This move would lower the cost of buying a home
Sanjiv Das
President of Pagaya/Former CEO of Caliber & CitiMorgage/ Board Member Two Harbors Investment
It’s a difficult time to shop for a new home. The cost of a?30-year fixed-rate mortgage?has declined recently but is?still hovering around 5%?-- almost double the rate in August 2021. Mortgage demand?has dropped?to the lowest levels since 2000. Put simply, Americans face the prospect of spending more of their hard-earned savings and wages to own a home.
Which makes this the time to take dramatic steps to make housing more affordable for all. The mortgage industry is a feast-or-famine business. Low rates are good for lenders. But when rates are elevated, as they are now, housing executives have little appetite for long-term investment. Nevertheless, housing executives and regulators should act countercyclically, adopting measures now that will help more Americans buy a home when rates eventually drop.?
It’s during these lean years when hungry mortgage executives should scrutinize costs and optimize processes. I know because I was the CEO of CitiMortgage after the 2008 financial-market meltdown. We were able?to avoid a total collapse?because the private and public sector worked together.
Housing can become more affordable by having better and more structured consumer data. Private lenders and federal housing agencies should work together to create such a program, which will ultimately lower the closing costs on a new loan for home buyers.
Here’s the problem: It’s expensive to originate a loan. The fully loaded cost of a retail originated loan can be as high as $8,500. This is largely because mortgage origination is an antiquated process that relies on paper verification of consumer data such as assets, income and cash flow of potential buyers. While there has been ample innovation in credit cards and buy now, pay later (BNPL), the fact is that mortgage lending technology has been stymied by disorganized consumer data.
The home buyers most affected by high costs are lower-income consumers. For example, the 30-year mortgage rate averaged about 2.75% in 2020 and 2021. While some 7 million homeowners refinanced in 2020, almost 2 million low-income homeowners did not.
High costs are not the only deterrent. Homeowners must navigate a complex refinancing process. They must provide many of the same documents to their lenders again and go through an elaborate reapplication process to lower their mortgage rate, with almost no reprieve on the cost of closing.
领英推è
One solution is to create a robust consumer financial data program. Federal housing agencies would have a secure national database that stored dynamic information on consumers such as asset, income and cash flow. Consumers could grant potential lenders access to this information via encrypted keys.
To prevent hacking, these keys could be stored in a decentralized manner, perhaps even leveraging the blockchain. Over time, this “mortgage key†would become part of the housing vernacular. Lenders would ask consumers for these keys. Such a system would reduce or eliminate much of the paperwork back-and-forth between consumers and lenders.
What’s more, since mortgage credit rules are mainly driven by the underwriting guidelines defined by the agencies, a majority of loans could be auto-underwritten for credit in real time — with manual intervention required to override the algorithmic bias of loans that are rejected. This would help to reduce the origination cost substantially and enable borrowers to overcome a big impediment to lock in lower rates, which would ultimately reduce their monthly mortgage payments.
Fannie Mae and Freddie Mac transformed the landscape of homeownership some 50 years ago by making available housing credit and lowering the interest cost. This was a paradigm shift for the housing market, and now almost two-thirds of Americans own their own homes.
This democratization must continue with a data digitization program. Better, more structured data will lead to more affordable housing.
Sanjiv Das?was the CEO of CitiMortgage and Caliber Home Loans
Data & Analytics Leader, AML, ML, Gen AI, LLM Ops, Team & Thought Leader, Data Governance, Risk Management, Digital Transformations, Automations, Fraud Detection, Process Optimizations, Data Security.
2 年Sometimes the face value of data is not we really want . The actual data in this case will have the promise to give better results but at consumer level the data is target for many opportunities that would have the cadence to be explored in not such true manners . Let’s be congnizant in actual data how we build these roadmaps . Great post Sanjiv Das always have admired your ability to point to things which lies in between layers.
Founder at Systems Behavioral Research
2 å¹´Yes, especially a study of long-term *individual* payment histories might reveal how things go bad and yield clues about how to avoid that outcome. One of, if not the biggest unaccounted risks that remains is the dependence on individual history. But that would require statisticians and data managers to give up their grandiose view that insight can be gained just from gross statistics. Sorry, been offering my method to account for dependence on individual history for a decade and am a bit disgusted.
Your Personal Mortgage Expert
2 å¹´I said the same thing about 5 years ago: https://www.dhirubhai.net/pulse/relinquish-all-hope-none-our-data-safe-adam-rosenblatt
Founder/CEO @ RedFile Technologies, Inc | Veteran, Patented Inventor, Author, Master of Smoke & Flame
2 å¹´Industry standard Classification + Positional Attribution + Cross Document Validation = 50-70% reduction in Operational Expenses. As Mario correctly points out, the really question is greed . . .