The Economic Health of a Borrower’s City Affects Loan Performance
Harlan Seymour runs a family office in San Mateo, California, with a primary focus on analyzing and investing in fixed income instruments, including marketplace loans.
Marketplace lenders like Lending Club and Prosper provide dozens of data points about a borrower’s financial situation. In an attempt to improve upon average loan performance and generate “Alpha”, sophisticated investors crunch this data to select loans that have been graded more harshly by the lender than the data warrant.
One of the data points provided is the borrower’s locality (city and state). Analyzing historical loan data shows that, all else being equal, loans issued to borrowers living in economically prosperous cities significantly outperform, while those from troubled localities underperform.
We have created a locality database containing geographic, economic and demographic information for the U.S. as a whole, for all 50 states (plus Washington, D.C.), for all of the over 3,000 U.S. counties, for over 50,000 cities, and for thousands of neighborhoods within larger cities.
Borrowers enter their city and state in their loan applications. Many borrowers use their neighborhood name as their city name. For example, Venice Beach, CA is actually a neighborhood of Los Angeles. Knowing the neighborhood is useful, since economic data (like real estate prices) is available down to the neighborhood level.
We examined the 333,134 loans issued by Lending Club through June 30, 2014. Using the locality database, we were able to match 99.1% of the cities entered, with many of 0.9% unmatched cities being misspellings (that we decided not to pattern match). 2.6% of the cities matched actually turned out to be neighborhoods:
15,144 individual cities and 161 neighborhoods were matched within the historical loan data. Using the latitude and longitude of these cities and the loan count for each city, we created a heat map of where borrowers are located (# of loans per city is color coded):
Locality Health Score (LHS)
The locality database contains a rich set of current and historical, economic and demographic values for the U.S. itself, as well as for the neighborhoods, cities, counties and states it contains. Such values include home prices, unemployment rates, labor force participation, per capita and household incomes, populations, etc. An optimized weighted average of these values was formulated to create the Locality Health Score (LHS) that runs from 0 (bad) to 1 (good) for each locality.
As of January 1, 2014, these are the top and bottom 8 U.S. cities with populations over 500,000, as measured by the LHS:
LHS vs. Loan Performance
We used Lending Club’s June 30, 2014, historical loan snapshot to analyze how the LHS relates to loan performance (overall default rates) over the lifetime of the loans. Specifically, we focused on grade A-D loans from 2010-2013. For the 2013 cohort only “seasoned” loans issued in the first half (H1) of 2013 were included in this study.
Cohorts were broken down by year and grade. In each cohort, loans with a high LHS (> .8) and loans with a low LHS (< .3) were compared to the overall cohort. We measured the fraction of bad loans in a cohort as:
(# in grace period + # in default/charged off) / (# loans in cohort)
The results show that high (> .8) LHS loans result in a reduction of the percentage of “bad loans” in every cohort (all years, all grades). Conversely, low LHS (< .3) loans experience an increase in the percentage of “bad loans” in every cohort (all years, all grades). Thus, the LHS is highly relevant for marketplace loan selection.
This chart shows that for every year, 2010-2013, an LHS < .3 (in red) significantly increases the fraction of bad loans vs. the overall fraction (in dark grey), and that an LHS > .8 (in green) significantly decreases the fraction of bad loans:
These charts show that these results are consistent across all years (2010-2013) and all grades (A-D):
How to Use LHS When Investing in Marketplace Loans
My family office invests in marketplace loans using highly tuned filters for selecting “good loans” that should default at a rate significantly lower than other loans of the same grade or interest rate. The LHS plays a part in these filters.
Since we invest in fractional loans (vs. whole loans), we mainly use the LHS to vary the amount we invest in a given loan: the higher the LHS, the higher the fractional investment. This biases our marketplace loan portfolios towards borrowers in prosperous localities. When the next economic downturn comes, borrowers in prosperous localities should be more resilient.
The city or neighborhood in which a borrower resides turns out to have a meaningful impact on the rate at which a borrower may default on his loan(s) versus loans to other borrowers of the same grade. Furthermore, in an economic downturn, borrowers in more prosperous cities may be more resilient than those in troubled cities. With the Locality Health Score (LHS), we have developed a single number that coalesces several economic and demographic values for a locality into a simple-to-use and understandable number, ranging from 0 (bad) to 1 (good).