What do we do after we have picked the fruit?

Back from another property asset management conference where the key-note speakers having highlighted the size of the cuts the public sector are facing, offered the assembled attendees the solution of selling/not renewing leases for underutilised properties; considering flexible working, reviewing office use generally and selling/not renewing leases for the freed-up space. At question time one attendee, hardly disguising his frustration, asked, and I paraphrase here, “we have picked all the low hanging fruit already but we still have to achieve more ‘efficiencies’. What do we do now?” The speaker, who happened to be a civil servant and fan of Yes Minister’s Sir Humphrey Appleby GCB, KBE, MVO, MA (Oxon), responded accordingly.

Before going to the conference I was doing some work on a client’s data where they had implemented our modelling software based and the single integrated property model. I calculated the efficiencies from integrating the data and consequently the required work, delivered ‘efficiencies’ of some 6% without comprising the quantity or quality of the work delivered. In addition further ‘efficiencies’ can be realised by using the outputs from the modelling to facilitate better planning and programming. These are achieved by introducing more effective procurement methods such as longer term framework agreements that allow the supplier to programme the work and plan resources more effectively. There are plenty of apocryphal accounts as to the existence of the ‘efficiencies’ that could be achieved by introducing more effective procurement methods but little hard evidence as to the scale of them. I was, therefore, interested to learn at another conference that in Hamilton, Canada the local authority had implemented strategic asset management together with longer term framework agreements and the latter had actually achieved 5% to 7% savings. If we put these two together we achieve an average saving of some 12%. Hopefully that is more useful than a Sir Humphrey solution.

Will lease liability influence estate strategy

Until now it has always surprised me how little the directors and senior management of most business and commercial organisations have engaged with its real estate. It is after all often the second biggest overhead after people for many of them. Perhaps this is all about to change with the recent changes to the International Financial Reporting Standards (IFRS) adopted by the International Accounting Standards Board (IASB) to ensure that leases appear on a company’s balance sheet. This change which looks like it will need to be implemented for the financial year commencing 2012 will mean that some potentially large numbers for lease liability will go on balance sheets. In addition it will result in an immediate negative impact on profit and loss and a consequential adverse effect on key corporate performance indicators such as gearing and EBITDA ratios.
In the majority of organisations the lion share of leases will be for land and buildings so it will be surprising if the Boards of these organisations, especially those that have had a policy of leasing property, a policy that has until now kept property off the balance sheet, don’t start to look more closely at their real estate assets. Although the catalyst for this interest in real estate assets is financial, the corporate real estate manager’s response will need to be comprehensive and also include the property and business aspects.
There are a whole range of possible reactions an organisation might want to explore including that of a move to a freehold portfolio. The challenge for the real estate manager is, that while they make the relevant parties aware that there will be significant consequences to the balance sheet, profit and loss and corporate PIs, to assemble the data necessary to produce the metrics for these consequences. This will entail the real estate manager bringing together the data associated with the financial, property and business aspects of real estate into a coherent holistic database. This will not always be as easy as it sounds as traditionally this data is stored in disparate databases with data of varied quality. A recent Deloitte’s survey revealed that 65% of respondents said they were not confident in the data in their lease database.
The financial, property and business data in an integrated real estate database, much of which should be able to be fed from other systems, such as the finance system, will be need to be structured around a single property model so that it can populate the forecasts and strategic option appraisals which will be required to feed the estate strategy and plans in these changing times.

Visual inspections for property asset condition assessments

Recently I’ve been looking at condition surveys again. As part of this my attention was drawn to some research on visual inspection of bridges in the States. As serious research in this area is generally hard to find I looked at it to see if there were any lessons to be learnt. I was impressed by the quality and thoroughness of the work. What they did was to get 49 practicing bridge inspectors from 25 states to carry out inspections using the national rating system on 10 bridges. Below I have taken the results for one of the bridges that appeared to me as being typical.

As you can see there are 10 ratings in the USA for bridges and only about 40% of the inspectors agree about the rating of each of the three elements for this particular bridge. Now before you start to say that would never happen here, remember all these guys had on average just over 10 years experience inspecting critical assets similar to this one. This evidence seems to point to the fact that these are typical results for visual inspection of built assets.
Some years ago I was told that we human beings were generally good at distinguishing between up to about seven single faceted entities. More than this our ability to differentiate between one and the next one deteriorates rapidly. With this in mind I tried amalgamating the ratings into five and the results were as follows:

Now with only five ratings, which is more like the number I’m used to, between 61% and 74% of the inspectors agree about the rating of each of the three elements. Although a significantly less variable result it is still one worthwhile remembering when reviewing visual inspection data.

Its the data, stupid

I spent a happy day at an asset management conference at a UK University recently. Several of the presentations were from doctoral and post doctorate research students. It was really uplifting to witness their enthusiasm to find ‘the answer’ to the problem they were investigating. For a moment the weight of cynicism from years of dealing with asset management lifted from me and I remembered my own days as a student and later years carrying out research.

Then the Professor came to the rostrum. In a forensic analysis of data he demolished the credibility of most of information the industry is currently using. I found one slide in the presentation particularly devastating. It recorded that in a trial (in the USA) approximately two-thirds of the assessors sent to look at a structure observed and recorded a painting defect, that is a third missed a painting defect! In addition less than five percent noticed a design defect which was suspected of being the cause of a recent notable failure, meaning over ninety-five percent missed it. Salutary stuff and it did have us questioning whether we were trying to run before we could walk.

How can we resolve problems without reliable information?

The example here was taken from a visual inspection. Sensors will obviously improve the situation and other information can be more objective. On the other hand factors such as the aging of data can introduce more uncertainty. The whole field is a minefield. One useful source of help is “Asset Information Guidelines: Guidelines for the Management of Asset Information” published by the Institute of Asset Management. But if you just want a few words of advice then proportionality is the first that comes to mind, clarity is the second, availability is the third and currency is the fourth.

Just do it

We have now been talking, it seems forever, about the need to implement serious property asset management.

More and more people I talk to are now saying just do it! In fact most add another word which I will omit here.

Where to start then? Data - but that is expensive and time consuming to collect. Perhaps there is a cheaper yet effective approach to managing data collection.

There are three principles that should drive any data collection strategy:
1. Move forward in an incremental way with each stage providing results that are an improvement on the previous situation
2. Make sure each stage in the incremental strategy is a step towards the desired final solution
3. Ensure data can be refreshed simply as part of business as usual.

I remember one client who adopted this approach to improve the economic sustainability of their estate. Unfortunately there was little coherent data to support this process. The estate was varied in terms of property size (23,000 to 14 m2), age (18th to 21st century), use and servicing (including laboratories, swimming pool and offices). Although there were nearly 150 properties in the estate, 30 of them accounted for 80% of the GIA. Therefore a 10% sample survey of these larger properties was carried, choosing properties that represented the various size, age, use and servicing characteristics of the whole estate. The data from the survey and backed up with consultations with the in-house team was used to model the other properties. Based on the results of this model a further 10% of properties that had not already been surveyed with the highest priority for action were identified and surveyed. This second 10% tested the robustness of the model as well as providing data for reworking the model.

The reworked results were used to populate the database differentiating between the actual and modelled data. This data was used to develop the first draft of the strategy for economic sustainability of the estate. As with all asset management documents it is dynamic and is regularly updated as the data is refreshed through post project handback and ongoing surveys. The policy for on-going surveys is to carry out annual assessments on about 20% of the estate based on the performance of the property, age of survey and reliability of data. A matrix such as the one below can be used to identify the urgency of assessments:

*or since remodelled
Red is the urgency for modelled data and green for surveyed data

In this case the surveys were assessing condition but the same approach can be applied to all the asset management assessments such as energy, occupancy, suitability and compliance

A tale of the tail wagging the dog

The other day I a saw a preliminary enquiry from an organisation with a large property portfolio for a Microsoft Access condition survey system. I held my head in despair. Why specify the technology and not mention what the condition survey is to be used for? I wonder if the same people would limit their choice of a new washing machine to a specific one at an early stage without considering whether it was needed for a person living on their own or someone who regularly washes all the gear for the local football team.

My experience is that with modern IT systems the issues are rarely technology but functionality and data. It is much more important that the reason for the system and the outputs from it are fully understood. It is also important that the system should adopt recognised data structures and standards. This latter point will enable data to be more easily exchanged with other systems, both internal and external to the organisation, as well as measure performance that can be readily understood. For any property system the property structure should comply with BS 7666, particularly BS 7666-2 Spatial datasets for geographical referencing: specification for a land and property gazetteer; and for condition surveys the asset structure should comply with BS ISO 15686, particularly BS ISO 15686-5:2008, Buildings and constructed assets: service life planning: life cycle costing. Any system that fully complies with these standards will naturally require a sophisticated relational database. Interestingly I failed to fully represent these requirements in a Microsoft Access database.

A few days after seeing this enquiry, on a lovely sunny morning, I reluctantly boarded a train from my local station, where on one side I watched contented cows in a field that runs down to the river and on the other young lambs gambolling, to go and give a presentation at a conference in London. Tearing myself away from this idyllic scene I was rewarded when I sat and listened to one of the other presenters describe planning and delivering an estate strategy. An essential part of this was collecting data to benchmark the estate, and one of the principal benchmarks was condition. Perhaps more importantly though the presenter interspersed his presentation with shots of the property users and quotes from them about the service being delivered. Not surprisingly then the principal conclusion was that the estates strategy should be service [business] led NOT estate led. In turn the data that feeds the strategy should be service [business] focused. With that approach there is a person who will deliver the efficient and effective property portfolio that meets the needs of the organisation.

So to recap:
• First: service [business] requirements;
• Second: estates requirements; and a long way behind
• Third: technology requirements.

Identifying condition based work for a property portfolio – where do I start?

For most portfolio holders with little data this is a big problem. The collection of appropriate data to measure the performance of the property portfolio and ensure that required actions and work can be programmed effectively can be both expensive and time consuming; but without data the consequences are very much more costly. The starting point, therefore, must be data.

The two principal issues with data are reliability and cost of collection. Unfortunately these two issues are in conflict, that is reliable data is generally more expensive to collect.

There are a number of ways property data can be collected but for the purposes of this discussion I have classified them into four:
1. Judgement forecasting – relying on local managers, surveyors and contractors knowledge to assess the condition of properties and forecast what actions and work is required to them;
2. Modelling based on existing data sources – building a symbolic model of each property and associating data from, for example, planned preventative maintenance records;
3. Non intrusive assessments – a typical condition survey; and
4. Schedule of work required – a list of tasks required to be carried out.
The reliability of these is illustrated below:

Many of the organisations I meet currently use judgement forecasting supported by an out-of-date assessment. This is not surprising as to move from this position to, say, one of up-to-date real-time assessments is both costly and time consuming.

Rather than attempting a big leap forward, with all is implications on cost, resources and risks, I often find myself recommending an approach that incrementally improves the position. This approach is to initially model the data using as much of the available reliable data as possible including any judgement forecasting data. The outputs from the model can then be used to identify properties where action is a priority. Assessments can then be carried out on the properties identified. Based on the assessments costed schedules of work can be prepared to finally feed the decision as to what should happen to each identified property. Throughout the whole of this process, which should be continuous, the model should be updated with the most reliable data available

This approach has the following benefits:
1. Concentrating money and effort where the benefits are highest;
2. Continuously improving the property data at the lowest cost;
3. Improving the situation almost immediately; and
4. Capturing structured data thus improving accessibility and usability.