# Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices
## 4 Discussion
This modeling approach provides a more comprehensive analysis of the determinants of housing prices by allowing for the analysis of the interactions between determinants of housing prices at different levels. Our study's results show that the hierarchical regression models outperform single-level OLS-based models in analyzing house prices. This approach takes into account the variation in housing prices across different levels of aggregation, providing a better understanding of the determinants of housing prices. Our study's use of hierarchical regression modeling allows policymakers and practitioners to gain a more nuanced understanding of the factors that influence house prices in a given area. This knowledge can inform decisions regarding housing policies, zoning regulations, and real estate development. Our study identified several characteristics of house price. The diversity of housing characteristics is a reflection of the production of housing, which is not a standardized process, and the participation of internal and external characteristics makes housing vary by case. Internal characteristics refer to the characteristics of the housing itself, such as size, number of bedrooms, and quality of construction. External characteristics reflect the characteristics of the surrounding environment, such as the quality of schools, access to public transportation, and crime rates. Different regions have different living cultures and different educational levels of residents, which makes buyers' preferences on characteristics, different. These findings provide a more comprehensive understanding of the factors that influence house prices, and highlight the importance of considering both internal and external characteristics of a house, as well as their interactions with time and region. Understanding the characteristics of house prices is crucial for policymakers and practitioners who are interested in developing effective housing policies and strategies. Our study's findings can help inform decisions related to housing affordability, real estate development, and urban planning. Our study has several original contributions to the literature on house prices. First, our hierarchical linear regression approach allows for the analysis of the inherently hierarchical attributes of the determinants of housing prices and their interactions at different levels. This approach resulted in a significant improvement compared with single-level OLS-based models. Second, our study used accessibility features measured at both the census tract level and the school district level, based on local data. This approach allowed us to better understand the factors that influence house prices at different levels of aggregation. Our approach also includes the ability to account for location-specific random effects, to handle unbalanced data (e.g., differing numbers of houses in various neighborhoods), and to flexibly model interactions between fixed factors (e.g., square footage, number of bedrooms). However, increased complexity, potential convergence issues, and challenges in selecting the best model structure, may impact the accuracy and reliability of predictions.