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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.

Many studies have analysed the impact of various variables on the ability of companies to raise capital. While most of these studies are sector-agnostic, literature on the effects of macroeconomic variables on sectors that established over the last 20 years like property technology and financial technology, is scarce. This study aims to identify macroeconomic factors that influence the ability of both sectors and is extended by real estate variables.

The impact of macroeconomic and real estate related factors is analysed using multiple linear regression and quantile regression. The sample covers 338 observations for PropTech and 595 for FinTech across 18 European countries and 5 deal types between 2000–2001 with each observation representing the capital invested per year for each deal type and country.

Besides confirming a significant impact of macroeconomic variables on the amount of capital invested, this study finds that additionally the real estate transaction volume positively impacts PropTech while the real estate yield-bond-gap negatively impacts FinTech.

For PropTech and FinTech companies and their investors it is critical to understand the dynamic with mac-ro variables and also the real estate industry. The direct connection identified in this paper is critical for a holistic understanding of the effects of measurable real estate variables on capital investments into both sectors.

The analysis fills the gap in the literature between variables affecting investment into firms and effects of the real estate industry on the investment activity into PropTech and FinTech.

German (corporate-) real estate management departments have been facing the challenge of poor data quality for years. This holds them back from generating efficiency potentials via the use of new methods from the field of digitalisation and from coping with the increasing requirements from the ESG context. The purpose of this paper is to explore why German (corporate-) real estate managements (do not) share data with their real estate service providers to address the data quality challenge and identify possible solutions.

To answer the research question, the reasoned action approach, an established theory from psychology for predicting human behaviour, is used. The relationships between the constructs are determined using linear regression. The study participants are almost exclusively from Germany.

The organisational milieu (perceived behavioural control) has a significant impact on the behaviour of sharing data with real estate service providers. Especially the change of contractual arrangements (data-driven contracts) seems to be crucial for the improvement of information logistics.

To the best of the author’s knowledge, for the first time, the reasoned action approach is used within the German real estate industry to predict organisational behaviour in the context of digitalisation.

Technological change drives transformation in most sectors of the economy. Industry 4.0 technologies have been applied at different stages of a building’s lifecycle. However, limited studies exist on their application in real estate facilities management (REFM). This study aims to assess the existing knowledge on the topic to suggest further research directions.

Scopus-indexed literature from 2013 to 2023 was examined and visualised using VOSviewer software to output quantitative (descriptive) results. Content analysis was used to complement the quantitative findings.

Findings indicated a concentration of research in China, Norway and Italy. The knowledge areas included three clusters: lifecycle integration and management, data curation and management and organisational and management capabilities. The benefits, challenges and support strategies were highlighted.

More collaboration is needed across countries and territories on technology integration in REFM. Future research using alternative methodologies is recommended, with a focus on adopting and non-adopting REFM organisations. Further, implications for facility managers, employees, technology suppliers or vendors, training, organisations and management exist.

Further, implications for facility managers, employees, technology suppliers or vendors, training, organisations and management exist.

The study reveals the knowledge base on technology use in REFM. It adds to the evidence base on innovation and technology adoption in REFM.

This study aims to provide a framework for assessing the technical condition of a house to determine its market value, including the identification of other price-setting factors and their statistical significance. Time on market (TOM) in relation to the technical condition of a house is also addressed.

The primary database contains 631 houses, and the initial asking price and selling price are examined. All the houses are located in the Brno–venkov district in the Czech Republic. Regression analysis was used to test the influence of price-setting factors. The standard ordinary least squares estimator and the maximum likelihood estimator were used in the frame of generalized linear models.

Using envelope components of houses separately, such as the façade condition, windows, roof, condition of interior and year of construction, brings better results than using a single factor for the technical condition. TOM was found to be 67 days lower for houses intended for demolition – as compared to new houses – and 18 days lower for houses to refurbishment.

To the best of the authors’ knowledge, this paper is original in the substitution of specific price-setting factors for factors relating to the technical condition of houses as well as in proposing the framework for professionals in the Czech Republic.

This study examines the relationship between environmental building certification Building Research Establishment Environmental Assessment Method (BREEAM-NL) and office rents in the Dutch office market.

A hedonic price model was used to assess the impact of BREEAM certification on office rents. The study is based on 4,355 rent transactions in the period 2015 to mid-2022, in which 331 transactions took place in certified office buildings and 4,024 transactions in non-certified office buildings.

The results provide empirical evidence on quantitative economic benefits of BREEAM-certified offices in the Netherlands. After controlling for all important office rent determinants, the results show a rental premium for certified office buildings of 10.3% on average. The green premiums highly differ across submarkets and vary between 5.1 and 12.6% in the five largest Dutch cities. Additionally, the results show significant positive correlation between BREEAM-NL label score and rents, whereby better performing buildings generally command higher rents.

The study contributes to the current literature on green building economics by providing, as one of the first, empirical evidence on the existence of financial benefits for BREEAM-certified office buildings in the Dutch office market.

This paper aims to establish a fundamental and comprehensive understanding of non-fungible tokens (NFTs) by identifying and structuring common characteristics within a taxonomy. NFTs are hyped and increasingly marketed as essential building blocks of the Metaverse. However, the dynamic evolution of the NFT space has posed challenges for those seeking to develop a deep and comprehensive understanding of NFTs, their features and their capabilities.

Utilizing common guidelines for the creation of taxonomies, the authors developed (over 3 iterations), a multi-layer taxonomy based on workshops and interviews with 11 academic and 15 industry experts. Through an evaluation of 25 NFTs, the authors demonstrate the usefulness of the taxonomy.

The taxonomy has 4 layers, 14 dimensions and 42 characteristics, which describe NFTs in terms of reference object, token properties, token distribution and realizable value.

The authors' framework is the first to systematically cover the emerging NFT phenomenon. This framework is concise yet extendible and presents many avenues for future research in a plethora of disciplines. The characteristics identified in the authors' taxonomy are useful for NFT- and Metaverse-related research in finance, marketing, law and information systems. Additionally, the taxonomy can serve as an information source for policymakers as they consider NFT regulation.

This study aims to see the moderating effect of board diversity on the relationship between ownership structure and real earnings management.

This study uses unbalanced panel data of 75 listed energy firms (346 firm-year observations) from three South Asian emerging economies (Bangladesh, India, and Pakistan) from 2015 to 2019. The two-step system GMM estimation is used for data analysis. This study also uses fixed effect regression to obtain robust findings.

The findings show that firms with a greater ownership concentration and managerial ownership significantly reduce real earnings management. In contrast, the data refute the idea that institutional and foreign ownership affect real earnings management. We also find that board diversity interacts significantly with ownership concentration and managerial ownership, meaning that board diversity moderates the negative link of the primary relationship that reduces real earnings management. On the other hand, board diversity has no interaction with institutional and foreign ownership, implying no moderating effect exists on the primary relationship.

To the best of the authors’ knowledge, this is unique research investigating how different ownership structures affect real earnings management in the emerging nations’ energy sector, which the earlier studies overlook. More specifically, this research focuses on how board diversity moderates the relationships between ownership structure and real earnings management, which could be helpful for future investors.

Financial stability has become a focal point for central banks since the global financial crisis. However, the optimal mix between monetary and financial stability policies remains unclear. In this study, the “soft” approach to such policy mix was tested – how often monetary policy (in inflation reports) analyses financial stability issues. This paper aims to discuss the aforementioned objective.

A total of 648 inflation reports published by 11 central banks from post-communist countries in 1998-2019 were reviewed using a text-mining method.

Results show that financial stability topics (mainly cyclical aspects of systemic risk) on average account for only 2%of inflation reports’ content. Although this share has grown somewhat since the global financial crisis (in CZ, HU and PL), it still remains at a low level. Thus, not enough evidence was found on the use of a “soft” policy mix in post-communist countries.

Given the strong interactions between price and financial stability, this paper emphasizes the need to increase the attention of monetary policymakers to financial stability issues.

The study combines two research areas, i.e. monetary policy and modern text mining techniques on a sample of post-communist countries, something which to the best of the authors’ knowledge has not been sufficiently explored in the literature before.

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.