Dashflow’s AI for Discounted Cash Flow (‘DCF’) models in real estate.

Using the MaaS approach (‘Modelling As A Service’)

Dashflow’s AI for DCF modelling represents a raising of the efficiency bar to obtain far safer and highly-accessible financial modelling for team members. It combines the familiar liberal environment of Microsoft Excel which all businesses depend upon, and leverages it with powerful AI and automation methods to simulate Analyst-type modelling labour. This proven approach is called MaaS: ‘Modelling As A Service’:

Vier Augen Prinzip (the four-eyes principle)

All Dashflow IRR and appraisal figures are double-checked via the Excel Models generated by Dashflow’s AI engine. It is effectively the “mathematical proof” the latest RICS Investment Valuation Report recommends is so vital in order to increase client confidence in real estate projections, especially via technology.

And this also gives more confidence to users. In the cover sheet of the DashModel (see ‘Dashflow discrepancy’ comments), you can read an audit comment. It helps verify that the cash flow modelling was calculated twice independently. This helps avoid traditional human error by ensuring the basics of modelling income and expenses over time are spread over as expected.

A typical deal is modelled by Dashflow’s AI from scratch in 30 seconds. It does not use a legacy file or butchered in-house model template.

No butchered corporate templates

Each DashModel for Excel is built from the ground-up with a newly-created blank workbook. Depending on the deal, Dashflow’s AI generates 10 to 20,000 or more formulae. The DashModel files are macro-free, unlocked and can be branded to suit each team’s requirements in record time.

Human and spreadsheet wins

Because AI writes only the minimum formulae required for the “mathematical proof”, DashModels tends to be 95% smaller/simpler than a typical corporate template that attempts to have modelling flexibility for any type of deal. As any Analyst will confirm, a corporate template often contains many cells, even entire spreadsheets, that are often irrelevant or only partly-used for a typical analysis.

As such, the 95% saving means that the resulting Excel file is smaller, leaner and faster to triple-check if any analyst wishes to review the file for themselves.

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