ABSTRACT

This study uses U.S. Census data on average home values in Southern California census tracts from 1960 to 2010. Using growth mixture modeling (GMM), 26 unique groups are detected capturing nonlinear change in neighborhood relative home values over this study period. There were seven broad patterns of changing home values: (1–3) decline and then rise (at high, mid, and low portions of the home value distribution); (4) rise and then decline; (5–6) a monotonic increase (either above or below the region average); and (7) a monotonic decrease. Multinomial regression models found that covariates exhibited a much stronger effect for distinguishing between the average level of home values in neighborhoods over the study period, rather than how home values changed over time.

摘要

本研究使用美国人口普查数据，了解 1960 年至 2010 年南加州人口普查区的平均房屋价值。使用增长混合模型 (GMM)，检测到 26 个独特的群体，捕捉了该研究期间邻里相对房屋价值的非线性变化。房屋价值的变化有七种主要模式：(1–3) 先下降后上升（在房屋价值分布的高、中、低部分）；(4)先涨后跌；(5–6) 单调增加（高于或低于区域平均水平）；(7) 单调递减。多项回归模型发现，协变量在区分研究期间社区房屋价值的平均水平方面表现出更强的效果，而不是房屋价值随时间变化的方式。