The sale of real estate is one of the most capital-intensive and slowest transaction processes in Germany. On average, it takes over 300 days for a property to be fully transferred - from the initial contact to the review of all documents to notarization and handover.¹

The fact that this process takes so long is rarely due to the property itself. It is the processes that slow it down. In an industry that is heavily characterized by individual coordination, paper files and isolated tools, there is often a lack of structured, digital workflows. Instead, manual checks, media disruptions and high coordination costs characterize everyday life.

Yet there have long been alternatives. Digital tools offer concrete solutions for processing transactions more efficiently, cost-effectively and reliably. In this article, we show where the greatest frictional losses currently occur in the transaction process - and how technologies such as natural language processing (NLP), predictive analytics and digital data rooms are changing these processes for the long term.

Purchase contracts, land register excerpts, declarations of division, energy certificates, floor plans - every real estate transaction generates dozens, sometimes hundreds of documents. These usually have to be viewed and checked manually. Depending on the volume, the review and classification alone can take several hours per party.

In addition, there is a high susceptibility to errors: different file formats, missing pages, duplicate versions or contradictory information make structured checking difficult and often lead to time-consuming queries.

Sellers, buyers, estate agents, notaries, banks and authorities usually work with their own systems, tools or simply by email. This results in numerous media disruptions: information has to be transferred manually, sent multiple times or requested repeatedly. Different versions of the same document often lead to misunderstandings, delays or unnecessary coordination efforts.

Another key problem is the lack of transparency in the process: buyers often do not know whether the financing bank has already provided feedback. Sellers have no insight into the status of the credit check. Brokers spend a large part of their time answering queries and keeping track of information.

The consequence of these structural deficits is a considerable coordination effort: deadlines have to be coordinated, queries answered, deadlines monitored and individual process steps documented - often manually. The resulting effort adds up and costs everyone involved valuable time. In markets with high demand, this loss of efficiency quickly becomes a clear competitive disadvantage.

Artificial intelligence (AI) can not only reduce the aforementioned hurdles, but also fundamentally improve many processes. However, the decisive factor here is not the mere introduction of new technologies, but their well thought-out integration into existing processes - always supplemented by human expertise and professional judgment.

With the help of NLP, contracts, land registers and declarations of division can be read automatically. AI recognizes relevant clauses, checks completeness, flags anomalies - and does so with an accuracy that in many cases even exceeds the human average.2

In due diligence (DD) in particular, this means less manual checking, more structured data and faster feedback.

Another advantage of AI-based tools is their ability to automatically extract data from different formats. Whether a scanned PDF, image file, Excel spreadsheet or email attachment - information such as floor space details, years of construction, ESG criteria or ownership can be systematically recorded and converted into structured data.

Creditworthiness analyses, fraud detection and location assessments can also be carried out faster and more data-based thanks to predictive analytics. Based on historical market data, payment flows and comparative transactions, realistic assessments of price trends, demand and potential risks are created - in real time.

The advantages are obvious - and can already be measured today.

When all parties involved work together within a standardized digital system, processes can be made much more efficient, which in turn leads to a significant reduction in transaction times.

Less coordination effort, automated document checking and clearer processes reduce personnel costs. At the same time, the use of external auditors or experts becomes more efficient, as only relevant issues need to be processed manually.

Structured data rooms, traceable workflows and automated alerts reduce the susceptibility to errors. Missing documents, incorrect document versions or forgotten deadlines are a thing of the past.

All parties involved - brokers, buyers, sellers, notaries, banks - can see the status of the transaction at any time. This creates transparency and significantly reduces queries.

A central storage location for all relevant documents - versioned, secure and with individually controllable access rights - forms the centrepiece of modern platform solutions. All parties involved work on a standardised database; changes are seamlessly documented, deadlines are automatically monitored and all communication can be efficiently bundled thanks to seamless email integration. In addition, structured deal documentation ensures that all process steps are mapped in a traceable and audit-proof manner.

Analysing contract texts is one of the classic strengths of modern NLP technology: the system automatically recognises paragraphs, deadlines, potential risks, missing attachments or formal defects - and provides structured, evaluable feedback. For brokers and investors, this means considerable time savings in the preliminary review, faster decision-making and quicker feedback to customers.

Market data, location assessments and price trends can be analysed on the basis of historical transactions and real-time data. Buyers and sellers receive data-based forecasts instead of gut feelings.

The seamless connection with external parties is another key success factor of digital platforms. For example, using APIs to connect directly to a CRM system, automate credit checks with banks or coordinate appointments not only saves valuable time, but also minimises potential sources of error throughout the entire process.

Even if many processes can be automated: People remain central.

Value is created where people conduct negotiations, build trust and develop strategies. AI provides the data - but no decisions.

‘For estate agents in particular, the digital transformation means a noticeable reduction in administrative tasks: Instead of sending exposés manually or chasing up missing documents on the phone, there is more time for what really counts - in-depth market analyses, targeted customer care and individual, advice-oriented support.’

Because they are based on clear information, structured data and comprehensible processes. This increases the quality of financial statements - and reduces subsequent corrections.

Traditional property sales are slow, expensive and error-prone - and not because of the property, but because of the processes. AI-supported technologies can help here: they enable faster processes, create transparency and minimise coordination effort.

Anyone who invests in digital transaction processes today benefits in several ways:

• Less effort, more speed: processes can be measurably accelerated - often by weeks or months.
• Reduce costs, increase quality: automation reduces errors, lowers checking costs and improves the quality of the transaction.
• Convince customers, create trust: Transparency, speed and structure increase the satisfaction and trust of everyone involved.

Relieve employees, utilise skills in a targeted manner: People do what machines cannot: Advice, negotiation and relationship management.

Those who actively shape this development now will not only secure a technological advantage, but also a stronger market position in an increasingly data-driven industry.

References
1 Drooms (2023): Real Estate Transaction Barometer
2 Hendrycks, et al., 2020. Measuring Massive Multitask Language Understanding.