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Investigating willingness to pay for green buildings in Nigeria and other developing nations is an urgent need since the world is advocating sustainable development, which the building industry must adopt while attempting to satisfy housing needs that are yet to be met in most developing countries. Recognizing that medium-income earners are victims of housing deficit who can afford housing to a reasonable extent in Nigeria, this paper aimed to investigate medium-income householders' willingness to pay for a green residential building to aid green building investment decisions in Makurdi. Three objectives were put in place to pursue the aim in which the study was approached quantitatively and adopted survey strategy using 300 questionnaires as the instruments for data collection as suitable for contingent valuation technique. Data collected were analyzed using the weighted mean, logistic model and multiple regression. The findings were that Makurdi medium-income earners find green features essential and are willing to pay a 3.3% premium price to purchase residential buildings with green features. Therefore, the paper recommends that the government and private investors strategically consider green residential buildings while creating more awareness of the importance of building green.

There is a prevalence of poor building maintenance practices in both the public and private sectors in Malaysia. To improve the current state of maintenance, effective decisions must be made by the building stakeholders. Unfortunately, the decision-making process for building maintenance in Malaysia is still traditional. The decisions are usually made based on the latest maintenance inspection without taking into consideration the trend of past data. This limits the building maintenance strategy to corrective (reactive) and preventive (expensive). Data-driven decisions improve building operations and create better predictive maintenance programs because the stakeholders can instantly identify problems and effectively act. Maintenance analytics is a structured and technological approach used to extract information from data and has proven to be an acceptable tool to improve building operation and maintenance. It is used to determine “what has happened?”, “why it happened?”, “what will happen?”, and “what needs to be done?” to enable decision-makers to take appropriate actions. In a country like Malaysia where maintenance practice is not data-driven, there is a need to identify the techniques to improve the maintenance process (especially decision-making). Therefore, this study aims to identify the various analytical techniques applied in existing maintenance analytics studies and determine the current direction of maintenance analytics studies. A comprehensive literature review was done to understand maintenance analytics, types of data and its sources, and the analytical techniques applied. Findings from the literature review revealed that the major data sources are CMMS, BIM, IoT, BAS. It was also noticed that the type of data used influenced the choice of analytical technical techniques. In addition, it was noted that certain studies did not use the major data sources and analytical techniques, and other studies used more than one data source. Overall, the general direction of the maintenance analytics studies was building performance and operation, end-user complaints, and work orders. There is a gap in the application of maintenance analytics to cost-effective decision-making in building maintenance. Which is recommended as the direction for future studies.

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