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On the Impact of Infrastructure Improvement on Real Estate Property Values: Evidence from a Quasi-natural Experiment in an Emerging Market

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Abstract

Prior studies show infrastructural improvements impact property values positively. The effect is often reflected soon after the announcement and continues until the project is complete. These studies, however, are primarily set in developed countries. Emerging markets pose unique risks where uncertainty around implementation and funding could dampen these positive effects significantly. We utilize a quasi-natural experiment around an inner-city road redesign and improvement project in Bangalore, the fastest growing city in India. We exploit the difference-in-difference regression approach to examine the project’s impact on residential and commercial property values around two sets of geographically proximate roads, one of which was chosen for the redesign. Unlike in developed countries, we find that property values are unaffected by the announcement when uncertainty is the highest but start reflecting the positive value of the infrastructure once construction starts and show significant gains upon completion. Our findings carry important policy implications for structuring value capture strategies in emerging markets.

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Notes

  1. S.U.R.E. is the acronym for "Specifications for Urban Roads Execution.".

  2. Kemp and Mollard (2011) examine similar issues for a more developed market like Australia.

  3. Hack (2002) suggested that the association between public transport investment and land value should be evaluated at multiple points in the project management phase.

  4. Cushman and Wakefield (2014, 2015, and 2016).

  5. See Hans (2013) and L J Hooker (2013).

  6. See Pandey (2016).

  7. 1 USD = 60.936 INR as per average rates in 2013, when Tender S.U.R.E. was announced (https://www.taxesforexpats.com/expat-tax-advice/historical-fx-rates.html).

  8. Sea Ray (2017) and DHNS (2017) outlining Tender S.U.R.E. project delays.

  9. See Zabel, J. E., & Kiel, K. A. (2000) for an exhaustive overview of variables that may be used for hedonic housing price models.

  10. Alexa is a web traffic analysis company that ranks websites based on popularity and traffic for each sector/keyword. Alexa ranks sites based primarily on tracking a sample set of Internet traffic—users of its toolbar for popular web browsers.

  11. 1 USD averaged around INR 55.911 in 2012, 60.936 in 2013, 63.469 in 2014, 66.768 in 2015 and 69.956 in 2016 (https://www.irs.gov/individuals/international-taxpayers/yearly-average-currency-exchange-rates and https://www.taxesforexpats.com/expat-tax-advice/historical-fx-rates.html).

  12. For brevity, we do not present the summary statistics only for the pre-project time period.

  13. For a brief review of the hedonic pricing model and the related literature, see Yen et al., (2018).

  14. We use area and the squared value of the area to account for potential non-linearity in the relationship between size and property values, but our results are qualitatively similar.

  15. We try alternative time windows instead of six months, but our results remain similar.

  16. Exp (coefficient of Log Distance to Metro * 1 km) from Tables 4 and 5 represents the factor by which the value of properties situated away by 1 km deviates from the value of properties closest to the rail metro. For residential rentals, the discount is exp (-0.096*1) = 0.90, or 10% lesser than properties closest to the rail metro.

  17. Using exp (coefficient of Bedrooms * 1 room) from Table 4 minus 1.

  18. Exp (-0.17) = 0.84 (or 16% lower on TS roads than on non-TS roads).

  19. See Table 3 for more details.

  20. Most of these studies (for example, Agostini and Palmucci, 2008; Golub et al., 2012) and Yen et al. (2018) document partial adjustment of property values on announcement due to uncertainty.

  21. RBI’s (Reserve Bank of India, India’s Central Bank) price Index for Bangalore City shows increasing real estate prices, which indicates that landlords may have the bargaining power (short supply, rising prices etc.), and may exploit the intervention through higher rents even at the early stages of the construction. However, we cannot verify this conjecture because of insufficient data. Future research may explore this in more detail.

  22. Exp (Coefficient of TS*Post-phase dummy) from Residential Rent regression in Table 9.

  23. We thank Special issue editor Gangzhi Fan for this suggestion.

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Acknowledgements

We thank Gangzhi Fan (the editor), Karthik Nagendra, seminar participants at the 2019 Asian Real Estate Symposium, an anonymous referee for their comments and suggestions, and Ekta Jalan for her excellent research assistance. We acknowledge Times Internet Limited and Info Edge (India) Limited for help regarding the listing data used in the paper. All errors remain our own.

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  1. Department of Finance, School of Business, University of Connecticut, Storrs, CT, 06269, USA

    Chinmoy Ghosh

  2. Finance and Accounting Area, Indian Institute of Management, Bangalore, India

    Venkatesh Panchapagesan & Madalasa Venkataraman

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  1. Chinmoy Ghosh
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Correspondence to Venkatesh Panchapagesan.

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Ghosh, C., Panchapagesan, V. & Venkataraman, M. On the Impact of Infrastructure Improvement on Real Estate Property Values: Evidence from a Quasi-natural Experiment in an Emerging Market. J Real Estate Finan Econ 68, 103–137 (2024). https://doi.org/10.1007/s11146-022-09938-5

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