Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Computer Science and Information Engineering Mathematics / Statistics

Yuxing Liu1,2, Wei Chong Choo1This email address is being protected from spambots. You need JavaScript enabled to view it., Keng Yap Ng3,4, and Shuang Jin1

1School of Business and Economics, Universiti Putra Malaysia, Serdang, Selangor, Malaysia

2Department of Economics and Management, Yuncheng University, Yuncheng, Shanxi, China

3Institute for Mathematical Research, Universiti Putra Malaysia, Serdang, Selangor, Malaysia

4Department of Software Engineering and Information Systems, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia

 

Received: September 14, 2024
Accepted: October 8, 2024
Publication Date: January 2, 2025

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202509_28(9).0016  


Individual investors in the real estate market exhibit a dual characteristic of limited attention and limited rationality, which makes thempronetocognitivebiasesanddecision-makingerrors. Anovelresearchframework is established in this paper to examine the time-varying impacts of news sentiment and investor attention on housing price volatility. By estimating the Time Varying Parameter-Stochastic Volatility-Vector Auto Regression (TVP-SV-VAR) model using monthly data from 2011 to 2021 for four super cities in China, this paper obtains the following observations: (a) the response of housing price volatility to both news sentiment and investor attention shocks is positive; (b) the positive effect of news sentiment on housing price volatility is also related to market performance, with the effect being more pronounced during periods of rising housing prices; (c) by comparing the results of the models for different cities, housing price volatility has a more robust pattern of response to news sentiment shocks than to investor attention; (d) in terms of response values, housing price volatility is more sensitive to news sentiment than to investor attention; and (e) the COVID-19 outbreak weakened the impact of news sentiment on housing price volatility. This paper integrates the mathematical model, data and artificial intelligence methods into the relationship analysis for capturing connections between housing price volatility and news sentiment/investor attention, which instructively enriches research instruments of market economy and expands the bounds of cognition.


Keywords: Housing price volatility; news sentiment; investor attention; TVP-SV-VAR model; impulse response


  1. [1] N.Miller and L. Peng, (2006) “Exploring metropolitan housing price volatility" The Journal of Real Estate Fi nance and Economics 33: 5–18. DOI: 10.1007/s11146 006-8271-8.
  2. [2] C. K. Soo, (2018) “Quantifying sentiment with news media across local housing markets" The Review of Financial Studies 31(10): 3689–3719. DOI: 10.1093/ rfs/hhy036.
  3. [3] J. Gao, P. Li, A. A. Laghari, G. Srivastava, T. R. Gadekallu, S. Abbas, and J. Zhang, (2024) “Incom plete multiview clustering via semidiscrete optimal trans port for multimedia data mining in IoT" ACM Transac tions on Multimedia Computing, Communications and Applications 20(6): 158:1–158:20. DOI: 10.1145/3625548.
  4. [4] J. Gao, M. Liu, P. Li, J. Zhang, and Z. Chen, (2024) “Deep multiview adaptive clustering with semantic invari ance" IEEE Transactions on Neural Networks and Learning Systems 35(9): 12965–12978. DOI: 10.1109/ tnnls.2023.3265699.
  5. [5] J. B. D. Long, A. Shleifer, L. H. Summers, and R. J. Waldmann, (1990) “Noise trader risk in financial mar kets" Journal of Political Economy 98(4): 703–738. DOI: 10.1086/261703.
  6. [6] G. A. Akerlof and R. J. Shiller. Animal spirits: How humanpsychology drives the economy, and why it matters for global capitalism. Princeton, NJ, USA: Princeton University Press, 2009. DOI: 10.2307/j.ctv36mk90z.
  7. [7] E. Granziera and S. Kozicki, (2015) “House price dy namics: Fundamentals and expectations" Journal of Eco nomic Dynamics and Control 60: 152–165. DOI: 10.1016/j.jedc.2015.09.003.
  8. [8] G. Kaplan, K. Mitman, and G. L. Violante, (2020) “The housing boom and bust: Model meets evidence" Jour nal of Political Economy 128(9): 3285–3345. DOI: 10.1086/708816.
  9. [9] J.R.Nofsinger, (2001) “The impact of public information on investors" Journal of Banking and Finance 25(7): 1339–1366. DOI: 10.1016/s0378-4266(00)00133-3.
  10. [10] R. J. Shiller. Irrational exuberance. Princeton, NJ, USA: Princeton University Press, 2016. DOI: 10.1515/9781400865536.
  11. [11] C. D. Carroll, (2003) “Macroeconomic expectations of households and professional forecasters" The Quarterly Journal of Economics 118(1): 269–298. DOI: 10.1162/00335530360535207.
  12. [12] S. V. Møller, T. Q. Pedersen, E. C. M. Schütte, and A. Timmermann, (2024) “Search and predictability of prices in the housing market" Management Science 70(1): 415–438. DOI: 10.1287/mnsc.2023.4672.
  13. [13] P. Li, A. A. Laghari, M. Rashid, J. Gao, T. R. Gadekallu, A. R. Javed, and S. Yin, (2023) “A deep multimodal adversarial cycle-consistent network for smart enterprise system" IEEE Transactions on Industrial Informatics 19(1): 693–702. DOI: 10.1109/tii.2022.3197201.
  14. [14] P. Li, J. Gao, J. Zhang, S. Jin, and Z. Chen, (2023) “Deep reinforcement clustering" IEEE Transactions on Multimedia 25: 8183–8193. DOI: 10.1109/tmm.2022.3233249.
  15. [15] L. Lambertini, C. Mendicino, and M. T. Punzi, (2013) “Expectation-driven cycles in the housing market: Evidence from survey data" Journal of Financial Stability 9(4): 518–529. DOI: 10.1016/j.jfs.2013.07.006.
  16. [16] C. B. Walker, (2014) “Housing booms and media cov erage" Applied Economics 46(32): 3954–3967. DOI: 10.1080/00036846.2014.948675.
  17. [17] A. Nowak and P. Smith, (2016) “Textual analysis in real estate" Journal of Applied Econometrics 32(4): 896–918. DOI: 10.1002/jae.2550.
  18. [18] L. H. H. H. X. S. W. Cai, (2019) “Monitoring housing rental prices based on social media: An integrated ap proach of machine-learning algorithms and hedonic mod eling to inform equitable housing policies" Land Use Policy 82: 657–673. DOI: 10.1016/j.landusepol.2018.12.030.
  19. [19] E. Beracha, M. Lang, and J. Hausler, (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. DOI: 10.22300/0896-5803.41.4.605.
  20. [20] O. I. F. E. Dulleck, (2021) “Market news and credibil ity cues improve house price predictions: An experiment on bounded rationality in real estate" Journal of Be havioral and Experimental Finance 31: 100550. DOI: 10.1016/j.jbef.2021.100550.
  21. [21] J. Li, Y. Wang, and C. Liu, (2022) “Spatial effect of market sentiment on housing price: Evidence from so cial media data in China" International Journal of Strategic Property Management 26(1): 72–85. DOI: 10.3846/ijspm.2022.16255.
  22. [22] C. Hannum, K. Y. Arslanli, and A. F. Kalay, (2019) “Spatial analysis of Twitter sentiment and district-level housing prices" 12(2): 173–189. DOI: 10.1108/jerer-08 2018-0036.
  23. [23] J. Shao, J. Hong, X. Wang, and X. Yan, (2023) “The relationship between social media sentiment and house prices in China: Evidence from text mining and wavelet analysis" Finance Research Letters 57: 104212. DOI: 10.1016/j.frl.2023.104212.
  24. [24] F. Liu, Y. Kang, K. Guo, and X. Sun, (2021) “The re lationship between air pollution, investor attention and stock prices: Evidence from new energy and polluting sec tors" Energy Policy 156: 112430. DOI: 10.1016/j.enpol.2021.112430.
  25. [25] Y.-J. Zhang and Z.-C. Li, (2021) “Forecasting the stock returns of Chinese oil companies: Can investor attention help?" International Review of Economics and Fi nance 76: 531–555. DOI: 10.1016/j.iref.2021.07.006.
  26. [26] D. Ballinari, F. Audrino, and F. Sigrist, (2022) “When does attention matter? The effect of investor attention on stock market volatility around news releases" Interna tional Review of Financial Analysis 82: 102185. DOI: 10.1016/j.irfa.2022.102185.
  27. [27] E. Beracha and M. B. Wintoki, (2013) “Forecasting residential real estate price changes from online search activity" Journal of Real Estate Research 35(3): 283 312. DOI: 10.1080/10835547.2013.12091364.
  28. [28] L. WuandE.Brynjolfsson. “The future of prediction: How google searches foreshadow housing prices and sales”. In: Economic Analysis of the Digital Econ omy. 2015. Chap. 3, 89–118. DOI: 10.7208/chicago/9780226206981.003.0003.
  29. [29] W.Zhang, C. Yu, Z. Dong, and H. Zhuo, (2021) “Rip ple effect of the housing purchase restriction policy and the role of investors’ attention" Habitat International 114: 102398. DOI: 10.1016/j.habitatint.2021.102398.
  30. [30] W.Zhou, M.Chen, Y. Gao, and R. Feng, (2022) “Re search on the impact of market concern for real estate policy on housing prices: Evidence from Internet search and hedonic price theory" Computer Modeling in En gineering and Sciences 131(3): 1635–1652. DOI: 10.32604/cmes.2022.018437.
  31. [31] M. Bełej, (2023) “Predicting housing price trends in Poland: Online social engagement- google trends" Real Estate Management and Valuation 31(4): 73–87. DOI: 10.2478/remav-2023-0032.
  32. [32] X. Zhang, G. Li, Y. Li, G. Zou, and J. G. Wu, (2023) “Which is more important in stock market forecasting: At tention or sentiment?" International Review of Finan cial Analysis 89: 102732. DOI: 10.1016/j.irfa.2023.102732.
  33. [33] G. E. Primiceri, (2005) “Time varying structural vector autoregressions and monetary policy" The Review of Economic Studies 72(3): 821–852. DOI: 10.1111/j.1467-937x.2005.00353.x.
  34. [34] J.Nakajima,(2011)“Time-varyingparameterVARmodel with stochastic volatility: An overview of methodology and empirical applications" Monetary and Economic Studies 29: 107–142.
  35. [35] J. V. Duca, J. Muellbauer, and A. Murphy, (2021) “What drives house price cycles? International experience and policy issues" Journal of Economic Literature 59(3): 773–864. DOI: 10.1257/jel.20201325.
  36. [36] J. D. T. Alan J. Hanna and C. B. Walker, (2020) “News media and investor sentiment during bull and bear mar kets" The European Journal of Finance 26(14): 1377 1395. DOI: 10.1080/1351847x.2020.1743734.
  37. [37] S. D. Tan and O. Tas, (2021) “Social media sentiment in international stock returns and trading activity" Journal of Behavioral Finance 22(2): 221–234. DOI: 10.1080/15427560.2020.1772261.
  38. [38] Z. Umar, O. B. Adekoya, J. A. Oliyide, and M. Gubareva, (2021) “Media sentiment and short stocks performance during a systemic crisis" International Re view of Financial Analysis 78: 101896. DOI: 10.1016/j.irfa.2021.101896.
  39. [39] L. Bork, S. V. Møller, and T. Q. Pedersen, (2019) “A new index of housing sentiment" Management Science 66(4): 1563–1583. DOI: 10.1287/mnsc.2018.3258.
  40. [40] S. Heinig, A. Nanda, and S. Tsolacos, (2020) “Which sentiment indicators matter? Evidence from the European commercial real estate market" 42(4): 499–530. DOI: 10.1080/08965803.2020.1845562.
  41. [41] H. Marfatia, C. André, and R. Gupta, (2020) “Pre dicting housing market sentiment: The role of financial, macroeconomic and real estate uncertainties" Journal of Behavioral Finance 23(2): 189–209. DOI: 10.1080/15427560.2020.1865354.
  42. [42] P. Bayer, K. Mangum, and J. W. Roberts, (2021) “Spec ulative fever: Investor contagion in the housing bubble" American Economic Review 111(2): 609–651. DOI: 10.1257/aer.20171611.
  43. [43] O.Cepni,R.Gupta,andY.Onay,(2022)“Theroleofin vestor sentiment in forecasting housing returns in China: Amachine learning approach" Journal of Forecasting 41(8): 1725–1740. DOI: 10.1002/for.2893.
  44. [44] S. Shen, Y. Zhao, and J. Pang, (2024) “Local housing market sentiments and returns: Evidence from China" The Journal of Real Estate Finance and Economics 68(3): 488–522. DOI: 10.1007/s11146-022-09933-w.
  45. [45] J. H. Gallin, (2006) “The long-run relationship between house prices and income: Evidence from local housing markets" Real Estate Economics 34(3): 417–438. DOI: 10.1111/j.1540-6229.2006.00172.x.
  46. [46] V. Mikhed and P. Zemˇcík, (2009) “Do house prices reflect fundamentals? Aggregate and panel data evidence" Journal of Housing Economics 18(2): 140–149. DOI: 10.1016/j.jhe.2009.03.001.
  47. [47] N.Vlastakis and R. N. Markellos, (2012) “Information demand and stock market volatility" Journal of Bank ing and Finance 36(6): 1808–1821. DOI: 10.1016/j.jbankfin.2012.02.007.
  48. [48] N. Vozlyublennaia, (2014) “Investor attention, index performance, and return predictability" Journal of Bank ing and Finance 41: 17–35. DOI: 10.1016/j.jbankfin. 2013.12.010.
  49. [49] F. Wen, L. Xu, G. Ouyang, and G. Kou, (2019) “Retail investor attention and stock price crash risk: Evidence from China" International Review of Financial Anal ysis 65: 101376. DOI: 10.1016/j.irfa.2019.101376.
  50. [50] S. Yin, H. Li, A. A. Laghari, T. R. Gadekallu, G. A. R. Sampedro, and A. S. Almadhor, (2024) “An anomaly detection model based on deep auto-encoder and capsule graph convolution via sparrow search algorithm in 6G In ternet of everything" IEEE Internet of Things Journal 11(18): 29402–29411. DOI: 10.1109/jiot.2024.3353337.
  51. [51] N.Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, (1953) “Equation of state calculations by fast computing machines" Journal of Chemical Physics 21: 1087–1092. DOI: 10.1063/1.1699114.

Latest Articles

Performance evaluation and sensitivity analysis of a low- capacity single-effect LiBr-H2O absorption cooling system using energy and exergy modeling
Performance evaluation and sensitivity analysis of a low- capacity single-effect LiBr-H2O absorption cooling system using energy and exergy modelingVolume 28, Issue 9

Read more: ...

Application of selective ensemble learning soft sensor modeling based on GPR in the solar thermal power collection system
Application of selective ensemble learning soft sensor modeling based on GPR in the solar thermal power collection systemVolume 28, Issue 10 (In Press)

Read more: ...

Experimental and kinetic analysis of the addition of zerovalent iron for promoting conversion of CO2 into CH4 by anaerobic sludge
Experimental and kinetic analysis of the addition of zerovalent iron for promoting conversion of CO2 into CH4 by anaerobic sludgeVolume 28, Issue 10 (In Press)

Read more: ...

Sustainable Water Futures: Enhancing Efficiency and Conservation in Madinah AL Munawara
Sustainable Water Futures: Enhancing Efficiency and Conservation in Madinah AL MunawaraVolume 28, Issue 10 (In Press)

Read more: ...

Fault diagnosis of power transformers based on dissolved gas analysis and multi-kernel graph convolution network integrated with dual-channel classifiers
Fault diagnosis of power transformers based on dissolved gas analysis and multi-kernel graph convolution network integrated with dual-channel classifiersVolume 28, Issue 10 (In Press)

Read more: ...

Applying the Least Squares Approach to Solve Fredholm Fuzzy Fractional Integro-Differential Equations
Applying the Least Squares Approach to Solve Fredholm Fuzzy Fractional Integro-Differential EquationsVolume 28, Issue 10 (In Press)

Read more: ...

Transforming Otilonium Bromide for Resilient Antimicrobial Combat
Transforming Otilonium Bromide for Resilient Antimicrobial CombatVolume 28, Issue 10 (In Press)

Read more: ...

Ultra-small Hysteresis IGZO Thin-Film Transistors with Room-Temperature Sputtered SiO2 as Dielectric Layer
Ultra-small Hysteresis IGZO Thin-Film Transistors with Room-Temperature Sputtered SiO2 as Dielectric LayerVolume 28, Issue 10 (In Press)

Read more: ...

Investigation Of Alkaline Solution Temperature Increment Influence Onto SS316L Surface
Investigation Of Alkaline Solution Temperature Increment Influence Onto SS316L SurfaceVolume 28, Issue 10 (In Press)

Read more: ...

    



 

2.1
2023CiteScore
 
 
69th percentile
Powered by  Scopus

SCImago Journal & Country Rank

You may also like these articles below...

Most Popular Articles

Research Categories

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.