粤港澳大湾区建设规划推高了珠三角地区房价吗?——基于双重差分方法的经验研究

本文基于双重差分模型,采用2012—2018年中国37个地级市城市(9个珠三角城市加28个其他省大中城市)的面板数据,实证检验了粤港澳大湾区建设规划对珠三角地区房价的影响及其异质性效果,然后运用莫兰指数探究珠三角地区内房价变动是否存在空间效应。结果发现:粤港澳大湾区建设规划对珠三角地区房价存在显著正效应,且对不同人口规模和房价水平城市的房价影响效应存在不平衡性;珠三角地区房地产市场尚不存在明显的空间效应,粤港澳大湾区建设规划的出台在短期内可能会进一步拉开区域内各城市房价水平差距。基于此,珠三角地区在把握粤港澳大湾区建设历史发展机遇的同时,不能忽视房价波动风险,应坚决执行房地产市场调控政策,确保区域协调与高质量发展。     

基于面板数据的江苏省房价影响因素分析

近些年来,房价的快速上涨引起社会各界的广泛关注。江苏省作为中国的经济强省，各市房价近些年也快速上涨。为了分析江苏省不同地区房价的主要影响因素，基于2016-2020年江苏省13个地级市房价及其房价影响因素年度面板数据，使用dbscan聚类算法将江苏省房价聚类成为两大类地区，构建双固定效应面板模型对江苏省房价进行分区域分析。进一步考虑各区域房价可能存在的空间相关性，并构建相应的空间计量模型，对江苏省房价影响因素进行分析，最后给出相应的结论和建议。     

Effect of COVID-19 lockdowns on city-center and suburban housing markets: Evidence from Hangzhou,China

In 2020, governments worldwide enforced lockdowns to contain the spread of COVID-19, severely impeding aspects of daily life such as work, school, and tourism. Consequently, numerous economic activities were affected. Before the COVID-19 outbreak, city-center housing markets in areas surrounding popular tourist attractions performed better than did suburban housing markets because of the output of the tourism industry. This study examines the changes in the performance of city-center and suburban housing markets in regions with popular tourist attractions after the lockdown. Specifically, the dynamics of city-center and suburban housing markets in Hangzhou, where West Lake is located, and the changes in the information transfer between these housing markets after the lockdown are explored. Transaction data from January 1, 2019 to September 30, 2020 are used to perform analysis, in which adjusted housing prices and asking prices are employed to measure market performance and sellers’ pricing strategies, and transaction volume and time on the market are used to measure market liquidity and transaction frequency. The results reveal that the effects of lockdowns differ between city-center and suburban housing markets. After the lockdown, a substantial structural change is observed in the suburban housing market; the volatility risk of housing prices decreases substantially, causing an increase in transaction premiums. Housing prices and transaction volume increase in the city-center housing market after the lockdown; this is possibly because of the influence from the overall housing market booms. In addition, because sellers raise their asking prices and the transaction time is extended, the sellers in the city-center housing market are particularly influenced by the disposition effect. This leads to a reversal in the lead–lag relationship between the city center and suburban housing markets in terms of informativeness. Specifically, before the lockdown, the city-center market transfers information to the suburban market, but after the lockdown, the suburban market transfers information to the city-center market. The COVID-19 pandemic has changed the world in many aspects; this paper finds that it will also change the development pattern of the real estate market in different locations.     

居住用地供给对上海住房市场的影响——基于动态存量一流量模型的实证研究

文章运用住房市场存量-流量模型,动态分析居住用地供给对新建商品住房供给及住房价格的影响,并据此模拟不同居住用地供给思路下商品住房市场的运行状况。基于上海数据的实证分析发现：政府新增居住用地供给对一定时期后的新建商品住房供给具有显著影响;居住用地供给能够影响远期住房价格,但很难对当期住房价格产生影响。模拟结果显示：增加居住用地供给所能起到平抑房价的作用较为有限。据此提出,政府居住用地供给应着眼于长期,不宜依据短期住房市场状况进行频繁调整。     

城市住房产品配置效率分析

随着我国人口向城市转移,城市住房需求矛盾日渐突出,解决城市住房合理供给问题十分迫切。城市住房产品具有准公共性,完全由市场提供或公共提供都会造成社会总收益的损失。优化我国城市住房产品配置效率措施应为采取混合提供方式,坚持市场化供给,加大小户型、低总价住房供给,平抑过高房价,保持房价平稳增涨。     

The Impact of Social Housing Developments on Nearby Property Prices: a Nelson Mandela Bay Case Study

Social housing projects often face substantial “Not‐in‐my‐backyard” (NIMBY) sentiment and, as a result, are frequently plagued by local opposition from communities who argue that nearby property prices will be affected adversely by these developments. International hedonic pricing studies conducted have, however, produced mixed results with some concluding that social housing developments may in fact lead to an improvement in surrounding property values. There is, however, a paucity of South African evidence. This study considers the validity of the most pervasive NIMBY argument, the claim that social housing developments negatively affect nearby property values, by considering the property prices of 170 single‐family homes in the Walmer neighbourhood, Nelson Mandela Bay, as a function of their proximity to an existing low‐cost housing development. The results of this study indicate that in the case of one Nelson Mandela Bay low‐cost housing development, a negative impact is exerted on the property values of nearby houses.     

城镇化水平、FDI与房价——基于泛长三角的空间计量研究

文章利用31个城市2000-2011年面板数据,基于空间距离加权矩阵,使用空间计量相关方法,研究泛长三角地区城镇化水平、外商直接投资(FDI)与房价的关系。研究表明:空间相关性是影响房价的重要因素。城镇化进程加快导致了浙江、江苏和安徽房价上涨;实际FDI与房价的关系在省际间表现出明显差异,其中,浙江和安徽实际FDI与房价呈显著正相关,而江苏实际FDI与房价的正相关关系不显著。     

The effect of gasoline prices on suburban housing values in China

By raising road transportation costs, an increase in gasoline prices should be expected to reduce housing demand in locations further from the central business district (CBD) relative to inner-city locations. This study uses a monthly real estate area dataset for 19 large cities in China over 2010–2018 to investigate the impact of gasoline prices on intra-city spatial differentials in housing prices. The findings suggest that higher gasoline prices on average lead to a relative decline in housing prices in outer suburbs, with a 1% increase in gasoline prices on average leading to a 0.004% relative reduction in home values for every additional kilometer from the CBD. The effect is larger in cities that have higher automobile ownership rates and that are less densely populated. The results are consistent with a conclusion that the rise of electric vehicles, autonomous vehicles, and working from home is likely to contribute to a lowering of geographical price differentials within Chinese cities over time.     

An Empirical Analysis on Regional Influential Factors of Housing Prices in China

Using the Pooled Data of housing market of 30 provinces during 1995-2003, this paper investigates the micro-level influence factors of housing prices fluctuation in China. We have found out that income, cost and lagged housing prices are important factors to housing prices fluctuation. The influence factors do not change with time, but vary in different regions. In addition, cost and lagged housing prices are not only the influence factors of the state housing prices, but also the notable impact factors to each region. Thus, we suggest that the government should take different policies in different regions, control the demander and supplier and guide fight expectations.     

Housing Markets in China and Policy Implications： Comovement or Ripple Effect

The overheated housing market has recently become a top priority of the Chinese authorities and whether the ripple effect exists is key to understanding this housing issue. The present paper uses a cointegration estimation technique for six first-tier Chinese cities during the 2003-2013 period to show that the comovements among housing prices in China are fully reflected in a long-run equilibrium. Using the Toda- Yamamoto causality test, the ripple effect is found to be characterized by a lead -lag relationship. More importantly, it is found that Beo＇ing is the main source of housing price appreciation in China, and should be targeted as the regulatory object to efficiently resolve the troubles in this increasingly high housing-price era.     

Development of Public Housing in Post‐reform China

This paper explores the dynamic nature of the transformation of public housing regimes in urban China since the abolishment of the urban welfare housing system in the late 1990s. We summarize the latest progress in the development of public housing in post‐reform China and investigate the driving forces behind these developments. A close examination of the public rental housing program in Shanghai helps to show that the recent revival of public housing in Chinese cities is mostly driven by the desire for economic growth. We conclude that the state provision of housing could be a short‐run state remedy to alleviate economic imbalance and social inequality. However, in the long run China needs to seek more effective solutions to solve the low‐income population's housing affordability problems.     

Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy

In a similar way to the stock market, the housing market in China has often been portrayed as highly speculative, giving rise to “bubble” concerns. Over the last decade, residential prices increased every year on average by double digits in Beijing or Shanghai. However many observers and researchers argue that fundamentals of the housing sector, both sector-specific and macroeconomic, may have been the driving force behind housing price volatility. While existing empirical work exclusively relies on the government housing prices which may suffer from the well-documented downward bias, this paper uses original high frequency unit price as well as transaction series for the residential resale housing markets of Beijing and Shanghai between January 2005 and December 2010 to test alternative hypotheses about housing prices volatility.We propose a sequential strategy in five steps integrating several techniques previously developed in a piecemeal and scattered way. First, we construct daily hedonic prices. Second, in order to search for the possible presence of bubbles on such high-frequency data, we propose using recently developed tests of an explosive root as an alternative to the unit root hypothesis. The third step is generated by the necessity of handling microstructure noise present at a daily frequency, thus filtering the raw data to extract a random walk component. The fourth step extracts a slowly changing monthly volatility component from the filtered daily hedonic real estate data. Finally, in so far as the presence of bubbles does not seem to characterize the residential housing market in major Chinese cities, such as Beijing and Shanghai, in a fifth step we show that fundamentals are able to explain slowly changing volatility, as well as transaction volumes in these first‐tier cities.     

基于灰色关联度的安徽省房价影响因素的分析

以安徽省2001-2010年房地产相关资料为依据,适当选取房价影响因素指标,引入灰色关联分析方法,构建房价影响因素的灰色关联度模型,定量地计算出各指标与房价的灰色关联度。结果表明:人口总数、居民消费价格指数、税收三大因素对房价影响最大,而住宅施工面积、住宅销售额、住宅投资完成额影响较小。     

35个大中城市住宅市场差异研究

本文以我国35个具有代表性意义的大中城市住宅市场为研究对象,选取相应的住宅市场发展指标和经济社会发展指标,采用因子分析和聚类分析方法将35个城市的住宅市场划分为供求极度紧张型、供求紧张型、供需两旺型、增长较快型、平稳发展型和缓慢发展型等六种发展类型,并对各类城市住房市场的发展特点进行了总结和分析。最后针对六种发展类型,提出相应的土地调控政策建议。     

住房贷款与房地产价格关系的实证研究——来自深圳的证据

本文围绕住房贷款与住房价格的关系展开研究,将住房贷款与住房价格视为内生变量,将购买住房成本、二手住房价格、住房供求缺口、收入住房价格比视为外生变量,采用深圳市2006年1月至2008年5月的月度数据估计了包括2个内生变量、4个外生变量的VECM模型。实证结果表明：深圳住房贷款与住房价格之间存在长期均衡关系,中央银行信贷数量调控效果要优于利率价格调控效果,银行业住房信贷政策整体上讲是稳健的,基于此并针对有关结论提出了政策建议。     

Housing Supply,Demand and Price: Construction Cost,Rental Price and House Price Indices

Housing prices are subject to the impacts of supply as well as demand. While supply is affected by construction costs, demand is determined by the renting/buying considerations of the public. As a result, the construction cost index (CCI) on the supply side and the rental price index (RPI) on the demand side should be closely related to the house price index (HPI). The present study adopts three price indices of the Taiwan housing market, the CCI, the RPI and the HPI, and examines long‐term and short‐term correlations among the three indices. Empirical results indicate that the relationships among three indices are nonlinear. More interestingly, this article finds that the HPI stimulates changes in the CCI and the RPI, although construction costs and rent are viewed as fundamentals in the existing literature. This phenomenon is rather obvious when deviations of the latter two indices from the HPI are greater. The corrective behavior of the HPI is more notable under these circumstances.     

房地产跌涨论

跌方：以上海房内环以内两室一厅95平米精装房,夫妻收入每月两万（在上海算是中上了）为例：     

PM2.5对浙江、福建、江西房价的动态影响研究——基于函数型线性模型

以函数型线性模型作为研究动态影响的工具,通过研究发现PM2.5对浙江、福建、江西房价的动态影响存在两种模式：一种是PM2.5上升最终会导致房价下降,PM2.5下降最终会导致房价上升,该模式在全年中占了243天,符合客观认知：空气质量越好房价越高,空气质量越差房价越低;另一种是PM2.5的上升最终会带动房价上升,PM2.5下降最终会导致房价下降,该模式在全年中占了122天,有些违背常理,应当是由其他因素引起而恰好与PM2.5的变动趋势重合。     

“十二五”开局之年的我国房地产市场走向评估

在2011年及十二五期间,基于价值规律的制导,房价构成要素的上涨、房地产市场刚性需求的继续释放,许多城市的房地产价格还存在上涨压力。要有效调控房价,必须从土地供应、房地产银行贷款、税收、保障性住房等方面进行综合的、互动的、配套的松紧适度和有差别的结构性调控,以此为改进与优化取向,藉以防止房地产市场与房价的大起大落,促进房地产市场的稳健发展。     

The impacts of distance to CBD on housing prices in Shanghai: a hedonic analysis

It is widely recognized that location is the primary determining factor of housing price. But to what extent the variation of housing price in Shanghai can be explained by the locational factor has not been empirically examined. In this paper, we examine the power of applying the hedonic method to the spatial-statistical analysis of housing prices in Shanghai. The data we use covers all new commercial residential housings sold in Shanghai during July 2004 and June 2006. The main focus in this paper is to examine the effect of geographical distance to city centre on the selling price of residential housings in Shanghai. We also discuss how the price gradient varies at different directions in Shanghai. Finally, we demonstrate the importance of applying quality control on the development of a housing price index. The statistical methodology and empirical results obtained in this paper carry interesting implications for other cities in China as well.