Search results

This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

This paper aims to propose a new housing finance mechanism through gold price as an alternative to interest rate in Islamic home financing, especially on Bai’Bithaman Ajil (BBA) contract.

This study using simulation approach to calculate the monthly installments for home financing using gold price references. In simple terms, propose a financing formula in the BBA contract by converting the selling price of the house to the gold price, and then the monthly installments also follow the actual gold price. The authors provide an example by simulating this formula using historical data and cases of housing financing at Indonesian Islamic banks. The authors compare housing financing models based on gold prices and interest rates. Finally, The authors can compare the two housing financing models that are affordable for low-income people.

The results show that in the initial period, monthly installments of BBA based on gold price were lower than home financing based on interest rate. This result makes it possible for low-income people who cannot access financing based on interest rates to access financing based on gold price. However, the total installments of financing based on gold prices are higher than the financing model based on interest rates.

The paper confines one contract, namely, BBA, as it is claimed to be more Shariah-compliant than others.

These findings suggest an alternative model for Islamic banks and regulatory authorities in Indonesia to replace the interest rate reference with the gold price in BBA contract housing financing. This model can offer competitive advantages for Islamic banks, including lower initial installments and inflation-protected profits, serving as a means of differentiating them from conventional banks.

Gold price-based housing financing model in Islamic banks will increase the affordability of housing financing for low-income people.

This paper tries to solve two problems, namely, first, the problem of assuming that Islamic and conventional banks are the same, and second, the problem of housing finance affordability. This study needs to be explored.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.

Green building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However, the performance competency and design knowledge of the practice's building control instrument process can be affected by its evaluation and the information management of building information modelling (BIM)–based model checking (BMC). These maintenance-planning problems have not yet been investigated in instances such as the Grenfell Tower fire (14 June 2017, approximately 80 fatalities) in North Kensington, West London.

This study proposes a theoretical framework for analysing the existing conceptualisation of BIM tools and techniques based on a critical review of GB maintenance environments. These are currently employed on GB maintenance ecosystems embedded in project teams that can affect BMC practices in the automation system process. In order to better understand how BMC is implemented in GB ecosystem projects, a quantitative case study is conducted in the Malaysian public works department (Jabatan Kerja Raya (JKR)).

GB ecosystem projects were not as effective as planned due to safety awareness, design planning, inadequate track insulation, environmental (in) compatibility and inadequate building access management. Descriptive statistics and an ANOVA were applied to analyse the data. The study is reinforced by a process flow, which is transformed into a theoretical framework.

Industry practitioners can use the developed framework to diagnose BMC application issues and leverage the staff competency inherent in an ecosystem to plan GB maintenance environments successfully.

This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.

Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users.

The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively.

This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation.

1. The study investigates customers' adoption of AI chatbots' recommendation.

2. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

3. The central and peripheral cues are generalized according to cooperative principle theory.

4. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

5. Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

6. Customers' mind perception positively moderates the central and peripheral paths.

The study investigates customers' adoption of AI chatbots' recommendation.

The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

The central and peripheral cues are generalized according to cooperative principle theory.

Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

Customers' mind perception positively moderates the central and peripheral paths.

This study aims to develop a database of existing academic information in house purchase decision (HPD) using systematic literature review (SLR), to facilitate worldwide advancement of research under HPD domain.

This research examined papers from two reputable databases – Scopus and Google Scholar – from 1992 to 2022 using a scoping review technique (Arksey and O’Malley, 2005) and a theme analysis method. Out of 374, 181 articles fit the inclusion parameters and were evaluated using the theme analysis approach.

Data from 181 articles was evaluated thematically to create a thematic map of HPD research. Five main themes and their sub-themes were identified: consumer behaviour, housing attributes, factors influencing purchasing decisions, investment analysis and demographics, which proved essential in understanding HPD and customer preferences for house purchase.

Data from 181 articles were evaluated thematically to create a thematic map of HPD research. This SLR intends to provide useful new insights on consumer concerns about home purchases in the rapidly developing residential real estate market and the issues that marketers, housing sector stakeholders, real estate industry and existing and future researchers should prioritize.

This research is unique such that it is the only 30-year-long SLR on the subject matter of HPD. This paper makes a significant contribution to residential real estate domain signifying the present state of research in HPD.

ESG (Environment, Social, Governance) has taken on increased importance in recent years for all stakeholders, with the S dimension now taking on a stronger focus in the real estate space. This paper proposes a new metric to be used in the S space to assess improvements in aspects such as gender equality and cultural diversity in real estate. It adds to the S metrics currently available to see the more effective delivery of the S dimension into real estate investment decision-making.

A new S metric in ESG is proposed and validated. Using this metric, examples regarding gender equality and cultural diversity are assessed among leading real estate players in Australia. This S metric is assessed over a number of time periods to demonstrate the improvements in gender equality and cultural diversity in these major real estate players.

This new S metric is seen to be highly effective and robust in capturing the changes in various aspects of the S dimension in ESG in the real estate space today; particularly concerning gender equality and cultural diversity. It is clearly able to demonstrate the significant changes in increased participation of women at the more senior leadership levels by leading players in the real estate space.

With ESG becoming a critical issue in the real estate sector, issues involved in the S space will take on increased significance going forward. This is critical, as the elements of the S dimension such as gender equality and cultural diversity are important aspects for an effectively functioning real estate industry. The S metric developed in this paper can be used for benchmarking purposes over time, as well as between real estate players, between sub-sections within a real estate organisation, and comparing against other industry sectors. It is also relevant in all organisations, and is not just limited to the real estate sector. Additional metrics in the S space are an important development to further empirically assess the effective delivery of the S dimension of ESG in the real estate sector and more broadly.

This paper specifically proposes this new S metric in ESG in the real estate industry. This is a key issue for the real estate industry going forward at all levels, as it will facilitate a more diverse real estate industry and more effective real estate investment decision-making. This S metric is applicable in all organisational sectors where the S dimension of ESG is important.

This paper aims to critically evaluate the impact of empowering leadership on safety behavior and safety climate during safety monitoring at a nuclear power plant (NPP) in the United Arab Emirates (UAE).

Data were collected using questionnaires filled out by 500 participants from the UAE nuclear sector. The relationships among the variables were analyzed using structural equation modeling.

The results indicated that empowering leadership has a positive impact on safety behavior, and a positive safety climate leads to increased levels of safety behavior (compliance and participation). The results also showed that safety climate partially mediates the relationship between empowering leadership and safety behavior.

This study contributes to the existing knowledge regarding empowering leadership, safety monitoring, behavior and climate. Because limited information is available on this topic, this study extends the research on the relationship between empowering leadership and safety research at an NPP. Specifically, it outlines that safety monitoring partially mediates the relationship between empowering leadership and safety behavior. This research enables NPPs worldwide to incorporate empowering leadership to enhance safety monitoring and ensure better safety behavior and climate.

Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City.

The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable.

Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyers consider this event as sporadic violence.

There are only a few studies of violence in housing prices using data from developing countries, and most of these studies are conducted with aggregated data at the municipality or state level. The authors are using geocoded information, both violence events and housing prices, to estimate more disaggregated effects. Moreover, the authors used different proxies to measure different characteristics of violence (magnitude and visibility) to estimate the heterogeneous effects of violence on housing prices.

The purpose of this study is to develop a financial performance measurement model for real estate business.

The study uses balanced scorecard (BSC) proposed by Kaplan and Norton (1996) as a theoretical support. The study, being exploratory in nature, uses survey method to collect data on several dimensions of BSC as well as on other performance measures used by real estate businesses in India. The survey data collected is analyzed using exploratory factor analysis (EFA) to explore the model constructs. This is followed by building an integrated conceptual model for measuring the financial performance of a real estate business. The model is tested using partial least squares structural equation modeling (PLS-SEM).

The study finds that the financial performance of the real estate business revolves around customer satisfaction, employee satisfaction and external networks. The right alignment of these components lead to superior financial performance. It also provides a competitive advantage to the real estate business. These three components (customer satisfaction, employee satisfaction and external networks) have direct and indirect influences on the financial performance of real estate business.

A small sample size (78 respondents), as well as the respondent’s geographical concentration in India, are the limitations of the study. Hence, generalization of findings may be difficult until the findings are validated across the globe.

The conceptual performance measurement model suggested in this research provides an effective tool to plan and strategize to achieve superior financial performance, particularly for stakeholders in the real estate business.

To the best of the authors’ knowledge and belief, this is the first attempt to develop a comprehensive financial performance measurement model for real estate business and test it using EFA and PLS-SEM.