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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.

In this two-part case, Richard Davis and Stephen Hodgetts, co-owners of D&H Management LLC, are trying to come to terms with changes in the real estate market‐changes that have made their rental homes worth less than their mortgages and at best yielding at most a break-even cash flow. In Part A Davis and Hodgetts are weighing the following options: (1) sell all of the properties, assume a loss (walk away with nothing), and avoid the negative cash flow; (2) walk away from all of the properties, assume a loss (walk away with nothing), and avoid the negative cash flow; (3) delay paying the mortgage on some of the homes, allow these properties, if necessary, to go into foreclosure, and in the interim use the positive cash flow to shore up some of the more positive cash flow homes; (4) contact all of the lenders and try to renegotiate the mortgages so as to have lower monthly rates.

In Part B Davis proposes that he and Hodgetts go their separate ways. Davis walks away with the two properties that have mortgages in his name, while Hodgetts obtains the four properties that have mortgages in his. From Hodgettsʼ perspective this is a losing proposition since (1) he would have to take over the management of four “loser” properties rather than Davisʼs two, an ʼunfairʼ split of the liabilities; (2) he had no interest in managing properties; and (3) he and Davis would be splitting up a long-standing team.

This paper aims to test two hypotheses related to the supposedly negative impact of rent control on residential mobility: the mobility of renters is, first, negatively related to how attractive their residential areas are and, second, relatively high for renters living in properties built after 2005.

This paper estimates logit and multinomial logit regressions and models household moves. The multinomial logit regressions separate between short- and long-distance moves and between moves to rentals and to owned dwellings. This paper uses the “relative income” of the tenants’ residential areas to proxy area attractiveness. This paper estimates regressions for entire Sweden and the three largest “commuting” regions and municipalities, respectively.

The full sample provides support of both hypotheses in all regressions. Hypothesis one gets stronger support for moves to other rentals than moves to owned dwellings but about equally strong support for short- and long-distance moves. Hypothesis one obtains strongest support in Gothenburg municipality while hypothesis two obtains strongest support in the Malmö region. Also, hypothesis two obtains stronger support for short-distance moves than long-distance moves and slightly stronger support for moves to owned dwellings than those to rented dwellings.

This paper does not estimate “how much” rent control affects mobility, and results cannot be used to design specific rent setting policies. Results may be sensitive to how different types of moves are defined.

Efforts to reform rent setting policies in Sweden are encouraged.

To the best of the author’s knowledge, this paper’s two hypotheses are not tested before in Sweden and can be tested without control groups.

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.

The purpose of this paper is to investigate how the pandemic affects tenants’ response to their lease obligations. This paper commences with examining the adopted tenant selection criteria during the COVID-19 pandemic. Then, this paper statistically tests if there is a relationship between selection criteria and response on whether the pandemic has effects or not. Then, this paper investigates the specific areas of impact on tenants’ ability to adequately keep to lease agreements in the Nigerian rental market. Finally, this paper proceeds to confirm if there is a relationship between selection criteria and the aspects of tenants’ deficiencies in rental obligations because of COVID-19.

Survey data, backed with interviews, is elicited from practicing estate surveyors and valuers and licensed property managers in Lagos, the largest property market in Nigeria and sub-Sahara Africa. Policy solutions and implications were solicited from personnel at the ministry of housing and senior professionals in the property sector. Data were analyzed using descriptive statistics, factor analysis and computer-aided qualitative data analysis, Atlas.ti.

Tenant’s health status is now accorded a priority together with others. Numbers of tenants are challenged with keeping to the prompt-rent-payment rule. Other areas of slight breaches included livestock rearing, subletting, alteration and repair covenants. Except for tenant reputation and tenant family size, there was no significant relationship between tenant’s health status consideration and the COVID-19 effect on tenant non-compliance with lease obligation. Tenants’ non-compliance with tenancy obligations has a connection with the tenants’ affordability, reputation, ability to sign an undertaking and health conditions during the pandemic. This paper recommends rental housing policy review.

It is recommended that the rental policy should be reviewed to give room for rental allowance or palliatives, private rental market regulation, exploration of the national housing fund and, if possible, social housing adoption policy in Nigeria.

This paper draws policymakers’ attention to the need to prepare for the future safety net that caters to citizenry welfare in challenging times.

What is the relation between the land system with Chinese characteristics and the country's high-speed economic growth in the past decades? There is a lack of rigorous academic research based on the general equilibrium theory of macroeconomics on this issue.

By building a multisector dynamic general equilibrium framework with land system, this paper explores how the land supply mode with Chinese characteristics affects China's economic growth as well as its transmission mechanism.

This paper confirms the importance of land system with Chinese characteristics in explaining the mystery of China's high-speed economic growth. Counterfactual analysis shows that if China adopts a land system similar to that of other developing countries, GDP will drop 36% from the current level under the baseline model.

As the industrial sector shrinks relatively and the output elasticity of infrastructure decreases, this inhibitory effect will become more apparent. China should improve its land supply mode, especially expand the supply of commercial and residential land and reduce the cost of land in the service sector. This can promote better economic development in the future and thus improve household welfare and the structure of aggregate demand, replace “land-based public finance” and thus inhibit the “high leverage” risks of local governments.

With revolutions in the telecommunication sector having led to wide unprecedented consequences in all facets of human life, this paper aims to examine the relationship between cell phone tower base stations (CPTBSs) and residential property prices within the City of Johannesburg (CoJ), South Africa.

The authors align their work with global literature and assess how the impact of CPTBSs influences residential property values in South Africa. The authors use a semi-log hedonic pricing model to test the hypothesis that proximity of CPTBSs to residential properties does not account for any variation in residential property prices.

The results show a significant impact that proximity of CPTBS has on residential property sale prices. However, the impact of CTPBSs’ proximity on residential property prices depends on their distance from the residential properties. The closer a residential property is to the CTPBS, the greater the impact that the CTPBS will have on the selling price of the residential property.

With international studies offering mixed findings on the impact of CPTBSs on residential property values, there is limited research on their impact in South Africa. The findings of this study offer crucial insights for the real estate practitioners, property owners, telecommunications companies and the public, providing a nuanced understanding of the relationship between CPTBSs and property values. This research helps property owners understand the effects of CPTBSs on their properties, and it assists property valuers in gauging the impact of CPTBSs on property values.

Although networked hospitality businesses as Airbnb are a recent phenomenon, a rapid growth has made them a serious competitor for the hospitality industry with important consequences for tourism and for tourist destinations. The purpose of this paper is to analyse the nature of the phenomenon, its potential further development in the next five years and the impact this developments will have on tourism, on hotels and on city destinations.

A literature study, combined with scenario workshops and a Delphi panel, were used to map current trends and uncertainties. With this input, future scenarios were elaborated using the Global Business Network (“scenario cross”) method.

Network platforms as Airbnb are often classified under something called the “Sharing Economy”, a denomination that obscures their true nature. Airbnb is a challenging innovation to which traditional hospitality will have to respond. Its impact has at the same time led to a call for regulatory policies. The definition of these policies and the evolution of tourism are variables that determine future scenarios. Attempts to ban the phenomenon mean a disincentive to innovation and protect oligopolistic markets; more receptive policies may have the desired results if tourism grows moderately but in booming destinations they may lead to a harmful commercialization.

Until now, Airbnb has been described in conceptual studies about the so-called “Sharing economy”, or more recently in empirical studies about isolated effects of holiday rentals. This paper contextualizes the evolution of networked hospitality and seeks to synthesize the sum of its impacts, thus enabling businesses and local governments to define positions and strategies.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.