Recovering house price trajectories from sparse transaction data using functional principal component analysis

In the real estate market, property prices are recorded only at the time of sale, leading to highly sparse transaction data. Recovering house price trajectories from such sparse data is therefore essential for accurate property valuation. This study models the latent price trajectory as functional data and applies functional principal component analysis (FPCA) to recover the underlying price trajectory. Real estate transaction data from the Kitsilano neighborhood in Vancouver is collected to validate the FPCA model. The first three eigenfunctions are employed to reconstruct the price trajectories, along with their corresponding confidence intervals. A 10-fold cross-validation procedure is conducted to assess the accuracy of the recovered trajectories. The results demonstrate that FPCA can reconstruct house price trajectories with high precision.