Matching Indices for Thinly-Traded Commercial Real Estate in Singapore
We use a matching procedure to construct three commercial real estate indices (office, shop and multiple-user factory) in Singapore using transaction sales from 1995Q1 to 2010Q4. The matching approach is less restrictive than the repeat sales estimator, which is restricted to properties sold at least twice during the sample period. The matching approach helps to overcome problems associated with thin markets and non-random sampling by pairing sales of similar but not necessarily identical properties across the control and treatment periods. We use the matched samples to estimate not just the mean changes in prices, but the full distribution of quality-adjusted sales prices over different target quantiles. The matched indices show three distinct cycles in commercial real estate markets in Singapore, including two booms in 1995- 1996 and 2006-2011, and deep and prolonged recessions with declines in prices around the time from 1999-2005. We also use kernel density function to illustrate the shift in the distribution of house prices across the two post-crisis periods in 1998 and 2008.
Unlike residential real estate markets where transactions are abundant, commercial real estate transactions are thin and lumpy. Many institutional owners hold commercial real estate for long-term investment purposes. The dearth of transaction data has led to the widespread use of appraisal based indices, such as the National Council of Real Estate Investment Fiduciaries (NCREIF) index, as an alternative to transaction-based indices in the U.S. However, appraisalbased indices are vulnerable to smoothing problems. Appraisers appear to systematically under-estimate the variance and correlation in real estate returns other asset returns (Webb, Miles and Guilkey, 1992). Despite various attempts to correct appraisal bias, it remains an Achilles’ heel of appraisal-based indices. Corgel and deRoos (1999) found that recovering the true variance and correlation of appraisal-based returns reduces the weights of real estate in multi-asset portfolios.