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Price impacts of signalling in Chinese residential land auctions

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Abstract

Auctions are used by local Chinese governments to raise revenue while releasing land for development. Because developers reveal their valuation of parcels through winning, bids for land use rights in subsequent auctions may be influenced by outcomes of earlier government sales. A log linear hedonic regression analysis of more than 900 residential land use rights auctions in Chengdu over eight years reveals that top developers’ winning bids signal other developers who adjust their bids in subsequent auctions. Developers appear to believe that the top companies possess superior knowledge through research, experience, or insider information. The results are higher government revenue and rising land prices, which can lead to lessened competition among housing developers, higher house prices, and affordability problems in neighbourhoods where the top developers buy land.

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Fig. 1
Fig. 2

Source: Reproduced with permission from Yang, Y., Zhang, D., Meng, Q., and McCarn, C. Urban Residential Land Use Reconstruction under Dual-Track Mechanism of Market Socialism in China: A Case Study of Chengdu, Sustainability (2015), 7, p. 16853

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Notes

  1. SOEs are companies in which the government owns a majority of the shares.

  2. Under the English auction process, at least 20 days prior to the date of auction, the local land bureau makes a public announcement with information such as the location of the property up for bid, land use restrictions, floor area ratio (FAR), the intended bidding date, reserve price, and the bidding deposit requirement (often 10 per cent of the reserve price). Interested buyers bid publicly at the specified time and place. At least three qualified bidders are required, and whoever offers the highest bid wins (Wang and Yang 2020). Winners are required to initiate development on the property within a set time period. Bids may be submitted by private companies or SOEs, by an individual company or a consortium as a joint bid. Results must be published within 10 days of sale (Yee 2005).

  3. To determine the length of time during which to measure the shock to auction prices caused by a top developer’s winning bid, we considered the time structure of the residential auction process in Chengdu. Under the English auction process, at least 20 days prior to the auction date, the local land bureau makes a public announcement of the parcel offered for sale. Suppose a signal occurs and results are published the next day. In that case, an effect could be observed in day t + 1 if another property in the area had been advertised for sale 20 days earlier, and participants in the auction could react quickly. However, if the results are not published until the deadline, 10 days after the auction, and the property is announced for auction in that area, 30 days will elapse from the signal to the subsequent auction date. If the time window is limited to 30–45 days, only properties already in the auction process or immediately advertised could be affected by the signal. At the other extreme, many leases require developers to begin work on acquired land within six months. Therefore, a time window that extends to five to six months after a signal auction may capture spillover effects from physical changes to the parcel and surrounding infrastructure rather than the immediate price shock from the winning bid. We chose to use the midpoint (90 days) between the auction date and the date development must commence under most residential leases in this market. In determining the area’s size to study as the competitive geographic market within which auction prices might have a direct and immediate influence, we considered political boundaries, the size of the parcels being auctioned, and the work of previous researchers in other markets.

  4. Some of the companies listed in the Top 100 are parent companies of local developers. In those cases, the ranking considers the total size of real estate investment in Chengdu by the parent company.

  5. To verify the Top 100 list as an objective measure of a developer’s influence, we surveyed 30 local developers to ask who the established developers in Chengdu are. They identified 58 developers. Almost all (88 per cent) of the observations are classified the same for the variable SIGNAL using either of the two approaches.

  6. Because White’s test indicates that our model suffers from heteroscedasticity, we use heteroscedasticity-consistent standard errors.

  7. Regression results based on the developers identified in the survey are similar to those obtained using the Sichuan Real Estate Yearbook’s objective classification. In addition, regression results are similar if SIGNAL is based on the Top 50 rather than the Top 100 developers.

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Authors and Affiliations

  1. Southwestern University of Finance and Economics, 819 Gezhi Building, 555 Liutai Avenue, Wenjiang District, Chengdu, 611130, Sichuan, People’s Republic of China

    Xiaorong Zhou

  2. University of Georgia, 205 Housing and Consumer Research Center (House C), 415 Sanford Dr, Athens, GA, 30602, USA

    Velma Zahirovic-Herbert

  3. University of the Witwatersrand, Georgia State University, 309 Welton Way, Peachtree City, GA, 30269, USA

    Karen M. Gibler

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  1. Xiaorong Zhou
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Zhou, X., Zahirovic-Herbert, V. & Gibler, K.M. Price impacts of signalling in Chinese residential land auctions. J Hous and the Built Environ 36, 1299–1321 (2021). https://doi.org/10.1007/s10901-020-09806-9

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