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This study aims to investigate the impact of international diversification on the value and operating efficiency of European real estate firms.

The study is conducted using a panel fixed effects regression model to estimate the relationship of international diversification with firm value and operating efficiency. International diversification is mainly measured via the negative of the Herfindahl–Hirschman Index (HHI) using property-level data. Firm value and operating efficiency are proxied by financial ratios observed annually from 2002 to 2021 at the firm level.

The results demonstrate that international diversification has a negative effect on firm value. Additionally, it lowers operating efficiency by weakening a firm's ability to generate operating earnings from its assets. By examining whether the reduction in operating efficiency is due to the rental income channel or the capital gains channel, the authors find strong statistical evidence that international diversification negatively impacts capital gains. International diversification is negatively associated with net gains from property valuations (unrealized capital gains) and net profits from property disposals (realized capital gains).

The empirical analysis is limited to Europe.

This paper extends the geographical diversification literature. While existing literature focuses on domestic diversification within the United States, this paper explores the effects of international diversification on European real estate firms. To the extent of the authors' knowledge, this is the first paper to examine the impact of geographical diversification on capital gains.

The purpose of this paper is to examine how advertising is used by real estate companies as an instrument for managing the adverse effects of a catastrophe.

Through a theoretical analysis, types of post-disaster advertising messages were identified. On the basis of the likely variations in post-disaster advertising, a content analysis was conducted of a sample of 4,150 property print advertisements to identify advertising messages related to the earthquake. Finally, the message changes in these earthquake-related advertisements were evaluated and compared with the dimension of time to explore the development of advertising strategies.

The authors found that 12 types of advertising messages were used by developers in response to the Wenchuan earthquake. The initial advertising strategy was mainly to manage public relations, then the strategy was to reduce or compensate for the increased earthquake risk perceptions of buyers.

The findings provide valuable references for helping enterprises adopt effective advertising messages and strategies to reduce the negative effects of disasters.

There are only a few studies on advertising campaigns, especially in the real estate industry, that have been conducted in the wake of catastrophes. This study sought to expand upon the scarce findings in this particular field.

The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies.

The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method.

The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach.

One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets.

The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City.

The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice.

The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

Due to the young age of proptech, little is known about the dynamics of its expansion. In particular, there is limited agreement about a definition of “proptech,” while different categorizations are popping up. A severe lack of information emerges for the proptech scenario in Italy. The goal of this paper is to systematize multiple proptech maps in the attempt to create a framework for comparison of country-specific trends and an overarching definition of proptech. The research examines the evolutionary stage of the Italian digital real estate sector and compares it to the international context.

An in-depth analysis of 12 proptech maps at both national and international level was conducted based on online research. A list of Italian proptech companies was composed through multiple methods. A map was built for a cross-country comparison.

Each country or organization tends to develop its own categorization. This creates a multifaceted context where comparison and analysis are challenging. The Italian proptech sector seems underdeveloped compared to neighboring countries. Big room for improving the proptech business in this country still exists.

The results are valuable for proptech start-ups, business investors and well-established real estate actors to build on new entrepreneurial initiatives. The opportunity to advance proptech mapping and categorization emerges as a prospect for future research.

This research adds an overview of cross-country proptech categories and proposes the first analysis of Italian proptech. This will contribute to support entrepreneurial opportunities.

The aim of this paper is to introduce a framework that helps to identify strategic themes on which real estate investors form their strategies. A holistic approach to strategic management in real estate management has enjoyed popularity in corporate real estate research, while similar research has been lacking from the investor-based real estate management.

The research design consists of two main parts: 1) formulating propositions based on existing literature and 2) attempting to validate the propositions through a qualitative interview study with major real estate owners in Finland.

The main finding is that the current real estate investors reflect the transient nature of competitive advantages and assess their strategies accordingly. The companies consider the traditional profitability and revenue growth aspects of their business but also a more long-term future growth dimension. As an outcome, the investors base their strategies on eight strategic themes which are “Innovation”, “ESG”, “Marketing and sales”, “Financial management”, “Leasing management and tenant satisfaction”, “Competitive environment and portfolio management”, “Outsourcing and strategic partnerships” and “Cost and operation optimization”.

This paper opens opportunities for future research concerning different strategies in real estate investment business and their impacts.

The presented framework provides support for real estate investors to create real estate management strategy or to evaluate their current strategy and to recognize operational actions and decisions that are relevant for their strategy.

This paper provides an extension to corporate real estate (CRE) literature by showing that the CRE theories are adaptable to real estate investment and provide value for their strategic management. This paper also contributes to real estate investment literature by providing a well-founded and empirically contested strategic management framework, the IREM framework, for identifying strategic themes on which real estate investors form their strategies.

Deepening supply-side structural reform is the main objective of the economic work since the Chinese economy entered a new stage of development. By adopting the fundamental principles and methodologies of Marxist political economy, the authors can provide clarifications on the three basic theoretical issues concerning the supply-side structural reform. The paper aims to discuss these issues.

First, the essential starting point for understanding the supply-side structural reform is the primacy of production, as well as the organic connection between production and consumption in social reproduction, rather than the supply and demand as superficially seen in exchanges. By identifying the right starting point, the authors can avoid alternating between demand and supply management, and between liberalism and interventionism.

Structural problems, which are closely related to the institutional structure of production and the purpose and nature of production, cannot be solely attributed to the imbalance caused by market failures. Chinese economy has suffered prolonged structural contradictions and structural problems.

To decide whether the financial and the real estate sectors are real economy or virtual economy, the key is to examine whether the monetary capital used in financial activities and real estate commodity (capital) go through the capital circulation process of from monetary capital to productive capital and further to commodity capital, and whether the capital gain is generated by the value appreciation of capital or the value transfer and distribution as a result of the transfer of ownership. With its emphasis on developing the real economy, the supply-side structural reform should foster both development of manufacturing, and parts of financial and real estate sectors that are the real economy.

The purpose of this paper is to investigate the functionality and effectiveness of the Carbon Risk Real Estate Monitor (CRREM tool). The aim of the project, supported by the European Union’s Horizon 2020 research and innovation program, was to develop a broadly accepted tool that provides investors and other stakeholders with a sound basis for the assessment of stranding risks.

The tool calculates the annual carbon emissions (baseline emissions) of a given asset or portfolio and assesses the stranding risks, by making use of science-based decarbonisation pathways. To account for ongoing climate change, the tool considers the effects of grid decarbonisation, as well as the development of heating and cooling-degree days.

The paper provides property-specific carbon emission pathways, as well as valuable insight into state-of-the-art carbon risk assessment and management measures and thereby paves the way towards a low-carbon building stock. Further selected risk indicators at the asset (e.g. costs of greenhouse gas emissions) and aggregated levels (e.g. Carbon Value at Risk) are considered.

The approach described in this paper can serve as a model for the realisation of an enhanced tool with respect to other countries, leading to a globally applicable instrument for assessing stranding risks in the commercial real estate sector.

The real estate industry is endangered by the downside risks of climate change, leading to potential monetary losses and write-downs. Accordingly, this approach enables stakeholders to assess the exposure of their assets to stranding risks, based on energy and emission data.

The CRREM tool reduces investor uncertainty and offers a viable basis for investment decision-making with regard to stranding risks and retrofit planning.

The approach pioneers a way to provide investors with a profound stranding risk assessment based on science-based decarbonisation pathways.

Previous research on automation and job disruption is only marginally related to the real estate industry and its characteristics. This study investigates the effects of digitization on jobs in German real estate sector, in order to assess the proportion of jobs threatened to be replaced by automation. Since Germany is the largest EU economy insights for the German real estate market allow a first approximation for Europe.

An extensive database of the German Federal Employment Agency containing job definitions and occupation titles is matched with real estate criteria to create a subset with the relevant real estate occupations. This data is combined with a database of the German Institute of Employment Research reflecting to what extent tasks within jobs can be automated by current technical capabilities.

For the 286 identified occupations within the real estate sector a weighted average of 47 percent substitution probability through current technological capabilities is derived for tasks within the examined occupations.

This contribution indicates the extent of the structural change the real estate sector has to face due to digitization: One out of two real estate jobs will have to be re-created.

This research quantifies the magnitude of the job killer aspect of digitization in the real estate sector.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.