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Multifamily Loan Portfolio Case Study


Analysis of Multifamily Loan Portfolio for Large National Financial Institution

A Community Bank got a better understanding of their multifamily loan portfolio due to Situs’ analysis and reporting.

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Client Need

A Community Bank (Bank) multifamily loan portfolio had 1,972 loans with 2,579 borrowers and an aggregate Unpaid Principal Balance (UPB) of approximately $2.4 billion. The Bank asked Situs to perform a historic analysis of the loss experience in their multifamily loan portfolio as justification for the heavy concentration in the business.

Situs Solution

Using a twenty-three year historic analysis of their business, Situs analyzed the expected loss rates from 1992 through 2014 and used this history to determine Expected loss rates for that portfolio, by risk rating and geography. This expected loss was then used in their business model.

The first step is to determine the historic Probability of Default (PD) as well as the Loss Given Default (LGD) for each year of the analysis. Once these two items are determined, the product of these determines the Expected Loss (EL) rates to be used for future loans. This information is kept at the loan, and therefore geographic level, so future business can be accurately priced using these new risk-based loss rates. The process involves maintaining historic credit risk ratings by loans and detailing each loan based on their performance. Situs then compared these results to other results Situs has seen within the same markets over the past two decades.

Next, Situs provides a summary of high-, medium- and low-risk loans to the client. This summary included detailed data about pricing—outlining what percentage of the full financial institution’s multifamily loan portfolio each sub-category accounted for, and providing weighted average LTV’s, DSCR’s, NOI’s, PD’s, LGD’s and total EL for each category, as a historic guide.

Using a twenty-three year historic analysis of their business, Situs analyzed the expected loss rates from 1992 through 2014 and used this history to determine Expected loss rates for that portfolio, by risk rating and geography. This expected loss was then used in their business model.

The first step is to determine the historic Probability of Default (PD) as well as the Loss Given Default (LGD) for each year of the analysis. Once these two items are determined, the product of these determines the Expected Loss (EL) rates to be used for future loans. This information is kept at the loan, and therefore geographic level, so future business can be accurately priced using these new risk-based loss rates. The process involves maintaining historic credit risk ratings by loans and detailing each loan based on their performance. Situs then compared these results to other results Situs has seen within the same markets over the past two decades.

Next, Situs provides a summary of high-, medium- and low-risk loans to the client. This summary included detailed data about pricing—outlining what percentage of the full Bank’s multifamily loan portfolio each sub-category accounted for, and providing weighted average LTV’s, DSCR’s, NOI’s, PD’s, LGD’s and total EL for each category, as a historic guide.

In conjunction with this analysis, Situs held interviews with senior management to discuss any operational or financial changes that had taken place in the past 12 to 18 months within the Bank. The findings indicated that management was mostly happy with their changes, which created fairly significant loan growth in the portfolio, improved risk management and created new growth challenges. Following the interviews, Situs completed a loan policy and guidelines review for the Bank to ensure compliance with the new pricing and growth expectations.

Outcome/ Benefits

Throughout the review process, Situs helped the Bank gain a better understanding of their multifamily loan portfolio loss rates and business model, while also helping regulators gain more transparency into the portfolio. Situs explained, in detail, the performances of each area within the multifamily portfolio and provided clarity in realizing more accurate loan loss estimates.