قیمت های مسکن و حجم معامله

We use annual, quarterly and monthly data from the US to show that the correlation between housing prices and transaction volume (number of existing houses sold) differs across different frequencies. While the correlation is high at the low frequencies it declines to the levels close to zero at high frequencies. Granger causality tests for different frequencies show that the way of causality in housing market changes from region to region. Our findings provide a litmus test for the existing theories that are proposed to explain the positive correlation between transaction volume and housing prices.

In this paper, we use US data to analyze the relationship between housing prices and transaction volume at different frequencies. Our analyses provide several tests to evaluate the theories offered to explain the comovement of housing prices and transaction volume documented in the literature. The first test in our analysis utilizes the different correlations observed at different frequencies. The theories proposed in the literature generate positive comovement at higher frequencies (in the short run) but generate negative comovement or non at lower frequencies (in the long run). In this respect, we investigate the relationship between housing prices and transactions by using spectral analysis to reveal how much different frequencies contribute to the correlation. Since both theories and data have implications about the correlation at different frequencies our paper proposes a new way of testing the existing theories in the literature which generate the comovement of housing prices and transaction volume. In addition to the correlation analysis we also explore the direction of the causality between the two series by using Granger causality test at different frequencies. This is important to evaluate the theories because the direction of causality between housing prices and transactions differs depending on the mechanism of the models. For our analysis we use annual, quarterly and monthly housing prices and transaction volume data from the US. We use HP and band-pass filters and dynamic correlations to obtain the correlations of the two series at different frequencies. In our analysis we show that the largest part of the positive correlation between housing prices and transaction volume comes from the low frequency components. However, at higher frequencies the correlation becomes smaller and sometimes negative. We, also, find that for the quarterly data at high frequency the way of causality between the two series is from transactions to housing prices. Our results are slightly different from Leung et al. (2002) findings which reveal the same relation at business cycle frequency. On the other hand, for the monthly data we do not find a way of causality dominating the other. For some cities transactions cause prices and for some cities prices cause transactions. There are also cities where both prices and transactions cause each other. While Granger causality tests provides small support for the search models, non of the theoretical models proposed passes the dynamic correlation test. Hence, our analysis poses a challenge for the existing theories. The paper is organized as follows. In Section 2 we provide a brief summary of the literature about housing prices and transaction volume and discuss what those theoretical models imply about the correlation of the two variables at different frequencies. In Section 3 we give a brief description of the spectral method. We describe our data set in Section 4. We provide the results and explain our findings in Section 5. Section 6 concludes.

In this paper, we use HP and band-pass filters, dynamic correlation to study the relationship between the housing prices and transaction volume in at different frequencies in the US data. We show that low frequency component is the major driver of the positive correlation. We also find that the way of causality at high frequency between the two series is from transactions to housing prices when we use quarterly regional data. For the monthly city-level data we do not find any direction. Taken together, we conclude that these findings pose a challenge for the current theories which claim to explain the positive correlation between two series.