This move would lower the cost of buying a home

This move would lower the cost of buying a home

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.

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.

[This article was published in MarketWatch]

Sanjiv Das?was the CEO of CitiMortgage and Caliber Home Loans

Gauri V.

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 å¹´

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.

赞
回复
Albert Galick

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.

赞
回复
John M.

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 . . .

赞
回复

要查看或添加评论,请登录

Sanjiv Das的更多文章

  • Private Credit: Continued Expansion in Consumer Lending

    Private Credit: Continued Expansion in Consumer Lending

    As banks deal with the increasing capital requirements and ongoing regulatory scrutiny, private credit has stepped up…

    3 条评论
  • 3 Ways AI Will Transform Banking in 2025

    3 Ways AI Will Transform Banking in 2025

    AI is revolutionizing banking, offering tools that are reshaping how financial institutions serve their customers and…

    5 条评论
  • How AI Can Help Banks Lend to Mainstream Americans

    How AI Can Help Banks Lend to Mainstream Americans

    AI is ushering in a new era for millions of Americans seeking access to credit. By leveraging advanced data analysis…

    2 条评论
  • How AI Helps Banks Keep Their Deposit Customers

    How AI Helps Banks Keep Their Deposit Customers

    AI is helping banks keep their edge in the fast-changing world. Banks face a perfect storm of challenges: mounting…

    2 条评论
  • America's secret weapon against inflation: The 30-year home mortgage

    America's secret weapon against inflation: The 30-year home mortgage

    I’d like to stick up for something that hasn’t received its fair share of attention: the 30-year fixed- rate mortgage…

    9 条评论
  • Stay ahead in housing with these 4 key research tools

    Stay ahead in housing with these 4 key research tools

    Whether you're a prospective homeowner, an industry expert, or involved in housing policy, having access to reliable…

    4 条评论
  • How private credit can benefit consumer lending

    How private credit can benefit consumer lending

    Imagine a world where more people can obtain the financing they need, and where dreams are within reach. This is the…

    4 条评论
  • Why Homeownership Needs to Remain the Cornerstone of the American Dream

    Why Homeownership Needs to Remain the Cornerstone of the American Dream

    Every Fourth of July, I reflect on what makes the United States special. As an immigrant from India, I was drawn by the…

    9 条评论
  • How AI Can Revolutionize the Mortgage Market and Boost Homeownership

    How AI Can Revolutionize the Mortgage Market and Boost Homeownership

    Homeownership is a cornerstone of the American Dream, yet the path to securing a mortgage can be fraught with…

    9 条评论
  • 3 Ways AI is Boosting Financial Inclusion

    3 Ways AI is Boosting Financial Inclusion

    How can we ensure that everyone has access to financial services? And I mean everyone -- no matter their socioeconomic…

    2 条评论

社区洞察

其他会员也浏览了