# Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices
###### Abstract
When the hierarchical structure of house prices is ignored, the characteristics of neighborhood attributes and regional attributes will be affected by heteroscedasticity and spatial autocorrelation, which will lead to the deviation estimation of standard deviation [27; 30]. However, few studies have focused on the impact of hierarchical regional characteristics on house prices. In addition, most studies focused on national, state, or city-level housing price research, and rarely on determining factors of local-level house prices. However, when a family chooses a house, they know which states and cities they are going to live in, or have even decided which county. Therefore, it is of great significance to understand the regional characteristics and internal hierarchy of local-level houses in order to uncover the determinants of house prices. Therefore, an appropriate method should be used in order to obtain more unbiased and effective house price predictions [31]. Through the above analysis, we know that house characteristics are multilevel, which can transform the hedonic price model into a multilevel regression problem. It is well known that all background information not fitted into the model will eventually be included in the individual level error term of the model [32]. Because the individual level error terms in the same background are necessarily related, ignoring the background factors means that the regression coefficients act equally in all situations, which reflects the misconception that \"the mechanisms of things are essentially the same under different background conditions\". The purpose of the hierarchical model is to estimate the value of the dependent variable based on a series of independent variables that are not at the same level, which meets the needs of house pricing. For details, refer to the articles by [PERSON] [33], and [PERSON] and [PERSON] [34]. The effectiveness of the multi-level model in housing price estimation has been verified [28], but it only discusses the feasibility of the scheme, and has not established a complete model. It only considers a two-layer model, with only one variable in each layer. We expand the explanatory variables of each layer on its basis, to establish a more complete model. As such, this paper adopts a multilevel modeling method (MLM), and analyzeskey issues, such as which factors are key to influencing the level of housing prices, and how the factors in the multi-level model are constrained. In this study, the selling price of the house is related to the individual characteristics of the house itself (the first level), and these individual characteristics will be grouped in the census tract area (the second level), which will correspond to different school districts (the third level). At the same time, considering that the influence of housing characteristics on the price will change with time, and that preferences of buyers for particular characteristics are different in different years, thus the variable of the year of a house purchase is added to the individual characteristics.