This is the first in a series on the end of the London Inter-Bank Offered Rate (LIBOR), a widely used financial metric that is integral to global finance. In this post, we cover why LIBOR is being discontinued, why that is important, and what is replacing it.

As we talked about in our previous post, the Biden campaign has proposed a first-time home buyer (FTHB) tax credit of up to $15,000, which will function as a true markdown in the price of a house.

Next, we can examine the potential variation in regional impacts via a methodology utilized by Mark Keightley, PhD, an economist at the Congressional Research Service. Keightley performed an analysis of the previous FTHB tax credit that was part of the American Recovery and Reinvestment Act of 2009. He used median home price data in conjunction with estimates of price elasticity of demand—the relationship between house prices and demand for housing—to forecast growth in housing demand.

The silver lining of the COVID-19 pandemic is that it provides a rare opportunity for sweeping economic reform in the new administration, and the Biden campaign ran on a promise to make quality affordable housing accessible to Americans in all ZIP codes. As part of a larger investment in American housing, Biden has proposed a first-time home buyer (FTHB) tax credit. 

With contributions by Olivia Hebner

Today’s post closes our three-part series on discrimination in mortgage lending and the analytical techniques that Summit uses to detect it. Part one discussed analytical techniques to detect redlining, the practice of denying loan applications to minority applicants based on the racial composition of neighborhoods. Part two examined loan pricing discrimination, the practice of charging higher interest rates to minority applicants due to their race.

In this installment, we talk about origination discrimination and predatory lending. 

Machine-learning models have become increasingly popular among financial institutions, with applications that include fraud detection, loan underwriting, portfolio management, and securities trading. They are beginning to replace traditional explanatory models, which examine the underlying drivers of modeled outcomes. For solving business problems, managers focus on effective results, using reliable predictions or estimates rather than underlying causal relationships.

Each year, on the third Monday in January, we remember Dr. Martin Luther King Jr.—a man whose courage shaped the civil rights movement that changed our nation and whose lifelong dream was to create a more just society. In some ways, the present may seem far from Dr. King’s dream.

With contributions by Natalie Patten

This is the second installment of our three-part series on discrimination in mortgage lending and the analytical techniques that Summit Consulting uses to detect it. Last week, we introduced redlining and showed examples of analyses we perform to identify potential redlining practices by a given lender. This week, we will explore how we identify and measure pricing discrimination.

With contributions by Natalie Patten

Today’s post kicks off a three-part series on discrimination in mortgage lending and the analytical techniques that Summit Consulting uses to detect it. We use Home Mortgage Disclosure Act (HMDA) data, internal data from banks, and underwriting documents to detect and estimate potential predatory-lending practices for our clients.

As the 2020 fall semester wraps up, Summit Consulting is excited to highlight its relationship with Virginia Tech’s Computational Modeling and Data Analytics (CMDA) program and its work with CMDA students on their capstone project.

Summit Consulting and its subcontractors successfully partnered with the U.S. Department of Agriculture’s (USDA) Office of the Chief Information Officer (OCIO) to develop and implement an approach to business modernization across the department.