Mapping Banking Data

Demonstrating the Banking Industry’s Affects on Communities

ESI Senior Vice President Lee Huang’s op-ed, How Neighborhoods Could Lose if Trump Succeeds in Rolling Back Bank Reforms, published in Next City demonstrates how reduced access to data could be detrimental to communities across the nation. He makes the case for the transparency and accountability derived from Dodd-Frank as the act strives to equalize fair provision of banking services.

In the wake of the Great Recession (December 2007 – June 2009), the Financial Crisis of 2008, and the subsequent Housing Market Crash, the sweeping, bipartisan Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank) and the creation of the Consumer Protection Bureau (CFPB) were passed to prevent devastating economic collapse. Trump’s administration, viewing that the banking industry is over-regulated, is considering rollbacks of Dodd-Frank. This would mean fewer consumer protections, less public accountability and oversight, meaning reduced transparency, therefore less data to access and share.

Lack of access to banking institutions means financial exclusion. This limits economic opportunity in underserved communities, impacting the ability of the households and businesses to build wealth, perpetuating and reinforcing divestment.

For example, current data on home mortgage performance does not include many of the most relevant data points banks consider when making lending decisions, such as credit score and debt-to-income ratio.  Provisions in Dodd-Frank are set to phase in those reporting requirements, which may yield a more accurate parsing of when disparities can be explained away by systemic differences in borrower characteristics and when they really are evidence of discrimination.

Many cities have adopted their own initiatives. Cities like Philadelphia, New York, and Dayton have all passed responsible banking ordinances (RBOs) to prompt banks to provide lending and investment in low-income and minority communities. Banks that decline to participate are disqualified from bidding on contracts with these cities for their banking and pension fund business.

Since 2008, financial and housing markets have experienced significant distress, making efforts towards oversight and accountability increasingly important. To support this effort, Econsult Solutions examined lending transactions and branch data from twenty-five US cities- many of which have already implemented or considered RBOS – to determine if spatial disparity and discriminatory practices exist. The data sources used in this analysis are all publicly available, including the 2014 American Community Survey, 2015 Home Mortgage Disclosure Act (HMDA), and the Federal Financial Institutions Examination Council (FFIEC).

Mapping the Analyses

To supplement our analysis, we created an interactive map using CARTO, a cloud-based mapping service. Our map illustrates several key factors, including home loan distribution, denial rate, and branch access by race and income.

The CARTO map is a fully interactive tool made up of multiple elements. The first is the layer selector, which allows the user to switch between different layers. There are 6 layers in total, relating to home loan penetration, application denials, and bank branch access.

Caption: Select and zoom within different layers to explore the data.

The numbers visualized for map layers were calculated by taking two numbers to produce a quotient value. For example, the first layer illustrates Home Loans in relation to Race/Ethnicity. The numbers here were calculated by taking the number of home loans in non-minority tracts and dividing it by the number in minority tracts. All other layers are essentially the same as they use two columns to produce a quotient value.

A city with a score of 3 means that a there are twice as many home loans in non-minority areas than in minority areas, on a per-household basis.

Another element of the map includes the actual points, each being a city with its own respective value. Visually, the data is broken into classes of varying symbol sizes and colors. In each layer, the radius and color of each circle is proportional to each city’s quotient value. The example pictured below shows two cities, Philadelphia and Buffalo, each with home loan penetration values of 2 and 4 respectively. Because Buffalo has a value of 4, it is represented by the larger and darker blue circle.

Here graduated symbols are used to show a quantitative difference between mapped features by varying size and colors of symbols.

The third main element of the map is the infowindow, which provides  detailed information about each city. We included pertinent information related to our analysis, including population, the numerator, denominator, and quotient. In the following example, the tooltip contains related information about Washington DC. We can see that it has a branch access quotient value of 1 (in this case, .8 divided by .8).

The infowindow includes important information related to this layer.

Many cities in the US have already passed or at least considered responsible banking ordinances. In theory the adoption of these ordinances is a positive thing, because they obligate financial institutions to lend fairly and meet the needs of the communities where they operate. However, as indicated by our analysis, it is clear that there is still a lot of work to do.

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Carlos Bonilla

Research Analyst Carlos Bonilla applies a strong background in spatial analysis, cartography, and data visualization, focusing on economic and environmental issues in urban areas. Prior to ESI, Carlos worked as a fellow for Azavea Summer of Maps, where he conducted research for The Food Trust and the Greater Bicycle Coalition to investigate food access and severe road crashes in Philadelphia.


Lee Huang is a Senior Vice President and Principal at Econsult Solutions, and is also on the boards of the Asian-American Chamber of Commerce of Greater Philadelphia, Sustainable Business Network of Greater Philadelphia, and Welcoming Center for New Pennsylvanians.

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