It’s natural to treat any financial projection with a healthy degree of scepticism by scrutinising any assumptions, identifying possible biases and challenging the conclusions. This critical approach has been a cornerstone of financial diligence but in today’s world of big data, advanced analytics and integrated systems, this traditional stance may be holding organisations back.
What if, instead of simply critiquing forecasts, reviewers actively contributed their insights and knowledge during the forecasting process itself? What if insights flowed more freely across all levels of an organisation – up, down and across silos – so that every forecast was enriched, not merely audited?
Untapped critical knowledge
In tiered or siloed structures, critical knowledge often enters the forecasting process either too late or gets trapped at one level. Senior leaders might retain too much discretion over the final forecasts which, although useful in collective bargaining, is potentially risky in investment appraisals. These challenges are especially problematic in sectors such as social housing where investment decisions have unique financial, operational and social implications.
Yet a major part of the solution is within reach. For investment appraisals, performance can be significantly improved through two simple changes:
- Clear, targeted training for non-financial managers and their teams;
- Better documentation of forecasting bases, assumptions and methodologies.
Our suggested training topics include:
- How to simplify complexity without losing accuracy;
- How to build and document forecast assumptions;
- Understanding net present value (NPV) and discounting cashflows;
- Basics of identifying financial risk and opportunity cost;
- And, most importantly, how to align forecasts with broader organisational strategies.
That last point around strategic and social alignment is often overlooked and deserves an article of its own; it’s not difficult but it is obscured by customary thinking and unnecessary complexity.
In social housing, forecasting is done by people with varying levels of expertise, serving different purposes. While individual forecasts may lead to efficient decisions and investments, their overall effectiveness can be reduced by a lack of collaboration and shared oversight.
Forecasting blind spots
Reflecting on my time in mergers and acquisitions at EY, I recall three simple yet striking examples of forecasting blind spots:
- Privatised bus companies underestimated cash flow delays. When private owners streamlined the cash-handling and banking process, they unlocked working capital that often exceeded their business acquisition price.
- Electricity boards had unnecessarily-frequent painting schedules for their substations. Rationalising these routines across the UK during preparations for flotation led to enormous cost savings.
- Post-privatisation tariffs allowed electricity companies to pass on all costs and trading losses for a number of years to maintain ongoing profits, highlighting the risks of carrying forward outdated assumptions into new operating environments.
These examples may not have direct parallels in social housing but they highlight how siloed or budget-driven decision-making, even in efficient organisations, can obscure real financial opportunities.
Across the housing sector, finance teams do excellent work managing treasury, budgets and compliance within a challenging regulatory environment and their forecasts serve these functions well.
Other forecasts are available…
But those aren’t the only forecasts that matter. Property maintenance and investment, spanning cyclical, reactive, planned and strategic investment work, relies on projections from both finance and asset management systems. These latter systems model future costs to maintain regulatory and local standards but vary widely in their data-quality and scenario-planning assumptions.
Major cost drivers such as damp and mould, fire risk, disrepair and energy upgrades now demand closer attention due to evolving regulations and public scrutiny. Yet these costs are still handled inconsistently; some are built into base forecasts, others buried in ‘what if’ scenarios.
Precedent vs. data
Meanwhile, property investment decisions often stem from historical precedent and/or managers and advisors’ experience rather than data-informed strategic models. These informal approaches coexist with more formal budgets and forecasts, creating a fragmented view of financial performance.
This is a heavy cognitive and analytical load for non-financial managers, and a missed opportunity for finance teams and senior management to provide strategic input early. Done right, forecasting in this era of big data should bridge gaps, not exacerbate them.
Investment in social housing is significant, even when constrained by tight budgets. Ensuring that these funds are spent wisely demands a more integrated and strategic approach to forecasting and decision-making.
At the core of improved financial performance is a sharper understanding of opportunity cost, itself requiring consistent forecasting methods and a robust assessment framework applied early, not just at the approval stage.
Poor interpretations
I’ve seen many investments succeed or fail through a poor interpretation of the available information. Many investments are made on a whim, based on poor advice from a senior party or the ill-founded ambition of a subordinate – neither should benefit in an effective organisation.
Applying these disciplines after investment proposals have been developed is too late. Authors and reviewers might introduce their own biases instead of shared goals and misunderstandings can go unchecked. The real power lies in shaping decisions as they emerge, ensuring they reflect not just efficiency but also effectiveness and long-term value.
Ian Ellis is the chairman and managing consultant of Asprey Solutions.
