Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

Liu, Lianyi; Wu, Lifeng

doi:10.1016/j.seps.2020.100916

Cited by 36 publications

(37 citation statements)

References 18 publications

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“…Various studies of the issue of identifying the determinants of affordable housing have been undertaken in different economic systems [19][20][21][22]. This study suggests the model that has been calculated for a specific economic system and adjusted for all of its local features.…”

Section: Methodsmentioning

confidence: 99%

“…Some authors suggest assessing to what extent a particular parameter affects the housing affordability through a univariate regression [20]. Liu and Wu [21] suggest using a model combining Holt's modified exponential smoothing and whale optimization algorithm to forecast the housing market environment. Alqaralleh and Canepa [22] believe that dynamic asymmetries in the housing market cycle can well be modelled using a logistic smooth transition model.…”

Section: Introductionmentioning

confidence: 99%

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Determinants of housing affordability in the region

Kleshcheva

2021

E3S Web Conf.

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The issue of providing the population with affordable housing is one of the most pressing social concerns. Each region necessitates a distinct approach. The purpose of this article is to identify the factors that influence a housing affordability level in the region, analyze the impact mechanisms inherent in them, and assess the quantitative impact of these factors on the housing affordability index. The following factors have a direct impact on housing affordability: the rate of inflation, the population size, the cost of housing, the loan interest rate, the rate of housing construction, the investment scale, the income level of the population, and the economic system's overall development level. The quantitative assessment of the cumulative impact of these factors on the level of housing affordability is based on building a regression model describing how the housing affordability index in the region depends on multiple factors and assessing its reliability. The simulation model confirmed that the supply, demand, and housing market conditions have the greatest impact on housing affordability in the region.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determinants of housing affordability in the region

Kleshcheva

2021

E3S Web Conf.

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“…Correspondingly, the momentum effect stems from within a system, in that it is an empirically observed phenomena whereby events which have been trending in a certain direction for some time would be expected to continue to do so [ 9 ], akin to the momentum generated under a physical system. The momentum effect of a system can be traced through historical data [ 10 ], or through prediction modeling [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts

Gao

Yang

Xiao

et al. 2022

Socio-Economic Planning Sciences

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This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57–18.67%) and a spillover effect (7.07–27.60%).

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“…Artificial Neural Networks (García et al, 2008;Selim, 2009), Support Vector Machine (Kontrimas & Verikas, 2011;Plakandaras et al, 2015), Genetic Algorithm (Gu et al, 2011), Decision Tree (Fan et al, 2006;Cupal et al, 2019), Random Forest (Antipov & Pokryshevskaya, 2012), and Rough Set Theory (d 'Amato, 2002'Amato, , 2004'Amato, , 2007. Recent papers are focused on the application of machine learning methods in the valuation of real estate, which has currently resulted in a significant increase in research focused on this area (Baldominos et al, 2015;Park & Bae, 2015;Hausler et al, 2018;Hu et al, 2019;Liu & Liu, 2019;Pérez-Rave et al, 2019;Liu & Wu, 2020). Valier (2020) states that "machine learning models are more accurate than traditional regression analysis in their ability to predict value.…”

Section: Introductionmentioning

confidence: 99%

A Mean-Variance Optimization Approach for Residential Real Estate Valuation

Guijarro

2021

Real Estate Management and Valuation

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This paper introduces a new approach to the sales comparison model for the valuation of real estate that can objectively estimate the coefficients associated with the explanatory price variables. The coefficients of the price adjustment process are estimated from the formulation of a quadratic programming model similar to the mean-variance model in the portfolio selection problem and are shown to be independent of the property to be valued. It is also shown that the sales comparison model should minimize the variance of the adjusted prices, and not their coefficient of variation as indicated by some national and international valuation regulations. The paper concludes with a case study on the city of Medellín, Colombia.

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Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

Cited by 36 publications

References 18 publications

Determinants of housing affordability in the region

Determinants of housing affordability in the region

COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts

A Mean-Variance Optimization Approach for Residential Real Estate Valuation

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