Conditionally Autoregressive Models for House Prices Data: Insights from a Comparative Simulation Study

Abstract

The modelling of property prices has been extensively studied in econometrics, with widely used approaches including generalised linear regression and geo- graphically weighted regression. These models commonly address local spatial correlations observed in property price data. However, despite its potential to capture spatial effects, the Conditional Autoregressive (CAR) model remains underutilised for this purpose. This study examines the robustness and predictive power of the CAR model, comparing it with established spatial models across three different datasets generation. An illustrative case study on Lombok house price data is also included. Simulation results showed that the CAR model demon- strates a distinct advantage, achieving lower bias and variability compared to other spatial regression models, effectively capturing neighbourhood-based spa- tial relationships, and exhibiting strong predictive power across various scenarios. In the Lombok case study, the CAR model outperformed other models, providing more precise estimates for property-related factors such as land size and built- up area. The results confirm that CAR’s spatial framework enables a nuanced analysis of property values across regions, enhancing accuracy in predictive mod- els. This study also reveals the distinct strengths and limitations of each model, offering insights into their predictive accuracy and applicability across diverse real estate contexts.

Haziq Jamil

Assistant Professor in Statistics

My research interests include statistical theory, methods and computation, with applications towards the social sciences.