The Dynamics and Measurement of Commercial Property Depreciation in the UK

Summary Report by:

Dr Tim Dixon

Director of Research

College of Estate Management, Reading

Additional Research by:

Victoria Law

Judith Cooper

March 1999 1999/1

Published March 1999 by the College of Estate Management Whiteknights, Reading, Berkshire, RG6 6AW © The College of Estate Management 1999 with the exception of CB Hillier Parker data which are the copyright of CB Hillier Parker ISBN 1 899769 72 2

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Foreword and Acknowledgements It is now some 12 years since the seminal CALUS study on depreciation which Francis Salway produced. Since then, others such as Andrew Baum, Richard Barras and Paul Clark have strengthened our knowledge and understanding of depreciation with research targeted towards specific locations. Clearly in the low growth 90s the issue of depreciation remains, and so this new research is designed to further enhance the property profession’s understanding of commercial property depreciation and the forces that shape its measurement, not just in selected areas, but nationally across the UK using rental value data. Further on, and having completed the research over a three year period, I can safely say it has proved to be one of the most challenging and thought-provoking projects undertaken by the College’s research team. The time and resources invested in this work has been substantial, not only from our team, but also from valuers and others in sponsor organisations. We are grateful to them for providing the information needed to carry out the research, which encompassed the analysis of more than 700 properties and 33 case studies. The research was generously funded by a range of leading investment organisations and advisors including Prudential Portfolio Managers lid, Boots Properties, Standard Life Investments, Henderson Investors, Royal Sun Alliance, Pat Allsop Trust and CB Hillier Parker. In reporting our results I am mindful of protecting confidentiality but also in presenting results which we feel are of most interest to our audience. To that extent whilst we report aggregated sector results for offices, standard shops and industrials, the focus is particularly on offices. Although the views contained within this report are those of the research team at the College of Estate Management, and not the sponsors, I would especially like to thank the following for their helpful comments during the course of the research: • Paul Mitchell and Paul McNamara at Prudential Portfolio Managers; • Peter Hobbs, Mike Dutton and Richard Bartholomew at Boots; • Francis Salway at Standard Life Investments; • Andrew Smith and Catherine Williams at Henderson Investors; • Ian Dowson, Anne Furlong and Stephen Ellis at Royal Sun Alliance; • John Oxley and David Law at Allsop & Co, • Allan Patterson, Guy Weston, David Martin, Tony McGough and Mark Teal at CB Hillier Parker. My thanks are also due to Professor Neil Crosby of Department of Land Management and Development, University of Reading, who assisted us in the development of ideas in the Pilot phase of this research, and which contributed to Chapter 2 of this report. I would also like to thank James Gallagher and Dr Ian Wilson of The University of Reading Statistical Services Centre for their detailed advice in formulating the statistical methodology. My thanks are also due to Alison Andrews for her typing of the final manuscript. My own personal thanks are due to Vicky Law and Judith Cooper who, during their time at the College as part of our research team, contributed a great deal to the successful outcome of the research, both

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in terms of data and statistical analysis and report production. This final report is partly the result of a great deal of hard work and good humour from them both. Finally, the College would like to dedicate this report to Norman Bowie whose trail-blazing in the early 1980s first brought the spectre of depreciation to the attention of the property world. Confidentiality To protect the confidentiality of sponsors and their property holdings, no address details are provided in this report nor are individual sponsors named in relation to data availability or data quality issues. Copyright The copyright of this report is held by the College of Estate Management, with the exception of the data supplied by CB Hillier Parker, the copyright of which is retained by CB Hillier Parker. Dr Tim Dixon BA(Hons) DipDistEd FRICS Director of Research College of Estate Management Whiteknights Reading RG66AW Tel: 01189861101 Fax: 01189577344 Email: t.j.dixon@cem.ac. uk December 1998

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Executive Summary

Previous studies of property depreciation have frequently focused on restricted geographical areas. This is partly due to data limitations, stemming from confidentiality issues and the complexities of assembling a comprehensive dataset from disparate sources. Using rental value data supplied by IPD, data from the Hillier Parker Rent Index, and other property-specific information from the research consortium of sponsors, the College of Estate Management undertook a large-scale, national study of rental depreciation in the commercial and industrial property sectors and ‘newer’ property types, such as shopping centres, retail warehouses and office parks. The research, which was carried out in 1996-97, sought to analyse the process of depreciation, its effect on the performance of rents, and the impact of capital expenditure on depreciation, and involved more than 700 properties. This report summarises the results from the research and focuses particularly on offices. • For the period, 1984-95 offices have the highest depreciation of 3.05% p.a., industrials depreciated

at 0.32% p.a. but retail ‘appreciated’ by 0.28% p.a. • Depreciation rates across all sectors appear to be lower in the ‘slump’ of 1990-95 than in the

‘boom’ of 1984-89. For example, offices depreciated by 6.03% during 1984-89 but 3.52% during 1990-95.

• For standard retail units and offices town type is the most significant factor in explaining

depreciation rate. In this respect, London offices suffered generally higher depreciation than other centres both during 1984-95, and in the boom and slump.

• Conversely, age (as represented by construction period) and whether a property is in a prime or

non-prime location are less important than town type in explaining depreciation rate. • Locational quality (LQ) change is a feature of both the office and retail markets. • No significant relationship between LQ change and depreciation rate could be established, but

limited evidence suggests, overall, that higher depreciation tends to be associated with LQ change.

• In the West End of London, limited evidence suggested refurbished office properties depreciated

less than original buildings in the period 1984-95 but data constraints made it difficult to analyse the impact of capital expenditure on depreciation.

• The data used is the best available to the research team, but the depreciation rate results should

be set against issues of data quality, especially the interpretation of ERV by valuers from boom to slump, In comparison with the Hillier Parker Rent Index. For example, a systematic ‘lag’ in ERVs against the HP Rent Index in the slump of 1990-95 could lead our results to show lower depreciation rates over this period than might actually be the case. Similar divergence in the 1980s boom would also lead to higher than expected rates during this period.

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CONTENTS FOREWORD AND ACKNOWLEDGEMENTS 2 EXECUTIVE SUMMARY 4 CONTENTS 5 1.0 INTRODUCTION 10 1.1 Summary, Aim and Objectives 10 1.2 Research Design and Methodology 11 1.3 Format of Report 12 2.0 PROPERTY DEPRECIATION AND ECONOMIC DEPRECIATION- A CRITICAL REVIEW 13 2.1 Introduction 13 2.2 Property Depreciation Studies 13 2.2.1 CALUS Report 13 2.2.2 JLW Study 15 2.2.3 Baum’s Studies 15 2.2.4 Barras and Clark 16 2.2.5 Weatherall, Green and Smith Study 17 2.3 Economic Depreciation Studies 17 2.3.1 Studies of Economic Depreciation in Commercial Real Estate 18 2.4 Issues Arising from the Literature Review 20 2.4.1 Definitions and Concepts 20 2.4.2 Economic Depreciation Methodologies 24 2.4.3 Patterns of Depreciation: Cause and Effect 26 2.4.4 Longitudinal and Cross-Sectional Studies 30 2.4.5 Building Quality 31 2.5 Summary 33 3.0 RESEARCH DESIGN, METHODOLOGY AND TERMINOLOGY 34 3.1 Introduction 34 3.2 Research Design and Choice of Rental Index 34 3.3 Methodology and Terminology 35 3.4 Case Studies 38 4.0 RESULTS 39 4.1 Introduction 39

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4.2 Sample Size Age Distribution and ADR Analysis 39 4.3 Regression Analysis and Variable Selection 39 4.4 Overall Patterns of Sector Depreciation (EDRs): 1984 Cohorts 42 4.4.1 Sector Comparisons 42 4.4.2 Market State 43 4.4.3 Data Quality Issues 43 4.5 Standard Offices 45 4.5.1 The Sample 45 4.5.2 1984Cohort 46 4.5.3 1990 Cohort 46 4.5.4 Other Descriptive Comments 46 4.5.5 Comparison of Cohorts 47 4.5.6 Locational Quality 47 4.5.7 Capital Expenditure 47 4.5.9 Case Studies 50 4.5.10 Data Quality Issues 50 5.0 CONCLUSIONS 52 5.1 Introduction 52 5.2 Main Findings 52 5.2.1 Sector comparisons 52 5.2.2 Market State 52 5.2.3 Age as a ‘Causal’ Factor 52 5.2.4 Locational Quality 53 5.2.5 Refurbishment 54 5.3 Data Quality Issues 54 5.4 Significance of the Research 57 5.5 Further Research 58 BIBLIOGRAPHY 59 APPENDIX A - STATISTICAL METHODOLOGY 62 APPENDIX B - DESCRIPTIVE STATISTICSIGRAPHS (ADRS) FOR OFFICES 69 APPENDIX C OVERALL EDR COMPARISON, BY SECTOR 71 Table C1 1980 Office Cohort - Overall EDR 71

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Table C2 - 1984 SSU, Office, and Industrial Cohorts - Overall EDR 72 Table C3 - 1990 SSU, Office and Industrial Cohorts - Overall EDR 73 APPENDIX D - OFFICES 74 Table D1 - Sample Size and Composition 74 Table D2 - Mean Age, Office Cohorts 75 Table D3 - Sample Size and Composition 76 Table D4 - Sample Size and Composition 77 Table D5 - Age Profile, 1984 and 1990 Office Cohorts 78 Table D6 - Movement of LQ Change (Offices) 79 Table D7 - Regression Results - Office Locational Quality Change 80 Table D8 - Refurbished Offices 1980 Cohort (No LQ Change) 81 Table D9 - Refurbished Offices, 1984 Cohort (No LQ Change) 82 Table D10 - Variable Selection: 1980 Office Cohort 84 Table D11 - Variable Selection, 1984 and 1990 Office Cohort 85 Table D12 - Variable Selection, 1980 Office Cohort 86 Table D13 - Variable Selection, 1984 Office Cohort 87 Table D14 - Variable Selection, 1990 Office Cohort 88 APPENDIX E - CASE STUDIES: OFFICES 89 Case Studies - Summary Table 89 Original Buildings: No Location Quality Change, Offices - Prime 90 Refurbishments 93

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TABLES AND FIGURES Tables 2.1 Summary of Previous Property Depreciation Research 14 2.2 Rates of Economic Depreciation 26 2.3 Rates of Depreciation 27 3.1 Case Studies’ Sector Breakdown 38 4.1 Variable Selection, 1984 and 1990 SSU Cohorts 41 4.2 Variable Selection, 1984 and 1900 Office Cohorts 42 4.3 EDR by Sector (1984-95) 43 4.4 EDRs by Town Type - 1984 Office Cohort 46 4.5 EDRs by Town Type - 1990 Office Cohort 46 5.1 Original Properties: EDRs (No LQ Change and LQ Change) 54 5.2 Previous Depreciation Studies: A Summary of Rental Depreciation 57 Figures 2.1 The Effect of Age, Inflation and Obsolescence on Age-Price Profiles 22 2.2 Efficiency Profiles for Different Depreciation Patterns 25 2.3 Age-Price Profiles for Different Depreciation Patterns 25 2.4 Geometric Age-Price Profiles for Offices and Industrials (Hulten & Wykoff) 28 2.5 Age-Rent Profiles for Offices (Cross-Sectional Studies) 28 2.6 Age-Rent Profiles for Industrials (Cross-Sectional Studies) 28 2.7 A Classification of Depreciation and Obsolescence 32 3.1 Cohort and Market State Frameworks 36 4.1 Number of Properties in Each Cohort 40 4.2 Mean Age of Cohorts (Original, No LQ Change) 40 4.3 Depreciation Rates (EDR5): 1984 Cohorts by Sector (1984-95) 44 4.4 Depreciation Rates (EDR5): 1984 Cohorts 44

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4.5 ADR by Age Group: Prime Offices 48 4.6 EDR: Standard Offices (Market State Comparison) 48 4.7 LQ Change: 1984 Office Cohort 48 4.8 Case Study 01/CSI4 London City Office (Prime) 1980-95 49 4.9 1990 Office Cohort - London City Offices 49 4.10 Office Properties: Comparison of HP Index and ERV 49 5.1 Retail Properties: Comparison of HP Index and Zone A 56 B1 Descriptive Statistics/Graphs of (ADRs) - Offices 70

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1.0 INTRODUCTION 1.1 Summary, Aim and Objectives Previous studies of property depreciation have frequently focused on restricted geographical areas. This is partly due to data limitations, stemming from confidentiality issues and the complexities of assembling a comprehensive dataset from disparate sources. Using rental value data supplied by IPD, data from the Hillier Parker Rent Index, and other property-specific information from the research consortium of sponsors, the College of Estate Management undertook a large-scale, national study of rental depreciation in the commercial and industrial property sectors and ‘newer’ property types, such as shopping centres, retail warehouses and office parks. The research, which was carried out in 1996-97, sought to analyse the process of depreciation, its effect on the performance of rents, and the impact of capital expenditure on depreciation. The aims of this main study, which follows the unpublished pilot study (CEM, 1996), are to: • analyse the process of depreciation and its effect on the performance of rents in the commercial

and industrial property markets, and • examine the impact of capital expenditure on depreciation in the same property markets. Based on two main sources (Investment Property Databank (IPD) and Hillier Parker data and case studies), which are examined in more detail in section 3.0 below, the objectives arising from these aims are as follows. IPD and Hillier Parker Data • analyse rental depreciation patterns over 1980-95 by town and property type (both ‘main’ sectors -

retail, offices and industrial - and ‘new’ sectors - shopping centres, retail parks and office parks); • differentiate rental depreciation rates within particular sectors on the basis of building

characteristics (eg. prime and non-prime, construction date, town type, etc.); • examine the issues of location and changes in location quality in relation to selected property

types, especially retail; • differentiate rates and patterns of rental depreciation between refurbished and non-refurbished

properties; • examine the importance of age as a ‘causal’ factor in rental depreciation; and • investigate the relationship between rental depreciation and market state over time. ‘Thin’ Case Studies • examine the process of rental depreciation and how it operates over time; • quantify the rate of capital expenditure and analyse its impact on depreciation; and, • differentiate rates and patterns of depreciation between properties with different building

characteristics (eg. prime and non-prime, construction date, town type, etc.).

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1.2 Research Design and Methodology Building on the pilot study (CEM, 1996), the current research was funded by a range of leading investment organisations and advisors which included:

• Henderson Investors; • Boots Properties; • CB Hillier Parker; • Pat AlIsop Trust; • Prudential Portfolio Managers; • Royal Sun Alliance; and • Standard Life Investments.

The first stage of the study comprised a detailed national, longitudinal analysis based on ERV, floorspace and other IPD data obtained for the period 1980-95. To measure rental depreciation rate the ERV of the subject property was compared over time with the prime HP Rent Index, which is market-based and uses 100% locations. Locational quality data was also obtained for each property direct from the sponsor organisations. The second stage of the research incorporated a sample of 33 case studies covering the main property sectors. Where possible, data going back to 1970 was used. Annual rates of depreciation were calculated in two main ways:

• Estimated Depreciation Rate (EDR) was determined from regression analysis; and, • Average Depreciation Rate (ADR) was calculated by using the ERV:HP ratio at the

start and end of a time series to produce a geometric mean. The study used the concept of ‘cohorts’ (or a separate group of properties studied from the same start point over the relevant time period) to maximise the use of the dataset and compare EDRs over time. The total number of properties used in the analysis was 728 (which included 33 case studies) The totals by sector were as follows:

1980 Cohort 1984 Cohort 1990 Cohort Standard Shops 210 84 Standard Offices 97 153 113 Standard Industrials 23 32 Retail Warehouses 6 Office Parks 5 Shopping Centres 5 The total of 728 represents 36% of all relevant properties initially supplied to the research team. Finally, although we believe that our data is the best available to us, the possibility of valuers differing in their perception of rental value over the market cycle remains very real. For example, a systematic ‘lag’ in ERVs against the HP Rent Index in the slump of 1990-95 could lead our results to show lower depreciation rates over this period than might otherwise be the case. This is explained in more detail in sections 4.4.3, 4.5.10 and 5.3 of the report.

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1.3 Format of Report The format of the report is as follows:

• Section 2 - a critical review of the property and economic depreciation literature; • Section 3 - research design, methodology and terminology; • Section 4 - presents the results of the research on a sector-by-sector basis. It

includes a comparison of the three main sectors (standard shops, offices and industrials) which were investigated in the research and focuses particularly on offices; and,

• Section 5 - summarises the main findings of the research and presents conclusions.

The Appendices and the selected office Case Studies are printed on pale blue paper at the end of the report.

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2.0 PROPERTY DEPRECIATION AND ECONOMIC DEPRECIATION - A CRITICAL REVIEW 2.1 Introduction A key objective of the pilot study (CEM(1996)) was to provide a conceptual framework for the main study. This required a critical examination of background theory relating directly to property but also to other fields, in particular, economic depreciation. From this examination it was then possible to identify a number of critical issues which extended the scope of previous studies and provided a basis for developing the conceptual framework and methodology for the main study. The literature breaks down conveniently1 into two main fields,

• property depreciation, and

• economic depreciation This part of the report therefore examines and reviews these two fields. For each field of study a summary and critique is included. Finally, both fields of study are synthesised by extracting the key issues from each in order to develop the conceptual framework, and help formulate a valid methodology for analysing depreciation. 2.2 Property Depreciation Studies The five key studies which have been carried out in this field are summarised in Table 2.1. A brief summary and critique of each study now follows, concentrating on the methodologies adopted. 2.2.1 CALUS Report The CALUS research (carried out by Francis Salway (CALUS, 1986)) was important in investigating the depreciation of commercial property for the first time. In particular, it sought to identify the impact of depreciation on property values and to better understand how analytical models could incorporate depreciation. The study examined users and property investors and was based on a cross-sectional analysis. In summary, the study comprised three areas of empirical research: • a survey of users’ views on the problems of older office and industrial buildings;

• a survey of property investors’ views and policies on depreciation; and

• a cross-sectional survey of differences in value between new and older office and industrial

buildings at one point in time (June 1985). This was supported by a limited longitudinal study. The cross-sectional study examined rental values and yields for hypothetical buildings of different ages in 32 office locations and 25 industrial locations. The principal variable was the age of the building - namely: brand new, 5 years old, 10 years old, or 20 years old.

1 To some extent the division is artificial because a number of economic depreciation studies examined property and real estate. Nevertheless, these studies emanate directly from the theoretical studies of economic depreciation, and not property depreciation and so fit most comfortably within the former category.

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15

Hypothetical buildings were used as the focus of the study to control for size and location, and, in essence, the age of the buildings was used as proxy for all factors contributing to building depreciation. The study did not, therefore, seek to isolate the impact of obsolescence (vis a vis age) on depreciation, and went on to suggest that further research was needed to expose the forces behind depreciation. Furthermore, by using a hypothetical, ‘Delphi’, approach it was clear that the study could not be seen as strictly market-based, because valuations of actual buildings did not form a part of the data set. Nevertheless, as a landmark study, it did act as a catalyst for other research in the field. 2.2.2 JLW Study Using data from the PPAS database the JLW study (1986) focused on rents, since capital values tend to reflect investors’ expectations about future rental growth. Again, this research concentrated on the relationship between ‘obsolescence’ and age, although the term ‘obsolescence’ was not defined or distinguished from ‘depreciation’ in the study. To cope with the variation of rents over time and location, the study expressed ‘Estimated Rental Value’ (ERV) for each property as a function of the ‘full market rent’ (FMR) for the same location and year. This fraction, known as the ‘rental obsolescence rate’ (ROR), was then compared with the age of the building. FMR was derived from the JLW 50 Centres Guide, and the analysis was carried out for both offices and industrial properties using a cross-sectional approach for each year during the period 1980-85. The market state in 1985 was found to have a substantial effect in the study which in these terms alone leave it open to criticism, although a strong relationship between age and ROR was established in the study. 2.2.3 Baum’s Studies Criticisms of these earlier studies led to Baum (1991) initiating further work to extend their scope and focus in three main related areas:

• the definition and classification of ‘depreciation’ and ‘obsolescence’ • the development of a model which could examine and measure the causes of

depreciation; and • the creation of a computer-based ‘depreciation-sensitive’ decision model which could

measure the sensitivity of individual property investments to depreciation factors. In order to achieve this, the empirical study related depreciation to age, before measuring the impact of building quality on depreciation. The study examined both offices and industrials and used a cross-sectional study supported by a longitudinal study of rents. Site variations and their potential impact were isolated by selecting well-defined study locations: City of London for offices and Slough for industrials. Property value was taken as a proxy for building value and the effect of site value was removed by holding it constant. A loss in real property value was measured by comparing the value of each building in the data sample with a hypothetical new building with similar qualities. Existing use value was isolated by assembling data free of changes in use, and changes in plot ratios were excluded by measuring property value on a unit of space basis. ERVs, yields and capital values were collected, and both tenure and site depreciation were excluded

16

to leave the real existing use value of the building for analysis. In essence, the empirical part of the study comprised an analysis of:

• cross-sectional rents (as at August 1986); • cross-sectional yields; • cross-sectional capital values; and • longitudinal rents (1980-86).

ERVs for each property were produced using a panel of three surveyors. This was based on the ERV for a typical new lease of a 10000 sq ft unit for each property. From this an ERV index was produced (ie the mean ERV for properties in the 0-4 yrs range). The data used was not an actual measure of market price, but Baum argued that actual letting values were not available because of the size of geographical area and the relatively low number of transactions which take place within a closely defined time frame. He also argued that transactions would be distorted by the presence of letting inducements and that the ‘panel’ or ‘Delphi’ approach mirrored the open market basis of setting rental values. The analysis was performed at two levels. Initially, using regression analysis depreciation was related to age and secondly to building qualities (i.e. external appearance, internal specification and configuration). This set of building qualities was derived from an analysis of occupiers, and buildings ranked on a scale of 1 to 5 by the panel according to these factors. These findings are also mirrored in Baum’s (1997) updated study of City of London Offices supplemented by the West End, in which he confirmed again that building quality was a more important factor in explaining depreciation than age. This study used a similar approach to the 1986 survey. A pattern of increasing depreciation over the period 1986-96 emerged, and Baum used both longitudinal and cross-sectional approaches. Interestingly, the former used the IPD Index (which is ageing) to deduct ‘market’ depreciation from overall depreciation (ie. average rental decline for the sample) to determine ‘age-related’ depreciation. Baum’s work is important because it focused on the causes of depreciation for the first time. The data limitations were recognised by the author and have been highlighted by others including Khalid (1992). In particular, the taxonomy of depreciation and obsolescence did not distinguish between obsolescence sub-groups particularly in relation to ‘functional’ and ‘technological’ obsolescence (see 2.4.1 below). Furthermore, further detailed analysis is needed into building quality for different property types, and taken beyond merely an occupier-based study. By using actual buildings, even though it still requires a valuation based approach, it did avoid the problems associated with the ‘hypothetical building’ approach in CALUS. 2.2.4 Barras and Clark It was partly to take account of these criticisms that Barras and Clark (1996) decided in their study to use valuation-based ERV data derived from IPD, which they felt provided a much closer approximation to the behaviour of the market than the ‘artificial judgements’ of agents using a set of ‘hypothetical buildings’. Their study was based on hypotheses which stemmed from Salters (1966) ‘vintage’ model which saw each investment in new capital as embodying an improved technique of production, which in turn lowered the unit operating cost of successive vintages. This has close parallels with the economic

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depreciation literature (see section 2.3 below). In particular, they examined the depreciation pattern of individual buildings through ERVs and yields. They also examined the impact of such patterns at a portfolio level, by testing how average rates of rental and capital growth might vary from the market area across age bands. The study was based on IPD City of London office data, and used both a cross-sectional (1980, 1989, and 1993) and longitudinal (1981-93) approach to analyse the data. In this sense, the study is valuations-based and so reflects valuers’ perceptions rather than market pricing, but avoids the problems of a purely ‘hypothetical’ approach. The performance of City offices which remained continuously in the IPD portfolio for the period 1981-93 was compared with the performance of the whole City portfolio, which acted as a market proxy. Refurbished buildings and those built prior to 1945 were excluded from the analysis. However, the study concentrated on one single geographical location and a single sector, and to that extent was more limited than either CALUS or Baum. Furthermore, the study failed to distinguish ‘obsolescence’ from ‘depreciation’, and used the two terms interchangeably. This criticism is examined in more detail in section 2.4.1 below. 2.2.5 Weatherall, Green and Smith Study This study did distinguish obsolescence as a cause of depreciation, and in terms of quantification defined depreciation as measuring the ‘declining relative worth of a building’, while obsolescence measured ‘its continued usability for a given purpose and its adaptability for another. Ultimately, however, the study measured depreciation rates. The study examined the relationship between building age and investment performance across offices, retail and industrials in the UK, using data from IPD. Each group contained broadly similar sub-groups for ease of comparison, and for each group the investment record of a series of age bands was examined for the period 1980-93. The study was therefore longitudinal, and examined rental growth, capital growth, total return and equivalent yield. However, a notional prime property was not used as the benchmark for performance: it was argued that a market-based return was a more valid measure, and so this was calculated for the relevant sub-sector or region. 2.3 Economic Depreciation Studies Hulten and Wykoff (in Hulten (ed)(1981:85) define ‘economic depreciation’ as a ‘decline in asset price due to ageing’. Previous property depreciation studies, with the exception of Barras and Clark (who built on Salter’s (1966) work), have tended to overlook this field, which stems from the early work of Hotelling (1925), and is largely based on theoretical and empirical studies in the USA. Many of these studies have concentrated their efforts at a macro- scale level, and the debate has centred around the rate and pattern of depreciation to include in any system of national income accounts, and associated tax allowances, in order to reflect accurately the impact on real assets, ranging from plant and machinery to real estate. The empirical studies that have been carried out can be classified according to the:

• type of asset studied (e.g. real estate, automobiles or machine tools); • statistical methodology adopted (e.g. observed age, hedonic pricing); and • basis of data used (i.e. asset price or rental price).

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Studies of economic depreciation have covered a wide variety of assets. For example, Wykoff (1970), Ramm (1971) and Akerman (1973) studied the depreciation of automobiles. Hall (1971) studied trucks, Griliches (1970) tractors, and Oliner (1996), machine tools. These, and other related studies, are reviewed in Jorgenson (1996) and Hulten and Wykoff (1996). There have also been real estate related studies. Residential housing was examined, for example, by Chinloy (1977) and Malpezzi, Ozanne and Thibodeau (1980) and commercial property by Hutten and Wykoff (1976, 1980, 198Ia, I981b), and Taubman and Rasche (1969). The methods employed to determine the rate at which structures depreciate also vary a great deal. These methods include:

• observed age method;

• macroeconomic or econometric models (for example, the perpetual inventory method); and,

• hedonic pricing methodologies. The observed age method (see, for example, Grebler et al (1956)) simply imposes a particular depreciation pattern on the average life of structures to derive the depreciation rate. Macroeconomic methods have been used in residential studies (Leigh (1979) for example) and general structures (for example, Young and Musgrave (1980)). The perpetual inventory method, for example, builds up the time-series of capital stock from time-series of investments and capital goods. Hedonic pricing models have also frequently been used. These use multiple regression techniques to derive the most important explanatory variables (including age) for price in terms of their correlation, and furthermore, attempt to determine how much price change is attributable to key variables. Hedonic pricing models have tended to use cross-sectional data, because of the difficulty for controlling for other influences over time. Hedonic price is the ‘implicit’ price of an attribute of a good, which is revealed through derived prices of differentiated products and the specific amount of attributes associated with them. Hulten and Wykoff (various (op. cit.)) used this technique as did Malpezzi et al (op. cit.) and Khalid (1992). Finally, the empirical studies may be distinguished by their use of price data (for example, Hulten and Wykoff (op. cit.) and Taubman and Rasche (op. cit.)) or non-price data, as used by the US Bureau of Economic Analysis (BEA), and Coen (1975). 2.3.1 Studies of Economic Depreciation in Commercial Real Estate Two important studies which are now highlighted are those by Hulten and Wykoff (various op. cit.) and Taubman and Rasche (op. cit.). Hulten and Wykoff (1976) in their seminal study of sixteen classes of ‘structures’ in the USA utilised used asset price data to determine depreciation rates over time. At the heart of their model was the price effect of depreciation measured by:

D(s,t) = q(s,t) - q(s,t + I)

where D is depreciation of an s-year old asset at time t, and q is price.

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To measure the effect of depreciation data was extracted from a survey of building owners conducted by the US Treasury in 1972. The survey contained information on various classes of structures, for example, shopping centres and offices, and included details on date of construction, acquisition date, floor area and so on. The aim of the study was to measure economic depreciation and then compare it with tax depreciation to generate new estimates of industry capital stock. To achieve this they subdivided the sample (which included 526 factories, 1654 offices, 1666 retail trade buildings and 580 warehouses) and ran equations in the following form:

Pt = F (Agest, t, x)

where Pt is the acquisition price in the year of acquisition, denoted by t; Age is the age of the building;

t is the year of acquisition; and

x is a vector of characteristics, including structural material variables, construction quality characteristics, and the business income and population of the geographic region in which the structure is situated.

Implicit in their model was the fact that the estimates of depreciation rate included quality changes, where these occur, due to obsolescence, or a ‘vintage effect’. This is because the independent effects of age, date and vintage cannot be separately identified econometrically (Hall (1968)). They also recognised that buildings are location-specific and can differ significantly in terms of quality, size, and subsidiary equipment (e.g. elevators, ventilation, etc.), and to deal with this, the ‘x’ variable in their model was included; acquisition price was dealt with on a per square foot basis; and acquisition prices were calculated net of land value. Moreover, because the data consisted of a cross-sectional sample taken at a single point in time, only surviving assets were included in the study. To overcome potential bias therefore, and to ensure that depreciation estimates reflected the performance of typical assets in each vintage, an allowance was made for ‘non-survivors’ using retirement pattern estimates. Nonetheless, the authors (Hulten and Wykoff (1976:36)) state:

'. . . it is obvious that we are dealing with a highly non-homogeneous group of assets and our results should be interpreted accordingly.’

To determine the depreciation patterns for assets they used a polynomial power series and Box-Cox power transformations to determine the speed and path of depreciation. In contrast to this work, Taubman and Rasche’s (1969) study of offices used rental price data. In the preamble to their study they acknowledge that the value of capital can decline due to ‘wear and tear’ and ‘obsolescence’, through technical change or outmoding. Furthermore, they include both wear and tear and obsolescence under the general heading, ‘depreciation’, which, they argue, can be measured by the sum of market value change plus the cost of repairs made. They used a sales revenue approach to calculate present values for offices of different ages, which they then converted into an expected future profile for a new building to determine its economic life and present value in each year of its existence. Using discount rates of 5% and 10% they calculated present values for, each cross-section profile of buildings from 1951-63, but Taubman and Rasche’s study has a number of limitations which are important to point out, for example, the study ignored inflationary effects and assumed inflation did not have a differential effect on prices and costs. Again, the answers derived are ex-ante measures, in

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that they assume the prevailing conditions for the cross-section would continue for a further 70 years. Finally, only four age intervals were used for the analysis, and the length of lease used in the US office market could have led to bias towards an increasing depreciation rate pattern, because rents remained constant over the period of the lease. 2.4 Issues Arising from the Literature Review A number of important issues are raised by the studies which have been described in sections 2.2 and 2.3 above. In turn, these can assist with developing both a conceptual framework and a valid methodology for the current study. 2.4.1 Definitions and Concepts Baum (1991:59) in his discussion of depreciation and obsolescence distinguished depreciation as ‘the loss in the real existing use value of property’ from obsolescence, which as one of the causes of depreciation, is defined as ‘a decline in utility not directly related to physical usage or the passage of time’. Physical deterioration was viewed by Baum as the other main cause of depreciation and this dual effect of obsolescence and deterioration is confirmed by Flanagan et al (1989), who distinguished obsolescence as a ‘relative loss of utility’ from deterioration, as ‘an absolute loss in utility’. However, there are a variety of sub-groups of obsolescence which have been further classified by Khalid (1992), including ‘functional’ and ‘technological’ obsolescence, which Baum did not differentiate. For example, functional obsolescence can occur as a product of technological change leading either to changes in occupiers’ requirements, or the introduction of new building products. Examples of this might include a defective layout, or an inability to accommodate new IT. The term is thus used in relation to the whole building, whereas technological obsolescence refers to components of a building which can become technologically inefficient; for example, mechanical and electrical services and facilities. Functional obsolescence tends therefore to be incurable, whereas technological obsolescence is often curable. Although Baum argued that legal and social obsolescence are separate sets of functional obsolescence, Khalid expanded Baum’s taxonomy of two types of obsolescence (aesthetic and functional obsolescence) to eight:

- economic; - functional; - aesthetic; - environmental; - legal and social’ - technological; - locational; and - physical.

Clearly, further work needs to be carried out in developing these precise taxonomies for different property types. Baum’s study was limited to offices and industrials, and Khalid’s to offices, and the ‘building quality’ factors which are a measure of obsolescence will certainly vary between building type. The property depreciation studies also failed to recognise the important work carried out by Hulten and Wykoff and others in the field of economic depreciation, although the theoretical debate in this field can assist in understanding how depreciation operates.

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In essence, depreciation theory involves distinguishing between the value of the stock of capital assets and the annual value of the asset’s services, and accounting for the decline in an asset’s value through economic depreciation and physical depreciation. ‘Economic depreciation’, in this sense, is the decline in asset price due to ageing (Hulten and Wykoff in Hulten (ed)(1981:85)), and ‘physical depreciation’ (or ‘mortality’) is the loss in productive capacity of a physical asset due to loss of in-use efficiency or to retirement (1-lulten and Wykoff (1981 b)). This work builds on Feldstein and Rothschild (1974), who define ‘depreciation’ as the fall in price of an asset as it ages, and ‘deterioration’ of a piece of equipment or asset, as the increase in real resource cost per unit of output as an asset ages. If the relationship between age and price is accepted, the value of a s-year old asset may be represented by point a on curve AB and the value of a s + 1 year old asset by point b (figure 2.1). Economic depreciation is therefore equal to the difference on the price axis between a and b, and the rate of economic depreciation as the percentage decline along the curve AB (or the ‘age-price’ profile). In fact, the move from a to b is driven by two factors:

• an ‘ageing’ effect, because as an asset ages it may lose some of its original productive

efficiency, and/or as it ages it moves closer to ‘retirement’ from service; and

• an ‘obsolescence’ effect, because newer assets may appear with technologically superior designs which reduce the price of existing assets when the cost savings of the newer assets become embodied in the older obsolescent ‘vintages’ (i.e. the year in which a cohort (or group of assets) is built). This idea was also explored by Salter (1966), but with the emphasis on lowering unit costs within a firm/organisation context.

Furthermore, suppose there is a shift in the age price profile from t = 1 to t = 2. This shift would be driven by:

• an ‘inflationary’ effect, through general price inflation and supply and demand stock in relative prices; and,

• an ‘obsolescence’ effect, caused as a result of improvements in the quality of new assets, if

those improvements are achieved at a cost. The overall effect of these changes is therefore a move from a to c in the figure. Extracting the differential impact of age, inflation and obsolescence can be very difficult, as Hall (1968) has pointed out. Although hedonic pricing models are an option therefore, the estimates of depreciation and inflation must implicitly include a ‘quality change’ or ‘vintage effect’ due to obsolescence. Economic depreciation has so far been defined in terms of the decline in asset price due to ageing. However, assets may also experience a fall in physical efficiency with age, and efficiency ratios may be calculated for different ages of an asset’s life. A new asset, for example, has an ‘efficiency’ ratio, or index, of I .0, based on the ratio of rent of a s- year old asset to a new asset. This presumes, of course, that rent is an accurate surrogate for efficiency, which is normally calculated by reference to a marginal product ratio. Depreciation, through efficiency decay, could therefore give rise to three types of pattern (figure 2.2):

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Figure 2.1 The Effect of Age, Inflation and Obsolescence on Age-Price Profiles

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• geometric decay, when the asset loses efficiency at a constant • percentage rate; • straight-line decay, when the asset loses efficiency in equal increments over its life; and • ‘one-horse-shay’ in which the asset retains full efficiency until retirement

The use of rental value data (e.g. Taubman and Rasche (op.cit.)) to map depreciation patterns over time is therefore an alternative to the use of age-price profiles. Figure 2.3 shows the corresponding age-price profiles for each asset efficiency profile: except in the case of the geometric pattern the profiles in figure 2.3 differ from figure 2.2 because of the differential impact of rents and yields in the net present value/price model. Hulten and Wykoff used this type of age-price profile analysis to map depreciation patterns. The economic depreciation literature is therefore useful in providing a further insight into how depreciation may be studied. Moreover it is useful to borrow the terminology which stems from this literature; in particular, the terms, ‘cohort’ (group of assets) ‘vintage’ (the year in which a cohort is built) and ‘age’ (year since construction or refurbishment). 2.4.2 Economic Depreciation Methodologies The economic depreciation literature also raises a number of issues which relate to the methodology for measuring depreciation. These issues were not pursued by the property depreciation literature. First, the issue of ‘censored sample bias’ or retirement of assets in the sample. In studies using market prices, the issue of the measurement of assets that do not survive the study period is raised. Hulten and Wykoffs (op. cit.) study includes price corrections for this retirement of assets by multiplying the asset price of surviving assets by a probability of that age of asset surviving (plus the asset value of retired assets multiplied by the probability of retirement). They assume a nil value for non surviving assets so the latter value is nil. Not taking into account censored sample bias will mean that the depreciation rates will only relate to the surviving assets of any age group. However, DeLeeuw (1981) suggests that this is only relevant for machinery, not structures. He bases this on the idea that retirement of structures is often redevelopment, refurbishment or change of use when the present value of the existing asset is greater, assuming the change, than if the asset remains in its existing state. The second issue is that of ‘lemons’. ‘Lemons’ are assets that are sold in the market but do not conform to the average of those which are kept until retirement. Where comparable market prices are used to determine the asset values of the sample assets, it is important that the transactions are good comparisons. If the only comparables on the market are those which are there because they do not conform to the rest of the population of assets, the valuations on which depreciation estimates are founded may be flawed. A third issue raised by the literature is that of ‘filtering’. Archer and Smith (1992) describe it as a change in the quality of the use of a structure and their analysis of office rents in Orlando and Jacksonville was tied into data on the changing use of ageing buildings. Tenants were graded by being in or out of the Fortune Five Hundred top office occupiers and the declining percentage for different age groups of buildings was recorded. Salway (CALUS, 1986) also highlights the issue suggesting it might provide a useful insight into the shape or pattern of depreciation for particular property types.

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Figure 2.2 Efficiency Profiles for Different Depreciation Patterns

Figure 2.3 Age-Price Profiles for Different Depreciation Patterns

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The economic depreciation literature therefore increases our understanding of the patterns of depreciation and the methodology for assessing its impact. 2.4.3 Patterns of Depreciation: Cause and Effect It is really only Baum’s study that has gone beyond age in seeking to explain the causes of depreciation. A number of studies have alluded to the building quality issue but most have resulted in descriptive assessments of the patterns of depreciation. Table 2.2 Rates of Economic Depreciation Retail Office Warehouse Factory Age 1 5 10 15 20 30 40 50 60 70

3.54 2.77 2.47 2.32 2.22 2.10 2.03 1.99 1.96 1.94

4.32 2.85 2.64 2.43 2.30 2.15 2.08 2.04 2.02 2.02

5.57 3.68 3.05 2.74 2.55 2.32 2.19 2.11 2.05 2.01

3.02 2.99 3.01 3.04 3.07 3.15 3.24 3.34 3.45 3.57

Best geometric rate 2.20 2.47 2.73 3.61 R2 (0.993) (0.985) (0.995) (0.997) (adapted from Hulten and Wykoff (1981)) For example, Hulten and Wykoff found an approximately geometric form of depreciation for age-price profiles ranging across all assets. This produced a ‘convex-to-the-origin’ pattern of depreciation, with prices declining more rapidly in the early years of an asset’s life than in later years. In fact, as the authors point out, there are variations in the depreciation rate over time, although these are relatively small (Table 2.2). The table shows this variation and the associated average rate of depreciation based on Box-Cox analysis which gives a good fit, as shown by the R2 values. The authors concluded that a constant rate of depreciation can serve as a reasonable statistical approximation to the underlying Box-Cox rates, despite the apparent pattern of accelerated depreciation in the early years of an asset’s life. They also found that there is reasonable stability of depreciation rates over time, which is surprising in view of non-systematic changes such as interest rates and tax, although this stability is probably the result of the slowness of investors reacting to changes in economic variables. Indeed, with the exception of Taubman and Rasche (op.cit.), and some of the automobile studies, the general conclusion from all the economic depreciation studies is that the age-price pattern of various assets has a convex-to-the- origin shape, represented by a constant depreciation pattern of geometric form. It is, however, interesting to compare these studies with the property depreciation studies. Firstly, as regards the average rate of depreciation, Hulten and Wykoff suggest, in terms of used asset prices,

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this is 2.2% per annum for retail, 2.47% for offices and 3.61% for industrials (see Table 2.2). The rate of 2.47% for offices compares with 1.6% for Barras and Clark (op. cit.), 1.22% for Baum (1991), and 2.4% for CALUS (City of London) (Table 2.3). Baum’s 1996 cross-sectional study found CV depreciation of 2.9% and ERV depreciation of 2.2% in the City, and 2.2% and 1.6% respectively in the West End. Table 2.3 Rates of Depreciation (ave % p.a.) [CV depreciation] (ERV depreciation) CALUS1 Baum(1991) Baum(1997)2 Barras and

Clark3 Hulten and Wykoff

Retail Offices Industrial

[-] (-) [2.4] (3) [-] (3.3)

[-] (-) [1.22](0.92) [-] (0.65)

[-] (-) (2.9](2.2) [-] (-)

[-] (-) [1.6] (1.2) [-] (-)

[2.2](-) [2.47](-) [3.61](-)

Notes: 1CALUS (op. cit.: 24) found a range of variation in capital value depreciation of up to 6.2% to

8.4% for offices and industrials, and 2.4% for City of London offices (and 1.4% rental depreciation in prime City offices). The CALUS study was for up to 20+ year old buildings; Baum and Barras and Clark looked at up to 35+ and 30+ year old buildings respectively, and Hulten and Wykoff up to 70+ year old buildings.

2 City of London figures only. The corresponding figures for the West End are 2.2% and 1.6%.

3Barras and Clark’s study focused on the City of London, as did Baum’s 1991 study. There is, however, a variation in the pattern of depreciation. Hulten and Wykoff suggest a convex or geometric pattern of depreciation, although they suggest depreciation rates are often higher in the first 20 years of an asset’s life. Hulten and Wykoff do not, however, offer any reasons for the observed patterns of depreciation, preferring to measure market results. The resultant geometric age-price profiles for offices and industrials are shown in figure 2.4. It should be noted that their study was cross-sectional, used capital values, and also included a factor for retirements. The shape of capital valuation depreciation in the Baum, CALUS and Barras and Clark studies indicates inconsistencies. For offices, Baum (1991) suggests that a high initial rate reduces between years 7 to 11 before accelerating again in years 11 to 26, reducing thereafter. On the other hand, in his 1996 study (Baum (1997)) he found that the fastest period of City Office depreciation was years 7 to 12, and in the West End, years 2 to 7. The former result indicates depreciation acting much earlier (ie. in the second review period) than 10 years before in the 1986 study. The CALUS study also indicated constant depreciation up to 10 years in the City of London, reducing thereafter. The study also looked at provincial offices and found that depreciation accelerated during years five to ten before reducing thereafter. Barras and Clark found appreciation in the first few years before depreciation sets in after 7 years, with higher rates between years 7 and 14 than after 14 years, showing a greater similarity with Baum’s 1986 study. The capital value estimates of central London office buildings in the 1990s would be affected by over-renting and new buildings entering the portfolio in the period would be affected by vacancy and/or lettings packages which may reduce capital values. This may explain the odd results for older buildings having higher capital values than newer ones in the Barras and Clark study. Generally speaking, using capital values tends in practice to produce a ‘convex -to the-origin’ pattern, and not a ‘one horse shay’ (which itself is akin to a static existing rent to new rent ratio) with an increasing capitalisation rate until the property is ‘retired’.

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Figure 2.4 Geometric Age-Price Profiles Figure 2.5 Age-Rent Profiles for for Offices and Industrials (Hulten and Offices (Cross-sectional studies) Wykoff)

Figure 2.6 Age-Rent Profiles for Industrials (Cross-sectional studies)

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Results of rental value analyses of depreciation can also be compared. As the CALUS study points out, the general expectation of rental depreciation patterns in commercial buildings (except retail) would be as follows (1 986:66):

‘a low rate of depreciation in the first five or so years of a building’s life, then a gathering of momentum between years 5 and 15-25 and thereafter either a levelling out of depreciation or, alternatively, a sharp rise if the building is entering a state of total obsolescence’:

The CALUS study found a fairly constant rate of depreciation over time for offices and industrials (see figures 2.6 and 2.7), with the highest rate of depreciation (3.4% and 3.9% respectively) occurring in years 5-10. There may, however, have been particular reasons why this pattern emerged in the CALUS study. Cross-sectional studies can be influenced and distorted by prevailing market conditions and the impact of obsolescence. For example, in a weak market there may be a wider than usual differential in rental value between new and 5-10 year old buildings. Again, the mid-1985 date for the CALUS study came shortly after raised void floors in offices had become common, and industrial properties were featuring a higher office content and more distinctive architecture. This could also lead to new buildings outperforming 5-10 year old buildings by a larger amount than usual. Taken together, these two factors can lead to differences from the expected pattern of depreciation. Baum’s 1986 study shows different rental depreciation patterns for offices and industrials from those of CALUS. As regards offices, the highest depreciation rate occurs in years 17-20, with a subsequent levelling off. Depreciation, for Baum (1991:116): ‘strikes hardest after the third and/or fourth rent reviews', and so his findings are at odds with CALUS, which found depreciation at its highest in years 5-10. Moreover, depreciation in general is much slower in Baum’s study for both offices and industrial than in the CALUS study (see figures 2.6 and 2.7). These general patterns were also confirmed in Baum’s 1986 longitudinal studies of offices and industrials, although the dataset for the latter group was limited in scope. On the other hand, Baum’s 1996 study found that the fastest period for rental depreciation was now earlier years 7 to 12 in the City and years 2 to 7 in the West End. Interestingly, Barras and Clark (op.cit.), in their study of offices suggest depreciation is at its highest during years 10-20, although their cross-sectional studies also confirmed the relationship between age and depreciation is not straightforward. This comparison of the patterns of depredation therefore holds a number of important lessons:

• the type of study (longitudinal or cross-sectional) appears to influence the pattern that emerges for any particular market segment;

• the timing of the study appears to generate different results for the same market segment;

• technological change can create building quality changes which can distort the age

depreciation relationship; and

• market state is important and can influence the pattern over time. The issue of longitudinal and cross-sectional studies is now explored in more detail.

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2.4.4 Longitudinal and Cross-Sectional Studies Longitudinal studies are those which collect information on subjects of study at two or more points in time (Goldstein (1979)). They may be prospective, following the subjects forward in time, or ‘retrospective’, by extracting multiple measurements on each subject from historical records. The defining feature of a longitudinal study is therefore a set of repeated observations on subjects which enables change to be studied directly. Such change may be over different ages, over time or over different cohorts. Indeed, it is this ability to separate ‘age’ effect (changes over time within individual subjects) and ‘cohort’ effect (differences among items in their baseline levels) which distinguishes longitudinal studies from cross-sectional studies (Diggle et al (1995)). In turn, the fact that longitudinal studies collect data on a number of variables also distinguishes them from time-series analysis, which collects data on only one or a few variables. Cross-sectional studies, or those that collect data at a single point in time can also be retrospective in nature. Again, ‘change data’ can also be analysed using a series of independent samples or cross-sections over time: these differ from longitudinal studies, however, because the same subjects are not measured over the time period involved. The CALUS (1986) study adopted a cross-sectional approach, but was supported by a limited longitudinal study, because it was felt that in a longitudinal study the ageing process could be too easily disturbed by the violent forces of market fluctuations. This is confirmed by Baum (1991) who also discussed other problems associated with a longitudinal analysis. His study was very much concerned with capital value change and longitudinal analysis makes it difficult to control for site value change over time in such studies unless study locations are tightly defined. Lack of suitable longitudinal data to measure performance, ERVs, yields and capital expenditure was also a problem in his study. Finally, building quality, measured by such factors as external appearance, internal specification, configuration and physical deterioration factors can only be retrospective in such studies: this makes it difficult to judge such factors and to isolate any changes satisfactorily. Cross-sectional studies, however, also have their problems. Although the impact of age and building quality may, in theory, be measured if location and property design are well-defined, in practice the fact that buildings at a single point in time will be heterogeneous makes it impossible to study building quality change over time with a single cross-sectional study. Indeed, the impact of sudden obsolescence due to technological change may be missed because of the study date selected, or for the same reason, a market imbalance may distort annual patterns of depreciation. Finally, Baum (1991) argues, it is impossible to examine the impact of capital expenditure over time on depreciation in a cross-sectional study, although to analyse such information longitudinally requires a good and accessible dataset. Ultimately, Baum suggested that both types of study have a place in estimating depreciation and obsolescence, and could be used together. Data availability was a limiting factor in his study, and so a cross-sectional study was used supported by a longitudinal analysis. This combination was also used in Barras and Clark’s study; but JLW and Weatherall Green & Smith used a longitudinal approach. As a way of analysing and mapping change over time, however, a longitudinal study has key advantages, if data to support such a study is available. Although it may be argued that a number of cross-sectional studies over time could analyse change, such an approach would suffer from the fact that the same individual properties were not being tracked continuously over the time period involved. Section 3.0 of this report deals with the implications of this for the methodology of the current study.

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2.4.5 Building Quality Of the various types of obsolescence discussed in the literature (for example Khalid (1992) identifies eight), only physical factors are related to age. However, age has been used in all the studies to determine the rate of depreciation. Baum (1991), in particular, highlights the limitations of this assumption and suggests that changes in building quality is a better explanatory variable than age. This links with the economic depreciation literature on vintages, which suggests that the introduction of new design and technology improves efficiency and increases the value gap between the best machine or structure and other cohorts. Building quality is the product of the individual attributes of a building. These attributes can change over time so that an attribute which would lead to retirement at one time may become less significant to the value at a later time. One example is the changing services technology which originally caused certain office building designs to produce inefficiency of operation due to the demand for increased floor to ceiling height. Recently, further advances in ‘cordless’ technology have reduced the need for this additonal height, potentially bringing previously functionally obsolete offices back into service (Oades, 1996). Baum produced a taxonomy of the breakdown of depreciation (Figure 2.7) and Khalid carried out a more detailed examination of these factors for offices. Satway (CALUS, 1986) produced an analysis of the factors most likely to render old offices and industrial properties obsolete, internal layout came top of both office and factory lists. Povall (1986) cited inflexibility as the key variable leading to obsolescence, Duffy (1986) identified four factors; service provision, floor to ceiling height, plan layout and building image; and Healey and Baker (1987) used survey work to isolate design factors. Much of this work is focused on offices. Baum isolated four factors as indicators of building quality: physical deterioration, external appearance, internal specification and configuration. Figure 2.7 gives a breakdown of the latter three factors. External appearance includes the aesthetics of the entrance hall. Internal specification includes the quality of finishes and services and configuration includes horizontal and vertical layout. As most of the property depreciation studies have concentrated on office and industrial properties, the taxonomies and discussion are restricted to these property types. The exception is Salway (CALUS, 1986) who discussed the factors concerned in creating obsolescence in a wider context, including retail. O’Roarty (1996), in her PhD study of retail rent determination, details survey work with isolates both retailers and valuers’ views of the property specific issues which determine rental value. However, this study concentrated on high street shops only. Only Baum’s studies have sought to identify the influence that quality factors have on depreciation estimates. The four quality indicators (physical depreciation, external appearance, internal specification and configuration) were each ranked and scored within the City of London office market study. The analysis revealed that internal specification and configuration were more important than external appearance and physical depreciation, and this distinction was confirmed in surveys of agents and occupiers. He found that these indicators explained the depreciation rate of buildings better than age.

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Figure 2.7 A Classification of Depreciation and Obsolescence

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The issue of quality change and how it might be incorporated into price indices has received some coverage in the non-property context. These studies (for example Cagan, (1965); Hall (1968)) attempt to isolate the effect of a change in quality from age depreciation, with much of the ancillary literature aimed at second-hand cars. The problem of isolating quality price change from market changes and depreciation has been addressed in two ways. First, by attempting to identify separate vintages which have the same quality characteristics, so isolating change due to market changes and age depreciation. The second is to identify quality indicators and determine their effect on price, the Baum approach. The literature on structure/building quality and quality change indicates that changing technology is a principal factor in depreciation. However, with the exception of Baum, the property depreciation literature has not addressed the problem empirically, preferring to adopt age on the grounds that increasing age implies decreasing quality. The problems with quality are twofold. First, any study will require detailed ‘property-specific’ data and second, any results will be difficult to incorporate into a property investment appraisal model. By its nature, technological change is a set of unique events which are impossible to forecast by reference to past experience. For these reasons the current study does not address the issue of building quality. 2.5 Summary This overview of both property depreciation and economic depredation has highlighted a number of issues which hold valuable lessons for any new study of depreciation in commercial property. The most important are as follows:

• distinguishing ‘obsolescence’ from ‘depreciation’ is vital, although measuring and isolating the effect of age, inflation and obsolescence can be difficult;

• the literature can provide useful badges and concepts for studying depreciation; for example,

‘cohort’, ‘vintage’ and ‘age’;

• the problem of ‘censored sample bias’, the ‘lemons’ issue, and ‘filtering’ needs recognising in any study of depreciation although a lack of relevant data meant an investigation of these issues was beyond the scope of the current study;

• the pattern of depreciation established in any study can be influenced by such facts as the

type of study (ie. cross-sectional or longitudinal), the timing of the study, and the market state;

• longitudinal studies offer key advantages over cross-sectional studies of being able to analyse change over time in the same individual properties and, potentially, to measure the impact of capital expenditure on depreciation;

• building quality is an important issue and further research (beyond the scope of the current

study) is needed to determine its importance (vis a vis age) in producing particular patterns of depreciation in different property types.

These lessons confirmed that the current research should comprise a longitudinal study of ERVs and that an examination of age as a ‘causal’ factor, set in the context of market state, should form a part of the study. The components of the research methodology are discussed in the next section of this report.

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3.0 RESEARCH DESIGN, METHODOLOGY AND TERMINOLOGY 3.1 Introduction The literature review has shown the need for a study to analyse:

• depreciation patterns of major property types over time; • the impact of capital expenditure on depreciation rates over time; • the importance of age as a key explanatory variable for depredation; and • how market state may influence the depreciation pattern of particular building types.

A longitudinal study can provide the basis for analysing change. Essentially, the longitudinal analysis requires the tracking of the rental values of actual buildings over time, set off against a rental value of the location. This may be based on a rental value, each year, of the prime building in that location. Given this, and the objectives set out in Section 1.0 of this report, the data requirements for such an analysis includes:

ERV history for ‘new’ buildings; • ERV history of actual buildings for relevant property types; • evidence of location quality; • details on age (i.e. date of construction or period since refurbishment); and • capital expenditure details for individual properties;

3.2 Research Design and Choice of Rental Index The first stage of the research therefore comprised a detailed longitudinal analysis based on ERV, floorspace and other data obtained for the 1980-95 period from IPD. To maximise the potential of the study, this data was for properties held by individual sponsors as at 31 December 1995, irrespective of the acquisition date. The basis for the measurement of rental depreciation is the relative difference in annual growth between ERV and a prime rental index benchmark over the relevant time period. It was agreed that the most suitable index was the Hillier Parker (HP) Rent Index, which is market-based and uses 100% locations. This was obtained for relevant centres for the period 1980-95, and for the case studies back to 1970 where possible. Other indices were considered, including:

• Healey and Baker PRIME; • JLW 50 Centres; and • IPD Town Service.

In detail, the Hillier Parker Rent Index is an index of rents aggregated into regions for each sector, based on a standard definition. Currently there are 731 rent points and the data is collected in £ psf for each town; however, this information is not publicly available. The series dates back to 1965 (although data was collected in May 1965, 1969 and 1972). From 1972 to 1990 data was collected in May and November and since 1990 the series has been published each quarter. The Hillier Parker data set has a number of advantages. It is a market based index which values hypothetical properties at least every six months over the study period, and it uses 100% locations. The basis of the rent is headline, and this accords with the majority of data held by the sponsors (see below). The major limitation with the data is that before 1984, the number of rent points making up the index was substantially less than currently. Before 1984, the rent points numbered 189 with 70 retail, 86 office and 33 industrial. The May 1979 issue of the Rent Index (Hillier Parker (1979) details the actual locations.

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Data quality issues are, of course, raised with the use of any index for property research. The HP Rent Index is based on what Hillier Parker term the ‘best rental value’ (BRV), which is on an unadjusted headline basis, and does not make any adjustment for the value of incentives. After detailed discussions with the sponsors and their in-house valuers, our understanding is that all sponsors’ ERV data is on the same basis, with one exception. The exception is from 1993, where one sponsor incorporates what can be described as a ‘provable, effective’, basis which strips out the value of rent-free penods over and above the usual period for fitting-out. The effect of this deduction was at its height in 1993 and 1994, but by 1995 it appears as though shorter lease terms were becoming common in a number of markets, with commensurate shorter rent-free periods. Clearly this difference could potentially impact on our data. However, having investigated the issue in detail across the sectors concerned, we found no evidence of a systematic distortion of depreciation rate created by including this sponsors ERV data. We therefore included all sponsors’ ERV data in our analysis, to maximise our final dataset, but we have also pointed out the data quality issues which need to be considered. Locational quality information for each property for the same period was also obtained from the sponsors. The basis for this information was a ‘prime’ or ‘non-prime’ category, based on how far off prime (as a percentage of a 100% location) a particular property was, in each year for the period 1980-95. It was agreed with sponsors that 85% would be adopted as the cut-off-point: any property less than 85% would be classified as ‘non-prime’, and prime as 85% or above. The second stage of the research used a sample of 33 case studies covering the main property sectors. ERV, floorspace and relevant capital expenditure and capital value data was available from IPD for 1980-95. Where possible, data for the period 1970-80 was obtained from the sponsors to top this data up for the relevant case studies. The intention in the case study part of the research was to use the information to examine the process of depreciation and the history of particular properties over time, against a market state framework. 3.3 Methodology and Terminology The IPD and Hillier Parker data was used in two main ways to calculate annual rates of depreciation for particular property types (this is discussed in more detail in section 3.4 below). Firstly, Ordinary Least Squares (OLS) Regression was used to determine an estimated depreciation rate (EDR). This is the ‘best fit’ depreciation rate (as a percentage per annum; see sections 3.0 and 4.7 of Appendix A) obtained by pooling data within particular sectors according to their characteristics, which comprised:

• prime/non-prime; • construction period (ie. Post-1975, 1966-74, Pre-1965), and • town type/geographical location.

The analysis was carried out at five levels:

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• Level 1: prime, town type and construction period all included; • Level 2: prime and town type disregarding construction period; • Level 3: Towntype, disregarding prime and construction period • Level 4: Prime and construction period, disregarding town type • Level 5: Prime disregarding construction period and town type.

In addition, overall EDRs for each sector without the three variables were also calculated. Another way of viewing this is that disregarding variables ‘averages’ their effect. For example, Level 3 produces EDRs by towntype ‘averaged over prime and construction period. This provided a powerful tool to explore the data and allow ‘slicing’ into relevant time periods or market states (ie. the end of the late 70s, early 80s downturn in 1980-83, the ‘boom’ of 1984-89, and the ‘slump’ of 1990-95). The choice of market state was influenced by the shape of the data and the need for a consistent comparative basis for the analysis. The rationale for the choice of sub-periods and the statistical methodology employed is discussed in more detail in Appendix A. It should be noted that in tables throughout the report a minus sign for a rate indicates depreciation (eg -2.5%) and no sign, appreciation (eg. 1.5%). Secondly, an average depreciation rate (ADR) may also be calculated for a single property or a group of properties using a geometric mean (or compounding formula) to compare an ERV:HP ratio at the start of the time series with the same measure at the end of the series. Again, an annual depreciation rate in percentage terms is produced, but the method, in contrast to OLS regression, ignores movement in depreciation in the intervening years. An ADR was calculated when data permitted. This is effectively a ‘relative’ measure of depreciation, which is consistent with the same ratio logarithmically transformed) used in the OLS regression. It should be distinguished from the alternative ‘absolute’ method of calculating ADR which the pilot study (CEM (1996)) and other studies such as Baum (1981; 1997) have used (see section 2.0 of Appendix A). To ensure consistency we have used this ‘relative’ measure of depreciation throughout the report to calculate ADR. Both EDR and ADR have their role to play in describing rental depreciation. EDR forms the main focus of the IPD data analysis, supplemented by ADR for towns and sectors, where data permits. ADR is also used in the case studies, because of the relatively small sample size. It is important to appreciate that for EDR to provide meaningful results, a longitudinal analysis requires data for each property for each year over the time period selected. So for a 1980-95 study, each property included in the analysis would require full data for 1980-95. This introduces the concept of cohort, which we define as a separate group of properties studied from the same start point over the relevant time period (Figure 3.1).

Figure 3.1 Cohort and Market State Frameworks

For example, for the period 1980-95 we would use the term ‘1980 cohort’. For the period 1984-95, ‘1984 cohort’ and for 1990-95, ‘1990 cohort’. Each property in the cohort must therefore have data for the full time period to be included in our analysis. Using cohorts in this way in our analysis enables us to:

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• maximise the use of the dataset; and • compare EDRs over time and over market state.

An increasing depreciation rate over time can also be simply a result of an ageing sample. To control for this, the mean age of each cohort is shown at the start date in the report, (ie. for the 1984 cohort, the mean age is given at 1984) and EDRs are rebased to enable comparisons between different cohorts to be made. Construction period was used as a proxy for age, and three construction periods were adopted for all sectors (post-1975, 1966-74 and pre-1965), which are comparable with the main grades of property in the market. The size of the dataset and the number of variables in our regression also suggested three periods was a sensible number to adopt. The key advantage of adopting construction period, rather than age, was that it logically fitted our longitudinal analysis and also made it easier to report our results in a systematic way. As properties in a cohort age over time, age and ‘market state’ have a tendency to interact. In particular, as the age profile of a cohort changes, what was a ‘young’ building in a period of ‘boom’ becomes an older building in a period of ‘slump’. Construction period, however remains fixed, so we have used this throughout our analysis, except where age could highlight particular features of our results. We have also used construction period as a proxy for analysing the importance of age as a ‘causal’ factor in depreciation: again, this fits the logic of the OLS regression and allows us to assess the relative importance of the other variables in our regression model (i.e. prime/non-prime and town type). Apart from the reasons associated with methodology it should also be noted that the tables in the results section, which show age in years against depreciation rate, are limited in scope because of the shortage of a sufficient age range of properties in the cohorts. Generally, a wider range of ages was found in the 1990 cohorts, so we have included these where possible. To examine the issues of Changes in locational quality and capital expenditure we also used further statistical techniques which are explained in Appendix A. The study focuses on ‘building-based’ expenditure on refurbishment, because it relates directly to physical deprecation. ‘Tenancy-based’ expenditure (eg. the cost of restructuring leases) is not therefore included in our analysis of capital expenditure, but the following items are included:

• refurbishment costs; • change to the fabric of building; and • initial development and redevelopment costs.

Finally, we distinguish in the report between an original building, or one that is not refurbished, and a refurbished building, or one that is refurbished (see Appendix A). In summary, within each sector, three groups of properties have been analysed where possible:

• original with no locational quality change; • original with locational quality change; and • refurbished with no locational quality change.

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Refurbished properties with locational quality change were not analysed because they were low in number and isolating the differential impact of locational quality change and refurbishment, alongside other factors in the OLS model, was not feasible. 3.4 Case Studies In order to enhance the understanding of the process of depreciation over time, 33 case studies were collected, which were intended to provide a greater level of detail of depreciation against different market states. An average depreciation rate (ADR) was calculated for each individual property using the averaging technique which compares the ERV:HP ratio over different time periods. An ADR was calculated for the longest period for which data was available and also for each market state and compared with the benchmark EDR. Where a property had been refurbished, average expenditure was calculated for the two years before and after refurbishment date, together with depreciation rates. A total of 48 properties were submitted for inclusion as case studies. However, as in the main body of the report, a number were excluded due to lack of data or inconsistencies. Data for the period 1980 - 1995 was available from IPD and sponsors were asked to provide a commentary on the property and historic data from 1970 to enhance the analysis. Data loss and other data inconsistencies, such as refurbishment dates with no matching expenditure, lack of Zone A, or no matching HP data, resulted in a final number of 33 case studies. The breakdown of the case studies is shown in Table 3.1 below. Table 3.1 Case Studies Sector Breakdown PROPERTY TYPE SECTOR NUMBER Original Buildings with no Location Quality (LQ) Change

Office 14

Retail 5 Industrial 3 Retail Warehouse 2 Original Buildings with LQ Change Office 5 Retail 1 Industrial 1 Refurbishment (No LQ Change) Office 2 TOTAL 33 No shopping centres were included in the case studies because of data loss, inconsistencies and unavailability of alternative benchmark series Where the centre was dominant in a town. For reasons of confidentiality, only a limited number of office case studies are presented in this summary report. These are shown in Appendix E.

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4.0 RESULTS 4.1 Introduction The results which follow in this chapter of the report provide a summary of the important features of the analysis of the IPD data together with supporting evidence from selected case studies. After an overview of the sample size, age distribution and ADR analysis of the sample, the regression analysis and variable selection is discussed. The overall pattern of depreciation rate (EDRs) for 1984-95 between sectors and over market state is then presented, together with a brief review of the data quality issues. A more detailed analysis is then provided for offices, and where relevant data is available, each cohort is examined. The cohorts within this sector are then compared over market state. In presenting this information we distinguish between inferential statements, based on formal statistical hypothesis-testing procedures, and descriptive statements which have been made from our general interpretation of the regression analysis output. These are presented separately. Locational quality change and the effect of capital expenditure on refurbishment are also discussed in general terms. Finally, data quality issues are discussed in detail. 4.2 Sample Size, Age Distribution and ADR Analysis In total, 728 properties were used in the analysis, which represents 36% of all relevant properties initially supplied to the research team. In general a substantial amount was attributable to missing or inconsistent data items for certain properties in the time series. Figure 4.1 shows that the largest cohort was the 1984 standard shop unit (SSU) cohort with 210 properties. However, the 1984 and 1990 office cohorts also had relatively high numbers: 153 and 113 respectively. In contrast, industrials (23 and 32 for the 1984 and 1990 cohorts), followed by retail warehouses, office parks, and shopping centres had much lower numbers of properties. Ultimately, these low numbers restricted the level of statistical analysis for these sectors. It should be noted that all these figures include original properties with and without locational quality(LQ) change, as well as refurbishments. In terms of average age, figure 4.2 shows the pattern between the three main sectors by cohort for original properties with no LQ change (ie those properties that form the basis for the main statistical analysis). The average age of offices is higher than, for example Baum’s (1991 and 1997) studies: 9.6 yrs in the 1986 study and 14 years in the 1996 study. However, Baum used a cut-off point of 35 years in each of his studies, which lowers the average age of his samples. We have not adopted a cut-off because we believe this masks the overall picture of depreciation when the detailed age profile of buildings is considered. Finally, the data for each of the 1984 sector cohorts was analysed to produce summary descriptive statistics of the ADR5. The details of this are shown graphically for offices in Appendix B (Figure B1: p65), and indicates a generally close relationship to the pattern of EDRs which emerged from the subsequent regression analysis. This is confirmed by the comparison of overall EDR and ADR in section 4.6 of Appendix A. 4.3 Regression Analysis and Variable Selection It is important to note that regression analysis has been used in this study as a powerful and flexible exploratory tool to supplement the ‘averaging’ (ADR) technique, and to maximize the use of the IPD

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data set. In this sense, the technique is a means to the end of providing an improved insight and understanding of the factors which affects the data. The details of this analysis are given in Appendix A. Of course, the data used in any regression analysis must be representative of the population studied, because without this the model and conclusions drawn from it are likely to be in error. A particular problem may arise when data has been extracted from historical records: ie ‘happenstance’ data (Box, Hunter and Hunter (1978)). For example, this type of data may be corrupted by outliers, ‘wild’ points and other inconsistencies, which in turn impact on the variable selection process, and could lead to model misspecification. In this study, however, we have taken great care to ‘filter the data and exclude properties which had missing or inconsistent items. We are confident therefore that we have a relatively sound dataset as our starting point, which is also assumed to be representative, on a sector-by-sector basis, of the total population. There remains, however, the possible influence of the ERV basis within our dataset, particularly during the 1990s (see below). With this in mind, we carried out significance tests to determine the importance of the three variables in our regression model:

• prime/non-prime; • town type;and, • construction period.

Table 4.1 Variable Selection, 1984 and 1990 SSU Cohorts 1984 Cohort Construction Period Town Type Prime 1984-1995 <0.001

[1] <0.001

[1] <0.001

[1] 1984-1989 0.62 <0.001

[1] 0.014

1990-1995 0.666 0.603 0.26 [1]

1990-1995 rebased 0.18 0.0038 [1]

0.053

1990 Cohort Construction Period Town Type Prime 1990-1995 <0.016 <0.0024 <0.001

[1]

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The results of this analysis are summarised in Tables 4.1 and 4.2 for the 1984 and 1990 SSU and office cohorts. The number of industrials and other sectors was too low to enable such testing. Full results are given in Tables D10 to D14, including the 1980 office cohort which is not summarised here in the text (because of the low statistical significance). In each table the figures represent p-values and shading means a variable is significant at the 5% level. [1] Means a variable is the most important in the model. For example, in Table 4.1, for 1984-95, all three variables are significant at the 5% level. For SSUs, however, the overall evidence suggests that town type is the most important variable, but that construction period and prime are also important. Table 4.2 Variable Selection, 1984 and 1990 Office Cohort 1984 Cohort Construction Period Town Type Prime 1984- 1995 0.028 <0.001

[1] 0.055

1984-1989 0.507 <0.001 [1]

0.074

1990-1995 0.356 0.200 [1]

0.762

1990-1995 rebased at 1990

0.183 <0.001 [1]

0.557

1990 Cohort Construction Period Town Type Prime 1990-1995 0.998 <0.001

[1] 0.187

Table 4.2 is more clear-cut, and shows that town type is consistently the most important variable for offices. However, we do not believe the statistical tests that particular variables from the model. From an intuitive to suggest particular variables are unimportant, dangerous. This is particularly the case where the data has some limitations, where F tests can give conflicting results for different cohorts and ‘market states’, and where, as in this analysis, the time-scale is over relatively short periods. For example, a variable may not be significant because the time period in a particular market state is too short to allow the detection of differences, not because the variable is ‘unimportant’. Nonetheless, because of its importance, and to enable a consistent comparison to be made, the results which are presented in this results section for the office cohort, focuses on town type as the most important variable (‘Level 3’ of the analysis: see section 3.3 above). Further ‘descriptive’ statements are made in a separate section for the sector, based on the other levels carried out within the analysis. 4.4 Overall Patterns of Sector Depreciation (EDRs): 1984 Cohorts 4.4.1 Sector Comparisons To produce a clear and simple comparison between the sector results the 1984 cohort data for offices, retail and industrial were analysed to produce ‘overall’ EDRs, ignoring the impact of construction period, town type and prime/non-prime. The results are summarised in Table 4.3 and

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Figure 4.3 shows the pattern that emerges for 1984-95 (full details are given in Tables C2 of Appendix C). Offices have the highest depreciation of 3.05% p.a. for this period, followed by industrial of 0.32% p.a., and retail ‘appreciating’ at 0.28% p.a. This pattern was also confirmed by the case studies (see relevant sections in each sector). Table 4.3 EDR by Sector 1984-95 1984 Office Cohort EDR (%p.a.) 1984 - 1995 -3.050% 1984 - 1989 -6.039% 1990 - 1995 -3.521%

1984 SSU Cohort EDR (%p.a.) 1984 - 1995 0.280% 1984 - 1989 -1.707% 1990 - 1995 -0.903%

1984 Industrial Cohort EDR (% p.a.) 1984 - 1995 -0.317% 1984 - 1989 -0.443% 1990 - 1995 -0.148%

4.4.2 Market State Using the same data, Figure 4.4 shows how depreciation appears to fall over all sectors from the ‘boom’ period (1984-89) to the ‘slump’ (1990-95). For example, for offices the depreciation rate was 6.04% from 1984-89, but 3.52% from 1990-95 (Table 4.3). The results from other cohorts and the case studies tends to support this view. Prior to this, the 1980 office cohort suggests for the period 1980-83, ‘appreciation’ of 0.54%. Again, the full results are given in Appendix C (Table C2). 4.4.3 Data Quality Issues Overall, two features of these results which stand out are:

• ‘appreciation’ in retail, and • lower overall depreciation in the slump than in the boom for all sectors

Although we believe these features partly represent the logical reality of what actually does occur, they are also likely to be partly fuelled by the nature of the data in 1990-95, and particularly by valuers’ differences in perception of ERV in comparison with the HP index. Indeed, from a purely statistical viewpoint, the interpretation of the 1990-95 slump in EDR for SSUs and the 1980-83 period for offices, is that the results are not significantly different from zero at the 5% level (Tables C1 and C2 of Appendix C). Moreover, the low number of industrial properties (23 original with no LQ change) makes it difficult to detect trends and draw firm conclusions for this sector, and this is paralleled by the relatively high p-values (ie not significantly different from zero at the 5% level) in Table C3 of Appendix C.

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Intuitively, however, the pattern of lower depreciation in the 1990-95 slump than the 1984-89 boom in all sectors could be explained by constraints on new development, and hence supply, which has led to a ‘levelling off’, or even decline, in depreciation in the slump. It is interesting to note that Barras and Clark’s (1996) study of Central London offices showed a similar fall in depreciation, although Baum (1997) found a rise in depreciation from boom to slump. The nature of the data in the 1990s will tend to have an impact as well, and this is discussed in more detail within each sector. In summary, although we believe that our data is the best available to us, the possibility of valuers differing in their perception of rental value over the market cycle remains very real. In particular, a systematic ‘lag’ in ERVs against the HP Rent Index in the slump of 1990-95 could lead our results to show lower depreciation rates over this period than might otherwise be the case, and overrenting might tend to underpin this. Research by Crosby and Murdoch (1997) supports this view. 4.5 Standard Offices 4.5.1 The Sample There were 648 standard offices in the IPD database. After data attrition the final number was as follows:

• 1980 Cohort1 56 original, no locational quality change (Mean age at 1980, 40 yrs) • 1984 Cohort 91 original, no locational quality change (Mean age at 1984, 42 yrs) • 1990 Cohort 100 original, no locational quality change (Mean age at 1990, 39 yrs)

Full details are given in Tables D1 to D4 of Appendix D. The analysis used for office town types throughout the report are:

• London City • London West End (‘London W.E.’) • London Other (ie mid-Town and fringe City) • London Suburbs and Satellites (‘London SS’) • Metropolitan (‘Met.’) • Metropolitan Suburbs and Satellites (‘Met. SS’). This applies to the 1984 and 1990 cohorts,

only. • Major Regional (‘Maj. Reg.’) • Minor Regional (‘Min. Reg.’) • Sub Regional (‘Sub. Reg.’) The sub-regional category only applies to the 1990 cohort.

As was shown above (section 4.3), town type was found to be the most significant factor in our model for offices. Because of this and to simplify our presentation, we have therefore summarised the results for each relevant office cohort at ‘Level 3’ of our analysis (ie by town type). We have also included a short descriptive summary of what the tables presented at other levels show. The statistical significance of individual EDRs has not, however, been tested. 1 Results not given because of low statistical significance (see section 4.5.5)

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4.5.2 1984 Cohort Table 4.4 shows that for the period 1984-95, London West End (4.32% pa) and London Other (4.33% pa) suffered the highest overall rates of depreciation. This is also the case in 1984-89, but in 1990-95, London City offices suffered the highest depreciation at 11.19% pa. Depreciation rates fell in London West End, London Other, Metropolitan, and Minor Regional from boom to slump, but rose in London City, London SS, Metropolitan SS and Major Regional centres. Table 4.4 EDRs by Town Type-1984 Office Cohort 1984-95 1984-89 1990-95 London City -0.943 2.251 -11.194 London West End -4.321 -10.712 -3.932 London Other -4.328 -11.018 -3.027 London SS -2.004 -2.718 -5.186 Metropolitan -2.649 -2.942 -2.532 Met. SS -1.414 -3.427 -5.557 Major Regional -3.288 -1.076 -1.358 Minor Regional 0.003 -1.431 -0.455

4.5.3 1990 Cohort Table 4.5 shows the 1990 cohort EDRs for 1990-95. This confirms the pattern of increase and decrease from boom to slump for the first five town types and for minor regional. However, there are some differences between the pattern of 1984-89 in the 1984 cohort and the pattern of 1990-95 in the 1990 cohort: Metropolitan SS and Major Regional this time show falls in depreciation (the Sub-Regional category only applies to the 1990 cohort). Again, it may be the case that other factors such as age and/or ‘primness’ differences are having an effect here. We suggest that these 1990 cohort rates for 1990-95 are better evidence for EDR by town type than the 1984 cohort rates for the same period. This is because the 1984 cohort is anchored in 1984 and so covers an ultimately older sample over a longer time period. The mean age of the 1984 cohort is 48 years in 1990. Table 4.5 EDRs by Town Type 1990 Office Cohort

1990-95 London City -11.505London West End -0.284London Other -2.109London SS -3.736Metropolitan -1.431Met. SS 5.201Major Regional 0.745Minor Regional 1.014Sub-Regional -3.711 4.5.4 Other Descriptive Comments The evidence from a full analysis of the 1984 cohort also suggests in a purely descriptive sense

• Prime offices overall suffered lower rates of depreciation (5.13% pa) than non-prime (6.6% pa) in the 1984-89 boom, but in the 1990-95 slump, prime offices depreciated at a similar rate (3.67% pa) to non-prime (3.44% pa).

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Again, although construction period does not appear statistically to have a significant effect on depreciation rate, an interesting pattern of ADR by age group for prime offices is shown in figure 4.5. This shows that in both the 1984 and 1990 cohorts depreciation rates were highest in years 16-25. 4.5.5 Comparison of Cohorts Declining overall depreciation (based on EDR) from the 1984-89 ‘boom’ to 1990-95 ‘slump’, which has also been highlighted in the case of standard retail units, is shown more clearly in Figure 4.6, where each cohort is approximately the same age at the respective start date of analysis (Table C2 of Appendix C). The ‘appreciation’ for 1980-83 is also shown for comparative purposes but this is not statistically significant at the 5% level, and so results are not given in detail for the 1980 cohort. The tendency to falling depreciation from the 1984-89 ‘boom’ to the 1990-95 ‘slump’ is also highlighted by comparing market states, across cohorts. Tables C1 and C2 of Appendix C shows that the rebased 1980 cohort (6.69% pa) and 1984 cohort (6.04% pa) give very similar overall EDRs for 1984-89. Tables C1 to C3 shows a less clear pattern in the slump of 1990-95, but the higher rates in the 1990 cohort (2.2% pa) in comparison with the rebated 1980 (1.28% pa) and 1984 cohorts (1.13% pa) could be explained by the distribution of the 1990 sample base within the London sub-groups. 4.5.6 Locational Quality Table D7 in Appendix D shows the results of the statistical tests. The 1980 cohort results indicate (B values in Table D7) that for London City properties which suffered locational quality (LQ) change an increase of 4.75% p.a. in depreciation rate is associated with a decrease of 1% p.a. in LQ (and vice versa), but for London Other, an increase in depreciation rate of 1.04% p.a. is associated with an increase in LQ of 1% p.a. The results suggest there is no clear-cut relationship between LQ and depreciation rate. Mean LQ change is shown in Figure 4.7 and the significant statistical results of the regression analysis for the 1984 cohort are shown in Table D7. For properties that suffered LQ change, these show that for London Other, an increase in depreciation rate of 1.06% p.a. is associated with an increase in LQ of 1% p.a. and for metropolitan an increase in depreciation rate of 1.15% p.a. is associated with an increase in LQ of 1% p.a. 4.5.7 Capital Expenditure Firm conclusions are difficult to draw, but for the 1980 cohort all the refurbished properties for the period 1980-95 suffered more depreciation than original properties represented by the overall EDR and specific benchmarks. There is some limited evidence to say that for non-prime, the post-refurbishment depreciation rate (3.02% p.a.) is less than the pre-refurbishment rate (11.48% p.a.). The 1984 cohort is, however, less diverse in its town type and four of the six properties are located in London West End. This shows that all refurbished properties in the group depreciated less overall in the period 1984-95 than original properties. Over both cohorts the capital spent on refurbishment ranged from 7% to 20% of capital value for prime and from 12% to 21% of capital value for non-prime.

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49

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4.5.9 Case Studies The summary details of the office case studies are provided in the Case Studies section at the end of the report (see Appendix E).

Generally There are 14 office case studies with no locational change, of which 6 are prime. As the previous analysis shows, there are generally higher rates of depreciation in the boom period of 1984 - 1989 than in the slump period of 1990-1995, particularly in prime original properties. However, the pattern for non-prime offices is inconsistent, with only about half the properties included in the case studies confirming this trend. For prime offices over the longest time period where data was available the range of depreciation rates was low, between appreciation of 1.16% p.a. and depreciation of 2.04% p.a. However, the range for non-prime offices was much greater, between 1980-1995 (from 1.26% p.a. to 8.12% p.a.). Other factors appear to offer little explanation. Age, town type and size do not appear to be consistent factors individually although in combination it is possible that some further insights could be made. Town Type and Market States The main analysis shows London City offices to have suffered particularly from depreciation in the period 1990-1995, and London West End properties to have suffered during 1984-1989. Case study 02/CS27 (Appendix E: p90) illustrates the latter point with depreciation of 7.85% over the period, and Case Study 01/CS14 (p89) illustrates the former point. This is not immediately obvious from the rates provided but when viewed graphically it can be seen that the latter property suffered marked depreciation over 1991 to 1993 (see figure 4.8). This particular property is within the 1984 Cohort - for which there are only 3 properties - but the 1990 Cohort has 7 properties and when these profiles are also graphed the trend is again visible (see figure 4.9). 4.5.10 Data Quality Issues In the analysis of offices, ‘appreciation’ was certainly not such a consistent feature of our results as in retail, except for the 1980 cohort, which had a relatively small number of properties (56), and for some case studies. Occasionally, at a town type level of disaggregation, ‘appreciation’ does occur in other cohorts, but this is infrequent and is often associated with relatively low numbers of properties in a particular category. Despite this, it should be stressed that the data quality issues discussed above are still important to consider in the office sector for the period 1990-95. In the case of offices, the proportion of the final dataset which is on a ‘provable, effective’ basis, supplied by one sponsor, is higher than with retail (for example, 10% of prime office properties in the 1984 cohort). To assess the impact of this data on final results, we ran a number of analyses for the 1984 and 1990 cohorts, one with all sponsors’ data included, one excluding this particular sponsor’s data, and then subsequent analyses, excluding other data on a sponsor-by-sponsor basis. We found only small differences in rates and no evidence of systematic variation between sponsors’ ERVs for 1993-94, when letting incentives were at their height. In summary, there is insufficient evidence to support the view that including this sponsor’s data affected our results significantly. For this reason, and to maximise the use of our dataset, we included all sponsors’ data in our analysis for all cohorts.

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The other factor of valuers’ interpretation of ERVs could not, of course, be examined directly in this study, but is again offered as a point to bear in mind in interpreting the results. For example, a systematic ‘lag’ in ERVs against the HP Rent Index in the slump of 1990-95 could lead our results to show lower depreciation rates in this period than might otherwise be the case, and overrenting might underpin this. There is some evidence to support this view from Figure 4.10, which shows an average depreciation rate (ADR) for offices of 4.09% pa for 1984-89 compared with an ADR of 2.43% for 1990-95. The latter reflects an overall decline in the HP Index for prime properties of 9.1 2%pa and a fall in ERVs of 11.38% pa over the same period. The graph also suggests a ‘lag’ between the HP Index and ERVs of about a year over the period 1989-90, and an overall decreased divergence between the two lines in the slump, in contrast to the boom. Also, within the period 1990-95, there is evidence of limited ‘appreciation’ during 1990-92 as the ERV and HP lines converge, but this is exceeded by subsequent depreciation. Similar patterns also emerge for prime and non-prime categories. Similarly, if ERVs also diverged from and lagged the index during the late 1980s for the same reasons, then depreciation rates would tend to be correspondingly higher during this period than might otherwise have been the case. This is examined in more detail in section 5.3.

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5.0 CONCLUSIONS 5.1 Introduction The overall aims of this research study were to:

• analyse the process of depreciation and its effects on the performance of rents in the commercial and industrial property markets; and

• examine the impact of capital expenditure on depreciation in the same property markets.

Ultimately, the study focused on rental depreciation, and did not examine the impact of capital expenditure on total return. Moreover, it became clear as the study progressed that data attrition from our original dataset left very few refurbished properties. Nonetheless, despite this and the issue of data quality, our results do enable us to talk in general terms about a limited number of the latter group, in addition to our main analysis. Based on the full analysis of IPD data and the case studies, this section of the report therefore summarises the main findings of the report before discussing in more detail:

• the data quality issues; • the significance of the research; and • the scope of further research.

5.2 Main Findings 5.2.1 Sector comparisons

The pattern that emerges for 1984-95 is as follows:

• Offices have the highest depreciation of 3.05% p.a. for this period, followed by industrial of 0.32% p.a., and retail ‘appreciating’ at 0.28% p.a.

5.2.2 Market State • Depreciation appears to fall over all sectors from the ‘boom’ period (1984-89) to the ‘slump’

(1990-95). • The results from the office cohorts and the case studies tends to support this view. 5.2.3 Age as a ‘Causal’ Factor As is pointed out in Appendix A (section 6.1), it is very difficult to establish a direct ‘causal’ link between construction period and depreciation, because of the complexity of interrelationships between the variables overtime. However, the evidence from our statistical tests for both SSU and office sectors suggests that construction period, as a proxy for age, is not as significant a factor in explaining depreciation in our OLS model as town type. The relative significance of each variable varies from sector to sector and between cohorts but, in general, prime/nonprime and construction date are consistently less important than town type.

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That is not to suggest that any variable should be excluded from the model, however, and in fact, in certain instances such as the composite age depreciation profiles of offices, interesting evidence of the nature of rental depreciation, emerges. For example, prime offices (1984 and 1990 cohorts) in years 16-25 (see Figure 4.5: p38). However, our statistical findings that town type is significant support the idea that the size/geographical location of a centre has an important influence on depreciation rate, although establishing cause and effect is, again, extremely difficult. It is certainly the case that the regional dimension of performance has been most clearly defined for standard offices and shops (IPD (1996)). For example, London led the cycle through the boom years of the 1980s with returns and rental values rising higher and earlier than the rest of the UK. Similarly, through the 1990s recession, returns and rental values fell furthest in London and the South, and least in the rest of the UK. These regional variations are closely paralleled by differences in depreciation rate by town type in our study. For example, from 1984-95, London West End offices, had the second highest depreciation rate in the office sector (4.32% pa) after Other London ( ie Mid-Town and fringe City) (4.33% pa)- see Tables 4.4 and 4.5. This tendency towards higher depreciation in London in these two sectors is also generally borne out by the relative pattern of rates between London and the rest of the country both in the late 1980s boom and subsequent slump. • In summary, the results for the OLS regression and statistical tests confirm previous

depreciation studies, which have found that other factois, such as building quality (not investigated in the current study) are more important than age in explaining depreciation patterns. In this study town type was generally the most important variable.

5.2.4 Locational Quality Offices appear to exhibit LQ change, particularly in the London region. Often this change is upward rather than downward, although offices in metropolitan centres also suffered LQ decline over the period 1990-95. The relationship between LQ change and depreciation is not clear-cut in statistical terms, however. For example, for ‘London City’ offices, locational quality decline was associated with an increase in depreciation, whereas for ‘London Other’ and ‘Metropolitan’ centres, increases in LQ were associated with increases in depreciation rate (see table D7 of Appendix D). It may be the case that local factors are operating in these centres to cause this pattern, or that the accuracy of qualitative historic judgements associated with LQ change may be variable. Making retrospective judgements on LQ change is problematic and may cause problems in properties where LQ change occurs or where valuers perceive no change when in fact change has occurred. Indeed, as Table 5.1 shows, there is mixed evidence in the case of offices. Our analysis of retail units also proved inconclusive. Although regional and district centres exhibited LQ decline no statistically significant relationship with depreciation was found other than for ‘London Other’. • The research has shown that locational quality (LQ) change is a feature of the office and

retail markets. The relationship between LQ change and depreciation is not clear-cut in statistical terms, however.

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Table 5.1 Original Properties: EDRs (No LQ Change and LQ Change) EDR (% p.a.)

No LQ Change EDR (% p.a.) LQ Change

Offices 1984 Cohort (1984-95) 1990 Cohort

- 3.05 - 2.2

- 2.32 (65) - 6.49 (15)

Note: Numbers in brackets are the total properties with LQ change in each cohort 5.2.5 Refurbishment For offices, limited evidence suggests that in London West End refurbished properties depreciated less overall than their benchmarks in 1984-95. The range of capital expenditure on refurbishment (as a percentage of capital value) varied from 7% to 20% for prime, and 12% to 21% for non-prime. Statistical tests revealed no significant differences between pre- and post-refurbishment rates. This was mainly the result of highly volatile annual depreciation rates and the low number of properties. The methodological issues are examined in more detail in section 5.5 below. • In the West End of London, limited evidence suggested refurbished office properties

depreciated less than original buildings in the period 1984-95. 5.3 Data Quality Issues Overall, two features of the results which stand out are:

• ‘appreciation’ in some sectors; and perhaps related to this,

• lower overall depreciation in the slump than in the boom. These features may represent the logical reality of what actually does occur. Indeed, Barras and Clark (1996) found evidence of falling depreciation from boom to slump in the City of London Office market. It is likely however that the nature of the data in 1990-95 is also having an impact. This is clear if it is recognised ‘appreciation’ implies that an old building gains in value against a brand new building in the a prime location. Although, of the three main sectors, retail would be expected to depreciate least, industrials also exhibit appreciation, although there are significantly lower numbers of industrial properties than retail on which to base this conclusion. It seems very likely therefore that the data used for our analysis in 1990-95 is exhibiting particular characteristics which could partly explain the two features highlighted. To shed light on this issue we examined the basis of our data in detail. The HP Rent Index is based on what Hillier Parker term the ‘best rental value’ (BRV), which is an unadjusted, headline basis and does not make any adjustment for the value of incentives over and above the usual rent free-period for fitting-out. Hillier Parker also hold limited ‘achievable rental value’ (ARV) data, which are adjusted for additional incentives over and above the usual package for fitting-out. ARVs are not, however, available in the public domain, and do not form the basis of the HP Rent Index.

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We know, from 1993, that one sponsor’s data was on a ‘provable, effective basis’, which did adjust rent for incentives to produce an effective, or ‘hardcore’, rent. We also know that 1993 and 1994 were the years when adjustment to this sponsor’s data would have had the greatest effect. This is because by 1995 the discounts from the headline basis had declined, perhaps reflecting more common, shorter lease terms with shorter rent-free periods. However, we are confident that for all sectors the relatively low proportion of properties held by this sponsor in our final dataset did not make any significant difference to our results. For example, there are only 3 properties in the 1984 SSU cohort, and 2 in the 1990 SSU cohort. Moreover, running our analysis without this sponsor’s data made no significant difference to our results, and a comparison of relevant data revealed no significant differences between sponsors’ data, or between ERVs within centres. We therefore included all sponsors’ data to maximise the use of our dataset. Furthermore, we understand that all other sponsors’ data is on a ‘headline’ basis, in line with the HP Rent Index, making any adjustment for the presence of incentives unnecessary. The other possible explanation relates to the view of ERV by valuers in the market during the period of study. There could have been a tendency for ERVs to be founded on a less ‘optimistic’, provable basis (based on open market lettings where they exist, or rent reviews and other settlements in their absence) than the market during the late I 980s boom, but a more ‘optimistic’, provable basis, based on rent reviews, in the 1990s slump. The evidence for this ‘lag’ effect over the cycle is provided by the slower ERV decline in the I990s, in comparison with the market, or HP index decline, both of which are contributing to the ‘appreciation’ during this period. Overrenting could also tend to bolster ERVs in the slump. In the case of standard shop units, for example, we found that there was evidence to suggest a slower fall in ERVs during the period 1990-95, than for the relative average HP benchmark. This is shown clearly in Figure 5.1, which points to a difference in the interpretation of rental value evidence during boom and slump for retail. For example, we found a slower fall in ERVs during the period 1990-95, than for the relative average HP benchmark. This is shown in Figure 5.1 where the average depreciation rate for retail was 1.44 % pa for 1984-89, compared with appreciation of 0.75 % pa for 1990-95. The latter reflects an overall fall in the average HP Index for retail properties of 2.23% pa and a slower fall in ERVs of 1.68% pa over the same period. There also appears to be a ‘lag’ effect during 1989-90 between the index and ERVs. Similar patterns emerge when the data is split into prime and non-prime. Whilst there was no ‘appreciation’ in the office sector, Figure 4.10 (p49) shows a similar ‘lag’ effect, and if this is a pattern of the data in 1990-95 it could also have a similar, though converse, effect on depreciation rates during the late 1980s boom, making rates higher than might otherwise be the case. Indeed, recent research at the University of Reading (Crosby and Murdoch, 1997) supports this view. They used rental valuation and case study data for commercial property, and found that in the rising market of the late I 980s new lettings were perceived as significantly higher that that provable at rent review. In contrast, in the post-1990 recession the process was reversed with new lettings falling below the level of review rents. Indeed, when adjusted for incentives the reversal was even more pronounced. The research also suggested different valuers may be applying a different basis of rent within the valuation of the same property: some may be assessing review rent and others what the perception of the new letting rental value may be. • In summary, although we believe that our data is the best available to us, the possibility of

valuers differing in their perception of rental value over the market cycle remains very real. In particular, a systematic ‘lag’ in ERVs against the HP Rent Index in the slump of 1990-95 could lead our results to show lower depreciation rates over this period than might otherwise be the case, and overrenting might tend to underpin this. Conversely, similar characteristics in the 1980s data could lead to depreciation rates being higher than expected during that period of the cycle. Research by Crosby and Murdoch (1997) supports this view.

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5.4 Significance of the Research This study has provided a new and up-to-date analysis of rental depreciation for the main property sectors. Using a combination of EDR (derived from OLS regression) and ADR, rental depreciation patterns by town, and property type and construction date have been provided. An insight into variations in depreciation rate over ‘market state’ has been presented, and locational quality change investigated in detail. The limited number of case studies have also supported the main study by providing further evidence on the process of depreciation over the market cycle. Table 5.2 Previous Depreciation Research Studies: A Summary of Rental Depreciation OFFICES INDUSTRIAL Study Year of Cross-

Section Depreciation Rate (% p.a.)

Depreciation Rate (% p.a.)

Baum (1991) Baum (1997)

1986 (1) 1996 (3)

1.1(2)

2.2

0.52 -

CALUS (1985) 1985 3.0(4) 3.3 Barras and Clark (1996)

1980 1989 1993

1.2 1.5 1.2

- - -

Notes (1) Longitudinal analysis (1979-86) showed 2.86% p.a. for industrials and 0.78% for City

offices. (2) Average of 1.1% p.a. Both 1991 and 1997 figures relate to City of London only. The 1997

study also examined the West End (1.6% pa rental depreciation rate) (3) Also supported by 1986-96 longitudinal study. (4) Unpublished CALUS data showed a prime City of London rate of 1.4% pa. In particular, the study has used real data on a national, longitudinal basis to examine rental depreciation. Comparison with previous research in the area is therefore difficult because of the difference in time period involved, but also because previous studies have frequently incorporated a cross-sectional basis and have often used an absolute method for calculating ADR. Nonetheless, a summary of previous rental depreciation results is included in Table 5.2 for comparison. Interestingly, this shows a similar pattern of falling depreciation in Barras and Clark’s study of City of London offices, although they do not cover the period 1993-95 in their study. On the other hand, Baum (1997) found a pattern of increasing depreciation in the City of London for the period, 1986-96. In fact, in the latter study, the longitudinal study analysis to support the cross-sectional study used the IPD index (which is ageing) to deduct ‘market’ depreciation from overall depreciation (ie average rental decline for the sample) and find the ‘age-related’ depreciation. Clearly much also depends on the year adopted for the cross-sectional analysis (see also section 2.4.3).

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5.5 Further Research The research presented in this report has provided: • a detailed literature review and background to depreciation; • a national study of rental depreciation for the main sectors for 1984-95; • an examination of data quality issues • a detailed examination of the late I 980s boom and early 1990s slump; • evidence to show that town type is more important than age and prime/non-prime in determining

depreciation rate; • an examination of locational quality change; and • age/depreciation profiles for the office sector. Further analysis into refurbishment is needed, however, to develop the general conclusions of this study: • in the short-term, and within market state, can depreciation be offset by refurbishment? and, • in the medium to long term is depreciation systematically higher in refurbished or original

properties? A key problem in such research is that if the focus is on isolating refurbishments within a particular market state, then, even if only two years’ data is used, pre- and post- refurbishment, unless the refurbishment occurs midway through the market state, it may be impossible to calculate relevant rates. It was also found that ADRs were quite volatile, and in the absence of detailed information on refurbishment duration, it was assumed that the ERV:HP factor in the year of refurbishment was not part of the ‘pre- and post-’ calculations. Any further research in this field should look at a small number of pre-defined locations, which would mean it would be much easier to control for other factors, and could also shed further light on the whole issue of building quality.

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BIBLIOGRAPHY Akerman,S.R. (1973), ‘Used Cars as a Depreciating Asset’, Western Economic Journal, Vol 11 (Dec), 463-74. Baird,G., et al (1996) Building Evaluation Techniques, McGraw Hill. Barras,R., and Clark,P. (1996), ‘Obsolescence and Performance in the Central London Market’, Journal of Property Valuation and Investment, Vol 14 No 4, 63-78. Baum,A. (1991), Property Investment Depreciation and Obsolescence, Routledge. Baum,A., (1997) Trophy or Tombstone? A Decade of Depreciation in the Central London Office Market, Lambert Smith Hampton and HRES Box, G., Hunter, W., and Hunter, J. (1978) Statistics for Experimenters, Wiley Cagan, P. (1965), ‘Measuring Quality Changes and the Purchasing Power of Money’, National Banking Review 3 : 217-36 CALUS (1986), Depreciation of Commercial Property, College of Estate Management CEM (1996), The Dynamics and Measurement of Depreciation in Commercial Property: Pilot Study, CEM(unpublished) Chinloy, P.T. (1977), ‘Hedonic Price and Depreciation Indexes for Residential Housing: A Longitudinal Approach’, Journal of Urban Economics 4, 469-82. Coen, R.M. (1975), ‘Investment Behaviour, The Measurement of Depreciation, and Tax Policy’, The American Economic Review, March, 59-74. Crosby, F.N., and Murdoch, S. (1994) ‘Capital Valuation Implications of Rent-Free Periods at Rent Review’, Journal of Property Valuation and Investment, Vol 12 No.2, 51-66. Crosby, F.N., and Murdoch, S. (1997) ‘The Influence of Procedure on Rent Determination in the Commercial Property Market of England and Wales’, RICS Cutting Edge Conference, Dublin, September. DeLeeuw, F. (1981) Discussion of Hulten and Wykoff (1 981 a) in Hutten, C.R. (Ed) Depreciation, Inflation and the Taxation of Income from Capital, Urban Institute Press Diggle, P.J., Liang,K.V., and Zager, S.L.(1995) Analysis of Longitudinal Data, Oxford Duffy, F. (1986), ‘The City Revolution - Its Impact on Office Space’, in The Workplace Revolution, Healey and Baker Fieldstein, M.S., and Rothschild, M. (1974), ‘Towards an Economic Theory of Replacement Investment’, Econometnca, Vol. 42, No.3, 393-423. Flanagan, R., Norman, G., Meadows, J., and Robinson, G. (1989), Life Cycle Costinq:Theory and Practice, BSP Professional Books. Goldstein, H.(1 979) The Design and Analysis of Longitudinal Studies, Academic Press

60

Grebler, L. et al (1956), Capital Formation in Residential Real Estate, Princeton University Press. Griliches, Z. (1970), ‘The Demand for a Durable Input: US Farm Tractors, 1921-57’, in Harberger, A. (ed), The Demand for Durable Goods, Chicago. Hall, R. (1968) ‘Technical Change and Capital from the Point of View of the Dual’, Review of Economics and Statistics, Vol.35 (Jan), 35-46. HalI, R. (1971), ‘The Measurement of quality change from Vintage Price Data’ in Gnliches,Z., Price Indexes and Quality Change, Cambridge, Mass. Healey and Baker (1987), National Office Desan Survey, Healey and Baker Hotelling, H. (1925), ‘A General Mathematical Theory of Depreciation’, Journal of the American Statistical Society, Vol.20 (Sept), 340-53. Hulten, C.R. and Wykoff, F.C. (1976), The Economic Depreciation of Non-Residential Structures - Working Paper in Economics No. 16, John Hopkins University, Baltimore, MA. Hulten, C.R. and Wykoff, F.C. (1980), ‘Economic Depreciation and the Taxation of Structures in US Manufacturing Industries: An Empirical Analysis’, in Ed. Usher,D., The Measurement of Capital, University of Chicago Press. Hulten, C.R. and Wykoff, F.C. (1981a), ‘The Measurement of Economic Depreciation’ in Ed Hulten, C.R., Depreciation. Inflation and the Taxation of Income from Capital, Urban Institute Press. Hulten, C.R. and Wykoff, F.C. (1981b), ‘The Estimation of Economic Depreciation using Vintage Price Assets’, Journal of Econometrics 15, 367-96. Hulten, C.R. and Wykoff, F.C. (1996), ‘Issues in the Measurement of Economic Depreciation: Introductory Remarks’, Economic Inquiry, Vol.34 (Jan), 10-23. IPD (1996), Property Investors’ Digest, IPD. de Jonge, H., and Gray, J. (1996) ‘The Real Estate Norm (REN)’ in Baird, G et al (1996) Building Evaluation Techniques, McGraw Hill. Jones Lang Wootton (1986) Obsolescence: The Financial Impact of Property Performance, JLW Jorgenson, D. (1996) ‘Empirical Studies of Depreciation’, Economic Inquiry, Vol.34 (Jan), 24-42. Khalid Abd Ghani, (1992), ‘Hedonic Price Estimation of The Financial Impact of Obsolescence on Commercial Office Buildings’, unpublished PhD thesis, University of Reading, Construction Management. Leigh, W.A. (1979), ‘The Estimation of Tenure-Specific Depreciation/Replacement Rates Using Housing Quantity Measures for the US 1950-70’, The Quarterly Review of Economics and Business, Vol.19, 49-59. Malpezzi, S., Ozanne, L., and Thibodeau, T.G. (1987), ‘Microeconomic Estimates of Housing Depreciation’, Land Economics, Vol.63, No.4, 372-85.

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Oades, R (1996), ‘Cordless IT Communications’, Architects Journal, 2 May, 36-37 Oliner, S.D. (1996), ‘New Evidence of the Retirement and Depreciation of Machine Tools’, Economic Inquiry, Vol.34 (Jan), 57-77. O’Roarty, B. (1996) A Critical Assessment of the Rental Valuation of Retail Property, Unpublished Ph.D thesis, Faculty of Engineering, University of Ulster. PovaIl, 5, (1986), ‘Building Design’. Paper presented at CALUS Conference, September 17 Ramm, W. (1970), ‘Measuring the Services of Household Durables: The Case of Automobiles’ in Proceedings of the Business and Economics Statistics Section of the American Statistical Assoc. Salter, W.E.G. (1966), ‘Productivity and Technical Change’, Cambridge University Press. Taubman, P. (1980), ‘Comment’, in Usher,D.(ed), The Measurement of Capital, University of Chicago Press. Taubman, P. and Rasche, R.H. (1969), ‘Economic and Tax Depreciation of Office Buildings’, National Tax Journal, 22 (Sept), 334-46. Weatherall Green and Smith (1985), ‘Depreciation for Investors’, Economic and Property Briefing, February. Wykoff, F.C. (1970), ‘Capital Depreciation in the Postwar Period’, Review of Economics and Statistics, Vol.52 (May), 168-72. Young, A.H., and Musgrave, J.C. (1980), ‘Estimation of Capital Stock in the USA’, in Usher, D.(ed)., The Measurement of Capital, University of Chicago Press.

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Appendices

Appendix A Statistical Methodology

1.0 Introduction This section describes the statistical methodology and its rationale in detail. The methodology was tested initially on a small sample of 52 regional offices and then replicated for other property sectors. Section 4.0 therefore describes the methodology used for the pilot sample and Section 5.0, the variations in detail from this for the other sectors analysed. 2.0 Regression Analysis There are two main ways in which depreciation rates can be calculated. Firstly, average depreciation rate, based on a geometric mean, may be found by comparing an ERV/HP measure at the start of the time series with the same measure at the end of the series. A simple compounding formula is used to calculate the annual depreciation rate. This method, however, ignores any ‘noise’ or movement in the time series in intervening years, and suffers from the fact that associative factors which may be linked to specific depreciation rates (such as construction period, type of town and prime/non-prime) are difficult to isolate and examine when there is a large data set. The formula used takes ERV and the prime benchmark (HP) at year 1 and year n in the future and calculates the annual multiplier i.e. ERV1/HP1 (in year 1) →ERVn/HPn (in year n) therefore: (ERV1/HP1) r

(n-1) = ERVn/HPn r(n-1) = (ERVn/HPn) /(ERV1/HP1) r = {(ERVn/HPn) /(ERV1/HP1)}

1/(n-1) where r = annual rate of change The rate produced gives the annual change from year 1 to year n. It measures the relative change between the ERV:prime benchmark (HP) ratio from year to year. A second method, Ordinary Least Squares (OLS) regression can be used as an exploratory tool for tackling such problems, although it also relies on a full time series for each property over the period of time to be examined. The output from this analysis is an ‘estimated’, or ‘best fit’, depreciation rate which takes intervening movements in ERV/HP into account. The technique allows flexibility in ‘slicing’ the data into relevant time periods, and also allows significance testing to be carried out to test the relationship between age and depreciation rate. OLS regression, in summary, provides a powerful and flexible exploratory tool to supplement the ‘averaging’ technique, and to maximise the use of the IPD data set. 3.0 The Simple Regression Model In simple terms, a straight line relationship could be used to model depreciation in the form, ERVt/HPt = ß0 + ß1t where ERVt is the ERV of the subject property at time t HPt is the Hillier Parker index at time t, ß0 is a constant, and ß1 is a constant depreciation amount.

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This, however, is a linear trend model which represents a constant level of growth or decline. The evidence from previous empirical research into depreciation suggests that a more realistic a priori assumption about the pattern of depreciation over time would be an exponential trend model, where depreciation is occurring at a constant compound rate. This has the added benefit that predicted rents would be constrained to be non - negative. An exponential model would take the following form: ERVt/HPt = e (ß0 + ß1t) where e is the base of the natural logarithm (= 2.718) This transforms through logarithms to, ln(ERVt/HPt)= (ß0 + ß1t) ln(e) = ln(ERVt/HPt)= ß0 + ß1t where ß1 is now a depreciation rate. In this way Ordinary Least Squares (OLS) regression can be used to estimate a depreciation rate for a pooled set of properties. It must be noted that the rate produced relates to the rate of change of the transformed data. All EDR rates quoted in this report are in this format. To convert the rates to be comparable to the original data the exponential must be taken. However as the betas produced are close to zero there is either no change or the change is very small. In isolated cases where the quoted depreciation rate is highest at around 10% the change incurred by taking the exponential would only be approximately 0.5% less. For this reason, it was not felt necessary to transform the rates (see section 4.7). The ADRs produced are however from the original and are not therefore strictly directly comparable to the EDRs, but as stated before the difference in rate is very slight. Regression analysis can also be used to incorporate a number of characteristics of a property. Incremental rates of the effect of these characteristics are produced and this is achieved through the use of dummy variables (see section 4.4). A pilot sample of 52 regional offices was used to test the transformation, the fit of the model and the methodology. In the full analysis, London and Regional offices were analysed together. 4.0 Statistical Methodology 4.1 Transformation and Fit of the Data A natural log transformation was used on the data to account for the exponential nature of depreciation. From an analysis of the data there was nothing to suggest that a different transformation would be more suitable. Ten properties were randomly selected from the test sample to assess the suitability of the log transformation by plotting the log of the ERV/Hillier Parker ratio against time. A straight line indicated a suitable transformation. The graphs produced did not fit a straight line well but given the market cycles involved in the time period, this was expected. The variation exposed supported splitting up the overall period of 1980-1995 into three segments 1980-1983, 1984-1989, and 1990-1995 (see section 4.5).

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The time periods use the inclusive periods 1980-1983, 1984-1989, 1990-1995. The rationale for using these sub-periods is that we looked in detail at the pattern of results emerging in all sectors early on in our analysis. We found that often there were, for a number of properties, sharp changes in the data between the year end 1983 and the year end 1984, and the year end 1989 and the year end 1990. We also found that because of these ‘jumps’ it is possible that by including the year 1983 and year 1989 for the second and third sub-periods, misleading results could be produced, by using the data for the same year twice. In summary, the evidence from our data suggested that the most appropriate market state periods for our dataset are: • 1980-83 (31 Dec 1980 to 31 Dec 1983); • 1984-89 (31 Dec 1984 to 31 Dec 1989); • 1990-95 (31 Dec 1990 to 31 Dec 1995). Having established the suitability of the transformation, the regression model was then fitted. Residuals and predicted values from the regression model were examined graphically to assess model adequacy. The residuals are calculated by the difference between the observed values and the predicted value from the fitted regression model. Examination of the residuals revealed no serious violations of the assumptions underlying the regression model, apart from non-independence of errors to some extent. This is not unexpected as each profile is for an individual building. Plotting residuals over time also revealed some systematic variation due to the time periods mentioned above. Despite this, OLS can still be used to produce an appropriate empirical description of linear trends. 4.2 Developing the Model The intention of the model is to identify rates of depreciation for a variety of chosen town types and construction periods and for prime and non-prime properties. The refurbished properties were removed to be analysed separately (see section 6.3 below). Refurbishment of a property lifts the value of the property above ordinary market movements. In this sense, it is more appropriate to fit two lines to the data, one pre refurbishment and one post refurbishment if possible. The ratio of ERV/Hillier Parker was first indexed to 1980 before taking the log transformation. The purpose of this was to standardise all the ERV/HP values and set 1980 as time zero. All future years are relative to 1980 and are therefore comparable. As 1980 is set to time zero, there will be no constant in the regression model. The regression is therefore constrained to pass through the origin to correspond to the constraints in the data. The plots previously referred to do not show anything to suggest that this is an inappropriate action. To achieve rates according to characteristics, correspondent variables need to be included in the model. The data fell into 4 town type categories: London (suburbs and satellite), metropolitan city, major regional and minor regional. Three construction periods are used: pre 1965, 1966-1974 and post 1975. The final category relates to whether the property is prime or non-prime. Inclusion of all these variables in one model involved the use of dummy variables. Five analyses were run with varying combinations of categories,

• Level 1: Prime, town type and construction period all included, • Level 2: Prime and town type disregarding the effect of construction period, • Level 3: Town Type disregarding the effect of construction period. • Level 4: Prime and construction period, disregarding the effect of town type, • Level 5: Prime, disregarding the effects of construction period and town type.

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4.3 Choice of Variables The construction periods chosen (i.e. pre-1965, 1966-74 and post-1975) were comparable with the main grades of building found in the property marketplace. Due to the small sample sizes the number of categories did not exceed three. The prime/non-prime classification relates to the locational quality of each property in 1980. A locational quality indicator greater or equal to 85% was classified as prime. The town type variables are taken from the IPD classification for key centres. London is dealt with separately. 4.4 Dummy Variables As referred to above regression analysis was used to establish rates of depreciation for different characteristics of property i.e. town type, construction period and whether it is prime. The rates calculated are incremental values on a baseline. For a baseline non-prime property, located in London (suburb and satellite), and built before 1965, the incremental effects can be calculated for a change in the town type, change in construction period, and a change from non-prime to prime. The regression equation is extended to include coefficients for the dummy variables, and provides equations for the corresponding set of lines. Example I In(ERVt/HPt) = bt + c(D1 t) + d(D2 t) + e(D3 t) + f(D4t) + g(D5t) + h(D6 t) where: D1 prime D2 = construction period 1966-1974 D3 = construction period post 1975 D4 = town type, metropolitan city D5 = town type, major regional D6 = town type, minor regional, are dummy variables and t = time ERVt/HPt is a ratio indexed to the start point of the time period under analysis. (Note: There is no constant in the equation as the regression is constrained to pass through the origin.) Each of the categories are given a value of 1 where a property relates to the category, and a value of 0 when it does not. Each of these variables must be multiplied by time. The regression model relates the dependent variable (the log of the indexed ratio), to the independent variables (the various town type and construction period categories, prime, and time). The results produced provide increments on the baseline. The baseline is set by the exclusion of one dummy variable in each category. In the above example the baseline has been set to a non-prime property located in London (suburb and satellite), built before 1965. The b coefficient in the above example gives the average rate of

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depreciation for this baseline group. The coefficients for the dummy variables provide the incremental effect of that dummy variable on the baseline. 4.6 Conclusion For the sample data there was no conclusive evidence to suggest the model was not an appropriate one to use for this data. However, it should be stated that the results give an estimate of what has happened over the 15 years and should be used as such. In this sense the depreciation rates produced are estimated depreciation rates. A limited comparison of the EDR output from the model with ADRs by sector revealed close comparability between ADR and EDR (see Table A1 below and Appendix B). For the ADRs and the EDRs to be comparable, the exponential of the EDRs has been taken (see section 3.0). For example, the untransformed office EDR is -3.05%. To transform this correctly the exponential is taken, which is 0.96996 (i.e. the ERVIHP ratio in year 1 is 96.996% of the ERVIHP ratio in year 0, and so on). The depreciation rate per annum is therefore -3.004%, which is very close to the untransformed EDR. Table A1 Comparison of ADR and EDR (adjusted) 1984 Cohort (% p.a. 1984-1995) ADR EDR (adjusted) Office -3.019% -3.004% SSU 0.400% 0.280% Industrial -0.256% -0.316%

5.0 Other Sectors As far as the other sectors were concerned, the analysis replicated the stages outlined above for the pilot sample. The town type categories for retail were based on Hillier Parkers’ ‘Shopping Centres of Great Britain: A National Survey of Retailer Representations by Trading Location, 1996’. Regional centre classification was divided into major and minor centres by taking the mid point of the regional centre category. Industrials were divided into London and the South East, Metropolitan and Other centres. The detailed results of these sectors are not reported in this report. 6.0 Further Analysis 6.1 Age as a ‘Causal’ Factor • The complexity of the interrelationships between independent variables such as construction

periods, town type and primeness and the response, or dependent variable, depreciation rate make it very difficult to establish a direct causal relationship between construction period (as a proxy for age) and depreciation rate. It is, however, possible to test for ‘association’ between construction period and depreciation rate using significance tests (t tests or F tests). Because of the complexity of the interrelationships causality cannot be inferred from these tests.

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Our significance testing was confined to testing the significance of the increment in explanatory variables over the baseline. A significance test was used to determine the importance of each of the explanatory variables within the model, and hence to suggest their respective importance in explaining depreciation. 6.2 Locational Quality (LQ) The issue of location quality was also fundamental to our study. In the longitudinal analysis for each cohort in our study we coded properties as to whether they were prime or non-prime in 1980, for the 1980 cohort, 1984 for the 1984 cohort, and 1990 for the 1990 cohort. A property greater to or equal to 85% ‘of prime’ was classed as a prime property. However, locational quality can change over time and can potentially impact on depreciation rate. For example, a prime property can become non-prime over the period 1980-95 and vice versa. To shed light on this issue, we separated properties with LQ change from those without LQ change. Because of the often continuous change in LQ and also because of the low numbers involved in such change, OLS regression was not used. For example, ‘primeness’ in such cases is itself a ‘moving’ variable which makes it difficult to formulate a meaningful categorisation where LQ changes frequently. It was therefore decided to analyse the group where there was LQ change separately within each sector. The analysis comprised two stages: • an assessment of the frequency and amount per annum of LQ change; and • a linear regression analysis to test the relationship between LQ change per annum and

depreciation rate per annum for ‘market state’ periods. This enabled us to draw conclusions between the amount of LQ change and depreciation rate for properties where there was LQ change. The regression was disaggregated by town type because this was considered to be more important than the other variables in its association with locational quality.

6.3 Capital Expenditure and Refurbishment We also isolated properties where capital expenditure had comprised a refurbishment of some kind, but where there was no LQ change. All properties identified from the LQ questionnaire that had corresponding expenditure were classified as refurbished. In addition, all properties with expenditure classified as expenditure code 1 (refurbishment), where expenditure in any year exceeded 5% of capital value, was also classified as refurbishment. Measuring the impact of capital expenditure individually on depreciation rate is problematic because a property’s performance is affected by changes in market state and other local economic factors over a particular time-period. One possible way to control for this is to take a pre- and post-refurbishment depreciation rate for each property within homogenous groups (by primeness, construction date or town type) over a short time period, for two years prior to refurbishment and for two years after refurbishment. The pre- and post-refurbishment rates can then be compared statistically using a paired t test. We also considered testing the relationship between change in depreciation rate and amount of expenditure. The low number of properties severely limited our analysis, however. Our analysis also attempted where possible to show capital expenditure on refurbishment over time for existing properties as a percentage of capital value in year(s) of capital outlay.

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It should be noted that the impact of capital expenditure on total return was not analysed because there was insufficient data to calculate total return in both the full analysis and the case studies. Acknowledgement Our thanks are due to Paul Mitchell of Prudential and James Gallagher of University of Reading Statistical Services Centre for their help in formulating this methodology. Software All statistical analysis was carried out in SPSS and Microsoft Excel.

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Appendix B

Descriptive Statistics/Graphs (ADRs) for Offices Initial ADR Analysis The data for each sector was originally analysed using summary descriptive statistics. The statistics provided a description of the behaviour/pattern of the data over the period of analysis, 1984-1995, with market states 1984-1989 and 1990-1995. Office 1984 Cohort All locations experienced depreciation over 1984-1995. The standard deviation ranged from 0.08% p.a. (Metropolitan suburbs and satellites) to 3.4% p.a. (Major Regional). Overall the standard deviation was 2.6% p.a. around the mean of 3.2 p.a. % depreciation. Analysis by market sector shows overall higher depreciation in the boom period of 1984-1989 and shows particular contrast in the depreciation rates by location between market state. For example, London City which showed least depreciation (compared to other locations) in the boom and the greatest depreciation in the slump (see figure B1 overleaf).

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71

Appendix C Overall EDR Comparison, by Sector

Table C1 1980 Office Cohort - Overall EDR

1980 Office Cohort Rebased at 1984 EDR (%

p.a.) p value

1984 - 1995 -3.32% <0.001 1984 - 1989 -6.69% <0.001 1990 - 1995 -3.62% <0.001 1980 Office Cohort Rebased at 1990 EDR (%

p.a.) p value

1990 - 1995 -1.28% 0.020

1980 Office Cohort EDR (%

p.a.) p value

1980 - 1995 -2.33% <0.0011980 - 1983 0.54% 0.2471984 - 1989 -5.67% <0.0011990 - 1995 -3.62% <0.001

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Table C2 1984 SSU, Office, and Industrial Cohorts - Overall EDR 1984 Office Cohort EDR (% pa.) p value 1984 - 1995 1984 -1989 1990 -1995 1990 -1995 rebased at 1990

-3.050% -6.039% -3.521% -1.134%

<0.001 <0.001 <0.001 0.005

1984 SSU Cohort

EDR (% pa.) p value 1984 - 1995 1984-1989 1990-1995 1990-1995 rebased at 1990

0.280% -1.707% 0.903% 1.134%

0.002 <0.001 0.077 <0.001

1984 Industrial Cohort EDR (% pa.) p value 1984-1995 1984-1989 1990-1995 1990-1995 rebased at 1990

-0.317% -0.443% -0.148% 0.128%

0.144 0.428 0.910 0.638

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Table C3 1990 SSU, Office, and Industrial Cohorts - Overall EDR 1990 Office Cohort EDR (% pa.) p value 1990 - 1995 -2.20% <0.001 1990 SSU Cohort

EDR (% pa.) p value 1990 - 1995 -1.11% 0.0203 1990 Industrial Cohort EDR (% pa.) p value 1990 -1995

1.98% <0.001

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Appendix D Offices

Table D1 Sample Size and Composition 1980 Cohort Full ERV Figures 180 less No LQ data Sold No HP data No construction date No ERV/floorspace Inconsistent 76 Usable Data 104 of 104, 56 original, no LQ change 36 original with LQ change 12(1) refurbishments of which: 7 no LQ change 5 LQ change

(1)Only 5 usable properties due to further missing items and inconsistencies.

Final Number Used : 97 properties

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Table D2 Mean Age, Office Cohorts 1980 Cohort at 1980 1984 Cohort at 1984 1990 Cohort at 1990 Mean Age All Prime Non Prime

40 30 44

42 39 44

39 35 41

Standard Deviation All Prime Non Prime

40.23 29.3 43.86

53.06 65.42 45.09

50.33 62.92 46.33

Maximum All Prime Non Prime

200 90 200

334 334 214

260 260 210

Minimum All Prime Non Prime

2 2 2

0 0 0

0 0 0

Range All Prime Non Prime

198 88 198

334 334 214

260 260 210

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Table D3 Sample Size and Composition 1984 Cohort Full ERV figures 275 less No LQ data

Sold No HP data No Construction date No ERV/floorspace Inconsistencies 101

Usable Data 174

of 174, 91 original, no LQ change

57 original, with LQ change

26(1) refurbishments of which:

18 no LQ change 8 LQ change

(1)Only 5 usable properties due to further missing items and inconsistencies.

Final Number Used: 153 properties

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Table D4 Sample Size and Composition 1990 Cohort Full ERV figures 138 less No LQ data Sold No HP data No Construction date No ERV/floorspace Inconsistencies 19 Usable Data 119

of 119, 100 original, no LQ change

13 original, with LQ change

6(1) refurbishments of which:

4 no LQ change 2 LQ change

(1) No usable data due to further missing items and inconsistencies.

Final Number Used : 113 properties

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Table D5 Age Profile, 1984 and 1990 Office Cohorts 1984 Office Cohort

1990 Office Cohort

Prime

Prime

Age (1984)

ADR % pa (84-95)

ADR %pa (84-89)

ADR %pa (90-95)

No. Properties

Age (1990)

ADR % pa

No. Properties

0-5 -2.445 -1.431 -2.895 12 0-5 -1.387 11 6-10 -2.080 -5.347 0.364 3 6-10 -4.393 1 11-15 -0.517 -1.801 -0.099 1 11-15 -7.762 1 16-20 -5.118 -6.931 -4.975 4 16-20 -9.997 2 21-25 -2.040 -6.871 1.227 2 21-25 -11.299 1 26-50 -5.772 -4.051 -10.663 3 26-50 -8.363 3

51-100 -2.734 -5.795 0.628 6 51-100 1.138 1 100+

Total

-2.898 -0.520 -3.769 2

33

100+ Total

-0.827 3

23

Non Prime Non Prime

Age (1984)

ADR % pa (84-95)

ADR %pa (84-89)

ADR %pa (90-95)

No. Properties

Age (1990)

ADR % pa

No. Properties

0-5 -3.156 -3.675 -2.837 3 0-5 -6.730 6 6-10 -3.146 -1.789 4.380 9 6-10 -2.717 13 11-15 -0.454 -4.301 2.579 3 11-15 -2.842 6 16-20 -2.967 -4.709 -2.954 4 16-20 -1.174 19 21-25 -2.419 -0.142 -3.456 8 21-25 2.626 1 26-50 -2.562 -5.302 -1.303 11 26-50 -2.243 12

51-100 -4.049 -7.043 -2.379 16 51-100 -2.186 11 100+

Total

-3.901 -7.402 -1.566 4

58

100+ Total

-0.462 9

77 Grand Total 91 Grand Total 100

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Table D6 Movement of LQ Change (Offices) LQ Change 1980 1984 1990 Within Non-Prime 20 28 7 From Non-Prime → Prime 2 3 - Non-Prime → Prime → Non-Prime 3 3 - Within Prime 5 8 3 Prime → Non-Prime 6 14 3 Prime → Non-Prime → Prime - 1 - 36 57 13 For example, 15 properties in the 1984 cohort were originally prime but became non-prime over the period 1984-1995.

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Table D7 Regression Results - Office Locational Quality Change 1980 Cohort 1984-89 R2 F P B London City 0.587 7.12 0.04 4.75 London Other 0.53 7.91 0.02 -1.04 1984 Cohort 1984-89 London Other 0.545 9.58 0.015 -1.06 Metropolitan 0.753 12.2 0.025 -1.15

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Table D8 Refurbished Offices 1980 Cohort (No LQ Change) Summary

Average %CV Spent

Average Pre-Rate Average Post-Rate

ADR 1980-95

PRIME 19.76 11.23 -6.93 -3.8 NON-PRIME 21.72 -11 48 -3.02 -2.82

EDR Benchmarks (1980 Cohort)

1980-83 1984-89 1990-95 1980-95

PRIME - 0.85 - 5.39 - 3.57 - 2.67 NON-PRIME 1.14 -5.78 -3.63 -2.18

Notes

1. The pre- rate and post- rates are volatile because they are the annualised ADRs over two years These were calculated on the basis of two years data before and after refurbishment. The low number of properties meant that a paired t-test revealed no significant difference in rate.

2. The ‘Average %CV Spent’ column is the average amount of capital expenditure as a

percentage of capital value in the year(s) of outlay.

3. EDR benchmarks in the summary table are for original buildings, and for the whole dataset, prime and non-prime. The EDR5 in the main table are by prime/non-prime, town type and construction date.

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Table D9 Refurbished Offices, 1984 Cohort (No LQ Change) Summary Average %CV

Spend Average Pre-

Rate Average Post-

Rate ADR

1984-95 NON-PRIME 12.13 3.98 6.38 -0.93 EDR Benchmarks (1984 Cohort)

1984-89 1990-95 1984-95

PRIME -5.13 -3.67 -2.77 NON-PRIME - 6.56 - 3.44 -3.21

Notes 1. The pre- rate and post- rates are volatile because they are the annualised ADRs over two years.

These were calculated on the basis of two years data before and after refurbishment. The low number of properties meant that a paired t-test revealed no significant differences in rate.

2. The ‘Average % CV Spent’ column is the average amount of capital expenditure as a percentage

of capital value in the year(s) of outlay. 3. EDR benchmarks in the summary table are for original buildings and for the whole dataset,

prime/non-prime. The EDRs in the main table are by prime/non-prime, town type and construction date.

4. No summary is made of prime because there was only one property.

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Tables D10 to D14 Note on interpretation Variable selection involves assessing the importance of each variable in the model. By leaving each variable out in turn, the significance of the individual variables, in terms of their contribution to the model, may be calculated. A 5% significance level has been adopted throughout. OLS regression provides an empirical tool for calculating EDRs. Significance tests (F and I tests) provide a guide for assessing the importance of each variable in the model. R2 were not provided because of the complexity of their interpretation in this analysis. Shading signifies a variable is significant at the 5% level.

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Table D10 Variable Selection: 1980 Office Cohort 1980 Cohort Construction Period Town Type Prime 1980-1995 <0.001

[1] <0.001

[1] 0.204

1980-1983 0.547 <0.001 [1]

0.765

1984-1989 0.55 <0.001 [1]

0.43

1990-1995 0.801 0.421 [1]

0.741

1984-1995 rebased to 1984 0.114 <0.001 [1]

0.675

1984-1989 rebased to 1984 0.354 <0.001 [1]

0.665

1990-1995 rebased to 1984 0.764 0.295 [1]

0.715

1990-1995 rebased to 1990 0.903 0.003 [1]

0.327

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Table D11 Variable Selection, 1984 and 1990 Office Cohort 1984 Cohort Construction Period Town Type Prime 1984 - 1995 0.028 <0.001

[1] 0.055

1984 - 1989 0.507 <0.001 [1]

0.074

1990 - 1995 0.356 0.200 [1]

0.762

1990 - 1995 rebased at 1990

0.183 <0.001 [1]

0.557

1990 Cohort Construction Period Town Type Prime 1990 - 1995 0.998 <0.001

[1]

0.187

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Table D12 Variable Selection, 1980 Office Cohort

Time Period Variable F p value 1980 - 1995 Construction Period

Town type Prime

10.99 4.75 1.62

<0.001 <0.001

0.204 1980 - 1983 Construction Period

Town type Prime

0.61 7.79 0.09

0.547 <0.001

0.765 1984 - 1989 Construction Period

Town type Prime

0.6 7.92 0.63

0.550 <0.001

0.430 1990 - 1995 Construction Period

Town type Prime

0.22 1.01 0.11

0.801 0.421 0.741

1984 - 1995 rebased at 1984

Construction Period Town type Prime

2.18 15.09

0.18

0.114 <0.001

0.675 1984 - 1989 rebased at 1984

Construction Period Town type Prime

1.04 28.44

0.19

0.354 <0.001

0.665 1990 - 1995 rebased at 1984

Construction Period Town type Prime

0.27 1.22 0.13

0.764 0.295 0.715

1990 - 1995 rebased at 1990

Construction Period Town type Prime

0.1 3.39 0.96

0.903 0.003 0.327

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Table D13 Variable Selection, 1984 Office Cohort Time Period Variable F p value 1984-1995 Construction Period

Town type Prime

3.59 12.95

3.70

0.028 <0.001

0.055 1984-1989 Construction Period

Town type Prime

0.68 28.86

3.22

0.507 <0.001

0.074 1990-1995 Construction Period

Town type Prime

1.04 1.41 0.09

0.356 0.200 0.762

1990-1995 rebased at 1990

Construction Period Town type Prime

1.71 4.10 0.35

0.183 <0.001

0.557

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Table D14 Variable Selection, 1990 Office Cohort

Time Period Variable F p value

1990-1995 Construction Period 0.00 0.998 Town Type 11.01 <0.001 Prime 1.74 0.187

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Appendix E Case Studies - Offices

Case Studies - Summary Table Case Studies with no Locational Change and no Refurbishment Ref Sector Region Const Prime Analysis Period Overall

ADR 1984-1989

ADR 1990-1995

ADR 01/CS14 Office L City 1973 Y 1975-1995 -0.1 -1.21 0.73 02/CS27 Office L W End 1925 Y 1980-1995 -4.09 -7.85 -5.57 03/CS17 Office Met 1900 Y 1975-1995 1.16 -7.38 4.73 04/CS12 Office Met 1975 Y 1975-1995 -2.04 -0.07 -2.9 05/CS31 Office Met 1981 Y 1981-1995 -1.22 -6.95 -2.26 06/CS44 Office Met 1900 Y 1980-1995 -3.34 -3.08 -1.82 07/CS21 Office L City 1965 N 1980-1995 -4.46 -8.39 -1.47 08/CS30 Office L Other 1930 N 1984-1995 -1.06 -9.71 6.06 09/CS24 Office L Other 1962 N 1980-1995 -8.12 -1.37 -16.63 10/CS07 Office Met 1960 N 1974-1995 -2.04 1.39 2.82 11/CS08 Office Met 1960 N 1975-1995 -0.25 -2.38 -1.12 12/CS18 Office Met 1960 N 1980-1995 -1.73 3.32 -3.58 13/CS19 Office Met 1963 N 1980-1995 -1.26 4.97 -6.69 14/CS20 Office Met 1930 N 1983-1995 -4.22 -0.97 -2.65 Case Studies with Locational Change and no Refurbishment Ref Sector Region Const Prime Analysis Period Overall

ADR 1984-1989

ADR 1990-1995

ADR 25/CS25 Office L City 1987 Y

(86%-95%) 1987-1995 -3.87 - -7.67

26/CS22 Office L City 1930 Y (100%-85%)

1980-1995 -4.41 -7.32 -4.63

27/CS23 Office L City 1974 N (70%-60%)

1980-1995 -2.83 -3.71 -7.34

28/CS28 Office Met 1973 N (75%-60%)

1980-1995 -4.52 -6.05 -2.36

29/CS29 Office Met 1974 N (65%-50%)

1980-1995 -2.06 -8.41 -1.96

Case Studies with no Locational Change and Refurbished Ref Sector Region Const Prime Analysis Period Overall

ADR 1984-1989

ADR 1990-1995

ADR 32/CS33 Office L Sub & Sat 1965 Y 1980-1995 -3.29 -2.57 -5.27 33/CS48 Office Min Reg 1978 Y 1984-1995 2.29 -0.75 -4.17

90

Original Buildings No Location Quality Change Offices - Prime

91

Reference: 01/CS14 Property Type: Office Town Type: London

Region: City Construction Date: 1973

Initial Location Quality: 95% (Prime) Location Quality Change: Constant

Refurbishment: No

Sponsor’s Commentary: Office property of 41,000 sq ft; Tenant upgraded accommodation which now provides top quality space. However, looks dated externally

Average Depreciation Rate: 1975 - 1995: -0.10% ANALYSIS: BENCHMARKS (EDR, % pa) Market State Property

Depreciation Rate (ADR, % pa)

Town Type

Prime Sector

Prime Sector & Construction

Date

Prime Sector & Construction Date

& Town Type Slump 1972-74 - - - - - Upturn 1975-77 6.9 - - - - Downturn 1978-80 -14.35 - - - - Stable 1980-83 2.27 - - - - Boom 1984-89 -1.21 2.251 -5.126 -3.635 1.606 Slump 1990-95 0.73 -11.194 -3.669 -2.708 -10.471

92

Reference: 02/CS27 Property Type: Office

Town Type: London Region: West End

Construction Date: 1925 Initial Location Quality 100% (Prime)

Location Quality Change: Constant Refurbishment: No

Sponsor’s Commentary: The building comprises 68,000 sq ft arranged on

basement - 8th floors. Originally constructed as a residential block and as a result the space is not ideal for office use, i.e. there are no suspended ceilings, raised floors or air conditioning.

Average Depreciation Rate: 1980-1995: -4.09% ANALYSIS: BENCHMARKS (EDR, % pa) Market State Property

Depreciation Rate (ADR, %

pa)

Town Type

Prime Sector

Prime Sector &Construction

Date

Prime Sector & Construction Date

& Town Type

Slump 1972-74 - - - - - Upturn 1975-77 - - - - - Downturn 1978-80 - - - - Stable 1980-83 -1.68 - - - - Boom1984-89 -7.85 -10.712 -5.126 -7.213 -11.808 Slump 1990-95 -5.57 -3.932 -3.669 -3.274 -3.394

93

Refurbishments

Reference: 32/CS33 Property Type: Office

Town Type: London Suburb & Satellite Region: South East

Construction Date: 1965 Initial Location Quality 100% (Prime)

Location Quality Change: Constant Refurbishment: 1983

Sponsor’s Commentary: An office property of approximately 12,000 sq ft; originally let to

one tenant on a long review cycles. Subsequently assigned and sub-let to various tenants. Refurbishment was undertaken in 1983 including the common parts. Following refurbishment the property was re-let to a single tenant.

Average Depreciation Rate: 1980-1995: -3.29%

ANALYSIS: BENCHMARKS (EDR, % pa) Market State Property

Depreciation Rate (ADR, %

pa)

Town Type

Prime Sector

Prime Sector &Construction

Date

Prime Sector & Construction Date & Town

Type Slump 1972-74 - - - - - Upturn 1975-77 - - - - - Downturn 1978-80 - - - - - Stable 1980-83 -1.72 - - - - Boom 1984-89 -2.57 -2.718 -5.126 -7.213 -3.628 Slump 1990-95 -5.27 -5.186 -3.669 -3.274 -4.455

REFURBISHMENT ANALYSIS:

Refurbishment 1983 Average Spend 1983 No expenditure prior or post 32.1% refurbishment date 1981/82 Average Depreciation -3.02% Rate 1984/85 Average Depreciation 0% Rate

94

Reference: 33/CS48 Property Type: Office

Town Type: Minor Regional Region: South East

Construction Date: 1978 Initial Location Quality: 100% (Prime)

Location Quality Change: Constant Refurbishment: 1994

Sponsor’s Commentary: Two office buildings totalling 130,000 sq ft comprising ground

and three upper floors. The accommodation is fitted with suspended ceilings and underfloor trunking. All floors are serviced by lifts. Multi-tenanted by major corporations.

Average Depreciation Rate: 1984-1995: 2.29% ANALYSIS:

BENCHMARKS (EDR, % pa) Market State Property

Depreciation Rate (ADR, %

pa)

Town Type

Prime Sector

Prime Sector &Construction

Date

Prime Sector & Construction Date & Town

Type Slump 1972-74 - - - - - Upturn 1975-77 - - - - - Downturn 1978-80 - - - - - Stable 1980-83 - - - - - Boom 1984-89 -0.75 -1.431 -5.126 -3.826 -2.065 Slump 1990-95 4.17 -0.455 -3.669 -4.446 -0.985 REFURBISHMENT ANALYSIS: Refurbishment 1994 Average Spend 1994 No expenditure prior or post 4.7% refurbishment date 1992/93 Average Depreciation -22.34% Rate