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Do Higher House Prices Crowd-Out or Crowd-In Manufacturing? A Spatial Econometrics Approach

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Abstract

This paper examines the hypothesis that higher house prices lead to greater manufacturing concentration in Chinese cities. There are several innovations in our work, such as allowing for feedback and spillover effects across cities via using spatial panel data models. We also address the endogeneity of house prices with a difference-in-differences approach that relies on house purchase restrictions imposed by some local governments that vary across cities and time, which limit the number of homes residents can purchase, and with an instrumental variables approach. Across various model specifications, we find robust evidence of significant crowding-in of manufacturing firms when house prices rise. This crowding-in impact tends to be dampened in cities with house purchase restrictions in effect. Our direct, indirect, and total effects of house price changes on manufacturing concentration imply significant feedback and spillover effects across cities when a city’s house prices change and when a city experiences a house purchase restriction. These findings have important potential policy implications for real estate markets when local policymakers want to increase their city’s manufacturing concentration. These include offering subsidies and/or other incentives for homeownership, and discouraging house purchase restriction policies by the local governments.

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Notes

  1. The Law of the People's Republic of China on Land Administration indicates all urban land will be owned by the government, but rural/suburban land (with the exception of state-owned land) is to be owned by “the collective”. While residents may use the land, they are not allowed to own land.

  2. The average residential housing selling price is 5190 yuan/m2 in 1998 and 72283 yuan/m2 in 2022. The data is taken from https://fdc.fang.com/index/.

  3. The Manufacturing base of both Foxconn and Huawei were located in the city of Shenzhen before. With the rapid rise of house prices in Shenzhen, the former moved to Zhengzhou in inland China, while the latter moved to Dongguan, located in the north of Shenzhen.

  4. Data source: Statistical bulletin of China’s national economic and social development in 2022.

  5. Sufficient working opportunities and relatively high earnings compared to those in the workers’ hometown guarantee the continuous inflow of manufacturing labor.

  6. Francke and Vos (2004) also consider spatial dependence in a very specific context of hedonic housing price indexes.

  7. In the estimation process, W is row standardized (all nonzero spatial weights are rescaled so that the sum across each row equals 1), and the diagonal of the W matrix is zero. Alternatively, we could have normalized the columns, although that would have changed the interpretation of the weights in an undesirable manner. The standardized row elements have the desirable property of reflecting how all other cities impact a given city, but standardizing the column elements reflect the opposite relationship.

  8. See Ambrose and Shen (2023) for a recent example of a hedonic house price modeling approach in the context of a quasi-experimental framework. Others, including Deng et al. (2022), have used the house price restriction as a quasi-experiment in other Chinese real estate studies.

  9. Due to Chinese city administrative level adjustment and data availability, the dataset does not include data from the Tibet Autonomous Region, Hong Kong and Macau SAR, Taiwan District, and eight prefecture-level cities in five Provinces. These eight cities are Sansha and Danzhou in Hainan, Tongren and Bijie in Guizhou, Haidong in Qinghai, Zhongwei in Ningxia, and Turpan and Hami in Xinjiang.

  10. China’s industrial above-scale firms involve all state-owned and non-state-owned firms whose annual main business income reaches a threshold level. The statistic caliber of the level changed twice during the sampling period. In 1999–2010, the threshold level is 5 million Yuan or more. After 2011, the level increased to 20 million Yuan or more.

  11. The shortest distances between the closet cities in our sample is about 24.5 km.

  12. Fourteen PE core cities are not manufacturing hubs. They are Taiyuan (Capital of Shanxi), Hohhot (Capital of Inner Mogolia), Changchun (Capital of Jilin), Haerbin (Capital of Heilongjiang), Nanchang (Capital of Jiangxi), Nanning (Capital of Guangxi), Haikou (Capital of Hainan), Guiyang (Capital of Guizhou), Kunming (Capital of Yunnan), Xi’an (Capital of Shannxi), Lanzhou (Capital of Gansu), Xining (Capital of Qinghai), Yinchuan (Capital of Ningxia), and Urumqi (Capital of Xinjiang).

  13. See, for example, Liang et al.(2016) and Huang (2022).

  14. In 2018, the Ministry of Land and Resources of China was abolished, and the Ministry of Natural Resources of China was established. Afterward, the Land and Resources Statistical Yearbook and the Land and Resources Yearbook were no longer published. Thus, the data on transferred (sold) areas of state-owned construction land are unavailable for 2018 and later. That’s why we use the data on the transferred (sold) area of state-owned construction land from 2000-2017.

  15. To examine the validity of the spillover mechanism assumption, we conducted a falsification test by replacing our actual weight matrix with six randomly selected “placebo” spatial weight matrices that are not based on geographical distance but have the same sparsity as the actual weight matrix. The between and overall R-squared values for models with all placebo matrices were much lower than those for the model with our existing weight matrix. Specifically, the overall R-squared value was 0.540 for the model with the actual weight matrix and 0.096 or less for the models with the placebo matrices. Similarly, the highest between R-squared value from the models with placebo W matrices is 0.068, which is much lower than the R-Squared value of 0.53 from the model with the original matrix. Furthermore, while the results from the SDM model with the actual weight matrix show that all three indirect effects of the key control variables are statistically significant, the indirect effects for the HPR variable and its interaction with the lnHp variable were insignificant with the use of five out of six “placebo” spatial weights matrices. The results of this test support hypothesis that spatial dependence plays a role in the relationship between house prices and manufacturing concentration. The results are available upon request.

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Acknowledgements

The authors benefitted from discussions with James LeSage, David Drukker, and helpful referee and editor comments. Any remaining errors are the responsibility of the authors.

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Authors and Affiliations

  1. School of Economics and Management, Changsha University of Science and Technology, Changsha, Hunan, 410076, People’s Republic of China

    Ping Feng

  2. Departmrnt of Economics, College of Business, University of Texas at Arlington, Arlington, TX, 76019, USA

    Mahmut Yasar

  3. Center for Real Estate and Urban Economic Studies, University of Connecticut, Storrs, CT, 06269, USA

    Jeffrey P. Cohen

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  1. Ping Feng
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Feng, P., Yasar, M. & Cohen, J.P. Do Higher House Prices Crowd-Out or Crowd-In Manufacturing? A Spatial Econometrics Approach. J Real Estate Finan Econ (2023). https://doi.org/10.1007/s11146-023-09956-x

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