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Exploring the Spillover Effects of Urban Renewal on Local House Prices Using Multi-Source Data and Machine Learning: The Case of Shenzhen, China

Land 2022, 11(9), 1439; https://doi.org/10.3390/land11091439

by Xiaojun Li^1,†, Jieyu Wang^2,†, Ke Luo¹, Yuanling Liang¹ and Shaojian Wang^2,*

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Reviewer 3:

Hrvoje Tomic

Land 2022, 11(9), 1439; https://doi.org/10.3390/land11091439

Submission received: 10 August 2022 / Revised: 25 August 2022 / Accepted: 27 August 2022 / Published: 31 August 2022

(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)

Round 1

Reviewer 1 Report

This study measures the added premium effect of urban renewal on local house prices through econometric models and multi-source data, and explores the spillover effect of urban renewal on house prices using an integrated model based on machine learning and Geo-detector analysis. In general, the topic is interesting, and this study is well written and organized. However, a few minor revisions are needed before publication as follows.

1. The research gaps which the paper will fill in and the contribution of this paper should be clearly and reasonably presented.

2. Some recent literature from the last few years should be added in the introduction section.

3. A brief description of the study area and a location map of Shenzhen should be provided.

4. Relevant policy recommendations are needed.

5. There are several language typos. The authors should have a careful proofreading.

Author Response

Response to Editor and Reviewers

Exploring the Spillover Effects of Urban Renewal on Local House Prices Using Multi-source Data and Machine Learning: The case of Shenzhen, China

Response to Reviewer 1:

(1) The research gaps which the paper will fill in and the contribution of this paper should be clearly and reasonably presented.

Response: Thank you very much for your suggestion. The existing literature researches neglected inquiry into the spillover effects of urban renewal on house prices. And previous studies have concerned mainly regression analysis based on traditional econometric models, constructing indicator systems mainly with static variables, with less consideration being given to variable omission and endogeneity. Therefore, the novelty of our research is measures the added premium effect of urban renewal on local house prices through econometric models and multi-source data, and explores the spillover effect of urban renewal on house prices using an integrated model based on machine learning and Geo-detector analysis. To better clarify the novelty and contribution, we added this emphasis in the last paragraph of “Introduction” section as follows:

In summary, many studies have focused on urban renewal and little attention has been paid to the mechanism of urban renewal's influence on house prices, which means that the existing literature evidences a relatively poor understanding of the link between urban renewal and house prices. Meanwhile, previous studies have mainly conducted regression analysis based on traditional econometric models, with less consideration of variable omission and endogeneity. Therefore, the purpose of this paper is to attend to this gap in the study field, and the contribution of this paper mainly includes two aspects. To be specific, first of all, we quantify the premium effect of urban renewal on house prices, since existing studies have paid insufficient attention to the spillover effects and mechanisms of housing prices in the context of urban renewal. Quantify the impact of urban renewal renovation is a relatively new research topic. Second, our study may provide policy tool in order the urban renewal through the impact on house prices. Explicitly, the empirical results of our study can provide a reference for refinements in management practice for urban development and assist in the delivery of sound city planning and construction. Third, compared with the regression analysis based on traditional econometric models in previous studies, this paper adopts multi-source data, DID and machine learning to provide a multi-faceted and dynamic analytical approach to study the impact of urban renewal on house prices, which can more accurately analyze the impact mechanism and the role relationship between urban renewal and housing price spillover. Therefore, this paper will contribute to studies related to the impact of urban renewal.

(2) Some recent literature from the last few years should be added in the introduction section.

Response: Thank you very much for your suggestion. According to your suggestion, we have added some recent literatures, including:

Liang, C.M.; Lee, C.C.; Yong, L. R. Impacts of urban renewal on neighborhood housing prices: predicting response to psychological effects. Journal of Housing and the Built Environment, 2020, 35: 191-213.

Albanese, G.; Ciani, E.; Blasio, G. Anything new in town? The local effects of urban regeneration policies in Italy. Regional Science and Urban Economics, 2021, 86: 103623.

Cho, G.H.; Kim J.H.; Lee, G. Announcement effects of urban regeneration plans on residential property values: Evidence from Ulsan, Korea. Cities, 2020, 97: 102570.

Jayantha, W. M.; Yung, E. H. K. Effect of revitalisation of historic buildings on retail shop values in urban renewal: An empirical analysis. Sustainability, 2018, 10(5):1-18.

Lee, C. C.; Liang, C. M.; Chen, C. Y. The impact of urban renewal on neighborhood housing prices in Taipei: An application of the difference-in-difference method. Journal of Housing and the Built Environment, 2017, 32(3):407-428.

Ahlfeldt, G. M.; Maennig, W.; Richter, F. J. Urban renewal after the Berlin Wall: a place-based policy evaluation. Journal of Economic Geography, 2017, 17(1): 129-156.

The related information has been presented in “1 Introduction” and “5. Conclusions and policy recommendations”, as follows:

For example, Liang et al. [23] estimated the impact of urban renewal on neighborhood housing prices through difference-in-difference methods and spatial econometrics, and found that the impact of urban renewal induces a sustained response in neighborhood housing prices even before reconstruction is completed. Using small and medium-sized cities in central and northern Italy as case studies, Albanese et al. [24] found that urban renewal projects led to higher house prices by improving public sector interventions. Using Ulsan, Korea as the study area, Uho et al. [25] found that house price increases were most pronounced in project locations where residents showed a high willingness to participate. In these areas, house price increases were found even before the final plans were released.

……

This finding is similar to the results of empirical studies for cities in Hong Kong, Taiwan, Berlin and South Yorkshire, which show that urban renewal can lead to significant increases in house prices, including commercial property rent [50-53]. This is due to the fact that urban renewal can eliminate neighborhood and housing negative externalities, and will also have a gentrification effect, raising local housing prices and squeezing out low-income earners, which in turn will continue to drive up house prices [54].

(3) A brief description of the study area and a location map of Shenzhen should be provided.

Response: Thank you very much for your suggestion. We have added a section describing the study area and added a study area map, as follows:

Study area

Shenzhen, located in the south of Guangdong Province in southern China, forms the research area addressed in this study. As part of China's reform and opening up, Shenzhen was established as a special economic zone in the 1980s and transformed from a small fishing village to one of the four first-tier cities in China after Beijing, Shanghai, and Guangzhou. Shenzhen has an area of 1997 km² and a population of about 17.68 million in 2021. With a gross domestic product (GDP) of RMB 3,070 billion, it ranks third in China. Shenzhen is the pioneer area for urban renewal in China. The long-standing space problem of insufficient land being available for urban renewal has made the transformation and development of the stock infrastructure an urgent issue to be addressed. Shenzhen became the first city in China to fully transform its land supply for stock renovation, and thus became an exemplar for good urban renewal research and practice in China. The Shenzhen Urban Renewal Measures promulgated by the Shenzhen Municipal Government defines urban renewal as "an activity of comprehensive improvement, functional change or demolition and reconstruction of a specific urban built-up area by a corresponding body". Specifically, the definition includes the following three categories: (1) Comprehensive improvement: without modifying the main structure and function of the building, improving fire protection facilities, improving infrastructure and public service facilities, improving street facades, environmental improvement and energy-saving renovation of existing buildings; (2) Functional change: changing part or all of the functions of the building, but without changing the main body of land use rights and period of use, retaining the original structure of the building; (3) Demolition and reconstruction: strictly in accordance with the provisions of unit planning for urban renewal of housing units, and implementation of an annual plan for urban renewal.

Fig.1 Location of the study area

(4) Relevant policy recommendations are needed.

Response: Thank you very much for your comment. We proposed the policy recommendations in the second paragraph of “5. Conclusions and policy recommendations”, as follows:

The above empirical results of our study provide practical evidence for the link between urban renewal and the real estate market. Results reveal more precisely the influence of urban renewal on local house price premiums, and can provide useful background information and act as a reference for urban renewal and refinement of urban developments in Shenzhen and other cities in China well into the future. To bespecific, the government should promote a sustainable urban renewal model, improve local and micro regulation of the real estate market while promoting urban renewal projects, establish and improve the long-term mechanism of the real estate market, and promote the healthy development of urban renewal and the real estate market.

(5) There are several language typos. The authors should have a careful proofreading.

Response: Thank you very much for your suggestion. We have the manuscript polished by a native English speaker. Also, we have carefully amended the expression of each sentence to avoid ambiguity as well as the grammar, spelling and punctuation of each sentence to avoid unnecessary mistakes.

Response to Reviewer 2:

The research question in this article is very interesting and it is explored using advanced analytical techniques. A general question is that, beyond the quantification of urban renewal influence on house prices, is there any quantification for certain factors, for example the transport improvements, in terms of what is adequate for this price change? This is important for policy making.

Response: Thank you very much for your recognition of the importance of our study. For quantifying transportation factors as you mentioned, we use two variables in this study, the amount of change in transportation facilities and the amount of change in road network density, to account for the impact of urban renewal on transportation and thus measure the impact of such transportation improvements on house prices. In addition, we also consider changes in commercial facilities (mid- to high-end hotels, business offices, restaurants), changes in public service facilities (leisure facilities, educational facilities, medical facilities), and changes in population characteristics.

Some other remarks are as follows:

(1) Some clarification is needed as to the data employed in the analysis (section 2.2). In the PSM-DID method, I believe it is houses; a description of the data set would be useful. In the RF regression, it seems that the urban renewal projects are the unit of analysis. In that case the way the urban renewal projects are related to the independent variables can be explained, for example what is the areal unit? In the geo-detector analysis, the 300 samples are houses? In general, since variables at different spatial scales are employed, how are they combined in each data set? For example, how transportation in incorporated in the analysis?

Response: Thank you very much for your comment. As you said, in the PSM-DID method, the sample N means houses. In the RF regression, the number of housing premiums around each urban renewal project is the dependent variable, and the number of changes in commercial facilities, public service facilities, transportation facilities, and demographic characteristics due to urban renewal are the independent variables. RF regression is used to explore the effect of the amount of change in each factor due to urban renewal on the housing premium, which is how we incorporated factors such as transportation in our analysis. In addition, in the geo-detector analysis, 300 samples are the number of urban renewal projects. We also have added these explanations in section 2.2, as follows:

In the PSM-DID method, houses are used as the sample N. In the RF regression, the number of housing premiums around each urban renewal project is the dependent variable, and the amount of changes in commercial facilities, public service facilities, transportation facilities and population characteristics due to urban renewal are the independent variables. In the analysis of geo-detector analysis, the study sample is urban renewal projects.

(2) At some points (for example line 172, Table 3 first row) the dependent variable is referred to as independent variable and vice versa.

Response: Thank you very much for your suggestion. We feel very sorry for our carelessness. We have corrected the relevant expressions, as follows:

The dependent explanatory variable (Price) indicates the unit price per m2 of a housing transaction, and the unit is 10,000 yuan

Table. 3 Index system for the impact of urban renewal on the housing premium

Dependent variable	Dimension	Independent variable
Housing premium for urban renewal projects	Business location	Change in the number of medium and high-end hotels (3-star and above) within the area of influence
		Change in the number of business office buildings within the area of influence
		Change in the number of restaurants in the area impacted
	Public Services	Change in leisure facilities within the area impacted
		Change in educational facilities within the area of influence
		Change in medical facilities within the area of influence
	Transportation	Change in traffic facilities within the area impacted
	Transportation	Change in the density of the road network within the area impacted
	Demographic characteristics	Average years of schooling (15+) for streets within the area of influence
		Average age for streets within the area of influence
		Population density for streets within the area of influence

(3) Line 311: I cannot see in Table 2 the figure of 13,900 yuan per m2. Also, in Table 2 the bottom line (N) should be explained for all columns.

Response: Thank you very much for your suggestion. The figure of 13,900 in Table 2 should be in the fourth column of the third row. The figure in the table reads "1.38708***". We have changed the figure in the text to 13,871. In addition, we added the explanation of N in the bottom line of Table 2, as follows:

Table. 2 Results of PSM-DID

	(1)	(2)	(3)	(4)
	OLS	FE	Weight	On_Support
Renew	0.87772^***	0.94245^***	1.38708^***	0.94255^***
	(8.66)	(85.37)	(101.49)	(85.37)
Transportation	-0.00092	-0.00624^***	-0.00759^***	-0.00624^***
	(-0.19)	(-27.32)	(-26.78)	(-27.33)
Medical	-0.00719^*	-0.00752^***	-0.00384^***	-0.00753^***
	(-2.33)	(-21.02)	(-9.44)	(-21.02)
Education	0.00313	0.00251^***	0.00160^***	0.00252^***
	(1.49)	(10.65)	(5.80)	(10.68)
Food & Beverages	0.00035	0.00031^***	0.00069^***	0.00031^***
	(0.50)	(5.04)	(9.17)	(5.03)
Leisure	-0.00151	0.00416^***	0.00237^***	0.00416^***
	(-0.57)	(11.07)	(5.72)	(11.05)
Business	0.00272	0.00473^***	0.00413^***	0.00473^***
	(1.49)	(31.43)	(22.43)	(31.42)
Cons	3.96364^***	4.19067^***	3.82565^***	4.19069^***
	(12.88)	(349.88)	(221.65)	(349.84)
N	101,914 (Total sample size)	101,914 (Total sample size)	67,069 (Number of matched samples)	101,898 (Number of matched samples)

(4) It would be useful to mention the software employed in the analysis.

Response: Thank you very much for your comment. The software used for the PSM-DID model is Stata, the software used for the random forest model is Matlab, and the software used for the Geo-detector is Geodetector. According to your suggestion, we have mentioned the software used in 2.2 section, as follows:

The software used for PSM-DID model, random forest model and Geo-detector are Stata, matlab and Geodetector respectively.

(5) Possible repetition in lines 298-300.

Response: Thank you very much for your comment. We feel very sorry for our carelessness. We have removed the redundant expressions.

Response to Reviewer 3:

The paper is exploring the spillover effects of urban renewal on local house prices using multi-source data and machine learning in the case of Shenzhen, China. The manuscript, in general, is well written and with the appropriate manuscript structure. The topic fits the scope of the journal, and the case is relevant. The manuscript describes applied research which has practical value, and the results and methods used are clearly presented. Overall, I propose to accept the manuscript for publication in its present form. I propose replacing Figure 5 with the interpolated one (surface instead of points) – maybe it would be easier to see the trends. Additionally, the discussion section could include the globally relevant discussion meaning showing how the results can be utilized elsewhere.

Response: Thank you very much for your recognition of the importance of our study and the two questions which can really improve our manuscript. Firstly, each point denotes an urban project renewal point. In addition, if we express it with a surface instead of a point, it is difficult to confirm the specific size of each surface, and it will cause mutual overlap, which may cause misunderstanding. Therefore, we did not make any modifications to Figure 5.

Secondly, according to your suggestion, we have also added discussions at the global level, as follows:

Firstly, during the period 2008–2018, there was a significant positive premium effect of urban renewal on the overall unit price of local housing transactions, with the premium concentrated in the range 9,000–14,000 yuan per m2. This finding is similar to the results of empirical studies for cities in Hong Kong, Taiwan, Berlin and South Yorkshire, which show that urban renewal can lead to significant increases in house prices, including commercial property rent [50-53]. This is due to the fact that urban renewal can eliminate neighborhood and housing negative externalities, and will also have a gentrification effect, raising local housing prices and squeezing out low-income earners, which in turn will continue to drive up house prices [54].

…….

The above empirical results of our study provide practical evidence for the link between urban renewal and the real estate market. Results reveal more precisely the influence of urban renewal on local house price premiums, and can provide useful background information and act as a reference for urban renewal and refinement of urban developments in Shenzhen and other fast-growing economies well into the future. To bespecific, the government should promote a sustainable urban renewal model, improve local and micro regulation of the real estate market while promoting urban renewal projects, establish and improve the long-term mechanism of the real estate market, and promote the healthy development of urban renewal and the real estate market.

Author Response File: Author Response.pdf

Reviewer 2 Report

The research question in this article is very interesting and it is explored using advanced analytical techniques.

A general question is that, beyond the quantification of urban renewal influence on house prices, is there any quantification for certain factors, for example the transport improvements, in terms of what is adequate for this price change? This is important for policy making.

Some other remarks are as follows:

1. Some clarification is needed as to the data employed in the analysis (section 2.2). In the PSM-DID method, I believe it is houses; a description of the data set would be useful. In the RF regression, it seems that the urban renewal projects are the unit of analysis. In that case the way the urban renewal projects are related to the independent variables can be explained, for example what is the areal unit? In the geo-detector analysis, the 300 samples are houses? In general, since variables at different spatial scales are employed, how are they combined in each data set? For example, how transportation in incorporated in the analysis?

2. At some points (for example line 172, Table 3 first row) the dependent variable is referred to as independent variable and vice versa.

3. Line 311: I cannot see in Table 2 the figure of 13,900 yuan per m². Also, in Table 2 the bottom line (N) should be explained for all columns.

4. It would be useful to mention the software employed in the analysis.

5. Possible repetition in lines 298-300.

Author Response

Response to Editor and Reviewers

Exploring the Spillover Effects of Urban Renewal on Local House Prices Using Multi-source Data and Machine Learning: The case of Shenzhen, China

Response to Reviewer 1: