Transactions Based Commercial Real Estate Indices: A Comparative Performance Analysis

1QIULIN KE, 2KAREN SIERACKI, AND 3MICHAEL WHITE

1UNIVERSITY COLLEGE LONDON, 2KASPAR ASSOCIATES

& 3NOTTINGHAM TRENT UNIVERSITY

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.

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

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

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.

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

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

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

Transactions & Appraisal Indices

Transactions & Appraisal Indices – Growth Rates

19851986

19871988

19891990

19911992

19931994

19951996

19971998

19992000

20012002

20032004

20052006

20072008

20092010

20112012

20132014

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TPIWDCV

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

West End Offices

0

100

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1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Chart Title

TWEI AWEI

West End Annual Capital Return

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1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Chart Title

DTWEI DAWEI

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