Housing affordability: a framing, synthesis of research and policy, and future directions

Galster, George; Lee, Kwan Ok

doi:10.1080/12265934.2020.1713864

Cited by 63 publications

(38 citation statements)

References 183 publications

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“…The sigmoid output of the input gate is multiplied with the potential cell state value, C t ', which is calculated with Equation (4). The product is then added to the previous cell state value factored by the output of the forget gate to produce the new cell state value, C t , calculated as Equation (5). At the output gate, calculated as Equation ( 3), the sigmoid function determines how the hidden state value should be updated.…”

Section: Application Of Machine Learningmentioning

confidence: 99%

“…Affordable rental housing policy is a promising solution to alleviate the housing cost burden in cities, as various types of public rental housing (PRH) policies are encouraged around the world [1,2]. Consequently, a number of innovative policies and strategies are being introduced, and the opportunities of PRH as a building block of sustainable urban development is rapidly growing [3][4][5][6]. PRH provisions and policies are connected to ecological, economic, and social aspects of sustainability [7].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)

2020

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Providing adequate public rental housing (PRH) of a decent quality at a desirable location is a major challenge in many cities. Often, a prominent opponent of PRH development is its host community, driven by a belief that PRH depreciates nearby property values. While this is a persistent issue in many cities around the world, this study proposed a new approach to assessing the impact of PRH on nearby property value. This study utilized a machine learning technique called long short-term memory (LSTM) to construct a set of housing price prediction models based on 547,740 apartment transaction records from the city of Busan, South Korea. A set of apartment characteristics and proximity measures to PRH were included in the modeling process. Four geographic boundaries were analyzed: The entire region of Busan, all neighborhoods of PRH, the neighborhoods of PRH in the “favorable,” and the “less favorable” local housing market. The study produced accurate and reliable price predictions, which indicated that the proximity to PRH has a meaningful impact on nearby housing prices both at the city and the neighborhood level. The approach taken by the study can facilitate improved decision making for future PRH policies and programs.

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Section: Application Of Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)

2020

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“…Housing affordability fundamentally depends on the relationship between the metropolitan-level distributions of household annual ability to pay ($A) and annual occupancy costs of residential units according to their quality ($O) (Galster & Lee, 2020). Therefore, housing affordability is not only an outcome of the housing market, but also the labour market.…”

Section: Housing Affordability and Inequalitymentioning

confidence: 99%

COVID-19 impact on city and region: what’s next after lockdown?

Kang

Choi

Kim

et al. 2020

International Journal of Urban Sciences

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COVID-19 is unique in that it is spread through everyday contact with other people. Therefore, social protective measures, beyond medical protective measures, such as social distancing, lockdowns, border closures, and human tracing are initiated to control the spread of COVID-19. Such responses have produced secondary issues such as drastic changes in people's way of life and work, housing instability, economic shock, and privacy issues. This paper examines the four domains of urban and regional issues related to the secondary impact of COVID-19, including (1) social distancing, urban structure, community, and density; (2) housing affordability; (3) lockdowns, border closures, reshoring, and regional economic recovery; and (4) smart city technology, contact tracing, and privacy. The following six recommendations have been proposed. First, institutional and cultural factors are more important than urban features, such as population density. To handle infectious diseases such as COVID-19, it is important to build systems, technology, infrastructure, and urban structures that can strengthen resilience instead of implementing a directionless policy of dispersion. Second, it is necessary to improve accessibility to essential services at the community level, including medical facilities and food supply. Third, continuous effort should be made to boost housing affordability, as it is directly related to people's basic life. Fourth, measures are needed to protect those people who are socioeconomically disadvantaged. There is also the need to restore global trade and economic relations. Fifth, since data technology-based COVID-19 control raises the human tracing and privacy issue, we must ensure the principles of privacy management, such as transparency and voluntary consent, are being met. Finally, since COVID-19 is spread through people, individuals may become anxious and fearful of others without grounds; this may increase prejudice and hatred, including xenophobia. Significant social effort is needed to overcome such ill-defined anxiety and fear and maintain a healthy civil society.

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“…Housing price is to be counted as the point of balance, in which the ability to compensate for a home fulfills the eagerness to sell a house (Qin & Han, 2013). House prices could be categorized as one of the key determinants of house ownership affordability (Galster & Lee, 2020), implying that changes in house prices influence the investment decisions of investors. A house price itself is affected by several factors, for instance, speculative pressure, supply condition of mortgage market innovation, and demographic development.…”

Section: Financial Considerationsmentioning

confidence: 99%

The Prominence of Financial Considerations on Housing Investors’ Purchase Decisions

Dananjoyo¹,

Cahaya²,

Riyadh³

2020

JAFEB

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As a basic element for sustainable development, the residential housing industry is vital and fundamental for every country in the world. Therefore, this study examines the impacts of financial considerations on house purchase decisions by housing investors in Auckland, New Zealand. 110 completed questionnaires were statistically analyzed. For testing the proposed hypotheses, Structural Equation Modelling (SEM) was used. The results show that house prices, income, and credit accessibility significantly influence housing investors' purchase decisions in a positive direction. It appears that more expensive houses offer more promising returns such that housing investors having higher levels of income and access to loans are brave enough to invest in such houses. This study aims to present the key factors influencing house purchase decisions from the viewpoint of housing investors as fundamental groups of stakeholders in the property market, which is rarely examined in previous studies. The implication of this study is to provide guidelines for housing regulators in New Zealand to develop affordable housing prices through the availability of land banks. This study also offers practical contributions to housing investors, particularly by providing key guidelines to make effective investment decisions.

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Housing affordability: a framing, synthesis of research and policy, and future directions

Cited by 63 publications

References 183 publications

Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)

Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)

COVID-19 impact on city and region: what’s next after lockdown?

The Prominence of Financial Considerations on Housing Investors’ Purchase Decisions

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