A guide for those who work at a real estate company and want their AI assistant to generate vacancy reports, net operating income reports, and other recurring follow-ups using the same definitions and the same layout every month.

Net operating income is the metric that determines a property's value. Here is the formula, a sample calculation, and a breakdown of what is actually included in operating costs.

From Tracking Net Operating Income to Actually Improving It—AI, Key Metrics, and Workflows A webinar on why companies with hundreds of proven ways to track their real estate data still use only a fraction of them—and how AI helps solve that problem. Kevin (Product Manager) and Niklas (Head of the Customer Team) will present the content, and Rasmus will moderate. 0:02 – Welcome and practical information 1:07 – Kevin and Niklas introduce themselves 1:58 – Background: Homepal has accumulated about fifty clients and hundreds of proven use cases, but each company uses only a fraction of them 8:00 – The four barriers to scaling up: from building everything in-house, to a dashboard catalog, to identifying relevant anomalies without manual work, to minimizing reliance on specific individuals and development time 3:00 PM – How Homepal uses AI to eliminate the fourth obstacle—industry knowledge and interpretation rules are fed into the AI, which then delivers ready-to-use, quality-assured reports for each company 8:00 PM – Why everything is translated into an impact on net operating income—a common metric for prioritizing among completely different types of deviations 25:00 – Concrete examples: external work orders, overdue rent, and why the time interval (0–30 days vs. 180+ days) matters 30:50 – Question: Are there similar discussions regarding the balance sheet and property value? (Short answer: not yet—the focus is on net operating income) 32:11 – AI-enhanced fault reports – how free-text fields can be automatically categorized instead of depending on who enters the data 38:17 – Question: How do you work with benchmarks for heating and energy? 40:25 – Follow-up question: How does property type (e.g., senior living facilities vs. schools) affect benchmark levels? 42:29 – Niklas takes over: This is what the interface looks like in practice—the Insights and Actions tabs, the biweekly dialogue with the customer, and how to move from insight to approved action 45:00 – Live demo: the “higher proportion of externally executed work orders” case study—nine flagged properties, a recommendation focusing on the three that account for 71% of the potential, and a calculation of the impact on net operating income 47:51 – Question: Is the case study calculated per BOA/LOA? (Kevin explains the difference between relative prioritization and absolute monetary valuation) 49:39 – Demo continues: AI chat linked to selected properties, chart view, and how action cards are created when an insight is adopted 56:46 – Question about the semantic layer – do all customers use the same definitions, or do they vary by company? 57:49 – About Homepal’s ~300 standardized metrics, and a brief overview of the newly launched Metrics MCP for AI assistants 1:01:33 – Final question: Which system do customers most often forget to enable before going live? 1:03:14 – Conclusion

A property in a portfolio of 100 can fall behind for several months before anyone notices. No one sits down and compares that particular property to all the others every week, and once the volume becomes large enough, it’s nearly impossible to do manually.

Stockholmshem is now placing 400–500 fewer orders per month than before, which has saved the company 10 million kronor in just six months. But none of those figures appeared in the financial statements at first. They were hidden in the figures underlying net operating income, long before work on the annual report had even begun.

AI-powered data enrichment can save you thousands of hours of analysis work. Keep reading to find out how.

In the debate about AI, we often talk about the models, the technology, and the possibilities. But in practice, many AI initiatives stall much earlier than that. Not because organizations lack data, but because the data is locked in systems that were never built to communicate with one another.

...and can you make sure they become more?

This blog post is intended for those of you who are responsible for monitoring and management at a real estate company and who can relate to the situation where the right information was available, but nothing happened.

This blog post is intended for those of you who are responsible for monitoring and managing a real estate company—and who recognize the feeling of having to explain an outcome that really should have been preventable.

The role of CFO in a real estate company has long been about monitoring net operating income, ensuring regulatory compliance, and delivering monthly reports. But that picture is changing—rapidly. A new trend report from Aspia identifies seven shifts that, taken together, describe what will be required of the finance function leading up to 2030. We’ve read it with the day-to-day operations of real estate companies in mind.

"AI insights for ongoing operating net work" shows examples of where and how you can find the biggest impact on your net work right now – while you can still do something about it. The e-book is not based on theory but on real, anonymized data that you can use: fault reports per home, comparisons between properties and trends that reveal which objects are actually pulling the result — up or down. You get to follow the whole way from problem to action: how the ongoing operating net work works, what insights Homepal's AI highlights, and how you translate them into actions before the financial statements close. Download the e-book through the form to see all insights in their entirety.

In this video, Axel talks about our new feature "Key Metrics" - a new way to collect, prioritize and work with the organization's most important metrics in one place. You will see how key figures can be structured, owned by teams, followed up over time and linked to concrete actions when something deviates. All to create clarity, responsibility and a better connection between analysis, decisions and actual work in the organization. 👉 Please note that this is a demo based on design files. Development is starting now and we are aiming for launch within a few weeks.

In this video, Axel shows the latest updates after our beta period – shaped by valuable feedback from our customers. The video also highlights some new additions since last time and briefly goes through a simple example of what an imagined user journey might look like. Everything is shown in a design prototype from Figma and not in the actual product. Hence there are some limitations and flaws. Now we are taking the step towards production with the goal of launching within a few weeks 🚀 We are super excited!

When Gladsheim got a new main owner the need for a tool that not only collected data but also made it possible to understand and act on it increased.

Watch the webinar with Kevin Kimaryo and Henrik Litsander at Olov Lindgren.

Homepal is now launching a closed beta phase where selected companies can test how strategic goals can be linked directly to strategic and operational key metrics – thereby creating consensus, commitment and measurable impact throughout the organization. Our new feature makes it possible to break down the company's most important goals and strategy into concrete key metrics that different groups can actually work with and influence – and then see how it contributes to the whole. Our beta customers are currently helping us shape how this will work in practice before it is released to all users.

Property companies sit on a goldmine of information hidden in their work order descriptions. For decades, the industry has tried to create structure through codes and categories – yet the real analytical value often remains out of reach. Homepal’s new AI-based classification turns that around: instead of forcing structure into systems, we let AI understand reality as humans describe it – and make it analyzable based on the decisions you actually want to make.

It's time to get to know our CTO, Love.

Most property companies today have access to more data than ever before. Dashboards are everywhere – from operations to management – and visual analytics have become a natural part of daily work. Yet, despite all this progress, we still see the same pattern across nearly all our customers: when the monthly report needs to be presented to the CEO, management team, or board, the information is still exported into PowerPoint or Excel. That’s where the real decisions happen – not in the dashboard. The reason is simple: dashboards weren’t built for reporting.

Every property company has goals. Net operating income should increase, costs should go down, and tenant satisfaction should improve. Management sets focus areas and reports track progress – but often, that’s where it stops. Because there’s still a gap between the strategy at the top and the reality of day-to-day operations.

When will Homepal introduce AI? What would Kevin have done if he wasn't a product manager? Why do you need to define what a rental process is (doesn't everyone know that?)? What's next for Homepal? Get to know Kevin in just under 9 minutes!

Head of Property Management: “We could see it in black and white: this is how much money we are leaving on the table in the form of vacancies. We don't have time to deal with this many inquiries. This is how much a full-time employee costs.”

Hear why Riksbyggen chose Homepal.