Abstract

Moving is by far the most common way to adjust housing consumption. Quantitative knowledge on the moving pattern can therefore teach us much about the typical development of housing demand, or in other words about the steps in housing careers. The paper argues that knowledge of local moving patterns can enhance our understanding of the supply of vacant housing market opportunities. As such the paper relates to the literature on Markov vacancy chain models.The paper uses a cross-section of households as a basis for empirical descriptions of moving matrixes. This type of data yields a censored sample of moves. Censoring is not random and direct estimates of the moving patterns give biased estimates. The reason is that the household sample only reports the last in a sequence of moves. The bias emerges because moves to some destinations (rented units) are more likely to be followed by a new move than moves to other destinations (typically moves into large owner-occupied units). A weighting procedure that gives unbiased estimates of the moving pattern is proposed and applied in the paper.

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