Automate Your Business Case
Our configurator combines project costs, time schedules and expected revenues to automatically generate a discounted cash flow (DCF) model that is tailored to the specific deal structure, equity share and exit scenario of your business case.
Our configurator combines project costs, time schedules and expected revenues to automatically generate a discounted cash flow (DCF) model that is tailored to the specific deal structure, equity share and exit scenario of your business case.
Project financials, the design and the programme all correspond to a single source of truth. This allows for diverse scenarios to be tested and project information to be updated simultaneously as a result of any changes.
Rabbit Real Estate & ZHAW win Innosuisse SME Innovation Project Grant
We are thrilled to announce that, together with our colleagues Konrad Graser, Evangelos Pantazis, Ph.D. & Pavel Sulimov from the ZHAW Zurich University of Applied Sciences, Rabbit Real Estate has been selected for an innovation project grant from the Swiss Innovation Agency Innosuisse. The purpose of the grant is to co-develop a digital end-to-end configurator for residential real estate development.
We are thrilled to announce that, together with our colleagues Konrad Graser, Evangelos Pantazis, Ph.D. & Pavel Sulimov from the ZHAW Zurich University of Applied Sciences, Rabbit Real Estate has been selected for an innovation project grant from the Swiss Innovation Agency Innosuisse. The purpose of the grant is to co-develop a digital end-to-end configurator for residential real estate development.
We are grateful to have been selected from among 270 applicants and look forward to fully using this opportunity to bring more innovation and digital integration to the real estate and construction industry.
Thank you to Innosuisse for this opportunity and to our partners Konrad Graser, Evangelos Pantazis, Ph.D., Pavel Sulimov and the ZHAW Zurich University of Applied Sciences for the successful application and collaboration. We look forward to working together!
We would like to echo the words of Innosuisse CEO Dominique Gruhl-Begin: "Dare to take the plunge, even when times are tough".
Rabbit Real Estate @ ZHAW
In November 2025 our co-founder Furio Valerio Sordini had the privilege of introducing members of the ZHAW CAS in Machine Intelligence course to Rabbit Real Estate and our digitally-integrated workflow.
Himself an alumnus of the ZHAW's MAS in Data Science, Furio shared insights into how we use various Python and machine learning tools in our end-to-end configurator for multi-family homes in systematised timber construction.
In November 2025 our co-founder Furio Valerio Sordini had the privilege of introducing members of the ZHAW CAS in Machine Intelligence course to Rabbit Real Estate and our digitally-integrated workflow.
Himself an alumnus of the ZHAW's MAS in Data Science, Furio shared insights into how we use various Python and machine learning tools in our end-to-end configurator for multi-family homes in systematised timber construction.
Focused on the feasibility study stage, our configurator screens plots of land, analyses market data, performs site analyses and configures design solutions within our industrialised construction approach to provide crucial information for early decision-making and a springboard for the project's planning phase.
We look forward to deepening our collaboration with the ZHAW as we strive to be at the forefront of innovation in the DACH real estate market.
Thank you to Pavel Sulimov from ZHAW Institute of Computer Science (InIT)ZHAW Institute of Computer Science (InIT) and ZHAW Datalab for having us!
Project Revenues with Confidence
Projected revenues drive real estate projects. And yet, at the project outset, revenue estimates often rely on a whole host of assumptions, such as floor plan efficiency and an arbitrary unit mix. We want to sharpen those assumptions in a systematic and data-driven way.
Projected revenues drive real estate projects. And yet, at the project outset, revenue estimates often rely on a whole host of assumptions, such as floor plan efficiency and an arbitrary unit mix. We want to sharpen those assumptions in a systematic and data-driven way.
Thanks to our partnership with Semanta.ai, Rabbit Real Estate delivers precise revenue estimates from the very first feasibility study. Semanta.ai collects millions of real estate advertisements, enabling market-based revenue and trend estimates per sqm, unit type and location.
Together with our configurator and kit-of-parts, which generate functional floor plans for various unit mixes and sizes, we can calculate precise revenue estimates. This allows for better-informed decisions regarding programme, floor area efficiency and the choice of design options to optimise the business case.
Top-down & Bottom-up
Optimising a housing project is a complex, iterative process. It requires a great deal of fine-tuning and cross-scalar trade-offs between different goals and parameters. Our configurator helps expedite this by automating and evaluating iterations according to a scoring system.
Optimising a housing project is a complex, iterative process. It requires a great deal of fine-tuning and cross-scalar trade-offs between different goals and parameters.
Our configurator helps expedite this by automating and evaluating iterations according to a scoring system. Building volumes are simultaneously positioned "top-down" in response to geo data, spatial analysis and planning regulations, whilst also laid out "bottom-up" using a single kit-of-parts within a modular construction system.
Together, this multi-scalar approach maximises plot utilisation and floor area efficiency, provides the ideal unit mix and thereby optimises risk-adjusted returns. This is just one way in which we want to make decision-making more dependably data-driven.
Automated Bill of Quantities
Prefabricated and systematised construction goes hand-in-hand with precise bills of quantities and element-based analyses. Gone are the days when projects had to rely on imprecise area and volume benchmarks that throw together different construction trades, materials and supply-chains.
Prefabricated and systematised construction goes hand-in-hand with precise bills of quantities and element-based analyses. Gone are the days when projects had to rely on imprecise area and volume benchmarks that throw together different construction trades, materials and supply-chains.
With our configurator and a kit-of-parts, we can accurately generate BoQs at the feasibility study stage and know exactly what goes into a project - element by element.
Stay tuned to see how we tie these to automated element-based cost, LCA and construction time estimates - all at a feasibility study stage.
Configure. Automate. Optimise.
Our Python-based configurator uses a kit-of-parts to generate a wide range of potential floor plan solutions based on project inputs (target unit mix & sizes, plot geometry and the maximum realisable area or volume) within minutes.
Our Python-based configurator uses a kit-of-parts to generate a wide range of potential floor plan solutions based on project inputs (target unit mix & sizes, plot geometry and the maximum realisable area or volume) within minutes. This automated process includes key output metrics such as floor area categories, unit and room sizes.
Stay tuned to see how we use this approach to further optimise floor area efficiency and economic performance, as well as providing reliable data on costs, LCA and construction time - all at a feasibility study stage.