Calculating Comparable Performance

Once the terminal nodes have been created, a servicer's performance in each node is compared to the performance of all the other Fannie Mae loans for that node. This section will walk through the steps necessary to calculate the relative performance of a servicer’s portfolio. Node 7 from Figure 2 represents a high LTV loan that is limited in terms of the solutions that can be solicited. Node 13 represents a low LTV loan that is no more than four-months delinquent and potentially more curable. We will refer to these terminal nodes as having a high or low curative opportunity in the following example to demonstrate how the Comp is calculated and the way a servicer’s portfolio stratification influences its performance.

In Table 3, the performance of Servicer A is compared to the performance of the Comp, excluding Servicer A's loans, and against Servicer B who has a similar population of loans but with different attributes. Note that there are 500,000 loans in the Fannie Mae denominator, and Servicer A has 38,500 of these loans and Servicer B has 40,000. Servicer A has had 865 metric events and Servicer B has had 900 metric events (Servicer Numerator column). Servicer A's Comp is the other 461,500 loans from the Fannie Mae denominator. Servicer B’s comp, while a similar number of loans, is different from servicer A’s based on the metric event opportunity of their portfolio.

Table 3:  Comparable Pool Construction

Servicer	Node	Event Opportunity	Fannie Mae Numerator	Fannie Mae Denominator	Servicer Numerator	Servicer Denominator	Comp Numerator	Comp Denominator
Servicer A	7 13	Low High	1,500 10,000	100,000 400,000	120 745	8,500 30,000	1,380 9,255	91,500 370,000
			11,500	500,000	865	38,500	10,635	461,500
Servicer B	7 13	Low High	1,500 10,000	100,000 400,000	250 650	16,000 24,000	1,250 9,350	84,000 376,000
			11,500	500,000	900	40,000	10,600	460,000

 

Table 4 shows how a servicer’s metric value is influenced by its portfolio makeup. Here, the denominator weight represents the portion of the servicer’s metric population represented by that node. The product of the metric and the denominator weight yields that node’s contribution to the servicer’s overall metric value. This helps to illustrate the importance of segmenting the portfolio based on key loan attributes to account for the servicer’s’ unique credit characteristics. Now we will take into account the key loan attributes and look at the performance in both the high and low opportunity nodes.

Table 4:  Understanding Node Weighting Based on Servicer’s Portfolio

Servicer	Node	Metric Event Opportunity	Servicer Numerator	Servicer Denominator	Servicer Metric	Denominator Weight	Contribution to Metric Ratio
Servicer A	7 13	Low High	120 745	8,500 30,000	1.41% 2.48%	22.08% 77.92%	0.31% 1.93%
			865	38,500	2.25%	100.00%	2.25%
Servicer B	7 13	Low High	250 650	16,000 24,000	1.56% 2.71%	40.00% 60.00%	0.62% 1.63%
			900	40,000	2.25%	100.00%	2.25%

 

A servicer’s metric ratio is determined by dividing the metric numerator by the metric denominator. While metric performance can be expressed in aggregate for each servicer or for each node, the Comp is created based on how other loans with similar characteristics perform. Notice in Table 4 that the total number of loans (servicer denominator) for each servicer is similar, and servicers’ absolute performance is the same 2.25%. However, the grouping of the two populations by nodes based on the servicer portfolio composition is different, as shown in the Contribution to Metric Ratio column.

Table 5:  Calculating Servicer and Comp Performance

Servicer	Node	Metric Event Opportunity	Servicer Numerator	Servicer Denominator	Servicer Metric	Comp Numerator	Comp Denominator	Comp Ratio
Servicer A	7 13	Low High	120 745	8,500 30,000	1.41% 2.48%	1,380 9,255	91,500 370,000	1.51% 2.50%
			865	38,500	2.25%
Servicer B	7 13	Low High	250 650	16,000 24,000	1.56% 2.71%	1,250 9,350	84,000 376,000	1.49% 2.49%
			900	40,000	2.25%

 

The same ratio calculation is made against the servicer’s Comp pool of loans to establish the performance of all loans in the Fannie Mae portfolio with those characteristics. Once the Comp ratio is calculated (Table 5), you can determine how many loans would have had a metric event if the servicer performed at Comp by multiplying the Comp ratio by the servicer’s denominator for both nodes as in Table 6 below. The servicer’s Comp for the metric is then calculated as the sum of the individual node Comp values.

Table 6:  Determining Metric Comp Values

Servicer	Node	Metric Event Opportunity	Comp Ratio	Servicer Denominator	Comp Value
Servicer A	7 13	Low High	1.51% 2.50%	8,500 30,000	128.20 750.41
				38,500	878.60
Servicer B	7 13	Low High	1.49% 2.49%	16,000 24,000	238.10 596.81
				40,000	834.90

 

Summary

For STAR metrics that measure relative performance, we segment the loan population for each metric based on loan attributes that impact servicer performance in the metric. Once the loan populations are segmented into their final, terminal nodes, the metric ratio for all other Fannie Mae servicers with similar loans is calculated. That ratio is then applied to the servicer’s loan volume to establish the Comp for that node. The results are then aggregated for all nodes to establish the servicer’s metric Comp which represents expected metric performance.