Earticle

초록

영어

Second-hand housing market is the barometer of the real estate market since the buyers of second-hand houses usually are those who really want to live, whereas financial investment and speculation are not their goals. So the price and determinants of second-hand houses reflect the real demand of housing market. In this paper KNN related algorithms are applied to study the problems associated with price of second-hand house. It includes using KNN and weighted-KNN algorithms to predict the price, using cross validation method to compute average deviation of prediction algorithm and compare KNN’s prediction effect with weighted-KNN’s, and using stimulated annealing optimization algorithm to compute the weight values of house attributes and evaluate the relative importances of them. Through the analysis of attribute importance it can show the influences of different house attributes on house price and the main concerns of buyers. All these results can give valuable information for manages, decision makers and appraisers of real estate.

목차

Abstract
 1. Introduction
 2. Data Set and KNN Algorithm
  2.1. Data Set Description
  2.2. KNN Algorithm
 3. Weighted-KNN¬¬ and Cross Validation
 4. Stimulated Annealing Algorithm
 5. Second and Following Pages
 References

키워드

저자

• Weikun Zhao [ School of Computer Science and Technology, Harbin institute of technology, Harbin, Heilongjiang, China ]
• Cao Sun [ School of Computer Science and Technology, Harbin institute of technology, Harbin, Heilongjiang, China ]
• Ji Wang [ School of Computer Science and Technology, Harbin institute of technology, Harbin, Heilongjiang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605