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by Property Forum | Interview

Real estate market players are starting to recognise the importance of data but finding the right way to collect, analyse and utilise it is still often a challenge. Matej Leskovar, Managing Director at Slovenia-based software solution provider Imagine d.o.o. talked to Property Forum about this topic in detail.

The real estate industry is slowly learning that data is key but collecting that data is often a challenge. What kind of building systems or upgrades would you recommend for owners and asset managers in order to collect a steady stream of data?

Data has become increasingly recognized as a crucial factor in the real estate industry, empowering informed decision-making. However, the challenge lies in effectively collecting this valuable resource. To overcome this hurdle, owners and asset managers should prioritize the adoption of advanced building systems and upgrades. By embracing IoT technologies, cloud connectivity, and AI-powered analytics tools, they can establish a seamless and continuous flow of data. Connecting this data with all the financial data from ERP software and all the smart building technologies data we can get an endless pool of new cross-combination KPIs. This integration enables the acquisition of real-time insights, empowering agile decision-making, optimizing operational efficiency, and driving strategic planning.

It is essential to select the appropriate real estate management platform, recognizing that data is key in today's real estate industry. By implementing a user-friendly platform, owners and asset managers can seamlessly navigate and utilize the system, leading to significant time and cost savings. Opting for a web-based and cloud-based platform empowers users with the flexibility to access critical data anytime, anywhere, and from any device. Moreover, the cloud-based infrastructure ensures secure and centralized data storage, safeguarding the valuable asset of data.

Matej Leskovar

Matej Leskovar

Managing Director
Imagine d.o.o.

Matej Leskovar graduated with a master's degree from the Faculty of Mechanical Engineering in Maribor and further pursued additional education in the field of computer science, specifically software development. He has been leading the company Imagine for over 23 years, covering various areas of expertise. His primary focus lies in the real estate sector, where software products have gained a majority market share in Slovenia. Additionally, he dedicates significant attention to information security within the company, ensuring uninterrupted business operations, and has earned the title of CIS - Information Security Manager. He has implemented all ISO standards within the company, including ISO 9001:2015 - Quality Management System, ISO/IEC 27001:2013 - Information Security Management, ISO 22301:2019 - Business Continuity Management System, and ISO/IEC 27017 - Security Techniques - Code of Practice for Information security controls based on ISO/IEC 27002 for cloud services. As a representative of the company, Matej Leskovar actively participates in various professional associations such as the Chamber of Commerce and Industry of Slovenia, the Association for Informatics and Telecommunications, the FIABCI International Real Estate Federation, the Slovenian Business Club, and others. More »

How can data patterns help landlords or portfolio managers make decisions regarding their assets? 

Data patterns play a crucial role in providing valuable insights for landlords or portfolio managers when making decisions regarding their assets. In the past, the real estate industry heavily relied on "hunch" or "experienced intuition" due to the limited availability of data. There is no need to emphasise how many decisions were wrong or at least based on unreliable data.

Nowadays, we can collect all the data! The first step was the development of technologies that provide us with data from multiple sources connected to our real estate asset: financial data, consumption of energy per sqm, per employee, insights into daily and seasonal variations, as well as information on repair events, maintenance works, and associated costs. The range of data types is extensive, and it is crucial not to overlook any of them, as the combinations of key performance indicators (KPIs) derived from these data sets can be immensely valuable. These data-driven KPIs are valuable insights that enable us to make informed decisions, optimize operations, and maximize the potential of our real estate assets.

Prediction, based on rich data patterns can give us trendlines and KPI benchmarks when we should act in the sense of selling an asset, investing, starting an energy reconstruction, … or even helping an HR team in one part of our portfolio. The data itself is not enough, but smart and experienced property owners will combine all their data with global trendlines of real estate and the financial world around them.

If we would like to put the range of possibilities into today’s analytics terminology, areas would be Predictive Maintenance, Energy Efficiency, Occupancy Optimization, Financial Performance, Tenant Satisfaction, Risk Management, Portfolio Optimization, Market Trends and Competitive Analysis, etc. But limitations are only in management's creative imagination.

By leveraging data patterns, landlords or portfolio managers can gain a deeper understanding of their assets' performance, identify areas for improvement, mitigate risks, optimize operations, and make informed decisions to maximize their return on investment.

Is there a role for AI in helping decision-makers navigate through massive amounts of data?

Absolutely! AI plays a significant role in helping decision-makers navigate through massive amounts of data. Here are some ways AI can assist in this process:

  • Data Processing and Analysis, where AI algorithms can handle large volumes of data and perform complex analyses at a speed and scale that surpass human capabilities. AI can automatically process, clean, and organize data. It can also uncover patterns, correlations, and insights within the data that may not be readily apparent.
  • Automated Data Exploration can automatically explore and discover patterns, trends, and relationships within massive datasets. Decision-makers can leverage AI to uncover hidden insights and gain a deeper understanding of the data, helping them make more accurate and informed decisions.
  • Predictive Analytics for predictive modelling and forecasting based on historical data, where decision-makers can utilize AI algorithms to identify trends, predict the future, etc.
  • Natural Language Processing (NLP) which allows AI systems to understand and analyse human language, including unstructured data such as documents, emails, customer feedback, and social media posts.
  • Recommendation Systems can be utilized in various domains, such as product recommendations, content recommendations, or investment strategies, assisting decision-makers in making more informed choices.
  • Automated Decision Support can analyse complex scenarios, simulate outcomes, and provide recommendations or decision options to assist decision-makers in navigating through complex and data-intensive situations.
  • Data Visualization - AI can aid decision-makers by generating interactive and intuitive visualizations of data. Data visualization techniques powered by AI algorithms can represent complex datasets in easily understandable charts, graphs, or dashboards.
  • Efficient Data Filtering and Prioritization, where AI can highlight critical data points or patterns that require attention.

Overall, AI can be a powerful tool for decision-makers to navigate through massive amounts of data. However, it is important to recognize that AI, at least for now, is just a tool—a tool with unlimited power and possibilities, but still a tool. In the end, the experience of Management should be the last stone in the new mosaic of decision-making, based on a massive amount of data. And we have to emphasize again: collecting all the data is key and software solutions to do that is the first and most important step also for entering the AI era.

How important is the data knowledge of tenants for landlords that want to have valuable information, in real-time, about their buildings?

The data knowledge of tenants can be highly valuable for landlords and a piece of “must-have” information to stay in touch with their tenants and ahead of the competition.

To effectively stay in touch with tenants, Access to Occupant Behaviour Data becomes essential. This data provides insights into space usage patterns, preferences, and needs, enabling landlords to better understand and cater to tenant requirements.

Real-Time Feedback and Issue Reporting are a necessity for tenants' satisfaction, and they can provide immediate feedback on building conditions, maintenance issues, or service requests. Energy Usage and Consumption should be on priority for ESG strategies. By encouraging tenants to provide energy usage data or implementing submetering systems, we can monitor energy patterns, identify opportunities for efficiency improvements, and engage tenants in energy conservation initiatives.

Tenants can contribute insights and suggestions based on their experiences (Collaborative Problem-Solving), which can help us make informed decisions, implement improvements, and align building operations with tenant needs.

We cannot forget Smart Building Integration. It can provide feedback on the usability and effectiveness of smart building features, such as lighting controls, temperature settings, or occupancy sensors. This information can help us fine-tune the functionality of smart building systems and maximize their benefits.

In summary, tenant data knowledge contributes to improving building performance, enhancing tenant retention, and the overall success of the property.

How can high-quality data collection and management help with the creation and execution of ESG strategies?

High-quality data collection and management is not some help, but the first step – base for creating ESG strategies and following them. It should be the top priority of every ESG strategy along with the software support to handle all the data. To be precise: without enough data, the organization cannot set an authoritative credible ESG strategy.

Baseline Assessment is key to identifying areas of improvement, setting targets, and establishing a starting point to measure progress against ESG goals. Reliable data is essential to understand the current state and identify gaps that need to be addressed. Identifying Material Issues enables the identification of material ESG issues relevant to the organization's industry, stakeholders, and value chain with a focus on the most significant ESG factors that have a material impact on their business and real estate assets. Performance Measurement allows organizations to track and measure their progress in implementing ESG strategies and initiatives to provide quantitative and qualitative indicators with KPIs, assess performance, set benchmarks, etc. Quality data collection supports credible ESG reporting and transparency for attracting responsible investment.

And on the end: continuous Improvement is our main goal for ESG strategy. By collecting data over time, we can track progress, evaluate the effectiveness of initiatives and investments, and make data-driven adjustments to improve the overall performance of our real estate portfolio.

In summary, high-quality data collection and management are fundamental for the creation and execution of ESG strategies and drive continuous improvement in our ESG practices.