The Dynamics and Measurement of Commercial Property Depreciation in the UK - Summary Report by: Dr Tim Dixon Director of Research College of ...
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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 1999/1Published 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
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
2in 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
3Executive 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.
4CONTENTS
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
54.2 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
6Table 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
7TABLES 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
84.5 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
91.0 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.).
101.2 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.
111.3 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.
122.0 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.
1
The literature breaks down conveniently 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.
13TABLE 2.1 SUMMARY OF PREVIOUS STUDY PROPERTY DEPRECIATION RESEARCH
STUDY DATE VALUE SECTORS LOCATION OF SAMPLE SIZE TYPE OF BASIS OF SITE AND OTHER
VARIABLES ANALYSES SAMPLE ANALYSIS ANALYSIS OTHER COMPONENTS
ANALYSED FACTORS OF STUDY
CALUS 1986 • ERV Offices Britain Offices: 32 Cross-sectional Hypothetical Not appropriate Survey of users
• Yield Industrial locations (1986) valuations of (see basis of Survey of
Industrials: 25 hypothetical analysis) occupiers
locations buildings
(rental values
& yields)
JLW 1986 • ERV Offices UK (outside Sample from Cross-sectional PPAS Not considered Rental
Industrial London) PPAS (1980-85) (not Obsolescence
appropriate) Rate (ROR)
ERV/’FMR’ ratio
Baum 1991 • ERV Offices City of London 125 (each Cross-sectional ERVs & Yields Smoothed Survey of
• Yield (CV) Industrial sector) (1986) – ERV – panel using ‘siting occupiers
& yield and estimates of score’ Study of ‘building
Longitudinal actual buildings quality’
(1980-86)
ERVs
Barras and 1996 • ERV Offices City of London 150 Cross-sectional IPD Data Not considered none
Clark • CV (1980, 1989 (analysis by
and 1993) and age band) –
Longitudinal valuation
(1980-93) based
Weatherall 1995 • ERV Offices UK Not known Longitudinal IPD Data Not considered Growth and
Green and • Yield Industrial (1980-93) (analysis by performance
Smith Retail age band) – oriented study
valuation
based
Baum 1997 • ERV Offices City and West 128 and 125 Cross- ERVs & Yields Smoothed Survey of
• Yield (CV) End sectional: City – panel using ‘siting occupiers
and West End estimates of score’ Study of ‘building
1996 actual buildings quality’
Longitudinal: and IPD index
City 1986-96
14Hypothetical 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
15to 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
16depreciation 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).
17Studies 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.
18To 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
19that 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.
20In 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):
21Figure 2.1 The Effect of Age, Inflation and Obsolescence on Age-Price Profiles
22• 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.
24Figure 2.2 Efficiency Profiles for Different Depreciation Patterns
Figure 2.3 Age-Price Profiles for Different Depreciation Patterns
25The 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 3.54 4.32 5.57 3.02
5 2.77 2.85 3.68 2.99
10 2.47 2.64 3.05 3.01
15 2.32 2.43 2.74 3.04
20 2.22 2.30 2.55 3.07
30 2.10 2.15 2.32 3.15
40 2.03 2.08 2.19 3.24
50 1.99 2.04 2.11 3.34
60 1.96 2.02 2.05 3.45
70 1.94 2.02 2.01 3.57
Best geometric rate 2.20 2.47 2.73 3.61
2
R (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
2
analysis which gives a good fit, as shown by the R 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,
26this 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)
1 2
CALUS Baum(1991) Baum(1997) Barras and Hulten and
3
Clark Wykoff
Retail [-] (-) [-] (-) [-] (-) [-] (-) [2.2](-)
Offices [2.4] (3) [1.22](0.92) (2.9](2.2) [1.6] (1.2) [2.47](-)
Industrial [-] (3.3) [-] (0.65) [-] (-) [-] (-) [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%.
3
Barras 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’.
27Figure 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)
28Results 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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