A COMPARISON OF BANDWIDTH AND KERNEL FUNCTION SELECTION IN GEOGRAPHICALLY WEIGHTED REGRESSION FOR HOUSE VALUATION

被引：9
作者：
Yacim, Joseph Awoamim [1 ]
Boshoff, Douw Gert Brand [2 ]
机构：
[1] Fed Polytech, Dept Estate Management & Valuat, Sch Environm Studies, PMB 001, Nasarawa 962101, Nigeria
[2] Univ Cape Town, Urban Real Estate Res Unit, Dept Construct Econ & Management, Private Bag X3, ZA-7701 Rondebosch, South Africa
来源：
INTERNATIONAL JOURNAL OF TECHNOLOGY | 2019年 / 10卷 / 01期
关键词：
Global model; Geographically weighted regression; House price; Kernel function; MASS APPRAISAL; PREDICTION;
D O I：
10.14716/ijtech.v10i1.975
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The study examines the influence of four spatial weighting functions and bandwidths on the performance of geographically weighted regression (GWR), including fixed Gaussian and bi-square adaptive kernel functions, and adaptive Gaussian and bi-square kernel functions relative to the global hedonic ordinary least squares (OLS) models. A demonstration of the techniques using data on 3.232 house sales in Cape Town suggests that the Gaussian-shaped adaptive kernel bandwidth provides a better fit, spatial patterns and predictive accuracy than the other schemes used in GWR. Thus, we conclude that the Gaussian shape with both fixed and adaptive kernel functions provides a suitable framework for house price valuation in Cape Town.
引用
收藏
页码：58 / 68
页数：11
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