低杠杆下的房价动态:以印度大城市为例

IF 1.5 Q3 URBAN STUDIES
Sudhanshu Sekhar Pani
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引用次数: 0

摘要

本文旨在研究新兴经济体中大城市的房价动态。本研究的目的是表征房价动态及其空间异质性。设计/方法/方法作者利用印度35个百万以上人口城市的数据,探讨了房价动态的空间异质性。研究方法采用面板计量经济学,允许空间异质性,横截面依赖性和非平稳数据。通过空间差异检验,分析了房价的收入弹性、建筑成本的作用以及商业银行对房地产业的贷款。长期基本面因素驱动着印度房地产市场,在印度,财富参数强于建筑成本或住房项目融资可获得性等供给侧参数。房价对家庭总存款(财富代理)的长期弹性在不同城市差别很大。但是,在0.39估计的弹性较低。系数最高的是卢迪亚纳(1.14),其次是布巴内斯瓦尔(0.78)。短期动态稳定且具有空间异质性。短期动量(滞后房价变化)的参数值为0.307。动量因素是短期内的关键动力。第二个驱动因素是长期均衡的回复率(估计为- 0.18),高于发达市场报告的比率。研究的局限性/启示本研究适用于需要住房服务买家提供一些房屋净值贡献的市场。实际意义利益相关者可以根据长期基本价值和短期房价动态来描述稳定的房地产市场。由于稳定的房地产市场有利于所有利益相关者,弱或不存在的均值回归动态可能促使政策制定者进行干预。城市规划者以及地方和区域治理的作用,对于消除可能导致价格失控的需求侧或供给侧因素的瓶颈至关重要。原创性/价值现有文献关注的是宽松的信贷规范带来的房地产泡沫风险。为了防止房地产市场出现泡沫,一些监管机构要求购房者以股权的形式缴纳更高的保证金。在业主权益要求较高的市场中,房价的动态变化与高杠杆市场不同。财富效应的影响是用新的数据集来检验的。这项研究以新兴市场为背景,引用了德国和日本等低杠杆发达市场的观察结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
House price dynamics under lower leverage: the case of metropolitan cities in India
Purpose This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics. Design/methodology/approach The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks. Findings Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets. Research limitations/implications This research applies to markets that require some home equity contributions from buyers of housing services. Practical implications Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices. Originality/value Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.
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CiteScore
2.80
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29.40%
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68
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