Pusat Pengajian Kejuruteraan Awam - Tesis

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Browsing Pusat Pengajian Kejuruteraan Awam - Tesis by Author "Adwan, Zubeida Ali Al"

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    Spatial hedonic pricing model for real property valuation in jordan using geographic weighted regression
    (2020-08-01)
    Adwan, Zubeida Ali Al
    Property valuation or assessment is a compulsory property tax activity to be imposed on all properties in Jordan. Currently, the lengthy, time-consuming and costly processes have been performed manually. Furthermore, the assessment needs to be updated from time to time in order to keep up with the current market value. As such, there is an increasing need to develop alternative valuation models that can estimate large-scale property values in a short time with little workforce and low cost. Hedonic property price models are increasingly used in economic methods that are used to assess properties automatically. Real property is a group of characteristics and utilities where the property parameters are related to the estimated total value of the transaction. By collecting value-added data on different buildings, a regression analysis can be used to determine the relationship or correlation between each of the characteristics of the valued price transaction. This study applies a spatial hedonic pricing model to the real estate market in Amman Jordan. The Geographic Weighted Regression (GWR) was used within the framework of GIS to correlate the adopted thirteen properties namely structural, location and neighborhood characteristics with their corresponding price and to obtain results in the form of reports and maps. As a first step in the GWR procedure, the exploratory ordinary least square (OLS) regression was carried out and the redundant property parameters were excluded. After the implementation of the GWR, coefficients showing the effects of property variables were identified, their values and standard residuals were mapped and analyzed. The random spatial distribution of the standard residuals shows that there is no spatial clustering in these residuals and therefore the model estimation is out of multicollinearity. Noticeable spatial variations in the values of both coefficients and their standard residuals were investigated. The predictivity test also shows spatial variation of the model predictivity goodness from location to location. The results verified the need for local models to measure spatial variations, as it was evident that spatial heterogeneity was revealed in output coefficient raster surfaces. The study has also suggested that more property variables be included in order to enhance the specified price model reality.