Mass Appraisal

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MASS APPRAISAL
Table of Content
*1, Introduction……………………………………………………………..*1 2, Discussion………………………………………………………………2 2.1 Examining and testing Normality of the data…………………………………3 *2.2 Assessment on *the performance of an assessment*…*………………………4 2.2.1 Testing the level of appraisal for the assessment…………………………..5 2.2.2 Measures of variability…………………………………………………………5 2.2.3 Examining Assessment Bias…………………………………………….…….6 2.2.4 Kruskal Wallis test for within Submarket group equity…………………..….6 *3 Conclusion*………………………………………………………………7 4 Reference…………………………………………………………………8 1 Introduction

The analyses will firstly using data set (camp2007fysales.xls) to examine and test the Normality of the data, and then the appraisal uniformity and measures of assessment bias will be considered, finally the secondly capital value data set (camplaterfysales.xls) will be used to identify that there is any evidence regarding to sale chasing. 2 Discussions

2.1 Examining and testing Normality of the data
Marano stated that once the assessment sales data has been validated, Stratified, checked for representativeness and adequate sample size the first thing to do in the statistical analysis phase is to determine the a/s ratios for each strata and determine if they satisfy the requirements of a normal distribution. If the ratios are normally distributed then parametric statistics should be used and if they are not then a non parametric statistics should be used (2008). However, in order to determine if a sample or any group of data fits a standard normal distribution, the histogram and the Normal Probability Plot are the simplest ways to check whether or not it is reasonable to assume that the random errors inherent in the process have been drawn from a normal distribution. Referring to this case, the frequency distribution histogram will be carried out to give a good visual but we still cannot allow to a robust decision to be made, therefore a normal Quantile plot needs to perform, the results are showed below: Before construction frequency distribution, the data set needs to be sorted into three Groups as referring to the requirement. {draw:frame}

Histograms:
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Normal Quantile* Plot*:
Whole LGA:
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{draw:frame} Group 1
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Group 3
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As can be seed from the results, we can determinate that the ratio of LAG, Group1, Group2, are quite normally distributed, and Group3 on other hand is not. Because: Histograms
*Normal Quantile* plot
In this test, we are unable to show the points on those plots to see the data is fitted into a nearly linear pattern or not, but other way we can use that is to compare the correlation coefficient to the table of normal probability plot correlation coefficient in determination of normality. For the LAG, Group1, Group2, their correlation coefficient are all greater than the standard the critical values at the 95% confident level, which means we cannot reject the null hypothesis that the data came from a population with a normal distribution. Group3 on other hand are different, its correlation coefficient is lower than the critical values at the 95% confident level, therefore, and we can reject the null hypothesis. *2.2 Assessment on *the performance of an assessment.

The International Association of Assessing Officers (IAAO) publishes a standard on ratio studies. The IAAO Standard on Ratio Studies suggests performance standards for the level of assessments and the uniformity of assessments. The IAAO standards are advisory and compliance is voluntary. The following content we will use IAAO standards as benchmarks to evaluate Campbelltown’s performance step by step. The statistical summary of A/S Ratio for the LGA as a whole and three socie economic groups showed below: {draw:frame}

2.2.1 Testing the level of appraisal for the assessment.
Weighted mean,...
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