Real Estate Price Dynamics Platform
Alex van de Minne, Research Scientist and Platform Lead
David Geltner, Professor & Faculty Advisor
The MIT Center for Real Estate’s Price Dynamics Platform (PDP or Platform) serves at the intersection between the Academy and the Real Estate Industry. We utilize the newest data combined with cutting-edge and interdisciplinary econometric techniques from many fields not just real estate, such as (space) navigation, weather forecasts, and healthcare. We develop innovative tools that potentially have a global impact for real estate stakeholders, resulting in a more transparent real estate market.
Economic models used for decisions about mortgage prepayments and defaults; measures of national and international wealth; the total value of collateral behind a portfolio of mortgage loans; real estate derivatives and asset allocations – among many other applications – are all informed by models of real estate valuation and price dynamics. To date, the real estate industry has been characterized by a shortage of data and econometric models that suit its specific needs.
Our research focuses on developing applications and models used for real estate price indices for markets with scarce observations; forecasting prices; and for the mass valuation of real estate using machine learning.
A Bayesian Structural Time Series Approach to Constructing Rent Indexes: An Application to Indian Office Markets
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.
The Effect of Green Retrofitting on US Office Properties: An Investment Perspective
Buildings are responsible for over one-third of all resource consumption, greenhouse gas emissions, and energy consumption. Commercial buildings represent approximately half of that total. In mature economies such as the United States, new construction annually represents only a small fraction of the existing stock of buildings. Hence, retrofitting of commercial buildings, through major renovation projects, is extremely important for sustainability within the built environment. Most studies of green building economics have focused on new construction. This paper is one of the first to focus specifically on retrofit green. Based on a larger sample than most previous studies of new construction, we quantify the magnitude of value enhancement created by green retrofit of US office buildings. This paper is also the first to consider the subsequent investment price dynamics effects of such sustainability. Methodologically, we introduce an innovative way to control for other effects to isolate the value impact and the investment risk and return impact of green retrofitting. We do this by applying a repeat-sales model of only (and all) buildings which will ultimately be retrofitted (in our sample). By using new real estate price indexing methodology, namely a structural time series model employing a hierarchical repeat-sales (HRS) specification, we can build statistically rigorous comparative price indexes of retrofit green, versus non-green, office buildings in the US, quarterly for the 2005-2014 period, even with relatively scarce transaction price data (441 pairs). We find substantial value enhancement in green retrofit projects (between 10% and 20%), and we find evidence that retrofitted green buildings provide investors with lower asset price volatility. But during and just after the Financial Crisis the premium dropped temporarily to near zero, suggesting that the demand for green property investment is income-elastic.
Revisions in Granular Repeat Sales Indices
Price indices based on repeat sales are the most widely used type of real estate index based on asset transaction prices. But such indices are particularly prone to revision. When a new period of transaction data becomes available and is used to update the repeat sales model, all past index values can potentially be revised. These revisions are especially problematical for commercial real estate (as compared to housing), because commercial transactions are relatively scarce and properties are heterogeneous, reducing estimation precision. From a methodological perspective, the magnitude of expected revisions is a particularly useful measure of the quality of the empirical index, as it directly reflects both the precision of the index and its practical usefulness in economic and business applications, since revisions themselves are problematical in practice. This paper focuses on random revisions for indexes in thin, commercial property markets, the type of market that is most challenging for empirical price indexing. We present multiple specifications of the repeat sales model, seeking to reduce revisions. With the objective of minimizing the expected magnitude of revisions, among the specifications we explore, the best result obtains from an index methodology that specifies the periodic returns as a first order autoregressive process, that also uses the periodic returns of an aggregate index as an explanatory variable for more granular indices, and that allows the variance parameters of the signal and the noise to be time-varying. In our small-sample test cases, this model reduces overall index revisions by more than 50%.