Real Estate’s New Era: Kirill Mozheykin on Big Data as a Game-Changer

Kirill Mozheykin is a Product Marketing Lead in the PropTech sector who has built and led high-performing marketing teams and launched new products and services throughout his career.

Kirill has been especially impactful in bringing data-driven solutions to the real estate industry, contributing to Kalinka Group’s recognition at the International Property Awards, in London, in 2019, 2021, and 2022.

Kirill, How Would You Describe The Shift You’ve Observed In The Real Estate Landscape, And How Has Data-Driven Decision-Making Played A Part?

Real estate is undergoing a major transformation, and I have been luckier than most to see it evolve from the inside. In the early part of my career, I relied heavily on instincts, personal networks, and conventional market reports, which were useful but not very insightful.

I always sensed that there were some patterns to be found in the market that were simply invisible to us when we were engaged in basic comparative analysis.

When I started to delve into big data and advanced analytics, it was clear right away that using them to make decisions that affect the operation of the company were something much more than just a buzzword. It was a completely new way to look at things.

With the help of historic stock sales data, the kinds of models we operated at Kalinka Group anticipated with a new level of confidence just where the market was headed, undulations and all and gave us real-time insights necessary to put together impending strategic decisions.

How Do You Define “Big Data” In A Real Estate Context, And What Types Of Information Are Most Useful In Your Work?

Real estate’s “big data” is the vast, varied sets of information that can be mined for insights we can act on, in contrast to opinions and a not-so-big sampling of sales. Property listing platforms are good examples of big data, especially the large online portals where you can find thousands of listings.

But the data government agencies publish, detailed property records, for instance, and tax data, add a layer of legal and financial certainty that makes us more confident in our assessments and valuations.

Another good data source is social media and online search trends, since they let us eavesdrop on the conversations people are having and on the search behavior that increasingly alerts us to emerging neighborhoods and the kinds of features buyers today care about, everything from eco-friendly building designs to the kind of walkable shopping districts that make a neighborhood desirable.

But perhaps the most underutilised data source out there is the Internet of Things (IoT), which is generating so many real-time metrics these days, on energy consumption, for instance, or security updates, or even foot traffic in commercial properties, that we really ought to be using all these data points to refine our property assessments in a way that just doesn’t happen with traditional methods.

Could You Explain How You Collect And Integrate Data Effectively So That It Becomes Useful For Your Organisation?

Collecting vast quantities of data is merely the initial step; effective integration is the real secret sauce. Based on my experience, I believe scalable and secure cloud-based systems are crucial. They allow teams to share and collaborate, which is essential when properties are as high-profile and high-stakes as the ones we deal with, at my company, Kalinka Group. Integration becomes much more complex with our next-level CRM systems.

They are crucial for centralising all specification, interaction, and insight data into one place, serving also as a collection point for the kinds of raw data an AVM or basic financial model might also ingest. But I insist that these kinds of systems only work if the data flowing into them is trustworthy.

What Advanced Analytics Techniques Do You Find Most Valuable In Today’s Real Estate Market, And Why?

By itself, data is merely raw material; it is the analysis that converts it into something significant. Two techniques I consider essential are predictive analytics and machine learning. These techniques allow us to model future market conditions, which is priceless when it comes to identifying not just undervalued properties, but also neighborhoods that are about to surge in demand.

Neural networks, and machine learning in general, can discover relationships that might go unnoticed when humans are doing the work manually. They can take into account everything from signals coming from the macroeconomy to local zoning changes in the places being studied and build a kind of forecast that incorporates those signals and might not even be noticeable to human forecasters.

I have used this approach when exploring potential new markets in places like the UAE, Cyprus, Thailand, and Turkey, potential markets where historical data of various types, along with demographic data, has helped us pinpoint certain places as viable options for strategic expansion.

What Are Some Practical Ways These Data-Driven Strategies Can Be Applied In The Day-To-Day Work Of Real Estate Professionals?

Data-driven techniques can be used in many different areas of real estate. One is portfolio optimisation. Here, we run many different scenarios to help clients understand whether they should hold, buy, or sell a property, given a range of quantifiable risks and returns. Another is dynamic pricing. Here, we use real-time data to understand how to price a property so that it doesn’t languish on the market or end up undersold.

Analytics is also crucial for market expansion. It directs investors and developers to growth areas, ensuring that they don’t miss the next emerging hotspot. It helps with customer segmentation and targeted marketing; by examining buyer behavior and demographic data, we’re making our messaging more relevant and risking less in our marketing campaigns.

These are just some of the examples that highlight the industry-wide shift toward data-driven approaches.

Which Technology Tools Have You Adopted To Support Your Data-Driven Initiatives, And What Role Do They Play?

I have worked with many essential technologies that greatly boost the effectiveness of data-dependent strategies. Software for visualisation, such as Tableau, depicts large datasets in an easy-to-read manner, which allows both teams and clients to quickly discern if something is amiss.

Power BI also serves a similar purpose, providing real-time dashboards that inform everyone of the current status of our market or any experimentation we are doing. If something shifts, these tools will often alert us in time to do something about it.

Platforms such as DataRobot have revolutionised predictive analytics because they make it easy to build, test, and iterate predictive models. The old way was to build a model and then test it, followed by an awful lot of iteration. These platforms have replaced that with a model-building routine that’s an awful lot more like Agile and a whole lot less like Waterfall. I also find IBM Watson especially helpful for doing the kinds of analyses I do with luxury properties.

Its natural language processing allows us to do really interesting work with valuation and cutting-edge analytics, linking everything from social media sentiment to news articles about new retail projects and infrastructure to the kind of model you wouldn’t dream of building without it, precisely because these platforms and Watson maintain a balance between innovation and data privacy.

Have You Encountered Any Significant Challenges In Adopting These Technologies, And How Did You Address Them?

The reliability of data flowing into these tools is one of the toughest nuts to crack. Your analytics software can be as cutting-edge as they come, but they aren’t going to produce accurate insights from inputs of poor quality.

My team and I work to get around this by applying rather stringent checks and balances. Each data source we use is scrutinised and passed along several gates to confirm that it is consistent, complete, and recent.

Another obstacle concerns getting people used to this new way of working. Some professionals fear that the algorithms will take over and their intuition will no longer be needed. In fact, the analytics does not eclipse but rather enhances human expertise.

If anything, it allows all of us to look better and be better pros when serving our clients. Most importantly, you do not have to be a forecasting guru to use these tools. In fact, they are used in many instances for looking backward rather than forward.

What Considerations Around Ethics, Privacy, And Bias Do You Keep In Mind When Working With Real Estate Data?

The large amounts of money and sensitive client information in real estate make ethics and privacy especially important. In my jobs, I’ve trained AI models on datasets that are as diverse and as inclusive as possible, which, for me, means working closely with people from different backgrounds and with different perspectives, so that the datasets better reflect the kind of situations and the kinds of people that exist in the world.

That, in turn, gives the AI a much better chance of working in an unbiased way and producing a balanced view of market conditions.

Being transparent is just as critical. When you inform clients about the ways in which their data figures into the larger picture and you take the time to illuminate the dark corners of data security, you build a trust that should last as long as your real estate business does.

This trust is particularly key in luxury real estate, where clients have a right to expect both discretion and service that clears the highest of hurdles. That makes conducting a thorough ethical review of new tools and data sources just good business.

What Measurable Results Or Recognition Have You Seen From Applying Data-Driven Practices In Real Estate?

Using clear data into benefits in many businesses is a proven practice. At Kalinka Group, bringing in AVMs and machine learning did wonders for our valuation accuracy, and it provided so many more reasons for clients to rely on our judgments.

And for us, just the way these numbers were presented,  the appearance of consistency and transparency,  wowed the folks at the International Property Awards that we’ve been honored by in 2019, 2021, and 2022 for Real Estate Marketing.

At Barnes International Moscow, we used AI tools to manage portfolios in real time. This allowed us to deliver advice with speed and precision. We could warn clients to get ready to do something, and then they would do it, often just before the market did something big. It was amazing to work with clients on advice this way, but it was also great to enjoy the stronger relationships that flowed from these capabilities.

How Do You See Data-Driven Strategies Evolving In The Future, And What Advice Do You Have For Those Looking To Adopt Them?

The real estate industry is ready to undergo more transformation, and data will be the propulsive force. While human intuition and know-how will always be part of the equation, data analytics will gradually empower agents and enable them to work at a scale and speed never before possible.

I see a future of ever-increasing machine learning capabilities, with models that will be not just predictive but also reliably so, on a consistent and frequent basis. And I see AI-infused customer relationship management systems and full-funnel data streams integrated from the Internet of Things (IoT) making real-time insights accessible to more people, almost all the time.

For professionals and businesses just embarking on this journey, my counsel is to direct resources toward investments in strong data architecture tied to secure cloud platforms and advanced solutions for customer relationship management.

Enlist or develop teams with the know-how of data science, and maintain a watchful eye on the horizon for new tools that might better serve your increasing and improving capacity to do business. And above all, cultivate a culture that values transparency, ethics, and collaboration. When done thoughtfully, these initiatives will move entire organisations closer to the conversion of opportunities into real, tangible achievements.