Estimation algorithm for predicting the performance of private apartment buildings in Hong Kong

Purpose: For the sake of public health and safety, a territory-wide evaluation of the quality of buildings in Hong Kong is crucial. However, it is a lengthy process to assess the performance of the whole stock of buildings in the city. To get around this predicament, this paper aims to propose a statistical approach for a fast and reliable building evaluation algorithm using the Building Quality Index (BQI) developed by The University of Hong Kong. Design/methodology/approach: Using the BQI assessment framework, the condition of 133 and 160 private apartment buildings in Yau Tsim Mong and the Eastern District respectively are assessed and rated. The data of the Yau Tsim Mong buildings are used to estimate a regression model associating the relationships between building performance, measured by the BQI, and other exogenous factors. The resulting model is then employed to predict the performance of the surveyed buildings in the Eastern District. Findings: The regression analyses on the Yau Tsim Mong data indicate that building age, development scale and building management mode are significant determinants of the existing condition of the sampled buildings, echoing the findings of previous studies. BQI scores of buildings in the Eastern District are estimated using the resulting regression model, and there is a highly positive relationship between the predicted BQI and in-situ BQI scores. Originality/value: The study is the first in the literature to provide an algorithm for estimating building condition in a densely developed high-rise urban area.