Analyzing housing supply location choice: a comparative study of the modelling frameworks
Abstract The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental module...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-51754-9 |
| _version_ | 1797349993920593920 |
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| author | Yu Zhang Eric J. Miller |
| author_facet | Yu Zhang Eric J. Miller |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | Abstract The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental modules. The supply of new dwellings in urban simulation models have evolved from static, exogenous inputs to dynamic, agent-based determinations. This study follows this trend to examine two approaches to modelling the spatial distribution of new housing supply: the first approach models the development choice of each location; the second approach models the location choice of each residential project. Multinomial logit and nested logit models are applied to a Toronto empirical dataset. The results show that although the first approach achieves higher goodness-of-fit and prediction accuracy, the second approach performs better in explaining the locational preference of individual projects. Project characteristics such as structure type and construction cost, as well as location characteristics such as housing price, number of sales, and population density affect the spatial distribution of new housing supply. Both approaches are evaluated regarding estimation, prediction, and microsimulation system integration. The findings enhance housing modelling literature and inform urban microsimulation’s housing supply model configuration. |
| first_indexed | 2024-03-08T12:38:22Z |
| format | Article |
| id | doaj.art-5bfb57bbf94e4795ae369937f6ab7b06 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T12:38:22Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5bfb57bbf94e4795ae369937f6ab7b062024-01-21T12:17:37ZengNature PortfolioScientific Reports2045-23222024-01-0114111410.1038/s41598-024-51754-9Analyzing housing supply location choice: a comparative study of the modelling frameworksYu Zhang0Eric J. Miller1Department of Civil and Mineral Engineering, University of TorontoDepartment of Civil and Mineral Engineering, University of TorontoAbstract The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental modules. The supply of new dwellings in urban simulation models have evolved from static, exogenous inputs to dynamic, agent-based determinations. This study follows this trend to examine two approaches to modelling the spatial distribution of new housing supply: the first approach models the development choice of each location; the second approach models the location choice of each residential project. Multinomial logit and nested logit models are applied to a Toronto empirical dataset. The results show that although the first approach achieves higher goodness-of-fit and prediction accuracy, the second approach performs better in explaining the locational preference of individual projects. Project characteristics such as structure type and construction cost, as well as location characteristics such as housing price, number of sales, and population density affect the spatial distribution of new housing supply. Both approaches are evaluated regarding estimation, prediction, and microsimulation system integration. The findings enhance housing modelling literature and inform urban microsimulation’s housing supply model configuration.https://doi.org/10.1038/s41598-024-51754-9 |
| spellingShingle | Yu Zhang Eric J. Miller Analyzing housing supply location choice: a comparative study of the modelling frameworks Scientific Reports |
| title | Analyzing housing supply location choice: a comparative study of the modelling frameworks |
| title_full | Analyzing housing supply location choice: a comparative study of the modelling frameworks |
| title_fullStr | Analyzing housing supply location choice: a comparative study of the modelling frameworks |
| title_full_unstemmed | Analyzing housing supply location choice: a comparative study of the modelling frameworks |
| title_short | Analyzing housing supply location choice: a comparative study of the modelling frameworks |
| title_sort | analyzing housing supply location choice a comparative study of the modelling frameworks |
| url | https://doi.org/10.1038/s41598-024-51754-9 |
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