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In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.

The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.

The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.

To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

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.

Corporate governance principles are living a positive momentum in light of the megatrends reshaping the world. An effective company based on sound governance principles can prevent issues and corporate scandals as the company ensures greater transparency and accountability. Accordingly, this paper aims to investigate the relationship between shareholder-oriented corporate governance mechanisms, value and performances in the real estate sector.

This paper investigates the relationship between corporate governance mechanisms, performance and value in a sample of 111 USA real estate firms. After collecting data from 2014 to 2018, this paper tests the research hypothesis using the linear fixed-effect model.

The results demonstrate a positive impact of shareholder-oriented corporate governance mechanisms on performance and value. In particular, firms with no chief executive officer (CEO) duality and staggered board mechanisms and recognizing excess variable compensation to the firms' executive have a significantly higher Tobin's Q, return on assets (ROA) and price-to-book performance.

The implications are twofold: on the one hand, this motivates shareholders to establish new corporate control mechanisms to maximize value, attract more capital and improve operating performance. On the other hand, this allows investors to direct the investors' resources toward real estate firms with effective corporate governance mechanisms that may return higher performance and value.

Focusing on the real estate industry, where governance is expected to have a lower impact due to solid regulation, especially in real estate investment trusts (REITs), the research allows the formulation of industry-specific inferences that may be generalized for the general market.

Real estate is a capital-intensive industry for which the asset values tend to be highly volatile and uncertain. Transaction costs in the industry are therefore high, and transparency for investors may be low. The need to signal reliable estimates of property assets, in the communication to external stakeholders, can therefore be expected to be of extra importance in this sector. The purpose of this paper is to investigate how real estate firms use big four auditors to signal quality.

The authors use Swedish firm level data containing all limited liability real estate companies in the country to determine the determinants of big four auditors. The data set consists of 34,306 observations and is analyzed through logit regressions.

The results show that big four companies are primarily contracted by large and mature companies, rather than new firms or firms with volatile financial records, although the latter could be expected to have a large need to signal quality. The authors also find that firms listed on the stock market and firms targeting public use real estate are more inclined to use big four companies.

Real estate is a capital-intensive industry for which the asset values tend to be highly volatile and uncertain. Transaction costs in the industry are therefore high, and transparency for investors may be low. The need to signal reliable estimates of property assets, in the communication to external stakeholders, can therefore be expected to be of extra importance in this sector. No prior study of this area has been detected.

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.

The purpose of the article is to improve the understanding of the role of institutional factors in real estate development. The authors take into account zoning (existence and type), type of right of disposal and type of buyer and seller of property in a multivariate econometric estimation. Dependent variable of the analysis is the time between acquisition of empty land and the application for a building permit, a period when many important development decisions have to be made. This indicator is closely related to debated phenomena like land hording and speculation.

The authors estimate a Cox proportional hazard model with the time between acquisition and application for a building permit as dependent variable and institutional indicators and a number of control variables as explanatory variables. Study area is the GZM Metropolis in the South of Poland. This region shows enough variability in institutional arrangements to allow for this type of analysis.

The analysis shows that institutional factors significantly influence the real estate development process. In areas that have not issued a zoning plan, the period until the building permit application is significantly longer. When the state is involved in a transaction (as purchaser or seller), it also takes longer until the building permit application is submitted. Although the instrument is usually intended to speed up development, perpetual usufruct implies a longer period until building permit application. Because of the results the authors get for control variables and for robustness checks, the authors are confident of the results of the analysis.

To the authors’ knowledge, this is the first study that deals with the question how institutional factors influence the timing of real estate development. By using data for a region in Poland, the authors also add to knowledge about real estate development in CEE countries.

The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities.When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.

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.

This paper is a bibliometric analysis of articles published on the influence of Covid-19 on consumer behaviour.

Biblioshiny and VOSviewer applications are employed for the bibliometric analysis and visualisation, respectively.

The most influential documents, authors, affiliations, countries and journals are presented. Citation, Co-citation and keyword co-occurrence analysis is conducted and presented in the form of a clustered network diagram.

This paper found three main themes of the research in consumer behaviour amid Covid-19 a) Food purchasing decisions and food wastage, b) Adoption of technology and c) Intrinsic and extrinsic influence on consumer behaviour.

The Covid-19 pandemic has shaken the world’s economy and left behind its adverse effect on almost every walk of life. Consumer behaviour is no exception, studies have reported paradigm shifts in the way consumers are reacting to marketing stimuli, making purchase and consumption decisions. For the marketers to sustain profitability, they need to understand the changing behaviour and tailor their offerings accordingly.

The article offers the emerging theme and sub-themes in the consumer behaviour research that leads to future expansion of this research domain.

This study investigates the factors influencing re-usage intention of online and mobile grocery shopping among young adult consumers in South Africa.

Data were collected from selected young adult participants using a stratified probability sampling strategy. Smart PLS was used to analyse the data.

The findings of the study indicate that perceived usefulness (PU), peer review (PR) and attitude (ATT) positively influence continuance intention (CI).

In line with the available literature, there are few prior post-adoption studies that delineate the influence of individual characteristics on digital commerce usage activities. There is high mobile penetration as a result of positive digital commerce and mobile application usage and adoption, creating the need to investigate and better understand the drivers behind, not just adoption and usage, but continued use of digital commerce platforms and applications. Since the sample size is relatively small, further future research studies can test the same model with bigger sample sizes to assess generalisability of the results in different locations.

This study adds to the current literature by concentrating on the extent to which systems and marketing elements influence young adult customers' intention to continue using online and mobile grocery shopping platforms in South Africa.

The study adds value from a theoretical standpoint, contributing to the antecedent factors of the technology acceptance model (TAM), theory of reasoned action (TRA) and stimulus-organism-response (S-O-R) model and giving marketing academics insights into what aspects drive re-use of online and mobile grocery shopping and on what should be the focus.