1-variable Hedonic Pricing Model of Housing Price in Chongqing

8/12/2019 1-variable Hedonic Pricing Model of Housing Price in Chongqing

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SCHOOL OF HUMANITIES, ARTS, AND SOCIAL SCIENCES

DIVISION OF ECONOMICS

HE3013 URBAN ECONOMICS

INDIVIDUAL PAPER

PREPARED FOR:

Assistant Professor Walter Edgar Theseira

PREPARED BY:

Chen Enjiao

DATE OF SUBMISSION:

27 February 2014


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Contents

Abstract ................................................................................................................................................... 3

1. Context: The Worlds Largest (And Little Known) City.................................................................... 4

2. Theoretical Background: Classic Theory of Bid-Rent Curves .......................................................... 4

3. Data and Empirical Model ............................................................................................................... 4

3.1 Summary Statistics .................................................................................................................. 6

3.2 Empirical Results & Interpretation .......................................................................................... 6

3.3 Limitations & Conclusion ........................................................................................................ 7

Bibliography ............................................................................................................................................ 8

Appendix 1 ............................................................................................................................................ 10

Choice of CBD .................................................................................................................................... 10

Appendix 2 ............................................................................................................................................ 13

Magnified Versions of Figure 1 and Figure 2 .................................................................................... 13


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AbstractThis project seeks to estimate the relationship between housing price and distance to Central

Business District (CBD) in the worlds largest city Chongqing. This is done by collecting data on the

listing price of new residential units on a property website.

A hedonic pricing model with 1-variable is then used to test the spatial trade-off in question. My

results show that decreases the further a unit is away from the CBD, as predicted by

Bid-Rent theory.


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1.Context: The Worlds Largest (And Little Known) CityDespite being one of the fastest growing urban centres1in the world boasting a population of

29.19 million as of 2011 (National Bureau of Statistics of China), little is known about the

dynamics of housing prices in Chongqing. Despite the prevalence of hedonic pricing models

in housing research, they are scarcely applied to Chinese cities (Chen & Hao, 2008), possiblybecause of issues with data availability2.

This paper aims to study how housing prices vary with locational characteristics in

Chongqing in light of such developments.

2.Theoretical Background: Classic Theory of Bid-Rent CurvesAccording to Bid-Rent theory, land valuation is based on the geographic distance from an

established point of maximum value (Hanks, 2011), typically represented by the CBD3

(Fujita, 1989). Such a relationship has been verified in many instances (Alonso, A Theory of

the Urban Land Market, 1960; Alonso, Location and Land Use, 1964; Muth, 1969; Mills,Studies in the structure of the urban economy, 1972; Mills, An Aggregative Model of

Resource Allocation in a Metropolitan Area, 1967).

In this traditional location theory, a spatial trade-off exists between housing prices and

commuting costs4., i.e. households paying higher prices are compensated by the lower costs

of commuting to the CBD and vice versa. I expect to see such a negative relationship5in my

empirical results.

3.Data and Empirical ModelSince no comprehensive dataset is available, I collect my data on 50 random housing unitsfrom Anjuke6, taking into consideration only new and unfurnished residential units for better

comparability. Distance to CBD (indicated by the red circle in Figure 1)7is then obtained

from Baidu Maps8. All data is finally fed into Google Fusion Tables9to generate the map

below:

1Annually, the size equivalent of Luxembourgs population enters Chongqing city in search of jobs (Watts,

2006).2The Chinese housing market has been freed for only about a decade. Previously, housing was allocated as a

welfare benefit. Firms were ordered to provide highly subsidised housing to employees and priority access was

given on the basis of rank and seniority. This only ceased with reform in 1998 (Chen & Hao, 2008).3This is due to the concentration of job opportunities in the CBD area according to the model.

4Housing and accessibility to employment centres are jointly purchased so households attain spatial

equilibrium by making trade-offs between accessibility and housing price.5To clarify, I am interested to investigate the negative relationship between housing price and commuting cost.

When housing price is higher, commuting cost should be lower and vice versa.6The sitehttp://cq.fang.anjuke.com/map/loupan/?from=navigationautomatically generates 50 random units

per visit.7Please refer to Appendix 1 for a detailed discussion on the choice of CBD in Chongqing.

8

Baidu Maps is accessible athttp://map.baidu.com/,it is preferred to Google Maps because it offers the mostupdated transportation information as far as China is concerned.9Google Fusion Tables is accessible athttp://www.google.com/drive/apps.html#fusiontables.
http://cq.fang.anjuke.com/map/loupan/?from=navigationhttp://cq.fang.anjuke.com/map/loupan/?from=navigationhttp://cq.fang.anjuke.com/map/loupan/?from=navigationhttp://map.baidu.com/http://map.baidu.com/http://map.baidu.com/http://www.google.com/drive/apps.html#fusiontableshttp://www.google.com/drive/apps.html#fusiontableshttp://www.google.com/drive/apps.html#fusiontableshttp://www.google.com/drive/apps.html#fusiontableshttp://map.baidu.com/http://cq.fang.anjuke.com/map/loupan/?from=navigation


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Source: Google Fusion Tables, 2013

Figure 1: A Geospatial Map of Housing Prices in Chongqing10

As can be seen, most purple and pink circles cluster around the red dot that is the CBD and

the Yangtze, as we would expect.

Figure 2 shows a scatter plot as well as line of best fit for my data. For magnified versions,

refer to Appendix 2.

Figure 2: Scatter Plot and Line of Best Fit for Data Obtained

10An interactive version of this map has been made available online athttp://bit.ly/1lm1sJk,containing

information on unit names and addresses.
http://bit.ly/1lm1sJkhttp://bit.ly/1lm1sJkhttp://bit.ly/1lm1sJkhttp://bit.ly/1lm1sJk


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The econometric equation in consideration is thus:

where is the housing price (measured in) and is the distance from

CBD (in ).

3.1Summary StatisticsMy sample consists of 50 listing prices. The mean housing price per square metre in the

sample is $9340 with a standard deviation of $2228.

Table 1: Summary Statistics

3.2Empirical Results & InterpretationTable 2 illustrates the Ordinary Least Squares estimates for the model.

(1)Ln (HousePrice)

Distance toCBD

-0.0121

(-8.83)

_cons 4.087(230.17)

N 50

tstatistics in parentheses*p< 0.05, **p< 0.01, ***p< 0.001

Table 2: Regression Results for 50 Housing Units in Chongqing City

The coefficient on is highly significant and shows that as distance to CBD increases by1km, listing price decreases by 1.2%. In contrast to the 5% drop observed for Shanghai (Chen

& Hao, 2008), this seems small. However, such a subtler effect might be due to my

examination of individual unit instead of zonal-level data11.

The negative relationship is as predicted. As time savings are capitalised in the value of

property, housing prices increase with greater proximity to the CBD, reflecting the price

premium people are willing to pay for convenience.

11Though I had tried to gather data only for housing units that are as comparable as possible, there remains

many individual dwelling characteristics that are not controlled for, which may render our results problematic.

Distance 50 10.71 7.325501 2.5 40.4

LnHousePrice 50 3.957095 .1128323 3.50515 4.176091

HousePrice 50 9339.76 2228.211 3200 15000

Variable Obs Mean Std. Dev. Min Max


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3.3Limitations & ConclusionAs mentioned at the outset, the lack of high quality and consistent data on housing prices inChongqing may introduce measurement errors into this empirical exercise12, possibly givingus an inefficient estimator13. Such attenuation bias may partially explain the small coefficient

on.

Furthermore, a 1-varaible specification is extremely liable to suffer from the omittedvariables bias, causing the problem of endogenity when the effects of other key variables are

captured in 14. Our estimator may therefore be inconsistent.

More fundamentally, the model implicitly assumes that the city is monocentric, but thereexist 2 other CBDs in Chongqing which may conflate our results15. A better model mayintroduce location-time interacting dummy variables instead of making a priori assumptionsabout the location of CBD, as employed in this Hong Kong study (Yiu & Tam, 2004).

Housing is often thought to be a basic right (United Nations) and an essential component ofsocial security. In the context of rapidly rising housing prices in China, greater attention mustbe paid to the underlying dynamics in order to inform policy. It is hoped that this paper hasprovided an empirical verification of the Bid-Rent theory in the developing market of Chinaand that more research in this underserved geography will continue. The urbanisation ofmillions of people is at stake.

12Indeed, our small sample size may not be representative of the population of housing prices in Chongqing,

leading to more noise and contributing to the attenuation bias aforementioned.13

This assumes that the error(s) in measurement is uncorrelated with residuals.14

For example, omission of the variable access to green space may exaggerate our OLS estimate. Other

variables like listing time may warrant attention as well.15

Suppose Unit A is 5km away from my chosen CBD but only 1km away from the Jiangbeizui CBD which isfurther up north, its housing value may be artificially higher due to its proximity to Jiangbeizui, therefore

causing an underestimation of .


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Yiu, C., & Tam, C. (2004). The estimation of housing price gradients: A comparison of different

approaches applied in Hong Kong.Adequate & Affordable Housing for All.Toronto:

University of Toronto.

ZenTech. (n.d.). Retrieved February 14, 2013, from Chongqing Map:

http://www2m.biglobe.ne.jp/~ZenTech/English/Map/China/Chongqing.htm

Zhang, Y. (2013, November 16). Chongqing News. Retrieved February 12, 2014, from CBD to be

enlarged: http://english.cqnews.net/html/2013-11/16/content_28672965.htm


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Appendix 1

Choice of CBD

The largest direct-controlled municipality in China, Chongqing is divided into 19 districts, 15 counties,

and 4 autonomous counties.

This really means that the Greater Chongqing Region looks like below:

Source: ZenTech, 2013

Figure 3: Map of the Greater Chongqing Region


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Of which, the urban area of Chongqing city proper like this:

Source: Chinese Tourist Maps, 2013

Figure 4: District Map of Chongqing city


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Source: Google Maps Engine, 2013

Figure 5: CBD Centres of Chongqing City

In particular, our CBD of interest is located in the Yuzhong Peninsula (, or "Central Chongqing

District") where government and international business offices are located (Chinese Tourist Maps).

The marker of this location is the green marker as above in Figure 5 where the Peoples Liberation

Monument is. It stands in the Jiefangbei commercial square which is like Chongqings version ofTimes Square New York. It is the most established CBD to date while the other 2 (Danzishi and

Jiangbeizui) are relatively recent developments (Zhang, 2013).

Distance to CBD is therefore obtained by calculating the driving distance from a selected housing

unit to the Peoples Liberation Monument.This warrants some explanation. First of all, Chongqing is

a hilly city so walking distance is highly undesirable as a measure of choice (since people tend to

avoid it altogether). Secondly, I assume that driving distance is a good measure since even if one

does not own a car, cabs are cheap in Chongqing (Taxi Auto Fare) even after taking disposable

income into account. Otherwise, driving distance is linearly related to traveling time by public

transport as Chongqing has a well-developed network consisting of buses and metro. Therefore it

remains the measure of choice.


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Appendix 2

Magnified Versions of Figure 1 and Figure 2

Source: Google Fusion Tables, 2013

Figure : A Geospatial Map of Housing Prices in Chongqing


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Figure : Scatter Plot and Line of Best Fit for Data Obtained

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1-variable Hedonic Pricing Model of Housing Price in Chongqing

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