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Chinese Housing Markets: What We Know and What We Need to Know
Joe Gyourko
Wharton School, University of Pennsylvania Paper presented at the International Symposium on Housing and Financial Stability in China. Hosted by the Chinese University of Hong Kong, Shenzhen Shenzhen, China─December 18-19, 2015
The views expressed in this paper are those of the author(s) only, and the presence of them, or of links to them, on the IMF website does not imply that the IMF, its Executive Board, or its management endorses or shares the views expressed in the paper.
Chinese Housing Markets:What We Know and What We Need to Know
Yongheng Deng, Joe Gyourko & Jing WuHousing and Financial Stability in China
Shenzhen ChinaShenzhen, ChinaDecember 18, 2015
1
Documentation—Prices and QuantitiesQ
• What Do These Data Show?– Strong trend price growth in aggregate in land and house values
• Economically large variation about that trend
– Substantial heterogeneity across markets• Extremely strong real price growth in Beijing, but real land values have fallen
substantially the past two years in Dalian
– Transactions Volume• Has fallen in recent quarters both in terms of land parcel sales and in the amount of
new housing soldnew housing sold
2
Documentation: A Cl L k t L d P i A M j M k tA Closer Look at Land Prices Across Major Markets
• Chinese Residential Land Price Indexes (CRLPI)( )– Wharton‐Tsinghua‐NUS collaboration (Gyourko, Wu, Deng)– 35 major markets, not just east coast or East region cities
• See next slideSee next slide
– Transactions‐based, not appraisal‐based– Full samples of land sold by local governments to private residential
developersdevelopers– Constant quality price indexes created
3
城市覆盖范围城市覆盖范围 City CoverageCity Coverage
•目前CRLPI指数覆盖全国35个大中城市国35个大中城市。Currently CRLPI covers 35 major cities in mainland Chinamainland China.
•这些城市占据了全国新 75%•这些城市占据了全国新建商品住房市场约50%的市场份额。These cities contribute
60%
65%
70%
75%
Market Share by Floor Area
Market Share by Total Value
These cities contribute to about 50% of the new home market in the
h l t40%
45%
50%
55%
whole country.30%
35%
2004 2005 2006 2007 2008 2009 2010 2011 2012 20134
Figure 1: Chinese National Real Land Price Index35 Markets, Constant Quality Series
(Quarterly: 2004q1 – 2015q3)(Q y q q )
500
400
300
200
100
2004q3 2005q3 2006q3 2007q3 2008q3 2009q3 2010q3 2011q3 2012q3 2013q3 2014q3 2015q3
5
Figure 2: Number of Parcels Sold35 Markets (Quarterly: 2004q1 – 2015q3)
800
600
400
200
0
2003q3 2004q3 2005q3 2006q3 2007q3 2008q3 2009q3 2010q3 2011q3 2012q3 2013q3 2014q3 2015q3
6
Chinese Housing Unit (Really Space) Growth2007 20142007‐2014
Figure 6: Year-Over-Year Growth in Floor Space Sold,
Newly-Built Housing Units, 2007(1)-2014(4)Newly Built Housing Units, 2007(1) 2014(4)
60%
80%
20%
40%
-20%
0%
-40%
2007
Q1
2007
Q2
2007
Q3
2007
Q4
2008
Q1
2008
Q2
2008
Q3
2008
Q4
2009
Q1
2009
Q2
2009
Q3
2009
Q4
2010
Q1
2010
Q2
2010
Q3
2010
Q4
2011
Q1
2011
Q2
2011
Q3
2011
Q4
2012
Q1
2012
Q2
2012
Q3
2012
Q4
2013
Q1
2013
Q2
2013
Q3
2013
Q4
2014
Q1
2014
Q2
2014
Q3
2014
Q4
National Level 35 Major Cities
7
Figure 3: Chinese Regional Real Land Price IndexEast, Middle and West Regions, Constant Quality Series
(Semi‐annually: 2004h1 – 2015h1)(Semi annually: 2004h1 2015h1)
5.00
4.00
50
3.00
1.00
2.00
2004h1 2005h1 2006h1 2007h1 2008h1 2009h1 2010h1 2011h1 2012h1 2013h1 2014h1 2015h1
East MiddleWest
8
1000
11002004=100
Beijing
700
800
900
1000
400
500
600
100
200
300
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
9
300 2004=100Dalian
200
250
100
150
0
50
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 20142004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
10
700 2004=100Chongqing
500
600
200
300
400
0
100
200
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
11
What Do We Need to Know?
• Better measures of price and quantity are not enoughp q y g– Cannot tell all that much just by looking at P and Q
• Intersection of supply and demand
• What do local market supply and demand fundamentals look like in Chinese housing markets?
12
Metrics on Supply‐Demand (Im)Balancespp y ( )
• Annual new construction relative to market size• Unsold inventory relative to sales volume in market• Vacancy rates in nine provinces• Price‐to‐rent ratios• Price‐to‐income ratios• Breakeven real appreciation expectations from Poterba user
cost equation
13
Annual New Supply As Share of 2010 Stock
8.00%
9.00%
10.00%
8.00%
9.00%
10.00%
3.00%
4.00%
5.00%
6.00%
7.00%
3.00%
4.00%
5.00%
6.00%
7.00%
0.00%
1.00%
2.00%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Beijing Tianjin Shanghai Chongqing
0.00%
1.00%
2.00%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Changsha Guangzhou Chengdu Xi'an
6 00%
7.00%
8.00%
9.00%
10.00%
1 00%
2.00%
3.00%
4.00%
5.00%
6.00%
14
0.00%
1.00%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Nanjing Dalian Hangzhou Wuhan
Unsold Inventory Held by Developers As Share of T ti V l i M k tTransactions Volume in Market
300%
350%
300%
350%
150%
200%
250%
150%
200%
250%
0%
50%
100%
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
B iji Ti ji Sh h i Ch i
0%
50%
100%
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Changsha Guangzhou Chengdu Xi'anBeijing Tianjin Shanghai Chongqing Changsha Guangzhou Chengdu Xi'an
250%
300%
350%
50%
100%
150%
200%
15
0%2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Nanjing Dalian Hangzhou Wuhan
Aggregate Space Delivered35 M j M k t35 Major Markets
million sq.m.
450
500
300
350
400
200
250
300
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
16
Longer‐Run Supply/Demand Trends2001 20142001‐2014
• Supply at least 10% below our projected demand– Beijing (87%), Hangzhou (79%), Haikou (79%), Jinan (86%), Ningbo (85%),
Shanghai (70%), Shenzhen (73%)
• Supply at least 30% above our projected demandpp y p j– Chengdu (230%), Chongqing (193%), Guiyang (162%), Harbin (160%),
Hohhot (178%), Lanzhou (143%), Qingdao (144%), Shenyang (154%), Taiyuan (148%), Tianjin (132%), Xian (130%), Xining (32%), Yinchuan (193%), Zhengzhou (191%)(193%), Zhengzhou (191%)
• Other Major Markets Somewhere in Between, with Most Looking Modestly Oversupplied
h h ( %) h h ( 9%) li ( %) h ( 9%)– Changchun (111%), Changsha (119%), Dalian (115%), Fuzhou (119%), Guangzhou (93%), Hefei (125%), Kunming (120%), Nanchang (102%), Nanjing (104%), Nanning (115%), Shijiazhuang (113%), Urumqi (94%), Xiamen (115%), Wuhan (129%)
17
18
19
20
Quarterly Price‐to‐Rent Ratios
45
50
55
60
45
50
55
60
25
30
35
40
45
25
30
35
40
45
20
2009
Q3
2009
Q4
2010
Q1
2010
Q2
2010
Q3
2010
Q4
2011
Q1
2011
Q2
2011
Q3
2011
Q4
2012
Q1
2012
Q2
2012
Q3
2012
Q4
2013
Q1
2013
Q2
2013
Q3
2013
Q4
2014
Q1
2014
Q2
2014
Q3
2014
Q4
Beijing Tianjin Shanghai Chongqing
20
2009
Q3
2009
Q4
2010
Q1
2010
Q2
2010
Q3
2010
Q4
2011
Q1
2011
Q2
2011
Q3
2011
Q4
2012
Q1
2012
Q2
2012
Q3
2012
Q4
2013
Q1
2013
Q2
2013
Q3
2013
Q4
2014
Q1
2014
Q2
2014
Q3
2014
Q4
Changsha Guangzhou Chengdu Xian
45
50
55
60
20
25
30
35
40
21
20
2009
Q3
2009
Q4
2010
Q1
2010
Q2
2010
Q3
2010
Q4
2011
Q1
2011
Q2
2011
Q3
2011
Q4
2012
Q1
2012
Q2
2012
Q3
2012
Q4
2013
Q1
2013
Q2
2013
Q3
2013
Q4
2014
Q1
2014
Q2
2014
Q3
2014
Q4
Dalian Nanjing Hangzhou Wuhan
Annual Price‐to‐Income Ratios
16
20
16
20
4
8
12
4
8
12
0
4
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Beijing Tianjin Shanghai Chongqing
0
4
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Changsha Guangzhou Chengdu Xian
12
16
20
4
8
22
0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Dalian Nanjing Hangzhou Wuhan
Conclusions
• Need more effort on measurement of prices in particular– China needs a S&P/Case‐Shiller price index– Ability to gauge land market is a real advantage
• Keep context in mind when examining prices and quantitiesChi i t t d f l b– Chinese prices started from very low base
– China is a high growth and high volatility market
• Do not see much value in trying to put the label ‘bubble’ on• Do not see much value in trying to put the label bubble on these markets– Very short time series, with imperfect data– Still, no doubt Chinese housing markets are very riskyStill, no doubt Chinese housing markets are very risky
• ‘Priced to perfection’ in the following sense: even in fundamentally strong Tier 1 cities, relatively small changes in expectations, absent countervailing rent increases, will lead to large negative changes in price levels per Poterba’s user cost framework
23
Conclusions
• Beyond ‘rich’ pricing, supply appears to have outpaced y p g, pp y pp pdemand over the last decade, not just the last year, in various markets
Primarily but not exclusively in the interior of the country– Primarily, but not exclusively, in the interior of the country – Any negative demand shock will occur in an environment of weak
fundamentals in these places—this combination always leads to large price declines in any durable goods market and housing is a durableprice declines in any durable goods market, and housing is a durable good
24