Emerging Consumer Cities - Mixed land use, amenities and housing prices in Shanghai

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Emerging Consumer Cities - Mixed land use, amenities and housing prices in Shanghai

A dissertation presented

by

Jingyi Zhang

BA, Peking University MPP, Harvard University

to

The Harvard University Graduate School of Design

in partial fulfillment of the requirements

for the degree of

Doctor of Design

Harvard University Cambridge, Massachusetts

October 2018

© 2018 by Jingyi Zhang

All rights reserved.

Dissertation Advisors: Jingyi Zhang Professor Peter G. Rowe, Richard B. Peiser, and Anthony Saich

Emerging Consumer Cities -Mixed land use, amenities and housing prices in Shanghai

Abstract

This research quantitatively studies how mixed land-use planning impacts the housing prices in Shanghai. To answer the question, I collected data and constructed a database on housing price and land use to measure the impacts of urban amenities and mixed land-use on housing prices in Shanghai. This work makes an important empirical contribution to existing studies in the field of consumer city, mixed land-use, valuation and housing prices, and the on- going debate on land market reform in China.

This study provides a key quantitative analysis of the efficiency of current land use structure in Shanghai and the level of willingness-to-pay for mixed land-use. This can shed light on a major policy debate about land efficiency in China, including Shanghai, and the land market reform which has been a key policy under the current administration. Based on the analysis, an oversupply of industrial lands intended to attract foreign investors and an inefficient public land market is found to have attributed to the distortion of land structure in China. This research quantifies the impact of land use pattern on housing prices and proposes improvements in land use planning. In terms of methodology, this research applies multiple regression models in

iii addition to the traditional hedonic models, in the estimation of willingness-to-pay for mixed land-use or amenities.

Based on the analysis of first-hand collected land use and housing price data of Shanghai, this study provides estimates for the land use’s impact on housing value and offers policy considerations on efficient land use. The 2013 China’s Third Plenum of the 18th Congressional

Conference has highlighted optimizing land use structure and city’s physical structure as a major reform objective; however, so far there has been limited quantitative studies that assess the relationship between land use patterns and housing prices in China, which reflects the lack of and the difficult access to related data. Using a novel dataset, the analysis produces a variety of quantitative results. One estimate is that one percent more land use in greenspace in a 500 by 500 meters grid attributes to an increase of RMB6,600 in property value. Similarly, having one percent more land use in shophouse and shopping center in such a grid also elevates property values, by RMB5,900 and RMB7,900 respectively.

The results drawn from Shanghai can serve as a good starting point to understand other cities in Yangtze River Delta economic zone – China’s most vibrant economic agglomeration.

The empirical and methodological framework developed in this study can be generalized in future research and applied to other cities.

iv

Table of Contents CHAPTER ONE: LITERATURE REVIEW ...... 1 CONSUMER CITY ...... 1 Global Context ...... 1 Local Context: Shanghai ...... 4 MIXED LAND-USE ...... 12 HOUSING PRICE - DETERMINANTS OF HOUSING PRICE ...... 16 Classic Hedonic Price Model ...... 16 Spatial Hedonic Model...... 21 MIXED LAND USE AND HOUSING PRICE ...... 23 CHAPTER TWO: METHODOLOGY ...... 26 TYPAL DISTRICTS IDENTIFICATION...... 26 Descriptive Analysis of Typal Areas ...... 35 MAPPING AND SATELLITE IMAGE PROCESSING ...... 47 Land Use Type ...... 47 Grid Scale ...... 47 Mapping Results ...... 50 OLS REGRESSION ANALYSIS WITH DISTANCE TO FACILITIES ...... 59 OLS REGRESSION ANALYSIS WITH LAND USE ...... 60 DIVERSITY INDEX...... 60 QUANTILE REGRESSIONS WITH DIVERSITY INDEX ...... 60 CHAPTER THREE: HYPOTHESIS AND DATASET ...... 61 HYPOTHESIS ...... 61 DATASET ...... 62 CHAPTER FOUR: MAPPING RESULTS ...... 64 LAND USE CLUSTERING ...... 64 AMENITIES CLUSTERING ...... 75 CHAPTER FIVE: QUANTITATIVE RESULTS ...... 80 ORDINARY OLS MODEL ...... 80 Regression Residuals ...... 84 Model Improvement - Spatially Lagged Variables ...... 86 Ordinary OLS Results Analysis...... 90 HEDONIC MODELS WITH LAND USE VARIABLES ...... 96 Results Analysis by Land Use ...... 103 HEDONIC MODELS WITH DIVERSITY INDEX ...... 107 Quantile Regressions with Diversity Index ...... 110 CHAPTER SIX: CONCLUSION ...... 112 BIBLIOGRAPHY ...... 115

v

List of Figures

Figure 1: Shanghai International Settlements Map in the 19th Century ...... 5 Figure 2: Consumer Equilibrium ...... 16 Figure 3: Production Equilibrium ...... 16 Figure 4 Administrative Districts of Shanghai ...... 26 Figure 5 Satellite Image of Shanghai ...... 26 Figure 6: Satelite Map of Selected Typal Areas in Grid Cells ...... 27 Figure 7: Photos of Selected Typal Areas ...... 29 Figure 8: Xinmin Road in Pudong 1988 ...... 35 Figure 9: Lujiazui Under Construction in 1996 ...... 35 Figure 10: Mixed Use Planning of Zhangjiang ...... 37 Figure 11: Current Development of Zhangjiang ...... 37 Figure 12: Xintiandi Before Regeneration ...... 38 Figure 13: Xintiandi Today ...... 38 Figure 14: Huaihai Road in 1920s ...... 39 Figure 15: Huaihai Road Today ...... 39 Figure 16: Catholic Church in Xujiahui ...... 40 Figure 17: Xujiahui Today ...... 40 Figure 18: Historical Nanjing Road ...... 41 Figure 19: Nanjing Road Today...... 41 Figure 20: Historical North Sichuan Road ...... 42 Figure 21: North Sichuan Road Today ...... 42 Figure 22: Wujiaochang in 1962 ...... 43 Figure 23: Wujiaochang Today...... 43 Figure 24: Zhenru in 2007 ...... 44 Figure 25: Zhenru Planned Future ...... 44 Figure 26: Shanghai Blower Factory in Daning ...... 45 Figure 27: Daning Lingshi Park ...... 45 Figure 28: Hongqiao in the 1980s ...... 46 Figure 29: Hongqiao Today ...... 46 Figure 30: Pie Chart of The Mean of Each Land Use Type as a Percentage in Each Grid Cell ...... 52 Figure 31: The Percent of Grid Cell that Has Each Land Use Type ...... 55 Figure 32: Point of Coffee Shops in Typal Areas ...... 56 Figure 33: Point of Interests Data of Restaurants in Typal Areas ...... 57 Figure 34: Point of Interests Data of Galleries in Typal Areas ...... 57 Figure 35: Point of Interests Data of Parks in Typal Areas ...... 58 Figure 36: Point of Interests Data of Shops in Typal Area ...... 58 Figure 37: Point of Interests Data of Schools in Typal Areas ...... 59 Figure 38: Residential Price of Year 2010 by Percentile ...... 64 Figure 39: Residential Price of Year 2010 versus Year 2015 by Percentile ...... 65 Figure 40: Clustering of Green Space ...... 66 Figure 41: Clustering of Industrial Land ...... 67 Figure 42: Clustering of High Rise Office ...... 68 Figure 43: Clustering of High Rise Residential ...... 69

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Figure 44: Clustering of Linong Houses ...... 70 Figure 45: Clustering of Villa Houses ...... 71 Figure 46: Clustering of Shopping Centers ...... 72 Figure 47: Clustering of Mixed Residential and Retail Uses ...... 73 Figure 48: Clustering of Office and Retail Mixed-Use ...... 74 Figure 49: Clustering of Restaurants ...... 75 Figure 50: Clustering of Shops ...... 76 Figure 51: Clustering of Parks ...... 77 Figure 52: Clustering of Galleries ...... 78 Figure 53: Clustering of Museums...... 79 Figure 54: Percentile Map of Regression Residuals (GeoDa) ...... 84 Figure 55: Moran’s I on OLS Residuals ...... 85 Figure 56: LISA Cluster Map on OLS Residuals ...... 86 Figure 57: Spatial Weight Mastrix and Housing Prices ...... 87 Figure 58: Moran’s I of Residuals of Spatial Lagged Model ...... 89 Figure 59: LISA Map of Spatial Clusters of Migrants ...... 94

vii

List of Tables

Table 1: Summary of Selection Reasons for Each Typal Area ...... 28 Table 2: The Mean of Each Land Use Type as a Percentage in Each Grid Cell ...... 51 Table 3: The Percent of Grid Cell that Has Each Land Use Type ...... 53 Table 4 Summary Of Ordinary Least Squares Estimation ...... 81 Table 5 Summary of Ordinary Least Squares Estimation without Count Variables ...... 83 Table 6: Regression Diagnostics without count variables ...... 84 Table 7: Regression Diagnostics with count variables ...... 84 Table 8: Spatial Lag Model with Distance Weight Matrix ...... 88 Table 9 Result of OLS Model ...... 90 Table 10: OLS Results of Variables on Count ...... 92 Table 11: OLS Results on Percentage of Migrants ...... 93 Table 12: Correlations between Housing Prices and Percentage of Migrants on New Properties Prices (Left) and Resale Prices (Right)...... 95 Table 13: Land Use Regressions ...... 99 Table 14: Land Use Regressions in Absolute RMB Amount ...... 101 Table 15: Diversity Index ...... 108 Table 16: OLS Results of Diversity Index ...... 109 Table 17: Quantile Regressions ...... 111

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Acknowledgements

I would like to express my deepest gratitude to Professor Peter Rowe at Harvard University, chair of my dissertation committee, for his academic guidance, invaluable insights of Shanghai urban history, and enthusiastic encouragement throughout this research work. Professor Rowe has provided extensive professional and personal guidance, which I benefited a great deal in advancing my academic research and supporting personal development. I am also greatly indebted to Professor Richard Peiser and

Professor Tony Saich, both members of my committee, for providing very constructive suggestions and sharing unparalleled knowledge in the field of land economy and political economy.

I would also like to extend my sincere thanks to Professor Albert Saiz from the Massachusetts

Institute of Technology and Professor Sumeeta Srinivasan from Tufts University for their academic guidance on urban economics and spatial analysis. I am also highly grateful to Dr. Hongwei Wang, Dr.

Wei Shi, Dr. Zhongwei Deng, and Mr. Junjie Xu from Shanghai Winchampion Enterprise Credit Service

Company for their assistance in housing transaction and land use data.

I would like to thank the following institutions for their generous assistance and grants to support my research: Harvard University Graduate School of Design, Ash Center for Democratic Governance and

Innovation at Harvard Kennedy School, Harvard’s Joint Center for Housing Studies, and Urban China

Initiative - A Joint Initiative of Columbia University, Tsinghua University and McKinsey & Company.

I also benefited greatly from the contribution and discussions with Ms. Yani Li, Mr. Enze Tian,

Dr. Qian Di, Dr. Chenghe Guan, Dr. Har Ye Kan, Ms. Yingying Lu and Dr. Congyan Tan.

Finally, the work would not have been possible without the consistent support and encouragement throughout the five years of my doctoral studies from my parents, parents-in-law, my husband and my son.

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Chapter One: Literature Review

Consumer City

Global Context

Traditionally, cities are considered as centers of production in urban economics. However, cities are not only about production. As city density increase and individual wealth accumulates, consumption amenities grow in value to city dwellers. Adam Smith said in his famous book

Wealth of Nations that consumption is the sole end and purpose of all production (Smith, 1776).

The comparative advantage of cities is the reduction of costs of interaction through proximity.

Cities are specialized in all kinds of entertainment, e.g. restaurants, museums, bars, movie theatres and concert halls. An obvious explanation is that these industries involve fixed costs, which can be spread over a larger consumer-base in cities, and because of dense urban areas, reduce the travel costs for consumers and producers (Glaeser & Gottlieb, 2006).

According to Max Weber, there are four types of city economics:

1. consumer city

2. producer city

3. industry city

4. merchant city

In reality, cities are usually a mixture of the four types mentioned above and are classified by their predominant economic components (Weber, 1922). Glaeser, Kolko and Saiz’s empirical study points out that cities with high amenities grow faster than those with low amenities

1

(Glaser, Kolko, & Saiz, 2001). According to the theory, four most important amenities for consumer city include:

1) A rich presence of various services and consumer goods, i.e. restaurants and theaters.

2) Aesthetics and cityscape geography, i.e. weather and architectural beauty.

3) Good public services, i.e. good schools and less crime.

4) Travel speed and low transportation cost.

Glaeser and Gottlieb argued that big cities are having a renaissance as places of consumption, not production. Desire to live in large consumer cities led to the resurgence of big cities worldwide in the last 20 years. On one hand, modernization and globalization enhances the importance of knowledge in the economy, denser and bigger cities appear to have a comparative advantage in facilitating the flow of knowledge. On the other hand, the rising income and abundance of urban amenities have greatly attracted consumers to live in these cities (Glaeser & Gottlieb, 2006).

The amenities which can attract high human capital residents grow faster, which is why governments decide on certain types of amenities to invest as these attract highly-talented consumers to live in the metropolitan area and boost economic growth. Based on this framework,

Glaeser and Gottlieb argue that successful cities usually share several common phenomena: rising populations, and housing prices that rise faster than nominal wages due to increase demand (Glaeser & Gottlieb, 2006). In addition, both empirical and theoretical work has documented that average wages are substantially higher in large cities than in small cities

(Glaeser & Mare, 2001). Lee (2010) also concluded that high-skill workers are willing to be paid less in exchange for living in large cities, which offer a variety of consumption.

2

Consumer cities have not been well studied in China, partly due to the fact that Chinese cities were positioned as the center of production since the establishment of the People’s Republic of

China in the late 1940s. However, it does not imply that there has been no relevant observation for consumer city in the Chinese history. Zhao and Zhang (2009) point out that the business capital cities during many dynasties have displayed consumer city characteristics, i.e. Yangzhou during Tang Dynasty, Hangzhou during Song Dynasty, and Shanghai in the 1920s. As consumer cities were deemed as cities in the capitalist world due to ideology issues in contemporary China, and to meet the demand of industrial growth after World War II, most consumer cities have been transformed by policy intervention into production cities. Suzhou, Shanghai, and Hangzhou are good examples to illustrate. This transformation is commonly associated with reduced competitiveness let alone the grave problem of pollution. As Shanghai’s average GDP per capita grows, the consumer market and the demand of consumption has been growing remarkably.

Housing prices have skyrocketed, which implies a greater willingness of consumers to pay for convenience (Glaeser & Gottlieb, 2006).

3

Local Context: Shanghai

Urban History of Shanghai

Shanghai is the most modern city in China and one of the largest metropolises in the world. It has always been a key commercial and financial center of China and in the Far East. Historically, it has been an important transportation hub along the Pacific Ocean. “Haipai” culture (ocean style culture) – the root of Shanghai city - is characterized by its inclusiveness and openness to foreign cultures. Shanghai’s urban form shows its political, economic and cultural evolvement in the history, comprising of three major periods (Zheng, 2005): the founding period before 1843; the mercantile period of the early 20th century; and the recent growth in the 21st century.

With a claimed Chinese history of more than 5000 years, Shanghai was formally founded in the

13th century as an administrative center, when it was a traditional Chinese city with canals and narrow streets. Its culture at that time was influenced by Kingdom Wu and Yue (now Jiangsu and Zhejiang provinces in the Yangtze River Delta). It was the foundation of Shanghai culture.

When China lost the Opium War against the British Empire, Shanghai was required to become one of the five port cities open to foreign trade in 1843 together with Guangzhou, Fuzhou,

Xiamen and Ningbo. Since then that Shanghai has started a centuries-long “East Meets West” journey.

Shanghai was famous for its variety of entertainment and consumption in the early 20th century.

From 1843 to 1949 (the establishment of People’s Republic of China) Shanghai developed

European characteristics due to concessions to foreign countries. Each concession had their own administrative systems. As shown in the 1884 map below:

4

Figure 1: Shanghai International Settlements Map in the 19th Century

Source: www.revolvy.com/page/Shanghai-International-Settlement

The British Concession - set up in 1845 - was located in the northern part of the city and highlighted in blue; the French Concession set up in 1849 to the south highlighted in faded red; and American Concession set up in 1848 to the north highlighted in faded orange; Chinese part of the city to the south of the French Concession highlighted in faded yellow. Later, British

5

Concession and American Concession merged into Shanghai International Settlement where the

British had major decision power and influence, while the French Concession still administered independently. The original purpose of the concessions was for international trade due to its geographic location on the Huangpu River. The urban development was focused on the port area along the Huangpu River during that period, which resulted in more trade activities in

International Settlement than in French Concession. However, French Concession was famous for its retail and consumer businesses. Due to its melting pot nature and burgeoning commercial scene, many upper-class Chinese and Europeans established their residences in French

Concession, which laid down the foundation for its retail business. The mix and conflict between foreign cultures and traditional Chinese culture in the concessions formed the foundation of

Haipai culture and the consumer city characteristics of Shanghai.

Since the establishment of the People’s Republic of China in 1949, the policy makers decided that cities should be the centers for production. In the period of 1949 to 1978, the central government controlled the supply of all consumption and forced Shanghai to transform into an industrialized production city. It was influenced strongly by former Soviet Union and that was the time when industrial zones were developed and many buildings were built with Soviet style.

The consumption characteristics of many cities including Shanghai, Beijing, Suzhou and

Hangzhou quickly diminished during this period.

With the People’s Republic of China opening up to the western world in late 1980s, Shanghai was gradually redeveloped to become the financial center of the country and a world metropolitan. The government, for the first time, prioritized the service industry for development. During this period, the Pudong District was developed, and the Huangpu River became the focal point of the city. From 1979 to 1991, Shanghai’s economy and industries grew

6 at a remarkable pace. The establishment of Pudong special economic zone in 1992 introduced

Shanghai to global competition, which led to rapid modernization and a transition back to a consumption city (Zhang, 2009). As a consequence to this shift to commercial and service industries, the industrial production as a total of GDP in the central nine districts fell from 58.1% to 13.8% during the period of 1990 to 2004.

As one of the fastest developing cities in the world, Shanghai experienced extensive urbanization and globalization over the past 30 years since the economic reform. Last year, Shanghai’s GDP has reached $353.9 billion, which is comparable with San Francisco’s $360.4 billion. This development is an astonishing achievement considering back in 2001, Shanghai’s GDP was only a quarter of that of San Francisco at $239.0 billion. According to international experiences, the consumption structure tends to experience a transformation in the next ten years after GDP per capita exceeds $10,000. As Shanghai’s GDP per capita surpassed $14,000 in 2014, its citizens’ increasing wealth lead to an increase in demand for more diverse city amenities and higher quality of life.

On the other hand, the domination of labor-intensive and export-based industries, the traditional drivers of growth, has started to wane in China after the 2008-2009 Global Financial Crisis.

Consumption is playing an increasingly important role for GDP growth, compared to export and investment. Consumption is projected to account for 43 percent of total GDP growth by 2020, compared with projected contributions from investment (38 percent of GDP growth) (McKinsey,

2012). This is an exciting moment that upgrades in economic structure are taking place and cities are transforming from the center of production to the center of consumption.

7

Corresponding to its economic structure, contemporary city planning and housing in Shanghai presents a combination of legacies from foreign concessions, socialist urban planning, and the more recently established market economy. Before China’s economic reforms of 1978,

Shanghai’s land use reflected a more egalitarian premise and heavily focused on industrial land to serve industrialization and production needs. In addition, the local government’s incentive to attract foreign investment resulted in numerous economic zones, which increased the proportion of industrial land use in cities. In contrast, recent economic growth has resulted in more mixed development than was planned (Yang et al, 2013). Over the past few decades, Shanghai’s housing market has been influenced by two forces—the political force which had led to housing privatization and the industrial development which had supported a continuous increase in personal wealth and an emerging service sector.

In addition to consumption related attributes, social economic factors such as immigration and population density will also shed light on how Shanghai is becoming a consumer city. According to the 2010 Shanghai’s statistical yearbook data, approximately 43 percent of Shanghai’s population are migrants. It is similar to New York City where 35 percent of its population is foreign born. Immigrants are attracted to consumption cities because such cities have a number of essential features, including existing concentrations of immigrants, making them very welcoming for new immigrants to live (Glaeser & Gottlieb, 2006). In most urbanized cities of

China, migrants are treated differently in housing, public services and social welfare benefits, and Shanghai is no exception. In such a big metropolis the current capacity of social welfare targeted at low-income groups and migrants is not in line with the huge influx of population. For one to enjoy all the public amenities in Shanghai or any city China for that matter, they need a

Hukou (household registration that permits house purchase and grants access to public services)

8 and these require a lengthy submission process and are limited in number. This adds to migrants’ difficulties in enjoying basic social welfare and residency that are directly linked with employment. Providing Shanghai is a growing consumer city, the backgrounds of migrants are diverse with a mix of high-skilled elites and low-income people who have limited access to urban amenities.

Why Shanghai?

Each city has its own unique characteristics. To study the topic of consumer city, Shanghai is no doubt a unique yet representative city economically, socially and culturally. The discussion above illustrates how Shanghai has been evolving between production centered and consumption focused at different stages of history. Shanghai is the leading Chinese city the transformation from industrialization to consumption and services. Its Haipai culture is unique which continuously incorporates foreign cultures without losing its own characteristics.

Shanghai is chosen for this empirical study due to its distinct and various land use patterns which can produce better samples for the chosen quantitative model. With a population of over 20 million and size of 6,340 square kilometers, Shanghai has 16 administrative districts and 105

Jiedao (sub-district communities). For example, Huangpu District has a legacy of French

Concession, while Pudong District mainly represents new developments over the past three decades. Nine typal areas are selected for this study which will be introduced in the next chapter.

There are a number of other Chinese cities that show consumer city characteristics such as

Hangzhou, Chengdu, Beijing, to name a few, however, the study of Shanghai can contribute to the understanding of smaller cities in Yangtze River Delta economic zone, because consumption cities always have radiation effects (Zhang, 2009). The Yangtze River Delta economic zone consists of 16 cities, and is considered as China’s most densely populated and economically

9 developed region. These cities are interconnected with Shanghai economically and demographically, and Shanghai serves as the main driver and innovation hub for this economic agglomeration of cities. Therefore, the study of Shanghai can also shed light on the future development and transformation of cities in the Yangtze River Delta Region. This rich hot-bed of activity is the perfect testing ground to provide empirical evidence, which can help to understand if consumer-related amenities play an important role in determining housing prices.

Future Urban Planning: Shanghai 2035

In January 2018, Shanghai Municipality published the Shanghai Master Plan 2017-2035 outlining how Shanghai will transform into an international metropolitan through an innovative urban planning process. The Master Plan provides a strongly support for my thesis of the importance of mixed land-use planning from the following policy perspectives:

1) Transformation from economic development oriented to people oriented. This highlights the meeting of demands from different groups of citizens (students, blue collar workers, tourists, and retirees) at the community level, suggesting that 99% of public service facilities will be accessible within a 15-minute walk, including convenience store, senior facility, hospital, restaurants, supermarkets, bus stop, metro stations, cultural center, kinder garden, schools, gyms, and children’s playground. More importantly, it emphasizes on the increase in the numbers of parks and plazas, and ninety-percent of those should be reachable within 5-minute walk from residential communities.

2) The Master Plan will reduce the percentage of industrial land use and increase the land use of green space, and public facilities through mixed land-use planning to improve land use structure.

The 15-minute community-life circle stipulates that 90% of residents will live within five

10 minutes from a (minimum 400sqm) park or a square. This cap also means that 23% of Shanghai should be covered by green spaces.

3) By 2035, Shanghai will extend its current 666km of metro lines to over 1,000km, compared with 402km of London and 373km of New York. This emphasis on metro system reflects the government embracement of transit-oriented development (TOD).

4) The Master Plan emphasizes on historical preservation. The Urban Planning Councils have shifted priorities for historical areas from “tear down, renovate, preserve” (拆改留) to “preserve, renovate, tear down” (留改拆). Under this plan historical architecture will be renovated and restored, in line with the consumer city theory that people value cityscape aesthetics and historical buildings.

Importantly, the Master Plan proposes ideas which are supported by consumer city theory. This study provides real-world application as it produces empirical findings to raise awareness of policy makers on where the real desire of its citizens lies.

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Mixed land-use

Mixed land-use is defined as a mixture of commercial, residential and industrial land within a certain area. With increasing awareness of urban sprawl and importance of amenities, mixing land use has become one the key planning principles in many cities in the world. According to

US Environmental Protection Agency, “Mixed land-use” is one of the 10 principles of Smart

Growth. According to the Congress of New Urbanism, “Neighborhoods [to] contain a mix of shops, offices, apartments, and homes; land uses are mixed—use within neighborhoods, within blocks, and within buildings” (CNU, 2002). Mixed land-use can increase vitality and economic activity by putting residential, commercial and recreational uses in close proximity to one another, because such land use pattern is pedestrian-friendly and result in higher foot traffic for retail centers and other businesses. In addition, in urban areas with mixed land-use, each neighborhood provides a variety of social and commercial functions, which allows a citizen to realize all of their daily activities without having to move to other parts of the city (Handy, 1992;

Breheny, 1995).

For comparison, in the United States, industrialization resulted in separation of residential areas from industrial factories, as to protect citizens from the pollution emitted from manufacturing factories. The emergence of skyscrapers in denser cities resulted in stricter zoning regulations to ensure that tall skyscrapers do not block sunshine. New York City was the first city that initiated this practice by the passing of the 1916 Zoning Resolution, which not only called for limits on building heights, but eventually called for separations of uses. Since the 1960s, zoning ordinances throughout the United States have had the effect of isolating employment, shopping and services from residential housing (Song & Knaap, 2004). In addition, owing to technological progress, especially in the transport sector, and changes in cultural behavior, land uses were

12 often separated (Grant, 2004). This resulted in systematic and vast urban sprawl and suburbanization, and created dependency on the automobile. As Jane Jacobs argued in her famous book The Death and Life of Great American Cities, separation of uses destroy communities and innovative economies by creating isolated, unnatural urban spaces, and proposed a mixture of uses as a vital and necessary recipe for a healthy urban area (Jacobs,

1961). Other advocates for mixed land-uses argued that the separation of land uses has led to a myriad of serious issues that include excessive commute times, traffic congestion, air pollution, inefficient energy consumption, loss of open space and habitat, inequitable distribution of economic resources, job housing imbalance, and loss of sense of community (Smart

Communities Network, 2002). Different from single-use zoning, mixed land-use usually consists of residential buildings with street-front commercial and retail spaces in close proximity to leisure areas of open and green spaces. The appearance of variety of amenities is another characteristic of mixed land-use. Reforms concerning changing zoning codes and land use development practices are taking place in many metropolitans in the U.S. to promote mixed land- use development (Victoria Transport Policy Institute, 2003).

Successful mixed land-use projects can be found worldwide, including Bay Street Emeryville of

California, Midtown Miami, Kop van Zuid of Rotterdam, and Puerto Madero of Argentina, among others. Bay Street Emeryville in California is a good case in illustration. It is a large mixed-use development in the city of Emeryville, which currently has 65 stores, ten restaurants, a sixteen-screen movie theater, 230 room hotel, and 400 residential units with 1,000 residents.

Through effective mixed-use development, this micro-scaled example now serves as a social hub for families and friends who can gather to shop, stroll, dine or be entertained (BSE Info, 2014).

Similar to the popularity of Smart Growth and New Urbanism in the U.S., the Compact City

13

Concept in Europe also considers mixed land-use as one of the backbones of this contemporary planning concept (Rowley, 1996). From the ancient Greek and Roman cities to the cities of the medieval times, Europe has a rich history mixing areas of working and living (Brueckner, Thisse

& Zenou, 1999). The European compact city concept emphasizes high-density, mixed land-use neighborhoods, as well accessibility by public transport, based on the premise that the integration of social functions into land use planning can result in livable and sustainable urban environment

(Burton, 2000).

Back to the Far East, metropolitans in China were originally planned in large parcels and with single-purpose zoning, however greater population densities and a boom in prosperous economic activities led to increasing mixed land-use in large metropolitans in China. Ying and Liu (2013) found evidence that land-use mix remains higher in the city center and much lower in the periphery; geographic extents of planned and actual urban activities largely overlap; and there exists a lack of residential and commercial activities along several axes in the city periphery using geo-referenced social media data in Beijing. This inefficient land use structure – lack of residential land and excess of industrial land in cities – has contributed to the skyrocketing housing prices and the problems associated with the unaffordability of housing in China.

Admittedly, from development or financial perspectives, mixed land-use imposes huge challenges for project financial viability and sustainability, because macroeconomic success requires that the many different uses all remain in business. Most developers are specialized in one single asset type, and joint venture requires more sophisticated skills in operation and cooperation. For example, Real Estate Investment Trusts (REITs) are designed for 19 standard real estate types, including office parks and strip malls, which require low-density, single-use zoning. On the other hand, construction costs for mixed-use development usually are higher than

14 those for similarly sized, single-use buildings due to extra costs in fire separations, sound attenuation, ventilation, and egress (Leinberger, 2001). Costs that may arise because of mixed compact development may have congestion effects and a rise in property costs (Breheny, 1992).

It is also argued that the benefits and costs of mixed use, as well as compact development have not been well understood (Burton, 2000).

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Housing Price - Determinants of Housing Price

Classic Hedonic Price Model

The Hedonic Pricing Model, a method used to explain the price differentiation of heterogeneous goods, is derived from Lancaster’s (1966) consumer theory and Rosen’s (1974) price model. The theory assumes that goods are not a direct object of utility, instead, they are the properties or the characteristics from which the utility is derived (Lancaster, 1966). Both theories try to establish an equilibrium model to explain the observed price differentiation with implicit prices. Each good within a comparable category is seen as a myriad of objective attributes, referred to as Z

(z1, z2, … zn), which bundles utility-affecting attributes that consumers pay at a different price.

Rosen established an equilibrium model from both sides of purchase and production, with the maximum utility and production revenue. Figure 1. illustrates two different buyers, and ‘θ’ is the price that consumers are willing to pay. With a given income ‘y’, buyers choose to buy at the point where the two curves are tangent to each other. The p-curve is convex, assuming that income increase leads to an unambiguous increase in all attributes consumed.

Figure 2: Consumer Equilibrium Figure 3: Production Equilibrium

Source: www.narfiyanstudio.com

16

The major difference between Lancaster’s model and Rosen’s model is that in Lancaster’s model products do not possess final attributes, but “are purchased as inputs into self-production functions for ultimate characteristics”. In this sense consumers are their own “middle man”, while in Rosen’s model, producers tailor their production to meet the final demand of characteristics, and each purchase is done discretely to maximize utility given the level of income and utility index. Meanwhile Lancaster assumed that there is a linear relationship between the goods’ attributes and price, and Rosen postulated that it would be a non-linear relationship since customers cannot arbitrage inherent attributes. Therefore, in terms of durable goods like real property, Rosen’s model is more applicable.

Hedonic pricing model was first adopted in housing market research in the late 1970s.

Previously, in the monocentric model, housing price is determined by its proximity to the employment center. The hedonic model stipulates that housing prices should be explained by a variety of the property’s’ attributes.

Since the 1980s, the hedonic model is widely adopted for empirical research in housing markets

(Ball, 1973, Chau et al., 2001). Early literature investigated the relationship between environmental quality and housing prices (Ridker & Henning, 1967), and later on, a variety of focuses sprung up from transit proximity and education, to neighborhood demographic characteristics. More recent research usually adopted the same system to categorize those features: locational attributes (L), structural attributes (S), and neighborhood attributes (N)

(Goodman, 1989; Williams, 1991).

Locational attributes usually refer to fixed location in regard to proximity to the urban center

(Ridker & Henning, 1968) and accessibility. Structural attributes include physical characteristics such as floor area and number of rooms and bedrooms, while neighborhood attributes primarily

17 include the social economic class of the neighborhood, available municipal services, and other externalities.

Research shows that influences of these attributes are very context specific, and can vary significantly from country to country, and from city to city. Within the context of China, the attributes should be selected very carefully because of its cultural and economic specificity. For example, neighborhood attributes can be extremely difficult to measure not only because of a lack of reliable data, but also because this concept usually doesn’t understand the special nature of Chinese residential communities. A majority of them are gated communities, where neighbor- to-neighbor interaction is rarely seen, and thus whether externalities have significant impact on housing price is highly questionable.

Apart from the selection of attributes, there are several important underpinning assumptions that would affect the applicability of this model in the real housing market, including perfect competition, no information asymmetry, and that the market is at equilibrium. In addition,

Feitelson, Hurd, and Mudge (1996) noted that in theory, the result of hedonic price research will be affected by the segmentation of housing markets. This is an issue in Shanghai as it is problematic to treat the entire housing market as a single entity.

The earliest introduction of hedonic model was in 1998 when Wu (1998) analyzed the transferability of hedonic model in the context of China. However, Despite the economic reform and marketization took place in the late 1970s, the commercialization and privatization of public housing products did not begin until the 1980s, before which housing provision was part of the welfare system of the planning economy (Wang, 2006). a free and formal housing market did not come into existence until the early 2000s. Due to the late lack of reliable data, it was not until the year 2003 that hedonic pricing model was adopted for empirical research.

18

The earliest introduction of hedonic model was in 1998, when Wu (1998) analyzed the transferability of hedonic model in the context of China. He pointed out that market segmentation could be a problem for hedonic model research especially for China. It should be noticed that during that period of time the housing market of China is considered premature.

Geographic reasons and lack of information might be barrier for customers to move between submarket and result in different demand or supply structure. In addition, the nature of housing market is sticky and it is difficult to ensure equilibrium. Also because of the rapid urban growth and redevelopment, expectation generated from future plan could also be an omitted variable and lead to skewed result.

As markets began to mature, data systems could gradually be finely tuned in top cities, resulting in empirical research being done in several of the most mature housing markets, including

Beijing, Shanghai, Hangzhou, Xi’an, and Shenzhen from the year of 2003. These studies adopted similar methodology and commonality components, and most of this research had a clear focus on the structural attributes of target residential properties and share the shortfall of lacking variables related to urban amenities. Several investigated the impact of subway construction on housing prices. Since this research will be conducted in the interest of Shanghai’s consumption function, the following literature review will focus on existing empirical research on locational attributes.

Most existing research adopted distance to Central Business District (CBD) as the locational variable. The first empirical research was conducted by Wen (2003) in Hangzhou, using 4063 transaction data in 290 residential communities. Apart from distance to the CBD, Wen also included distance to West Lake as an aesthetic element. In Ma and Li (2003), a different categorizing method was used. Three categories are set as construction cost, customer

19 preference, and living cost. Construction cost include land price, in which distance to CBD is used as a substitute and structure cost which is assumed to be constant. Customer preference is intended to reflect the amenity provision, including parking, green space, hot water supply, telecommunication, completed or ongoing construction, number of floors, and schools. However, schools and green space are again taken as constant. Living cost refers to management fee.

Evidence was found that among the variables, management fee and distance to CBD have the most significant impact on housing price. He (2004) conducted a research on the housing price impact of Metro Line 13 construction in Beijing. Results show that the housing units within 0.5 km of stations have significantly higher (12 percent) price than those within 0.5 to 1 km range.

Comparing housing prices before and after the construction, the average price was raised by 16 percent.

Wang (2006) adopted the LSN categorizing system, but the only explanatory variable related to location is the distance to CBD. Most of the variables still focus on the physical quality of the residential community, ranging from building height, interior design, facilities within district, etc. In Wang and Huang (2007), apart from the distance to CBD, new variables were added to the model including distance to transit station, commercial center, supermarket, elementary and secondary schools, view of Huangpu River, and green space. However after inspecting collinearity, most of the amenity variables were excluded from the model. The seven effective variables are:

1) Distance to CBD

2) Floor area

3) Distance to metro station

4) Number of floor

20

5) Interior design (dummy variable)

6) River view

7) Green space

The above empirical research in China is silent on the impact of urban amenities on housing price, either due to limited data variation in the data or collinearity problem.

Spatial Hedonic Model

As an improvement on the traditional hedonic price model, recent studies have further developed to address spatial autocorrelation on housing. Housing price is spatially correlated mainly for two reasons. First, neighborhoods developed by the same developer at a certain time period usually share similar physical characteristics including structure, dwelling size, design, etc. Second, these neighborhoods tend to enjoy similar amenities including restaurant, school and park. (Basu,

1998). This results in the residuals from ordinary hedonic price model being spatially correlated, which leads to difficulty to hold the assumption that residuals are not independently distributed in an ordinary least-square regression model, causing biased t-statistics and difficulties in interpreting the significance of the coefficient. This has been true for Shanghai’s case according to the results in the following session.

In order to address spatial correlation, some researchers have used dummy zones defined by administrative boundaries to capture spatial attributes. However, the definition of boundaries are not necessarily the correct factor to differentiate characteristics among houses. Others, for example, Dubin (1988), assigned the observations of transacted points into 500 feet grids and measure the distance between grids to capture spatial correlation. This allows a spatial regression to address spatial correlation. Can (1990) constructed weighted matrices for the spatially lagged

21 dependent variable and its estimated coefficient measures the degree to which nearby property values have an absolute influence on the price of a particular house. By using the Lagrange multiplier and Moran's I statistics, Can further tests for the presence of residual spatial autocorrelation and for omitted spatially lagged dependent variables. Her research presents evidence of spatial autocorrelation in the estimated residuals. Furthermore, Pace and Gilley’s

(1997) model for spatial dependence in hedonic house price residuals by using a simultaneous autoregressive model. This technique predicts property values using information on nearby properties. They report that the spatial autoregression residuals are 44 percent lower than OLS residuals (Basu, 1998).

Spatial econometric models have been widely used in the environmental policies where researchers include one additional variable of air quality in the hedonic price model, because houses with better air quality tend to have a higher value (Freeman, 2003). However, to account for spatial autocorrelation and spatial heterogeneity, spatial dependence has been incorporated, leading to the so-called spatial hedonic model. An instrumental variable is often identified to address this problem. Recent research has addressed the problem of endogeneity by using the contribution of distant sources to localized air pollution metrics as an instrument to measure air pollution at the county level (Bayer 2006). Others focused on a separate source of endogeneity of the air quality variables in the hedonic specification. For example, Anselin (2008) used the polynomials in the coordinates of the house locations as instruments to correct the problem of endogeneity in the pollution variable. As the dependent variable is an individual house transacted point, and air pollution variable is collected by a finite set of monitoring stations, Anselin tackled this mismatch in scales by using spatial interpolation and a spatially lagged dependent variable,

22 based on five categories covering house specific characteristics, locational accessibility, neighborhood characteristics and interpolated air pollution values.

Mixed Land Use and Housing Price

Given the constraint on data availability in land use and housing prices, there is limited comprehensive and quantitative study on mixed land-use and its impact on housing price. An empirical study based on data from Tuscan, Arizona, suggests that increasing amounts of economic activity leads to higher surrounding property values, thus a mixed land-use should be planned in order to stimulate economic activities surrounding residential neighborhoods (Cao &

Cory, 1981). However, there is no conclusion on the question of what an optimal mixed land-use should be. Grether and Mieszkowski (1980) did not find a systematic relationship between non- residential land use and housing prices in a study that employed data from 16 market case studies from the New Haven metropolitan area. Li and Brown (1980) reached the conclusion that housing prices rose due to accessibility, but fell due to problems such as congestion, pollution, or unsightliness. The attributes they tested include aesthetic attributes, pollution levels, and proximity. However, the studies above have the limitation of focusing on point data or a single land-use type. A more comprehensive analysis on land use pattern and housing price can be found in Geoghegan (1997) on the central Maryland area. By integrating indices on diversity and fragmentation of land uses into traditional hedonic price model, he shows that in the highly developed suburbs of Washington, D.C., diversity and fragmentation of land uses are valued positively since diverse and fragmented land uses result in amenities such as walkable access to small shopping areas and public infrastructures such as schools. Similarly, Irwin (2002) found a price premium associated with permanently preserved open space.

23

With development of Geographic Information System (GIS) and remote sensing technology, measuring the impact of mixed land-use on housing prices becomes possible. Song and Knaap

(2004) constructed their comprehensive and quantitative measures of a mixed land-use database using GIS and data from Washington County, the western portion of the Portland metropolitan area. They grouped mixed land-use into the following five categories:

1) Neighborhood commercial stores

2) Multi-family residential units

3) Light industrial sites

4) Public institutions

5) Public parks

They use GIS tools to calculate: (1) distance from each type of land use to single family property; (2) proportion of each land use within a Traffic Analysis Zone; and (3) a diversity index. Song and Knaap incorporated this quantitative measure of mixed land-use into a classical hedonic price model while controlling for the physical characteristics of the property, public service level of the neighborhood, distance to CBD and major public transportation nodes, and other socio-economic indicators such as population density. Their GIS-developed land use measures distinguish this study from previous studies as they found that housing prices increase with proximity to—or with increasing amounts of—public parks or neighborhood commercial land uses.

Using a similar methodology, Koster and Rouwendal (2012) investigated that households in

Rotterdam City region are willing to pay about 2.5% more for a house in a mixed neighborhood.

They discovered this market-applicable information by including in their hedonic equation a

24 variable that measures the diversity of land uses and variables that measure the presence of mixed land-use. Given the numerous explanatory variables in a hedonic function, multidimensionality emerges as the explanation power of each variable is reduced. Different from Song and Knaap’s fully parametric estimation, the contribution from Koster and

Rouwendal’s research consists in the introduction of a nonparametric estimation of mixed land- use in the hedonic price model while keep the rest parametric. This semi-parametric approach shows that there is substantial heterogeneity in the willingness to pay for different aspects of mixed land-use. The data on jobs in different sectors is used as a proxy for mixed land-use, and the result shows that sectors compatible with residential use are business services, education and healthcare, leisure and retail.

Although there has not been empirical research on mixed land-use and housing prices in

Shanghai. Hot debates over land use structure and housing prices have been around since 2005 when housing prices started to rise (Zheng, 2008). Peiser and Du (2014) found out that land price is positive and significantly correlated with the magnitude of land hoarding by local governments, resulting a distortion in land structure. As China has proposed to reform the fiscal system in its new reform blueprint, it is the time to understand the land use structure and its relationship with housing prices.

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Chapter Two: Methodology

Typal Districts Identification

With population over 24 million, Shanghai is the largest city in China and the third largest on

Earth in terms of area. Located on the east coast between the Yangtze River Delta and

Hangzhou Bay, Shanghai has an area of 6340 square kilometers and 16 administrative districts.

The downtown area is bisected by Huangpu River with the historic center (Puxi) located to the west of the river and the new development zone (Pudong) to the east of the center where the financial center Lujiazui is located.

Figure 4 Administrative Districts of Shanghai Figure 5 Satellite Image of Shanghai

Source: ResearchGate

The 16 districts can be further divided into four groups due to its location:

• Central Areas: Huangpu, Jing’an, Xuhui, Changning, Yangpu, Hongkou, Putuo

• New Development Zone: Pudong

26

• Suburban Areas: Minghang, Baoshan, Jiading

• Remote Suburbs: Songjiang, Jinshan, Qingpu, Fengxian, Chongming

The nine typal areas of representative districts from the first three categories are selected to conduct focal analysis about land use and amenities. The typal areas are selected based on the three major criteria: 1) Functionally, they represent the economic and industrial structure of their districts; 2) Planning wise, they contain a variety of land uses; 3) They are unique yet representative in historic development of Shanghai. The selection of typal areas enables the researcher to perform analysis in each district. This effectively excludes the differences in social economical characteristics across districts, and it shows how mixed land-use impacts each district differently.

Figure 6: Satelite Map of Selected Typal Areas in Grid Cells

27

Table 1: Summary of Selection Reasons for Each Typal Area

District Typal Areas Reason

1 Pudong • Lujiazui 陆家嘴 Suburbs redeveloped into

• Zhangjiang High Technology offices and new tech industry

Industrial Park 张江

2 Huangpu • Xintiandi 新天地 Famous mixed-use project with

• Huaihai road 淮海路 office, commercial and residential uses. Vibrant

commercial area in former

French concession

3 Xuhui Xujiahui 徐家汇 High density residential district

4 Jingan Nanjing Road 南京路 Commercial district

5 Hongkou Sichuan Bei Lu 四川北路 Middle to low end commercial

district

6 Yangpu Wujiaochang 五角场 Old industrial area redeveloped

into university and offices

7 Putuo Zhenru 真如 High density residential areas

with new shopping malls. One

of the four sub center planned

by the government

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Table 1: Summary of Selection Reasons for Each Typal Area (Continued)

8 Zhabei Daning 大宁 Previous industrial land

redeveloped into residential use

and offices

9 Changning Hongqiao 虹桥 Transportation hub

Figure 7: Photos of Selected Typal Areas

1 Lujiazui 陆

家嘴

Source: www.ljz.com.cn

29

Figure 7: Photos of Selected Typal Areas (Continued)

2 Zhangjiang

High

Technology

Industrial

Park 张江

Source: www.zjpark.com/

3 Xintiandi 新

天地

Source: www.shuionland.com

30

Figure 7: Photos of Selected Typal Areas (Continued)

4 Huaihai road

淮海路

Source: www.sohu.com

5 Xujiahui 徐

家汇

By

Ricky Qi

31

Figure 7: Photos of Selected Typal Areas (Continued)

6 Nanjing

Road 南京

西路

Source: www.district.ce.cn

7 Sichuan Bei

Lu 四川北

路

Source: www.shanghai.zunrong.com

32

Figure 7: Photos of Selected Typal Areas (Continued)

8 Wujiaochan

g 五角场

Source:www.sh.eastday.com

9 Zhenru 真如

Source: www.chinatoday.com.cn

33

Figure 7: Photos of Selected Typal Areas (Continued)

1 Daning 大

0 宁

Source: www.shobserver.com/

1 Hongqiao

1 虹桥

Source: www.thepaper.cn

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Descriptive Analysis of Typal Areas

Lujiazui Lujiazui is the international financial center of Shanghai and China, considered as the Chinese

Wall Street. It is located in Pudong New Development District on the eastern bank of Huangpu

River. It is a peninsula on the bend of the river, which becomes the new CBD of Shanghai compared to the Bund across the river. The total size is about 6.89 square kilometers with population of around 160,000. The core area – International Financial and Trade Center – has an area of 1.7 square kilometers, while the average GDP per square kilometers is as high as USD2.8 billion, which ranks the first in China. Its over 400 skyscrapers forms Shanghai’s skyline, from which the 632-meter Shanghai Tower, the 492-meter Shanghai World Financial Center, and the

468-meter Oriental Pearl Tower stand head and shoulder above the rest.

Figure 8: Xinmin Road in Pudong 1988 Figure 9: Lujiazui Under Construction in 1996

Source: history.eastday.com

Historically, Pudong District was mainly covered by farm land, fishery villages, warehouses, and factories. Until the 1980s, Lujiazui was a low-built area. In 1986, the Shanghai Master Plan proposed Pudong New Development District, and in 1991 a subsequent international competition

35 for the planning of Lujiazui International Financial Center was held. The principal for the design of Lujiazui Financial Center was “Harmony between Chinese traditions and foreign characteristics; combination between Puxi and Pudong; Connection between history and future”.

This brought Shanghai’s “Haipai” culture into the modern China for the first time. Major domestic financial institutions including the Bank of China, China Construction Bank, Shanghai

Stock Exchange were the first to establish their presence in Lujiazui and started the development momentum. Soon after foreign investors and financial institutions, such as HSBC and Citi Bank, followed the lead.

Zhangjiang Hi-Tech Park

Zhangjiang Hi-Tech Park is a technology park located the south of Pudong New Development

District, considered as Chinese Silicon Valley specialized in internet information, biomedical technologies, and integrated circuits. Established in 1992, with its total area of 25 square kilometers Zhangjiang Hi-Tech Park consisted of 22 parks and more than 70,000 enterprises. Its planning includes four functional areas: the Technical Innovation Zone, the Hi-Tech Industry

Zone, the Scientific Research and Education Zone, and the Residential Zone. The development is featured by the integration of mixed land-use facilities to the technology centers.

36

Figure 10: Mixed Use Planning of Zhangjiang Figure 11: Current Development of Zhangjiang

Source: K R I T Z I N G E R + R A O

After 22 years of development, in 2017, Shanghai Municipal Government decided to expand

Zhangjiang Hi-Tech Park into Zhangjiang Science City with total area of 94 square kilometers.

The government identifies the bottleneck for Zhangjiang’s Development as the lack of public amenities and transport accessibility. The new plan proposes low-density land use and emphasizes on livable city amenities. (Fu, 2017)

Xintiandi

Xintiandi is a unique pedestrian area of 30,000 square meters that consists of shops, restaurants, galleries, exhibition, and entertainment venues located to the south of Huaihai Middle Road - the core business area in downtown Shanghai. It is an exemplary example of an old town regeneration project carried out by Shui On Land during 1999 and 2001, where “History Meets

Tomorrow in Today’s Shanghai”.

Historically, Xintiandi area was comprised of more than 9000 Shikumen (Stone Gate) houses - a special old form of building architecture only found in Shanghai. Shikumen houses can be dated back to the mid-19th century. In early 20th century, Shikumen houses accounted for more than

60% of the total Shanghai residential area and were the main residential houses for local citizens, which accounted for more than 60% of total Shanghai residential area. It is a mixture of

37 traditional Chinese Linong and western architecture characteristics, yet another representation of

Shanghai’s Haipai culture. The Shikumen-style exprienced its most prosperous time in the 1920s and 1930s, but faded in the 1990s.

Figure 12: Xintiandi Before Regeneration Figure 13: Xintiandi Today

Source: Shui On Land

The development of Xintiandi kept the facade of the Shikumen-style while upgrading its internal structure and facilities to meet the modern commercial and residential demands. With the preservation of the Site of the First National Congress of the Communist Party of China,

Xintiandi becomes the scenery embodying both the historical and unique architectural features of the city. Xintiandi also serves as a good illustration of how mixed land-use influence housing prices. Starting at USD$1000 per square meters, it is one of the most expensive property markets in China and the world, with some apartments costing more than those in Tokyo, New York, and

London.

Huaihai Road

Huaihai Road, together with Nanjing Road, is one of the two most prosperous shopping streets in

Shanghai. Distinguishing itself from Nanjing Road with upscale shops, Huaihai Road, considered the Chinese Champs Elysees, comprises three sections with the 5.5-kilometer-long

38 main section being Middle Huaihai Road in the former French Concession. It’s also the home of

Shanghai’s most exclusive garden villas and famous national heritage conservation places, such as Former Residence of Songqingling and Former Residence of Sun Yat Sen.

Figure 14: Huaihai Road in 1920s Figure 15: Huaihai Road Today

Source: Archive Bureau of Luwan District & Google Images

Historically, it was constructed in 1901 under the name of Avenue Joffre by the French and the high street was once the main road of Shanghai French Concession. In the 1920s, Huaihai Road was famous for the garden houses that were built by the large community of Russians fleeing the

Communist revolution in their homeland. At that time, 50.2% of expats in French Concessions were Russians. And 552 European style houses were built during 1920 and 1921. Along both sides of the street planted parasol trees.

Xujiahui

Xujiahui is one of the four sub-centers of Shanghai, together with Huamu, Zhenru, and

Wujiangchang. Located in Xuhui District, Xujiahui has a size of 4.04 square kilometers.

Xujiahui is a traditional business center since the 1990s when the metro system and shopping malls started to thrive – it is now one of the densest residential areas in Shanghai.

39

Historically, Xujiahui was a symbol of Catholics in East Asia. The French Missionary the St.

Ignatius Cathedral in 1847, followed by orphanages, monasteries, schools, libraries and the

Xujiahui observatory. However, from 1949 and onwards, many of these large houses and estates were converted and Xujiahui became an industrial area. During the 1990s, the state-owned factories were sold off and it became a major residential area.

Figure 16: Catholic Church in Xujiahui Figure 17: Xujiahui Today

Source: www.archive.shine.cn

Nanjing Road

The 5km-long Nanjing Road, located in the center of Shanghai, connecting the Bund to

Hongqiao, with the People’s Square as the mid-point, is one of the world’s busiest shopping streets. It comprises two sections: the east section was renovated into a pedestrian street in 2000 for middle class shops and for tourists; the west section became high-end commercial space for shops and office buildings. Along the road are mainly multi-level shopping malls, historic stores, specialty stores, world-class hotels, attractions, Shanghai largest bookstore, and theaters.

Nanjing Road is also a famous location for tourists and locals to celebrate important holidays such as the Chinese New Year, New Year's Eve, and Christmas.

40

Figure 18: Historical Nanjing Road Figure 19: Nanjing Road Today

Source: www.district.ce.cn

Historically, Nanjing Road was built in 1862 by the British in the International Settlement. It is a representative area of International Settlement and foreign influence. At the end of World War II, the foreign settlements were abolished. Nanjing Road gradually converted into a commercial street, that now handles an annual traffic of about 200 million people.

North Sichuan Road

North Sichuan Road is a 3.7km-long middle to low end shopping area of Hongkou District,

Shanghai. Its proposition of mid to low end retails made it prosperous when people sought affordable goods; however, low-end consumer goods were the commodities most affected by the emergence of e-commerce and online shopping. The demolition of bike lanes further deteriorated the business activities. Its residential properties are mainly Linong and workers’ unit, further adding to its reputation as a lower class neighborhood.

41

Figure 20: Historical North Sichuan Road Figure 21: North Sichuan Road Today

Source: www.shanghai.zunrong.com

Historically, it was once the third largest commercial district after Nanjing Road and Huaihai

Road until the end of 20th century. With the operation of Songhu Railway in 1898, commercial activities started to thrive in North Sichuan Road. From the Battle of Shanghai in 1937 until end of World War II, North Sichuan Road was under Japanese control, resulting in many Japanese stores, restaurants, tea houses, hospitals, schools, and public facilities.

Wujiaochang

Similar to Xujiahui, Wujiaochang, at 3.1 square kilometers, is another sub-center of Shanghai located in the north. It’s characterized not only by its commercial center but also university campuses and innovation centers. The name is made after its centrally located roundabout which has five road exits. Its underground development in the roundabout is successful in terms of transit-oriented development model. The Knowledge and Innovation Community (KIC) integrates the innovation of Silicon Valley with the creative spirit of Left Bank.

42

Figure 22: Wujiaochang in 1962 Figure 23: Wujiaochang Today

Source: Chinese University of Hong Kong and Google Image

Historically, Wujiaochang area was mainly farm land. It was first planned for development in

1929 by the Republic of China in the Greater Shanghai Plan. Designed to be the new center of

Shanghai to compete against the former concession areas, the development was halted due to the

Japanese invasion of China. Until end of 1980s, Wujiaochang mainly consisted of low-level buildings due to the presence of the nearby Jiangwan Military Airport. In mid 1990s, the military airport was relocated moved out, and Wujiaochang began its new life as a northern business center.

Zhenru

At 2.43 square kilometers, Zhenru is the fourth and last remaining area of developable land within the sub-center of Shanghai. Zhenru stands out by its premium location adjacent to the transportation hub of Shanghai West Railway Station and an intersection of five metro lines.

From this railway station, major surrounding cities can be reached by an hour. Thus, it is an important transportation and the main logistic center hub in the Yangtze River Delta. The major housing types in Zhenru is mostly workers units built in late 1990s. However, with the regeneration plan, high-end residential housing and mixed-use commercial properties are being

43 developed, such as the Zhenru Mixed-use Sales Center design by KWG Property and Greenland

Group.

Due to its closeness to the river, Zhenru can be dated back to the 10th century as part of the

Kingdom Wu. Traditionally a commercial town with a business focus on fruits and seafood,

Zhenru was on the decline until 2017 when the shanty town was regenerated into a sub-center.

With the railway station, the Zhenru development follows the TOD model with high density commercial and residential land use surrounding the stations and underground shops.

Figure 24: Zhenru in 2007 Figure 25: Zhenru Planned Future

Source: www.chinatoday.com.cn

Daning

Daning is located in Zhabei District, bordered with Hongkou District to the east, Suzhou River to the south, Putuo District to the west. It is positioned as livable residential area with large presence of green space. Planned as one of the 14 municipal commercial centers1, Daning is undergoing a few large mixed-use commercial development projects including Daning Ever

1 The 14 municipal commercial centers are Xujiahui, Zhongshan Park, North Sichuan Road, Lujiazui, East Nanjing Road, West Nanjing Road, Wujiaochang, Daning, Zhenbei, Middle Huaihai Road, Yuyuan, the Bund, Disneyland, Hongqiao.

44

Sunshine Department Store to be opened in 2019. In terms of housing types, workers’ units built in the 1990s are located in the south part of Daning and newly developed commercial condominium after 2000 are concentrated in the north.

Historically, the Daning area lagged behind economically and used to be shanty towns. Before the 1990s, it used to be an industrial area, but the development of Daning International Business

Plaza in 2006 has added new life to the area. In 2005, Daning Lingshi Park was built, and is the largest green space in Puxi. At that time, the average resale housing price was at 9,836 RMB per square meters. In 2017, the average unit price was as high as 71,445 RMB per square meters, a seven-fold increase compared to 2005.

Figure 26: Shanghai Blower Factory in Daning Figure 27: Daning Lingshi Park

Source: www.shobserver.com Hongqiao

With its major airport and high-speed train station, Hongqiao serves as a vital transportation hub of Shanghai. The core area of the business district is about 4.7 square kilometers, containing offices, shops, hotels, conference centers and various kinds of entertainment. The eastern part focuses on aviation companies, while western part is developed with high-end residential properties and international conference centers. From north to south, the northern part is a hotbed

45 of automobile manufacturing, and southern part is export oriented. It targets at lower commercial to residential ratio of 1.3 to 1, compared to 3.5 to 1 in Lujiazui, aiming at attracting more residents.

Figure 28: Hongqiao in the 1980s Figure 29: Hongqiao Today

Source: www.thepaper.cn Historically, Hongqiao can be dated back to the Ming Dynasty (1368-1644). It used to be the most important agricultural center of Shanghai in the period of 1880s to 1980s. There were 26 factories, more than 700 households settled in several villages in the area before the 1980s. In

1982, Hongqiao was opened out to absorb foreign investment. It absorbed the highest foreign direct investment – more than USD 2000 per square meter in the 1990s. In 2009, the Hongqiao

CBD was set up, and in 2011 Hongqiao was positioned to be the Shanghai International Trade

New Platform. In 2015, the total traffic in Hongqiao transportation hub reached 310 million- person trips, which makes it world’s largest transportation hub.

46

Mapping and Satellite Image Processing

The two key data sources for this research are the land use data and housing transaction data.

Land use data for Shanghai is not publicly available. Thus, urban planners processed land use data through satellite image. The free satellite image has its limitations from lower or medium resolution type. And some researchers acquired latest high resolution satellite image which is beyond the budget for this research. Enlighted by Methodological Notes on the Spatial Analysis of Urban Formation (Rowe 2013), land use data is processed through Google Earth’s satellite images, which provides an open source alternative with clear views of buildings, roads, green spaces, among others, within a manageable budget. Google Earth provides the latest satellite image which has spatial resolution less than one meter. This is sufficient to offer researchers a clear view in seeing the different land uses (Malarvizhi, 2015). In addition, Google Earth supplies images taken at different years and it has continued to upload new images. This allows future research on time series analysis and predictions.

Land Use Type

Different from ordinary land use categories published by the government, there are 26 land use types used in this study in order to focus on mixed land-use. The 26 land use types are: residential land (high-rise, slab house, linong, villa), office land use (high-rise, low-rise), shopping land use (shopping center, shop house, wet market), mixed land-use (mixed office and retail, mixed residential and retail, mixed office and residential, mixed office, residential and retail), hotel, under-development, green space, farmland, industrial, school, hospital, government, theater, museum, gym, road and other.

Grid Scale

47

Grid cell is the basic unit in this study to assess the correlation between land use and housing prices. Grid-based analysis is a method for arranging data into a specific unit, with integrating land use data with housing prices data. The size of each grid is defined by users and normally determined according to purpose, scale, or the available data format. If the grid cell size is not appropriate, it will result in spatial information loss or data redundancy, so appropriate grid scales are vital (Dong 2017). There are two types grid cell used in this research: 500 meters by

500 meters (500m*500m) grids to process land use data through satellite image; and 1500-meter- diameter buffer to calculate number and density of infrastructure facilities and consumption related amenities.

There are two considerations to use 500m*500m grid cell to capture land use:

On one hand, grid cell should be big enough to capture the variation in land use pattern, and at the same time it should also be discernible in Satellite Image. Given the size of residential community, 500m*500m is appropriate to capture different land use pattern within walking distance from the residential building. If the grid cell is too small, the adjacent grid cell may have similar residential and land use information.

On the other hand, grid cell size should be small enough for walking distance to determine the land use adjacent to one residential property, and it should not be too big in order to avoid having more than one residential community within one grid cell. Studies show that it is the average absolute value of relative errors grow substantially with the grid size increasing (Don, 2017).

In addition to the 500m*500m grid cells for land use, a 1500-meter-diameter buffer is created to calculate the number and density of infrastructure and consumption related point of data. The

1500-meter-diameter buffer is in line with Shanghai Planning Guidance on 15-minute

Community-life Circle published by Shanghai Planning and Land Resources Administration

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Bureau in August 2016 as part of Shanghai 2035 Master Planning. The Guidance proposes that

99% of residents to be able to reach essential public services with a 15-minute walk, which includes supermarket, schools, metro stations, parks, restaurants, etc. On average, it takes an adult about 15 to 20 minutes to walk 1500 meters. Thus, the1500-meter-diameter buffer is created to capture the amount and density of facilities.

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Mapping Results

All typal areas are divided into 500 meters by 500 meters grids and the grid overlay is used with

Google Earth images. Each of the grid cell is labeled with a unique number and later on it is matched to each transaction data. Then the percentage of each different land use within each grid cell is counted. The overview of the land use data is presented as below:

Table 2 shows the average land use composition for grid cells. Slab houses take up the largest proportion (27 percent) on average in a grid cell, followed by high-rise residential structures (16 percent) and roads (14 percent). School, areas under development, and industrial land-use also constitute significant proportions, with 7%, 5%, and 5%, respectively.

Another reading of the data is provided by Table 3, which show the percentage of all the grid cells that possess a particular land use. This determines how common a land-use for all grid cells.

Unsurprisingly, 98 percent of all grid cells have roads in it. Slab houses, high-residential buildings, consistent with the previous table, are present in more than 80 percent of all grid cells.

Finally, 71 percent of grid cells have shop houses and schools.

One important observation to note from the statistics is that underdeveloped area and industrial land use rank very high. They both take up an average of 5 percent in each cell, and they are present in 43 percent and 35 percent of all grid cells, respectively. For Shanghai, a city comparable with San Francisco in terms of GDP, the excessive presence of industrial land and underdeveloped land is an important observation. This also supports the argument that the

Shanghai government oversupplies industrial land with the intention to attract foreign investment, which limits the supply of residential land. This may be indirectly correlated with high housing prices.

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Table 2: The Mean of Each Land Use Type as a Percentage in Each Grid Cell

Land Use Type Mean as % in each grid cell

Slab House 27%

High-rise residential 16%

Road 14%

School 7%

Under development 5%

Industrial 5%

Linong 4%

Shop House 3%

Other 3%

Green Space 2%

High-rise office 2%

Low-rise office 2%

Villa 1%

Shopping Center 1%

Hospital 1%

Government Agency 1%

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Figure 30: Pie Chart of The Mean of Each Land Use Type as a Percentage in Each Grid Cell

Shopping Center Hospital Government Low-rise office 1% 1% Agency 2% Villa High-rise office 1% Green Space 1% 2% 3% Other Slab House 3% 28% Shop House 3%

Linong 4%

Industrial 6%

Under development 6%

School High-rise 7% residential 17%

Road 14%

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Table 3: The Percent of Grid Cell that Has Each Land Use Type

Land Use Type Percent Cells which has this land use type

Road 98%

Slab House 86%

High-rise residential 80%

Shop House 71%

School 71%

Industrial 43%

Other 42%

Low-rise office 38%

Under development 35%

High-rise office 34%

Green Space 30%

Linong 24%

Government Agency 23%

Shopping Center 18%

Hospital 16%

Wet Market 13%

Hotel 12%

Villa 12%

Mixed residential and retail 9%

Mixed office and retail 4%

Farmland 3%

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Table 3: The Percent of Grid Cell that Has Each Land Use Type (Continued)

Mixed office and residential 2%

Gym 2%

Mixed office, residential and retail 2%

Theater 1%

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Figure 31: The Percent of Grid Cell that Has Each Land Use Type

Road 98%

Slab House 86%

High-rise residential 80%

Shop House 71%

School 71%

Industrial 43%

Other 42%

Low-rise office 38%

Under development 35%

High-rise office 34%

Green Space 30%

Linong 24%

Government Agency 23%

Shopping Center 18%

Hospital 16%

Wet Market 13%

Hotel 12%

Villa 12%

0% 20% 40% 60% 80% 100%

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In order to enhance the accuracy of satellite data reading, Point of Interests data (POI) was also clipped into typal areas and overlay with the Google Earth map. POI data can better identify the land use from Satellite images. Selected POIs maps are presented below:

Figure 32: Point of Coffee Shops in Typal Areas

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Figure 33: Point of Interests Data of Restaurants in Typal Areas

Figure 34: Point of Interests Data of Galleries in Typal Areas

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Figure 35: Point of Interests Data of Parks in Typal Areas

Figure 36: Point of Interests Data of Shops in Typal Area

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Figure 37: Point of Interests Data of Schools in Typal Areas

OLS Regression Analysis with Distance to Facilities

Linear regression is a statistical method for testing relationships between variables. I begin by estimating a model that predicts housing value using ordinary least squares (OLS) regression.

The dependent variable (y) is the sale price per square meters by each community. And the independent variables (x) are: distance to different infrastructure facilities as described in the data session; average price per square meter by community for newly constructed properties; migrant as percentage of total residents at sub-district level. A simplified model can be described as below:

𝑝 = 푎0 + 푎1푥1 + 푎푛푥2 + 푎푛푥3 + ⋯ + 푎푛푥푛 + 휀

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OLS Regression Analysis with Land Use

In this set of OLS regressions, the dependent variable (y) is the sale price for each unit, and the independent variables (x) are the land use variables, and different type of structures. These regressions intend to shed light on the statistical relationships between land use and housing prices.

Diversity Index

The previous models intend to shed light on how each type of land use corresponds to housing prices. However, to understand how the diversity of land use attributes to housing prices there is a need to construct a variable that captures the variation in land use for within a grid. Following

Song and Knaap (2004), a Shannon’s index is developed to measure the extent of mixed land- use. And OLS regression analysis is carried out to explore the relationship of mixed land-use and housing prices.

Quantile Regressions with Diversity Index

The OLS regression captures the mean response of unit sales prices to the diversity index, but it is likely that the correlation between prices and diversity may vary due to different levels of diversity. One possibility is that mixed land-use may only have large premiums for low diversity areas, so a quantile regression is employed to shed light on this issue. By design quantile regressions will present the coefficients of diversity index on unit sales price at different level of diversity.

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Chapter Three: Hypothesis and dataset

Hypothesis

Based on research from the most recent and substantial studies, four of the top amenities for a consumer city include:

1) A rich presence of variety of services and consumer goods

2) City-scape aesthetics and physical attributes

3) Good public services

4) Travel speed and low transportation cost

Although current studies touch upon public infrastructure or particular types of land use and housing prices in China, most of the existing empirical studies ignore the impact of urban amenities and mixed land-use on housing price, for reasons that either there is limited differentiation in the data, or the land use data was never available to public. I contribute to the existing consumption city and hedonic price model literature by introducing mixed land-use variable and a quantile regression. My research studies quantitative results measuring the impacts of mixed land-use and amenities on Shanghai’s housing prices. By understanding the impacts from mixed land-use, policy makers can have a quantitative tool when planning land use; real estate developers can project where is the best location to develop one additional office building, residential properties or shopping center.

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Thus, the following hypothesis are tested to analyze how the mixed land-use and consumption related amenities attributes to residential housing price in Shanghai:

• Consumption related amenities – These are restaurants, parks, theaters, shopping malls and have positive impact on residential housing prices.

• Mixed land-use will have positive impact on residential housing prices, which indicate that people are willing to pay more for mixed land-use.

• Good public service facilities – Being close to public facilities has positive impacts on residential housing prices, as people value more about such services.

Dataset

• Housing price – 224,351 housing transaction prices in selected typal areas (see below) in

Shanghai between January 2010 and April 2015, including both new unit transactions and

resale transactions. Most transactions contain information on address, total price, total

area, property type (apartment, garden house, house, townhouse, Linong, and old

apartments built in 70s), transaction date, new or second hand, land use type (apartment,

houses, house plus apartment, mixed commercial and residential, mixed commercial and

office, office, commercial, and other), floor to area ratio (FAR), green coverage ratio,

developer, and property management company. However, some transactions may lack

information in one or more categories, which are dropped during regression.

• Infrastructure facilities – Geo-referenced points data of different infrastructure facilities

are downloaded from online sources. The infrastructure facilities include point locations

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of subways, parks, schools, hospitals, restaurants, nightlife leisure, office buildings,

highways, tourist points, government offices, shopping and CBD. Distance from each

transaction point to each of these infrastructure facilities is calculated. In addition,

surrounding each transacted point, a 1500 meters buffer is created. By using ArcGIS, I

count the total number of restaurants, bus stops, office, schools and hospitals in that

1500-meter radius respectively, as proxy of density of selected public infrastructure

facilities.

• Land use data - According to Methodological Notes on the Spatial Analysis of Urban

Formation (Rowe, 2013), land-use data can be analyzed using satellite image data. From

Google Map or Bing Map, different land use types are discriminable from aerial

depictions and can be geo-referenced to major coordinate systems, allowing the surface

area of Shanghai to be described in its entirety. Then by drawing a buffer of 500-meter

radius surrounding one transacted property, it is feasible to calculate the proportion of

each land use type in that buffer ring. The proposed land use types include high

residential towers, Linong houses, slab houses, office, strip shops, shopping centers,

mixed commercial and residential uses, schools, factories/industrial land, green space and

roads.

• Social-economic data - Population density, immigrant density, as well as other social

economics data is necessary to be controlled for the regression.

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Chapter Four: Mapping Results

Land Use Clustering

Figure 38: Residential Price of Year 2010 by Percentile

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Figure 39: Residential Price of Year 2010 versus Year 2015 by Percentile

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Figure 40: Clustering of Green Space

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Figure 41: Clustering of Industrial Land

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Figure 42: Clustering of High Rise Office

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Figure 43: Clustering of High Rise Residential

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Figure 44: Clustering of Linong Houses

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Figure 45: Clustering of Villa Houses

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Figure 46: Clustering of Shopping Centers

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Figure 47: Clustering of Mixed Residential and Retail Uses

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Figure 48: Clustering of Office and Retail Mixed-Use

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Amenities Clustering

Figure 49: Clustering of Restaurants

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Figure 50: Clustering of Shops

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Figure 51: Clustering of Parks

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Figure 52: Clustering of Galleries

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Figure 53: Clustering of Museums

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Chapter Five: Quantitative Results

Ordinary OLS Model

Given the spatial characteristics of housing price, GeoDa performs the regression with the following diagnostics. First, how the model fits the data is examined and presented in Table 4 below.

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Table 4 Summary Of Ordinary Least Squares Estimation

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As observed in the first section of the results, the R-squared is as high as 0.84, which indicates the model fits the data well. R-squared is a statistical measure that captures the part of variation in the variable of interest that is explained by the regression model. The higher the value of R- square, the better the model fits the data. Therefore, this means 84 percent of the dependent variable is explained by this linear model. In addition, the probability of the F-statistic is 0, which also confirms the significance of the test.

Notably, the multicollinearity condition number is around 39 in the above model, indicating that the variables in my regressions are too correlated and provide limited separate information.

Though multicollinearity condition number is not a test statistic but a diagnostic to suggest problems with the stability of the regression, multicollinearity is a statistical phenomenon in which two or more explanatory variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a non-trivial degree of accuracy.

This is not surprising as the distance to bus variable can be correlated to the variable of the count of bus stops within 1500-meter buffer. The 1500-meter-diameter buffer is in line with Shanghai

Planning Guidance on 15-minute Community-life Circle published by Shanghai Planning and

Land Resources Administration Bureau in August 2016 as part of Shanghai 2035 Master

Planning. The Guidance proposes that 99% of residents to be able to reach essential public services with a 15-minute walk, which includes supermarket, schools, metro stations, parks, restaurants, etc. On average, it takes an adult about 15 to 20 minutes to walk 1500 meters. Thus, the1500-meter-diameter buffer is created to capture the amount and density of facilities.

Therefore, a new OLS regression is performed without the variables on “count”.

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Table 5 Summary of Ordinary Least Squares Estimation without Count Variables

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With the variables on “count number of certain amenity within buffer ring” removed, the multicollinearity condition number fell from 38 to 24. According to Anselin, an indicator below

30 does not suggest problems of multicollinearity.

Table 6: Regression Diagnostics without count variables

Table 7: Regression Diagnostics with count variables

Regression Residuals

A close examination of regression residuals can lead to insight, improvement of models and further hypothesis.

Figure 54: Percentile Map of Regression Residuals (GeoDa)

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In the quantile map of OLS residuals, it is difficult to tell whether the residuals are clustered or not. The clustering effects can be further studied by Moran’s I number of the OLS residuals, showed as below.

Figure 55: Moran’s I on OLS Residuals

In order to better verify the clustering of OLS residuals, I use Local Moran’s I which provides a statistical method to evaluate local clustering. According to the Global Moran’s I map, the value is very close to zero, which indicates a lesser level of clustering. This means there is no indication of significant global autocorrelation of the residuals, which suggests that my assumption of independently distributed residuals is correct.

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Figure 56: LISA Cluster Map on OLS Residuals

In addition, the LISA cluster map above indicates significant local clustering of like-values of

OLS residuals. It can be observed that the high-value residuals – highlighted in red – are clustered in the southwestern part of the city; whereas the low-value residuals – highlighted in blue – are clustered in the northwestern and southeastern part of Shanghai. In other words, the model fit with observations in the red points areas is not as good compared to those dark blues points area. For those red points, my model under-predicts its housing price value given the independent variables in the model. It can be explained that the majority of the independent variables are infrastructure and amenities related. There might be many other factors that may affect the housing price in those areas such as social economic factors, congestion, pollution, securities, etc., which are not included in this model.

Model Improvement - Spatially Lagged Variables

As discussed in the in-depth study, spatially lagged variables are an essential part of the computation of spatial autocorrelation tests and the specification of spatial regression models. By

86 constructing a spatial weight matrix by distance (the threshold distance of letting each observation at least have one neighbor), I can calculate a new independent variable WIGHT2P which amounts to an average of housing price per square meter(s) of the neighboring communities. The scatterplot below shows a statistically significant positive correlation between housing price and the average price of its neighboring communities.

Figure 57: Spatial Weight Mastrix and Housing Prices

This confirms the discussion in the study that a spatial autoregressive model might provide a better result than a classic OLS regression, because it provides a framework to account for the spatial autocorrelation present in lagged models and produce consistent parameter estimates. A spatial context of each observation will improve the model and analysis.

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Table 8: Spatial Lag Model with Distance Weight Matrix

The R-squared in the spatial lagged model is not directly comparable with the R-squared in the

OLS model, though it is as high as in the OLS model. Rather, a closer comparison of the result of

Log-Likelihood, AIC and SC shows a decrease in value of the spatially lagged model compared to the OLS mode, which indicates the improvement of model. The coefficient on the lag

88 parameter is 0.33 with a range from 0 to 1, indicating that the relative importance of the spatial context to the model. Also, it is significant using the 95% confidence interval. Compared with the Global Moran’s I of the spatial lagged model and the OLS model, a smaller value of Global

Moran’s I is found, which indicates less clustering of the residuals.

Figure 58: Moran’s I of Residuals of Spatial Lagged Model

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Ordinary OLS Results Analysis

The first hypothesis focuses on the negative or positive correlation between housing price and distance to different amenities. As the spatially lagged model has dropped statistical significance of several variables, I decide to use the OLS model result to analyze my result, which provides a closer investigation of the coefficient to understand the correlations between housing price, migrant, and public infrastructure.

Table 9 shows that for most public infrastructure prices increase as the distance between house and infrastructure shrinks. This reflects the utility that infrastructure can bring to the house buyers. The result also supports Alonso’s monocentric model that housing price increases with shorter distance to CBD. In general, my result supports the hypothesis that residents prefer to pay higher price for houses with closer distance to certain public infrastructure facilities, including office building, shopping, tourist points, banks, hotels, CBD, hospitals, subways, university, restaurant, bus and expressways. On the other hand, strategic investment in public infrastructure can increase home value, which may result in gentrification process.

Table 9 Result of OLS Model

Variable Coefficient Std. Error t-Statistic Probability CONSTANT 12785.05 1236.013 10.34378 0 Distance to Office Buildings -0.14 0.15 -0.90 0.37 Distance to Shopping Centers -4.61 1.18 -3.92 0.00 Distance to Tourist Points -0.61 0.26 -2.34 0.02 Distance to Banks -1.48 0.67 -2.19 0.03 Distance to Night Life Amenities 5.51 1.20 4.58 0.00 Distance to Government Agencies 1.28 0.55 2.30 0.02 Distance to Hotels 1.18 0.60 1.98 0.05 Distance to CBD -0.17 0.04 -4.76 0.00 Distance to Schools 0.62 0.45 1.35 0.18

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Table 9 Result of OLS Model (Continued)

Distance to Hospitals -1.16 0.49 -2.37 0.02 Distance to Parks 0.13 0.14 0.92 0.36 Distance to Subway Stations -0.01 0.05 -0.29 0.77 Distance to Universities -0.22 0.16 -1.41 0.16 Distance to Restaurants -0.83 0.78 -1.07 0.29 Distance to Bus Stops -0.56 0.51 -1.09 0.28 Distance to Expressways -0.03 0.07 -0.39 0.70 New Properties 0.54 0.02 27.35 0.00 Percentage of Migrants -514.34 1552.61 -0.33 0.74

The study discovered that there are five types of infrastructure facilities show contrary explanations. As the distance increases between the house and the location of night life leisure, government, hotel, school and park, price increases. It indicates that home buyers prefer not to live close to these locations. The first four locations share common characteristics in high noise level, and this can be one of the reasons that impacts people’s choice. The regression results that the closeness to schools and parks, which usually have positively impact on housing price, also have negative implication on prices, are counter intuitive. However, the t-statistics of both variables are not statistically significant, which may change if larger sample of observations were used.

In general, regression results above verify my first hypothesis that housing price is positively correlated with a certain number of infrastructures, and negatively correlated with others.

Notwithstanding the robust results above, not all the variables are statistically significant at 95% confidence level. The table above illustrates that only distances to shopping, tourist, bank, night life leisure, government, hotel, CBD and hospital have a large enough t-statistics value. Another

91 five variables assessing the convenience level of five different infrastructure were tested.

Convenience levels provide a quantitative measure of how public infrastructure offers different choices that makes it convenient for users. Therefore, contrary to measuring the distances between the houses and the locations, I counted the total number of the infrastructure of one kind in a buffer zone with 1500-meter radius. The more choices in this radius, the higher convenience level it indicates. Most of the new variables are statistically significant as described in Table 10 below.

Table 10: OLS Results of Variables on Count

Variable Coefficient Std. Error t-Statistic Probability

Total Count of Bus stops 37.15 17.12 2.17 0.03

Total Count of Restaurants 17.03 6.83 2.49 0.01

Total Count of Offices 16.06 28.50 0.56 0.57

Total Count of Shops -10.31 2.63 -3.92 0.00

For those statistically significant variables, I can interpret the coefficient as the following:

• By having one additional bus stops within 1500-meter radius of the house, the housing

price will increase by 37 RMB/m2

• By having one additional restaurant within the circle, the housing price will increase by

17 RMB/m2

• By having one additional shops in the circle, the housing price will decrease by 10

RMB/m2

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In addition, there is a statistically significant positive correlation between new construction housing price and resale housing price, which supports my third hypothesis that resale price is positively correlated with new constructed housing price.

Here I test my hypothesis that housing price is negatively correlated with migrant as percentage of total population at sub-district level. In order to mitigate multicollinearity, I drop all counts variables and do a new regression with distance variables and percentage of migrants. The results show that migrants as percentage of total population is statistically significantly negative correlated with housing price. By one percent increase in the migrants of total population, the housing price will likely drop by 514 RMB/m2. This suggests that a sub-district with more migrants tends to have lower housing price, which supports the segregation theory in urban economics that people prefer to live close to who have similar social economic status. However, this is not statistically significant. This could be the reason that density of migrant exhibits limited impact on the housing price, but it could also be underlying data issues that jeopardize the result. Therefore, I further explored the correlations between migrant density with a newly constructed housing and resale housing. In the two scatterplots below, it can be observed that newly constructed housing is more sensitive to migrant density. One factor could be that with newly constructed housing are sold to higher income groups given similar locations, so higher income people have stronger preference of living far away from migrants.

Table 11: OLS Results on Percentage of Migrants

Percentage of Migrants -514.3389 1552.613 -0.331273 0.7406123

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The study also maps Local Moran’s I with ArcGIS to observe the clustering of housing price.

Local indicators of spatial association (LISA) are used to evaluate the clustering in those individual units by calculating Local Moran's I for each spatial unit and the statistical significance for each I. LISA map identifies spatial clusters of sub-districts of Shanghai with high values of migrant density or low values of migrant density. As the map below shows, sub- districts with high migrant density are clustered in the western suburban part of the city, whereas sub-districts with low density migrant are clustered in the central districts.

Figure 59: LISA Map of Spatial Clusters of Migrants

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Table 12: Correlations between Housing Prices and Percentage of Migrants on New Properties

Prices (Left) and Resale Prices (Right)

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Hedonic Models with Land Use Variables

Hedonic model regressions are performed to analyze how different land use attributes to sale prices. Two regression analyses are carried out: one is on the “elasticity” that shows how property valuation varies with proportion of land use; the other dedicates to the “presence” of land use that analyzes how property valuation differs with having a particular land use versus not having one.

More specifically, a first regression is carried out to show property sale price changes corresponding to one percent more in the proportion of a particular land use. The dependent variable is total property sale price (RMB10,000), and explanatory variables are the percentages of land use include: green space, shophouse, shopping center, school, hospital, government, road, low-rise office, high-rise office, high-rise residential, slab house, linong, and areas under development. The control variables include: the square meters of the unit, whether it is a new property, property type, year and location of sale.

The second column in Table 11 shows the first regression results. The first row shows that the coefficient on greenspace is 0.66, with strong statistical significance2. This can be interpreted as having one percent more land used as greenspace in the grid attributes to an increase of

RMB6,600 in property value. Similarly, having one percent more land use in shophouse and shopping center also attributes positively to property values, with a RMB5,900 and RMB7,900 increase respectively, supposedly due to the convenience for shopping nearby. Having more land use in school, government agency and road reduces property value, which are the land use that could be associated with more traffic and noise. Regarding the land use in other types of

2 Significance at 1%.

96 properties, more office buildings also corresponds to higher property value, with low-rise office building posting a high premium of nearly RMB50,000. More land used as high-rise residential buildings, linong, or land under development reduces value. More slab house nearby increases property value.

The second regression uses a set of binary variables as explanatory variables. These are constructed to indicate whether a type of land use is present in a grid (if present it takes on the value of one, otherwise it is zero). For instance, the variable of green space shows one if there is greenspace identified within a grid. The dependent variable is the same—total property sale price

(RMB10,000), and explanatory variables include the above indicators of land use of green space, shophouse, shopping center, school, hospital, government, road, low-rise office, high-rise office, high-rise residential, slab house, linong, and areas under development. The same control variables are used.

The "Elasticity" Regression estimates the coefficient of having one percentage more of a proportion of a land use, whereas the "Presence" Regression estimates the coefficient of having presence of a land use; dependent variable is the total unit sale price in RMB10,000. Control variables include a list of dummies including years of sale, districts and locations of sale, property types, and residence mixed types.

The third column in Table 11 shows the results from the first regression. The first row shows that the coefficient on greenspace is 38.25, with strong statistical significance3. This can be interpreted as property sales in grids with:

3 Significance at 1%.

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• Greenspace are on average RMB382,500 higher than those in grids without greenspace

• Shopping venues and centers are associated with RMB126,700 and RMB100,060 higher

in property value

• Land use in school, government agency, road and industrial use reduces property value

• Hospitals are associated with higher property value

• Low-rise office buildings posts a large premium of RMB513,200

• Having high-rise residential building, slab house, linong, and area under development

reduces value

Notably, compared to the results from the first regression, the results for having a slab house is not so clear-cut, since the presence of a slab house reduces property value, but having one additional percent of slab house is associated with an increase in property value.

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Table 13: Land Use Regressions

Variables of interest: "Elasticity" "Presence" Notes

Regression Regression

Greenspace 0.66*** 38.25*** Having greenspace adds property value

Shophouse 0.59*** 12.67*** Shophouse adds value

Shopping Center 0.79*** 10.06*** Shopping center adds value

School -1.88*** -6.92** Having school reduces value

Hospital 0.30 4.22* The presence of hospital adds value

Government

Agency -4.97*** -25.02*** Government agency reduces value

Road -0.60** -7.26* Roads reduces value

The presence of Industrial use area reduces

Industrial 0.01 -24.83*** value

Low-rise office 4.96*** 51.32*** Low-rise office area adds high value

High-rise office 0.39*** 0.33 High-rise office area adds value

High-rise residential -0.90*** -3.96* High-rise residential area lowers value

Slab house 0.32*** -15.38** The presence of slab house reduces value

Linong -1.45*** -36.16*** Linong reduces value

Having area under development reduces

Under development -1.70*** -39.76*** value

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Table 13: Land Use Regressions (Continued) Control variables:

Property sq meters 4.72*** 4.62*** control variables first hand 83.20*** 81.24*** control variables

Year of sale yes Yes control variables

Districts and locations of sale yes Yes control variables

Property type yes Yes control variables

Residence mixed type yes Yes control variables

No. observations 224,263 224,263

R-squared

(percent) 67.4 67.3

***denotes statistical significance at 1%, **significance at 5%, *significance at 10%.

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Table 14: Land Use Regressions in Absolute RMB Amount

Variables of interest: "Elasticity" "Presence" Notes

Regression Regression

Greenspace 6600*** 382500*** Having greenspace adds property value

Shophouse 5900*** 126700*** Shophouse adds value

Shopping Center 7900*** 100600*** Shopping center adds value

School -18800*** -69200** Having school reduces value

Hospital 3000 42200* The presence of hospital adds value

Government

Agency -49700*** -250200*** Government agency reduces value

Road -6000** -72600* Roads reduces value

The presence of Industrial use area reduces

Industrial 100 -248300*** value

Low-rise office 49600*** 513200*** Low-rise office area adds high value

High-rise office 3900*** 3300 High-rise office area adds value

High-rise residential -9000*** -39600* High-rise residential area lowers value

Slab house 3200*** -153800** The presence of slab house reduces value

Linong -14500*** -361600*** Linong reduces value

Having area under development reduces

Under development -17000*** -397600*** value

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Table 14: Land Use Regressions in Absolute RMB Amount (Continued) Control variables:

Property sq meters 47200*** 46200*** control variables first hand 832000*** 812400*** control variables

Year of sale Yes 382500*** control variables

Districts and locations of sale Yes 126700*** control variables

Property type Yes 100600*** control variables

Residence mixed type Yes -69200** control variables

42200*

No. observations 224,263 -250200***

R-squared

(percent) 67.4 -72600*

***denotes statistical significance at 1%, **significance at 5%, *significance at 10%.

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Results Analysis by Land Use

More Green Space, Higher Prices

The regression shows that having one percent more land used as greenspace in the 500m*500m grid cell attributes to an increase of RMB6,600 in property value; and property sales in grids with greenspace are on average RMB382,500 higher than those in grids without greenspace. This correlation strongly suggests people’s willingness to pay for more green space, which is considered as a feature of consumer cities. Simply, residents like to spend their leisure time in parks close to their homes.

More Shops, Higher Prices

There are two variables in this regression related to shops: shophouses and shopping centers.

Shophouses refer to middle end shops on the ground level of a building along the streets.

Shopping centers are complex of shopping malls, usually located in business centers. The regression shows that having one percent more land used as shophouses or shopping center in the

500m*500m grid cell attributes to an increase of RMB5,900 or RMB7,900 in property value respectively; and property sales in grids with shophouses or shopping centers are on average

RMB126,700 or RMB100,600 higher respectively than those in grids without shops. This correlation strongly suggests people’s willingness to pay for shops, which shows strongly the consumer city feature –residents like to live close to shopping areas.

More Government Agencies, Lower Prices

The regression shows that having one percent more land used as government agencies in the

500m*500m grid cell attributes to a reduction of RMB49,700 in property value; and property sales in grids with government agency are on average RMB250,200 lower than those in grids

103 without government agencies. This negative correlation strongly suggests people’s willingness to stay away from government agencies, which is considered as production-related amenities.

Being close to government agencies means higher traffic volume, noises and sometimes protests.

Different from the Beijing culture, Shanghai’s Haipai culture tends not to have an emphasis on close ties with government.

More Roads or Under Development Area, Lower Prices

These two types of land use represent higher level of noises and more pollution. The regression shows that having one percent more land used as roads or under development area in the

500m*500m grid cell attributes to a decrease of RMB6,000 or RMB17,000 in property value respectively; and property sales in grids with roads or under development area are on average

RMB72,600 or RMB397,600 lower respectively than those in grids without them. This correlation strongly suggests people’s willingness to avoid noise and pollutions.

More Offices, Higher Prices

There are two variables in this regression related to offices: low-rise offices and high-rise offices.

High-rise offices refer to skyscraper type office buildings while other offices are considered as low-rise in this regression. The regression shows that having one percent more land used as low- rise or high-rise offices in the 500m*500m grid cell attributes to an increase of RMB49,600 or

RMB3,900 in property value respectively; and property sales in grids with low-rise or high-rise offices are on average RMB513,200 or RMB3,300 (but not statistically significant) higher respectively than those in grids without shops. At first glance, this is counter intuitive to the hypothesis that people do not want to live close to production centers. However, this correlation can be explained by the following possible reasons: first, on the ground floor of office buildings,

104 especially low-rise offices, usually exists a variety of shophouses which are consumption related amenities; second, people prefer to live close to office in order to reduce commuting time and increase leisure time for consumption.

More Residential Buildings, Lower Prices

The regression shows that having one percent more land used as high-rise residential or linong in the 500m*500m grid cell is associated with a reduction of RMB9,000 or RMB14,500 in property value respectively; and property sales in grids with high-rise residential or linong are on average

RMB39,600 or RMB361,600 lower than those in grids without them. This negatively correlation can be explained as denser residential area may result in higher noises and traffic, which residents are likely to avoid, and residential buildings are not consumption related amenities.

More slab houses? It depends.

The results for having slab house is not so clear-cut, since the presence of slab house reduces property value but having one additional percent in slab house increases property value. Slab houses are mainly workers unit system (单位) built in the 1970s to the 1990s by the government for its workers in the factories. Slab houses are usually 4 to 5 stories without elevators. although considered as inferior to the commercial condos built after the 2000s, they are mostly centrally located, where a large variety of shops and restaurants are present. This presence helps to explain why more slab houses in a grid cells are associated with an increase of RMB3,200 in property value. The results also show that residents in general prefer not to have slab houses in their communities as they are associated with lower social economic groups.

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More Schools, Lower Prices

The most interesting counter intuitive result is about schools. The regression shows that having one percent more land used as schools in the 500m*500m grid cell attributes to a reduction of

RMB18,800 in property value; and property sales in grids with schools are on average

RMB69,200 lower than those in grids without schools. This negative correlation contradicts with many studies which suggests strong positive correlations between schools and housing prices.

Due to the Shanghai’s large population, there are multiples schools in the same district with different quality and reputations, so it is possible that a low ranked school is located close to a luxury residential community. And residents would prefer to avoid huge traffic and higher noise level associated with school commutes and parents’ daily pick-up and drop-off of pupils.

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Hedonic models with diversity index

The previous regressions show how each type of land use corresponds to housing prices, but they do not provide a holistic view on how the overall diversity of land use attributes to housing prices. Following Song and Knaap (2004), I construct a Shannon’s index to proxy for the level of mixed land-use:

푘

Diversity Index = − ∑ 𝑝푖푙𝑜푔𝑝푖 푖=1

Here ‘p’ denotes the proportion of each land use. Theoretically the diversity index takes a value in the range from zero to one; and higher value indicates more diverse land use. In line with the construction in Song and Knaap (2004), the study used groups of land use to have seven broad land use categories in total. These are grouped into commercial (shopping center, shop house, wetmarket, hotel, gym), residential (high-rise residential, slab house, linong, villa), public

(school, hospital, government agency, theater, museum, road), mixed (mixed office, retail, residential), park (greenspace), industrial, and others. The constructed diversity index from the sample averages 0.55, with a standard deviation of 0.13.

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Table 15: Diversity Index

Mean 0.55

Standard deviation 0.13

Max 0.90

Min 0.05

Number of observation: 215,958.

Note: The construction of which follows Song & Knaap

(2004). There are seven categories: commercial (shopping

center, shop house, wet market, hotel, gym), residential (high-

rise residential, slab house, linong, villa), public (school,

hospital, government agency, theater, museum, road), mixed

(mixed office, retail, residential), park (greenspace), industrial,

and others.

Table 14 below shows the OLS regression results, where the dependent variable is the unit’s total sales price and the explanatory variable is diversity index, with the same list of variables in previous regressions as controls. The overall diversity index has a positive effect on housing prices, which shows that higher diversity is associated with higher unit sales prices.

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Table 16: OLS Results of Diversity Index

Dependent variable: total unit sale price

Variables of interest:

Diversity index 65.00***

Control variables:

Property sq meters 4.73***

First hand 64.66***

Year of sale yes

Districts and locations of

Sale yes

Property type yes

Residence mixed type yes

No. observations 215,958

R-squared (percent) 66.4

***denotes statistical significance at 1%,

**significance at 5%, *significance at 10%. The regression estimates the coefficient of having a unit increase in diversity; dependent variable is the total unit sale price in RMB10,000. The explanatory variables are the diversity index.

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Quantile Regressions with Diversity Index

In addition to the OLS regression that provides the mean response of unit sales price to the diversity index, quantile regression is a useful tool to investigate whether the coefficient can vary at different level of diversity. The reason behind exploring the non-linearity is that mixed land- use could have large premium for low diversity areas.

Table 15 shows the results of quantile regressions, carried out at four quantiles of diversity—

20th percentile, 40th percentile, 60th percentile, and 80th percentile. Although all the coefficients are significantly positive, confirming that more diversity is associated with higher property value. It also shows that at a high level of diversity over 60 percentile, property value is more responsive to the change in diversity; but there is no strictly monotonic pattern observed in coefficient across diversity quantiles.

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Table 17: Quantile Regressions

20 percentile 40 percentile 60 percentile 80 percentile

Variables of interest: diversity index 17.11*** 11.51*** 16.04*** 25.19***

Control variables:

Property sq meters 2.07*** 2.54*** 3.30*** 4.27*** first hand 57.71*** 59.08*** 60.08*** 35.26***

Year of sale yes yes yes yes

Districts and locations of sale yes yes yes yes

Property type yes yes yes yes

Residence mixed type yes yes yes yes

No. observations 215,958 215,958 215,958 215,958

Pseudo R-squared 37.4 45.2 51.0 57.7

***denotes statistical significance at 1%, **significance at 5%, *significance at 10%. This presents results from quantile regressions at the specified quantiles (20, 40, 60, 80 percentiles).

The coefficients present results from each quantile regression. Higher Pseudo R-squared means better fit of the model to data. dependent variable is the total unit sale price in RMB10,000.

Control variables include a list of dummies including years of sale, districts and locations of sale, property types, and residence mixed types.

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Chapter Six: Conclusion

This research provides an analysis of the relationship between different amenities, land use, and housing prices in Shanghai at transaction level. There is evidence that consumer-related amenities such as restaurants, tourist points, and shopping places have larger impact on housing price than production-related amenities such as office buildings and subways. In addition, considering government as a proxy of production activities given the large role played by government in the business activities in China, the results show that the closer the government, the lower the housing prices are. This is another good illustration that production amenities have negative impacts on housing price. And the spatially lagged regression addressed the autocorrelation issue and improved the model.

This study presents a number of important policy implications. As shown clearly in the regressions, most of the amenities have positive impact on housing prices and diversity index returns a positive value. Citizens in Shanghai value consumption related amenities and diversity in land use. Easier access to public transportation system such as subway, open space, universities, and planned public facilities surrounding subway leads to higher density and higher floor-area-ratio in proximity to transportation nodes, hence shortening the home-to-work distance for migrants living in suburban areas. This provides empirical support for transit- oriented development (TOD), which could help Shanghai reduce traffic congestion, protect open space, promote public health and increase housing options. Specifically, Shanghai municipality could direct public financing resources toward infrastructure development in suburban sub- districts with high migrant density, which will provide easier access to services and improve their livelihood.

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It would be important for Shanghai government to adopt mixed land-use planning approach to facilitate Shanghai’s transition from production city to consumption city. Reducing production related land use such as industrial land and increasing mixed land-use could help meeting the demand from Shanghai citizens. This shift can become an additional driver of economic growth as China aims to transform its economy from one that is investment-driven to consumption- driven.

Furthermore, this study could be used as a cost-benefit analysis model for real estate developers in Shanghai. My regression shows that by having one percent more land use in greenspace in a

500 by 500 meters grid attributes to an increase of RMB6,600 in property value. Similarly, having one percent more land use in shophouse and shopping center also attributes positively to property values, with RMB5,900 and RMB7,900 in magnitudes respectively. Therefore, when faced with choices between which amenities to be included in a development projects, real estate developers could apply this model to help quantify the projects’ financial returns.

Shanghai’s modernity is not limited to its skyscrapers - it is deeply rooted in its culture and history highlighting the importance of a holistic understanding of Shanghai through multiple angles. The commercial mentality is culturally rooted in its citizens’ mind, who put high values in commercial services, business activities, leisure life, freedom, and openness. People from

Shanghai are usually described as “petty bourgeoisie” by others, and it reflects in their attitude of enjoying life. The tension between political desire and citizens’ preference are always in a dynamic balance. The Shanghai 2035 Plan is a positive development that shows citizens’ preferences are heard and acknowledged by the State. Shanghai’s urban planning styles are always in evolution, but in the end, it is Shanghai’s culture that deeply yet invisibly influences the urban trajectory of the city. The spirit of Haipai culture continues to live on.

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“You never change things by fighting the existing reality. To change something, build a new

model that makes the existing model obsolete.” - Buckminster Fuller

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