The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal (original) (raw)
Related papers
International Journal of Technology
The study examines the influence of four spatial weighting functions and bandwidths on the performance of geographically weighted regression (GWR), including fixed Gaussian and bisquare adaptive kernel functions, and adaptive Gaussian and bi-square kernel functions relative to the global hedonic ordinary least squares (OLS) models. A demonstration of the techniques using data on 3.232 house sales in Cape Town suggests that the Gaussian-shaped adaptive kernel bandwidth provides a better fit, spatial patterns and predictive accuracy than the other schemes used in GWR. Thus, we conclude that the Gaussian shape with both fixed and adaptive kernel functions provides a suitable framework for house price valuation in Cape Town.
Journal of Geographical Systems, 2007
Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.
Discovering and Applying Location Influence Patterns in the Mass Valuation of Domestic Real Property
This thesis addresses the important question of how to incorporate location into a model for the mass valuation of residential (domestic) real property. Two new methods for doing so are described. The first is a process for detecting distinct market segments within a defined study area. It makes use of Geographically Weighted Regression to construct a three dimensional point pattern surface throughout the area for a home of identical characteristics. When the surface displays a discernable spatial structure, evidence is provided for the existence of market segments. The segments detected by this technique are used in a system of models that account for large-scale effects of location on value and thereby improve the predictive accuracy of this model system as compared to a single global model of the same form. The second is a modification to the comparable sales method of valuation such that it provides a prediction accuracy superior to both ordinary least squares (OLS) estimates and the comparables sales method itself. It is formed by taking an optimal linear combination of the OLS model and the comparables sales model based on an examination of the spatial structure of the localized residual errors of the OLS. In combination, these two methods provide improved predictive accuracy and a reduction in the spatial autocorrelation of the residual errors of the resultant predictions when compared to alternative model structures
2006
There is an identifiable theoretical relationship between the comparable sales method (CSM) of valuation as practiced by mass appraisers and the recent developments in geostatistical valuation models. The CSM is shown to be a special case of a spatially lagged weight matrix model. There is a less formal but clear relationship with Geographically Weighted Regression as well. The predictive accuracy of CSM is compared to several Ordinary Least Squares Model configurations, and results obtained from Geographically Weighted Regression via empirical studies on diverse datasets. An example of a comparable sales weighting scheme as practiced by mass appraisers is provided. In addition, particular interest is focused on how well each method is able to model the spatial variations in property values. This is done by examining the local and global spatial autocorrelation in residual errors of the predicted values.
Land
The traditional linear regression model of mass appraisal is increasingly unable to satisfy the standard of mass appraisal with large data volumes, complex housing characteristics and high accuracy requirements. Therefore, it is essential to utilize the inherent spatial-temporal characteristics of properties to build a more effective and accurate model. In this research, we take Beijing’s core area, a typical urban center, as the study area of modeling for the first time. Thousands of real transaction data sets with a time span of 2014, 2016 and 2018 are conducted at the community level (community annual average price). Three different models, including multiple regression analysis (MRA) with ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), are adopted for comparative analysis. The result indicates that the GTWR model, with an adjusted R2 of 0.8192, performs better in the mass appraisal modeling of r...
Pacific Rim Property Research Journal, 2007
Locational Value Residual Surface (LVRS) has been suggested as an alternative to resolving the difficulty in the traditional modelling of locational influence on property values in a particular area. The objective of this paper was to compare the relative performance of models that apply locational value residual surface (LVRS) and the traditional multiple regression models in the prediction of residential property values. A controlled sample of 125 single-and double-storey residential properties was used to construct regression models. It was found that models applying LVRS were marginally better than the traditional models in predicting property values.
An empirical evaluation of spatial regression models
Computers & Geosciences, 2006
Conventional statistical methods are often ineffective to evaluate spatial regression models. One reason is that spatial regression models usually have more parameters or smaller sample sizes than a simple model, so their degree of freedom is reduced. Thus, it is often unlikely to evaluate them based on traditional tests. Another reason, which is theoretically associated with statistical methods, is that statistical criteria are crucially dependent on such assumptions as normality, independence, and homogeneity. This may create problems because the assumptions are open for testing. In view of these problems, this paper proposes an alternative empirical evaluation method. To illustrate the idea, a few hedonic regression models for a house and land price data set are evaluated, including a simple, ordinary linear regression model and three spatial models. Their performance as to how well the price of the house and land can be predicted is examined. With a cross-validation technique, the prices at each sample point are predicted with a model estimated with the samples excluding the one being concerned. Then, empirical criteria are established whereby the predicted prices are compared with the real, observed prices. The proposed method provides an objective guidance for the selection of a suitable model specification for a data set. Moreover, the method is seen as an alternative way to test the significance of the spatial relationships being concerned in spatial regression models.
Mass Appraisal: An Introduction to Multiple Regression Analysis for Real Estate Valuation
Journal of Real Estate Practice and Education, 2004
The real estate tax assessment process is used to provide an introduction to multiple regression analysis. The tax assessor's office in a small west Texas county has always assessed properties through manual market comparison analysis. This manual process uses recently sold properties that are in close proximity to the subject property to make corresponding weighted adjustments. After going to a seminar on multiple regression analysis for mass appraisals, the county tax assessor employs a university professor to explain how multiple regression analysis works for real estate valuation and mass assessment, as well as what its relative benefits are over the existing manual system. He invites his staff, the county commissioners, and others to a one-night seminar that explains multiple regression analysis. This seminar presented by a university professor to Texas participants is used educate case readers about real estate appraisal and multiple regression analysis. Exhibits Multiple regression handout presented in Appendix. Availability This case is available through the ARES clearing house.
Spatial Econometrics Revisited: A Case Study of Land Values in Roanoke County
2005
Omitting spatial characteristics such as proximity to amenities from hedonic land value models may lead to spatial autocorrelation and biased and inefficient estimators. A spatial autoregressive error model can be used to model the spatial structure of errors arising from omitted spatial effects. This paper demonstrates an alternative approach to modeling land values based on individual and joint misspecification tests