Small-Area Population Forecasting: A Geographically Weighted Regression Approach

Chi, Guangqing; Wang, Donghui

doi:10.1007/978-3-319-43329-5_21

Cited by 18 publications

(12 citation statements)

References 33 publications

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“…In general, GWR applications use continuous kernel functions that produce smooth decreasing weights as distance increases (Chi and Wang, 2017). We use adaptive Gaussian kernel:…”

Section: Methodsmentioning

confidence: 99%

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Local and global determinants of office rents in Istanbul

Bera

Uyar

2019

JERER

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Purpose This paper presents a hedonic office rent model under the decentralized structure of Istanbul Office Market. The data set in the study includes 2,348 office spaces for the first quarter of 2018. This study aims to find determinants that affect the level of rent and examine whether the effects of office rent determinants are global or not. Design/methodology/approach To consider both global and local effects, the paper uses mixed geographically weighted regression approach in hedonic office rent analysis. Findings The empirical results indicate that office rent determinants such as physical, locational, neighborhood and market operational characteristics have significant impacts on the level of the rent. The findings also show that one of the office rent determinants has a global effect and the other determinants have local effects. According to the estimation results, local effects and statistical significances of these determinants vary from lower quartiles to upper quartiles. Originality/value To the best of the authors’ knowledge, this is the first paper to consider global and local effects of office rent determinants on the level of rent, with mixed geographically weighted regression approach. The paper provides new insights into the hedonic valuation of commercial real estates, especially for decentralized office markets.

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“…In general, GWR applications use continuous kernel functions that produce smooth decreasing weights as distance increases (Chi and Wang, 2017). We use adaptive Gaussian kernel:…”

Section: Methodsmentioning

confidence: 99%

“…If there is no spatial variability, the corresponding independent variable should be treated as a global variable, i.e. with no local effect, and GWR should be re-fitted (Nakaya et al, 2009;Chi and Wang, 2017).…”

Section: Methodsmentioning

confidence: 99%

Local and global determinants of office rents in Istanbul

Bera

Uyar

2019

JERER

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“…In this study, Mean Absolute Percentage Errors were found to be slightly lower than those of several simple extrapolative models. In later work, a geographically weighted regression approach was proposed and tested (Chi & Wang, 2017 ). When applied to minor civil divisions in Wisconsin, the geographically weighted regression model was found to be slightly less accurate than several simple extrapolative forecasts.…”

Section: Small Area Population Forecasting Methods 2001–2020mentioning

confidence: 99%

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Wilson

Grossman

Alexander

et al. 2021

Popul Res Policy Rev

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Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet, the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper, we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001–2020. The key themes covered by the review are extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socioeconomic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used. Supplementary Information The online version contains supplementary material available at 10.1007/s11113-021-09671-6.

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“…In this study, Mean Absolute Percentage Errors were found to be slightly lower than those of several simple extrapolative models. In later work, a geographically-weighted regression approach was proposed and tested (Chi & Wang, 2017). When applied to minor civil divisions in Wisconsin, the geographically-weighted regression model was found to be slightly less accurate than several simple extrapolative forecasts.…”

Section: Incorporating Socio-economic Variables and Spatial Relationshipsmentioning

confidence: 99%

Methods for small area population forecasts: state-of-the-art and research needs

Wilson¹,

Grossman²,

Alexander³

et al. 2021

Preprint

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Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are: extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socio-economic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.

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Small-Area Population Forecasting: A Geographically Weighted Regression Approach

Cited by 18 publications

References 33 publications

Local and global determinants of office rents in Istanbul

Local and global determinants of office rents in Istanbul

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Methods for small area population forecasts: state-of-the-art and research needs

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