Overview:
- Quantitative commercial real estate modeling is becoming more widespread, but is still limited in several crucial ways
- You may be measuring variables unlikely to improve the decision while ignoring more critical variables
- Some assessment methods can create more error than they remove
- A sound quantitative model can significantly boost your investment ROI
Quantitative commercial real estate modeling (CRE), once the former province of only the high-end CRE firms on the coasts, has become more widespread – for good reason. CRE is all about making good decisions about investments, after all, and research has repeatedly shown how even a basic statistical algorithm outperforms human judgment<fn>Dawes, R. M., Faust, D., & Meehl, P. E. (n.d.). Statistical Prediction versus Clinical Prediction: Improving What Works. Retrieved from http://meehl.umn.edu/sites/g/files/pua1696/f/155dfm1993_0.pdf</fn><fn>N. Nohria, W. Joyce, and B. Roberson, “What Really Works,” Harvard Business Review, July 2003</fn>.
Statistical modeling in CRE, though, is still limited, for a few different reasons, which we’ll cover below. Many of these limitations actually result in more error (one common misconception is merely having a model improves accuracy, but sadly that’s not the case). Even a few percentage points of error can result in significant losses. Any investor that has suffered from a bad investment knows all too well how that feels. So, through better quantitative modeling, we can decrease the chance of failure.
Here’s how to start.
The Usual Suspects: Common Variables Used Today
Variables are what you’re using to create probability estimates – and, really, any other estimate or calculation. If we can pick the right variables, and figure out the right way to measure them (more on that later), we can build a statistical model that has more accuracy and less error.
Most commercial real estate models – quantitative or otherwise – make use of the same general variables. The CCIM Institute, in its 1Q19 Commercial Real Estate Insights Report, discusses several, including:
- Employment and job growth
- Gross domestic product (GDP)
- Small business activity
- Stock market indexes
- Government bond yields
- Commodity prices
- Small business sentiment and confidence
- Capital spending
Data for these variables is readily available. For example, you can go to CalculatedRiskBlog.com and check out their Weekly Schedule for a list of all upcoming reports, like the Dallas Fed Survey of Manufacturing Activity, or the Durable Goods Orders report from the Census Bureau.
The problem, though, is twofold:
- Not all measurements matter equally, and some don’t matter at all.
- It’s difficult to gain a competitive advantage if you’re using the same data in the same way as everyone else.
Learning How to Measure What Matters in Commercial Real Estate
In How to Measure Anything: Finding the Value of Intangibles in Business, Doug Hubbard explains a key theme of the research and practical experience he and others have amassed over the decades: not everything you can measure matters.
When we say “matters,” we’re basically saying that the variable has predictive power. For example, check out Figure 1. These are cases where the variables our clients were initially measuring had little to no predictive power compared to the variables we found to be more predictive. This is called measurement inversion.
Figure 1: Real Examples of Measurement Inversion
The same principle applies in CRE. Why does measurement inversion exist? There are a few reasons: variables are often chosen based on intuition/conventional wisdom/experience, not statistical analysis or testing; decision-makers often assume that industries are more monolithic than they really are when it comes to data and trends (i.e. all businesses are sufficiently similar that broad data is good enough); intangibles that should be measured are viewed as “impossible” to measure; and/or looking into other, “non-traditional” variables comes with risk that some aren’t willing to take. (See Figure 2 below.)
Figure 2: Solving the Measurement Inversion Problem
The best way to begin overcoming measurement inversion is to get precise with what you’re trying to measure. Why, for example, do CRE investors want to know about employment? Because if too many people in a given market don’t have jobs, then that affects vacancy rates for multi-family units and, indirectly, vacancy rates for office space. That’s pretty straightforward.
So, when we’re talking about employment, we’re really trying to measure vacancy rates. Investors really want to know the likelihood that vacancy rates will increase or decrease over a given time period, and by how much. Employment trends can start you down that path, but by itself isn’t not enough. You need more predictive power.
Picking and defining variables is where a well-built CRE quantitative model really shines. You can use data to test variables and tease out not only their predictive power in isolation (through decomposition and regression), but also discover relationships with multi-variate analysis. Then, you can incorporate simulations and start determining probability.
For example, research has shown<fn>Heinig, S., Nanda, A., & Tsolacos, S. (2016). Which Sentiment Indicators Matter? An Analysis of the European Commercial Real Estate Market. ICMA Centre, University of Reading</fn> that “sentiment,” or the overall mood or feeling of investors in a market, isn’t something that should be readily dismissed just because it’s hard to measure in any meaningful way. Traditional ways to measure market sentiment can be dramatically improved by incorporating tools that we’ve used in the past, like Google Trends. (Here’s a tool we use to demonstrate a more predictive “nowcast” of employment using publicly-available Google Trend information.)
To illustrate this, consider the following. We were engaged by a CRE firm located in New York City to develop quantitative models to help them make better recommendations to their clients in a field that is full of complexity and uncertainty. Long story short, they wanted to know something every CRE firm wants to know: what variables matter the most, and how can we measure them?
We conducted research and gathered estimates from CRE professionals involving over 100 variables. By conducting value of information calculations and Monte Carlo simulations, along with using other methods, we came to a conclusion that surprised our client but naturally didn’t surprise us: many of the variables had very little predictive power – and some had far more predictive power than anyone thought.
One of the latter variables wound up reducing uncertainty in price by 46% for up to a year in advance, meaning the firm could more accurately predict price changes – giving them a serious competitive advantage.
Knowing what to measure and what data to gather can give you a competitive advantage as well. However, one common source of data – inputs from subject-matter experts, agents, and analysts – is fraught with error if you’re not careful. Unfortunately, most organizations aren’t.
How to Convert Your Professional Estimates From a Weakness to a Strength
We mentioned earlier how algorithms can outperform human judgment. The reasons are numerous, and we talk about some of them in our free guide, Calibrated Probability Assessments: An Introduction.
The bottom line is that there are plenty of innate cognitive biases that even knowledgeable and experienced professionals fall victim to. These biases introduce potentially disastrous amounts of error that, when left uncorrected, can wreak havoc even with a sophisticated quantitative model. (In The Quants, Scott Patterson’s best-selling chronicle of quantitative wizards who helped engineer the 2008 collapse, the author explains how overly-optimistic, inaccurate, and at-times arrogant subjective estimates undermined the entire system – to disastrous results.)
The biggest threat is overconfidence, and unfortunately, the more experience a subject-matter expert has, the more overconfident he/she tends to be. It’s a catch-22 situation.
You need expert insight, though, so what do you do? First, understand that human judgments are like anything else: variables that need to be properly defined, measured, and incorporated into the model.
Second, these individuals need to be taught how to control for their innate biases and develop more accuracy with making probability assessments. In other words, they need to be calibrated.
Research has shown how calibration training often results in measurable improvements in accuracy and predictive power when it comes to probability assessments from humans. (And, at the end of the day, every decision is informed by probability assessments whether we realize it or not.) Thus, with calibration training, CRE analysts and experts can not only use their experience and wisdom, but quantify it and turn it into a more useful variable. (Click here for more information on Calibration Training.)
Including calibrated estimates can take one of the biggest weaknesses firms face and turn it into a key, valuable strength.
Putting It All Together: Producing an ROI-Boosting Commercial Real Estate Model
How do you overcome this challenge? Unfortunately, there’s no magic button or piece of software that you can buy off the shelf to do it for you. A well-built CRE model, incorporating the right measurements and a few basic statistical concepts based on probabilistic assessments, is what will improve your chances of generating more ROI – and avoiding costly pitfalls that routinely befall other firms.
The good news is that CRE investors don’t need an overly-complicated monster of a model to make better investment decisions. Over the years we’ve taught companies how incorporating just a few basic statistical methods can improve decision-making over what they were doing at the time. Calibrating experts, incorporating probabilities into the equation, and conducting simulations can, just by themselves, create meaningful improvements.
Eventually, a CRE firm should get to the point where it has a custom, fully-developed commercial real estate model built around its specific needs, like the model mentioned previously that we built for our NYC client.
There are a few different ways to get to that point, but the ultimate goal is to be able to deliver actionable insights, like “Investment A is 35% more likely than Investment B at achieving X% ROI over the next six months,” or something to that effect.
It just takes going beyond the usual suspects: ill-fitting variables, uncalibrated human judgment, and doing what everyone else is doing because that’s just how it’s done.
