Can Web Search Queries Predict Prices Change on the Real Estate Market?
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...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9423962/ |
| _version_ | 1818731131283963904 |
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| author | Nina Rizun Anna Baj-Rogowska |
| author_facet | Nina Rizun Anna Baj-Rogowska |
| author_sort | Nina Rizun |
| collection | DOAJ |
| description | 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 Trends data as an extra variable from demand side to improve the prediction accuracy if it is included in the model which is based on the existing housing prices determinants. |
| first_indexed | 2024-12-17T23:12:48Z |
| format | Article |
| id | doaj.art-60c6417ae454495eaf4b509e3d6a7c8b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T23:12:48Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-60c6417ae454495eaf4b509e3d6a7c8b2022-12-21T21:29:05ZengIEEEIEEE Access2169-35362021-01-019700957011710.1109/ACCESS.2021.30778609423962Can Web Search Queries Predict Prices Change on the Real Estate Market?Nina Rizun0https://orcid.org/0000-0002-4343-9713Anna Baj-Rogowska1https://orcid.org/0000-0002-8694-4461Department of Informatics in Management, Gdañsk University of Technology, Gdañsk, PolandDepartment of Informatics in Management, Gdañsk University of Technology, Gdañsk, PolandThis 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 Trends data as an extra variable from demand side to improve the prediction accuracy if it is included in the model which is based on the existing housing prices determinants.https://ieeexplore.ieee.org/document/9423962/Big data utilizationcross-correlation analysismachine learning classificationGoogle trends (GT)house price index (HPI)prediction |
| spellingShingle | Nina Rizun Anna Baj-Rogowska Can Web Search Queries Predict Prices Change on the Real Estate Market? IEEE Access Big data utilization cross-correlation analysis machine learning classification Google trends (GT) house price index (HPI) prediction |
| title | Can Web Search Queries Predict Prices Change on the Real Estate Market? |
| title_full | Can Web Search Queries Predict Prices Change on the Real Estate Market? |
| title_fullStr | Can Web Search Queries Predict Prices Change on the Real Estate Market? |
| title_full_unstemmed | Can Web Search Queries Predict Prices Change on the Real Estate Market? |
| title_short | Can Web Search Queries Predict Prices Change on the Real Estate Market? |
| title_sort | can web search queries predict prices change on the real estate market |
| topic | Big data utilization cross-correlation analysis machine learning classification Google trends (GT) house price index (HPI) prediction |
| url | https://ieeexplore.ieee.org/document/9423962/ |
| work_keys_str_mv | AT ninarizun canwebsearchqueriespredictpriceschangeontherealestatemarket AT annabajrogowska canwebsearchqueriespredictpriceschangeontherealestatemarket |