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Article
Publication date: 13 September 2023

Xueqi Wang and Graham Squires

This paper aims to define intergenerational housing support and assesses and synthesizes the existing literature on intergenerational support for housing to identify trends and…

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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

Property Management, vol. 42 no. 2
Type: Research Article
ISSN: 0263-7472

Keywords

Article
Publication date: 27 June 2023

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.

Details

International Journal of Contemporary Hospitality Management, vol. 36 no. 4
Type: Research Article
ISSN: 0959-6119

Keywords

Abstract

Details

A Neoliberal Framework for Urban Housing Development in the Global South
Type: Book
ISBN: 978-1-83797-034-6

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