Spatial Analysis of Housing Prices Using Geographically Weighted Regression: A Case Study of Hedonic Housing Price Data in Isfahan

Abstract

Housing is a fundamental human need that plays a critical role in the economic stability of families. Housing prices have always experienced significant fluctuations, with numerous factors impacting them. Investigating said factors can lead to a greater understanding of the housing market and more accurate planning. The present study investigated the impact of five factors: floor area, percentage of individuals with professional careers in the studied area, number of bedrooms, building age, and whether the property is a villa or an apartment, using data from 100 residential properties registered in the Isfahan city real estate and documents system. The study employed methods: ordinary least squares regression and geographically weighted regression. In addition to highlighting the importance of floor area and percentage of professionals (which stems from the spatial location of the studied area), the findings demonstrate the superiority of geographically weighted regression over ordinary least squares regression in spatial analyses.