Please use this identifier to cite or link to this item:
https://repository.iimb.ac.in/handle/123456789/7730
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jalan, Ekta | |
dc.contributor.author | Panchapagesan, Venkatesh | |
dc.contributor.author | Venkataraman, Madalasa | |
dc.date.accessioned | 2017-04-05T06:12:32Z | |
dc.date.accessioned | 2019-05-27T08:27:31Z | - |
dc.date.available | 2017-04-05T06:12:32Z | |
dc.date.available | 2019-05-27T08:27:31Z | - |
dc.date.issued | 2017 | |
dc.identifier.other | WP_IIMB_537 | - |
dc.identifier.uri | http://repository.iimb.ac.in/handle/123456789/7730 | - |
dc.description.abstract | We examine whether internet search intensity, as captured by Google s Search Volume Index (SVI), predicts house price changes in an emerging market like India. Emerging markets have low internet penetration and high information asymmetry with a dominant unorganized real estate market. Like in developed markets such as the US, we find that internet search intensity strongly predicts future house price changes. A simple rebalancing strategy of buying a representative house in the city with the greatest change in search intensity and selling a representative house in the city with the smallest change in search intensity each quarter yields an annualized excess (over risk-free government t-bills) return of 4 percent. | |
dc.language.iso | en_US | |
dc.publisher | Indian Institute of Management Bangalore | - |
dc.relation.ispartofseries | IIMB Working Paper-537 | - |
dc.subject | Google trends search volume index | - |
dc.subject | House price index | - |
dc.subject | Search intensity | - |
dc.subject | Housing price forecasting | - |
dc.title | Does internet search intensity predict house prices in emerging markets?: a case of India | |
dc.type | Working Paper | |
dc.pages | 27p. | |
Appears in Collections: | 2017 |
Files in This Item:
File | Size | Format | |
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WP_IIMB_537.pdf | 414.93 kB | Adobe PDF | View/Open |
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