Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data

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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Internet search query data improve forecasts of daily emergency department volume

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocz154 ◽

2019 ◽

Vol 26 (12) ◽

pp. 1574-1583 ◽

Cited By ~ 2

Author(s):

Sam Tideman ◽

Mauricio Santillana ◽

Jonathan Bickel ◽

Ben Reis

Keyword(s):

Predictive Power ◽

Linear Models ◽

Data Sources ◽

Weather Data ◽

Volume Data ◽

Internet Search ◽

Search Activity ◽

Search Volume ◽

Day Of The Week ◽

Google Search

Abstract Objective Emergency departments (EDs) are increasingly overcrowded. Forecasting patient visit volume is challenging. Reliable and accurate forecasting strategies may help improve resource allocation and mitigate the effects of overcrowding. Patterns related to weather, day of the week, season, and holidays have been previously used to forecast ED visits. Internet search activity has proven useful for predicting disease trends and offers a new opportunity to improve ED visit forecasting. This study tests whether Google search data and relevant statistical methods can improve the accuracy of ED volume forecasting compared with traditional data sources. Materials and Methods Seven years of historical daily ED arrivals were collected from Boston Children’s Hospital. We used data from the public school calendar, National Oceanic and Atmospheric Administration, and Google Trends. Multiple linear models using LASSO (least absolute shrinkage and selection operator) for variable selection were created. The models were trained on 5 years of data and out-of-sample accuracy was judged using multiple error metrics on the final 2 years. Results All data sources added complementary predictive power. Our baseline day-of-the-week model recorded average percent errors of 10.99%. Autoregressive terms, calendar and weather data reduced errors to 7.71%. Search volume data reduced errors to 7.58% theoretically preventing 4 improperly staffed days. Discussion The predictive power provided by the search volume data may stem from the ability to capture population-level interaction with events, such as winter storms and infectious diseases, that traditional data sources alone miss. Conclusions This study demonstrates that search volume data can meaningfully improve forecasting of ED visit volume and could help improve quality and reduce cost.

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Sentiment-based predictions of housing market turning points with Google trends

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-12-2014-0058 ◽

2016 ◽

Vol 9 (1) ◽

pp. 108-136 ◽

Cited By ~ 5

Author(s):

Marian Alexander Dietzel

Keyword(s):

Real Estate ◽

Housing Market ◽

Turning Points ◽

House Price ◽

Data Set ◽

Content Type ◽

Forecasting Models ◽

Search Volume ◽

Out Of Sample ◽

Google Search

Purpose – Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts. Design/methodology/approach – Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability. Findings – The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes. Practical implications – The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value – This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

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International Real Estate Review

International Real Estate Review ◽

10.53383/100302 ◽

2020 ◽

Vol 23 (2) ◽

pp. 267-308

Author(s):

Are Oust ◽

◽

Ole Martin Eidjord ◽

Keyword(s):

Real Estate ◽

Housing Market ◽

House Prices ◽

House Price ◽

Search Term ◽

Housing Bubble ◽

Search Terms ◽

Search Volume ◽

Google Search ◽

The U.S

The aim of this paper is to test whether Google search volume indices can be used to predict house prices and identify bubbles in the housing market. We analyze the data that pertain to the 2006?2007 U.S. housing bubble, taking advantage of the heterogeneous house price development in both bubble and non-bubble states in the U.S. Using 204 housing-related keywords, we test both single search terms and indices that comprise search term sets to see whether they can be used as housing bubble indicators. We find that several keywords perform very well as bubble indicators. Among all of the keywords and indices tested, the Google search volume for ¡§Housing Bubble¡¨ and ¡§Real Estate Agent¡¨, and a constructed index that contains the twelve best-performing search terms score the highest at both detecting bubbles and not erroneously detecting non-bubble states as bubbles. A new housing bubble indicator may help households, investors, and policy makers receive advanced warning about future housing bubbles. Moreover, we show that the Google search outperforms the well-established consumer confidence index in the U.S. as a leading indicator of the housing market.

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Associations between COVID-19 mobility restrictions and economic, mental health, and suicide-related concerns in the US using cellular phone GPS and Google search volume data

PLoS ONE ◽

10.1371/journal.pone.0260931 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0260931

Author(s):

Catherine Gimbrone ◽

Caroline Rutherford ◽

Sasikiran Kandula ◽

Gonzalo Martínez-Alés ◽

Jeffrey Shaman ◽

...

Keyword(s):

Mental Health ◽

Real Time ◽

Economic Stress ◽

Warning Signs ◽

P Value ◽

Volume Data ◽

Search Volume ◽

Search Data ◽

Google Search ◽

At Home

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic’s social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.

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

Journal of Property Investment and Finance ◽

10.1108/jpif-01-2014-0004 ◽

2014 ◽

Vol 32 (6) ◽

pp. 540-569 ◽

Cited By ~ 15

Author(s):

Marian Alexander Dietzel ◽

Nicole Braun ◽

Wolfgang Schäfers

Keyword(s):

Real Estate ◽

Commercial Real Estate ◽

Google Trends ◽

Internet Search ◽

Price Indices ◽

Search Query ◽

Content Type ◽

Forecasting Models ◽

Search Data ◽

Google Search

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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