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외지인은 부동산을 비싸게 매입하는가?: 제주도의 아파트 시장에 대한 실증분석
방송희 ( Song Hee Bang ),이용만 ( Young Man Lee ) 한국부동산분석학회 2013 不動産學硏究 Vol.19 No.3
There is a belief that out-of-state buyers pay more for real estate than in-state buyers in Jeju Island. We investigate whether the belief is true in condominium market, in which information on transaction prices is diffused more quickly compared with other types of houses. And we explore whether the premium is driven by anchoring-induced bias or by search cost if out-of-state buyers pay more. For the study``s purpose, we adopt a SPAR model and a repeat sale price model as alternative models instead of a conventional hedonic price model. The data of transaction prices from July 2006 to March 2013 is used to estimate our models. The analysis results reveal that the rate of premium paid by out-of-state buyers is about 4.2% for condominium. And we find that out-of-state buyers from non-high-priced regions as well as out-of-state buyers from high-priced regions pay more for condominium than in-state buyers, and that out-of-state buyers from high-priced regions pay much more than out-of-state buyers from non-high-priced regions. From the results we conclude that the premium paid by out-of-state buyers in Jeju Island is driven by anchoring effect(anchoring-induced bias) as well as by search cost.
경매시장의 주택가격지수 추정에 관한 연구 -강남3구의 아파트를 중심으로-
이해경 ( Hae Kyeong Lee ),방송희 ( Song Hee Bang ),이용만 ( Young Man Lee ) 한국부동산분석학회 2010 不動産學硏究 Vol.16 No.2
The purpose of this paper is to develop a housing price index in the real estate auction market and to analyse the relationship between auction market and private negotiation market. We use the data on auction for condominium in Gangnam-gu, Seocho-gu and Songpa-gu, Seoul from Q1 2001 to Q2 2009. The SPAR(sale price to appraisal ratio) index model is adopted to estimate the index. After estimating the index, it is compared with the Transaction-based Housing Price Index which is made and released by the government to trace housing price change in the private negotiation market. We test null hypothesis for the equality of both indices and check up the cross correlation between two indices. We find that the null hypothesis are not rejected and the auction price index is coincident with the Transaction-base Housing Price Index. Next, we compare the auction price index with the KB Housing Price Index which is one of the appraisal-based index for the private negotiation market. We find that the auction price index is more volatile than the KB Housing Price Index and leads the KB Housing Price Index by 1 quarter. The result seems to come from the smoothing of the KB Housing Price Index.
매월 조사되는 주택 가격 변동률의 이상치 탐색 방법에 관한 연구
육태미 ( Tae Mi Youk ),방송희 ( Song Hee Bang ),이재성 ( Jae Sung Lee ) 한국부동산분석학회 2013 不動産學硏究 Vol.19 No.4
In statistical theory, an outlier is a value that is numerically distant from overall pattern of a distribution. It may be a meaningful observation, but it comes from an error in survey, data entry and process in most cases. Detection and handling are needed because outliers by errors debase the statistical quality leading to biased parameter estimation. Generally, traditional Box-plot or Z-score are very useful for univariate outlier detection and a Median rule could be applied in the non-Gaussian case. These methods calculate the tolerance interval that defines the range of acceptable observation values. Outlier detection for periodic surveys would consider the past view, because it is based on a ratio of value comparing the current time with previous time. If time period, however, is short, a state to get many unchanged values can occur. In this case, the ratio is centered at 1, and therefore outlier detection method reflecting this factor is required. This paper considers Quartile Method with power transformation and Hidiroglou-Berthelot(1986) method that is efficient in periodic data. The methods were applied to housing sales price. We suggest an outlier detection method for real-world data. In addition, we also analyzed data using Tukey Algorithm of United Kingdom``s office of National Statistic(ONS).