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

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.