Improving Sales Comp Selection
Tenant Credit Profile adds a crucial layer to finding the “perfect” comp
A comprehensive, fact-based valuation of a multifamily property increases the likelihood of profitability and long-term success of an investment. Multifamily investments can be financially rewarding if done right but can also backfire if the fact base has gaps or the analysis is not thorough.
But to get the valuation correct, the underwriting process requires selecting the correct sales comparables. That’s why underwriters spend significant time and effort trying to find the “perfect comp” as input into the valuation.
In practice, however, finding the right comp is challenging for a variety of reasons. Property and local area characteristics vary highly. Geographies suffer a dearth of recent trades. High quality, recent data can’t be found on the subject and potential sales comparables. Underwriters can’t ensure they are picking the most suitable comp set.
All the data typically used is backward-looking. Ideally, underwriters would include leading indicators of asset performance at the property-level – if they could find it.
Adding a Dash of TCP
GeoPhy’s Tenant Credit Profile (TCP) provides a leading indicator of future rent payments by aggregating the financial health of all a building’s current tenants. It provides a forward-looking overlay on the comp process.
As a test, we looked at a real apartment building in St. Petersburg, Florida. The property’s appraisal had identified 5-10 potential sales comps using traditional criteria: proximity, size, age, amenities, class. So – can you get additional useful insight from understanding tenant financial health?
Here’s an example of how we would take this analysis a step further by using TCP to compare the credit profiles of the tenants within the subject and comparable properties.
Credit Scores Differentiate Buildings
As you can see in the chart below, our subject property’s median credit score was just 589 – quite poor. It is significantly worse than the median credit score for the metro area. That tells us to look for sales comparables with similar credit characteristics.
Median Credit Score: Subject Property
As Table 1 below indicates, tenants’ median credit scores in sales comp 1 are much healthier at 724 than both St. Petersburg’s metro area and the subject property. It wouldn’t be an ideal comparable.
Comp 2, on the other hand, has a median credit score of 583 – almost identical to the subject property. It would be an almost perfect comp.
Table 1: Median Credit Scores for Comps
We can take our credit analysis a step further by comparing how many tenants applied for a “payment holiday” since the start of the pandemic back in March 2020.
The subject property has had 6.6% of it’s tenants ask for a payment holiday vs comp 2’s 5.1%, further justifying comp 2 as an almost perfect selection. Additionally, despite comp 1’s healthy median credit score, nearly 11% of their tenants have asked for a payment holiday, which can act as a leading indicator for the number of tenants expected to have possible issues paying rent in the future.
Payment Holiday: Subject
Table 2: Payment Holidays for Comps
Taking Additional Steps
We could continue the analysis working through each of the 10 potential comps using a variety of other TCP capabilities including:
- How do delinquency rates compare?
- Are tenants’ monthly debt payments trending up and threatening their rent?
- Have tenants recently applied for more credit, signifying concerns about their current income?
Ultimately, the TCP data adds certainty and precision to the valuation process. By comparing the credit profiles of the tenants within the subject and comparable properties, it ensures the comps are similar. TCP helps analysts get smarter in winnowing a selection of 10+ potential comps to the final three “perfect comps.”
Sign up for a demo and we’ll walk you through how TCP can improve your selection process.