This paper aims to propose a guiding hedonic model for urban transformation and planning efforts by presenting an approach that can be used to examine the changing demands of users and investors following disasters such as pandemics and earthquakes, and their effects on housing prices, through a case study at the neighborhood scale.
The housing data and model were developed by synthesizing systematic literature review results and area analysis. Data from major real estate platforms were analyzed using hedonic pricing, with coefficients log-transformed for percentage-based interpretation. Correlation, regression and significance tests were conducted via SPSS 27.
The model identified eight statistically significant housing features affecting prices. In the study area, user preferences showed thresholds at buildings over ten floors and older than 15 years. Each additional floor reduced price by 0.17%, and each year of age by 0.9%, while a one-square-meter increase in area raised price by 0.6%. These numerical differences emphasize the contextual nature of housing preferences, suggesting that localized analysis yields more accurate insights for urban renewal and new developments.
The mathematical findings are limited to the research area and the period during which the data were collected; however, this limitation does not undermine the overall applicability of the model.
In contrast to other studies using similar methods, this research focuses on the smaller scale of the neighborhood, linking user preferences to the characteristics of the study area through their willingness to pay.
