Information content of transaction volume: the housing market in the United Kingdom
According to search theory, transaction volume possesses the function of price discovery and reflects information more rapidly than price does. However, the findings of previous empirical studies differ considerably. In this study, a theoretical model is first established to analyze the potential information lag of transaction volume during pessimistic speculation. Data on the UK housing market are collected to conduct an empirical analysis of the responses of housing transaction volume to different market conditions. The results show that transaction volume responds to market information more quickly than does housing prices. However, under increasing market uncertainty, transaction volume lags four periods before reflecting the effect of the uncertainty. Moreover, this study performs a rolling window bootstrap Granger causality test, revealing that price leads volume during the period in which transaction volume fails to reflect an immediate rise in market uncertainty. An increase in market uncertainty reduces transaction volume. In addition, once transaction volume drops below a specific threshold, it loses its information content and price discovery function, extending the lead-lag gap with housing prices by two periods. The present study proposes a simple method for determining the informative-ness of housing transaction volume.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Andrew, M., & Meen, G. (2003). House price appreciation, transactions and structural change in the British housing market: a macroeconomic perspective. Real Estate Economics, 31(1), 99-116. https://doi.org/10.1111/j.1080-8620.2003.00059.x
Aye, G. C., Balcilar, M., Dunne, J. P., Gupta, R., & Eyden, R. (2014). Military expenditure, economic growth and structural instability: a case study of South Africa. Defence and Peace Economics, 25(6), 619-633. https://doi.org/10.1080/10242694.2014.886432
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593-1636. https://doi.org/10.1093/qje/qjw024
Balcilar, M., & Ozdemir, Z. A. (2013). The export-output growth nexus in Japan: a bootstrap rolling window approach. Empirical Economics, 44(2), 639-660. https://doi.org/10.1007/s00181-012-0562-8
Bansal, R. & Yaron, A. (2004). Risks for the long run: a potential resolution of asset pricing puzzles. Journal of Finance, 59(4), 1481-1509. https://doi.org/10.1111/j.1540-6261.2004.00670.x
Bijsterbosch, M., & Guérin, P. (2013). Characterizing very high uncertainty episodes. Economics Letters, 121(2), 239-243. https://doi.org/10.1016/j.econlet.2013.08.005
Case, K. E., & Shiller, R. J. (1989). The efficiency of the market for single-family homes. American Economic Review, 79(1), 125-137.
Case, K. E., & Shiller, R. J. (1990). Forecasting prices and excess returns in the housing market. Real Estate Economics, 18(3), 253-273. https://doi.org/10.1111/1540-6229.00521
Clark, P. (1973). A subordinated stochastic process model with finite variance for speculative process. Econometrica, 41, 135-155. https://doi.org/10.2307/1913889
Clayton, J. (1998). Further evidence on real estate market efficiency. Journal of Real Estate Research, 15(1), 41-57.
Clayton, J., Miller, N., & Peng, L. (2010). Price-volume correlation in the housing market: causality and co-movements. Journal of Real Estate Finance and Economics, 40(1), 14-40. . https://doi.org/10.1007/s11146-008-9128-0
Copeland, T. (1976). A model of asset trading under the assumption of sequential information arrival. Journal of Finance, 41, 1149-1168. https://doi.org/10.2307/2326280
De Wit, E. R., Englund, P., & Francke, M. K. (2013). Price and transaction volume in the Dutch housing market. Regional Science and Urban Economics, 43(2), 220-241. https://doi.org/10.1016/j.regsciurbeco.2012.07.002
Dzielinski, M. (2011). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, 9(3), 167-175. https://doi.org/10.1016/j.frl.2011.10.003
Eforn, B. (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics, 7(1), 1-26. https://doi.org/10.1214/aos/1176344552
Engle, R. F. & Granger, C. W. J. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica, 55(2), 251-276. https://doi.org/10.2307/1913236
Epps, T. & Epps, M. (1976). The stochastic dependence of security price changes and transaction volumes: implication for the mixture of distribution hypothesis. International Review of Financial Analysis, 44, 305-321. https://doi.org/10.2307/1912726
Gallant, A., Rossi, P., & Tauchen, G. (1992). Stock prices and volume. Review of Financial Studies, 5(2), 199-242. https://doi.org/10.1093/rfs/5.2.199
Genesove, D., & Mayer, C. (2001). Loss aversion and seller behavior: evidence from the housing market. Quarterly Journal of Economics, 116(4), 1233-1260. https://doi.org/10.1162/003355301753265561
Gu, A. Y. (2002). The Predictability of house prices. Journal of Real Estate Research, 24(3), 213-233.
Hort, K. (2000). Prices and turnover in the market for owner-occupied homes. Regional Science and Urban Economics, 30(1), 99-119. https://doi.org/10.1016/S0166-0462(99)00028-9
Hoshi, T. (2011). Financial regulation: lessons from the recent financial crisis. Journal of Economic Perspectives, 49, 120-128. https://doi.org/10.1257/jel.49.1.120
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254. https://doi.org/10.1016/0165-1889(88)90041-3
Johansen, S., & K. Juselius. (1990). Maximum likelihood estimation and inference on cointegration – with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169-210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551-1580. https://doi.org/10.2307/2938278
Kang, W., & R. A. Ratti. (2013). Structural oil price shocks and policy uncertainty. Economic Modelling, 35, 314-319. https://doi.org/10.1016/j.econmod.2013.07.025
Karpoff, J. (1987). The relation between price changes and trading volume: a survey. Journal of Financial and Quantitative Analysis, 22, 109-126. https://doi.org/10.2307/2330874
Ko, J.-H., & C.-M. Lee. (2015). International economic policy uncertainty and stock prices: wavelet approach. Economics Letters, 134(C), 118-122. https://doi.org/10.1016/j.econlet.2015.07.012
Leung, C., Lau, G., & Leong, Y. (2002). Testing alternative theories of the property price-trading volume correlation. Journal of Real Estate Research, 23(3), 253-264.
Mantalos, P., & Shukur, G. (1998). Size and power of the error correction model cointegration test: a bootstrap approach. Oxford Bulletin of Economics and Statistics, 60(2), 249-255. https://doi.org/10.1111/1468-0084.00097
Mantalos, P. (2000). A graphical investigation of the size and power of the granger-causality tests in integrated-cointegrated VAR systems. Studies in Nonlinear Dynamics & Econometrics, 4(1), 1-18. https://doi.org/10.2202/1558-3708.1053
Miller, N. G., & Sklarz, M. A. (1986). A note on leading indicators of housing price trends. Journal of Real Estate Research, 1(1), 99-109.
Shi, S., Young, M., & Hargreaves, B. (2010). House price-volume dynamics: evidence from 12 cities in New Zealand. Journal of Real Estate Research, 32(1), 75-99.
Stein, J. C. (1995). Prices and trading volume in the housing market: a model with down-payment effects. Quarterly Journal of Economics, 110(2), 379-406. https://doi.org/10.2307/2118444
Tsai, I-C. (2014). Ripple effect in house prices and trading volume in the UK housing market: new viewpoint and evidence. Economic Modelling, 40(C), 68-75. https://doi.org/10.1016/j.econmod.2014.03.026
Zhou, Z.-G. (1997). Forecasting sales and price for existing single-family homes: a VAR model with error correction. Journal of Real Estate Research, 14(2), 155-167.