We introduce a hedonic price model which enables us to disentangle the value of a property into the value of land and the value of structure. For given reconstruction costs we are able to estimate the impact of physical deterioration, functional obsolescence and vintage effects on the structure...

This paper concerns the estimation of granular property price indices in commercial real estate and residential markets. We specify and apply a repeat sales model with multiple stochastic log price trends having a hierarchical additive structure: One common log price trend and cluster specific...

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...

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...

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...

This paper deals with unobserved heterogeneity in hedonic price models, arising from missing property and locational characteristics. In specific, commercial real estate is very heterogeneous, and data on detailed property characteristics are often lacking. We show that adding mutually...

In this paper we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will...

Combining granular data on temperatures across the continental US with micro-level commercial real estate (CRE) data from 1980 to 2020, we study the impact of exposure to extreme temperature shocks on investment performance of CRE at the individual asset level. We find that exposure to extreme...

Using several centuries of data on house prices from Amsterdam, the Netherlands, and seven key house price determinants, this paper shows that the historically recent increases in house prices over the past decades can be explained by 1) the increasing effect of interest rates and income on...

In this paper we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will...