1.1 Project background

1.2 Purpose of Project

1.3 Broad approach

1.4 Benefits of this Project

1.5 Key points

2.1 Principles for developing and selecting KERs

2.2 KER subject areas

2.3 KER drivers

2.4 Future possibilities

2.5 Key conclusions

3.1 Standardisation and the Data Matrix

3.2 Defining a core dataset

3.3 Data availability

3.4 Key definitional issues

3.5 Practical guidelines for successful data management

3.6 Key conclusions

4.1 Summary results

4.2 Variability of KERs

4.3 A selection of KERs

4.4 Comparison with other sectors

4.5 Key conclusions

5.1 Assessment of progress made

5.2 Consolidating this Project

5.3 Extending the analysis

5.4 Key conclusions

From Chapter 3

From Chapter 5

1.1.1 Information is now central to all management. Institutions need to organise their information to manage their operations, make key decisions, direct their strategy and monitor progress towards key objectives. The role of information is emphasised by Michael Earl of the London Business School:

"The way in which management in the future, therefore, has to change is that managers must understand information as a resource. We have to be able to manage information as an asset, both as a level for business development and as a process for managing organisations. In short, every business becomes an information business and every manager becomes an information manager."

1.1.2 Estates and facilities now comprise approximately 20% of the annual operating costs of higher education institutions (HEIs) and are the second largest resource cost after people. In addition, property is central to the operation of HEIs, as it affects the everyday activities and success of each institution.

1.1.3 Good quality estate statistics should therefore be of fundamental importance to all HEIs. Relevant and accurate data will allow individual institutions to manage their estates better for the benefit of their businesses. The sector as a whole will also have much enhanced tools to help it decide on priorities and other issues.

1.2.1 The origin of this Project was an expressed desire of directors of estates within HEIs to transform the quality of their data, so that useful indicators of performance could be developed, and better comparisons made between institutions. Only with accurate and appropriate data could institutions answer key questions about estate performance. It was felt that a coherent, consistent and relevant set of data needed to be developed in order to improve the quality of estate management and promote interchange and learning between institutions on estates matters.

1.2.2 The Project intended, therefore, to produce an essential management tool for the higher education sector by identifying a framework for the collection and analysis of certain types of data.

1.2.3 Accordingly, the Higher Education Funding Councils for England, Scotland and Wales commissioned a report on the subject of estate management statistics. The Project Team was commissioned in early 1998 to undertake the following main tasks:

1. identify important estate management statistics;
2. develop robust definitions of those statistics;
3. produce preliminary comparative information for the sponsor institutions; and
4. recommend how the Project might be extended to the rest of the sector.

1.2.4 The Project focused on the achievable, and concentrated on building up a framework which could be adapted in the future as the sector’s priorities change. Consequently, the Project focused on the more ‘traditional’ role of estate management of strategy, maintenance, new development, running costs and effective use of space, and not on some of the wider facilities issues such as service delivery.

1.2.5 The Project built on what has gone before, but has been a pilot in the sense that we did not consult with the sector as a whole, but with a group of 39 institutions which we called ‘sponsors’ (Appendix 1). The output of the Project should therefore be seen as a consolidation of past efforts and an opportunity to take the sector forward in this very important area.

1.3.1 Our approach was based upon a combination of literature review, analysis of questionnaires developed and issued as part of the project, site visits, detailed data collection, and an extensive consultation process. The staged approach outlined in Figure 1.1 below was considered necessary both to improve the quality of the proposals (by making them more relevant and usable) and to win acceptance in the sector as a whole.

1.3.2 The approach adopted was needs - based, as will be explained in later chapters. We went through a process, supported strongly by the Steering Group (see Appendix 2 for a list of members), of identifying common objectives, then analysing ways of measuring progress towards those objectives and examining the data requirements. There then followed a period of preparing consistent definitions before collecting and validating the data and preparing the report.

Figure 1.1 The Project approach

Consultation

1.3.3 We consulted extensively with the 39 sponsors. There were nine specific consultation stages during the Project, as set out in Table 1.1. The Project also included 14 site visits.

Table 1-1 The consultation process

Stage	Focus
1. Initial survey	Identifying what sponsors required from Project
2. Seminar	One-day seminar
3. Objectives	Identifying principal objectives
4. Key ratio definition	Prioritising key ratios
5. Definitions	Developing and proposing definitions
6. Data structures	Data framework and redraft of definitions
7. Data collection	Collecting data
8. Validation	Producing draft results and approving with each sponsor
9. User satisfaction survey	Assessment of the overall project process

1.3.4 Clearly, considerable support was required from the sponsors, especially in the case of data collection. We are very grateful to the sponsors for all their efforts. This was their Project, not only because it could not have been done without them, but also because they and their counterparts elsewhere in the sector will be the principal users of the data and beneficiaries of the new data framework.

1.3.5 Another aspect of the Project was the very strong assistance the Project Team received from the Steering Group, which included several sponsors as well as finance, administrative and other experts (Appendix 2). The Steering Group met five times and provided much good advice in helping us to refine the approach and make it relevant and practical for estates departments.

1.3.6 We would also like to express our thanks to the three Funding Councils for their help throughout the course of this Project.

Principles

1.3.7 The Project was based upon a number of working principles agreed with the Steering Group:

• produce an enabling framework which will allow institutions to assemble performance-related data linked to their own objectives;
• identify Key Estate Ratios which most institutions would find helpful;
• use a simple, achievable methodology;
• make the output relevant to the business as a whole;
• develop robust, common-sense definitions;
• build on existing definitions as far as possible;
• prioritise the data requirements to maximise benefits and minimise inputting demands;
• ensure that the results are transparent;
• make the framework capable of being extended to all HEIs; and
• allow for comparison with other sectors and internationally.

A note on terminology

1.3.8 Definitions are often a matter of opinion and in order to save time, we have taken a simple approach and used terms we hope are not contentious.

1.3.9 Thus, we have identified key statistics, such as the number of taught students or the amount of floorspace. When these statistics are combined, they are called Key Estate Ratios (KERs); for example, space provision per student is a KER. In some cases, such as the percentage of buildings in good condition, the statistics and the ratios are the same.

1.3.10 The ratios collected as part of this project are not performance indicators or measures in their own right, but they are capable of being turned into performance indicators. For example, an institution might have the objective of increasing the proportion of good quality buildings from 50% to 70% of the estate over a five-year period. It would be quite legitimate to give this to the estates director as a target, with the 20 percentage points uplift as a performance indicator, provided also the means to implement the objective were forthcoming.

1.4.1 We see the main benefits of developing Key Estate Ratios as:

• enhancing property performance;
• linking estate management to the main business;
• demonstrating the added-value of the estates team and raising their profile;
• providing essential input to the estates strategy;
• advancing common interests across the sector;
• keeping in touch with best/good practice;
• awareness of strengths and weaknesses;
• identification and development of priorities; and
• learning from others in discussion groups.

1.4.2 Not all these benefits will be available in full immediately. Some will take a number of years to build up, depending on how the statistics are shared in the future. In addition, it will be important to learn from experience elsewhere in the public and private sectors, as well as internationally.

1.5.1 The Project has an important role to play in helping the sector to build up management information which will have considerable benefits for individual institutions in terms of both the operation of their estate and their general management. The approach adopted has been consensual and methodical, concentrating on the priority statistics which will help estates directors to manage their property more effectively in the interests of the institution as a whole.

1.5.2 The report is structured as follows:

• The KERs are outlined in Chapter 2, including a discussion on how they were selected. The KERs are discussed first because they effectively determine the statistics required – i.e. it is necessary to identify the key estate management needs in order to establish what data are required to support those needs.
• Chapter 3 discusses how to assemble and structure the data needed to support the KERs.
• Chapter 4 provides a summary analysis of the KER results.
• The ways in which the outputs from the project can be taken forward are considered in Chapter 5, including a series of recommendations for next steps.
• Finally, Chapter 6 provides a summary of the recommendations made in the report.