Real Estate Condition Analysis (VZ1)

Problem In real estate transactions, buyers often face uncertainty regarding the true condition of a property. Traditional sales documentation – comprising photos, descriptions, and renovation histories – is subjective and inconsistent, making it difficult to assess renovation needs or estimate future costs. This lack of clarity can lead to poor investment decisions, unexpected renovation expenses, and mistrust among stakeholders.

At the same time, sellers, agents, and financial institutions lack a standardized, data-driven method to objectively assess or communicate the physical condition of a property or its components. While tools for estimating renovation needs do exist, they typically rely on statistical averages and theoretical lifespans rather than the actual, observed condition of building elements. This gap undermines trust, slows down decision-making, and increases risk in financing.

Objective Property buyers should be able to realistically assess the condition of a property – or its individual components – before making a purchase. Using AI, images and textual information from sales documentation are analyzed to visually evaluate the condition of specific elements.

• Image Analysis: Automatically detects and assesses the condition of elements (e.g., windows, roof, kitchen) based on photos.
• Text Understanding: Processes construction year, renovation dates, and descriptions from the sales documentation.
• Condition Matching: Compares with a component lifespan table (e.g., windows: 30 years, roof: 40 years).
• Assessment: Evaluates the condition and categorizes it into one of four options used in hedonic pricing models (e.g., condition: like new / used but in good condition / significant wear and tear / in need of renovation).