Use of Geographically Weighted Regression (GWR) Method to Estimate the Effects of Location Attributes on the Residential Property Values

Abstract

This study estimates the effect of locational attributes on residential property values in Kuala Lumpur, Malaysia. Geographically weighted regression (GWR) enables the use of the local parameter rather than the global parameter to be estimated, with the results presented in map form. The results of this study reveal that residential property values are mainly determined by the property's physical (structural) attributes, but proximity to locational attributes also contributes marginally. The use of GWR in this study is considered a better approach than other methods to examine the effect of locational attributes on residential property values. GWR has the capability to produce meaningful results in which different locational attributes have differential spatial effects across a geographical area on residential property values. This method has the ability to determine the factors on which premiums depend, and in turn it can assist the government in taxation matters. Abstrak Studi ini memperkirakan pengaruh atribut lokasi terhadap nilai properti residensial di Kuala Lumpur, Malaysia. Regresi berbobot secara geografis (GWR) diperkirakan memungkinkan penggunaan parameter lokal daripada parameter global , dengan hasil disajikan dalam bentuk peta. Hasil penelitian ini mengungkapkan bahwa nilai properti residensial terutama ditentukan oleh atribut fisik (struktural) properti, namun kedekatan dengan atribut lokasional juga berkontribusi

Making the Third Dimension (3D) Explicit in Hedonic Price Modelling: A Case Study of Xi’an, China

“…Such kinds of consequences should be taken into consideration and well-addressed before establishment. GWR outperforms OLS with a higher R 2 , which is consistent with previous findings [53,70]. However, the significance of 2D factors differs from previous literature.…”

“…However, OLS has been criticised for its multicollinearity issues, omitting variables, and possibly containing biased results [47,48], so in the current literature body it always serves as a basic model to be compared with other more advanced models (see papers as follows [46,[49][50][51]. In contrast, GWR, a locally weighted regression model, is found to be advantageous over OLS in existing studies [38,[52][53][54][55], highly appreciated for revealing the spatial heterogeneity in property values and the factors. It also has a better model performance and accuracy compared to OLS [56].…”

Identifikasi Faktor-Faktor Yang Mempengaruhi Nilai Properti Residensial Berdasarkan Persepsi Penilai Pemerintah

“…Properti merupakan komoditas multidimensi, yang dicirikan oleh daya tahan yang relatif lebih lama, struktur yang relatif tidak fleksibel serta imobilitas. Setiap properti memiliki paket atribut yang unik yang menentukan permintaan dari properti tersebut seperti aksesibilitas, transportasi, fasilitas, karakteristik struktural, dan kualitas lingkungan (Dziauddin & Idris, 2017;Kain & Quigley, 1970;Ridker & Henning, 1967;So, Tse, & Ganesan, 1997;Stegman, 1969;Yan, 2020) Para peneliti menggunakan dua pendekatan yang diterima secara luas dalam membuat model permintaan properti. Pendekatan pertama adalah model monosentris, di mana harga rumah diasumsikan sebagai fungsi jarak antara lokasi properti dengan pusat perekonoman (di beberapa penelitian disebut dengan Central Business District).…”

Geographically Weighted Regression Model: A Potential Approach For Better Management Of Urban Growth

“…Abdullah, 2003;Jaafar, 2004;Rainis & Noresah, 2004;Tahir & Roe, 2006;Samat, 2007;Lee, Lim, & Nor'Aini, 2008;Osman et al, 2008;Tan, Lim, MatJafri, & Abdullah 2009). On the other hand, studies which used GWR include modelling urban spatial structure Noresah and Rainis, (2009), analyzing land use change (Noresah, Gairola, & Talib, 2010), assessing the rental value of shop houses (Eboy, Ibrahim, & Buang, 2006) and examining the locational attributes effect on residential property values (Dziauddin & Idris, 2017). This indicates that studies on understanding the spatially varying relationship between urban growth patterns and determinants using the GWR approach in the Malaysian context are yet to be explored.…”