INNA

INNA is a Nordic expert services group that specializes in the brokerage and management of apartments, real estate, and business premises. They provide customers with comprehensive solutions in real estate in the housing and business premises sectors.
 
Pricing apartments is typically a time-consuming, manual process that requires pulling data from numerous sources each time a new price is needed. The process relied heavily on emails, spreadsheets, research, and the knowledge of individual employees. Finding a way to automate this process would speed up pricing and improve customer satisfaction through faster response and better accuracy. 

Vincit explored developing a digital solution to simplify and automate the apartment pricing process.

The project began in 2025 with a discovery and design phase. During this stage, the team focused on understanding the needs of interview participants, including current customers, potential customers, and internal stakeholders. 

From this research, Vincit developed a clear concept and prioritized product package. We identified both the opportunities and constraints of the problem space. At the time, pricing apartments was a fragmented, largely manual process, requiring people to gather information from multiple sources and conduct extensive analysis before making pricing decisions. The vision was to create an AI-powered solution that could automatically analyze relevant factors and generate accurate pricing recommendations.

Following concept development, we moved into the requirements, design, and response-planning phases. We created a user interface prototype and tested directly with users to validate assumptions and gather feedback.

A key challenge was data availability and quality. The required data existed, but it was distributed across multiple systems and formats. Significant effort was needed to collect, harmonize, and prepare the data before it could be used effectively.

The implementation phase involved extensive experimentation and machine learning model development. Customer data, apartment characteristics, location information, contract details, and external market data from sources such as Oikotie were combined to build predictive models. The resulting solution is able to generate apartment referrals, thereby supporting customer satisfaction and improving overall business performance.

The entire system was developed within the Microsoft Fabric ecosystem. The front-end interface was built using Power BI. By leveraging existing platform capabilities rather than building everything from scratch, we were able to deliver a cost-effective and scalable solution tailored to business needs.

Automated and accurate pricing

The AI-powered pricing tool helps INNA to set automated and accurate rental rates by continuously analyzing large volumes of market and property data. The system can evaluate factors such as location, apartment size, amenities, building condition, local demand, seasonal trends, vacancy rates, competing listings, and historical leasing performance to estimate an optimal rental price.

AI pricing allows INNA to respond more quickly to market conditions, reduce the risk of underpricing or overpricing units, and maintain competitive rental rates while improving overall portfolio performance.

A competitive advantage for INNA

Customers using INNA can now make faster, more accurate pricing decisions across their portfolio. By continuously analyzing a wide range of factors, INNA can optimize rents in near real time rather than relying on manual reviews or static pricing models.

INNA expects higher occupancy rates, increased rental revenue, and reduced vacancy periods, while also allowing the company to react more quickly to changes in local market conditions. INNA can also demonstrate to clients that they have a tool that solves a common pain point – reducing the manual work needed for pricing apartments.

In the future, the tool will allow for 100% automated pricing of rental apartments.