Bricks, Taxes and Spending

9. Balancing act: How a national initiative to address regional imbalances amplified local housing inequality

Cheonghum Park
Korea Institute of Public Finance

Abstract

This chapter investigates the interplay between a national policy initiative and local housing inequality by examining Korea’s Innovative City development project. While the national policy intended to address housing inequality at a broad level, the development project may have unintentionally contributed to local housing inequality by creating a gap between newly constructed housing units and existing neighbourhoods. The study illustrates the importance of considering both national and local perspectives to achieve balanced housing outcomes, focusing on housing price inequality. Results show that Innovative Cities have intensified housing price inequality within their host municipalities. Closer inspection shows this rise is specifically in the villages where Innovative Cities are located, while nearby areas see stagnant or declining prices. The findings strongly indicate that the national development plan inadvertently amplified local inequalities, highlighting the necessity of a comprehensive approach that integrates both national and local perspectives.

The opinions expressed and arguments employed herein are those of the author and do not necessarily reflect the official views of the OECD, its Member countries, or the KIPF.

9.1. Introduction

Housing inequality is a persistent issue that requires a comprehensive understanding at both the national and local levels. This chapter explores the interplay between national policy and local housing inequality through the lens of a community development policy in Korea, called the Innovative City development project. The project aims to reduce housing and quality of living differences across regions, which has been considered a central socioeconomic problem in Korea for decades. Figure ‎9.1 illustrates how population growth in Korea has been concentrated in the Seoul Capital Area since the Korean War. While the national policy intends to reduce regional imbalance at the national level, it may lead to unintended consequences for local housing inequality as the development project introduces a gap between the newly constructed housing units and the existing neighbourhoods. By analysing this interplay, this study aims to shed light on the importance of pursuing both national and local objectives to achieve balanced housing outcomes.

From a national perspective, the development of planned communities across several provinces serves as a strategic approach to reduce housing and quality of living gaps at the national level. National policymakers use data on regional disparities, economic indicators and demographic factors to identify areas in need of intervention. The goal is to promote equitable access to affordable housing, amenities and infrastructure across regions. By investing in community development, the national policy aims to create a more balanced housing landscape, foster socioeconomic development of underdeveloped regions and improve overall living standards.

At the local level, the implementation of the planned community development policy can have varying effects on housing inequality. While the national policy aims to reduce disparities, there is a need to carefully consider the potential implications for local communities. The establishment of new developments may lead to increased housing inequality within specific neighbourhoods. Factors such as rising property values, gentrification and segregation can contribute to local housing disparities.

While various factors contribute to housing inequality, including affordable housing provision, neighbourhood segregation and access to amenities, this research focuses on housing price inequality. This deliberate choice is motivated by several reasons. First, housing price inequality serves as a comprehensive measure that encapsulates the overall level of inequality within a given area. By analysing housing prices, we can gain valuable insights into differences in wealth distribution among local residents. Second, by concentrating on housing price inequality, we establish a unified framework for analysis. Examining multiple factors simultaneously can lead to complex and intricate analyses, often lacking a cohesive framework. By focusing on housing prices, we provide a consistent and standardised measure that enables us to compare and evaluate the extent of inequality across different regions and time periods. Lastly, housing price inequality carries significant implications for individuals and communities. It affects affordability, geographical segregation and social mobility. As such, understanding the dynamics of housing price inequality can inform policymakers and stakeholders about the broader implications of housing challenges and guide the formulation of effective interventions to address these issues.

By examining the impact of the community development project on local housing price inequality, this study aims to contribute to the existing literature on housing inequality and provides valuable insights into the interplay between national policies and local housing dynamics. Understanding the effects of such projects on housing price inequality can inform policy decisions and facilitate the development of targeted interventions aimed at promoting more equitable housing outcomes at both the national and local level.

The empirical analysis in this chapter leverages administrative housing price data within a difference-in-difference framework, contrasting housing price inequalities in municipalities hosting Innovative City projects with the rest of the municipalities before and after the establishment of housing units in the new urban centres. The findings reveal a rise in the housing price Gini index within municipalities undertaking innovative city projects, while geographically distant municipalities remain unaffected. Further investigation at the village level elucidates this municipal housing inequality shift. Through an event-study analysis, we find that the surge in housing prices within villages hosting innovative city housing units, alongside the concurrent decrease in housing prices within neighbouring villages, contributes to the increase in housing inequality at the municipal level. These results indicate that the national balanced development initiative generated housing disparities at the local level.

The subsequent sections of this chapter are structured as follows: the next section offers an overview of the Korean Innovative City development project. Following this, the chapter explains the research methodology, encompassing data selection and regression specifications and then presents the empirical findings. The final section concludes the chapter with a summary of the results, along with a discussion on relevant policy measures.

Figure ‎9.1. Population growth has been concentrated in the Seoul Capital Area

Source: Seoul Institute (2013[1]), Geographical Atlas of Seoul, https://www.si.re.kr/node/53240

9.2. Korean Innovative City development

The Innovative City development project is a response to the large underlying regional imbalances within the country. Korea has been facing a significant population imbalance, with over 50% of its population concentrated in the Seoul Capital Area. This disproportionate distribution of people across regions gives rise to a range of challenges, such as unequal infrastructure development, limited employment opportunities and unequal access to public services outside the Seoul Capital Area. In light of these disparities, the government has implemented a range of policies to promote a more balanced and inclusive development approach.

The Innovative City development project stands out as a prominent initiative in this endeavour. It aims to establish regional growth centres by strategically relocating public institutions and subsequently creating residential and business areas in regions outside Seoul. The primary objective is to create self-sustaining communities equipped with adequate housing, industries and amenities. By doing so, the government aims to attract businesses and residents to these areas, fostering local economic growth and addressing regional disparities. By promoting economic diversification and providing quality housing, the project aims to create a more equitable distribution of resources and opportunities across the Korean regions.

The Innovative City development project started in June 2003, operating within the framework of the "Government's Policy for Local Relocation of Public Institutions for Balanced National Development." The selection process for the 10 innovative cities was finalised in 2005 and the building of housing units in these cities commenced in 2013. By 2019, a total of 134 public institutions, employing 48 000 individuals, had successfully relocated to these newly-established cities. As of 2022, approximately 90 000 housing units had been provided, accommodating a population exceeding 230 000 residents. The budget for the development of innovative cities surpassed USD 7.7 billion by the end of 2015 (KoChangsu & LeeHwanoong, 2020). Notably, the distribution of the 10 Innovative Cities is fairly balanced throughout the country, except for the Seoul Capital Area (Figure ‎9.2).

Figure ‎9.2. Locations of the municipalities with Innovative Cities

Source: Ahn et al. (2021[2]), Impact of Innovation City Projects on National Balanced Development in Korea: Identifying Regional Network and Centrality, International Journal of Geo-Information, 10(3):169, https://doi.org/10.3390/ijgi10030169

Table ‎9.1 presents an overview of the distinct phases in the development of the Innovative City project. Phase 1, spanning from 2007 to 2014, marks the initial stage of the project and involves the relocation of public institutions and associated companies. This phase lays the foundation for the subsequent phases by establishing the necessary infrastructure and administrative backbone. Phase 2 focuses on attracting private industries and research institutes, including universities, to participate in the development project. By involving diverse stakeholders, this phase aims to foster collaboration and knowledge exchange to further enhance the innovative ecosystem within the cities. Finally, Phase 3 represents the spread of innovation throughout the cities, characterised by the continuous growth and expansion of innovative industries, businesses and research activities. This phase embodies the ultimate objective of the Innovative City project, which is to create dynamic and sustainable centres of innovation and economic development.

Table ‎9.1. Innovative City development plan

Development phase	Years	Specifics
Phase 1: Relocating public institutions	2007~2014	Approximately 2 500 to 4 000 employees related to the relocation of public institutions and affiliated companies, with an induced population of 15 000 to 25 000 people.
Phase 2: Industries and research facilities	2015~2020	Approximately 4 000 to 8 000 employees in private companies, universities and research institutes, with an induced population of 25 000 to 50 000 people.
Phase 3: Spread of innovation	2021~2030	The number of jobs and induced population resulting from the spread of innovation clusters varies depending on the region and scale.

Source: Ministry of Land, Infrastructure and Transport (2022[3]), Introduction to Innovative City, http://innocity.molit.go.kr/content.do?key=2208172074710

The development project, which involves the simultaneous development of ten small to medium-sized cities, has led to a diverse range of studies investigating the impact on housing prices and land values. Researchers such as Min & Shin (2021[4]) found that housing prices in Innovative City locations were notably higher compared with non-Innovative City locations at the municipality level. Similar findings were observed by Lee (2015[5]), who analysed housing price trends in Innovative City areas. Various studies have also examined the fluctuations of land values in Innovative City locations.1 While previous studies have primarily focused on the impact of Innovative Cities on housing prices and land values within their immediate locations, this study takes a broader perspective by investigating whether the influence of Innovative Cities extends beyond their boundaries and affects housing price developments in surrounding areas. By exploring this aspect, the study aims to gain insights into the potential implications of the Innovative City project on inequality.

9.3. Methods and data

The yearly housing price data from 2008 to 2019 come from the official housing prices provided by the Korean government for tax purposes. These housing price estimates encompass every housing unit across the country, enabling a comprehensive analysis of housing inequality at any geographical level. While the prices are not actual market prices but estimated values, they are credible estimates updated every year that both the government and the taxpayers rely on for property taxation. I combine the housing price data with the distance between the geographic centre of each village and the nearest Innovative City housing unit computed via GIS. This allows for an examination of the spatial relationship between housing prices and the proximity to Innovative City development projects.

To identify the causal effect of the Innovative City development on local inequality at the municipality level, a two-way fixed effects model is employed. This model controls for municipality fixed effects and year fixed effects, allowing for an analysis of the causal relationship between Innovative City construction and changes in local housing price inequality in a difference-in-difference framework. The analysis focuses on comparing treated municipalities, where Innovative Cities are constructed, with control municipalities that are distant from the newly constructed cities. Considering the potential spill-over effect of the construction on neighbouring municipalities, the effect of development is assessed based on the distance of each municipality to the closest Innovative City. The regression specification for this model is as follows:

$Gin{i}_{it}={\sum }_{\mathrm{d}\in \{<!--\; -->\mathrm{0,10,40}\}}{\mathrm{\beta }}^{d}Distanc{e}_i^d\times Pos{t}_{it}+\mathrm{\gamma }\mathrm{P}\mathrm{o}\mathrm{s}{\mathrm{t}}_{it}+{\mathrm{\lambda }}_i+{\mathrm{\mu }}_t+{\mathrm{\epsilon }}_{it}$

(1)

where $Gin{i}_{it}$ is municipality $i$’s Gini index in year $t$; $Distanc{e}_i^0$ indicates whether there is an Innovative City in municipality $i$; $Distanc{e}_i^{10}$ and $Distanc{e}_i^{40}$ indicate whether the distance between municipality $i$’s housing-weighted centre and the closest Innovative City housing unit from it is between 0~10km and 10~40km, respectively; $Pos{t}_{it}$ indicates whether the first housing units of the Innovative City closest from municipality $i$ has been supplied in year $t$; ${\mathrm{\lambda }}_i, {\mathrm{\mu }}_t$ are municipality and year fixed effects, while ${\mathrm{\epsilon }}_{it}$ is the robust standard error. The coefficients of interest are ${\mathrm{\beta }}^0, {\mathrm{\beta }}^{10}$ and ${\mathrm{\beta }}^{40}$. Coefficient ${\mathrm{\beta }}^0$ estimates the treatment effect of the Innovative City development project on the inequality level of the municipality where the new city is located, while ${\mathrm{\beta }}^{10}$ and ${\mathrm{\beta }}^{40}$ measure the spill-over effects.

Table ‎9.2 presents the distribution of municipalities based on their proximity to the nearest Innovative City. The grouping of municipalities into three distance categories allows for a comprehensive analysis with sufficient comparison groups for each Innovative City.

Table ‎9.2. Number of municipalities by distance to the closest Innovative City

Closest Innovative City	Treated	0km < Distance < 10km	10km < Distance < 40km	Distance. > 40km
Gangwon	1	1	4	36
Gyeongnam	1	1	8	8
Gyeongbuk	1	1	5	3
Daegu	1	2	11	8
Busan	1	10	9	2
Ulsan	1	3	4	3
Jeonnam	1	1	12	12
Jeonbuk	2	3	8	13
Jeju	1	1	1	-
Chungbuk	2	2	12	66
Total	12	25	74	151

Note: “Distance” indicates the distance from the centre of the municipality to the closest Innovative City housing unit. “Treated” indicates that an Innovative City is located in the municipality.

After identifying the impact of Innovative City development on housing price inequality at the municipality level, a more granular event study analysis is conducted to examine housing price changes at the village level. This approach aims to understand how housing prices have changed within municipalities around the time of construction, contributing to the overall change in municipal housing price inequality. The analysis focuses on the heterogenous impact of city development on housing prices at the village level by considering the proximity of each village to the Innovative City. This allows us to understand how the development of Innovative Cities has influenced housing prices in nearby villages compared to those further away, resulting in changes in overall inequality. The corresponding regression specification is as follows:

$log{\left(Housing Price\right)}_{it}={\sum }_{\mathrm{d}\in \{<!--\; -->-7,-6,\dots ,6\}/-1}{\mathrm{\beta }}^d{\mathrm{\delta }}_{it}^d\times Treate{d}_i+{\mathrm{\lambda }}_i+{\mathrm{\mu }}_t+{\mathrm{\epsilon }}_{it}$

(2)

where $log{\left(Housing Price\right)}_{it}$ is the log of village $i$’s average housing price in year $t$; ${\delta }_{\mathrm{i}t}^d$ is the event time dummy; the year before the construction of the nearest Innovative City is normalised to zero;$Treate{d}_i$ indicates whether municipality $i$ is within the given distance from the nearest Innovative City; the control group comprises the villages that are more than 10km away from the nearest Innovative City housing unit; ${\mathrm{\lambda }}_i, {\mathrm{\mu }}_t$ are municipality and year fixed effects, while ${\mathrm{\epsilon }}_{it}$ is the standard error clustered at municipality level. This event study analysis is conducted separately for different distance levels between villages and Innovative cities: villages where Innovative Cities are located, villages between 0~1km, 1~3km and 3~5km.

9.4. Results

The analysis of housing price inequality reveals interesting findings regarding the impact of Innovative City development on municipalities. Figure ‎9.3 shows the regression coefficients and confidence intervals based on regression specification (1). When comparing municipalities where Innovative Cities are located to a comparison group located farther than 40km from the Innovative Cities, there is statistically significant evidence of an increase in the housing price Gini index. Specifically, municipalities hosting Innovative Cities experienced an increase in the housing price Gini index by 0.006 after the introduction of housing units within the Innovative City development. The median of the standard deviation of the yearly Gini index from 2008 to 2019 at each municipality is 0.011. Hence, the size of the effect of Innovative Cities on municipal housing inequality is half of the size of the standard deviation of the municipality level ten-year variation. The findings suggest that the presence of Innovative Cities has contributed to a notable increase in housing price inequality in these municipalities.

Regarding the spill-over effect, the analysis considers municipalities located at varying distances from the Innovative Cities. For municipalities situated between 0 and 10km from the Innovative Cities, there is a slight increase in the housing price Gini index by 0.002. However, this estimate is statistically insignificant, suggesting that the spill-over effect of Innovative City development on directly neighbouring municipalities is not robust. Municipalities located between 10 and 40km from the Innovative Cities did not experience an impact of Innovative City development on housing price inequality. This finding suggests that the influence of Innovative City development on housing price inequality is limited to municipalities within close proximity to the Innovative Cities, rather than affecting a wider range of municipalities located at greater distances.

Figure ‎9.3. Impact of Innovative City development on local housing price inequality at the municipality level

Note: Each bar corresponds to the difference-in-difference estimator of the treatment effect of the Innovative City development on the local Gini index at the municipality level by the distance from the centre of the municipality to the closest Innovative City housing unit. “Treated” indicates that an Innovative City exists in the municipality. The control group comprises the municipalities that are more than 40km away from an Innovative City. The line for each bar spans the 95% confidence interval of the corresponding coefficient.

Sources: Ministry of Land, Infrastructure and Transport (2022[6]), National Spatial Data Infrastructure Portal (database), http://data.nsdi.go.kr/dataset/12645 and http://data.nsdi.go.kr/dataset/20200305ds00001

Figure ‎9.4 presents the regression results at the village level based on specification (2), analysing the housing price dynamics within municipalities where Innovative Cities are located. The top-left panel displays the results for villages where the Innovative Cities are located, while the other panels illustrate the housing price changes in neighbouring villages.

The findings reveal interesting patterns. Firstly, in the village where the Innovative City is located, an immediate increase in housing prices of approximately 10 per cent is observed. This suggests that the presence of the Innovative City has led to a surge in housing prices within these villages. The estimates persist over the subsequent years, suggesting that the effect of Innovative City projects is not a temporary shock on housing prices.

The neighbouring villages show a different trend. The regression results indicate that these villages did not experience a similar increase in housing prices as observed in the villages where the Innovative Cities are situated. Instead, there is some evidence suggesting a decrease in housing prices in the neighbouring villages after the development of the Innovative City when compared with villages sufficiently distant from the new housing units. This finding implies that the newly developed Innovative Cities may have absorbed some housing demand from surrounding areas, leading to a decrease in housing prices in those villages.

Overall, the changes in housing prices within and around the Innovative Cities contribute to an increase in housing price inequality at the municipality level. This increase is driven by the rise in prices within the villages where the Innovative Cities are located, while the neighbouring villages experience a decline or no significant change in housing prices. These findings demonstrate how a national policy designed to address spatial inequality unintentionally resulted in a rise in local inequality concerning housing prices.

Figure ‎9.4. Impact of Innovative City development on local housing price at the village level

Note: Each bar corresponds to the difference-in-difference estimator of the treatment effect of the Innovative City development on local housing price at the village level by the distance from the centre of the village to the closest Innovative City housing unit. “Treated” indicates that at least one Innovative City housing is within the village. The control group comprises the villages that are up to 5km away from the nearest Innovative City housing unit. The line for each bar spans the 95% confidence interval of the corresponding coefficient.

Sources: Ministry of Land, Infrastructure and Transport (2022[6]), National Spatial Data Infrastructure Portal (database), http://data.nsdi.go.kr/dataset/12645 and http://data.nsdi.go.kr/dataset/20200305ds00001

9.5. Conclusion

The analysis of the impact of Innovative City development on housing price inequality reveals important insights into the unintended consequences of national policies aimed at reducing regional imbalances. The results demonstrate that municipalities hosting Innovative Cities experienced a significant increase in housing price inequality compared to distant control municipalities. This highlights the local effects of Innovative City development on housing price dynamics.

Moreover, the village-level analysis within Innovative City areas reveals that housing price dynamics contributed to the observed increase in inequality, while neighbouring villages did not experience similar changes. These findings highlight the need to consider the local dynamics and spill-over effects when implementing national development projects. Policymakers should carefully evaluate the potential implications of such policies on local inequality and consider targeted interventions to mitigate unintended consequences.

To address the local housing price inequality problem while still achieving balanced growth initiatives, a potential policy approach from a national perspective involves promoting the availability of high-quality housing options across a wider geographic region. This could be achieved through the construction of new housing units and infrastructure improvements to enhance living standards. Collaboration between national and local authorities is crucial in co-ordinating these efforts effectively.

In addition to addressing housing price inequality, it is essential to examine other factors contributing to housing inequalities, such as neighbourhood segregation, affordability and access to amenities. In-depth research and engagement with local stakeholders can help identify specific challenges and tailor interventions accordingly. Strategies may include implementing affordable housing quotas, rent control measures, community land trusts, or measures to preserve the existing affordable housing stock.

Bridging the gap between national policy and local housing inequality requires an ongoing dialogue and knowledge exchange. Collaboration between national policymakers, researchers and local stakeholders is key to understanding and mitigating the potential negative effects of community development projects. National policymakers can provide guidance, support and resources to address housing disparities within the new developments, while local communities contribute valuable insights to inform research and policymaking.

Ultimately, this research enhances our understanding of the complex relationship between national balanced development initiatives and local inequality. It underscores the importance of carefully considering the local context and implementing targeted interventions to achieve more equitable outcomes in housing. By adopting a holistic approach and fostering collaboration between national and local stakeholders, policymakers can work towards reducing housing disparities from both the local and national perspectives.

References

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[7] Kang, H. and M. Jun (2010), “Empirical study on land price increase through the increase of expectation value in the development process of Administration city, Innovation cities and Enterprise cities”, Journal of the Korean Regional Development Association, Vol. 22/1, pp. 53-66, http://uci.or.kr/G704-000688.2010.22.1.007.

[8] Kim, M. and E. Kim (2018), “Influence of Innovation Cities Development on Officially Announced Land Price”, Journal of the Korean Urban Geographical Society, Vol. 21/3, pp. 93-107, https://doi.org/10.21189/JKUGS.21.3.7.

[5] Lee, J. (2015), 혁신도시 부동산 시장 10년, pp. 69-82, https://www.reb.or.kr/reb/common/nttFileDownload.do?fileKey=9174eaa181bba0a2c12f4a9cf4f3708f (accessed on 27 June 2023).

[9] Lee, Y. and C. Kim (2020), “A Study on the Performance Evaluation and Validation of Innovation City Development”, Journal of The Korean Regional Development Association, Vol. 32/1, https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002574452.

[4] Min, H. and S. Shin (2021), “The Evaluation of the Impact of Innovative City Development Policy - Focusing on Local Real Estate Prices -”, Journal of the Residential Environment Institute of Korea, Vol. 19/3, pp. 165-183, https://doi.org/10.22313/reik.2021.19.3.165.

[3] Ministry of Land, Infrastructure and Transport (2022), Introduction to Innovative City, http://innocity.molit.go.kr/eng/content.do?key=2209054164788 (accessed on 22 June 2022).

[6] Ministry of Land, Infrastructure and Transport (2022), Multi-unit Housing Prices, http://data.nsdi.go.kr/dataset/12645 (accessed on 28 March 2022).

[1] Seoul Institute (2013), Geographical Atlas of Seoul 2013, Seoul Institute, https://www.si.re.kr/node/53240 (accessed on 27 August 2023).

Note

← 1.  For example, Lee & Kim (2020[9]), Kim & Kim (2018[8]), Kang & Jung (2010[7]). The studies confirm the rise in land values associated with the development.