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Does Planning Matter? Effects on Land Markets

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Abstract

This paper uses a spatial multi-intervention difference-in-difference method to examine the opening and planning impacts of transport improvements on land markets in a mega-city of China. The results suggest the significant heterogeneity in the capitalization effects from changes in rail access on prices for different land uses in affected areas versus unaffected areas. Residential and commercial land parcels receiving increased station proximity experience appreciable price premiums. However, such effects vary widely over space. These results add to the evidence that public investments have an important role to play in spurring the spatially targeted land market.

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Notes

  1. To mitigate the inflation effect, we have adjusted the land prices by using the CPI index. All monetary figures are constant in 2009 RMB. Also, we have trimmed the land price distribution by only keeping parcels in each year whose price is between the 5th and 95th percentiles of the whole sample price distribution.

  2. Recall that this study simplified the analysis through the use of the flexible distance bands. But it is important to note that there is a long tradition of the land value gradient, suggesting that land prices decline smoothly with distance from a new station and therefore the actual treatment effects are not discrete across either time or space. (2km _ station ≥ 2008/2009)

  3. The employment accessibility is measured by using the non-parametric gravity model (see Ding et al. 2010).

  4. BRICS is the title of an association of leading emerging economies (Brazil, Russia, India, China and South Africa). See http://en.wikipedia.org/wiki/BRICS for details.

  5. The Beijing Municipal Committee of Transport’s official website http://www.bjjtw.gov.cn/ contains informative details of subway lines in Beijing. Anticipated announcement effects would give us more insights on the ways that land markets capitalize changes in amenities. However, there were many versions of subway development plans in Beijing. The first version of the plan had been announced in the late 1980s (under this plan, most of the current new subway lines should have been built in the 1990s), but nothing happened until the early 2000s. Meanwhile, the land and housing markets have not been fully transformed from the socialist welfare system into the market-pricing system until the early 2000s. In the robustness result not reported, we do test the anticipation effects for subway stations (i.e. Line 14 and 16) that had been announced but the exact completion time would be no early than 2015. The insignificant coefficients of the results suggest that it is difficult-to-measure the announcement effects of new subway lines given data limits and the large uncertainties associated with the proposed timetable.

  6. Note that the term of “bedroom communities” represents places where commuters perform most professional and personal activities in another location, maintaining their residence solely as a place to sleep. See http://en.wikipedia.org/wiki/Commuter_town for details.

  7. See the seminal work by Rosen (1974), and see a recent survey by Gibb and Pryce (2012) about future directions for housing economics. We adapt the classic scenario to China (Zheng et al. 2009).

  8. It is important to note that this paper does not provide a unified framework for linking sorting with institutional roles (Gibb and Pryce 2012).

  9. Recall that our analysis does not attempt to account for the impact of financial changes on the real estate markets (Deng and Liu 2009; Jackson and Orr 2011).

  10. In this study, we have tried estimating flexible-form models with Box–Cox transformation but could not reject a strong log–log relationship between land prices and key explanatory variables.

  11. Standard errors are clustered at the zone level to allow for heteroscedasticity and spatial-temporal correlation in the error structure within zones.

  12. In the presence of nested treatment groups, our study’s estimates provide new insights about each treatment effect conditional on the subsequent treatment scenarios. One major concern is to test whether there are spillover effects among treatment groups when adding all of them into one model specification. As a robustness check, we have tried to add each treatment group subsequently in different model specifications, but the difference between their coefficients won't tell anything about the spillover effect because the sum up value of the treatment coefficients remains the same as when adding all of them into one model specification.

  13. βj1 represent a set of baseline categories (Treatment j*Period 1 ) that are omitted in the estimating result tables.

  14. Note that the net planning effect includes a combination of the potential negative construction effect and the positive anticipation effect for planned stations (Knaap et al 2001).

  15. Intuitively, the control group is places that have never been within a 2 km radius of a rail transit station. For a more nuisance assessment, we have also used the propensity score matching techniques to select the control group without 2 km distance band of a rail station based on local demographic characteristics, and the results are virtually similar.

  16. Due to the lack of census panel data, we cannot measure demographics dynamics in treated places relative to observationally identical control places as a result of transport improvements.

  17. To further control the spatial-temporal effect, we al (station ≥ 2003) so include the interactions between time trends and parcels in each treatment group that only meet the first treatment selection criteria—parcels that experienced distance reductions to the closet stations(Treatment Criteria 1* Time); and interactions between time trends and parcels in each treatment group that only meet the second treatment selection criteria—the parcel-station distance is now within the distance bands(Treatment Criteria 2* Time).

  18. Recall that the distance bands are cumulative, which make the interpretation of the results more straightforwardly. For example, for residential parcels we find a positive effect within 1 km of a station. But the next band is within 2 km of a station and the results show a stronger positive effect. This result implies that the proximity impact of rail stations is determined by the mix of properties within 1 km and between 1 km and 2 km. Of course, researchers can further disaggregate the distance band selection into the 0.5 km range, or choose to define the bands as 0 to 0.5 km, 0.5 km to 1 km, 1 km to 2 km, and 2 km to 4 km. Recall that our purpose here is to shed light on the importance of considering the distributional proximity impacts of rail stations on land prices over space.

  19. Note that treatment dummies have insignificant signs, which can help explain the pre-opening effect of station in 2008 is not caused by the price-growing trends in the treated places.

  20. See McDonald and Osuji (1995) and McMillen and McDonald (2004) for a detailed discussion.

  21. The estimated coefficients of these interaction terms are not reported. The results remain robust by controlling the interactions between time trends and distance-to-stations.

  22. Note that land parcels located more than 4 km away from a new station might also benefit from the improvements in rail access and would be far enough from the localized congestion nuisances at the station areas. We have tested this hypothesis and find little evidence to support this claim.

  23. Note that while the qualitative nature of the results is relatively robust, the estimated coefficients of treatment effects within the central city have lower magnitudes than those within the suburbs. In the results not reported, we also find that the opening and planning effects are more pronounced in station areas that look to be getting several new lines rather than just one.

  24. There are no significant spillover effects within groups when using the 1 km and 4 km distance bands.

  25. Note that we have also interacted the residential land parcels in each treatment group with both of treated and control commercial land parcels. Because the estimating results are not significant, they were dropped from the table.

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Acknowledgments

We thank conference participants at the 2013 European Real Estate Conference held in Vienna for insightful discussions, and Steve Gibbons, Paul Cheshire, Henry Overman, Olmo Silva and anonymous referees for excellent comments. Wenjie Wu thanks the Carnegie Trust for Universities of Scotland, and the Adam Smith Research Foundation, University of Glasgow. This paper is also supported by the National Natural Science Foundation of China (Project No. 41230632), Fundamental Research Funds for the Central Universities (12JNYH002), and New Century Excellent Talents in University (NCET-110856).

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Authors and Affiliations

  1. Urban Studies, School of Social and Political Sciences, University of Glasgow, 25 Bute Gardens, Glasgow, G12 8RS, UK

    Wenjie Wu

  2. School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK

    Guanpeng Dong

  3. College of Economics, Ji Nan University, No. 601 Huangpu Road West, Guangzhou, 510632, China

    Bing Wang

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  1. Wenjie Wu
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  2. Guanpeng Dong
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  3. Bing Wang
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Correspondence to Bing Wang.

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Table 9 Descriptive statistics of variables
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Wu, W., Dong, G. & Wang, B. Does Planning Matter? Effects on Land Markets. J Real Estate Finan Econ 50, 242–269 (2015). https://doi.org/10.1007/s11146-014-9455-2

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