Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends
Various research methods can be used to collect housing market data and predict housing prices. The online search activity of Internet users is a novel and highly interesting measure of social behavior. In the present study, dwelling prices in Poland were analyzed based on aggregate data from seven...
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| Format: | Article |
| Language: | English |
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Sciendo
2023-12-01
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| Series: | Real Estate Management and Valuation |
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| Online Access: | https://doi.org/10.2478/remav-2023-0032 |
| _version_ | 1827589949542629376 |
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| author | Bełej Mirosław |
| author_facet | Bełej Mirosław |
| author_sort | Bełej Mirosław |
| collection | DOAJ |
| description | Various research methods can be used to collect housing market data and predict housing prices. The online search activity of Internet users is a novel and highly interesting measure of social behavior. In the present study, dwelling prices in Poland were analyzed based on aggregate data from seven Polish cities relative to the number of online searches for the keyword dwelling tracked by Google Trends, as well as several classical macroeconomic indicators. The analysis involved a vector autoregressive (VAR) model and the Granger causality test. The results of the study suggest that the volume of online searches returned by Google Trends is an effective predictor of housing price dynamics, and that unemployment and economic growth are important additional variables. |
| first_indexed | 2024-03-09T01:08:49Z |
| format | Article |
| id | doaj.art-9dfaf0b825894172ad09ef635b903dcd |
| institution | Directory Open Access Journal |
| issn | 2300-5289 |
| language | English |
| last_indexed | 2024-03-09T01:08:49Z |
| publishDate | 2023-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Real Estate Management and Valuation |
| spelling | doaj.art-9dfaf0b825894172ad09ef635b903dcd2023-12-11T07:38:15ZengSciendoReal Estate Management and Valuation2300-52892023-12-01314738710.2478/remav-2023-0032Predicting Housing Price Trends in Poland: Online Social Engagement - Google TrendsBełej Mirosław01Departament of Spatial Analysis and Real Estate Market, University of Warmia and Mazury in Olsztyn, ul. Oczapowskiego 2, 10-719Olsztyn, PolandVarious research methods can be used to collect housing market data and predict housing prices. The online search activity of Internet users is a novel and highly interesting measure of social behavior. In the present study, dwelling prices in Poland were analyzed based on aggregate data from seven Polish cities relative to the number of online searches for the keyword dwelling tracked by Google Trends, as well as several classical macroeconomic indicators. The analysis involved a vector autoregressive (VAR) model and the Granger causality test. The results of the study suggest that the volume of online searches returned by Google Trends is an effective predictor of housing price dynamics, and that unemployment and economic growth are important additional variables.https://doi.org/10.2478/remav-2023-0032calendar effectsbehavioral financereal estate marketr20r30r31 |
| spellingShingle | Bełej Mirosław Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends Real Estate Management and Valuation calendar effects behavioral finance real estate market r20 r30 r31 |
| title | Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends |
| title_full | Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends |
| title_fullStr | Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends |
| title_full_unstemmed | Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends |
| title_short | Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends |
| title_sort | predicting housing price trends in poland online social engagement google trends |
| topic | calendar effects behavioral finance real estate market r20 r30 r31 |
| url | https://doi.org/10.2478/remav-2023-0032 |
| work_keys_str_mv | AT bełejmirosław predictinghousingpricetrendsinpolandonlinesocialengagementgoogletrends |