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Property management in commercial real estate (CRE) is an important operational function that needs to be managed because it brings large cost implications to the organization. As India aspires to become a developed real estate market, analysis of the growing importance of automating property services and technology acceptance by stakeholders are two key concerns that need to be explicitly addressed. This study aims to examine the extent of property technology (PropTech) adoption in India and propose a technology-enabled stakeholder management model in Indian CRE.

The research is qualitative in nature and follows the grounded theory approach. Research data were collected by conducting a series of semi-structured interviews with 18 property management professionals from different prominent Indian companies using PropTech.

The findings suggested the nine most typical automated property management functions in Indian CRE. The result of this research is the automated property services model for stakeholder management in CRE. The model demonstrates the value of implementing technology in property services in India.

The study provides useful insights into how artificial intelligence (AI) in property management can be applied to address property-related challenges, various stakeholder needs and improve property performance in accordance with energy efficiency policies.

This paper attempts to add to the limited body of literature on technology in the property management domain. The model demonstrates how automated property services meet the needs of different stakeholders in CRE and provides remote working procedures within the COVID-19 pandemic context.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.