Model for predicting price change patterns in multi-family houses post renovation work in South Korea

Renovation work on deteriorated multi-family houses (MFHs) is often undertaken to improve their physical performance. However, due to uncertainties in economic benefits from renovation, many MFHs frequently withdraw their renovation plans in South Korea. Despite this problem, there has been very little research on countering this issue. With this background, this study aims to develop a model for predicting the price change patterns (MPPCP) of deteriorated MFHs upon renovation in South Korea. An artificial neural network (ANN)-based MPPCP was developed to detect the relationship between project attributes and price change patterns due to renovations. By combining the parameters of the ANN method, 108 candidate models were identified and a final MPPCP was proposed after conducting simulation tests to verify the level of correct for the candidate`s models. The results of model application to actual MFH renovation cases show that the developed model can facilitate a project owner's decision-making by estimating price change patterns for the deteriorated MFH in the project planning stage itself.