Trojan Horse: How a Phenomenon Called Measurement Inversion Can Massively Cost Your Company

measurement inversion


  • A Trojan horse is anything that introduces risk to an organization through something that appears to be positive
  • Measuring the wrong variables is a Trojan horse that infiltrates virtually every organization
  • This phenomenon has a real cost that can be measured – and avoided

The Trojans stood at the walls, drunk from victory celebrations after they had previously watched the Greek fleets set sail away in retreat, having been defeated after nearly 10 years of constant warfare. They had little reason to suspect treachery when they saw the massive wooden horse just outside their gates, apparently a gift offering from the defeated Greeks. Because of their confidence – or overconfidence – they opened the gates and claimed the wooden horse as the spoils of war.

Later that night, after the Trojans lay in drunken  stupor throughout the city, a force of Greek soldiers hidden in the horse emerged and opened the gates to the Greek army that had not retreated but had actually lay in wait just beyond sight of the city. Swords drawn and spears hefted, the Greek soldiers spread throughout the city and descended upon its people.

The end result is something any reader of The Illiad knows well: the inhabitants of Troy were slaughtered or sold into slavery, the city was razed to the ground, and the term “Trojan horse” became notorious for something deceitful and dangerous hiding as something innocuous and good.

Organizations are wising up to the fact that quantitative analysis is a vital part of making better decisions. Quantitative analysis can even seem like a gift, and used properly, it can be. However, the act of measuring and analyzing something can, in and of itself, introduce error – something Doug Hubbard calls the analysis placebo. Put another way, merely quantifying a concept and subjecting the data to an analytical process doesn’t mean you’re going to get better insights.

It’s not just what data you use, although that’s important. It’s not even how you make the measurements, which is also important. The easiest way to introduce error into your process is to measure the wrong things – and if you do, you’re bringing a Trojan horse into your decision-making.

Put another way, the problem is an insidious one: what you’re measuring may not matter at all, and may just be luring you into a false sense of security based on erroneous conclusions.

The One Phenomenon Every Quantitative Analyst Should Fear

Over the past 20 years and throughout over 100 measurement projects, we’ve found a peculiar and pervasive phenomenon: that what organizations tend to matter the most often matters the least – and what they aren’t measuring tends to matter the most. This phenomenon is what we call measurement inversion, and it’s best demonstrated by the following image of a typical large software development project (Figure 1):

measurement problems

Figure 1: Measurement Inversion

Some examples of measurement inversion we’ve discovered are shown below (Figure 2):

measurement inversion examples

Figure 2: Real Examples of Measurement Inversion

There are many reasons for measurement inversion, ranging from the innate inconsistency and overconfidence in subjective human assessment to organizational inertia where we measure what we’ve always measured, or what “best practices” say we should measure. Regardless of the reason, every decision-maker should know one, vital reality: measurement inversion can be incredibly costly.

Calculating the Cost of Measurement Inversion for Your Company

The Trojan horse cost Troy everything. That probably won’t be the case for your organization, as far as one measurement goes. But there is a cost to introducing error into your analysis process, and that cost can be calculated like anything else.

We uncover the value of each piece of information with a process appropriately named Value of Information Analysis (VIA). VIA is based on the simple yet profound premise that each thing we decide to measure comes with a cost and an expected value, just like the decisions these measurements are intended to inform. Put another way, as Doug says in How to Measure Anything, “Knowing the value of the measurement affects how we might measure something or even whether we need to measure it at all.” VIA is designed to determine this value, with the theory that choosing higher-value measurements should lead to higher-value decisions.

Over time, Doug has uncovered some surprising revelations using this method:

  • Most of the variables used in a typical model have an information value of zero
  • The variables with the highest information value were usually never measured
  • The most measured variables had low to no value.

The lower the information value of your variables, the less value you’ll generate from your model. But how does this translate into costs?

A model can calculate what we call your Overall Expected Opportunity Loss (EOL), or the average of each expected outcome that could happen as a result of your current decision, without measuring any further. We want to get the EOL as close to zero as possible. Each decision we make can either grow the EOL or shrink it. And each variable we measure can influence those decisions. Ergo, what we measure impacts our expected loss, for better or for worse.

If the variables you’re measuring have a low information value – or an information value of zero – you’ll waste resources measuring them and do little to nothing to reduce your EOL. The cost of error, then, is the difference between your EOL with these low-value variables and the EOL with more-valuable variables.

Case in point: Doug performed a VIA for an organization called CGIAR. You can read the full case study in How to Measure Anything, but the gist of the experience is this: by measuring the right variables, the model was able to reduce the EOL for a specific decision – in this case, a water management system – from $24 million to under $4 million. That’s a reduction of 85%.

Put another way, if they had measured the wrong variables, then they would’ve incurred a possible cost of $20 million, or 85% of the value of the decision.

The bottom line is simple. Measurement inversion comes with a real cost for your business, one that can be calculated. This raises important questions that every decision-maker needs to answer for every decision:

  1. Are we measuring the right things?
  2. How do we know if we are?
  3. What is the cost if we aren’t?

If you can answer these questions, and get on the right path toward better quantitative analysis, you can be more like the victorious Greeks – and less like the citizens of a city that no longer exists, all because what they thought was a gift was the terrible vehicle of their destruction. 


Learn how to start measuring variables the right way – and create better outcomes – with our two-hour Introduction to Applied Information Economics: The Need for Better Measurements webinar. $100 – limited seating.


These Two Problems Are Why Innovation Initiatives Fail

innovation problems


  • The most common reason organizations fail at innovating might not be what you think
  • Defining what innovation means to an organization is critical for success
  • Even the best-defined innovation initiatives fail if they aren’t properly measured

Innovation has captured the imagination of business leaders everywhere. Everyone wants to create that ground-breaking, disruptive product or service that turns the industry on its head. Or, at the very least, they want to move toward changing anything – paradigms, processes, markets, you name it – that will take the organization to that next level.

Unfortunately, many innovation initiatives fail, a failure being an outcome which is disproportionately smaller than expectations, allocated resources, or both. They happen quite commonly, and business literature is rife with reasons why, ranging from not having ideas that are “good” to not having the right people, enough budget, or an accepting culture.

The main reason an innovation initiative fails, however, is more fundamental: companies aren’t doing a good enough job defining what innovation actually means and then measuring it.

The Innovation Definition Problem

Decision-makers that this very moment are spending a staggeringly-high percentage of their revenue trying to innovate often don’t have a firm definition of what innovation means – and this isn’t just academic. 

Innovation is a vague, mostly meaningless term that obscures what you’re really trying to accomplish. It can mean almost anything. Everyone has a definition; here’s 15 of them.

What we’ve found when faced with these terms (and there are a lot of them) is that either the decision-makers know what they want to accomplish, but they don’t know how to measure it, or they think they know what they want to accomplish, but they’re measuring the wrong things (or even pursuing the wrong goal).

So, how do you define the problem? When organizations want to innovate, they’re largely looking to do something that they couldn’t previously do, for the purpose of taking the company to a level it couldn’t previously reach.

How they achieve this is largely a function of two things: impact and timeliness. The earlier a company undertakes an initiative, and the more impact that initiative has, the more innovative a company will be, as shown by the Innovation Time/Impact Square in figure 1:

innovation measurement

Figure 1 – Innovation Time/Impact Square

In our experience, the companies that find the greatest success compared to their peers come up with better ideas – impact – and do so usually before anyone else – timeliness. If your products or services are producing a lot of return, but you are behind the curve, you’re mainly just catching up by implementing best practices. If you’re getting there first, but your concepts aren’t particularly high-value, then you’re underachieving given your abilities. And if your concepts are low value and based only on what others have done before you, you’re falling behind.

Any of the three states of being may be acceptable, but none are examples of innovation. 

What does “impact” mean? One way to define it is to select an objective – revenue growth, higher stock price, more sales, greater market share, or some other desired outcome – and determine the growth target. 

Of course, no organization is going to spend a significant portion of their budget on merely adding a percent or two to their growth, not if they can help it. The allure of innovation is substantial, paradigm-changing growth.

What that growth looks like specifically depends on the firm, but the reality is simple: spending significant resources on innovation – a difficult and costly process – needs to be worth it.

“Timeliness” can mean a variety of things as well. Increasing the quantity of product concepts produced over a given period of time is one definition. Identifying new trends before anyone else is another. Focusing on speeding up the pace at which you create does have value in and of itself, but investing too much in accomplishing this goal can result in lower overall return.

Framing innovation in this way gives you the basis to make better decisions on anything from how much to increase the R&D budget to who you need to hire, what technology you need to acquire, or what you need to do to improve the quality of the ideas your organization creates.

Once you’ve defined what innovation means to your organization, you then have to measure it.

The Innovation Measurement Problem

The innovation measurement problem is simple: companies, by and large, don’t know how to measure this concept. In practice, this means most firms can’t:

  1. Evaluate how “good” they are at innovation, whatever that means
  2. Figure out what it takes to get “better” at innovation, whatever that looks like
  3. Determine the cost of doing those things to get “better” and forecasting ROI

innovation measurement

The first major attempt to accomplish the first task came in 1976, when Michael Kirton produced a paper identifying the two types of creators: adaptors (those who make existing processes better) and innovators (those who do something different). From this effort came the Kirton Adaption-Innovation (KAI) Inventory, which basically provides a person with where he or she falls on this adaption-innovation continuum.

The effort is a noble one, but we don’t have any sense of objective value. Are people at the Innovation end of the scale better at growing a company than the ones at the other end, and if so, by how much? 

These kinds of problems aren’t specific to just the KAI inventory; they’re found in almost every attempt to quantify the processes, impacts, and probability of innovation initiatives. 

For example, some organizations also use what academics call semi-quantitative measures (we call them pseudo-quantitative ones) like the “innovation Balanced Scorecard” promoted in 2015, and the “Innovation Audit Scorecard,” promoted in 2005. The flaws of these particular methods are explained in How to Measure Anything; they include the following:

  • Ranges of values on scorecards are largely arbitrary;
  • Weighting scores is also arbitrary (i.e. how do you know this component, weighted at 15%, is twice as important as one weighted 7.5% Are those individual values accurate?);
  • Estimates are usually subjective and uncalibrated, even from experts;
  • It’s impossible to perform meaningful mathematical operations with ordinal scales (i.e. is something that is a 4 really twice as effective as something that’s a 2?)
  • They don’t incorporate probabilities of outcomes; and
  • Using one gives you the illusion of improving decision-making, even though doing so may actually introduce error (a concept called the analysis placebo)

McKinsey, to its credit, promotes two quantitative metrics to evaluate effectiveness of R&D expenditures (ratio of R&D spending to new product sales and product-to-margin conversion rate), but even this approach doesn’t speak to whether or not the innovation problem lies within R&D – or if focusing on improving these two metrics is the best action to take

Plus, R&D is only one way a company can innovate, per how we defined the concept above, and it doesn’t exist in a vacuum; it is accompanied by a host of other factors and strategies.

There’s a bigger problem, though, with measuring innovation: even if you come up with “good” metrics, no one tells you which “good” metrics have the most predictive power for your organization. In other words, each variable, each measurement, each bit of information you gather has a certain value. The vast majority of the time, organizations have no idea what the value of these pieces of information are – which leads to them measuring what is easy and simple and intuitive rather than what should be measured.

For example, one common metric that is bandied about is the number of patents created in a certain period of time (i.e. a quarter, or a year). Refer back to the Innovation Time/Impact Square above. More patents increase the chance that you’ll get there first, right? Maybe – but that may not make you more innovative. What if you modeled your creative process and actually estimated the market potential of an idea before you developed and patented it and found that your ideas, as it turns out, have consistently low market potential? Then your problem isn’t “How do we create more ideas?”; it’s “How do we create better ideas?”

It doesn’t matter if you know you need to create ideas that are more timely and more impactful if you can’t measure either. You won’t be able to make the best decisions, which will keep your organization out of the rarified air of the innovators.

The bottom line: focusing on defining innovation for your firm and creating better measurements based on those definitions is the only proven way to improve innovation. 


Learn how to start measuring innovation the right way – and create better outcomes – with our two-hour How to Measure Anything in Innovation webinar. $150 – limited seating.


What the Manhattan Project and James Bond Have in Common – and Why Every Analyst Needs to Know It

monte carlo simulation


  • A powerful quantitative analysis method was created as a result of the Manhattan Project and named for an exotic casino popularized by the James Bond series
  • The tool is the most practical and efficient way of simulating thousands of scenarios and calculating the most likely outcomes
  • Unlike other methods, this tool incorporates randomness that is found in real-world decisions
  • Using this method doesn’t require sophisticated software or advanced training; any organization can learn how to use it

A nuclear physicist, a dashing British spy, and a quantitative analyst walk into a casino. This sounds like the opening of a bad joke, except what all of these people have in common can be used to create better decisions in any field by leveraging the power of probability.

The link in question – that common thread – gets its name from an exotic locale on the Mediterranean, or, specifically, a casino. James Bond visited a venue inspired by it in Casino Royale, a book written by Ian Fleming, who – before he was a best-selling author – served in the British Naval Intelligence Division in World War II. While Fleming was crafting creative plans to steal intel from Nazi Germany, a group of nuclear physicists on the other side of the Atlantic were crafting plans of their own: to unleash the awesome destructive power of nuclear fission and create a war-ending bomb.

Trying to predict the most likely outcome during a theoretical nuclear fission reaction was difficult to say the least, particularly using analog computers. To over-simplify the challenge, scientists had to be able to calculate whether or not the bomb they were building would explode – a calculation that required an integral equation to somehow predict the behavior of atoms in a chain reaction. Mathematicians Stanislaw Ulam and John Von Nuemann, both members of the Manhattan Project, created a way to calculate and model the sum of thousands of variables (achieved by literally placing a small army of smart women in a room and having them run countless calculations). When they wanted to put a name to this method, Ulam recommended the name of the casino where his uncle routinely gambled away large sums of money<fn>Metropolis, N. (1987). The Beginning of the Monte Carlo Method. Los Alamos Science, 125-130. Retrieved from</fn>.

That casino – the one Fleming’s James Bond would popularize and the one where Ulam’s uncle’s gambling addiction took hold – was in Monte Carlo, and thus the Monte Carlo simulation was born.

Now, the Monte Carlo simulation is one of the most powerful tools a quantitative analyst can use when incorporating the power of probabilistic thinking into decision models.

How a Monte Carlo Simulation Works – and Why We Need It To

In making decisions – from how to make a fission bomb to figuring out a wager in a table game in a casino – uncertainty abounds. Uncertainty abounds because, put simply, a lot of different things can happen. There can be almost-countless scenarios for each decision, and the more variables and measurements are involved, the more complicated the calculations become to try and figure out what’s most likely to happen.

If you can reduce possible outcomes to a range of probabilities, you can make better decisions in theory. The problem is, doing so is very difficult without the right tools. The Monte Carlo simulation was designed to address that problem and provide a way to calculate the probability of thousands of potential outcomes through sheer brute force.

Doug Hubbard provides a scenario in The Failure of Risk Management that explains how a Monte Carlo simulation works and can be applied to a business case (in this context, figuring out the ROI of a new piece of equipment). Assume that you’re a manager considering the potential value of a new widget-making machine. You perform a basic cost-benefit analysis and estimate that the new machine will make one million widgets, delivering $2 of profit per unit. The machine can make up to 1.25 million, but you’re being conservative and think it’ll operate at 80% capacity on average. We don’t know the exact amount of demand. We could be off by as much as 750,000 widgets per year, above or below.

We can conceptualize the uncertainty we have like so:

  • Demand: 250,000 to 1.75 million widgets per year
  • Profit per widget: $1.50 to $2.50

We’ll say these numbers fall into a 90% confidence interval with a normal distribution. There are a lot of possible outcomes, to put it mildly (and this is a pretty simple business case). Which are the most likely? In the book, Doug used an MC simulation to run 10,000 simulations – or 10,000 scenarios – and tallied the results for each (with each scenario representing some combination of demand and profit per widget to create a loss or gain). The results are described by two figures: a histogram of outcomes (figure 1) and a cumulative probability chart (figure 2)<fn>Hubbard, D. W. (2009). The failure of risk management: Why its broken and how to fix it. Hoboken, NJ: J. Wiley & Sons.</fn>:

Figure 1: Histogram of Outcomes

Figure 2: Cumulative Probability Chart

You, the manager, would ideally then calculate your risk tolerance and use this data to create a loss exceedance curve, but that’s another story for another day. As Doug explains, using the MC simulation allowed you to gain critical insight that otherwise would’ve been difficult to impossible to obtain:

Without this simulation, it would have been very difficult for anyone other than mathematical savants to assess the risk in probabilistic terms. Imagine how difficult it would be in a more realistically complex situation.

The best way to sum up the diverse benefits of incorporating MC simulations into decision models was written by a group of researchers in an article titled “Why the Monte Carlo method is so important today”:<fn>Kroese DPBrereton TTaimre TBotev ZIWhy the Monte Carlo method is so important todayWiley Interdisciplinary Reviews: Computational Statistics 201466): 386– 392.</fn>

  • Easy and Efficient. Monte Carlo algorithms tend to be simple, flexible, and scalable.
  • Randomness as a Strength. The inherent randomness of the MCM is not only essential for the simulation of real-life random systems, it is also of great benefit for deterministic numerical computation.
  • Insight into Randomness. The MCM has great didactic value as a vehicle for exploring and understanding the behavior of random systems and data. Indeed we feel that an essential ingredient for properly understanding probability and statistics is to actually carry out random experiments on a computer and observe the outcomes of these experiments — that is, to use Monte Carlo simulation.
  • Theoretical Justification. There is a vast (and rapidly growing) body of mathematical and statistical knowledge underpinning Monte Carlo techniques, allowing, for example, precise statements on the accuracy of a given Monte Carlo estimator (for example, square-root convergence) or the efficiency of Monte Carlo algorithms.

Summarized, Monte Carlo simulations are easy to use, not only help you more closely replicate real-life randomness but understand randomness itself, and are backed by scientific research and evidence as to how they make decision models more accurate. We need them to work because any significant real-world decision comes with a staggering amount of uncertainty, complicated by thousands of potential outcomes created by myriad combinations of variables and distributions – all with an eminently-frustrating amount of randomness haphazardly mixed throughout.

How to Best Use a Monte Carlo Simulation

Of course, knowing that an MC simulation tool is important – even necessary – is one thing. Putting it into practice is another.

The bad news is that merely using the tool doesn’t insulate you from a veritable rogue’s gallery of factors that lead to bad decisions, ranging from overconfidence to using uncalibrated subjective estimates, falling victim to logical fallacies, and making use of soft-scoring methods, risk matrices, and other pseudo-quantitative methods that aren’t better than chance and frequently worse.

The good news is that all of those barriers to better decisions can be overcome. Another piece of good news: you don’t need sophisticated software to run a Monte Carlo simulation. You don’t even need specialized training. Many of the clients we train in our quantitative methodology don’t have either. You can actually build a functional MC simulation in native Microsoft Excel. Even a basic version can help by giving you more insight than you know now; by giving you another proven way to glean actionable knowledge from your data.

On its own, though, a MC simulation isn’t enough. The best use of the Monte Carlo method is to incorporate it into a decision model. The best decision models employ proven quantitative methods – including but not limited to Monte Carlo simulations – to follow the process below (figure 3):

decision analysis process

Figure 3: HDR Decision Analysis Process

The outputs of a Monte Carlo simulation are typically shown in that last step, when the model’s outputs can be used to “determine optimal choice,” or, figure out the best thing to do. And again, you don’t need specialized software to produce a working decision model; Microsoft Excel is all you need.

You may not be creating a fearsome weapon, or out-scheming villains at the baccarat table, but your decisions are important enough to make using the best scientific methods available. Incorporate simulations into your model and you’ll make better decisions than you did before – decisions a nuclear physicist or a secret agent would admire.

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Are We Already In a Recession?

recession risk


  • Fears of a recession are rising as experts attempt to predict when a recession will officially occur
  • Forecasting a recession, for most practical purposes, is irrelevant to decision-makers
  • Decision-makers need to ask the right questions that will help them mitigate the risk a recession poses

A Google search of “risk of recession” uncovers a treasure trove of prognostication, hand-wringing, and dire predictions – or sneering dismissals – involving whether or not the U.S. economy will soon take a nosedive.

Global Recession Will Come In 9 Months if Trump Takes This One Step, Morgan Stanley Argues,” from MarketWatch; “Global Recession Risks Are Up, and Central Banks Aren’t Ready,” from The New York Times; “Current U.S. Recession Odds Are the Same as During ‘The Big Short’ Heyday,” from Forbes; all of these articles reflect one of the nation’s most present fears: that the economic wave we’ve ridden for the past 8 years will soon crest, then crash onto the beach.

It’s surely a worrisome time. Even though the economy appears to be going strong – unemployment is still low, credit spreads are stable, etc. – there’s a tremendous amount of uncertainty when it comes to what the economy will do. If we knew when the recession would hit, we’d be able to do something about it, although “do something” is vague and means different things for different people and, frankly, we as a nation aren’t particularly good at knowing what that “something” is, let alone doing it.

Throw in the fact that the formal announcement of a recession always lags when the recession actually began, and our need to be able to predict the expected downturn only grows.

But two things are very possible, maybe even probable:

  1. The recession has already begun; and
  2. Asking when the recession will happen is completely irrelevant.

It Doesn’t Matter If We’re In a Recession Right Now

If a time traveler came to you from ten years from now and told you that this day marked the official beginning of the Great Recession Part 2: Judgment Day (or whatever clever name economic historians will bestow on it), would it make a difference?

Probably not, because it would be too late to take actions to avoid the recession, since it’s already here.

But even if the time traveler instead said that the recession would start three months from now, or six months, or 12 months, would that make a difference? Possibly – but it’s also very possible that the economic risks that collectively cause and make up a “recession” have already started impacting your business.

And if you knew that the recession was six months down the road, maybe you put off taking the actions that you need to take today (or needed to take X months ago) in order to mitigate the damage your organization could incur.

No matter how you slice it, asking “When will we be in recession?” or “Are we already in a recession?” is not only mostly irrelevant, but also largely counterproductive because it takes our focus off what we should already be doing: asking the right questions.

Questions we should ask instead are:

  1. What impact will a recession actually have on my organization?
  2. What specific economic risks are most likely for me?
  3. When would these risks start impacting my organization? Can I tell if they already have?
  4. What can I do today to mitigate these risks as much as possible?

All of these questions are completely independent and not reliant on knowing when a recession will happen. Remember, what constitutes a recession is completely arbitrary. It’s also one broad term for dozens of individual risks that tend to happen in clusters during recession periods but may all begin or end at wildly different times, and have different severity.

Developing answers to the above questions is far more productive than trying to discern when the recession will happen by reading news articles, watching percentages go up and down on TV, or hiring shamans to study chicken entrails. If you can find those answers, you’ll be far ahead of the curve and increase your chances of being in the minority of organizations that not only weathers economic downturns, but actually grows during them.

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Going Beyond the Usual Suspects in Commercial Real Estate Modeling: Finding Better Variables and Methods

commercial real estate modeling


  • 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</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 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:

  1. Not all measurements matter equally, and some don’t matter at all.
  2. 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.

measurement inversion examplesFigure 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.)

solving measurement inversion problemFigure 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.


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