주관적 사전확률을 반영한 부동산 대량평가모형 개발
Incorporating Subjective Priors into Mass Appraisal Modeling
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초록
본 논문은 지가 정보의 제공, 과세표준 산정 등 다양한 행정 목적에 활용되는 공시지가(公示地價)의 정확성 제고방안에 대해 검토하였다. 현행 공시지가는 헤도닉 가격모형의 일종인 비준표(比準表)를 통해 가격을 산정하며 이러한 비준표는 고전적 통계접근법(classical or frequentist approach)인 최소제곱법(ordinary least squares)을 통해 작성된다. 그러나 고전적 통계접근법은 모형에 투입한 데이터의 질(quality)만큼 좋은 결과를 산출할 수밖에 없으며, 데이터의 질이나 양이 충분하지 않은 경우 모형의 성능은 악화될 수밖에 없다. 본 논문에서는 부동산 전문가가 보유한 지식, 즉 주관적 사전확률(subjective priors)을 모형에 반영하여 이러한 데이터의 제약을 극복하였다. 모형 적합 결과는 교차검증(cross-validation)에 기반한 시뮬레이션을 통해 개선된 성능을 확인하였다. 전문가의 머리 속에 있는 지식을 ‘주관적’인 것 또는 ‘정성적’ 판단이라 하여 배척하기보다는 그러한 주관적 지식이 어떻게 모형에 반영되는지 적어도 투명하게 설명할 수 있다는 점에서 베이지안 모형은 매력적인 접근법이라 할 수 있다.
Abstract
The government-announced land price has a variety of usage from providing land information to market to serving as assessment value for property. In this study, we investigate an alternative model to enhance the accuracy of this government-announced land price. The current model for the price estimation is based on a standard comparison table of price, which is a special case of hedonic pricing models. This comparison table is constructed from the ordinary least squares result, which is one of frequentist approaches. The frequentist approach, however, can only produce an output as good as the quality of input data and if the data is not enough in amount or its quality is poor, its output necessarily deteriorates. We overcome this constraint of data by incorporating substantive knowledge of real estate experts (subjective priors) into mass appraisal modeling. The improved accuracy of our model is verified by the cross-validation simulation method. Attractiveness of a Bayesian model lies in the fact that it provides a logically transparent method for combining prior information with data, rather than rejecting the knowledge in experts’ heads just because it is subjective or qualitative.
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