IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v44y2025i1p216-241.html
   My bibliography  Save this article

Forecasting house price index with social media sentiment: A decomposition–ensemble approach

Author

Listed:
  • Jin Shao
  • Lean Yu
  • Jingke Hong
  • Xianzhu Wang

Abstract

Social media sentiment influences housing market trading and policy‐making in China. To explore the multiscale relationship between social media sentiment and house price index (HPI) and improve prediction performance, a sentiment‐based decomposition–ensemble approach is proposed for HPI forecasting. In this approach, five steps, that is, sentiment analysis for massive Weibo textual reviews about house prices, data decomposition for bivariate time series integrated by HPI and the sentiment index (SI), data smoothing for high‐frequency components, component reconstruction for all individual modes, and all components prediction and ensemble, are involved. For verification, the National‐level and two city‐level house price indices are used as the sample data. The empirical results illustrate that the proposed approach can achieve better performance than all considered benchmark models at multi‐step‐ahead prediction horizons, indicating that it can be used as an effective tool for HPI forecasting.

Suggested Citation

  • Jin Shao & Lean Yu & Jingke Hong & Xianzhu Wang, 2025. "Forecasting house price index with social media sentiment: A decomposition–ensemble approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 216-241, January.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:1:p:216-241
    DOI: 10.1002/for.3188
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3188
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.3188?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cindy K Soo, 2018. "Quantifying Sentiment with News Media across Local Housing Markets," The Review of Financial Studies, Society for Financial Studies, vol. 31(10), pages 3689-3719.
    2. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    3. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).
    4. Yu, Lean & Ma, Yueming & Ma, Mengyao, 2021. "An effective rolling decomposition-ensemble model for gasoline consumption forecasting," Energy, Elsevier, vol. 222(C).
    5. Shao, Jin & Hong, Jingke & Wang, Xianzhu & Yan, Xiaochen, 2023. "The relationship between social media sentiment and house prices in China: Evidence from text mining and wavelet analysis," Finance Research Letters, Elsevier, vol. 57(C).
    6. Bangzhu Zhu & Xuetao Shi & Julien Chevallier & Ping Wang & Yi‐Ming Wei, 2016. "An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(7), pages 633-651, November.
    7. Xiaolong Liu, 2013. "Spatial and Temporal Dependence in House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 47(2), pages 341-369, August.
    8. Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
    9. David A. Mascio & Marat Molyboga & Frank J. Fabozzi, 2023. "The battle of the factors: Macroeconomic variables or investor sentiment?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2280-2291, December.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    12. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    13. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    14. Huang, Yanmei & Hasan, Najmul & Deng, Changrui & Bao, Yukun, 2022. "Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting," Energy, Elsevier, vol. 239(PC).
    15. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    16. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    17. Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
    18. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).
    19. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
    20. Wang, Jue & Athanasopoulos, George & Hyndman, Rob J. & Wang, Shouyang, 2018. "Crude oil price forecasting based on internet concern using an extreme learning machine," International Journal of Forecasting, Elsevier, vol. 34(4), pages 665-677.
    21. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    22. Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
    23. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Zicheng & Gao, Ruobin & Wang, Piao & Chen, Huayou, 2023. "A new perspective on air quality index time series forecasting: A ternary interval decomposition ensemble learning paradigm," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    2. Chuan Zhang & Ao‐Yun Hu & Yu‐Xin Tian, 2023. "Daily tourism forecasting through a novel method based on principal component analysis, grey wolf optimizer, and extreme learning machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2121-2138, December.
    3. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
    4. Jiang, He & Hu, Weiqiang & Xiao, Ling & Dong, Yao, 2022. "A decomposition ensemble based deep learning approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 78(C).
    5. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    6. Zheng, Li & Sun, Yuying & Wang, Shouyang, 2024. "A novel interval-based hybrid framework for crude oil price forecasting and trading," Energy Economics, Elsevier, vol. 130(C).
    7. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).
    8. Tang, Ling & Zhang, Chengyuan & Li, Ling & Wang, Shouyang, 2020. "A multi-scale method for forecasting oil price with multi-factor search engine data," Applied Energy, Elsevier, vol. 257(C).
    9. Jindai Zhang & Jinlou Zhao, 2022. "Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting," Energies, MDPI, vol. 15(10), pages 1-15, May.
    10. Li, Jingjing & Tang, Ling & Wang, Shouyang, 2020. "Forecasting crude oil price with multilingual search engine data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    11. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
    12. Qin, Quande & Xie, Kangqiang & He, Huangda & Li, Li & Chu, Xianghua & Wei, Yi-Ming & Wu, Teresa, 2019. "An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction," Energy Economics, Elsevier, vol. 83(C), pages 402-414.
    13. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    14. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    15. Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024. "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, vol. 92(C).
    16. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
    17. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    18. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    19. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.
    20. Julien Chevallier & Bangzhu Zhu & Lyuyuan Zhang, 2021. "Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 537-575, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:44:y:2025:i:1:p:216-241. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.