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Can volume be more informative than prices? Evidence from Chinese housing markets

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Abstract

This paper examines pairwise intercity price–volume dynamics in China using novel daily transaction price and volume dataset from 32 Chinese cities. Despite geographical disparity and time variations, the volume–volume dynamic relationship plays a noticeably more significant role than price–price or price–volume relationships, suggesting that volume may be more informative than prices in China’s pairwise intercity housing market spillovers. We further propose a new spillover/connectedness measure to summarize both price and volume information and better measure such spillovers. We find that the new measure can be significantly explained by economic fundamentals, which attests to the soundness of the basic finding.

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Notes

  1. Noteworthy, the accounting literature pays much attention to the informational role of trading volume of stocks around earnings announcements. See a review by Bamber et al. (2011).

  2. In the literature, the possible candidates for informativeness of volume or prices on the housing may include the information that can generate more accurate forecast on changes of the housing price (Bollerslev et al. 2016), on the price volatilities (Chinloy and Larson 2017), on the spillover of housing prices (Han et al. 2018; Yang et al. 2018). The specific type of information we examine in this study is related to more accurate (in-sample) forecast on changes of housing prices or volume, most noticably in an intercity spillover context. We thank the referee for making the insightful suggestion.

  3. This is defined by all possible price–volume, price–price, and volume–volume dynamic relationships between a pair of cities, which totals eight, as discussed below. Thus, in addition to its contribution to the literature on the price–volume dynamics, this study also substantially extends the related literature on housing market spillovers. Existing studies on housing market spillovers in China (e.g., Yang et al. 2018) or in other countries generally examine only intercity price-price dynamic relationships in a bivariate or multivariate context, with a noticeable exception of Yang et al. (2021), which, however, examines intercity volume-volume dynamic relationships alone.

  4. Unlike more developed housing markets, both prices and transaction volumes in Chinese housing markets have been to a varying degree explicitly subject to various government intervention policies in China to fight against a speculative mania on these markets. See, e.g., Du and Zhang (2015), Glaeser et al. (2017), and Yang et al. (2021).

  5. The possibility of a leading informational role of housing transaction volume over prices is also being informally recognized in a very few recent studies using international data. For example, although they do not present any empirical evidence, Badarinza and Ramadorai (2018, p. 546) implicitly admits the possibility in their statement that “We view this as encouraging evidence that our method is successful at identifying foreign demand effects that impact volume, and ultimately prices in the London residential real estate market.”.

  6. As our main interest is to shed more light on the informational role of volume versus prices in the pariwise intercity context, the effort nauturally leads to the measurement of pairwise housing market interactions, which nevertheless has the obvious limitations that it is only in the bivariate (rather than multivariate) context and further filtered by certain criteria (i.e., “geographical closeness”). Nevertheless, such a bivariate framework is still used in the recent literature on China’s housing market network (e.g., Hurn et al.,2022). As discussed further below, such limitations may not undercut the inference of the main interest, although they do not offer a complete (multivariate) picture for housing market spillovers, despite its additional use of the information of volume. We leave a more comprehensive multivariate modelling of housing market interactions for future research, as it is beyond the scope of the current paper.

  7. In addition to the nonexisitnce of the official housing price indexes at the daily (or weekly) frequency, the city-level average housing transaction prices in China behaves better than official housing price indexes in accurately relecting the genuin housing price changes, at the comparably monthly frequency. By comparing the self-built housing price index from the mortage data with the Chinese official housing price index and the average transaction prices used in this study, Fang et al. (2016, p. 130) conclude that: “…the average price…exhibits highly synchronized comomoverments with our index……our index is capturing similar fluctuations as the straight calculation of average tansaction prices.”.

  8. Many cities might only report the transaction information in the past few months or never publicly report such information on their official websites.

  9. The longest public holidays in China, like the Spring Festival, are 7 days. But for some smaller cities, there might be up to ten days without housing registrations because of local government’s extended holidays. Thus, if the observations are missing for one month or longer, it can hardly be attributed to public holidays, but instead reflects some irregularities of the concerned housing market.

  10. See Fang et al. (2016) for city tiers and Yang et al. (2018) for city administrative levels and city regional groups in China. These studies also provide more details about Chinese housing markets.

  11. That is, 100 × (log(current price or volume)-log(previous price or volume)). Thus, they are the logarithm differences of the price or the volume, although we still refer to these as price and volume below.

  12. There is an alternative meaning of asymmetry in the context of Granger causality tests in the literature (e.g., Wang and Ngene 2018), where asymmetry is based on the differences between positive and negative changes in the variables involved.

  13. There are price and volume data even over the weekends. The results are basically the same when the longest lag is set at 14 (2 weeks) or 30 (1 month), because only just a few cities have selected optimal time lags approaching seven.

  14. More specifically, let P1, V1, P2, and V2 denote the housing transaction price and volume of city 1 and city 2, respectively; “P1→P2” denote Granger causality running from prices in city 1 to prices in city 2, and others are defined similarly. The eight possible intercity dynamic relationships thus are P1→P2, P1→V2, V1→P2, V1→V2, P2→P1, P2→V1, V2→P1, and V2→V1.

  15. Whether two cities are located in the same province or in adjacent provinces can be seen either from Table 1 or from Fig. 1. To ensure sufficient observations for the examination, at least three-month (one quarter) observations need to coexist for each pair of cities.

  16. In the US, only about 6% of owner-occupied homes are traded per year (Piazzesi and Schneider 2009).

  17. To ensure sufficient observations for the examination, again, at least three-month (a quarter) observations exists in a certain year for that city.

  18. The weights are calculated from Tables 3 and 4. The number of significant Granger causalities out of the 73 city pairs is18 for “P1→P2”, 25 for “P1→V2”, 25 for “V1→P2”, 48 for “V1→V2”, 22 for “P2→P1”, 24 for “P2→V1”, 22 for “V2→P1”, and 58 for “V2→V1” (see Tables 3 and 4). Take the first city pair “Anqing—Bengbu” in Table 5 in year 2009 as an example, its ICHTC at year 2009 is thus calculated as \({\mathrm{ICHTC}}_{2009}=\frac{0\times 18+0\times 25+0\times 25+0\times 48+0\times 22+1\times 24+0\times 22+1\times 58}{18+25+25+48+22+24+22+58}\times 100\). We also calculate ICHTC without weights, i.e., \(\mathrm{ICHTC}=\frac{Total number of significant Granger caucalities found in the eight intercity correletions}{8}\times 100\), the estimation results are similar (available on request). Given the finding, it may be easier to use the ICHTC without weights in the future research.

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Acknowledgements

We thank the editor and particularly two anonymous referees for extensive comments. An earlier version was presented at the 30th Annual Conference on Pacific Basin Finance, Economics, Accounting, and Management. We acknowledge financial support from the National Natural Science Foundation of China (Nos. 72001119 & 72103105) and the National Social Science Foundations of China (No. 20BJL049).

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Authors and Affiliations

  1. Business School, University of Colorado Denver, P.O. Box 173364, Denver, CO, 80217-3364, USA

    Jian Yang

  2. Department of Social and Political Science, University of Chester, Chester, UK

    Meng Tong

  3. School of Finance, Nankai University, Tianjin, China

    Ziliang Yu

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Correspondence to Jian Yang.

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Appendix

Appendix

See Tables 16 and 17.

Table 16 Panel-Granger causality test for intracity price–volume dynamics, Romano-Wolf adjusted p-values
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Table 17 Panel Granger causality tests for intercity dynamics of price-price, price–volume, and volume-volume, Romano-Wolf adjusted p-values
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Yang, J., Tong, M. & Yu, Z. Can volume be more informative than prices? Evidence from Chinese housing markets. Rev Quant Finan Acc 61, 633–672 (2023). https://doi.org/10.1007/s11156-023-01161-4

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