CBPM Conference 2026 · Vol. 2026, Issue 1

A Predictive Model for Estimating the Service Life of Floor Finishes in Public Office Buildings in Nairobi: Implications for Maintenance Practice and Policy

Olivia S.O Mwembe , Njeri Wachira- Towey, and Anthony Oduor Ralwala

Keywords: Floor Finishes,Lifecycle Costing, Maintenance Planning, Predictive Modelling, Public Buildings

Abstract

Service life prediction, a key component of lifecycle-based asset management, remains underdeveloped and underutilized in Kenya’s public building sector. The absence of empirical prediction tools has contributed to reactive maintenance leading to inefficient use of resources in public office buildings. The main aim of the study as to develop a predictive model for estimating the service life of floor finishes in public office buildings in Nairobi. The study examined the common types of floor finishes and their service lives, determining the factors influencing service life and examining relevant regulatory frameworks using a mixed-methods approach. Quantitative data was collected from 42 government-owned office buildings through a questionnaire to facility managers as well as observations on the floor conditions. Qualitative insights were obtained from officials at the State Department for Public Works, Kenya Bureau of Standards (KEBS), and the Kenya Association of Manufacturers (KAM). Multiple regression analysis was used to examine the influence of nine determinants on observed service life. The regression model was statistically significant (F(9,32)=2.313, p=0.039) and explained 39.4% of the variance in service life (R²=0.394). Management practices (?=-0.580, p=0.005), design deficiency (?=0.447, p=0.021), accidents/force majeure (?=0.356, p=0.041), and poor workmanship (?=0.383, p=0.043) were statistically significant predictors. Management-related factors had the strongest influence, highlighting the role of institutional culture in material performance. The study recommended stronger implementation of the existing maintenance policies while standardizing maintenance practices across all public office buildings. The proposed model provides a practical decision-support tool for lifecycle-based procurement and predictive maintenance scheduling for efficient use of public resources.