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This study examines the long term effects of macroeconomic fundamentals on apartment price dynamics in major metropolitan areas in Sweden and Germany.

The main approach is panel cointegration analysis that allows to overcome certain data restrictions such as spatial heterogeneity, cross-sectional dependence, and non-stationary, but cointegrated data. The Swedish dataset includes three cities over a period of 23 years, while the German dataset includes seven cities for 29 years. Analysis of apartment price dynamics include population, disposable income, mortgage interest rate, and apartment stock as underlying macroeconomic variables in the model.

The empirical results indicate that apartment prices react more strongly on changes in fundamental factors in major Swedish cities than in German ones despite quite similar development of these macroeconomic variables in the long run in both countries. On one hand, overreactions in apartment price dynamics might be considered as the evidence of the price bubble building in Sweden. On the other hand, these two countries differ in institutional arrangements of the housing markets, and these differences might contribute to the size of apartment price elasticities from changes in fundamentals. These arrangements include various banking sector policies, such as mortgage financing and valuation approaches, as well as different government regulations of the housing market as, for example, rent control.

In distinction to the previous studies carried out on Swedish and German data for single-family houses, this study focuses on the apartment segment of the market and examines apartment price elasticities from a long term perspective. In addition, the results from this study highlight the differences between the two countries at the city level in an integrated long run equilibrium framework.

The purpose of this paper is to investigate the potential effects of the so-called sharing economy on growing city tourism as well as on urban property markets.

Official statistical data and a geo-information system (GIS) are used on a small scale in order to identify concentration processes among overnight visitors and the potential concomitant conflicts with other interest groups.

Currently, the effects of the sharing economy on housing markets and city tourism are barely measurable and are limited to a few central locations. However, a growing demand can be discerned in housing-like accommodation concepts which can be operated via booking platforms. As there is likely to be strong future growth in this area, continuous market observation (monitoring) is urgently advised.

Official statistics only allow an analysis of overnight guests staying with larger accommodation providers. Booking platforms for holiday homes and other temporary accommodation options have such little interest in data transparency that the overall phenomenon of city tourism can be addressed only in part.

Associating various data within the GIS enables municipal administrators and urban planners to identify potential sources of conflict within the property markets in good time and effectively counteract these where possible.

Increases in property prices directly attributable to growing city tourism may lead to the displacement of less financially secure members of the established population as well as businesses.

The sharing economy is a relatively new research topic which will become increasingly important in future. The identification of potential sources of conflict due to tourist accommodation has therefore not yet been comprehensively carried out on a small scale.

The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility.

The competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models.

Results reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances.

The research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making.

To the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.

The purpose of this paper is to develop an effective process to market high-rise luxury condominiums in a middle-income country in Asia like Sri Lanka, based on empirical evidence.

The case research methodology used to address the four research issues used multiple sources of data. In stage 1, qualitative data were collected in interviews with managers and salespersons of six condominium developments that ranged from successful to failure. In stage 2, quantitative data were collected in a survey of the buyers of the six cases.

The authors contributions to knowledge include the first evidence-based findings about what influences the success and failure of high-rise luxury condominium developments in a country like Sri Lanka. In addition, a comprehensive marketing model of an effective marketing process is developed for forward-thinking professionals in the field to use to successfully market their luxury high-rise condominiums projects in the future.

Detailed steps for successful marketing are outlined, from the Board of Management down to salespersons.

This is the first academic research paper to examine the effective marketing of high-rise luxury condominiums in a middle-income country like Sri Lanka.

This study aims to bridge the gaps in the extant literature on memorable tourism experiences (MTEs) and Airbnb by exploring the sources of negative memorable Airbnb experiences among Finnish guests.

This study used a qualitative approach and used data collected through semi-structured interviews. The sampling criteria for selecting participants were that they must be Finnish nationals who had booked and stayed in an Airbnb accommodation within the past 36 months. The participants were selected using a criteria-based snowball sampling technique. A qualitative empirical study was conducted using self-structured interviews that were completed by 18 Finnish tourists who had stayed at an Airbnb property in the past three years.

Three main sources constitute the conceptual framework of a negative memorable Airbnb experience: unclean accommodations, unpleasant host behaviour and poor customer service.

Airbnb management should make efforts to reduce guests’ negative experiences. This goal can be achieved by developing service-quality-management policies and strategies that are standardised, clear and universally applicable to all hosts. In addition, Airbnb management should recruit qualified customer service personnel and equip them with service recovery skills through training and control mechanisms. Such training should focus on upgrading their ability to handle complaints.

This study responds to the need to examine negative incidents that are a part of MTEs and the sources of negative memories. The originality of this study includes the extension of the existing literature on MTEs and Airbnb, as a conceptual framework of negative memorable Airbnb experiences that comprises three main components: unclean rooms, unpleasant host behaviour and poor customer service has been proposed.

This paper aims to explore the effects of a surge in tourism short-term rentals (STR) on housing prices in municipalities within Portugal’s two largest Metropolitan Statistical Areas.

This study applies the difference-in-differences (DiD) methodology by using a feasible generalized least squares (FGLS) estimator in a seemingly unrelated regression (SUR) equation model.

The results show that the liberalization of STR had a significant impact on housing prices in municipalities where a higher percentage of housing was transferred to tourism. This transfer led to a leftward shift in the housing supply and a consequent increase in housing prices. These price increases are much higher than those found in previous studies on the same subject. The authors also found that municipalities with more STR had low housing elasticities, which indicates that adjustments to the transfer of real estate from housing to tourism were made by increasing house prices, and not by increasing supply quantities.

The study suggests that an unforeseen consequence of allowing property owners to transfer the use of real estate from housing to other services (namely, tourism) was extreme housing price increases due to inelastic housing supply.

This is the first time that the DiD methodology has been applied in real estate markets using FGLS in a SUR equation model and the authors show that it produces more precise estimates than the baseline OLS FE. The authors also find evidence of a supply shock provoked by STR.

This chapter explores the concept of authenticity in the context of today’s highly competitive hospitality industry. Drawing on the multi-sited ethnographic case study of Ziferblat, the world’s first pay-per-minute cafe franchise, the author examines how the imperative of authenticity is addressed by small international enterprises falling in between the categories of chain and independent. By tracing how Ziferblat’s original concept, shaped by the personal and socioeconomic background of its founder, was subsequently transformed by the local teams and adapted to different cultural-geographical contexts, this chapter adds new empirical evidence to the dynamic and pluralistic notion of multiple authenticities.