MSCI on calculating the risk to real estate income

This article is sponsored by MSCI

In June, investment solutions provider MSCI and international real estate services firm Savills led a funding round into Income Analytics, a data technology firm that provides investors with proprietary global rental default risk measures on commercial real estate income. Such information is increasingly crucial for managers and capital providers as they seek to determine the volatility of future cashflows in a market that is still in a state of flux, explains René Veerman, MSCI’s head of real estate.

Should commercial real estate investors be paying more attention to the security of their cashflows?

They should, for two reasons: firstly, due to lockdowns and travel restrictions, many firms in sectors like travel and leisure have been unable to pay their rent. I know of investors who have received less than half their expected rental income in a given month.

If you accept that the value of an investment is equal to the discounted future stream of cashflows, that should have a significant impact on the value of the asset. Those segments of the market may not be negatively impacted by structural trends; however, they are suffering temporarily because of covid-19.

Secondly, there are sectors of the market subject to long-term secular changes, notably retail and logistics, where covid-19 has accelerated those trends. Predictions that online shopping would increase from 20 percent of sales to 30 percent over a decade have come true in a year. In some respects, we have jumped 10 years into the future.

Those changes mean that investors in real estate will have to adjust their strategies and their expectations. Because of the massive volatility of cashflow over the last year, investors have realized they did not have the tools or the data to properly compare the riskiness of real estate rental income across assets, portfolios and regions. That is ultimately what we are trying to do through our investment into Income Analytics.

Especially at the core end of the market, where capital tends to be invested in stabilized assets, the majority of long-term returns are generated from income. In aggregate, at the portfolio or market level, income return tends to be relatively stable while capital values can fluctuate enormously over the cycle.

At the more opportunistic end of the spectrum, where you are redeveloping sites, for instance, returns are more reliant on the revaluation of the asset after work is
completed.

Even in such cases, a significant proportion of the value creation stems from the leasing of the building to future tenants – the quality and duration of those future income streams still matters. When income stops being predictable, an investor can suddenly find themselves invested in an asset is not doing what it is supposed to do – and is potentially less valuable.

How risk has increased in the wake of covid-19

MSCI research shows a dramatic rise in the probability of leisure asset rental defaults.

Research published in September reveals that the level and durability of rental income generated from constituent properties of the MSCI UK Quarterly Property Index declined between December 2019 and June 2021, with some segments hit much harder than others. Rental income from leisure properties saw the 10-year cumulative probability of default increase by over 100 percent in that time, while the office and retail sectors saw only marginal shifts in their default probability.

The income-weighted INCANS Global Tenant Scores for segments of the MSCI UK Quarterly Property Index have declined consistently across the major property types since June 2020, following the onset of the pandemic.

Shifts in income-weighted cumulative probability-of-failure curves from before the market turmoil of the pandemic to the present are most starkly illustrated by the hard-hit leisure segment. In Q4 2019, the probability of default over one year was below 0.3 percent and over 10 years it was around 1.8 percent. These probabilities of default climbed to 1 percent and just under 3.9 percent, respectively. This is particularly notable when considering that the all-property benchmark exhibited a similar default curve to that of leisure in Q4 2019, but shifted to a much lesser extent by Q2 2021.

Why do investors need to measure default risk on commercial real estate income?

Credit ratings that gauge the relative risk of tenant default over the short-run are already available and are widely utilized in the real estate industry. These are typically useful as a snapshot of a given tenant’s current creditworthiness. However, income risk is not relative.

Either rent is paid, or it is not. Consistently tracking risk levels over time, comparing across countries and translating that immediate risk to a longer-term view that aligns with the length of a lease requires more work.

The utility of such risk scores is maximized when you can apply them in cashflow analysis. Income Analytics uses Dun & Bradstreet data to measure the likelihood of failure of a certain tenant, then normalizes that data to derive an INCANS score which corresponds to the percentile risk the company sits in versus the global universe of companies over recent history. By examining the relationship between these scores over time and the subsequent failure of companies, Income Analytics can derive probabilities of failure that can be translated into ‘equivalent bond scores,’ facilitating the comparison of real estate income risk with other asset classes.

When you can apply probabilities of default of your tenants to your cashflows, it means you can assess the impact in cash terms on the valuation of your asset, and on your expected returns. It allows you to run through your financial model more quickly and precisely because it enables you to calculate the riskiness of a rental income stream of a specific asset that contains a specific list of tenants.

There is an enormous variety of types of assets in real estate – millions of different buildings, each with its own tenants in a specific location. Indeed, specificity in real estate is so high that getting information that is this granular, and yet specific, is of great value.

How can that information be utilized?

Cashflows form the basis of modelling at nearly all steps of the investment process from acquisition, to financing, leasing and hold/sell decisions. Quantifying income risk in a probabilistic way means it can be engrained in a systematic way throughout the entire investment process.

From a portfolio monitoring perspective, it allows managers to be alerted when there are significant changes in the risk to their income streams at lease, asset or portfolio level. It allows them to understand concentration risk across the income stream too; is too much of my income coming from one sector of the economy despite being spread across multiple property types? When I look through to the ultimate parent company is my exposure too high and am I breaching my own fund investment policies?

Meanwhile, beyond the direct uses for clients, we can embed the INCANS score in our solutions. MSCI measures investment performance and risk and attributes it to particular asset classes. Income Analytics enables us to also assess the risk to income and begin to understand the contribution income risk has to assets’ relative investment performance in the context of a benchmark, tying together analysis of both risk and return rather than having them treated separately.

Why is it important for investors to have access to that information in real time?

When everything is going smoothly in the wider world and economy, an investor with long-leased assets might think they can afford to look away for a while, and periodic reporting is fine. However, if they want to play into new themes as they appear because of structural changes, then by waiting for those patterns to show up in the data after months or years they will miss opportunities.

Furthermore, changes in forecast income may have an immediate impact on value. If you accept that the value of an asset is effectively a discounted cashflow, that means a change in the riskiness of the income causes a change in valuation. Real time data about that risk allows investors to evaluate immediately what impact the change in income could have on the valuation of their asset.

One can draw a parallel with a company generating earnings. A very large part of the value of that company when its shares are traded on the stock market is derived from future earnings. When something happens to cause a dip in those earnings the market reaction is often minimal, because its future cashflows look almost the same.

For managers or investors with a large, complex portfolio, understanding quickly your exposure to a single tenant, corporate group or industry sector can be difficult and time consuming. Being able to calculate this in seconds allows investors to spend their time on investment decision making and managing the risk rather than spending hours measuring it.

How will performance analysis in the real estate sector evolve in the future?

Clearly, greater transparency is good for investors and lenders. They can see what is happening to their capital and predict what they can expect with greater accuracy.
That is the case for all asset classes, but in private markets there is less transparency because data is often very closely guarded. As a consequence, there is less data and less ability to analyze it.

However, data is gradually becoming more widely available, at a higher frequency, which allows for more sophisticated analytical tools.

If you were to go back 10 years it would be very hard to compare the rental income riskiness of an asset in the UK with an asset in Sweden. That is now possible because you can combine the available data sets and run the analysis.

Owners of private commercial real estate tend to be large institutional investors which have invested to meet liabilities such as future pension payouts.

In order to ensure that those monies are invested appropriately and that performance and risk is assessed accurately, there is a secular drive towards more transparency. In the long term we would expect the data available to private real estate investors to resemble that which is available in the public markets.