Forecasting the housing vacancy rate in Japan using dynamic spatiotemporal effects models

Sachio Muto, Shonosuke Sugasawa, Masatomo Suzuki

Research output: Contribution to journalArticlepeer-review

Abstract

This study attempts to predict and forecast the future heterogeneous increase in the vacant house ratio among prefectures in Japan using spatial panel models with unobserved dynamic spatiotemporal effects. The study formulated models with autoregressive and random-walk spatiotemporal effects, referring to the dynamic spatiotemporal effects (DSE) models. We estimate the model parameters and latent spatiotemporal effects via Markov Chain Monte Carlo algorithm. Simulation studies demonstrated the superior performance of the DSE model in terms of future prediction when spatial and temporal correlation exists. The model is then applied to the prefecturewise ratio of vacant houses to the non-rental housing stock in Japan, and the results imply existence of spatiotemporal correlations that cannot be captured by explanatory variables. Furthermore, it is revealed that the DSE models can provide better forecasting than the existing spatial panel models.

Original languageEnglish
Pages (from-to)21-44
Number of pages24
JournalJapanese Journal of Statistics and Data Science
Volume6
Issue number1
DOIs
Publication statusPublished - 2023 Jun
Externally publishedYes

Keywords

  • Bayesian inference
  • Markov Chain Monte Carlo
  • Spatiotemporal correlation

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Theory and Mathematics

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