CHAPTER 7 / QUESTIONS

Question 1

For model calibration, in valuation work models are primarily calibrated to produce either predictive or explanatory results.

Answer 1

For model calibration, in valuation work models are primarily calibrated to produce either predictive or explanatory results.​

Question 2

PREDICTIVE MODEL

Answer 2

Developed to produce the highest quality overall prediction of market value - for example, achieve the best possible estimate of selling price, but not necessarily the most reliable estimates for the individual coefficients.

Question 3

EXPLANATORY MODEL

Answer 3

Developed primarily to explain the value that each variable contributes to market value - for example, the value per square foot of living area. In other words, rather than focussing on the outcome of the model overall, this type of model focuses on developing the most accurate possible values for the coefficients.

Question 4

In an explanatory model, the model builder wants to _ _ _ _ _ _

Answer 4

In an explanatory model, the model builder wants to maximize the accuracy of the values of the coefficients.

Question 5

COEFFICIENTS & T-STATISTICS

Answer 5

As discussed in Lesson 6, the significance of the coefficients is indicated by the t-statistic and its associated significance level.

Question 6

HIGHER T-STATISTICS & LOWER SIGNIFICANCE LEVELS

Answer 6

Higher t-statistics and lower significance levels increase the reliance the model builder can place on the statistical significance of the coefficients.

Question 7

A HIGH T-STATISTIC

Answer 7

A high t-statistic leads to the acceptance of the hypothesis that the coefficient is significantly different than zero, meaning you are confident the coefficient number is accurate.

Question 8

T-STATISTIC CRITERIA

Answer 8

As mentioned in the previous lesson, the criteria for this is usually to have a t-statistic over 2 and a significance level of less than .05. This would indicate that the probability of this coefficient being equal to zero is 5 % or less, meaning you are confident it is a reliable result.

Question 9

IN A PREDICTIVE MODEL THE MODEL BUILDER WANTS _ _ _ _

Answer 9

In a predictive model, the model builder wants to ensure the R-square is high and the standard error of the estimate is minimized.

Question 10

MRA MODEL BUILDING
STEP 1

Answer 10

STEP 1: Specifying the Model

The additive general model that is often applied to residential property is: ​MV = LV + BV

Question 11

MRA MODEL BUILDING
STEP 2

Answer 11

STEP 2: Reviewing the Variables

The next step in the model development process is to review the variables available in the database and group the variables according to the factor the characteristic represents in the general model (e.g., in this case living area, location, or amenities).

Very often, some characteristics to be included in the model will be represented by more than one variable and sometimes it is necessary to use a combination of more than one variable to correctly represent the characteristic or factor needed in the model.

Question 12

MRA MODEL BUILDING
STEP 3

Answer 12

STEP 3: Examining the Variables

To get a sense of the important variables so we can get a feel for what to expect out of the final model

To exclude variables from the regression model that are of no use

To avoid multicollinearity, excluding any variable strongly correlated with another variable

Question 13

STEP 3: Examining the Variables

In this section, we will examine the variables in our database, their relationship to sale price, and their relationship to each other. There are a number of reasons for testing these relationships:

Answer 13

To get a sense of the important variables so we can get a feel for what to expect out of the final model.

To exclude variables from the regression model that are of no use. For example, they may have little or no statistical relationship to the sale price (within the data being analyzed) or they have too few occurrences

To avoid multicollinearity, excluding any variable strongly correlated with another variable

To fmd variables that might be useful, but need to be changed into a useable format.

To fmd variables that might be useful, but are multiplicative in nature

Question 14

Statistical tools for examining variables and their relationships

Answer 14

Many of the statistical tools for examining variables and their relationships were shown in previous lessons, including:

descriptive statistics - frequency distributions and crosstabulation tables;

charts or graphs, such as scatterplots and boxplots; and

more advanced statistics such as correlation coefficients and simple linear regression.

Question 15

SCATTERPLOT & GRAPHICAL ANALYSIS

Answer 15

A scatterplot between sale price and year of sale should tell us if a time adjustment is necessary.

Question 16

EXAMPLE SCATTERPLOT
& GRAPHICAL ANALYSIS

Answer 16

The graph shows the market has been flat for almost four years in this market area. The regression line is nearly flat and the correlation between sale price and year of sale is only 0.004. No time adjustment is necessary.

Question 17

STEP 3 OF THE MRA PROCESS

Answer 17

STEP 3: Examining the Variables

Question 18

REASONS TO EXAMINE VARIABLES?

Answer 18

We will examine the variables in our database, their relationship to sale price, and their relationship to each other. There are a number of reasons for testing these relationships:

To get a sense of the important variables so we can get a feel for what to expect out of the final model

To exclude variables from the regression model that are of no use

To avoid multicollinearity, excluding any variable strongly correlated with another variable

To fmd variables that might be useful, but need to be changed into a useable format

Question 19

STATISTICAL TOOLS FOR EXAMINING VARIABLES

Answer 19

descriptive statistics - frequency distributions and crosstabulation tables;

charts or graphs, such as scatterplots and boxplots; and

more advanced statistics such as correlation coefficients and simple linear regression.

Question 21

A GOOD PREDICTIVE MODEL

Answer 21

A good predictive model can be used to directly estimate sales prices. In a predictive model, the model builder wants to ensure the R-square is high and the standard error of the estimate is minimized. This is normally achieved by including all variables that reduce the model’s standard error, regardless of the t-statistics or significance levels for the variables. This leads to a model with the lowest overall error possible, but it does not necessarily produce reliable individual variable coefficients.