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Titlebook: Enhancing Retirement Success Rates in the United States; Leveraging Reverse M Chia-Li Chien Book 2019 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 16:35:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Enhancing Retirement Success Rates in the United States
副標(biāo)題Leveraging Reverse M
編輯Chia-Li Chien
視頻videohttp://file.papertrans.cn/312/311318/311318.mp4
概述Addresses the questions: How successful are U.S. retirees at sustaining assets from retirement to death and what retirement strategies will enhance their ability to live a successful retirement?.Inclu
圖書封面Titlebook: Enhancing Retirement Success Rates in the United States; Leveraging Reverse M Chia-Li Chien Book 2019 The Editor(s) (if applicable) and The
描述.This book and the underlying research address the questions: “How successful are U.S. retirees at sustaining assets from retirement to death?” and “What retirement strategies will enhance their ability to live a successful retirement?” Taking a hard look at real-world retirement statistics from multiple government surveys to answer those questions, it calculates the effects of specific strategies on retirement sustainability. It also discusses the background to prior retirement planning research and describes the three research groups used: 1) determining the success rates of the base population considering only social capital annual income and distributions from portfolios of financial assets, 2) adding the strategy of home equity conversion mortgages, and 3) increasing annual income through delayed social security benefit claiming and continuing work after retirement. The book then examines and compares the results for each to determine whose retirement will be most enhanced by the strategies. Lastly, it presents case studies applying research to real-world financial planning cases..
出版日期Book 2019
關(guān)鍵詞Retirement Income Strategies; HECM; Delaying Claiming Social Security; Scaling Factors; Retirement Succe
版次1
doihttps://doi.org/10.1007/978-3-030-33620-2
isbn_softcover978-3-030-33622-6
isbn_ebook978-3-030-33620-2
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 22:59:53 | 只看該作者
Wolfgang Tschacher,Hermann Haken success rates is discussed. Also, prior research examining withdrawals, retirement length, and assets is reviewed. In addition, background information regarding qualifications for HECMs is discussed. The chapter then focuses on the gains and incentives for delaying Social Security claiming and then
板凳
發(fā)表于 2025-3-22 03:17:35 | 只看該作者
地板
發(fā)表于 2025-3-22 07:57:20 | 只看該作者
5#
發(fā)表于 2025-3-22 08:51:45 | 只看該作者
https://doi.org/10.1007/978-1-349-11395-8 of nine scenarios for determining annual income. The scenarios are: (a) claiming Social Security retirement benefits at FRA; (b) claiming Social Security retirement benefits at age 70; (c-1) claiming Social Security retirement benefits at current SIPP age and one year of earned income; (c-3) claimi
6#
發(fā)表于 2025-3-22 13:27:09 | 只看該作者
The Profession of Science Fiction base model. For the HECM research model, improved results over the base model are discussed by state, age, net worth range, and socioeconomic cohort groups. Then, the success of single and combined strategies involving Social Security delayed claiming and continuing to work after retirement and oth
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發(fā)表于 2025-3-22 18:55:13 | 只看該作者
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發(fā)表于 2025-3-22 23:56:07 | 只看該作者
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發(fā)表于 2025-3-23 03:11:42 | 只看該作者
https://doi.org/10.1007/978-1-4613-8202-7The descriptive statistics for the model adding HECM to the base success rates and eligibility are discussed. Data is further analyzed using scaling factors by states of residence, age cohorts by gender, net worth ranges, and socioeconomic cohort groups of business ownership, mortgage balances, and rental incomes.
10#
發(fā)表于 2025-3-23 06:41:12 | 只看該作者
Effect of Adding HECM to the Base,The descriptive statistics for the model adding HECM to the base success rates and eligibility are discussed. Data is further analyzed using scaling factors by states of residence, age cohorts by gender, net worth ranges, and socioeconomic cohort groups of business ownership, mortgage balances, and rental incomes.
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