Credit Unions Excel At Meeting Members' Auto Loan Needs

Since year-end 2017, credit unions have gone from financing 19.7% of auto loans on the market to 20.5%.

 
 

Total auto loans at credit unions expanded 10.1% year-over-year, the fastest of any major loan product, and added $34.0 billion to the industry’s total loan portfolio in 2018. By year-end, auto loan balances totaled $369.8 billion, with $220.9 billion, or 59.7%, coming from used auto loans and $148.9 billion, or 40.3%, coming from new.

The share of the credit union loan portfolio held by auto loans increased 34 basis points year-over-year to 35.0% as of Dec. 31, 2018. Since year-end 2017, credit unions have gone from financing 19.7% of auto loans on the market to 20.5%. By the end of 2018, one in five credit union members, or 21.2%, held an auto loan with their cooperative. That’s an increase of 74 basis points year-over-year.

Growth in new auto loans was up 11.7% in 2018; however, that is a slowdown of 1.3 percentage points year-over-year. Growth in used auto loans followed close behind at 9.1%, which also is a deceleration of 1 percentage point from the year prior. Despite slower growth in 2018, the average auto loan balance for the industry increased $265 since year-end 2017 to $14,834 by Dec. 31, 2018.

Is Your Auto Lending Portfolio Growing, Too?

Total auto loans increased 10.1% year-over-year, adding $34.0 billion to 2018’s total loan portfolio. Are you curious where you stack up amongst your peers? Peer-to-Peer can help you analyze your data with ease.

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Indirect balances, which consist primarily of auto loans, increased 14.3% annually to $223.6 billion at year-end. This is down 3.5 percentage points from Dec. 31, 2017. As production slows in both the new and used auto segments, credit unions are also slowing the rate at which they originate loans through point-of-sale relationships.

Year-end delinquency in the auto portfolio improved 2 basis points year-over-year to 0.66%. Total delinquency for this segment has remained within 10 basis points since credit unions started reporting it in 2013. Used auto loan delinquency was nearly double the rate of new auto loans — 0.82% versus 0.43%, respectively. Delinquency in indirect lending improved 5 basis points from year-end 2017 to 0.67% at year-end 2018. Overall, auto lending delinquency remained the steadiest of any product in the credit union loan portfolio.

For more on auto lending, read 6 Graphs About Credit Union Auto Lending.

LEADERS IN AUTO LOAN PENETRATION

FOR U.S. CREDIT UNIONS >$100 MILLION IN ASSETS | DATA AS OF 12.31.18
© Callahan & Associates | CreditUnions.com
Rank Credit Union State Auto Loans
/Members
Total Auto Loans/Total Loans Total Auto Loans* 12-Month Auto Loan Growth Indirect Loans Outstanding
1 Partnership Financial IL 126.63% 26.75% $21,789,714 39.84% $0
2 Citizens Community IA 106.65% 90.81% $150,560,585 11.75% $129,627,242
3 Mission City CA 105.98% 27.40% $19,022,508 23.08% $2,221,939
4 Atlanta Postal GA 97.94% 56.13% $888,366,428 1.64% $0
5 Cooperative Center CA 95.46% 51.65% $36,000,084 -17.88% $31,271,759
6 Solano First CA 88.21% 56.74% $51,335,942 -4.27% $25,359,499
7 Corporate America Family IL 74.14% 47.33% $232,821,617 30.87% $28,198,596
8 United IL 71.93% 56.86% $54,340,418 26.16% $43,382,969
9 Associated GA 71.06% 69.93% $822,344,265 4.27% $178,276,953
10 Community Credit Union of Lynn MA 70.74% 20.95% $24,482,810 -22.04% $21,370,406
* Total auto loans >$3 million.

Case Study: Data Diving Drives Auto Lending

CU QUICK FACTS

Oregon Community Credit Union
Data as of 12.31.18

HQ: Eugene, OR
ASSETS: $1.7B
MEMBERS: 157,876
BRANCHES: 11
12-MO SHARE GROWTH: -1.41%
12-MO LOAN GROWTH: -0.43%
ROA: 1.12%

Oregon Community Credit Union ($1.7B, Eugene, OR) has infused data into its lending to drive auto loan production.

Declining indirect lending share in a competitive market prompted the Eugene-based credit union to explore a new approach to rating risk and pricing loans. OCCU kicked off the project by challenging some assumptions — including that FICO scores and loan length are both good predictors of risk — baked into an auto rate sheet that the cooperative hadn’t fundamentally changed in years. Turns out, the former assumption was still true, the latter not so much.

Business intelligence manager Casey Foltz’s team put together 20 different models that used algorithms and machine learning techniques. The lending team contributed supporting documents, testing, and validation, as well as deep institutional knowledge about members and the market.

The project began in late 2016, and OCCU put new rates into place in May 2017. The effects were swift. The credit union posted record months in auto loan volumes in the following two months.

Building on experience, OCCU pricing continues to incorporate such factors as overhead cost of paying the dealer reserves, probability of default, how much interest the note is likely to generate, and member loyalty.

“It all goes into one simple number where we can see where we’re breaking even and where we have room to adjust,” Foltz says. “We use that science to support lower rates and properly price those with higher risk, so pricing as a whole is more in line with the real risks."

But OCCU’s approach to lending isn’t solely about hard numbers.

“We balance that science with what our gut tells us about pricing loans, what our competitors are doing, how our dealers will react, and what we know about each member we deal with,” says vice president of lending Ethan Nelson.

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June 3, 2019


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