A Bayesian Structural Time Series Approach to Constructing Rent Indexes: An Application to Indian Office Markets

34 Pages Posted: 14 Nov 2017

See all articles by Sheharyar Bokhari

Sheharyar Bokhari

Massachusetts Institute of Technology (MIT)

David Geltner

Massachusetts Institute of Technology (MIT); MIT Center for Real Estate

Alex Van de Minne

University of Connecticut - Department of Finance

Date Written: November 10, 2017

Abstract

We introduce new methodology for constructing real estate rent indices. Using unique data on contract rents from six Indian metropolitan markets, we pair subsequent rented units in the same building to create over 12,000 pseudo repeat rent pairs. We impose an autoregressive structure on the log rent returns in a structural time series variant of the repeat sales model widely used in real estate price indexing. We also allow for time-varying index signal and noise variance parameters. This method has several advantages, including low statistical estimation noise (even in small samples), fewer historical revisions, and the ability to capture changes in market volatility and its subsequent effect on the rent index and estimation error. Finally, we estimate the model using full Bayesian inference that gives the entire posterior distribution. The resulting indices are robust to property heterogeneity and omitted variables, and present well behaved quarterly depictions of the recent history of office market rents in the six cities.

Keywords: commercial real estate, Bayesian inference, time-varying parameter model, rent index

JEL Classification: R32, C01

Suggested Citation

Bokhari, Sheharyar and Geltner, David and Van de Minne, Alex, A Bayesian Structural Time Series Approach to Constructing Rent Indexes: An Application to Indian Office Markets (November 10, 2017). Available at SSRN: https://ssrn.com/abstract=3068857 or http://dx.doi.org/10.2139/ssrn.3068857

Sheharyar Bokhari (Contact Author)

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

David Geltner

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
Cambridge, MA 02139
United States

MIT Center for Real Estate ( email )

77 Massachusetts Avenue
Cambridge, MA 02139
United States

Alex Van de Minne

University of Connecticut - Department of Finance ( email )

School of Business
2100 Hillside Road
Storrs, CT 06269
United States

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