Analyzing the rent-to-price ratio for the housing market at the micro-spatial scale

Abstract

The rent-to-price ratio is one of the popular indicators for monitoring the property market. This study explores micro-scale spatial dynamics of the ratio for houses at the individual property level in Seoul, South Korea. We match the apartment unit sold and the one leased based on the carefully chosen criteria and apply a Bayesian multi-level modeling approach to this matched dataset. We employ the Integrated Nested Laplace Approximations (INLA) algorithm in order to estimate relevant parameters in the multi-level model. The ratio determinants found in the study include property age, apartment unit area, interest rate, and floor. This study also presents the importance of taking into account the hierarchical structure of apartment units, as well as seasonal and spatial variations when estimating the ratio and predicting future trends in the property market based on the ratio.

Keywords

• rent-to-price ratio
• Bayesian multi-level model
• hierarchical structure
• seasonal variation
• spatial variation
• apartment unit