From an FD’s viewpoint, setting asset management budgets (as with others) is heavily dependent on collective bargaining for resources from asset managers whose bias will be to seek more resources, and there is always a way to spend budgets. Confidence in budget needs is difficult to attain when disparities between the accounting lives of components (determined by stated accounting policies) and their actual lives (determined by quality of manufacture, servicing, installation, and tenant behaviour) can severely disturb reported profit expectations through less orderly component replacements.
Unfortunately, asset management records and systems are largely forecasting tools that aim to predict and maintain condition standards of assets and, sensibly, use component lives that derive from actual average life and cost expectations based upon experience. The need to achieve scale economies with large works programmes further distorts component lives, sometimes even shortening them.
There is a simple predictive tool that can mine component accounting data and related forecasts at asset level and calculate, using expected actual replacements in asset management systems, the related anticipated replacements and profits or losses on disposals for at least the next two years. This can be used for predicting depreciation adjustments recalibrating component accounting outputs in advance rather than taking unexpected I&E account adjustments every year or month.
Unfortunately, even resolving this leaves an unhealthy divide between effective asset management and effective financial management, when budgets are spent intuitively rather than through an organisation-wide investment option appraisal and proposal methodology.
It is becoming apparent to many landlords that asset management systems and finance budgets are inadequate for effective management of an asset portfolio. Historically, combining these with in-house and third-party consultants’ experience is a poor basis for sound custodianship.
Without a soundly-based projection detailed to asset level, that is well utilised, maintained and shared centrally, the relative future net earnings value of each asset and the true opportunity costs of investment/divestment plans can often be down to guesswork, custom and bias. Additional bias is also introduced through investment justifications based upon ad hoc social and strategic factors without a structured rationale that has consensus in the organisation.
Massive administrative effort would be required to establish day to day cost and revenue allocation in an accounting system down to asset level with any accuracy, but modern BI tools can mine electronic data and spreadsheet sources outside of the finance system to build sensible year zero and onward projections of net revenues at asset level. These can be reconciled to recent year TBs as a gross error check and calibration.
Sources such as current rent roll, asset management planned & cyclical maintenance projections, recent years’ bad debts, voids and day to day maintenance records can provide asset level costs and revenue bases that can be extrapolated with logical forecasting algorithms. Asset registers and various analysis indexes from housing or asset management systems can be imported for reporting on the resulting projections and user defined inflation and discount factors applied.
Within Asprey’s system, a sound methodology addresses the impact of social and strategic factors of import on investments or divestments and even identifies opportunity costs of competing investments in social and strategic terms, as well as financial terms. This completes the toolkit for an organisation wishing to properly manage the accretion of financial and strategic value in its underlying assets. Crucially, such a system bridges the gap between effective financial management and effective asset management and builds consensus across an organisation over asset values and investment prioritisation, not only within business streams but also at corporate portfolio level.
As with any projection, its day to day use identifies improvements in data and in forecasting expertise, that in turn build continuous improvement into prudent management of the asset portfolio at all levels.