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Cited by 26Housing Market and EconomicsStock Market Forecasting MethodsForecasting Techniques and Applications

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Can Web Search Queries Predict Prices Change on the Real Estate Market?

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Rizun, Nina, Baj-Rogowska, Anna·2021·IEEE Access
DOI 10.1109/access.2021.3077860
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Abstract

This study aims to explore whether the intensity of internet searches, according to the Google Trends search volume index (SVI), is a predictor of changes in real estate prices. The motivation of this study is the possibility to extend the understanding of the extra predictive power of Google search engine query volume of future housing price change (shift direction) by (i) the introduction of a research approach that combines the advantages of the complementary use of cross-correlation analysis and machine learning classification algorithms; (ii) applying the multi-class HPI values classifier which allows predicting the housing price increase, decrease or relative stability; (iii) exploiting the SVI that relates to interests in both `real estate' and `credit to buy real estate'; (iv) evaluation of the introduced approach in the context of the Polish real estate market. The main theoretical contribution of our work is a confirmation that the freely available information regarding Google user searches can provide an in-depth insight into enriching the generally accepted statistics on supply and demand in the real estate market. From the practical perspective, this research confirms that SVI can be associated as a sole determinant to anticipate the housing price change with time-lag sufficient for making decisions regarding the purchase (sale) of individual property or the real estate market control. Such findings can be also helpful for researchers who intend to use Google Tre…

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Research works printed but not linked: 89

  1. bui, 2019, A lasso and elastic-net regularized generalized linear model for predicting blast-induced air over-pressure in open-pit mines, Inzynieria Mineralna, 21, 41
  2. 10.1080/00420980600990928 · DOI
  3. 10.1016/S1874-8651(10)60092-0 · DOI
  4. breiman, 1984, Classification and Regression Trees
  5. 10.1016/B978-0-12-801460-8.00002-1 · DOI
  6. 10.1109/ICACA.2016.7887926 · DOI
  7. 10.1016/j.econmod.2014.10.050 · DOI
  8. 10.2139/ssrn.1865276 · DOI
  9. fishbein, 1977, Belief, attitude, intention, and behavior: An introduction to theory and research, Philosophy and Rhetoric, 10, 130
  10. 10.2139/ssrn.1438286 · DOI
  11. 10.1016/j.energy.2006.11.010 · DOI
  12. quinlan, 2008, Data mining tools see5 and C5.0, Proc Rulequest Res, 1997
  13. cara, 2017, The potential of applying machine learning for predicting cut-in behaviour of surrounding traffic for truck-platooning safety, Proc 25th Int Tech Conf Enhanced Saf Vehicles (ESV) Nat Highway Traffic Saf Admin, 3
  14. niedzielska, 2018, Using Google trends to predict the rate of return of WIG20 index, Econ Century, 3, 82
  15. 10.2807/1560-7917.ES.2020.25.21.1900221 · DOI
  16. 10.1002/jtr.2348 · DOI
  17. 10.1080/10835547.2011.12091318 · DOI
  18. 10.1086/258464 · DOI
  19. bulczak, 2014, Zastosowanie Google Trends w prognozowaniu zmian na rynku nieruchomo?ci, Finan Manage, 12, 79
  20. 10.2139/ssrn.2022293 · DOI
  21. 10.1111/j.1467-9868.2005.00503.x · DOI · Paper page
  22. 10.1080/00401706.1970.10488635 · DOI
  23. 10.1111/j.2517-6161.1996.tb02080.x · DOI · Paper page
  24. 10.1016/j.neucom.2011.06.013 · DOI
  25. 10.1063/5.0002759 · DOI
  26. 10.1016/j.neucom.2018.01.038 · DOI
  27. 10.1109/ICSSS.2019.8882842 · DOI
  28. 10.1109/ISBAST.2014.7013130 · DOI
  29. 10.1016/j.jad.2016.10.038 · DOI
  30. 10.1109/ICCUBEA.2018.8697402 · DOI
  31. 10.1038/s41598-018-29796-7 · DOI
  32. 10.1016/j.eswa.2005.01.006 · DOI
  33. 10.1080/10835547.2013.12091364 · DOI
  34. 10.1108/PM-01-2017-0003 · DOI
  35. 10.3390/socsci8040111 · DOI
  36. baumöhl, 2020, Fear of the coronavirus and the stock markets, Finance Research letter, 36
  37. 10.1108/JES-11-2017-0346 · DOI
  38. 10.1016/j.eneco.2017.10.035 · DOI
  39. 10.1016/j.resourpol.2019.101542 · DOI
  40. 10.1142/S0219477519500068 · DOI
  41. wo?k, 2020, Advanced social media sentiment analysis for short-term cryptocurrency price prediction, Expert Syst, 37
  42. 10.1007/978-981-15-3380-8_2 · DOI
  43. 10.1109/ICICOS.2017.8276357 · DOI
  44. 10.1139/cjfr-2014-0562 · DOI
  45. 10.1016/j.rser.2020.110114 · DOI
  46. hastie, 2003, The Elements of Statistical Learning
  47. 10.1007/978-3-030-38651-1_32 · DOI
  48. chaturvedi, 2021, Parameterized Comparison of Regularized Regression Models to Develop Models for Real Estate, IOP Conf Ser Mater Sci Eng, 1099, 10.1088/1757-899X/1099/1/012016 · DOI
  49. 10.1109/ICIMIA48430.2020.9074952 · DOI
  50. hastie, 2014, Glmnet vignette
  51. 10.18637/jss.v033.i01 · DOI · Paper page
  52. 10.1007/s00366-019-00833-x · DOI
  53. 10.1002/asi.24255 · DOI
  54. choi, 2012, Predicting Initial Claims for Unemployment Benefits
  55. 10.1108/JPIF-01-2014-0004 · DOI
  56. 10.1111/j.1475-4932.2012.00809.x · DOI · Paper page
  57. 10.1016/j.techfore.2017.11.009 · DOI
  58. 10.1080/20477724.2020.1767854 · DOI
  59. 10.1371/journal.pone.0233855 · DOI
  60. 10.1007/978-3-030-23943-5_19 · DOI
  61. 10.1109/ICTS.2016.7910283 · DOI
  62. 10.1109/ICECDS.2017.8389853 · DOI
  63. 10.3390/ijerph16173201 · DOI
  64. grover, 2017, Gradient boosting from scratch
  65. 10.1016/j.jad.2019.05.030 · DOI
  66. 10.3390/su11102848 · DOI
  67. 10.1177/1354816618793765 · DOI
  68. 10.18374/JIMS-15-3.6 · DOI
  69. 10.1038/srep06822 · DOI
  70. 10.1371/journal.pone.0040014 · DOI
  71. 10.1038/nature07634 · DOI · Paper page
  72. 10.1016/j.pacfin.2014.01.003 · DOI
  73. 10.1016/j.techfore.2020.120026 · DOI
  74. 10.1111/coin.12198 · DOI
  75. 2020, National Association of Realtors Report
  76. 10.1109/ICICCT.2018.8473231 · DOI · Paper page
  77. 10.1109/BigData.2018.8622152 · DOI
  78. 10.1007/978-3-030-30967-1_9 · DOI
  79. áeh, 2018, Estimating the performance of random forest versus multiple regression for predicting prices of the apartments, ISPRS Int J Geo-Inf, 7, 168, 10.3390/ijgi7050168 · DOI
  80. jamil, 2020, Machine learning price prediction on green building prices, Proc IEEE Symp Ind Electron Appl (ISIEA), 1
  81. surowiecki, 2005, The Wisdom of Crowds
  82. 10.1016/j.eswa.2014.08.029 · DOI
  83. 10.1016/j.eswa.2014.11.040 · DOI · Paper page
  84. 10.1007/978-981-15-3859-9_28 · DOI
  85. 10.1109/IJCNN.2007.4371433 · DOI
  86. webb, 2009, Internet search statistics as a source of business intelligence: Searches on foreclosure as an estimate of actual home foreclosures, Issues in Inform Syst, 10, 82
  87. 10.1080/07350015.2020.1791133 · DOI
  88. 10.1016/j.eswa.2014.07.040 · DOI · Paper page
  89. 10.1016/j.ijleo.2013.09.017 · DOI

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Metadata

Authors
Rizun, N., & Baj-Rogowska, A.
Affiliations
Gdańsk University of Technology (PL)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
9
Pages
70095-70117
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2021.3077860
OpenAlex
W3159731117
Cited by
26
References
1
Citations by year
2026: 5 · 2025: 6 · 2024: 8 · 2023: 3 · 2022: 4
FWCI
2.86
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Housing Market and Economics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
Google Trends, search volume index, real estate market, web search queries, cross correlation function, real estate credit, Polish real estate market

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