This study investigates the applicability of a large language model (LLM) to housing price appraisal in terms of predictive power and explainability. We first transform a hedonic dataset into a convertible format to construct the LLM-based appraisal framework using the established prompt engineering. We then compare the results to those obtained using a traditional hedonic pricing model. Our findings reveal that LLM outperforms the traditional benchmark model concerning two accuracy measures (i.e., root mean square error and R2 value) in appraising housing prices. This outcome indicates the substantial capability of LLM for seizing nonlinearity in the hedonic dataset. Furthermore, the LLM-based appraisal framework provides three-dimensional interpretations, including (1) the directional impacts, (2) the qualitative importance of the hedonic variables concerning housing prices, and (3) narrative reasoning for the appraised prices. These findings reinforce that the proposed LLM-based valuation model is a potential tool for understanding the mechanism of housing prices. Investors can implement our framework to estimate properties and support decision-making through explainable LLM results. Moreover, policymakers can benchmark our results when developing monitoring systems and designing transparent real estate markets.