Sentiment-Based Predictions of Housing Market Turning Points with Google Trends

Dietzel, Marian Alexander; Chang, Kuang Liang; Chen, Nan‐Kuang

doi:10.15396/eres2015_3

Cited by 2 publications

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“…The cited author investigated the relationship between online search activity for mortgages and real housing market activity. Dietzel (2015) examined whether Google search volume data can be used as a leading indicator of consumer sentiment to predict turning points on the US housing market. The study demonstrated that the Google model was not always accurate in timing, but that it correctly predicted upcoming turning points in housing prices.…”

Section: Discussionmentioning

confidence: 99%

“…The study demonstrated that the Google model was not always accurate in timing, but that it correctly predicted upcoming turning points in housing prices. In other studies, Google Trends was described as a reliable tool for forecasting housing prices (Castelnuovo & Tran, 2017;Dietzel, 2015;Huarng et al, 2020;Wu & Brynjolfsson, 2009). A somewhat different approach was adopted by Limnios and You (Limnios & You, 2021;Limnios & You, 2016) who combined linear pricing models for the housing market with Google Trends data to determine whether crowd-sourced search query data can improve the models' forecasting ability.…”

Section: Discussionmentioning

confidence: 99%

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Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends

Bełej

2023

Real Estate Management and Valuation

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends

Bełej

2023

Real Estate Management and Valuation

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“…Fluctuation in housing prices touches the nerves of policymakers, ordinary residents, and financial institutions (Dietzel, 2015) and has become an issue of general social concern. Any imbalance or contraction in the housing market can lead to financial instability, which in turn poses a macroeconomic threat (Lee & Park, 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

Wang

Liu

2022

International Journal of Strategic Property Management

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Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarters from 2016 to 2020, the spatial effect of market sentiment on housing price is empirically analyzed by calculating direct and indirect effects. The results show that market sentiment had a significant positive effect on housing prices in the local and neighboring cities over the research period. However, the impact of market sentiment on housing price was heterogeneous in terms of geographical region; the direct effect was stronger in the eastern region than in the central and western regions, and the indirect effect was significant only in the eastern region. These findings can provide references for government to formulate housing market regulation policies and measures based on market sentiment.

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Sentiment-Based Predictions of Housing Market Turning Points with Google Trends

Cited by 2 publications

References 0 publications

Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends

Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

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