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
