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The purpose of this study is to introduce a new perspective on determinants of cross-border investments in commercial real estate, namely, the relative attractiveness of a target market. So far, the literature has analyzed only absolute measures of investment attractiveness as determinants of cross-border investment flows.

The empirical study uses a classic ordinary least squares estimation for a European panel data set containing 28 cities in 18 countries, with quarterly observations from Q1/2008 to Q3/2018. After controlling for empirically proven explanatory covariates, the model is extended by the new relative measurement based on relative yields/cap rates and relative risk premia. Additionally, the study applies a generalized additive mixed model (GAMM) to investigate a potentially nonlinear relationship.

The study finds on average a ceteris paribus, statistically significant lagged influence of the proxy for relative attractiveness. Nonetheless, a differentiation is needed; relative risk premia are statistically significant, whereas relative yields are not. Moreover, the GAMM confirms a nonlinear relationship for relative risk premia and cross-border transaction volumes.

The results are of interest for both academia and market participants as a means of explaining cross-border capital flows. The existing knowledge on determinants is expanded by relative market attractiveness, as well as an awareness of nonlinear relationships. Both insights help to comprehend the underlying transaction dynamics in commercial real estate markets.

Whereas the existing body of literature focuses on absolute attractiveness to explain cross-border transaction activity, this study introduces relative attractiveness as an explanatory variable.

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.

Within spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m² cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.

The findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).

These results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.

The paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

The paper employs a unique online user-generated housing search dataset and introduces a novel measure for housing demand, namely “contacts per listing” as explained by hedonic, geographic and socioeconomic variables.

The authors explore housing demand by employing an extensive Internet search dataset from a German housing market platform. The authors apply state-of-the-art artificial intelligence, the eXtreme Gradient Boosting, to quantify factors that lead an apartment to be in demand.

The authors compare the results to alternative parametric models and find evidence of the superiority of the nonparametric model. The authors use eXplainable artificial intelligence (XAI) techniques to show economic meanings and inferences of the results. The results suggest that hedonic, socioeconomic and spatial aspects influence search intensity. The authors further find differences in temporal dynamics and geographical variations.

To the best of the authors’ knowledge, it is the first study of its kind. The statistical model of housing search draws on insights from decision theory, AI and qualitative studies on housing search. The econometric approach employed is new as it considers standard regression models and an eXtreme Gradient Boosting (XGB or XGBoost) approach followed by a model-agnostic interpretation of the underlying effects.

This paper aims to examine whether there are differences between the long and short-term relationship of house prices and interest rates. The elasticity of house prices to monetary policy changes, e.g. via interest rates, is from a theoretical perspective and in the long-run negative. However, house prices adapt in the short-run dynamically to economic, financial, institutional and demographic factors.

In this paper, the authors confirm the aforementioned elasticity for the Nordic housing markets but provide evidence of drastic deviations from the negative relationship. This is done by using rolling regressions in search for time-varying betas.

The empirical results show that recessionary and expansionary policy regimes play a much more important role in the development of house prices in Finland, Sweden and Norway, than in Denmark.

Further, it is shown that the relationship between house prices and monetary policy is discontinuous over time, with large deviations from the long-term beta during the past decade. This holds true especially since the beginning of the financial crisis and the expansionary monetary policy in Europe.

Given the growing market awareness concerning responsible investments in recent years, the purpose of this paper is to bridge the gap between real estate companies which implemented a corporate social responsibility (CSR) agenda and the possible role within a multi‐asset portfolio optimisation framework. The behaviour of the asset class sustainable real estate (SRE) together with its diversification characteristics are the main focus.

The study is an explorative empirical analysis applying a portfolio optimisation algorithm. First, the authors developed a sustainable real estate index comprehending listed real estate companies from 2004 until 2010 acting in line with a CSR agenda. Second, the authors introduced SRE into the opportunity set of an UK investor and finally, generated the theoretical optimal asset allocation of SRE within different risk‐return portfolios.

The unique risk‐return pattern of SRE enables the asset class to be allocated across all portfolios ranging from low to high risk along the efficient frontier. In the low‐risk levels, SRE behaves as a diversifier whereas in the medium‐ to high‐risk portfolios SRE is represented as the main allocated asset. Sustainable real estate thus offers opportunities to numerous investors in view of their investment preferences and corporate strategies.

The results could encourage institutional investors to take investments in CSR‐driven listed real estate companies into account and to rethink their strategic asset allocation approach in view of the identified asset characteristics and the behaviour within a portfolio framework.

The paper provides a first insight in the field of portfolio management by introducing SRE into the opportunity set of a UK investor. The study raises SRE to an aggregated level and delivers theoretical as well as empirical evidence of the role sustainable real estate is playing within a multi‐asset portfolio.

The purpose of this paper is to investigate the effect of energy consumption on the financial performance of German residential buildings in a large panel framework. The authors provide evidence that energy efficiency in the residential sector is a relevant factor affecting both tenant investment decisions and consequently the performance of investor portfolios.

Based on the IPD Database and information from the German statistical office, the authors create portfolios of buildings across several energy consumption levels in order to describe the energy pricing mechanism in the context of total return and rent price. Furthermore, the authors apply conditional and unconditional regressions over the period of 2008 and 2010, to accurately quantify the energy price premium in the German residential market.

The descriptive portfolio results show that energy‐efficient buildings yield an up to 3.15 percent higher return and 0.76 €/m² higher rent than inefficient buildings. Furthermore, the regression results indicate that a one percent decline in energy consumption affects the total return of buildings positively by +0.015 percent. The hedonic results additionally show that one percent energy conservation boosts rent prices by +0.08 percent and market value by +0.45 percent, ceteris paribus.

Overall, the study presents an alternative methodology for describing and estimating hedonic datasets and offers some initial empirical evidence on the energy price premium in German residential markets. The paper contributes to prior European studies regarding the use and implications of energy performance certificates and confirms their significant impact on residential housing performance variables.

A green agenda has become a growing subject throughout an increasing number of European listed real estate companies over the last decade. The focus on sustainability is presumably not only goodwill or legislation driven but is rather a benefit driven action to achieve an economic surplus. The purpose of this paper is the development of an adequate sustainability definition, the investigation of the effect of a sustainability agenda on a company level, and the identification of possible financial benefits.

This is an explorative qualitative and quantitative study. First, the authors developed a four‐bottom‐line real estate sustainability agenda in accordance with the guidelines of the European Public Real Estate Association and the Global Reporting Initiative. Second, the study examines 80 European listed real estate companies from 2006 until 2009, and third, the study applies a panel analysis with conditional and unconditional regression techniques.

After classifying firms across different levels of sustainability intensity and quantifying the impact of an intensive green agenda the authors found a positive linkage between a green agenda and a green performance, especially in terms of an increased ability to generate revenues and a decreased level of idiosyncratic stock volatility. As a result, green commitments are not merely altruisms but are economically driven instead.

This paper gives, to the authors' knowledge, a first insight of how European real estate listed companies behave in terms of corporate social responsibility. The study contributes to the theoretical literature of corporate sustainable real estate companies by establishing an economic transmission mechanism as well as providing empirical evidence in favour of responsible activities.

This paper aims to investigate the effect of corporate social responsibility (CSR) ratings on the ex ante cost of capital of more than 2,300 listed US companies in a panel from 2003 to 2010. It examines whether financial markets value continuous investment in CSR activities through higher market capitalization and lower cost of capital.

The measure of the cost of capital reflects the perceived riskiness of individual companies expressed in the unobserved internal rate of return that investors expect to hold a risky asset. Based on descriptive portfolio estimations, panel and quantile regressions, the authors model the cost of equity capital as a function of CSR strengths and concerns obtained from the KLD-database and accounting controls.

The authors show that firms' CSR strategies differ significantly across industry sectors. Customer-orientated companies such as telecommunications and automobile outperform asset-driven sectors such as real estate or chemical companies. Furthermore, the authors find a 10-bp positive effect for one standard deviation of firms' intensive allocation of resources in sustainable activities.

Since the authors are interested in the effect environmental, social and governance activities have on the firm's perceived market valuation rate, the authors apply the Fama-French model because of its efficiency in explaining realized returns, rather than incorporating analyst's long-term growth forecasts into the proxy for the equity premium.

Managers of companies with low or intermediate CSR scores may consider the financial benefits of improving their social and environmental performance. A good starting point is usually to draw up a company-wide CSR agenda, possibly guided by a dedicated CSR task force, mapping out the potential costs and benefits of such measures. In addition, by improving their CSR ratings, a company may get access to additional resources, ranging from the growing ethical investment industry to employees for whom CSR performance matters when choosing an employer.

The authors expand the existing literature by considering firm's CSR level to be in relation to the overall CSR performance and decompose firm's CSR agenda into strengths and concerns rather than counting the number of activities a firm is involved in. The applied methodology allows a better understanding of firm's CSR agenda and its implication for capital markets and investors on both long and short investment terms.