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The purpose of this paper is to test the asymptotic properties and prediction accuracy of two innovative methods proposed along the hedonic debate: the geographically weighted regression (GWR) and the generalized additive model (GAM).

The authors assess the asymptotic properties of linear, spatial and non-linear hedonic models based on a very large data set in Germany. The employed functional form is based on the OLS, GWR and the GAM, while the estimation methodology was chosen to be iterative in forecasting, the fitted rents for each quarter based on their 1-quarter-prior functional form. The performance accuracy is measured by traditional indicators such as the error variance and the mean squared (percentage) error.

The results provide evidence for a clear disadvantage of the GWR model in out-of-sample forecasts. There exists a strong out-of-sample discrepancy between the GWR and the GAM models, whereas the simplicity of the OLS approach is not substantially outperformed by the GAM approach.

For policymakers, a more accurate knowledge on market dynamics via hedonic models leads to a more precise market control and to a better understanding of the local factors affecting current and future rents. For institutional researchers, instead, the findings are essential and might be used as a guide when valuing residential portfolios and forecasting cashflows. Even though this study analyses residential real estate, the results should be of interest to all forms of real estate investments.

Sample size is essential when deriving the asymptotic properties of hedonic models. Whit this study covering more than 570,000 observations, this study constitutes – to the authors’ knowledge – one of the largest data sets used for spatial real estate analysis.

In the literature on determinants of disclosure, scholars generally tend to investigate the existence of relations in “global” terms by considering the whole range of observed values pertaining to both dependent and independent variables involved in the descriptive model. Despite the different methodologies used coherently to this approach, a hypothesis can be only accepted or rejected entirely. This paper aims to contribute to the literature by proposing a data-driven method based on smooth curves, which allow scholars to detect the existence of local relations, significant in a limited interval of the dependent variable.

The employment of smooth curves is simplified by conducting a study on goodwill disclosure. The model derived by the adoption of the locally weighted scatterplot smoothing (LOWESS) curves may provide an accurate description about complex relations between the extent of disclosure and its expected determinants, whose shape is not completely captured by traditional statistic techniques.

The model based on LOWESS curves provided a comprehensive description about the complexities characterizing the relationship between disclosure and its determinants. The results show that in some cases, the extent of disclosure is influenced by multi-faceted local relations.

The exemplificative study provides evidences useful for standard setters to improve their comprehension about the inclination of companies in disclosing information on goodwill impairment.

The adoption of smooth curves is coherent with an inductive research approach, where empirical evidence is generalized and evolves into theoretical explanations. The method proposed is replicable in all the field of studies, when extant studies come to unclear and contradicting results as a consequence of the complex relations investigated.

The purpose of this article, starting from linear regression, was to estimate a switching regression model, nonparametric model and generalized additive model as a semi-parametric model, perform function estimation with multiple nonlinear estimation methods and conduct comparative analysis of their predictive accuracy. The theoretical importance of estimating hedonic functions using a nonlinear function form has been pointed out in ample previous research (e.g. Heckman et al. (2010).

The distinctive features of this study include not only our estimation of multiple nonlinear model function forms but also the method of verifying predictive accuracy. Using out-of-sample testing, we predicted and verified predictive accuracy by performing random sampling 500 times without replacement for 9,682 data items (the same number used in model estimation), based on data for the years before and after the year used for model estimation.

As a result of estimating multiple models, we believe that when it comes to hedonic function estimation, nonlinear models are superior based on the strength of predictive accuracy viewed in statistical terms and on graphic comparisons. However, when we examined predictive accuracy using out-of-sample testing, we found that the predictive accuracy was inferior to linear models for all nonlinear models.

In terms of the reason why the predictive accuracy was inferior, it is possible that there was an overfitting in the function estimation. Because this research was conducted for a specific period of time, it needs to be developed by expanding it to multiple periods over which the market fluctuates dynamically and conducting further analysis.

Many studies compare predictive accuracy by separating the estimation model and verification model using data at the same point in time. However, when attempting practical application for auto-appraisal systems and the like, it is necessary to estimate a model using past data and make predictions with respect to current transactions. It is possible to apply this study to auto-appraisal systems.

It is recognized that housing price fluctuations caused by the subprime crisis had a massive impact on the financial system. The findings of this study are expected to serve as a tool for measuring housing price fluctuation risks in the financial system.

While the importance of nonlinear estimation when estimating hedonic functions has been pointed out in theoretical terms, there is a noticeable lag when it comes to testing based on actual data. Given this, we believe that our verification of nonlinear estimation’s validity using multiple nonlinear models is significant not just from an academic perspective – it may also have practical applications.

This study aims to analyze whether urban tourism affects Berlin housing rents. Urban tourism is of considerable economic importance for many urban destinations and has developed very strongly over the past few years. The prevailing view is that urban tourism triggers side-effects, which affect the urban housing markets through a lack of supply and increasing rents. Berlin represents Germany’s largest rental market and is particularly affected by growing urban tourism and increasing rents.

The paper considers whether urban tourism hotspots affect Berlin’s housing rents, using two hedonic regression approaches, namely, conventional ordinary least squares (OLS) and generalized additive models (GAM). The regression models incorporate housing characteristics as well as several distance-based measures. The research considers tourist attractions, restaurants, hotels and holiday flats as constituents of tourism hotspots and is based on a spatial analysis using geographic information systems (GIS).

The results can be regarded as a preliminary indication that rents are, indeed, affected by urban tourism. Rents seem to be positively correlated with the touristic attractiveness of a particular location, even if it is very difficult to accurately measure the real quantity of the respective effects of the urban tourism amenities, as the various models show. GAM outperforms the results of OLS and seems to be more appropriate for spatial analysis of rents across a city.

To the best of the authors’ knowledge, the paper provides the first empirical analysis of the effects of urban tourism hotspots on the Berlin housing market.

The purpose of this paper is to assess the existence of critical tipping points for explanatory variables (age, government grants, education and household size) – in particular, household debt service-to-income on multidimensional poverty.

The paper applies a generalized additive model (GAM) using regression splines on National Income Dynamics Study data to establish threshold effects of the explanatory variables on multidimensional poverty.

The results show that the tipping point at which debt is associated with improved household welfare is 42.5 percent (level of debt service-to-income). With significant findings, household heads younger than 60 years of age and more children are associated with lower multidimensional poverty. Government grants may suffer from fungibility as they do not seem to be an effective tool for multidimensional poverty eradication. The ideal household size with negative significant correlation to multidimensional poverty is less than four members. And lastly, education proves to be the best instrument for households to escape multidimensional poverty.

High household indebtedness is a severe social problem. Its effects include deteriorating physical and mental health, relationship difficulties and breakdown. Significant social costs arise such as medical treatment and indirectly, reduction of productivity. Further effects on society include rising criminal behavior, children dropping out of school thereby transferring poverty to succeeding generations. Non-performing loans increase and in turn lead to reduced credit availability. The overall health of the economy is impacted due to reduced aggregate demand.

Macro studies have demonstrated the presence of thresholds on debt analyses. However, such is not known in micro analyses, this paper attempts to bridge this knowledge gap by applying GAM for analysis of debt-poverty nexus at the micro level.

This paper investigates the relationship between information quality and stock returns during the International Financial Reporting Standards (IFRS 9) pre-adoption announcements and examines the influence of modern technology on these relationships across 24 emerging countries.

This paper conducts an event study using data obtained from the DataStream, Osiris, International Telecommunication Union (ITU) and the World Bank databases from 2009 to 2014. The non-linear generalized additive model (GAM) was implemented to test the study hypotheses.

Results indicate a significant positive non-linear relationship between low information quality and stock returns during IFRS 9 pre-adoption announcements. This result implies that IFRS 9 announcements have a positive impact on corporations with low pre-adoption quality information. This result is also more pronounced in small rather than large corporations and financial rather than nonfinancial institutions. Furthermore, modern technology plays a significant decisive antecedent role, while industry type has a moderating effect on the relationship between information quality and stock returns. The codified legal system has a positive impact on stock returns across emerging countries.

Data unavailability in some emerging countries.

The empirical evidence provides useful guidelines for corporate managers, investors, international accounting standard-setters and regulators to improve financial reporting practices.

This paper extends the work of Armstrong et al. (2010); Onali et al. (2017) by including the impact of non-linear relationships using GAM analysis and the role of modern technology across emerging countries.

The paper aims to estimate the effect of energy efficiency on the Market Value of office buildings and consider whether this effect increases over time.

The authors analyze a dataset of office building valuations from 2009 to 2011, provided by the German Investment Property Database. The authors use hedonic regression models to determine the effect of energy efficiency and energy consumption on Market Values. Using generalized additive models (GAM) for modeling nonlinear covariate effects, the authors control for further building characteristics and location. Due to the small sample size, the authors introduce an innovative econometric approach that mitigates this problem.

Mainly due to the small sample size, and in spite of the newly developed econometric methodology, the authors do not find clear evidence of the relationship between energy efficiency and the Market Value. However, the study nonetheless provides interesting insights into the composition of office building Market Values in Germany.

In addition to the empirical results for the German office market, the main contribution of this paper lies in the econometric methodology. Beside the application of cutting-edge statistical techniques, the authors develop a method for handling datasets, for which the variable of interest is rarely observed, leveraging on the total available data. Thus, the methodology offers promising prospects for future research in similar settings.

The valuation of artworks is challenging since their value encompasses economic, social and cultural values. This study examines two specific questions about the economics of Indian art market: first, the determinants of the price of paintings by Indian artists and second, the risk and return characteristics of investment in Indian paintings. The authors also analyze the role of local context for both questions.

This study uses 8,865 paintings that are auctioned between January, 2000 and June, 2018. A generalized additive model (GAM) is employed to identify the determinants of auction prices and estimate art market price index.

The results indicate that the price of paintings in the Indian market is impacted by both global and local factors. Consistent with the previous research, this study finds that provenance, literature, living status of an artist, artist reputation, auction house, location and gender determine prices. However, the unique behavior of artwork medium and art movement affiliation in the Indian art market signifies the importance of local context in the valuation of artworks. An analysis of the second aspect of the study, i.e. risk and return characteristics of art investment, suggests that though overall art market returns are not lucrative, there are sub-sections in the market that outperform stocks and other assets. Further, the Indian art market shows a weak or negative correlation with other assets, thus making it a good candidate for a diversified portfolio. One of the important findings of this study is that artworks created by artists associated with the Bombay Progressive Artists' Group (PAG) command a significant price premium over all other artworks. Moreover, the average return on investment in paintings by artists affiliated to the Bombay PAG is not only significantly better than other art movements but also higher than all other art assets.

This study contributes to the growing literature on the economics of art market by providing a comprehensive analysis of the economics of Indian paintings. This research highlights the importance of local factors in price determination and on the risk and return characteristics of art investment. To the best of the authors’ knowledge, it is the most comprehensive study of the economics of Indian painting market and the first study to identify the relationship between Indian art movements and prices of paintings and returns on investment in paintings.

The purpose of this paper is to verify the influence of the characteristics of boards of directors on the level of environmental disclosure by Brazilian companies. The board is one of the main mechanisms of corporate governance and its characteristics can influence the level of environmental disclosure.

The study used a sample of 152 companies listed on the São Paulo stock exchange, the BM&FBovespa. The level of environmental information was obtained by conducting a content analysis of company sustainability reports and websites. The generalized additive models based on negative binomial distribution were used to verify the influence of the characteristics of boards on environmental disclosure.

The results indicate that the number of independent board members had statistical significance, suggesting that board independence can improve the control of the administration and encourage greater environmental disclosure. The age variables showed that environmental disclosure increased on boards with average age up to 60 and then decreased.

It is expected that this study will contribute to the literature on corporate governance promotion by producing empiric data about the impacts of the characteristics of boards of directors on the level of environmental disclosure of companies that operate in a country with an emerging economy, Brazil. Another contribution may be related to the process of choosing board members because a board of directors must be composed of members with various characteristics to reduce the asymmetry of information and improve a company’s environmental disclosure policies.

The purpose of this paper is to quantify the impact of 100-year flood risk on both property rents and values in Germany, exemplified by the market of the historic city of Regensburg, and therefore supports investors in understanding market behavior patterns in both rental and investment context.

The authors construct two generalized additive models for rents and purchasing prices with spatial components and under inclusion of both typical property characteristics (as control variables) and a 100-year flood risk parameter in order to estimate its effect on the rents and property price structure. The authors apply the methodology to a four-year data set of more than 16,500 observations.

The analysis shows that flood risk is a highly significant parameter when estimating both the rent as well as the sales price model. The authors also find that purchase prices for one square meter of living area are, on average, EUR299 lower if the property is located in the flood risk zone. In addition, also rental markets come with a respective, but rather low, discount.

The authors provide transparency to investors in terms of the impact that a flood risk location has on property rents as well as purchasing prices. The study supports investors by providing evidence on reaction patterns in German real estate markets and helps quantifying the financial impact that comes with flood risk in Germany.

This is the first study that aims to empirically test and to quantify the impact of flood risk on property rents and purchasing prices in Germany. Related research has been performed for the USA, Ireland and New Zealand and largely refers to event-driven work or rather conceptual in the context of property valuation.