成年:住房市场的装修溢价

Mari O. Mamre, Dag Einar Sommervoll
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引用次数: 0

摘要

我们依靠对房地产清单的新颖文本分析,并在挪威交易数据集中识别翻新住宅,以估计城市住房市场的翻新溢价。利用经典回归方法和随机森林算法,在享乐框架下估算了改造费用。后者的优势在于,它允许更复杂的装修费用和解释变量之间的相互作用。我们估计,翻新住宅的正面翻新溢价为5 - 7%,而未维护/被忽视的住宅的负面翻新溢价为9 - 10%。这些平均值掩盖了这些溢价随时间的显著变化,特别是反周期效应。忽略装修信息也会影响对短期房价增长的估计。未经维护的住宅往往在第四季度交易更多,这表明文献中报道的部分季节性价格变化是由于装修方面的成分变化。如果不受控制,这种构成效应会使价格变动估计向下,因为未维护的住宅交易价格要低得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Coming of Age: Renovation Premiums in Housing Markets

Coming of Age: Renovation Premiums in Housing Markets

We rely on novel textual analysis of real estate listings and identify renovated dwellings in a dataset of Norwegian transactions to estimate the renovation premium in an urban housing market. The renovation premium is estimated in a hedonic framework by classical regression approaches and a random forest algorithm. The strength of the latter is that it allows for a more complex interplay between the renovation premium and explanatory variables. We estimate a significant positive renovation premium of 5–7 percent for renovated dwellings and a negative premium of 9–10 percent for unmaintained/neglected dwellings. These averages mask significant variations in these premiums over time, particularly, a counter-cyclical effect. Omitting renovation information also has implications for estimated short-term house price growth. Unmaintained dwellings tend to transact more in the fourth quarter, indicating that parts of the seasonal price variation reported in the literature are due to compositional variation with respect to renovation. This composition effect bias price movement estimates downward, if uncontrolled for, as unmaintained dwellings transact at significantly lower prices.

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