购买小区住宅的利弊分析

一般来讲,购买有小区规划的住宅,价格相对便宜。这是因为小区规划建房,少则十几万,多则近百万,土地面积大,建筑总量高,发展商获得的土地单位面积成本要比较便宜,单位面积的建筑成本也会因建筑总量走高而相对摊薄,房价自然而然也就比单楼独栋的要低。     

投机性需求、刚性需求与房价上涨的非线性关系——基于空间计量模型的实证研究

本文利用2000~2008年24个省份的季度面板数据,采用空间计量经济模型构建基于当期收入、预期资产收益变动以及利率的住宅价格模型,以资产升值预期和收入变动的视角来研究住宅价格上涨的投机性需求和刚性需求。实证研究结果表明货币政策对房价的影响极为重要;可支配收入对房价的影响次之,资产升值预期对房价的影响与刚性需求相差不大,表明投机性需求已经逐渐成为主导我国住宅价格走势的关键性因素。     

上海市住宅价格影响因素及未来走势研究

本文采用实证分析方法,以2000~2009年上海市宏观经济数据为样本选取上海市33个经济社会指标,通过主成分分析得到影响上海市商品住宅价格的4个主成分并以指标载荷得分为其命名,即:需求因素,供给因素,市场因素,土地因素。选取主成分代表指标,通过多元回归方法求出其与上海市商品住宅价格的回归方程,并以此为依据进行预测分析。分析结果显示,需求因素为影响上海市住宅房价的最主要因素,供求矛盾推动了上海市商品住宅价格的持续走高,并在未来一段时间仍然保持上升趋势。文章最后从政府监管和需求者角度分别给出了相关建议。     

基于ESDA方法与空间计量模型的房价溢出效应分析

一个地区房价的波动可以通过时间滞后传递到其他地区，从而产生房价溢出效应。本文以我国31个省际区域2005～2014年的数据为样本，运用探索性空间数据分析（ESDA）对31个省的房价空间分布格局及演变态势进行分析。然后运用空间计量模型分析我国省际房价变动的影响因素，并从地理因素和经济因素两个方面研究房价溢出效应。研究结果表明：中国31个省域间的房价存在明显的空间相关性；地区的城市化率、城镇就业人员平均货币工资、二、三产业产值占GDP比重对房价有显著的影响，房价存在显著的空间溢出效应，经济距离对地区房价影响比地理距离的影响更显著。     

交易者结构与市场行情：来自深圳股票市场的经验证据

证券市场微观结构理论的核心问题是证券价格的形成与决定。众多学者从不同角度探讨证券市场价格的影响因素,其中投资者结构是影响证券价格的一大重要因素。为了探讨影响中国证券市场价格的微观因素,本文使用各种类型投资者数量作为衡量投资者结构的指标,运用协整检验和Granger因果关系检验方法研究深圳股票市场中交易者结构与市场行情间的内在关系。实证结果表明,在深圳股票市场中,市场价格与个人交易者总数之间存在长期稳定的协整关系。个人交易者总数的变化与市场价格变化间存在着显著的单向因果关系,即交易者总数的变化可以通过市场价格的变化来解释。而市场价格与机构交易者总数之间既不存在长期稳定的协整关系,也不存在单向或双向的因果引致关系。     

住宅小区设计风格应结合室外景观

业内人士近日指出,室内设计要结合整个小区的外景观环境,这是影响项目的外在的自然因素,也是在项目策划时期不可忽视的重要因素。 自然环境的因素有可能决定整个项目的方案设计、投资成本及将来的房价。室外景观之所以重要的另一个原因还在于它属于公共的、普遍性的、硬性的因素,     

2008年:房地产市场难以出现拐点

对于2008年房地产市场价格的走势,中国社科院发布的2008年经济蓝皮书做出了明确的预测。由于支撑我国房地产市场持续发展的因素将长期存在,我国房价的上涨趋势不可避免。此外,目前我国地价房价倒挂现象严重,一些通过招拍挂出让的土地的楼面地价已经接近或高于同地段在售新建房屋的价格,必然使得未来房价大幅提高,这又造成很多地方开发企业通过推迟开盘时间、放缓销售速度等方式捂盘惜售,进一步拉高了房价。     

限价房定价模型实证研究

自2006年推出限价房政策以来,对我国的房价起到一定的平抑作用,但在定价方面存在不尽合理的问题,例如价格参考和价格波动风险等.限价房的定价机制是影响限价房政策执行效果的重要因素,建立科学合理的限价房定价模型并对其进行实证性的研究极其重要.文章通过综合分析各地限价房的定价方法,在前人研究基础上以中低收入家庭的月供及当地租赁价格为两个主要计算指标,对得出的数值进行赋权研究,建立了限价房定价模型.     

城市价值与房价新论

在应对国际金融危机的过程中，中国经济刚刚企稳，房价又超乎寻常上涨，原因何在？学者、官员和媒体纷纷探究。在现代市场经济条件下，影响房价的有供求关系、土地价格、通胀预期、居民收入水平、经济政策等多种因素，但最根本因素是城市价值。城市价值决定城市总体房价，城市价值转移是房价形成的特殊规律，是科学分析判断房价水平及其趋势的重要方法和依据。     

2004年上海市郊区房价走势分析

2003年,上海市郊区的房价快速上涨,平均价格较上一年上涨了20%。2004年一季度,郊区的房价涨幅为3.8%,涨幅明显趋缓。与此同时,上海市出台了相关政策,调控房地产市场的发展,这些政策的出台势必会影响全年的房价走势。下面,从商品房的开发成本、房地产市场的供求情况以及投资者的投机行为等方面,对2004年上海市郊区房价走势作一分析。开发成本新建商品房开发成本包括土地成本、建筑安装工程成本、各类固定收费、期间费用和开发利润五个方面。土地成本由土地拍卖价格决定;建筑安装工程成本主要受建筑材料费用影响;各类固定收费包括六项:人防工…     

成都市金沙遗址博物馆对周边住宅价格影响分析

通过分析成都市金沙遗址博物馆周边一定区域内住宅到博物馆距离与住宅价格之间的关系,建立Hedonic模型,进一步研究影响城市住宅价格因素中旅游景区这一特征对住宅价格的影响。研究结果分为1)在影响住宅价格的因素中,显著性较强的是周边市政设施、物业费、建筑装修程度和到金沙遗址博物馆距离;2)成都市金沙遗址博物馆对城市住宅价格的影响可以用半对数模型表达式表示。进而得出结论:1)影响城市住宅价格因素中旅游景区特征对周边住宅价格的提升具有正作用;2)运用Hedonic模型可以有效地将旅游景区特征对住宅价格的影响作用进行量化;3)基于旅游景区的正向作用,国家应充分重视旅游房地产业。     

Risk and Return in the U.S. Housing Market: A Cross-Sectional Asset-Pricing Approach

This article carries out an asset-pricing analysis of the U.S. metropolitan housing market. We use ZIP code–level housing data to study the cross-sectional role of volatility, price level, stock market risk and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the dynamic role of volatility and housing returns within submarkets over time, our risk–return analysis is cross-sectional and covers the national U.S. metropolitan housing market. The study provides a number of important findings on the asset-pricing features of the U.S. housing market. Specifically, we find (i) a positive relation between housing returns and volatility, with returns rising by 2.48% annually for a 10% rise in volatility, (ii) a positive but diminishing price effect on returns and (iii) that stock market risk is priced directionally in the housing market. Our results on the return-volatility-price relation are robust to (i) metropolitan statistical area clustering effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density.     

Urban flight: A short-term phenomenon or long-term trend?

We investigate whether the urban flight from more to less dense locations identified by previous housing studies for the early COVID period is a temporary, pandemic-induced phenomenon, or long-term trend. We focus on the period of 2017–2022, 14,961 single-family home transactions from Southwestern Ontario, and three housing market metrics. Our results for sales price in the early pandemic periods are in line with previous studies. However, our results for sales price in the last pandemic phase (2022), marketing time, and visit activity suggest that the urban flight was a temporary phenomenon characteristic of the early COVID phases.     

Housing Attributes Associated with Capital Gain

Housing units are heterogeneous goods. Rates of change in housing prices are typically modelled as if they arise from factors unrelated to the housing unit itself. For example, housing price increases in the latter part of the 1970s and early 1980s are argued to have arisen primarily from demographic factors and the differential effects of inflation on the effective rate of taxation on income from corporate capital and on owner-occupied housing. Cross-sectional variation in price inflation is not addressed. Consumers who purchased housing units are not indifferent to their attributes. To the extent that expectations vary within regional housing markets as a consequence of variation in housing attributes, standard linear hedonic price regression may generate biased estimates of implicit prices. This paper identifies sufficient conditions for the estimates of implicit prices in linear hedonic price regressions to be unbiased and generate unbiased estimates of implied price changes. Finally, this paper identifies living space (house size) as a significant attribute related positively to the increase in individual housing prices in a regional market.     

User Cost and the Demand for Housing Attributes

A number of studies have related changes in the demand for housing to changes in user cost. All have treated housing as a composite good rather than as a bundle of characteristics. We consider the effect of changing user cost on the demand for the component characteristics of owner-occupied housing, and, given information about the supply of the characteristics, we predict implicit price responses. An empirical test of our model indicates that reductions in user cost result in higher real prices for the non-replicable attributes of housing, examples being location and access to fixed amenities. In contrast, the price of attributes that are perfectly elastic in supply are not affected by changes in user costs. We conclude that the effects of changing user cost are not uniform across housing types and locations, thus generating the appearance of housing submarkets.     

Marking Single–Family Property Values to Market

This paper is the text of the 2001 Presidential Address for the American Real Estate and Urban Economics Association. House price prediction accuracy is a function of: (1) the procedure used to identify within–metropolitan–area housing submarket boundaries, (2) the econometric technique used to estimate the parameters of the house price model and (3) the characteristics of the local housing market. This paper empirically examines several procedures for delineating within–metropolitan–area housing submarket boundaries. In addition, the paper examines the increase in prediction accuracy that can be achieved by employing spatial econometric techniques to spatially adjust ordinary least squares house price predictions. Finally, the paper examines the influence that local housing market characteristics have on house price prediction accuracy. Housing market characteristics examined here are: (1) structural characteristics of neighborhood properties, (2) heterogeneity of the neighborhood housing stock and (3) housing market liquidity. Prediction accuracy is examined using hedonic house price equations with over 40,000 single–family transactions for Dallas, Texas.     

Another Look at Tenure Choice, Inflation, and Taxes

The purpose of this paper is to explore the impact of inflation on the demand for housing. The first part of the paper presents a theoretical model that identifies the effects of inflation on the after-tax cost of housing and the choice between rental and owner-occupied housing. The second part discusses the results of a simulation model that measures the effect of inflation on the aggregate demand for housing, the aggregate homeownership rate, and the price of housing. The paper concludes that while inflation can be expected to increase the aggregate demand for housing and the price of housing relative to the general price level, inflation should ultimately lead to lower rates of homeownership. A corollary that is probably more relevant today is that lower inflation rates should reduce the real value of the housing stock and increase the homeownership rate. The paper also contains forecasts of the impact of the Tax Reform Act (TRA) of 1986 upon housing demand and the probability of homeownership for a variety of households.     

山西省“十二五”公共租赁住房建设研究

针对山西省公共租赁房还只停留在政策制定层面上,没有规划也没有具体实施等问题,界定了公共租赁房的内涵,采用定性分析与定量分析相结合的方法,预测分析得到城镇居民人均可支配家庭收入、商品住房市场均价,确定了"十二五"期间城镇保障公共租赁住房居民家庭所对应的年收入保障线标准和需要保障的比例,从而预测出"十二五"期间城镇公共租赁住房的年均建设数量,并提出了公共租赁住房建设的政策建议.     

Flooding and Liquidity on the Bayou: The Capitalization of Flood Risk into House Value and Ease‐of‐Sale

The existing literature focuses on how perceived flood risk affects house value. Search theory, however, implies that flood risks will be capitalized into both house price and liquidity. This article draws on search theory to develop an empirical approach for estimating flood risk capitalization into both price and selling time. The results show the mix of price and liquidity capitalization varies by level of flood risk as well as across housing market phases. Regardless of the specific capitalization pattern, the results illustrate that focusing solely on price without allowing for concomitant liquidity capitalization can yield estimates that understate the full impact of flood risk on house transactions.     

Do Conspicuous Consumers Pay Higher Housing Premiums? Spatial and Temporal Variation in the United States

This study is the first to examine the relationship between conspicuous demand and housing price dynamics. We hypothesize that conspicuous consumers would want high‐end homes to signal their wealth and this housing consumption behavior would induce greater deviations from fundamental house prices. We test this by using a unique dataset that matches the consumers’ appetite for nonhousing luxury goods from Google Insights for Search to housing premiums that they pay for high‐end houses in U.S. Metropolitan Statistical Areas (MSAs) during 2004–2011. The estimation results demonstrate that controlling for a wide range of MSA demographic and economic characteristics, conspicuous demand has a significant, positive relationship with housing premiums. This relationship varies spatially and temporally. Conspicuous demand has a stronger relationship with a price increase in high‐end homes in MSAs with a steady, higher housing premium than in MSAs with a volatile, lower premium during the boom period. In MSAs with a steady, higher housing premium, the relationship remains significant even during the bust period, potentially contributing to maintaining higher housing premiums.