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1 – 3 of 3This paper aims to define intergenerational housing support and assesses and synthesizes the existing literature on intergenerational support for housing to identify trends and…
Abstract
Purpose
This paper aims to define intergenerational housing support and assesses and synthesizes the existing literature on intergenerational support for housing to identify trends and possible areas for future research.
Design/methodology/approach
The methodology employed in this paper is a systematic literature review. A total of 32 articles were chosen for assessment. Upon thorough review, summary and synthesis, general trends and three specific themes were identified.
Findings
The review of 32 papers found that intergenerational support is a crucial strategy to help younger generations achieve homeownership. However, it also highlights the potential for social inequity resulting from unequal distribution of housing resources within families, especially regarding housing. Several potential gaps in the current research are identified, including the need for explicit attention to the provider's intention, exploration into the size and form of financial support for housing, understanding how parental housing resources differ in their transfer behaviors, and examining how parental motivations influence them to provide housing support.
Originality/value
This paper provides recommendations for further research on the topic, while also adding perspective to understand the micro-social mechanisms behind the intergenerational reproduction of socioeconomic inequality, especially in the housing market.
Details
Keywords
Fatemeh Binesh, Amanda Mapel Belarmino, Jean-Pierre van der Rest, Ashok K. Singh and Carola Raab
This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.
Abstract
Purpose
This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.
Design/methodology/approach
Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.
Findings
The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.
Research limitations/implications
This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.
Practical implications
This study produced a reliable, accurate forecasting model considering risk and competitor behavior.
Theoretical implications
This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.
Originality/value
This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.
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Sampa Chisumbe, Clinton Ohis Aigbavboa, Erastus Mwanaumo and Wellington Didibhuku Thwala