Understanding the Digital Platform Economy: Effect of Airbnb on Housing in Indian Cities

ABSTRACT

This article answers the question: What is the effect of Airbnb on rents and housing prices? Based on theory and empirical evidence, we expect that in cities where Airbnb is active, rental accommodation and housing would be relatively more expensive. This is because we assume that such units were withdrawn from the housing market and excess capacity of houses can be used. We distinguish between the rents of one- , two- , and three-bedroom apartments, in addition to housing prices in major Indian cities. Accounting for the endogeneity of Airbnb density, we find that the Airbnb density has a significant effect in terms of raising rents of apartments of different sizes as well as increasing housing prices. The magnitude of our estimates implies an increase of up to 0.08% in the rent of two-bedroom apartments, 0.14% in the rents of three-bedroom apartments, and 0.39% in housing prices per square foot, for every 1-percentage-point increase in Airbnb density. These effects are higher than those found in some existing studies, but lower than those found by others. The policy implications of the research and caveats of the data and research are summarized.

KEYWORDS:

• Housing—Indian cities
• Airbnb—Indian cities
• housing affordability—rents in Indian cities
• digital platform economy—Indian cities
• Airbnb—rents and housing prices

Acknowledgments

I sincerely thank K. C. Smitha and Shivakumar Nayka for their help with managing the data set for this article. I thank Paavo Monkkonen at the University of California Los Angeles for guiding me to the Global Human Settlements data set. I thank the participants at the ADB conference on platform economies in Asia, where an early version of this article was presented, for their comments. I thank the anonymous reviewers and the Editor of the journal for their comments, which have added value to the article. Any errors remain mine.

Disclosure Statement

No potential conflict of interest was reported by the author.

Notes

1. Gandhi et al. (2020) attributes this to rent control, poor public services, and inadequate judiciary.

2. This is defined as annual gross rent divided by house price, and is expressed as a percentage.

3. All prices in Indian rupees have been converted into U.S. dollars at the rate of 1 U.S. $ = INR 72.59, as per the official exchange rate at fbil.org.in as of February 15, 2021.

4. https://www.numbeo.com/cost-of-living/city_price_rankings?itemId=100

5. Gajewski and Li (2015) show how the internet reduced information asymmetry in the French financial market.

6. As per the Census of India 2011, nearly two thirds of India’s urban population is in the workforce (15–59 years of age). It should be noted that there are accommodations for younger floating population in Indian cities who wish to stay there for a job or for education, similar to rental units; these accommodations are known as PGs (paying guest accommodations) and are usually part of houses, but they are more informal in nature. To date, no systematic data exist on PGs.

7. Of course, it would be good to have information on the number of tourists who visit a city, as that likely determines the share of both hotels and Airbnb, but these data are not available at the city level.

8. Typical is a term used by Google for the rentals of various kinds of star hotels. When one clicks on hotels by star rating, and then clicks on what you’ll pay, Google lists a typical price, which is what is used here (the sample of cities is much smaller for four- and five-star hotels, since these hotel types are not found in all cities. So in the interests of keeping the sample size reasonable, we do not consider the rents of four- and five-star hotels as instruments).

9. To convert the PPP$ to INR, an exchange rate of PPP1$ = INR 18.38 is used.

10. The AirDNA active listings are current as of September/October 2020.

11. See Gandhi et al. (2020) for an analysis of the effect of rent control at the state level on vacant houses in Indian cities.

12. Cities above the 100,000-population threshold are assigned a value of 0 for this distance variable.

13. We computed a Pearson’s correlation matrix on the variables of interest. The results are within reasonable ranges. For reasons of space, we omit the correlation matrix from the article.

14. More details can be found at https://www.airbnb.co.in/help/article/1380/responsible-hosting-in-india, which lists the rules that represent responsible hosting. They include Airbnb policy on reminding guests about keeping noise down, pets, smoking, and so forth. Implementation of such guidelines and norms requires cooperation from both guests and hosts.

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Kala Seetharam Sridhar

Kala Seetharam Sridhar is Professor at the Institute for Social and Economic Change (ISEC), Bengaluru, India. During May-June 2021, she was a visiting Fulbright Nehru Fellow at the University of California Los Angeles Luskin School of Public Affairs, Department of Urban Planning. She has been a visiting scholar at UNU-WIDER, Helsinki, Finland, multiple times. She has authored or edited 9 books published by Oxford University Press, Palgrave Macmillan, Springer, Routledge and Sage. She has published in journals which include Regional Science and Urban Economics, Urban Studies, Applied Economics, Area Development & Policy and Environment and Environment and Urbanization ASIA, among others. She is on the Editorial board of journals such as Area Development and Policy, Urban Science, Urban India, and is frequently consulted by the Asian Development Bank for research on urbanization. She is consistently among the top 10% of authors globally on the Social Science Research Network.