Lending Club Loan Listings, Issuance, and Impact on Yield from Non-performing Period
Mad Rush at Loan Release Time: Part II - Loan Performance with Time to Fund
Mad Rush at Lending Club Loan Release Time: Part I
The objective of this study is to investigate the rate at which loans are funded after being listed and the performance of loans with time to fund. The study was conducted to determine the validity of claims such as Good loans get funded very quickly and Only way to invest in good loans is to use automation or be online at the time new loans are released.
The loans listed between July 10th and October 2nd, 2013 (about 3 months) were used for analysis. The loan listing and historical data used for this study was captured by PeerCube. The historical loan data files were downloaded on June 3rd, 2014. These files were used to determine the loans that were actually issued after being listed between July 10th and October 2nd, 2013 and the performance of such loans.
There were 37,456 loans listed between July 10th and October 2nd, as detected by PeerCube. Historical loan data files show 23,673 (63.2%) of loans listed in this period were issued.
Time to Fund
Each loan listing includes a list date and time. This data and time was assumed to be the time when the loan first became available on Lending Club primary platform. The last recorded time when loan was detected by PeerCube as available on the platform was assumed to be when the loan was fully funded. The difference between these two times was considered to be the time to fully fund a loan.
The longest time required for funding of a loan for the loans in dataset was 15,583 minutes (about 11 days). The shortest time required was 2 seconds. As this study looked at funding time in minutes, any loans that were funded in less than a minute were assumed to be funded instantaneously.
Because the focus of study was to determine whether “early bird gets the worm” is true for Lending Club loans, study divides the loans based on time to fund into Eleven groups with each group containing at least 1,500 loans:
- 0 - 1 min: The loans that were funded within 0 to 1 minutes.
- 2 - 3 min: The loans that were funded within 2 to 3 minutes.
- 4 - 10 min: The loans that were funded within 4 to 10 minutes.
- 11 - 60 min: The loans that were funded within 11 to 60 minutes.
- 1 - 3 hrs: The loans that were funded within 61 to 180 minutes.
- 3 - 5 hrs: The loans that were funded within 181 to 300 minutes.
- 5 - 12 hrs: The loans that were funded within 301 to 720 minutes.
- 12 - 15 hrs: The loans that were funded within 721 to 900 minutes.
- 15 - 18 hrs: The loans that were funded within 901 to 1,080 minutes.
- 18 - 32 hrs: The loans that were funded within 1,081 to 1,920 minutes.
- 32+ hrs: The loans that took more than 1,921 minutes to get fully funded.
Figure 1 below shows the loan count funded with Time to Fund groupings. 30.46% of loans were funded within 3 minutes , 40.38% within 10 minutes, and almost 50% of loans were funded within the hour of being released on the platform. The volume of fully funded loans decline with time for up to 3 hours after loans being released. There is an increase in volume of loans funded between 3 - 5 hours. As the new loans are released about 4 hours apart (6am, 10am, 2pm, 6pm, and 10pm PT), this increase may be due to some of the older loans being fully funded along with the newly released loans.
Table 1 shows the loan listings by Credit Grade. 61.67% of listings are for Grade B and C loans. Only 29.31% of listings are for low quality loans; those carrying Grade D through G.
|% of Total||9.02%||33.58%||28.09%||14.88%||8.18%||4.84%||1.40%||100%|
Figure 2 below shows the Listing Volume by credit grade with Time to Fund. 57.07% of loan listings that carry Grade B and C and 42.9% of listings that carry Grade D through G are funded within 10 minutes. This appears to confirm the general consensus that the loans with Grade D through G make up the high proportion of loans that are fully funded soon after being listed.
Figure 3 below shows the percentage of loan listings that were finally issued with Time to Fund groupings. In aggregate, 63.2% of listings were finally issued. The issuance percentage for loans that are funded within 10 minutes after loans being listed is one to three percentage points higher than that in the aggregate.
Table 2 shows the loan issued by Credit Grade. While 29.31% of loans listed are with Grade D through G, only 26.21% of loans issued are with Grade D through G. The percentage of listings finally issued by Credit Grade are similar to the ones published in the previous post Lending Club Loan Listings, Issuance, and Impact on Yield from Non-performing Period (same listing dataset).
|% of Total Issued||9.73%||35.74%||28.32%||13.94%||7.17%||4.06%||1.04%||100%|
|% of Listings||68.16%||67.28%||63.71%||59.19%||55.37%||53.03%||46.96%||63.20%|
Figure 4 below shows the Issued Volume by credit grade with Time to Fund. The trend is very similar to the Figure 2 for listing volume by credit grade. 59.94% of issued loans that carry Grade B and C and 38.39% of issued loans that carry Grade D through G are fully funded within 10 minutes of being listed.
Time to Issue
Figure 5 below shows the Median and 90 Percentile Time to issue in Days with Time to Fund groupings. In aggregate the median time to issue for loans after listing is 7 days with 90 percentile of loans are issued within 15 days. It appears that median time to issue is a few days longer for loan fully funded very early or very late than that in the aggregate.
Figure 6 below shows the Median Time to Issue by Credit Grade with Time to Fund. It appears that median time to issue is independent of credit grade.
- Over 40% of loans listed on Lending Club platform are fully funded within 10 minutes. Half of loans listed are fully funded within the hour of being listed.
- The higher percentage of loans funded early are finally issued though may take a little longer to be issued.
- The loans with grade D through G are fully funded much more quickly. The lenders targeting such loans may need to lend quickly to access larger pool of low quality loans.
The above analysis shows that early bird gets the worm. In the next part, we will further analyze this dataset to review the performance of the loans, i.e. the quality of worms.
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