台灣基於需求的社會住房提供和投資途徑

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：66

、訪客IP：3.137.200.58

姓名

維蒂亞(Vidya Trisandini Azzizi) 查詢紙本館藏

畢業系所

土木系營建管理博士班

論文名稱

台灣基於需求的社會住房提供和投資途徑
(NEED-BASED PROVISION AND INVESTMENT PATHWAYS OF SOCIAL HOUSING IN TAIWAN)

相關論文

★ 運用深度神經網路建立H型鋼構件自動辨識系統之研究	★ 運用關聯法則探討協力廠商對營造廠報價行為之研究
★ 探討影響台灣工程顧問公司落實法律遵循反貪腐關鍵因素之研究	★ 以分包商角度探討對營造廠報價行為策略之研究
★ 運用SOMCM分群演算法開發設計雲端智慧平台之營運維護產業介面-以桃園市某園區為例	★ 專案管理在履約爭議處理機制之比較與研析
★ H型鋼構件智慧塗裝路徑優化研究	★ 以資料包絡分析法評估大型統包營造廠之經營績效-以上市櫃公司為例
★ 運用組織特徵映射圖動作軌跡相似度測量法探索預鑄工項生產效率與資源規劃之研究	★ 預鑄專案成本估算策略之研究
★ 新建工程建造執照查核缺失要項之探討--以台北市為例	★ 灰關聯分析探討古蹟與歷史建築再利用之研究
★ 營造工地管理人資量化與預測	★ 公共建設專案現金管理與控制之研究
★ 營建業ERP整合PDA模型之研究	★ 水庫營運效益評估之研究-以石門水庫為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

台灣的樓市，尤其是台北，是全世界最難負擔、最複雜的樓市之一。這既讓買家對他們的購買感到不滿意，也疏遠了一部分需要適當住房的人口，但他們的需求無法在傳統的住房市場上得到滿足。本研究利用數據驅動的住房市場細分，使用群啟發演算法(Swarmed-Inspired Projection) 作為框架來進一步了解住房市場動態，並使用多元回歸分析進一步解釋每個房地產特徵的影響。以台北市39個特徵的5000宗房地產交易為例，可以總結出台北市共有5個住宅板塊。住房市場最大的人口統計數據是主要發生在大同區的流動性集群（由 1,019 筆交易組成，平均單價最低），因為它們主要由工作時間長的通勤者組成。使用基於電子表格的計算，可以得出結論，到2040 年台北市需要增加社會住房供應，因為台北市有超過10 萬個未滿足需求，包括顯性需求（無家可歸人口）和明顯需求（有租金問題的家庭）。該住房人口的優先住房特徵改編自住房細分中以流動為導向的集群，即地鐵站、零售服務和以圖書館提供形式出現的機構包容。通過各種方案的保障房投資路徑，可以推斷出需要私人和公共資金的混合才能獲得最具成本效益的保障房項目。資本補助資金與運營和租金補貼相結合，提供了克服租金收入受限和 20 年期間社會住房供應實際成本的答案。這產生了有形資產，這些資產反過來又可以實現關鍵的社會目標：經濟生產力、社會福祉和環境可持續性。

摘要(英)

Housing market in Taiwan, especially in Taipei, is among the most unaffordable and complicated in the whole world. It both left buyers feel unsatisfied with their purchase as well as alienating a segment of its population, which is in need of proper housing, but their needs are incapable to be met in the conventional housing market. This research utilizes data-driven housing market segmentation using Swarm-Inspired Projection (SIP) as a framework to further understand housing market dynamics, with the impact of each real estate characteristic is further explained using multiple regression analysis. Using 5,000 real estate transactions with 39 characteristics in Taipei City, it can be concluded that there are 5 housing segments in Taipei City. Among the biggest demographics for housing market is the mobility-oriented cluster that mainly take place in Datong District (consisted of 1,019 transactions with the lowest average unit price) for they are mainly consisted of commuter that has long work hours.
Using spreadsheet-based calculation, it can be concluded that by 2040 social housing provision in Taipei City need to be increased because there are more than 100,000 unmet need in Taipei City which comprises of both manifest need (homeless population) and evident need (households in rental distress). Prioritized housing characteristics for this housing demographic is adapted from the mobility-oriented cluster in the housing segmentation, which are MRT stations, retail services, and institution inclusion which take form as library provision.
Through various scenarios of social housing investment pathways, it can be inferred that a mixture of both private and public funding is required to obtain the most cost-effective social housing project. A capital grant funding, combined with operating and rental subsidy, provides the answer of overcoming constrained rent revenue and the real cost of social housing provision during 20 years timeframe. It produces tangible assets which in turn can deliver key societal objectives: economic productivity, social wellbeing, and environmental sustainability.

關鍵字(中)

★ 數據驅動的住房市場細分
★ 保障房供應
★ 購買力平價
★ 投資途徑

關鍵字(英)

★ data-driven housing market segmentation
★ social housing provision
★ PPP
★ investment pathways

論文目次

ABSTRACT i
LIST OF PUBLICATION iv
ACKNOWLEDGEMENT v
TABLE OF CONTENT vi
TABLE OF FIGURES x
CHAPTER I INTRODUCTION 1
1.1. Research Background 1
1.2. Problem Statement 4
1.3. Research Objectives 4
1.4. Research Scope and Limitation 5
1.5. Methodology Overview 6
1.6. Research Organization 7
CHAPTER II LITERATURE REVIEW 9
2.1. Factors Influencing Real Estate Price 9
2.1.1. Real Estate Price Theories 9
2.1.2. Facilities Influencing Real Estate Price 13
2.1.3. Data-Driven Housing Market Segmentation 16
2.2. Real Estate Appraisal 17
2.2.1. Sales Comparison Approach 18
2.2.2. Income Approach 19
2.2.3. Hedonic Price Approach 20
2.2.4 Dynamic Impact Appraisal Function Based on Attributes 23
2.3. Social Housing as Infrastructure 28
2.4. Funding and Financing of Social Housing as Public Infrastructure 31
2.4.1. Public Infrastructure Financing Instruments 31
2.4.2. Social Housing Funding and Financing 34
2.5. Effective Investment Pathway for Social Housing as Infrastructure 37
2.6. Current Housing Condition in Taiwan 39
2.6.1. Current Housing Market Condition 39
2.6.2. Social Housing Units and Policy in Taiwan 42
2.7. Summary 44
CHAPTER III FACTOR DETERMINATION AND CLUSTERING METHODS 46
3.1. Factor Determination of Real Estate Clustering 46
3.2. Clustering Data Management 49
3.2.1. Clustering Data Collection 49
3.2.2. Clustering Data Pre-processing 50
3.3. Clustering Methodology 52
3.3.1. Swarm-Inspired Projection Model Development 52
3.3.2. Multiple Regression Analysis 55
CHAPTER IV NEED-BASED REAL ESTATE CLUSTERING 58
4.1. Development of Need-Based Real Estate Clustering 58
4.2. Quantifying Influences of Real Estate Characteristics 67
4.2.1. Cluster C0 67
4.2.2. Cluster C1 69
4.2.3. Cluster C2 71
4.2.4. Cluster C3 73
4.2.5. Cluster C4 75
4.3. Cluster Evaluation 76
4.4. Summary 77
CHAPTER V SOCIAL HOUSING NEED ASSESSMENT AND INVESTMENT METHODOLOGY 79
5.1. Housing Need Assessment Method 79
5.2. Data Collection for Social Housing Assessment 83
5.3. Multi-Criteria Appraisal for Social Housing Investment Pathways 84
CHAPTER VI SOCIAL HOUSING NEED ASSESSMENT AND INVESTMENT PATHWAY MODEL DEVELOPMENT 89
6.1. Social Housing Need in Taipei City 89
6.1.1. Current Social Housing Condition in Taipei City 89
6.1.2. Implications and Rationale in Providing Facilities for Social Housing 91
6.2. Need-Based Social Housing Assessment 95
6.2.1. Population Projection 95
6.2.2. Wage Rate Projection 98
6.2.3. House Price to Income Ratio 99
6.2.4. Need-Based Social Housing Estimates 99
6.3. Cost Estimation of Social Housing Provision 103
6.4. Potential Rental Payment Contribution 104
6.5. Social Housing Investment Cost Modelling 107
6.5.1. Indexation and Operating Costs Assumptions 108
6.5.2. Social Housing Funding Gap 109
6.5.3. Cost Modelling Scenario to Fill Social Housing Funding Gap 110
6.6. Recommended Investment Pathway and Model Evaluation 115
6.7. Summary 119
CHAPTER VII CONCLUSION 121
7.1. Conclusion 121
7.2. Suggestion 123
7.3. Expected Contribution 124
REFERENCES 125
APPENDIX 136

參考文獻

Adair, A. S., Berry, J. N., & McGreal, W. S. (1996). Hedonic modelling, housing submarkets and residential valuation. Journal of property Research, 13(1), 67-83.
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications. Paper presented at the Proceedings of the 1998 ACM SIGMOD international conference on Management of data.
Apple Daily. (2014). Apple poll: Housing Prices in Taipei are Too High, Do You Think Your Next Generation Can Afford It? Retrieved from https://tw.appledaily.com/headline/20140422/IWPXEPTNMQKWXHADC4L6QFIP74/
Azmi, D. I., Karim, H. A., & Amin, M. Z. M. (2012). Comparing the walking behaviour between urban and rural residents. Procedia-Social and Behavioral Sciences, 68, 406-416.
Barfod, M. B., Salling, K. B., & Leleur, S. (2011). Composite decision support by combining cost-benefit and multi-criteria decision analysis. Decision support systems, 51(1), 167-175.
Barton, H., Grant, M., & Guise, R. (2003). Shaping neighbourhoods: a guide for health, sustainability and vitality: Taylor & Francis.
Bochnovic, M. A. (2014). An Analysis of Bid-Rent curve variations across American cities: Arizona State University.
Cai, S. (2018). Taipei′s Affordable Housing Challenge. Taiwan Business Topics. Retrieved from https://topics.amcham.com.tw/2018/10/taipeis-affordable-housing-challenge/
Camilleri, D. (2011). A long-term analysis of housing affordability in Malta. International Journal of Housing Markets and Analysis, 4, 31-57. doi:10.1108/17538271111111820
Central News Agency. (2020). President Tsai pledges 120,000 social housing units by 2024December 2020. Retrieved from Taiwan News website:
Cervero, R., & Duncan, M. (2006). ′Which Reduces Vehicle Travel More: Jobs-Housing Balance or Retail-Housing Mixing? Journal of the American planning association, 72(4), 475-490.
Chan, C., Forwood, D., Roper, H., & Sayers, C. (2009). Public infrastructure financing-an international perspective.
Chang, C.-O., & Chen, S.-m. (2018). Dilemma of Housing Demand in Taiwan. International Real Estate Review, 21, 397-418.
Chang, C.-O., & Hsieh, B.-M. (2018). 6 Changes in housing policy, housing wellbeing and housing justice in Taiwan.
Chang, C.-O., & Yuan, S.-M. (2013). Public Housing Policy in Taiwan. The Future of Public Housing: Ongoing Trends in the East and the West, 85-101. doi:10.1007/978-3-642-41622-4_6
Chen, J.-H., Ji, T., Su, M.-C., Wei, H.-H., Azzizi, V. T., & Hsu, S.-C. (2021). Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city. Journal of Housing and the Built Environment, 1-25.
Chen, J.-H., Su, M.-C., & Annuerine Badjie, B. (2016). Exploring and weighting features for financially distressed construction companies using Swarm Inspired Projection algorithm. Advanced Engineering Informatics, 30(3), 376-389. doi:https://doi.org/10.1016/j.aei.2016.05.003
Chen, J.-H., Yang, L.-R., Azzizi, V. T., Chu, E., & Wei, H.-H. (2020). Establishing dynamic impact function for house pricing based on surrending multi-attributes: evidence from Taipei city, Taiwan. International Journal of Strategic Property Management, 24(2), 119-129.
Chen, J.-H., Yang, L.-R., & Su, M.-C. (2009). Comparison of SOM-based optimization and particle swarm optimization for minimizing the construction time of a secant pile wall. Automation in Construction, 18(6), 844-848.
Chen, Y.-L. (2011). New Prospects for Social Rental Housing in Taiwan: The Role of Housing Affordability Crises and the Housing Movement. International Journal of Housing Policy, 11(3), 305-318. doi:10.1080/14616718.2011.599133
Chen, Y.-L. (2015). The Factors and Implications of Rising Housing Prices in Taiwan [Press release]. Retrieved from https://www.brookings.edu/opinions/the-factors-and-implications-of-rising-housing-prices-in-taiwan/
Chong, S., & Poole, E. (2013). Financing infrastructure: a spectrum of country approaches. RBA Bulletin, 65-76.
Chou, H. Y. (2012). The Study on Factors of Decision-Making of Purchasing House-Examples as Taipei City and New Taipei City. (Master′s Degree), China University of Science and Technology, Taiwan.
Chuknyisky, P., Kasarova, V., & Kanaryan, N. (2014). Fine Tuned Income Approach in Real Estate Valuation in Emerging Europe: The Case of Bulgaria.
Chung, I. H. (2015). School choice, housing prices, and residential sorting: Empirical evidence from inter-and intra-district choice. Regional science and urban economics, 52, 39-49.
Cordera, R., Coppola, P., dell′Olio, L., & Ibeas, Á. (2019). The impact of accessibility by public transport on real estate values: A comparison between the cities of Rome and Santander. Transportation Research Part A: Policy and Practice, 125, 308-319.
Crookham, J. (1995). Sales comparison approach: revisited. The Appraisal Journal, 63(2), 177.
Cui, N., Gu, H., Shen, T., & Feng, C. (2018). The impact of micro-level influencing factors on home value: A housing price-rent comparison. Sustainability, 10(12), 4343.
D Benjamin, J., Guttery, R., & Sirmans, C. (2004). Mass Appraisal: An Introduction to Multiple Regression Analysis for Real Estate Valuation (Vol. 7).
Demyanyk, Y., & Van Hemert, O. (2011). Understanding the subprime mortgage crisis. The review of financial studies, 24(6), 1848-1880.
Department of Information Services. (2016). Social housing policy aims to achieve social justice [Press release]. Retrieved from https://english.ey.gov.tw/Page/61BF20C3E89B856/60e1e497-8704-488b-8abc-753686b6af56
Department of Information Services. (2018). Government agencies identify more land resources for social housing [Press release]. Retrieved from https://english.ey.gov.tw/Page/61BF20C3E89B856/bdb2a3ed-109b-418d-aaba-411d6c3f09a0
Dixon, T., McNamara, P., Lorenz, D., & Lützkendorf, T. (2008). Sustainability in property valuation: theory and practice. Journal of Property Investment & Finance.
Duca, J., & Muellbauer, J. (2014). Tobin lives: Integrating evolving credit market architecture into flow-of-funds based macro-models. In A flow-of-funds perspective on the financial crisis (pp. 11-39): Springer.
Efron, B. (1992). Bootstrap methods: another look at the jackknife. In Breakthroughs in statistics (pp. 569-593): Springer.
Ewing, R., Hamidi, S., Tian, G., Proffitt, D., Tonin, S., & Fregolent, L. (2017). Testing Newman and Kenworthy’s Theory of Density and Automobile Dependence. Journal of Planning Education and Research, 38, 0739456X1668876. doi:10.1177/0739456X16688767
Flanagan, K., Martin, C., Lawson, J., & Jacobs, K. (2019). Is Social Housing Essential Infrastructure? How We Think About It Does Matter. Retrieved from The Conversation website: https://theconversation.com/is-social-housing-essential-infrastructure-how-we-think-about-it-does-matter-110777
Forrest, A. (2019). Vienna′s Affordable Housing Paradise. This New World. Retrieved from https://www.huffpost.com/entry/vienna-affordable-housing-paradise_n_5b4e0b12e4b0b15aba88c7b0
Goodman, J. L., & Ittner, J. B. (1992). The accuracy of home owners′ estimates of house value. Journal of Housing Economics, 2(4), 339-357. doi:https://doi.org/10.1016/1051-1377(92)90008-E
Guo, B., & Sun, K. (2010). Research on Real Estate Price Based on Hedonic Analyze Among Cities. 2010 International Conference on System Science, Engineering Design, and Manufacturing Informatization.
Henn, L., Sloan, K., Charles, M. B., & Douglas, N. (2016). An appraisal framework for evaluating financing approaches for public infrastructure. Public Money & Management, 36(4), 273-280.
Henn, L., Sloan, K., & Douglas, N. (2012). Towards a framework for evaluating financing alternatives for High Speed Rail along the East Coast of Australia. Paper presented at the Paper to Australasian Transport Research Forum 2012 Proceedings.
Hodgetts, D., Stolte, O., Chamberlain, K., Radley, A., Nikora, L., Nabalarua, E., & Groot, S. (2008). A trip to the library: Homelessness and social inclusion. Social & Cultural Geography, 9(8), 933-953.
International Monetary Fund. (2015). World Economic Outlook. Retrieved from Washington:
Jha, A. K. B., Jennifer Duyne; Phelps, Priscilla M.; Pittet, Daniel; Sena, Stephen;. (2010). Safer homes, stronger communities : a handbook for reconstruction after natural disaster : Safer homes, stronger communities : a handbook for reconstructing after natural disasters. In.
Jordan, D. K., Morris, A. D., & Moskowitz, M. L. (2017). The minor arts of daily life: Popular culture in Taiwan: University of Hawaii Press.
Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: issues, challenges, tools and good practices. Paper presented at the 2013 Sixth international conference on contemporary computing (IC3).
Kelley, K., & Bolin, J. H. (2013). Multiple regression. In Handbook of quantitative methods for educational research (pp. 69-101): Brill Sense.
Kim, K. S., Park, S. J., & Kweon, Y.-J. (2007). Highway traffic noise effects on land price in an urban area. Transportation Research Part D: Transport and Environment, 12(4), 275-280. doi:https://doi.org/10.1016/j.trd.2007.03.002
Kong, F., Yin, H., & Nakagoshi, N. (2007). Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City, China. Landscape and urban planning, 79(3-4), 240-252.
Kontrimas, V., & Verikas, A. (2011). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11(1), 443-448.
LaFlair, G. T., Egbert, J., & Plonsky, L. (2015). A practical guide to bootstrapping descriptive statistics, correlations, t tests, and ANOVAs. Advancing quantitative methods in second language research, 46-77.
Lan, F., Wu, Q., Zhou, T., & Da, H. (2018). Spatial effects of public service facilities accessibility on housing prices: A case study of Xi’an, China. Sustainability, 10(12), 4503.
Law, S. (2017). Defining Street-based Local Area and measuring its effect on house price using a hedonic price approach: The case study of Metropolitan London. Cities, 60, 166-179.
Lawson, J., Pawson, H., Troy, L., van den Nouwelant, R., & Hamilton, C. (2018). Social housing as infrastructure: an investment pathway.
Lee, C.-H. (2009). An Assessment Model of Residential Collateral Using Data Mining Techniques. (Master′s), Shih Hsin University, Taipei, Taiwan.
Lee, H. Y. (2014). Customer-oriented Weight Determination for Assessing House Price in Urban Areas using SIP Clustering Approach. (Masters of Science), National Central University, Taoyuan, Taiwan.
Leishman, C., Costello, G., Rowley, S., & Watkins, C. (2013). The predictive performance of multilevel models of housing sub-markets: A comparative analysis. Urban studies, 50(6), 1201-1220.
Lin, Y.-P. (2003). A Knowledge-based Decision Support system for Customer’s Expected Preferences of Housing Market Product Consumption. Chaoyang University of Technology, Taichung, Taiwan.
Liu, Z., Liao, L., & Mei, C. (2018). Not-in-my-backyard but let’s talk: Explaining public opposition to facility siting in urban China. Land use policy, 77, 471-478.
Manganelli, B., Pontrandolfi, P., Azzato, A., & Murgante, B. (2014). Using geographically weighted regression for housing market segmentation. International Journal of Business Intelligence and Data Mining 13, 9(2), 161-177.
Martínez, C. F., Hodgson, F., Mullen, C., & Timms, P. (2018). Creating inequality in accessibility: The relationships between public transport and social housing policy in deprived areas of Santiago de Chile. Journal of Transport Geography, 67, 102-109.
Mathews, N. (2012). Indian Market Takes A Breather. Engineering News Record. Retrieved from Engineering News Record website: https://www.enr.com/articles/1137-indian-market-takes-a-breather
Matthews, M. R. (2019). Feng Shui Practice. In Feng Shui: Teaching About Science and Pseudoscience (pp. 65-90): Springer.
Meen, G. (1999). Regional house prices and the ripple effect: a new interpretation. Housing studies, 14(6), 733-753.
Ministry of Finance. (2019). Statistical results of housing price affordability indicators [Press release]. Retrieved from https://pip.moi.gov.tw/Upload/CustomFile/Doc/108Q4%E8%B2%A0%E6%93%94%E8%83%BD%E5%8A%9B%E7%99%BC%E5%B8%83%E5%85%A7%E5%AE%B9(%E5%B9%B3%E5%8F%B0).pdf
Ministry of Interior. (2019). 2019 Housing Demand Trends Survey [Press release]. Retrieved from https://pip.moi.gov.tw/V3/E/SCRE0101.aspx#
Mooya, M. M. (2016). Real Estate Valuation Theory. Springer Books.
Muddiman, D. (2000). Theories of social exclusion and the public library. In Open to all?: The public library and social exclusion (pp. 1-15): London: Resource: The Council for Museums, Archives and Libraries.
Muller, P., Chepeleva, K., & Shemeleva, Z. N. (2020). The Segmentation of the Residential Real Estate Market on the Affordability Criterion: Methodical Aspect. Paper presented at the International Scientific Conference" Far East Con"(ISCFEC 2020).
Nikolaos, K., Dimitra, V., & Agapi, X. (2011). Real estate values and environment: A case study on the effect of the environment on residential real estate values. International Journal of Academic Research, 3(1), 861-868.
Office of Information Services. (2015). Housing policy aims to help young people realize dreams [Press release]. Retrieved from https://english.ey.gov.tw/Page/61BF20C3E89B856/83e233cc-b271-4650-893d-6d62904e3fdd
Oikarinen, E., & Schindler, F. (2015). Momentum and mean reversion in regional housing markets: evidence from variance ratio tests. International Journal of Strategic Property Management, 19(3), 220-234.
Pan, Y., Zhang, Y., Li, F., & Luan, W. (2012). Bubble and Its Burst: A Double-Chaotic Model of Dynamics and Control in The Estate Market. Journal of Convergence Information Technology, 7, 349-359. doi:10.4156/jcit.vol7.issue14.40
Ratchatakulpat, T., Miller, P., & Marchant, T. (2009). Residential real estate purchase decisions in Australia: is it more than location? International Real Estate Review, 12(3), 273-294.
Roback, J. (1982). Wages, rents, and the quality of life. Journal of political economy, 90(6), 1257-1278.
Rosen, H. S. (1979). Housing decisions and the US income tax: An econometric analysis. Journal of Public Economics, 11(1), 1-23.
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of political economy, 82(1), 34-55.
Rosmera, N. A., & Lizam, M. (2016). Housing market segmentation and the spatially varying house prices. Social Sciences (Pakistan), 11(11).
Rothenberg, J., Galster, G. C., Butler, R. V., & Pitkin, J. R. (1991). The maze of urban housing markets: Theory, evidence, and policy: University of Chicago Press.
Rowley, S., Leishman, C., Baker, E., Bentley, R., & Lester, L. (2017). Modelling housing need in Australia to 2025.
Scheurer, J., Curtis, C., & McLeod, S. (2017). Spatial accessibility of public transport in Australian cities: Does it relieve or entrench social and economic inequality? Journal of transport and land use, 10(1), 911-930.
Shonkwiler, J. S., & Reynolds, J. E. (1986). A Note on the Use of Hedonic Price Models in the Analysis of Land Prices at the Urban Fringe. Land Economics, 62(1), 58-63. doi:10.2307/3146563
Su, M.-C., & Chang, H.-T. (2001). A new model of self-organizing neural networks and its application in data projection. IEEE Transactions on Neural Networks, 12(1), 153-158.
Su, M.-C., Su, S.-Y., & Zhao, Y.-X. (2009). A swarm-inspired projection algorithm. Pattern Recognition, 42(11), 2764-2786. doi:https://doi.org/10.1016/j.patcog.2009.03.020
Su, M.-C., Zhao, Y.-X., & Lee, J. (2004). SOM-based optimization. Paper presented at the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541).
Suksri, J., Raicu, R., & Yue, W. L. (2012). Towards sustainable urban freight distribution-a proposed evaluation framework.
The Economist Intelligence Unit. (2017). Rethinking Social Housing [Press release]. Retrieved from https://country.eiu.com/article.aspx?articleid=1876044571&Country=Taiwan&topic=Economy&subtopic=Forecast&subsubtopic=Policy+trends
Thrun, M. C. (2018). Projection-based clustering through self-organization and swarm intelligence: combining cluster analysis with the visualization of high-dimensional data: Springer.
Tiwari, P., Nair, R., Ankinapalli, P., Rao, J., Hingorani, P., & Gulati, M. (2015). Living Environment. In P. Tiwari, R. Nair, P. Ankinapalli, J. Rao, P. Hingorani, & M. Gulati (Eds.), India’s Reluctant Urbanization: Thinking Beyond (pp. 153-173). London: Palgrave Macmillan UK.
Tsai, I.-C., & Peng, C.-W. (2011). Bubbles in the Taiwan housing market: The determinants and effects. Habitat International, 35(2), 379-390.
Tsai, P.-M. F. H. (2020). Housing transactions in 6 largest cities up 25% year-on-year. Retrieved from Focus Taiwan CNA English News website:
Wang, B. (2011, 8-11 Jan. 2011). On the analysis of income approach for real estate. Paper presented at the MSIE 2011.
Weisberg, B. (1993). One city′s approach to NIMBY how New York city developed a fair share siting process. Journal of the American planning association, 59(1), 93-97.
Wu, C., Ye, X., Ren, F., & Du, Q. (2018). Modified data-driven framework for housing market segmentation. Journal of Urban Planning and Development, 144(4), 04018036.
Xiao, Y. (2017). Hedonic Housing Price Theory Review. In Y. Xiao (Ed.), Urban Morphology and Housing Market (pp. 11-40). Singapore: Springer Singapore.
Yip, N. M., & Chang, C.-O. (2003). Housing in Taiwan: state intervention in a market driven housing system. The Journal of Comparative Asian Development, 2(1), 93-113.
Yu, C.-M., & Chen, P.-F. (2018). House Prices, Mortgage Rate, and Policy: Megadata Analysis in Taipei. Sustainability, 10(4), 926.
Zhong, Q., Karner, A., Kuby, M., & Golub, A. (2019). A multiobjective optimization model for locating affordable housing investments while maximizing accessibility to jobs by public transportation. Environment and Planning B: Urban Analytics and City Science, 46(3), 490-510.

指導教授

陳介豪(Jieh-Haur Chen)

審核日期

2021-8-24

推文