نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناس ارشد شهرسازی، بخش شهرسازی، دانشکده هنر و معماری، دانشگاه شیراز، شیراز، ایران
2 استادیار بخش شهرسازی، دانشکده هنر و معماری، دانشگاه شیراز، شیراز، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction:
Housing is a critical decision and a major investment for households, characterized by heterogeneity and the complex interaction of multiple intrinsic characteristics. Its value arises from a set of structural, spatial, and environmental characteristics. In developing countries such as Iran, rapid urbanization and policy challenges exacerbate housing issues and make it essential to understand the determinants of housing prices. This study addresses a research gap in Shiraz by quantitatively analyzing the impact of spatial variables on housing prices in Valiasr neighborhood using the Hedonic Pricing Method (HPM). The research classifies the variables into internal (physical-structural) and external (accessibility and environmental-spatial) indicators to build a reliable pricing model.
Methodology: This applied research has a descriptive-analytical and correlational approach. Data were collected from 117 housing transactions in Valiasr neighborhood from April to October 2022. Primary data collection included structured questionnaires and semi-structured interviews with real estate agents. From an initial set of 38 spatial variables, 27 variables were selected for the final analysis after applying scientific criteria.
Dependent Variable: Price per square meter (MPrice and its log form LnMPrice).
Independent Variables: Physical-structural indicators (e.g., usable area, year of construction, type of structure), accessibility (e.g., distance to metro, bus, parks), and environmental-spatial indicators (e.g., road width, security).
Spatial analysis was performed using GIS and Open Street Map (OSM)-based network analysis to calculate walkable distances to urban services. Statistical analysis and HPM estimation were performed in SPSS 26 software, and both linear and semi-logarithmic forms were tested through Enter and Stepwise regression methods.
Results: The comparison of the estimated models revealed that the semi-logarithmic model developed using the stepwise method (Model 4) was the most effective in explaining housing price variations.
Out of the 27 independent variables, only 6 were statistically significant in the best-fitted model (Model 4). The final estimated semi-logarithmic regression equation is as follows:
Ln(MPrice) = -6.954 + 0.018*(Build_age) + 0.012*(Width) + 0.155*(Security) + 0.002*(N_Area) - 0.649*(H_Typo) - 0.079*(Structure)
The results indicate:
Positive and Direct Effects: The variables floor area ratio(N_Area), year of construction (Build_age), road width (Width), and security (Security) have a positive and significant relationship with housing prices. This means newer buildings, larger usable areas, wider roads, and more security (e.g., alley guards) increase property values.
Negative Effects: The variables housing type (H_Typo) and structure type (Structure) have a negative and significant effect. Specifically, apartments (H_Typo) and concrete structures (Structure) were associated with lower prices per square meter compared to other types.
Discussion: The results highlight the important role of spatial variables in housing prices. The superiority of the semi-logarithmic model is consistent with some previous studies but contradicts others, highlighting the suitability of the model for any context. The positive effects of usable area and building age reflect preferences for spacious, modern houses. Road width and security emphasize the value of spatial and environmental quality. The negative associations with apartment type and concrete structures may reflect local market preferences or perceptions of quality and safety. The integration of HPM with GIS-based spatial analysis effectively decomposes housing value, with stepwise regression effectively identifying key predictors.
Conclusion: This study provides a measurable model that identifies key spatial drivers of housing prices in Valiasr, Shiraz. The most influential factor is the building's construction year. The findings are valuable for:
Urban Planners: Informing evidence-based policies and development priorities.
Investors: Identifying opportunities and understanding market dynamics.
Policymakers: Designing targeted interventions and effective housing regulations.
Future research should expand the geographical scope, consider more detailed environmental variables, and apply spatial econometric techniques to address potential autocorrelation.
کلیدواژهها [English]
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