Difference between revisions of "EzModel"
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| − | + | In recent decades, there has been a surging interest in modeling housing prices among economists, planners, and policymakers due to the significant role of properties in household wealth and national economy. In Singapore, public housing accommodates more than 80% of its citizen and citizens either choose to buy a new Housing Development Board (HDB) flat or purchase a HDB resale flat, second-hand flats with less than 99 years left on the lease. | |
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| + | Our project will focus on modelling the HDB resale flat prices which are shaped by market forces. As many previous hedonic pricing models that uses linear regression fails to take into account spatial variations among the observations in the local surroundings, our project will be building a modeling tool based on the geographically weighted regression (GWR) model to analyse the effects of spatial variation on housing prices. Our application will provide users with the option of using a mixed geographically weighted model as well as allows users to add new spatial attributes into the GWR model that will be computed on the go based on the dataset uploaded. We hope that this modelling tool will help users more accurately investigate the impact of variables on HDB resale flat prices in Singapore. | ||
Revision as of 22:27, 25 February 2019
| Project Description |
In recent decades, there has been a surging interest in modeling housing prices among economists, planners, and policymakers due to the significant role of properties in household wealth and national economy. In Singapore, public housing accommodates more than 80% of its citizen and citizens either choose to buy a new Housing Development Board (HDB) flat or purchase a HDB resale flat, second-hand flats with less than 99 years left on the lease.
Our project will focus on modelling the HDB resale flat prices which are shaped by market forces. As many previous hedonic pricing models that uses linear regression fails to take into account spatial variations among the observations in the local surroundings, our project will be building a modeling tool based on the geographically weighted regression (GWR) model to analyse the effects of spatial variation on housing prices. Our application will provide users with the option of using a mixed geographically weighted model as well as allows users to add new spatial attributes into the GWR model that will be computed on the go based on the dataset uploaded. We hope that this modelling tool will help users more accurately investigate the impact of variables on HDB resale flat prices in Singapore.
