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

1. Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data Marian Alexander Dietzel | Nicole Braun | Wolfgang Schaefers ERES Conference Bucharest 2014

2. AGENDA Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data 1. Motivation andTheoretical Background 2. Research Design andMethodology 2.1. Data 2.2. Models 4. EmpiricalResults 5. Conclusion

3. Motivation andTheoretical Background Beracha, E. and Wintoki, J. (2012), “Predicting Future Home Price Changes Using Current Google Search Data,” Journal of Real Estate Research, forthcoming. Hohenstatt, R., Käsbauer, M. and Schäfers, W. (2011), “’Geco’ and its Potential for Real Estate Research: Evidence from the U.S. Housing Market”, Journal of Real Estate Research, Vol. 33 No. 4., pp. 471-506. Hohenstatt, R. and Käsbauer, M. (2013), “GECO’s Weather Forecast’ for the U.K. Housing Market: To What Extent Can We Rely on Google ECOnometrics?”, Journal of Real Estate Research, forthcoming. Wu, L. and Brynjolfsson, E. (2009), “The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales”, Working papers, Wharton School, University of Pennsylvania Housing Market Predictionswith Google Trends Data All studies find empirical evidence that Google Trends data have predictive power and improve the forecast accuracy for housing markets (USA and UK).

4. Motivation andTheoretical Background Can Google Trends data also improveCommercial Real Estate Market Forecasts? Research Question

5. Motivation andTheoretical Background GraphicalInspection in annualdifferences CoStar Composite Index Google

6. Motivation andTheoretical Background GraphicalInspection in annualdifferences CoStar Composite transactions Google

7. MotivationandTheoretical Background • prediction of outcomes • investment appraisal formulation of a decision-making strategy • search for suitable properties settingofinitialinvestmentgoalsanddecisioncriteria Stage 1 • detailed search for alternative investment opportunities • choosing rational criteria for asset selection • formulation of investor specific strategy Transaction Processand Internet Research Stage 2 • unspecific: internetsearchformarket/investmentclimateandcomparables (yields, rents etc.) • listingservices • real estateagents/propertynewswebsites • specific: internetsearchformarket/investment • climateandcomparables (yields, rents etc.) • comparisonofotheranalyses Stage 3 • internetsearchforactualproperties • listingservices • real estateagents (JLL, CBRE etc.) objectrelated interest: marketrelatedinterest Stage 5 information input (analysis of market conditions) Stage 4 • analysis of economic, political and investment climate for national and regional markets • Stage 6: application of decision criteria; Stage 7: trade-off between properties; Stage 8: project screening; Stage 9: investment selection; Stage 10: deal resolution and post investment activity Investment Process after Roberts andHenneberry (2007)

8. Research Design andMethodology Google Data Search Volume Indices (SVI) derived from Google Trends (http://www.google.com/trends/) Normalized values, scaled measured between 0 and 100 The weekly data covers search queries conducted from Sunday to Saturday. Google Trends makes the newest weekly data available with an approximate two day delay.

9. Research Design andMethodology Macro Data Commercial Real Estate Data: CoStar Commercial Repeat-Sale Indices CCRSI Moody‘s/RCA Commercial Property Price Indices CPPI Macroeconomic Data: US unemployment initial claims US construction expenditures National Financial Conditions Index (NFCI) Chicago Fed National Activity Index (CFNAI)

10. Research Design andMethodology Google Data

11. Research Design andMethodology Models

12. Research Design andMethodology Model Specification VAR (6)-Model endogenous variables exogenous variables

13. EmpiricalResults Price Forecasts * The reduction of the MSE and U1 Theil is always measured in relation to the best baseline model (usually b2). A positive value stands for an improvement in forecasting accuracy in comparison to the baseline model.

14. EmpiricalResults Transaction Forecasts * The reduction of the MSE and U1 Theil is always measured in relation to the best baseline model (usually b2). A positive value stands for an improvement in forecasting accuracy in comparison to the baseline model.

15. Robustness Check Robustness across Real Estate Sectors * The reduction of the MSE and U1 Theil is always measured in relation to the best baseline model (usually b2). A positive value stands for an improvement in forecasting accuracy in comparison to the baseline model.

16. Robustness Check Robustness across Real Estate Sectors * The reduction of the MSE and U1 Theil is always measured in relation to the best baseline model (usually b2). A positive value stands for an improvement in forecasting accuracy in comparison to the baseline model.

17. Robustness Check Clark-West Forecast Improvement Significance Tests

18. FindingsandConclusion Main Findings Google datahelp in improvingtheforecastaccuracyforthecommercial real estatemarket g2-models havethelowestmeansquaredforecasterrors  a combinationofmacroand Google datayieldsthebestforecastingresults • Models based on Google dataonlyoutperform non-Google modelsin mostcases(78%)  Google databyitselfhassignificantexplanatory power towardsthecommercial real estatemarket

19. Questions Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data Thankyouforyourattention! Questions?

20. BACK UP Granger Causality Test

21. BACK UP Lag Order

22. BACK UP Clark-West ForecastSignificance Test

23. BACK UP Impulse Response Function

24. BACK UP Macro Data