纳入环境、社会和治理因素的年均房价时间序列的对数模型

Q4 Business, Management and Accounting
Jason R. Bailey, W. Brent Lindquist, Svetlozar T. Rachev
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引用次数: 0

摘要

利用 2000 年至 2022 年的数据,我们分析了美国八个主要城市每年新建房屋数量以及四个可用的环境、社会和治理 (ESG) 因素对房屋年平均销售价格的预测能力。我们将 P-样条线广义加法模型(GAM)的预测能力与常用广义线性模型(GLM)的严格线性版本进行了对比。由于年度价格和预测变量的数据构成了非平稳时间序列,我们对每个时间序列进行了适当的转换,以产生平稳序列,供 GAM 和 GLM 使用,从而避免分析中出现虚假的相关性。算术收益率或首次差分是预测变量的适当变换,而平均价格响应变量则使用 AR(q)-ARCH(1) 拟合得到的创新序列。根据 GAM 结果,我们发现 ESG 因素的影响因城市而异,反映了地域多样性。值得注意的是,空调的存在是一个强有力的因素。尽管受可用时间序列长度的限制,这项研究代表了将环境、社会和治理因素纳入房地产预测时间序列模型的关键一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hedonic Models Incorporating Environmental, Social, and Governance Factors for Time Series of Average Annual Home Prices
Using data from 2000 through 2022, we analyze the predictive capability of the annual numbers of new home constructions and four available environmental, social, and governance (ESG) factors on the average annual price of homes sold in eight major U.S. cities. We contrast the predictive capability of a P-spline generalized additive model (GAM) against a strictly linear version of the commonly used generalized linear model (GLM). As the data for the annual price and predictor variables constitute non-stationary time series, we transform each time series appropriately to produce stationary series for use in the GAMs and GLMs in order to avoid spurious correlations in the analysis. While arithmetic returns or first differences are adequate transformations for the predictor variables, we utilize the series of innovations obtained from AR(q)-ARCH(1) fits for the average price response variable. Based on the GAM results, we find that the influence of ESG factors varies markedly by city and reflects geographic diversity. Notably, the presence of air conditioning emerges as a strong factor. Despite limitations on the length of available time series, this study represents a pivotal step toward integrating ESG considerations into predictive time series models for real estates.
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来源期刊
CiteScore
4.50
自引率
0.00%
发文量
512
审稿时长
11 weeks
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