The real estate industry is intensely debating the use of Artificial Intelligence to accelerate analyses and processes. However, institutional investment decisions involving millions of euros are critically dependent on the quality of the underlying data. ProOmea GmbH identifies fragmented data, manual review processes, and incomplete decision-making bases as significant bottlenecks in the current market. These weaknesses not only impair the efficiency of traditional acquisition due diligence but also the reliability of generative AI, which relies on structured and qualitative information.
If crucial information such as building law, eligibility for subsidies, construction costs, rental rates, or realisation scenarios is missing, a complete decision model cannot be created. AI systems fill such gaps with statistically plausible assumptions, leading to convincing-sounding, yet potentially flawed, results. The main risk here is not the absence of an answer, but a plausible answer based on an inadequate data foundation. AI can thus accelerate decisions, and this also applies to erroneous decisions.
Decision Intelligence as a Solution
ProOmea, a company based in Stuttgart, addresses this problem with its Decision Intelligence Platform. This platform integrates market data, subsidy structures, project parameters, and real transaction data into a structured analysis process. The aim is to create reliable foundations for acquisition and investment decisions in residential construction. An internal IT team ensures the continuous development of these digital solutions. In recent years, ProOmea has supported the realisation of over 1,000 residential units in Germany.
Jeremy Justin Jahn, Managing Partner at ProOmea GmbH, emphasises that the actual bottleneck is not a lack of technology, but the fragmentation and quality of data structures. He points out that many market participants still review real estate investments with high manual effort, heterogeneous data sources, and traditional Excel models. In a volatile market environment with fluctuating construction costs, complex subsidy programmes, and high demands from institutional investors, this represents an increasing risk.
Challenges in Affordable Residential Construction
ProOmea's approach proves particularly relevant in the field of affordable housing. Here, rental models, eligibility for subsidies, construction costs, regulatory requirements, and return expectations must be coherent from an early project stage. Many socially meaningful and politically desired projects are not yet structured in a way that allows investors to make a valid acquisition decision. Jahn highlights that while AI works with probabilities, an investment decision is binary. Therefore, in addition to technology, mathematics, market knowledge, subsidy expertise, and experience from real transactions are needed to structure complex projects.
ProOmea's Decision Intelligence Platform integrates various analysis and review layers, including market and rental data, location assessments, project parameters, subsidy programmes, regulatory requirements, and economic scenarios. This enables a robust overall picture to be formed from diverse individual pieces of information. It allows for early identification of whether a project, in its existing form, is economically viable or if alternative use, subsidy, or realisation scenarios would be preferable. Particularly in residential development projects, this early overview can be crucial before key decisions such as the space mix, floor plans, energy standards, or subsidy eligibility become difficult to modify.
From ProOmea's perspective, the final decision will continue to rest with humans. Technology supports data structuring, risk presentation, and the preparation of use scenarios. The responsibility for deploying third-party capital, however, cannot be delegated. Simon Bender, Technical Product Engineer at ProOmea, stresses that real estate investments are a matter of trust and liability. It is crucial that results remain comprehensible and that individuals understand the underlying data, assumptions, and models. For ProOmea, the progress of data-based real estate analysis lies not in autonomous purchasing decisions, but in creating improved foundations for decision-making. Those who integrate market, location, subsidy, and project data early can review residential construction projects more quickly, assess risks more precisely, and prepare investment decisions more thoroughly.
