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Intercity Information Diffusion and Price Discovery in Housing Markets: Evidence from Google Searches

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Abstract

We provide an innovative measure of information flow in Chinese housing markets based on search records from the Internet search engine Google. The measure depicts a substantial flow of house-price related information from national “superstar” cities, such as Beijing and Shanghai, and regional “star” cities, such as Tianjin and Chongqing, to other “normal” cities. The empirical results based on Granger causality test and turning point detection analysis both suggest that such information diffusion is a key factor that influences the intercity house price discovery process in the short run. The “superstar” and “star” cities lead the country in terms newly-built house prices changes in the sample period between 2006 and 2011.

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Notes

  1. CNNIC is a non-profit organization with support from China’s Ministry of Information Industry and the Chinese Academy of Sciences. Since 1997, CNNIC has been publishing the “Statistical Report on Internet Development in China” semi-annually. The latest version of the report was published in January 2012 and is available on CNNIC’s official website (www.cnnic.cn).

  2. Estimates of the number of Internet users in China by other institutes, such as the National Bureau of Statistics of China and the World Bank, are very close to CNNIC’s figures.

  3. This figure has ranged between 70 and 80 % in CNNIC’s annual survey over the last 5 years.

  4. There are two potential problems. First, the pattern of information diffusion revealed via web search records may over-sample inexperienced market participants, since professional participants are more likely to use channels such as market analysis reports or the business media, instead of web search engines. However, given that buyers in the current Chinese housing market largely consist of inexperienced households, we believe the potential sampling problem will not substantially affect our results. Second, a measure of web searches based on web search records may underestimate the density of information flow from some of the largest cites with very developed housing markets. For example, cities such as Beijing and Shanghai are the focus of well-known professional property websites (e.g., www.soufun.com), which Internet users may visit without searching the broader Internet via search engines. Moreover, for mega cities like Beijing, people may search directly for information on house price changes in their districts (e.g., “Haidian” + “house price”, instead of “Beijing” + “house price”). We acknowledge this as a limitation of the information flow index and leave it for future research.

  5. The leading web search engine in China, Baidu, also provides a similar service named “Baidu Index” (index.baidu.com). However, in most provinces, this index only date back to 2008. In addition, its calculation formula is opaque. Therefore in this study, we choose to rely on “Google Trends”.

  6. More technical details on the “Search Volume Index” are available on the “About Google Trends” webpage (www.google.com/intl/en/trends/about.html); or see discussions in Da, Engelberg and Gao (2011).

  7. In the original work by Da, Engelberg and Gao (2011), web search frequency is adopted as a measure of “attention”. Here, we treat it a proxy of information flow, with the assumption that Google users will browse at least some of the search results (websites) and hence get some related information.

  8. In this paper, we use the keyword “house price” (fang jia). The evidence from “Google Trends” suggests that this word is used much more frequently by Chinese users than other options. The number of search requests for these terms- “price of house” (zhu fang jia ge), “real estate price” (fang di chan jia ge) and “building price” (fang wu jia ge) – is only 1.5, 2.0 and 3.5 % respectively of the number of searches on “house price” (fang jia).

  9. We explicitly exclude the term “hotel” (jiu dian or bin guan) in our search, since in Mandarin, the term “hotel rate” is also “fang jia”.

  10. In early 2010, Google closed its business in mainland China; after that, users in China could still access the Google server in Hong Kong.

  11. Ideally, a city-level index would reflect the spatial pattern of information diffusion more clearly than the provincial-level indicator, especially in describing information flows within a province. However, the total number of Google search requests for “house price” is not large enough to report in many cities currently, leaving us unable to calculate city-level index.

  12. There are 31 provinces (including 4 municipalities and 5 autonomous region) in mainland China. The Xizang (Tibet) Autonomous Region is not included in the following analysis because the volume of Google search requests for “house price” from that region is too small to report.

  13. The detailed results are available on request.

  14. We adopt the key word “home price” in the US analysis. The results based on other key words like “house price” are consistent.

  15. As a preliminary test of this explanation, we calculate provincial level indexes for house price information in Hong Kong. The results suggest that few people in mainland China (even in Guangdong Province, which is very close to Hong Kong) search for information about house prices in Hong Kong. This finding is consistent with the fact that the movement of house prices in Hong Kong does not share the same common trend with cities in mainland China (mainly because the central government’s policies do not apply to Hong Kong).

  16. Another possible explanation is that more people from other provinces plan to purchase housing units in these “superstar” or “star” cities, and thus search for related information in advance. The last column in Table 2 calculates the average proportion of home buyers from other provinces, and the pattern is consistent with this explanation. But, the differences between various tiers are only marginally significant.

  17. The 35 major cities include all the 3 “superstar” cities defined above, 15 of the 16 “star” cities with Suzhou as the only exemption, and 17 of the 268 “normal” cities. So far the constant quality house price indicator is not available for other cities.

  18. More details about this index and its comparison with the official house price indicators are reported in Wu, Deng and Liu (2014).

  19. The total volume of newly-built housing units transacted in these 35 cities in the sample period reaches 8.39 million, or 3330 units per city per month on average.

  20. The standard Granger causality test augmented with error correction terms, or the VEC approach, is suggested in some studies if the price levels (in log term) in the two cities are cointegrated. However, in this analysis, the sample period is too short to test for cointegration and the focus of the analysis is just the short-run house price dynamics. Accordingly the standard Granger causality test without error correction term is adopted.

  21. As a preliminary attempt, we collect the quarterly series of all provinces’ PIFI on Beijing’s house prices, which is the only available continuous time series of PIFI so far. The empirical tests suggest that, on one hand, a sharp change in house prices (either an increase or decrease) in Beijing will lead to house price information flows to other cities in the following one to three quarters (i.e., higher PIFI). On the other hand, such information will immediately affect house prices in the lagging cities. However, a more definite conclusion requires evidence from more cities, and we leave this for future research.

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Acknowledgments

We thank the referee and the editor, Kelvin Wong, Yuichiro Kawaguchi and participants in the 2012 Asia-Pacific Real Estate Research Symposium, the 2012 GCREC Annual Conference and the 2012 AREUEA-AsRES Joint Conference for helpful comments. The authors thank the Institute of Real Estate Studies at National University of Singapore for financial support. Wu also thanks the National Natural Science Foundation of China for financial support (No. 71003060 & 71373006).

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Authors and Affiliations

  1. Hang Lung Center for Real Estate and Department of Construction Management, Tsinghua University, Beijing, China

    Jing Wu

  2. Institute of Real Estate Studies, NUS Business School, and School of Design and Environment, National University of Singapore, Singapore, Singapore

    Yongheng Deng

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  1. Jing Wu
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Correspondence to Yongheng Deng.

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Wu, J., Deng, Y. Intercity Information Diffusion and Price Discovery in Housing Markets: Evidence from Google Searches. J Real Estate Finan Econ 50, 289–306 (2015). https://doi.org/10.1007/s11146-014-9493-9

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