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The purpose of this paper is twofold: the authors initially survey a sample of literature published after the Great Recession that address macroeconomic and commercial real estate forecasting methods related to the Great Recession and compare significant lessons learned, or lack thereof. The authors then seek to identify new models to improve the predictability of commercial real estate early warning signals regarding cyclical turning points which result in negative appreciation rates.

The authors develop a probit model to estimate quarterly probabilities of negative office appreciation returns using an alternative methodology to Tsolaco et al. (2014). The authors’ alternative method incorporates generally publicly available macroeconomic and real estate variables such as gross domestic product, office-related employment sectors, cap rate spreads, and commercial mortgage flow of funds into a probit model in order to estimate the probability of future quarterly negative office appreciation rates.

The authors’ models demonstrate the predictive power of macroeconomic variables typically associated with office demand. The probit model specification shows probabilities of negative office appreciations rates greater than 50 percent either as the quarterly office returns become negative, or in some cases several quarters before office returns become negative, for both the Great Recession and the recession occurring in the early 1990s. The models fail to show probabilities greater than 50 percent of negative office returns until after they occur for the recession in 2001. While this indicates need for further improvement in early warning models, the models do predict the more severe periods of negative office returns in advance, indicating the findings useful to real estate investors to monitor the changes in economic and real estate data identified as statistically significant in the results.

The Great Recession is a unique laboratory of significant contractions, recessions, and recoveries that challenge pre-recessionary real estate cycle models. The models provide guidance on which historical economic indicators are important to track, and gives a framework with which to calculate the probability that office prices are likely to decline. Because the models use macroeconomic indicators that are publicly available from at least one quarter in the past, the models or variations of them may provide real estate professionals with some indication of an impending decrease in office prices, even if that indication comes only one quarter in advance. Armed with this information, property owners, investors, and brokers can make more informed decisions on whether to buy or sell, and how sensitive their real estate transactions may be to timing.

The authors introduce several new models that examine the ability of historical macroeconomic indicators to provide early warning signals and identify turning points in real estate valuations, specifically negative office appreciation rates caused by the Great Recession. Using data from at least one quarter in the past, all the data in the models are publicly available (excluding National Council of Real Estate Investment Fiduciaries data) at the observed return quarter being predicted, which gives practitioners rational insights that can provide at least one source of guidance about the likelihood of an impending decrease in office prices.

Investment in non-listed real estate funds (NREFs) in an emerging economy like India has its own challenges that entail a detailed understanding of the risks. The purpose of this paper is to identify the key risk factors across the life cycle of a NREF, based on a considered feedback of various real estate fund management stakeholders. It is important for the investors and fund managers to appreciate these risk factors to make informed investment decisions.

The present study based on the literature survey and discussion with experts identifies 39 risk attributes, which were further summarized using factor analysis into a smaller set of factors impacting NREF returns (risk). The relative importance of each risk attribute was examined and ranked using the relative importance index (RII). Further, cluster analysis using Euclidian distance was used to partition these risk attributes in various segments depending on their importance.

The risk attributes are summarized as five risk factors, i.e. regulatory RISK, foreign direct investment risk, entry risk, business risk and project risk. Whereas the top five perceived risk attributes are investee/partner risk, project entitlement risk, title risk, legislative and regulatory risk and project execution risk.

This study has significance to the industry practitioners and the academic community in developing an understanding of the dynamic nature of risks across the life cycle of the NREFs in India and classifying them at the macro-meso-micro levels.

This paper is one of the first attempts to understand the risks impacting NREFs in India. It will help investors develop a better strategic understanding of the risks across the life cycle of an investment.

This paper aims to identify the economic stimulus measures that ensure stability of the Lithuanian housing market in the event of an economic shock.

The econometric analysis includes stationarity test, Granger causality test, correlation analysis, autoregressive distributed lag models and cointegration analysis using ARDL bounds testing.

The econometric modelling reveals that the housing price in Lithuania correlates with quarterly changes in the gross domestic product and approves that the cycles of the real estate market are related to the economic cycles. Economic stimulus measures should mainly focus on stabilizing the economics, preserving the cash and deposits of households, as well as consumer spending in the case of economic shock.

This study is beneficial for policy makers to make decisions to maintain stability in the housing market in the event of any economic shock.

The aim of the paper is to discuss developments in non-residential real estate in Greece.

Given the lack of existing literature, the authors start by discussing at length the data sources available, and analyzing the stylized facts of non-residential real estate activity in Greece. Finally, the authors examine the degree of covariation (using the index of concordance methodology) between non-residential real estate and the business cycle.

The results indicate that the structure of non-residential sector is highly fragmented into various sub-categories and at the initial stages of its developments, it was strongly affected by the preparations for the 2004 Athens Olympic Games. Finally, despite its small share of total GDP, non-residential real estate exhibits a significant degree of covariation with the business cycle.

The extracted information may be a useful resource for those interested in the developments in non-residential real estate in Greece and the covariation of key variables with the business cycle.

The paper constitutes a systematic research approach for the role of non-residential real estate in the Greek economic activity.

This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania.

The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations.

The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle.

Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary.

This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.

The purpose of this paper is to examine information and volatility linkages among real estate, equity, bond and money markets in Australia.

A novel rational expectations framework of financial contagion (Kodres and Pritsker, 2002), along with a combination of robust statistical methods including simple and dynamic correlations and generalized impulse response (Fereidouni et al., 2014) have been employed using data covering three dynamic pre-pandemic economic cycles, namely, global financial crisis (GFC) period, pre-pandemic housing boom and pre-pandemic housing downturn from 2008 (February) to 2019 (December).

Results reveal information linkages across real estate, equity, bond and money markets through correlations in return and volatilities of these series. Finding indicates that the three financial markets (equity, bond and money markets) are interdependent and integrated through information and volatility linkages during the GFC period and pre-pandemic housing downturn period. Financial markets have stronger associations with real estate market during pre-pandemic housing boom. The findings contribute to the general notion that the performances of three financial markets are closely related to the “boom” phase of the real estate cycle.

This research provides an extension of existing literature regarding the information and volatility contagion of the expanded set of core investment markets in Australia. The findings could assist household buyers and investors in designing strategic investment portfolios/hedging strategies and minimizing asset specific risks through diversification over short-term and long-term. In addition, results could support the maintenance, growth and development of a combination of competitive balanced investment markets including real estate, equity, bond and money markets in post-pandemic economy.

The purpose of this paper is to develop an operational framework with guidelines and lessons to improve the current real estate portfolio disposal procedures of freeholds, based on empirical evidence from the banking sector.

The empirical research is based on a comparative analysis of four case studies, representing approximately 80 per cent of the Dutch banking sector. The case studies comprise a systematic document review of corporate business and real estate strategies and semi-structured interviews with decision makers who steer the organisation’s corporate real estate (CRE) portfolio composition.

This research shows a strong relationship between organisation characteristics, legacy and strategy, disposal drivers and CRE disposal strategies. The weighing of drivers and order of steps in strategy execution strategies largely depend on organisational objectives.

This paper reports empirical findings from Dutch case studies. To generalise, further research is needed in different legal, financial and economic contexts and in other sectors. This paper suggests a more thorough study of the relationship between space-use efficiency and technological innovation implementation..

The framework proposes strategy improvements and a proactive approach to corporate real estate management (CREM) to create value through real estate portfolios.

This paper provides a thorough analysis of the CREM of the Dutch banking sector and is applicable to CREM in this and other sectors.

The purpose of this paper is to look at the lessons learnt from the previous real estate cycles based on a sample of investors, occupiers and academics and seek to understand the practical challenges the industry faces in the current cycle.

The paper summarises the results of qualitative research and interviews conducted and analysed by BNP Paribas Real Estate and Ipsos MORI.

The paper considers the crisis of 2008, its impact on performance, lessons learnt by the industry as a result and the future challenges. Whilst the industry felt well prepared to withstand future uncertainty and change, there was concern that subsequent generations of industry professionals will not be well equipped to deal with the pace and magnitude of change.

This is a practical study that seeks to place a greater emphasis on the drivers of market sentiment rather than focussing on quantitative forecasts.

There is much attention given to quantitative property market forecasts; however, there seems to be little appreciation of the need to evolve our process in today’s fast paced, structurally changing market which will behave differently to how it has in the past. Economic forecasts have received much criticism recently and these provide the basis for property market forecasts. The consideration of sentiment and the qualitative aspect of the future drivers of performance have never been so critical.

PropTech has shown important implications for real estate; however, it remains a poorly understood phenomenon in both academia and practice. This paper aims to advance the understanding of PropTech by disentangling what the real estate sector needs from technology and, in parallel, observing closely the activities of PropTech businesses in two different countries.

The paper adopts a mixed-method approach relying on qualitative data collected through interviews with PropTech players and real estate operators, and quantitative data collected through multiple sources on the Finnish and Italian PropTech ecosystems. The analysis is exploratory and descriptive.

This study underscores the potential benefits that digital technologies, introduced by PropTech, can add to the real estate sector: data integration; decisions informed from data; balance of new and old approaches to problem-solving; change in vision; adaptability of technologies and business models; and new professional profiles. These benefits are obtained when technology enables innovation from cross-pollination of different sectors. This becomes evident through the analysis of NACE activities, which demonstrates that innovation not only depends on interactions between property and technology, but the phenomenon covers a much broader spectrum of activities and industries.

Our exploratory analysis brings up new evidence that not only the real estate sector and property sector are affected by PropTech but the phenomenon covers a much broader spectrum of activities and industries. This paper contributes to the debate on technology innovations and value chain in the real estate and construction sector, while adding a cross-country perspective.

The purpose of this chapter is to investigate the adoption and use of building information modelling (BIM) for residential real estate development in Nigeria (using Lagos as a case study), with a view to providing information towards improving BIM uptake, which could enhance sustainable housing delivery in the country. A quantitative research methodology was adopted involving the use of questionnaire survey to collect primary data. The data were obtained from private real estate developers in Lagos State. The self-administered questionnaire was distributed to all the 72 active real estate developers in the study area, and the response rate was 62.5%. The collected data were analysed using statistical tools such as frequency and percentages, mean rating and chi-square. The results revealed a low level of awareness and usage of the transformative and contemporary BIM technology (6D BIM version) by real estate developers. It was established that the 2D and 3D BIM traditional versions were the most utilised across the phases of real estate development process. It was also found that the level of BIM utilisation has a significant relationship with the age and asset base of the real estate developers. The chapter concludes by advocating increase in the asset base and organisational profile of real estate developers to enhance BIM adoption, especially, the 6D BIM, which could facilitate sustainable real estate development.