ENTES VINCULADOS

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.

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

55

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.

57 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).