The essence of AI-driven predictive maintenance in public housing in Nigeria

By Akinpelu, Akinwale Olusola

Housing investment across multiple climes and perspectives have distinctively portrayed itself to being a cost intensive venture marked with unique traits which presents it as a social and economic medium embroiled with a mix of profit and risk.

Players in the property industry including public developers and private investors in the bid to supply quality housing units and other associated housing services usually incur high financial burden in the cause of site acquisition, development and management.

Concerns towards delivering affordable and sustainable housing to satisfy the peculiar needs of the society have continued to intensify especially in urban settlements grappling with population explosion and rapid urbanisation. Issues of housing deficit have become prominent in urban centers despite the commitment of huge capital sum to develop adequate housing product.

The shortfall in housing supply in developing countries have stemmed mostly from qualitative issues as against or over quantitative problems. Despite numerous impressive housing policies formulated by several public authorities to bridge the widening gap between housing demand and supply, the broader objective of bolstering housing provision have seem to record only partial successes due to low budgeting and poor housing management strategy including lack of enhanced maintenance practice.

Aside developing housing stock to mitigate homelessness, the need to ensure high maintenance standard remains indispensable. Public housing performance in developing countries have seldom met the physical and economic expectation of end users due to consistent maintenance needs which worsens when ignored by managing authorities. The absence of sound maintenance culture, plan and schedules have led to high-value project being functionally obsolete.

Maintenance practice which is a key aspect of property and asset management have not been well appreciated by developers operating in the public sector. Housing investment scheme in the public domain have been undertaking majorly with the focus of providing housing which upon its completion is left without laid down plans to execute routine maintenance.

The failure to identify subsequent repair and maintenance need after housing development puts housing on the verge of under-performance due to potential structural risk emerging from poor maintenance policy. Improved maintenance culture when cultivated in public housing management practice have the possibility of increasing property values and reducing risk.

Although the bane of maintenance lies in not having the right professionals with technical expertise to make quick decisions that is based on large housing data gathered through periodic condition survey. The difficulty in getting the right solution to address maintenance problem is now gradually getting resolved through computer-aided assistance.

Automatic systems in property management enabled by innovative technological solution such as Artificial Intelligence (AI) have become effective in making efficient housing maintenance decision through swift prediction of maintenance needs and rapid generation of practical solution based on critical analysis of enormous housing data.

Artificial intelligence with the ability to study patterns from large data set on a given subject matter is currently employed in designing maintenance plans and solutions that is based on historic data collected from condition survey over time. AI-enable programs and software including Big data, Blockchain, Artificial Neural Networks (ANN) and Deep Learning (DL), among other disruptive technologies, provide reliable judgment on the right approach to be taken towards ensuring quality maintenance of inspected properties.

Information obtained from the thoroughly analyzed data from housing condition can be used by professionals to determine the cheapest and most effective way to maintain public housing.

Disruptive technologies in the digital era is currently transforming traditional management approach to a responsive rather than reactive practice.

The government through the adoption of new information and communication technology (ICT) can reduced the inefficiencies in making maintenance budget and plans for already developed housing stocks. Investing in property technology to aid rapid decision-making would not only improve property values through predictive maintenance practices but will as well increase the competence of government employees in the housing and land ministries such as the estate surveyors and valuers who engage in the management of public properties owned by ministries, departments and agencies (MDAs) of the government.

In order to develop sound data bank for predictive maintenance, the government should facilitate sustainable collaboration between advanced technology companies and professional institutions in real estate industry such as the Nigerian Institution of Estate Surveyors and Valuers (NIESV). These institution through their directory across federation can engage registered professionals in data collection for the development of reliable data banks.

ESV. Akinpelu, Akinwale Olusola is a Public Affairs Analyst.