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Beijing Land Market

by David Wang TP_WangDavid

 

Beijing Land Market

Over the last two decades, Beijing’s housing market has experienced tremendous growth that coincides with China’s overall economic growth and new free market housing policies.  This rapid growth is an interesting case study into the development of modern cities, as old housing is privatized or demolished to make way for new infrastructure, commercial developments, and housing projects.  As the Central Business District expanded, the city began to spread out towards the urban fringes.  Zheng and Kahn seek to examine the classic urban monocentric model in a city experiencing massive new development.  Building upon the monocentric model, Zheng and Kahn also consider Brueckner, Thissé, and Zenou’s (BTZ) theory of amenities to explain the similarities of Beijing to European cities, where high-income residents locate close to the city-center.  Beijing is quite similar to Paris, as the city center (designated to be the Tiananmen Square area) and nearby CBD contain many urban amenities that attract higher-income residents.  In fact, much of Beijing’s current urban form can be explained using the monocentric city model.  In addition, the capitalization of local public goods adds further insights into this developing urban form.

Before explaining the findings from the test of the monocentric model in Beijing, it is important to explain the three data sets used in the tests.  The first, a housing project data set, is a record of 920 new housing projects, which contain an average of 791 housing units each, in the Beijing market between 2004 and 2005.  These data are representative of the housing units purchased by Beijing homebuyers as there is little re-sale present in the market.  Since the projects are spread geographically around Beijing, their prices can be used to test the relationship between the housing prices and distance from the City Center.  The second, a land parcel data set, includes information about all land parcel auctions from 2004 to June 2006.  These auctions are the first step for developers to lease land to build a new housing project.  Once again, the selling prices of these open-auctions of land are used as a proxy for real estate prices.  The third data set is a detailed analysis of housing projects and their proximity to various local public goods, including public transit (subways, bus stops), high schools, major universities, crime levels, and environmental amenities (air quality, parks).

The testing of the monocentric model in Beijing is a fairly straightforward process. However, the controls used in the equation are extremely important.  The estimation equation is

TP_WangDavid-1

where j is a parcel or project at location q in year t.  The controls are dummy variables for the region of Beijing in which a land parcel (or housing project) is located and the date of the land parcel auction (or housing project sell date).  These regions are defined by four quadrants using Tiananmen Square as the origin.  The inclusion of these controls is an appropriate way to control for any inherent differences in the regions that a simple distance measure would not be able to capture.  For example, by controlling for quadrant (region of Beijing), the results show that land prices in the Southeast are 41% cheaper relative to the Northeast.  If this region control were omitted, bias could be introduced into the coefficient for the effect of distance on land price.  This estimation equation is run twice, once for the land parcel data and once for the housing project data.  With the estimation on land parcel data, it was found that an extra kilometer of distance from the CBD reduces land price by 4.8% (including both commercial and residential land).  However, when restricting the regression to only residential parcels, the land price gradient falls to 4.3%.  As Zheng and Kahn suggest, this result may be due to agglomeration economies, where “land closer to the CBD is more valuable for non-residential users.”  When estimating the equation for the housing project data, only a slight decrease in price of 2% per kilometer away is found with an R2 value of 0.175.  Although Zheng and Kahn do not explain this result, it is possible that since controls for transportation were not included, there could be limited conclusions to be made about the housing prices.

Another interesting characteristic of Beijing’s urban form is the relationship between the zoning rules provided by the Land Authority under the Beijing Master Plan.  Using a regression similar to (1), but replacing the dependent variable with FAR, the floor-to-area ratio (a measure of density on a parcel of land) declines with distance from the City Center.  However, this decline in density is limited to commercial land parcels and is not significant with residential land parcels.  In other words, tall commercial buildings are located towards the City Center, while residential building heights are relatively flat across the city.  Government urban planning forces may be causing this flat construction density.  With Tiananmen Square, a historic landmark, at the City Center, the city’s urban planning commission set restrictions on the height of nearby buildings.  At the same time, the planners want to increase building height as the distance from Tiananmen Square increases, in order to create a skyline for the city.  Thus, the flat density gradient of the residential buildings may result from the combination of the urban planning pushing up building height and market forces pushing down building heights with distance.  Therefore, to summarize, land and real estate prices decrease with respect to distance from the City Center.  However, the zoned density of residential projects does not fall with distance from the Center.

The final portion of the paper concerns the monocentric model with additional considerations for the capitalization of local public goods in the real estate prices.  Since homeowners in China do not pay residential property tax, the value of the public goods should have a higher effect on property prices.  Normally, a concern with studying the capitalization of public goods is whether they are exogenously or endogenously determined.  Zheng and Kahn argue that due to the former planning economy, Beijing is a good candidate for such a study.  The central or city government, without necessarily any consideration for market forces, planned the locations of public goods, such as schools, parks, and universities. Other local public goods measured include public transportation stops, air quality, crime levels, university distance, and also university quality (through entrance exam scores). Zheng and Kahn use standard hedonic methods to estimate the capitalization effects.  Using ordinary least squares, equation (2) is estimated, where the dependent variable is the log of the price per square meter of housing in project j located in community q at time t:
X1j represents the physical characteristics of an average unit in a new project.  Zheng and Kahn claim that this control succeeds due to the conformity amongst the housing units in the projects, where each unit has similar building structure, internal space, and decoration.  In addition, since these projects are all new construction, they should all be of approximately the same age and this factor should be already controlled.  Another interesting variable added to the regression is a dummy variable controlling for whether the project is built by a state-owned enterprise (SOE).  Since SOEs are owned by the state, they do not have an incentive to aggressive push for sales above expected market values, resulting in projects that are on average 10% cheaper than private housing projects.

Zheng and Kahn examine the individual effects of the public goods by running the regression multiple times, adding a variable for an additional local public good with each subsequent regression. For example, the first regression includes only the variable for the nearest subway stop distance, while the second regression includes both the subway stop distance and also distance to the nearest bus stop.  With the addition of each public good, the regression’s R2 value increases.  The regression’s explanatory power increases when controlling for distance to local public goods.  From the analysis, we find that air quality, parks, universities, and schools affect home prices, while transit and crime have no significant effect.  Crime may not have any effect because it is mostly concentrated around the city fringe, where migrant workers congregate.  These workers have huge demand for living and may outweigh any negative capitalization of crime.

Zheng and Kahn’s testing of the monocentric model in Beijing provides a consistent explanation of the shape of Beijing’s urban development.  Her collection of data is broad and consideration of alternate regressions shows the completeness of her analysis.  Her use of local public goods in Beijing to study capitalization effects is interesting and her assumption that these goods are exogenously determined is quite reasonable, as China was centrally planned.  However, the location of some public goods, such as the old universities, Tsinghua University and Beijing University, were determined even before the central planning.  In the past, these universities perhaps were located due to endogenous reasons, though it is unlikely that any capitalization effects carried over to the current state after the revolution of the twentieth century.  In addition, we cannot easily attribute causality in any of the regressions.  It may be appropriate to run some difference-in-difference estimators to compare directly the differences between land parcels.  This may further parse out causal effects of distance.  It is difficult to think of additional data that could be collected, but perhaps further exploration could be done into the transportation systems.  Since Beijing recently changed the subway system to a flat fee, transportation costs from far distances in the city should be decreased and may affect some of the results in the monocentric model view of the city.  This sudden change in transportation cost may provide an opportunity for some fixed effects estimators of before and after prices.  Nevertheless, Zheng and Kahn’s study provides valuable insights into the urban development of a large city within an economy in transition.  The unique data available after China’s many market reforms gives a great starting point to test the monocentric city model without bias from historical norms.

 

 

 

 

 

 

Works Cited

Zheng, Siqi, and Matthew E. Kahn. “Land and Residential Property Markets in a Booming Economy: New Evidence from Beijing.” Journal of Urban Economics 63.2 (2008): 743–757.

 


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