FinTech Disrupting The Mortgage Industry

photo by www.cafecredit.com

photo by www.cafecredit.com

Researchers at the NY Fed have posted The Role of Technology in Mortgage Lending. There is no doubt that tech can disrupt the mortgage lending business much as it has done with others. The abstract reads,

Technology-based (“FinTech”) lenders increased their market share of U.S. mortgage lending from 2 percent to 8 percent from 2010 to 2016. Using market-wide, loan-level data on U.S. mortgage applications and originations, we show that FinTech lenders process mortgage applications about 20 percent faster than other lenders, even when controlling for detailed loan, borrower, and geographic observables. Faster processing does not come at the cost of higher defaults. FinTech lenders adjust supply more elastically than other lenders in response to exogenous mortgage demand shocks, thereby alleviating capacity constraints associated with traditional mortgage lending. In areas with more FinTech lending, borrowers refinance more, especially when it is in their interest to do so. We find no evidence that FinTech lenders target marginal borrowers. Our results suggest that technological innovation has improved the efficiency of financial intermediation in the U.S. mortgage market.

The report documents the significant extent to which FinTech firms have already disrupted the primary mortgage market. They also predict a whole lot more disruption coming down the pike:

Going forward, we expect that other lenders will seek to replicate the “FinTech model” characterized by electronic application processes with centralized, semi-automated underwriting operations. However, it is unclear whether traditional lenders or small institutions will all be able to adopt these practices as these innovations require significant reorganization and sizable investments. The end result could be a more concentrated mortgage market dominated by those firms that can afford to innovate. From a consumer perspective, we believe our results shed light on how mortgage credit supply is likely to evolve in the future. Specifically, technology will allow the origination process to be faster and to more easily accommodate changes in interest rates, leading to greater transmission of monetary policy to households via the mortgage market. Our findings also imply that technological diffusion may reduce inefficiencies in refinancing decisions, with significant benefits to U.S. households.

Our results have to be considered in the prevailing institutional context of the U.S. mortgage market. Specifically, at the time of our study FinTech lenders are non-banks that securitize their mortgages and do not take deposits. It remains to be seen whether we find the same benefits of FinTech lending as the model spreads to deposit-taking banks and their borrowers. Changes in banking regulation or the housing finance system may affect FinTech lenders going forward. Also, the benefits we document stem from innovations that rely on hard information; as these innovations spread, they may affect access to credit for those borrowers with applications that require soft information or borrowers that require direct communication with a loan officer. (37-38)

I think that the author’s predictions are right on target.

 

Uses & Abuses of Online Marketplace Lending

photo by Kim Traynor

 

The Department of the Treasury has issued a report, Opportunities and Challenges in Online Marketplace Lending. Online marketplace lending is still in its early stages, so it is great that regulators are paying attention to it before it has fully matured. This lending channel may greatly increase options for borrowers, but it can also present opportunities to fleece them. Treasury is looking at this issue from both sides. Some highlights of the report include,

 

 

  • There is Opportunity to Expand Access to Credit: RFI [Request for Information] responses suggested that online marketplace lending is expanding access to credit in some segments by providing loans to certain borrowers who might not otherwise have received capital. Although the majority of consumer loans are being originated for debt consolidation purposes, small business loans are being originated to business owners for general working capital and expansion needs. Distribution partnerships between online marketplace lenders and traditional lenders may present an opportunity to leverage technology to expand access to credit further into underserved markets.
  • New Credit Models and Operations Remain Untested: New business models and underwriting tools have been developed in a period of very low interest rates, declining unemployment, and strong overall credit conditions. However, this industry remains untested through a complete credit cycle. Higher charge off and delinquency rates for recent vintage consumer loans may augur increased concern if and when credit conditions deteriorate.
  • Small Business Borrowers Will Likely Require Enhanced Safeguards: RFI commenters drew attention to uneven protections and regulations currently in place for small business borrowers. RFI commenters across the stakeholder spectrum argued small business borrowers should receive enhanced protections.
  • Greater Transparency Can Benefit Borrowers and Investors: RFI responses strongly supported and agreed on the need for greater transparency for all market participants. Suggested areas for greater transparency include pricing terms for borrowers and standardized loan-level data for investors.

*     *     *

  • Regulatory Clarity Can Benefit the Market: RFI commenters had diverse views of the role government could play in the market. However, a large number argued that regulators could provide additional clarity around the roles and requirements for the various participants. (1-2)

As we move deeper and deeper into the gig economy, the distinction between a consumer and a small business owner gets murkier and murkier. Thus, this call for greater protections for small business borrowers makes a lot of sense.

Online marketplace lending is such a new lending channel, so it is appropriate that the report ends with a lot of questions:

  • Will new credit scoring models prove robust as the credit cycle turns?
  • Will higher overall interest rates change the competitiveness of online marketplace lenders or dampen appetite from their investors?
  • Will this maturing industry successfully navigate cyber security challenges, and adapt to appropriately heightened regulatory expectations? (34)

We will have to live through a few credit cycles before we have a good sense of the answers to these questions.

Loose Credit. Plummeting Prices.

"Durdach Bros Miller Lite pic4" by MobiusDaXter

Christopher Palmer has posted Why Did So Many Subprime Borrowers Default During the Crisis: Loose Credit or Plummeting Prices? to SSRN. While this is a technical paper, it is clear from the title that it addresses an important question. If it can help us get to the root causes of the foreclosure crisis, it is worth considering. The abstract reads,

The surge in subprime mortgage defaults during the Great Recession triggered trillions of dollars of losses in the financial sector and accounted for more than 50% of foreclosures at the height of the crisis. In particular, subprime mortgages originated in 2006-2007 were three times more likely to default within three years than mortgages originated in 2003-2004.

In the ensuing years of debate, many have argued that this pattern across cohorts represents a deterioration in lending standards over time. I confirm this important channel empirically and quantify the relative importance of an alternative hypothesis: later cohorts defaulted at higher rates in large part because house price declines left them more likely to have negative equity.

Using comprehensive loan-level data that includes much of the recovery period, I find that changing borrower and loan characteristics can explain up to 40% of the difference in cohort default rates, with the remaining heterogeneity across cohorts caused by local house-price declines. To account for the endogeneity of prices — especially that price declines themselves could have been caused by subprime lending — I instrument for house price changes with long-run regional variation in house-price cyclicality.

Control-function results confirm that price declines unrelated to the credit expansion causally explain the majority of the disparity in cohort performance. Counterfactual simulations show that if 2006 borrowers had faced the price paths that the average 2003 borrower did, their annual default rate would have dropped from 12% to 5.6%.

Ok, ok — this is hyper-technical! The implications, however, are important: “These results imply that a) tighter subprime lending standards would have muted the increase in defaults, but b) even the relatively “responsible” subprime mortgages of 2003–2004 were sensitive to significant property value declines.” (40) It concludes that, “In reality, cohort outcomes are driven by both vintage effects (i.e. characteristics bottled into the contracts at origination) and path dependency in that exposure to economic conditions affect cohorts differently depending on their history.” (40)

So, the bottom line is that loose credit and plummeting prices were both causes of the defaults during the crisis. Mortgage underwriters and policymakers are on notice that they need to account for both of them in order to be prepared for the next crisis. This paper’s contribution is that it has quantified the relative impact of each of those causes.

 

 

Treasury Gives RMBS a Workout

The Treasury has undertaken a Credit Rating Agency Exercise. According to Michael Stegman, Treasury

recognized that the PLS market has been dormant since the financial crisis partly because of a “chicken-and-egg” phenomenon between rating agencies and originator-aggregators. Rating agencies will not rate mortgage pools without loan-level data, yet originator-aggregators will not originate pools of mortgage bonds without an idea of what it would take for the bond to receive a AAA rating.

Using our convening authority, Treasury invited six credit rating agencies to participate in an exercise over the last several months intended to provide market participants with greater transparency into their credit rating methodologies for residential mortgage loans.

By increasing clarity around loss expectations and required subordination levels for more diverse pools of collateral, the credit rating agencies can stimulate a constructive market dialogue around post-crisis underwriting and securitization practices and foster greater confidence in the credit rating process for private label mortgage-backed securities (MBS). The information obtained through this exercise may also give mortgage originators and aggregators greater insight into the potential economics of financing mortgage loans in the private label channel and the consequent implications for borrowing costs.

While this exercise is very technical, it contains some interesting nuggets for a broad range of readers. For instance,

The housing market, regulatory environment, and loan performance have evolved significantly from pre-crisis to present day. Credit rating agency models appear to account for these changes in varying ways. All credit rating agency models incorporate the performance of loans originated prior to, during, and after the crisis to the degree they believe best informs the nature of credit and prepayment risk reflected in the market. Credit rating agency model stress scenarios may be influenced by loans originated at the peak of the housing market, given the macroeconomic stress and home price declines they experienced. The credit rating agencies differ, however, in how their models adjust for the post-crisis regime of improved underwriting practices and operational controls. Some credit rating agencies capture these changes directly in their models, while other credit rating agencies rely on qualitative adjustments outside of their models. (10)

It is important for non-specialists to realize how much subjectivity can be built into rating agency models. Every model will make inferences based on past performance. The exercise highlights how different rating agencies address post-crisis loan performance in significantly different ways. Time will tell which ones got it right.