Implementing Advanced Analytics in Real Estate: Using Machine Learning to Predict Market Shifts

Machine learning has shook up many industries, but its effects in the real estate market fluctuation forecasting area have been utterly dramatic.

We’ve spoken with Kirill Mozheykin, a CMO in the real estate industry, who believes that in the coming years, machine learning won’t be just a utility; it will be a strategic advantage in how one thinks about real estate. Kirill has a wealth of experience at the high end of the real estate market, particularly in the international markets of Cyprus, the UAE, and China.

The Basics of Predictive Analytics in Real Estate

Traditional real estate market analytics methods are being replaced by algorithms that analyze thousands of variables, like property size and location, social media sentiment, infrastructure development plans, demographic shifts, neighborhood walkability scores,  climate change impacts and even proximity to cultural hubs or transit lines. In other words, do things previously thought to be impossible.

According to Kirill, the crew at Barnes International Moscow could “see in advance” neighborhood appreciation rates using ML models. These models harness the massive scale of integrative data, from local key economic indicators to online consumer behavior, all in real time.

Data Sources and Integration Challenges

Machine learning operates on a variety of qualitative data, which is only effective in a strong data infrastructure, says Kirill. Such infrastructures capture and integrate information from all sorts of sources. At Kalinka Group, the team envisioned data acquisition comprehensively, with a detailed roadmap chart that spanned the entire domain of available data or potential data/

According to Kirill, they weren’t drawing a straight line from A to B, but widening the channel of approach, working to guarantee the well-ordered flow of quality data into their models. Kirill notes that it is just as important to integrate data effectively: they present rigorous validation checks that ensure each data source contributes nothing less than the highest degree of accuracy. This is necessary to transform the raw data they collect into actionable insights and avoid a situation commonly referred to as “garbage in, garbage out.”

Advanced Analytical Techniques

An ML model can learn nonlinear relationships between your continuous predictor variables. According to Kirill, At Kalinka, these models were used to show whether or not the opening of a transit line led to changes in property value around that neighborhood.

Other sophisticated techniques included:

• Gradient Boosting: Amalgamating a number of weak predictive models to achieve a strong, accurate model.
• Random Forest Algorithms: Algorithms based on random forests predict with an improved accuracy when compared to other models, such as decision trees. 
• NLP: Using unstructured data such as news articles or social media posts to determine trends and risks.

The techniques enabled the group to yield extremely accurate predictions of market volatility and assisted customers in making investment choices.

Practical Applications in Real Estate

Real estate has been thoroughly penetrated by machine learning, which offers investors, developers, and property managers very real benefits. Here are just a few of the benefits highlighted by Kirill:

Predicting Market Trends

According to Kirill, his team used ML on a project in the UAE to parse economic data to determine an upsurge in demand for upscale, sustainable, rental apartments. This gave clients the opportunity to pluck an emerging, ripe market from the proverbial tree.

Increasing Property Value

Comparable sales and broker intuition formed the basis for traditional real estate valuation methods. These have now been extended to hundreds of variables, thanks to machine learning, which make for more accurate and more nuanced property valuations. At Kalinka Group, Kirill recalls using Automated Valuation Models (AVMs), which allowed to work with increased speed and transparency.

Optimizing Commercial Portfolios

Predictive analytics are powered by machine learning in such a way that they predict three important things: vacancy rates, maintenance costs, and the effects of infrastructure on property value. As Kirill notes, this was proven to be the case in a project in Thailand, where a multinational client used ML not to predict the future, but to understand the present. Their real estate investments were rebalanced, and they exited underperforming markets that only looked good on paper.

Identifying Emerging Markets

As for noticing potential development hotspots, Kirill recalls European projects like those in Cyprus and Turkey, where they advised clients on entering markets with an impressive amount of confidence.

This confidence came from a combination of the team’s own data and analytics powered by machine learning. According to Kirill, with machine learning algorithms (and a good deal of human ingenuity mixed in), his team developed models to identify potential infrastructure development. That potential is often a marker for economic growth.

Ethical Considerations and Challenges

Data Privacy

The real estate analytics sector handles delicate financial and personal data. It is necessary to strike a balance between the use of this data to drive innovation and the strong privacy protections that clients expect. At Kalinka Group, Kirill built encryption and compliance processes that keep client data safe while allowing for the kinds of advanced analytics that drive business goals.

Algorithmic Bias

Kirill stresses that bias and discrimination can also happen in the machine learning models themselves. The solution, according to him, is to train them on datasets that are both diverse and representative of the sorts of populations you find in the real world. Predictive algorithms at Barnes International became clearer, more transparent and, most importantly, more inclusive.

Conclusion

Kirill sees the future of machine learning in real estate as boundless. He expects to see even better predictive accuracy with the arrival of quantum computing and more powerful AI models. In his view, success hinges on a solid data infrastructure, data literacy, and ethics.

If the real estate industry is to thrive in the current environment, professionals must be able to marry technological knowledge with some good old-fashioned intuitiveness. In other words, seeing and leading through changing times demands a data-driven way of working that is also data-guided at its heart.