Commercial real estate is an inherently competitive industry.

Brokers – sales, leasing, debt and otherwise – earn their living through commissions.

Even where brokers work as part of a team, they’re very much incentivized to make their own success within that team.

Deals are increasingly being made off-market. Therefore, in order for a broker to be truly successful, they need to be more proactive in identifying and analyzing their own opportunities.

Easier said than done, right?

That’s where “big data,” predictive analytics, and property intelligence come into play.

In this article, we take a look at how commercial real estate brokers are using predictive analytics to bring their business to new levels.

Winning in CRE with Predictive Analytics

“Predictive analytics” might sound like a term that only certain IT professionals would understand.

But truth be told, this buzzword simply refers to analyzing massive data sets (“big data”) to spot trends and predict future outcomes.

IBM offers a straightforward definition:

“Predictive analytics brings together advanced analytics capabilities spanning ad-hoc statistical analytics, predictive modeling, data mining, text analytics, optimization, real-time scoring and machine learning.

These tools help organizations discover patterns in data and go beyond knowing what has happened to anticipating what is likely to happen next.”

In commercial real estate, predictive analytics can include:

  • Demographic trends
  • Housing trends
  • Rental trends
  • Sales trends
  • Consumer spending trends
  • And really anything that effects how commercial properties are transacted and interacted with.

Predictive analytics can either be on a market-level, or individual property-level.

Market-Level Analytics

For one, predictive analytics can help you understand how markets and asset types are likely to trend in the future.

This could mean sales volume, sales prices, and so on.

It also helps investors hone in on where to invest, how much to invest, the types of asset classes to invest in, etc.

Predictive analytics can help investors, brokers and other service providers identify areas of risk (and opportunity) sooner and with more accuracy than their competitors.

Property-Level Analytics

Predictive analytics can also be used, however, to understand where a single asset and its owner will trend in the future.

What does that mean?

A broker can use, say a property intelligence tool like Reonomy to find the predictive analytics they need to identify whether an owner is likely to sell their property or not.

How to Use Predictive Analytics to Make Your Own Success

Commercial real estate professionals can use predictive analytics in countless ways.

We take a look at a few specific examples of how to use predictive analytics to grow your CRE business.

Finding Properties Likely to Sell

They key for brokers is to get in front of potential sellers before anyone else.

Reonomy

Reonomy’s property intelligence platform allows you go off-market to analyze multifamily or commercial properties and owners to see if they’re likely to sell.

By arming CRE pros with sales history, debt history, and information on full owner portfolios, you can analyze an owner’s history and intentions.

This can be done by analyzing property sales history and debt history, along with an owner’s portfolio (to see trends specific to that owner, if there are any).

You can also access a list of comparable properties to get a better idea of how similar assets are being transacted, and at what rate.

Reonomy Property Likely to Sell

ProspectNow

ProspectNow is another platform that aggregates data to provide insight as to when a property is likely to sell.

The website does so by looking at the characteristics of properties that have recently sold, and then uses those metrics to help determine the likelihood of other properties selling within the next year.

The platform can do this for both residential and commercial properties, making it a great resource for brokers looking to connect with rental property owners off-market.

There are other ways to find properties likely to sell. There are three primary indicators that could cause an investor to sell:

New Construction: If there’s been an influx of new product coming to market, an owner may sense the need to upgrade their own property (or lower rents) in order to remain competitive.

Those who are not interested in doing so may be interested in selling – or alternatively, refinancing.

Area Sales: Using predictive analytics, brokers can closely monitor the sales history for commercial real estate – both locally, regionally, and at the national level.

A broker can use this information to pitch an owner to take advantage of a particularly hot market.

For example, a well-informed broker may approach the owner of a Class C warehouse facility and explain the opportunities to sell the property to a value-add investor who wants to capitalize on the proliferation of “last mile” warehouse facilities needed by companies like Amazon.

Occupancy Trends: Shifts in occupancy are an early indicator that a commercial property may be ripe for sale.

For example, if a broker notes that retail occupancy is on the decline, he may approach the owner of a retail strip center to discuss selling the asset before conditions worsen.

Identifying Properties in Need of Refinancing

Let’s continue down the path of new construction, for a minute.

Reonomy

Again, you can rely on Reonomy property intelligence to show you properties likely to refinance in the near future.

By seeing the most recent mortgage origination date on a property, or a mortgage maturity date (plus any combo of additional filters), lenders and loan originators can glean whether an owner is likely looking for some extra cash on-hand.

Searching Off Market Properties in Need of Refinancing on the Reonomy Platform

A broker could then approach the existing CRE owners directly to discuss their options.

For example:

One option could be for the current CRE owner to refinance into a lower rate.

Facing competition from new construction, the owner may feel the need to lower their rental rates.

By refinancing into a lower rate, the owner can save money each month, ideally covering the cost (or more) than the cost of lower rental rates.

A second, alternative approach: instead of refinancing into a lower rate, the owner might want to pursue a cash-out refi.

A cash-out refi could help an owner invest in property improvements to remain competitive with the new construction coming online.

DataTree

DataTree is another aggregator of big data and offers many tools for CRE professionals looking for properties in need of refinancing.

The website creates property “finance prediction scores,” which includes the Refi Intel Score.

The “Refi Intel Score” allows mortgage originators to market directly to prospects or current customers who are most likely to refinance in the next three to four months.

These scores can be tailored by loan program, including scores for FHA loans, conventional loans, and cash-out refinancing.

DataTree Screenshot

Finding Properties in Need of Renovation

People often talk about the ways in which real estate brokers and loan officers can use predictive analytics to grow their business.

But other CRE service providers can use predictive analytics to their benefit, as well.

Let’s consider the case of a commercial contracting company:

This general contractor specializes in renovating 50 to 200-unit apartment buildings.

The contractor can use a property intelligence platform like Reonomy to identify properties in need of renovation.

Using Reonomy, the general contractor can search for multifamily properties with at least 50 units.

They can then filter the results to show only those that have not been renovated in a certain period of time (say, 50 years) and that have not sold for some time (say, 20 years).

If the properties have been owned by the same person for two decades, they’ve likely built up substantial equity in the property and could leverage that equity to bring the property into a more modern condition.

This data is especially helpful when combined with other predictive analytics.

That same general contractor can refine their outreach by looking at other metrics, such as population growth, rent growth and inventory.

Let’s say he’s based in the Boston area, where population growth and rent growth both continue to rise.

While there has been an influx of new construction, there is still not enough inventory to keep pace with demand.

As such, vacancy rates for multifamily buildings remain at historic lows. In this case, the general contractor might realize that property owners with Class C multifamily buildings in well-located areas might not be motivated to renovate at this time.

Given market trends, the owner can continue to push rents without investing in improvements.

Instead, the general contractor might want to look at some of Boston’s peripheral towns where multifamily occupancy rates are a bit lower than the downtown core.

Identifying Properties in Need of New (or Better) Management

Predictive analytics can also be used by property management companies, or owner seeking efficiencies in how their properties are managed.

CRE tech blogger Michael Beckerman notes that predictive analytics will “forecast future maintenance issues, use real time data and statistical modeling to reduce energy management costs and respond to tenants’ needs before they actually surface.”

A property management company, for example, can look to see how long it takes for a property to re-lease upon vacancy.

Let’s say data indicate that in Buffalo, NY it takes approximately $2,500 to make a unit “rent ready” and then 15 days to fill a vacant apartment.

A property manager can use this information to their advantage when pitching to potential customers.

If that property manager has a track record of turning units faster for less money, this could result in a lucrative new contract for their company.

Service providers can also use predictive analytics to grow their business. Let’s say trends indicate that most commercial properties in Denver, Colorado rely on natural gas.

A commercial solar installer sees that the price for natural gas is increasing compared to the cost of solar infrastructure.

A simple cost-benefit analysis can inform whether a property owner would save money by investing in solar – cost savings that can then be passed on to tenants when signing NNN leases.

Predictive Analytics for CRE Investors

One thing that many people overlook is that commercial real estate is not valued based on the value of the land or building alone, but rather, on the income-earning potential of that property.

For example, at first glance, a 1950’s era roadside motel might not drum up much fanfare among CRE investors.

However, someone who looks at the underlying data may realize that the property sits on 20-acres.

The parcel is in an area subject to a mixed-use overlay district, which allows for denser development on the site – including office, retail and residential uses.

While the land, as is, and motel might only be worth $750,000, a savvy real estate investor might be willing to pay double or triple that amount (or more) if they see this as an opportunity to redevelop the property into a mixed-use project.

But will a mixed-use project in this area be viable? Here’s another example of why predictive analytics can be so powerful.

Predictive analytics can help CRE investors make investment decisions based on where the market will be in the next 5 to 10 years.

They can anticipate whether property values will increase, decrease, or remain the same. They can tell us occupancy rates and rental trends.

Let’s say the project has a restaurant component to it. We can even look at consumer spending and consumer habits to learn more about how locals spend their dollars.

Where do people eat out?

Are fast food chains, fast casual, sit-down restaurants or fine dining establishments most popular?

How often do people go out to eat, how long do they stay at the establishment when they do, and how much do they spend while there?

This information is highly valuable for a CRE investor looking to make decisions about the type of restaurant space to incorporate into their property, and how to approach leasing based on local trends.

Another example: predictive analytics indicate that a suburb of Los Angeles is rapidly expanding. There are three new light rail stops, with office buildings springing up along the new rail corridor.

Despite a recent boom in commercial construction, residential construction has been limited. Therefore, constrained residential supply has pushed prices upward.

A discerning real estate investor would identify this as an opportunity, and would start approaching land owners off-market to see about buying property that can be redeveloped for denser multifamily development.

Using Data to Land CRE Deals

The data provided through predictive analytics is only so valuable, unto itself. The real power of predictive analytics is using this data to source off-market leads that convert to deals.

This is how you grow your CRE business!

Once you’ve found properties of interest, use Reonomy to uncover the property owner’s information – including their name, phone number(s) and email address.

Be sure to reach out and highlight your industry knowledge. And don’t wait! Get a head start on your competition.

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