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This paper aims to decompose the value effects of green retrofits on commercial real estate. The paper disentangles various sources of value capture mechanisms that can be attained through green retrofit actions and profiles the extent to which green retrofit solutions can be effectively capitalised using transaction evidence from the Munich housing market. The insights offered can help real estate owners and investors during their ex ante analysis of future energetic retrofit investments.

The authors offer their reader both a conceptual framework and the results from an empirical analysis to identify the value effects of retrofits and the associating gains in energy efficiency. The conceptual framework theorises the different value components that a deep retrofit has to offer. The regression analysis includes a multivariate analysis of 8,928 dwellings in the Munich residential real estate market.

This study’s framework disentangles the total retrofit value effect into three components: the capitalisation of energy savings, the exposure to the value discount because of stricter standards and the value uplift because of indirect benefits (health, employee satisfaction, marketing etc.). The regression results indicate that the value gains because of energy efficiency improvements are in the range of 2.4–7.4%, while the indirect benefits and reduced exposure to stricter standards amount to another 3%.

While numerous studies have investigated the upside value effects of energy efficiency in the real estate sector, there is scant academic research which has sought to evidence the value of green retrofit solutions and the extent to which this can be capitalised. Instrumentalising the various value effects of energetic retrofit that have been identified is not straightforward. At the same time, inadequate value capture of energetic retrofit effects could delay intervention timelines or aborting of proposed retrofit actions which should be of primary concern to policymakers and stakeholders tasked with the decarbonisation of real estate assets.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level.

Using methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices.

The empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finally, the explanatory power increases when regressing on prices for condominiums compared with houses, implying that the indicators may rather reflect investor sentiment.

To the best of the authors’ knowledge, this is the first paper to extract both the news coverage and news sentiment from real estate-related news for regional German housing markets. The approach presented in this study to quantify additional qualitative data from texts is replicable and can be applied to many further research areas on real estate topics.

This paper aims to explore the in-sample explanatory and out-of-sample forecasting accuracy of the generalized additive model for location, scale and shape (GAMLSS) model in contrast to the GAM method in Munich’s residential market.

The paper explores the in-sample explanatory results via comparison of coefficients and a graphical analysis of non-linear effects. The out-of-sample forecasting accuracy focusses on 50 loops of three models excluding 10 per cent of the observations randomly. Afterwards, it obtains the predicted functional forms and predicts the remaining 10 per cent. The forecasting performance is measured via error variance, root mean squared error, mean absolute error and the mean percentage error.

The results show that the complexity of asking rents in Munich is more accurately captured by the GAMLSS approach than the GAM as shown by an outperformance in the in-sample explanatory accuracy. The results further show that the theoretical and empirical complexities do pay off in view of the increased out-of-sample forecasting power of the GAMLSS approach.

The computational requirements necessary to estimate GAMLSS models in terms of number of cores and RAM are high and might constitute one of the limiting factors for (institutional) researchers. Moreover, large and detailed knowledge on statistical inference and programming is necessary.

The usage of the GAMLSS approach would lead policymakers to better understand the local factors affecting rents. Institutional researchers, instead, would clearly aim at calibrating the forecasting accuracy of the model to better forecast rents in investment strategies. Finally, future researchers are encouraged to exploit the large potential of the GAMLSS framework and its modelling flexibility.

The GAMLSS approach is widely recognised and used by international institutions such as the World Health Organisation, the International Monetary Fund and the European Commission. This is the first study to the best of the author’s knowledge to assess the properties of the GAMLSS approach in applied real estate research from a statistical asymptotic perspective by using a unique data basis with more than 38,000 observations.

The purpose of this paper is to provide a dating system for the Italian residential real estate market from 1927 to 2019 and investigate its interaction with credit and business cycles.

To detect the local turning point of the Italian residential real estate market, the authors apply the honeycomb cycle developed by Janssen et al. (1994) based on the joint analysis of house prices and the number of transactions. To this end, the authors use a unique historical reconstruction of house price levels by Baffigi and Piselli (2019) in addition to data on transactions.

This study confirms the validity of the honeycomb model for the last four decades of the Italian housing market. In addition, the results show that the severe downsizing of the housing market is largely associated with business and credit contraction, certainly contributing to exacerbating the severity of the recession. Finally, preliminary evidence suggests that whenever a price bubble occurs, it is coincident with the start of phase 2 of the honeycomb cycle.

To the best of the authors’ knowledge, this is the first time that the honeycomb approach has been tested over such a long historical period and compared to the cyclic features of financial and real aggregates. In addition, even if the honeycomb cycle is not a model for detecting booms and busts in the housing market, the preliminary evidence might suggest a role for volume/transactions in detecting housing market bubbles.

Overtourism is a term that has emerged in media over the past few years. Issues of carrying capacity that were limited to tourism sites have recently spread to places with no tourism background. The development of new technologies and network hospitality (NH) has enabled a blurring of roles. Residents and tourists are more than often using the same infrastructure and spaces creating tensions. This reinforces issues related to ownership and citizenship within a new context. However, there is only a limited number of studies linked to urban overtourism, and a categorisation of cities is necessary to apprehend the phenomenon. The purpose of this paper is to analyse the current narratives of overtourism in cities and their impact on selected stakeholders.

This conceptual paper uses a qualitative approach to investigate the case of several cities bearing signs of overtourism. It uses data from public and private sources (statistics, press, city marketing, etc.) from six cities of various size in Europe in which the media reported overtourism syndrome. The data were analysed through a thematic analysis, enabling a categorisation and a typology of urban overtourism.

Findings show that overtourism is a notion constructed from various aspects, including recently added supply sources such as NH and low-cost carriers. The urban morphology and branding strategy play a major role in the sentiment of overtourism.

The study indicates the need for further research considering the urban destination in a holistic manner, rather than approaching it at the tourist site scale. A further quantitative research could test the model of urban overtourism taxonomy.

The developed urban overtourism typology and framework of analysis. The argument of using the urban morphology understanding and technology to address urban destination overtourism.

If the only difference between cities lies in their initial housing prices, the initially lower-price cities should eventually catch up with the initially higher-price ones, i.e., “absolute convergence.” Alternatively, if the major determinants of housing prices are city-specific, cities will converge to parallel growth paths of housing prices, i.e., “conditional convergence.” This study tests for the existence of absolute and conditional convergence in house prices among cities in China. The strong evidence for conditional convergence suggests that each city possesses its own steady-state housing price to which it is converging, which depends on the city's own socio-economic characteristics. In other words, differences in these socio-economic characteristics among cities can create permanent differences in housing price among them. The differences in steady-states house price across cities reflect differences in the level of socio-economic development among them. The findings inform the kinds of interventions and resources that are most likely to be effective in reducing income disparity.

The purpose of this paper is to examine the market liquidity (time-on-market (TOM)) and its determinants, for rental dwellings in the largest seven German cities, with big data.

The determinants of TOM are estimated with the Cox proportional hazards model. Hedonic characteristics, as well as socioeconomic and spatial variables, are combined with different fixed effects and controls for non-linearity, so as to maximise the explanatory power of the model.

Higher asking rent and larger living space decrease the liquidity in all seven markets, while the age of a dwelling, the number of rooms and proximity to the city centre accelerate the letting process. For the other hedonic characteristics heterogeneous implications emerge.

The findings are of interest for institutional and private landlords, as well as governmental organisations in charge of housing and urban development.

This is the first paper to deal with the liquidity of rental dwellings in the seven most populated cities of Europe’s second largest rental market, by applying the Cox proportional hazards model with spatial gravity variables. Furthermore, the German rental market is of particular interest, as approximately 60 per cent of all rental dwellings are owned by private landlords and the German market is organised polycentrically.

Germany has a polycentric city structure. This paper aims to reduce complexity of this structure and to find a reliable classification scheme of German housing markets at city level based on 17 relevant market parameters.

This paper uses a two-step clustering algorithm combining k-means with Ward’s method to develop the classification scheme. The clustering process is preceded by a principal component analysis to merely retain the most important dimensions of the market parameters. The robustness of the results is investigated with a bootstrapping method.

It is found that German residential markets can best be segmented into four groups. Geographic contiguity plays a specific role, but is not a main factor. Our bootstrapping analysis identifies the majority of pairwise city relations (88.5%) to be non-random.

A deeper discussion concerning the most relevant market parameters is required. The stability of the clusters is to be re-investigated in the future, as the bootstrapping analysis indicates that some clusters are more homogeneous than others.

The developed classification scheme provides insights into opportunities and risks associated with specific city groups. The findings of this study can be used in portfolio management to reduce unsystematic investment risks and to formulate investment strategies.

To the best of the authors’ knowledge, this is the first paper to offer insights into the German housing markets which applies principal component, cluster and bootstrapping analyses in a sole integrated approach.