18TH

ANNUAL PACIFIC-RIM REAL ESTATE SOCIETY CONFERENCE

ADELAIDE, AUSTRALIA, 15-18 JANUARY 2012

BACKWARD-BENDING NEW HOUSING SUPPLY CURVE:

EVIDENCE FROM CHINA

SIQI YAN*, XIN JANET GE

Faculty of Design, Architecture and Building, University of Technology, Sydney

ABSTRACT

This paper tests the existence of a backward-bending housing supply relationship in China, and estimates price

elasticities of new housing supply for 35 major Chinese cities. Based on the panel data model of 35 cities, it is found

that the response of housing supply to price change is relatively insensitive in China, and the supply elasticities have

decreased with the rise in housing price. As a result, the remarkable increase in housing price in China can be at least

partly attributed to inelastic housing supply. The results from this paper may inform Chinese government to take

effective measures to reduce the large amount of idle land so as to increase the new housing supply.

Keywords: backward-bending housing supply relationship; price elasticies of housing supply; China

INTRODUCTION

Since the abolition of welfare housing allocation in 1998, there has been a considerable price appreciation in China’s

real estate market, with the annual average growth rate of housing price from 1999 to 2008 reaching as high as 7.3%

(China Statistical Yearbook, 2009). As a result, housing affordability in China has become a pressing social and

economic issue in recent years. In some large cities, the ratios of property value to annual gross household income have

exceeded 10, much larger than those in western countries (Stephen, Lennon, & Winky, 2007).

What has caused the escalation in housing price in China? Existing studies have attempted to answer the question from

different perspectives. Chen et al. (2011) found that the growth in urban household income contributed to the increase

in housing price in the whole country, and urbanization level and the number of floating population were positively

related to housing price in inland provinces. Wang et al. (2011) examined the linkage between urban economic

openness and property prices based on the quality of life theory and Balassa-Samuelson (B-S) effects, using panel data

of 35 large Chinese cities. Their research demonstrated that urban economic openness accounted for about 15.9% of the

housing price appreciation from 1998 to 2006. Wang (2011) developed a theoretical framework to examine how the

privatization of housing assets that were previously owned and allocated by the state affected the housing prices in

equalization. Her empirical research suggested that housing reform in China have alleviated price distortion, thus

allowing households to increase their consumption of housing and leading to a rise in equilibrium housing prices. These

studies have paid more attention to the factors causing the increase in demand when explaining the surge of housing

price in China. By notable contrast, very few researches have converted emphasis to supply-side factors.

Rising demand leads to rising price under inelastic housing supply. For, example, Vermeulen (2008) found that

influenced by government intervention in land and housing markets, Dutch housing supply was almost fully inelastic,

and that had contributed significantly to remarkably high level of house price. In July 2011, a report that cited statistics

from the Ministry of Land and Resources showed that, by the end of 2010, a total of 11,944 hectares of residential land

had been left standing idle in China, amounting to one tenth of the quantity of annual land supply (Xinhua News, July

28th, 2011). Calculated by a floor area ratio of 2 90 square meters per unit, there would be an increase of 2.7 million

units of housing if these lands are developed. Given the situation, it would be reasonable to suspect that the response of

housing supply to price change might be insensitive in China, and that can be a source of the rapid growth in real estate

prices.

Supply curves are generally upwards sloping as suggested by classical microeconomic theory, i.e. the correlation

between price and supply is positive. However, some researches have provided the theoretical justification and

* Yan Siqi. E-mail address: siqi.yan@student.uts.edu.au.

18th

Annual PRRES Conference, Adelaide, Australia, 15-18 January 2012 2

empirical evidence for the feasibility of a backward-bending housing supply curve (Mayo & Sheppard, 1991, 2001;

Pryce, 1999), which means housing supply might become negatively related to housing price when housing price rises

above a certain level. If the backward-bending supply relation indeed exists, the price elasticities of housing supply can

be quite low or even negative when price is quite high, which represents a sluggish response of housing supply. Hence

through testing the existence of a backward-bending housing supply relationship, the responsiveness of housing supply

can be examined. This paper seeks to test the existence of a backward-bending housing supply relationship in China,

and estimate housing supply elasticities for 35 major cities. It is expected that the results can shed new light on the

source of the rapid growth in the housing price in China. Section 1 reviews literature on backward-bending housing

supply relationship, the estimates of housing supply elasticities, and the impact of land supply on housing supply.

Section 2 explains the methodology and the panel data of 35 cities used for estimation. Section 3 presents the empirical

results. Section 4 offers concluding comments.

LITERATURE REVIEW

Backward-bending Housing Supply Relationship

Mayo and Sheppard (1991, 2001) first provided the theoretical justification for the backward-bending housing supply

curve through the analysis of developers’ decision-making process. They suggested that for an individual developer,

both the profit from immediate development (π0) and the value of vacant land (V0) can be viewed as a monotonically

increasing function of the housing price (P), developers won’t provide housing unless π0 is greater than V0. If the

increase in V0 from the housing price growth is greater than the increase in π0 (i.e.0 0/ /V P P ) in the

neighborhood of P0 (where 0 0V ), an individual developer would offer zero supply when price rise above P0. Figure1

provides the illustration for the situation. When 0P P , an individual would choose to develop the land, when

0P P ,

he would be indifferent between developing the land and leaving it idle, when0P P , he would choose to leave it

vacant. Thus 0P can be viewed as a cut-off price, and for each developer, there would be such a cut-off price.

Figure 1 An individual developer’s housing supply decision

Figure 2 illustrates the situation for the whole house-building industry. Initially, housing supply in the market ( Q ) is

positively related to housing price ( P ). As the price rises, some developers would choose to reduce their housing

output towards zero, and with the further price increase, more and more developers would follow their steps to hold

land vacant. Thus it can be observed that when housing price rises to a level higher than P , housing supply would

become negative related to the price, namely backward-bending housing supply relationship. In this case, the price

elasticities of housing supply can be quite low or even negative when price is quite high, so the existence of backward-

bend supply relationship can be taken as an evidence of inelastic housing supply.

Another explanation of backward-bending housing supply curve was provided by Pryce (1999). His analysis was based

on Evans’ point that developers would be likely to hold land vacant if they could foresee that housing price is likely to

decrease. Assume that suppliers’ expectation about future price is based on past price behavior which has followed a

strong cyclical pattern, then in period t, it is conceivable for the developers to expect a decrease in price in period t ,

0

0V

P0P

0 0,V

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Annual PRRES Conference, Adelaide, Australia, 15-18 January 2012 3

where represents the delay between the start and completion of a house structure. Thus the number of starts can be

negatively related to current prices during a boom. To test the backward-bending supply relationship, Pryce developed a

cross-sectional model using data at English local authority district level, and found that housing supply “bends

backwards” during the boom period.

Figure 2 Backward-bending housing supply curve

In different perspective, from the Cobweb Theorem, Ge (2004) and Ge, et al. (2006) developed a generic system for

predicting discontinuous changes in housing prices for Hong Kong using cusp catastrophe theory. In her model,

vacancies are assumed a proportion of total supply of housing in a stable market system. Since a time lag between the

decision to start work and the date of building completion for housing, the lag supply may not meet the increase in

demand for housing and leads to a higher price of housing in the short term. Alternatively, if there is a sharp decrease in

demand, housing price fall and the lag supply increases because of expectations of supply. Vacant housing may be

increased suddenly and supply curve becomes “backward-banding”.

The Estimates of Housing Supply Elasticities

Existing studies focus on two basic approaches to estimate the price elasticity of housing supply: the structural approach

(where supply is generally expressed as a function of housing price and input prices) and the reduced form approach

(which estimates supply elasticity indirectly).

Follain (1979) expressed the quantity of new housing construction as a function of housing price and input prices. The

null hypothesis in his study is that housing supply is infinitely elastic. In that case, the long-run equilibrium quantity is

determined entirely by demand, and housing price and input prices would not directly affect the quantity of new

housing supply. Using aggregate annual data for American housing market, the empirical results showed that there is no

significant positive relationship between housing price and quantity supplied, and failed to refute the hypothesis of

completely elastic housing supply. Poterba (1984) considered the impact of credit rationing and used an asset market

approach to model the housing supply. He regressed the investment supply against real house price, the real price of

alternative investment projects, real construction wages and net deposit inflows into savings and loan institutions (as a

measure of credit availability). He estimated alternative linear models which produced elasticities raging from 0.5 to

2.3. Topel and Rosen (1988) examined whether the current asset prices are sufficient statistics for housing investment

decision. Their model was based on dynamic marginal cost, and the estimated short- and long-run supply elasticities

were 1 and 3 respectively. Differing from previous studies, DiPasquale and Wheaton (1994) and Mayer and Somerville

(2000) incorporated the land market into theoretical structure. DiPasquale and Wheaton (1994) indicated that new

construction only occurs when the current housing stock differs from the long-run equilibrium level (which depends on

housing price and input prices). They estimated a stock-adjustment model which includes housing price, lagged housing

stock and various input prices as independent variables, and the estimates of price elasticities ranged from 1 to 1.2.

Mayer and Somerville (2000) estimated new housing construction as a function of changes in housing prices and costs

rather than as a function of the levels of those variables, their study reported a stock elasticity of about 0.08 and a flow

elasticity of about 6.

P

Q

P

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Annual PRRES Conference, Adelaide, Australia, 15-18 January 2012 4

Malpezzi and Maclennan (2001) drew inferences about supply elasticities based on housing demand parameters from

the literature. According to a three-equation flow model of the housing market they developed, the price elasticity of

housing supply can be expressed as a linear combination of the income elasticity of housing price and the elasticities of

demand with respect to housing price and income. They estimated the reduced form equation of housing price to get the

income elasticity of housing price first, and then calculated supply elasticities based on the assumption about he

elasticities of demand with respect to housing price and income. Given housing’s durable nature, construction lags and

significant transaction costs, the underling assumption embedded in the flow model that all adjustment takes place in a

single year may not be true, hence they also developed a stock adjustment model to estimate income elasticity of

housing price and the supply elasticities. According to the flow model, in the prewar United States price elasticities of

housing supply were between 4 and 10, postwar they were between 6 and 13. In the prewar UK supply elasticities were

between 1 and 4, postwar they were between 0 and 1. Stock adjustment models yielded different elasticities, ranging

from 1 to 6 for the United States, and from 0 to 1 for the UK. Harter-Dreiman (2004) also used the results from

estimation of the reduced form equation for housing price along with assumptions regarding income and price

elasticities of demand to draw inferences regarding the long-run supply elasticities, in spirit related to Malpezzi and

Maclennan’s. The distinguishing feature of her study is the vector error correction (VEC) approach, which is used to

examine the relationship between house price and personal income and estimate a three-equation model of those two

variables. Utilizing a panel data set consisting of 76 metropolitan statistical areas in United States from 1980 to 1998,

the research reported that supply elasticities were between 1.8 and 3.2.

It is noteworthy that little work on housing supply has been done outside the United States. Given the institutional

features affecting housing market outcomes vary a lot from country to country, more efforts on the estimation of supply

elasticities in other countries would be necessary.

The Impact of Land Supply on Housing Supply

It is quite common for the governments to exert control over the supply of residential land through land use planning.

For example, in many countries, change in land use requires government approval. Furthermore, governments can

directly control the flow of new land available for development, which is what happens in Hong Kong and Mainland

China. In Hong Kong, government is the sole owner of the territory, who leases land to private developers. In Mainland

China, rural land is collectively owned by peasants, while urban land is owned by the state. The municipal

governments, as a representative of the state, sell the urban land-use rights to developers for a fixed period through

auction, tender, or negotiation. Peng and Wheaton (1994) proposed two theories to explain the correlation between

government’s land supply and housing supply. The “myopic” theory suggests that land supply should be positively

related to housing supply, as an increase in land supply enables developers to get the land they need to complete desired

housing production. On the contrary, the “rational” theory argues that housing production mainly responds to variation

in housing prices rather than that in land supply, so more restrictive land supply won’t alter housing production in the

shot run. In the long run, when the market returns to equilibrium, higher housing and land prices encourage the

substitution of capital for land and hence raise the density of development. They developed a stock-flow housing model

using time series data of 1965 to 1990 for Hong Kong, and found that restrictive land supply led to the rise in housing

prices but not lowered housing supply. Zheng (2008) developed a panel data model of housing supply, using provincial

data of China, and found that 1-year and 2-year lagged land supply had significant effects on housing supply, 1-year

lagged land supply had significant effects on housing price.

METHODOLOGY AND DATA

Based on the provided literature, a model that reflects the relationships between housing supply and housing prices can

be developed first. The developed model is then tested by empirical study for the China real estate market. The results

from the test then can be discussed.

Compared with the traditional regression model of housing supply, where housing supply is expressed as a function of

the level of house price or the change in house price (Follain,1979; Poterba,1984; DiPasquale and Wheaton,1994;

Mayer and Somerville, 2000).The existence of backward-bending supply relationship can be tested by including a

squared term for price in the model. As mentioned in the section of literature review, government’s land supply might

have significant effects on housing supply, thus we include land supply as one of the independent variables. Estimation

equation takes the following form:

2

0 1 2 3 4 1 5 2+it it it it it it itQ P P LS LS LS (1)

where itQ is the quantity of new housing supply at city i in year t, P is the real house price, LS is the quantity of

government’s residential land supply, 1tLS and 2tLS are one-year and two-year lagged land supply respectively.

0 n is the coefficients. The price elasticities of housing supply can be calculated as:

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Annual PRRES Conference, Adelaide, Australia, 15-18 January 2012 5

1 2/ 2it it it itE Q P P (2)

If the backward-bending supply relationship exists, estimation results would show that the supply curve is concave,

with a significant negative coefficient on the squared term for housing price. The cut-off price above which housing

supply would become negative related to the price can be calculated by making the first-order partial derivative with

respect to price equal zero:

1 2/ 2 0it it itQ P P (3)

1 2/ 2itP

For example, assume 1 equals 100 and

2 equals -1, then the estimate of the cut-off price would be 50, which indicates

that when housing price reaches a level higher than 50, the correlation between housing price and housing supply would

become negative.

Data

Panel data of 35 major Chinese cities from 1999 to 2008 will be utilized in the empirical research. The 35 cities are 4

municipalities directly under the central government (Beijing, Tianjin, Shanghai and Chongqing), 22 capital cities of

provinces and autonomous regions, and 5 sub-provincial cities which are not provincial capitals (Dalian, Qingdao,

Ningbo, Xiamen, and Shenzhen).

Chain price index and average housing prices for each of the 35 cities can be obtained from China Real Estate Statistics

Yearbook (CRESY) and China Statistical Yearbook (CSY). To calculate the real house price, we convert the chain price

indexes to fixed-base indexes (normalizing the index to 1 in the chosen base year 1999) first, and then for each city,

multiply the index value in each year by the average housing price in 1999. The quantity of new housing supply and

government’s residential land supply are collected directly from CRESY and CSY respectively. Table 1 presents the

variable definition and descriptive statistics for the variables involved.

Table 1: Variable definition and descriptive statistics

Variable Definition Source Mean Std. Dev.

Q The quantity of new housing supply CRESY 604.86 555.58

P Real house price Authors’ computation 3877.13 2063.46

LS The quantity of residential land supply CSY 380.35 362.55

EMPIRICAL RESULTS

Table 2 presents the estimation results for the housing supply model. The value of adjusted R2 is around 91%, the

probability of F-Statistic is 0, suggesting a reasonable explanatory power of the model. The negative coefficient on the

squared term for housing price has a significant t-value, offing a clear evidence of supply curve being concave. The

quantity of government’s land supply and its lagged value have significant positive effects on housing supply,

consistent with the “myopic” theory proposed by Peng and Wheaton. It can be calculated that the cutoff price is

9796.82 yuan, within the sample range of real house price (sample maximum for Pit is 11648 yuan).

Table 3 reports estimates of city-specific price elasticities of new housing supply in 1999 and 2008. The findings

suggest that new supply of housing is relatively inelastic in 35 cities in China. The elasticities range from 0.05 to 0.35 in

1999 and from -.13 to 0.31 in 2008. As mentioned previously, a substantial amount of land being left idle might be one

of the major causes of the inelastic housing supply in China. According to the statistics from the Ministry of Land and

Resources, 60 percent of the idle land has been left unused due to government-related causes. When the developers

acquire the land from the municipal governments, some land have been ready for development (electricity, water supply

and paved roads are accessible, the ground has been leveled), whereas some land still have some old buildings left on

site. To initiate development programs on theses land, developers have to demolish old houses and relocate the former

residents first, which can be difficult and time-consuming and often induce sever delay in housing projects.

Governments’ planning adjustment can also cause delay in property development. The floor space ratio or building

density for the residential land might be changed, sometimes even the residential use can be converted to make way for

the municipal construction. Apart from the government-side factors, developers’ land hoarding also contributes to the

increase in the amount of idle land. Many developers prefer to keep the land undeveloped until its value has risen

significantly, then they can choose between developing it and reselling it. For example, a parcel of land only 4

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Annual PRRES Conference, Adelaide, Australia, 15-18 January 2012 6

kilometers away from Beijing’s CBD was acquired by China Resources Land in 2005, after left vacant for 6 years, the

land price per floor area has increased from 5067 yuan/sq2 to around 20000 yuan/sq

2 (Li, 2011). If the land was resold

by the developer, a profit margin of 300% can be obtained.

Table 2: Estimates of new housing supply of 35 major cities in China, 1999-2008

Dependent variable: new housing supply

Variable Coefficients Std. Error t-Statistic Prob.

Constant (C) -845.7525 73.64980 -11.48343 0.0000

P 0.419304 0.029020 14.44870 0.0000

P2 -2.14E-05 2.21E-06 -9.677567 0.0000

LS 0.296708 0.048532 6.113658 0.0000

LSt-1 0.137698 0.052727 2.611512 0.0096

LSt-2 0.253735 0.049937 5.081113 0.0000

R2 0.924441 F-Statistic 75.28999

Adi.R2 0.912162 Prob.( F-Statistic) 0.000000

Table 3: Estimates of city-specific price elasticities of new housing supply

City E(1999) E(2008) City E(1999) E(2008) City E(1999) E(2008)

Beijing 0.08 -0.08 Ningbo 0.29 0.13 Nanning 0.31 0.26

Tianjin 0.27 0.18 Hefei 0.31 0.27 Haikou 0.28 0.23

Shijiazhuang 0.34 0.31 Fuzhou 0.25 0.19 Chengdu 0.28 0.21

Taiyuan 0.3 0.26 Xiamen 0.16 0.04 Guiyang 0.33 0.3

Huhehaote 0.35 0.31 Nanchang 0.34 0.28 Kunming 0.29 0.27

Shenyang 0.32 0.25 Jinan 0.3 0.24 Chongqing 0.35 0.31

Dalian 0.25 0.18 Qingdao 0.32 0.21 Xian 0.3 0.26

Changchun 0.31 0.28 Zhengzhou 0.3 0.27 Lanzhou 0.33 0.29

Haerbin 0.31 0.27 Wuhan 0.29 0.22 Xining 0.33 0.3

Shanghai 0.23 0.07 Changsha 0.32 0.28 Yinchuan 0.34 0.31

Nanjing 0.29 0.21 Guangzhou 0.1 0.04 Wulumuqi 0.32 0.29

Hangzhou 0.23 0.07 Shenzhen 0.05 -0.13

The second finding is that elasticities of all 35 cities have been declined from average 0.27 in 1999 to 0.21 in 2008. This

implies that the willingness of providing housing was further declined for the period, and the more and more sluggish

response of new housing supply have contributed to the rapid increase in housing price.

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Annual PRRES Conference, Adelaide, Australia, 15-18 January 2012 7

Third, Beijing and Shenzhen, among other cities, are shown negative elasticities in the year 2008, -0.08 and -0.13

respectively. Although there was an upward trend in the amounts of new housing start in the two cities in the early

2000s, the quantities have decreased considerably from around 2004 to 2008. In Beijing, the quantity of new housing

supply declined from 22.07 million square metres in 2004 to 15.65 million square metres in 2008. In Shenzhen, the

quantity went down more notably, from 9.82 million square metres to 4.72 million square metres with an annual

decrease rate of 13%. Apart from negative elasticities, near zero easticities are reported for the city of Shanghai and

Hangzhou, which also implies rather inelastic housing supply in the two cities.

It is also found that the estimates of supply elasticities in China generally as a whole are much lower than those for US

(ranging from 0.5 to 13) and those for UK (ranging from 0 to 4, Table 4). One plausible explanation for this is that

rural-urban land conversion is strictly restricted in China, thus the new land available for housing development is very

limited, which would adversely affects the responsiveness of the housing supply. Mayo and Sheppard (1996) examined

the housing supply in three rapidly growing countries, also found that the countries with more restrictive planning

systems had smaller supply elasticities. Apart from Beijing, Shenzhen, Shanghai and Hangzhou, the range of supply

elasticities of cities in China are from 0.18-0.35 which are coincided with the literature (Potarba, 1984; Harter-Dreiman,

2004).

Table 4: Estimates of price elasticities of new housing supply for US and UK

Author Country Supply elasticities

Poterba (1990) US 0.5 to 2.3

Topel and Rosen US 1 (short run), 3 (long run)

DiPasquale and Wheaton US 1 to 1.2

Mayer and Somerville US 0.08 (stock elasticity), 6 (flow elasticity)

Malpezzi and Maclennan US 4 to 10 (prewar), 6 to 13 (after war)

Malpezzi and Maclennan UK 1 to 4 (prewar), 0 to 1 (after war)

Harter-Dreiman US 1.8 to 3.2

CONCLUSION

This paper tests the existence of a backward-bending housing supply relationship in China, and estimates price

elasticities of new housing supply for 35 major cities. It is found that the response of housing supply to price change is

relatively insensitive, and the supply elasticities have decreased with the rise in housing price. In particular, estimates of

the supply elasticities for Beijing and Shenzhen are negative, those for Shanghai and Hangzhou are close to 0.

Recently, to curb soaring housing prices, China’ s central government has issued a range of policies such as the

property-purchasing limitations and regulations on home mortgage loan. However, the policy goals can’t be achieved

by merely restraining the demand. Governments also need to take effective measures to reduce large amount of idle

land so as to increase the supply on housing market. It is essential for the planning authority to avoid unnecessary

planning adjustment which can have adverse effects on property development. On the other hand, the policies aiming at

regulating developers’ land hording should be implemented more strictly.

REFERENCES

Blackley, D. M. (1999), The long-run elasticity of new housing supply in the United States: Empirical evidence for

1950 to 1994. Journal of Real Estate Finance and Economics, 18, 25-42.

Chen, J., Guo, F. and Wu, Y. (2011), One decade of urban housing reform in China: Urban housing price dynamics and

the role of migration and urbanization, 1995–2005. Habitat International, 35, 1-8.

Dipasquale, D. and Wheaton, W. C. (1994), Housing-Market Dynamics and the Future of Housing Prices. Journal of

Urban Economics, 35, 1-27.

Follain, J. R. (1979), Price Elasticity of the Long-Run Supply of New Housing Construction. Land Economics, 55, 190-

199.

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Ge, X. J. (2004), Housing price models for Hong Kong. PhD thesis, University of Newcastle, Australia.

Ge, X., Runeson, K.G., Leung, A.Y. and Tang, C. (2006), A cusp model of housing price in Hong Kong, HKU-NUS

Symposium on Real Estate Research, Hong Kong, July 2006 in 2006 HKU-NUS Symposium on Real Estate Research,

ed K W Chau, Seow Eng Ong, Hong Kong University, Hong Kong, China, 1-30.

Harter-Dreiman, M. (2004), Drawing inferences about housing supply elasticity from house price responses to income

shocks. Journal of Urban Economics, 55, 316-337.

Li Shanshan (2011), A profit margin of 300% has be obtained by China Resources Land through land holding.

http://cn.reuters.com/article/cnInvNews/idCNCHINA-4510720110627.

Mak, S., Choy, L. and Ho, W. (2007), Privatization, housing conditions and affordability in the People's Republic of

China. Habitat International, 31, 177-192.

Malpezzi, S. and Maclennan, D. (2001), The long-run price elasticity of supply of new residential construction in the

United States and the United Kingdom. Journal of Housing Economics, 10, 278-306.

Mayer, C. J. & Somerville, C. T. (2000), Residential construction: Using the urban growth model to estimate housing

supply. Journal of Urban Economics, 48, 85-109.

Mayo, S and Sheppard, S. (2001), Housing supply and the effects of stochastic development control. Journal of

Housing Economics, 10, 109-128.

Peng, R. and Wheaton, W. C. (1994), Effects of restrictive land supply on housing in Hong Kong: An econometric

analysis. Journal of Housing Research, 5, 263-291.

Poterba, J. M. (1984), Tax subsidies to owner-occupied housing - an asset-market approach. Quarterly Journal of

Economics, 99, 729-752.

Pryce, G. (1999), Construction elasticities and land availability: A two-stage least-squares model of housing supply

using the variable elasticity approach. Urban Studies, 36, 2283-2304.

Topel, R. and Rosen, S. (1988), Housing Investment in the United-States. Journal of Political Economy, 96, 718-740.

Wang, S., Yang, Z. and Liu, H. (2011), Impact of urban economic openness on real estate prices: evidence from thirty-

five cities in China. China Economic Review, 22, 42-54.

Wang, S. Y. (2011), State misallocation and housing prices: theory and evidence from China. American Economic

Review, 101, 2081-2107.

Zheng, J. (2008), Study on the Effects of Land Supply Mode and Land Supply Amount on Real Estate Price. PhD thesis,

Zhejiang University.

Email contact:11309325@uts.edu.au

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Pacific Rim Real Estate Society 18th Annual Conference, January 15th – 18th 2012,

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Property: Nexus of Place & SpaceThe PRRES Conference is the signature event for the Pacific Rim Real Estate Society and this conference has achieved an ‘A’rating with the Excellence in Research for Australia (ERA) initiative.

The 2012 18th PRRES Conference will be hosted by the School of Commerce at the University of South Australia. The conferencewill include presentations from Australian and International speakers at the forefront of research and policy-making and brings togethera multi-disciplinary group of leading property researchers and policy makers from throughout the Pacific Rim region. It also advancesunderstanding and cooperation between educators and the property industry in an increasingly complex industry. The PRRESconference attracts delegates from Australia, New Zealand, Singapore, Malaysia, Philippines, Japan, Hong Kong, Taiwan, Taipei,South Africa, England, America, Canada, Germany and Spain.

The conference theme ‘Property: Nexus of Place & Space’ which focuses on the contribution of property to economy and tocommunity, seeks to cover a wide range of research areas including property development, property investment, property education,property management, valuation and property markets; urban, regional and rural. Other topics will include;

Regeneration of business & retail space through place makingIconic buildings - development, investment performance & importance to placeThe contribution of transport to place making & urban sustainabilityUrban security - the conflict between individual security & liveable placesThe loss of ‘place’ in generic property educationSpatial tools for space makingThe risk of place making - what happens when the water disappears or the sea risesReporting on the sustainability of buildingsHousing renewal; its contribution to sustainable community and property value

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Andrew McAnulty (CEO Mission Australia) Housing renewal - sustainable communities,affordable housing & the new opportunities for deliveryNatalya Boujenko (Strategic Consultant Intermethod) Evaluating neighbourhood centres& transport networksTim Horton (SA Commissioner for Integrated Urban Design) Iconic buildings -development, performance & importance to placeStanley McGreal (Professor University of Ulster, Editor JERER) Urban security &performanceGilbert Rochecouste (Village Well Melbourne) – Regeneration of business & retailspace through place makingMartin Haese - Rundle Mall General Manager SA

The 18th PRRES Conference offers a challenging professional development experience and networking opportunity for currentundergraduates, post graduates, academics and industry professionals including a) over 100 academic papers covering widespreadresearch activities in property; b) a mentored PhD Colloquium program; c) exciting PRRES Case Study competition; d) IndustryDay; e) networking opportunities at the Welcome reception, Women’s breakfast, Conference Dinner, Taste SA function &Saturday night welcome for those coming early; and f) opportunity for members to publish and promote their research in PRRES’srefereed Journal, the Pacific Rim Property Research Journal.

As well, for families there is a once in a lifetime opportunity to see the Pandas at Adelaide Zoo or to participate in the AustralianDown Under Community Bike Ride!

Conference Registration Details will be available at http://www.prres.net/Conference/2012Conference.htm

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Submission Deadlines

CLOSED Submission of Refereed and Non Refereed Abstracts

Finalised Acceptance of Abstracts

CLOSED Submission of Refereed Papers

Finalised Acceptance of Refereed Papers

Conference Coordinator: Dr. Geoff Page

Email: prres2012@prres.net

School of Commerce, University of South Australia, City West Campus Way Lee Building 4-27GPO Box 2471, Adelaide South Australia 5001 Mobile: 0414950645

Sponsors

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Proceedings from the PRRES Conference - 201218th Annual Pacific Rim Real Estate Society Conference

Adelaide, Australia, January 15-18, 2012

Key Note SpeakersDelegate Papers - Listed by Author

Delegate Papers - Including Keywords and AbstractConference Program

The Conference in Pictures

PRRES Conference - 2012, Keynote SpeakersAndrew McANULTY CEO Mission Australia has amassed fifteen years experience with three diverse UKhousing Associations and the Stonebridge housing Action Trust one of London’s most successful regenerationcompanies. Andrew took the helm of MA Housing in November 2009 with 97 homes in management. By June2011 the organization had grown to 1,100 homes in management and anticipates growing to 1500 homes byDecember 2011 and 3000 in two years. Andrew’s experience will highlight the innovation and vision required tocreate cutting edge outcomes for projects which link Government, the private sector and the community housingsector – in order to actually deliver high quality affordable housing.

Natalya BOUJENKO Director Intermethod is a founder of Intermethod, is a strategic consultant specialising inintegrated street design, city development and transport planning. Natalya’s portfolio spans a vast range of localand international projects and policy work in London, Birmingham, Belfast, Adelaide, Canberra, Port Augusta andAnangu Pitjantjatjara Yankunytjatjara Lands. She has led a number of innovative projects and programmesfocussed on integrating multi-modal street needs and developed bespoke methodologies in achieving 'streets forpeople' objectives. Natalya is currently engaged in a number of strategic street and transport studies as well asalso developing a new Compendium for street design for South Australia.

Gilbert ROCHECOUSTE Managing Director Village Well is recognised both nationally and internationally as aleading voice in sustainable communities and businesses. His catalyst ideas have regenerated iconic places andenlivened many urban and rural communities. Gilbert has worked with hundreds of main streets and businessesover the past two decades to create more vibrant, connected and resilient communities. He is one of the world’sleading placemaking practitioners and passionately promotes living, playing and working in ways which supportthe cultural, social and environmental elements that are unique to each place. As one of the first Al Gore ClimateLeaders, he sees the potential of placemaking to inspire a deeper environmental awareness and stewardshipwhere people can make a difference both locally and globally.Professor Stanley MCGREAL Director of the Built Environment Research Institute University of Ulster hasresearched widely into issues relating to housing, urban development and regeneration, planning, globalisation,property market performance, and investment. Professor McGreal has been involved in major research contractsfunded by government departments and agencies, research councils, charities and the private sector. Analysis ofhousing, urban renewal strategies and regeneration outputs in the property sector and the investment markethave been a central theme of this research with particular implications for policy.

Timothy Horton is the South Australian Commissioner for Integrated Design, providing independent adviceto the Premier and Cabinet. The Integrated Design Commission is Australia’s first state level multidisciplinarydesign, cross-government advisory team. Timothy is an award winning architect and urban designer, withexperience spanning the public and private sector in Australia and internationally. Common throughout has been adeep interest in civic space and the role for design in shaping human-centred urban policy. He has held positionsas state President of the Australian Institute of Architects, has acted as a member of the editorial board for theAustralian Urban Design Protocol and is currently a Board member of South Australia’s leading craft and designbody, the Jam Factory.

Martin HAESE is presently General Manager of the Rundle Mall Management Authority (RMMA) and asFounder and Managing Director of Retail IQ has worked nationally and internationally as a retail advisor. Martinis a retail entrepreneur, retail thought leader and widely recognised champion of the retail industry. In 2010 theRMMA engaged Retail IQ to assist with the revitalization of the Rundle Mall precinct in Adelaide. Rundle Mall is

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RMMA engaged Retail IQ to assist with the revitalization of the Rundle Mall precinct in Adelaide. Rundle Mall isthe retailing heart of South Australia and is home to 700 retail stores, 200 serviced based businesses, 15 uniquearcades and SA’s largest 3 department stores. It attracts in excess of 23 million pedestrian visitations per annumand remains the most concentrated place for pedestrian traffic in SA. Martin will be sharing the bold, innovativeand purposeful steps that he has taken with Rundle Mall and how those strategies are transforming the precinctand stimulating property development, public engagement and greater retail market share.

David SMITH Director Residential Energy Efficiency Team Renewable and Energy Efficiency DivisionDCCEE Canberra will speak on Building sustainability assessment and mandatory disclosure regulations.

Delegate PapersPapers shown as "refereed" have been refereed through a peer review process involving an expert international board of refereesheaded by Dr Valerie Kupke. Full papers were refereed with authors being required to make any changes prior to presentation at theconference and subsequent publication as a refereed paper in these proceedings. Non-refereed presentations may be presented atthe conference without a full paper and hence not all non-refereed presentations and/or papers appear in these proceedings. Allauthors retain the copyright in their individual papers.

Sandy Bond, Assessing Nz Householders’ Energy Use Behaviours: A Pilot Survey Refereed

Steven Boyd, Functional Learning For Property Students Refereed

L. E. Bryant and A. C. Eves, Infrastructure Charges And Increases To New House Prices: A Preliminary Analysis Of The USEmpirical Models Refereed

Chai Ning and Oh Dong Hoon, Case Studies Of The Effects Of Speculation On Real Estate Price Bubble Forming: Beijing AndShanghai (2001-2010) Refereed

Ian Clarkson, Will A Carbon Price Change Private Farm Forestry In Australia? Refereed

Neil Coffee and Tony Lockwood, The Property Wealth Metric As A Measure Of Socio-Economic Status Refereed

Greg Costello, "Building Age, Depreciation And Real Option Value - An Australian Case Study" Refereed

Greg Costello, Chris Leishman, Steven Rowley and Craig Watkins, The Predictive Performance Of Multi-Level Models Of HousingSubmarkets: A Comparative Analysis Refereed

Lucy Cradduck and Andrea Blake, The Impact Of Tenure Type On The Desire For Retirement Village Living Refereed

Lucy Cradduck, Place And Space: Community In The Internet Economy And What This Will Mean For Property Refereed

T. K Egbelaki, S. Wilkinson and P.B.Nahkies, Impacts Of The Property Market On Seismic Retrofit Decisions Refereed

Chris Eves, An Analysis Of Nsw Rural Property Market: 1990-2010 Refereed

Xin Janet Ge, Heather Macdonald, and Sumita Ghosh, Assessing The Impact Of Rail Investment On Housing Prices In North-WestSydney Refereed

Xin Janet Ge, Grace Ding and Peter Phillips, Sustainable Housing – A Case Study Of Heritage Building In Hangzhou ChinaRefereed

Eckhart Hetrzsch and Christopher Heywood, Climatic Influence On Life-Cycle Investing For Sustainable Refurbishments InAustralia Refereed

Hui Huang and Xin Janet Ge, "Effects Of Property Channel On Real Estate Market In China – Case Of Guangzhou, China"Refereed

Simon Huston, Sebastien Darchen and Emma Ladouceur, TOD Development In Theory And Practice: The Case Of Albion MillRefereed

Judy Line and Karen Janiszewski, Growing Affordable Housing For Women

Seung-Young Jeong And Jinu Kim, The Spatial Factors And Retail Units’ Prices In Seoul Refereed

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Shen Jianfu and Frederik Pretorius, Real Estate Development With Financial Frictions And Collateralized Debt

Nicole Johnston, Chris Guilding and Sacha Reid, Examining Developer Actions That Embed Protracted Conflict AndDysfunctionality In Staged Multi-Owned Residential Schemes

Ernie Jowsey and Jon Kellet, The Contribution Of Housing To Carbon Emissions And The Potential For Reduction: An Australia-UK Comparison Refereed

Valerie Kupke, Peter Rossini and Sharon Yam, Identifying Home Ownership Rates For Female Households In Australia Refereed

Matti Kuronen, Mikko Weckroth and Wisa Majamaa, Public-Led Urban Development Projects – Comparing Victoria AndScandinavia Refereed

Veronika Lang And Peter Sittler, Augmented Reality For Real Estate Refereed

Alice Lawson, Social Sustainability Through Affordable Housing In South Australia

Rita Yi Man Li, Chow Test Analysis On Structural Change In New Zealand Housing Price During Global Subprime FinancialCrisis Refereed

Rita Yi Man Li, A Review On 10 Countries’ Sustainable Housing Policies To Combat Climate Change.

Tony Lockwood and Peter Rossini, Efficacy Of The Geographically Weighted Model On The Mass Appraisal Process Refereed

Fionn Mackillop, Balancing The Need For Affordable Housing With The Challenges Of Sustainable Development In South EastQueensland And Beyond.

Damian Madigan, Prefabricated Housing And The Implications For Personal Connection Refereed

KF Man and Peter PY Mok, An Empirical Study On The Impacts Of Express Rail Link On Property Price-Hong Kong Evidence

KF Man and SH Fung, Operation Efficiencyassessment Of Hong Kong Public Funded Universities-A Dea Approach

Vince Mangioni, Defining The Role Of Valuations In Mortgage Lending Refereed

Tajul Ariffin Masron and Hassan Gholipour Fereidouni, The Effect Of FDI On Foreign Real Estate Investment: Evidence FromEmerging Economies Refereed

Anas Matsham & Christopher Heywood, An Investigation Of Corporate Real Estate Management Outsourcing In MelbourneRefereed

John McDonagh, The Christchurch Earthquakes Impact On Inner City “Colonisers”

Abdul Rahman Mohd Nasir, Chris Eves and Yusdira Yusof, Education On Plant And Machinery Valuation For The Real Market:Malaysian Practicality Refereed

Timothy O’Leary, Residential Energy Efficiency And Mandatory Disclosure Practices Refereed

David Parker, Property Education In Australia: Themes And Issues Refereed

Alan Peters, Urban Planning And Its Impact On Housing Markets – The Sydney Example In Theoretical Perspective Refereed

Melissa Pocock, "Holdouts, Site Amalgamations And Renewal Of Urban Areas: A Call For Legislative Reform" Refereed

Mohammad Anisur Rahman and Nicholas Chileshe, "Attitudes, Perceptions And Practices Of Contractors Towards Quality RelatedRisks In South Australia"

Janek Ratnatunga and David Parker, A Capability Approach To Real Estate Valuation And Value Enhancement

Wejendra Reddy, Determining The Current Optimal Allocation To Property: A Study Of Australian Fund Managers

Damrongsak Rinchumpoo, Chris Eves and Connie Susilawati, "The Validation Of The Rating For Sustainable SubdivisionNeighbourhood Design (RSSND) In Bangkok Metropolitan Region (BMR), Thailand" Refereed

Peter Rossini and Valerie Kupke, Granger Causality Test For The Relationship Between House And Land Prices Refereed

Steven Rowley, Housing Market Dynamics In Rural And Regional Australia Refereed

Jane H. Simpson, An Exploratory Study Of The Performance Characteristics Of The Property Vehicles Listed On The NewZealand Stock Exchange (NZX)

Garrick Small, Government Space Procurement Options: Testing The Myths Refereed

Connie Susilawati and Deborah Peach, Challenges Of Measuring Learning Outcomes For Property Students Engaged In Work

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Integrated Learning Refereed

Stephen F. Thode and Robert Culp, A Proposed Mortgage To Help Avoid Another Housing Bubble Refereed

Gan Seng Keong and Ting Kien Hwa, Corporate Real Estate Practices Of A Diversified Conglomerate: A Case Study On TheUEM Group Refereed

Pamela Wardner, Understanding The Role Of ‘Sense Of Place’ In Office Location Decisions Refereed

Clive M J Warren, Ann Peterson and David Neil, Academic Integrity: Achieving Good Practice In Undergraduate Property AndPlanning Programs Through Online Tutoring Refereed

Hao Wu and Chuan Chen, Urban “Brownfields”: An Australian Perspective Refereed

Sharon Yam, Corporate Social Responsibility And The Malaysian Property Industry Refereed

Siqi Yan, Xin Janet Ge, Backward-Bending New Housing Supply Curve: Evidence From China Refereed

Jia You, Sun Sheng Han And Hao Wu, "Spatial Distribution Of Affordable Housing Projects In Nanjing, China" Refereed

Yusdira Yusof, Chris Eves and Abdul Rahman Mohd Nasir, Space Management In Malaysian Government Property:A Case Study

Paul Zarebski, A- REIT Fiduciary Remuneration And The Global Financial Crisis

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