10 ways data analytics reduces CRE risk

Data analytics is yet another new age buzzword that agitates eye rolling and yawns. Our belief: it has been unnecessarily complicated. The principles are intuitive and probably in use by you already. This piece simply empowers you with words and examples. Read on, and realise that you’re actually a data analytics champion now.
Data analytics commercial real estate

Data analytics, along with Blockchain, AI, IoT and ML, is one of the dreaded buzzwords.

It has been positioned as an elitist, mysterious marvel. This positioning is incorrect!

Data analytics is used subconsciously by you, dear reader, every day. Because of this, it is actually understandable, accessible and relevant. And, when used in business, it is the “secret sauce” that helps us make more money, and fail less.

Risk can have many forms – but it is linked, in its heart, to two levers we hold dear: greed and fear.

Here we tie fear – or, more accurately, our desire to manage risk – to data analytics.

And further, to make this really live, we bind data analytics to commercial real estate (CRE).

What is data analytics

Example

To explain data analytics, we’re going to lean on an accessible, real life example, powered by the world’s oldest and most sophisticated computing machine (i.e. your brain).

Data analytics is a two-step process, with the steps best summarised into two roles.

The “Miss Moneypenny” (step 1) world of data analytics is the “low glamour” bedrock of the discipline: data collection, organisation, and storage.

The more glamorous analysis and translation piece (step 2) is better understood as the “James Bond” of data analytics.

And, as we know, James Bond cannot succeed without Miss Moneypenny.

The unexciting “Miss Moneypenny” processes that form the bedrock of the discipline (data collection, organisation and storage) are easily understood. (Incidentally, the human brain handles this deceptively complex function exceedingly well.)

The glamorous “analyse and translate” James Bond role is less accessible, so it is unpacked as follows.

Data analytics example

As a committed first year varsity student, you wake up on a Sunday morning with a brand new, pounding headache.

Your brain automatically starts working through the four, sequential analysis and translation steps of data analytics.

1. Descriptive analytics

It kicks off by running descriptive analytics (i.e. what) on its various databases. As a result, it mines out the following gold: “I have various pains (but the greatest is located between my ears), as a result of a, somewhat bewildering, but impressively varied range of actions starting yesterday and ending about 4 hours ago”

2. Diagnostic analytics

Your (sore) brain moves on to diagnostic analytics (i.e. why): “Is it malaria? (No, I was in the bush over 3 weeks ago). Is it sport related? (No formal contact sport yesterday). Booze? (Jackpot). But why the intensity? Mixed drinks (check), no water between drinks (check), started dehydrated and on an empty stomach (check, check), felt it necessary to smoke cigarettes (urgh, check), went to bed late so limited sleep (check).”

3. Predictive analytics

Fast forward a few years. Hopefully, after collecting and storing sufficient structured data samples (aka benders), you start moving into the world of predictive analytics (i.e. what is likely to happen). For example, every time I have this combination of activities, involving this selection of people, in this location, and a commencement state, it is likely (95% probability) that I will be awoken by a jackhammer pounding my brain.

4. Prescriptive analytics

Prescriptive analytics (i.e. what action to take) to prevent this recurring: you clearly define an algorithm to your saintly partner (i.e. when these specific variables interact, an action should happen). Your request may be to kindly trigger you with an exit suggestion, when circumstances are such that high-risk stimuli are in play, while you are still receptive to rational decision-making. (E.g. Mike, your soon-to-be-28th-best-mate, is bringing out the tequila again, now is a good time for us to exit).

To all readers, now feeling like highly accomplished data analytics experts out there, well done. And yes, only now will it dawn on your parents that your behaviour, more fairly termed “field research”, was always fueled by a deep love of data science.

Going back to relevance, this process is not only used for booze. If we are fortunate to be a reasonably competent adult, we use this data analytics-driven learning loop throughout our life, and in all aspects of our life – from relationships through to business.

Housekeeping

Now is when we fuse together the concepts of risk (remember the fear and greed), and data analytics, and apply them to real estate.

For purposes of simplicity, we’re going to focus on a subset of our noble and beloved asset class – commercial real estate (CRE).

Here we call out 10 ways in which data analytics helps reduce risk in commercial real estate, unpacked against four select industry verticals, namely: brokers (sections 1-2), property funds/owners (sections 3-6), debt funders/banks (sections 7-8) and insurers (sections 9-10).

Data analytics commercial property

Data analytics for commercial real estate brokers / advisors

1. Deal risk

Remember the significant financial pain caused by the failure  of a lost “dead-cert” deal? How does data analytics help reduce this risk?

First, the Miss Moneypenny bedrock stuff… All vacancies data needs to be collected. Next, the data needs to be accurate, standardised and stored in a highly structured and precise format so that it is search friendly.

For example, vacancies data:

  • must comprise units linked to properties, short-expiring leases by property;
  • these properties need to be bound to geographies/co-ordinates;
  • units must contain meta data such as combinable and sub-divisible attributes, category attributes, availability dates, tenant incentives, expenses and parking information.

Now for the glamorous James Bond part…

Enter search criteria (dates, locations, minimum and maximum values) into a sophisticated engine so as to identify those results matching your criteria.

Filter out properties from the universe of options, and output the info into a tenant-friendly brochure/report (linking back to a website), specifying decision-relevant property attributes.

This complimentary “dream team” of roles, working in tandem, means you don’t fail to identify the perfect sub-divisible property located in an overlooked suburb deep in your target area. Result – your client thinks you are amazing, and you also do the deal.

Your competition does not. And wonders how you did.

2. Competitive and reputational risks

Time is money, and you are not operating in a competitive vacuum. Your clients expect this entire analysis and translation process to happen in seconds, not hours. And your data needs to be reliable: incomplete or inaccurate data wastes time, and takes a scalpel to your reputation.

So, if you want to succeed, tech-driven data analytics is the only solution.

Hopefully it is now clear that the easily overlooked data analysis “bedrock” of collection, organisation and storage is non-negotiable. Without it, there is no ticket to proceed to the James Bond world of analysis and translation.

Data analytics for property funds

3.  Pricing risk and investment risk

As the saying goes, “junk in, junk out”. Or, the converse: high quality data equals high quality decisions.

A decision about any new lease or lease renewal is principally a data-driven decision, leaning on data analysis and translation. If you, wearing the hat of landlord, position your vacancies or lease renewals above market value (greed), you stand to either miss or lose high quality tenants, or go for periods when the space remains vacant and you lose income. Under-price your product (fear), and your widows-and-orphans shareholders get hurt, while tenants pay below market rentals.

The Miss Moneypenny role of data analytics ensures that a complete, accurate and well-structured population of relevant information is available. The James Bond solutions empower decision-makers to make data-driven decisions, quickly.

4. Compliance risk

Compliance is boring, and so is how you go about preventing this risk…

A  powerful antidote to this boredom: the consequences of acting negligently or recklessly when reporting wrong information to shareholders and regulatory bodies.

Risk mitigation here talks to the low glamour Miss Moneypenny of data analytics: the collection, organisation and storage piece.

Property funds face two issues:

Firstly, their data is  not recorded completely and accurately at point of capture. This means that critical bits of missing information are not collected, organised and stored at inception.

Examples of this info include:

  • tenant lease deal incentives;
  • relationship tracking of tenants from previously-leased space to new space;
  • tenant “real world” unique identifiers;
  • tenant relationships to holding company entities;
  • and key decision makers in lease transaction and actions and processes.

By the time you want to report on this data, it’s too late and your ability to report is hamstrung. “Band-Aid” solutions simply dial up risk. Without recording correctly at point of capture, you’re playing catchup in a race that you have already lost.

The second issue is the risk-exacerbating violation of best practices:

  • your data is siloed with multiple versions of the same data, and is not combined into in a single, reporting-friendly analysis environment, like a data warehouse.;
  • such data is not visible to operational personnel (result: issues only get identified in the time-compressed reporting window);
  • data is not assigned “real world” unique identifiers (e.g. tenants are not assigned to unique identifiers, meaning an increased risk of data duplication. This means that tenant data can’t be identified between silos, tenant data can’t be easily enriched, and identifying trends or stories in tenant data is difficult;
  • “Golden record” principles and supporting workflows/processes don’t exist.

Data silos (sometimes-duplicated clusters of data used by various divisions / in various processes) are a major issue for property funds, which often have masses of data spread across numerous, disparate systems.

Data collected, organised and stored correctly ensures that the right data can be reported timeously, and that processes to correct data early on can be implemented.

5. Continuity / operations risk

What happens when key individuals move on? When valuable, proprietary information lives in human brains, instead of being stored digitally, in structured systems?

Data assets cannot be backed up, “data debt” is created, and unhealthy human dependencies arise.

6. Profitability risk

Two drivers here. Firstly, data is not stored correctly at point of capture. Secondly, data reporting is manual (instead of managed by automated, efficient processes). Therefore, the problem has to be “Band-Aided”.

The problem: missing data analytics best practices – the Miss Moneypenny correct data collection, organisation and storage, and James Bond analysis and translation solutions.

The sub-par solution: a non-scaling “Band-Aid” of employing highly skilled, expensive, high-opportunity-cost-of-time staff, working in near perpetual motion, doing largely high-risk “hands work”.

Ignoring the opportunity cost of poor utilisation of highly skilled humans (these humans are expensive!), the “Band-Aid” solution of employing humans is more expensive than the maintenance of a well-structured data analytics solution.

This difference in costs results in reduction in profits. The effect: reduced distributions.

Data analytics risk debt funders

Data analytics for debt funders / banks

7. Credit risk / timing risk

In  commercial real estate, unlike residential property, individual asset values are bigger. Portfolio valuation issues don’t “come out in the wash”, and aren’t solved by the laws of large numbers. Debt funders need to know the value of individual assets. And debt funders need this data sooner rather than later.

Two examples of this discipline enabling more proactive credit risk management / reducing delays in action are laid out below.

Data analytics example A

In a loan to value (LTV) ratio, assuming all funding comes from a single source (and assuming no cross-collateralisations), the debt funder can be certain of the debt amount.

The property value, however, is prone to variation. Borrowers, in terms of their funding terms and conditions, can be required to submit data to their lenders on an annual or as-required basis. In certain instances, such submissions may not coincide with financial reporting periods, or worse, arrive too late…

Factors that can hurt property values can be categorised into two groups:

Group 1

Data points that are internal to the property. Such data can be further subcategorised:

  • public domain data such as property vacancies, loss of key tenants, and the property’s general condition;
  • proprietary data such as changes in tenant credit risk/covenant quality, reduction in maintenance, unfavourable lease renewal terms, reducing trading densities.

Group 2

External factors – i.e. generally, public domain data

Macro nodal negatives – such as increased vacancies, declining property rentals, changing uses and nodal regulations – are one example. These factors can and will reduce the value of all properties located within a node. Another example would be a key retailer, facing profitability or insolvency challenges, hurting various customer portfolios values.

Provided data is collected, stored and organised correctly, the foundations are in place for decision-relevant, proactive analysis and translation interventions. For example, borrowers are bound to legal entities, which in turn are categorised into parent entities, property assets are geolocated to enable hyper-accurate geospatial analytics, property category composition is accurate, tenants are uniquely identified and bound to portfolios, etc.

Descriptive and predictive data analytics builds on these foundations. By presenting lenders with user-friendly dashboards, both internal and external property value issues can be revealed. Such data enables lenders, in near real time, to identify exposure to, and mitigate, credit risk.

Ignoring the foregoing preventative measures, such data, delivered at lower cost and greater frequency than traditional property valuations, can also assist lenders in impairing loan assets.

Data analytics example B

The ability to call out a property’s true (over-)indebtedness and identify high risk borrowers using an expanded view of their debt exposure.

In less information-perfect markets, it is possible for borrowers to secure debt against a property from various sources. This action increases the sum of loans against a property’s value. In certain instances, existing borrowers may not be aware/notified of such additional gearing.

While seniority of debt reduces risk to primary funders, it can constrain cash availability for a borrower. Such cash constraints can divert funds from critical repairs and maintenance expenditure and incentivise the appointment of lower-quality service providers. This “death spiral” can result in reduced property values.

Furthermore, over-indebted borrowers will likely have a shorter loan duration and will generally attract higher client management costs.

A property portfolio is not a static asset. Property owners are free to make gearing decisions with various third parties, on various assets under their control.

When a debt funder has a holistic view of a borrower’s portfolio, by property and loan value, it provides that lender with a view of the borrower’s financial stability.

A borrower with a falling property portfolio value and a higher portfolio level of debt can be tempted to “rob Paul to pay Peter”. Across a distressed portfolio, high quality assets can tend to cross-subsidise lower quality assets. As the profitability of higher quality assets increases, it constrains resources of a property fund’s management team to successfully operate all assets.

When a lender has a view of all property and debt in a borrower’s portfolio, and access to debt and value changes / trends within the portfolio, such a lender has a greater ability to proactively act on risks. Descriptive data analytics surfaces such insights to decision-makers, while predictive and prescriptive analytics mitigate future risk.

8. Competitor risk

In the instance of a high performing property fund, property values should increase over time, and operator risk should decrease.

Assuming palatable LTV ratios are fixed, as property values rise, so too does the value of potential or “unsweated” increases in debt funding.

By way of example: a highly entrepreneurial and hyper-competent property fund purchases US$10bn of distressed assets, funded by interest-only debt at an LTV of 50%, 3 years ago. That property fund has actively managed those assets, and the value is now US$15bn. At a 50% LTV, on the new values, the debt funder can safely increase funding by US$2.5bn.

Where the debt funder fails to step in, others can, and will.

Data analytics can identify these patterns, unveil borrowers with a higher propensity to borrow, and equip prospective lenders with the data to approach such prospective borrowers.

While this is not risk related, savvy debt funders can extend this model. Given the correct data, one can identify financing opportunities in competitor portfolios, and act on these.

Further, as data availability matures, it becomes possible to understand how active the market is, identify loan transactions by competitors, understand the terms, and actively counter moves made by competing debt funders.

Additionally, referencing the points regarding improved data availability in section 7 above, and applying them to competitor risk, the benefit of high quality, decision-relevant data is transaction speed, and reduced friction. When banks have access to complete, standardised, high quality (up to date, accurate) borrower information, such data speeds up the credit process, allowing that bank’s clients to get money quicker, and enable banks to disburse funds faster.

Data analytics insurance commercial property

Data analytics for insurers

9. Pricing risk, competitor risk

Here’s an example: red sports cars are expensive to insure. But a red sports car driven by a 25-year old female accountant with a clean driving history is significantly better risk than the average. Further, a white family saloon, driven by a 25-year old male with a criminal record, carries more risk than the red sports car.

The point of this? Making risk calls on high level data, rather than granular, risk-specific data will always create risk mispricing.

A data-driven approach to CRE risk empowers insurers to price risk more accurately and avoid the spectre of good risk subsidising bad risk.

As always, it starts with the role of Miss Moneypenny – how data is collected, organised and stored.

In processing the following non-exclusive list of fields (and ignoring office, industrial and retail), a property insurer is able to evaluate the risk of a generic property. This means not outpricing the red sports car driving accountant, and correctly evaluating the white family saloon driving ex-convict’s risk.

Applying algorithm-driven predictive and prescriptive analytics enables risk to be more accurately priced. Good risks get good pricing and are attracted. Higher risks are accorded higher pricing. Assuming policy holder decision makers are rational, these (unattractive) policies will flow towards those businesses who price based on the white family saloon data alone.

10. Financial risk

Insurers win when claims are prevented, and policy holders win when policies are cheaper. In this regard, both parties’ objectives are aligned. Data analytics can be activated to incentivise policy holders to reduce their risk, and encourage risk-reducing behaviours.

Examples of positive Miss Moneypenny-style data analytics interventions:

  • data collection, organisation and storage;
  • risk-relevant property data is captured / updated by the policy holder into a common, specific data environment;
  • IoT is used, independently of policy holder’s actions, to harvest data identifying high risk behaviour, which is entered into the above data environment;
  • based on the inputted data, the James Bond data analytics interventions can now play a starring role

Predictive analytics: “As a result of the data we have available, your risk has reduced, your policy now falls within tier X, and you pay a reduced premium”

Prescriptive analytics: “Your next steps to further reduce  your policy risk, ordered by ease of execution, and based on what our claims data is telling us, will be to take the following actions”

This level of proactive, data-driven reporting is a direct benefit of high-quality data analytics.

Data analytics commercial real estate

Because data is the oil that CRE runs on, the industry provides a highly fertile environment for data analytics to reduce risk – and increase revenues.

All of the above feels like a when-not-if…so why the delay? Why is the real estate industry not benefitting, now, from these solutions?

Firstly, CRE is exceedingly complicated: assets are not homogenous, and industry players have not been gifted with the protocols and tools to standardise how their data is stored and structured. Various verticals within the CRE industry own and manage data uniquely. The domain is deceptively complex, and insiders and outsiders alike often don’t-know-what-they-don’t-know.

Interventions by industry outsiders have followed a try and fail pattern, burning the fingers of passionate innovator/early adopter project sponsors inside of these CRE organisations. As those who have paid for this lesson have found out, competencies for engineers skilled in banking, insurance, FCMG and logistics industries are not automatically transferable to CRE. “Once bitten, twice shy” – it is understandable that decision-makers are reluctant to engage in high-risk, career-limiting behaviour.

Secondly, everyone assumes data analytics is a silver bullet. However, it is a two-step process – with the first step being time-consuming, expensive and slow to deliver the impactful, action-packed James Bond second step insights.

The first step involves the Miss Moneypenny disciplines of data being collected at source correctly, organised appropriately and stored right. Such processes are deceptively complicated, highly unglamorous and oftentimes more expensive and time-consuming than is obvious from the outside-in.

What happens: the “hard yards” work is skipped or constrained, in a quest for the exciting wins. Unfortunately, the biblical parable of the builders comes to mind – those who build their houses on sand fail, while those who build on solid rock succeed.

Thirdly, “If it ain’t broke, why fix it?”

While the solutions are in a relatively immature stage of creation, the benefits of data analytics (while growing) are neither obvious, nor significant. At current levels of solutions maturity, the cost-risk-return model is unattractive. As is standard with all tech adoption lifecycles, only when the benefits are undeniable, and the innovation is mainstreamed, will we see data analytics playing front and centre in CRE.

The three points above makes the US$29tn commercial real estate industry one of data analytics’ last frontiers. This fusion of new age technology with hard-won CRE experience is pioneering, and tremendously exciting.

James Bond villains carry guns and come in all shapes and sizes. Data analytics villains are the business evils of risk, inefficiency, and lower profits. In both instances, hard work, passion and superior wit will ensure that the heroes defeat the villains.

Hopefully after reading this you feel less intimidated about the discipline, have your beliefs reinforced about where your energies can be well spent, and are inspired to take steps forward in this rewarding and exciting journey.

We wish you good luck!

About the author

Related posts

Covid and commercial real estate
Industry Intel and News
SA CRE and Covid-19: the industry’s experts talk

These are stressful times for South Africa – where we are seeing the very best and worst of human behaviour. A panel of some of SA’s top commercial real estate (CRE) industry professionals gathered on Wednesday 15 April 20 to collaborate and plot a way forward through the weeks and months ahead. This is a summary of the 75 minute session.

Industry Intel and News
Johannesburg blue chip office node performance

An efficient snapshot of South Africa’s office property market as at October 2019 – using, as a proxy for the larger industry, the performance of three blue chip nodes in Johannesburg. We also reveal two new-to-industry metrics: average space density, and performance by owner type.

Leave a Reply

Your email address will not be published. Required fields are marked *

Fill out this field
Fill out this field
Please enter a valid email address.
You need to agree with the terms to proceed

Menu