SpringerLink
Log in

Sentiment-based indicators of real estate market stress and systemic risk: international evidence

  • Research Article
  • Published:
Annals of Finance Aims and scope Submit manuscript

Abstract

We propose sentiment-based indicators of real estate market stress for the USA, the UK, Canada, Australia, India, and on the global scale. The global and country-level indicators are based on a novel methodology synthesizing textual analysis of real estate research and Google search data. Using mixed frequency vector autoregressions, we show that in the USA, the UK, Australia and India, the sentiment-based indicators are found to mediate the relationship between real estate prices and systemic financial risk. In particular, for the UK, there is a vicious circle involving the interaction among the three variables: the sentiment-based indicator of real estate market stress unidirectionally leads systemic risk, the latter impacts real estate prices, whereas the prices drive the stress sentiment. Canada appears the only sample country where real estate market stress sentiment is unrelated to real estate prices and systemic risk. On the global scale, there is a bi-directional linkage between the stress sentiment and real estate prices. Overall, our empirical findings suggest that policymakers and real estate market participants should account for sentiment regarding real estate market stress in their decision-making.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. The language issue also pre-determined the choice of countries for our analysis. We conduct it for the countries where English is the native language and which have deep real estate markets.

  2. See https://www.internetworldstats.com/stats7.htm and https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/.

  3. See https://vlab.stern.nyu.edu/srisk for more details.

  4. In a nutshell, the literature on real estate market synchronization is largely inconclusive and sensitive to the sample composition and level of analysis. For example, one can mention the studies which uncover stronger synchronization both across international real estate markets, e.g. Vansteenkiste and Hiebert (2011) for major European economies, and across regions within a country, e.g. Cipollini and Parla (2020) for Italy.

  5. Since our indicator is built on the scientific sources, one may argue that some terms and expressions extensively used by practitioners may be underrepresented. In light of potential bias, we conduct interviews with several international real estate investors in order to add to and/or to remove the items from our baseline dictionary. Following their advice, we remove “high loan-to-value ratio” and “Evergrande” and add the following expressions: “high vacancy rate”; “low occupancy rate”; “high mortgage rates”; “mortgage refinancing”; “MBS restructuring”. Then we derive alternative sentiment-based indicators for all the sample countries and on the global level. We compare the performance of the baseline indicators and the alternative measures by conducting conventional and nonparametric Granger causality tests. It appears that our baseline indicators either Granger cause the modified ones or there is a bi-directional linkage between them. Overall, the evidence suggests that our baseline indicators are not outperformed by the modified ones, both globally and for the sample countries. The results are available from the authors upon request.

  6. In order to conduct the causal analysis and derive impulse responses, we adopt the MATLAB code developed by Kaiji Motegi http://www2.kobe-u.ac.jp/~motegi/Matlab_Codes.html.

  7. It is reasonably hard to offer a coherent and uniform theoretical explanation for these linkages. Regulatory issues as well as differences in economic policies make generalizations complicated. However, for the countries where the SB-IREMS drives systemic risk, in particular, the UK, it is the financialization of real estate sector that could account for such relationship. Hofman and Aalbers (2019) argue that the UK adopted an economic model heavily reliant on the private housing debt. Plakandaras et al. (2020) find that the real estate sector in the UK is largely driven by monetary policy shifts and susceptible to bubbly dynamics. The conclusion is corroborated by Miles and Monro (2021) who relate real estate price dynamics in the UK to the changes in risk-free real interest rates. Hence, in the countries where the financialization of real estate sector is less pronounced, the impact of economic agents’ expectations regarding real estate market stress may not affect systemic risk as a whole.

References

  • Abildgren, K., Hansen, N., Kuchler, A.: Overoptimism and house price bubbles. J Macroecon 56, 1–14 (2018)

    Article  Google Scholar 

  • Acharya, V., Pedersen, L., Philippon, T., Richardson, M.: Measuring systemic risk. Rev Financ Stud 30(1), 2–47 (2017)

    Article  Google Scholar 

  • Adrian, T., Brunnermeier, M.K.: CoVaR. Am Econ Rev 106(7), 1705–1741 (2016)

    Article  Google Scholar 

  • Agnello, L., Schuknecht, L.: Booms and busts in housing markets: determinants and implications. J Hous Econ 20(3), 171–190 (2011)

    Article  Google Scholar 

  • Algaba, A., Ardia, D., Bluteau, K., Borms, S., Boudt, K.: Econometrics meets sentiment: an overview of methodology and applications. J Econ Surv 34(3), 512–547 (2020)

    Article  Google Scholar 

  • Allen, F., Carletti, E.: Systemic risk from real estate and macro-prudential regulation. Int J Bank Acc Financ 5(1/2), 28–47 (2013)

    Google Scholar 

  • Anastasiou, D., Drakos, K.: European depositors’ behavior and crisis sentiment. J Econ Behav Organ 184, 117–136 (2021)

    Article  Google Scholar 

  • Anastasiou, D., Kapopoulos, P., Zekente, K.-M.: Sentimental shocks and house prices. J Real Estate Finance Econ Forthcom (2021). https://doi.org/10.1007/s11146-021-09871-z

    Article  Google Scholar 

  • Anastasiou, D., Giannoulakis, S., Koutoupis, A., Tzomakas, C., Davidopoulos, L.: European banks’ profitability and sentimental cycle. Rev Behav Econ 9(3), 223–250 (2022)

    Article  Google Scholar 

  • Anundsen, A., Gerdrup, K., Hansen, F., Kragh-Sørensen, K.: Bubbles and crises: the role of house prices and credit. J Appl Econom 31(7), 1291–1311 (2016)

    Article  Google Scholar 

  • Aroul, R.R., Sabherwal, S., Saydometov, S.: FEAR Index, city characteristics, and housing returns. Real Estate Econ 50, 173–205 (2022)

    Article  Google Scholar 

  • Baker, S.R., Bloom, N., Davis, S.J.: Measuring economic policy uncertainty. Q J Econ 131(4), 1593–1636 (2016)

    Article  Google Scholar 

  • Baur, D., Heaney, R.: Bubbles in the Australian housing market. Pac. Basin Financ J 44, 113–126 (2017)

    Article  Google Scholar 

  • Beracha, E., Lang, M., Hausler, J.: On the relationship between market sentiment and commercial real estate performance—a textual analysis examination. J Real Estate Res 41(4), 605–638 (2019)

    Article  Google Scholar 

  • Bhatt, V., Kishor, N.K.: (A)synchronous housing markets of global cities. J. Real Estate Finance Econ Forthcom (2022). https://doi.org/10.1007/s11146-022-09903-2

    Article  Google Scholar 

  • Brownlees, C., Engle, R.F.: SRISK: a conditional capital shortfall measure of systemic risk. Rev Financ Stud 30(1), 48–79 (2017)

    Article  Google Scholar 

  • Brunnermeier, M., Rother, S., Schnabel, I.: Asset price bubbles and systemic risk. Rev Financ Stud 33, 4272–4317 (2020)

    Article  Google Scholar 

  • Cao, Y.: Measuring systemic risk and dependence structure between real estates and banking sectors in China using a CoVaR-copula method. Int J Financ Econ 26, 5930–5947 (2021)

    Article  Google Scholar 

  • Chauvet, M., Gabriel, S., Lutz, C.: Mortgage default risk: new evidence from internet search queries. J Urban Econ 96, 91–111 (2016)

    Article  Google Scholar 

  • Chiang, S., Chen, C.: From systematic to systemic risk among G7 members: Do the stock or real estate markets matter? J Int Financ Mark Inst Money 79, 101594 (2022)

    Article  Google Scholar 

  • Choi, H., Varian, H.: Predicting the present with Google Trends. Econ Rec 88(s1), 2–9 (2012)

    Article  Google Scholar 

  • Cincinelli, P., Pellini, E., Urga, G.: Leverage and systemic risk pro-cyclicality in the chinese financial system. Int Rev Financ Anal 78, 101895 (2021)

    Article  Google Scholar 

  • Cincinelli, P., Pellini, E., Urga, G.: Systemic risk in the chinese financial system: a panel granger causality analysis. Int Rev Financ Anal 82, 102179 (2022)

    Article  Google Scholar 

  • Cipollini, A., Parla, F.: Housing market shocks in Italy: a GVAR approach. J Hous Econ 50, 101707 (2020)

    Article  Google Scholar 

  • Das, P., Fuss, R., Hanle, B., Russ, I.N.: The cross-over effect of irrational sentiments in housing, commercial property, and stock markets. J Bank Finance 114, 105799 (2020)

    Article  Google Scholar 

  • Deng, Y., Zeng, Y., Li, Z.: Real estate prices and systemic banking crises. Econ Model 80, 111–120 (2019)

    Article  Google Scholar 

  • Dietzel, M.A., Braun, N., Schäfers, W.: Sentiment-based commercial real estate forecasting with Google search volume data. J Prop Invest Finance 32(6), 540–569 (2014)

    Article  Google Scholar 

  • Diks, C., Panchenko, V.: A new statistic and practical guidelines for nonparametric Granger causality testing. J Econ Dyn Control 30, 1647–1669 (2006)

    Article  Google Scholar 

  • Dissem, S., Lobez, F.: Correlation between the 2014 EU-wide stress tests and the market-based measures of systemic risk. Res Int Bus Financ 51, 100939 (2020)

    Article  Google Scholar 

  • Du, Z., Huang, A.G., Wermers, R., Wu, W.: Language and domain specificity: a Chinese financial sentiment dictionary. Rev Finance 26(3), 673–719 (2022)

    Article  Google Scholar 

  • Duca, J.V.: Making sense of increased synchronization in global house prices. J Eur Real Estate Res 13(1), 5–16 (2020)

    Article  Google Scholar 

  • Duprey, T., Klaus, B.: Early warning or too late? A (pseudo-)real-time identification of leading indicators of financial stress. J Bank Finance 138, 106196 (2022)

    Article  Google Scholar 

  • Geanakoplos, J., Axtell, R., Farmer, J.D., Howitt, P., Conlee, B., Goldstein, J., Hendrey, M., Palmer, N.M., Yang, C.-J.: Getting at systemic risk via an agent-based model of the housing market. Am Econ Rev Pap Proc 102(3), 53–58 (2012)

    Article  Google Scholar 

  • Gentzkow, M., Kelly, B., Taddy, M.: Text as data. J Econ Lit 57(3), 535–574 (2019)

    Article  Google Scholar 

  • Ghysels, E., Hill, J.B., Motegi, K.: Testing for granger causality with mixed frequency data. J Econom 192(1), 207–230 (2016)

    Article  Google Scholar 

  • Hausler, J., Ruscheinsky, J., Lang, M.: News-based sentiment analysis in real estate: a machine learning approach. J Prop Res 35(4), 344–371 (2018)

    Article  Google Scholar 

  • Heinig, S., Nanda, A.: Measuring sentiment in real estate—a comparison study. J Prop Invest Finance 36(3), 248–258 (2018)

    Article  Google Scholar 

  • Heinig, S., Nanda, A., Tsolacos, S.: Which sentiment indicators matter? Evidence from the European commercial real estate market. J Real Estate Res 42(4), 499–530 (2020)

    Article  Google Scholar 

  • Hoesli, M.: An investigation of the synchronization in global house prices. J Eur Real Estate Res 13(1), 17–27 (2020)

    Article  Google Scholar 

  • Hofman, A., Aalbers, M.: A finance- and real estate-driven regime in the United Kingdom. Geoforum 100, 89–100 (2019)

    Article  Google Scholar 

  • Hohenstatt, R., Käsbauer, M.: GECO’s weather forecast for the UK housing market: To what extent can we rely on Google econometrics? J Real Estate Res 36(2), 253–282 (2014)

    Article  Google Scholar 

  • Hohenstatt, R., Käsbauer, M., Schäfers, W.: “GECO” and its potential for real estate research: evidence from the US housing market. J Real Estate Res 33(4), 471–506 (2011)

    Article  Google Scholar 

  • Jang, H., Song, Y., Sohn, S., Ahn, K.: Real estate soars and financial crises: recent stories. Sustainability 10(12), 1–12 (2018)

    Article  Google Scholar 

  • Kanas, A., Zervopoulos, P.D.: Systemic risk, real GDP growth, and sentiment. Rev Quant Financ Acc 57(2), 461–485 (2021)

    Article  Google Scholar 

  • Koelbl, M.: Is the MD&A of US REITs informative? A textual sentiment study. J Prop Invest Finance 38(3), 181–201 (2020)

    Article  Google Scholar 

  • Koetter, M., Poghosyan, T.: Real estate prices and bank stability. J Bank Finance 34(6), 1129–1138 (2010)

    Article  Google Scholar 

  • Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J Finance 66, 35–65 (2011)

    Article  Google Scholar 

  • Mahalik, M., Mallick, H.: What causes asset price bubble in an emerging economy? Some empirical evidence in the housing sector of India. Int Econ J 25(2), 215–237 (2011)

    Article  Google Scholar 

  • Mbarki, I., Omri, A., Naeem, M.A.: From sentiment to systemic risk: information transmission in Asia-Pacific stock markets. Res Int Bus Financ 63, 101796 (2022)

    Article  Google Scholar 

  • Meng, H., **e, W.-J., Jiang, Z.-Q., Podobnik, B., Zhou, W.-X., Stanley, E.: Systemic risk and spatiotemporal dynamics of the US housing market. Nat Sci Rep 4, Article Number 3655 (2014)

  • Miles, W.: Has there actually been a sustained increase in the synchronization of house price (and business) cycles across countries? J Hous Econ 36, 25–43 (2017)

    Article  Google Scholar 

  • Miles, D., Monro, V.: UK house prices and three decades of decline in risk-free real interest rate. Econ Policy 36(108), 627–684 (2021)

    Article  Google Scholar 

  • Nyman, R., Kapadia, S., Tuckett, D.: News and narratives in financial systems: exploiting big data for systemic risk assessment. J Econ Dyn Control 127, 104119 (2021)

    Article  Google Scholar 

  • Pavlidis, E., Paya, I., Skouralis, A.: House prices, (un)affordability and systemic risk. N Z Econ Pap 55(1), 105–123 (2021)

    Google Scholar 

  • Plakandaras, V., Gupta, R., Katrakilidis, C., Wohar, M.E.: Time-varying Role of macroeconomic shocks on house prices in the US and UK: evidence from over 150 years of data. Empir Econ 58, 2249–2285 (2020)

    Article  Google Scholar 

  • Preis, T., Moat, H., Stanley, H.E.: Quantifying trading behavior in financial markets using Google Trends. Nat Sci Rep 3, Article Number 1684 (2013)

  • Ruscheinsky, J.R., Lang, M., Schäfers, W.: Real estate media sentiment through textual analysis. J Prop Invest Finance 36(5), 410–428 (2018a)

    Article  Google Scholar 

  • Ruscheinsky, J.R., Kandlbinder, K., Schäfers, W., Alexander, M., Dietzel, K.: Forecasting the German real estate market with dictionary-based textual analysis. In: 25th Annual European Real Estate Society Conference. ERES: Conference. Reading, UK (2018b). https://eres.architexturez.net/doc/oai-eres-id-eres2018-198

  • Salim, M.Z., Daly, K.: Modelling systemically important banks vis-a-vis the Basel prudential guidelines. J Risk Financ Manag 14(7), 1–20 (2021)

    Article  Google Scholar 

  • Shi, S., Valadkhani, A., Smyth, R., Vahid, F.: Dating the timeline of house price bubbles in Australian capital cities. Econ Rec 92(299), 590–605 (2016)

    Article  Google Scholar 

  • Tan, J., Cheong, S.A.: The regime shift associated with the 2004–2008 US housing market bubble. PLoS ONE 11(9), e0162140 (2016)

    Article  Google Scholar 

  • Vansteenkiste, I., Hiebert, P.: Do house price developments spillover across Euro area countries? Evidence from a Global VAR. J Hous Econ 20(4), 299–314 (2011)

    Article  Google Scholar 

  • Wang, X., Hou, S., Shen, J.: Default clustering of the nonfinancial sector and systemic risk: evidence from China. Econ Model 96, 196–208 (2021)

    Article  Google Scholar 

  • Wu, J., Deng, Y.: Intercity information diffusion and price discovery in housing markets: evidence from Google searches. J Real Estate Financ Econ 50(3), 289–306 (2015)

    Article  Google Scholar 

  • Zhang, B.: Economic policy uncertainty and investor sentiment: linear and nonlinear causality analysis. Appl Econ Lett 26(15), 1264–1268 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

The research was supported by MGIMO University “Priority-2030” programme.

Author information

Authors and Affiliations

  1. Department of Applied Economics, Moscow State Institute of International Relations (MGIMO-University), 76 Prospekt Vernadskogo, Moscow, 119454, Russia

    Mikhail Stolbov

  2. Department of Theoretical Economics, National Research University Higher School of Economics (NRU HSE), Moscow, Russia

    Maria Shchepeleva

Authors
  1. Mikhail Stolbov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Shchepeleva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mikhail Stolbov.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 5, 6, 7, 8, 9, 10 and 11.

Table 5 Descriptive statistics
Full size table
Table 6 Terms and word collocations determining the SB-IREMS index dynamics Australia
Full size table
Table 7 Terms and word collocations determining the SB-IREMS index dynamics Canada
Full size table
Table 8 Terms and word collocations determining the SB-IREMS index dynamics India
Full size table
Table 9 Terms and word collocations determining the SB-IREMS index dynamics UK
Full size table
Table 10 Terms and word collocations determining the SB-IREMS index dynamics USA
Full size table
Table 11 Terms and word collocations determining the SB-IREMS index dynamics Global
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stolbov, M., Shchepeleva, M. Sentiment-based indicators of real estate market stress and systemic risk: international evidence. Ann Finance 19, 355–382 (2023). https://doi.org/10.1007/s10436-023-00429-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10436-023-00429-y

Keywords

JEL Classification

Search

Navigation