Loan Portfolio Analysis
Brean Strategic Advisors’s loan portfolio offering serves to aggregate, extract, enrich and analyze as much historical data as our client has about the historical performance of their loan portfolio, including paystrings for active, prepaid and charged-off loans as well as all relevant origination characteristics. When origination characteristics and performance data are combined at the loan-level and regressed, analytical insights result which inform regulator and business line decision making and reporting.
Brean Strategic Advisors’s approach to loan data aggregation, enrichment and analysis is detailed below.
1. Aggregate dataset
Many of Brean Strategic Advisors’s banking clients have stitched together businesses over a series of acquisitions and bulk pool purchases. Origination loan tapes captured, retained and aggregated from retail origination may be limited to those required by the core solution, such as balance, coupon and reset. Wholesale channel loan origination was generally accompanied by full bid tapes yet this data is often retained on separate spreadsheets for each pool and may have loan ids which are not mapped to the core deposit solution or servicer reporting.
The first step in data aggregation and normalization is capturing a comprehensive set of origination characteristics normalized across origination channels and into a single source. For example, Brean Strategic Advisors ingests loan tapes acquired in hundreds of transactions by multiple predecessor banks. In some cases, such as lower volume retail origination to local customers, the only way to extract the relevant information may be scanning loan files stored in a warehouse and using a combination of programmatic or human review to extract the relevant document metrics.
The second step of aggregation is pulling historical performance. The difficulty of this exercise will vary with the nature of the assets and the volume of servicers. Many smaller bank clients purchase whole loans on a servicing retained basis, meaning there could be hundreds of servicers. In some cases bank clients direct all of the servicer cashflows to a third party to aggregate but the data is aggregated at the pool rather than the loan level.
Brean Strategic Advisors tailors the extent of historical data aggregation to our clients’ budget. For clients with clean data sets and fewer servicers we may be able to pull in decades of historical data. For a client with a more limited budget and messier data we may only be able to bring in historical data for the largest whole loan pools and going back only a few years.
b. Monthly ongoing updates
Going forward, Brean Strategic Advisors will aggregate monthly servicer reports. Brean Strategic Advisors ingests, normalizes and enriches the servicer loan tapes. For example, Brean Strategic Advisors will dynamically update home price-adjusted loan-to-value ratios. We will aggregate servicing reporting across sectors and subsectors and marry the performance data with the origination data.
On a monthly basis, Brean Strategic Advisors tracks the composition of the loan pool by key origination metrics. For consumer loans, these include FICO, LTV and DTI, among others. For commercial loans this includes debt-service coverage ratio and sector. Performance data is also tracked. This includes trends in prepayment speeds, default frequency and loss severity. We break performance data into buckets by sector, product and credit characteristics, among others.
Brean Strategic Advisors regresses the origination data against performance trends to highlight macro and micro drivers of performance within the portfolio. The regression analyses inform the application of the regulatory stress scenarios to our clients’ portfolio. To the extent we identify drivers such as differences in underwriting or servicing which are proven to account performance differences, these are reflected in the results of our regression analyses.
Brean Strategic Advisors uses the insights from the performance trends to drive growth in the underlying businesses. In some cases we develop customized models, such as a client concerned about entering a new geography who desires an assessment of how the new region varies from the bank’s existing geographic footprint. In other cases, a bank may be interested in expanding its underwriting guidelines and altering loan terms and is interested in how to price this risk and implications for potential losses.
Clients concerned about expanding volume in today’s low rate environment increasingly engage Brean Strategic Advisors to perform probability-weighted credit risk adjusted return analyses. The objective is to determine the likelihood of various macroeconomic drivers occurring, such as the probability of various home price and unemployment paths. Given these weightings, Brean Strategic Advisors is tapped with calculating the expected losses, existing coupon and the target return. Our objective is to determine if our client would benefit from pricing certain risks more aggressively or should reduce its exposure or increase prices for other risks given the economic outlook.
Once Brean Strategic Advisors has ingested our client’s data, developed a framework to track origination trends, portfolio composition and performance on a monthly basis, our goal is to report this information in the formats customized by clients.
Reporting options include:
a. Detailed Loan Analysis
i. Report detailing historical data regarding the performance of the portfolio, including paystrings for active, prepaid and charged-off loans as well as all relevant origination
ii. Track trends in origination over time by key credit metrics and sector.
iii. Track performance over time by sector.
iv. Stratification of the portfolio by sector, performance and origination credit metrics.
b. Credit Stress Testing
i. Expected losses using different macro scenarios.
ii. Documentation of data sources and methodology in level of depth sufficient for regulators.
c. Interest rate and prepayment stress testing.
i. Use of various interest rate scenarios to evaluate total return and optionality.
ii. Down 100 bps parallel shock to up 300 bps.
i. Expected one year losses in the 99.95th percentile scenario.
e. Incorporation into institutional what if scenarios.