How technology and data can help your DEI efforts
Dr. Martin Luther King, Jr. spoke about the real cost of our failure to address diversity, equality and inclusion (DEI) in housing back in 1963, saying “it didn’t cost the nation one penny to integrate lunch counters or the right to vote. But we’re facing issues that cost the country billions of dollars.”
Today, fair lending, nondiscriminatory practices and Home Mortgage Disclosure Act compliance are givens in the quest for DEI. Yet there is one area where the mortgage industry could be doing so much more to make fair housing a reality and society more equitable: data management.
Technology can have a tremendous effect on efficiency and saving time and money. Data can also make a significant difference in ensuring companies are as inclusive as they may seem. In the right hands, technology can push our industry’s diversity, equality and inclusion efforts in the right direction.
Data management and analytics tools allow us to track and predict trends along a multitude of specific points, such as age, gender, education, family structure, marital status, employment history, credit history, Audible history, Netflix preferences and more – you name it and it can be tracked.
Census and marketing/survey data can be aggregated and then visualized to make areas apparent that need focus. Those might include population density, racial makeup, diversity factors, income, wealth, access to health care centers, density of potholes, or average police response times. If you can imagine it (and gather data on it), technology can represent it.
These visualizations are more than just pretty bar charts and colorful graphs. They expose gaps in how the people of our nation are served. We can use data visualization to better understand our products and services, and their effect on the communities we serve.
Data can even tell a company if it is missing any areas, programs or features our customers want. Are we constantly improving our turn times? Is there feedback we have not addressed? Are we continuously aligning with the voice of the people? Is our customer experience improving?
Another area where data visualization can tell us what we’re doing well and what we may need to look at a little harder is quality. Traditionally, quality assurance (QA) and quality control (QC) are manually intensive, repetitive tasks — stare and compare, repeat. Technology-driven solutions using optical character recognition (OCR), automated document recognition (ADR) and bots to reduce manual workload early in the cycle have all made QA and QC activities less cumbersome.
Here, data visualization tools can help companies accurately pinpoint gaps in serving homebuyers and homeowners fairly.
Data analytics play a role in the public arena as well. Housing and property law steadily evolves and transforms in response to our elected legislator’s views — the Homestead Act, the post-Civil War return of plantation land to former slave owners, the Fair Housing Act and more. As the courts and regulators interpret those laws, data analytics may play a role – the battles over state election redistricting and arguments over disparate impact in fair lending or employment.
The evolution of law is rarely a direct line — some backsliding and a few bumps are to be expected. But we must continue to take steps forward, and technology can help in that process.
Gathering the information needed to make the most balanced decisions requires effort. Some data is easily collected during census surveys to understand population demographics in specific areas. Other data points require a more in-depth understanding of human nature and society.
Is there a relationship between lot acreage and the age of the homeowner? Is there a link between family composition of homeowners and proximity to a golf course, theater, hiking trail, schools, fire stations? Do rising unemployment trends among younger generations influence purchase of larger homes by empty nesters since their adult children are moving back in? How do we correlate the availability of hospitals to the overall health of a community? Is there a correlation between the locations of Whole Foods and teacher-to-student ratios? Do we need better infrastructure in this subzone? How can we align inflation, cost of living and minimum wage?
Listen, learn, correct, listen, tweak, repeat. This process will make us efficient in our data analysis and give our DEI efforts real substance. We have the tools to truly learn what people want — at the community, county, state and national level. Now, it is up to us to wield them properly.