Time to Close Stats: Expected vs. Unexpected

September 14, 2017 George Neal

With PrecisonLender clients using the software to price commercial deals at a rate of nearly $400B a year, we have access to a great (and growing) amount of loan data to analyze. We’ve recently started taking a closer look at these data sets in order to build predictive models that help our clients manage pipeline expectations, and thus, revenue. As we analyze this data, we periodically come across findings that we think you’ll find interesting.

In this post, we’ll look at “time to close” data.

First, A Definition

Before we dive into what we found, it is helpful to understand what we, at PrecisionLender, mean when we say “time to close.” We define time to close as the amount of time between when the loan is priced and when the loan is funded. We look at it this way because those are two data points we have available to measure within the PrecisionLender application. This view differs slightly from the traditional bank view, but while the clock might start ticking a little sooner in the PrecisionLender view, we don’t think it introduces any significant skew into the results.

THE EXPECTED

What did we find?  In some cases, exactly what you would expect.

Larger Loans Take Longer

Larger loans took longer to close. Loans of less than $100k closed in an average of 49 days, whereas $3MM and above loans tended to take closer to 61 days.  

Time to Close vs. Amount

LOCs Close Fast

Line of credit renewals are very fast to close. Also, lines of credit are the fastest loan to close, with a median time to close of roughly 30 days. Again, no surprise there. 

Median Time to Close by Product

The data also shows that practice makes the process more consistent and faster — in some cases dramatically. For example, the more commonplace loans (1- and 5-year) have median times to close on the order of 35 days and represent just over half the sample, whereas loans of other durations that are used less often have median close times in excess of 50 days and as high as 78 days. That’s interesting to know, and helpful if you want to improve your time to close. But again, no surprise.

THE UNEXPECTED

There were, though, a few things we found that differed from what we expected. 

CRE Doesn’t Take Quite So Long

One was that real estate secured loans, while taking longer to close, did not have the significant difference we expected to find. Based on conversations and readings, we expected CRE loans would take a month or more longer than non-real estate secured loans. The minimum expectation of the people we spoke with was two weeks. 

However, the data did not support that expectation. When evaluating commercial real estate vs. non-real estate loans, the average time to close difference was only one week!

Median Time to Close by Product Type

A Wide Range of Closing Outcomes

Another thing that caught our attention was the degree of variance in time to close between institutions, and even within institutions. 

For example, in loans between $300k and $500k we saw an average time to close of approximately 54 days across all banks. But almost 25% of those loans had times to close of 80+ days! To put that in borrower-centric terms, 25% of customers are getting access to their funds a month later than the average, and that level of variance was common across loan amounts.

Time to Close vs. Amount

To us, this looks like an opportunity for banks to distinguish themselves and establish a competitive advantage. That thought begs the simple question, “Do banks that close faster actually close more loans?” 

The answer is a simple “YES.”

Granted, the large group of loans in the 0-10 day group is typically LOC renewals (as we mentioned above). But leaving that group aside, it remains clear that more loans are booked in the faster closing groups.

Histogram of Time to Close

More to Come

In the coming weeks, as our data scientists continue to comb through the pricing data that flows through PrecisionLender each day, we’ll have more insights to share. Just as with this post, some of these insights may confirm what you already suspected. But others may cause you to question old assumptions and look at your bank’s pricing strategies and tactics in a different way. 

 

About the Author

George Neal

George is the rare combination of banker, data scientist, and educator. He has held executive roles in both retail and investment banking focused on risk management, predictive analytics, and profitability. George’s love of data and all the ways it can be used to make better financial decisions brought him to PrecisionLender, where he heads our Data Insights Team.

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