房地产市场周期文献综述及外文文献资料

本份文档包含：关于该选题的外文文献 、文献综述 一、外文文献 文献信息

标题: Toward a New Metrics for the Evaluation of the Social Added Value of Social Enterprises

作者: Evans, Richard D

期刊名称: Business Perspectives；第21卷； 第1期；页码：44-49 年份: 2012.

Real Estate Cycle Conditions & Their Main Economic Driver

Evans, Richard D

Commercial real estate in the Memphis area is weathering a recession, as are markets nearby and across the nation. Integra Realty Resources (IRR) provides an annual real estate market cycle analysis that listed the Memphis, St. Louis, and Tulsa office markets as being in that company's stage two of recession at the beginning of 2011, while Nashville had moved into stage three - the last and lowest point of recession. Recession in their taxonomy is characterized by high-and increasing vacancy rates and moderate-to-low new construction. Construction being completed generally would not have been deemed feasible if conditions had been anticipated as construction began. Recession definitions also include low absorption, where new sales and rental rates may be so low that there is negative net absorption.

The IRR real estate market cycle description also includes a non-real estate statistic. \estate cycle definition of recession. Other real estate perspectives give employment trends the highest status in explaining real estate demand. For example, candidates for status as Certified Commercial and Investment Members (CCIMs) of the Realtors learn to forecast demand in every sector of commercial real estate by forecasting local area employment in their required course, \Analysis for Commercial Real Estate, CI 102.\Real estate brokers learn how to identify base industries for local areas by studying employment proportions across industries, to compare those

proportions to national figures (for \quotients\and to study trends in employment that show local area industry strengths and weaknesses relative to overall national trends in employment and trends within industry categories.

This article describes two sets of real estate market cycle reports for the various commercial real estate sectors - from Professor Glenn Mueller's Cycle Monitor - Real Estate Market Cycles (downloadable free from its sponsor Dividend Capital at www.dividendcapital.com) and from Integra Realty Resources' IRR-Viewpoint. IRR Memphis data come from Walter Alien, noted member of the Appraisal Institute and a member of the Board of Integra Realty. Since the labor market is recognized as the key demand driver for all real estate, this article also shows how Shelby County labor markets have developed over the last ten years, applying the analysis now learned by many CCIMs.

Real Estate Cycle Conditions

The IRR-Viewpoint 2011 showed stage two recession conditions in not only the Memphis office market, but also the retail market, as well as the St. Louis retail market. Nashville was deeper in recession, at stage three, but the Tulsa retail market had moved from recession into the first stage of recovery. The Memphis apartment market was rated as having moved through the recession trough to the first of three steps of recovery, along with Tulsa and St. Louis. Nashville was still in stage three recessions. The Memphis industrial market was the only one of these four regional markets to show the first signs of recovery at the start of 2011.

The most recent Mueller report is for the third quarter of 2011. The Dividend Capital Cycle Monitor rates national markets in one of sixteen stages of his model of the real estate cycle, where stage one is at the trough between the low point of the recession and the first onset of recovery, and stage sixteen is just before the end of recession. Stage sixteen comes just before stage one. Across the U.S., office markets were in the trough at stage one, including Memphis, New Orleans, Oklahoma City, and St. Louis. Nashville was one step out of the trough at stage two. Retail markets in Memphis, Nashville, St. Louis, and the overall U.S. market had just moved from stage one to stage two, joining Oklahoma City, but leaving New Orleans. Apartments were

just out of the trough at stage two in Memphis, Oklahoma City, and St. Louis. New Orleans stayed in cycle trough conditions, while the overall U.S. retail market and Nashville moved to stage three. Industrial and warehouse markets in the U.S., Memphis, Oklahoma City, and St. Louis stayed in stage two, while Nashville and New Orleans industrial markets stayed in the trough of the real estate cycle. In Mueller's cycle model, feasible new construction seems far away, at stage eight. Given the long lags common in real estate development, builders may start tentative planning and acquisition steps soon, hoping to time development so as to be \ready\

After the second quarter of 201 1 , Memphis office markets are in Mueller's stage one trough, along with St. Louis, Oklahoma City, New Orleans, and the nation on average. Nashville is one step ahead in beginning its office market recovery. Memphis industrial markets are in stage two, along with the national average and St. Louis and Oklahoma City. Lagging behind at stage one in the industrial markets are Nashville and New Orleans. For apartment markets, Memphis is in stage two, as are Oklahoma City and the national average. Nashville leads in this asset type in stage three, but St. Louis and New Orleans remain in the trough of the recession. Retail is largely still in recession trough conditions for the national average and for Memphis, Nashville, and New Orleans. In these data, Oklahoma City rated stage two status in its recovery, reflecting second quarter 201 1 conditions in retail.

To summarize the real estate cycle conditions, many Memphis regional commercial real estate markets are still in recession, some are in the trough between recession and recovery, and a few have taken the first tentative steps out of the trough. Commercial real estate markets have suffered recessions in other cycles from speculative overbuilding. It seems that the latest cycle is different, despite high vacancy rates. A view of the labor markets shows why this real estate recession was caused by a decreased demand for space for workers in the office and industrial sectors. A lack of employment growth also led to decreased demand for apartments and for the consumer activity that supports the retail real estate sector.

Labor Markets Driving Real Estate Markets

The Bureau of Labor Statistics provides up-to-date labor market data at its Economy at a Glance site. After selecting a region, state, or local area, the most current labor market data are provided above a set of tools and \at the bottom of the site. Table I is derived from a report generated by selecting \Quotient\from the calculator bar on the site for Shelby County. Using the NAICS industry sector categories, Shelby County employment figures for 2010 are compared to national employment levels.

For example, Shelby County employment in Construction (NAICS code 23) was 15,196 averaged over the four quarters of 20 10. That represents 3.81 percent of total Shelby County private employment. U.S. Construction employment averaged 5.17 percent of total private employment. The under-representation of that sector of the Shelby County economy gives it a \less than 1.00, 0.74 = 3.81/5.17.

A quotient larger than one indicates that an industry sector has locally stronger representation than do national proportions. The 3.48 ratio for Transportation and Warehousing means that the local economy is apparently exporting those services to the rest of the nation. Wholesale Trade, Administrative and Waste Services, and Real Estate and Rental and Leasing round out the four sectors of the Shelby County economy that have stronger employment proportions than are typical for the U.S. economy.

Some readers may be surprised with the industry sectors that are close to 1 .00- indicating no more than normal employment in the local economy. Retail Trade (now 0.95) was once a base industry for Shelby County, with many consumers from surrounding counties traveling to Memphis for their shopping. Health Care and Social Assistance, with a location quotient of 0.99, does not seem consistent with the area's reputation as a center for medical care. The 0. 95 location quotient for Accommodation and Food Services would counter arguments that the area is a tourism and convention center, as would the 0.61 figure for Arts, Entertainment, and Recreation. The area's reputation for several decades as a financial center is put in doubt by the 0.76 location quotient for Finance and Insurance in 201 0. Many readers

already know that the Manufacturing sector is small in the local area, confirmed by a 0.81 location quotient.

To explain some of these surprising results, it is useful to exploit the added detail available in the labor market data. Table 1 shows location quotients calculated from the \is for three-, four-, or five-digit definitions. International Paper has its headquarters in the local economy, and the location quotient for Paper Manufacturing (NAlCb 322) is 3.62, despite Manufacturing (NAICb 31- 33) having only a 0.81 location quotient. Miscellaneous Manufacturing (NAICS 339), Beverage andlobacco Product Manufacturing (NAICS 312), Chemical Manufacturing (NAICS 325), Petroleum and Coal Manufacturing (NAICS 324), Machinery Manufacturing (NAICS 333), Electrical Equipment and Appliance Manufacturing (NAICS 325), and Printing and Related Support Activities (NAICS 323) are in a set of seven classed as exporting manufacturing subsectors with location quotients higher than one.

NAICS code 492 is Couriers and Messengers, which has a 12.87 location quotient. 1 he proportion of focal employment being more than twelve times the national proportion tells the importance 01 FedEx to the local economy. Air lransportation (NAICS 48l), Water Transportation (NAICS 483), Truck Transportation (NAICS 484), and Warehousing and Storage (NAICS 493) all contribute more than double the proportion of employment locally than is typical in the U.S.

NAICS code 441 is Motor Vehicle and Parts Dealers, with a 1.34 location quotient. AutoZone s headquarters in the local economy explains part 01 how this sector of retail is not below average, despite the 0.95 location quotient tor aff of Retail. Only hve subsectors of retaif are not befow 1.00, including Home Furnishing Stores (NAICS 442), Clothing and Clothing Accessories Stores (NAICS 448) , and Sporting Goods, Hobby, Book. and Music Stores (NAICS 451).

Hospitals (NAICS 622), with a location quotient of 1.27, are an export sector, while the other subsectors of Health Care and Social Assistance are relatively small, with a location quotient of 0.99 for NAICS 62. Likewise, the 0.76 location quotient lor Finance and Insurance (NAlCS 52) obscures the importance of NAICS 523,

Securities, Commodity Contracts, and Investments, with a 1.13 location quotient. The low employment representation for tourism-related sectors seems valid. Only one three-digit subsector related to tourism, Accommodations and Food Services, Arts, Entertainment, and Recreation, has a location quotient above one. Museums, Historical Sites, Zoos, and Parks (NAICS 712) has a 1.30 location quotient.

Proponents of location quotient analysis of labor markets as applied to anticipating and explaining changes in real estate markets call for a chain of causation as described as follows. Identify the local economy's base industries as sectors with location quotients above one. Forecast changes in base industry employment. Anticipate that the other sectors will grow to support the growth in the base /exporting industries. Forecast residential real estate demand based on total growth in jobs. Forecast industrial real estate demand from growth in industrial, warehousing, and total employment. Forecast retail real estate demand from growth in total jobs and the income from those jobs. Office demand is sensitive to total employment growth, but more so to growth in employment in the sectors that are focused on office work - -sectors for Financial Activities, for Professional and Business Services, and for Information.

Trends in Employment

Data on employment growth in a local economy may support shift-share analysis, a trend analysis for explaining an anticipated growth that is closely related to location quotient analysis. Another Bureau of Labor Statistics site provides data for an analysis of changes in employment over long periods. The Census of Employment and Wages shows that Shelby County lost 34,386 jobs between 2000 and the end of 20 10. Total employment in Shelby County grew from 414,307 in 1991 to 498,780 in 2000, but then stalled for five years of mixed decline and zero net growth. In 2006, there was a marked rebound to the high point of employment at 508,434, but 2007 experienced small decreases that foreshadowed much larger decreases each year through 2010. Over the 2000-2010 decade, 6.9 percent of employment in Shelby County dissipated. The employment figures include public employment and other differences that make them not perfectly comparable to figures in Table 1.

A free site from the University of Georgia uses the Census of Employment and Wages data to perform a classical labor market, shift-share analysis. The site generates tables such as Tables 2 and 3 for the user's choice of geographic area. The purpose of shift-share analysis is to segment local area employment change into three components - the part that is just proportional to growth or decline across the U.S. as a whole, the part that just reflects national changes in particular industry sectors that are represented in the local economy, and the part that may be attributed to specially developing local competitive strengths or weaknesses in those industry sectors.

Table 2 ranks eleven industry sectors by employment importance in the Shelby County labor market in 2 000. The employment changes by 2010 shown in the table become the input for the shift-share analysis. The Trade, Transportation, and Utilities sector ranks as the local market's most dominant employment source in both years, but it also shows the greatest decrease in employment - 23,320 lost jobs. Education and Health Services rank as the second largest sector in both years and gained 15,143 jobs over the decade. The relatively small Manufacturing sector lost 22.3 percent of its jobs in Shelby County, while Financial Activities lost 14.9 percent.

Table 3 shows the results of the shift-share analysis of the employment change data in Table 2. The sectors have been resorted to show the sectors with the strongest \\two plausible causes of change in a local economy.

The first set of calculations translates the number of jobs that would be created or destroyed in the local economy if each sector had the same percentage change as seen for the overall U.S. employment figure. Between 2000 and 2010, total U.S. employment decreased 1.618 percent. Shelby County Manufacturing employment was 44,944 in 2000. Using the national overall employment decrease, that would hypothetically explain a 727 decrease in Shelby County Manufacturing employment. This hypothetical percentage change is repeated in each line of Table 3 to reflect how Shelby County employment in each sector would have suffered job losses if they were proportional to the overall national decline. If Shelby County had just replicated the

losses of overaU jobs in the U.S. economy, the losses would have summed to 8, 065 jobs.

The second set of calculations reflects how each employment sector changed nationally (net of the -1.618 percent taken across all sectors in the prior step). Manufacturing decreased 33.6 percent nationally, 32.0 percent beyond the overall job loss percentage. If Shelby County Manufacturing employment had suffered decline in the same proportion as all Manufacturing, then there would have been an additional 14,366 jobs lost in the local Manufacturing job market. Each sector had its own national change during the decade. Table 3 details the application of each sector's net performance to Shelby County employment in 2 000. The sum of these adjustments is positive - 4,006.

The first two calculations for Manufacturing employment in Shelby County call for a sum of 727 + 1 4, 366 = 1 5 ,093 lost jobs. The actual change in local Manufacturing employment was only 10,015 lost jobs. Manufacturing actually did better than could be expected in Shelby County, where expectations are based on how national overall employment and national Manufacturing employment trended in the 2000^2010 period. The \Share Component\of Table 3 's shiftshare analysis shows that Manufacturing \trend decomposition analysis shows that Manufacturing was the premier growth sector by number of local jobs relative to expectations. Proponents of shift- share labor market analysis would anticipate that whateyer locd competitive advantages the Memphis area exploited over the 2000-2010 period could persevere in the future to give better than normal growth in Manufacturing employment. This situation would translate into increased real estate demand for that sector. As a caveat, the Manufacturing sector is small in Shelby County. This sector did decrease in employment by 22.3 percent during the historical period of study.

All the other Competitive Market Component calculations for Shelby County over the 2000- 2010 period are negative. These results do not support great growth anticipations for employment or real estate markets. Retail and residential real estate demands are both sensitive to total employment. Office Market real estate demand

growth will be dismal if the negative Competitive Share Components in Information, Professional and Business Services, and Financial Activities all repeat the recent decade's performance. All three sectors decreased in the ten-year period. As a group of three, they decreased locally more than by expectations based on national and industry trends.

The shift-share analysis of a summed Competitive Share Component of -30,327 points to special local employment problems and disadvantages that caused more weakening of the labor markets than could be explained by the national economy's decrease and the mix of industries represented in Shelby County. The nature of the problems and disadvantages are not identified in shift-share analysis.

The most negative conclusion from the shift-share analysis is for the most important sector of the local economy. Trade, Transportation, and Utilities would have lost 1.6 percent of local employment just keeping pace with national employment trends across all sectors, and then another 4. 1 percent because this sector slipped in national employment relative to other sectors. Shelby County employment in its most important sector declined an extra 9.0 percent beyond those expectations. The problems and disadvantages in the local economy that caused this negative Competitive Share Component will need to be reversed to avert the stagnation that would come from an ailing sector with very high location quotients.

The current mix of industry in the local economy is not a negative factor. In fact, the sum of the Industrial Mix Components is positive. Memphis has industries that did well relative to the overall U.S. economy in the period studied. 二、文献综述

房地产市场周期文献综述

摘 要

房地产市场在发展过程中客观存在着周期波动现象，分析房地产市场周期，不但具有理论上的开拓意义，而且在实践中有助于引导房地产市场的主体——投资者、开发商进行投资决策。房地产投资决策是整个房地产开发过程中的关键环

节，决策的合理性、科学性将直接影响开发项目能否顺利进行。如何运用房地产市场周期理论指导投资决策，是决策者迫切需要解决的难题。本文旨在从房地产市场周期的角度探讨房地产投资的决策方法，以降低投资风险。 关键词：房地产市场；周期波动；房地产价格 1 引言

宏观经济具有周期性特征，房地产市场在运行中，也因为受资源约束或消费约束而出现经济收缩阶段，由于资源供给充裕或消费需求拉动而进入经济扩张阶段，周而复始、循环往复，由此构成房地产市场周期波动现象。房地产市场周期波动对房地产投资收益、风险以及随时间而变化的投资价值有着深刻的动态影响，从而影响着房地产投资项目的成败。因此，房地产投资者和房地产研究者越来越关注于房地产市场周期理论和周期模型分析在投资决策中的应用。房地产市场周期的研究往往是基于经济周期研究基础上。

米切尔在长期研究的基础上发表的著作《商业循环》(商业周期)被认为是测定经济周期波动研究的开创性著作。米切尔又出版了《商业循环：问题和调整》(商业周期：问题和它的设置)一书。这本书详尽地总结了自本世纪初以来经济周期波动测定与景气指数建立等方面的进展和成果，对运用景气指标监测宏观经济周期波动的问题进行了理论探讨，特别是详尽地讨论了利用经济变量的变动时差，超前反映经济波动的问题。虽然整个商业和土地经济学界早期非常重视房地产市场周期研究，但其对房地产投资决策的应用研究却一直为房地产学术界和实践者所忽视，直到九十年代，这一情况才有所改观。近些年来，房地产学术界、产业研究者和决策制定者对房地产市场与财富周期的兴趣日渐浓厚，大量文献应运而生。公开发表的有关房地产市场周期的文献数量和诸如美国房地产协会（ARES）、国际房地产协会（IRES）、欧洲房地产协会（ERES）、亚洲房地产协会（ASRES）以及太平洋房地产协会（PRRES）等组织年度会议提交的论文情况充分反映了有关房地产市场周期知识的迅速增长。 2 国外房地产市场周期研究

国外房地产市场周期研究首先产生于 1929 年大萧条后的美国，经过七十多年的沉淀，形成了众多的成果。

2.1房地产市场周期与国民经济的相关性研究

Grebler 和 Bums(1982)分析了美国总体建设、公共建设、私人建设和住宅建设1950~1978 年的数据，发现了 6个住宅周期和 4个非住宅周期，并发现 GNP（经济周期）领先房地产市场周期 11个月达到峰值。Brown(1984)考察了美国 1968~1983 年家庭住宅的销售情况，在消除季节影响和趋势影响之后，发现房地产市场周期依然存在，并与国民经济周期具有强相关关系。Pritchett（1984）分析了 1967~1982 年间国民经济对房地产市场周期的影响，试图从房地产现金流量中找出一个关键变量来反映周期的变化。得出的结论是，当房地产市场周期达到峰值时，需求领先于供给，而当房地产市场周期降至谷底时，需求滞后于供给；并且标识房地产市场周期的最佳变量是空置率。通常，空置率在衰退阶段达到很高的水平，在扩张阶段逐渐下降，在房地产市场周期的顶峰达到最低点。Kling和McCue(1987)研究了宏观经济因素对写字楼建设的影响。他们利用回归模型对写字楼月建设量、货币供给、名义利率、GNP和综合物价指数进行了分析。他们的结论是：名义利率的下降导致写字楼的过量建设和市场周期波动。Downs(1993)的研究得出结论，不同的市场有不同自然空置率水平的原因是供给和需求的不同。动态市场往往比静态市场有更高的空置率水平。他研究了房地产市场周期与一般经济周期的关系。Janssen, Kruijt 和 Needham(1994)研究了 1976~1989 年 14 年间荷兰的住宅市场。结论是，无论是全国市场还是大城市市场都存在着周期波动，不同城市的周期不同，且与全国市场存在着很大的差异。1997 年，Green 的分析表明，住宅建设投资带动 GDP 的波动，而非住宅物业的投资落后于 GDP 的波动。原因在于：住宅建设能带动国民经济的增长，而国民经济的强度和结构的变化会导致商业性房地产价格和租金波动。上述文献研究，证明了房地产市场周期的实际存在性及房地产市场周期与经济周期的“领先/滞后”关系。

2.2全球房地产市场周期研究

在对 1985~1994 全球房地产市场周期波动的解释中，Bertrand(l996)分析了形成周期的国际因素和国内因素，认为国际资本流动(日本的对外投资)、各国资本市场自由化、金融管制的放松、扭曲的财政政策和土地利用制度是全球房地产市场周期波动以及泡沫崩溃的主要原因。虽然全球房地产市场周期研究尚处于起步阶段，（完整内容请到百度文库）但是人们将会研究出越来越多的世界周期模

Grebler 和 Bums(1982)分析了美国总体建设、公共建设、私人建设和住宅建设1950~1978 年的数据，发现了 6个住宅周期和 4个非住宅周期，并发现 GNP（经济周期）领先房地产市场周期 11个月达到峰值。Brown(1984)考察了美国 1968~1983 年家庭住宅的销售情况，在消除季节影响和趋势影响之后，发现房地产市场周期依然存在，并与国民经济周期具有强相关关系。Pritchett（1984）分析了 1967~1982 年间国民经济对房地产市场周期的影响，试图从房地产现金流量中找出一个关键变量来反映周期的变化。得出的结论是，当房地产市场周期达到峰值时，需求领先于供给，而当房地产市场周期降至谷底时，需求滞后于供给；并且标识房地产市场周期的最佳变量是空置率。通常，空置率在衰退阶段达到很高的水平，在扩张阶段逐渐下降，在房地产市场周期的顶峰达到最低点。Kling和McCue(1987)研究了宏观经济因素对写字楼建设的影响。他们利用回归模型对写字楼月建设量、货币供给、名义利率、GNP和综合物价指数进行了分析。他们的结论是：名义利率的下降导致写字楼的过量建设和市场周期波动。Downs(1993)的研究得出结论，不同的市场有不同自然空置率水平的原因是供给和需求的不同。动态市场往往比静态市场有更高的空置率水平。他研究了房地产市场周期与一般经济周期的关系。Janssen, Kruijt 和 Needham(1994)研究了 1976~1989 年 14 年间荷兰的住宅市场。结论是，无论是全国市场还是大城市市场都存在着周期波动，不同城市的周期不同，且与全国市场存在着很大的差异。1997 年，Green 的分析表明，住宅建设投资带动 GDP 的波动，而非住宅物业的投资落后于 GDP 的波动。原因在于：住宅建设能带动国民经济的增长，而国民经济的强度和结构的变化会导致商业性房地产价格和租金波动。上述文献研究，证明了房地产市场周期的实际存在性及房地产市场周期与经济周期的“领先/滞后”关系。

2.2全球房地产市场周期研究

在对 1985~1994 全球房地产市场周期波动的解释中，Bertrand(l996)分析了形成周期的国际因素和国内因素，认为国际资本流动(日本的对外投资)、各国资本市场自由化、金融管制的放松、扭曲的财政政策和土地利用制度是全球房地产市场周期波动以及泡沫崩溃的主要原因。虽然全球房地产市场周期研究尚处于起步阶段，（完整内容请到百度文库）但是人们将会研究出越来越多的世界周期模

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