评估住房空置的驱动因素:1960年至2010年美国大城市的纵向分析。

Galen Newman, Ryun Jung Lee, Donghwan Gu, Yunmi Park, Jesse Saginor, Shannon Van Zandt, Wei Li
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引用次数: 12

摘要

住房空置已成为城市人口减少或萎缩的一个主要问题。然而,所有城市地区都存在一定程度的空置住房。小比例是允许流动性和足够增长空间的必要条件,也是健康城市化的一个指标。相反,普遍的住房空置可能表明由于房产被遗弃而导致的结构性危机。土地面积和人口变化、就业变化、人口趋势、发展强度和经济状况是房屋空置的主要驱动因素。这些相互关联的因素的影响程度因城市而异。本文探讨了影响住房空置率的因素之间的关系,以确定潜在因素的变化。该研究调查了1960年至2010年间人口超过10万的美国城市,以10年为周期进行了多变量回归分析,并进行了纵向面板分析。回归分析考察城市住房空置因素随时间的变化,而面板模型评估哪些因素保持一致。面板模型结果表明,人口变化、非白人人口百分比、失业率和密度是一致的,是住房空置率的重要预测因子;增量回归模型表明,失业率和区域位置也是住房空置率的有力指标。这些结果虽然有些探索性,但提供了对长期数据的见解,城市应该长期跟踪这些数据,以确定抵消住房空置的最佳政策方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Evaluating drivers of housing vacancy: a longitudinal analysis of large U.S. cities from 1960 to 2010.

Evaluating drivers of housing vacancy: a longitudinal analysis of large U.S. cities from 1960 to 2010.

Evaluating drivers of housing vacancy: a longitudinal analysis of large U.S. cities from 1960 to 2010.

Evaluating drivers of housing vacancy: a longitudinal analysis of large U.S. cities from 1960 to 2010.

Housing vacancies have become a major issue in depopulating, or shrinking, cities. All urban areas, however, are subject to some degree of vacant housing. A small percentage is necessary to allow mobility and sufficient space for growth, and is an indicator of healthy urbanization. Conversely, widespread housing vacancies may indicate structural crisis due to property abandonment. Land area and population changes, shifts in employment, demographic trends, development intensity, and economic conditions are primary drivers of housing vacancies. The degree to which these interrelated factors contribute can fluctuate by city. This paper explores relationships between factors contributing to housing vacancies over time to identify changes in underlying factors. The research examines U.S. cities of over 100,000 population over the period of 1960-2010, conducting multivariate regression analyses in 10-year periods and performing longitudinal panel analyses. The regressions examine changes in urban housing vacancy factors over time while the panel models assess which factors have remained consistent. The panel model results indicate that population change, percent nonwhite populations, unemployment and density are consistent, significant predictors of housing vacancies, The incremental regression models suggest that unemployment and regional location have also been strong indicators of housing vacancies. These results, while somewhat exploratory, provide insight into long-term data that cities should track over time to determine the optimal policy approaches to offset housing vacancies.

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