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Online Information Search, Market Fundamentals and Apartment Real Estate

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  • Prashant Das
  • Alan Ziobrowski
  • N. Coulson

Abstract

We examine the association between online apartment rental searches and fundamental real estate market variables namely, vacancy rates, rental rates and real estate asset price returns. We find that consumer real estate searches are significantly associated with the market fundamentals after controlling for known determinants of these variables. In particular, we show that apartment rental-related online searches are endogenously and contemporaneously associated with reduced vacancy rate. However, the association between the searches and rental rates is not significant. The searches are also contemporaneously associated with positive returns on the appraised values of multifamily assets. There is some evidence that the searches are fundamentally associated with REIT returns in the short run and that REIT investors watch the online search trends to inform their stock pricing decisions. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Prashant Das & Alan Ziobrowski & N. Coulson, 2015. "Online Information Search, Market Fundamentals and Apartment Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 51(4), pages 480-502, November.
    • Handle: RePEc:kap:jrefec:v:51:y:2015:i:4:p:480-502
    DOI: 10.1007/s11146-015-9496-1
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    1. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    2. Matthias Bank & Martin Larch & Georg Peter, 2011. "Google search volume and its influence on liquidity and returns of German stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(3), pages 239-264, September.
    3. Jim Clayton & David Ling & Andy Naranjo, 2009. "Commercial Real Estate Valuation: Fundamentals Versus Investor Sentiment," The Journal of Real Estate Finance and Economics, Springer, vol. 38(1), pages 5-37, January.
    4. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    5. Theodore M. Crone & Richard Voith, 1988. "Natural vacancy rates and the persistence of shocks in U.S. office markets," Working Papers 88-4, Federal Reserve Bank of Philadelphia.
    6. Merton, Robert C, 1987. "A Simple Model of Capital Market Equilibrium with Incomplete Information," Journal of Finance, American Finance Association, vol. 42(3), pages 483-510, July.
    7. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    8. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    9. Rosen, Kenneth T & Smith, Lawrence B, 1983. "The Price-Adjustment Process for Rental Housing and the Natural Vacancy Rate," American Economic Review, American Economic Association, vol. 73(4), pages 779-786, September.
    10. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 50(4), pages 1001-1040, September.
    11. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    12. Neumann, John J. & Kenny, Peppi M., 2007. "Does Mad Money make the market go mad," The Quarterly Review of Economics and Finance, Elsevier, vol. 47(5), pages 602-615, December.
    13. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
    14. Ralf Hohenstatt & Manuel Käsbauer & Wolfgang Schäfers, 2011. ""Geco" and its potential for real estate research: Evidence from the US housing market," Journal of Real Estate Research, American Real Estate Society, vol. 33(4), pages 471-506.
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    3. Julia Gabriele Harten & Annette M Kim & J Cressica Brazier, 2021. "Real and fake data in Shanghai’s informal rental housing market: Groundtruthing data scraped from the internet," Urban Studies, Urban Studies Journal Limited, vol. 58(9), pages 1831-1845, July.
    4. Paul M. Anglin & Yanmin Gao, 2023. "Value of Communication and Social Media: An Equilibrium Theory of Messaging," The Journal of Real Estate Finance and Economics, Springer, vol. 66(4), pages 861-903, May.
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    6. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
    7. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    8. Nafeesa Yunus, 2023. "Co‐movement among oil, stock, bond, and housing markets: An analysis of U.S., Asian, and European economies," International Review of Finance, International Review of Finance Ltd., vol. 23(2), pages 393-436, June.
    9. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    10. Yunus, Nafeesa, 2020. "Time-varying linkages among gold, stocks, bonds and real estate," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 165-185.

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