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Online Information Search, Market Fundamentals and Apartment Real Estate

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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.

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Notes

  1. See http://www.businessweek.com/articles/2012-08-09/google-central-banks-new-economic-indicator. Retrieved February 9, 2014.

  2. See “Getting real-time: Internet economic indicator”s, June 14 2011 issue of The Economist.

  3. See http://forbesindia.com/printcontent/22482. Retrieved February 9, 2014.

  4. See http://libertystreeteconomics.newyorkfed.org/2012/01/forecasting-with-internet-search-data.html. Retrieved February 9, 2014.

  5. See Joseph et al. (2011) and Ghysels, Plazzi, Torous, and Valkanov (2012) for further details

  6. See Bolster et al. (2012) and Neumann and Kenny (2007)

  7. The MSAs in our sample include: Phoenix (AZ), Los Angeles (CA), San Diego (CA), San Francisco (CA), Denver (CO), Washington (DC), Fort Lauderdale (FL), Orlando (FL), Tampa (FL), West Palm Beach (FL), Atlanta (GA), Chicago (IL), Baltimore (MD), Bethesda (MD), Charlotte (NC), New York (NY), Portland (OR), Austin (TX), Dallas (TX), Fort Worth (TX), and Houston (TX).

  8. They measure illiquidity as the ratio of the absolute daily return to trading volume summed over a month.

  9. See https://support.google.com/trends. Retrieved February 9, 2014.

  10. See http://support.google.com/insights/bin/answer.py?hl=en&answer=94792. Retrieved February 9, 2014.

  11. Rental rate and operating expense data for San Francisco for the 2010 Q3 was imputed by straight-lining the adjacent quarter values. The I4S data from Miami was applied to Fort Lauderdale, FL. The Washington DC I4S data was applied to Bethesda, MD. Also, the same I4S data was repeated for Dallas, TX and Fort Worth, TX metros.

  12. Ticker symbols of REITs included are: AIV, AVB, BMR, CPT, CUZ, DEI, EQR, EQY, ESS, FRT, HME, MAA, PIR, PPS, UDR, VNO and WRE.

  13. See appendix Note 1 for details.

  14. Details are available upon request.

  15. We also examine the model that replaces the average of the last 4 quarters of the I4S by the quarterly averaged I4S. The results are similar.

  16. Although not reported, results from such models are similar and are available upon request.

  17. See Derwall et al. (2009) for the expected coefficient signs.

  18. Detailed results are available upon request.

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Acknowledgments

An earlier version of this paper won the best manuscript award at the American Real Estate Society (ARES) annual conference in Seattle (2011). Authors are thankful to Jonathan Wiley, Karen Gibler and the anonymous reviewers for their intellectual contributions. The paper has benefitted from the participants at the AREUEA mid-year conference (2011, Washington DC). Feedback from the following persons is highly appreciated: Vivek Sah, Julian Diaz III, Tim Riddiough, Jay Hartzell, Alan Tidwell, Philip Seagraves, Julia Freybote, Dongshin Kim, SungHan Ro, Frank Gyamfi-Yeboah, Kenneth Soyeh, Patrick Smith, Alan Ferguson, Yu Liu, Paul Seguin.

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Authors and Affiliations

  1. Ecole hôtelière de Lausanne, Route de Cojonnex 18, 1000, Lausanne 25, Switzerland

    Prashant Das

  2. Georgia State University, 35 Broad Street Suite 1404, Atlanta, GA, 30303, USA

    Alan Ziobrowski

  3. University of Nevada Las Vegas, BEH 530A, 4505S Maryland Pkwy, Las Vegas, NV, 89154, USA

    N. Edward Coulson

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  1. Prashant Das
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  2. Alan Ziobrowski
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  3. N. Edward Coulson
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Correspondence to Alan Ziobrowski.

Appendix

Appendix

Grenadier (1995) andVoith and Chrone (1988) detail the association between vacancy rates and natural vacancy rates. The current deviation of vacancy rates from the natural vacancy rates determines the degree to which a real estate market is out of equilibrium. The observed vacancy rate (VAC) in a market i during quarter t is the sum of the natural vacancy rate (Vn) and the deviation from it. Local fixed effects and time-varying macroeconomic factors determine the natural vacancy rate. For estimation purposes, the macroeconomic factors are shown as a polynomial of time. Grenadier (1995) proposes a fourth degree polynomial (i.e., j = 4). The deviation ε i,t exhibits persistence (ρ) such that it is an MA(1) process.

$$ \begin{array}{c}\hfill {\mathrm{V}\mathrm{AC}}_{\mathrm{i},\mathrm{t}}={\mathrm{V}}_{\mathrm{n},\mathrm{i},\mathrm{t}}+{\upvarepsilon}_{\mathrm{i},\mathrm{t}}\hfill \\ {}\hfill {\mathrm{V}}_{\mathrm{n},\mathrm{i},\mathrm{t}}={\upalpha}_0+f\left(\mathrm{t}\right)\hfill \\ {}\hfill f\left(\mathrm{t}\right)={\displaystyle \sum_{\mathrm{j}=1}^{\mathrm{J}}{\upalpha}_{1,\mathrm{j}}\;.\;{\mathrm{t}}^{\mathrm{j}}}\hfill \\ {}\hfill {\upvarepsilon}_{\mathrm{i},\mathrm{t}}={\uprho}_{\mathrm{i}}\;.\;{\upvarepsilon}_{\mathrm{i},\mathrm{t}-1}+{\mathrm{u}}_{\mathrm{i},\mathrm{t}}\hfill \end{array} $$
Fig. 3
figure 3

Comparing Old versus New I4S Series. Notes: Both graphs depict the Google Insights for Search (I4S) Indices extracted over different Time-frames. The Solid line depicts the new I4S sub-category series titled “Apartments and Residential Rentals”. The dotted line depicts the series from a corresponding older sub-category series (based on thee taxonomy was phased-out in 2011) titled “Rental Listings & Referrals”. The two time-series shown in the graph exhibit a correlation of 92.5 %

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Fig. 4
figure 4

Comparing New I4S Series extracted over Different Time-Frames. Notes: Both graphs depict the Google Insights for Search (I4S) Index in ‘Apartments and Residential Rentals’ sub-category extracted over different time frames. The data for solid lines is extracted for January 2004 – December 2011 time-frame. The data for dotted lines is extracted for January 2004- December 2010 Time Windows. The two time-series exhibit a correlation of 98.7 %

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Das, P., Ziobrowski, A. & Coulson, N.E. Online Information Search, Market Fundamentals and Apartment Real Estate. J Real Estate Finan Econ 51, 480–502 (2015). https://doi.org/10.1007/s11146-015-9496-1

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