日本经济分析：辨别短期内土地价格可能上涨的地区1023.ppt

,(1),(2),(3),(4),(5),2012 年 10 月 19 日Issue No:12/18日本经济分析研究报告辨别短期内土地价格可能上涨的地区Naohiko Baba,我们在本文中基于日本各县详细数据对土地价格的决定因素进行了模型分析，并辨别出了中期内地价很有可能上涨的地区。我们的主要结论如下：我们的时间序列分析和横向分析均显示，对日本地价影响最大的人口指标是人口红利（劳动力人口/受赡养人口）。随着人口红利的上升（下降），适龄劳动力人口获得更多（更少）的资源用于建设生产活动，由此在经济体中形成良性（恶性）循环，地价随之上升（下降）。人口红利对于商业地价的影响大于其对住宅地价的影响。,+81(3)6437-9960 高盛证券株式会社Chiwoong Lee+81(3)6437-9984 高盛证券株式会社Yuriko Tanaka+81(3)6437-9964,高盛证券株式会社综合考虑人口红利以及劳动力人口人均名义 GDP 等指标，我们的模型可解释日本地价变动的 95%左右。此外，劳动人口人均名义 GDP 受到人口红利的显著影响，这意味着日本地价的唯一最重要影响因素其实是人口红利。我们对各县土地溢价的计算显示，东京的住宅和商业用地的溢价都最高。东京商业用地的溢价尤其高，溢价幅度约为大阪（位居第二）的近三倍。这体现出东京商业活动高度集中的集群效应。我们基于人口红利前景筛选出了地价可能会上涨的地区。东京和大阪地价上涨的可能性位居第一，其次是大东京区（神奈川县、埼玉县和千叶县)、大大阪区、九州（不包括佐贺县和长崎县）、宫城县和石川县。投资者不应视本报告为作出投资决策的唯一因素。有关分析师的申明和其他重要信息，见信息披露附录，或参阅,1,1,2,2012 年 10 月 19 日,日本经济分析,Identifying areas where land prices are likely to rise in near futureJapanese land prices are showing signs of ending a downtrend that dates from the early 1990s,when Japans asset bubble burst.In the benchmark land price report published in late September(prices as of July 1),there were substantial increases in the number of locations where residentialand commercial land prices were either flat or higher year on year in the four largest populationcentersTokyo,Kanagawa,Osaka,and Aichi(see Exhibit 1.)Demographics is the fundamental determinant of land prices.In the report we published beforethe release of benchmark land prices in late September,we focused on the productivepopulation ratio(productive population/total population),using momentum in this ratio(differencebetween productive population growth and total population growth)as an indicator of where landprices are likely to rise in the near future.We found that an increase in nationwide land prices isunlikely,but there is scope for a rebound in Tokyo on a comparatively short horizon.Exhibit 1:Clear halt in land price decline in the four biggest population centersPercentage of locations where land prices are rising or flat,Residential land prices,Commercial land prices,40353025,(%),UnchangedRising,35302520,(%),UnchangedRising,20,151050,151050,2011,2012,2011,2012,Note:Share calculated after weighted by nominal GDP of each prefecture.Source:MLIT,Cabinet Office,GS Global ECS Research.We have since been asked many questions about the application of our hypothesis to other partsof the country and about the potential for land prices outside Tokyo to rise.In this report wetherefore seek to identify more robust implications by using data for the past 30 years for Japansall 47 prefectures.We combine information from time series data with information from crosssectional data for the 47 prefectures.In seeking to predict near-term price trends,we analyzeresidential and commercial land separately,whereas in our previous report we used the“allcategory”price.The analytical model we present in this report is based on demographics,which represents themost significant structural change in the Japanese economy.The model has strong explanatorypower for historical land prices,so it should be effective in long-term forecasting.However,forecasting the demographics of individual prefectures is much more difficult than forecastingnational demographics because these forecasts need to incorporate population inflows andoutflows for each age segment.In this report we therefore confine ourselves to medium-term(three-year)forecasts based on current demographic momentum.See the September 5,2012,Japan Economics Analyst:Demographics implies a rebound in Tokyo land price,albeit a fallnationwide.高盛全球经济、商品和策略研究,2,2,3,2012 年 10 月 19 日,日本经济分析,In addition to price movement rooted in demographics,there may be a spillover from non-fundamental factors.Land prices are asset prices and,as such,are strongly influenced bysentiment.For example,a rise in closely watched Tokyo land prices could change investor anddeveloper sentiment in other parts of the country,leading to an increase in local prices.We aresaving the results of such analysis for our next report.Preliminary considerations(1):Time series observationsBefore commencing our model analysis,we set out our basic data.Exhibit 2 shows how majorpopulation centers experienced a massive bubble in land prices from the late 1980s to early1990s,albeit to different degrees,and a subsequent,prolonged correction that is now beginningto show signs of ending.The correction has been more severe for commercial land,which rosemore than residential land during the bubble.Exhibit 2:Decline halting for residential and commercial land prices,Residential land price,Commercial land price,10009008007006005004003002001000,(Thousand yen/sqm),TokyoOsakaKanagawaAichi,9000800070006000500040003000200010000,(Thousand yen/sqm),TokyoOsakaKanagawaAichi,1975,1980,1985,1990,1995,2000,2005,2010,1975,1980,1985,1990,1995,2000,2005,2010,Source:MLIT.The key underlying factor is a demographic trend we discussed in our previous report.There aremany demographic indicators.Exhibit 3 picks out the so-called demographic dividend.Thedefinition of demographic dividend is productive population divided by dependent population,where the productive population is ages 15 to 64 and the dependent population is all other ages.It is a measure of how many persons in the productive population are shouldering the burden ofproviding services(pensions,healthcare,etc.)for the elderly and raising childreni.e.,providingfor two segments of the population that do not generate their own income.If the dividend is three,for example,there are three members of the productive population for each dependent.Theburden in this case is obviously lighter than if there were only two,enabling more resources to beallocated to constructive production activity.If per-capita income rises as a consequence,a virtuous cycle develops,with stimulus for domesticdemand such as consumer spending,capex,and housing investment so that demand for landincreases as a factor of production,resulting in an increase in land prices.Conversely,if thedemographic dividend shrinks,land prices decline in a reversal of this mechanism.For the remainder of this report we use the“official land price”published by the Ministry of Land,Infrastructure,Transport andTourism in March of each year,which shows prices for January 1.The“benchmark land price”is based on surveys by local authoritiesand shows prices for July 1.The official price is said to have a stronger urban skew and the benchmark a more regional skew,but thetrends are almost the same if the difference in survey timing is discounted.Because the official prices are for January 1,we treat themas data for the year prior to publicatione.g.we treat the 2012 price as 2011 data.高盛全球经济、商品和策略研究,4,2012 年 10 月 19 日,日本经济分析,The demographic indicator we used in our previous report was the productive population ratio(productive population/total population).The demographic dividend basically moves in the sameway as the productive population ratio.Because its denominator is the dependent populationrather than the total population,which is the sum of the productive population and the dependentpopulation,however,it can capture the above-mentioned mechanism more vividly.In fact,as weshow later,the demographic dividend also has higher explanatory power for land prices.The current trend in the demographic dividend is a halt to the ongoing decline and the beginningsof a rebound(see Exhibit 3,which shows the demographic dividends for the locations covered inExhibit 1).The correlation coefficient with land prices is generally high.We provide more rigorousevidence with our full model later in this report.Exhibit 3:Leveling off/pickup in demographic dividend lies behind the halt in land price decline,Demographic dividend(productive population/dependent population),Correlation coefficient for demographic dividendand land prices,3.02.72.42.1,(x),Tokyo,TokyoOsakaAichiKanagawa,Residentialarea0.610.740.760.74,Commercialarea0.580.660.670.61,Osaka,1.81.5,KanagawaAichi,1975,1980,1985,1990,1995,2000,2005,2010,Source:MIC,MLIT,GS Global ECS Research.Preliminary considerations(2):Prefectural cross sectionobservationsNext we observe the relation between demographics and land price using prefectural cross sections.The average values we obtain for 1970 to 2011 are shown in Exhibit 4.The demographic indicatorswe use are:(1)the productive population ratio(productive population/total population),(2)thedependent population ratio(dependent population/total population),(3)the elderly population ratio(elderly population/total population),and(4)the demographic dividend(productivepopulation/dependent population).There are two points of particular interest.(1)All the demographic indicators show strong linkagewith land prices in the expected direction,but the demographic dividend has the highest explanatorypower measured by coefficient of determination(R2).(2)Demographics strongly influencescommercial land prices,not only residential land prices.In fact,the slopes of the regression lines inthe scatter diagram indicates that demographics has a greater impact on commercial land pricesthan they do on residential land prices.高盛全球经济、商品和策略研究,15,14,15,14,5,2012 年 10 月 19 日Exhibit 4:Strong link between demographics and land prices can also be found in prefectural cross sections,日本经济分析,Productive population ratio and land prices,Dependent ratio and land prices,15,(land price;ln),15,(land price;ln),1413,Residential areaCommercial area,y=20.7x-1.1R=0.53,1413,y=-20.7x+19.6R=0.53,Residential areaCommercial area,12,12,1110,y=18.1x-0.8R=0.55,1110,y=-18.1x+17.3R=0.55,0.62,0.64,0.66,0.68 0.7 0.72(productive population ratio;x),0.74,0.25,0.27,0.29,0.31,0.33,0.35 0.37 0.39(dependent ratio;x),Elderly ratio and land prices,Demographic dividend and land prices,1312,(land price;ln)y=-14.9x+14.8R=0.34,Residential areaCommercial area,1312,(land price;ln)Residential areaCommercial area,y=2.3x+8.1R=0.57,1110,y=-13.7x+13.2R=0.38,1110,y=2.0 x+7.2R=0.59,0.07,0.09,0.11,0.13,0.15,0.17 0.19 0.21(elderly ratio;x),1.5,1.7,1.9,2.1,2.3 2.5 2.7(demographic dividend;x),Source:MIC,MLIT.Our analytical model makes full use of time series and crosssection informationBased on the preliminary observations above,we undertook a rigorous assessment of land pricedeterminants,incorporating all our time series and cross section information in panel analysis.Inchoosing a model,we need to take account of the following three characteristics of land priceformation.(1)Path dependency(stickiness).Unlike financial assets such as stocks and bonds,land cannotbe turned into a standardized product.As a result,its liquidity is very low,and price moves arevery sticky.(2)Investors and developers are nonetheless very sensitive to whether prices are rising orfallingi.e.price momentum.In this context,Japans bubble can be seen as the product of self-fulfilling expectations.(3)There are likely to be regional premiums based on factors such as economic clusters.Japanspolitics and economy are heavily concentrated in the Tokyo area.This may be creating premiumsover and above the impact from productive population inflow.In this report we use a partial adjustment model to take specific account of path dependency(stickiness).In this model,land prices do not adjust to the theoretical level suggested by高盛全球经济、商品和策略研究,3,3,6,2012 年 10 月 19 日,日本经济分析,fundamentals in any given year.They adjust by only a certain proportion of the differencebetween the theoretical level and the preceding years price(adjustment speed is between 0 and1).We can estimate the adjustment speed together with other parameters.Change in land price=adjustment speed x(that years theoretical land price level previous years land price)In accordance with our earlier findings,we use the demographic dividend as the key determinantof theoretical land prices.To take account of other determinants,we also incorporate prefecturaldata of(1)nominal GDP per head of productive population as a proxy variable for productionactivity and(2)the preceding years change in land price(hereafter price momentum)as anexplanatory variable.The reason for using price momentum is to take account of self-fulfillingexpectations in land price formation.As a macro factor common to all the prefectures,we test significance of the divergence of theMarshallian K from its trend(hereafter Marshallian K).The definition of Marshallian K is moneysupply(M2+CD)/nominal GDP and deviation from its trend on the upside(downside)denotesexcess(insufficient)liquidity supply for the economy as a whole.We thus have a variable thatshows whether there is too much cash in the economy or not enough.Finally,to capture the level of each prefectures land price premium stemming from factors suchas cluster effect,we added a dummy variable for each prefecture.The dummy variable capturesthe land price divergence for each prefecture(sample period average)when the nationwideaverage is set to zero,which can not be explained by demographics,production activities andland price momentum.Our sample period is 1970 to 2011.(For prefectures where land price data is not available from1970 we used data from the year when data became available.)Results:95%tracking performanceThe results of our model analysis are summarized in the following four points.(1)Tracking performance is very high,with a 0.95 coefficient of determination for all specifications(see Exhibits 5 and 6).That is to say,our model can explain 95%of Japans prefectural pricemovement over the last 30 years.(2)Adjustment speed is 0.13-0.17 for residential land prices and 0.14-0.15 for commercial landprices,which indicates high path dependency(see Exhibit 5).This signifies that whendemographics and other fundamentals change,only about 15%of the change is reflected in oneyear.Change is incorporated subsequently slowly with lags.(3)The significant variables for theoretical land prices are the demographic dividend,nominal GDPper head of productive population,and land price momentum(see Exhibit 5).Marshallian Ksometimes had a theoretically-correct positive sign in some specifications but it was not statisticallysignificant.(4)As expected,Tokyo has the highest premiums for residential land and commercial land(seeExhibit 7).This is particularly so for commercial land,where the premium is almost three timesthat for second-ranked Osaka.We see the cluster effect at work here,reflecting extremeconcentration in Tokyo.Commercial land premiums show stronger correlation than residentialpremiums with GDP per unit area(see Exhibit 8).Furthermore,Tokyo concentration is the highestglobally(see Exhibit 9).Differences are smaller for residential land,which has high premiums forthe greater Tokyo area(Kanagawa,Saitama,Chiba)and the greater Osaka areas(Kyoto,Hyogo,Osaka).We used dynamic generalized method of moments.高盛全球经济、商品和策略研究,7,2012 年 10 月 19 日,日本经济分析Exhibit 5:Combination of demographic dividend and nominal GDP explains land pricesResidential land price modelEstimation equation,(1),(2),(3),Population bonus,23358.98,21985.18,10872.17,Nominal GDP per personMa