"Income data transparency key to unlocking billion-dollar inflows of institutional capital into commercial real estate", says property data firm Income Analytics

Commercial real estate could attract billions of pounds of passive capital in the way fixed income and equities do if the sector reported income data more accurately and transparently to investors, according to Matthew Richardson, the chief executive of property data firm Income Analytics.

Passive capital invests by replicating a specific benchmark or index such as the S&P 500 in order to match its performance. While MSCI can provide similar benchmarks the inherent illiquidity of commercial real estate prevents investors from using them to deliver tracker strategies.

The level, duration and quality of the rental income paid is the most reliable guide to the long-term performance of commercial real estate, but currently the sector does not provide this data consistently to investors.

Instead, real estate investors are largely guided by subjective labels such as Core, or Core-plus, which do not truly capture the risk and volatility of investments.

Matthew Richardson, founder and CEO of Income Analytics, said: 

“We are the only industry that I can think of that pre-labels what our fund is, most other people look at the actual performance and then relate the risk to the historic performance and likely future performance.

“When you actually look at the long term performance of real estate in the developed market around the world, the bulk of your long-term returns comes out of your cash flow – out of your income. And that’s also the most stable part of your income, and the bit you can control. What an investor really needs to have is a number for default or a failure number. I need to know the percentage probability that my tenant or counterparty in the agreement is going to go bust and leave me high and dry.”

Moving to a reporting standard for income is particularly crucial for commercial real estate compared to residential as income is a much more important factor for investors to understand as lot sizes are larger, with space treated as a commodity.

Income Analytics is able to offer investors essentially an Experian style credit check of a commercial real estate investment by assigning a score (INCAN score) to companies, giving a forward-looking and data-based benchmark.

The company feeds Dun & Bradstreet data, a primary global credit rating agency, into its analytical models to produce unique reports into the creditworthiness of a business and understand its likelihood of failure.

The Income Analytics’ reports include:

• 10-year tenant income risk forecast;
• Global tenant scores;
• Bond equivalent ratings; and
• Global corporate family tree.

This data enables investors to better understand who the counterparty is, what relation they have to a parent company and where responsibility lies for the payment of rent, in addition to establishing what percentage of their cash is at risk if the counterparty fails or defaults.

Stronger commercial real estate data will also potentially enable funds to automate parts of their due diligence processes, meaning they will be able to more efficiently allocate capital to commercial real estate.

Matthew Richardson, founder and CEO of Income Analytics, added:

“It’s no secret that Covid-19 has severely impacted the world’s financial markets, and the hunt for yield we saw from investors post-global financial crisis is only going to increase, especially as blue-chip investments like gilts yield ever lower, and sometimes even negative returns.

“But commercial real estate runs the real risk of missing out on billions of dollars worth of capital if it does not provide the levels of data many investors will need to make any investment stack up. Given that the yields the sector can offer will be at least 2 or 3 percentage points higher than gilts, commercial real estate really needs to get its house in order on data reporting to ensure it does not miss out.

“What we have set out to do is to restructure how that credit agency data works, so we can convert that number into a percentage which can then be forecast forward and move us away  as a sector from subjective labelling to something driven by the data.”

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