This paper develops a new asset pricing model designed to address the limitations of the Federal Housing Finance Agency's repeat sales method, particularly in accurately measuring housing prices in low-volume trading areas. In classical asset pricing models, the dynamic programming approach assumes a recursive relationship in which decisions are based on state variables from the previous period. While this approach is more reliable with abundant data, limited data can introduce significant bias, as the model may struggle to accurately capture the underlying dynamics in low-sample scenarios. Using Bayesian inference, this paper aims to propose a robust framework that overcomes this bias.