住房市场的重新绘制:2011年基督城地震后的保险赔付和住房市场复苏

Cuong Nguyen, Ilan Noy, D. E. Sommervoll, Fang Yao
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引用次数: 2

摘要

2011年2月22日,新西兰克赖斯特彻奇市的大部分住宅被一场罕见的破坏性地震破坏。几乎所有的房子都投保了。我们想知道保险是否能够充分减轻损失,或者地震造成的损失以及相关的保险支付是否导致了城市住房市场的空间重新排序。我们发现保险赔付与地方房价之间存在负相关关系。我们还发现证据表明,这一结果背后的机制是,在某些情况下,房屋并没有被修复(即,业主已经将付款装入口袋)——实际上,主动修复的保险索赔(而不是直接支付)并没有导致价格的任何相对恶化。我们使用一种遗传机器学习算法,旨在改进标准的快乐模型,并确定城市住房市场的动态,以及三个数据集:所有住房市场交易,所有提交给公共保险公司的地震保险索赔,以及所有地方当局的建筑许可数据。我们的研究结果很重要,不仅因为巨灾保险的效用经常受到质疑,还因为了解灾后房地产市场会发生什么,应该成为全面评估灾难本身影响的一部分。没有对这些影响进行量化,就很难设计出能够以最佳方式预防或减轻灾害影响的政策。
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Redrawing of a Housing Market: Insurance Payouts and Housing Market Recovery in the Wake of the Christchurch Earthquake of 2011
On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from the earthquake, and the associated insurance payments, led to a spatial re-ordering of the housing market in the city. We find a negative correlation between insurance pay-outs and house prices at the local level. We also uncover evidence that suggests that the mechanism behind this result is that in some cases houses were not fixed (i.e., owners having pocketed the payments) - indeed, insurance claims that were actively repaired (rather than paid directly) did not lead to any relative deterioration in prices. We use a genetic machine-learning algorithm which aims to improve on a standard hedonic model, and identify the dynamics of the housing market in the city, and three data sets: All housing market transactions, all earthquake insurance claims submitted to the public insurer, and all of the local authority’s building-consents data. Our results are important not only because the utility of catastrophe insurance is often questioned, but also because understanding what happens to property markets after disasters should be part of the overall assessment of the impact of the disaster itself. Without a quantification of these impacts, it is difficult to design policies that will optimally try to prevent or ameliorate disaster impacts.
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