Discussion Paper No. 928

DO BANK LOANS
AND LOCAL AMENITIESEXPLAIN
CHINESE URBAN HOUSE PRICES
Daisy J. Huang
Charles Ka Yui Leung
Baozhi Qu

March 2015

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanDo bank loans and local amenities explain Chinese
urban house prices?
Daisy J. Huang, Charles K. Leung, and Baozhi Qu
This version: February, 2015

Abstract
Based on Chinese city-level data from 1999 to 2012 and controlling for geological,
environmental, and social diversity, this study suggests that credit plays a significant role indriving up house prices after the Great Recession, whereas property prices only influence banklending before 2008. Local amenities such as higher education, green infrastructure, healthcare,and climate also positively affect house prices. Moreover, the impacts of bank loans on housing
prices tend to be related to the level of amenities, suggesting an integrated approach (i.e.
combining macroeconomic and urban economic variables) of housing market for the future
research.
Keywords: endogenous income and endogenous amenities; bank loans and monetary policy;
multicollinearity; clustered standard errors
JEL Classification: G21; O18; R11

Acknowledgements: The authors would like to thank Belton Fleisher, Pohan Fong, Xu Han, Vikas Kakkar,
Kamhon Kan, Fred Kwan, Jennifer Lai, Huimin Li, Mi Lin, Chung Yi Tse, Yi Wen, Isabel Yan, Zan Yang and theparticipants at the 2013 ASSA-CEANA Meeting, the Seventh Biennial Conference of the Hong Kong Economic
Association, the 2012 AsRES-AREUEA International Conference, the GCREC 2012 Annual Conference, WEA
conference (2012), and at the City University Brown Bag seminar, and especially anonymous referees for theircomments. In addition, we are grateful to City University of Hong Kong for its financial support. The work
described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong
Special Administrative Region, China [Project No. CityU 144709]. Part of the research is conducted when Leung
visits the ISER, Osaka University, whose hospitality is gratefully acknowledged. The usual disclaimers apply.
Corresponding authors: Huang, HKUST Jockey Club Institute for Advanced Study, The Hong Kong University of
Science and Technology, Clear Water Bay, Kowloon, Hong Kong. Email: huangjur@gmail.com; Leung, Departmentof Economics and Finance, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong. Email:kycleung@cityu.edu.hk; Qu, China Merchants Group. 38th floor, Shun Tak Centre, 168-200 Connaught Rd. C., HK.
Email: baozhiqu@cmhk.com.
1

1 Introduction
This paper studies the housing market of China and is built on several strands of theliterature. First, this paper is related to the debate on whether credit aggregates cause the property
market boom, or the other way round. Although the relationship between credit aggregates andhouse prices is significantly positive and economically strong in many countries, the direction of
causality and the propagation mechanism remain to be resolved empirically. For instance, somestudies of the 2006 U.S. real estate market collapse suggest that the subprime mortgage
expansion was responsible for bursting the bubble (e.g., Mian and Sufi 2009; Pavlov and
Wachter 2004, 2006). However, other authors argue that no conclusion concerning causality
should be drawn (e.g., Wheaton and Nechayev, 2008; Davis and Zhu, 2011). Similarly, Collyns
and Senhadji (2002) find evidence for a channel from credit to property price appreciation during
the 1997 Asian Financial Crisis. Yet, Gerlach and Peng (2005) claim that property prices inHong Kong during that period were weakly exogenous, and the causality went from property
prices to bank loans, rather than the other way around.
The China’s housing market, which has received significant domestic and international
attention recently, faces the same set of questions as other markets. According to the data fromthe CEIC and the National Bureau of Statistics of China (NBSC), the GDP has grown by aphenomenal rate of 479% between 1999 and 2012, house prices have been up by 200%, but theloan balance has increased tenfold astonishingly. The banking system issued loans worth
RMB10 trillion in 2009 alone; this was equivalent to almost one-third of the country’s GDP inthat year. Together, these figures1 raise great concerns in business, policy, and academic circles
about the sustainability of China’s housing market. It is natural to ask whether these housing
loans are created by market demand. Or, the bank loans are simply being injected into theeconomic system by policy decisions? Will China experience a housing market collapse
followed by decades of recession, as happened in Japan? Many researchers have focused on the
housing markets at the national or provincial levels. 2 Although these aggregate-level studies
obviously have merits, research at a more disaggregated level would clearly be complementary.
For instance, different cities or different regions within the same country may display different

1
2

These figures are in nominal terms.
For instance, see Wang and Wen (2012) and the reference therein.
2

patterns, or have different trends in the macroeconomic variables. City-level data not only
provide us with a larger sample size, but also are less vulnerable to aggregation bias (Hanushek
et al. 1996). Furthermore, the structure of panel data enables us to gain insights on the debatefrom both the cross-sectional and the temporal dimensions.
Since this study focuses on city-level data, it naturally connects to the second literature,
which relates the house price to the local amenities. As housing is a durable and immobile
consumption good, location and local amenities matter. Other things being equal, people preferhousing in locations that offer a higher quality of life. Rosen (1979) and Roback (1982) haveestablished that average wages and real estate prices in different places adjust to reflect thevalues of spatially tied features. Following Rosen and Roback’s pioneer work, there have been
many studies testing and confirming the existence of compensating differentials in wages andresidential property prices in both developed and developing economies (e.g., Blomquist et al.
1988; Gyourko and Tracy 1991; Shapiro 2006; Berger et al. 2008). China is large enough toinclude places characterized by very different climates, environments, and types of social
development. The compensating differentials literature contains enlightening research on China,
including studies by Zheng and Kahn (2008), Zheng, Fu, and Liu (2009), Zheng, Kahn, and Liu(2010), and Zheng, Cao, and Kahn (2011). Our paper complements previous research byincluding a broader set of amenities and with a focus of their impacts on housing prices.
Although bank loans and local amenities are seemingly unrelated, we combine analysisof both factors in one research for simple economic reasons. First, it is more than likely that boththe credit availability and the geographical distribution of urban amenities have impacts on thecity-level demand. And as a matter of fact, loans and some amenity measures may be interacted.
While some amenities such as climate may be less influenced by human activities, otheramenities such as public health service, or “local fiscal climate” by Gyourko and Tracy (1991),
clearly depend on economic activities. Not surprisingly, the second type of amenities and loans
can be both tied to the level of economic development, and hence creates some relationships
among them. While some previous literature emphasizes how the differences in amenities
explain the cross-sectional variations in housing prices, some other literature focuses on how thecyclical movements in bank loans lead to house price fluctuations. Yet in the context of a panelof city-level house prices, as both cross-sectional and temporal components are present, it is not3

clear a priori which factor will dominate. Integrating these two factors in a simple yet unifying
framework should provide a more complete picture of the housing markets.
To achieve the stated objective, it is necessary to select a diverse sample that is also
representative of China. Our dataset includes 29 cities; most are provincial capitals that are
scattered across the whole country. In addition, most are among the largest cities in their regions,and have a relatively large number of housing transactions. The house price figures are therefore
more representative and less likely to suffer from a small sample bias. However, an analysis
using simple regression, even with all of the relevant variables included, would still deliver
biased estimates, partly due to the multicollinearity among the amenity measures, and partly
because the effect of amenities on house prices is also indirectly reflected in income. For
instance, both number of doctors and number of hospital beds shared by every 10,000 local
population are indicators of healthcare condition; these two measures have a correlation
coefficient of 0.78. We tackle the multicollinearity problem by the principal component analysis
technique (PCA hereafter). The idea is to reduce various measures to a few variables, and at thesame time retain most of the variance in data. In this analysis, 17 amenity measures areconsolidated into five categories: climate, air pollution, crime rate, health care, and social
development. The formal recognition of the local-amenities differences has important
implications for the empirical strategies. For instance, in a city with “better” amenities, theseamenities would affect the house prices directly (as people are willing to pay for them), and also
affect the house prices indirectly, as an improvement of local amenities may enable the samegroup of employees to generate more output, as well as attract more capable people to that cityand eventually raise the aggregate income of the city. 3 Additionally, it seems reasonable toconjecture that with growing income, households would be more willing to pay for certain
amenities.4 It follows that a two-step procedure is necessary to disentangle the direct and theindirect effects. We first regress income on the principal components of the amenities, and retain
the residuals. We label these residuals as filtered income, as they are the part of the income notpredicted by local amenities. Then the filtered income is added to the property price regression,
together with the normalized loan, the amenities, and the other variables. The natural logarithmsof real house prices and real incomes, as well as the normalized loan are further detrended to3
4

Among others, see Davis et al. (2014), Wang and Xie (2011) for more discussion on this.
In other words, the income elasticity of some amenities could be higher than unity.
4

obtain reliable estimation results. And in order to adjust the within city correlation, clustered
standard errors are employed for statistical inference.
Our empirical findings suggest that credit plays a significant role in driving up house
prices after the Great Recession, whereas the reversed effect of property prices on bank lendingis only present before 2008. More specifically, before the 2008 financial crisis, we find loan
volume does not have any significant impact on home prices. However in the post-recession era,such impact is positive and significant at the 5% significance level. If the normalized loan
expands by 1%, then the real home prices would be higher by 16.1 basis points next year. In
terms of standardized coefficient, an increase of one standard deviation in loan GDP ratio would
predict a following 0.179 standard deviations’ expansion in real housing prices in the next year.
On the other hand, property prices significantly influence bank credit before 2008, but not after.
In case of 1% appreciation of real home prices, the bank lending would be greater by 49.3 basis
points. In other words, a rise of one standard deviation in house prices would lead to an increaseof 0.371 standard deviations in loans in the following year.
Our empirical results also confirm that location is economically very significant. Not
surprisingly, people are willing to pay more for housing units located in cities with rich resourcesin higher education and great green infrastructure. People prefer to live in big cities because of
the better economic opportunities, in spite of the crowding and higher crime rates. If the social
development component combining higher education, green coverage, and population density
has a score greater by one standard deviation, the property prices would be 0.401 standard
deviations higher. The amenity indicators capture the location heterogeneity so well that theregional dummies are no longer significant. In an alternative specification, amenity variables
such as climate, health care, and social development are found to be significantly and negatively
interacted with the normalized loan. It suggests home prices in cities with more desirable
amenities are less sensitive to credit expansion.
The organization of this paper is simple. The next section provides some background on
the housing markets, bank loans, and local amenities in China. The literature review is followed
by a discussion of the data, and then by a description of the methodology. The results are thenpresented, and the conclusions are given in the last section.

5

2 Background on the Housing Markets, Bank Loans, and Local
Amenities
For many households throughout the world, a house is not only a shelter, but also themost important investment the household has. Yet for the older generation in mainland China,
there was no such thing as private property. Most people lived in apartments assigned by theiremployers. These arrangements continued until the 1980s when the government launched its
housing reform. The administrative housing allocation system was officially abolished in 1998.
Since then the government has introduced a series of macro land-use policies to encourage thedevelopment of the new private housing market.5 Under the joint forces of the private market andgovernment stimulation policies, real estate prices began to increase in 2001 6 , and thenaccelerated until 2007. In Figure 1, we use floor space completed as a proxy for new supply inthe real estate market. Floor space sold is intended to stand for demand. When the floor spacecompleted is greater than that sold, the amount of housing unit inventory increases and property
prices are kept low. When the new supply does not meet the demand, the amount of inventory inthe housing market decreases and the price rises7. At the more disaggregated level, 21 out of the
29 major cities in China had an average housing price growth rate of over 10% annually from1999 to 2012. An average-sized 120-square-meter apartment in Beijing cost 36 years ofdisposable income for an average resident in 2004, and by 2012 it cost 52 years of disposable
income. Furthermore, as the China Real Estate Index System reported, in 2010 alone the house
prices in Beijing surged by 37.1%, and by 47.1% in Hangzhou.8

5

For a detailed discussion of these policies, please refer to Leung and Wang (2007), and Zhao et al. (2008).
Coastal cities like Hangzhou, Shanghai, and Shenzhen first experienced the rise of housing prices in around 2001
or 2002. Prices in interior cities started to appreciate a little bit later (in around 2003 or 2004).
7
Due to the existence of pre-sale of housing units, the comparison between housing completion and housingtransaction is probably an imperfect supply-demand indicator. On the other hand, different real estate developments
may have different pre-sale periods and to our knowledge, there is no official data which corrects this "pre-sale gap".
In addition, pre-sale means that the ownership of the unit is shifted from the developers to the buyers before the unit
is completed. In other words, the existing stock at the time of pre-sale does not increase. The buyers still need to buy
or rent other housing units as shelters. It only provides a "hedge" of the house price fluctuations. And there are
instances that developers actually go bankrupt after pre-sale and hence are unable to deliver promised units. Beingaware of all these limitations, we proceed with this comparison as a proxy.
8
Soufun developed the China Real Estate Index System (CREIS) to monitor real estate prices in one hundred cities.
For more, please refer to Anonymous (2011 May 11). The property report: China’s tougher mortgage policies. WallStreet Journal.
66

As property prices boomed, so did real estate investments. From 1986 to 2012, nominal
investment in China grew at an annual rate of 18%. In contrast, nominal real estate investment
expanded at a nearly doubled rate; it grew 32% per year from RMB10 billion to 7,180 billion.
The proportion of real estate investment over total investment soared from 2.6% in 1986 to 28%
in 2012. As investments made up 47.8% of China’s GDP, real estate investment accounted for
14% of the total economic activity. Figure 2 provides a visualization of these observations.
[Insert Figure 1, Figure 2 about here.]
In addition to the frenzied house prices and real estate investments, we also provide abrief review of the monetary policy after 1978. The broad money and outstanding loans
increased at an average annual rate of 23% and 19% respectively. Both were faster than thenominal GDP growth which was on average 16%. From Figure 3, we can see that the proportionof loans issued over GDP has increased to 123% in 2013 from 52% in 1978. The evolving pathof the ratio of M2 to output is even more startling. In the late 1970s, M2 accounted for less than
25% of gross production, whereas at the end of the sample period, it was almost 190% of GDP.
In 2009 alone, the new loans issued were close to RMB10 trillion, a 33% expansion over theprevious year. In 2010, the credit aggregates increased by another RMB8 trillion. The total loan
portfolio was expanded by over 50% right after the 2008 financial crisis. Together with the
RMB4 trillion stimulus package, these loans were supposed to finance infrastructure projects, butmany ended up going to giant the state-owned enterprises where they were used to finance thepurchase of real estate instead. 9 According to Wu et al. (2012), residential land auctions
witnessed land prices doubled in eight major cities in 2009, which in their opinion can be
attributed to the massively leveraged purchases by the state owned enterprises (SOEs hereafter).
They also show that on average, state-owned corporations pay 27% more than other bidders forthe same land parcels. In some ways, the current situation in China’s housing market resembles
the pre-crisis U.S. property market. The overheated real estate market and the rapid credit
expansion have drawn a lot of attention, as the affordability of housing is of great concern to thegeneral public, and the bursting of a real estate bubble could cause serious problems for thebanking system and the macroeconomy.

9

See Davis, B. (2011 Jun 27). China real estate concerns rise. Wall Street Journal.
7

[Insert Figure 3 about here.]
China has achieved great economic success since its economic reform and opening up.
But it has always been a great country with huge cross-sectional variations in amenities.
According to the Encyclopedia Britannica, China is the third largest country by area. It stretchesfrom the Russian border in the north, to the South China Sea. In the west, it extends into the
Pamir Mountains, the “Roof of the World”. The landscape is vast and diverse and the climaticand geological environments vary from region to region. Thus, it may be interesting toinvestigate how the local housing markets behave given such geographical diversity. Our sample
of 29 large cities, most of them provincial capitals, is highly representative for such diversity.
For example, our sample includes Kunming, the “City of Eternal Spring” in the south-western
Yunnan province 10 , and the “Ice City” of Harbin, located in the north-eastern Heilongjiang
province, which has the coldest and longest winter in China. Temperatures in Kunming range
from 31.3°C (88.3°F) in the summer to -4°C (24.8°F) in the winter, while the lowest recorded
temperature in Harbin is -37.3°C (-35.1°F) in the same period. The geology not only shapes thediversified climate, but also affects the “colors” seen in satellite photos. Western China iscovered by deserts, mountains, and plateaus, and has an arid climate. In contrast, the east,
consisting of the Northeast Plain, the North China Plain and the Middle-Lower Yangtze Plain, ischaracterized by humid warm weather. Correspondingly, the green coverage ratio is higher in theeast than in the west. The average urban green coverage ratio is over 36% in eastern cities in our
sample, whereas this figure is only 29% in the western cities.
These broad climatic and geological environments are reflected in large cross-sectionalvariations in the cities. The availability of higher education also varies dramatically acrossregions. In 1995, the central government launched an initiative called “Project 211” to promote
China’s higher education by providing funding to universities. So far, only 112 of the 2,358
universities and colleges in the country are enrolled in the program11. Therefore, a designation asa 211 Project institution is an indicator of educational quality. The number of universities
10

There is a special article in the December 8, 2012 issue of iMoney magazine called “New favorite mainland cityof Hong Kong capital, Kunming” (in Chinese). It gives a very detailed description of Kunming’s climate andgeography, and how these features are going to attract Hong Kong capital. In that article, Kunming is compared to
the warm and beautiful Florida.
11
There are more and more colleges and universities in China nowadays, but the 211 Project probably will not
expand any more.
8

sponsored by the Project in a city may be a proxy for cultural richness, higher education quality,
or even the human capital environment. The distribution of these universities is uneven acrossthe country, with 26 in Beijing, ten in Shanghai, eight in Nanjing, seven in Wuhan, and seven in
Xi’an. In total, these five cities are home to half of the best universities and colleges in the nation;
there is only one such institution in most other major cities, and none in Ningbo andShijiazhuang.
The same pattern can be seen in the distributions of the health workforce and facilities.
There are 3.6 and 3.4 doctors per 1,000 people in Guangzhou and Beijing respectively, which iscomparable to the standards in European countries such as Germany, France, and Denmark.12 In
contrast, the doctors density in Chongqing and Shijiazhuang is only 1.4 and 1.8, respectively.
The density of hospital sickbeds is also high in Beijing and Guangzhou and low in Chongqing
and Shijiazhuang. In Guangzhou, there are 5.7 sickbeds serving every 1,000 people. The figure is
4.8 in Beijing. But 1,000 people have to share 2.4 sickbeds in Chongqing, and 3 in Shijiazhuang.
Air pollution also varies significantly across cities. The World Health Organization(WHO hereafter) issued in 2011 a database13 recording outdoor air pollution in 91 countries and1,100 cities. According to this database, 20 among the top 100 most polluted cities are in China.
In particular, Beijing ranked the fifth within the country and fiftieth world-wide. In fact,according to the state-run Xinhua news agency and the air quality daily report14 by the Beijing
Municipal Environmental Protection Bureau, Beijing’s air pollution hit dangerous levels on tendays in January 2013, and on one day, the PM2.515 reading was 993 micrograms per cubic meter.
Although air pollution is challenging sustainable growth everywhere in China, the south remains
in relatively better condition. In the WHO database, the top five polluted cities, including
Lanzhou, Xining, Urumqi, Jinan, and Beijing, are all in the north, and the least polluted fivecities 16 , Haikou, Nanning, Fuzhou, Kunming, and Guangzhou, are all in the south. As the12

The worldwide data are available on the WHO website
http://www.who.int/gho/health_workforce/physicians_density/en/index.html.
13
The full dataset can be downloaded from http://www.who.int/phe/health_topics/outdoorair/databases/en/.
14
The data are available on this webpage: http://www.bjepb.gov.cn/air2008/Air.aspx?time=2013-1-28. Historical
data can be obtained by modifying the enquiry date.
15
PM2.5 measures the number of tiny particles with diameters less than 2.5 micrometers. Such particulate matterscan enter deep into human lungs and cause potential cardiovascular and respiratory diseases. The recommended
standard for PM2.5 in the U.S. is below 35.4 micrograms per cubic meter. If the measure rises beyond 210.5, the air
quality is rated as hazardous.
16
Lhasa in Tibet, the Hong Kong Special Administration Region, the Macau Special Administration Region, and9Chinese grow rich, so are their marginal valuations on quality of life. The three large-scale local
protests17 in 2012 against environment-harming industrial projects are convincing evidence ofpeople’s willingness to pay for desirable amenities.

3 Literature Review
Changes in the real estate and credit markets can be mutually reinforcing. House priceappreciation can cause credit expansion. As illustrated by Kiyotaki and Moore (1997) and otherauthors, as both households and firms use property as collateral for borrowing, the net worth ofhouseholds’ collateralizable assets increases with house prices. Such change in net worth enables
them to borrow even more. From the banks’ point of view, real estate values affect their capital
positions. In good times, the loans secured by real estate collateral are even safer, and the banks’direct holdings of property assets also appreciate. This perception increases financial institutions’risk-taking capacity and their willingness to extend credit, so they pour more loans into theeconomy. Supporting empirical research of such a cause and effect link from house prices tocredit volume includes studies by Gerlach and Peng (2005) and Peng et al. (2008) 18. Gerlach andPeng (2005) study the housing market of Hong Kong in the 1982 to 2001 period. Their cointegration analysis suggests that the property prices in Hong Kong are weakly exogenous, andthe causality goes from property prices to bank loans. Peng et al. (2008) investigate therelationship between the property market and the macroeconomy in mainland China from 1998
to 2004, using panel data covering 25 provinces and six major cities. Their results show a twoway linkage between property price inflation and GDP growth. Furthermore, property pricegrowth has a positive and significant effect on investment, especially real estate investment, but
not on consumption. Thus, the causal relationship also seems to go from property priceappreciation to credit expansion.
As the supply of housing stock is typically inelastic in the short run, an increase in bankcredit may drive up house prices. Studies supporting this hypothesis include Mian and Sufi
(2009), Collyns and Senhadji (2002), and Coleman IV et al. (2008). Mian and Sufi (2009)
cities in Taiwan are excluded.
17
Two of the three protests occurred in July 2012, one in the eastern coastal city Qidong in Jiangsu Province, andone in Shifang in the south-western Sichuan Province. The third protest happened in October 2012, and was also in
the eastern coastal city Ningbo.
18
Among others, see Chen and Wang (2007) for loan-level evidence and more discussion of this point.
10

conduct a within-county analysis using detailed zip code-level data from 1991 to 2007. They findthat mortgage credit grows unprecedentedly in subprime zip codes despite the relative income
growth declines. Moreover, the negative relationship between mortgage credit and income is still
significant in Metropolitan Statistical Areas (MSAs) with elastic housing supplies, implying thatthe subprime lending expansion is not due to lenders’ expectations of higher future house prices.
The authors suggest that the relative house price appreciation in subprime communities is theresult of the increase in credit supply. Collyns and Senhadji (2002) report strong relations
between credit growth and asset price booms in Indonesia, Malaysia, Philippines, South Korea,
Thailand, Hong Kong, and Singapore. Using panel regressions and individual country VARs, theauthors also argue for a credit channel to property price appreciation. Coleman IV et al. (2008)
run a pooled cross-sectional analysis of 20 MSAs in the 1998-2006 period to investigate housing
price dynamics. In the earlier years of their sample, economic fundamentals such as
unemployment, population, income, and capital cost are found to be the primary driving forcesof the house price dynamics. However, they lose significance towards the end of the observation
period. The authors also discover that Government-Sponsored Enterprises (GSEs hereafter) are
the main supplier of mortgage capital prior to 2004, whereas from late 2003 GSEs are squeezed
out of the market by private issuers who wildly increase subprime lending. All of these studies
suggest that credit expansion is the main driving force of house price appreciation, especially inthe recent years.
Other studies find mixed evidence regarding the direction of causality. For instance,
Wheaton and Nechayev (2008) use time series techniques to examine whether the appreciationof house prices between 1998 and 2005 can be explained by economic fundamentals. They
estimate a single-stage univariate model for each of 59 U.S. MSAs from the 1970s to 1998, andthen use actual economic data from 1998 to 2005 to forecast house prices. They find large
magnitude of errors in the forecast. However, the explosive growth in credit availability brought
about by the subprime lending, as well as the purchase of the second and investment home,predict the forecast error significantly.19

19

In a cross-country study, Davis and Zhu (2011) also find a two-way relationship between bank lending andcommercial property price using panel error correction model.
11In addition to the literature on the link between house prices and bank credit, there isanother strand of research in urban economics, compensating differentials, that is relevant to ourstudy. Rosen (1979) and Roback (1982) lay down the basic framework for this line of research.
They argue that people compete for amenable places to live, but as wages and home prices areadjusted to reflect spatially tied amenities, people have no incentive to move from one location toanother. They set up a simple general equilibrium model to derive the comparative statics ofwages and real estate prices. They also employ the hedonic pricing variant to test the hypothesis
empirically. They find that housing prices are higher in cities with better amenities. However, thesame is not necessarily true of wages. In their studies, pollution, extreme climate, and crime are
documented as dis-amenities, and mild weather and, surprisingly, population density as desirable
amenities. They construct a quality of life index using the implicit price estimates.
Building on Rosen and Roback’s pioneer work, Blomquist et al. (1988) modify theirmodel by including more than one county in each urban area to account for agglomeration
effects. With a large cross-sectional dataset of 253 counties in 185 U.S. metropolitan areas, they
derive new measures of quality of life, which reveal substantial variations both within andamong urban areas. Their list of amenity variables is more extensive than that used in earlier
research; it includes teacher-student ratio, violent crime rates, and even climate andenvironmental attributes. Gyourko and Tracy (1991) re-estimate the compensating differentials
using the same census data as Blomquist et al. (1988). Although they shift the focus to central
cities of metropolitan areas and work with an even longer amenities list including police, fire,
health, education services, and local tax rates, their estimates of the characteristic variables,
especially the pure amenities like climate, almost always bear the same signs as in Blomquist etal. (1988). Additionally, their study indicates that public safety, and fire and health services havea statistically significant effect on local wages. More recently, Shapiro (2006) demonstrates thathuman capital, as an indicator of local amenity, also has a causal effect on wages, rents, andhouse prices. But the author suggests that the influence on rents and home values is much larger
than that on wages.20
The hypothesis that the concern for quality of life may rise with income, has inspired a
growing number of studies on the compensating differentials in emerging markets. For example,
20

Among others, see also Albouy (2009), Albouy et al. (2013), Glaeser (2008).
12

Berger et al. (2008) apply the Rosen-Roback framework to Russian labor and housing markets.
Their results are consistent with those found in developed countries, and confirm theapplicability of the Rosen-Roback framework to transition economies. In addition to the
traditional amenities such as climate, environment, crime rates, and health services, they alsofind that ethnic conflicts matter in Russia.
When it comes to the research on China’s housing market, there have been many
interesting studies as well. Leung and Wang (2007) apply the DiPasquale-Wheaton model toChina and conduct policy analyses. Leung et al. (2011) propose simple models for house priceand construction dynamics, and estimate model implications with data from four major cities.
Shen (2012) develops a new housing affordability measure in terms of permanent income, andargues that housing affordability in China is fairly strong according to their new measure. Ren,
Xiong, and Yuan (2012) test the existence of rational expectation bubbles. Wang, Yang, and Liu(2011) inspect how economic openness affects real estate prices. Liang and Cao (2007), Liangand Gao (2007), Peng et al. (2008), Zhang, Gong, and Bu (2006) contribute to the topic ofinteraction between bank lending and property market. However, these articles carry out theirinvestigation at the national or provincial level. City-level research in this field has been rare due
to data limitations. Deng et al. (2011) suggest a channel through which credit is poured into realestate investment by SOEs using land auction data from eight major cities. Their paper focuseson the land markets. With a manually collected dataset, we attempt to complement to theanalysis of the relationship between bank loans and house prices from the disaggregated citylevel.
Efforts have also been made to estimate compensating differentials in the context ofChina, including Zheng and Kahn (2008), Zheng, Fu, and Liu (2009), Zheng, Kahn, and Liu(2010), Zheng, Cao, and Kahn (2011), among others. Building on the same Rosen-Roback
framework, Zheng and Kahn (2008) use two micro-datasets of land sales and project-wide house
prices in Beijing to estimate the capitalization effects of local public goods. The authors findproximity to public transit, parks, core high schools, and universities all have significantly
positive effects, and air pollution has a significantly negative impact on prices. Following Zheng
and Kahn’s (2008) early work on Beijing, Zheng, Fu, and Liu (2009) extend the research to 85
Chinese cities. Using China’s Urban Household Survey, the authors perform a similar hedonic
13

analysis of the amenity premium in 1998 and in 2004. This study incorporates more amenity
variables than Zheng and Kahn (2008), such as climate and healthcare. The findings indicate astrong increase between 1998 and 2004 in urban resident’s willingness to pay for amenities.
Another study of compensating differentials by Zheng, Kahn, and Liu (2010) uses cross-citypanel data from the 1997 to 2006 period. They document that green space and air pollution havesignificant effects on house prices, but in opposite directions. In contrast to Shapiro (2006), theyfind the effect of their measure of human capital insignificant. The effect of climate is also
insignificant. Zheng, Kahn, and Liu (2010) contribute to environmental economics byestablishing that FDI inflows reduce pollution levels. Zheng, Cao, and Kahn (2011) put evenmore weight on environmental economics; they study both air pollution production and its
capitalization effect on real estate prices. The same 85 cities as in Zheng, Fu, and Liu (2009) inthe 2006 to 2008 period are sampled, but an IV strategy is adopted to address the endogeneity ofpollution. The authors find air quality and hospital beds have significant effects on house prices,
but climate and teacher-student ratio do not.
Building on these authors’ insights, this paper attempts to complement the literature byexamining how a wide range of amenities are reflected in property prices, even after bank loans
and other control variables are taken into account. Zheng and Kahn (2008) focus their analysis
on Beijing. Zheng, Kahn, and Liu (2010) and Zheng, Cao, and Kahn (2011) concentrate on
environmental issues. Zheng, Fu, and Liu (2009) mainly concern the compensating differentials
in wages and housing rents. In addition, previous studies incorporate at most five aspects ofamenities or disamenities. For each aspect, only one measure is adopted. The indicators of air
pollution in these studies may serve as an illustration. Zheng, Fu, and Liu (2009) select sulphur
dioxide emissions only, while in Zheng, Kahn, and Liu (2010) and Zheng, Cao, and Kahn (2011),
the authors use particulate matter concentrations. However, both sulphur dioxide and particulatematter are important pollutants. Restricting their choice to either one means loss of information.
Such practice is probably due to the concern that if more measures of the same amenity areincluded, estimation will be complicated by high collinearity among the measures (Ridker andHenning 1967). With the help of principal component analysis, we are able to mitigate themulticollinearity problem and include multiple measures for every amenity. In total, we
incorporate 17 measures covering seven aspects of amenities or disamenities in this article:

14

climate, air pollution, crime rate, health care, urban crowding, higher education, and green
infrastructure, the last three of which are consolidated into one principal component.

4 DataWhile previous studies on China’s real estate market are interesting and inspiring, there
are rooms for further improvement. First, they adopt the provincial level data in their research,which may combine information from both urban and rural areas. Their measure of real estate
prices mixes residential, commercial, and industrial real estates. In addition, the composition andthe relative importance of different types of real estates may vary across cities. Thus, a moredisaggregated dataset with a price series exclusively focused on residential sector would provide
more insights into China’s municipal housing markets. In light of these considerations, wecollect annual data for the 1999-2012 period for 35 major cities representing all the provinces in
China except Tibet, the Hong Kong Special Administration Region, the Macau Special
Administration Region, and Taiwan. Six cities including Haikou, Hohhot, Nanning, Shenzhen,
Xining, and Yinchuan are excluded due to missing variables and data reliability problems. There is

at least one city in the sample for most provinces, and for areas such as Liaoning, Fujian,Zhejiang, and Shandong province, there are two cities allowing for further analyses. These cities
are scattered throughout China, and demonstrate large cross-sectional variations. Together, they
represent China’s geological, environmental, and social diversity.
The variables we collect can be divided into two groups. One group contains house prices,loans issued by the financial institutions of each city, per capita21 disposable income, long-term
interest rates, and stock market returns. As all of these variables are nominal, we convert theminto real ones using city-level CPI and inflation rate 22 . The second group is variables forlocation-tied amenities. Specifically, we include local population density, the number of 211
Project universities in each city, green coverage ratio of each city, number of doctors andhospital sickbeds shared by every 10,000 people, number of suspects arrested or prosecuted by
21

Variables involving population, such as per capita disposable income and population density, are calculated basedon the population with “Hukou”. The authors try to collect data on total population from municipal statistical
yearbooks. But some cities choose to report total population, while others still report officially registered population
only. The lack of consistency makes our efforts fruitless.
22
There is currently no city-wide or province-wide fixed based CPI. We obtain the December CPI of each city withprevious year as base from CEIC. City-level inflation rate is generated by CPI minus 100. Assuming the 1999 CPI
equals to 100 for every city, we add the inflation rate back to derive an alternative CPI series.
15the local procuratorate every 10,000 people, the amount of yearly sunshine and precipitation,
extreme temperatures, relative humidity, and the concentrations of PM10, SO2, and NO2 in the air.
As the data come from various sources, we provide detailed variable descriptions and data
sources in Appendix A. The descriptive statistics reported in Table 1 give an overview of how
these variables evolve during the sampling period.
[Insert Table 1 about here.]
It is clear from Table 1 that real house prices, real bank loans, and real per capita
disposable income all increased enormously as the economy developed23. Average real property
prices and real per capita disposable income almost tripled. The real loans volume experienced afivefold increase. Housing prices grew the fastest in Ningbo 24 , a beautiful coastal city in
Zhejiang province, at an average growth rate of 13.6% throughout the sample period. Shenyang25
witnessed the slowest average growth rate, 4.9%, in the same period. The growth rates in realbank lendings were much larger, around 15% in most cities in our sample. The fastest growth
was in Changsha, Hefei, Ningbo, and Taiyuan, each of which had a rate of over 20%. The
growth rates in real income were similar to that of the house prices, but with smaller variances.
Income in most cities grew at around 10%. The income in Urumqi grew at the slowest pace
(5.7% annually). To avoid scale effect, we normalize loan volume by GDP. And since it is highly
likely that there exist long-run trends in these three series, we detrend these series in conjunction
with deflating and logging.
For our amenity variables, we consider seven aspects: urban crowding, higher education,
crime rates, health care, green space coverage, air pollution, and climate. Following the literatureand considering data availability, we use per square kilometer population density to measureurban crowding, and green coverage ratio to represent local green infrastructure. The datacollection methods for healthcare resources, air pollution, and climate are consistent withprevious research, but we include more dimensions to capture more information. As we
23

Note that in China, most bank loans are made by the four major, state-owned banks. Thus, the total bank loans area sum of the loans made by private banks and state-owned banks. Our regional dataset, however, does not provide us
with the information necessary to differentiate the two. See Geiger (2006) for more discussion of this problem.
24
Zhejiang is one of the most advanced eastern provinces in China. Ningbo is the second most famous city in thisprovince, and its importance is only second to the provincial capital, Hangzhou.
25
Shenyang is the provincial capital of the north-eastern Liaoning province, which is economically less developed
than Zhejiang province.
16

discussed earlier, the previous literature has examined the effects of particulate matter andsulphur dioxide. We include both pollutants, and add nitrogen dioxide26 due to its adverse healtheffects on human respiratory systems. For the same reason, we include the number of doctors
and hospital sickbeds per 10,000 people as measures of the municipal healthcare resources. Our
measures of climate include extreme temperatures, temperature difference27, amount of annual
sunshine and precipitation, and relative humidity. Unfortunately, there is no crime data at the citylevel, so we use the number of suspects arrested and prosecuted by the local procuratorate per
10,000 population in each province as a proxy. These raw measures of air pollution, health care,climate, and crime rate are first treated with principal component analysis before entering theregression. The method will be discussed in detail in the next section.
While class size or student-teacher ratios in elementary schools are often used asmeasures of educational resources, we use the number of 211 Project universities by populationin the municipality instead. There are at least three reasons for such a substitution. First, thelocations of these universities are arguably more exogenous than other variables such as theaverage student-teacher ratio or class size. These universities were established a long time ago,
and are not the product of recent economic developments28. Second, the variations are larger intertiary education than in primary and secondary education. There are 26 universities in Beijingthat are funded by “Project 211”, ten in Shanghai, eight in Nanjing, seven in Wuhan, seven in
Xi’an, one in most other major cities, and none in Ningbo and Shijiazhuang. And finally, living
around a key higher education institution means beneficial exposure to well-educated peers andeasy access to university campus where large open space, sports facilities, and libraries areavailable. In fact, Zheng and Kahn (2008) have demonstrated that proximity to major universities
is a desirable feature for home-buyers, whereas the insignificance of student-teacher ratios isdocumented by Zheng, Cao, and Kahn (2011).
Our panel dataset might have an additional merit. Some previous studies on therelationship between amenities and house prices focus on the cross-sectional differences. While26

The health effects of nitrogen dioxide can be found on the website of the United States Environmental Protection
Agency: http://www.epa.gov/oaqps001/nitrogenoxides/health.html.
27
The temperature difference is defined as maximum temperature minimum temperature ⁄100 .
28
In fact, it is well-known that increasing teachers’ salaries or reducing student-teacher ratios do not necessarily leadto “better” education outcome. See Hanushek (2006, 2009). In addition, Hanushek and Yilamz (2007) analyze
household mobility when school qualities differ across communities.
17these cross-sectional differences are clearly important, it might overlook the fact that local
amenities do change over time. For instance, the health care provision in many cities improves
over the sample period, especially in Changsha, Chengdu, Guangzhou, Hangzhou, and Jinan.
Some places have cleaner air (e.g., Chongqing, Kunming, Nanjing, Wuhan, etc.) in the sense thatthe pollutant emissions are reduced as time passes by. At the same time, public security has been
deteriorating in all cities. One possible explanation is that the growing income gap between therich and the poor drives up the expected benefit of committing a crime. We provide a
visualization of the increase of hospital beds in our sampled cities from 1999 to 2012 in Figure 4.
Appendix E reserves more details about the temporal change in amenities for interested readers.
These “stylized facts” further reinforce the view that amenities would change with income andtherefore it is important to adopt a unifying framework including both amenities and bank loans
to study the house prices in different cities.
[Insert Figure 4 about here.]

5 Empirical Framework
5.1 Principal Component Analysis (PCA)
A typical challenge for empirical research on local amenities is that we either do notinclude enough measures of the amenities, or we do have many measures and yet
multicollinearity exists among them, which makes the econometric results a bit difficult tointerpret. For example, indicators for climate, such as the amount of precipitation and relative
humidity, are naturally highly correlated. The same is true for other amenities. Suchmulticollinearity has been documented by Ridker and Henning (1967). When all of our amenity
measures are included in the regression analysis, most of them are insignificant and display
counterintuitive signs. Simply dropping some will result in the loss of valuable information. To
mitigate the multicollinearity problem while preserving the information in the data, we use
principal component analysis29, a popular statistical method that reduces the data dimensionality
and at the same time retains as much variance as possible.

29

Principal component analysis and related methods have been used widely in previous studies. See Jolliffe (2010)
for a review of the literature on this subject.
18

The PCA recommends preserving five principal components out of 17 raw measures.
These five components jointly account for over 70% of the total variance. Each component canbe thought of as a weighted sum of the standardized amenity variables with scoring coefficientsin Table 2 as weights. Since most weights are assigned to minimum temperature, temperature
difference, sunshine, and humidity, we can roughly regard component 1 as climate indicator. By
the same token, component 2 measures air pollution, component 3 stands for crime rate, andcomponent 4 represents local healthcare condition. Each component proxies one kind of amenity
except the last one, which mixes urban crowding, higher education, and local green infrastructure
together. We term component 5 social development.
[Insert Table 2 about here.]

5.2 Two‐step Regression Analysis
Although PCA is adopted to mitigate the multicollinearity problem, a simple regression
with PCA-consolidated variables may still fail to give accurate estimates of the effects of local
amenities, as those amenities may affect house prices directly as well as indirectly by changing
the local income. To have a better understanding of the mechanism through which amenities
affect the house price, our regression analysis is conducted in two steps. First, we regress the logtransformed and detrended real income on local amenities and retain the residual. The residual
could be interpreted as the “filtered income”, i.e. the income that is not predicted by local
amenities. We put this residual term together with other explanatory variables into the secondstep regression to get more consistent estimates of income and amenity variables.
Step 1:,(1),

is the log transformed and detrended real per capita income.

is the row vectorof five principal components including climate, air pollution, crime rate, health care, and social
development. Although some of these amenities may not be strictly exogenous, they can beconsidered as predetermined (Keane and Runkle, 1992). The amenities level, such as health careprovision, is the result of past fiscal policies, and is presumably independent of future income
shocks. According to Greene (2012), predetermined variables can be treated “as if they were
exogenous in the sense that consistent estimates can be obtained when they appear as19

regressors.”30 We calculate the residual from the regression (the part of income unexplained byamenities) and denote it as the filtered income. The subscript is an index for city, for year.
Step 2:
,
,

,
,

,

,

is the log transformed and detrended real housing prices.
detrended loan GDP ratio.

(2)is theis the residual from the first-step regression.

is the real long-term borrowing rate, and serves as the proxy for mortgage rate.
is the current-value-weighted aggregated market returns with cash
dividend reinvested. It is included since the stock market return may reflect the market sentimentor represent the future productivity growth31.

is the same row vector of amenities as in the firststep. In order to tackle the cross-sectional heterogeneity in Chinese cities, we also include theregional dummies. Each city is placed in one of the four categories, i.e., northeast, east, west, andcentral China, according to the division criterion of the NBSC. A similar practice has been
adopted by other authors.
For the purpose of alleviating the endogeneity problem, we include the one-year-lagged
instead of contemporary

in the second-step regression. Limited by the sample size, we canafford to consider one lag only. In order to justify our approach of combining bank loans andlocal amenities in one framework, we conduct model comparison by carrying out three housing
price regressions, all with log transformed and detrended real house prices as dependent variable.
The first regression includes macroeconomic variables as regressors. The second regression
includes urban amenities as controls. And the third regression integrates both, the specificationof which is provided in Equation (2). The goodness-of-fit statistics of the three empirical models
are summarized in Table 3. Compared with the first two columns, the third has the highest
adjusted R-squared and F-statistic, the lowest AIC, BIC, and root mean squared error (RMSE).
And as will be shown by the same regression on standardized variables in column (2) of Table 4,30

We will show in section 6.3 that endogeneity in these produced amenities may not be a serious issue in the current
sample. For more discussion on endogeneity issue in econometrics, see Angrist and Pischke (2010), Sims (2010),
among others.
31
See Greenwood and Jovanovic (1999), Hobijn and Jovanovic (2001) for more discussion.
20

apart from the lag term of bank credit, urban amenities also have larger standardized coefficients
than some macroeconomic variables, which means amenities have relatively greater impacts on
detrended real house prices than filtered income, long-term interest rate, and stock market return.
Together with the significant interaction between loans and amenities to be reported in Table 7,
these evidence suggest that the specification in Equation (2) is a better empirical model.
[Insert Table 3 about here.]
Since housing prices within the same city tend to be correlated over the years, it isprudent to use clustered standard error to deal with the possible intra-class correlation. On theother hand, clustering analysis tends to inflate the standard errors even more thanheteroskedasticity robust standard errors. The statistical significance of the point estimates are
expected to be affected.

5.3 The Reversed Effect of House Prices on Loans
To inspect the effect of house prices on credit, we run the loan regressions.32 Following
Peng et al. (2008), fixed asset investment is included to capture the investment demand for bankcredit. The stock market return is taken into account for two reasons. During a hot stock market,
on the one hand, as households draw deposit from banks to invest in the stock market, the banks
will have to reduce the loan supply. On the other hand, firms also turn to the stock market as analternative financing source. Both reasons suggest the loan volume may be negatively related tothe stock market return. We fit the loan equation with a fixed effect model.
,

,

,
,

,
,

(3)

refers to the annual fixed asset investment over GDP ratio. Other
variables carry the same definition as in Equation (1) and (2). Contemporary
replaced by its one-period lag to address the endogeneity concern.

32

In this regression, income enters the right hand side without filtering.
21

is6 Empirical Results
6.1 Main Results
As previously discussed in Section 5.2, we fit three empirical models for housing prices
to facilitate model comparison. The findings from the full model which combines bank loans andurban amenities in one unifying framework will be discussed in detail in this section. Results of
the other two models are reserved in Appendix B. To justify our use of the PCA technique, we
perform a regression with the raw amenity measures and provide the results in Appendix B as
well. Regression output with PCA-consolidated amenity variables and unfiltered income can also
be found in Appendix B.
Our discussions begin with the outcome of the second-step regression with PCA
technique applied on the full sample from 1999 to 2012. Column (1) of Table 4 reports the point
estimates and their associated p-values. Column (2) documents corresponding standardized
coefficients. Since standardized coefficients inherit the same statistical significance as point
estimates, we omit the p-values and significance stars in column (2). In the full-sample outcome,the elasticity of the house prices with respect to the detrended and filtered real income is 1.017,
which indicates the detrended real housing prices would be 1.017% higher if the income
increases by 1%. In terms of standardized coefficient, real property prices will rise by 0.324
standard deviations if the income goes up by one standard deviation. Such effect is statisticallydiscernible from zero at the 1% significance level. The coefficient of real stock market return onreal house price is around 0.021, which is statistically significant at the 1% significance level.
This confirms previous findings that the stock market and housing market tend to co-move.33
However, the coefficient estimate of the normalized loan is not significantly different from zero,detailed discussion of which will be provided in the subsample analysis. The long-term interest
rate is not significant either.
[Insert Table 4 about here.]
Column (1) of Table 4 also shows that estimates for crime rate and the principal
component representing higher education, green space coverage, as well as urban crowding are
33

Among others, see Leung (2007) on the relationship between the stock market and the housing market in
equilibrium.
22

highly significant at the 1% significance level. All amenities except crime rate display the
anticipated signs. A comparison with Table B4 confirms our conjecture that income is notcompletely exogenous. Cities with more desirable amenities would attract people with higher
abilities and incomes. Statistically speaking, we need to adopt a 2-stage procedure to address
such “implicit selection”. Notice that the point estimates are elasticities of house prices withrespect to corresponding component score. The interpretation should be how a one-score change
in the principal component would influence the property prices. For example, an improvement of
one score in the social development component would push house prices up by 12.8%. One
standardized deviation increase in the social development indicator would result in 0.401
standardized deviations appreciation in the housing prices. Recall the social development
component is made up of higher education, green infrastructure, and urban crowding. Ourfindings are consistent with existing evidence. The premium for green infrastructure has beenpreviously documented by Zheng, Fu, and Liu (2009), and Zheng, Kahn, and Liu (2010), while
similar positive influence of education or human capital can be found in Blomquist (1988),
Gyourko and Tracy (1991), Zheng, Fu, and Liu (2009). Our paper is closest to Zheng and Kahn
(2008) in the sense that both inspect the impact of higher education. Conventionally, a higher
population density implies crowding and should suppress house prices. However, recent studies
suggest that a more concentrated population may be associated with better learning and matchingin the labor market (Laing et al., 1995; Bleakley and Lin, 2012), better matching in the marriage
market (Gautier et al., 2010), more product variety (Schiff, 2012), etc. Thus, a higher population
density can have a positive effect on house prices. In fact, Roback (1982) also documents asignificantly positive influence of population density on residential site prices. We believe thesame reasoning applies to public security. Great and economically successful cities usually also
breed more crime. The positive capitalization effect of crime is also reported by Blomquist et al.
(1988), Gyourko and Tracy (1991), and Berger et al. (2008). The amenity variables capture thelocation heterogeneity well so that regional dummies are no longer significant.
Running regression on standardized variables, we directly compare coefficients to assess
the relative strengths of corresponding variables in explaining the variance in housing prices. In
column (2), one-year lag of normalized credit has a standardized coefficient of 0.094, and real
income 0.324. Among all the explanatory variables, the long-term interest rate has the smallest
standardized coefficient, -0.008, which means one standard deviation increase in the interest rate23

almost does not alter the property prices. All the amenity components have greater standardized
coefficients than the mortgage rate proxy do. The standardized coefficients of social
development and crime rate are even larger than those of bank lending and income. Clearly suchcomparison is possible only when both bank loans and urban amenities are included in a unifying
framework.
To investigate how housing prices may influence loans, we fit equation (3) to data using
fixed effect method and report the estimation results in column (1) of Table 5. As the column
shows, home price lag significantly and positively affects loan GDP ratio. The point estimate is0.53, and the standardized coefficient is 0.414. In plain language, 1% appreciation in theproperty price would induce the future credit volume to expand by 53 basis points, while one
standard deviation increase in the property price would correspond to 0.414 standard deviation
increase in bank lending in the following year. The impact of the normalized fixed asset
investment is larger in elasticity, but smaller in terms of standardized coefficient. If the fixed
asset investment over GDP ratio is higher by 1%, the demand for bank loans would be almost
1% greater. If the fixed asset investment goes up by one standard deviation, the credit wouldgrow by 0.356 standard deviations. The first instinct about interest rate, a measure of borrowing
cost, is that higher interest rate should suppress borrowing. However, a more careful reasoning
casts doubt on this simple intuition. Interest rate is an endogenous variable. When there is hugedemand for capital, the borrowing cost will naturally climb up. And when the economy“overheats”, the central bank will also raise the interest rate to cool it down. As a result, weobserve a positive relationship between the interest rate and the credit34. Our negative estimate of
the real stock market return is evidence of our conjecture that stock is another investment vehicle
to households, and the stock market is an alternative financing source to firms. Despite being
statistically insignificant, the estimate for detrended real income is of a negative sign, whichechoes the finding of Peng et al. (2008). Cities with higher income tend to have more diversified
financing channels and rely less on bank loans.
[Insert Table 5 about here]34

It may take a more structural model to investigate the relationship between interest rate and property market
formally and hence we leave this for future research.
24

6.2 Subsample Analysis
As is shown in Figure 1 and Figure 3, both house prices and loans have gone throughseveral different stages. It is therefore natural to wonder whether our results derived from the fullsample also hold in different sub-samples. In this section, we will discuss our findings for
subsamples.
Due to the limitation of sample size, we can only afford to divide the whole sample into
two subsamples, which are Before the Great Recession, and After. A further attempt to break the
pre-crisis subsample into before and after 2003 will result in too few observations for the periodprior to 2003. As our sample begins in 1999, the first subsample (i.e. Before the Great Recession)
has more observations than the second one. In fact, both the qualitative and quantitative results
for the period between 1999 and 2008 are very similar to those in the full sample. Therefore, wewill put more effort into documenting the findings for the post-recession era.
In the housing price regression for the data after 2008 reported in column (5) and (6) of
Table 4, we find that bank credit plays a significant role in driving up home prices, which isconsistent with Liang and Cao (2007), and Liang and Gao (2007). Furthermore, the magnitudes
of both point estimate and standardized coefficient are larger compared to the results in the othercolumns of Table 4. To be more specific, if the loan GDP ratio goes up by 1%, the house prices
in the following year would appreciate by 16.1 basis points. And one standard deviation increasein the current financial institutional lending would lead to a 0.179 standard deviations growth in
the real house price in the next year, with everything else held constant. In contrast, thecoefficient on the real income changes in the opposite direction. Both the coefficient values andthe statistical significance decline. The influence of the stock market return becomesinsignificant as well. We believe such failure of precise estimation is caused by the small amountof identifying variance (Griliches and Mairesse, 1995) in the stock market return from 2009 to
2012. The other variables have the same pattern as documented in the last section.
In sum, we find the bank lending has contributed to the property price inflation in a timeof easy credit, but not before. Nevertheless, the pattern of statistical significance is reversed in
the case of how the real estate prices have influenced the loan volume. From 1999 to 2008, ourestimate suggests that the housing prices significantly and positively affected the loan GDP ratio,
rather than the other way around. Peng et al. (2008) reached the same conclusion for the period
25

from 1998 to 2004. In the second sub-sample, the impact from the previous year real house priceon bank loan switches from significant to insignificant. Even income does not appear to have any
power in explaining loans after the Great Recession. Again probably because of the reduced
amount of variations in the interest rate and stock market return, their estimates lose significanceas well in the second subsample. The only statistically significant variable is the normalized
fixed asset investment. In spite of this, the adjusted R-squared surprisingly jumps to 0.97 incolumn (5). Putting the small sample issue aside, the results here are in fact consistent with arecent literature, which suggests that the “Stimulus Package” implemented by the Chinese
government after the Great Recession, which tends to focus on fixed asset investment, may playa key role in explaining the bank loans in China.35

6.3 Endogenous Amenities
This section shifts the attention to amenities. Obviously, some amenities are completely
exogenous such as climate, while others are not. Urban crowding, green infrastructure, publichealth care, crime rate, and air pollution are produced by human society (Rosen 1979; Roback
1982; Blomquist et al. 1988; Gyourko and Tracy 1991), and may not be strictly exogenous. Thus,
to examine the effect of amenities on the house prices, one needs to address such endogeneity
concern. In this section, we conduct the same two-step analysis on amenities as we do on income.
Notice that our amenity variables are linear combinations of all raw amenity measures, and thus
there is measurement error in them when they are viewed as indicators of amenities. For example,
component 1 is mostly about climate, but it also contains other information, and cannot bethought of purely exogenous. We therefore apply the filtering process to every component. We
regress the produced amenities against detrended real income and retain the residuals as “filtered”
amenities in the first step, which are then inserted into the second-step regression together withthe other variables specified in equation (2).
Overall, our findings are not weakened by this “cleaning procedure” applied to theamenity variables. Regarding how the bank loans have affected the housing prices, the results
remain the same as in Table 4. The empirical results on how amenities affect house prices
actually become sharper. Due to such similarities, we focus our discussion on the post-recession
35

Clearly, it is beyond the scope of this paper to survey this growing literature on the impact of the post-crisis
response of China. Among others, see Csanadi et al. (2013), Garcia-Herrero and Santabarbaram (2011), IMF (2014),
Wong (2011).
26

estimation reported in column (5) and (6) of Table 6. After removing the “endogenous
component of amenities”, the effect of income on home prices is much greater. If one city isricher than another by 1% of filtered income, it would also have the house prices higher by
3.792%. Such a huge effect is not unlikely if housing is considered as a “luxurious good” by thehouseholds. Then an increase in income would lead to a higher proportion of expenditure shareon housing in the total budget. Since the housing supply is quasi-fixed in the short run, an
increase in the expenditure share will be translated into a higher housing price at the equilibrium.
Among the amenities, social development and crime rate are still highly significant. Now, otheramenity components also become significant. The standardized coefficient of the climate
component is 0.369, which is larger than that of the normalized loan and almost four times of theprevious estimate for climate in Table 4.
Notice that such an important impact of climate we have obtained is different from what
Zheng, Kahn and Liu (2010) and Zheng, Cao, and Kahn (2011) have found. These two studies
documented a negligible climate effect. The difference of the results may be due to themeasurement issue. These studies measure climate by a function of temperatures, while thispaper includes a more inclusive measure through the principal component method. Thus, ourresult may suggest that other aspects of climate such as sunshine, precipitation, and relative
humidity matter for the housing market. In addition to climate, the healthcare component has
become marginally significant as well. The associated standardized coefficient has also grownfrom 0.018 in Table 4 to 0.078 in Table 6, an over three times’ increase. This finding about theimportance of local healthcare condition is consistent with existing literature. Zheng, Fu, and Liu(2009) show that people are willing to pay a positive rental premium for more doctors in town.
Zheng, Cao, and Kahn (2011) have documented the number of sickbeds in hospitals and clinics
positively affect newly-built house prices.
[Insert Table 6 about here]

6.4 Interaction Effects
We have argued that loans and some amenities may interact with each other through theirrelations to a common third variable. In this section, this thesis is examined more formally. Morespecifically, we incorporate the interactive terms of loans and other variables in the estimation.
The new regression becomes
27

,
,

,
,

,

,
,

,

(4)

Overall, this regression supports the position that credit and amenities are indeed interrelated. Here are more details. Compared with Table 6, the goodness of fit improves in Table 7.
The estimate for the loan GDP ratio gains significance even in the full sample. The amenity
variables remain as significant as in Table 6. More importantly, the interaction effects between
climate, health care, crime rate and bank lending are statistically significant, with signs in linewith our expectation. The multiple of social development and bank lending is almost significant
as well. Notice that bringing in interactive terms in the regression demands more cautions in theinterpretation of the results. Here are our interpretations. The influence of credit on housing
prices now varies across the level of amenity. The negative coefficients on the interactive terms
suggest that home prices are less sensitive to credit expansion in cities with better amenities suchas more pleasant climate, more green space coverage, better health care and higher education. In
addition to the full-sample results, we also briefly review the subsample findings. From 1999 to
2008, the main effect of the normalized loan is statistically indistinguishable from zero, the sameas the overall effect in column (3) of Table 6. Though the main effects of amenities are very
similar to their full-sample counterparts in column (1) of Table 7, the interactive terms seem tobecome more important in the econometric model. In fact, after 2008, only the interactive termof pollution and loan ratio is significant. The statistical significance of other interactive terms
could be “reduced” due to the small sample issue. We can only wait for future research toconfirm this speculation.
[Insert Table 7 about here]

6.5 Panel Estimation
To further exploit our dataset which has both cross-sectional and temporal variations, we
perform additional panel data estimation. Consistent with our previous results, the impact ofcredit on house price is found to be significant only after the Great Recession. Therefore, to save
space, we limit our discussion to the post-recession results in this section. The fixed effect modelhas the advantage of allowing the unobserved individual effects to be correlated with the28covariates. The tradeoff is “using the most restricted amount of variance for the identification of
the various coefficients” (Griliches and Mairesse, 1995). In our case, we run the Hausman test to
determine whether random effect or fixed effect model should be adopted. The Hausman test
does not reject the consistency of the random effect model for the data after 2008. We present theresults from the fixed effect model as well.36
The first two columns of Table 8 report the fixed effect and random effect estimations ofequation (2), corresponding to the last two columns of Table 4. The two columns in the middleof Table 8 are the panel estimation counterparts of column (5) and (6) in Table 6. And the lasttwo columns of Table 8 can be compared to those in Table 7. After a comprehensive comparison,
we find the random effect estimates of the lagged normalized loan are very close to previous
ones. Although the within transformation reduces the amount of variance in data, the coefficienton the loan variable is still significant in column (5) of Table 8, and is marginally significant incolumn (1) and (3). However, the same parameter is slightly bigger in the fixed effect model thanin the random effect model. When it comes to the real income, the random effect estimates are
greater than the ordinary least squares estimates. The fixed effect estimates of income are quite
different across the three specifications. Unlike previous regressions, the coefficients of interest
rate become statistically significant, and carry stable values using either the fixed effect or the
random effect estimator. The negative sign further suggests that interest rate has an impact on the
housing price. Among the amenity variables, both the significance pattern and the magnitude of
the significant estimates are similar to those in Table 4, 6, and 7. The interaction effects are moresignificant in the full sample, just the same as the case in Table 7.
[Insert Table 8 about here]

7 Conclusions
As the world economy struggles to move out of the recession, the Chinese economy canno longer depend on the driving force of exports to prevent it from slowing down. In fact, somedeveloped countries are expecting to export their goods and service to China. If the Chinese real36

As we present the earlier version of this paper in different conferences, there are audiences interested in thefindings from the fixed effect model. For a more engaging discussion of the fixed effect model and the random
effect model, see Hsiao (2003).
29

estate market crashes at this time, China and many of her trading partners would suffer serious
setbacks; this scenario is of great concern to both researchers and the general public37. Most of
the existing research on the relationship between credit expansion and house price surge relies onaggregate data at the provincial or national level, leaving out the issues caused by geographicalheterogeneity. This study uses a unique, hand-collected city-level dataset to take the first step inassessing the interactions among bank loans, property prices and local amenities. Since bothcredit and amenities influence house prices, and since the effects of these two factors areinteracted, we combine them in one simple yet unified framework. And our model has thecapability to evaluate the relative importance of different factors.
To conclude our findings, the housing prices had significantly affected the financial
institutional lending before the Great Recession, whereas after the crisis the loan expansion has
been an important force driving up the home prices. When the real property prices went up by
1% before 2008, the loan GDP ratio would be greater by almost 50 basis points in the next year
according to our estimate. If instead the house prices experienced an increase of one standard
deviation, the future normalized loan would expand by 0.371 standard deviations. On the other
hand, in the post-recession era, 1% incremental change in the loan ratio would boost the real
property prices by 16.1 basis points in the following year. In terms of standardized coefficient, an
increase of one standard deviation in the loan ratio would lead to a growth of 0.179 standard
deviations in the future house prices. Such empirical results imply that although the loans wereonce truly demanded by the market, they begin to influence the asset prices when an enormous
amount of them are poured into the economy. Besides bank lending, local amenities such as
climate, health care, higher education, and green space coverage are also capitalized into house
prices. And the amenities’ impacts are by no means weaker than that of the credit. For example,
the standardized coefficients of climate and social development (the composite component of
population density, higher education, and green infrastructure) are 0.357 and 0.71 respectively.
Both are greater than that of the bank lending, which is 0.179. Comparing our results with theliterature also suggests that “climate” is more than temperature. Sunshine, precipitation, andrelative humidity all matter for the housing market. The healthcare indicator has a standardized
coefficient of 0.075, also larger than that of the interest rate (0.06) and the stock market return (-37

See Economist (2013) for a special report on this topic.
30

0.057). Last but not the least, the credit and some amenities including climate, crime rate, healthcare, and social development are negatively interacted with each other, suggesting that property
prices are less sensitive to lending growth in cities with more desirable amenities.
The evidence we have documented is likely to carry important policy implications. First,
monetary policy is important for the house price stability as it would impact the credit expansion.
Since the major banks in China are all national banks, bank credit is indeed a part of themonetary policy. Second, the local government investment in amenities can lead to increase inhouse prices. One possible reason for why amenities affect property prices is that cities offeringhigh quality of life can attract immigrants, whose living needs push up housing demand much
more than the supply is able to adjust. Apart from contributing to home price, the influx of newlabor, especially the high quality human capital, is beneficial for promoting productivity of thecity as well (Peri, 2012). On the other hand, amenities via housing demand can influence local
land sale revenue, which in the current context of China is an important determinant of local
government financial position.
One limitation of our study is there may be concern for external validity. The current
sample covers only relatively developed cities in China with more active financial markets. The
link between the bank credit and the housing market may be stronger in these cities. As
urbanization is still an undergoing process for China, meaning that small and medium cities oftoday could become big cities in the future. Understanding the housing market, urban amenities,
and government policy could be very important. Thus, this study may also shed light on thoseemerging cities. In addition, the China experience may shed light for other developing countries
as well.38
Clearly, our analysis is far from being the last word on this issue. Household-level data
and the mortgage loan data, when become available, should be used to confirm our tentative
conclusion. Since there has been evidence of improved regional capital mobility in China as the
economy develops (Chan et al., 2011), future research could also investigate whether there exists
spatial spillover among regional housing markets, and what role does regional capital flow playin such spillover effect.
38

Clearly, it is beyond the scope of this paper to survey the literature on the urbanization in developing countries.
Among others, see Glaeser (2014), Malpezzi (2012), Romer (2014).
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38

Figure 1 National Housing Market
6,000

1,170,000

5,000

930,000
4,000

810,000
690,000

3,000

570,000
2,000

450,000
330,000

1,000

210,000
90,000
1998

0
2000

2002

Floor space completed

2004

2006

2008

Floor space sold

Data source: CEIC

39

2010

2012
Housing price

RMB/Sq meterThousand square meter

1,050,000

Figure 2 Ratio of Real Estate Investment over Total Investment
30%
25%
20%
15%
10%
5%
0%
1986

1991

1996

2001

REI over Investment

2006

Residential REI over Investment

Data source: CEIC

40

2011

Figure 3 Ratios of Broad Money and Loans over GDP

180%
160%
140%
120%
100%
80%
60%
40%
20%
1978

1983

1988

1993

M2/GDP

1998

2003

Loan over GDP

Data source: CEIC

41

2008

2013

19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12

Number of Hospital Beds per 10,000 Population
10
30
50
70
90

Figure 4 Boxplot of Chinese Cities' Hospital Sickbeds

Notes: The lower hinge of each box represents the 25th percentile, the upper hinge represents the75th percentile, and the line within each box indicates median. Outside values are excluded.

42

Table 1 Summary Statistics
1999
Variables

Mean
2016.286
114.0101.422
7310.339
7.986
0.192
29.6750.332-1.07636.181-9.924
1958.747
945.49467.294.
69.818
.
.
5.867
6.132
27.88443.035624.3383.029
31.057

Real House Price (Yuan per Square Meter)
Real Loan (Billion Yuan)
Loan GDP Ratio (Proportion)
Real Income (Yuan Per Capita)
Real Long-Term Interest (%)
Real Stock Market Return (Proportion)
Real Fixed Asset Investment (Billion Yuan)
Fixed Asset Investment over GDP (Proportion)
City-level Inflation Rate (%)
Maximum Temperature (°C)
Minimum Temperature (°C)
Yearly Sunshine (Hours)
Yearly Precipitation (Millimeter)
Relative Humidity (%)
PM10 (Micrograms per Cubic Meter)
SO2 (Micrograms per Cubic Meter)
NO2 (Micrograms per Cubic Meter)
Days with Good Air Quality
Suspects Arrested per 10,000 Population
Suspects Prosecuted per 10,000 Population
Doctors per 10,000 Population
Hospital Beds per 10,000 Population
Population Density (Persons per Square Kilometer)
211 Project Universities
Green Coverage (%)

43

2012
Standard
deviation
946.322110.8130.689
2894.575
1.566
0.017
36.369
0.128
1.430
2.341
8.504
570.851574.347
10.507.
56.316
.
.
2.008
2.357
10.31715.477477.825
4.662
7.860

Mean
5862.114
732.9301.696
21156.735
3.969
0.024
262.895
0.659
2.711
36.291-10.118
1914.536
982.347
65.93388.68635.17140.600330.824
7.629
11.19334.35458.913736.278
3.029
40.034Standard
deviation
2976.219
622.4610.534
5717.690
0.418
0.004
161.467
0.219
0.412
2.933
10.882598.991557.980
11.60823.17814.4969.829
24.7492.976
4.212
16.90915.576641.709
4.662
4.554

Table 2 Matrix of Scoring Coefficients
Raw Measures
Maximum Temperature (°C)
Minimum Temperature (°C)
Temperature Difference
Yearly Sunshine (Hours)
Yearly Precipitation (Millimeter)
Relative Humidity (%)
PM10 (Micrograms per Cubic Meter)
SO2 (Micrograms per Cubic Meter)
NO2 (Micrograms per Cubic Meter)
Days with Good Air Quality
Suspects Arrested per 10,000 Population
Suspects Prosecuted per 10,000 Population
Doctors per 10,000 Population
Hospital Beds per 10,000 Population
Population Density (Persons per Square Kilometer)
211 Project Universities per 10,000 Population
Green Coverage (%)
Cumulative Proportion of Variance

44

Comp1
0.156
0.472
-0.456-0.4550.319
0.433-0.076
0.078
0.037
0.025
0.029
-0.0670.037-0.0240.133-0.092
0.029

Comp2
0.214
-0.0120.052-0.081-0.056-0.030
0.491
0.478
0.321
-0.528-0.042-0.072-0.039
0.085
0.081
0.142
-0.217Comp3
0.116-0.0070.045-0.0080.003-0.055-0.119-0.003
0.366
0.083
0.613
0.637
0.014
0.017
0.189
-0.037-0.085Comp4-0.242
0.043-0.0900.124
0.079-0.015-0.0040.095
0.020-0.0150.052-0.0180.671
0.664-0.047
0.016
0.065

Comp5
0.214
0.036
-0.0010.110
0.086-0.0460.061-0.2530.057-0.078-0.046-0.020
0.032
0.007
0.405
0.603
0.569

0.227

0.393

0.526

0.637

0.723

Table 3 Empirical Model ComparisonnkAdjusted R-squared
RMSE
F
AIC
BIC

(1)
Macroeconomic Variables
223
7
0.80
0.18
28.41
-116.17-88.9145(2)Local Amenities
223
8
0.79
0.19
41.12
-104.27-73.61

(3)
Both
223
12
0.85
0.16
69.65
-166.74-122.45

Table 4 Housing Price Regression: Main Results

Dependent variable:log(Real House Price)
Lagged Loan GDP Ratio
Filtered Income
Real Long-Term Interest
Real Stock Market Return
ClimateAir Pollution
Crime Rate
Health Care
Social Development
Constant

Full sample
(1)
(2)
Coef.
Std. Coef.
0.079[0.238]1.017***[0.001]-0.002[0.758]0.021*[0.083]0.010[0.575]-0.004[0.734]0.174***[0.000]0.006[0.744]0.128***[0.000]-0.034[0.621]

0.094
0.324
-0.0080.036
0.044-0.017
0.677
0.018
0.401

2008 and before
(3)
(4)
Coef.
Std. Coef.
0.081[0.297]0.885***[0.006]0.009[0.403]0.021[0.146]0.024[0.284]-0.008[0.583]0.157***[0.000]0.018[0.610]0.122***[0.000]-0.089[0.221]

0.104
0.285
0.038
0.043
0.114
-0.035
0.677
0.049
0.420

After 2008
(5)
(6)
Coef.
Std. Coef.
0.161**[0.043]0.965***[0.006]0.014[0.527]-0.047[0.542]0.016[0.478]0.040[0.246]0.183***[0.000]0.005[0.806]0.125***[0.000]-0.079[0.471]

0.179
0.304
0.060
-0.057
0.062
0.111
0.606
0.013
0.341

Observations
223
138
85
Clustered Standard Error√√√
Adjusted R-squared
0.85
0.83
0.88
F
69.65
41.20
59.95
Notes:
1. Clustered standard errors are computed to draw inference. p-values are in the brackets. The stars*, **, and *** indicate the significance level at 10%, 5%, and 1% respectively.
2. House price is measured in terms of Yuan per square meter. The measurement unit of income is
Yuan per capita. These two variables are first deflated, then transformed into natural logs, andfinally detrended. Loan GDP ratio is detrended as well. Income is further filtered by urban
amenities.
3. We subtract city-level inflation rates from nominal long-term interest rate, and nominal stock
market return to obtain corresponding real values. Long-term interest rate is in percentage,
while stock market return is in proportion.
4. Climate, air pollution, crime rate, health care, and social development are all principal
components. Social development mainly captures population density, green space coverage, andhigher education.
5. Coefficients for regional dummies are suppressed to save space.

46

Table 5 Loan Regression

Dependent variable: Loan
GDP Ratio
Lagged log(Real House
Price)
log(Real Income)
Fixed Asset Investment
over GDP
Real Long-Term Interest
Real Stock Market Return
Constant

Full sample
(1)
(2)
Coef.
Std. Coef.
0.530**[0.047]-1.288**[0.041]0.928***[0.000]0.023*[0.079]-0.063***[0.009]0.660[0.109]0.414-0.6440.356
0.090-0.0832008 and before
(3)
(4)
Coef.
Std. Coef.
0.493**[0.016]-1.643***[0.008]0.828**[0.037]0.023*[0.085]-0.068***[0.001]0.910***[0.006]0.371-0.8430.217
0.094-0.100After 2008
(5)
(6)
Coef.
Std. Coef.
-0.054[0.728]-0.723[0.378]1.182***[0.000]-0.004[0.712]0.009[0.859]0.978***[0.008]-0.045-0.3470.637-0.017
0.009

Observations
377
261
116
Clustered Standard Error√√√
Adjusted R-squared
0.75
0.77
0.97
Notes:
1. Clustered standard errors are computed to draw inference. p-values are in the brackets. The
stars *, **, and *** indicate the significance level at 10%, 5%, and 1% respectively.
2. House price is measured in terms of Yuan per square meter. The measurement unit of income
is Yuan per capita. These two variables are first deflated, then transformed into natural logs,
and finally detrended. Loan GDP ratio is detrended as well.
3. We subtract city-level inflation rates from nominal long-term interest rate, and nominal stock
market return to obtain corresponding real values. Long-term interest rate is in percentage,
while stock market return is in proportion.
4. City dummies are included but not reported to save space.

47

Table 6 Housing Price Regression: Endogenous Amenities

Dependent variable:log(Real House Price)
Lagged Loan GDP Ratio
Filtered Income
Real Long-Term Interest
Real Stock Market Return
ClimateAir Pollution
Crime Rate
Health Care
Social Development
Constant

Full sample
(1)
(2)
Coef.
Std. Coef.
0.079[0.238]3.792***[0.000]-0.002[0.758]0.021*[0.083]0.090***[0.000]0.014[0.287]0.463***[0.000]0.027[0.177]0.316***[0.000]-0.034[0.621]

0.094
1.207
-0.008
0.036
0.369
0.055
1.260
0.078
0.838

2008 and before
(3)
(4)
Coef.
Std. Coef.
0.081[0.297]3.559***[0.000]0.009[0.403]0.021[0.146]0.101***[0.001]0.010[0.523]0.435***[0.000]0.037[0.285]0.303***[0.000]-0.089[0.221]

0.104
1.146
0.038
0.043
0.455
0.043
1.324
0.106
0.901

After 2008
(5)
(6)
Coef.
Std. Coef.
0.161**[0.043]3.799***[0.000]0.014[0.527]-0.047[0.542]
0.098***[0.000]0.059*[0.087]0.478***[0.000]0.026[0.190]0.318***[0.000]-0.079[0.471]

0.179
1.196
0.060
-0.057
0.357
0.161
1.094
0.075
0.710

Observations
223
138
85
Clustered Standard Error√√√
Adjusted R-squared
0.85
0.83
0.88
F
69.65
41.20
59.95
Notes:
1. Clustered standard errors are computed to draw inference. p-values are in the brackets. The stars*, **, and *** indicate the significance level at 10%, 5%, and 1% respectively.
2. House price is measured in terms of Yuan per square meter. The measurement unit of income is
Yuan per capita. These two variables are first deflated, then transformed into natural logs, andfinally detrended. Loan GDP ratio is detrended as well. Income is further filtered by urban
amenities.
3. We subtract city-level inflation rates from nominal long-term interest rate, and nominal stock
market return to obtain corresponding real values. Long-term interest rate is in percentage,
while stock market return is in proportion.
4. Climate, air pollution, crime rate, health care, and social development are all principal
components. Social development mainly captures population density, green space coverage, andhigher education.
5. Coefficients for regional dummies are suppressed to save space.

48

Table 7 Housing Price Regression: Interaction Effects

Dependent variable:log(Real House Price)
Lagged Loan GDP Ratio
Filtered Income
Real Long-Term Interest
Real Stock Market Return
ClimateAir Pollution
Crime Rate
Health Care
Social Development

Interactive Terms
Climate by Loan Ratio

Full sample
(1)
(2)
Coef.
Std. Coef.
0.125**[0.042]
3.437***[0.000]-0.001[0.802]0.016[0.182]0.080***[0.001]0.004[0.781]0.435***[0.000]0.028[0.209]0.295***[0.000]-0.082**[0.022]
Air Pollution by Loan Ratio-0.036[0.158]Crime Rate by Loan Ratio-0.059*[0.060]
Health Care by Loan Ratio -0.089***[0.001]Social Development by-0.075*
Loan Ratio[0.070]

0.149
1.094
-0.006
0.027
0.329
0.015
1.183
0.080
0.783

-0.178-0.088-0.082-0.164-0.0952008 and before
(3)
(4)
Coef.
Coef.
0.044
[0.523]
3.127***[0.000]0.009[0.376]0.007[0.539]0.082***[0.003]0.012[0.432]0.421***[0.000]0.026[0.404]0.276***[0.000]-0.113**[0.010]-0.023[0.392]-0.112***[0.000]-0.154***[0.000]-0.103**[0.020]

0.057
1.007
0.035
0.015
0.369
0.050
1.281
0.073
0.820

-0.285-0.065-0.185-0.241-0.162After 2008
(5)
(6)
Std. Coef.
Coef.
0.233**[0.017]3.408***[0.000]-0.020[0.333]0.085[0.289]0.082***[0.001]0.008[0.728]0.454***[0.000]0.049***[0.008]0.288***[0.000]-0.014[0.693]0.136***[0.001]-0.011[0.856]-0.018[0.565]0.035[0.645]

0.258
1.072
-0.083
0.103
0.300
0.023
1.039
0.145
0.642

-0.0240.241-0.012-0.0390.029

Observations
223
138
85
Clustered Standard Error√√√
Adjusted R-squared
0.87
0.86
0.91
F
115.8
86.66
748.4
Notes:
1. Clustered standard errors are computed to draw inference. p-values are in the brackets. The stars*, **, and *** indicate the significance level at 10%, 5%, and 1% respectively.
2. House price is measured in terms of Yuan per square meter. The measurement unit of income is
Yuan per capita. These two variables are first deflated, then transformed into natural logs, andfinally detrended. Loan GDP ratio is detrended as well. Income is further filtered by urban
amenities.
3. We subtract city-level inflation rates from nominal long-term interest rate, and nominal stock
market return to obtain corresponding real values. Long-term interest rate is in percentage,49

while stock market return is in proportion.
4. Climate, air pollution, crime rate, health care, and social development are all principal
components, which are further filtered by income. Social development mainly captures
population density, green space coverage, and higher education.
5. Constant and coefficients for regional dummies are suppressed to save space.

50

Table 8 Post‐recession Housing Price Panel Regression

Dependent variable:log(Real House Price)
Lagged Loan GDP Ratio
Filtered Income
Real Long-Term Interest
Real Stock Market Return
ClimateAir Pollution
Crime Rate
Health Care
Social Development

Main Specification
(1)
(2)
FE
RE

Endogenous Amenities
(3)
(4)
FE
RE

Interaction Effects
(5)
(6)
FE
RE

0.210
[0.111]0.700[0.276]-0.024*[0.072]
0.095[0.110]-0.018[0.708]0.051[0.210]0.119[0.105]-0.031[0.337]0.166**[0.015]0.210[0.111]2.755[0.117]-0.024*[0.072]
0.095[0.110]0.041[0.582]0.065[0.140]0.333*[0.082]-0.016[0.617]0.305**[0.027]0.258**[0.018]4.411***[0.010]-0.039**[0.022]0.152**[0.018]0.101[0.180]0.068[0.166]
0.509***[0.008]-0.010[0.734]0.427***[0.003]0.231***[0.001]
3.962***[0.000]-0.035**[0.011]0.131***[0.007]0.080***[0.000]0.017[0.403]0.489***[0.000]0.022[0.236]0.309***[0.000]-0.080***[0.002]0.049[0.426]-0.033[0.566]-0.020[0.426]0.039[0.561]-0.031[0.252]
0.091**[0.016]-0.019[0.733]-0.020[0.429]0.049[0.417]0.160*[0.055]1.261***[0.000]-0.025**[0.025]0.080*[0.056]-0.003[0.860]0.023[0.222]0.196***[0.000]-0.016[0.371]0.143***[0.000]Interactive Terms
Climate by Loan
Air Pollution by Loan
Crime Rate by Loan
Health Care by Loan
Social Development by
Loan

0.160*[0.055]4.193***[0.000]-0.025**[0.025]0.080*[0.056]0.082***[0.000]0.043**[0.022]0.501***[0.000]0.006[0.750]0.342***[0.000]Observations
85
85
85
85
85
85
Clustered Standard Error
√
√
√
√
√
√
R-squared
0.77
0.77
0.83
Number of Cities
29
29
29
29
29
29
F
1.859
1.859
17.14
Notes:
1. Clustered standard errors are computed to draw inference. p-values are in the brackets. The
stars *, **, and *** indicate the significance level at 10%, 5%, and 1% respectively.
2. House price is measured in terms of Yuan per square meter. The measurement unit of income
is Yuan per capita. These two variables are first deflated, then transformed into natural logs,
and finally detrended. Loan GDP ratio is detrended as well. Income is further filtered by urban
amenities.
3. We subtract city-level inflation rates from nominal long-term interest rate, and nominal stock51

market return to obtain corresponding real values. Long-term interest rate is in percentage,
while stock market return is in proportion.
4. Climate, air pollution, crime rate, health care, and social development are all principal
components, which are further filtered by income. Social development mainly captures
population density, green space coverage, and higher education.
5. Constant is suppressed to save space.

52

Appendix
This appendix provides some supplementary materials to the main text. Appendix A is acomplete list of variables used in our analysis. In appendix B, there are four housing priceregressions, including one with the macroeconomic variables only, one with the PCAconsolidated amenities only, one with the 17 raw amenity measures, and one single-step
regression with consolidated amenity components and unfiltered income. And several graphs inappendix C are to show that amenities are not constant but vary over time.

53

Appendix A. Variables Description and Data Sources
Table A1 Data Description
Variable

Description

SourceHouse Price

Housing price in Yuan per square meter. Data for the 35 cities are available
after 1999. Data for some metropolises can be traced back to earlier.

China Real Estate Statistical Yearbook

Loan

Total loans issued by the financial institutions of each city. The data are interms of 100 million Yuan. We change the unit to billion Yuan.

China Statistical Yearbook of Regional
EconomyIncomePer capita disposable income of urban household in Yuan.

CEIC

Rent Inflation

Percentage change compared to previous year. The data are derived fromrent price index. But the index is not updated anymore after 2010.

CEIC

Long-Term
Interest

Benchmark loan borrowing rate for five years or longer determined by the
People’s Bank of China.

The People’s Bank of China

Stock Market
ReturnCurrent-value-weighted annual aggregated market returns with cash
dividend reinvested.

CSMAR

Fixed Asset
Investment

Fixed asset investment in terms of 100 million Yuan. We change the unit tobillion Yuan.

CEIC

GDP

GDP in terms of billion Yuan.

CEIC

City-level CPI

December CPI of each city with the previous year as the base period.
Inflation rate is generated by CPI minus 100. Assuming the 1999 value
equals 100 for every city, we add the inflation rate back to generate analternative CPI series.

CEIC

Population

Registered population of the entire municipal district in terms of 10,000
persons. Data of Beijing, Shanghai, Tianjin, and Chongqing are obtained
from China Statistical Yearbook for higher reliability.

Urban Statistics Yearbook & China
Statistical Yearbook

Land Area

Land area in terms of square kilometers for the municipal districts.

Urban Statistics Yearbook

54

Table A1 Data Description (Cont’d)
Variable

Description

SourcePopulation
Density

Population density in terms of number of people per square kilometer. It is
calculated by total population over the land area.

Author’s Calculation

Green Coverage

Percentage coverage of green space in the city. The data are for the confinedcity proper.

Urban Statistics Yearbook

Hospital Beds

Number of hospital beds to serve patients in the municipal district. We
divide the number of hospital beds over 10,000 population to constructhospital beds density among local population.

Urban Statistics Yearbook

Doctors

Number of doctors to serve patients in the municipal district. We divide thenumber of doctors over 10,000 population to construct doctors density
among local population.

Urban Statistics Yearbook

Suspects
Arrested

Number of suspects arrested by the provincial procuratorate. We divide it
over every 10,000 provincial population to construct the correspondingarrest ratio. City-level data are not available, and hence are proxied by theirprovincial counterpart.

The Provincial Procuatorate’s Annual
Reports

Suspects
Prosecuted

Number of suspects prosecuted by the provincial procuratorate. We divide it
over every 10,000 provincial population to construct the corresponding
prosecution rate. City-level data are not available, and hence are proxied bytheir provincial counterpart.

The Provincial Procuatorate’s Annual
Reports

Provincial
Population

The population of the province in which the city is located. They arecollected to compute the crime rate in every 10,000 people.

China Statistical Yearbook

PM10

Particulate matter in terms of micrograms per cubic meter. Little data isavailable before 2003.

China Statistical Yearbook on Environment& China Environment Yearbook

SO2

Sulphur dioxide in terms of micrograms per cubic meter. Little data isavailable before 2003.

China Statistical Yearbook on Environment& China Environment Yearbook

NO2

Nitrogen dioxide in terms of micrograms per cubic meter. Little data isavailable before 2003.

China Statistical Yearbook on Environment& China Environment Yearbook

55

Table A1 Data Description (Cont’d)
Variable

Description

SourceDays with GoodAir Quality

Days of air quality of grade II or beyond among the whole year. Little data isavailable before 2003.

China Statistical Yearbook on Environment& China Environment Yearbook

211 Project
Universities

The number of universities in the municipal district that are sponsored by
Project 211. It is documented as of 2009 and no time variation is taken into
account.

Ministry of Education

Maximum
Temperature

The annual maximum temperature in degrees Celsius. Little data is available
for Ningbo.

China Statistical Yearbook on Environment& China Meteorological Yearbook

Minimum
Temperature

The annual maximum temperature in degrees Celsius. Little data is available
for Ningbo.

China Statistical Yearbook on Environment& China Meteorological Yearbook

Temperature
Difference

It is the squared difference of annual extreme temperatures divided by 100.

Authors’ Calculation

Relative
Humidity

The annual average humidity in percentage for 35 cities after 2003. Little
data is available for Dalian, Qingdao, and Xiamen.

China Statistical Yearbook on Environment

Sunshine

The annual sunshine hours.

China Statistical Yearbook on Environment& China Meteorological Yearbook

Precipitation

The annual average precipitation in millimeters.

China Statistical Yearbook on Environment& China Meteorological Yearbook

56

Appendix B. Model Comparison
This appendix provides four house price regressions, one with the macroeconomic
variables only, one with the PCA-consolidated amenities only, one with the 17 raw amenity
measures, and one single-step regression with consolidated amenity components and unfiltered
income. All four sets of results point to equation (2) as a better empirical model.
To facilitate the model comparison in Table 3, we run a simple regression with themacroeconomic variables only and a hedonic type of regression including PCA-consolidated
amenities only. To give an overview, the regression with consolidated amenities (reported inTable B2 and column (2) of Table 3) has the lowest adjusted R-squared, but the highest root
mean squared error, AIC and BIC statistics. The adjusted R-squared of the regression with themacroeconomic variables (reported in Table B1 and column (1) of Table 3) is larger than the allamenities specification, but not as large as what are documented in the main text (reported incolumn (1) of Table 4 and column (3) of Table 3). Table B1 also has the in-between root meansquared error, AIC and BIC statistics. Clearly our empirical model combining both factors fits
the sample better.
We now fill in more details about the regressions. Our discussion starts with the model
controlling the macroeconomic variables only. Compared with column (1) of Table 4, estimatesare greater and more significant when the amenity variables are omitted, probably because they
absorb part of amenities’ impacts on property prices. Recall that in Table 4, the point estimate for
one-year-lagged normalized loans is 0.079, and the standardized coefficient of the same variable
is 0.094. The point estimate increases to 0.129, and the standardized coefficient goes up to 0.154.
The same pattern holds for the stock market return and the interest rate. Replacing filtered
income with the original detrended log real income, we see quite a lift in the point estimate from1.017 to 1.445, and a huge raise in the standardized coefficient from 0.324 to 0.854. Holding themacroeconomic variables constant, we find the real house price in northeast China is 14.2%
higher than the price in the central part of the country.
In the following, we discuss results from the amenity regression in Table B2. Estimatesfor climate and health care are larger and more significant than those in the first column of Table
4. Though staying on the same order of significance, the coefficients of social development and57

crime rate are smaller in magnitude. The estimate for air pollution changes to the wrong positive
sign, but it remains insignificant. If the amenity level is held the same, home price in the east willbe 25.2% higher than its counterpart in central China. The significant regional effects in this andthe above table suggest there are other factors that can help explain housing prices in either Table
B1 or B2.
As a demonstration of the multicollinearity problem, the R-squared values from
regressing each amenity measure on the other 16 are summarized in Table B3. From this table, it
is clear that high correlation among amenity measures is a serious issue. Eleven out of the 17 Rsquares are greater than 0.6, among which seven are around 0.9. In a housing price regression
including all 17 amenities (reported in Table B4), other than the crime rates, only the variables
not contaminated by multicollinearity are statistically significant. Essentially, multicollinearity
means reduced amount of variations available for analysis. Together with our moderate sample
size, it also works to inflate the sampling variance of the ordinary least squares estimator39. In
light of this, we are not able to distinguish whether the insignificant estimates for other amenities
are due to no effects indeed or whether we simply fail to find the true effects by the specificationin Table B4.
Considering the compensating differentials of house prices for local amenities are partly
hidden in income, we adopt a two-step strategy in Table 4. For comparison, here we provide theresults of a single-step regression with amenity components and the original detrended log real
income in Table B5. Coefficients on macroeconomic variables in these two tables are by andlarge the same. However, estimates for social development and crime rate are both quantitatively
smaller and statistically less significant than those in Table 4. Estimates for climate and healthcare are even with the wrong signs in Table B5.

39

In fact, some econometricians think multicollinearity and the problem of small sample size are just two sides ofthe same coin. See Goldberger (1991).
58

Table B1 Housing Price Regression with Macroeconomic Variables

Dependent Variable: log(Real House Price)
Lagged Loan GDP Ratio
log(Real Income)Real Long-Term Interest
Real Stock Market Return
Eastern Dummy
Northeastern Dummy
Western Dummy
Constant(1)
Coef.
0.129**
1.445***-0.0050.033**
0.050
0.142*
0.002-0.027(2)(3)
p-values Std. Coef.
0.023
0.000
0.352
0.016
0.704
0.084
0.978
0.721

0.154
0.854
-0.022
0.057
0.060
0.115
0.002

Observations
223
Clustered Standard Error√
Adjusted R-squared
0.80
F
28.41
Notes:
1. To facilitate the comparison of empirical models, we restrict the sample tothe same one as in Table 4.
2. Clustered standard errors are computed to draw inference. The stars *, **,and *** indicate the significance level at 10%, 5%, and 1% respectively.
3. House price is measured in terms of Yuan per square meter. The
measurement unit of income is Yuan per capita. These two variables are
first deflated, then transformed into natural logs, and finally detrended.
Loan GDP ratio is detrended as well.
4. We subtract city-level inflation rates from nominal long-term interest rate,
and nominal stock market return to obtain corresponding real values. Longterm interest rate is in percentage, while stock market return is in
proportion.

59

Table B2 Housing Price Regression with Local Amenities

Dependent Variable: log(Real House Price)
ClimateAir Pollution
Crime Rate
Health Care
Social Development
Eastern Dummy
Northeastern Dummy
Western Dummy
Constant

(1)
Coef.
0.030
0.004
0.113***
0.045
0.112***
0.252**
0.110-0.085-0.108(2)(3)
p-values Std. Coef.
0.206
0.775
0.000
0.121
0.000
0.027
0.331
0.335
0.131

0.130
0.016
0.439
0.129
0.350
0.299
0.089
-0.091

Observations
223
Clustered Standard Error√
Adjusted R-squared
0.79
F
41.12
Notes:
1. Clustered standard errors are computed to draw inference. The stars *, **,and *** indicate the significance level at 10%, 5%, and 1% respectively.
2. House price is measured in terms of Yuan per square meter. The variable isfirst deflated, then transformed into natural logs, and finally detrended.
3. Climate, air pollution, crime rate, health care, and social development are
all principal components. Social development mainly captures population
density, green space coverage, and higher education.

60

Table B3 Multicollinearity among Amenity Measures
Independent Variables
R-squared
Maximum Temperature (°C)
0.92
Minimum Temperature (°C)
0.99
Temperature Difference
0.99
Yearly Sunshine (100 Hours)
0.74
Yearly Precipitation (100 Millimeter)
0.38
Relative Humidity (%)
0.75
PM10 (100 Micrograms per Cubic Meter)
0.89
SO2 (Micrograms per Cubic Meter)
0.41
NO2 (Micrograms per Cubic Meter)
0.34
Days with Good Air Quality
0.88
Suspects Arrested per 10,000 Population
0.89
Suspects Prosecuted per 10,000 Population
0.88
Doctors per 10,000 Population
0.68
Hospital Beds per 10,000 Population
0.63
log(Population Density)
0.49
211 Project Universities per 10,000 Population
0.28
Green Coverage (%)
0.32
Notes: R-squared in the first row is obtained from theregression of maximum temperature on all the othervariables in this table. Other R-squared values are obtainedin the same way, where the variable explicitly listed in therow plays the role of a dependent variable, and all the othervariables in the table are independent variables.

61

Table B4 Housing Price Regression with Raw Amenity Measures

Dependent Variable: log(Real House Price)

(1)
Coef.

Lagged Loan GDP Ratio
Filtered Income
Real Long-Term Interest
Real Stock Market Return
Maximum Temperature (°C)
Minimum Temperature (°C)
Temperature Difference
Yearly Sunshine (100 Hours)
Yearly Precipitation (100 Millimeter)
Relative Humidity (%)
PM10 (100 Micrograms per Cubic Meter)
SO2 (Micrograms per Cubic Meter)
NO2 (Micrograms per Cubic Meter)
Days with Good Air Quality
Suspects Arrested per 10,000 Population
Suspects Prosecuted per 10,000 Population
Doctors per 10,000 Population
Hospital Beds per 10,000 Population
log(Population Density)
211 Project Universities per 10,000 Population
Green Coverage (%)

0.068
0.892***-0.0030.025*
0.006-0.0000.002
0.004
0.004**-0.002-0.050-0.004***0.005**-0.0010.047**
0.024*
0.006-0.0030.266***
3.575
0.004

(2)
(3)
p-values Std. Coef.
0.383
0.004
0.674
0.083
0.789
0.986
0.924
0.565
0.042
0.496
0.783
0.001
0.012
0.322
0.016
0.069
0.323
0.380
0.000
0.581
0.184

0.081
0.261
-0.0110.043
0.044-0.010
0.048
0.046
0.062
-0.046-0.030-0.1870.156-0.092
0.334
0.225
0.127
-0.092
0.410
0.033
0.062

Observations
223
Clustered Standard Error√
Adjusted R-squared
0.86
F
169.9
Notes:
1. Clustered standard errors are computed to draw inference. The stars *, **, and*** indicate the significance level at 10%, 5%, and 1% respectively.
2. House price is measured in terms of Yuan per square meter. The measurement
unit of income is Yuan per capita. These two variables are first deflated, thentransformed into natural logs, and finally detrended. Income is further filteredby the raw amenity measures. Loan GDP ratio is detrended as well.
3. We subtract city-level inflation rates from nominal long-term interest rate, andnominal stock market return to obtain corresponding real values. Long-term
interest rate is in percentage, while stock market return is in proportion.
4. Constant and coefficients for regional dummies are suppressed to save space.

62

Table B5 Housing Price Regression in One Step

Dependent Variable: log(Real House Price)
Lagged Loan GDP Ratio
log(Real Income)Real Long-Term Interest
Real Stock Market Return
ClimateAir Pollution
Crime Rate
Health Care
Social Development
Eastern Dummy
Northeastern Dummy
Western Dummy
Constant

(1)
Coef.
0.079
1.017***-0.0020.021*-0.019-0.011
0.068***-0.0010.059***
0.009
0.019
-0.002-0.009(2)(3)
p-values Std. Coef.
0.238
0.001
0.758
0.083
0.356
0.392
0.003
0.948
0.003
0.944
0.855
0.980
0.901

0.094
0.601
-0.0080.036-0.082-0.0430.265-0.004
0.184
0.010
0.015
-0.002

Observations
223
Clustered Standard Error√
R-squared
0.85
F
69.65
Notes:
1. Clustered standard errors are computed to draw inference. The stars *, **,and *** indicate the significance level at 10%, 5%, and 1% respectively.
2. House price is measured in terms of Yuan per square meter. The
measurement unit of income is Yuan per capita. These two variables are
first deflated, then transformed into natural logs, and finally detrended.
Loan GDP ratio is detrended as well.
3. We subtract city-level inflation rates from nominal long-term interest rate,
and nominal stock market return to obtain corresponding real values. Longterm interest rate is in percentage, while stock market return is in
proportion.
4. Climate, air pollution, crime rate, health care, and social development are
all principal components. Social development mainly captures population
density, green space coverage, and higher education.

63

Appendix C. Changing Amenities
In this appendix, we provide several graphs to show that amenities are not constant butvary over time. Figure C1 to C5 are the box plots for the amenity components. It is clear fromthese plots that crime rate, healthcare condition, and social development indicator have been
increasing. Air quality generally has been improved. And there is no pattern in climate. Figure
C6 to C8 include three marked evolving paths of certain amenities in individual cities as specificexamples.

64

-3

-1

Climate1

3

5

Figure C1 Boxplot of Climate

2003

2004 2005

2006 20072008 200920102011 2012-3-1

Air Pollution
1

3

5

Figure C2 Boxplot of Air Pollution

2003

2004 2005

2006 2007

65

2008 200920102011 2012

-3-1

Crime Rate
1

3

Figure C3 Boxplot of Crime Rate

2003

2004 2005

2006 20072008 200920102011 201220102011 2012-3-1

Health Care13

Figure C4 Boxplot of Health Care

2003

2004 2005

2006 2007

66

2008 2009-3

Social Development-1
1

3

Figure C5 Boxplot of Social Development

20032004 2005

2006 20072008 200920102011 2012

Notes:
1. The vertical axis stands for the corresponding principal component.
2. The lower hinge of each box represents the 25th percentile, the upper hinge represents
the 75th percentile, and the line within each box indicates median.

67

30
19
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Number of Hospital Beds per 10,000 Population
40
50
60
70

19 700
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Population per Square Kilometer
800
9001000 1100 1200

1300

Figure C6 Population Density in Beijing

Figure C7 Number of Hospital Beds in Chengdu

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19
99
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15

Green Space Coverage (%)
25
35

45

Figure C8 Green Coverage in Chongqing69