The relationships between rent multiplier and user cost – a case study of Taipei

Abstract

The value of Rent Multiplier (RM) for the city of Taipei has been in extraordinary magnitudes and remains to be a myth to most housing economists. Why does the RM in Taipei exhibit such a peculiarity? Is it because the populace there are so peculiar in their housing behaviours that can be held to account for such an extraordinary phenomenon or because there are logically consistent economic factors behind the scene that might have led the people to make their housing choices rather differently from the way usually envisaged by the conventional wisdom in economics? In this article, we try to uncover the myth by examining whether the economic factors such as user cost, vacancy rate and people's disposable income can be held to account for the above-mentioned consequence through a vector error correction model. More specifically, we examine whether there are long-term relationships between those explanatory variables and the RMs in question. The results show that our argument that the extraordinary RM phenomenon can be explained with user cost is empirically verified.

Notes

¹As a matter of fact, the rate of homeownership in Taiwan is so high that there are only few countries in the world that can be its competitor. On the contrary, the percentage of renters among all the households is only 13%, a figure that is hardly remarkable.

²The actual tax burden for those property owners, as shown by Peng et al. (2007), is roughly only one-tenth of the nominal rates stipulated by the authorities concerned, ranging from 0.09 to 0.13% for a couple of sampled districts in the city of Taipei. Viewing from the user cost perspective, this incredibly low tax incidences for the property owners must have profound impact on the house owners' cost of capital and would obviously affect their owning/renting decision in favour of owning instead of renting and hence push up the magnitude of RMs.

³Maximum eigenvalue test shows one cointegration vector, significant at 5%.

⁴The values in parentheses are t-statistics: *** denote t-statistics significant at 1% levels, respectively.

⁵As the industrial adjustment is made quarterly at a speed of 7%, the time required for short-term adjustment to long-term equilibrium is quarters, meaning that 14.29 × 3 = 42.87 is needed for complete adjustment.