Liquidity in commercial property markets: deconstructing the transaction process

This paper draws from a wider research programme in the UK undertaken for the Investment Property Forum examining liquidity in commercial property. One aspect of liquidity is the process by which transactions occur including both how properties are selected for sale and the time taken to transact. The paper analyses data from three organisations; a property company, a major financial institution and an asset management company, formally a major public sector pension fund. The data covers three market states and includes sales completed in 1995, 2000 and 2002 in the UK. The research interviewed key individuals within the three organisations to identify any common patterns of activity within the sale process and also identified the timing of 187 actual transactions from inception of the sale to completion. The research developed a taxonomy of the transaction process. Interviews with vendors indicated that decisions to sell were a product of a combination of portfolio, specific property and market based issues. Properties were generally not kept in a “readiness for sale” state. The average time from first decision to sell the actual property to completion had a mean time of 298 days and a median of 190 days. It is concluded that this study may underestimate the true length of the time to transact for two reasons. Firstly, the pre-marketing period is rarely recorded in transaction files. Secondly, and more fundamentally, studies of sold properties may contain selection bias. The research indicated that vendors tended to sell properties which it was perceived could be sold at a ‘fair’ price in a reasonable period of time.

Expert judgement in the processes of commercial property market forecasting

In this paper we investigate the role of judgement in the formation of forecasts in commercial real estate markets. Based on interview surveys with the majority of forecast producers, we find that real estate forecasters are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self-censorship’ or are ‘censored’ following in-house consultation. It is concluded that the forecasting process is more complex than merely carrying out econometric modelling and that the impact of the influences within this process vary considerably across different organizational contexts.

Re-thinking commercial real estate market segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

An analysis of commercial real estate returns: an anatomy of smoothing in asset and index returns

In this article, we investigate the commonly used autoregressive filter method of adjusting appraisal-based real estate returns to correct for the perceived biases induced in the appraisal process. Many articles have been written on appraisal smoothing but remarkably few have considered the relationship between smoothing at the individual property level and the amount of persistence in the aggregate appraisal-based index. To investigate this issue we analyze a large sample of appraisal data at the individual property level from the Investment Property Databank. We find that commonly used unsmoothing estimates at the index level overstate the extent of smoothing that takes place at the individual property level. There is also strong support for an ARFIMA representation of appraisal returns at the index level and an ARMA model at the individual property level.