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The growing demand for housing and infrastructure, as well as the requirement for affordable housing, has been a significant factor, necessitating investigation for sustainable approaches and implementation of alternative construction innovations. Hence, this study aims to identify and assess the drivers for implementing modular construction systems (MCS) in developing countries.

The study adopts a quantitative research approach to seek respondents’ opinions on the factors that can drive the implementation of MCS in developing countries. Accordingly, a structured questionnaire was used as an instrument of data collection based on five Likert scales. The data was analysed using the mean score, one sample t-test, Kruskal–Wallis, factor analysis (FA) and Pearson correlation analysis.

Results show that 15 of the 16 major identified drivers were statistically significant towards implementing MCS, which indicates that the drivers are crucial for implementing MCS in developing countries. However, the Kruskal–Wallis test reveals that the respondents have varying opinions on the identified drivers. FA categorised the drivers into four categories, namely, “management and sustainability”, “key performance”, “know-how and logistics” and “regulations and policies”. A strong relationship among the four categories of drivers was established using Pearson correlation, which indicated that all the drivers’ categories are essential for implementing MCS in developing countries.

This study identified and assessed the drivers towards implementing MCS in developing countries. The study concludes that the identified drivers are essential for implementing MCS in developing countries. Also, the study considers the government the most placed player in driving the implementation of MCS in developing countries.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

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.