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Past Experiences and Investment Decisions: Evidence from Real Estate Markets

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Abstract

This paper investigates how market participants form risk perspectives through a sequence of information shocks. Guided by a theoretical Bayesian learning model, we exploit a natural experiment afforded by the fracking boom in Pennsylvania in the late-2000s. We empirically examine whether familiarity with historical conventional gas explorations affects the willingness to pay for houses near fracking wells. We find the local real estate market is very efficient with participants rapidly collecting and processing market–relevant new information. We also find that participants discount historical events and rely on current information to estimate the risk of a change in market conditions.

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Notes

  1. Gold, Russell and Tom McGinty, “Energy Boom Puts Wells in America’s Backyards” The Wall Street Journal, October 25, 2013.

  2. “Fracking: So where’s the economic boom that was promised?” https://www.dispatch.com/article/20140128/NEWS/301289852

  3. See Viscusi et al. (1997) for a more general discussion of the Bayesian learning model.

  4. Although there are approximately 1700 Zip Codes tabulation areas in PA as of 2018, approximately 55% of the housing stock in PA are in those 200 Zip Codes. “2010 Census of Population and Housing” https://www.census.gov/prod/cen2010/cph-2-40.pdf.

  5. We report the annualized gain as the monthly gain multiplied by twelve months. Monthly gains are estimated by taking the sum of the corresponding base term coefficients, the interaction term coefficients, and multiplying the net return by the median house price in our data ($209,800).

  6. The median sale prices in this sample is 158.89 thousand dollars.

  7. For example, one of the accidents has the following description in the DEP report: “The well erupted late Tuesday, sending thousands of gallons of chemical-laced and highly saline water spilling from the drill site, heading over containment berms, racing toward a tributary of a popular trout-fishing stream and forcing seven families nearby to temporarily evacuate their homes.”

  8. Conventional selection criteria (AIC, HBIC, and SBIC) suggest that the most appropriate number of lag months is zero. Standard errors are calculated using two-way clustering by county–year.

  9. We also tested several alternative models control for county-level household income, the count of fracking wells in a Zip Code, the AGI and Wage and Salary Income. None of those additional specifications drastically altering the results reported in the current Table 6. Results from the alternative specifications are available upon request.

  10. The model specifications used to produce Column (1)-(2) corresponds to those in Column (1) Table 4 because local characteristics data at the Zip Code level are not available for this period.

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Acknowledgements

We thank Kun Bi, Chris Cunningham, Kris Gerardi, and seminar participants at the 2020 FSU-UF-UCF Real Estate Symposium, the Pennsylvania State University, the American Real Estate Society’s 33rd Annual Meeting, the 2016 Southern Finance Association annual meeting, and the 2018 Energy and Commodity Finance Conference for their helpful comments and discussions. We sincerely thank the helpful comments made by our anonymous referees. Special thanks to Gary Frederick for providing background information about the PA real estate market during the study period. All errors are our own.

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  1. Pennsylvania State University, Pennsylvania, USA

    Brent W. Ambrose

  2. Clemson University, South Carolina, US

    Lily Shen

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  1. Brent W. Ambrose
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Correspondence to Lily Shen.

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Ambrose, B.W., Shen, L. Past Experiences and Investment Decisions: Evidence from Real Estate Markets. J Real Estate Finan Econ 66, 300–326 (2023). https://doi.org/10.1007/s11146-021-09844-2

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