Fundamentals of effective real estate fund modelling

Fund modelling: Unpacking the black box

Financial modelling is crucial to a firm’s critical decision-making and financial modelers are under pressure to be quick, exhaustive, accurate and understandable. However, over time, proprietary fund models can become black boxes.

This lack of transparency can lead to missed variables in calculations, use of outdated assumptions, and other small items that, when overlooked, compound into large errors. Very few firms take the time to do an audit to understand what needs to be done in order to achieve modelling efficiencies.

Financial modelling versus fund modelling

Financial modelling refers to the process of creating a mathematical representation of the financial and operational characteristics of a business. Fund modelling is the application of financial modelling to the investment fund industry. The resulting fund models have a higher degree of complexity because, by definition, an investment fund is a legal entity that pools capital together to invest in one or more asset classes. The scale advantage created by this pooling of assets and consolidation of portfolio operations becomes the great challenge of fund modelling.

The four design components of a typical fund model are:

1. Historical data providing an understanding of the current state of business

2. Assumptions about the future of the operations

3. Mathematical formulas to forecast the financial and operational behavior of the business

4. Metrics and charts to assist in making business decisions

Fund modelling can support decisions ranging from a business plan for a new investment thesis to monitoring the cash balance of funds under management. In other words, you could encounter as many financial models as you have strategic decisions or business processes in the life cycle of an investment fund.

• Fund creation: Determine your optimal fund structure by modelling the impact of differing fee and investor return structures on your fund thesis.

• Investor cash flow: Understand how to prepare for investor cash flows using distribution and waterfall models.

• Investment phase: Make sound acquisitions and dispositions based on models that are responsive to due diligence.

• Business planning: Business planning cycles are rapid and continuous, and models must be sensitive to real-time updates and scenario analysis and provide contextual reporting.

• Fund close: Closing your fund seamlessly and opportunistically requires ongoing modeling of the influences of market factors on your investments.

Decisions that are supported by fund modelling:

• Business valuation

• Underwriting

• Acquisitions

• Management and operations decisions

• Capital budgeting

• Financial statement analysis

• Determining the firm’s cost of capital

Drivers of fund modelling complexity

Building an influential fund model is a complicated task that must account for the quantity and quality of data, the complexity of fund structures, and the resulting reporting on business intelligence and performance and risk metrics. Driving the level of complexity are these key factors:

Least common denominator of reporting lines across assets

Consolidating the financial data of a portfolio of assets is easier if all calculations are done in the exact same way. As soon as one or more assets require specific calculations or pro forma line items, the complications increase. For example, if an asset model requires 100 pro forma lines, of which 10 are specific to one asset class and the fund invests in four asset classes, the model will consolidate 130 pro forma lines, some with empty data.

Granularity of assumptions

Granularity refers to how exhaustive a model is. Managing the quantity of manual inputs increases rapidly as the fund model allows for multiple scenarios based on asset level assumptions. For example, if you have a fund with 15 multi-let assets with an average of 10 historical leases and five new leases, each with 30 inputs, that is 6,750 tenancy schedule inputs to manage.

The number of structuring options

Fund managers are faced with a matrix of products (UCITS, SIFs, SICARs, RAIFs, etc.), legal forms (SICAVs, SICAFs, etc.) and jurisdictions to accommodate all types of investors at the time of fund inception. Moreover, when underwriting a new acquisition, fund managers must navigate a large set of ownership chains between the fund and the asset, to balance operational costs with tax and distribution optimizations. The three financial statements of all structuring options should be accurately forecasted within a fund model.

External constraints on fund operations

Pressure to comply with a continuous stream of new regulations and investors demands for transparency are leading to additional metrics within a fund model. For example, the AIFMD Annex IV transparency reporting requires all authorized and registered AIFMs to periodically report to their local regulators on each AIF they manage or market within the European Union. The reporting is composed of 302 data fields for each investment fund that should in theory be computed in the fund model.

Flexibility of analysis

A fund collects an immense amount of data to use in analytics. The ability to slice and dice this information is what made PivotTables in Excel so successful. Allowing that level of flexibility in fund models requires computing power and centralization of data. For example, flexibility often translates into complex formulas that work as database queries, collecting and rearranging data according to the selected variables for each analysis.

How effective is your fund model?

• Is your model quick to update?

• Has your model appropriately incorporated the industry best practices (e.g. INREV, EPRA, IFRS)?

• Does your model forecast the three financial statements of each underlying structure of the ownership chain?

• Does your model calculate performance metrics at asset, holding companies, fund and investors level?

• Does your model address the drivers of returns?

• Is your model easy to understand?

• Are your modelling forecasts built using the actual data from your property managers and asset managers?

• Does your model have good documentation so that someone else could operate it if your key modeler was unavailable?

If the answer is ‘No’ to any of these questions, evaluating your modelling practices will help you identify and address the overall risk level, compliance to industry best practices and operational pain points of your current fund model.

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