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This study evaluated the effect of macroeconomic factors on residential real estate (RE) investment returns in the cities of Abuja and Lagos, Nigeria, with a view to guiding RE investors and researchers.

A survey research design was employed using a questionnaire to collect RE transaction data from 2008 to 2022 from estate surveying and valuation firms in the study areas. Rental and capital value data collected were used to construct rental and capital value indices and total returns on investment. The macroeconomic data used were retrieved from the archives of the Central Bank of Nigeria (CBN). Granger causality (GC) and multiple regression models were adopted to evaluate the effect of selected macroeconomic variables on residential RE investment returns in the study areas.

The study found a progressive upward movement in rental and capital values of residential RE investment in the study areas within the study period. Total and risk-adjusted returns on investment were equally positive within the study period. Only the inflation rate, unemployment rate and real gross domestic product (GDP) per capita were found to be the major determinants of residential RE investment returns in the study areas within the study period.

The secrecy associated with property transaction information/data by RE practitioners in the study areas posed a challenge. Property transaction data were not adequately kept in a way for easier access and retrieval in many of the estate firms and agent offices. Consequently, there was a lack of data that spanned the study period in some of the sampled estate firms or agent offices. This data collection challenge was, however, overcome by the excess time spent retrieving the required data for this study to ensure that the findings appropriately answer the research questions.

Inflation and GDP per capita have been found to be significant factors that influence residential RE investment performance in the study areas. Therefore, investors should pay attention to these identified macroeconomic factors for residential RE investment in the study areas whilst making investment decisions in order to mitigate a possible loss of income or return. The government should formulate and implement economic policies that would address the current high unemployment and inflation rates in Nigeria at large.

This study has extended and further enriched the existing body of knowledge in the field of RE investment analysis in Nigeria. To the best of the authors' knowledge, this study is the first to adopt the Cornish Fisher value-at-risk and modified Sharpe ratio models to analyse risk and risk-adjusted returns on residential RE investment, respectively, in Nigeria. It has therefore redirected the focus of RE researchers and practitioners to a more objective approach to RE investment performance analysis in Nigeria.

The purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added value of analyzing tree-based ML models with interpretable machine learning (IML) techniques.

Data on Belgian residential rental properties were collected. Tree-based ML models, random forest regression and eXtreme gradient boosting regression were applied to derive rent prediction models to compare predictive performance with a LR model. Interpretations of the tree-based models regarding important factors in predicting rent were made using SHapley Additive exPlanations (SHAP) feature importance (FI) plots and SHAP summary plots.

Results indicate that tree-based models perform better than a LR model for Belgian residential rent prediction. The SHAP FI plots agree that asking price, cadastral income, surface livable, number of bedrooms, number of bathrooms and variables measuring the proximity to points of interest are dominant predictors. The direction of relationships between rent and its factors is determined with SHAP summary plots. In addition to linear relationships, it emerges that nonlinear relationships exist.

Rent prediction using ML is relatively less studied than house price prediction. In addition, studying prediction models using IML techniques is relatively new in real estate economics. Moreover, to the best of the authors’ knowledge, this study is the first to derive insights of driving determinants of predicted rents from SHAP FI and SHAP summary plots.

Specifically, this study aims to examine residents’ perceptions regarding the following: the sociocultural, environmental and economic impacts generated by the presence of Airbnb and the irritability caused by the presence of Airbnb based on Doxey’s Doxey (1975) irritation index (i.e. index).

Twenty-one semistructured in-depth interviews were conducted between February and March 2021 with residents of three condominiums in the Huancaro residential complex. Data were analyzed using the qualitative data analysis software ATLAS.ti 8.

Results showed that participants perceived negative economic impacts regarding investments, jobs, real estate prices and overall cost of living; negative sociocultural impacts regarding criminality, social conflicts and cultural exchange; and negative environmental impacts regarding sanitation in the context of the pandemic and the state of the Airbnb apartments. Further, it was found that participants related to the following three of the four stages of irritability: euphoria, apathy and annoyance.

It is necessary to complement the information with the perceptions of the residents about the city’s authorities and managers in the hotel business before the stage of the COVID-19 pandemic and the current stage.

The study identifies improve Airbnb operations like establishing health paraments and defining cohabitation rules at the condominiums.

The residents consider that visitors’ returns produce positive and negative impacts on the quality of life being important for understanding their perceptions.

Short-term rental companies, such as Airbnb, generate a range of impacts on urban residents, particularly when travelers encroach on areas of the city beyond the traditional “tourist bubbles.” This study explored the perceptions of Airbnb’s impacts on activities among residents of Huancaro, a residential section of Cusco-Peru, in the context of tourism reopening after a year of an almost complete halt in tourism activities because of the COVID-19 pandemic. The study also highlighted the heterogenetic responses to Airbnb within the community.

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.

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 article aims to analyze the behavior of commercial real estate prices in Europe, with a focus on the post-coronavirus disease 2019 (COVID-19) pandemic period. The authors use national and city-level data for the various commercial real estate sectors in ten countries, as well as listed real estate data, to assess any differences across property type and space.

The authors analyze the behavior of commercial real estate prices after the COVID-19 pandemic, emphasizing differences across property types. For that purpose, the authors use national and city-level direct real estate data for the ten largest countries in terms of market capitalization, as well as listed real estate data. The article then turns to discussing the likely trajectory of commercial real estate prices in the future.

The recent rise in interest rates and geopolitical instability have affected prices differently across sectors. Industrial properties benefited from the pandemic, although prices declined significantly in 2022. Residential properties continued their upward price trend and have been the best-performing property type during the last two decades. Retail real estate continued its downward price trajectory. Thus far, office markets do not appear to be significantly affected by structural changes in the sector. The data for listed real estate markets in Europe suggest that markets bottomed out in early 2023.

This paper provides for a better understanding of the behavior of commercial real estate prices in Europe since the COVID-19 pandemic. The authors assess whether the effects found during the COVID-19 crisis were temporary or long-lasting. Also, many economic and political uncertainties have emerged since the beginning of the Ukraine war in February 2022, and it is important to analyze the effects of such uncertainties on commercial real estate prices.

Using data spanning 145 years for Sweden, the authors investigate the benefits of holding multi-family properties for investors who aim to hedge wage growth.

The authors assess the risk-adjusted excess return that results from adding multi-family properties to a mixed-asset portfolio that aims to track wage growth. The authors also analyse the macroeconomic determinants of asset returns. Finally, the authors test whether a causal relationship exists between the growth rate of real wages and that of real net operating income.

The benefits from holding multi-family properties are the greatest for low-risk allocation approaches. For more risky strategies, the role of real estate is more muted, and it varies greatly over time. Holding real estate was most beneficial during the first two decades of the 21st century. Multi-family properties are found to be the only asset class to be positively related to wage growth. The authors show that the net operating income acts as the transmission channel between wages and property returns.

The paper assesses whether the growing interest of pension funds for multi-family properties is warranted in the context of a portfolio that aims to track wage growth.

Using long term data makes it possible to use a rolling windows approach and hence to consider multiple outcomes for an allocation strategy over a typical investment horizon. This permits to assess the dispersion of performance across several periods rather than just one as is commonly done in the literature. The results show that the conclusions that would be drawn from looking at the past two or three decades of data differ substantially from those for earlier time periods.

This study aims to investigate the impact of housing knowledge, housing challenges and housing policy on the renting intention and satisfaction of young people.

A questionnaire survey helped collect data from young people in the study area, which were then analysed using the Statistical Package for the Social Sciences (SPSS) 27 software. A descriptive analysis and the Cronbach’s alpha test were adopted to analyse the data. The confirmatory factor analysis confirmed a significant relationship between housing knowledge, housing challenges and housing policy and renting intention and satisfaction.

The overall findings revealed that most young people intend to own a home one day, and a minority of them decided to continue renting. The findings suggest that there is a significant relationship between housing knowledge and housing intention. However, housing challenges and housing policies do not appear to impact renting intentions. On the other hand, housing knowledge and housing challenges were found to be associated with housing satisfaction, while housing policy does not show a significant relationship.

This study, however, poses limitations as it uses a limited model and location and involves only a cross-sectional study. Future studies can use the methodology used in this study to conduct further investigations on housing intention and satisfaction in other regions of the country, thereby validating the findings of this study.

In terms of practical implications, this study has made a valuable contribution to the field of housing literature by shedding light on two crucial elements, namely, housing intention and satisfaction, which have been understudied. Understanding the determinants of housing intention and satisfaction is vital in efforts to implement appropriate policy reforms.

Findings from this study offer valuable insight related to managerial and practical implications, with the former implicating a need to prioritise initiatives that enhance renters’ housing knowledge. Implementing educational programmes and providing accessible resources can empower renters with a better understanding of the rental process and other important housing information.

This paper is relevant because it provides a guideline for policymakers to initiate regulations concerning housing and implement appropriate policy reforms. This study can also help housing providers develop more affordable housing that meets the needs of young people currently renting because most have expressed their housing intentions. Understanding housing intention and satisfaction determinants is vital to implementing appropriate policy reforms.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

This study aims to examine the zakat al-mustaghallat acceptance index (ZAMAi) through the examination of its predictors identified in this work, including attitude, social influence, self-efficacy, amount of information and Islamic altruism, at best.

Drawing from the attitude-social influence-self efficacy model, this study evaluated the effects of these factors on ZAMAi using an empirical investigation surveying 184 respondents who were identified as the owners of residential properties in Malaysia.

In the core model, this study found significant outcomes for the effects of attitude, social influence, self-efficacy, amount of information and Islamic altruism, along with the demographic items tested. For post hoc analysis, this study found two significant outcomes drawn from the role of attitude as a mediating variable in this study.

The results obtained from this study should be used with caution owing to its limited applicability and the constraints of subjects and variables in the framework developed.

The results obtained can become a yardstick to gauge the success of zakat al-mustaghallat acceptance in Malaysia.

This study introduced new measures of ZAMAi, where Malaysian landlords are brought into play.