AI, Tech Platforms, and Shiny Objects

AI, Tech Platforms, and Shiny Objects

AI (artificial intelligence) does work across dozens of industries. Just ask Amazon, Starbucks, Target, and Walmart. What do those four have in common? Years of operational data, the money to invest in such an initiative, and experts in the discipline who know how to balance the science and the art of that discipline.

So is real estate different? Well, it depends on the individual aspect of the business under analysis, but it still starts with people, money and time. It also requires the same discipline to balance science and art, and to avoid chasing shiny objects just because they’re, well, shiny. Worse yet, some of those shiny objects could be classified as “me too” solutions, including recently publicized tech platforms being rolled out by so many brokers and franchisors.

AI is such an overused term these days since it’s often mistaken for data analytics and there are some good data analytics solutions out there. However, none other than Robert Reffkin told the assembled audience at Inman Connect in NYC this past January (at the 9:20 mark) that “I hope you don’t think that data analytics is going to help you make more money.” While that may have been a broad brush comment on his part, data analytics does work in real estate – but one still needs people, money and time.

But what comes after the data?

It’s one thing to have all that data but one needs to know what to do with it and one also needs to know the industry at the LOCAL market level. There are similarities across various markets throughout the country but anyone who thinks that market dynamics are basically the same in Silicon Valley, Phoenix, Dallas, Washington, D.C., and New Jersey is either new to the business or uninformed about local markets, or both.

Some people back at headquarters may be great data crunchers but without a thorough understanding of each market’s nuances, they could go down a rabbit hole and waste valuable time. One size does not fit all and it takes longer than a lot of people realize for data analytics initiatives to pay off in this business. For example, the need for knowledgeable managers in local markets becomes greater to validate and act on the data analysis by the people back at headquarters. And that also takes time.

Starting at the top and working down

Let’s start with Realogy and Zillow. If you’re Realogy, your primary business is either a company-owned brokerage operation or a franchisor. If you’re Zillow, it’s selling leads and advertising to agents and brokers with an emerging iBuyer business.

Realogy and Zillow have a decided advantage over others in the data arena. As Ryan Schneider said in that same ICNY video, Realogy has TWENTY years worth of data at its fingertips, and we all know that Zillow wouldn’t be as successful in pricing zipcode-based leads without the mountains of data available to it. Both of these companies are years ahead of the competition with respect to the amount of data that they have access to.

Both have a nationwide footprint and access to all that data with one large similarity and one large difference:

  • They both have access to many years’ worth of real estate transactions across the country and relationships with tens of thousands of agents.
  • Realogy has access to granular agent compensation data on a huge scale and Zillow does not. Zillow has access to agent buying habits on a massive scale and Realogy does not. But those differences are also what drive their respective business models and revenue.

What does this mean to franchisors?

This type of activity also applies to firms that are exclusively in the franchising business but still have a large geographic footprint.

Franchisors, specifically RE/Max & Keller Williams, have to convince their franchisees of the value associated with the data and the franchisor’s analysis of said data – easier said than done. Before that happens, the analysis needs to be tested in enough markets to establish not just the methodology but also the value, both intrinsic and perceived.  Again, it depends on which markets are parts of that test. And again, that takes time in addition to people and money.

They also need a (new and improved?) tech platform for presentation of some of the data analytics to their respective customer bases. More people, money and time.

What about brokers?

Few brokers have the available resources to commit to a wide-ranging initiative. Regional brokers, say, in the top twenty of the Real Trends 500, probably have the data necessary to do some number crunching specific to their individual markets and interests. But the smarter ones have been crunching data already, albeit under other banners (e.g., commission split analysis, company dollar analysis, agent productivity, office productivity).

What about old and new business models?

Discounters historically have garnered at most a high single-digit market share. The analysis for that model has been rehashed more than a few times and still comes out the same – one doesn’t need to see that tree fall in the forest to know what sound it makes when it hits the ground. People and money are the big hurdles for these brokers.

The iBuyer model relies heavily on Zestimate 2.0. Regardless of the source or methodology, it still requires a large investment of people, money and time. But as many others have pointed out, that’s only the starting point. The path down that road is where Zillow has a huge near-term advantage over Opendoor and other entrants in that marketplace, an advantage that they have built up over the past 12 years. People, money and especially time are required, even if you’re one of the industry’s unicorns.

So is Robert Reffkin right? Is the ROI really there for such a data analytics initiative along with the tech platform when it comes to broker/agent adoption? Are these brokers and franchisors really looking to increase market share in the near term for their own individual financial interests or is this just another example of the shiny object syndrome so prevalent in this business?

There is a third business model in this mix – the self-described “tech-enabled brokerage” – two of whom are Redfin and Compass. That in turn raises the following questions:

  • Why is Redfin pouring so much money in 2019 into classic real estate broker marketing?
  • Why is Compass acquiring other brokers left and right?
  • And how do these two situations square with both companies’ professed allegiance to tech platforms in general as meaningful competitive advantages in this business?

The people, money and time being invested by these two companies in tech platforms is astounding but are they really building better mousetraps? Or have they become dazzled by a couple shiny objects and mistaken them for better mousetraps?

Summing up

So is it really artificial intelligence at work in the real estate industry or is it really just good old fashioned data analytics? Fifteen years ago, we called it data analysis and that process hasn’t changed much. Will it make a difference at Realogy now under Ryan Schneider. That remains to be seen.

Was Robert Reffkin correct back in January about the value of data analytics? Ask Zillow, Opendoor and Realogy – they would disagree with him.

An old boss once shared an adage with me about the finite resources of people, money and time in the business world: “Talented people are valuable but still fungible to a large degree, especially in technology; money is valuable but always fungible; time is as valuable as the other two but it’s not fungible.”

Without the advantage of time, the odds are not that great for KW and RE/Max to achieve the needed adoption rates to make much of a difference with their respective tech platform projects. Without the advantage of time, Compass and Redfin also face huge hurdles in catching up to the leaders in their market.  And there lies the real danger of the shiny object syndrome when it comes to topics such as data analytics and tech platforms in real estate – chasing that shiny object eats up valuable time regardless of how much money or people one has.

[Image via https://steemit.com/]

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