1. Transactions Based Commercial Real Estate Indices: A Comparative Performance Analysis 1 QIULIN KE, 2 KAREN SIERACKI, AND 3 MICHAEL WHITE 1 UNIVERSITY COLLEGE LONDON, 2 KASPAR ASSOCIATES & 3 NOTTINGHAM TRENT UNIVERSITY

2. Background Commercial real estate assets are infrequently traded, heterogeneous, vary spatially and have no central trading exchange. In response to this and to aid understanding of market performance, appraisal based indices have developed.

3. Appraisal Based Indices Appraisal index smoothing reflecting index construction methods and valuation process Thin markets and limited comparables may further impact/bias appraisal indices Reliance on heuristics in valuation decision making – anchoring and slow adjustment (Diaz and Wolverton, 1998; Gallimore 1994) Smoothing and lagging in appraisal based indices

4. Transactions Based Indices Indices based upon transactions should not be subject to the above inaccuracies. Hence they may provide a more accurate reflection of market volatility and investment risk. However transactions of commercial property are infrequent, this problem becoming more acute in smaller markets Without detail on property characteristics, it is not possible to control for heterogeneity and hence average transactions prices in different years may not be directly comparable – price differences may simply reflect property differences in each annual sample

5. Methods of Index Construction Hedonic analysis has been used to control for heterogeneity in the context of transactions based indices - Devaney and Diaz (2011) Alternatively Clapp (1990) used appraisals on land parcels in his area of study to overcome a lack of data on property characteristics. However the above approach can lead to endogeneity in estimated models.

6. Sample Selection A sale occurs only when the selling price meets or exceeds the seller’s reservation price (Gatzlaff and Haurin, 1998) A transactions dataset contains only those properties that meet the above condition Differences in reservations prices are unobservable but we do observe the selling price – but not for properties that do not sell Typically a Heckman correction procedure can be followed to account for sample selection effects – assuming we have data for properties for sale not just those sold However Fisher, Geltner, and Pollakowski (2007) find that the impact of sample selection bias is insignificant which contrasts with Devaney and Diaz (2011) who find significant sample selection effects present

7. Data and Approach Transactions data are from Property Archive Recorded transactions begin in 1969 The time period covered here is 1985 to 2014 We focus on Central London offices and due to small sample sizes we do not use earlier data before 1985 leaving us with ~30 years observations of just over 5,100 transactions About 85% of this is concentrated in the City of London (1,767), and West End (2,530) markets

8. Hedonic Regression Approach The dataset is limited with respect to property characteristics variables However it includes details on property owners – 2,920 comprising pension funds, insurance funds, property companies Other variables include sale price, postcode district, floorspace, and yield (although the sample is more complete in later years for this variable) Repeat sales are also identified in the dataset. Some are ‘flips’ and we do not know if some of these properties have been refurbished – not used as basis for analysis We compare appraisal and transactions based indices particularly with reference to turning points and unconditional volatility

9. Transactions & Appraisal Indices

10. Transactions & Appraisal Indices – Growth Rates

11. Volatility The unconditional volatility of the transactions index (sd = 58.832) is greater than the appraisal index (sd = 45.149) MA transactions index also have greater volatility (sd = 49.687) than the appraisal index

12. West End Offices

13. West End Annual Capital Return

14. Conclusions and Further Research Preliminary analysis suggests that the transactions based index predicts market turning points, sometimes with a lag, and has greater volatility than the appraisal based index Sample size smaller at start of period and also varies procyclically – assets transacting in bust periods may be different average quality from those selling in boom periods Next step to control for heterogeneity and further detailed analysis of the dataset to evaluate the role of owner type