Separating the Age Effect from a Repeat Sales Index: Land and Structure Decomposition
Since real estate is heterogeneous and infrequently traded, the repeat sales model has become a popular method to estimate a real estate price index. However, the model fails to adjust for depreciation, as age and time between sales have an exact linear relationship. This paper proposes a new method to estimate an age-adjusted repeat sales index by decomposing property value into land and structure components. As depreciation is more relevant to the structure than land, the property’s depreciation rate should depend on the relative size of land and structure. The larger the land component, the lower is the depreciation rate of the property. Based on housing transactions data from Hong Kong and Tokyo, we find that Hong Kong has a higher depreciation rate (assuming a fixed structure-to-property value ratio), while the resulting age adjustment is larger in Tokyo because its structure component has grown larger from the first to second sales.
A price index aims to capture the price change of products free from any variations in quantity or quality. When it comes to real estate, the core problem is that it is heterogeneous and infrequently traded. Mean or median price indices are simple to compute, but properties sold in one period may differ from those in another period. To overcome this problem, two regression-based approaches are used to construct a constant-quality real estate price index (Shimizu et al. (2010)).