Temporal transferability of the housing price component of an integrated land use and transportation model

Housing price is a critical component of integrated urban models (IUMs). The housing market in Canada was affected by COVID-19 including significant price hikes. This poses the question: how transferable are our pre-pandemic travel and land use models to the pandemic period? This question is specifically relevant for IUM's land use modeling components, such as housing market, since these models tend to make longer-range pre-dictions. With this motivation, this study investigates temporal transferability of housing price micromodel of an IUM. Separate latent segmentation-based autoregressive (LSA) models are developed for pre-pandemic and pandemic periods using sales data from 2016 to 2021 for Central Okanagan region. The key feature of LSA models is to accommodate unobserved heterogeneity, spatial autocorrelation, and temporal correlation. The parameter estimation results of the two models suggest significant differences exist between two periods. For example, buyers are likely to spend more to reside in mixed land use areas during pre-pandemic period. During the pandemic, people are willing to spend more for areas with homogeneous land uses such as residential areas. Furthermore, the pre-pandemic model is applied to pandemic context to examine temporal transferability based on statistical and predictive measures. Results reveal significant differences between the two models. Findings provide empirical evidence on the need for re-estimation or re-calibration of the pre-pandemic model parameters prior to applying for the pandemic period. Developed model will be included in an IUM which is expected to enhance the capacity of IUM to test impacts of socioeconomic shocks like the COVID-19 pandemic on housing market.

Automated summary

This study investigates the temporal transferability of a housing price micro model within an integrated urban model. The analysis of pre-pandemic and pandemic period sales data reveals significant differences between the two periods. The results suggest the need to re-estimate or re-calibrate the model parameters prior to applying it to the pandemic period.