Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Prediction of apartment prices per unit in Daegu-Gyeongbuk areas by spatial regression models

공간회귀모형을 이용한 대구경북 지역 단위면적당 아파트 매매가격 예측

  • Received : 2015.03.06
  • Accepted : 2015.03.23
  • Published : 2015.05.31
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Abstract

In this study we predict apartment prices per unit in Daegu-Gyeongbuk areas by spatial lag and spatial error models, both of which belong to so-called spatial regression model. A spatial weight matrix is constructed by k-nearest neighbours method and then the models for the apartment prices in March, 2012 are fitted using the weight matrix. The apartment prices in March, 2013 are predicted by the fitted spatial regression models and then performances of two spatial regression models are compared by RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error).

이 연구에서는 공간회귀모형 중 공간시차모형과 공간오차모형을 이용하여 대구 경북 지역 단위면적당 아파트 매매가격을 예측하였다. k-최근접이웃 (k-nearest neighbours)을 이용하여 공간가중행렬을 구축하였으며, 이를 이용해 2012년 3월의 단위면적당 아파트 매매가격에 대한 모형을 적합시켰다. 적합시킨 공간시차모형, 공간오차모형을 이용하여 2013년 3월의 단위면적당 아파트 매매가격을 예측하였으며 RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error)를 통해 두 모형의 성능을 비교하였다.

Keywords

References

  1. Anselin, L. (1988). Spatial econometrics: Method and models, Kluwer Academic Publishers, Dordrecht.
  2. Anselin, L. (2001). Spatial externalities, spatial multipliers and spatial econometrics, Regional Economics Applications Laboratory, Illinois.
  3. Chun, H. (2002). A study on the living culture of apartment, Master Thesis, Yonsei University, Seoul.
  4. Chung, K., Kim, S. and Lee, Y. (2012). A comparative study on the goodness of fit in spatial econometric models using housing transaction prices of Busan, Korea. Journal of Korean Association of Geographic Information Studies, 15, 43-51. https://doi.org/10.11108/kagis.2012.15.1.043
  5. Hur, Y. (2007). A study on the estimation of house price in regard of spatial effects. House Studies Review, 15, 5-23.
  6. Park, H., Jyoung, S. and Rho, T. (2003). An spatial econometric analysis of apartment sale prices and spatial effects. Journal of Korea Planners Association, 38, 115-125.
  7. Stakhoych, S. and Bijmolt, T. (2008). Specification of spatial models: A simulation study on weights matrices. Papers in Regional Science, 88, 389-408.

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