
Understanding costs to reduce costs
Using state-of-the-art OCR technology and machine learning, invoice data is automatically extracted, checked and classified into cost items. This creates a precise data basis for comprehensive analyses at building and portfolio level.

Reconsider costs
In the real estate industry, costs are often analyzed based solely on accounting data. However, conventional charts of accounts and accounting logic usually do not provide the necessary level of detail for a precise cost analysis. ERP systems and BI solutions frequently do not provide sufficiently granular and consistent reports for informed decisions. With costREview, real estate costs are classified using artificial intelligence according to a standardized DIN cost structure down to the level of individual invoice items. This detailed analysis reveals optimization potential and provides a deep understanding of cost structures and developments.
Step by step towards greater cost efficiency
In times of crisis, not only do budgets shrink, but also the scope for action in management. In order to secure and increase the profitability of real estate, cost savings are essential. Especially in a tight rental market, optimizing ancillary costs becomes crucial to retain existing tenants and enable new rentals. At the same time, regulatory requirements are increasing the pressure to manage real estate more sustainably and cost-efficiently. This is where costREview comes in: It helps you create more cost transparency in simple steps and provides the tools to manage your properties more cost-efficiently.
In the costREview portal, you can easily upload your invoices via drag & drop. Using cutting-edge OCR technology and our machine learning-based classification model, over 60 cost groups are precisely assigned according to DIN cost structures.
Data extraction can also be done directly via our invoice management tool SmartInvoice to create synergies in invoice processing.
Our high quality standard of 100% is guaranteed by an innovative, hybrid system. We rely on a combination of advanced machine learning models and the expertise of our subject matter experts based on the proven “human-in-the-loop” approach. This combination enables a thorough check of the output, so that even the smallest deviations can be reliably detected and corrected. In this way, we ensure that our results always meet the highest quality standards.
A deep understanding of cost distribution is key to identifying key cost drivers. Cost groups with the largest relative share of total costs or the highest cost increases in recent years offer valuable starting points for more in-depth analyses.
Example: Over the last five years, maintenance costs have accounted for one third of total costs and are therefore considered a major cost driver. Reducing these costs has a direct impact on returns.
A property or portfolio benchmark can be used to evaluate the costs of a property in comparison to individual properties or entire portfolios. Costs can be analyzed, for example, according to technical system groups or types of insurance and compared according to location, type of use and size of the property.
Example: For air conditioning systems, I pay 40% more per square meter in maintenance costs compared to other properties or portfolios.
All cost items can be analyzed per service provider and location across the entire portfolio to identify the most cost-efficient providers. In this way, savings potential can be tapped through switching providers, framework agreements or “market standard checks” of offers.
Example: Previous analyses show that I am paying too much for air conditioning repairs compared to my other properties. The vendor benchmark gives me an overview of my service providers so that I can realize savings potential by switching to the most cost-efficient provider.