Housing price modeling using fixed and adaptive kernels in geographically weighted regression (A case study for district ۵ of Tehran metropolitan city, Iran)
Publish place: 2nd International Conference on Geographic Information Science of Interdisciplinary Foundations and Applications
Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
GISCIENCE02_048
تاریخ نمایه سازی: 3 بهمن 1400
Abstract:
Housing is one of the main human needs in satisfying comfort and tranquility. In recent years, due to sharp changes in housing prices, especially in Tehran, estimating the price of apartments has become one of the most attractive topics among citizens. Fixed and adaptive kernels in geographically weighted regression have been used to analyze the spatial distribution of housing prices in District ۵ of Tehran Municipality and the factors affecting it. The sample size includes ۷۴۶۴ apartment housing prices that were sold in ۲۰۱۸, ۲۰۱۹ and ۲۰۲۰. To compare the results of geographically weighted regression in each of the fixed and adaptive kernels, gaussian, exponential and bi-square functions have been used. The mean adjusted r-squared coefficient using gaussian, exponential and bi-square functions in fixed and adaptive kernels is ۰.۷۶۱ and ۰.۷۵۵, respectively. The results of this study show that due to the uniform distribution of data in the study area, the fixed kernel performs better than the adaptive kernel.
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Authors
Saeed Zali
MSc. Student, School of Surveying and Geospatial Information, College of Engineering, University of Tehran, Tehran, Iran
Parham Pahlavani
Associate Professor, School of Surveying and Geospatial Information, College of Engineering, University of Tehran, Tehran, Iran
Behnaz Bigdeli
Assistant Professor, School of Civil Engineering, Shahrood University of Technology, Shahrood, Iran