in southern Africa. It surveys accommodation providers and tour operators across the region to establish a baseline of current tourism activity. The surveys found that tourism is an important industry for the region, with many lodges and camps hosting thousands of visitors annually. However, the sector is still relatively small and could see significant growth if the TFCA increases conservation and promotes sustainable tourism development.
This document analyzes land use trends in seven metropolitan areas in Michigan. It finds that 22% of new income property development in the current real estate cycle has located in walkable urban places, which make up only 2.7% of the total land area. This share is up significantly from previous cycles and indicates pent-up demand for more walkable development options. Rents and home prices are significantly higher in walkable urban areas, further suggesting demand that is not being fully met under current development patterns that have been dominated by drivable and suburban formats for decades. The report examines existing walkable places and their potential for further growth, as well as opportunities and challenges across the different metro regions.
The Mott Foundation’s 2012 Annual Report examines the phenomenal growth of the community foundation field as well as Mott’s long-standing commitment to its spread and vitality. Timed for release as the field begins a year-long celebration in 2014 of the 100th anniversary of the community foundation movement, the publication includes a narrative section that describes Mott’s contributions to the field, our current focus and the lessons learned over the years.
For nearly nine decades, the Charles Stewart Mott Foundation has been guided by our founder’s strongly held belief that every person exists in a kind of informal partnership with his or her community. Our latest annual report explores the power of such intimate collaboration, illustrated by four inspiring “portraits” of people who, with the help of Mott grantmaking, have engaged with their communities to create positive change.
The report also features a joint message from William S. White, the Foundation’s chairman and CEO, and Mott President Ridgway H. White, who reflect on the value of partnership and the understanding that “no single institution has the knowledge, resources or agility to single-handedly address complex social issues. That power must lay in the collective hands, hearts and minds of people working together, often in new and creative ways, to make good things happen.”
Toward Sustainability: Helping communities engage with and protect the environment
From the coastal sand dunes of Michigan’s Lower Peninsula to the warm waters of the Amazon River Basin, the Mott Foundation has long supported the stewardship of the world’s most precious natural resources. Featuring beautiful photography and first-hand perspectives of those working on the ground, the Foundation’s latest annual report highlights five examples of that work and introduces an important and exciting new focus in Mott’s grantmaking — advancing climate change solutions.
The report also features a message from William S. White, the Foundation’s chairman and CEO, who reflects on how this new focus builds on decades of exploration and learning. That experience, White says, “has taught us that this grantmaking must seek practical ways to simultaneously build strong economic, environmental and social conditions for all people — in a word, ‘sustainability.’”
Read more at http://www.mott.org/AR13
This digital marketing plan proposes strategies to increase brand awareness, sales, and customer retention for Nevada Bobs, a golf retail company in Ireland. It includes research on the target audiences and development of four key persona groups. A multi-channel marketing strategy is outlined utilizing SEO, PPC, display advertising, social media (Facebook, Twitter, Instagram, Pinterest), email marketing, blogging, and Google My Business. Goals are set for each channel to drive reach, engagement, leads and conversions over 12 months. Performance will be measured through key metrics like website traffic, social media followers, click-through rates, dwell time and repeat visits. The plan aims to position Nevada Bobs as the premier retailer for quality customer service
Housing data for Q4 2010 showed a continued fragile recovery in the housing market. Single-family housing permits increased and starts remained steady, although completions fell. Multifamily permits, starts, and completions all fell slightly. Both new and existing home sales rose in Q4, while median prices for new homes remained steady and prices for existing homes fell slightly from a year ago. House price indices estimated a decline in prices in Q3 from Q2 and a year ago. Inventory of homes for sale decreased in Q4.
This document provides guidance for Knights of Columbus council leaders, including Grand Knights, District Deputies, and Financial Secretaries. It outlines organizational structure, award programs, duties of officers, protocols, and instructions for conducting activities. Key sections include qualifications for membership, duties of specific officer roles, instructions for conducting degree ceremonies and audits, and details on featured Supreme Council programs that councils can participate in for awards, including Refund Support Vocations Program, Habitat for Humanity, Special Olympics, and Food for Families. The document aims to equip leaders with practical information to grow membership, conduct charitable activities, and ensure compliance.
The document provides guidelines for formatting Water Research Foundation research reports. It outlines the required sections for reports, including the front matter (half title page, title page, disclaimer page, table of contents, etc.), text (introduction, methods, results, conclusions, recommendations chapters), and back matter (appendices, references, abbreviations). It provides direction on formatting aspects like page dimensions, typeface, headings, pagination, and placement of tables and figures. The document also addresses using copyrighted materials and publishing or presenting project results. Examples of each section are provided in appendices to demonstrate the proper formatting.
Rapport de la Banque Mondiale sur la Production et la Consommation du Charbon...Stanleylucas
This document summarizes a national assessment of charcoal production and consumption trends in Haiti. It was conducted by an interdisciplinary team led by the World Bank and included field research, interviews, and data collection on charcoal production and trade over three sampling periods. The results found that charcoal production and trade is highly concentrated around Port-au-Prince and varies significantly within days of the week and across regions of Haiti. Production in more remote areas is supplemented by "feeder roads" that transport additional charcoal into urban areas.
The series of currency crises which hit several developing countries in the 1990s did not leave the emerging market economies of Central and Eastern Europe unscathed. The roots of the crises in European Transition Economies were usually less sophisticated and easier to identify. Most crisis episodes in the former communist countries fit nicely with the ”first generation” canonical model elaborated in 1979 by Paul Krugman and developed in 1980s by other economists. In this model, fiscal imbalances are the main factor leading to depleting international reserves of the central bank and speculative attacks against national currencies.
Authored by: Rafal Antczak, Marek Dabrowski, Malgorzata Markiewicz, Artur Radziwill, Marcin Sasin
Published in 2001
This document provides a summary of the Editorial Toolbox Templates freeware which contains templates for newspapers and online editions. It includes templates for basic graphics and charts such as bar charts, pie charts, timelines, and flow charts. It also contains templates for financial and political graphics like currency symbols, flags, and trend arrows. The templates are for non-commercial use in programs like InDesign. The document provides instructions on using and modifying the templates effectively.
Global Employment Trends for Youth 2013 | A generation at risk the share of women in total (female and male) unemployment, which is primarily driven by differences in labour force participation rates. At the regional level, the female youth labour force participation rate in North Africa is the second lowest in 2012 – only 19.7 per cent of young females of the working-age population participate in the labour force while 46.8 per cent of young males participate. The gap between male and female youth participation is not expected to become much smaller in the medium term. It is projected that in 2017, 20.1 per cent of young women will be in the labour force. There is great heterogeneity between countries in terms of youth unemployment by sex. In 2011, the youth unemployment rates for males and females in Morocco were fairly close, with young men facing a slightly higher unemployment rate (18.1 per cent) than young women (17.4 per cent). In Algeria, on the other hand, young women were far more likely to be affected by unemployment than young men. The female youth unemployment rate in this country was 37.5 per cent in 2010, while the male youth unemployment rate stood at 18.7 per cent (ILO, 2011a). Skills mismatches are a structural labour market problem in North Africa, which can be illustrated using unemployment rates by educational attainment. The unemployment rates for persons with tertiary-level education are among the highest in the world, at 21.4 per cent, 18.9 per cent and 17.4 per cent in 2010 in Algeria, Egypt and Morocco, respectively. In Algeria and Egypt, they are higher than for persons with primary or secondary education, pointing at a mismatch between the supply of and demand for skills and education. In most advanced economies, persons with higher levels of education are less likely to be unemployed, but this does not seem to apply to North African economies, as prospects of finding jobs for those having completed tertiary education are grim. 2.2.8 Sub-Saharan Africa Although the regional youth unemployment rate in Sub-Saharan Africa is lower than in most other regions, it is significantly higher than the adult unemployment rate. Compared with an adult unemployment rate of 5.9 per cent in 2012, youth are twice as likely to be unemployed, with an estimated youth unemployment rate of 11.8 per cent in 2012. Youth unemployment rates much higher than the regional average are found in South Africa, where over half of young people in the labour force were unemployed in the first three quarters of 2012, and in Namibia (58.9 per cent in 2008), Réunion (58.6 per cent in 2011) and Lesotho (34.4 per cent in 2008; ILO, 2011a and 2013b). On current trends, the youth unemployment rate is projected to remain close to 11.7 per cent in the coming years. Similarly to South Asia, the relatively low regional youth unemployment rate in Sub- Saharan Africa is linked to the high levels of poverty. The region has by far the highest rate of working povert
This document summarizes the 2014 Matanuska-Susitna Borough Housing Needs Assessment. It analyzes population demographics, household characteristics, housing stock, housing costs, and affordability over time to determine if there is an affordable housing issue in the borough. The assessment finds that if current trends continue, housing may become unaffordable for median and lower-income households due to rising housing costs and stagnating incomes. It projects future housing needs based on past growth patterns to help identify potential risks to housing affordability. The goal is to determine the extent of any affordable housing issues and inform housing policy in the borough.
This document presents a five-year economic development strategy for Washington D.C. It was created through collaboration between local government, businesses, universities, and other organizations. The strategy aims to create 100,000 jobs and generate $1 billion in new tax revenue over five years. It identifies seven key sectors - federal government, professional services, technology, hospitality, retail, real estate, and education/healthcare. For each sector, the document analyzes strengths and opportunities. It then outlines strategic initiatives to support business growth and workforce development in order to diversify and strengthen the local economy. The strategy reflects the Mayor's vision of a prosperous and equitable D.C. for all residents.
The perceptions of the role of women and men in families have changed over the past few decades. Men are no longer perceived as the economic providers to families. The role of men in the family has undergone many “diverse demographic, socio-economic and cultural transformations” impacting the formation, stability and overall well-being of families. In light of this development, DESA’s Division for Social Policy and Development (DSPD) launched a new publication on “Men in Families and Family Policy in a Changing World” on 17 February focusing on the shifting roles and views of men in families.
The Situation of Adolescents and Youth in the Middle East and North Africa Re...Nicholas Cooper
This document provides a review of key trends related to youth in the Middle East and North Africa region based on available data. It covers 10 domains: demography, poverty, health, HIV/AIDS, education, economic participation, migration, civic engagement, child protection, and conflict/emergencies. While data availability is limited, key findings include relatively low poverty rates but high youth unemployment; health issues like injuries, mental health problems, and tobacco use; gains in education but challenges with quality and transition to work; and gaps in child protection. The review emphasizes the need for improved data collection on youth to inform policies that maximize their potential and enable a successful transition to adulthood.
- The document presents the findings of a baseline assessment of small-scale and artisanal gold mining conducted in Central and Eastern Equatoria States of South Sudan between February and May 2015.
- It finds that artisanal gold mining provides critical income to around 60,000 miners and indirectly benefits almost half a million people, but that current practices also have negative health, environmental, and social impacts.
- There is an inadequate legal framework and lack of government oversight of mining activities. As a result, artisanal miners and local communities have little awareness of rules or benefits meant for them.
This document is a mayoral campaign book written by Sam Liccardo outlining his vision and plans for San Jose if elected mayor. It discusses three main challenges facing San Jose - public safety, the city budget, and jobs. For public safety, Liccardo proposes hiring more police officers, leveraging technology like data analytics, emphasizing gang prevention, and restoring community policing. For the budget, he advocates for public-private partnerships, "Fresh Start" budgeting, and paying down debts. For jobs, he wants to help small businesses, use incentives to spur manufacturing, implement congestion pricing at the airport, and leverage libraries as job skills centers. The book concludes by laying out Liccardo's vision for San
This document provides a summary of key findings from a survey of living conditions in Anbar Province, Iraq in June 2008. The survey included interviews with 1,200 randomly selected households across Anbar. It collected demographic data on family size and composition. It also gathered information on employment status, household income levels, standards of living, access to services, and security conditions. The survey findings provide insight into how residents of Anbar were faring in terms of their social, economic and living conditions in mid-2008 after years of war and instability.
The document discusses various aspects of life in the British Isles. It covers the etymology, geography, flora and fauna, demographics, history, politics, culture, transportation and education systems of the region. Specific topics examined include the structure of the government and parliament, main political parties, local governance, healthcare system through the National Health Service and university system.
This document analyzes tourism in the proposed Kavango-Zambezi Transfrontier Conservation Area (TFCA), which would span parts of five countries in southern Africa. It surveys accommodation providers and tour operators across the region to establish a baseline of current tourism activity. The surveys found that tourism is an important industry for the region, with many lodges and camps hosting thousands of visitors annually. However, the sector is still relatively small and could see significant growth if the TFCA increases conservation and promotes sustainable tourism development.
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Read more at http://www.mott.org/AR13
The Stockholm Institute of Transition Economics (SITE) has the pleasure to invite you to a presentation on Friday January 25, 12.00 – 14.00 with Maurizio Bussolo, lead economist in Europe and Central Asia Chief Economist Office at the World Bank.
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Similar to NCRC_Home_Mortgage_Lending_in_St._Louis_Milwaukee_Minneapolis_and_Surrounding_Areas_WEB (20)
2. 2
ABOUT NCRC
NCRC and its grassroots member organizations create opportunities
for people to build wealth. We work with community leaders,
policymakers and financial institutions to champion fairness in
banking, housing and business development.
Our members include community reinvestment organizations,
community development corporations, local and state government
agencies, faith-based institutions, community organizing and civil
rights groups, minority and women-owned business associations,
and social service providers from across the nation.
For more information about NCRC’s work, please contact:
John Taylor
President and CEO
johntaylor@ncrc.org
(202) 688-8866
Jesse Van Tol
Chief of Membership and Policy
jvantol@ncrc.org
(202) 464-2709
Jason Richardson
Director of Research and Evaluation
jrichardson@ncrc.org
(202) 464 2722
Eric Hersey
Director of Communications
ehersey@ncrc.org
(202) 524-4880
ORIGINAL RESEARCH BY:
Jason Richardson
Director of Research
and Evaluation
Bruce Mitchell
Senior Research Analyst
Nicole West
Research Fellow
ACKNOWLEDGMENTS:
John Taylor
President and CEO
Jesse Van Tol
Chief of Membership
and Policy
Eric Hersey
Director of Communications
Ryan Conley
Communications Coordinator
Richard Lynch
Graphic Designer and
Publications Manager
Gerron Levi
Director of Policy and
Government Affairs
Nicole Barden
Director of Membership
and Organizing NCRC Board Members Bethany Sanchez, Director of Fair Lending,
Metropolitan Milwaukee Fair Housing Council, Dave Snyder,
Coordinator, Minnesota Asset Building Coalition, and Elisabeth Risch,
Director of Education and Research, Metropolitan St. Louis Equal
Housing and Opportunity Council, provided invaluable assistance
with this project.
4. 4
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
St.LouisandSurroundingAreas
EXECUTIVE SUMMARY
St. Louis, Missouri and its suburban community of Ferguson gained
national attention as a site of protests in 2014. These events arose in
part out of a history of racial discrimination and economic decline
that has afflicted the broader metropolitan area. The urban core of
the City of St. Louis was previously industrial, and has lost 63 percent
of its population since 1950. The impact of deindustrialization,
population loss, and an aging infrastructure has left large areas of
the North City portion of St. Louis derelict and abandoned. The city
is also hyper-segregated.1
The predominantly white community
in the southern portion of St. Louis and predominantly African
American community of North City are residentially isolated. Most
neighborhoods of North City have over 80 percent African American
residency, while many neighborhoods in the southern portion are majority white. African
American-majority neighborhoods also extend from North City to suburbs in the northwest,
such as Ferguson, and across the river into East St. Louis in the state of Illinois. These areas form a
contiguous segregated zone of disinvestment and concentrated poverty.2
NCRC has found an extensive mortgage lending
imbalance in St. Louis, with mortgage credit distribution
heavily swayed by income levels and the racial makeup
of neighborhoods. These trends are noteworthy,
especially within the City of St. Louis. While median
family income is a crucial factor, lending is concentrated
in majority white neighborhoods and scarce in majority
African American neighborhoods. Additionally, sections
of the St. Louis metropolitan area, like East St. Louis,
have poor access to conventional banking resources,
with a large proportion of residents either unbanked or
underbanked.3
Isolation from financial services impacts
the ability of majority African American neighborhoods
to build wealth, concentrating poverty. This perpetuates
a cycle of disinvestment, reinforcing the likelihood that
lenders will not invest there.
To help address this problem, NCRC is calling for stronger enforcement and expansion of the
Community Reinvestment Act (CRA) and the preservation and strengthening of the affordable
housing goals in the secondary mortgage market.
1 Douglas S. Massey and Jonathan Tannen,“A Research Note on Trends in Black Hypersegregation,”Demography 52.3
(2015), 1025-1034.
2 Tanvi Misra,“America Has Half as Many Hypersegretated Metros as it Did in 1970,”CityLab, May 21, 2015.
3 Ethan Geiling,“The Most Unbanked Places in America,”Corporation for Enterprise Development, December 14, 2011.
The Ville neighborhood in North St. Louis City,
at Bishop L. Scott Ave. and Maffitt Ave. (Photo:
Elisabeth Risch, EHOC)
The cover image is
a map of St. Louis
and surrounding
counties. Darker blue
colors indicate more
loans per housing
unit in a given
census tract.
5. 5
ANALYSIS NCRC • 202-628-8866 • www.ncrc.orgSt. Louis and Surrounding Areas
KEY FINDINGS
• In the St. Louis metropolitan statistical area (MSA) as a whole,
72 percent of the population is white and 21 percent is African
American. While there is evidence of extensive segregation,
with areas to the northwest and east of the City of St. Louis
showing very high concentration of African American residents,
the variable which best predicts home loan activity in the MSA
(or region) is not race but in fact the median family income of
the neighborhood.
• In the City of St. Louis itself, the racial composition of the
neighborhood is a strong predictor of mortgage activity,
becoming nearly as important as neighborhood income in
its predictive capability. As the percentage of white residents
increases, so does the amount of mortgage lending. Conversely,
a higher proportion of African American residents correlates with fewer mortgage loan
originations. This relationship of race and lending in communities significantly enhanced the
predictive capacity of our regression model. This is visible in the map of lending in the city
as well, with neighborhoods in the white, southern half of the city receiving higher levels of
mortgage investment while virtually none was extended to the North City portion of St. Louis,
where neighborhoods are typically 80 percent or more African American.
• The City of St. Louis and its inner ring suburbs like Ferguson show strong indications of hyper-
segregation. There are many census tracts in which the population is 75-98 percent African
American – concentrated clusters of segregated neighborhoods. Within these areas less than
one percent of homes received a home purchase loan for the 2012-2014 period. This lack of
lending is not fully explained by differences of income, meaning that credit is flowing more
to neighborhoods with higher percentages of white residents with the same income profile.
• Other studies suggest that it is
difficult for individual qualified
borrowers of any race to secure
credit in high-poverty, hyper-
segregated areas.4
These areas place
a strain on public services due to
their decreased tax revenue, aging
infrastructure, and increased demand
for services. As a result, public
services may fail or be scaled back,
with the segregated areas becoming
areas of concentrated poverty, to the
detriment of other parts of the MSA.5
This can lead to unproductive zones of
abandonment in urban centers, which
provide insufficient revenue to support their residents’needs.
4 Elvin K. Wyly, Mona Atia, Holly Foxcroft, Daniel J. Hamme, and Kelly Phillips-Watts,“American home: Predatory
mortgage capital and neighbourhood spaces of race and class exploitation in the United States,”Geografiska Annaler:
Series B, Human Geography 88.1 (2006): 105-132.
5 Tanvi Misra,“America Has Half as Many Hypersegretated Metros as it Did in 1970,”CityLab, May 21, 2015.
The Ville neighborhood in North City, at St. Louis Ave. and Taylor Ave.
(Photo: Elisabeth Risch, EHOC)
The Wellston neighborhood at Etzel Ave.
and Stephen Jones Ave. (Photo: Elisabeth
Risch, EHOC)
9. 9
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
METROPOLITAN ST. LOUIS
The history of St. Louis over the past 50 years is similar to many of America’s suburbanized and
deindustrialized cities in the late 20th
century. Reaching a maximum population of 856,000
residents in 1950, the urban core shrank to merely 319,000 by the 2010 U.S. Census. Meanwhile,
the population in the surrounding suburbs grew from approximately 825,000 to 2.4 million
(1950 and 2010 decennial U.S. Census). The flight of more affluent white residents from the city
center resulted in the concentration of African Americans and impoverished residents within the
city and aging inner-ring suburbs of the metropolitan complex. Very high levels of segregation
are borne out in its high index of dissimilarity of 70.6 for African American and white residents.
Groups Index of Dissimilarity*
Asian/white 40.8
Hispanic/others 30.6
Black/white 70.6
*Taken from a Brown University study (2010 U.S. Census data): http://www.s4.brown.edu/us2010
This analysis of the St. Louis area will first describe the relationship between mortgage lending
and demographic and socioeconomic characteristics in the overall MSA, and then focus on the
City of St. Louis in contrast to its surrounding suburban counties. Throughout the study, we
accounted for areas of empty lots and abandonment by normalizing the number of loans by the
number of housing units counted in the 2010 U.S. Census.
The St. Louis MSA includes the City of St. Louis and surrounding counties in Missouri and across
the Mississippi River in Illinois. The first table displays descriptive statistics for the region.
DESCRIPTIVE STATISTICS
Minimum Maximum Mean Std. Deviation
Hispanic % .0021 .7177 .024962 .0337131
White % .0019 .9878 .722783 .3124266
Black % .0000 .9856 .213720 .3149884
Asian % .0000 .2318 .019387 .0278541
OwnerOcc % .0026 .9826 .697069 .1961753
Poverty % .0028 .6321 .151116 .1263145
hs_grad % .5160 .9995 .884369 .0778097
Loans_Unit .0000 266.5120 31.252728 35.6801006
Loans_POP .0000 105.5406 13.054707 13.7713962
Count_POP .0000 .2874 .075810 .0515345
Minority % .0122 .9981 .277217 .3124266
N=654
10. 10
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
The high concentration of African American or white residents in census tracts reflects the
pattern of segregation indicated by the high index of dissimilarity. In fact, 85 of the 654 census
tracts in the St. Louis MSA have African American population percentages of 75 percent or more.
The entire northern portion of St. Louis, North City, is over 75 percent African American.
This demographic polarization is manifested in the lending patterns throughout the area,
with the percentage of African Americans located in a census tract significantly and negatively
correlated with the sums of mortgage lending. Additionally, there were positive and significant
correlations between higher socioeconomic status and lending, with greater median family
income having especially strong relationships with higher home mortgage lending.
A scatterplot of the relationship between higher median family income and mortgage lending
reveals a very strong linear relationship, with an r2
of 0.679.
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner
Occupancy
%
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R Loans
per Unit
-.397** .397** -.412** -.061 .322** .491** -.543** .598** .824**
Spearman’s
rho
Loans
per Unit
-.362** .362** -.408** .216** .585** .570** -.761** .761** .802**
N=654
11. 11
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
In order to explore the relationships between mortgage lending patterns and social variables,
a regression model was developed using variables for race and ethnicity (percentages of
white, African American, Hispanic, and Asian residents per each census tract), education level
(percent of high school graduates), and indicators of wealth (median family income, homeowner
occupancy) and poverty levels. Of these variables, the single most significant one was median
family income per tract, with the percentage of white residents in the tract an additional
predictor. The relationship of median family income and home mortgage lending per housing
unit in the area has an adjusted R-squared of .679, a very high value considering this is a complex
socioeconomic relationship with a potential for many factors to come into play. Essentially,
higher median family income explained almost 68 percent of the mortgage lending in the St.
Louis MSA. When white race is considered there is an indication of confounding with the income
variable in the model. The variable for white percent has a negative correlation coefficient,
which is not present in the bivariate correlations analysis. The strong bivariate correlation
and regression value for income and home purchase mortgage lending indicate a stronger
association than does race.
MODEL SUMMARYC
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .824a
.679 .679 .56659743
2 .834b
.696 .695 .55236476
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(White)
c. Dependent Variable: Zscore(Loans_Unit)
COEFFICIENTSa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 1.024E-013 .022 .000 1.000
Zscore(Tract_TO_M) .824 .022 .824 37.176 .000 1.000 1.000
2
(Constant) 1.022E-013 .022 .000 1.000
Zscore(Tract_TO_M) .922 .027 .922 33.937 .000 .634 1.578
Zscore(White) -.161 .027 -.161 -5.919 .000 .634 1.578
a. Dependent Variable: Zscore(Loans_Unit)
While white residents are 75 percent of the population in the St. Louis MSA, they received 83
percent of the mortgage loans, resulting in a 112 percent disparity ratio. In contrast, African
American residents are 18 percent of the population, but received only four percent of the loans,
creating an unfavorable disparity rate of 26 percent for them.
12. 12
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
MARKET OVERVIEW LOAN NUMBERS
Count of Loans
Race and
Ethnicity
Population Applications Originations Approval Rate
Disparity Ratio
Loans/Population Size
White 1,997,776 99,721 69,881 70% 112%
Hispanic 68,263 1,689 981 58% 46%
Black 487,527 7,896 3,956 50% 26%
Asian 56,510 2,289 1,505 66% 85%
Total 2,661,885 126,555 83,409 66% 100%
Geography St. Louis MSA 2013
Lender All HMDA
Filters (State is IL and MSA is St. Louis, MO-IL MSA) Or ( State is MO and MSA is St. Louis, MO-IL MSA)
Notes Figures may not equal 100% due to rounding
When loans made in 2013 are analyzed relative to both race and borrower and census tract
income, we find consistent patterns of greater loan approvals for upper-income applicants in
upper-income areas. Additionally, the lowest approval rates were for the most segregated minority
neighborhoods with the lowest income levels.
LOANS BY BORROWER INCOME
Percent of Applications Across
Different Census Tracts
Low- and Moderate-Income (LMI) Middle- and Upper-Income (MUI)
As a Percent of All Approval
Rates
As a Percent of All Approval
RatesApplications Originations Applications Originations
All Tracts 36% 32% 60.24% 64% 68% 70%
Minority Level
< 10% Minority 47% 49% 63% 50% 50% 71%
10-19% Minority 24% 26% 64% 31% 32% 73%
20-49% Minority 17% 16% 58% 14% 14% 68%
50-79% Minority 6% 5% 50% 3% 3% 72%
80-100% Minority 5% 3% 37% 1% 1% 44%
Tract Income Level
Low - < 50% MSA/MD Median 3% 2% 40% 1% 1% 44%
Moderate - 50-79.99% MSA/MD Median 18% 16% 53% 7% 6% 56%
Middle - 80-119.99% MSA/MD Median 54% 55% 62% 40% 39% 65%
Upper - 120% or More MSA/MD Median 25% 27% 65% 52% 55% 71%
Geography St. Louis MSA 2013
Lender All HMDA
Filters
(State is IL and MSA is St. Louis, MO-IL
MSA) Or ( State is MO and MSA is St. Louis,
MO-IL MSA)
Notes
Figures may not equal 100% due to
rounding
13. 13
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
THE CITY OF ST. LOUIS
The relationship between mortgage lending, race and socioeconomic status shifts when we
focus our analysis on the highly segregated area of the City of St. Louis. Here, lending takes on
a different and more racialized pattern. Within the city, 52 percent of the population is African
American and only 39 percent is white. The socioeconomic status of residents within the
city is also substantially lower than residents of the suburbs. The mean of tract-level median
family income is 64.9, well below the average of 100 and within the moderate income range.
Additionally, less than half of the residents of the City of St. Louis own their homes, with a mean
homeowner occupancy level of only 46 percent. Residents of the city have substantially less
wealth as measured by these two socioeconomic indicators than residents of the suburbs do, as
we’ll see when we turn to the analysis of suburban patterns.
DESCRIPTIVE STATISTICS
Range Minimum Maximum Mean Std. Deviation
White % .9329 .0031 .9361 .397597 .3382666
Black % .9754 .0103 .9856 .524294 .3696598
Asian % .1699 .0000 .1699 .025643 .0359682
OwnerOcc % .8869 .0026 .8896 .464460 .1824598
Poverty % .5974 .0347 .6321 .281140 .1427111
Tract_TO_M 153.7100 .0000 153.7100 64.960159 34.4988948
hs_grad % .4108 .5887 .9995 .822085 .0873702
Loans_Unit 93.2104 .0000 93.2104 11.978153 14.0534507
Loans_Pop 28.5988 .0000 28.5988 6.541962 7.1213846
Minority % .9329 .0639 .9969 .602403 .3382666
N=126
Within the city, the bivariate correlations between mortgage lending per unit and white and
African American race have significant and higher correlation coefficients than when the MSA
was examined. These patterns had been obscured by their aggregation in the larger data set.
Socioeconomic factors like education, income, and poverty continue to be significant and have
the highest correlation coefficients.
14. 14
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
BIVARIATE CORRELATIONS
Scatterplots indicate that the relationship between mortgage lending and median family
income continues to be strong, with an r2
of .586. The scatterplots for the race variables, percent
African American and percent white, also show strong linear relationships, presenting near-
mirror images of each other with significant r2
of 0.474 and 0.475, respectively.
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner
Occupancy %
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R
Loans per
Unit
-.689** .689** -.688** .130 .433** .277** -.602** .718** .766**
Spearman’s
rho
Loans per
unit
-.825** .825** -.815** .538** .656** .305** -.722** .755** .823**
N=126
15. 15
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
The regression model in this case is more complex, with variables for both socioeconomic
status and race coming into play. The model which explains the relationship of home mortgage
lending and the explanatory variables most fully, and that is also the most parsimonious,
includes both higher median family income and lower percentages of African American
residents. This model has an adjusted R-squared of .640. This means that neighborhoods with
higher median family income and lower numbers of African Americans explained 64 percent of
the mortgage lending in the confined areas of the City of St. Louis.
MODEL SUMMARYe
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .766a
.586 .583 .64585547
2 .804b
.646 .640 .59974270
3 .816c
.666 .658 .58510748
4 .825d
.680 .670 .57480234
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(Black)
c. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(Black), Zscore(hs_grad)
d. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(Black), Zscore(hs_grad), Zscore(Asian)
e. Dependent Variable: Zscore(Loans_Unit)
16. 16
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
COEFFICIENTSa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 1.007E-013 .058 .000 1.000
Zscore(Tract_TO_M) .766 .058 .766 13.254 .000 1.000 1.000
2
(Constant) 1.006E-013 .053 .000 1.000
Zscore(Tract_TO_M) .552 .071 .552 7.737 .000 .566 1.766
Zscore(Black) -.325 .071 -.325 -4.561 .000 .566 1.766
3
(Constant) -1.002E-013 .052 .000 1.000
Zscore(Tract_TO_M) .401 .089 .401 4.501 .000 .344 2.904
Zscore(Black) -.280 .072 -.280 -3.915 .000 .535 1.868
Zscore(hs_grad) .231 .086 .231 2.689 .008 .372 2.690
4
(Constant) -1.002E-013 .051 .000 1.000
Zscore(Tract_TO_M) .405 .088 .405 4.626 .000 .344 2.905
Zscore(Black) -.210 .077 -.210 -2.737 .007 .451 2.217
Zscore(hs_grad) .231 .084 .231 2.742 .007 .372 2.690
Zscore(Asian) .138 .059 .138 2.327 .022 .757 1.321
a. Dependent Variable: Zscore(Loans_Unit)
Race and income are the strongest factors among the variables which we examined in this
segment of the analysis for the City of St. Louis. In both the bivariate correlations and in the
regression model, income and race have the strongest relationship to home mortgage lending
among variables which we examined in this segment of the analysis for the City of St. Louis. We
will see the importance of race diminish as we turn to an analysis of the St. Louis suburbs.
17. 17
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
ST. LOUIS SUBURBS
The suburbs were then isolated from the MSA and examined separately for comparison with
the city. African Americans comprise a smaller percentage of the population, though they live
in very segregated neighborhoods. Here, 44 of the 548 census tracts (many in East St. Louis) had
percentages of African American residents greater than 75 percent. Meanwhile, 444 of the tracts
had percentages of African American residents less than the MSA mean level of 21 percent.
The suburbs are also more affluent than the city, with the average median family income at
106 percent of the MSA average, versus 65 percent in the city. They also have much higher
percentages of homeowner occupancy, at over 74 percent compared to 46 percent in the city.
DESCRIPTIVE STATISTICS
Range Minimum Maximum Mean Std. Deviation
White % .9860 .0019 .9878 .788020 .2630550
Black % .9840 .0000 .9840 .151738 .2638063
Asian % .2318 .0000 .2318 .018218 .0261488
OwnerOcc % .9773 .0053 .9826 .745772 .1584583
Poverty % .6231 .0028 .6258 .124461 .1032315
Tract_TO_M 288.3300 .0000 288.3300 106.246077 40.6737512
hs_grad % .4835 .5160 .9995 .897792 .0681037
Loans_Unit 266.51 .00 266.51 35.0924 37.41496
Loans_Pop 105.54 .00 105.54 14.3060 14.37662
Minority % .99 .01 1.00 .2120 .26305
N=548
In the St. Louis suburbs, the importance of race in home mortgage lending diminishes in the
bivariate correlations. Home mortgage lending is most strongly associated with socioeconomic,
rather than racial and ethnic factors. This is indicated by the high correlation coefficients for
education level, income, and poverty. Race is still significant, and shows a positive correlation
with the amount of mortgage lending to neighborhoods with higher percentages of whites, and
a significant negative relationship with the percentage of African Americans.
18. 18
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
Scatterplots show the very strong linear relationship between mortgage lending and median
family income, with an r2
of 0.702. The scatterplots of race (African American) and lending are
much weaker than they were in the city, however.
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner
Occupancy
%
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R Loans per
Unit
-.320** .320** -.348** -.051 .395** .471** -.535** .592** .838**
Spear-
man’s Rho
Loans per
Unit
-.195** .195** -.258** .193** .632** .539** -.745** .739** .801**
N=548
19. 19
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
A regression model was constructed to determine which variables may act as predictors for
home mortgage lending. The significant variable with the strongest relationship to higher
amounts of home mortgage lending was tract median family income. With an adjusted
R-squaredof .701, increased median family income provided the model with the best goodness-
of-fit of all the variables. While white race also achieved significance, its negative coefficient is
inconsistent with the bivariate correlation results, indicating that it is collinear and should be
removed from consideration, especially since it enhances model goodness-of-fit negligibly.
MODEL SUMMARYd
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .838a
.702 .701 .54669957
2 .845b
.715 .714 .53516200
3 .847c
.718 .716 .53269809
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(White)
c. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(White), Zscore(OwnerOcc)
d. Dependent Variable: Zscore(Loans_Unit)
20. 20
ANALYSIS St. Louis and Surrounding Areas NCRC • 202-628-8866 • www.ncrc.org
COEFFICIENTSa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) -1.022E-013 .023 .000 1.000
Zscore(Tract_TO_M) .838 .023 .838 35.835 .000 1.000 1.000
2
(Constant) -1.022E-013 .023 .000 1.000
Zscore(Tract_TO_M) .903 .026 .903 34.188 .000 .750 1.334
Zscore(White) -.132 .026 -.132 -4.980 .000 .750 1.334
3
(Constant) -1.019E-013 .023 .000 1.000
Zscore(Tract_TO_M) .877 .028 .877 30.798 .000 .640 1.562
Zscore(White) -.160 .029 -.160 -5.569 .000 .629 1.589
Zscore(OwnerOcc) .074 .030 .074 2.460 .014 .580 1.726
a. Dependent Variable: Zscore(Loans_Unit)
CONCLUSION
This analysis shows that income and race are significantly associated with home mortgage
lending patterns throughout the St. Louis MSA. Our mapping and bivariate correlation analysis
indicated that lower income areas and areas with larger proportions of African American
residents have significantly less home mortgage activity than affluent or majority white areas at
all scales of analysis. This pattern is much more significant in the City of St. Louis, however, where
income and race are both strong predictors in the regression models. The statistical model for
the suburbs is less clear, and income and race may be collinear, making it difficult to examine
their influence separately. This is interesting since it indicates that income and race may have a
different impact on lending in the suburbs and urban core of St. Louis. It could also reflect higher
median family income of suburban African Americans families in the region.
21. 21
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
MilwaukeeandSurroundingAreas
EXECUTIVE SUMMARY
Within the City of Milwaukee, Wisconsin, nearly 90 percent of African Americans live in the inner
city. The Milwaukee metro area has one of the lowest rates of African American suburbanization.1
The city itself is“hyper-segregated,” with most African American neighborhoods concentrated
in the central and northern portions of the city.2
Majority African American neighborhoods
stop at the northern boundary of the city, where the suburbs become overwhelmingly white.
This hyper-segregated reality has even impacted the transportation policy of the region,
further spatially isolating African American communities.3
Additionally, comparing the City of
Milwaukee to its suburbs reveals an example of extreme income inequality. In 2013, Milwaukee
Mayor Tom Barrett stated:
“We are the only metropolitan area in the entire country where you’ve got that ratio [city
poverty to suburban poverty] higher than four, meaning that the poverty rate is four
times greater in the city than it is in the suburbs.”4
The combined effect of institutional, individual and policy decisions manifest in the minority
areas of the city, which form a single, contiguous, segregated zone of disinvestment and
concentrated poverty.5
NCRC has found an extensive mortgage lending imbalance in Milwaukee, with mortgage
credit distribution skewed heavily toward majority white neighborhoods. While median family
income is a critical factor, lending is concentrated in majority white neighborhoods and scarce
in majority African American neighborhoods. Within the City of Milwaukee, the race of the
neighborhood is a significant variable associated with the amount of mortgage loan activity. This
further concentrates poverty, impacting the ability of majority African American neighborhoods
to build wealth and increasing the likelihood that lenders will not invest there. This process
creates more segregation, as African American families are concentrated in neighborhoods that
see little to no investment from lenders.
To help address this problem, NCRC is calling for stronger enforcement and expansion of the
Community Reinvestment Act (CRA) and the preservation and strengthening of the affordable
housing goals in the secondary mortgage market.
1 Ray Sanchez,“Race and Reality: The scourge of segregation,”CNN, December 1, 2015.
2 Douglas S. Massey and Jonathan Tannen,“A Research Note on Trends in Black Hypersegregation,” Demography 52.3
(2015), 1025-1034.
3 Ray Sanchez,“Race and Reality: The scourge of segregation,”CNN, December 1, 2015.
4 Tom Kertscher,“Milwaukee’s city-suburban poverty disparity worst among U.S. metro areas, Mayor Tom Barrett
says,”Politifact Wisconsin, October 3, 2013,
5 Douglas S. Massey,“Residential Segregation and Neighborhood Conditions in U.S. Metropolitan Areas,” America
Becoming: Racial Trends and Their Consequences 1.1 (2001): 391-434.
22. 22
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
KEY FINDINGS
• With an index of dissimilarity of 84.4,
Milwaukee is the third most segregated
major city in the U.S., behind Detroit,
Michigan and Gary, Indiana.6,7
• In the City of Milwaukee, African
Americans comprise 40 percent of the
overall population. Suburban areas of
the Milwaukee metropolitan statistical
area (MSA) are 88 percent white.
Both geographies display signs of
extensive segregation, regardless
of the size of the African American
population.
• Examining the number of loans in the
Milwaukee MSA in 2014 reveals that
the market favors white applicants.
Whites represent 70 percent of
the population, yet received 81
percent of the loans, resulting in a
118 percent disparity ratio. African
Americans are 16 percent of the
population yet only received four
percent of the loans, an unfavorable
disparity rate of 24 percent.
• Loans in the Milwaukee MSA are
heavily concentrated in majority
white and middle- and upper-income
neighborhoods.
• Across both the city and the MSA the
level of owner occupancy and the median family income were significantly correlated with
lending activity; however, in the City of Milwaukee the areas with a higher percentage of
white residents also had a higher likelihood of seeing mortgage activity.
• Other studies suggest that it is difficult for qualified borrowers of any race to secure
credit in high poverty, hyper-segregated areas.8
These areas place a strain on public
services due to their decreased tax revenue, aging infrastructure, and increased demand
for services. As a result, public services may fail or be scaled back, with the segregated areas
becoming areas of concentrated poverty to the detriment of other parts of the MSA.9
6 Douglas S. Massey,“Residential Segregation and Neighborhood Conditions in U.S. Metropolitan Areas,” America
Becoming: Racial Trends and Their Consequences 1.1 (2001): 391-434.
7 “Segregation: Dissimilarity Indices,”CensusScope.
8 Elvin K. Wyly, Mona Atia, Holly Foxcroft, Daniel J. Hamme, and Kelly Phillips-Watts,“American home: Predatory
mortgage capital and neighbourhood spaces of race and class exploitation in the United States,”Geografiska Annaler:
Series B, Human Geography 88.1 (2006): 105-132.
9 Tanvi Misra,“America Has Half as Many Hypersegretated Metros as it Did in 1970,”CityLab, May 21, 2015.
South 10th Street (Photo: Matthew Wisla courtesy of Milwaukee NNS)
Lindsey Heights (Photo: Mark Doremus courtesy of Milwaukee NNS)
25. 25
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
MILWAUKEE MSA ANALYSIS
With an index of dissimilarity of 84.4, Milwaukee is the third most segregated major city in the
U.S., behind Detroit, Michigan and Gary, Indiana. This is apparent when examining the racial
composition of the population in the overall MSA, where the City of Milwaukee is just 36 percent
white and the surrounding MSA is 88 percent white.
Groups Index of Dissimilarity*
Asian/white 48.1
Hispanic/others 60.6
Black/white 84.4
*Taken from Brown University study (2010 U.S. Census data): http://www.s4.brown.edu/us2010
Milwaukee MSA data shows that the area underperforms in the factors associated with wealth
building. Homeownership is low in the urban area, at 57 percent owner-occupied units.
Additionally, education measured by the rate of high school graduation was low. Rates of
high school graduates were also low, with an average of only 87 percent graduates per tract.
Conversely, more than 18 percent of Milwaukee MSA residents live in poverty (U.S. Census 2010).
DESCRIPTIVE STATISTICS
Range Minimum Maximum Mean Std. Deviation
Hispanic % .7617 .0103 .7720 .101397 .1678788
White % .9672 .0027 .9698 .632489 .3442419
Black % .9565 .0009 .9573 .212926 .3186603
Native % .0180 .0000 .0180 .004461 .0035123
Asian % .4146 .0000 .4146 .029825 .0371004
OwnerOcc % .9774 .0027 .9800 .574853 .2365981
Poverty % .8815 .0029 .8843 .185610 .1685155
Tract_TO_M 303.4700 .0000 303.4700 92.866400 45.4447320
hs_grad % .6139 .3861 1.0000 .870481 .1188406
LOANS_UNIT 167.54 .00 167.54 30.2474 32.31902
LOANS_POP 65.66 .00 65.66 12.7243 12.66047
Minority % .97 .03 1.00 .3675 .34424
N=425
26. 26
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
Examination of bivariate correlations showed significant and positive correlations between
home mortgage lending socioeconomic factors of median family income, homeowner
occupancy and education. Race, especially where there are increasing percentages of white
residents, had significant and high correlation coefficients, while African American percentages
showed significant negative associations with a lower coefficient.
A regression model was created to further examine the relationships and produced a high
adjusted R2
of .772 for home mortgage lending and median family income, homeowner
occupancy and lower percentages of Native American residents. Since Native Americans
comprise less than .5 percent of the MSA population, the variable was excluded despite the
improvement in model goodness-of-fit. The result is that socioeconomic considerations become
the dominant factor in home mortgage lending for the region.
MODEL SUMMARYe
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .843a
.710 .710 .53891375
2 .871b
.759 .758 .49226825
3 .880c
.774 .772 .47711296
4 .885d
.783 .781 .46842958
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc)
c. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc), Zscore(Native)
d. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc), Zscore(Native), Zscore(hs_grad)
e. Dependent Variable: Zscore(LOANS_UNIT)
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner
Occupancy %
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R
Loans per
Unit
-.645** .645** -.500** -.321** -.076 .765** -.651 .604** .843**
Spearman’s
rho
Loans per
Unit
-.879** .879** -.802** -.460** .028 .854** -.903** .859** .933**
N=425
27. 27
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
COEFFICIENTSa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 1.007E-013 .026 .000 1.000
Zscore(Tract_TO_M) .843 .026 .843 32.201 .000 1.000 1.000
2
(Constant) 1.003E-013 .024 .000 1.000
Zscore(Tract_TO_M) .609 .035 .609 17.462 .000 .470 2.127
Zscore(OwnerOcc) .321 .035 .321 9.217 .000 .470 2.127
3
(Constant) 1.002E-013 .023 .000 1.000
Zscore(Tract_TO_M) .574 .034 .574 16.674 .000 .453 2.207
Zscore(OwnerOcc) .306 .034 .306 9.039 .000 .467 2.142
Zscore(Native) -.132 .025 -.132 -5.314 .000 .874 1.145
4
(Constant) -1.005E-013 .023 .000 1.000
Zscore(Tract_TO_M) .659 .040 .659 16.613 .000 .329 3.041
Zscore(OwnerOcc) .326 .034 .326 9.700 .000 .457 2.187
Zscore(Native) -.152 .025 -.152 -6.117 .000 .839 1.192
Zscore(hs_grad) -.143 .035 -.143 -4.093 .000 .421 2.375
a. Dependent Variable: Zscore(LOANS_UNIT)
Examining the number of mortgage loans in the Milwaukee MSA in 2014 reveals that the market
favors white applicants. Whites make up 70 percent of the population and received 81 percent of
the loans, resulting in a disparity ratio of 118 percent. African American applicants fared worst in
this mortgage market. While African Americans are 16 percent of the population, they received
barely four percent of loans, resulting in a 24 percent disparity ratio. Hispanic borrowers also
fared badly. The Hispanic population is over nine percent of the area total, yet they received only
four percent of loans, resulting in an unfavorable disparity ratio of 43 percent.
MARKET OVERVIEW LOAN NUMBERS
Count of Loans
Race and
Ethnicity
Population Applications Originations
Approval
Rate
Disparity Ratio
Loans/Population Size
White 1,073,109 29,190 20,724 71% 118%
Hispanic 147,503 1,758 1,032 59% 43%
Black 255,779 2,102 998 47% 24%
Asian 45,588 901 595 66% 80%
Total 1,555,908 37,674 25,365 67% 100%
Geography Milwaukee MSA 2014
Lender All HMDA
Filters
Property Type is One to Four-Family and (Purpose is Home Purchase or Refinancing) and
(Occupancy is Owner Occupied) and (Action is Originated or Approved Not Accepted or Denied or
Withdrawn or Closed Incomplete)
Notes Figures may not equal 100% due to rounding
28. 28
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
When lending is analyzed according to applicant income and the socioeconomic status of
residents in the tract, many more loans (77 percent of loans to low- and moderate-income
buyers, and 96 percent of loans to middle- and upper-income buyers) were made in census
tracts with higher proportions of middle- and upper-income residents. Additionally, tracts with
a mostly minority population received 15 percent of loans made to low- and moderate-income
buyers, and only three percent of loans to middle- and upper-income buyers. This indicates a
highly segmented market, which favors lending to middle- and upper-income purchasers in
middle- and upper-income neighborhoods.
LOANS BY BORROWER INCOME
Percent of Applications Across
Different Census Tracts
Low- and Moderate-Income (LMI) Middle- and Upper-Income (MUI)
As a Percent of All Approval
Rates
As a Percent of All Approval
RatesApplications Originations Applications Originations
All Tracts 33% 29% 54.97% 67% 71% 66%
Minority Level
< 10% Minority 34% 36% 59% 55% 57% 68%
10-19% Minority 26% 27% 59% 30% 30% 67%
20-49% Minority 18% 18% 58% 10% 10% 61%
50-79% Minority 10% 9% 47% 3% 2% 56%
80-100% Minority 13% 9% 39% 3% 1% 30%
Tract Income Level
Low - < 50% MSA/MD Median 7% 4% 34% 1% 1% 32%
Moderate - 50-79.99% MSA/MD Median 22% 20% 50% 6% 5% 46%
Middle - 80-119.99% MSA/MD Median 48% 51% 58% 39% 38% 58%
Upper - 120% or More MSA/MD Median 23% 25% 59% 54% 56% 62%
Geography Milwaukee MSA 2014
Lender All HMDA
Filters
Property Type is One to Four-Family
and (Purpose is Home Purchase or
Refinancing) and (Occupancy is Owner
Occupied) and (Action is Originated
or Approved Not Accepted or Denied
or Withdrawn or Closed Incomplete or
Purchased)
Notes
Figures may not equal 100%
due to rounding
29. 29
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
THE CITY OF MILWAUKEE
The lower socioeconomic status of neighborhoods in the City of Milwaukee is evident in
descriptive statistics related to homeowner occupancy, high school graduation, and income.
Additionally, high levels of segregation are present, with neighborhood clusters containing
high proportions of African American residents. In fact, the City of Milwaukee rapidly
transitioned from white majority in 1990 (60.8 percent) to majority-minority (57 percent) by
2010. Deep segregation, rapid demographic transition, and decreased socioeconomic status of
neighborhoods separates the urban areas of the city from their surrounding suburbs.
DESCRIPTIVE STATISTICS
Range Minimum Maximum Mean Std. Deviation
LOANS_UNIT 78.37 .00 78.37 9.1717 10.97035
LOANS_POP 42.69 .00 42.69 4.2376 5.23456
Minority % .90 .09 1.00 .6336 .31271
White % .9049 .0027 .9076 .366403 .3127136
Black % .9500 .0073 .9573 .411819 .3589288
Native % .0179 .0000 .0179 .005525 .0037481
Asian % .4146 .0000 .4146 .034341 .0480178
OwnerOcc % .9472 .0027 .9498 .424416 .1951722
Poverty % .8684 .0159 .8843 .304251 .1654068
Tract_TO_M 195.8500 .0000 195.8500 63.462260 36.6438415
hs_grad % .6077 .3861 .9938 .798987 .1309655
N=208
Significant bivariate correlations between home mortgage lending and the two predictor
variables of higher median family income and increasing numbers of white residents showed
very high coefficients of correlation.
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner
Occupancy %
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R
Loans per
Unit
-.743** .743** -.521** -.176* -.083 .532** -.612** .607** .810**
Spearman’s
rho
Loans per
Unit
-.813** .813** -.653 .164* .170* .624** -.831** .769** .894**
N=208
30. 30
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
The regression analysis showed an adjusted R2
of .732 for home mortgage lending and the
predictor variables of median family income, homeowner occupancy, and the race variable
of white. The variable for Native Americans was also significant and negative, but due to the
low percentage of residents in this category and minimal improvement to the model, it was
excluded from the final analysis.
MODEL SUMMARYE
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .810a
.657 .655 .58741028
2 .839b
.703 .700 .54735771
3 .858c
.736 .732 .51745380
4 .863d
.745 .740 .50955341
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(White)
c. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(White), Zscore(OwnerOcc)
d. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(White), Zscore(OwnerOcc), Zscore(Native)
e. Dependent Variable: Zscore(LOANS_UNIT)
COEFFICIENTSa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) -1.000E-013 .041 .000 1.000
Zscore (Tract_TO_M) .810 .041 .810 19.847 .000 1.000 1.000
2
(Constant) -1.001E-013 .038 .000 1.000
Zscore (Tract_TO_M) .585 .055 .585 10.651 .000 .479 2.086
Zscore (White) .312 .055 .312 5.679 .000 .479 2.086
3
(Constant) -1.001E-013 .036 .000 1.000
Zscore (Tract_TO_M) .488 .055 .488 8.818 .000 .422 2.371
Zscore (White) .316 .052 .316 6.078 .000 .479 2.086
Zscore (OwnerOcc) .204 .041 .204 5.038 .000 .787 1.270
4
(Constant) -1.001E-013 .035 .000 1.000
Zscore (Tract_TO_M) .463 .055 .463 8.377 .000 .410 2.439
Zscore (White) .349 .053 .349 6.639 .000 .453 2.209
Zscore (OwnerOcc) .214 .040 .214 5.338 .000 .781 1.280
Zscore (Native) -.099 .037 -.099 -2.716 .007 .937 1.068
a. Dependent Variable: Zscore (LOANS_UNIT)
31. 31
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
MILWAUKEE SUBURBS
The Milwaukee suburbs provide further indications of hyper-segregation, with the high mean
percentage of white residents at 88 percent and low mean percentage of African American
residents of just over two percent. The census tract average median family income is 121
percent, compared to the city level of 63 percent. Poverty is just over seven percent in the
suburbs versus the city’s 30 percent. Education levels of nearly 94 percent high school graduates
versus nearly 80 percent present another dimension. All socioeconomic indicators point to
substantially better conditions in Milwaukee’s suburbs in contrast with the city.
DESCRIPTIVE STATISTICS
Minimum Maximum Mean Std. Deviation
Hispanic % .0103 .3074 .047425 .0437550
White % .5608 .9698 .887539 .0732762
Black % .0009 .3323 .022283 .0354887
Native % .0000 .0180 .003441 .0029355
Asian % .0014 .1161 .025497 .0213026
OwnerOcc % .1051 .9800 .719050 .1752052
Poverty % .0029 .2612 .071891 .0545669
Tract_TO_M 44.7000 303.4700 121.051014 33.7120753
hs_grad % .7861 1.0000 .939010 .0405683
LOANS_UNIT 2.04 167.54 50.4490 33.12538
LOANS_POP 1.50 65.66 20.8590 12.35087
Minority % .03 .44 .1125 .07328
N=217
Bivariate correlations are significant, with high coefficients for median family income,
homeowner occupancy and also high school graduate levels and the amount of home
mortgage lending in the suburbs. Scatterplots for median family income and mortgage lending
produce an r2
of .722.
32. 32
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner
Occupancy %
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R
Loans per
Unit
-.451** .451** -.201** -.533** .061 .759** -.650** .654** .850**
Spearman’s
rho
Loans per
Unit
-.572** .572** -.452** -.758** .028 .830** -.769** .741** .874**
N=217
33. 33
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Milwaukee and Surrounding Areas
Regression modeling results in a very high adjusted R2
of .792 for mortgage lending and
the predictor variables for median family income and homeowner occupancy. Race is a less
significant predictor of lending in the Milwaukee suburbs than it was in the city.
MODEL SUMMARYc
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .850a
.722 .721 .52859806
2 .891b
.794 .792 .45565515
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc)
c. Dependent Variable: Zscore(LOANS_UNIT)
COEFFICIENTSa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 1.008E-013 .036 .000 1.000
Zscore (Tract_TO_M) .850 .036 .850 23.623 .000 1.000 1.000
2
(Constant) -1.005E-013 .031 .000 1.000
Zscore (Tract_TO_M) .617 .041 .617 15.062 .000 .573 1.746
Zscore (OwnerOcc) .356 .041 .356 8.680 .000 .573 1.746
a. Dependent Variable: Zscore (LOANS_UNIT)
CONCLUSION
Home mortgage lending in Milwaukee’s suburbs is mostly predicted by socioeconomic
considerations: higher median family income, high rates of owner-occupancy, and higher
educational attainment. This situation contrasts with the statistical model of the city, where race
and income seem to matter the most. The maps and statistical results for Milwaukee reflect the
hyper-segregated and impoverished circumstances in many city neighborhoods.
34. 34
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
MinneapolisandSurroundingAreas
EXECUTIVE SUMMARY
Seeming to buck the trend of many industrial U.S. cities, Minneapolis, Minnesota has developed
a vibrant economy while increasing its racial and ethnic diversity. Contrasted with the other cities
in this report, Minneapolis is not hyper-segregated and is more economically prosperous. This
growth provides some indication, discussed below, that Minneapolis continues the process of
suburbanization of its outer ring communities.
In both the city and the surrounding counties, median family income appears to be the
driving factor in determining the level of mortgage activity. However, a pattern emerges when
viewing home purchase lending at the metropolitan statistical area (MSA) level. An outer ring
of communities in semi-rural areas surrounding the I-694 perimeter is clearly visible, in which
mortgages per unit over the three-year study period stand at well over 30 percent. This is twice
what is seen in the city, with the exception of downtown Minneapolis. At this level, these“exurbs”
are the site of a disproportionate amount of investment, drawing capital away from the city.
NCRC analysis indicates that the variables most correlated with loan activity are the median
family income and owner occupancy rate of the neighborhood. In addition, the number of
loans made to African American and Hispanic families falls short of the size of their share of the
population. In outer neighborhoods, certain trends of investment and disinvestment become
visible, trends which seem to put the area on the track towards a bifurcated metro economy with
significant disparities.
NCRC has found an extensive mortgage lending imbalance in Minneapolis. Within the City of
Minneapolis, the race of the neighborhood is an important predictor of the amount of mortgage
lending activity. Although median family income is significantly correlated and a stronger
predictor of mortgage lending, race (white) is also an important predictor in our model. This
relationship between race and income is probably due to the higher median family income of
white families in the neighborhoods analyzed. This means that as lenders concentrate lending
in wealthier neighborhoods they tend to ignore the lower income neighborhoods, which are
home to more African American families. If unchecked, this process creates more segregation,
as African American families are concentrated in neighborhoods that see little to no investment
from lenders.
To help address this problem, NCRC is calling for stronger enforcement and expansion of the
Community Reinvestment Act (CRA) and the preservation and strengthening of the affordable
housing goals in the secondary mortgage market.
35. 35
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
KEY FINDINGS
• With an index of dissimilarity of 64.5 for African Americans and whites, Minneapolis is
one of the more desegregated cities we have reviewed, including St. Louis, Milwaukee,
and Baltimore.1,2
• In the City of Minneapolis, African Americans comprise 18 percent of the overall population
and Latinos are 10 percent. Suburban areas of the Minneapolis MSA are 83 percent white.
While there is evidence of segregation in Minneapolis, with the southern part of the
city nearly 100 percent white, the variable that best predicts home loan activity is not race
but the median family income of the neighborhood.
• Examining the number of loans in the Minneapolis MSA in 2014 reveals that the market
favors white applicants slightly. Whites make up 78 percent of the population, and
they received 80 percent of loans. This is a slightly favorable disparity ratio of 102
percent. African Americans, who make up just over seven percent of the population
and received two percent of loans, fared the worst with an unfavorable disparity
ratio of 28 percent. Hispanic borrowers, who make up five percent of the population
and received a little over two percent of loans, also had an unfavorable disparity ratio of
41 percent.
• While loans in the City of Minneapolis and surrounding areas were visibly less in lower-
income census tracts, this was not uniformly so, and some areas of moderate income in fact
saw extensive investment.
• Across both the city and the MSA, the level of owner occupancy and the median family
income were significantly correlated with lending activity; however, a large imbalance
in lending toward the exurban ring outside of the beltway cannot be ignored. This
imbalance is draining the tax bases of communities such as Minneapolis and St. Paul.
• Other studies suggest that it is difficult for qualified borrowers of any race to secure
credit in high-poverty, hyper-segregated areas.3
These areas place a strain on public
services due to their decreased tax revenue, aging infrastructure, and increased demand
for services. As a result, public services may fail or be scaled back, with the segregated area
becoming one of concentrated poverty to the detriment of other parts of the MSA.4
1 Douglas S. Massey,“Residential Segregation and Neighborhood Conditions in U.S. Metropolitan Areas,” America
Becoming: Racial Trends and Their Consequences 1.1 (2001): 391-434.
2 “Segregation: Dissimilarity Indices,”CensusScope.
3 Elvin K. Wyly, Mona Atia, Holly Foxcroft, Daniel J. Hamme, and Kelly Phillips-Watts,“American home: Predatory
mortgage capital and neighbourhood spaces of race and class exploitation in the United States,”Geografiska
Annaler: Series B, Human Geography 88.1 (2006): 105-132.
4 Tanvi Misra,“America Has Half as Many Hypersegretated Metros as it Did in 1970,”CityLab, May 21, 2015.
39. 39
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
MINNEAPOLIS/ST. PAUL MSA ANALYSIS
The Twin Cities of Minneapolis/St. Paul differ from the other cities in this survey by their high
rates of economic and demographic expansion. They have a growing population, increasing
from 1.6 million in 1960 to 2.9 million in 2000. This urban growth corresponds with economic
expansion and low rates of unemployment. Both Minneapolis and St. Paul have retained
substantial numbers of predominantly white neighborhoods in their downtowns, resulting in
lower levels of segregation. The index of dissimilarity for Minneapolis/St. Paul is only 64.5 for
African Americans and whites. For Hispanic and white residents, however, the index is higher at
50.3 than either St. Louis (36.7) or Baltimore (40.3).
Groups Index of Dissimilarity*
Asian/white 38.5
Hispanic/others 50.3
Black/white 64.5
*Taken from a Brown University study (2010 U.S. Census data): http://www.s4.brown.edu/us2010
Lower levels of African American/white segregation are borne out by the descriptive statistics,
which show the highest percentage of African American residents in a census tract is 72 percent.
Overall, minorities comprise 23 percent of the population of the metro area. The poverty rate is
also low, with a mean of 11.9 percent, and the median family income level almost matches the
average, with 99.5 percent being the mean for the area.
DESCRIPTIVE STATISTICS
Minimum Maximum Mean Std. Deviation
LOAN_UNIT .0000 243.6340 47.449463 35.4193320
LOAN_POP .0000 89.2957 19.233658 13.2797864
Hispanic % .0058 .4493 .059370 .0656219
White % .0279 .9816 .767938 .2009658
Black % .0008 .7200 .082550 .1105514
Native % .0000 .3641 .006977 .0162754
Asian % .0011 .4290 .057431 .0644321
hopi % .0000 .0266 .000397 .0010927
OwnerOcc % .0221 .9823 .705844 .2192497
Poverty % .0000 .7206 .118903 .1142826
Tract_TO_M .0000 263.5900 99.564344 35.9813687
hs_grad % .4445 1.0000 .919000 .0786242
Minority % .0184 .9721 .232062 .2009658
N=785
40. 40
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
Considering mortgage lending per unit of housing, we find significant positive correlations with
socioeconomic factors of median family income and homeowner occupancy, both indicators
of greater wealth. Racial and ethnic factors also have significant and negative associations with
home mortgage lending. The coefficients of determination associated with both the African
American and Hispanic population of the tract are significant, but only to a moderate degree in
comparison to owner occupancy and median family income.
Examination of scatterplots depicting loans per tract per unit of housing revealed much stronger
linear relationships for the socioeconomic than for the racial and ethnic variables. The median
family income level per tract had a significant positive r2
of .678, while the relationship of African
American residents per tract with loan volume was significant and negative with an r2
of .213.
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner Oc-
cupancy %
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R Loans per
Unit
-.506** .506** -.462** -.407** -.230** .623** -.579** .546** .823**
Spearman’s
rho
Loans per
Unit
-.616** .616** -.600** -.615** -.226** .758** -.795** .745** .882**
N=785
41. 41
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
A regression model was prepared to examine the relationship of home mortgage lending with
the socioeconomic and demographic variables. The strongest model combined per-tract median
family income and levels of homeowner occupancy with an adjusted R2
= .701 when examining
the entire Minneapolis/St. Paul MSA as one unit. Another model including white residents
achieves a slightly higher adjusted R2
; however, the white variable switched signs to become
negative. This indicates multicollinearity in the model, or a confounding of the variables. Due to
this inconsistency, and to achieve a more parsimonious model, the white variable was excluded
from the final model.
MODEL SUMMARYe
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .823a
.678 .677 .56820710
2 .838b
.701 .701 .54712834
3 .844c
.712 .711 .53746093
4 .845d
.715 .713 .53549709
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc)
c. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc), Zscore(White)
d. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc), Zscore(White), Zscore(Poverty)
e. Dependent Variable: Zscore(LOAN_UNIT)
42. 42
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
COEFFICIENTSa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 1.013E-013 .020 .000 1.000
Zscore(Tract_TO_M) .823 .020 .823 40.562 .000
2
(Constant) 1.014E-013 .020 .000 1.000
Zscore(Tract_TO_M) .705 .025 .705 28.667 .000
Zscore(OwnerOcc) .194 .025 .194 7.905 .000
3
(Constant) 1.029E-013 .019 .000 1.000
Zscore(Tract_TO_M) .765 .027 .765 28.773 .000
Zscore(OwnerOcc) .253 .026 .253 9.560 .000
Zscore(White) -.149 .027 -.149 -5.421 .000
4
(Constant) 1.025E-013 .019 .000 1.000
Zscore(Tract_TO_M) .786 .028 .786 28.379 .000
Zscore(OwnerOcc) .287 .029 .287 9.759 .000
Zscore(White) -.111 .031 -.111 -3.560 .000
Zscore(Poverty) .095 .037 .095 2.596 .010
a. Dependent Variable: Zscore (LOAN_UNIT)
The analysis of mortgage loans from 2014 indicates some disparity between white and African
American borrowers. Whites make up 78 percent of the population, and they received 80
percent of loan, resulting in a disparity ratio of 102 percent. Meanwhile, African Americans, who
make up just over seven percent of the population and received two percent of loans, fared the
worst with a disparity ratio of 28 percent. Hispanic borrowers, who make up five percent of the
population and received a little over two percent of loans, also had an unfavorable disparity ratio
of 41 percent. However, approval rates for these groups were higher, relative to whites, than for
other urban areas like Baltimore, Milwaukee and St. Louis. African Americans and Hispanics had
approval rates of 57 percent and 65 percent, respectively.
MARKET OVERVIEW LOAN NUMBERS
Count of Loans
Race and
Ethnicity
Population Applications Originations
Approval
Rate
Disparity Ratio Loans/
Population Size
White 2,522,066 77,318 56,411 73% 102%
Hispanic 176,887 2,433 1,589 65% 41%
Black 238,353 2,559 1,452 57% 28%
Asian 186,172 4,507 3,038 67% 75%
Total 3,223,495 100,042 70,452 70% 100%
Geography Minneapolis MSA 2014
Lender All HMDA
Filters
Property Type is One to Four-Family and (Purpose is Home Purchase or Refinancing)
and (Occupancy is Owner Occupied) and (Action is Originated or Approved Not Accepted or
Denied or Withdrawn or Closed Incomplete)
Notes Figures may not equal 100% due to rounding
43. 43
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
Looking at applicant and neighborhood economic status reveals preferences for middle- and
upper-income tracts among all racial and ethnic groups. Approval rates for low- to moderate-
income applicants and middle- to upper-income applicants were closer than in Baltimore,
Milwaukee or St. Louis, at 68 percent and 73 percent, respectively. A majority of originations
were for mortgages on properties located in middle- and upper-income level neighborhoods,
at 76% and 92% for low- to moderate-income and middle- to upper-income borrowers,
respectively. Additionally, applications from people of all incomes indicate preference for
neighborhoods with lower percentages of minority residents, though loan approval rates there
were higher than in other metro areas.
LOANS BY BORROWER INCOME
Percent of Applications Across
Different Census Tracts
Low to Moderate Income Middle to Upper Income
As a Percent of All Approval
Rates
As a Percent of All Approval
RatesApplications Originations Applications Originations
All Tracts 40% 39% 67.76% 60% 61% 73%
Minority Level
< 10% Minority 31% 31% 67% 36% 36% 73%
10-19% Minority 30% 31% 69% 39% 40% 74%
20-49% Minority 28% 29% 69% 23% 23% 73%
50-79% Minority 9% 8% 64% 1% 2% 130%
80-100% Minority 2% 1% 56% 0% 0% 114%
Tract Income Level
Low - < 50% MSA/MD Median 4% 4% 61% 1% 1% 61%
Moderate - 50-79.99% MSA/MD Median 20% 20% 66% 8% 7% 66%
Middle - 80-119.99% MSA/MD Median 56% 57% 69% 45% 45% 70%
Upper - 120% or More MSA/MD Median 20% 19% 68% 46% 47% 72%
Geography Minneapolis MSA 2014
Lender All HMDA
Filters
Property Type is One to Four-Family
and (Purpose is Home Purchase or
Refinancing) and (Occupancy is Owner
Occupied) and (Action is Originated or
Approved Not Accepted or Denied or
Withdrawn or Closed Incomplete)
Notes
Figures may not equal 100%
due to rounding
44. 44
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
THE CITIES OF MINNEAPOLIS/ST. PAUL
A comparative analysis of the urban core and surrounding suburbs was conducted to
determine whether there were substantial differences between the inner-city areas and
suburbs. The descriptive statistics indicate lower percentages of whites live in the city, at
just 57 percent. Urban residents also have lower socioeconomic status, with only 50 percent
homeowner occupancy and a lower tract mean of median family income at 78.9 percent.
DESCRIPTIVE STATISTICS
Range Minimum Maximum Mean Std. Deviation
Hispanic % .4349 .0144 .4493 .101194 .0904595
White % .8879 .0279 .9158 .572246 .2511032
Black % .7109 .0091 .7200 .183501 .1560880
Native % .1820 .0000 .1820 .013305 .0163148
Asian % .4135 .0155 .4290 .092747 .0978701
OwnerOcc % .9103 .0221 .9324 .500757 .2307537
Poverty % .7137 .0069 .7206 .234001 .1530870
Tract_TO_M 237.6500 .0000 237.6500 78.881320 43.6515445
hs_grad % .5555 .4445 1.0000 .861463 .1171667
LOANS_UNIT 134.78 .00 134.78 27.9497 25.41003
LOANS_POP 55.41 .00 55.41 12.9253 12.08856
Minority % .89 .08 .97 .4278 .25110
N=197
Next, an analysis of bivariate associations for the number of mortgage loans was conducted.
This shows that higher median family income is a significant indicator of higher rates of
mortgage lending. It also has the strongest relationship, as measured by the much higher
coefficient of correlation.
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner
Occupancy %
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R Loans per
Unit
-.671** .671** -.549** -.362** -.419** .669** -.699** .593** .856**
Spearman’s
rho
Loans per
Unit
-.761** .761** -.714** -.441** -.554** .774** -.866* .719** .859**
N=197
45. 45
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
A regression model was then constructed, indicating that the same two variables which were
significant at the MSA level were also the best predictors at the city level: median family income
and homeowner occupancy. The Asian race variable was also significant, but rejected from the
model due to collinearity and its marginal improvement of model goodness-of-fit.
MODEL SUMMARYf
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .856a
.733 .732 .51760409
2 .897b
.805 .803 .44419673
3 .901c
.811 .808 .43807434
4 .905d
.819 .815 .43021339
5 .907e
.822 .818 .42695892
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc)
c. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc), Zscore(Asian)
d. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc), Zscore(Asian), Zscore(hs_grad)
e. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc), Zscore(Asian), Zscore(hs_grad), Zscore(White)
f. Dependent Variable: Zscore(LOANS_UNIT)
COEFFICIENTSa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 1.005E-013 .037 .000 1.000
Zscore(Tract_TO_M) .856 .037 .856 23.164 .000 1.000 1.000
2
(Constant) 1.002E-013 .032 .000 1.000
Zscore(Tract_TO_M) .696 .037 .696 18.820 .000 .735 1.360
Zscore(OwnerOcc) .311 .037 .311 8.413 .000 .735 1.360
3
(Constant) 1.002E-013 .031 .000 1.000
Zscore(Tract_TO_M) .660 .039 .660 16.813 .000 .636 1.572
Zscore(OwnerOcc) .313 .036 .313 8.578 .000 .735 1.360
Zscore(Asian) -.087 .034 -.087 -2.542 .012 .832 1.202
4
(Constant) 1.006E-013 .031 .000 1.000
Zscore(Tract_TO_M) .731 .046 .731 15.922 .000 .448 2.230
Zscore(OwnerOcc) .327 .036 .327 9.047 .000 .721 1.387
Zscore(Asian) -.130 .037 -.130 -3.525 .001 .693 1.442
Zscore(hs_grad) -.136 .048 -.136 -2.849 .005 .413 2.419
5
(Constant) 1.007E-013 .030 .000 1.000
Zscore(Tract_TO_M) .689 .050 .689 13.709 .000 .369 2.713
Zscore(OwnerOcc) .333 .036 .333 9.234 .000 .717 1.394
Zscore(Asian) -.105 .039 -.105 -2.724 .007 .622 1.609
Zscore(hs_grad) -.213 .061 -.213 -3.478 .001 .247 4.047
Zscore(White) .138 .069 .138 1.984 .049 .193 5.171
a. Dependent Variable: Zscore(LOANS_UNIT)
46. 46
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
MINNEAPOLIS/ST. PAUL SUBURBS
In contrast with the city, the surrounding suburbs have a higher percentage of white residents,
with a mean of 83 percent, and greater wealth, with a homeowner occupancy rate of 77
percent and median family income that is 106 percent of the average. This could be due to the
lower levels of disparity within the suburban tracts with their lower standard deviation for the
socioeconomic variables of median family income and homeowner occupancy.
DESCRIPTIVE STATISTICS
Range Minimum Maximum Mean Std. Deviation
Hispanic % .4009 .0058 .4067 .045357 .0472800
White % .8074 .1742 .9816 .833502 .1254193
Black % .5106 .0008 .5113 .048728 .0601844
Native % .3641 .0000 .3641 .004857 .0157152
Asian % .3227 .0011 .3238 .045598 .0422807
hopi % .0266 .0000 .0266 .000381 .0012112
OwnerOcc % .9533 .0291 .9823 .774555 .1660685
Poverty % .3816 .0000 .3816 .080341 .0606878
Tract_TO_M 263.5900 .0000 263.5900 106.493861 30.0208359
hs_grad % .4343 .5657 1.0000 .938277 .0467900
LOANS_UNIT 243.63 .00 243.63 53.9825 35.91101
LOANS_POP 89.30 .00 89.30 21.3472 12.99666
Minority % .81 .02 .83 .1665 .12542
N=588
Bivariate correlations show significant and positive relationships between homeowner
occupancy and median family income with home mortgage lending. Race and ethnicity are
also significant factors, though the negative relationship between higher percentages of African
American and Hispanic residents in a tract and lending had a lower coefficient of correlation.
Minority
%
White
%
Black
%
Hispanic
%
Asian
%
Owner
Occupancy %
Poverty
%
HS Grad
%
Median
MFI
Pearson’s R Loans per
Unit
-.370** .370** -.373** -.147** -.029 .563** -.559** .558** .838**
Spearman’s
rho
Loans per
Unit
-.429** .429** -.432** -.529** -.028 .704** -.721** .711** .864**
N=588
47. 47
NCRC • 202-628-8866 • www.ncrc.orgANALYSIS Minneapolis and Surrounding Areas
A regression model produced the best goodness-of-fit when the predictor variables of higher
median family income and homeowner occupancy were considered. The adjusted R2
of this model
was .715, showing a slightly weaker model than the one for the city areas.
MODEL SUMMARYd
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .838a
.702 .701 .54644879
2 .846b
.716 .715 .53417789
3 .849c
.720 .719 .53039216
a. Predictors: (Constant), Zscore(Tract_TO_M)
b. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc)
c. Predictors: (Constant), Zscore(Tract_TO_M), Zscore(OwnerOcc), Zscore(Hispanic)
d. Dependent Variable: Zscore(LOANS_UNIT)
COEFFICIENTSa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) -1.008E-013 .023 .000 1.000
Zscore(Tract_TO_M) .838 .023 .838 37.146 .000 1.000 1.000
2
(Constant) -1.003E-013 .022 .000 1.000
Zscore(Tract_TO_M) .760 .027 .760 28.654 .000 .692 1.446
Zscore(OwnerOcc) .141 .027 .141 5.313 .000 .692 1.446
3
(Constant) -1.002E-013 .022 .000 1.000
Zscore(Tract_TO_M) .783 .027 .783 28.558 .000 .637 1.569
Zscore(OwnerOcc) .165 .027 .165 6.008 .000 .634 1.577
Zscore(Hispanic) .079 .026 .079 3.063 .002 .716 1.397
a. Dependent Variable: Zscore(LOANS_UNIT)
CONCLUSION
Despite its lower levels of segregation, and the diffused pattern of home mortgage lending in the
maps, Minneapolis was impacted by some of the same factors as the other cities of this report (St.
Louis and Milwaukee), albeit not as consistently. The bivariate correlations for race and ethnicity
with mortgage lending were significant, but had much lower coefficients of correlation when
contrasted with income. In the regression model, median family income and owner-occupancy
were the strongest predictors of lending, though ethnicity (Hispanic) was an important
predictor in the suburban model. In conclusion, Minneapolis/St. Paul undergoes continuing
suburbanization as it expands its outer-ring suburbs, a process which intensified segregation
during previous eras of U.S. urban development. This possible trend should be closely analyzed as
the cities continue their expansion.