GECO's Weather Forecast for the U.K. Housing Market: To What Extent Can We Rely on Google Econometrics?

Hohenstatt, Ralf; Kaesbauer, Manuel

doi:10.1080/10835547.2014.12091387

Cited by 15 publications

(7 citation statements)

References 30 publications

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“…We assume that forecasting models benefit particularly from the well-displayed consumer demand information provided by Google search queries. This result is in line with existing research such as Hohenstatt et al (2011), Hohenstatt andKaesbauer (2013), Wu and Brynjolfsson (2009) and Beracha and Wintoki (2013). The evidence provided in Tables VII and VIII indicates quite clearly that the inclusion of Google Trends data is robust in improving the in-and out-of-sample accuracy of price and transaction forecast models for US commercial real estate markets.…”

Section: Forecast Testssupporting

confidence: 89%

“…In addition, they confirm empirically the time setting for the home buying process derived from the existing literature and the National Association of Realtors' "Profile of Home Buyers and Sellers 2009". Building on their first article, Hohenstatt and Kaesbauer (2013) confirm their results for the UK housing market. By splitting their sample into downturn and upturn phases, they provide evidence that the relatively robust subcategory "Real Estate Agencies" works particularly well during upswings.…”

Section: Sentiment-based Commercial Real Estatesupporting

confidence: 59%

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Sentiment-based commercial real estate forecasting with Google search volume data

Dietzel

Braun

Schäfers

2014

Journal of Property Investment &Amp; Finance

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Purpose – The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Design/methodology/approach – This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Findings – The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively. Practical implications – The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value – This is the first paper applying Google search query data to the commercial real estate sector.

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Section: Forecast Testssupporting

confidence: 89%

Section: Sentiment-based Commercial Real Estatesupporting

confidence: 59%

Sentiment-based commercial real estate forecasting with Google search volume data

Dietzel

Braun

Schäfers

2014

Journal of Property Investment &Amp; Finance

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“…Finally, Kristoufek (2013) posits that more frequently searched stocks are riskier, and demonstrates how this specific feature can contribute to lowering the overall risk of a portfolio by assigning lower portfolio weights to risky stocks. In addition to financial markets, Dietzel et al (2014), Hohenstatt et al (2011), Hohenstatt andKaesbauer (2014), Beracha and Wintoki (2013) and Wu and Brynjolfsson (2014) demonstrate Google's predictive abilities for the real estate markets at both national and state levels for commercial and residential real estate markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Outperforming the benchmark: online information demand and REIT market performance

Rochdi

Dietzel

2015

Journal of Property Investment &Amp; Finance

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Purpose – The purpose of this paper is to investigate whether there is a relationship between asset-specific online search interest and movements in the US REIT market. Design/methodology/approach – The authors collect search volume (SV) data from “Google Trends” for a set of keywords representing the information demand of real estate (equity) investors. On this basis, the authors test hypothetical investment strategies based on changes in internet SV, to anticipate REIT market movements. Findings – The results reveal that people’s information demand can indeed serve as a successful predictor for the US REIT market. Among other findings, evidence is provided that there is a significant relationship between asset-specific keywords and the US REIT market. Specifically, investment strategies based on weekly changes in Google SV would have outperformed a buy-and-hold strategy (0.1 percent p.a.) for the Morgan Stanley Capital International US REIT Index by a remarkable 15.4 percent p.a. between 2006 and 2013. Furthermore, the authors find that real-estate-related terms are more suitable than rather general, finance-related terms for predicting REIT market movements. Practical implications – The findings should be of particular interest for REIT market investors, as the established relationships can potentially be utilized to anticipate short-term REIT market movements. Originality/value – This is the first paper which applies Google search query data to the REIT market.

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“…They do not find clear evidence for house prices. Hohenstatt and Käsbauer (2014) extend their research by applying Google search data to the U.K. housing market and find that the subcategory Real Estate Agency is an indicator of transaction volume, especially during economic upturns. They posit that the home financing subcategory works as a potential stress indicator and evokes twice as strong a response in transaction volumes as it does in house prices.…”

Section: Literature Reviewmentioning

confidence: 98%

“…Some studies, such as Hohenstatt and Käsbauer (2014), Hohenstatt, Käsbauer, and Schäfers (2011), and Wu and Brynjolfsson (2015), have examined if Google search frequency of a specific real estate term or online queries categorized by Google into predefined real estate subcategories, such as “Real estate listings,” reflects people's attitudes toward real estate. These studies find that Google search frequency is directly related to future housing sales.…”

Section: Introductionmentioning

confidence: 99%

FEAR Index, city characteristics, and housing returns

Aroul

Sabherwal

Saydometov

2020

Real Estate Economics

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We use Google search frequency to construct a measure of aggregate sentiment in housing markets—Financial, Economic, and Real Estate (FEAR) Index—and analyze its relationship to housing returns. We find that housing markets react inversely to changes in FEAR Index, which captures negative sentiment, and that market characteristics affect the strength of this relationship. More financially distressed markets, as measured by bankruptcy rates and mortgage default double trigger, are more responsive to changes in FEAR Index than less distressed markets, and cold markets (markets with slow price appreciation) are more responsive than hot markets (markets with rapid price appreciation). We also examine these characteristics jointly and find that cold markets with financial distress are the most responsive to negative sentiment. Finally, we show that home prices are more sensitive to negative sentiment during recessionary periods.

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GECO's Weather Forecast for the U.K. Housing Market: To What Extent Can We Rely on Google Econometrics?

Cited by 15 publications

References 30 publications

Sentiment-based commercial real estate forecasting with Google search volume data

Sentiment-based commercial real estate forecasting with Google search volume data

Outperforming the benchmark: online information demand and REIT market performance

FEAR Index, city characteristics, and housing returns

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