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The sharing economy has enjoyed rapid growth in recent years, and entered many traditional industries such as accommodation, transportation and lending. Although researchers in information systems and marketing have attempted to examine the impacts of the sharing economy on traditional businesses, they have not yet studied the rental housing market. Thus, this research aims to investigate the impact of the sharing economy (i.e. home-sharing) on traditional businesses (i.e. rental housing market).

The authors assemble rich data from multiple sources about the entry of a leading Chinese home-sharing platform (i.e. Xiaozhu.com) and local housing rental price index. Then, econometric models (i.e. linear panel-level data models) are employed for empirical investigation. Instrumental variables are used to account for potential endogeneity issues. Various robustness checks are adopted to establish the consistency of the findings.

Overall, the estimation results show that the entry of a home-sharing platform will decrease the local housing rental price. Moreover, this impact would be strengthened in a more developed city. Additionally, this impact would be strengthened with higher prices of new houses or second-hand houses.

First, this research is one of the first to study the impact of the sharing economy (i.e. home-sharing) on traditional markets (i.e. housing rentals). Second, it contributes to the relevant literature by documenting that the impact of a platform's entry is not uniform but contingent on city and housing market characteristics. Third, practically, the findings also offer important implications for platform operators and policy makers.

This paper investigates one of the potential costs of rising segregation in American cities by evaluating empirically the extent at which ethnic-based segregation contributes to the onset and the speed of propagation of the COVID-19 pandemic.

Regression analysis based on matched data on early incidence of COVID-19 cases, segregation and covariates. Identification resorts on variations in segregation across MSAs and heterogeneity in the geography and timing of stay-at-home orders.

One cross-MSA standard deviation increase in segregation leads to a significant and robust rise of COVID-19 cases of 8.7 per 100,000 residents across urban counties.

Combines spatial data on COVID-19 cases and segregation; use of a new segregation measure; focus on early incidence of the pandemic and its drivers.

This paper seeks to define a theoretical model for the urban regeneration of mass housing areas based on citizen initiative, self-management and self-financing in the form of the neighbourhood cooperative. This paper aims to identify mechanisms for economic resource generation that enable the improvement of the urban surroundings and its buildings without assuming disproportionate economic burdens by the local residents based on two principles, the economies of scale and service provision.

The research is structured in three phases: a literature review of the different trends in self-financing for urban regeneration and the conceptual framework for the definition of a cooperative model; the definition of theoretical model by analysing community ecosystem, neighbourhood-based services and the requirements for its economic equilibrium; and the discussion of the results and the conclusions.

The results show the potential of the cooperative model to generate a social economy capable of reducing costs and producing additional resources to finance the rehabilitation process. The findings show not only the extent of economic advantages but also multiple social, physical and environmental benefits. Its implementation involves the participation of multiple actors, which is one of its significant advantages.

The main contribution is to approach comprehensive urban rehabilitation from a collaborative understanding, overcoming the main financing difficulties of the current practices based on public subsidy policies. The model also allows an ethical relationship to be built with supplier companies by means of corporate social responsibility.

Buildings and their use is a complex process from design to occupation. Buildings produce huge volumes of data such as building information modelling (BIM), sensor (e.g. from building management systems), occupant and building maintenance data. These data can be spread across multiple disconnected systems in numerous formats, making their combined analysis difficult. The purpose of this paper is to bring these sources of data together, to provide a more complete account of a building and, consequently, a more comprehensive basis for understanding and managing its performance.

Building data from a sample of newly constructed housing units were analysed, several properties were identified for the study and sensors deployed. A sensor agnostic platform for visualising real-time building performance data was developed.

Data sources from both sensor data and qualitative questionnaire were analysed and a matrix of elements affecting building performance in areas such as energy use, comfort use, integration with technology was presented. In addition, a prototype sensor visualisation platform was designed to connect in-use performance data to BIM.

This work presents initial findings from a post occupancy evaluation utilising sensor data. The work attempts to address the issues of BIM in-use scenarios for housing sector. A prototype was developed which can be fully developed and replicated to wider housing projects. The findings can better address how indoor thermal comfort parameters can be used to improve housing stock and even address elements such as machine learning for better buildings.

The purpose of this paper is to investigate the role of distinct types of heating technology and their price impact in German residential real estate markets, considering a wide range of other housing market determinants. The authors aim to test and to verify specifically, whether the obsolescence of heating technology leads to a significant price discount and whether higher technological standards (and environmental friendliness) come with a price premium on the market.

The authors create housing market models for rental and sales segments by constructing generalized additive models with explicit multi-layered spatial components. To elaborate a profound and contemporary answer using these models, the authors perform large-sample regression analyses based on more than 400,000 observations covering German residential properties in 2015.

First and foremost, the heating system indeed shows significant explanatory importance for measuring housing rents and purchasing price. Second, the authors find that it makes a difference whether clean “green” technologies are implemented or whether “brown” systems with obsolete technology or fossil energy sources is on hand. Ultimately, the authors conclude that while low energy consumption indeed comes with a price premium, this needs to be interpreted together with the property’s heating type, as housing markets seem to outweigh the “green premium” by “brown discounts” if low energy consumption figures are powered by a certain type of heating technology system.

Aside of a possible omitted variable bias, the main research limitation is constituted by the integration of asking prices in the analysis, as actual transaction prices are not systematically transparent on national level in Germany. Limitations are discussed at the end of the paper.

This work supports investors who face the challenge of making environmental- and energy-related decisions as well as appraisers who deliver financial fundamentals for such. Third, the paper supports both asset managers as well as investment strategists in argumentation pro-environmental investments beyond all ecological necessity.

This paper contributes to the current discussion on climate change and the eclectic role of real estate in this context. The authors deliver evidence on pricing effects as a measure of socioeconomic acceptance of progressive heating technology and environmental friendliness as an imperative of twenty-first century societies.

This is the first study on “green premiums” or “brown discounts” that includes heating technology as a potential and distinct driver of value and rents. It is a contemporary contribution and delivers original information on the quantitative impact of contemporary and anachronistic technology in heating to researchers as well as investors and appraisers.

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 study aims to explore the spatial externalities of apartment vacancy rates on housing rent by considering multiple vacancy durations.

This research uses smart meter data to measure unobservable vacant houses. This study made a significant contribution by applying building-level smart meter data to housing market analysis. It examined whether vacancy duration significantly affected apartment rent and whether the relationship between apartment rent and vacancy rate differed depending on the level of housing rent.

The primary finding indicates that there is a significant negative correlation between apartment rent and vacancy duration. Considering the spatial externalities of apartment vacancy rates, the apartment vacancy rates of surrounding buildings did not show any statistical significance. Moreover, quantile regression results indicate that although the bottom 10% of apartment rent levels showed a negative correlation with all vacancy durations, the top 10% showed no statistical significance related to vacancies.

This study measures the extent of spatial externalities that can differentiate taxation based on housing vacancies.

The findings indicate that landlords have asymmetric information about their buildings compared with the surrounding buildings, and the extent to which price adjusts for long-term vacancies differs depending on the level of apartment rent.

The primary aim of this research is to examine the effects of the Renovation, Conversion, and Extension (ROT) tax deduction for renovations on the scope and quality of renovations and its subsequent impact on house prices across various Swedish municipalities.

This study utilises a two-way fixed effect instrument variable (IV) spatial Manski approach, analysing balanced panel data from 2004 to 2020 at the municipal level (290 municipalities) in Sweden. The methodology is designed to assess the impact of the ROT subsidy on the housing market.

The study reveals that the ROT subsidy has significantly influenced house prices, with noticeable variations between municipalities. These differences are attributed to the varying amounts of tax reductions for renovations and the extent to which property owners utilise these subsidies.

The research is limited to the context of Sweden and may not be generalisable to other countries with different housing and subsidy policies. The findings are crucial for understanding the specific impacts of government subsidies on the housing market within this context.

For policymakers and stakeholders in the housing market, this study highlights the tangible effects of renovation subsidies on property values. It provides insights into how such financial incentives can shape the housing market dynamics.

The research underscores the role of government policies in potentially influencing equitable access to housing. It suggests that subsidies like ROT can have broader social implications, including the distribution of housing benefits among different income groups and regions.

This study contributes original insights into the field of applied real estate economics by quantitatively analysing the impact of a specific government subsidy on the housing market. It offers a unique perspective on how fiscal policies can affect property values and renovation activities at the municipal level in Sweden.

This paper explores how Airbnb hosts' experiences with and responses to the coronavirus disease 2019 (COVID-19) health crisis may differ according to their motivations to host and to the type and spatial layout of their Airbnb accommodation. Based on these insights, the paper reflects on the lessons that are learned for the future of short-term rentals.

This is a qualitative multi-method small-scale case study, which relies on in-depth interviews and a focus group discussion carried out with a group of hosts affiliated to the Airbnb Host Community in Aarhus, Denmark. Informed by an interpretivist approach, the study aims to make sense of people's subjective experiences with hosting on the Airbnb platform, and how they have continued and adapted their hospitality practices during the pandemic.

Participants' adaptive practices vary according to their motivations to host and the type of accommodation that they rent out. Although all hosts in this study now implement more intensive cleaning practices, hosts who stay with their guests onsite tend to take stricter preventative measures to avoid contamination and transmission of the virus in their social interactions with guests. On the contrary, hosts who rent out their entire properties and have minimal contact with their guests found themselves less affected by the pandemic's impacts and have had a continued demand for their properties.

The COVID-19 pandemic has unevenly affected Airbnb hosts. Hosts who share their homes with guests require different adaptations to their daily behaviour and cleaning practices at home than hosts who do not stay with their guests and rent out entire properties. However, unlike professional hosts who largely or solely rely on Airbnb for their income, occasional home-sharing hosts tend to be more flexible in coping with cancelled or fewer bookings.

This study provides novel insights into the uneven impact of the COVID-19 pandemic on participants in the platform economies of tourism. It contributes to existing literature on the impacts of the pandemic on Airbnb's operations by showing how hosts' adaptive practices are informed by their subjective living conditions and the type of accommodation they can offer their guests.