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The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.

This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.

The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively.

The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions.

This is the first paper applying Google search query data to the commercial real estate sector.

Recent studies on foreign investment in US real estate provide evidence that fluctuating exchange rates are likely to reduce the potential gains from international diversification by making these investments more risky. However, other research has suggested that forward currency contracts may provide an effective mechanism for offsetting exchange rate volatility and thus restore the diversification benefits. Examines the use of forward contracts as a means of hedging the currency risk associated with foreign investment in US real estate. Indicates that, although continuous hedging of US real estate with forward contracts allows foreign investors to eliminate most of the risk induced by currency instability, the improvements are insufficient to produce diversification gains for all foreign investors in the context of meanvariance portfolio performance.

The purpose of this paper is to understand what affects the liquidity of individual commercial real estate assets over the course of the economic cycle by exploring a range of variables and a number of time periods to identify key determinants of sale probability.

Analyzing 12,000 UK commercial real estate transactions (2003 to 2013) the authors use an innovative sampling technique akin to a perpetual inventory approach to generate a sample of held assets for each 12 month interval. Next, the authors use probit models to test how market, owner and property factors affect sale probability in different market environments.

The types of properties that are most likely to sell changes between strong and weak markets. Office and retail assets were more likely to sell than industrial both overall and in better market conditions, but were less likely to sell than industrial properties during the downturn from mid-2007 to mid-2009. Assets located in the City of London more likely to sell in both strong and weak markets. The behavior of different groups of owners changed over time, and this indicates that the type of owner might have implications for the liquidity of individual assets over and above their physical and locational attributes.

Variation in sale probability over time and across assets has implications for real estate investment management both in terms of asset selection and the ability to rebalance portfolios over the course of the cycle. Results also suggest that sample selection may be an issue for commercial real estate price indices around the globe and imply that indices based on a limited group of owners/sellers might be susceptible to further biases when tracking market performance through time.

The study differs from the existing literature on sale probability as the authors analyzed samples of transactions drawn from all investor types, a significant advantage over studies based on data restricted to samples of domestic institutional investors. As well, information on country of origin for buyers and sellers allows us to explore the influence of foreign ownership on the probability of sale. Finally, the authors not only analyze all transactions together, but the authors also look at transactions in five distinct periods that correspond with different phases of the UK commercial real estate cycle. This paper considers the UK real estate market, but it is likely that many of the findings hold for other major commercial real estate markets.

Divides investors into two types: tax‐paying investors and tax‐exempt pension funds. Tax‐paying investors will worry about the expected after‐tax variance of return on real estate, while tax‐exempt pension funds will worry about the expected pre‐tax variance of return. States this is an important observation because the after‐tax variance of return is apt to be significantly less than the pre‐tax variance of return (particularly during the early 1980s when tax‐paying investors were able to use real estate losses to offset other income). The model developed by the author suggests this reduced expected after‐tax variance of return helps explain the seemingly irrational construction that took place in US office markets during the 1980s.

Both neoclassical and Keynesian economists have widely favored the use of equilibrium models to understand economic activity, but dramatic periods of change such as the current global economic downturn are poorly understood by assuming equilibrium. The economist Joseph Schumpeter tried to inject dynamism and disequilibrium into economic models by arguing for the role of entrepreneurs in creating microeconomic change, and for examining long-term macroeconomic change as represented in business cycles. No economist, including Schumpeter, has ever connected these two approaches to change and these approaches are not typically used as alternative and complementary ways of viewing transformation over time. We suggest that these theories can be connected in a “mesoeconomic” institutional analysis rooted in economic sociology; we demonstrate this connection by examining the US commercial building industry. This industry has changed in qualitatively distinct ways over the past two centuries in what we call market orders, economic orders sometimes lasting for decades or more. In each market order, entrepreneurs of different sorts are able to flourish and push forward institutional changes that result in long-term economic shifts. Credit and finance have been pivotal influences in each market order, a factor supporting Schumpeter's focus on entrepreneurial action and speculation and one not largely discussed today. We view the recent disruption of financial markets as a signal of the destruction of a reigning market order.

The article analyzes the effects of the coronavirus disease 2019 (COVID-19) pandemic on commercial real estate prices, with a particular focus on European markets.

The authors start by highlighting caveats to bear in mind when referring to direct real estate indices. The authors then analyze the behavior of commercial real estate prices during the pandemic, emphasizing differences across property types. For that purpose, the authors use data for both direct and listed real estate and further discuss changes in the main factors affecting commercial real estate pricing. The article then turns to discussing the likely trajectory of commercial real estate prices in the future.

The authors report that retail and hospitality properties and to a lesser extent office buildings have been affected the most by COVID-19, while the residential and industrial sectors have been less affected by the crisis. The authors maintain that the future trajectory of prices will vary across sectors and that the type and location of assets will become increasingly important in their valuation.

This paper provides for a better understanding of the behavior of commercial real estate prices during the COVID-19 pandemic.

The purpose of this paper is to fill a gap in the real estate academic literature by defining the essence of real estate in smart urban environments. Space has traditionally been a silent component of real estate. Smart technologies powered by Ubi-comp are turning space into an active part of real estate, which represents a paradigm shift for commercial real estate. This shift requires new concepts and tools to analyse and model real estate in smart cities.

The paper defines the notions of smart space and smart real estate. Several concepts and tools are formulated, starting with a model of space users in smart cities, called the Cyber-Dasein inspired by Heidegger’s existential phenomenology of space.

The paper then analyses smart space’s attributes and proposes several metrics for commercial real estate in smart environments. After introducing three regression models for constructing a price index of smart real estate, the paper concludes by advocating that commercial real estate take an active role in the current debate about smart cities.

The paper does not provide any empirical analysis of smart real estate.

Smart environments offer real estate a unique opportunity to set up methodologies, concepts and tools for new properties in new cities. Now is the time to think carefully about the impact smart technologies will have on commercial properties before other stakeholders (in particular smart cities vendors and multinational technology giants) have fully modelled smart space and its nexus with smart real estate.

This paper is the first paper to provide a conceptual framework for the analysis of commercial real estate in smart cities.

Real estate is an asset that is traded in highly segmented, illiquid and informationally inefficient local markets. A short sale in real estate is almost infeasible and therefore impedes informed rational arbitrageurs to trade against mispricing. Thus, real estate returns are prone to sentiment-driven behaviours. Will the impacts on asset returns be identical for different types of sentiment?

This study argues that not all sentiment effects are created equal. Using the bounds test of the autoregressive distributed lag (ARDL) models, this paper examines how occupier sentiment versus investor sentiment contributes to the short-run and long-run dynamics of commercial real estate returns in Australia.

The empirical evidence suggests that investor sentiment and occupier sentiment influence return asymmetrically after macroeconomic conditions are controlled for.

The sectoral analysis further reveals that sector-specific sentiment plays a significant role in explaining commercial real estate returns. Furthermore, notable improvement is found in producing more accurate prediction in returns, given that measures of occupier and investor sentiment are appropriately specified in the forecast.

This study is novel in the sense that it acknowledges the impacts of occupiers' and investors' sentiment may be fundamentally different. The unique innovation and contribution of this study to behavioural finance literature are based on a new dataset from the Royal Institute of Chartered Surveyors which includes a survey-based measure of investor sentiment and occupier sentiment.

Examines the way in which the 1986 Tax Reform Act affected the status of US real estate as a tax shelter. Demonstrates that because tax benefits previously were an important component of total returns from income producing real estate, its immediate effect is to reduce after‐tax returns from real estate. Argues that if market values fall and rents rise, however, after‐tax returns from income producing real estate should be sufficient to attract individual US investors. Adds that the 1986 Tax Act should attract more US pension funds and foreign investors to US investment real estate given that the buying advantage of US investors has been reduced.

This paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories in Hong Kong were analyzed from February 1993 to February 2019 to test whether volatility clusters are present in the real estate market. Real estate price determinants were also investigated.

Autoregressive conditional heteroscedasticity–Lagrange multiplier test is used to examine the volatility clustering effects in these four kinds of real estate. An autoregressive and moving average model–generalized auto regressive conditional heteroskedasticity (GARCH) model was used to identify real estate price volatility determinants in Hong Kong.

There was volatility clustering in all four kinds of real estate. Determinants of price volatility vary among different types of real estate. In general, housing volatility in Hong Kong is influenced primarily by the foreign exchange rate (both RMB and USD), whereas commercial real estate is largely influenced by unemployment. The results of the exponential GARCH model show that there were no asymmetric effects in the Hong Kong real estate market.

This volatility pattern has important implications for investors and policymakers. Residential and commercial real estate have different volatility determinants; investors may benefit from this when building a portfolio. The analysis and results are limited by the lack of data on real estate price determinants.

To the best of the authors’ knowledge, this paper is the first study that evaluates volatility in the Hong Kong real estate market using the GARCH class model. Also, this paper is the first to investigate commercial real estate price determinants.