Abstract

Conventional real estate price indices provide a single measure for the path of asset prices over time (controlling for the quality of the representative or average property). But it could be that properties have different price dynamics based on the price segment they are traded in. On the demand side, investors at different price points are differentiated by the amount of capital the investor has at their disposal and the type and source of financing. Smaller, private investors cluster at lower price points, while large institutions dominate the high price points. On the supply side, properties at different price points may serve different space markets with different types of tenants, and may reflect different supply elasticity and land/structure value ratios. This paper uses an unconventional approach, quantile regression, to estimate price indices for different price segments in commercial real estate. Our results show that there are indeed large differences in price dynamics for different price points. These differences are suggestive of a lack of integration in the property asset market, because we find apparent differences in the risk/return relationship. Lower price point properties exhibit less risk (in the form of volatility and cycle amplitude), but without evidence of lower total returns. Lower price point properties also show greater momentum and hence, predictability.

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