Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices

Abstract

For decades, researchers have been concerned with house price modeling, and recognition has grown for the necessity of considering environmental and contextual variables in the process. This research examines the association between neighborhood characteristics and all individual house sale prices in Lucas County, Ohio, from 2012 to 2016, through a multilevel modeling (MLM) approach. Although there are various ways to define neighborhoods, census tracts and school districts are used in this study. Neighborhood characteristics include a foreclosure score, race heterogeneity, median household income, and built environment variables, such as walkability indexes. School district characteristics include student performance, tuition expenditures per pupil, and percent of expenditures spent on classroom instruction. The advantage of the multilevel model, is that it allows us to derive reliable estimates of place differences, representing a considerable improvement over the single-level model. Significant correlations were identified between house prices and foreclosure score, student performance, and tuition expenditures per pupil. The MLM results indicate that house prices not only lay in house characteristics themselves but also in neighborhood features, thus MLM offers good prediction accuracy and high explanatory power.