Spatial effect of market sentiment on housing price: evidence from social media data in China
Abstract
Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarters from 2016 to 2020, the spatial effect of market sentiment on housing price is empirically analyzed by calculating direct and indirect effects. The results show that market sentiment had a significant positive effect on housing prices in the local and neighboring cities over the research period. However, the impact of market sentiment on housing price was heterogeneous in terms of geographical region; the direct effect was stronger in the eastern region than in the central and western regions, and the indirect effect was significant only in the eastern region. These findings can provide references for government to formulate housing market regulation policies and measures based on market sentiment.
Keyword : housing price, market sentiment, sentiment analysis, social media, spatial Durbin model, spatial effect
How to Cite
Li, J., Wang, Y., & Liu, C. (2022). Spatial effect of market sentiment on housing price: evidence from social media data in China. International Journal of Strategic Property Management, 26(1), 72-85. https://doi.org/10.3846/ijspm.2022.16255
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Balcilar, M., Bouri, E., Gupta, R., & Kyei, C. K. (2021). High-frequency predictability of housing market movements of the United States: the role of economic sentiment. Journal of Behavioral Finance, 22(4), 490–498. https://doi.org/10.1080/15427560.2020.1822359
Bayer, P., Mangum, K., & Roberts, J. W. (2021). Speculative fever: investor contagion in the housing bubble. American Economic Review, 111(2), 609–651. https://doi.org/10.1257/aer.20171611
Beracha, E., Lang, M., & Hausler, J. (2019). On the relationship between market sentiment and commercial real estate performance–a textual analysis examination. Journal of Real Estate Research, 41(4), 605–638. https://doi.org/10.22300/0896-5803.41.4.605
Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity, 2003(2), 299–362. https://doi.org/10.1353/eca.2004.0004
Case, K. E., Shiller, R. J., & Thompson, A. K. (2012). What have they been thinking? Homebuyer behavior in hot and cold markets. Brookings Papers on Economic Activity, 2012(1), 265–315. https://doi.org/10.1353/eca.2012.0014
Cerchiello, P., Giudici, P., & Nicola, G. (2017). Twitter data models for bank risk contagion. Neurocomputing, 264, 50–56. https://doi.org/10.1016/j.neucom.2016.10.101
Cerchiello, P., & Nicola, G. (2018). Assessing news contagion in finance. Econometrics, 6(1), 5. https://doi.org/10.3390/econometrics6010005
Clayton, J., Ling, D. C., & Naranjo, A. (2009). Commercial real estate valuation: Fundamentals versus investor sentiment. The Journal of Real Estate Finance and Economics, 38(1), 5–37. https://doi.org/10.1007/s11146-008-9130-6
Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1–32. https://doi.org/10.1093/rfs/hhu072
De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703–738. https://doi.org/10.1086/261703
DeFusco, A., Ding, W., Ferreira, F., & Gyourko, J. (2018). The role of price spillovers in the American housing boom. Journal of Urban Economics, 108, 72–84. https://doi.org/10.1016/j.jue.2018.10.001
Dietzel, M. (2015). Sentiment-based predictions of housing market turning points with Google trends. International Journal of Housing Markets and Analysis, 9, 108–136. https://doi.org/10.15396/eres2015_3
Elhorst, J. P. (2014). Spatial econometrics. Springer. https://doi.org/10.1007/978-3-642-40340-8
Fischer, M., & Stamos, M. Z. (2013). Optimal life cycle portfolio choice with housing market cycles. The Review of Financial Studies, 26(9), 2311–2352. https://doi.org/10.1093/rfs/hht010
Gao, Q., & Zhao, T. (2018). The influence of home buyer sentiment on Chinese housing prices–based on media text mining. International Journal of Economics and Finance, 10, 145–145. https://doi.org/10.5539/ijef.v10n9p145
Gao, Y., Zhao, K., Wang, C., & Liu, C. (2020). The dynamic relationship between internet attention and stock market liquidity: a thermal optimal path method. Physica A: Statistical Mechanics and its Applications, 550, 124180. https://doi.org/10.1016/j.physa.2020.124180
Giannini, R., Irvine, P., & Shu, T. (2019). The convergence and divergence of investors’ opinions around earnings news: evidence from a social network. Journal of Financial Markets, 42, 94–120. https://doi.org/10.1016/j.finmar.2018.12.003
Granziera, E., & Kozicki, S. (2015). House price dynamics: fundamentals and expectations. Journal of Economic Dynamics and Control, 60, 152–165. https://doi.org/10.1016/j.jedc.2015.09.003
Hausler, J., Ruscheinsky, J., & Lang, M. (2018). News-based sentiment analysis in real estate: a machine learning approach. Journal of Property Research, 35, 344–371. https://doi.org/10.1080/09599916.2018.1551923
Heinig, S., & Nanda, A. (2018). Measuring sentiment in real estate – a comparison study. Journal of Property Investment & Finance, 36(3), 248–258. https://doi.org/10.1108/JPIF-05-2017-0034
Huang, R., Zuo, W., & Bi, L. (2015). Predicting the stock market based on microblog mood. Journal of Industrial Engineering/Engineering Management, 29(01), 47-52+215. https://doi.org/10.13587/j.cnki.jieem.2015.01.006
Huang, Y., Hong, W., & Yu, H. (2019). How does the market sentiment affect urban housing price. Economic Theory and Business Management, 07, 75–88 (in Chinese).
Hui, E. C. M., & Ng, I. M. H. (2016). Access to mortgage credit and housing price dynamics. International Journal of Strategic Property Management, 20(1), 64–76. https://doi.org/10.3846/1648715X.2015.1103802
Hui, E. C., & Wang, Z. (2014). Market sentiment in private housing market. Habitat International, 44, 375–385. https://doi.org/10.1016/j.habitatint.2014.08.001
Lam, C. H. L., & Hui, E. C. M. (2018). How does investor sentiment predict the future real estate returns of residential property in Hong Kong? Habitat International, 75, 1–11. https://doi.org/10.1016/j.habitatint.2018.02.009
Lambertini, L., Mendicino, C., & Punzi, M. T. (2013). Expectation-driven cycles in the housing market: evidence from survey data. Journal of Financial Stability, 9(4), 518–529. https://doi.org/10.1016/j.jfs.2013.07.006
Lee, C., & Park, K. (2018). Analyzing the rent-to-price ratio for the housing market at the micro-spatial scale. International Journal of Strategic Property Management, 22(3), 223–233. https://doi.org/10.3846/ijspm.2018.1416
Leitch, D., & Sherif, M. (2017). Twitter mood, CEO succession announcements and stock returns. Journal of Computational Science, 21, 1–10. https://doi.org/10.1016/j.jocs.2017.04.002
LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Chapman and Hall/CRC. https://doi.org/10.1201/9781420064254
Li, J., Hu, Y., & Liu, C. (2020). Exploring the influence of an urban water system on housing prices: case study of Zhengzhou. Buildings, 10(3), 44. https://doi.org/10.3390/buildings10030044
Li, J., Zheng, L., Liu, C., & Shen, Z. (2021). Information spillover effects of real estate markets: evidence from ten metropolitan cities in China. Journal of Risk and Financial Management, 14(6), 244. https://doi.org/10.3390/jrfm14060244
Liang, C.-M., Lee, C.-C., Lin, Y.-H., Yu, Z., & Yeh, W.-C. (2020). The impact of luxury housing on neighborhood housing prices: an application of the spatial difference-in-differences method. International Journal of Strategic Property Management, 24(6), 456–473. https://doi.org/10.3846/ijspm.2020.13649
Ling, D. C., Naranjo, A., & Scheick, B. (2014). Investor sentiment, limits to arbitrage and private market returns: investor sentiment, limits to arbitrage and private market returns. Real Estate Economics, 42(3), 531–577. https://doi.org/10.1111/1540-6229.12037
Luo, Z. Q., Liu, C., & Picken, D. (2007). Housing price diffusion pattern of Australia’s state capital cities. International Journal of Strategic Property Management, 11(4), 227–242. https://doi.org/10.3846/1648715X.2007.9637571
Marcato, G., & Nanda, A. (2016). Information content and forecasting ability of sentiment indicators: case of real estate market. Journal of Real Estate Research, 38, 165–203. https://doi.org/10.1080/10835547.2016.12091442
National Bureau of Statistics of China. (n.d.). Statistical data. www.stats.gov.cn/tjsj/
National Geomatics Center of China. (n.d.). National catalogue service for geographic information. https://www.webmap.cn
Oliveira, N., Cortez, P., & Areal, N. (2017). The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 73, 125–144. https://doi.org/10.1016/j.eswa.2016.12.036
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015). The effects of Twitter sentiment on stock price returns. PloS One, 10(9), 1–21. https://doi.org/10.1371/journal.pone.0138441
Ren, J., Dong, H., Padmanabhan, B., & Nickerson, J. V. (2021). How does social media sentiment impact mass media sentiment? A study of news in the financial markets. Journal of the Association for Information Science and Technology, 72(9), 1183–1197. https://doi.org/10.1002/asi.24477
Ruscheinsky, J. R., Lang, M., & Schäfers, W. (2018). Real estate media sentiment through textual analysis. Journal of Property Investment & Finance, 36(5), 410–428. https://doi.org/10.1108/JPIF-07-2017-0050
Siikanen, M., Baltakys, K., Kanniainen, J., Vatrapu, R., Mukkamala, R., & Hussain, A. (2018). Facebook drives behavior of passive households in stock markets. Finance Research Letters, 27, 208–213. https://doi.org/10.1016/j.frl.2018.03.020
Sinyak, N., Tajinder, S., Madhu Kumari, J., & Kozlovskiy, V. (2021). Predicting real estate market trends and value using pre-processing and sentiment text mining analysis. Real Estate: Economics, Management, (1), 35–43. https://doi.org/10.22337/2073-8412-2021-1-35-43
Soo, C. K. (2013). Quantifying animal spirits: news media and sentiment in the housing market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2330392
Tian, H., Gao, C., Xiao, X., Liu, H., He, B., Wu, H., Wang, H., & Wu, F. (2020). SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4067–4076). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.374
Vega, S. H., & Elhorst, J. P. (2015). The SLX model. Journal of Regional Science, 55(3), 339–363. https://doi.org/10.1111/jors.12188
Vergos, K., & Zhi, H. (2018). Is it a curse or a blessing to live near rich neighbors? Spatial analysis and spillover effects of house prices in Beijing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3289359
Wang, Z., & Hui, E. C. (2017). Fundamentals and market sentiment in housing market. Housing, Theory and Society, 34(1), 57–78. https://doi.org/10.1080/14036096.2016.1196240
Wu, Y.-L., Lu, C.-L., Chen, M.-C., & Chu, F.-N. (2017). What forces drive the dynamic interaction between regional housing prices? International Journal of Strategic Property Management, 21(3), 225–239. https://doi.org/10.3846/1648715X.2016.1254120
Yu, J., & Yuan, Y. (2011). Investor sentiment and the mean–variance relation. Journal of Financial Economics, 100(2), 367–381. https://doi.org/10.1016/j.jfineco.2010.10.011
Ardèvol-Abreu, A., & Gil de Zúñiga, H. (2017). Effects of editorial media bias perception and media trust on the use of traditional, citizen, and social media news. Journalism & Mass Communication Quarterly, 94(3), 703–724. https://doi.org/10.1177/1077699016654684
Bailey, M., Cao, R., Kuchler, T., & Stroebel, J. (2018). The economic effects of social networks: evidence from the housing market. Journal of Political Economy, 126(6), 2224–2276. https://doi.org/10.1086/700073
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market (Working Paper No. 13189). National Bureau of Economic Research. https://doi.org/10.3386/w13189
Balcilar, M., Bouri, E., Gupta, R., & Kyei, C. K. (2021). High-frequency predictability of housing market movements of the United States: the role of economic sentiment. Journal of Behavioral Finance, 22(4), 490–498. https://doi.org/10.1080/15427560.2020.1822359
Bayer, P., Mangum, K., & Roberts, J. W. (2021). Speculative fever: investor contagion in the housing bubble. American Economic Review, 111(2), 609–651. https://doi.org/10.1257/aer.20171611
Beracha, E., Lang, M., & Hausler, J. (2019). On the relationship between market sentiment and commercial real estate performance–a textual analysis examination. Journal of Real Estate Research, 41(4), 605–638. https://doi.org/10.22300/0896-5803.41.4.605
Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity, 2003(2), 299–362. https://doi.org/10.1353/eca.2004.0004
Case, K. E., Shiller, R. J., & Thompson, A. K. (2012). What have they been thinking? Homebuyer behavior in hot and cold markets. Brookings Papers on Economic Activity, 2012(1), 265–315. https://doi.org/10.1353/eca.2012.0014
Cerchiello, P., Giudici, P., & Nicola, G. (2017). Twitter data models for bank risk contagion. Neurocomputing, 264, 50–56. https://doi.org/10.1016/j.neucom.2016.10.101
Cerchiello, P., & Nicola, G. (2018). Assessing news contagion in finance. Econometrics, 6(1), 5. https://doi.org/10.3390/econometrics6010005
Clayton, J., Ling, D. C., & Naranjo, A. (2009). Commercial real estate valuation: Fundamentals versus investor sentiment. The Journal of Real Estate Finance and Economics, 38(1), 5–37. https://doi.org/10.1007/s11146-008-9130-6
Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1–32. https://doi.org/10.1093/rfs/hhu072
De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703–738. https://doi.org/10.1086/261703
DeFusco, A., Ding, W., Ferreira, F., & Gyourko, J. (2018). The role of price spillovers in the American housing boom. Journal of Urban Economics, 108, 72–84. https://doi.org/10.1016/j.jue.2018.10.001
Dietzel, M. (2015). Sentiment-based predictions of housing market turning points with Google trends. International Journal of Housing Markets and Analysis, 9, 108–136. https://doi.org/10.15396/eres2015_3
Elhorst, J. P. (2014). Spatial econometrics. Springer. https://doi.org/10.1007/978-3-642-40340-8
Fischer, M., & Stamos, M. Z. (2013). Optimal life cycle portfolio choice with housing market cycles. The Review of Financial Studies, 26(9), 2311–2352. https://doi.org/10.1093/rfs/hht010
Gao, Q., & Zhao, T. (2018). The influence of home buyer sentiment on Chinese housing prices–based on media text mining. International Journal of Economics and Finance, 10, 145–145. https://doi.org/10.5539/ijef.v10n9p145
Gao, Y., Zhao, K., Wang, C., & Liu, C. (2020). The dynamic relationship between internet attention and stock market liquidity: a thermal optimal path method. Physica A: Statistical Mechanics and its Applications, 550, 124180. https://doi.org/10.1016/j.physa.2020.124180
Giannini, R., Irvine, P., & Shu, T. (2019). The convergence and divergence of investors’ opinions around earnings news: evidence from a social network. Journal of Financial Markets, 42, 94–120. https://doi.org/10.1016/j.finmar.2018.12.003
Granziera, E., & Kozicki, S. (2015). House price dynamics: fundamentals and expectations. Journal of Economic Dynamics and Control, 60, 152–165. https://doi.org/10.1016/j.jedc.2015.09.003
Hausler, J., Ruscheinsky, J., & Lang, M. (2018). News-based sentiment analysis in real estate: a machine learning approach. Journal of Property Research, 35, 344–371. https://doi.org/10.1080/09599916.2018.1551923
Heinig, S., & Nanda, A. (2018). Measuring sentiment in real estate – a comparison study. Journal of Property Investment & Finance, 36(3), 248–258. https://doi.org/10.1108/JPIF-05-2017-0034
Huang, R., Zuo, W., & Bi, L. (2015). Predicting the stock market based on microblog mood. Journal of Industrial Engineering/Engineering Management, 29(01), 47-52+215. https://doi.org/10.13587/j.cnki.jieem.2015.01.006
Huang, Y., Hong, W., & Yu, H. (2019). How does the market sentiment affect urban housing price. Economic Theory and Business Management, 07, 75–88 (in Chinese).
Hui, E. C. M., & Ng, I. M. H. (2016). Access to mortgage credit and housing price dynamics. International Journal of Strategic Property Management, 20(1), 64–76. https://doi.org/10.3846/1648715X.2015.1103802
Hui, E. C., & Wang, Z. (2014). Market sentiment in private housing market. Habitat International, 44, 375–385. https://doi.org/10.1016/j.habitatint.2014.08.001
Lam, C. H. L., & Hui, E. C. M. (2018). How does investor sentiment predict the future real estate returns of residential property in Hong Kong? Habitat International, 75, 1–11. https://doi.org/10.1016/j.habitatint.2018.02.009
Lambertini, L., Mendicino, C., & Punzi, M. T. (2013). Expectation-driven cycles in the housing market: evidence from survey data. Journal of Financial Stability, 9(4), 518–529. https://doi.org/10.1016/j.jfs.2013.07.006
Lee, C., & Park, K. (2018). Analyzing the rent-to-price ratio for the housing market at the micro-spatial scale. International Journal of Strategic Property Management, 22(3), 223–233. https://doi.org/10.3846/ijspm.2018.1416
Leitch, D., & Sherif, M. (2017). Twitter mood, CEO succession announcements and stock returns. Journal of Computational Science, 21, 1–10. https://doi.org/10.1016/j.jocs.2017.04.002
LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Chapman and Hall/CRC. https://doi.org/10.1201/9781420064254
Li, J., Hu, Y., & Liu, C. (2020). Exploring the influence of an urban water system on housing prices: case study of Zhengzhou. Buildings, 10(3), 44. https://doi.org/10.3390/buildings10030044
Li, J., Zheng, L., Liu, C., & Shen, Z. (2021). Information spillover effects of real estate markets: evidence from ten metropolitan cities in China. Journal of Risk and Financial Management, 14(6), 244. https://doi.org/10.3390/jrfm14060244
Liang, C.-M., Lee, C.-C., Lin, Y.-H., Yu, Z., & Yeh, W.-C. (2020). The impact of luxury housing on neighborhood housing prices: an application of the spatial difference-in-differences method. International Journal of Strategic Property Management, 24(6), 456–473. https://doi.org/10.3846/ijspm.2020.13649
Ling, D. C., Naranjo, A., & Scheick, B. (2014). Investor sentiment, limits to arbitrage and private market returns: investor sentiment, limits to arbitrage and private market returns. Real Estate Economics, 42(3), 531–577. https://doi.org/10.1111/1540-6229.12037
Luo, Z. Q., Liu, C., & Picken, D. (2007). Housing price diffusion pattern of Australia’s state capital cities. International Journal of Strategic Property Management, 11(4), 227–242. https://doi.org/10.3846/1648715X.2007.9637571
Marcato, G., & Nanda, A. (2016). Information content and forecasting ability of sentiment indicators: case of real estate market. Journal of Real Estate Research, 38, 165–203. https://doi.org/10.1080/10835547.2016.12091442
National Bureau of Statistics of China. (n.d.). Statistical data. www.stats.gov.cn/tjsj/
National Geomatics Center of China. (n.d.). National catalogue service for geographic information. https://www.webmap.cn
Oliveira, N., Cortez, P., & Areal, N. (2017). The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 73, 125–144. https://doi.org/10.1016/j.eswa.2016.12.036
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015). The effects of Twitter sentiment on stock price returns. PloS One, 10(9), 1–21. https://doi.org/10.1371/journal.pone.0138441
Ren, J., Dong, H., Padmanabhan, B., & Nickerson, J. V. (2021). How does social media sentiment impact mass media sentiment? A study of news in the financial markets. Journal of the Association for Information Science and Technology, 72(9), 1183–1197. https://doi.org/10.1002/asi.24477
Ruscheinsky, J. R., Lang, M., & Schäfers, W. (2018). Real estate media sentiment through textual analysis. Journal of Property Investment & Finance, 36(5), 410–428. https://doi.org/10.1108/JPIF-07-2017-0050
Siikanen, M., Baltakys, K., Kanniainen, J., Vatrapu, R., Mukkamala, R., & Hussain, A. (2018). Facebook drives behavior of passive households in stock markets. Finance Research Letters, 27, 208–213. https://doi.org/10.1016/j.frl.2018.03.020
Sinyak, N., Tajinder, S., Madhu Kumari, J., & Kozlovskiy, V. (2021). Predicting real estate market trends and value using pre-processing and sentiment text mining analysis. Real Estate: Economics, Management, (1), 35–43. https://doi.org/10.22337/2073-8412-2021-1-35-43
Soo, C. K. (2013). Quantifying animal spirits: news media and sentiment in the housing market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2330392
Tian, H., Gao, C., Xiao, X., Liu, H., He, B., Wu, H., Wang, H., & Wu, F. (2020). SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4067–4076). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.374
Vega, S. H., & Elhorst, J. P. (2015). The SLX model. Journal of Regional Science, 55(3), 339–363. https://doi.org/10.1111/jors.12188
Vergos, K., & Zhi, H. (2018). Is it a curse or a blessing to live near rich neighbors? Spatial analysis and spillover effects of house prices in Beijing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3289359
Wang, Z., & Hui, E. C. (2017). Fundamentals and market sentiment in housing market. Housing, Theory and Society, 34(1), 57–78. https://doi.org/10.1080/14036096.2016.1196240
Wu, Y.-L., Lu, C.-L., Chen, M.-C., & Chu, F.-N. (2017). What forces drive the dynamic interaction between regional housing prices? International Journal of Strategic Property Management, 21(3), 225–239. https://doi.org/10.3846/1648715X.2016.1254120
Yu, J., & Yuan, Y. (2011). Investor sentiment and the mean–variance relation. Journal of Financial Economics, 100(2), 367–381. https://doi.org/10.1016/j.jfineco.2010.10.011