Start Date
31-10-2017 3:15 PM
End Date
31-10-2017 4:00 PM
Description
Faulty valuations used for property tax assessments lead to a wealth of costs for tax payers and local governments alike. For taxing authorities, such inaccuracies result in a higher number of tax appeals. More accurate, research-backed valuations help governments defend and explain their assessments, ideally (for governments) resulting in fewer appeals, more appeals settled out of court, and more court cases won. Automated valuation models (AVMs)- often referred to as computer-assisted mass appraisal or “CAMA” models-have been steadily increasing in across the globe – primarily due to advancing technology, with increased computational power, speed, and methodologies continuously making valuations more accurate and easier to execute. While there are a number of types of models and algorithms that fall under the AVM/CAMA umbrella, the most common types are based on ordinary least squares (OLS), or multiple regression analysis (MRA). Geographically weighted regression (GWR) has been shown improve the predictability power of traditional ordinary least squares-based regression models and significantly help correct problems of spatial autocorrelation (e.g. Brunson et al., 1996; McMillen 1996; Brunson 1998). Specifically, with respect to International Association of Assessing Officers statistical standards, GWR improves equity and uniformity of valuations (Borst & McCluskey, 2008; Moore 2009; Moore & Myers, 2010; Lockwood & Rossini, 2011; McCluskey et al., 2013; Bidanset & Lombard, 2014b). This paper will add to the existing literature base by executing the industry's most cutting-edge spatial AVM techniques (e.g. geographically weighted regression, spatial lag models, response surface analysis) in international markets (selected from the United Kingdom, South America, Asia, Canada, and the U.S.). All models within this research will be compared by not only goodness-of-fit attainment (e.g. AIC, adjusted R2), but by COD and PRD scores at both aggregated (city-wide) and disaggregated (neighborhood) levels within the same dataset. By examining equity and uniformity scores, this research will help identify optimal methods for promoting the accuracy and efficacy of tax assessments and real estate valuations to policy makers and practitioners worldwide, thereby promoting government accountability, mitigating administrative and legal financial costs to taxing jurisdictions and tax payers, and decreasing unjust tax burdens on property owners.
Publication Date
October 2017
Recommended Citation
Bidanset, Paul and Fasteen, Daniel PhD, "AVMs the world over: A comparison of appraisal models in international markets" (2017). International Research Symposium. 16.
https://researchexchange.iaao.org/irs/irs17/sessions/16
AVMs the world over: A comparison of appraisal models in international markets
Faulty valuations used for property tax assessments lead to a wealth of costs for tax payers and local governments alike. For taxing authorities, such inaccuracies result in a higher number of tax appeals. More accurate, research-backed valuations help governments defend and explain their assessments, ideally (for governments) resulting in fewer appeals, more appeals settled out of court, and more court cases won. Automated valuation models (AVMs)- often referred to as computer-assisted mass appraisal or “CAMA” models-have been steadily increasing in across the globe – primarily due to advancing technology, with increased computational power, speed, and methodologies continuously making valuations more accurate and easier to execute. While there are a number of types of models and algorithms that fall under the AVM/CAMA umbrella, the most common types are based on ordinary least squares (OLS), or multiple regression analysis (MRA). Geographically weighted regression (GWR) has been shown improve the predictability power of traditional ordinary least squares-based regression models and significantly help correct problems of spatial autocorrelation (e.g. Brunson et al., 1996; McMillen 1996; Brunson 1998). Specifically, with respect to International Association of Assessing Officers statistical standards, GWR improves equity and uniformity of valuations (Borst & McCluskey, 2008; Moore 2009; Moore & Myers, 2010; Lockwood & Rossini, 2011; McCluskey et al., 2013; Bidanset & Lombard, 2014b). This paper will add to the existing literature base by executing the industry's most cutting-edge spatial AVM techniques (e.g. geographically weighted regression, spatial lag models, response surface analysis) in international markets (selected from the United Kingdom, South America, Asia, Canada, and the U.S.). All models within this research will be compared by not only goodness-of-fit attainment (e.g. AIC, adjusted R2), but by COD and PRD scores at both aggregated (city-wide) and disaggregated (neighborhood) levels within the same dataset. By examining equity and uniformity scores, this research will help identify optimal methods for promoting the accuracy and efficacy of tax assessments and real estate valuations to policy makers and practitioners worldwide, thereby promoting government accountability, mitigating administrative and legal financial costs to taxing jurisdictions and tax payers, and decreasing unjust tax burdens on property owners.